diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 15e7d963..e69de29b 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -1,16 +0,0 @@ - -## Notes (skill-compounder) - -- **2026-09-03** Executing a docs/tutorials launch notebook locally runs its Colab '%pip install ... git+...@dev-1.0' cell, which overwrites the venv's editable hypertools with the stale REMOTE branch mid-run (seen 2026-09-03 as '48 dimensions ... static plots support at most 2'). scripts/execute_tutorial.py now tags 'pip install' cells skip-execution in memory; after any other notebook run, check 'pip show hypertools' says Editable, and 'pip install -e .[dev]' if not. -- **2026-09-03** Regenerating a docs/tutorials notebook from its example script (Plan 4 pattern, used 5x on 2026-09-03): generate cells from the script's section markers with a scratchpad script (install cell carried over byte-identical), never leave a HyperAnimation as a cell's last expression (its repr embeds an 89 KB video), execute with scripts/execute_tutorial.py (skips pip-install cells, disables HF progress bars), save the GIF at <=15 fps / ~300 frames (a 900-frame GIF is 13 MB), record the measured visible-output set in tests/test_examples_are_native.py, re-measure the budget once, commit script+notebook+GIF+gate together. Gallery pages for show=False examples need the HyperAnimation scraper in docs/conf.py. -- **2026-09-03** When a push will trigger hosted CI, commit the session-note/memory update BEFORE the push (seen twice on 2026-09-03: note committed right after each push, so every CI cycle had to be re-run or cancelled on the note-only head; PR #283). -- **2026-09-03** Bluesky launch thread (notes/bluesky-launch, gitignored): re-verify atproto lexicon limits with curl before posting (video cap moved 100->300 MB between July and Sept 2026), count post graphemes with the regex module's \X (not len), render clips from the examples' construct_artifact() via the scratchpad render script, and expect tutorial 'Full code' links to 404 until RTD builds master. -- **2026-09-03** Yahoo v8 chart 'range=max&interval=1d' silently returns 3-MONTH bars (AAPL: 169 rows since 1984); pass explicit period1/period2 epoch bounds to get daily. SEC XBRL needs a User-Agent with a contact (the project's pyproject email); 'companyconcept' can be EMPTY for a filer (ABT, KO) whose 'companyfacts' has the concept (measured 2026-09-03). -- **2026-09-03** Tutorial notebooks are GENERATED (scripts/generate_tutorial_notebook.py) then executed (scripts/execute_tutorial.py); after changing SPECS (dpi, prose) ALWAYS regenerate before re-executing -- on 2026-09-04 a dpi revert was made in the generator only, and two 10-minute re-executions wrote the stale dpi. Video size is CRF-bound now (was a fixed 1800 kbit/s), so dpi does not trade size. -- **2026-09-04** After a library behaviour change (new error text, a warning removed, a new return type), grep docs/tutorials/*.ipynb for prose or stored outputs demonstrating the OLD behaviour and re-execute those notebooks (2026-09-04: io.ipynb taught hyp.load(df) as a TypeError after load gained passthrough; align/reduce stored the glyph warning; lsl_streaming stored the liblsl ERR line). -- **2026-09-05** When the finding IS that a file/match is absent, chain ls/grep with '|| true' (or test -e) so the expected exit 1 is not logged as a tool failure -- **2026-09-05** Guard sed -n "$((n-12)),..." on a possibly-empty $n: an empty grep result makes sed see '-12' as an option; use grep -B/-A context instead -- **2026-09-05** A 'clean' docs build (sphinx -W -E -a) does NOT remove sphinx-gallery's generated docs/auto_examples/ (gitignored): after deleting or renaming an example, rm -rf docs/auto_examples first or -W fails on 'document isn't included in any toctree' for the stale pages (2026-09-05, after the gallery consolidation). -- **2026-09-05** Subagent dispatch prompts must state the no-mocks rule verbatim ('no mock objects, no monkeypatching library functions as spies; prove behaviour with real observables'): on 2026-09-05 an agent verified 'never touches the network' by monkeypatching requests.get and seaborn_dataset; replaced with mtime/.part/uncached-URL-raises observables. -- **2026-09-05** Never assert absolute font-metric numbers (probe heights, figure inches, pixel rows) in tests: CI's matplotlib (3.11.1) hints text differently from the local 3.10.8 -- 2026-09-05 two new tests failed on every CI job while green locally; assert against the library's own probe on the same axes, or relative tolerances. - diff --git a/.claude/compound/lessons/bluesky-launch-thread/SKILL.md b/.claude/compound/lessons/bluesky-launch-thread/SKILL.md new file mode 100644 index 00000000..36f009fd --- /dev/null +++ b/.claude/compound/lessons/bluesky-launch-thread/SKILL.md @@ -0,0 +1,10 @@ +--- +name: bluesky-launch-thread +description: Use when preparing or posting the Bluesky launch thread (notes/bluesky-launch, gitignored). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Re-verify atproto lexicon limits with curl before posting (the video cap has moved, 100 to +300 MB). Count post graphemes with the regex module's \X, not len. Render clips from the +examples' construct_artifact() via the scratchpad render script. Expect tutorial "Full +code" links to 404 until Read the Docs builds master. diff --git a/.claude/compound/lessons/commit-notes-before-push/SKILL.md b/.claude/compound/lessons/commit-notes-before-push/SKILL.md new file mode 100644 index 00000000..3d0b27fb --- /dev/null +++ b/.claude/compound/lessons/commit-notes-before-push/SKILL.md @@ -0,0 +1,8 @@ +--- +name: commit-notes-before-push +description: Use when about to push a branch that triggers hosted CI and a session-note or memory update is still uncommitted. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Commit the session-note or memory update BEFORE the push. A note committed right after the +push moves the head to a note-only commit, and every CI cycle has to be re-run or cancelled. diff --git a/.claude/compound/lessons/datatype-gate-no-raw-isinstance/SKILL.md b/.claude/compound/lessons/datatype-gate-no-raw-isinstance/SKILL.md new file mode 100644 index 00000000..545e1076 --- /dev/null +++ b/.claude/compound/lessons/datatype-gate-no-raw-isinstance/SKILL.md @@ -0,0 +1,15 @@ +--- +name: datatype-gate-no-raw-isinstance +description: Use when writing or delegating hypertools library code that checks whether an input is a DataFrame, Series or array. +created: 2026-10-09 +origin: project hypertools, session f69d921d +--- +Classify inputs with the shared helpers in hypertools/_shared/helpers.py +(is_frame_dataset, is_series_like, is_array_dataset, as_pandas_dataframe), never with +`isinstance(x, pd.DataFrame)` or `isinstance(x, pd.Series)`. tests/test_datatype_gate.py +scans the package and fails with "datatype check(s) outside the shared coercion layer" +for each raw check, so polars and other frames are treated like pandas ones. +An agent's targeted test run usually does not include that file, and the failure then +shows up only in the full suite: put `tests/test_datatype_gate.py` in the test list of +any prompt that adds input handling. To tell a row-labelled pandas Series from a polars +one, use `is_series_like(x)` plus a non-callable `x.index`. diff --git a/.claude/compound/lessons/docs-site-check-every-page/SKILL.md b/.claude/compound/lessons/docs-site-check-every-page/SKILL.md new file mode 100644 index 00000000..85e4f6c8 --- /dev/null +++ b/.claude/compound/lessons/docs-site-check-every-page/SKILL.md @@ -0,0 +1,18 @@ +--- +name: docs-site-check-every-page +description: Use when asked to build the hypertools Sphinx / Read the Docs site and confirm it looks correct, or when reviewing built docs pages in a browser. +created: 2026-10-09 +origin: project hypertools, session f69d921d +--- +Check EVERY built page with a scripted browser, never a hand-picked list. Build the RTD +way (from docs/: READTHEDOCS=True READTHEDOCS_GIT_IDENTIFIER=master READTHEDOCS_OUTPUT=$OUT +sphinx -T -b html -d _build/doctrees . $OUT/html, then python docs/post_build.py), serve +$OUT/html with `python -m http.server`, and run check_built_site.py (beside this lesson; +playwright is in .venv; see also scripts/verify_docs_playwright.py). It reads each page's +::before/::after content for ERROR/WARNING, broken images, console errors, leaked RST and +overflow at 1400px and 400px, and saves screenshots to look at. +Why: a sampled review passed the site while docs/hierarchy.html showed furo's red +"ERROR: Adding a table of contents" box from a `.. contents::` directive. That box is CSS +::before text, so sphinx -W (0 warnings), HTML text scans and link checkers cannot see it. +Before trusting 0 findings, plant a defect on a temporary copy of a page and confirm the +checker reports it. diff --git a/.claude/compound/lessons/docs-site-check-every-page/check_built_site.py b/.claude/compound/lessons/docs-site-check-every-page/check_built_site.py new file mode 100644 index 00000000..305d34c5 --- /dev/null +++ b/.claude/compound/lessons/docs-site-check-every-page/check_built_site.py @@ -0,0 +1,97 @@ +import json +import os +import sys +from playwright.sync_api import sync_playwright + +# usage: python check_built_site.py HTML_DIR BASE_URL OUT_DIR (serve HTML_DIR first: python -m http.server) +ROOT = sys.argv[1] +BASE = sys.argv[2].rstrip("/") + "/" +OUT = sys.argv[3] +os.makedirs(OUT, exist_ok=True) +pages = sorted( + os.path.relpath(os.path.join(d, f), ROOT) + for d, _, fs in os.walk(ROOT) + for f in fs + if f.endswith(".html") and not d.startswith(os.path.join(ROOT, "_")) +) +shots = [ + p + for p in pages + if ( + ("/" not in p and not p.startswith("hypertools.")) + or p.startswith("tutorials/") + or p == "auto_examples/index.html" + ) +] +shots += [ + "hypertools.plot.html", + "hypertools.predict.html", + "hypertools.describe.html", + "hypertools.io.LSLStream.html", + "hypertools.HyperAnimation.html", +] +JS = """() => { + const out={}; + out.broken=[...document.images].filter(i=>i.complete&&i.naturalWidth===0).map(i=>i.getAttribute('src')).slice(0,5); + out.overflow=document.documentElement.scrollWidth-document.documentElement.clientWidth; + const pseudo=[]; for(const el of document.querySelectorAll('article *')){ for(const ps of ['::before','::after']){ const c=getComputedStyle(el,ps).content; if(c&&/ERROR|WARNING|unnecessary/i.test(c)) pseudo.push(el.tagName+'.'+el.className+': '+c.slice(0,80)); } } + out.pseudo=pseudo.slice(0,3); + out.sysmsg=document.querySelectorAll('.system-message, .problematic').length; + out.videos=[...document.querySelectorAll('video')].length; + const txt=[...document.querySelectorAll('article p, article li, article dd, article dt, article h1, article h2, article h3')].map(e=>{const c=e.cloneNode(true); c.querySelectorAll('code,pre,.math,script,style').forEach(x=>x.remove()); return c.textContent;}).join('\\n'); + out.leaks=(txt.match(/[^\\n]{0,30}(``|:[a-z]+:`|\\.\\. [a-z-]+::)[^\\n]{0,30}/g)||[]).slice(0,3); + return out; }""" +res = {} +with sync_playwright() as pw: + b = pw.chromium.launch() + for theme in ["light"]: + ctx = b.new_context(viewport={"width": 1400, "height": 1800}) + pg = ctx.new_page() + errs = [] + pg.on( + "console", + lambda m: errs.append(m.text[:160]) if m.type == "error" else None, + ) + pg.on("pageerror", lambda e: errs.append("PAGEERROR " + str(e)[:160])) + for p in pages: + errs.clear() + try: + pg.goto(BASE + p, wait_until="load", timeout=60000) + pg.wait_for_timeout(400) + except Exception as e: + res[p] = {"goto": str(e)[:100]} + continue + r = pg.evaluate(JS) + r["console"] = sorted(set(errs))[:4] + res[p] = r + if p in shots: + pg.screenshot(path=os.path.join(OUT, p.replace("/", "__") + ".png")) + # phone width overflow on every page + ctx = b.new_context(viewport={"width": 400, "height": 800}) + pg = ctx.new_page() + for p in pages: + try: + pg.goto(BASE + p, wait_until="load", timeout=60000) + pg.wait_for_timeout(250) + res[p]["phone_overflow"] = pg.evaluate( + "()=>document.documentElement.scrollWidth-document.documentElement.clientWidth" + ) + except Exception as e: + res[p]["phone"] = str(e)[:80] + b.close() +json.dump(res, open(os.path.join(OUT, "results.json"), "w"), indent=1) +bad = { + p: r + for p, r in res.items() + if r.get("broken") + or r.get("overflow", 0) > 2 + or r.get("pseudo") + or r.get("sysmsg") + or r.get("leaks") + or r.get("console") + or r.get("phone_overflow", 0) > 2 + or "goto" in r +} +print(len(res), "pages checked;", len(bad), "with findings") +for p, r in bad.items(): + print(p, {k: v for k, v in r.items() if v and k not in ("videos",)}) diff --git a/.claude/compound/lessons/figure-qa-three-separated-agents/SKILL.md b/.claude/compound/lessons/figure-qa-three-separated-agents/SKILL.md new file mode 100644 index 00000000..6d14db34 --- /dev/null +++ b/.claude/compound/lessons/figure-qa-three-separated-agents/SKILL.md @@ -0,0 +1,13 @@ +--- +name: figure-qa-three-separated-agents +description: Use when reviewing the figures of a hypertools notebook or the feature tour for correctness. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Use three SEPARATED agents: one writes each figure's EXPECTED appearance from the notebook +code, prose and docstrings without seeing images; one describes the OBSERVED renders +without seeing code; one adjudicates with probes. This finds gaps solo eyeballing misses +(ax= palette ignored, z-label outside the tight bbox, a 2-D frame on the data, faded +recoloured forecasts). Render plotly outputs from their notebook JSON with kaleido (the +scratchpad review/manifest and tour_out/extract.py pattern), and re-run only the changed +figures each round. diff --git a/.claude/compound/lessons/forecast-index-is-not-dataset-index/SKILL.md b/.claude/compound/lessons/forecast-index-is-not-dataset-index/SKILL.md new file mode 100644 index 00000000..46fa7968 --- /dev/null +++ b/.claude/compound/lessons/forecast-index-is-not-dataset-index/SKILL.md @@ -0,0 +1,12 @@ +--- +name: forecast-index-is-not-dataset-index +description: Use when writing hypertools code that indexes schedules, lines or runs by a forecast. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Forecast index != dataset index. A predict=[...] collection's forecasts are MODEL-MAJOR +(forecast i = model i//n_datasets, dataset i%n_datasets), and hue=/cluster= regrouping +makes RUN index != dataset index. Any code that touches a forecast must translate through +_model_forecast_owner / forecast_datasets (forecast -> source dataset) and _forecast_owner +/ _seg_ds (dataset -> final run) before indexing. The bug hides when the counts coincide +and shows as an IndexError in animated reveal lookups or wrong plotly hyp_dataset tags. diff --git a/.claude/compound/lessons/global-setting-as-context-manager/SKILL.md b/.claude/compound/lessons/global-setting-as-context-manager/SKILL.md new file mode 100644 index 00000000..c0631de8 --- /dev/null +++ b/.claude/compound/lessons/global-setting-as-context-manager/SKILL.md @@ -0,0 +1,12 @@ +--- +name: global-setting-as-context-manager +description: Use when changing hyp.set_autoinstall or adding another process-global setting that is both a direct call and a context manager. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Model it as LIVE handle records (weakrefs, in construction order) plus a BASELINE folded +in by the weakref callback of a handle that dies unentered; take the newer of the top +record and the baseline by call order; mark exited records finished. The failure modes are +a restore-order race, retained handles, and a construct-then-enter race across threads. +Test overlapping blocks across threads, construct-then-enter interleaving, 100k discarded +direct calls (tracemalloc), and a block handle dying after exit. diff --git a/.claude/compound/lessons/legend-marker-on-line-artist/SKILL.md b/.claude/compound/lessons/legend-marker-on-line-artist/SKILL.md new file mode 100644 index 00000000..202ad87b --- /dev/null +++ b/.claude/compound/lessons/legend-marker-on-line-artist/SKILL.md @@ -0,0 +1,9 @@ +--- +name: legend-marker-on-line-artist +description: Use when a hypertools draw path puts a line's markers on a separate artist (fmt split, truth= overlay). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +A line whose markers are drawn on a SEPARATE artist shows a marker-less legend glyph. Keep +marker= on the line artist with markevery=[] so the legend handle carries it, and check +every marker-splitting draw path for this. diff --git a/.claude/compound/lessons/lsl-test-unique-stream-type/SKILL.md b/.claude/compound/lessons/lsl-test-unique-stream-type/SKILL.md new file mode 100644 index 00000000..e2b6ac3a --- /dev/null +++ b/.claude/compound/lessons/lsl-test-unique-stream-type/SKILL.md @@ -0,0 +1,10 @@ +--- +name: lsl-test-unique-stream-type +description: Use when writing or debugging a hypertools LSL test that resolves a stream by type. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +A test that resolves by type='EEG' fails whenever ANOTHER process advertises an idle EEG +outlet (a notebook kernel running the tutorial's synthetic outlet), because lsl_stream() +takes the first match. Give a test outlet a unique stream TYPE, as with the unique names, +and check pylsl.resolve_streams() for foreign outlets before blaming the library. diff --git a/.claude/compound/lessons/no-absolute-font-metrics-in-tests/SKILL.md b/.claude/compound/lessons/no-absolute-font-metrics-in-tests/SKILL.md new file mode 100644 index 00000000..b1210276 --- /dev/null +++ b/.claude/compound/lessons/no-absolute-font-metrics-in-tests/SKILL.md @@ -0,0 +1,10 @@ +--- +name: no-absolute-font-metrics-in-tests +description: Use when writing a hypertools test that asserts text or figure measurements. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Never assert absolute font-metric numbers (probe heights, figure inches, pixel rows). CI's +matplotlib (3.11.1) hints text differently from the local 3.10.8, so such tests pass +locally and fail on every CI job. Assert against the library's own probe on the same axes, +or use relative tolerances. diff --git a/.claude/compound/lessons/notebook-pip-cell-overwrites-editable/SKILL.md b/.claude/compound/lessons/notebook-pip-cell-overwrites-editable/SKILL.md new file mode 100644 index 00000000..93ac8bd4 --- /dev/null +++ b/.claude/compound/lessons/notebook-pip-cell-overwrites-editable/SKILL.md @@ -0,0 +1,11 @@ +--- +name: notebook-pip-cell-overwrites-editable +description: Use when executing a docs/tutorials notebook locally in hypertools. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +A launch notebook's Colab cell `%pip install ... git+...@dev-1.0` overwrites the venv's +editable hypertools with the stale REMOTE branch mid-run (symptom: "48 dimensions ... +static plots support at most 2"). Execute with scripts/execute_tutorial.py, which tags +'pip install' cells skip-execution in memory. After any other notebook run, check that +`pip show hypertools` says Editable, and run `pip install -e .[dev]` if not. diff --git a/.claude/compound/lessons/pin-matplotlib-backend-for-colab/SKILL.md b/.claude/compound/lessons/pin-matplotlib-backend-for-colab/SKILL.md new file mode 100644 index 00000000..62547d8c --- /dev/null +++ b/.claude/compound/lessons/pin-matplotlib-backend-for-colab/SKILL.md @@ -0,0 +1,10 @@ +--- +name: pin-matplotlib-backend-for-colab +description: Use when hypertools code or a notebook uses a matplotlib-only return API (fig.axes, .canvas, HyperAnimation .draw_frame/.on_frame/.n_frames, an internal hyp.plot call). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Colab and Kaggle auto-select the PLOTLY render backend, so matplotlib-only return APIs +break there while passing locally (plot_stream's head plot, the tour's animation clock, +launch notebooks). Pin backend='matplotlib' at such call sites and test them under +hyp.set_interactive_backend('plotly'), the same preference Colab sets. diff --git a/.claude/compound/lessons/plotly-scene-sized-by-height/SKILL.md b/.claude/compound/lessons/plotly-scene-sized-by-height/SKILL.md new file mode 100644 index 00000000..67c8c618 --- /dev/null +++ b/.claude/compound/lessons/plotly-scene-sized-by-height/SKILL.md @@ -0,0 +1,13 @@ +--- +name: plotly-scene-sized-by-height +description: Use when sizing or calibrating a Plotly 3-D scene or its camera distance in hypertools. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Plotly sizes a 3-D scene by its domain HEIGHT only and clips at the sides: the same +267x209 px cube appears in 600x300 and 1200x300 scenes, and a 300x600 scene's cube is 415 +tall, cut at 300 wide. At hypertools' default eye the cube is 0.89 x height wide and 0.70 +x height tall, and apparent size goes as 1/eye-distance only when backing OFF (an eye +nearer than about 0.8x clips the front). Name a calibration constant by its reference: +SCENE_CUBE_WIDTH_PER_HEIGHT = 1.4 means width per CUBE height, and reading it as width per +SCENE height backs the camera off 1.4x in every square panel cell. diff --git a/.claude/compound/lessons/readme-is-lowercase/SKILL.md b/.claude/compound/lessons/readme-is-lowercase/SKILL.md new file mode 100644 index 00000000..673fe98b --- /dev/null +++ b/.claude/compound/lessons/readme-is-lowercase/SKILL.md @@ -0,0 +1,9 @@ +--- +name: readme-is-lowercase +description: Use when a hypertools test or script opens the repository readme by path. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +The repository's readme is lowercase `readme.md`. A test that opens 'README.md' passes on +macOS (case-insensitive) and fails on every Linux CI job. Check the tracked file name with +`git ls-files` before hard-coding a path in a test. diff --git a/.claude/compound/lessons/regenerate-before-reexecute/SKILL.md b/.claude/compound/lessons/regenerate-before-reexecute/SKILL.md new file mode 100644 index 00000000..82023d98 --- /dev/null +++ b/.claude/compound/lessons/regenerate-before-reexecute/SKILL.md @@ -0,0 +1,10 @@ +--- +name: regenerate-before-reexecute +description: Use when changing SPECS (dpi, prose) for the generated tutorial notebooks in hypertools. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Tutorial notebooks are GENERATED (scripts/generate_tutorial_notebook.py) and then executed +(scripts/execute_tutorial.py). After changing SPECS, ALWAYS regenerate before +re-executing; otherwise a 10-minute re-execution writes the stale value. Video size is +CRF-bound (not a fixed bitrate), so dpi does not trade against size. diff --git a/.claude/compound/lessons/regenerate-tutorial-notebook/SKILL.md b/.claude/compound/lessons/regenerate-tutorial-notebook/SKILL.md new file mode 100644 index 00000000..b54f92b5 --- /dev/null +++ b/.claude/compound/lessons/regenerate-tutorial-notebook/SKILL.md @@ -0,0 +1,15 @@ +--- +name: regenerate-tutorial-notebook +description: Use when regenerating a docs/tutorials notebook from its example script in hypertools. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +1. Generate cells from the script's section markers with a scratchpad script; carry the + install cell over byte-identical. +2. Never leave a HyperAnimation as a cell's last expression (its repr embeds an 89 KB video). +3. Execute with scripts/execute_tutorial.py (skips pip-install cells, disables HF progress bars). +4. Save the GIF at <=15 fps and about 300 frames (a 900-frame GIF is 13 MB). +5. Record the measured visible-output set in tests/test_examples_are_native.py and + re-measure the budget once. +6. Commit script, notebook and GIF and the gate together. +Gallery pages for show=False examples need the HyperAnimation scraper in docs/conf.py. diff --git a/.claude/compound/lessons/release-verification-pipeline/SKILL.md b/.claude/compound/lessons/release-verification-pipeline/SKILL.md new file mode 100644 index 00000000..2703e439 --- /dev/null +++ b/.claude/compound/lessons/release-verification-pipeline/SKILL.md @@ -0,0 +1,11 @@ +--- +name: release-verification-pipeline +description: Use when running the full hypertools release verification (notebook re-execution, full pytest, sphinx -W gallery, example smoke gate). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +The pipeline runs about 50 minutes, past the Bash tool's 10-minute cap. Write it as a +scratchpad zsh script with step markers, launch it with nohup, and poll the log for the +DONE marker from run_in_background watchers (each at most 9.5 minutes). After any sphinx +build, `git checkout` the two autosummary stubs FrameContext and LSLStream, which are +regenerated with whitespace-only changes. diff --git a/.claude/compound/lessons/relpath-strings-use-forward-slash/SKILL.md b/.claude/compound/lessons/relpath-strings-use-forward-slash/SKILL.md new file mode 100644 index 00000000..58463d70 --- /dev/null +++ b/.claude/compound/lessons/relpath-strings-use-forward-slash/SKILL.md @@ -0,0 +1,9 @@ +--- +name: relpath-strings-use-forward-slash +description: Use when a hypertools test compares repo-relative paths as strings (allowlists, rosters, scanner findings). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Build such paths with `os.path.relpath(...).replace(os.sep, '/')`. Otherwise the test +passes on macOS and Linux and fails every Windows job on 'docs\tutorials\align.ipynb' +against 'docs/tutorials/align'. diff --git a/.claude/compound/lessons/remove-auto-examples-before-docs-build/SKILL.md b/.claude/compound/lessons/remove-auto-examples-before-docs-build/SKILL.md new file mode 100644 index 00000000..9e4c8620 --- /dev/null +++ b/.claude/compound/lessons/remove-auto-examples-before-docs-build/SKILL.md @@ -0,0 +1,10 @@ +--- +name: remove-auto-examples-before-docs-build +description: Use when building hypertools docs with sphinx -W after deleting or renaming a gallery example. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +A "clean" docs build (sphinx -W -E -a) does NOT remove sphinx-gallery's generated +docs/auto_examples/ (gitignored). After deleting or renaming an example, `rm -rf +docs/auto_examples` first, or -W fails on "document isn't included in any toctree" for the +stale pages. diff --git a/.claude/compound/lessons/stale-notebook-prose-after-behaviour-change/SKILL.md b/.claude/compound/lessons/stale-notebook-prose-after-behaviour-change/SKILL.md new file mode 100644 index 00000000..2ba8a484 --- /dev/null +++ b/.claude/compound/lessons/stale-notebook-prose-after-behaviour-change/SKILL.md @@ -0,0 +1,12 @@ +--- +name: stale-notebook-prose-after-behaviour-change +description: Use when a hypertools library behaviour changes (new error text, a warning removed, a new return type). +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Grep docs/tutorials/*.ipynb AND the gitignored notes/colab/*.ipynb (the Colab feature +tour) for prose or stored outputs that demonstrate the OLD behaviour, and re-execute those +notebooks. Examples of what goes stale: a tutorial teaching hyp.load(df) as a TypeError +after load gained passthrough, a stored glyph warning, a stored liblsl ERR line, a tour +note saying "pip install predict-hf first". Before editing an install claim, verify it in +a FRESH venv (python -m venv, pip install ., then the call). diff --git a/.claude/compound/lessons/windows-os-replace-retry/SKILL.md b/.claude/compound/lessons/windows-os-replace-retry/SKILL.md new file mode 100644 index 00000000..2e097404 --- /dev/null +++ b/.claude/compound/lessons/windows-os-replace-retry/SKILL.md @@ -0,0 +1,10 @@ +--- +name: windows-os-replace-retry +description: Use when writing a cache or atomic-write rename with os.replace in hypertools, or reading a PermissionError from Windows CI. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +os.replace onto a file other threads are reading or renaming raises PermissionError +(WinError 5) on Windows only. Wrap cache and atomic-write renames in a short retry that +accepts an existing identical destination, as _replace_retrying in +hypertools/io/sources.py does. Expect Windows CI to be the only place such a test fails. diff --git a/.claude/compound/lessons/yahoo-sec-api-traps/SKILL.md b/.claude/compound/lessons/yahoo-sec-api-traps/SKILL.md new file mode 100644 index 00000000..9426ab60 --- /dev/null +++ b/.claude/compound/lessons/yahoo-sec-api-traps/SKILL.md @@ -0,0 +1,10 @@ +--- +name: yahoo-sec-api-traps +description: Use when fetching price history from the Yahoo v8 chart API or filings data from SEC XBRL. +created: 2026-10-03 +origin: notes kept in CLAUDE.md +--- +Yahoo v8 chart with `range=max&interval=1d` silently returns 3-MONTH bars (AAPL: 169 rows +since 1984); pass explicit period1/period2 epoch bounds to get daily data. SEC XBRL needs +a User-Agent with a contact (the project's pyproject email). `companyconcept` can be EMPTY +for a filer (ABT, KO) whose `companyfacts` has the concept, so fall back to companyfacts. diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e62101b0..40dc171a 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -64,11 +64,14 @@ jobs: - name: Install system dependencies (Ubuntu) if: matrix.os == 'ubuntu-latest' run: | - sudo apt-get update + sudo apt-get -o Acquire::Retries=3 update # fonts-noto-cjk (GH #205): ubuntu-latest ships no CJK-covering font # at all, so tests/test_multibyte.py's `requires_covering_font` # skipif would silently skip every CJK-dependent test on this OS. - sudo apt-get install -y ffmpeg fonts-noto-cjk + # --no-install-recommends: ffmpeg's recommends (VA/VDPAU drivers, speech + # models) are never used here and tripled the download; a slow mirror + # (182 MB at 111 kB/s, 2026-10-08) pushed docs-clean past its timeout. + sudo apt-get -o Acquire::Retries=3 install -y --no-install-recommends ffmpeg fonts-noto-cjk fc-cache -f - name: Install system dependencies (macOS) @@ -108,6 +111,12 @@ jobs: # wheels for all three CI platforms/Python versions here. pip install -e ".[dev,torch]" + - name: Check repository lint (Ubuntu Python 3.12) + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + run: | + python -m pip install "ruff==0.15.20" + python -m ruff check . + - name: Pre-fetch headless Chrome for kaleido (Plotly image/GIF export) # tests/test_animation_export.py exports Plotly figures to GIF/MP4 via # kaleido 1.x, which drives a headless Chrome. Pre-fetching it here (into @@ -253,13 +262,16 @@ jobs: python-version: '3.11' - name: Install system dependencies run: | - sudo apt-get update + sudo apt-get -o Acquire::Retries=3 update # ffmpeg: mp4 rendering of animated gallery examples (matches RTD). # fonts-noto-cjk: CJK-covering font for the multibyte gallery text. # pandoc: nbsphinx renders the hand-authored tutorials/*.ipynb via # pandoc (Read the Docs' build image bundles pandoc; a bare GH runner # does not, so declare it here or the notebook stage fails). - sudo apt-get install -y ffmpeg fonts-noto-cjk pandoc + # --no-install-recommends: ffmpeg's recommends (VA/VDPAU drivers, speech + # models) are never used here and tripled the download; a slow mirror + # (182 MB at 111 kB/s, 2026-10-08) pushed docs-clean past its timeout. + sudo apt-get -o Acquire::Retries=3 install -y --no-install-recommends ffmpeg fonts-noto-cjk pandoc fc-cache -f - name: Export a pristine source tree (tracked files only) run: | @@ -289,6 +301,18 @@ jobs: run: | cd /tmp/docs-clean/docs python -m sphinx -b html -W -E -a . _build/html + - name: Run the docstring and guide examples (sphinx doctest builder) + env: + MPLBACKEND: Agg + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # the gallery was just built; the doctest builder only needs the + # `>>>` examples in docstrings and the guides (release audit + # 2026-09-07: two `hypertools.load` examples failed under this + # builder while the HTML build was green) + HYPERTOOLS_DOCS_PLOT_GALLERY: '0' + run: | + cd /tmp/docs-clean/docs + python -m sphinx -b doctest -W . _build/doctest - name: Release gate -- generated gallery notebooks install the PyPI package # docs/auto_examples/*.ipynb are gitignored and GENERATED by the build # above from docs/conf.py's branch-aware install-cell (so they don't exist diff --git a/CHANGELOG.md b/CHANGELOG.md index f60f160c..2ee243bd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # Changelog -## 1.1.0 (2026-09-04) +## 1.1.0 (2026-10-09) Hierarchical (`MultiIndex`) DataFrames become a first-class input. A frame whose **columns** carry a hierarchy now expands into one trace per group @@ -8,12 +8,41 @@ plus per-level means (the row axis has done this since 1.0); `hyp.predict` forecasts a hierarchy one group at a time with explicit model ownership; and `predict=` forecasts every plotted trajectory, derived means included. -Five previously-accepted inputs are now **rejected** -- see *Changed / -validation*. One of them (duplicate timestamps) is not hierarchy-specific -and reaches flat `hyp.predict` callers. +Seven previously-accepted inputs are now **rejected** -- see *Changed / +validation*. Three of them are not hierarchy-specific: duplicate timestamps +(which reaches flat `hyp.predict` callers), `ax=` combined with `animate=`, +and a matplotlib Axes passed as `ax=` under the plotly backend. ### Added +- **Forecasts use observation times.** Timed rows are sorted before fitting. + An index with a regular calendar (a stored or inferable frequency, a + `PeriodIndex`, month starts/ends, business-day sessions) is stepped on that + calendar; otherwise one future step is the median positive timestamp gap + (formerly the minimum gap), independently per dataset. Every forecaster + accepts a `step=` override, including calendar aliases such as `'B'` and + `'MS'`. GaussianProcess fits actual times; discrete-time models linearly + interpolate irregular observations onto a regular grid, with a warning. + Fitted reuse preserves the learned time scale. Series plots fit their signal + columns together using the time index, including animated forecasts, instead + of forecasting each `[index, value]` display pair. See the API guide for the + interpolation policy and its limitations. +- **Integer timestamp arithmetic preserves elapsed times.** Signed and + unsigned integer indexes use overflow-safe subtraction and addition, + including large epoch offsets. Discrete-time forecasting no longer rejects + valid unsigned timestamps because their past offsets wrap into the future. +- **Backtests score matching observation times.** GaussianProcess evaluates + held-out timestamps directly; discrete-time forecasts are linearly + interpolated to them, with a warning when needed. Training rows alone + determine the model and its step. Returned forecasts carry the same index + as the truth, and `horizon` reports held-out observations rather than the + number of generated grid steps. +- **Manipulator input and row-wise fixes.** A 1-D array or flat numeric + list/tuple consistently means one column in `manip`, direct classes, + `Pipeline`, and fitted reuse. Row-wise ZScore/Normalize work on multiple + datasets while preserving each dataset's statistics, index and column names. + MatrixColormap's exact interpolation now honors `set_gamma()`. + - **A column MultiIndex frame expands into one trace per group.** The innermost column level is the feature axis; every level above it groups, so a `(Market, Sector, Ticker)` frame draws one trajectory per sector plus @@ -135,8 +164,12 @@ and reaches flat `hyp.predict` callers. row versus column semantics, the plot/predict divergence, hue forms, mean construction, limitations, dual-axis and list inputs, return shapes, the unfitted/fitted ownership table, backend parity and feature - correspondence. All 138 of its examples are executed by the test suite - rather than merely read. `docs/pipeline_order.rst` gains hierarchy + correspondence. Its worked examples are `.. doctest::` blocks, run by + Sphinx's doctest builder (`make doctest` in `docs/`, and the docs CI + job), which is what checks the printed outputs and the error messages + the guide quotes; the test suite pins the guide's section list, its + links from the API reference and the tutorials page, and its comparison + table (`tests/test_docs_hierarchy_guide.py`). `docs/pipeline_order.rst` gains hierarchy expansion and mean construction as a side branch, in the prose and in the regenerated diagram: expansion runs before `format_data`/`analyze`, so every leaf gets the identical canonical pipeline, while mean construction @@ -204,12 +237,15 @@ and reaches flat `hyp.predict` callers. pipeline output's own coordinates (for `reduce=None` the raw columns) with fixed joint limits for animations, on both backends; `xlim=`/`ylim=` set them explicitly. `axis_scale='unit'` (the default) is unchanged and now - documented: 2-D plots are mean-centred, rescaled into the unit box and - pinned to (-1.1, 1.1) (GH #285). -- **`ndims=1` is a time-series mode.** Each column becomes one line against - the row index (a DatetimeIndex gives real dates; arrays use 0..n-1), 2+ - columns are allowed and legend-named by column, `axis_scale` defaults to - `'data'`, and animations reveal along x. Previously `ndims=1` drew one + documented: 2-D plots are mean-centred and rescaled into [-1, 1], inside + a frame square of half-width 1.125, with both axes pinned to +/-1.2375 + (10 % beyond the square) (GH #285). +- **`ndims=1` is a time-series mode.** With `reduce=None`, each column + becomes one line against the row index (a DatetimeIndex gives real + dates; arrays use 0..n-1); 2+ columns are allowed, and `legend=True` + names each line by its column. With the default `reduce=`, the data are + first reduced to one component, drawn as one line. `axis_scale` defaults + to `'data'`, and animations reveal along x. Previously `ndims=1` drew one column rescaled into [-1, 1] at 0..n-1 and refused 2+ columns (GH #285). - **`truth=` beside a forecast.** `hyp.plot(train, predict='Chronos', t=30, truth=held_out)` draws the actual continuation in the same space, styled @@ -249,7 +285,8 @@ and reaches flat `hyp.predict` callers. Per-panel axis labels come from DataFrame columns as in the single-axes call. On plotly the panels are `make_subplots` scenes and `return_model=True` returns the same bundle as matplotlib, which adds - `axes`, `panels` and `panel_models` (GH #285). + `axes`, `panels`, `panel_models` and, for a shared fit, the one fitted + `pipeline` (GH #285). - **Title styling on every frame.** `title_kwargs=dict(size=, weight=, family=, color=, y=)` is applied by hypertools' own title updater, including per-segment `title=` lists; `title_color=` takes one colour per @@ -271,7 +308,11 @@ and reaches flat `hyp.predict` callers. 'lorenz' | 'blobs' | 'moons' | 'swiss_roll' | 's_curve', random_state=..., n_datasets=...)` generates seeded example data (scikit-learn kwargs pass through; `n_datasets > 1` returns a list), replacing the random-walk, - helix and blob generators every tutorial wrote by hand (GH #285). + helix and blob generators every tutorial wrote by hand. Any keyword + `hyp.load` does not use itself is collected into `**source_kwargs` and + handed to the synthetic or web resolver that matches the name; with any + other kind of source, already-loaded data included, it raises `TypeError` + that quotes the keyword instead of dropping it (GH #285). - **Web sources.** `hyp.load('wikipedia:')` (plain-text extract; `'A|B'` returns a list), `hyp.load('yahoo:<TICKER>', start=, end=, interval=)` (daily OHLCV through explicit epoch bounds) and @@ -290,13 +331,20 @@ and reaches flat `hyp.predict` callers. - **Several forecasters in one call, backtests, and imputer scoring.** `hyp.predict(x, model=[...])` returns `{name: forecast}`; `hyp.predict(x, model=[...], holdout=k)` fits on the head and scores the - held-out tail (MAE / RMSE / MAPE, `per_column=`, `return_forecasts=`) against - every model plus an always-present naive last-value baseline, with - `scores.attrs['best']` and `['beats_baseline']`; `hyp.impute(x, model=[...], - truth=full)` scores imputers on the damaged cells only, with a column-mean - baseline and an `unscored` column for rows a model left NaN (GH #285). -- **`Smooth(center=False, min_periods=)` and a `Delay` manipulator.** A - trailing (causal) boxcar identical to `pandas.rolling(...).mean()`, and a + held-out tail against every model plus an always-present naive last-value + baseline, with `scores.attrs['best']` and `['beats_baseline']`; + `metrics=` picks which of MAE / RMSE / MAPE to report (all three by + default; the first one ranks `best`), `per_column=True` gives one row per + model and column, and `return_forecasts=True` also returns the forecasts + that were scored. `hyp.impute(x, model=[...], truth=full)` scores imputers + on the damaged cells only, with the same `metrics=` and `per_column=`, a + column-mean baseline and an `unscored` column for rows a model left NaN; + `return_imputed=True` also returns the scored imputations, the baseline and + the truth (GH #285). +- **`Smooth(kernel='boxcar', center=False, min_periods=)` and a `Delay` + manipulator.** A trailing (causal) boxcar identical to + `pandas.rolling(...).mean()` (`center=False` needs `kernel='boxcar'`; the + default savgol kernel has no trailing form and raises `ValueError`), and a Takens time-delay embedding (`hyp.manip(x, model='Delay', tau=, dims=)`) (GH #285). - **Alignment quality score.** `hyp.align(..., return_score=True)` returns the @@ -328,6 +376,42 @@ and reaches flat `hyp.predict` callers. so one `hyp.load` call can be the entry point over mixed names and in-memory data; other types still raise `TypeError`. Previously any non-string raised. +- **Input datatype handling defers to datawrangler.** The shared coercion + layer (`format_data`, `get_type`/`get_dtype`, `as_dataframe`, the + `predict`/`impute` normalisation, `io.streaming.is_stream`) classifies + inputs with `dw.zoo` predicates and converts with `dw.wrangle` instead of + its own `isinstance` ladders, so polars DataFrames, LazyFrames and Series + are accepted wherever pandas is -- `plot`, `reduce`, `align`, `cluster`, + `normalize`, `manip`, `predict`, `impute`, `analyze`, `describe` -- with + identical results (polars nulls become missing data), and whatever + datawrangler adds later comes for free. The same holds throughout: + `plot`'s `hue=`, `labels=`, `truth=`, matrix `palette=`, `panels=` and + its axis/legend labels; `manip` (and the Manipulator classes and + `Pipeline` steps directly), `align`, `stack`, `damage`, `apply_model`, + the fitted `Normalizer`, `save`/`load`, `text2mat` and the impute + backtest's `truth=`/`mask=` read any frame backend datawrangler + recognises through the shared predicates; a one-column DataFrame of + labels as `hue=` no longer raises `IndexError`, and `text2mat` accepts a + Series of documents. A static test (`tests/test_datatype_gate.py`) keeps + hand-rolled pandas/numpy type checks out of the library, and + `tests/test_polars_inputs.py` and `tests/test_polars_inputs_wave1.py` + compare the polars results against pandas. +- **Palettes from images read as gradients, and a data matrix is a + palette.** Colors extracted from an image are put in a deterministic + order -- by value, dark to bright -- when the image is used as a plot + palette (`palette_sort=` or a spec's `?sort=` picks `'value'`, `'hue'`, + `'lightness'`, `'columns'` or `'original'`; `image_palette()` itself and + a per-dataset image's lead color keep the salience order). A t x k data + matrix (array, nested list or DataFrame) passed as `palette=`, + `forecast_palette=` or a per-dataset entry is reduced to three dimensions + with `hyp.reduce` (`palette_reduce=`, default 'PCA', with + `palette_manip=`/`palette_normalize=`/`palette_align=` passed through), + each reduced column is scaled to [0, 1] as an RGB channel, the rows are + sorted (default along the first component) and the result is a colormap + resampled by interpolation to however many colors the plot needs + (`hypertools.plot.colors.matrix_palette`, `sort_colors`, + `MatrixColormap`). A 2-D array with 3 or 4 columns and every value in + [0, 1] stays a list of colors. - **Optional extras install themselves on demand.** The first call that needs plotly, kaleido, HF text embeddings, skaters (`Laplace`), chronos-forecasting (`Chronos`), torch (autoencoder reducers), gensim, @@ -338,9 +422,13 @@ and reaches flat `hyp.predict` callers. `pyproject.toml` stays the single declaration of every extra (`hypertools._shared.lazy_import`). Static image export with the plotly backend provisions kaleido's Chrome the same way, plus the four system - libraries a fresh Colab/Kaggle image lacks. `HYPERTOOLS_AUTO_INSTALL=0` - disables installation: a missing extra then raises `ImportError` naming - the manual `pip install "hypertools[<extra>]"` command, as before. + libraries a fresh Colab/Kaggle image lacks. `hyp.set_autoinstall(False)` + turns installation off, for the session or for one block as a context + manager (the same two forms as `set_interactive_backend`): a missing + extra then raises `ImportError` naming the manual `pip install + "hypertools[<extra>]"` command, as before. The environment variable + `HYPERTOOLS_AUTO_INSTALL=0` sets the starting value for images built + ahead of time. ### Changed / validation @@ -470,6 +558,32 @@ previously ambiguous or silently lossy. ### Bug fixes +- **NumPy 2-compatible optional dependency floors.** The `gensim` and + `density3d` extras require gensim>=4.4.0 and scikit-image>=0.25.0, + respectively; dev/docs requirements match. Earlier advertised minimums + predate upstream NumPy 2 support. +- **Backtest model ownership.** Forecast backtests fit an independent copy + of an unfitted model instance for each dataset, instead of reusing the + first dataset's learned parameters on subsequent datasets. Forecast and + imputation scoring leave caller-owned instances unchanged and reject + already fitted instances, which may have seen the held-out truth (GH #285). +- **Concurrent URL caching.** Threads downloading the same URL use distinct + temporary files, preventing `FileNotFoundError` during atomic replacement + (GH #285). +- **Delay column collisions.** `Delay` rejects duplicate column labels and + distinct labels with identical string representations, instead of silently + overwriting embedded features (GH #285). +- **Bundled font precedence.** The bundled Noto Sans faces take precedence + over same-family system fonts, keeping rendering consistent across machines + with an additional Noto Sans installation (GH #285). +- **Release documentation.** Plotting and scoring features shipped in 1.1 + are identified as 1.1 in their API documentation, correcting leftover 1.2 + labels. Installation guidance distinguishes Kalman imputation from + Kalman/ARIMA forecasting. The forecast example and its executed tutorial + use native URL caching; the browser verifier checks versioned notebook + links, rendered install cells, and real Plotly frame transitions without + requiring autoplay (GH #284, GH #285). + Each of these was found while building the above, and each affects FLAT input too. @@ -511,12 +625,17 @@ input too. - **A plotly `save_path=` to a raster/PDF format on a machine without Chrome failed with kaleido's bare `RuntimeError`.** kaleido 1.x renders through a headless Chrome, which a fresh Colab or Kaggle kernel does not - have. The failure is now a `HypertoolsIOError` naming the file, the - cause, and the ways out: `import plotly.io as pio; pio.get_chrome()` - (about 150 MB) plus, on Colab and Kaggle, the four system libraries the - downloaded Chrome needs (`apt-get install -y libatk1.0-0 - libatk-bridge2.0-0 libatspi2.0-0 libxcomposite1`; measured 2026-09-04), - installing Chrome, or saving with `backend='matplotlib'`. + have. With installation on (the default), hypertools now fetches a + Chrome for kaleido on first use, plus (on Debian/Ubuntu images where it + can run `apt-get`) the system libraries that Chrome needs; see *Optional + extras install themselves on demand* above. When that fails, + or `hyp.set_autoinstall(False)` is in force, the failure is a + `HypertoolsIOError` stating the cause and the ways out: + `import plotly.io as pio; pio.get_chrome()` (about 150 MB) plus, on + Colab and Kaggle, the four system libraries the downloaded Chrome needs + (`apt-get install -y libatk1.0-0 libatk-bridge2.0-0 libatspi2.0-0 + libxcomposite1`; measured 2026-09-04), installing Chrome, or saving with + `backend='matplotlib'`. - **Under the plotly backend, a figure kept in a variable was not displayed in a notebook.** `fig = hyp.plot(x)` drew nothing (on Colab, where `backend='auto'` resolves to plotly, 29 of the feature tour's plot cells @@ -605,10 +724,9 @@ input too. - **plotly discarded the per-trace alpha under a continuous `hue=`** for the same figures, from the other direction: the colour serializer drops the 4th channel and nothing set the trace `opacity`, so a hue plot that - matplotlib drew at `alpha=0.7` rendered fully opaque on plotly. Line - colours now carry the alpha; **marker** colours deliberately do not, - because matplotlib's per-point marker colours carry none either, and - parity is stated against matplotlib. + matplotlib drew at `alpha=0.7` rendered fully opaque on plotly. Line and + marker colours now carry the alpha on both backends (hue-coloured markers + ignored `alpha=` on matplotlib too until the release review). - **With `ndims=1`, matplotlib drew the `predict=` overlay at x = 0..t** instead of continuing the observed series: the overlay was plotted with no @@ -689,13 +807,901 @@ input too. the passed-in pipeline too (in place, on the same object the bundle returns), unless it already carries one of its own. +### Fixed during the release review + +Found by the pre-publication review of the 1.1.0 draft against 1.0.0. +Because 1.1.0 had not been published, they ship in it. + +- **A model that left values missing can no longer be named the best.** + `hyp.impute(x, model=[...], truth=)` and + `hyp.predict(x, model=[...], holdout=)` picked `scores.attrs['best']` as + the lowest score regardless of coverage, so a model scored on fewer, + easier values could win: `PPCA`, which cannot fill a fully-missing row, + was "best" with an MAE of 0.94 over the 10 cells it filled while + `SimpleImputer` scored 63.7 over all 25. Only complete models (`unscored` + of 0 over every dataset and column) are ranked now. Incomplete models keep + their rows, are listed in the new `scores.attrs['incomplete']`, and the + warning says they are excluded from the ranking. When no model is + complete, `attrs['best']` is `None`, `attrs['best_score']` is NaN and + `attrs['beats_baseline']` is `None`; `attrs['beats_baseline']` is also + `None` when the baseline row is itself incomplete. Each case warns. +- **`hyp.impute(truth=, mask=)` matches labelled data by label.** `truth` + and `mask` were checked for shape and then compared cell by position, so + a truth DataFrame with its columns or rows in another order was scored + against the wrong cells without a word (an MAE of 18.83 or 1.83 where the + answer is 3.67). When the data and `truth`/`mask` are both labelled, the + same labels in a different order are now reordered to the data's, and + labels that differ (or are repeated and not in identical order) raise a + `ValueError` naming the axis and the labels. A default `0..n-1` index + counts as labels; a polars frame is matched by column name and row + position. Bare arrays are compared by position as before, so + `truth.to_numpy()` is the way to compare two differently-labelled frames + cell for cell. A wrong-shaped array `truth` now gets hypertools' shape + message instead of a pandas internals error. +- **A backtest pairs forecast and held-out columns by label.** A custom + `Forecaster` that returned the training columns in another order was + scored against the wrong columns; it is reordered now, and one that + returns different column labels raises a `ValueError`. The built-in + forecasters were not affected. +- **An installed extra that is too old is upgraded, or reported as what it + is.** On-demand installation only asked whether an optional package + imported, not whether it met the requirement hypertools declares. In an + environment that already held plotly 5.24.1 next to kaleido 1.3 + (`plotly>=6.1.1` is required), saving a plotly figure as an image failed + with plotly's "Image export using the "kaleido" engine requires the + kaleido package", although kaleido was installed. Every package of an + extra is now checked against its declared requirement, once per process. + One that is too old is upgraded with a notice (`hypertools: upgrading + plotly 5.24.1 to plotly>=6.1.1 ...`) as long as it has not been imported + yet; if it has, or after `set_autoinstall(False)`, the call raises + `ImportError` naming the installed version, the requirement and the + command (`pip install "hypertools[interactive]"`, plus a restart when the + old version is already imported). With `backend='auto'` such a plotly + gives a warning and a matplotlib figure. `backend='plotly'` no longer + imports plotly before that check. A correctly resolved + `pip install "hypertools[interactive]"` was never affected. +- **`explore=True` hover labels stay inside the window.** The label was + always drawn up and to the left of the hovered point, so a point near the + left edge of a native window had its label cut off. It now opens toward + the centre of the axes. +- **plotly names and groups every leaf of a nested dataset list by its + group.** For `hyp.plot([[a, b], [c, d]], backend='plotly')` only each + group's first leaf carried the group's label; the others fell back to + their flat leaf number, so the leaves were named `'1'`, `'2'`, `'2'`, + `'4'`. Hover named the wrong group, and the legendgroups paired a leaf of + group 1 with a leaf of group 2, so a legend click toggled the wrong + lines. Every leaf is now named by its outer group (or by its entry in a + per-group `legend=` list), as a hierarchy's leaves already were, in static + and animated figures. Matplotlib was not affected. +- **3-D plotly markers on a line (`fmt='-o'`) draw at matplotlib's size.** + These markers are given per-point sizes, and `go.Scatter3d` draws a + per-point size at half the diameter of the same single size. The + conversion did not allow for that, so the markers drew at half + matplotlib's diameter. With `markersize=3` they were about 2 px dots under + a 1.4 px line, and most were hidden. They now match matplotlib within the + existing marker-parity tolerance (for example 4 px, previously 2 px, against matplotlib's + 5 px at `markersize=3`). Marker-only 3-D traces and all 2-D traces were + already correct. + +- **An animated forecast now starts where that frame's line ends.** + `predict=` with `animate=` drew each frame's forecast from the last raw + observation at or before the drawn head. An animation is paced on a + refined frame grid, so the head usually sits *between* two observations: + the forecast hung back from the line's tip and stood still until the head + reached the next observation, then jumped. On an 8-row trajectory over 160 + frames it lagged by up to 45% of the plot box and stalled for 23 + consecutive frames. It now starts at the vertex the frame actually draws + last, on both backends and for 1-D, 2-D and 3-D plots. The predicted + points themselves are unchanged, so `t=` still counts raw steps on from + the last observation. + +- **An animated `truth=` no longer shows the answer before the forecast + gets there.** A time-progressing animation (`True`/`'parallel'`, + `'serial'`, `'window'`, and `hue=`/`cluster=` regrouped reveals) drew + the whole held-out continuation from frame 0. Each truth row now appears + on the first frame whose drawn forecast of that dataset (any model's, + for `predict=[...]`) reaches or passes its position, and stays on every + later frame; with no forecast drawn yet it is hidden. On the feature-tour + series (24 hourly rows, `t=3`, 9 frames) the truth was on all 9 frames + and is now on the last; on a 20-row walk forecast 12 steps out it now + grows 0, 0, 0, 0, 1, 3, 5, 7, 9, 12 rows over 10 frames. Both backends + agree frame for frame (plotly's base trace holds frame 0's state), the + `'truth'` legend entry is on every frame, and static plots and + `animate='spin'` still draw it in full. + +- **Default forecast group colours no longer repeat an observed line's + colour.** `forecast_hue=`/`forecast_cluster=` without + `forecast_palette=` started the palette over, so forecast group 0 was + drawn in exactly dataset 0's colour: `hyp.plot([a, b, a + 2, b + 2], + predict='Kalman', t=6, forecast_cluster='KMeans', forecast_n_clusters=2)` + drew its two groups in `#db5f57` and `#57d3db`, the colours of datasets + 0 and 2. The groups now continue the figure's `palette=` past the colour + slots the observed data takes (its datasets or `hue=`/`cluster=` groups, + plus any an earlier call took on a reused `ax=`): the same call draws + `#dbc257` and `#57db80`, the free slots of 'hls' at 8, and `palette='Set2'` + gives Set2's fifth and sixth colours. Same on both backends, static and + animated; `panels=` resolves the default against the whole grid, so each + label keeps the single-axes figure's colour. An explicit + `forecast_palette=` overrides exactly as before. + +- **A continuous `hue=` no longer repaints an explicitly coloured + forecast on matplotlib.** Under a continuous `hue=` the matplotlib + backend recoloured every forecast in its trace's final hue colour, + discarding `forecast_hue=`/`forecast_cluster=`/`forecast_palette=` (and a + colour letter in `forecast_fmt=`), static and animated, while plotly kept + them -- the two backends drew the same call in different colours. The + explicit colour now wins on both, as `forecast_fmt=`'s docs say. + +- **No leftover "install the extra first" instructions.** Every optional + dependency goes through the on-demand installer, and the stale prose + that told users to install an extra by hand is gone: the `plot()` + reducer docstring's `pip install "hypertools[torch]"`, the autoencoder + and gensim gallery examples' "pre-install it with ...", and the LSL + tutorial's inline command; the `reduce()` torch error now says the + on-demand install was tried. The API reference gained a *Set + autoinstall* entry (`set_autoinstall`, how it works, and a pointer to the + guide). The `projectile_kalman` and `streaming_data` tutorials no longer + show a pip upgrade notice with a local interpreter path as the output of + their Colab install cell. +- **`hyp.load(..., offline=True)` never downloads and opens no network + connection.** The hosted example datasets (`'spiral'`, `'weights'`, the + `*_model` pipelines, ...) bypassed `offline`: a cache miss downloaded, + and a cached file failing its SHA-256 pin was deleted and re-downloaded. + Offline now serves only a hash-valid copy from `~/hypertools_data` and + raises `HypertoolsOfflineError` naming the file for a missing or corrupt + one, leaving the file in place; any other source that cannot be served + from disk raises it too. URLs skip the seaborn dataset-name listing. + That listing fetch now has a timeout, and a failed fetch is remembered + for 5 minutes (`hypertools.io.sources.reset_seaborn_names_cache()` retries + sooner). Also fixed the `hypertools.load` docstring example, which failed + under Sphinx's doctest builder (`NameError: hypertools`); the docs CI job + now runs that builder. +- **Plotly animation export honours `hyp.set_autoinstall(False)` and + raises the documented exception types.** The frames of a plotly + animation's GIF/PNG/video export are rendered in a separate worker + process, which now inherits the caller's installation setting: with + installation off, a missing kaleido raises the `ImportError` naming the + manual command and no pip runs (the worker used to start a fresh + interpreter that installed anyway). That `ImportError` reaches the caller + as an `ImportError`, and a missing Chrome as `HypertoolsIOError`, instead + of a `RuntimeError` wrapping the worker's traceback. +- **`alignment_score` rejects degenerate input with a clear error.** A + 1-D series, a non-numeric array, or NaN/inf values raise `ValueError` + naming the dataset (they hit numpy's own shape errors or returned a NaN + score), and `metric='dispersion'` on all-constant datasets raises like + `'isc'` already did instead of returning NaN with a RuntimeWarning. +- `docs/doc_requirements.txt` carries the same core floors as + `pyproject.toml` (scikit-learn 1.5.2, pandas 2.2.3, matplotlib 3.9.2, + scipy 1.14.1, numpy 2.1.0, statsmodels 0.14.3). +- **A list of `{category: color}` dicts works with a regrouping `hue=`.** + Each dataset naming its own categories (the documented per-dataset dict + form) was rejected as "2 per-dataset palettes but 4 dataset(s)" once + `hue=` split the datasets into more runs than dicts; the dicts name + categories and now resolve by name on both backends. +- **Overlapping `set_autoinstall` blocks keep the newest setting in force, + and direct calls are no longer retained.** Two `with + hyp.set_autoinstall(False)` blocks open at once (two threads, say) used + to switch installation back on inside the later block when the earlier + one exited, because exit restored a value saved before either. A block + now removes only its own setting; the setting is process-global and + documented as such. Every direct call also kept a handle alive until a + block's exit removed it, so a long session of direct calls accumulated + them; a superseded direct call is now released. A handle made for a + `with` block is never collapsed before it is entered, so creating a block + in one thread and entering it later is safe. +- **The plotly backend honours the colour letter of a data `fmt=` string** + (`'r-'`, `['g--', 'b:']`) exactly as matplotlib does, including animated + plots and `panels=` cells; it drew the palette colour before. +- **Matrix colormaps, image palettes and mixed `manip` lists.** The + matrix colormap honours every inherited Colormap operation (integer + sampling, `resampled()`, `reversed()`, `set_under`/`set_over`, bad and + NaN entries per element); image palettes interpolate exactly at any + count, so more than 256 categories still get distinct colours; a polars + `forecast_hue` Series is partitioned under `panels=` like a pandas one; + and `manip` lists mixing an unnamed array with named frames keep every + frame's index (dated or irregular) while lists of named frames pass + through untouched. +- **Series and mixed lists through the manipulators.** A + pandas or polars Series keeps its index and name through the Manipulator + classes and `hyp.Pipeline` (a `Pipeline([Smooth, Resample])` on an + irregularly sampled Series resampled at positions 0..n-1 instead of the + Series' own; `ZScore`/`Normalize`/`Resample` used directly never took a + Series at all), and a polars Series beside an array in a `hyp.manip` + list works. A mixed list keeps every frame's own feature names for every + model: the shared-statistics manipulators (`ZScore`, `Normalize`) match + columns by position themselves when labels differ (an unnamed array + beside a named frame, or two frames named differently) and reject + different widths with a clear message; the independent ones never + relabel anything. A 1-D array is one column (n observations of one + feature) for `hyp.manip`, as it already was for `hyp.normalize`, + `hyp.reduce` and the Manipulator classes; `hyp.manip` used to read it as + a single row. `MatrixColormap` follows matplotlib's full extreme-colour + rules (under/over/bad keep the alpha they were set with, `-inf`/`+inf` + are under/over rather than bad, and an `alpha=` override reaches the + extremes but not a transparent bad colour). `legend=` accepts a polars + Series (any series-like) of labels, on both backends. +- **A repeated metric in `metrics=` raises `ValueError` that says which + metric is repeated.** `hyp.predict(..., holdout=k, metrics=['mae', 'MAE'])` + and the matching `hyp.impute(..., truth=)` call used to fail with a + `TypeError` from inside the scoring code. +- **`holdout=True` with `t=0` reports `t` as the problem.** `holdout=True` + takes its size from `t`, so the error now says that `t` must be at least + 1 row. +- **The "left N scored value(s) missing" warning is attributed to the + caller's line**, like every other warning `hyp.predict` and `hyp.impute` + emit, instead of to a line inside the library. +- **`return_score=True` works on ragged input that `hyp.align` trims.** The + "before" score is computed on the row-trimmed datasets, the same ones the + aligner sees. +- **`HypertoolsOfflineError` and `HypertoolsTrustError` are importable from + `hypertools`** (`HypertoolsOfflineError` from `hypertools.io` as well), + so a caller of `hyp.load(..., offline=True)` can catch the error without + reaching into `hypertools.io.sources`. The API reference documents + `HypertoolsTrustError` and `io.synthetic_outlet` under these public + names; their source-view links pointed at anchors that did not exist. +- **`panels=` partitions every per-dataset and per-forecast argument.** A + per-dataset list of palette names (`palette=['viridis', 'magma']`), a + `legend=` list, an `alpha=` list, `forecast_fmt=`, `forecast_palette=`, + a model-major `forecast_hue=` and a forecaster fitted on every dataset + (`hyp.predict(x, return_model=True)`) now reach each panel as its own + entry, in both `panel_fit` modes and on both backends, matching the + single-axes figure; previously they raised inside the panel or drew + every forecast in the first colour. Shared-fit grids also hand back + their one fitted pipeline as `bundle['pipeline']` and in every + `panel_models[i]['pipeline']`, so held-out data can be projected without + refitting (it was `None`). +- **`panels=` keeps forecast labels that share a colour.** `forecast_hue=` / + `forecast_cluster=` with a `forecast_palette=` that gives two labels the + same colour (`['red', 'red']`, or a palette name that cycles) raised + `ValueError: palette= supplies N color(s)` inside the panels; each panel + now receives one palette slot per label, matching the single-axes figure. +- **`panels=` on 2-column or 1-column data with the default `ndims=` draws + 2-D cells** on both backends, instead of raising `Trace type 'scatter' is + not compatible with subplot type 'scene'` (plotly) or drawing flat + trajectories inside cubes (matplotlib). +- **`panels=` no longer re-clusters each panel.** Every cell reuses the + clustering already fitted for it, so seeded memberships equal the + individual call's on both backends in every fit mode and no extra + clusterer fits run (each panel used to re-fit its clusterer without the + seed the first fit used); `return_model=True` bundles report + `models['cluster_labels']`. Plotly composition keeps the palette offset + across a `color=` or categorical `hue=` call. Independent panels mixing + one- and three-column datasets draw the series as row index against value + on the 3-D cell's floor instead of crashing. +- **`panels=` keeps the joint figure's cluster colours, forecasts narrow + panels in their own space, and a marker-only hue no longer advances the + palette.** Shared clustered panels keep the joint cluster-to-colour + mapping and legend names when a slice lacks a cluster (two panels each + drew red); a 1-/2-column panel of a mixed-width independent grid + forecasts, reads `truth=` and reports its bundle on its own analyzed + rows (the individual call's numbers; the display padding used to feed + the forecaster) and only its drawing is lifted into the 3-D cell; a + categorical `hue=` with a marker-only fmt consumes no palette slot on a + composed axes/figure/cell, and its group colours beat a fmt colour letter + on the marker path as on the line path. +- **`panels=` decides each cell's projection from the analyzed data** (after + `manip=`/`pipeline=`/`reduce=`), not the raw column count: a Delay-expanded + 2-column dataset reduced to 3 components draws 3-D panels again in the + shared, independent and reducer-comparison modes on both backends, keeping + the requested `ndims` and each panel's fitted pipeline with no second + fit. Composing a call into a figure, axes or cell after a `fmt='r-'`, + `color=` or `hue=` call continues the palette from the slots actually + consumed, identically on both backends. +- **`panels=` fixes.** Works with `predict=` plus `truth=` in both + `panel_fit` modes; shared mode keeps a DataFrame's dates and column names + under `ndims=1` and accepts three-column frames; nested `hue=` and + `labels=` narrow per panel in both modes; `ndims>3` draws 3-D panels on + both backends; `save_path` accepts `~` and `pathlib.Path` and fails + before drawing when the directory is missing; plotly panels return the + same figure wrapper as a single-axes plot and display once per cell. +- **`panels=True` picks its grid from the figure's aspect ratio** and + prefers a grid with no spare cell: three panels form a row in a + default or wide figure (they were a 2x2 with a hole, and in a wide + figure each square 3-D axes shrank to the short cell height), four + form 2x2, six form 2x3. Explicit `(nrows, ncols)` and column counts + are unchanged. +- **`panels=` on the plotly backend keeps each panel whole.** The plotly + grid (`plotly.subplots.make_subplots`) received only each panel's + traces, so 2-D panels lost their unit frame, hidden ticks and + DataFrame-column axis labels; `legend=True` merged every panel into one + legend ('1, 1, 1' for three panels, the digit groups listed twice for a + reducer comparison); and two `colorbar=True` panels drew both colorbars + on the same spot. Each panel now moves into its cell with its axis + layout, frame square and `labels=` annotations re-referenced to that + cell, its own legend beside the cell (plotly's multiple legends), its + own colorbar (on whichever side `colorbar=` asked for), its `title=` as + formatted, styled and positioned by the ordinary title path + (`title_wrap=`, `title_kwargs=`, with the multi-line top margin that + path computes), and its `font=` materialized on the cell's own text; + 3-D cells back the camera off so the cube stays + inside a narrow cell; room for the legends/colorbars is reserved + beside every cell (the default-sized figure is widened by it, an + explicit `size=` is honoured verbatim). +- **`panels=` colorbars on matplotlib take their room from their own + panel.** A `colorbar=True` panel grid ran the single-axes + figure-widening placement once per panel, stacking every colorbar over + the last panel and leaving `tight_layout` warning about axes it could + not place; a colorbar drawn into a caller-supplied `ax=` (every panel, + every `hyp.subplots` cell) now uses matplotlib's own `ax=`-attached + placement, so each panel keeps its colorbar and the figure its size. +- **`panels=` on plotly is laid out like the matplotlib grid.** The plotly + grid used `make_subplots`' default spacing (10-15 % of the figure + between cells) and full-height cells, and backed the camera off ~1.5x + further than a narrow cell needed (the constant was the cube's width + relative to its OWN height rather than the scene's), so three 3-D + panels sat far apart with small cubes and titles floating well above + them. 3-D cells are now square and centred (an `Axes3D`'s equal box + aspect), the gaps are `tight_layout`'s 20 px (40 px between 2-D cells, + for tick labels), each row reserves what its titles need, and the cube + fills its cell within a few pixels of the matplotlib panel's. +- **Default-size `panels=` figures make room for their legends and + colorbars.** Three 10-entry legends beside three default-size panels + shrank the cubes to 1.3 in; the matplotlib figure now widens by the + same 1.1 in per column the plotly grid reserves, and an explicit + `size=` is honoured verbatim. +- **`palette=` colour lists behave as in 1.0.0 again.** A list shorter than + the dataset count cycles when there is no `hue=`; an empty palette raises + `ValueError` instead of `StopIteration`; a per-dataset list whose entries + are `{category: color}` dicts merges them by category name; and any other + per-dataset form under a categorical `hue=` raises an error carrying the + real dataset and category counts. +- **NaN in a continuous `hue=` no longer poisons the colour range.** The + `vmin`/`vmax` of the colour scale and the colorbar are computed over the + finite values only. +- **Legend and colour details.** `legend_kwargs={'fontsize': ...}` is + honoured together with `font=`; `bundle['colors']['categories']` contains + RGB tuples for the blend kind when `legend_colors=` is passed; and a nested + `hue=` whose sub-list does not match its dataset is identified in the + error. +- **`dataset_fade=` and `on_frame=` mutations reach the drawn collections + under a continuous `hue=` on matplotlib.** A fade or a per-frame artist + change was a silent no-op there. +- **`loop=True` accepts a per-segment `rotations=` list** of the documented + `2(n+1)-1` length. +- **`companion=` panels and `{index}` titles advance monotonically under + `order='serial'`** with several datasets, and the `start` in + `FrameContext.window_bounds` reflects the comet-head window on serial + reveals. +- **Animation errors say what went wrong.** A bad `companion=` or + `dataset_fade=` value raises an error that quotes the keyword; an + `on_frame=` hook that raises during `.save()` propagates its own + exception; and a `title=` callable that raises no longer leaves an + "Animation was deleted without rendering anything" warning behind it. +- **`title_wrap=` applies to dynamic titles** (a callable, or a `{index}` + format) and preserves explicit newlines. plotly draws a `\n` in a title as + a line break and reserves top margin for every title line at the + requested size. +- **Labels and titles validate their input.** A nested tuple `labels=` + annotates like a nested list; `labels='str'`, a bad `label_anchor=`, a + title list with a non-string entry, a title callable that returns a + non-string, `title_color=` alongside `title_kwargs={'color': ...}`, a + static `{index}` title with no index to fill it, and a malformed `{index}` + format each raise an error saying so. +- **`yahoo:` bars are dated by the exchange-local trading day.** The + exchange's `gmtoffset` is applied to the bar timestamps; Sydney and Tokyo + tickers were dated one day early. +- **Synthetic datasets accept more seed types.** Every synthetic dataset + accepts `random_state=np.random.RandomState(...)`, and the scikit-learn + backed ones (`blobs`, `moons`, `swiss_roll`, `s_curve`) also accept a + `Generator`, a `SeedSequence` or a NumPy integer with `n_datasets=1`; + reusing one `SeedSequence` across calls gives the same data each time. + `n_datasets=1.5` raises instead of being truncated to 1. +- **`hyp.load(..., streaming=True)` on a source other than a Hugging Face + dataset raises `ValueError`** instead of returning the whole dataset as if + the keyword had not been passed. +- **`hyp.text_windows` accepts NumPy integers** for `size=` and `step=`. +- **`hypertools.tools.text2mat` reads a flat list of strings as one + dataset.** Since 1.0 it returned one `(N, d)` matrix followed by one empty + `(0, d)` matrix per string. Ragged nested lists work, mixed inputs raise, + and a dict `semantic=` spec with a gensim vectorizer warns and skips the + step like the string form does. +- **Warnings raised while formatting input data are attributed to the + caller's line**: the PPCA missing-data fill and the mixed text-and-numbers + notice now point at the `hyp.plot`/`hyp.analyze` call that triggered them. +- **`fit()` returns the fitted model** on the manipulator and aligner + bases (the imputer base already did), so sklearn-style chains such as + `Smooth().fit(x).transform(y)` and `HyperAlign().fit(xs).transform(ys)` + work instead of raising `AttributeError` on `None`. +- **`predict='ARIMA'` on an animated plot no longer crashes.** The early + frames reveal two-row histories, and statsmodels raised an `IndexError` + on them. Forecasters now carry a `min_history` (ARIMA derives its own + from its order), `fit` raises a clear `ValueError` for a shorter history, + and the animated schedule waits until enough rows are revealed. The same + fix covers `predict=['Kalman', 'ARIMA']` under `animate=`. +- **A datetime-like `t=` works inside `hyp.plot`** (static and animated), + resolved against each dataset's `DatetimeIndex`, as the docstring said. +- **`predict=` lists and dicts work on row- and column-MultiIndex frames** + (one forecast per trace per model; the bundle is keyed by model name) + instead of failing an internal consistency check. +- **`ndims=1` on a dated column-MultiIndex frame** draws dates for every + leaf, not only the first. +- **`forecast_hue=` with a model collection** is one value per dataset, + shared across the models; a model-major list is also accepted, and a + mismatch names both counts. +- **Series-mode `return_model`** returns one `(t, n_columns)` forecast array + per input dataset in `predict['forecasts']`, the shape `hyp.predict` + returns. +- **`ndims=1` fixes.** `fmt=` lists are one entry per drawn column; `xlim=` + on a date axis accepts date strings and datetimes on both backends + (floats are matplotlib day numbers on both); no `'dataset 1'` y label + after a reducing `reduce=`; a 3-D `ax=` with `ndims<=2` raises instead + of drawing a flat 3-D line; a `TimedeltaIndex` is drawn in a readable + unit with a labelled axis. +- **NaN rows introduced by a trailing `Smooth(center=False)`** are reported + as the manip stage's doing, with the `min_periods=1` hint, instead of + the all-features-missing message. +- **Docstrings:** `font=` explains weights (bold resolves to the bundled + Bold face); `HyperAnimation.drawn_extent` documents its parameters; + `HyperAnimation.save` lists the supported extensions plainly. +- **A marker-plus-line format string keeps its marker in the legend.** A + dataset drawn with `'s--'` (or `'o-'`) is split into a smoothed line and + markers at the raw sample points; the legend handle showed only the + line. It now shows the marker and the line, on static and animated + plots, and the line itself still draws no markers. +- **`return_model=True` no longer fits the pipeline a second time.** The + bundle's `pipeline` is the one the figure was drawn with (the + cluster stage, which runs on the reduced scores, is appended as a + fitted step), so a UMAP or Isomap plot with `return_model=True`, and + every `panels=` grid built with it, fits once and warns once. +- **Seeded UMAP no longer warns about `n_jobs`.** A `random_state=` + hypertools injects made umap-learn override `n_jobs` and say so; + hypertools now passes the `n_jobs=1` umap uses anyway, unless the + caller chose one. +- **Isomap fits stay quiet about scipy's sparse-matrix efficiency.** The + dozen `SparseEfficiencyWarning`s scikit-learn's graph completion + triggers are silenced during the fit; sklearn's own warning about a + disconnected neighbour graph (the user's `n_neighbors`) still shows. +- **A `truth=` overlay's legend glyph shows its markers.** The truth is + drawn as a solid line with a marker on every observation, but its + `'truth'` legend entry was a bare solid line in the trace's own colour, + identical to the observed trace's entry. The curve now keeps the marker + (drawing none of its own) so the legend can tell them apart. +- **2-D `density=` layers fade out inside their own grid.** Each KDE grid + stopped 15% past its own dataset's bounding box, where the density is + still clearly visible, so a wide, flat cloud's glow was cut off in a + hard band well inside the frame. The grid now also reaches four kernel + widths past the data on both backends, where the density has faded to + nothing, while staying local to its own cloud (so a small cloud beside + a huge one keeps its resolution). +- **`hyp.subplots(..., backend='plotly')` and `ax=<cell>`.** The + compose-it-yourself grid (`fig, axes = hyp.subplots(); hyp.plot(d, + ax=axes[i])`) had no plotly form: `ax=` took a plotly Figure to append + traces to, but could not target a `make_subplots` cell. `hyp.subplots` + gained `backend=` and, on plotly, returns the grid figure plus a flat + array of cells that `hyp.plot(..., ax=cell)` draws into -- the whole + panel, `title=` included -- returning the grid; several cell calls in + one notebook cell display the grid once. +- **The slow-forecast-schedule notice no longer fires from timer noise.** + Its projection drew a slope through the first two timed fits, one row + apart at 2 and 3 rows -- tens of milliseconds each -- and on a slow CI + runner projected 10 s for a 30-row schedule that finished in well under + one. It now fits every timed length by least squares and waits for a fit + of at least 10 rows before projecting. +- **A fitted forecaster reuses its parameters on a short context.** The + minimum-history check added for fitting (`Forecaster.min_history`) was + also applied when an already-fitted model was passed back as `model=`, + so a fitted `ARIMA(order=(4, 0, 0))` refused two new rows it can + condition on with its learned parameters. Only the refit path is held + to the fit floor now. +- **Every `predict=` forecast is listed in the legend.** Only a collection + of models was; `predict='Kalman'` drew its faded continuation with no + key, and `truth=` then listed `observed` and `truth` beside an unnamed + dotted line. The single-model form now lists its forecast once, under + the model's name (the name `hyp.predict(x, model=[spec])` gives it), + static and animated, on both backends, in the order data, forecasts, + truth. The entry's glyph wears the forecasts' own style, in their colour + when they share one and in a neutral gray when one model's forecasts of + several datasets are drawn in several colours (the first dataset's + colour used to pose as the model's). +- **A collection of models keeps each dataset's colour and takes a + linestyle per model.** `predict=['Kalman', 'ARIMA']` coloured every + forecast by model from a `'husl'` palette whose first colour was the + first dataset's own, so two datasets under two models were four lines + in two indistinguishable pairs, and which series a forecast continued + could not be read at all. Forecasts now inherit their dataset's colour + (as the single-model form always did) and cycle solid, dashed, dotted, + dash-dot by model; `forecast_palette=` opts back into one colour per + model, and `forecast_fmt=` still replaces the cycle. +- **`truth=` on plotly marks every observation, not every vertex.** The + antialiased truth curve carried a marker on each of its ~900 drawn + vertices, so it rendered as a thick line; markers now sit on the raw + rows only, at the size the matplotlib overlay draws them. +- **A caller's axes draw in the palette, and a second call continues + it.** `hyp.plot(x, ax=ax)` and every matplotlib `panels=` cell drew + the datasets in the colour cycle their figure was created with + (matplotlib's default blue/orange) while `return_model`'s `colors` and + the plotly grid reported the hls palette; the axes now take the + palette. Drawing a second time into the same axes or plotly figure + restarted the palette, so two composed walks were both red; the second + call now continues it from where the first stopped, on both backends. +- **A recoloured forecast keeps its trace's alpha.** `forecast_palette=`, + `forecast_hue=` and `forecast_cluster=` recoloured the forecasts and + still halved their alpha, so Set1 forecasts at 0.35 over a hierarchy's + 0.7 leaves could not be found; the colour is what tells them apart, so + they are drawn at the trace's own alpha. The forecast legend glyph is + never drawn below 0.8 alpha either (it copied its forecasts' 0.35 and + vanished). +- **The 2-D frame square clears the data.** Static 2-D plots rescale the + data into the unit box and drew the frame square AT its edge, so the + extreme observations sat on the frame line and looked clipped; the + square now has a 12.5 % margin (the axes stay 10 % beyond it) on both + backends. +- **A 3-D figure's axis labels are inside its tight bbox.** `Axes3D` + measures its axes for layout only, dropping the labels, so a + `bbox_inches='tight'` save -- every notebook's inline render -- cut the + `zlabel=` off at the right edge; a figure artist now carries the three + labels' extents into the bbox. +- **`legend_kwargs={'loc': ...}` places the legend there.** A `loc=` + without a `bbox_to_anchor=` kept hypertools' outside-right anchor, so + `'upper left'` hung the legend off the right edge; the anchor is + dropped when a location is named. +- **Plotly legend keys are readable for tiny markers.** A `'.'` marker's + legend key reproduced the 2 px dot; legends now use plotly's constant + key size, as a matplotlib legend does. +- **A collection of models under `hue=`/`cluster=` regrouping continues + the right run.** One dataset split into two runs under two models gives + two forecasts for two runs, so the step that matches each forecast to + its run -- which only ran when the counts differed -- was skipped and + forecast i continued run i: Kalman took the earlier run's colour, ARIMA + the final run's. It runs for every collection under regrouping now. The + animated modes also looked the reveal schedule up by forecast index + rather than by source dataset, so two models x regrouping x + `forecast_trail=` raised `IndexError` on both backends. +- **A forecaster fitted on several datasets animates.** The animated + schedule forecasts each dataset's revealed history on its own, which a + `hyp.predict([a, b], return_model=True)` forecaster refused as a + dataset-count mismatch; `Forecaster.for_dataset(i)` now binds the view + the schedule needs. +- **Plotly honours a colour letter and markers in `forecast_fmt=`.** + `forecast_fmt='ro:'` drew red dotted forecasts with round markers on + matplotlib and inherited-colour dotted lines without markers on plotly, + static and animated, legend keys included. +- **Plotly animations keep a recoloured forecast's alpha too.** The + animated branch computed the halved alpha before the recolouring rule + applied, so `forecast_palette=` forecasts animated at 0.35 while the + static figure drew them at 0.7. +- **A second call into the same plotly grid cell continues the palette**, + as a second call into the same matplotlib axes does. +- **Plotly forecast legend keys compare colour, not opacity.** With + `alpha=[1, .4]` and an all-red forecast palette every key turned gray + because the RGBA strings differed only in alpha. +- **`legend_colors=` keeps its contract beside forecasts.** Explicit + `(label, color)` pairs define the legend outright, so no forecast or + `truth` entry is added to them (and on plotly the data traces stay out + of it too); a plain colour list is applied to the FINAL legend, after + the forecast/truth entries, instead of being refused against the data + entries alone. +- **Matplotlib panel legends clear their colorbars.** A panel with + `legend=True` and `colorbar=True` drew the attached colorbar under the + outside-right legend (~10 px overlap); the colorbar is padded past the + legend's measured overhang. +- **Plotly grid cells keep an explicit legend position and multi-line + title room.** `legend_kwargs` x/y are translated into the cell instead + of being replaced by the default placement beside the cell, and + re-laying out the grid keeps the top margin a multi-line title had + reserved. +- **A `forecast_fmt=` colour letter survives a regrouped animation.** + Under `hue=`/`cluster=` the animated modes repaint each live forecast + in its head run's colour unless the colour is pinned, and only + `forecast_hue=`/`forecast_cluster=`/`forecast_palette=` counted as + pinning: `forecast_fmt='ro:'` forecasts animated cyan under red legend + keys on matplotlib, and plotly's per-frame colours halved the alpha a + recoloured forecast keeps. +- **Mixture-hue legends list forecasts and `truth`.** A matrix `hue=` + builds its legend from swatches (clearing `legend=` on the way), and + the forecast/truth entries were only added when `legend=` was still + set, so those legends read `1, 2` alone on both backends. +- **Repeated calls into one axes, figure or grid cell compose.** The + forecast and `truth=` overlays styled themselves from the FIRST call's + lines on a reused matplotlib axes (three walks, three red forecasts), + and the legend accumulated one `truth` per call while losing earlier + forecast keys; the overlays now take this call's lines and the legend + is rebuilt by role -- the data entries, one key per model over every + call's forecasts, one `truth` -- on both backends and in plotly cells. +- **A plotly cell's legend and colorbar from separate calls sit side by + side**, and a multi-line title re-lays out the rows at once. A colorbar + added after a legend used to land on it, and a three-line title widened + the top margin but left the rows 37 px apart; the grid now keeps track + of what each cell has drawn beside and above it, and sizes the space + beside every cell for the busiest one. +- **Plotly draws a marker-only `forecast_fmt` as markers**, as matplotlib + does, and its animated collection traces tag their source dataset in + `trace.meta['hyp_dataset']` rather than the model-major forecast index. +- **A `hyp.subplots(backend='plotly')` grid grows its legend room only + when a cell asks for it.** The grid reserved 118 px beside every cell + up front (it cannot know which cells will draw a legend), so a + legend-less pair of cells sat left-heavy with cubes three quarters the + size of the matplotlib pair's. It is now built as tight as `panels=` + draws it, and the first cell that receives a legend or colorbar + re-lays out the grid with that room, moving the cells already drawn + (their legends, colorbars and titles included). +- **Streaming plots work when plotly is the render backend.** Colab and + Kaggle select plotly by default, and there every streaming `hyp.plot` + (a generator, a Hugging Face `IterableDataset`, `hyp.io.lsl_stream()`) + raised `AttributeError: 'HyperPlotlyFigure' object has no attribute + 'axes'`, as did any stream after `hyp.set_interactive_backend('plotly')`. + The head plot now always renders with matplotlib, as the streaming + docstring states. The `streaming_data`, `lsl_streaming` and `io` + tutorials failed at their streaming cells on Colab because of it. Present + since 1.0.0. +- **`xlabel=`, `ylabel=` and `zlabel=` join the font-coverage scan.** The + scan that picks an installed font for characters the default font stack + lacks read `labels=`, `legend=`, `title=`, `hue=` and the colorbar text, + but not the axis labels. An axis label in such a script (Javanese on + stock macOS; CJK on a Linux machine whose CJK font is outside the stack) + drew as empty boxes, while the same text as a title rendered. Present + since 1.0.0. +- **A `hue=` surface matches the points beneath it.** Each hull vertex + blended every point in its dataset with inverse-squared-distance weights; + in 3-D the many distant points outweighed the near ones, so the hull took + the dataset's washed-out mean colour. Vertices now blend their nearest + points, on both backends. +- **Markers sit on the observations.** `'o-'`, `markers=` and + `forecast_fmt='ro:'` put a marker on every antialiased vertex (about 900 + for a 40-row path), drawing the line as a solid tube; now only the samples + are marked, static and animated (in an animation, the frame-grid vertex + nearest each sample), on both backends. An explicit `marker=` wins over + the fmt marker on matplotlib, and a continuous hue with `'o-'` in 1-D/2-D + shows its markers on plotly. +- **Hue transparency.** Continuous-hue markers honour `alpha=` on both + backends, and translucent plotly 3-D lines keep their colour instead of + washing out to cyan. +- **Plotly hover labels name what you point at.** They read "trace 0"; every + data trace now carries its legend label (category, dataset, series column, + model or 'truth'), a lone unlabelled dataset shows only its coordinates, + and animated legends no longer grow entry by entry. +- **Plotly subplot cells.** Colorbars no longer land on the next cell, an + untitled call keeps the cell's title, a dimensionality mismatch raises a + clear error, your own traces are left untouched, and a plotly `ax=` + implies the plotly backend. +- **Plotly `frame_kwargs=`, `zoom=` and legend position.** `frame_kwargs=` + styles the plotly frame, static figures ignore `zoom=` (animation-only, as + documented), and `legend_kwargs={'x': 0, 'y': 1}` anchors the legend at + that corner. +- **Plotly date axes show the same dates in every time zone.** Numeric dates + were drawn in the viewer's local time, so a series starting at midnight on + 1 January began on the evening of 31 December in New York. +- **Composing into `ax=` no longer repeats a palette colour.** `'hls'` drawn + 2 + 2 now gives the four-colour `'hls'` set, and the bundle's `colors` and + the colorbar show the colours actually drawn. +- **Dict-list palettes colour marker plots.** `fmt='o'` ignored a per- + dataset list of `{category: color}` dicts. +- **Per-dataset and nested `labels=` survive `hue=`/`cluster=`** instead of + crashing on both backends; label arrays and Series are accepted. +- **Legends.** A nested-list input's legend names its outer groups instead + of four leaves in two colours; cluster and integer-hue line legends list + categories in order (0, 1, 2), as the marker path did; `legend=False` wins + over `names=`; `panels=` splits a plain `legend_colors=` list per panel; + and `legend_colors=` accepts one colour per data entry beside forecast and + truth entries. +- **Label connectors and box edges are visible on matplotlib.** Under the + seaborn style they were drawn white, so labels floated with no visible + link and cut notches through markers. +- **Caller-axes and panel titles and axis labels use the Noto Sans stack** + instead of DejaVu Sans. `font='Noto Sans'` (the bundled face) works in a + fresh process. +- **Two-column data draws into a 2-D `ax=`** instead of raising "the plot is + 3D". +- **0-255 colour lists raise `ValueError`.** `palette=[[255, 128, 0], ...]` + was silently read as a data matrix, reordering and rescaling the colours; + the error says to divide by 255 or pass a DataFrame. +- **The NaN-hue warning counts observations** (it counted antialiased + vertices) and points at the caller's line. +- **Forecasts and truth keep their own dataset's style with `'o-'`.** Each + marker-plus-line dataset was drawn as two artists, so three datasets' + forecasts came out red, red, green. +- **A one-column trace is drawn against its row index.** Antialiasing put a + 40-row line at x 0..936, squashing its forecast 24x; `axis_scale='data'` + also gave the value range to x. +- **`ndims=1` `truth=` takes one column of values per trace.** A two-column + truth used its first column as x, stretching a date axis back to 1970; it + now raises `ValueError`. +- **Marker-only `hue=`/`cluster=` always refuses forecasts and warns**, even + when the category count equals the dataset count (the forecasts were + silently drawn in the wrong category's colour). +- **The 'truth' legend key is gray when truths span several colours**, + instead of always showing dataset 0's colour. +- **Animated forecasts on two-column data no longer crash** with "too many + values to unpack". +- **`xlim=(None, date)` works on date axes**; the open side takes the data + bound. +- **`panels=` accepts `forecast_trail=`** alongside `predict=`. +- **`transform=` fixes.** A bare array is one dataset instead of crashing, a + DataFrame with its own index no longer gives all-zero forecasts, and a + polars frame no longer raises `SchemaError`. +- **A shuffled time index is drawn in time order**, so the forecast joins + the end of the line (with a warning). +- **`ndims=1` date ticks no longer collide**; matplotlib uses concise date + labels. +- **Regular calendar data are forecast on their own calendar.** Business- + day, month-start, weekly, quarterly and tz-aware daily indexes, and + `PeriodIndex` data, are fitted on their own rows and forecast onto the + next business days, month starts or periods. Before, business-day bars + were interpolated onto calendar days and forecast onto weekends, month + starts drifted, a fall DST change duplicated a day, and periods came back + as timestamps. +- **A fitted forecaster works across index kinds again.** A model fitted on + an array and reused on dated rows, or the reverse, raised an error about + `step`; it now steps in the new data's own units, as 1.0 did. +- **ARIMA's minimum history includes `seasonal_order`.** A short seasonal + fit gets the "needs N observations" message instead of a bare `IndexError` + or `LinAlgError`. +- **Time warnings appear once, and only when they apply.** A stacked panel + warns "not sorted" once per call instead of three times, and an explicit + `step=` on evenly spaced data no longer calls them irregular. +- **`yahoo:` intraday bars keep their timestamps.** `interval='1h'` put + every bar at midnight, so `hyp.predict` rejected the index; intraday bars + are tz-aware in the exchange's time zone. +- **A dict model spec with a flat parameter raises instead of silently + running defaults.** `{'model': 'PCA', 'whiten': True}` or + `cluster={'model': 'KMeans', 'n_clusters': 4, 'random_state': 0}` dropped + the extra keys; they now raise `ValueError` naming them and showing the + `'kwargs'` form, in `reduce` (including streaming), `cluster`, `manip`, + `align`, `impute`, `Pipeline`, `apply_model` and `text2mat`. The + documented `n_clusters` shortcut still works. Outer `**kwargs` next to a + dict spec now reach `manip`/`align` models, and a spec's `'args'` reach + streaming and `text2mat` models. +- **`hyp.plot(x, pipeline=p)` draws the pipeline's clusters.** A fitted + trailing cluster step colours the figure with the fit figure's colours; it + was dropped silently. +- **Aligner classes accept arrays.** `HyperAlign().fit(xs).transform(ys)` on + a list of NumPy arrays, or a single array, raised "Unsupported datatype"; + the aligners accept what `hyp.align` does and return each dataset in its + input's form. +- **`alignment_score(metric='dispersion')` rejects all-constant datasets.** + Datasets each constant at a different value scored exactly 1.0; they now + raise, like `'isc'`. +- **Rows a `manip=` stage empties stop the pipeline at that stage.** A + trailing `Smooth(center=False)` no longer triggers misleading PPCA + imputation warnings or sklearn NaN errors; the error names the stage and + suggests `min_periods=1`. +- **A fitted `Normalizer` accepts 1-D data.** A 1-D array, Series or list of + numbers is one column in both fit and transform. +- **Offline errors say what happened.** A missing 25+ character bare name + lists the full resolution chain instead of a Google Drive cache miss, a + cached copy that fails to parse raises `HypertoolsIOError` naming the + file, and an extensionless remote `.npz` reports the `trust=True` error + instead of a parquet one. +- **`load()`'s TypeError names polars frames**, which it accepts. +- **`set_autoinstall` handles are quiet at exit.** A live handle printed + "Exception ignored ... TypeError" at interpreter shutdown; a re-entered + handle keeps its creation order. +- **Align, impute and manip warnings point at your own line**, so deprecated + spellings are no longer hidden inside the library. +- **`[density3d]` needs `scikit-image>=0.25.0`**, the first release with + Python 3.13 wheels. +- **Core dependency floors have wheels for Python 3.10 through 3.13.** + The floors rise to numpy 2.1.0, pandas 2.2.3, scipy 1.14.1, matplotlib + 3.9.2, scikit-learn 1.5.2, statsmodels 0.14.3 and pillow 10.4.0, the first + release of each with CPython 3.13 wheels (the previous floors shipped + cp310-cp312 only, so a lowest-version install on 3.13 built them from + source). The README and `docs/doc_requirements.txt` match. +- **`animate_market_sectors` reads ExxonMobil's full share history.** SEC's + ticker map now sends XOM to a new holding-company CIK (2115436) with a + single 2026-06-30 share count, so every earlier XOM month was + back-filled from that one value. The example pins XOM to Exxon Mobil + Corp's CIK (34088), whose reported counts start on 2009-06-30, and its + SEC cache files are keyed by CIK so a file fetched under the old mapping + is not reused. The example and tutorial also note that HON's 2026-06-30 + SEC share count (316,940,010) is half its 2026-03-31 count (633,653,119). +- **Animated lines keep every observation.** Lines were resampled onto one + row per frame, so a dataset with more rows than frames was drawn through + only some of its points (a 36-row helix in a 9-frame animation became a + zig-zag star at 46% of its radius, and labels were dropped). Every + observation is now a vertex of the animated line, `animate='spin'` draws + the rows unchanged, and reveal timing and frame counts are unchanged. +- **Every morph transition frame moves.** Transitions sampled their own + endpoints, so a 2-frame transition only repeated the hold clouds. + Transition frames now fall strictly between the clouds in position, colour + and `alpha=`. The default morph dot is 4 pt on both backends (it was 1.5 + pt, sub-pixel on plotly). +- **Titles set in an `on_frame=` callback are visible on 3-D animations.** + With no `title=`, the `plot()` docstring's own example drew its title + above the canvas on matplotlib and cut it off on plotly; `on_frame=` now + reserves the title margin. +- **`companion=` panels use the trajectory's colour** instead of + matplotlib's default blue; an explicit `color=` still wins. +- **Short streams warn about clamped samples.** The warning needed 20 post- + head samples, so a short stream could draw most of its points on the box + surface silently; the clamped fraction is now checked again when streaming + stops. +- **Plotly animations keep a continuous hue on the moving data.** 3-D + windows were painted in the trajectory's first colours, and 2-D lines + never animated: the whole trajectory stayed on screen while one segment + flickered. Heads and trails now carry their own colours in every reveal + style. +- **Plotly 3-D lines are as thick as you ask.** WebGL drew Scatter3d lines + at half the requested width; they now match the 2-D line and matplotlib, + and plotly animations default to the documented 1 pt. +- **Plotly 3-D density no longer speckles the cube.** The density volume + extended past the scene and broke the cube edges into dots; it is now + clipped to the cube. +- **Plotly Play/Pause sit below the axis labels.** On a date or data x axis + they covered the tick labels. +- **Forecast time warnings name your line, once.** The interpolation and + "not sorted" warnings pointed at hypertools' own files (tutorials printed + `.../hypertools/predict/common.py:435`), a calendar step read + `step=<BusinessDay>` and a float step `0.04000000000000001`, and a + shuffled index under `hyp.plot` warned twice. They now point at the + caller, print `step='B'` or `step=0.04`, and appear once per `plot()` + call. +- **`hyp.describe`'s `'average'` is the average.** It was the curve for + every dataset stacked into one point cloud, which adds the + between-dataset distances, so the dashed "average" line was not the + average of the curves drawn beside it (on `weights_sample` at two + components: 0.756, against individual curves of 0.732, 0.781 and 0.829). + `'average'` is now the element-wise mean of the `'individual'` curves + (0.781 there), and the stacked curve is + still returned, as `'pooled'`, without being drawn. For one dataset all + three are equal. The x axis is ticked at whole component counts on both + backends (it showed 2.25, 2.5, ...). A bare array passed with + `format_data=False` is one dataset; it used to be split into rows and + raise `IndexError`. +- **`hyp.describe(show=False)` returns its figure**, undisplayed, like + `hyp.plot(show=False)`: a matplotlib figure is closed out of pyplot but + stays savable, and a plotly figure is not shown. It used to skip drawing + and return `fig=None`, so `show=False` now costs the (small) drawing time + as well. +- **matplotlib `axis_scale='data'` plots have no grid.** Seaborn's + `whitegrid` style, which hypertools draws under, left a grey grid on + data-scale axes (static, animated and `ndims=1` series plots) + that the plotly backend never drew. Both backends now draw none. A + caller's own `ax=` keeps whatever grid it already has. +- **Two `plot()` docstring corrections.** A datetime `t=` on a flat list + was documented as having to resolve to the same number of steps for + every dataset; each dataset is in fact forecast up to that time on its + own index (only a hierarchical input needs one shared count). And the + `animate=` entry now says how large an animated plotly figure gets: a + 3000-row, 300-frame 3-D animation is about 6.7 MB of JSON, and + `resample=1000` brings it to about 2.3 MB. +- **`hyp.describe` sweeps the dimensionality of a reducer instance.** A + configured instance such as `reduce=PCA(n_components=2)`, and a dict spec + that pins it (`{'model': 'PCA', 'kwargs': {'n_components': 2}}`), were + reduced to that one dimensionality at every point of the sweep while the + curve labelled the points 2, 3, 4, ... (on 60 x 6 random data: 0.739 four + times, against 0.739, 0.852, 0.930, 0.974 for `reduce='PCA'`; also in + 1.0). The sweep's dimensionality now always wins: an unfitted instance is + cloned for each point with `n_components` set to that point, keeping its + other settings and leaving the instance you passed unmodified and + unfitted, and an `n_components` in a dict spec's kwargs is ignored. Both + give the same curve as the name, without the "Unequal values passed to + dims and n_components" warnings. An already fitted model (including the + `Reducer` or `Pipeline` that `hyp.reduce(..., return_model=True)` + returns), whose dimensionality is fixed by its fit, and an instance with + no `n_components` parameter now raise a `ValueError` that says to pass a + name, a dict spec or an unfitted instance; they used to draw the flat + curve. `reduce=None`, which leaves nothing to sweep and drew a flat 1.0, + raises a `ValueError` too. +- **`sklearn.base.clone` works on a `hyp.Pipeline`.** `Pipeline` is a + scikit-learn `BaseEstimator`, but `clone(pipeline)` raised `RuntimeError: + Cannot clone object ... as the constructor either does not set or + modifies parameter steps` (also in 1.0). It now returns an unfitted + pipeline with the same step names, order and settings and independent + copies of every step, including nested pipelines and the pipelines that + `return_model=True` returns. `hyp.apply_model(data, pipeline, + stack=False, return_model=True)`, which clones the model for each + dataset, therefore returns one fitted pipeline per dataset and leaves the + one you passed unfitted; it used to return the same pipeline for every + dataset, holding only the last dataset's fit (the transformed data is + unchanged). `set_params(steps=...)` resolves and names + the new steps as the constructor does (it used to store them raw), and a + nested `<step>__<parameter>` name is refused with an error that says to + use `named_steps`. +- **`Normalize(mode='isotropic')` docstring correction.** It said a rotated + copy of a cloud is rescaled by the same scalar. The scalar is the largest + absolute coordinate deviation from the centroid, which depends on the + cloud's orientation (9.03 for a 200-point 2-D cloud, 7.21 for the same + cloud turned 45 degrees). The shape is preserved either way; the + behaviour is unchanged. + ### Documented limitations - Ragged groups (unequal feature counts per group) are rejected by both entry points, by an error naming the missing and unexpected features. That error's escape-hatch remedy is spelled for `hyp.plot`, so a `hyp.predict` caller has to translate it: group with `group_columns(df, - feature_correspondence='position')` and forecast the leaves + feature_correspondence='position')` (`from hypertools.core.hierarchy + import group_columns`) and forecast the leaves (`hyp.predict([leaf.to_numpy() for leaf in leaves], model, t)`, verified). - Unequal-length row groups are averaged over their overlapping prefix, with one aggregated warning. @@ -730,15 +1736,12 @@ input too. datasets. Pass `reduce='PCA'` when block order must not matter. - Continuous `hue=` over a **row** hierarchy is still warned-and-ignored; only column hierarchies honour it in 1.1. -- A forecast under a continuous `hue=` takes its source trajectory's **final - observed hue colour**, in the animated case as well as the static one, on - both backends. (Animated forecasts briefly wore the per-dataset palette - colour instead -- the colour of the hidden artist driving the reveal, - which nothing visible is drawn in, so the forecast appeared to continue a - colour its trajectory never had and a paused animation disagreed with the - static plot of the same call.) A **categorical** regrouping is unchanged: - there the live forecast still takes the colour of the run drawing the - head, which is what the viewer actually sees. +- Under a **categorical** `hue=`/`cluster=` regrouping, an animated live + forecast takes the colour of the run drawing the head, which is what the + viewer actually sees, so its colour can change as the head crosses a + category boundary. Under a continuous `hue=` a forecast instead takes its + source trajectory's final observed hue colour, animated and static alike + (see *Added*). - Duplicate innermost feature names inside one group are **kept** rather than rejected or de-duplicated, and matched across groups by `(label, occurrence)`: all such columns are plotted and forecast. Rename @@ -754,16 +1757,20 @@ input too. ## 1.0.1 (unreleased) -Small, additive plotting features and fixes. Public APIs are unchanged; two -items under **Changed** below alter how existing figures LOOK. +Small, additive plotting features and fixes. Public APIs are unchanged; +three changes alter how existing figures LOOK: smoothed lines +(`antialias=True`, the new default, under **New features**) and the two +items under **Changed**. Where 1.1.0 later refined one of these behaviours, +the entry below says so. > 1.0.1 was never published on its own. These changes were developed as a > patch release and now ship as part of 1.1.0, which is what `pyproject.toml` > declares; they are kept in their own section because they are separable > from the hierarchy work above. Because 1.0.1 is not a version anyone can > install, every guide and docstring that dates one of these behaviours dates -> it to **1.1.0**; this heading is the only place the shipped package names -> the patch line. +> it to **1.1.0**; this section is the only user-facing place that names +> the patch line. If you are upgrading from 1.0.0, everything from here down +> to the `## 1.0.0` heading is new to you as well. ### New features @@ -808,8 +1815,8 @@ items under **Changed** below alter how existing figures LOOK. - **`predict=` now works with the time-progressing animations too** (`animate=True`/`'parallel'`/`'serial'`/`'window'`). The forecast is - recomputed from the history revealed so far and re-anchored on the last - revealed observation, so the forecast trace grows with the animation instead + recomputed from the history revealed so far and drawn from the endpoint of + the current frame, so the forecast trace grows with the animation instead of standing still. Because the data is static -- all of it known before the first frame, merely revealed over time -- every forecast the animation will ever draw is computed up front. Two things follow: the whole fan is folded @@ -852,10 +1859,14 @@ items under **Changed** below alter how existing figures LOOK. grows with the DATA, not the frame count: 3 datasets x 60 rows x 900 frames is 177 fits (~5 s), while 3 x 500 x 900 is 1497 fits (~330 s) -- a longer series has both more distinct histories and a costlier fit each. `plot()` - now times the first real fit and warns if the projection exceeds - `slow_warning_seconds=` (default 10; pass `None` to silence), so a long - wait is expected rather than mysterious. The notice arrives before the - wait, not after it. + now times the fits as they run and warns if its projection of the total + exceeds `slow_warning_seconds=` (default 10; pass `None` to silence), so + a long wait is expected rather than mysterious. The notice arrives before + the wait, not after it. (As first written the projection came from the + first timed fit; 1.1.0 waits for fits at two or more history lengths, + one of them at least 10 rows long (or the longest the schedule has), and + projects from a least-squares line through their timings -- see *Fixed + during the release review*.) Deliberately NOT solved by sampling the reveal: striding the schedule would render a different animation than the one asked for. The outcome is not @@ -866,7 +1877,11 @@ items under **Changed** below alter how existing figures LOOK. the data.** Inheritance stays the default -- a forecast is its observed trace projected forward at half its alpha -- and each of these replaces exactly one aspect of it, so observed and forecast data may differ in - style, grouping, palette, or any combination. + style, grouping, palette, or any combination. (1.1.0 refines this: a + forecast recoloured by `forecast_palette=`, `forecast_hue=` or + `forecast_cluster=` keeps its trace's full alpha, and a collection of + models takes one linestyle per model; see *Fixed during the release + review*.) **`forecast_cluster=` clusters the forecast ENDPOINTS**, so a forecast's colour answers *which of these series are heading to the same place?* -- @@ -1010,14 +2025,15 @@ items under **Changed** below alter how existing figures LOOK. - **Per-dataset `alpha=`, alongside the existing per-dataset `color=`/`linewidth=`.** Inputs that assign alpha internally (row - `MultiIndex` frames, nested lists) keep their own values and now say so - with a warning instead of losing silently. + `MultiIndex` frames, nested lists of varying depth) keep their own values + and now say so with a warning instead of losing silently. - **Per-segment `title=` for serial-style animations, on both backends.** Pass a list of strings (one per dataset) to name each segment of a serial-style animation as it is revealed; for `animate='morph'` the holds are named and the transitions are left blank automatically. Anywhere else - a non-string `title=` raises `TypeError`. + a `title=` that is neither a string nor a callable raises `TypeError` + (1.1.0 added callable titles; see *Titles that follow the data*). - **`simplify=` on `plot()` (default `True`).** Today it governs `animate='morph'` tractability only: over clouds larger than 2000 points @@ -1037,7 +2053,11 @@ items under **Changed** below alter how existing figures LOOK. `alpha=` is matplotlib's opaque 1.0, so the default forecast alpha is `0.5`). Per-dataset styling carries through dataset by dataset -- `alpha=[1.0, 0.4]` gives forecasts at `[0.5, 0.2]`, and a dotted dataset - gets a dotted forecast. + gets a dotted forecast. (1.1.0 refines this: a forecast recoloured by + `forecast_palette=`, `forecast_hue=` or `forecast_cluster=` is drawn at + its trace's own alpha, and a collection of models keeps each dataset's + colour and cycles the linestyle per model; see *Fixed during the release + review*.) This is a **visible change to existing forecast figures**, and it deliberately replaces the previous rule: every forecast used to be drawn @@ -1144,8 +2164,9 @@ items under **Changed** below alter how existing figures LOOK. style, or a static plot with the default `antialias=True`). Bridged labels now grow in lockstep with the bridged data. -- **`title=` no longer stringifies a list onto the axes.** A non-string - `title=` now raises `TypeError` instead of drawing the literal +- **`title=` no longer stringifies a list onto the axes.** A `title=` that + is neither a string nor a callable (1.1.0 added callable titles) now + raises `TypeError` instead of drawing the literal `"['a', 'b', 'c']"` text, and the check runs before the analyze pipeline, so streaming plots (`plot_stream`) get it too. diff --git a/CLAUDE.md b/CLAUDE.md index 0d46a4fb..eaf00601 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -67,7 +67,7 @@ The dev-1.0 refactor moved several tools into their own top-level subpackages (e - `hypertools/predict/` - Forecasting models (`predict.py`, `arima.py`, `autoreg.py`, `gp.py`, `kalman.py`, `laplace.py`, `chronos.py`, `common.py`) - `hypertools/impute/` - Imputation models (`impute.py`, `ppca.py`, `kalman.py`, `sklearn_imputers.py`, `common.py`) - `hypertools/core/` - Shared config/exceptions and `apply_model()`/`Pipeline` (`configurator.py`, `exceptions.py`, `model.py`, `pipeline.py`, `shared.py`) -- `hypertools/_shared/lazy_import.py` - On-demand installation of optional extras: `lazy_import(module, purpose=)` imports a module and, if it is missing, pip-installs the hypertools extra that provides it (requirement strings read from the installed package metadata, so `pyproject.toml` is the single declaration; only the import-name -> extra map lives in the module), then imports again. `HYPERTOOLS_AUTO_INSTALL=0` disables it. `ensure_kaleido_chrome()` provisions Chrome (and, on Debian/Ubuntu images, its system libraries) for plotly static export. Every optional-dependency site goes through it; never hand-write a `pip install` hint elsewhere. +- `hypertools/_shared/lazy_import.py` - On-demand installation of optional extras: `lazy_import(module, purpose=)` imports a module and, if it is missing, pip-installs the hypertools extra that provides it (requirement strings read from the installed package metadata, so `pyproject.toml` is the single declaration; only the import-name -> extra map lives in the module), then imports again. `hyp.set_autoinstall(False)` (public, also a context manager) disables it; `HYPERTOOLS_AUTO_INSTALL=0` sets the starting value. `ensure_kaleido_chrome()` provisions Chrome (and, on Debian/Ubuntu images, its system libraries) for plotly static export. Every optional-dependency site goes through it; never hand-write a `pip install` hint elsewhere. **Plot Module** (`hypertools/plot/`) - `plot.py` - Main plotting interface and logic diff --git a/RELEASE_CHECKLIST.md b/RELEASE_CHECKLIST.md index c40a6076..3cf41942 100644 --- a/RELEASE_CHECKLIST.md +++ b/RELEASE_CHECKLIST.md @@ -1,75 +1,109 @@ # HyperTools 1.1 release checklist -The docs, notebooks, and README deliberately ship in **dev form** on the -`dev-1.0` branch and must flip to **release form** at publish. Several of those -flips cannot be done earlier (PyPI has 1.0.0 but not 1.1.0; the `v1.1.0` tag does -not exist yet), so they are done here, on `master`, in order — and the -**`release-gate` CI job** (runs only on `master` / tags) hard-fails until every -flip is done, so nothing can be forgotten. +The docs, notebooks, and README ship in **release form** on `master` (the 1.1.0 +draft: `master` == tag `v1.1.0` == `96ac8b7f`, gallery published under +`docs-notebooks/v1.1.0/` from that commit, a DRAFT GitHub release with wheel + +sdist attached, nothing on PyPI yet). Fixes found by the 1.1 release review +land through PR #286 (`fix/1.1-release-review`), so the release is **re-cut** +from the merge commit: the gallery manifest pins an exact `source_commit`, and +the `release-gate` CI job (runs only on `master` / tags) hard-fails until the +manifest, the tag and the artifacts all point at the final commit. Run everything from a **clean `master` checkout on the `master` branch** (not a detached tag checkout — the notebook migrator detects the branch via `git rev-parse --abbrev-ref HEAD`, which returns `HEAD` when detached). -## 0. Pre-flight (on `dev-1.0`) +## 0. Pre-flight (on the fix branch, before merging) -- [ ] **Push `dev-1.0` and open the integration PR (`dev-1.0` → `master`) FIRST.** - The 1.1 line was developed with no hosted CI at all (the remote branch - last moved 2026-07-23; ~185 commits since), so the PR's matrix CI is - the first time these commits meet Linux, Windows and every supported - Python. Nothing below happens until it is green. -- [ ] `dev-1.0` CI fully green (push + PR workflows). -- [ ] Full suite green locally: `pytest` (3700+ passed, 0 failed — including - `tests/test_examples_are_native.py`, the Plan 4 gate, at 0 failed). +- [ ] PR #286 CI fully green (matrix, `wheel-smoke`, `docs-clean`, + `dataset-gate`, `live-source-gate`). +- [ ] Full suite green locally: `pytest` (about 6,800 passed, 0 failed — + including `tests/test_examples_are_native.py`, the native-usage gate, + at 0 failed). +- [ ] Example smoke gate: `HYPERTOOLS_EXAMPLE_SMOKE=1 pytest tests/test_examples_are_native.py` + runs the six launch/forecast examples (`STATED_ARTIFACT`: + `animate_conversation`, `animate_forecast`, `animate_market_sectors`, + `animate_morph_zoo`, `animate_painting_embeddings`, + `animate_weather_decades`) end to end with their real loaders. The + other gallery scripts run in the sphinx-gallery build below. No CI job + runs the smoke gate, so it is a manual pre-release step. - [ ] Local release validation, all green in the same tree: `ruff check .`, - `cd docs && MPLBACKEND=Agg ../.venv/bin/python -m sphinx -b html -W -E -a . _build/html` - (0 warnings), `pytest tests/test_packaging_artifacts.py`, and the five - launch examples headless (`MPLBACKEND=Agg python examples/animate_*.py`). -- [ ] Decide the release date and the version (`1.1.0`; `pyproject.toml` - already says so, and `CHANGELOG.md` has a `## 1.0.1 (unreleased)` section - that its own note explains was never published on its own — leave it). + `cd docs && rm -rf auto_examples && MPLBACKEND=Agg ../.venv/bin/python -m sphinx -b html -W -E -a . _build/html` + (0 warnings; delete `auto_examples/` first or stale pages of removed + examples fail `-W`; this build executes all 51 gallery scripts), + `pytest tests/test_packaging_artifacts.py`, and the five launch + examples headless: `for f in animate_market_sectors animate_weather_decades animate_painting_embeddings animate_conversation animate_morph_zoo; do MPLBACKEND=Agg python examples/$f.py || break; done` + (the `examples/animate_*.py` glob matches 11 scripts, not these five). - [ ] Notebook hygiene: every committed tutorial was executed with `scripts/execute_tutorial.py` (which skips the Colab install cell so the - venv is not overwritten by the stale remote branch — see its docstring) + venv is not overwritten by a stale remote branch — see its docstring) and `pip show hypertools` still reports an editable install afterwards. + Stored outputs carry no `/Users/...` paths (`git grep -n '/Users/' -- docs/tutorials`). +- [ ] **Candidate feature tour on Colab (before sign-off and merge).** The + tour is a maintainer-local review artifact, not part of the repo: + `notes/colab/` is gitignored, so a clean checkout does not have it. + Its working copy is `notes/colab/hypertools_1.1_feature_tour.ipynb` in + the maintainer's checkout (kept in sync by + `scripts/update_feature_tour.py`). Set its `REVIEW_COMMIT` to the + candidate commit, which must be pushed (the tour installs + `hypertools[...] @ git+…@<REVIEW_COMMIT>`), and upload ONE copy to + Colab, saved as `notes/colab/hypertools_1.1_candidate_<short-sha>.ipynb` + (one is kept per reviewed candidate; the newest is the current one). + Run it in a fresh runtime. It tests a Git candidate with comprehensive + extras, not a published wheel or missing-extra installation. Run it + locally too: `scripts/execute_tutorial.py --out-dir /tmp/tour-check + notes/colab/hypertools_1.1_feature_tour.ipynb`. Retain the executed + notebook, JSON/CSV reports, source hashes, dependency versions, decoded + exports and visual verdicts. Inspect early previews after Run all, then + exercise the shared interactive viewer and downloads. Resolve + failures, blocked checks, and any explicitly deferred manual checks + before release sign-off. +- [ ] **Missing-extra policy.** In an isolated clean environment, check a + friendly error with autoinstall disabled and a real installation with + it enabled: `python scripts/verify_optional_install.py --output /tmp/optional-check` + installs the current source into a NEW temporary venv and never + touches the caller's interpreter. The comprehensive tour's eager + extras are not evidence for this behavior. Never remove packages from + a working research environment to manufacture the missing-extra + condition. - [ ] **conda-forge** is no longer a prerequisite: the feedstock (`conda-forge/hypertools-feedstock`) exists since 1.0.0 and its bot bumps on each PyPI release (step 7). ## 1. Merge to `master` -- [ ] Merge `dev-1.0` → `master` (the integration PR from step 0). Do NOT - delete `dev-1.0` yet (the pre-release notebooks still reference it until - step 2 runs). +- [ ] Merge PR #286 → `master`. Its description carries `Closes #284` and + `Closes #285`, so both tracking issues close on merge; confirm they did. ## 2. Flip everything to release form (on `master`) - [ ] **Notebooks → PyPI spec + clean note (automated).** `python scripts/add_colab_install_cell.py` - Retargets every committed tutorial install cell `... @ git+…@dev-1.0` → + Retargets any committed tutorial install cell `... @ git+…@<branch>` → `hypertools[<extras>]` (extras preserved) and strips the - `(<branch> preview)` / "On release this becomes …" note. The gallery + `(<branch> preview)` / "On release this becomes …" note. The 1.1.0 + notebooks are already in this form, so this is a no-op check. The gallery (`docs/auto_examples/*.ipynb`) is gitignored and REGENERATED by `docs/conf.py` on each build, which emits the identical PyPI line on a `master`/tag build (the migrator only retargets any on-disk copy, and the `docs-clean` CI job release-gates the generated gallery). -- [ ] **README images: commit SHA → `v1.1.0` tag (8 URLs).** - `sed -E -i '' 's#(/ContextLab/hypertools/)[^/]+(/images/)#\1v1.1.0\2#g' readme.md` - (drop the `''` after `-i` on GNU sed). This retargets WHATEVER ref is - pinned (robust to the SHA having drifted), not just `fc2429cb`. Verify the - POSITIVE: `grep -c '/ContextLab/hypertools/v1.1.0/images/' readme.md` → 8 +- [ ] **README images: pinned to the `v1.1.0` tag (8 URLs).** The 1.1.0 + README already pins `v1.1.0`, so this is a verify-only step: check + `grep -c '/ContextLab/hypertools/v1.1.0/images/' readme.md` → 8 and `grep -Ec '/ContextLab/hypertools/[0-9a-f]{7,40}/images/' readme.md` → 0. -- [ ] **CHANGELOG date.** Edit `CHANGELOG.md`: `## 1.1.0 (unreleased)` → - `## 1.1.0 (YYYY-MM-DD)` with the real release date. -- [ ] (Optional prose) `docs/tutorials/stock_forecasting.ipynb` has a - free-text "hypertools 1.0 preview" comment the migrator does not touch — - reword if desired (not gate-enforced). + Only if a ref has drifted, retarget it: + `sed -E -i '' 's#(/ContextLab/hypertools/)[^/]+(/images/)#\1v1.1.0\2#g' readme.md` + (drop the `''` after `-i` on GNU sed), then re-run both greps. +- [ ] **CHANGELOG date.** Edit `CHANGELOG.md`: the `## 1.1.0 (YYYY-MM-DD)` + heading must carry the date of the FINAL release commit (the draft is + dated 2026-09-04; a re-cut on a later day updates it). The release + gate fails a heading dated earlier than the release commit. - [ ] **Verify the file-content gates locally BEFORE committing.** Exclude the gallery-resolve gate — it checks the *remote* `docs-notebooks` branch, published in the next step, so it cannot pass yet: `HYPERTOOLS_REQUIRE_RELEASE=1 pytest -v tests/test_notebook_install_gate.py tests/test_release_readiness_gate.py -k 'not gallery_colab_notebooks_are_published'` → all green (no branch installs, no preview note, images on the tag, - CHANGELOG dated). + CHANGELOG dated no earlier than the release commit). - [ ] Commit all of the above on `master` in one release commit. - [ ] **Publish the gallery notebooks NOW — before any release gate needs them (this is what breaks the publish-order deadlock).** The gallery "Open in @@ -79,8 +113,9 @@ detached tag checkout — the notebook migrator detects the branch via be published before you push, not after. Build the gallery and publish: `cd docs && make html` then, from the repo root, `python scripts/publish_gallery_notebooks.py --ref v1.1.0 --notebooks-dir docs/auto_examples --push` - (the `docs-notebooks` branch already exists from 1.0.0; this adds the - `v1.1.0/` namespace and writes `v1.1.0/manifest.json`). Publishing static + (the `docs-notebooks` branch already exists; the script deletes and + rewrites the whole `v1.1.0/` namespace and its `manifest.json`, so a + re-cut republishes cleanly over the draft's publish). Publishing static notebooks before PyPI is harmless: their `%pip install hypertools[...]` cells resolve 1.1 the moment PyPI is updated (step 6). Both the `master` "latest" docs and the `v1.1.0` "stable" docs resolve to @@ -109,9 +144,11 @@ same artifacts you verify are the ones you publish. `dist/hypertools-1.1.0.tar.gz` + `…-py3-none-any.whl`. - [ ] `twine check dist/*` → PASSED. - [ ] Artifacts bundle the fonts + all license materials (font OFL, Apache-2.0 - license + third-party notices for the vendored brainiak/ppca, CHANGELOG): + license + third-party notices for the vendored brainiak/ppca): `tar tzf dist/*.tar.gz | grep -E 'NotoSans|OFL|LICENSE-APACHE|THIRD_PARTY|CHANGELOG'` - (5+ hits) and the same on the wheel via `unzip -l dist/*.whl`. + (6 hits: the CHANGELOG ships in the sdist only, via `MANIFEST.in`) + and `unzip -l dist/*.whl | grep -E 'NotoSans|OFL|LICENSE-APACHE|THIRD_PARTY'` + (5 hits). - [ ] Fresh-venv smoke: install the wheel in a throwaway venv, `import hypertools`, `hypertools.__version__ == '1.1.0'`. - [ ] Record artifact digests: `shasum -a 256 dist/*` (keep with the build commit; verify these exact files are the ones uploaded in step 6). @@ -127,12 +164,17 @@ same artifacts you verify are the ones you publish. ## 5. Tag the green commit + wait for tag CI -- [ ] `git tag -a v1.1.0 -m "HyperTools 1.1.0"` at the **exact commit that just - went green** on `master`. -- [ ] `git push origin v1.1.0` (the workflow's `tags: ['v*']` trigger runs CI - on the tag). -- [ ] Wait for the `v1.1.0` tag CI to go GREEN (same jobs; `release-gate` + - `docs-clean` gallery scan run on the tag too). +- [ ] The `v1.1.0` tag already exists on `origin` at the draft commit + (`96ac8b7f`). Move it to the **exact commit that just went green** on + `master`: `git tag -fa v1.1.0 -m "HyperTools 1.1.0" <sha>` then + `git push --force origin refs/tags/v1.1.0`. A moved tag is safe ONLY + because nothing has been published from the old one (PyPI still has + 1.0.0, the GitHub release is a draft); once PyPI has 1.1.0 the tag is + frozen. +- [ ] Confirm: `git ls-remote origin refs/tags/v1.1.0^{}` == the new sha. +- [ ] Wait for the `v1.1.0` tag CI to go GREEN (the workflow's `tags: ['v*']` + trigger runs the same jobs; `release-gate` + `docs-clean` gallery scan + run on the tag too). ## 6. Publish to PyPI (the already-verified artifacts) + smoke @@ -147,8 +189,17 @@ same artifacts you verify are the ones you publish. stale artifact): `twine upload dist/hypertools-1.1.0.tar.gz dist/hypertools-1.1.0-py3-none-any.whl`. (The static notebooks briefly resolving the previous PyPI release before this upload is harmless.) -- [ ] Create a **GitHub Release** for the `v1.1.0` tag with the 1.1 release - notes (from `CHANGELOG.md`). +- [ ] **GitHub Release**: the DRAFT release for `v1.1.0` already exists with + the draft commit's wheel + sdist attached. Replace both assets with the + step-3 files (`gh release upload v1.1.0 dist/hypertools-1.1.0.tar.gz dist/hypertools-1.1.0-py3-none-any.whl --clobber`), + replace its body with `notes/release_notes_v1.1.0_draft.md` (re-check + it against the final `CHANGELOG.md` first, and curl its "Links": the + `/en/stable/tutorials/` pages 404 until the Read the Docs tag build + below, and the PyPI release exists once + `https://pypi.org/pypi/hypertools/1.1.0/json` stops answering 404 (the + HTML project page answers 200 for any version number); the draft body + attached to the release predates the release review), confirm it + targets the moved tag, then publish it. - [ ] `pip install hypertools` in a clean env → installs `1.1.0`; run the README quick-start snippet. - [ ] **Gallery notebooks: confirm still resolved.** They were already @@ -158,12 +209,34 @@ same artifacts you verify are the ones you publish. (Publication is a MANUAL step today — there is no CI job for it; a `contents: write` `publish-gallery-notebooks` job on master/tags could automate it once token/environment handling is decided.) -- [ ] **Read the Docs**: trigger/confirm a build of the `v1.1.0` tag (and - point the "stable"/default version at it, replacing `v1.0.0`). The released docs' Colab - install cells must show `%pip install "hypertools[interactive]"` - (no `git+`) — `docs/conf.py` emits this automatically on a tag build. +- [ ] **Read the Docs: build BOTH `latest` and the `v1.1.0` tag by hand.** + Pushes do not reach RTD at the moment: the GitHub → RTD webhook + (`https://readthedocs.org/api/v2/webhook/github/hypertools/`) last + answered HTTP 400 in GitHub's delivery log, and RTD has built nothing + since the 1.0.0 release (`latest`/`stable` at 647ce929, 2026-07-24), + although `master` moved on 2026-09-05. Re-sync the GitHub + integration in the RTD admin (Admin → Integrations, + https://app.readthedocs.org/dashboard/hypertools/integrations/), then + trigger builds of `latest` (master) and of the `v1.1.0` tag from + https://app.readthedocs.org/projects/hypertools/builds/, and point the + "stable"/default version at `v1.1.0`, replacing `v1.0.0`. Verify: + `curl -sI https://hypertools.readthedocs.io/en/latest/optional_dependencies.html | head -1` + → `HTTP/2 200` (it is 404 until `latest` is rebuilt, and the README + and PyPI page link it), and the same page under `/en/stable/`. The + released docs' Colab install cells must show + `%pip install -q "hypertools[interactive]"` (no `git+`) — + `docs/conf.py` emits this automatically on a tag build; the tutorials + carry the version-guarded `%pip install -q "hypertools[...]>=1.1.0"`. - [ ] PyPI project page renders the README with all 8 images resolving (they now point at the `v1.1.0` tag). +- [ ] **Published-wheel smoke (only AFTER approved publication).** In a fresh + environment, install `hypertools[interactive]==1.1.0` with + `--only-binary=hypertools --report wheel-install.json`; do not fall back + to a tag/source checkout. Check the report's wheel URL/hash, installed + version and import path, then run representative plotting and forecasting + examples. Keep this evidence separate from candidate Git verification + (the Colab tour and the missing-extra check run in step 0, before any + upload). ## 7. conda-forge bump (the feedstock exists since 1.0.0) @@ -177,19 +250,27 @@ feedstock automatically, usually within hours of the PyPI upload. 1.0.0 tag and carry any NEW or RAISED floor into `run:` by hand (`matplotlib` stays `matplotlib-base`; `noarch: python`; the three bundled licenses stay in `license_file`). -- [ ] The `[predict]` / `[predict-hf]` / `[lsl]` extras stay pip-only - (`skaters`, `chronos-forecasting`, `pylsl` are not on conda-forge); the - base package is unaffected. +- [ ] The `[predict]` and `[lsl]` extras stay pip-only: `skaters` and + `pylsl` are not on conda-forge (checked 2026-09-11). `chronos-forecasting` + is (2.3.2 on 2026-09-11), so `[predict-hf]` could be expressed in the + recipe if wanted. The base package is unaffected either way. - [ ] Merge; after the feedstock builds, verify in a clean env: `conda install -c conda-forge hypertools` installs `1.1.0` and `import hypertools` works. -## 8. Cleanup +## 8. Announce + +- [ ] Bluesky launch thread from `notes/bluesky-launch/` (gitignored): re-verify + the atproto limits with curl before posting (they have moved between + drafts), count graphemes with the `regex` module's `\X`, and expect the + tutorial "Full code" links to 404 until Read the Docs has built the tag. + +## 9. Cleanup - [ ] After the release is confirmed good, delete the `dev-1.0` / - `dev-1.0-refactor` branches if desired (maintainer's call; 1.0.0's - checklist deferred it too) (the released artifacts no longer - reference them; the `release-gate` guarantees this). + `dev-1.0-refactor` / `fix/1.1-release-review` branches if desired + (maintainer's call; the released artifacts do not reference them; the + `release-gate` guarantees this). ## What the `release-gate` enforces (so you can't forget) @@ -203,7 +284,7 @@ run with `HYPERTOOLS_REQUIRE_RELEASE=1` by the `release-gate` CI job on | notebook install-cell note | no `(… preview)` / "On release this becomes …" | | README image URLs | `…/ContextLab/hypertools/v<version>/images/…` — the tag EXACTLY equal to `v` + pyproject version, not any semver or a commit SHA | | README branch refs | no `dev-1.0-refactor` / `hypertools.git@dev…` | -| CHANGELOG heading | `## <version> (YYYY-MM-DD)` — version == pyproject, and a REAL calendar date (not `(unreleased)`, not `2026-99-99`) | +| CHANGELOG heading | `## <version> (YYYY-MM-DD)` — version == pyproject, a REAL calendar date (not `(unreleased)`, not `2026-99-99`), and not earlier than the release commit's date (a stale draft date fails) | | generated gallery (`docs-clean` job) | every built `docs/auto_examples/*.ipynb` carries the PyPI spec (covers every published notebook, at the build layer) | | gallery Colab notebooks published | `docs-notebooks/v<version>/manifest.json` present, its `source_commit` == the release HEAD, its inventory == the built gallery, and the branch's actual `.ipynb` set (one GitHub tree request) == the manifest — so stale (old-RC), partial, or mismatched publishes all fail. Requires step 2's publish to have run FROM the release commit, BEFORE the master/tag push — see the deadlock note there. | diff --git a/docs/_gallery_scrapers.py b/docs/_gallery_scrapers.py new file mode 100644 index 00000000..a61ebf2c --- /dev/null +++ b/docs/_gallery_scrapers.py @@ -0,0 +1,66 @@ +"""Image scrapers the sphinx-gallery build uses (see ``image_scrapers`` in +conf.py). + +Kept in its own module, free of conf.py's import-time side effects, so +tests/test_docs_site_fixes.py can drive the scraper the way sphinx-gallery +does (once per code block) and count what it emits. +""" +import weakref + +#: every animation already handled for the running build. Weak, so an +#: animation that an example rebinds and drops is forgotten with it (an +#: ``id()`` set would mistake a new animation at a recycled address for an +#: old one and skip it). +_SCRAPED = weakref.WeakSet() + + +def matplotlib_and_hyperanimation_scraper(block, block_vars, gallery_conf, + **kwargs): + """sphinx-gallery's matplotlib scraper, plus the animations it misses. + + sphinx-gallery's matplotlib scraper walks ``plt.get_fignums()`` and pairs + each MANAGED figure with any ``matplotlib.animation.Animation`` in the + example's namespace. A ``hyp.plot(..., show=False)`` animation leaves its + figure unmanaged by pyplot, so the launch examples (which bind the + ``HyperAnimation`` wrapper the call returns) rendered NOTHING in the + gallery -- measured 2026-09-03: their pages carried no image block at + all. This scraper therefore also renders, through sphinx-gallery's own + animation writer, every ``HyperAnimation`` (or bare ``Animation``) in the + namespace that the matplotlib scraper did not. + + Each animation is rendered exactly ONCE. Two things used to break that + (built site, 2026-10: examples/animate.py showed five videos for its two + ``hyp.plot`` calls) when this ran as a separate scraper AFTER the + matplotlib one: + + * the matplotlib scraper closes every pyplot figure when it finishes, so + by then the animation it had just rendered no longer looked "managed" + and was rendered a second time; + * an example's namespace persists across its code blocks, so every + animation bound by an earlier block was rendered again in each later + block. + + So the set of managed figures is read BEFORE the matplotlib scraper runs, + and every animation handled (by either path) is remembered. + """ + from pathlib import PurePosixPath + import matplotlib.pyplot as plt + from matplotlib.animation import Animation + from sphinx_gallery.scrapers import _anim_rst, matplotlib_scraper + from hypertools.plot.hyper_animation import HyperAnimation + + managed = {plt.figure(num) for num in plt.get_fignums()} + pending = [] + for value in list(block_vars['example_globals'].values()): + ani = value.animation if isinstance(value, HyperAnimation) else value + if not isinstance(ani, Animation) or ani in _SCRAPED: + continue + _SCRAPED.add(ani) + if ani._fig not in managed: # else the matplotlib scraper has it + pending.append(ani) + + rst = [matplotlib_scraper(block, block_vars, gallery_conf, **kwargs)] + for ani in pending: + image_path = PurePosixPath(next(block_vars['image_path_iterator'])) + rst.append(_anim_rst(ani, image_path, gallery_conf)) + return '\n'.join(part for part in rst if part) diff --git a/docs/_static/custom.css b/docs/_static/custom.css index 52be82f4..1d05c0ff 100644 --- a/docs/_static/custom.css +++ b/docs/_static/custom.css @@ -10,82 +10,142 @@ font-size: 2rem !important; } -/* Gallery styling improvements */ -.sphx-glr-thumbcontainer { +/* ---- Gallery cards -------------------------------------------------- + sphinx-gallery emits, per card: + + <div class="sphx-glr-thumbcontainer" tooltip="..."> + <img .../> (docs/post_build.py wraps this in + <a class="hypertools-thumb-link">, + which opens the notebook on Colab) + <p><a class="reference internal" href="X.html"> + <span class="doc">Title</span></a></p> + <div class="sphx-glr-thumbnail-title">Title</div> + </div> + + and its own stylesheet stretches the <p><a> invisibly over the whole card + while showing the (unlinked) title <div>. An earlier rule here hid that + <p> outright, which removed the ONLY link to the example page: the card's + title and body did nothing when clicked. Instead, show the <p><a> as the + card's visible title link, drop the duplicate title <div>, and stretch the + link's hit area over the card. The thumbnail's Colab link sits above that + hit area, so: image -> notebook on Colab, anything else -> example page. */ +.sphx-glr-thumbcontainer p { + position: static; + width: auto; + height: auto; + margin: 0; + text-align: center; + font-size: 14px; + line-height: 1.4; +} + +.sphx-glr-thumbcontainer p a.reference { + position: static; + display: inline; + width: auto; + height: auto; + padding: 0; + color: var(--color-foreground-primary); + text-decoration: none; +} + +.sphx-glr-thumbcontainer p a.reference span { + display: inline; +} + +.sphx-glr-thumbcontainer p a.reference::after { + content: ""; + position: absolute; + inset: 0; + z-index: 1; +} + +.sphx-glr-thumbcontainer:hover p a.reference, +.sphx-glr-thumbcontainer p a.reference:focus-visible { + color: var(--color-brand-content); + text-decoration: underline; +} + +.sphx-glr-thumbcontainer .sphx-glr-thumbnail-title { + display: none; +} + +.sphx-glr-thumbcontainer .hypertools-thumb-link { position: relative; - display: inline-block; - cursor: pointer; - margin: 10px; + z-index: 2; + line-height: 0; } .sphx-glr-thumbcontainer img { - cursor: pointer; transition: opacity 0.2s; } -.sphx-glr-thumbcontainer:hover img { - opacity: 0.8; +.sphx-glr-thumbcontainer .hypertools-thumb-link:hover img { + opacity: 0.7; } -/* Fix tooltips being cut off */ -.sphx-glr-thumbcontainer[tooltip] { - position: relative; +.sphx-glr-thumbcontainer:hover { + border-color: var(--color-brand-primary); } -.sphx-glr-thumbcontainer[tooltip]:before { +/* Tooltip (the example's intro paragraph): a floating box below the card, + in the theme's own colours so it is legible in light and dark. This + replaces sphinx-gallery's in-card tooltip -- a ::before veil plus an + ::after text layer that cover the thumbnail and title -- rather than + adding a second one on top of it (both used to show at once). Below, not + above: above, the first row's tooltips ran off the top of the page. */ +.sphx-glr-thumbcontainer[tooltip]::before { + content: none; +} + +.sphx-glr-thumbcontainer[tooltip]::after, +.sphx-glr-thumbcontainer[tooltip]:hover::after { content: attr(tooltip); position: absolute; - bottom: 100%; + top: calc(100% + 6px); + bottom: auto; left: 50%; transform: translateX(-50%); - background: rgba(0, 0, 0, 0.9); - color: white; + box-sizing: border-box; + width: max-content; + max-width: min(280px, 80vw); + max-height: none; padding: 8px 12px; + /* show the whole intro (sphinx-gallery clamps it to 6 lines) */ + display: block; + -webkit-line-clamp: none; + overflow: visible; + border: 1px solid var(--color-background-border); border-radius: 6px; + background: var(--color-background-secondary); + color: var(--color-foreground-primary); font-size: 11px; - white-space: normal; - max-width: 280px; - width: max-content; - word-wrap: break-word; line-height: 1.3; + text-align: left; + white-space: normal; + overflow-wrap: break-word; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3); + pointer-events: none; visibility: hidden; opacity: 0; transition: opacity 0.3s, visibility 0.3s; z-index: 1000; - box-shadow: 0 2px 8px rgba(0,0,0,0.3); - pointer-events: none; } -.sphx-glr-thumbcontainer[tooltip]:hover:before { +.sphx-glr-thumbcontainer[tooltip]:hover::after { visibility: visible; opacity: 1; } -/* If tooltip would go off-screen, position it differently */ -.sphx-glr-thumbcontainer[tooltip]:last-child:before, -.sphx-glr-thumbcontainer[tooltip]:nth-last-child(2):before { - left: auto; - right: 0; - transform: none; -} - -/* Make the entire thumbnail container clickable */ -.sphx-glr-thumbcontainer { - cursor: pointer; -} - -/* Handle click events on the entire container using JavaScript */ -.sphx-glr-thumbcontainer p { - display: none; /* Hide the broken reference text */ +/* A touch screen has no hover: tapping a card would flash the tooltip over + its neighbours while the page navigates. */ +@media (hover: none) { + .sphx-glr-thumbcontainer[tooltip]::after, + .sphx-glr-thumbcontainer[tooltip]:hover::after { + display: none; + } } -/* Style the thumbnail title */ -.sphx-glr-thumbnail-title { - text-align: center; - font-size: 14px; - margin-top: 5px; - color: #333; -} /* ---- ContextLab brand (ported from ContextLab/scheduler, based on the lab's website): Nunito Sans everywhere, lowercase thin headings with slight letter-spacing, lab green for links/brand ---- */ @@ -105,12 +165,25 @@ h1, h2, h3, h4, h5, h6, font-weight: 300; } +/* ...except where the heading is a Python name, which is case-sensitive: + the lowercase look turned the API pages' titles into names that do not + exist ("hypertools.io.lslstream", "hypertools.hyperanimation"). An + autosummary page is a <section> whose <h1> is the object's dotted name + and whose next child is the object's <dl class="py ...">; inline code in + any heading is exempt for the same reason. */ +section:has(> dl.py) > h1, +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + text-transform: none; +} + a { color: var(--color-brand-content, #007030); } +/* set per theme in conf.py (a fixed dark green is unreadable on furo's + dark background) */ a:hover { - color: #005524; + color: var(--color-link--hover); } pre { @@ -135,3 +208,31 @@ article .sphinx-contrib-video-container video { max-width: 100%; height: auto; } + +/* plotly figures are laid out at a fixed pixel size (640px wide by default), + in a wrapper <div style="width:640px"> -- wider than a phone's content + column, where the right-hand side was simply cut off. Let the wrapper + shrink to the column and scroll the figure inside it. No effect on + desktop, where the column is wider than the figure. */ +article div:has(> .plotly-graph-div) { + max-width: 100%; + overflow-x: auto; + overflow-y: hidden; +} + +/* Tutorial pages: the notebook's install cell, moved under the title and + collapsed by docs/post_build.py */ +details.hypertools-setup { + margin: 0.5rem 0 1.25rem; + border: 1px solid var(--color-background-border); + border-radius: 0.25rem; + padding: 0.35rem 0.75rem; +} +details.hypertools-setup > summary { + cursor: pointer; + font-size: var(--font-size--small); + color: var(--color-foreground-secondary); +} +details.hypertools-setup[open] > summary { + margin-bottom: 0.5rem; +} diff --git a/docs/_static/gallery-fixes.js b/docs/_static/gallery-fixes.js index ab1ef1e3..a3cde439 100644 --- a/docs/_static/gallery-fixes.js +++ b/docs/_static/gallery-fixes.js @@ -4,3 +4,44 @@ // The previous runtime click-handler here targeted sphinx-gallery <= 0.16 // markup (.xref spans) that no longer exists, so clicks silently did // nothing. + +// plotly figures are drawn at a fixed pixel size (640x480 by default) inside +// a wrapper <div style="width:640px; height:480px">. On a phone the content +// column is narrower than that; custom.css caps the wrapper at the column +// width and lets the figure scroll inside it, but a drag on a 3D figure +// rotates it rather than scrolling, so half the figure stayed out of reach. +// Redraw each figure at the width it actually has (same aspect ratio), and +// back at its natural size when there is room again. Desktop is untouched: +// the wrapper is as wide as the figure there, so nothing is relaid out. +(function () { + function fitPlotlyFigures() { + if (!window.Plotly) { return; } + document.querySelectorAll('.plotly-graph-div').forEach(function (gd) { + var wrap = gd.parentElement; + var full = gd._fullLayout; + if (!wrap || !full || !full.width || !full.height) { return; } + if (!gd.dataset.hypNaturalSize) { + gd.dataset.hypNaturalSize = full.width + 'x' + full.height; + } + var natural = gd.dataset.hypNaturalSize.split('x').map(Number); + var width = Math.min(natural[0], wrap.clientWidth); + if (width < 50 || Math.abs(width - full.width) < 1) { return; } + var height = Math.round(natural[1] * width / natural[0]); + wrap.style.height = height + 'px'; + window.Plotly.relayout(gd, {width: width, height: height}); + }); + } + + var pending = null; + function schedule() { + clearTimeout(pending); + pending = setTimeout(fitPlotlyFigures, 150); + } + window.addEventListener('load', function () { + fitPlotlyFigures(); + // figures whose plotly.js arrives after `load` (slow CDN) + setTimeout(fitPlotlyFigures, 1000); + setTimeout(fitPlotlyFigures, 4000); + }); + window.addEventListener('resize', schedule); +}()); diff --git a/docs/_static/pipeline_order.svg b/docs/_static/pipeline_order.svg index 12462478..e049da7f 100644 --- a/docs/_static/pipeline_order.svg +++ b/docs/_static/pipeline_order.svg @@ -21,251 +21,251 @@ </defs> <g id="figure_1"> <g id="patch_1"> - <path d="M 0 324 -L 889.92 324 -L 889.92 0 -L 0 0 + <path d="M 0 324 +L 889.92 324 +L 889.92 0 +L 0 0 z " style="fill: #ffffff"/> </g> <g id="axes_1"> <g id="patch_2"> - <path d="M 106.169123 156.046154 -Q 117.819218 156.046154 127.904066 156.046154 + <path d="M 106.169123 156.046154 +Q 117.819218 156.046154 127.904066 156.046154 " clip-path="url(#p6d43bddd9f)" style="fill: none; stroke: #1b1b1b; stroke-width: 1.4; stroke-linecap: round"/> - <path d="M 121.504066 152.846154 -L 127.904066 156.046154 -L 121.504066 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The forecast is recomputed from the history revealed so far and -re-anchored on the last revealed observation, so the forecast trace grows with -the animation instead of standing still: +drawn from the endpoint the current frame draws, so the forecast trace grows +with the animation instead of standing still: .. code-block:: python @@ -427,8 +427,16 @@ the animation instead of standing still: ``t`` is measured in **raw observations of the analyzed data** -- not in animation frames, and not in drawn vertices. ``t=1`` forecasts the next observation. Because an animation is paced on a resampled frame grid (see -``duration``/``frame_rate``), an animated forecast joins the drawn trajectory -to within one raw observation rather than exactly. +``duration``/``frame_rate``), a frame's drawn head usually falls *between* two +observations. The forecast starts exactly there, so it meets the trajectory it +continues on every frame; the points it predicts stay where the model put +them, ``t`` observations on from the last one revealed. + +.. versionchanged:: 1.1.0 + The forecast used to hang off the last raw observation at or before the + head, so it stood still for as many frames as the head took to reach the + next observation and trailed the line's tip by up to a whole step -- most + visibly on data with fewer rows than frames. Everything is computed before the first frame ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -480,8 +488,10 @@ An observed line with no ``alpha=`` set is matplotlib's *opaque*, i.e. 1.0, so the default forecast alpha is 0.5. Per-dataset styling carries through dataset by dataset: ``alpha=[1.0, 0.4]`` gives forecasts at ``[0.5, 0.2]``, and a dotted dataset gets a dotted forecast. Both backends apply the identical -rule (on plotly, colour/width/dash with the alpha baked into the ``rgba(...)`` -line colour and echoed in ``meta['hyp_forecast_alpha']``). +rule. On plotly the forecast trace copies the colour, width and dash, and +carries the alpha in the ``rgba(...)`` line colour of a 2-D trace or in the +trace ``opacity`` of a 3-D one; either way the value is echoed in +``meta['hyp_forecast_alpha']``. .. versionchanged:: 1.1.0 Before 1.1.0 every forecast was drawn ``linestyle='--'`` at a hard-coded @@ -493,6 +503,27 @@ line colour and echoed in ``meta['hyp_forecast_alpha']``). a floor proportional to it -- so a retained forecast is never more opaque than the live forecast it decays from, however faint the dataset. +A forecast given its **own colour** -- by ``forecast_palette=``, +``forecast_hue=``, ``forecast_cluster=``, or a colour letter in +``forecast_fmt=`` -- keeps its trace's alpha instead of halving it: the colour +is then what tells it apart, and fading it as well hid it among translucent +traces. + +A **collection of models** (``predict=['Kalman', 'ARIMA', ...]``) draws one +overlay per model on every trace. Each keeps its dataset's colour (which series +it continues) and takes a linestyle per model -- solid, dashed, dotted, +dash-dot, in model order -- so the two questions are answered by two +encodings; ``forecast_palette=`` colours by model instead, and +``forecast_fmt=`` (one entry per model) replaces the cycle. + +Every ``predict=`` form lists its forecast **once in the legend**, static or +animated, under the model's name (the same name ``hyp.predict(x, model=[...])`` +gives it), after the data entries and before ``truth``. The key wears the +forecasts' linestyle and their colour when they share one; when one model's +forecasts of several datasets wear several colours the key is a neutral gray, +and it is never drawn below 0.8 alpha, so it stays legible however faint the +forecasts are. + Animated forecasts under ``hue=``/``cluster=`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -563,7 +594,8 @@ Everything they do not name stays inherited. * - *(nothing)* - the identity of the observed trace it continues * - ``forecast_palette=`` - - the same, in a palette of its own + - one colour per forecast from a palette of its own (one per *model* + for a collection), drawn at the trace's own alpha * - ``forecast_hue=`` - a grouping you supply, one value per forecast (see below) * - ``forecast_cluster=`` @@ -575,7 +607,8 @@ regroups the data: ``plot()`` forecasts every final trace, so a hierarchy wants one value per leaf group **plus** one per derived mean. ``forecast_fmt=`` sets the line/marker style, in the same format-string -grammar as ``fmt``, and changes nothing else: +grammar as ``fmt``, and changes nothing else -- unless the string carries a +colour letter (``'r:'``), which recolours the forecast too, on both backends: .. code-block:: python diff --git a/docs/api.rst b/docs/api.rst index e567f544..d2083a79 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -114,6 +114,91 @@ also groups by the outer levels but treats the innermost one as the time axis, which survives as each group's index. The result is a list of forecasts, one per group -- see :doc:`hierarchy`. +.. _observation-times: + +Observation times and future steps +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Forecasting uses each dataset's own observation times. Datetime, timedelta, +period and unique numeric indexes are sorted together with their values before +fitting. Periods are fitted on their start timestamps and forecast as periods of +the same frequency. Duplicate time +stamps are rejected; repeated numeric row IDs retain their positional meaning +(for example, stacked runs). Arrays and categorical row labels use observation +order. Shuffling timed rows therefore does not change the fitted forecast. + +A datetime index with a **calendar frequency** -- stored in ``index.freq``, +inferable with ``pd.infer_freq`` (business days, month starts, weeks, quarters, +hours, tz-aware days across DST), or a ``PeriodIndex``'s own -- steps on that +calendar, so it is regular: it is fitted on its own rows and forecast onto the +next business days, month starts or periods. Weekday-only sessions that skip a +few weekdays (exchange holidays) step in business days. Otherwise one future +step is the **median positive gap** between sorted timestamps. +Override it with ``hyp.predict(data, step='1h')`` for datetime/duration indexes +(or a calendar frequency such as ``step='B'`` for datetime indexes), +or ``step=0.5`` for numeric coordinates. Each dataset gets its own inferred +interval. A fitted model keeps its training interval when applied to new data, +so a learned one-hour transition never silently becomes a three-hour transition; +reused on a different kind of index (fit on an array, applied to dated rows, or +the reverse) it steps in the new data's own units. + +GaussianProcess fits the actual times, expressed as elapsed multiples of the +model's step. Kalman, ARIMA, AutoRegressor, Laplace and Chronos assume regular +steps: irregular data are **linearly interpolated** column by column onto a +regular grid ending at the latest observation, with a warning. The grid stays +inside the observed time span; training values are never extrapolated. Existing +missing values are not imputed by this operation. Interpolation can smooth +short-lived changes; choose the interval for your data, or use GaussianProcess +to retain the original observation times without interpolation. Models retain +their existing univariate/multivariate behavior. + +``hyp.plot(..., predict=...)`` follows the same policy. In ``ndims=1`` mode, +the time index supplies predictor coordinates rather than becoming another +signal column to forecast. The columns of each dataset are forecast together, +then split into lines for drawing. For a step override in a plot, use +``predict={'model': 'Kalman', 'kwargs': {'step': '1h'}}``. Animated forecasts +use only the observations revealed so far and wait until the model has enough +history, including enough interpolated grid points. If preprocessing changes +the row count and discards the corresponding timestamps, pass the analyzed +data with its updated index explicitly instead of guessing its times. + +Backtesting at observation times +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``hyp.predict(..., holdout=...)`` sorts timed observations before holding out +the last rows. The model and its interval are fitted on the remaining training +rows only. GaussianProcess evaluates predictions at the actual held-out times. +Regular-grid models forecast far enough to cover those times, then select +matching grid points or **linearly interpolate predictions** between them. +For a held-out time before the first full forecast step, interpolation starts +at the last observed training value. Missing endpoints remain missing; +held-out values are never used in fitting or interpolation. + +Returned forecasts, the naive baseline and ``'truth'`` share the held-out +index. ``horizon`` in the scores table counts held-out observations, which can +differ from the number of regular forecast steps. Arrays, categorical row +labels and repeated numeric row IDs are scored by observation position. +Choosing trading-day positions instead of calendar dates is therefore an +explicit modeling choice; the stock-forecasting tutorial demonstrates both. + +For example, these observations are scored at times 12 and 20, rather than +being compared with the model's next two regular steps at times 9 and 10: + +.. doctest:: + + >>> import pandas as pd + >>> import hypertools as hyp + >>> from sklearn.gaussian_process.kernels import DotProduct + >>> timed = pd.DataFrame({'value': [0., 1., 2., 4., 7., 8., 12., 20.]}, + ... index=[0., 1., 2., 4., 7., 8., 12., 20.]) + >>> scores, evaluated = hyp.predict( + ... timed, model='GaussianProcess', holdout=2, return_forecasts=True, + ... kernel=DotProduct(sigma_0=1, sigma_0_bounds='fixed')) + >>> evaluated['GaussianProcess'].index.tolist() + [12.0, 20.0] + >>> evaluated['GaussianProcess'].index.equals(evaluated['truth'].index) + True + Plot ------------------ @@ -142,6 +227,26 @@ Colors .. autofunction:: hypertools.plot.colors.continuous_colormap +Colors extracted from an image are put in a deterministic order before they +become a plot palette -- by value, dark to bright, unless ``palette_sort=`` +(or ``?sort=`` in an ``'image:<path>'`` spec) asks for another key -- while +``image_palette`` itself, and the lead color of a dataset an image stands +for, keep the most-salient-first order. + +.. autofunction:: hypertools.plot.colors.sort_colors + +A t x k data matrix is a palette too. ``matrix_palette`` reduces it to three +dimensions with ``hypertools.reduce`` (``palette_reduce=`` in ``plot``, +default ``'PCA'``, with ``palette_manip=``/``palette_normalize=``/ +``palette_align=`` passed through), scales each reduced column to [0, 1] as +an RGB channel, sorts the rows (default ``'columns'``: along the first +component) and returns a colormap that a plot resamples by interpolation to +as many colors as it needs. + +.. autofunction:: hypertools.plot.colors.matrix_palette + +.. autoclass:: hypertools.plot.colors.MatrixColormap + Set interactive backend ------------------------ @@ -150,6 +255,26 @@ Set interactive backend set_interactive_backend +Set autoinstall +------------------------ + +.. autosummary:: + :toctree: + + set_autoinstall + +The optional features (the plotly backend, text embeddings, ``Laplace`` and +``Chronos`` forecasting, the torch autoencoders, gensim models, Kaggle and +Hugging Face loading, LSL streaming, 3-D density iso-surfaces, ``.xlsx`` +files) are ``pip`` extras that install themselves on demand: the first call +that needs one installs that extra's requirements, prints a one-line +``hypertools:`` notice and carries on. ``set_autoinstall(False)`` turns +this off (for the session, or for one block as a context manager); a +missing extra then raises ``ImportError`` naming the manual ``pip install +"hypertools[<extra>]"`` command. See :doc:`optional_dependencies` for the +extras, the Chrome step behind static plotly export, and how to +pre-install everything. + Analyze ------------------ @@ -223,14 +348,21 @@ I/O io.lsl_stream io.LSLStream - io.lsl.synthetic_outlet + io.synthetic_outlet Exceptions ------------------ -HyperTools' I/O, backend, and remote-load/trust errors derive from -`hypertools.HypertoolsError`. Input-validation errors (invalid parameters or -data shapes) raise standard `ValueError`/`TypeError` with actionable messages. +HyperTools' I/O and backend errors derive from `hypertools.HypertoolsError`. +`HypertoolsOfflineError` (``hyp.load(..., offline=True)`` with no cached copy +to read) is a `HypertoolsIOError`, importable from ``hypertools`` and +``hypertools.io``. `HypertoolsTrustError` is raised by `load` when a remote +payload would have to be unpickled (a pickle, or an object-array .npy/.npz) +and ``trust=True`` was not passed; it subclasses `ValueError`, not +`HypertoolsError`, and is importable from ``hypertools`` (it is defined in +``hypertools.io.sources``). Input-validation +errors (invalid parameters or data shapes) raise standard +`ValueError`/`TypeError` with actionable messages. .. autosummary:: :toctree: @@ -238,6 +370,8 @@ data shapes) raise standard `ValueError`/`TypeError` with actionable messages. HypertoolsError HypertoolsBackendError HypertoolsIOError + HypertoolsOfflineError + HypertoolsTrustError Tools ------------------ diff --git a/docs/conf.py b/docs/conf.py index 1e21a4ed..56b60fd5 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -108,10 +108,12 @@ def _install_notebook_cell(): 'sphinx.ext.autosummary', 'sphinx.ext.viewcode', # provides the `.. doctest::` directive docs/hierarchy.rst uses for its - # worked examples. Those examples are EXECUTED by - # tests/test_docs_hierarchy_guide.py (via doctest.testfile), not by this - # builder -- the extension is here so the directive renders instead of - # raising "Unknown directive type", which -W turns into a build failure. + # worked examples. The html build only RENDERS them; they run under + # `make doctest` (the `-b doctest` builder) in this directory. The + # pytest suite pins the guide's structure, links and quoted messages + # (tests/test_docs_hierarchy_guide.py) but does not execute the blocks. + # The extension is here so the directive renders instead of raising + # "Unknown directive type", which -W turns into a build failure. 'sphinx.ext.doctest', # (see doctest_global_setup below -- running `-b doctest` from the repo # root used to litter it with the files those examples write) @@ -157,6 +159,10 @@ def _install_notebook_cell(): # rendering), just without the broken per-method stub links. numpydoc_class_members_toctree = False +# The autosummary class stubs already list each class's methods and attributes; +# numpydoc's own tables put a second copy of both on every class page. +numpydoc_show_class_members = False + # Never execute notebooks during the docs build: tutorial notebooks are # committed pre-executed (with outputs), and 'auto' also re-executed every # sphinx-gallery-generated .ipynb -- doubling build time and hanging on @@ -274,12 +280,14 @@ def _install_notebook_cell(): 'light_css_variables': { 'color-brand-primary': '#007030', 'color-brand-content': '#007030', + 'color-link--hover': '#005524', 'font-stack': "'Nunito Sans', -apple-system, BlinkMacSystemFont, " "'Segoe UI', Helvetica, Arial, sans-serif", }, 'dark_css_variables': { 'color-brand-primary': '#4CAF50', 'color-brand-content': '#4CAF50', + 'color-link--hover': '#81C784', }, 'footer_icons': [ { @@ -361,38 +369,31 @@ def _install_notebook_cell(): ] -def hyperanimation_scraper(block, block_vars, gallery_conf, **kwargs): - """Scrape the animations ``hyp.plot(..., show=False)`` returns. +# The gallery's matplotlib scraper lives in docs/_gallery_scrapers.py (a +# side-effect-free sibling module, importable by the test suite): it is +# sphinx-gallery's own matplotlib scraper plus the ``show=False`` +# HyperAnimations that one cannot see, each rendered exactly once. +from _gallery_scrapers import ( # noqa: E402 (needs the sys.path entry above) + matplotlib_and_hyperanimation_scraper) + - sphinx-gallery's matplotlib scraper walks ``plt.get_fignums()`` and pairs - each MANAGED figure with any ``matplotlib.animation.Animation`` in the - example's namespace. A ``show=False`` plot leaves its figure unmanaged - by pyplot, so the five launch examples (which bind the ``HyperAnimation`` - wrapper the call returns) rendered NOTHING in the gallery -- measured - 2026-09-03: their generated pages carried no image block at all, and no - ``.mp4`` for a thumbnail. This scraper finds every ``HyperAnimation`` (or - bare ``Animation``) whose figure the matplotlib scraper will not see and - renders it through sphinx-gallery's own animation writer, so the page - gets the same embedded video as the managed-figure examples. +def _quiet_gallery_only_warnings(gallery_conf, fname): + """sphinx-gallery ``reset_modules`` hook, run before every example. + + The docs build renders with the non-interactive Agg backend, so + examples/explore.py's ``explore=True`` makes hypertools warn -- correctly + -- that hover labels need an interactive backend. sphinx-gallery prints a + warning into the page's output block together with the build machine's + absolute path to the example. The warning describes the build + environment, not anything a reader of the page did, and the page's own + text says the picture is a static render; so it is filtered here, for the + gallery build only. Library behaviour is unchanged. """ - from pathlib import PurePosixPath - import matplotlib.pyplot as plt - from matplotlib.animation import Animation - from sphinx_gallery.scrapers import _anim_rst - from hypertools.plot.hyper_animation import HyperAnimation - - managed = {plt.figure(num) for num in plt.get_fignums()} - seen, rst = set(), [] - for value in block_vars['example_globals'].values(): - ani = value.animation if isinstance(value, HyperAnimation) else value - if not isinstance(ani, Animation) or id(ani) in seen: - continue - seen.add(id(ani)) - if ani._fig in managed: - continue # the matplotlib scraper renders it - image_path = PurePosixPath(next(block_vars['image_path_iterator'])) - rst.append(_anim_rst(ani, image_path, gallery_conf)) - return '\n'.join(rst) + import warnings + warnings.filterwarnings( + 'ignore', category=UserWarning, + message=r'explore=True shows labels on hover, which needs an ' + r'interactive matplotlib backend') # Gallery page order. sphinx-gallery's default (`NumberOfCodeLinesSortKey`) @@ -453,11 +454,15 @@ def _gallery_order(filename): 'hypertools GALLERY_ORDER'), # Abort on first failure 'abort_on_example_error': False, - # Execute code to generate plots - 'plot_gallery': True, + # Execute code to generate plots. HYPERTOOLS_DOCS_PLOT_GALLERY=0 turns + # the gallery off as a real boolean (the doctest builder in CI and the + # local release pipeline use it; `-D plot_gallery=0` on the command line + # is a string and makes sphinx-gallery warn about the type). + 'plot_gallery': os.environ.get('HYPERTOOLS_DOCS_PLOT_GALLERY', '1') + not in ('0', 'false', 'no', 'off'), # execute EVERY example (the sphinx-gallery default only executes - # files named plot_*, which left animate*/chemtrails/precog/explore/ - # save_*/analyze pages with code but no rendered output) + # files named plot_*, which would leave the animate*/explore/save_*/ + # analyze pages with code but no rendered output) 'filename_pattern': r'.*\.py', # render matplotlib FuncAnimations (exposed as variables in the # examples) as embedded HTML5 video via ffmpeg @@ -470,10 +475,25 @@ def _gallery_order(filename): 'expected_failing_examples': [], # Performance optimizations 'capture_repr': ('_repr_html_',), - # scrape BOTH matplotlib figures and plotly figures (the plotly scraper + # scrape BOTH matplotlib figures (including `show=False` animations, + # see docs/_gallery_scrapers.py) and plotly figures (the plotly scraper # renders interactive figures into the gallery via kaleido) - 'image_scrapers': ('matplotlib', plotly_sg_scraper, - hyperanimation_scraper), + 'image_scrapers': (matplotlib_and_hyperanimation_scraper, + plotly_sg_scraper), + # A plotly figure that is a block's last expression was shown TWICE: once + # by the plotly scraper above (hyp.plot shows the figure, which is what + # the scraper collects) and once more through `capture_repr`, because a + # plotly Figure also has a `_repr_html_`. Keep the scraped copy -- it is + # the one that also yields the page's thumbnail PNG. + # (hyp.plot returns `HyperPlotlyFigure`, a plotly `Figure` subclass; + # sphinx-gallery matches this against `str(type(obj))`.) + 'ignore_repr_types': r'plotly\.graph_objs\._figure\.Figure' + r'|HyperPlotlyFigure', + # `# sphinx_gallery_thumbnail_path = ...` lines configure the build; they + # are not part of the example and should not be shown in its code + 'remove_config_comments': True, + # the defaults, plus the gallery-only warning filter defined above + 'reset_modules': ('matplotlib', 'seaborn', _quiet_gallery_only_warnings), # Limit memory usage display 'show_memory': False, # Ensure proper thumbnail linking @@ -493,7 +513,60 @@ def _gallery_order(filename): } +class _GallerySourceLink: + """A per-page `source_edit_link` / `source_view_link` for gallery pages. + + furo's "Edit this page" / "View this page" buttons build their URL from + `source_directory` + pagename + suffix, which for a sphinx-gallery page + is `docs/auto_examples/<stem>.rst` -- a file that is generated at build + time and gitignored, so every gallery page's link 404'd. The theme + consults `theme_source_edit_link` (`theme_source_view_link`) FIRST and + calls its `.format(filename=pagename + page_source_suffix)`, so this + object stands in for that string on gallery pages only (set from + `_gallery_page_context` below) and maps the generated page back to the + tracked source under examples/: `auto_examples/<stem>` -> + `examples/<stem>.py`, and the gallery index -> `examples/README.txt`. + """ + + def __init__(self, url_template): + self.url_template = url_template + + def format(self, filename): + page = filename.rsplit('.', 1)[0] # strip the .rst suffix + stem = page[len('auto_examples/'):] + source = 'README.txt' if stem == 'index' else stem + '.py' + return self.url_template.format(path='examples/' + source) + + +_GALLERY_REPO = html_theme_options['source_repository'].rstrip('/') +_GALLERY_BRANCH = html_theme_options['source_branch'] +_GALLERY_EDIT_LINK = _GallerySourceLink( + f'{_GALLERY_REPO}/edit/{_GALLERY_BRANCH}/{{path}}') +_GALLERY_VIEW_LINK = _GallerySourceLink( + f'{_GALLERY_REPO}/blob/{_GALLERY_BRANCH}/{{path}}?plain=true') +_EXAMPLES_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), + '..', 'examples') + + +def _gallery_page_context(app, pagename, templatename, context, doctree): + """Point gallery pages' edit/view links at examples/ (see + `_GallerySourceLink`), and drop the links on the generated pages that + have no tracked source at all (`sg_execution_times`, a stale page whose + example was deleted): furo hides both buttons when + `page_source_suffix` is empty.""" + if not pagename.startswith('auto_examples/'): + return + stem = pagename[len('auto_examples/'):] + source = 'README.txt' if stem == 'index' else stem + '.py' + if '/' in stem or not os.path.exists(os.path.join(_EXAMPLES_DIR, source)): + context['page_source_suffix'] = '' + return + context['theme_source_edit_link'] = _GALLERY_EDIT_LINK + context['theme_source_view_link'] = _GALLERY_VIEW_LINK + + def setup(app): + app.connect('html-page-context', _gallery_page_context) # Keep the strict (-W) docs-clean CI gate robust to TRANSIENT third-party # doc-site outages: sphinx-gallery fetches each `reference_url` site's # searchindex.js to hyperlink API names, and a 503 there would otherwise diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 1052e0a7..249db441 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -7,13 +7,15 @@ nbsphinx>=0.8.0 jupyter_client>=6.0.0 wikipedia ipython -# Main package dependencies -scikit-learn>=1.4.0 -pandas>=2.2.0 +# Main package dependencies (same floors as pyproject.toml, which is the +# declaration: the docs image installs the package first, so these only +# need to agree with it) +scikit-learn>=1.5.2 +pandas>=2.2.3 seaborn>=0.13.0 -matplotlib>=3.8.0 -scipy>=1.13.0 -numpy>=2.0.0 +matplotlib>=3.9.2 +scipy>=1.14.1 +numpy>=2.1.0 umap-learn>=0.5.5 requests>=2.31.0 ipympl>=0.9.3 @@ -32,12 +34,12 @@ kaleido>=1.0 # (e.g. `brew install ffmpeg` / `apt install ffmpeg`) before building docs. # hyp.predict/[predict] models used by gallery examples (plot_predict, plot_impute) pykalman>=0.11 -statsmodels>=0.14 +statsmodels>=0.14.3 skaters>=0.11 # plot(..., density=True) 3-D iso-surfaces ([density3d] extra); gallery # examples build with it installed so the docs show the iso-surface path # rather than the scatter-fog fallback -scikit-image>=0.22.0 +scikit-image>=0.25.0 # hypertools.reduce.autoencoders (GH #162) and hypertools.tools.gensim_models # (GH #198) import torch/gensim unconditionally at module level, so both are # needed for autodoc/autosummary to import those modules (docs/api.rst's @@ -45,7 +47,7 @@ scikit-image>=0.22.0 # examples/plot_autoencoders.py and examples/plot_gensim_text.py in the # gallery build torch>=2.0 -gensim>=4.3 +gensim>=4.4.0 # hyp.load('kaggle/<owner>/<dataset>') (GH #116), used by # examples/plot_datasets_tour.py kagglehub>=0.3 diff --git a/docs/hierarchy.rst b/docs/hierarchy.rst index e6142d05..4856ca19 100644 --- a/docs/hierarchy.rst +++ b/docs/hierarchy.rst @@ -20,10 +20,6 @@ anything else: Everything below follows from that. Every example on this page is executable and its output is checked by ``tests/test_docs_hierarchy_guide.py``. -.. contents:: On this page - :local: - :depth: 1 - Row versus column semantics ---------------------------- @@ -741,9 +737,17 @@ unless it already carries one of its own. [(40, 3), (40, 3)] Bundled forecasts always correspond to ``trace_data``, so ``forecasts[i]`` -equals ``hyp.predict(trace_data[i], model=..., t=t)`` for every ``i`` -- +equals ``hyp.predict(trace_data[i], model=..., t=t)`` for positional input -- including the means, each forecast from its own averaged trajectory rather -than from an average of its members' forecasts: +than from an average of its members' forecasts. With a column hierarchy and +an observation-time index, attach that original index to the trace before +calling ``hyp.predict`` to reproduce the same fit. In ``ndims=1`` series +mode, ``trace_data`` contains display pairs ``[time, value]``; forecasts +contain only predicted signal values. Use the value column and the original +observation-time index to reproduce those forecasts. See +:ref:`observation-times` for the sorting and interpolation policy. + +For this example's positional input: .. doctest:: diff --git a/docs/hypertools.FrameContext.rst b/docs/hypertools.FrameContext.rst index 679e192d..65d2c34c 100644 --- a/docs/hypertools.FrameContext.rst +++ b/docs/hypertools.FrameContext.rst @@ -1,4 +1,4 @@ -hypertools.FrameContext +hypertools.FrameContext ======================= .. currentmodule:: hypertools diff --git a/docs/hypertools.HypertoolsOfflineError.rst b/docs/hypertools.HypertoolsOfflineError.rst new file mode 100644 index 00000000..d5863f5a --- /dev/null +++ b/docs/hypertools.HypertoolsOfflineError.rst @@ -0,0 +1,6 @@ +hypertools.HypertoolsOfflineError +================================= + +.. currentmodule:: hypertools + +.. autoexception:: HypertoolsOfflineError \ No newline at end of file diff --git a/docs/hypertools.HypertoolsTrustError.rst b/docs/hypertools.HypertoolsTrustError.rst new file mode 100644 index 00000000..3ff0cd8e --- /dev/null +++ b/docs/hypertools.HypertoolsTrustError.rst @@ -0,0 +1,6 @@ +hypertools.HypertoolsTrustError +=============================== + +.. currentmodule:: hypertools + +.. autoexception:: HypertoolsTrustError \ No newline at end of file diff --git a/docs/hypertools.io.lsl.synthetic_outlet.rst b/docs/hypertools.io.lsl.synthetic_outlet.rst deleted file mode 100644 index c87c3bae..00000000 --- a/docs/hypertools.io.lsl.synthetic_outlet.rst +++ /dev/null @@ -1,6 +0,0 @@ -hypertools.io.lsl.synthetic\_outlet -=================================== - -.. currentmodule:: hypertools.io.lsl - -.. autofunction:: synthetic_outlet \ No newline at end of file diff --git a/docs/hypertools.io.synthetic_outlet.rst b/docs/hypertools.io.synthetic_outlet.rst new file mode 100644 index 00000000..328976ee --- /dev/null +++ b/docs/hypertools.io.synthetic_outlet.rst @@ -0,0 +1,6 @@ +hypertools.io.synthetic\_outlet +=============================== + +.. currentmodule:: hypertools.io + +.. autofunction:: synthetic_outlet \ No newline at end of file diff --git a/docs/hypertools.set_autoinstall.rst b/docs/hypertools.set_autoinstall.rst new file mode 100644 index 00000000..34371600 --- /dev/null +++ b/docs/hypertools.set_autoinstall.rst @@ -0,0 +1,22 @@ +hypertools.set\_autoinstall +=========================== + +.. currentmodule:: hypertools + +.. autoclass:: set_autoinstall + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~set_autoinstall.__init__ + + + + + + \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 6cc7bcf6..a536a79f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -25,8 +25,8 @@ and ``Chronos`` forecasters, autoencoder reducers, gensim vectorizers, Kaggle loading, LSL streaming, 3-D density iso-surfaces, ``.xlsx`` loading) are ``pip`` extras of ``hypertools``, and they install themselves on demand: the first call that needs one installs that extra's requirements and carries on, -printing a one-line notice. Set ``HYPERTOOLS_AUTO_INSTALL=0`` to disable -this; a missing extra then raises ``ImportError`` with the manual +printing a one-line notice. ``hypertools.set_autoinstall(False)`` turns +this off; a missing extra then raises ``ImportError`` with the manual ``pip install "hypertools[<extra>]"`` command. See :doc:`optional_dependencies` for the full list. @@ -51,6 +51,9 @@ Some key features of HyperTools are: 7. Applying topic models and other text vectorization methods to text data +What changed in each release is listed in the +`changelog <https://github.com/ContextLab/hypertools/blob/master/CHANGELOG.md>`_. + .. toctree:: :maxdepth: 2 :caption: Contents: diff --git a/docs/optional_dependencies.rst b/docs/optional_dependencies.rst index 48d59d2f..84db63e2 100644 --- a/docs/optional_dependencies.rst +++ b/docs/optional_dependencies.rst @@ -5,7 +5,7 @@ Optional dependencies ``pip install hypertools`` installs everything the core functionality needs: plotting with matplotlib, dimensionality reduction, alignment, clustering, -normalization, and ``Kalman``/``ARIMA`` forecasting and imputation. The +normalization, ``Kalman``/``ARIMA`` forecasting, and missing-data imputation. The heavier model families are declared as ``pip`` extras of ``hypertools`` in ``pyproject.toml``. You can install them ahead of time, or let hypertools install them the first time a call needs one. @@ -65,7 +65,7 @@ The extras the plotly backend renders a volume either way) * - ``io`` - openpyxl - - ``.xlsx`` support for ``hyp.load`` + - ``.xlsx`` reading with ``hyp.load`` and writing with ``hyp.save`` Extras combine: ``pip install "hypertools[interactive,torch]"``. The ``dev`` extra holds the test and development dependencies and is not installed on @@ -89,15 +89,77 @@ If the install fails (no network, no permission to write to the environment), the call raises ``ImportError`` naming the manual command, e.g. ``pip install "hypertools[interactive]"``. +An extra that is installed but too old +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``pip install "hypertools[interactive]"`` always resolves versions that +work together. An environment that already held an older copy of one of +those packages (a notebook image with an older plotly, say) may not. So an +installed extra is also checked, once per process, against the requirements +hypertools declares, and every package of the extra is checked, because a +feature needs them together: kaleido 1.x cannot export a static image with +a plotly older than 6.1.1. + +A package below its declared requirement is upgraded the same way a missing +one is installed, with a notice naming the installed version and the +requirement:: + + hypertools: upgrading plotly 5.24.1 to plotly>=6.1.1 (needed for the plotly backend) ... + +This happens only while that package has not been imported yet. Python +cannot replace a package that is already imported in a running process, so +if your code (or another library) imported the old version first, nothing +is installed and the call raises ``ImportError``:: + + plotly 5.24.1 is installed, but hypertools needs plotly>=6.1.1 (needed + for the plotly backend). plotly is already imported in this Python + process, so hypertools did not upgrade it in place. Run `pip install + "hypertools[interactive]"` and restart Python (in a notebook, restart + the kernel or runtime). + +With installation turned off (below) the call raises ``ImportError`` with +the same three facts: the installed version, the requirement, and the +command. That command upgrades the extra's packages and leaves hypertools +itself as it is. With ``backend='auto'`` (the default), where plotly is +chosen only because the environment has it, a plotly that is too old and +cannot be upgraded produces a warning with that message and the plot is +drawn with matplotlib instead. + +A development or pre-release build of the required version (``6.1.1.dev0``, +``6.1.1rc1``) counts as that version, and a package whose version cannot be +read (an untagged source build reporting ``0+unknown``) is never reported +as too old. + Turning it off ~~~~~~~~~~~~~~ -Set the environment variable ``HYPERTOOLS_AUTO_INSTALL=0`` (``false``, -``no`` and ``off`` also work); it is read at each call. A missing extra -then raises ``ImportError`` with the manual ``pip install -"hypertools[<extra>]"`` command, and nothing is installed. -This is the setting to use in locked-down environments, in CI images built -ahead of time, and anywhere pip should not run inside a Python process. +Call ``hypertools.set_autoinstall(False)``. A missing extra, or one that +is installed but older than hypertools requires, then raises +``ImportError`` with the manual ``pip install "hypertools[<extra>]"`` +command, and nothing is installed or upgraded. It works like +``set_interactive_backend``: called directly it applies to the rest of the +session (``set_autoinstall(True)`` turns installation back on), and used +with ``with`` it applies to one block:: + + import hypertools as hyp + + hyp.set_autoinstall(False) # for the rest of the session + + with hyp.set_autoinstall(False): # for one block + hyp.predict(data, model='Chronos', t=5) # ImportError if missing + +This is the setting for locked-down environments and anywhere pip should +not run inside a Python process. Where no Python runs before hypertools is +imported (a CI image built ahead of time), the environment variable +``HYPERTOOLS_AUTO_INSTALL=0`` (``false``, ``no`` and ``off`` also work) +sets the starting value; a ``set_autoinstall`` call overrides it. +The setting is process-global (shared by every thread; the newest call +still in force decides, and a ``with`` block removes only its own setting +on exit), and it also reaches the subprocess that renders a plotly +animation's frames for export, which starts from the parent's effective +value, whichever of the two set it. That export raises ``ImportError`` +naming the manual command when kaleido is missing and installation is off, +like any other call. Chrome for static plotly export ------------------------------- @@ -116,7 +178,7 @@ provisions what is missing: - a Chrome build for kaleido (about 150 MB), via ``plotly.io.get_chrome()``. Both steps print a one-line ``hypertools:`` notice, and both are -skipped when ``HYPERTOOLS_AUTO_INSTALL=0``. When no working Chrome could be +skipped after ``set_autoinstall(False)``. When no working Chrome could be provided, the export raises ``HypertoolsIOError`` with the commands to run yourself: ``import plotly.io as pio; pio.get_chrome()`` and, on Debian/Ubuntu, ``apt-get install -y libatk1.0-0 libatk-bridge2.0-0 diff --git a/docs/post_build.py b/docs/post_build.py index 81e46c93..50d9a61e 100755 --- a/docs/post_build.py +++ b/docs/post_build.py @@ -98,7 +98,8 @@ def find_build_dirs(): # Also check environment variables that Read the Docs might set rtd_output = os.environ.get('READTHEDOCS_OUTPUT', '') if rtd_output: - possible_build_dirs.insert(0, rtd_output) + # Read the Docs writes the site to $READTHEDOCS_OUTPUT/html + possible_build_dirs[:0] = [os.path.join(rtd_output, 'html'), rtd_output] for build_dir in possible_build_dirs: if build_dir and os.path.exists(build_dir): @@ -324,6 +325,141 @@ def inject_notebook_badges(): return n +def strip_rst_markup(text): + """Plain text for a gallery tooltip: drop the RST inline markup + sphinx-gallery leaves in an example's intro paragraph. + + sphinx-gallery's own ``_sanitize_rst`` only recognises markup that is + preceded by whitespace, so a literal that follows ``(`` or ``/`` keeps + its backticks, and a tooltip is a plain HTML attribute -- the backticks + were shown literally on 15 gallery cards. A role with a ``~`` target + keeps only the last dotted name, any other role keeps its target, and + double- or single-backtick literals lose their backticks. ``text`` is + the (HTML-escaped) attribute value and stays escaped: only backticks and + role prefixes are removed.""" + def _role(match): + target = match.group(1) + if target.startswith('~'): + return target[1:].rsplit('.', 1)[-1] + return target + text = re.sub(r':[\w:+.-]+:`([^`]+)`', _role, text) + text = re.sub(r'``(.+?)``', r'\1', text) + text = re.sub(r'`([^`]+)`_{0,2}', r'\1', text) + return text + + +def clean_gallery_tooltips(gallery_html=None): + """Strip RST markup from every gallery card's ``tooltip="..."`` attribute + (see ``strip_rst_markup``). Returns the number of tooltips changed.""" + gallery_html = gallery_html or GALLERY_HTML + if not gallery_html or not os.path.exists(gallery_html): + print(" Skipping tooltip cleanup (no build dir)") + return 0 + with open(gallery_html, encoding='utf-8') as f: + html = f.read() + changed = 0 + + def _clean(match): + nonlocal changed + cleaned = strip_rst_markup(match.group(2)) + changed += cleaned != match.group(2) + return f'{match.group(1)}{cleaned}"' + + html = re.sub(r'(<div class="sphx-glr-thumbcontainer"[^>]*?\stooltip=")' + r'([^"]*)"', _clean, html) + if changed: + with open(gallery_html, 'w', encoding='utf-8') as f: + f.write(html) + print(f" Cleaned RST markup out of {changed} gallery tooltips") + return changed + + +# plotly's notebook renderers store two things in a notebook's saved outputs +# that do not survive being embedded in a Sphinx page (nbsphinx copies the +# stored HTML verbatim; the tutorials are committed pre-executed): +# * `<script type="module">import "https://cdn.plot.ly/plotly-X.Y.Z.min" +# </script>` -- the renderer's one-time "notebook_connected" bootstrap. +# The URL has no `.js` and the CDN answers 403. Each figure's own output +# carries a regular `<script src=".../plotly-X.Y.Z.min.js">`, which is +# what draws it, so the bootstrap is dead weight. +# * `<script src=".../mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG">` +# in every figure. The page already loads MathJax 4 (Sphinx's default), +# whose `window.MathJax` configuration object MathJax 2 then trips over +# ("Cannot read properties of undefined (reading 'Startup')"). +_PLOTLY_MODULE_IMPORT = re.compile( + r'<script type="module">\s*import\s+"https://cdn\.plot\.ly/plotly-[\w.-]+' + r'\.min"\s*;?\s*</script>') +_PLOTLY_MATHJAX2 = re.compile( + r'<script src="https://cdnjs\.cloudflare\.com/ajax/libs/mathjax/2\.[\d.]+/' + r'MathJax\.js[^"]*"></script>') + + +def clean_notebook_plotly_scripts(build_root=None): + """Remove plotly's dead CDN bootstrap and its MathJax 2 <script> from the + notebook-rendered tutorial pages (see the note above). Returns the number + of pages changed.""" + if build_root is None: + if not GALLERY_HTML: + print(" Skipping notebook plotly cleanup (no build dir)") + return 0 + build_root = os.path.dirname(os.path.dirname(GALLERY_HTML)) + changed = 0 + for page in sorted(glob.glob(os.path.join(build_root, 'tutorials', + '*.html'))): + with open(page, encoding='utf-8') as f: + html = f.read() + cleaned = _PLOTLY_MODULE_IMPORT.sub('', html) + cleaned = _PLOTLY_MATHJAX2.sub('', cleaned) + if cleaned != html: + with open(page, 'w', encoding='utf-8') as f: + f.write(cleaned) + changed += 1 + print(f" Cleaned plotly notebook scripts in {changed} tutorial pages") + return changed + + +# Every tutorial notebook opens with its HyperTools install cell (so "Run all" +# works on Colab), and nbsphinx renders cells in order: the page showed 15 +# lines of setup code ABOVE its own title. Move that cell under the title and +# collapse it. The notebook itself is not changed. +_SETUP_CELL = re.compile( + r'(?P<cell><div class="nbinput[^"]*docutils container">\s*' + r'<div class="prompt.*?</div>\s*</div>\s*' + r'<div class="input_area.*?</div>\s*</div>\s*</div>\s*)' + r'(?P<title><section[^>]*>\s*<h1.*?</h1>)', re.S) + + +def collapse_tutorial_setup_cells(build_root=None): + """Move each tutorial page's leading install cell below its title, inside + a collapsed <details>. Returns the number of pages changed.""" + if build_root is None: + if not GALLERY_HTML: + print(" Skipping tutorial setup cells (no build dir)") + return 0 + build_root = os.path.dirname(os.path.dirname(GALLERY_HTML)) + changed = 0 + for page in sorted(glob.glob(os.path.join(build_root, 'tutorials', + '*.html'))): + with open(page, encoding='utf-8') as f: + html = f.read() + start = html.find('<article') + match = _SETUP_CELL.search(html, start) if start >= 0 else None + # only the FIRST thing in the article, and only an install cell + if (not match or html[html.find('>', start) + 1:match.start()].strip() + or 'install' not in match.group('cell') + or 'hypertools' not in match.group('cell').lower()): + continue + moved = (match.group('title') + + '\n<details class="hypertools-setup"><summary>Setup: ' + 'install HyperTools (run this cell first on Colab)</summary>\n' + + match.group('cell') + '</details>\n') + with open(page, 'w', encoding='utf-8') as f: + f.write(html[:match.start()] + moved + html[match.end():]) + changed += 1 + print(f" Collapsed the setup cell on {changed} tutorial pages") + return changed + + def main(): """Main function to run post-build processing""" print("Running post-build script to fix animated thumbnails...") @@ -334,6 +470,9 @@ def main(): inject_notebook_badges() wrap_thumbnail_links() + clean_gallery_tooltips() + clean_notebook_plotly_scripts() + collapse_tutorial_setup_cells() if success: print("✅ Post-build processing completed successfully!") diff --git a/docs/superpowers/plans/2026-07-28-hypertools-1.1-examples-and-tutorials.md b/docs/superpowers/plans/2026-07-28-hypertools-1.1-examples-and-tutorials.md index 65eb08f9..3f6808cc 100644 --- a/docs/superpowers/plans/2026-07-28-hypertools-1.1-examples-and-tutorials.md +++ b/docs/superpowers/plans/2026-07-28-hypertools-1.1-examples-and-tutorials.md @@ -160,7 +160,7 @@ happened: - [x] the example and its notebook stop being called `*_forecast` where compatibility permits, and at minimum no displayed prose (title, gallery caption, docstring, axis labels) says "forecast". Renaming the FILES touches the gallery index, the tutorial notebook, both budget - tables, `STATED_ARTIFACT`, and the release notebook checker — costed at implementation time. + tables, `STATED_ARTIFACT`, and the release notebook checker — costed at implementation time. *(Decided 2026-09-03: the FILES keep their names. `auto_examples/animate_market_forecast.html` and `tutorials/market_forecast.html` are published 1.0 documentation URLs, so a rename breaks links for no reader-visible gain; the minimum, no displayed prose saying "forecast", is met, and a comment at diff --git a/docs/tutorials.rst b/docs/tutorials.rst index cca4b86e..5a22800c 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -192,7 +192,7 @@ spread tightens ~30%. See the full gallery example, :doc:`auto_examples/plot_story_trajectories`. .. image:: _static/thumbnails/sphx_glr_plot_story_trajectories_thumb.gif - :width: 400 + :width: 200 :alt: Animated hyperaligned brain-activity trajectories through a spoken story A quarter century of the market: six sectors, one space @@ -200,7 +200,7 @@ A quarter century of the market: six sectors, one space Three library calls: ``hyp.reduce`` takes each sector (a months-by-stocks matrix of growth curves, four or five stocks each) to three dimensions on -its own, ``hyp.align(..., align='HyperAlign')`` hyperaligns the six paths into +its own, ``hyp.align(..., model='HyperAlign')`` hyperaligns the six paths into one shared space, and ``hyp.plot`` draws them with a seventh, heavier path -- the market, their mean -- coloured through the mixture hue by each sector's share of the basket's capitalisation. The title is the current @@ -210,7 +210,7 @@ share counts from the SEC. (For the column-MultiIndex route to a market hierarchy, see :doc:`hierarchy`.) .. image:: _static/thumbnails/sphx_glr_animate_market_sectors_thumb.gif - :width: 400 + :width: 200 :alt: Six hyperaligned sector paths and the heavier market mean in one turning 3-D box, titled by date .. toctree:: @@ -235,7 +235,7 @@ and the mean temperature against time, drawn segment by segment on the same colour scale. .. image:: _static/thumbnails/sphx_glr_animate_weather_decades_thumb.gif - :width: 400 + :width: 200 :alt: A century of twenty cities' temperatures as one trajectory, coloured blue-cold to red-hot .. toctree:: @@ -257,7 +257,7 @@ and the title colour run on the public ``on_frame`` hook and read the schedule the library publishes; thirty seconds, two camera turns. .. image:: _static/thumbnails/sphx_glr_animate_conversation_thumb.gif - :width: 400 + :width: 200 :alt: The Mad Tea-Party revealed one turn at a time, each path coloured by speaker .. toctree:: @@ -279,7 +279,7 @@ box, each painting's name with its artist and year, the description that was embedded, and a thumbnail of the canvas. .. image:: _static/thumbnails/sphx_glr_animate_painting_embeddings_thumb.gif - :width: 400 + :width: 200 :alt: Five clouds of painting-description embeddings spinning in 3-D, each in its canvas's colour .. toctree:: @@ -287,6 +287,36 @@ was embedded, and a thumbnail of the canvas. tutorials/painting_embeddings.ipynb +Palette order, and a data matrix as a palette +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +``image_palette`` lists a canvas's clusters most salient first, and that is +still the colour a dataset leads with when a painting stands for it in a +per-dataset list. Used as the palette of one plot, though, the same colours +are put in a deterministic order -- by value, dark to bright, so a continuous +``hue=`` reads as a gradient -- and ``palette_sort=`` (or ``?sort=`` inside +the spec) picks ``'value'``, ``'hue'``, ``'lightness'``, ``'columns'`` or +``'original'`` instead. A t x k data matrix is a palette too: ``hyp.plot`` +reduces it to three dimensions (``palette_reduce=``, default ``'PCA'``, with +``palette_manip=``/``palette_normalize=``/``palette_align=`` passed through), +scales each component to an RGB channel, sorts the rows along the first +component and resamples the result to however many colours the plot needs. A +2-D array with three or four columns and every value in [0, 1] is still read +as a list of colours:: + + import numpy as np + import hypertools as hyp + + rng = np.random.default_rng(0) + walk = np.cumsum(rng.normal(size=(200, 3)), axis=0) # a 3-D random walk + weights = walk @ rng.normal(size=(3, 12)) + 0.5 * rng.normal(size=(200, 12)) + + hyp.plot(walk, hue=np.arange(len(walk)), palette=weights) # PCA, sorted along PC1 + hyp.plot(walk, hue=np.arange(len(walk)), palette=weights, + palette_reduce='FastICA', palette_sort='hue') + # an image's colours, ordered by hue rather than by value: + # hyp.plot(walk, hue=np.arange(len(walk)), palette='image:starry_night.jpg?sort=hue') + Morphing through the shapes zoo ------------------------------- @@ -298,7 +328,7 @@ it just above the cloud). The loader degrades to five parametric clouds when the zoo cannot be fetched, so the notebook always renders. .. image:: _static/thumbnails/sphx_glr_animate_morph_zoo_thumb.gif - :width: 400 + :width: 200 :alt: A cloud of dots morphing through the shapes zoo, titled per shape .. toctree:: diff --git a/docs/tutorials/align.ipynb b/docs/tutorials/align.ipynb index c5c2798c..1e050e6f 100644 --- a/docs/tutorials/align.ipynb +++ b/docs/tutorials/align.ipynb @@ -5,17 +5,28 @@ "execution_count": null, "id": "bccb6420", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:27:53.822078Z", - "iopub.status.busy": "2026-07-17T07:27:53.821840Z", - "iopub.status.idle": "2026-07-17T07:27:53.827070Z", - "shell.execute_reply": "2026-07-17T07:27:53.826417Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -29,7 +40,11 @@ "\n", "Alignment can be particularly useful in exploring statistical properties and/or similarities of datasets that are not in the same coordinate system (such as fMRI data from visual areas of participants watching a movie, and the movie data itself).\n", "\n", - "Alignment algorithms use linear transformations to rotate and scale your datasets so they match as best as possible. This tutorial covers the three algorithms, using `align=` inside `hyp.plot` and `hyp.analyze`, and reusing a fitted aligner on held-out data with `return_model=True`." + "Alignment algorithms use linear transformations to rotate and scale your datasets so they match as best as possible. For example, take the three datasets on the left below. Each has a similar shape (an S), but they are scaled and rotated differently. Aligning them finds the transformation that minimizes the distance between them, giving the overlapping copies on the right.\n", + "\n", + "![Three rotated and scaled S-shaped datasets before alignment, and the same three overlapping after hyperalignment](img/alignment.png)\n", + "\n", + "This tutorial covers the three algorithms, using `align=` inside `hyp.plot` and `hyp.analyze`, and reusing a fitted aligner on held-out data with `return_model=True`." ] }, { @@ -46,10 +61,10 @@ "id": "25fc5684", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:43.229831Z", - "iopub.status.busy": "2026-09-05T10:23:43.229679Z", - "iopub.status.idle": "2026-09-05T10:23:46.761598Z", - "shell.execute_reply": "2026-09-05T10:23:46.761069Z" + "iopub.execute_input": "2026-10-08T17:10:12.794209Z", + "iopub.status.busy": "2026-10-08T17:10:12.793960Z", + "iopub.status.idle": "2026-10-08T17:10:16.661024Z", + "shell.execute_reply": "2026-10-08T17:10:16.660454Z" } }, "outputs": [], @@ -76,16 +91,16 @@ "id": "ab9d3fd9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:46.763057Z", - "iopub.status.busy": "2026-09-05T10:23:46.762901Z", - "iopub.status.idle": "2026-09-05T10:23:47.076563Z", - "shell.execute_reply": "2026-09-05T10:23:47.076038Z" + "iopub.execute_input": "2026-10-08T17:10:16.662511Z", + "iopub.status.busy": "2026-10-08T17:10:16.662357Z", + "iopub.status.idle": "2026-10-08T17:10:16.935902Z", + "shell.execute_reply": "2026-10-08T17:10:16.935504Z" } }, "outputs": [ { "data": { - "image/png": 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", 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ZovHnn3+ioKAAZ5vTfV0a+hkihLgWGgY6RsbLCOLKywGVHGCfqREz//rXv1RtmtQVSvRbXGCJ6spBntTeyYG7RPOnT5+u0ugfeeQRVXMo4/qk9l5+1GvrUXAqJC1fDpDlvv/2t7+pmsNvvvlGHbjKAZ3JZKry3CXKIWN35HwZzSimgvOIIBlbKAen1ZHby/1KRFleN7ltQ56P/C9jj2TskKQJyusj4/nk7+qmjYy5fP3115XAqi1Ff+PGjWrkk+yDRBPEJBCzQ0SLlvYvr4dkHfzlL39RIkai63LQLoaIXE8cf4lG3HHHHcoAkPpLSWusjzP9PKsjownleUn/A+kRIFEbiZ5IeqXUhV511VV4++23ceGFF1ZcLtkO8pmTy7WIjYYIHxkBKUZDSkqKGikqESG5fW3I/sl9/d///Z/qwzBx4kT1fGT/JXNDG1spozslu0DMAslOEdErnwVnE4MQQogDMbFlXX3//ffVGiWp4lL+J6N/G0Jt67YYvA3RG82JrDuyFkpJmhghzsEKDTE0xFiQdVgOVh9++GFl6N98883qcllHZJ2WTUYxinEu64qclucieqY+mrqeV0f2SfTBa6+9pjIFxTQQ8/z555+v0s/hbHG6r0ttwQxZz2W0pmQsyGewepCBEOICXN11kdSNTAx46KGH1Agb6QovnYq1brPSXb86V199teocrHGqSQN79uyxX3bZZap7sYzokW64MsXg6NGjFdeRbsXSaV7G7cj4H+k8L+PqZJxhXZzqcffu3Wu/+OKLVddl6cI7aNAgNRapOjKCsF27dmpcj9a1X0ZMDhs2TL0eMurpxhtvtJ84cULdj3Q71pAO/XI96SYsr19qamqDn490EX766afV1Aa5jkwXkHGHF154YZXpAWPHjq3RJdoZGeUkUwhkNJN0bo6Li1MdkWW0kjMyDWDAgAHqOUnX51GjRtkzMzPVZTJ+SNtX2Z9nn31WdRk+1ft8Jp9nbcjrecMNN6iJF9q+yagoDemGLSOq5HMq4w5lBKjsuzbe0rkb8rfffqvGXMlzl5GOctr5M1jbWCnhyy+/VGMT5bnJPkh3beexojLqSd57ee3lszJx4sR6x3QRQoinI+MMZVyirKny+/7AAw+oNbu6XKxtSkJd63ZD9IY2reD9999v0H7KGiiapzqy5tS2fslUAOeRixra48oYP5nsJLpE1uJzzjmnxkg/0WSvv/66Wivl+ckmI51lcpBGXfqsqet5beufrO8PPvigPTY2Vt2X7MO8efPsbdu2rTElobb3SM6Ty041kau217i2KQan87pU162CjGOU9Vw+g+PGjav1tSSEnF0M8o8rjApCWjJWq1XV2EsDw4b0TyA1kaZaksK4cOHCOhsYEkIIIWciy+CPP/5QmXbOSCRcsuA++OCDimyClopkhTz66KMqu4MQQs4k7EBCSBOQ9EXpguxce0+aBj1LQgghzYWUDX7//fdVeh+52zq0YcMGVQIg5XyEEHKmYQ8DQppA//79T2suNSGEEEKaD+mRIJMDpN+N9BmS3gHugGQQSL8C0SHyvPbt24fHH39cNUScPHmyq3ePEOKG0DAghBBCCCFuhZQLykhdaXT722+/qclA7oAYBW+99ZaauCBjkKU8Usr6ZBKEZD4SQsiZhj0MCCGEEEIIIYQQUgP2MCCEEEIIIYQQQkgNaBgQQgghhBBCCCGkBjQMCCGEEEIIIYQQUgMaBoQQQgghhBBCCKkBDQNCCCGEEEIIIYTUgIYBIYQQQgghhBBCakDDgBBCCCGEEEIIITWgYUAIIYQQQgghhJAa0DAghBBCCCGEEEJIDWgYEEIIIYQQQgghpAY0DAghhBBCCCGEEFIDGgaEEEIIIYQQQgipAQ0DQgghhBBCCCGE1ICGASGEEEIIIYQQQmpAw4AQQgghhBBCCCE1oGFACCGEEEIIIYSQGtAwIIQQQgghhBBCSA1oGBBCCCGEEEIIIaQGNAwIIYQQQgghhBBSAxoGhBBCCCGEEEIIqQENA0IIIYQQQgghhNSAhgEhhBBCCCGEEEJqQMOAEEIIIYQQQgghNaBhQAghhBBCCCGEkBrQMCCEEEIIIYQQQkgNaBgQQgghhBBCCCGkBjQMCCGEEEIIIYQQUgMaBoQQQgghhBBCCKkBDQNCCCGEEEIIIYTUgIYBIYQQQgghhBBCakDDgBBCCCGEEEIIITWgYUAIIYQQQgghhJAa0DAghBBCCCGEEEJIDWgYEEIIIYQQQgghpAY0DAghhBBCCCGEEFIDGgaEEEIIIYQQQgipAQ0DQgghhBBCCCGE1ICGASGEEEIIIYQQQmpAw4AQQgghhBBCCCE1oGFACCGEEEIIIYSQGtAwIIQQQgghhBBCSA1oGBBCCCGEEEIIIaQGNAwIIYQQQgghhBBSAxoGhBBCCCGEEEIIqQENA0IIIYQQQgghhNSAhgEhhBBCCCGEEEJqQMOAEEIIIYQQQgghNaBhQAghhBBCCCGEkBrQMCCEEEIIIYQQQkgNaBgQQgghhBBCCCGkBjQMCCGEEEIIIYQQUgMaBoQQQgghhBBCCKFhQAghhBBCCCGEkFPDDANCCCGEEEIIIYTUgIYBIYQQQgghhBBCakDDgBBCCCGEEEIIITWgYUAIIYQQQgghhJAa0DAghBBCCCGEEEJIDWgYEEIIIYQQQgghpAY0DAghhBBCCCGEEFIDGgaEEEIIIYQQQgipAQ0DQgghhBBCCCGE1ICGASGEEEIIIYQQQmpAw4AQQgghhBBCCCE1oGFACCGEEEIIIYSQGtAwIIQQQgghhBBCSA1oGBBCCCGEEEIIIaQGNAwIIYQQQgghhBBSAxoGhBBCCCGEEEIIqQENA0IIIYQQQgghhNSAhgEhhBBCCCGEEEJqQMOAEEIIIYQQQgghNaBhQAghhBBCCCGEkBrQMCCEEEIIIYQQQkgNaBgQQgghhBBCCCGkBjQMCCGEEEIIIYQQUgMaBoQQQgghhBBCCKkBDQNCCCGEEEIIIYTUgIYBIYQQQgghhBBCakDDgBBCCCGEEEIIITUw1zyLENdTVFSEX375BfPmzcP69etx6NAhmEwmlJSUwNvbG35+fggICEBQUBBCQ0MRHh6OqKgoxMbGIj4+HomJiWjTpg3at2+P4OBgVz8dQgghhDQRWftnz56NOXPmKE1w8OBBpQmKi4sbpQnatm2rLieEENJwDHa73d6I6xPSLOaACIG5c+diw4YNOHz4MAoLCysu9/HxUVteXh4GDRqkLsvPz1f/i1goLy+HxWKp9zFEWHh5ecHX1xf+/v5KVISEhFSIipiYGMTFxaFVq1ZKVLRr1w6RkZEwGpmEQwghhJxNc0CMgd9++w3r1q1TAYOCgoKKy0UPiEGQk5ODfv36oaysTOkD0QRyW/n7TGgCMRtEE7Ru3VppgujoaGoCQohHQsOAuEQIyCbmgEQJqpsDCQkJSgScc845uPjii9WB+1133YW3334bx48fV4t2XcZDUlKSMhyOHj2KlJQUpKWl4cSJE8jMzER2dnaFqBCjQRMV9XlmYhiIqJD9ElERGBioREVYWJjaL9kXzWgQUSHRCzlNo4EQQgipH1mHxRiQoIGYA6IJnM0ByR4QTdCnTx+lCS666CJ1IP/oo4/iueeew7Zt29CzZ8869YazJkhOTq5TE4h+aKgmMJvNp9QEss+a0SCn5TaEENJS4S8YaTZk8RVjQDIH1q5dWyNKIEJADq41ISDmQF1mQEMOwGXx7tatm9oagwgEEROyf/L/sWPHlKgQc0ITFbm5uWrf5bwjR46orIb6RIXBYKgQFRIJEVEhpREiKiIiItTzFNGjiQoxGiSzQV4TQgghxN0QTTB//nxlDogmOHDggMoW1JD1Tw62J0yYoDbRBFJO0FRNINkDXbp0UVtjNYGYC6IJZL2X06mpqcpoyMjIqKIJ5DzRDXIbm812Sk0gz1G0ipRP1KUJpHxC0wTyHAghxNXQMCBnTAhIvwExB9asWaOiBNWFgCz8IgImTpyoogR1CYH6qG9BbiqyiEsUQLbG7osYC5qoEKNBRIWYCiIqsrKyKkSFnJaMBzEaTiUqJFXSuSZTRIXUXIqoqF6TKaJCNjEkCCGEED0gB9ALFizArFmzlCYQc0Ci+RqSuSfmwJgxY5QuEE0g5rleNIEcrMvW2H0RA8FZE8i6X10TiDYS00H0g2inUz0HzWgQ86A2TSDlE2I0SABG0wTs3UQIOZPQMCBNEgISJRAhoEUJqgsBOaAVISDmgEQJZCE7HeRAurnEQVORCIc8T9lGjBjRqNuKcBBTRdIlnUVFenq6ukxqM0VUyCbniaiwWq313qdmNDjXZDo3fxJRIfuq9WmQhpByGSGEEHI6mmDRokUqc2D16tXYv3+/OjB2PuAVc2DkyJEVmQONPRhvKZpADH3Zhg0b1qjbyrovpRPOmkAMheqaQAIQYj40VBNofRq0hpDV+zQ4awIJmshlLKkkhFSHhgFpkBD49ddflTlQXQjIYiSL46hRozB+/HhccsklTYoStERxcDrIoizbwIEDG3U7EQsiKqr3aXAWFWLeyPXktLxfIioa0qdBMxq0mkzZP6nJdG4IKe+tCAsRGRQVhBDiWcgavGTJEqUJVq1apdYYWWuczQHtgFnTBI3N3msI2vrjbpqgf//+jbqd9F6QjAYxGpz7NIgmkJLK09EEWp8GMRokY6G6JpCsBm3yBDUBIe4NDQNSQwjIOEMtSlCbEBg+fDjGjRvXbELAE8RBU5GDeWnwVFeTp7qQaISICTEaqtdkOjd/ElEh54v4aEifBhEVWk1mbX0anJs/iaiQMgr2aSCEEP0j6+3y5cuVJhBzYN++fUoTaOuCRLBFEwwePFhpAskc6NSp01nZN3cLIjQVWXt79OihtsZATUAIaQw0DDwUWWSXLl2qygr++OMPZQ7IQaOzOSAu8pAhQ1SUQIRAhw4dXLa/mjjgFNCmIQfp8v419j2Uz4lkMWipkprR4FyTKUaDpErK33J5Q/s0SPRCsho0o6F6nwat+ZOWKinCiBBCyJlHfrPFFJg5c6b6f+/evUoTOJsDYgJLmaGUG0rAoLHNBM8k1AT60wQSfHDu3SR/n44mkExH9m4iRB/QMPAA5Id65cqVKkog5oBECaoLATEHJD1eogTSfMiVQqA2GE1wDZLZIaUIskn9aWM+cyIctFTJ2vo0aM2f5H85X0RFY/o0aDWZzn0atIaQzn0a5HJCCCGVv8+SRSiaQLSBmAPym+ysCeT3VIIFY8eOVQGDxk4fam6YddiyNEFjejdJEEKyH9m7iRD9QMPAzYXAnj17ao0S6FkI1AbFQctC3i+pdZRt0KBBjbqtiAWtT4OIColQaKLCuSZTZmeLyBADTHptnGp/xGhwrsms3qdBMxq02dnyPWGfBkJIS9cE69evx88//4wVK1YoTSC/o5omkN84+a0TPSCZA6IJGlvy5goYRGh5NEfvpuqaQPRuc/RuEk0gAQlqAuKp0DBwAyHw008/qcyB3bt3V4kSaEJAsgZGjx7dYoRAbVAceA6Siti7d2+1NYaSkhIVudBEhdb8qXqfBhEV2oxtMRpOJSqkPEczGjRRIX0aRFRU79MgokJOy20IIeRsa4KNGzeqsgIxB0QTSKmYsyaQ3y3RA7JJNmHfvn1b5JvEIILncDq9mzRNoPVuqk0TaL2b5Hqn0gRa7ybRBDL62lkTSPmEGA1iLIjRICWVognkf2oC0tKhqm1BQuDPP/+sEAISJahNCEiEQDYRAo094NIzNAzIqZBIgZTSNLacRgSCZiBUbwgp3zERFVpNppwnZoTc5lQ1mdrsbDEatNnZzg0hnfs0SENIKaGQ50AIIQ1h8+bN+PHHH5Um2LVrl/q90n6X5DdINIFMMJLUcQkYNLYDv55hDwNyKmT9lSacjW3EKeu71qdBNIFkOtalCRrTu0nTBGI01KUJJNNR0wRiNlATEL1Aw0CnaFEC6VCsRQm0HyPNHBAhIFGCCy+80K2EQG0wmkCaC1nE5WC9sXPB5fsoAkIzGpxrMrWGkJqokJRJiWw0pE+DJiq0Pg3VG0KKsNBEhdanQa5DCHFftm/fjh9++EFpgp07d6p0bGdzQH4fZIKRmAMSMBgwYIBbp09TE5DmQtZgyRhs7Ihw+T7K2u/cu0lrCHk6vZtkfySrQTMa6urdpGkCMRvYu4mcaWgY6ICtW7eqsoJly5YpIVBblECEgBgEYg64uxCoL5pAiF6Q76As1NrM8cYgBoLWZbp6TaZz8ycpn5DfAxEVp+rTIP1JnGsynfs0iKhwrsnUJk/Ib4un/ZYQond27NihMgc0TSDGpLMmkO/00KFDlTkwY8YMNc3I077H1AREb2hlwLLJd7IxUBMQvUPDwAVCwDlKUJsQkIOPESNGqCiBNIzzNCFQGyxJIO6EuP9SO9zY+uGioqIqfRo0o6G2Pg0S3ZCO1KeqydQiGPI7JGVMmzZtOs1nRwhpKFJeKJpAxhyLPpDvshZxlHVPUpYHDx6szIELLrhABQ+oCSqpLw2cEE/SBFIaUV/vpsZoAglAyHU6d+6syp0IoWHQjMiXTIsS1CUExIUUc0CiBGIUUAjUz6l+5AhxZyRzQKaaNHayiQgEMRgkVVK2hQsXYt26dcpwkGaRWvaC1ER/+umnuP7665vpGRDiuchEF80ckBIDSUl21gRy0CAd5MUUEE0gJgE1Qe2wJIGQ09MEWu8mMRsWLVqENWvWqPOKi4srfpekJPrll1/GAw88wJfbw6FhcIaFwJIlS5Q5QCFwZqE4IKTpEbgFCxbgs88+U6NWRRBoxpuULIhRKdlMUspw6623qv4JhJDTQ4T4999/rzTBtm3blCZwLisSc0B6D8n3TzIHZKwhzYGGw6aHhDRdE0ggU4ID8r9kHmiZOtIPSTKbpfxZyhcvvfRSagKioGFwGkJg8eLFFVECZyEgmQMiBCRz4LzzzqMQOAOwJIGQhiELv5Q8ffLJJ0oMSENGTQxIXwOJXooYuPHGG1UJlMbHH3+s/pc+CISQhiMpwRIwkCidaAJpdOasCcSY69OnjzIHzj//fDXqmGPWTg8GEQhpODJ6XdZ4MTDl90rLIJAmilLyJL9LN998s+q/oPHLL7+o/6kJiEDD4BTIF+u7776riBJIbVD1KIEIARHhEiWQkYYUAmceigNC6mb9+vV4//331e+UGJqaGJB0RUlxFuPylltuUQ0a60IaKwrMMCCkbiQaJ+aAlPWIJhBzQPvuaOaA9AKRpoTnnnsuzjnnHGqCZoBBBELqRsoLRRPI75TWt0CQSQvSJ0F+m0QTSBZBXVATEGdoGDghkTgRApI5IJMLtDFo1aME0ndA3LgJEyZQCJwlKA4IqUSimO+9954qNThw4EDF75SUFcjByvTp05UYaMyoyLKyMvU/owmEOJAeH9KHSET3li1b1N/OmkDSd3v06KHMAfnOTZo0iYbbWYJBBEKq9kwTg2D+/PmqRFpbz318fNC9e3dMmzZNaYIOHTo0+GUrLS1V/1MTEI82DCRKoAkBiRLUJgR69uypzAGJzokQYOaA62C9IvFkRACIQfD7779j7969FQu5ZAN07doVkydPVmKgS5cuTX4MLQIhAoMQT0MCBFpZgUTnRBNoolsr5xHhLem7Ep2bMmUKzQEXQk1APBnJJBSDYO7cuWrSijQv1g7uO3XqpI5ZRBOIoXm6moBZh8RjDANNCDhHCWoTAhIlEBeOQkB/MJpAPC3bSRMDMn7VWQxIhEDSnEUMSDbBmcI5IkGIOyMTi7SAgZgDEkBw1gSBgYHKfBNzQDIHpk6dqrJ3iH6gJiCehBy3iCaYPXu2aqwukwwECWS2a9cOEydOxE033aRKEM8UWmCCmoCoz5q7CgFJ1RVzoC4hIJkDYg5QCLQMWJJA3N3U/OCDDzBr1iwlBmRmsjYLWcSANEmThkTyu9VcaL+TjCYQdyIjIwM//fSTStXVzAFNCGtNv2TWuHQGF00g2QM0B/QPNQFx998t0QS//vqryoIuKCioMMratm2r+qWJQSCjV5sLlikStzEM5AulmQObNm1S48LqEgISJZCNQqBlwmgCcSeysrJUx+Kff/5ZGZv5+fkVn/PExERccskluOGGGzB69OizNmqNJQnEHb5XM2fOVKU7Yg4cPXq0IjtH0wSSoSOaQIIFUm4ojUFJy0P7XdRGxBLSksnLy1OaQH6/5HhG/taMMWlMKE3Vr7/+etU7jZqAuAJzSxICWpRAvkwSJaguBDp27KjScbQoAYWA+0UTCGmJSHRAxhzKb5j8fuXk5FR8rhMSEpSZee2116pyKFfNYmc0gbQk5DskY7/mzZuHP//8s4Y5IOu/ROKczQHJMCTuATUBackUFRXh888/V+XS8vuVnZ1d8bmOi4tTPQhEE8ixDDUB0QNmvQoBcdnEHKhLCEiarpgDFAKeAaMJpCUhv1dffvklvv/+ezXyUAxPTQzExMTgoosuwlVXXaWiBnpppqplGDALi+gNibZJNo5kDogmkB4fWg2vNipMzIH+/fsrTSBTjKRxMXF/bDabq3eBkAYZ8t988w2+/fZbrFu3TmVIa0RHRystcOWVV+Liiy/WjSbQGsGzhwER9PGpBFSajdTpiFngPK1AEwIDBgxQ0TcKAc+G4oDoVQyIOSCCQMSA9FLRiIqKUhkEV1xxBS677DLd9gigOCB6Qg78N27cqDSBcx8iMbRat26tzAHRBCK0Q0NDXbqv5OzDMkWiZ8SAl4zCr7/+GmvWrFF9ijQiIiLUb5dogssvv1y3Jj01AdGdYfDCCy9g8eLFNc6XaJx86aQ3gYgGidS99dZbiIyMVCk7Utcjc8alJlHKEcSlc1XqDmle2OCI6M24knRoySJYtWqVEgNaLW14eLhKJ7z00ktVFkFLKY1i00OiF9555x1ValAbVqtVdQzPzc1V2QZyXWdNIGaCaALpX0RN4L5wrCLRmyaQqUZffPEFVq5cqX6jNE0ghqYERSV7QMoMWkppFMsUie4Mg+3bt6v/pQRBZotKCYL0KBARLmk7UtsjTcHEODh48GBF6mxtSFdxieCJSJdxifJFFTEhwkFqhUVMSMaCzCkVUaGX1B9SP4wmEFeLAUmHlppDEQPy+6SJgZCQENWxWMoMpClRS02FlgMxQa/RDuI57Nq1S/0vTY2lnEdKEKprAilTEFEumqEhmkCyFevSBBJ40DSBXjOASFWoCYirNcGSJUvw6aefYvny5eq4RcuAFQ0g0wtEE0jz4paaAcUyReKMLo6WZUEXRHTLfPGGIBkH+/btUwaCCAYxE0Q8SCqwCAyJPoioSE1NVS5ZXZ10xaWW2eYiksX1E/EvEUIRE1rEQgyG9u3bq3GMLfWL39JhNIGcbUQESNfipUuXqgMWTQzI78Tw4cNVKrSMOpTfC3eAGQZEL2hGvvQpkgP6hiAGwt69e5UmOHz4sDIYnDWBaAbZxHRoqCaQZsrVNYGYDM6awF2+/y0N9jUiZ5vVq1fjww8/VEZBUlJShckuvxODBw9WJdOiCeS3wh3QShLk95AQXRgGmqMvjcIa6u7Lgbt0P5atoU6ZGAtiMoiYkC+7mAwiJiRiIUJCBIdcJqJD+yGoS8xIExAtiyEsLExFLGJjYysiFiImpExCIhcskzh9GE0gzY2UPIkYkPIoOejQfgPkey49VKRb8S233IL4+Hi3fDO058usK+JqNIHq3M/oVEhUTwwG2RqqCUQHyHov676YgqIJjh8/XkUTyPmiG+rTBBL0EE0gBw6aJpA6ZU0TyKhU0QRSJiGn+R07fVimSJobGc36/vvvY9GiRThw4EBFxF2ylfr27VuhCSSw6I4ww4DozjDQxIFzY6MzjSzQknIoW0MRM0GEgvxQaCmRkrGQnp6uIhYiJuS0pCLJF6uuiIUc7Mpz1FIiJWIhYkKaocnBh5YSKQaDCIqWUvN8NqE4IM1RCiViQEqh5DuuHZxIZLF3796q6dptt92mvpuegPb8aXASPQURmlMTSAmCbA1FjARNE4jZUF0TSGajXEfOl+9TQzSBltkomkDLYhBTwVkTtJSa57MJgwikOUqhNE0g33PtmETMwO7du6tGhaIJGvOb0ZJx1kSE6MowaE5x0BRk8ZZtxIgRDbq+7P/+/fuVmNB6MUhKpEQsMjMzVcRCejGIuNi5c2e9Hf/lNdEiFhI5kYiFGAxaSqSICRktKWJCohjuLvIpDsjpIgJAxIA0U5OoYmlpacXBiaQWS6NCEQNy2hNpTDSXkLOhCbTvqF6QTELZhg0b1mBNIHpA0wQSeBCDobomkNNysFKfJhCDQ4S7BBTEYJAsS00TSODBuQG0/O3umoBliuR0EdPv3XffxW+//Ybdu3dXHIPI7498jyZPnqwyCHr06OGRLzbLFInuDAMtPU9v4qCxyGLes2dPtTUEEQdiKMjBi4gJLWIhYkJMBRETErFoSGMnEQdiMDg3dpKIhcx8j4uTLIZEZTC01MZOFAeksYg4/+CDDzBnzhwlxrW57fJ7I2Jg4sSJqt5QUgtJZUkCIa5Gr4ZBUzSBHGw09IBDNIH0WKiuCeQ80QRas0cxHaSMoj6TTzSB1gBaMhScmz2KoSBZDKIJ5LdQNEFLiyIyiEAai2hpCRrMnj1bBe2KiooqNIF8F2SSgWiChpY1eYomaGnHC8SNDQO9ZhicjQVPap8aU/8kYkGyGGQTMaFlMYiY0CIWYjI4GjtJSqSt0Y2dJGPBeWSlHho7uXu0hJw+UkIkYmDWrFmq3KCwsLCivlialI0bNw433XRTg6ODngYzDIhe0ARqSzcMmrLOycG8bGPHjm3QbQoKCqpoAjFKq2sC0Q0SiJDfxbqyGDRNIIEHMRgks7G6JpDfUTmwEk0g5oMroSYgp0LKgz766CM1Annr1q3qu6J9dkTfyndMNIFMNCA1YZki0Z1hoIkDCtZTI4t4//791XYqPvsoHQvnZaKgMBnjJ+fD5JVaISbEUBAxoUUsGtPYSSIWsh9axELEhNaLwbnZ45ls7MQeBqQ6IoRFDMycORNbtmxRqb2aGJDomcw8ljGHIgooLk+NfPe17xkhroSaoOHIwb1kSTU0U0oMAzEWxGCQ5q5yWtMEzmOsJbPBudFbfZpAMhurawIpldBKJ0UTyP/N0eyxrj4RxPMQLSuTjUQTSMNCCZ4Jsq6J4SVTDK677jo1jY2a4NTU990nnoeuDANPiyY0N336+WPx/Fy0bt0efftEYOSY4EY1dnIeTyViwnlkZUMaO8mPtDb/WrIYtDIJrdmjFrFoSGMnph8SiQ589tlnajb7xo0blWGgfc7k8zRt2jQlBqQxEcVA46E4IHqBmqD5kN9GOXiXraHIuu+sCSSzUdMEEniQAzX5X6ZMNEQTOI+xdtYE0p9J9ksyG0UTiAlRFwwiECkp+OKLL/DDDz9gw4YNyuzSPhtiWokxcM011+C8886jJmgC1AREd4aBu/Qw0BvdevjhyedboU1bn0ZFDhvb2Ekao0gkQgSF1F2KkVB9ZKVELER0NKSxU23NHrWDwwULFiihIWLCExo7eTJSovT111/j22+/VWJABKmGiIELL7wQV111FWbMmMExZWdIHDDDgOgBagJ9IaUJQ4YMUVtDNYE2xlprAO2sCeTATgxg0QR79uxp0Bhr55GVogm0njRLly5V2Y3Sn0mCENQE7ot8rr755hulCWQMspTdaEjpjGQQXHHFFbj00kupCc4A7GtEdGcYMJrQXK+rEW3b+Z6V969bt25qa0xjJ22ihKREipjQxlNpZRJyHbnMuVTlzTffVFv1xk61N3uMU0JCi1hIJkNLa+zkaQes33//vRIEa9asUeJSQwSiZBBceeWVuOyyy9iEpxlgSQLRC3KA2NyjlknzoU2eaejEGdEE8nuvaQLJYqhNE8h1pHzSeYy1ZJ3JVpsm0Jo9OmsCrdmjlsVATaBf5H3++eef8dVXX2H16tVKE2rIeyrZhKIHRBfwfTzziPZmEIHo0jBgDwPPa+w0evToBt1GTIJ77rkHDz74oIoua+OptJRIKZPQBIWIzPoaO2njqZybPTqPp5IyCRETErEQ15o0D/IeSYPCzz//HKtWrVJiQBOB8p5IOuEll1yiUgqlbwZpXphhQPQCNYHnaQJZ12VraAM6yT6TDLPbb79dHfhLFkP1kZWSxSCGw44dOxqsCbRmj5JlKZrAuQG0BB1EEzCLoXmQ92ju3LmqzGDlypWqt4amCcT4GT9+vOpNJKWH9ZWwkjMDSxKIbg0DliQ0L/LD21LdQokWCJISKQeRDVl4REDIeCqpuxSDQSIW2sjKxjZ20sZTaSmR2ngqqbkUMSEmgxgMzdXYyR2Q90RKSiQatGLFCvXaa2JAjBsxj7RGhfXVrpLmgRkGRC8ww4CcCs1E7tevnzINGrL+yJojZRKy5os+0PozOTd7FJ0gmqExmsB5ZKVoAsls1DSBGA3UBHW/J1JS8umnn2LZsmXqPdGMHXldxTySkkOZZCCvMTm7UBMQZ3Q1VpHph83Djm1FWL0yH9u3FuHp/7RGULAJLY3GNj3UxubI1lCknlKEhKRFas0etSwGrdmjiApxvRvb2Ml5PJWWEikTJSRl050PjiVK8OGHHyoxIOUl2vsnr4v0yLjggguUGHD1iC7CDAOiH6gJyKlobNND0QRyIC/bhAkTGnQbyVLQmj1KLwYxE2T9r64JRCeIfq1PE2hjrKtrAi2LQQwGTRO488GxlBbIdKMlS5YonaXVyUt2x6BBg1SDwptvvllpJeJamHVIdGcYaNEEZhg0Dzu2FmHlMsfIuT/XFWDsxJBmeqTmNwyac4SSLOCyyaLV2MZOsvBpIysli6F6s8eGNnbSRlZqWQza/GsxGbSUSDmt15RIaU4oBsGiRYvUa6NFaeR5yShQEQO33HKLKv0g+oLRBKIXmGFA9KAJ5MBd9EBDNYGsd86aQExyMRO0LAbRBFI6KZdJ9mNDNYFzZqNoAi2LQQwGKcfQsybYunUr3n//fZVd6JzNKVmjMgpUehOJJpDnQ/QFmx4S3RkGrFdsXgYPD8TcWTnw9TWgsLBhbrze0OMIpaY0dhLRIEJBa/aoZTHI+WIs1NXYqTp1NXaSXgwiJkRASHaFpESKydBcPQC2b99eIQYkM0PrQyKRlJ49e2Lq1Km49dZbGzXCi7gGGgZEb4YB+xqRljRqWQ7yZc2VraHI2i8BBdEEsu5rmkBKJzVNIKclXf9UmkCyGLQG0NrISi2LQQ7IRRNoY6ybSxPIc3nvvfcwf/58pXW0zGH5TktjbGlUKJqgMa8RcQ3MMCC6MwwYTWheZKzi/Q/FoXtPPzU5oSWiR3HQlOcgi7dsDW3sJHOGtc7R2shKLYuhKY2d5LsmBoPzyEoRE2IySEqkHNiLmKirsZPsh4iB3377TYkBLStIzAu53eTJk1W0oKETM4h+oGFA9AI1AWloEKE5MwzOBtoY6xEjRjR43LCzJhAjobomkMxG0QQ7d+6sVzOJwVDbGGtNEzg3e5TMhto0gQQ+RBNIs8Ldu3er/dPuW24nzYvFIJAAAmlZ0DAgujMMWK/Y/Atr3/4BaMm01GaNp4tEAXr37q22huDc2EnEhJbFoDV71FIim9LYSe5Dm30t5oMICakFFTEgqYWkZSOfHb2mtRLPQtMEzDAgdeGpv1Va9l5DD8Dld10MBTH469IE0otBruNcRljXay4Gg5bZKNkREtTQNIEEHEQTSF+ihpZxEP1CTUB0ZxgwmkD0UK/oDjg3dmooYiA4l0loEQsRE9rISjlfvqeSPSBiQBoWEveCGQZEL1ATkIbSkrMOz5YmkD5IsjVGE0gWg2y1aQIxGeQ8CSjccMMNuPHGGxs8Ipu0HJhhQHRrGHDm59lBDrql147Z3HKi9u5QkqBXJBVx4MCBaqsv4idNC6VfAXFfw8BTo3ZEf1FUgZqA1AWDCM2rCWS9l62+72jXrl3x8ccfN+OeEFciettTs3uJzg0DjlVsXrKzLFg0PxdrV+VjzPhgnDsjHC0Fd6lXbKnI686Fw72hYUD0ApseklPBIILroSZwb6gJiDO6CCexh8HZobzcjtk/ZyP9hAUrlua3yINvZhi4DooD94b1ikQvaJOTGEQgdcEggmthEMH9YZki0Z1hoKUfssFR8xId44VuPfwgwfrYeC8UtaARi4wmuB4aBu4NDQOiF6gJyKmgJnA91ATuDTMMiG5LEliv2PxcfUMk/P1NCI/QxVvfYBhNcD0UB+4NxQHRC+xhQE4Fexi4HmoC94YZBsQZXRw1Mppw9miV6DBnWhqMJrgWph+6P8wwIHpBRrQJDCKQuqAmcC0tsaSVNA5qAqK7kgStXpElCaQuGE1wPYwmuDcUB0RvUBOQumDWoeuhJnBvqAmI7gwDLcOADY7OPnt2FaO0xNZiFiY2PXT9e0DcE/lumUwmV+8GIRXQMCB1wQwD10NN4N6wTJHoriRBSz+UDyc5OxxNKsUXn6Rjz64SXH51BKaeF6brl54ZBq6H4sC9YTSB6A1qAlIX1ASuhWWK7g+DCER3GQbaDz+jCWfxNTcZsHd3iTr92+wclJbqO8tA+4wQ10HDwL2hOCB6+72hJiB1QU3geqgJ3F8T8D0musow0KA4OHsktPLGoCGBOJJUivMuDIPZrO90c5YkuBZGEzzjPWZJAtETbHpITgXLFF0HDybdGwYRiG4NA4qDs8t1t0TB398Io1HfZoHA9EPXQ3Hg3rAkgejt94aagNT3+RDYrd91UBO4NzQMiDM0DDyYwMCW0+CMDY5cD8WBe0NxQPQGexiQumAQwbUw69Az3mOW/hAN3RSGM5qgD4qL9dnLgOLA9dAwcG9YkkD0BDUBqQ8GEVwPDybdGwYRiC4NA4Hph65DRit++Uk6HvlbEnKyLdArrFd0HTQM3BsaBkRP0DAg9cEgguuhJnBvWKZIdGkYUBy4li8/y8CCebnIzrLijZdTYbPZoSdYr+h6GE1wb2gYED1BTUDqgxkGroUlCe4PNQFxhoYBUVx8WTjCI8wwexkwYXKo7hohUhy4FooD94figOjtN58ZZaQuGERwPcwwcG/k99ds1lWrO+JCzHr64WGDI9cREmrGfX+Pg9ViR/uOvtAbTD90PRQH7m8YUBwQvcAMA1If1ASuh5rAvWHTQ+IMMwxIBW3a+tQwC+QHY9niPJSVubYZIjMMXA9LEtwbGgZETzCIQOqDmsD10DBwb6gJiG4NA2YY6I+1qwrw8Xsn8I/7j2DThkKX7QfTD10PxYF7w5IEorcDQmoCUt/nQ/vdIq6BmsC9YYYB0aVhID/+nJKgL6xWO77/OlOdzsqywD/AdR8XRrddD8WBe8NoAtETDCKQ+qAmcC3sa+T+UBMQXRoGIg7Y4EhfmEwGPPCPePQd4I8u3XzRpZtflcsz0suRnXV2RjAy/dD10DBwf0wmk6t3gRAFMwxIQ2CGgeugJnBvaBgQXTY9ZIaBPklo5Y37/x5faw+DH77JxIa1BRg5JhgXXx6BoODmO9hg+qHroThwbygOiJ6gYUBO9fkQGGhyHdQE7g3LFIluMwxYr6hfvL2rflTST5Rj3eoCWCzAujUFOLl2N/vCRHHgOigO3B9OSSB6gWMVyak+HwIzDFwHNYF7wyAC0aVhwGhCy8LX14ip54XCz8+Icy8IQ0Bg86YyUxy4HooD94eGAdELNAxIfbARsuuhJnB/WKZIdFmSwOhxy0HKDy69MhJTzw2Dt48j+u/sSqYkl6tyhjMNowmug+LA/fHy8nL1LhCiYBCB1AdLElwPNYF7wwwD4gwzDMhpERhkqlGusHhBHp74xxFs3XTmxjBSHLgedqV2XzSzlhkGRE+RLQYRSF0w69D10DBwfxhEILozDDglwT1IOlSKrz5NV70NXn8pFSnJZWfkfikOXA/FgfuijbSlYUD0AjMMyKk+HwKzDl2DvO4MIrg/LEkgujMM5EPJpoctn1atvdF/UKA6PXFKKOLiz0yKMzMMXA8NA/elrMxh7NEwIHpBfvN5MEjq+3wIzEJxHdQE7g8zDIgGexiQM4rJZMAd98Sg38AADBsReMYWFDY4cj2MJrgvJSUl6n+KA6IXWJJA6oMZBq6HmsD9YYYB0WWGAZ1i9zENho8MOqPuM8WB66E4cP8MA2/vM9+olJCmQE1A6oOawPUww8D9yxQZRCC6Mww4JcG9yc6y4M1XUlGQb23S7Xmw6nooDtyX0tJS9T9LEoheoCYgp/p8ENdCTeC+MOuQVIclCaTZkcaHLz6XgqxMCwoL0vDgI/EqC6ExsF7RdWiZPxQH7gvFAdFjhgF7GJBTwc+I66AmcF/Y14hURzcWLdMP3RcvLwMsFrs6nZZapoyDxsL0Q9dBw8D9KS8vV/8zw4DoBfksskyR1AWDCK6HhoH7wiACqQ4NA9LsREV74e6/xqJHLz88+Vyi+rux0DBwHTQMPKckgT0MiF5ghgGpD2oC10PDwH1hEIHotiSB4sC96dzVT5UiNHWBYTTB9YYBa0bdFzY9JHqDWYekPmgYuB4aBu6fYcAgAtFghgFpEYsLxyq6DmYYuD9sekj0BoMIpD4YRHA9NAzcF2oComvDgM1rPAfpafDTd5mYOyu7UbfjZ+TsQ8PA/WFJAtFjDwP+3pO6YIaB62HWoftnHXKsItFdSQLFgWeZBU//3zEkHSqFyQT06uOPxNY+9d6G0QTXwZIEz6lXpDggeoGagNQHDQPXwwwD94VBBKLbDAPOXPYczGYDevT0U6elPH7ntuJT3obiwHUww8D9YQ8DojeYdUjqg5rA9dAwcF8YRCDVYYYBcQkzLg1HQYEN50wNOWV2gUBx4DpoGLg/FAdEb3DEJ6kPagLXQ8PAfWGGAakODQPiEry9jbjp9ugGX58lCa6DhoH7wwwDojdYkkAasz4R12QBEfeETQ+JbksSKA5IfbC5juuwWCx8DzxEHPj4nDrbh5CzATUBIfqGGQbun3XIsYpEd4YBnUpSH0w/dB3MMPAcU4hND4leYEkCaQicpHH2oSbwnKxDBhGIBksSiEux2ez47qtMHE8th3+AEbfeGVPr9WgYuF4c0NRzXygOiN6gYUAaAg2Dsw8NA/eHfY2Irg0D4nkYjQasWJqHwgIboqLr/gzQMHAdFAfuD8UB0RvMdiENgYaB6zLSWJLgvrCvEdFtSYKIA/7weya+vo6PodWxBtUKmx66Du17SXHgvjDDgOjVMGBTO1If1I1nH+07yd5S7guDCES3hgHTnT2XF15vg1febov7/h5X53WYYeA6KA48RxywXrHlHjS9/fbb6NatG44dOwZ3QNMEWjSTkNpwNpSsVivSjp9Acup+lJQW8gVrJph16P4wiNCysTeDJtBNHQDTDz27LCEs3Ky2U8FowtmH4sD9YTSh5ZKfn4+///3vKCoqcquIn9adW4QrO3WTurAU5CNr1kw8v70E6Xt7AMX+KLv2a9i6HEBUjhFdi2MwPGoM+nSe5FbfD1dCTeD+0DBoueQ3kybQza8nDQNSHyxJcB0UB55jGPj6+rp6V1oMJSUleP755zFy5Ej06dMHF154IQoKCtRlu3fvxnXXXYfevXtjxIgR+M9//lMhwITx48dj0aJFuOmmm9Rtp02bhgULFqjL5s6di4EDB1a5vkROhw0bhu+//77Gfhw8eBDR0dH44IMP3Coar2kCeZ0J0UhJTcN3389F2juvq7+LdmxHxo/fosxSqswCx4fG8TuWHmrDirhU/Mf4De7412x89PXcit860nSYdej+cHJS4ylxc01g1FvTQ3cSPOTMwZIE10Fx4P5QHDSeJ598EitXrsRrr72G2bNn4/7770dgYCAKCwtxyy23oHPnzvj111/xxhtvYMWKFXj66aer3P6JJ57AlVdeiTlz5uCSSy7Bvffei61bt2LChAkqk0puo7FhwwYVNTjnnHNq7IeIi6eeesrtovA0DIhGUvIWfLf6JfzlXz/jn3/Lw5yZHZG0aZO6zA47igKAnH2DKq5vyAxHRK4RRquj/45xd2eU7emO5b90xj2PLEHx3t18cU8DBhHcH2YYNJ4n3VwTmPVmGIhDIy8w8QyWLsyFxWJH67Y+6NDJFyaTodbr0TBwHRQH7o/eMgwK1q1B5szvYDuL0WWjry8iLroMgYOGnvK6eXl5mDVrFt5//30MGDBAnZeYmKj+l/NloX7kkUfU71bbtm3x2GOPKcHw4IMPIiQkRF1v6tSpFYu9RBUWLlyIn3/+GY8//jgmTZqkogoiFASJNAwfPhyhoaHwFDRNUFpa6updIWcRiZzt3rMP63bsQl7sauz1z0BmiA2IAszhMTDbHHGu1RFd1P/SkzewEDDHpMB6tB3sBhus/bcg19+KvunhaGWMxbKtHaF9ivobVyP5+Y8RPuMShJ1/EZv5NgFqAs8JIujFiKYmmORyTWDWYzSBhoHnsGh+Lo4eKYP0t3r30w51Xu9063DElBDM5toNCVI3FAeeIw70Yhhk/zYL5akpZ/UxrScftyGGQVJSknrNevToUWs6YNeuXav8ZvXs2VOZMocOHULfvn3VeWIkONO9e3d1uXD++efjzjvvVOuhNKIUM0GiFZ6Epgmc0zBJy0A+6wWFhSjIL4DVUAygEKWWIpSWF6O0vAj7dnqjqNSCAKMVU0IAa2EhbAX5KM9Ix5M5fVB+oCvspm4ofWw24FXZ1NDWZR+wrStgMaNDUWrF+QYfH7QJysShkFBY404AoXmwwICNsdnYYs1Cn152BCf6IDkpB+MP7lK3yZr5Pax5eYi85gaaBo2EmsD90VuGATXB+S7XBLoxDDQXi/WKnkVJiUMM+PoZ6z2YP90Mg317ivHBOyfwl/ti0bGTPg6KWgraa86GUe6fYaCXaELYtPOQ+dPZzzAIm3reaY8aFSOh+u+U9rdzr57q5Xci0DRxNmTIEAQHB2PZsmWIi4tDVlYWJk6cCE9C+ywyw0B/5OWfwMy5G7BzvwV5eb54yD5HPsCwl5fBXlqG79t2xe7tl6vrlk9ZCOvItY4bytfFG/D5+WEYLF4wRaVgUP7bVe7b1KYz5NfIYDXDkBwPQ+IRtM3yQ190wsCuE/BKdCry93dCSlS4ur45JhZtX3oTjwcGqd+x1RuXYuvhcKyPzUSprwFWowE7v74EdpMV0y5JQni/y5H103cqNSF30e/w7dQFQUOHn/0XsQXDMkX3R2+Tk6gJhrhcE+jGMNCEFBvSeBYDhwQiMsoLffufbFZ0CppsGOwtQWaGBW+/koYX32yjJjOQhsFogvujN8NAovwNifS7Cik/kLF/O3bsUI2HnOnUqRMWL16svjeaybZt2zaVYt+mTZuK6x04cKDK7fbs2YP+/fur03K76dOnY/78+eqxRo8e7XGZdzQM9IXVasGq7T9iSf4K7I7Oh/nPG2A84ijDORxRgsTC3IrretudGguWV5pkFXiVAxYv2Mr8alwU5XMCJxKTkBCfj5EBozGs9VD4dQlSl3341Vzk7++sTq/OFHPvSXgntILNxxf/ef1n7N3UDihuh9ti0zCmxIY/hpixKsqRuWiwmvDbbxG4/L1JMIeG4cBX72JVXHukLN2DZ2kYNAoaBu6P3koSqAmMLtcEuuxhQDyHy6+ObPLM5YZSkG/Fpg2OmcxZWRb1d3CIbj76LWbhYIaB+8L3uHGEhYVh8uTJeO6551TjoqioKOzcuVMt4rKoy/zjZ555RnVFzs7OxrPPPovLL7+8ygI/b948DB06VJUoiAgQU0HuT0PKEqS3gZQ/yP+eBnsY6IPi4mL8tOhnrI5dgIxQGxAg5xpg7XiwwjBICklAW28TjN7eMHj7ICbIFz6tkmAyWxFiLENUaiS8DV7whpf6P23wXpjsXgjyNyC2x/0wBgTA6B8Ir4gIPBUYVGeJwFH7Hlh7l8GYlIhRY1Lw6YeOIMKr33+EPavHw2B33K7YywvROaWYuMyCvj5H8XP7lUg7OBLB0cfV5cGjxuL1H0yw74yD3Zs9MhoLDQP3R299jfROmAdoAt0cNTGaQM50hkFBgRVzfsnG4vm5iIjywjU3OswJH1/dDAdpETDDwP1hZlfjEVEgo5Fuv/12Ne9YMgtGjRql0gY//fRTJQhmzJihBJcIhoceeqjK7aUJ4nfffYeHH35YiQu5rw4dKvu4SB+EiIgIlYkwbtw4eBqaJuBn0zVYy8vw5qe/YdPaVrCX9ULpg0ulQERdFlxgQPdW6Yi/8jCGD+iGVq0er3Lbq09uDibVvPMRTdun2NaZ2D1kizodkt5H/V9SUoCNA1cBUcnw/vF8+AblIrBLXwR3C4AxMBCRYeG4xTcASbYMjB5e2VHcLqMVxPrwYo+MxkJN4P7oLcOgJfC0m2sC3ZUksF7Rs1m3Oh/9BgbCy8tw2oaBNDpcMC8XlnI7MtPLMWRYEIKCTWdwbz2D+uq1ift0JieNQyIDIhCqj0YS2rdvjw8//PCUEYlPPvmk3uv06tULHTt2hJ9fzdTt2pCyBneBQQTXYLfZULB2lZpSstN3DJAfoloPmNYORJvuf+K84EkY3Ot8mPqeffk4PnEallreU6dnBzuMg/ySHIQZE4F2R2C48y1Mzx8Ng+94hPXuXqErpY94x2rlFdYp82FPi4dfjpRD9jvrz6UlQ03g/uitTLElEOjmmkA3hgGjCWTBbzn48tMMdOyUi1v+Eo3YeO/TMgxCQ80YMz4YyxbnYcSY4Cb3P/B0tGiC1GwT96R6Az7ieqQJ4tKlS1UaoydCw+Dsm4YlWzcj88dvUXbsiDpvUvwi/GrqgYDEI7igfVdMHn4rXEnntsMxbMXPWB13AqUne7H5egXAYLfDbjCgxN+Ob/anw/u7ANh99qBP9/W4sUc4/Hr2gk+bdhWm95GUrSjvsxvosxvt02TM6SUufV4tDZYkeI5hoJWGEdfjak2gm08CMww8m9wcC378NlOd3r+vBNnZlgYbBhnp5apHwZgJwfD2NlbJMNi5rQhXXRuJ8ZMcs89J46E48AxxwAwSfVBQUKC2d999FyEhIRg7diw8EY5VPHv8+ttSzJpvwBjTPAzNcJgFwtDQeLS7OBf9B9RSVuAiru/zANYdexi23Aj1t693IK7NGYHv/FahxBcwHktQ5xtKfRGclYTMH34HfvhaGQbRt/wFPomtsf/EVuCkJGhjjHfl02nRBjPXDPeFWYf6oUAnmkA3hgGjCZ5NSKgZDz2WgP+9cRw9evmhWw//BjU9FJNh1sxsdTom1gu9+znqEgXpX5CaUo7PP05Hebkd50wN4XSEJsB6RfeH4kA/LF++XNU2Sv3ja6+95rHNRrVxXuxh0HzsPrQSH67+A+m/Xqz+XhozCYPt78KvQwdEXHIF/Lv3guPwWz+EBsci6LOrUHJYantfRWFREaYNug0Ty67BtgNLsSwwFwfaH0BhehT6ZR2quF1p0iGkvPQ82vz3NRwqPlhhGLQPdkxdIA2HmsD94e+ufliuE01Aw4DohvYdffH0fxNlPHIV5GB/9s9Z6rTVWvXCtu0qZ8Ru+rOwwjCQTIS0VEdKlXy3vv48Q22SVf/Ox+2rZCKQ+tGyOjz1wMVTIkaMFp09ZOxiXUybNk1tng6DCM2D1WbFn7vnYF7WfOyMLQAGGuC9Jh3GE1GwWXyBW+5Cq5Ejdf17EBZSgtSTp/MLitT/3t7+GNBtGgZ0q1y3yo8/j+Kd2/H7kgUoMNkQWl6IsJQkHMwvA6KMgMmGjvEDXPdEWijMOnR/GEQ4uyxuAZpAd4YBXS3Pxsen5kHp9q1F+OVHRxZBQX7VWuueffzRsZMvevfzx8AhleNJROzcdle0yjqIiDRj6aJcHNhXCuntZjbrVwjpEUYT3B+KA6LXDAOp2ySnT3pGJj77cSX2lGYib9pyIPbkBUY7fMcsQP+C/rj5qskVpSB6pl1CObKKtootALO99v4rogG8Y+NhiorBb9+FwZ4TBrvBhsFe/kj79mr4lHvB2OYwIp9rfdb3v6VDTeD+sEyR6NYwYL0iqYt1qwsqThcWWGsYDI893apOwTDjknB1et/eEvW/VDQYjTQMGgOjCe4PMwyI3qAmODOUpaYgeckc/GtpfyCvB+zmcmDsesC/GCH5Bkwp64dzz78NXl4tZ976lR1jMH7xW3hfOpOfIhNi6cr1sOc4Rir7tjqK/ILWQImfmvzga2Uj36bAKQnuDxshE90aBkw/JHVx+dUR6NLNF59+CwQENS0t/qbbovkCNxFGE9wfGgZErxkGFK5NG41YtG0LchfMQ9F2x/jBoHZ+yM8bCoPFC3EbumLGgGiM6HUxzOaWNzbN4JQFIc+1PvbZ56HsKm+YtvVA31ZAYXIKglpnoTA/FtHRhWdhb90PBhE8I+tQz2VJxIMNA6YfkroIDTNj7ARHh6KAANbRn23Yw8D9oTggeoOaoPGcOJGBT3/4A/sPBOCvmR/CjMqeP2Os67GmewgumhSP4UPvQ4vGqZVRfeOSrVYLNoYmwdbKDkOXPbgq7r+wLlmJ+058ry6PHtnCXwcXwSCC+0OjlujeMOCHlNRHfeKANA8UB55hGLCpJdETNAwazuFjmzHn8HdYt3AYDLt7qPOWtumGicd3whwZhZAJk3Hh6LG4JKCyz09LxlbqKDFUGOoOImw/sAR5gQ7N0CUjCCFdYpCWmlxxuW+rxObdUTeFGQbuD4MIRLeGAesVSUOgYXD2oThwfygOiN6gJjg1m3bPw8+Zv2JPTJFqYmgcvAneux1jAo4EdkHcFdPg36c/DG424eajnTnY4vMA7PgOmeVV+xo5s/LEkormjsN8HdMQyo+nqf9z/LyQlXwCA2LjYJLxSaTBMIjg/rBMkejWMGA0gTSXYVBQYMXbr6ap0oZuPfwwamwwX+xGQHHg/tAwIHrD19fRhI9ZhzWZNW8pZi+yIO/SebCLWXASU5sDiBuwCTPGdcSQgZfDXUlJN8KeKw2NDTCZTXX+pm3c0RoGmxWmqDQM63q+Ot/g7chm/bbVSKS/HAz47UCri75Hz8hgdIvoiy5thsPXxzGemdSvw2i0uC/UBKQ6NAxIizx4bQz795Zg5/Ziddrbx9AiDANZkPXScEYbuceUdfeFJQktl4MHD+Kll17C6tWrYTabMXToUDz66KOIiYlBS0YLInDUciUl+/fhlV+XYM+68epv07IRsFw4B+F5Bowr643JPa5G8N/dv8FvaVAm7MF56rSfr+NzUp0Vq9fDumQifJYAgV12IvBfEer8sGnnY1leKk7sGakmJdjLvbG/Uwr2m4/iZ+yA6dAXaLU/AdHJQ3DBxF7o2KH9WX1uLQEGEdwfGgYtl4PNpAl0k6fGHgakuTIMsrMs8PV1HHz36avvyIHVaseC33Lwn6dTMPP7TGzZ5PouzhQH7g/FQcvlhRdeQLdu3fDzzz/jyy+/RGZmJv75z3/CXTIMaBgAtuIinPjkfRx75v8w9Nhq2H0dNfzeh1vhhqyxeL33u7h06F8RHOT+ZoHFUoacictQ+vfXYTDaKyZsVWf91uMVpzs6TV4O6NMP7W+4D10G7YN/24MwtD8AmCvLGqxmA5KPdsa2JT3w9P+V4403v4atrKx5n1QLg2WK7o9kdjFI1DJ5oZk0gW4MA+1Hv4w/zOQMGwbjJobg9ffa4b4H49Cjt5+uX18pNV22JA+7dxbjlx+zsWyxI4riSjglwf1hhkHjKSkpwfPPP4+RI0eiT58+uPDCC1FQUKAu2717N6677jr07t0bI0aMwH/+858qa9v48eOxaNEi3HTTTeq206ZNw4IFC9Rlc+fOxcCBA6tcX96fYcOG4fvvHd3dnXnnnXdw9913o3Xr1ujUqRNuueUWbNiwAS0dTRN4umFQfiINR5/+P+QtXaT+TswqRvvuyzFs2h6883J/TBl8Q4scjdhU9h9dh1JJKjhFAl5vvzy0bz8fke3WYnBnR3aBRtcu3fDIPTPw9r8n4aMnp+Lfvvfj2uxhGJwaqbI1jDu7Oq5oALrsWYHkfz8Fa1Fl6YenwyCCZ7zHeslybSmUuLkm0I1hwPRDcirkx6upTQ+9vY3oNzBA/a/35zj9/DB1esjwQNx8h+sjRhQH7g/FQeN58sknsXLlSrz22muYPXs27r//fgQGBqKwsFAt0J07d8avv/6KN954AytWrMDTTz9d5fZPPPEErrzySsyZMweXXHIJ7r33XmzduhUTJkxQv3NyGw1Z7PPz83HOOeeccr9ycnIQGRmJlg57GAC79uzDU2/NQ0lyinpNDL6+iLzqOjz+t9tx+3XTK3STJ7EtbV3FaVVSUIcmGBQRiKtSluOO1Fno7V93U0OJorZt1RfTB92OB0a9iDf6fIA7rvZCq/470KrdKrTPz0Lpwf1Ie+Ml2E+W53k6DCK4P2x62HiedHNNoJseBkw/JJ44JaGszAaz2QCjsdLJHTwsEK1aeyOxtT7EoGYYsMGR+6LHDIM52fMwJ+f3U16vnU8b/D3+/irnvZDyKg6VJp3yttNDJ2N62JRG71teXh5mzZqF999/HwMGOLqvJyY6RrTJ+RIdf+SRRxwHI23b4rHHHlOC4cEHH0RISIi63tSpUysWe4kqLFy4UKUQPv7445g0aZKKKohQECTSMHz4cISGhta7XxKB+Pjjj3HVVVehpSO1l56cYXAsORkvvJYFW9ZIvNkmGPeaVqH1fQ/CO+Zk238PZat1f+Uf9QQRvBMq6xCK9+1B4KChDbp/WeeGDx2gttIjSUh+YRVs+fko3rUDWb/+hIgLL4WnwyCC+6PHIAI1wVyXagLdKERGE8iZ5sTxclgs+jUYsjIteP5fyfjx28wq53/1aQZ++TELc2dlK0PB1TCa4BniQG+GQZGtGFnW7FNuedb8GreV8xpyW3mMppCUlKQiMD16OGbeV2841LVr1yqvZ8+ePdWB76FDhyrOEyPBme7du6v7Fc4//3wsXrxYpTjK90/MBElRPNV7KHWKUVFRuPHGG9HS0V4/TzQMysqK8OKib2DNdmSbFeS2gu9f7vN4syA/Lx9HPr8JXt/NQOCfXWGopy7Br2s3OfpXpws3rIW9CQ2TfVq3Qdw9f1O1gqIkvt2+Eb/OWwJPR5tcorc1g7h3EIGaYLFLNYFuMgxYr0jOZIaBXO+Nl1KRn2/FxMkhmHpeGEwm/bilpSU2PPXYUeRkW3HoQCnatPPF4KGB6rJNGwqRlWXB9i1FmDK9fvfwbMAGR+6PHg0Df6Mfwk2OA6b6CDYF1XpeQ24rj3E6v0O1RWBETFf/ndL+9vLyqnK96pEALcV8yJAhCA4OxrJlyxAXF4esrCxMnDix3v2RzITk5GR89NFHbpUNpEfDQPZJ3q+AgIBm+S6+ufZxpA3PgDGiGN5zzsEDd4dUZLB4MguWr4Mhtw1MW0MRXG6uVxOYAgLh37MP0vZtxtLwaPRbsQLjxoxp9GP6de4K/xkX4qVt2cjYPhbrkgrRreMBdOrYAZ5Kfb9/xD3Qo2FATRDsUk1Aw4C4pWFw5HAZjh5xNAjZuKEQ0y849cHD2cTH16hMjK8/y0BklBmxcY4Difw8K3JyHAcSbdv7VilVcBUUB54hDvR2kCmlAk0pFxCqlyicaeTgTV6vHTt2qMZDzkiTIckOcDZhtm3bplLs27RpU3G9AwcOVLndnj170L9/f3Vabjd9+nTMnz9fPdbo0aNVLWRd30+pfdy3bx8+/PBD+Pv7w53QxrrqhQOHDuHZl1JhzY5AVJe9uOPqHmd09N53a1/CurgMddrYfh8evH8MenY+2YTPwzl4LA12n1gYSn3Qo5PxlJpgS5ce+Gb7ZBi2B+CYdTfGNd4vUERNnYHCFctgsBuBgiC8NGslXrwtFIEBVZspegoMIrg/egwiUBNMd6km0M2nwZPTD8mZNwzkoLtdBx8Eh5gwYnSwLp3wSVNDcPk1EXjyuUS0buOILAYFm/DeZx3w/Mutce2N+mhcRnHgGd8rPX5H9EpYWBgmT56M5557Dps3b1YuvtQUlpaWqgN9Och95plncPjwYWzatAnPPvssLr/88ioL/Lx581SfgpSUFHzyySfKVJBGRxpSliBzlKWJUn2ph0899ZR6jBdffFFFvSXyIJu7TBzSmyb4cc8nKGt7DLAbkLGzGz76aDkyf/z2jHTRX/znt/g5akfF37eUTELPzqNO+37dhcuSt+KuoGcxpPuHmD6+7ykbIY8YORooc5jxWYdbo7i4aSVIkhn0j3u6wBCSDcuYlciZvgTPbn0E2TnJ8ETYw8D90WOGgZ4J8wBNoJsMg7rSNAlpimHQp1+A2vRCRno5UlPK0KtP5T6J2Jl6bs3MB2mCGBevnzFZFAfujyxmnthx/XSQDscyGun2229HUVGRyiwYNWqUKiX49NNPlSCYMWOG6s8jguGhhx6qcntpgvjdd9/h4YcfVjWGcl8dOlSmOUsfhIiICJWJMG7cuFr3QUTJV199pU5XT0+U8U4XXXQRWjLyG6k3TbAnPgmW+BMw5IbAeCQB7S0HMXuNDaZ1GxFYXo5frDPgYy6EFSYU9dsL4+HWMJyIQvk136JfiRF/G/FfmEw1pdfi5Wvw6eedYLxoD2ydDuK8E10xdvjVLnmOesSSk4Oyo0kIB3BulAGxsfGn1ATBwUEI7bgPWWbA2mMXtuzviaG9zm3S47dtnYh/PJSFfxcvhcUIHIoqxd+OPoop2/tjYvdrER6uryzG5oRBBPdHjxkGeudpN9cENAyIR4xVdCWb/yzE+28fh9VqV9kEsToyAxoCmx66PxQHjUciAyIQqo9GEtq3b69SAU8VkZAoQn306tULHTt2hJ9f7b0WEhISVCmDO6M3w0DhX4zyS36B8XgUVnx+BQzl3kBcMrzsVtiPx6Pk5NWMEUUwbe+uTvu8fDd2+JbgesujGOwF9AnqgyEdpwInMnFg5RZ8Nr87DBYveH1+BdqeOxtXXtG8ZTUtjeKd2ypO+/foXXH6VJrgwgsK8L9AxzzzzWkFGIqmGQZClw598PcDN+OVgg+RHwAU+QG/bAPmvJ+MqA4rcd2UePQa1N/ts7VYpuj+UBM0nkA31wS6s4/0ln5I9EVLNAzWry1AYaENJSV2fPtV1YkI9XH4UAlyT/YzcCWMJnjG90pvPQw8HUkfXLp0Kc477zx4KnLgpbceBlMKTx6sBhTB1j4JsDm+N3aLF0KDq43yLK9scikYSnxhnDMN6+ePxzuB8/H3bfdh90uPwWfx9whou11dxzsmBQ+edxuje9UoObCv4rRf9x4N1gS92lY2LjhqdPSGOB26dxiF5xKfRP+0MMAGmFYNgaHUV5WnJH/xCZL+fi/Sv/oUxXt2NWkyQ0uAmsD9oWGgP8pcrAl0lWGgx/RDoi9aomFw3U1ROHSwFHFxXrjpjugG3ebP9QV4983jaNXKGw8/ngAfH9d5e4wmeIY4oGGgDwoKCtT27rvvIiQkBGPHjoWnokdNcOmwB9Bl3yJsTV+HUlsZDvTeBbMR8PPKQ3D2QaTD0XPA2uEgys+fi/Jp82FeNwCG5DiYDraDMTkBdoMdsBqQHmXE4tFmnP+bBcPC5mDnYDv+cctUBAXV3szKk3nxYBDs8dMQa0/DPa3aNDjrMCK0FbxT7CjzNqDA68wEpKLC2+Khka9gx571+DDqODJzg+AdfhydctMhn9bc+b+p7Yeug3CitAuG9vHBeZNH1hkVbGmwTNH9oWGgHwp0ogl0ZRgIehMH5MxiLy9H8f69KNm3B5bsLDX6yK9HL/h16QZDA+qlGmIY2Gx2vP5SKkJDzWjb3gdjJ4TgbCGPLaMSwyPMVSYi/PPxBAQGGRuUqlhWZsNXn2agrNSOgwdKMffXbFx4qeu6MTOa4P5QHOiH5cuXq9pGqX987bXXPDrSrEfDQOjdaYLaFCOrXvbn5m2YOf8gUjtsRXlQoTrPMnmx48IvLoNpd2cY7AYYjiQCwfk4GlyKqOtm4PK+A+AV7pld90+FZJkcP9gNKPXFiaA8eAUHN1gTSNaqPT8ICC/AmQ439OgyCC8/AWRlZWPvqsPw29ULxbt3yg6rx9qb0wO2wx0xdxcw1+9p9PEvx7Cw4RjQZSq8vHzRUtFec5rM7guDCPphuU40ga4MAz2mH5LTpzwzA0VbN6No6yYU7dwOe2lplcuzZ/8M74RWiLzqevj36FXj9ha7BWZD/TOXncnLs2Lzn46O1X2y/c+aYbBnVzG++TxDPf4z/20NP//KL7VMP2go3t5G3P/3ODz7xDH0GxiAc2dImyfXoX0n3b0u05NhScLZRcYu1oV0P66vA7InoVfDoD4G9O2lNqt1Oo4d34ncwnT1PIwwoniGH0KDHOvCM49ep/4viDuGkFfcM4uk9GgSfl8wBwsPtoXJbENoYhriuhyBt8EL3gZvBJoDUZSaCD+vIESEB2Fwp7bwCguvETzYvXefMgsE/4iqZQWn0gRbt++C6ZX7YfQthrH7DmDQmX+e0vBw6LnTgHOnwVpYgKItm3Bo22pYVydAVk17SC5K26dgrQFYi1/gt/sX9D7SFUMizsHwoQPQ0mAQwf1hEOHssrgFaALdGQYtTRx4KrJIWzLSUX48VY2TshUVwlZcDHtZGWxlZbCXlcKam4OyY8dQlnLslPdXlnwMKS88i8irrkPopGkVj/F77iLMz12Ep1s9VnHeqcjJrvwMhYWdvY/42tUFqvRA+PbLDNxwa8PKD2ojsY2PapAYE+fl8gN1igP3h9EEokfkt0/7/WlpyCSENvGVzfk07BYLPvhIGls5TILouFy4K8ffeQPHvY3I2z9J/Z0eko9dsUeqXMf7q74wpsXAbrIg2nw3TF5eMEdFwSsqBn+ERSDZEg2fmGMoeXiHul7f/Mp1tSFr45xlBwH0hKHEDzF+zR/Vl6zJoOGj0Hv4KLx+RR5+nb8Ku/MO4Wg5UHay33GxH/Dnzg7Y8mcIPopdhlsmFmLwxPEw+raMrAOWJLg/1ASkOjQMmgFJKReN4+vrfqmklqxM5C5egPxVy2HJyjrj95/x9eeqRMEeF4X3TnyMVQVr1flvn/igwQfOrdv44NX/tVXGgW8z1P7L+7tjazG69/Kr0ltg0tQQLFmQi4REbwweevo1qDJNQUocPv/oBDp28UOv3v6NylQ4U3BKgvvDDAOiRyT10p2CCFKGl/LS8xhz7CgO9ilE6pbp6NTaG5kZWYiIdG0mWXP8ppSlJsMnMbbyTK+a76VBRg3I/37FMFrEUClHeWqK2tZ0mIjMHTJlojvMY/xhOWcp+hV0bPA+SPCi//G1OJ5oQMGxbpgxqbJZ4tkgJCQY1146RZ0uKbkW6/fMwar8NdgWWADTNse+lGdFwufnD3Fo9ncIGjIcwWPGw6ddB5cHCuqDmsD9oSYg1aFh0Az8ua5QNaxr3dYb088Pw5DhQXAHoyB79i/IXbZI1ec1GIMBvh06omR/ZYfjerHbkZS8Ge9aViO5PKXi7Fiv6AZnGBiNBtW/QLYzzYqlefjsw3SUl9tx530xGDys8r2NjfPG3x+JR9cefmofzgRHj5Rh8YI87NpRjM8+OOGSsYyMJrg/8r0ym3W1HBDidmWKYraXHTuqxlPduGc1ngsfiqU/d8LSn7NguucJRBlLEWUJQowpEsHlrREb0AODB/ZFS+1XJOt5v+TjSGz/AQIuugIGrw7wtsWizFKCkvIi5JflYlvfw8gvMMIEC/y9B8CSfhzl6SdUtmJBWVjF/Vn7blX/LyhajpHWy1QGx6k0Qd6yReiaeghdcQhZI0agW5d74Cp8fQMxqs/lGIXLkZ5xHJ+NWIOd26IQHHgEQWllsFtkfxer7dueo+AT2ApXXTAE8XFOhotOYNah+8OSBFIdXSnElph+WJDvEDPePgZ4eRlOllU4FrAjh8vUgV5LNgwkIpI951fkLl0oHSkrLzCZ4Ne1O3zatocpMAgmf38Y/fxh8Pau2ExBwaqJk8HLCykv/0f1MDgVW3oY8Wv4ryg9OV7Tz+CLv8TcgsGBA3Ejrj1rUxKyMi3Ysa1IZQpI00KN6BgvZRYIq/8oqGIYCN17+Z/R/di5rQghoSZce2MU/vtsCg4fLj3rhgGjCe6PvMee3FyP6BN3K1MM6DcQuQvnqfI9+bYZ7TaZzAe7uRyFUfkoNAKHS/NgXtkDplV9YSj1wapxM3H/7ReipaH1IfCz2tAp0Aet6jA+pg2q/ffImpuLK7Zuw9rYrdhuzIM9ypHRuD+6BF+vfQHXDP9nxXXryi4Q7aLR54LzoReiImPwt9svUGZY9r5w2FbnIX/tKthLSlDkbcDe/YNhyAnDP1dmo+sNr2JC/HD07zwFZvPZXfubY3KS473NwZotW7FmVw6ycwHv9rtRnnAY5UYrbLDDbgAKZs2AyWqGv38JHu5cAFNIKMwhIer/E2YTgsJj3C4rR08wiEB0bxi0NHHw8fsnVEaB8OIbbRAZ5aXq5hPbeCM7y4JLrqjseixGgtVqd+mIvIZiyclB9txfkLdkoSNScBKDry9CJ05GyMQpMIdWuv+nIqD/wHoNgzIz8PsEM9YOko+k4/Fae7fCX2PvRpx3pcN+NgyDH7/NxKyZ2ep0SIgJvfsFVFzWoZMv4hO81P+Dh5350VcFBVYsXZiLPv0CVB+DCZND1KQHyV4478Iw9B9YuS9nC83EY0dk94XigOgRMbHcKcPAt30HtH3tXZSnJqMsLRWd1qXhRGYWSmCBdzGQHwCY502E8XBr2BJS1BjG5MOFOHjXzfg2bgSSi+MRHlaCCyZ2UI0VdY3JpDIMJctA+jY0VguaQ0MxbvQoWOxLsW1vFgyHE2FvlQyYbZgbsRuDDq6ud6xi5m+/wJrjWMcD+g+CT6JjFKOekDU1smtXoGtXRF55HQrWrcbMTetgWO3QVrZ2R7ClbRq24Cf47/wJ/XJj0dtnPIb3GwcvL68WkWGQnZODVWu3YFhpFkr270HJgf2wFRZgYcfBOLzdYeKUx6bAGl1c5XY+R9rCUOaDorAM5Bz4usplr7ebjrxdwwC/owg7bybionMRbQhHjE8sovzaIMi7Pdq3bePS16ilwyAC0bVh0BLFgZb23rufP3JyrMow6NnHHz37tFaGQWCQqaLu/Z3Xj2PThkJcdFk4Bg0NRNxZjhQ3hLLUFOTMn4v8P5arlEANg7cPQiZORtjUc1XmQGMJHjUWmd9/BVuhw1yBLDQnFx2LEfjwWm8kJ1QuPqODRuDmqOvgY/Sp3IcGzFwWtm4qhNUmDQ9NaN3Wp97ygLSUshoRe+mBoCFZBs6GgdlswHMvNY/w2LenGP99JkVlMKSmlOPWO2PUxIRuPRxZCxdf7pqRW8wwcH9Yr0j0iGiClpZ1eCqM3t7wadNObf8c4nzJBSgqzsUj9jnIjMiCIT0S/q1247LMhbCVleBobhSKjnZEgRww7cvF//1jOzq26wm9Iuv1M6aHYC/1gTkjEx828X5Wb8yF11pHg0jztFkoGLoVhtVD8N8tDn1SmybYvHUHXlsWieHtEjE2KRnhF14KvSMND4NHj8P1o8ehy/BN+GXhEaR12XYyfAIU+QN/+Kdh/f988PH/diKk1TE8Pt4XwT16wSuq6Q2Wz/TkpMzso9h2eDl2FezElhV9Uby9Dwy2BPhHfIXEwsoGnwmWNBw+edqQVy34YjMos0Aw+xQCVb0ElFpOXr84AGnxeUiNFGNItgMwJCXB5/32sJsOoE3ntbgjOE010JTXyBQVhd12Izq274CwsNAz+6K4GfK7yzJFolvDoCVmGEiKunDTbdEV5oCwZVMhPn7vBAYOCUTnLr5Yt7pAmQXCT99lYfvWIjzyZKsq97V3dzFSU8rQvac/oqLPnjMqC27xzu3I+X1ujSwAKS0IGT8JodPOgzm46eMJDSYTzOGRKBPDwGRCu9ffQ/asmciZNxtJrY0VZoHBDtwSfQPGB4+pdTFqiGHw47dZSDpcqjyJD77oUOt1du0owszvs7B3dwmee6k14hMqTQNpZtixky969PZTYw3PFm3a+cDb26AMA/m8XH1DJPz9z36Tw+qwXtH9YYYB0SMtMYhwOvj7heD5uy7EulUr0TbjAHwL8mBJb69q+ktLQmH3K4blnCWw9t2GDWmddW0YCPZyLxjKvWG3NF3PJB+rLPu7Z8AkvPVdIsq294V8KixWWw1NIJ+X/32RCntKW6xMvQ2lE1bj7sTWaEkMHdRPbWVl52D1rl+wOn8tdkRkw5IfBuOxBHWdwpwAZH/yP3WYbI6Khm/b9pgXHAv/4EgM7tsZia0Smi0r0FkTHDh0CEtW7cShjBzkjViE9FAbIG9ZEGAO6wSzzaHtdgS1RmLhNhVw8m7TFsMTY1HaehdaxwWhXZu+aB93Lnx9g2AwGNV7WvBuITKyMmEpikO88f9UiYqUMlhycxBzrBhpCUdRlh8Me2hOlX0zZDmyMwxWM7xL89WIS40CX+DN4sdgKM0A4rYg+vIvEWkJUNkJ0d4x8C1rh5CAGLRNbIWICM8ud2AQgejeMGhp0YShIwKVORBabXxfWmo5crKtWDgvF3t2FuP6W6Kw4WTpglBcbKvyxZTnvnxJHlYuy1fnPfJkAjp3dXQPbi7Uj/L6Ncj+dSbKjlUddSSlB9KtN2zq+So1sKlY7BYcKk1CJ98OMGhN1Ww2GP38YCstUX/6lNnRc6cVxb5Al302dLf+iZxO+QjoNwDesfE19vlUZJ8cqyjZH3VlF4hRIJvWyPDyqyMrLgsIMOGxp6uaOWcKmXqwe0cxVizLQ0Irb5w7o3JRkmyCiVNCUFRkw8TJIbowCwRmGLg/NAxaLsuXL8dHH32ELVu2qHWka9eueOihh9C3b8tslufOTQ8bgp+fH8ZMOKfG+W8WF+OjJW9jRYftgMkOs0FX8q1WDFEnYCv3hTGgaaMjpQ/BZP+Z2NazLbJsCejT4wpclV2Ij3faoArd7YCt1DHKWOP3td+hqKy16hFhCMzDVRedh5aKt7c/xvS5EmNwpco+mbdyARa334/8Y4mIDjwI5DmuZ0k/gfz0E1gafgeQ0gpzvimF4eHbEF1iQnR5IGJNUfApbAtzWQwSYiPQrWMbBAaHNKoHgXwPc9LT4ZediYItm9V56V99gqdWtIchqwvsJitKz1ki71rFbWxtkmCM7Izw6Ex06N0PrYfdDK/oGPW4Ynv0OMWECdlq4ymn0yWlbyA1fS9Ssw8irSgZe0u9cKTdAZTkBSPOnlnldsdD/GHIcYyutPqV4UhkGY6grCI7weubWJi2S0ZnFq6NfwodvY0wh4XBFBqGbUEB2JwbjqhQb3To4I/2bWIQFdZavUfuCA2DlsvyZtIEulpxWmI0ISTUjL8/WvWgVigrtcHP34jYWC/cdEc0Elv74J9PJODg/hKVLi+Xa8hEhdTkchUVFyTKLHXr1dGMhTPVzDDtf2+gZM+uKuebIyJVfwJJjTMFnF50XcyCV9LewpbCbbgz5lZE5zvqCY3+/qq2sXDTn+rvVil2XPFTZZ+EQqxH4cb1yPzuK4ROnoaIy6+peN4NMQykb4SUg2jmutxGbuZsHsgB+W+zsx39JlrXfK2bi/w8K158PkVVY4SFmzD1vDCYTJX7deGlrik7qA9OSfAM2KOiZbJkyRJMnz4djz76KHx9ffG///0Pd9xxB/74448W/566Y0nC6RgJ6SEHlVkgdArXd3aB4HXtZ6ovg3++rHFTG3370oP70ftoOnojHUGjHHpk7MjB+HPLD4g7tB7fworyjHSlZ8xh4cgvyMRM/99R9hcDTGsGY1pgO4SHD4C7ZJ9cdM4luOgcoLS0FJn7bfA9EIqiHdvU65TtY4E9PRrySkvUvTTQiiOyQZpFZsE8Px7m5Y5Mi7GJL2FUbhKMvn4qeLMpOgxzU0bDaLIioNNeBHXfUfG4pUYLjn91HWx5IfAOT8VD2R8ib99Bx4V5+fCPSkJxVgQMVhNMRxKQEJqKLpZ4dA/tjR6jRiF4WlSzvi6+PgFo16qf2hROZT522xCVlSAZOrJlHk9CqO8eFOYHwBidUlHuoWHIq8xmic7JQLnNgvI0x7SuNR0H4MD2Uer0gum/wxr+MZAP+BcDQcUmlHx/FXx8ShEXUYDb25tgCg5R2RSmoCAcswMBIWGIioxoMX0VGERouSxpJk1Aw+AMUNtB/HkXhqvNmS7d/NRWnf17S5CR7oiKz7gkHKWlNhVtFg4dKFH17JkZ5SgpseHSKysj4aczIvHYc0/CkpFecZ5Ph04ImzwdAQMGqfKBplBmK8O24h3o499LRT/m5y7Cn4WOdLDXj78D3/NCMfCbUSgs747p69JhKsiGHCtvmxSPDf194fvVIIxN3YT40r2qV5Ic5efMmwNzZBRCJ05pcA+D0eOCK5pM/rm+AL/+lI1JU0MwYnSlWx0QaMJjT7VSpQhnagRiQw0m6Xex+c8ilJXZVSaKZBroGWYYuD8UB42npKQEr7zyCubMmYP8/Hy0b98en3/+OQIDA7F7924899xz2Lx5M4KCgnD++efjr3/9K7y9Hd/18ePHq8X8yy+/xJ9//omEhAR1+TnnnIO5c+fi8ccfx6pVqyquL0b6yJEj8cADD+DSS6vWYz/xxBNV/r722mvxww8/ICcnBxER+jMgG4OIGxoGwL9e/hkHj4Wh9BrHWhWaZ0DPPuOgdwySBXCy631TKNpVeeAqU5k0Hrh9BpL/vUWdtlmtSP/8Y8Td+zd8sell5MfJuXZ067ATV4/8C9wRHx8fxPfoBfTohfDzL4LdakXI0YMYsXwPDqVYUWAqQHm+ATlB9loPhiPK8lSPKqtseblI9fdDWXKiuqygTQqSYwqrPl6JHwylvijLdRz8a/dqNJvRKyIZaQO80btrECYMuR9hYQ1vhn02JnWIkSSbX+euyksYclHl5SUlFyMtYz9Scw4irfAYNiekI8e3GOVlJgQjShlR9pMZLPmqzsKBPdiRCSwU+QFFdi/4Hm0DuWZZ8X5kbfmkyn78J/4aWA52hd2YC6+73kCgVzGCLN4ItPnCnBmPokOdEBxoxKA2AejXJgGmYDEagmH0DzhjQcLGwgyDxlPi5pqAhoGLkWaI3j5GdYDcqbOvMgycWbUyHwt+q0znGzshpNb+BqoPwe6d6mDft1OXOn9krEVFSHn53xVmgWQURF17I/z79D+tHyYxC948/h7WFW5AkDEQ10RciUnBE7C/5BBWFaxxXCcrDhuyJ6vTmdv+xJF/OFLDxP027msP7z29cAi9MGa4EefHr0X27F/UpRlffQafVq0bPSUhN8eCN15KU6d/+TEbQ0cEVYnmt0o8e5kFzkw/PwzDRwWj34AANYpT77CHgWfABkeN48knn8S2bdvw2muvITo6GgcPHlTCoLCwELfccgumTJmCp556CllZWWqxLygowNNPP11lUZdNrjN//nzce++9+PbbbzFhwgT83//9H1asWKFOCxs2bFACRMRDfRw/fhxvvfWWEhIt3SxoqVmHZ5ptO3fj0LqeKnLsNfM8lF8wB9NsXWAyuVa+Fe/ZhRP7t8Depyvatqo91dUuTYkclkGTHqNk7+5aDQMpb4y990HgyefVvRdu2YhdB1ZgecwxuRReZXbc3PkeeAqi+yLbdsJtbTs5nXs1SkoKkHxiN45l78P2xDKkGrejoNAL7QJi4B3oBVtJCWwlxSgxaVqsDkJzYTBa4ROSC+9ekxEQuQ1YtR5xt9+Niy+7DC0VX99A9dnVPr8XVmlC6tCqMgJVjIMLDhzAjta7kZFrhVSLlqSFIsdUjFyfMuQXVpYleJurmi2CpdSRHWOwmVAQUYQCsw1pqpNjMUzJreG1xpEtVNJ+FqJT3q+4XWaYGe8UPACTTwn84lLQeswfCII/go1BCPYKgSUrAb6mcERHhqB7u9bwDQ4+owYDNUHjeNLNNYHuDANPiyZIJsFzL7ZWPQ0kXb06hw9Wrc9buyq/St27Ru6i35HxRaWr6RUXj/LjaapfQNh5FyLs3BlqUUl74yWUHTvquE50DBL++YRyX0+HElsp/pPyMnaV7IEhLQqlW/rgp50x6PSIDXfH36bGI36T9QNsickVt0mVHC0nTBv7VJzuNTQaEYOvhN1mQ87cWThuicKy59fCZrWjpLjqa1RaYsPj/ziqsgnatvPBPX9T4QVFRKQXunTzxZ5dJfDzMyIn26LOczWdujRvb4ozDTMMPAO9iYN5c7Lx+xxHQ6vb7oqpmBYipJ8ox3NPysEB0H9QIK69sWra66svpCDpkOO385W321W5THqW/PSdo7b16huiMHBw48ej5uXlYdasWXj//fcxYIAj5Tkx0RGhk/MlCvDII4+oNa1t27Z47LHHlGB48MEHERLiaB47derUisX+pptuwsKFC/Hzzz8rITFp0iQVVdDEwYIFCzB8+HCE1tFP5plnnlERhOLiYnWbV199Fe6AvH4trRHymaYgLxs+ndJQkh0H074OiJg7AdMfne7yNSHlxecwe6wda4p+Q7ev+uHCvlPQq3vXiutIeYCUIwghJbWvu8XF+dhzZDU6tx6iUu6rs7IICAqOQjzK4RVRNbsy32aH1WhEmdEAu9kLb/2+E/YR/kBAMc7N7YW47p3h6cgBcYfWA9U2pk+VsEWV691rtaKwsAhFxcUwm6fA27uyfERq9H1fqFqe6lv8nvrf2ELS608HKdnw9kvAiPgEjBhVOdHEGTE1s7vlIDntOIzFbRFn/rvK3LDm58OSn4vIpEwUmKywWE0ILrGj0NcOq/nkgb2T2RBkq2o2ZPn4AKnBsOYHIzc8DxtjtWkQDrzmXArTbsfY8VvC/4bY0uKTZRDBWBYdiw0nusDPz4LEzifQpo0FId5hCPGPRGhANHy94hARLiWxphaTdUhNMNelmkBXnwZPNAw05IBWturICMZVK/KxYmm+aojnXNKQkV4Os5dBNffT+gFolKc66q4EmUYgmzPGoCDEPfCP0zYLhG8zf1BmgeC9YRAMa/pDciJ2bC1CfEIoZoSfi9y5s/Db0DyUXfIz7PFpsEdmVN3fi37FtMGdcHxzqIq8CxGXXImyo0eQvtmOFWWjYLcbUJSWgRMfv4fAIcPg17UHvLwNOJ7mqEQLDTPV6PNw/kXhykzo3dffZaldLZ36RigR90Fv4qC4yIbsLMdnT77D1ZuHapcVFdY0WsV81S6vjpR8aZeVlTYt8pmUlKQOZHv0qNm2S6IK0mTIeUZ5z549UV5ejkOHDlU0HhIjwZnu3burywVJV7zzzjtViqOkH4uZcP/999e5P3fddZdKO0xJScFnn32GK664Aj/++KOqX2zJsCQB6O3rg78f/RblMMI+YRS636qDNHu7HUXdErFuQDLs5T44sGgcXpxlREjHuXj+6rbw79INy1dtg/ei62CLT0VEwvFa7+bZTX/H/lArTN8cxC09WmHc4HOrXD533+UwnOyy77vpRviXm5BQGoIuvp2wdGEk7DYTcqyR+G/Y5Shf3gk+a0fCb8xCXHTt7WflZXAX5HsWHByktsYEEVp6j5QzhbwOkZERaquNF6v8NU4d5xQVZyMnPxWHe2YiKfggsnJLMSSkK0LKW8Oalwdrfh7KpSFjRj7sxf6Af1GN+zUUVho54cVF0rQB1pxstR3wiUHuvs5Kix9texgrwjZW3rDAAJ8nHgGMGfCPP4znxpUhbNr5tT4vPUFNcKdLNYGuFKJ8OD09/bA6ElWT7eY7YqqcX5DvaJ5nMhpUM0W/Lt1RvGNbg+7T4OWF+PsegndsZTT+dBobLs//Q532shlxQ2I5PltrV+mTWXuPorhDmhIXwxfmIm6fEYVd0pDQ+0rEecVi44lV+KZkNuxyIOplxS/t/otpfSfDZLrSsZ9GI2LvfgA+z8wE9qlMQ1htduQtW6w26VwbNGI0wsMGwwaj6kuw+o8CfPjOcfj4GnHFNZEV/QxI02FJgmegN8NAmsZKY1DBrEVjTiJ9R7TL/ANqipqgYFPF5dXx8am8X2+fpplgmmCuzUQTI6F66ZT2t3PDq+qR87KyMiUEhCFDhiA4OBjLli1DXFycSmGcOHFinfsjdcOytWnTBoMGDVKbpC+eKl1R73hyEEGQLLusk2a/F2yI6V0lTOwyZG3u8JeHMX3Tu5i/LRQ4eeBiLC9E6r+fgner1ths6gBj0gQYk1oj4JyqzZUFq9WCYxY/eL97CYzHY/DJQgtsw1/G8MnnwrdDJxzbtQsGW2V/nxwvL+QElSEFmVhfkg/fLQ85LrAD1jxH4MNQ6oPRwV3h5dWyjTK9Q01w+r9rgQERamsVC4wcXPv1ZJKEHMbLcVFuXgQKSjojp+A4coszkVuahR0dTyA7PA9lpQaEBPSoMBpkK0Jl5oI9sFqZRJGfo7+I1axMhqyfvkfopGmVk8xOrll6a9BITeBaTaArhcj0w4bz2UfpSEtxRNbfe/s4/vq382AKCUHpoYPIX7VcNbSpi7i/PgTfjs61bo1Hshs+/SAd/aYXofCk89l1VzkSZ83C5ebdiDMmI2BTEZJPjsAVWd0+yYbgdl0QHehI4Y31HQPjZzPx5eWVomBu7u+4JvJyGA2OqILRxwfDHrgEUSv34ssfbPAzOEYhCuKi5sz5BXfiF/h27IygIWPwZ35viOdUVOi5IvNMw5IE90Y7cNWbOJgyPUxttSF9XKqXGjhz/99rTq7RGDU2WG2ng5QfiMG9Y8cODBs2rMplnTp1wuLFi5Wo1rIMpK5RDBlZvDUOHDhQ5XZ79uxB//791Wm5nXQ5ljpGeazRo0erWsiGII/rLsa7p2cY5C1dhNID4pYDXrHxCOw/CHpBSgiuGv4Qxrc/jveKV+Pg9jYYW7JKXSZjmjPadYfdYIPBbkS/bjWDE9KDYdiRcViV7fiO22PSsS52E9o8s87pWs9UnDKkRwEJx2CXJsW+ZbAM2AT84Mh2sBrsiGm3Gha7Fy6/sGoDMI28vHyUZWbAvzAP5RkZsGRmYPmR/fg9dQh6di3G3Te13PGLZxtqgrP/OxgeFolwRKI1KqejnFvl5+C8Ku/PQxkZSEpOw4mMXJgDOqEkOxR55bnItxYgsxA4kngYlhI/BPvnImzkBVXMAufH1RPUBNNdqgl0Zxh4sjhoDBdfHo49u6TezIDLropQX/aQMeOBMeMRfcMt6gdDehXISJi0t6rWrqT891n5JVCTB8IvvBTGRqaoHDxQgheeTVHpQck5pcDN8uYBbY843ruOJofAqUsARVx6Jez+vvhtyyf4bVLVj+A5weMrzAKN0DAzBp7XHUZp6JiYgJg770PB6pUo3LJJ9WgQSvbvVVuBoS/iAs+B1SsQQac3FbICeS3L01JRcnC/6gtRfuK4cnAFk38AjIGBMIdFwCs6WvWF8IqKhjEwyG1S+BlNcG8ksq3HDAM9I8795MmTVddjaVoUFRWFnTt3qkVcDvTffvttVUN43XXXITs7G88++ywuv/zyKgv8vHnzMHToUFWiICJATAW5Pw0pS5DeBlL+IP/XRkZGhurCPG7cuIqog4xQki7M1Y2MlognGwYHDyXh+VlGTIxojQGZRxB1/c0qO1BvxMbG4PH7Z6C0pARlGy9GzqLflclx56F5OJC4Apsje2HYkNobEN5+zfno1W0p3l59BLaENGyNSEByzGD0xjYMydoLa8JBmA60h7XvVoyBDyb63I3Hyt9StzVmSlaBQTVUvDzwQ0RlliPZPwy/f7gBpX1aI708A5nIRdqxGOTOnwYUBaJD+3m4MmWluv2y6M5YnnolDOVeWH/MhvJry3VnmuoVjlrWN6I9I6Oi1FYnU7QTI+q8Cr8PDSfMAzSBrhSiJ4uDxkxVWDQ/V0XIHvxnPIJDTGpUX20/GD6JrdVBrPQrsOVXjoFRWK3I+X2O2iQ6L/0MTP6VKUz10SrRG+HhZiQXlaEg1w7khgBhuUhItakxMNE33gZbUSEskhqVl4vc+b9Vuf2q2a/j50G5SG4rdY2V5sAV4Zeofgd1oQ7ADQYEDR6mNktuDvJX/4H8lUsrGjl2t29Gd8tmwAIYPwlA6uqu8O3cDb4dOsI7PgGmwFPX6NktFpQeOYySfXtQvHc3SvbuqTAIGtMoxxwV4zARIqPVNArZvE7+bwxw3bicM5l+TVo+UhMnUBw0DhEF//nPf3D77bejqKhIZRaMkrnjwcH49NNPlSCYMWOGqhkUwfDQQydTqE8iTRC/++47PPzww0pcyH116NCh4nLpgyBdjSUTQRb/2pD7PnLkCO6++241MikgIEA1YZSaxYZGH/SMaILGTMZxJ179aAfKUztjruFWZAxdjbu61eyXoSd8fH3hM3wkgoaPRFlaKgrW/IGgtFSMnTS13kjl8AFjUWT4BB/474b3/25GQVoM/jD0gn+vF2Hc7sjIMR5si6Xnz8XSk2aB0GbaTJhetcHsXYRZ3pNQnNcPyANKL38b9rBNgPHkulUSDJ8ix3chz+5oEJYSa8CymM4wHnEYBEHtDsDLi00SGwozDDwDBhEax9NurgloGLQgdu8sxrtvpqmGXZs2FOKvD8fX2ijRGaOPLxL/72kUbt7oWMRXr1RjYpxRkfl1qxEy1tF9syGTHe64NwbzZudg14QvkWfKhdFqR8xxO/wH9ELgoMrZNLayMuQtWYhCUzn2djRiZ1cTdnbdCzj5Ql3zI3Bdt3vQ3rdqE7DacBaP5pBQhE2ZjtDJ01CadAj5K5Yif80q2AoLHI9dVKiaQTo3hJTIv1dEBExBITAFBcHg5S2pLYDRoMwNS0YGypKPwl7uKPdoKvIalx05rLbaMPj6whweoZ6D7IfaHzV71/l0sPpfzeJ1aqB2tqE48AzDgOKgccjiKwLBeSyShsxf/vDDD08Zkfjkk6rzuqvTq1cvdOzYEX5+fnXug8x9dlc8NYiwctMs5BSEKjvd4FuMCy5p2NqsF6Q/UviMSxp8/Yn9b8DxOT5YkHayV1N4FvxyveEdcxRlaW1g7bYHXp9fAXt0OizT5gMmO7J2d1P9C4LKi+DjVaiG1AmGrHCY54+Hte922HrsBkKzgaA8eAXmITzCiJB+5+Gt+N9RFrMQpsAShBxIxAuPjW2eF8JNYdahZ7y/1ASNI9DNNQENgxaAdATftaMYWZmWiu7eMobR27tmxHfhvBxExXhVmQrgFR2rGpoI0dfdjJJDB5C78Hfk/7G84nZG38aN+gtL34Jz/TdioyFd/R2Tbpe+hY6DbznIlANukwmlhw7AVl6O436h+Nl7JGxFJwDbNuX+t0q24dzVvhh9/7MNLouoLdokz9O3bXu1RV55HYq2b0X+mj/U/7aCqpkV8ndptfNOhRyw+3bqDN9OXeDTKlG9nuawMCmdVOaEtSBfGQ3l6cdRfuLEyf+PqxpJrWSixvMoKUF5SrLaTr0DRpUZUWEiaJvz3xUGQwiM/v5n1GDg4uHeSPd+gRkG+isVWbp0qUpj9FQ8McNAmgF+Y52JsjvsMK0ZhJGGVmiV0BvuzoSBE7DghzSg2B9+CfvQZ286BoT/hC+m27GjvT+8Zp4H09pBMJR5o/yCuShYPQo2qwlZCEOU4TiK4g/CzysP2XMmwZgVDmNSIu7rMBK9+gyE1/uV+iL1xF6k5i1QpwMH/oEXr3mjTvFNaodBBM8oU6Qm0BdlLtYEuuth4GnioD7ktdiyqQg/fpOJo0eqNjG8/uYomEyGCvOgpNiGOb9mY+E8GaIC3P1AbJ3zxX3bdUBhZNXaJv/ejlFfwm85C3CsLBm7infD2+CN88OmoaNXZ0T7Vo5gPP7+2+qg929LgbxAGSXr2Bejtw/yV61A2gfvICXGjk2jw5F0YSyyv74b5hOO2xoLzJiUtxb9N1sRd9utDTYLxBg41edDejkE9O2vNukwXZaajJI9u1XWQFlqiurpYMnNVSUZdTwIzJFRqkzDT5kEXeGd0KrOA3Ap45CeBWhXmTbkXNpgycqoaLBkycpEeUa6+l/9nZnRsEwGaVgiM33zHO/tKTEa4dOmHWL/cq/qqXC6UBx4RoaBt3dl81HiOgoKCtT27rvvIiQkBGPHem700xMNg5Xbv0dGqMNoTuy8BbcPuQOewL/fWQsUd1On7TkxaPfhF6ox4pQfP8H+10dWXC+27R4UbOmAYhk1ZwCMpnJcfkC6K2+CKToGLwRMQFFWOAxFAZizdgP696y8rRASFANzlh0WswE2ox0+3pyo0Fg4atm9YdahvijQiSbQlWHgqemHdSHNyz/7MF1lFjjzytttERZurlKq8NoLqVWus/nPwjoNAzmQLVi/puLv8Isvr9K/YF3BBuwq2VPx9+up/4P327eoLsYTzvPHiI4dUT60B0zL/pTMQARLBUB+ENZYeyJtzxD456/DvpvMSI2Vg+xClTYYZixAsc2xPxcsz0IPOA7YS5OPofB/b6Boy0a1X37deyH2zntVKUWt+34K8fj73BwkH5PxZAZcdGkE/BIS4ZOQWOM+pFxB+hLYyy3qgNxut8EUIM0Lw2vtFtsU5H4kG0G2up6LmC7aGBznkTh1nW6owSCZHYWb/6zILDkd2ODIMzIMmH6oD5YvX65qG6X+8bXXXquYtOCJeKImWFi4HDjZsPfC4Gke8f6v3v4Tjk/6A155oTBkhmHExAPYezASCTEdMfDiG4Dv91dcNzyzC/59+wwsjF6Nr2ba4QunyUknjuOikDn4At1hDyjErvAM/LTmdVw09N4q0x06ZwRhZ2wB8gOAHftXoncXzzXlmgJLEtwb9jXSF8t1ogl0Zxh4WjShPry8DLjw0nB8+D9HaH7A4AD4+soM8aqzUo8cLq1x25XL8nHldZEIqGVGec6C3ypS4X3atkfY9AsqLrPYLThUmlTl+sZdXWFMiwXSYrE4+yjm3fY0MBLwGxGMYTt9MOrXdGSXh2OhZRqQBFgiwmHL7QwEHwP8S+BdbkeH8HUIyxbjQMyC3ZX7MueXKo8lxkHp4UPw6+KINDSW7VuKsG2LY8zjjIsrMyKqZyqIOSCbK5H9MPj5qQaJDckEaIzBYAoLR+Dg4WdkP5lh4N5QHJx9ZOxiXUybNk1txGFieZImKCsrwqGwIsBihnFPJ/SbWtHK3K3ZmbMN9thMlN32MQwnojA3/jjmzwmGeXUQELAfgzotxLrM0WifsBzT9u8Giidh8riR8PUxIzAsDDG33YW8FUtRvGsHDoaHAMcBQ2EAzGsG4btbP0PeH0/jhhH/V/F4Pa0DsHehaJvO+C4hHb2r9h4jp4CawL1hScLZZ3EL0AS6Mgw8TRw0hOGjHF39R4wOglHmD1c/gLRDTU2ojXWrCzBuYkiV82wlJcieffIg3WBwjGpycqvMBjNeafNv7C85gGNlKfg260egzAt2/yIYivxhGfMHTOv7wdpnG4q9y7C4RxkKew/ClRvbwfijDTabEeaNfWHf0gteEakY2+ZT9NtTAp+yxTgeb8AfQ0z4rq8je8CvyI4LfitHz12VESSZNe3boVOdr8epPh+lJZX35ePrXpGZxhoMZwqKA/emtNRhOLJekegNT9MExSX5sGVFwvv7C5VJf9vcbXhs8B+Iat8Dvh07wTsh0aUNcJuLKZ0ux46kF5ESboE9XqYnAYZsx0QDFAahZ+Y+jC37Ez4nJzbnr16J0HOmqtPy6QgaPkpt5eknYJyzAEjOAfJCYe2/RV1nQcR+XGMpg9nsKLsa1P4c/PK/IhjsRqRYOf2nsTDDwDM0AbMOiW4NA/YwqIn0KZARis5kZ1kwa2YWdu8qwVP/TsT4c0JUA0TpcxAcbERqajnOmRKCnr0dZQYFBVbV4yAyyguFGzdUTBEQt8FWVARbaUmVEoAwcygGBQ7AIAzAheHnIaNNJraM2YOPFi+GaUM/mHZ1gSE9ApZpC9X111q3wT7cB77ex5EdkgTvLy+FITcElhOtgKx+MPmswvxxZiwfUfXjVuxvwJYepiqGgV+XrnWWBDRkrN8d98aisNCK0lI7zGYKgTMBxYFnlCSwhwHRG56WdRgSHIP2q0bimGT0iUCLOA6sWI30Fasrput81XoCSs3B6NLOBxPHdEFcdDu0dBJiuuLlmA+Qcnw3DqRtwaGC/djqn4/cuGSUFwYitjAfJ1s2weDlpTIja0P6Cd1yw9W4/upyzFmwEt+02QnfEqB/dizMXSt7tCQmtoI5aiWsJ2Jht5hQUlgI34CTdSDklHDUsmdkGFATEN0aBuJmeVq9YlPYs6sYixfkqdO7dhRhxiW1p95rZsELz6TAYrHj6f8mwlZSdaRiygvPqmkGsgD7de2uRiuqJn5ORHpFYEL0cAS27Yl3fnaUR5jWDIZl2HogzJHd8GfhJli7OfoSlF39PQI+uwKJRWno7b8G79zgjePRtUdFxq2s2p+haJsjIlAXpxKP4RFmtZEzBzMMPCOaQHFA9IYnRrj+9cD5+GXuMixeXYa4wJ1VLpOStINHO8GeHYmjWyyY0+sZhB+zoWthNIZHjkK/zlNUo8CWSnxMV7WNkj9GVJ5vK/sU1twcFeAwhYaqccT1BREkW2rGtHGYgdpnlQvXdN6MwJINCCspganwVYCGQYNhEMG9YYYBqQ1drSyeln7YVPbvK6nocXDieNUD7uq8/9ZxJB0uleoDpKaUI27EKBTv3Y2CtatUhoHCakXpgX1qk/PbPP+ycvHlvXBekAcPCcLOm/dh6ddmlF/8a4VZoO7iZBNDwR6fhoK7PkA362Ck5Q/F8eiNMNgBuwEw202wGKyIMkfiiYMTYWm3Hz6jEpG76HdYsrJgLXL0H6gLfj7OPjQM3Bs2PSR6xRM1gWRVXHTeeFx0nownnoCSpEMo2b8PJfv3IjUpCfajkep69rg0wGxFVjCwKvg4VuEHxK/8HXe1fgAd2rX8rANnjN7eMFYLZGg09fPRuSQPxWUOLWX0d20/o5aG9pp7oqHnCTCIQGpDV992TxQHTaF3X3/06uOPzl394OdnrFGuIGiNERPb+KjRjBddFo6ISDOMPkbE3nEP0s65CgfXHoRv+gF4b/wNvgZHlNGSkY7S3HwsXWvAvj0luO2uGPj4OB5DzINLx3fF7wn3Ad6Ox0nwikdyeQrCTWHIsmZX7khQAX7CYiAUCDWFYGjgIIwPHoPWPokosZXCZrfCv60/MB6wZGch8/uv1c18Wrc5rbGKpPlGKHlCt25PhOKA6BVPPyAR495PRvx27AxgOuIAPLr/AFZv3Ie00mPIOeGLpPBiWCVf32pE+oJz8ZbXDrz8hHsZBmdaE9glSHLogDpt9A+A0WlKFDk1nJzk3rBMkdSGWW/OOjk1vfvWXmtXUmLDq/9NRW6uBff+LQ7tO/qi34AAHDtaiukXhFVpmrjzgBmf/RoGYCDO80tFH/tadX7AgEHIKg3EN58fUX//JysZDz+WUNFAMNAUgG7BHdTYxUvDL8RFYedj+54cFJ4wY+AwP9hhx89Zs/FjduX0g3tj/4Lufl3V6WJbMbYV7UCRrRhjg1XiIYq2bq647qmmI9QnDiTzYsvGQtUoMi6eM+XPFKxXdG/YEZnoFQYRatKpYwe1ObgFRcW5eP6HT5C0rTeMRxJRmFh1ypG70xTDoPjgPtgKC9Vp/5693LKRZHPCrEP3hiUJRPeGAcXB6fHN5xmq/ED46L0TqiFih06+yjyoPmEhO7uylCHQkgWYAL9uPdR4opyCysUzOtoLGRkWJLSqPAC/MuJSPJ78DJbmrUBxoQ0Ln+ylzh+/KxhXXheFSyMuRIRXOOZk/44Sewks5aUo2L4WZaVFuDvmM9gMdnTwblthGBRsXF9x3wF9+9f5/E7V9PC3Wdn4c10hZs3MxgP/iKvTWCGNg/WKnhFN8PHxcfWuEFIFT88waAjWLdvR6eBRHDk+Uf3ds2v9ZX3uREMaIdfGi79tR3LiZWjtswdXde9+xvfL3aFh4N4ww4DUhq5WY4qD00PKDlKSy3AkqRR33hdbYRJUNwsEmaBQun0TMg6eQLghA349eyPu3gdVraCfzabGOA4aEojZv2TjiX8cUY0Vp5wbpiYPtPFJhBlmpFsyMHfTXnjDYRgcOVIKzaiX8gPZhMyfvkParz8hM8wA212OgxLjroPY+dQVSI80ICHNESEwBYfAp50WOWlcNKEg36qaQQqhYSZ07e7X9BeS1PqasyTBPWFHZKJXOOqz/t/lnN9mI/O7LzFcggCdvsZByxDccd0UeBJNyTBI2hcHa3oM9qE3DNcyG7GxMIjg3nDUMtG9YSDigDXqTSc4xIyHHktA8rEyxCdUXQQzM8oREmquGDXYpZsfgiI2oejon+rv6Ov/pcwCQfoi3HpnDJIOleLAvhLVG1GmMkyYFKpu72P0Qf+AvlhXuAG2tkdhS0iBoSAAF13dAVarHd98kYGsDAsio8wq48BiBpaMNGHRWK+K/dnXwYj/3u+Dtkk2XPO9I8IZNGL0KVMD6/p8BAaZ8N/X2mDe7BxERXvB25sphmcKGgaeEU3gwRnRG/xM1k55WRmyv/kceYsXVJx3ZUJrRFw7BSZz5Trr7jSlh0HS0QOw5IVClJA5JhVt2zgyHUnDoSZwbxhEILo3DNjD4PRRGQBtfWr8uL/1ShqysiwYPS4YF10Woc43+vqq/1dZRmLl1wXwjzTisqscHZiFNu18MHJMEDasLcQ9D8TCz7/yIPyKiItxuDQJJ4LSUfaXjxBsCkK3tq87zIX5uTJ4AYmtvWGz2/DxgCPYXFxTxJT4GjBgsxVbuxuxvr8J1309C74dOiFw4OAmiQN/f1PFcyNnDkYTPEMcsCSB6A1NE1gsFmYgniQvLx//eG41Eo0FuPTkeeEXXoqw8y9qcoq+J7EjbSFK/7kWxqTW6J2ZCDiGOJJGwKaH7g2DCKQ2dBWGZTShedi8sQgHD5QiJ9uKlGOOgwPBp3Vb9f8m6yAsXWXDiqX5NW4rB+APPRaPdh0c5oJGvHccnkl8vOJvmZYgYkXKHyTaLxQW2vBj1i/YXLxd/S2jFavz/QwvfHeRNw61NaHUB0h782UU7XRcn+gDRhPcGzY9JHpF0wQlJY7xd55OcXExHnpmLYoOt8fuQ1OxOLY7om+9E+EXXOyRZkFTMgw2l+4AzDbYOhzG5MH1l0CS2qEmcG84OYno3jBgD4PmQcYp9h8UAAnWSNaAhm+Hjup/fzi6BefnWZGX5xihpyHjGaubBRomp4/P7pK9WJK3XJ3+v6db4T9vJqDTA39UTEswwICbZvtixmxH+rNGmU+lyCkIdJzOX72y1serTRBtWFeA0hLbKV8D0nQ4c9m9keitwAwDolfDQDO1PJ0P1zyPwtACxx9e5YgePQrBI0a7erdaVGR8X0ieOu1XDHRtN9LVu9QioWHg3lATEN2XJGjigOmHZ5bWbXzUpITiYhts1ko3XjUYNJkw2Ws2TJHR6PbogwgKariHFGAKwLjg0cookHGKh0qTME7ex7BivJr6KvbbD1Zcd/quaLTbkgS0rjsKEpzn2LewKefWeR3naIIYHO+8lobQMDOuvyWKUxGaCYoD94YZBkSveJ/sq8MMAyApZSv+aJUE+6VHgFZHcVV8P0ydKCuu+3M84wB2HVuDAO9ghPolIsg/HlGRjvLDxmQY7D20FsUn+yG3zw2CyaQrCdxisErNKRshuy3UBKQ2zHpNPwwMDHT17rgd0szQGWly6NOmHeIP7geyUhDiXQSDIbhR93lz1HXwMpiRVHoU10Veqc77Mftn7C+tNAuuOzEInX9coU53yAzAdX7nYlnxWhyxpcJ+cpfuer8UkV0HIOLiy+Cd0KrOx3MWBzJCUtatzAwLtm0uomHQTLCHgWdEE3xP9jQhRG+aQEuR9WTWJM2HPcphuE/pWIipwz0jOl5SWohHk59GwUlJaNzWBd5fXQq7ORPFJaXwtliQ/tlHMEdFwysqGjttZvTq1Q1+fpWTkuwWC9798jesXRMNc9vzYB2+Dt294lz3pFo4DCK4N+xrRHRvGGglCUw/PHv4de6CUjEMpD5y7x4EDhjUqNubDWbcFHUdymxl6rTgZfBCF9/O2FOyV/3d+aPVFdePufVOtE/oj2mYgfLMDOSvWiG1Bgi8bxC84xMaVa9YXGRDXLyXavTYqSvHKDYXFAfuDcUB0SuaJmCGAXDCklHxuvSLGQaPwW6H1dEWSWFeMVT9b7CImWSHrbwcuYvnq/PKYcRbZU8CxsPwbXsQfSeuRKcjBrTdmIHdvhNhz46AWbYDbTH6OWl4SJoCgwjuDZseEt0bBmxwdPbx69INOfPmqNPFe3Y22jDQ8DY6UkezMi0IWDoRvRNTcDTgOPptypQQZsX1fDt2qjjtFRGJ8PMubPK+DxoaqDbSvNAw8AxxoKV/E6IXtM+k9hn1ZPINxRWnw4I8Jzru6xuIfwbehsct78GQGQbj0coDfSMkgFAZREgODIEhwwjYjCjytmNZq1QsawWE9LCj+x8rsTGpH3zij+Kem6WkIdpFz6jlQ03g3jCIQHRvGGjigOmHZw/fzl1VhF9c/MJNfyLyimthMDatF6atpAR/LkzBrzOlCWEABgd2xyTLkirXKU9Lg6lj0FnriExOH0YT3Bs2OCJ6hSUJleRv7g9vOVgu8UHy9UWI86Dj3c5th+OTkt54/vefkXTyPL9Wu+FjNMEYEICEx56CJf0Eco6lwD7TBoOtqobJDTFg9bQ8RJsX4JlrrkVwcNM0CHGg6TBPnMzhCbBMkejeMGCGwdnHFBAI/x69UbR9i1pwcxfMQ8g5U2o1DexWK8qOHYElOwuWnGxYsrNhzc5GecYJlKWmwJqdhc1lMhm6j7p+r7I9VeZwBI+fBO/2DRtjtGdXsZqAcPX1UVX3gYbBWYfRBPeGGQZErzCI4ERBKIxJrdXJ5OPHMBCehWQa/Ouua/B+yBxs2GjGnVnf4LvyctjLyuDXsTPQsTM2fDa7wiww5gTh8h/KsamvEXs7mmA4koDclRPwwN6NePLvHdEqof4SSHLqIAInm7knLEkgujcMtB8fph+eXUImTVWGgZDx9WfI+X0OAocMh3+PnvDt1AVGH19lFhz916PIS0pFkq09fA3FiDccg5ehstxAuMDrB7SzHsA+W1fEGlIqzve96WF8uCQKl+wrRacu9fcb+PrzDPw+J0edHjIsEB07+50ywyAn26IMhh69/BEXz9TqMwkzDNwbRhOIXmGGQSVxUQakioEbWIDjGenwVG69Zjou77YeJ960yrxmWEuKYS0qgs3LiHXh62CLCYfxeDSC/Iow/KpncWHrtpi34UN8/VMPGGwmWNIS8MonS/Dv2yapJomk8TCI4N5QExDdGwaMJriGgN59EXLOVOQu+E39bcnKRM5vs9QmYxd923cEjEaUHTmMmeXX4oCti7reXd4vIcyQXXE/ufYQHLG1RZwxBT1NW1Slg5BtiMRX34QiO6sEf6zIV4ZB8tFS5Ofb0K6DD7y9DVVS26KiKj+Wq/8owLGjZRgwqPZeBcnHyjB/bg7WrSlQTRCvuzmKhsEZhuLAvWGGAdErmiZgI2Tg/CGJ6LDv32iTVYCosrHwZIIHDELx8FEwfDNTlVOWHNiH7CgziroeATq9j/j13fDClX+DyeToljhl4M0ILNuADz7KhzH8BK4+9hveevkYUn07YNzIOEyZ4BkTJ84U1ATuDccqktqgYUAUkVddB/+evVW34aJtWySs7LjAakXJvj0Vr1KW3TH7WOhy/63wCgtF2s5kWH98B4fL2mOW5WJ12UTzXAw1r8JeaxdYY9sh+6hjbu+BfSWw2ex44+U0FBRY8bd/xOObLzLw0GMJMJkcpsH4SSHY9Gch+g0IQNv2Pnjm8WR883kGLOXSLduK/72ehlvvilHX9zIbsHtnsTILhOgYxxgucuYzDIh7RxOYXkr0Bg2DSlp37gFLejFgBUr2OSYQeTK/GXxhNRphOrk+eZlPjoU12RHcLqnCLNAYOXwgIsL3IueHhSjL9cHmQ5eo87/ZBbT2W4Xuw4ef/SfRQmHWoWcEEThqmTjTtO52zQQzDFyHRPgD+vRD/F8fRrtX/4eYO+5F8NiJ8Iqt2o15ink2zhtVghGjgxDYtx++nBeA/34ZhhOWKKTbYyqul9gxRP2/yDIFR81dK84vzLdhx7Yi+PoZUVpix78ePYY9u0oqshEEo9GABx+Jx8QpoVg0P0+dV1JiR1m5Hfl5VqxZVYAtmwrV+dGxXpg0LbTitqFhVUUCOX3YN8IzDANC9AY1QSVGb2/4tG2vTpenpcCa51gbPZFjyclYunIQSmwBKDT6wqtNW0SGtUGgQxZgf3gBcvOO17hdt66dMfj+JxDVqrJ/gd2vCF/s+fVs7n6LhxkG7g1LEojuMwy0CBfTD12LKTgYQUOHq00QYVJ65DBKjx5BTHg4Agf3UAbDrzOzsHJZvviQ+LrsOszw/h4J552D5HQTQgx+wGGo0oSVSRMq7jsry4KXnk/FgEEB8PU1quwAITPDgqjoyuwArUShvNym3OyU47/Aai1EeXkhfl9yHY6kdcD5Mwagb9++OHHCIaKE9WsK0CrR5yy+Wu4PDQP3hj1jiN57GFATOPDr1AWlB/ap08X79jR5DHJL59XP/sDBPckoK8uD3W7B1AsvQvv27ZHtewxZg40I65CI/62bjYevv7nGbU2BgWj38P9h0kv/xG/5U1B++Uwc9T7pNJAGQcPAvWGZItG9YcD0Q/0aCFKuIJsz50wOxeYNhUg6WIRJ5jlobUjCfqMN+/aU41hhIm4AMMq8FN1vvATvvONoYtiztx/OnRGOLt188dqL0sLJwayfs3HTbZUNiMQk+Prrr/HZ169j48aNsFotykQwmbxxPH0N0k6sxpJlXzjtjRHe3r6YtzQMz/4nFm3atEHnzp3Rs2dPDBw4EJ06dYKxieMiPR0aBu6N1eooFyJEb2iagKaWg8I27fFD/FAkW1sjdnkSHvUgw0A0wQ8//IDn/v0vbN22G3aLreIzsmTJEixevLjK9WfiEzx5x92IiIhAbGwsWrdurXSAf2Aw8lodxLGLymAwfamuG5XFUsbGQMPAMzQByxSJbg0DHx9HZLi0tNTVu0IagI/ZgluG7cSOI8vQznRQNT38dW4JikskOyAKJ7xjEBdehiFjIjFghKP3gfQv8PZ2HLjfflcs/v1UMnz9DLjimgglCL7//nu89tprWL9+vUqLEpOgS5cuuOmmm3DPPfdU1FRlZWWp62zZsgW7du3Cvn0HceJEKjIyMrB161b8+eefNfZXhEVgYCAiIyORkJCgIhLdunVTWQoDBgxAaGhlaQOphIaBeyMHY5ynTfSIpgmYYeDAlNgGuw86jPXi0mS4O6IJZs6ciVdffRVr166tMI4C2wah7UUd8fep1+C6iQ9UaAJZ9//72pc4tKMABYXHUGI5iqLCgjo1gcHLCG8/M+IiYrGpzTi0a9euQhMMGjSImqCe90VgEMY9oUFLdG8YMMNA/1gsdmRmlCNl7S4U//gRoo3H0e5k24AQQy5uvzMSr72SiQ7GfbDBAFNgkLrMbNaaFFQ2K/DzN+KBf8Tit3kzMWnS61i3bl3FwYtkB9xwww24//77a228Eh4ejsmTJ6utrgVNjAQRCdu3b8e+fftw5MgRHD9+HMnJydi/f7+KSjgji5+fn58SCTExMRURCclSEENBhIQnLpBseujeMMOA6BUaBlVp1SoBxvA1sGVFojwnHAX5eQgMCoa78csvv+Dll1/GmjVrKsyiDh06IHFsOMLv7givQG90OOGLq4fcW0UTnHPOORg5ciTue2wN7FY77iz4BEF2K0wBgVgS6odf109BZvYOZJdtRl7YMngl21FaYEP6iUwcSTqGpUuXVtkP0SL1aYKuXbt6ZBSWGQbuDQ0DUhtmPdYr8sOqT6xWO+b8ko2Z32fJMEb0Nw3BNGNlsyDvhFboOzgMzz9tQ8Ezn6rzTCG9aj0AFUHwyiuvqKiBCAJZmDt27Ijrr78ef/3rX+Hv739a+yoH9j169FBbXeTk5ChDYfPmzcpcOHjwoDITJEtBTAYphajtM6plKcTHx1dEJPr06aMiEiJa3A1mGLg3WiYPIXqDPQxq0m7kWuyMT4M97jj2JV+Gfl2nwB2YO3cuXnzxRaxataoiy1SyAK+99lo8+OCDePf7X7G+zyogsEA1N7y/6z9gMtWUsHKA/9RDPZDx3lvwzXGYDda8XIwoyMWKnmaE5Y1Btz4dcdvVryA0JLLKbfPy8rBhw4ZaNcGOHTuwadOmOjWBlD5omkCMBE0TiFZwN2gYuDcMIhDdGwbMMNA3a1cXnDQLHBTZqx7Ui2EgmDf+XnGeT5t26v99e4rx8Uc/YemK/2/vPqCjrLM+jt8kBFIhtNB7ERAEpGPHwqKuvNjbKqKoq65tLYjYFRF7XV3XtStYllXXgohIURRFkN57CZ0QQkJCkvf8/vGJQ2aGmjIz+X7OmUMKkpjyzH3uvf97X7Hp0wuTBKIkgQKCW2+91T3pliVVDU4++WT3CESJDXUmKIBQAmHRokW2cuVK16Wwbt06W7p0qU2cODFgRaJatWpFFQn9P3oViXbt2oVdRYKEQWRjSwJCvcOAIsIfurVOtLkphfN/5m2caZ0tfBMGY8eOtccff9y+//57y87Odm/TDfcll1xit99+u1WtWtg98dlX39msr7talSlHWu65Y+zapl2tdo2mQf/dunVSrc6QYbb1f/+1HZMmWH7mTktqdYTd3CPK2vXqaLGxXQP+d/p4ffr0cY9gMYGe9xUTzJ49e6+YIC0tzSUYJk2a5BcTqEtS8UZqaqo1atTIdSmomKGYQLFBuMUE3g1lRey4rAi43iKQkLpK0X4Y2saPLRxcKI0bxVjPTs1t3oYbLG/GZEuN2mDJVeIs7R/P2c6ffij8S9HR9n32Hnu2Tx+bPPl727OnMEnQqGEzG3TlZS5J4AUEoUhPhpqfoEcwqkioE0EViXnz5hVVJDZt2uRe19uLU3AQrCKhAY0KKkIJCYPIpuCPDgOEImICf+0b9jLbOcW9vLhgtYWbcePGuSTB5MmTi5IEGlJ88cUX2x133OE3NyBn21b75MsoiyqINstMtNbzO1nXfmfu9+NExcZazQHnuYfnj2WKhx4T6GZfj4suuijg39m5c6eLCdSNoC4FJRjWrFnjuhQWLFjg5i4FiwnUoejFBIo7vJhAQxtDCR0GkY2YAIGQMMA+bUzLtRXLs23b1jzrdWyyW4W4MyPf7n+0sJvgmr8stpw951lK1Fa7YfJT7m2T1663V+cssF+3bLPdr7/v3pac1NCaNTrLune92l567SiLjY2MFmglPE488UT3CFaRWL58uatIaPCSV5FQNUJVCb1PgVOgioS6FIpXJI4++mg76qijyrQiQcIgsnEkAaGKDgN/9VPbWvIGs4xEs+UpmW6DUKDW/FCiDQYjR4501fesrMJVyuq+u/DCC+3OO+8MepSvID/fNv3zRRucvcxeb3C+FURXtrv/doaFMt34H3/88e4RLCZQDKChzepSWLhwYVFMoEKDXp4ypTAhFCgmqF27tosJ1LnoGxN4HbplgYRBZKPrEIGE1LMMMwxCz5Mj1tmGtFyLiTH7x+vN7ZS+KW6WgZ7AMjPzLGdPYUva1g3jbeCC7+yXjZtsd16+X9UgPy/Jvp+0w6KjoyImWXCgFQkNa9LjggsuCPh3du3aVVSRUFeCb0VCwYQSDcUpYZCYmOi6FOrVq2dNmzZ1XQoKHLp3716iFQkSBpGNagJCFccUAz+nNN5Yx+ZnpVjeykb2864Z1rNb6K1XVHJgxIgR7tienuNEN7p6HlSS4EDO9m/95GPLmj/XtGPptrhPrNpd9xfFieH8/VMHgR7nn39+wL+jr5figeIxgRIKKjoo0RAsJlDyxYsJvC4FHX1o2LCwyFMSSBhENooICPmEAe2Hoadl6ziXMNCRtTWrc6x5iziLiSm84X/x+S9tyrQnbPXqqZbz+3GDhrVq2kWX/sWG3HOPX9XgzwMibyBgSdCAR0121iNYRWL16tWuIuF1KaxYsWKvioTOgPpSQke/T16XgoIF34qEgogDrUiwJSGykTBAqPJiAipee4tb0tsqTywc6PtD5bnWM0TyBXoeevTRR922gczMTPc2rTC+5pprbMiQIQd13C5n3Vrb9tmYwleio63BdTdZfIi15pdmTHDMMce4RzDa+uTbpaCYYP369bZlyxb3Pg2PDBQTqCvSNybQXCUvJgi0kSoQYoLIRkyAkE8YeBcrBm6Ejh69kqxW7UrWtHmc1atX2bXKKSD45psJlpNT2FqoJ57zzx5gQ4bd49rlUPIVCXVr6HHuuecG/Ds6C+pVJDTN2bciocGNgSoSMTExASsS6lLQdGevIkFwENmoJiBUERME1q1DHZv7+7zdFWvKt+KuTUfDhw+38ePHFyUJ9HwyaNAgGzp06CF3u70+5r/Ws1KBVcmJskVHdbEJCyfbiVV2WKumPUr4/yA86UiHHuecc07QmEDzEtS9qC4FrZJWTLBx40b3suKEr776KmBMUL169b1igg4dOriYQB9P6DqMbMQECPmEAR0G5Wvh/CxbsWy3RceYde2eZNVrVLKjOidaZvYsG3jFfbZw0fe2e3dha2G1anWtRZMLrEO7a+z2oe3d3/e1eVOufTx6q517YQ2rWSu8WwjDJbDu1auXewSjqoPWSKpLwbcisXXrVtfBMHXq1ID/rpcw+NOf/uSOVqhLoXPnzu5xoBUJhC59f5l2jVBEwiCwXt062ydHjrKGtsI65W+zgoJ+ZTq4VDN5lCT45ptvLCMjw71NiQGtRb777rvd4L7D8dvUH23q5L72Q2wfi8qNNfvRLC99gU1s9ZI9ubmW1anVooT+TyL7d6dHjx7uEYwSCIoJlFgoHhNoePOPP/4YNE6Xvn37uphAXQpeTHC4K7FR/ugwQMgnDLwWadoPy8eMXzLtq88LNyFsT59l777/lKsaaOqvxMelWptW59i3Ex+znN017Lkn1tslA2tZ5y6Je/07GpI44sG1lp1VYEsWZdld9zW0GjVD6ketQlckBgwYEPD9WnXpVSRUffAqEgok5Ouvv/arLKgioQBBXQoKGFWRaN26dVGXgs6scjMa2qgmIFR5MQFdh/43bbfFrbGsufMLvz4b1lvluod3k74/el5QkkBbDrQdSLQ6WCsQlSQ43DPyS5Yus52zZphN+9Ge3Vp4tt8lC34Xva6e5ReYxURTgCgp+p7p0b9//6AxgQoMvjGBigvqWhT9LCguCBQTqEtBMYE6I327FPQ6MUFoI2GAQELqLo6JyOVrydIZ9u2Ux239hh/szdGFrYW64J9w3AWWUOkKS0io495Wp05dN7zwsWeaWKVK/lWNho2qWJ26lW3l8t1u9sG2rXtIGIRJcK4ndD18nX766a51UZXodevWFW180Ioo33OTqkioPbU479ykhlwpOPGtSOjsJBWJ8qXvK2sVEYooIgSXcORRljW38KjZzp+mWo3+gVvTD4eu8w8//LC7KUxPT3dv0/l3DS5UkkA3f4dr48bNNvKVqbZpQWurnFrNcjYNsqg9hUmBgkq5VlA/zQqSMyy/4Tq7u/JVVqtGYVs8yub3T2sd9fCl7RajR492zx2apaSYQMUGzVfS5ifFCYoJ9Oe0adMCxgTJycl7xQRt27Ytigm0aQLlh4QBQj5hQPth2QtUNahRPdX+b8Aldt99dxedWbvjppW2cUOu/XlAdVOHenS0FSUL9uwp2CtxoJcHX5dq475MtwsurWkJCTHl8H+GkuLbVaBW07POOss9glUk5syZU1SRUCVCFQmdm1RyQUkG/az5UrWheEXC61JQoKJp0lQkSjc44OuLUOT9XNJh4C+pRy/b8uF7ukDbjonjrfoZ/S2qBNbt6vr9yCOPuCTx9u2FHYeaTXTllVe6JIGuxyUla3OaDXtxiuUs7GSKIHIzalpUbI7ZnlgriNlju29/3i7O7mAnt7/QEhOrl9jHxeHxnWuk5+wzzzzTPYJ1sGmGgo4+6GdLXQoa1KyYQH+qg1FHW4LFBOpi8WICdSkoJlCCgees0kNMgEBIGFRAh1I1uOaGOla5SpQ1avzH+TUlCka9s9l+/nGn3T+8kZt54NtlcMXVBz4RWX7+aadN/HaH7crMs9PPqu43FwHl42AGHKkioQqBHsEoUPAqEkog+FYk1K2gyc+B/l1VJBS4aup28+bNXZdCp06dXABBReLQERwg1JEw8Bdbs5YldDzafl6x1sbHHmet3vjC/nZV4ETu/syfP9/FBF9++aVt27bNvU0rewcOHGjDhg1zN2iHIi1tg81ZuNByEubbmt1rbX3UFtscm217FrWxqDkdLbPBOtt9yUSLmb7WKk3sbfVrzLHcyvmWVi3B8rrOsJaZBXZW778e0sdGaMQEWveo5L8ewWiFtJ73vZhAxQXFBHq718EQLCZQl4IXE6hLwYsJ1NWIQ8MxRYR8woD2w9Kzv6pBo0ZNAx4v8LRo5T/c7qNRW+ybrwoTDrdct8LeGNXykD+//PwC++ar7bZwfrZ7PXNnnu3ene+OM9Std2Dr/1A6SnoispJTOuagx4FWJDSwccOGDe5PVSQ0W8OXqg3x8fF+FYn27du74KFVq1ZUJIJg6CFCHXONAlvZ7Tgb82Mt9/L0tGyb3mWWdekc/MbMl66jigk+//xzN+RONIvmsssuc9sNdO78QOXn5lrOqhWWvWyp5axfa7nr1toDG46xnPVNrKBSqu2+93Wz6N+fRwrMqkw4zaIyE802pZqdNsnyus2w0zZNtwFdrrJplRfbv6qPdX+1RVrzA/4cEL4xgW76+/Xr5x7BnqOU1PJiAnUuejGB5izp9W+//TZgTJCSkuK6INQtqzhAXQpdunRxP990KQTGMUWEfMKA9sOSdSBVg+VLs+2j97ZYnbqb7LIr/+gIWLQgy7Kz8t2WhGCaNvuj28C76ddsg0Oh/+76m+va/Xetsa1b97hjD8+MXG9r1+TYnfc0sAYNSRqUl7JeoXQgFQkFuF5FQj/nXpeCKhLqoFFgESghqU6EQBUJBRAKLCoizisiHH5G4a/XMT1t9Nef2/aFR1h+Srq9lPWeXTi9m/XpdKnFxPiHd1q3+9BDD9n//vc/19ElSrJ6gwt1PSz+dd+wcZOt37DJcm2L5cdsth27t1lGbobtyMuw+ROOtp074q1S8ka7edmne/23lWp3shwNLtxTyaK2pVhBzcIYRGcP8uulWcySFmYx+Ra1tboV1N5iPf58tVVtdqzNmfi52e+nD9rXDN6phooTE+jeQNuZ9AhGhTA978+cOdPFBMuWLXNzlRQTaK10oJggNjbWdSkoLlZMoOM2vjGBEmgVEV2HCPmEgYf2w0OnTKuSBAoI9lc1yMrKt8ceLtxmsGBelvU9I8UNK9RRg9f/udHWr8u1zl0T7err6lh8QuFZUl+1aldyxxRydhfYdTfVOeRkgadqtUp2/4hGlpQUbaPe3mzz52a5t2sbw/AnG1tMTNmtjcIfQnHnsn6mtdJJj4OtSCiIUNfChAkTglYk1KXgVSTUpaDgQYFEJFYk9LXSZGsgFGkgJzFBcEOv72HDXploO/qPs5yEbHvNvrMxv020YzM7WJe6Z1nlSjEuSfDZZ5+5myfRNe6iiy5ySYJgN2GbP3zfPpix3KYt08aCeMs9ZbHlnfi9XixSZdkAi8pKsIKUapYbYxbrk9epF7/eVjdItqopO61jeltrk1zPGqe2s5krJ9rcdqst/shtNrHLWNd5UG1nlLXq0NP9d7998H9WOSnLClostfaXnVBCP0WI9JhAP9Mnn3yyewR7nlMcoOMNigk0oFEzFBQTqNigZNp3333nd+3ZV0zQpk0bV+CINHQdIpCQ/Emn/fDg6EKnJIECAt+qwcUXX+w6CYpXDTzx8dH2pzOq238/2uq2GKRvz7M6dc2mTd3pkgWSvn2PVYkLfKPesnW8vfx6c3v8kXW2ZfMe275tj6VUP/Afqaxd+RYXH7XXhPaqVQtvXAacV9MdT9CgxcHX1yFZUM7BQbhN0T/cioQ3uDFQRcLrUtAASK8i0bFjR7ddIhwrEgQHCHXEBMGlptaykTf2shdmf2dzEgqP9K3KyLB7Hn7f1k5+xHbvLiwcVE1OtvPOO8/FBPvq3pL8rCzb/sWnVt1nI0HULp9Mwe8KErJcwiBqT4zt6N3T2jRrZ5UbNbHK9RrYPUEm3fet1cKOb5dlN9/zg8Uu72+5/cbZCVlNXEfE/IWLrWBLbYveYlZ5T5TFxyUf4E8IymvoYTjFBCqY7euojQZ/63lfMYGORXoxwaZNm9wQ5xkzZgSNCdSl4MUESiR4MYFihXBDhwHCJmFANWH/1IKt84effvqpu5iJsqBad6OqgTKgB0JdBVWrxdjxJ1UtmmHQ69gk0/3hB+9ttsuurL3PzoFff8l0nQB66PjAVX+t424w09bnWr36wY8RZGfn24iH1lqTZlXsskG1/eYnqKPh1iH1bPv2PGvSdO+jDyhboVhNCJWKxMSJEwNWJKpVq1ZUkWjZsmVRRUKDGkOtIqH/z1D7nADf3ykSBvuWUq2+DWx6p93496vs63GTLHNbYXdebKUka9ywr110Rj27KjvLopOTLWHKBEvbtNG21GtobY5oFbC7KD9rl9u+UDd7u8U1WGJVEvOtVkKONdt4hCXHJFtylRSrWiXFcgdGWa3qUda8WUeLiTl6v88jOWtWWebMX+2x2em2e82xFrPGrNL62tZ/RDv3d5bPW2RRKbWsYHtNq9+gcD4SQk+kxgQalHjiiSe6R7DnSsXe3mppLybQYEY9lGCYNGmS3/VLG+AUb2h+U6NGjVyXgooZigkUG4Ta8y/HFBFIaP2U/v7LxXnFwHRhUpLgk08+cZPmRTcmB1I1yMnJt3++sMH6nFrN2nVI2KvLQG8r/j3odWyydeuZtM9BiDJzembRy917JtmXn22z8V+nu2GFT7/U1F7/5yZr0KiytWsfb+3a//FxX3hqva1cvts98vMKrH7Dytb7uGRLSfnjR7JaSiX3QPmK1OCgtCsSel1vL07BQbCKhAY0KqgoS3QYIJQREwSnxKVigv/85z/uhkV0Jrvvn/pYs/an2e5dx1teRjUbEPuIWbZZfkaG7Zw21Sau2GKTV11mljTL6vacap2P3GVtana0Ixr3sri4JMvPLuxUaJSZbg90WmZ1Bl93SN87xXKffDnRGm1cbrXn/Gp7thZ2QB6fmmwfVOphVhBl55yRW7QyscvmldY6+21bXbOaNTx+8CF9TJS+cOwwKKmYQLO/9NBWsUB27tzpnvfVjaAYQIUFDWZU56I2QGjuUrCYQB2KXkzgrZbu3r27G9pYljimiEBC8m6MasIfdKFRQDBmzBhX2fSyoOecc46bSbCv9XW+N3yvvbzRfpmWabNm7rJb7qxnbY/84+Y9mP0lC+TKa1Ndl8L0aZkuETFn1i7bvGmPe99Ho7bajOmZ7rFzZ95eCYPT/1zd5s3JcsMNp0zMcG9btjjbbri13n4/JspWRU0YlHZFQr/Pet/kyZMDViSUDCxekdDvu4KIkqxIkDBAKKPDYG+6dgwfPtw++ugjt4ZWdLPRv39/u+uuu6xHjx57/f35C+Za/bWX2665sy1r/hx33GBhpd83Gu1MtlWp6bai9kLtUrLoVe9YzU3xVvDZGVar/gk2YMMPVrXKoXX4/W/sJPvPZ5Usf3Nja9Z8gV3ye7JAjtiYYS06T7GeRx9pp530x7Uza8E892fj3Cxr3rHjIX1clD5iguD0u3jssce6R7Dn29WrV7uhzV5MoDWS+r1WoUHxwZQpU/yugVWqVHFdCtpupphAnYu+MYG3Za4ksCUBYZEwIDgIXjUYMGCACwh0LupgaCjhjvTfpxFFFW4kKMnvV6PGVdxDevROtq+/TLejOiW4JIEnP8/sxx8yrHr1SnZE23iXXFCyoXHTKvb0iPVuM8L0nzNtzerd1rARRxBCSUWtJpRVRWLXrl2uSyFQRUKrzxRUFKeEQWJioutSqFevnjVt2tR1KShwUJeCqhQHimoCQhkxgbmOwkcffdQ+/PBD18Ek+v0/88wzbciQIXbMMccE/fq1bXOkWZsjrdrJp1lBXp5lL19qDcfNsh25KyxrY13Lb1T470l+dJRtzqlpVVa0tey6Sfbf1jusV1KunXaQ37OVa3+z0etnW8zmwqG0a3a1Mav0gyW0OdISOh1tiR0727219+6kyt2y2fZsKRzKGNeilUXFxh7kR0VZCce5RqEUE2jtsx7nnntuwL+TnZ3t4gE9NDvBiwmUUNAxSW19CBYTqEvBiwnUHak1krpnaNiw4QF/jsQECISEQZhWDQ5Glbho+/td9e2d1zdZx86J7oY92PGCdWtzrFOXRKtXP/aQnhCat6xiI59tYql1Yt2TiroN1NXw9r832aQJO6xrjz8+/jHHV3V/dumRaBvScu3s82qQLAhBBAelKyEh4bArEt9//33AioTXpaBgwbcioeMPXkWCDgOEsop6JEEJQyUJPvjgA3ez4F0rTj/9dLvzzjvt+OOPP+h/MyomxuJbtrYbW7Yumhe1av1Am7/hF1ucvdRWVdlmm9bWdu/bk9bIlqc1so4dVh/0xxm7/GPLPXaV5efEWdzc1nZMj93W/P9etej4wLGHZC1eUPRyXOs2B/0xUXboMChd6jDs1auXewSjrU8a2qyYQIUFxQS6d9Dgc73vhx9+CPjvqivSNybQXCUvJtD7RddbDXMEfEUVhNhvvn5IdWaneABckaoGJ5100n6rBqXh2SfW24xfCmcSPPBoIzeQsCTk5xfYNZcvs9zcAqtRo5I99VLTvd7PDWloO+6442zq1KkcFQph+6pIaM6C3l+chp0pQMjMzHRHHpSIAEKNfka1iSTQhPJIo1XII0aMsFGjRrkkoWiI6gknnOCSBMGOPpWkrKwsmz13oc1ZvM7q1a5q/U4JnMjcl4en/M3m1M0wKzB7MnmoNahbmKDYl7uf+NA2bqtsqfGrbHC/7ta0y6EXSVC6+vXrZ2PHjqX7MITl5OS4eQnqXlRMoFXSigl035Genm67d+/2S/z4xgQ69qDEA+Chw6AcqgaPPfaYjR49uiggONyqQUlZs2p30SDERk1K7jyUjkD0+3OKxcVFW936sS6B4Hssgta20BZiOUWUQEXiyy+/dIOZFBjIm2++ydcVIdvCG8kdBlrvqpjg/fffd91CXpLgtNNOszvuuCPoFpfSoo/dvWsn9zhUcQW/VyejzPbkFW5s2J+0FQ0sb3OqrYlqa8nXht8quoqEmCD0qYNQRxH2dYRZx5+9+UpKAOllLyZ47bXXyvCzRTgIyYRBpAUHqhooIFDVwMvY6Um5b9++LiDo06dPmXweeXkFNuGbdOvZO9mSkv1XKZ1zQU3LytKKtagSnXMgZ59fs0T/PZQdgoPIoLON33zzjb311ltukrPOPCpR+dxzz7kZC0AoisQZBur6efzxx+3dd991g0+9pN8pp5xit912m4sNwlm1qGRFPu7ljemrrEmDfQ8w3Kz5BdtrKL9glWpvtJq19t+RgPJDTBAZNERxwoQJ9u9//9tdk9RhcOqpp9qzzz7rurqAkE8YREJwoKrByJEj7b333iuqGigg0C/j7bff7v4sS2tX77ZXXtxgq1bk2MTxO+z2YQ2satW9kwY9j9GTPLA3joyEN7Uk3nLLLW7ysuYVKHGguSj3339/iU5WBkpDpBQRlKR74okn7O2333brV0VzRlQw+Pvf/+6Sd5GijjW16NkJFr2+rn2flGHd2u3776/d/KvtHjbKojbXtI5pWiF38McgUHZIGIS3OXPm2M033+ySBYoJNOvo1ltvdcPWvTkGQFgkDMJ1KnuwqoFaChUQ6NxXeYmLj7b07YVBV3p6njsWABwIgoPwo2voK6+84makeEef2rdv717XZHUgnI4khGtMoCTBU0895ZIEOkMsStJpJoGSeBpqHIlqRHewyqNT3MvLWu1/NsqK7YvMauZbQd1N1q7y3jOOEHqICcLT66+/bg899FDR/Yk2K+n1YNsagJBOGCg4CKcOg2BVAw0uVMYuVILzmrVi7a57G9jIR9bZoGtSLSWlUsAnAOYJoDiCg/A6/qTkpGakaHiZhsieffbZ9vTTT1vjxo3L+9MDIn6Ggdak6vdNc0GUJND1U7+HGh7rJQn0/xTJunQ+0l6NWWlReZVs55Ya+/37q3IKt0BI0+psSAh1dB2GVyFTR5/feecdN59ARxF1X/LMM89wFBHhnTAIh/ZD36qBppHr4hkOVYO69Svb8Ccbu+GDvn76IcOtXOzWM8kuvry2m2EAeEgYhD5tlVGiYNq0ae77pbVJQ4cOddtWFCAA4SocEgbaQqIA/I033nDbRrwkQe/evV3rr5J2kZ4k8FUlOsqatxlrSZZuLbLXW0FBj30WI7ZGZxS93KD2EWX0WeJQEROEPq1h1v2I1ivq+1WzZk33+j333MNRRBySkIskQzU4CFY10O70cKoaFE8WyMrluy0jI9++HbfD0tbn2m1D65f40EOEL6oJoUlt2romPfnkk27/smifsmanlPVkdaCixQRKEjz//PNuYJj2oOs6qeRcz5497aabbrLzzjsvLGKCkrZr3hzb9NZrdkla4TWpcsPG++1c3LasqUWnaRp0llVtwoaEUEfCIHRjghdffNHFAFqhKB06dHCrWiNpRgrKR0gmDELlvGJFqRpoM0JiYrTVq1/Z+p6RQrIAeyE4CC1paWkuSTlmzBi3S1lHoC6++GKXPFBnARBJQikm0G7zF154wa0cmz9/flGSoHv37va3v/3NLrroooiICQ7Ft5N+tA//l2H9s/5rLTO2uLdFx8db6sDB+/1vd0w9zipvrWUFsTkWezpD10IdMUHorWtXh+EHH3zg7lvU8ay5BIoJGjZsWN6fHiJESCYMyrOaUBGrBpdfleoeQCB0GISGcePG2Z133mkzZsxwr9evX99tXLnxxhsj7poEeLTqqzwTBkoS/OMf/7B//etfNm/ePPe56HPq2rWr3XDDDXbppZdW6N+/Bcun2BuTfrG0LzWvqZb9r/EpdnPGaPe+ejffYXEtW+3338jPqeL+jIrLLvXPF4cvVBJ4Fd2kSZNcomD69OkuTqtTp47dd999bjUrRxFR0kgY/B4QvPTSS/bqq6/uVTXo1q2bqxqoeleRAwIA5UMDYIcPH+6qmps2bXKtvb169XLHEPQnEOn03Jubm1vmv3cvv/yy/fOf/7S5c+cWJQk6d+5s119/vV1++eUVPiaYt3SivbbyY1u/o75FxSZYpYRMi9qVaJnpDSwjtrItS65t7475ze4bsu997gX5+dY5dZztTI23SglKHBxdZt9nHBqKCOVH1yINWlf3gLoNFRPoKKLeduKJJ5bjZ4ZIF5IJg7LIXipJoLVjShL4BgRdunRxVYO//OUvFT4gAIT2w7K3atUq19H0+eefu5ul+Ph4u/LKK11QkJJSuK4MqAjKqutQSQIdNVA3wezZs11MoI/dsWNHu+6662zgwIFU7X736bQX7b0aP1vlj66zyltrWEHKdos6cYIdsSDK/rRpur1S9SLLWtfSKu3ZsN+ve176djtj3a/u5YSOJAvCATFB2Vu3bp07Cv3JJ5+4+xetbNd9igaw16rF3A+UvgqVMFBAoIqBEgVz5szZq2rw17/+1a644gqSBEAx+j1h3WbZUDCg7QZqfZYmTZq416+66iquTaiQ9BxdWjcourbp+KGSBL/99ptLTCgG0aCwa665xgYPHkySIIBZOQsKX6iaYba1hkVtT7G+SxfbcUm1bFrNLpb9cyvTmMOqNbbv93uQvWxJ0cuV69Urwe8uSgsJg7IzduxYtxZx1qxZ7vUGDRq4o4nqdKLzGVbREwa6sS/pqoHaC/UL51s1uPbaa23QoEEEBMA+EByU/twUnTtUt9O2bdvc9UmthWo57NSpEz+bqNBKeoaB/i1tO9I08ZkzZxYlCY488ki7+uqrXVzA+d996193gO3a+IFtaLLUaqdk2NFtk+y0Ex+xlGop9tO4UbYn7nuL2lHVmjXcvd/vx85fphW9HN+2fQl8h1HaOJJQunTf8uCDD7qj0lu2bHEFm2OOOcYdRezRo0cpf3QgTBIGCg40+ftwUDUASg4Jg9KhoapqMdQwQ920JCcnu+NQjz76qCUlJZXSRwUqXsJA//0777zjZoH8+uuv7vdNQbiSBOreUYehJovjwHRo2cc9rLf/+3YlLLM9fRe5lzvuPG2f/870GbPsrY3JdlqdZGu7M9/i2x7JtyAMEBOUjuXLl7uY4IsvvnBJg4SEBNflpDWJHEVEeYuYIwlUDYDSQTWhZL3//vuuo2Dx4sXu9ZYtW9r9999vl1xySQl/JCD8HU5MoN81bT3SFHEF4EoStG3b1s0DUXKOJEHJW1mwvujlZqkdgv69tA0b7aXXd1jexmPsg5ge1vvUX+wakjZhgYRByfr4449t2LBhtmBB4VGfpk2butd1nQJCRaVwPq9I1QBAONi1a5cNGTLE3njjDcvIyHDXub59+7pjB7qBAVAyMcGHH35ozz77rP38889FSYIjjjjCHT/U1iMNC0PpWZOw0/1ZOcesQbPA17Y1aXPt8YX/sNyoi037p6Krptv5/6e1jAgHJAxK5ijiPffc49a1bt++3SVGTzrpJHvmmWfsqKOOKoGPAFSAhMG+qglUDYCyRXBw6DQ3RdsOtC9Z1y61Fd5+++3ufCI3LkDJxAT/+c9/XKA9bdo0t1VESYLWrVu7zQY33nija+1F6du6bYNtr6KwMtfqp1exmJi9Q8y8vD325a//sg8Sp1pOgyizge9Z3Lvn2OBzq1p1tr+EDboOD51Wt+vYwfjx44uOIuoapaOIXKcQysIiYUDVACg/BAeHNkPl4YcftpUrV7q3qYvgkUcesQEDBpTK9wiIVBpAGChpOWbMGNeh89NPP7k1Y0oS6HjP5ZdfbrfccgvBdzmY/ON8q/LWHVZQY6vFdZxn1uuP9301foqNzfrc1rfRkQXtUDCrmb/Lbr+5hjVt2LE8Pl0cIooIB08zVHT0cOnSpe71Vq1a2QMPPGAXXXQRP4cIC5VC9bxisKqBfslUNVDVjmwcUPoIDg6M2grVPfDee++5Iwi60enfv7+7qWnWrFkpf5eAyKSYwLsGffrpp+73aerUqUXDkZUk0D7yW2+9lWGh5WzJqnSLKmhoUVtqWkpUDSvYs8eW//yTPfZxtu1e08TyG59mdsSbLl/Qc32qXd1lqCUkpJT3p42DRExwYHbu3OlWIL711lvuZRVE+/Xr545M6V4GCCchmTDQ2Z4qVaoUnT9UQHDZZZe5gIAkAVC2CA72TRVOVTR//PFH97WqVauWSxxoaBHr2YDDoyBbHQSxsbFFK5ebN29elCSoWrUqX+IQkRgfYwVRBVa55VxrtfRbW3bDGMvLyrbcpBvc+6NXNbIaP7exQZ3bW9fjmFkQrug63DdtYlFMMGXKFFf8rF69upthpI4CBq0iXEUVhNjdgDJxain0TSAkJiZa7dq1rXHjxm54UceOHd1O0vbt27v3Ayg9aqdftWqVZWZm8mX+nYIATV9//PHHbe3ate5tui499thjbpghgJIxevRou/DCC/1iAiXmGjVq5GKCTp06Wc+ePd2fxATlR5sPJrz6hHVdsur3QweFvqx7lP26/RQ7utsGu/rS01xBCOGra9euNnv27MNegR5pMcErr7xiI0aMcPGSaG3r8OHD7ayzzirvTw+IvISBl51TtW7mzJluV/nq1att8+bN7oal+HwDPfEoe1evXj1XdejQoYN169bNevfuzd5SoASQMPjDxo0bXVVTa5DUCaVqgeYSPPXUU1a/fn1+3oBSoFhAnTwzZsxwMYEC8k2bNgWNCapVq+Z+HxUTKGjXDc6xxx5rNWrU4PtTyrKXLrbN779tOWtXW3RCosW1bmNxXbpbYsfOrksE4a9Lly42Z84cEgYa9Ll1q9122202atQoy8rKcj/jZ555pjtOrSInEClCMmGwv4D9+++/t19++cXmzp1ry5Yts7S0NEtPT3dti8VbGZOSklx3QpMmTVwlonPnzq47QS9TiQD2r02bNrZmzRp3Bq+i+u6771xQoGSmLpl169Z1iYO///3vXEeAcqRiQvGYYP369W6mSPGYQM/5XkzgdSwqJlCBoV27dvwuAwfg6KOPtnnz5rmkeUWla46e/zVjTTGBrinadqCjBxxFRCQKu4TBvuh8oyoQP/zwg/3222+2ePHiou4EDSEr/r+qtWbqTlAlQnMSdMShR48e1qtXL4YnAb9TUK22+4qWMND1REcOVClQolLzVNS99OSTT7pqJYDQ/x1Wd4I6FhUTFO9YDBQTaPWpYoIWLVq4mEC/8yoyMCsBKKSjPwsWLKhwCQN1M2noqmIAJSW95MnIkSPt5JNPLu9PDyhVEZUw2B9VSZVMmD59uqtErFixwnUn7Nixw21hKN6doP2oqamprjtBbdm6MKgSoUCC7gRUFNpnrifHjIwMqyjXCe1J1kR2XRd0E3HBBRfYE0884c5NA4gMev73uhNUMV2+fLm71qljMVBMoO4ELyZQ55UXE2jiOTEBKgrN61m0aJFrwa8o1wl1FGpzm+Y26NjT2Wef7Y4iqtsQqAgqVMJgX9S6+PPPP7tKxKxZs1x3gm4ctmzZ4i6Kxb9M8fHx7jxkgwYNXHeCZido6FL37t3Z5ICIomBYT5iRnjD44osvXDuhhjmJBqrp9WuvvZabAaACxgQqLmh2groTdIPkxQSBOhYVE/h2LB511FEuHlDXohINQKTQz7Zi5EhPGIwfP95tPFLnsmhWmo4mqqBAghAVDQmDA7Ry5Uq3IkVnmOfPn+8qEWpTVneCt+rJo/NLal9UJaJp06bubKQqEWpjVmUCCCcKfr2f9Ui8KdCqo5dfftkNL9KxA7Ufq+1Qg9IAIBANXpw6depeHYsbNmxw3QmBYgKvY1ExgToWNTvhuOOOs2bNmvEFRliJ5ISBfne12eCFF15wg1UVEyjxp2MIig2AioqEQQlQtUGDT/RQJWLJkiXuzLduQIpfUHXxUSWiZs2arjtB7d66+KoKoYsSO1oRanQER0+ckZQwWLp0qd100002duxYFyBoTZv2umstImeVARwOne3WMQclFNSx5HUsejGBb3eCYgIde/JiAiVo1fLtxQR6HxBK1FGr51DFvpGUAFRM8Pnnn7vjSIrTtc5VRxHZrgKQMCiTISnqRvC6EzQoRpUIVWzV4p2Xl7fX39dKFt2w6FyU153grYRibRvKK2GgIWGqnIW7jz76yIYNG+aGn4nWrun1K664orw/NQAVhGICzU5QTKDZCepgVHeCYoJgHYt16tRxMYHWRKpjUdVO1rahPGgYqLaRRELCQLOKhg4d6rqERL9Ten3w4MEcOwB80GFQzjR5XlUIdSdor60qEevWrbNt27b5TaBVJSIhIcENXmvYsKHrTlAlQrMTtBeXVS4oDbqp1rndcE0YKKhRUuC1115zXRI6e9inTx+3/UDBNwCE0vVKs5Q0O0HdCV7HomKCQB2LiglUAdXMFc2bUceiNj0pJqBjEaVBz5tKeoVrwkBHEe+77z575ZVX3O+VYgIdD9IQQyXjAPgjYRDi3QmqhGqzgyoRelltU+pOUKKheHeCgoNq1aq57gTd5Kk7QUGDLoQ6OwkcCp2x1ZOq9pqHE1UM1GI4YcIE97uk341BgwbZww8/zGBSAGFH1zG1gqtjUYPYvI5FHRkL1rHoxQTqTlBlWDGBOhaZ7o5DpdhSXTFaTRpOVJC78cYbbdy4ce53RcNIL7vsMncUkcGkwL6RMAhjuoFTMkHbHdSdoEBCK6F0c6fVL76UQVUlonbt2q4Soe4EDV1Sd4IqEnQnIBgFmvpZC5eEwZtvvmkPPviga5mUI444wr1+/vnnl/enBgClRh1U6lhUTKBtT7oG7qtjUbNbNDtBbdhex6K6Ezp16kRMgKA0tFPFq3BJGLz//vuuo0AJA++Y5b333uuSBQAODAmDCK5EKImg4EGVCK87QWfR1Z2g9/vSXllVIrQ2RhdTtZx5sxMY+FKxKWGg4wgKOkM5UL7zzjvt7bffdkGMEmB9+/a1Z5991v08A0BFpud8bXjS7ISZM2e6mEBVYnUnBIoJvI5FxQRex6KGMGp2go5FomInDFavXu1+bkKVjkvcdddd9vrrr7vum5iYGDv55JPdUUR9/gAODgmDCkqJA687Qa3bqkSoO0FVZJ3vKt6doHYtdSeoEqGKrboTevfu7YII9tFGNq0C1Q15KCYMNIn8lltucUGwJo8ruXXttde6agLndwHgwGiDg2ICXVN9OxYVEwTqWFR3gtex2KZNm6LuBHUsEhNENn2/tfUjFBMG6qy5+eabbeLEiS4JlpKSYldddZU99NBDbBwBDgMJA/jRlGZVIDR4SWsiVYlQNllJBlVvfVdCed0J1atXd1scVInQyp1u3bq5SgQr6sKfkkQKDBRQhgIFAS+99JKNHDnS/VyKfuZGjBhhp59+enl/egAQUXTN1Y2Y17G4aNEid+1Vd4JigkAdi7pR82ICzU5Qd4KKDHo7wpuKRhrEGSoJA/38/fvf/3bzidQ14yU19Po555xT3p8eEBFIGOCgpaWluYquKhFaCaVpuapEqG1d+2t9qQ1M3QkauqhKtS7iOh+pow6a6EwlIvSFSsJACavbbrvNRo8e7c7jaqDXWWed5VoMtTUEAFD2NIjZNyZQd4LiBMUEwToWFRN4HYuaTK/uBLWKExOEPs27UMynVv/ypO6XO+64w9599113BEFHEVU0UEygYc0ASg4JA5QoBQfTp093K6HUnaAhM6pEaC2fLujFuxPi4uJcG7kqES1btnTtjKpE9OjRg6m1IUItp/re6XtYHjQR/NZbb3XBqH5+FGiq5fD2229nMBcAhHjHoroS1LGoP31jgkAdi4oJvI5FxQTqTvA6FplkHxrKO2Ggo7SKAdTxop8fDe684YYbbOjQoRxFBEoJCQOUKQ1e1EVeSQXNTtBKqA0bNrhKhAILX8oWJycnuxtEDd5T9UGzE7Qmkuxx2VH1XhV9VfjLssXwiSeesKefftpVqjTRW1UoHUPo06dPmX0eAIDSoy0O3jwldScoJtDNqObmBOpY9GICr2NRayJ11EHDbelOKBtK5KirRN+jsowJnn/+eXv88cfdcQhRgUlHEfv161dmnwdQUZEwQMjQTamqyEoozJ4921UiNFhHrfBZWVl7VSJ0A6lKhDLLDRo0KOpO0JpIdSjofSgZ+vpq6FVZJAwUPKpy8Mknn7huFZ2FPffcc+2pp55yQSIAoGLQc4ASCepY1AwFzU7wYoJAHYvx8fFFHYs68qjZNooHFBdorTRKhpIzigdU6CltSkzoKOKHH37oYkQNM+7fv787dqDvM4CyQcIAYUOzEnRO8tdff3WVCA23UXeC2uICdSdo4GKdOnVcd4LWRKpCrbZGnZvEgdOTsio9GnBVWsaOHevWIuoYi5ekUJBw4403UjUCAPhRDOBtdtDKSK9jUZXvYB2LXkygjkXFBOpYVLcCQith8N1337kYQPGeEkN169a1m266yc0soJMEKHskDBARVG3QGclp06a5SsSSJUtctdrrTvCl7gRVG1SJ0Pl8VSLUnaChS2pvZB2ff8JAwZcy/SVJ/6amGL/wwgvuPKu+L6oE6RiCZlgAAHAoVI32jQnUsahW9mAxgboTvI5FxQRaE6nnoa5du9KxWIw2X+jrqKGDJR0T6MiBugcUb+j7opjsySeftOOPP75EPxaAg0PCABFPZ980tVnD8zR0acGCBa4SoYq5uhPy8vL2+vuavl+tWjWX0VYlQkOX9KSlzQ56W0VTr1499zUqqYSBqkLqHPjiiy9cgKDkzcUXX+wCBVZuAQBKm2ICdSwqJvA6Fr1z+YFiAq9jUfOT2rVr52ICdSxWxA09+hps27atxBIGOmaio4ifffaZO4aiI6Xnn3++SxTUqlWrRD4GgMNDwgAVngIEzU3QWUlVIpYtW+a6E/SEqCrFXr8wUVGWmJjoKhHqTtBKKFUiVBnXQEa1PUZiwkBJF7V6Ho4xY8bY3Xff7VpHRW2gw4YNs0GDBtFiCAAICVojrLkJ6lCYM2eO61hUd0KwmEBJb93YKnngdSd4HYuRGBOokKLjCPp6HA4VDYYMGeJmVom+fjqaeN111xETACGGhAGwD7pR1g2uEgo6S7dw4UK36UGVCAU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"text/plain": [ "<Figure size 1100x450 with 2 Axes>" ] @@ -103,10 +118,13 @@ " rotation, _ = np.linalg.qr(rng.standard_normal((3, 3))) # a random 3-D rotation\n", " copies.append(scale * base @ rotation)\n", "\n", - "fig, axes = hyp.subplots(1, 2, size=[11, 4.5])\n", - "hyp.plot(copies, ['-', '--', ':'], reduce=None, ax=axes[0], linewidth=2,\n", + "fig, axes = hyp.subplots(1, 2, size=[11, 4.5], backend='matplotlib')\n", + "# backend='matplotlib' on the grid and on each call: fig.tight_layout() is matplotlib's,\n", + "# and on Colab the default backend would be plotly\n", + "hyp.plot(copies, ['-', '--', ':'], reduce=None, ax=axes[0], linewidth=2, backend='matplotlib',\n", " names=['copy 1', 'copy 2', 'copy 3'], title='three rotated, scaled copies', show=False)\n", "hyp.plot(copies, ['-', '--', ':'], reduce=None, ax=axes[1], linewidth=2, align='HyperAlign',\n", + " backend='matplotlib',\n", " names=['copy 1', 'copy 2', 'copy 3'], title='after hyperalignment', show=False)\n", "fig.tight_layout()" ] @@ -129,10 +147,10 @@ "id": "41da41bc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:47.077696Z", - "iopub.status.busy": "2026-09-05T10:23:47.077594Z", - "iopub.status.idle": "2026-09-05T10:23:47.088686Z", - "shell.execute_reply": "2026-09-05T10:23:47.088199Z" + "iopub.execute_input": "2026-10-08T17:10:16.936930Z", + "iopub.status.busy": "2026-10-08T17:10:16.936866Z", + "iopub.status.idle": "2026-10-08T17:10:16.945478Z", + "shell.execute_reply": "2026-10-08T17:10:16.945070Z" } }, "outputs": [ @@ -167,10 +185,10 @@ "id": "13bc3af3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:47.089886Z", - "iopub.status.busy": "2026-09-05T10:23:47.089811Z", - "iopub.status.idle": "2026-09-05T10:23:47.152515Z", - "shell.execute_reply": "2026-09-05T10:23:47.151942Z" + "iopub.execute_input": "2026-10-08T17:10:16.946325Z", + "iopub.status.busy": "2026-10-08T17:10:16.946273Z", + "iopub.status.idle": "2026-10-08T17:10:17.022497Z", + "shell.execute_reply": "2026-10-08T17:10:17.022036Z" } }, "outputs": [ @@ -214,10 +232,10 @@ "id": "d20fd423", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:47.153667Z", - "iopub.status.busy": "2026-09-05T10:23:47.153582Z", - "iopub.status.idle": "2026-09-05T10:23:48.432894Z", - "shell.execute_reply": "2026-09-05T10:23:48.432375Z" + "iopub.execute_input": "2026-10-08T17:10:17.023827Z", + "iopub.status.busy": "2026-10-08T17:10:17.023741Z", + "iopub.status.idle": "2026-10-08T17:10:18.279839Z", + "shell.execute_reply": "2026-10-08T17:10:18.279384Z" } }, "outputs": [ @@ -258,10 +276,10 @@ "id": "b7b30683", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:48.433942Z", - "iopub.status.busy": "2026-09-05T10:23:48.433864Z", - "iopub.status.idle": "2026-09-05T10:23:48.521899Z", - "shell.execute_reply": "2026-09-05T10:23:48.521500Z" + "iopub.execute_input": "2026-10-08T17:10:18.281230Z", + "iopub.status.busy": "2026-10-08T17:10:18.281144Z", + "iopub.status.idle": "2026-10-08T17:10:18.367576Z", + "shell.execute_reply": "2026-10-08T17:10:18.367062Z" } }, "outputs": [ @@ -308,18 +326,18 @@ "id": "45bb35c9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:48.523025Z", - "iopub.status.busy": "2026-09-05T10:23:48.522963Z", - "iopub.status.idle": "2026-09-05T10:23:48.626063Z", - "shell.execute_reply": "2026-09-05T10:23:48.625620Z" + "iopub.execute_input": "2026-10-08T17:10:18.368767Z", + "iopub.status.busy": "2026-10-08T17:10:18.368695Z", + "iopub.status.idle": "2026-10-08T17:10:18.475567Z", + "shell.execute_reply": "2026-10-08T17:10:18.475162Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 1104.62x450 with 2 Axes>" + "<Figure size 1105.66x450 with 2 Axes>" ] }, "metadata": {}, @@ -330,10 +348,10 @@ "pair = [data[0], data[1]]\n", "pair_aligned = hyp.align(pair, model='Procrustes', index=0)\n", "\n", - "fig, axes = hyp.subplots(1, 2, size=[11, 4.5])\n", - "hyp.plot([d[:100] for d in pair], ax=axes[0], names=['subject 1', 'subject 2'],\n", + "fig, axes = hyp.subplots(1, 2, size=[11, 4.5], backend='matplotlib')\n", + "hyp.plot([d[:100] for d in pair], ax=axes[0], names=['subject 1', 'subject 2'], backend='matplotlib',\n", " title='two subjects, unaligned', show=False)\n", - "hyp.plot([d[:100] for d in pair_aligned], ax=axes[1], names=['subject 1', 'subject 2'],\n", + "hyp.plot([d[:100] for d in pair_aligned], ax=axes[1], names=['subject 1', 'subject 2'], backend='matplotlib',\n", " title=\"Procrustes: subject 2 rotated onto subject 1\", show=False)\n", "fig.tight_layout()" ] @@ -354,10 +372,10 @@ "id": "f7aac5ff", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:48.627102Z", - "iopub.status.busy": "2026-09-05T10:23:48.627033Z", - "iopub.status.idle": "2026-09-05T10:23:48.788067Z", - "shell.execute_reply": "2026-09-05T10:23:48.787587Z" + "iopub.execute_input": "2026-10-08T17:10:18.476625Z", + "iopub.status.busy": "2026-10-08T17:10:18.476557Z", + "iopub.status.idle": "2026-10-08T17:10:18.632679Z", + "shell.execute_reply": "2026-10-08T17:10:18.632319Z" } }, "outputs": [ @@ -391,10 +409,10 @@ "id": "207bdf65", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:48.789280Z", - "iopub.status.busy": "2026-09-05T10:23:48.789198Z", - "iopub.status.idle": "2026-09-05T10:23:49.002851Z", - "shell.execute_reply": "2026-09-05T10:23:49.002463Z" + "iopub.execute_input": "2026-10-08T17:10:18.633705Z", + "iopub.status.busy": "2026-10-08T17:10:18.633646Z", + "iopub.status.idle": "2026-10-08T17:10:18.841728Z", + "shell.execute_reply": "2026-10-08T17:10:18.841438Z" } }, "outputs": [ @@ -444,10 +462,10 @@ "id": "b23526d4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:49.004222Z", - "iopub.status.busy": "2026-09-05T10:23:49.004141Z", - "iopub.status.idle": "2026-09-05T10:23:49.698072Z", - "shell.execute_reply": "2026-09-05T10:23:49.697717Z" + "iopub.execute_input": "2026-10-08T17:10:18.842894Z", + "iopub.status.busy": "2026-10-08T17:10:18.842832Z", + "iopub.status.idle": "2026-10-08T17:10:19.524966Z", + "shell.execute_reply": "2026-10-08T17:10:19.524597Z" } }, "outputs": [ @@ -460,9 +478,9 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "<Figure size 1101.55x450 with 2 Axes>" + "<Figure size 1102.59x450 with 2 Axes>" ] }, "metadata": {}, @@ -479,10 +497,10 @@ "\n", "held_out_aligned = hyp.align(held_out, model=aligner) # reuse: transform only, no refit\n", "\n", - "fig, axes = hyp.subplots(1, 2, size=[11, 4.5])\n", - "hyp.plot(group_averages(held_out), ax=axes[0], names=['group 1', 'group 2'],\n", + "fig, axes = hyp.subplots(1, 2, size=[11, 4.5], backend='matplotlib')\n", + "hyp.plot(group_averages(held_out), ax=axes[0], names=['group 1', 'group 2'], backend='matplotlib',\n", " title='held-out timepoints, unaligned', show=False)\n", - "hyp.plot(group_averages(held_out_aligned), ax=axes[1], names=['group 1', 'group 2'],\n", + "hyp.plot(group_averages(held_out_aligned), ax=axes[1], names=['group 1', 'group 2'], backend='matplotlib',\n", " title='held-out timepoints, fitted aligner reused', show=False)\n", "fig.tight_layout()" ] @@ -501,10 +519,10 @@ "id": "275606c5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:49.699415Z", - "iopub.status.busy": "2026-09-05T10:23:49.699320Z", - "iopub.status.idle": "2026-09-05T10:23:49.702332Z", - "shell.execute_reply": "2026-09-05T10:23:49.701889Z" + "iopub.execute_input": "2026-10-08T17:10:19.526164Z", + "iopub.status.busy": "2026-10-08T17:10:19.526092Z", + "iopub.status.idle": "2026-10-08T17:10:19.529241Z", + "shell.execute_reply": "2026-10-08T17:10:19.528825Z" } }, "outputs": [ @@ -512,7 +530,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "ValueError: aligner was fit on 18 dataset(s) with [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 1 ...\n" + "ValueError: aligner was fit on 18 dataset(s); got 1 dataset(s) with [100] column(s)\n" ] } ], @@ -520,7 +538,8 @@ "try:\n", " hyp.align([data[20][150:]], model=aligner)\n", "except ValueError as e:\n", - " print('ValueError:', str(e)[:120], '...')" + " fitted, got = str(e).split('; ') # the fit-time shape, then what was passed\n", + " print('ValueError:', fitted.split(' with ')[0] + ';', got.split(' (fit-time')[0])" ] }, { @@ -532,7 +551,7 @@ "\n", "- The [analyze tutorial](analyze.ipynb) chains `manip`, `normalize`, `reduce`, `align` and `cluster` in one call.\n", "- The `plot_align` gallery example is the one-figure version of this tutorial.\n", - "- The market-sectors and weather tutorials use `align=` inside animated plots." + "- The market-sectors tutorial hyperaligns its sector trajectories with `hyp.align` before animating them." ] } ], @@ -552,7 +571,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/analyze.ipynb b/docs/tutorials/analyze.ipynb index 947a34ec..7129db4a 100644 --- a/docs/tutorials/analyze.ipynb +++ b/docs/tutorials/analyze.ipynb @@ -5,17 +5,28 @@ "execution_count": null, "id": "f3654c06", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:28:00.257780Z", - "iopub.status.busy": "2026-07-17T07:28:00.257701Z", - "iopub.status.idle": "2026-07-17T07:28:00.260653Z", - "shell.execute_reply": "2026-07-17T07:28:00.260195Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -44,10 +55,10 @@ "id": "34b669cb", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:50.916904Z", - "iopub.status.busy": "2026-09-05T10:23:50.916761Z", - "iopub.status.idle": "2026-09-05T10:23:54.427804Z", - "shell.execute_reply": "2026-09-05T10:23:54.427309Z" + "iopub.execute_input": "2026-10-08T17:10:20.741452Z", + "iopub.status.busy": "2026-10-08T17:10:20.741360Z", + "iopub.status.idle": "2026-10-08T17:10:24.211279Z", + "shell.execute_reply": "2026-10-08T17:10:24.210767Z" } }, "outputs": [], @@ -76,10 +87,10 @@ "id": "63cc3edc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.429251Z", - "iopub.status.busy": "2026-09-05T10:23:54.429096Z", - "iopub.status.idle": "2026-09-05T10:23:54.432484Z", - "shell.execute_reply": "2026-09-05T10:23:54.431962Z" + "iopub.execute_input": "2026-10-08T17:10:24.212898Z", + "iopub.status.busy": "2026-10-08T17:10:24.212661Z", + "iopub.status.idle": "2026-10-08T17:10:24.216393Z", + "shell.execute_reply": "2026-10-08T17:10:24.215941Z" } }, "outputs": [ @@ -112,10 +123,10 @@ "id": "238c1d10", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.433413Z", - "iopub.status.busy": "2026-09-05T10:23:54.433349Z", - "iopub.status.idle": "2026-09-05T10:23:54.576253Z", - "shell.execute_reply": "2026-09-05T10:23:54.575675Z" + "iopub.execute_input": "2026-10-08T17:10:24.217291Z", + "iopub.status.busy": "2026-10-08T17:10:24.217234Z", + "iopub.status.idle": "2026-10-08T17:10:24.412059Z", + "shell.execute_reply": "2026-10-08T17:10:24.411613Z" } }, "outputs": [ @@ -141,7 +152,7 @@ "source": [ "## Manipulation\n", "\n", - "`manip` is the first stage: a per-dataset transformation applied in the data's native space, before anything is pooled. The manipulators are `Smooth` (kernel smoothing over time), `Resample` (interpolate to a new number of rows), `ZScore` and `Normalize` (per-column rescaling); a *list* of specs chains them. Below we smooth each timeseries with an 11-timepoint boxcar and resample it from 100 to 300 rows. `analyze` with only `manip=` returns the manipulated data unchanged in dimensionality (still 100 columns, now 300 rows), so we pass it on to `hyp.plot` to reduce and draw." + "`manip` is the first stage: a per-dataset transformation applied in the data's native space, before anything is pooled. The manipulators are `Smooth` (kernel smoothing over time), `Resample` (interpolate to a new number of rows), `ZScore` and `Normalize` (per-column rescaling), and `Delay` (a time-delay embedding, `tau=`/`dims=`); a *list* of specs chains them. Below we smooth each timeseries with an 11-timepoint boxcar and resample it from 100 to 300 rows. `analyze` with only `manip=` returns the manipulated data unchanged in dimensionality (still 100 columns, now 300 rows), so we pass it on to `hyp.plot` to reduce and draw." ] }, { @@ -150,10 +161,10 @@ "id": "8100aaca", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.577429Z", - "iopub.status.busy": "2026-09-05T10:23:54.577350Z", - "iopub.status.idle": "2026-09-05T10:23:54.729979Z", - "shell.execute_reply": "2026-09-05T10:23:54.729594Z" + "iopub.execute_input": "2026-10-08T17:10:24.413247Z", + "iopub.status.busy": "2026-10-08T17:10:24.413183Z", + "iopub.status.idle": "2026-10-08T17:10:24.514042Z", + "shell.execute_reply": "2026-10-08T17:10:24.513538Z" } }, "outputs": [ @@ -203,10 +214,10 @@ "id": "7111353f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.731206Z", - "iopub.status.busy": "2026-09-05T10:23:54.731122Z", - "iopub.status.idle": "2026-09-05T10:23:54.771114Z", - "shell.execute_reply": "2026-09-05T10:23:54.770686Z" + "iopub.execute_input": "2026-10-08T17:10:24.515006Z", + "iopub.status.busy": "2026-10-08T17:10:24.514942Z", + "iopub.status.idle": "2026-10-08T17:10:24.554660Z", + "shell.execute_reply": "2026-10-08T17:10:24.554282Z" } }, "outputs": [ @@ -243,7 +254,7 @@ "\n", "To normalize and reduce the dimensionality of the data, pass the `normalize`, `reduce`, and `ndims` arguments together. The `reduce` argument specifies the reduction method and `ndims` (int) the number of dimensions to reduce to.\n", "\n", - "Supported dimensionality reduction models include: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, UMAP, and the torch autoencoders.\n", + "Supported dimensionality reduction models include: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, UMAP, NMF, LatentDirichletAllocation, and the torch autoencoders.\n", "\n", "Below, `reduce='PCA'` with `ndims=3` finds the 3 orthogonal directions along which the data vary most, so each 100-component dataset collapses to the 3 components that explain the most variability. This output is already three-dimensional, which is exactly what `hyp.plot(..., reduce=None)` is for: it draws the coordinates as they are." ] @@ -254,10 +265,10 @@ "id": "d946b215", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.772138Z", - "iopub.status.busy": "2026-09-05T10:23:54.772065Z", - "iopub.status.idle": "2026-09-05T10:23:54.847454Z", - "shell.execute_reply": "2026-09-05T10:23:54.847115Z" + "iopub.execute_input": "2026-10-08T17:10:24.555700Z", + "iopub.status.busy": "2026-10-08T17:10:24.555634Z", + "iopub.status.idle": "2026-10-08T17:10:24.630241Z", + "shell.execute_reply": "2026-10-08T17:10:24.629916Z" } }, "outputs": [ @@ -302,10 +313,10 @@ "id": "de84055b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.848759Z", - "iopub.status.busy": "2026-09-05T10:23:54.848672Z", - "iopub.status.idle": "2026-09-05T10:23:54.887125Z", - "shell.execute_reply": "2026-09-05T10:23:54.886769Z" + "iopub.execute_input": "2026-10-08T17:10:24.631502Z", + "iopub.status.busy": "2026-10-08T17:10:24.631436Z", + "iopub.status.idle": "2026-10-08T17:10:24.668276Z", + "shell.execute_reply": "2026-10-08T17:10:24.667841Z" } }, "outputs": [ @@ -335,7 +346,7 @@ "## Normalize, reduce, and align\n", "\n", "Next, we normalize, reduce and then align all in one step. The `align` argument accepts:\n", - "+ `'hyper'` / `'HyperAlign'` - the [hyperalignment](https://doi.org/10.1016/j.neuron.2011.08.026) algorithm (Haxby et al., 2011, *Neuron*)\n", + "+ `'HyperAlign'` - the [hyperalignment](https://doi.org/10.1016/j.neuron.2011.08.026) algorithm (Haxby et al., 2011, *Neuron*). `'hyper'` is a legacy alias for the same name; it still works (here silently -- `hyp.align(model='hyper')` is where it warns that it is deprecated)\n", "+ `'SRM'` - the shared response model (Chen et al., 2015)\n", "+ `'Procrustes'` - a single rotation onto one reference dataset\n", "\n", @@ -348,10 +359,10 @@ "id": "8e41c59a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.888258Z", - "iopub.status.busy": "2026-09-05T10:23:54.888196Z", - "iopub.status.idle": "2026-09-05T10:23:54.929455Z", - "shell.execute_reply": "2026-09-05T10:23:54.929040Z" + "iopub.execute_input": "2026-10-08T17:10:24.669209Z", + "iopub.status.busy": "2026-10-08T17:10:24.669150Z", + "iopub.status.idle": "2026-10-08T17:10:24.708885Z", + "shell.execute_reply": "2026-10-08T17:10:24.708506Z" } }, "outputs": [ @@ -379,7 +390,7 @@ "source": [ "## Adding clustering\n", "\n", - "`cluster=` is the last stage. `analyze` still returns the transformed *data* (never labels), so the labels live in the fitted pipeline: pass `return_model=True` to get it back, and recover the labels with its `'cluster'` step. The labels come as one flat sequence over the row-stacked datasets, exactly as `hyp.cluster` returns them for a list, so we split them per dataset by row count before coloring." + "`cluster=` is the last stage. `analyze` still returns the transformed *data* (never labels), so the labels live in the fitted pipeline: pass `return_model=True` to get it back, and recover the labels with its `'cluster'` step. The labels come as one flat sequence over the row-stacked datasets, exactly as `hyp.cluster` returns them for a list, and `hyp.plot` takes that flat sequence as `hue=` directly (one label per stacked row). K-means starts from random centroids, so the spec seeds it (`'random_state': 0` in its `kwargs`) to make the cluster sizes below reproducible." ] }, { @@ -388,10 +399,10 @@ "id": "1d322729", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:54.930619Z", - "iopub.status.busy": "2026-09-05T10:23:54.930555Z", - "iopub.status.idle": "2026-09-05T10:23:55.035337Z", - "shell.execute_reply": "2026-09-05T10:23:55.034722Z" + "iopub.execute_input": "2026-10-08T17:10:24.709841Z", + "iopub.status.busy": "2026-10-08T17:10:24.709783Z", + "iopub.status.idle": "2026-10-08T17:10:24.821406Z", + "shell.execute_reply": "2026-10-08T17:10:24.820829Z" } }, "outputs": [ @@ -400,12 +411,12 @@ "output_type": "stream", "text": [ "pipeline steps: ['manip', 'normalize', 'reduce', 'align', 'cluster']\n", - "cluster sizes: [74 65 41 20]\n" + "cluster sizes: [20 74 41 65]\n" ] }, { "data": { - "image/png": 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", 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4CGeEN7E8vk84ZjiBQW6P8XeJqjB87pUqVVK/F+y/EJp64IEHVOUmtkecfOB/bBOp7vlDiOcEDQ7ccGSwo8cBDWdtKDs0lnVbAX702ElALOBHj4MKyjejbcCXSDEDsJNE+ARhE1zgVGEnjB2/WRjOalCSigMMDsr4rPDaOHhC+AVLmDTjk08+UZ8zSvSN6LwXHAjxFwd59BKCA4RE2IYNG/qdsafqO4E7hQMNytUhfvE9QFzA0TOWoyPfBAcsbL/o8QOxA+cDpddG8N0hZAd3APfjYIRtH4m8KKnV4P1DDOK7x2eH50TyK8J/AGf6+G3gfmzTOHDpMBTEYqI/N4RR8J4REoG4wneGxF64ZNh2ogHbFEIqSNzG9oIDNRwjbHNmvXysIJ5tBf2YsK7YXiFuAL5H3G4GxAw+qyeffFI5WnClkPCN7b5Lly6+5ZCoj1wf/N7wfSM0BoFndIKwT4SgRlgPnznK2yEu4UpCxGJbxWsBiB2je0aIHUhDqVOqV8LpwLZFIikadgXmcRBC7AcO5DiwwyUkhLgDR5Vt2x1qQ0LsD1wshFh14zhCiDvwRMiJEOJNULGG/BsktCLchOomhOPwF/lBhBD3QEFDCHEtyPVAkivycZB8jvwxlOYjT8XYQJMQ4nyYQ0MIIYQQx8McGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4HgoaQgghhDgeChpCCCGEOB4KGkIIIYQ4nkKpXgFCEsGxY8dk/fr1UrJkSalevboUKVKEHzQhhLgYChriCvHy2WefyUcffSTLli2TLVu2yMGDB6VQoUJy/PhxtUxaWpoULFhQihYtKsWKFZNSpUpJ6dKlpWzZspKRkSEVK1aUypUrS9WqVZUAOuOMM6RmzZpSokSJVL89QgghEZCWl5eXF8mChNgBCJR58+bJhx9+qMTLzz//LAcOHPDdDxFz2mmnSXp6uqxdu1Z69uypRMnevXvljz/+UMseOnRIcnJy5M8//5S//vpLTpw4EfI1IYTg8EAIwfHBc0MIlS9f3ieEqlSpItWqVVNCqFatWup+QgghyYOChthavCxcuFDef/99Wbp0qWzevDmfeIGYaNSokbRr106uvvpq5aqAiRMnyk033SRz586VTp06hX2t/fv3y7Zt22T79u2yY8cO2bVrl+zevdsnhHA/hNCRI0fk6NGjSghp9ycYBQoUUEIIrhBEFYRQmTJllBCqUKGCZGZm+oTQ6aefrta9UqVK6nGEEEKigyEnYgtOnjwpX3zxhbz33ntKvGzatEmJCKN4gQBo0aKFXHzxxcp5qVu3btDn06IgUgMSjsq5556rLtEApwciCGLIKISysrIkOztb9u3bp8JfEEK//vqrbN26VQmhUOuFdcf71UII4TEIoXLlyikhBNEDFwrhMS2E8D8eQwghXoV7QJIS8bJ48WLlvHz99dc+8aIP8gjx4KDdvHlzueiii5TzUr9+/aheAzkz+rUSCQTHWWedpS7RAFEDAQSBg787d+5UQmjPnj0+IQQ36vDhw0ocYRm4QqGEEN4zRA1cIawXwmPIE4IQQp4QPlNjnlCNGjVUiAyhNEIIcToUNCShQFAsWbJEOS9fffWVbNy4UR2sA8ULQkbaeYlWHJhh97ANhAecFR0ii+bzhPCBEPrll1+UEPrtt9+UEPr99999eUIQQviccR+EUDhhp4WQzhMKTJiGOwYhhPAYhBAuWIYQQuwCBQ2xDBw0ly9fLu+++64SLxs2bFAHWC1eIDIgXiBc4LxAvCD/JZEk2qFJNvgMkXeDS6tWraJ6LL4LnScEIRQsTwhiCOII1WORJEwXLlxYCaHAPCEthBAe0wnTEHAIm9ldcBJCnAcFDYlZKHz33Xcye/ZsJV7Q8wWhEqN4wYELwqVNmzZy1VVXRZ2fEg865MQivn+AyMDlvPPOi+qzhMCBCNIJ08gF0kII3zmEEPKEsBzcIVSeQQiFyxOCEAqXJ6QTpuEI4X8KIUJIMChoSER8//33MmvWLCVe1q1bp87g9QEL4gEHIQiX1q1bK+cl2oOm1egDn9scmlQAsdGwYUN1iQY4PBBAcIUQHoMQQggsMGEaQggCCctEmicEIVS8eHFfPyGzPCEthOAMsbEiIe6Hgobk44cfflDOCxJ3tXjRwgAHFBw4EO6AgOnRo4c0bdrUdmfOyUoKJsGBiKhdu7a6RAO+M4TDdHhMCyFjnhCEEMJjEEZYNlyekG6siHWCEDLLE9L9hHTCNC4QTIQQZ0BB43FWr16tnJdFixYp8YKzZ6N4QYgCpdJwXrp37y4XXHCB7cSLFWXbxF7fHcJLuGC7ixRstxA7SJg25glBCAU2VoQgwrYebWNFnSdkbKxo7CekGyviPkJIcqGg8RAQLBAv6PeyZs0atZPXO3OIF9j2ECxwXyBeWrZs6QjxYgZDTt5D523hgpL/aIDAgSOEC4QQXCEthIx5QugnhFAZmjxG0ljRmDCt84SMjRWNeUJImIZL5NTfHCGphoLGpaDC6J133lHOC1wYnI0axQvOMJs1ayYXXnihEi84E3bTjpRJwSQaEH4655xz1CUaMD7D2FhRCyGzPCGEzbBcuMaK2HZ1wnSwPCFjY0WExjiAlRAKGleAqpKZM2cq5wXiBTtU49kjxAuSdOG4XHnllaps2k3ixQw6NCQZwH1B08doGz/i96nzhIz9hCCEkCdkbKyI6xBKkeYJcQAr8Sp0aBwG8gPgvCxYsECJF1SHBIqXxo0bK+fliiuukEsuucSTLfGZFEzsDH6TcFdwiQYIGggcY56QTpgOzBPCX9zOAazEK3jvSOcgcOYG8TJ//nxZtWqV2nEZxQvi8bDIkbTbtWtXueyyyzwpXsxgUjBxI7o5JS7Id4uGaAawIlzGAazEafDoZxNwpoWE3c8//1yJF12KqkEM/eyzz1Y7MTgvHTp0oHgJgdtDaoREi50GsGoXFRc4SSyPJ1ZAQZMCsEOAeIHzgp4vcF7QhEyDstAGDRrI+eefr5yXyy+/nI3BYoR9aAhJ/QDWhQsXyscff6wG0cIFgtjRF4TIkQdISLxQ0CQYnMUgbATnBeIFZzBG8YIzEyQUwnnp3LmzdOrUidOPLYBVToSkjp9++kkmTZok8+bNU2JFh8qRRN2kSRO1r7v++uvViRsqugixAgoaC0GyHpwX/IhXrlypxEtubq7vfnQnrVevnuqRgR803Bf8wIn1sMqJkOS2iXjppZfks88+k40bN/pO2lBxBWenY8eOMnTo0HzVYHBQGR4mVkFBEyNIosN4gE8//VQ5L7BWA8VLnTp1VK8XLV5g3ZLkwConQhIHcmogYObOnasG06IfD4DbglEXKFCAgAnX1wchJxYyEKugoIkAZP/PmTNHiRcMaYR40T9gAKGCducQLwgZIWmXSW6phVVOhFib9/fyyy/Lhx9+qLqMI1EYoO8NOhyjPcR1110XdeUVBA2egxAroKAJANn67777rnzyySfy3XffKfFy9OhR3/3o3InOnBjIiGTdbt26qQokYi8YciIkPgd68uTJal+IfBg0+NO/K/TOueiii2Tw4MHStm3buEJGFDTESjwtaPAjNYoX2KiB4gUtxSFeEANGl12UPhL7w6RgQqI7kXvttddUGB35fyil1r8jjFiA63zttdeq/aCVOS8UNMRKPCNoYJG+9957Srx88803Srxo2xQgORfiBSMCEP/t0aMHJ+Y6GDo0hAQHIfM33nhDZsyYoU7m4MhoAYMBme3bt5drrrlGncQlMseFgoZYiSsFDYQKYr1IWPv2229VQyg0fdIg8x7iBQ2mcMZx1VVXUby4DObQEPIPqDqCeHnrrbdkxYoVqjuwpmLFiqpooW/fvtKrV6+k9rxiUjCxkkJuONP46KOP1AXiZcuWLfnECyxT9D7QzgvahhN3wyon4mXQ9wWO9JtvvilLly5VM510597y5curTuO9e/eW/v37p7R1BAUN8aygwVkGuk3CfVm+fLnqQKmT1QDOLCBe4LxceumlynmBfUoIIW4G/VwQTp86dap89dVXqgeWFjCY+XbxxRer/eHAgQNtVcTAkBPxhKCBeMEPFGGjZcuWKecFLbON4uW0005TsV5cevbsKVWqVEnpOhP7wJATcbuAWbx4sbz66quyaNEiVY2px3xgdMqFF16o3Oh//etftg+nsw8NcaWgwQ8yMzNTZdgbBzNig4fzglJBOC8QL9WqVUvpuhJ7w6Rg4jbgSqOUGnOR4E6fOHHC1wcLPbBQiYRmdk5ypenQENcKGpxRYHyAWTwYVUmYSL1gwQJ56KGH1FlIuXLlpEKFCkoEQfAg0RcN7jBeAP9T+XsXOjTELfOQMAcO85D0SR5yXtCBt0uXLkrAnHHGGeJkOMuJuFLQQLTgxwr7FFNZ8SNGhRLiwZhIjcx8lBfCwYHwgcDBjzzYmHoc1PBjQT8ZdO5FLDkjI0MlBSNcBdGDLpdaBLG7r3tgUjBxGtjnYZwAOpJjNpKeh4Tw+plnnhl0HpLT4YkncaWgwYaNsBNcF1xatmwZcaUTxA8usGJ/+eUXJYCQ2Q/hAwGE/Jvs7GxZt26dL9YcbB0gqmDjQgBpFwgCCDk6OBvCrBLMacJ1DlazJ3RoiN3BfgoODCo0sV+KdR6S06GgIa4UNJjpEcxtCQUESMOGDdUlEiBodu/erabCQgTBEYLbg9vgAu3bt091zoQogkNkzOcxO3DiDAoCCA4POglDAMEFguBBm3CMSoAAwk6K07WTg5tzaI5kH5dDu49JeuUiUjLDVj9hEgLsXzAP6YMPPpDVq1dbNg/JqejfJkNOxCpcIWhiOdhBbOCCcsZIQHn45s2b1QUuEAQQBrZBAMH5wQBLiCC4QthZBTuQIhSCHzD64+gwGFwgCCAk8yHZGQIIOzhYyxBHJLbvGCRje0ommxYclGWTsgRvKy1NpMX1laRuO/uU4ZJ/QHgcVUgYbIt8GF2liW0T4W7se6yYh+RUkBsJOJySuFLQwHq06wEI4gP9bXCJBAga5ARBAKHkHP9DAOEsDWEwuEDYwcEdwjL6x20GfvAQQMgFQg8JCCDkAkEA6WRoCCC4QPhLC9edOTRwZrSYAfi7dFKWVGlcgk6NDcBJz5QpU9Q8pO+//z7fPCQk8WIeEobaelHABKJzhOjQEKugoEkQ2GFBXOASzRkdEgMhcBBfh9iBAMrKylIuEBwg/EWSdKhkaOxAEQZDeMuYDI0W53ClIIB0LhCSoe3UaMsq3OjQIMyU7+3kiSx5cY9cdm/VFK2Vd0HOCzrxYqQAupQb5yGh8jJZ85Ccis4Z4mdDrMJWvzI7OzTJAA2wED+PNIaOMxyEvyCC8BcCCGIHYS8IH7hAOGvEjhZVE7pvRbDPHi5QyZIllcBBLhAEEHbMCINBAEGc1a1bV123+xmm3dcvFpAzY8ZvPx2VvZv/lIp1QrewZ+5NfOD3NnPmTDUPCT1hAuchwYHBPCSMFEjmPCSnQoeGuFrQMJYaHdhpIs8m0jJOhF/g9iAZGgIosCReJ0PDFUKIDGGwUCXxeH1dEg8BBBcIAgguEEQPBJB2gVKVDO2mkBMSgM9oUVK2L/tnVplm74ajIQWNMfcGnHdNhjTqVi6Rq+t4sO1gHhKmUmMeEn4XgfOQMMwRLgyKAkh05Obmqr8Uf8SVgsbrDk2igQhB3g0uSESMBFRiIAQGFwgiR7tAxmRouEC4vmbNmpDJ0LokXrtAOCjoknjtAmkBhNvjcVncGHICDbqWMxU0FesXjzj3Bnw/LVskTaTRFRQ1Gmy76AGDeUhffvllvnlI6FSOeUiDBg1yZZg22TDkRFwtaJgcZj9w5ok+GJH2wsBBQSc6B5bEG5OhcbBAsnS4ZGhdEo/O0LokXrtAuiQeYTAIIWMs3o1JwQAuTK226bJl8T9zzU6/oGRId8Y09+ZvUVPzwnRPJxRjHtIrr7yi5iEhb01vL3AdMQ8J+S8opbb7PCQnotthcL9PrMJWezImhzkfOCNwWnBBUmQkwOWBAwSBg1AYxA4qwhAe052hIYQQGkPcPVQytO4MrW1s9P344YcffC6Q7gkEEQSB5ERa/ztTSpQvJKvf3aeu/7LiiAopBSvfLlg0iNOVd0rseEnQfPPNN2oe0vz58/PNQ2ratKl07dpVNbPjoNvkhZy43ydWYas9GTdsbwJh0bx5c3WJBLg6OhkaeUAIhUEEQQDBBYJAggACcIfgEoVLhtYuEEridTI0Sm3hAhnng9kh2RghpNXvnXp/ijDl2ydyg7tUv2/JlcoN3Zv/gZ5QGCcwb94803lInTp1kmHDhjl+HpITMY52IMR1gkZbj7B97XDgIPYEIgQOCy7BQCOzxo0by+jRo2Xs2LFK7Oj5YAgtQOgEzgfD/xA/kSRD44AIARQ4H0yHwXQuUCKSRbM2HlUixo88kb0bj0rJlumm1VGIwHkh7ITvGOMEPvnkE5X8bkw8RfI8EnkhYNw2D8mJMCmYWI0tBQ0OKFTtJB4Ck4IhOHBp1apVxAmLOg8ocD6YToZGLhBE0Nq1a0Pm6ujO0GbJ0HCBjPPBkLAdTsyfyg6KHIgVdBSGixONEHLSPKS5c+eqeUhHjx71iV58npdeeqnKgYm0ISZJvqBhDg1xtaDBwYSChsRDvEnBcGAaNWqkLpGA10Hejy6JRxgMLhAEkLEkHsvg/kiSoZELZEyGhiCDAKpYrors+rWEZJY6QzJLnS6FChZRKqdiveCVTsivOXn8pCx/5XdxMnDaIGD0PKQjR474PjM4Y5iHNGTIkIgH25LUocN/3NcTV+fQQNCwLJLEQ7LLtvF6SDrGJVIgcPR8MOQCmc0HQygMFWKhkqGhZgoXKiw3zT1VEo8wGFygwPlgp2WcIYdzi0mpooZk6DBCyC7zkN5991358ccf881D6tmzpyqjxlwkhqmdmUPD3EnieoeGkHhwQh8aiPbzzjtPXSIBro5xPtjmDdtk28Ydsjd7j+w/gjyg/XLk6GEVhkEuSajO0AXSCkqhAkWkZKkS8v/OL+M3H0yXxCMZGmEbhMSSddBBT6PXX39dZs2apeYhQdRpxw3rxXlI7nNoEI4lxLWCRm/ohMSKG8/WISqQa4OLX/dfQ7V24ARuVH3pkni4QFs3/SLbNv4qv2dnyZHjB+TI0UNqGbhDoeaD4fPUJfFwgXRn6MD5YBBASIZGH5do5iFhpADmIcGZ0kBc9ejRQ3Xi7d69O8/kXQarnIirBY2OpdKhIVbhtsZ6obr/Alxf9vI/JdzIvcEFOSUQQEtXZ0nb09GN79TyLYf9I370b884HwxCxzgfTCdDIxSEJNxQn69xPhjCYHo+GC5IsEZ/IOTEaDgPyVswKZi4WtBoW1tv6IS4OeQUD8G6/4K8k/kb5kEAmVU5BfavQTL0WWedpS6RAEGDHB9dEaZ7AgXOB8N1OERG9xV5PpdddplcffXVMmDAAM5D8hg6MZ4hJ+Jqh4aChsSL2wVNqN4yaQXyT+aGwMlXsm1Bt2B8zgg54RLpfDCIJQgfY3iJeA9O2yZWU8COgoY5NMQqQePWkJPuLQPxYgTXWwytlE+gKIFj1sAmLb/4SVZJPfE2TAomVkOHhrgSfdB0q0MDkPuCcBEcFsxrwogDLU52r8lR/2thg78tA5vrpYm6LdldgvGdUNQQ7dAw5ERcKWiYQ0Oswu0hJw3EiFGQ+Cqf8vJXPGkBhM7AAP1n3DLygDg3h4adgomrQ07MoSHx4vaQE5J84cLgb7DKJ13xZFwGAgZCpljpgqlY7b/Xiw4N+SeHhg4NsQpbnZ7pDTtUW3hCvO7QBHNhzCqfAiueQjk4ycKtIpNEB0cfEKuhQ0NciVsFTSgXRlc++ZEmcvTgCXV/JA5OsmAODWHZNnG1oNGxVG1FEhIrbg05hXNh/Cqf/hY3Xz67R2aP2CbrPt4X9LHJxG0ik8Tn0KD3ESGuDTlR0JB4cePog2D9Z4x9Z4yJv4v/u8dX0YTl1809EFHPmkTDHBpidGg4bZtYRQE7Chr2oSFW4TY3INCFMes7g/9V0q+JG9Oga9mQj00GbvtOSGzQoSFWQ4eGuBK35tAE9p8x9pqJxMk5q1NZdQn12GTAHBrCpGDiakHDHBpiFW4WNGb9Z8zur9kmXbYsPuS7rWbrdL9Ge6nCrd8JiS3k5NbwMEk+ttqSGHIiVuHWpOBIQeXS1i//ETNg61eHZO/mP/P1r0k2zKEhgKkFxNUODQUNsQq3OzSxVkN9PGanyq1JVQ8aL38nxJ8TJ07wIyHudWh0+R6VO4kXrwsa0540wAY9aABzaAgbqBJXCxqdQ0NBQ+LFrSEns5EHZgTrSZPqHjReFpnEH+znKWyJa0NO2qFhHxoSL250aKIdWxA4jfuTMTuD9q9JJsyhIYAhJ+Jqh0YLGm7oJF7cJmhiHVsAp6ZywxJSsU6xsP1rkgnPzAlCTtwOiJXQoSGuxG2CJpLBk1b0r0kGbvlOSHzwxJW42qEpVOjUDpbJYoSET/KNNmQENyfVYgYw5EQAHRriaodGw6RgYhVucQN0ki/CTHBmAkNG4cRKtPk3icQt3wmJ36FhyIm4XtDQoSFW4aYqp2Aho3BiJVj+DZ4rFU4NHRoC6NAQV4ecNBQ0xCrc5gboJF+jMxMuWThU/k2q4Jk5oUNDXC9osKNjyIlYhdsETSCRiBUr8m+sxO3fCYlc0HCOE3G1oAF0aIhVuP3gGYlYMWuyd1bnspJK6NAQOjTE9YIGOzqW8xGrcLugCRQrwfrLIKfmqgk1pEHXMur62g/3y+wR21T+TbJx+3dCIoOChlgNk4KJq3FTUrAV/WXWfXQg3zynZCcHMymYAIaciOsFDRwahpyIVXjFDYAgCSdKrGjOZxUMORE6NMQTIScKGmIVThc0kQ6jjAS7JAc7/Tsh1rmnTAomrndomENDrMLJB0+rm+GFa86XTOjQEIaciOsFDRQ7BQ3xOolqhhc4gftE7kn1WsnOoSGEDg1xvaBhyIlYiVMPnonMd8Hjd/2Yk7JRCPhOGGogFDTEEzk0dGiI1wVNIvNdIukuTEiiYciJuF7QMORErMSpgibS/jKxkOpRCCzbJno7oFNHXB9yokNDvC5ogvWXCTdVOxr3x/jRJLPaiYKGAOznixYtyg+DuFfQ0KEhVuL0xnrG/jJWVT3ZqdqJeBc6NMRqbLcHo6AhVuJkhyaRVU/RdBe2Gjo0RJ9sFCxYkB8GcbegcfpZNbFP+NItgiYRVU+RdBdOFOxDQ1jlRDyRFExBQ6zCLYLG6qonKzsQe/U7IfFBh4Z4wqFhUjCxCrccPK3Me7G6A3Es0KEh+G0y5ERcL2jo0BCvY1bNZEXeS6hcHJCMnBrm0BC9HVDQEFcLGmzgHE5JvOzQhHJQ4s17CZaLs+7j/bLuo/1Jc23o0BCGnKxn+fLlMnDgQFmzZo0UKmS7w3vCYQ4NcTVOEzSJ7uIbLBdn7Yf7E/aaTv9OSGKgQ2N/Bg0aJNu3b7f8eXfu3ClDhw6VJk2aSOvWreWFF15wp6CBQ8OQE/HqwTPRXXzNOhCf1blMvuUS3TmYDg2hoLE3R44ckZUrVybkuW+66SblIM2YMUMeeughmTx5ssyZM8d9goY5NMTLgiaRM5w0CCVdNaGGdBhTRf09q1O5hL+mETZUI3o7cFNY5Pgf2ZKzbo36m2j+/PNPeeSRR5S70bhxY+nRo4ccPnw433J333233HHHHX63TZgwQfr16+e7PnPmTOnYsaOcffbZ0q5dO+WW7N69W92Wm5srHTp0kPr166twFli/fr0MGDBAvS6Wnz59uu+5Zs+eLTfccIMsXbpU2rdvLxdccEG+dVq9erWsXbtWxo4dK3Xr1pWLLrpIBg8eLFOnTo37c7Hd1kSHhni5D02yuvgG5uKwczBJNm5yaA4uWiBZUyaditempUmlwddL6YvaJez1HnjgAVm1apU8++yzUqlSJdmyZYuUKlUq6ufZsWOHjBs3TomY2rVry7Zt2+TYsWNSsWJFeemll5RQguCpVq2apKeny8GDB2XIkCHSv39/GT9+vGzatElGjhyp7m/Tpo16ToiViRMnytNPPy2ZmZn5XhP3V61aVa23plmzZvL888+r1y5SpIi7BI3TDkLEvjhxW0pFF99kviarnIibHBo4Mj4xA/LyJOu1l6XE2Y2lUPkMy18PouKDDz6QSZMmSdOmTdVt1atXj+m54MAgxQPCo3LlyuqiKV36VFFAmTJlpHz58ur/WbNmqWWGDx+urkPIdO7cWd5//32foMnKypIpU6YogWTGvn37pGzZsn63lStXTrVr2b9/v5/QiRbbbU3YwJlDQ7wsaFJFMjsHM4eGADc4NMf27Paf9ApOnlS3J0LQIEkXlcANGzaM+7nq1Kkjt956q/Tp00cuvvhiue6666RBgwZBl9+4caO6wFExiiIk9xrFSTAxA8yO71rYxrtfKGTHHBoehNwHzmLwAy+SWTnfjzzUffHixG3JDo3vEgkdGuImhwb7rXzj6wsUOHV7AtD7tFgP/jk5OX7XUW3UvXt3mTZtmir5Rj7LiBEjgr528+bNVZjKiHFqeriQUYUKFeTAgQP5XBuIW7hB8WC7rQkbuBMPQl4nlCgJFV+OJPYcj+Bx2rZk9RBKQuyKW3JosE/CfgthJjgzEDOVBg1NiDujw0v43NBrpmXLliGXLVmypPz6669+t8FhMRMZN998s0rivfHGG5WggbkAjJ374egsXLhQhagKFy4c0/oj+RjrtHfvXpWrA1BNhcTjePJngO32kEwKtj+BAiOUKAkVXwbhYs/hnluvBwgUPU5MCk7EEEq7QYeGaGI9KNoN7JOw30qU02wEIR1UID388MPKKYEoQKJt27Zt8y3bqFEjldS7aNEiFQb64osv5Ntvv/WFlbZu3SorVqxQuTglSpSQxYsXq7wYgLwZCIy5c+dKr1691P1dunRRCcT33XefDBs2TDkzGzZsUDkxxjBUKM4880w599xz5T//+Y9KKN61a5dKQMb/8WK7PSQdGnuixUPu1p8le+Z0n8DI6HWNZM+cFlSUhIov6+XN7sNj/9yyOajgyVn1o/99miRUGCSjbNv4thJZQp0qmENDgBtCThrssxIpZIxAyDz66KOqRBohJJQ/t/k7KddIt27d5KefflLl2yj1hugZNWqUSioGECxI6H3sscdUbgvckyeeeELdV6xYMbnnnntU9RH6xEDItGjRQl5//XVVMt67d291clKzZk259957o1p/VGeNHj1aevbsqaqn0MCvb9++cX8uaXk2O4XFl4Iado4/sA9+LkkggUffv8nod62kN2+h/t82ckS+I3TmjTdJ4YoVZee4MflizzWemHBKsLz6kun6ZP77Ftnzwn/N1+fv56/x5AQpVilTLmpxgXww6SXfWVMi83Wsmt2068ecfGXbicihMZsXlQxwplerVi3Vj4J4F4haJKO+9dZbqV4V4hJsJ4/p0NiLfCGjQILcnj19qmS/9YZyS/ziy0oAnZQ9Lzyr/k+/sI0cWvqVX+wZqNc0A3FdvGQoHZ53UvZ/9rEUL1hQ6h/aL7seHffPay350s9dKlqzVsrFjVkSMBreJVJsRJJ4nCjBw5ATcaNDQ1KP7bYmbuD24dDxbNmTtUz2Vc6Tk0VESvwhUvxQFE/wd4gIjgsuRzdvlD3PP+t3P8RMtdFj5WRurk9YoNtmMDcIgqd43XpBnSHN/k8+ktKFC50SP/q1vl7s99rZM95MeZgqWBIwBE3lhiVSlnic6Eorhpy8jXbgub8nVmK70Qd0aOzBmoMLZMq2ETK30FRZOkRk+bUiC28S2XFu8MeUbndZ/hsNOTEF000OiCdPKjFT4qyGPpfEVwZpJC1Nqo0Zp0QHloO7Epo8KaCeIy9i4ZWMluXJnt0Uy2smekAmIegI66akYGIPbCdo2Ck4tY7Mjpw1svvPzbIga5LkaTGgtUUBkVWdRY6mmzy4QAEp3fqi/ELE0I/BVKiY9GvQZZAqvPT3MrherFYd3zLlOl8hGb1DiJq/XyfiBDFjorLLZjdF+5qJFlkMOREkqAIKGuJqQcMNPLWOzJxd42TGzjH/iJlACojkZKRJequ2/oJj0FAlOPIJEUM/BlOhEqRfA5wYhKmqjBqj/pqFg8p17ibV/vNgfpGEpORe18jBv47LSf02ChTwX2ezt2ZoDpUszKZfJ2J2UzSvmQyRpXtcEG87NAw5ESthDg1RzoyfIxPC10jLS5P6I8ZJ2Qp1JKNnn3wVQ+H6MUTTryGSMkgtooxJxxm9+ikHJ+fECVlbopQSRfq1sM6HvlmmkpYDQegrmeikW+SuJDoJOJrZTckYkMkcGm+DdvmAJ7DE1YKGG3jy2X9sd3BHxkCaFJB2mUOlbOk6IQVHOCFidb+GYCIJB81jkqbyc4yvjXJyVGAlq1W5XccbhJrdlMhhlQw5ETo0xBOCRluQaPJDWzpxjgxETNkilSW9UIb6myZpJqImTc4r00XqpLeQ4ydzpczfy9uRYCLJrM1SsluVO3W8QSKHVdKh8TbaoYm31T0hRuyz9wwQNCjr48aemFwZHV6CiGlX6XppWLqd+js/C71fjAIgT1YemCuNy3WyrZAJR7C+kclsVe7F8QaEhIJJwSQR2C4zT4sYbUmSxOXK4O+CrJfV7RA1l2fenO8xeXJSDhxLfvVPMlwAiBhjubibK5vsBENO5K+//lIfAlMMiKsFjd7AtYInic2VMQqW04rXU65NYN4MQk1OxWaTPVJW2WQ3GHLyNgw5JYbly5erqdVeHR1UwK4hJwoa68nK3ZLvNgiWQgWKqv4zAKEn3Kbva1dpqGPDTXYE+TO71+T4Kps6jKmi/iY7ITiV0KEh2qFh2ba9GTRokGzfvt3S58Sx/bnnnpPLL79cTQNv3bq1GlR55MiRuJ+7kF0dGq3giTUgrLQke1q+22uXbC4z/+47o3NqBteYoFwbOycBO9GhsUNlUywkYqYTHRpvQ4fG/hw5ckRWrlxp+fPu3r1btm3bJvfdd58aUrtnzx41DRwTuKOd2m17h0YLGubQJKc0e/ORFflyakC1Eg0dL2Zw0LSLoLHLOAHtEEX6uhBhs0dsk8/G7VJ/cd0KKGi8jRsFje60jr+JBi7HI488otyNxo0bS48ePeTw4cP5loNQuOOOO/xumzBhgvTr1893febMmdKxY0c5++yzpV27dvLCCy8o0YHb8D116NBBhbEQzgLr16+XAQMGqNfF8tOnT/c91+zZs+WGG26QpUuXSvv27eWCCy7It041atSQxx9/XNq0aSNVq1aV8847Ty6++GLZunWr+xwavYHTobEWlGabY55T43Qxo7GLoLFDZVO0DlEiy8spaLyNzvFwS1JwsOrRRPHAAw/IqlWrlKtRqVIl2bJli5QqVSrq59mxY4eMGzdOiZjatWsr5wRmQsWKFeWll15SQgmCp1q1apKeni4HDx6UIUOGSP/+/WX8+PGyadMmGTlypLofAgWsXbtWJk6cKE8//bRkZmaGfH20Z/n+++9l7ty5ctddd4nrBI2OqVLQJILwwxqdngRsR0EDYXD04Il8tyezsikWcZIoEcYcGqL370VTMG4kWdWjp5donJATQ4iKDz74QCZNmiRNmzZVt1WvXj3m7wGiAsKjcuXK6qIpXfrUyU6ZMmWkfPny6v9Zs2apZYYPH66uQ8h07txZ3n//fZ+gycrKkilTpiiBFAq4N3CVYGJAFHXr1k1cJ2jo0CQu5BRczJwSOm5LArZDyMnoivgKyPKSX9kUizjR5eXGx1klwujQeBs3JQWHqh5NxL4USbpwuBo2/KcDeqzUqVNHbr31VunTp48K+1x33XXSoEGDoMtv3LhRXZo1a+Ynipo0aeK7Xq5cubBiRoenkKezbt06efLJJ2X//v0yYsSIuN6PbQWN3uCJNQTvBozjbJp0zLxZlW27RcxoUiloAl0RLWTa3JwpFesVT2qZdiziJJEznShovI3OkXSDQ2O2b02k0633abH+hnJycvyuDx06VLp37y7Tpk2TgQMHyuDBg4MKC7x28+bNVZjKiPF7jDQvCvkzoF69elKwYEEVRotX0NguKZgOTWKAUEFc1wycTZQoWNp1YibVBHNFipUumPSeM7H2vkGOTSLKyznWxNvoHBo3CBq9b01WuwuElyAA1qw51WojFCVLlsyXLAyHJZAKFSrIzTffrMqpJ0+e7PcbPXHihJ+jg+RdhKgQbtIX5NzEuz1YcfJpO4eGZduJI6PI6Z7Im7FLyCmRIZtYiHXgpNUznZhDQ7RD45akYCQAI2cmGe0uENJBBdLDDz+snBKICSTitm3bNt+y6POCpN5FixapMNAXX3wh3377rS+sBHGyYsUKlYtTokQJWbx4sRIoAHkzMBiQsNurVy91f5cuXVQCMUquhw0bpgTphg0bpGzZsn5hqFB88sknKtR0zjnnqERmVE0hudmKHBrbOjQs27Y+C3/GzjGm99Upeb7Fr0bs2hEYr125YeqHYDLk5G3cFHLSQMQkq90FhMy5556rSqTRoA4iI8/k5A0ioWfPnqp8+4orrpBvvvlGRo0a5Xe8RUIvcmggVlA59cQTT6j7ihUrJvfcc4+89dZb6jXgCEG4vP7667Jv3z7p3bu3ev7nn38+qnVHkvGMGTOkb9++ctlll8lDDz2kngevFS9peanOmgzg7bffVm906tSpqtadWJOFP2XbCNP8GU0ySg2TDc4o0Lhp9erVrmtM52Rgl1900UWyYMGCVK8KSRFjx46V+++/Xx1gIz2zJyQcttu7MocmeU31jOB+TNsunFbcVcnBqdLrgSLGyUIm+6/jsjv3mFQuWkQyCrNTMIkfXfQBF4AQq7DdXlZbkAw5JbrCyawnTZ58sudZ17g1qcqhceqIAzMW/HFQJu3KUlsKtpjrq1SSduXjey/MoSFuSgom9sF2OTQcfWA9cFvOTD/V9MiYNxM4WVujG0Mlo4V3okmGoDGOE7DLiINQbsuawznqbyTLajED8PflXVkRPTYczKHxNvqE1U2jD0jqsa1D49Xx54kAwmT9oS/9bvv5yDdyYUY/WZL9lirbDgS3/XZ0o+wvWFo5PE4MQSXjoBnoxpzVpUzKRxxY5bYgzBQoB0/+fXu8oScKGm+jQ050aIgnBA1DTonvZJlZtLaarA3h8ume/+YLSenbnByCSqRDY+bGrJt7IN9yqSzVDue2NE4vEVScIGcmMDBZ4O/b44WCxtvoE1Y6NMTVIScKmsTl0PiT5uuXUC+9pV9jKN2jP3A2ye4/NydtmmwqBE2003KDNc5r0LWsrUq1w7ktwYDQgYujtwr8HVqlkiWJwRQ03oZJwcQTDg1zaBLXyRJVTMbz7V9yfvS5LsbGUDknDqrk4EBH51Qfm8Q5NhAScJNChbgiWUYvV6REAUkrlJewabnBGued1amsutipVDtWtwUhKbg4rHIiVkKHhiSC1O9pA9BlfMyhsRaIFf8DWv6JsPiLC8SA+dynfxwbiCN0Hq5crI4l6xeJoIhUdOjlylQrImVqHlfXQ4mTWKflQqicfm22bJtaTtLyCkhe2kk5Y8A+KZlx6jOJVMgkoiw6mNuCMNPJKNyWRK0bHRpv46bhlMQ+FLJryInDKVM3EVY7Ojion0oYNi/xhmPTKqOfVCpa2+eYhHNQzO6PRFBEKjqMy53KCQ4vTmKdlovX+qneMyJ3pEvB7Aw5kZEtP5U5LOcdnxBxEnUiyqKtclsStW4s2yY8YSWecmgoaJIzbXvD4SWms0eMIahCBYrKzJ1jTB2br7Onqf/w3CgNRzVVMAclmMMSiaCIVHT4Lfd3lVM4cRLttFwtyo6eOHjqMWUOyvEyB333hxNC8STqBnueSEUK7o/kua1at2BwOKW3oaAhnkgK1lnvFDTWggMsyrQDWXNwvhqL8N2+9/MlxOrZJAgrnZrUHbwMGgf2dYcW53NQ9PMFc1hwu1nScqCgiGQZ0+Xywg/fjGZaLkQZPq85u8apKrB4Bn3Gkqhr5qKM2LBNxm3bpf7iuhV9aaxYt1BQ0HgbCprEsHz5cqlfv75nP1/bCRrm0CQOhIbMyPvbacFBGgdrHLQDK37gpPSuNi6kqMn/vKeckUgclnCCIlLRYVxOJ+wGEydG8P5Qwn5VlTHqL9ypQIFnJspOfR5pYYVQqERdibEsOt7Gd6HEULzrFg7m0Hgbrx5wncagQYNk+/btCXnuvXv3Sv/+/eXaa691b8hJJ4nRoUle2MmITvg9lTVzKjR0oSFPpr1fbk1ojG5FuLCOMcRlFgKLdBnjcv9vZx1JO5gWcTWWToqOJjSGT+nyzFukRMHSIdfJDIRu2pRJl8UHDvlua10mPeKQTjAXZeORo9KybHrI0BQIFVIKlURsRaIwBY23gaDhNmBvjhw5IitXrkzIc3/33Xdy2223qeHBJ06ccK9Do61oChrr0e5FeJclz8+FMLo3QDsZzcr28HMnzkpv6+egXJjRV4kAOBuROizhiqx1GCwwEdgsXHb8aJ6cPFVMEZTAx0YbGtPvP1oxAyAMvjSIGfDVgUMROyxmLgr47849+UJPgW7Mx7/vCxtSQgLw2FrV5NrMDPUX1+MNcWmSdTAzhtSiGfsQ+FhiLTiIuU3QJHN7+fPPP+WRRx6R1q1bS+PGjaVHjx5y+PDhfMvdfffdcscdd/jdNmHCBOnX75/0g5kzZ0rHjh3l7LPPlnbt2skLL7wgu3fvVrfl5uZKhw4dVBgL4Sywfv16GTBggHpdLD99+nTfc82ePVtuuOEGWbp0qbRv314uuOAC0/VftGiR/Oc//5Fu3bpZ+KnY0KHRUNAkBu1e/LjvY1l5YG5ETotGH9whaA4c3yPf7X/3bwlyysVpWu4KaZHRRzkoe3K3yJLsaflcjmAOSyx9YCJ5XKjGemaPLV0oM2hoDEIqf/WXxDzQM97RAtpFMTotZm6LWWhqbrZJR+OAkFJgldM1mRkybU92xInCoZwcfTBLRFm4fs4tR3P91lfX6umKLWPFFzD+D8H3UfaBpFSfeRG3hZySWa0IHnjgAVm1apU8++yzUqlSJdmyZYuUKlUq6ufZsWOHjBs3TomY2rVry7Zt21SX/ooVK8pLL72khBIET7Vq1SQ9PV0OHjwoQ4YMUaGi8ePHy6ZNm2TkyJHq/jZtTs0LXLt2rUycOFGefvppyczMNH3d22+/3SeAPCForLShiD8QEq0rDpDG5ToZxMd0v4N0MK9Ez3gyuhi4FY+vl36hT6TM2fVg0BLrQCcj1j4w4R4Xatp2sMf2qjY2otBY4LiISNfZ6tEC2GkWL5gmz+7YE1QYBRNOXTPKykfZ+/3u+/FQjnpOMxFkFAdmrxPNDh7fTbwHATMxZHzOQIzvBcsYbzNrTCABy59erIjUKXGqCpPEh5tCTomuCAwEouKDDz6QSZMmSdOmTdVt1atXj+m54MCcPHlSCY/KlSuri6Z06VO/xTJlykj58uXV/7NmzVLLDB8+XF2HkOncubO8//77PkGTlZUlU6ZMUQIp2dhW0NChSTxaXMB5gBjRzgk6CAfLk9Eho1AJvtH2dYm1D0ysjwv12OMnc/1cmGAJyhjaGetrx9vsLpB6JYqHFEbBhFOLMqWUoBGTHbGZCDI74JsJsEh28IgERnIQCObgmIkhPDaYmAkkkvcWeP+YLTvp1FiEm0JOiRziagaSdCEIGzZsGPdz1alTR2699Vbp06ePXHzxxXLddddJgwYNgi6/ceNGdWnWrJmfKGrSpInverly5VIiZmwraLChu82StDtG58QYGjK6N/rgflrxeiFdjGj7ukS7fKSPC+XQmD82TfXciST5ONZ1TsRogXDCKNj9uSdPBt0Rm4kgs+tmAgxJyeF28IdPnJTiYZYJ5uAEE0w3VUe4MHHQqbEONzk0iRziaobep8X6+eXk5PhdHzp0qHTv3l2mTZsmAwcOlMGDB8uIESOCvnbz5s1VmMqIcWp6KgeO2i4pWENBk1q0c4O8GGM5Mw724RJ8o+nrEsvy0TxO//gDk38DH6uWlTzVQBC5NWbJx1assxk4gDcsFZ89jQP9hPo1ZEyNKupvYOjG7P5QpdmBgynNdp24DWLMCEQIkpIDCdzBbzqaG3IZM9GC69qxMRNMp3oOmfNPcb3//9GinZpYk6HJKRDmcIugSeQQVzMQXipYsKCsWbMm7LIlS5bMlywMhyWQChUqyM033yzPPfecTJ482a9Ax5j+AUdn69atKkSFcJO+IOfGDtjWoWEOjX0wy3sJ52JEWmId6/KRPE7vMIMlDuOCeVQzdo72PSaaXJhY1zlRhOsCHHh/uNLszCKFVHUTnJyDJ06EzNMxEyFmTo6uAEkrENgk0f8gsDEnv8uT9/ftZiE2XK9YpHC+99M3M0NqFy+aL/EXuUJ6OWPWWOgMsuTkSHgBNzk0iRziagZCOqhAevjhh5VTAjGBRNy2bdvmW7ZRo0YqqRdVRQgDffHFF/Ltt9/6wkoQJytWrFC5OCVKlJDFixcrgQKQNwO3Ze7cudKrVy91f5cuXVQC8X333SfDhg1TzsyGDRukbNmyfmGoVGHLXyNDTs4VOtHcH+/ykTyuQCEJmTj818n8TkE0uTCxrrOdd8TB8lPC2epmzgm4uXqmtCxzqi+Oryw84GBmXEYRIkM3UIgF5rjAgQp2YNHXA9+3Xjcz4WMm0hKZI+EFcMLqtm7RkY4VsQIImUcffVSVSCOEVLduXV9SrhGURf/000+qfBul3hA9o0aNUknFAIIFCb2PPfaYcs1Quv3EE0/4mtzec8898vzzzyvXBkKmRYsW8vrrr6uS8d69eysHvGbNmnLvvfeKHUjLC1XXmiIKFy6s4nRLlixJ9aoQB5ORkSFlKpaQWz9sle8+hNAQUkIICv11AnNhEF5zslAJJNLyaCyH/jKBwgUiwehqaDfHGNoK9Viji1OhSGE5vfMV0vLRp0yX0csN37DNb90ggSYaltuc86eM3rLTbxmz54oXvA7EUuCOsn9mhlxRsZxlr+Ml4BAguRXN2wixCltKZDo0xLLtKPdUmMnvdkPyrpW5MHYlmmZ4oSo2wuXpRJJLoP/XZ+fB8g1wfViVSn55L9cHLIdQmCRw3pQGpdr9MvNvD9P3ZLPpXoy4qcqJ2Adb+qXY0GF/ERIv6BQcrgzbbrkwqeyREa5iI5ytHmkuwQVl05UwCrVMuOcKt65WNu1DHk4gDDvFDvbvbgs5kdRjS0GDDZ1VTiRedNl2JILF6bkwVvXIsKI3TijRo09UihUsqCq74nmuUOtqdedWM/EE0I04kvdB3J9DQ1KPLQUNq5xIOJD7guZ46Adj1nkY9xUs/E8fGrcKlkT0yMCBH11xNxw5KvVLFre0O64WNFaFG8xcnER0bsXjEHZCt+TAsNOFZSMfKEpOQUFDEoEtf4XMoSGxzm8y3pdRr5AcPdXh3rPE4rgkci6N1YLGzMUJNYW8dKGCMYeggoWdgk03J97oQ0Psgy0FDaxI5tCQaOc3Ab8ZU2kiJauk+aZ9e5VoemQkei6N/l0nMtwQrMsxGv7FI9KChZ3wvEdP5nF4ZZQODZrDEWIltgxiYmfHxnok2vlNgffpM0Dc53Ui7UYcKufGrg5NIMG6HAeKNN3kL9rnDVzzWJ/PyyAUzBwa4hmHhoKGxDS/yXAfjpl5JyXq+UpuJNKKn0TPpdHJ/okONxhdqUi6HFs53ZyEB/v3QoX4WREPODRMCibBCNU3xmw+0+HfTno63BRtH5pEz6VJRsgp0JXSoxICWX04R9YczonaWcHzBZKWwGGEboRl2yQR2FIiM4fGW0RSsWS8L1QZtvG+RzdeKsePebufUSw5MYmcS5OMkFOkFUpzft+vLrHk1Jjl0pDIoaAhnhE0SBZjUrA3iLRiKfC+UGXY+r6TTGmIug9NYOUQBBFcDKuETSoETbAKJYky8VmH7Q4eP2E6OJMhp+i2AyYFW8/y5ctl4MCBahK3F0N6tgw5MYfG2xVLuD3UfdE21vMyOidGYsiJiSZUZXdBY/Y5RJP4bPwsUNUUiJV5Rl4Av0sKGvszaNAgNXPLat577z01OBPDMNu1aycvvfSSuwUNHRr3E03FkvE+Ejmx5sQEC1XFW8mTzBwas88hGKFyYMw+CyyflqA8Iy/AkJP9OXLkiKxcudLy54VAmjJlitx8883y+eefy3333acmeusJ4PFgy18gQ07uz42JtmIp8L5IoEOTPyemaIECaqgjDtKhDsCxhqrs6tAAfAbBcl+6ZJQN+r7MPgtcv6V6ppQuGHujPi/jRofmSPZxObT7mKRXLiIlMxK7Pfz555/y9NNPy0cffSSHDh2SWrVqydSpU/Mtd/fdd6vKwieeeMJ324QJE2TJkiUyffp0dX3mzJny8ssvy65du6RixYrSq1cv6dGjh1x99dWSm5srHTp0UMu9/vrrcsEFF8j69evlwQcflFWrVklGRoZcf/310q9fP7XM7Nmz5dNPP5XBgwfL6NGj5fDhwyoMZuSMM86QOXPm+K5nZmZK27Zt5ZtvvpErrrgirs/Flr9COjTOJ1T+i1HsXJhxjSzJnm46ODLcUMlwUND8Aw64Px7KibgDcKLKt7WgScXBzEyYALzPThXKBi1xD/ZZoNqJQiY23JZDs2nBQVk2KUsQ4YZWb3F9Janbzpru2mY88MADSlA8++yzUqlSJdmyZYuUKlUq6ufZsWOHjBs3Tl544QWpXbu2bNu2TY4dO6aEDcJAEDYQPNWqVZP09HQ5ePCgDBkyRPr37y/jx4+XTZs2yciRI9X9bdq0Uc+5du1amThxohJcECuRsG/fPqlbt67Eiy0FDR0aZzsvobr5YvlAsXNhRj/JLFo7ZMWS26Zg273ayWxkAiqFdJ5JrAfyVDo0cKfMuLLCP+5MsLEP8Q7sJO51aODMaDED8HfZy1lSpXGJhDg1EBUIz0yaNEmaNm2qbqtevXpMzwUHBr9JCI/KlSuri6Z06VOCrEyZMlK+fHn1/6xZs9Qyw4cPV9chZDp37izvv/++T9BkZWWpkBIEUiR8//33snr1aiWQ4sWWv0gKGnsKlUicFxAu/yVQ7CzJfksG15hg+RRsOjTxhZCMoaqfj+aqsud45zvpJO1UCBqE2sw4o1jRsKIvkaXsXsRNggZhpsDaAzT0xO2JEDTIQUEYqWHDhnE/V506deTWW2+VPn36yMUXXyzXXXedNGjQIOjyGzduVJdmzZr5iaImTZr4rpcrVy5iMQOH6JZbbpH7779fqlat6l5B4/XqlGQTiVAJ57xEkhsTSuxY7cBw+F38ISR94H5w2y5L5julKik4kllMmUUKhRR9gUMwSey4KeSEnBnVldyw8aQVOHV7Ioj3pCAnJ8fv+tChQ6V79+4ybdo0VfKN/JcRI0YEfe3mzZurMJWRokX/aYtQpEhk7/u3335TrwURhdd3bZUTHZrEAmGyI2eNrwQ60hLpSCuPIEyQGwNRA4z5L1rsGIk22TcaKIzj7wAcbr6T7lUTSQVUKkNOwSqdtEBDSCrWEncS/e/SLX1S4MIgZwYiBuBvi6GVEpYYjPASjpHoNRN23UqWVIm5RuCwBFKhQgVVdfTcc8/J5MmT/U46jGOI4Ohs3bpVhagQbtIX5NxEw+7du5V46tu3rxI1VmHLLYoOTXKdmNKFMiNyTcJVJRlfY0n2NN9yF2b09WuIF2+yb6TQofEn1rBJKHcnWM6JHQWNxsylwVptOHJUrsnMkOl7spkrk2DcFHICSABGzkwyqpwQ0unYsaM8/PDDyimBmEAibtu2bfMt26hRI5XUu2jRIhUG+uKLL+Tbb7/1hZUgTlasWKFycUqUKCGLFy9WAgUgbwZuy9y5c1XlE+7v0qWLSiBGqfWwYcOUM7NhwwYpW7asXxgqFHv27FFiBu+hZ8+e8scff/ju07k6sUJB46G8GGDmxPSqNjYioRKJGAl0ewByZOqlt4podAFJLIFhk0iGVpolCMPdAdGOVUhlyCkwRyaQqXuyldhBknB6wYJSv2RxqVOiWJLX0hu4yaHRQMQkulxbAyHz6KOPyg033KBCSKgQavN3Uq4RNK/76aefVPk2Sr0hekaNGuXr+QLBgoTexx57TP020ehOl3gXK1ZM7rnnHtUjBq4NhEyLFi1U+fYjjzwivXv3Vt9jzZo15d5774143SGwkAeEpGZcjEAcxUNang09ecTofvzxR1U+RqxzY84t00VWHvgw33JXVRkjB47vySdUAnNojKIlmBhBKGvOrnGmr1GtRPxJbNFQo0YNOXDggCoJJPmJ1l0JFD8IM6FzbiBjalRRQyHNxNK6devU2eHQkXfI+EceiUhchRJd4QSZ8X78NVvfUMCxwdgEJgJbC9wZJKHOnz/f4mcmXsaWEhnK3YY6yzFAcPx2dKPMz/qnnTREzQ8H5uZbVjsxEBuRuiahKo8iDUsR5w2tDHR3YglFfZ19QC339cEjapSAvj3Y8qFEV7D7tIhBZRbCR/p+lJ0Hrm+4IZN6oGU8lV3EGw4NST223KIoaKxxZQKB+3Je2a6ycv9c05BRNCXSwUq8k5kjEw6WbUtCOwFHG4o6vVgReSfrd/3l+N0ebPlgoivYaxw5ccJXXm4E19/ak+2XIxPNxOx4KruIORQ0xGps+ctkUnBsmOWvGIG4aFy2k7rEk78SrsTbLjkyTAoOjlWdgM0SjRGKCpZ0mxeQFOy7PdjyIaqrzO4zEzPG+2sVLyoT6teQjTlH5dkd+YdMhsKK0Q/kFHRoSCKw5S+Tyj06tFty9MTBkGIm0I2JhUh70cTTEM8q6NBE767EcrCONBSFJFs5+XcPjQJpfrcHWz6Y6NpnUiIeznHRj8W6YgZTtIQaYEmih/t54hlB4/Ucmki69pq5JWZCpmPmTXJa8XqWCIxkNsYjicXMXYmk6ilWsYSKoe4ZZeUztVQBv9vNli9XuJAaGvlR9n61xenb9UwqI7iv79/hJLM9R6BgCya6Qj0HwlV0Z6zDTWXbxB7YVtB4GbPqpHPLdfIJBi12Chcoms8tOXUeeeqadmXqpbe0bN2clPRLhyY6dyXaqqdYet40K30q/6VdRhm5uVY13+TvwOUhWpA0rNela0YZ6VShnHqsvt33PYvI2FrVlDAqVbBgvvlTCDMFCrRgogvr0apsunz8+36fkNIJxVdUPPX6xBoKFy7Mj5JYii2Vg5cdGrOQDkqtcWlfaZi67Z/7zUz2PLk88xYpUbB0XPkrTkj6DQdzaOKvekJiLkRHNKXUoe7XfWiO5OXJmC0784kn7RIFrsvc7ANK0JglM+cZ5jRF0zww2LL4O+C0CmoCN2c3WY/eBihoiGcEjVcxC+lotIj4h/zLQWDEG15yStJvOOjQxF/1NHrLzlOfZYSl1CDU/fpg9s3hHGkQpIIoVAVWJMnM0cxcCrUsZzclBt1fzMv7eZIYbDnLycvK3WzWkcZfzBjJPzMpViKd64TXQO8au4oZEh1aKARDi47NOX+aOjl6jlMwp0ffrwVN4K7HWL1kti7GhN5YZ1IRewkaL+/nSWKw5V7Ayxt6YEjHCASL2W0YXXD8ZG7MbokxvMSkX28SmFMSat5RqP414frbBJsUbHRZwlVgxTqTitgDtOAHdGiI1RSyc/b78ePHPbnR65DOj/s/lu/3f+SX4AsC81cqF6tjWXhJT8l2QtJvOBhyig6jUMDkaZ3jEmkpNQgXEtIOTYuy6XLob7Fj5rKEEy0MBzlf0Hj5xJUkBluqBb2hY8MvVaqUeBE4La0rDDBtgmdV/opZeGlJ9nS5MKOfGihp96TfcDApOHqMQiFY6XUo9yScu6IFTb1SJWRw/RohXRaKFnfy119/qb8UNMRTgobDKc0b1MXTtC6S8FJm0doyuMYE2yf9hoOCJj6CuSTh3JNQ92tBg++GgsWbMOREPCVoMNLcuOETa4gmvGSHTr9W4NXyf6sIJjrCiZFg9+vvo0ABW9YjkCQ6NHo/T4hV2HKvovNmcnNzU70qriFUeAkiBjg5vGQGHRr7gbw4wO/Gu+j9uhfzI0lisX0ODYkdL4WXzGBSsP3QISc6NN5FCxo6NMQTgkZv6NqaJNHjxfBSIHQB7Eewsm3iHRhyIp4KOWmHhiGn2PBieMkMHjTtBx0aovfrrHIinnJoKGiiFzK/Hd0o2bk7PBdeMoMhJ/sKGk5a9i50aIinBA0dmthCTPOzXgp6v9vDS4EsXbpUtm/frkRx586dpU6dOtKwYUM577zzpHHjxozfpwiGnAgdGuJJh4Z9aCJ3ZsKJGbeHl8A333wjDz/8sHz++edy+PBhdVuxYsXk448/zrcsHIKSJUtK+fLl5bTTTpMaNWpI/fr15ZxzzpHmzZtLtWrVUvAO3A9DTkTv15kUTKyGgsYFIMwUjDYZ10qd9BauFTPff/+9PPTQQzJv3jw5dAjN9EUqV64s1157rdx7770+YfLLL7/Id999Jz/99JNs2LBBtm3bJr/99pv88ccfsmPHDuXoBAIxVLp0aalYsaJ6ntq1ayuXp0mTJuqC+0l0GBvrEW/CkBPxpKBhDk38uFHM/PDDD0rEfPbZZ3Lw4EF1W6VKleSaa65RIub000/P9xjchkuPHj2CnjX++OOPSiCtWbNGNm/eLDt37pSsrCz5+eefZe3atfma9MHlKVGihHJ5IKLg8tSrV8/n8pxxxhkJ+gScCxvrEU7bJp4SNBx9EB2nFa9nenurjGtcI2ZWr14tDz74oHzyySdy4MABdRuck6FDh8ro0aPjFg8Q0RAhuARj165d8u233yqXZ/369T6XJzs7W3799VdZvnx5vscULVpUuTwVKlTwuTwNGjRQDg/yeSCIvARDTkQ7NPhtEOJ6QcM+NNEB0dK+0jCZnzXJN+e4VUZ/aVruCnEycErgxCAHZv/+/eo2CIN//etfct999ylxkEyqVKki3bp1U5dgZ54QXtrl2bRpkwpnweWB+IEIQmjMCBrMQdSUK1dOuTwQZtrladasmdSsWdNVTeiYFEzo0BBPCRqt3JkUHDkNS7ezbAp3KkF+C0TMhx9+KPv27VO3IaQzaNAgJWLq1q0rdgVCHK4LLsGAuIHLg/AWBM7WrVuV8wOXB24PEpvNnjc9PV05UlWrVpVatWopl+fcc89VosdJE+np0BA9/oIODbEaChoX4dSSbDgZWsTgwA7gWPTv31+JmLPOOkvcAnJ9UEaOS7CdPfJ1kMAMtwe5PEho3rNnj/oLwTd//ny/x8DBKV68uPrMMjMzfS5Po0aNlOCBCLSLy8OkYMIqJ+IpQcMcGvcDZ2LcuHHywQcfyO+//65uK1u2rPTr10+JGFQTeREM7EO4CZdg4POC4IHLs27dOp/Lg9uR34P7zFweODkI2WmXB0IRLk/Tpk3VZ58MmBRMtKChQ0M8IWj0hs5ZTu4Cje6Q2Pvee+/J3r171W1lypSRPn36qOqkUAdx8g8QJR07dlSXYC4IhI52eeCAaZcHyctwfRYuXGjq8kDYwOVBRRicHbg8EDwQP1a4PCdOnPC9HvEmDDkRTwoa5tA4HxxI0exuzpw5Kn8EoOrn6quvVk4MHAJiLRALcLhCuVxIsobgQQk8xM+WLVuU2IHLoxObzZxT7fIgQRoJyxA66LyM6jDkOoWDISfCPjTEk4KGDo0zQf8WLWJ2796tbkNS61VXXSX33HOPyusgqQVOTPv27dUlmPCAs4MEZgicjRs3KocNLg/CW+jNs2jRIr/HoFkeXB64btrlwcgJ7fIgkZlJwYQODfGUoOHoA+eBg9wjjzwis2bNUtU6AGfz3bt3V+GkUP1diD1dHoyCwCUYaGoIJwcuDxKZtcuDcCKu43az5wUjRoyQ//73v8rlOfPMM5XLA6GLpGnibpgUTDzp0GglT+wJQkioTnrnnXeUoAGYj4Q+LXfffbe0bNky1atIEghChxdffLG6mAE3BgnLuhkhXB6UpcPpQcjr66+/li+//DKfy4OREnB5IG6qV6+ucnn0YFHkWSFxmjgX7bxzdAixGlvuGfSGzpCT/UCOBZyYGTNmqNCSFjFdunRRIqZ169apXkViE+DGoPkhLkj8Bi+++KLceOONavuB8MUQUTg5K1euVK4OQlnYrrCdoUQdQigQCBpscxkZGb7BonB5IHbOP/981aCQ2Bd9okpBQ6yGgoaEBQMcx48fL2+99ZbqfAuQK3H55ZfLqFGjgp6hExJIYA4NwpIQwcGEMJbHNgdnR7s86LqM3CyEtuD2wOkJdHng8mqXByMnkMujXR6EtzjpOfWChk4b8YSg4eiD1IOQwKOPPirTp09XBw0tYjp06CB33XVX0GRSQqzsQ4Pl0CgQF1TGmfHnn38qhwcXjJzQLg8EDxKbV61ale8xGCwKlweVWdrlQb6QHiyqp7QT62EqAfGkoOGGn3wR8/jjj8u0adPUWbC2hS+99FK54447gvY9ISRSElG2jW0U+VqhcrbQPgBl6nB5EMrSg0XhPsIBWrp0qenzIk8IIyf0YFG4PBgsigtDJrHBVALiKUGjz9644SceVKo88cQT8uabb6oqFQC7vl27djJy5MigLfoJcVKnYJSQ49KjR4+glTfovKwHi6L5IFweJL7D8UF+j153o8uDwaJweZC3A5dHDxaFy4OEZjYQzA9PVImnBI2GgiYxIBHzySeflKlTp6qdtXbFkAtz2223BZ0mTUi82LUPDbZ/iJBQ7QVQyacrtjBYVLs8mD+GcvXly5fnewxODuDyoBmhdnnQjwcOD/J5IIi8BgUN8aSg4YZvHTk5OfLUU0/J66+/rs4+cbaJnXjbtm3l9ttvlyuvvNLCVyPEfbOc0B0ZYj+Y4IfLo7ssw+VB/g7CWXB5IH4ggubNm+f3GHwOEDUYLAqXRw8WhcuDvjzo0+PEzyrcft3KkCMhjhA0eu4LiV3EPPvsszJlyhS1c8XBBO3rW7VqJbfeequy3922syT2xs2jD3CCANcFl2BA3MDlQXgLAkcPFoXLA7cH1Vxmz4tO28jl0YNF4fJgbAhEDyrFnAT368RzggY7PIacogcVH+jA+uqrr6rkR4gYlEe2aNFCbrnlFunVqxdFDEkZTnZorABl5MhLC5abBvcC+Tp6sCjcVD1YFH/xm54/f77pYFG4PBg5oV0ejJyA4EFjQjt93nRoiOcEDWDIKTJgdU+YMEEmT56szvq0iEGTsZtuukn69etnqx0a8S6cth0a/G4Rbgo1eR5NByF44PJgsKh2eXA78ntwn5nLoweLapcHg0Xh8mDOFmZ7JXMbcKNDR1KPbQUNNngKmtAi5vnnn5eXX37ZV4GBqguckWFOzoABAyhiiO2wa1Kwk4AoQQuFYG0U8BlD6GiXB+Fm7fIgeRmuz8KFC01dHggbPVgUzo4eLArxY9V3RoeGeFLQMNaaf0fwv//9T1566SWVdIgdF0QMYvbDhw+XQYMG8UBBHBFy4hl64oDwQL8cXEL1nILgwdgJiB89WBQuj05sDgT5d9rlQYI0EpYhdNB5GdVhKF+PBAoa4klBQ4fm1I9/0qRJagYOOp5qEQOr+P/+7/9k8ODBbCFOHOfQsO19aoETg27fwTp+43uCs4MEZggcjJxAx3C4PAhvod3DokWL8u2z4fJg5IR2eTByQrs8SGTG986QE0kUFDQ2FTGvvPKKcmMQJ8fOBWddiKtjsN91113HAwJxJF5PCnYK+H4wCgKXUE054eTA5UHYW7s8GDmB67jd7HmxP4PbQ4inBI2XQk74kaMy6YUXXlA7Arx3/PhxdnPDDTfIsGHDKGKI43Fz2bbXQMNANOMMNpwW3zUSluHyYLDt559/rpp6gvvuuy/Ja0u8AAVNCsEPHt16J06cqAbraRGD2Pf111+vhAynAhM3QYfGWy0ksG+D2ww3B6Hyyy67TPXGQu4NIZ4RNDiwu9GhgYjB8EeUWcOu1QlyiC8PHTpU/v3vf1PEENdCQeN+kHODxp2opML+Djk1GG47btw4DvQk3hQ0bkoKxo/67bffVg3vYL9qEYP49JAhQ1SvGE7uJV6AZdvuBSFziBaEmcCZZ56prl999dWpXjXiEWwtaJzs0GDH/c477yh7Fe3M0fUY7wkdPFGZhDMYihjiNejQuAuEku666y5544035MiRIyrPr2vXrurkDWXdhCQT2woanQ3vJLC+c+bMkWeeeUZN3tUiBqWL6BGDSdZenK5LiIZJwe4AJ2nYny1ZskSJ1IyMDDXkdvTo0QyZk5Rha0HjFIfmvffeU5Osly1bpjr4AoiYa6+9Vv3InTY8jpBEQYfG2WL0ueeek8cee0x27typbkMrifHjx0unTp1SvXqEUNDEyocffqhEDM5QcnNz1W2Yj6JFDEoaCSH+UNA4D3QPHjlypMyYMUNVLqHyEnkxTz/9tFSrVi3Vq0eIDzo0UfDpp5/K448/Ll9//bX6YQPEifv37y933nknRQwhYWBSsHNYvHixEjIYkQAhiu6/999/v8qZYWNEYkdsLWjskEMzb948JWK+/PJLn4g544wz5JprrlE/7GROqSXE6dChsTfY5yKkhGKG3bt3qxxAjC148sknpW3btqlePUKcKWjQhClVgmbBggXqR40zlKNHj6rbMJekb9++MmrUqIiHsBFC/NF5cTzDtxeYz4TKS+QDIg8QFZgDBw5UQgbDKAlxArYVNNjhoUooWUC8ILkNA9dycnLUbdWrV5c+ffooEcMfNSHWOTQ4YSGp5+OPP5a7775bfvrpJ3W9atWqan83fPhwik7iODxd5YRcmEceeUS++OIL1UNB/6AxN+mee+6RSpUqJfT1CfEaDDmlHjgwDz74oDz//POSnZ2twkqtWrVSSb7NmzdP9eoR4j5Bk6iQE/rDPPzwwzJ//nyfiDnttNNUx957771XKleubPlrEkJOwaTg1IEOvjfffLN88sknqlt5yZIl1bw4ONPMBSRuwNaCRp/NWdEECiLGOO0VwgXN7jD1tUqVKpa8DiEkNHRoks+sWbNUw7v169f7KjOx37vuuutSsDaEeFTQxOPQYPDjQw89pKqUDh06pG5D2eGAAQNUOAlJvoSQ5EJBkxxQkTlmzBiZNGmSHDhwQIXw27Vrp6qXGjVqlKS1ICS5uKpsG4ltGIb22WefqRkjAHkwqE7CGQnKrQkhqYMhp8Sybt06ueWWW1SlJnIQ0eAT1Us4uePYFeJ2HO/QYFQ9fqyIC+/fv1/dVrFiRWWnQsRwQBoh9oEOTWJ4/fXXZezYsfLzzz+r6xiC+8ADD0i/fv0S9IqE2A9H5tDgLARZ+ig53Ldvn7oNw9EwxRqx4tq1ayd5bQkhkUBBYx3IB0SJNcQM/sc+EzOVEFaqW7cuN0jiOWwraDCG3ihoNmzYoJyYjz76SP744w91GxrcYXYSnJj69euncG0JIZFAQRM/yA/EpOuvvvpKudjlypVTeYFwZDBniRCvYvscGrguGASJfgkAP16MHYATc9ZZZ6V6NQkhUcAcmtg/txdffFH1zdqxY4e6Dcm9OMnr1q0bt0FC7Cxo0KUXQ9Fee+01JW7QK6ZDhw7SsWNHadOmDae8EuJA6NBEB9zoO+64Q9566y01hqVw4cLSo0cPeeaZZ1ipSUgAaXlWNXtJAIgDo+QQ8WE9U0mD7pbI2kfYCeIHy55zzjnSsmVLNUyN1ish9mPo0KEyefJk9XvGvCASvIs5Jl2vWLFCiUAUOqApHsYUIBxPCMmPrX8ZmzZt8rNccX3JkiWycuVKlRi8fft22bt3r+r+i9uN4EymTJkyqoFejRo1lD0LodO6dWt2AyYkRTDkFPqzwfgBDIT87bff1G3nnXeeGpTbvn37pH1HhDgVWzs00YC+M0uXLlVdgdGPZsuWLWqCLKqg0GQq0N1B229URqHBHkocGzdurNydc889l2dAhCQI5MQhjIzBs3QaTrF79265/fbbZfbs2ZKbmytFixaVnj17KmHDUSyEeFDQhDvzgaMDG/eHH35QFVPa3UE4K7DfDcJVcHeQt1OrVi1p0KCBnH/++WqAG6duExI7GDeCMmM0fUNunJfBPLk777xTOc4AI1gQZkIjPK9/NoTEgicETSSJdwhZffvtt7Jq1Srl7sDyRaM+nDEZwY4G7g5i2sjdOfPMM33uDnJ4uCMiJDgDBw6UqVOnWjanzWlgKCTmyk2cOFGdUMEtvuCCC+Spp55S+xBCSOxQ0IQB7g1CWAhn4Uxq48aNqmwSOyNM6w50d2AXY3Itzrbg7iB3B+7OhRdeyIm2xPNgltqbb77pOUHzyy+/qJEE6KOFcFvx4sVV+wnkx6CwgRASPxQ0cZKVlaVCWXB31q5dq1qPIyaO6qxjx475LQv3plSpUmq+FHJ30AwQSX84M0NPHbo7xO30799fpk2b5hlB8/7778u9994ra9asUdcxTw5N8K6//nr+3gmxGAqaBNvLcHWWLVum/qJKC+7O77//Ljk5Ofl26ihjReNAuDt16tRR7k7z5s1V7g6EECFOB67E9OnTXS1ocCJz//33q0Z4KErAiQp6Z6F3DIoOCCGJgYImhaAKC+3L0UAQ7s7WrVuVu4OKLdjSRjCnJT09Xbk7OMtD7g7K0BHKwuwqujvECWBYIprEuVHQ4IQFvWLmzZunkp5xEoKqLnT35QkJIYmHgsbGZ3koQUePnR9//FHtLHfu3KkSmM3cHcTkEYvX7g4SlJG706JFC9WAkBA70LdvX3n77bddJWjgOMGR0X2z8PvDdeQLEUKSBwWNQ0HZua7MQkn6tm3bZM+ePcrdQajLCPp9wN3JzMxUTQaRr4PcHdjgcHsISRa9e/eWmTNnOl7Q4KQCuTCvvvqqHDp0SDmol156qWqMxxlzhKQGChoXgkaCyNtB23RUaOHM8ddff1XujtkICbg7aDJYtWpVNUICZegoJW3WrBnb0xNL6dWrl7zzzjuOFTT4PaFPzKJFi1SFIyoakeA7duxY/lYISTEUNB4DO2Hk6qAyC7k769evV24P3B2caSL2H+juoMmgdncaNmyocneQqFytWrWUvQ/iTK6++mqZNWuWowQNfjOvvPKKPPjgg+q3AuDCYNI1BkUSQuwBBQ3xA52T0XMH7s7q1auVuxNqhATyc+Du6AGhuslgkyZNOCCU5AMt/dHi3wmCBo010ckXZeYIMUHcd+nSRYWVatasmerVI4QEQEFDojpTxdgI5O58//336n80DEMvHgihQHcHA0JhyWMeDQ4ARneHM2q8yVVXXSVz5syxtaBBIj5mK0HYYz0h2EeMGCGjR4/m/ClCbAwFDbH0jNbo7hgHhJqNkIC7g9lYxgGhqMrigFD3ghDNu+++aztBA7E+YcIEefzxx1W+GcD2+Oijj0rHjh1TvXqEkAigoCFJO2BA5EDwYEAocnfg7qDJYLABoXB39IBQuDtoMti6dWu2incwV155peqeaxdBA3cRbgzyehBSxXYH0YXZSmiBQAhxDhQ0xBZA2CCUhd47aBNvHBAabISEdnfQZBCuDnJ30F2ZTQbtyxVXXCEffvhhygXNF198IXfccYcKnWJdEAK97bbb1G3cfghxJhQ0xPagrw7KZSF40GQQA0K1u4NkTbMBoXqEhB4Qqt2d0qVLp+x9EJGuXbuqAY2pEDTYjhBSwggCODNIakdrArgx2DYIIc6GgoY4HoyL0GXocHf0CAm4O2YjJMwGhGKEBP7n2XliQZXQxx9/nE+EJhJ02EbvmA8++EC5fZiZ1qdPH3niiSeUy0cIcQcUNMTV4ACGwaDI3dEjJDAgNDs7O+yAUJShw93BCAmEsziPJ346d+4sn3zySVIEzdy5c+Xuu++WVatWqetoLXDXXXfJv//9bwpXQlwIBQ3xNAhdQexod0ePkDhw4EC+ERJ6QCiaDOoBoXB3EK5AWTrdnfB06tRJPv3004QJGgjYcePGyfPPP686YyOshDYB6B2D8BIhxL1Q0BASBFS9YFYWBA/O8vUICbg7GCERbEAoRkhgQCHKfvWAUDg/ROTyyy+Xzz77zHJB8/PPP8stt9yixBKEaMmSJeXaa69VZdfMmyLEG1DQEBIjeoQEKmWMA0IxQsJsQCgOrMjdgZtjHBCKXB6vgJ4u8+bNs0zQYC4UGt6hySNAEjiu/+tf/7Lk+QkhzoGChpAEgPwc44DQzZs3qyaD4QaEYj4WcnfOOecc5eygOgu9UdxChw4d5PPPP49L0OCzHTNmjEyePFmFBhHqa9eunapeQr8iQog3oaAhJMngYI4QyVdffaUSltFkEO7O3r17TQeEYoQE3B30SjEOCEXujtOav1122WUyf/78mAQNcpxQrbRgwQL1eAxNHTJkiBoaia7ThBBvQ0FDiM04ePCgyttBk0G4O8YREsEGhKL82DggFGXoGBCKUJeduPTSS2XhwoX5RFsoXnvtNRk7dqz6HADK63G9d+/eCVxTQojToKAhxEHAmUC+DgSP2YBQsxEScDLg7ugREqj2QeUP8nmSTfv27VWX3nCCBqJu1KhRMnXqVDly5IgSZsi/efbZZ6V27dpJW19CiHOgoCHERaCZIEJZqM7C7CyEtvQICbMBoagG0u4OnA+MkNADQhNRho5cl0WLFgUVNFhvzFbCe0AVGarGbrzxRrn//vtdlUtECLEeChpCPALcG4Sw4O4gdwcjJNBkELk7cEHMRkjA3dEjJODuoAwd4SwIjVi45JJLZPHixX6CBq/7v//9T8aPH6/WB5x99tnqOhrxEUJIJFDQEEIUCFuhDB0uydq1a5W7gxESqCQKNiC0YsWKfgNCIXYaNGgQ1N256KKL1GugrB0VX3BjZsyYoSq/kPzcrVs3Va2Eai9CCIkGChpCSFggQODqoBQdIySQu6NHSMDdMRshUbZsWeXuIOdFDwhFF9/ly5erKi0IJzwOuTyoXrrzzjttl8RMCHEOFDSEkLhBFRamoaMyC+4OytCRu4Pk3sABoWrHk5amGgs+9thjKq+GEELihYKGEJJQEK6C0IEzgxwehKauueaalFRZEULcCwUNIYQQQhyP9XWZhBBCCCFJhoKGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIY6HgoYQQgghjoeChhBCCCGOh4KGEEIIIeJ0/j9n4/hW9DUUWQAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -418,7 +429,7 @@ "full_pipeline_data, pipeline = hyp.analyze(\n", " weights, manip={'model': 'Smooth', 'kwargs': {'kernel': 'boxcar', 'kernel_width': 11}},\n", " normalize='within', reduce='PCA', ndims=3, align='HyperAlign',\n", - " cluster={'model': 'KMeans', 'n_clusters': 4}, return_model=True)\n", + " cluster={'model': 'KMeans', 'kwargs': {'n_clusters': 4, 'random_state': 0}}, return_model=True)\n", "\n", "print('pipeline steps:', [name for name, _ in pipeline.steps])\n", "labels = pipeline.named_steps['cluster'].transform(full_pipeline_data)\n", @@ -436,7 +447,7 @@ "source": [ "## Replaying a fitted pipeline on new data\n", "\n", - "The `Pipeline` returned above remembers every fitted parameter (the smoothing kernel, the z-scoring means and standard deviations, the PCA loadings, the per-dataset alignment rotations, the cluster centroids). Passing it back as `pipeline=` applies those parameters to new data with no refitting. Here we hand it the *full* 36-subject dataset averaged into two new groups -- data it has never seen -- and draw the result in the space the pipeline learned." + "The `Pipeline` returned above remembers every fitted parameter (the smoothing kernel, the z-scoring means and standard deviations, the PCA loadings, the per-dataset alignment rotations, the cluster centroids). Passing it back as `pipeline=` applies those parameters to new data with no refitting. Here we hand it two new group averages from the 36-subject `weights` dataset (subjects 10-18 and 28-36, first 100 timepoints) -- data it has never seen -- and draw the result in the space the pipeline learned." ] }, { @@ -445,10 +456,10 @@ "id": "c104127a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:55.036964Z", - "iopub.status.busy": "2026-09-05T10:23:55.036856Z", - "iopub.status.idle": "2026-09-05T10:23:55.104822Z", - "shell.execute_reply": "2026-09-05T10:23:55.104214Z" + "iopub.execute_input": "2026-10-08T17:10:24.822807Z", + "iopub.status.busy": "2026-10-08T17:10:24.822702Z", + "iopub.status.idle": "2026-10-08T17:10:24.888696Z", + "shell.execute_reply": "2026-10-08T17:10:24.888074Z" } }, "outputs": [ @@ -509,7 +520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/animate_forecast.ipynb b/docs/tutorials/animate_forecast.ipynb index 83f81627..b22ac48b 100644 --- a/docs/tutorials/animate_forecast.ipynb +++ b/docs/tutorials/animate_forecast.ipynb @@ -3,11 +3,29 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -17,8 +35,8 @@ "# Forecasting three regions' weather while it is drawn\n", "\n", "An animated forecast: `hyp.plot(..., animate=True, predict='Kalman')`\n", - "refits the forecaster on the history revealed so far and re-anchors it on\n", - "the last revealed observation, so the prediction grows and bends with the\n", + "refits the forecaster on the history revealed so far and draws it from the\n", + "endpoint of the current frame, so the prediction grows and bends with the\n", "animation instead of standing still. `forecast_trail=True` keeps the\n", "earlier forecasts on screen as a fading fan, so you can watch the\n", "prediction *change* as history accumulates.\n", @@ -40,7 +58,10 @@ "colour, the Americas have their own), `forecast_palette=` gives that\n", "grouping its own colours, and `forecast_fmt=` draws every forecast dashed.\n", "Everything they do not name is inherited from the trace a forecast\n", - "continues, drawn at half its alpha.\n", + "continues. A forecast that inherits its trace's colour is drawn at half\n", + "the trace's alpha; these carry their own colours from\n", + "`forecast_palette=`, so each current forecast is drawn at full opacity,\n", + "and with `forecast_trail=True` only the earlier fits fade.\n", "\n", "`slow_warning_seconds=` is the one keyword here that changes no pixel.\n", "An animated forecast needs one fit per distinct revealed history length,\n", @@ -53,9 +74,10 @@ "**Data & graceful degradation.** The archive is fetched once and cached.\n", "If the network is unavailable the example says which error it hit and\n", "synthesizes three seasonal regions (a hemispheric mix in each, a slow\n", - "drift) so it always renders; `HYPERTOOLS_OFFLINE` makes the fetch refuse\n", - "rather than degrade, which is how the test-suite proves the import path\n", - "fetches nothing.\n" + "drift) so it always renders. `HYPERTOOLS_OFFLINE` -- an environment\n", + "variable this example reads, not a hypertools setting -- makes the fetch\n", + "itself refuse rather than try, which is how the test-suite proves the\n", + "import path fetches nothing; the loader then falls back as above.\n" ] }, { @@ -72,24 +94,21 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:44.873278Z", - "iopub.status.busy": "2026-09-05T10:29:44.873137Z", - "iopub.status.idle": "2026-09-05T10:29:48.405999Z", - "shell.execute_reply": "2026-09-05T10:29:48.405472Z" + "iopub.execute_input": "2026-10-08T17:10:26.083407Z", + "iopub.status.busy": "2026-10-08T17:10:26.083221Z", + "iopub.status.idle": "2026-10-08T17:10:29.451567Z", + "shell.execute_reply": "2026-10-08T17:10:29.451058Z" } }, "outputs": [], "source": [ "import os\n", - "import tempfile\n", - "import urllib.request\n", "from typing import NamedTuple\n", "\n", "import numpy as np\n", "\n", "import hypertools as hyp\n", "\n", - "CACHE = os.path.join(tempfile.gettempdir(), 'hypertools_gallery_cache')\n", "ARCHIVE = ('https://raw.githubusercontent.com/ContextLab/'\n", " 'hypertools-paper-notebooks/master/data/temperatures.csv')\n", "# three regions, six cities each, every region spanning both hemispheres:\n", @@ -120,10 +139,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:48.407442Z", - "iopub.status.busy": "2026-09-05T10:29:48.407266Z", - "iopub.status.idle": "2026-09-05T10:29:48.412009Z", - "shell.execute_reply": "2026-09-05T10:29:48.411452Z" + "iopub.execute_input": "2026-10-08T17:10:29.453123Z", + "iopub.status.busy": "2026-10-08T17:10:29.452985Z", + "iopub.status.idle": "2026-10-08T17:10:29.456737Z", + "shell.execute_reply": "2026-10-08T17:10:29.456282Z" } }, "outputs": [], @@ -140,20 +159,11 @@ " or ``None`` (announced with the error) when it cannot be fetched.\"\"\"\n", " if os.environ.get('HYPERTOOLS_OFFLINE'):\n", " raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch')\n", - " os.makedirs(CACHE, exist_ok=True)\n", - " dest = os.path.join(CACHE, 'temperatures.csv')\n", " try:\n", - " if not os.path.exists(dest):\n", - " req = urllib.request.Request(\n", - " ARCHIVE, headers={'User-Agent': 'hypertools-gallery/1.1'})\n", - " with urllib.request.urlopen(req, timeout=60) as response:\n", - " payload = response.read()\n", - " with open(dest + '.part', 'wb') as handle:\n", - " handle.write(payload)\n", - " os.replace(dest + '.part', dest) # never a truncated cache\n", " # the archive carries '<City>' (absolute) and '<City>_anomaly'\n", " # columns; its complete rows end in August 2013\n", - " recent = hyp.load(dest).dropna().tail(N_MONTHS)\n", + " # GH #285: the native URL loader owns download and atomic caching.\n", + " recent = hyp.load(ARCHIVE, cache=True).dropna().tail(N_MONTHS)\n", " return [recent[cities].to_numpy(float)\n", " for cities in REGIONS.values()]\n", " except Exception as error:\n", @@ -202,7 +212,7 @@ "source": [ "## 3. One call: an animated forecast with a fading fan\n", "\n", - "`predict='Kalman'` with `animate=True` refits on every distinct revealed history and re-anchors the forecast on the last revealed month; `forecast_trail=True` keeps the earlier fits as a fading fan. `forecast_hue=`, `forecast_palette=` and `forecast_fmt=` restyle only the forecasts, and `slow_warning_seconds=None` silences the long-schedule notice for a wait that is known.\n" + "`predict='Kalman'` with `animate=True` refits on every distinct revealed history and draws the forecast from the current frame's endpoint; `forecast_trail=True` keeps the earlier fits as a fading fan. `forecast_hue=`, `forecast_palette=` and `forecast_fmt=` restyle only the forecasts, and `slow_warning_seconds=None` silences the long-schedule notice for a wait that is known.\n" ] }, { @@ -210,10 +220,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:48.412975Z", - "iopub.status.busy": "2026-09-05T10:29:48.412909Z", - "iopub.status.idle": "2026-09-05T10:29:48.415126Z", - "shell.execute_reply": "2026-09-05T10:29:48.414748Z" + "iopub.execute_input": "2026-10-08T17:10:29.457603Z", + "iopub.status.busy": "2026-10-08T17:10:29.457547Z", + "iopub.status.idle": "2026-10-08T17:10:29.459533Z", + "shell.execute_reply": "2026-10-08T17:10:29.459216Z" } }, "outputs": [], @@ -230,13 +240,15 @@ " # all three are dashed, and each still continues the path it belongs\n", " # to. `slow_warning_seconds=None` silences the long-schedule notice:\n", " # the 180 fits this clip needs measured about 6 s, a known wait.\n", + " # backend= is pinned: the frames are drawn and saved through the\n", + " # matplotlib animation, and on Colab the default would be plotly.\n", " return hyp.plot(\n", " data.regions, '-', names=data.names,\n", " animate=True, duration=DURATION, frame_rate=FRAME_RATE,\n", " predict='Kalman', t=HORIZON, forecast_trail=True,\n", " forecast_hue=['New World', 'Old World', 'Old World'],\n", " forecast_palette=['#d62728', '#1f77b4'], forecast_fmt='--',\n", - " slow_warning_seconds=None,\n", + " slow_warning_seconds=None, backend='matplotlib',\n", " title='Three regions, one year ahead', size=(8, 6), show=False)\n" ] }, @@ -252,10 +264,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:48.415994Z", - "iopub.status.busy": "2026-09-05T10:29:48.415935Z", - "iopub.status.idle": "2026-09-05T10:29:54.370223Z", - "shell.execute_reply": "2026-09-05T10:29:54.369743Z" + "iopub.execute_input": "2026-10-08T17:10:29.460312Z", + "iopub.status.busy": "2026-10-08T17:10:29.460260Z", + "iopub.status.idle": "2026-10-08T17:10:35.231856Z", + "shell.execute_reply": "2026-10-08T17:10:35.219495Z" } }, "outputs": [ @@ -289,10 +301,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:54.371571Z", - "iopub.status.busy": "2026-09-05T10:29:54.371393Z", - "iopub.status.idle": "2026-09-05T10:30:00.565746Z", - "shell.execute_reply": "2026-09-05T10:30:00.565344Z" + "iopub.execute_input": "2026-10-08T17:10:35.234467Z", + "iopub.status.busy": "2026-10-08T17:10:35.234274Z", + "iopub.status.idle": "2026-10-08T17:10:41.325148Z", + "shell.execute_reply": "2026-10-08T17:10:41.324702Z" } }, "outputs": [ @@ -306,7 +318,16 @@ ], "source": [ "anim.save('animate_forecast.mp4', dpi=100)\n", - "print('saved animate_forecast.mp4')\n" + "print('saved animate_forecast.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('animate_forecast.mp4', embed=True))\n" ] }, { @@ -335,7 +356,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/animate_forecast.mp4 b/docs/tutorials/animate_forecast.mp4 index b3bfc3dc..18f05134 100644 Binary files a/docs/tutorials/animate_forecast.mp4 and b/docs/tutorials/animate_forecast.mp4 differ diff --git a/docs/tutorials/cluster.ipynb b/docs/tutorials/cluster.ipynb index baf49b1d..cb2b6139 100644 --- a/docs/tutorials/cluster.ipynb +++ b/docs/tutorials/cluster.ipynb @@ -3,22 +3,35 @@ { "cell_type": "code", "execution_count": null, + "id": "6b848940d91f", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:30:39.752660Z", - "iopub.status.busy": "2026-07-17T07:30:39.752507Z", - "iopub.status.idle": "2026-07-17T07:30:39.755801Z", - "shell.execute_reply": "2026-07-17T07:30:39.755385Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "49879b738baf", "metadata": {}, "source": [ "# Clustering with Hypertools" @@ -26,6 +39,7 @@ }, { "cell_type": "markdown", + "id": "49fefb9bf6c5", "metadata": {}, "source": [ "The cluster feature performs clustering analysis on the data (an array, dataframe, or list) and returns a list of cluster labels. \n", @@ -37,6 +51,7 @@ }, { "cell_type": "markdown", + "id": "f7a0e4c93912", "metadata": {}, "source": [ "## Import Packages" @@ -45,13 +60,14 @@ { "cell_type": "code", "execution_count": 1, + "id": "ace106146b25", "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:17.710637Z", - "iopub.status.busy": "2026-09-05T10:24:17.710444Z", - "iopub.status.idle": "2026-09-05T10:24:21.195316Z", - "shell.execute_reply": "2026-09-05T10:24:21.194852Z" + "iopub.execute_input": "2026-10-08T17:10:42.511420Z", + "iopub.status.busy": "2026-10-08T17:10:42.511206Z", + "iopub.status.idle": "2026-10-08T17:10:45.931025Z", + "shell.execute_reply": "2026-10-08T17:10:45.930535Z" } }, "outputs": [], @@ -64,6 +80,7 @@ }, { "cell_type": "markdown", + "id": "e045ac38d92e", "metadata": {}, "source": [ "## Load your data" @@ -71,6 +88,7 @@ }, { "cell_type": "markdown", + "id": "d79d923ce59e", "metadata": {}, "source": [ "We will load one of the sample datasets. This dataset consists of 8,124 samples of mushrooms with various text features." @@ -79,13 +97,14 @@ { "cell_type": "code", "execution_count": 2, + "id": "a8fb2c34a99c", "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.197162Z", - "iopub.status.busy": "2026-09-05T10:24:21.196981Z", - "iopub.status.idle": "2026-09-05T10:24:21.300367Z", - "shell.execute_reply": "2026-09-05T10:24:21.299967Z" + "iopub.execute_input": "2026-10-08T17:10:45.932657Z", + "iopub.status.busy": "2026-10-08T17:10:45.932500Z", + "iopub.status.idle": "2026-10-08T17:10:46.032812Z", + "shell.execute_reply": "2026-10-08T17:10:46.032153Z" } }, "outputs": [], @@ -95,6 +114,7 @@ }, { "cell_type": "markdown", + "id": "965109ff74c7", "metadata": {}, "source": [ "We can peek at the first few rows of the dataframe using the pandas function `head()`" @@ -103,12 +123,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "a48d73c2aadd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.301607Z", - "iopub.status.busy": "2026-09-05T10:24:21.301533Z", - "iopub.status.idle": "2026-09-05T10:24:21.310465Z", - "shell.execute_reply": "2026-09-05T10:24:21.310045Z" + "iopub.execute_input": "2026-10-08T17:10:46.034088Z", + "iopub.status.busy": "2026-10-08T17:10:46.034013Z", + "iopub.status.idle": "2026-10-08T17:10:46.042774Z", + "shell.execute_reply": "2026-10-08T17:10:46.042401Z" } }, "outputs": [ @@ -325,6 +346,7 @@ }, { "cell_type": "markdown", + "id": "2688eb9f251b", "metadata": {}, "source": [ "## Obtain cluster labels" @@ -332,9 +354,10 @@ }, { "cell_type": "markdown", + "id": "3aadc6bfe3ae", "metadata": {}, "source": [ - "To obtain cluster labels, simply pass the data to `hyp.cluster`. Since we have not specified a desired number of clusters, the default of 3 clusters is used (labels 0, 1, and 2). Additionally, since we have not specified a desired clustering algorithm, K-Means is used by default.\n", + "To obtain cluster labels, simply pass the data to `hyp.cluster`. Since we have not specified a desired number of clusters, the default of 3 clusters is used (labels 0, 1, and 2). Additionally, since we have not specified a desired clustering algorithm, K-Means is used by default. K-Means starts from random centroids, so `random_state=0` fixes them: the labels, and the counts below, are then the same on every run.\n", "\n", "Every column of this dataframe holds categorical strings, so `format_data` dummy-codes them first (one 0/1 column per category value, turning the 22 original columns into 117) and K-means sees that numeric matrix." ] @@ -342,12 +365,13 @@ { "cell_type": "code", "execution_count": 4, + "id": "d7bb9fec4841", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.327628Z", - "iopub.status.busy": "2026-09-05T10:24:21.327545Z", - "iopub.status.idle": "2026-09-05T10:24:21.385255Z", - "shell.execute_reply": "2026-09-05T10:24:21.384748Z" + "iopub.execute_input": "2026-10-08T17:10:46.059385Z", + "iopub.status.busy": "2026-10-08T17:10:46.059301Z", + "iopub.status.idle": "2026-10-08T17:10:46.118835Z", + "shell.execute_reply": "2026-10-08T17:10:46.118123Z" } }, "outputs": [ @@ -363,12 +387,13 @@ } ], "source": [ - "labels = hyp.cluster(mushrooms)\n", + "labels = hyp.cluster(mushrooms, random_state=0)\n", "set(labels)" ] }, { "cell_type": "markdown", + "id": "905669eb1f89", "metadata": {}, "source": [ "We can further examine the number of datapoints assigned each label." @@ -377,19 +402,20 @@ { "cell_type": "code", "execution_count": 5, + "id": "75720e62ef2f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.386996Z", - "iopub.status.busy": "2026-09-05T10:24:21.386887Z", - "iopub.status.idle": "2026-09-05T10:24:21.389789Z", - "shell.execute_reply": "2026-09-05T10:24:21.389030Z" + "iopub.execute_input": "2026-10-08T17:10:46.120323Z", + "iopub.status.busy": "2026-10-08T17:10:46.120219Z", + "iopub.status.idle": "2026-10-08T17:10:46.122892Z", + "shell.execute_reply": "2026-10-08T17:10:46.122415Z" } }, "outputs": [ { "data": { "text/plain": [ - "Counter({0: 5067, 1: 1761, 2: 1296})" + "Counter({2: 5067, 1: 1761, 0: 1296})" ] }, "execution_count": 5, @@ -403,6 +429,7 @@ }, { "cell_type": "markdown", + "id": "aa2b2c7b94d8", "metadata": {}, "source": [ "## Specify number of cluster labels" @@ -410,6 +437,7 @@ }, { "cell_type": "markdown", + "id": "037830697102", "metadata": {}, "source": [ "You can also specify the number of desired clusters by setting the `n_clusters` argument to an integer number of clusters, as below. We can see that when we pass the int 10 to n_clusters, 10 cluster labels are assigned. \n", @@ -420,12 +448,13 @@ { "cell_type": "code", "execution_count": 6, + "id": "9f2257f3d58a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.391058Z", - "iopub.status.busy": "2026-09-05T10:24:21.390971Z", - "iopub.status.idle": "2026-09-05T10:24:21.425267Z", - "shell.execute_reply": "2026-09-05T10:24:21.424732Z" + "iopub.execute_input": "2026-10-08T17:10:46.124254Z", + "iopub.status.busy": "2026-10-08T17:10:46.124170Z", + "iopub.status.idle": "2026-10-08T17:10:46.159467Z", + "shell.execute_reply": "2026-10-08T17:10:46.158616Z" } }, "outputs": [ @@ -441,12 +470,13 @@ } ], "source": [ - "labels_10 = hyp.cluster(mushrooms, n_clusters = 10)\n", + "labels_10 = hyp.cluster(mushrooms, n_clusters=10, random_state=0)\n", "set(labels_10)" ] }, { "cell_type": "markdown", + "id": "70457b4aae6f", "metadata": {}, "source": [ "## Different clustering models" @@ -454,6 +484,7 @@ }, { "cell_type": "markdown", + "id": "6a37f518dc46", "metadata": {}, "source": [ "You may prefer to use a clustering model other than K-Means. To do so, simply pass a string to the cluster argument specifying the desired clustering algorithm.\n", @@ -464,13 +495,14 @@ { "cell_type": "code", "execution_count": 7, + "id": "6b473d346ef5", "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:21.426650Z", - "iopub.status.busy": "2026-09-05T10:24:21.426563Z", - "iopub.status.idle": "2026-09-05T10:24:25.117591Z", - "shell.execute_reply": "2026-09-05T10:24:25.117058Z" + "iopub.execute_input": "2026-10-08T17:10:46.161556Z", + "iopub.status.busy": "2026-10-08T17:10:46.161378Z", + "iopub.status.idle": "2026-10-08T17:10:49.709719Z", + "shell.execute_reply": "2026-10-08T17:10:49.709319Z" } }, "outputs": [], @@ -481,18 +513,19 @@ { "cell_type": "code", "execution_count": 8, + "id": "79ba2766abd6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:25.118882Z", - "iopub.status.busy": "2026-09-05T10:24:25.118813Z", - "iopub.status.idle": "2026-09-05T10:24:25.549116Z", - "shell.execute_reply": "2026-09-05T10:24:25.548727Z" + "iopub.execute_input": "2026-10-08T17:10:49.710868Z", + "iopub.status.busy": "2026-10-08T17:10:49.710801Z", + "iopub.status.idle": "2026-10-08T17:10:50.223501Z", + "shell.execute_reply": "2026-10-08T17:10:50.223086Z" } }, "outputs": [ { "data": { - "image/png": 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", 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HKIRcCTtNCGk+IVvDNKNjFEJMj1EI8cZ1BEEQggURNILf4Elz1apV+OSTT5R42bZtmzrBauKFIoPihcKFkReKF/pfAkmgIzRNDd9D+m54O+usszx6Lv8Wmk+IQsiRT4hiiOKI1WPuGKajoqKUELL1CWlCiOkxzTBNAce0WbALTkEQQg8RNILXQuGnn37C/PnzlXhhzxemSizFC09cFC7Dhw/HlClTPPan+IKWcpIivkYoMng77bTTPHovKXAogjTDNL1AmhDi35xCiD4hrsfoECvPKIRc+YQohFz5hDTDNCNC/L8IIUEQHCGCRnCLn3/+GR999JESL1u2bFFX8NoJi+KBJyEKl2HDhqnIi6cnTX+jnfjCLULTHFBs9OrVS908gREeCiBGhZgeoxBiCszWME0hRIHEddz1CVEIxcXFmfsJ6fmENCHEyJA0VhSE8EcEjWDH+vXrVeSFxl1NvGjCgCcUnjiY7qCAmTx5MgYOHBh0V85NZQoWHEMR0aVLF3XzBP7NmA7T0mOaELL0CVEIMT1GYcR1XfmEtMaK3CcKIT2fkNZPSDNM80bBJAhCaCCCpoXz66+/qsjLsmXLlHjh1bOleGGKgqXSjLxMmjQJQ4YMCTrx4o+ybSG4/nZML/HGz5278HNLsUPDtKVPiELItrEiBRE/6542VtR8QpaNFS37CWmNFfmYIAhNiwiaFgQFC8UL+71s2rRJHeS1gznFC8P2FCyMvlC8nHHGGSEhXvSQlFPLQ/Nt8caSf0+gwGFEiDcKIUaFNCFk6RNiPyGmytjk0Z3GipaGac0nZNlY0dInRMM0o0Sh+p0ThOZGBE2YwgqjDz/8UEVeGIXh1aileOEV5qBBg3DmmWcq8cIr4XA6kIopWJ/aonLU5JcgOjcZkRkJTfxXCV6Yfurbt6+6eQLHZ1g2VtSEkJ5PiGkzrueqsSI/u5ph2pFPyLKxIlNjMoBVEETQhAWsKvnggw9U5IXihQdUy6tHiheadBlxueiii1TZdDiJFz0kQmNPyZLtKJy7gnk4njWRPf1MJI/u3gx/nfCB0Rc2ffS08SO/n5pPyLKfEIUQfUKWjRV5n0LJXZ+QDGAVWioSoQkx6A9g5GXJkiVKvLA6xFa89OvXT0VeLrzwQpx99tktsiW+mIKtqdp5BIUv/tC4wGhE4UsrEN+vjTlSI9GbpoPfSUZXePMEChoKHEufkGaYtvUJ8SeXywBWoaXQ8s50IQSv3Chevv32W2zcuFEduCzFC/PxDJHTtHvBBRfg3HPPbZHiRQ8xBcMsUoq/3IwTX/xq/ybVG1G5vRBJZ3SS6E2IoDWn5I1+N0/wZAAr02UygFUINeTsFyTwSouG3cWLFyvxopWiajCH3qdPH3UQY+Rl7NixIl6cEO4pNbdTTJZRGR0KnlmK6n3HcPzTXwCj4+iNP5AIUPMSTANYtSgqb4wkSXm84A9E0DQDPCBQvDDywp4vjLywCZkGy0J79uyJ008/XUVezjvvPGkM5iUttQ+NXYrJCcc/+cV+Yb1RmYf9JWjEvxO6+GMA63fffYcvv/xSDaJlFIhiR7sxRU4foCD4igiaAMOrGKaNGHmheOEVjKV44ZUJDYWMvIwfPx7nn3++TD/2Ay25yskkHtwTMw6JMKhKKL+JK+5PgCNAQvDwyy+/YO7cuVi0aJESK1qqnCbqAQMGqGPd9OnT1YUbK7oEwR+IoPEjNOsx8sIv8bp165R4qa6uNj/O7qTdu3dXPTL4hWb0hV9wwf+01ConO/HgDQYge9qZfhEbDtNeNhEgSUeFfpuIF198Ed988w22b99uvmhjxRUjO+PGjcO0adPsqsH4/ZT0sOAvRNB4CU10HA/w9ddfq8gLQ6u24qVr166q14smXhi6FZqGlljl5I5nxh3azroAsV2z/Ceu9DAAETGmw4+ko0IPemooYBYuXKgG07IfD2G0haMuWKBAAeOqrw8jqFLIIPgLETRuQPf/xx9/rMQLhzRSvGhfYEKhwnbnFC9MGdG0Kya35qWlVTl54plxigGITIsPvLgyAgce/AKpF/Wx9vBIOipofX8vvfQSPv/8c9VlnEZhwr437HDM9hA33XSTx5VX/H5yG4LgD0TQ2EC3/ieffIKvvvoKP/30kxIvlZWV5sfZuZOdOTmQkWbdiRMnqgokIbhoSSknf0VmFEb4bAZ2Gpmxei1jkxiSBe8i0C+//LI6FtIPwwZ/2veKvXNGjhyJ66+/HiNGjPApZSSCRvAnLVrQ8EtqKV4YRrUVL2wpTvHCHDC77LL0UQh+Woop2G+RGT+Zgf0lrrR0lL8Qj47rC7nXX39dpdHp/2MptfY94ogFRp2vvfZadRz0p+dFBI3gT1qMoGGI9NNPP1XiZc2aNUq8aGFTQnMuxQtHBDD/O3nyZJmYG8K0hAiNXyMzJMLgkxnYn+Kqvtr54EdPEI+OPUyZv/XWW3j//ffVxRwjMpqA4YDMMWPG4KqrrlIXcYH0uIigEfxJWAoaChXmemlYW7t2rWoIxaZPGnTeU7ywwRSvOKZMmSLiJcwIdw+NXyMzBiDnjlGI657ttZjxS6l4oErGXYx8aAmw6oji5d1338Xq1atVd2CNrKwsVbRwxRVX4NJLL23SnldiChb8SWQ4XGl88cUX6kbxsnv3bjvxwpApex9okRe2DRfCm3CucvKreCBGoFVybFBEZohfS8bntsyScfZ9YUT67bffxsqVK9VMJ03cp6enq07jl112Ga6++upmbR0hgsa/rFq1Ctddd50ybrfE6rHIULvKYLdJRl/4h2MHSs2sRnhlQfHCyMs555yjIi8MnwpCuMCTr2lith836kNExN9pr8wbh/plArjTfjxhWDJO4c50+ptvvonvv/9e9cDSBAxnvo0aNUodD3myC6YiBkk5BTdTp07FzJkz0aFDB79vm1HCu+66S0XT+bkNa0FD8cIvKNNGP/74o4q8sGW2pXhp3bq1yvXydvHFFyMvL69Z91kIHsI15cRIgjr5+kBsz1xUbS1QkQpffDN+NyRz3zpnNm3JeBPMsAqUgFm+fDleffVVLFu2TFVjatFIjk4588wzVTT6hhtuCPp0ekuMJIQC5eXlyiAeCOjb+sMf/qDandTV1fltu0H1SeIXMicnRznsLQcz8gPPyAtLBRl5oXhp27Zts+6rENyEoylYpUYOHvd5O7m3j1A/fUmz+N2Q7CczsNsiKwRLxhmVZik15yIxOq2dCNgHiz2wWInEZnahFJUOpwhN7bEi1BTkIzonF5HpGQG3Wjz99NPKasELfQqDN3WiHPfdd59KPz711FPmZc8++yxWrFiBd955R93/4IMPVI8h9hqin4o+KorhSy65RDWLZXqSvPHGG6rPEBspPvzww2qIckZGhhphceWVV6p1tGazLOl/4IEHVAaFn1tbKML/9re/qeo69ngLS0HDN5HjA2zhH4RVSZxIvWTJEvzjH/9QVyFpaWnIzMxUIoiCh0Zf/mE5XoD/F+Xfcgm3CI1VasRHeMKO79U6aDwzfi0ZD6IZVv6ah8Q5cJyHpF3k0fPCDrwTJkxQAiYQ6YCmJBxmOZUsW4LC1+Y2pi6vn47kkaMD9noPPfSQEhTPPPOM8oQyg5GYmOjxdhjZmzVrFp5//nnV4ZkFNMyOUNiwEzTPyRQ8DCDwnEsBcuONNyrv1WOPPaaGjd59993q8eHDh6ttbt68GbNnz1aCi+dmPf74xz+aBZA/CSpBQ9HCLyvfZL5R/BLzDWY+mBOpmXNjeSEjOBQ+FDj8kjs6afGkxi8L+8nwj81cMhUlPwBMV1H0sMulJoKku2/4EE6mYL/MZ/JTjxe/G5I1fEh9MXJVub0wqGZYeQOPeTyJ8AqXs5G0eUhMr59yyikO5yGFOqF+4cnIjFnMaKnL119CfJ9+AYnUUFQsWLBAiV32SCM8l3kDIzBaZoSRPcvonua14nlTS1tyViHXuf3229V9ChmO9vnss8/MgoYDmV977TUlkJqayGD7YPPNZdSFtzPOOMPt8BvFD28Mxf72229KANHZT+FDAcSwXFFREbZs2eL0JMd9oKhiGJd/SC0KRAFEjw6vhviH4pwm3pfBasFJOERoeKI+/uUmHP98U1CkdUyGZP8JK5I6qS/i++T5lvryU+TKXzOs3IXHKZ6UmDbgccnbeUihTqgLGqaZ7D5/9fVqeSAEDS/8mbXo1auXz9vq2rWrMuZefvnlyjjO8RWcgO4IDh7ljSlOS1HEKmINnjObQ8yQoPokMZfqzQmIAoR/XHf/wBQ0+fn56g9DEcSIEKM9XMYoUHFxsVLBFEWMEFn6efROnLyCogBihIedhCmAGAWi4GGbcI5K4AeHf2SZrt00hLqHxhwJ8bce8zKlQjFz4tttft8fihmmv5o9cuWnGVbO4PGFXgVeXf/6669+m4cUqmjfzVBPOdEzwzSTlajheYHLA4B2jtSi0J5SYdFQllA0T5o0CfPmzVNVcPS/zJgxw+FrDx48WKWpLGF7FI2m7GMUloLGm5MdxQZvVKXuQHPTzp071Y1RIAogmqgogBj54QBLiiBGhXiwcnQi5YeQX2B+ALQ0GBUtBRBDeQzhUQDxAMfQMsWR4N3fOFQjNP5OMfma1vFnFCRYy8X9McPKFqbHWYVE0yP9MFqVJj+bTBHw2OOPeUihCqMMJNRNwYzC0DPDNBMjMxQz2VOnBcwYzM8O3zP2mnGVxUhISFCWDUt4IW8LzzN33HGHEtO33nqrEjTaZ9KyCokX5jSlM0UVjEI0MthCj8F6AqL4YH8b3tyBgoahQQogGrb4fwogXqUxDcYoEA9wjA5xHe3LrQc/vBRA9AIxr0kBRC8QBZBmhqYA4oeNP0M9hNuSPTSBqB5KGtMDSWd28iqtE2ziypz68rcpOcKA+rhjKN7xM+IyeyA2ravHm+BFD70DNDr+/PPPdvOQaOLlPCQOtW2JAsYWzSMUjCdGT6EBmJ6Zpqhy4vGffqpHHnlERUpo4KURd8QIU/WiJb1791amXlYVMUOwdOlS1YBWSyvx4pydo+nFYZaBrQC0CmL6ZhhtYesUVj7xcX6GaSC+//77cfPNN6vzEv1ezExYpqGai6A68wWzoPEUHrAoLnjz5IqOxkAKHObXKXYogGiyYhSIESD+pOJ2ZobmAZQfRKa3LM3Q/OAzKkUBpHmBaIYOpkZbLTlCY26a508MQMaUfkFTmp04rDOSz+7uU1fe4i83+2FPjBZd9gyIObca+Rsaw+hJ7UcgZ+BtTrdAzws78XKkAE8SlvOQeAXbVPOQQhXNMxQu7w1FTKDLtTUoZB5//HHccsstKoXUrVs3synXkokTJ6roIMu3+X5T9Nx7770q7Ul4nqCh94knnlAXf3369DGXePP88Ze//AVz5sxR7QIoZIYOHarKtx999FHVaZrHV57j/vrXvyIYMBiD6IjPeneWKIbaVXVzXuFQYVME8ScFEMUO014UPowC8aqRH3iu66yBEQ8qVNsMUVLgUHFTAPHATMVOAcQPLr84vB/sV5jsfcAvH69i+KUMBSo2HcahWV/5vWmeN51vGZk58MDn8Dc5d45C0hnui/yA7pfBiKQrshHVMRbHdr9k93DbUQ9bRWr4HeLVLuch8fNlOw/p9NNPV/OQeKBvTh9BqMCINSNXTHWw/FgQfCWopHGo51KbGh406bNxt4yTQpHRHuZQKYBsS+I1MzSjQpqT3llJPF9fK4mnAGIUiAKIUSCKHgogLQrUXGboUBLHyk9iay5srqZ5gSjNBtQATG/xv2/GgMrSdSjdrV9F9tvS/8MmTFFTqTkPid8L23lIDMUzCsNwvOAZrI4hIv6EsBQ04ZRyCkYoQrReA3r5Vj0Y3WEKjFEgihwtCmRphmYUiPdpUnNmhtZK4rUoEE8KWkm8FgXSBBCX+xIFCsWUE8VHxpUDUTRvbfM2zQuEZ4Yl2hf09nq//JOO4y9lURliMKLWsNN8l5/d/63PxyfL9mHt5iMoOFYJo/Et9RjTtuxUznlInG8TjmnapibcUk5C8xNUn6RwMIeFG7zyZB8Md3th8KSgGZ1tS+ItzdAURTRLuzJDayXx7FKplcRrUSCtJJ5pMAohywNjqJqCY7pkNm+n3UB0ACYRBqSe77i/hSsxU/rjHh+rrIxAbiFQkK0iMxQzGLQJq/fsx0ff7sGqzUdw+Ei5ytSRhNhIDOiRifNG9sfdj7wX9POQQhGtHYYc94WwFDSi1EMfRkYYaeGNpkh3YJSHESAKHKbCKHaYX2d6TOsMTSHE1Bh9DM7M0FpnaC2Mzb4f69evN0eBtJ5AFEEUSMGZdrLwrDZB1VDADMkaEUDCxETURhxGJFxXD1UV70Tl0W2q2qhmXb1FybhNhMVTBm/Bhl3L8eHivVix/QAOvl+GugYFExfTCr26pGP0oDxcek4n5KSbUkgJeWeImAlwykmO+4K/EEEjNDsUFmzWxJs7MKqjmaHpA2IqjCKIAohRIAokCiDC6BCjRK7M0FoUiCWRmhmahkVGgSzngwXaDK3STlcNQtHbbqadDEDOHaOUN8WXHir+mOKtR9QZNTiZuQLlMdUoX/oh4nL6o82Z9zlcv+CnOSj9bbnpTkUM8PlIU0RF4bmY2X78AN7dvQw/5G/Gbx8WoLbOFLGLiYpAjw4pGHlaa1x+bme0ydafg5PWfYLHrym4h+VoB0EIO0GjhR6ZJgj2Khqh+aAIYYSFN0ewVLFfv35q4uvMmTOV2NHmg7EknkLHdj4Y/0/x444Zml4gCiDb+WBaGkzzAnljFk27sA/qymvsp0HrRW6MQKvkWJ8bwvlnIKNtBMWoxAziTVfipLJgPQ6ueBxtzrzX7tkn9i5tFDPkaKqFmHGPPaUFeH/3Miw/vBF7SwtQU29KaUZFRKJTTiqGDcrEFWO7oHMb178vS7e96UcjuIeYgoUWIWh4QhHVLviCrSmYgoO3s846y23DouYDsp0Pppmh6QWiCGJTK2deHa0ztJ4ZmlEgy/lgNGxz3zkSwE7QGIN7OjTaHQYOtLbyqFiKGY3KgnUqrWQpFuwiM9s7ANs6unzJQ+VFeG/XMiw7vBE7Sw+hus7ky4g0tEL7xCycmdMTl3YegZ5p7YALl+nujx5ZA25FSkf3uogLvgka8dAIYS1oeDIRQSP4gq+mYEZg2GWTN3fg69D3o5XEMw3GKBAFkGVJPNfh467M0PwuxNa3QnxkLJKj45EWnYiM2GR0OO0UpP5Wg3bxmeiQnItBv5/ol3b9KuXkKx0OA/23A2XxQGKFU/FQ+tsPZkFjFZnZ3QZYw5ls+pGZo1Ul+GDXcnx7aD22nziAyjpT2qKVIQJt4jMxNPsUXNJ5GAZkdrWIGBmBwfriSo9WcZkiZprQFCzHeiEsBY1mDqOgkbJIwReaumybr0fTsdY23B0ocLT5YPQC2c4HKy44itITpThadQIn6+tg5In5t1XWG1l4v9kMrZXEMw3GKJDtfDB6gdizSK9ixy9Rnqg6k2hwQzhomSSryExRsp2YOV5dho/2fI9FB9dh6/H9KK81lfpGwIDW8ekYnNUDUzqdhSFZPWzS1EZg2M+mfXIhrmyJSXOcyhT876ERU7AQ9hEaQfCFUOhDQ9F+2mmnqZuz6iOtSR5SYqzmgzENZjkfTBuQyuX0CznrDK3NB6PHR3WGTkpGfMFJZMakIDsuFW3iM9AuMUulbdokZCAywsWhgm83hYMNsWndUVVsPwwvud1ZupGZspPV+GTvD/j6wE/YVPwbSk+atkmJkx2XhlF5fTGpw5kYkdvbhc/OYBIz2SZzuCdUH9vh8XME7yM0lpOaBSHsBI32QRcEbwkXUznTSZYpJXpteHMXCh2tJF6LAmnzwTQzNNdRZmjOB3OwHQMMiIpohZhW0YiPjEFyVBxSYxKRHpOE7Pg0dBt1GnL3RyI7Kh8dWiciMS5KVTTFZ/VR26y2EDU0257Y/Y0SM1U1tViw+BC+/HwTNh77L47XlJvXy4pNwdDs03BhhyE4t80AF6LKvmmensByh7qqo3YeH8H/SJWT/1m1ahWuu+461eS0JUa+guo31nKpEqER/EWoNdbzNzQf83bGGWc4XY+RoL0zPkDVyWocKD+KfWUF2F92FPmVx1BQeVylvSg2SmoqUFFbhd9qyrCr5DDqNQm0fbHdNiNbvY/oqAjExUQiKTEe6emZyM7NQ2bGYuzZ8gO27D2BYycaU0HpMYkY1bovLmh/Os5vdzqiW7l7eDICXfcBuzq4NCS7S8n+Ro+PEBjEFBz8TJ06VVWJsnAhUDz00EN455131NTusBI0mqLUPuiCEM4pp2CCUaDs6Wei8KUV6BqZh64peS6fk3JBb2RcNRAH9m7Gpp+XY8++g8g/dhIH9u/Dns3/w7GSapSU1aCs4iSOFJXiQMEJ1G5sTOekJEbjrH45OH9gJ1x44jplgG7Ew7/bqXtNNzcMye5ATSQEFs0YLymn4KS8vBzr1q0L6Gtw0vfWrVv9tr2gjNCIoBF8RQSN53AqtyEuCgXPLHW9sgGIOzMeBWv+g5pDP6JbK6BbZwCd2RX4DJQf0q9S4gTrPgOHY//BQqx9Y3JjmfYCWx+FJ/1nDCYhQ7+Mm0KmNhI4GQNEVQORtgVnRsC4vx7o58EuCF6nnMKlbLu2sgg1ZfmITsxFZFxGQF+LWYynn34aX3zxhWofQcP/m2++abfefffdp4TjU089ZV727LPPYsWKFSoqQjhBnh3V6cdjU1EOXJ08eTIuueQSdS7mEFbyxhtvYMiQIUqAPPzww9i4caPqwTV9+nRceeWVap358+fj66+/xvXXX696gHHOH9NgejAVzn3hvnFCfdgKGvHQCP4SNC095eTVNGzbid8RBtPQzHd+ghp2FGFAzLnVyN8wS3cb5Yd+dLh9jjQwGOuVJ8dMaYJvIw2c+GX0hEtJGnC0TWNFd+ZBIFnzDvPX/g0oWf81WlXHIePiK7zfL6HFmIJL9i5B4bq5Zi9X9oDpSO44OqBpGgqKZ555RlUz0iOXmKjf7doZ9M7NmjULzz//vPLm0WdHoUlh8+KLLyphQ8HDSkk2EmXRwY033oirr74ajz32mBIld999t3p8+PDhapvsyzV79mwluNhxXQ8KnT/84Q9KGFEU+YugFDQSoRH81YdGUk7ep5408cIZUdEDIpDaOh2RdXmIzIpzKGZcUVdThrqTldb6JancwzlNmthy7pexFS5p+UDScYtlDZvg/ehKILYSwM+cl2F6qHjBJ4iIi0fa+Ile/a6CexGaUBc0jMw0ihliROH6lxCf0y8gkRqKigULFmDu3LkYOHCgWsaxLN7Acy0v+ig82OKBNw2tdYrWBoJ89NFHap3bb79d3aeQGT9+vEodaYKGBQevvfaa0+IFRm8uuOACFfFhoUJYChrx0Aj+QlJOvqWe4vu1MZeLF+19HYVLG0cSRFcwt+QdlUe2qJ9W0oVihI3vdBvqWYgXMw0K5Yz1QOZxXTHDyIytcCnOBaridF7CABzqCmRuB5Jtjq1F789D0tCzEJke2BRCS/bQhHrKiWkmO8+XsV4tD4SgYesGvne9evH74htdu3bFXXfdhcsvvxyjRo3CTTfdhJ49ezpcn41DeRs0aJCVKBowYID5PufhORMzTF3Rn3PLLbfA3wSVoJEIjeAvJOXkn3Jxli9bzVfiAfz4brea01UX2/dziYxL05+B2fkgkHsU2NQJ2N3eWonoGoQNQGyNQ88M00x6wqUyxcEOM1LTDYhPAiJLrR+q3LkdSac7rxITWm6Ehp4Zu8+pIaJhuf/Ros5aFNpTKiqs07PTpk3DpEmTMG/ePFXyTf/LjBkzHL42hwgzTWWJ5d/QVednRm84RsZWkFFI3XbbbQ5fO+QEjfamOGsLLwjuIBEa/5C/do7Hz2GfmZyBt+HQisdQUbDeanltdYk6KOoeiylO2hcCu21LRA0e95mhZ0Y3i6WzKTMRQFUboFUlEHXMQtgYgaraIlTU5CM+OhexkRKt8QfhMvqAURh6ZphmYmSGYia7/7SAGYOZXmJjTPaacdWOISEhAQcPHrRaxgiLLWztcMcdd6gU0K233qpEhXYMtWzQyYjOd999p1JU3kbWXn75ZSufLFNUjAx98sknPvtpgkrQSIRG8BciaHyH0ZnaskMePSe5y/nI7jtV/T/vzPvUNmgEjsvsoZYdWPpAw3Wsg6tLPT8NxUvfbcAvPdzuM0MDMD0zTDO5LWqMQOGUhq7H9UDmQiB5gwHF7Y9i697/mJ/UM3s62iYHzvDZUginsm0agOmZaYoqJ6Z0xo0bh0ceeURFSmjgpRF3xIgRdutyFh1NvcuWLVNpoKVLl2Lt2rXmtBLnyq1evVp5cdg1fPny5ebxLfTN8Jy8cOFCVfnExydMmKAMxPfffz9uvvlm9bdj/5jU1FSrNJQzOnXqZHWf2yXdu3f3+b0JqnaqmuLTQpGC4C2ScvKd0t++9/g5HGlgCZvTpXWboH5S2BBGzB0GyzU/jdYIRhMvp+wDLlgGnL3a9JMpKifQQ0OTb9wJBy+jNxHBYHFEjACOjgfipl2EreXzrAyfmwvnqoiN4BvaVTpnkIUDFDHxWb0CXrJNKGT69++vfCjnnXeeEhlGnVzuxIkTcfHFF6vy7QsvvBBr1qzBvffea36cgoWGXnpoKFZYOaWVePPv8pe//AXvvvuueg1GhChc6IHhsF2WWnP7c+Z4HsUNFAZjEJWBUAnyTX3uuedULk0QvIVt/Rm+5OeInyfBcwp/eR0lu750e/3IxDx0PPdfDh/fv/RvagTCmNs+R9Hxaqyfd7HjjbE3jRtN8twpy1a44zXWIS22F4qrNtktb5M8Br2ypzt/suCU3/3ud3jhhRfUyZEnSkEIqwiNFnqUPjSCvwgivR5y2EZbXJE7yPFFCAdRavOc1J/ElZ+RIsZBo7zIhDyzcPntFOBwZ9NP3terblLixbYdkfa4ttxBuyI9MUMOlnwrURofCbcIjdD8BKWgkZST4CviofEdDpB0Fxp+Hc0+KvhpDo6se8Fqmbdt9CITspE7+DZd4cL7x9N1Ns6jXInOxrieAUhZCeS85/m0heOV9uZKoeWZgoXgIahMweKhEfyFCBrfYETFtlzbGSmdTe3Ria0R2HY7pgiN55ImtftkZPa6XP2/Jj0NMNgYYQxASbajHXSw3ACcGAJEFvvWrFjw3hSsfVcFIawEjaScBH8hpmDvYUTFEzFDSn8zTae2fS59NbZ40hPYMgKkiRmS0GkYULHAfkVHG3ZRrn3sTC/GRERl6S6XEm/3EGuB4G9E0AhhiURomiYyo8Fqar3n6pZ9W/iaYtK6m701jsgacCtSOo6yWhYVnwU4bkOjjxMVZUz1XGmVVO1GXX21VW+aAyVLsLnwRfM6PbNvlhJvB1j2NxGEsBM0mjksmJX7zooqbCuvRI+EOHSNFzNbsCKCxjWWqSG96Ion1JYddrsiSskZg0nMtBs1064Bn21kxlbMkOhWSfA7HoaNthx9xUq4ZMb3sxIzhPe5XJrx2SMNVIUW4aEJVkEzZ38Blp9o7Is+IiUJt7XTnyYqNC+ScnKOrXiJy+mPSgeiwhVRCXkOBYkuDX1oEtsMcdiAz1Jo6RFhCC4jKYXLKZk36j62q2g+euVIibctPM57275fEEImQhOMVU6MzFiKGcL7YzNSJFIThEiExjF685lciRl2AC7b/wPqa+zLhaJS2+NkufsdhY0N5USaeCEULpbixZGQIRsL5uBw6XLvDTkBIr/0B93lB0uXoEvGFInS2CApJ8HfRASjoAnGD/r3xXp1n8ATew4osSMEF6EsaIpKarFpb4X6GQi0jr2e9qTpPOFFlSayTQlFxqZ5tjEjENEqyqloccSJqp2NYsbZ7MoAkhLTKMQsOV7t6H01qjlQgn3KSSI0gj+RCI2bnHAgskqMwAO7D0j6KcgINUFD8ZJ/rAa7D1dj3ndFpvEABmD6+GyM7p/s19eKiErwaP3olI5m8UHPi633hvc96ShsiEpEhLHx+1R7rAg1BfmIzslFZHqGw4qhyJPA1sMv6WzQSaQmABGcEw6Fi2NaRYT+vCJ/E4wXrkJoE1SCJjIyMmjNYimtWjl9XNJPgrcsWV+CuQsLLYt/FLz/0sJC9Oscj4xk/31V609yAKT7pHW/yOq+bXrI0wZ8hlbfw2AwRTVLli1B4WtzGwY8GZB9/XQkjxytWzGUVASU2jTOO1KUh4IjHZGTtVfd1/6flXHItdjxgZyEYSgod3/WFauhBGskQuN/Vq1aheuuu07NXdLOpy2JoPyNg9EUPCwtGV85SDtp/GPPAbzay/MwuhA4gj1Cw8iMnpjRqDcC+cU1fhU0lt4VS5Laj0Tpb8tslhoQl9HdIz+OqwZ8RuM/zJEZs5ghRiMKX38J8X36oTbZZLS1pNRm5t/yVVOwc+8AnaFNRnTtuA4jhsxvXORnIgyRaJcyDvtPfO3O2irCJNhHaCTlFNxMnToVM2fORIcOHfy2zWeffRazZ8/WfWzJkiVo04YtwMPAQ6MRjBEalmizqskZlUZg6TEH432FZqG+3sGQniCBaSZnmivCAOSm+beih9EVRkpsoZiJzexlscSAjF5XoqYsH7WV+tOl89d6Nmm3ZP8PSmTyRMY0k90vX1+vlrsaK8DITKOYMe2r5f/5GNcJFIfLlioxkxzTzcWaEeiZPU0MwTpIhCa4KS8vx7p16/y+3WnTpmHlypVWt/vvv1+JptatW/u0bRE0bkKhsrfKtfn3lUNHfPqDCC0rQpObHu1UzEwbn+3X6AyFSemBlYjPGaAa1tlSdXST8szkDXsQGb2vQtGmd3Do+1nY+9UMlOxdYrWdoq0f6zfOc0bD34OCJqJhdpst9dWuv2dMLTkPvRiwY89pKK/wr//IlpLqHQ4fS4/rixEdn5XGei0kQkO/17GKTU0ytLSqqgqPPvoohg0bhn79+mHy5MkoKyuzW+++++7DPffcYxchufLKK833P/jgA4wbNw59+vTB6NGj8fzzzyM/P18tq66uxtixY9GjRw+VziJbt27FNddco16X67/zzjvmbc2fPx+33HKLEiljxozBkCGm1gyWxMXFIT093er24YcfqlSZr2Mwgi7lxA94sKWc7ti2F4Un3Ysa1TSIn1HpjobHCE1JsAuaDbsdt7udeGaaXw3BFCSF66zTOHrUnNiLI+tfwskyVuZo758RhevmIj6nHyoKNri1HT2S2w8z/03qq/V9Jfn/fhJJN04CnARYTJ4ZZ+YYI7buGoptu07HWYM/RffOP6GpOVb5C6priyU640TQhMscJ5Pfa675M9kze3pAhexDDz2EjRs34plnnkF2djZ2796NxMREj7ezf/9+zJo1S4mYLl26YO/evaptSlZWFl588UUllCh42rZti6SkJJSUlODGG2/E1Vdfjcceeww7duzA3XffrR4fPny42ubmzZtVSunpp59GTo7rPm3/+9//cPjwYUyZMgW+EnSCJthSThQn7ooZjTUl5SJogoRgFjTKP/NFocPHP11RjHNPS/FLhIYRFU9EyMmywzpLjchfMxtVRVu82gfOdYo0psHY8P1mVZMq5bL5G9WeAhRWfoL0Q8Cx1vqahabftrlbcSD/FAeixrTMiAj8sPYitMndgYR45x64QLD96LsY3PaBJn/dUCBcIjSMyDSKGWLE5sKXAtYhmqJiwYIFmDt3LgYOHKiWtWvXzqttMQLDtDyFR25urrppJCebLqZSUlJUFIV89NFHap3bb79d3aeQGT9+PD777DOzoCksLMRrr72mBJI7vPLKK7j00ksRHx8PXwk6ecwPeHOU81G4PLn3kJ0HhuLEUwYne1YWK7RMQbP9QKXTFirGBkOwP6APxh+4I2Zi0vVNxIkRp2Pv3TNQV1aK+rJSlG74AalXTwDiGtcpGQT8djFwuItJzJTvysOvW8/U9cPk5exxy/FrNEagpMz/JxZ3KK76tUlSEKFIuAgaU48h229yfcB6D+3bt09d9PfqZel3846uXbvirrvuwuWXX64iLYyuOGP79u3qNmjQIPPt008/RUFBgXmdtLQ0t8XMli1bsHr1alx77bXwBxKhsUkp/VRWgflHivGfHszRA6cmxKplnvBh4TGJ0AQJwW4KdoY/DcHRiZ5X2UTGZaG20jNPWERsBlqfficK1s216j4cn9Yfx1//VKk0FZRvBRQVzzPpkbEGYJ0RtcXA0XHAkWOmUuxDBZ0sIjDWlUsUOKXlyW7WZBtx9FgeWmdTADU9PLHJLKfwTTmZKthsOzwGrrJNu0jzVgxWVFTYmXQnTZqEefPmKR/L9ddfjxkzZjh87cGDB6s0lSUxFn646Gj3j1kvv/yy8ur4agbWCLpPE/9ITZly0ksp8b4WqekU5/kAyqO1dXh+f6NiFZqPYI3QuOoC7G9DcGRcBlJ7TNZ9LCZNv1Insd0wj1+nvqpIGYiT8k5HavIkYCO/ZEDFa+vNqSX1b5TBQocYgX5AVSdg+eopWLD4d1i9YbxNOqmxconl2lxny05t/xpD/foYsPaXcQE3CDtCmuqFd4SGYpWemcbTaWAr25heatWqleo144qEhAQ7szAjLLZkZmbijjvuwHPPPadEBtHEpmXGhBGdPXv2qBQV003ajZ4bT6Fv5ssvv1QCyl+0eEHjKKX0wqEjStTkxnh3hbzsRCmKPPTeCC1D0LCR3ozZe/HMx/qi987JOXh2Rke/GYLpn6k4sgmJrU35dluy+k1FdLJ1Dj4mvRtaRXtuMmw0EL+E4x98Auxm5z2bhy21jEYEcChGrxTb+om79vV1Wq4djGknaarnOHoaDhEaQgMwK9oG5T0Y8Mo2pnQY1XjkkUewfv16HDx4EIsWLVJ+GFt69+6NFStWYNmyZThw4ADeeustrF271vw4xcl7772HnTt34tChQ1i+fLkSKIS+GUZbFi5cqFJKpaWlmDBhggo4sMyaz6GpePHixVbbdJfXX39dVVb17dsX/iKipXtonPldKGr+b/cBjEzx7sD+2iHHhk+hKTCgsi4uYDORAtFIjyQntPJbZIaVTYyYsPT6wFJ9cyqX15Tst1pWfWwX6ut88e/UA46+WrpZIgPW1p/hIn1kRHl5qled8gyGeiQnNoeXRZrqhXvKSYMRmfT4Xk2SXmTKp3///qpE+rzzzlNVSkadg8rEiRNx8cUXq/LtCy+8EGvWrMG9995rfpyChYZeemgoVlg59dRTT5l+n9hY/OUvf8G7776rXoMRodTUVLzxxhsoLi7GZZddprY/Z45nvagIxdH777/v1+gMMRiD7BKWbyLNTj/91HRlltf9ulOVWzuiQ0wU9lV7V0r+XI+OyIgKSqtSWKLNRPp1TwWuHtcefcfejR5nTg3ITCRv4NDJWW8fcppqYnTGX5VNFDNNPr0RDS/JiQhaSxl2MchkPhY4/b8fIyoqAj+8dpHpksoI/HPnH1B80mauge5GPZllYFqPYuasQU1Zuq3toyn1EMir9VCGkQD2Uzl69Ghz74oQJkS2dA8NuTEvS0VjHOGtmCHLi09gcnbzhLpbGkzlvGhbBk0TaoBmInnbSE+nUjkgvhlTZZN/xEx87umoyF/t4NGGX8hSY1i+bH8A7S3O8y8DhnoDsIhJfuCnuH4ojnMlZhpex/zTHXFjxNlnvIvszP1NXLJtRIfUC9Ah9XwxA7eQlJMQHES09JQTfTJfFh0P2PYP+SCGBM8iM7Zihp8lU01N40yk5oZihdEiihfCn1ePzsCD1+T51TfjbWWTIxyLGWIjZrQjS0JDZEYTM5r20WwvjN4UAVuMvbxIIxnQpcNPLgRbhHq0OfrP7Du+sMlfM9QIt5ST0PwEXYSGH/CmEjSedAD2lqFe+m8Ez3u62MOzZ+O9JetOoKS8Dt3bxjVrpIaihdEiCiyWZQdqX1RlU9cLcHzn5wg4toESVsvTb9/BXqtoZdsa2ScPYStO9VDU1KNd6+3YtU/f6KzRfDU0pj4kUq7tGLodWK0jCGEraJoqQuNNB2BvKK1t+iaBguXZrFHR/LCpXN3IzRP846nRPDtMJXkiTLhuU4iq1K7nN42gscz+UMxsoFIB0MNJQIfFFOlAeWIccMRz6REZWdPwYo6u8utVuqm5kHJt5/A4b9m/RBDCTtA0VYTGmw7A3sBhlTLXKfBkpUbZLTPA4LBsmyMHfPXU0LOjVSzRF+Nv43HtsSI1eZojAiLTQ8CHpYma5SyTAg4Mz8O+Yx3RIW4vCqqysKWsF05N3AQj5gPshnCa6Tnlh71peR6Bxd+zu2iDq9gci7E2AjdHuklDyrWdw++mpJwEf9JiBQ3LtT3tAOwNMqyyaaiu0ekIrJp26QsaLmXKx1tBs/NQlZVnx9/G45JlS1D42twGs60B2ddPR/LI0W6LHVY40RRcV93EJ3S+5ZHAhylTsN7cL0b7GxiwtexUVNU9ikTUmTVITY03gsZURdT4opY7UI8Lxryg5j01BbGROaiqte0pJOXa7piCJeUkhL2gaYp29YyavHL4CGqaoKJVhlUGnphovbSDg3Ii7TlREd5HZnSGSmrGY18FDcWKWcwQo1Hdj27XHrGdu7oUO6ap2o2Tf5uUeuBATR7WpzlqkEejNlBTb2pY+c8dWrm2hy/itJ4hArV1TZfKoJhJi+2J4qotVuXa4p9xjlQ5Cf6mxQoacmNr5+Xa/kKGVQaWBSuLMW+JfdM0yyonPY4cP4muebHeNcYL4OwlRl7shJjRiAOzHkTGpVeh6IN51mLn9ZcQ36efitSYpmpbT/5tUgzA94lnNkTHHGAETiIKPx3r50bvGXs6tt2AfQf6qUnawdJEr7hqMzqlTUJGXB81w0fEjGskQiO0CEHTVFVOSZHisA91PnMgZtyJ0HgDDcB6m+Qp2V89ZCJolNRrVmM0WosZjfp6JYIoaPzZe0Zjcf4o/Fh8uvr/0DRT+fam0t7olfQrzsldarXuh4em4NeEfi63aTS2ws/HB3oVQYqPK8dZgz/FD2svUiMNTBEbEtGs3pk9xZ+gXcq5ImbcRKqchBYhaJoqQvPj8dImeR1JOQUGRkscixnNQ+PgIUCVb3sKq5n0mHVDW4+jPXpYpZP00O3IF6G8NKTiyK/wJzO3PIAaI9M3pvdy6bExDY8YsPRYDlYUD8PfTn1YLTlQkYf1JwY4j86YfgnUIRL7OI3S47SYEV06/KL8MW1yd6j5TFo0Rvt/cxqBj1duR24SRzgIrhBBI4S9oKFJrKk6BefFRgNNUO0kKafAwGiJSxzogukT9KMp7pRh21qNef5OS/Ttq1S1eyfK1/+M4s/me/ZEemimTjOnm45v+wT+4r+7rrcSMw0vaPV/Pv7G3ivUhUgrQ41b4sT03mnd9YwejDQwomuHdWazL4WLpXhpSiGTHtsXx6p+abLXC0ck5eR/Vq1aheuuu07NXYqMDLrTe8Bp0RGaEWkpeK+wOKCvwWJiKdtu+jECMJ8ijbrTrM/omeRVGbZKOdk8z+ijGbhg7hyU/sBaZ9/wZ7rJNjLjGAO2V/TSESfOsJKD6J24HvsqOqC0Ps3ps9rH7sbktvPZXLjZSYs7VUfQGJAa172Z9ij0kAhN8DN16lTMnDkTHTqwQ6b/WLp0Kf7973+rid0pKSkYM2aMGoQZF+d51NySiGCM0DSVoNlYGvjoDAcfPLir+Zp7hTO2YwTsBzjbqx29VBOjMis3l6oybAuvrSrDtp3UrYkof5mBGZnxScw0mIJZGWUadWDwi2fGPTGjdsBBNZOrpzT2jak2xqBfygaXTxqY+jNaObPXNaH/ueJkAXpm32xxCGVl03Txz3iACJrgpry8HOvWrfP7dgsKCjBjxgyMHDkS33zzDZ599lmsXLlSCRxfiWjJEZqmaq63o7IaP5WUNclrtTQYQeEMJM5CuuE8jnO2xL4PzVm9E60iKYzKzJi9F898bNtHxH7+k5aOuvLsDKtZTL6YgSu3b/PqedY7Wo+ybT+pCE1Gryt9FjU0/HouZjTce21WoGnr7yg/BdX1zsqsjUiJLMbA9A2IrXAiXJqwQj0yIk5N0R7R8VkMyntQ/ZSp2p4LmnBKixTXFmFbxSb1M9BwSvmjjz6KYcOGoV+/fpg8eTLKyuzPMffddx/uueceq2UUEFdeyeOEiQ8++ADjxo1Dnz59MHr0aDz//PPIz89Xy6qrqzF27Fj06NFDpbPI1q1bcc0116jX5frvvPOOeVvz58/HLbfcogQKoy5Dhgyx26dDhw7h5MmTuOmmm5CXl4fTTjtNrctoja9EtuQITVZU0/3660srMDBZ5joFAoqJDbsr8OrXR+3Ltm0iNFeenWlfgu3gBGkZebFNR111dgY658X4PIsprrvebAAPaQ8cPfwKwIwTDEhsexbKDnxvt1psZk9UHd3scnOsXqLh15WfxXsFYftcA9acGOpg3XoMSP4ZF7f9RBUzRa4HMvOBoyMdXI65alHjJ/KSz1I/WZ4tJdreEU4Rmh9KlmBe4VyV4maH8quyp+Os5MbeUP7moYcewsaNG/HMM88gOzsbu3fvRmKi5+eX/fv3Y9asWUrEdOnSBXv37kVNTQ2ysrLw4osvKqFEwdO2bVskJSWhpKQEN954I66++mo89thj2LFjB+6++271+PDhw9U2N2/ejNmzZ+Ppp59GTg6PI9aceuqpav3//ve/+OMf/4h9+/Zh0aJFdsIrbASNo3b1/qYppyz1T/KmG6rgDkqYMF1k94h1hIbzmyzFh6MSbNvIi63w4c93vitSkSFfy7TZKC/prBGep51YUJXYkNPsb6kPjLpihkQnt0N0YmuU7P3W6aZZir302Ch+G52IFu/DIY2mYMsl+ts7O/M7jMn6ziTWmJWqApJ3AdG7gUM32g/ETPkBODEssNGa1kkjkBLbNXAv0EIIlwgNIzKamCH8+U7hS+gZ3w9pkf4fWUJRsWDBAsydOxcDB5qGs7Zr186rbTECwwAChUdubq66aSQnm/yD9Likp5uaX3700Udqndtvv13dpzAZP348PvvsM7OgKSwsxGuvvaYEkh6xsbF47733VITmiy++QHFxMR5//HGcd9558JWg+zTxA95UERqKjEXFga+M6BYXI9GZAE/a1tUlFh6aG8/LtDP4OjIVjxmQjHN7JaOurB4nimqRX2ovfPzVFZjkTL9NpZ5qj9invXRpbyFiPAiUlOz+2u19ikA96gN1eDAaLXxIzn4BI3okbjM9zAs9ZqWqTI/EHgZar8nB4UEFpohMPZC5EEheb3rc36KmS9rlaBURjbS4HiJm/Eg4RGgKa/Ltig/47TlSkx8QQcOIBiuBe/WiGd83unbtirvuuguXX345Ro0apURGz549Ha6/fft2dRs0aJCVKBowgJ3BTaSlpTkUMxpvvvmm+h1+//vf47vvvsOTTz6Jjh074pRTTgkvQUMPTVNFaJgCyo5shcIATsTmMfWu9q0Dtn3B2XtvarNPkuLsD5wUI0wdvW3Ty2bNkhLsfbPEnF4699oM9TOiBoiqBk7GAMZo/3QFpin4+HeL3RczsTYRGQ9Fjbvl2vUqOuPJhj3dCdO6w9KW4fvikTrPNaJ/yjq0jW+Yx8SHeW440bhG3DcFaL8SYLPhqGNAZENbqYxlQEQ1UHwO/Mau4veUCVgiM/4jXCI02dG5DceaxvNWBCKQFd0Y7fAn2vmx0YfmGRUV1jMMp02bhkmTJmHevHmq5Pv6669Xpl1Hrz148GCVprLEcmp6dLTz4+KSJUvUa9EQTPFz8cUXq/QW94PiJirKftBwyJqC+QFvKkFD2sUGduaLGoJY7Ua/FMFrHDbIMzRGAxyt07m19d8/ugLIOGA1WQCL3irCuekJaL8VyNsD9fP8Nkk+R2dYrn1g5gMo+591t12nJHrtw3WLBzbPxP5qXl1pAtDd76JnzfG0n2dkrlbCxXJZXsx+3NrxeVzSxqInDx/W8VpSxMTtaxQzGmk/Av1OMiflPzYXzkVVExg+Wwrh4qFhFIaeGYoYwp9XZk8LSHRGSy/xfWOvGVckJCTYmYUZYbElMzMTd9xxB5577jm8/PLLapk2Cd2ycz8jOnv27FEpKqabtBs9N+7C57du3VqJGY0zzzwTR44cUZVVvhDRkk3BpCrAr8XDfG6M71fygmMoLOiPsW//xnxSvSrtdiQ+LMuwk44BbXbpxArqgZ1Ly62CIlu/LlXpKG/xulybx6YA6f3/2/xni4Z3pLG02jWe7RSvLgckr0VKVIkSLhQw5+d8oX7e1uW/aBt3yHoc1W/W0Rl3SInp3FBa7S+MqhOw4D98uRoPJmgAntXxWdyV96D6GUhDMIUAK5AeeeQRrF+/HgcPHlSm2urqart1e/fujRUrVmDZsmU4cOAA3nrrLaxdu9ZKXNDPwgojVh8tX75cCRRC3wyjLQsXLlSl1qWlpZgwYYJKFd1///3qOTQVL1682GqbrqDvh2Zi+my476ya+uc//6lSaKmpqeElaJoyQlN0shabKhqS8gGiiUcDtljoj5n9e1P59nO/76j+X1t1HKn1W+28M3pl2FEngcyDHiRY6oGNP9qLGm5z094Ku/41fivX5sfV9+pGuw8oIzMnkeQg/OPOu2JpwHbxqTfSdF2Pi9t+al7E1NJZ6SvRtuAQsAPAMtMt4mC66f8N3hhPqK+uVqXUQ9o+rPM7RKBbxlWeb1TwK+GQctJgRKZ7fK+ARWYsYcqnf//+qkSaZlpWKRl1zpsTJ05UKR2Wb1944YVYs2YN7r33XvPjFCw09NJDQ7HCyqmnnnrKbN5ls7t3331XvQYjQhQcb7zxhjLyXnbZZWr7c+bM8Wjfud/0zHzyySfqNW+44QZlPGZllK8YjE2Z33EDOqVZw94U4w82lVVg1t6GHH0AuSAjBde0dj8kJ/jvYMlQJq86bLEtw57QNRmb53tuEOdzJ0/PxqDRyW51GraM0DDd5BX00Yz1n72FkRl9MeM5GVEFKDppX6ppyUcP9UKn1rFY+Mz51vtoqYvWN0Rl6JXp2x+VvzhQNAYDUidMwvHPP7ZeHhGBjk89q0ZCkAMlS7C58CVzXXfP7GlK7Jyo2olVB2z/Dgb0z70H6/OftFs+ouNsKdP2E4zS8UTKE6Yg+IMWHaFhKqgpenEtLDqhokFC06P3WdIrw/56c4lX53M+9+OXCrFhc5lbnYZty7U9FjJso6Nn+zJ6J2YcR2a8wYjsGNUMxzmGBsWnCRetGallposDuxtmfVLMpF04CXn3PoiMy65u7DLMOVbXT0fmJZdbL4+IMM+30nDUBI8mX72Ov9mJA6UTcBMQThEaofmJbMkf8IyoSEzISMHnRR4m5z2kvsEYzNcTml/Q6PWfUU4qL3U000//fjPfZNgNUGm3y1JtZ8udVDO5n1JyBwO2lPVxsY5RlYSrV2TWbV+DSLPPCAEJjWXaxQs+QcrZ5yL+1F5IGnomagry1YRxTbSkjb9Qd7kljprgUdxkxvdDRU0+4qNzzes4Wi74jhaBF0Ej+JMWHaEh52c6H4jnrzdZjMFNj16nYEfzmGJ8KERTr1APxJYBrdjozo0ZTx6Zgh2VatviaLkDDlZTJfkzRml0cUgxlWLzp3r/TzoxOlNh2hQ8VO40GXIpVihsbEWLo+XuQLGSHt/LTrQ4Wi74BrvRhpMpWAgOIlpyp+CmeoOn5WVLdKaZ0Pss2Q615M8Lh6X6Vsm2r7GkO+UIEFcGXDMswyo6U1NUi8MrS7F9ZSkK1+0KTKm2B6KmTQxNKnazw93fL7vnORdHF+Z8al2KrVVAVzWknrSCw/rGrsCWlK/7yct9E4INziIiImgEfxJ0OZCm/oAHskfMnW1z0D0hTsRMM+JIHNOs269zvEoJHdlUjUVv+tZfxDJ4kp5v+vnD80XIrm2lDMNHlpRgz9xCdd43WUe6oF3kQHSPdOMkrUUw3A2muLneLV1eUx4a+9/A0yZ57B1zAIer26iEkv4u1eOU5O2NVU61BusybGqrwoY0EyMzOsWHZT/+gNpLr/QqAiMEZ4RGUk5CWEdomvoDHkhjsIiZ5sdZtI/Rk7aJ0UrMWK3m4wfCYGMYPrqzyixmLD2vP9VOQrkxGWX1ecg/eab6qUs2AkYEav3Qqc+A4Znf46LWnyrhYqLe/H/+5GMpkSXAAZOgMVTqbIYihrrSUScFo1F5ZITQR+uZIhEawZ+0+AhNII3BXx4tlnLtIPTQWHI0X2dApR8znjQMb1tWgiijnufVgILsO5CyPwaU1dV1RhRFrEOH6Pl2/pnyymSUlGYgOakICfElAR5x4N0shUFpP6Fb4g4U1WQgI7pIbabopOn/KXtLgNUmsaLK2l1sK2HoWSj/8QfrhRERyvArhD4SoRFahKDRIjTsFqy1Xm4KY3AgBA3LtbltqW5qPlwJmsxc/QGV/uT7RSUYpSMZWNyTsj/WItljQEL9AJTV/4jEiIb+SInA9j0D8cOai1Q6h9GOswZ/iu6df0J5hfcix5Rq0iqcjBYiRvu/aY/cox7t4/erpzEKw+6/GinRDf/vZgB224TBDAYkjxmLksU2QzMNBmRddhVi23dE0QfzTH8cnVJsIfQjNK7m/ghCWAgalvU11YedgmNMWjK+9WHyNp0/FgUuCinXDn5Bs2NDRUDFTAcH1db82UO3WtmAgpMXIjHmv+p+eUkyvj9wkTk7TFHzw9qLUF0Ti7UbxtmJHD2OFOWh4EhH5GTtRVbGIZ1ybVtRQ06iFepQp0JEzoSNESMzllmJGEfrJV9wDko++tb0ig09ZJJHjkZUegaK3p/XsCum5RQu7pRiC6GJmIKFFiFoNBHDkGRTqvcp2ek+CRpbMUOkXLt5cTWNlmMLPqa3xQXeDrPWq7a22j8Hz0s1tlV+GoOhDKvKzrOzuhmNEVizYZyVyPl+zUVok7vDFKmxCLAsXzUFO/cOMIuWrh3X4WCSqVybJeba9PC6KNtJWFGog6vvnxG9kn7FuTkmkeIUQwTSx0xG+pmTgbc+REz79krMkLTxE5E09Cxd4cL/i5AJP06eNB0xxUMjhLUpWPuAawq+qWCUZlKmb4OxYNVvVMq1gz1Co+uf8VDMaM17G5rauqy2dgemnvacHIb3qu/B3vq+DvbK9qsbgU3bz2gY794YmWkUM6Yt836bk/lIOma0mh7OwZy2e+EKlmFf2e49N36hCGT3n4bIuAZxYjAgIjLKbz1khNBDUk6BYdWqVejRo0eTjA4KRoI25dTUgoacm5GKT44e92kbv2+bg+TIVqp6SrwzwQujM+UldS7Xc1bEbJtOWt/Q+BYNAifay+gOU5UbjX3caDZj/fimbWehV/eVSMg1jXFgmkkvLjQoYgfWH2xnFTnKPGhERZIBdW53TbAow7YhZ/CdaBWTjIjIGNTXViM6MVeJGUuR6Sp6JrSMCI2UbQc3U6dOxcyZM9GhA492/oHn9pdffhkLFixQE8A58HLUqFFqEGZCAvs2hJGg0SI0eqPQQ6EnjZRqB3+EZu2SEpVqctc7YysfktJa4WRxnV06iaXYBTyh2wgd3f1yUFekGuT2jUPVL3p1zY6erS2NQElZhtkgbFRbs3+lfft7qiiQ9e9oQEy5ERWp7vWh6Z30q6kM2w4D4jK6WwkYPUTQtGwkQhP8lJeXY906bdCa/8jPz8fevXtx//33o3PnzigoKFDTwJ955hn89a9/Dc+Uk1bW15T4Op5gZEqiRGWCvGxb8814agS2PL2XFtfpppP4ZeruwjdDHL109Wlx6PpwWwy6xfm0akdiw2CoR3KiqUEgvTNrNkzQjdAcL9Uvfe6VuBFZUYeRE80KK6Py2JjGOdjXtQ/L+F53N1K7TnApZkz7KhGalkw4Cpqi2jJsqjigfgYaRjkeffRRDBs2DP369cPkyZNRVmb/uhQK99xzj9WyZ599FldeeaX5/gcffIBx48ahT58+GD16NJ5//nklOriMf6exY8eqNBbTWWTr1q245ppr1Oty/Xfeece8rfnz5+OWW27BypUrMWbMGAwZMsRunzp27Ignn3wSw4cPR5s2bXDaaaepCM2ePXt8fl+CLkKjfcCbI0LDFFGSwYBSL8teyuq0pmJCsGAraNzxzbjDSQdxjE4ua4JMFpfWNsv5nFYGA6LSIrFve6VX3XoH9f1aRWfsvTO26MusARnrcFGbHerej1vHYPPWxmLzI22MKE03RW/6xv6KVqUxKEeyTbl4BFK7nu/W3oqgadloHo9wMQUvKdmMuYVLYeScMhgwPXsURif3DNjrPfTQQ9i4caOKamRnZ2P37t1ITLSZjusG+/fvx6xZs5SI6dKli4qcMJiQlZWFF198UQklCp62bdsiKSkJJSUluPHGG3H11Vfjsccew44dO3D33XerxylQyObNmzF79mw8/fTTyMlxfnHG9iw///wzFi5ciD//+c8IO0Gj5VSbQ9AUnaxFmQ9nu8HJvuX/hMALGn/0nbH0ztiil7Cx7PDyKyM8jAbqrFORGIEnZuxt2DdHYsSR0DEgM93Uu0bfO+MKA0rKaG/eofrbbNkw0irOlHWwHkM6LUFsaQS2rBmJL9HHulzcwvirsX9nFfZuq0THHnFo17XRNi0eGkE7vsfExIT8m8GIjCZmCH++VLgU/eLbIyPSc5HhCooK+k/mzp2LgQMHqmXt2rXz+u9AUUHhkZubq24aycnJ6mdKSgrS09PV/z/66CO1zu23367uU8iMHz8en332mVnQFBYW4rXXXlMCyRmM3jCqxCAGRdHEiRMRdoKmOSM09NCEz1hMQS/llJIRiZGTUrH0Y3vzt9N+uQ2KxLYU29E2bHvO8H7VsAS0L65H8qZKq3X480i8ASuXl7kQWs6iNvXoce5NKFizHr8divMqwhMdVaH+x2Z99jOZIhB1KBFbdg82P6b1xOk3bgRyunS1EjMfzinAz8sp3UycNiIJl9zWeLUmEZqWTTiZgvNrjpvFjEY9jMivOREQQbNv3z4V4erVq5fP2+ratSvuuusuXH755Srtc9NNN6FnT8eRpe3bt6vboEGDzMt4rh4wgBFhE2lpaS7FjJaeok9ny5Yt+Oc//4njx49jxowZPv0+QStotA98c8x18lbUvHzoCEalp/h5rwRf0AQNJ11X5dcgNjcacYls9+8aTQ5cdGMmThmYiA0/lGLtvCK3Yid6PWfKdlQhs6DOTuispKCpNLoRNXL8yqedUYqlX2Rh/Q/93Eh66Ud4ak7Gq/9FRvJigulTa1Gzbfdg3Z441YY+iIwzPVeLzFiKGcL7Q8aavhv8PWuq5NKhJaN5JMMhQpMbnarSTJaihu0uc6NTAnpM8/aioKLCdOGiMW3aNEyaNAnz5s3Dddddh+uvv96hsOBrDx48WKWpLLH8O7rri6J/hnTv3h2tWrVSaTRfBU3QmYKb20PDuU7eQgm29Jj/RygIvsFJ1+tv34utsw6pn1F79D9bjg4PWW2iVWRnxMQ0dB+ZqCt4XcVD+PgBCzFj+ZqqFsmt87vlWALrrRQVRmH9D9VeihnTYzmZ+7B990B8vvhWB4cGLrPxiRmAjFzrAxjTTHp89kohnn/ggDoo5v92UkVxhJbtoQkHQcMoDD0zFDGEP6dljwpIdEZLL1EAbNq0yeW6CQkJdmZhRlhsyczMxB133IHnnntOlVQTbfRQXV2dVUSH5l2mqJhu0m703Pj6eXDV1T0kBU1zlm0Tzl7yhTUl5X7bF8E3eAXTqi4Se1606Qb8fZm5ER7lK4Oj/brpH1gNEdYn7KHn6jdf1IvsGS1+Hk9rpUq6bdehPIh20JjPwW+lu3TfrjifxAw7CMfHlZlnRjnehs1jxnqUHvjBKjpz4qh+dPXg7obKxQaPEKM2XF9ouRGacDEF0wD8bMfr8GDeJPUzkIZgpnRYgfTII49g/fr1OHjwIBYtWqR7zuzduzdWrFiBZcuWqZ4vb731FtauXWt+nOLkvffew86dO3Ho0CEsX75cCRRC3wwDDDTssrS6tLQUEyZMUOKDJdd8Dk3FixcvttqmK7766ivlxaGh+PDhw/juu++UuTmsPTTNUbbtD8QYHFzEGRtTIRo8JdPiRkeHaQgAYNxRrZZbdl1gRHfytGwVnSlcegLFa8pRUmUfZXEkF3i/Iq8V2k1MR17bGCx74IBqvtfPItbBdU7XacynjzPB4ir87Ki/DMXMaowYsgCHCzq5EDN6rxGBPT9+jdzuvfHJq7V2qSbHe2MwR21uf4R/BaElEU4pJw1GZAIVlbGFKZ/HH39clUgzhdStWzezKdcSioRffvlFlW+z1HvEiBG49957lalYO9/S0PvEE08oczBLt5966in1WGxsrGp2N2fOHBW1YSXU0KFD8cYbb6iS8csuu0xFVTp16uRR/xiajF999VUlyCjCaDLmfmpG47ASNNoHvDk8NL4210tqFSEemiAjsl7/CrArr3RsPC88re6mKdYATLzB5JuhmNlwx15UF5pC5PqSAPitofrJlqgusegyKgW/rDSd6ClYChoE1WCb1+/f8JhtzOLsKan4br5vHaydeWfSU4+qyqbiExm61uie3X7A5h3DnPS+OYo9v+bj5+UenJwMjVEbRmksq6CE8EdmOfkGS7Qpamy9LEOGDMG2bdvM95ma+vvf/65ullx11VVmH8vbb7/t8HW4nrauBpvhscJKjylTpqibM8444wx1CwRBl3JqTg+NpTHYG8rr6lXptxAcZERlIbPWtuOLCUsxA5tuv0zlar4ZRmY0MaOto0eKg5RTdIcY/O+LYhTlNwplCpYaB69vVxtgANp28fVk7zzdVF0Vj/cX3IMf103S3avkpKMOvDv1OGvQp0hIKMOhA555zyw7Fe9z4LlpCqp270TxV1+on0LTCxpGAQTBXwRthKa5Uk6aMfjzIs/NvfUNER6Z4dT8lO2sQmIr9lHQN5o5Or1T6NAlU1ZSp7oKM83kCm6Lp3PbUuwdSRHY/Jbd1EenjfkoaHZpURoDMGV6ttoP73FuV06IK8YvW0c6STXxu6h30jEiN2sXundZh9hOtyCmgKF29/wwRpt96tCD/h//Ubb+J1RsWI/4fv2R2N/Up4NQtFRu34a47j0Q27krCubOQekPy82Px/XtjzZ/vM+v+yKEvylYCB6CTtA05+gDS2OwN4LG4IfxCYJnoqV0WyWSesQh0SZlweWqlNJD5zz/hky8vPtMAXiaHZflXrzOMnVEYVtzXjI2f1XicWM+ygpKg9MvTMEZ56WpKNE37zNC4h3RUWWoOel47nd5pV6sygKDEXX10bqRm/wjXbGz7Aksf5RCxrN91EpO2Z/G23STrUAh+x9+ENU7Td2OS75bhJiu3dDugVk64mUAKn+xnlNT+ct6HPzXY2ZRo7d9wT9ox/dwGn0gND9BJ2g0xd6c488ZYbkgIxWfF3nmWxibnizRmSZi95wCHLUwoGaOSELnhsZt7DkTEWUwdQT2cLvGhlOzJjpwxPNSQoqS8iP6n19Xjfkohlhk2aZzLI5uLMeva8qR2t7bg74RbfN2YPe+05ys43gYpcFgxLmXxqNkR6lD2/Pyz72rUkpMjcTvHm7rtZixFShJZ41AwuAhZjGjwfvHvvrcal1iK2Yal69XQubEt99I9KYJUk4SoRFahKBp7iqn8zNT8UURO0C6zzfHStAhNhaj000to4XARWYsxQzh/eyxKaj8rcaiTNvzNok091a7EB2u4CuWHK5R4sX2dJ/oQsxsaHjOgVePIL2kHoxXGn+qAPtwaqdgprcyG4SX4ziiEUkJx5DbpRN2Oy+dcrhH3TutxqIPTofReJFF/xn/DJWMiorwKTJjK1B4v7ZE/904sXSJx2Lp5GHTGAlH0RvBN7QLVonQCGFtCg4WQcMozVU5rqcG257IXjpUKMbgAMN0kh4H5x+z6jnjScopbWg8Mm7NUqLBmehwhfZqnQ7VYpxO5ZPmnbF9Dl/3m4YqKAoWihnbCiwup7DhyMg+DT8bG45bc8qAGvxpdlf0G801vaFedQY2Gg0+HSqiHWoW74UR00B6OPpL1+VbixNX2IoZ2+iN4DtiChZahKAJBg+NRue4GK+NwULgaJWg/7E98XOF/SwnNyM0xfm1eOW/R8CWUnRL+NKz0mBTNWV5Tk9wUN100iKac2pelO46p1n0zbEVOrZ07p1nNVvJM+rRuweb5UV4LUJad4jEgFGJqHGQkUpJ8z44TE+LHqljxiLQVO7QF1OCZ0iERmgRgkYr42tOD40vJdx8Q8UYHFjvzN4Xjri1ru18FRKdpT/H6fDeGowxAqyJaZw3a49ToRNnWYxsbfLVcKcv6rFDJ3Vfh4lMPaHD9JMtWuWQozEE+hgxoPdXuPzCp9Cr+0r7MQcekJASiXXLyhy+Tlyi94KGBl16ZizhfVY0Zd9wM/xBTNfuusvjuumLKaHlDqcUgoegEzTN3VjPl9lOfDOn5WWLMbgJvTPOsEw55VyYgrQhCag50jiXxJIsm+iJVwkRDpi0WcT72ifZbDS2gbJBK+5mCulUhy3w9NNVtkXPPQbEKSHDhnWZuZ60ljcgNakYCfEl6tbv1GVex6rSs6N0n3ru5Wl+mbadM/02tP3bw8i48lr1M2XMWNVPJrpde+Te9Sfd58R06aa7PKpNOztx1O6BmaqM23a5VDv5h2C4YBXCj8hgjdAEg6Bxt4Sbh+Y72uage0KciJlm8M44xPKkWWtE8SrHPWUYt3F1inU1gFK9pM79KCfVTUYLI3CKTUrJndfXmvHR2ZHeJQo1lUZsW1epbt6Q2G4YYNiiZjQN7PcdDhSdiaJCz1Kv6dmR6NwrHqsX24vPI4dqrAbfeYNlOXXauAm6JdkUH7bG4ci0dGX4tuXkwf1KBJ0syDdHYCiOMiZdAky6RKWZuFzEjP8QQSO0CEGjud6DRdC4U8LNcu0zUpOadL9aIo68M84xhQlqip1fETouXm7cCiWCqxGQjsqwHRmN1wDQLKiZXk5rMjRs/9AuX78zRvQcORzbfuqOTavL0Ov0RLTr0QpFhY5SR9a0bh+FM8anYtCoFIfNANf/zyQq8/d55zPTa4ZHs65tSXasTsqofO0qh9ut3LoVWVdcrSuO2vzxXq/2VXCMCJrAsGrVKlx33XVqEndLTOcFXcopmDw0liXczk40Xx8rwZJj+k3UBP9Ruc+zcRiah4Y9alzJEFfRF2OrRjFjmUkxOpmybVmG7ai6SYsZqcomLxM8fI57ksM5U27Owdz/O4BPXqnEjl9bqZ+b17i/5dSsKCVmCBsCTrk52+GbeqKo3uNJ23rl2rZixrzuzu0ebfvEVwtUWbZevxouF/xLMB3fBcdMnToV+/a57PvgMZz8PW3aNAwYMADDhg1Tgy/DUtBoqjJYIjRalGZ6XrbTE56UawcONsor2VSBukp9k2pkiv5fprK+HHsMO1TDveg0fTOwhstUUp21v0ZrwLcUwDYbIaI9/qPF9GxH1U0JFqXYbbz07hx0e+iAPp1OjcW9z3UEIow4ZjG3ijiqUtLj1MH8bRoZNDoZ987uiCHn6vdlcjXDyXbOUuEbryCQOBJHUq4dGEHjq49KCCzl5eVYt06/AaWv/P73v1fn+vfffx//+Mc/1DTvjz/+OPwEjZZbDyZBQ9gsb3aPjrggI0X3pCPl2oHhyJISrJ+xF1tnHcLRpfrRgtjWDjrpGniiN5VyR2V4H3515F0hjBmddPC4ZRLSkTW3mwvfjDv7Zt0b13PGXJKuIiqb3Zhb5cw3o0VnLOF223Sx9+DwZLbrV+sye0uY+jkw8wEUvfum+skoSc1ezkJvHqRc27/U1dWFnaDhYOJNZRVN0oesqqoKjz76qIpu9OvXD5MnT0ZZmf3x8b777sM999xjtezZZ5/FlVdeab7/wQcfYNy4cejTpw9Gjx6toiX5+flqGYdEjx07Fj169FDpLLJ161Zcc8016nW5/jvvvGPe1vz583HLLbdg5cqVGDNmjJr+bcuvv/6KzZs3Y+bMmejWrRtGjhyJ66+/Hm+++abP70vQJtmCTdBokZprWmdhaEoSHth9wOoxKdcOTGRmz9xCl3mY+hr9FSrqyxDJXBH/72G6SoNb3gugo43oMDZ4Xtg8b7+O6NEiOM6qm9AwCNMXMcPOxp5PHbMmKiYCuzZVoJUnBVEWjJqcirGX6xWPAx/OKcDPupVpBmVcZtrJtmOwJ6klf0PvjV66Ssq1/Uu4pZxoOZh7qNB8HGBEP5Ad4x966CFs3LgRzzzzDLKzs7F7924kJlo2iHCP/fv3Y9asWUrEdOnSBXv37lU94LKysvDiiy8qoUTB07ZtWyQlJaGkpAQ33ngjrr76ajz22GPYsWMH7r77bvX48OHD1TYpVmbPno2nn34aOTmmcTSW8PE2bdqo/dYYNGgQ5syZo17bl+7RkcGs4IOVrvGxuDkvW6WZGJmRcu3AUJVf45appGK3vcE0JjsSZcZS5Bhb4/AXxag+6vkBNL5LDCrPSMCGt45ZlXUbdZrbaV4ag43QcDW7yeBlhEh7DV8Dwr2HJOCFBw/AwxmeVsQn6qfzKFb0xUxj2TbTTraCxlEn4ECjTdtmNMhSQEm5tv8Jp5QTIzKamLHsGN8vKT4gVa8UFQsWLMDcuXMxcKBpmny7dtatB9yFEZj6+nolPHJzc9VNIznZJMhSUlKQnk6HH/DRRx+pdW6//XZ1n0Jm/Pjx+Oyzz8yCprCwEK+99poSSHoUFxcjNZWXco2kpaWpc/7x48ethE7YCJpgjNBYQvXNDyy7ArORXiA+uC09OlN50PNKmMRTYpE5KgnZo1KQ/U5rdDB2wf43i7zahzZT0vBDQ1pkMb+8DV2Erb+K1lO2t7KCp0HMpOdEIrag1qfpR450hlYO7gtDx6Vg1TcnfBIz5NiRk7qRFudN/QxWDQDd6QTMZnfVHpp9PSG+l2lMBEWNKg2Xcu2AEU4pJ54DjA4sCIE4L9CkS0HYq1cvn7fVtWtX3HXXXbj88ssxatQo3HTTTejZs6fD9bdv365ujKhYiiKaey3FiSMxQyigHHlnff1MBOVZmL9UKIQk+WEVIRMY34w7qSY90gYnKDHDJnzJESlujz6wJSqzFVrFRsBQ2fj8Aw3GXT1Bg4ZIXZFFCmjU5DQUuNnVWA/tlQ0OHvNsQpE1oyalIS4pwmcxQ378qkTdThuRhEsaJp6TjjpixfI7zvX1BlRqnYBtp2mzmd7e+/6IWg9nM7krjCzTStwH6TsTOMIpQqN1lDc2kQVBaxbq7ftXUWHtXWO10aRJkzBv3jxV8k0/y4wZMxy+9uDBg1WayhLLqemuUkaZmZk4ceKEXdSmVatWKhoUVqZgjVAQNELz+WYcUX3kpBIzx38uh8Hg3Qk7IhY4WVSnjMiZS0vNAyYHuBiLoPWcITxZH11frjuWwB2421VOxAztu9YuLs9Y9mkxvvQycuUIppfcLcXObR9jJX6cdQLOuulWRLfroKImuTffprt+ZFsm/uwxJFr3h5IuwMEBr9LDRdBoVbDayTTQFgSml3jyZ68ZVyQkJNiZhRlh0RMZd9xxB5577jlVcWRZoGNp/2BEZ8+ePSpFxXSTdqPnxl1oPj548CCOHGm82GM1FY3Hvk5fD9oITTB7aATfRQv9MbG50Yi2qT5y1zfjiMKvStSNeHO4TOoXi9Jfqsz7oA2YrHSjGomvytP5lFuzVMXP4r/v9zrdxOfpDao2NqS0mNpyRUJyBMpL9EvdfY3MxCUaUFlmv5HvPy/GlXe1dplyio1r5bILMCMkJ779xq6Rnl4X4NoDv8GQlARjqbVnx1hWqoy+CYOHSBfgICKcIjRNbUFgSocVSI888oiKlFBM0Gg7YoT1fDPSu3dvZepdtmyZSgMtXboUa9euNaeVKE5Wr16tvDjx8fFYvny5EiiEvhkKjIULF+LSSy9Vj0+YMEEZiO+//37cfPPNKjKzbds25YmxTEM545RTTkH//v3xt7/9TRmKDx06pAzI/L+vBK2gkQhNC0gnGYBO07ORNTrZLHROlvhbyHp25q4pqLV7SoQbYoZPYbeUtOxI9O2TgKKVpchrF43SbdVeiRpnjf5aN0SKnJmCR01KRWxiBL56S5sS5T84RbvTKXGYr5NO27iqHOOLalW5dpyTzs6JyfaHHtsuvfH9+qNig20X4PWqey+jNkdefsHqMVsxo8GqpcyrrtMVR/TLCE0PL1h9GX/R0i0IFDKPP/64KpFmConlz8MbTLmWTJw4Eb/88osq32apN0XPvffeq0zFhIKFht4nnnhCRc0YPXnqqafMTW7/8pe/qOojRm0oZIYOHYo33nhDlYxfdtllKgXVqVMn/PWvf/Vo/1md9cADD+Diiy9W1VNs4HfFFVf4/L4YjFpCLoiIiopSeboVK1Y0964IfoSChT1lbLvQ9Z/dESc2VHifatKb2sjmTUuvR1J0Mh458z++7LbaNGVBhpPHyxuMw+cPjkfM2grz/jgSJq7GLByPBVKr7EvBbe8v1SnbPntKKk4crXNYYeQryekRKDnmeBL3lXfmYNu6Coev/+h73XDVVVfh7bfftorMsN+Mu8SfPhQVq9m60D0iW+eh9rC990ZGGzQPjBDQ3MrmbYLgL4JSIkuEJjzRTScZgYPzj/nkm8mdlOrwc+QPvU4RYZoRbQ+3vqdBzDBFZClmtOd6QkkMkHlrFlrfkqMiMJaloHrN+/Q8OmnZUb6LGSc77kzMkKL8Gt3XH3FhiqkjMedytWrlU6m2J2KG6IkZ9bq/rDN3IhaajnCqchKCh6AVNHqlXUJoExET4TAN5YtvJv/j405nObncr3iD118US6/LsLMS3f49HL1i3OkJ6DIqBceLTqp0EiMwhx2sy5ey/ZbQjLxjvfdXvTFxppuj3yMmzsV7ZWDLBf2HElMjVSpKrWZzMnNYqt2FvZQDi3QBbnp4fA+3lJPQ/ATlJ4ofdPHQhBcULZsfdFCX40oEGIDWkx0VSzt7mmtB0/aadCR0sG/N7y6W5dO7fijzRZep57YZaqrK2bvFVC1U3eCX0ZMRluMVcttH4XcPt1WVQyXF3l8M1BuBagde3vbdYnDj/Sxcd8yU6dk45TTrmU4a7DmjXajYChqtVNuuIunBWSot5A8ciSPpAtz0hKOHRmh+gvITJVVO4YUvpdgdbsxEu6sycPgT/SiMU3jSdJFyOvDWMZQ2iAdP4ZZ57qdM4ym3r49jDI62i0JdbAROFNUit72pfDHdhddGs/zGxkeYe7oMPNu6VNkT4uL1l3c8JQa3znLejXT0xWlqICX3g5EiS7SeM44EjW2pNn/yPmnzx3uVCViPmK76kR0aft0RR9IFuHkQQSMEAqlyEgKOL6XYJ0/U4dBHxV4914FX2G9w+z+zNbgPAyYtm+dl7j+JxbMOmSd0k04uXl+LxSSlNn6VWTL+ydwjqPewYMwQAZxzWZZu9VKrSAMWvHYEBoPjd7THgMbIDCNFQ8amqNEGjMxoYsuZoHHW0C5l+ChUbd2s22zv4L8eV14Y2+V63X4pjqQLcPMTTn1ohOAhKAUNQ5HioQkf2G/GW+prvE+fMOVUbXTWft83tEZ6TML40m/G8v/seVPQ0M+GQsndxnzDLkjD0Z1VOLqtEpk94lT5eNFh95tTtusejd/NNDWnWzq/GMcKrZ+769cqdXNEj/7xdl1/ed92mfa99ibdQJGSMmaslUgpri1C2a0XIOXwOYjZmW8lXhyJI+kCHBwRGltjuCCEraCRxnpC5ogkGKK8v4rjFWAUvPfHOINxig1MeTSIDmdl2J4Q0ZBmOtSwXVe9b441pHPyvzmB+uWlan01RyrOoMYwuEtiUuOh4J7/dMTapSfw89IS7N3qekp5j/5xmHpfnluv4ypC4wyKl8LcamS3H4rYyAz8ULIEbxe+aH588ulX4dy0rvbPqclHdnQu0iIdFd4LTQ2rD8VDI/gbETRCwCnd7lmUJGNEInLGpiKxa6waY3B4vhf+mQAnnbY0CA9nk7Td6Tfj7LlHXfSx+S07EjfckQsWJe164IDVBPBTK43Y3xDpYYwksSGa5CjGcupgayMv01aV5fUuBY3WFdldNLO/p4KG4mVe4Vxl8mbkbVLGVfi4qLGPDfm4aJ767c9Nu9D8HEvBc3X2zTgrebRHrysEBl6wagMJBcFfiClYCHh1065nmESxJ30YT7P2pA5IUGKG8GdCN++iLDzxBYr2bnYP9lTMcLmW7NG3u5pg55T2F6Siproe+zm3SueLzXeXc6jGMSXV8FObS2VJenakriipLHNtwqkq9ywl6E3KiVEWTcwQ/rQVMxpcvrdqp3qOpZgh3AaXC82PlG0LgSAoJbJ4aMKsukmvDPuiVMR3iMGx78vsHkvqbj2lOTLZs1x72tAEJPeMA/5nOvkFAsYzXMklbyIzWil2dcOIA0eih5VVm15hDAfIBnCGThfhCJsIkq1HJzkjAudcmqErZp66Y6+dj0YPGn49wZuUE1NGnvwdnzjwAAYnUsJZw21sLPsJI1LHur0tITCIoBFaTISGZjExBYdvdRNnNx3+9Lh95KZhtpPlwEqmnCKiPYu0JHaLRULnWFPCKUCTPdzZI76yTmNkl1EdipluTsTMYYtxBx10xIy2f3r9a7TIDWnTMVZXzNA/446Y0UqxAy1oYiI8j9CtKfted/m7R1/BomLTHBuh+eDnQEzB/mfVqlVqanVL7eMWlIJGTMFhVN2kc6a16wxsALrcmaNmOmmDKsnuOQXY/MABFK/0rPNtUo84VB85CYMhwqsITVJvz07SztATGq7WZ/SnjaNeNRQ8Dam6WCceHq7b2YGg0hr5Fh89qcSLLZvXuH6/6Z1habaneCNoqutdG5M9gWmpRcWf+XWbgmfwQkMETfAzdepUNXPL32zfvh3XXnst+vXrh5EjR6pp2/64+AxaQSMRmtCHkRZGXMyfsgggd0Kq7lk2KrmVXWTmqBfziNKGNPpvvLUEp5+u7+3xFGcpJzh5rM7Bc9mNp/OfcpGeHaXuJ7ox+FJPUJmeDeTvO6l6zjC9ZElPG4OwXmTGEyOwrx4aVij5p4bM2kAsfprmQ1JOwU95eTnWrWvs7+QvKisrceONN6Jt27Zq6vc///lPvPvuu3j99dfDU9BIyil8YMSl/7MdccqDeepn7vmpunmQkyV1ynOjUbrN8/4xrSelotsf6DxBQ5rKdadgvVLxtIG+CZq0ofrtdpXh1+Bc6HAgZYWDx8sigTefzMfK+cWqpDvCi5SW1jvHEqaX9CI1evQdGu9VZMb3sm3r37Qe0ahGprrx/96wu3K7V88TfCccIzRFJbXYtLdC/Qw0VVVVePTRRzFs2DAV5Zg8eTLKymy/2cB9992He+65x2rZs88+iyuvvNJ8/4MPPsC4cePQp08fjB49Gs8//zzy8/PVsurqaowdO1alsZjOIlu3bsU111yjXpfrv/POO+ZtzZ8/H7fccgtWrlyJMWPGYMiQIXb7tHr1ahw/fhz/93//h/bt22PQoEFqH9944w2f3xcxBQsBh5EXy+gLozZ7Xio0nV0b2t0qP02Dh4YiiGkjT8i7OA1tLzX1GWFKa8+LhaZp2x7EaLr9KdcsZmI7RKFqn4Mpiy4oLjxpJyx4n/U1x4z6/hg+voPpHoqGfrEwbqiyW6dtrck709si+qQXgdF7ba2r8J70Vqg6Zl+99O37x8xRF2cpp6Q03w4ZmqBx92RWVFuGNaVblGiJQI1aVo0clFu9i0YkYAdilN3ZJHbqEIdWqDQ/Rwguws1Ds2R9CeYuLFTXT9Tq08dnY3T/xvS5v3nooYewceNGPPPMM8jOzsbu3buRmOj5hdj+/fsxa9YsJWK6dOmCvXv3oqamBllZWSoNRKFEwcNoSlJSEkpKSlR05eqrr8Zjjz2GHTt24O6771aPDx8+XG1z8+bNmD17Np5++mnk5OToirHY2FhERzdeiHTu3BkHDx5UQic11fO5fUEtaCRCE95QsKT0i1f9aayMwUaoqig+xrQRoyXupp1qy+tUmio6LdJcWeXutG21T+ckW0Vm4jvFeC1odu4+Cdv+tGq0AXvsOBk0mZDTCvf+rR1KimuxfMMBdcq2JMJCzGjPcQdN5CRck47IAzXAUvsruRPH6rB/Z5Uy+cYlOQ7c9j3Lt4O0JxGaJSWb8WLhdw33TleiJQrFNmJGbU0t42MnkWbxuBFx2IM4HNTdfue47j79LoL3hFOEhhEZTcwQ/nxpYSH6dY5HRrL/T7EUFUzVzJ07FwMHDlTL2rVzPmfNEYzA8DtJ4ZGbm6tuGsnJpu96SkoK0tPZdQv46KOP1Dq33367uk8hM378eHz22WdmQVNYWIjXXntNCSQ9evfurUTNe++9h0svvVSJGIofwuW+IIJGaBYYsaFvxg4jkP9lMdpfk4XOt+Uge2wKDn9WjOLVzo2qhV+VqFty3zhzdkJFaNxMOSX3irMyIx9bbn/Sd4fovnHY/0slujgRLnpwL/NOT8SODRVY+GIhcpxEWpzhaB0uW/bhMRxycrzY8EMJ3vtPvsMKJ2+qmuz2r+Hv4UrQMDLTKGbUM5RQiVEF6/q/YSlOQR2SrSRfZcM0LFtR0z9hiHQObkbCSdDkH6uxy2xzan1+cU1ABA1Nuqxi6tWrl8/b6tq1K+666y5cfvnlGDVqFG666Sb07NnTqZmXN6aJLEXRgAGNQ1/T0tIcihnSpk0bzJw5E4888oiKDjEic91112Hp0qVm4RR2giZQ5bZC8BARox8JyF94ArnnpynRU/lbDYodpEAyz07E0e+shUfJL5U2jfXc+BxZ9L7x1oyc0C0KHabmoC4tEidu36u8MO4OrOQe5lMwDEnCwgcOqAZ4jkSJnmDRlimPTq9YRG6yT1epMQkuLn4K9tfoipm8LlG46IYcn8WMJ6bg7ZUsTrfFgGo4vhK1FjOwETV1iMExcwpqffkqZQqWcQjNQzilnHLTo1WayfKUFWEActO8n2Hnj4sCR1RUWLv0pk2bhkmTJmHevHlKWFx//fWYMWOGw9cePHiwEiKWxMQ0tlawTCU5YsqUKeo1jx07pgTQl19+qdJc7jzXGWIKFpoF+lw2P8irbR3qTT1szI35dDRJp5uzEdfWVX8Sppxc0+6qDLPHxxszMskdn67SZEwXEdYGbHNDTmlips3N2Ti2u8rpKAVL4WKJ5o+p6hFjFjOW6/D/WxMMDsceaNTW6u9tSmqUX8SMr7OcXOO4XWEluuE4Tlf+Gw0xBTcfPDGGy+gDRmHomaGIIfw5bXx2QKIzWnqJYnDTpk0u101ISLAzCzPCYktmZibuuOMOPPfcc3j55ZetLjos5yoyorNnzx6VomK6SbtRjHgKt8/X5e/C9BMNxr4StIJGIjThizOhoogw9bBx1JiPPWvow2mV4Pzj6+45c/+8IlNvHJ7US123+9dDi/DsbRBEsQ2jC1ztQnGHSIx6riMGjU5G/fE6t7oL663DdyJ2W7XVOnxOZb845PwpF72udT63m+mkgaOS3Zrz1BSCpntc6wAMrjClrbytihL8RzilnAgNwM/O6IgHr8lTPwNpCGZEgxVITNmsX79emWkXLVqkUj96fpUVK1Zg2bJlOHDgAN566y2sXbvW/DjFCcXEzp07cejQISxfvlwJFML0DyMmCxcuREFBAUpLSzFhwgSV7rr//vvVc2gqXrx4sdU23YH7wkoqGojvvfdeJbJuvfVWn98bETRCk+NIqCgigE7TTN2CdRvzRZjEA30ue1844vK1jEY3Zg01mJHVIMxPPB+EyWiRFuHJzDV1eWEm2J0Tcl18K6Q0PLfdaQm6b4s73hmtYMwS3s/pG4+OLsrQ+5yRoEqxWeXEuU7uzHkKdMopIzIRw5OcTbPyFoOqgCJiCm4+wilCo8GITK8OgTEC28KUT//+/VWJ9HnnnaeqlIw6No2JEyfi4osvVuXbF154IdasWaMEhAYFCw299NBQrLBy6qmnnlKPsRLpL3/5i+oRw9dgRIh+F5ZXFxcX47LLLlPbnzNnjsf7T9MwIzL07LCqipVUeXl5Pr4rgMEYhKEQ5ug2bNigflEhPCM062+3buamMAA9Z7U1N8Yzl2BrJd4NYieufbTqIOwM+mt+98h0FFUdxZyz33R7BlTxj551Jc4YlYicc0yTwdcuKcHHc03VDmc2zFhyBr94VRemYOTVjeHaJX/ch8RD9tVVzgzCfGvq+schcn2l1TpcnnVrFk6W12PVmjJsczA5+8zzU3DB1MZ9YE+aLWvKVWTGn2KGbNmyRZkOeaDkFaYlHCq5s3Ibusb1QFJkLm7f63ujLXuMSMZ6TM2+SiZvNyOMztCE+u233zbnbghhRlBKZCr3INRZQqAxAvXV9bol3ozqMGLDSMjhL9gz17XhWK9sm/1qDJHAwffst1G8yjMxQ4qWlqlb8pAEfLy6XIkZSgBXGWXu1XoAV5yXZrU8tW88ag+5bnJn9smcEY+uE9KR2TUWa+YUoG55qQq78l2syI7E0ReOqHVPaRhIqdfEvN9ZHIfZCEWMv4WMhpaPP1F3HIuLv1DipWNsV7xeMAerSpeb18uOYhWFZ72I3MOAm3L+iCFJjis5hMATjhEaofkJyk+UCJrwhv1ndDE0zH9y0ZgvtiGt4wj2r8kclmwy0dgIY2O9EYfed5BWMgK5F6Qi/3PP004lq8pVjQ2lCN0qzrwwhQ2m4fE3Z5vTTeToTpNt191ybS5L7hqnxAwZfFsOjo5NwdFtlYhKiDCLGb1J2/4sxfYm5fRj2VIYi46p//eM64/NlZR3jew/yQjeqQHYAyOyoqwFnNA8iKARWoSgEVNw+KJ18dWj3ZWN1UbOqPhNP3WS3C8WbS/NNKesSlBsFaHhnKfDHzsRKxFA+tBE5H9x3KshUJkNguaok/LqPQB63ZmDEd3jrMQMoyv1y0stet82Vi9td2Aw5jqZNh2VKW542/pFscNJ2xQ0eZ2jcdGN2U0qZshXRR/bmYJtxQyJQomb7iFPMCIWv6G23mQAF5oPidAIgSAoTcGi3MO8uskWA9Du6gy0nmidfnFEpY7HhEQmR1r5bwpaHcSBmn1od20Gej7cFjFZzsVSuysyTCkvB2Imrp3zyFCfkYkwRJimXOvBUzMTLgnJjUZgLTJjKWa0dNLqhlEImpixLcWOYCRKR5Bwe+X7a5xO2k5Jiwy4mKEnhmkl/iSLihdgTckKt6qc2C+GnYEbf2ujnwzBichSwy6F5kaO80KLiNBIyik8cViGfUcOMs5wPw0Q3yEax/5nvzxjqH01T7WxCq0npCkxlf+Fc29KQpcYh83+2l6TjgNvmVIkjmjXPga/vzsRvz3JzjL6qGnXMRHYtakCmbnRStgwRaQXTSHsBWopdFTC5owEdJ2QpitmLCM9ziZt+7MUWw9bT8yAhCFYX74axoaUkzuXUpzNxHEGHEBp6r3sOyeRgXq46l8kNAXhVLYtBAdBK2iE8MNchm20L8N2F5Zr63XyTewWYzcl23L0gdNScdveNzrUV7qOELAvTl2+4/lPap5S/zi88OAB8xC7ydOz0bFHnEpV2VYoqd/BfjeR3JBWssU20uNo0ra/S7FtYUTGUsyQdeWmSb1aFb1B60LmAkZqYnAUlSru5Z/0U37NCVUWLjQvUVHOI56CEDYpJ6lyCj/oj+E0bfOnzqLnjDs4GkvA6EnPWfYt8S3TGnpm48YVXfe+iUpxfTVZV16Pko3WbcUtie8fj9UbKq2G2H38UiGi0iJV+kgTMfy5gaMKdBItvF9xpNZsILZEL9IDm22OuiYd9/ynIwLJmpIfHD+ozdmyOPLUIhGVyFM/3Us/eQ9r33KjAyfmBPeN4SJoBH8TlKEQidCEL3pl2O7iaCyBIz+GZYSGr8OeMSyxtqXjLVnIauiSq4ku2943kSkRbkVoTqy338fYvEh0vi0XBdX1MK63FjyMWBTl15grlDZ9exzLviszVyKtb6hOirA4ndd+dQK7vjqBPSOS1PM0MnUiPeo1OJgSwJibs1VH4kBjNDgWHrVGU2RJS/uUoRtq1DgCjicwIhLHkKycQ/bpp1YoR4nT4RCuxcy07FESnWlmtP5icpwXWoSgEeUe3tiWYbtLkk1Fj6vlttVVemJGi6z42vuGpeK22zFvb0yKGlp55Cf916enhuz9rQbfLC2zikOwb0xq/zh0bx2Fmi9LrPw07DlDEaSXfrKE6467OA39m0DMkNOThmHpia/sllO8lNWbev1UR7RBCXqi1qqnskHd53JN1HBMQTXSUY84RKBSVSlVuT3202KfErpgatYwETNBJGjkOC/4GxE0QsjACiYKB8u0E+9bVjZ5VF3lRBDZii5nc6PSRyWi8605KiWmx/7SOnw6Y69tSxwzJ6vrcaKo1txl2JJr/5SLUwcmqjJsW3cO9+jQ58XIvKu105QTN9l2QGBNwJakRNpXq1UhyxSJMe4yR8+sxYyGaTnXNyBCzV6y7coThSM4qVoXui9qese1FTETJFRVmb4nEqERWoSg0dzvHIIlH3rBks635SB7bIpKP1GIOBMzWsop/0vHfWXcEUSOjMgaCe1iHAoudhB+89PjDsUMvSQZudE4ml+ju05MrElIsVGeXleWylXlSrBRfOmtw/u1Axqb7zUF359Ypzwx7CUTiTKrtBIbGzamCTWHuL2oqVDF6tB9jGImEZtQZlUD5pyBiYH1DQmeCxqJ0PifVatW4brrrlNzl1riuTMof2Ptg84PfmKiVCMI1lA4uBIh2kmzlSHSYeffbn/KtauMcteI7CjCYyu4lG/GwUgFipnJ0xq7Bds2NtbEjm3TPattGIGi7ZU4sK7CvI5lUz7OeDrr3jZoKuYULMby0m0sxm/wxBShFhmNwqNB0DSWIzgSJM6ECgVPO2UUto/g2G9levbZEp0JIk6eNMUaRdAEP1OnTsXMmTPRoUMHv273008/xcsvv6ymfWdlZeGKK67AzTffHN6CRoZTCr5AQRPp4COee0GKSzHjzIiskdI/3k5cWQquuqJaB/sG3Dqzrbm5HUUNS7hZ9USjsCZ2ThbXuizFLsyvsWvMpw2m7BLA8mxbdlYVNIgZy/SRhZhp6BBresi3Eux6JKMatUjFalSgDWrQ1m6di9MGY3RKTxEzQYaknEKD8vJyrFvHIS3+Zd++fWra9h133IE+ffrg119/xd13343WrVurieBhV7bNkeaWH3xB8JZa40ndDnO557vXldiZ4Tilfxx63OfdyHue1+mdsYQVSH9+tiOmPZinfvK+y1JsA1Cv8yvyi80p203J96Uc0mCL9Z416hlfe8qYvDY0DcfjoO6ripgJ7giNdpwPF+iDY8NM/gw0PDc++uijGDZsGPr164fJkyejrMy+6OC+++7DPffcY7Xs2WefxZVXXmm+/8EHH2DcuHFKXIwePRrPP/888vPz1bLq6mqMHTsWPXr0UOkssnXrVlxzzTXqdbn+O++8Y97W/Pnzccstt2DlypUYM2YMhgwZYrdPjPZ8/PHHOOecc5CTk6PWGzFiBNasWROeERot98c3UxC8RRlPjbW6ZdjeVFlZ0vHWLGS7Ef3Y52AQJ8/nTCfZwkiN5VgEZ6XYbF03eno22rWPxq751rOb6nXmPAUaJ9XaZowN07Z9jdA0bAQnkYw4HMLFab3wcfFm1MMo5dlBjnZcDyePx9olJWZTv9YwM5AtEh566CFs3LgRzzzzDLKzs7F7926v7Bn79+/HrFmzlIjp0qUL9u7dqzIjTAO9+OKLSihR8LRt2xZJSUkoKSnBjTfeiKuvvhqPPfYYduzYoaIrfHz48OFqm5s3b8bs2bPx9NNPK8HiDsXFxejWjelj3wh6D40geItmCg5E7xtHJdq2B7n5DgZxjrsqw0q4eArlwJQbM9G14aDJnjQs42ZkhnvWysGcp0ByVnJ3fFnyi9N1rE3BvmJUkmZI0ghcmjEKo1MGqS7AbJwnnYCDX9CES4TGtkJRa5jZrV+8T99xR1BULFiwAHPnzsXAgQPVsnbt7BuLuvu3YKNDCo/c3Fx100hONh1bUlJSkJ7OikTgo48+Uuvcfvvt6j6FzPjx4/HZZ5+ZBU1hYaFKKVEgucPPP/+s0k4USGEpaLQPuhaaFARvsDxpNnXvG+0gZ79TwHlXZmDEhe6lvBylnChajEmN3Yu1xnxcn5GZphQzxbVFKKzJR3Z0LrIjk1FY62SadcNR393RB84xIMmQjak5t6l7FDEiZIKfcEs56VUoag0zAyFo6EFhBXCvXqzy842uXbvirrvuwuWXX45Ro0bhpptuQs+ePR2uv337dnUbNGiQlSgaMGCA+X5aWprbYoYRojvvvBN///vf0aZNm/CO0EjKSfAFf0QBvO1946gM+8o7ctDHg0Gcjkqx2XbuYpsZWBQxTR2VWVT8GT4umqf+z9EFJWg8sOliHlLlnwhNbpQMmgw1tON6uFQ5ccisowrFQKAZ6709vlVUWHcrnzZtGiZNmoR58+apku/rr78eM2bMcPjagwcPVmkqS2JiGr+H7grVw4cPq9eiiOLr+4OgFDTaGyKCRvAFy9EHTdX7xtVBrr0Hgzgty7U1UcOfv3Icws2N5d7NxaLiBWYxQ9gMzxXGhumUhlb+qUc4LfF0v2xHaDrCLULjqEIxUN9PppfYq429Zs444wyn6yYkJODgQWvTPCMstmRmZqqqI5p4b731ViVoIiJM39E6zffWENH57rvvVIrKF0FK0zHFE8u1KWr8RVAKGonQCL5Clz1DsxTFzPHyi8gQ7Wmnnabc+Z4eTN3tfeOvg5zt5GytFNtwYQouOS+tycUM00q7K7crQdUlrrta9nHR21bruHO9aK5y8oueMSC/NigPYUILitAQGoDpmWGaiZGZQH4/mdJhBdIjjzyiIiU08NKIO2LECLt1e/furUy9y5YtU2mgpUuXYu3atea0EvvArF69Wnlx4uPjsXz5cuWLIfTN8Di5cOFCXHrpperxCRMmKAPx/fffr/rGMDKzbds2pKamWqWhnFFQUKDEDH+Hiy++GMeOcQyvCc2r4y1BeTTQTjbSh0bwBJb98Uu+ePFicwljbGwsvvzyS7t1eYXDqxd+gdj/oGPHjqo0sW/fviqkqn2pm+sgp+edoQZITrWugmq6SIy1eDkreYzdejE4gmq0cSptjA2Tlr0dMGmHnzYjNB3acT1cIjSOKhQDCYXM448/rkqkmUJihdDwBlOuJRMnTsQvv/yiyrdZZEPRc++99ypTsfY3oKH3iSeeUOZglm4/9dRT5mPnX/7yF8yZM0c1waOQGTp0KN544w1VMn7ZZZepCHinTp3w17/+1e19p8DixSZNzbxZQnHkCwajP2Lyfuall17C9OnT8cknn+Ciiy5q7t0Rghg65P/xj39g0aJFKC01+Vzowme5Ib9kmjD57bff8NNPP6kvN780LE9kDpdXB2wgZRlW1eAXmk5/XgFxO7zCYZSHBjje+HigYIRm1wMH7Eqxuz7cNqA+GUuDb1pkhpVHxh0sxxzosf/rdVgx42UMfuRqdL7UebjcHR5uewm6xrpXGioEB6+++qoq/f3www/VFbog+IugjtCIh0bQY/369UrEfPPNN6qEkbAXw1VXXaVETPv2nMZsDZfxRqHj6Kpxw4YNSiAxN71z504cOHBAlSDu2rVLhXRttT+jPAzDMspDEcUoT/fu3c1RHl/ahVO0NHUp9g8lSzCvcC6MqhzagEkZV3kkZkg89iESpahQnXt1/EJ+DNCMSOohYiYEkWnbQosSNDL6QLCFfQoefvhhfPXVVzhxgq3moCIndOg/8MADPs8aoYimCOHNEYcOHVL5Z0Z52C1Ti/IUFRUp453WSdMS5pgZ5aHpTovyMH/NCA/9PBREjmjKUmxGZjQxQ/jzExdihl166xCHVqhEBGpQjRyL2UpGp6Zg+Fi2fX5yX0zNtg+xC6FjCrasjBGEsBU00odGIIyUMBJDD8zx46YBkxQGN9xwgzKludvrwF/k5eWpnDRvjq48Kby0KA+7aLLPAqM8FD8UQUyNWcJKAooaGv0Y5aEw06I8NNl16typSUqxmWbSxIyG7X1LbMVLHPagEp0sQi/6k7Q1PeNrSX2HGM6IEkIRidAILUrQaMpdTMEtD/pbKGI+//xz1Q6bMKXDqa8UMf5ojx1IIc6oC2+OoLhhlIfpLQocVhkw8sMoD6M9evNMuF22HWdEis2nOnfurKI8/fv3V6LHHxPpYyLcv1quQZrNlGuDjZiBY1Hjp8Z65fUmY6kQerApHJEIjeBvRNAIzQ4jGZqI4YmdMGLBeSEUMaeeeirCBXp9WEbOm6ODPf06NDAz2kMvDw3NLHXkTwq+b7/91i7KExcXp94z9ofQojws2aTgoQjUeko4orpef24am+VxXlIUShCJMiemX/2IjO1yf03b7hHX2qfnC81HuFY5Cc1PUAoa8dCEP4xMsPSQ5YNHjx5Vy9jLgFNgKWL80dY7FOHAPqabeHME3y8KHkZ5tmzZYo7ycDn9PXzMFp48GMlhyk6L8lAoMsrDHhTZibl23pdSnIKTyGyIwBgRiWNqwrW+o1dPzOiIGh+7nBIxA4eHoJEIjdAiBI32QZdZTuEFew/Q2Pvpp5/iyJEj5sFnnCPC6iRnJ3GhEYoSNqXiTQ/2k6DQ0aI8jIBpUR6alxn1YbdPSyJjo5DWLRlJeXFIzItHTOs2iM5LRHxeBeLz0hGTnohagyMxQ1wJFNPj9XVmE43Hf9JTYvJwTdaZUtkU4kjKSWiRgkY8NKEPT6Rsdvfxxx8r/whh1c8ll1yiIjGMEAj+heklRricRblosqbg+Wzr9/ixbCcqDhWj/PAxVB06gGPL8lF6aA/qqhpFT6uYKMS1TkVCXjriW6chPi8N8a3T1c+EvDTEtU5DZKwbKQRt2rbTDJh+tEfETHgQbqMPhOAhqAWNRGhCE/Zv0UQMZ3YQmlqnTJmiOk+62yJbCBxM7/UfOQQvddiGTiqtpGFEAraizHgKaorLUX64GBWHjinBU3HY9PPEznwc/t9mVBVaT9VmFMdW6Gj/Z5QnNiPRnNEyOFQ0pUjGTvRMmIwfy/eal0qaKXyQCI3QogSNjD4IPejhYDvsjz76SFXrEHo2OEWV6SRn/V2E5iG/5rhOabYBtUhSGSEKFN7Se7XTfX5d9UlUFpxQQqecgsdC9BR8vxXlh46hrrKxGikiKhJRiaaLlR1vLcfxbQeV0EkwR3zSkBBfgqnZV+Gs5NHYWVWAbZWHlQFYugGHD2IKDhyrVq1Sc5LYNoJ+vJZGUEdoNCUvBCdMIbE6iS3MKWgI5yOxTwtnh7iaBCs0LzEResMBjahWXX5dwzRUYvtMddODFU01JyqshM6hbzci/4etqDxyAns+/BGVhSesRpK3imqFDyL/rbxVrAjjZGFWaWmDRemzaokH6nBCi7wHcnSI4B/YLmPmzJk+Ny7VY8WKFWo+FP1+nDP13HPPYeTIkT5tMyiPDNoHXVJOwQcraRiJef/991VqSRMxnMJKETNs2LDm3kXBTarrTSeWQE17ZCVTTGqCuqX1NIkkQ2QrJWj63zsJbcb0RV1NLaoKT6hoTsTuI4hYfkR9rvg5Y4k6q7ZsoaDhZy4jI8M8WPSUU05RYuf0009XDQqF4EW7UBVBE9yUl5dj3bp1Adk2p3r/6U9/wh/+8Ad1UczPgrOu6e4igkZwCQc4PvbYY3j33XdV51vCvifnnXeemtw6atQoeRdDkNzoVCdDCgKDNvpA89C0io5EQtsMJLRNxyNT7kbnB9rZVWzxM8eGgxQ327dvV12X6c1ipRwr53744Qd7IRUTY47ycORE165dzVGefv36iSE1CASNRNq8hxGNp59+Gl988YUayss2DG+++abderzI5PutTdAmzz77rIqOvPPOO+bp1xwIzSg7m3deeumlauYdCzc4T3Hs2LFqPU7ZHjJkiGoIymrVjRs3qosKDpJmuw0yf/58fP3117j++uvVSJqysjLdkTCcFP7ggw/iggsugD8JSkEjow+aH1bB8EPHDz1PGpqI4Yf7z3/+M8aMGdPcuyj4gaYUM1bDKe08wQYUnKxG51j7ii2Gu3njAdbRwZ1XkrzRO8BhoozyUPCwZJ0HXls4WJRRHnah1qI8PXr0MA8W1aa0C/4nXK0ENUW1qMqvQWxuNKIzAntqfeihh9Tn+plnnlGifffu3V51DOfFAvuBMfXDUTK8WKDHicLmxRdfVMKGgoffBxZ2cBgwJ6Wz6Skvcvn9uvvuu9Xjw4ebZquxMejs2bOV4GKjT73KV74O95fbZ0d42hNYMMIK2LAVNOH6wQ9mEfPkk09i3rx56gNHGAo855xzcM899zjseyKErim4yfFTp2BL+BnlAdGZZ4sHUZapM8rDVJY2WJTRRx7UV65cqbtdHmB5cNcGizLKw8GivEnKxDvC0UpwZEkJ9swtNHcc6DQ9G1mjfTs5O4Kigg1J586dq5piEnrNvIERGEZBKTyYqrVM12rigpFOCn/Cog+uc/vtt6v7/F6w6/lnn31mFjT0Vr722msOZ+3xYoMXKq+88ooSZnV1dern3/72N/z73/9G2AkarU17OH7wgw1+ORiOfPvtt5XKJwzXjx49WilvRy36hXA1BQcWo7kPjbWg4b3ucYHzvrRv317deEWoB69K2XlZGyzK5oM88PLgzIgPrzrNYxssojzM+/Ngz4M8ozzaYFFGeXiScTVyoiUSbheqjMyYxQwxAnteKkRKv/iARGoYMed76I9u6l27dsVdd92lmpvSOnDTTTepOXGOYMqXN8vWGxRFFPgaHMHibHAwLyL4XWJ0iesSnmtuu+029Xv5kooMSkGjIYImMDCv+c9//lPlXHmw1qJi/EDTpOVomrTQEkzBgcXoYPTB9OyzkRHp+5BNb+HnnyLEWXsBegw4WJRRHvoItCgP54+xA7OeV4AXB7zSZXdnLcrDEwZPAPTz+MMIGWqEm6Bhmskud1tvWh4IQePoO+QuFRUVVvenTZum2mswMs+Sb/pfZsyY4fC1+R1hmsoSyzEWrhomMrLJ8UaamCFM+/JzwQtsLRoUdoIm3D74zQk/xP/617+UsYtXn/xg8oM3YsQI/PGPf8RFF13U3LsoNIsp2KDTiyaAaFEOi8jFhSn9MTrZ8VVhsJCXl6fEviPBzygPR01oUR76C5jOYpSH4ociaNGiRVbPYQSHooYHd0Z5tMGijPLwKrhTp05hF+Xhcd2XWV7BBj0zdu76iIblAYCRP0YH+Rlz1RojISFBiW1LGGGxhYL7jjvuUKbfW2+9VQka7XPHlJBlRIdjU5ii0mYuegpnyPF8xP3XokxMCWvRTl8IakFj+UYKnsMPDcN6zGfy4EoRww/hWWedpcKMDL+H28FScB9GRKZnj8JLhUtR30SipvHqsnFZlCGoD0NuwwsERl14cwTFDaM8TG9R4GiDRRnlYbSH1Vx626Uhk14ebbAoozwcG0LR440ZtDkJt+M6ozD0zDDNpEzvEUCnadkBMwZT/NLPyG7sjJTwc8GU6IgRI+zW7d27tzL1Llu2TEUHly5dqj5/WlqJn7/Vq1crLw4FBcupNUM8xQU/ewsXLlSVT3yc7TloIObYmptvvllFZuhJY+dxdzvA8zN87rnnqiooGot5IUADMV/DV4L2SEIFLyknz2HFx3/+8x+8+uqr6oPGEwhzkkOHDsWdd96pPjQiYgQNRkb6xbfH9sp8PFPwdeDfGJ1w+WmJHVvMH4QVKfSlOfKmMXrBk5M2WJTRVG2wKH/yO/3tt99aPYffZ1Yg8kTHK2ctysOTGU8ybEwYTN/5cIvQEBqA6ZlpqionChlWod5yyy3qwpV/4+ENplxLGE1kipTl2zw3UPSw1QZNxYSChYbeJ554QpmD+/TpYy7xZmqIlUdz5szByy+/rIQMzyOM8rMX2WWXXabOL4wishu8J1DIsP8Mq6Uois4//3zlo/EVg9HW6RYk8AvI6ppvvvmmuXcl6KHCZW8Bfuh41aeJGKru3//+96pHQDAd0ITgZEHxOrxdtCKgr7HlxUX45clPcfbbdyL79G5qRtNtOecE9DXDDTYdpOBhlIddVrUoD5fTH6d3IcgTFyM5TC1oUR6G/hnl4XGCV9hNBbvBsneQWAoEfxPUERr5wDsXMVTObIikVWAwr8orMuY/r7nmGhExgkdcmDZA+WnmFdmXMPsL7fqpZ1wb3NX2EpnR5AUUJUw5OGqjwCttCh0tysN0sxbloZ+CUR/6IPSiPBQ2jPKwIoxX/YzyUPBQ/PjroigcIzRCcBDUgibccq3+OBC88MILquERDVU8cFHEMGfPvgCcuyGRGMEXJqadpsTGAwc+DMwb2VC23TuxvYiZAMFjAM2Wzsp62XOKgmf9+vVK/LBlA8UOozyasdkW+u+0KA8N0kw1UOiw8zIrX9w1dIqgEVqkoJEIjenLzwZK//3vf1VnSE3EMFT8u9/9TpXYSQtxwZ9wsvXN2WfjxULrq3h/oFVUdY7P9vu2BfdhJIbdvh11/OZxhpEdGkgpcFgZw/4njPIwvcV2DzSa2h6zGeVhIzYtysOqGC3KQyMqj1W8UJUIjRAIRNAEqYhhF0VGY5gn58GFV10s5WRJHZsfiYgRAm0WjjNE4ZkC3zxsl6cNRUpUHOYWfmdqotow+iAtOrQqc1oaPN5wFARvjmDPEEZyGOVh2luL8nDkBO9zud52eTzztuRXEEJW0LSklBO/5KxMopOcBwL+7vzy8+qGTnaWyImIEZqS7nGtfXo+XRIjUnqo8nBWUuXXnMCbKYexEZ/LFXoYwIaBbMbpaDgtj2k0LDPKw8G2ixcvVqZlwrJfQfA3ImiaEX7h2a2Xg7w4WE8TMcx9c4IphYyrrouCECgoRJh60qIrjjg78VS0jkm1MxNbdv/lT94Y9SHi9Qp/WCbMYxujzYzmMFXO/iPsjUXvjSC0GEHDA144RmgoYthimmXWDNdqBjnml9mCmvMsRMQIwdinZnvlYXxZ8ovdOn0T2uOMpK44K6m7Wg8Nc5n0RhloVU4iaMIXem7YuJOVVDze0VPD4bbsnSIDPYUWKWjCyRTML/V7772nGt4x/KqJGOanOYqdvWLkiy4EKxQmFCwUKV+V/GIVrbEcKqmt5+q7QETQhB9MmVO0MM1ETjnlFHX/kksuae5dE1oIQS1oQjlCwwP3hx9+qMKrbGfOZlf8ndjBk5VJvIIRESOE3qiEs82jEiJgwLTsUR4NlZQITXjBVNKf//xnvPXWWygvL1c+vwsuuEBdvLGsWxCakqAVNJobPpTg/n788cf497//rSbvaiKGpYvsEcNJ1i1xuq4QfikoGnxzo1M8npCtfaelbDe04UUaj2crVqxQIjUjI0MNueV8HkmZC81FUAuaUInQfPrpp2qS9Y8//qg6+BKKmGuvvVZ9yUNteJwgOEMz+HqDRGhCF4rR5557Ts39OXDggFrGVhKcy8NZPELzs2rVKlx33XWq8WpLrIoN2t842AXN559/rkQMr1Cqq6vVMs5H0UQMSxoFQbBGBE3owe7BHBz4/vvvq8olRmDoi+GEZG0ysxCeTJ06FTNnzlQDT/0Ji19sh6xSLHN+oy+IoPGAr7/+Gk8++aQarMYvNmGemBND//SnP4mIEQQXiCk4dFi+fLkSMhyRQCHK7r9///vflWdGTN3hT3l5uWonEgjYfPHhhx+26lTtj0xG0I5gDhYPzaJFizB27FjV0vu8885TqpJfbI5VLy4uVt0x6eSXiIwguEYiNMENj7lMIbVu3VpNxaaY4dgCjjnIz8/HfffdJ2ImCOAF9aOPPophw4apWVqTJ082Ny20hH8vlsxbwpYhV155pfn+Bx98oAad9unTB6NHj1bNXfm35jJmH3j+Y0Uu01lk69atavgxX5frv/POO+ZtzZ8/X/VPW7lypRIrQ4YMcRj1YzCA87+0mz+8V0EboWETpuYSNEuWLFF5Yl6hVFZWqmWcS3LFFVfg3nvvdXsImyAI1mhpZLnCDy44n4mVl/QD0gfICkx6Mf75z3+qYZSCa2qLylGTX4Lo3GREZiQE9C176KGH1Gw/VtFmZ2erC+tELyIc+/fvVxfkFDFdunTB3r171d8/KytLDUGmUKLgYWoxKSlJVbWx1QizEhS+nPfFKB4fHz58uNomx16woSJTkrz4dyRoeC7l+ZX7P3HiRJXe4nk/LAUND3isEmoqKF74B+KVSEVFhVrWrl07XH755eqNly+1IPgvQuPrgUvwD19++aW6iv/lF1PDxDZt2qjj3e233y6i0wNKlmxH4dwV/ICzhA/Z089E8ujuAfmYUlQsWLBADS1m9Ew7V3kDIzAMHFB45ObmqpuGlnVgY0TtIv6jjz5S6/DzQShkxo8fj88++8wsaAoLC/Haa68pgeSIN954Qx0DWPXLeYWPP/64Ou/OmDEDYStoAm0KpheGYbulS5eqfKH2hebcJKaUqBwFQfAfknJqfngFTv/CnDlzUFRUpErozzrrLHVFPXjw4ObevZCMzJjFDDEaUfjSCsT3axOQSA2nnrM5K0fk+ErXrl1VZI4X7pzJxcHH7FrvCE5d523QoEFWomjAgAHm+2lpaU7FDLFcn/tQWlqK119/PXwFTaBSTswDPvLII8oLo4kY5osZRvvrX/9qpVAFQfAvYgpuPtjB94477sBXX32lTogJCQnK78DIdGpqajPuWWjDNJNZzGjUG9XyQAga7aLA215OFQ0ZCA2O3Jk0aZIaycM0Ixu/OhIWfG2KXqapLImJiTH/3xsvDP00FNdhawqmoNH+cP5oAsVcIHOAQ4cOVeEx/p+lYxx3z/wxO1uKmBGEwCIRmqaHaQIOg2RbCbabYHripZdeUibSF154QcSMj9AzwzSTFREG0/IAwL8fz4/sNeOKhIQEO7MwIyy20FJBscvS6ZdfftnK52aZKWE0hcKYKSqmm7QbPTe+QC9Ox44dfdqG2meEaYSGgx8vvvhilQc8/fTT8cknn6g/7q233qpCdocPH1Z/vLy8PL/utyAIjhFB03RVMGwlwcgLe8bwJMaKFBpJaSBlakHwD4zC0DNDEaOIMCB72pkBMwYzpcMKJGYa1q9fry7KWY1b3dAPzZLevXurXmn0hrIZIkdUcJ6gBsUJ5wzu3LlTXdjTS6r1FtIqjxYuXIiCggKVFpowYYKK7t1///3qOTQVL1682GqbrqBpmEJ6y5Yt6jUZYPjvf/+rIkW+EhlOZds0tjEU9s033yjjFKEPhtVJ/AP4uzmQIAieISmnwMKTxJ133qkqNXllzQs6eiT+8Y9/yNiVAEIDMD0zTVXlxPMcjbRMGTKF1K1bN7Mp1xJWD/G8SOM3Re6IESOU6ZumYkLBQkHBql5+N1m6/dRTT6nHWOlGLym9VozasBKKGQ4aeuk9veyyy9QFCtNFtGu4C03GFEAcZkoRxsjMgw8+qPbVVwxGf+V1/AwNT1SPtvk+vVH1/LIyL3z8+HG1jOEvvjkUMTIgTRCCB6Z5eWBkvlzaH/gPnmTY0XXXrl3qPofgsrTXst+IIIQ7kaHooeFVCF36LDlkczvC4Wg0M3E4miuHtSAIzYOknPwHvRG82qaY4f95zORMJfYm4RW7ILQ0glbQcLCWpaDZtm2bisR88cUXOHbsmFrGKzzOTmIkhp0MBUEIbkTQ+A79gZx0/f3336s0AT0VTA0wIiOTroWWTNB7aBh1oTNfK+nil/eqq65SkRg69wVBCB3EQ+P9+0bjJL0LNGJqhk9e5PnDeyAI4UDQChqWpnGOCJvtUNywVwxnStDdTfOTTHkVhNBDIjSewWg0Z/G8++67qk18VFSUakHx73//W41jEQQhBEzBhHngEydOqPywNlNJg02F2DaZaSeKH67bt29fnHHGGaodtIReBSH4YGkmKyb4fWYVheC4izln5KxevVqJQBY6sE8Iq1WYjhcEwZ6g/maw2Y5lyJX3WVPPkeY0BrOfDMeQs/svl1vCKxmWh7FZHsvCGJ6l0OF0UmmgJwjNg6ScnL83HD/AgZDsk0VOO+00VVLLycWCIIRwhMYT2HeGI8vZFZh192wexaY9rIJi/b1tdIdN9lgZxbAtSxw5Cp3Rnf79+8sVkCAECHrimEbm4FmJNJjIz8/HH//4R8yfP1/15WAbeTYFpbCRiy9BaIGCxtWVDyM6DOOysyIrprToDtNZtg38mK5idIe+HbYL57AudhvmADeZui0I3jN16lRVZsymb1pr9ZYK58mxmy8jzoRdy5lmYiO8lv7eCII3tAhB447xjikrdi/UWoMz5MtGfbbtpHmgYXSHOW16d0455RRzdIceHjkQCYJjOPzuzTff9NuctlCDbePZsn727NnqgorR4iFDhuBf//qXOoYIguA9ImhcwOgNU1hMZ/FKijNRWDbJgxGnddtGdxgu5vwUXm0xukPvDqM7Z555pgyBE1o811xzDd5+++0WJ2h+++03NZKAfbSYbouLi1PtJ+iPkY7JguAfRND4SGFhoUplMbrDoVtsPc6cOKuzampqrNZl9CYxMVHNl6J3h80AafrjlRl76kh0Rwh3rr76asybN6/FCBrOyeGcG20yMufJsQne9OnT5fsuCH5GBE2Aw8uM6vz444/qJ6u0GN05evSomlFle1BnGSsbBzK6wzHtjO4MHjxYeXcohAQh1GFU4p133glrQcMLmb///e+qER6LEnihwt5Z7B3DogNBEAKDCJpmhFVYbF/OBoKM7nAYJ6M7rNhiWNoSzmlJSkpS0R1e5dG7wzJ0prI4u0qiO0IowGGJbBIXjoKGFyzsFbNo0SJleuZFCKu62N1XLkgEIfCIoAniqzyWoLPHzoYNG9TB8sCBA8rArBfdYU6euXgtukODMr07HPfOBoSCEAxcccUVeO+998JK0DDixIiM1jeL3z/ep19IEISmQwRNiMKyc60yiyXpe/fuRUFBgYruMNVlCft9MLqTk5OjmgzSr0PvDsPgjPYIQlNx2WWX4YMPPgh5QcOLCnphXn31VZSWlqoI6jnnnKMa48mMOUFoHkTQhCFsJEjfDtums0KLV44HDx5U0R29ERKM7rDJYJs2bdQICZahs5R00KBB0p5e8CuXXnopPvzww5AVNPw+sU/MsmXLVIUjKxpp8J05c6Z8VwShmRFB08LgQZheHVZm0buzdetWFe1hdIdXmsz920Z32GRQi+706tVLeXdoVJYBoYKnXHLJJfjoo49CStDwO/PKK6/g4YcfVt8VwigMJ11zUKQgCMGBCBrBCnZOZs8dRnd+/fVXFd1xNkKC/hxGd7QBoVqTwQEDBsiAUMEOtvRni/9QEDRsrMlOviwzZ4qJ4n7ChAkqrdSpU6fm3j1BEGwQQSN4dKXKsRH07vz888/q/2wYxl48FEK20R0OCGVInvNoeAKwjO7IjJqWyZQpU/Dxxx8HtaChEZ+zlSjsuZ8U7DNmzMADDzwg86cEIYgRQSP49YrWMrpjOSBUb4QEozucjWU5IJRVWTIgNHxhiuaTTz4JOkFDsf7ss8/iySefVH4zws/j448/jnHjxjX37gmC4AYiaIQmO2FQ5FDwcEAovTuM7rDJoKMBoYzuaANCGd1hk8Fhw4ZJq/gQ5qKLLlLdc4NF0DC6yGgMfT1MqfJzR9HF2UpsgSAIQugggkYICihsmMpi7x22ibccEOpohIQW3WGTQUZ16N1hd2VpMhi8XHjhhfj888+bXdAsXboU99xzj0qdcl+YAv3DH/6glsnnRxBCExE0QtDDvjosl6XgYZNBDgjVojs0a+oNCNVGSGgDQrXoTnJycrP9HgJwwQUXqAGNzSFo+DliSokjCBiZoamdrQkYjeFnQxCE0EYEjRDycFyEVobO6I42QoLRHb0REnoDQjlCgv+Xq/PAwiqhL7/80k6EBhJ22GbvmAULFqhoH2emXX755XjqqadUlE8QhPBABI0Q1vAExsGg9O5oIyQ4ILSoqMjlgFCWoTO6wxESTGfJPB7fGT9+PL766qsmETQLFy7Efffdh40bN6r7bC3w5z//GbfddpsIV0EIQ0TQCC0apq4odrTojjZC4sSJE3YjJLQBoWwyqA0IZXSH6QqWpUt0xzXnn38+vv7664AJGgrYWbNmYc6cOaozNtNKbBPA3jFMLwmCEL6IoBEEB7DqhbOyKHh4la+NkGB0hyMkHA0I5QgJDihk2a82IJSRHwE477zz8M033/hd0OzatQt33nmnEksUogkJCbj22mtV2bX4pgShZSCCRhC8RBshwUoZywGhHCGhNyCUJ1Z6dxjNsRwQSi9PS4E9XRYtWuQ3QcO5UGx4xyaPhCZw3r/hhhv8sn1BEEIHETSCEADoz7EcELpz507VZNDVgFDOx6J3p2/fviqyw+os9kYJF8aOHYvFixf7JGj43j744IN4+eWXVWqQqb7Ro0er6iX2KxIEoWUigkYQmhiezJki+f7775VhmU0GGd05cuSI7oBQjpBgdIe9UiwHhNK7E2rN384991x8++23XgkaepxYrbRkyRL1fA5NvfHGG9XQSHadFgShZSOCRhCCjJKSEuXbYZNBRncsR0g4GhDK8mPLAaEsQ+eAUKa6golzzjkH3333nZ1oc8brr7+OmTNnqveBsLye9y+77LIA7qkgCKGGCBpBCCEYmaBfh4JHb0Co3ggJRjIY3dFGSLDah5U/9PM0NWPGjFFdel0JGoq6e++9F2+++SbKy8uVMKP/5plnnkGXLl2abH8FQQgdRNAIQhjBZoJMZbE6i7OzmNrSRkjoDQhlNZAW3WHkgyMktAGhgShDp9dl2bJlDgUN95uzlfg7sIqMVWO33nor/v73v4eVl0gQBP8jgkYQWgiM3jCFxegOvTscIcEmg/TuMAqiN0KC0R1thASjOyxDZzqLQsMbzj77bCxfvtxK0PB1X3jhBTz22GNqf0ifPn3UfTbiEwRBcAcRNIIgKJi2Yhk6oySbN29W0R2OkGAlkaMBoVlZWVYDQil2evbs6TC6M3LkSPUaLGtnxRejMe+//76q/KL5eeLEiapaidVegiAIniCCRhAEl1CAMKrDUnSOkKB3RxshweiO3giJ1NRUFd2h50UbEMouvqtWrVJVWhROfB69PKxe+tOf/hR0JmZBEEIHETSCIPgMq7A4DZ2VWYzusAyd3h2ae20HhKoDj8GgGgs+8cQTylcjCILgKyJoBEEIKExXUegwMkMPD1NTV111VbNUWQmCEL6IoBEEQRAEIeTxf12mIAiCIAhCEyOCRhAEQRCEkEcEjSAIgiAIIY8IGkEQBEEQQh4RNIIgCIIghDwiaARBEARBCHlE0AiCIAiCEPKIoBEEQRAEIeQRQSMIgiAIQsgjgkYQBEEQhJBHBI0gCIIgCCGPCBpBEARBEEIeETSCIAiCIIQ8ImgEQRAEQQh5RNAIgiAIghDyiKARBEEQBCHkEUEjCIIgCELII4JGEARBEISQRwSNIAiCIAghjwgaQRAEQRBCHhE0giAIgiCEPCJoBEEQBEEIeUTQCIIgCIIQ8oigEQRBEAQh5BFBIwiCIAhCyCOCRhAEQRCEkEcEjSAIgiAIIY8IGkEQBEEQQh4RNIIgCIIghDwiaARBEARBCHlE0AiCIAiCEPKIoBEEQRAEIeQRQSMIgiAIQsgjgkYQBEEQhJBHBI0gCIIgCCGPCBpBEARBEEIeETSCIAiCIIQ8ImgEQRAEQQh5RNAIgiAIghDyiKARBEEQBCHkEUEjCIIgCELII4JGEARBEISQRwSNIAiCIAghjwgaQRAEQRBCHhE0giAIgiCEPCJoBEEQBEEIeUTQCIIgCIIQ8oigEQRBEAQh5BFBIwiCIAhCyCOCRhAEQRCEkEcEjSAIgiAIIY8IGkEQBEEQQh4RNIIgCIIghDwiaARBEARBCHlE0AiCIAiCEPKIoBEEQRAEIeQRQSMIgiAIQsgjgkYQBEEQhJBHBI0gCIIgCCGPCBpBEARBEEIeETSCIAiCIIQ8ImgEQRAEQQh5RNAIgiAIghDyiKARBEEQBCHkEUEjCIIgCELII4JGEARBEISQRwSNIAiCIAghjwgaQRAEQRBCHhE0giAIgiCEPCJoBEEQBEEIeUTQCIIgCIIQ8oigEQRBEAQh5BFBIwiCIAhCyCOCRhAEQRCEkEcEjSAIgiAIIY8IGkEQBEEQQh4RNIIgCIIghDwiaARBEARBCHlE0AiCIAiCEPKIoBEEQRAEIeQRQSMIgiAIQsgjgkYQBEEQhJBHBI0gCIIgCAh1/h+HSbaPywTmFgAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -520,6 +553,7 @@ }, { "cell_type": "markdown", + "id": "5c3e014303a2", "metadata": {}, "source": [ "Feeding the cluster labels back in as `hue` colors each point by its cluster. The labels here are strings (`'cluster 0'`, `'cluster 1'`, and so on), which sends `hue` down the categorical path, so each cluster gets its own discrete color; `legend=True` then names those clusters in the legend.\n", @@ -544,7 +578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/conversation_shape.ipynb b/docs/tutorials/conversation_shape.ipynb index 9a364bf5..2c7d2767 100644 --- a/docs/tutorials/conversation_shape.ipynb +++ b/docs/tutorials/conversation_shape.ipynb @@ -3,23 +3,38 @@ { "cell_type": "code", "execution_count": null, - "id": "7867b212", - "metadata": {}, + "id": "a2acff0f", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"\n", - "%pip install -q sentence-transformers\n", - "\n", - "%matplotlib inline" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", - "id": "4972be2a", + "id": "1c884951", "metadata": {}, "source": [ - "# The shape of a conversation: per-speaker paths, revealed one turn at a time\n", + "# The shape of a conversation: one path per turn, coloured by its speaker\n", "\n", "A conversation as geometry, in one `hyp.plot` call on raw dialogue. Each\n", "**turn** (a contiguous run of speech by one speaker) is cut into sliding\n", @@ -40,16 +55,18 @@ "margin, sized to the tallest wrapped title, so the figure never has to grow\n", "by hand); the legend maps colours to names.\n", "\n", - "The one bespoke effect left is a **recency fade** across turns: the current\n", - "turn is opaque, earlier turns recede slowly (a turn keeps most of its\n", - "opacity for several exchanges before settling at a visible floor, so the\n", + "The one effect written out by hand is a **recency fade** across turns: the\n", + "current turn is opaque, earlier turns recede slowly (a turn keeps most of\n", + "its opacity for several exchanges before settling at a visible floor, so the\n", "conversation's recent past stays legible), and unspoken turns are hidden.\n", - "Nothing in 1.1 fades across already-revealed datasets, so it is real custom\n", - "work -- but it runs on the public `on_frame` hook and reads the schedule\n", - "the library publishes (`ctx.current_index`, `ctx.revealed_counts`).\n", - "Before 1.1 this example monkeypatched `ani._func` and re-derived that\n", - "schedule by hand; the hook replaces both. The clip runs 30 seconds with two\n", - "full camera rotations.\n", + "1.1 ships the same fade as one keyword -- `dataset_fade={'floor': FLOOR,\n", + "'decay': DECAY}` gives every head and trail exactly these alphas -- and\n", + "this example keeps it as a hook to show what the public `on_frame` hook\n", + "can do: it reads the schedule the library publishes (`ctx.current_index`,\n", + "`ctx.revealed_counts`) and assigns every artist on every frame. Before\n", + "1.1 this example monkeypatched `ani._func` and re-derived that schedule by\n", + "hand; the hook replaces both. The clip runs 30 seconds with two full camera\n", + "rotations.\n", "\n", "Here the conversation is Lewis Carroll's *Mad Tea-Party* (Alice in\n", "Wonderland). The turns are bundled inline -- quoted verbatim from the\n", @@ -74,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "03efba12", + "id": "a352f4c0", "metadata": {}, "source": [ "## 1. Imports, the speakers, and the turns\n", @@ -85,13 +102,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "0939dd31", + "id": "b2a24d49", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:03.756845Z", - "iopub.status.busy": "2026-09-05T09:10:03.756774Z", - "iopub.status.idle": "2026-09-05T09:10:07.316619Z", - "shell.execute_reply": "2026-09-05T09:10:07.316154Z" + "iopub.execute_input": "2026-10-08T17:10:51.292106Z", + "iopub.status.busy": "2026-10-08T17:10:51.291920Z", + "iopub.status.idle": "2026-10-08T17:10:54.665658Z", + "shell.execute_reply": "2026-10-08T17:10:54.665145Z" } }, "outputs": [], @@ -157,9 +174,9 @@ "FLOOR, DECAY = 0.18, 0.7\n", "# Title size and wrap width. At 14 pt a character is ~8.7 px wide at 100 dpi,\n", "# so a 64-character line is ~560 px over the 800 px-wide axes -- centred with\n", - "# clear margin either side -- and the longest turn (118 characters) wraps to\n", - "# exactly two lines; no turn needs a third (verified by rendering turns\n", - "# 15-17 and 22, the long ones).\n", + "# clear margin either side -- and the longest turn (117 characters, 119 with\n", + "# the quotes the title adds) wraps to exactly two lines; no turn needs a\n", + "# third (verified by rendering turns 15-17 and 22, the long ones).\n", "TITLE_SIZE, TITLE_WIDTH = 14, 64\n", "# How many seconds of the current turn's reveal the opaque comet-head spans\n", "# (hyp.plot's tail_duration; default 2). Raised so the head covers more of\n", @@ -169,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "1bb1eb1c", + "id": "6ae3a124", "metadata": {}, "source": [ "## 2. Windows and the payload\n", @@ -180,13 +197,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "63418e32", + "id": "7bf6d20a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:07.318149Z", - "iopub.status.busy": "2026-09-05T09:10:07.317978Z", - "iopub.status.idle": "2026-09-05T09:10:07.321923Z", - "shell.execute_reply": "2026-09-05T09:10:07.321180Z" + "iopub.execute_input": "2026-10-08T17:10:54.667221Z", + "iopub.status.busy": "2026-10-08T17:10:54.666982Z", + "iopub.status.idle": "2026-10-08T17:10:54.670512Z", + "shell.execute_reply": "2026-10-08T17:10:54.670034Z" } }, "outputs": [], @@ -204,9 +221,10 @@ "\n", " ``min_windows`` prevents a real rendering artifact: ``hyp.plot`` draws a\n", " ONE-ROW dataset as a dot (there is no line through a single point), and\n", - " with a fixed 6-word window, 12 of the 28 turns above collapse to a\n", - " single window and would show up as stray specks. Shrinking the window,\n", - " and the step if needed, keeps every turn a real path.\n", + " with a fixed 6-word window stepping by 2, 12 of the 28 turns above get\n", + " fewer than three windows -- 9 of them none at all, 3 a single window\n", + " that would show up as a stray speck. Shrinking the window, and the step\n", + " if needed, keeps every turn a real path.\n", " \"\"\"\n", " words = text.split()\n", " n = len(words)\n", @@ -233,24 +251,24 @@ }, { "cell_type": "markdown", - "id": "edd1c41a", + "id": "2709f731", "metadata": {}, "source": [ - "## 3. The recency fade and the title hooks\n", + "## 3. The recency fade, on the `on_frame` hook\n", "\n", - "`recency_fade` fades earlier turns on the public `on_frame` hook; `speaker_title` tints the title with the current speaker's colour on every frame; `make_room_for_title` grows the figure so a two-line title clears the box.\n" + "`turn_alpha` is the fade's formula and `recency_fade` assigns it to every head and trail on the public `on_frame` hook. It is the hand-written form of `dataset_fade={'floor': FLOOR, 'decay': DECAY}`, which gives exactly these alphas in one keyword. The title needs no hook: `title_color=` tints each turn's title with its speaker's colour, and the library reserves the wrapped title's margin.\n" ] }, { "cell_type": "code", "execution_count": 3, - "id": "526b65ae", + "id": "23cd4401", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:07.330754Z", - "iopub.status.busy": "2026-09-05T09:10:07.330643Z", - "iopub.status.idle": "2026-09-05T09:10:07.343313Z", - "shell.execute_reply": "2026-09-05T09:10:07.342764Z" + "iopub.execute_input": "2026-10-08T17:10:54.671390Z", + "iopub.status.busy": "2026-10-08T17:10:54.671329Z", + "iopub.status.idle": "2026-10-08T17:10:54.674228Z", + "shell.execute_reply": "2026-10-08T17:10:54.673909Z" } }, "outputs": [], @@ -276,9 +294,11 @@ " \"\"\"The one bespoke effect left: earlier turns recede as the talk moves on.\n", "\n", " ``chemtrails``/``precog``/``bullettime`` fade WITHIN one trajectory;\n", - " nothing in 1.1 fades ACROSS already-revealed datasets, so this is real\n", - " custom work -- but it runs on the public per-frame hook and reads the\n", - " library's own published schedule instead of re-deriving it.\n", + " this fades ACROSS already-revealed datasets. It is the hand-written\n", + " form of ``dataset_fade={'floor': FLOOR, 'decay': DECAY}`` (same\n", + " formula, heads and trails alike), kept here to show the public\n", + " per-frame hook reading the library's own published schedule instead of\n", + " re-deriving it.\n", "\n", " ``ctx.artists`` is NOT one artist per dataset. It is heads first, then\n", " trails (animation_context.FrameContext), so with ``chemtrails=True`` it\n", @@ -316,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "96362f20", + "id": "54f03884", "metadata": {}, "source": [ "## 4. One call\n", @@ -327,13 +347,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "e2b14cf0", + "id": "31e7c896", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:07.344523Z", - "iopub.status.busy": "2026-09-05T09:10:07.344445Z", - "iopub.status.idle": "2026-09-05T09:10:07.349704Z", - "shell.execute_reply": "2026-09-05T09:10:07.349310Z" + "iopub.execute_input": "2026-10-08T17:10:54.675148Z", + "iopub.status.busy": "2026-10-08T17:10:54.675096Z", + "iopub.status.idle": "2026-10-08T17:10:54.677988Z", + "shell.execute_reply": "2026-10-08T17:10:54.677584Z" } }, "outputs": [], @@ -350,7 +370,9 @@ " titles = [textwrap.fill(f'\\u201c{text}\\u201d', TITLE_WIDTH)\n", " for text in data.texts]\n", " # THE hypertools call: raw dialogue in, one disjoint trajectory per\n", - " # turn, coloured by speaker, revealed ONE TURN AT A TIME.\n", + " # turn, coloured by speaker, revealed ONE TURN AT A TIME. backend= is\n", + " # pinned because the hook sets matplotlib alphas, and on Colab the\n", + " # default backend would be plotly.\n", " anim = hyp.plot(\n", " data.turns, '-',\n", " vectorizer=data.vectorizer, semantic=None, corpus=None,\n", @@ -366,14 +388,14 @@ " title_kwargs={'size': TITLE_SIZE},\n", " title_color=[SPEAKER_COLOR[s] for s in data.speakers],\n", " duration=30, rotations=2, frame_rate=16, elev=16, size=(8, 8),\n", - " show=False)\n", + " backend='matplotlib', show=False)\n", " anim.on_frame(recency_fade)\n", " return anim\n" ] }, { "cell_type": "markdown", - "id": "605e9473", + "id": "fb56a393", "metadata": {}, "source": [ "## 5. Load the data and build the animation\n" @@ -382,13 +404,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "66b53bc9", + "id": "0c8d5dbb", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:07.350817Z", - "iopub.status.busy": "2026-09-05T09:10:07.350745Z", - "iopub.status.idle": "2026-09-05T09:10:20.739288Z", - "shell.execute_reply": "2026-09-05T09:10:20.738485Z" + "iopub.execute_input": "2026-10-08T17:10:54.678823Z", + "iopub.status.busy": "2026-10-08T17:10:54.678766Z", + "iopub.status.idle": "2026-10-08T17:11:06.694268Z", + "shell.execute_reply": "2026-10-08T17:11:06.693880Z" } }, "outputs": [ @@ -413,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "28a4bf0e", + "id": "f4981dff", "metadata": {}, "source": [ "## 6. Save the animation\n" @@ -422,13 +444,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "2c97845b", + "id": "6fd53c43", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T09:10:20.740867Z", - "iopub.status.busy": "2026-09-05T09:10:20.740648Z", - "iopub.status.idle": "2026-09-05T09:10:31.857791Z", - "shell.execute_reply": "2026-09-05T09:10:31.857339Z" + "iopub.execute_input": "2026-10-08T17:11:06.695731Z", + "iopub.status.busy": "2026-10-08T17:11:06.695648Z", + "iopub.status.idle": "2026-10-08T17:11:17.229714Z", + "shell.execute_reply": "2026-10-08T17:11:17.229324Z" } }, "outputs": [ @@ -442,15 +464,24 @@ ], "source": [ "anim.save('conversation_shape.mp4', dpi=100)\n", - "print('saved conversation_shape.mp4')\n" + "print('saved conversation_shape.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('conversation_shape.mp4', embed=True))\n" ] }, { "cell_type": "markdown", - "id": "c05a4584", + "id": "6c8e0a07", "metadata": {}, "source": [ - "<video controls loop muted autoplay playsinline src=\"conversation_shape.mp4\" title=\"The shape of a conversation: per-speaker paths, revealed one turn at a time\" style=\"max-width: 100%\"></video>\n", + "<video controls loop muted autoplay playsinline src=\"conversation_shape.mp4\" title=\"The shape of a conversation: one path per turn, coloured by its speaker\" style=\"max-width: 100%\"></video>\n", "\n", "[Download the clip](conversation_shape.mp4)\n" ] @@ -472,7 +503,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/conversation_shape.mp4 b/docs/tutorials/conversation_shape.mp4 index 01f2461d..ba008c28 100644 Binary files a/docs/tutorials/conversation_shape.mp4 and b/docs/tutorials/conversation_shape.mp4 differ diff --git a/docs/tutorials/conversation_trajectories.ipynb b/docs/tutorials/conversation_trajectories.ipynb index 105cb79e..dd9a23c2 100644 --- a/docs/tutorials/conversation_trajectories.ipynb +++ b/docs/tutorials/conversation_trajectories.ipynb @@ -4,25 +4,54 @@ "cell_type": "code", "execution_count": null, "id": "dd301e09", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, + "outputs": [], + "source": [ + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d069839f1e2", "metadata": { "execution": { - "iopub.execute_input": "2026-07-17T07:39:00.057725Z", - "iopub.status.busy": "2026-07-17T07:39:00.057435Z", - "iopub.status.idle": "2026-07-17T07:39:00.063683Z", - "shell.execute_reply": "2026-07-17T07:39:00.062982Z" - } + "iopub.execute_input": "2026-10-08T17:11:20.738324Z", + "iopub.status.busy": "2026-10-08T17:11:20.738050Z", + "iopub.status.idle": "2026-10-08T17:11:20.743920Z", + "shell.execute_reply": "2026-10-08T17:11:20.743315Z" + }, + "tags": [ + "tutorial-prerequisite", + "prerequisite-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive]\"\n", - "\n", "# This tutorial also uses convokit (not a HyperTools dep) for the Reddit\n", "# corpus; self-install it so a top-to-bottom run works headlessly too.\n", "if importlib.util.find_spec('convokit') is None:\n", - " %pip install -q convokit" + " %pip install -q convokit\n" ] }, { @@ -47,21 +76,21 @@ "an embedding is requested.\n", "\n", "(The [conversation-shape tutorial](conversation_shape.ipynb) is the\n", - "companion piece: it follows each *speaker's* path through a scripted\n", - "conversation with `order='serial'`; this one follows a real Reddit thread\n", + "companion piece: it draws one path per turn of a scripted conversation,\n", + "coloured by speaker, with `order='serial'`; this one follows a real Reddit thread\n", "utterance by utterance with `animate='serial'`.)" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "f4f30816", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:42.053422Z", - "iopub.status.busy": "2026-09-05T10:27:42.053253Z", - "iopub.status.idle": "2026-09-05T10:27:45.599475Z", - "shell.execute_reply": "2026-09-05T10:27:45.598836Z" + "iopub.execute_input": "2026-10-08T17:11:20.745437Z", + "iopub.status.busy": "2026-10-08T17:11:20.745303Z", + "iopub.status.idle": "2026-10-08T17:11:24.299966Z", + "shell.execute_reply": "2026-10-08T17:11:24.299405Z" } }, "outputs": [], @@ -91,14 +120,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "5fb984f4", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:45.601148Z", - "iopub.status.busy": "2026-09-05T10:27:45.600845Z", - "iopub.status.idle": "2026-09-05T10:27:54.013410Z", - "shell.execute_reply": "2026-09-05T10:27:54.012900Z" + "iopub.execute_input": "2026-10-08T17:11:24.301406Z", + "iopub.status.busy": "2026-10-08T17:11:24.301150Z", + "iopub.status.idle": "2026-10-08T17:11:32.703416Z", + "shell.execute_reply": "2026-10-08T17:11:32.702957Z" } }, "outputs": [ @@ -106,7 +135,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Dataset already exists at /Users/jmanning/.convokit/saved-corpora/reddit-corpus-small\n" + "Dataset already exists at ~/.convokit/saved-corpora/reddit-corpus-small\n" ] }, { @@ -115,7 +144,7 @@ "ConvoKitMeta({'subreddit': 'reddit-corpus-small', 'num_posts': 8286, 'num_comments': 288846, 'num_user': 119889})" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -145,14 +174,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "22215c3d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:54.014560Z", - "iopub.status.busy": "2026-09-05T10:27:54.014478Z", - "iopub.status.idle": "2026-09-05T10:27:54.875355Z", - "shell.execute_reply": "2026-09-05T10:27:54.874846Z" + "iopub.execute_input": "2026-10-08T17:11:32.704669Z", + "iopub.status.busy": "2026-10-08T17:11:32.704596Z", + "iopub.status.idle": "2026-10-08T17:11:33.632054Z", + "shell.execute_reply": "2026-10-08T17:11:33.631623Z" } }, "outputs": [ @@ -247,14 +276,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "aee29fb7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:54.876578Z", - "iopub.status.busy": "2026-09-05T10:27:54.876491Z", - "iopub.status.idle": "2026-09-05T10:27:54.878904Z", - "shell.execute_reply": "2026-09-05T10:27:54.878425Z" + "iopub.execute_input": "2026-10-08T17:11:33.633038Z", + "iopub.status.busy": "2026-10-08T17:11:33.632972Z", + "iopub.status.idle": "2026-10-08T17:11:33.635192Z", + "shell.execute_reply": "2026-10-08T17:11:33.634848Z" } }, "outputs": [ @@ -297,14 +326,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "b35bea63", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:54.880065Z", - "iopub.status.busy": "2026-09-05T10:27:54.879977Z", - "iopub.status.idle": "2026-09-05T10:27:54.882144Z", - "shell.execute_reply": "2026-09-05T10:27:54.881738Z" + "iopub.execute_input": "2026-10-08T17:11:33.636019Z", + "iopub.status.busy": "2026-10-08T17:11:33.635963Z", + "iopub.status.idle": "2026-10-08T17:11:33.637700Z", + "shell.execute_reply": "2026-10-08T17:11:33.637345Z" } }, "outputs": [ @@ -348,25 +377,25 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "6046a2dc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:54.883013Z", - "iopub.status.busy": "2026-09-05T10:27:54.882947Z", - "iopub.status.idle": "2026-09-05T10:28:00.646793Z", - "shell.execute_reply": "2026-09-05T10:28:00.646382Z" + "iopub.execute_input": "2026-10-08T17:11:33.638532Z", + "iopub.status.busy": "2026-10-08T17:11:33.638479Z", + "iopub.status.idle": "2026-10-08T17:11:39.563393Z", + "shell.execute_reply": "2026-10-08T17:11:39.562803Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 667.768x480 with 1 Axes>" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -411,14 +440,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "51c49fed", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:00.648328Z", - "iopub.status.busy": "2026-09-05T10:28:00.648198Z", - "iopub.status.idle": "2026-09-05T10:28:05.586202Z", - "shell.execute_reply": "2026-09-05T10:28:05.585496Z" + "iopub.execute_input": "2026-10-08T17:11:39.564743Z", + "iopub.status.busy": "2026-10-08T17:11:39.564648Z", + "iopub.status.idle": "2026-10-08T17:11:43.703724Z", + "shell.execute_reply": "2026-10-08T17:11:43.703213Z" } }, "outputs": [ @@ -426,7 +455,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "video: 0.9 MB\n" + "video: 0.8 MB\n" ] } ], @@ -437,7 +466,16 @@ " zoom=1.5, linewidth=2,\n", " save_path='conversation_trajectories.mp4', show=False)\n", "\n", - "print(f\"video: {os.path.getsize('conversation_trajectories.mp4')/1e6:0.1f} MB\")" + "print(f\"video: {os.path.getsize('conversation_trajectories.mp4')/1e6:0.1f} MB\")\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('conversation_trajectories.mp4', embed=True))\n" ] }, { @@ -459,7 +497,7 @@ "\n", "Try a longer conversation (raise `MIN_UTTERANCES`), a different sentence\n", "window length, or another ConvoKit corpus entirely (`movie-corpus`,\n", - "`supreme-court-arguments`, ...). You could also compare *several*\n", + "`supreme-corpus`, ...). You could also compare *several*\n", "conversations at once by passing multiple trajectories to `hyp.plot`, or use\n", "`hyp.align` to map different conversations into a shared space." ] @@ -481,220 +519,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "3d172f197494482f88fd5cce86528b05": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "94b082499a4b4fd4a9959bd296d3bf0b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "f0ba33a2ac02421ca971f09587956e6d": { - "model_module": "jupyter-matplotlib", - "model_module_version": "^0.12", - "model_name": "MPLCanvasModel", - "state": { - "_cursor": "pointer", - "_data_url": null, - "_dom_classes": [], - "_figure_label": "Figure", - "_image_mode": "full", - "_message": "", - "_model_module": "jupyter-matplotlib", - "_model_module_version": "^0.12", - "_model_name": "MPLCanvasModel", - "_rubberband_height": 0, - "_rubberband_width": 0, - "_rubberband_x": 0, - "_rubberband_y": 0, - "_size": [ - 0, - 0 - ], - "_view_count": null, - "_view_module": "jupyter-matplotlib", - "_view_module_version": "^0.12", - "_view_name": "MPLCanvasView", - "capture_scroll": false, - "footer_visible": true, - "header_visible": true, - "layout": "IPY_MODEL_94b082499a4b4fd4a9959bd296d3bf0b", - "pan_zoom_throttle": 33, - "resizable": true, - "tabbable": null, - "toolbar": "IPY_MODEL_f390ff897c89407c861752bc7b7770f5", - "toolbar_position": "left", - "toolbar_visible": "fade-in-fade-out", - "tooltip": null - } - }, - "f390ff897c89407c861752bc7b7770f5": { - "model_module": "jupyter-matplotlib", - "model_module_version": "^0.12", - "model_name": "ToolbarModel", - "state": { - "_current_action": "", - "_dom_classes": [], - "_model_module": "jupyter-matplotlib", - "_model_module_version": "^0.12", - "_model_name": "ToolbarModel", - "_view_count": null, - "_view_module": "jupyter-matplotlib", - "_view_module_version": "^0.12", - "_view_name": "ToolbarView", - "button_style": "", - "collapsed": true, - "layout": "IPY_MODEL_3d172f197494482f88fd5cce86528b05", - "orientation": "vertical", - "tabbable": null, - "toolitems": [ - [ - "Home", - "Reset original view", - "home", - "home" - ], - [ - "Back", - "Back to previous view", - "arrow-left", - "back" - ], - [ - "Forward", - "Forward to next view", - "arrow-right", - "forward" - ], - [ - "Pan", - "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", - "arrows", - "pan" - ], - [ - "Zoom", - "Zoom to rectangle\nx/y fixes axis", - "square-o", - "zoom" - ], - [ - "Download", - "Download plot", - "floppy-o", - "save_figure" - ] - ], - "tooltip": null - } - } - }, - "version_major": 2, - "version_minor": 0 - } + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/conversation_trajectories.mp4 b/docs/tutorials/conversation_trajectories.mp4 index 6b6ac9ba..abbdf775 100644 Binary files a/docs/tutorials/conversation_trajectories.mp4 and b/docs/tutorials/conversation_trajectories.mp4 differ diff --git a/docs/tutorials/hierarchy.ipynb b/docs/tutorials/hierarchy.ipynb index a4fdf6d9..b779a5a9 100644 --- a/docs/tutorials/hierarchy.ipynb +++ b/docs/tutorials/hierarchy.ipynb @@ -3,15 +3,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "85880937b9a3", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "e09aad8e0af8", "metadata": {}, "source": [ "# Hierarchical DataFrames: plotting and forecasting a column MultiIndex\n", @@ -26,6 +46,7 @@ }, { "cell_type": "markdown", + "id": "055f793606ac", "metadata": {}, "source": [ "## Import packages" @@ -34,12 +55,13 @@ { "cell_type": "code", "execution_count": 1, + "id": "a9f6411ae54d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:55.969055Z", - "iopub.status.busy": "2026-09-05T10:24:55.968916Z", - "iopub.status.idle": "2026-09-05T10:24:59.503014Z", - "shell.execute_reply": "2026-09-05T10:24:59.502495Z" + "iopub.execute_input": "2026-10-08T17:11:47.218655Z", + "iopub.status.busy": "2026-10-08T17:11:47.218485Z", + "iopub.status.idle": "2026-10-08T17:11:50.518414Z", + "shell.execute_reply": "2026-10-08T17:11:50.517989Z" } }, "outputs": [], @@ -56,22 +78,24 @@ }, { "cell_type": "markdown", + "id": "cd91312fdb27", "metadata": {}, "source": [ "## Build a column hierarchy\n", "\n", - "`weights_sample` holds three subjects' brain responses while they listened to a story: three arrays of 300 timepoints by 100 features, and the 100 features mean the same thing for every subject. That is exactly the shape a column hierarchy describes. The arrays are smoothed first with `hyp.manip` so the trajectories are legible rather than a tangle, and then `hypertools.tools.stack` builds the column hierarchy in one call: the innermost column level names the **features** (shared across subjects), `Subject` groups them, and a constant top level, `Group`, is what gives the hierarchy a mean to draw. Feature correspondence across groups is by **name**, so every group must carry the same innermost labels." + "`weights_sample` holds three subjects' brain responses while they listened to a story: three arrays of 300 timepoints by 100 features, and the 100 features mean the same thing for every subject. That is exactly the shape a column hierarchy describes. The arrays are smoothed first with `hyp.manip` so the trajectories are legible rather than a tangle, and then `hyp.stack` builds the column hierarchy in one call: the innermost column level names the **features** (shared across subjects), `Subject` groups them, and a constant top level, `Group`, is what gives the hierarchy a mean to draw. Feature correspondence across groups is by **name**, so every group must carry the same innermost labels." ] }, { "cell_type": "code", "execution_count": 2, + "id": "f8a79e4907bd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:59.504677Z", - "iopub.status.busy": "2026-09-05T10:24:59.504509Z", - "iopub.status.idle": "2026-09-05T10:24:59.626686Z", - "shell.execute_reply": "2026-09-05T10:24:59.625993Z" + "iopub.execute_input": "2026-10-08T17:11:50.519906Z", + "iopub.status.busy": "2026-10-08T17:11:50.519764Z", + "iopub.status.idle": "2026-10-08T17:11:50.635753Z", + "shell.execute_reply": "2026-10-08T17:11:50.635309Z" } }, "outputs": [ @@ -109,6 +133,7 @@ }, { "cell_type": "markdown", + "id": "a0c8a1b178b6", "metadata": {}, "source": [ "## One call: the leaves plus their mean\n", @@ -119,12 +144,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "82bc7eec0ace", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:59.644245Z", - "iopub.status.busy": "2026-09-05T10:24:59.644143Z", - "iopub.status.idle": "2026-09-05T10:24:59.793936Z", - "shell.execute_reply": "2026-09-05T10:24:59.793540Z" + "iopub.execute_input": "2026-10-08T17:11:50.651901Z", + "iopub.status.busy": "2026-10-08T17:11:50.651824Z", + "iopub.status.idle": "2026-10-08T17:11:50.795481Z", + "shell.execute_reply": "2026-10-08T17:11:50.795148Z" } }, "outputs": [ @@ -145,6 +171,7 @@ }, { "cell_type": "markdown", + "id": "cb85ae67b9cf", "metadata": {}, "source": [ "## The style belongs to the hierarchy\n", @@ -155,12 +182,13 @@ { "cell_type": "code", "execution_count": 4, + "id": "9a59f1846ee8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:59.795194Z", - "iopub.status.busy": "2026-09-05T10:24:59.795113Z", - "iopub.status.idle": "2026-09-05T10:24:59.850379Z", - "shell.execute_reply": "2026-09-05T10:24:59.850020Z" + "iopub.execute_input": "2026-10-08T17:11:50.796449Z", + "iopub.status.busy": "2026-10-08T17:11:50.796388Z", + "iopub.status.idle": "2026-10-08T17:11:50.848889Z", + "shell.execute_reply": "2026-10-08T17:11:50.848501Z" } }, "outputs": [ @@ -192,6 +220,7 @@ }, { "cell_type": "markdown", + "id": "55ab7979631d", "metadata": {}, "source": [ "## A continuous `hue=` carried through the hierarchy\n", @@ -202,12 +231,13 @@ { "cell_type": "code", "execution_count": 5, + "id": "453cd12e6ac9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:59.851508Z", - "iopub.status.busy": "2026-09-05T10:24:59.851441Z", - "iopub.status.idle": "2026-09-05T10:25:00.677805Z", - "shell.execute_reply": "2026-09-05T10:25:00.677348Z" + "iopub.execute_input": "2026-10-08T17:11:50.849868Z", + "iopub.status.busy": "2026-10-08T17:11:50.849807Z", + "iopub.status.idle": "2026-10-08T17:11:51.617813Z", + "shell.execute_reply": "2026-10-08T17:11:51.617474Z" } }, "outputs": [ @@ -243,6 +273,7 @@ }, { "cell_type": "markdown", + "id": "a1fe43c8a751", "metadata": {}, "source": [ "## Forecasting the frame with `hyp.predict`\n", @@ -253,12 +284,13 @@ { "cell_type": "code", "execution_count": 6, + "id": "ccbb88051af5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:00.679039Z", - "iopub.status.busy": "2026-09-05T10:25:00.678948Z", - "iopub.status.idle": "2026-09-05T10:25:12.458208Z", - "shell.execute_reply": "2026-09-05T10:25:12.457624Z" + "iopub.execute_input": "2026-10-08T17:11:51.618961Z", + "iopub.status.busy": "2026-10-08T17:11:51.618893Z", + "iopub.status.idle": "2026-10-08T17:12:03.259931Z", + "shell.execute_reply": "2026-10-08T17:12:03.259491Z" } }, "outputs": [ @@ -282,6 +314,7 @@ }, { "cell_type": "markdown", + "id": "99f8126c1194", "metadata": {}, "source": [ "## `predict=` inside `plot`\n", @@ -292,12 +325,13 @@ { "cell_type": "code", "execution_count": 7, + "id": "e1498c44b479", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:12.459336Z", - "iopub.status.busy": "2026-09-05T10:25:12.459251Z", - "iopub.status.idle": "2026-09-05T10:25:13.560029Z", - "shell.execute_reply": "2026-09-05T10:25:13.559525Z" + "iopub.execute_input": "2026-10-08T17:12:03.261131Z", + "iopub.status.busy": "2026-10-08T17:12:03.261052Z", + "iopub.status.idle": "2026-10-08T17:12:04.325681Z", + "shell.execute_reply": "2026-10-08T17:12:04.325298Z" } }, "outputs": [ @@ -312,7 +346,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -331,6 +365,7 @@ }, { "cell_type": "markdown", + "id": "8d65f16c70d5", "metadata": {}, "source": [ "## What `trace_data` and `trace_metadata` hold\n", @@ -343,12 +378,13 @@ { "cell_type": "code", "execution_count": 8, + "id": "fef17d005dab", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:13.561080Z", - "iopub.status.busy": "2026-09-05T10:25:13.561015Z", - "iopub.status.idle": "2026-09-05T10:25:13.566258Z", - "shell.execute_reply": "2026-09-05T10:25:13.565874Z" + "iopub.execute_input": "2026-10-08T17:12:04.326726Z", + "iopub.status.busy": "2026-10-08T17:12:04.326663Z", + "iopub.status.idle": "2026-10-08T17:12:04.331594Z", + "shell.execute_reply": "2026-10-08T17:12:04.331233Z" } }, "outputs": [ @@ -379,6 +415,7 @@ }, { "cell_type": "markdown", + "id": "d95f51219d44", "metadata": {}, "source": [ "## What is refused, and why\n", @@ -389,12 +426,13 @@ { "cell_type": "code", "execution_count": 9, + "id": "4f4f0fb185fa", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:13.567149Z", - "iopub.status.busy": "2026-09-05T10:25:13.567082Z", - "iopub.status.idle": "2026-09-05T10:25:13.581829Z", - "shell.execute_reply": "2026-09-05T10:25:13.581528Z" + "iopub.execute_input": "2026-10-08T17:12:04.332376Z", + "iopub.status.busy": "2026-10-08T17:12:04.332321Z", + "iopub.status.idle": "2026-10-08T17:12:04.347642Z", + "shell.execute_reply": "2026-10-08T17:12:04.347280Z" } }, "outputs": [ @@ -439,6 +477,7 @@ }, { "cell_type": "markdown", + "id": "160d271f415f", "metadata": {}, "source": [ "## The row-MultiIndex form, for contrast\n", @@ -449,12 +488,13 @@ { "cell_type": "code", "execution_count": 10, + "id": "39e6098f483c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:13.582815Z", - "iopub.status.busy": "2026-09-05T10:25:13.582753Z", - "iopub.status.idle": "2026-09-05T10:25:25.598869Z", - "shell.execute_reply": "2026-09-05T10:25:25.598398Z" + "iopub.execute_input": "2026-10-08T17:12:04.348602Z", + "iopub.status.busy": "2026-10-08T17:12:04.348541Z", + "iopub.status.idle": "2026-10-08T17:12:16.193171Z", + "shell.execute_reply": "2026-10-08T17:12:16.192735Z" } }, "outputs": [ @@ -505,6 +545,7 @@ }, { "cell_type": "markdown", + "id": "b6149e727881", "metadata": {}, "source": [ "## Where to go next\n", @@ -529,7 +570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/hugging_face_embeddings.ipynb b/docs/tutorials/hugging_face_embeddings.ipynb index d96d8d82..dd0cbbcc 100644 --- a/docs/tutorials/hugging_face_embeddings.ipynb +++ b/docs/tutorials/hugging_face_embeddings.ipynb @@ -2,22 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "e09bedb2", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:37:05.647993Z", - "iopub.status.busy": "2026-07-17T07:37:05.647722Z", - "iopub.status.idle": "2026-07-17T07:37:05.654080Z", - "shell.execute_reply": "2026-07-17T07:37:05.653339Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -48,10 +57,10 @@ "id": "eac39684", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:16.418902Z", - "iopub.status.busy": "2026-09-05T10:27:16.418809Z", - "iopub.status.idle": "2026-09-05T10:27:19.850640Z", - "shell.execute_reply": "2026-09-05T10:27:19.849902Z" + "iopub.execute_input": "2026-10-08T17:12:17.443079Z", + "iopub.status.busy": "2026-10-08T17:12:17.442918Z", + "iopub.status.idle": "2026-10-08T17:12:20.858848Z", + "shell.execute_reply": "2026-10-08T17:12:20.858314Z" } }, "outputs": [], @@ -91,10 +100,10 @@ "id": "3871ae82", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:19.852247Z", - "iopub.status.busy": "2026-09-05T10:27:19.852074Z", - "iopub.status.idle": "2026-09-05T10:27:21.627626Z", - "shell.execute_reply": "2026-09-05T10:27:21.627114Z" + "iopub.execute_input": "2026-10-08T17:12:20.860431Z", + "iopub.status.busy": "2026-10-08T17:12:20.860263Z", + "iopub.status.idle": "2026-10-08T17:12:22.483630Z", + "shell.execute_reply": "2026-10-08T17:12:22.483120Z" } }, "outputs": [ @@ -136,10 +145,10 @@ "id": "ae675f75", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:21.628852Z", - "iopub.status.busy": "2026-09-05T10:27:21.628688Z", - "iopub.status.idle": "2026-09-05T10:27:26.639161Z", - "shell.execute_reply": "2026-09-05T10:27:26.638780Z" + "iopub.execute_input": "2026-10-08T17:12:22.484686Z", + "iopub.status.busy": "2026-10-08T17:12:22.484549Z", + "iopub.status.idle": "2026-10-08T17:12:27.569612Z", + "shell.execute_reply": "2026-10-08T17:12:27.569221Z" } }, "outputs": [ @@ -185,16 +194,16 @@ "id": "43a34765", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:26.640363Z", - "iopub.status.busy": "2026-09-05T10:27:26.640259Z", - "iopub.status.idle": "2026-09-05T10:27:28.853178Z", - "shell.execute_reply": "2026-09-05T10:27:28.852585Z" + "iopub.execute_input": "2026-10-08T17:12:27.570687Z", + "iopub.status.busy": "2026-10-08T17:12:27.570617Z", + "iopub.status.idle": "2026-10-08T17:12:29.876116Z", + "shell.execute_reply": "2026-10-08T17:12:29.875465Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -229,16 +238,16 @@ "id": "1fafa74d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:28.854668Z", - "iopub.status.busy": "2026-09-05T10:27:28.854574Z", - "iopub.status.idle": "2026-09-05T10:27:38.552094Z", - "shell.execute_reply": "2026-09-05T10:27:38.551498Z" + "iopub.execute_input": "2026-10-08T17:12:29.877831Z", + "iopub.status.busy": "2026-10-08T17:12:29.877710Z", + "iopub.status.idle": "2026-10-08T17:12:39.117595Z", + "shell.execute_reply": "2026-10-08T17:12:39.117236Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -285,220 +294,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "52a633392a154d29aa60adbe8a2baa2d": { - "model_module": "jupyter-matplotlib", - "model_module_version": "^0.12", - "model_name": "MPLCanvasModel", - "state": { - "_cursor": "default", - "_data_url": 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"grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - } - }, - "version_major": 2, - "version_minor": 0 - } + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/io.ipynb b/docs/tutorials/io.ipynb index a8493c16..b7cd5170 100644 --- a/docs/tutorials/io.ipynb +++ b/docs/tutorials/io.ipynb @@ -3,15 +3,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "6e115fbdec40", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "d403040ef188", "metadata": {}, "source": [ "# Loading and saving data with `hyp.load` and `hyp.save`\n", @@ -21,6 +41,7 @@ }, { "cell_type": "markdown", + "id": "2581d6b80bc0", "metadata": {}, "source": [ "## Import packages" @@ -29,12 +50,13 @@ { "cell_type": "code", "execution_count": 1, + "id": "d2c9ada8785c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:26.811970Z", - "iopub.status.busy": "2026-09-05T10:25:26.811767Z", - "iopub.status.idle": "2026-09-05T10:25:30.301577Z", - "shell.execute_reply": "2026-09-05T10:25:30.301089Z" + "iopub.execute_input": "2026-10-08T17:12:42.610368Z", + "iopub.status.busy": "2026-10-08T17:12:42.610026Z", + "iopub.status.idle": "2026-10-08T17:12:45.943434Z", + "shell.execute_reply": "2026-10-08T17:12:45.942793Z" } }, "outputs": [], @@ -54,6 +76,7 @@ }, { "cell_type": "markdown", + "id": "57500cdd5cdf", "metadata": {}, "source": [ "## Hosted example datasets\n", @@ -64,12 +87,13 @@ { "cell_type": "code", "execution_count": 2, + "id": "7d0c9908b6e2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:30.303403Z", - "iopub.status.busy": "2026-09-05T10:25:30.303197Z", - "iopub.status.idle": "2026-09-05T10:25:30.421802Z", - "shell.execute_reply": "2026-09-05T10:25:30.421332Z" + "iopub.execute_input": "2026-10-08T17:12:45.944717Z", + "iopub.status.busy": "2026-10-08T17:12:45.944578Z", + "iopub.status.idle": "2026-10-08T17:12:46.104565Z", + "shell.execute_reply": "2026-10-08T17:12:46.104171Z" } }, "outputs": [ @@ -102,6 +126,7 @@ }, { "cell_type": "markdown", + "id": "274072e4ac60", "metadata": {}, "source": [ "## Synthetic datasets\n", @@ -112,12 +137,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "837d96bf244e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:30.439565Z", - "iopub.status.busy": "2026-09-05T10:25:30.439461Z", - "iopub.status.idle": "2026-09-05T10:25:30.620466Z", - "shell.execute_reply": "2026-09-05T10:25:30.620005Z" + "iopub.execute_input": "2026-10-08T17:12:46.121233Z", + "iopub.status.busy": "2026-10-08T17:12:46.121130Z", + "iopub.status.idle": "2026-10-08T17:12:46.320180Z", + "shell.execute_reply": "2026-10-08T17:12:46.319776Z" } }, "outputs": [ @@ -153,7 +179,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640.513x480 with 1 Axes>" ] @@ -189,6 +215,7 @@ }, { "cell_type": "markdown", + "id": "3096da2ee9ab", "metadata": {}, "source": [ "## scikit-learn's bundled datasets\n", @@ -199,12 +226,13 @@ { "cell_type": "code", "execution_count": 4, + "id": "96cbabdcda8c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:30.621572Z", - "iopub.status.busy": "2026-09-05T10:25:30.621511Z", - "iopub.status.idle": "2026-09-05T10:25:30.676675Z", - "shell.execute_reply": "2026-09-05T10:25:30.676214Z" + "iopub.execute_input": "2026-10-08T17:12:46.321220Z", + "iopub.status.busy": "2026-10-08T17:12:46.321156Z", + "iopub.status.idle": "2026-10-08T17:12:46.370108Z", + "shell.execute_reply": "2026-10-08T17:12:46.369811Z" } }, "outputs": [ @@ -322,22 +350,24 @@ }, { "cell_type": "markdown", + "id": "3016bc58a9be", "metadata": {}, "source": [ "## Local files: `hyp.save` round trips\n", "\n", - "`hyp.save(obj, path)` picks the format from the extension: `.csv`/`.tsv`/`.txt` are delimited text, `.npy` a numpy array, `.npz` an archive with one array per list element (or per dict key), `.json` and `.parquet` and `.xlsx` are DataFrame formats, `.mat` a MATLAB file, and anything else -- `.pkl` included -- a pickle. Writes are atomic, so a failed save never destroys the file already at that path. `hyp.load` reads every one of them back; `.xlsx` needs the `io` extra (openpyxl), which is installed on first use." + "`hyp.save(obj, path)` picks the format from the extension: `.csv`/`.tsv`/`.txt` are delimited text, `.npy` a numpy array, `.npz` an archive with one array per list element (or per dict key), `.json` and `.parquet` and `.xlsx` are DataFrame formats, `.mat` a MATLAB file, and anything else -- `.pkl` included -- a pickle. Writes are atomic, so a failed save never destroys the file already at that path. `hyp.load` reads every one of them back; reading and writing `.xlsx` need the `io` extra (openpyxl), which is installed on first use." ] }, { "cell_type": "code", "execution_count": 5, + "id": "a379f9ec8e18", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:30.677670Z", - "iopub.status.busy": "2026-09-05T10:25:30.677601Z", - "iopub.status.idle": "2026-09-05T10:25:31.076357Z", - "shell.execute_reply": "2026-09-05T10:25:31.075957Z" + "iopub.execute_input": "2026-10-08T17:12:46.371229Z", + "iopub.status.busy": "2026-10-08T17:12:46.371166Z", + "iopub.status.idle": "2026-10-08T17:12:46.544259Z", + "shell.execute_reply": "2026-10-08T17:12:46.543867Z" } }, "outputs": [ @@ -460,6 +490,7 @@ }, { "cell_type": "markdown", + "id": "d60637ee2410", "metadata": {}, "source": [ "## A URL\n", @@ -470,12 +501,13 @@ { "cell_type": "code", "execution_count": 6, + "id": "24b857322c1d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:31.077495Z", - "iopub.status.busy": "2026-09-05T10:25:31.077428Z", - "iopub.status.idle": "2026-09-05T10:25:31.083855Z", - "shell.execute_reply": "2026-09-05T10:25:31.083505Z" + "iopub.execute_input": "2026-10-08T17:12:46.545220Z", + "iopub.status.busy": "2026-10-08T17:12:46.545164Z", + "iopub.status.idle": "2026-10-08T17:12:46.550764Z", + "shell.execute_reply": "2026-10-08T17:12:46.550333Z" } }, "outputs": [ @@ -591,6 +623,7 @@ }, { "cell_type": "markdown", + "id": "1cdd8265cc27", "metadata": {}, "source": [ "## Web sources\n", @@ -601,12 +634,13 @@ { "cell_type": "code", "execution_count": 7, + "id": "e1c10894cebf", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:31.084860Z", - "iopub.status.busy": "2026-09-05T10:25:31.084800Z", - "iopub.status.idle": "2026-09-05T10:25:33.060132Z", - "shell.execute_reply": "2026-09-05T10:25:33.059568Z" + "iopub.execute_input": "2026-10-08T17:12:46.551608Z", + "iopub.status.busy": "2026-10-08T17:12:46.551553Z", + "iopub.status.idle": "2026-10-08T17:12:48.612713Z", + "shell.execute_reply": "2026-10-08T17:12:48.612187Z" } }, "outputs": [ @@ -645,6 +679,7 @@ }, { "cell_type": "markdown", + "id": "2f4a46c9bfc7", "metadata": {}, "source": [ "## A Hugging Face dataset, streamed\n", @@ -655,12 +690,13 @@ { "cell_type": "code", "execution_count": 8, + "id": "e52a61cb3d1c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:33.061368Z", - "iopub.status.busy": "2026-09-05T10:25:33.061283Z", - "iopub.status.idle": "2026-09-05T10:25:35.468579Z", - "shell.execute_reply": "2026-09-05T10:25:35.468152Z" + "iopub.execute_input": "2026-10-08T17:12:48.613796Z", + "iopub.status.busy": "2026-10-08T17:12:48.613724Z", + "iopub.status.idle": "2026-10-08T17:12:50.826217Z", + "shell.execute_reply": "2026-10-08T17:12:50.825765Z" } }, "outputs": [ @@ -696,6 +732,7 @@ }, { "cell_type": "markdown", + "id": "4e42ee5d0bf4", "metadata": {}, "source": [ "## Data you already hold passes straight through\n", @@ -706,12 +743,13 @@ { "cell_type": "code", "execution_count": 9, + "id": "1ec8bf5d4369", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:35.469839Z", - "iopub.status.busy": "2026-09-05T10:25:35.469750Z", - "iopub.status.idle": "2026-09-05T10:25:35.496606Z", - "shell.execute_reply": "2026-09-05T10:25:35.496215Z" + "iopub.execute_input": "2026-10-08T17:12:50.827465Z", + "iopub.status.busy": "2026-10-08T17:12:50.827374Z", + "iopub.status.idle": "2026-10-08T17:12:50.852922Z", + "shell.execute_reply": "2026-10-08T17:12:50.852584Z" } }, "outputs": [ @@ -721,7 +759,7 @@ "text": [ "True the frame comes back as the same object\n", "2 datasets: the in-memory array and the hosted spiral\n", - "hypertools.load: dataset must be a string (a dataset name, file path, or URL), a path-like object, an already-loaded pandas DataFrame or numpy array, or a list/tuple of those; got dict\n" + "hypertools.load: dataset must be a string (a dataset name, file path, or URL), a path-like object, an already-loaded DataFrame (pandas, or polars DataFrame/LazyFrame) or numpy array, or a list/tuple of those; got dict\n" ] }, { @@ -749,22 +787,24 @@ }, { "cell_type": "markdown", + "id": "ca276922bc96", "metadata": {}, "source": [ "## Saving figures and animations with `save_path=`\n", "\n", - "`hyp.plot(..., save_path=)` writes the figure in the format the extension names -- any `matplotlib.pyplot.savefig` format for a static plot -- and the plotly backend writes `.html`. For an animation, `.gif`, `.png`/`.apng` and `.svg` need nothing extra; the video formats (`.mp4`, `.mov`, ...) need FFmpeg on the machine. Every frame is rendered and encoded, so pass a short `duration=` for a quick export. The animation below is saved next to this notebook as `io.mp4` and its last frame is shown in place; the still images go to the scratch directory." + "`hyp.plot(..., save_path=)` writes the figure in the format the extension names -- any `matplotlib.pyplot.savefig` format for a static plot -- and the plotly backend writes `.html`. For an animation, `.gif`, `.png`/`.apng` and `.svg` need nothing extra; the video formats (`.mp4`, `.mov`, ...) need FFmpeg on the machine. Every frame is rendered and encoded, so pass a short `duration=` for a quick export. (`backend='matplotlib'` keeps the returned animation a matplotlib one on Colab, where the default backend is plotly; `.draw_frame` and `.figure` are matplotlib's.) The animation below is saved next to this notebook as `io.mp4` and its last frame is shown in place; the still images go to the scratch directory." ] }, { "cell_type": "code", "execution_count": 10, + "id": "1076475a7b73", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:35.497674Z", - "iopub.status.busy": "2026-09-05T10:25:35.497612Z", - "iopub.status.idle": "2026-09-05T10:25:36.336225Z", - "shell.execute_reply": "2026-09-05T10:25:36.335720Z" + "iopub.execute_input": "2026-10-08T17:12:50.853912Z", + "iopub.status.busy": "2026-10-08T17:12:50.853852Z", + "iopub.status.idle": "2026-10-08T17:12:51.776222Z", + "shell.execute_reply": "2026-10-08T17:12:51.775726Z" } }, "outputs": [ @@ -774,26 +814,25 @@ "text": [ "iris.png: 20,507 bytes\n", "iris.pdf: 4,270 bytes\n", - "iris.html: 4,859,358 bytes\n" + "iris.html: 4,859,577 bytes\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "io.mp4: 60 frames, 339,452 bytes\n" + "io.mp4: 60 frames, 429,087 bytes\n" ] }, { "data": { - "image/png": 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+ "image/png": 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", "text/plain": [ "<Figure size 640x504 with 1 Axes>" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ @@ -804,14 +843,24 @@ "for name in ('iris.png', 'iris.pdf', 'iris.html'):\n", " print(f'{name}: {os.path.getsize(os.path.join(workdir, name)):,} bytes')\n", "anim = hyp.plot(subjects, animate=True, duration=4, frame_rate=15, save_path='io.mp4',\n", - " title='three subjects, saved as io.mp4', show=False)\n", + " title='three subjects, saved as io.mp4', backend='matplotlib', show=False)\n", "print(f'io.mp4: {anim.n_frames} frames, {os.path.getsize(\"io.mp4\"):,} bytes')\n", "_ = anim.draw_frame(anim.n_frames - 1)\n", - "anim.figure" + "display(anim.figure)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('io.mp4', embed=True))\n" ] }, { "cell_type": "markdown", + "id": "cedbdd4c3f73", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"io.mp4\" title=\"Three subjects, saved with save_path=\" style=\"max-width: 100%\"></video>\n", @@ -821,6 +870,7 @@ }, { "cell_type": "markdown", + "id": "ecabecf05165", "metadata": {}, "source": [ "## When loading or saving fails: `HypertoolsIOError`\n", @@ -831,12 +881,13 @@ { "cell_type": "code", "execution_count": 11, + "id": "d88b7f9a3def", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:36.337665Z", - "iopub.status.busy": "2026-09-05T10:25:36.337568Z", - "iopub.status.idle": "2026-09-05T10:25:36.341255Z", - "shell.execute_reply": "2026-09-05T10:25:36.340833Z" + "iopub.execute_input": "2026-10-08T17:12:51.777242Z", + "iopub.status.busy": "2026-10-08T17:12:51.777173Z", + "iopub.status.idle": "2026-10-08T17:12:51.780152Z", + "shell.execute_reply": "2026-10-08T17:12:51.779547Z" } }, "outputs": [ @@ -866,6 +917,7 @@ }, { "cell_type": "markdown", + "id": "bc5a8516a3db", "metadata": {}, "source": [ "## Clean up" @@ -874,12 +926,13 @@ { "cell_type": "code", "execution_count": 12, + "id": "8b1ad5aa4afa", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:36.342371Z", - "iopub.status.busy": "2026-09-05T10:25:36.342299Z", - "iopub.status.idle": "2026-09-05T10:25:36.345238Z", - "shell.execute_reply": "2026-09-05T10:25:36.344874Z" + "iopub.execute_input": "2026-10-08T17:12:51.781194Z", + "iopub.status.busy": "2026-10-08T17:12:51.781132Z", + "iopub.status.idle": "2026-10-08T17:12:51.783803Z", + "shell.execute_reply": "2026-10-08T17:12:51.783513Z" } }, "outputs": [], @@ -904,7 +957,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/io.mp4 b/docs/tutorials/io.mp4 index b0e9c75d..e531bdb6 100644 Binary files a/docs/tutorials/io.mp4 and b/docs/tutorials/io.mp4 differ diff --git a/docs/tutorials/lsl_streaming.ipynb b/docs/tutorials/lsl_streaming.ipynb index c2aeb563..5ae6089d 100644 --- a/docs/tutorials/lsl_streaming.ipynb +++ b/docs/tutorials/lsl_streaming.ipynb @@ -2,22 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "fb8c6829", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T08:26:23.034697Z", - "iopub.status.busy": "2026-07-17T08:26:23.034624Z", - "iopub.status.idle": "2026-07-17T08:26:23.037617Z", - "shell.execute_reply": "2026-07-17T08:26:23.037292Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive,lsl]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive,lsl]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -40,8 +49,8 @@ "\n", "`pylsl` (which wraps the native `liblsl` library used by essentially every\n", "LSL-speaking device/app) is HyperTools' `[lsl]` extra. `hyp.io.lsl_stream()`\n", - "installs it on demand the first time it is called; to fetch it ahead of time,\n", - "run `pip install \"hypertools[lsl]\"`.\n", + "installs it on demand the first time it is called (the *Optional dependencies*\n", + "page of the docs covers pre-installing extras and turning this off).\n", "\n", "This tutorial doesn't require any real hardware: it starts a **synthetic**\n", "LSL outlet on a background thread (publishing multi-channel oscillating\n", @@ -57,10 +66,10 @@ "id": "c66e4b8a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:12.269779Z", - "iopub.status.busy": "2026-09-05T10:26:12.269537Z", - "iopub.status.idle": "2026-09-05T10:26:15.749852Z", - "shell.execute_reply": "2026-09-05T10:26:15.749294Z" + "iopub.execute_input": "2026-10-08T17:12:55.267468Z", + "iopub.status.busy": "2026-10-08T17:12:55.267233Z", + "iopub.status.idle": "2026-10-08T17:12:58.614986Z", + "shell.execute_reply": "2026-10-08T17:12:58.614552Z" } }, "outputs": [], @@ -88,10 +97,10 @@ "id": "b2a01944", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:15.751261Z", - "iopub.status.busy": "2026-09-05T10:26:15.751123Z", - "iopub.status.idle": "2026-09-05T10:26:15.764217Z", - "shell.execute_reply": "2026-09-05T10:26:15.763826Z" + "iopub.execute_input": "2026-10-08T17:12:58.616493Z", + "iopub.status.busy": "2026-10-08T17:12:58.616343Z", + "iopub.status.idle": "2026-10-08T17:12:58.634927Z", + "shell.execute_reply": "2026-10-08T17:12:58.634607Z" } }, "outputs": [ @@ -101,14 +110,6 @@ "text": [ "synthetic LSL outlet 'HypertoolsTutorialStream' running on a background thread\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-09-05 06:26:15.761 ( 0.001s) [ 67105F0] api_config.cpp:126 INFO| Loaded default config\n", - "2026-09-05 06:26:15.761 ( 0.001s) [ 67105F0] common.cpp:78 INFO| git:64988c6a14b8dc3b3f270ece58eab4f480bfab43/branch:refs/tags/v1.17.7/build:Release/compiler:AppleClang-17.0.0.17000013/link:SHARED\n" - ] } ], "source": [ @@ -148,22 +149,19 @@ "id": "cd338b3d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:15.765142Z", - "iopub.status.busy": "2026-09-05T10:26:15.765083Z", - "iopub.status.idle": "2026-09-05T10:26:24.932157Z", - "shell.execute_reply": "2026-09-05T10:26:24.931653Z" + "iopub.execute_input": "2026-10-08T17:12:58.635936Z", + "iopub.status.busy": "2026-10-08T17:12:58.635871Z", + "iopub.status.idle": "2026-10-08T17:13:05.907575Z", + "shell.execute_reply": "2026-10-08T17:13:05.907000Z" } }, "outputs": [ { - "data": { - "text/plain": [ - "(600, (600, 3))" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "samples: 600 projected shape: (600, 3)\n" + ] } ], "source": [ @@ -172,7 +170,16 @@ "fig = hyp.plot(stream, stream_init=200, stream_chunk=20, stream_max=600,\n", " title='Live LSL stream (synthetic outlet)',\n", " save_path='lsl_streaming.mp4', frame_rate=5, show=False)\n", - "fig.stream_info['n_samples'], fig.stream_info['xform_data'][0].shape" + "print('samples:', fig.stream_info['n_samples'], ' projected shape:', fig.stream_info['xform_data'][0].shape)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('lsl_streaming.mp4', embed=True))\n" ] }, { @@ -210,10 +217,10 @@ "id": "7d86a2fc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:24.933915Z", - "iopub.status.busy": "2026-09-05T10:26:24.933811Z", - "iopub.status.idle": "2026-09-05T10:26:25.459331Z", - "shell.execute_reply": "2026-09-05T10:26:25.458612Z" + "iopub.execute_input": "2026-10-08T17:13:05.909160Z", + "iopub.status.busy": "2026-10-08T17:13:05.909048Z", + "iopub.status.idle": "2026-10-08T17:13:06.422725Z", + "shell.execute_reply": "2026-10-08T17:13:06.422006Z" } }, "outputs": [ @@ -271,7 +278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/lsl_streaming.mp4 b/docs/tutorials/lsl_streaming.mp4 index 3b10c419..d135eed8 100644 Binary files a/docs/tutorials/lsl_streaming.mp4 and b/docs/tutorials/lsl_streaming.mp4 differ diff --git a/docs/tutorials/manip.ipynb b/docs/tutorials/manip.ipynb index 9f3622d0..6ef70c43 100644 --- a/docs/tutorials/manip.ipynb +++ b/docs/tutorials/manip.ipynb @@ -3,24 +3,45 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "7f4198705b8e", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "f452cc15b5d2", "metadata": {}, "source": [ "# Manipulating data with `hyp.manip`, and animating windows and trails\n", "\n", - "`hyp.manip` applies per-dataset manipulations -- `Smooth`, `Resample`, `ZScore`, `Normalize` -- and runs first in the library's canonical pipeline (`manip -> normalize -> reduce -> align -> cluster`). They run in native space: `Smooth` and `Resample` treat each dataset on its own, while `ZScore` and `Normalize` fit one shared set of statistics across a list. The same specs go into `hyp.plot(..., manip=)`. The second half of this notebook animates the manipulated data with a sliding window (`animate='window'`, sized by `focused=`) and with the three trail styles." + "`hyp.manip` applies per-dataset manipulations -- `Smooth`, `Resample`, `ZScore`, `Normalize`, and `Delay` (a time-delay embedding, `tau=`/`dims=`) -- and runs first in the library's canonical pipeline (`manip -> normalize -> reduce -> align -> cluster`). They run in native space: `Smooth` and `Resample` treat each dataset on its own, while `ZScore` and `Normalize` fit one shared set of statistics across a list. The same specs go into `hyp.plot(..., manip=)`. The second half of this notebook animates the manipulated data with a sliding window (`animate='window'`, sized by `focused=`) and with the three trail styles." ] }, { "cell_type": "markdown", + "id": "b89d9f99d7d4", "metadata": {}, "source": [ "## Import packages and load the data" @@ -29,12 +50,13 @@ { "cell_type": "code", "execution_count": 1, + "id": "5c56b010853b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:48.599977Z", - "iopub.status.busy": "2026-09-05T10:25:48.599837Z", - "iopub.status.idle": "2026-09-05T10:25:52.111076Z", - "shell.execute_reply": "2026-09-05T10:25:52.110555Z" + "iopub.execute_input": "2026-10-08T17:13:07.597702Z", + "iopub.status.busy": "2026-10-08T17:13:07.597524Z", + "iopub.status.idle": "2026-10-08T17:13:10.980878Z", + "shell.execute_reply": "2026-10-08T17:13:10.980405Z" } }, "outputs": [ @@ -63,22 +85,24 @@ }, { "cell_type": "markdown", + "id": "40d0e0bf5581", "metadata": {}, "source": [ "## `Smooth`\n", "\n", - "`Smooth` is a Savitzky-Golay filter along the time axis (`mode='savgol'`; `kernel_width` must be odd, `order` is the polynomial degree). It returns a DataFrame with the same shape. Below, the raw and smoothed versions of one subject are drawn in the same space." + "By default `Smooth` is a Savitzky-Golay filter along the time axis (`kernel='savgol'`, with `order` the polynomial degree; `kernel='gaussian'` and `kernel='boxcar'` are the other kernels). `kernel_width` should be odd: an even width is bumped up by one, with a warning. It returns a DataFrame with the same shape. Below, the raw and smoothed versions of one subject are drawn in the same space." ] }, { "cell_type": "code", "execution_count": 2, + "id": "85cfac86a0db", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.128863Z", - "iopub.status.busy": "2026-09-05T10:25:52.128681Z", - "iopub.status.idle": "2026-09-05T10:25:52.360089Z", - "shell.execute_reply": "2026-09-05T10:25:52.359416Z" + "iopub.execute_input": "2026-10-08T17:13:10.997444Z", + "iopub.status.busy": "2026-10-08T17:13:10.997266Z", + "iopub.status.idle": "2026-10-08T17:13:11.222261Z", + "shell.execute_reply": "2026-10-08T17:13:11.221745Z" } }, "outputs": [ @@ -109,6 +133,7 @@ }, { "cell_type": "markdown", + "id": "692860de6a49", "metadata": {}, "source": [ "## `Resample`\n", @@ -119,12 +144,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "56e587d47e81", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.361199Z", - "iopub.status.busy": "2026-09-05T10:25:52.361123Z", - "iopub.status.idle": "2026-09-05T10:25:52.391017Z", - "shell.execute_reply": "2026-09-05T10:25:52.390588Z" + "iopub.execute_input": "2026-10-08T17:13:11.223195Z", + "iopub.status.busy": "2026-10-08T17:13:11.223122Z", + "iopub.status.idle": "2026-10-08T17:13:11.251094Z", + "shell.execute_reply": "2026-10-08T17:13:11.250669Z" } }, "outputs": [ @@ -143,22 +169,26 @@ }, { "cell_type": "markdown", + "id": "60e11e316a77", "metadata": {}, "source": [ "## `ZScore` and `Normalize`\n", "\n", - "`ZScore` gives every column zero mean and unit variance; `Normalize` rescales every column into `[min, max]` (`0` to `1` by default). On a list, both fit **one** set of statistics across all the datasets, so a column's mean is zero over the stacked rows, not within each dataset." + "`ZScore` gives every column zero mean and unit variance; `Normalize` rescales every column into `[min, max]` (`0` to `1` by default). On a list, both fit **one** set of statistics across all the datasets, so a column's mean is zero over the stacked rows, not within each dataset.\n", + "\n", + "A one-dimensional array, Series, or flat numeric list is one column of observations. Direct manipulator classes, `hyp.manip`, and `hyp.Pipeline` use the same shape convention. With `axis=1`, `ZScore` and min-max `Normalize` scale each row across its features; a list of datasets is handled separately per dataset, including datasets with different numbers of features. These row-specific statistics cannot be reused on new observations.\n" ] }, { "cell_type": "code", "execution_count": 4, + "id": "a85b4d55f326", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.391950Z", - "iopub.status.busy": "2026-09-05T10:25:52.391890Z", - "iopub.status.idle": "2026-09-05T10:25:52.432926Z", - "shell.execute_reply": "2026-09-05T10:25:52.432360Z" + "iopub.execute_input": "2026-10-08T17:13:11.251983Z", + "iopub.status.busy": "2026-10-08T17:13:11.251926Z", + "iopub.status.idle": "2026-10-08T17:13:11.307070Z", + "shell.execute_reply": "2026-10-08T17:13:11.306616Z" } }, "outputs": [ @@ -182,6 +212,54 @@ }, { "cell_type": "markdown", + "id": "64c31403abb9", + "metadata": {}, + "source": [ + "### `Normalize(mode='isotropic')`\n", + "\n", + "The default `mode='minmax'` rescales every column on its own, so each feature gets its own offset and scale and the data's shape is distorted on purpose. `mode='isotropic'` centres the whole table on its centroid (the per-column mean) and divides every column by the **same** scalar, the largest absolute deviation from the centroid over all entries, before mapping the result onto `[min, max]`. All pairwise distances are scaled by one constant, so angles and distance ratios are preserved; the centroid lands at the midpoint of the range and at least one coordinate touches an end of it. On a list, one shared centroid and one scalar are fit across all the datasets, so every dataset is moved and rescaled identically. Below, the same subject is normalized isotropically into `[-1, 1]`, and the ratio of two between-row distances shows the shape is unchanged." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3cd7e71c10ac", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-08T17:13:11.308134Z", + "iopub.status.busy": "2026-10-08T17:13:11.308057Z", + "iopub.status.idle": "2026-10-08T17:13:11.358432Z", + "shell.execute_reply": "2026-10-08T17:13:11.358018Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "isotropic: min -0.761 max 1.0 | centroid at 0.0\n", + "distance ratio, rows 0-1: 0.2263 | rows 5-40: 0.2263\n" + ] + } + ], + "source": [ + "isotropic = hyp.manip(subject, model='Normalize', mode='isotropic', min=-1, max=1)\n", + "print('isotropic: min', np.round(float(isotropic.min().min()), 3), 'max', np.round(float(isotropic.max().max()), 3),\n", + " '| centroid at', np.round(float(isotropic.mean().mean()), 3))\n", + "\n", + "\n", + "def distance(a, i, j):\n", + " return float(np.linalg.norm(np.asarray(a)[i] - np.asarray(a)[j]))\n", + "\n", + "\n", + "# one scalar scales every distance, so this ratio is the same for any two pairs of rows\n", + "print('distance ratio, rows 0-1:', np.round(distance(isotropic, 0, 1) / distance(subject, 0, 1), 4),\n", + " '| rows 5-40:', np.round(distance(isotropic, 5, 40) / distance(subject, 5, 40), 4))" + ] + }, + { + "cell_type": "markdown", + "id": "6401e2ace0fa", "metadata": {}, "source": [ "## Chaining manipulations\n", @@ -191,13 +269,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, + "id": "8166e971c0cc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.434183Z", - "iopub.status.busy": "2026-09-05T10:25:52.434093Z", - "iopub.status.idle": "2026-09-05T10:25:52.632235Z", - "shell.execute_reply": "2026-09-05T10:25:52.631795Z" + "iopub.execute_input": "2026-10-08T17:13:11.359533Z", + "iopub.status.busy": "2026-10-08T17:13:11.359457Z", + "iopub.status.idle": "2026-10-08T17:13:11.528198Z", + "shell.execute_reply": "2026-10-08T17:13:11.527823Z" } }, "outputs": [ @@ -912,7 +991,7 @@ "Pipeline([smooth=Smooth(kernel_width=25), resample=Resample(axis=[0, 0, 0], n_samples=[150, 150, 150]), zscore=ZScore()])" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -928,6 +1007,7 @@ }, { "cell_type": "markdown", + "id": "b4463ca477a8", "metadata": {}, "source": [ "## `manip=` inside `plot`\n", @@ -937,13 +1017,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "id": "d41864e6f39a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.633275Z", - "iopub.status.busy": "2026-09-05T10:25:52.633197Z", - "iopub.status.idle": "2026-09-05T10:25:52.840949Z", - "shell.execute_reply": "2026-09-05T10:25:52.840444Z" + "iopub.execute_input": "2026-10-08T17:13:11.529083Z", + "iopub.status.busy": "2026-10-08T17:13:11.529025Z", + "iopub.status.idle": "2026-10-08T17:13:11.746801Z", + "shell.execute_reply": "2026-10-08T17:13:11.746365Z" } }, "outputs": [ @@ -966,22 +1047,24 @@ }, { "cell_type": "markdown", + "id": "26a267335c59", "metadata": {}, "source": [ "## A sliding window: `animate='window'` with `focused=`\n", "\n", - "`animate='window'` shows only a moving window of each trajectory: what is inside the window is drawn opaque and everything else is hidden. `focused=` is the window's length in **seconds** of animation time (the same unit as `tail_duration`); it defaults to `tail_duration`'s value, so passing it explicitly is what decouples the two. The clip is saved next to this notebook as `manip.mp4`, and its middle frame is shown in place." + "`animate='window'` shows only a moving window of each trajectory: what is inside the window is drawn opaque and everything else is hidden. `focused=` is the window's length in **seconds** of animation time (the same unit as `tail_duration`); it defaults to `tail_duration`'s value, so passing it explicitly is what decouples the two. The clip is saved next to this notebook as `manip.mp4`, and its middle frame is shown in place (`backend='matplotlib'` keeps the returned animation a matplotlib one on Colab, where the default backend is plotly: `.draw_frame` and `.figure` are matplotlib's)." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "id": "c19e0954d756", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:52.842129Z", - "iopub.status.busy": "2026-09-05T10:25:52.842056Z", - "iopub.status.idle": "2026-09-05T10:25:53.940692Z", - "shell.execute_reply": "2026-09-05T10:25:53.940205Z" + "iopub.execute_input": "2026-10-08T17:13:11.747747Z", + "iopub.status.busy": "2026-10-08T17:13:11.747689Z", + "iopub.status.idle": "2026-10-08T17:13:12.787502Z", + "shell.execute_reply": "2026-10-08T17:13:12.786981Z" } }, "outputs": [ @@ -994,27 +1077,36 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x504 with 1 Axes>" ] }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ "anim = hyp.plot(subjects, manip='Smooth', animate='window', focused=1.5,\n", - " duration=8, frame_rate=15, save_path='manip.mp4',\n", + " duration=8, frame_rate=15, save_path='manip.mp4', backend='matplotlib',\n", " title='a 1.5-second window sliding along each smoothed trajectory', show=False)\n", "print(f'{anim.n_frames} frames')\n", "_ = anim.draw_frame(anim.n_frames // 2)\n", - "anim.figure" + "display(anim.figure)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('manip.mp4', embed=True))\n" ] }, { "cell_type": "markdown", + "id": "e6a58925ff97", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"manip.mp4\" title=\"A sliding window along three smoothed trajectories\" style=\"max-width: 100%\"></video>\n", @@ -1024,6 +1116,7 @@ }, { "cell_type": "markdown", + "id": "98eef158a041", "metadata": {}, "source": [ "## Trails: `chemtrails`, `precog` and `bullettime`\n", @@ -1033,24 +1126,25 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, + "id": "44746ca4d446", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:53.941968Z", - "iopub.status.busy": "2026-09-05T10:25:53.941882Z", - "iopub.status.idle": "2026-09-05T10:25:54.048850Z", - "shell.execute_reply": "2026-09-05T10:25:54.048399Z" + "iopub.execute_input": "2026-10-08T17:13:12.788888Z", + "iopub.status.busy": "2026-10-08T17:13:12.788798Z", + "iopub.status.idle": "2026-10-08T17:13:12.892591Z", + "shell.execute_reply": "2026-10-08T17:13:12.892142Z" } }, "outputs": [ { "data": { - 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", 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", "text/plain": [ "<Figure size 702.975x480 with 1 Axes>" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1059,7 +1153,7 @@ "trails = hyp.plot(subjects, manip='Smooth', animate=True, focused=1.5,\n", " chemtrails=[True, False, False], precog=[False, True, False], bullettime=[False, False, True],\n", " names=['chemtrails', 'precog', 'bullettime'],\n", - " duration=8, frame_rate=15, show=False)\n", + " duration=8, frame_rate=15, backend='matplotlib', show=False)\n", "_ = trails.draw_frame(trails.n_frames // 3)\n", "trails.figure" ] @@ -1081,7 +1175,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/manip.mp4 b/docs/tutorials/manip.mp4 index b6dc5065..201e9299 100644 Binary files a/docs/tutorials/manip.mp4 and b/docs/tutorials/manip.mp4 differ diff --git a/docs/tutorials/market_sectors.ipynb b/docs/tutorials/market_sectors.ipynb index ea1c4149..ceb0e9b1 100644 --- a/docs/tutorials/market_sectors.ipynb +++ b/docs/tutorials/market_sectors.ipynb @@ -3,19 +3,35 @@ { "cell_type": "code", "execution_count": null, - "id": "4c136738", - "metadata": {}, + "id": "a1a2f66b", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"\n", - "\n", - "%matplotlib inline" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", - "id": "0361afae", + "id": "0143207a", "metadata": {}, "source": [ "# A quarter century of the market: six sectors, one space\n", @@ -24,8 +40,8 @@ "as seven paths through one shared 3-D space. Each **sector** is handed to\n", "the library as its own matrix -- months down the rows, that sector's stocks\n", "across the columns (four or five of them; the counts differ on purpose) --\n", - "and each cell is the stock's **trailing twelve-month return**. Three library\n", - "calls turn that into the figure:\n", + "and each cell is the stock's **cumulative log return since the first\n", + "month** (a growth curve). Three library calls turn that into the figure:\n", "\n", "1. `hyp.reduce` takes every sector from its own handful of stocks to three\n", " dimensions **separately**, so a sector is a trajectory in a space made\n", @@ -43,12 +59,16 @@ "row, so it keeps its own colour; the market path's weights are each\n", "sector's **share of the basket's market capitalisation** that month\n", "(reported share counts x price), so its colour shifts toward whichever\n", - "sectors dominate -- tech-blue-red as the 1990s bubble deflates, more\n", - "financial-gold before 2008, and back again. The title is the **current\n", - "date**, tinted by the basket's own trailing twelve-month return: red when\n", - "the market is below where it stood a year earlier, green when it is above.\n", - "The camera makes three turns over one minute, and nothing that has been\n", - "drawn fades, so the last frame is the whole quarter century.\n", + "sectors dominate -- away from technology red as the dot-com bubble deflates\n", + "(about a third of the basket in mid-2000, a fifth by 2004), toward\n", + "financial gold before 2008 (the largest sector in 2006), then back to\n", + "technology, about half the basket through the 2020s. The title is the\n", + "**current date**, tinted by the basket's own trailing twelve-month return:\n", + "red when the market is below where it stood a year earlier, green when it\n", + "is above. The camera makes three turns over one minute. Only the last six\n", + "seconds of path are drawn at full strength, but nothing disappears: older\n", + "path stays on as a faint trail, so the last frame is the whole quarter\n", + "century.\n", "\n", "**Zero padding, verified.** `hyp.align` zero-pads datasets with different\n", "numbers of columns to a common width automatically (its `trim_and_pad`\n", @@ -57,19 +77,27 @@ "shared fit), which is exactly why the reduction here is per sector -- the\n", "sectors do not share columns, and should not share a projection.\n", "\n", - "**Data & graceful degradation.** Adjusted and unadjusted daily closes come\n", - "from Yahoo Finance's chart endpoint (full history, month-end decimated so no\n", - "future observation reaches back into a bar) and share counts from the SEC's\n", - "XBRL company-facts API (quarterly, from 2009; earlier months back-fill the\n", - "first reported capitalisation along the adjusted price). Everything is\n", - "cached on disk. If the network is unavailable the example falls back to a\n", - "seeded synthetic basket with the same sector structure and share counts, so\n", - "it always renders, and the technique is identical either way.\n" + "**Data & graceful degradation.** Daily closes and split events come from\n", + "Yahoo Finance's chart endpoint (full history, month-end decimated so no\n", + "future observation reaches back into a bar). Both of its closes are\n", + "split-adjusted -- `adjclose` also reinvests dividends -- so the share\n", + "counts, which the SEC's XBRL API (the per-concept endpoint, falling back to\n", + "company facts; quarterly, from 2009) reports as they stood on the day, are\n", + "multiplied by every later split before they meet the price. Earlier months\n", + "back-fill the first reported capitalisation along the adjusted price.\n", + "XOM's counts are read from Exxon Mobil Corp's filings (CIK 34088), not the\n", + "holding company the SEC's ticker map now names. HON's 2026-06-30 count in\n", + "the SEC data (316,940,010, from a 10-Q/A) is half its 2026-03-31 count\n", + "(633,653,119).\n", + "Everything is cached on disk. If the network is unavailable the example\n", + "falls back to a seeded synthetic basket with the same sector structure and\n", + "share counts, so it always renders, and the technique is identical either\n", + "way.\n" ] }, { "cell_type": "markdown", - "id": "2e397b7e", + "id": "e56c84f4", "metadata": {}, "source": [ "## 1. Imports, a disk cache, and the universe\n", @@ -80,13 +108,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "289781fb", + "id": "7c22a51a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:29.965245Z", - "iopub.status.busy": "2026-09-05T03:21:29.965178Z", - "iopub.status.idle": "2026-09-05T03:21:33.837099Z", - "shell.execute_reply": "2026-09-05T03:21:33.836650Z" + "iopub.execute_input": "2026-10-08T17:13:14.085287Z", + "iopub.status.busy": "2026-10-08T17:13:14.085136Z", + "iopub.status.idle": "2026-10-08T17:13:17.444821Z", + "shell.execute_reply": "2026-10-08T17:13:17.444322Z" } }, "outputs": [], @@ -140,24 +168,24 @@ }, { "cell_type": "markdown", - "id": "3c61e642", + "id": "72420eda", "metadata": {}, "source": [ "## 2. Prices and share counts, with a synthetic fallback\n", "\n", - "Yahoo Finance supplies adjusted AND unadjusted daily closes (an explicit date window: `range=max` silently degrades to quarterly bars); the SEC's XBRL API supplies reported shares outstanding, which are not split-adjusted, so market cap multiplies them by the unadjusted close. Both are cached on disk. These are the only functions that touch the network; `HYPERTOOLS_OFFLINE` makes them refuse rather than degrade, which is how the test-suite proves the import path fetches nothing.\n" + "Yahoo Finance supplies daily closes and split events (an explicit date window: `range=max` silently degrades to quarterly bars). Both of its closes are split-adjusted -- `adjclose` also reinvests dividends -- while the SEC's XBRL API reports shares outstanding as they stood on the day, so every count is multiplied by the splits that came after it before it meets the price (and the odd filing slip, a count 100x off its median, is dropped). Both sources are cached on disk. These are the only functions that touch the network; `HYPERTOOLS_OFFLINE` (an environment variable this notebook reads, not a hypertools setting) makes them refuse rather than fetch, which is how the test-suite proves the import path fetches nothing.\n" ] }, { "cell_type": "code", "execution_count": 2, - "id": "d1b011f9", + "id": "742d6e96", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:33.838817Z", - "iopub.status.busy": "2026-09-05T03:21:33.838648Z", - "iopub.status.idle": "2026-09-05T03:21:33.845194Z", - "shell.execute_reply": "2026-09-05T03:21:33.844726Z" + "iopub.execute_input": "2026-10-08T17:13:17.446473Z", + "iopub.status.busy": "2026-10-08T17:13:17.446322Z", + "iopub.status.idle": "2026-10-08T17:13:17.453319Z", + "shell.execute_reply": "2026-10-08T17:13:17.452820Z" } }, "outputs": [], @@ -187,8 +215,9 @@ "\n", "# --- the data half: the ONLY code here that reaches the network -------------\n", "def fetch_prices(sectors=SECTORS):\n", - " \"\"\"Daily ADJUSTED and unadjusted closes for every ticker, or ``None``\n", - " if anything (network, parsing) goes wrong.\"\"\"\n", + " \"\"\"Daily dividend-and-split-adjusted and split-adjusted closes for every\n", + " ticker plus its split events, or ``None`` if anything (network,\n", + " parsing) goes wrong.\"\"\"\n", " # outside the try, so it raises instead of being caught and quietly\n", " # downgraded: a test that sets HYPERTOOLS_OFFLINE is asserting that no\n", " # fetch happened, and a swallowed exception would hide one\n", @@ -196,55 +225,74 @@ " raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch')\n", " os.makedirs(CACHE, exist_ok=True)\n", " try:\n", - " adjusted, raw = {}, {}\n", + " adjusted, close, splits = {}, {}, {}\n", " for tickers in sectors.values():\n", " for ticker in tickers:\n", " # an explicit window: `range=max` silently degrades to\n", " # 3-month bars (measured 2026-09-03), a period does not\n", " result = _cached_json(\n", - " f'yahoo_daily_{ticker}.json',\n", + " f'yahoo_daily_splits_{ticker}.json',\n", " 'https://query1.finance.yahoo.com/v8/finance/chart/'\n", - " f'{ticker}?period1={PERIOD1}&period2={PERIOD2}&interval=1d'\n", + " f'{ticker}?period1={PERIOD1}&period2={PERIOD2}&interval=1d&events=split'\n", " )['chart']['result'][0]\n", " stamps = pd.to_datetime(result['timestamp'], unit='s').normalize()\n", " quote = result['indicators']\n", - " adjusted[ticker] = pd.Series(\n", - " quote['adjclose'][0]['adjclose'], index=stamps, dtype=float)\n", - " raw[ticker] = pd.Series(\n", - " quote['quote'][0]['close'], index=stamps, dtype=float)\n", - " return pd.DataFrame(adjusted).sort_index(), pd.DataFrame(raw).sort_index()\n", + " adjusted[ticker] = pd.Series(quote['adjclose'][0]['adjclose'], index=stamps, dtype=float)\n", + " # the chart's `close` is SPLIT-adjusted as well (only\n", + " # dividends are left in: GE's 2009-12-31 close comes back as\n", + " # 72.51, as-traded 15.13), so the splits come along to put the\n", + " # SEC's as-reported share counts in the same units\n", + " close[ticker] = pd.Series(quote['quote'][0]['close'], index=stamps, dtype=float)\n", + " splits[ticker] = [(pd.Timestamp(s['date'], unit='s').normalize(), s['numerator'] / s['denominator'])\n", + " for s in result.get('events', {}).get('splits', {}).values()]\n", + " return pd.DataFrame(adjusted).sort_index(), pd.DataFrame(close).sort_index(), splits\n", " except Exception as error:\n", " print(f'price history unavailable ({error!r})')\n", " return None\n", "\n", "\n", - "def fetch_shares(tickers):\n", + "def fetch_shares(tickers, splits):\n", " \"\"\"Reported shares outstanding per ticker from the SEC's XBRL API, as a\n", " month-end series (forward-filled between filings, NaN before the first),\n", - " or ``None``. Counts are as reported -- NOT split-adjusted -- which is why\n", - " market cap below multiplies them by the UNADJUSTED close.\"\"\"\n", + " or ``None``. The counts are filed as they stood on the day; each is\n", + " multiplied by the ratio of every LATER split in `splits` (ticker ->\n", + " [(date, ratio)]), which puts it in the units of Yahoo's split-adjusted\n", + " close -- the pair multiply to the true capitalisation, even in the\n", + " months between a split and the next filing.\"\"\"\n", " if os.environ.get('HYPERTOOLS_OFFLINE'):\n", " raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch')\n", " try:\n", + " # an explicit override where the SEC's ticker map names a filer that\n", + " # does not hold the ticker's share history: it maps XOM to ExxonMobil\n", + " # Holdings Corp (CIK 2115436), which has filed one share count\n", + " # (2026-06-30), so every earlier XOM month would be back-filled from\n", + " # that single value; Exxon Mobil Corporation (CIK 34088) reports\n", + " # counts from 2009-06-30 (both measured 2026-10-08)\n", " ciks = {row['ticker']: row['cik_str'] for row in _cached_json(\n", " 'sec_company_tickers.json',\n", - " 'https://www.sec.gov/files/company_tickers.json').values()}\n", + " 'https://www.sec.gov/files/company_tickers.json').values()} | {'XOM': 34088}\n", " shares = {}\n", " for ticker in tickers:\n", - " facts = _cached_json(f'sec_shares_{ticker}.json',\n", + " # the cache is keyed by CIK as well as ticker, so a file fetched\n", + " # under a ticker's earlier CIK is never reused for a new one\n", + " facts = _cached_json(f'sec_shares_{ticker}_{ciks[ticker]}.json',\n", " SEC.format(cik=ciks[ticker]))['units']['shares']\n", " if not facts:\n", " # the per-concept endpoint comes back EMPTY for a few filers\n", " # (ABT, KO -- measured 2026-09-03) whose complete\n", " # company-facts file carries the same concept; read it there\n", " facts = _cached_json(\n", - " f'sec_facts_{ticker}.json', SEC_FACTS.format(cik=ciks[ticker])\n", + " f'sec_facts_{ticker}_{ciks[ticker]}.json', SEC_FACTS.format(cik=ciks[ticker])\n", " )['facts']['dei']['EntityCommonStockSharesOutstanding']['units']['shares']\n", " # one value per period end: the LATEST filing wins over amendments\n", " frame = pd.DataFrame(facts).sort_values('filed')\n", " frame = frame.drop_duplicates('end', keep='last')\n", - " series = pd.Series(frame['val'].to_numpy(float),\n", - " index=pd.to_datetime(frame['end']))\n", + " series = pd.Series(frame['val'].to_numpy(float), index=pd.to_datetime(frame['end']))\n", + " series *= [np.prod([r for day, r in splits[ticker] if end < day]) for end in series.index]\n", + " # filings carry the odd slip (measured 2026-09-11: ORCL 2012-09\n", + " # 4.8e15 shares for 4.8e9, MRK 2009-06 and KO 2009-10 zero); in\n", + " # split-adjusted units a count never strays 100x from its median\n", + " series = series[(series / series.median()).between(0.01, 100)]\n", " shares[ticker] = series.resample('ME').last().ffill()\n", " return pd.DataFrame(shares)\n", " except Exception as error:\n", @@ -254,7 +302,8 @@ "\n", "def synthetic_market(sectors=SECTORS, days=7000, seed=0):\n", " \"\"\"The same sector structure, seeded, so the figure renders offline:\n", - " daily closes (adjusted == unadjusted) and a constant share count.\"\"\"\n", + " daily closes (no dividends, no splits: both closes are one series) and a\n", + " constant share count.\"\"\"\n", " rng = np.random.default_rng(seed)\n", " index = pd.date_range('1999-01-04', periods=days, freq='B')\n", " tickers = [t for ts in sectors.values() for t in ts]\n", @@ -270,7 +319,7 @@ }, { "cell_type": "markdown", - "id": "ced1a06d", + "id": "04aee8b1", "metadata": {}, "source": [ "## 3. Growth curves per sector, market-cap weights, the basket's return\n", @@ -281,18 +330,18 @@ { "cell_type": "code", "execution_count": 3, - "id": "a2eaaff0", + "id": "cf021a0f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:33.846135Z", - "iopub.status.busy": "2026-09-05T03:21:33.846071Z", - "iopub.status.idle": "2026-09-05T03:21:33.849725Z", - "shell.execute_reply": "2026-09-05T03:21:33.849447Z" + "iopub.execute_input": "2026-10-08T17:13:17.454325Z", + "iopub.status.busy": "2026-10-08T17:13:17.454266Z", + "iopub.status.idle": "2026-10-08T17:13:17.457742Z", + "shell.execute_reply": "2026-10-08T17:13:17.457398Z" } }, "outputs": [], "source": [ - "def assemble(adjusted, raw, shares, sectors, source):\n", + "def assemble(adjusted, close, shares, sectors, source):\n", " \"\"\"Month-end trailing returns per sector, market-cap weights per sector\n", " and the basket's own return, on one shared monthly index from START.\"\"\"\n", " # month-end levels; a month still in progress is DROPPED (resample\n", @@ -305,9 +354,10 @@ " # month, so a sector's matrix is a set of growth curves and its 3-D path\n", " # is a journey rather than a tangle of month-to-month noise\n", " paths = levels.loc[months] - levels.loc[months[0]]\n", - " # market cap = UNADJUSTED close x reported shares; before the first\n", - " # filing, the first known cap is carried back along the ADJUSTED price\n", - " cap = raw.resample('ME').last().reindex(months) * shares.reindex(months).ffill()\n", + " # market cap = split-adjusted close x split-adjusted shares; before the\n", + " # first filing, the first known cap is carried back along the ADJUSTED\n", + " # (dividend-reinvested) price\n", + " cap = close.resample('ME').last().reindex(months) * shares.reindex(months).ffill()\n", " first = cap.apply(lambda col: col.first_valid_index())\n", " for ticker in cap:\n", " known = cap.loc[first[ticker], ticker]\n", @@ -326,13 +376,13 @@ " try:\n", " prices = fetch_prices(sectors)\n", " if prices is not None:\n", - " shares = fetch_shares([t for ts in sectors.values() for t in ts])\n", + " shares = fetch_shares([t for ts in sectors.values() for t in ts], prices[2])\n", " except RuntimeError:\n", " pass\n", " if prices is None or shares is None:\n", " return assemble(*synthetic_market(sectors), sectors,\n", " 'synthetic basket (offline)')\n", - " return assemble(*prices, shares, sectors,\n", + " return assemble(*prices[:2], shares, sectors,\n", " 'Yahoo Finance closes, SEC share counts')\n", "\n", "\n", @@ -343,24 +393,24 @@ }, { "cell_type": "markdown", - "id": "954404cb", + "id": "57db878c", "metadata": {}, "source": [ "## 4. Reduce per sector, hyperalign, draw seven paths\n", "\n", - "Three library calls: `hyp.reduce` per sector (its own stocks, its own space), `hyp.align(..., align='HyperAlign')` into one shared space, and `hyp.plot` on the six aligned paths plus their mean, coloured through the mixture hue. The `on_frame` hook only sets the title: the date under the head, tinted by the basket's return.\n" + "Three library calls: `hyp.reduce` per sector (its own stocks, its own space), `hyp.align(..., model='HyperAlign')` into one shared space, and `hyp.plot` on the six aligned paths plus their mean, coloured through the mixture hue. The `on_frame` hook only sets the title: the date under the head, tinted by the basket's return.\n" ] }, { "cell_type": "code", "execution_count": 4, - "id": "1641529a", + "id": "e30c4856", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:33.850704Z", - "iopub.status.busy": "2026-09-05T03:21:33.850647Z", - "iopub.status.idle": "2026-09-05T03:21:33.854559Z", - "shell.execute_reply": "2026-09-05T03:21:33.854194Z" + "iopub.execute_input": "2026-10-08T17:13:17.458582Z", + "iopub.status.busy": "2026-10-08T17:13:17.458527Z", + "iopub.status.idle": "2026-10-08T17:13:17.462317Z", + "shell.execute_reply": "2026-10-08T17:13:17.461981Z" } }, "outputs": [], @@ -381,11 +431,13 @@ " # path's rows are the sectors' shares of the basket's capitalisation.\n", " hue = [np.tile(np.eye(len(names))[i], (n_months, 1))\n", " for i in range(len(names))] + [data.weights.to_numpy()]\n", - " # THE call: seven paths, one minute, three turns of the camera, and\n", - " # a trail as long as the clip so nothing drawn ever fades.\n", + " # THE call: seven paths, one minute, three turns of the camera; a\n", + " # six-second bright head, and a chemtrail that keeps everything older.\n", " months, basket = data.weights.index, data.market.to_numpy()\n", " # the title is restyled per frame below; the library reserves its margin\n", - " # at build time from rcParams, so the size is declared here as well\n", + " # at build time from rcParams, so the size is declared here as well.\n", + " # backend='matplotlib': the hook and legend below use the matplotlib\n", + " # figure, and on Colab the default backend would be plotly\n", " with plt.rc_context({'axes.titlesize': TITLE_SIZE, 'axes.titleweight': 'bold'}):\n", " anim = hyp.plot(aligned + [market], '-', hue=hue, palette=SECTOR_COLORS,\n", " hue_mode='mixture', linewidth=[1.1] * len(names) + [3.4],\n", @@ -393,7 +445,7 @@ " tail_duration=TAIL, duration=DURATION, frame_rate=FPS,\n", " rotations=ROTATIONS, colorbar=False,\n", " title=f'{months[0]:%B} {months[0].day}, {months[0].year}',\n", - " size=(8, 8), show=False)\n", + " size=(8, 8), backend='matplotlib', show=False)\n", " ax = anim.figure.axes[0]\n", " ax.legend(handles=[Line2D([], [], color=c, lw=2, label=s)\n", " for s, c in zip(names, SECTOR_COLORS)]\n", @@ -416,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "f74a02eb", + "id": "85060e6c", "metadata": {}, "source": [ "## 5. Load the data and build the animation\n" @@ -425,13 +477,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "0f4a53e2", + "id": "ee426656", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:33.855383Z", - "iopub.status.busy": "2026-09-05T03:21:33.855331Z", - "iopub.status.idle": "2026-09-05T03:21:34.494279Z", - "shell.execute_reply": "2026-09-05T03:21:34.493793Z" + "iopub.execute_input": "2026-10-08T17:13:17.463211Z", + "iopub.status.busy": "2026-10-08T17:13:17.463154Z", + "iopub.status.idle": "2026-10-08T17:13:18.038190Z", + "shell.execute_reply": "2026-10-08T17:13:18.037689Z" } }, "outputs": [ @@ -439,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "market data: 316 months x 27 stocks in 6 sectors, May 2000 - Aug 2026 (Yahoo Finance closes, SEC share counts)\n" + "market data: 317 months x 27 stocks in 6 sectors, May 2000 - Sep 2026 (Yahoo Finance closes, SEC share counts)\n" ] } ], @@ -456,7 +508,7 @@ }, { "cell_type": "markdown", - "id": "8d93a053", + "id": "f22ada08", "metadata": {}, "source": [ "## 6. Save the animation\n" @@ -465,13 +517,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "a5a52921", + "id": "b566ef8e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:21:34.495348Z", - "iopub.status.busy": "2026-09-05T03:21:34.495275Z", - "iopub.status.idle": "2026-09-05T03:26:02.319037Z", - "shell.execute_reply": "2026-09-05T03:26:02.318536Z" + "iopub.execute_input": "2026-10-08T17:13:18.039144Z", + "iopub.status.busy": "2026-10-08T17:13:18.039079Z", + "iopub.status.idle": "2026-10-08T17:17:45.157651Z", + "shell.execute_reply": "2026-10-08T17:17:45.157207Z" } }, "outputs": [ @@ -485,12 +537,21 @@ ], "source": [ "anim.save('market_sectors.mp4', dpi=100)\n", - "print('saved market_sectors.mp4')\n" + "print('saved market_sectors.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('market_sectors.mp4', embed=True))\n" ] }, { "cell_type": "markdown", - "id": "61fe70c0", + "id": "815f8f27", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"market_sectors.mp4\" title=\"A quarter century of the market: six sectors, one space\" style=\"max-width: 100%\"></video>\n", @@ -515,7 +576,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/market_sectors.mp4 b/docs/tutorials/market_sectors.mp4 index 0ebcf58b..14a08b05 100644 Binary files a/docs/tutorials/market_sectors.mp4 and b/docs/tutorials/market_sectors.mp4 differ diff --git a/docs/tutorials/modern_sklearn_dynamics.ipynb b/docs/tutorials/modern_sklearn_dynamics.ipynb index c5e72748..1b0e9894 100644 --- a/docs/tutorials/modern_sklearn_dynamics.ipynb +++ b/docs/tutorials/modern_sklearn_dynamics.ipynb @@ -2,22 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c597c07b", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:35:44.441872Z", - "iopub.status.busy": "2026-07-17T07:35:44.441602Z", - "iopub.status.idle": "2026-07-17T07:35:44.447775Z", - "shell.execute_reply": "2026-07-17T07:35:44.446930Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -27,7 +36,7 @@ "source": [ "# Modern scikit-learn models and dynamical systems\n", "\n", - "HyperTools 1.0 exposes the latest scikit-learn models directly through its\n", + "HyperTools exposes the latest scikit-learn models directly through its\n", "plotting interface. In this tutorial we'll use sklearn's built-in\n", "[HDBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.HDBSCAN.html)\n", "for density-based clustering, Gaussian mixture models for *soft* clustering,\n", @@ -41,10 +50,10 @@ "id": "d81bf0db", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:55.253458Z", - "iopub.status.busy": "2026-09-05T10:25:55.253209Z", - "iopub.status.idle": "2026-09-05T10:25:58.787810Z", - "shell.execute_reply": "2026-09-05T10:25:58.787220Z" + "iopub.execute_input": "2026-10-08T17:17:46.351186Z", + "iopub.status.busy": "2026-10-08T17:17:46.351045Z", + "iopub.status.idle": "2026-10-08T17:17:49.804369Z", + "shell.execute_reply": "2026-10-08T17:17:49.803856Z" } }, "outputs": [], @@ -78,16 +87,16 @@ "id": "a013092c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:58.789800Z", - "iopub.status.busy": "2026-09-05T10:25:58.789509Z", - "iopub.status.idle": "2026-09-05T10:25:58.942734Z", - "shell.execute_reply": "2026-09-05T10:25:58.942345Z" + "iopub.execute_input": "2026-10-08T17:17:49.805843Z", + "iopub.status.busy": "2026-10-08T17:17:49.805633Z", + "iopub.status.idle": "2026-10-08T17:17:49.988699Z", + "shell.execute_reply": "2026-10-08T17:17:49.988288Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -123,16 +132,16 @@ "id": "7b14433a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:58.943925Z", - "iopub.status.busy": "2026-09-05T10:25:58.943857Z", - "iopub.status.idle": "2026-09-05T10:25:59.028627Z", - "shell.execute_reply": "2026-09-05T10:25:59.028117Z" + "iopub.execute_input": "2026-10-08T17:17:49.989727Z", + "iopub.status.busy": "2026-10-08T17:17:49.989666Z", + "iopub.status.idle": "2026-10-08T17:17:50.068271Z", + "shell.execute_reply": "2026-10-08T17:17:50.067643Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -166,10 +175,10 @@ "id": "0ee8fb27", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:59.030339Z", - "iopub.status.busy": "2026-09-05T10:25:59.030263Z", - "iopub.status.idle": "2026-09-05T10:25:59.062386Z", - "shell.execute_reply": "2026-09-05T10:25:59.061852Z" + "iopub.execute_input": "2026-10-08T17:17:50.069629Z", + "iopub.status.busy": "2026-10-08T17:17:50.069530Z", + "iopub.status.idle": "2026-10-08T17:17:50.099061Z", + "shell.execute_reply": "2026-10-08T17:17:50.098568Z" } }, "outputs": [ @@ -217,10 +226,10 @@ "id": "4b80c94e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:59.063753Z", - "iopub.status.busy": "2026-09-05T10:25:59.063655Z", - "iopub.status.idle": "2026-09-05T10:25:59.562583Z", - "shell.execute_reply": "2026-09-05T10:25:59.561916Z" + "iopub.execute_input": "2026-10-08T17:17:50.100470Z", + "iopub.status.busy": "2026-10-08T17:17:50.100385Z", + "iopub.status.idle": "2026-10-08T17:17:50.518823Z", + "shell.execute_reply": "2026-10-08T17:17:50.518276Z" } }, "outputs": [ @@ -266,16 +275,25 @@ "id": "f06cfdd1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:59.563695Z", - "iopub.status.busy": "2026-09-05T10:25:59.563563Z", - "iopub.status.idle": "2026-09-05T10:26:01.066810Z", - "shell.execute_reply": "2026-09-05T10:26:01.066114Z" + "iopub.execute_input": "2026-10-08T17:17:50.519774Z", + "iopub.status.busy": "2026-10-08T17:17:50.519709Z", + "iopub.status.idle": "2026-10-08T17:17:51.959862Z", + "shell.execute_reply": "2026-10-08T17:17:51.959390Z" } }, "outputs": [], "source": [ "anim = hyp.plot(trajectory, animate=True, duration=20, frame_rate=15,\n", - " save_path='modern_sklearn_dynamics.mp4', show=False)" + " save_path='modern_sklearn_dynamics.mp4', show=False)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('modern_sklearn_dynamics.mp4', embed=True))\n" ] }, { @@ -317,220 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "053e6e8c388043b8a1251fbf3489404e": { - "model_module": "jupyter-matplotlib", - "model_module_version": "^0.12", - "model_name": "ToolbarModel", - "state": { - "_current_action": "", - "_dom_classes": [], - "_model_module": "jupyter-matplotlib", - "_model_module_version": "^0.12", - "_model_name": "ToolbarModel", - "_view_count": null, - "_view_module": "jupyter-matplotlib", - "_view_module_version": "^0.12", - "_view_name": "ToolbarView", - "button_style": "", - "collapsed": true, - "layout": "IPY_MODEL_6e8e9d7825ec42e4924540fd85c46239", - "orientation": "vertical", - "tabbable": null, - "toolitems": [ - [ - "Home", - "Reset original view", - "home", - "home" - ], - [ - "Back", - "Back to previous view", - "arrow-left", - "back" - ], - [ - "Forward", - "Forward to next view", - "arrow-right", - "forward" - ], - [ - "Pan", - "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", - "arrows", - "pan" - ], - [ - "Zoom", - "Zoom to rectangle\nx/y fixes axis", - "square-o", - "zoom" - ], - [ - "Download", - "Download plot", - "floppy-o", - "save_figure" - ] - ], - "tooltip": null - } - }, - "457aeba9025b418b9bec589f7230e643": { - "model_module": "jupyter-matplotlib", - "model_module_version": "^0.12", - "model_name": "MPLCanvasModel", - "state": { - "_cursor": "pointer", - "_data_url": null, - "_dom_classes": [], - "_figure_label": "Figure", - "_image_mode": "full", - "_message": "", - "_model_module": "jupyter-matplotlib", - "_model_module_version": "^0.12", - "_model_name": "MPLCanvasModel", - "_rubberband_height": 0, - "_rubberband_width": 0, - "_rubberband_x": 0, - "_rubberband_y": 0, - "_size": [ - 0, - 0 - ], - "_view_count": null, - "_view_module": "jupyter-matplotlib", - "_view_module_version": "^0.12", - "_view_name": "MPLCanvasView", - "capture_scroll": false, - "footer_visible": true, - "header_visible": true, - "layout": "IPY_MODEL_4ab1f21f3f8048b991d1960d144a19f1", - "pan_zoom_throttle": 33.0, - "resizable": true, - "tabbable": null, - "toolbar": "IPY_MODEL_053e6e8c388043b8a1251fbf3489404e", - "toolbar_position": "left", - "toolbar_visible": "fade-in-fade-out", - "tooltip": null - } - }, - "4ab1f21f3f8048b991d1960d144a19f1": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "6e8e9d7825ec42e4924540fd85c46239": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - } - }, - "version_major": 2, - "version_minor": 0 - } + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/modern_sklearn_dynamics.mp4 b/docs/tutorials/modern_sklearn_dynamics.mp4 index c1cc086d..6a2e49a5 100644 Binary files a/docs/tutorials/modern_sklearn_dynamics.mp4 and b/docs/tutorials/modern_sklearn_dynamics.mp4 differ diff --git a/docs/tutorials/morph_shapes_zoo.ipynb b/docs/tutorials/morph_shapes_zoo.ipynb index d8377b93..693645fd 100644 --- a/docs/tutorials/morph_shapes_zoo.ipynb +++ b/docs/tutorials/morph_shapes_zoo.ipynb @@ -3,19 +3,35 @@ { "cell_type": "code", "execution_count": null, - "id": "f3d46c11", - "metadata": {}, + "id": "535f895e", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"\n", - "\n", - "%matplotlib inline" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", - "id": "239b4163", + "id": "696852df", "metadata": {}, "source": [ "# Morphing through the shapes zoo, with titles\n", @@ -59,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "c26eb2e8", + "id": "92e131d3", "metadata": {}, "source": [ "## 1. Imports and the sampling constants\n", @@ -70,13 +86,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "2fb9c8d3", + "id": "7a301474", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:19:30.632456Z", - "iopub.status.busy": "2026-09-05T03:19:30.632272Z", - "iopub.status.idle": "2026-09-05T03:19:34.168005Z", - "shell.execute_reply": "2026-09-05T03:19:34.167434Z" + "iopub.execute_input": "2026-10-08T17:17:53.166382Z", + "iopub.status.busy": "2026-10-08T17:17:53.166272Z", + "iopub.status.idle": "2026-10-08T17:17:56.434272Z", + "shell.execute_reply": "2026-10-08T17:17:56.433738Z" } }, "outputs": [], @@ -117,10 +133,11 @@ "# clear of the axes box: measured over all 600 frames (title bbox bottom vs\n", "# the highest projected box corner, 2026-09-03), 0.90 collided by 7 px at\n", "# the box's near-top-corner azimuths and 0.93 clears them everywhere. The\n", - "# family is named explicitly because hypertools' bundled default (Noto Sans)\n", - "# ships only a Regular face, so ``fontweight='bold'`` alone silently falls\n", - "# back to regular (checked with font_manager.findfont, 2026-09-03); DejaVu\n", - "# Sans Bold ships inside matplotlib itself, so it is always available.\n", + "# family is named explicitly so the clip keeps the face it was designed in:\n", + "# DejaVu Sans Bold ships inside matplotlib itself, so it is always\n", + "# available. (hypertools 1.1 also bundles a Noto Sans Bold face, so\n", + "# ``fontweight='bold'`` alone renders bold too; the override is a choice of\n", + "# typeface, not a workaround.)\n", "TITLE_FONTSIZE = 24\n", "TITLE_FONTFAMILY = 'DejaVu Sans'\n", "TITLE_Y = 0.93\n" @@ -128,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "3a6cad33", + "id": "88d6b74f", "metadata": {}, "source": [ "## 2. The shapes zoo, sampled and loop-closed, with parametric stand-ins\n", @@ -139,13 +156,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "dd80d72f", + "id": "987a3bef", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:19:34.169316Z", - "iopub.status.busy": "2026-09-05T03:19:34.169174Z", - "iopub.status.idle": "2026-09-05T03:19:34.174003Z", - "shell.execute_reply": "2026-09-05T03:19:34.173640Z" + "iopub.execute_input": "2026-10-08T17:17:56.435592Z", + "iopub.status.busy": "2026-10-08T17:17:56.435458Z", + "iopub.status.idle": "2026-10-08T17:17:56.439823Z", + "shell.execute_reply": "2026-10-08T17:17:56.439426Z" } }, "outputs": [], @@ -215,7 +232,7 @@ }, { "cell_type": "markdown", - "id": "4f850fae", + "id": "b57c1229", "metadata": {}, "source": [ "## 3. One morph call, with restyled titles\n", @@ -226,13 +243,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "dbeffd67", + "id": "2a0e6e51", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:19:34.174980Z", - "iopub.status.busy": "2026-09-05T03:19:34.174920Z", - "iopub.status.idle": "2026-09-05T03:19:34.180917Z", - "shell.execute_reply": "2026-09-05T03:19:34.180562Z" + "iopub.execute_input": "2026-10-08T17:17:56.440809Z", + "iopub.status.busy": "2026-10-08T17:17:56.440755Z", + "iopub.status.idle": "2026-10-08T17:17:56.443064Z", + "shell.execute_reply": "2026-10-08T17:17:56.442776Z" } }, "outputs": [], @@ -252,10 +269,12 @@ " # THE hypertools call: black pixel-sized dots morphing through the zoo.\n", " # title= names each shape while its hold plays and is left blank by\n", " # hyp.plot itself during every transition -- no hand-rolled schedule.\n", + " # backend= is pinned: the hook below restyles a matplotlib title, and on\n", + " # Colab the default backend would be plotly.\n", " anim = hyp.plot(data.clouds, fmt='.', color='k', markersize=1.6,\n", " animate='morph', rotations=rotations, morph_samples=N,\n", " duration=30, frame_rate=20, size=(6, 6), show=False,\n", - " title=data.titles)\n", + " title=data.titles, backend='matplotlib')\n", "\n", " def restyle_title(ctx):\n", " \"\"\"Runs AFTER the library's title updater on every frame: re-apply\n", @@ -272,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "c61531b2", + "id": "0470c4d2", "metadata": {}, "source": [ "## 4. Load the data and build the animation\n" @@ -281,13 +300,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "4c434477", + "id": "c32ae10a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:19:34.182059Z", - "iopub.status.busy": "2026-09-05T03:19:34.181999Z", - "iopub.status.idle": "2026-09-05T03:19:38.608791Z", - "shell.execute_reply": "2026-09-05T03:19:38.608338Z" + "iopub.execute_input": "2026-10-08T17:17:56.443880Z", + "iopub.status.busy": "2026-10-08T17:17:56.443825Z", + "iopub.status.idle": "2026-10-08T17:18:00.584565Z", + "shell.execute_reply": "2026-10-08T17:18:00.584052Z" } }, "outputs": [ @@ -310,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "05b172d5", + "id": "89f158f0", "metadata": {}, "source": [ "## 5. Save the animation\n" @@ -319,13 +338,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "c08c9392", + "id": "7859d4cc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:19:38.610166Z", - "iopub.status.busy": "2026-09-05T03:19:38.610096Z", - "iopub.status.idle": "2026-09-05T03:19:42.369883Z", - "shell.execute_reply": "2026-09-05T03:19:42.369402Z" + "iopub.execute_input": "2026-10-08T17:18:00.585996Z", + "iopub.status.busy": "2026-10-08T17:18:00.585913Z", + "iopub.status.idle": "2026-10-08T17:18:04.398804Z", + "shell.execute_reply": "2026-10-08T17:18:04.398430Z" } }, "outputs": [ @@ -339,12 +358,21 @@ ], "source": [ "anim.save('morph_shapes_zoo.mp4', dpi=100)\n", - "print('saved morph_shapes_zoo.mp4')\n" + "print('saved morph_shapes_zoo.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('morph_shapes_zoo.mp4', embed=True))\n" ] }, { "cell_type": "markdown", - "id": "7baf3aca", + "id": "9a68dd3b", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"morph_shapes_zoo.mp4\" title=\"Morphing through the shapes zoo, with titles\" style=\"max-width: 100%\"></video>\n", @@ -369,7 +397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/morph_shapes_zoo.mp4 b/docs/tutorials/morph_shapes_zoo.mp4 index 4c5e0ae2..029b46f5 100644 Binary files a/docs/tutorials/morph_shapes_zoo.mp4 and b/docs/tutorials/morph_shapes_zoo.mp4 differ diff --git a/docs/tutorials/normalize.ipynb b/docs/tutorials/normalize.ipynb index 35c50867..ba5e91e5 100644 --- a/docs/tutorials/normalize.ipynb +++ b/docs/tutorials/normalize.ipynb @@ -3,22 +3,35 @@ { "cell_type": "code", "execution_count": null, + "id": "5cfe780da48d", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:28:05.819120Z", - "iopub.status.busy": "2026-07-17T07:28:05.819058Z", - "iopub.status.idle": "2026-07-17T07:28:05.821745Z", - "shell.execute_reply": "2026-07-17T07:28:05.821350Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "af11b59078ea", "metadata": {}, "source": [ "# Normalization" @@ -26,6 +39,7 @@ }, { "cell_type": "markdown", + "id": "8ca1d4f67704", "metadata": {}, "source": [ "The `normalize` function is a helper to z-score your data. This is useful if your features (columns) are scaled differently within or across datasets. By default, hypertools normalizes *across* the columns of all datasets passed, but also affords the option to normalize columns *within* individual lists. Alternatively, you can also normalize each row. The function returns an array or list of arrays where the columns or rows are z-scored. Note that a list containing a single array is returned as a bare array; lists of two or more arrays come back as lists." @@ -33,6 +47,7 @@ }, { "cell_type": "markdown", + "id": "8400f7093895", "metadata": {}, "source": [ "## Import packages" @@ -41,13 +56,14 @@ { "cell_type": "code", "execution_count": 1, + "id": "390d553b5ff3", "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:12.821629Z", - "iopub.status.busy": "2026-09-05T10:24:12.821310Z", - "iopub.status.idle": "2026-09-05T10:24:16.318632Z", - "shell.execute_reply": "2026-09-05T10:24:16.318081Z" + "iopub.execute_input": "2026-10-08T17:18:05.464669Z", + "iopub.status.busy": "2026-10-08T17:18:05.464511Z", + "iopub.status.idle": "2026-10-08T17:18:08.818669Z", + "shell.execute_reply": "2026-10-08T17:18:08.818246Z" } }, "outputs": [], @@ -60,6 +76,7 @@ }, { "cell_type": "markdown", + "id": "5aa7d87739ef", "metadata": {}, "source": [ "## Generate synthetic data" @@ -67,6 +84,7 @@ }, { "cell_type": "markdown", + "id": "b934434d3765", "metadata": {}, "source": [ "First, we generate two sets of synthetic data. We pull points randomly from a multivariate normal distribution for each set, so the sets will exhibit unique statistical properties." @@ -75,13 +93,14 @@ { "cell_type": "code", "execution_count": 2, + "id": "d1fb62c7c930", "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:16.320335Z", - "iopub.status.busy": "2026-09-05T10:24:16.320153Z", - "iopub.status.idle": "2026-09-05T10:24:16.323377Z", - "shell.execute_reply": "2026-09-05T10:24:16.322986Z" + "iopub.execute_input": "2026-10-08T17:18:08.820059Z", + "iopub.status.busy": "2026-10-08T17:18:08.819925Z", + "iopub.status.idle": "2026-10-08T17:18:08.822754Z", + "shell.execute_reply": "2026-10-08T17:18:08.822359Z" }, "scrolled": true }, @@ -106,6 +125,7 @@ }, { "cell_type": "markdown", + "id": "e23c352f3285", "metadata": {}, "source": [ "## Visualize the data\n", @@ -116,12 +136,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "92010c60518b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:16.324241Z", - "iopub.status.busy": "2026-09-05T10:24:16.324180Z", - "iopub.status.idle": "2026-09-05T10:24:16.411947Z", - "shell.execute_reply": "2026-09-05T10:24:16.411432Z" + "iopub.execute_input": "2026-10-08T17:18:08.823653Z", + "iopub.status.busy": "2026-10-08T17:18:08.823596Z", + "iopub.status.idle": "2026-10-08T17:18:08.951363Z", + "shell.execute_reply": "2026-10-08T17:18:08.950949Z" }, "scrolled": true }, @@ -143,6 +164,7 @@ }, { "cell_type": "markdown", + "id": "da054a2e7c2c", "metadata": {}, "source": [ "## Normalizing (Specified Cols or Rows)" @@ -150,6 +172,7 @@ }, { "cell_type": "markdown", + "id": "5566d7c40063", "metadata": {}, "source": [ "By default, calling `hyp.normalize(data)` z-scores the columns across all passed lists (equivalent to passing `normalize='across'`). To specify a different normalization, pass one of the following arguments as a string, as shown in the examples below.\n", @@ -161,6 +184,7 @@ }, { "cell_type": "markdown", + "id": "534eddd1062b", "metadata": {}, "source": [ "### Normalizing 'across'" @@ -168,6 +192,7 @@ }, { "cell_type": "markdown", + "id": "1bc52376d35e", "metadata": {}, "source": [ "When you normalize 'across', all of the data is stacked/combined, and the normalization is done on the columns of the full dataset. Then the data is split back into separate elements." @@ -176,12 +201,13 @@ { "cell_type": "code", "execution_count": 4, + "id": "79420c297493", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:16.429175Z", - "iopub.status.busy": "2026-09-05T10:24:16.429093Z", - "iopub.status.idle": "2026-09-05T10:24:16.451162Z", - "shell.execute_reply": "2026-09-05T10:24:16.450821Z" + "iopub.execute_input": "2026-10-08T17:18:08.967543Z", + "iopub.status.busy": "2026-10-08T17:18:08.967466Z", + "iopub.status.idle": "2026-10-08T17:18:08.987686Z", + "shell.execute_reply": "2026-10-08T17:18:08.987296Z" } }, "outputs": [ @@ -203,6 +229,7 @@ }, { "cell_type": "markdown", + "id": "9714674e09e1", "metadata": {}, "source": [ "### Normalizing 'within'" @@ -210,6 +237,7 @@ }, { "cell_type": "markdown", + "id": "2877a56b016a", "metadata": {}, "source": [ "When you normalize 'within', normalization is done on the columns of each element of the data, separately. " @@ -218,12 +246,13 @@ { "cell_type": "code", "execution_count": 5, + "id": "8c6d9d7b14ea", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:16.452395Z", - "iopub.status.busy": "2026-09-05T10:24:16.452322Z", - "iopub.status.idle": "2026-09-05T10:24:16.472824Z", - "shell.execute_reply": "2026-09-05T10:24:16.472474Z" + "iopub.execute_input": "2026-10-08T17:18:08.988600Z", + "iopub.status.busy": "2026-10-08T17:18:08.988540Z", + "iopub.status.idle": "2026-10-08T17:18:09.008033Z", + "shell.execute_reply": "2026-10-08T17:18:09.007664Z" } }, "outputs": [ @@ -245,6 +274,7 @@ }, { "cell_type": "markdown", + "id": "46ef241b0d1f", "metadata": {}, "source": [ "### Normalizing by 'row'\n", @@ -255,12 +285,13 @@ { "cell_type": "code", "execution_count": 6, + "id": "7ad75255ac82", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:16.474016Z", - "iopub.status.busy": "2026-09-05T10:24:16.473948Z", - "iopub.status.idle": "2026-09-05T10:24:16.497017Z", - "shell.execute_reply": "2026-09-05T10:24:16.496690Z" + "iopub.execute_input": "2026-10-08T17:18:09.008917Z", + "iopub.status.busy": "2026-10-08T17:18:09.008857Z", + "iopub.status.idle": "2026-10-08T17:18:09.030990Z", + "shell.execute_reply": "2026-10-08T17:18:09.030657Z" } }, "outputs": [ @@ -297,7 +328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/painting_embeddings.ipynb b/docs/tutorials/painting_embeddings.ipynb index 38244e1a..034ed762 100644 --- a/docs/tutorials/painting_embeddings.ipynb +++ b/docs/tutorials/painting_embeddings.ipynb @@ -3,20 +3,35 @@ { "cell_type": "code", "execution_count": null, - "id": "84299d92", - "metadata": {}, + "id": "41904f0e", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"\n", - "%pip install -q sentence-transformers\n", - "\n", - "%matplotlib inline" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", - "id": "16192191", + "id": "49d77988", "metadata": {}, "source": [ "# Five paintings, described in words, drawn in their own colors\n", @@ -54,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "0bcf4556", + "id": "59246015", "metadata": {}, "source": [ "## 1. Imports, a disk cache, and the five paintings\n", @@ -65,13 +80,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "ad843599", + "id": "91f797ca", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:17:11.039190Z", - "iopub.status.busy": "2026-09-05T03:17:11.039061Z", - "iopub.status.idle": "2026-09-05T03:17:14.502728Z", - "shell.execute_reply": "2026-09-05T03:17:14.502234Z" + "iopub.execute_input": "2026-10-08T17:18:10.102971Z", + "iopub.status.busy": "2026-10-08T17:18:10.102787Z", + "iopub.status.idle": "2026-10-08T17:18:13.576700Z", + "shell.execute_reply": "2026-10-08T17:18:13.576160Z" } }, "outputs": [], @@ -177,24 +192,24 @@ }, { "cell_type": "markdown", - "id": "2cecab4c", + "id": "d4d4caf7", "metadata": {}, "source": [ "## 2. Windows, canvases and colours\n", "\n", - "Each description is cut into overlapping ten-word windows. The canvas is downloaded once (the only network access here) and `image_palette` picks its most salient legible colour; offline, the fallback colour and a flat swatch stand in. `fixture_data` takes every colour from the one committed thumbnail and embeds with TF-IDF, so no test fetches a canvas or a model.\n" + "Each description is cut into overlapping ten-word windows. The canvas is downloaded once (the only network access here) and `image_palette` picks its most salient legible colour; offline, the fallback colour and a flat swatch stand in. `fixture_data` is the test-suite's path: it takes every colour from a thumbnail committed next to the example script (`examples/data/`, not shipped with this notebook, so it is not called here) and embeds with TF-IDF, so no test fetches a canvas or a model.\n" ] }, { "cell_type": "code", "execution_count": 2, - "id": "331bba23", + "id": "cab0069a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:17:14.504375Z", - "iopub.status.busy": "2026-09-05T03:17:14.504216Z", - "iopub.status.idle": "2026-09-05T03:17:14.510213Z", - "shell.execute_reply": "2026-09-05T03:17:14.509814Z" + "iopub.execute_input": "2026-10-08T17:18:13.578088Z", + "iopub.status.busy": "2026-10-08T17:18:13.577927Z", + "iopub.status.idle": "2026-10-08T17:18:13.583864Z", + "shell.execute_reply": "2026-10-08T17:18:13.583464Z" } }, "outputs": [], @@ -314,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "ee517969", + "id": "1a07f8ee", "metadata": {}, "source": [ "## 3. One call, plus the annotated column\n", @@ -325,13 +340,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "4ae7c961", + "id": "13dfff19", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:17:14.511238Z", - "iopub.status.busy": "2026-09-05T03:17:14.511176Z", - "iopub.status.idle": "2026-09-05T03:17:14.516926Z", - "shell.execute_reply": "2026-09-05T03:17:14.516535Z" + "iopub.execute_input": "2026-10-08T17:18:13.584965Z", + "iopub.status.busy": "2026-10-08T17:18:13.584904Z", + "iopub.status.idle": "2026-10-08T17:18:13.590640Z", + "shell.execute_reply": "2026-10-08T17:18:13.590303Z" } }, "outputs": [], @@ -339,9 +354,10 @@ "def construct_artifact(data):\n", " \"\"\"`data.descriptions` / `data.colors` / `data.images` in, the animation\n", " out. Returns the HyperAnimation wrapper, never the unpacked pair.\"\"\"\n", - " # labels= annotates per OBSERVATION, not per dataset: one sub-list per\n", - " # cloud, carrying the painting's name on its MIDDLE window (roughly the\n", - " # centre of a text trajectory) and None everywhere else.\n", + " # labels= in its per-OBSERVATION form: one sub-list per cloud, carrying\n", + " # the painting's name on its MIDDLE window (roughly the centre of a text\n", + " # trajectory) and None everywhere else. (1.1 also takes one label per\n", + " # dataset, placed by label_anchor=; this form names the window itself.)\n", " labels = [[name if i == len(cloud) // 2 else None\n", " for i in range(len(cloud))]\n", " for name, cloud in zip(data.names, data.descriptions)]\n", @@ -351,8 +367,10 @@ " # puts every window into one shared UMAP space so the clouds are\n", " # directly comparable. n_neighbors=12 keeps one description's windows\n", " # together, min_dist=0.25 lets a clump pack closely, random_state=42\n", - " # fixes the stochastic layout. 15 fps: the side panels' antialiased\n", - " # text compresses badly in a GIF, and 240 frames at 20 fps was 7 MB.\n", + " # fixes the stochastic layout. 15 fps dates from when this clip was a\n", + " # GIF (240 frames at 20 fps was 7 MB); the mp4 keeps it. backend= is\n", + " # pinned: everything after the call annotates a matplotlib figure, and\n", + " # on Colab the default backend would be plotly.\n", " anim = hyp.plot(\n", " data.descriptions, '.',\n", " vectorizer=data.vectorizer, semantic=None, corpus=None,\n", @@ -361,7 +379,8 @@ " ndims=3, color=data.colors, markersize=5, labels=labels,\n", " animate='spin', rotations=2,\n", " title='Descriptions of five famous paintings',\n", - " duration=12, frame_rate=15, size=SIZE, zoom=BOX_ZOOM, show=False)\n", + " duration=12, frame_rate=15, size=SIZE, zoom=BOX_ZOOM,\n", + " backend='matplotlib', show=False)\n", " fig, ax = anim.figure, anim.figure.axes[0]\n", " ax.set_position([0.15, 0.0, 0.6, 1.0]) # roomy: nothing clipped while measuring\n", " ax.title.set_visible(False)\n", @@ -425,7 +444,7 @@ }, { "cell_type": "markdown", - "id": "e3da0756", + "id": "f29d8348", "metadata": {}, "source": [ "## 4. Load the data and build the animation\n" @@ -434,13 +453,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "b80741d2", + "id": "b1b52722", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:17:14.517783Z", - "iopub.status.busy": "2026-09-05T03:17:14.517730Z", - "iopub.status.idle": "2026-09-05T03:17:29.464948Z", - "shell.execute_reply": "2026-09-05T03:17:29.464353Z" + "iopub.execute_input": "2026-10-08T17:18:13.591450Z", + "iopub.status.busy": "2026-10-08T17:18:13.591399Z", + "iopub.status.idle": "2026-10-08T17:18:27.699414Z", + "shell.execute_reply": "2026-10-08T17:18:27.699024Z" } }, "outputs": [ @@ -465,7 +484,7 @@ }, { "cell_type": "markdown", - "id": "0dacbd7c", + "id": "193fba00", "metadata": {}, "source": [ "## 5. Save the animation\n" @@ -474,13 +493,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "1468d3ba", + "id": "1d6bd1be", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T03:17:29.466433Z", - "iopub.status.busy": "2026-09-05T03:17:29.466217Z", - "iopub.status.idle": "2026-09-05T03:17:47.276642Z", - "shell.execute_reply": "2026-09-05T03:17:47.276072Z" + "iopub.execute_input": "2026-10-08T17:18:27.700801Z", + "iopub.status.busy": "2026-10-08T17:18:27.700726Z", + "iopub.status.idle": "2026-10-08T17:18:45.232656Z", + "shell.execute_reply": "2026-10-08T17:18:45.232167Z" } }, "outputs": [ @@ -494,12 +513,21 @@ ], "source": [ "anim.save('painting_embeddings.mp4', dpi=100)\n", - "print('saved painting_embeddings.mp4')\n" + "print('saved painting_embeddings.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('painting_embeddings.mp4', embed=True))\n" ] }, { "cell_type": "markdown", - "id": "11b513ee", + "id": "8af1c2e2", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"painting_embeddings.mp4\" title=\"Five paintings, described in words, drawn in their own colors\" style=\"max-width: 100%\"></video>\n", @@ -524,7 +552,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/painting_embeddings.mp4 b/docs/tutorials/painting_embeddings.mp4 index 52741443..aa9b7574 100644 Binary files a/docs/tutorials/painting_embeddings.mp4 and b/docs/tutorials/painting_embeddings.mp4 differ diff --git a/docs/tutorials/pipelines.ipynb b/docs/tutorials/pipelines.ipynb index 4429f296..0c206faa 100644 --- a/docs/tutorials/pipelines.ipynb +++ b/docs/tutorials/pipelines.ipynb @@ -3,15 +3,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "34bbf2590077", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", + "id": "e62fe7490660", "metadata": {}, "source": [ "# Fitted models and pipelines: `return_model=True`, `.transform`, `pipeline=`, `apply_model` and `Pipeline`\n", @@ -23,6 +43,7 @@ }, { "cell_type": "markdown", + "id": "716e99f36f89", "metadata": {}, "source": [ "## Import packages and split the data" @@ -31,12 +52,13 @@ { "cell_type": "code", "execution_count": 1, + "id": "d17830914e68", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:39.866104Z", - "iopub.status.busy": "2026-09-05T10:25:39.866038Z", - "iopub.status.idle": "2026-09-05T10:25:43.322867Z", - "shell.execute_reply": "2026-09-05T10:25:43.322463Z" + "iopub.execute_input": "2026-10-09T17:29:07.655330Z", + "iopub.status.busy": "2026-10-09T17:29:07.655041Z", + "iopub.status.idle": "2026-10-09T17:29:10.961376Z", + "shell.execute_reply": "2026-10-09T17:29:10.960939Z" } }, "outputs": [ @@ -65,6 +87,7 @@ }, { "cell_type": "markdown", + "id": "bb998ea04b97", "metadata": {}, "source": [ "## `manip`\n", @@ -75,12 +98,13 @@ { "cell_type": "code", "execution_count": 2, + "id": "e5a8090faa37", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.341023Z", - "iopub.status.busy": "2026-09-05T10:25:43.340808Z", - "iopub.status.idle": "2026-09-05T10:25:43.585322Z", - "shell.execute_reply": "2026-09-05T10:25:43.584844Z" + "iopub.execute_input": "2026-10-09T17:29:10.977999Z", + "iopub.status.busy": "2026-10-09T17:29:10.977842Z", + "iopub.status.idle": "2026-10-09T17:29:11.205763Z", + "shell.execute_reply": "2026-10-09T17:29:11.205214Z" } }, "outputs": [ @@ -103,6 +127,7 @@ }, { "cell_type": "markdown", + "id": "3e9750a26864", "metadata": {}, "source": [ "## `normalize`\n", @@ -113,12 +138,13 @@ { "cell_type": "code", "execution_count": 3, + "id": "52b7ab8f7b97", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.586486Z", - "iopub.status.busy": "2026-09-05T10:25:43.586408Z", - "iopub.status.idle": "2026-09-05T10:25:43.600090Z", - "shell.execute_reply": "2026-09-05T10:25:43.599743Z" + "iopub.execute_input": "2026-10-09T17:29:11.206819Z", + "iopub.status.busy": "2026-10-09T17:29:11.206754Z", + "iopub.status.idle": "2026-10-09T17:29:11.219764Z", + "shell.execute_reply": "2026-10-09T17:29:11.219365Z" } }, "outputs": [ @@ -141,6 +167,7 @@ }, { "cell_type": "markdown", + "id": "80b26cddc30b", "metadata": {}, "source": [ "## `reduce`\n", @@ -151,12 +178,13 @@ { "cell_type": "code", "execution_count": 4, + "id": "fe551cf34c92", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.601105Z", - "iopub.status.busy": "2026-09-05T10:25:43.601044Z", - "iopub.status.idle": "2026-09-05T10:25:43.605844Z", - "shell.execute_reply": "2026-09-05T10:25:43.605510Z" + "iopub.execute_input": "2026-10-09T17:29:11.220594Z", + "iopub.status.busy": "2026-10-09T17:29:11.220537Z", + "iopub.status.idle": "2026-10-09T17:29:11.224815Z", + "shell.execute_reply": "2026-10-09T17:29:11.224519Z" } }, "outputs": [ @@ -176,6 +204,7 @@ }, { "cell_type": "markdown", + "id": "6549cccabefb", "metadata": {}, "source": [ "## `align`\n", @@ -186,12 +215,13 @@ { "cell_type": "code", "execution_count": 5, + "id": "2460b6f95402", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.606756Z", - "iopub.status.busy": "2026-09-05T10:25:43.606705Z", - "iopub.status.idle": "2026-09-05T10:25:43.771269Z", - "shell.execute_reply": "2026-09-05T10:25:43.770781Z" + "iopub.execute_input": "2026-10-09T17:29:11.225666Z", + "iopub.status.busy": "2026-10-09T17:29:11.225616Z", + "iopub.status.idle": "2026-10-09T17:29:11.384432Z", + "shell.execute_reply": "2026-10-09T17:29:11.384074Z" } }, "outputs": [ @@ -204,7 +234,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640.581x480 with 1 Axes>" ] @@ -223,22 +253,24 @@ }, { "cell_type": "markdown", + "id": "9b7a79bb8796", "metadata": {}, "source": [ "## `cluster`\n", "\n", - "`hyp.cluster` returns one label per row of the stacked input. The fitted clusterer assigns held-out rows to the training clusters through `model=`." + "`hyp.cluster` returns one label per row of the stacked input (`random_state=0` seeds K-means, so the counts below reproduce). The fitted clusterer assigns held-out rows to the training clusters through `model=`." ] }, { "cell_type": "code", "execution_count": 6, + "id": "2d8717dbce36", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.772298Z", - "iopub.status.busy": "2026-09-05T10:25:43.772215Z", - "iopub.status.idle": "2026-09-05T10:25:43.806083Z", - "shell.execute_reply": "2026-09-05T10:25:43.805432Z" + "iopub.execute_input": "2026-10-09T17:29:11.385483Z", + "iopub.status.busy": "2026-10-09T17:29:11.385416Z", + "iopub.status.idle": "2026-10-09T17:29:11.416405Z", + "shell.execute_reply": "2026-10-09T17:29:11.415946Z" } }, "outputs": [ @@ -247,18 +279,19 @@ "output_type": "stream", "text": [ "Clusterer(model=<class 'sklearn.cluster._kmeans.KMeans'>,\n", - " params={'n_clusters': 4}) training labels: [ 35 211 169 185] held-out labels: [ 0 95 86 119]\n" + " params={'n_clusters': 4, 'random_state': 0}) training labels: [128 117 225 130] held-out labels: [70 57 82 91]\n" ] } ], "source": [ - "labels, kmeans = hyp.cluster(train, cluster='KMeans', n_clusters=4, return_model=True)\n", + "labels, kmeans = hyp.cluster(train, cluster='KMeans', n_clusters=4, random_state=0, return_model=True)\n", "test_labels = hyp.cluster(test, model=kmeans)\n", "print(kmeans, 'training labels:', np.bincount(labels), ' held-out labels:', np.bincount(test_labels))" ] }, { "cell_type": "markdown", + "id": "f5c376fdc007", "metadata": {}, "source": [ "## `predict`\n", @@ -269,12 +302,13 @@ { "cell_type": "code", "execution_count": 7, + "id": "13f3ce155e5d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:43.807520Z", - "iopub.status.busy": "2026-09-05T10:25:43.807427Z", - "iopub.status.idle": "2026-09-05T10:25:46.728578Z", - "shell.execute_reply": "2026-09-05T10:25:46.728025Z" + "iopub.execute_input": "2026-10-09T17:29:11.418056Z", + "iopub.status.busy": "2026-10-09T17:29:11.417971Z", + "iopub.status.idle": "2026-10-09T17:29:14.253722Z", + "shell.execute_reply": "2026-10-09T17:29:14.253219Z" } }, "outputs": [ @@ -294,6 +328,7 @@ }, { "cell_type": "markdown", + "id": "aa3a9e452342", "metadata": {}, "source": [ "## `impute`\n", @@ -304,12 +339,13 @@ { "cell_type": "code", "execution_count": 8, + "id": "393ce0baf6c5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:46.729699Z", - "iopub.status.busy": "2026-09-05T10:25:46.729631Z", - "iopub.status.idle": "2026-09-05T10:25:46.751108Z", - "shell.execute_reply": "2026-09-05T10:25:46.750710Z" + "iopub.execute_input": "2026-10-09T17:29:14.254796Z", + "iopub.status.busy": "2026-10-09T17:29:14.254734Z", + "iopub.status.idle": "2026-10-09T17:29:14.274404Z", + "shell.execute_reply": "2026-10-09T17:29:14.273995Z" } }, "outputs": [ @@ -335,22 +371,24 @@ }, { "cell_type": "markdown", + "id": "9e278665f489", "metadata": {}, "source": [ "## The `plot` bundle\n", "\n", - "`hyp.plot(..., return_model=True)` returns a dict instead of the figure: `fig`, `xform_data` (the analysed data), `trace_data` and `trace_metadata` (the drawn trajectories; the metadata is `None` unless the input was hierarchical), `animation` (the raw handle, `None` for a static plot), `pipeline` (a fitted `hyp.Pipeline` covering whichever stages ran), `models` (the reduce/align/cluster/impute specs) and `predict` (`None` unless `predict=` was set). The pipeline's steps are the stages, in the library's canonical order." + "`hyp.plot(..., return_model=True)` returns a dict instead of the figure: `fig`, `xform_data` (the analysed data), `trace_data` and `trace_metadata` (the drawn trajectories; the metadata is `None` unless the input was hierarchical), `animation` (the raw handle, `None` for a static plot), `pipeline` (a fitted `hyp.Pipeline` covering whichever stages ran), `models` (the reduce/align/cluster/impute specs, plus `cluster_labels`), `colors` (the colours drawn, with the palette and colormap behind them) and `predict` (`None` unless `predict=` was set). The pipeline's steps are the stages, in the library's canonical order." ] }, { "cell_type": "code", "execution_count": 9, + "id": "b7d5465c434d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:46.752112Z", - "iopub.status.busy": "2026-09-05T10:25:46.752055Z", - "iopub.status.idle": "2026-09-05T10:25:46.982384Z", - "shell.execute_reply": "2026-09-05T10:25:46.981890Z" + "iopub.execute_input": "2026-10-09T17:29:14.275424Z", + "iopub.status.busy": "2026-10-09T17:29:14.275364Z", + "iopub.status.idle": "2026-10-09T17:29:14.422384Z", + "shell.execute_reply": "2026-10-09T17:29:14.421860Z" } }, "outputs": [ @@ -360,13 +398,13 @@ "text": [ "keys: ['animation', 'colors', 'fig', 'models', 'pipeline', 'predict', 'trace_data', 'trace_metadata', 'xform_data']\n", "pipeline steps: ['manip', 'normalize', 'reduce', 'align']\n", - "models: {'reduce': {'model': 'PCA', 'params': {'n_components': 3}}, 'align': {'model': 'HyperAlign', 'params': {}}, 'cluster': None, 'impute': None}\n", + "models: {'reduce': {'model': 'PCA', 'params': {'n_components': 3}}, 'align': {'model': 'HyperAlign', 'params': {}}, 'cluster': None, 'cluster_labels': None, 'impute': None}\n", "trace_metadata: None\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -386,6 +424,7 @@ }, { "cell_type": "markdown", + "id": "472a998b8116", "metadata": {}, "source": [ "## Replaying a fitted pipeline with `pipeline=`\n", @@ -396,12 +435,13 @@ { "cell_type": "code", "execution_count": 10, + "id": "1705509c0f25", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:46.983494Z", - "iopub.status.busy": "2026-09-05T10:25:46.983406Z", - "iopub.status.idle": "2026-09-05T10:25:47.228893Z", - "shell.execute_reply": "2026-09-05T10:25:47.228462Z" + "iopub.execute_input": "2026-10-09T17:29:14.423345Z", + "iopub.status.busy": "2026-10-09T17:29:14.423278Z", + "iopub.status.idle": "2026-10-09T17:29:14.673085Z", + "shell.execute_reply": "2026-10-09T17:29:14.672693Z" } }, "outputs": [ @@ -414,7 +454,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -431,6 +471,7 @@ }, { "cell_type": "markdown", + "id": "06cf474a6b1c", "metadata": {}, "source": [ "## `apply_model` and its modes\n", @@ -441,12 +482,13 @@ { "cell_type": "code", "execution_count": 11, + "id": "d58fa62fe326", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:47.230099Z", - "iopub.status.busy": "2026-09-05T10:25:47.230007Z", - "iopub.status.idle": "2026-09-05T10:25:47.247471Z", - "shell.execute_reply": "2026-09-05T10:25:47.246891Z" + "iopub.execute_input": "2026-10-09T17:29:14.674218Z", + "iopub.status.busy": "2026-10-09T17:29:14.674148Z", + "iopub.status.idle": "2026-10-09T17:29:14.693473Z", + "shell.execute_reply": "2026-10-09T17:29:14.692916Z" } }, "outputs": [ @@ -482,6 +524,7 @@ }, { "cell_type": "markdown", + "id": "33059060c383", "metadata": {}, "source": [ "## `supported_models()`\n", @@ -492,12 +535,13 @@ { "cell_type": "code", "execution_count": 12, + "id": "c869e787c10e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:47.248787Z", - "iopub.status.busy": "2026-09-05T10:25:47.248728Z", - "iopub.status.idle": "2026-09-05T10:25:47.251488Z", - "shell.execute_reply": "2026-09-05T10:25:47.250939Z" + "iopub.execute_input": "2026-10-09T17:29:14.694729Z", + "iopub.status.busy": "2026-10-09T17:29:14.694667Z", + "iopub.status.idle": "2026-10-09T17:29:14.697140Z", + "shell.execute_reply": "2026-10-09T17:29:14.696702Z" } }, "outputs": [ @@ -548,6 +592,7 @@ }, { "cell_type": "markdown", + "id": "5b1e8557c99f", "metadata": {}, "source": [ "## `Pipeline` used directly\n", @@ -558,12 +603,13 @@ { "cell_type": "code", "execution_count": 13, + "id": "d6f0616ba909", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:47.252882Z", - "iopub.status.busy": "2026-09-05T10:25:47.252801Z", - "iopub.status.idle": "2026-09-05T10:25:47.343485Z", - "shell.execute_reply": "2026-09-05T10:25:47.343047Z" + "iopub.execute_input": "2026-10-09T17:29:14.698219Z", + "iopub.status.busy": "2026-10-09T17:29:14.698164Z", + "iopub.status.idle": "2026-10-09T17:29:14.787696Z", + "shell.execute_reply": "2026-10-09T17:29:14.787081Z" } }, "outputs": [ @@ -598,6 +644,7 @@ }, { "cell_type": "markdown", + "id": "d27c7db5dfce", "metadata": {}, "source": [ "A hand-built pipeline with a raw scikit-learn step replays through `hyp.plot(pipeline=)` too: unfitted, it is fit on the plotted data; fitted, it is applied to each dataset as-is.\n" @@ -606,12 +653,13 @@ { "cell_type": "code", "execution_count": 14, + "id": "fb0ccf42520e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:25:47.345236Z", - "iopub.status.busy": "2026-09-05T10:25:47.345136Z", - "iopub.status.idle": "2026-09-05T10:25:47.400345Z", - "shell.execute_reply": "2026-09-05T10:25:47.399869Z" + "iopub.execute_input": "2026-10-09T17:29:14.789033Z", + "iopub.status.busy": "2026-10-09T17:29:14.788945Z", + "iopub.status.idle": "2026-10-09T17:29:14.843001Z", + "shell.execute_reply": "2026-10-09T17:29:14.842490Z" } }, "outputs": [ @@ -625,7 +673,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640.581x480 with 1 Axes>" ] @@ -639,7 +687,7 @@ "\n", "raw = hyp.Pipeline([('reduce', PCA(n_components=3))])\n", "print('fitted before plotting:', raw.is_fitted)\n", - "fig = hyp.plot(test, pipeline=raw, names=[f'subject {i}' for i in range(len(test))],\n", + "fig = hyp.plot(test, pipeline=raw, names=[f'subject {i + 1}' for i in range(len(test))],\n", " title='a raw-PCA Pipeline fit and replayed by hyp.plot')\n", "print('fitted after plotting:', raw.is_fitted)\n" ] @@ -661,7 +709,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/plot.ipynb b/docs/tutorials/plot.ipynb index 47f3e4e1..8969be47 100644 --- a/docs/tutorials/plot.ipynb +++ b/docs/tutorials/plot.ipynb @@ -5,17 +5,28 @@ "execution_count": null, "id": "35e6f3cc", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:28:39.411839Z", - "iopub.status.busy": "2026-07-17T07:28:39.411673Z", - "iopub.status.idle": "2026-07-17T07:28:39.414707Z", - "shell.execute_reply": "2026-07-17T07:28:39.414320Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -44,10 +55,10 @@ "id": "540d8df5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:28.007713Z", - "iopub.status.busy": "2026-09-05T10:23:28.007502Z", - "iopub.status.idle": "2026-09-05T10:23:31.834373Z", - "shell.execute_reply": "2026-09-05T10:23:31.833942Z" + "iopub.execute_input": "2026-10-08T17:18:57.130575Z", + "iopub.status.busy": "2026-10-08T17:18:57.130408Z", + "iopub.status.idle": "2026-10-08T17:19:00.558445Z", + "shell.execute_reply": "2026-10-08T17:19:00.557860Z" } }, "outputs": [], @@ -75,10 +86,10 @@ "id": "776d929c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:31.835784Z", - "iopub.status.busy": "2026-09-05T10:23:31.835645Z", - "iopub.status.idle": "2026-09-05T10:23:31.936165Z", - "shell.execute_reply": "2026-09-05T10:23:31.935714Z" + "iopub.execute_input": "2026-10-08T17:19:00.560110Z", + "iopub.status.busy": "2026-10-08T17:19:00.559919Z", + "iopub.status.idle": "2026-10-08T17:19:00.667953Z", + "shell.execute_reply": "2026-10-08T17:19:00.667360Z" } }, "outputs": [], @@ -100,10 +111,10 @@ "id": "b3d961ab", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:31.937356Z", - "iopub.status.busy": "2026-09-05T10:23:31.937286Z", - "iopub.status.idle": "2026-09-05T10:23:31.946034Z", - "shell.execute_reply": "2026-09-05T10:23:31.945669Z" + "iopub.execute_input": "2026-10-08T17:19:00.669335Z", + "iopub.status.busy": "2026-10-08T17:19:00.669261Z", + "iopub.status.idle": "2026-10-08T17:19:00.678189Z", + "shell.execute_reply": "2026-10-08T17:19:00.677781Z" } }, "outputs": [ @@ -334,10 +345,10 @@ "id": "0603611f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:31.946942Z", - "iopub.status.busy": "2026-09-05T10:23:31.946889Z", - "iopub.status.idle": "2026-09-05T10:23:32.212107Z", - "shell.execute_reply": "2026-09-05T10:23:32.211612Z" + "iopub.execute_input": "2026-10-08T17:19:00.679121Z", + "iopub.status.busy": "2026-10-08T17:19:00.679062Z", + "iopub.status.idle": "2026-10-08T17:19:01.005870Z", + "shell.execute_reply": "2026-10-08T17:19:01.005318Z" } }, "outputs": [ @@ -372,10 +383,10 @@ "id": "9bfbf542", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:32.213278Z", - "iopub.status.busy": "2026-09-05T10:23:32.213178Z", - "iopub.status.idle": "2026-09-05T10:23:32.311640Z", - "shell.execute_reply": "2026-09-05T10:23:32.311213Z" + "iopub.execute_input": "2026-10-08T17:19:01.007276Z", + "iopub.status.busy": "2026-10-08T17:19:01.007178Z", + "iopub.status.idle": "2026-10-08T17:19:01.110690Z", + "shell.execute_reply": "2026-10-08T17:19:01.110192Z" } }, "outputs": [ @@ -400,10 +411,10 @@ "id": "1fea871c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:32.312855Z", - "iopub.status.busy": "2026-09-05T10:23:32.312774Z", - "iopub.status.idle": "2026-09-05T10:23:32.407245Z", - "shell.execute_reply": "2026-09-05T10:23:32.406742Z" + "iopub.execute_input": "2026-10-08T17:19:01.111686Z", + "iopub.status.busy": "2026-10-08T17:19:01.111620Z", + "iopub.status.idle": "2026-10-08T17:19:01.213683Z", + "shell.execute_reply": "2026-10-08T17:19:01.213179Z" } }, "outputs": [ @@ -438,16 +449,16 @@ "id": "3911a079", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:32.408310Z", - "iopub.status.busy": "2026-09-05T10:23:32.408240Z", - "iopub.status.idle": "2026-09-05T10:23:32.496405Z", - "shell.execute_reply": "2026-09-05T10:23:32.495874Z" + "iopub.execute_input": "2026-10-08T17:19:01.214784Z", + "iopub.status.busy": "2026-10-08T17:19:01.214721Z", + "iopub.status.idle": "2026-10-08T17:19:01.308363Z", + "shell.execute_reply": "2026-10-08T17:19:01.307887Z" } }, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -480,10 +491,10 @@ "id": "f9aaf2b2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:32.497474Z", - "iopub.status.busy": "2026-09-05T10:23:32.497410Z", - "iopub.status.idle": "2026-09-05T10:23:34.718220Z", - "shell.execute_reply": "2026-09-05T10:23:34.717874Z" + "iopub.execute_input": "2026-10-08T17:19:01.309507Z", + "iopub.status.busy": "2026-10-08T17:19:01.309433Z", + "iopub.status.idle": "2026-10-08T17:19:03.481673Z", + "shell.execute_reply": "2026-10-08T17:19:03.481310Z" } }, "outputs": [ @@ -520,10 +531,10 @@ "id": "d61a0559", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:34.719545Z", - "iopub.status.busy": "2026-09-05T10:23:34.719457Z", - "iopub.status.idle": "2026-09-05T10:23:34.760076Z", - "shell.execute_reply": "2026-09-05T10:23:34.759649Z" + "iopub.execute_input": "2026-10-08T17:19:03.482961Z", + "iopub.status.busy": "2026-10-08T17:19:03.482887Z", + "iopub.status.idle": "2026-10-08T17:19:03.532048Z", + "shell.execute_reply": "2026-10-08T17:19:03.531678Z" } }, "outputs": [ @@ -560,10 +571,10 @@ "id": "1c0f4f7e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:34.761324Z", - "iopub.status.busy": "2026-09-05T10:23:34.761258Z", - "iopub.status.idle": "2026-09-05T10:23:34.870831Z", - "shell.execute_reply": "2026-09-05T10:23:34.870410Z" + "iopub.execute_input": "2026-10-08T17:19:03.533279Z", + "iopub.status.busy": "2026-10-08T17:19:03.533205Z", + "iopub.status.idle": "2026-10-08T17:19:03.650057Z", + "shell.execute_reply": "2026-10-08T17:19:03.649720Z" } }, "outputs": [ @@ -601,10 +612,10 @@ "id": "d2480d95", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:34.872049Z", - "iopub.status.busy": "2026-09-05T10:23:34.871959Z", - "iopub.status.idle": "2026-09-05T10:23:35.013129Z", - "shell.execute_reply": "2026-09-05T10:23:35.012665Z" + "iopub.execute_input": "2026-10-08T17:19:03.651240Z", + "iopub.status.busy": "2026-10-08T17:19:03.651175Z", + "iopub.status.idle": "2026-10-08T17:19:03.802876Z", + "shell.execute_reply": "2026-10-08T17:19:03.802421Z" } }, "outputs": [ @@ -639,10 +650,10 @@ "id": "82bac278", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.014269Z", - "iopub.status.busy": "2026-09-05T10:23:35.014200Z", - "iopub.status.idle": "2026-09-05T10:23:35.132037Z", - "shell.execute_reply": "2026-09-05T10:23:35.131526Z" + "iopub.execute_input": "2026-10-08T17:19:03.803979Z", + "iopub.status.busy": "2026-10-08T17:19:03.803912Z", + "iopub.status.idle": "2026-10-08T17:19:03.914792Z", + "shell.execute_reply": "2026-10-08T17:19:03.914316Z" } }, "outputs": [ @@ -678,10 +689,10 @@ "id": "db3be955", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.133202Z", - "iopub.status.busy": "2026-09-05T10:23:35.133120Z", - "iopub.status.idle": "2026-09-05T10:23:35.608855Z", - "shell.execute_reply": "2026-09-05T10:23:35.608393Z" + "iopub.execute_input": "2026-10-08T17:19:03.915894Z", + "iopub.status.busy": "2026-10-08T17:19:03.915823Z", + "iopub.status.idle": "2026-10-08T17:19:04.344392Z", + "shell.execute_reply": "2026-10-08T17:19:04.344082Z" } }, "outputs": [ @@ -703,6 +714,82 @@ " title='Mushrooms colored by row position')" ] }, + { + "cell_type": "markdown", + "id": "2284e276", + "metadata": {}, + "source": [ + "### A data matrix as a palette\n", + "\n", + "A palette does not have to be a colormap name or a list of colors: a `t x k` **data matrix** (a numpy array, nested list or DataFrame) works too. `hyp.plot` reduces it to three dimensions with `hyp.reduce` (`palette_reduce=`, default `'PCA'`; `palette_manip=`, `palette_normalize=` and `palette_align=` are passed through to the reducer), scales each reduced column to `[0, 1]` and reads it as an RGB channel, sorts the rows (`palette_sort=`, default `'columns'`: along the first component) and resamples the result by interpolation to however many colors the plot needs -- one per dataset, one per category, or a gradient along a continuous `hue`. Below, a random walk is mixed into twelve noisy columns and that matrix colors the walk: the three directions of greatest variance in the matrix become the red, green and blue channels.\n", + "\n", + "The same ordering applies to colors extracted from an image with `palette='image:<path>'`. `image_palette` (in `hypertools.plot.colors`) lists them most salient first (and that is still the color a dataset leads with when an image stands for one dataset), but as a plot palette they are sorted by value, dark to bright, so a gradient reads as one. `palette_sort=` (or `?sort=` inside the spec) chooses `'value'`, `'hue'`, `'lightness'`, `'columns'` or `'original'` for image and matrix palettes alike. A 2-D array with 3 or 4 columns whose values all lie in `[0, 1]` is still read as a list of colors." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "625eac30", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-08T17:19:04.345564Z", + "iopub.status.busy": "2026-10-08T17:19:04.345488Z", + "iopub.status.idle": "2026-10-08T17:19:04.541953Z", + "shell.execute_reply": "2026-10-08T17:19:04.541501Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 663.736x480 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "walk = np.cumsum(rng.normal(size=(200, 3)), axis=0) # a 3-D random walk\n", + "weights = walk @ rng.normal(size=(3, 12)) + 0.5 * rng.normal(size=(200, 12)) # mixed into a 200 x 12 matrix\n", + "fig_matrix = hyp.plot(walk, hue=np.arange(len(walk)), palette=weights,\n", + " colorbar={'label': 'time step'},\n", + " title='A 200 x 12 data matrix as the palette')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "030a0925", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-08T17:19:04.543024Z", + "iopub.status.busy": "2026-10-08T17:19:04.542960Z", + "iopub.status.idle": "2026-10-08T17:19:04.777234Z", + "shell.execute_reply": "2026-10-08T17:19:04.776843Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 663.736x480 with 2 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_ica = hyp.plot(walk, hue=np.arange(len(walk)), palette=weights,\n", + " palette_reduce='FastICA', palette_sort='hue',\n", + " colorbar={'label': 'time step'},\n", + " title=\"palette_reduce='FastICA', palette_sort='hue'\")" + ] + }, { "cell_type": "markdown", "id": "33350757", @@ -715,14 +802,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "55305509", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.609953Z", - "iopub.status.busy": "2026-09-05T10:23:35.609882Z", - "iopub.status.idle": "2026-09-05T10:23:35.721359Z", - "shell.execute_reply": "2026-09-05T10:23:35.720881Z" + "iopub.execute_input": "2026-10-08T17:19:04.778301Z", + "iopub.status.busy": "2026-10-08T17:19:04.778236Z", + "iopub.status.idle": "2026-10-08T17:19:04.887314Z", + "shell.execute_reply": "2026-10-08T17:19:04.886857Z" } }, "outputs": [ @@ -755,14 +842,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "7e0e2268", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.722484Z", - "iopub.status.busy": "2026-09-05T10:23:35.722404Z", - "iopub.status.idle": "2026-09-05T10:23:35.858024Z", - "shell.execute_reply": "2026-09-05T10:23:35.857476Z" + "iopub.execute_input": "2026-10-08T17:19:04.888498Z", + "iopub.status.busy": "2026-10-08T17:19:04.888429Z", + "iopub.status.idle": "2026-10-08T17:19:05.019799Z", + "shell.execute_reply": "2026-10-08T17:19:05.019216Z" } }, "outputs": [ @@ -799,20 +886,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "0b8c94a1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.859206Z", - "iopub.status.busy": "2026-09-05T10:23:35.859128Z", - "iopub.status.idle": "2026-09-05T10:23:35.982229Z", - "shell.execute_reply": "2026-09-05T10:23:35.981789Z" + "iopub.execute_input": "2026-10-08T17:19:05.021038Z", + "iopub.status.busy": "2026-10-08T17:19:05.020952Z", + "iopub.status.idle": "2026-10-08T17:19:05.152697Z", + "shell.execute_reply": "2026-10-08T17:19:05.152383Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -841,20 +928,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "dc33e54b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:35.983361Z", - "iopub.status.busy": "2026-09-05T10:23:35.983295Z", - "iopub.status.idle": "2026-09-05T10:23:36.150296Z", - "shell.execute_reply": "2026-09-05T10:23:36.149553Z" + "iopub.execute_input": "2026-10-08T17:19:05.153850Z", + "iopub.status.busy": "2026-10-08T17:19:05.153789Z", + "iopub.status.idle": "2026-10-08T17:19:05.305710Z", + "shell.execute_reply": "2026-10-08T17:19:05.305066Z" } }, "outputs": [ { "data": { - "image/png": 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fls8//1x59GrgPRUjLpdffrmy9kTHrtVwMP0bnoBijRkEFBEoBBRribwFFNZ9EM9Zs2Yphw0YyxMSDjjj7s2i+raOnWA0QDBcJtRQgwmWFVK/4Yz03LFtjxLPIdmZIR2frA66ZZHde+qpp+TTTz9VZvfa3wNciubOnasEFNFoKuxZdjJSNQY+jhBQ732gAAKKMRZvAbVCYZqQVPXyDcVdI6rUHCqagGKtocarw3d+aZEpIz3hHB8kx+jEwOqzshAd7Fv+5z//qYIUWPp5++dqpTHURVNxVaSToqrP3XffLatXr9a9DVEqzqiQ3//HP/6hPCCx8gdii5opiuXaIlx07aKN24ppCmIdemOkaqaAoX4KkYhFKOJRQ/WOouf1L4npOSJNSbsS1Cxlhsh89NFHqrkIUw/e/rlYmQb/XNRDMebSG7ZdORwO1YCabCx3SoVt9JqFFSJVjL1o+/i0TfWooaLzDM1I2FaADfVGb45I8Wn797Tde3gBQYThIYnWb4jwiBEjVPcvd5H2LXqTqHp7+ZqFdw3RSjVUMWukxwTv43j4H8fqn4vZe/iOe/vnTpkyRc4999yg/rmpjNPptEQQZTlRhQACiOD48ePDXoyLIjteRLAPhAfvrl27lOCiToAIF4YO8OWFQMMiy3sHnz/oZMPmGLwQEQ1jRx+iYbwQtWgYIjxy5EjlpMQUdOrSW0TV7BEV70g1HkvPY+H8/sUyPj/XUiM9ZvofRwKCDxguvPvuu+q9DwGGt3/uvHnz5KabbuoTjm9Opn/10c402tvbI36c/w6+UL8AiC0EFi9GWGohGoYIa9EwvH3RAYe0CYr6Rm/A2jb6nJwcJcKIhktLS1XHHAz0IcJ4UUOER48enRJF/75CbxDVeHT5mpHORH0xHj9dCGqsIz1mCqqZ/sehwLw9rP9QG8XooLd/Lt77zjzzzJj8c1P9b9lugR4by0Wq2ryTf5OS2Wizq7ig+SkcYLuFKBgvbETFEGFEwxBhLRrGgDSEeMOGDUGNBSDCiIbz8vJUUwCiYYgwPIURDUOEUROGCCNCtsKLpTfSG0T17cM9rmBmYZZbUjyOK9aRHu0EpHKvU4btdsiOwWmyb1Dovy+j+8fDzUkDGTdscsEuZj3/XJjXoCaKXcx9HafTyZpqsEhVS2NYCUSYxx57rLqE+0uG8KIuDDFGNIyOO3hkQnghwoiGIcwQaW31kR4QVYiwFg2j9otoGEPYEOGqqiolwmjQwh+bFWoLqUCqiyqirjdrepY9mMEPBlfI6Nwcy6RWzRR6TVAvfLVDJq91do8LOWTVeLu8dJ7x30yw++O4+u91SO3WQ5IzJk+yR+bG7J8LK9V169Yl3D83lXFSVM1N/1oRCCFSv7hgm004QGSRkoYIo/6L1LPWoFVTU6MatCDG+OND6iecBi1Ew/4NWhBhRMMQYKSKekP3X1+bU43HmMoVFaUyvagg6sfHYxb1/P4lMj4/J6YaqndEj4hTE0iAj/h82fFOTwSqRaWH+tmlotpheP8Dg+zys3fTpHH5Ts/XKTi5SCpuGRzWMaHM9Mgjj8jLL7+s6qPaPmbNPxdjLgsXLkyYf24q43K5GKn2dlGNBqSCseUel3BAdIsIGEKMaBcijGhYa9BChzT+UJFGQto6WIMWzoa1Bi0chzauBBHWasPolEZKGoJsBWuyWElVUdWagGJhcGaG7O3oVPOVEIrLK0rlnLKSqI8HZhNmzMZ6c3x+rlw2oNSkud16JZbz3uvwCKQGPp+wrkv2DcoMiEr976vd/8YtmVLQmC1tyw/73Nb4Ub0UzS3VjVg1/1wY2sA/V9uIpfnnwrXoqquuUrVRimhksFHJAO2Nuq+KajQ/L0Sbkayjg9hCYNGghWgYIoxoGClpbVwJ90HqOtS4Ek6CkJJGNKylpLUGLUTDEGGtQQsjTVYjVUXVjCagf6uqMM3YIR7pXnB+eT/TIvorn26XMd+4dEUS5DU5Zd47PYIKjO4LMj5pkjZxb2bxZ///7pLhfz1a1UCffPJJee6553T9c2Fic8UVV1jGPzeVcTFSTb2aam8B0Scu2BEbDnhjgADjgmgYUS9EWIuGIcKIhvGGgWaKYA1a2riSf4MW3mAgwtq4EkQYwhzvs/VUFVUzTAYgqLEaO8RrbMaMkR7vOuoVzwQXVBz/pI0Q0di+XoejQxbtXixvfLRI1j29Xg4dORzgn3vZZZfJt7/9bfY9mAxF1QC84QKtVZwkH9RlsdUn3M0+EFXUfLWUtDaupDVoISWN2jE+x23BTqAgqlo0rI0r4YQAIoxOaYgwGjgQqSMixrH2FTRP3mgxo2MVHb7xElQzHIpQR0XK9+itwQU1VFTqf3+b12v9vb3vy6s735RVNavlcNthz/MV5xfL6aefrgQ0VfxzUxkXI9XgkSpFNXWBECLKxAXbgsIBtSZ0SePiPa4EEUaDltYpjeh4/fr1htEw0mqIhv0btLRxJRwTUtIQYTxHX03/miFY8fTzrdrrlG/X50pedofIyNDC37a1RVo3NXu6b7WRno+218lpK7qCCmZtgUg/d39QSPCa+fjAUnll5xvyxaGVcrAVUbr7N1GQkS8nlE2V06vmyIXDzpMR3x8tRaf2vQbAZIHfjRVS6Mk/AgNHJdZU+xaot2LWLtx5O/wBIcr1HleCCCP69W7QgjgjbW00rgRvVPwh4mTO30FL85NGU5a3n7QV/nAhiJFGqhBSpwmjKWY1Shlx+3tpkr0M85gdsuelupDdtAf/tls1B2k0nZgnfzzdIcet6pKfvhlcUPHzKwkhqJ9XfyEvbn9Zlh/6QvY3HxBnd4o4Lz1XJvefKKcNmi2XDL9AirOKfR7nbDZuCiTmw0g1RPo32MwmIYiGIXC4hAtqvmjQ0saVfvnLX6ooduzYsZ4GLXwMx08aIhzMTxrHpflJx2PjBwQRK8iwbizcKuD3qiqkMD0t5tEUM92S/A0VLm3Olexl7maecLppEaF6CyrIW94sI4dmyHlvBRdUoHf7mpq18sK2l+XT6mWyt3mfOFxuccxJy5Zx/Y6R2ZWnyCXDL5SK3PKgz42omSQWK5zwJv8I/GBNlcQLCB+G5rXB+TvuuEMJKtZgBfOThhBDhNGghU5pzUEL0TDS1hBiWMaF6yetNWhp40po0IIQa3Vh/D+cBi0sxMZez9u37fG5Xi+CxbONzove0MFso33gP7ry9UibTD6hQPS81Kof3idDfj8y4PqGpYEjPHi+MVsdYg9T9TfXbZZntr0oSw98JruadkuXy31Cn2XPlDFFo+SUgbPk0hEXyaD8QWF/b4iuYzGBIJHDSNUAiipJJMFqqtH4SUNwkZL2H1eCCGt+0rgPBDqUn7TWoIVo2HtcCSlpCC/S0rZBVdJlz5F0ryYYPOPZpcXyVk2daeles7ty9QwY0Ezk6NB3h+rY1qaiUk2o8H8ILa73B9//4RKbT0ORN9sbd8pz216Uj/Z/Ijsad0iH090ol2HPkOGFw2RmxUly2VEXy4jC8LMg3pTdXMlaahLA3xIj1SA1VTYqkVRzVIIQIvLEJRI/aU2ENfMObVxJa9DS/KSDNWip7yc9XdIysyQ9J0e+6FcixSUlkl9aKkMrK+XjwYNl97BhHj9ppKpjGVeKtckJKV89A4bOXcZd/41L65So+tdQRed5Jm7o+Tnta94vz37zgny4/2PZ2rhN2h3uGfh0W7oMya+SkyqmyyUjLpRjSo6WWMmZkEdBTSIUVR1YUyWJJNndv0gHY0UXLpH4SWtLHdCgteKb7fLV7t3SVlsrnQ31kt7aKgf27ZOdIfyk4aDlPa6ElLS3n7RWG9YatMz0k0YN1cityAh02erVUP053FYjr7/3oizZ9y/ZUv+NtDrcxv5ptjQZlFsp0ytOlItHXCCTSsP7mUeCy5Ga3eS9ARdHavRh+pck+g8xVf2k/btxjZyREO1q0TBEGNGw97hSNH7SWXl50padI1lFxZJVWirZpWWSO3CA5A4YJAVDhkjBsBGSGaRBC565GRNzpWuN2zDem+zR2dK2OTCtWzijRBo/CdzIU9deJy9uf0Xe3fu+fF23WZq7mt0/K7HJwNyBMrXsNLlw+PlyYtnxcTcTaVvf4pOmJsnJdCYTyzUqacP7dFQiiSDVRNWIYM5IqMdG6ieNKFibGYYQIyUN0YUIo0GrvqFBWtCotWunuII0aNnS0sSekalS0ul5eUpos4r7yYQhg+X+4UOlqD1Pyo/0l2EFQ2RQ7iAlRv2vrpSaF6qldXWTT+NP/eIaFaU2dbbIKztek0V7FsuG2q+lsdM9E2MTm5TnlMmplSfL+UPPlpMHzAwqokY111jBvCxFNTkg+5JsLCeqrKmSRJHqW2riWZdCyheXUOYP2khPW+0RadyxXZr37JHmfXuk5eABaTt8WNqP1EhHfb10NjWKs65Wmg4ekLquLln8qUsW6zwnhDHjjxmSnZEt+YX5UpRbKKXlpVJWXy57V+6RzfVbpK6jp+O3LLu/TCs/Qc4eOk9OHzRHMuzhvaXFS1CBPS/5b+x9lQxGqsaRKudUCbE23iM92SX91KVs8hTD+98xrFLG5bvTonXrjsjGj9bLHvs+2bVnl6x/fp1Utx2SmrYaqW2vU9Fnc32zHKmrkU27N6taMgS3JKtYTh14spw95EyZN/gMyUzLjEok4yWogKYPiUdr4KOo6sD0L0kkjFQTM1rjbYuode+WSYm6nDhiipw3eZ7hY6vuGiEzz5wlq7etkeXnf5RwkYyUzkP0LU80WhBmhfSv5bbeUlRJIqGommOX6P+mgmXn2puL95xs/QdHArp39WZN/WuUrs7USdM3LKqVmmcOJPsw+hRtbe7XkJkd6r2mpqrNGbFRiSSipkrMtUvUBPSkI+kyeVuB1A/LkAFjC9X9gs2X2grt4mrQj3odTQ5xNCXetjTS7TXe1L5yWOy5aVJybpnJR0WCiaoVIlXLiapGMMs3QsyCkao5tdWJBbmekZ6uB/bLnm7xhJN318nNUn9MXtD50txx+dL8WYPuba0bm9WMKmqqiSTWr1bz1EEpmFEs6aXJH/Po7XR0rwq1QqRqufSvBiNVkggoquaASBRNSHk7OwLEE58fum9f0MeXnNVfcibpG9Cnl2TEbWdrKFwx3qd1c+AcLjEfbauZFRqVLCuq7P4l8YYjNeZz4G+7I35MRmWmmuscdNtwyZ2U73Mb5lMhuC5Xj09wIgm1Xg+3fTbF7TNsdA8Yc6xvalEfSXwjVdoUBoGRKkkEjFTNA05CXfvc5vSRkDOxJ0KtvG1YwMJx1GKVaNmSM3dalytS3KL//LiutsQunx3vkulf+Am/TeTLcqfct2mH5/hQf0a6nMQnUrVC+jfdqhEEa6okEVBUzaPxk8A1bOEA+0FvIKTe22hUOtkVXDbjJaj4qhuPsUu/OpeM2Yq6buDtZ73nEKdNZN1Yu4zb6HSn/2wiudcNkPtaj3iiWHzEYnfUn83cGETEI6qMVIOIKtO/JN4w/WsuLlvklc9Qe0cblrq9ftGolAwgoohAPzjJLu/PypCZyzpl/NcuwbfqLfPY3Trua6fctzBTsjpdcvzIEqkcmCUuv2w4HvNS9RG5cVDwBeckusymFSJV1lQJIabgH3GGs3e04pbBQe/TuqY57undUODrnvqpU8ZudsiKKeny92szZd3ZuYHzuS6R8Ru7ZPuwNHm+q0G+qO/xLvZmSW0D66u9OP1rSVFlpEpI6oGIM2eyb6NRsAi16NR+Qe8Do4jOfR1ekWpssppzXHjHZiisSx1y/ROdcvOjHVJ/wP0m7s+MZU4pbHBH1Usb3CcEemD8iPTORiXLiiprqiQRsKZqHmgoal3VE52lVxqPNxTNLfX8H3XT2jcPqY/ez+U9hhPr7wkiXnpheClXo6/kneo96QuH4Rvq4D2hrRuz4ryCrq/RYaE51eTLug6MVEmiXmcUVXPQsx8M1gmsrUfzd1lSNVZdo4joRRVpZi0qbh+fLZlr2wxjXjUic7w9sJPXj2C3TV7VKeuPCe7sc6ijU0bmupeHkNjhnGoIsAORkSpJBBTV2PGPKsNdj+bp7A3DKMI7+Zs1Onwx8k8z7/23/rJjUPBotK7YFmL2NDijt4snBWxEo8OhO7vKmdbUb1RipEr6NBRV8yPUcNej1b5xOOz7azVVGEUM/u1I2feHHdLitcQ8VITqwSby0MJsufKZdsMRmfneIzJfO1W6N5KKrkoB73bI+nHGb68P73d/796zq9hPe/++as99buJMa8TpXys4KllWVLX9eFbmg/qPZUXzSpmaN0VOLZqV7MMhEcL0b/j4GzKAYAb5ocgYkCU1jx8M/wGoqdp6jCL8TSJqXjgoraubQzZCFXQbrj9xWZZU7nWqEZljN7qUEOqNyDx+SYZc9Vxn5M0nYS5r0GZXi9LtPoIK8Dn21TJNHH6kmpUFt+nkYklRTYX07/d3/Eyqu9x/BCtbVstLta/LX4b9d7IPixDT0at7otEoWkFNr8wUV0dkJ82a4HmP7XibRMDiUE/4/cm094hdU4FNjch8Ms0mw3Y7VITqDYS1ap8jYkHFd7a7yh7R93b3Lv1VcVgAz4g1NGxUSvHuX0SomqBq4HNcz4g1dWCkGhqjuqejwxUy9dq0rEFadVK0A26pkua1wVO3/qgtNWn2oEYR3iKrx992H5SP6hvV/6es6pLz3upSwolU72dT0gJSvPh8/AZ98Q+WDl4x2S4NheZN1dKFKbVE1ZJ93VaPVJHy1eP+Q4/I1rZtCT8e0ndrqvFubEHkp4ejriNoJIrU66DbhgXMrXoclDoji1TteXaxpUcvVFtb2jyCiiai8990C6r3iIz/s+PzsiOBTU3BBBW3fTjLuK43IjvyN3085+aW1ogf15fo6nK//pn+TVFRRQ0VKV9/nOKU2/f8Vk4umCG3VNyYlGMjvV9UIaAwD9jW2i5PHayJq1k7unT1yBmbL+1fuxdD+5M7sUdIB/080CAf5B1XKLUvhd+oZMu2ia2hR8o6u2qkveOAZGUOkIz0nplX758PdrvCY7erplPeX7VfCnNdKoIcsjtwXMZIJHE95B8/Y/+6qz+IeF+dnx40St3W1hHdVpzUe5kmFDYqhSGqVm5UQor34UOPS4fo/4F81LhU5hbNkZHZIxJ+bCSy9G+qgQ5RpAP1oqcH42DWji5dPdLy0yRnUr5uerdwRnHItGz94pqwjwHRrazq+V0daXhf9lU/4JG4yvIbpV/hbN2fz5Wb0uXoF5pktkvk1G7Ra8uMLp0XMkKdbpeVk0P/7OeUFMr7tQ26Onl2aZG8XuObbsfXHJ2XE9lB9zG6LBSpMv0bJdeVXRX09nv2/W+0T02ILojA9ARVwxkH+ztEl0bXq/Suzv7TYHXNaMZw0BSlZRQQofYIKnDJvuoH1fX+Px+keUe/0OS5K9K8qKMOOOiMKvALZQZxyqc9FoXBaOjskn+rLNO9DYI6KifL87W0DAS32oQXqVpBVC3b/WvlSFWLVu879JDh7XXOBjYupQCplP6FYLpCnCEj5WkmEEgIpbcIZk/MlLq12yTfUaqE1Tu9mzasVZpa1uumZaMdw2lYWufJLCDlG5gLdarrDzhzfG4pPeKeMfUGn0P84pGjCGc+FaxoalEXiOfW1vaA72ZLa7v8dMgAybbbPSlsEl6kaoVGJUv+tqwuqujyfatuccj7QXSdNqfMLjwlIcdFenf3bzC/WNxyQxwiGghm5tBsKbs5T6WC61ftkNY1LrGtsUmjHBZ7xQEZ9L0xUnJWmTstu0M/LRuLUQRmVLXfk92uH4k4nW0BJxQ1/eyqzuktrM54p+ciKClAPI/Ly5EvmwObkJ7cXyN/GjPU5IPrvXRyTjU4aWlpnh+SledTw+H+6kdkYu54KU0PvpGDJIdUEVWtVmjEb0dUmW4S4B9VIkLtXJcttu44Dx9dBzNkz+3bJG9WjtScEZiWLcidqCLWWIwiCmeWqN+T2xRGfzvMrgN3S2X5TbKgYrJq3gJoGEIN1TM6E+f1ca4I51OBnqCCfZ2d8syBGrlsQGC0TwLhSE2KRqp686nhsLl1S1yOh/SNRqVQtVTNoD3e86ltazo8gupP88ctInsGuD/ZM1DSlp4gsqdCpWX1nitcMJ6DlLJDialLpZWNpBEiPvtIm1y92qXckgAah+75XpY8fWGG/GumPWZRxe/A6PfwybQ0U+dTXzlcy72rYaJNi2RnJ39JgWUj1WSKqpH9oNF8amhS4827L5IKkSpmFF0Jfok1fuKuY+qaMOh+MZukrTlGbMuOl7TVE9R90sUljeuzJXuY8V5RUDSvn3Tu79D18s295oBs2vFr6eqqFaerXer3rpWyI7fIobTHRYoafO7b8cLFsn91jYwWkVEismq8XV46L1NGfeOQ8zCXGuGyc737Buv+XTs2+GaaaHi5+ojcMCi8lXV9mY7uRiUriKplu3+T9WaH9C5qoZhDxUd8rgGRjYZXa98y8QhJn6uphjhENXKRa+7Ihcum/0VtJV3d5vaBZLWP9giq+7hs0vxxq3Q2uE0XjCiYUay8fHMm+XYaI6Vcnfd3t5TD+teRJrW39ZPGP5VI9j3fk7QvJnnu+9H2BVKwerRP1+zktU6ZuLrLI6ja9eH8xnGfvRX6t+md7uN5szrNfy29V9vAaDWCSJVLyi0WqQazHwSIWjMl8u6yHR07ZGVToFkEST5WF1XllBQktLLHaeTC7bEbOA1b9ZMxknEU1qL53obPszIrdaPY2lUbDL9O5qh06arcrkZi4N9bddcIKbmiQPrdLpJ5xTeeY3A5bWLryhCbq1seXTZJf3W+SH2B7HVWStfOUbpR5UVv9Aiq9/WhwH0qDwYKKD7/8CR7YP+xzd0YFQ82G9RdSQ9aDw4CsmTD9K8XRundRw896RFVzKcGG6UxYnXLVzIlv+fMmiQfq9dUjYwewIkFeTK3tChuIxeoY3ZN+soTeUI0HZO+kub++2XQf06RXb9fJ84N/Ty3pU09KJnFY6VVAhuJnIWNAhnSE9zmkW9Jw77PPd3CUi6yX+533+htuOTAm6WvxEFg7TX9ZF3OMbJ3MLx7A60GY3mLxWOXT7bJ1FXuLTb46u/MTpN9lXbV5XvKpw6Pd3AoJ6WYsPbL1BJ0Wqix1ZKiihA+GRFEWbr+QHabtCkhxSaa7w+4Oarn3tm2O8ajI30pUg3VnLSisVmuriwzXVA1+z+Ho0G6Ln5duqZ9IWk7B4tj6G6Rqv2CNaD7Dz8ssqC7Iclz20EpaP6ByCLfFK4S45mfi60jS9I2I5q0+d6Gx3Z/hkYj/eQqAlPMlvqpi80lztIjkt2QrlKvtYUiJQ3matC24eny4Sy7mnmt3O+SM5Yg8nW4BXZOuuwbaFMRqhmCOjI7U7b62RjGI7XfG3FYyNbWkqKarJqqQ4KbkiMV/MzhF6J67s0dW1R9luvhkouPZ2yGTfLy3NfpGRVY2ehBc08yU1R97f+ATQmpo2q//gP8bjuY9/8kfdI5kr56gie8cozeooS3a7a7hNIjrO7IF8/h+10ZYHeKZLrc4uqyq49d570laVuOkrmvjhebqzOiJqRIRmTcgmmXhU+0eyJffITA3vP9LNMiVAjq8KxM2dne4ZmnjcfscW+kk5Fq6Eg1GTXVSbkT5d2GfwW9z/q2jVE/P9fDJRe3aHSnFkVk+FEZ0q/UJZt2fDfAqCDZIK0brKnGbPekQPs/EOGJ7Z6B4hp4UNqGvyYDc6+U1nXt0rpmlNg2j1adwM7KfdJ+wauS1pYreWMHSFu/10M+HwS54JhhYre9J5LeKO233qtSvohQQdY93/PUWaORtlB+vhpwSrJH6aAUCdvbO+imFGWkapVyTrpVG5WSEami5jkqa6Rsad9qeB+jzsdwWdH8JXeuJgG3aPQIKrDZ0f0baFRgBRCdoAEJJvnepgWuOEUw+vZ/4ffLpr9wjs8oTdP4I9K5NsOnEzht3yCxv1ypItSGGa+F/Xytb7vE0dT9TEWN4ixydxPbtw3taVwKQzQjGZFRz99tddhQmGY4nxuJg1K40E0peptCK2BJUU1WTRVdvh0Gji1mMTXvuLg+P9GnuXWzvlx4XmZOaWndLGlphYa+tYkGa9ywdUZbYwa8V5qZSY+pgt7+G2PKSi6SQ2s/DRil6VibbjDNalP3Ra3WN/XrF6H6PZ+rwy6S4Suga4uqZLKfDaE2R4ur9peLDKjuEc5I5Q/P0Z7hftSuwe6OX+/nQINSpA5K4QA3Jex/Ndshq7eLqo2RqrUi1UjtB6Mh15bDKDVJ6L7B23wblXYf/LPn3lZJB0M8vQU0XvU1nETgew5MAQcDkb5DpWj9IznDyE4TVjQ4GYiq3vOpx3WlqREaRKsNrkLZvmWOHJ3plNz2ntOBhvJmqZm8WxZVTlTNS9c/Ebor1KuCbDh7irrpK2d5WR7GueN3aV0jRTUCmP61WKQarf1gpLS4WuVvBx/gAvMkkJsDnx1f1JmtwUZoM9PBwRZqWwmcRGRnDpFte24P6/75OZPkcN0rItnHqpSvf2cv0BNH1fWb3WL4vOgI1ns+zKlm3f09cY7bKCW7hspFjb7bZvD/wup8+bJKVFduYYMtwFA/8Fjc9oJpXS6Z/oXv8/nPnsLycMtRaSolbFbHr/GBWbMr3ap0WShSTf6krAVENXr7wcjBAvOtbdsS9vWIMe5I1ehW9zoxM5qj0Ai1Y9+d6iM+jwcQbqxcw8dIbvO/TyQ0ta5SH9F4pBepOsZs1u1BUJFqW5Cdq+gqnvSV72O7/2sTu6StHyfpjXkG6WWRlp3jpHKvS47d0CUfnpTm6Sn2b8HaNtj9cdYyh0xb6ZR1R7tFGBhFovh8+zDzPH7PLCnUvX6GwfVEH47UhCDRVlNDMocoW8JEsbRhmYzMHpGwr0e0Rhw//NK//rS2b5P83HFR//ha2rb6NUfFpyHKdxTGN3Ud7Dajx0eKUWTZ9a1PxFVSJ+nLTggyn6pP1+kfSNrq8Z7jUZGqVyRilF7GdzBllVPmvwfz/8BExLYhNll+fLrUFtnk5kc7ekZkXCLjNrnkvmszJKtT4h+JdrOotkHtVcUaOI2TiwqY+o0iUrWCm5JlG5USXVPNTwty1hwHmNhJPHp7OJX3b5DHHKx5WooLTopKAHuESj8CNktUA0dhINwPKOEGgbf5irre4yOmO7L0d1/S5lhtjQWStn5swG3BwNgMotJoGOAVkPunh4fvcsmiOTZVK9VbYA5BRSSaSCCoN1eWSbPDKWPyciioUYARTKukfy0pqhkZGQn9emN06m3xZGbhtIR+vb7OodrX5WDNUyG6f8U0AdSf+dSwd3faxnMUxiWHa9+WgrzJOrf5fk/GozSRoee+5Lnt8peky8d9KbigqqMsPeIxenB/R+FF0bYwbh+62yE7la2h7/29u30TzX37DslNleUU1ChhTdVi6V+kYkdlHZWQr4Wvw9Rv4jhU+5ocrIF3s05tL0T6F2+50QhgsJnPyvIbTItSYWX4TWep6oQNuK3+re7oPNAN1/t7am2Hab1JIDKd8bm+aAa7zR8I8Lqx0nXyUiWsCmypMWFeHI+CoCJSDdbtmwxgS6kWKJCIYfevxSJVUJhWkKCvwwaERIGIUS9C1QiVLqooXRCVABrNfPYvOc+0MR1vs32b/FTmp78ik9O9G+6c0tl5SEqLzlIC67YAtPuIeqifT8LZM1AyXjtT7Psqe1LF4zaKa8gecb3iW7MNNrITjMP9RPYNskthgyugMziem2bCwdW9kWZ6cWLei3pb+tdukZqqNY7CIFJNpFUhmpUSAc0fEkdY3bsuY0EtKzknqk5aiFZF6eUB1x+ufS3o48JlT9NWeWDfQa8qqE3e6jrPL2K1qbnbmvo3lKAW5k2TEVW/9RF1PUOMZAEHpaz7rlOuSz4uTOvHiqO0xv29mpCZLa0VJahoQkJ3b6hu33gwNpipgzXKgimHgzaF4Ykq8uSZmeb5mwbj48alCfk6JHEYuwQFH6kpL7lUykrODbg+nE5ajRzdckLsTUq7D/5N1tTtFpdc73M9lpNt6DpWjklfJ4W2hoDvuaF5mTQ0L/c5Zsu8f/s5KHmjhLWmVCSzXWydxttaNCelUCAy1awHEzp36sXXLW3K3/fuXb4nfdxIE5uoMlINgiakbW1tkgiwQPywI/YIIhz+efjphHwd0uMS1JOQ8V8dpl9Tdbo6g4zH+HbSGkWePYJuXpMSjqG+8SPpZ68RW8BGF5e855gvf2m/VT7tnGHwDL7HrGeIkQzSvh4ZZETGPX7jsrnEluY0rKW608WhM1v+KV733GniBBXgO+hwuVRjkj3Oy+b7Cg4Liaqla6oQ1cLC+NcgV7eskUTR4mpRIs6F5YkBURlGSBAhtnXslQPYBeo9UqMjqoX5x8U0HqM5KCEFfLDmGd16ZjQ0t25yH5+tQeanv6pSvu712d59rHZ533GmisKnp+tlX5zS0LRSsjIHKYEvLTq7O0WcBOoL1OiMC/aDOri6N9tkvHCOSNujmLULKr7tVz4r9s4MyXj2Qp9xHC2KNU7xxkdQc2wirUZ9T65Ab2cKavRwpCbM9G9Hh+/C3mSufDOTZY0rKKoJBELW2LJGDhx+xOd6PZfCwrwTJTd7ZNTjMf4pYtRmc7JGmGJRmJczxvN/NCUdlbZFpXwRofpik/e7zpBxaWu7U8G+qCXj3ffD8RlZEGZkVkht/TsSD9K+mCTpr85370YN0smLGmswy0PgmX09+ht1+tJ1YKmkfzjTHb1i7+rc98U1aL8y4F+Zd4bBV9I2mJrHNQPL1KiMHqPzcnS9nUl0sFEpzEi1vT2+G2M0EDXm2RJnAFFp4pwiCY2RMLrnVHuu6198vgwZ+KOox2P0jBRgIGGW5y/EPjuzx4kLgokaqt5yb0SwR5yhviaO7ykpKQwUmqbW1ZKZ3t9QdGOivsAjqEDr9PXHZ0uNgfCqCHX0RrXHFbVZNDx5BBViO/ZrccxaJs4RO2Vc6VL5QfEandeBU06yfxjTvG5ZRmC0/Y99h5Q7kv+pANK+FFLz078wDbIClk//Joqr+l8u9x16KCFf6+Qio5oXiQdY6aY/p9oTqg7of530L54b/nhM8XlS2DVRWjaul8yKAZLer9RAgM11UEr3G8mCsM5Oe0elfL3TmBAK1F69QXcwhDbD1i6drix1Ox5f26AfjUJwhwy4VcxGuSX57UHVaqJGLkruuN9XniCaeEzW5rFi23yMskrUnkv7mLZhrDKe0OZjR+blyiVd6+SFpnHqxAM/J6TSEfln2wbL+61HRZUOPtTpkCsr+skTB4/4HPPH9Y1y65ABqoaKKxChUlB7d6SabuVGpURFqqAgQXOq0c7Xkegxij+8u38zDH7/7vGYBd0GEj0crn1VGu99RVR21WaT8mtvlJwZE3UE2BwHJTQp1TYslSZ4VPvZAJ2UsVR9L0j5eguFd+p3VdcUnRqsU+akv2NQe3U/+a4Dd4vZ+LslheeYFNjd6/7c7iOiodbMHax5XJBE/16W+wRDO7EAJ7kelcLC78qrDQOiilm9BbXnqEV1+SI6RQ2VxE9UE20aZIQ1jsIP7YfT2Rl6F2KqNSvhzeNAx0EpTe+XkK9HUIs06HL1dP/agnbCoiYa+FiXOEtE0tT0ikuqH3tQSpsvk/TNLulCiVNpl00qK2J3UMIYDbp+tWP2/x4AhBE1VH+h0CLUHkH1fhK7LOk6U51YQJgTRlGjdJ33VkBNNdgJp5HxVaiTVCPzfvx8CtUvz5djO/4q3zrqXtnWkS1/3n1Q9zmrMjNkT0dk700w6kBTEqPU3h+pWuMoLBCpolkpEeBNYEBmRUK+FvEerbkpwF5dM9TH2E0w4dMdj3GK2L0DE6dTap57StJXi2TdK5LxuEjWX0WliGNBG6MJBwjFsLTtAc1JENoeQfXHJu87ztC1OownjuNXuxuIVMrX/c8f3zqq75Ya/ftoKWH3deGa9/uT1bVVphcVyByjtWzF+RE9n7dbEomfqLKmajFRRbNSpmRIh8Q3Op5bOIdRapJHa7R07I5tk6SutjOodaDPeAxmjG0uJajpb4nYGvUfg+vT1G0uaVyxTAqmTlM1V73nDdXEpI3RxIK7thqsuxXGEePkmPT1ut3CcQHNSu/MDlJDxYjMM54RGV95daeCkUJ2Vu0V++4qnw04Rsb+4aJJ94Xl/WRJbUPAbSeXFMmOtg5Z3tAc3RMT06GoWlBUa7qOxF1QweKGJTI0e7DMLjwl7l+L+ALx8haw1habdAYxUPcfj0lf4hLbfneEaiSo/tQ8/bjUPPOEqrkWnhL+jlO9MZrYCF6vfM9xlixxzPM07cR7NlV2DQoqqJ4RmT0DxXHsRpGXXAEjMqjNIpWsHJn8RDRrZLm0tAUTVJuUFM3VGRnqKQUgVYta6IP7qj2nJDd0d+7+aMhAeWRvtbyjI7poTKJbUmJBGccq6V9Ld/8msqaKOmciwBvGg9WPycTc8YxYLYzeeEzXbHdqN1xB9dBdc80dP1GQZQ2149R/jKao4OSwU8DdT+mmu2fKPVoTTFS1ReB2VXvF/Gs8ItZwZlNVhHrmO5Jms0v6P78taZtH9aSGM7qk/dZ73ULqTffeVm8y0vOlovSK7oUBWhNUzw9GO5HB2FDPfQINOoIZNCwcVC79MzPkqYNub2JNdKcU5huKMYkPjFTDjFQTZf4AUOcMNg9nJk5xslnJAhg5KgHd8Ri7iLOfltqNEKdTOg4ekI6MhojHbgZX3CJ52cfIvkP3GYunN9rn3Vqi2Roa11UD51v1mnjiMZvqXUtVAzLlByVr0RkBt6tvpStDpDE/UFT1vlzTUsnIKJMxw/7qk/L3T7ljaQIW0QdLxQczaDinrEROKi4IEF26JfXdSNUaR+FHVlZWwtO/6MY9q8jIbcVc7GJns5IFCLb6Tbc5yWXzbU6KBLtdWoq3yZ6Df9G7MeTYTXPbhoDr0rpCjIF1H75ma9jjFxzsxDFwvjW+s6leTUXDt4u92n1yq93uweUWWKR5w+Vw7StS17hU8nPHeVL/2v+9Mbo+XCCk4/IDO3uNrie9e6TGkqKajEgVzCsJHP6Ph6DeUH4NU78WwShSDTTjt8uA/AWRp367KTjrW3KgWUszRra43KgD2JHRGHbzC+qk38u6R67MeEhmpH1gvLlH4oM2m+oNPkczUue8xdJ+88PiOnprECtCt8jqjccEA6ldM9btEev/HafRUclaNdV4g7eK71d8R0bnjKKgWoRQS8q9O4bTWrLEdahd0r69QGqef9p4cNKAuq+WiOhM1xQ4JgeM3bRt2yqtmzdJzugxkj1ipCkdwN6zmRi7mZK+Qr7snCKfOOckJv3rP5uKhqPz3lLNSA75Rt3FoZo8AlPCns+zO6Lq5sXe2OKC6eZ9L8RyuCiq4aV/Ex2pxrNZ6aziM2V6wYlxe35ibk1VAxFk69I1cuDRB9xCarNJ6SULpG3HN9L8+bKwv5ZKG+tMtTTavpTtv/lSKi66SXUHH3zgb9K4tCcqLZhxshRcFWEGJZQ5UbfAHpexUpa2f8un1qpnb2jmbKpj1DcqFezp3PVvOJr0lWe3qkoJj94irqN2iOu1DpH8CEdYuuEkS+/HRVG1pqjGs1lpbPbRpj8niW+kCrqO1Ei1JqjA5ZKaF56Wqtt/6xFVV4G7gSnYqA2uty8XcfoHTGh+KhHVHWwvKvIRVIDPi+bMlYyO/tKZeVjMxH+FnJ69oekUNYozSKNR18Wv68+ZYp9qiF9XdtYYaWv3j+qDu2WR3oGLompNUdWald6oX2T6c99z4M9yY/m1nE+1GKEiVXTsBqR6nU5pXvOl+m/XJOmxJew2hYCrkh4Zn4u0n+gXrWrOTE6nNBlEvk1rvhTbisMiC0OOm2qTIWGjrZDTszdMGmpExrf7Wv2etCxw1mhpa8eSBF+KC2aIK//4oCMypHficrnYqBROTbWrq0sSTbyalbT5VJhMkNSJVLGBRi9Eqn31JXEO9BJUYHd/jsjVHy2aTXvfa1ObnzNT06cf6x5Dy6ovxb5PxP6Vz3ir0YrXyHAZ2xsml8Actru2apPKsqt1H1GUP0WNyGCMZljlHTJm2L1B3bJI78FlIVG1xlFYJFLVotU5hafKkgZ0SEZHib1Y6px1OtOInE9NNVrWrtFtSvKJUEPMsgZEs+9LRM5MHbt2qI+Zr4s4vxBxDBZlIuGc5ndH9+IZfWE1qrNGUXDUVsjFP7J1SUnhaVLbsMT9t2Rz+zTDEANezvuq7/fcE59rEam/cxbp/biY/g1OdnZ2Urt/L+x3bkyiWuusk2J7sdQ7633qs5xPTa1GJU89VSfq1BVUHaP9gPsimo3WmQkPhxjvFxUldyCV7LcGLm2piGOGzrFphkL+IgoRdgeAYeG9Qs57F2l8sEt5vwvUxSavSHbmAE/k6e/lTBEl6RaJVO1WjlSTJaqIVs8s9B01iBREqifnz1BCCjifmnrpX916KnSon7Gg+hvt6963O5qNBSWufulgfJ7xoUjmI7qmTZL2id/1ThHb1vC/pv8KOc3W0LwNN97q3lMPdQsmtgqlm2raQHoPLkaq4Zk/JKOmqnFOv7NkUcOSmJ6jydks9w67R43qoLOYO1StR7BI1d59chfWeAxEbWlgk5Luff3XxkUJ0sHFo66UQ588IWm73UILbE36UWnaZpH0L0U6Z4o4J7uPyTUq/ChVb4WcGXOtJYVzpCj/JEMrwUhq4KTvktHdi5NsrBEvWyz9Cza3bon5OabmHaeElGJqTYK9STd8+L5u6lc9rskm6e+7pAvJDO0pbCLOGSKuL3si1ewxY6Vt00YVvfp3CEfrzOQNZlhzM4arrmLvZijJ1Y+OOxa6G6U0QdWOO1z0PITNmGstKfyWqpNqGEWeFFWSCulfaxyFgagmo1HJTMbnjUv2IZAgGL1JB8ym+gMfWkSFtuBNShBUgOg17ZvQs6yRUHb9zVI061R1rHrNULr1U7uIY3b0RZ94zbU6ne1hZRQoqiQYFNUwfjjJTP/CTjBWkPZllJp6jUpGtdRY0ro9i8vNoevwIWnZuF6lqPWaoTzCqhOxBt1XnvC51tDLBDQoqiQYTP8GQVvhk0xRNQPUUUnq4ZlNNRBW75lTT+QXYVrXVlQkrvr6oPdJKysXx6Fq3dtqX31RXYBzqL542r4ScR0bQSdwhB7C0WOLypyBokr00HSChvphkMyaaqw+wDeVL2SUmsojNXrX22zSNdEVZObUexF2cIIJau6J0yU9v0galiyKyVcYgmpfJeKc4v99iFTmXSddmc1SXfuswQHGyTTXZZMRg+9UKV//ZiT/RQI+D2P6lxjQ1tamPjJSDYNkRqqx+gC3dLWafkzEXIwiH5X+1cGV7yWofjOnZedfJ/mTpyiziOpHekwJoqKtXRqWh2+VqVLLy0UcOr7C9h1+jUnqG7FJdskIqd+zVBKKiuZdknl1iaT3841O9RYJVNx4i0dsXU6nuFK8x4LEB633xiqiask5VStEqrEuLX/iyDPyfsOHph4Tia+o4s27dtGb0nn4kO79naXGM6eZlYOUUGDTzIAf/jSm42rp9hWOhHR0ADt1ZlP3uNPS3taIOY3DZdvu26VG3jYOrOM0vWI/KFK7+O2An7veIgFcD7Hd89vbRRwO6Tq4X31OiF6kapVGJbuV3/AcDmxYTB6x+gDfX/0IvX5TBO3Nu+aZx+XQQ/fp3id/6FRd4bLXdtdhtftNmiIZlYOiOo6cYydE9ThEq/7iqdV40X2MaDrjcbcxRGvBdp9RoDgsZdKne6yn5sCbnq5lgJSvHt7Rq5aN1sSWEI329nYff4NkYw1pNyDZjUqIVifkjJOvWtdH/RzsALZ+TVUvUtKj9cMVkl4fOHPa/8wrVJTqXRPMGjpCOvftDftYUEftd8ZZkl5cIjt+/O9RfT/BRne07mMHmppsrvAbl2KprRqM9XTNd0nLwc1S2M+dr7bn5ek+vHP/Pr9r3E/WumVTQM2V9F209K9VIlVrHIXBG16yRRUbZWIRVMAOYOunf40ipZDCVQtBXSAl888JqAmmlUfW+Z0zYqQSCu8ILhqyK8dKe/d8rGFTk57YGQlnOJ3Cwcz6DYTVVeT+r//PLdgX0Z6mdcNaKTnjrDAeQ/pSpJrBmqr1RTXWDuAFpd9mB7DFycTrrEa/hmqEivp22WTwD++Skvnn6ka6jurIXjs5o8aoj61bAveEhkvuxOOCCqqHSNO9oSJVozV02mP9U+Yum+SWjg47Q6B9Ca0E3rJmNVPAxLKRKmuqYXQAR8tRWcOjfiyJP0McnTIgK1Pq341iKb3LJc7uM+SGz2Lros07HutmRDVJtW3fFvXz5E6YFPI+hssAYgFdxl8aCKvTb4csZlMrblSjNJFkCDy737pBCthsWtq2yqHaN9XHcO+/79Cjsrf6sbAfQ8yHNdUUilRRU11Qeok8WfNcxI+FGDP1a10QJZW6HLI52i4dm10cDQ0qXdtVXxv1cRSffYE4amvcHa4xgBEUjPQcfuKRoG5QRjOt4ddPYXKM7QG+j7PViqQt8bNB7BbUjGUi6etF0icMkaqFP1eCqurP2/T9tTMGD5HO3bsCv7ItMLKPBghgc+smycsZ4/Ec3n3wb1Lf2BM15+dMkmGDbgv7/rUNb0tRwckyuMI9BkQSB0U1hbp/wTkl86WmsybijTXRzrcS8whmJoDrVckv2l+TyykH//7nmI6v/9XXSfawETEJataoMVJ2+VWe7w9Rb/OKZSG7hHX3wQarnzpFMl4WKZ+9UA6ufDhgmQDEFB3GRbknSsfkZmnKXufxGra1uWvRjqW7xDGnVo4seTZo2hfjSf6i6n1YOIGItlHJXwwhhKVFc32uA02tq2XH3j9IWnphgNiWl14ccH+A6/BcEF49ISbxHb20Sk3VGkloA6vCZEeqGlPzj49qDRw7f5NHKDOBzppD3eYeyaOrvk6OvPpSTM+Rf/wJSmDqP/5AmleuEHtBQcjHQOAwL4rxFn/vYp8l55qSaSncFpHat98Qm0O/+SjrO1OlbPTVsmnHd32uh4CjuQuCjlR5qDqqo1HfAjGtoECqfnVX1IIKofMXQ3yeZsdan0AgrHrXte3fY/g1IKQ19YsNo15iPoxUI4hUnU7/DofUclfa1r5dxuWOjdtxEX2MzASK5syV+iWLe26DvW+Ev1PMkbauw3bw2KmLUVDV8YwaIzt+9n3pqtb3CDYCu1f9V9LlfF4mrg8PqZ2rqL3aOkRcmSKugW7nKIddpNNZ3VMj9Yt0GzK+kIyVhSKlLsPtPY4wUuWuLv0Mlc3limmUBoKn+/VckfVNdDkOG95W37RM2tq36Ea9FNb4RqpsVEqBmqp/bTVSnq55geYPScCoAebwC8/4iK1Rv44tL1/3+uJzL5DSC78tVgHRd/vePRELqoZmCpH30TAZlvZTGTr/Nz3dzTvdwosaLATV25pRpXTX6TyhzSVHdizRN8jQtveEWDSO7ynnaP0T0fT80FF4MJCK1aOkcIaKJs3AX1C9hZXNTPHt/s3KyhIrYLdy+tcKNVWNEVF08jrFGfNYDokMd2pXP5Jo26CjBDqBalph9xClH43LP5U9d95hiV9JyYWXSulFl8rhp/8Z0/NAQIsHzZL8o/xd94N0C2POdLzOqAx+nJPdSwb0nJ1A8/LPgu6IBXWvvax7e0ZxicQCapuooXqDz3E9osjsrMjWPWZnHW1KpEzMEVU6KqWYqEaTAraLnR3ACSR8IwE3ejXVjKrB4jQwbnccjP0EKXPIMOnYtSPm56l96Vl1MQOtk1ZvkYBht7BRwImVc/vdEXCkS9nbd+/U/f0hQyBPvGDK6jd056KZSK+JqCh/mm6kmZbWXxw6Kd9+hafKvkNfB1yfkzVaWts3hx0pk97VqGTZSNVKNdVoDPYhqDeUX0PzhwQRiZGAB6+aau7xUyV77DHSuWd3xMYN4WLv198UQTWTnAmTVbocPz8sPA/pKexzo06k7+oRUqSQwxVU9XQGrdhp3el4s/apQkjLSs7ydOlqs6lGolfR72Ld9HBD0zLdyPeowb8NuL8WEZPen/61dPevlSJVzWD/jfrQRgE/qPiOjM4ZRUFNIBEZCXhHqt3v4+kl/aXhixUST5xHjBtcEk3G8KPE1doqrV+tUpdou4XL8i6QQ62vesLZov1TpL0x8p8jaqkF02fqGnF0Hj5syj5V/zGXwNnUyUr8/DuE9x26T1dUUSfFY0ZU3eV5XgCRxthNuVzMsZo+GKlaWlStFKlq0SqWjz9Q/WjQNDAFNfFgFjVS8B6t/RZNG67B2XK305KV6dz+TczdwsXVJ0rFzEsl75uR0rRnteRXTZKmjcskku8+65hxUnbx5Z6uXoirf8ah4d23RZxOHYP98NEzd/AfmWlqXaWu7198gRyueznkeI32mHK5SEW+eiI9bNDPoz5mkpqRqmXTv2lpaZaLVMHswlPkzqo7gtoXvl23OKHHRES9KWcOxgqW8HGbP7g8UVLMZGcbC6pJqctk4r1CbsCO86Vq5o9UHfvAnXdL0yPvqo9tWyPzLk7LyvYZk8EsMWZRi06fF3BfR0N9VJ6/R+o/0DV30APX+wtqKPZVP6w7AwvBxSgNiS/alAhFNQUjVY2R2SPkxvJrDYX1zbp3OEqTIGAT2LJxvfqYOXiI7n3shYW619enpcvh9g71Ro439vSBlbEdTPeyZD1sMY6DWAXUSIdec5f0n3uZbh2761Bk9ei846YGXKd+F6X9fa7TfChCef76+/ciekT6Np60dWyTusZPdG/jKE3iIlWmf1PIUckoYp2YO14eP/SMLGv+3Oc2pIbpphR/Gj58X6offcDjNZg1crTu/TIqq6S9YUPA9S6bXTq9mmNyj50gDTGkGIPhMnAJSkW0RQKNn+kLSbikl5dL0axTdW/LGNCz9F0Dotqyfp3h2jf/9GteziRpNohIzSaYgQTqrWxSih9M/0aQ/rVqpOpdY72q7LKA6zlKE38QmXoLKmjXST1mDBosGWXlus/RkZam0r8azWujc0qy5eov2e6VdC8SaFq9Utp2bo/qKbLHjVczqcP++y+GJ0sH/nxP4Je22VRTlV4KWC/9GkxQ09N8I+FYcRtITNa9jaM08UULvqwyp2rZmqqV0796zUsQUsBRmsSgZirDcMPv3Ltbmj7+QPe2usxsybDb3CvX1EhJhH+UeflKHCpu6kObSboXCRz437ulPYqOa1B0ymzDCFXvZEnDs6RcJwUcqbFCMKvBaEFTEkdpktf9a5WaqmW7f1MhUvVPBSPlC5MICC2JL2qmUrXvRte1W77wJhnwk59Iv8xMqXnm8eiOISdHiUNjEJegcMifdaqh8PdKgvzK1JJ2g9+p1uult/bNKBo0MmIwGy3FC2cmbqhJTqSajUZBC2DZSDWVRBVASGGeT0GNP0gPKrvAKAS1+LwLZdif/k81NfXr7IhpkKbwpJnmvNZToIkJiwRMwWYTW1amJzsQ0UPFZrj2zciCEEYMqK1GSkHeNN3rs7P06/beou5tLkESF6laJf1r6UjVu95FSKj0YDjYuhtKevapRvk8WVlSfOppqvPY2RHbXGqXgVexadixJib6E1SkuLMGVckeE7bzZB01UqWO9VbygWA/y+xRo3zuG8qCEKATuKL0Ymlrn6bbBWwUyZYUzBK7PTNg9yq+BsZkvEdy6JZkDVHNtkikamlRTaVIlZgnmqiXZlYMkPR+pVHXUo2ofe1FqX39Jck/6WQVNbmiTNfmjhwtO37y3ZiOBbFX+cIbpeEL3+7xSMkYPFQ6d+80vD2tX39xHA6+ySatP+6jL+7NX3wuhx7+h8RKv8uulCPPPKG7kk+LPls3rNd9LH7KGXmhI3pEh/puSZMC3JK0Paf+Igl2HbhbKstv0vUJZorXWnRZrFHJ0qLKSLUPj8jYbFJ+7Y1SeAr2jvUAsY0Zl0ualn7orn1EoYkZJaUxRcsgb+p0KZgxUzoPHJCM0v5iPOEaGmeQ+VgQSlBzJkyS/KnT5NBD+vOcLWu+lFhBRGo0143GI01UMyqNZ4WD2RR61zGBntkDunNxgSmDdh3EF5aCcITyZ1/1/TJm2P+pVK7/19DEm1gnUrUjI2MBKKrEmmldl0uqH3tQcsdPVBGrFsHqmb4rgjUtGdzm9oOPXBgbV34eY4QKd6BanzRotKSVlIgjQsMF/9Ru29cbDAXVDPJOmKbStocNturYvUaSsgwMPIKJqn9Ump05Qvd+mphq7N9TKV/s7JKxY1dLnkFvYXPrZikumK4b+XLpuDVwWMx5z9KiyvRv30E3ret0qutb1q4JHRka3maTojPOlvpFr+vcFF36t2vvbomVtk2BK8MiIj1dyq65QZzNzVF3LwdbtxY5eitr3KQVlwRdy+dsaVYfde/TnbGQx57VjUT05lPhcBSKt1+4UNavxlypTT542yXDR2fJRVc/GXC/I/WLJTOjTDfy3bH3j/T2tVCkahWsES/rkJ6ezvRvH0KldXUikdYNa8NLtRqmBl36gurx/pWEkzdVv7M0EjKGDFXjPNEsEvDBhB9AxqAqyZ91iuHtWYOHBhVujMgYre4b8INbPSUAvUjVaD41M904jYwIVRNUNzbZvvloeemfVwbct6Vto9QaWhCu8tghkuTRZTHnPYoqsQz+9VNQ+8arYQlq/okzIv56kaR/00pinz3OGDpMBvzwp5I7YVLsz1VWpj6mF5fEVOcsjHGRAJqkXJ0dhnO2+BqIpoMdA+qpRqv7Or0Wp+uJqtF8atWAW1Rnrx57dw7T2bJuk22bxyjBNeoY1yNS0wkSH1E1a9dur0//slGpb3r4+hBCUPtffZ3kT5oiR97Wj0aDgT9EV5jdvpFuX9Gjc+cOU+qooHD6LDXOA8vASEmvHCQDbviOpznIXtJPnLVHojoOW1qadFZX6+5rrbhqofoa9QaCW3zuBdL/wkvV/40ibm+jB703Tm0+1X/0BdcX5p+oOy4z9phKlfLVE9a9O4fKwCpf/+fiwhnS0blfd7MNLQiTT5fFItV0K6d/Se8n1rnTzIGDVM214b13In5suOlfq7kdZVYNdnvjxvAz8zZQwCKBaL9HW4b+GENGcbHna7Tv2hmyaxn39d+l6m/0YBSN+M+nal25RoI3fvwkmTApR75a3Rpw25ixvhkJTaDdozd/9Gl24nyqdRqVbIxUQ8Oaat8gprlTm011A1c/cn+0D09JOvbE1ihV6Ff/bN8RnTG+Er2jj5FDWwKbruz5BbLvf++WvClY7Wb0+/W9Hh3CmFnFiA0iVH/npGBvnHojLsGi2B/fJvKnP+6Vr1b1COuMkwtk+glXSUvb9ACB1rx9aUFoPboYqYYHI9W+gdGIDJZU17/7tvEDu7tC27aH7vSUIG/pbQ6HFJw6Rxo/WCJ9gcyhw1W6HI1BqGNG0+hkKyuTQd/5gUf0YKbR5Z0Ctts9kW/L6pUiOTm6z1M4LbCei+fUsyFUXzeKsyCjKPZITZfMO7tEZp9WJAcPdMqoMTkyYqTbkSfYDCrnU62Hg5FqeDBS7Rt0HjoU0WLxkosulczyAZIzarSaXz380nNRf227oKbq6jOCWjjnTCm/6tqA0RVbYVFEz2Pr6vIRPqxwQ920+csVKkINSCW3BqZZjTx8g37dKEQV4nnwQKVUDBgmudnuktKH7zfII/f3nAR8e0GpR1B9H9chFQMypV8pS1FWpouNSuHBSLVv42yoNxy9KThhuufz/EnHSd1rL8UkrH0BNHQVz56rO7riMvhZG5F//IkB12G8BxekfINRePo8KZw+I2JBjcYxB+L56APVmkGXXHtjuYyfmOsjqOC5p2pUgX3+OSUBj9NEd/650XdZk74lqpYdqcnIyEj2IZAEoI2G+GCzScYAgzlDE+dKG7KzJd1unT/GeJLW7ZtrNLoSNunpUn7Ftbo3IVrtqN4f9OEZ/ftHJaggkjdORJrewoiP+HzVyibd+z/3ZI1s29oW8Dh121M18tbrtVEdM4k/MAmykqhaNq9BUe1DozQ6tVKsZgvAZlNpX2+iEQnY5pWcebZ0XXttUswfEo7XujVHi76oSJijRQOuv1n3th0/+75vXdUAvV2o4RLJGydSt/6/W3z+6UeNho/57e175NQ5hbqvieefqpGjx/bUXYm1IlW7RXx/gXWORGdO1YqdXSSOozQ2m1Tdcaf6r9qXqiO2/ptr7Hk9vrHhkn3UKGWaUNbSFHXgC0P8VEFbtwY7w7rXXo76eQpPmmUYoYYjqNHUUb2J5I0TtVA9vtkafE3fB0v0537xMoXoMmK1Hk6LRap2q0eqbSE2cJBeNErjcqnGJSOx9XdcQsNNNEbwiJbw9ZXPfpSymj/1BLE6tpxc5eDUvnWLKc8Hswm9xeLNK1eEtfYt2C5Us0UVzUVnnl0sZoM08VuvMRVsJbpYUw0Pimof9PrV3jR1xNbZ7hthGHnFhiJ34iQVLblXyClVjRzUfPvr1IItROao0XLU3x9Wq+XMAg1he357uzqZ8cY9i2pM1shR0u/Ms2P++pFGI3PnFcdlFvn5p2tU7ZVYZ6TGzvRvaLSFsx0dHXH/pZDEgzSu2j6i/THY7VJ+zQ3umqn/O2G3yUOstVTsDa380W3q/111tUH2qgShOw3tL/KRklbaX9IHGpu+S1ps7Q42l/vEo3VbbFFqznGBgomTGb2IVY+8aTNl8O3ulH60aNuqIhXVtWta4lIzx3OiZkusgdNi6V/Lj9S0x/jmRawL0rnYl4pULCJHrV4K0cIuVax+U7hcqsbqvbQ8UtMC7A3FyIenQeqR+yNO/xbNO0dKTj/Ts981FtRi8s3G69+yjhol7Zs3Rv38jqYmFVXGAmqg9txcCZwyFWlcttRTHw2W/rWlxV5hikZU0cnrPzpjJllZlq2c9TkcjFTDg+nfvgEEKnfsOJ8GJAhn1e2/9b1j99JyTcw0r9hw0XZ2ehqkVPI3srPbvAmTPMeJj1kjo+9kdXYGj3RCCqrNJiXnnG94c9cBX1P4SIHZPWqgnfV1urd7R4DpemNR3eRPDZxpjbeoYs4UTUXxpL29+4SPJB2n02mp9G+61dO/jFT7Jrrp1e6l5ZqwaV6xtUvekealHwd9vs7Dh1TKUj2vlyKEnR602brrsD3Yi/Rdn0KRVloqHVH67YLsCRMl7+hxUvP80xIvYHaP2mnL58t0b7fnuEdLdt91h2EjFGqpsEQ0S1TDeeNErTOeEar7OIy7i0nisVqkmm71SNVqW91Jcj2B27dvU5GtBkZj8iccF1JUG95dpC6oq2q407/hUXrJAp9oGoLTGkbXq14XbPbQ4bLvj9HXGXNGHe0W1BgLhhB3R41+GttRVyvNBoIKOnbvkqbVK3UFNaNqsJRefJkpghpppLpls16y2lyuuaGc1oUWwslINTJRZaTa99A1heim5oWnpWDaSUrgfPawKoXsFhmbTYrPOk+cHe3SsNjXlL/1q9V+q99CC1PptxdIyfxzYu48zjzK3QWrUtjexxvp85QPiElQsVi8eO48yRpUJYeff0baNq7TuVdwAcs7bqo0GYhuVrdpv1los+pWaUaB1SGxDk6Kangw/ds3CblftTsFDHzup4TVLhU3f89jtg8HoWCgphpKmvpfdZ3kT/YViOp/PizRkDfpOPURx4a6cUMURv7lC28KcJWKFFgFtn29wXDGF7VqW66xcKSXl6umr65a/XnNjIqBYiaRpH9Hjc6J5XwlLLZubpUTprttH0nycTqdlvKKt04i2g+Kat8k5H5Vu13VNvXNI5ySVlioRAvRZGfN4aBfSwt8sscea3ifw48/LDt+8l0VFQM8b8eO6NbNFXk1VuUE+Zp6pA8YKMP+9H9KjJUonzpHogUWkHqRNszuq351l6pVey8Q93nsqNFqKw0omqnfKGZ0fSLSvzB9OGlWfAWvLzhbphJOp9PjwGcFLC+qrKn2LfybgXzonmWFqBiZR+B61DsxTtIQZB9ryXkXSVN2rkr/6qc//TqPH31ARdHRGtKjU1eryUYzjpM1ZJhPTRcjN9GAKDMtNz+k2X3rxvW693F4Radq1njhTT6343N/K8lEiioalT792NjfN1ZwCIiGiXVwMv0bHqypEn8wZqO96WvmEZ551m7BhalDOPXO9P5lUtDWGn7U4XJJ7eK3peCEaVH9Ympff0W6jhyRnNFHB09vG1By5lk+nxuJnhHpQ4ZKyenzVNrWyLjBnpunUuYZAwYoT2XH4cBdtxnl5WHNGidSVDGTunlTq4wek6NGXeKZ+sX6OO5XtRZOi0Wq1klE+0FHpb6JVi8NZ8xG7w09VB1Vqxk6m5t1u38RwWK9We2LzwY8rv6dN6Vk7jz1+GgalfCYxk8/jlhQ9YzoDVfjGZB79DEe8wttxtf7e0AEG46Pcv+LLw+4Dj/3eIipv6g2Nbpk0Zu1Sjy1bTEP/O2gLPXaPHPUaP2ucTM4+dR8OWV2dGNUJH4wUg0T1lT78CiNXqeJzpyo3ht6MKelwrlnSuG0mUpUEK3pdf+6HE6pe+05/SdwuZSAl150aVTiqD1HuGQMHiIVC2/S3eySNURnNV4I20JvtBnf1i2bVIQajqDGumUmVlH9fFmz2DrcqfMZJxfInLlFPoIKvtkcPwe2YSO49s2KOBmphkdW95wiV7/1HXxGZMJY+6YH3vThqdu1X89RyOYRBXysLyjyuTXv+BOl7s1XggifW9hDNlN1H3OseciyBdfoihhqxpFGygXTZwRch+fGJVR0nz1hkvQ//+KECCpqovDVhbmClmZ942W3kNq8WkAgpp2diXU1oqhaE5fLxfRvJDVVGur37VGa/ldfp2YeI0kv5h47QRp0RdX3uQ+UD1TXlF5+lVoHh/Ry8xfLjZ+4u6TnaZLSEc3is8+X9h3bpHXdVxIrepF5NDOywSLMcEz30woKEiKosBd89IFqz9gx6peYCV28qFa3prp+rdt6MlHQmtCauCiqkUWqFNW+gVH0lzlwUESCqqV19UDq1xtt7rHkDHcTUMjNK93pXyV2OseKcRRQ98YrQZ8m/+RvSdNH/wp6H4iztsPUuwEo0u5j70UC0Ua8BVOja86KNELVBBXgIz6/cmF/w4i/uUkSCk30rYmLohoeFNW+hW701z0iEy7BREIvWtMiH63RAQvSg+I9I6sDIt32nTtCHmeagQWjBqwUM8vK1XysFrZpG3oi3c6jLRKINuI1y783HHtBnTW6suKzRnGJ1v2b3AlARqrWxGmxmqrd6qLKOdW+vV813CjVSCS8DQ388RbVkNjCm5Ft2bA26NNkDhshOePGB51nrcCokJ9blLahJ9LtPNoiAX9CRbwwmxjww5/GvAs1XIz2k369sV1cru7fTxJtCmmib11cjFTDg+YPfY9YZh6NRMLb0CCUqNqyjDePVN0R3oyst7ewHuVXX2cYEaN+XDx7rkr5BoRtXht6cIKQffQxYXXsaosEIMTeJxbBIt5gKeN40RVkb4YWqYbyI46noNJE39qimm4hm0LrHIkfTP/2TaKdeTQSCTQgGeEtqsFM/M2akdVS0EaimpZXoKLRjn17g27uwbEe0jtW7Fg9+zxxtAcuEkAUjxEa7+5noy7p9t07JdFMOi5PXntJ30tYO8GIdP+tGUw5IU+uuKaMhg8WxsVINTyY/iWRoGdoEGquUmtU6jh8OLjLkU2/tut/AgAnIiPyZ50qFdffrP6va4hvs0lXzWHZcd9fDI8Dwh5Ol7SRuGMm1fvnEW6XdCKAmUN5ebpUV7s30njjdDnc/0lCTXXcsTkUVIvjYqQaHtnZ7kFrzqmScPE2NECEGmoMRItUOw7uDyKoaBIKXdsNFelmVQ0O8Mz1XltXesnlUvP8U8bHEWyRgF+XtJG4t25YG1ans3+XdCL4+IN6XUFNdqQ6aYq+TzKxFmkWalSybPqXNVUSDZqhQThoourqXt7gT8lFl6rNMqEENeS6Op00tH/6OKihhH/TVpAuaSXuj9yv+zQta1YrMa1fsjiiLul442816I/WqJRoUV14E31+UwEXI9XIIlV2/5J4oaV/Ha2turfnjHTvZQ1F65bNQQU1d+IkXaEKq35ss4W1SADXK3E3EFSNhmVL9buk586TwmkzEi6oMMMPJqg+jUoJ7P695QcV3JmaQqSzUSk0jFRJvNEi1fSy8phnZIPNnFb+6Lbon8DlCqtJyiPuYTyfHhmlxl3S8WT1l+G4InWnf+MgqoMGZ8je3b6txzjXGsn1bimDy2KRqt3qUQRrqiTur7Wi4phmZI1GcfpddqUM+vFtsW3nCbJIIHfsuIg6pZHaLZw+M+Iu6XiS7nYjDYpLmyO2mV83u/7fKuTbV5R6gmCOz6Qm6RYSVesciQFM/5J44T1SE8uMbOcBfUGMJLIy2s5TesmCsI+ls1Z/+XnOpOOk9NwLPZFopF3S8QTG+aHwOCqZXFPFpht0HeMy7aSCACN/kjqkU1TDh5EqiRf+5g+JnJENuZ2nuyO4ZP45YR9H29cbDb9Pb9GMtEs6nowKK82qdf+al1g7+pgsufGWCs/nEFKKaeqSTlENH0aqJN4lhrBsCk2ekQ3aOYzmpDvujEjs8DzuxeUrA27LO26q7jEnU0y1FW8b1uo3iXnj2XlrYqA6sDJ0hEysT1eXewyLohomOMN2OLoHvwkxmYi8f0MQbfSnO0qj05wUjGBjNOnl5Qm3HAzV7fv2G7WyYln4a9t6RmrMi1QLC60z10iiR9tipq0KtQKWLx4w/UtSQVSjjf5i3c5jNEaTfewEKTjxpKQKKgR086ZWGT0mR9UtQ82jGqNFquaFqv3LrfMmTKKHohrFmx5FlZjN0qVL5b/+67/k3XffVa+xIUOGSE5OjhQVFUlFRYX6fOTIkXLsscfKlClT5JhjjolbeinY3Gk4GI3RFM36lhScOF2Shb+ATpiUI1+tDp3qDRqpmiiqu3aGnwkg1qWtrU19ZPo3TCiqxCw+++wz+cMf/iBLliyR5mZ36rGyslKmTp0qTU1NsnfvXjl06JBs2LBBVq8O3DSDP9r8/HwpLS1Vjxs+fLgcffTRMnHiRDn++OOlvLw86mOLpfPYkORtSZPVK5sCItJoBdWNlkkwL/2bDMtDYj6MVCOENVUSCytWrJDf//738t577ynhBAMHDpSFCxfKL37xCyWOeiAdvH37dvniiy/kq6++ks2bN8vOnTvlwIEDcvDgQXXbxx9/HPBahQsYol0I7ODBg2XUqFEybtw4Oe6442TChAlBz6aj7jyGOb9/+thmU25QiWw40kZRPny/QR65v9rUr+GMg/fvtBkFpj0XSR7t3b0HjFTDhJEqiZQvv/xSfve736nUbmOjO1oaMGCAXHXVVfLLX/5SqqqqwuoKPuqoo9Tl0ksv1b0PRBoR7apVq1R0+80338iePXvk8OHDsmnTJiXG/uAPPy8vT0W7EPdhw4apaBeCe8IJJ6jjjDp9/OiDcElwL1MPYwGAGUBAH32gWtsJIJcsKJXnntSflY2J7i01ZqV/tflU0ntENdPAvzsZWLpRiZEqCQeIG4R08eLF0tDQoK5DtLhgwQIlpKiRmg1SwTNnzlQXo2h39+7dKlrWot0dO3aoaBdpZkS+qO36v96x8lCLdnECgNquFu0i1az35hGX9HEYEaomqAAf4yKo3iM1MUaqpf3t8u8/rKSg9iI62P0bGYxUiRHr1q2Tu+66SxYtWiT19fXqurKyMrnhhhvk9ttvl6FDhyb1h4doF8eAy8UXX2zYZIFIF5f169d7ol2I7pYtW2Tt2rW6K64Q7fbr188T7Y4ZM0ZFu6gPVyVAUMGWza3BdgiYitaoFGv3b2VVFgW1l0aqGRypCf+NyaxxB5L6bNy4UQnpW2+9JXV1deq6/v37qxrpf/zHf6h0bSqBGuz06dPVxYhdu3bJypUrVbSLtDKi3f3798uRI0dUJIwGLL3nLSwsVCcZiHbxc0G0O3nyZHXRNkClDpqhfmyNSnNOLzLpeIjVzIEymf4ND0aqBEKC1O6bb76phAQgSrvmmmuUkKIZqDeD1DUuF1xwgWH6a82aNaqWjGh369atKtqtrq5WkS/qvT3p055oNzc3V/0cUcdFtDt69GhPtIsmK81tKjZ7QXPQvH/tEr1hw8hRWVw43gtpZ6NSZLCm2jdB6hNC+sYbb0hNjbtOV1JSIldccYUS0rFjxyb7EC0DztAhhLgYsW/fPk8n89dff+2JdvGzxSjR8uXLAx6D2i6iXWQCtGgX87qIdFHf7Veaqxp+ojNziIwem8LI079l5Xa54ppyCmovpZ2NSpGBs2XaFPYNMKZy5513yuuvv646aEFxcbFcfvnlSkiRviTRgdGhc889V12Mol3UqLVoFyc1SC0j2oUAQ4jRTe1NWlqG9Cs+VvJyKyUvb6D7Y26l5Oe6/5+ZWWRat26PqEae/p08JZ+C2ovpZPo3MlhT7d2gAxY10ldffVU15wB0vmKMBV27SEeSxES7iD5xMQICi2gXqeZ1a7fKls31cqTGKc0t+6Wmdq366HT2LPtOT8+VvG6BdV8GSr4SYPf/c3MGSFpamGMQHu/fyJk2ozCKR5FUoYPdv5HBSLX3gcYbGDK8/PLL6o0aIM2IDllEpJMmTUr2IRIdMOIzf/58ycueKZvWVMsxsDge6duh29ZWI00t+6RZXfZLc7P7/0dq18vuve9KW7u7Ju7GJjnZZW7hzfONcrXoNyuzpDvaDa9R6fgT8+SL5T1G/ZxH7TuRahb2EVsES8+pUlR7B2ic0YQUc5qgoKBALrzwQuVsBJs/Yn0wm2rklgTBy8kpU5ey0om69+nqapXm1gMesVXC2/2xtnajEmSn0711BKSlZSuBtdvc5ve79y5RX6cn+kW0634zXXhTuZwyu1CZ+G/Z1Cqjuk38Se+mnY1KkcH0b+qC5hiY1r/44ouqKUYzTDj//PNVajdYYw2xJphNjYX09BwpKhiuLka1U0SzbqHd1y2++6X6kHtH7P7qZbJ73xKfx2Rn95fszDx575MuzzIEdIQfrDlWWtunqKa2UJ3MJPXTv1mMVMMDrf+cU00dkM5F1+4LL7ygRBXAqAANMrfddlvQeUxCkOrNyS5Vl/79xnt+INt3viEfLfuRnHjcr2X4kPnS3HKgR3hxaftcao5s9jRb+QNjAJzQoZNZW4YAsYVDFU7uMFpEUpOu7iXlFNUwwRmmljMn1gSduohIn3vuOZXm1YT0rLPOUkJqZONHUg/Mpvp79ycCl1dNFenewoKh6qLxq7uqPKlenISjexlNVRBZbRkCFiHgRA+zux9++GGgmCdp9R+JDTYqRQhrqtYEJgxYo/bMM8+o0QsAM4F58+bJz372Mzn11OQtxibxA1toTpqVmNlUH0JsqWlvd/q8Z8C2ERcj4A+NiBae0TDH2LZtW1JX/5HoYaNShDD9ax1gC/jHP/5Rnn76aXXmD3B2f8YZZyghnT17drIPkSSgUSnhguoVqRqZP2RlRVYzRbc5TvyMTv6SsfqPRAcj1ShE1d9ijSRWSO+++2556qmnlAkAwBvHaaedJrfeeqsSVNJ3iLVRKVpcTkfYkaoZpNLqv75OV3dN1Up+1pY+dWKkmniQGrvnnnvkySefVGkxrQkAkehPfvITNatISCLRvH/1HJXQ2IsF6YnGSqv/+jKdnFONDI7UJAacdf/P//yPPP744+qMG+CPF+mxH/3oR4b2dqRvkaxGpR7zB1uAoF5zQ7mq9VqNpK3+q6qSvpj+zbTQyYb1Xo1eMFKNHy0tLfKnP/1J/vnPf6rNJkiz44V58skny49//GM577zz4vjVSSoC8br2xnJ59MFqzTkwoftUbV5bao6fmisLrrWmoFpl9Z93tIsOZi3atVKqNFY4UhMhrKmaL6R//vOf5dFHH1VnwhBSzPDNmDFDfvjDH6r1YhyUJ8GAa9H4ibny+bJGeeZx9waheOMJjL3Sv8eMz01pQU3E6j/8H9ctWrRIN9rF5ifvaHf8+PEq2sXXSxU6aagfGSjss1EpNpBi+stf/iKPPPKIOtPFzxM/V5wd/+AHP1CpKQopiQSI2QnTCuTZJ2oSkwrWIlWv9O+kKfkJ+MJ9Y/XfsmXLDFf/eS+611b/4YIROivQxUalyMCbPUU1cnD2+te//lUeeugh2bhxo0dITzzxRPn+97+vOhoppMSUVPAD1fEXVm1OFaJqE1l4Y2qnfVNp9R/Gh/AesnjxYt1F94h20bWM2rF3tDt06NCEvMcwUo0QCAFtCsMDfxh///vf5cEHH1R/GBBSvPAxmP7d735XrrzySgopiUsq+IP36+W1F2vj3v075/RiufmWYRTUJK/+Q7QLsUUEjGgXHz///HPdaLegoMBn0T3sIbVF9+igNktUrTQDbJ0j0cFKPygrgtTHfffdJ/fff78SUpyAQEjxgv33f/93ueaaayikJK4gYjx1dlGcRdUdqfYvy6SgJnn1n9FIHd6LMK+7cuVKFfWinosmK5hl4CNKT++9957PYxDJatEu7CER3Y4ePVpFuwgGIMKhol2Hwz3DbCUsrVqsqeq/eB944AH5xz/+oVruNSHFHtLvfOc7ct1111FIScKFFavXjNbCmdX926/UOmMTJPC9GmM9EyZMMPzRwBgDootoFyllLdrF9ZjfRSSsF0Vr0e6gQYNkxIgRKtrF+x2E14re8JYWVXb/9gjpww8/rKJSvCAhpDiDwwv45ptvluuvv55RPbFEKvjvf9kvWza1R/08o8dkyWVXlcnuXR3y2IPV4nSiluqOVIuK3XtVSWrSv39/5cJm5MSG9zWIrRbtorarRbvoaMbn77//fsDjrNYfYmlR7cuRKl5g6NhFnRR2aEhz4MWDzRn/9m//JjfddBOFlFguYv3O9wfKj//dbWkZDRBUbJzBBSJ98ECHvPRyiXz+pfXePIm52O125R6FixGIan/605+q9ZIwrQE33nijpX4VlhfVviakcDVC5y5cVjQhxYsMLxyIqZWcQwgxSgUH6wq++rr+aiTmxWdrfAz6Z5xc4Fnhpj0XLjnv2HQdlUjfYdeuXcrd7fXXX1cpXyzzWLhwobJUtdo+XEurFowJenukCiGFYf29996rWtqR6sWbB2bCbrjhBrnlllsopCQlU8ErljXK0zoGEQMHuRuObrylQubMLZItm1pl1JgcH0H1RnsPYKTa93jjjTfkF7/4hUoHA2z/wecIMKz6erB8TbW3Cumzzz6rTBlQnNeEFBsrUB9F525vshIjfQ+I5tRpBfKMn0GEvwG+luoNp8PTqm+ixPzxwN/85jeqh6S2tlb93mGf+v/+3/8LOvZjFSwfqfYmIUUdADaB2FyBFAaEFAPTSGPAlIFCSnqjQYTWcBStAb42q05R7d1s2bJFubzBaAInUphjRaYOe5zNmGlNFJYW1VSvqeLN4NVXX1VnWMuXL1dnYBBSLC++9tpr1QvIKnZfhMQzFYyGI0So0Tghaelf1lR7J88++6z86le/UuvxAOZT8fnVV18tqUhKRKpIj6aSwL722mtqAww2SGiribAp4qqrrlIbYFLprIuQWNEajqJFi1RT6T2AhF7u8R//8R9qVBA7nFHqO/3001UmD3OoqYylX6XaHxGEyep/UG+99ZbqRPv000+lvd09p4dBZQjprbfeSiElJErYqNR7WLduncrQffDBB+pkCQvZ8f5455139pryV0pEqti0YsU06TvvvCN33323LF26VB0jGD58uFxxxRVqlgpbHgghsaFFqkz/pi4PPfSQ/O53v1MuSgBNmRBSowXuqUxKiKqWQrUCS5YsUYXzjz/+2COk8KxcsGCB/OxnP5Pi4uJkHyIhvQpGqqlJQ0ODek984oknpLm5WWUbzz77bDX1gOCjt2JpUdWMDrR0arKANdZ///d/y0cffSStra3qOizyveyyy+TnP/+55YaPCelNUFRTixUrViijBpTC8LsrLS1VvSS33357n5i5t7SoanVULSJMJBDQP/zhD/Lhhx+qoro2eIxdpBBS+FgSQuIPR2pS43f0f//3fyqLh8XnAN7keA+dN2+e9CVSIv2bqEgVtdH/+q//UkV0pCsANiPAveO2225T648IIYmFkap1gRfvT37yE3nuuedU8JOZmanqpBgjxA7VvoilRVVLFcSzpor5UQgpaqWaQXNlZaVyNoIdFrbaE0KSBxuVrAcyeRBTbJTBSQ/2of76179WNdS+btKREqJqdqQKa8Df//73amluY6Pb0BviiWFjzE5BVAkh1oCRqnVObtBbgllS7D9FN/aUKVPkf/7nf5SNIEmhmqoZogqzegjpu+++q7rSAM6uMP4CIe2rqQpCrA5FNblgkfgPf/hD5Q6HrCHmSRGAQEzZW5KikWq0292/+uorueuuu5SXZH19vboOdVE0G0FIMQpDCLE2bFRKDm+//bbqJcH7qNZfgiZNLPzo6ynelBfVSCJVOHZgyHjRokVSV1enrisrK1M1Ughpb56PIqQ3wkg1cSASRSDyt7/9TWpqalSKd8aMGarxaOrUqQk8ktTF0qKalZUVVqPSxo0b1QsBZ1ZYFQQwGwXTesxGwaCZEJKacPVb/IHTETZlIRiB13peXp6aesBIDA1t+khNddOmTSoiffPNN+XIkSPqOpgwINf/y1/+Uq1UI4T0nki1t+5XTiYvvviiCjy+/vpr9TkyecjoIbNHenGkijMn8M033yi/SGyDR2oClJSUeJqNUn27ASEkEKZ/zQXzpHfccYc88MADqtcE9dHZs2errt5jjz2WL8G+UFO9//771SYDDBoDpCMuv/xyJaTjxo1L8lESQuIJG5XMAWUybIiB7SpS6lj4ga5eZPysuLAkVbG0qGJeFIVyeEgC5PkRjWI2avLkyeqFgT84dqIR0nthpBob//znP+W3v/2tyvSB0aNHq88xBUH6mKgef/zx8p//+Z/yyiuvqAYkRKqYN4V5g3+aGGnggQMHqh2m48ePV51qJ510EovshKQ4FNXIgTscxl8gqPg/6tHz58+X//3f/5VRo0bF4bdENGwu7RWbQlRXVyufXojr+vXrZdu2bcrhA/UB/05hvJjy8/PVWA3mUtHAhCgXbeL4P6NcQqwN5iIx4nHo0CGaDYQAQQc2xHzyyScqi4fmTXTx/uY3v+kTG2KsQEqKajDQ1LRq1SqVMl6zZo1s2bJFdu/eraJcbJvx/3bhDoIoF6nmkSNHqij3hBNOkOnTpysxJoQkl1tuuUX+/ve/q+ZErlkMBOL5j3/8Q3mY470OoOEIn2N/KUksvU5UQ7Fnzx4luDCCRpS7Y8cOFeXCutDfuQlRbkFBgXJhQpSLeu5xxx2n0sqYfWWUS0j8+c53viP33XefKgFxZrIHjBLC1P7ZZ59Ve56x1eucc85RRg3Y90ySQ58T1WAgdYwFu8uWLVPWXIhyIcI4Q8aL1v9HlZOTo86cYd+lRbnTpk1TkS676Qgxh5tvvllFYijvoGO1r4PSF8T0888/V+9JOOn/3ve+pywFtdl+kjz4G/ACNQfUWnHRY+fOnapWgboF2tPhQoL6Lj7HC9znB5uert4A8IIfNmyYHHPMMSrKnTlzJj2HCYkAjtS4fwaIQGFiv3//fvVzwfsJtsbMmTOHrycLwUjVJFCvhbDiglru1q1bZe/evSpFgyjX54dus6koF1aK2I6DbrwJEybIiSeeqKJcNhQQ0sONN94oDz74oDQ3N/e5DBBKU2g8evnll5WzHCYdLrroIiWwOGEn1oOimqCzTES1WpQLSzBEvQcPHlT7XDVvUw3URhDlYserFuVivAhRLne9kr7GDTfcIA899JA6OUVjYV8AKyoxEoOmS4C/+5/+9KfKn5e9HNaGomoBMEf22WefqSgXW3ZQy8UOQzRmwFLMP8rF2Tr2GCLKxSD3xIkTVS0XphisqZDexnXXXSePPPKIitR6cxYHkwvY+fzXv/5VjQ/hbx3Zqz/96U9qGoGkBhTVFIhysTwAHcuIcvH/Xbt2qVouxNg/ysWbTlFRkYpyYYSBKBdGGKgTM11EUhFsm3rsscdUd35vPGnE3zPsA7EcBN8jSkMLFixQ9VKOEKUeFNUUBztjIbjoWkaUCysyNDIgyvXf7oO0EaJcGGEMHjxYRbkwwkCUi5pub3zDIqnPNddco5yBcALZm1Kfr776qtqotWHDBvU5xvZ+8YtfqBpyb/o++xoU1V4e5UJokVpevXq1quXirBhGGIhyta5KDTRBIMqF3SPmcLGsQKvl8oyZJAusc3z88ccDRtpSEZRzfv3rX6sNMTjxhXjOmjVL2QdOmjQp2YdHTICi2oeBuGpRrmb3iCgX0a+/3SP++DW7RwyWa3aPMMJAipln1iReXHnllfLkk0+mtKiiTwJNRmhAQsSNvyWkteF6ROe23gVFlRg2TSC6hREGRoRQy9XsHjHaoGf3CLcbdCkiyoVNmlbL5cA+iQXsS37qqadSUlSffvppFZlCVAFMYvA5ThRI74SiSqKen9OWGqAmhJEhXIcoV8/uEWfjmt3j0UcfrVJdSCtjRpdRLgkGmnYgTqkiqphZR20UHcsYmcPr/7TTTlOzpbA6Jb0biioxHaSOMV+HWq73UgPYPRotNUDNVltqgKYpmGBgnICpMXL55ZfLM888Y3lRhbUpln5/+OGHql8BmRs0HWF3aV+ZryUUVZIE0CwFwfVeagAjDHi7Iu3sDTqStaUGMMLAmb62ug9pZtL7ueyyy5RpvBVFFeL58MMPy1133aUMXQBeo7/73e/kggsuSPbhkSTASJVYrjsSKWWI7tq1az1LDTS7R+83VgzHa1EujDC0KFdbasDooHfw7W9/W55//nlLiSrKHHA4Qq0X2Rec/J111lkqxTt8+PBkHx5JIhRVklKgdotarrbUQItyUbvSi3LRJFVRUaGiXIwIwYQcUS5XY6UOl1xyibzwwguWENXly5fLj3/8Y3XSh+OBsxmWqN9+++2c8yYKiirpNSBiQLcy7B5R38JSA9g9Gi01gBEGolwYYWhLDWAHB7vH3myHl2pcfPHF8uKLLyZNVJHivffee+Xuu+9WSzIArEH/+Mc/yhlnnJGUYyLWhaJK+gR4Y4TbFJYaoIkKRhiIcuGxarTUQLN71JYaYEQIc7lcapBYsJXlpZdeSriowgoUUSkEHWUJnGihTgovXr4GiBEUVUJEpKGhQaX0NLtHLco1WmqQl5enVvchyoURhrbUAE1UtHs0lwsvvFCtPkuUqH7wwQdy6623qhIDviZOrCCuWAzO8S8SCooqIWFEuajfQnT1lhr42z1qSw1g96gtNUDjFGq5qMGRyEB0+Morr8RVVFGPR3oXdoH4veLECZkJLAXHPDUh4UJRJcSETlCkldG17L3UANfrLTVAlKstNYARBqJc1HJR02UkFMh5550nr732WlxEFZ3lmC3F88O0BB3jl156qdxzzz08ASJRQVElJI4gikXTFKJc1HI3b96sjDBQy4Xdo95SA5gGaEsNYPeoLTXA9X2Rc845R9544w1TRfWtt96S2267TY1tAZzg4PObb76ZJzYkJiiqhCQRpBq1BfWwe9SWGsAIw2ipAYwwtKUGGBFClAvDgd4a5Z599tlq12isooqf53/+53/KfffdpzrCkeLFyQoaj3DiQogZUFQJsSio8yG69V9qALtHo6UGJSUlHrtH76UGqWz3CFOFt99+OyCqDxek47EE/J133lE/U6Tfr7rqKjUSw2UPxGwoqoSkKOhO1lb3IcrFiBCiXHQy6y010OwetaUG6FRGpIY0s5Wj3Pnz58uiRYsiFlUYRsCUAScjAE1jv/rVr9TSc0LiBUWVkF4IUp0QWzgAoaarLTVA2lNvqUFOTo7uUgOMCcEkI5nMmzdPRZnhiCq+NwjpQw89pE4ucLIwe/Zs1dULRy1C4g1FlZA+CMzfEeViRMh7qQGEyGipgWb3iPotarmzZs1SUW+8OfPMM2Xx4sVBRRXfA1K8//rXv9T9MNJ0/fXXy5133pn0kwLSt6CoEkJ8gNkFGqdQy9XsHr2XGvi8gdhsKsqFEcagQYOU3SNGhLC2D80/Ziw1gBXgu+++qyuqjz32mFqthgYvgOYtfA4TfkKSAUWVEBJx4w+WGqCJCrVcRL3oYjZaaoCo0XupAbyV0TyFzULhMHfuXHnvvfc8oopo+uc//7k8/vjjqmELXwPC++c//5nrAEnSoagSQkwDDlOo4+otNdCze0RqVrN71KJcjAihiUpbanD66afLkiVL1HP+6Ec/UoKOmjBqwJgr/fWvf80FCMQyUFQJIQkBkSYapuA+tXr1arXUQItyIcZ6Sw1geIHmI0SkGuPHj5c//OEPqiuYEKtBUSWEWALYOmpLDeB0hDqpFuVCXNEYhS7ecNPGhCQDiiohhBBiEtad+CaEEEJSDIoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEIIRZUQQgixFoxUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCEmQVElhBBCTIKiSgghhJgERZUQQggxCYoqIYQQYhIUVUIIIcQkKKqEEEKISVBUCSGEEJOgqBJCCCFiDv8fUk8LhcRwTJIAAAAASUVORK5CYII=", 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0auA9FSMuV155pbL2RMeu1Whn+jc0AcUaMwgoIlAIKNYSeQoorPsgnrNmzVIOGzCWJyQUcMbdlUX1+FIdO8FIwDy9M/oaakDBskDqN6SRnrt3KfFMH5AZ1PHJ6qBbFtm9Z555Rj766CNldq/9PcClaP78+UpAEY0mw55lByNVY+DjCAH13AcKIKAYY/EUUCsUpglJWi/fIPS7d4iaQ0UTUNQ11Bh1+BYsLDFnpCeU44N4Gp0YWHxWFqKDfcv/+te/VJACSz9P/1ytNIa6aDKuinRQVPW57777ZP369bq3IUrFGRXy+//85z+VByRW/kBsUTNFsVxbhIuuXbRxWzFNQaxDV4xUzRQw1E8hEtEIRUxGeTRsIkULols0EXZK2hmfZikzRGbVqlWquQhTD57+uViZBv9c1EMx5tIVtl21t7erBtREk/jqsg/YRq9ZWCFSxdiLto9P21SPGio6z9CMhG0F2FBv9OaIFJ+2f0/bvYcXEEQYHpJo/YYIDxkyRHX/chdp96IriaqXl69JeNYQrVRDVZgy0mOC93EM/I+j9c/F7D18xz39c6dMmSLnn39+QP/cZMbhcFgiiLKcqEIAAURw3LhxIS/GRZEdLyLYB8KDd9++fUpwUSdAhAtDB/jyQqBhkeW5g88XdLJhcwxeiIiGsaMP0TBeiFo0DBEeNmyYclJiCjp56SqiavqIikekGpOl51FQeEEPyR6Xa6mRHjP9j8MBwQcMF9555x313ocAw9M/d8GCBXLLLbd0C8c3B9O/+mhnGs3NzWF/ne8OvmC/AIgtBBYvRlhqIRqGCGvRMLx90QGHtAmK+kZvwNo2+qysLCXCiIZLSkpUxxwM9CHCeFFDhEeMGJEURf/uQlcQ1Zh0+ZqQzkR90fSmJERd43KjH+kxU+xN9D8OBubtYf2H2ihGBz39c/Hed/bZZ0fln5vsf8t2C/TYWC5S1eadfJuUzEabXcUFzU+hANstRMF4YSMqhggjGoYIa9EwBqQhxF9//XVAYwGIMKLhnJwc1RSAaBgiDE9hRMMQYdSEIcKIkK3wYumKdAVRrTary9cTk9ySTMeMkR6zT0Bi4OakgYwbNrlgF7Oefy7Ma1ATxS7m7o7D4WBNNVCkqqUxrAQizLFjx6pLqL9kCC/qwhBjRMPouINHJoQXIoxoGMIMkdZWH+kBUYUIa9Ewar+IhjGEDRHu16+fEmE0aOGPzQq1hWQg2UUVUVfNm9F3+XrS8/v9JWtEtmVSq2YKfawi+vICka1Hq2VkTpYMy86M2j8XVqobN26Mu39uMuOgqJqb/rUiEEKkfnHBNptQgMgiJQ0RRv0XqWetQauqqko1aEGM8ceH1E8oDVqIhn0btCDCiIYhwEgVdYXuv+42pxqLqKvk6p6SN6Mg4q+PxSxq0QU9JCvKGqqZEb2Wf1K5I7vIpotz5dnjh923zy7Ik9v79wzpsVBmeuyxx+TVV19V9VFtH7Pmn4sxlxtuuCFu/rnJjNPpZKTa1UU1EpAKxpZ7XEIB0S0iYAgxol2IMKJhrUELHdL4Q0UaCWnrQA1aOBvWGrRwHNq4EkRYqw2jUxopaQiyFazJoiVZRdXdBBQFaf3TpfVgi0slbCIlV/aUovNKIz4emE2YMRvrSfbUXCm5opc5Eb1Zx2YTeeD6dDklI0uye2fIs62de5XBqpo6mV9SoBuxav65MLSBf662EUvzz4Vr0TXXXKNqoxTR8GCjkgHaG3V3FdVIni9Em+Gso4PYQmDRoIVoGCKMaBgpaW1cCfdB6jrYuBJOgpCSRjSspaS1Bi1EwxBhrUELI01WI1lF1YwmoJ7f7meesUMs0r1w9rmgzHIRvd0pktHqlFf7Nou06r9P/XHfIfnrqMGqBvr000/LCy+8oOufCxObq6++2jL+ucmMk5Fq8tVUuwqIPnHBjthQwBsDBBgXRMOIeiHCWjQMEUY0jDcMNFMEatDSxpV8G7TwBgMR1saVIMIQ5lifrSerqJphMgBBjdbYIVZjM2aM9MSqjuqwiVQV678u21taZP/yZbJq2Rvy7KaNcqzS3z/3iiuukMsuu4x9DyZDUTUAb7hAaxUniQd1WWz1CXWzD0QVNV8tJa2NK2kNWkhJo3aMz3FboBMoiKoWDWvjSjghgAijUxoijAYOROqIiHGs3QbNkzdCzOhYVR2+sTovMcGhyKw6aodnvhLUJQtTpTbf5n6tH3zvXdn75hKpWv+FNFXi+7mekNzCQjnzzDOVgCaLf24y42SkGjhSpagmLxBCRJm4YFtQKKDWhC5pXDzHlSDCaNDSOqURHW/atMkwGkZaDdGwb4OWNq6EY0JKGiKMx+i26V8TBCuWfr44vsNXFIpktcswCS78OxqaZGt9o7v7VhvpMaOOilfa23NTpLyPXY4Wimzb8KHs/dliObruM2msOIJ3c3W/tNw8KZ12svSbd6YMWnSRfG/EEJlTHHnTFwkP/D1bIYWe+CMwcFRiTbV7gXorZu1CnbfDHxCiXM9xJYgwol/PBi2IM9LWRuNK8EbFHyJO5nwdtDQ/aTRlefpJW+EPVwliuJGqvUMhohxNMatRyoj138yT5cUtUpvfJLLrQNBu2r/vP6KagzSu3JoqY146YVoE/cfBX8jSNxfL0c/WSv3hQ3jxqetTs3Okx4RJ0nfuGTL4wkslo7DQ6+vq26NLXZPwYKQaJP0baGaTEETDEDhcQgU1XzRoaeNKv/jFL1QUO3r0aHeDFj6G4icNEQ7kJ43j0vykY7HxA4KIFWRYN+ae8QhCz+/2k5T81KhHU2LpliRnFcpLg2Bw4EqvBuumRYTqKah9Djpk9IsnojqEL6s2yEu7XpWPKtbIwfpyaXe6OuZTMrOk+KQx0uf0uTL4oksluyzw2AyiZhJfrHDCm/gj8IE1VRIrIHwYmtcG5++++24lqFiDFchPGkIMEUaDFjqlNQctRMNIW0OIYRkXqp+01qCljSuhQQtCrNWF8f9QGrSwEBt7PQ/ctcv7Br0I1i6SNSLHNNefaI329UC9cvfMTJEWfze1R8sr5H+GDfC7fvXxznGWKV+0yaI321zzo2Gw7fg2eW7Xy7L68Mey78R+aXO6Tugz7OkysmC4FMyfI8U3XiG5ffuG/JiIrqMxgSDhw0jVAIoqiSeBaqqR+ElDcJGS9h1XgghrftK4DwQ6mJ+01qCFaNhzXAkpaQgv0tL9bH0lq80u2akeTTBOkcJzS+T4W1WmpXvN7spVj4F/bK4RFa0B6KjU6953V1OLiko1ocL/IbS4HuTXOkMW1N11e+WFXS/LqkMfyp66PdLicDXKpdnTZHD+IDmt56lyxdBLZEj+YHVc9383w92YFAq39illLTUB4G+JkWqAmioblUiyOSpBCBF54hKOn7Qmwpp5hzaupDVoaX7SgRq0QKotVTJS0iUrNUuKPiuWosIiKcktkT4D+0j/D/rLoP2D3H7SSFVHM64UbZOTs6P5Z8PYVCk55lAjKkq4Wow7wVcfr1Oi6ltDBTM+0RdUfJ/y+kPyws6X5P1DH8iOul3S3N7sfr4G5PaTU3vOkEuHXCQnFY1S13ech/h1+obC+JwsCmoCoajqwJoqiSeJ7v5FOhgrunAJx09aW+qABq3dn+6U/V/tl+qmaqlprZXG1EYpP1wuu/cG9pOGg5bnuBJS0p5+0lptWGvQMtNPGjKFbloIVm1+iIulnU6/GqpWR525pjP1XtlUJS/ufFneLf+3bK/ZKY3tja6f15YifbP7yIyep8glQy6UiSX+zzleDU9eliZt6a5Z1HAEFbQnaTd5V8DJkRp9mP4l8f5DTFY/ad9uXCNnJES7WjQMEUY07DmuFJGfdEaOZDVlSmFGgZRklEhpZon0yu4tfbN7yYC8ATIkb5Dkpwdo0LKL9OqfLbvF35FoRGambNPZUjWzKF8+rPYWVNRRT3+lUh7bvVjeOfiebDm+TerbXClku9ikd3ZvmVZ6hlw0+AI5pXRq0OgcEgpB3T0oRKH3YVNDk1eamiQm05lILNeopA3v01GJxINkE1UjAjkjoR4brp80omBtZhhCjJQ0RBcirMw7amqluqFa9p7Y5+6O1QPRYbo9TaWkc1JzlNAWZxRK//EDZODzg6U5p0CO9SiTvAGDJLtvXyVG1/bpIS9VVMn6E64IU2v8WV5Vo6LU1oYTsue1xXJo6TJ5a8PX8tNWl9DaxCZlWaUyp89suWDguTK712lBRVQzdQjFLSlUMC9LUU0MyL4kGsuJKmuqJF4k+5aaWNalkPLFJaj5Q8dIz7Gmatldt0cO1B+QA/XlcrjhiFQ2VUpV8zGpaamRutYTUu04LodPHJG2423iXP6RyHKdB7XZ5HdpaZKWmSn5ubmSnV8gZSUlUlNWKusOHJSa7dukpcOEHpRm9pDpZSfLeQMXyJl950mqPfS3NPzmP5qaIjPWtXs1S4Wb8vUlJ4XbZBIFI9UAkSrnVAmxNp4jPcWZReoypXSS4f373D1Isse4lipsPHZcVm3aLPbyA3Jg3z55ceMmaTpaIU1VVdJ8vFpa6+qkpr5ejlcdk/3btroatGw2ySgskt6z58iAs8+Vk2acLT9/wOkVafpGnoHAfVefmqouXs1SUULTh/ijNfBRVHVg+pfEE0aqcRqt8bBFdHfvFpWqy5App8ikBYsMv/TeIf3k7Fmnya4v18sFq9a6r0+tdYpTmr1ENBxJRDwJMUX9NORmqY7xnUAifDRABzOJDVoQxvSvDhRVEk8oqjGwS7S7drNWPXvEb0525TFXXdQTbdY0UI2yVSdND2GLJtEarH6qJ57KYOKtNq908bpJ3innZdW1kpmSIlf0Koni6Eg4YJMWMLNDvcvUVLU5IzYqkXjUVInJdokdAloxPVt2jrTJ0BN2GTQwT91Pb75UI99uk1qHfn37RHu7nGjzb4aC2EHYIHCh4Oy4hDKD6unOhB9ryTmpsn1oiltQAT7ic1zv+ziLK6slO8Uu55cWhXZwxBRRZaQagECWb4SYBSNVc2qr2RPy3CM9DzYck1W7DrhuTBeZ3dAqJ9kyDQUVjMnNlo9r9d2UNtc3ujbB+JwEQcggjHpOSprO+tZbH7ghXS0YD1Q/9XVnwkd8/trZTj8Bx+f997fLpjH+8ckzR6pkZmGelKRZLnbpcrR0rAq1QqRq2TY1RqokHlBUzQGRKJqQ9mS1+4knPn+gvHNZtx7n9CiSiQYG9EVKlPTDUaReIZS+lV29hiW82UFQXTVU4ywFRNL3jRGfn7+sXfcoLn+1TUW2emxr6BwLIrFD22pmhUYly4oqu39JrOFIjfn8ff/hsL+mT3qamuu8c3BfmZjrvcgb86kQXKdKDesLIYRSTwR9hTbUGVTMu+qBr9S7RUWyb7WpCNcPp8uYo2HTCfWRxDZSpU1hABipknjASNU84CRU3hr+ysYJHhHqnYP6+C0cRy0W6mQUW+rVVvH5299IkbP+Hf4M6r7+9rBGcwC+x5TPW2Xd5DT398C/Az9tlj2P7nWHzqg/I11OYhOpWiH9m2rVCII1VRIPKKrm8eFx45ppIGA/6AmE1HMbDdLHzgAq566t6nTl+hn2hwDut/gc/Vqtp+G+L/M+dMg3PmxWiwI+OjVNbs0qloZHyzsz105Ri91RfzZzYxARt6gyUg0gqkz/kljD9K/Jz2cE7lTB9o6uru7YlxrksSGg6ML1FdCwDPs7mpRGbGuT/BMiL5+bKpe80ebX7PT5OJHJG/SFFded/V67XJBbIIWDU+SI72E7RY69UiFlN4e+m5WEntlkpBoAiiohyQUizqWaCJq0d/RLdP4qgidkwxFQwxlUj+gU3/Fgb5v0PdSZesbHqRtEaidmSu76Jl1hxX2aXzsmJ2bqp8JrV1RL8UVljFZNhOnfIDBSJST5QMQ5KTdLvvAwwg8UoQYTVBhFlGvuRGqkJrrjm5ybJZ+faNQ1cFAzqD7pXnw7T0H1JG99k/zjhnQpqnHKFa+06t6nfrXxCQbGj5gC7pqNSpbs/mVNlcQL1lTNAw1FnoLaJ8B85vySTkFF3fTNo9Xqo+djeY7hRPt7gohfVFaiItQL3vQ2cMDnE77Sr5/6mkV5Xn/pkhbZ38/V1BQu9gxLvvUmLS0WmlNNvKzrwEiVxOt1RlE1Bz37wUCdwNp6NF+XJYjfSTk6RhFRaKpnmnl2ZarYfHa4QiDPXGlsNmMUIPc4JjLtk5aIIpPWoy2SOcx7fIhEDudUg4AdiOz+JfGAoho9vlFlqOvRtM7e0IwiOmuqIzJdxvyh4JtmPjczT/d+kQgjjmbyF5GpfXtdu+7sKmdaI4ONSkFgpEriBUXV/Ag11PVob1RWh/4FHTVVGEX8elh/+e2ecll/oiGCRqjICrNGbVL53kFvyFQ+esh9ONrsqtpP+2C5+z5lt3CmNdz0rxUclSyb/tX241mZmg9WSv26TyVnyjQpmDUn0YdDwoTp39DxNWQAgQzyg9ErI02ePFIV8v21PiXNKMLXJOKlI1Wy3t0pbNwIlZIX+nhNKFIc9UqGjtlVe0Gql6ACfI59tUwThx6pZmSEnsXoVqKaDOnfPT/9nrRVVKj/N6xfJ9WvvyKD/u/PiT4sQkxHr+6JRqNIBbVPeqq0GGykCRYrehpFeJpEwOJQT/h9saUHTvSG66RkCk6Rw/ft070JC+AZsSZXo5IlW9Cs3v2LCFUTVA18jutJ8sBINThGdc83Ko8FTb1OzNU3yL+9Xy853PEmGDJOp6TYbQGNInDbOaVFhvc58vf9bvEyknQrLgNEJEvf4MBQVJM8UkXKV4+jjz0oTbt2xP14SPetqca6sQWRnx7HW9oDRqJIvd45qK+aW9VzUGoNs7qTY7dLahT7b5t2NEjdqpqgozKxJn2I8UmBIU6Rxm2B68fdnbY2V6c5079JKqqooSLl64fDIQd+fZfkzZwtPW++PRGHRrqBqEJAYR7QvKtRqp45ElOzdnTp6jE6N0u2NOl36Uzw2DTzs0H6adnJ+TnyShiNSpk2m9R6iGprW5U0txyWjPRekpZaovv8YLcrDBaqWtuk8qly8a2masJqi2MauGVX5yyuLoZqn3yv03jCRqUQRNXKjUpoSjr65KP4TereXrd6lRTMmy+ZQ4bF/dhIeOnfZEN1iD7kYdLu2fDysPlm7ejS1SM3JUWld9fruCfNLDQ2yNdYXtUZNQYD0e0XHr+qY7XvSXnFQ27p61N2sxTnz9V9fmpn5sjTI1rl1i0tYTcgxUpY8+cVSe171bo6WXBuidS87tPAZRPJGpETgyPpOrRZKFK1ZE3V6pEqKL3mxoC3l//x/rgdC+keIALTFVQNh8v+zkwQXRpdj/Sub900mEF+JGM4aIrSMgqIUDsFFTilvOJhdb3e85O/ul6+/VhLROIYq1OuttpWKf12H93bIKgZw7M6v3lHBoKWhqFFqlYQVct2/1o5UnVHq488YHi7o/a4alziqI21Sab0rxLMQIdrF5XyNBMIJITSUwQn5KTKV8d3S3t7sRJWz/TuwPR6OdGwSTctG+kYzurjte7MAlK+/k+CQ11vP5yh+/xYLXJo+PSEukA8m3c0+h1z8/ZG6fWTAWLPtLtT2CS0SNUK3b8U1QiAWB5f/lbQ+0F0bQ6H5J/uSk0Ra5Fs3b8B/WLtImU3mR/RQDAHZqbLrTmlKhW8rma/fFmfJV/WpyEPKz1TKuW7A4eqrlukZbeW66dlozGKwK9I+z3Z7fonDQ5Hk2RGeEKRkDGaDvHMmpwjjZ/X+91W9fQhGfiHkQk4quSklXOqgUlJSXE/SVaeTw2FiscelOxxEyS12P+snSSeZBFVd63QgH6/HmK6SYBvVIkI9esmpHs7c5NH2jPkrl0H5LT8dJnT4p+WzcueoCLWaIwiTivKV78nlymMfnp73+H7pE/ZLVJy1ThX81YY4pnIyrqeoILW8lapeu6wlFzRK+7HlIy0cE41OdO/evOpodC4fVtMjod0j0aloLXUDoP2WM+nflmPE1395+zD2mY56Oit/n/Q0UfWtJ4qBx29VFpW77FCBeM5SCm3KzF1qrSy0TFAxCu+4ZBjp+pH68nx2+6kenEl51NDROvBycyMYGSpu0SqiRRVI/tBo/nUoCTbX3M3IhkiVTWj6Izvi+zD43Vh9sTaZGPrePnMNl02OCa57tPulO2VGTI4K/B+1QXFBXKopVXXy/ebRftl656/S1tbtTiczbKvdqPU5X9HnDX/knybdwfxqt1XSNvKY3K0n12usWAtNRKOvVohZTf1TfRhJE2kmklRNY5UE/VmF8h+0HA+NQjVby2RvJNnmH6spLvUVIMcoxq5MDf1azN4XorsLVLtSNcV1tbUkbKhtYdXevjD2hYpST0RsHI5s9DVMfzb3Qe9/HuRUk6v+bv6WhyOQ1LkrvLe4hS72OQOWZi6WCaluv4eW166TM5cP0Rs0i5OaZd9fW3S/yDumdzUvlstxReWsVkpxEiVS8otFqkGsx9UUWsE3WUte/bIiQjEmMQeq4uqyynJFrhBKQYjFy6PXf9h2B8PGipDM/QcCpySmdFb91g/P7bZ8GcYnpkivWSnGomBf++9Q/rJ1aWZ8os+zXJl/hb393E4bdIm6UpQXd/NJm+1LZJaZ77Igd6St3645xSKDDjolEPQ9y5A4zb9uivpROvBQUCWaJj+DcV+8OnH3aKK+dRAozRGNHy1XnInTon090S6YU3V0OgBWZNT8qRgfknMRi5Qxxxn/6IzlStO9XlRy275r8FT5Dc7t8qW1lL3bVMzyqUw7SScmvo9Vq66Tj9SHdj6muwp/7izW1hEBtc9qG7rWI6maHfizdL7iYDAHnOUSNH6oWLzeWx81qdSugjWfp1agVYLNbZaUlQRwicigkgtxZuEDk1NSkiRCu516/cieuym/XujOzjSrSLVYM1J9Z/WSem15keomv1fe3utLEp/RaY61sj+9oHSP2Wv9LWXy6FKkUOVj8olKSIHbX06b7MdlraMH8oyKfJ5RKdMT/9YWloyZaeM8hEIp/pa7f9oNFIOFjrYcb3NOwqxiVOK7VXSLCdJWheQovRhmdKyoynmqf2uSLuFzIIsKaoJq6m2Bf7FIBVc+dJzET10y/Ztql7L9XCJxcszNs0mOTmu6/SMCixt9NDhnmSmqHrb/wGbElJc9PC9LfX472Wc/SKP6FZkqGyR/e2DZHb6v8XWapMdzpFeka/3YxuXfGzikDRbu/roqqk6ZGHqEtnZPlzWj54mt65pSzoR9QWCmj44Q1r2NrueihjNHndFWhmpBo9UE1FTzZ4wUWr//U7A+zRt3hTx42v1WbosJQaXaLhSi2Dw0DQpLnHK1j3f8TMqSDTKGSnQKhWT3ZP87f9AeCe2GKXpaTssA1NfkYKiq2VjQ5t8WT9KdraPVp3AveSAnJv6kjQ5s+WkvJ6S37Q46ONBkMflDxC77V1JtdXJdzPuVylfRKjgL813iLOvXTaOapexW1BptY65QyS07G6mm1KEkapVyjmpVm1USkSkippnxrDh0rxju/Gdojyu+s8/pagmAJdodAoqsNnR/etvVGAFEJ2gAQkm+Spq0d4vnLGJYPTt/0JfkrakxTNCdcrYuhrZ2OTZJWyTw9Jf3mjrpyLU6U2vhPx471Y75UQ7olORfFut5Ke4bAv3tA+WvFqblBxrl6Xz06U5vUWmfJX8wko3pchtCq2AJUU1UTVVRJGOZnOH6H3JmTwtpo9P9Klv9DfgUG//7peZQxoat0lKSr6hb228wRo3bJ3R1pgBz5VmZtJpqqCz/iYApUUXy/qqj71Svmpm1UtQPbGp+6JWa5RWRoTq+3gtDuxT9a6p9vmip9yxpFnsatxG5O15qbJxdLuM3ewtrNpPlSzCCjcl7H812yGrq4uqjZGqtSLVcO0HI8GWlc0oNUHovr3bvBuV9h/5k/veVkkHQzw9BTRW9TWcROBn9k8BBwKRfrtK0fo/w4Hky6YanIxEVf/xRNolVY3QIFqVmjzJWzJPbB2HCrk9e0WbfHJqg2xbuFdGvjXa72iqCkWKjyeHsNatPk5RTdL0b+KHeiwQqUZqPxguzsYGOfIQhtlJvMnOGuF3nfojNFgIra0TMwM8Dja3mPV4sQInEUP63RPy/XOzJkrl8cWSaas3iHCN/oadkmnzd0/S6J+yx/9rnU5pkzT5c/Md8kTTDfLyodvE5vQfozn5o2wpq4IBhP/3hqCumWSNN95g6B0/SY5IlaIajf1gBGCBedOuHXH7fsQYV6RqdKtrnVi0qM0te74je8rvUR/xeSwIJNyhiLp2n3A40fiF+tjkzNGNVIdJp3mD721oVjICESzqrp5f2/k/u+yXoXKwOFe3VxhHUbh2jN/cqqcphJXIP7tI//qZ+tcTfThSE4R4W02lDxgQkf1gpNSuWS2ZQ4bF7fsRrRHHB5/0ry+NzbskN3tMxE9fQ9MOn+ao2DREeY/CeKeuA91m9PXhoiLLdt+vdcqs9H9LYVu1fOaYEWA+VZ9vpL0rG5ondn5dx5Yajdp8u6qhIuXrL+c2w5+kT0XgnxERop4gx4raZdWuvarbO+0Z82YXMPUbQaRqBTclyzYqxbummpKdK/HFWmfL3QG9PZzK+zfA1xypelYK806NSAA7hUo/AjZLVP1HYSDcDynhBv63eYu63teHixZZ+rovqTnW9HI50ZwnW5xjA8yn+oOxmWCJtNUzUqXX4XaZtMn/mPf0Fxm83/9rgglmPAVVA4JaemsfcdS3S9bIHApqBGAE0yrpX0uKalpafIeds0bEdxlw/vTT4vr9ujtHq1+XI1XPBOn+FdMEUH/mU8Pe0Wkby1EYp1RWL5W8nEk6t3n/TMajNOGh576kcUnG83LQ8YHubUZgDlUzenD9SE7dAPOdeekycVOz100Om8jSM9LltsdakqIpCRx9oFzKbulDQY0Q1lQtlv5FKjZj6PC4fC98H6Z+48fR6tfkSNXTusLh2/3rjy0iAQw089mn7CbTolRYGTp3FKlOWF+qat7qiM5tAUW9sXmnmAXEcnrax7qiGeg2vZGar9vGyqkp7ythFfez6Z1CBrX5Nll8TqoSUoCPSxamSnlfuyybm5JUOSHYUroWKJBk7v5lpNpBSh62csSelPz4fB/iihj1IlSNYH+EPUuuikgAjWY+exQtMm1Mx9NsP9P2PWld9Ka0T13vcQ+HtLYelZKCc5TAar53nqIe7PmJNxDTZS3nySHp537+Rtk2Sj/7flmsIlXf6VMX6yalyvahKVJyzCFVxXYltGA1lpXbbHLWe21qltXyOF0bafJmFCb6SJIy/Wu3SE3VGkdhEKnG06oQzUrxgOYP8SOk7l2nsaCWFp0XUSctRKtnyZV+11dWv2bKWE1d+XapeOigRxnUJqlLFvpErDY1d1tV84YS1Pyc6TKk36+9RF3PECNRwEHpsZbb5JD09zJ9QC222H60Y9mbMRDS3YNS3ILqWXe9/7sZsvjsVMtErZmjA5k6WCPaSjbaLRSpWlpU42k9Vbf6g7h9LxIfOiPG8EZqyooul9Ki86Maj8nKGBqTMZ39R/4u+zc+rITUE5vTLikbR3sIq/cPVlu/RnYduNvrmK3xFqTnoOSJTY45ekiatPhEqr4YSyaEtjHLOj9v05YG5e/rBzfSRCWqjFQDkN6xCLypyWcNUozAAvH2qvgsX6x89l9x+T6k0yWo89zR523VoKbqcLYGGI/x7qQ1ijz1BT26JiUcQ03dKnGUHBOnzTuLg6NKWzpfMu77rqR8MN3gEbyPWc8QIxFsb/NdCSd+4zd2p0NS1M9sJJ62IFtuAo/RxBWniLPFqRqT3C/NGC2b7y60W0hULV1Thajmx6EG2fClZy0qtjgbGpSIc2F5fEC6EyMkiBCbWg7K4cpHvUdqdEQ1P3dyVOMx2no5pICPVD2nW8+MhPrGra7/FNTJifnvSc7bZ6j3ZM/JS5vYJfXteer/7bPW6B5z7Yl1kpHeVwl8ScG5HSni+APLQYzOtBkmzJzSWw7IkpaLpcn5uKRISkDxvSztSWmTdHml9XKfJJxT9vW3G86uxmyMJssm0mjsKuXr7UxBjRyO1ISY/m1pia25fTgr38yk7tM1FNU4AiGra/hSDlc+5nW9nkthfs4pkp05LOLxGF8jBdRmszKGmGLSn5PVOfp1qO8RWSoZMlraZL60+4lE6vK50j5+kxJgX7BkXLsnjs/IgjAtvadU17wtseCLtinyVtuijpEZ40jRVWPtuN0w/euafR2R4toudcTxvqxuP139ftA9PDf1beldUi51F/SUvMVz1YlHPEwfSq/rrUZl9MgakaPr7Uwig41KIUaqzc3NEg8QNdqyXS/yeJDeq0/cvhcxFkbXnGrndT0KL5ABvX8Y8XiMnpECDCTM2noDsc9MH6L+X1RSJSdsTtksqfp2fU67HN14ktTVBMr04PiekaL8s/xuOdG4XtJTexiKbrQRaqegqqMNsHau46Ph6JNThsomtccVtVk0PK1un9MRqTplpG2TzEhdLYNSdkv61DWS+aPNfulepNLbTv8wqjRwSqm/MB79Z7lyR/LVaqR9KaTmp39hGmQFrJGEDpD+jRc9rrwmbt+rYObsuH0vImqlm/6cameo2qvHjdKrxxUhNzv1KFwk0jpbNm9qkGNVbQEE2BwPYY3UlHwllNVVJTJ7/ttKWN8T/3lMCO0rS8+VB++/QzZ8NsV1ZU2e2HcNFDnQ2/VRNTU5pbpWPxqF4Gak9RWzQcq3U1BDq4mq59UvUsVP7ZCdMkbebV+ouod9V8ahexhiq5E5PFfSrtjqrkkrQV30lrSf+b6knBnYOjEQ7UdbpfibPf0Or+6DGul1xwDp+f3+0vP7/WTQ30aqtC/pupFqqpUbleIVqYKUPP8B+phgkbbv7oRha4tHAJSWkhdgPOaqDgOJTv797gFZvmSPy+jHJnL9zWVy6uxeUldTINVVxSqSzCuoNc1BCU1K1bWr5eMPUmT5kjvE6bSLzeZQwmqzO2TF0rNkbkd1EnLxlqRKnYrwbLJ8ySIZ1ZgmucvnqghWS3UqQZn/nkHtFThl3+H7xGz83JI6vlfA/lx1s956Obt3RBtkzdyRqidF4JjYP0/sVcWq6UtLkTec/pQU5t4mTYtLIjKZOvbUEd3jPnzfPhWdUkxjK6rxNg0ywhpH4YP25LS2xs9dJG7NSk6ntBw5LKnFiV+C3V3IMepydXf/2gJ2wqIm6gkixeVLzncLMj4+8XCF1J8okRefheDZlODNX/SanLVwctSpX4zRoOvX9X1dgur6vnZZtfwsuerbD8jTNrtscmZIsTjkmNhdgtpBrtMmucuxe9R1nVY7hMAGbmqKDdiHujB1iU5NNZIO3WAnqQbm/QV14tCpNx+f8g8ZOufP0rYrQ478ab/+iVa/dGk9EF6/B4w60JTEtG/Xj1StcRQWiFTRrBQXbDZJ72me9ysJdbTmFp83YJvbUB9jN4GEzzMFDGHbunGsW9g04FPywjNVSlABbn/ntQtUijgatDEagJSv7/fF5zu3jJRR474USMReSfESVACh9d096tXU9PZcXavDWDIpdZ18I+Xtjrgax6Z3fB5CqjICQe7j/ryzph2Keb8vzdk7JW9GgeTP01+/ljuzMGK3JBI7UbVKTdWSkWoiRFWNuKSli7TGtuM4f958RqkJHq3R0rF7dk2U49WtAa0DPcdj3n17W0eE6jvIog+E9tM1dTJtep4Ul6TqPm6wJib3GE1HcxIiYG9hdcqalfO8aoxak452fIhctWv1QDcsjCPax27W7RaOBWhWeq8dDVLG4zSXpf3LY0TGUzxdPxtSyH1s++Wgc4DXBpypqfrG/qGi/VaLLyqT2hXVfjcWzC6Slj1NUr+2NsJHJmZDUbWgqLYdq4q5oILaFcsls/9AyT/dHA9YEjoQL08Ba2ywSWurcfHMczwGtVJX6jVQ/c6fZ5+skueeqlI119Pn5oe841RvjAY12vmLlqgaqb6wI/p2yimnvydrVno/XmAvIqcyjkhddoaracfLQzg2s6kH2vsGFFRtRAZNRqNtG901VQgpItw+9nJVm0UqGffxFdHh2aXS0BRIUG1SVDBfZ2SosxSAVC1qoRUPl7vPVcpucnXu9v7hAKl47KDUvu0vumhMQh3V9/qsEYHsCUk0oIxjlfRvqpW7f+NZU0WdMy44nVLxxMOSPW4CI1YL4zseg+ajTkEND63mOm5CtuQV1ATdceo7RlOQN9udAh43dZ0MGr5dtm4cIyuXnqPzvewdCtoZm6r0b4Dj86qxLlko7cN3xiRiDW021SnzUt5U0fNzzVfLDmen21KatMp3M+5XQuqJ2tvqE5GmpeZKz5KrOxYGaCcfnbVb7UQGY0Od9/E36Ahk0FB2Q19J65EuVc8c0b5ciW7ulHxDMSaxgZFqiJFqvMwf1PdEndPIDNZsHA42K1kAI0clvfEY/dRr6CAVfORwi9jT9cZ7Au9t7d/zdsnJPEnKjz7gjlhHjt0kK5cu0In2nLLm33O9UsHVYZgbQFhVV6zJomo8m+q9zq1UDsmK9nN0bndKmy1d6py5fqKqR82J1ZKWViojB/3VK+Xvm3LH0gQsog+Uig9k0FB0XqnknVroJ7p0S+q+kao1jsKHjIyMuKd/0Y1bcJb/mX9MsNvZrGQBAm218J1PhZBNmflRxN8Lf+8ZuZ/KgSN/1rs16NhNfdPXXp/jeBZe/J7PbKe3YSF+vHMvf1au/Mnvpe2CNztnMwN8H9zj64NYvRav2dTOiH2Q7JSj0lt3RMbVpO0ajwmVyurFcrxuteRmj3Gn/rX/e2J0fahASLPH5PoJr9H1xHw4UmPBSBUUzV8gNcti7INqt0vZdTcx9WsRjCJVzYwfqVkt4lt43mD57MPIvs8ZZ2fIzt2vSWvLII8Z1tAWl3t2AHty0qSV0n/I53JwX3+prOjZEaF6/mx2yc5pUN9rvaTIx84MQT9ri9hkorTJJHHonlWvenu+9B2/weMYYzObis8vTXtSbaFBPXR/+yDZ095pEemNayBYdzwmAEjtFubNNG0xPLHu33EKu3+tVVONPTbpedv3JGv4CAqqRQi2f9GzY7i+rkyOVebIZVc1y4vPYnQmvO+1/C1kXW7tiCAxw7pE1UbbGs+UPkXeYze7djTJtq2NMmJklgwZlunVAewLhG/UuE1SV7Nf1q6c45WexveBgLvmW12pV00mD0m67JE2uVi81yviq4s6xnfMFFXf2VQIKj5Xfr0dnr2Kdv+UsPZ5hq05om5e7I0tzJthxo9BLIqTohpa+jfekWosm5UKzz5H8k7hH3ay1FQ1EOF8tCpNHn+oQpzOGpVSvfSqEtmzs0k+WRPu3GHnDOvbixd1XAdnpD3u7uCH/n5EVq/qrGfOnJ0nV9/o6gDW7Am9I139zmBNuHH9vl2DdWvBByRFHD47YhCTH+uoIcdiNnVoynaVCtY6dz2BYKLrt9Nq0ClDbVtlsH2nvCYtkmuLbM6TgyxdHydF1ZqiGstmpcxRo01/TBLbSBXA19clqK7P8fGlZ6vkrl/3i0BUPbH7dQcXFNi9BBXg83nz+8nm9WfJWy/P9BJMdAJ7gs979DokB/cOkr4D90jvfq6oziWQvpOqTmUWATvDhb72hjG00oSQ5qcYR8CL0l+RqQ7/OVOb0xnU4TMzY6Q0NftG9YHdskjXwElRtaaoas1KsairHv7T/VJ2/c2cT7UYwSJVdOz63gWdvF9+bq47Dh7zkzUndG/78vMT8uars9w9PSrSXbJIpY89I1YY5/tGqp3Cq+ebK7JeUmWnpHjbGzrNT/+Gg2tE5pBXS5X6PXX8CJkZI6SpGV3U3qB26sydGnBEhnRNnE6nZbx/7Vauqba1edd74tWsFMv5VGUyQZImUu3ZK103Qlryis/Qvwl89IG+qH7xeYOOG59dyvdh16gLd93UwxcYn2sp40BJUAipp72hVotNLP6OVa6RIJv0Kb1W9ysKcqeoERmM0Qzqc7eMHPSXgG5ZpOvgpKhaM1LVotX8OZrtW2TYC4v038Q65lNJ8rDhy4a4jC4HYt8e/b+DL9ae4v6/kS8wrj9yEKvPgv0Q2u0OGX7SRjlRl2t8V22FXMz9gp1SlH+G+ltSR2dz+TTDEMPl5dwJPtci0mhHZEjy4WT6NzCZmZkJ7f4tPv8iqV25IuKvdxyvFnthoThqarzrs5xPTapGJa2eGhrBvYDN5sCewXLoQB9VO01LR3exf+fs3p1D5JNVp+scm//x9hu0Sz3mtk3jZdumcTJm4hey4JJXvO6T8tlE5bqkVshpu0hjZmtol7LiC9XFJoslM72XO/L09XKmgJJUpn+DR6qJElUVrc47O6rHcBw/LrlYRq65fHA+NenSv3r1VH20TSvxxiYH9w5UtdSnH3CN6/jeDkHVd4Hyvy8E1dN4YdP6SUq0AT5+9e433ILqaWtoXsTqua2msx7qEkyMInnXzBiREg1GqiGaPySipqpRfM55UrtiWVSP4ag/IYPu/4tK+aKzmDtUrUegSDUjI9SWg9C21sSC0SPPkQf+ZPy99Y33tfVovsM0dl3RXr9muhLYgeKQk6XVdFvDovx5UpB7qqGVoPt7xbArmSQ/aR29OInGGu1SFkv/gsbt/t2F4ZIzeZoSUoqpNQn0Jv3+e7WGqV/9qSt/L9uRozNl6+YmiRWYYc3PyxOnM5Ahgp7g2ryEFI1JJ038XDZ9McVPfLdtOknK9w1S1+utkEMK2FGCydbIKcr/hqqTahilcimqJBnSv9Y4CgNRTUSjkpnkjBmX6EMgATB6k/adTfXFdb2RWHVeH0tB/datpTJrToE61sAYnTjY5LzLn5OsnAZZv/ZkHUF13UcTVIDuYM+5Vq2mqm20CWROEQiHozmkjAJFlQSCohrCk5PI9C/sBKMFaV9GqcnXqBRaLdVYrOJB5dE22bypIYwUtTeITvsM2K+6fLdtGhvk53HqzrWefvWTUjZqRwgzsoEIvkyg85iZ/iXGMP0bAG2FTyJF1TSHJpJ0aLOpsRylKSiwSU1N4G9QWpYiRyvadW9b8nK1ukTKlJmrVTS5dWMgQdWPyBGx1kmK7KsqlTLZYTgjq5lT+EewtojMGSiqRA9NJ2ioHwKJrKlGO09adsMtjFKTeKRG7+qgQuu5ySwIgQT1lBnZkluQKiuWxcrRyCHnnXeSZOUWSt+BHwRpsjK63il9B+4NOiO7Z/twnwj2NVl0wQUq5evbjOS7SMD78Zj+Jfo0NbnKLIxUQyCRkWq0PsBtjQ2mHxMxF6PIB+lfPVwvBb0uWZFrbuwhk6bkKrOIxx4MdbZVn6ZmkbVRCar2mtX7+ZxyyumrpLTnHNm8uU4+/eDUiB5/yIitbm9hNSOLXa1ewto5I+sZwcJe8awzB0lxiXc7h94igZtv7+kWW4fDKS0tCXbhIJakpaP3xiqiakmbQitEqtEuLT/23FNS+z6WSJNkEVW8eS97s1oqj+q/7mw244iuT990JRTYNPODn0SX9v8StoRRYZMePSF4eiJkk+KiXvK3P34uf/ndQNm2aWLAaNTo8QcM3emupT7zz1s7BNUZfEbWaZPlS6v9nne9RQK4HmL767sOSHu7yJFDbepzQvQiVas0Ktmt/IbXjr+kBBKtD3DFYw/S6zdJ0N68n3uySh554KjufU6eka4rQNBm1GE1Jk7JlT59IztrHjs+S6LHKZVHehvPrto2qrnTcGupvqlf31qq7/1d1yOy9+btN497dS0j5auHd/TqOh5NbAnRaG5u9vI3SDSWFVUrNCohWs0aMz6qx6DXr/VrqnqRkh5rP0IE6+dsr/arIkrVIl18HDjE5QoWKqij/vLefnLjt10pz9j9aTtl7QenBe1Szsg02sLjlMEjtqjUr14t1fe+esfhdNqk/GCl+/OcHP3HOFTukzHoOOTtBiJMuida+tcqkao1jsLgDS/RooqNMo2bvorqMdgBbP30r1GkZPBVHl8vcumVPWTheUV+NcGynilhHcuQYa7mnOBzp4EZOTpDtm4ONPdpk+rK4MKdnVcrzU05fmYQhUVH5eJrn1afuTbZ6Lk1+RtheB2BzSEFxfjaXn7PmyGao76IfL2hUc46B0srCBF3pMqaahKIarRRZsllV7ED2OLYbOlSdTT81xkE9e57+snC84t0I92KI+GVLoaPdKV9t2+LPAqbMDk7iKBqBJv3dEr1Ub30sU0Kil31UKR+d2zRn+WePOPDgIKKDuBevXqFnCHQjsm1+k3ky/UNTAETN4xUk6imGm0HcMbgoaYfEzGP9tYBkpHeLO8sqwn7a/GSaG521Qs/Xh3d6MvUUxARikodHz8e+Ynk+InZJjQ5BeocFintXe5l9OAPUuqiG8HOO2+JDBu1Q0YOv0yN0mzbGvqcrUeg6k4B+47dREtD0w6pb9wqOVkjvWwTA93/eN2HKp1dlD8zpK8hXb+myvRvoCenuERKLr1Kql5wpbvCwmZj6tfCIEpytpeIOCPzeLbZRWpr21W6tqY6ciE898JCqa5qV01S0YARFIz0PPVYZRSmFU7JLzgmtTVGZgwOGTl2izzzz28HqKU6JSe3XjfK3bP1PLni8h5KUPH879quH5X3H5Am+/f5d2BrkapnZG+WeO4/8nepqVvlvk9u1kQZ1PfOkO9fXbtUCvJmS/+et0d8XCQyKKpJFKmCooXnSWtVVfgbaxK92ZoENBNw1VGx/Dqy35PTIfKPP0U33nHtjT1k0JDMqAR1+MgMufKaUvfPh6j30zVGTUbBsEltTXHA23NSfyxOp3E0PGNWhvTr11f3tp3bUmT/nhxZsTxwHRXjSf6i2hn54gQi0ijVVwwhhCUF872uAyca18ueg7+VlNR8P7EtK7nE7/4A1+GxILzhRr0k+tFLq9RULRupwqow0TVVjdyp0yJaA0fv38QRzEygCrOoqlEpcSc/NcfbZMkr0W14mXpyrhKYD1bWyLpP6yUvL5qG/mDr62zy2suNBvdzyLW3bJVZs0+VlR/CpWmM7mMhVR6sjlpXq38ynZeXojqkIxVUCJ2vGOLzFHu27v0hrHrXNR0yPgmCkFbVLDeMeon5MFINI1J1OPxn3JKpttq8e5dkj8abC4knRmYC8+YXyIrlNe7b1Ft+mL9TzJFu/MqckY4lrxyP+jGQBv3p9/ZIRUW0J6BOGTCoSfbtCZxWPXzISHjt0tT2kaxY3k+ef/Lbhh3ANdXBs09t7UbWkbao6qgQPKPHDYe29s5xIF9qTqyRpubtulEvhTW2kapVRmosO6dqhe5f39pquFS99CzNHxKA0YjMS89V+oit/ss/J1f/Tfb8CwvlostCM3+PB4i+Dx5ojlpQR4xulZ//N5ygRkfxKA5lV/j8k7mGZhDqGlvwn2nUaH1hz82L7k0TqVg90GSEaNIMfAXVU1gRKZPYdf9mZIQ3G97tRBXpXyvUVDUyBg8J/4scDpo/xBmV2q3Utxn8eqO/E49eTJRfoD9juvajOrnn7ugaisziosuL5OLLS+TZfxlHTaEyZWovGTkyGtMJh5x1wRJpbckIGvWt/bg+4I5Y8Nqr+hF8YVF0NTPUNlFD9QSf43pEkZkZw8N6vMyMUaZEysQcUWX3b5KJakQpYLudHcBxJGQjAQ2dmmq//mnS0qJfdjgS5uypHgMGpcu+PfqG/eHwyvPV6mIGWiet0SIBQ2wOmXfuazJs1Db3ijejhQOhsH9vs+7vDxmCp14yZ/UbunPRTKTXRFSQO1030kxJ6SHtOinf4vw5Un50i9/1WRkjpLF5W8iRMulajUqWjVStVFONyGDfbpey626i+UOcCM9IQPxqqlOnZcvokzLlwP7WsI0bQqW4h90UQTWT8ZOyVLocz1/YC8+ddikp1Xakivo4afqaiI/FKMrNyU0xdZ8qhLS06Bx3l+7R6jfVRyPR61l8iW56uPbEGt3Id2j/X/vdX4uISddP/1qjspsEkapmsF+z7I2g9+t52/cla/gICmocCc9qsAN4/3ZEqkU9UuWzT2O1v9TFsUrrnCQOHpomjY1O+eqLRnWJBLgjDR82S5ode90Raq+e4aVEPWupM07L0zXiqKxsNWWfqu+Yi/9s6iQlfr4dwuVHH9AVVdRJ8TVD+t3rflwAkcbYTZlcwrGabhipWlpUrRSpatEqlo9XPP5QwDQwBTX+YBY1XFxmAh2/xzA7QI3AyXKHwYul2b0z2rWKTjn3ohoZPmShbN06RbZ8fUxGnVQsx4/h+TwR8qOcNCZDLrmyc84W4uqbcXhnaS3aE/wN9sNAz9zBd2TmROMX6voehRdK5fFXg47XaF9TJheryFdPpAf1/VnEx0ySM1K1bPo3JSXFcpEqyD99rvS7+56AbYzHly+N6zERGNJnSv+B4dqUubbUaFFStGRmGguqSZlLi+CUi6/Mkosvnabq2P/7qzp59fk09XHHtvDWsmVkpniNyWCWGLOoZy4o8LtvbU17RJ6/x2pW6po76IHrfQU1GOUVj+rOwEJwMUpDYos2JUJRTcJIVSNzyDApu/5mw3fK42+/yVGaOAGbwM2bGtTH/gP0RTU/X//cMSW1RppbKtUbOd7Ye/eJLnHTsStZl9y8rqOqv7y3v5y3qJ9uHfvokfDGeyZPc/kee4LfRUkP39+Fa+Y12No3zxopQPSI9G0saWrZpTyA9eAoTfwiVaZ/k8hRyShizR43QY4+96TUf+LTnOF00k0pDrz/Xq08/lCFOxM/bIR++qdPvzSp/do/hLTZneJ0dqYUx47PlkPlsamr1tV2HdtK9yKBD8NrDPOlrCxVZs3xj0hBr1569TGbbNrYYLj2zTf9mpM1UeoNIlKzCTRKhHorm5RiB9O/YaR/rRqpetZYS6+4xv8GjtLEHESmnoIKdmzzF86+/dOktEy/gSElpUWlfzU2fBWZZ252TteJQkNdJLB+3QnZuzv8VCwYMy5TzaT+358HGZ4s/el+/7WLaFRCU5VeClgv/RpIUFNTeoiZuAwkJunexlGa2KIFX1aZU7VsTdXK6V+95iUIqYKjNHEBM5WhjAwf3N8qH6zUb5xJzzwudluaWrmmRkrSw/tzyMl1GRbc0uEp3B3QFgn88b7Dsm1rZB1Zp88tMIxQ9U6WOnGdvOilgMM1VghkNRgpaEriKE3iun+tUlO1bPdvMkSqvqlgGOjDJAJCS2ILZiqjWHUrN9xSJj/+cS9JTy+W556siugxsrLsShzWfhxdGnTWnFxD4e+KBPqVYUm70e9UW/2mt/bNKBo0MmIwGy3FC2cmbqhJTKSaiU5BC2DZSDWZRBVASGGeT0GNPUgPwi4wEkFddFGh/OFvg1RTU2sL1pxFXus89TQ4CEVPbp6+LaKVwCIBM8CJUEa6zZ0dCPeLjda+GVkQwogBtdVwycuZrnt9ZsYI3es9Rd3TXILEL1K1SvrX0pGqZ72LkODpweA4Ozq23ftUI3ygjAybzDmjUHUet3Q07kRKVWVsG/JQmYjm/BQp7r79MmTjV9H7Hg8dlqFSx3or+UCg53L48Eyv+wazIAToBO5Zcok0NU/X7QI2imSL8maJ3Z7ut3sV3wNjMp4jOXRLsoaoZlokUrW0qCZTpErME03US3v2SpfiktSIa6lGvPZytbz+SrWcOju3YyLKGVG6dtiIbPnxd/ZEt4veJnLDzWXy2SfRdRz3H5gm+/caGyMU90iRyorAM989cJ9K/ft89km9PPrPoxItV3yzWJ576pjuSj4t+vx6k9HIjFNy8oI75iA61HdLmujnlqTtOfUVSbDv8H3Sp+wWXZ9gpnitRZvFGpUsLaqMVLvviAwE7/qby+T0ud4pVohttODxV7+PGqY9ouRvUUlaVNEymDYjR2aelieHD7dKSQ+IRWSdtKCpKfDJZzBBHT8xS6ZNz5VHHtAXzi8/b5BoQURqNNeNxiNNVPv0MRbOQDaFnnVMoGf2gO5cXGDKoF0H8YWl4IkD/p3C5RUPyshBf1OpXN/voYk3sU6kateaRRMMRZVYMq2Lj088XCHjJmSriFWLYI1M3wM1LRnfFlmn07pP6qKLUOEOVN3ulQaNlKKiFDkaxQIApHa3fN1kKKhmcPL0HJW2feV5/Y7b7JzO32n/ARlhi6pvVJqZrr+mURNTjYMisqZutYxtb5Ncg+9Z37hNCvNm6Ea+XDpuDdot5rxnaVFl+rf7oJfWRfYf12/4siFoZGh4m03krHMLZNnr/kbtkaZ/D+6Pvga6dUvkkSlITRW57qZSqa93RNy9HGjdWtj4b9FzU1iUEnAtX0O9K9LWu4+WsXjief1IRG8+FQ5HvuK5X1Kkv7RL347rlkiqbBA0iNnk3YbPZaikypXi/3s9VrNc0tNKdSPfPQd/R29fC0WqVsEa8bIOqampTP92I5DW1QtEvt7QGFKq1TAz6BRdQe34KveWmngybbq/NV+4DBiYpsZ5Ilkk4IkZvYB9+6XJrNNzA9R8MwIKN0ZkjFb3ff+OXu4SgF6kajSfmp7axy2ej0mGvCtp6iM+h8hqgqqxU1LlOZ0Yo6Fps1QbWhB+4bZDJImjzWLOexRVYhl866fgjSXVIQnqKTON39QDfGXIqlJUHP3Yy8BBafKDn/SS8ROzo36s0lJX7bGwKPJkk2uRQH7UTVKtLU7DOVt8D0TTgY4B9VSj1X1HDndGIXqiajSf2q/X7VKdMcpHPG2yQVJlo4+gauzoEFxfbEEsCEniRdVmoY0Vlk7/slGpe3r4ehJM8669sYdMnJIrS1/37ioNBdcfojOkbt9wt6/osXdPqyl1VDBjVr4a54FlYLj06ZsqN93Wy90cVFRsl+pjkXXap6TYpKKiVXdf6zU3uBYVfLBSP1Nw/oWFctHlLrtAo4jb0+hB741Tm0/1HX3B9bW500Wa9/h9TWH2qSINn+p+P6SJ+4r3c1qYP1NaWg/pbrahBWHiabNYpJpq5fQv6fpEO3fau2+6qrm++3YkYymhpX+t5nbUr3+68saN5jnzNFDAIoFIf8a0dP0IobAwzf099u1tDtq1jPv67lL1NXowikZ851O1rtyRWfpGDacXL5CjzmZZ3/iV322jsyaINH7uJ9Cu0ZvfeTU7cT7VOo1KNkaqwWFNtXsQzdypcufJsMtjD1ZE9vU6KcBk4MB+16qrSJl1unfKd8/uyDx8IXqjTsqU7Vv8O4dz8+zyx/vKZQpWuxn9fn2uR4cwZlYxYoMI1dc5KdAbp96Iy7DMITI7b6asqlvtvg6f4/o7+/5IfnfwD/KFh7Ditpk9bza0GYS3Ly0IrUcbI9XQYKTaPTAakcGS6neWGjUYdXaF7t4VTVrWKe3tTTJnXp6sXGFCB2wSMHBwukqXozEIdcxIGp1KS21y2/f7ukUPZhoVFZ0pODTpapHv+nUNkmXwLabr1HPxmHo2hCCSaOT2njfL/IJ5srVxu4zMGq4EFVS1HZNzixbIGQVz5XDrEa/bAs2gcj7VerQzUg0NRqrdg6NHW8NaLH7x5UVS1itdho/IUvOrr7xQGd0eM6ez2wjqvLPz5Zrry/xGV/ILwhOrtjabl/BhhRvqpp9/Wq8iVN9UcqNOD5KRh28gIhFViGezo1mm502TklR4PYu8V/u+PFjxmPs+V5Vc5hZUz6873HJEeqX3dH8dsSZtbFQKDUaq3ZvaGofh6M3JM/Lcn0+cnCuvvXI8OmHtBqCha+78Qt3Rldqa8PLvU0/x77TGeA8uSPkG4swF+TJjZn7YghqJYw7E86GKx1XdHKn+m8uulwnZ47wEFTxT9YIqBJxXtNDv6zTRPb/jNmI92iwmqpZ9R0lLC+7zSZIfbTTEE/x99DKwqzNzqjQzs1bstu7REJfTsQnHaHQlVNA/ePX1Zbq3IVqtOBS43tujR2cDU7iE88aJSNNTGPERn6874e2qpPF01Quyo2mX39dpovt69VsRHTOJPTAJspKoWvYdhaLafUZp9GqlWM3mC25D2teTSEQCtnlnn1sk11/flhDzh3jjuW6t4UTklm4YLfrWrb10b/vp9/Z41VWN0NuFGirhvHEidev7u8Xnq+o+Mvyauw78Wublz9F9TTxT9aKMzhrllyYm1ohU7Rbx/QXWORKdOVUrdnaR2I3S4D3z7nv6qf9jX6qe2Ppursnx8I0NlaHDM5VpQsOJ0ohjXxjiJwvaujXYGb72auSp8lNn5RtHqCEIaiR1VE/CeeNELVSPHc07A37ditqVutdDaCG6jFith8Nikard6pFqU1P0Q/ckOUZp8Dkal4zE1tdxCQ03kRjBI1rC91cjNRHO80w7ORIHp/iSlW1TDk47tkc2MuMLzCb0Fouv+7Q+pLVvgXahmi2qaC46t/BsMRukiV9jKthStLGmGhoU1e7n9au9Z+qJbbPP8mojr9hgTJiYraIltUIOmhr+oavj7qFTC7YSw0ekyz8eHapWy5kFGsJ+fdcBdTLjiZpFDcCw4Rly9rnRd9CGG40sKJwfk1nkZ6teVLVXYp2RGjvTv8HRFs62tEQ36E6sCdK4SOdqfwv4eN1NZapm6vveqZk8RFtLxd7QH97pMlo/Xo10ZWg2hb7HguP2FflwKemRIr37GLc0pETZ7YB+V5x47NoeXWPS5Gn+NVCczOhFrHpMPy1H7rqnf1THoG2rCldUv2zYEJOaOR4TNVtiDRwWS/9afqSmudmc1BWxHkjnYl8qUrGIHLV6qVr19XCFWv2mRaqosXouLQ/XtAB7QzHyoTVIwYUp3PTvgvMK5Myzi9z7XaMBi8m3BVj/NnR4hmzbHPlr/8SJdhVVRgNqoNnZOJnxF+Y1q+vc9dFA6d8Ue/RvdpGIKjp5fUdnzCTDbrz3lcQXRqohwvRv9wACNXqMaxG5BoTzrl+7GpY0tKXlmphpXrGhou3s1BqkFGGe3Y6fmOM+TnwcNjLyN9bWlsCRbjBBxaGfd0GR4e2Hy6MTfZjdowZac1w/fewZAZaWGp+bT9OZaY21qGLOFE1FsQSGEsQaOBwOS6V/U62e/mWk2j3RS69qS8s1YdO8Yle8XS2rPwjcLFN5tFWlLPG4nsFpqOlBvJ+rOqwHBQauT8EoKUmRPbsjL2uMn5Apo8bkyIvPRr6cPBgwu0ft9JM1Dbq3Z2W6fvZ7795v2AiFWiosEc0S1VDeOFHrjGWEqo5D7IbdxST+WC1STbV6pGq1re4ksZ7Au3c1q8hWA6Mx4yfnBhXVd5bVqgvqqhquJpbQRPXSq0q8omkIzrpPw69Xogt24OBM+d09gZ2HAjF8VJYS1GgXjEPcq6r051aPV7fLJ2uMn9P9+1pk/boTuoLar3+aXHJFiSmCGm6kuq1xu8Sam8quo3WhhXAwUg1PVBmpdj/0TCE0Xnq2SqafmqcEznMPK95vNZHB/89ZVCgtzQ5ZvtR7JdxX6z2F0BbSzt7LriqRhecVRd15PHR4uuqCRQra83jDBd7H0QgqFovPX1AofftlyIvPVcrmjTq13SD6NXlajnyyRn9d3MDB5kSoGtqsulWaUWB1SKyDg6IaGkz/dk+C7VfVUsDA835KWO0it363p9tsHw5CAQlhSfk1N/aQST4C8a9HI1s1N3Gya/QEx4a68coV4e+AveEWV4d0NMAqcMvXTYYzvq4GJWMBKytLVU1f1aqD2p+evc0dNwon/Tsia7jKQMTSKWtb4w6ZkXdyzB6fhP/6sJJXvHUS0T5QVLsnwfar4n0VtU1d8wgHttukKNFCNFlVGbh0oM0wjh5r7PLz5KOV8uPv7FFRMcDj7tkVWT105mxX97Hre4YnjL16p8of/jZIiTF+vjnz9N2NQgEWkHqRNszuf3lvP1Wr9lwg7jv/iq004DSPn8cTo+vjkf6F6cOsvFMltnR9a8tkwuFwuB34rIDlRZU11e6FbzOQJ9osK0TFyDwC16PeiXGSd3xSv54surhIMrNPqPSvbvrTA4g3omJE0ZEa0qNTV6vJRjKOM2BQhldNFyM3kYAoMzs3JajZ/eZN+j9ndXVnDRbHg8jZE3zuayUZT1FFo9IHAfx9owUnYoiGiXVwMP0bGqypEl8wZqO96WvmEdo8qya4MHUIpd7ZozRVmhrzQo46IKzLl1bLydNDH+Px5PXF1XLsWJuMGJUVML1txNnneI/PGImeEQMGpsqZC4pU2tbIuCE7x65S5r16pSlP5cqj/k1MZWVpIc0ax1NUMZO6tXGbjMwaoUZdYpn6xfo47le1Fg6LRarWSUT7QEel7olWLw1lzEbvDT1oHbWjZlhf79Dt/kUEi/LMy8/7P87bb9bI/AVF6usjaVTC13z0QV3YgqpnRG+0Gs+IUSdlu80vtBlfz58BEWwoPsqXXNnD7zo877EQU19RrXOekDerlynx1LbF/P3IQ7KqbrX7viMyhsbsOObkzpa5+afH7PFJZDBSDRHWVLvvKI1eZ6zenKjeG3ogp6X5Z+fL9NNcC7Jd0Zp/92+7wymvvaC/yQV3hYBffHlJROKoPUao9B+QJjfc0lN3s8uAARlh2xZ6os34bt/aqCLUUAQ12i0z0YrqmvpPpKXK9XPMzpsp8wvmeQkq2BZkC000DMl01ZKJtXAwUg2NjAzXmwZXv3UfPEdkQln7pgfe9OGpe0jPUcjmul27X15BjdfNU0/JkTcXHzcWvg5hD9ZMpR1ztHOkV11XqitiqBmHGynPmOmftsZj4xIsuh8/MVMuuKRHXAQVNVH46sJcQUuzvlr1hutGD8tDiGmrI74z7BRVa+J0Opn+DaemSkP97j1Kc+2NPdTMYzjpxbHjs+VQuU6Tks9jl/U+rK688poStQ4O6eXP1hobHmhv6VqTlJ5onntBoezZ1Swbv4rOyF77Pr5EMiMbKMIMxXQ/Ly8lLoIKe8GHKh5XNVGk5lG/xEzosurlujXVDfWbJJ7QmtCaOCmq4UWqFNXugVH017tveM0vWlpXD6R+PdHmHs/qaAIKtnlFS/9C7PSOFeMo4I3FgReBz/5Grqz6t75xgqc4aztMPRuAwu0+9lwkEGnEOy3C5qxwI1RNUAE+4vMbenzTMOI/IcH3uJoJTfStiZOiGhoU1e6FXvSnjciESiCR0IvWtMhHa3TAgvRAeM7I6oFId++e4EbrGRmBOxVhpVhalq7mYzW3KG1DT7jbebRFApFGvGb594ZiL+jbtYvPP677VMThut5mwsabaGCkak0cFqup2q0uqpxT7d77VUONUo1EwtPQwBdPUQ2GLcQZ2a836BvQawwaki5jxmUFnGe9/uaefm5R2oaecLfzaIsEfAkW8cJs4gc/6RX1LtRQMdpPurl5izg1UU2gptJE37o4GamGBs0fuh/RzDwaiYSnoUEwUc1IN37XvjvEGVlvb2F/rr2xzDAiRv147vxClfL1TXl6bujBCcKokzJD6tjVFglAiD1PLAJFvIFSxrGiVQIYYnSIajA/4lgKKk30rS2qqRayKbTOkfjA9G/3JNKZRyORQAOSEZ6iGsjE36wZWS0FbSSqOXkpKhotP9gScHOP61j9BRU/zrmLiqS5ud1vkQCieIzQeHY/G3VJ798b/12hk3MmyivVr+ne5j7BSECoenLOFLmu9GoaPlgYJyPV0GD6l4SDnqFBsLlKrVGpsrIloMsRUr+hzMjCiciIWXNy5Vu3uiJFPUN86EVVZZs88GdXHdVI2EPpkjYSd8ykej4foXZJxwOYOZSllklFm/+JjbO9w1EpAcWqsVljKKgWx8lINTQyM11//JxTJaHiaWiACDXYGIgWqR45ZDx3qpqEQqjtBot0+/XP8PPM9Vxbd+mVJfLiM8Y7UgMtEvDtkjYS9683NIbU6ezbJR0PVtZ8oCuoiY5Up+ROjPv3JOFjpUYly6Z/WVMlkaAZGoSCJqrp6fpKdvHlsCQsCCqowdbV6aWhfdPHgQwlfJu2AnVJQ9wfe1BfnL5c36DEdMXymrC6pGONr9WgL1qjUrxF9ZayGxilJgFORqrhRars/iWxQkv/Njb6G8eDYR17WYOxfVtjQEGdMDFbV6hCqR9DR0JZJIDrIe5GgqqxZnWtrqDOX5Av02e6LBzjCczwAwmqIgHdv9/veTt3piYRqWxUCg4jVRJrtEi1tCw16hnZQDOnP7yzT8Rfj2MKpUlKE/dQHk+PkgBd0rHk8/r1we/kDlTNV9X+aX1lf+tBv27fEVnDTP9epHtEqnarRxGsqZJYU1Boj2pG1mgU54pvFsuP7uwb0mMYGUoEWiQwekx2WJ3SSO3OMKiXBuqSjiVpIVSgHFqkGoOy2bd7fkuuLrnMvbCe4zPJSaqFRNU6R2IA078kVniO1EQzI3v4sP6ITDiRldF2nkuvKgn5WKqr9I9j4uQsOf+iEnckGm6XdCyBcX5QYlRTxaYbdB3jcmredD8jf5I8pFJUQ4eRKokVvuYP8ZyRDbadR+sIXnie93LyQGzZ3GT4c3qKZrhd0rFkRNbw4HfSnhcTbQpPyhglt/e82f05hJRimrykUlRDh5EqiXWJIRSbQrNnZAN1DkNQ776nszkpFPA4anH5Ov/bJk/L0T3mRIqptuJtQ+PXQe+r7bw1M07tk97bxEcjiQ66UimqoYEz7PZ2/c5MQqIlHO/fYEQa/emN0ug1JwUi0BhNWVlq3C0Hg3X7vlG9VNbUfxry17hHakyMVPNT4j+LS8xH22KmrQq1ApavqTL9S5JBVCON/qLdzmM0RjN2fKaccmpeQgUVArq1cZuMzBqh6pbB5lENiUFJtSyth3kPRhJGC0U1/Dc9iioxm9WrV8v//u//yjvvvKNeYwMGDJCsrCwpKCiQnj17qs+HDRsmY8eOlSlTpshJJ50Us/RSoLnTUDAao5n1jQI5ZUbs96Aa4SugE7PGy/rGryJ6rM4tNeap6t7mfaY9FkkcTU2uPgKmf0OEokrM4uOPP5bf/va3smLFCqmvdy237tOnj0ybNk1OnDghBw8elKNHj8rXX38t69f7z07ijzY3N1dKSkrU1w0ePFhGjRolEyZMkKlTp0pZWVnExxZN57ERidw8uu7Eer+INFJBVThiMACYyD1yxDQYqYYJa6okGj799FP5n//5H3n33XeVcILevXvLDTfcID//+c+VOOqBdPDu3bvls88+k6+++kq2bdsme/fulcOHD8uRI0fUbR988IHfaxUuYIh2IbD9+/eX4cOHy5gxY2Ty5Mkyfvz4gGfTkXYew5zfN32Mz+EGFc+GI20U5b3a9+XBisdM/R5Op8N0IZyZN920xyKJo7nZtVGJkWqIMFIl4fL555/Lb37zG5XaratzdeP26tVLrrnmGvnFL34h/fr1C6kreOjQoepy+eWX694HIo2I9osvvlDR7c6dO+XAgQNSWVkpW7duVWLsC/7wc3JyVLQLcR80aJCKdiG4J598sjrOSNPHjz9cIdAebHIJZQGAGUBAH6p4XJziVOYJV5VcKk9XvWD693F29Cqalf7V5lNJ1xHV9PTo3c+6RaMSI1USChA3COny5cultta1ygzR4lVXXaWEFDVSs0Eq+LTTTlMXo2h3//79KlrWot09e/aoaBdpZkS+qO36vt6x8lCLdnECgNquFu0i1az35hGL9HEoEaomqAAfYyGo6rG1MDxKTe1hL5Ef9PkPCmoXooWNSuHBSJUYsXHjRrn33ntl2bJlUlNTo64rLS2Vm266Se666y4ZOHBgQp88RLs4BlwuueQSwyYLRLq4bNq0yR3tQnS3b98uGzZs0F1xhWi3uLjYHe2OHDlSRbuoDxeXBI/EzWBb43a3oMaazkal6B6nX0YfCmoXjVTTOFIT+huTWeMOJPnZvHmzEtK33npLjh8/rq7r0aOHqpH+53/+p0rXJhOowc6YMUNdjNi3b5+sW7dORbtIKyPaPXTokBw7dkxFwmjA0nvc/Px8dZKBaBfPC6LdSZMmqYu2ASpp0ALVKOdUzyyYZ87xEMuZA6Uz/RsajFQJhASp3TfffFMJCUCUdt111ykhRTNQVwapa1wuvPBCw/TXl19+qWrJiHZ37Nihot2KigoV+aLe606fekS72dnZ6nlEHRfR7ogRI9zRLpqsNLepqOwFzUIzf0iJvP13eMYwLhzvgjSzUSk8WFPtniD1CSF94403pKqqSl1XVFQkV199tRLS0aNHJ/oQLQPO0CGEuBhRXl7u7mTesmWLO9rFc4tRorVr1/p9DWq7iHaRCdCiXczrItJFfbcku1g1/ERk5hAmbpvCCALVMnupXFd2NQW1i9LMRqXwwNkybQq7BxhTueeee+T1119XHbSgsLBQrrzySiWkSF+SyMDo0Pnnn68uRtEuatRatIuTGqSWEe1CgCHE6Kb2JCUtRQrHFkt2nxzJ6Z2jPqpL71z1Mb0g3bRuXbeoRhCoTsmdREHtwrQy/RserKl2bdABixrpkiVLVHMOQOcrxljQtYt0JIlPtIvoExcjILCIdpFq3rBjo2yr2S5VjmPScKheqjdUqY+O1s7+h9TsVMn2Elt8zJWcjv9n9cqWlPTQFqRqY6qRtP/OzOc8alemhd2/4cFIteuBxhsYMrz66qvqjRogzYgOWUSkEydOTPQhEh0w4rNw4ULJPC1HvqzYKsPkJBnm06HbVNUkDeUnpKG8XolsPT6W10v1pmNy8J390nzMYzWdTSSzNEsJbI5PlKtFv+lFGa5oN8RGpVNypsra+s/cn3MetftEqhkZGWIVLD2nSlHtGqBxRhNSzGmCvLw8ueiii5SzEWz+iPXBbKqRWxIEL6s0S11KJpTq3qetsU0aD3eKLYRX+1i9uVoJsqOlM9pNyUxRAmtLc+V9D67Yr76PO/rtlSMpGa5o95ayG2Ru/ukdJv7bZWTWcI7PdAOa2agUHkz/Ji9ojoFp/csvv6yaYjTDhAsuuECldgM11hBrgtnUaEjNSpW8wQXqYlQ7RTSrhLa8Q3wP1cvRda6MRsWaQ1K+Yr/X12T2yJT0nEz5sO1d9zIEdIRXjT0izVMaVVNbsE5mkvzp3wxGqqGB1n/OqSYPSOeia/ell15SogpgVIAGmTvvvDPgPCYhyj+5JEtdisd1rmbb+8ZuWfPDVTL5V6fIgIWDpeFwR4TbcWn6pF6ObatyN1v5AmMAnNChk1lbhgCxhUOVMswoLuaTn6S0dSwpp6iGCM4wtZw5sSbo1EVE+sILL6g0ryak55xzjhJSIxs/knxgNhUev/FyUnLj7v61qXRv3sB8ddG4t98v3alenISjexlNVRBZbRkCFiHgRA+zu++//76fmCdq9R+JDjYqhQlrqtYEJgxYo/bcc8+p0QsAM4EFCxbIT3/6U5kzZ06iD5HEAGyhmZV3alxmUz1xe1cYjOg0O1xWddp7BmwbcTEC/tCIaOEZDXOMXbt2JXT1H4kcNiqFCdO/1gG2gL/73e/k2WefVWf+AGf3Z511lhLSuXPnJvoQSRwaleItqIog5g8Z9vA6P9FtjhM/o5O/RKz+I5HBSDUCUfW1WCPxFdL77rtPnnnmGWUCAPDGccYZZ8gdd9yhBJV0H6JtVIoUR7sz5EjVDJJp9V93p62jpmolP2tLnzoxUo0/SI3df//98vTTT6u0mNYEgEj0xz/+sZpVJCSuaFtqdJp47WJXC9LjjZVW/3VnWjmnGh4cqYkPOOv+/e9/L08++aQ64wb440V67Ic//KGhvR3pXiSuUcn1wdf2EIJ6U9l1qtZrNRK1+g9C3B3Tv+kWOtlgpNpNaWhokD/84Q/yr3/9S202QZodL8zZs2fLj370I1m0aFGiD5FYDIjXzWXXy8MVj4sjjsKq7VOVlE5RnZY9Va4vu8qSgmqV1X+e0S46mLVo10qp0mjhSE2YsKZqvpD+6U9/kscff1ydCUNIMcM3c+ZM+cEPfqDWi3FQngQCrkUTssfJmrpP5Mmq5+L0ZPmnf8dln5TUghqP1X/4P65btmyZbrSLzU+e0e64ceNUtIvvlyy00lA/PFDYZ6NSdCDF9Oc//1kee+wxdaaL5xPPK86Ov//976vUFIWUhAPEbHreyfJU1fNxSQVrhvqe6d8pufSINmv135o1awxX/3kuutdW/+GCETor0MZGpfDAmz1FNXxw9vrXv/5VHnnkEdm8ebNbSE855RT53ve+pzoaKaTEjFTwQxWPx1xYtQEAiCpk9eayG7pFlGqF1X8YH8J7yPLly3UX3SPaRdcyasee0e7AgQPj8h7DSDVMIAS0KQwN/GH84x//kIcfflj9YUBI8cLHYPp3vvMd+eY3v0khJTFJBb9Xs1Jern4tds9uR031zMJ5cvugWymoCV79h2gXYosIGNEuPn7yySe60W5eXp7XonvYQ2qL7tFBbZaoWmkG2DpHooOVnigrgtTHAw88IA8++KASUpyAQEjxgv2P//gPue666yikJKYgYpxbMCe2otoRqpam96CgJnj1n9FIHd6LMK+7bt06FfWinosmK5hl4CNKT++++67X1yCS1aJd2EMiuh0xYoSKdhEMQISDRbvt7e1iNSytWqyp6r94H3roIfnnP/+pWu41IcUe0ttuu01uvPFGCimJu7Bi9ZrRWjizun9L0pnytfJ7NcZ6xo8fb3gfGGNAdBHtIqWsRbu4HvO7iIT1omgt2u3bt68MGTJERbt4v4PwWtEb3tKiyu7fTiF99NFHVVSKFySEFGdweAHfeuut8q1vfYtRPbFEKvjPh/4hW5sjd14amTFCrim9Qva17JeHK54QhzjE5nQ1KBWm6a+MI8lBjx49lAubkRMb3tcgtlq0i9quFu2ioxmfv/fee35fZ7X+EEuLaneOVPECQ8cu6qSwQ0OaAy8ebM749re/LbfccguFlFguYv1e79vkP/b8KOLHgKBi4wwuEOnDLUfk1aJX5HP5xHJvnsRc7Ha7co/CxQhEtT/5yU/UekmY1oCbb77ZUr8Ky4tqdxNSuBqhcxcuK5qQ4kWGFw7E1ErOIYQYpYIDdQXf2ONaNRLzfNXLXgb9s/Nmule4aY+Fy9u2LF1HJdJ92Ldvn3J3e/3111XKF8s8brjhBmWparV9uJZWLRgTdPVIFUIKw/q//OUvqqUdqV68eWAm7KabbpLbb7+dQkqS1CDiU3my6lm/2/um91ZieXvPm2V+wTzZ2rhdRmYN9xJUT7T3AEaq3Y833nhDfv7zn6t0MMD2H3yOAMOqrwfL11S7qpA+//zzypQBxXlNSLGxAvVRdO52JSsx0l0NIqbJU1XPeUWsvgb4Wqo3lA5Pq76JEvPHA//rv/5L9ZBUV1er3zvsU//f//t/Acd+rILlI9WuJKSoA8AmEJsrkMKAkGJgGmkMmDJQSEnX9Ap2NRxFaoCvzapTVLs227dvVy5vMJrAiRTmWJGpwx5nM2Za44WlRTXZa6p4M1iyZIk6w1q7dq06A4OQYnnx9ddfr15AVrH7IiSWqWA0HCFCjcQJSUv/sqbaNXn++efll7/8pVqPBzCfis+vvfZaSUaSIlJFejSZBPa1115TG2CwQUJbTYRNEddcc43aAJNMZ12ERIvWcBQpWqSaTO8BJPhyj//8z/9Uo4LY4YxS35lnnqkyeZhDTWYs/SrV/oggTFb/g3rrrbdUJ9pHH30kzc3N6joMKkNI77jjDgopIRHCRqWuw8aNG1WGbuXKlepkCQvZ8f54zz33dJnyV1JEqti0YsU06dtvvy333XefrF69Wh0jGDx4sFx99dVqlgpbHggh0aFFqkz/Ji+PPPKI/OY3v1EuSgBNmRBSowXuyUxSiKqWQrUCK1asUIXzDz74wC2k8Ky86qqr5Kc//akUFhYm+hAJ6VIwUk1Oamtr1XviU089JfX19SrbeO6556qpBwQfXRVLi6pmdKClUxMFrLH+7//+T1atWiWNjY3qOizyveKKK+RnP/uZ5YaPCelKUFSTi08//VQZNaAUht9dSUmJ6iW56667usXMvaVFVaujahFhPIGA/va3v5X3339fFdW1wWPsIoWQwseSEBJ7OFKTHL+jv/3tbyqLh8XnAN7keA9dsGCBdCeSIv0br0gVtdH//d//VUV0pCsANiPAvePOO+9U648IIfGFkap1gRfvj3/8Y3nhhRdU8JOenq7qpBgjxA7V7oilRVVLFcSypor5UQgpaqWaQXOfPn2UsxHssLDVnhCSONioZD2QyYOYYqMMTnqwD/VXv/qVqqF2d5OOpBBVsyNVWAP+z//8j1qaW1dXp66DeGLYGLNTEFVCiDVgpGqdkxv0lmCWFPtP0Y09ZcoU+f3vf69sBEkS1VTNEFWY1UNI33nnHdWVBnB2hfEXCGl3TVUQYnUoqokFi8R/8IMfKHc4ZA0xT4oABGLK3pIkjVQj3e7+1Vdfyb333qu8JGtqatR1qIui2QhCilEYQoi1YaNSYli6dKnqJcH7qNZfgiZNLPzo7inepBfVcCJVOHZgyHjZsmVy/PhxdV1paamqkUJIu/J8FCFdEUaq8QORKAKRv//971JVVaVSvDNnzlSNR9OmTYvjkSQvlhbVjIyMkBqVNm/erF4IOLPCqiCA2SiY1mM2CgbNhJDkhKvfYg+cjrApC8EIvNZzcnLU1ANGYmho001qqlu3blUR6ZtvvinHjh1T18GEAbn+X/ziF2qlGiGk60SqXXW/ciJ5+eWXVeCxZcsW9TkyecjoIbNHunCkijMnsHPnTuUXiW3wSE2AoqIid7NRsm83IIT4w/SvuWCe9O6775aHHnpI9ZqgPjp37lzV1Tt27Fi+BLtDTfXBBx9UmwwwaAyQjrjyyiuVkI4ZMybBR0kIiSVsVDIHlMmwIQa2q0ipY+EHunqR8bPiwpJkxdKiinlRFMrhIQmQ50c0itmoSZMmqRcG/uDYiUZI14WRanT861//kl//+tcq0wdGjBihPscUBOlmojp16lT57//+b1m8eLFqQEKkinlTmDf4pomRBu7du7faYTpu3DjVqXbqqaeyyE5IkkNRDR+4w2H8BYKK/6MevXDhQvnjH/8ow4cPj8FviWjYnNorNomoqKhQPr0Q102bNsmuXbuUwwfqA76dwngx5ebmqrEazKWigQlRLtrE8X9GuYRYG8xFYsTj6NGjNBsIAoIObIj58MMPVRYPzZvo4v2v//qvbrEhxgokpagGAk1NX3zxhUoZf/nll7J9+3bZv3+/inKxbcb3x4U7CKJcpJqHDRumotyTTz5ZZsyYocSYEJJYbr/9dvnHP/6hmhO5ZtEfiOc///lP5WGO9zqAhiN8jv2lJL50OVENxoEDB5TgwggaUe6ePXtUlAvrQl/nJkS5eXl5yoUJUS7quZMnT1ZpZcy+MsolJPbcdttt8sADD6gSEGcmO8EoIUztn3/+ebXnGVu9zjvvPGXUgH3PJDF0O1ENBFLHWLC7Zs0aZc2FKBcijDNkvGh9n6qsrCx15gz7Li3KnT59uop02U1HiDnceuutKhJDeQcdq90dlL4gpp988ol6T8JJ/3e/+11lKajN9pPEwd+AB6g5oNaKix579+5VtQrULdCeDhcS1HfxOV7gXk9saqp6A8ALftCgQXLSSSepKPe0006j5zAhYcCRGtdzgAgUJvaHDh1SzwveT7A1Zt68eXw9WQhGqiaBei2EFRfUcnfs2CEHDx5UKRpEuV5Pus2molxYKWI7Drrxxo8fL6eccoqKctlQQEgnN998szz88MNSX1/f7TJAKE2h8ejVV19VznKYdLj44ouVwOKEnVgPimqczjIR1WpRLizBEPUeOXJE7XPVvE01UBtBlIsdr1qUi/EiRLnc9Uq6GzfddJM88sgj6uQUjYXdAayoxEgMmi4B/u5/8pOfKH9e9nJYG4qqBcAc2ccff6yiXGzZQS0XOwzRmAFLMd8oF2fr2GOIKBeD3BMmTFC1XJhisKZCuho33nijPPbYYypS68pZHEwuYOfzX//6VzU+hL91ZK/+8Ic/qGkEkhxQVJMgysXyAHQsI8rF//ft26dquRBj3ygXbzoFBQUqyoURBqJcGGGgTsx0EUlGsG3qiSeeUN35XfGkEX/PsA/EchD8jCgNXXXVVapeyhGi5IOimuRgZywEF13LiHJhRYZGBkS5vtt9kDZClAsjjP79+6soF0YYiHJR0+2Kb1gk+bnuuuuUMxBOILtS6nPJkiVqo9bXX3+tPsfY3s9//nNVQ+5KP2d3g6LaxaNcCC1Sy+vXr1e1XJwVwwgDUa7WVamBJghEubB7xBwulhVotVyeMZNEgXWOTz75pN9IWzKCcs6vfvUrtSEGJ74Qz1mzZin7wIkTJyb68IgJUFS7MRBXLcrV7B4R5SL69bV7xB+/ZveIwXLN7hFGGEgx88yaxIpvfvOb8vTTTye1qKJPAk1GaEBCxI2/JaS14XpE57auBUWVGDZNILqFEQZGhFDL1eweMdqgZ/cItxt0KSLKhU2aVsvlwD6JBuxLfuaZZ5JSVJ999lkVmUJUAUxi8DlOFEjXhKJKIp6f05YaoCaEkSFchyhXz+4RZ+Oa3eOoUaNUqgtpZczoMsolgUDTDsQpWUQVM+uojaJjGSNzeP2fccYZarYUVqeka0NRJaaD1DHm61DL9VxqALtHo6UGqNlqSw3QNAUTDIwTMDVGrrzySnnuuecsL6qwNsXS7/fff1/1KyBzg6Yj7C7tLvO1hKJKEgCapSC4nksNYIQBb1eknT1BR7K21ABGGDjT11b3Ic1Muj5XXHGFMo23oqhCPB999FG59957laELwGv0N7/5jVx44YWJPjySABipEst1RyKlDNHdsGGDe6mBZvfo+caK4XgtyoURhhblaksNGB10DS677DJ58cUXLSWqKHPA4Qi1XmRfcPJ3zjnnqBTv4MGDE314JIFQVElSgdotarnaUgMtykXtSi/KRZNUz549VZSLESGYkCPK5Wqs5OHSSy+Vl156yRKiunbtWvnRj36kTvpwPHA2wxL1u+66i3PeREFRJV0GRAzoVobdI+pbWGoAu0ejpQYwwkCUCyMMbakB7OBg99iV7fCSjUsuuURefvnlhIkqUrx/+ctf5L777lNLMgCsQX/3u9/JWWedlZBjItaFokq6BXhjhNsUlhqgiQpGGIhy4bFqtNRAs3vUlhpgRAhzuVxqEF+wleWVV16Ju6jCChRRKQQdZQmcaKFOCi9evgaIERRVQkSktrZWpfQ0u0ctyjVaapCTk6NW9yHKhRGGttQATVS0ezSXiy66SK0+i5eorly5Uu644w5VYsD3xIkVxBWLwTn+RYJBUSUkhCgX9VuIrt5SA1+7R22pAewetaUGaJxCLRc1OBIeiA4XL14cU1FFPR7pXdgF4veKEydkJrAUHPPUhIQKRZUQEzpBkVZG17LnUgNcr7fUAFGuttQARhiIclHLRU2XkZA/ixYtktdeey0moorOcsyW4vFhWoKO8csvv1zuv/9+ngCRiKCoEhJDEMWiaQpRLmq527ZtU0YYqOXC7lFvqQFMA7SlBrB71JYa4PruyHnnnSdvvPGGqaL61ltvyZ133qnGtgBOcPD5rbfeyhMbEhUUVUISCFKN2oJ62D1qSw1ghGG01ABGGNpSA4wIIcqF4UBXjXLPPfdctWs0WlHF8/nf//3f8sADD6iOcKR4cbKCxiOcuBBiBhRVQiwK6nyIbn2XGsDu0WipQVFRkdvu0XOpQTLbPcJUYenSpX5RfaggHY8l4G+//bZ6TpF+v+aaa9RIDJc9ELOhqBKSpKA7WVvdhygXI0KIctHJrLfUQLN71JYaoFMZkRrSzFaOchcuXCjLli0LW1RhGAFTBpyMADSN/fKXv1RLzwmJFRRVQrogSHVCbOEAhJquttQAaU+9pQZZWVm6Sw0wJgSTjESyYMECFWWGIqr42SCkjzzyiDq5wMnC3LlzVVcvHLUIiTUUVUK6ITB/R5SLESHPpQYQIqOlBprdI+q3qOXOmjVLRb2x5uyzz5bly5cHFFX8DEjx/vvf/1b3w0jTt771LbnnnnsSflJAuhcUVUKIFzC7QOMUarma3aPnUgOvNxCbTUW5MMLo27evsnvEiBDW9qH5x4ylBrACfOedd3RF9YknnlCr1dDgBdC8hc9hwk9IIqCoEkLCbvzBUgM0UaGWi6gXXcxGSw0QNXouNYC3MpqnsFkoFObPny/vvvuuW1QRTf/sZz+TJ598UjVs4XtAeP/0pz9xHSBJOBRVQohpwGEKdVy9pQZ6do9IzWp2j1qUixEhNFFpSw3OPPNMWbFihXrMH/7wh0rQURNGDRhzpb/61a+4AIFYBooqISQuINJEwxTcp9avX6+WGmhRLsRYb6kBDC/QfISIVGPcuHHy29/+VnUFE2I1KKqEEEsAW0dtqQGcjlAn1aJciCsao9DFG2ramJBEQFElhBBCTMK6E9+EEEJIkkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIoagSQggh1oKRKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCHEJCiqhBBCiElQVAkhhBCToKgSQgghJkFRJYQQQkyCokoIIYSYBEWVEEIIMQmKKiGEEGISFFVCCCFEzOH/A9IUvQ7l17f9AAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -877,7 +964,7 @@ "For quick, easy data normalization of the input data, pass the normalize argument.\n", "\n", "You can pass the following arguments as strings:\n", - "+ across - columns z-scored across lists (default)\n", + "+ across - columns z-scored across lists (the default of `hyp.normalize`; `hyp.plot` does not normalize unless asked)\n", "+ within - columns z-scored within each list\n", "+ row - each row z-scored\n", "\n", @@ -886,14 +973,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "325ea544", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:36.151790Z", - "iopub.status.busy": "2026-09-05T10:23:36.151687Z", - "iopub.status.idle": "2026-09-05T10:23:36.304878Z", - "shell.execute_reply": "2026-09-05T10:23:36.304163Z" + "iopub.execute_input": "2026-10-08T17:19:05.307423Z", + "iopub.status.busy": "2026-10-08T17:19:05.307323Z", + "iopub.status.idle": "2026-10-08T17:19:05.457983Z", + "shell.execute_reply": "2026-10-08T17:19:05.457429Z" } }, "outputs": [ @@ -927,7 +1014,7 @@ "\n", "To do so, pass one of the following to the `align` argument:\n", "\n", - "+ `'hyper'` / `'HyperAlign'` - hyperalignment algorithm (default). See: https://doi.org/10.1016/j.neuron.2011.08.026\n", + "+ `'HyperAlign'` - hyperalignment algorithm (default). See: https://doi.org/10.1016/j.neuron.2011.08.026. `'hyper'` is a deprecated alias for the same name; it still works, with a `DeprecationWarning`\n", "+ `'SRM'` - shared response model algorithm. See: https://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model.pdf\n", "+ `'Procrustes'` - a single Procrustes rotation of every dataset onto one reference dataset\n", "\n", @@ -936,14 +1023,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "d6eb0755", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:36.306492Z", - "iopub.status.busy": "2026-09-05T10:23:36.306407Z", - "iopub.status.idle": "2026-09-05T10:23:36.440985Z", - "shell.execute_reply": "2026-09-05T10:23:36.440545Z" + "iopub.execute_input": "2026-10-08T17:19:05.459566Z", + "iopub.status.busy": "2026-10-08T17:19:05.459472Z", + "iopub.status.idle": "2026-10-08T17:19:05.510215Z", + "shell.execute_reply": "2026-10-08T17:19:05.509836Z" } }, "outputs": [ @@ -993,14 +1080,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "4fecb5f6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:36.442141Z", - "iopub.status.busy": "2026-09-05T10:23:36.442064Z", - "iopub.status.idle": "2026-09-05T10:23:36.498203Z", - "shell.execute_reply": "2026-09-05T10:23:36.497705Z" + "iopub.execute_input": "2026-10-08T17:19:05.511288Z", + "iopub.status.busy": "2026-10-08T17:19:05.511227Z", + "iopub.status.idle": "2026-10-08T17:19:05.560758Z", + "shell.execute_reply": "2026-10-08T17:19:05.560441Z" } }, "outputs": [ @@ -1015,13 +1102,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jmanning/hypertools/hypertools/tools/format_data.py:495: UserWarning: Missing data: filling missing values with PPCA (observed values are preserved exactly; only the NaN entries are reconstructed). Pass impute= to choose a different imputation model -- see hypertools.impute.\n", - " warnings.warn('Missing data: filling missing values '\n" + "<cell>:14: UserWarning: Missing data: filling missing values with PPCA (observed values are preserved exactly; only the NaN entries are reconstructed). Pass impute= to choose a different imputation model -- see hypertools.impute.\n", + " full_r, damaged_r = hyp.reduce([full, damaged], ndims=3)\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 688.591x480 with 1 Axes>" ] @@ -1063,14 +1150,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "c9589275", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:36.499347Z", - "iopub.status.busy": "2026-09-05T10:23:36.499266Z", - "iopub.status.idle": "2026-09-05T10:23:39.829030Z", - "shell.execute_reply": "2026-09-05T10:23:39.828673Z" + "iopub.execute_input": "2026-10-08T17:19:05.561801Z", + "iopub.status.busy": "2026-10-08T17:19:05.561745Z", + "iopub.status.idle": "2026-10-08T17:19:08.721599Z", + "shell.execute_reply": "2026-10-08T17:19:08.721134Z" } }, "outputs": [ @@ -1105,20 +1192,20 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "aa4b0cf7", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:39.830446Z", - "iopub.status.busy": "2026-09-05T10:23:39.830364Z", - "iopub.status.idle": "2026-09-05T10:23:39.957516Z", - "shell.execute_reply": "2026-09-05T10:23:39.957151Z" + "iopub.execute_input": "2026-10-08T17:19:08.722599Z", + "iopub.status.busy": "2026-10-08T17:19:08.722536Z", + "iopub.status.idle": "2026-10-08T17:19:08.841678Z", + "shell.execute_reply": "2026-10-08T17:19:08.841219Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1100x450 with 2 Axes>" ] @@ -1128,9 +1215,11 @@ } ], "source": [ - "fig, axes = hyp.subplots(1, 2, size=[11, 4.5])\n", - "hyp.plot(full, '-', ax=axes[0], title='250 rows', show=False)\n", - "hyp.plot(full, '-o', resample=40, ax=axes[1], title='resample=40', show=False)\n", + "# backend='matplotlib' on the grid and on each call: fig.tight_layout() is matplotlib's,\n", + "# and on Colab the default backend would be plotly\n", + "fig, axes = hyp.subplots(1, 2, size=[11, 4.5], backend='matplotlib')\n", + "hyp.plot(full, '-', ax=axes[0], title='250 rows', backend='matplotlib', show=False)\n", + "hyp.plot(full, '-o', resample=40, ax=axes[1], title='resample=40', backend='matplotlib', show=False)\n", "fig.tight_layout()" ] }, @@ -1141,19 +1230,19 @@ "source": [ "## Animating\n", "\n", - "Any plot becomes an animation with `animate=True` (a trajectory that draws itself in while the camera rotates; `'spin'`, `'serial'`, `'window'` and `'morph'` are the other styles -- see the [animation guide](../animation.rst)). `duration` and `frame_rate` set the length and smoothness. With `save_path=` the animation is encoded to a file (`.gif` needs nothing extra; `.mp4` needs FFmpeg) and `show=False` keeps the notebook from also embedding a live copy. The returned `HyperAnimation` exposes the finished figure as `.figure`." + "Any plot becomes an animation with `animate=True` (a trajectory that draws itself in while the camera rotates; `'spin'`, `'serial'`, `'window'` and `'morph'` are the other styles -- see the [animation guide](../animation.rst)). `duration` and `frame_rate` set the length and smoothness. With `save_path=` the animation is encoded to a file (`.gif` needs nothing extra; `.mp4` needs FFmpeg) and `show=False` keeps the notebook from also embedding a live copy. The returned `HyperAnimation` exposes the finished figure as `.figure`. (On Colab the default backend is plotly, whose animations are plotly figures; `backend='matplotlib'` asks for the matplotlib `HyperAnimation` this cell uses.)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "a0629326", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:39.958797Z", - "iopub.status.busy": "2026-09-05T10:23:39.958726Z", - "iopub.status.idle": "2026-09-05T10:23:40.627011Z", - "shell.execute_reply": "2026-09-05T10:23:40.626547Z" + "iopub.execute_input": "2026-10-08T17:19:08.842682Z", + "iopub.status.busy": "2026-10-08T17:19:08.842621Z", + "iopub.status.idle": "2026-10-08T17:19:09.471163Z", + "shell.execute_reply": "2026-10-08T17:19:09.470783Z" } }, "outputs": [ @@ -1166,21 +1255,29 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x504 with 1 Axes>" ] }, - "execution_count": 23, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "anim = hyp.plot(full, animate=True, duration=6, frame_rate=15,\n", + "anim = hyp.plot(full, animate=True, duration=6, frame_rate=15, backend='matplotlib',\n", " title='A random walk, animated', save_path='plot.mp4', show=False)\n", "print(f'{anim.n_frames} frames written to plot.mp4')\n", - "anim.figure" + "display(anim.figure)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('plot.mp4', embed=True))\n" ] }, { @@ -1205,14 +1302,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "f39332df", 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).then(function(){\n", " \n", - "var gd = document.getElementById('a74e1add-7641-4b48-b2a1-94fbcc5d9fcd');\n", + "var gd = document.getElementById('d0be8fa5-5323-43cc-ae37-367a78b1af44');\n", "var x = new MutationObserver(function (mutations, observer) {{\n", " var display = window.getComputedStyle(gd).display;\n", " if (!display || display === 'none') {{\n", @@ -1345,20 +1442,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "be0f171a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:40.786756Z", - "iopub.status.busy": "2026-09-05T10:23:40.786665Z", - "iopub.status.idle": "2026-09-05T10:23:40.835076Z", - "shell.execute_reply": "2026-09-05T10:23:40.834672Z" + "iopub.execute_input": "2026-10-08T17:19:09.629589Z", + "iopub.status.busy": "2026-10-08T17:19:09.629509Z", + "iopub.status.idle": "2026-10-08T17:19:09.679625Z", + "shell.execute_reply": "2026-10-08T17:19:09.679012Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1386,14 +1483,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "id": "dee81a4a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:40.836733Z", - "iopub.status.busy": "2026-09-05T10:23:40.836649Z", - "iopub.status.idle": "2026-09-05T10:23:41.757693Z", - "shell.execute_reply": "2026-09-05T10:23:41.756696Z" + "iopub.execute_input": "2026-10-08T17:19:09.680950Z", + "iopub.status.busy": "2026-10-08T17:19:09.680871Z", + "iopub.status.idle": "2026-10-08T17:19:10.139822Z", + "shell.execute_reply": "2026-10-08T17:19:10.139368Z" } }, "outputs": [ @@ -1402,7 +1499,7 @@ "output_type": "stream", "text": [ "clusters.pdf: 125 kB\n", - "clusters.png: 36 kB\n", + "clusters.png: 35 kB\n", "coords.csv: 473 kB\n", "reloaded coordinates: (8124, 3) -- identical: True\n" ] @@ -1439,14 +1536,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "id": "fc606a88", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:41.759716Z", - "iopub.status.busy": "2026-09-05T10:23:41.759587Z", - "iopub.status.idle": "2026-09-05T10:23:41.854109Z", - "shell.execute_reply": "2026-09-05T10:23:41.853647Z" + "iopub.execute_input": "2026-10-08T17:19:10.140866Z", + "iopub.status.busy": "2026-10-08T17:19:10.140802Z", + "iopub.status.idle": "2026-10-08T17:19:10.147485Z", + "shell.execute_reply": "2026-10-08T17:19:10.147137Z" } }, "outputs": [ @@ -1469,7 +1566,7 @@ "\n", "fig, ax = plt.subplots()\n", "try:\n", - " hyp.plot(full, animate=True, ax=ax)\n", + " hyp.plot(full, animate=True, ax=ax, backend='matplotlib')\n", "except ValueError as e:\n", " print('ValueError:', str(e).splitlines()[0])\n", "plt.close(fig)" @@ -1487,14 +1584,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "id": "4171caa5", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:41.855176Z", - "iopub.status.busy": "2026-09-05T10:23:41.855107Z", - "iopub.status.idle": "2026-09-05T10:23:41.857199Z", - "shell.execute_reply": "2026-09-05T10:23:41.856804Z" + "iopub.execute_input": "2026-10-08T17:19:10.148316Z", + "iopub.status.busy": "2026-10-08T17:19:10.148263Z", + "iopub.status.idle": "2026-10-08T17:19:10.149982Z", + "shell.execute_reply": "2026-10-08T17:19:10.149648Z" } }, "outputs": [], @@ -1517,14 +1614,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "id": "e6fba04e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:41.858098Z", - "iopub.status.busy": "2026-09-05T10:23:41.858043Z", - "iopub.status.idle": "2026-09-05T10:23:41.911031Z", - "shell.execute_reply": "2026-09-05T10:23:41.910587Z" + "iopub.execute_input": "2026-10-08T17:19:10.150735Z", + "iopub.status.busy": "2026-10-08T17:19:10.150687Z", + "iopub.status.idle": "2026-10-08T17:19:10.271440Z", + "shell.execute_reply": "2026-10-08T17:19:10.271014Z" } }, "outputs": [ @@ -1560,7 +1657,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/plot.mp4 b/docs/tutorials/plot.mp4 index bcb91b06..e877c03f 100644 Binary files a/docs/tutorials/plot.mp4 and b/docs/tutorials/plot.mp4 differ diff --git a/docs/tutorials/projectile_kalman.ipynb b/docs/tutorials/projectile_kalman.ipynb index a5eb0c65..6c3ecaf7 100644 --- a/docs/tutorials/projectile_kalman.ipynb +++ b/docs/tutorials/projectile_kalman.ipynb @@ -33,11 +33,16 @@ "\n", "## Why a Kalman filter for projectile motion?\n", "\n", - "A **constant-acceleration state-space model** is a very natural match for\n", - "ballistic flight: under gravity alone, each coordinate's *acceleration* is\n", - "constant (zero horizontally, `-g` vertically), so position, velocity, and\n", - "acceleration together form a simple linear-Gaussian dynamical system --\n", - "exactly what a Kalman filter models. That makes the Kalman filter a\n", + "Ballistic flight is a textbook **linear dynamical system**: under gravity\n", + "alone, each coordinate's *acceleration* is constant (zero horizontally,\n", + "`-g` vertically), so position, velocity, and acceleration evolve linearly\n", + "-- the kind of system a Kalman filter is built for. HyperTools' Kalman\n", + "models are not hand-built projectile models, though; they learn linear\n", + "dynamics from the data. `hyp.impute(model='Kalman')` fits one scalar\n", + "linear-Gaussian state per column and smooths across time, and\n", + "`hyp.predict(model='Kalman')` fits a delay-embedded linear-Gaussian model\n", + "(each observation regressed on up to five before it, by least squares),\n", + "which is flexible enough to follow a smooth arc. That makes them a\n", "principled choice for two related but distinct tasks on this trajectory:\n", "\n", "1. **Imputation** (`hyp.impute(data, model='Kalman')`): fill in missing\n", @@ -56,42 +61,55 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "cbe537e6", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T08:26:02.793125Z", - "iopub.status.busy": "2026-07-17T08:26:02.793064Z", - "iopub.status.idle": "2026-07-17T08:26:13.722310Z", - "shell.execute_reply": "2026-07-17T08:26:13.721578Z" - } + "tags": [ + "hypertools-install" + ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m~/hypertools/.venv/bin/python -m pip install --upgrade pip\u001b[0m\r\n" - ] + "outputs": [], + "source": [ + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b27b1874e66", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-09T17:28:43.562891Z", + "iopub.status.busy": "2026-10-09T17:28:43.562743Z", + "iopub.status.idle": "2026-10-09T17:28:43.567211Z", + "shell.execute_reply": "2026-10-09T17:28:43.566803Z" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "tags": [ + "tutorial-prerequisite", + "prerequisite-install" + ] + }, + "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[predict]\"\n", - "\n", "import importlib.util\n", "\n", "if importlib.util.find_spec('py7zr') is None:\n", - " %pip install -q py7zr # for decompressing the SportVU game archive" + " %pip install -q py7zr # for decompressing the SportVU game archive\n" ] }, { @@ -110,14 +128,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "1463c819", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:08.068034Z", - "iopub.status.busy": "2026-09-05T10:27:08.067824Z", - "iopub.status.idle": "2026-09-05T10:27:08.404497Z", - "shell.execute_reply": "2026-09-05T10:27:08.403981Z" + "iopub.execute_input": "2026-10-09T17:28:43.568487Z", + "iopub.status.busy": "2026-10-09T17:28:43.568419Z", + "iopub.status.idle": "2026-10-09T17:28:43.911609Z", + "shell.execute_reply": "2026-10-09T17:28:43.911186Z" } }, "outputs": [ @@ -125,8 +143,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[download] using cached /Users/jmanning/.hypertools_cache/01.01.2016.CHA.at.TOR.7z (5984493 bytes)\n", - "[extract] game JSON: /Users/jmanning/.hypertools_cache/sportvu_extracted/0021500492.json (103997567 bytes)\n" + "[download] using cached ~/.hypertools_cache/01.01.2016.CHA.at.TOR.7z (5984493 bytes)\n", + "[extract] game JSON: ~/.hypertools_cache/sportvu_extracted/0021500492.json (103997567 bytes)\n" ] } ], @@ -176,14 +194,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "ca26072b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:08.405828Z", - "iopub.status.busy": "2026-09-05T10:27:08.405679Z", - "iopub.status.idle": "2026-09-05T10:27:09.937353Z", - "shell.execute_reply": "2026-09-05T10:27:09.936875Z" + "iopub.execute_input": "2026-10-09T17:28:43.912716Z", + "iopub.status.busy": "2026-10-09T17:28:43.912600Z", + "iopub.status.idle": "2026-10-09T17:28:45.393954Z", + "shell.execute_reply": "2026-10-09T17:28:45.393124Z" } }, "outputs": [ @@ -273,7 +291,7 @@ "0.160 72.83623 41.94126 3.62254" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -321,25 +339,25 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "5132bd21", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:09.938529Z", - "iopub.status.busy": "2026-09-05T10:27:09.938439Z", - "iopub.status.idle": "2026-09-05T10:27:13.762233Z", - "shell.execute_reply": "2026-09-05T10:27:13.761738Z" + "iopub.execute_input": "2026-10-09T17:28:45.395242Z", + "iopub.status.busy": "2026-10-09T17:28:45.395150Z", + "iopub.status.idle": "2026-10-09T17:28:49.064305Z", + "shell.execute_reply": "2026-10-09T17:28:49.063875Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -393,14 +411,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "f137ca7b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:13.763458Z", - "iopub.status.busy": "2026-09-05T10:27:13.763371Z", - "iopub.status.idle": "2026-09-05T10:27:13.768802Z", - "shell.execute_reply": "2026-09-05T10:27:13.768506Z" + "iopub.execute_input": "2026-10-09T17:28:49.065482Z", + "iopub.status.busy": "2026-10-09T17:28:49.065413Z", + "iopub.status.idle": "2026-10-09T17:28:49.070890Z", + "shell.execute_reply": "2026-10-09T17:28:49.070465Z" } }, "outputs": [ @@ -516,7 +534,7 @@ "0.839 76.66918 38.04769 13.60741" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -543,14 +561,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "5f8ea7ea", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:13.769913Z", - "iopub.status.busy": "2026-09-05T10:27:13.769857Z", - "iopub.status.idle": "2026-09-05T10:27:14.102983Z", - "shell.execute_reply": "2026-09-05T10:27:14.102495Z" + "iopub.execute_input": "2026-10-09T17:28:49.071771Z", + "iopub.status.busy": "2026-10-09T17:28:49.071714Z", + "iopub.status.idle": "2026-10-09T17:28:49.359505Z", + "shell.execute_reply": "2026-10-09T17:28:49.359081Z" } }, "outputs": [ @@ -613,20 +631,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "5fc9ac10", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:14.103987Z", - "iopub.status.busy": "2026-09-05T10:27:14.103921Z", - "iopub.status.idle": "2026-09-05T10:27:14.317146Z", - "shell.execute_reply": "2026-09-05T10:27:14.316690Z" + "iopub.execute_input": "2026-10-09T17:28:49.360703Z", + "iopub.status.busy": "2026-10-09T17:28:49.360639Z", + "iopub.status.idle": "2026-10-09T17:28:49.550579Z", + "shell.execute_reply": "2026-10-09T17:28:49.550230Z" } }, "outputs": [ { "data": { - "image/png": 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wyn1peidZvFLOggFbIiIiovSH5RGIiIiIiIzMw8MDtWrVUuUppDbuokWLVA3hb7/91thDIyIiIiIjYNCWiIiIiMjImjRpgg0bNmD+/Pmqjm65cuVU4FaauhERERFR+sOatkREREREREREREQmhDVtiYiIiIiIiIiIiEwIg7ZEREREREREREREJoRBWyIiIiIiIiIiIiITwqAtERERERERERERkQlh0JaIiIiIiIiIiIjIhDBoS0RERERERERERGRCGLQlIiIiIiIiIiIiMiEM2hIRERERERERERGZEAZtiYiIiIiIiIiIiEwIg7ZEREREREREREREJoRBWyIiIiIiIiIiIiITwqAtERERERERERERkQlh0JaIiIiIiIiIiIjIhDBoS0RERERERERERGRCGLQlIiIiIiIiIiIiMiEM2hIRERERERERERGZEAZtiYiIiIiIiIiIiEwIg7ZEREREREREREREJoRBWyIiIiIiIiIiIiITwqAtERERERERERERkQlh0JaIiIiIiIiIiIjIhDBoS5QCnj17hnbt2iFz5szQaDSYMWMG552IiCiN4n6fiIgo/bh58yYaNGgAR0dHdb6/ZcsWYw+J0giNTqfTGXsQRGldly5dsG3bNowZMwbZsmVDhQoVcPv2bZw4cQLff/+9sYdHRERESYj7fSIiovSjSpUquHPnDr7++mtkzJgRtWrVwqFDh/D8+XMMHjzY2MOjVIxBW6IUIIHaevXqYeXKlZHrPv/8c/zyyy/g9yZERERpC/f7RERE6UNgYCBsbW1VwHbChAmR65s1a4ZLly7h7t27Rh0fpW4sj0CUAuQbNvnGjYiIiNI+7veJiIjSBy8vL/WT5/uUHBi0JYrnG7MiRYqoRW7rvXz5EtmzZ1eXQISHh8c7f0uXLlU1bSSbVrJq5bYsPXv2VPeFfp0sREREZBz79+9X++LNmzfHeGz16tXqsaNHj8a7De73iYiIUg/JgjU8H4++vIuUOnR1dVW3R4wYoX4nb968qjzCjh07cO/evchtyXqixDJP9G8QpRM2NjZYtmwZqlatqi51+Pnnn9X6zz77DG/evFEnZmZmZvFuo0aNGlixYgW6d++O+vXro0ePHmp9gQIF8PjxY/z111/qcSIiIjIuOcHKnTs3Vq1ahdatW0d5TNbJvtvDwyPebXC/T0RElHq4uLjEOB8PDQ3FkCFDYGlp+c7fb9Omjcqwled37twZTZo0gb29Pezs7FTM4OHDh5g+fbp6rqwnSiwGbYniUalSJYwcORKTJ09WJ3DSDXrt2rWYMWMGChcu/M65y58/v1okaCvP79atW+Rjcl+CtobriIiIyDgkC0b2yfIlrZxoSQdo/WWPe/bsUV/gvgv3+0RERKmHBFejn49Lkpafn586V3+XUqVKwcHBQQVty5UrF2VbOXPmxKtXr3i+Tx+E5RGIEnDJQ/HixfHRRx/h008/Rc2aNTFo0CDOGxERURojV8QEBwdjw4YNkevWrVuHsLAwnnQRERGlccuXL8evv/6KKVOmoHbt2sYeDhGDtkTvIpdFLF68GHfu3IGvry+WLFnC+rNERERpkNSxd3d3V+UQ9OR25cqVUbBgQaOOjYiIiJLPuXPnMGDAAFXmYOjQoZxqMgnMtCVKgN27d6ufQUFBuHnzJueMiIgoDWfbHjx4UNWhu3XrFo4dO8YsWyIiojRMyhi0bdtWlTBcuHChsYdDFIlBW6J3uHDhAsaNG4devXqhbNmy6NOnj6p196ES0o2SiIiIUlanTp1Uo9E1a9aoLFsLCwt07Njxg7fL/T4REZHpiYiIQNeuXfH69Wts3rwZtra2SbJd7vcpKbARGVE8pHNkz549kSNHDsycOVOVSJDLJqXQuJRM+NCi50J2DtJxkoiIiIzP2dkZjRs3xsqVK9UVNo0aNVLrPhT3+0RERKZn7Nix6sranTt3Il++fEm2XdnvJ0WyF6VvDNoSxWPChAmqts3ff/+NDBkyqO6Q3333Hb755hu0a9cOTZo0ee/5K1++vPopTc0aNmyosnoku4eIiIiMXyJB9vNi/PjxSbJN7veJiIhMy8WLF9V+vkaNGnj+/Ln6wtZQt27dPmi/L81MpT6uJH7Z29ujefPmSTBqSk80Op1OZ+xBEJmiM2fOoFKlSvjkk08wa9asyPXh4eHw8PDAo0ePcPny5QRlycqlEZ999hnmzJkTZTuSsbt27Vp4e3tD/lfk/45ERETGFxISgmzZsqlLJp8+fQpra+tE/T73+0RERKbvwIEDqF27dpyPJ+T8/O7duypDd+rUqRg+fHjken9/f/Tr1w9//vmnurrW1dVVPZcoMRi0JSIiIiIyEBYWpkojSUbMokWLODdERERElOLYiIyIiIiIyMCWLVvg5eWlyiQQERERERkDM22JPuDSyZcvX8b7HEdHR9jY2HCOiYiIUoHjx4/jwoULqr6dNB+TUkl63O8TERGlH4GBge9sJJYpUyZYWlqm2Jgo/WEjMqL3dOTIkXjr34glS5agZ8+enGMiIqJUYO7cuaoJSZkyZbB06dIoj3G/T0RElH5IE7FevXrF+5z9+/ejVq1aKTYmSn+YaUv0nl69eoXTp0/H+5zixYsje/bsnGMiIqJUjvt9IiKi9OPJkyeq8Xh8ypcvDycnpxQbE6U/DNoSERERERERERERmRA2Ivs/nU4HHx8f9ZOIiIjSNu73iYiI0g/u94koNTJq0PbBgwdo2bIl7O3tkSVLFowYMQKhoaHqMWnw1KFDB2TMmBGZM2fG4MGDERYWFue29u3bh7Jly6qmT0WLFsW2bdsSNRZfX1/VNEp+EhERUdrG/T4REVH6wf0+EaVGRg3atmjRQgViT506hV27dqkizhK4FR9//DHu3bun1m3duhWbN2/GxIkT46w10rx5czRr1gwXL15E//790b59e9y4cSOF3xERERERERERERFRKq1pe/XqVRQrVgx37txB3rx51brjx4+jdu3a6rF8+fLh33//hYeHh3ps2bJlGDlypArQarVRY80//fQTFixYgGvXrkWuk+1IUehp06YlaDxSGkEybd+8eQMHB4ckfa9ERERkWrjfJyIiSj+43yei1MhombYBAQHqp5Q/0JOyBoGBgVi7dq0KzFasWDHysRo1auD58+e4e/dujG2dOXMmMrhr+PwTJ07E+frBwcHqH27DhYiIiIiIiIiIiCjdBm2LFCmiAraSCSslEvz8/DB06FD1WEhIiHrMzMws8vnOzs7q57Nnz2Jsy9vbW9W9NSTPj+25epMmTVKZtfold+7cSfjuiIiIiIiIiIiIiFJZ0NbOzg6rVq3CokWLYGtrC1dXV5QsWVI9FhEREeP5FhYW6qdGo4nxWFzPj+25eqNHj1alEPSLNEUjIiIiIiIiIiIiMjZzY754kyZNVI1aKXvg5OSkGpKZm5ujTJkyKpAqwVh9/VovLy/108XFJcZ2smbNipcvX0ZZJ8+P7bl6VlZWaiEiIiIiIiIiIiIyJUbLtDWUJUsWlRk7f/581KlTB9WrV4f0Rzt58mTkc44ePYpMmTIhf/78MX5fat/K44bkvru7e4qMn4iIiIiIiIiIiChNBG0fPXqkyhJcuXIFw4cPVw3IpNasBGc7duyIL774AufOncORI0cwcuRI9OvXT5U88Pf3R6FChTB79my1nS5duqiM3TFjxsDT0xOzZs3C3r170bt3b2O+PSIiIiIiIiIiIqLUFbRdt26dypytVq0arl27hsOHD6NcuXLqsV9//TXysRYtWqBZs2YYN25c5O9K8zJ9LVtpOrZt2zZs3boVJUqUwNy5c1UAWG4TERERERERERERpSYandQhIPj4+MDR0VHV0nVwcOCMEBERpWHc7xMREaUf3O8TUWpk1EZkRERpXUhwMPbffImK+TLDyc7S2MMhog9wy8sPz3yCUKWAM+eRiIiIiFJcSFgErj31waNXgch4fR3MLSzhlLcU8harqG5T2sKgLRFRMgkK8MONWa1wwy8njlYcie9bFOdcE6ViE1fsRMWXW3G/Yid0atHM2MMhIiJKc3x9fdGzZ0+cPn0ad+/ejfKY9K2ZOHEizp49i8DAQGzevBlNmjQx2liJUkJ4WBiun9qLN+e348mbEIzyaa0Ct+Jfq1nIqXkBnAVeb7bHdecGyFyzHwqW9OCHk0YwaEtElAyCAnxxc2YLlAo+g4JmF3CrwDDOM1Eq95XtRhT02Ym1J3wx284NA+sWMvaQiIiI0oyrV6+idevWyJMnT4zHdu3ahW7duqmg7cKFC2Frawt7e3ujjJMoJfj6vMbFP2ahoOcSFMNLte6FLgNCwlrAydYS+V3scSmwDl4HXUfuEE9khB8qeW8CNm7C2R2VkLnFeOQpVokfVirHmrb/xxo3RJRUAv19cWtWM5QIPocAnRXuNFqG4h6NOcFEqX2/f+8osKQRAnWWqBQ8B33ql8MgBm6JiIiSxOzZs2FhYYEiRYqobFvDTNvixYvjm2++QefOnd9r2zzfp9QiyO8VzmyYCre7K5AZPmrdG9jhhmNVIF9NZPbohnxZHKHRaKJk4145sg0hJ5aijO9BmGl0OKArB8ePN6FsHicjvhv6UMy0JSJKQv7BYei38ix6Bpghn9Ya9xovR/HKDTnHRGlBnspAluKweX4Zbc3+wc9/2UPauX5Rjxm3REREH2rgwIHq54EDB6Ksv3XrFm7cuKG+ZC1Xrhy8vb1Rt25dTJ8+HRkzZox1W8HBwWoxDNoSmTKdToftF54g+I9haBf+p1r3WJMNj0oOQOmmn8DdyjrO3zUzN0fJGq2BGq3x5PYlXPp9Aka8agXz5aew+dOqyJ3JNgXfCSUlbZJujYgoHQsOC0ff5afw7x1fjNAOw91Wm1GMAVuitEMyGtx7q5uDHP+Rw2tM33sDM/beMPbIiIiI0qw7d+7AzMwMP/30E+bOnYu1a9fizJkz6N+/f5y/M2nSJHVFjX7JnTt3io6ZKDEu3fNCh9+OYuCas5jk3xyXNIVxuvyPyPb1Jbi3GQLLeAK20WXPXwJVBq9Ejuw54e0Xgp5LTsDniSc/kFSKQVsioiQQER6O1Ytm4Mgtb9hZmmHxx1VQvGwVzi1RWlOqI2BpD6eAu5jn/lytmrH3Jn49wINhIiKi5ODl5aWyEH///XdUqlQJVapUUUHZTZs2ISwsLNbfGT16tCqBpF8ePHjAD4dMjvfj2zg7vS2eLWyPk3dfwdpCix713FFg9DGUb/4JtOYW77VdOytzLO7pjuyO1qj18ndY/1YZfhe3J/n4KfmxPAIR0YfS6XDqtwHo9Xw9LC3qIU/3eSjH2kFEaZNVBsD9Y+DfmWh0ZzJGNdqBH3ddx5Rd1+FgbYFulV2Bg1Pkmxyg9mhjj5aIiCjVk6ZjlpaWyJw5c+Q6aVYmAdtXr17BxcUlxu9YWVmphcgUBQf549y6iSh5eyHKaoIRodVgQNEgfNSqCbI72iTJa2RztMbSnu6499tkWCIU2g3dsW57a5hptYCFDfa69IS9tTky21mifrGsqJA3E49hTRCDtkREH+j4qrGo9Hy9ul24YgO4F4p54EhEaUiVQcCJBYDfUwzQrYdf7baYs98T3269hPJ3F6DotdlA7a+NPUoiIqI0oUyZMvDz81MlEaSmrfD09ISdnV2sAVsik6XT4fzfa5H53+9RSfcU0ABXzIsBTaZgVLnqSf5ybtkdEPrxauxa1heNwvahY/BGBOgsYasJwZMXb/BTeEf1vN8O3cbM7HvQ8tVSHsOaGAZtiYg+wOnt81HJc7q6faTAEFRpHndtLSJKI+ycgS/vqmxb7J+IYbXM4OPRFI4npqPotQ24VfwLFKg50tijJCIiShNcXV3RunVr9OnTB8uWLVMNxr7++mv07dvX2EMjSrBnT+7j+Yo+KB1wXN33ghPulB2FCs36QWuWfJVLS+R2RpHRG3H32AZkOTYRtr531fqBFlvRMEcwfnP5CrkuzEbLV79jsWUX1C/5OVgB2nQwaEtE9J4uHtqEkidHqW9Ij2bpCI+u33EuidILcyvg/4FZzf6JGKudAo1FGH4KbYffzntgcRlvVCvkbOxREhERpQkSrB00aBCqV68Oa2trdOjQQdW1JTJ1YeERWHb0Hn7dcwGbcAchGjOcytEVpTuPQ0UHpxQZg7mZFnmrdgAqtwHOrwaOzAG8r6Ow1y789HIfYB6C7do6OO6XFWt/+xsL+9VFnsy2KTI2ip9GJxW9CT4+PqqrpBQpd3Bw4IwQUbyuntiLPDu6wE4TjFP2tVF2yEbV1ZaIEmbKlCn48ssvVUfovHnzYuXKlZg6dSquX7+O7NmzY8CAAerx2Lx+/RpOTlEPcmUfLutTfL8fGghMzBZ518s8O44Fu+K8pgiadvoUZYu5vf+2iYiIKEnwfJ+M4cbFExi2LwAXn/ir+x2zP0X/huWQv0hZ434gEgZ8dglYUAcIDwHMLBGUpyas7/yFMJ0WB8wqo1jXqchRoIRxx0nMtCUiSqyt5x5hy/ZzWKgJwUXr8ig5cA0DtkSJcPDgQWzbti3yvtSl+/nnnzFu3DhUqFABp0+fRufOnZE7d2506dIlxu8/efJEZdkYdoLWSlMFY5CmYwZcwp6gudkTNMcxhKxbiUcVRyBnky8BjcY44yMiIiKiFBUaEozTy0ehwoOlKBfWHfesm2J0k6LoWKEJtFoTOCaU49LrOyMDtvLTOtwPYU4FYP7qFupFHMGblY0Q2H8fbLIVNvZo0zUjneEQEaU+wWHh+GbLRXyx9hz2hxTFbOdvUfDzzbCySpoOn0TpwdOnT/Hxxx9j/vz5kesKFiyomou0bNkSOXPmRIsWLdC4cWMcOnQozm3I85ydnSOXTJkyxfu6Uv9OsmwMlyQJ2B7++W3Dhu/fANWGqtXheWvA08INlpow3D35Jy4/8P7w1yIiIiIik/fo1iXcnlINlR8uhrkmAg0ye2Hf8FroXDGPaQRs9cew+ye+PYb91uvtz/tHYV6mE15034dryAdHnS+er+r3NiuXjIZBWyKiBHh85xpu/FgTR44fU/cH1SmIgZ8NhY29I+ePKIHCw8PRsWNHfP/99yhatGi8z33x4gWyZfuv7IChZ8+eqWzbfPnyqeBtgwYNcPTo0Xi3J3XvpByCfpEs3iQ72NU3Has3Rt03u3sIeSq3xlTHr9E7aAg6Lz6D8w8SXrqBiIiIiFKf00f3wW5FQ7iF3YAP7HCq4nRUHboGzvZWMBmxHcPKT7m/fyIyP/wbr5ovQoDOCq6+Z/Hs3+XGHnG6xpq2/8caN0QUl0uH/0DuvQPgCH+cQHEEdN6CWm5ZOGFEiTRy5EhVd1afZavRaCJr2ho6cuSICsRevnxZdYyOLigoCPv27VOPBQQEYMmSJWo5f/48ChcuHGemrSyG+30J3L53Tdv9kwCt2X8Hu9EPhiPC4esxHD2XnMTpe69gb2WG35uaoWjF+ol/LSIiIvogPN+n5PbP33+gzKF+yKAJxA3zwsjYcy2y5CpgehOfgGNY1B6NDTOGoN3rxTho3wQ1h68xxkiJQdv/8B9xIorNqe0LUPrkl7DQhKudr8NHa5Atd0FOFlEibd26FWPHjlUBWalHG1fQVu5Xq1YNP/74I7p3757g7ZcuXRqtWrVSr2FK+32/4DB8vOQE6j+chT7mO3Gz4ngUajLI6AfiRERE6QnP9yk5bf33POruaQB7TRA8bcsg92dbYWWXMVVP+u0nXvhuzhIcDi+OFR9XRPVCLsYeUrrE8ghERHE4deRvlDw5WgVsTzvURZ5hBxiwJXpPM2bMUJmw9vb2MDc3V4u+nq3UuBXSWKxu3boYPnx4ogK2ws3NDc+fPze5z8feyhxLe1eCi9PbA/cCx7/D8bU/QBcRkSTb9/d9jWOrJ+DI3E+wY+XPOHXnubq07cGW7/HgZQBCwyOiXgonAV0iIiIiShIrj93DF9seYnxYd1zPUBn5v/gz1QdsRf7sLihUuam6PWH7VYTpjykpRbE8wv/xmzciMvTkyUPofquJHPDGRftqKD7kD2jNGOwgel+SQevv7x9lXcmSJbF7924UL14cOp0OtWrVQr9+/VQZhcSQ3y1VqhR69+6NIUOGmOR+PygkDKfmfoxqr7ao++cy1kexfktgaZvhvbf5wPMizFa1RQ7ds8h1f4eXxbmIAhhmsQE/hbbDb2iHMY7b0TVgJW6V+AI5WoyBjSX/LSMiovSF5/uUHJb9cwNjdtxUt3tXzYdvm7pBk4a+IH8dEIJa0w7APMAbU8u9QO0OA409pHSHQdv/4z/iRKQnmWkHpnVG/cBdeKTNAZehR2Bp78QJIkrqg5D/l0ewtLREzZo10bZtW5Vla8jZ2Rl3795FjRo1VLZumzZtMHv2bGTPnh1ly5ZFSEgIZs2ahU2bNuHatWtwcnIy2f2+ZNceXTUeFT1nqG7Cd81cYdZ5FXIXLJnobT25dx2aJU2QDd54Chc8ylITeXxOYZtzH/wNd9R9vgy9Q1arhr8aDfA4IhPu6bIhRGMJWzt7ODjnRLba/eCYv0KyvFciIiJTwvN9SmoH1/6EbFcWo3PIN+hQsyy+bOSmjm3Tmt8PnkajfU1Vrd7jLu1gVbAGshYojayuRaG1MKEGa2nU22sTiYjSq1jqP/781w2setUWG6wuI3uxOgzYEiWzBQsWwNPTE5MnT1ZL9CxaWcLCwtRP4eLiomrX3r59W5VbqFy5Mg4fPpzggK2xaLRaVOk+BqcPlofr/k+RN/weHqxog19qbEOPqgWQwdoiQdt55fUEoctaIw+8cU+bC/b9d6N81lwyWegtmR7qhKEydGPXQYNw9Ts5tC+RAy/fbiBQ0nSBoYtdEOCmQQf3XKhVOAu02rR3okFERESU1A6tGI+at6apgqMz3S6iWqNOaTJgK9rVKIcjF5uhqvfvqOS1AZDlKBCm0+KpeTZcydwQbp1+QO5MtsYeaprETNv/4zdvROmUvs5j7a9V4Pafm17ovugEBpptUpcX69cTUdpi7P3+s0d38GJZd6zxL48V4Q1gY2GGpkUc0C3ncxSv0ggWlm+btRl6ExiKq098sHrjZkzw+xYBGjto+uxG1tg6E0fWsDUHIsKAEu2gc2uMZy/f4MYjL/jeO4/hb9ohEG9fp4/jKTTKCxRtOgh2DqYd/CYiIkpt+31KGySB4PDSr1D93q/q/pmc3VCuz5y3lzWlYXK12Lm/1yL8yjZk8LmJnGEPVNM1MTW0A/506oodg6rB1pJ5oUmNQdv/4z/iROnY/4MbgeX6YuY5wCL4FQO2RGmcKez3I8LDse3CI8zadxu3vPzRSnsYMyx/RaDOEvct8iPIPAPMI4JhHe6HzOFeaBE8Dvd1WdXvVrJ7iikdysPVrcw7v4yKcf//JAC84fRDbDx1F39EDEIerRd8YIdLOTugeOsv4eicPSWng4iIKE3v9yl1U2WuFg5BlcdL1f1TefuhwkdT0nzANjZhYeF48OAOnt88jTEnzXHN1xrfNC2KPtXzG3toaQ6Dtv/Hf8SJ0rfwPWNgdmRG5P3QGqNhUWeUUcdEROljvy9ZG+cfvsHj3TPg8XARnOAT6/N6hHyJq3YVUa2gM0Y1LoKsDjGzceMK0Ma5HoB/QCDO75iHXFcWII/u0dt1OmtcyNMdxdt9BQfHTEn8jomIiNLvfp9Sp2MLh6Dyw8Xq9olCQ1Cx6/fGHpJJWHPiPkZvuog8mWxxcEStNFsmwlgYtP0//iNOlH7pQoNwZ3p95A+48Pa+1gKa77yNPSwiSof7/fDwcDy4cRbedy9BF+QDjYUNrOydYJMpJ7IVKAl7O/tE1+mOEriNCAdqj44z8/f836thf3w6CoXfUuteIQNOFv8WNVv3gZV52umGTERE6Yup7vcpdViy9xzq/9MWuTTeOOY2CpU7x34slR4FhoRjyoQRaIn9sG01HYXL1TL2kNIUBm3/j/+IE6VTOh0u/9oFxb3+VHcjNObQ6sJYy5YojeN+P24R4RE4u2cZXE5ORZ6IR2gbPAbemcrim6bFUK9oFmZQEBFRqsP9Pr2v/defo/fSk8ime4EfSz9Fzc7sdxLd6Z9ao7zvPhzL2gmVP/mNf2xJSAsjOnbsGKpXrw4bGxs4OzujU6dOePbsGZYuXapOCGJbDh48GOu2MmbMGOO5r1+/TvH3RESpy/nV30YGbG/nbAHtmBdvA7ZyGbFkpRERpTNaMy3KN+6FnF+dxz+V5uG+fSncexGAvstP4Zc5U3D32jljD5GIiIgo2b0OCMHQdeckzwd1KpVlwDYO2pJt1c+8z/5WtX8pDQRtg4OD0axZMxQuXBiXL1/G3r17cefOHXzyySfo3LkzvLy8oiybN29WlzOUK1cuxrYCAwPVZQ5XrlyJ8jvyfCKiuFzYuwqlb85Wtz2dqiN/3xVvH5DLihm4JaJ0zszcAtUbd8b+4bXwSa0CyGv2Ah97T0OONXVwfMEgBAX4GnuIRERERMnmwtIhqBm0H4Wz2GFM8+Kc6TgUrdYKATorZIMXbp77h/OUhMxhJC9fvsSLFy8wcOBA5M//tsNct27dMGfOHFhZWanF0OLFi9GnTx9kyJAhxraePn0KCwsLFClSJMGX7EnQWBbDyyWIKP245eWHbf+cQgkAD2yLocCgbVGfoK8HKfUfiYjSMXsrc3zZqAi6umlwc30ZlAo8gUqPluHR1N14Xe8nFK/azNhDJCIiIkpSt88dRI3nK1HDEjhXvTEszY16obpJs7a1x5kMlVHO7yBentoAlKtp7CGlGUb7q8uePTsqV66MadOmISgoCI8fP8by5cvRt2/fGM+9du0adu3apQK8sZGSCtJ5uVChQmq7NWrUwI4dO+J9/UmTJqlMXP2SO3fuJHtvRGTafINC0W/5KSwIrodvM09HjiEHYv/CR2Xcssg8EZHIlc8NJUfuwSmPX/EMmZBT9xTF/+qK4zO64s1LNm8kIiKitEEXEY6wHW+TeI45NEAZ9xrGHpLJ0xVtoX7mevIXSyQkIaN+VbBnzx4VcM2bN68qk9C1a1cMHz48xvN++ukntGrVCq6urrFup2LFiti2bRs2bdqEnTt3ok6dOmjRokWc9W/F6NGjVUkF/fLgwYMkfW9EZJqkO/rotcdwy8sf2RysMbhXV1hYRs3sJyKi2MkXXBUadoX14FM4nrmVWlfp9XYEzKqEQ5fvvfvf4IgIVdYqLCyMU0xEREQm6ey2uSgceg1+Ohvk7TjN2MNJFdyqt0WwzgK5dE9w9+pJYw8nzTBaeQSxcuVK3Lp1C+PGjVNNySZPnowSJUqgXr16kc+RoO6KFSuwf//+OLej1WrRqFGjyPtlypTB2bNnVUmFmjVjT8uOrQQDEaV9J5aNwhd3d+CG+TBM6d4WLhn47wARUWI5ZsyMSgOX4dqxnbDbPRS/h1TBrBWX0LmiD75uWlSVVNALDw/H8+fPVVksw9JUZmZmquyVXCVla2vLD4GIiIiMzuf1C7iefduQ+nz+fqiaM/bkQYrK3sEJx+2r4aWPH7xveiMfSwCn7qDt+fPnMWjQINU8TMoa9OvXD+vWrVMZsvfu3YOLi4t63uzZs1G2bFl4eHgkavtubm64ePFiMo2eiFKj83tXo/L9+eoag/FlA1Emd0ZjD4mIKFUrUrkxAktWh//ft4AjD7HmxH08un4KX1bPjOLVWuDVq1e4f/9+rJm1Esx9/fq1WpycnFSpKulRQERERGQsl1ePhgfe4L4mJ8p3ZKm8xHhQezaGb7gAtzsZ0D3ZPqH0xWjlEW7evKmyKiRgqycZtnLJnL5Ugb+/P+bOnYshQ4YkevuXL19WJReIiMSDG+eQ/5+h6vYx5zao1PpzTgwRURKwsbPHty1KY3XfSnB1NMfowJ9RfG93HJ31EW5cvZSgUggS3JUv8qVkFREREZExnL98GeWfbVC3fWqNg7W1DT+IRKhXLCsszbS4/swXR26x30GqDtq6u7sjJCQEo0aNwu3bt3H9+nXVaCxnzpwoVqyYeo6UN7C3t0fbtm2j/K4EcyXYK1m4YtWqVVi2bJnahqenJ8aOHavKKcTVuIyI0hffNy8RsbYrMmgCccWiBMr1nWfsIRERpTlVCjhjx0APvHYpr+57vNyCbHv6w+tpwvoGSHBXjuMkM1dq3xIRERGllKdvgtB3yxN0C/kKf2fughI123HyEymjrSU6VcyNXBovYFV7PLh5nnOYWoO20lRs69atOHToUGT5A7k8bteuXbC2tlaXzE2fPl0FXqXmWWwH9voD+qxZs2LOnDkqECxNyWSbshQsWNAI74yITK3xmOdv3eAa8RDPkQkuH6+BJetZExElC3t7B3gMXIp9JX/CM10muOIRyp8YhJMXL0Gn0yVoG15eXrh69SoCAgL4KREREVGye/YmEJ0XHMNz32C8cnFH5X5vEwQp8YbVK4xfbX9DlYgzcFjVGKe2/QYdv4x/bxpdQo+g0zgfHx84Ojqqy/IcHByMPRwiSiL/LBuD6ndmIERnjjstNsCtfG3OLRFxv5+MgoKCVJmqwNfPkPPf0cgXcRdeOkeMcZiAtpWLIKN1zC/jY6PRaNQVWFmyZFG3iYiI3hfP9ykuXg9vwntxZ3wR2Af+joWxtl9l5M7EBqkfwvvpA7xY2BZuYdfV/QvW7nDuOAc58hXhH2JqybQlIkpu+649w9BrhXEyojDOl/6OAVsiohQgZayETcaseF1/Jp5a5YeL5g2Gv5mIIXte4PSToCjPz3ZjBbJfXxpjO5JX8PDhQ9UHQUpqERERESUlr4eeCFvUBEUjbmKa9RKs7VuJAdsk4JwtN/KNOISjrgNU8lSpoJNwWlodx1aNZdZtIjFoS0Rp0m0vP3yx5hy8dBnxR9kFcG/zhbGHRESUbjJt9SIsM+BZzal4Y5sX+bVP8VHYBvxw+DUWnPFBcLhOBWxzXl8CnSbu7FtfX1/VpEyalRERERElBe9njxC4qAWy657jgSY7nHutRu7MdpzcJGJpZQ2PXpPxtOvfuGxZGjaaEFS++TPWzf8BwWHhnOcEYtCWiNIcP59XWLr4F/gGh6GCqxO+bV7K2EMiIko3ojcRC7NygmfdxbhfqCeGWWzAQLNN2HvLB5d3/6YCto/ceuFp4e7xblN6HUjj2nv37qnbRESUNskXddKIPG/evHE+59NPP2XZHPogoSFBeLmwDfLoHuEpnGHWcxty5CnAWU0GeQqXQbFRB3C86Ff4N6IEvr1bEl0XHMfrAF5FlRAM2hJRmhIu3cfndsK4wB8w0m47fu1WDpbm/KeOiCilSEPZ2HgV6aECtBK4vWHVA33D12N6WDvMRxtEJLDFgre3N5uUERGlUdKEUpqLS+A2LqtWrcK5c+dSdFyU9pxcNBSFQ6/hjc4OIV02IodrIWMPKU3TaLWo1PFLRHTbpI4TT917hS7zj+HlyxfGHprJYySDiNKUkwsHoUzgMQTpLFCvaUdkyRB78ICIiJKHjY1NnI89z9caEp7VaqB+rg6rg2XnfTHu0Ct4ByQsgzY4OBjXrl3Dixc80CciSkv27t2LwYMH46uvvor1cWlyOWbMGEyfPj1B+wppPma4EIlrR3egyrNV6vadalNUJiiljOqFs2LDgCpwtrNEG+9f8XpOHfi85vFcfBi0JaI048Tm2aj89O0O+FLFSShcrpaxh0RElO7Y2dnFGbjNcmczNNJkTLIuAPxrMxj5zLxx8XkIhu7xxj/3A+NtTmbYpOzu3bu4f/++uk1ERKnfwIEDMWDAgFgfk+zbjh07YuHChciaNes7tzVp0iQ4OjpGLrlz506GEVNqExQajjGHA7Ar3B3HnVqgTP1uxh5SuuOWLQN+714IrcyPIn/EXdyd1x6hIcHGHpbJYtCWiNKEq8d3o8y5Mer20Vx9UKFpX2MPiYgoXdJoNMiZM2eM9fqmY1Ii4VKdlQjXWsJSF4K9lsNQO+Nz+IfqMOP4G9z+e/47m5PpeXl54caNGwgLC0umd0NERKagT58+6NKlC2rVSlhSxujRo/HmzZvI5cGDB8k+RjJ98w7ewvEXtvjWehSKfDzf2MNJt/LlzYtXrVYiQGeFUkGncWrJUGMPyWQxaEtEqd7jO9eQbWcfWGrCcca+Bir1mmLsIRHRO0yZMkUF9yRbUsjPxo0bI0OGDMiePTsmTpwY7++vW7cObm5uqi5WuXLlcPToUc65CXFwcECWLFliDdhK07EQuxy4XGcFws2sYKYLxaLg4Rhc4DkGmW9C+4C1+BXtscexQ4Jey8/PD9evX0dICBtaEBGlRbNmzVKZthKITSgrKyu1LzJcKH179iYAvx28rW5/16wYHO3jLudEya9Qmeq45vH2vL3S41W4dnIvpz0WDNoSUarmFxyGjeuWwgk+8DQrgKKfrIbW7N3ZWURkPAcPHsS2bduirGvTpg3Mzc1x7NgxLF68GFOnTsWyZcti/f0zZ86gW7duGDRoEC5evIj69eujWbNmePXqVQq9A3oXCcjnypUr8hJWjS48MmCrF2rjgkt11yDMzAZaXRi+eDwUQ803YL62A6YEtVZ1bldf8kV4xLvLHwQFBamMWwZuiYjSHqlhu3v3blhYWKhjhYIFC6r1cnvcuHHGHh6lEg+W9MZEzEb9nKFoViq7sYdDAMo16omTjg2h1eig2f01dBERnJdoNDoWAlOkMLnUupFLJ/gtHFHqICfy/Zafwt/XnqOT3RkM6dUFWXMVMPawiCgeT58+RbVq1VTQtlixYrhz545qKFWhQgU8fvxYZdmKsWPHquecOnUq1pp3EqCTEzi9/PnzY+jQofj8888TNP/c76cMOcx8+fKlqj0bEceBuDbUD2V2t1aB3QitBY423Iml533x1+239W2LOltgcKWMcLY1S1BmVZEiRdSJPBERpU4HDhxAz549I6/Gif6lnBwvNGzYUH1xK1d1GF7ZERfu99O325eOIe/vjVRw8GqzrShagb1PTIX343uw/c0dtppgnKk8UwVy6T/MtCWiVGvKzksqYGtlrkWnngMZsCUyceHh4aqJyPfff4+iRYtGyZzNmzdvZMBW1KhRA+fOnYs1c1KeX6VKlSjrqlevjhMnTsT52uwibbyM28yZM6sAvTQoi7M52f8DttqIUJQ6PgSTtPMwtLIjbM01uOodimF/eeP8s+B3NiiTz9nT0zPOADEREaU+hQsXRokSJSIXuS/kdkICtkR+279WAdtT9nUYsDUxzjlccT53V3jrHHDg4l02mI2GQVsiSpVObJ6FJsd7IAteYWr70iiTO6Oxh0RE7yC16KQOrZQ2MOTt7a0Ce4acnZ1VkFeycKOL6/nPnj2L87XZRdq4JANWPvscOXJEWW9Y6/Zs0914XKg7Mry8COeHu9H96Y+YWj8zCjiZwy9Eh9f/LnrboOwdh6/+/v64d+8eD/qJiIgI5w9tQamgUwjRmSF7m/h7JpBxuLX9FvUifsGsF+447OnNj8EAg7ZElOpcObYLZc59j9La25jmdhUtSkcNAhCR6dm6dSv27t2rmolEF1tWpNSt02dqJvT5sT1Xj12kjU8+H8mmlvIFEsSN3pxMPCncA/4Ob2sVOj39B1Uvf4uJtTPjZ+dtGGqxAT+FtsMY35YI18Vf51ZKMkhwn4iIUp9atWpFlkaIjVydwyqPlBBSI9Xu4Nu6x2eytkXO/MU4cSYok1MmtHJ/W+Zw3sFbxh6OSWHBLyJKVR7fuYbsu/rCUhOOM/Y1UK0Hmw8QpQYzZszA+fPnYW9vH2W9NBOREy9XV9co6728vGBmZoZMmTLF2JY0t5KgXPTnu7i4xPn6EiSUhYxPyiRIeQxfT6sYzcmgNcO1GvPgdngg7F9fRcbnx1FpZ31odBHYn7krfnnUFBF3AhESrsPnFR1hFk+g/sGDB+q1bG1tU+aNERERkUm5engTioXfgr/OCoXajTX2cCgefarnw8pjd2B7ezeuX9DArVQlzhczbYkoNfF98xLBKzrACT7wNCuAop+shtbs3Y1piMj4Fi9erIK2UqdWv4g///wT+/btUxk10qRM7+jRoyhVqhQsLS1jbKtixYrqcUNy393dPQXeCSUFCchnbDUZto3GqNtRaLS4Xm0OHhfu8fauLkKVRHDw6I2hHhlhpgEO3Q/CgjM+8WZayWO3b99WZTaIiIgo/Qk9Mk/9PJe1DTJn4dWZpiyXky3mZ9uGBZY/w28Py1josTwCEaUK4WFhuDWvM/JF3IMXnJCh1++wsctg7GERUQLly5cvShMRWYQ0E6lZsyY8PDzQv39/XLlyBbt27VI1aOW+kIBunjx5sGnTJnW/T58+2L9/P3799VfVdEpKHzx8+BCdO3fm55HKODk5qSZlGTJE+/dco4FO818wV4MIlPi7MzxyWWNo5YyQ/Nq/bgdi9+3AeJuTSWOyR48eJffbICIiIhNz6dEbdH/dD1PDOiJfsxHGHg4lQL56fdTPsr6HcP/mBc4Zg7ZElFocWTwSZQKPIUhngVctliJrrrc1b4gobVi/fj1CQ0NRoUIF9O7dG4MHD44M2krGZFhYWGRWpQT5Vq9erUoulCxZEnv27MH27dvjLY9ApkuyqQsVKoRcuXJF1iU2rHd7t/QIRGjMYBX4HIWODEPlXNboVvJtmQ3bC0vfNiczCPBGJ6UzfH19U+z9EBERkfHNPXgLPrDDoxKfIEcenjumBvmKueOcTWVoNTo83TnF2MMxCRodK3grPj4+cHR0xJs3b+Dg4GDsz4WIDKw/9QCzNu7FQouf4FfpC1Ro2pfzQ0QfhPt90+Tn5we/Hd8h29VFUerdakP9UODkGDi8OAufzGVxw2Ma7uxbgPYBazEX7VG0YX9ksIz7AjKppezm5paC74SIiEwJ9/vpy72n3qg98xgidBrs/KI6imZnjCe1uHZ8D4rsbI8QnTne9D8DlxxR+16kNyyPQEQm7eTdl/h680U81GXBrirrGLAlIkrDJLjq4pwJ3iX7R2lQFmFhj5tVflIBWwnclt9eTwVsf9N2wOSg1ph76k289W1VMNjPL4XeBRERERmT97qB2GTxLfq6PmfANpUpUqkBrloUh6UmDLf+mIz0jkFbIjJZzx/dxdLlixEarkOTktkwqEExYw+JiIiSmVndb5C5zWRkzpw5xmOelX+EhGY10EEHDbK6t4O5Bjj+KBi7bwXGu93Xr18n46iJiIjIFHg/uoVSL3ejjPY2WpXPY+zh0HsI8RikfpZ8shGvvf9rVJweMWhLRCYpOCgAr5Z0xOzwCRjkdAzT2peGVvu21iEREaVtUtvW1dU1RuA2q+da1YRMH7htfqo7Pikaoh5bet4Hd1+HqtuxNScLCXn7PCIiIkq77mybDAtNOC5ZlEIx9zrGHg69h1K1OuCWWX7c1mXDlsNn0vUcMmhLRCbp3IJP4BZ2DX4aW3Rs3wm2lubGHhIRERkhcKvvNWDYnOxyraUI11rALDwYg+/2Q9MsLxEaAfx87DWcry2PtTkZg7ZERERpm6/XfZR8skndDvH4IrLBKaUuGq0WdxutQPOQifjprBneBL79Uj49YtCWiEzOic2zUenFFlU4/k7NmciZv7ixh0REREYgJ1v58uVDrlurIwO2Uus2OEMeXKm9DOFaS5hFhGCW72C4Wz9Em4Df4XpzaZQmZnpWVlb8DImIiNKwO1smwloTistmRVCmZhtjD4c+QO3yxVE4awb4Bodh2ZG76XYuGbQlIpNy6+IxlDo3Vt0+nrc/Stdub+whERGREZmbm8MpowMeRwvEhthmw6W6qxFmZg0zXRjW4UsMs9iAn0Lb4XfrdjG2Y2trm8IjJyIiopQS9OI+ijzaoG6/cB8BrRnDXamZlEb8vE4h2CEQYYdnws/nFdIj/hUTkcnwef0CVpt6qm9Hz1u7o1KPH4w9JCIiMgGWDcZAW3tUjPVh1plwqd466KCFFjqEwRyzw9vgt9M+eOoXFuW5jo6OKThiIiIiSkk3ds6FJcJwRlMcHvVac/LTgKYls2O97RQM1a3AxS0/Iz0yatD22LFjqF69OmxsbODs7IxOnTrh2bNn6rHBgwerS+IMlxkzZsS5rX379qFs2bJqW0WLFsW2bdtS8J0Q0YfS6XTYsWomcume4Cmc4dpnJbRmUesREhFR+pU1a1bY2dnFWO9ydws0iECE1gLmCMP4DJvROOIgZhx9hdAIaVkGdZxpbW1thFETERFRcguP0GHQ43r4JOQLPKowChbmPI9MC8y0GgSU+kjddru9FAF+b5DeGC1oGxwcjGbNmqFw4cK4fPky9u7dizt37uCTTz5Rjz958gTfffcdvLy8IpcBAwbEui15bvPmzdX2Ll68iP79+6N9+/a4ceNGCr8rInpfi/+9i9EPKuKrsL5402wBMjpn42QSEVEk+QI/f/78sLS0jFxn2JzsbNPd6mf30N/xs+U8fOn/IzZfeKaenyNHDs4kERFRGrXz0hPcfRmEo9bVULd+E2MPh5JQ2aZ98UiTFZnggwtb407kTKuMFrR9+fIlXrx4gYEDB6oD8DJlyqBbt24qgCuePn2KQoUKqcwI/RJXhsTq1auRO3dujB8/HgULFlRZuh4eHpg/f34Kvysieh8n777EpD+vyik53Jp8DrcKdTiRREQUgwRg5fhQ6twaBmz1tW7l56ts1dTtemZn0f/eUPj7+8DCwoKzSURElAauznzz5o1K3Hv8+LFafJ/dxeL9b+NIPavkha2lubGHSUnI3MISj0q8Te4seHNxuqtta7Sgbfbs2VG5cmVMmzYNQUFB6n+25cuXo2/fvupxKZPw1VdfqUvhSpQogW+++QaBgYGxbuvMmTMqSGuoRo0aOHHiRLyZvj4+PlEWIkp5Tx94wntZd9hF+KJZqezo4eHKj4GIiOIkX+IXL14c9jbWUQK2erfdx8ErbwsEamyRX/sU+XZ1w4tHtzijREREqVRERAQePXqE8+fPw9PTU8WPJHCrlt+H47eXvdHU4jR6VOa5ZFpUptknKtvWGa9xaeVIpCdG/Qpiz549aNOmDfLmzQs/Pz9MmDBBZcmKtWvXwt/fH5kzZ8alS5cwbNgw9T/m4sWLY2zH29sbJUuWjLJOMnP19XFjM2nSJIwd+7ZDPREZR1CAH3yXdkRjnSccMoSjbLvt6vJXIiKi+EimrWOrH2EdHAxrPz8EBAQgLCxMZeLK4lR6CcL8XuDerLpwjXiA+0vbwWnEEWgtbTixREREqYgk3N2+fVvt66Oz8nuIAt5/w0wTgbzZs+LZg9uwy58fVlZWRhkrJQ9LK2u8rPUjcu7vBfdnv+PamW4oUq5muphuozYiW7lyJW7duoVx48ahQ4cOmDx5sqptK6RcQtWqVVGkSBG0a9dONSFbsWIFQkNDY/3WJTq5DC6+4M/o0aNVWr1+efDgQRK/OyKKjy4iAhfn9UShcE+8Qgbk6zqTl7IQEVGiyEmZfMEvZbLy5cuHnDlzwsXFRQV1rTNmRXjXjfDWOSJP6G1cXTaIs0tERJSKhIeH4+bNm7EGbIXdpWUwQwT2hZdFyZJl1fMkxhRbjIhSt5I12+CUQz3sjKiIgbv98Mo/BOmB0YK2ktY+aNAg7N69G/369VMZtBKYbdGihWo6Fp2bm5vKoJBauNFJCYXo62UbctAe30G+g4NDlIWIUs7x1ePg7vMXwnRaPKo/FznyunH6iYgoSeUv4IYL7j/CR2eL+fey4vyD15xhIiKiVOLhw4cq01Zkv75U1bPXs/K9D1ev/W/v2Lug1IO3j0lZTSblpU2F+izBtAyjcPMNMGjtWYRH6JDWGS1oK9+W2NraqmYSevXq1YvzfzBpUObo6KgCtNFVrFgRR48ejbJO7ru7uyfT6InoQ5zbtx7uN992fjxVZARKVG3OCSUiomRRu2lnfJ9/LbaGeagDfN+gmFdtERERkWmRcplSClNPpzFTDUj1gVvHK8ugRQQ8I3KgTuAe9bie/F5sV2lT6ubo4IB5PSrA2kKLf256Yf3mjUjrjBa0lYBqSEgIRo0apeqTXL9+HQMHDlSXtUmTsi+//BInT55UAdw//vhD1bQdMWJE5P+8EuydPXu2ut+lSxdVgHrMmDGqKPWsWbNUmYXevXsb6+0RURyunj6Iwgc/h5lGhxMZm6BSx1GcKyIiSjZSLmtM+yrImdEG914EYMLG46pEDxEREZkuifEYksaj0oBUAre5L85CzucH1PqC2sexNiZ99epVio6XUkaRbA6Y3KoYfrWYiY4X+uD8/t/T9NQbLWjr6uqKrVu34tChQyhbtiw8PDzw+vVr7Nq1C3Z2dqoOSbNmzVC4cGFVf1aCtvJTT0ol6OuUSNOxbdu2qe2VKFECc+fOVY3M5DYRmY6bz3zx5bbbeKFzwCXrcij76RJotEYtrU1EROmAo60FZnYqg9pm5zH0Rlec+mOusYdEREREcZDatNJ7KDp94DbL3S3Q4u2l8Rfz9IgRsBU+Pj6c3zSqZXlXZM6SA1qNDq4HB8Pr8V2kVRqdTpf2i0AkgPwPLeUX5B8G1rclSnqPXwei7dwjePImCLVyRuDXnlVhmyETp5qIjIL7/fTp6JJR8Lg3F746G7z86CBc87OeOhFResD9fuoiV2PHlylbalsDWCAMoTDHheZ7Yn2OxHUMy3FS2hIcFID7U6urxuanM9RG+WFbkBYxxY2Ikt3rV974af4iFbAt4GKH6b0bMmBLREQprmL38bhhUQQZNIF4vboPglnvjoiIyKQEBQXFG7DNeHmZCtgG68zVT8PmZIaYn5i2WVnbQtNiFsJ1GpT33Y/L/+5AWsSgLRElq6AAPzz+tSV+9P8One3PYPnHleBkZ8lZJyKiFGdmbgGnbksRCCuUDruAA8vH81MgIiIy4Vq2hnJd+gUFbi/DT6Ht0MJ+DR4V7hmlOZkhGxubZB4pGVvB0lVxyrnl2zsHJyMtYtCWiJJNRHgErv7SCcVCLyFIY4n+reqrRjBERETG4uJaFHfLfaVu17r/K44f/5cfBhERkQkIDg7Gy5cvY31MArNZ72xUmZVPkQlti9rhqVuPyOZk0QO3GTNmTKFRkzG5tvwGoTozFA85j2sn96a5D4NBWyJKNvuWj0dZ/38QojPHvQZLkLdEZc42EREZXdHmX+CmgwesNKFw3PkZnr+M2eyEiIhSnq+vL9q2bYu8efNGWb9y5UqULl0a1tbWyJcvHyZPTptZdend06dP43zMMvBZ5O3HtsVQLptVlOZkGl145OMWFhawt7dP5tGSKciWpxDOOjXE1Yjc+OvcbaQ1DNoSUbI4dXQ/qt+dpW5fK/0lSlRpzJkmSsfCw8MxZ84clClTRp1wZcmSBR07dlSXwH3//ffQaDSxLvfu3YuxrdevX8d4HrMpKFE0GuTuuQhvNA64HJ4LozacRUQEe/MSERnT1atX4e7urgK3hjw9PfHzzz9j3LhxuHXrFmbOnKlur1692mhjpaQXEhKCFy9exPm4WaC3+rk5ojoqFS+kjv/0JHD7xK1n5P2sWbNGeZzSNvs2M9A45EfMvJMLXr7BSEsYtCWiJOfl7Y0suwfAShOGK441UKr1CM4yUTonDSWOHj2KsWPHqpOy3bt3q4Dsp59+ipEjR8LLyyvKIidkBQsWRO7cuWNsSwK9Evg1fL50GSZKDOtMOfGy+z58g8+x77Y/5h26xQkkIjKivXv3YvDgwfjqq7clbPTkeODMmTNo2bIlcubMiRYtWqBx48Y4dOiQ0cZKyZNlq28elv360ijlDuxeXkYmrxMI02kRZm6Ptv7r4tyOubk5nJ2d+RGlI8XyZEXZPE4Ii9Bh/akHSEvMjT0AIkpbwiN02LViCrrjKZ5pXZD/4yUqo4mI0jc5eF61alWUdZ07d8Zvv/0GW1tbtRhatGgRvvjiC2i12lgP6uWkjQfk9KHy5S+EsS2sMXLjBfy05zqq5LJAmYKunFgiIiMYOHCg+nngwIF3PlcyMkuUKBFvbVRZ9Hx8fJJolJRcWbbe3m8zaYVOY6bq1OqzaLNdW6puX43Ig06aP/FI2yvObUmWrZmZGT+odKZbJVdcv/8Ur/9divDq38PMPG2EO5lpS0RJav6h2/j2WQ38EPERwlotgLUDv+UkopjOnz+PefPmoXv37jEekyzc+/fvo1ev2A/Inz17prJtpaadBG8bNGigsnjjIyducsJmuBCJ9hVyoVNxW8w3mwrt6g7wCQjkxBARmbAjR47g5MmTcR4niEmTJsHR0TFyie3KHTLNLFvDOrUSuHU9NxUZX5xGmE6DkmZ38aBwT/V4bCRY6+LikoIjJ1PRtGRW/GX9Jb4Om41LhzYgrWDQloiSzIWHr1WmEqBBweYjkLNUbc4uEUVRrlw5WFlZqZp10mhk9OjRMWZo2rRp6Nu3L+zs7GKdvVatWuH333/H9u3bsWXLFnXZZJ06dXDjxo04Z5snbxQXqXn3db2cqGR2HaUiruHowmFRThyJiMh03LlzB+3bt8fcuXPh6hr3lRFyfPHmzZvI5cGDtHXJdFoSGhoaJcs2euDW+cFOyF7ZXKPDP1m64rlbjzi3JT0TmGWbPllbWuBB1npv75xchLRCo+NRqSIZN/INnPyD7uDgYOzPhSjVCfD3weYZQ/CDb2PULJkPv3Qpx+Lvyczf3181ZAoKClJZhCn5z7kEOSTwJnVFpQFUXME1ougePXqk9rlS1/a7775TwdslS95e/ibOnTun1kmN2sRkxUhHaQnmSs3chF4mKdvnfp/07hxYgXwHPkeEToNDleejVuMOnBwiIiOQ8gg9e/bE3bt3o6yXwGvNmjVVGYUhQ4Ykaps83zdd8rk+f/48zsdLb28Ac10YQmCOM013w0Ibe+k9KalVsmRJVdOW0qeHnpeQa2VVdSz35KN/kTN/caR2/Gsmog+mi4jAjfkfoWvoPhSzuYJ8rQ4wYJuMAgMDVeBLgk3GHof+cib50ksuU7exsTHqmMj0yd+JLEWLFkX27NlRpUoV1RHayckpMsu2Xbt2ib6M0c3NLd4DfvmSQRaiuOSr1R2Xr+9D8SebUPzYcNwpWgH58ubnhBERmYCHDx+idu3aGDBgQKIDtmTaWbbSUDYuLteWq4BtsM5cNbnO7bkyztIIkmXLgG36lqtgCVywdkepoJN4sHs2cn4yD6kdyyMQ0Qc7u+Y7lHmzD6E6M1jU+xYZ7RgYSc7s2uvXrxs9YBudjEfGJeMjSsyButBniUumxbp16xJ9Mia/L5m7hQsX5uTTByna8xc8MM8LF80bvF7ZE0HBIZxRIiIje/z4sQrYype6vXv3VpfS6xdK3aRPQVxXC+Y5/xPy3FyKn0LboYp2JR4U6qlq3Ga7sSLWLFsJ2hJFuPdRk1D02R8I9PdN9RPCoC0RfZCrB9ag3M3Z6vaRIqNQomoTzmgyZrbevHkT4eHhJjnHMi4Znz4DN7kdP35cZVeGhYV90HZq1KiBTZs2vffvT58+PdZmWnqnT59G8+bN1eVagwYNQnq1f/9+zJkzRzUgk0xxuS/z0aRJE2TKlEk9Z8aMGahYsaJaDMnlkXny5In8nGbPno0NGzbg1q1bKlj76aefqixbuZSS6ENorWxh23U5AmGFsmHncWLxME4oEZGRLViwAJ6enpg8ebJqMmW4UNrMss12fTlc7u9Qt/1gg9ZF7PC8SI/I5mTRA7fyt2BhYZEi4ybTVrJmOzzWZIUj/HF+6wykdgzaEtF7e3LjDPIcGKxu/5OxFWp0GsHZTEYS6DLVgK2ejE/GSf/5/vvvUapUKfz111/48ssv0+3USPmDzZs3q0yZfPnyoUePHqhatSpWrVoVma0tJ2WxZdlKBoYE5/WZGHJgLrVrZV5r1aqlMnAOHz4cWWKB6ENkzlcat6v8iIc6Z0y+74Y9l59yQomIUpDs2w3r2Y4ZM0YdA8S2UOrOso2IiIj1MWu/t43jAnWW+MeiKurms43SnEyjC4/SayNr1qwpNGoydWbm5nhYfICqa3v75lUEhpj2+fO7sKYtEb0X31fPoFvTGXYIwgWLUnAf8Bvr2CYjKTtgaiUR4iLjlPGyOdl/XY5HjBiBbNmyIT0rU6YM/v777zgfl7rI0iQkNhLklcCsXqdOndRClFyKN+iNHwPL4PLRJxi6/jw2fmIHt2wZOOFERERJQL6Mj7OWrU4HK/+H6uaK8PqoXiInLM3+az4WvaYts2wpurItPkP3m5nx7xtn+B27i341CiC1YqYtESVaeIQOk9cfgDYiGI+QBVk/Xgdra2vOZDJ6/fp1qh5vcHAwxo8fDw8PDxW869Wrl7q03dDu3bvRsmVLVUZAOgPv2bNHrQ8KCsKkSZNQrVo1lC5dGq1bt4afn1+M1xw1ahSGDx8eZZ1cRt+5c+fI+y9fvsTnn3+utiPb+/3332M0p5KyC23atFFZnI0bN44ch97ixYsjxzJ06NA4MwSuXbuGhg0bqku/+vbtq0o5SBMNGeeiRYuwZMkSVQZAxiOkbIBc3i/rKlWqpAK9Mm9Cfk/m7ejRo6qcgMyRBC2lBqyUHJBmXu7u7hg9enRknVjx5MkT9OvXD2XLllVjlufqs1IksC6Zv/Ja8njbtm1x7ty5eD9XovRkaNMyqJQvE/yCw/Dz4hV48fy/Lw6IiIgoebJsHZ8dhf2b6wjQWWGNWQvUz/82yzY2zLKl2FhYWqFlvTrq9q8HbsEn6L/zo9SGQVsiSrQf/ryKlXfs0SFiIvzarUHWbDk4i8lMApepebxSg0yCob/88gu2bNmCAgUKqMvj9cHdY8eOYdiwYSrAumvXLkybNk0FTfXlBeTS95kzZ2L79u0YPHgw7O3t32tc48aNw/3797F69WoVOJXMTwls6kkQVLoSS0B0586d6N+/vxqX1OoVBw8exM8//6yCwzKWcuXKYfny5bG+lrzHNWvWRAaPJeCaI8fb/1fWr1+PS5cuqaCxjElI8FgC0lKrdf78+eo9y+N6UitYXlvmUsoMSNC1Y8eOKqAr72fWrFn4888/sW3btsgMBgkWy2v+8ccfqvauvK40+hLy/iV7de3atdi6dauqy5s9e/b3mleitMjSXIt53cqjq+NFzAr+Fl4L2iAoIOYXRkRERJRwcowqvQj0sl9f+l+NWp1O3RfLwhvgW+dDyOu5LM5tOTs7w9LSktNPMbQplxMFXOyQMfA+Dmych9SK5RGIKFF2nb6ORYfvqNsj29eGWwkGbFNCSEhIqh2vBBclULhw4UIV5BRfffUV9u3bpwKf3bp1U0HHRo0aRV7ynjNnTvVTLpeXIKTUOi1fvrxalzt37vcak2xLsnklWFm8ePHIgLBk9epJULN69ero0KFD5DgkoLljxw4VLJYgqoyzVatW6nEZ+969e2OtNSzNEPQNtiTIrL8tJIP2xx9/jNIwoUiRImoR0nRLMmdv3LgRZZsff/yxyrIVkhk7depUfPPNN2r7efPmVVmz0phLSJBYMou//fZbmJmZqXmToLgEcGWeJbBubm6ugroSMJbXJKKonOws0a91AwSvnYUioVdx7pdOKDlki6qXRkRERB+eZavTmKnmYiIoQ17Y+XjCT2cNCzMt6rxYhUfOveLMsk3v5ccobuZmWoyrDFTaMwKhN8zh9bgFXHK4IrVhpi0RJZj3lQOosq0WmmmP4rPaBdCsFAO2KSW1fYNsOF7JXpVv1PWBUqHVatV9fVDy9u3bKFGiRIzt3Lt3L8bvvi/JSJUDRMNtyYFehgz/1amU8ezfvx8VKlSIXE6ePKkOLoVk6UYfZ6FChRI9Fskijt7h9syZMyrLt27duipgK0Ht6MF6qe2qJzWDJRBsmHUs6wICAiLfi2QySyBX/17mzZsX+V6kbIIc7NarVw9z585NNTWTiVKaa5FyuFd/IUJ05ijj/w9O/tYfujgu6SQiIqK4SRkvwyxbw+ZiErh1fPIP7iM7LkbkRR/NVrU+eg1bvcyZM6e6cyRKWVU8asDTwg02mhDc3vBtqpx+pgkQUYJEvLoPiw094IAAdLI/h0p1x3LmUlBqqxlsOF59jdXoHX4lgKo/0Iqr+69+vQQX34c+gGlIAsaGJNvU8PWaNWuGzz77LMpzbG3/q6UVfSyGv59Q0Q8wpUSDZNG2b99e1biVplxSx/ddfwfRA7+G5L24urqqLOXYxpsxY0ZVn1eCxfKzfv36WLZsGYoWLZro90OU1pWs2gSnXk9GhZPDUNlrAw6vyIlqH70tbUJEREQJ8/Tp01hr2eoDsxK4DdaZI4/ZE9wr1BPecQRsBbNs6V00Wi0i6n0P7OqI8i+24d71c3B1K4PUhJm2RPRuIf54uagdHCPe4IouL3L2XAwLczPOXAqSAFtqHa9kh0pwUWq46snB2pUrV5A/f351Xy7tv3z5coztyCX9cml/bI9FJ1mm0RuUGZYXkFIHEnCVBmF6Uj7AsGlawYIFVdZvrly5oiz60gYyHsPfF3fuvC0X8iGkCZkEk6VshGTyyuvI2D6EZABLMFgycQ3fS/QDXClZIQ3K6tSpE6WGLhFFVaFpH5wsNETdrnZnJo5tmMkpIiIiSiC5gszLyyvOxx8V7IYQmMNKE4YwmMO7SI94a9lGbyZMFJtilRvhnK0HzDURePHHN0htGLQloqj2TwIOTvnvvk4Hn7X94Ox3Hf46K2jyVkO+HFk4aylMApKSfZkayDhlvHoSNJQM0h9++AGnT59WJQ8mTpyoMnBbtmypniM1ZKXxlwQNpTmWBDElUOvk5ISGDRuq3z137hwePXqEv/76S9WEjU6CnUeOHFHNwqQUwsqVK3Hq1Kko42rQoIGqJSt1X2/duqWagBlmvUpjr+vXr2PKlCm4e/euWqSerT74265dO3VfavHKa8h4T5w48cFzJnVlJStYSiLIdqVcgaen5wdts0qVKipIK43UZC5lXqW52YEDB9Tj8j4OHTqk1ksgWt73+9YLJkov3LuMwcnsXdXth+f3YtfF/xoZEhERUfxZtnFdXef49F9kPjgKlghT5YjMEfZfc7JYMMuWEsOx2XiE6zQo5/8Prp36G6kJg7ZEFO1fBTNg/8TIwG3Y3vFwuL0dYToN7DTBKJKPzYqMRTJFJevUlMn49E3EDEkGaa1atTBo0CBVfkCCoNKYTB/clWZg48ePV03CJLAqJQL038TL+jJlyqB///6qCZgENGM74GvRooVqzjVq1Cg0b95c1aL98ssvozxHtiUlA3r06IGPPvoINWrUUPVj9eSxpUuXqqCxBJRle6tWrYqc99q1a2PkyJGYPn26eh/Hjh3D119//cHzJjVu5f3J2KXJ2atXrzBkyNuMvvcl2c1S9sDBwQE9e/ZE48aNVcBaXydXguYTJkxQQfE+ffqojFtprEZE8dBoUKHvHKzJPQYjQvth0LpzOOLpzSkjIiKKhyRceHvHsb+MCIfruako4HcSB8JLYaLb5sgat7EFbqWWLbNsKTHyFXPHaafG6nbw3pgl6EyZRhfXVx3pjHQVlywsacQiJ7hE6ZoEbCVwW7ozcH5N5Gr/ql/Crv5XRh1aeufv74+bN28iPDwcpkYCm3JJvmGWLZGp4n6fPkRYeAQ+XXUGe648g72lBuvaZ0HxkhU4qUREJor7feOSq9devHgR62P5T46B09N/EKizRH3NPExp7Aorc40K2ErgNnozMmkqnNr6fZDxPfC8BOsVTbAuvDZaDf0VuTKljnNWZtoSUUw1RwK1v1YB23Dd238mbpf4ggFbEyABUTc3N5MrlSDjkXExYEtE6YG5mRazu5RFrYKOmKSbiTwbm+HWhX+NPSwiIiKTExQUFGfAVrJs7b3PqpuzwtqgUVEXFbAVEqiVgK1G91+yivSZYMCW3kfugiUwNNcaTAvrgA1nHiG1SHzLayJKF47l/hjldJNhKYXgNRbI345dsk2FjY2NapglWbfSREsOhOSSo5S8cEIaesllSXLQJE3HGKwlovTGytwMv3YujTsz/JAhNBChmzrhcYa/kCNfEWMPjYiIyGRIY9y4ZH64BxZhfvDWOWC7ZSNMzWcb5XHDDFt9Hwii99WibB78c+s19l59hsH1CiM1MGrQVmoBjhgxQjWKkRP+evXqYebMmciaNSt27dqF77//XtUVlG9TOnfurGrxmZvHPmQJGkhpA0NSEzC1dVwnMjqfx3iw/UecvOqHymZhCIUFLHShb0smSAYumQz5d5PBUiIi47G1y4Bcn/0Bz9n1UTD8FjxXdkHQ8H9gbZM6LrkjIiJKToGBgXj58mXsD0b812xsXlhzNCvrDAuzt1m2sXF2dmYtW/ogtdyywEIThgxPjuLZ0/zImi1mLxZTY7TyCJIVJk1cChcurLpa7927F3fu3MEnn3yigq3StObTTz9VtRulCYws0s07rn8IJGB75coV1bhGv5ja5cNEJi/YF28WtkLuG8sw0GwjNmfsifCvn70tlWDQnIyIiIjecsyYGfY91+MVMqjA7flFn3NqiIiIADx+/DhyHrJfXxqlsZjzg12wDnwKL50jslsGomvw+niv8suePTvnlD6ISwYrbLD/GWssJ+Le4XVIDYwWtJVvW6SuycCBA5E/f37VGVy6VksA18nJCWfOnFHdvXPlyqU6jnft2hWHDh2KdVtPnz5VXbKLFCmivn3RL/I/dnxBYylGbrgQpWsR4bi/oDMcfa6ru/ud2qPZwOmwtjD7r8YtA7dEREQxZMtdEA9rz1K33b0249qpvzlLRESUrvn5+alSbno6jZlqLKYP3L6wK4SDurK4FJEXH+s2QaM1i3NbLi4usLS0TJFxU9oWkKOy+mlx+y+kBkYL2sq3JJUrV8a0adNUPUb5Bmb58uXo27dvrM+XAG+2bNlifezZs2eqlqN0LZft1qhRAzt27Ij39SdNmqQycfVL7ty5k+R9EaVW5xZ9jjze/yBMp8WpzC1Rc+ACWJgZ/BOhD9xG/FcInoiIiN4qWbMNTjo2glajg8WfwxAWFsapISKidEniM48eRW32pG8spg/crn2WA6fCCqC22Xk8LNwzRv1aPa1WG2csiCixXMo1Vz/d/E8jOCgApk6jS8nONdH4+vqiTZs2uHjxovoWZsKECRg8eHCM592+fRulS5dWJRQqVaoU4/GIiAjs2bNHFaWW21u2bMH48eOxb98+1KxZM85MW1n0JNNWArdSZsHBwSGJ3ymRaTu+cSYqXfxO3d5ZZBIadfwk3kx1IqLUTvb78qUt9/uUlF4+f4Rbv7bH1OA2aNysLXpVzccJJiIyAdzvpyw5vvL09Iz1MQnYSuA2RGeuml4fy94NFhV6x7kt6XkkV2ATJQVdRDhejMsPZ7zGpbrLUaJ6S5gyozYiW7lyJW7duoVx48appmSTJ09GiRIlVEMywzIKLVq0wJdffhlrwFb/zUujRo0i70uphbNnz2Lx4sVxBm2l67ksROndpTOHUfrCeEADHM7VF407fWrsIREREaVKmbLkxM7Ga3Bi8yVc3XMDzUrlUPXTiIiI0nOWraFQa2dEQKsCtiEwh1n5XnE+l1m2lNSkDMedjB5wfr0Tfpd2AiYetDVaeYTz589j0KBB2L17N/r166cCrDNmzFABWmkiJqT+SYMGDVC3bl188803idq+m5sbnj9/nkyjJ0obvHyDMXfHCQTBEpftKqNq78nGHhIREVGq1sk9D0rmdIRvcBhm7Thp7OEQERGlKEm8k2bxsdGEhyDnpV+gRYQqy2eJMOS4uTLeLFtzc6PmGlIaZFa4gfqZ3eswTJ3RgrY3b96Era2tqkOrJxm28j/3gwcPVDq9BGwlu1aCuYklDc0KFy6cxKMmSjsiInT4Yu1Z7PB3w6cZZiJvnxXxFn8nIiKidzPTajC2RTF8arYVo662wbWTezltRESULki5SulXFJf8p76HRXgAfHXW6Oy4MkqN2+jMzMxU0JYoqRXwaK6+NHCNeIAn9942YjdVRgvauru7IyQkBKNGjVI1a69fv46BAwciZ86cKFiwIBo2bKgCumPHjlVNyLy9vdUi/wj4+/urx2bPnq22tWrVKixbtkxtQ+qmyO/s379fbY+IYrf8X08cufUCNhZmGNu9EeycsnCqiIzs999/R506dd7796XxkVxpcvz48SQdFxElTjnXTKjl4gs7TTDMd41EOJuSERFROiBXTUucJzbZry1BxufH1O1pYR3RuUzmGM3JDEnzMQncEiU1RycXzHccjGbBE/D3Y0uYMqMFbV1dXbF161YcOnQIZcuWhYeHhyqHsGvXLmzevFmdcK5evRouLi5Rlvv370eemEoAV8i3L3PmzFGB4IoVK6ptyiLBXyKK6cGtK6iztwkaak/iq6ZFUShrBk4TJStpEDl9+nSj1dXq0aMHli9fjvQkKCgItWvXxuHDpn/ZD1FaVLDzVPjADgXDb+HE8tHGHg4REVGyCg8Px5MnT+J83Pb124zGJ7pMeJKrCVwdLdR9feBWowuPfK6URJD4D1Fy0ZXtiku6/Nh5+RlMmVGLg0j5A1mik2ZkH330Uby/e+fOnShlFQyblxFR/DtT37V9UUzzHIPt98LN/TtOFyU7CRzKlRTGsG3bNnUA2aVLF6Qn1tbW+OKLL/D999+r+vHMVCBKWZmy5sLJst/A/exoVLq3ABcOVEapWm35MRARUZr09OlTda4ZVy1b89e31O35ES3RrmSmqL9buHuU+9mzZ+exKyWrFqVzYNqe6/jX8wVuPfdFgSymmchmtExbIjKOk+sno1joJfjrrJGxy0JozfjPQGonmaQBIWEpushrJtTIkSNV4HTevHnq0n0piyOkDMDZs2cxYsQIlC5dWmXCPnz4UD3n3r17UbYh644cORJ5X67GaNOmDUqVKoXGjRtjz549cb7+ypUr0a5du8gmBt27d1dXdUyePFld6TFp0iS13sfHB8OGDUP58uVVPfUJEyZEubxL6nN9/vnnKFeuHCpUqKDel55sr2nTpupLRynvI2UO9OQ9yetI4LhTp07qOVevXlWP/fHHH2oe5H18/PHH8PPzizJ2ef3x48er8cjrDh8+XI1TT2rAy5ecJUuWVF9eylUmhs0aZG7kKhZZb2xyEC9XxZQpU0YFlLNkyYKOHTtGZmS0atUKGo0myiIZ2nFZt26d+ruQbcncHD16NAXfDVHCuLf8FMcztYBWo0OeA4Nw/8Y5Th0REaU5oaGhePbsv4zF7NeXRil34HRvB+xDX6gsW4/MAShxP2YNWz0rKytm2VKyy53JFj3y+2GWxWz4LWqB8/t/x8NblxEcHGRSs882fETpyJtX3ihy/Rd1+1LxYaiUt4ixh0QfSIKn7eYdxel7r1J0Liu4OuH3AR4qsPYu3377rSptI8G6fv36qQMxva+++gpt27ZVwVJ7e3sVYHwXCVQOGDAAo0ePRtWqVXHy5En1+5s2bYrS3FI8f/4c58+fjwzM6kmphlq1amH79u3qdcWQIUNgY2OjAq4BAQFq+1I7XbYtwdNevXqpIOH69evV86Vhprhw4YIKPMv7rFKlCi5duoRvvvlGBRObN2+uniPbGzx4MPr27auCtpkzZ1YNOb/88kv1u/Xr18e5c+cwbtw4ZMjw37e8EjiW50nddgsLC5U1K8vPP/+sHh86dCjs7OxUWSFp5Dlx4sTI0kFC5rpatWr466+/VKkEY3r16pUKrErdeQlSy2f9ySef4NNPP1Xjl+DtggULVPBWz8HBIdZtnTlzBt26dVONSuWKnYULF6JZs2aqrr2Tk1MKviuidyvddx6u/3QdbmHXEbCmA7w/PwrnzJk5dURElGbIcZxhUodOY6bq1OqzaDcHuyNDWBMUMHuOBq/X4VHWXnFuK0eOHAk6xyD6UN0r5kKehydhGRwGHOyj1oXrNHiqyYwXFlnxIGt9VM6hRUY7G6Dmfwk7kQ5OASLCgdrJVwaLQVuidOTK+rHwgB/uanOjfOvBxh4OJRFTP6SRIKQEHCWAmClT1EuhihQpgj593u4gRUKCthI0rV69Ojp06KDuS9kFqZG+Y8cOFRg1JAFVCcrmz58/ynpHR0cVlNWThpgSUDx27FhkoFACrD/99JMK2v77778qe2Djxo2RQV69pUuXolGjRpHjyZUrF27cuIElS5ZEBm2FBCMNS/ls2LBBBbJ79+6t7ufOnVu9vj5jVLJuJRAtS+HChdU6eX+SWRscHKzKBMn7kyxjqRMvJBO3a9euUcYnryHjNjZnZ2fVONRQ586d8dtvv0VeUid/D/K8d5G5lQzlzz77TN2XrGkJtsv2JRuayJRY29jBue8mPJhXH4tC6uH06qtY268y7Kx4GE5ERKmfJAy8fPky1nIHErgNDddhyY2G6KmzRR3NKVW/Nno5BD1bW1t+AU8ppmDpqrgSuAqB/85DJv/byB7+GNaaUGSDN7KFeuP3O+Vx7V4gBpv9jrDwCJjX/hLQf6EgAdv9E4HaXyfrGBN9tCjZUnJiGf2bD/lWRbKf8uTJk5TjI6Ik8uzhLZR9vEZF+F5X+Rp5LUy7SyIljPxbLBmvgaGx149KLjYWZknyDbiUGUgsCYhKqQTD35WmW9JhNrZv/aVZZfSxSgmE6NuUA04JBBpezi/Zq/qgruzfogds9Y8ZZocKyaaV7M/43qvsT4sXLx5lnWQK64O2EpSVS80Ma/HKGGXdixcv1D5XxqMP2Ap9cDd6TbD4mkIYi2RAS8kMaRInJCgut/39/VWGhWTSSpA6tlq8kmkbvSa+BPJPnDgR5+tJoFsWPcMyE0TJLXPWXPDrfxh/LDiDl4/eYOCas5jfvTzMWaKIiIhSOTmmiq2WrQrM6nTIe2MpTmlWwsoiDA8L98SzOAK2+mQMZtlSSipWuREgi8Q1I8LxyusxXj7yxJsnnvB6lBtLb4cjPEKHYYcmIeT0clh2XQ3c3PNfwDa2DFxjBm3z5cunTv6kFp0h+WZFHour8DQRGdeRHcvRWhOKKxYlULpOR34caYgc2Nhaps6MLUvLd395IKUFon9JKJfC67MsDb+Zj06yVWMLtEZ/XdmmlEaIq4aqPB7XAWRYWFiMdRJclezi+F5TaLVRa0ob/o7+EjOpyRv9Pcg++PLlyzF+37CerWGmc/RaucYk9Wdl7PL+pC6wPuN5//79KkAr71VqFkuGswRwv/suZrNEb29vVWLCkGToSmmKuEiJDCnNQGQsrtkyY+FHFdB5/jGcunYHu5buQrOPv+UHQkRkwNfXFz179sTp06dx9+7dyPVyW8oqSXNbOVaQK2u+/jp5M9woYfRJDrF5Aztk12lgpQlDuMYcz9zeflkfG7naLa7SWEQpQaM1g1PW3GoBaqOsTocmF5/im83m+Ch8D5z9H0E3v+bbK12rDk72gK1I9Fl+XCeuckIo9fuIyPTceOaLYXcrYiXGYHyLitBEC/QQJTfZbxjWWo2L1GfVH7AbZsEaKliwoDqQlzIE7yKBXAn8vYtkuEpwWGrXRi+lIPLmzauao8UWBJbfvXjxYpR1cj+27RiSq1auXbsWI2vX8DUliOvl5aXKBsT2+5LZIA3SJDM1+u/ryZhjC2gbizSlk3FLMzYJyD569EiVO/Dw8Ih8TrFixVSpDKlZG1vQNra/JZmr+DIzJDgsNYD1ZAwyh0QpqVweJ8xqVxR5NzWF24OHOLbGDJU7f8UPgYgIUMcGrVu3jvXqXWlAK1mYUkpKrlaSEktyLChlo8i44mpQrAkPRr6bi1QzznBoYaYLU83J4iqNIJ8vkSnRaDRoWio7KubLhCm/z8eke51hpnn79x52ZiXMXasAhRsm6xgSHLmREx1ZZNDSbEV/X5YvvvhCdYCWunlEZHom77yGCB3gUqwmipWrZuzhUDokl+hLXVi5pF9ql8ZFmkjJAdv8+fPVc6Vmq9QrNcwolf3N9evXMWXKFJV1IYvUs40e3BVSGkGakcV1MGkYCJaGXVITVi69lzFKgzPZrpDHpB6vNBi7deuWKl3w999/q8fkZEEafa1duxYPHz7Erl27VHbsxx9/HO9rSgO2U6dOqedK4HL37t3qd/Uk00BOUKQ5mWSVyJikpIDUbhUSyJUyDNKYTMYkJzpSgzd6OYHYro4xJvl8ixYtqt6bNB6TmsDSpCw6afomn11s5HONXjtNgtsuLi5xvq7UVNZncDCTg4ypYZm8eFWwjbrtfm0Kzu9byw+EiAjA3r17VWkkaVRrSL6sP3v2rDo+lNJSjRs3Vg1kpWFsXKQkknxBa7hQysp29DvY6ILgo7PB5ip/qFq2UuNWArfRyXG2KSUZEBlyyWCFyfkvqIBtKN6ea5kHegOrO8B34yAg7L8SbEYL2so/krLIia9kEOnvyyKZQqVLl1YnXkRkWi6cOYbL167CTKvBiEZuxh4OpVP9+/dXP5s2bYpff/013udOnTpVBWzluRJE7d69e5S6rXJb9jcSwGzZsqUKfkoDqthqn5YqVUodpEuQ9V1mzpypgqBSdqF+/frqkrs3b96ox+RKkkWLFqkMYHk9WeRyfiFfWM6aNQtr1qxRDclk/PKFptyOjwQlf/75ZzV2OfmQBmsTJ06M8hwJEst25ORFmpjJiYxhfdo5c+aorNN27dqpywTlcsLo2SkyT7KPNkVSn1fEFlSXEgqx1egVFStWjKz9qyf33d3dk2mkREmrUrexOOnUVB38Fzo4CLcuHOEUE1G6N3DgQAwYMCDGPMgX6nIFkiQB6NWoUQPnzp1TV0nFVRZJGs/qF15dk3ykFFd02a4tRo5XJ9XtjQ49kC+zrcqwjS1wK4mB+qvGiEzSwSnQHPhB1bD1HvIIO5z+K/OR4eIyLFu1BK/8Y/+36ENpdO9KP4qmV69e6sQ2rdUakZN6+cdcTtDT2nuj9CsiPBw3J3nANfQ2NuQbi249o9YAJUoPpElYkyZN0K9fP6Q3ciJTtWpVTJgwAQ0bJu+lO+8iQW4JxErDMKk/K5nRkiUjmbcyvp07d6rPSTIt/vnnHxWElqB0165dVTa1nJxJuQTJ0L1y5YqqjStBb2lIJgH1X375RWUcx5dta4j7fTK20JBgXP+pAUoEn8MzZIbZgANwzsaGvkREBw4cUF9E62vaSgB248aN6golPUkkky/npUyUYTA3vgakErjl+X7Sk5CSJAkY9jfKcnA4cvucwSOdMy7WXg6XDP+V0pSArUYXjiduPd8+N0sWBtXJdB2cEmvTsWfbxiHr6Z9wNLwIOod+h84V82BSm5JJ/vKJLmwpdeckqOnp6aku5dQXnU5k7JeIUsCZHfPhFnYdEdCiUYNmnHNKlyRTd8OGDemyUaYEQqUGb926dY09FFX6YvPmzahdu7ZqXNqjRw8VUJZMY32gVrKJpXTCtGnTIgO2+mMMafimP9aQmrerV69WQdySJUtiz5492L59e4IDtkSmwMLSCrkHbMR9bU5kxQu8WNgOQQGm0zSQiMhUxFXLXsRVz55lkVKOfAaGpbgiQoNg4/O2z8LhzB2iBGyFZNzqA7bSRDdbtmwpOFqiRIoIjxGwFVmbf6fW5ynXEB0r5EarMjlMoxGZ1JBr3769ypiR/zlv3rypmq1I7T45IZN6ekRkfAF+b5DnzBR1+0K+Pqic47/Ly4nSE8nM3LJli6o5qw8CppcsWynbMHbsWHVAbGxSRkJfBzi6jBkzqgBzXCTIK5k00T9XWYhSM0cnZ/h2WY83KxvAPtQbU37fj297NIu3qR4RUXoTVy17KY0lX/yS8Um2s2QxS2PfF+e3wx2vVZZtjvLN4/09OcbTB+CJTFLt0XE/VnMkpH3e5GR8+URn2ko9PfmfSjo2GhaKlsYwhg1UiMi4Liwbhix4iceaLCjT8Wt+HJRuSfBjxYoV6SpgKywtLVWQVMoKEJHpylWwBO43WoG2oROw+KoWs/d5GntIREQmRWrZS6kEw2a2UsteyiPI8Q6ZxvG2JPPpzK3wzeMqGBXaB0dz9oattVWcvyPlKliakiiJg7ZyCaJ08s6VK1eU9YUKFcK9e/cSuzkiSgZXjuxAZa+3Hea9a0yCtY0d55mIiMhElaxcF4NbVVO3f/7rBnafvmHsIRERmQwJznp4eKjGtlLXXpLFpM6tvtEtmQYpSXHwmQW8g7U4YFMfuco2jPN5RYoUiVJSgYhil+jrJf39/aNk2OrJ5QryPx8RGZe/zytk/Guwun0iU3NUrN2OHwkREZGJ61QxD24+98PLI8tR+Y++uKldg0Jlaxp7WEREJmH9+vXo27cvKlSooMoqyRXADNqalufe3lj2j3zpqMXXzUogfw5L+Pr6qp4EstjY2Kjm79JvgWWAiJIpaCtdn5cvX47x48er+/I/mxQGnzJlimouQkTGNX73bWQLrYa2FkdRrOdsfhxERESpxFeNi+Di5TNwDPRHyNaP8DzLfmTJmc/YwyIiSlG1atVS5RAM5cyZE3/++Sc/CRN2c/UIbNf8g6XZvkCz0k1iNCgjohQI2kpwVrpQnzp1SjU5GTlyJC5fvqwybf/999/3GAIRJZUNpx9i7emn0GraweOjicjt4MTJJSIiSiXMzLQoMGAt7s6sibwR9+G5uC0yDD0IG7sMxh4aERFRnG5dO4eKL7bCQhuOTpXyMZOWyFg1bUuUKIEbN26gatWqaNmypSqXIN2bz549iwIFCiTVuIgokW7duILxW06r20PqFUalQjk4h0RERKlMBsdMsOz+O17BAQXDb+Hq3C6ICA839rCIiIji9GrrV7DQhOOiXWUU9mjGmSJKIhqdFBch+Pj4qPoqb968YQdDSnX8fV/jxfQq8A8zw4JcEzCtTwtotRpjD4uIyGRxv0+m7urx3SjwZ2dYasJxNFdvePSZbuwhERGlWtzvJ5/z//6J0n91RphOi2dd9yNn4TLJ+GpE6UuiM23FP//8g27duqFKlSp49OiRWrdixQocPnw4qcdHRO+gi4jA1QUfI0/EI2TW+uGbVhUYsCUiIkrlilZqiPNlx6rbHg8X4/DeLcYeEhERURTh4RGw2jdG3T7n0pIBWyJjB203btyIhg0bqs5/Z86cQXBwsFovGao//PBDUo+PiN7h+LpJqOCzV32z+bLxb8iUJSfnjNKc2bNno3PnzjAl8qVltWrVIveDqd306dPRvXt3Yw+DiAy4txqIY9m7Y2ZYa3x8wAJn7r/i/BARkck4uWMRioTfgD+sUbDDBGMPhyjNSXTQdsKECZg3bx4WLFgACwuLyPVS41aCuESUci4e3AT3a1PV7VOFh6BIpQacfjJZo0aNwvDhw6OsW716tQp8PnjwAKmNdDHet28frKysjD0U1KtXD2FhYcYeBhElg4p9ZuFS4c8RHAb0W34aj14Hcp6JiMjoAkPC8ejcX+r25Xy9kTFLLmMPiSjNSXTQ9vr166hRo0aM9VIP9vXr10k1LiJ6h/s3zsF1/2cw0+hwImMTVOr8DeeMUpW9e/dixowZ6kvA3LlzIzWytLQ09hBw7969VBn0JqKE0ZppMaNjGRTN7gBfP18cmjtQ1bInIiIypsX/3sGwgI8w0GIcSrX/ih8GkSkEbbNlywZPT88Y66Webf78+ZNqXEQUjzcBoXi59jM4IADXLIqh9IBF0Gjfq0Q1pRUh/nEvoUGJeG60DK64nveBzp07h9GjR2PmzJkoWrSoWrd48WI0bdoUpUuXRt26dbFlS9z1G+vUqYOdO3fio48+QsmSJdGgQQMcOnQIf/31lyrhU6ZMGfTo0QNPnjyJ/J3z58+jZ8+eqFixIipVqoQRI0ZEljZ4+PCh+p3jx4+jefPmKFWqFFq1aoXTp0/HGyx1c3NTv6vPJP7ll1/wzTffoGzZsvDw8MD8+fPVPrNLly5qm/L+5DX09L8jV7GUK1cONWvWxJw5cxARERE5LnkNeS1Dsu7IkSO4fPky2rVrp9YVL148ynjkddq0aaNet3HjxtizZ0+Ubch8S5azzPfQoUMjX5OITI+dlTkWflQB821+Qefg33FjbmdEhIcbe1hERJROefsFY+6BW+p23SbtYG2bwdhDIkqTEh3l6du3L7744gt1MqjRaPD48WOsWrVKXfL6ySefJM8oiShSaHgEPlt9BgMC+uOgthJc+qyHlbUtZyi9+yFH3Mv6aHVKpxaM+7kr3wYAI80oGfvzPsDt27fV/mLs2LEqsKmXOXNmfPfdd9i1axc6deqkgp+vXsVdv1ECnRKYleCtBCaHDRuGhQsX4qeffsLvv/+Oly9fqqCwnpQxaN26NTZs2KCCqfJlozxPLzAwEJMmTcL333+PHTt2IE+ePCqwmxi//fYb8uXLh23btmHAgAFqLAMHDkS/fv3w559/okiRIipYbUjGkjVrVmzduhVjxozB0qVLVUA1IQoXLqzGLP79918cPXoUOXLkUJm38voyjzI//fv3V/Nz8+ZN9dyDBw/i559/Vvvu7du3q4Dx8uXLE/VeiShl5cxog6xNRyNYZ4GyAUdwfOFgfgRERGQUezf8Bptgb5TM6YgWpT/s3ICIPjBoe+HChcgMHDnZlIwhyYLy8/NTpRL69OmjTgjlxJSIko9Op8OojRdx2NMbbyyywKXPBmTOmjovK6f0ydvbW+0z8ubNiyZNmkR5rGXLlioDNnv27OjatStCQ0NjZJgakoxa2RflypULvXv3ho+PDz799FOUKFEChQoVUpmyV69ejXy+BEzlNSQYK9ml7u7uuHHjRowvJsuXL6/KNUigVZqNxRc4jk62/fHHH6sxSRawvb09ateujVq1akWuk23KWPVkLuR15TUlg7hbt27xZhkbktryGTK8zWzImDEjMmXKBK1Wi/Xr16N69ero0KGDqr0rc1GhQgUVjBYSrG7UqJFaL68rrynZwURk2opUqIuLFSaq2x5PluPkH/OMPSQiIkpn7t28iDZ3vscBqyH4voYDtFqNsYdElGaZJ+RJciInl5hmyZJFlUA4efKkyj6SSz4lcFusWDF1YppYx44dU9s5deoU7OzsVCMVyYqSjCPJkJIsIbmc08zMTHW0njZtGszNYx+yNIORLKJr166pE+ApU6aoS1yJ0pIji0Yi+LYVzLRV8EvXsiiWw8HYQyJT8dXjuB/TmEW9P8IznudG+y5v8EUkJfn3XrJsJbtUMl71l/bLFxLLli1TGapPnz6NLFug/xkbyWjVk32IcHV1jbIuICAg8r40y5TXlWxTCZpKZm2zZs2ibLNAgQKRt/XBUH9/f9y6dUsFVvX0wc/4xqQfg+yToo9TxuXg8Pb/X8PH9WUOFi1ahA8hwWgpnyCBWr2goCBV4kjcv39flU4wJIFu2YcSkWmr0Lw/jj29gsqPlqLE6W/hmbc0Cpb676oFIiKi5OS15Su4asJxw6YsypcuxckmMnbQVrJ37ty5o4K2d+/eVVm30nxFgrXvS07E5WRZsp7kRF1OoCVbV07mN23apDKVpPTC/v371QmzZF05OTmpS0ejk4CyBGilJp9kD8mlnu3bt1cZwnLpKFFacPz3aaj6cD48LDTYXbMO6hTJauwhkSmxtDP+cxNAsmM/++wzVQph4sSJKqtVAp1SEmDevHmqPIJkxMoXdFKnNj7W1tYx1sX1xZ7sJ2S/IvuGzz//XDXP1JcVMGRjYxPr70vdXAko68n+UDJmo5MSDAkdk15YWFiU+yEhIbFuR88wEB0XCYLLPlbm2pCt7X+lVKTEUWLGSUSmo2Kvn3Bh2mWUCjoJm80f4VWOf+DkzOMCIiJKXpeP7UEF/0MI12ng0CLmsTQRGaE8Qtu2bVVzFDmxlpM8ydyRjNvYloSSTNoXL16okgrye9IARi7PlKYqXl5eqrafdBWXLF9plDJu3Dj8+uuvsTZKWb16tbq8c/z48ShYsCAGDx4c2QCGKC0499dqVLg0Qd0+nqcvGtepY+whEb0XfaBQaq3K5ftyhYQEKeXKC/nyTRp1SbarBB2TkjQhk7IBX331lSqfIPsM2Q8llARRpbyBfknKAGf05p6S7SplFgwzc319fSMfj17SQd6XMNw/yr5QagcbjlkWKZ8g5P1Hz6qVL2eJKHXQmpsjb7/VeKzJCpsIf/y0bhfCwtlMkIiIkk9EeATM9n6rbp/N3BR5ilbkdBMlswSddUrwUy6jlBPLQYMGqUtE9ZeNvi+pWVi5cmVV8kAax8jJszRBkW1LV3E5CZUO33pSO/f58+cq0zd6cFgueTVsZqN/vmTpxpfpa3jZrWF9QSJTcuXYLrgd/gJmGh1OODVD5V6TjT0koiQhmbZytYU0xJJ9gvxbLoFDySSdPn26qteaVKQ5l2xXSunIFRiSNSv7tOilCYxBMnbnzJmj5kLe/5o1azBy5Ej1mFxhIjVpZT8s5YSkvu7kyZMjA7VCX/Lgjz/+QNWqVdXVMR07dlRfaEqpIKlrK+RLUSmBIO9fylJIU1F5vnxpKg3MTpw4oUozEFHq4JApC162X4W9636C0+ND+HGnO75pFu0quINT5CwbqB21ASIREVFind69DO5h1xCgs0K+9j9wAolSQIJThaRhiTh9+rQ60fvQoK2QerUSDJaTZqmNK53AJUtWTljlpFNq2eo5Ozurn8+ePYsRtJXGNnLpqiF5vjw3LnJZrHQuJzJlnuf/Re6dPWGjCcF5m0oo9+kSaAyCNUSpmexHJDgr5W/k32TJDJXApQQhv/76a7x58ybJXqtUqVKqBM+oUaNURqrse4YMGYKLF5O2Zu/7kOxi2V/Je5dAtWQh62v9iqlTp6r9ozxP5kb2k4bN0SRrVkoLyZegEtCVJmT6khPyu6tWrVKZwW5ubuqKFCHN0SQwLPMvV71IQzeZ84Q2QCMi05C3mDtCihdC4Suz8NNR4I8c36BFuXz/BWz3TwRqf23sYRIRUSoXHByI7Cd/VLcvuvZApez/9ZEgouSj0SX1NaiJMHfuXHVCKSfRcmnszp07sWLFCnXyKoFhCcYaNlCRWoOSDSQZuobq16+vOoHLCaue1EaU8gpxNVWJLdNWTnwlSKBvDkNkTPfueiLD0trIBB9csSyJ/IN3wdo28Q3/iMh0yf4vPDxc7QspZcl+X2obc79PacHhRSNQ7cF8+Ohs8azTThR6vue/gG3Nt5n7RETpGff7H2bJvksI3zcBTc1PIuPwM7Cxd0yiT4aI4mO0riNSX1BKLVy5ckVdrtmvXz+sW7cOLVq0UI3J5CRKsqH0l4BKnVvh4uISY1tZs2aNUZtQnh/bcw3rE8bX6IXImB6/DkSXNffQI6w66lpdQ65PtzJgS5RGGfG7UyJKIzx6Tob3D1vgHP4cGdbVfLuSAVsiIkoCrwNCMP3QI/iEdUfGZhPQjgFbohRjtOusb968qbpYS8BWr169eggMDIxsQnPy5MnIxyTDVhqoxNbsTGrfyuOG5L67u3syvwuipPfCLxjdFh3HozdBWOfYFxk/2wuHjJk51URERBQrMzMtLPvvg3wFJO0eI6BBRLXhnC0iIvpgs/d5wicoDEWyZUDrigU4o0TpIWgrAVXpGC6Xhkodw+vXr2PgwIGq4UqxYsVUExUpkSBNyY4cOaJq70k2rnQe9/f3V8He2bNnq2116dIFT548wZgxY1RjmVmzZmHv3r3o3bu3sd4e0XvxffMS++Z8ikder5DD0Ror+laG8/+7vRMRpWZSBkIarknjM2tra2TJkkXt62X/LVauXKlKHcljUpNX6vPG5fXr1+p4wHCRWvhE6ZnD1TUqYCvJ+1rocO+X5sYeEhERpXIP71xDjRMDUEJzG6ObFIWZVvY0RJTmg7aurq7YunUrDh06hLJly8LDw0OdhO3atUudsP36668qq7ZatWqqZEKzZs0wbty4yN8PCwtT5RP0TcekE7hsr0SJEqpW7tq1a9VtotQiKMAP939pgfZBGzDbZj5W9KmEnBltjD0sIkpGP/74Y5R67GmZNE+Tq2CkCejVq1exe/du3Lt3D59++qn6wvXnn39W+/lbt25h5syZ6vbq1atj3ZYEeuVYQUoh6Rf5Apgo3TJoOnau2Ai1Kt/Lw3i6sp+xR0ZERKnYk01foab2PH502IiaheMuP0lEabARmSlhYXIyppDgIFyZ2QplAo7CT2eDp202oGDpavxQiChNk+Dsb7/9purbR9euXTv1paw0Fo1u//796Nu3rwr2vi/u9yktBmyl6ZguIgIXprdGad8D6mF/989h13SisUdJRGRU3O8n3rUzB1HkjxaI0Glwt92fyF+ySjJ8MkRkkpm2RPRfwPbyzNYqYBuks8D9RksYsCWiNE8akkpAtnv37rE+/uLFC2TLli3Wx549e6aybaWMgpRVatCgQYza9tEFBwerEzbDhShNiAiP0nRMo9WicL+leKDNibPhBbDrijfCwt9enUZERJQQ8gVg2K5v1O0zGRswYEtkJObGemEiAoKDAnB1VhuUDTiKYJ0FbtSah1IejTk1RJRmlStXDpcvX1YNR6Ve/ejRo2M8R2rZSzPSpUuXxrqNVq1awcHBQZVaCggIwJIlS1CnTh0VCC5cuHCsvzNp0iRVmoEozakd8/8hmwxOCP34b3T77Tz8X4TD868b+LJREaMMj4iIUp+zf69DuZAL6hw1dzterUFkLCyP8H+8XIJSWnBYOM5MbwcP/30qw/ZmnfkoWbMNPwgiStMePXqk9rlS1/a7775TjUkl6Kp3584dVc9e6v3GlYUbG2liJsHcuAKzkmkri56MIXfu3Hjz5o0KABOlRdsvPMbnq8/CCiFYWjcCHvXbGntIRERGwfP9hAsLDcHDSWWRN+IhjuXogcr93jaAJ6KUx0xbIiMFbAesOI1XL2thgeVpPK07GyVrtORnQURpnpQzkKVo0aLInj07qlSpopqQOTk54cGDB6hbty6GDx+eqICtcHNzw/Pnz+N83MrKSi1E6UmzUjlw9vZzVDs9CBUPX8BJ/6dwb/WZsYdFREQm7Pi2Baga8RCvkAHFOnxv7OEQpWusaUtkhIDtJyvPYP91L1wzL4xbnf9lwJaI0qXQ0FD1U0olPHz4ELVr18aAAQMwZMiQRG1Hfl8yd+MqjUCUnn3VrATsnHPBTKOD+7mvcGwNL3MlIqLY+QeHYejlgvg6tDcuFR8Oh4yZOVVERsSgLVEKF3Q/Mbsnnl8/BitzLRZ/5I7KRXLzM6A0r2/fvtixYwfSgnv37qmsTgkyUsLt378fc+bMUXVnpUSC3B80aBCaNGmCoKAgFbBt164devfuDW9v78hF3L17F3ny5MGmTZvU/dmzZ2PDhg24deuWCtZ++umnKsu2Z8+e/EiIojEzN0eFz1fgWJaO6n7l61NwbOFQdUxCRJSaHDt2DNWrV4eNjQ2cnZ3RqVMn1ZyUks6Cf27jmX8YDmdsgUqtB3FqiYyMQVuiFHRs6ShUf/MHllpOweKuxVGloDPnn1LW/knAwSmxPybr5fFk8Ouvv6Jp06Ywtl9++QW///47TNmTJ0/QpUsXpDVS/mDz5s0qOJsvXz706NEDVatWxapVq7BgwQJ4enpi8uTJcHFxibLoM2nDwsLUTyHrpXZtqVKlUKtWLTx+/BiHDx9Wr0FEMWnNzFBpwDwcdf1E3a/8cBGOz+vPwC0RpRpSl75Zs2bqqhppaLp3715VB/+TT97+u0YfzsvrGZYcuq5uj2xYBJbmDBcRGRv/LyRKIVeP70ale/PV7dulhqFq0Tyce0p5WjNg/8SYgVsVsJ349vFkYGFhAVPw77//wtRJ8FEfnExLypQpg7///hsvX75ESEiIql8rQfSMGTNizJgx6j3HtggJ8kpgtm3bt02UJLPm4sWL8Pf3Vxk2W7duRaFChYz8DolMm0arhUevH3G82DeI0GlQ+fl6lkogolRDjh9evHiBgQMHIn/+/Oq4olu3biqAS0nj7pph2IYh6JH1DpqUzMZpJTIBDNoSpYCgQH/Y7R4CrUaHExmboGLbwZx3Mo6aI4HaX0cN3OoDtrJeHk8GnTt3Vpe0i+PHj6Nx48b4888/UadOHZUtKeUTvLy8MG7cOLi7u8PDwwPTpk2L/H35nYYNG+LAgQOoV68eypUrh379+kW5JE4aV02fPj3K644aNUo1tRJdu3bF6dOn8c0336jyBvrxSDfhYcOGoXz58qhUqRImTJiggop6ly5dUpftlyxZUmV4XLt2Ld73Gt/2pKSCnGTI+2nevLl6761atVLjEuvWrcP333+PM2fOqDHK/Ojfx6JFi7BkyRJUrFgRn3/+uSorMH/+2y+C9FasWKEyT9Ni0JeIkkalDiNwotBgPNNlxNIrOlx69IZTS0QmT5qXVq5cWR0fSlkl+TJ3+fLl6hgyrsxcOSYzXChu96+dQrkX25FH64WOHoWh0Wg4XUQmgEFbohRwduVXyBPxCN7ICLceszjnZDqB2/EuyR6wjasu7MaNGzFv3jysXLlSBUJbtmwJOzs7Vbf022+/xcKFC1VwU08yMyUoKXVRJbgpgdA+ffokOEApWZ1ywD969GgcPXpU/a6QpldyYC9lEyQwKq+pD+jKa0iA1NXVFdu2bVMZobNmxf//cHzbE4GBgZg0aZIKzkqdX6nVOmLECPVYixYt8NFHH6F06dJqjFJOQG/9+vUqgCzbleC2PHfnzp1RXnvPnj0qIM4DbSKKT6Uu32FywdXYHV4ew9afR2g469sSkemT4xz5wj5v3ryqTIJ8Ia//cj46OdZydHSMXHLnZh+R+Lze+pVqWHnatjqKV26QTJ8gESUWg7ZEyezO5eOo8HCFun3fYwIcM72t0UhkVBKgNbMEwkPe/kzBgK0IDw/H4MGD1QG3ZJtKFq1Wq1UBTzmolizSAgUKqCZThr8jB+ZFihRRl8JL4PLGjRsqkJkQchm+vIYEhjNlyqSaWNy+fVsFR3/44Qd1qV2JEiVUxsYff/yhfueff/7Bq1evVP1UOUGQLGDJ8I3Lu7anJ+skE1feq2xPGnPJ68iYZDEzM1NjlJMMPQkE//jjjyqALI9Jpq7MjzTp0l82KBm7ErQlInpXqYSvW1dAJjtLXH/mi+VH7nDCiMjkyRf90oRUjgE7dOigauFLbdvYyJf0b968iVzky3+K3dWjf6JU4HGE6szg3OoHThORCTE39gCI0rKICB2ub5+BfJpwnLGrjnINuxt7SET/lUTQB2zlp9xP4cCt1CnVk0Bqrly5VFDVcF1AQECU35HAqZ5kqNrb2+P+/fuqdMH7kKBvREREZBkCfXBYsmGFHOBLkFReR08Cze+7PT0JSOtlyJBB/ZT6rPE10pLgtmFt4Jw5c6rAr5SZ+PTTT1W92Bw5cqjnERG9S2Z7K4xoUAhn/vgFdf4ejldF9sHJJTsnjohM0vnz5zFo0CBcuXJFfXkvX3rLlVdy5ZFcwaVvXqpnZWWlFoqfLiICmr/HqdunnZujcmEeRxKZEgZtiZLRqhP3MeZVF/SyyoH+XT7nXJNpiF7DVn9fpGDg1traOtHNysLCwiJvS1kEuR/fAbkEfS0tLeN8XLYhma1btmyJ8zmGgWRhbm7+QdsT8pzEiu19yImKZJ1I0Pavv/5SGcpERAnVoXxOVNqzB/nCH+Pk2pFwH/j2yiAiIlNz8+ZN2NraRmk8Kn0O5Itx+ZI9etCWEub0X2tQIewqAnWWKNjubfCWiEwHyyMQJZMHLwMwZec1RECLPA0HIkvO/7IKiYwmtqZjsTUnM1Genp6Rt+/cuaMaUUjGrT4z18/PL8YBviGp9SqZr3py4C+BXaldK5m+houQn5K9YZgpKyUQ4vKu7SWEjFGydROiUaNGqjyClEmQsgwM2hJRYpiZWyCg7o/qdnnvbbh78V9OIBGZJClRJcdX0pxVjsWuX7+OgQMHqiuPihUrZuzhpUph4RG4f2qHun0hVxc4Z3c19pCIKBoGbYmSQUhwEA4u/BJhwX4olycjulXmDpBMRER47E3H9IFbedxEScbr1KlTcfnyZXWwPn78eJQtWzayXIHUj5XGXKdOnVIlE2bMmIGHDx9G2YY0Itu/fz+ePHkCLy8vFCxYENWqVVO1cs+cOYOnT5/i5MmTqkGYqFGjhipfIK8lwduzZ89i/vz5cY7xXdtLCBmjlFm4ePHiO+uvSc3bmjVrqvcq9XGl3i8RUWKUqNIYJ+zrQKvRIfiPYYgw+GKLiMhUSLmqrVu34tChQ+r4z8PDA69fv8auXbtiXL1FCbPpzCMM9e2KPprvUaz9t5w2IhPEoC1REpOTnYtzOqNbwHKssf4RszqVgZlWw3km01B7dNwlEFTgdjRMlTTn6tatm8qqkLIAko0qQVy9nj17qgN4KRXQpk0b1Zirf//+UbYxdOhQFQht0KABNm7cqNbNnDlTBXw/++wz1K9fH19//bVqWKEvSbBgwQKV1duyZUt8++23GDNmTLwnB/FtLyEkW1beR48ePWKMPzYyFwcOHGCWLRG9t1wdpsFfZwW30Ks4vnUuZ5KITJIcvx05ckQdV8lxntT1l2MuSryg0HDM2HtD3a5UuyUyZHTmNBKZII1OCvARfHx8VMaS7AAcHBw4I/TehdxPzO2LSl4bEKIzw/W6i1CyRmvOJtEHOn78uApiSvMJCd7SfyRruFatWurExbDBGcWP+32iqE6u/BbunrPgp7OBb5ftyO5WgVNERGkG9/tR/fnHenx3JAzmDllxYEQtWFvw+JrIFDHTligJA7bHFn6hArbigvuPDNgSJTF+zxjT7t27Ubx4cQZsieiDlOv4DS5blIAtgrB0218qC4uIiNIeP59X8DgzDAetBmNMhRAGbIlMGIO2REnk2LLR8Hi8XN0+XvQrVGjWj3NLRMnm+fPnOH36tKqx26tXL840EX0QMwsrZPp4A0ZpB+M371I4vGBY3M0pVVPLSZxxIqJU6NKGH+AEH7zQZka92nWNPRwiigeDtkRJ4MTm2fC4N0/dPlZoKCp1/JLzSkTJSgK1AwYMUPV7mzdvztkmog+WPVt2NOv8GTQa4PwjX2D/RGBj31gCthMBLS+lJSJKbV57PUbJe28TjZ65j4CFhaWxh0RE8TCP70EierdzD15jymkd5pg54W7uNqjcdQynjSiJVapUCdevX+e8GtixYwfng4iSXI3CLviibiEs2tsIH5nvgfPF9UDQG6Dr+v8CtrW/jrupJRERmawbG8aioiYIN80KoHzDj4w9HCJ6BwZtiT5AcFg4hq47h9thBTClwCJM61GL80lERESp2sA6hXDt0UvsvVkOncwPADd3A2MzAbpwBmyJiFKp5w9vo8zTDYAGCKj2NbRs7ktk8lgegegDLNp/Fbe9/eFsb4UxnWpwx0dERESpnplWgxld3LEx50hMC23/dqUEbDVaoMogYw+PiIjew+3NY2GpCcMVixIoVbM155AoFWDQlug9Pbl3He0PN0FPs134tqkbHG0sOJdERESUJlhbmGHhRxWRy8la3dfp5D8RCJ9dHgh4aezhERFRItx/EYDLz0MQrDOHps7X0GgZCiJKDfh/KtF7erTxK7ho3qC93Vm0KJ2T80hERERpiuPJ6ejktwK7XT5Gl9CvEaQzh5nPQ7yZWx86FcUlIqLUYPa+mxgf2g3Dcq1GUY8mxh4OESUQg7ZE7+HGmQOo4LMXEToNrJr+yG8qiYiIKG0xaDrW8LOf8UnPXvjE9if8r707Aa/pWsMA/J0hc2SUSUISCRHzPNYQilJVRakWpdWrpYaiXOoqvVWXDlRdtK6hVVqlSqmiNKZSlFAzEUNmITOZz77PWmnSjMbEmb73eXbPOXufYa199PzZ/177X1E6VzimhWP9J2NxMz1L360kIqJ7uHrzNjaGRcv7r3Zvyf1FZET0lrRdtWoVVCpVmUtISEi5265du1bqvZKTk0s9z8nJSS/9ItOn6HTI/fkdef+YUzcENnpC300iIiIiqli64pOOdajthqUTh2BTx+34VPc8YpLS0X/JQSTezuaeJyIyYGEb5iJYuYxOQW5oUsNZ380hogegUvR0bVNWVhbS0tKKrTtw4ACGDRsmE7M5OTnFtq1duxafffYZLly4AHWJ+ivnzp1D06ZNERkZWbhOPMfFxeW+25OamgpHR0ekpKTAwcHhoftFpu/YtuVodmQCMhRLpL72Ozx8AvTdJCIiekCM+0QP73JCOl5efhjNUnfhafdb6Db+C+5OIjJo5hr3o8JPwXN1B2hVOpzr9yuCGzTTd5OI6AFooSdWVlZyKWrFihUYMWKE/DEtafny5Rg3blyphK0QFxcHb29vVK1atVLbTJSUEAv/IzPljjhR42W0YcKWiIiIzEyAmz2+fNoeAd8vBpKBk/t7oVH73vpuFhERlRC3ZRZ8VDqctGmFRkzYEhkdg6lpe/78eWzfvh1jxowptW3Hjh24fv06hg8fXuZr4+PjERsbC39/f5m87datGw4dOnTPkb7ibFvRhehusnN1WLpuI+yV27ii9kWzwe9zhxEREZFZCmjQCn9UfU7edwidDl1u8avkiIhIvyIvhKFJ8i5536bbv/h1EBkhg0nafvzxx+jTpw98fX1Lbfvoo4/w2muvwc7OrszXitetX78eW7duxaZNmxAYGIjOnTvj4sWL5X7enDlz5IjegqV69eoV2h8yLZk5eRj7TRg+j/LD68o06J77ApZW1vpuFhEREZHeBL4wF8mKPfx113Bq8yf8JoiIDEjCT+9Bo1IQZtMWtZu013dziMhYk7ZipOzq1avx1ltvldp24sQJ7Nmzp8wRuAWsra3Rs2dP1KtXDy1atMDixYtRu3ZtrFmzptzXTJ06VdazKViK1sMlKio1+RamffE9tp+Jg6VGjaEvDUVAg9bcSUREDyAvLw+LFi1C48aNZdx2d3fHwIED5ZUywtWrV9GjRw9UqVIFXl5emD179l3fb926dQgKCpLvJera3+sKGyKqeE5VPXCi9pvyvv+phchKTeBuJiIyANcvhKFxSqi8b//UDH03h4iMOWkrJhhr0qQJ2rRpU+Yo2/79+z/wSFhxIHfjxo1yt4t6uqIAedGFqKT4qMu4tbAzJt+YjFpWSVg1vAU6BblzRxERPaCkpCSZWJ01a5acQFSUPhITj44aNUpu79u3L7RaLX7//XdZ4/7DDz/El19+WeZ7HT9+HIMHD8bYsWNx6tQpdO3aFb169ZKfQUSPV6t+E3EJvnBAOiK+m8rdT0RkAOJ/+gBqlYLjtu1Qq1HpPAsRGQe9J21v376NJUuWlDnKVox+FSNpytp2N4qiyANCMdqW6GFdOXMYqv89CX/dVWhVCpb0C0DbQE52R0T0MMRkoeIKmGeffVbWoBcnawcNGoQLFy7g2LFjCAsLwxdffCGvmhEjbkXsFyd1y7Jy5UpZBmn06NGoVasW5s6dK0sd3e0KG9ayJ6ocNtaWuNbqXXm/VtT3SIuP4K4mItKjywnp2HqrGmIVFzh048k0ImOm96StGE1jb2+Pfv36ldq2YMECtGzZUi5FiUsoa9SogY0bN8rH4qBuw4YNuHz5skzWilE7YpTtsGHDHls/yLREXjoJx/X94Y5EXFVXR87wnQhsyDOUREQV5eTJk1i6dCmGDBkiR876+fnJsggFOnToIEskZWdnl3qteH7btm2LrWvfvj2OHDlS7uexlj1R5enU7Tl8bfk8Xsx6B4uOZXFXExHp0ZI9l/FlXje86/8tAhuzli2RMVPru77d/PnzZb1ajUZTbJuoM7ts2bIyR9mKkbS5ubnyVnBzc5OXWzZs2BCdOnVCTEwMDhw4AGdn58fWFzKtkgjaNf3gglSEawLg/GYovHyD9N0sIiKTIOrPihJFoga9OGEraszfvHkTrq6upUbmir8Tbt26Veo9ynu+qJFfHtayJ6o8Wo0abs++jyNKMD7fF4EJ353Axfg07nIioscsKukONoVFy/tvdOaVx0TGTqvPDxeJ2oiIsi+hEpc5pqamlrlNXFYpErMFXnjhBbkQPaqM22lIX9kfAUhApKoanP/xIxxd3LhjiYgqyJYtW2R8F1fGzJgxA9HR0QgMDCz1PAsLC3mrUqlKbdPpdGU+v6znFhCJYrEQUeXoVtcDb4YEYlFoOMLD9uHDE5sR69UZA5tXR+/G3nC0yf9/moiIKs/pDR+gJ3RICngaTWpwEBuRsdNr0pbIkIiR27M3HsYLOTokqh2gHbYJrh4++m4WEZFJ8fb2lktwcLAshyDKHMybNw+JiYnFnpeQkCBP7rq4uJR6Dw8PjzKfL668ISL9ECdNJnUPwjOeSfDd9AosdZnYHn8AozaPx0c7L2JGr7ro1+yvv6v2zgN0eUAIay0SEVWUhLjr6Bj1OZ6yzMbpuiztR2QK9F7TlshQ/G//FXx9JgsDc2ch6pl1LIlARFTJcnJy5G2rVq1kvfq4uLjCbYcOHZJljywtLUu9TtS6F9uLEo9FyQUi0q+g+s1h3aAP1Cqgp/YI1tl9hJSMHExcfxJrDl/LT9iGzgbUxUujERHRownf/CFsVNm4qK2Nem2e5u4kMgFM2hKJg/3TlzHn53NyX0zu1RgNmxWf4IaIiB5NaGgoFi1aJCcgEyURxOOxY8eiZ8+ectKxNm3aYOTIkTh79iy2b98uJw4Tj8uagHTEiBHy9YsXL0Z4eLisVxsVFYVBgwbxayLSN40W6LMEaD1KPmyVdxx73D4S1zQhYeu/8xO2Ie8AHSfru6VERCYj9dYNNIz5Tt6/03oCVGqmeohMAf9PJrMXc/UCgjZ0xBj193i+aTUMbeNr9vuEiKiiiclBf/jhB4SEhMja9EOHDkW7du2wZs0auf27776TI2+bN2+OV155BePHjy9M2pacgLRu3bpYu3YtFixYgAYNGmDnzp3YunUryyMQGQqRLOj+AdB5unzol3YcEdZDMF6zHmvtBkPX/m19t5CIyKSc3fwh7FSZiFD7oVHngfpuDhFVEJVScARk5sSkKGLys5SUFDg4OOi7OfSY5ORk4/LcDqiTe05eRlJj4h5Y29hx/xMRmTjGfaLH5PDnwM/5o2pzFTUCs77Gf/o2wAsta/ArIKLHxpTj/u3UJOR+Ug+OuI0/WnyC5k+/qu8mEVEF4UhbMmtHVk2VCds02KDKkK+ZsCUiIiKqSJkp+bcqNbQqHcZoNuKjnReQmplf05qIiB7NmR/ny4TtdZU3mnR/mbuTyIQwaUtm68+D29E6arm8H97y35x4jIiIiKgiFUw6JmrYzkhEbsdpmGixAYMyvsWi3Ze4r4nogR0/fhy9evWCq6srLCwssHTpUrPei1m5eVh5xRlHdEGIbfgGNFqtvptERBWISVsyS7cSb8F15xhoVAqOO3VHk56v6btJRERERKaZsBWTjqlU0IZMQUT9cTJxG3L4VVy5eVvfrSQiIyImM+3SpQs6duyIgwcPIjIyEgMGDIA523AsCj/fro1xNnPQpFf+BJBEZDqYtCWzI8o4n1kxCt64gTiVO4Jf/VzfTSIiIiIyLbq8vxO2RdRs9qS8baM+i2Nr3tVT44jIGE2bNg2TJ0/G22+/jaCgIHh6esLFxaXM52ZlZck6tkUXU5OnU7BsX4S8P6J9TVhaaPTdJCKqYEzaktn56tA1/JzkjXTFBlnP/Bc2VZz13SQiIiIi0xIytVTCVvLvgJttpsm7/ZOW4fjOrx9/24jI6GRkZOCXX35B7dq10a5dO/j4+KBfv36Ijo4u8/lz5syRE48VLNWrV4epOfnL1+iT8hX8rO/ghRam1z8iYtKWzMyl+DR8sO0cvsnrgh9DdsC3aTd9N4mIiIjIrFTtPgXH3PvL+0EHJ+Lm5RP6bhIRGTiRnM3JycG8efMwe/ZsbN68GUlJSTJxK66kLGnq1KlISUkpXEQpBZOiKHA++gnGazfivep/wM6KtWyJTBFH2pLJycvLQ1paGpKTk3H79m3k5ubK9VkZaXh3bSiycnXoWNsNgzo21HdTiYiIiMxS/Vf/i5PahrBDJrK+GYy8zDR9N4mIDFhCQoK8Xb58OTp16oRmzZph0aJFOHz4MK5du1bq+VZWVnBwcCi2mJKLv2+Df24E7ihWCO41Tt/NIaJKwtMxZDJEnSJxBvbOnTultjnZWkD901h8nHYNk2wn48Pnu0ClUumlnURERETmzsrKGg5Dv0bc8k7wzo3ExRWvovYb6+SEZUREJdna2spbUce2QI0aNQoTun5+fma107L2fypvw1x7op1HNX03h4gqCUfaktHT6XS4fPkyLl26VGbCVp2TDvufRsI/PQz2yMDYjr5wr2Ktl7YSERERUT7/Gr4403YBUhVbfB4TgLDIZO4aIiqTqGUrErf79u0rXBceHi5vzS1hG33pBBrcOQydooJX94n6bg4RVSImbcmoifpFERERshSC14VV8Ly4uth2TXYqAg5Ogkf6WWQqWiysOhNu1euUWfeIiIiIiB6vzt16Y1bgOnyf+wTeXBuGW+lZ/AqIqBQbGxuMHDkSEyZMkCURzpw5g3HjxqFPnz5wc3Mzqz12/ZfF8vaEbWvUDGqg7+YQUSVi0paMWlRUlCwsLygqDbwvrCxM3GqzklH74AQ4pF6Uj7eqQ9CxZTNZsP7WrVt6bTcRERERiWoIKszo3wb+Ve0QnZyBqat/RU56IncNEZUyd+5c9O/fH8888ww6dOiA6tWrY8WKFWa1p8ScLXXit8n72hbD9d0cIqpkKoVDDgvroTo6OsoEoKkVKTfl70yURChKJGxF4jY2cBAcbxyBbepluf7L3G6w6jgBtVws5WOtVov69etDo9Hope1ERKRfjPtEhuVSfBpm/Hcl5qs+QbJTPdQZv5X1bYmowphK3F+/LwzKznfR2iIcPu+chFproe8mEVEl4khbMkq5ubm4evVqqfVxtYcgOmg4vMK/gU1qhFy3Irc7zgW9WZiwLXh9wQhdIiIiItKvWh5VMKZbPTgjHXVSDuDw1zP4lRARFSHG2y07lorJuSOxu/OPTNgSmQEmbckoRUZGIicnp8xtInGrU1tABQXZigbrHEegbx27Mi8tISIiIiLD0PaJLjgc9La83+ryQhzZtEjfTSIiMhi/RyTiYnw6bC016NvcV9/NIaLHgElbMjqJiYmwOvRJqUnHVHnZcIz7DbUOToRal4MsRQtLVR4Wum+BRq0q9T4ZGRmPsdVEREREdC8dBk3GEY+B8n6zsOkI21n87z0iInN1ePdG1FdF4LnG1eBow7IIROaASVsyKiLReu3atVKTjkGXi5rH3kPg0X/B4VYYDubVRVDWVzjg/hKCrnxVKsErsJ4tERERkYFRqdBi5BIcceoJjUpBvd/G4/S+TfpuFRGRXsUkZ+CpqE+x1Wo6Rrud5LdBZCa0+m4A0f3Ky8tDREQEdDqdLIEgiMQtFB1s0q/DKf6gXHcBfngxZzra+FjBuuUriL5kmf+8v0onFLC0/LvGLREREREZBpVag6ajv8KxBX3R7PY+pO/+EMdqdEAzPxd9N42ISC92hIZiuDoSudCiWvNn+C0QmQkmbcloiq6LiccyMzML18kErKLA++KqwnXXtX7onv4BXKzVGNnUESqVqjBRq1Lyir2nvb39Y+wBEREREd0vrYUF6o/9Dpv+OwHv3OgCzaqjWDeyDYK9jHfWdyKih5Gbp4Ny6nt5/6ZXB3jaOHFHEpkJlkcgoxAdHY3k5OTiKxUFFtlJhQ910KBD+gfy/pstHVHF6u9/3iJxGxs0rNgoWycnBjsiIiIiQ2VlZYNub36GOr7VkJqZiyHLj+BqdKy+m0VE9Fjtv5iAkNz98n7VVoO494nMCJO2ZPASEhIQHx9ffKUYYXvuC7hf3Swf6lQaqJGHMZqN6FXLFo08rO76nh4eHnIULhEREREZLltLLVYMa4G6nlUwIGMdrJa1R1z0VX03i4josfn94K/wV8cjR2UFbXBP7nkiM8KkLRm0W7duIeeX90pPJKZSwTYlXN7NsPPB03bf4uOc/phosQGTre8+WYUoi+Dm5laZzSYiIiKiCiJmSf9qaH0MsjwALyQgfXkfpCQncv8SkclLvJ0Nt6tb5P0M/66AFUv8EZkTvSVtV61aJUc6lrXs3bsXffr0KbV+06byk3Hr1q1DUFAQrK2t0bRpUxw6dOix9ocqXmJioqxjq6g0ciKxoolbcd/h5jF5P1TbAedu5uB/qn447zcUNS6tKp3k/YtarYafnx9H2RIREREZkaouLrAc9gMS4YhA3RVcX9IX2Vl/z3VARGSKNoVFo6XqrLzv0IKlEYjMjd6StoMGDZKXvRddfvjhBzg6Osqka2xsLJYtW1Zse8+eZV8KcPz4cQwePBhjx47FqVOn0LVrV/Tq1QtJSX/XOyXjmnQsJiYGV65cKaxHGx00XCZu/Y/9G+rcO6hyM0xuO+A+GKPie8n7o1s44naDYfK5JScdK+Dj4wMrq7uXTiAiosqxfft2tG7dGjY2NvD29sakSZOQm5uLmTNnlnsi99q1a6XeR9Q4L/k81iknMn0evnWQ9Nwa3FGs0CArDCeXDJd/NxIRmZTQOcDeefLuphPReC77PfzSbCkQ+GT+erGdiMyCVl8fLBJnJZNnK1aswIgRI1ClShXExcWhTp06qFq16j3fa+XKlejcuTNGjx4tH8+dOxfr16/HmjVr8Oabb5b5mqysLLkUSE1NfeQ+0aPLy8uTo2tLTjomEre2KRfhEhMK55g9UEHBcZ8hGHa5h9zeJ8gObXysC59bFgcHh/v690RERBVPnEidNm0axo8fL2N2eHi4PIHr4uKCyZMnl4rXa9euxWeffYbq1auXei9xYldcWRMZGVnsSgoiMn0BjdrjZOJi1N8zAi2St+HopkVo8dwYfTeLiKjiqDVA6GykZGTjz6gmUKs0aBLSFzj4qVyPkHe4t4nMhN6StiWdP39ejsARB2iCmHhq6NChuH37NqpVqyZH0ooDPY1GU+ZI227duhVb1759exw5cqTcz5szZw5mzZpVCT2hh5WdnS0P4jMyMkptc47eDae4g/K+SNjmqSww+FpP5CoK2vpY46UGd6/to9VqWRaBiEiPnJ2dZbwueuXDSy+9hH379slkrq2tbbHnL1++HOPGjSszGStO7IqRug9yIo4na4lMR6OQ/vj92jG0vroY9U+8h6hGXeFTs46+m0VEVDE6TpY3jqGzMUbTH3/4voaqx4okbP/aTkSmz2CGpXz88ceyjq2vr698HBoaim+//VbeirIHs2fPlktZbt68CVdX12LrxIGcSPyWZ+rUqUhJSSlcio7WoccvPT0d586dKzNh6xh7AP5hc2SyVhAJW42Sg1d136OOqwXGtHSEWqW66/vXrFkTFhYWldZ+IiJ6uMkmPT09S63fsWMHrl+/juHDh5f5OhHfxWhbf39/mbwVJ27vVctenKwVJZgKlrJG8BKR8Wgx5H0ctO6AGbnDMO3XJJZJICLT0nEydlj3kBNtr4npwYQtkZkyiJG24uBr9erVMkFboE2bNoX369atKy+XX7BgAWbMmFHq9TqdrtQ6kaATNe4epDwD6e+gXdQs9Dy/Uk46VrS8gcONo6h5/N9QKfnf8UX/IXjx6tMYlLNBBrC+nvZI1Ay96/uLA3pRcoOIiAxHREQENmzYgF27dpXa9tFHH+G1116DnZ1dma8VJ3lFyRtxovfOnTuFZZJOnjyJ2rVrl3uydsKECcXKIjFxS2S8xNV31UZ8i83z9yE7PBE7z8aje73SJ4GIiIxRamYOLqTboLtWjLTTARpLjrAlMkMGMdJWlERo0qRJsURtSUFBQbhx40aZ2zw8PJCYmFhsnZi4zM3NrcLbShVHTBwRFRUla9iK+yJhKyYb87y4Wm63v3USAUf/BbUuRz6+4dgQg64+jZsZOmy0fR6XA16G/+VVhc8vi5iYRvz7ICIiwyFidu/evTFlyhS0atWq2LYTJ05gz549GDOm/BqVop6tmJy0Xr16aNGiBRYvXiyTtaKWfXnEiVqR6C26EJFx86tqhxHt/eX9j7YcQ2Z68TkRiIiM1ZGIRDyv/mtQm1oL5GUXTk5GROZD7yNtRc3aJUuWyOVuzpw5U+7omZYtW5a6LFI8fv311yu0rVSxE45duXJFlqYoUDDCViRuhRTPtlBU+ecVouwboMvNqcjK08G7igYzO7og2eZlRGvVUCl5ZX6GqI/o5+d31xHXRET0eIkrZ0Q5gy5dumD69OlljrLt37//A4+CvdvJXSIyXaNDAhH9x1b8M+O/OL/mSTQeuUzfTSIiemQ2+96Dlzop/8FbZ4HjX+aXSBBY05bIbOg9abtixQrY29ujX79+hevCwsLw888/y1E0Ylbp/fv344MPPsCiRYvkdjEys0OHDrJcQt++fTFixAg0bdpUjrQRB4Ji8hIxglPMSk2GR0wGIyYcy8zMLLWtaOLW69LXcpTtbqeBeDXuWTE2F409LDGhtRPsLPOTuUVLKZQsjxEYGFjmxHVERKQf4kSdiNNidK2I4SWJ+vLr1q3Db7/99kDvK67WEHXRX3nllQpsLREZAzsrLV5o7Q+vA4lwj1mPmLOvoFrd8q/eIyIyeHvnoV3c1/JuslM9OFXx+DtRy8QtkVlR63u05fz58+UlkEWTawWJ2ieffBLBwcFy1I1I2IpZpgsOznJzcwsnHBA1b9euXSsPABs0aICdO3di69atLI9ggNLS0uSBdVkJW8Eq/TrSXRtBp7aQCdscaP9K2AI9A20x7QnnwoRtecRM4yJhy4nHiIgMh6gh2717d9SqVQuzZs2S9czFRKJiKahNL+K4uHpGLEWJk7U1atTAxo0bC8sqiXq4ly9fljFl1KhRcpTtsGHD9NI3ItKv1l364pBtJ2hUCu5sGg9FV/ZVWERExiAjKxsXdD7yvkWd7n9vEInbkHcA/sYRmQ29jrQViVoxEUlJYmIRMdK2PGK26JiYmGLrxIhbsZDhEqUwLl26JJPtXhdWlZp0zPJOHGofehvazJtQQ0E2tLBELsZpNyKz0TB0q2l7z88QpRBEwlaURiAiIsPxww8/4PDhw3IRJ1qLEuVynJ2dsWzZMnkFTkklT9aKmvUi8Sv+hhBX67Ru3RoHDhyQ70FE5kf8/VdtwMdIX/kEArPP4+h3/0GLF97Rd7OIiB7KHo+haKf6r7xvV//p4htZGoHIrKiUgiMgMydGADk6OspLNzk5SeWURDh//rw86BbE5GGiBEJ00HCZuLXIvImg38bB6k6s3H4MQeiX+S4mWv6AMer1hc+7n4Qtvz8iIroXxn0i03Ng7Rw8cfE/yFY0OPfUOjRq01XfTSIiA2FMcf/9DQfhc2I+ujpEwnvSb4CaJf+IzJXea9qSeRAjowsStiVr16pzM+AUf6gwYftHXm30z3lXTjjm/8RriI6xL5ycrLzErUjYBgQEGHwAJiIiIqLK0e6FKTj+ySE0Td8Lr+2v4aD1ZrRt0oi7m4iMSui1bFzOHQbPns3gzYQtkVlj0pYqXU5ODpKS/pr5sgiRgFXpclDtUn6RdeGPvFronzMTzb2sMK6VI2wt1IWJWpVSdn0yrVYrE7biElkiIiIiMk8qtRr13vgSUQtCcCTTG9PWX8NsnSv6NcuvDUlEZOhupGbicsJtqFRA65ou+m4OEekZk7ZU6ZKTkwvrEBaj5ME29UrhQ3EpW/+cWegfbIeB9eyhFpHqL+WNsLWxsZEJWysrq8ppPBEREREZDSs7Z7iP3Q3bVTPxWtxmTFyvQXxaJt7oGCCvzJL2zsufyCdkqr6bS0RUzLFzF9FafRZ3PJrDydaSe4fIzKn13QAyj5G2ZVHp8qBOvZ7/HEUDS1UevvXbhkH1qxRL2JbHyckJQUFBTNgSERERUSHLKq7o1sAHEy02YKzmeyT8sgCzN4flDyIQCdvQ2awRSUQGKevkRnxr+T4+yfuPvptCRAaAI21Jb0nb3LCv4ZARidW5XfCZ5T+wsvpPaH3tK0RftLjnpGNeXl5yKRwxQURERET0F3WnKWLSA0wQCVoAO46dw+HEpmh9/Qsg5B3OwE5EBqlafKi8Vfw76LspRGQAONKWKp2lZfHLOizTruPWgf+hZezX+DinP9Y5v4G5T7ois+EwRAcNl5OOeV5cXeZ7iSStv78/qlWrxoQtEREREZWv42Sg8UvybnfNHzJhG93kLSZsiYzcvHnz5LHg1atXYUoiomPROPeUvO/V8jl9N4eIDACTtlTpik0QdvMCau15HdVv7ceCnL647D8Y/+rgDAer/H+KYoStSNyWNemYhYWFLIfg4sKC7ERERER0H/osBtR/X1y4+LwtMnPKntyWiAzf3r17sWXLlsr7gNA5+WVUyvxwUV5lTqV99JUD62XJwFitD+y961ba5xCR8WDSliqdnZ0dtFotEiPPIvDQJFgjE4lwRG7joXi1iQO06uIlDkTiNjZoWLF1tra2qFOnjnwvIiIiIqL7Iicdy4Wiyj/smX7nQ6zeeYg7j8gIxcXF4dVXX8UXX3xReR+i1uTXvS6ZuH0M9bDdwjfI23jfZyrtM4jIuLCmLVU6tVqNW+dD0ebsHNirMnEKtXCj7XsIcXO4r9eLkbW+vr7yfYiIiIiI7ktBkiXkHajajUfGR3Vhk5mA7oeH4WrLMPi5VeGOJDISeXl5GDhwIGbOnIng4OB7Pj8rK0suBVJTU++/rIrwVz1s+bjIb0nh9goWce44GuachE5RwSfk1Ur5DCIyPsyCUaXKvJOGI5++iM7n3s1P2GrrI7nLx6jh5nRfrxe1a/38/JiwJSIiIqL7VzLJorWE9avbkAsNaqgTcHnVSCiKwj1KZCSmTp0qS+UNHjz4vp4/Z84cODo6Fi7Vq1e//w8Tvxnit0P8hvzbrdITtuK36Lfdm+X9U/ZtUdWnVqV8DhEZHyZtqdJcTkjHuU96oWXST/KM4ZEaI2A9YDlsbYvUuC2HRqNBQEAAvLy8OOEYERERET0YXV6pJIvKrTZu9fgcX+d1w6kUK2z9M5Z7lcgIbN68Gbt27cLChQsfKMmbkpJSuERGRj7Yh4rfDlFWJS87vy52JSVshR/CovGvqJZ4JWcKnHq9V2mfQ0TGh+URqFLOFK47GolZW86iSV5PzLe8joSun6LlE73lths3bsglOzu7zNdXrVpVjrAVE48RERERET2wkKllrvZo9TxupjXCgl2X4Lb1LFrVdIF7FWvuYCIDtmDBApw8ebL4BNcAAgMD8fLLL2P58uWlXmNlZSWXRxqtr+jy7+ty8x9XQuL22LUk/GvTaXm/aZfn4RvMUbZE9DcmbalCJSfdwv82bMGiy275KwI6Quk7EvVd88shqFQqeHh4wN3dXdYVyszMRE5OjtwmJhkTgZjJWiIiIiKqLK93DJCjbB0SjiN64fuwev1bOLp6cocTGagVK1bg9u3bxdY1aNAA27ZtQ7169SqvvEpwb+Dcj4CDd/EatxXkz7DDyN40AQ7ZI9EkMAhvdAqssPcmItOgUljMSRIJRFHrRlw64eBwfxNkUXGnD++G089vwElJwbM5czCge0e81r4m1GoVdxURERkUxn0i83YlPgmWS1rAGwmIUleDZsgGePlXQvKHiCqFGAx05coVOf9Jhcb9ovWwGw4APm0EqC2AJ8YD+z6ssNq2fx4JRfWfhsBZlYbj1q0QPOFn2FhqHvl9ici0sKYtPbI7d25j/5I3EbytH3wQj3S1A5YOqIWRHQOYsCUiIiIig+Pv4YzsAd8iFlXho4uB3ZddcfqXr/TdLCIypHrYTr5AFS9AlwPU7JS/Xmx/RH8e+Ak1fxokE7aXLeug7qi1TNgSUZlYHoHuLnQOoNaUfTZxz1zcjDiB9Mg/0V6JAlTAccduCBq+BJ5OVblniYiIiMhg+ddtjpgRu3Bh1UAE5V5A/d/G4Oj5Xaj3yiLY2vHKOyJDVmkXDBeth61SATVaA2d+AK4fqpARtid//RZBe9+EtSoH56waw3/MZljb55cSJCIqiSNt6e5EwlZcHiIuEykic9ccYM8HqHp9G/yUKCTBAWfaL0bTt9bDjglbIiIiIjIC1Xz84ff2Xhxwf0k+bnFrM2591ArHL1zVd9OIyBDUaJN/e/33R36rw5uXou7eUTJhe8K2DWq+tY0JWyK6KyZtH8Gcbeewad4rOLRyCq6dPw6TJM4mistA/krc6nQKTq99B9YH/oPf8+ogXbHGHo+XYTE+DPW65P+xS0RERERkLKysbPDEqMU4FbISCXDBsVw/9Ft1Bu9vPYvMnEe/FJqIjJhvO6D2U0C95x5pVPDSXy/A/thiWKjycMyxK+qN3wwra7sKbSoRmR5ORPYIE5IMWbwby+IHyjNlwjV1dcR4dIR9nS4IbN4VNnZVYDJ2zgAOfopcaKBFHj7O6Y+9zn3xrx610KJebX23joiI6IFwIjIiKktK0k18vOM8vjqRIh83c83G7A52qNOqG3cYkRHTV9zPyM7DPzf+ic0nYuCJW/iw5gm0e/VDqDWcdIyI7o1J20f4EU9LS8G5XV/C6uIWBN85BkvV32fisxUNfrQfiAt1x6CBjxMaVnOAr6stVGojG9ycl4uboYtgd3AebHS35apsRYuVnY9geDt/WGqNrD9ERERM2hLRPew+F49/fv8nZmfNQTfNMRyrEgKv5z5AtZp1ue+IjJA+krbXzx3Fth/X4T9JIdCoVZj+dLA8hiYiul9M2lbQj7g4K39p/wYoEXtQPfkoPHETc3IG4fO8Z+R2X1Uctlj9C5GWNZFeJQBK1SDY+9SDZ0BjuHpWN8hkbvypX5H309uolhleuC4XWmiR+/eMmkREREaII22J6F6S09JxccU/0DxxG9QqBTmKBicdOqJKh9EIat4lf5IiIjKPuJ90DYg6CjTof8+n5uXm4vC6uWh6cb68KvdN9XQMHvwKWtd0fbjGE5HZYtK2Eg7eFJ0O0RFnERaXhaO3rPBnVAr847ZjvmZhmc9PhS2+qvIPRPn1Q22PKgiuqkUtJ8DV3fvRk7mhc/InEysrwSomF9PlFZ8hE8CFPw8jZ8cM1L+dX2w9Q7GAjSoHCc0nwK3Xu/mvEzVumbglIiIjxaQtEd2vy6d+x+2f3kHDzD8K14VrA3GrxUQ0ffIFWGgMb/AFEVVg3M9IAubVFAf6wJjjgGtAuU8NP30YeZvHIijnvHz8p3ULeL68Eu5e1fmVENED0z74S+heRKLVJ7A+fAKB/HG2QE52M4SfewrJ1/5ETtw5WCeHwzXjCrx1sXBQ3cHpW8D2hEj53BB1GFZafogkOCDG0hdpjsHQ1mgOz+B28K5Z98ESuSJhKxKsQtHEbdHEK4A72bnYdioO3x65Du/ILfjU8nfkKmpctwxAzZxL8nluBa8vuC3rfYmIiMqxfft2zJw5EydPnoSLiwsGDRqE//znP9BqtejTpw82b95c7Pk//PCDXF+WdevWYcaMGbh27Rrq1q2L//73v2jT5q8ZnomIKlBAg9ZAg924/OdvuPXrIjRK+gWBueFYu+8gJoR5YmK32ujT2BtqNUfeEpkkG2cgoDMQvgs4+Q3QeXqpp0RePoMbW2ahcdJOaFQK0hUbnKs/Ac37TYRKHJMTET0EjrTV84ibzIw7iI04hXN3HHEuSYULcWmoE70e4zM/l5dhlZQCO3zh9g40tZ5EU19ntPBzgZ3VPXLvRRO0Lf+Rf//IF8ip3QuXLevgRswV/H7LDouznpJPt1Ar+NxrC2p0HYXA2G0PPFKXiIiopKSkJHTp0gXjx49H586dER4eLpO2Y8aMwbRp09CqVSu89tprxZK0Ih5bWlqWeq/jx4/L5y9YsADdunXD//73P7mI93R2dr6vnc+RtkT0sG7GR+PCts8wNaotrt/OT8Y87x6NV5/wR52WXbljiQzQI8f9098DG14BqlQDxoYBFtZQFAVHriRixYEITAkfgprqWPnUMPsO8By4AF7Vyx+RS0Rk0EnbVatWYfjw4WVu27NnDyIjI/Hhhx/iwoUL8PLywuuvv44pU6aU+fzk5ORSB2niB1msv1+GdvCWcTsd0eEnkHTlBPKiT8A56RT8ci7DSpWDbllzcVHJv7xioHYvhtkcQLJnW7g06olaTTqWOROlsmcuVHs+KPfzopSqeMluGQa0qIH+zXzg4WBdqf0jIiKaNGkSTp8+LUfg+vr6Ys2aNXjiiSfuuWNEovfixYvYsWNH4bqaNWtiwoQJePPNN8t8TVZWllyKxv3q1asbTNwnIuOTmZOHlb9dxdLQC1ivTEJtdTSOuPVDvaGfwK6Kk76bR0QVebyfmwVlYROoUqMR7dcPpxV/LLjRCOeS8o+9X9TsxgsOp2Dx5HQEN+vIfU9Exl0eQYyu6dWrV7F1Bw4cwLBhw+Qlk2+99Rbee+89NG/eHMeOHZPPFwdXL774Yqn3io2NhbW1tUz0FlAb4MReD8LGzh6BjZ4AxPKX7KxMXDp7BEMzvHE8Kg2HIxLR/vZJBOecASLFsgyJWx1wzbY+oLFClqLBQse3EZuSiejkhjivAcRVWyJNH654I15xwh0rN9i7esO9VjOEdmoPtdZCr/0mIiLzcevWLXh6esr78fHxGDp0KG7fvo1q1aph8ODBclSupowTkWKkrRhhW1T79u1x5MiRcj9rzpw5mDVrViX0gojMlbWFBm90CsCAhk64/FUjIDkaLRO+R8wn+xH5cijq+Pnou4lE9Aj2XkzAprBopGXmID41C3VS+uND1afwvvo9vAH8kjMS1y0745lG1TC83UzU8uRJYCIykaStlZWVXIpasWIFRowYgQYNGsgDsgLe3t7o0aMH9u3bV2bSNi4uTj6natWqMGWWVtao1aQDagEYLEbPKgpirlTDkePboL0ailppR+GiSoXLnYPy+TpFhfM3+yMRDhij2SgTttnQwlKVi9SAZ+HV4x0EuNnru1tERGSGIiIisGHDBuzatUs+Dg0NlQlae3t7HD58GBMnTpQJXFG3tqSbN2/C1bX4DMzibwAxarc8U6dOlSNxS460JSJ6VK4urnAd/w3+3LcJVUPfxr6cujh5PBFzmLQlMmof7biAU9EphY9PoRXUmn9giPV+WNg4YGBwbbzX9UnYWnKqICKqHAbz63L+/Hl5eeRnn31W7mic+vXrl7lNjM4Ro239/f2RnZ2NevXqydE0d5uQpKzLJI2NSqWCd806cgEmICc7C2f+2I30yFPIy8lCVpUamFmtORpHrUGNkxuQ23EaLEOmyFq0zURd27POnESMiIgeu8TERPTu3VuWPRK1aYWiMVtMLCZKHImatWUlbXU6Xal1FhYWMi4+yMliIqKK1LBDH6Q36QSr07F4q54/dy6RkZvXvyEOXLop55BxtbdEgJsdarj0gKXWuK/qJSLjYTBJ248//lhOPiJq2pV08OBBHD16VNbBLYt4nahLI157584drFy5Uk5yImanrl27ttlcJmlhaYV6bXsCEEuRycJOzpeTkGkLJhMruBWJ26KPiYiIKplIxorSBmJSsunTS8++XCAoKAg3btwoc5uHh4dM/BaVkJAANze3Cm8vEdGDsK/ihOfasJ4tkSkI9nKQCxGRvhjEKSIxUnb16tWyjm1JV65cwfPPP48lS5aUmdAVRD3bnj17yhG2LVq0wOLFi2WyVkxocrfLJEUR8oKlaD1ck6LLkwnbUolZ8VisF9uJiIgeAxFvRcJWjK4Vo2jv5syZM+WeeG3ZsiUOHTpUbJ14LP4GICIiIiIiMgUGMdJWlERo0qRJqXIGIpEqRuKI2aWHDBnyQO95txE6ZnWZZMjU8rdxhC0RET0mogxR9+7dUatWLXmliyh7VODatWvYsWOHPAErJiPdv38/PvjgAyxatEhuv3r1Kjp06CATvX379pX175s2bSpP0ook8PLlyxEVFSUnLSUiIiIiIjIFeh9pKyYZEaNoS46yFQdfISEheP3118scgXs3YoKuc+fOlTtCh4iIiB6vH374QU4wtnbtWlnGoOiSnp4uE7VPPvkkgoOD8dFHH8mE7UsvvVQY13Nzc+VtQc1b8T4iiSsmL925cye2bt3K8ghERERERGQyVErBEZAeR9mKgzMxi7SYNVqIiYlBx44d0a9fPznKtuTs0CVH3Ij38PLykqN1xURkCxcuxMaNG+XkZs7Ozvc9AsjR0VFeuinq4xIREZHpYtwnIiIyH4z7RGSM9DrSNi8vD/Pnz8eYMWMKE7bCsmXLEB4ejrlz55YajVPWiBuxXlxq2bBhQ3Tq1EkmfQ8cOHDfCVsiIiIiIiIiIiIiQ6H3kbaGgmfeiIiIzAfjPhERkflg3CciY6T3mrZERERERERERERE9DcmbYmIiIiIiIiIiIgMCJO2RERERERERERERAZEq+8GGIqC0r6i1g0RkaGoUqUKVCqVvptBZHIY94nIEDHuE1UOxn0iMsa4z6TtX9LS0uRt9erVH883Q0R0H1JSUuDg4MB9RVTBGPeJyBAx7hNVDsZ9IjLGuK9SCk45mTmdToeYmJgHOrstRuWKJG9kZKRJJVVMtV+m3DdT7Zcp9+1++8URN0SVg3H/4X6TjA37ZXzM/Ttj3CeqHIz7D/ebZGzYL+Nj7t9ZFY60vT9qtRo+Pj4P9WWIL8CU/nGZer9MuW+m2i9T7pup9ovI0DHum9dvEvtlfPidEVFFYtznb60pMNXYaMp9c3jEfnEiMiIiIiIiIiIiIiIDwqQtERERERERERERkQFh0vYRWFlZ4d1335W3psRU+2XKfTPVfply30y1X0SmzJT/vzXVvrFfxoffGREZClP9PTLlvrFfxoff2d1xIjIiIiIiIiIiIiIiA8KRtkREREREREREREQGhElbIiIiIiIiIiIiIgPCpC0RERERERERERGRAWHSloiIiIiIiIiIiMiAMGn7kBITEzFgwAA4OTnB1dUV48ePR25uLoxRWloa+vXrBz8/v2Lrr169ih49eqBKlSrw8vLC7NmzYUy2b9+O1q1bw8bGBt7e3pg0aVLhd2SsfcvLy8OiRYvQuHFjWFtbw93dHQMHDkRsbKxR96ukefPmQaVSyf6YQr/E74PoT9FlwYIFJtE3InPBuG/4GPeNN4Yw7hORoWHcN3yM+4z7ZnG8r9BD6dOnj9KyZUvl+PHjyv79+5UaNWooM2fONLq9efbsWSUoKEjp2rWr4uvrW2xbkyZNlF69eimnT59Wtm3bpjg6OiqrVq1SjEFiYqJs/5dffqlERkYqoaGhiqenpzJ79myj7ltCQoLy4osvKps2bVIiIiLkv79WrVrJf4/G3K+i9uzZozzxxBOK+Hm6cuWKSfRrwIAByowZM+T3V7BkZGSYRN+IzAXjvmFj3DfeGMK4b3zfGZE5YNw3bIz7jPvmcrzPpO1DuHHjhqJSqZSDBw8WrhM73d3dXcnLy1OMycKFC5UlS5bIpGbRpO0ff/whk2YxMTGF60RSulmzZoqxmjhxotK9e3eT69uCBQuU4OBgk+hXbGysEhAQIE8mFCRtTaFfHTp0UFavXl1qvSn0jcgcMO4bJ8Z9w8e4z7hPZIgY940T477hY9xXHvh4n+URHsKJEyegVqvRsmXLwnUdOnTAjRs3Ci/nNhZjxozB66+/Xmr98ePHZbkEMXy7aB9F37Ozs2GMbt26BU9PT5Pq28mTJ7F06VIMGTLE6PslSj+IUg8zZ85EcHBw4Xpj75cQHx+PadOmwcPDA/Xr18f06dORkZFhEn0jMgeM+8b5e8S4b9gY9/Mx7hMZHsZ9xn1DweP9bJj78T6Ttg/h5s2bspatRqMpXFe1atXCL8tU+ihq9RYl+ij+wBYHQcYmIiICGzZswBtvvGESfWvatCmsrKzQokULWY946tSpRt8v0YegoCAMHjy42Hpj75fw7bff4ptvvsHevXtlUnr16tUYPXq0SfSNyBww7hvf7xHjvuF/Z4z7+Rj3iQwP477hx5CSGPcN/ztj3H+4uK+9r2dRMTqdrtQesbCwkLei4LApMKU+iiLyvXv3xpQpU9CqVSvs2rXL6Pu2ZcsWpKam4ty5c5gxYwaio6MRGBhotP3avHmz/F4OHjxokv8WxcRxBerUqSNP+IiJDMUZOGPvG5E5MIXfIXPqI+O+4X9njPvG//8ZkSkzpZhoDn1k3Df874xx/+H/P2PS9iGIIc8pKSnyh06USRASEhLkrZubG0ylj+LHryjRR5FscnFxgbFITk5Gt27d0KVLl8IEmSn0zdvbWy6ijIAYat+2bVs587Kx9kvMrCgu/bC3ty+2XiSiRe1tX19fo+xXecSI4tzcXFhbWxvtd0ZkThj3jef3iHHfOGII4/7fGPeJDA/jvmHHkKIY9xn3Tf14n+URHvLSdJFIOnr0aOG6Q4cOyZ1es2ZNmAJRr1fU542LiyvWx4YNG8LS0hLGQCTWRcJWjK4VBwem1LeicnJy5K3op7H2a8WKFTJpK2q7FCzCtm3b8Ouvvxptv8pz5swZODo6okePHibXNyJTxLhvHL9HjPvGE0MY9xn3iQwZ475hx5ACjPuM+2ZxvF+pU8OZsBdffFFp1aqVEhYWpvz222+Kr6+v8s9//lMxVqGhobIPRbVt21bp3bu3cubMGeXnn39WnJyclKVLlyrGICUlRX4/4ntKSEgotuTl5Rlt33799Vfls88+U06cOKFERUXJx40aNVJ69uwptxtrv8oifp6uXLli9P2KiYlRJk+erBw5ckS5fv26snnzZqV69erK+++/b/R9IzInjPuGjXHf+GMI4z4RGRLGfcPGuM+4by7H+0zaPqTk5GRl0KBBip2dneLq6qqMHj1ayc7OVkwpaSuSgj169FBsbGwULy8vZebMmYqxWLVqlfzjv6xFJAKNtW/iJEHnzp0VZ2dnxcLCQvHx8VFGjRqlJCUlye3G2q97HbwZc7/EHxT9+vVT3N3dFWtra6Vu3brKggUL5MkDY+8bkTlh3DdsjPvGH0MY94nIkDDuGzbGfcZ9czneV4n/VN6gYCIiIiIiIiIiIiJ6EKxpS0RERERERERERGRAmLQlIiIiIiIiIiIiMiBM2hIREREREREREREZECZtiYiIiIiIiIiIiAwIk7ZEREREREREREREBoRJWyIiIiIiIiIiIiIDwqQtERERERERERERkQFh0paIiIiIiIiIiIjIgDBpS0RERERERERERGRAmLQlg6QoCv7xj3/AxcUFKpUKJ06cgLHYvXs3goODkZeXB2PQunVrfP/99/puBhERmTHG/ceHcZ+IiPSNcf/xYdw3bkzakkHavn07Vq1aha1btyI2Nhb169eHsZg8eTKmT58OjUYjH2/cuBFdu3aFm5sbHBwc0KZNG+zYsaPYa2bOnCmT00WXOnXq3PVzxGsaN25cav3Vq1cfKNEt2vrPf/4TOp3ugfpJRERUURj3GfeJiMh8MO4z7tP9YdKWDNLly5fh5eWFtm3bwtPTE1qtttRzsrOzYWgOHDgg296vX7/Cdfv27ZNJ223btuHYsWMICQnBM888g7CwsGKvrVevnkxQFyzivR6HHj16IC0tDT///PNj+TwiIqKSGPcZ94mIyHww7jPu0/1h0pYMzrBhwzBmzBhcv35djhj18/OT6zt16oQ333wT48ePR9WqVdG9e3e5/pNPPkGDBg1gZ2eH6tWrY9SoUUhPTy98PzFi18nJSY7aDQoKgq2tLfr37487d+7gyy+/lO/v7OyMsWPHFitpkJWVhUmTJsHb21u+d6tWrbBnz567tv3bb7+VCVpra+vCdQsWLJCjb1u0aIFatWrhgw8+kLdbtmwp9lqRmBYJ6oJF9LGi9mfJUbxiKeiLGBHcs2dP2XYiIqLHjXGfcZ+IiMwH4z7jPt0/Jm3J4Hz66ad477334OPjI0ecHj16tHCbSLJaWlrit99+w9KlS+U6tVqNhQsX4syZM3L7r7/+KpOkRYkErXiOSEyKSzFEwvK5556To1/Fsnr1anz++efYsGFD4WtEgvjQoUPyNX/++Seef/55PPXUU7h06VK5bd+/fz+aN29+1/6JMgRiZKuo11uUeN9q1aqhZs2aeOmll2TSuqL2Z9ERvOPGjYO7u3ux8gstW7aUbSciInrcGPcZ94mIyHww7jPu0wNQiAzQ/PnzFV9f32LrOnbsqDRp0uSer12/fr3i6upa+HjlypWK+KceHh5euG7kyJGKra2tkpaWVriue/fucr1w7do1RaPRKNHR0cXeu0uXLsrUqVPL/WxHR0flq6++umv75s6dqzg7Oyvx8fGF67Zt26Z89913ysmTJ5Xt27crbdq0UWrUqKGkpqaW+z7vvvuuolarFTs7u2KL6Jfob1hYWKnXfP/994q1tbVy4MCBYus3b94s3ysvL++ubSciIqoMjPuM+0REZD4Y9xn36f6ULhRKZMCaNWtWat2uXbswZ84cnD9/HqmpqcjNzUVmZqYcXStKIQjiNiAgoPA1Hh4esiyCvb19sXU3btyQ90+dOiVLJdSuXbvYZ4mSCa6uruW2LyMjo1hphJLWrl2LWbNmYfPmzXK0a9G6sgUaNmwoSzH4+vriu+++w6uvvlru+4lyDz/++GOxddHR0bKUREmihu6QIUOwaNEitGvXrtg2GxsbOQJY9E/cJyIiMgSM+8Ux7hMRkSlj3C+OcZ+YtCWjImrLFnX16lX06tULb7zxBmbPni1LDogJvESiU0xUVpC0tbCwKPY6UdO1rHUicSmImrii1quYOEzcFlU00VuSqEOblJRU5jZRZmHEiBFYv349nnzyybv2U9TgFQnj8PDwuz5PlIoIDAwstq6sSdvi4uLQu3dv+fllJYETExPlvmXCloiIDAnjfnGM+0REZMoY94tj3CcmbcmoiaSqSLR+/PHHsratIEanPqomTZrIkbZi5G379u0f6HVnz54ttf6bb77BK6+8IhO3Tz/99D3fRySNxYyaYmTsoxKjjp999llZw1ZM2laW06dPy7YTEREZMsb9e2PcJyIiU8G4f2+M+6aNSVsyamKUaU5ODj777DM888wzxSYoexRilKuYDGzo0KEyISwSmgkJCdi9e7csX1Be4rV79+5yMrSSJRFefvllWXBdlD0Qo14FMarV0dFR3p80aZJsvyiJEBMTg3fffVeO8B00aNAj92XkyJGIjIyUbRd9KCBGJYszd4KYhKxbt26P/FlERESViXH/3hj3iYjIVDDu3xvjvmnLH5pIZKQaNWokR4/OnTsX9evXx5o1a2R924qwcuVKmbSdOHGirCXTp08fHD16FDVq1Cj3NSLRe+bMGVy4cKFw3RdffCHr7I4ePRpeXl6Fy7hx4wqfExUVJRO04nMGDBgg6+b+/vvvcHNze+R+7N27F7Gxsahbt26xzz948GBhDVxxf/jw4Y/8WURERJWJcf/eGPeJiMhUMO7fG+O+aVOJ2cj03QgiU/L222/LCdE+//xzGIMpU6bIOrwiuUxEREQPhnGfiIjIfDDu0+PEkbZEFeydd96RZQ4KJjUzdO7u7vj3v/+t72YQEREZJcZ9IiIi88G4T48TR9oSERERERERERERGRCOtCUiIiIiIiIiIiIyIEzaEhERERERERERERkQJm2JiIiIiIiIiIiIDAiTtkREREREREREREQGhElbIiIiIiIiIiIiIgPCpC0RERERERERERGRAWHSloiIiIiIiIiIiMiAMGlLREREREREREREZECYtCUiIiIiIiIiIiKC4fg/FUpsgGrWxIQAAAAASUVORK5CYII=", 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", "text/plain": [ "<Figure size 1400x400 with 3 Axes>" ] @@ -637,7 +655,9 @@ ], "source": [ "frame = np.arange(len(arc))\n", - "fig, axes = hyp.subplots(1, 3, ndims=1, size=[14, 4])\n", + "# backend='matplotlib' on the grid and on each call: suptitle/tight_layout are matplotlib's,\n", + "# and on Colab the default backend would be plotly\n", + "fig, axes = hyp.subplots(1, 3, ndims=1, size=[14, 4], backend='matplotlib')\n", "\n", "for ax, col in zip(axes, arc.columns):\n", " miss_idx = np.flatnonzero(missing_mask[:, list(arc.columns).index(col)])\n", @@ -650,7 +670,7 @@ "\n", " hyp.plot([occluded, true_series, imputed_series, imputed_pts],\n", " ['-', '-', '--', 'x'], color=['0.8', 'C0', 'C1', 'C1'], linewidth=[12, 1.5, 1.5, 1],\n", - " reduce=None, ndims=1, ax=ax, title=col,\n", + " reduce=None, ndims=1, ax=ax, title=col, backend='matplotlib',\n", " xlabel='frame (25 Hz)', ylabel='feet' if col == 'x_ft' else None,\n", " legend=(['occluded frames', 'true (recorded)', 'Kalman-imputed', 'imputed entry']\n", " if col == 'x_ft' else False),\n", @@ -690,14 +710,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "4b9c50fd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:14.318333Z", - "iopub.status.busy": "2026-09-05T10:27:14.318244Z", - "iopub.status.idle": "2026-09-05T10:27:14.408699Z", - "shell.execute_reply": "2026-09-05T10:27:14.408180Z" + "iopub.execute_input": "2026-10-09T17:28:49.551651Z", + "iopub.status.busy": "2026-10-09T17:28:49.551577Z", + "iopub.status.idle": "2026-10-09T17:28:49.633644Z", + "shell.execute_reply": "2026-10-09T17:28:49.633147Z" } }, "outputs": [ @@ -712,7 +732,7 @@ }, { "data": { - "image/png": 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+rnSM2LMfOPTPP/+Yv2mZ2obHFgMT++Zl/tiyZW2iZyd2++ZDWywDm8D448DR6+agBXv8AcGBNhy4kFSTK98TjixNi7SUi5ih4WeRHept8Trrzfo+cwAAAzX7+rFK7nZHGGjYjyJl4Gs/4TPfQx4TtiNAeX5lUGKLZeX9rd0MbC/WAJqPRf/++2+Sx0NGyIrzXFo+h/xRzcE+DGgzY3J6EbfJzPHEz+ZDjiziyYj9gfjLnoEBR/stXrzYBDEZlcLmB5IBHKeXYF8J/p8UloVp/19//dWUgSfZ1ExuyS8fBqwMfNiczBM1f0HzfwYmHBmVUvxFzNFb9913n/l1ylGR1mYQBsPsy8WTCpuRk2u+TQ/WAwNg/oLliDd+aTI45ii+jMBmlBkzZpggjl8mzBpMnjzZjGxz1OyZWjzG2IzLUX0cdcgAmc3FX3zxhTkerCN2U7qfKx0j9sc6m4T4Zc0vPo6sZD8fZjc4pUJqsambkwgzG8vsNrMj7Cdm7YNqxedifzk+F7+A+UXI5+bxYvvDiVkMTrXC7Aoz8OzLxiCMU5FwhCDrnDh6l/30+Nysl99//91kguz7ednie8PzB18rRzqn5vWmtFz2GNwz0GA2i+cvfjY5+tiaTbf2GeRr4XvMEeIMHHjMcH+2FnD0dHK3O8Ljjd0R+Fz8EcKy8ji1zzCxiwaPJ74+/kBmUMyuDPbZSmY+58+fb7Lh3J+fQ2bcGEAzY8fWDOvKDcxg8n3maFZ+djNjkvesOM+l5HPI94OtBOx7ygwtP1scGc6sLF+/iMcGc9YTB4M69kHgB4XNM/xy5AmYX+qc4ygjsUM1+54xWLDt4OsImzn4pcTAiV8q7Ltn/+WVnDfeeMNM38E+YF9++aXZVqlSJRMIpRa/KNk/g8EcT9psfua0ADyBMvDg6+G0Hvzi/e2335AZ+OXC7AhPrGz6YDDD18KmYJYtvfh4DOA4pQi/TBnA8UuZ01xkBH4xM5jnlzUvPN5Yb5zShlNRpHY/VztGbDNMPDb4XjGjwQCIASp/PKUFs1Wca43NSvzCY2DGIIKfW/vBAmwK5Q80TgPCYL927dqmDJyOwhbrkc2AfAwGFWyiZNcH20EevJ3TDvHHF7+4ea5gcF+yZMlEy8oyspM6jxkGNKldKiol5XKEP7b4uWA9MWvEY5nnGw7msQZMbB5k+XlMcV8GamzW5H4puT2x8jJbzznR2K+MgQl/hHCwAgNKKwamDPoY5HM6Fmb02ETIANJ2Hj0GJzwW+Xr4XvLYZ93zmLIdAMNjle8zg0jWEQM9Pn9SZU0LBk9ZcZ5L7nPIH9T8HHDaGmvfRgZ9rDt3XClFsgcvDmmFB2LTA08CPAmxQ7WIZD4G5vyyZzDB7g4iIpJN+sw5A9Pi/KWr5X5Eso51cAyzRiIiks2aWbMC+5uw78OWLVtM0y37/SgtLpI52KeOfYh4YVMU+1qxaY9dAzSFg4hIxvGoYI5BHL9g2P+KfZ4S60gsIunHPnLsv8UfUOzLxc8dm1Y5EltERDKOx/aZExEREckOPLbPnIiIiEh2oGBORERExI0pmLPBFmdO9qqWZxEREXEXCuZscAZzLlOV2OLvyeH9uOxTWu+f3ag+VB86NvRZ0blDJPMpmMtAzOhxBnRl9lQfOj70WdG5Q+dSkayiYE5ERETEjSmYExEREXFjCuZERERE3JiCORERERE3pmBORERExI0pmBMRERFxYwrmRERERNyYgjkRERERN6ZgTkRERMSNKZgTERERcWMK5kRERETcmII5ERERETemYE5ERETEjSmYExEREXFjCuYy0Nojwfj9wDVcuRmZkQ8rIiIikijfxG+S1LgaHoVHp2/BzcgY7AzZjJ8fa47c/qpeERERyVzKzGWQPP4+KB6Q0/y/4/RljPxpG6KiYzLq4UVEREQcUjCXQby8vPDlAw2R29fLXF++/wLGzN8Di8WSUU8hIiIikoCCuQxUo0Q+vNCqAPx8YgO6nzacxJf/HMnIpxARERGJR8FcBqtbLAc+vKdu3PWxfx3Ab1tOZ/TTiIiIiBgK5jJBzwal8OJdNeKuv/jbTqw6FJQZTyUiIiIeTsFcJhl2eyU81Ly8+T8qxoInZ2zFofNXMuvpRERExEMpmMvEARFv9qiNjrWKm+tXwqMwZOpmhFyLyKynFBEREQ+kYC4T+Xh7YXz/BqhVMsBcPxlyHU/M2IKIKE1ZIiIiIhlDwVwm48TBkwc1QZG8Ocz1DcdC8Mb83ZqyRERERDKEgrksUCrAHz93jEBvv3Vo7r0Xv248ge/XHM+KpxYREZFsTutNZba984HFL6Jq2Fl86sO2V+CspRDe+XMgKhUdjjuqF8v0IoiIiEj2pcxcZgdyMwcCYWfjbS6BEEz0/Qzzfvoahy9ohKuIiIiknYK5zBITbTJyQMLlvLxjF4jAC5iCR6dsQKhGuIqIiEgaKZjLLCfWJsjIxat4L6CUVzCKX9qGJ3/cishojXAVERERNw/mJk2ahOrVq+P06djlr+bNm4cePXqgbt26aN++Pb799ttE7xsWFmbua3tp0qQJnObqeQT6+GCvv1+iF95eDJew7mgw3lqwx3llFREREbflMgMgNm7ciBUrVsRdP3HiBKZMmYJRo0aZYG737t149tlnUbJkSXTv3j3B/YOCgpAjRw78888/cdu8vZ0Xqwb6+qFbmVKIsLapOuAfY0HlE37ATWDG+pOoWiwfBrWskKXlFBEREffmEpk5BmKvvvoq3nnnnbht5cuXx9y5c3HnnXeiePHi6NChA9q2bYtNmzYl+hjcr1ChQnGXAgUKwFlCi1ROMpAj3t6mQ5u468zOLd17PgtKJyIiItmF0zNz0dHRGD16NEaMGIHKlSsnuW9oaCiqVq3q8Lbg4GAT0LE5NjIy0uw3cuRINGzYME1lSgvr/fg3JuG4B4fuqF4cV69445uVx8x9Rv68FT8OaYoGZZ0XiGYU2/oQ1YeODX1WMuPc4ePDOZ9EPJuXxWJJYdiROT766CNcuXIlLivHvm7Lly9HmTJl4u23detWDBkyBAsXLkTp0qUTPE54eDjWr1+PUqVK4caNG5gzZ465sN9dxYoVU1QWnji2b9+eIa/r+I3jePPIm8nu92blN1EuZ3l8tuEy1py6abYF5PDG/9oXQom8To+1RURcWuPGjZ1dBBGnc2q0sGzZMqxbtw6//PJLkvudOnUKTz31FN544w2HgRyxv9ztt98ed71evXomAFywYIHpd5ca7KOXll97DAZ37dpl7p/jUg7gSPL3qV6tOmoWrolJdWPwyJRN2HAsFGHhMfho43XMerw5Cufxh7uyrQ/9elZ96NjQZ0XnDpFsGMxNnToV+/fvT9AU2qlTJ/Tq1Qvvv/8+AgMD8fDDD5usHLelBjNybH5NLQYe6Qk+eF9vn5R1R+R+3D+3jw++HXgb7v1qLQ5duIoTwdfx2PStmD6kKfLl9IM7S299ZjeqD9WFjg19VkSyTTDHYI1NorY4UpVTkLDP27lz5zBw4ED079/fBHSpwdbjo0ePok+fPnAX+XP5Ycrgpug9cQ0uXAnH9lOXMOj7jWZbgLMCOk5+zDnzrp4H8hYHyrdkBOqcsoiIiIhrBXNly5Z1uL1ChdjpORjIde7c2QRkISEhcbdzpCrnohswYABeeeUVk8mbPn06ihYtilq1apkBENOmTTNZud69e8OdlC6QC1MHN8X9k9bj0vVIbD15CQ99txFTH7kNBXJnTpNrVHQMft9+FpuOhcDLC8jt74sCuf3Q5MZqNN77AXJcP/ffzgGlgLs+BGr1yJSyiIiISOq4bA/7WbNmmbnmOJEwL7YOHDhgMm9RUVHmrzXAmzhxoulflzt3btSvXx8//fQT8ufP75TyF8xREP4+/oiIjjDX6x6LwSNLY/BDR2/sqhjbBMvbuZ+9miUD8NPQ5hjw3QaEXIvAjlOXcO/X6zBtcFOUKpArQ8t5PSIKD3+/CRuP/xcsU2fvjRjh91nsFZsZVixc1WLmQFzv9T3yNLgnQ8siIiIibjia1ZVYR7M2aNAgzQMgbO8feDUQoeGhbPMFhr4Ir/1HYKlRGZj8IZgCYyBXMm/JRB/vwLkrJqALuhJurpfMn9Nk7aoVz4eMEBNjMUuJLd5zzm7ywRiszjEKJRASt45svPtZgHMojNElp6Fbg7LoUqcECufNkeH1md2oPlQXOjb0WRHxqMxcdsBAjZerq1bj1P7Yoa0M6MruC0XeNq2TvX/1Evnw27CWGPj9BhwPvo7Ayzdx39fr8O1DjdGsUuF0l+/TZQfjArm8OXzxWb8GKFMoF7yOr4bX0svY75N4P72C0ZfgdXIdXj9+GW/O34OWlQujZ4PS6FavJHL6KXATERHJKgrmMhkTn0Hjx3NtMabCzF9ez9O6FbzYQS0Z5QrnxuwnWuKRHzZh15nLuHwj0vSnG9WhKobfUQV+KRw1a2/e9jOY8Pdh8z+zbxMeaIg7qhcz1wPPBKZoKbLGx84CEbUQHWPBqkMXzeV/i/bhgWblMKB5eRTJ496jcEVERNyBSyznlZ1dW70GN3fvjg3kKCbGXOf2lCqSNwd+fqw52lQtEvsQFuCzZYfQ/pN/8NOGk6bfW2psOBqMF2bvjLv+SpeacYEchfrlTNFSZH071sET7SqjTMH/+vEFX4swQWLrD//Gq7/vxoVrqSubiIiIpI6CuazKysWr9djsXGq6K7IZdMojTfFMx2rwuRVonQq5gVfm7kKz95abps7dZy6bfnCOynHxajjWHw3G+GWHzOTE4VGxwWW/JmUxpLXdChkl66eoTKVqNMOLd9XAqhfuwOxhLdC9fin43ipbZLQFv2w6jRF/XsRLc3bhRPC1FL9WERERSTk1s2ZFVs6eTXYuJX3nrBjEsXm1XfWiGPvXAdOsSVfCozBl7XFz4YoRlYvlRS4/H5Oxu3IzCqdCruNaRMI1Du+oXhTv9KqTsLnXPvhMzK39eP8mFQqZy7kuNTFt3XFMX3fClCvaAszacgZztp3FQ83L46kOVVHQjVe1EBERcTXKzGV2Vi6xfnFeXqnOzlnVK1MA04c0w4IRrU1mLaefd7xmzo3HQvDvwSBsOh6K/eeuOAzk7mlYGl8NaAx/34w9BErkz4kX7qqB1S+2x6j2lZHbL/b1s18dg812H/+DH9YcQ2T0rWZnERERSRdl5jKJJTISkYGBsdOSONzBgshz58x+Xv5py1TVLZMfH95bz/R5m7/jDP49eBGbjoeYQRJW/j7eKFkgJ8oVyo0KhfOgTukA3FahECoVzYvMlD+3n8nCNQm4gs1h+TBp1XHciIw2ZXtrwV5MX38C7/aqg5aVY/sBioiISNoomMsk3v7+qDh7FqJsVq5IUPmFC5v9MiJweqhFBXMhNq9GRMWYlRwyOvOWWnn8vE1Q90CzCvjor/2Ys/WM2X406BoemLQBfRqVwatda6KQml5FRETSRMFcJvIrWdJcshqDuExa+Stdza/j+jbAoBYV8OaCPdh28pLZ/tvW0/h7/3m82rUW+jQqnaLpWkREROQ/6jMniS5FlpTEliJLTv2yBcxEyGxizZcz9rdE6PVIPDdrB4ZO3YwLV27qHREREUkFZeYkAa5asbDXwtilyBKR3FJkSfH29jKTCneqVRxvL9yLhTsDzfbl+y+g86cr8V7vuuhSN+szmiIiIu5IwZwkuRRZZioWkBNfPNAIvRqcx0tzduLi1QiTpeN6sT0blMLbPeqY/oAiIiKSODWzitPdWas4/nq6Le6uUyJu27ztZ9H5s5VmomMRERFJnII5cQmF8+bAlw82wmf9GiDgVl+6c2E38cCk9fh8+SEzT52IiIgkpGBOXAZHsvZqWBp/jW6LlpULm22M4cYtPYiHvtuAC2EaHCEiImJPwZy4nJL5c5kVLrgO7a2lXrH2SDC6fL4Kqw4FObt4IiIiLkXBnLgk6zq0Pz3aHMUDcphtHCAx8PuNGLfkgJpdRUREblEwJy6teaXCWDSqDdpVL2quc3W0z/8+jMFTNuHS9QhnF09ERMTpFMyJWwyO+H7QbXjp7hpxza7/HgxCtwmrsfvMZWcXT0RExKkUzIlb4ETDw26vjBlDmsWt43o69Ab6fLUWs7ecTvR+MTExCA8PNxcL03oiIiLZjCYNFrfSskoRLBzZGk/8uBU7Tl1CeFSMWQps+6lQjOlWG/6+3oiOjkZwcDCCgoJw8+Z/I2B9fHyQJ08elChRAvny5XPq6xAREckoysyJ2ylVIBdmPt4cDzQrF7dtxvqTeHDyepw4H4Ldu3fj1KlT8QI5YpAXFhaGgwcP4ujRo4iMjHRC6UVERDKWgjlxSzl8ffB+77r46N56JhtHm46Hos83G3Aw6Eay9w8NDcW+fftw7dq1LCitiIhI5lEwJ26tb5OymPV4CxTPd2v6kusxeHVFMFafTD6gY2buwIEDpklWRETEXSmYE7dXv2wBfNmrPKoV8jPXI6KBTzdcxoxdVxCdzKAHDoo4fvw4Tp8+rQESIiLilhTMiduLiooCbobh7XaF0L5Crrjtc/dfw4drLuFaZEyyj3H+/HkcPnw49rFERETciII5cXvXr183f/18vPBkkwAMaZAvbj66bYE3MHPpSuDwUuS9uB2wRCf6OBwcsX//fty4kXwTrYiIiKvQ1CTi9mwHMXh5eaFL1TwoE+CLHeuX4AWvqSgVHQLsi709ImdRnKozHJdKtnX4WJyPjgFdxYoVUaBAgax6CSIiImmmzJy4PW/vhIdx25gN+NT7U5TwCom33e9mECptfhMFAlcmOdHwkSNHEBgYqH50IiLi8hTMidvz84sd+BDHEo2yuyc6PMDZ+sohEWV4exJNrnT27FkzHx3npxMREXFVCubE7eXOnTve9bzBu+B/M8gEbokd9DluBiHqzPZkH/vSpUum2dV+AmIRERFXoWBO3F7OnDkREBAQd90vPGXzxv225QR2XQhPdj8GcgzoLl++nK5yioiIZAYFc5ItcL1Vq8gchVN0n5NRBfD2ylAsPHgt2b5xbGrl1CXnzp1TPzoREXEpCuYkW8iXLx/KlClj/r9auK4ZtZpYeMbtQV6FsTmmGpp67UXwrr+wbM0ahKdgjrkzZ87gxIkTCuhERMRlaGoSyTaKFy9uJv1l9ozTj3DUKgM3275z1gAvsmIHbD4xGgWibzXJhgIX/iyMM3WHI6ZCuySfh8t/cSmwSpUqwcfHJ/NekIiISAooMyfZSunSpVG+fHlcLnU7jjZ5E5E5i8a7ndfPV+6LkkdnIr81kLuliCUY9Xe+jSsH/k72eTjB8MGDB01QJyIi4kwuFcxNmjQJ1atXN+tkEv8OHToUDRs2ROvWrfHVV18lef9Fixahc+fOqFu3Lnr37o1t27ZlUcnFlRQpUsQcR9fKdcCuO3/CgRbjcLTRq+bvrg7TUejMCrOf/WhX66oR1Q58hSWHr6Ro5YkDBw6YiYZFRETg6cHcxo0bsWJF7Jes1ciRI+Hr64uZM2fivffew3fffYe5c+c6vP+ePXvw/PPPY+DAgViwYAFatmyJYcOGaQSih8qTJw9q1KiBPHkDcLVIA4SW7mD+5g3Zm/S0JV5AKa9g7NixCd9suYzImKQHRlhXjNDUJSIi4tHBXFBQEF599VW88847cdt2796NvXv34u2330bVqlVx++234+GHH8b06dMdPsacOXPQvHlzPPjgg6hQoYIJ7PLmzWsCO/FM/v7+qFatGgoXLpzqaUuK4RKWHL2Bd1eG4lpkTJL7sp8em1yVoRMREY8cAMEpH0aPHo0RI0agcuXKcdsZyLH/U7FixeK2NWnSBF9++SUiIiLMF7V9Zo5Nsba4/86dO9NUprS+lvTcP7txlfrgKFfORceRqCmdtiTYK3Zd1t1BEXh9RQhea1MQhXIlPtiBfecY0PGHR4IVKVysPlyB6kL1kVHHhwYhibhAMPfJJ5+YRc179uwZb3toaGiChc4LFixoPtycld82yEtq/0OHDqW6TLt27Ur1fTLy/tmNK9WHddoSv0SaWtmoGpmjCIbUyoPSu9eaueg2Xq6Bl/8OxmttCqFsQOIfGf7I4I8Kd6oPZ1NdqD7Se3w0btw4w45HEXfl1GBu2bJlWLduHX755ReHi53bY/858vJK+DXsaNJX6/6pxQEUafm1x0CTJ5+03j+7ccX6YFPoueDRKLvulUSnLfGOiUD7vS+jPTsh+ANnLYXw1s2BePXvZnipVUHUKho/K2yPPyLKlSuX4Dh1xfpwFtWF6kPHh0g2CeamTp1qOo9ztKqtTp06meCsVKlSCbJv/BLMnz9/gsdivyhm7GyFhISgUKFCqS4XnyM9X7bpvX9240r1wXVcS985DOf9/VBow0dmMIRVlF8AfCPD4BMZFu8+JbxC8JXfZ3gi8mm8vbIpXm5dEPWL50j0OXicsr+mffbYFevD2VQXqg8dHyJuHsy9//77uHHjRrxt3bt3x7fffmv6xHFkKgdHFC0aO1cYpxrhlBP2/eWoXr162L49/sLpvN6/f/9MfhXijgFE8XaP4ly1Lgjb/ZcZFBHpXxAVt38ARDqYsoSZYi/gDb/pWBreBB+uuYQxbQuiRpHEM3ScVoerUuTKlSvTX4+IiHg2p45mLVu2rBltaHshjkZt2rQpGjRogDFjxpg1MVeuXGmCvH79+sV9WbZr1w5Lliwx1++77z5s2LABP/74o1luiX3xuBJAt27dnPkSxUWxCbRk6TIo0fxehJXrBHh5w//mxcSnLEHslCVNvfcjPNqC91aH4uTlxCcMZmb52LFjDrsLiIiIZLupSRIzfvx4M+1Dnz598Morr2DQoEFxmTZ+WfI2a1+5KlWqmABu2rRpJru3Zs0afPPNN2lqZhXPwSZ7zkeXOzp+02pimuS/av5ej7Tgw7WXcC0i8WCNWeezZ89mWFlFRERccjSrPc6ob7vWJleFSCyrt3r16gR97XgRSQ1OW1KqWkNgY/L7dq5bBn/s9MWxS1E4dzUaEzZdxostCzgclEPnz583PyjYV09ERMTjMnMiWcWnUhtYAkrBkkhDK/O/nNIkomgdfFDzBO7zX4vm3nux5ewNrDgRv9+nPTb7OxptLSIiki0zcyJO4e0Dr7s+hGXmQBPQecVNVPLflCUhpe9A3eUPmRGwzWymLflg+yBcLNYFRXL7JLqGKwfy2K5EISIiklGUmROxqtUDXn2nAQEl49VJZM6iOF+5L4ofmWkmG7ZVAiH4zPtTbF33V5LZN7P6RGTiAyZERETSSpk5EfuArkZXWE6sQfDxvQiO8MPVQrVMRo4STFviBcRYgEHXfsAvx9vi9op5HdYnR7VqMISIiGQGZeZEEnwqfOBVsS0Kt3sc3pXaIm/IXtO0mui0JV6x05bs2rkJYeExSU4mLCIiktEUzIkkgiNUuW5wrujLKaqjfFGhmLojZVOciIiIZBQFcyJJ4Pq+RSvUSVEdXUAB/HPiJnacD09yP41sFRGRjKRgTiQZuWp0QFSe4jbjW+Pj9jDfItgYU8Nc/2ZLGMKjLEkuMi8iIpJRFMyJJPsp8YF3l7HmX/sQzXr9Qv0RqFnY18w91+zGKmzdug6wOA7atMSXiIhkJI1mFUkB79o9cSP8W/gsedUMhrCdtuRUneFmcMTsyBHI5X8x9obzwPUlRRBYbwQulWwb77ESWy1CREQkLRTMiaRQrkb9cLxAE4Qf+gd+4cGIzFEYVwvXRYFza1Bp85sJ9s8ZcdFsP9rkzXgBnZ+fn+pcREQyjII5kVQoU648doc1xlVrvzdLNMrunuh4DrpbzbC8/VKJVoCX4xUiRERE0kN95kRSObq1dOnScdfzBu9Kcg46buft3M/bWx83ERHJePp2EUmlIkWKoFChQuZ/NremhN/NYJQsGX+ZMBERkYygYE4klTiAoXz58sibN6/pN5cSR1DKBIEiIiIZTcGcSFo+ON7eqFatGgrW74KInEUTnYOO67aetRTG67uK4srNKNW1iIhkOAVzIunI0BUrURI+3TgHnRcsdj3neJ2zkLwV+RDOXY3C2CUHVdciIpLhFMyJpJNPnd7w6jsNXgHx+8R5BZRCSLfvsMq3hbn+08ZT2HcxQvUtIiIZSlOTiGSEWj2AGl2BE2uBq+eBvMWB8i1R2NsHz908hrcX7jW7fb35Mu5pF4PcPpqmREREMoYycyIZxdsHqNgGqHtv7F9eBzCoZQXUK5Pf/H/6SjS+XXlUdS4iIhlGwZxIJvPx9sL/7qlr/tKX/xzBkaCrqncREckQCuZEskDtUvkxpFUF839EtAUvz9mFGA51FRERSScFcyJZZFT7KiiWJ7bpdeOxEMzackp1LyIi6aZgTiSL5PL3weONAuKuv/fHPgRdCVf9i4hIuiiYE8lCDUrkQK8Gpcz/YTej4ka5ioiIpJWCOZEs9kqXGiiQ28/8v2DHWazYf0HvgYiIpJmCOZEsVjiPP17rWivu+mu/78a1cC31JSIiaaNgTsQJ+jQqjZaVC5v/z1y6gU+XaqkvERFJGwVzIk5a1/X93nWRwzf2I/j9mmPYfeay3gsREUk1BXMiTlKhSB6M6lDV/M8p516asxNR0TF6P0REJFUUzIk40WNtK6F68Xzm/91nwjBl7XG9HyIikioK5kScyM/HG//rUxdesSt94ZMlB3Eq5LreExERSTEFcyJO1qhcQTzUvLz5/0ZkNF6ftxsWi5b6EhGRlFEwJ+ICnu9cHSUCcpr//zkQhIU7A51dJBERcRMK5kRcQL6cfnirZ+24628t2IPL1yOdWiYREXEPCuZEXETn2iXQuXZx8//FqxH435/7nF0kERFxA77OfPLo6Gj8/PPPmDVrFo4ePYq8efOiWbNmeOWVV/Drr7/iiy++cHi/v//+G6VLl463LSwsDLfddlu8bfny5cPmzZsz9TWIZKS3etTBmsPBuBoehV82nULvhqXRrFLs5MIiIiIuF8xdvnwZ27Ztw8iRI1G9enVcuXIFb775Jt566y18/PHHePDBB+Ptv3DhQsyYMQMlS5ZM8FhBQUHIkSMH/vnnn7ht3t5KPIp7KZE/J168qzpen7fHXH957i78+VQb5PD1cXbRRETERTk1mCtUqBA++eSTeNu6du1qsnK5cuUyF1uzZ8/GwIEDHQZpDOaKFy9uHlPEnT3YrDzmbDuDbScv4WjQNUxccQTPdKzm7GKJiIiLcmowZ2///v345Zdf0KtXrwS3rVq1CoGBgbjnnnsc3jc4ONgEdO3bt0dkZCSqVq1qMn4NGzZMU/NvWljvl9b7Zzeqj7TXx3s9a6PHxLWIirHgyxWH0blWMdQoETu5cHagY0P1kVHHh4+PstYiXpY0TGjFD9iBAwdMcMWmUfZ1K1asGGrWrAk/P79U12rv3r1x6NAh8/+QIUMwevToBPs88sgj5vFfeOEFh48RHh6O9evXo1SpUrhx4wbmzJljLvPmzUPFihVT/Lq2b9+e6vKLZIafd1/B7H3XzP+VC/rif+0Lw8f71uzCImI0btxYNSEeL1XB3KZNm0yftX///Rc3b95E7ty5zSCDq1ev4tq1a/D390erVq1MU2iLFi1SXLnnz583j3HkyBF8/vnnqFu3Lv73v//F3b5v3z7ce++9WLZsmcP+conp0aMH7rzzTowaNSpVwRyfPy2/9nj/Xbt2pfn+2Y3qI331ER4Vg54T1+LQhavm+nOdquKJ2ysjO9CxofrIqOND51qRVDSzPvvss1i5ciXuvvtuE3A1aNAAAQEBcbczGONghuXLl+Opp55CmzZtEvSHSwz7uvFSuXJlFC1aFP3798dLL72E/Pnzm9u/++47dO7cOVWBHDEjx+bX1OLJIT0niPTeP7tRfaStPnL7+GDsffVxz5drEGMBPl9+BJ1rl0TVW2u5Zgc6NlQfOj5E0i/Fwz0ZbK1YsQJvv/022rZtGy+QIza1MoDjaFRHU4ekVFRUlPlrTRiyKffPP//Eww8/nKrH4f053UlKm1hFXFGDsgXwaJtK5v+I6Bg8P3snohnZiYiIpDaYY181Bmy///67GWBgLzQ0FPPnzzf/c79nnnkm2cdkHzc223LgA5taef3dd9/F7bffjgIFCph9pk6dalLt9erVi3ff06dPo127dliyZIm5Pn36dCxevBgnT540zbUMKpmVY388EXc2umM1VCqSx/y//dQlfL/6mLOLJCIiLiTVE7G9/PLLpn+cvYiICIwZMyZVj8Vm1KVLl5o+dh06dMCLL76IRo0amTnmiIMrZs6c6TArx8wbs3jWDB6nJJk4caLpJ/fQQw/hwoUL+Omnn+KaakXcVU4/H3x0bz143Rr78PGSAzgaFNuPTkREJMV95tatW2f+Mnjiqgp58sRmCigmJsZkyAoXTt1M9RydysxbYji4YuvWrQ5vK1u2LFavXh1vfjpeRLKjJhUK4eGWFfDDmuNmYMSLv+3Er4+1gLdGt4qIeLwUB3NcfcEa0I0YMSLebV5eXmZwApfhEpHM8Xzn6li+7wJOhlzHpuOhmLbuOB5upT6hIiKeLkXB3M6dO/HGG2+YkWddunQxE/sWLFgw80snInFy+/viwz71cP+k9eb6h4sP4PbqxVDxVn86ERHxTCkK5oYOHYpZs2ahfPnyqFOnDnLmzJn5JRORBFpULowBzcthxvqTuBEZjVE/b8NvT7SEv6/WIRZJDPt5X7p0ycyPygnm0zBXfpqx5YrrhvN7kwP7bLsoiWR5MysHI9CiRYvMQAf7dVNFJGu8fHdNrD0cjKMXr2HXmctmQMQrXWqq+kXscDWgM2fO4PLly04vB507d84MyuPUXfoOlSwP5jhhL0ecVqtWzfyiefzxxxOd9PTHH3/M0AKKSHx5cvji8/sboveXaxAZbcG3K4+idZUiaFutqKpKxCYbx2UiXW2tbAaWnGSf64dnVZZuw4YN5jt8z5498PVN+5LsnGP26aefTnSN9OR8+umnZlAjpxJzZMuWLWZasePHj+OOO+4wCxRIyqToXbVOFHz48GHs3bvXrIWnXxUizlOndH68eFcNvPvHPnP9mZk78OdTbVA0Xw69LeLxmAlzxUDOiuVi+apXr67vUhsM5Din7KRJk1z2vXPrYI5t/h07djQXTsT7xBNPmImBRcR5BreqiNWHL+KfA0G4eDUcI3/eihlDmsHXR/3nxLOxadXVgwGWj+WsUqWKs4viMo4dO4bnn38eJUqUcHZR3E6Kz/pcyotee+21FAVyXMdVRDIP55j7+L76KB4Qm41bfzQEY/86oCoXeHrzqrP7yKUUy2k/CT8HaLzzzjto0aKFWQP9kUceMasa2frrr7/Qs2dPszoSV0yyroTEAR7/+9//0Lp1a9SvX9+sgMQmXXtc+/y5556Lt23ChAm4//77466HhISYacj4OHw8DoLkQA775ls2uTKbxnXbreWw+v777+PKwlWhOCetI1wFit25uLrUo48+ajKWXOWJ5eTa7D/88AOaNm0aNy3ajh07zGIC3NasWTMTALLeiPdjvXEqNc6+wTrieu+nTp3CF198gZYtW+K2224zCyDYrmbFpUMfe+wxNGzY0JSZ+1oHyvA94qIGfC7e3qdPH2zfvh1uGcyNHTvWrKzAARDWzpz2eCBxHdVBgwaZA0pEMleRvDnw5YON4Htr8uBvVh7Fn7sCVe3isThq1Z3L++GHH5ogiSsacfnMypUrm/5u1v247OWzzz5rAi8uYckVk6zLXbKZkpPpjx8/3swNy/5taW1FY/cqLo/JlZQYUC1fvtwEPFYMjoYNG2YCJX7vsy89y8XmY/r3338xbtw4EzSyLFzdadq0aQ6fi6/x559/jgsqGYiVKlXKXOcqULt37zbBJMtEDCoZqM6ePRvffvutec283YoxCp+bdTl37lwTjPXr188Eenw97IvHWGbBggVmf64mxSCSz8llSdm3j8/766+/mtv5+s+ePWumZZs3b56JhTi3ritJcU9IHlR8QawcRsGsfL4YHiisKI7SYZ86Dr1mpTHCFZHM17h8IbzerRbemL/HXH9u1g5ULZ4PVYqpK4R4HiYV3LW8/C5lADF58mQT/BAn4//7779NQDRgwAATjNx1110miCKOjKWwsDATnLC/Gfu1W1dKSgs+FrN//M6vXbt2XKDILKAVg502bdqgb9++ceVgoPPHH3+YIJLBFcvZq1cvczvLvmzZMofN335+fmZJTmJMYf2fmHH74IMPzD5WNWrUMBcqV66cybQdPHgw3mMOGTLEZOWImTQmpKwtixUqVDBZtn37YvscM3hkJvL11183gztZbwyWGdixnvkeceAIgz0GknxOV5PiYM7f39/0lWP0zRQnJxJmAMcUbpEiRUy6k78OmN5Mz2gZEUm9gS3KY9vJUPy+/SyuRURj2IwtmDe8lRn5KuJJuE64u5aX2S5miawBFHl7e5vr1mDl6NGjuPfeexM8zokTJxLcN62YwWKTqO1jsR8bl9i0YnnWrl2LJk2axG1j0GPt78asnv2oV47gZZNqajCusA3kiCNimZFjFpCB540bN9CtW7d4+1Ss+N/qOBw1zADRNkvJbdevX497Lcx8MsCzYhMsYxticorNxHfeeSceeOABc3G1dd9TfabngcU2Y15ExDVwkNL799TF/nNXzOXwhat44bed+OL+huY2EU/BxIP1S9pdymtl7cNlP6kxAyvrfolNeGzdntbPu6M64/e9LdtEDZ+PAdTw4cPj7ZM7d+64/+3LkpZEj239EJt6mXW77777TB86BlX/c9Cty35xA/uA0BZfCxdFYFbTUXnZ4sj+fwwi+ZeDQbmuPNeXdxUa9iaSjZb7+npAY+TLGXsC+mNnIL76N37HaZHszt1WKLItL7NJDDrYR8w2kOOUYJUqVTLX2UTI+eLssWmQTYSObrPHrJT9wAjbZko2mTIQs82isRnStn8fR+EyS1imTJl4F2sTKctjn4XjaNX0Yssgg0w2P3NFKj5PSEhIuh6TGUMGiczc2b4W+1G1bPrmwIj27dvH66PnChTMiWQjFYrkwbi+DeKuf7T4gAZEiEdhFsVdy8tgghmn999/30ygy6bT9957z2TsOHqV2EeNAw4YTLBTPoMbBnBcL50jQnlfjrTktCdLly6NG+Vpi0EQm0g5SIFNqjNmzMDmzZvjbme2q1OnTqavGvuVcTQtBx/YZsnYN/7AgQP46KOPzCS/vLC/nDUoZFMwr7OvH5+D5d24cWO664v91phFZD9CPu5XX31l+uunB0e4MnjjAA7WJeuVgyr++ecfcztfB2fo4HYGqHzdae2P6DLBnDulr0U8UcdaxfFcp2px10fP3I6dp91rhJ9IWjHr5Gr9mRLDctqvAsGMU7t27TBq1CjTjMngiAMirPtxEAKnLuHgBAZcbGoMCgoyt3E7+62zbzsHHzDQcdQs26NHDzMogFN/dO/eHZs2bTJTb9jiY7HpkSNpOUMFFw7gQAMr3jZlyhQTTDLQ5ONxBSjr6lBcweGFF14wI0P5OjgK99VXX013nbEPHV8fy87BFaGhoRg9enS6HpPZUDafBgQEmClPOM0KA1lrf0YG0++++64JlrlWPTN0HNDhSrwsqVxxmAcKD6auXbuaA86+PdudcZQNf9HwNSa2XFlm3j+7UX04rz74sX521g7M2XrGXOfKEBwQUaqAa6yprGND9ZGZxwc7xDN74soTB/N1aQUIcVpmjnO3sIMjh/ByUkPOIcMJhTmKRkRcA/u7/O+eumhaIbb/StCVcAyduhk3Ilz3y00ko3C5SfaDctUf1SwXy6dlMcVpwRw7/nHkCNvbObkf05JsS2ebM1Ooa9asSXSWZxHJOjl8ffD1Q41RvnDs6LK9gWF4de6uREfDiWQnbJZk5svVmlxZHpbLvnlVJD180/PLggEc52Xp0KEDpk+fjt9++810duRcNGxn5/BhV/1lJOIJCuXxx+SBTdBz4hpcj4jGnG1n0KBcAQxsUcHZRRPJdMx8cdQlJ+PlSEzOg8YBAVn5g4ZZck40y1GrHOygIE5cJpjjB4GdGbkcBkfLsN8c+9Cxw2a1atXMCJBPPvnETBrIToMi4jxcDWLsvfUx/Ket5vo7C/eiSflCqFUqQG+LeAQGUAqiJDtLdTDHJT0YwPEXDmdDZtDGvnO2kwtyO4f5PvjggwrmRFxA13olse1kRUxefQyR0RY8P3sHfh/eCn4+mp1IRMTjgjnOs/Lyyy+bgC2pyRmLFSsWb5kPEXGu5++qjpWHgnDw/FXsORuGyauO4Yl2lfW2iIi4uVT/LOd6aJwzJrlZtjkL9DfffJOesolIBg+I+Pi++vC+tcLOxBWHEXw14YSiIiLiXtTGIsm6tnYtjnTtZv6Ke6tXpgD6NomdufxqeBQm/J2+mdNFRMT5FMxJsoNdLoz7FBFHjpi/mtbC/T3TsRpy+cWOMp+x/gROBmtVFxERd6ZgTpJ0bfUa3Ly16DP/8rq4t2IBOfFom4rm/6gYCz5b/t8C2yIi4sHB3LJly3D16tWMejhxAczCBY0fD1hHKnt7m+vKzrm/oW0rIX8uP/P/79vO4ND5K84ukohIis2aNcssYpBWUVFRZvLmDRs2ZItaz7Bgjov9Nm/eHI888ghmz56dUQ8rrpCVs67oEROj7Fw2EZDTD8Nujx3JGmMBxi1Vdk7EFfz+++9mcXpn4A91Tvg/bdo0eJKbN2/ijjvuwOrVqwFPD+a2bNmCn3/+2Yx03b9/f0Y9rLhKVs5K2blsY1DL8iiSN4f5/8/d57D7zGVnF0nE4zkzoFiwYAECAwPxwAMPeNT7kDNnTjz11FNmHt3o6GjPDuY4u3bdunXRp08fvPbaaxn1sOIqWTkrZeeyjdz+vhhxx3/zzH285IBTyyPi6V544QUTUH399demCfCll14y29mcuG3bNjz//POoX7++yZydPn3a7HPixIl4j8FtXDvdis2I99xzD+rVq4e7774bS5YsSfT5Z8yYgXvvvRe+vrFT0D700ENYvHgxPvzwQzRs2NCsy05hYWF49tln0bhxY7OkJ1d6ioiIiDcfLVvrGjVqZOab5euy4uNxxag6deqgc+fOprnUiq+Jz8OAsn///mafffv2mdvmz59v6oGvg0uF2nfrioiIwDvvvGPKw+d97rnnTDmtTp06hUGDBpk4hfPkrly5Mu51EuuGS75xu0dMGsyMzbx587CbneGvXUtwu/XNlmyQlfPy4pWEO3h5mdvztG5l1h0U93V/s3KYtOoYzly6gX8OBGHT8RDcVqGQs4sl4pFef/11swxmgwYN8Nhjj5k1Xa1eeeUVkyxhEJU3b14TeCSHAcywYcPMRP+tWrXCpk2bzP3nzJmDqlWrxtv3woUL2LFjR4LvcDb5tmvXzqy7zuel0aNHm3VvGYhdv37dPP6ECRPMYzOoYncrBpUzZ840+1++HJv137lzpwlI+Tq5tjvjCCZ/mBnr3r272YeP9/TTT+PRRx81wVzhwoVx6NAhvPjii+a+HTt2xPbt2/H222+bdeCt3n33XbPf1KlT4efnZ7JsvIwbN87c/swzz5ik09y5c3Hjxg289957iLFJVrCuW7dubVa4YpNrtg/mWIE8ELiEl21FSvZhiYxEZGCg40DO7GBB5LlzZj8vf/+sLp5k8ETCT3Woihd+22muj/3rAH59rLmCdMmW/tgZiHFLD+BaeNY1peXJ4YNnO1VHl7olk92X36kMRBhYcOJ9WzVq1MDQoUPjrqckmGMw1aZNG/Tt29dcL126tEnG/PHHHyZgssVAi8FapUqV4m3Pnz+/Cdasjh49inXr1pn12QMCYtd3ZuDFpT0ZzK1Zswbnz5/Hb7/9Fhf8WU2ZMgV33XVXXHm47OfBgwfxww8/xAVz1KtXL5M9s2I/fAa4gwcPNtfLli1rnp/loKtXr5q4hBeuD098fczEhYeH49ixY+b1MStZvnx5czszd1xy1Bafg+V2R6kO5pgi/eKLL8wBItmTt78/Ks6ehaiQkET38S1c2Own7u+eRqXx9b9HcPTiNWw8FoLFu8/h7hR88Yi4m29XHsGRoIQtSpntm5VHUxTMJSUty2MyUGKTq+192dm/RIkSCfZl02bx4sUT/JBjU6r9YzKjZTuSlP3MmO2yBnvlypVLEMhZb2OgZovZt8mTJyf5WpmtrF27drxtzCxag7ljx44hMjIyXl8/lpHbgoODTYaS5bEGcmQN+myVLFnS1INHBHMczluxYuwcVZJ9+ZUsaS6S/fn6eOO5ztXx5I9bzfXX5+1Gs0qFUSiPgnXJXh6/vTI+WZL1mbnH28bPdqWFfwp+PLOJ0r7LDAclDh8+PN723LlzJ7gvs1uOAjD75+VjsomVo24d4e2Jdb9h/GCPQRezkUk9J3nbDcazvY/lVisS+/zZvwauE79nz54E97ftL2ebGXXXKdZSHcyx4yI7aD7xxBOZUyIRyXJ31ymBjrWKY+ne87h4NQJj5u3GhPsbqrlVshVmx9KbIctsDIRs+3Ilhv2/6MqVK/GyZraqVKliZppgc2ZyGOA56gdvjxkxBo3sG2ffJEsVKlQwgzIcBYe8765du+Jt43VHj2OLzar2s2Qwy2f7nH5+fggKCjLN0Y7uz8EQHJhRqlSpBPe3YpkdBbrZcjQro9xvvvnGBHWjRo0yo1RsLyLifvgF8l7vOiiQO/bX7sKdgZi5+ZSziyXicdjUx35nbBo8d+5covsVLFjQ9IH79ttvzb7sE8ZRp7YZqH79+uHAgQP46KOPcPz4cXNhfzn7oI/YxMpBEMlNCs8AkQMF2Ods69atpowcWMHHJd7G/n4c2HDkyBHTBLp8+XJzG/uwcYDBL7/8YkaustsWs2kcnZoUDvzYvHmz2ffMmTP466+/zH2tAgICzIhd9unn1C4sEwdzWEfKMsBjcy4HRLBMHCHLPn4+PrHLGlqxiZUxjkdk5njQcAhvRmA7O+emY4UzSmYUz2HFHLXDCn3yySfjDgKriRMnxusYaWvRokUYP368ib55wI0ZM8YMcxbPE3g1EKHhoQ5vu3jjImABiuQukmB7WEQYAvwCHN7m6D7J3UYFcxREybyunQ2gYvly4r1edTH8p9jm1jHz9qBemQKoWTK2k7OIZL7HH3/cBEpMmLB/GQOUxIwdO9aM4uS+7AfHTv+hof+d99hHjIMOuN+PP/5omhY5ypRTeNjjlB/MXjH4Si5Txu9ZBohsvmU2iwHoww8/bG7jyNTvvvvOlItBGIPLLl26oEOHDmaAweeff25GyPJ2BpAcZcpBEUlhmTkq1fq8t912mxmNals3r732mhlRy/ghJCQERYsWRe/eveNuZ19/jqLl1CsMNhkf2PePYwDIqV/ckZfFiWszscL5hjA45JvFdDEjZ74JDNruu+8+M+qFB4EVAz5H7elsE+e+fCM5BJsBIkfAcPQKR+OkNLjkkGcecPYRe1bcP7txVn0wkOv2ezdERP8375Ez+fv4Y2GvhSiWq5hbHB+v/74b09fHzl1VsUgezB/RCvlyxu/Tkl76rKg+dHy4HgaPDLw4LYqniYiIMLEDg0zOf+cRkwbzRPzvv/9i0qRJJupP64zVjI6Z6mSmjW3atWrVMr8w+MuALl68aAZbcD/rJbFOoBySzOXEONSY7eecj4aBH/v3iWdhRs5VAjliWRLLErqi17rVRJ3Ssdm4Yxev4aXfdmk9XhEPwEmCmQRx11UQ0uPPP/80MYNt8ihbN7OyTZ3t22zvZqDFxB7b4ZnO/eqrr0y6Na3YwZFt6dahywzmOFEghzyz2bVHjx6mzd1RVoOZObbV2w9vZj+C1ErrgWy9nyd+EFypPmKik+88nNVYJnc5Pny9gAn9G6DHxLW4cjMKf+wKROM1BTCoxX/D+tPLXeoiq6g+0l4frpzldjfsd8ZRqvwetp+DLbtn5T7//HO89dZbDke5uoNUl5opSPZH45ttHU3DNnM2b/I2No+mFtu1OXMzMVBknwHikiX8oHJ0Cduy2bmTo2i4TIg99hMoUKBAgg6i1sdNDfvRNll9/+wmq+vj+I3jcDUHDh5AeK5wtzo+nmiUFx+tjZ2Y9P0/9iHX9fOoVjhjpytxl7rIKqqP1NeH/Txokr6BUNOnT/e4KvT390/QPz/bB3OcgPDXX3+NC+SIqUkuUsuOhWnBdegYEHKUCaNjZv+4pIjt4AUGkOxTx6U6HAVzjrr+pTXC5tptae0zx5NPWu+f3TirPnIE5wCOwKVUr1Yd1QpUc6vjo0EDIMR7PyavPo4oC/D5lutYMKIBCuZOf0Cnz4rqQ8eHSMbxTUvk7mjiP6YpUzKpoSMc0cJL5cqVzeAHLrDLBYbtBy6wWZezOTvC9dvslzfhAAv7JVFSgl+06fmyTe/9s5usrg9vn/hdQesei8EjS2PwQ0dv7KqYstvScp+kbmOZrHXgTsfHi3fXxI7Tl7HpeCgCL9/Ec7N34ftBt8HbO2PW5HWnusgKqg/Vh0iWDIDggrscgco+c7ZLbbz//vsZMgLEGig6yrSxyZSDGxzhsGqOFLTF68yCiAezWHD/vzEoEwzzN956s4ndlpb7JHebm/Lz8caE+xuh8K3VIP45EIQv/zns7GKJiEh6gjnO5cKlPDgCtWnTpma+FwZxbHZlNi01uFAuJwHkwAcuzMvr7Hd3++23m7ni2PzKyf34//z5881kxdaFhhlMMrDk1CPEaUw2bNhg5tLh7NMcJcuJA7mUiXiu+scsqHJrKiH+5fXkbkvLfZK7zZ2VyJ8T4/tzNYjY6+OWHjRruIqIiJs2s7Lpk0EVJ/nlFCLMpHGJjuQmGUzssTgbNPvJcWADm0q5eO/o0aNN/zjO+PzDDz8gPDzcZOQ44R9HtFozd3xuawaPfeoYwPHCgRK8znKmpZlV3Bsn6eXcbhFR4ei3MgbRXoCPBeYvr++oGBuVOLytAlJ/n2Ru8/fNYcrkzlpXLYKnOlTFZ8sOIcYCPPXLNiwa1QYFtX6riIjTpXkMLoO3tARwtmrWrGkGNDjC5TkmT56c6H05L539/HadOnUyF/FsXG2Bk/SGrvwbXoHvxm1nkMWM2bf5h5sVGwoEfp7gtq8CO6Fw4OJU3Sep234t9jIKtm1vyuTu03CMbF8V644EY8OxENN/7vnZOzFpYGOt3yoi4g7BHOdeYVaMy3JwUkEOgkgMpxMRcbYSeUrg5ve/4ybXKbRdtNrbG8VnLDfHcILbvLxQZNa/sKTmPsncluv731Hi7geQHfh4e5nm1rvHr0To9Ugs23ceU9cex8OtKjq7aCIiHi1FwVypUqXiFu9t1KiRfomLy7u2eg1u7t6d8IaYGITv2eP4ThYLLDdupO4+ydzGMrAsedvEn9DanfvPfdK3PgZP2Wyuv79oP5pUKIQ6pVO2ZJ6IZC6uT8opxLjuuatgNyouALBmzRoz68Vvv/2Gfv36mbndcuTIYfrbs9vUxx9/bPbn8p7sYtWyZUtnFz17BXOPPvpo3P/szybiytiPMmj8eJNpc/qIUi8vU5Y8rVshu2hfoziGtq6IyauPISI6BiN/3oYFI1sjbw73nDldJEkx0cCJtcDV80De4kD5lpxrKNMqzT6woZ9++glffvmlCdDYxcjdcG5arsY0c+ZMc7106dL4+++/0zydmWTAaFauW8bBCfZOnTqlkaPiEiyRkYgMDHR+IGcKY0HkuXOmTNnJC3fVQL0y+ePWbx3zu4MsqIi72zsf+KwOMLUb8NuQ2L+8zu1ZZNmyZfjss8/MWujuGMgRFwRgqx4HJvJCCuQyVqp/Sp85cwYxtv2Cbrl8+TIC+QUq4mTe/v6oOHsWokIcT58RdfGiyZj5Fi6cYHt0WBh88ud3eJuj+yR3G3E7y+TuAyBs+fty/rmG6Pr5alwNj8KcbWfQskoR3Nu4jLOLJpIxGLDNHMhfZPG3hwXGbu87DagVO7tCZuFcqS+//LKZ8YEDBq2+//5701TJKbqKFCmCkSNHxq1pbo8zRDz//PNmCc6tW7ea9dM5xRhniWD2j9OCcZ5WzgJhXVudy2d++umn2Lt3r+lW1bZtWzNtGJtE+Zyc8ouzRXAbpwLjYEj2q3e0tBpXbGIWjue/RYsWmeuclYKDFdnMWqaMzhlZGsxZF93lG8u1U21nbeebxAl9W7RokSGFEkkvv5IlzUUyT/nCefD+PXUx6udt5vrrv+9Gw3IFULloXlW7uH/T6uIXEwZyBrd5AYtfAmp0zbQmV07/9cQTT5gBiPbfrZzGa8yYMShXrhwWLlxogjPOz8r1yB1h0PX222+bCf+Z5Xv22WdNAMapvBigsfvU+PHj8cEHH5j9uY1rpvM+XPd82LBhmDVrFgYMGGBuv3Hjhlly880330SxYsUwduxYEzAyaEvsudl0zP05Ty2X7BQnBXN801etWmUidkbffEOsGOD17dsXXbp0yeDiiYgr61G/FNYcuohfN5/CjchojPhpG+Y+2RI5/bREl7gx9pELO5vEDhYg7EzsfhXbZPjTX7x40UyQz/lVHX2v9uzZM16ihRk2ZsgSC+Y4sT+7SNHgwYOxYMECPPnkk6hTp47Zxqwet1nVqFHDXIgBIxcHOHjwYIK+9NZM3GOPPYY+ffqYwM++DAUKFDBNqhxEqXlfXSCYK1GihFllgZ3LmSLNmTNnJhZLRNzFmz1qY8vJUBy+cBX7AsPw/qJ9eLtn7JeEiFviYIeM3C+VOGE+s3LffvstZs+ejXvvvTfuNn4Hc35WBl9c5YjNpWT96wjXNbfiak1Uvnz5eNs44tSKzbF8bra4hYWFmUyc/WpKXEvdKl++fObvtWvXEg0oxQWCuYiIiLjOiq1btzYL2Cc1jYmIeI5c/j744oGG6PnFGoRHxWDauhNoWbkI7qpTwtlFE0kbjlrNyP1SiZm04cOHm+ZUNo0yA2YNyKZMmWKWumQzK7Nnvr6+yU6W7yj5wvs5wr7vQ4YMMckb9m/jSk1sUrVn2zonbhLMMT27YsUK8+azM6WjSYP5a4HbuZaqiHiWGiUCMKZ7Lbw6N3ZU6wuzd6BO6QCUKZjb2UUTST1OPxJQKnawg8N+c16xt3O/TGD9ju3fv7+ZM4593DiAgUkVrmHevXt3dO3a1exz/PjxDH1udqVik+grr7wSt40JHK7KJG4ezHGOGGsUb13YXkTE1gNNy2Ht4WD8sSsQYTej8NQv2/HLY83h55PqGZBEnIuDGu768NZoVi+7gO5WMuOuDzJ1vjkrZubYR27cuHFmDjqOOGUzKNdGZ9MoR536+fll2POxdY2Py8EM1apVM825hw8fNv33xHWl6Cxr23TKzpBJXUTEMzGbwNGtZQrGNr9sORGKz5bF7zQt4jY47QinHwmwGxXPjFwWTEti2x+NAduMGTOwcuVK05cub968JsDjKFQusWk7bUl6cZoSzljBwJEDIzioQYsFuD4vC9tHU4Fzy9SuXdv0naOJEydi8uTJJpDjiJqqVavCXXGKFc7r06BBg3hTr2TV/bMb1Ydn1se2k6G47+t1iIph1wtg+uBmaF21iEfWRUqpPly4PrJ4BQiRtEh1+weX47AOhmD/OI6qYYDH4I7pYBHxbA3LFcTznaub//lT8elftyPoSuIj7URcGgM3Tj9S997YvwrkJDsEc0FBQXFDmjkLNVO8TZs2NXPd7Nq1KzPKKCJu5tE2ldC2WlHz/8Wr4Xhm5nbExLjA8moiItlQqoM5dr7kzNRca40jXK0rQ3AeGo6AERHx9vbCuL71UTRfDlMZqw5dxLerjqpiREQyQaqjL85KzaU9uNQHO2JaZ3TesmULqlePbVoRESmSNwc+69fA9Jujj/86gK0nQ1UxIiLOWgHCihMJNm/e3EwkbDsDNJtarYMiRESoVZUiGN6uCr5YcdgMiBj50zYseqoN8voriy8iklHSdEYtW7asWVx3586dZhAE56ThHDRa/UFE7D19Z1U0KR+7xM+ZSzfw0m87zSTjIiLipMxcZGQk3nnnHcyZMwdRUVGxD+Lra2aqfuGFF+JGuoqImPODjzfG398QXcavwuUbkfhz9zn8vKkQaulUISLinMwcJy/ctm0bvvvuO9NPjgsCc0FeLjEyduzYjCmViGQrpQvkwth768Vdf2/RfpwOi/0xKCIi6ZPqYG7+/PkmM9esWTPkyZPHzETdsmVLs+2PP/5IZ3FEJLvqVLsEHmoeO63RzcgYjN9wCRFRMc4uloiI5zWzXrlyJW4Eqy1u4/QkIiKJeaVLTaw9chFHgq7h6KUofLb8EF7uUguBVwMRGp74SNeCOQqiZF67ZZVERCRtwRyXV/nxxx/x8ssvx9s+ffp0NGrUKLUPJyIeJJe/D8b3b4jeX65BZLQF3646hrrlgTe2PYSI6IhE7+fv44+FvRYqoBORdPn888/x888/4+bNm/jggw/wxRdf4K233jLxC8cCfPbZZ2YNXOKiCNzuDmvTprqZlUEcm1M7dOiAESNGYPjw4Wjfvj2WLl2K119/PXNKKSLZRp3S+fFMx2rmfw5qffvPjUkGcsTbk8rciWQ0Zov3Bu9N9MLbMwMXuH/uuefibfvpp5/M1F+nTp0y1zmn6wMPPJBgVPiGDRvM97EVgxPuy2U4HT3PhAkTzP9TpkxB/fr1zb68P2eocCau+T5r1qwMf9w9e/bgyy+/NEEcu4y1atUKv/32W7ZIRKU6M1ejRg0sWbLEVMSxY8fMiNY2bdqga9euyJcvX+aUUkSylaGtKuCPLcewOygCF69GIE/syl9G3WMxeGRpDH7o6I1dFTUfnWQ9Bmrdfu/mEtniZcuWmWwR10HntGBWXD7z999/NxP4J4UzTHzyySfo2LEjChaMnSLI3sMPP4yAgADzPH///Tecbc2aNcm+rrQ4cuQIChcujNtvvx3ZTZrOlLlz5zZTkTBLx2wc/1cgJyIpPvF4e2Fk0/zIn8sv/g0WC+7/NwZlgmH+mtSdSBZjFtgVssXbt28337Pjx49HzZo1493GFZg4g0RYWFiyXaNq1aqFcePGpeq5meW7++67sWjRIpOtq1evHh599FGzPvvbb7+N2267DS1atMDHH38c7z6dO3fGP//8gzvvvNNkvB577DGcP38+bh82XXJWjMSykVwilDNlvPbaayZTaM0e8nU+++yzaNy4sRmA+e6775rFC+j06dNo2LAhAgMDTTxSp04dMweuLc668cYbb+DixYvmcVkOatu2rclgemQw99dff5klvbp06WKi56eeegrr1q3L+NKJSLZVJLcP3u1ZO962+scsqHKr9Yp/eV3EE3ENdAZs7M/FoMlejx49TIDHbFpyGHwtXLjQTPSfGidOnDDNkF9//TVmzJiB/fv3o2fPnmYmCwZATOZMnjzZBHFWbApmH3r2Rfv1119NwMVlQFM6UTibWLkGPINYxhW8L7HfWnh4uGl+5dRofE5roEdsGn766afN/sxmVqlSBbbYLM1gsGjRouZx2dyanaQ6mGPHQUbQTMn27dvXRO6cNJjR97Rp0zKnlCKSLXWpWwJ31iwWe8ViQb+VMYi+tZYr//K6snPiaZg9YlDClZWYNEkMAz02te7duzfJx2PzLPu3v/nmm4iJSfl0QNHR0SZAqlatmsnMMevm7e1tAis+JsvGZT1ts2C8D2MEdsmqWrWqCSQPHjyI3bt3p+g5CxQoYJ6DASNnyciVK5cJbBmAvf/++6hUqZLJvDFLyO5etnr16mUygiVKlICfX/ysP6dRY6siH5uPm91aE1MdzP3www9mTrmPPvrItLMziGN7PCt50qRJmVNKEcm2Hm9bOV5WzufWD3j+VXZOPBEn4+/Tp48J0mbPnp3ofmXKlMHIkSNNUJdc5ovf19znl19+SVVZKlasGPc/Ayw+JwMi2232AyYYhFqVK1fOBFInT55EWjEYZBDK5t4mTZqYC5tMbZtvids9VaoHQLC9vGnTpgm2sx2bc9CJiKR2uhLbrJw1mLPNzu2oeCtdJ+IBmAFjJo2d9d977z3z/WobVNkaOHAgFixYYII+Bk6JYQsaEzHM+N11110pLkvOnDnjXbfPeDliXeqTGEDyeo4cORLdn8FgUkuB8jGYoWMWMin+HrycaKozc4yMN27cmGA752XJjiNERCRzFfQPQOOj8bNyVtbsHG/nfiKewMsr9scLO/Nztgj29bJ29rfn4+NjAj4Okrh8+XKSj8vmSfZzz+ylNw8fPhz3P2e94Jxu1kCTmbyrV6/G2//QoUMJXj+ba63YXMuAj3XAzKDtRdKYmWNHTB447GDIVCojbr5xTAtzFAon5LNN93JwhIhIYkoEHcUry8IRBV94IWEGzgKLub3Ew0eBfDp5i2fh9y0HHXA0Kkd9OsKBENzHdkBAYkaNGoXu3bubjFvp0qUzvLxsgmWw+Oqrr5psGrOBHGnKfnfWgJLz5rG/fbFixcxACo5GrV37v8FQHACxYsUKkyBiRpGDGTjPHvvicZRrqVKlzECLCxcumGnRJA2ZuXnz5pmOjfwFsGPHDjMJH0eY1K1b14yU2bRpkwnseOHwYhGRpFgunQWuezsM5Mhsv+4du59IFuDycZxHLim8nftlNnbU51QeHE1qXZnAEfadYwYsOQywOBDCNhvGOewYdLEPGuejS8+kwcwUDhgwwJSHI27Z1802E8i+e0wKPfnkk7jnnnsQEhKCxx9/PN5jPPPMMyZY69SpkxlNS8w8MhBk8zPLyGAxuUykJ/GypHS8sAdgWpfz+nBeHh6QWX3/7Eb1ofpI0bFxci0iv+yBqPDEPzO+OaPh98R8oGIbZEf6rLhefWi94NTjdCHsw8eBG/oOdPFmVhGRDFW+JfxKloBfGCeYS/jbklu8Akqb/USyCld2yOzVHbIr5Yiynrezf30xdcy2fjbTMvXKOW3YDm5t0mWalrdx4AVncE4MZ4fmrM62F08epiziNrx9gLs+vHUlflNrjCV2mrnTzd6I3U9ERFwrM8f27m3btpm2dQZfnNqEbfmcM+eFF14wi/+ysyaDOU44yBE97BjJzpuOpkzh0GcuI2JlOxeOiLiwWj2AvtOAxS8CYf/1jTuHwngr8iEEbiuNOc1j4Oujz7SISLqDubNnz5qRJPbY8ZKdJ8uXL5/ix+IszJxw2BZHpnAJED7O3Llz47YXL17crKHGARaJBXPch48pIm4a0NXoCpxYC1w9j4hcRfHwvGgcDLoBnL6MH9Ycx6NtKzm7lCKSCK6ZeuDAAdWPOwRzHTp0MMtqcMkNW5cuXTLLe9mu0ZZaXPeNs1NzSQ5HQkNDzXwzjgQHB5uAjs2xkZGRZj9m/DgkOrVs57dJy/3Sev/sRvWh+kjTsVEutm8cG1XfuycUfb/dYJpaP1l6AO1rFEGFwnmQ3eizkvb6UEd7kVQEcxxezE6NvPB/2/Xd+IFbs2ZNmps1OYmhdZj0kCFDEgxTpq1bt5qm1g8++MDhY3A9Ni4ZwqzhjRs3zNw1gwYNMv3uEps5OzG7du1K0+vIqPtnN6oP1Udajw2eUe6unBuLDl/HzcgYjJq+AW/eXhDetyZVzW70WUl9fXB1BBFPl+KpSTivCyf2S8oTTzxh+rilFptnOSP0kSNHzKTD7CP3v//9L+52zjfzwAMPmD5ziWXtHOHgCQZ5KS2TdTg8nz+tU5Pw5JPW+2c3qg/VR0YcG9fCo3D356tx5lLsHFrPdqyKJ9vFrueaXeizkvb60LlWJBWZuaVLl+LEiRO47777TKDFiQytmJErUaJEmpfWYF83XipXroyiRYuaJUw403X+/PkRGBhoJhlkxi41gRwxI8fm19TiySE9J4j03j+7UX2oPtJzbATk9sFH99bHgO9im1s/XXYIjcoXQqsqRZDd6LOi+hDJ9D5zHJTAmaK5LEdmBSvWBXqZMDx37pyZgJDBHQO61OD9jx49ij59+mRKOUUk6zBwe6pDVXy27JCZrmT4T1sx8/EWqFb8vx+VIiKeKtWd3DjI4eLFixny5OvXrzfzzHHgA5taef3dd98167FxiTAGcp07dzYBGZf8sF6ITb7t2rXDkiVLzPXp06dj8eLFOHnypGmu5RQnzMqxP56IuL9R7avi9mpFzf+XrkdiwOQNOBWS9mWHREQ8NpjjYrhcjzUjsBmVzbcM2jhK9sUXX0SjRo3w8ccfY9asWaZZd9KkSWYyYduLNfPGLJ61yx+nJJk4caLpJ/fQQw+ZiYe5mC+fQ0Tcn7e3F754oCHqlYn9TF+4Eo4HJq/HucvJr0cpImnzxhtvJDlhv8THdW05kwZjmebNm+PMmTNo3bq1SVARu5A999xzcftzjt21a9ciy6cm+eyzz8wqDczOcRSRv3/8xYjLli2b4seqWbOmabZ1ZMSIEeaSGD7P6tWr481Px4uIZF/5cvphyiNN0febdTh84SpOhdzAg5PX49fHW6BI3hzOLp5IurB1iQvIc0Ag+4//+OOPpnvTK6+8gm7duqFly8xd0m7z5s1mejAmVKxef/31DOlWxbjhr7/+Mv9zYOKECRPgTIMGDcLbb7+dqrlxU4Lz5O7cuRMzZ84010uXLo2///47QayU0VIdzN19991m6g+O+iSvW1MEMEPG//ft25fxpRQRuaVQHn/MGNLMBHQnQ67jSNA1PPTdRvzyaHPkz+2nepIMd23tWpx7732UePUV5MnEgOquu+5CwYIFTWsVVzPy9fU1I3vZBYnBXGZbtWpVgm0sQ0ZgIohZKbao2QaLznDt2jWz+lRmYDcvZuWqVKkSty2zA7k0NbMuXLgQy5cvj7ssW7bMXKz/i4hkthL5c+LHoc1QMn9Oc31fYBiGTN2EiKj/5r8UyQhMVFwY9ykijhwxf7NyEfmIiAiTQGFT3SOPPGKa5DiHKnGmh8cee8xMjM9mvC+++CKubJy8n8Ef+6NzxaQ6deqY5TPZBDhmzBgzuX79+vVNf3TrXH4Mtr755hssWLDAPA+7KxGbBBmEsQ96rVq1zJyvtjj1F5ffTK5MjjA7x/KMGzcOTZs2Neupv//++2bwIx+HZWQXrEWLFsW7D7OFX3/9tVlxgtnKd955x9RVUk2XfM3svsXHZl98Nnt26tTJ7Gtd7ID1NWDAAPO83P/nn3+Ouz/rnXPgctEElonPbY+tidyP5eXjsqzsLsb/k5vaLcuDOaYMk7qIiGSFsoVym4DO2ry6+UQoPlq8X5UvGera6jW4uXu3+Z9/eT2rMKPDZk9iYMBAgkEas1uPPvqomSR//vz5+PTTT02zHpv4rBgAciAgm23ZN539x3PlyoVKlSqZx/rjjz/Mdzb7xNHQoUPRpUsXE+jweb788st4ZSlcuLDps/7nn3/GbWNwuHLlShMwpqRMjvz+++8mEJs9e7YJyqZNm4b777/fvE4GRQy4+BqYTbPi43MwJB+fQejy5cvjzU2bFDZfW/sAMrjja2UmLSwsDIMHDzavkXXDgHHs2LHxspV79+41ASpfG8tgjwM4mV211iHrNKukOn86fvz4JG9/6qmn0lMeEZEUq1Q0L74b1AT3fr0WkdEWTF59DLdXL4o2VWNHvYqkB7NKQfzO4+pGXPXI29tcz9O6VVwXo8xmXW+cKxxZ/2eAwWDG2p+NfcgZADHA4FRe1kDrySefNJ3wrVhm22m+7rnnHtNZ3/r4DB45b2xia5wzaGMgwz58fCw2BefJk8dkx5gJS65MjuTIkcMsCODn54dy5crhq6++MnPEcjAjMUD8/vvvzUwV7GdPOXPmNNlANgGzz9vw4cNNMMfnTm4lKpYtICDA/M8A1/paf/vtNzNfLh+LOG8ug1uWv02bNmYbB1ZOmTLFzInrCJc5Ta4OXSaYYwdJW+w/xzQiI+vUTuorIpJe9csWwKtdauLNBXvN9Tfm78Hip9rC3zdtywuKOMrKGTExcdm5vG1aO62iDh48aNZDt23q45rkRYrEn0ibzZa2eB9m3Bh4cS1zfm/bNk8mhwMXmMljUysHQHLABpuBGSCltEz2GPQxkLNicFihQoV418k2M8f72Pblq127trmdTcHMvKUFy8+LbZ2xKdZ2fXf2Z0wskHO2VAdznM/NHl8w06N8oSIiWW1QywpYsDMQW06E4mjQNUxZewyPtXXNk664aVbOygnZOUdlY0aKU3clNVjBvuM9+74xu/Tyyy+bJtYdO3bE9XdLCWbv7rjjDhPEMUv277//xsUEKS2To8ycveTuY11cwCriVkDq6LGsmKlMCst/2223mVgmsfJlxUCGtMqQn658seycyfZnEZGsxi/Vt3rUhvW7deKKIwi7Gak3QtKflbMN5Oyyc1l5fHNUq1XVqlXNYAMGV2wOtF7YTJgUdvTnGuqtWrUy2S/r3Ge2zxNj/3odNLWyDx4DOS7DWa9evXSVKS3Y5GqbUdy/f79p4rQ2n+bOnRtXrlyJu50BLLOGVtamWNs65ejTY8eOmddkW/60ZvqyWoa1QzCdan9giIhklTql8+OehrHrQ1++EYkpa46r8iV9WbnEMm9eXub2rBrZWrJkSRNAcaUk9ktjHzUGGuxrxkn8z549a+ZdZR+25B5nxYoVJuhivzsONrC/nVN2cHoNDqBwhP3H2L3qhx9+MIGdVVrLlBYM5DjYgMEXm3y//vrreP3yOHqXc9jydRw4cMCMmLXN9rE/G7NsHGDBOmXgx3lqmfHjYIvDhw/j1KlTZoYO+65l2SaY44gT+ws7BPINtG+fFxHJSqM6VIGPd+wX8ORVR01QJ5JalshIRAYGMqpLZAcLIs+dM/tlJDZfWifL5/QX7I9OHHDAwKhjx45m9Cj7mHFQADNRHNDAfmsffPBBsv3fOEku54jlCFH2neN8b7YDBh588EETkHFghHWUqz0GQRytySZa22AuuTKNHj3aTHvCAIrTmaQHm0PZrYsB3JAhQ0ymkYM9rDiKl1lGTr3CKU4YaNr25eMACjY1c6QwR58y+GRmj8FtaGioWbaUAzDsR/S6Mi9LKn9acO4Ve+ygyEiYb1axYsXgrphy5YHeoEGDNM14nd77ZzeqD9WHM46N52ftwKwtsXM6jb6zGp66sypcnT4rrlcfDOaibq0F7ohv4cLwy4QmREmadYoWLtcp6RgAwWUpRERc1Yj2VTBn2xlEx3CqkqN4uGUFrQwhqeZXsqS5iOvJyomb3UWa+8yxjZnrj7HjYXKjREREskr5wnlwb6PYvnNXbkbh21VHVPkikq15p2WgAzsTcpZktitzbjkuw8HOiKmZr0ZEJLOM7FAFfj6xfed+WHMcF69qcJaIZF+pDuY4+zNHu3z33XfYsmWLGenBpTG4EDCXvhARcbYyBXPjgablzP/XI6Lx1T/KzolkB1yxwnbNVEljMMelLTipHkeGcOAD55SxLnTL9cxERFzB8DuqIKdf7Clu+voTOH7xvxnkRUQ8OphjXzlHa45xG+eeERFxBcUCcuLhlhXN/xFRMXj1913qOC0i2VKqgzkOFf/xxx8TbOeSHo0aNcqocomIpNvI9lVQukAu8/+aw8GYs9XxRKgiIh41NQkn2hs6dKiZGZlrs3GI8L59+8y8QJxxWUTEVeTJ4Yt3etXG4Cmxs7i/88detKlaxGTtREQ8NjNXo0YNLFmyxMyqzIV6ue4a/1+4cKFZ601ExJW0r1Ec3erFzhd26XokXppzq7k1Jho4tgrYNTv2L6+LiHhCZo5zynER2379+sXbzmlJeOFSHyIiruTtnnWw/miImaLk7/0XsGbBD2h9eCyuHbyIc1vzo0Sjy8hTrQhw14dArR7OLq6ISOZm5gYMGGDWhrPHbQ888EBqH05EJNMVyuOPD/vUNf939t6IlltGI+byWVzYGYCIMD/z13I5EJg5ENg7X++IiGTvYO7o0aOoXLlygu21atXCoUOHMqpcIiIZqkPN4ujXuCSeyDkd+/z9sP9ibtwMiW1J4N99F3Nhr78fApe8pCZXEcnewRznlQsKCkqwndvY/Coi4qoer34MQ8rmRv/SJXBkbwFExy4SYf7yer9SxdEtvzcCDyx0dlFFRDIvmOvatStef/11s+ID55y7evWq+Z/bOnbsmNqHExHJMjdvnEWEtxfqH7OgSiDgc2u9bv7ldW7n7aFhJ/WuiEj2HQDxzDPP4PLly2Z6Ek5HQt7e3ujcuTNeeumlzCijiEjGyFUIsFjQb2WMycZZgznidW7fUdErdj8RkewazOXIkQMffPABXn31VZw4ccIM8S9btiwKFCiQOSUUEckoJevHZeXs2Wbn0LW+6lxEsm8wZ5UvXz7UqVMnY0sjIpKZvLxM9i0mkT4m3M7bLSP0NohINu4zJyLitiKjUCQs8RMftxcOA96btxsXrtzM4sKJiGRxZk5ExO34++Hlh30QcD3xXS7nBk6dDkPPL9ZgyiNNUb1EvqwsoYhIqimYExGPEhzgheCA5HYCAi/fxP2T1mPOEy1RoUieLCqdiEjqqZlVRDxGwRwF4e+T9JKD/t7+qFm0hPk/5FoERv2yDdExNsNeRURcjDJzIuIxSuYtiYW9FiI0PDTJgC+3TxH0/nINjgZdw87TlzFz8ync37RclpZVRCSlFMyJiMcFdLwk54N76qHvN+vM/xNXHMa9jcvAz0eNGSLienRmEhFxoGnFQmhbraj5/3ToDfy+7YzqSURckoI5EZFEPNWhStz/k1cdM5Oki4i4GgVzIiKJaFy+EBqXL2j+P3D+ClYeuqi6EhGX49Rgjmu7zpgxAz179kTdunXRokULPP3007hw4YK5/fTp02YN2IYNG6J169b46quvkny8RYsWmTVi+Vi9e/fGtm3bsuiViEh29WibinH/T1511KllERFxuWDu8uXLJuAaOXKkCcS+++47nD17Fm+99Za5ndt9fX0xc+ZMvPfee+b2uXPnOnysPXv24Pnnn8fAgQOxYMECtGzZEsOGDTPPISKSVh1rlUD5wrnN/6sOXcS+wDBVpoi4FKcGc4UKFcInn3yCO++8E2XLlkWtWrXQtWtXHDt2DLt378bevXvx9ttvo2rVqrj99tvx8MMPY/r06Q4fa86cOWjevDkefPBBVKhQwQR2efPmNYGdiEha+Xh7YXCr/7Jzk5SdExEX41JTk+zfvx+//PILevXqZQK50qVLo1ixYnG3N2nSBF9++SUiIiLg7++fIDPHplhb3H/nzp1pav5NC+v90nr/7Eb1ofrILsfGPQ1LYtzSg7h8IxILdpzFcx2ronhATo+tj8yWmvrw8fHJghKJuDaXCObYv+3QoUPm/yFDhuDxxx/HN998gwIFCsTbr2DBgubDfenSpXhBHoWGhjrc3/q4qbFr1640vY6Mun92o/pQfWSHY6NDeX/M2R+JyGgLPpi7EYPqJ7cmWPauj6yQkvpo3LhxlpRFxJW5RDD39ddf4+rVqzhy5Ag+//xzMwCiXLmEs62z/xx5eXkluM3RlAHW/VOLAyjS8muPgSZPPmm9f3aj+lB9ZKdjo1Slm1h46F9ERFuw6PANPNKhPuqVye+x9ZGZVB8ibhjMFS9e3FwqV66MokWLon///qbPm/3gBWbfeKLLnz/hCbRw4cImY2crJCTE9MtLLT5Hek6o6b1/dqP6UH1kh2OjZME8ePz2ypjw92FExVgw6IdNGNO9Nno3LG361XlafWQF1YeIm84zFxUVZf7Wr18fZ86cQVBQUNxtHPlavXr1BP3lqF69eti+fXu8bbzOX7oiIhlh+B1V0OTWvHNhN6Pw3KwduHPcv5i69jiu3IxUJYuI5wVz69evN/PMceDD+fPnzfV3333XjFy97bbb0KBBA4wZMwaHDx/GypUr8e2336Jfv35xc9C1a9cOS5YsMdfvu+8+bNiwAT/++CNOnDhhRsmeO3cO3bp1c+ZLFJFsJKefD75/5DZ0r18qbtuxi9fwxvw9aP7+coyZtxtnL91wahlFxPM4NZhjc+nSpUvN3HAdOnTAiy++iEaNGuHjjz82t48fP95k6vr06YNXXnkFgwYNMk2w1j5yvM3aV65KlSomgJs2bRq6d++ONWvWmEEUaWlmFRFJTEBOP0y4vyF+GtoMraoUjtt+LSIa09adQOdPV2LRrkBVoIh4Rp+5mjVrYurUqYnezn50kyZNcngb56VbvXp1vG2dOnUyFxGRzNayShFz2X8uzARxc7eeQXhkJLqd+RP5B7+OrYP7odHjTwPe6gMnIh7WZ05ExJ3UKBGA93vXxeY+17E5z2g8tncBCl25goLTv0TUJ7WBvfOdXUQRyeYUzImIpNfe+cjz+2D4nQ7DzZDYAVr8e+NwCDBzoAI6EclUCuZERNIjJhpY/KLpv3txVz7A69acl16x12N4dfFLsfuJiGQCBXMiIulxYi0QdhbXzuWIzcpZbs05Z/Ey16+f8wfCzsTuJyKSCRTMiYikx9XzOOvtg5O7A2CxZuVu4XVu5+3cT0QkMyiYExFJh0BfP7wSWRJewX7wsmblbuF1buftZ9O4vKCISHIUzImIpENo4UroszoGMYnczu28feX1/+akExHJSArmRETSIzoGRcISP5lye+Ew4Jf1J+ImORcRyUjK+4uIpIe/H15+2AcB1xPf5XJu4FTQdaw5HIzWVYuovkUkQymYExFJp+AALwQHJLcT8Omyg2YJMC+v+H3rRETSQ82sIiJZZMuJUKw6dFH1LSIZSsGciEgWevePvYiISmy4hIhI6imYExHJAlWK5jV/D56/ikmrjqrORSTDKJgTEUmHgjkKwt8ndj3WxPD2V+5qDO9bXeXGLz+E4xevqd5FJENoAISISDqUzFsSC3stRGh4aJIBH/cb3Coak1cfM82sr/6+CzOGNNNgCBFJNwVzIiLpxECNl+SM7lgNf+4+hzOXbphpSuZsPYM+jcuo/kUkXdTMKiKSRfLk8MU7vWrHGwwRci1C9S8i6aJgTkQkC7WvURxd68Zm8UKvR+KN+XtU/yKSLgrmRESy2BvdayEgZ2wvlwU7zmLBzkC9ByKSZgrmRESyWLGAnHinV52462Pm7UHwjWi9DyKSJgrmREScoGeD0uhev5T5P+xmFCZuuoyYGIveCxFJNQVzIiJO8k7P2igekMP8v+N8BCavOa73QkRSTcGciIiTFMjtj7H31o+7PvavA1h7WGu3ikjqKJgTEXGittWKYsQdlc3/bGUd8fM2Mw+diEhKKZgTEXGyUe2roGGJ2CXBOO/cEzO24GakBkSISMoomBMRcTIfby881awAyhbMZa7vPH0ZL/62ExaLBkSISPIUzImIuIB8/t74ekAj5Pb3MdfnbT+Lr/896uxiiYgbUDAnIuIiapTIh3F9/xsQ8dFf+7F833mnlklEXJ+CORERF3JXnZIYfWc18z9bWZ/6ZTsOnb/i7GKJiAtTMCci4mJGdagSt37r1fAoDJ22GZeuRzi7WCLiohTMiYi4GC8vL4y9rx5qlQww108EX8fwn7YiMjrG2UUTERekYE5ExAXl9vfFpEFNUDhP7JQlaw4H470/9jm7WCLighTMiYi4qNIFcuHrhxrDz8fLXJ+y9jh+3njS2cUSERejYE5ExIXdVqEQ3u1VJ+76mHm7sfFYiFPLJCKuRcGciIiL63dbOTzcsoL5PzLaYlaIOB9209nFEhEXoWBORMQNvNa1JlpXKWL+D74Wgad+2YZoLuYqIh7P6cHcypUr0bdvX9SrVw9t2rTBhx9+iKioKEyYMAHVq1d3eDlz5kyCxwkLC0uwX5MmTZzymkREMpqvjzc+v78hSgTkNNfXHw3BhL8PqaJFBL7OrIPLly/j008/xaBBg9C8eXOcOHECzz77LPLnz4+hQ4fiwQcfjLf/woULMWPGDJQsGTv/kq2goCDkyJED//zzT9w2b2+nx6oiIhmmUB5/THigIfp/u95k5cYvP4Q2VYuicfmCqmURD+bUaIdB29y5c9GrVy+UKFECzZo1Q/fu3bFp0ybkypULhQoVineZPXs2Bg4c6DBIYzBXvHjxePsXKFDAKa9LRCQzB0SMvrNq3AoRL/62E+FR0apwEQ/mcqmrS5cuoWjRogm2r1q1CoGBgbjnnnsc3i84ONgEdO3btzfNtYMHD8a2bduyoMQiIllr2O2VUb9MfvP/4QtX8dU/R/QWiHgwpzaz2jt16hQWL16MKVOmJLjt+++/x3333YfcuXM7vO+dd96JvHnzolSpUrhx4wbmzJljmm/nzZuHihUrpqoc0dFp+5VrvV9a75/dqD5UHzo2Muezwlnn/te7DnpMXIuoGAu++fco+jcpg6L5csDT6sPHxycLSiTi2rwsFibqXSMjN2DAAHTp0gVPPvlkvNv27duHe++9F8uWLXPYXy4xPXr0MEHeqFGjUrQ/Txzbt29PddlFRJxh8rYw/Hn4uvn/rsq58Wij2OW/PEnjxo2dXQQRp3OJzBxHorJZlIMg7AM5+u6779C5c+dUBXLEjBybX1Orbt26afq1x2Bw165dab5/dqP6UH3o2Mjcz8qbVcLx7ycrcT0iGsuO3cDzPRujQuE8cHc6d4i4WTB35coVE8jVr18fr776aoLb2U/uzz//xM8//5yqx2XC8ejRo+jTp0+qy8STaXqCsfTeP7tRfag+dGxkzmeleP7cGNqmEj5ffsg0t074+wg+698Q2YXOHSJuMADi6tWrGDJkCMqXL4+RI0ciNDQUISEh5hITE2P2mTp1qvm1ynnobJ0+fRrt2rXDkiVLzPXp06eb/nYnT57EkSNH8Oabb5qsXO/evZ3y2kREssJjbSuZKUto3o6z2H8uTBUv4mGcmplbunQpduzYYS6cQ87W8uXLzdQlM2fOxPvvv+8w88bJha1d/jgVycSJE80gCg6SYKbvp59+Mo8hIpJd5c3hiyfbVca7f+wzU5V8suQgJg3UhOkinsSpwRyzZsllzrZu3epwe9myZbF69eq46127djUXERFPM6B5eUxedQznwm5i6d7z2HYyFA3LaSJhEU/hcvPMiYhI6uT088HIDlXirn+85ICqUMSDKJgTEckG+jYpi3KFYufhXHM4GGsOX3R2kUQkiyiYExHJBvx8vPFMx2px19+cv0fLfIl4CAVzIiLZRPf6pVC3dOygr0MXrmLckoPOLpKIZAEFcyIi2YSPtxc+urcefL254BfwzcqjmLf9jLOLJSKZTMGciEg2UrNkAF7tWjPu+vOzdmLF/gtx16+tXYsjXbuZvyKSPSiYExHJZh5uWQH3Ny1r/o+IjsHj07dg+b7zZl7OC+M+RcSRI+aviyzNLSLppGBORCSb8fLywts966BrvZJxAd2wGVuw5ueFuLl7t9nGv9dWr3FySUUkIyiYExHJpqNbx/drgB71S5nrkVExCJkwARav2P508PZC0PjPlJ0TyQYUzImIZFO+Pt4Y17e+Cegeu7gAVUNPwcvatBpjwc3de3Dtp4+dXUwRSScFcyIi2ZgJ6OqcwP37lgFedn3kvCwImvg1LHvmOat4IpIBFMyJiGRnMdG4+f3LuBniD1huNbFaWbzM9muTXzL7iYh7UjAnIpKNWY6vwdkNkbDA8chVbje3H9dgCBF3pWBORCQbC7ywD8E3/eAFu6zcLdweHO5n9hMR9+Tr7AKIiEjmuZQ7L15+xAcB1xPf53Ju4KvceRE77lVE3I2CORGR7KxkfQQHeCE4IPn9RMQ9qZlVRCQ78/bO2P1ExOXo0ysiIrhwJVy1IOKmFMyJiAh+XH9CtSDiphTMiYgI/t5/AfsCw1QTIm5IwZyIiJhZ6N77Y5/WahVxQwrmRESysYI5CsLfxz/pnSy+sETlwerDFzFr8+msKpqIZBBNTSIiko2VzFsSC3stRGh4aKL77D0dhef2nzL/j5m/G9VL5EP9sgWysJQikh4K5kREPCCg4yUxtQoDGw9ZMHPzadyMjMGQqZvw86PNUbV4viwtp4ikjZpZRUQE7/Sqg2YVC5mauHg1Av2+XY/dZy6rZkTcgII5ERFBDl8ffPtQE9Qtnd/URsi1CPT/dj2W7T2v2hFxcQrmRETEyJ/bDz8+2gxNyhc016+GR+HR6ZsxccVhjXIVcWEK5kREJE5ATj9MH9IMXevG9rGzWICxfx3AEzO2IuxmpGpKxAUpmBMRkXhy+fvgiwca4rlO1eK2Ld5zDt0nrMaes+pHJ+JqFMyJiEgCXl5eGNG+Kr4b1AT5c/mZbSeCr6P3l2vx88aTanYVcSEK5kREJFEdahbHwpGtUa9M7MCIiKgYvDxnF56duQPXI6JUcyIuQMGciIgkqWyh3Jg1rAUeal4+btucbWfQa+Iarecq4gIUzImISIqmLuFcdJ/f3xC5/X3MtoPnr5p+dB8t3o+bkdGqRREnUTAnIiIp1qN+Kcwf0RrVb60OERVjwZf/HMGd4/7FzE2nEBkdo9oUyWIK5kREJFWqFMuL+SNbYVSHqvDz8TLbTofewAu/7VRQJ+IECuZERCRNza7PdKyGRaPaoE3VInHbOeKVQV27sf9gxvoTCI9S86tItg/mVq5cib59+6JevXpo06YNPvzwQ0RFxY6QevLJJ1G9evV4l2XLliX6WIsWLULnzp1Rt25d9O7dG9u2bcvCVyIi4nmqFs9nJhmePawFWlf5L6g7c+kGXvt9N9p//C+WakkwkUzlCye6fPkyPv30UwwaNAjNmzfHiRMn8OyzzyJ//vwYNmwYgoKC8O6776JDhw5x98mbN6/Dx9qzZw+ef/55vPLKK2jVqhVmzZplHmPJkiXm8UREJPM0qVAIM4Y2w5YToZjw9yH8cyAoLqh7dNpmdKxVHG/3rI2S+XPpbRDJTpk5Bllz585Fr169UKJECTRr1gzdu3fHpk2bzO0XL15ExYoVUahQobiLv7+/w8eaM2eOCQgffPBBVKhQwQR2DPwWLFiQxa9KRMRzNS5fEFMeaYr5I1rFy9QxO3fXZ6uwaFegU8snkh05vZnV3qVLl1C0aNG4YO7FF19Ey5YtTcD3/fffIzo6OtHMXMOGDeNta9KkCXbu3Jkl5RYRkf/UK1MA04c0NVOZFMmbw2y7fCMST/64Fc/N2oFr4ZpwWCRbNLPaO3XqFBYvXowpU6aY69OmTYOPjw9y586NHTt2mP50169fx4gRIxLcNzQ0FAUKFIi3rWDBgjh06FCqy5FYwJjS+6X1/tmN6kP1oWNDn5WudYqjdeVCeG3eHizadc5UyOwtp7H9ZCgmPtDQjIxNz7mD3xEins7XlTJyTzzxBB599FHUr1/fbLPNtFWpUgVXrlzB1KlTHQZzFoslwTZf37S9vF27dqXpfhl1/+xG9aH60LGhz8rg6hZUzpkfk7eF4UaUBYeDrqH3xDX4/O4iKJjTJ83njsaNG2fo+UrEHblEMBcWFobBgwebPm8cwZoY9p8LDg52eFvhwoVNQGgrJCTE9LNLLY6GTcuvPf6K5MknrffPblQfqg8dG/qs2OLv856tr2HEz9ux/9wVXI+yIGexCmhQqbDOHSLuHMwx28ZAjtm4V199Ncl92WTKwQ2OcGqT7du3x9vG6/379091mRiIpScYS+/9sxvVh+pDx4Y+K1ZVigdg7pOt8OOGE/D19kLzSkXg4+O4+7bOHSJuMADi6tWrGDJkCMqXL4+RI0eafm/MpvGye/dufP3119i3bx/Onj2L+fPn45tvvsHQoUPNfU+fPo127dqZqUfovvvuw4YNG/Djjz+aKU4++eQTnDt3Dt26dXPmSxQRETu5/H0wtE0lPNyqInwTCeRExE0yc0uXLjUDG3hZuHBhvNumT5+OzZs344cffkB4eLjJyL3++uvo0aNHXB85Ti5s7SvHPnUM4HjhQAleZ/CXlmZWEREREXfh1GCOqzTwkpimTZsmelvZsmWxevXqeNs6depkLiIiIiKeQvltERERETemYE5ERETEjSmYExEREXFjCuZERERE3JiCORERERE3pmBORERExI0pmBMRERFxYwrmRERERNyYgjkRERERN6ZgTkRERMSNKZgTERERcWMK5kRERETcmK+zC+BKLBaL+RsdHZ2m+1vvl9b7ZzeqD9WHjg19VrLi3OHt7Q0vL680PZdIduBlsUYwgoiICOzatUs1ISLiRho0aAAfHx9nF0PEaRTM2YiJiUFUVJR+5YmIuBFl5sTTKZgTERERcWMaACEiIiLixhTMiYiIiLgxBXMiIiIibkzBnIiIiIgbUzAnIiIi4sYUzImIiIi4MQVzIiIiIm5MwVwGuXTpEp566ik0adIEzZo1w3vvvWcmIPYUV69exciRI9G+fft420+fPo2hQ4eiYcOGaN26Nb766itkdytXrkTfvn1Rr149tGnTBh9++GHcseBp9cHlmGbMmIGePXuibt26aNGiBZ5++mlcuHDBI+vD1qRJk1C9enVTB55cFzxXsh5sL1OmTPHoOhFJLa3NmkFeffVV8wU1bdo0XL9+Hc8//zzy58+PESNGILs7cuQIhg8fjlKlSiW4jQFe8eLFMXPmTJw9exbPPvssSpQogd69eyM7unz5Mj799FMMGjQIzZs3x4kTJ8xr5rEwbNgwj6yPbdu2mdfNL+krV67gzTffxFtvvYWJEyd6XH1Ybdy4EStWrIi3zVPrIigoyJw/BgwYELctT548Hl0nIqnGtVklfYKDgy3Vq1e3bN26NW7bnDlzLC1atLBER0dn++qdNm2a5aeffrKsX7/ecscdd8Rt37Vrl6VatWqW8+fPx22bMGGCpXfv3hZP8sEHH1gGDx6s+rhlypQplrvvvttj6+PChQuWO++803L48GHz+k+dOuWxdUEPPvig5ffff0+w3ZPrRCS11MyaAfbt22fWBmSzmhWbW4ODg3HmzBlkdw899BDuv//+BNv37t2L0qVLo1ixYvHqZf/+/YiIiIAnNcEXLVpU9QGY9/6XX34xza6eeHyw2Xn06NEmY1+5cuW47Z5YF1YXL1402eyWLVuiW7du5v+bN296dJ2IpJaaWTNAaGgo8uXLBx8fn7htBQsWjDtRlS1bFp5aLwUKFIi3jfXCLzQGOLYn6ezq1KlTWLx4sekDtH79eo+tDzaLHTp0yPw/ZMgQPP744/jmm288rj4++eQTVKxY0QSztjz5s8LgjV1T+Pp5jLCPKbusVKhQwWPrRCS1FMxlgJiYmATb/Pz8zF8vLy94Kkf14uvr6zH1wi+cJ554Ao8++ijq16+PtWvXemx9fP3112aQDPtXfv755+bLuly5ch5VH8uWLcO6detMZtKeJ39WatasGfc/s5X8UcxBMvzseGqdiKSWgrkMUKRIEfNFxRMym1spJCTE/C1UqJBH1ws7wNtnIHiy5oCA7CwsLAyDBw82gyCefPJJeHp9sBM7L/yyZpNz//79zSAhT6qPqVOnmiZCjsy01alTJ/ZdTjCAKDvXRVKYueTob39/f486PkTSQ33mMkCtWrXMyXjXrl1x2ziCj00EntrESpyKgn0GOVrNtl44qpEn6uyKIzYZyDEbx1HOnl4f9qzTtLB+PKk+3n//fcybNw+///573IW+/fZbE+h5Ul0khU2t7LbStm1b1YlICimYywAM2u6++24zXxIHQ2zduhUff/yxmWvMk5sDatSogQYNGmDMmDE4fPiwmX+NX1z9+vVDdsUMLfuElS9f3kyrwEwCs7S8VKtWzePqg/0EOc8cM1Lnz5831999913cfvvtuO222zyqPvjDjseA7YXYN6xp06YeVRdWbG4fO3Ysdu7cicDAQCxfvtz0meNnyBPPHyJp5cUhrWm+t8Sxzp/1999/m1/SXbt2xcsvvxzXd84TbNiwwbxm1oEVv8Bfe+01M68Wf22zeS07z703d+5cvPTSSw5v4xcVjwdPqg/+uPnggw/MX3ZyL1y4sJlYmiM6AwICPO74sMfMG4+LMmXKeGRd8MfPK6+8gs2bN5v/GfDyRzBHyLPLiifWiUhaKJgTERERcWNqZhURERFxYwrmRERERNyYgjkRERERN6ZgTkRERMSNKZgTERERcWMK5kRERETcmII5ERERETemYE5ERETEjSmYExEREXFjCuZE0igmJgavv/66WWO0SZMm2L59u+pSRESynII5kTRatmwZFi5ciO+++w6zZs1CrVq1XLYuQ0JC0KpVK7NGKs2bNw89evRA3bp1zVqpXMDcVs+ePc26obYX633tzZkzB23btk2wPSoqytyPa/YmZf/+/WjevDkuXryYrtcoIuKpfJ1dABF3deTIEVStWhX16tWDqxs3bhxat26NmjVr4sSJE5gyZQpGjRplgrndu3fj2WefRcmSJdG9e3ezf1BQkAlSbQPU/PnzZ0rZatSogTvuuAMff/wxPvjgg0x5DhGR7EyZOZE0ePfdd/Hll19ix44dJvv00ksv4fTp02jYsCECAwPRv39/1KlTx2SzLBYLxo4di06dOqF+/fro2rUrVq1aFfdYvO/EiRPx2muvmfu3aNHCZMoOHz6MBx54wASLvI99huvHH380WTU+5oMPPohDhw45LOvly5cxf/58UyYqX7485s6dizvvvBPFixdHhw4dTGZt06ZN5vbo6GiEhoaiSpUqKFSoUNzFx8cnXccKX6d9tm/ChAnmtr59++KPP/4wzysiIqmjYE4kDZ566ik89NBDJpBat26d6TtH169fx9NPP42hQ4eaZlgGRF5eXihTpgw+/PBDLF682ARrzz//vAnyrL755htUrFgRCxYswLBhw/DJJ59g5MiReOyxx7Bo0SKTvXr55Zfj9mfz7ldffYV33nnH/F+7dm08/vjjiIiISFBWBo558+Y1gWJiGEQVLVo0rkmW/QEHDRpkmmbvu+8+E/ylF+uIdcULA9EcOXLg9ttvN7exbAEBAfj333/T/TwiIp5GzawiaZAvXz7kypXLZKuYtSJrVqlXr14m62Xr/vvvj/u/X79+mD59uukjZg2gypUrhyFDhpj/GUR9/vnnpumxXbt2cdsYtIWFhZmgh/dn0Mdgi1544QX8+uuv2LJliwkWbTF7mFRT8NatW01Tq7WJk2ViMyyfx9fXF+vXr8eYMWNMUMrX5siFCxfMIJCk5MmTx1yY+WNg+8gjj8QrF//ftWtXos8hIiKOKZgTyWD2QU14eLhpNmWmjkEPr1u3WzErZ4tBT4UKFeJdt2b+GGQdPHjQZO8+++yzeM9z/vz5BOU5d+4cSpQo4bCsp06dMlnGN954A6VLl47bbhsQsjmU+82ePTvRQKtIkSL45Zdf4m1jdq9jx44J9p08eTJu3ryJ4cOHx9teqlQp00QtIiKpo2BOJIP5+/vHu87+cv/8849pJmXQxsEFAwcOjLcPmxwTfDh9E/94somWfdA4qMFWwYIFE+x79epV00/OHgOnhx9+2GQEk8uGsdxr1qxJ9HZvb2/TlGw/mtUe+/V9/fXXppnVvp7YFHzlypUkyyEiIgkpmBPJZGymHDBggBloQBmRfWJfvDNnziQIoBzJnTs3rl27liBbx4CSgyIY0CWHgzFsM4VpweDuxRdfxODBgx1O48IyWjOQIiKScgrmRDIZp/xgVov96BhEceRqejEA4+hXZtxatmxpsm8cWMC+efYZL45YtQ0g2RTLQK5z587o06ePGfBgxf5/HDDB6UuaNm1q+gWuXLnSNLH+8MMP6SozB3mwmZh9Bq3PyYykNYBjGYsVK5au5xAR8UQK5kQyGbNRvNx9992muZLTmnDwQnp069bNjFzlXHBvvvmmGZDRqFGjeAMtbAcWLFmyJO46JzhmsDZp0iRzsXXgwAHT/42B1/jx481gBc6lx5GzyQ1wSM5vv/1msolt2rSJ29a7d++4gRccqDF69Oh0PYeIiCfystjOjyAi2Q6zYJxHjv3UOJWKK2Igx7nyVqxYETfCV0REUkbzzIlkc2w65aTD9qNNXcnMmTNNs68CORGR1FMwJ+IBOA8d+76xGdXVcITr8uXLTVO0iIiknppZRURERNyYMnMiIiIibkzBnIiIiIgbUzAnIiIi4sYUzImIiIi4MQVzIiIiIm5MwZyIiIiIG1MwJyIiIuLGFMyJiIiIuDEFcyIiIiJuTMGciIiICNzX/wGlj+BRHY22KAAAAABJRU5ErkJggg==", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -761,7 +781,7 @@ "## Part 2: Forecasting -- extrapolating the arc's remainder\n", "\n", "Now the other direction: given only the **first 30 frames** of the arc\n", - "(the rising portion through just past the apex), can `hyp.predict` with a\n", + "(the rising portion, stopping two frames short of the apex at frame 31), can `hyp.predict` with a\n", "Kalman model extrapolate the remaining flight? We fit on frames 0-29 and\n", "forecast `t=20` steps ahead, then compare against the actual, recorded\n", "final 20 frames." @@ -769,14 +789,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "e04b421b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:14.410080Z", - "iopub.status.busy": "2026-09-05T10:27:14.409985Z", - "iopub.status.idle": "2026-09-05T10:27:14.444891Z", - "shell.execute_reply": "2026-09-05T10:27:14.444474Z" + "iopub.execute_input": "2026-10-09T17:28:49.634582Z", + "iopub.status.busy": "2026-10-09T17:28:49.634522Z", + "iopub.status.idle": "2026-10-09T17:28:49.666829Z", + "shell.execute_reply": "2026-10-09T17:28:49.666516Z" } }, "outputs": [ @@ -784,10 +804,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " MAE[x_ft] = 6.469 ft\n", - " MAE[y_ft] = 0.901 ft\n", - " MAE[z_ft] = 0.379 ft\n", - "Overall MAE (all axes): 2.583 ft\n" + " MAE[x_ft] = 3.267 ft\n", + " MAE[y_ft] = 1.590 ft\n", + " MAE[z_ft] = 0.452 ft\n", + "Overall MAE (all axes): 1.770 ft\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<cell>:7: UserWarning: Irregular observation times were linearly interpolated onto a regular grid with step=0.04 before fitting this discrete-time forecaster. Pass step= to choose the grid interval; GaussianProcess uses the actual observation times without interpolation.\n", + " forecast = hyp.predict(first30, model='Kalman', t=HORIZON)\n" ] } ], @@ -813,36 +841,49 @@ "source": [ "### Forecast, visualized (side view: x vs. z)\n", "\n", - "Solid blue is the observed rising portion the model was fit on; solid\n", - "green (with circle markers) is the *actual* recorded continuation, drawn\n", - "via `truth=`; dashed orange (with x markers) is the Kalman forecast\n", - "`hyp.plot` computes itself from `predict=`/`t=` -- one call rather than a\n", - "separately-fit forecast hand-assembled into a three-dataset column stack.\n", + "All three series share the trace's colour and the legend names them. The\n", + "plain solid line is the observed rising portion the model was fit on; the\n", + "dashed, half-opacity line continuing it is the Kalman forecast `hyp.plot`\n", + "computes itself from `predict=`/`t=` (`forecast_fmt='--'` dashes it); the\n", + "solid line with round markers is the *actual* recorded continuation,\n", + "drawn via `truth=` -- one call rather than a separately-fit forecast\n", + "hand-assembled into a three-dataset column stack.\n", "`axis_scale='data'` keeps the axes in real court-position/height feet\n", - "rather than hypertools' normalized unit frame." + "rather than hypertools' normalized unit frame. The forecast here is fit\n", + "on the two plotted columns only, and it stalls near the apex instead of\n", + "following the descent -- worth seeing rather than hiding: twenty steps is a\n", + "long horizon for a model that saw only the rising half." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "cc09b7c9", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:14.446039Z", - "iopub.status.busy": "2026-09-05T10:27:14.445978Z", - "iopub.status.idle": "2026-09-05T10:27:14.544609Z", - "shell.execute_reply": "2026-09-05T10:27:14.544209Z" + "iopub.execute_input": "2026-10-09T17:28:49.667837Z", + "iopub.status.busy": "2026-10-09T17:28:49.667777Z", + "iopub.status.idle": "2026-10-09T17:28:49.758588Z", + "shell.execute_reply": "2026-10-09T17:28:49.758236Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<cell>:1: UserWarning: Irregular observation times were linearly interpolated onto a regular grid with step=0.04 before fitting this discrete-time forecaster. Pass step= to choose the grid interval; GaussianProcess uses the actual observation times without interpolation.\n", + " fig = hyp.plot(first30[['x_ft', 'z_ft']], predict='Kalman', t=HORIZON,\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -850,8 +891,8 @@ "source": [ "fig = hyp.plot(first30[['x_ft', 'z_ft']], predict='Kalman', t=HORIZON,\n", " truth=actual_tail[['x_ft', 'z_ft']],\n", - " reduce=None, ndims=2, axis_scale='data', fmt='-o',\n", - " names=['observed (fit, frames 0-29) + Kalman forecast'], legend=True,\n", + " reduce=None, ndims=2, axis_scale='data', fmt='-', forecast_fmt='--',\n", + " names=['observed (fit, frames 0-29)'], legend=True,\n", " xlabel='court position, x (ft)', ylabel='ball height, z (ft)',\n", " title=f'Kalman forecast vs. actual, {HORIZON} steps ahead',\n", " show=False)\n", @@ -877,20 +918,28 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "63ea5b38", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:14.545797Z", - "iopub.status.busy": "2026-09-05T10:27:14.545726Z", - "iopub.status.idle": "2026-09-05T10:27:14.605122Z", - "shell.execute_reply": "2026-09-05T10:27:14.604649Z" + "iopub.execute_input": "2026-10-09T17:28:49.759540Z", + "iopub.status.busy": "2026-10-09T17:28:49.759473Z", + "iopub.status.idle": "2026-10-09T17:28:49.815466Z", + "shell.execute_reply": "2026-10-09T17:28:49.815140Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<cell>:1: UserWarning: Irregular observation times were linearly interpolated onto a regular grid with step=0.04 before fitting this discrete-time forecaster. Pass step= to choose the grid interval; GaussianProcess uses the actual observation times without interpolation.\n", + " fig = hyp.plot([first30], predict='Kalman', t=HORIZON, reduce=None,\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -912,14 +961,19 @@ "source": [ "## Honest takeaways\n", "\n", - "A constant-velocity/acceleration Kalman model is a genuinely good fit for\n", - "this problem *because* the underlying physics matches its assumptions:\n", - "during free flight, gravity imposes constant acceleration, so position,\n", - "velocity, and acceleration evolve linearly and the noise is well-described\n", - "as Gaussian measurement jitter around a smooth trajectory. That's exactly\n", - "why it filled the fully-occluded frames well above (something the default\n", - "`PPCA` imputer structurally cannot do, per GH #169) and extrapolated the\n", - "ball's continuing arc reasonably from only its rising half.\n", + "A linear-Gaussian Kalman model is a genuinely good fit for this problem\n", + "*because* the underlying physics matches its assumptions: during free\n", + "flight, gravity imposes constant acceleration, so the trajectory evolves\n", + "linearly and the noise is well-described as Gaussian measurement jitter\n", + "around a smooth path -- even though neither HyperTools model is told\n", + "about gravity (the imputer smooths one scalar state per column, the\n", + "forecaster learns lag dynamics from the data). That's exactly why it\n", + "filled the fully-occluded frames well above (something the default\n", + "`PPCA` imputer structurally cannot do, per GH #169). Forecasting from only\n", + "the rising half is much harder: the three-column forecast follows the\n", + "height over the apex far better than the horizontal travel (compare the\n", + "per-axis MAEs printed in Part 2), and the side-view fit stalls near the\n", + "apex.\n", "\n", "But the same assumptions that make it work here are exactly where it\n", "**breaks down**:\n", @@ -929,8 +983,7 @@ " smooth linear-Gaussian model cannot represent -- it will \"round off\" a\n", " bounce into a soft curve rather than a sharp reversal.\n", "- **Spin-induced effects** (Magnus force from backspin, rim friction on\n", - " contact) are nonlinear and not part of the constant-acceleration state\n", - " space at all.\n", + " contact) are nonlinear and not part of any linear state space at all.\n", "- **Air drag** technically makes free-flight deceleration horizontally\n", " velocity-dependent rather than perfectly constant, which the model\n", " approximates rather than models exactly (a minor effect at typical shot\n", @@ -971,7 +1024,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/reduce.ipynb b/docs/tutorials/reduce.ipynb index 1245797b..896329c8 100644 --- a/docs/tutorials/reduce.ipynb +++ b/docs/tutorials/reduce.ipynb @@ -5,17 +5,28 @@ "execution_count": null, "id": "e7de6193", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:27:47.309945Z", - "iopub.status.busy": "2026-07-17T07:27:47.309800Z", - "iopub.status.idle": "2026-07-17T07:27:47.313502Z", - "shell.execute_reply": "2026-07-17T07:27:47.313089Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -27,7 +38,7 @@ "\n", "The `reduce` function reduces the dimensionality of an array or list of arrays. The default model is IncrementalPCA, but a variety of models are supported. Note that `ndims` defaults to `None`, which means no dimensionality reduction is performed unless you explicitly request a target number of dimensions via `ndims`.\n", "\n", - "Supported models include: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, UMAP, and six torch autoencoders (Autoencoder, DeepAutoencoder, SparseAutoencoder, ConvolutionalAutoencoder, SequenceAutoencoder, VariationalAutoencoder)." + "Supported models include: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, UMAP, NMF, LatentDirichletAllocation, and six torch autoencoders (Autoencoder, DeepAutoencoder, SparseAutoencoder, ConvolutionalAutoencoder, SequenceAutoencoder, VariationalAutoencoder)." ] }, { @@ -44,10 +55,10 @@ "id": "51f3db60", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:56.315910Z", - "iopub.status.busy": "2026-09-05T10:23:56.315707Z", - "iopub.status.idle": "2026-09-05T10:23:59.792180Z", - "shell.execute_reply": "2026-09-05T10:23:59.791617Z" + "iopub.execute_input": "2026-10-09T17:28:51.607998Z", + "iopub.status.busy": "2026-10-09T17:28:51.607574Z", + "iopub.status.idle": "2026-10-09T17:28:54.955848Z", + "shell.execute_reply": "2026-10-09T17:28:54.955273Z" } }, "outputs": [], @@ -75,10 +86,10 @@ "id": "235c29ff", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.793703Z", - "iopub.status.busy": "2026-09-05T10:23:59.793535Z", - "iopub.status.idle": "2026-09-05T10:23:59.796499Z", - "shell.execute_reply": "2026-09-05T10:23:59.796210Z" + "iopub.execute_input": "2026-10-09T17:28:54.957172Z", + "iopub.status.busy": "2026-10-09T17:28:54.957027Z", + "iopub.status.idle": "2026-10-09T17:28:54.960203Z", + "shell.execute_reply": "2026-10-09T17:28:54.959806Z" } }, "outputs": [], @@ -102,10 +113,10 @@ "id": "57e63367", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.797636Z", - "iopub.status.busy": "2026-09-05T10:23:59.797563Z", - "iopub.status.idle": "2026-09-05T10:23:59.799453Z", - "shell.execute_reply": "2026-09-05T10:23:59.799160Z" + "iopub.execute_input": "2026-10-09T17:28:54.961152Z", + "iopub.status.busy": "2026-10-09T17:28:54.961092Z", + "iopub.status.idle": "2026-10-09T17:28:54.962832Z", + "shell.execute_reply": "2026-10-09T17:28:54.962495Z" } }, "outputs": [ @@ -135,10 +146,10 @@ "id": "846259bc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.800241Z", - "iopub.status.busy": "2026-09-05T10:23:59.800191Z", - "iopub.status.idle": "2026-09-05T10:23:59.804126Z", - "shell.execute_reply": "2026-09-05T10:23:59.803826Z" + "iopub.execute_input": "2026-10-09T17:28:54.963580Z", + "iopub.status.busy": "2026-10-09T17:28:54.963528Z", + "iopub.status.idle": "2026-10-09T17:28:55.014275Z", + "shell.execute_reply": "2026-10-09T17:28:55.013788Z" } }, "outputs": [ @@ -173,10 +184,10 @@ "id": "001b6b95", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.805055Z", - "iopub.status.busy": "2026-09-05T10:23:59.804994Z", - "iopub.status.idle": "2026-09-05T10:23:59.809348Z", - "shell.execute_reply": "2026-09-05T10:23:59.808994Z" + "iopub.execute_input": "2026-10-09T17:28:55.015229Z", + "iopub.status.busy": "2026-10-09T17:28:55.015172Z", + "iopub.status.idle": "2026-10-09T17:28:55.019470Z", + "shell.execute_reply": "2026-10-09T17:28:55.019150Z" } }, "outputs": [ @@ -209,10 +220,10 @@ "id": "e4535c21", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.810320Z", - "iopub.status.busy": "2026-09-05T10:23:59.810263Z", - "iopub.status.idle": "2026-09-05T10:23:59.948663Z", - "shell.execute_reply": "2026-09-05T10:23:59.948207Z" + "iopub.execute_input": "2026-10-09T17:28:55.020393Z", + "iopub.status.busy": "2026-10-09T17:28:55.020335Z", + "iopub.status.idle": "2026-10-09T17:28:55.156384Z", + "shell.execute_reply": "2026-10-09T17:28:55.155981Z" } }, "outputs": [ @@ -247,10 +258,10 @@ "id": "41349927", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.949818Z", - "iopub.status.busy": "2026-09-05T10:23:59.949751Z", - "iopub.status.idle": "2026-09-05T10:23:59.954421Z", - "shell.execute_reply": "2026-09-05T10:23:59.954068Z" + "iopub.execute_input": "2026-10-09T17:28:55.157523Z", + "iopub.status.busy": "2026-10-09T17:28:55.157462Z", + "iopub.status.idle": "2026-10-09T17:28:55.161952Z", + "shell.execute_reply": "2026-10-09T17:28:55.161547Z" } }, "outputs": [ @@ -287,10 +298,10 @@ "id": "482ae05d", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.955335Z", - "iopub.status.busy": "2026-09-05T10:23:59.955279Z", - "iopub.status.idle": "2026-09-05T10:23:59.991993Z", - "shell.execute_reply": "2026-09-05T10:23:59.991597Z" + "iopub.execute_input": "2026-10-09T17:28:55.162836Z", + "iopub.status.busy": "2026-10-09T17:28:55.162781Z", + "iopub.status.idle": "2026-10-09T17:28:55.197231Z", + "shell.execute_reply": "2026-10-09T17:28:55.196837Z" } }, "outputs": [ @@ -326,18 +337,18 @@ "id": "fcfd16ca", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:23:59.993256Z", - "iopub.status.busy": "2026-09-05T10:23:59.993189Z", - "iopub.status.idle": "2026-09-05T10:24:06.822393Z", - "shell.execute_reply": "2026-09-05T10:24:06.821996Z" + "iopub.execute_input": "2026-10-09T17:28:55.198100Z", + "iopub.status.busy": "2026-10-09T17:28:55.198043Z", + "iopub.status.idle": "2026-10-09T17:29:01.723307Z", + "shell.execute_reply": "2026-10-09T17:29:01.722821Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 640x480 with 2 Axes>" + "<Figure size 860x480 with 2 Axes>" ] }, "metadata": {}, @@ -368,10 +379,10 @@ "id": "95049b26", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:06.823831Z", - "iopub.status.busy": "2026-09-05T10:24:06.823728Z", - "iopub.status.idle": "2026-09-05T10:24:09.091906Z", - "shell.execute_reply": "2026-09-05T10:24:09.091430Z" + "iopub.execute_input": "2026-10-09T17:29:01.724480Z", + "iopub.status.busy": "2026-10-09T17:29:01.724410Z", + "iopub.status.idle": "2026-10-09T17:29:03.833711Z", + "shell.execute_reply": "2026-10-09T17:29:03.833309Z" } }, "outputs": [ @@ -408,7 +419,7 @@ "source": [ "## How many dimensions do you actually need?\n", "\n", - "`hyp.describe` is the companion to `hyp.reduce`: for each candidate number of dimensions it reduces the data and correlates the pairwise distances of the reduced data with those of the full data, so you can see how much structure each extra dimension buys. Called with the defaults it draws the curve; with `show=False` it returns only the dict of scores (`'average'` over the datasets, `'individual'` per dataset). For these data the curve starts around 0.8 at two dimensions, passes 0.9 by five, and keeps creeping toward 1 -- so three dimensions carry most of the pairwise-distance structure, and each further dimension buys a little less." + "`hyp.describe` is the companion to `hyp.reduce`: for each candidate number of dimensions it reduces the data and correlates the pairwise distances of the reduced data with those of the full data, so you can see how much structure each extra dimension buys. It returns a dict of scores -- `'individual'` (one curve per dataset), `'average'` (the mean of those curves, drawn as the dashed line), and `'pooled'` (the curve for all datasets stacked into one point cloud, which is not drawn) -- plus the figure under `'fig'`. Called with the defaults it draws and shows the figure; with `show=False` it still returns the figure, without displaying it. For these data the curve starts around 0.8 at two dimensions, passes 0.9 by five, and keeps creeping toward 1 -- so three dimensions carry most of the pairwise-distance structure, and each further dimension buys a little less." ] }, { @@ -417,16 +428,16 @@ "id": "a4c76a44", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:09.093085Z", - "iopub.status.busy": "2026-09-05T10:24:09.092987Z", - "iopub.status.idle": "2026-09-05T10:24:09.251834Z", - "shell.execute_reply": "2026-09-05T10:24:09.251373Z" + "iopub.execute_input": "2026-10-09T17:29:03.834762Z", + "iopub.status.busy": "2026-10-09T17:29:03.834690Z", + "iopub.status.idle": "2026-10-09T17:29:04.104626Z", + "shell.execute_reply": "2026-10-09T17:29:04.104210Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -438,7 +449,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "average correlation with the full data at 2..6 dimensions: [0.792, 0.844, 0.88, 0.915, 0.93]\n" + "average correlation with the full data at 2..6 dimensions: [0.781, 0.852, 0.888, 0.914, 0.928]\n" ] } ], @@ -455,7 +466,7 @@ "source": [ "## Reusing a fitted reducer with `return_model`\n", "\n", - "`return_model=True` also returns the fitted reducer. Passing it back as `reduce=` applies the *same* projection to new data (via `.transform`) instead of refitting -- so two datasets reduced separately land in one shared coordinate system, exactly as if the model had seen both. Here we fit PCA on the first array only, then project the second through it, and compare against the joint fit from earlier." + "`return_model=True` also returns the fitted reducer. Passing it back as `reduce=` applies the *same* projection to new data (via `.transform`) instead of refitting -- so two datasets reduced separately land in one shared coordinate system, exactly as if the model had seen both. Here we fit the default reducer (IncrementalPCA) on the first array only, then project the second through it." ] }, { @@ -464,10 +475,10 @@ "id": "3b09890a", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:24:09.253075Z", - "iopub.status.busy": "2026-09-05T10:24:09.252975Z", - "iopub.status.idle": "2026-09-05T10:24:09.299350Z", - "shell.execute_reply": "2026-09-05T10:24:09.298975Z" + "iopub.execute_input": "2026-10-09T17:29:04.105749Z", + "iopub.status.busy": "2026-10-09T17:29:04.105653Z", + "iopub.status.idle": "2026-10-09T17:29:04.150743Z", + "shell.execute_reply": "2026-10-09T17:29:04.150285Z" } }, "outputs": [ @@ -527,7 +538,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/stock_closes_cached.csv b/docs/tutorials/stock_closes_cached.csv index ca913f0a..bdcdb80f 100644 --- a/docs/tutorials/stock_closes_cached.csv +++ b/docs/tutorials/stock_closes_cached.csv @@ -1,28 +1,4 @@ date,AAPL,MSFT,NVDA,JPM -2024-09-05,222.3800048828125,408.3900146484375,107.20999908447266,217.6300048828125 -2024-09-06,220.82000732421875,401.70001220703125,102.83000183105469,212.4600067138672 -2024-09-09,220.91000366210938,405.7200012207031,106.47000122070312,216.80999755859375 -2024-09-10,220.11000061035156,414.20001220703125,108.0999984741211,205.55999755859375 -2024-09-11,222.66000366210938,423.0400085449219,116.91000366210938,207.22999572753906 -2024-09-12,222.77000427246094,427.0,119.13999938964844,206.60000610351562 -2024-09-13,222.5,430.5899963378906,119.0999984741211,204.32000732421875 -2024-09-16,216.32000732421875,431.3399963378906,116.77999877929688,207.86000061035156 -2024-09-17,216.7899932861328,435.1499938964844,115.58999633789062,209.25 -2024-09-18,220.69000244140625,430.80999755859375,113.37000274658203,207.52999877929688 -2024-09-19,228.8699951171875,438.69000244140625,117.87000274658203,210.47999572753906 -2024-09-20,228.1999969482422,435.2699890136719,116.0,211.08999633789062 -2024-09-23,226.47000122070312,433.510009765625,116.26000213623047,211.44000244140625 -2024-09-24,227.3699951171875,429.1700134277344,120.87000274658203,211.58999633789062 -2024-09-25,226.3699951171875,432.1099853515625,123.51000213623047,210.19000244140625 -2024-09-26,227.52000427246094,431.30999755859375,124.04000091552734,209.77999877929688 -2024-09-27,227.7899932861328,428.0199890136719,121.4000015258789,210.5 -2024-09-30,233.0,430.29998779296875,121.44000244140625,210.86000061035156 -2024-10-01,226.2100067138672,420.69000244140625,117.0,207.0399932861328 -2024-10-02,226.77999877929688,417.1300048828125,118.8499984741211,207.2899932861328 -2024-10-03,225.6699981689453,416.5400085449219,122.8499984741211,205.22999572753906 -2024-10-04,226.8000030517578,416.05999755859375,124.91999816894531,211.22000122070312 -2024-10-07,221.69000244140625,409.5400085449219,127.72000122070312,210.92999267578125 -2024-10-08,225.77000427246094,414.7099914550781,132.88999938964844,210.75 2024-10-09,229.5399932861328,417.4599914550781,132.64999389648438,213.4199981689453 2024-10-10,229.0399932861328,415.8399963378906,134.80999755859375,212.83999633789062 2024-10-11,227.5500030517578,416.32000732421875,134.8000030517578,222.2899932861328 @@ -501,3 +477,26 @@ date,AAPL,MSFT,NVDA,JPM 2026-09-02,324.9599914550781,496.82000732421875,224.41000366210938,356.2200012207031 2026-09-03,328.2099914550781,510.1199951171875,228.4499969482422,362.05999755859375 2026-09-04,319.9700012207031,499.70001220703125,230.36000061035156,358.6400146484375 +2026-09-08,316.2200012207031,493.95001220703125,225.72999572753906,353.510009765625 +2026-09-09,315.3399963378906,491.6499938964844,223.6699981689453,354.7099914550781 +2026-09-10,326.57000732421875,492.44000244140625,218.36000061035156,353.55999755859375 +2026-09-11,332.2699890136719,495.6300048828125,218.2899932861328,356.2300109863281 +2026-09-14,333.0799865722656,505.4100036621094,210.9600067138672,350.1300048828125 +2026-09-15,331.3399963378906,497.1199951171875,212.1699981689453,352.489990234375 +2026-09-16,332.4100036621094,490.29998779296875,213.89999389648438,348.9200134277344 +2026-09-17,337.0,497.75,219.33999633789062,349.30999755859375 +2026-09-18,336.1300048828125,493.7799987792969,222.27000427246094,349.6700134277344 +2026-09-21,338.9800109863281,501.6099853515625,227.3800048828125,352.0400085449219 +2026-09-22,339.75,498.0,228.8699951171875,340.0 +2026-09-23,337.0199890136719,500.5899963378906,225.50999450683594,337.5299987792969 +2026-09-24,335.9200134277344,497.92999267578125,224.5800018310547,338.55999755859375 +2026-09-25,341.07000732421875,516.1699829101562,225.07000732421875,343.05999755859375 +2026-09-28,338.3999938964844,509.2200012207031,228.86000061035156,336.5899963378906 +2026-09-29,329.3999938964844,508.9599914550781,227.2100067138672,334.9800109863281 +2026-09-30,333.0199890136719,512.9000244140625,228.3800048828125,330.8299865722656 +2026-10-01,330.32000732421875,512.7999877929688,230.86000061035156,333.17999267578125 +2026-10-02,333.69000244140625,517.530029296875,233.9499969482422,332.3800048828125 +2026-10-05,332.8900146484375,525.1799926757812,238.89999389648438,332.3800048828125 +2026-10-06,333.6300048828125,529.2999877929688,239.24000549316406,331.2799987792969 +2026-10-07,336.6700134277344,529.760009765625,237.47000122070312,329.5799865722656 +2026-10-08,340.4200134277344,522.6099853515625,230.47999572753906,331.4200134277344 diff --git a/docs/tutorials/stock_forecasting.ipynb b/docs/tutorials/stock_forecasting.ipynb index 97712ffb..63261cba 100644 --- a/docs/tutorials/stock_forecasting.ipynb +++ b/docs/tutorials/stock_forecasting.ipynb @@ -39,25 +39,28 @@ "execution_count": null, "id": "7df04baa", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:36:30.354512Z", - "iopub.status.busy": "2026-07-17T07:36:30.354248Z", - "iopub.status.idle": "2026-07-17T07:36:30.362194Z", - "shell.execute_reply": "2026-07-17T07:36:30.361405Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " # On Colab (or any fresh environment), install hypertools. Every\n", - " # forecasting model used below either ships with the core install\n", - " # (pykalman, statsmodels) or installs itself on first use (skaters for\n", - " # 'Laplace', chronos-forecasting for 'Chronos'). hyp.load('yahoo:...')\n", - " # uses `requests`, which ships with hypertools's core install -- no\n", - " # separate yfinance install is needed.\n", - " %pip install -q \"hypertools[interactive]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -67,7 +70,7 @@ "source": [ "## Downloading real price data\n", "\n", - "We'll grab about two years of daily adjusted closes for four large, liquid,\n", + "We'll grab about two years of daily closing prices (Yahoo's `close`: split-adjusted, with dividends not reinvested) for four large, liquid,\n", "very different stocks: Apple (`AAPL`), Microsoft (`MSFT`), Nvidia (`NVDA`),\n", "and JPMorgan (`JPM`), via `hyp.load('yahoo:<TICKER>', start=..., end=...)`.\n", "\n", @@ -95,10 +98,10 @@ "id": "ce8ebbf0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:26.665518Z", - "iopub.status.busy": "2026-09-05T10:26:26.665377Z", - "iopub.status.idle": "2026-09-05T10:26:31.258212Z", - "shell.execute_reply": "2026-09-05T10:26:31.257760Z" + "iopub.execute_input": "2026-10-09T17:28:00.294737Z", + "iopub.status.busy": "2026-10-09T17:28:00.294668Z", + "iopub.status.idle": "2026-10-09T17:28:07.271217Z", + "shell.execute_reply": "2026-10-09T17:28:07.270509Z" } }, "outputs": [ @@ -106,7 +109,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "data source: hyp.load('yahoo:...') (live, 2024-09-05 to 2026-09-04)\n" + "data source: hyp.load('yahoo:...') (live, 2024-10-09 to 2026-10-08)\n" ] }, { @@ -145,39 +148,39 @@ " </thead>\n", " <tbody>\n", " <tr>\n", - " <th>2026-08-31</th>\n", - " <td>316.850006</td>\n", - " <td>507.290009</td>\n", - " <td>220.779999</td>\n", - " <td>356.019989</td>\n", + " <th>2026-10-02</th>\n", + " <td>333.690002</td>\n", + " <td>517.530029</td>\n", + " <td>233.949997</td>\n", + " <td>332.380005</td>\n", " </tr>\n", " <tr>\n", - " <th>2026-09-01</th>\n", - " <td>325.130005</td>\n", - " <td>501.019989</td>\n", - " <td>217.440002</td>\n", - " <td>354.950012</td>\n", + " <th>2026-10-05</th>\n", + " <td>332.890015</td>\n", + " <td>525.179993</td>\n", + " <td>238.899994</td>\n", + " <td>332.380005</td>\n", " </tr>\n", " <tr>\n", - " <th>2026-09-02</th>\n", - " <td>324.959991</td>\n", - " <td>496.820007</td>\n", - " <td>224.410004</td>\n", - " <td>356.220001</td>\n", + " <th>2026-10-06</th>\n", + " <td>333.630005</td>\n", + " <td>529.299988</td>\n", + " <td>239.240005</td>\n", + " <td>331.279999</td>\n", " </tr>\n", " <tr>\n", - " <th>2026-09-03</th>\n", - " <td>328.209991</td>\n", - " <td>510.119995</td>\n", - " <td>228.449997</td>\n", - " <td>362.059998</td>\n", + " <th>2026-10-07</th>\n", + " <td>336.670013</td>\n", + " <td>529.760010</td>\n", + " <td>237.470001</td>\n", + " <td>329.579987</td>\n", " </tr>\n", " <tr>\n", - " <th>2026-09-04</th>\n", - " <td>319.970001</td>\n", - " <td>499.700012</td>\n", - " <td>230.360001</td>\n", - " <td>358.640015</td>\n", + " <th>2026-10-08</th>\n", + " <td>340.420013</td>\n", + " <td>522.609985</td>\n", + " <td>230.479996</td>\n", + " <td>331.420013</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -186,11 +189,11 @@ "text/plain": [ " AAPL MSFT NVDA JPM\n", "date \n", - "2026-08-31 316.850006 507.290009 220.779999 356.019989\n", - "2026-09-01 325.130005 501.019989 217.440002 354.950012\n", - "2026-09-02 324.959991 496.820007 224.410004 356.220001\n", - "2026-09-03 328.209991 510.119995 228.449997 362.059998\n", - "2026-09-04 319.970001 499.700012 230.360001 358.640015" + "2026-10-02 333.690002 517.530029 233.949997 332.380005\n", + "2026-10-05 332.890015 525.179993 238.899994 332.380005\n", + "2026-10-06 333.630005 529.299988 239.240005 331.279999\n", + "2026-10-07 336.670013 529.760010 237.470001 329.579987\n", + "2026-10-08 340.420013 522.609985 230.479996 331.420013" ] }, "execution_count": 1, @@ -255,17 +258,17 @@ "id": "5c71eee1", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:31.259361Z", - "iopub.status.busy": "2026-09-05T10:26:31.259273Z", - "iopub.status.idle": "2026-09-05T10:26:31.262780Z", - "shell.execute_reply": "2026-09-05T10:26:31.262437Z" + "iopub.execute_input": "2026-10-09T17:28:07.272423Z", + "iopub.status.busy": "2026-10-09T17:28:07.272330Z", + "iopub.status.idle": "2026-10-09T17:28:07.276526Z", + "shell.execute_reply": "2026-10-09T17:28:07.276144Z" } }, "outputs": [ { "data": { "text/plain": [ - "{'AAPL': (502, 1), 'MSFT': (502, 1), 'NVDA': (502, 1), 'JPM': (502, 1)}" + "{'AAPL': (501, 1), 'MSFT': (501, 1), 'NVDA': (501, 1), 'JPM': (501, 1)}" ] }, "execution_count": 2, @@ -275,9 +278,9 @@ ], "source": [ "# One DataFrame per ticker, in log-price space. We index by trading-day\n", - "# position (0, 1, 2, ...) rather than calendar date -- weekends/holidays\n", - "# would otherwise leave gaps that complicate `t`-step-ahead forecasting;\n", - "# trading-day position is the natural \"clock\" for daily equity data.\n", + "# position (0, 1, 2, ...): one step is one observed trading day. This\n", + "# deliberately gives weekends and holidays no extra model time. Keeping\n", + "# the original dates instead models elapsed calendar time; see below.\n", "HOLD = 30 # trading days held out for backtesting\n", "\n", "datasets = {}\n", @@ -322,9 +325,10 @@ "dollars, unlike the plots later in this tutorial (which convert forecasts\n", "back with `exp` for display). `scores.attrs` carries the verdict directly:\n", "`scores.attrs['best']` is the model with the lowest average MAPE among the\n", - "real forecasters (never the naive baseline), and\n", + "real forecasters that forecast every held-out value (never the naive\n", + "baseline, and never a model listed in `scores.attrs['incomplete']`), and\n", "`scores.attrs['beats_baseline']` says whether it actually beat that\n", - "baseline. We report whatever comes out below, with no cherry-picking." + "baseline. We report whatever comes out below, with no cherry-picking. (Depending on the data, `GaussianProcess` can print scikit-learn's `ConvergenceWarning` -- \"lbfgs failed to converge\" -- while it optimizes its kernel; the fitted model is still used and scored. `Chronos` samples its forecasts, so the cell seeds torch first to make its rows reproducible.)" ] }, { @@ -333,13 +337,25 @@ "id": "3b472d8e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:31.263883Z", - "iopub.status.busy": "2026-09-05T10:26:31.263821Z", - "iopub.status.idle": "2026-09-05T10:27:04.801243Z", - "shell.execute_reply": "2026-09-05T10:27:04.800815Z" + "iopub.execute_input": "2026-10-09T17:28:07.277416Z", + "iopub.status.busy": "2026-10-09T17:28:07.277350Z", + "iopub.status.idle": "2026-10-09T17:28:40.222342Z", + "shell.execute_reply": "2026-10-09T17:28:40.221927Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "~/hypertools/.venv/lib/python3.12/site-packages/sklearn/gaussian_process/_gpr.py:670: ConvergenceWarning: lbfgs failed to converge after 17 iteration(s) (status=2):\n", + "ABNORMAL: \n", + "\n", + "You might also want to scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + " _check_optimize_result(\"lbfgs\", opt_res)\n" + ] + }, { "data": { "text/html": [ @@ -387,9 +403,9 @@ " <th rowspan=\"4\" valign=\"top\">Kalman</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>1.10</td>\n", - " <td>0.06</td>\n", - " <td>0.07</td>\n", + " <td>0.79</td>\n", + " <td>0.05</td>\n", + " <td>0.05</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -397,9 +413,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.69</td>\n", - " <td>0.23</td>\n", - " <td>0.24</td>\n", + " <td>0.33</td>\n", + " <td>0.02</td>\n", + " <td>0.03</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -407,8 +423,8 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>0.74</td>\n", - " <td>0.04</td>\n", + " <td>0.85</td>\n", + " <td>0.05</td>\n", " <td>0.05</td>\n", " <td>30</td>\n", " <td>0</td>\n", @@ -417,9 +433,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.22</td>\n", - " <td>0.01</td>\n", - " <td>0.01</td>\n", + " <td>0.82</td>\n", + " <td>0.05</td>\n", + " <td>0.06</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -428,8 +444,8 @@ " <th rowspan=\"4\" valign=\"top\">ARIMA</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>1.00</td>\n", - " <td>0.06</td>\n", + " <td>0.88</td>\n", + " <td>0.05</td>\n", " <td>0.06</td>\n", " <td>30</td>\n", " <td>0</td>\n", @@ -438,9 +454,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.73</td>\n", - " <td>0.23</td>\n", - " <td>0.24</td>\n", + " <td>0.32</td>\n", + " <td>0.02</td>\n", + " <td>0.03</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -448,9 +464,9 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>1.07</td>\n", - " <td>0.06</td>\n", - " <td>0.06</td>\n", + " <td>1.28</td>\n", + " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -458,9 +474,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.24</td>\n", - " <td>0.01</td>\n", - " <td>0.02</td>\n", + " <td>0.56</td>\n", + " <td>0.03</td>\n", + " <td>0.04</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -469,9 +485,9 @@ " <th rowspan=\"4\" valign=\"top\">Laplace</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>1.39</td>\n", - " <td>0.08</td>\n", - " <td>0.09</td>\n", + " <td>0.86</td>\n", + " <td>0.05</td>\n", + " <td>0.05</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -479,9 +495,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.84</td>\n", " <td>0.24</td>\n", - " <td>0.25</td>\n", + " <td>0.02</td>\n", + " <td>0.02</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -489,9 +505,9 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>1.00</td>\n", - " <td>0.05</td>\n", - " <td>0.06</td>\n", + " <td>1.33</td>\n", + " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -499,9 +515,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.20</td>\n", - " <td>0.01</td>\n", - " <td>0.01</td>\n", + " <td>0.65</td>\n", + " <td>0.04</td>\n", + " <td>0.05</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -510,9 +526,9 @@ " <th rowspan=\"4\" valign=\"top\">GaussianProcess</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>0.99</td>\n", - " <td>0.06</td>\n", - " <td>0.07</td>\n", + " <td>1.53</td>\n", + " <td>0.09</td>\n", + " <td>0.10</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -520,9 +536,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>2.16</td>\n", - " <td>0.13</td>\n", - " <td>0.14</td>\n", + " <td>1.70</td>\n", + " <td>0.11</td>\n", + " <td>0.12</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -530,9 +546,9 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>0.62</td>\n", + " <td>0.49</td>\n", + " <td>0.03</td>\n", " <td>0.03</td>\n", - " <td>0.04</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -540,9 +556,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.33</td>\n", - " <td>0.02</td>\n", - " <td>0.02</td>\n", + " <td>0.83</td>\n", + " <td>0.05</td>\n", + " <td>0.05</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -551,9 +567,9 @@ " <th rowspan=\"4\" valign=\"top\">AutoRegressor</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>0.71</td>\n", - " <td>0.04</td>\n", - " <td>0.05</td>\n", + " <td>1.21</td>\n", + " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -561,9 +577,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.16</td>\n", - " <td>0.20</td>\n", - " <td>0.21</td>\n", + " <td>0.82</td>\n", + " <td>0.05</td>\n", + " <td>0.06</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -571,7 +587,7 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>1.13</td>\n", + " <td>1.10</td>\n", " <td>0.06</td>\n", " <td>0.07</td>\n", " <td>30</td>\n", @@ -581,9 +597,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.74</td>\n", - " <td>0.04</td>\n", - " <td>0.05</td>\n", + " <td>0.35</td>\n", + " <td>0.02</td>\n", + " <td>0.03</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -592,9 +608,9 @@ " <th rowspan=\"4\" valign=\"top\">Chronos</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>0.86</td>\n", - " <td>0.05</td>\n", - " <td>0.06</td>\n", + " <td>1.21</td>\n", + " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -602,9 +618,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.06</td>\n", - " <td>0.19</td>\n", - " <td>0.20</td>\n", + " <td>0.36</td>\n", + " <td>0.02</td>\n", + " <td>0.03</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -612,9 +628,9 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>1.25</td>\n", - " <td>0.07</td>\n", + " <td>1.28</td>\n", " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -622,9 +638,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.43</td>\n", - " <td>0.03</td>\n", - " <td>0.03</td>\n", + " <td>0.84</td>\n", + " <td>0.05</td>\n", + " <td>0.06</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -633,8 +649,8 @@ " <th rowspan=\"4\" valign=\"top\">naive</th>\n", " <th>AAPL</th>\n", " <th>log_close</th>\n", - " <td>1.00</td>\n", - " <td>0.06</td>\n", + " <td>0.90</td>\n", + " <td>0.05</td>\n", " <td>0.06</td>\n", " <td>30</td>\n", " <td>0</td>\n", @@ -643,9 +659,9 @@ " <tr>\n", " <th>MSFT</th>\n", " <th>log_close</th>\n", - " <td>3.73</td>\n", - " <td>0.23</td>\n", - " <td>0.24</td>\n", + " <td>0.34</td>\n", + " <td>0.02</td>\n", + " <td>0.03</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -653,9 +669,9 @@ " <tr>\n", " <th>NVDA</th>\n", " <th>log_close</th>\n", - " <td>1.07</td>\n", - " <td>0.06</td>\n", - " <td>0.06</td>\n", + " <td>1.31</td>\n", + " <td>0.07</td>\n", + " <td>0.08</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -663,9 +679,9 @@ " <tr>\n", " <th>JPM</th>\n", " <th>log_close</th>\n", - " <td>0.23</td>\n", - " <td>0.01</td>\n", - " <td>0.02</td>\n", + " <td>0.56</td>\n", + " <td>0.03</td>\n", + " <td>0.04</td>\n", " <td>30</td>\n", " <td>0</td>\n", " <td>30</td>\n", @@ -677,34 +693,34 @@ "text/plain": [ " MAPE MAE RMSE n unscored horizon\n", "model dataset column \n", - "Kalman AAPL log_close 1.10 0.06 0.07 30 0 30\n", - " MSFT log_close 3.69 0.23 0.24 30 0 30\n", - " NVDA log_close 0.74 0.04 0.05 30 0 30\n", - " JPM log_close 0.22 0.01 0.01 30 0 30\n", - "ARIMA AAPL log_close 1.00 0.06 0.06 30 0 30\n", - " MSFT log_close 3.73 0.23 0.24 30 0 30\n", - " NVDA log_close 1.07 0.06 0.06 30 0 30\n", - " JPM log_close 0.24 0.01 0.02 30 0 30\n", - "Laplace AAPL log_close 1.39 0.08 0.09 30 0 30\n", - " MSFT log_close 3.84 0.24 0.25 30 0 30\n", - " NVDA log_close 1.00 0.05 0.06 30 0 30\n", - " JPM log_close 0.20 0.01 0.01 30 0 30\n", - "GaussianProcess AAPL log_close 0.99 0.06 0.07 30 0 30\n", - " MSFT log_close 2.16 0.13 0.14 30 0 30\n", - " NVDA log_close 0.62 0.03 0.04 30 0 30\n", - " JPM log_close 0.33 0.02 0.02 30 0 30\n", - "AutoRegressor AAPL log_close 0.71 0.04 0.05 30 0 30\n", - " MSFT log_close 3.16 0.20 0.21 30 0 30\n", - " NVDA log_close 1.13 0.06 0.07 30 0 30\n", - " JPM log_close 0.74 0.04 0.05 30 0 30\n", - "Chronos AAPL log_close 0.86 0.05 0.06 30 0 30\n", - " MSFT log_close 3.06 0.19 0.20 30 0 30\n", - " NVDA log_close 1.25 0.07 0.07 30 0 30\n", - " JPM log_close 0.43 0.03 0.03 30 0 30\n", - "naive AAPL log_close 1.00 0.06 0.06 30 0 30\n", - " MSFT log_close 3.73 0.23 0.24 30 0 30\n", - " NVDA log_close 1.07 0.06 0.06 30 0 30\n", - " JPM log_close 0.23 0.01 0.02 30 0 30" + "Kalman AAPL log_close 0.79 0.05 0.05 30 0 30\n", + " MSFT log_close 0.33 0.02 0.03 30 0 30\n", + " NVDA log_close 0.85 0.05 0.05 30 0 30\n", + " JPM log_close 0.82 0.05 0.06 30 0 30\n", + "ARIMA AAPL log_close 0.88 0.05 0.06 30 0 30\n", + " MSFT log_close 0.32 0.02 0.03 30 0 30\n", + " NVDA log_close 1.28 0.07 0.08 30 0 30\n", + " JPM log_close 0.56 0.03 0.04 30 0 30\n", + "Laplace AAPL log_close 0.86 0.05 0.05 30 0 30\n", + " MSFT log_close 0.24 0.02 0.02 30 0 30\n", + " NVDA log_close 1.33 0.07 0.08 30 0 30\n", + " JPM log_close 0.65 0.04 0.05 30 0 30\n", + "GaussianProcess AAPL log_close 1.53 0.09 0.10 30 0 30\n", + " MSFT log_close 1.70 0.11 0.12 30 0 30\n", + " NVDA log_close 0.49 0.03 0.03 30 0 30\n", + " JPM log_close 0.83 0.05 0.05 30 0 30\n", + "AutoRegressor AAPL log_close 1.21 0.07 0.08 30 0 30\n", + " MSFT log_close 0.82 0.05 0.06 30 0 30\n", + " NVDA log_close 1.10 0.06 0.07 30 0 30\n", + " JPM log_close 0.35 0.02 0.03 30 0 30\n", + "Chronos AAPL log_close 1.21 0.07 0.08 30 0 30\n", + " MSFT log_close 0.36 0.02 0.03 30 0 30\n", + " NVDA log_close 1.28 0.07 0.08 30 0 30\n", + " JPM log_close 0.84 0.05 0.06 30 0 30\n", + "naive AAPL log_close 0.90 0.05 0.06 30 0 30\n", + " MSFT log_close 0.34 0.02 0.03 30 0 30\n", + " NVDA log_close 1.31 0.07 0.08 30 0 30\n", + " JPM log_close 0.56 0.03 0.04 30 0 30" ] }, "execution_count": 3, @@ -713,6 +729,16 @@ } ], "source": [ + "# Chronos SAMPLES its forecasts (the only hypertools forecaster that does), so\n", + "# seed torch's generator to make its rows reproducible. torch arrives with the\n", + "# chronos-forecasting package hyp.predict installs on first use; if it is not\n", + "# installed yet, this run's Chronos rows are simply unseeded.\n", + "try:\n", + " import torch\n", + " torch.manual_seed(0)\n", + "except ImportError:\n", + " pass\n", + "\n", "# model name -> the spec handed to hyp.predict(model=...). A plain string\n", "# names a model with its defaults; a dict carries keyword arguments.\n", "model_specs = {\n", @@ -753,10 +779,10 @@ "id": "ebcc94d0", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:04.802451Z", - "iopub.status.busy": "2026-09-05T10:27:04.802369Z", - "iopub.status.idle": "2026-09-05T10:27:04.809669Z", - "shell.execute_reply": "2026-09-05T10:27:04.809310Z" + "iopub.execute_input": "2026-10-09T17:28:40.223519Z", + "iopub.status.busy": "2026-10-09T17:28:40.223437Z", + "iopub.status.idle": "2026-10-09T17:28:40.230004Z", + "shell.execute_reply": "2026-10-09T17:28:40.229673Z" } }, "outputs": [ @@ -805,60 +831,60 @@ " </thead>\n", " <tbody>\n", " <tr>\n", - " <th>GaussianProcess</th>\n", - " <td>0.99</td>\n", + " <th>Kalman</th>\n", + " <td>0.79</td>\n", + " <td>0.82</td>\n", " <td>0.33</td>\n", - " <td>2.16</td>\n", - " <td>0.62</td>\n", - " <td>1.03</td>\n", + " <td>0.85</td>\n", + " <td>0.70</td>\n", " </tr>\n", " <tr>\n", - " <th>Chronos</th>\n", - " <td>0.86</td>\n", - " <td>0.43</td>\n", - " <td>3.06</td>\n", - " <td>1.25</td>\n", - " <td>1.40</td>\n", + " <th>ARIMA</th>\n", + " <td>0.88</td>\n", + " <td>0.56</td>\n", + " <td>0.32</td>\n", + " <td>1.28</td>\n", + " <td>0.76</td>\n", " </tr>\n", " <tr>\n", - " <th>AutoRegressor</th>\n", - " <td>0.71</td>\n", - " <td>0.74</td>\n", - " <td>3.16</td>\n", - " <td>1.13</td>\n", - " <td>1.44</td>\n", + " <th>Laplace</th>\n", + " <td>0.86</td>\n", + " <td>0.65</td>\n", + " <td>0.24</td>\n", + " <td>1.33</td>\n", + " <td>0.77</td>\n", " </tr>\n", " <tr>\n", - " <th>Kalman</th>\n", - " <td>1.10</td>\n", - " <td>0.22</td>\n", - " <td>3.69</td>\n", - " <td>0.74</td>\n", - " <td>1.44</td>\n", + " <th>naive</th>\n", + " <td>0.90</td>\n", + " <td>0.56</td>\n", + " <td>0.34</td>\n", + " <td>1.31</td>\n", + " <td>0.78</td>\n", " </tr>\n", " <tr>\n", - " <th>naive</th>\n", - " <td>1.00</td>\n", - " <td>0.23</td>\n", - " <td>3.73</td>\n", - " <td>1.07</td>\n", - " <td>1.51</td>\n", + " <th>AutoRegressor</th>\n", + " <td>1.21</td>\n", + " <td>0.35</td>\n", + " <td>0.82</td>\n", + " <td>1.10</td>\n", + " <td>0.87</td>\n", " </tr>\n", " <tr>\n", - " <th>ARIMA</th>\n", - " <td>1.00</td>\n", - " <td>0.24</td>\n", - " <td>3.73</td>\n", - " <td>1.07</td>\n", - " <td>1.51</td>\n", + " <th>Chronos</th>\n", + " <td>1.21</td>\n", + " <td>0.84</td>\n", + " <td>0.36</td>\n", + " <td>1.28</td>\n", + " <td>0.92</td>\n", " </tr>\n", " <tr>\n", - " <th>Laplace</th>\n", - " <td>1.39</td>\n", - " <td>0.20</td>\n", - " <td>3.84</td>\n", - " <td>1.00</td>\n", - " <td>1.61</td>\n", + " <th>GaussianProcess</th>\n", + " <td>1.53</td>\n", + " <td>0.83</td>\n", + " <td>1.70</td>\n", + " <td>0.49</td>\n", + " <td>1.14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -867,13 +893,13 @@ "text/plain": [ "dataset AAPL JPM MSFT NVDA mean\n", "model \n", - "GaussianProcess 0.99 0.33 2.16 0.62 1.03\n", - "Chronos 0.86 0.43 3.06 1.25 1.40\n", - "AutoRegressor 0.71 0.74 3.16 1.13 1.44\n", - "Kalman 1.10 0.22 3.69 0.74 1.44\n", - "naive 1.00 0.23 3.73 1.07 1.51\n", - "ARIMA 1.00 0.24 3.73 1.07 1.51\n", - "Laplace 1.39 0.20 3.84 1.00 1.61" + "Kalman 0.79 0.82 0.33 0.85 0.70\n", + "ARIMA 0.88 0.56 0.32 1.28 0.76\n", + "Laplace 0.86 0.65 0.24 1.33 0.77\n", + "naive 0.90 0.56 0.34 1.31 0.78\n", + "AutoRegressor 1.21 0.35 0.82 1.10 0.87\n", + "Chronos 1.21 0.84 0.36 1.28 0.92\n", + "GaussianProcess 1.53 0.83 1.70 0.49 1.14" ] }, "execution_count": 4, @@ -895,10 +921,10 @@ "id": "978a8ecc", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:04.810685Z", - "iopub.status.busy": "2026-09-05T10:27:04.810623Z", - "iopub.status.idle": "2026-09-05T10:27:04.816184Z", - "shell.execute_reply": "2026-09-05T10:27:04.815815Z" + "iopub.execute_input": "2026-10-09T17:28:40.231021Z", + "iopub.status.busy": "2026-10-09T17:28:40.230957Z", + "iopub.status.idle": "2026-10-09T17:28:40.236609Z", + "shell.execute_reply": "2026-10-09T17:28:40.236201Z" } }, "outputs": [ @@ -947,60 +973,60 @@ " </thead>\n", " <tbody>\n", " <tr>\n", - " <th>GaussianProcess</th>\n", - " <td>0.06</td>\n", + " <th>Kalman</th>\n", + " <td>0.05</td>\n", + " <td>0.05</td>\n", " <td>0.02</td>\n", - " <td>0.13</td>\n", - " <td>0.03</td>\n", - " <td>0.06</td>\n", + " <td>0.05</td>\n", + " <td>0.04</td>\n", " </tr>\n", " <tr>\n", - " <th>Chronos</th>\n", + " <th>ARIMA</th>\n", " <td>0.05</td>\n", " <td>0.03</td>\n", - " <td>0.19</td>\n", + " <td>0.02</td>\n", " <td>0.07</td>\n", - " <td>0.08</td>\n", + " <td>0.04</td>\n", " </tr>\n", " <tr>\n", - " <th>AutoRegressor</th>\n", + " <th>Laplace</th>\n", + " <td>0.05</td>\n", " <td>0.04</td>\n", + " <td>0.02</td>\n", + " <td>0.07</td>\n", " <td>0.04</td>\n", - " <td>0.20</td>\n", - " <td>0.06</td>\n", - " <td>0.09</td>\n", " </tr>\n", " <tr>\n", - " <th>Kalman</th>\n", - " <td>0.06</td>\n", - " <td>0.01</td>\n", - " <td>0.23</td>\n", + " <th>naive</th>\n", + " <td>0.05</td>\n", + " <td>0.03</td>\n", + " <td>0.02</td>\n", + " <td>0.07</td>\n", " <td>0.04</td>\n", - " <td>0.09</td>\n", " </tr>\n", " <tr>\n", - " <th>naive</th>\n", - " <td>0.06</td>\n", - " <td>0.01</td>\n", - " <td>0.23</td>\n", + " <th>AutoRegressor</th>\n", + " <td>0.07</td>\n", + " <td>0.02</td>\n", + " <td>0.05</td>\n", " <td>0.06</td>\n", - " <td>0.09</td>\n", + " <td>0.05</td>\n", " </tr>\n", " <tr>\n", - " <th>ARIMA</th>\n", - " <td>0.06</td>\n", - " <td>0.01</td>\n", - " <td>0.23</td>\n", - " <td>0.06</td>\n", - " <td>0.09</td>\n", + " <th>Chronos</th>\n", + " <td>0.07</td>\n", + " <td>0.05</td>\n", + " <td>0.02</td>\n", + " <td>0.07</td>\n", + " <td>0.05</td>\n", " </tr>\n", " <tr>\n", - " <th>Laplace</th>\n", - " <td>0.08</td>\n", - " <td>0.01</td>\n", - " <td>0.24</td>\n", + " <th>GaussianProcess</th>\n", + " <td>0.09</td>\n", " <td>0.05</td>\n", - " <td>0.10</td>\n", + " <td>0.11</td>\n", + " <td>0.03</td>\n", + " <td>0.07</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -1009,13 +1035,13 @@ "text/plain": [ "dataset AAPL JPM MSFT NVDA mean\n", "model \n", - "GaussianProcess 0.06 0.02 0.13 0.03 0.06\n", - "Chronos 0.05 0.03 0.19 0.07 0.08\n", - "AutoRegressor 0.04 0.04 0.20 0.06 0.09\n", - "Kalman 0.06 0.01 0.23 0.04 0.09\n", - "naive 0.06 0.01 0.23 0.06 0.09\n", - "ARIMA 0.06 0.01 0.23 0.06 0.09\n", - "Laplace 0.08 0.01 0.24 0.05 0.10" + "Kalman 0.05 0.05 0.02 0.05 0.04\n", + "ARIMA 0.05 0.03 0.02 0.07 0.04\n", + "Laplace 0.05 0.04 0.02 0.07 0.04\n", + "naive 0.05 0.03 0.02 0.07 0.04\n", + "AutoRegressor 0.07 0.02 0.05 0.06 0.05\n", + "Chronos 0.07 0.05 0.02 0.07 0.05\n", + "GaussianProcess 0.09 0.05 0.11 0.03 0.07" ] }, "execution_count": 5, @@ -1037,10 +1063,10 @@ "id": "9df34c56", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:04.817071Z", - "iopub.status.busy": "2026-09-05T10:27:04.817014Z", - "iopub.status.idle": "2026-09-05T10:27:04.819337Z", - "shell.execute_reply": "2026-09-05T10:27:04.819015Z" + "iopub.execute_input": "2026-10-09T17:28:40.237493Z", + "iopub.status.busy": "2026-10-09T17:28:40.237434Z", + "iopub.status.idle": "2026-10-09T17:28:40.239831Z", + "shell.execute_reply": "2026-10-09T17:28:40.239478Z" } }, "outputs": [ @@ -1048,9 +1074,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best model by average MAPE: GaussianProcess (1.03%)\n", - "Naive last-value baseline: 1.51%\n", - "-> GaussianProcess beat the naive baseline by 0.48 percentage points of MAPE, on average across these 4 tickers.\n" + "Best model by average MAPE: Kalman (0.70%)\n", + "Naive last-value baseline: 0.78%\n", + "-> Kalman beat the naive baseline by 0.08 percentage points of MAPE, on average across these 4 tickers.\n" ] } ], @@ -1064,7 +1090,12 @@ "\n", "print(f\"Best model by average MAPE: {best_model} ({best_mape:.2f}%)\")\n", "print(f\"Naive last-value baseline: {naive_mape:.2f}%\")\n", - "if beat_naive:\n", + "if beat_naive is None:\n", + " # no verdict: no model forecast every held-out value (they are\n", + " # listed in scores.attrs['incomplete']), or the baseline did not\n", + " print(\"-> No verdict: models that left held-out values missing \"\n", + " f\"({scores.attrs['incomplete']}) are not ranked.\")\n", + "elif beat_naive:\n", " print(f\"-> {best_model} beat the naive baseline by \"\n", " f\"{naive_mape - best_mape:.2f} percentage points of MAPE, on \"\n", " f\"average across these {len(tickers)} tickers.\")\n", @@ -1088,6 +1119,157 @@ "forecasting pipeline should run before trusting a model's predictions." ] }, + { + "cell_type": "markdown", + "id": "a79cada2", + "metadata": {}, + "source": [ + "## Calendar time versus trading-day position\n", + "\n", + "The comparison above deliberately uses trading-day positions: Friday to Monday\n", + "is one step, just like Monday to Tuesday. HyperTools also accepts actual\n", + "calendar timestamps. The following backtest keeps the original dates for one\n", + "ticker and holds out its last 30 observations.\n", + "\n", + "HyperTools recognizes these dates as business-day trading sessions, so the\n", + "model interval is one business day (`step='B'`), inferred from the\n", + "**training timestamps only**. Kalman fits the training closes on that\n", + "business-day calendar; market holidays are the only gaps, and they are filled\n", + "by linear interpolation before fitting (the warning below says so).\n", + "GaussianProcess evaluates the actual held-out timestamps. No held-out price\n", + "enters fitting or interpolation. All returned predictions carry the same index\n", + "as the held-out truth, so the scores compare matching times, and the reported\n", + "`horizon` is the 30 held-out observations.\n", + "\n", + "The filled holiday values are synthetic, not observed market closes. Choose the\n", + "time axis that matches the question you want to model; changing it can change\n", + "the fit and the scores." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f2845e12", + "metadata": { + "execution": { + "iopub.execute_input": "2026-10-09T17:28:40.240795Z", + "iopub.status.busy": "2026-10-09T17:28:40.240740Z", + "iopub.status.idle": "2026-10-09T17:28:40.315685Z", + "shell.execute_reply": "2026-10-09T17:28:40.315164Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First held-out time: 2026-08-27 00:00:00\n", + "Last held-out time: 2026-10-08 00:00:00\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<cell>:2: UserWarning: Irregular observation times were linearly interpolated onto a regular grid with step='B' before fitting this discrete-time forecaster. Pass step= to choose the grid interval; GaussianProcess uses the actual observation times without interpolation.\n", + " calendar_scores, calendar_backtest = hyp.predict(\n" + ] + }, + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>MAE</th>\n", + " <th>RMSE</th>\n", + " <th>MAPE</th>\n", + " <th>n</th>\n", + " <th>unscored</th>\n", + " <th>horizon</th>\n", + " </tr>\n", + " <tr>\n", + " <th>model</th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " <th></th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>Kalman</th>\n", + " <td>0.0602</td>\n", + " <td>0.0676</td>\n", + " <td>1.1084</td>\n", + " <td>30</td>\n", + " <td>0</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>GaussianProcess</th>\n", + " <td>0.0653</td>\n", + " <td>0.0722</td>\n", + " <td>1.2018</td>\n", + " <td>30</td>\n", + " <td>0</td>\n", + " <td>30</td>\n", + " </tr>\n", + " <tr>\n", + " <th>naive</th>\n", + " <td>0.0714</td>\n", + " <td>0.0781</td>\n", + " <td>1.3138</td>\n", + " <td>30</td>\n", + " <td>0</td>\n", + " <td>30</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " MAE RMSE MAPE n unscored horizon\n", + "model \n", + "Kalman 0.0602 0.0676 1.1084 30 0 30\n", + "GaussianProcess 0.0653 0.0722 1.2018 30 0 30\n", + "naive 0.0714 0.0781 1.3138 30 0 30" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "calendar_data = np.log(closes['NVDA'].dropna()).tail(60 + HOLD).to_frame('log_close')\n", + "calendar_scores, calendar_backtest = hyp.predict(\n", + " calendar_data, model=['Kalman', 'GaussianProcess'], holdout=HOLD,\n", + " return_forecasts=True)\n", + "for name, prediction in calendar_backtest.items():\n", + " assert prediction.index.equals(calendar_backtest['truth'].index), name\n", + "print('First held-out time:', calendar_backtest['truth'].index[0])\n", + "print('Last held-out time:', calendar_backtest['truth'].index[-1])\n", + "calendar_scores.round(4)\n" + ] + }, { "cell_type": "markdown", "id": "e71d61fa", @@ -1095,25 +1277,25 @@ "source": [ "## A closer look at the foundation model\n", "\n", - "`Chronos` forecasts by *sampling*: it draws `num_samples` plausible continuations (20 by default) and reports their median, so its forecast is a distribution summary rather than a fitted curve. Because it needs no fitting, the same call works on any series length and any column count. Below we forecast one ticker directly in price space -- the trailing 60 training days as an `ndims=1` series (a real `DataFrame`, its own index the trading day, so the x-axis is in actual trading-day units rather than a hand-built `(day, price)` column stack) -- and let `hyp.plot`'s own `predict=`/`truth=` draw the Chronos forecast next to what actually happened, in one call instead of three hand-assembled datasets." + "`Chronos` forecasts by *sampling*: it draws `num_samples` plausible continuations (20 by default) and reports their median, so its forecast is a distribution summary rather than a fitted curve. Because it needs no fitting, the same call works with at least two observations and any column count. Below we forecast one ticker directly in price space -- the trailing 60 training days as an `ndims=1` series (a real `DataFrame`, its own index the trading day, so the x-axis is in actual trading-day units rather than a hand-built `(day, price)` column stack) -- and let `hyp.plot`'s own `predict=`/`truth=` draw the Chronos forecast next to what actually happened, in one call instead of three hand-assembled datasets." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "afe740e2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:04.820239Z", - "iopub.status.busy": "2026-09-05T10:27:04.820185Z", - "iopub.status.idle": "2026-09-05T10:27:05.705331Z", - "shell.execute_reply": "2026-09-05T10:27:05.704874Z" + "iopub.execute_input": "2026-10-09T17:28:40.316779Z", + "iopub.status.busy": "2026-10-09T17:28:40.316702Z", + "iopub.status.idle": "2026-10-09T17:28:41.059265Z", + "shell.execute_reply": "2026-10-09T17:28:41.058658Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1136,7 +1318,7 @@ "fig = hyp.plot(train_tail, ndims=1, reduce=None,\n", " predict={'model': 'Chronos', 'kwargs': {'model_name': 'amazon/chronos-t5-tiny'}},\n", " t=HOLD, truth=held_out,\n", - " names=['observed (train + Chronos median forecast)'], legend=True,\n", + " names=['observed (train)'], legend=True,\n", " xlabel='trading day', ylabel='price ($)',\n", " title=f'{tk}: Chronos zero-shot forecast, {HOLD} trading days ahead')" ] @@ -1163,22 +1345,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8976768f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:05.706485Z", - "iopub.status.busy": "2026-09-05T10:27:05.706408Z", - "iopub.status.idle": "2026-09-05T10:27:05.979169Z", - "shell.execute_reply": "2026-09-05T10:27:05.978814Z" + "iopub.execute_input": "2026-10-09T17:28:41.060645Z", + "iopub.status.busy": "2026-10-09T17:28:41.060548Z", + "iopub.status.idle": "2026-10-09T17:28:41.350011Z", + "shell.execute_reply": "2026-10-09T17:28:41.349499Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "<Figure size 640.322x480 with 1 Axes>" + "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, @@ -1222,20 +1404,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "43ac9c3e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:05.980564Z", - "iopub.status.busy": "2026-09-05T10:27:05.980475Z", - "iopub.status.idle": "2026-09-05T10:27:06.425019Z", - "shell.execute_reply": "2026-09-05T10:27:06.424546Z" + "iopub.execute_input": "2026-10-09T17:28:41.351280Z", + "iopub.status.busy": "2026-10-09T17:28:41.351144Z", + "iopub.status.idle": "2026-10-09T17:28:41.800566Z", + "shell.execute_reply": "2026-10-09T17:28:41.799916Z" } }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "<Figure size 1200x800 with 4 Axes>" ] @@ -1245,7 +1427,9 @@ } ], "source": [ - "fig, axes = hyp.subplots(2, 2, ndims=1, size=(12, 8))\n", + "# backend='matplotlib' on the grid and on each call: suptitle/tight_layout are matplotlib's,\n", + "# and on Colab the default backend would be plotly\n", + "fig, axes = hyp.subplots(2, 2, ndims=1, size=(12, 8), backend='matplotlib')\n", "context = 60 # trailing days of history to show for context\n", "\n", "for ax, tk in zip(axes, tickers):\n", @@ -1260,8 +1444,9 @@ " # backtest above, so the drawn forecast is the model applied fresh\n", " # to exactly what's plotted.\n", " hyp.plot(train_tail, reduce=None, ndims=1, predict=best_model, t=HOLD,\n", - " truth=held_out, ax=ax, title=tk,\n", - " names=[f'observed (train + {best_model} forecast)'],\n", + " truth=held_out, ax=ax, title=tk, backend='matplotlib',\n", + " # names= draws a legend by itself, so it goes on the first panel only\n", + " names=[f'observed (train + {best_model} forecast)'] if ax is axes[0] else None,\n", " legend=(ax is axes[0]),\n", " xlabel='trading day', ylabel='price ($)',\n", " show=False)\n", @@ -1294,14 +1479,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "d9b2b4bd", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:27:06.426229Z", - "iopub.status.busy": "2026-09-05T10:27:06.426126Z", - "iopub.status.idle": "2026-09-05T10:27:06.434562Z", - "shell.execute_reply": "2026-09-05T10:27:06.433984Z" + "iopub.execute_input": "2026-10-09T17:28:41.802872Z", + "iopub.status.busy": "2026-10-09T17:28:41.802717Z", + "iopub.status.idle": "2026-10-09T17:28:41.813737Z", + "shell.execute_reply": "2026-10-09T17:28:41.812324Z" } }, "outputs": [ @@ -1310,8 +1495,8 @@ "output_type": "stream", "text": [ "Fitted on pooled ['AAPL', 'MSFT', 'NVDA'] -- is_fitted: True\n", - "Reused-forecaster MAPE on JPM: 3.41%\n", - "(for comparison, JPM's own freshly-fit AutoRegressor MAPE: 4.25%)\n" + "Reused-forecaster MAPE on JPM: 1.81%\n", + "(for comparison, JPM's own freshly-fit AutoRegressor MAPE: 2.08%)\n" ] } ], @@ -1349,11 +1534,13 @@ "id": "fabd9617", "metadata": {}, "source": [ - "As expected for a lag-regression fit on *other* stocks' price dynamics and\n", - "then pointed at a new one, this is not a substitute for fitting directly on\n", - "the ticker you care about -- but it demonstrates the mechanics of\n", - "`return_model=True`: the forecaster's learned parameters were reused\n", - "as-is, with no re-estimation, on data it had never seen.\n", + "Whether the pooled fit lands above or below `JPM`'s own fit on a given run\n", + "(compare the two MAPEs printed above) says little on its own: it is one\n", + "30-day window, and a lag regression fit on *other* stocks' price dynamics\n", + "is not in general a substitute for fitting on the ticker you care about.\n", + "What the cell demonstrates is the mechanics of `return_model=True`: the\n", + "forecaster's learned parameters were reused as-is, with no re-estimation,\n", + "on data it had never seen.\n", "\n", "## Next steps\n", "\n", @@ -1387,7 +1574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/stock_volumes_cached.csv b/docs/tutorials/stock_volumes_cached.csv index f6d33c85..e0fa71e8 100644 --- a/docs/tutorials/stock_volumes_cached.csv +++ b/docs/tutorials/stock_volumes_cached.csv @@ -1,28 +1,4 @@ date,AAPL,MSFT,NVDA,JPM -2024-09-05,36615400.0,14195500.0,306850700.0,8067900.0 -2024-09-06,48423000.0,19609500.0,413638100.0,7777000.0 -2024-09-09,67180000.0,15295100.0,273912000.0,8935100.0 -2024-09-10,51591000.0,19594300.0,268283700.0,28406900.0 -2024-09-11,44587100.0,19266900.0,441422400.0,13658700.0 -2024-09-12,37455600.0,17395700.0,366052700.0,9054400.0 -2024-09-13,36766600.0,15874600.0,238358300.0,10226700.0 -2024-09-16,59357400.0,13834700.0,248772300.0,8634900.0 -2024-09-17,45519300.0,18874200.0,231925900.0,7573300.0 -2024-09-18,59894900.0,18898000.0,310318900.0,8259900.0 -2024-09-19,66781300.0,21706600.0,293506400.0,11635200.0 -2024-09-20,318679900.0,55167100.0,382462400.0,20885000.0 -2024-09-23,54146000.0,15128900.0,206228500.0,7223500.0 -2024-09-24,43556100.0,17015800.0,354966800.0,7323200.0 -2024-09-25,42308700.0,13396400.0,284692900.0,8976500.0 -2024-09-26,36636700.0,14492000.0,302582900.0,7807500.0 -2024-09-27,34026000.0,14896100.0,271009200.0,7032500.0 -2024-09-30,54541900.0,16807300.0,226553700.0,8663800.0 -2024-10-01,63285000.0,19092900.0,302094500.0,8540100.0 -2024-10-02,32880600.0,16582300.0,221845900.0,5810900.0 -2024-10-03,34044200.0,13686400.0,277118000.0,7251300.0 -2024-10-04,37245100.0,19169700.0,243678100.0,10211700.0 -2024-10-07,39505400.0,20919800.0,346250200.0,6718900.0 -2024-10-08,31855700.0,19229300.0,285722500.0,6021800.0 2024-10-09,33591100.0,14974300.0,246191600.0,7024000.0 2024-10-10,28183500.0,13848400.0,242311300.0,7927100.0 2024-10-11,31759200.0,14144900.0,170209500.0,18320700.0 @@ -500,4 +476,27 @@ date,AAPL,MSFT,NVDA,JPM 2026-09-01,53167400.0,21046900.0,109756200.0,5079000.0 2026-09-02,33776400.0,15336100.0,157104700.0,5341400.0 2026-09-03,37225800.0,24125900.0,134681600.0,5414200.0 -2026-09-04,39551800.0,18074400.0,134946800.0,4873500.0 +2026-09-04,39606900.0,18101800.0,135352400.0,4873500.0 +2026-09-08,35477100.0,18882300.0,122965600.0,5631400.0 +2026-09-09,65640000.0,12889600.0,82955500.0,6318200.0 +2026-09-10,70011900.0,16038800.0,105768000.0,4184200.0 +2026-09-11,50716900.0,14510500.0,89060100.0,5354600.0 +2026-09-14,39269100.0,23091900.0,132267200.0,9609900.0 +2026-09-15,31748200.0,17794600.0,88059700.0,14026500.0 +2026-09-16,35981000.0,16670300.0,96563600.0,8303400.0 +2026-09-17,36700200.0,17841600.0,94191300.0,6480100.0 +2026-09-18,86588200.0,39622300.0,190287400.0,18312400.0 +2026-09-21,34999200.0,27959900.0,109806100.0,6414700.0 +2026-09-22,40711800.0,21652800.0,95746700.0,11485600.0 +2026-09-23,31658800.0,19445600.0,89547300.0,7068500.0 +2026-09-24,24733100.0,16657600.0,77469900.0,6934300.0 +2026-09-25,30002500.0,38193100.0,89947700.0,11460300.0 +2026-09-28,32820800.0,19626900.0,142344300.0,10483700.0 +2026-09-29,38478000.0,21341800.0,101494900.0,7254900.0 +2026-09-30,49988600.0,27223300.0,121732200.0,9113300.0 +2026-10-01,36306300.0,19731900.0,98591400.0,8160000.0 +2026-10-02,33278600.0,17822300.0,135167800.0,6658200.0 +2026-10-05,34400900.0,26604800.0,127171500.0,5346100.0 +2026-10-06,30449000.0,21107500.0,101701900.0,6286800.0 +2026-10-07,34147900.0,16790200.0,81772400.0,7003000.0 +2026-10-08,35279900.0,20201700.0,118311700.0,8060800.0 diff --git a/docs/tutorials/streaming_data.ipynb b/docs/tutorials/streaming_data.ipynb index 274bc482..8b4780b2 100644 --- a/docs/tutorials/streaming_data.ipynb +++ b/docs/tutorials/streaming_data.ipynb @@ -2,37 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "deaeac7f", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T08:25:39.598683Z", - "iopub.status.busy": "2026-07-17T08:25:39.598627Z", - "iopub.status.idle": "2026-07-17T08:25:50.178595Z", - "shell.execute_reply": "2026-07-17T08:25:50.177791Z" - } + "tags": [ + "hypertools-install" + ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.2\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m~/hypertools/.venv/bin/python -m pip install --upgrade pip\u001b[0m\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -71,10 +65,10 @@ "id": "ff217396", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:02.272935Z", - "iopub.status.busy": "2026-09-05T10:26:02.272709Z", - "iopub.status.idle": "2026-09-05T10:26:05.757444Z", - "shell.execute_reply": "2026-09-05T10:26:05.757022Z" + "iopub.execute_input": "2026-10-08T17:20:16.862409Z", + "iopub.status.busy": "2026-10-08T17:20:16.862222Z", + "iopub.status.idle": "2026-10-08T17:20:20.290796Z", + "shell.execute_reply": "2026-10-08T17:20:20.290311Z" } }, "outputs": [], @@ -106,22 +100,19 @@ "id": "6461311e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:05.759043Z", - "iopub.status.busy": "2026-09-05T10:26:05.758894Z", - "iopub.status.idle": "2026-09-05T10:26:05.983169Z", - "shell.execute_reply": "2026-09-05T10:26:05.982763Z" + "iopub.execute_input": "2026-10-08T17:20:20.292214Z", + "iopub.status.busy": "2026-10-08T17:20:20.292064Z", + "iopub.status.idle": "2026-10-08T17:20:20.559030Z", + "shell.execute_reply": "2026-10-08T17:20:20.558644Z" } }, "outputs": [ { - "data": { - "text/plain": [ - "(3000, (3000, 3))" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "samples: 3000 projected shape: (3000, 3)\n" + ] } ], "source": [ @@ -141,7 +132,16 @@ "fig = hyp.plot(live_feed(), stream_init=500, stream_chunk=100,\n", " stream_max=3000, save_path='streaming_data.mp4', frame_rate=5,\n", " show=False)\n", - "fig.stream_info['n_samples'], fig.stream_info['xform_data'][0].shape" + "print('samples:', fig.stream_info['n_samples'], ' projected shape:', fig.stream_info['xform_data'][0].shape)\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('streaming_data.mp4', embed=True))\n" ] }, { @@ -175,10 +175,10 @@ "id": "2b2c8fa2", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:05.984465Z", - "iopub.status.busy": "2026-09-05T10:26:05.984380Z", - "iopub.status.idle": "2026-09-05T10:26:05.986881Z", - "shell.execute_reply": "2026-09-05T10:26:05.986482Z" + "iopub.execute_input": "2026-10-08T17:20:20.560298Z", + "iopub.status.busy": "2026-10-08T17:20:20.560218Z", + "iopub.status.idle": "2026-10-08T17:20:20.562997Z", + "shell.execute_reply": "2026-10-08T17:20:20.562599Z" } }, "outputs": [ @@ -223,29 +223,36 @@ "id": "764de189", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:05.987864Z", - "iopub.status.busy": "2026-09-05T10:26:05.987809Z", - "iopub.status.idle": "2026-09-05T10:26:06.149722Z", - "shell.execute_reply": "2026-09-05T10:26:06.149147Z" + "iopub.execute_input": "2026-10-08T17:20:20.563952Z", + "iopub.status.busy": "2026-10-08T17:20:20.563879Z", + "iopub.status.idle": "2026-10-08T17:20:20.720956Z", + "shell.execute_reply": "2026-10-08T17:20:20.720576Z" } }, "outputs": [ { - "data": { - "text/plain": [ - "(750, 3000)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "points on screen: 750 samples retained: 3000\n" + ] } ], "source": [ "fig = hyp.plot(live_feed(seed=7), stream_init=500, stream_chunk=100,\n", " stream_max=3000, stream_window=750,\n", " save_path='streaming_data_window.mp4', frame_rate=5, show=False)\n", - "len(fig.axes[0].lines[0].get_data_3d()[0]), fig.stream_info['xform_data'][0].shape[0]" + "print('points on screen:', len(fig.axes[0].lines[0].get_data_3d()[0]),\n", + " ' samples retained:', fig.stream_info['xform_data'][0].shape[0])\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('streaming_data_window.mp4', embed=True))\n" ] }, { @@ -287,10 +294,10 @@ "id": "ffb5fffa", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:26:06.151120Z", - "iopub.status.busy": "2026-09-05T10:26:06.151024Z", - "iopub.status.idle": "2026-09-05T10:26:08.763813Z", - "shell.execute_reply": "2026-09-05T10:26:08.763421Z" + "iopub.execute_input": "2026-10-08T17:20:20.722217Z", + "iopub.status.busy": "2026-10-08T17:20:20.722130Z", + "iopub.status.idle": "2026-10-08T17:20:23.208312Z", + "shell.execute_reply": "2026-10-08T17:20:23.207774Z" } }, "outputs": [ @@ -355,7 +362,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/streaming_data.mp4 b/docs/tutorials/streaming_data.mp4 index fac25054..1dede5d3 100644 Binary files a/docs/tutorials/streaming_data.mp4 and b/docs/tutorials/streaming_data.mp4 differ diff --git a/docs/tutorials/streaming_data_window.mp4 b/docs/tutorials/streaming_data_window.mp4 index b14f622d..35ee95fb 100644 Binary files a/docs/tutorials/streaming_data_window.mp4 and b/docs/tutorials/streaming_data_window.mp4 differ diff --git a/docs/tutorials/text.ipynb b/docs/tutorials/text.ipynb index f42bde94..a18e2310 100644 --- a/docs/tutorials/text.ipynb +++ b/docs/tutorials/text.ipynb @@ -2,22 +2,31 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d7504a02", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:38:24.718043Z", - "iopub.status.busy": "2026-07-17T07:38:24.717977Z", - "iopub.status.idle": "2026-07-17T07:38:24.721043Z", - "shell.execute_reply": "2026-07-17T07:38:24.720709Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -35,10 +44,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:26.755717Z", - "iopub.status.busy": "2026-09-05T10:24:26.755562Z", - "iopub.status.idle": "2026-09-05T10:24:30.262835Z", - "shell.execute_reply": "2026-09-05T10:24:30.262296Z" + "iopub.execute_input": "2026-10-08T17:20:26.741173Z", + "iopub.status.busy": "2026-10-08T17:20:26.740936Z", + "iopub.status.idle": "2026-10-08T17:20:30.084326Z", + "shell.execute_reply": "2026-10-08T17:20:30.083864Z" } }, "outputs": [], @@ -65,10 +74,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:30.265003Z", - "iopub.status.busy": "2026-09-05T10:24:30.264797Z", - "iopub.status.idle": "2026-09-05T10:24:30.785096Z", - "shell.execute_reply": "2026-09-05T10:24:30.784092Z" + "iopub.execute_input": "2026-10-08T17:20:30.085738Z", + "iopub.status.busy": "2026-10-08T17:20:30.085594Z", + "iopub.status.idle": "2026-10-08T17:20:30.648968Z", + "shell.execute_reply": "2026-10-08T17:20:30.647761Z" } }, "outputs": [ @@ -76,7 +85,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "dog article: 51,491 characters in 5 chunks; cat article: 50,234 characters in 5 chunks\n" + "dog article: 51,491 characters in 5 chunks; cat article: 49,885 characters in 5 chunks\n" ] } ], @@ -144,10 +153,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:30.787351Z", - "iopub.status.busy": "2026-09-05T10:24:30.787194Z", - "iopub.status.idle": "2026-09-05T10:24:30.792104Z", - "shell.execute_reply": "2026-09-05T10:24:30.791335Z" + "iopub.execute_input": "2026-10-08T17:20:30.650784Z", + "iopub.status.busy": "2026-10-08T17:20:30.650692Z", + "iopub.status.idle": "2026-10-08T17:20:30.653576Z", + "shell.execute_reply": "2026-10-08T17:20:30.653097Z" } }, "outputs": [ @@ -181,16 +190,16 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:30.793628Z", - "iopub.status.busy": "2026-09-05T10:24:30.793507Z", - "iopub.status.idle": "2026-09-05T10:24:30.972893Z", - "shell.execute_reply": "2026-09-05T10:24:30.972456Z" + "iopub.execute_input": "2026-10-08T17:20:30.654711Z", + "iopub.status.busy": "2026-10-08T17:20:30.654638Z", + "iopub.status.idle": "2026-10-08T17:20:30.873109Z", + "shell.execute_reply": "2026-10-08T17:20:30.872670Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 800x600 with 1 Axes>" ] @@ -210,7 +219,7 @@ "source": [ "Now, let's add a third very different topic to the plot.\n", "\n", - "This call also passes `labels=hue`. `labels=` draws a per-point text call-out and takes exactly one entry per observation (row), so every text chunk is annotated individually with the article it came from. That is distinct from `legend=`, which labels whole groups: one entry per drawn group, not per point." + "As above, the three articles are passed as a list of datasets and `legend=` names them, one entry per dataset. This call also passes `labels=`, which is distinct: `labels=` draws a per-point text call-out and takes exactly one entry per observation (row) across all the datasets, so every text chunk is annotated individually with the article it came from." ] }, { @@ -220,16 +229,16 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:30.974002Z", - "iopub.status.busy": "2026-09-05T10:24:30.973938Z", - "iopub.status.idle": "2026-09-05T10:24:31.443278Z", - "shell.execute_reply": "2026-09-05T10:24:31.442931Z" + "iopub.execute_input": "2026-10-08T17:20:30.874101Z", + "iopub.status.busy": "2026-10-08T17:20:30.874039Z", + "iopub.status.idle": "2026-10-08T17:20:31.380802Z", + "shell.execute_reply": "2026-10-08T17:20:31.380424Z" } }, "outputs": [ { "data": { - "image/png": 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0KsiLL75oL7/8stt0FDuBW32zGldz4oknutWPbAwnBWvHGVBxynJKuJ9//rlb4/3uu+9cSFI509ODq2nTpu4k+v3339894DZ2qyVQHghOQH5T0NCxWv3793crITNnziy+rmHDhm623wknnGDHH398EGeOLl972gXBKctC0hdffGEDBgxwE04Vknzp0jekKSRpwqkPSdpyCWQjghOQX/QapnaR//73v+68t2nTphWf96bBx+pN0uuWlt9CPFJr+drglI3VsFjZfe828gE2aNAgV0lSSPr9999dSPIPMoUklS7Vj9S5c2e33Lb99ttn+m4DkRGcgPxYEdF5bzrGROMCYs97U7uI+mlLn/cWqpVrB2pScUoDPZC0zKaQpLCkQV2xIUlNbwpJqiJpe6USeXkO7AIygeAE5B690X/22WddYNrQ895CtWLtgE0qTikISWp607quHmBjxoyxuXPnlghJjRs3dlUk7XBTGt9pp53K+24AGUdwAsKnsQBPPvmkO+9NI23K47y3UK2k4pSa9U9NL50xY0bxx7TkpiSuIZKqIikkaYebbteqVSvmJiFnEZyA8IwePdoFJY23UQtJ6fPe1NB97rnn5uUmpBUxR7pks6B6nLTbTaGpZs2abseAHnCadKovthq9ddFyXWmqQmlitz5PDXNaC9bnqxFcAyl1cK6C1rbbbuserCHsPgCY4wRkP50eoaDkz3uL3Tmm15xDDjmk3M97C9UKglP584FGk00fe+yxda7X0SY+QGlt+M8//3S7Dv766y+bM2eOC14aWqktm6tWrSpusov3gqQ1Vv17ClpqwmvQoIFbU1ZlSyVTDbdUyFJDOYfvAgBErzkKStr9NmrUqHXOe9PON/Uopfq8txCtWrXK/apCRzYLMjj5L25pCjlaE9YlCgUnBSwFLa0t6/d60KuqpcFhajDXESrayaBJ4X7tOR4tGarUqnVpBS1VtRSoFLQ0LNMvH+odhn7N9uY3ZD+W6oDsOe9NA5N13ps/m1QrHXreV79tJs57C9EKKk7lz6fQRMGprPTAVojRJSo1omtdWkFr4sSJNnnyZBe0Zs2a5a7ThHFVt6ZOnerup29aj/eip/+Pr2rVrVu3uKql5UMdBKz7paqWerUUxoDSj18A6aVVC+1603lvI0aMWOe8N+1603iATJ/3FqKVa/u9qDiVI/8gTFb5STWFmz333NNdoj4QtMatsKWgpeVDhSoFLV/V0jsUhS6tf/utp/GoSqWqlprh69SpY/Xr13c7CNWrpR9YX9XS0E79mR/a3EbFCUjfeW+vvfaa/fDDDyXOe9OKgs57O+WUU+zwww/nObecglO2r8hk971LIJPBqayUnNUHFXW4ppYPFaq0fKjlQYUtBa3p06e7oKWApXc4uo1CmL4Wiapaviley4elm+K1fOib4n1Vi6b4sBCcgNS8eL/00kvuvLehQ4euc96bKkonnXRS1p73lgtLddWqVbNsFuR3vbyW6rKRwo7exegSdU1c74h80FJ1S8uHClpqgvdN8apqqUlezYpRmuJV1fJN8erVUtDS8mGLFi1c0FKvlp5EqGplDsEJKL/z3nQ4rs57ix134897O+6449zhuLy5TC2aw1MopIpTOmjMguZXRR30qeCkhnctHypsKWjpzwpaCle+KV6VrtiBbIma4mNHPWj5UEFLoUpBS7sPFba0fKjqVravXYeE4ASUnZ7/NBpA573ppInS570dcMAB7giuUM97C9lKBmCm7sWC4LRxVCVSoNFFDYxRqGoVW9VSqNITTmxTvJ+zpQd/sqZ4zS+J1xSvKpuWDxW0WrZs6ZY3c+H8pVQhOAFlO+/tq6++cm8UfdVdvaKq7B911FFu5xvPN5m1inEEqUNwSj89oWjuSNTZI/oexTbFa9SDlg/9qAc/U0sf/+233yI1xauq5Zvi/agHX9VS0NLyoRrk82X5kOAExKezS5955hn77LPP1jnvbffdd7cjjzwyZ897C9lKKk6pQcUpDAo7Wp7TJSoFKu0sVFVLje9aPlTY0vKhb4rX7/WOcX1N8apqqSleQUtVLfUqaAeiRj0oaClgKWjpEmo5nuAElDzv7dNPPy0xc8+f99atWzc3nVs/+8heBWu/bzSHlzOCU+5SFUmXffbZJ9LtdXRB6aZ4LR/GNsWrV0th69dff03YFC8KWn7Ug5ri/agH3xQfe/6hKl3ZUNXKhvsAZILeYPXt29c++uijdc57a9Omjf3973/P2/PeQrYy5vuYzYIMTsmWdZA/1CfVrl07d4lCwUnBSkuDClp+UryvailoqVdLH9P1yZaEFVr8pHhVtWJHPShoaY6WH/WgqhtN8cCG08/qE088YR988IGNGTNmnfPeDjroIDvnnHMiPxcgO61kAGZqUHHChlLYUaDRJSqNcVDQ8ucf+qqWP/9QQUsX9W6t7/xD3xSvqpZvivdVLT/A1J9/qOvWh6U65Cr9jOkYkwEDBrhqsUauCOe95bYClupSg+CEdFL/U/v27d0l6g9+7PmHClragahgpaClUQ/q1VIPl5YYklVP/fmHaoovff6herX0b4iWKhW6GMaHUKm/Uc3cb7/9tv3888/rnPe233772RlnnGGdOnXK9F1FCtEcniIs1SGbKbyoalSW3gq9aMSef6im+NLnH+o2+ni88w+1VBF7/qGWD1XVUtCKPf9QVS2NetDt1asValM8wqc3DxoP8Prrr7vGbj3GS5/3dtppp7klOHr58q/iVL16dctm9DgBWdIUv9dee0V+V6Yq0wUXXGBffvmlXX755SXOP/RN8Qpd6geJev5hbFO8P//Qj3pQ0FL44kUMG0JLbRo4qfPevv/++3XOe+vSpYsbOMl5b/lt1do5TjSHlzM9cdMcjnymqpJ2Dvmt1ffee2/S26vvShUsvwNRS4nxzj/UbXRdsiONYs8/9KMeFPr8+Ye6T74pXpWtbH/niNRQuH/llVfcMSbDhg1zPYGlz3s74YQT7Nhjj2WJGcVoDk8RluqA//0s+GCUrBKk61Qt0iVqj4gqBGqK9wdNqydLQUtVLd8U7w+bLsv5h6Wb4nWf/PmH2n3IQMJwl1jeeust69+/vxs+qZEgfklZu00POeQQO/744znvDUn5oki2v+EKbqmOihNQtuC0IdSQrsGBukSh+6BG+NhRD6pqlT7/UH1asQMK13f+YeykeH/+oY7l0fKhP/+Qpvj00/f7ww8/tOeffz7ueW/777+/9ejRw/Up0UuHqKg4pYheIJINMgTyRTaNI/C7n3SJSr1YaorXEqJvivcztXxVS4FLL8rxmuI93xTvzz9Ur5aqHLHnHypg+fMPdT3KRs+5X3zxhWvoVl9d6fPeNLS2e/fu1rNnz0ijNIBkFadsfzOU3fcuDipOwLoVpxCpMqFzw3SJ+m5U1SxVrLQDUcuHflJ86ab4spx/6JviY0c9+Jla6tNS8MrHpnh/3psOyFWwLX3emxq5dd6bvmZAefD9ldn+80ZwAgLln1xCDU5lpaqSKka6RKGviwKV79XS8qGqWgpaftSD79NSCFvf+Ye+KV5VLT8pXr1afvlQYUvLhwpbCmSh0fwknff28ccfr3Pe284772yHHnoo570hpZJtTMkmBCcgUKFXnFJNYUfLdbp07NgxclO8qllaQlR1S0Gr9PmHGs6o5cQoTfGqavnlQz/qQRUaP1PLn3+o+5jud9n+vLeBAwe6cFn6vLeuXbu6Y0zKclA3sDFC2TEfZHDihQL4H34eyo8qRWU9/1Dhygct3xSv0Q7+/ENVtfQxBbIoTfF+1IOCVuz5h75Xyw9YLev5h7pvCkrvv//+Oue96e87+OCDOe8NGbWKilPqglOyJx8gX1Bxyo7nI82u8jO1olCQil0+jHf+oQKXwpeqQMma4v35h7FN8X7Ug4KW/h71KKk65s97U0BTo/wBBxzgepSi9pgB6ag4VciiTS85U3HSD70vKQP5LN96nHKFAk6HDh3cpSznH/pRD/HOP1RTfLzzD/UipIuCXefOnTnvDVmtIJCiSHDBiaU6YA0qTvlhQ88/PPHEE+2TTz5xL0bZvksJED1WQ6g4VQyx4sQ7bIDghPWffyiEJoSiMJClOoITECgqTkgmUW8UkK0KqDilBhUnYA16nJAMlXmEppCKU+peLHgnBWTXkSvIDO2Me/XVV+NepzEJG0Ofr2GjakyXhx9+2PVNAalCxSmFjZK8kwJYqkNyvMFEaIqKioJ4QxhkjxNPCABLdUiO50mEpoAep9QgOAFr0BwO0ZBLDbLUeXLdunVzZ83FBqfXX3/d9t13X9tjjz2sV69ebgCn99NPP1nPnj3ddXvuuaddccUVtmLFCr6wyIhCepxSg+AElERlIb/pYN4ePXrYe++9ZyeddJJdfPHFbhCmf1x8+umn9uyzz1q/fv1s4sSJdsMNNxR/rs6l6969u7322mvuOJZBgwYl7JkC0rFUVzGAmWOVQ+xx4oUCoOKENbp06eIqTXLKKae4ipOCkH+evP76690RLHL11Ve726jqpAnmrVu3dhfRdHEdv6IJ5UAmFAZScSI4AYFiHAFEB//GatWqlTuWxfOhyV+nQKUjWxSchg8f7ipNqlApTC1btswOO+wwvrDIWHCqGEDFKfvvYSlUnIA16HGClK7A6896bMSrzPuP6frp06e73ihVmh588EF74403rFOnTnxRkTGFBKfUoMcJWIPgBJkwYUKJL8TYsWOtefPmxSEpdp6TrtM7+s0339w1huv311xzjbVr1859zty5c/miImOKAulxyv57GKfiBIClOqzx+eef24ABA1xAeuGFF+yHH36wY445pvjLc9ttt7m+pTFjxtjdd99tXbt2tTp16thmm21mS5cutc8++8x97uOPP27jxo3jy4qMKQokOAWXQqpUqUJzOMDkcKz16KOPutBz7bXX2hZbbGEPPPCAbbvttsXPkwcddJBbklu+fLlbirvxxhvdx3faaSc777zzrHfv3u4F6+ijj7ZLL73URo4cydcWGVEYyFJdhdWBbVHTtlutxQd2t4FypxfKO+64w3755Rdr27YtX2GUoMrSwIEDOWkBwWjatKkLTuq/y2bZH+3i9DgB+N9SHW8iEA+PC4SmKJCluuy/h3GW6gDQHI7kCE4IMThVCqA4Emxw4qBf5DvmOCEZghNCU0TFKbXBaeXKlSn6F4Cw8CYC8fh5TkAoiqg4pXYcgXaIAPmMOU5IhkCNEMN+JZbqyp+vOBGckO98cGJJBvHwuEBoiliqSw2W6oA16HFCMgQnhGY1FafUqFq1qvt1xYoVKfoXgDCwVIdk6HFCaIrocUptjxPBCfmOihOSoeKE0Kym4pQa9DgBa7BjCut7EQJCsprglNqlulWrVqXoXwDCwORwJENwQmhWE5xSgx4nYA16nLC+FyGqkgjtMVt5bTtONgt2cjg9Tsh39DghGSpOCM1qglNqUHEC1qDihPW9CAEhWc1SXWrQ4wSs+2QDxHsRAkKymopTaoMTZ9Uh37FUh2TocUKIKnHkSvljcjiwBkt1SIaKE0KzmopTalSrVs39SnM48h0VJyRDxQkhqrJ2A1g2C3aprqCgINN3BcgoDvlFMlScEKJKLNWlruJEjxPyHRUnJENwQoiqUHEqfzSHA2sw3BDJsFSHEFVmAGb5o+IErEHFCclQcUJICta23xCcUhic6HFCvqPHCckQnBCS5cuXu18JTilAxSkcnTp1sjfeeCPTdyNnUXFCMizVIcTgVIUep/LH5HBgDeY4IRkqTgjJypUrS7zGZ7PgxhFQcQJK4gUSQOhWrFjhfmWpLgWqV6/ufl21alUq/npsoLlz59qFF15oO++8s3Xs2NFeffXV4pArQ4YMsR49etiOO+5onTt3tr59+5Z4wZ82bZqdccYZ7vqDDz7YPvroI/v73/9u3377Ld+TBFiqQz4s1RUVrbbRo5ba4G8WuV/1Z+RuxalKAEt1lS3Q4ERzeHa55ZZb7M8//7T+/fu7dwz333+/TZ8+3V2nX88991z7xz/+YQ888IC73dVXX21FRUV2/vnnu9tceeWVrkT71ltv2dKlS+3222+3SZMmZfh/FUZwouKEeHLhcfHzj0vs2b6zbN7cwuKP1W9QyU7u2dg67FE7o/cN5YuKUwrR45R9Fi5c6CpEvXv3trZt29r2229vN910U3FVUGFKHz/vvPOsefPmts8++9jFF19szz//vAtP48aNs2HDhtnNN99s22yzjas66e8qLPzfkyXWRY8Tcjk4rVheZFu3rG4rV5T8fyhEPdJnhn0/dHHG7hvKHz1OKeTXP1mqyx5TpkxxAUjhyGvWrJnVqVPH/X7ChAklrpN27drZnDlz3GXixInutgpV3rbbbpvG/0GYWKpDri7VaTnulusm24C35tqKFUVxb9O/32yW7XKw4lQ1gObw4JbqPJbqsveFvHTI1ffKh6jYJdfddtvN/Vq3bl3XGxVL/VG6vvTn4X+Y44RcrTiNHb3Mpk5ZZVOnLEh4m7lzCtzt2rStmdb7htRgqS4NCE7ZY/PNN3cv4mPGjCnRLK65HE2bNrXTTjttnWCkgxzPPvtsF4xat25txx57rOtt8pYtW+Y+b9NNN03r/yXkoAqUFmrFacH8wnK9HbLfqrWtHSFUnIJ95iU4ZQ9VjLQT7q677rLRo0fb+PHj7c4777QTTzzRWrRoYbvssotNnjzZ9TEtWLDALe2NGDHCttpqK/f5Ck+bbLKJjRo1yhYvXux6phTCQn7HnA4s1SEZLZ+Hqm69SuV6O4RTcarCrrrUvYuixym73Hrrra65W1UiLb898sgjrrl7yZIlriLVpUsX+/77711VSVWovffe2xo0aFD8+R06dLBZs2bZzJkzXTVqhx12sBkzZljNmpThE2GpDrna47R9mxpu91zsbrrSGjSs7G6H3LAqoIoTPU4ot6pTnz59El6vZvHDDjss4fX6Ydliiy1KvFtW43iTJk34DiVAxQm5qmLFCm7kgHbPJXLS6Y3c7ZAbVjI5PLX0Loqt6rnN9zvFDtFESYwjQK5WnERzmi68rJmrPJWuNOnjzHHKLSsZgJl69DjlFvVA1a5d22rUqOFCsR9RQHBKjAGYSCYXegQVjtp3qOV2z6kRXD1NWp6j0pS7S3XVAnizHORSnd5FEZxyLwRoorjedajHSaHJN48jd5uAkTqhV5w8hSRGDuS+lVScUovglHvUQK4LoqPiBCBXrAqo4lQx1BcMepzy8x3JIYcckum7kTXocUI+VJyQXxWnagSn1KA5PD9pnIEGY2INKk7I9R4n5I+VAS3VBVlxYqkufJoqriGZHTt2dFPFu3fv7oZfPvPMM3booYe6jx144IH21ltvudt/8cUXdsEFF7g5TzpEWBdVHf/zn/9YvmJyOJKh4oSQFBQUuF+pOKUIFafy06lTJ3vjjTcs3W666SYbNGiQPfjgg/buu+/aJZdc4nbVNWzY0G644Qb78MMP7YQTTrDrrrvO5s2bZ3vttZddccUV1rhxY/vuu+/cZdq0aXm9SYClOgC5YmVAFafKob7TZidRuHSkyoABA+zJJ590B/mKzqT7+uuv3dBLBaI//vjDnV/3r3/9y/r27euOZBk3bpz7vmviuMYVfPDBBy44Pfzww+7vuOiiiyyfsFSHZKg4ISQFa98E6+SJbBdkcKLiFLZJkya5H5K2bdsWf0xLcTpiRct1n332mfu9QpPodvvtt58LWjrPSH1OmjK+zz77uL4nnYmXj5gcjmTocUKIu+qqUHFKDXbVbZi5c+e6ZTBVdjQn6eKLLy6xnjxkyBC755577LfffnNLZieddJKdc845xUtCqgQ9/vjj7pw5Hdar8+UGDx7sQk3z5s3L/ITu/16FobFjx7pS7UsvveTuY+vWra1y5cru8GAtz+n+6t/Q5+rf1hEu+gHT36GhmfmIihPWh111CC04VafilBoEpw1zyy23uCGT/fv3d6Hk/vvvt+nTp7vr9Ou5555r//jHP+yBBx5wt7v66qvd0tj555/vbnPllVdau3btXEO3jkRRAFOIKSsFIA25HDVqlOtdmj9/vvt39OfDDz/cNYeLluvk888/t19++cV+/vlnF5zyua8pFj1OSEY/UwQnhKIgoKW6IHfV0eO0YX1FH330kfXu3dstfWlXmhq0fcpXmNLHzzvvPBdstAymitTzzz/vnoDVXzRs2DA788wzXfWnadOmLkCVpddsxIgR9tdff1n9+vXdPKY77rjDfvzxR7csN378eFflGj58uAtMClFXXXWVC1jbbbed23WnUKXApuU5HdGS77O8qDgByBWr1r4W6cD3bBdscMr3F82ymjJligs5sX1FWu5SCJIJEyaUuE5UXVKzti7+7LgmTZoUX68m7Q1166232i677OKC2imnnGI//PCD20WnnXVHHnmkXXrppW4pcMstt3T9TPq32rdvbzvssIOrnB1//PHuB40+Do5cQXw0hyPEilPVAIJTkM3hVJw27msXS0t2/kFbOoT4P6uXKF5AUTVoQykgKTzpIho/MHr0aNdjVbNmTVeZUuVJ59W1atXKZs2a5fqpDjroIDviiCNcmFIYVAVKy4p169Z1wS6fZhtRcUIyBCeEWHGqGMBzeJDBSS/Y9LmUjc6BU7/DmDFjbPfddy9uFld/kWg5TEtgsUaOHOmWz+rVq+cqP4sWLXKBRs3aovlKyQZcanecGsq1pKfPif2B0NKhAo92yCmYqZKk5UCNKdAPkCpMahCfOnWqW0asVauWtWjRwi3zxf6f2rRp40KXHhOnnnpqEO9Wygu76gDkisKAVpGCDE5UnMpOFRntULvrrrvstttucwFD84980NDS1wsvvGCPPfaY6yVSqHnooYfsrLPOcterJ0pLa5rUrSZyBZ5vv/02YfOplt7UOK5lN1WC1HzuB5z5XXQKPgpI+rOWChWUDjjgAHcbVZ/0dx933HHuB0r3R6MKunXr5v4vous1XVyXfOS/9ixXIh4qTgix4hSC7K+JxUFw2jBaFtPS12mnnWann366mxruq09qCH/22Wftq6++crva1ESu0HLGGWcUf77mKqnSp8D0/vvvu56oeC/aCkhq3vaVJu2SUEjyjeRadlMFabPNNnPXKQhp6U1N4vr7FMpUkVKFSaMGtKynihcBYd2fAwDIBQUB7ZYOsuKkZRkmh5edAkqfPn1KfOzoo48u/r0qSpqjlIjClUYSlC6tln4B15Ke+CU9UWXL91MpGCk4xdKf9Y5DFy3z6bax21LzdVZTMizVIRkqTggtOFVIsIKRbYJ8y0pwyg7qdxK/M89TqPXDKcu6tKQwEO/6UH6g0onmcCRDcEJICgKqOAUbnFi2ST8t1f30009uJ5uW23RMis6Y0zlypatHusyePbvED4X/wdCuuSVLlpT4HPUvKWz5SpNuq94nT1UolMQATAC5orCwMJg3yEEu1dHjlBkKNC+++KJr4FaztvqW9t1333Vup2C06667uh17ClkKRAo+WupToNIuPX1MS3r6O/UDoyCssQP+87fZZhsXpvz3WtWtjRl/kIuoOCEZKk4ISUFAS3VBBidVJag4pd+FF14Y+bbqb9LoADWKK/goMCkk6cVeAUiDNPWDouv0MY09iA1G2m3nr9cPk0KaAlc+jRtYH4ITgFxRENBSXZDBiR6nMF7UFXJig05sE7m/fn2f7yko60DiEE7OTheaw5EMFSeEpDCgpboge5yoOOUfVZ44tDQ+qq9I9LgI5YUIKGCpLrUS7bxC7tDIAlUW/UYA/VkvAswu+h8qTkiG4ISQFK1t2whBkEt1VJzygxrLfZVJ3/N0zHJaXVRky8aOtsIF861S3XpWY/s2ViFLf5iZHI6ojxEg2xVQcUotglPuU0hK99DLFVMm27T77rTCeXOLP1apfgNrfHJPq91hD8s2NIcjGaryCEkhPU6pRXBCeVsxdYpV26K51dp1txIfV4ia8UgfW/z90Kz7oodS1kbmUHFCSMGpYiDPaUEu1TEAE+W9PDftX3dY1c22sIJ5c+LeZnb/flarfYesWrajxwnJ0OOEkBRScUotf+YZcu8HRwcNH3LIIWn9d11P07y5tmzUz7Zq2tS4tymYO8fdLpuwVAcgVxQF1Bwexr0shaW63KRjWNQQrh106aRG8PK8XboQnJAMFSeEpDCgilOQpRuGIIbx7mHcuHH2+OOP2zfffOPCkI5Rue++++yll16yTz/91B2j0qhRI7v44ott//33twEDBtgTTzzhzqjbfvvt3d8zduzYlN9X7Z4rz9ulC0t1WJ9QXoiAQnqcUoszy7LfxIkT3aHAkydPtgcffNDq1q3rgpJ+VYDab7/9rHbt2vbmm2/addddZ19++aV16tTJpkyZYu+++6698cYbaVuSrdF6B6vUoKEVzo3f3ySVGzR0owmyCeMIkAwVJ4SkKKClOipOSMk8jkmTJtmgQYPsqaeesr322qvE9SeccELxO4yFCxe6CpQCls6j07Eq+uHRWXbpDCBbXHOTTbrin3q1iXubRiednlWN4cJSHYBcUURwSs9SXUhf6HyyfPlymzFjhgtGbdu2LfEOWB/v16+fqzDNmTPHVq1a5a7T8lwmVWnU2Frc96hNvuNGK5z9V4lKk0JTNs5x8vRzAJRGxQkhKSoqCmY1KeiKkyobyQ6KRXb1WCg0Pfnkk24p7uqrr3Z9TOqDuuKKKywbVG7QwFrc+9CaXXbz52X95HAqTlgfepwQUnCqHMiO+TDuZSn+i6vKBsEp+2i5TUttevcwatSo4qU6LcspKB1xxBHWvXt39/37/fffiz/Pn0WXyQqK7kPN1jtYCAhOSIaKE0JSFFDFKTvfSkesOOmFF2XzxRdf2OjRo1P+/dlqq61szz33tFtvvdWGDh3qApR+rVWrln3//ff266+/2meffWb//e9/i7+fCsFqHp89e7a7Tn1PWu5DfOyqQzIEJ4SkiOCUWgSn9NNgyrIErq233touv/xy23LLLe2CCy6w448/3u2UO/HEE23x4sV23HHHudEEqj61atXKfU6dOnWsY8eObsddr1693OcQjhOjvw/rw1IdQlEUUM9y5ZCDU6YbipGYSq5qDP/3v/9d4uOqIp188sl2xhlnFH/s1FNPLf79tttu62Y5Yf1YqsP6EJwQiqKAKk5BBiff17Ry5cpM35WsNnfuXLvhhhvs66+/dtUcVXhiE71mJmk4pXa31axZ03bccUdr37598ZOtgukff/xhixYtcrvfVCn6888/bbPNNnN9ZmrO/+6771yfkm5bv359O/roo92ynJbi1NOkv1dLdq1bt3aznT744AP3eQ8//LD7Ny666CILQdHqIhu9bKzNL1xg9SrVtTY1treKFTL77ojghPUt1QGhKKLilJ7mcCpOyd1yyy0u6PTv3999zRSStthiC3edwpAmde++++7WtWtXW7BggZvmrSfb7bbbzsaPH+96kvTivMcee7iGbwWhBg0auBCl41E0UmDevHlucGXjxo3d7xVqddtddtnF/Tp16lTXr6QApdvss88+rsdJS3ahWF643Aqs0F6Y84pNWPGH+1iDSvWtZ+OTbY/aHTJ2vwhOWB8qTgjF6tWrg6k4hbGgmKDiRHBKTCHno48+st69e7slM239VzXI71gbOXKk2/nWoUMH15CtXiRVhn766Sd3TIqqS/qcww47zFWmFJR8aFLI0ou2lt3Uk6SGb/25RYsW7u9WQGratKlrENffrwZvfY5+KLTMqifzGjVquEu2W1y42FZXWG2zC2bbdZtfaW2qrzkKZm7hPOsz4xHr91d/G7V0tKtIpRvN4UiG5nCE9nitzDiC1CE4rZ/CjkJS7ADKevXqFT8wVR1SwImlIKWPf/vtt64PSUekaFluk002cc3h+rrPnz/fGjZs6MKQ/n5N+/YTwHWEipbhtFQ3c+ZMF778rrgQd8cpDF0/+VbrWu8g+3rRt3Z8wx529Wa9XGD6cenP7jYfLBjoLpmoQFFxwvpQcUIoigJaqgvjXiYITn7qNBIr/UBcX5VCAUjXKQR5+r2W3fQkrMN6Fb78E7J+1ffDB6kRI0a4g3l33XVXN6tJu+dS8c6kcNkyWzHlT1s6epStTtHcJ/U0TS+Yac/O/q+NWzHB7pn+gP209Be7fNN/2pZV1yx5er4CNXTx95YuBCesD8EJoVgdUMUpyOBEj9P6qRKkJ80xY8YUf0wVIN9Qr6rRrFmzSnyOqkTVq1d3v1cYUvXIL4fqRVqfq94ofVy3U6CK/Tv0702bNs0t+2nEgJb2StNtNqZptXDJEls1c4atnPKnzX3rNZt29602sdeFtvj7oVbe1Agea9XqVfbAjEft+dkv2ryCktd5/Wb3T9uyHUt1SIalOoT2eK1IxSl1WKpbP/UtHXzwwXbXXXe5+Utq9n7zzTeLH5hawtOgyWHDhrn+IzWRDxkyxPUsKdwoMCkYaVedlu80T0m79BSatAtPFah27dq5HXtaFtT1uq36nfT36qJQ9dVXX5Vo+NPfuXTpUvfv6d8ty5Tw1QUFVrFGDVdhKly00FZOneI+Xjhvrs14pE+5hyftniut0Apt4IJPbVHRorifM6dgrqtUpRO7pxCKAw44wF599dW412nOmzaabCg9D6kvUweMi3buhrQJJd+tDqjiFMa9LEUv2sI4guQ0tfvmm2+20047zX3N7r///hLB6qijjrJBgwa5XW66XmFKO+i0+05hS83e06dPt+HDh7swpZEGemArIKmitdNOO7nvwccff+wClapYhx56qL333nv2yiuvuJCkXXSqdHn6vDZt2tiHH37oApVmOEU5Nkc/VCsmT3Lnx1lRoa2Y+IerPMWa3b+f1WrfodzOltPIAfUuaRluYypVqcJSHdaHpTqEYnVAu+qCDE70OEWjcNSnT5+E1zdr1syOOeaYdT5+4YUXugqVZi7pxVlnzf3222+umqQKkUYaKFhpSU7hSf1M2iHnn6R79OjhqlQKVfpBUK+TqlSi2xx44IHuUharly+3SrXrWNGypbZkxA+24NOBatQqcZuCuXPcAb012/yvIX5jaE6TGr7Vu7SxlapUCKWsjcxgqQ4hWU3FKT2Tw6k4pYYqS5rDpIunKpEqR9oxp6pRy5YtE36+qlKxO/YUthTiNqYMu9pW28oZ023J8GG2eOhgK1ryvypWrMIF8zf431jn3ywqsnaTa9gFy7rYS3UG27wK8f/NWA0rN3CVqnSg4oQQK07amXvWWWe5SreeSy699FI76KCD3HV6o/X666/bAw884NoF9t13X7vxxhvdzl7RuBRVzjVgV/83Le3ddtttxasQCFvlQJbqgnzLylJdemhOk+9v0hwnTf5WqCrrk5R6mrTTbmNGEqycMcNmPHyfLfz8E6u6+RbW6OSeVv+Io5UeStzOLeWVg6W/jnJN52o+3/yhd+3SO2bbOW9Wsy7Ld0r6eac3OintE8XpcUJIVcknn3zSVaW1pH/SSSfZxRdf7E4fEI1A0SBejT/p16+fe87R6QeenntUwX7ttdesb9++rtUgUc8UwrKapbrUIjil70lXTdx+yU2hSUMt10dzoDbddFO3q07vGjU5XO8kNAxzQ1XfqoVVrFXbClfOtYrVqlvdAw92H6+2VQub8fhDZgUFVrlBQ6uxfRvbWIWLF1v1bba1alttbUvnzXUfq7jabKtRC2yrUUNtu0uPtJc3GVqi90mVJoWmTEwSL0uDPfJHti7VdenSxbp16+Z+f8opp7geSQUhNXZrxMz111/vnj/k6quvdrfRmBRVnXR0ky6iVgGdfKA2AuSGyoFUnMK4lwmCE3OcUktldF025MGv3XaqUmlZVX+HSu4bQw3fjU/u6XbPLR35o818/CFrev5FVmvXDrbZpVfa9Afvs0Ynnb7RjeFanpt8y7XW6NgTreHRx9nSH39Y5zZb9RtkD9/7oI1Z8XtWnF1HxQkh2XrrrUv8WaNLVN1WcNKYEx+a/HV6fOvoJgUnbVRRpUkVKoUpzZXT6QYI32p6nFKLHqfspp15upS32h32sGYXXmZ/vfCcLR422IpWLLdmF/WyGm3aWos+j7jm8Y2l5vKCWTNtxqMPqOEi7m3UhL7it7HWtpya0DcWwQkhVZxKP16T3U9/W12vHb7qjTr22GPdBhb1Td55551puc9Ij8pUnFKHilP+UnjSyAEFHDWCr5w2xapt2aJcQtM6zeVJerLKswl9YxGckEg2BqcJEyaU+LP6H/3RUOqn1Dwmfxi5rlPLgKrWGpOi319zzTXFn6vZcr5xHOGrsnbjV7bLvs7BCOhxym9ajtPIgTp/28eqb7V1ub44RG0uL68m9PJAcEJIwenzzz+3AQMGuID0wgsv2A8//FA8FkWjZrRLTn1LOvXg7rvvtq5du7r+Sp2bqY0m6pnU5+ow8nHjxmX6v4NyFMocpyCDkz8WREMXgfKk5vJK9dc9KiZWeTWhlxeawxHSUt2jjz5qb731lmsQV3DS6IFtt93WXaf5bhpNoCU5DcfVEF4N8RXNjDvvvPOsd+/ebnivdvxqlAHCV7D2tTyUilOF1QG+XdW7jebNm9sll1xSYho2UB50dIua0BNRn5WWDLOBXhj1bp0t2bl1LMkFF1zgenniHUuioKGjjNZHL0Layfrdd9+VeO5UOBk4cKDbIatjSbQL9sUXXyz3/wcQlSqJGrB89tlnu3EV2S7oihMDMJEKvgm9dOVJlaZsCk1egO99kCbZWHECSvOv5aFUnCqHHJwYR4B0NaGrp0nLc+V1Dh6QDgQnhGD58uVBBacgXwXocUK6m9D1a7aGJipOuccfS7Lzzju7XiANifT8sSSajaaxH7169XIzjTwdS9KzZ0/XN/Lzzz/bFVdc4QbRAtlqxdrHZyjjCLLzlWA9/BeX5nCA4JSLyuNYEgWs7bbbjmNJEMxSXdWqVS0EYcS7BFiqAwhOuai8jiWpXbu2O6ybY0mQzVZQcUofghNAcMqnY0kk2bEkomNJzj//fHeo9ogRI9zco3RupFEVbNSoUXGvGz16tKuUbSiFQ+0E1FKmDBkyxAVKhG1lYM3hQS7VeXpiAPIdPU65Z2OPJdEBuJqy3a5dO+vUqVNa7jOwsRWnUJbqgg5OVJwABmDmonjHkmh2XeyxJLHX+WNJ1BgeeyxJjRo13LEk6dSwYcO0/nvIneBUJZCKU7A9Tnp3RXM4ykPR6iIbtXS0/bp8jNlqs7Y129gONVpbxQphvK+g4hTGUMuLLrrINW3HW74aPHiwnXHGGescS7Lrrrval19+6Y4lufHGG90uOX8syWWXXeamxt9xxx22zTbbuAq8jiVZsmSJ3Xrrre7v0fKdlrc0gTtdtJSoxvR33nnH/fvqu/rb3/7m7qN/7v7111/d/1n3WdWxzp07Fx+lNWPGDHedmuBFgzr19QtlxxU2vAgSSsUp2EciwQnlFTpGLPnJ7p/xqBXYmrH/b84fYLUr1rZzm/S0PWp3yPqfA4JT7tGxJDqL7dprr3UH3vpjSRScNPlbowi0JKfq01577eXChz+W5PDDD3fjChSq9OZSx5KMHDkybfddjex77rmnu1+6r1o+VN+VKlEKVZoQPWfOHLdrUPdx2LBhLhjuvffe7vMVoDSGoVGjRq4S8fXXX9sff/zhdgiqmta4cePiM81q1qxp9eplz7mRyI/m8DDuZQL0OGFjjFs+3rau1sJ2qbWTndH4FHvyr+eKr1tctNj6zHjELmt2YdaHJ4JTblEztzz99NPrXHf00Ue7iyh4iCpKasj2dASPgstxxx1nLVu2tJNPPrn4OoUwLe15qoLpUp4UhLSbz99PBT4tPepAXoWcZcuWuUqabiMKSToCZrfddnOhqX79+u4iOty3QYMGLmgpOOmxrkZi/5j34RBhW0XFKT2oOGFjl+fum/awNa3axLas2tz+Kpgd93b9/upvHWq1z+plO4JTGBYsWOCW3bR8pe9ZOpavMjU5vFmzZuuEKf2/NWbhvvvus9NOO634OvVhaelR5+q1adPGVaf69u3r5lYpFCpoHXbYYe7rpa/h7bffbvvtt59bAtSZfTprr2vXrhn4X6K8sKsuTQhO2Bijl421eUXzbczy32zgwk9txNKf4t5uTuFcd1tgYz333HNuzMARRxxhO+64o3344YeukuIPOdVy1FFHHeUu2m6vPidPAUmzmVRF0lLcn3/+6YLW+mQqOJXHrsAHH3zQHWrMrsD8WaqrtvaNQrYLdqmO4ISNMb9wQUpumwlUnMKgClJsL9L48eNd+FEvj6pMCghamhL1MCk06AVFLya6jS5St25dt4POh65sDE7xdgW2bdu2xK5ALRuW3hX4zTfflNgVKNoVqOoSctcqlurSQz9cWt8GNkS9SnVTcttMIDiFwY8T8NQsraUoBSJVlHxo8tfp+6rr1QytSowaqBWWFKbU16PBl9kanMqyK/Duu+92S236/2tXoKpv6vPS/09/h3qj0rkrEOm3MrABmFSckJfa1Nje6les55brkmlYqYG7bTYjOOXeZpbY5atFixbZ22+/7So2OtRXFahBgwZF+nsyFZzKsitQlTaFKl+JO++886x3794uVKnBPN27ApF+BWsb/FmqSzE9IbCrDhtKzd5nNDnF7ZxL5vTGJ2V1Y7gQnMJQevlq9uzZ1qRJk+IXDn/WnKiypOc4VWF01Ip+r7DhqWE6yotMJoJTWXcFlnbJJZe4Szzp2BWI9FsZWMUpu18R1rNUR3DKfauLimzp6FG2aPA37lf9eWOGXH6zaLD7VX/WmAGNG6hdsdY6t69TsXYQowiY4xQOLV998cUXbilKZ7lp+W2HHXZw12kukXaIKTApUKmipAqNwpHCk3pA1DyucKUdalGmgSdryAayMThVozk8tag45b7CJYttyl232qrJk4o/Vql+A2t8ck+r3WGPpJ+rgLVi6mSrtnlzm7/sL7v5rz42o2Bm8fUNKtW3no1PdsFI4waYHI50LF9pm736fTQcslu3bm5G0cyZM928JTWOa0lO1SeNG1AzuTRt2tRt1f/kk09cGNKWfW3NnzVr1nr/TYITQmoOrxZIcKqwOtA6v3aWqKlSO1OQe5b++otV3WxzK5g/36bcdPU61ze78LKE4Wn5HxNs+kP/ssJ5c23mQe2s/Ym9bNKKP+2mqXeuc9sQqkrJqFKhmTZ+eQSIDU0afaAwBmSz66+/3m0YUC+bDqbOdizVIeuoWjTzycds8s3X2ty3Xo17m9n9+8Vdtls1a6ZV37ql1e6wpxVVMOvX5ne7bco9NmPVLKtka45piNVvdn+3bBeyQN/7IE0tDUC2K6A5PD0YR5C7lo0d7apFsnTtr6UVzJ3jblezzZrZMKIgNeXOm612+91VirGJW1a0hZtUsIUr/7Dxs9ZtVJU5BWsGXOpg31ARnJAIS3UIqcepKof8phbBKXcVLpi/QbfzgWvBpx+5Py9qWzEnBlyuD8EJiRCcEIKCwCpOwdZx2VWXuyrVrbdBtysdpOoszo0Bl8nwwggeH8iV5vDq1atbCIIOTkwOz001tm/jds8lU7lBQ3e7ZEGqxZ9FtsnC1SrJJPx7GlbO/gGX60PFCYkQrBGCVYEduVIx5N1EzHHKTRUqVnQjB5JpdNLp7nbJAlfF1WaHfrTmBzJReDq9UfYPuFwfghMSITghpKW6qgSn1FeceMHIXRo1oJEDpStPqjQlGkUQL3C1HVtkJ762yjZZtG6lKfRRBB4/B0iEXXXpVVS02kaPWmqDv1nkftWfEb3ipDMbQxDGvYyDpbrcp3BUq32HNU3fC+a7pThVlUpXmkp/joLVXy88V7wzT+Fpx9m1bO6pB9mKbTZ1PU1angu90iRMDsf6Hh9Ijx+HL7F+T82yeXP/dyZh/QaV7OSeja3DHrX5NiQR2upR5ZCX6uhxyn0KSbEjBzYmcLXM0Zk2VJyQCMEpPVauLLLmW1W1TTapXCI46feP9JlhF17WjPAUoeIUimBfSQhOiBK46vxtH/drsipV6AhOSITglHpajrvjxik2d3aBHXhI/B26/fvNZtkuQo9TKKg4AYEjOCERglPqjR29zCb+sdJuv3FqwtvMnVPgbtembc003KMwg1OFgJaVg6448YKBfEePE5KhOTz1FswvLNfb5aOCwCpOBCcgcLyBQCIhvYsPVd16lcr1dvnaHF4hoMdqsMFJ2xZpDgeAdfnnxpBejEK1fZsabvdcMg0aVna3Q3xUnNKEpTpgDSpOSBScWKpLvYoVK7iRA8mcdHojdzvER49TmhCcAHqckPiFSAhO6aE5TRo5ULrypEoTowhyb6mucshLdbzTBoB1sVSXmfDUvkMtt3tOjeDqadLyHJWm3Ks4EZyAwPEGAiEFJ809ytVwof8HIwc27PGajY/VRAhOQMAYR4CQepxmzlxp99w61ebM5lgShFtxyq6fqjL2OAGg4oQwKk4T/1huDRtWse7HNizxcX8syfdDF2fsviHzPU4VsyzkJ0PFCQgcS3XI9uCk5bkH751u9epVttqbVEp4LIl6hHJl2Q7R0RyeJlWqVEnXPwVkLZbqEEJwUk+TKkuxB+CWxrEk+auQXXXp21UHgIoTsr/HKdeOJcnlBvdMPV4rZsljNYpg0wfBCQDCqDjl0rEk6sV64bm/SlTPNL9JQzA1kgC53+MUzj1NsFTHsSsAkN3BKVeOJVm5ssjee2veOkuONLjnV8UpnHuaoOIU2hk3QHmixwkhLNXlwrEkWp6bNnWlXXPT5lYnSYO7boeyoeKUJlWrVnW/Ll++PF3/JJCV2FWHbKg4KTCMHrXUBn+zyP1aOkCEfiyJeppuu36KPf7wTFu8qDBpgztyOzhVDn2O08qVKzN9V4CMIjgh0WMiXcEpat9PyMeS6P5qgWP4sCXrvR3K/ngNKTiFc09LoeIEsFSH5BWndA0KXrlideS+H38syd/2qeN+DSE05VqDe7YpDKziFM49TdAcvmLFikzfFSCjqDihNN/7meqKk5bjnn5iplWoqH/LcrrvJ1ca3LNREc3h6Q1O9Dghn2XLrinkZ3O4lty+/nyRPfHwTFu7OpizfT+50OCezY/XSgEdoxZ8xWnVqlWZvitARlFxQqaaw3NtsOX6hN7gnq2KAqs4BdsczlIdsAbBCZkKTvnY9xNyg3s2P4dVCqjiVDn05nB6nJDPmOOEZGE61e/ifd9PsjPocrHvxze4Iz8rTuHc01IITgCQ2YoTfT/Ix4pTsMGJHicAyPzkcPp+kG/N4SzVAQFjqQ7ZMDmcvh/kU8Up+ODE5HDkO5rDkQ1HrtD3g415DvPnz4Yg2KW6atWquV9pDkc+o+KETDaHA+WB5vA08emUOU4AsO4LkTAgFSFYTcUpvRUnluqQz6g4IdPN4UC+9TgF+1NFcALWoMcJmT7kF9hY9DilAcEJoOKExKfNC0t1CMFqlurSO8fJnwIOAFiDpTqEZDVLdelBxQmg4oT4aA5HaCozjiD1CE4AEB/jCBCS1SzVpUf16tXdr4wjQL6jORylUXFCaCpTcUo9epwAmn8RHz1OCPU1PQTBjiOg4gSsQcUJpVFxQmgqBTQ6g+AEBIwBmIiHHieEFvKrUHFKX8WJyeEAEP/FiDlOyHYrV650v9LjlAb+i+wHvQH5iIoT4qHHCaEFpypUnFLPn8HErjrkM4ITki3VUXFCtlu+fLn7leCURgQnACiJihNCC06VAxpHEM49TYAjV5CPRo8ebbfddpvNmzfP/Qw0bdrUmjRpYltssYVtu+22tsMOO1j79u1t5513Lu4HRP6gxwmhFT+qBLRUR3ACAjF27Fi7/fbb7b333rO5c+e6j9WvX98aNWpkCxYssHHjxtkvv/wSd5tvrVq13G033XRTa9GihW2//fa244472u67725bbrllBv43SMdSXUjv4pGfllNxSi+t31NxQi77/fffXVh69913bc6cOe5jCkAnn3yyXXvttdamTZt1PufPP/+0H374wX7++WcXtiZOnGjTp093YWvKlCk2ePDguEcYbbLJJta4cWNXtdpmm21c1WrXXXd1l5o1a6bl/4vy4TfN0OOEUCpOVatWtVAE/3aE4IRc88cff9itt95qAwYMsNmzZ7uP1atXz0488UQXltq2bZv081VB0qV79+4Jd7H89NNPNnz4cBs1apSrVClQzZo1yyZMmOCWAQcOHLhO1UrhSaGtWbNmttVWW5WoWunPfsMGMo8eJ4RixYoVwVVHw7mncVBxQq6YNGmS61l6++237a+//nIfq1u3rh1//PF2zTXX2E477VRu/5be2Sns6JLItGnT7Pvvv3dVqzFjxhRXrVT10nVDhw6NW7WqU6eOWzr0VStVxFSxUr9V7dq1y+3/gOTocUJowakqFaf0IDghZFpSu+OOO+zNN9901R7RctkxxxzjKku77LJLxu7bZpttZkcccYS7JKpaqZ/KV620pDh58mT3/1AI1BLhJ598UuJzVJHyVSs1s6tK1apVK1e16tChgwtaVK3KB5PDEYpVLNWlPzgxABMh0ZKYD0szZsxwH1OV5uijj7arr77aBYgQ6N2hqki6JKIQpaqVlgVVtdISpKpVqlqpeqXr4v29vmq1+eabW8uWLV3VSiFSXxsFS6wfS3UIreJUhV116UHFCSFQWLjzzjvt9ddfd4FBtGx11FFHWe/evW3PPfe0XKTxCN26dXOXRP2Jv/76q2tkV/VKvVaqws2cOdNVr3777Tf77LPPSnyOKlI1atRwPV/qtVIv13bbbeeqVrvttpvru6JqxQBMhGMVFaf0ouKEbKVqi8LSq6++alOnTnUf00gALX0pLO21116W79QMqt6tZP1bao5XsFLVSk3rvmqlj6v/SteVpneuqlo1bNjQVa223nrr4qqV+roUunIdFSeEYgUVp/Si4oRsohdzhaVXXnnFLcmJenpUcdEyXMeOHTN9F4OjJbtDDjnEXRIFBAUqX7VSr5WvWilgjR8/3r744ot1njd81Uq9Vr5q1a5dO1e10hiG0KtWNIcjFKuoOKWXntz8EwSQCZqNdNddd9lLL73klpdEL8pdu3a1q666yjp37sw3JsXPARrPkGxEw/z5812w+vHHH13I0sgFVQEVdNXYPmLEiLhVKy2nqmqlRnlVrVq3bu0msatqpUCXzWgOR2iH/FZlV116UHFCJuiF+O6777YXX3zR7SDzYenggw+2K6+80g488EC+MVlElSV9TxJ9X/TmS5UqNauraqXeKn1fVbVST5qC1ldffbXOc4+OstHfrV6u5s2bu6qVApyqVqpeZXIuDUt1CC04VaE5PH3vNtlVh3RYuHChC0v9+/d3M41EL5xdunSxyy+/POFSEsJ4HlFTuS7Jvv8avaCqlRrafdVKM7dUxVIPVmkKTqpaNWjQoLhqpX9DVas99tjDBa5UV5yYHI5QluqqVatmoQh+ACbBCamiF8t//etf9sILL7gXSv/DfcABB1ivXr0S7hZD7tEYBC27Jlp6VYVHjet+aKivWmnkhDYKKGwPGjQobtVKg0511I2qVjqgWVUrjXlQ0/yGLl9QcUIoVlJxSi96nFDeFi9ebPfdd5/95z//cY3FohcvvWBeeumlCQdCIr/puUgDPHXRtPdEjy1VrNRTpaqVHl/aRKBeKwWtkSNHxq1aaTemqlaxBzT7XitVsuKhxwmhHZtWjYpTerBUh/KwdOlS69Onjz3//PNulpBedBSWOnXqZJdddpkdeeSRfKGx0bRsp52ViXZXqkqkDQbDhg0rrlqpUqWqlcKVdgt+++2361St/AHNWvrTUTeqWvlJ9FTkEUrFqRrBKT2oOGFDLV++3O6//3577rnnXGOwwpKaE/fZZx+75JJL3AG5oW9JR1j0eNMxNLro2J1Ej1tVrHTRjkBftVKvlUK/mttjqUKqA5pVtdJRN7FVK39As8YxAJmykl116UXFCWWhF52HH37YnnnmGXeWmsKSlkI0jPLiiy92L1aEJWQz9UTp8ZpsgKoqUzfddJM9++yztv/++7vHvXYHanSGQtbgwYPX+RxftVKvlT+gWfOsdECzdgnq3wVSoYCluvTSOynmOGF972YeeeQRe/rpp93uJx+WtKvpoosushNPPJGwhJyiCpLGIch1113nNjOU/pnQLkB/QLMqVQpUWt7TJgj9nAwcOHCd51p/QLOOulFVLLZqpT/zpgMbggGYaaYfVJ9WgdgXhscff9yeeuop98KgsKQnfh0Se+GFF9opp5zCkzxyWrLmcPXvKezokoimrvsdgjqgWb1WqlrpgGZdN3To0LhVK39As69a6agbVa30s6fgBZRGxSnNqDgh9ofv3//+t/Xt29eFJVUi9fjQtu7/+7//s9NPP52whLyxseMItFtP/VGJdpHqzYn6qXzVSn2CamxX1UpjGLQU/sknn5T4HN0XX7XSUTeqUrVq1cpVrRSsFLSoWuVvj1P1gJaDg57jRHN4flNYevLJJ+2JJ55wW7l9WNJhrhdccIGdeeaZPBEjL6V6HIGqVnpToksiClGqWmlZUFUrf0CzqlaqXum6eH+vr1rpgOaWLVu6qpX/t9SHhdyyirPq0ouKU36GJTV3q7qkJ2SFJb04aFjg+eefb2eddVZGj7oAskE2HPKr8QgaEptoUKx+ljXPyh/QrF4rf0Czqlcax/DZZ5+V+Bz9rPsDmtVr5Q9oVtVKTezqu6JqFeZSXXUqTulBcMqfFwHtEFLfkgYIajaNnhzVAHveeefZueeeS1gCAhuAqTc4esOjSyKaX6VgpTdJalr3VSt9XP1Xuq40jRVR1UoHNKtqpaNuNI1dlWiFK4UuZF9wqhzQG95w7mmC4OSfIJB7YUnTu7UjTjNrfFjSE+A555zjAlNIp2kD6ZQrR65oyU7nQCY6C1L/TwUqX7VSr5WvWilgac7VF198kbBqpaqYr1rpjZiClcYwhP51C0lBgBu8gg5O9DjlFj0J6hBdzVpS06l+oLTUoCeys88+2/7xj38QloAcqTiVB/9mSpdE5s+f74KVqtUKWf6AZlWt1NiuN2bxqlaa9K6qlRrlfa+VP+pGR+CgfHucQhJ0cKLilBth6eWXX7aHHnrINYv6sNS6dWvXr6QdcSGtfQPZIBt6nLKFKksHHniguyT6WqlSpecfVa38Ac2qWqmJXUHrq6++KvE5+rqqaqUDmrVDUOMXVLVSgFPVStWrkJaeMqmAilN66YHJUl149ET12muv2YMPPujO5dI7Dj0RqbHzjDPOsH/+85+EJWAj5EvFqTzoa6TnHl0SWbhwoauCq2qlhnZftdJRN/qzPh7v9UlVK1WnfK+V3hCqaqXxC1omhBGc0o3m8LDC0ttvv+3OhxsyZIib3aGwpHdpPXv2dEeeMCAPKB8Ep/KlMQidO3d2l0TPb2pc90NDfdVKBzRrLIMGiH799dclPkfPf6qmq2qlENW8eXM3y0pVK41eUNN8PvRxFqxdZQhJ0LVEluqy3zvvvGN9+vSx7777rnjQmU5vP/XUU+2yyy5z78gApGapjuWi9FWtFHp0Of744+PeZvHixa4ypZ4qVan8Ac3qtdLAUAWu0vT90wHN6rXyBzSraqVQpaqV+q9CV1hYaKEJOjixVJed3n//ffvXv/5l3377ra1YscJ9TM2VCkuXX345YQlI04tRaO/kc5neJHbs2NFdEoVdza9S+4KvWqlSpaqVlgRVwfrmm29KfI6+vzrqxlet1GulN6Z+/IKWBbO9R7SAilN6EZyyx0cffWT33nuv+8HWaeyiNf2TTz7ZrrjiCib+AmnEUl2YVSsdQ6PLMcccE/c2em5VxUoX7Qj0VSsFKzW46wSFeCszqlqp18pXrdTPpaqVdggqbGU65FcILOAHX3FC5nz66ad29913u7V7H5b0Q3/SSSfZlVdeyaA5IEMITrlJ1aO99trLXRLRHCuNX1DVSkuA/oDmuXPnuoqW2ibi/b3q42rcuHHxAc2qWumAZl1SWbUqoOKUXvQ4pZ+OQLjnnnvc9txly5a5j2mA3AknnGBXXXUV802ALJArAzBRdno+1qV79+5xr1evqSax+wOaddSNqlZqYlcFS/1XpXer67VWm3dUtWrWrJmrWumAZl+1UmP7hj7WCE5pRsUpPRSS7rrrLvvyyy9t6dKl7mP6QVETpMKSpvsCyB5UnJCIduop7OiSiKau+x2COqDZV610QPPUqVPdzujS1GulqpVeD3zVSsOLVbHSLsFEu6ZZqkszTXdljlNqqFfpzjvvdMcVLFmyxH1Ms0h01Env3r2ZQQJkMYITNoZ26x1xxBHukqhq9csvvxRXrdRfpWVAP3pBYevjjz8u8TmqSCk81a9f31Wt1NahqpX6s+hxSiMqTuVL7yLuuOMO17vkw5J+gDTB++qrr3YPdgDZj6U6pLpq1b59e3dJRCFKVSstCypI+QOaVbVS9Uq7Bz2NWAhJ0N3VBKeNpwe2wtInn3xiixYtch/TzgtN8FZYyoU5IUC+VpxCeyeP3NGkSRPr1q2bu8R69tln7dZbb3VBSvSG/IknnrCQEJzykMqrCksqpeooAdF5SxodcO2112Z8eyqAjcNSHbLJwoUL3U7r//73v241Q0WPQw891B27pV6o0ATf4+S78qk+Jacmv9tuu80GDhxoCxYsKH5HoAZvhSWtNwPIDSzVIRsMGzbMLr30UjcMWWFeu/L05+uvvz7o42RyIjhphhBHd6xLzXu33367ffjhhzZ//nz3Mc3pUM+SwpIGVALIPVSckMnQ/uijj7qxNRpzIDvuuKPbmV162S5UQQcnX2UiOP3P6NGjXWXpgw8+sHnz5rmP6ZwjHaR73XXXBVkWBVA2BCek2+zZs61Xr172yiuvuNdkFTZ69OhhDzzwQM61f+RExckfHpuvNB1WlaX33nvPTYcVlURPO+00u+aaa9x4fQD5g6U6pHPOX69evdy0cgV2tYDccMMN7qitXG2hCfp/5ddI/UGy+URzMxSW3n33Xbe9UzQfwzd4t2nTJtN3EUCGK065+sKFzAfze+65xzV36xBi7d7UaAId7t65c+ec//bkTI9TPtD2TW3jHDBggCuLSr169ezEE090YUlnCwEAFSekguYwXXLJJfb222+7lR6dYXfqqae6wKRKU74IOjj5d1O5XHGaNGmS61nSA1UTVqVu3bpuN5yW4XRWEADEIjihPKlnVidGaHe2P0VCS3EXXXRRXp6HmBNLdatWrbJcotOtNWfpzTffdNNXRWcAHXPMMa6ytMsuu2T6LgLIYjSHY2OpoqQ37Y899phrB9Fy3N577219+vSxPffcM6+/wDkRnHKh4qRtmz4sac1Y6tSpY0cffbSb4N2hQ4dM30UAgSA4YWNaQv75z3+6MTaakajz5c4++2y79957XWsICE4ZpYCksPTaa6+5s3tE86iOPPJIF5byPdUD2DAs1aGsXn/9dTeyRufKiYYi689nnnlmXi7H5WzFKcQeJy293Xnnnfbqq6/a1KlT3cdq1aplhx12mFtD3meffTJ9FwEEjooTotDGKk3xfvLJJ92JEgpI+++/v5u9RP9sjganUHqctANOYUmDwfwkVZU/NUVVlaWOHTtm+i4CyCEEJ6xvULJ2x3366adWWFjo2kK0PKcRN5zCkePBqVq1allbcdIgSo2Yf+mll2zy5MnuYzVq1LCuXbvaVVddlRezLgBkBsEJ8eiQ3ZtuusnGjx/v/rzddtvZzTff7EbaIM8qTtkyOVznwd1999324osvujECPiwdfPDB7mToAw88MNN3EUAeBSdg8eLF7s36888/735fqVIl9wb+oYcecsEJeToAM5MVp4ULF7qw1L9/f5s4caL7mIaCdenSxS6//HI75JBDMnbfAOR3czjy1/Dhw+3SSy+1QYMGuceDTpZQH60qTL7ogDwMTpmqOCksaVLqCy+8YBMmTCheNjzggAPcmT25cgI0gDBRccpPCkhPPPGEaxPRPEDRiRLavX3EEUdk+u7ljJzocdKsiVRTifO+++6z//znP8Xrwwpu6lVSqudBCSBbEJzyi3pqtcKhntply5a51Zju3bvb/fff78YKoHzlRHBKVcVp6dKl7oHXr18/GzdunHsyUljq1KmTXXbZZW7eEgBkG4JTfvjmm2/cKsfQoUPd97xx48ZuOU7HcXHAc+rkRI9TeQYnzbXQDIvnnnvOfvvtN/dg1L+jUfPavqlJ3gwDA5DNtMUcubscpzf0WgHxg5N33XVXN9mbDUjpEXRwKq+Kk8LSww8/bM8884yNHTvWhSWl9b/97W928cUX27HHHktYAhAMKk65RydNaKXjjTfecBui9PqnMQI6O65Zs2aZvnt5JejgpN1rG9rjpLD1yCOP2NNPP+2GgfmwtMcee7gTn/WApLIEIEQEp9yhIZVXXHGFjRgxwv150003dctz6q3lNSoz8qripNs9/vjj9tRTT9mvv/7qSp6aaaEDdC+88EI75ZRTeCACCB7BKWwqBmgnnN7c//XXX1ahQgV3dqmW5ziWK/Ny/sgVPQD//e9/W9++fW3UqFHFYUlrwv/3f/9np59+OmEJQE4hOIVJIwTUHvLee++51zUNUD7jjDPc+JsGDRpk+u4hlypOpYOTwpIOLdQ8i5EjRxaHpV122cUuuOAC69mzJzsOAOQsglNY3nnnHbcTTm/uZcstt3R/Puecc3hjn4VypsdJFzV3q7r0008/ubCk9V+d8Hz++efbWWedRVgCkBcITtlPrSM33nije4M/b94893q13377uWbv9u3bZ/ruIYkKqwP+CdNuOJUyN9lkEzeg0oeldu3a2XnnnWfnnnsuYQlA3tGMOVUxAn56z1m///67W44bOHCgGxtRu3ZtO+2009zRXfo9sl/wPU716tVzh+uKQlPdunVdkteThoZWKrmrmW7rrbfO9N0FgLTgrLrs8/LLL9v111/vgpNss802dsMNN7jQhLAEHZwUlAYPHuzmWmhtWEFp6tSpNmnSJBszZox99NFHxbfVrgRVp9Rgt8UWW7hToXfccUfba6+93K46v+wHAKGj0pQddPrE1Vdfbc8++6wtWrTI9doefPDBbshymzZtMn33kI9Ldevzxx9/2Ndff+3mX2hW08SJE23WrFnuAVx69pNmOKla1bRpU2vRooU7GHG33XZz1SoFLQAIxaGHHmoffPABlacM+fnnn91JE19++aX7Hmhl5Oyzz7Zbb72VN+k5IKeDUzLqiRoyZIg740cPclWrpk2b5g5LVO9ULFWratasaQ0bNnQhqlWrVrbzzju7uRoKV34sAgBkg27dutmHH35IcEojBSRtULr99tvdm3Rp3bq13XbbbdajR4903hWkWN4Gp/X9AGgd+ttvv7Xhw4e7ZT8t/6lapcBV+hwonWWnapUmuqqXaocddnDLf6pWMQofQLr9/e9/d60K9Dqlnnpsr7zySnvhhRfc0pxWLxRctRxHb21uIjhtgIULF7pTqYcNG2a//PKLjR8/3lWr9AMUr1pVq1Yta9SokTVv3ty23357V63SOXiaK8UJ1gDKW9euXd2uLYJT6uj5X8tx3333nesp04qETqDQ/CVWIXIbwamc6YlKx7moWqXeKh0arGmwGpvvRybE2xmoalXLli1db5WqVfvuuy+TYgFsEDUgf/LJJwSncqbnbx0If++997qNSKI3wnfeeaer8iE/EJzSTD1UgwYNsu+//97tBJwwYYKrVi1YsMCdeF1616CqVY0bN3aTZFWt0lEx2gmoWVUc8AggnoMOOsgdDkvFqXyoTePyyy+3V1991a0q6A2vZmVpOW6zzTbjQZhnCE5ZRDv91KiuEQs//vijq1ZNnjzZZs+ebUuWLFnnSVBHztSvX99VqzQTRGFq9913t7333ttVsQDkpy5dutjnn3++Tj8myuaLL75wgUm9rlqOU8+qhleqp4k3rvmL4BTYux7fW6XlQFWrZsyY4apVGvoZS/NCNIW2SZMmxdUqDQNVqNLv+aEHcteBBx7oXvQJThv2BlZLcaom6TlXfaraPX3fffdZp06dUvDdQmgITjn0w/7DDz+4apXO6vvtt99sypQprlqlnR6lN09q4KeqVSozb7vttm4Y6B577OGWARn7D4TtgAMOcDOECE7R6flSzd4DBgxwb0T1HHnccce5wKTNPYBHcMqjJwU1rCtcqbdKc0ZUrdIOwVWrVq1TrapTp46rVmkYqGaRqFrVsWNHt72WahWQ3fbff3/76quvCE4RvP/++9a7d28bOXKk+7Nm9V111VX2j3/8g+c6xEVwgnt3peU/VavUY6UZVgpac+bMsWXLlq1TrfJH12y++eauWqVdJapW6aJBoQAyq3Pnzu7UBCpOiZ/zNMX7sccecxt2tBynuXt9+vRxfaJAMgQnrJeGf2onoBok1Vvlj65RtSre0TWbbLJJcbVKw0B9tWqrrbbiqw2kwX777ef6IUv/fOY7HcP1z3/+001V19dGu5ZPOeUUu+eee9zzFhAFwQkbRf1TOrZGF/VW+YOW9S5O1aoSD7a1By37o2t00PJOO+3khoHqXR5D44DyoSZmLc0TnNZ47bXX7Prrr3enQIhaDvTnM844g4ccyozghJTR+AS9w/PVKj1pxR60HO/oGr3r05bf2IOWVa1iVgoQnQboaqJ1PgcnzVu69tpr7emnn3Y7j9Wbqd4v7ZbT6BZgQxGckDGapK4nd1Wr1JjpD1qeN29ewoOW/dE1qlb5o2sUrji6BvgfvdlQz2I+BidtftHuOD/HSm/GzjzzTHf4Lj2YKA8EJ2RttUoDQNWnEXt0TaKDlrXMp4OWVa3S0TXqrdLynxo+1W8F5BM97vWGpPSO2VzWr18/u+WWW9x8O2nVqpX78/HHH5/pu4YcQ3BCkHSgsno4Yg9anj59uqtWJTq6JvagZR2w7A9aZrwCco0G3epnI9eDkzaoaJTA888/705XUOVZ5/Q99NBD7jQFIBUITsjJapXClJYBSx+0nOjoGlWr1EflD1rWaAW9+GjsAhAaDbLVeZi5GpzUM6nlOFWk9fOsn9Pzzz/fbrzxRjaZIOUITsg7mqauJ1x/0LKqVRoGqipW6aNrVI3SJPXSBy0rVGk5kGoVstGee+7p3jSUfjyHTAHp3//+t911113uDE/RiQd33HGHHXbYYZm+e8gjBCcghpppdcCyP7qm9EHL8Y6u0YHKqlbFHrSsHhPmwiBTVDHV4zgXgpNGm/Tq1ctefvllN+JEu28PP/xwu//++92bGSDdCE5AGagy5atVGgaqcQvqrdJ253hH1/iDljX80x9do2qVdgVSrUKqKLwr+IccnPRzdtlll7leLb1h0c+RhlfqOBR20SKTCE5AOdGLlM4CHDJkSImDlnV0TbyDljUMNPagZQ0DVaVAyywctIyN0aFDBzfio/RGiRCW43Soro4+0ZsU0bgRTfbWwcVANiA4AWmiBnU1rMcetDxz5kxXrYp3dE3sQctt2rRxvVUabKipx0AuBSeFJDV7v/nmm+4NiDZs9OjRwy3HMU4E2YbgBGQBDfzU8p+ClV7w/EHL/uia2GqVhoGqt0pH18QetKxKlSpWug75TVUa7SzN9uD08ccf25VXXun6sUTV1yuuuMItybGUjWxFcAICoF4q9Xz4g5Z18LKqVTq6JtFBy02bNnXVKX/QsqpVOiMQuU/fbz1OSk/gzwZ6vGqK9yOPPOI2XeiNgGaqaYlOYxSAbEdwAgKn/intAlRvlT+6RgctaxhovIOWdeyE5t74o2vUW6UXLFUpOGg5N2hZd/To0VkVnBT2tRz33nvvuY0UehyedNJJdu+997qdqUAoCE5ADlOzreZU6aBlzfXxBy1rGGiig5b90TXqrdJ4BX/Qsj6GMGgivr7X2RCc3n77bbvmmmtcBUy0w1R/Pvvss1mOQ5AITkAe05EV6qvSlu+ff/7ZnfOV7KBlHV2j3irNz9FZYOqtUrVKL9RsEc8e+r5oV2fpimO66LGjKd59+/Z1g2XVr9SpUyfX7K3HChAyghOAhNUqLfeot0rNuxoGquUWVat00HLpo2u0zKcll9iDltWsrmGgOicQ6aPlV20wSHdw0mPk4osvtk8++cRVM7UztGfPnm66NyM2kCsITgA2iHb86aBl7QaMPWhZFYZEBy3r6Br1VmkYqK9W6UWeHVRhB6cXXnjBbr75ZvdvinrnVHE6+eST0/LvA+lEcAJQ7lSN0tKfP2hZy0Y6uibZQcv+6BpVq9Rb5Q9apnG47HSGm4KsNg6kiqqO6lV67rnnXL+cJuV36dLFHnzwQXemI5CrCE4A0m7WrFkljq7xBy1rGGiig5Y1CNEftKzt9qpWaTAo1ap1KXiqXy0VwUmBWMtxX331lQvACrbnnnuuqzgxQwz5gOAEIOvm/KhKpREL+lXLP6pW6eiaRActxx5dE3vQcr721ai/TP1o+nqVBwWkp556yvUq6e/1/8Ztt91m3bt3L5d/AwgFwQlAULTrT71V2gmoapXGK6i3SjsE4x207I+u8Qcta7yClgC32WabnK1WqRKnI342NjipX+3yyy+3F1980VWvtHPy0EMPdctx+noC+YjgBCBnaJlPgUrDQLWk5A9aViN7ooOWNQxU1So1NKs3SL1VmmStAY0hBydV6dSHtCH09bv00ktd1U9fM+2KvPDCC+3aa69l7ATyHsEJQN7QMpPfCahRC/6gZVWrEh20rKNr/EHL/uiabK+2qLKmwFiW4KTluIceeshN8lZVTzRz6a677rJDDjkkhfcWCAvBCQDWDm0cOnSoq7KoWqWja2IPWi7xxFmhgqtW+YOWVa3yBy136NAh403SaqDXsTtRgpMa9VVdev31190YCc3jUt9Snz59XCUOQEkEJwCIQDv/tBNQDev+oGWFDlWr4h1dU/qgZfVWqWE9HQcta6q7+r40JiCRzz77zK688kp3cLSW4zS49LLLLrNevXrlbO8XUB4ITgCwkVTZUV+QqlUaBhp70HK8o2vUP6W+IYUoX63yBy2Xx9E1iYKTliPvvvtutySn0Kf7oh2I9913nzuPEMD6EZwAIIXUO6SjSNRbpaNr1FulHW8KLgpc8apV/ugaVavatm1bXK2KetCywpjv3RItOWr20oABA9zOQy0zHn/88S4wqTkeQHQEJwDIIG359wctjxw5ssRBy/GOrvHVqtiDljVeQces+GqV5lkpmL300kvWu3dv9/eKjrvRn88//3yW44ANRHACgCyuVqmfqvRBy7Nnz0560LJCl59ppeU4LcOp2VuN6wA2DsEJAAKlABV70LKqVept0k5ALcH16NHDjRNQozqA8kFwAgAAiIg9pwAAABERnAAAACIiOAEAAEREcAIAAIiI4AQAABARwQkAACAighMAAEBEBCcAAICICE4AAAAREZwAAAAiIjgBAABERHACAACIiOAEAAAQEcEJAAAgIoITAABARAQnAACAiAhOAAAAERGcAAAAIiI4AQAARERwAgAAiIjgBAAAEBHBCQAAICKCEwAAQEQEJwAAgIgITgAAABERnAAAACIiOAEAAEREcAIAAIiI4AQAABARwQkAACAighMAAEBEBCcAAICICE4AAAAREZwAAAAiIjgBAABERHACAACIiOAEAAAQEcEJAAAgIoITAABARAQnAACAiAhOAAAAERGcAAAAIiI4AQAARERwAgAAiIjgBAAAEBHBCQAAICKCEwAAQEQEJwAAgIgITgAAABERnAAAACIiOAEAAEREcAIAAIiI4AQAABARwQkAACAighMAAEBEBCcAAICICE4AAAAREZwAAAAiIjgBAABERHACAACIiOAEAAAQEcEJAAAgIoITAABARAQnAACAiAhOAAAAERGcAAAAIiI4AQAARERwAgAAiIjgBAAAEBHBCQAAICKCEwAAQEQEJwAAgIgITgAAABERnAAAACIiOAEAAEREcAIAAIiI4AQAABARwQkAACAighMAAEBEBCcAAICICE4AAAAREZwAAAAiIjgBAABERHACAACIiOAEAAAQEcEJAAAgIoITAAAAwQkAAKB8UXECAACIiOAEAAAQEcEJAAAgIoITAABARAQnAACAiAhOAAAAERGcAAAAIiI4AQAARERwAgAAiIjgBAAAEBHBCQAAICKCEwAAQEQEJwAAgIgITgAAABERnAAAACIiOAEAAEREcAIAAIiI4AQAABARwQkAACAighMAAIBF8/9TXLSwZ0ILjAAAAABJRU5ErkJggg==", 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"text/plain": [ "<Figure size 800x600 with 1 Axes>" ] @@ -251,8 +260,8 @@ "\n", "bball = chunk(bball_text, chunk_size)\n", "\n", - "hue = ['dog'] * len(dog) + ['cat'] * len(cat) + ['bball'] * len(bball)\n", - "fig = hyp.plot(dog + cat + bball, 'o', hue=hue, labels=hue, size=[8, 6])" + "labels = ['dog'] * len(dog) + ['cat'] * len(cat) + ['bball'] * len(bball)\n", + "fig = hyp.plot([dog, cat, bball], 'o', legend=['dog', 'cat', 'bball'], labels=labels, size=[8, 6])" ] }, { @@ -280,10 +289,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:31.444482Z", - "iopub.status.busy": "2026-09-05T10:24:31.444414Z", - "iopub.status.idle": "2026-09-05T10:24:33.385766Z", - "shell.execute_reply": "2026-09-05T10:24:33.385356Z" + "iopub.execute_input": "2026-10-08T17:20:31.381845Z", + "iopub.status.busy": "2026-10-08T17:20:31.381783Z", + "iopub.status.idle": "2026-10-08T17:20:33.211176Z", + "shell.execute_reply": "2026-10-08T17:20:33.210830Z" }, "scrolled": false }, @@ -338,10 +347,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:33.386938Z", - "iopub.status.busy": "2026-09-05T10:24:33.386872Z", - "iopub.status.idle": "2026-09-05T10:24:44.909398Z", - "shell.execute_reply": "2026-09-05T10:24:44.908713Z" + "iopub.execute_input": "2026-10-08T17:20:33.212331Z", + "iopub.status.busy": "2026-10-08T17:20:33.212268Z", + "iopub.status.idle": "2026-10-08T17:20:44.997045Z", + "shell.execute_reply": "2026-10-08T17:20:44.996478Z" } }, "outputs": [ @@ -381,7 +390,7 @@ "source": [ "## Visualizing State of the Union addresses\n", "\n", - "In this example we will plot each State of the Union address from 1989 through 2018 (29 addresses, loaded with `hyp.load('sotus')`). Each address is plotted as one dot, colored by its chronological position (the hue sweeps from red/orange for the earliest addresses through green, blue, and violet for the most recent ones). Addresses delivered close together in time -- usually by the same president -- tend to use similar language, so dots with similar colors tend to fall near one another in topic space (though the separation is far from perfect)." + "In this example we will plot each State of the Union address from 1989 through 2017 (29 addresses, loaded with `hyp.load('sotus')`). The hosted list is grouped by president rather than sorted by date -- George H. W. Bush (1989-1992), George W. Bush (2001-2008), Bill Clinton (1993-2000), Barack Obama (2009-2016), Donald Trump (2017) -- so the cell below spells out each address's year, and each address is plotted as one dot colored by that year (the hue sweeps from red/orange for the earliest addresses through green, blue, and violet for the most recent ones). Addresses delivered close together in time -- usually by the same president -- tend to use similar language, so dots with similar colors tend to fall near one another in topic space (though the separation is far from perfect)." ] }, { @@ -391,10 +400,10 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:44.911014Z", - "iopub.status.busy": "2026-09-05T10:24:44.910937Z", - "iopub.status.idle": "2026-09-05T10:24:45.090180Z", - "shell.execute_reply": "2026-09-05T10:24:45.089821Z" + "iopub.execute_input": "2026-10-08T17:20:44.998507Z", + "iopub.status.busy": "2026-10-08T17:20:44.998407Z", + "iopub.status.idle": "2026-10-08T17:20:45.168550Z", + "shell.execute_reply": "2026-10-08T17:20:45.168219Z" } }, "outputs": [ @@ -407,7 +416,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "<Figure size 1000x800 with 1 Axes>" ] @@ -420,10 +429,11 @@ "sotus = hyp.load('sotus')\n", "print(f'{len(sotus)} State of the Union addresses')\n", "\n", - "order = np.arange(len(sotus), dtype=float) # chronological position\n", - "fig = hyp.plot(sotus, 'o', hue=order, size=[10, 8],\n", - " title='State of the Union addresses (1989-2018), '\n", - " 'colored early to late')" + "# the year of each address, in the hosted list's order (grouped by president)\n", + "years = np.r_[1989:1993, 2001:2009, 1993:2001, 2009:2018].astype(float)\n", + "fig = hyp.plot(sotus, 'o', hue=years, size=[10, 8],\n", + " title='State of the Union addresses (1989-2017), '\n", + " 'colored by year')" ] }, { @@ -443,16 +453,16 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:45.091387Z", - "iopub.status.busy": "2026-09-05T10:24:45.091305Z", - "iopub.status.idle": "2026-09-05T10:24:52.232972Z", - "shell.execute_reply": "2026-09-05T10:24:52.232583Z" + "iopub.execute_input": "2026-10-08T17:20:45.169693Z", + "iopub.status.busy": "2026-10-08T17:20:45.169632Z", + "iopub.status.idle": "2026-10-08T17:20:51.392592Z", + "shell.execute_reply": "2026-10-08T17:20:51.392190Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1000x800 with 1 Axes>" ] @@ -462,7 +472,7 @@ } ], "source": [ - "fig = hyp.plot(sotus, 'o', hue=order,\n", + "fig = hyp.plot(sotus, 'o', hue=years,\n", " reduce={'model': 'UMAP', 'kwargs': {'random_state': 42, 'n_jobs': 1}},\n", " size=[10, 8],\n", " title='State of the Union addresses (UMAP)')" @@ -485,16 +495,16 @@ "metadata": { "collapsed": true, "execution": { - "iopub.execute_input": "2026-09-05T10:24:52.234325Z", - "iopub.status.busy": "2026-09-05T10:24:52.234223Z", - "iopub.status.idle": "2026-09-05T10:24:52.422957Z", - "shell.execute_reply": "2026-09-05T10:24:52.422455Z" + "iopub.execute_input": "2026-10-08T17:20:51.394012Z", + "iopub.status.busy": "2026-10-08T17:20:51.393923Z", + "iopub.status.idle": "2026-10-08T17:20:51.577435Z", + "shell.execute_reply": "2026-10-08T17:20:51.576891Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 1000x800 with 1 Axes>" ] @@ -504,7 +514,7 @@ } ], "source": [ - "fig = hyp.plot(sotus, 'o', hue=order,\n", + "fig = hyp.plot(sotus, 'o', hue=years,\n", " reduce={'model': 'UMAP', 'kwargs': {'random_state': 42, 'n_jobs': 1}},\n", " corpus='nips', size=[10, 8],\n", " title='State of the Union addresses (NIPS-trained topics)')" @@ -535,7 +545,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/weather_decades.ipynb b/docs/tutorials/weather_decades.ipynb index 11e3d0ef..f60ac732 100644 --- a/docs/tutorials/weather_decades.ipynb +++ b/docs/tutorials/weather_decades.ipynb @@ -3,19 +3,35 @@ { "cell_type": "code", "execution_count": null, - "id": "92728ad8", - "metadata": {}, + "id": "9c08067b", + "metadata": { + "tags": [ + "hypertools-install" + ] + }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", - "%pip install -q \"hypertools[interactive]\"\n", - "\n", - "%matplotlib inline" + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", + "import importlib.util\n", + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { "cell_type": "markdown", - "id": "bdbf962d", + "id": "40dc9fca", "metadata": {}, "source": [ "# A century of weather: twenty cities as twenty features, one hot path\n", @@ -56,14 +72,16 @@ " months revealed so far growing over it as a line coloured **segment by\n", " segment by the mean temperature it is drawn at** (same colormap and\n", " range again), and a head marker on the current month coloured like the\n", - " head of the path. The raw monthly mean swings by ~15 °C\n", + " head of the path. The raw monthly mean swings by ~12 °C\n", " every year (the coloured line and the marker bounce with it), so a\n", " trailing 12-month rolling mean is drawn in plain black over the revealed\n", " months to let the warming drift show through the seasons.\n", "\n", - "The callback derives the current month from the frame index (a parallel\n", - "animation exposes no reveal count) and assigns every artist's state from it\n", - "on every frame, as the hook contract requires. (Before 1.1 this example\n", + "The callback derives the current month from the frame's position in the\n", + "clip: a parallel reveal does publish `ctx.revealed_counts`, but it counts\n", + "rows of the path as drawn -- resampled onto the 2400-point frame grid --\n", + "not months. It assigns every artist's state from that month on every frame,\n", + "as the hook contract requires. (Before 1.1 this example\n", "monkeypatched `ani._func` to redraw a second panel every frame; the public\n", "hook is what replaced that reach, and it now drives three.)\n", "\n", @@ -79,7 +97,7 @@ }, { "cell_type": "markdown", - "id": "3cd85204", + "id": "2525f7c7", "metadata": {}, "source": [ "## 1. Imports and a disk cache\n", @@ -90,13 +108,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "920cbb04", + "id": "a4e1d0b8", "metadata": { "execution": { - "iopub.execute_input": "2026-09-04T13:41:06.650915Z", - "iopub.status.busy": "2026-09-04T13:41:06.650782Z", - "iopub.status.idle": "2026-09-04T13:41:10.309453Z", - "shell.execute_reply": "2026-09-04T13:41:10.308827Z" + "iopub.execute_input": "2026-10-08T17:20:55.068559Z", + "iopub.status.busy": "2026-10-08T17:20:55.068414Z", + "iopub.status.idle": "2026-10-08T17:20:58.478508Z", + "shell.execute_reply": "2026-10-08T17:20:58.478078Z" } }, "outputs": [], @@ -133,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "3b730d7b", + "id": "1acc9575", "metadata": {}, "source": [ "## 2. Fetch the paper's archive and the coastlines, with fallbacks\n", @@ -144,13 +162,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "2c6296cc", + "id": "2cd2b08c", "metadata": { "execution": { - "iopub.execute_input": "2026-09-04T13:41:10.310936Z", - "iopub.status.busy": "2026-09-04T13:41:10.310786Z", - "iopub.status.idle": "2026-09-04T13:41:10.318360Z", - "shell.execute_reply": "2026-09-04T13:41:10.317825Z" + "iopub.execute_input": "2026-10-08T17:20:58.480039Z", + "iopub.status.busy": "2026-10-08T17:20:58.479904Z", + "iopub.status.idle": "2026-10-08T17:20:58.485992Z", + "shell.execute_reply": "2026-10-08T17:20:58.485627Z" } }, "outputs": [], @@ -273,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "8273ed24", + "id": "fe86efaf", "metadata": {}, "source": [ "## 3. One call, three panels\n", @@ -284,13 +302,13 @@ { "cell_type": "code", "execution_count": 3, - "id": "621c7862", + "id": "7e1f333e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-04T13:41:10.319427Z", - "iopub.status.busy": "2026-09-04T13:41:10.319359Z", - "iopub.status.idle": "2026-09-04T13:41:10.325751Z", - "shell.execute_reply": "2026-09-04T13:41:10.325288Z" + "iopub.execute_input": "2026-10-08T17:20:58.486944Z", + "iopub.status.busy": "2026-10-08T17:20:58.486880Z", + "iopub.status.idle": "2026-10-08T17:20:58.492615Z", + "shell.execute_reply": "2026-10-08T17:20:58.492265Z" } }, "outputs": [], @@ -309,13 +327,15 @@ " # is also the path's resolution: 20 fps x 120 s = 2400 points, more than\n", " # the 1645 months, so the 12-month loop keeps every one of its vertices\n", " # (a 300-frame grid aliased it into chords -- measured 2026-09-03).\n", + " # backend= is pinned: the panels and the hook below are matplotlib's,\n", + " # and on Colab the default backend would be plotly.\n", " anim = hyp.plot(\n", " data.temps, '-',\n", " hue=mean, palette='RdBu_r',\n", " colorbar={'label': 'Average temperature ($^\\\\circ$C)'},\n", " manip='Smooth', normalize='across',\n", " animate=True, chemtrails=True, rotations=1,\n", - " duration=120, frame_rate=20, size=(14, 7), show=False)\n", + " duration=120, frame_rate=20, size=(14, 7), backend='matplotlib', show=False)\n", " fig = anim.figure\n", "\n", " # Layout: the library's 3-D axes and colorbar take the left ~55%; the\n", @@ -375,8 +395,9 @@ " line_ax.spines[['top', 'right']].set_visible(False)\n", "\n", " def on_frame(ctx):\n", - " # A parallel reveal exposes no reveal count: the head sits at\n", - " # fraction frame / (n_frames - 1) of the path, hence of the months.\n", + " # ctx.revealed_counts counts rows of the frame-grid-resampled path,\n", + " # not months; the head sits at fraction frame / (n_frames - 1) of\n", + " # the path, hence of the months.\n", " i = min(round(ctx.frame / max(ctx.n_frames - 1, 1) * (n_months - 1)),\n", " n_months - 1)\n", " fig.suptitle(f'{MONTHS[data.months[i] - 1]} {data.years[i]}',\n", @@ -394,7 +415,7 @@ }, { "cell_type": "markdown", - "id": "85fa902f", + "id": "4253a9f2", "metadata": {}, "source": [ "## 4. Load the data and build the animation\n" @@ -403,13 +424,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "0e0243a8", + "id": "9bf479f6", "metadata": { "execution": { - "iopub.execute_input": "2026-09-04T13:41:10.326735Z", - "iopub.status.busy": "2026-09-04T13:41:10.326675Z", - "iopub.status.idle": "2026-09-04T13:41:10.663390Z", - "shell.execute_reply": "2026-09-04T13:41:10.662927Z" + "iopub.execute_input": "2026-10-08T17:20:58.493414Z", + "iopub.status.busy": "2026-10-08T17:20:58.493357Z", + "iopub.status.idle": "2026-10-08T17:20:58.804297Z", + "shell.execute_reply": "2026-10-08T17:20:58.803873Z" } }, "outputs": [ @@ -432,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "a5a6807a", + "id": "5521af50", "metadata": {}, "source": [ "## 5. Save the animation\n" @@ -441,13 +462,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "04cf8db7", + "id": "c060858b", "metadata": { "execution": { - "iopub.execute_input": "2026-09-04T13:41:10.664703Z", - "iopub.status.busy": "2026-09-04T13:41:10.664613Z", - "iopub.status.idle": "2026-09-04T13:46:04.985421Z", - "shell.execute_reply": "2026-09-04T13:46:04.984989Z" + "iopub.execute_input": "2026-10-08T17:20:58.805633Z", + "iopub.status.busy": "2026-10-08T17:20:58.805567Z", + "iopub.status.idle": "2026-10-08T17:26:29.192813Z", + "shell.execute_reply": "2026-10-08T17:26:29.192426Z" } }, "outputs": [ @@ -461,12 +482,21 @@ ], "source": [ "anim.save('weather_decades.mp4', dpi=100)\n", - "print('saved weather_decades.mp4')\n" + "print('saved weather_decades.mp4')\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('weather_decades.mp4', embed=True))\n" ] }, { "cell_type": "markdown", - "id": "3f7dd452", + "id": "af774be0", "metadata": {}, "source": [ "<video controls loop muted autoplay playsinline src=\"weather_decades.mp4\" title=\"A century of weather: twenty cities as twenty features, one hot path\" style=\"max-width: 100%\"></video>\n", @@ -491,7 +521,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.14" } }, "nbformat": 4, diff --git a/docs/tutorials/weather_decades.mp4 b/docs/tutorials/weather_decades.mp4 index 439b6e40..af7cdb92 100644 Binary files a/docs/tutorials/weather_decades.mp4 and b/docs/tutorials/weather_decades.mp4 differ diff --git a/docs/tutorials/wikipedia_embeddings.ipynb b/docs/tutorials/wikipedia_embeddings.ipynb index 02d19aa2..83617557 100644 --- a/docs/tutorials/wikipedia_embeddings.ipynb +++ b/docs/tutorials/wikipedia_embeddings.ipynb @@ -5,19 +5,28 @@ "execution_count": null, "id": "a9336158", "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:37:32.307632Z", - "iopub.status.busy": "2026-07-17T07:37:32.307409Z", - "iopub.status.idle": "2026-07-17T07:37:32.312909Z", - "shell.execute_reply": "2026-07-17T07:37:32.312119Z" - } + "tags": [ + "hypertools-install" + ] }, "outputs": [], "source": [ - "# Install hypertools (run this first on Colab)\n", + "# HyperTools setup: use 1.1 or newer; retain a current local checkout.\n", "import importlib.util\n", - "if importlib.util.find_spec('hypertools') is None:\n", - " %pip install -q \"hypertools[interactive]\"" + "from importlib.metadata import version, PackageNotFoundError\n", + "from packaging.version import Version\n", + "from pathlib import Path\n", + "try:\n", + " _hypertools_version = Version(version('hypertools'))\n", + "except PackageNotFoundError:\n", + " _hypertools_version = Version('0')\n", + "if _hypertools_version < Version('1.1.0'):\n", + " _spec = importlib.util.find_spec('hypertools')\n", + " if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / '.git').exists():\n", + " raise RuntimeError('Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.')\n", + " %pip install -q \"hypertools[interactive]>=1.1.0\"\n", + "else:\n", + " print('Keeping HyperTools', _hypertools_version, 'in this kernel. Optional extras are loaded when requested.')\n" ] }, { @@ -38,9 +47,8 @@ "\n", "The embedding model runs through HyperTools' `[text]` extra\n", "(sentence-transformers). You do not have to install it first: HyperTools\n", - "installs it on demand the first time an embedding is requested. The install\n", - "cell above fetches it ahead of time so the first plot does not pause for the\n", - "download." + "installs it on demand the first time an embedding is requested, so the first\n", + "embedding cell pauses for that download (and the model's) once." ] }, { @@ -49,10 +57,10 @@ "id": "3b90dd05", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:09.085726Z", - "iopub.status.busy": "2026-09-05T10:28:09.085584Z", - "iopub.status.idle": "2026-09-05T10:28:12.536792Z", - "shell.execute_reply": "2026-09-05T10:28:12.536203Z" + "iopub.execute_input": "2026-10-08T17:26:30.372294Z", + "iopub.status.busy": "2026-10-08T17:26:30.372156Z", + "iopub.status.idle": "2026-10-08T17:26:33.790946Z", + "shell.execute_reply": "2026-10-08T17:26:33.790520Z" } }, "outputs": [], @@ -73,9 +81,8 @@ "## Loading the sample Wikipedia corpus\n", "\n", "HyperTools ships with a sample corpus of Wikipedia articles, available through\n", - "`hyp.load('wiki')`. The loader returns the raw data directly -- a list\n", - "containing one array of documents, where each entry is the (preprocessed)\n", - "text of one Wikipedia article." + "`hyp.load('wiki')`. The loader returns the raw data directly -- a flat list\n", + "of 3,136 strings, each the (preprocessed) text of one Wikipedia article." ] }, { @@ -84,10 +91,10 @@ "id": "8fb4294e", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:12.538301Z", - "iopub.status.busy": "2026-09-05T10:28:12.538125Z", - "iopub.status.idle": "2026-09-05T10:28:12.700865Z", - "shell.execute_reply": "2026-09-05T10:28:12.700289Z" + "iopub.execute_input": "2026-10-08T17:26:33.792435Z", + "iopub.status.busy": "2026-10-08T17:26:33.792279Z", + "iopub.status.idle": "2026-10-08T17:26:33.944030Z", + "shell.execute_reply": "2026-10-08T17:26:33.943610Z" } }, "outputs": [ @@ -136,10 +143,10 @@ "id": "570e900f", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:12.702103Z", - "iopub.status.busy": "2026-09-05T10:28:12.702014Z", - "iopub.status.idle": "2026-09-05T10:28:12.706376Z", - "shell.execute_reply": "2026-09-05T10:28:12.705938Z" + "iopub.execute_input": "2026-10-08T17:26:33.945213Z", + "iopub.status.busy": "2026-10-08T17:26:33.945135Z", + "iopub.status.idle": "2026-10-08T17:26:33.949018Z", + "shell.execute_reply": "2026-10-08T17:26:33.948580Z" } }, "outputs": [ @@ -189,16 +196,16 @@ "id": "4e817c13", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:12.707464Z", - "iopub.status.busy": "2026-09-05T10:28:12.707391Z", - "iopub.status.idle": "2026-09-05T10:28:44.985342Z", - "shell.execute_reply": "2026-09-05T10:28:44.984802Z" + "iopub.execute_input": "2026-10-08T17:26:33.949920Z", + "iopub.status.busy": "2026-10-08T17:26:33.949860Z", + "iopub.status.idle": "2026-10-08T17:27:05.182049Z", + "shell.execute_reply": "2026-10-08T17:27:05.181591Z" } }, "outputs": [ { "data": { - "image/png": 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", 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aTIauPXp/4gI6nGMhTwhtQsnrkgxdfwPPNV2bdN0lbxTIuhEbF3xeaNNCxzSwEQG9/lBWDK0j1ICDRORoQwhHglzMdF+RiA2EzjUlzZHXYTBoYyVm25LXhq7xwSCRo80KXaf02SU/KNGM7o2Kioqjfg/kLqawBiWKnQmcUZYgfbi0Y6fUaTFwL0KLJd2cJCKUAXY00CJAliC52ciCHCrhgsSVujLQ3yDLhC5WuumSExMo+YAuTLrRKNhNFypZLHQDUACbdotXXXUVy5Qjq4bS1mlxIeE40gIhugwptkE3C+20KW6Q7DYb7PdIQMVMQrohkxNBhgO9BlmU5Eam16CbXYxLJUPvm84RWZz0Pkk8KahPG5dkN5MInSuq6aOdOWXp0muSRUy/QxYixVPJlUWLFsWTaCNByQ60i6f6qsFckrQpoXNOn0GqTFmyFmhDQQs4uYrpnIiZrwRZ2/QeyMNA8S2y7MktS5YDuatoV58q+Up87WTo2kzlSqfNE3kiqISGEjFoA0UbK/qaPlM6H8cLEiTyBtDnQK9N3gOySugzOppjoXuPMjCpTIfccPQziqENvP4o4Yx+h1zW119/PVtY6Zo7XpYXuUvJ0iEXOYkHhQNok0xJY8keBAobkJeHxI+uLfpsKGmHrmHRchuKo6kPpPdGbnK6B+hep80aHaeYXET3DJ2XoaDrjASQ1jrKwE0FbSzILU/ZzKk2SrQ2kKfopZdeYp/T0UD3DEH33pnAGSWCtIiQBUP1ZqmyDSk4TJ0/hlsakQzdrJR0Q3ECugDJX54KWoQpcYAyP8VsVFo4k12ftGhTDIIyvej1yK1BiyUds3jB0uJDmW3k0qN0drIg6cKl30mVbJHs9iWRJSGiv0F/nxYlinEMVR9EqeUkxGT1kjVH1iO5+Qam0Q8FCRvt9sXieCr7SCWCtImgRCO66WkxEBfYwTYXtEiQyNE5pdck65QWVLLU6TMVNzn0udB7JquNdqlUe0gL7GCLEf0ulc5QzDQ5SUk8H8l/n6wKOuaBLa3oeZR4Qn+XRINc3/Ta9Du0KaDzPpBUZRm0aRns/VOZAYk2nQMxjkwWBe3mjxe0EaHXp42emAxGGz4qkUmOvQ3nWCg5iX6fNnG0eaFrgK4Nui/Jmhah+5Dib7SJoJ/T5oVKMsT79HhA9w69N9rc0TmmTRYt+vQ+ROjeI+Gj1n50L5Lw0b0ollQdbyjZhM4NbQzoWEj8aT2ha4WujVSx2WRIzCnJiO6ZwdYhyo4ni53ugVTQJoDWqTfeeKNfRvBwoM04rSvDiYGeDkgSg0VoOUcNpWLTzU8LImfkQ1Y17cRpY5FKrDhnLuQ1ooL5gWVBnKGhDShttMk7dSSL9XThjIoJjgT4nuL0geqdyGohC5OsR87ogFzXFCYgS4pzdNC9Ql4XstjPFM4odyiHc7SQO4rimBRDJDcf58yCwgmUjEVhEnLrUr0uuV/JvT1UAgrncCjzlxIFSQhTlR2drnB36HF2h1JAeuAUBQ6Hc2qgvqYUnqCkKbHWl+J9VDYwsHifMzrhIsjhcDicUQuPCXI4HA5n1MJFkMPhcDijFi6CHA6Hwxm1cBHkcDgczqiFiyCHw+FwRi1cBDkcDoczauEiyOFwOJxRCxdBDofD4YxauAhyOBwOZ9TCRZDD4XA4oxYughwOh8MZtXAR5HA4HM6ohYsgh8PhcEYtXAQ5HA6HM2rhIsjhcDicUQsXQQ6Hw+GMWrgIcjgcDmfUwkWQw+FwOKMWLoIcDofDGbVwEeRwOBzOqIWLIIfD4XBGLVwEORwOhzNq4SLI4XA4nFELF0EOh8PhjFq4CHI4HA5n1MJFkMPhcDijFi6CHA6Hwxm1cBHkcDgczqiFiyCHw+FwRi1cBDkcDoczauEiyOFwOJxRCxdBDofD4YxauAhyOBwOZ9TCRZDD4XA4oxYughwOh8MZtXAR5HA4HM6ohYsgh8PhcEYtXAQ5HA6HM2rhIsjhcDicUQsXQQ6Hw+GMWrgIcjgcDmfUwkWQw+FwOKMWLoIcDofDGbVwEeRwOBzOqIWLIIfD4XBGLVwEORwOhzNq4SLI4XA4nFELF0EOh8PhjFq4CHI4HA5n1MJFkMPhcDijFi6CHA6Hwxm1cBHkcDgczqiFiyCHw+FwRi1cBDkcDoczauEiyOFwOJxRCxdBDofD4YxauAhyOBwOZ9TCRZDD4XA4oxYughwOh8MZtXAR5HA4HM6ohYsgh8PhcEYtXAQ5HA6HM2rhIsjhcDicUQsXQQ6Hw+GMWrgIcjgcDmfUwkWQw+FwOKMWLoIcDofDGbVwEeRwOBzOqIWLIIfD4XBGLVwEORwOhzNq4SLI4XA4nFELF0EOh8PhjFq4CHI4HA5n1MJFkMPhcDijFi6CHA6Hwxm1cBHkcDgczqiFiyCHw+FwRi1cBDkcDoczauEiyOFwOJxRCxdBDofD4YxauAhyOBwOZ9TCRZDD4XA4oxYughwOh8MZtXAR5HA4HM6ohYsgh8PhcEYt8lN9ABzOiSAcDuPgwYPQ6XTIz8+HUqnkJ5rD4RwGF0HOGSF4H3zwAd5++21s2rQJNTU1cLvdkMvliEaj7DkSiQQymQwqlQpqtRp6vR5GoxFmsxnp6emw2WzIyspCbm4uE83CwkIUFxdDq9We6rfH4XBOIJJEIpE4kX+AwzmekKitXr0ab731FhO86upquFyu3p+T8GVnZ8NgMODAgQO48sormZB1dnbCbrez53o8Hvj9fgSDQUQiEcRisSH/JoknWZIknmRZ0muTeKalpfWKZ05ODvLy8ph4lpSUsJ9zOJyRDxdBzogWvDVr1uCNN97Axo0bUVVVdZjgkQBNnjwZZ511Fq666ipmvRF//vOf8Y1vfAPvvPMOVq1adcS/5XQ6UVdXh/r6ejQ2NqKlpQVtbW294kk/J/H0+XwIBAJMPEUrczCkUikTT7I+SYhJPE0mExNPq9WKzMzMXvEsKChgx56RkcF+j8PhnBy4O5QzIojH41i7di1ef/11JniVlZVMeJIFj0Rj/vz5WL58ObPwysrKBn09UUiG6+ggy2369OnscTSQRUnCSQKaLJ4dHR3o7u6Gw+FgrlkSz+bmZtTW1jLxHOq46Njp/YriSa5bEk+LxcLEk4SSrF1y3YriSV/T73A4nKOD3zWcUyJ4n376KbPwPvvss17BE4WB3I+00M+ZMwfLli1jFt64ceOO6m9QDFD8WycSEqkJEyawx9FAQkiiSaJI/zY1NTHxbG9v7xVPsnq9Xi8TVHoOWZ9DiSe9ZxJCsj7puMh1S3FPEk+Ke9I5TY57FhUVMfctuXk5nNEKF0HOCYVEaMOGDczCW79+PSoqKtgCP1DwyJ0pWnhHKyipGOkuRRIrsuBE9+3RnE8SSxLPhoYGJp6tra1MPLu6unrjniSedJ7pZySeR9oMiOIpxj0HJg2RFU7iSa5bEk960HM4nNMdLoKc4wYttJs3b8Zrr73GBO/QoUNsURYFj4SJBI/Ejiw8EjyK551ITrQleLKhc0hxRHosWrToqH6XPgsx7kniOVjckwSUBJWyboeTNKRQKJh4Dox7iuJJrlsxaYhEn1y6I32Twhk9cBHkHLO4bN++Ha+88goTPKrJIzdesuDRYkdit2TJElxxxRVHHW/7PIjuUJ783AcJEz1mzpx5VOeSRJGEU0waotimKJ70mZN4UtyTnkdWKGXskngeKe5J4nmkuKeYNESWJ33NxZNzvOEiyBkWO3bswMsvv8wEr7y8nFkK4iJHgkMLF4nd4sWLmYV3tAvt8UZcLM80S/BUQAI1adIk9jgayJIk0STrk1y3JJ7knh2YNETiSaJKzxlu3JPEU6PR9NZ7pop7iuJJFihvlsAZDC6CnMPYtWsXs/AoeUUUPFFMaBGixYZccSR6l19+OWbNmjXidugnKzGGMzgkPKWlpexxNNBnRq5a0XUrimdy3JPEk1y3JKb03CPFPcVmCXRMJJ6p4p5ivaeYNEQPElnOmQ0XwVHOvn37mIX3ySefMMGjXXqy4JH7jMoSyMK77LLLMG/evBEneMejRIIzsj47cn3Sg6674ULXLQkkJQ0lxz1JPAc2SyARpWv9aJsliHHP5GYJyfWeYrME+hnn9ICL4CiCRI4Ej+rx9u/fzxYGcQEgwSOXEokcWXkkeAsWLDgtBC8V3B06+hDj0PSg8pqjgUSRLE96kHiS9SmKZ3Lck+o9yY1LjRuG0ywhOWlIjHsmN0tIjntS0hBZo6frPXe6wkXwDIUyM1966SVm4ZG1R7veZMGjnezs2bOxcOFCJni04z6Tbj6eGMM5Gsg1OnXqVPY4Gqj1XnKzBFE8U8U9yaVLzztSswS6dsWkocHinsnNEshty5vEHztcBM8AKBvvxRdfZBYeCR7dhMm7VBI8SlQhy+7SSy9lJQpnkuClgluCnJMBWXnUyOFomznQ/SnGPZPrPUk8Ke6Z3CyB/p/EdbhxT94k/ujgIniaQfEOsvA+/vhjJniUVTdQ8KZNm8YsvIsvvhgrVqwYle20eGIMZyRD9yRZcfQ4GkgESRST455i0tDAuCf9S9/nTeKHZvStjqcRtEMkwfvoo4+wd+9edrEnCx7FF8h9Q4krF110EVauXDkqBS8VPDGGcyYiNpygB8Xvj4ajaRLf3d09aprE8xVzhEA7Okpa+fDDD5ngiWnfIhQTmDJlCrvwycI799xzueANwUi6yTickcBIahIvemvoQRbrqSxF4SJ4CqCLiASPLDyqySMLjwqLRWg3NXHiRMydO5dZeOeffz4v9j1GeJ0gh3Pqm8SvWbMG7777LmuWT9YmCaT4oPAN5TWcKrgInmBot0QuTbLwSPBop5QseLQDoqA6WXgXXHABm33Hu/p/fnh2KIdz6tizZw/+8Y9/sAHYJHCiW5XWthkzZrC17o477mCbfcqEPZVwETyOUMCaLDz64Hfu3MkELxQK9f6culSMHTuW1TDRRUBWHhe8EwPPDuVwTm5J1t///nd88MEHbFKMuNGnWCFZkOeddx5uv/32w7JoyVNzqkMXXASPEQokU2ux999/n1l4ZPYPFLwxY8awWjxR8MitwDk58OxQDufEUV9fz0TvnXfeYc3zqV6SIKuO2uRRkh6J3pHqLskdeqqT+bgIDgPKmnr11VeZ4FEjaRI88UMnSNyoVRIJHrkzKXGF9xw8tfDsUA7n+OYx/POf/8Rbb73Fuk1RsgxBdYmU8UmlWF/60peOOmOVRJBe41TCRXAAlOVE8/Dee+89NiqIBC8QCPT+nDo4UIcGahpNCSuXXHIJHy46AuHuUA7n83m6/vWvf7G1kOJ7VLQv3ldU6kAj0m655RYsXbr0c7kzuQieYuiDTRY8MvEHCh61IyLBI582dVuhNGPOyIcnxnA4R7f5/89//sNCPJTPQGUL4n1E7dnIu3XTTTexdfB4xvC4CJ5EyHx//fXXmeBt3bqVCZ5o0hOUoEKCR+3FyJ9NI4J4J/jTF24JcjiDQ+GcZ555Bv/73/+YAUCWnyh61MT77LPPxvXXX882/icyZsdF8ARB4ka+awrabtu2jRV5UiGnCGUskeBR0SjtbGjqORe8MwseE+Rw+qBsTRK8559/Hlu2bGFdYkRsNhtL3Lvuuutw9dVXn9SaZJ4Yc5x2NG+//TZ7kODV1NQcJnhkzlNtimjhUdsezpkNzw7ljGaoLo88X88++yw2btzIeoiKHVxow08dp6655hrccMMNp7RMi4vgMexmqOsAWXmbN29mnQjEgC1BOxgSPLLwzjnnHGbhkWnP4XA4ZzJUb0ehnqeffhrr169nNcqi6FEvT5ocQ+vhzTffPKIS+RI8O3RowaMPlVyamzZtYhYetdtJFjyaqUW+a3pceeWVrFErh0NwdyjnTBe9Tz/9FP/+97/ZzFDKYhdbBFLbRZoiQ16vW2+9dcSHeuS8TrAP+hCp6zhlJiU3j6aTRBYepeWShUeCR13JOZzB4IkxnDMN8n5R2QL14SQvmDgkm+qUqUaZMjipQP108n4luCXYH9q5UOuxVP5tcXYWzdH75S9/yXY7NGlZHNdBIknJLlS0Tq3J6OtTvcPgnDq4Jcg5U/pvUt9h6r8pGgYUw6NOLBdeeCETvcLCQpzOKHjv0D5I6OgDJtOeuo3TB0+ZneTfpkkLyUMjSSxJFOnCGGxkBy2EdIKp3o86uJBvPD09nSXGkCuVhJK6HYjCybu8nDnwxBjO6QatedSKjDpTUS9Osf8mhX7Gjx8/aP/N0x05d4f2PxnkEiXrjh4LFiwYdoYoCSY9yE1Aw2jFacskluKkZZp9VV5ePuR4HToGEmJyMZBoitYmiSbFHGnXRb3xqC8o/f+pbv7KSQ23BDkjHVqnyNKjzHZal461/+bpjpyLYB/UQ+5IgxhTQaI1adIk9hgOJII0FJK6nZNwkuVJVqU4ZVkcEklCSpZocnxysMnKJJpkSVJHGRJNsjZJJKnFELVZI9GkC5tPjTg58JggZ6RB6wv133zzzTexb9++49Z/83Ql3mOMcHfocRDBY1kgSaDoQanDw4FKMaqqqtiDrE0STWoqS6JJFiY12SbhJOuTLvDBrE1y09GHTvWLoouWrE0STQpoU8IPiSbdFOT2IEHlHNtnTJyM64nDSQWFbih7k5rvU3xPzG6na5NCMbT2HI/+m6cr0Z4Zg7yB9gCzeKQuWiRYVH9Ij+FAIkgxThJNKu+gr0k0aTdILlqyNummICuUniNeEKmgi4REk2KbVONDokmxTRJNMSGIRJOsTfr3VLsXRgI8Jsg52dBG+cknn2T9N2nazMD+m5TIQv03qfH+aBS9gYgxT24JniYieLTQRU6CRI+j2TlScJxEkeIFJJAkmjSdnqxNsjTpX0oUGiohiG46ctGS6zU5IYjaI5H1S6IpxjYpIWgkFc8eL7glyDnRUAyPOrJQOzLqVpXcf5My1k9W/83TlWBPDPRUn5sR9cmcSSJ4LFBRK8UDhhsToJ0UuWZJOOlfEk0SSHLJkliStUm7U7o5KdtMrCsa7NyTtUnDgEkUKbZJokk3M7loSTRJ0MvKytj/j/Sd7Eg/Ps7pB91vL774Iuu/STV7A/tvkqVH/TepHdnJ7L95uhLmluDhnGrf8OkG3WgUNxxuyjS5aMmqpIQgEs2B5SdiQhBZn+S+JRftUOUn9PfF8hMSTbI2STTJ2iShJNEUrc1TlRA0VCbwSCCSiEAKCWSSEbUf5fRcO9R/k6YtUP9Nui8G9t+khtNk7VFiHOfoCIVC7N9TvWEYUXfeaLcETzQkXBRHpAcF44cDZbCRe5asTRJG0dpMTggia5P+nyZOD5UQJJafiNYmLSRi+YlobYqiSd//PNbcSHSH+mJeuKIOlPv3oFBdArVUi0eafgI6RKPcjHAihFsyv4bx2imn+lBHJXTtUo0e9d9ct27dYf03qWMV9d/84he/eEaGEE423B2aglMdIOUcDu1wqU5puLVKtJCIyT4Dy0+SE4JogaGEoSMlBInlJ9QhSCw/Ea1NsfyEXLQknsmxhZGWGOOM2nF/3d2IQEgGIBboVyCUEHbDXdF29u/b9pdwwL8HtcEK1AQrsMh4Nq7PuP2UHfeZDvXffOKJJ1j/TYrDi9cLeTeo/ybF86hsYaT33zwdifSUnp3qdX/EWYKc0xuywMiiowclBgwHsibJ0iRRJDctCSRl0pLrVuwQROJJbluKIwyVECR2CBJdLFSXtWvXrl5rU6zZJOEkUT1ZfOZa008AiY3eNf3+3yyzoDp4iD1Etng+5SJ4HKGB2tR/86OPPjqs/+asWbPYXD0qUOfN+E+eO/RUr/sjSnVO9cngnBpIjObMmcMew4GsRzEhiOKa5KYl4STRJGuTRJVEkyArlKzRIyUEidYmlZ+ICUGU1k7WZnI/2mN10a52vD7oz2bq5uPajNugkqjw0/pvwxmzQyPRIpDwQy3hsabPA9XsUiuy1atXp+y/uWrVKtx5552nff/N05FwUlu4U8mIUh3RLCaXBM/u4wwlXGTJ0WMwqDh52rRpuO+++/Czn/2MCaTYj5bcXiSOA/vR0tckmMNJCKJFlERzYD9a0UUrxjZJXHd6NyM8wAoUGaMeD41MC1fEgfXuj1g8cIVpFV7qfgqVgQNQSBV4o+sFfOr+ADZ5Fr6a+0MYZEZ4Y27EEjGY5BZ+oSRBnzG1IqMxbJQAlpx8QQlklMxConem9d88HQnxxJjBRZAWoVO9O+Cc3gxMjCGRoseiRYuGHbQX45oD+9GKCUEU2yThPHDgwJCxR6lcAplKBrlGBqVeAZVJCY1FBU2GGlsz9kGfq8XLBS9BX6iG1qaGPdKFdEUGck0FkCZkeM/5Knud+nA1Nrk/wUb3WrRFmtn3vmC7HfMNS6GQKkd1/02aO0r9NwOBQO9GidzeNHqNYnrDbXLBOfkiyGOCSYgngxYgLoKcz8PnTYwhS2/y5MnsMRzo71AcUyw/EUd/tba3YmfTNgScQUR9Mfg7QnA3+ZCIDp61KpG+BKlCCoVGDo1ODalBArVFCb1Vh62ZB6DKlMGQr4e5UI+nw49jl2kLvpF7L0YDZNGT6In9N30+X28SFVng1H/ztttuG3bzfc6pQ3RNn+q1fkTGBEkEeQoy5/Nwsksk6O9R4k2qYc9toRa0RpqQJrfiwaY+sYp4InA2eOGq98DT5EOgLQRPhw9BewgBZwhhdwQxbwLuTi/ikTgSiY7Uf1zyEr6neKC3/IRctJTNOLAfLcU2yQ14OmU6iv03X3vtNezevfuw/ps0YJtKFqgPJw+hnJ4xQTlPjEltCXI4n4eRUie4yf0pnu54DGPVk3Bx+jX9fqYwKHDJ/MtRNb0c49VTcZXtJkQTUez2bsMLXU8gX1mEr+bcgwTigrszBjyz5wm8svu/cDZ64G0OwNfhh78rCJ3HCLfTg4A3AFeDi8XGhuoQJPajpZjlwH60YvkJiSa5FClp5GQtVFRz+tRTT+Hll19m/TfJ7Sxa9nRcvP/mmWcJqlSqU3ocI1IEhxpdxOEMh5FiFWzzrEcCCRwK7sPYwERM1s7AQf8+RBGBBBJcmHYV0hR9k0IUUKJAXYxQLIga/yHcW30XookwjHIL7i16ELfMvBM3z7gd0UQErpgDDzb+mL3+AsMKfOx6m73Gd3MfQKlmHKoC5fjV3nvhrvdjme8CuJu9vTWbyeUnRzugmqxNsUPQwH60JJpHM6Ba7L9J7cio/ybFW0VIkC+//HLWkeWyyy475RYD5/jCs0NTIPqGuSXIOV6cqmL5cDyM17r/S/LR+73VjjcRTARglWewLNAFhuX9BPCgfy8qAgeggQbSBEmkBJEEuYwScEXt6I50QivTQyqRQilRwSbNwm+KH8d2z0b8p+Mvva/zZtcLqAlVMgtSnaZijwbpPvy86E9QS4duX0f3XnI/WhLH5H60YkIQCehwBlSL/WjJRSv2o6UHJRlR/SaJsQjvvzm6CPHEmMMRd3riyeFwTld36C7fFqx1vce+HqOegKpgORNAYqxmEq7PuOMw0fxLy4OIIQqLNJ19jyy86bq5aArXwwA9/lv5V2Rr83FjydeZEBJyiRyt4f51kBWhA4cdjy/uwXbPBiwynTXkcVNcccKECexxNAOqxUxasWZzYD/aVAOqKTZJ09Ovuuoq3Hjjjbz/5igj2tMtirtDU1iCXAQ5QxGJ+SGXanozQE+mCNLrkSvySCUJhSrqDaph1lyZRhBB+vo7uT9FiXrsYc8PxP2QSWSIJaJwxLqZAUnvripcDk/cDVdADmVYDnu4E12hNmSoc9AVaYdCosJKyyWIIQZ31IWt3vWDHtOznX/HWuf7uDnrLuSrhj/ia7gDqofbj5YElsQy2fXJGX2E+RSJwUWQxwQ5g1HreB2V3c/Bqp2JmTk/OKIIHk93KAngH1p+wYrYb8z4MhYYl6d8HsXi2iOt+GXRX5mlJoOMiU66wjqo+Gz1rGcNtAlB3AXx9saFbMioMoZsSS4KdaWwqrJYE+4/tfwKCokSPyl4GFdab0JV4OCQIkg0R+qxzvXhYZboyWSozQtn9BDhiTGHwy1BTiq6/LvZv1btNHT797Kv7YF9w1poj6clSPG5isB+9vV+/66UIkiNsn/X/DPmyvREXTg/7XL2/en6oVvCUYxQ49BCLzPirqzvwRHtxmbPJ5iimwV/zIccZT7GaPtclF2Rjt5jckWdrLheLzP0/nyqdhb2+Lezr0XJEc9EtjIfpxL6TLgQcsI9liB3hybBY4KcgZDo7Wj5Nft6Vs6PUZJ2FdyttZBIZPCFW6BT5hyTO5Rajq11vsfakmkkOtaBZbZ+IbJVh9f5iSilKlxvu4NZYRdYrkz5nDgt8JAwEVRLNEN+oO3hFvyu+QFopFp8N+9neLj4X+z7JBB0HBN10wb9XRLgYDzAhK9YLbSPy1Lm4r78h+CP+1CqHoe/t/4Ou/1besVPtC/fs7+KxcazRm2XGc7Iigkq+BSJPrglyBFpcH0Ab6gB6Zq+EU4SSJFIhBGNC11Cuvw7jyiCg7lD3+1+Ge86X+n3vfccr+DWzG9itmHhoB/EYtPZ7DEY5YHdiEP4mxnK7CE/UBJTT8zFHvXBaiZia5zvYIJ2GibpUrf56gq3s3IJpVSJlZaLD/t5jkqw8kLxIBNAERmkvcflibtQF6pmscpTAbcEOQS3BFMgmsVDzZjjnPn4I2042PkE+5oSYObk3s++tmgmIBYPI1O/AJGYB1n6wcXqSJag6NZM9f1kESRr64XOf0MKKa613cqswUg8jD81/wpt4SZcmH41lprOZfG8v7U+zNyY9FyK1WUOIoLuqBPvOV5FhiIbU3VzoJVqMVY7CU+2/ollla5zrsajY548bNr8Gse7eLnzKdgUmbiv6BGWSCPijXqw3bsJKqkS8wxLoZKqka8sQWO4llmal6V9AbWhClQHDkEuUeKlzv/gvLTLMFM/HyebkTLjkXNq4W3TUsAtwVOHPxbCc92bsMFbiS9al8As1+IzTwXOM01BiTrjpB1HNO7HrpZHeiNZZvVYJn4iMqkS07K+dcTXOZIIUoytd26fRChSJ9G6NP16ZpWRwFgVWdjsXsdm+hFknZFotEVaUB08yL5HAkmTHEgcD/XEKS+0XI3FprMGnfDwvuM1fOJ6n31NlqcxbMCr+5+AyWhi38tS5UKKPoETaQk1sH+pXpBigbIedyvFB3+/8z5M/ywPDaVdUJ2lwSTtdDSGa9jPx2omYon5HCzBOez/76n5MrM+3+x+4ZSIIMFjgpwoL5E4HNE3LJrJnBNLPBFHRbANbWEn/tm5FtGe6NEbzu3ojngRSESwx9+IebpSqKRyXJs+H9IUmX1dEQ/ecO7ARE0u5uvHHPPxVHT9F23eDQhGu9j/l1iuQMYREkoGI9kd6vJVobbjLWSZ5yPLMh/xeAyVHZ9ApZAiJItDkpDgKtvNWGw+h01poDZnhAxyVrdH/7LYm0p4b7nKAkzQTMOBgJCw4445MU+7FOM1U9iIo42eNXjX8TLuyv4+JutmHHZs5PakU613arDe8TZioQh8UTcyAjl4YPofYJKlwdPcDm9zBzJnTIBMKdwXF1mvgUamwxjNeFZ+IUKiOGdNIcaUZ2Lcnmyol+uYu5Tilrt927DCvKrf39dLjfBG3VBj6ML5E8WpbmXHGRlEkmY7nkpGpDuUi+CJxRn1oT1Ci3Ul3nML2ZbJuKIBhBKCS9oV8+M99x72tSPmw1czz8Ff2j9kv/uljGVYYZyI/9k341PPIax27ce0kgJojiHhIhYPoc75BvtaLbdCp8hFnkmwXD5v27TK1hfg9B1Ct2cfE8E9Bx7DEocWfkkCb2d6kJAAz3X+i2VfNoXq+o4JwjmII8bKHUTrhQrVZxkX4EBQEEGd1MDcj9/M/TFLdnmg4Tvs+/9u+xPmG5bhSttNvcXtxBLTSlQ7ytFmr0AQLthMuUwEx6ZPhVWRiWgwhJ1/+x8S0Rj8XU6MuXApIr4gPBurkZVhxtvml5glOF0/l41VSpPbkDGhDCh3Q15kwhjDRPZ3Lkq/mj1qA5V4vv1fWGhagQJ1CRwRYZPRGKpDe7h1ULftiYLHBDnJliCfIpFCBHmd4InDEwviW3XPIIQoshXm1M+JB5OSKfp27U1hO1vAyE1K31/rKsdr9u29gpkpN+Etx07M0BVhjDrzqI5LJlUh33guOvzbMcH2JdhSWFDHAh2vRT+BiWACEbR0fwancy8rRlex1mRCxqRWqmPW3qXpX0BHpJW5I7USHfYHduEs8wWHue9IgMr9e5nbdIpuZu/3Kc43TjOZuUZpMvxa97usS4uYsEJ0VdRiQXAeXpFVQBIDFhWfjxLrRChlwvUfl8QhlcsQi8YgVwkbivq3N6B9037EpXG0fakBbyVehDPqwCtdT7Of33/+75F2lhkyrfKwY32y7c/ojnSgOnAQPy56CItMZ+Nj59ssi5U+45MNtwQ5BLcEU8AtwaOnJezAZm81dvkbMEaVgRutiw5bBMldudlXjdnaIvy29R0mgERrxAkpJP2EbrIyD+XhFsQQhxIyBBCBUaphrs6LLTPYa99qW4qN3iqUqGx4y7mL/d5y/Xi0RV142bEN77r24ImSwYuxQ1EnDnb+h32dazwb6dqJcAYrYNKUYZzti5AmJXx8XktQIo0jkvAgIY2zYz/Q+jiUMgPisSgysxfjz2NuYu5ESh7RynTsd+7K/B5c7dXQmXOgzP5qyten59+W9Y1+C/sa57vojLRhufE8VAfKWTZmgaqEJbKI2KsbsOspYUiuZFEC9gwvoJb2CmCVvxx/bP4ljJfr8GXlN5A3TrDqVJaeGkCdHEq5mlmTJNyEXKJg7k+5PrVbqVBVykSwUF3K/v9y2w2YoJuK1kAjPmx/HStsFyFTnTrL9kTBY4KcCJ8neDg8Jnh0RBMx/LjxRRa7Iw4FWxFLJPC+ew/b5d+XcxkmanPwSNu7qA114ml81ls4LUICeGP6QpSpsxCMR9AZdWNfZxP7WbbKDKVEjgvM0zBXLyygxErTZPZwxwJoDHWjOeLEWu/BXqvCJu8r2k5Fs/tjtPs2sq/pX5XMhlCsk/1/k/NjzM0XskHjiSg6vFthUBUOWgpxJBGcMC+ERucHzPGfiCdAvs9IwUzM0C2CxTSOPSddYev3u3U730J75QYoNUbMvPReSJJcmYPRGm7Cy11Psa/Jonyk9EnWKSbZDUpIkty0OrURWZZxKDX0Jf5Q1ii5YR1aF5ozu5EvlbJiec8CJSaWXQG9LR1LdF/rFV5yaa51Utbo0/hSduqEoVuyv45LotchXW7rFSDKRn2i+lHm6qXXv63o2zhZcEuQk+wOPdUTX0ZkTJC7Q4fHPn9TrwCKiPE7KtZe5z3IRNCQNDkgVUpCsdLKrLdchRlTtQW9378qbS5m6ooG/fv0uhHE0BUVWnupIccPcy9CgapvMkIqLGrBuhERBZBwhap6v67ufhG1ztchk6iwrPhxyFNMQPCEGoV6Qt00+MOtMKpLmCUp3lihYNI7lgJ1iQT2eNYgzzwPaYO074pFBHdwLBoWTtgwunxZ5Okwy9JYkkyRugwKiZDMQsTDMThe3g2JRgHLJZNReMtyPN32GOyZXlxqXtCvaL1AWYJy3x5WYhGKhfCtqpuhhIL1Fp2lX4hViiuQmchhQkaPSv9+doy7vVsHPTYSYquif4YvlXEUaEtR569Ak68WXaF2WFVH58I+VnhMkDOS1nkugqcx5AIdDHJlXm6Zzb7+TvYqrPccwiv2bbDHhEJzcmWSdWiVG7DFX4NNXkF85ulL8UjB9Wzdz1GmTvEXiSRiOBBo6f3/ybp8jNUMnmRhDxxAVff/4I+09x2nzIJwzNH7/5m6uUgk4oglQkzIU+EO1qLTtwNZ+vnY3Hgf4okwlDITwjEXMvXzkKaZDLNciNMd3CLHWedPQ5dnG/t/mnKnhJK1GaPFuLL1ObgDNRifeyv06lz2nOJZl8JgLYDRVtLPchsKjUyLnxb9gSWsiG5VEc/6ajheFxKQ1MVpyJpUioAkTmY4spR9HWp8MS8+crzFrPilppXY69+ORDgGQ6cawUxgn28Htns34Ny0y3CJ9VpWEN8aamLPF7vGsL8XdeEf1Q8hvV6DyxbeBZNemEoRqOiA+4OD0C8tRWBvK1Y5Z+Ffs2vgUbmw27EZZ2ddgpMBtwQ5xFBDn0etCIqpsiNlhzDSuSJtNla79rH43UAuscxEhsLIvlZLFTjLOAktYSeL5c3XleJm22IEaRqCRIanOvuaLtsjXkzXD279JaOUynGX7Sz8u+tTlhyTo0ydaCOyv/1xBKJ9AkiQAEqhRhzUPDoBs2Ys1tZ+FZG4EzmG5ZhCdXSqwn5W4PbmXyIS98IROMQEQKwvJNq9W9Du2YxsvVAjR9n403K/jo8O3sacv9aEBN/Ouh1WZQYC4Q40dL/LntfU/SHG536RfS1XapBV1r8QPxLwIOh1QG/NHzSeRS3Y6L+BqArTKPMHEqUMimwTK3P4TsudCLndKCicAu+BBthX74Ju0RgozErE4gGkN2kw+c0iLG+1QOWXI1iiwBPXfwSTXYOuRCNgFWKBZOF1RtoxVS9seIiDvr2Y/h8LCmrS0bLxQ5juvxadL26H62VBiD2b64CIcM0sMc9CxZRWjNueCX9+CzRTshFpcsL9aTV0swugGXdiakR5TJATHSFNUUaUCPLJ8keHUabBEyW3M3GrCXXgHeduNEcEq2qxYWxv3JDY6KnCOy4hpV8lU7BFiARir78JC/RjsMZ9gJU2FKpsLJEmTU7DW4/sB1xumoDZ+mKWZFM6hDut1fMZIjFvyp+RAM7Mvhc1jpdZraAgiECHdzMmZ97FLIdq+8vM3TnWegPUinREQl5olZkYa70JFd3PwuE/0NNYTYU4gqzTDHvteBxSqQJjbNegquMF5jrUKQQLV6VIR5puMjzBOmSY5g567OQW3fPG7xEN+VEwaxWyJw1vZJCIutSK4sevgUQmhVSjgL+hDd1vbGDHcvCzQ1CpzIi0u+FsbMTkb8/CPPNiGP/dDl9dHZTUKo76kNZE8JWOOyB5rAJQy7Dj/k8RMsTww4JfMxesTZHV+/cmaKfiUECYKagJCCEG++t7IBP9upE4pCY1EsEozl70Bcx85wC8n5ajBeWQ6JVIeIU6Xedb+yC36pH7k/OhsA1vUvxw4JYgRzR2RsJmaERagrxOcPiopAoUq23scbZpEiqDbdBJVchSmtEZcePexheZpXhFj2uUCMYES/u3LW/hYLAV49XZuDptHhYayvCiYws+dh/AQn0Zvpl17pB/+13nblYuQUX0U7SDTyZwBiuxt/1PKX9mUBZjTNq1CETa4OzpwkJQ8n5xzwQGb7gJ1fYXhWtEYcWcvAfgDTWy+N/u1t/D3jNlgiiyXMCyTE1KSja5mS24rkAVMkyzYdVPZYJi1AiWLsUOZ5b88IjnOBGLIhoW4oThgBD/PFpkekGMCIXFAAkVwIeiSISjUBSbEOpwoXxMC3a667DIvBzGBWUIHGhB3B9m1q4iywSTywxW4ReMoetfG7F/VguUy1SYZeqzWsOtLnTc/xbSpHoYr5oA43LBTaqdmYfgFsF1SqjG2KBI10KeZUSkxdX3XnsEUPgfINrpRf3dLwMKGWQaBWy3L4R+1uebQsFjghyCu0OHEMGRcnJORyjLU4RifmLNn0GmxrVp81ih/HVWoVWWLy5YXJXBdiaG+wNNvTFDEtMj8UzXBiawr9q3DSmCCqk2aYZBHxm6eZiUcQf2dfwNrkBlz3elyNDNwQTbrVDJBfeqRmGDVpGNQKSTxfvINWrWlLH/D0fdva+XpV+EorSL2c/F/pR542LY3PgTJqqLih5m1uPRIldpMWHll+CzNyOjbHCLcbgoDDqM+9FtaH39EyQiUSgzzIhXVsEYUyFDmYNcdRG0s3QwzCpGqNmBSKcH2ql58G2shVSlRFyWQHGVDTmNZjxX8hRLyLF8FIRncy1kGiXi3lCvG1ZhFSy4/O+sRO2dzyPmFq6HwPZG0Jx7/4FWZNy2EC2/eJ/ScdnPZFl6mFdOQPfTPck2sQSZw4gFo2h76COUPnsz+TPZR3qsO/mRYAFwTi3kDh0J1wG3BM9gqGj9fNNUxBJx1s6MrEYqg9jnb2RRxKWG8ZAmJPjYs5+VORhkGlyTPg8fuQ9gmWH8EV//LONErPMcwjLj0NMIdMpcLCp4FJsb/w/RhBcaeQamZ3+XlT40uT5Ep09IWiE0chumZ/dP1ydRW5j/MNq86xGOuZklQa3V1tfdjURSPHSc7cbe2CFlh46dXIb0XEpScSFBs9d7htYS9BqBSABaJQn0kTFmlbDH8UKu1yL/BqGdWc1fX2D/FralY1XpT/o9T5VrYQ/qHtP57GbEg2SpCQLUku9EKBrA7pYNmPJizwYjDsgMWuhm5iHc6UHdd19BtNkNmUmNtC/MQuc/N5KPuNclGe50QZFpQN7PL0Tzz95DIhRFPBBBzC7EWFNhf2kXS/SRyKWw3jIXprOEUpPhwt2hnJFk7IzIeYIjJWB6ukNJL7fYlvQuPPv9TSxDdH+wufc5mXIjfpF3FerCXRivyWG/UzrMbi/UNo0ew0EqUWC89WZ0+rcj33weE0AiTTsFank6JJAjx7gM2YOMMery78K+9r8J7ytHB6Xc1E8AiVbPehRZLmJf+wI+3PXDOyCRSqAMGTCpeC4Mqr7yj5d2/BcVHeVYXLocy8Yee3u240H2JcvRtW4HTNOEOG4qAlUdiLkDrMxDMzEHWV9fhv2ul1DgCWDKEyTkggWPhBTaKblIROPofnZLb4lH1BWA+8ODKP7bNYgGI9j/6/9C1yVlfVMb7nsVaRdOR3y8HpLdTthlDphK9IBMIliBSUhMKjhe7SnDCceYqBpXjD2qHT13h3IIbgkOAc8OPb7QoNcnOz/FB+7Dp7G3R92sd+ZQ7sxjwRX1468dH7H45A2mEuxoEgrg5+X/AkZ1ce/ztIpMLC36yxFfTyHrS8zY1foICkznI8ewAp2enYiAXKJx2P37e0VQLpMj4PdDp9fDopwLm34GPO4m7N31d6jUaWh0CbG9uu5qLOuZrjAcOqq3I+DsQO7k5ZCrhh6aOxhUtO/64CCz7syrJkKTl4n8L6w6TCj82xtZbaF2Uja2YRsUNi/0AQ3yb5oLuVGDy4w3IdLtRV3zC4K3OSZB5leWwLCwGDVfFaxL9n16xBMIVXcjUN4Oxwd7oO+WCi5N+jV3EN3PbENLvgPO6QFElDGMG29F6dM3I1TbjXCjA873yhFzBmC7axHsz21HuL6nrCUOhKo6oS4bfhYptwQ5oiXI3aGDwC3B48tz3RtTCqBaIscV5tksy/R4Q6UYu3vqGOfKo71WG7kxjegTweFi0YzDgoLfYHfrH+GPtKDJ/THOKv0nkAW0eTaiw7cdxZZLeptxb2/9CSbNq8GmNw2Y+gWhoXV31z6Ewx72OK/sclS7OjCnaPCZhAMJurtQs+El9rVUKkP+jKETh5Lp/mwX/HXNyDhvEaKNXnQ9uZl9X56mhWHh4W5W3+Z6tP3+E/Z13Y0SOAqCOHRRBeRSBb6fK4xcIhTpehgWlMLzWQ3rhhP3R4TIK53uGCBRSZlVKJHJWM2jMteEYL3QQJulBzMEMYwo44jJE1h4yTUwptvgWn0Q/r0tTDjjnhDkmQbW0zT/15eg7Y9r4dtUz34v3OI+ahEcCYsf59TCLcEjnBzO8YOyRAnqEzpPV8LmA1KRPMUJBy5GvlgIBwLNmKTNg/YYpkGITNMWwCrXs7KLSeblsMukcAYPwRNsZi7QVN1fjgS5UMfZbkCN/VXkGc/q/X6WYQF7JA/l9YYboNHKkZ4dYO+xzbMJEZ0bJksJK42I1JgxM6cU2abht2OTq/VQqPWIBL3Qpg1/8kLUF0Dr62vY15QVaps/h9UNkggpMgZpMSfvK9Kv7T6EBdMuRZrGhpL0CZBJ+/dWzfjyUkikCnjWVqLzqY3w7q9H3BdiBSOGJWORftV0SBQyZvlRg231mEwE9ve4xEkHFQnYfrgcwdqDKMu0IWfMOISbnej81yb2FGlPZmu03YOWX32A4sevRdzXV8urSBLl4cJFkBPjluDgNwd3hx5fvmhbworZOyMerDBNwtQhXJ+/bX0Lh4JtmKzJw325lx7z38xWmvHnIqH4nGiJdqHduwntiU2IxN0Yn3HzMb2uTTeTPYZCr8xHkeViPPu/x9C8Jw/eG5uwp/WP7Gdji6+HfVs+KrdSh5wqXHr3hVDr+soXhkKuVGP6Zd9HLBKAUtt/4Y8nYmhqWs2sqvy8lf36jTr2HqBQHahJjMQpg/vTQyh49DJmVcnTdanfw+wCGL87Fx9VvgFljgWlmZMxWZk6M9X94SEmgFT+EVdF4dvfACglkITliCsAubl/8k/ut89F6+OfwLe1hkaFMNp//yGUsQSCsiYEswvR9WRfG7b0L8xCuMkB17vlkNKUCoUMgcqOvheMxdF431vMrZpzz0ooc4YWRe4O5YgieKr7ho64xBgRbgkeXyxyGgnUhHAihpftW5kIUpLMGnc5a4Q9LqnVWSguWOGh+PHt2kNZnSLynukHJwoSoLHW6/Gzr92A6667jsUTqf8oZYeqFTaYrEInHY1BDYXq6G4BmULJHgPp6tqFmtpXhNfV2GCzzuxd8O1b9lAgEIpWI/wtdaC8S/fmaujmFcAwvRi6SX2t05LJmDMBX5gzeOZtsKoT7X9eR61jhL+FhFDs7gsjLpcgJo1DkXW4pUliFbcH+/dEjSeQkCSYUtc/8DKUBqHVmmqMFaazxyIejUG/sJhZrlK1AoblY+B57yB0i4tZgkyoSnCx+nY0MhEM1duZG5YaBaSCW4KcGLcEMejNMVJSZ88kaPjtWnd5b+nDX9s/QnfUix3eOjxWcgsrnyB+kHMhtvvqMF1bgJfsW6CSyFGgtMIdD2CRvuywqQjDZbztZlborpJZkG86+3O/n1DUhb1tf4ZMqsaUrK8N6l4lEaJ6w8XFv2cda/SqXGAmkFFkg1qvhkze37UYi0ewq+pRBMNdmFryTRiGmTCk1WRAwkZASaDR9MXH2l/+DPEKP5RhfY/mCOmaMYcfro8PwP1ZBcb86VZIKD4nlaD14TVMULLuXgbNxL6aTyqap1Zm8jQdvFvr2X1CGZ+RNkrwSfQ2xfablCi7+wKWUBPp9kE/SeiHSkTtPvb87jf2IniwQ8j+JOETfhmQ9nwtASJdNGZLDu3cfNT938sIN9oh0ShR+DNqYKBBrF3o/iNTK6AelwH9/CImrpSUE6ztRtOP3mQ/z7n3XGin9nc5c0uQQ3ARHAJuCR5/aAYgPUQy5EbYo14EEmHs9NVjvmEM+z61SyPr8BP3Qbxk7z+ZIByPsq40xwJZY2Xp1+B4YPeXM/dmOO7s+f/9yNDPSvlcsWheJTexh4ghLXUbMG+gEU7vIfZ1p3N7rwi2+1zY19WEmZlFsKgPt2T1+nwsmPcg+1qpFCxNIlDTKnwhFQSnt+BAKmGWmyJdh4P3PQZZtxq5d6yEf4cwxsqzobafCHb9dztze7LXYUXtqRJLJDD41dCUCiKszulrgB4PRtDw/TcQ94WhHk8/l0KVb0X+A6tYXSDVDtY/+CainS720gllHPFYHK5PDiDaLQhewh+G66MDsF03H6E6O/tesKoL9hd2smM1nTuevVa8ydn3dwNJHWh64IkxHIK7QweB7XB5YswJ44WuTXjfuRerzFPRHLGzGYBlKeoC85QW9jNKpqGuMDR3kJJchsK+yYva5zvgWxzG7MtLoJUde2LNUDQ6VyMcExZavaoASuXgDb+P1uowaAuRlbYAgVAnstP7Mkf/uWctugNeHOhqxjdmpc4KTRY/IuoIIFIehkSqgmXlZNjf282SYVjfVnkC2jEF8ErqEPX7EEsPwrm2CkGtDPJQHIqJmXBub0TXJ1XIOHc8K2MhJEo5EsFkV3Xy+0tAU9I3G9H5bjncn1TBdMl4eNsaEIEHMqigKrXCdus8SM0atP9lHaR6Bfz1rYh5/YJLlCw8o5Il10TcQnE+JdlQMo9+tpDJmv2d5fB8VsvKPZxv7xeOTadE59/WQ6KSI+PLi9i/urlCPehAuDuUE+Pu0NRwETz+HPA3wxXzY55+DN5w7GCCRs22/1HyJcEakR4eF6OC+YeD1yFQGYF8mRQ+TZiJZVWwjblHaYLEQFresCPWFof0DeCeGc9jhr4IN9oWpnz9z0OuaTmbRG/VTsd78Rz8qf55XJ0+D5daZiKciPYT68FEsD3swrvOPSyLdYxcioauD5BpmgOrYSomFd1x2PONSg0TQeOA2sA2vxM17g7MtBZDLRdcyvZ6Dw6tbkB2ngYIkHxoIdeboJyQjmBDGyQROSRxKYK7u6BcYkPU74XUq0KgtglhrZTqLxBodaN7fQ2i7iAiDj/i5H6UyaGbkoO8Ly9E7e3Ps7+VbA+mXTUdlsum9v5/93PCKKa21z5BROpmkyeUejMkCqHjTOc/NsK/rQFxeRQxbRiQJYQsASq1oM40PS8uz9LDML0MaaumQGZUI9zmhrIwDbayDPi2N8K9+iD7PnWZSUTiSETCkFt10E7J6WeJUixxqM+EM7qIs+b2PDHmMHhM8PNBC8yLnVtQG+zENH0Bnu/ayBJiyJK4MzMKs0zL+oOSUHyn/r9wxXz4ce6lh80BjAXjaHiwCzSEIsNuxIQv5eAPre+zuYNTNHm4N+/wzNGM80zY/0wTds9uRFfEiw8d+9ERcuOHBUIB+/HCrJ2E6QW/ZZMzdrW8x763zlWOF7uF2rsVholsTFT+BTMHXXD/27kBW321+Ni1H9+QOeD0HUKrfQP09vGYMO1amCx93WX+27EB23W1WJo+DnapF893bMI1tnlsA/HInncRiIVR4+nEjWWLhIkXTx1AoM2PfYeUWHjnfIT2tcL+yn7I80ywLixCIpKAZ20VpEY50hYsQP0fIlAGY5BlGmCgdmWROKI7WmGeU4CujytgnpUPxyv7IYsBwW3N6FRthW5+Efw0EikBKIvSWEG9+ZLJrJWZiHHlOPZ3oq4wYAEkUSkrrne8uQeejTXQThDihSTILDYoFtaHJTBfMA3Od3cJ10J3AK639yDW6YEqzwb7CzugzLcg/7eXQDcrH0V/u1YQuEQCUbsfMr0Smkl911PnvzfB9X45TBdOgu0mIcOVW4KcGM8OTQ3tDHhizLHTEXHjje6d7Ov2iIsNvhVbV5P19n95l2GDtwpZchP+1PZB74T6gSIokUsgN8kQscegsir61Rt29EySH0jaUj2eztiAor1WFFSnobHUAVeM2jQLvNW9ExX+NnwhYwGyVUPPHhwMGg11T/0LvSOjet930jF94ilnteLzf/tFxO4XCs4HUqzOYCJYoEyHRZXBRBBBGYL+brQ1besngmtd5QhLYvgsXMV6r8Jfh3cdezBLVwiFVIZAjIYDC9aud2cHdPu7QFFDv1uDiifsSFe1I+GPIFTRxer5Cn+6HBk3zmR1ezReSfLDsxF1+KEvy4D9me3wbm5AsNoOY0Eapv/9Olbk7nt1v+D4TCTgXV8DloPTI/DKLCOsN8057D3abpoD642zUX/3K4g0SqHISYN8ghqBA62s1MF22zxEYh54DzYAbgWgjkISkUIhMyLzivlIWzYJUV8Q7X9cg0jQDZlFi3CDvXdaBZVGgMo8TH3Wse2Lh5dxUMYoQR1wcBMNTeaWIAfcEhwM7g79fKQr9BiryUJDqBurLNOwyVuJIrUNM/XFmKjJYeeXhvGud1f0OtNyFWmHvY5ULsG03xQi2BGBrlioo/t61kqs91Sw6fPJ1AQ7WGu2IrUVE3bnYNlrQkPl5h86kLBK8JmrAhO1uXi+Q7DUjHINbs9efkzvj0RIFMDkuRTk4hX/h2Yi1oY70b7pENJ7JiMM5LL0WVhsHAuzXAu5RIa8tLNQe/ADuHWNyM6fw5KA3nPsQZpchxtsC/GafTvaw8ImQCVRMLfrZl8NHpp8LSq9HViQPobFx9zbuhBnxXdxyKJ+aBNuxKK0EaFEGCliERkSsQQkchkTg8b6D9F9cB/UnxSipV0Fy9n5UI4JIFRlh+ezemR9eR77m4YlJXB/SLWA9L5pqoPwPigz03zx5EHPFyXaUJG7RCpH9t0r2DzD+m++hHCtU4gZVlcyN6iyNB2yNgViLj90C3LQ8tBHbN5g5jeWIv+BSxFudkA9JgNRe4BlqGqm5jDXp39/GzSTsljN42Bk3LEQrg8PscQZgt73SHCDcU4t3B06CNwd+vmgBf0nRZf1/v/KtP4LZCgawcu7t8ETDUBiJEsEyBnEKpPrZdDr+xY3mlF4VfrcflZZub8Fv24S0uF/nHcJLs+ZBTu8LOXeqjPg8S6hU8qP8y9BmSYTNYFOzNCnTpYYDnqZGl/NPAcVwVY285Aszf2ubSiGHekyDRy+PZiYNh967aUwjtPi4ksvHvS1rIq+Gjq10owJU4Xs1dXOfXi++j0EEhEmrOeaJuN620L8ruU91nD6jqxl+Mh5APO6CvBQxVvoVPuwNVSDL3fNgePjFkorYVKlkoUgidJLyCBRKhEIp8FXrYLx0y5YV9jgrK1FTf1LgBmI5gehaZsAf4UTebfMQPcr+6CbkQPHx3UwzMxC5u3zoJ9bgJbfroXUrIbSooJuei7Srpw25PmKeYJ9dYDBKCtzQFyYKtj1wk5IbUrEJSHEGwOAJwL1hExWD9j2yFr2a74t9Uy8NGOFTFWFVcesTirPqPvmS0I7NasORX++etBj0E7NZQ+C+qVSfDAelbBYp8IyvCkenDOPGHeHpoa7Q48/ZKXt6qqHTqFC0BfB/lYhDf/OzOWYlJsLm6J/VuNwIEvpnrr/oSPSN5CVOs34xgWx9N5xsFmMqEnrBBpJEqQwybX4SeFlzGKTHWOtocgS4zj2EAm2/Q7hmAvtPf9fZ38XY2vGY82Vu+GavAXlrU+h2Hox1D0T5ZOJJ+LY2lwLrUKJKZlCOcTTnZ8hHolDEZUhKo+jOezELVlL8dOCy1ksNV+VjpKPtGj7XxOu1pTg8e/uw8FAC1Q5OkjVMsTDMSY6hlmZkBvlkCqliKmM8L3azsQn4hYaEng/C0Ji0iKh80PRZoZMAeTdOQXaMWZoJ56Fmns+RqjOBde4NBQ9sAy6qdkY8+Q1LHGGpmMMB8PSUibkUoOqt3A974FVsL+xD76tjZC2a5Bz90p0/m0DEohCPcYGzYQsqErSmVhpZ+SlFNaGu1/tLX+gOKBIIhJjLd8Gi/n597YiHokhEY+h4XuvoPgvPfFEzqgjkUhAJhvcgzBqO8ZwS/D4s6WjBs9Ufsa+nqTJgSWhBbQSzMgshElxbDtxssAo5kjM1hVjkbEMj7V/zNyErelO/CD3IkxGHh4svg5KiQy2nvIBGSSo+Vs7HFt8KLrdhvSFg/TOPAq0ymyEAy50Qws1orDqZsO11g+FLg7l1R+j0UXtu8OYlH17v9/r9OxARVc13qigptNSfH3eShxKtLJ4aXvABWlcCnVAirvKhD6lyXFT13ahREMTkCM9rMWq3JlQWrQY99hSoY6P8kzUUtTcswGhJi/SLy+DRE6xVinSFgjuZ8vcHHT++kIk8g8hnGuHLGRgAiguEKHMNjgn7kPCO7H375Ib9WigeKJxhTBdXkRdZkPGl+ajS62EIlOPwJ42ZiVSok/Q7EDHjs3I+/kq1nSbHQvF/uIJof8oK9yPCLWFPX1F1eMzEenysokTbY+ugcyigem8CTCeMw7yAS3pVMXpfa5sXwQxV5CL4Ci2BFWq4bUsHFUiyC3B449S3G0lAN/uEDQBJXJLLaiNdGKqugDSY+job1MYcGvGEtSFunCldQ5rzfaWcxd8nlqM97UiHPFCqdAjV9Xf+qJ4WNdaIYmla53nuIhgg3opGgJudMCABkk6vqYsQdYFZjz3r+cw0a6E2hqCUd2/ltATrMeupt+xr3N0U9DqL4QPQfy741PhexILzvvbRBjcGsh+IAGm9/+b2jID/NV+SGQSPDThBsg0wjmWafpuqag3DG+4Ef7zqhDa1Q6NMhfFDyyAKkO48eM6GZwzKiGfKEz4iE0VLCrnp81ofnwvPFfvR1TthjtXqMM7nsjNGmR9bRF8HW2o+vvTkGWrIE9o0LVHaJCgSktD2uTJzO3ZeI9QZJ97/ypmTdIQ3qzvrmBNtp0flLMyi3ZviE3EYOFKewD253bA/vwOaKZkI/OrS3r7l9LflWgVkEqVsN25iL0WZ3SSGCGx4VN/BAPgInj8mZFeiG9OXokCTRrkUWGxPtjZij/UfYC13eXDfp2wPYoopUL2cI5lMm7PWs4EkPhxzgW4wNcIrWMXqhvfSP0iUkB7tQrRojj+O30jnmwVROfzoA2XYzJacBYO4Sa5HWOlAahsCvxo3bfwr4fbsbT0D8i39J8ZKJNqIOnpHn1u2Vx8d9EFKDYK0zVoS3CNai5MTi2kcQk85X0ZriK51+ej4K4SjP3VpF4BHIhcr0TwC4cQnFgP13mbEfNFEG4Vht9GXBH4trihNvdltaplNtQ/shutz1ayzYJ8XzakUgWy8oc/7uload++AVG9H6FCB2JasoiF/yIB4TjDTU7EHAFWbxg8JDqcAc34DLg/rkDcKcQcZUYVDEv6J0yRIAb2tMKzruawv0tWpeu9fXC8sbvv6dE4XB+Xw7dDGNHEOfMTY2TcHZpaBMVWV5zj52Iea87GnWNXoDvTi+YWB56TbBQuxAHT2Qeje6MH1X8QFsG8O7VotDzNFssZk74ClVJoR6aUqqBSGBEKO6BWCW6vgax1lOPJceuACQnEJAnUODtwc9biY+5JSkw3TsE+3zrmZJOFDmJPUy3OHv8vIdM4koBacfixaJUZWFj6IA56a/Ce14WzdQ7YpAV4pPh6VmOol6rQdr0DgeYwMlcdnjhEcT7DFBOaX2iHtiiAzAtSv19r7lQ0tDZBhTR0fudVRI2HMC1xN5r/U4/uygOQznRB5tBCUVOG3Bnno21rJfs9dZ4B2XMuRvq5hycRBWvtsL9+AIZ5+TAsOPYkI8IyZjyclQcgD2ghb5EjND7ByjbMZYILVTMhE+ZLpyBOfUGXCa31CGqbFu0Q2qkR0S4fdDPykP3Ds9H6uzUUNAYUUiAaR6inrIJIxONCa7VwCOFGJ7r/twPmi6eyz8q15iC6ntrAnpf/qyugyj88a5lz5hDnxfKp4SJ4fN0NnWvciLhj8E8K4501u6BSKfDFa5cgP56O7ogXM41FCHVEWBcRpUWe8jWi4SC81aHe77XXHIB3AmVBAnZnBbIz5vQOmp0/5V4EQt0waFNPRnBGBJdfIg7ka9OwyDT2cwlgPJqA+tBUzMx9AG34CK2udYjHI2h1CSI/VE2aVpmJ/9o/RnvQhUpfG35VejXMSh0UPRZi9iVDL8Jtb3ah+1MXe+jGaph46Ir7N/IeU3AlCrLPxcH6fyPobIXdsxfVzS+hdfZmSJEHqSKGmDGEoqsnw6C2otPYwD6LwntmQ2lN3RS889mdCOxrh297U0oRDFR0wvHWfhgXl7CM0qGwlE1EZL0H7g0HmemW7poM2y3zoTKn9cYUrV84vC8rJc+YzhsPz8Y6JpBkxRFsuG5E+FpmUDHXqJfaq311CRM6EkASQspKJmvQeO743iQaKcUc2XxDKVyflEM7MRf6mYO3xOOc3sS5JZga7g49fngrg6j9e6fwPyuFf0KhCAKBMNISOuTpLPAeCODgz1vYwjvloQKos4RMvaaarXB2NyDgcsPn6EDxtBUw1OQwQS1cMZs6JzM70prWv6G2Qq5lj8G4wDaN9RTNUVkwxTC8CQ0DiSXieLz+Y7SFnPjCtgXwvBmARAWM/eMqtLrWs+dEeornBxNBeh8yrRSzDUV4z78HwVgE9x56EX+e/MVhx0gNk3To+MAOVaYSB39ej0QIKP5KNqzLzIc1D49IA0hI46w3Z0PruwA1WFnYBbnMiCg8qAm8iOjbZkTdUcj0cijSBk8Y0E3PYSKonZp6sG/nU1vZJIrAvtYjiiB7vak5rDm3MteC/J9d2JsAkwybYrGuBppxGVAVpbEEH9ut85F25XT4tjVAO13Y9Ph3NbEZico8M8tMdby2F4bFJb1CJ9OpWP9TyggtePgKyMwaNP3sTYRbnIj7o6w1nLosDe4P97FH4e9uQLjZzmYqKtJ4/PBMIsGzQ1PDLcHjB1l2UpUE8XACpTMyEY8nYDZq0V7nxsbVldAZVJhvKUZw6k4kMjuw9zMrxp91NtQ6LSr3Cu3IEKd8Thk87iZM/klfjeAM3HVMx0Qjm86z9vW3PBaagw5sc9Wyr5s8dpigQcTSCv+BQkyf/F2Eot3IMfdNzBhI92ceVP+pHaoMOa55eB5Wt+9jBgiVfQhzHg4XwVg8TCNr+41ssswxYsYT4xF2RLHv2zW9cdN+f2u9A7V/bUL0GgPipTHWnSwRFSxfEsQInKw5tV6dC5lWjrguAKlFnXpIRA9pF02A+ZwxrEF1KvSz8pkI6mamtsYHQq3PSp+8gTXIHqz0ouuZbXB/XMn+Zsk/r+sVSuoZajxrbN/7fW4Hot0+ITtWLmPlFFFXT62iiEwCuVHDZhMGazoRrGwXLECaPEwZqhWC253E0vXxHjjf2wmpToWih26BVDHicvk4xwgXwUGgQCmfInF8oOSQ6X8qQjwUZ19bIeykN34oxJ18nhDcsiYkclvZouv3tWP7m09j0vKLYLTkwu1oQV7JbMTCEeSPm48TTTxCnVQG7yvpcvux7pVDkBfJML2gAG1hF8ouyEJT52cIXPIm9sfkWKJ+ECpFXwF5KkuQLGQi1BFF1BvHXQVnY3X3PqyyTU1ZwxiIdGFj3b2IJyKYW3B/v0xTmVoGTbYMZT/IQ7A1jIyVSeOLElF0b3PC8e0/Aho/W+Sph2tCIXSQYSOLJAlML/o20gyT4M6qx6F5L5APEsXh8dCp+s/hS2ao2rq0y6fAfMEESI9iYHAq66/f39MJTcmp4wyVTJAohptdkCilsFw6lVmIrg8OCgIoEfqWulYfYkk1rnf2w3rD7H4CK37GqqJ0GJaNRaTdDXVRBiu5cH68C3EXzSYcI9Qd0ucYjiIeCMF/sBaa0jzItP0bmXNOP+LcHZoabgkeXxRGWtz6L3AzFhZBoZDBlmOEPgjYt3/W14MsIUXd+l2QblwK49I9aN1XgXELz4HOmDrxI9wdhb8xBNMULSsXoNZhxECLgn1fMrjAObb7UPVoKzR5Skz8ZT5r2zaQPf9uQMZGA2KyOC55dBaKSoTib/fiBCh/MyGLIhoPItmRmEoEsy+zsOxLit8p0+SYgxLMMQsjglIeW1s9onEhW9IdrDms3IIwzzQgEvNhV+ujCEddsOlmoKbrVejOKwAkfvIcCw92nhOs7Sd9aWieBetUQbSD0XYkJMITA+EOVv/o2d4FmUEJ3bi+WYjsfUXj8B+0Q1VohNxw+MiqoxHAI0GfXbTbD5lZC9sd8+Hf0wLX+xRDFAjsbYXtjoWwv7635xcARbYREqUM0U4P9AuKWSap3KJB4GBHb/xQjDlm3t7fapeZpOh89lO4N+1D/k+ugzInjQlkx4vvwX+gGqq8TOR/+4vH7f1xTg3cEhzCEuTZoScWtVaB2ctK4K8PIeSOYszss1H9yRZIZAnEFQH4g51QSNsQCTQKI3fqq5BZLPR9HGi57fthA7Omsi+1wLrMgPKfNLH44qRf5jOBITrWuFD3j04ozDJMfbQQMvXh1pZrj59NrPDXhxFxRnubdidjSdPBjxBiijgyDH1dbiYvuxgWuwlqpRU6Vd8g2sFEUGmWo+hLfdPfBz4/5ouzlnFEqCsC99M2qNKXAsowMq5ZwL4fbA2h4rcNkBtlGHdPISuTqOt+F13eneyc+cKCoHkhTHpgks42AAkU2i5A99Yu+IwHgWwv7L4DSNNNRIZpLkojXZBKFEjXT4VzXTua/iqUsJQ9NBfq/L5hvm3P7IfjvTooMrQY84ezEA9E0fnUDqEx9vXT+k2TEKGxTPZX9rGYnmn54KI/EJo84d1Qx752f1jBLEGpVoF4MCq0Y4vE0fXsdsRdfipKBaIxtP9uba9Ll0YvOV7d0/t68XCUzUQkMaSepIosU3/RpvNEU+6Z6zjGrEVlXjoSYaFAP97zL+f0JsFjgqnhluDJgRb3fT9qZNaJ5kI7JDEZa8osVZFVIYFanoY001yEtK0omDw79YskEizeKI5e8h4MMkEkfNVBKHumt7e85GB/hyZSUMmBvvTwrMfsC82IuKLsZ6kEkJh0Qx6cU33Q5KmYkItIpXLkW88a5BCPbmIBiXXnx242Fir3cgv2fqcB8XAcGqxgMcQOox/ZF6ng2O5BqD2MUDu91wCMk/UwafosxAz9DLR6hCQdsS5xjPUawUpS6NExkWb9OeGBC9vrf4PlpY9BodKh2HYJqyes/dkWBAN2JKCChFy0AzQtxobdUguzMHtNz/o6uNcKcVLd1CwoC8yItHqgmWDrtcodrx+A8z1qnA5oJ2eyPqBDEenwwP7aPpbNKeLf1SxMr1DJoRxjZY2z6dVpnJPrnQPQzy1kUy76PgAILtJ+Hc/pmwk0/t9bCNfamejlP3wZlNlGwZW6ZCJz98rMOnS88C7CzZ0wLZmBzBsvhm9fJbTjSxCx22H/ZC00hYUwzjw8e5Uz8kkkEpDLT32M99QfwQC4JXiSSNIGvS4HvmALdNY0TLxwGWuXJR2iiJWsNpoin3m+CRN/ngdvVRDpiwxM6NzlQhcV41QhZhNsC8M4XYOujzxQZSl6J1IMRJWhQNndqbMdRWgxt0wThHU4sAnuRymCrr1CCUf3eg8MkzQ9Ii9BxkojOla70PKinblqrYtN6F7vYtYrZYcSGYbZWFLyJ3acKpkZMqkKHZ7trGQjlgjgUOd/WBIMWYhaOTWUFsRJEpLi0H1bMPFXyyBVSOHb342W8W8hOKkBptrpyGq5GHJT//OWdesUaErN0E2ysr+nGW+DVCNniSuKbAPqfvAB4t4IrNdMQvoVE4RzXCKUPcitWla+cCS6X9jFpseLFh37RzydoSjCFZ0sAzTzq4vZt6zXz2TJMO5Z+Wh6bB0U4TjkWcZeFzn9rvnyqcBrUsj1alYnKHw/gdaHPmQ9SBVWPfJ/cSEM84TesFG7MLkj0u2C3KCDaYHQuqf9tVfh2bObPXQTJ0Km5jHC040EtwQHF0E+b+zEQ4kyJGBUI+jc7oOxcxFKLsmETNnfCqN4X/2TXTCMUyPvmnSW/VjxUCsbDOtt9kA3N4z0CRnMxdn4fBe613vZYhcPJpiLdD9Zm7RoX2pG58cuNP63GwU3CrG8k8HRXksld2Wg8pFW5hKt+bOQpagtViL70jT23mKBONTZSkR9cfjrBAvp4C+aMeGn+ez9maZqoS8TFuSJWbfBop2AvS1/TjKE4uz8mLTFKLVehYZtnyHcGIC7ZCe6PipFxvkF0E1KRwTd7He8mkbY13SwzNGcm4p7j5PigOkX9nVoURWYUfqPK5jFGA/GkAgKCSUxr2AxEsZFRdBOymQuUym5LXvOj29HCwL72+B8/xCMS4uhn18IzYQMqMfamAgqCywwrRqPwI5mRDq8sN4yB87X9yFwoA1ym565N6VKeW9f04RJDVk0wZJ/Ym2enqlPUmhm5CD9mhlIfBGIdnqhm1sAX4+bNdomlLREWlzw7qiB87NtUBdkIvuuq+A/UAPTgv4ZxZqiYnh274IqOxtS5anvP8k5erglOAhkHvOY4MlBdEsy4aI0+E/dyLu6fwJM+7suePYH2CPjHBMan+tmAkh4g81wb/SjfWsTFvx0JbrXCQJIOLZ6mXtTxLHZi5gngfb3nCdFBMk9+42pP4BVk84skeFOXTBO0sI0TQf7Ri8LTTE7UCZhLtqpfyhCzB+DOksJ94GkyQlxoP7fHawpeOubDsx6opT9vUQijmCkExJKeU0IopBADGqFFeMzb4FcqkEgz4mq3P+w13E1TUIGCiDTKTBt4jfR2r0e4ReEGKcq+8iWjhgHpPrH/PuXIVjjgGlZEfwHu5gCa8dZWe/OZDwb6tH2J6FLC71b99pquNdUQzcnHznfXcb+9e9pZYN7NTdkounprXDubUH2D85C4z1vwfHyHkSaXci6e1nfORyfCcfsAkR3NwudY3quCemFE4XEKGrITbHCOqGTTLwnBCji/HQ3wq2dCDd1wrhwMrTjCyAz9vcAGKdPh37CeDaiirmLOaclMt42LXVMkFuCJw9tvhKGCWpWLpA293BXo2WuDt0bPNCXqVliCz2fbBSlVQa/VwlY/JD11M4V3GxFy2sOFkMLNkfgKe+rD7MuN8D+mQ/WpZ+v4JlqHSu2tUIqlaBsVtag2abd6zy4ftyX2NcVD7ei6DbboLHGZOjaU1rl0I1RIftSM/x1YViXGHszbYVsW8AwQYOiL2fAVxlE7tVpaHtbcO0p0+UIxZzYWvszRGLe3qzSsek3ozuwC3b/PhhUBUwAifTCMlTXypiF2FH8EXLCU6FTZiNNP4E9YvdEEXGGoc4VGhDQiCd/pQeaEj0rzxgMTVk6e/j2d6Dx59RSDii4fym0E2wIRroRjfmhV+f3q0dUFVkQaXUjEYrC3uWFxBuA9BUqWq9kz1OfXQLfvlaEdrVAV5aBoDfEQpU+dxAPbK1AoUGDm8flsXu46Dsr2Gv697Wi4s+fwqGRoXFvPc5ZX8XOMVmNsjQtIi3ufgJIhA94gAwFpAag6bGngXgMaSsXI23lkr7PKRaFa/dGSNVaGKfMHfQ64IxcEjwmOLglyEXw5EE9MCfcP3hRtXm6DrOf7HO7ZV1shmWOji32za8Z4Cp3o/hmIZaXNk/PHuQy3fv9BuY6JBHV5quQc4kFuZelLrM4GhrKu7D7jQbIo1LYGz2YsrwQWrOy3yIY6oyw2F40HoVMIoNzhx/NRjuyL7Yg6o3BMG5wqyrcGUXbm4KgufcGmXimgv5exgoTQA967xcm0GXdCkNOAdzBbgQiPZ16emjq+hiSDgv0NeegZNHlvd83qosxPf972NH0G/iiTWh1r8cYa9+A2kPhNrzUvhmx5jjGmLKw+B0r3Jvt0I4zoPRHkyBRDT67jxHtU5hwhw+Nz6xF43VvIiGLYVret5E+azpyfrCMxQjDTS60P76JOW7XFugRfnE7Lv5QcFfGJRI4S9OgWlMDaQxo/OdGdCkliBQZYb+wDB0+PzoCYVxUmAkrDe7tgabO+40qaD0haCu74ansEgzDWBzeaJwlJZC7lMRUplciEohAFpWzsVjp/p5zKKGJHD3JNT24929H97p32NfKNBs0ecPPduWMDBI8JpganhgzMujc2YqwJ4TshQWQyqVw7vCxInPjNA0rMSDxzL/Ginwc7tqk0ojpfy1CIpqAXPf5h2b66kOQ66ToWO+Gs86H3DoLW/g7P/Xi7W07MfnsPIybn8MsVrLQWl61M9ckPccTd8MgM0FpkzNhppBc6bcykb4gtUVK4m6YqIG/NsTEfrg0VW2F29MA96EGzCu7CznGJYglQoiHEuh27UdM60coqwkSfTUcn54H41hBPFnSyN5cmNMmISTvgvLARHiz/dCXadnA378eXM2OmaZZbG2vxsQOJROOcLMX+2/5BKYFGSj41uRBj0s3LRN59yxiQhJucCHsdTEBJJyV1XA8UgtVkRmFPz9b+AWZFFGpBJ0ZWpjcff1i315ZiO6QHzfTqChvhJVB5HYLP/copciIK1Fk0CBtYBF/AlBGYoLQxRNw6oX4naRnQgW9F/EKifojWL00H6V1buTHa4QsVMou1thQ69Fj8qF6KFrKoS0ug8Lcs6GS00SOLi6CpykKxakfqDziskO5JXjq8Ta7Ufk/YcYdCaBtWi5LhqEFreVVB9u2T/ltASts7/rMja4NbuRekgbDuL6eoTKVFP2q1o+RrvUeVP+5ndUeRuOxfu47aUwKWVSK7gYv9r3VgFBLFFKthMU1Kc4phRRGmSA2zDXbk+lPSS+DQfG/CT+hzM2jw5o7Fh2N5TBZ86BWmzA558v9fl7R8TzqHG9A1VyGtGUmOHd4oExXwL3fj8anOiCRXoGC88zoeNeODmktpv1tHOSGnkQTFptMQBaT4tUFVZiZm4nCSgWUXsCzU0igGQr99CxW06fM0sO0vRTyXUpozrFC+XoaPPEGhGociPnC0JRZUfLY5Qh4Qyhr7GRdYqzTi7Hd5cFBVRxXfFwHqS/KShrcNGlCLkVTtg4ygwb3T0nd6JpioxO+vQLbP6vCThUw8WVHz3uSwnfZZMg/qICyZxpFd7oGRU0emN1BqHRh9llTb51WTTbirV40vPoK0uMdcG5bh9ybvgx5mhFRtx0dHz4PbdEEyHW8t+jphpyXSBwOzw499cg1CiZ+8WgcKosGUpWUWXfUHYYRJ/eUYE3UPtHGMkFjvhgm/fToxvoE2yLo+sTNXKYDpy+IiH+TknEkKsqgAEznqhGPJRDYGoLCo4ahXs0EUHiiEMf01QbR+FE7tArBmgs0hDH2R9mIumJIX3z8F0trThnmLf0Wqv7QhspNrSj9Vhbz80mUElZSMTbjOhSnXwL5WA063negavMniIw/BNWGeZAjix23a6cgBlKFBBFnhLmTV2RMxMetBxCXAvIE4NfHsLu4Cy1yJcpkGky8WCh/GArqLFP7843Mei/5zVKoMoV4XfhiL+BPQEMJM0bh/MfcIbT+aDWoS2zhL89hWadnJxKYEY4gHJDCs8/O9iE6fxS//+pMlBk0+GZ+apexiK4gDYvy5sBR2YyGQBz4H6DONKA6R4/K5QUYf6gLS3P1sPmisH4iZBN3GDOhlHvRIbdBEgMScsAv0SJdHgPkYTS/+AeqpO9xpMbhbToA87h5x+GT5JxMuAgOclJ4TPDUUv8PByQ1uchYroM0pGUlAwU3pkOdp4S3IgiZRgrDeCGuljbXgK51bvbv0VLzt3ZWYN+51o0Zj/Wl/yeTtcoEiVwCdaYCUpUgnLYVJkSDMbRvcrHnSGV95mH+9Va0vOxA96deqOUavOp4Bndc9hUmfNTa7URi3+yFr1JwEba/40TTi3Yo0qQYe78VGqsWCpmW1U02PNsB/7feBeQxJJQh6F+4jq3loTZhUU9bYsKBHwiF78rLpJBmC+JPQmCUa7Aoeyy2+g6hfHkEC+Zpsb7226xP6Zz8n0CjOFyQulfXAzEaY5RA4+92YMxvlsC9sRmdLx2E5dxiWM7ri6eFWzysAwwRavYwESS3cjo1UThrPFz+KDo/OITNszOR2+yEyd6CDr0OeYU2VFa1s8zucWOz2e9027147Z1d0GqUMJi1qKvvxrL5YwC5FAm5FB3eAGIyCRTyMAIbhI4yEqkOibgUJUvn4sDGQxhX242YzIddU/OhmrgYhrgJnvKtgCRC/lUwhZRQZvNToARgfdFUSJW8ZvB0Qc4twZF5UkY7vuoQJDE5Qg1A/b5OhNqjCDSFWTNuSnJJpvTL2Si5PYu5EY8WTbaCiaA4vikVZL1QNxlKttn19TpmhdKxyNJlmHpjPkKeKHKXpKFrjQdygxTWxUa0fyCIY7O3EXWxGkQMMUgMJz570DJbz7rNUAZp1B9n5RDe7N34rPM1KO0mLBrzIKstTEQkkFeXIDq2Eoq6nqQjZlgLx9i1Rjh+IqfRCFmmlP382rL5WJAvTGxYVDwOSrkc3YHt8Efa2Pfs/v3INS3vd0ydbzfAtaFDaNhNwtZE7eni6Hq1gsUVO188iLQeEQzWOlmxffo1k4W+pnP6u4Vj/gjcOQY8fd14KIIRLNjeyOywt97bDe00KqVoYs9TKuUoKc7A+3sbEAhG2KPbRd1dger6Lvav1+5H9sZGlIW8MHuFLGLWT7Unj8dgM0AjllbE4rjixgVQWA2Ih8dAlZmLrg2vApJwT20FPaLoWP9PdG0zIefcb0Bt/XzDhjknFnFIwkhY70/9EQyAW4KnnjF3Z8G+0YOMlSYWk2t7w8myPgfjWASQKLojAxnnmqDJPbwB9GF/Qy6h4QqsJq9trQuxWByKmAxyvRTZcyzIWmWGY6cP279SA8tMHab9uRArcqfhmavfRevHTjgOejH/F30jf04EZK1OfaSwd15hoMOHVkszIIsjDAc2vPR3pGknIOvCWYgFbwH2ROHvisEvpRZzctjOMaLlxW4o5yvRVN0JjcYFWaEE359+AdyxEMan902V0KkF96VVNw2Z+vlIJGLI0AvDjZPpeKUOCQoqisHUKLW6iyPt/BJ0vFCOtPMFCzzU4kHdvR8zPcn++hwY5uYg0umHIksndN6Jx1F/z7swd/iwYF4m9k2wwGHWwuL0ozNNC68nANGW16iFz7NNq0CYeozKpbh4ShFqGrsxd3ohSwYi7Sp0tMEWdCBGXXQ8Wkj0IdgLIoiOn4Qxs/IxrTQdnW/vgiHNiM4nt7KWbMalpdCXTUb3jtfZSCrDhHnwVH8i+OhlccSjdjS98TMYxi1B+qyrIFcPv8MQ5+QRDgtNHHhiTAp4TPDUY5yoYeJC7dByr0hjnWJSTXUQ8dWF0PhsJ0xTdawMQSQei7M6Om2BmhVwp0qaGCoWSJMlWDmGRc5ijiwExMb6SBB3CZXsUU8crt0+2JaZ0LnWhYgrho41bhTeZINFnQ5bIgPSsBTqpLT9kwFZg7mXZUK2fSW6nTGE/QHE/DLY/ZWQxjJQsHgC9v1xN6RePSRxJeRpCuRcZoNiiQrby+tQm9+M6VVBoAGQ1tsxYXpqAaf5htNyvjXocWRcWoiudxsR84WASAxZN4yFTCOH+axCeMrdaH+1EY51bYh2UaMDSsRJIBaIoPaeNQi3eJFx/SSkX1IGfyiKYLef7ZrNzhBL1mnIT0NDngUBnQIJiQS2BWPQ4gmgNhGHPhzFVePz8KFCjtjORmzcUIHLLpmJDJvQH1RBWaQKCRAUxgjaJ5qQHqgANXbbq8oTro00HXQ3LULzr1YjsKcF/p1NMCwpQchBHYt6xmF1CSUcwoiSni9lCXhqPoWn9jOUfOGv7D2F7DVQpZVAKju51wEnNcGg8PlxEUzBSDgpox2yFA78XxNL7/ccCqL0K5lDPr/1TTvc+wLsYTvbCLlWyGqk5tPkSpUbpZjxePFRFTRX/LaFTZSgeOOkn+cL1qaQA4GCS61Q5SrQ9HQ3fA0h1P67k5U8ZJ1vYTFD80wda+OmlKkg7ekmkjmn/ygiIuyMomuzB5bpOmh6+n8eLygx58CPyD2owNjv3IH6PfvgMm1lP3PUt6A77VME794CicME4x+/iYhDSDT66LP9aO1wwqDQQaaKIR6NQZ8p9PwcCMXfmp5qha/KD+VtlXCqdiMvdCn8b2pgnm+GZZ4FtksKYb2oAKH2IOQmOeQ9jcfJJepaL2T8hloCkCLe4zKVQJltQLhdqMsLNvS4lsMRvHdOAc5b3YixVW5smZUBr0GBVYUZeKtZcHHW7m6H1hvFc8EwXtYo8PM547DCpMd7YWH3smZ7La4+byriNKlDq8QhWzYcai3cKg1mF8SR2E8+1wQmjy/EJ3saUJhpQlGmiQ39DextYf/SNaTNHQfLzPMRtrcBMg0SiRAibuG99CMRhePAq3DVfoJ4yAVt1jTkLPn+cf2cOZ/PEuTu0BSMhJMy2iGXI41JIro/8TDL0Lasb3TRQKjTjGOrD8bJGpY049zlZeIUbI+whZVNlqBci6MoGZT2jFuiUgtKlGpf7YK2WIWMs42wrTCyxdA9PQBfY4i5SgnjBA2mPtgXC2r01OGx5t/gkQd+B9uiw4+/4q+tcJUH0PK+E7MfTZ2YMxRiOzbK4oz6Yv060lDGrEj9U50Id6VDkTcJcZ0PMYsDEWu98BpmN3KuMcA6y4Z4LIpMTR0keheUpumY84VzWOs1OZvskWKjck8VQi1BJKQxNEafYQLibwpBt/ESOLc7mQiyv/+nKjg32pF+dgby7yhGoN6L6l/tQ1ymgUwVR8JHgieBVCeHusgETZkFud+bh8CBLqStEmKWpQYtxtoMkPdUl2R0h9CdqcVYqxETgiFkOyMY/46QzEMjEfdNNSEQiyEvPw0RrQLRWBy6dB2qnD4mgt5IDJoxmWirlsEa6gL2HxJe9/KL8UlIif0VTdh8sBnfuXIezOeNh+nsMlbDyF5fKkP63ItQ89h9iIcCUOeWIBLoBhQ9MUKZ0KpNkzcdjoNv9J6zaEBo08Y59YRCoRFj9Iw4xRkJJ2W0E3XHemvqCF9NCLa+1pCHkTbPwB5E2BHFoYdp3A5gnKJFxB5H9sXm3rgh/bz8p81MQCbcnztoK7Oy72Wx2j7TZC2zJsUuLoGycK9FmX91OgxlamgLVCyBJplI1I/rb5qNeKwZGUsPtwIJcWYguX7FZuEVv2llBfPjfpwj1DqmgES56tE25q4tvNWK5pccrPRCLMKnn5Ngl30/G61vO+A9ICSFyJvyETe4Ectqh7wxH7GoDEW5FyL/UqFjT1vDXiBQgQwtMGGyDjKlkCnt2OaFwiTrbczNzqM9gmCLsJumFmRpielwSHYjXTKTPIwwTu0TfV+FUHrhq/TAvduB5v9UI+YSZvJFooDmailMeTnInDMNjkoXHn/1APaXKPH9S0uh0At/UyaV4MJVk1DnjmFtmx37xpuxIsuCv1e3oDscQXcYGCuTQBpLIKZTYH62Ba2BMPaGwrj20lnY1tSNaflW7O12C7ObJcC8haXQzypGtKMd3leoNZsE6rxcpHWGIE3EYFGp2N8V3+NAlNZsBJtroMrIY51zAk2VSF98GXwNG6FML4Bt7lVo/Oh+hN3NMBQshGXipUjEIvC1bofKXAyFfmgPB+fEwS3BYViC5OqhHoSckw91V8m6xMymS+hK1Gyu3nAhNyTF/6ggnYQx8xxzv597ygMItQsLsOdAAKqlgggGWsKIeKII1EcQaAyhe6MHCVpYf5TD+pZSf1Pq45ncxYXilGmzUyc+1Da8h5tvm8d6jYYjXigVhz+v7K4sZK4IwNDTSNy5zcdikfTw14UgLfDA52+B1TIFUmnfIkyxScc2HxN6x2YfE0D2HhrCSMxP4OADzfAcDKLoThuzqpOxzcmGQ96GoEcJxfaZcHTnI/RQhFmzjY/GgMUKyNQSGC1CEgzVUdb+rZ3Fu6Y8UgR1tgKeUB1U1nTkXJsJf10Atusy4DePx0yVDNIyGWJzY5Bq+u6dwq+Vouk/DYBCiupfl7M23gTJS2BWOTomf8qafOs6H0TzAwexhAz35UaU5/uRL5EjYg9ClaeHRCZF8fXT0dDlhNzhhWNfA+SxOCQZRniMCox9aDlePdCMPdoo0OXCuk4X+xuTjFo0NXSjaV0FvOMyhCHDEily9BqozDIg04ho0beYCMp0OsxTu5H90adIhIMItmRBk1sgnPdEAsF6B5Q2HWQ6FXKvuAsRtx0Ks7V3bFbH2icRbK1DsK0e1tmXI3/lL8n3C4lUWFc6d/0Hrur36eKBaeyF0GdOh8pcBKmcT6I4FZagUnnqY7QjVgQphXYknKDRCAmAdYkBBdcLLdFocYlHE0x0Oj50ofVNJ3Iut8C2/HAXI7lDpz1cjNa3HKj/dyeCTREU3tJXu2aeoYOmQMnKHEj4CBrntO/7DWw230Bq/tzGCs/1Y9Uo+Uommzs4HBJRwYLweSOQSVNfRzKlFIYxahz4YzNzaWZMM7ANgH6MGqoiCdbvfBDxRBhFueehJOcCNH5oh1wthXWaAfk3psO1J4D8a9OE99IURtZFZja9guKohHtPAEqDDGLXy+JvZkBlVCDw7BQoAnFWF0iFFC2v22GepUOiTQftK1ewRuRafU9bsAFh1Hrn26joehZKmQkLzv4dOn/sQscP6/DKNTKsnG3DeVlmyHpisiKxUAL+emHjIWE+aaFbJ3WhMZ+TBhpilEhEURt8FZGCNCjrMpGnV2GRUY/q736CqDOEzC9ORPoqwWW8zGrGU/saWONsugK8gTCuL8uDOl2PcapsrK1oQpZGhY5AiDy0UEiluHx1JayOAFprhGJ7k1KOe9cfQK5eg2/NLIXcYEA8FEXFT95FxNUGdY5w1kLNDb0i2P3OAXQ8v5PFNW1XTIX57LFQWvquLRJCdWYJPFVboLTkQiLr6SlLUzz6rgzhn3gEroOvwXXoNSj02Sg899FhXVec4wNPjBkCUfjIXOYiePJx7/Pj4C9a2NeTfp0HbZEKh37dAvfeAEq+koHmV+xsQnzt4x1w7vKh+M4MuHb5WdxQYRYWG4VRDs++ICvQ7vrE008ESSTZKKY40P6+C9alRiRiiV4BlKokLCFHqpawuJrcJGMuypif5veFMO5HR25p5nI0orliFyRSA959eQ8uu2DwzZT7UACunrFIwYowZHEptGcp0b3Tw+JuUADRYIQV5te8LMwXrHqyHUq9HOMeUGB3x69hKMjH5CW3Metm/y8aEUccugIVmy5BruVQVxSW2TrYFpqw9wcNzGIklGkyhO0xdH7kgcIiZ0lFJKIZy/oSYej8KAwyltSiyVEi2CEkoURiHvgavazhN20LVr0eg+5/bXDfo4RxYl9TgMrKNjQ32WEm128kgexrCuArd8K9zQ65UY6xU66AyZeFOsfbaAuth+I2A2Z5fonJM9IR90cQdYWYbPgPOaCbbGWZpQqrBoWZFjS3OgGZBPfOKsMHDg/W2d24sSATf5o7Ht5IFD/eVc3ifzNtJih0KsARQFBBFirJLxCNJ1Dv9sMZiiBdo0S4y4dgMyXiqKFImwRtkQHGabN730ukk6zvBOAJous/W1i2q+Wi/n1TTROXQVc0AzK1DpIUnqT0KdfD17ID0UBfg/OItxX+jv3QZkw64rXFOT5EIpERE/4acZageFJop6DX8xqfkw0NixUh4aHJ6mTREB0fu1m/0IgjILgCN/ng2FZLdcpQ5ykw9eG+pJS869KZYKayFulntY+3I+ZP4ODPm1m3GGppRhmS1PKs9TUHWt9wMkEc+91sVD7axgr41TnD8wxIJTIh0zEug88v3GyDYRyngWm8BhFvHPGWOBKBBFSZCgScCcg/uAqJtG7k3XY2vUUhC58erDQjhpbOLfAFW9ijJOsiaFWZ8FRRogpNb5cJ9Y+5wORf57O/RZav2A+USF9uRMe7LmaF0vkkMSSyzg8zFzD7kxIJzDP77oPS9KuhlJthUo+BokEHy4IwnJ4IjLTpIJfudi8TQRq51PBKG/bsqYN7bB3G5lihac6DYZIZ/tk70XXuGyg0nw+ZXI4c01K4QjXwhhth0BbCOFbwANBUB+v3ZmH7qxUo2NQB96YOlp5b8IPZWDYzH1MKrGjwBvHLnVWISCUIqpXI0ahwTZ4NwViCNFfoMxqJ4ux7L8Sz7+7CDqMSeBAwqpSYlG5AvlHb23RblWNExkWTEGp1IfuqWVCm9e/wk3H1NEjVcrjf2U8KCllPq7eByLVGhD1tCLsaoMuZ2esKJWIBB6KURENZsJYShJ3VTJQdB1/mIngS4e7QYbhDRXOZc3IhESr9RiZrWE0DZomiL9ng2OFjFh8t1qK1xhDbiYb756fThHV6pIIK7ymmRjP/aDoEYZ7WF+sTrUKyECm2RQk0wdYINPnDE0HfbimUG+ej3L4fBytp4R4cuUaGqT/qK3AncSPxIhGRyCZAla6APkc4tgW/LmNZs+2fuKDOVEJXsBiuunIYNPnQqjKYYI39ZjbsW73seLfcVAV9iRr516ez8U2VD7ci2BKBukiBifflscScrHNNiHhi8Pa4UNkp9aTwC/egkOlQknYZa7xd/vNm9r3xP82Fq9jLYqnWFUY4d3gR6Ayi9a0uZJR2wLDwTfgWArLHboVrtw1N49ciLgmhxb8OZbhMeA3bF1FgPg8aRUbv36r2BfE7lR9TMlQoqCJrWfiMfU0e7C1Uo1Crwm67m1lniliCTW2aZhLOVY5Wha+OzUVHIIK5aQbIVEpcdfEsTHf58Bf2PqS4c1r/jFw6f9lX9J8gnwzFATOvm4m0s8ci6gxAPSb1cOZ4NITGD3+MRDQI8/iLYZ1yXe/PEvFo7/swFa2AszqAiKcZSlM+YiEPokEHVCbB/co58ZbgSKgGOPVHMIglKO4UOCcX1idyUf8+oNQ5xnaWEXu+Xc+G72asNLIeot6Kvs+o8EtHNy2e3Kjpi/W90+2TITciWWO6YlVvzaG2MHXiQiwagaurGSZrDmRyQSSd5U5IXWaMk8yDJPF8b1yTLNvk0U70PXLNipmlyUNzpQopclf2r89TpwuvX3ytKBRmLJr4i37PSZupZ3HG7XfVslIBEjeKjU7+TQHLOiUR1NiUvZmpzIUslaD+P4KbM32JAaYZfZsHisEGOyPIvSyNuZJFkjcdiQiQd70Nj9W3w/hUA4q9NVAodJDIzJBoon1dZiapkL6UeoFehbqud1FoPbff507DfJNZ3eWEMxrDxgVmBLRSzIvJUWLR4Y95CTTXtTIp+XpBJmrdfigUMnxlUhHSVEp2Xrs3NSIzFMWn9k5kr65Ca5YR4+69AFOtgmdgODWj0Xgc7x5oAk2bumBiHhNOds5sevYYnETvGKaBmUlKYy5yl/4fEzt93nwYChYj7GmBQpeB+tV3Ix72wDb9dpiKzzni8XGOHW4JDkMExRRazsiAShwmP1jAMifVuQq2iNU+0YHOD9zs5+1vumCZpmcLYMPTHaxovei2zEFLIGhSQrL1NzDDVJUhR+ubDmSeZ+q1SFOx55OX4OpshN6Shdnn3cy+l39xPhO27zz6XcSUglVV/Yd22Dd5kXOlhY1aohKNA/c3wVcVQsnXMmGaokHT83aocxQswUVcpGk2YfdmL2LeeO+YJRIzoutTN9rec7JkHWoBp+gRWIqJps83wLnVywTKNEN4n2O/nw1vTRDSjCB2rXkBOrMNY6av6ImJCou2pufcEn5qBPDPzt5zknt5nyibpmtZCQY9lxqDB2JxbHR4cWnmPviueIXVIMzN+iV0ystgTxRCJlHA+qXp7HezMBdZZpoT0ccH7Q683NKNXJ0K3y3JgU4uw+I0I3a7vPArpNg224xtSCBfpUBXoM/F7Egk8NN5/SdZuA90oubfO9nXpblSqKghd6MT4XY31HmWYYvgtvoubKlrQliqgk4lxzlj+9rGDYVUrkb+OT9HyFkHfW7/98nOsa3veCVyFdSWYiaKJIBE2NuGzj3/YeUU1ik3stfjHF+4CA6BmAzDLcGRB1kiFBMMdQXhq/HCPEPTK4IKiyAA3RvsaF9LcRag5XUZir/UZ104tnvRudaD7EvMMCTVvKWCEm8oRkjF39MeTT2rjvA42liszutsR83f2pB3nRW6XB3G3jYWn3xtLe5Z8jO0veNkk+YJ124/E0GyCsWJD+49fpbd2blGeC8kermXWVhGatML/QusXfv8vY0Dav7ewdzB/pow/I2NmPZI33E6t/qYAFJpR/616Yi4ogg7Y6j6YzsCRbsRKWyAs6MBOSVTobNYMfGBPARbw0hb2GeFU7IM1TBSswGqhUyGRMQyq88a0sikuCzLgsrpccxkT0hAogE+cHsgxRh0hMKobGpgWZnXZKWjUNP3evSzF1u6EY4nUOUJ4rV2O27ItWGyQYurNSZUv9mE1mw5Ksar0BSKYoFWA3zYDr9GiqzSw51JCqOKWbdkwlWMtUIRiyGtMA2qnL5ymYEiuK7Vjtdr27Ak24Lz8zOwuqIVlvK3cHPXfuzVT0Sd/SxUdrhQlpG65nMgZPHRw1X7Mfztu5E24SqoTEJsNhVytQXZ87+PkLsBCm0G2rf9iX1fbSmFsUgYPcU5/g20eWJMCkQfMRfBkQlZUOX370bUHWYuS+NEPVR5Gaycglx3bas7e9P6NbkyeA4F0PySnZVGNDwluPwotjj7qRJWJ+jaG0DmuSbWQFqEZhWygn1KlhjEkhSx5paio+EgJN0WdH3qRbgrhvH3CRmkDy18HDPT5rK/m3OVhbkisy4UFmJyRxbeZmPHkH2ZhZVptPa0ZfNXhVD5SBuK7kgaS0QDXqcfRKNmHTS+VdDrsqDQyxBxCscptj0T0ZUq4T0YYpPu/fUh7L+3USgSp8k/kixIi+qhT7dBoxeOhxJhxGQYEcoKpckdlDiTfH4G48rsdPzGOx9rA0qYlCasa5CgJtDdv5tYKMxcnL8sy0erPwyFVIIfVDQiigTrSheTAh91u3B9jlB7l/+OHWlbhMSoqn1BHDrPjFm7vFBtscO+Yjf8DfsRN13PkpFEtPkmTP3Z2ajzBrDX3o29ZYIF+2A4gky14C5NFkHKIH2/oQPeWBzvNXWjyxNCZZMd1zqEDjRFkVZs6/CgrsODe86bBr1qeBmF8VgYnTv/ydyj9Pey5t095PN12bPYI+LvglRpoOAi1Gllw/pbnKNDXN9VqlNfnzniYoLcEhz5UNIIqUW43c8eedfno/G5LnS8T5aUDKoCEzJXWpB5jg0Hf97ChMZNXVPoaotSKzQJqzs89BtqhJxgAjTmm1m9r09lAmJyzEBhGMiE+RcjXTUdda8JiSXJ2Zd6RZ9VpZ0Yh266nHVaOfSinU2dIPGlB0HlB5N+nY99P6lENKcFsrYs1D/XyUrMqDg+IY3CP3ct/H6gtk6GKZNuZrHTtreFTjbJcTzaKFDfUyLQGEawINL7foyT1FBYilB00VzIUzQVT2V9J8cCj8S3SnJQ7rXg+dYuNAdSx9U7QhH8X3kD2hwhKKjIXJFgvbOj1NCarJ+YBDW/q0UiFIeuVNNb51hYH0bZYx0szuaZ3gDPzCp4ElXo8ExHpkEoL6AyEfYaGTqMtWkxIx7ETqcPYmHEwxUNLMbnp3hfSzfebO6EQSaDIxZj5RaSeAJ1wRCkEgm22JbhAk0bnOlTEGuTICaVYGeLHUuKM+Ft3QHnoTdhGXcJdNkzUr5PiVQBjW0iAp0HoM2cNuxzqNBaUbzqsZ7XGHFL5BlBhCfGHFkExZPEOXWEuiKsKTZ1aRETSqhXZvrCIrS/3wqpzAU5TRCIy9Hxvlh3JUHO+fkskYZIX6CH52AA6QsN0OQr0PGBG3nXp7FBuCqbkChCXVCSIetv7D3ZCDSHkXHO0O4v2uFnziyA7GsehDsjrNONyKs1/8XXpn4fplIZ9m78gC3D6t3zIekwIdQZ6R17JKItUCK0eBNi6R2QOI3QfnA++37WpSa0vu6ErDkHsdxWmNRlOPhgC+LROEvuIZEs/rKQLENWDlmyNPkiGIhAaZWz80cZouy1KN7Y0wrsREBu0ZkmHba7vGgOpbiHEoCPxiIFQ6A9eISssjhV5gFxuQShRALXuTRwbRFqIo0zc1H03XFwbuiCOk+L9hcbWKH9/pxiZGI3ZLI49rb/HTtaAlBIdVhc/FNoFELcj4Tsm6W52Ov2waSQQyWVYr9byDL1ReP4uN2OUDyBUCwCmWgZSoBcgwZfnFEKuXQaS4bJiETxcsceZtE2Ob2I+uVo2/g7Zla3bngIhoIlyJzzlZTXRs7iHyMRj/ROj4hF/Oja/SQkcjVsU28eVOS4+J1YxJwPbgmmgFuCI4fyB5pZMbZzuw4lX0mHVCmHRC6FcYoBnkMRBFuNCLsSaHzeAamavEcJlH03G5YZ+l5B6FznZi5G0zQt6v/VyVx7NX/qQMybwKRf5TMxojjjQMzTdeyRTPX/DqJrZztKrhwH2+w+y5GwLu6f0erY6cUPZvyMLYRhH7loe5yCWsE6Spt/eHYhs9YiPU2a2VghQJWlQN5VVmScY4av7g4YpynR8Y4Prp1CrDCar8SUB/PZ0F9pukToKbrVxxJsSr6ewcSfRC/7kuG3nvu87Hb7UOkNsLdc6JMi7IijJQ8wyKWYb9Fjj8ePjkgEERmglUsQiSdYucPdY3PhicaQGZGiosCFeCgO0wwTVBkqmOeks89zvzyMPW0uNGWbsd1/JxYbnkd6rIklY4aiLnT5DiDbuBCheJwl19D5n2rqO9cXZ6fjP/QxyOWwqZXopgkTlMzJPgDqLiPBipx0aBR94lTu8CE/0wSjVIoF7hdR9+5uyqzqraXxNKxDxqw7UgoX/X3qHCPibdoAT+On7Gtd1nToslgEtRdH5ZtwVLyBtPFXwFy66kR8PBz0xQS5CKaAl0iMHESLJebpxP4vr2Y9G4u+fwEqH2llwiZl7rwE/DWh3qkJVNJA7kBvZRD2zV74esooujd4kLZYh87Vnp7CcBdzRQ6cVD8Y9JodW4W/27mt7TAR9FQG2PGKJRck3mLcSRHMgMo5TWg43WRD6d2ZLHuTsi+pdk9ulMG20oimZ+3ImnI2oqF25F8wHtobdaxUgl638ekuOLb4mOWnoH6XPUR9UZT/shme/QFmtVLiDEEWLiXV0KT7k80LLd1oCUWhDgCrHotDEQXcM+UoNKhQeKUFC3IN+GVFM+JywKCWQ51IYHa6AUa5DIkDQTRucqPoa8UItoew755qmKbpUfpNYYzR8ouLYf3pQcTWBrDzsjqYJ7chlpCxhtfsvMfC+N7BWiZu3yrKwWxz/83GFbk2FjJuC0ew3x9kGbfsKmL9RAGyD6q8AYzrqTeMxOJ4cn8D6+c+I8OEuEtIuqJaPk3GRPjb98GQv2DYlpsmfQKkrHxEyZpoD8RZ9S7LEu0+8D/osmax0gmCNgCexvXwtW2HIXc+9LnzP/fnNJrhQ3WH2TaNc2IgQQk2h5i707HFhczz06EtPDxbc8JPc5krM1h1EN1VCYQ7vIh6g70T3uN+YeEjtyBB8TMSP9ceH1peFrMqaSK8F+49XUhETbCdk866v+Re2ZfuTxakY7OXWYQD6wFDrhD2P7RLcLOuKIS7yo7clf3dmB0fOVH3DyHpxrbSgKJbM5ggPXTvo8hNz8M3n7od5T9VItoU6o3/ERTHpLpHegQ7uhFzx+HaEsfsp+awRBdRAAk6ZsK1x4+om2bvCTEuaiHHOuiQEO8PMBdvoEFwQ8o0Embp0gYgfbERqp7SihMFayAdjmKhxYCmYDcyIYOcYm3k1twRgRMR1sez+HYbxmhV8EZjrNdBRySG1zscWNCkQOXvWoBYHIHmEKqUIWQH43BsdrPmCNTYW5qQIFYtvN8cYyVi0hjb1ATjemRorFCpJ6MrZMfyLe0IrmtD5KY5UJgPv7bkPS3NZpj0mGDQwhUKY49DiD7OSuuz6uVSCXOPNnoCKDJqkTXvG/A2boJpzLlCUXv/rmlHhDJGiy/8R7/4JSXQ0KQJlbkQaeMuR+fufyERDaD7wPPImvNN9hx/x250bKcyf8DXvAlq21QYCxbDkL/kqOZkcgS4O3QIRPOYxwRPHLWP1aN7HfV9lLFeysGWECY8IMyNS4YWPl2pGvaNGYDGASh1cGyPwzBBy9L5w13CYk/jg2jsUbgrisZnumGYnCRkCpoGQm2qhOlMnnIti8VRXdzBXzQza8wwSQPHRh+bpDD+vhzoxqh7J9m3rW5H2BUAJDG0rnbBWGaBsaT/ZIrG//aVMZClSQXqU35biL8eeBglJSX4tvJO5H8hjWV8UpF5iKxEqQSuXcJiLsYDo84YrMuNaHq+m8UAqRZv3D1CbRrVRlKNJAlgjwIKJK1/JHgUHxShzjt7v9fAzmPLKw7M/FcJKxugwnhKCCq81XZUSS+DQT1OW3Z78ZLCjR3xILRSKZ6cVspicm6TH4GWCFrW1cNj2gfVpEXY6fKhmibNk1BL2CB2zLToUPub9h4PowSyMSqsyQhjqUsC+RRhSDFlcb7RaYfrKhMU+/yoks3D1FgNZFIS1ziWlj6AaCyEG6XbYK23Q+nRovuzWmRdOLH3WCk+yPqGArgxPwPn2ISazPfa7FCpQrg6z8aab4vQz74zsxS+SBRGlhVqhTbjKJVvAKL4iTR/+jOEHNUwlqyEbdotcNV9hLCrrjczlEosnJVv9fudYOce9pApdCyjlHN08N6hQ8BjgicWx/Zu2Ne3Cf9DTS4hhX6cdZBG2k2QSHxIJGjxKWHfb33d0RfA6YHiXyQq1CybyDjXzNyc1Ie0+1MPEtQAQRIB4ureMUpUZE5JN+yYNgkWQCKaYLMGtSUqlHzVwgreTRMsaF0ThzjN1V3lgH2zh41ccu72sYG+AxEtMfaaPZ1DNPmqXpctjYgyTtH0vgXDJDUTO6lcWBzLH6CJ8GBdcUSoByodr3GSBqaZWrS+4egrixAHE0T7dHH8/bnQ5Kh6i+BZh5d4As5dfnR+JNQjNksimHNrNtRqwb06sHRgMKq9QTzX0IVpZi0uzknD6kcbYNkVxQQLsOMOwB+LY7vDgzlpRtZowDgJqBn7MvyhKtSq9kGS+FHva/UcHko0Klhmk1XtQtYqCwputGFhhxKHxobwxRzh+vhjZSsCvtUoGrMb+4qWoSk+ETmqL8AcW4MptrPYc3Y1vwhvYiO8V0qRu2YGDBP6z+z7e1O70MxFIoE9KrisY4kEXmjqZKfxrVY7xhn6N0egmYKCAJ4Ywl5KAkrA2/QZVKZC5C6+T0imkWsQcjWiZcOvB/1dmerku7vPJBFUq099I4IRawlyd+jxJeqJoP3terS9Qb00JZBIopBLGiCRJWCe3NeJw7PfiYYnaqCw6SCBF1IIO/e4LIuChL29QiUGIEGzWhMSZnmVfS8Hqkw51NlKpM81sAfhqwsh2ECv3zNaPi6UQChMSZde/7ajCDS7sP8n1YDUDV1BGpvlF6c+ojIFJH4Tqn/f1w/UX2OHwiZDTMzjJ1nX9Axi7ZkxRyjNcuRdm8bGHFGtINUvimRfbOkVQIIstPb3XP2SZyxz9TBOdsNXFWRiFnXEDz92aq4to9czsSn3xPif5LIyioxzjEzUKWYpM0oR9MdR7g1Ce8CNWTMteGd/F9bVOHHu+DSs6KmrE/ms3Y01rS5ckG/BzHQ9XmvqZkJIjw1dbkzzREFpN4pI3/EU6/q7IOUyYbGRSdSYl6aHSiqBViZFeygCdySGZTYjlLdLUXizrbeN3NWZPeOcemj0h7BQvglySQRTlTsxN20xLs4aA6mkr5hciPDR/iqO7vMaUKXcgOZ9+zCr4CpkGsswUUcCl4BKJsX5NiFZiDJDF6UbsdXhwcL0oxeVRCKOeMQPmfLYGu6njbsU3fufQTziQ+euf8B+6DUkYsGeDjKDb0rk2gxeR3iM8MSYIeBt004Mra/WovO9RiBBmwwpJIj2Dld17WqFYaoghM3/3YdIawPCrTqYZkyAZxepixTWJUboxmhQz9p4CeNs6PcTULDWY+S+zL2i/6JJjP12Nqr/3IRAM41GAnRjVOy3KJuTWqNRzWDbOy4hQzAeh3NXAObZWti3BSBXtSDU3gKJdwIksSwkosrDFiXrMgOLI4qF+ETBTX2WrSiCRE5S2zEqY2CipZBAnaVkTawj9iibdUjdWai3aTK+6mDvNA33/h4LMflQeqzjBCRoeZUmQwBFt9hgGKthDxFquTbhdwV47vkGREJxZGcJ4rSjycPq53Y0eg4TwVfqu+GJxPCvinZ8bHSi3BPs/dttwSic5wJtlYBusga35OkwL80AnaL/TMEZ2d+CPVAOi2YC2xzMsgiC0b/hGeCJOuBwdaIgfexhQ63vKs3CtpYVkMe2YUdkNjq7nJhk0jHZK9EK72N63lUIhF1oce9HrnkKyts+QiIhwWdVT0Ejn4izbdPYoZfpNKxsgth4sAHNOypx3eRiLDhKESQBbFr7E4Sc1T09P8/G0WIsXAZP4ycIe5pZsDuWNGZJ3FVI5DokosJOS6axIhboSpkcQ8fjOPgKIoEu5kalhJ28pQ9Arun/mY52wj05HyNhXN6ItQTFnQLn+KDJF8sNSLykkKgViIc0kEjC6P64Frk3zGCTwyVxLyTUckvihda4AzHtfugXXYDs62xsmC5NkIBcgriPlnsijvwb+7tT/fVBOLZ62Sy82r/VIlBDLlW61LRMMPd8ox5THinsFYeSr/R3ibR/4ISv2oaYn2rSyI2ohiSqYoJFbcT0E9TwHAgi+0Izs+LYxPH2CAINIdbDU0x8Gcq1SFMdpv+liMUhpUoJdn+jDlFPHIW3WpF5npklD9HECJlKEAJq5k1uX8r61I9XIeeKNFQ83MJaowl/rOdfwdfHyiRIBFOh1clxyy3FbOq9gs3XAy6ebMWmOjeWjekf7ySWZRrxVpMDiYQXVR4aY6VmmZREkU4FnUmKLy/OZjWCgyGXaZGhHzp2FY6G/r+9swCPs8za8DPuGYm71lJ3L/VCgeIui8Miy+7C4iw/uvgui/vi7oWiLfVSb9O0aRt3mYy7/td5v0ykTdICLU2b976Yi2SSmcxMZ77nO/YcfL7hZYTCQYzOmYFCwxRWC5S1mZgP0KkwYNDZWNIyHzX1ZhYR31dWy7ZHXJuZjGlGHVtgPK3gzwhHgqxTalNjJSR+K/yiEBzYjsoWIdVMArtsWx2Kqyywe1vh9AawfEcFZo0Q0u41Xj+KHW5MoZRup3GJfYmGA/DbytnXXvOuXy2CJFpeczESRlzCOkEb1v8XAevetp92FH9jAsheS6URcm0qPM1FcNashi5zavvPvC3FsJR83OVvNG18FuGgG9r0iTAOPJU30qAjHcpHJLqBp0MPDwmz0iHRyVDx5Gb2feLMBFh/3NXmst/m9UgbHC4ajYYPt8IwMRMt773Jrova96Bl6WA0fGZl3w99IAONn7fCXuRC+llJ++0M3PNoHWsioXGBYEusyzcKqUHCmk9IbKiJJOwJw11Gw/i69hQc7dyres0MkTgCdVIU0vhkhBLS4N7tZ80kVIejJppRz+ewFGdM7HIu7V5wOkeC+xKzIiNfT/LnJJp/cjCrs4qXmllHKJlrU9RKj2/Ek9kIOkJMGOj71JOMaP7BzrpbWb2R/lRYiHbTT+/9zF8iEbFLjJHpOnbpjkXZ8bD69iAJzyEclaJJfAuiYhMmJmgx3NC9CflvJfZ6uZp8WPOvSkhVYkx6IBvlkiAer2pAhkKOf2Snwh0OM4n4pFl4T1j9bjTZW5EYl8ks1CRiGep8ZNGmAOUHolExwmEp0g2Cfyf9lc/WlkAqDSJep0GiXoPpQ3PaH8PDe6qZjdoetxd/yet5kTKZWyeP/TM8LTtgHHTar36+NGPYvFlwh8mc/QjSJv0DlUuuir0a3d7G3y6SQEvR211EUK5Lh1imRSRItQIBElnCYq+ENm0C+53+TqgtyOGRYDdwETx8GMclQXLrWIQ8IViWrUY06oGIvEJCHfZa2sJkDPi/BexrUXQhnJuKkXDSTNQv6fgdqp/l3yB8kEOeMBuAj3U5kvhRdMX+LRNkyL4kH6WP1yDoELO9hA2fNSHkCgLiCHbeVYmwKwx9fjHkahtESTMR9Mexmpk4UIqI24qA2wqvawASZiayxhTqQKVo1FHkgbdO8AIlj82emGqczQb2qSGHOjszzjW1r2eKQfXJgbcIy3tp63vlK80sYiXo78QG8cmEe/e/6pk12/DHs5B5dgK72IrcqHqtBeFAFNpcBbOAi4n6oWJWkhu7WiIQiwI4LSMKk7prw8lvZUdlBdbuLMaEwUMwMi8fi8ZdBourCZItqaiADSFvBH57GBukbrjDEez2+Jj36JkpQuqb0qDmQAju6jewxNuMgcnjMaXgFPazVIUM2qRZsFs3YWraMIxIGgo5G1w/HxKxGDqNExF27qHArWd2NanWSiVMBGl2kQiFo2i2BpEa37FlIwY5xtDlt9AxSE+jPFJIlXFQmAbCb9nT/jsUvTlqVyHsaWVFX5FExkYoCFXCoPbfo1ELW9kSxOXOZZFiTFw7R5TN215DxrS70d8J8saYnuE1wcNL3MhEOIv2wl2ymUp9iIaTYZo5GrY1uxE3Jg8SVUeOPmHhDHYh5Ovbam5iwdg51vSy8+5a1i9Da31o9RE1yUR8dGSLQqIRQZOjxshnhAOF3xyAp1w4Q275QRiboLRruG4rvAgjvNsOT+BU6Eao4S7OhkTSiHCYIjwF0k41MmsyV4mPLdotvq2G3Z7+VnYPacdpKbNwdfbfUfFsRyONPEkC1aBGlraUivNgW+9G4mw9M/jWDVbCUeSFdrCKzfU5d3nZ343hYk0xQMgehm2LG4kz4tC83M7mAzMvSIBp3G9rzDgY0vUz4A9bIBVrWF3vULGyaDusLhdWbN/ORNCkTWaX0KwIon4xRAluhOPNmCtJRLXXjyzaHt+pU3N0nBCJvlcm/Lt6/HY0L98FsUKK+IkFuCafPEWHwlFWgqavP4ZptFBHa3W1IiG+CpGICCMyZnZ5TCRy/xyUjSqvDwO1QqfoI2/VYXe1Dwsm6nHBgu7/vQ8GijIdlUsR9tsRlzuP1fWow5OaamIRmlyT3CaCovY6o6thA8JU8ZWqkDXrAdStepCJZuKIS7q40dj2fsW+Tpn4d2gzp7OIMGHoRahdcTdrvPG1FDOxjNm49VdCPBLsmVjLLK8J/jEYZ42Eu6gUtuVboJ84AFk3LOzyc1dJGcRyGdLPyGDD3mpadNu2EJZqcJSipHNc6oB0bHO1Nd2QAbId5u8trC6ozRcOZHKTDHHDtXCXeWCabEDKwkSU/bcekSY1xCIHJOIWiGU2yNRaRMLJ8LkXwTBeg4QZOrSuccE4TsNSlTQXJzNJ2KB6d5ZrMWwBa/vYgUQrZoIpNjSg9PN32c/F9QsQbkhjA/5D7snAoNvTmP0ZNa8wT9I2c+3212qcBnVtq5XIRECVKkfF80ITBW2wKLw3ndUaDyUN9k2osaxCXuJ8DEg4G4eacQMHYdWOIowf1BHREFKlGKkLpfi6+CmEdwUxOfcS3JnX1WKsMwuGXYo6617EN5rQ8O02dp0iMQ66fCFibVr+DUJuF8wuF/s3cfldEIsjLE06NGt/USPLtUJdR6q3tllIq9c0/T4TDW/LDtYBSlh3f4asuU9AnSiYf8eIH3YBpOoEqBIKoU4azq4zDjwFlpJPYMg/HjJNMnIW/He/+1boc5hpN4kjfU2pT3fjFtSvewQSdRIi9gpItSnwtZbAXv4D9HnzoE4agf5IkBto9wwflv/tNHy4Bu7dDZAlJEORqEXy6SMg2qfDj9ANH4C8Wy4TbMYK81HyV6GxYN9ucMeWnaj492vs6wH3/RXJx2d0+blpso4NYtOC3GBjLeTiMkSiOoSjQqMMdY+al9mgzpSi9YtvIVarMPD2Oe2PyVHsgac6Apl4KJTyFcJ83Z0JUOUmQ2ZoZZZaGefFYzfZku3yMqu1Uc/mslTjsIczEXZH2EhGT+y0bcetO6/Bkm+XsLVPNIdoK9vW8V4zKeFpoDnHtkYasajX1U2qdDlMU7Vw7/WxOqhML2FTH1QHJMQKMSwbXaj9yIKk2XFIWbB/kwthWe9CzTtmJBwXd8Da4Y7atxEIu+AP2jF94D9xqBk/aDC7dEcw7BUaXEg8gsK2jJ4waVLZxRVqQYtoB2uykus75v10BUNh2fYLvpUKkXUkLIfHq0WGKQdJ+q6duN3x13NTsanEjTnjft9cnlRJr3eb308kiKC7iTXEuOo3sAYbQ8GJkCoNrL4Y9lraT6LismZQwhSuho3Mek1h6NgdGfLZ0LD2MSZ+mXMfh7NqOax7F8NYcCJspV8j5BbMyAmRSILmra+y62h3Yc78p9AfCfWhxsc+Gwlyx5iDh/ka7qhE61c/se89JbWIQg+x3IeEBWMgVnQIRTQUhr+uBdoheexAReTffRbcu+uhG52LaDiCise+greyGbL4jtpZtM1+K0jWVmIR/JVV8OytQOrCGZDGaVH6gFD8F4vdkKTnIdQogkguQ+KceNhXr0frYtriAChzs6ApFKIOcoahofVQUxxEwvw4RCE/68jsnOKkTQzsvhUiFqnVfWJhA+exLfE9oZMrIJU5EPY1QiTOQMBjQcVXH7FUaNqMeUidMJ75iaoyDy41RSJZcENXz9LRT+fAQVFhmhyaHAXKX26GpzqA6vdbexRBWvLrbwqh/lPLAUUwVT8OVZafkWr4Y11JQpEoRGIDRCFaQeRFun7/De3doc1JxJBbToZIIoFM19H1mzJjAZKmzoVi8Tfse6VcDZfHgBKPDUs2bcGsEYXQKrsOyXdmUJaKXX4vZJuWNe8/sJd9A6kqAarEoUzEGn95or3blCLB2p/vRMBRA03aRHiatkKbMQXO6uWskYy20MtUCVCnjmE+orUr7mkXOmflclj3fMq+dlR8z+6/MyF3M5QJhez3yZu0vxLiItgzfJXSrxfAqsfegquIork20RKHIBHXo/mjUnjLSpH9twvbf7/m2U/g3LgLuvFDkPUXIb0mI/Pk8fkQSSUINNnh2lENv8iGqDsAeTQexolDoMnPhqesGWX3fg6RKAIJ9jIxCTtcSL3sbKRdOBwtX5dCPyENhvEdw/fs4YTTmUUbpVUd25ph/rEGqedPhDxBh8F3piMaTYN9lY4dONVDu6bliNyrk5njCvmL7ritis3iEdaN7h5FMGiz49NzL4BKKkfp028ged5xiBudC4lLSKdJZXIWwdIaJ0qHavKU7Pt9ocYfEl/bZg+UKbL9DL+pwzSh0zb45Ll6VL9r7nUFVMrxBlS3mJEws/tu0M4My7gAhenndllae7gx+4O4p7gGak89kq3UICRGVUsdDIm5CEQjyFX17vIh76FjVSyR4Kr5c3Ez1abVJHjC672xYjeK63fh2vmnI07927tdnbVr0bL1FVbnSxp9Zc+PT5uMxJGXdjwuqQoShQFhvw0ybRqFqQg4G9rHLqJhPxNASl2SIPotpfBjD1x1ayCWKNoFUKI0wtupoYYIB7w0WEgehOzzQtGnt3kbMmY/CiV5n/ZTQlwEe4bXBH8dUX8Q7qIyiBBBlLmyiCA3+BB2B9n0Q9jV4YxC+OuFGlaA5rzaIsOWL3+G+csVUGQkI/eeq2CYNgANa1YJP1dKkXTSFOE2TbQWSagBSgxahB1OyFOFVJY6x4Ds68Z1+xhVBbkY8N+H4G91o+zOL9h1MqMGaRdNZl9TuskwvedIgwbxJW0bK0KOELRUd0sG/K0B7H2yDqoMJdvT17nrM2i3Qy1rS3NGwwg6XbAuXwmJV1jeKhMLP9vzeAMbgqcB/YE3p+0XsVW9aYZUL2HNMJQ3GfNcLmRx3QsSWcFVv2NG0hw9ss7pOUIlJ5ruVjn1+Pz3EcBINAynrw46RRrEbdsTWpwlLNWWoP11m9Aj0QjqrRUwqBNR3PAzSps2IClpDtzhDPikRuSqjcxZRh6XjFv2Ctscbs1JxahO9bpfg04lRHMmnRbzRg5FtbkJVeZKBEPA5vIa5KekIDOh+wj6QJBQUfOJo/InJI6kdP/BnTiIpQpkz3uSRYRynfAeSJ10E5sDVBgL4Cj/lkWC+rwFzJmm6rsbEAnRthARZLp06LJnM1NtiUIPn2V3l/sO2NtKDftgL/0GyrHXoL8S4iLYM1Jp2+wWX6p7UIiVcqRcvBD2X3bAX9eAiNeHsN3JZrYNx01A0mlz0FTSAoVOAUN6HDKvPxO21duhzEpC5YPPM6H07qEBZjE8lY1wVjQi8+oFCMm8cFfWI+/K06HKEoROPzEPQYuL7RQ0TB+AsM0OedrBtepLtBqE9pIAC0ItVvV+gAoHwii5txwhewgDbs1ldl6u3R6WvqTZQmqQIYcX63onrOs9rAM9o5PwqLIy8djqFRiRnoWLrv8bZDop6t/5jrmbiLXUrDOgfaRD+H9bYa8T9mJhLRITQPagYsPw3WNe5WQG2uYVzh4H5Q8Fv1Q9jRbXDiiliUjSDkelZSUb+qbXdUbBzYjXFBz0fW2uWIHNVSuhkCohk7sQjoYQcO3EguShCEaBczLOYRZnez0+ZqBt8DWhvjWEUbru64gHCw3Lzxw2BKHwQLz8tRLVzW58vYHm78rw90XT8dXGTbB7PLh41jQkG3pfrBzDMOBkhP0OaNMmHrQAtj8emRpyWUc6VpMyml0IXcakdtNtiVyDrHlPwF2/kaU6STTpZySKZLrdI7RipZO7utIomAL0ZxEU9ZHtG32uJhizauIiePA4Nm6Ad08pdONHIvnsRah74QOIFTKkXrAQVZubseq59azJ5NTHj4cuMxkp585DzdNvwrObzlIFE+2wWIpacTKq7vsc4/66EJmnzWK1PpFUikggiNr/vI1AixWZN17AIkZCuo8/Za81y1+2wVdH0afQaKEbLjTZ0FhF/RdWpJ9mYjN/MYpuLEbIJhw0Kl+qRuH9A5H35xTUvN8MmSbCokFPjQ9hVwjRkATK9K51PfqAfbmnBKvMzbh5zmS49+4VFtaJRMi96hrIjUKDBi0BtqxzseW3+5J5bjxsmwQhJNLPNLV7ntKAPaVo3VUBlL7UBONoDTLPjkfd5xYkHic0b4RCfvgDdmjUB2786I16SyV+3PoxTNokLBx3AWyeKna9J2BGeevydveY2GvdGV/QjT1N65Cky0GKPp/9PByJQEobRNj+P2EeMhQJYWL2Cag0b8OIjNlIM3SIeLW5Gh63FRdpgL3167HXAjQY4pBq6Bo578vOr7agcWcdCk8ajZShXQfEYwdAslTbssUEhQ6QxQVZt+jrP+1AdaMCECmwbHslTh4EeJt3wFBwQq/2Y9TlqZ71EA4VPksp2yFoL/8OcTlzmL0aNdJo0yezzs4YuswpcNX/wrx1aX6Qaor2sh8RdAljPMITFd7LUnUy23Yvb9tT2JmQl8aGRIhGQrCVfQN18ihokkfhWCRMZgtcBHuHi+DBI4gZ4NywDRlXX4i8e66FffUmlN3yEKwmOmOXs20GFF3FkAwZjg3r4qAxyDBpYgghTxDhtu0SzSu2wPrfVZCnJyPvwVvgr2mEe0cp+5ljfRES20TwQNDCWftmC6QKCxqee5tdl3bBqdCMKIQyXTiYNX1nR9gVYanEziJIac8YNLJAaAeoIFUFYdtgh2OHjZl/i8JhJJ+Uut9m+RjsoO/xoPaVVyEKBJF6/nlQpQsH5Lo3v4GnvBYZV5zK6n3tf9sVQtGtFQj7I9ANV8NZ5IdYSdvhhcfnawliG61ICkTZbCE54LSscCLv0kQU/lMQ90gkhDXr/wWf34JBA85AdsZxOFjIfIDmECnalRulqG7eg1A4gGZ7LRpt5QgG4hCKBphFWSTibe92HJt10X7p0M3V36K0eQNLqZ4x+k68vvQHWN0unDttFjKTUpCbPg0GdQKS4tKQoEvF4JSufpjldgu+3PAp+wuD0jrqteJ91hHtSyQcQc3GCuF13lLZrQgGXS2oXfYwJqdMQql/NMQKH1y+APwBcfuikp+3eZDb+A7ipa1MJFImCPv9DjWelmL4bZVsJpBcaKgWWLfy3vafkxg6qpa2N8YYB5zc/jMapchr21EYw5C3AOYd78K298su1ytNBd0KoM9ajtrld7WlWDMQdFTBXv498he99auj2qOBEE+HHtyZAufgMMyYANvPa6HISIWozWfRvPgnhOxWaG1rMSBhEESWRvhWSuAcXICw14daZyJsEQdsFqDqh+UwiszIGToW0uw8aOzVoIm/QH0zIv4AlNmp0I0fhmCLBfopB39mWvnsHti3WKFM8rN1OFRPVGYltwsgkXG2iXmSpi7qGEon8m/KR90H9Qi0+OHYboenwgN1rhryeIr4AogGQohE2xo0BNuR/YhtkQg5nQg7nexAHrIJrf7e2iZYV65n+lH32uco+OdVbLsFNcE0LLYiYGlb9ZIoRdrtJtYQI2nz+QxahSiQ0OYpEfZFYRyrZnZqMcLhAHx+wVLMVleLFGWw1/GLzpQ938S22JMwj/xPNoZkjoPdY4FJl4w9Lcvh9tO/jhKDUqaizPwzImwlFpCs7djbF0OrEF5XpUwLh8+HVqfQhlve1IgXWoJo9gdxYVY+CnX71+HotXu3yQY12aBFw4iLS8XpGUMhlUiRrO/aJbsvYokYA+YUorG4DjlTOoTZbBciT38wAnfDdoQ8rZhm+BonD5VgZa0R3+42smamSMTP0q9U53y78VxcmfIqULsW9sShv8kkuzcohVq/+kHBPNtvQ8KwCxBpi5AJuT4HcTmzYN4u2AjSLCBhr/gB1t2fsxlCfd78/e43fuh5rK4YsFdApkmBNnMqG5vojpCnuc3CkLasCO8b+r5lxzuIBlwwFZ4NmXr/lWdHKyGeDj0wPBI8eNIvOxfJZ50EiUbdnmIwzp2Cxjc+YKkyg6sUUVEIlh+Xo3WJMEaRdPZFiEtSQqWIIK6B0pRhSPZsBnZvhmjcSMjTkhC/cDYkbZ2AGTect9/fjQRD7IxfqhRSkdZdVSh68mNoMhIx+s4LOuYOZQbkPXwLoqEQZClJCLa6IIsXGkNomJ4u+2Ico4dULcbue4VuO3uRg4lg1sWZsK6oQsQXhiIZSFyQySzVekORnIzUCy5AoKkJ8bOFvXdyYxzl3tmBRpmZjIbFFlS/3QLDWA1Sjjeh/nMze/wJ0/SIK1SzjfJkmRY/RYvUU43IuzIRQXsY3sYAZAYJUuZ0rVvJZGqMGn4F6reVwPv8IBRFqzHymZxeLd46Xljhf2TiTeg1JiwYcy77enuNDw22XYhERdhZ/wvEYhkkEvLxFEEq6dq5SgxPn4V0wyDolCbIJErMGjYKLQ47RuYNwDu7ha7Gak/3A+g73F4U+yOQJ89EsjiIa7NHsM30ByIYDqOiqQUZE/OQN6Nr7fDFb4SsRWkDNbCsYV+TDVnFjqX4puFihCJi1JnpBWhb/SQJwgslljQfj0Upi1l3JokSdXQq4npPx3YHNb/UrXqAZiHY3kCpKp5Zp9H9UVMNmWMT6qSRSJl4M/v8aFLHsY0QbOOwSAJ1Mo2NANY9X7Lo1Lrni25FkD6L6VNvh7tpK7uNVNHxHqHbNW54FkFPE3sPJo25BgnDL2Yp1YDbDEeZsMTXUfZNe82SFv4eK4R5OrR36M3Tl8LlI03I7WFRlLSXjjyprmu3oWnONKgH5CDQZIZl6Qp4infD5o+DGanIFu1GfIYGlz41DK7txah+PIyy8FCUBYeiULIFWeu3sPsIO9qG97rBb3Nh4x0vIeT1Y/SdFyOuIB3mzXsR9gXgKK2Dr8WG3OsGskhQV6iHzChnB/XSuz6Fr9qC1AsmIeEEwY2jJ7QDtEicn4igNYiEGULTC9U2k0/ORMt3dUg5JQMJs3qOSDrvEzROFTpcY0g0Kgz8140INLVCMygHJQ/VsetJ7Ab9IwMj/0MjIyKWjqz9qJU1uwRaQqj90ILUU4xInq2HfacX1R8KDjItK51I3yeaTUoYjqAtHXU+CyKiKKJt0eOByLs2iY1/xBWq4DIHYan1I32YBhKpCMMzFkCvTkGNpQSV5q0IhyWsrhYKS9DkaECmKX+/1yBe25GKnFbY8Zpfny/CHpcXC5O7Pm6i1uPDc5/tQJw7BPdwAy4bVXBQAkh8vm4ztlZUI0mvw40nC+Kwo8KDyiY/VCJhG4M06offQulSEUQyFRp96QhFpAhH2kzR286gdGIvLH4DtrrGIEddjTn6AlQuvY+JRcZx/4S5xYak9Bxo4vZ/Dt1Bac6gU9hkQVFaXPZMlv5Mn/5/sJR8RK2i7a+bRKFjHac0+hD0tK1XioYR9jsBbSrzFLXuESLBniBLNhq035fW4g/ga93Z/n3ThqeROulm+O2VTADlcVlQJgxhtdCgqx6qxP2j/KOZEI8ED+5F4gC+hmbsvOsx+ENy5F15FpKndU1HRkIhuEv2QpWduZ8QKrMy2CWgTcKS0mo0u3TsgOkbORsjR7UNrA8vRPq1l+HbFwCfX4w94jHINbSwDlPz54uhyEiBdtT+YuVtaEXQKTSNOMrqmAhmzB0LT30rtFlJUKWY2IHENLUjSqNxDF+tkOrxlHfe2dYVx6ZK2DdWIOGEEci+dP9ZqtTTc9jlYOhpiwTtLpQZ4yA3CWfnaaeYWIozNt9HOwYJ6yY36j8RHjPVBckphobm2WuXo4A6S46gLQzDqO4HvVNPNjDDbVW6rL22SY9p8+tNsJR6MeqiZCQO6XpbGvUgX9JwMIpPb61AwBPB0HkGjDsjgXUiZsePRqp+CGRiJYrrNiPChCOCpcVf4OyJf4ZKfnANS+OMWnbpjjX1NsAeZG1TE/xSDD7IJijC17YrzhsQUsp2dwgPvVvPGmunGsvYdVlyikLzWYSTOecupEYl+PGteljdbW4uiLK/ffasArzyrZ1cFJCdKIZE2Za2jUaw7ZeVMDc2QKWJw4Jz/3xQj6091UjHGH/HSZ6zZiXc9evZheYB5doUNG18BiFPC7yte5A58wGEAy7WmKM0CeldSsv+mtQsdfC2bPtfm6h1tUsjj9G6lf8HsVzIilADTtacR9ltaICfhJo9Zp8NQY8ZCkPefvsejyYikQhvjOkNOnjymqCAv7EZnoASfqhR/Mp3SJgwDBJ5R1NvwzufwPLTCsgS4jHoiXu7fWPtLJGj0Sl8uOinTS0dKTn6ff2ksRjx7Xco2q3GgMhmpN9wGarvf4I1cro2betWBPWDspBz2gwEnB6kzBDSQ6pkI0b+4xy0frUEZX+/DYlnngb1kEGQxulYl6lYLkXWX+bCVVyHxBN79kysfvYnVvOjcYy824UGhMavrbD84kLm+QnQDT64A3JP3Wfukj2ofep5yJOTkH3nzRDLZKh83cx2BcpNXiTO6EhbkTcpbaqn5r7CezPYAt8YlK4d9UjvA89k8bavB2mAGoG2CobTdZuc+4lgB1FEwoKIh2lpXyfkUiWGZ87D9uodHc/LF8COmm0Yn7//stdfy9yseGzIbkbEFcSZo7va5R2I0yePY5FgSXUET39ZgjmFSZBLAH8ISEwQ3ofKuESkTLoKyoQCyDQJkEWjMGnqQIZEJOgGqQ1D0yXIzRiEF/6ehFAwALXqb+y21ChChteuEto32QCp7OBqrYSt/Lv2r30WQZDZ42HCJmLpUbJNE64bCJenBQpDLqp++Dsz3U6bcttvPniTAw25yLD7NuYja96/2TC9be/ncDdsZMJOw/Q0eqHLnIGAo5alWtUpY6DLmIwwzSj+8FdEQz6Wvs2c8yhk6sM3inM44ZHgAeDp0A7iRhZCNyQP/l2NkOnU7VZnMerqJdgZmomBLsHBpUu/fBuDx+mw4QcLHJYQ67pTKUPtEUnAF4HI2oyxJ2cg2rgG6bkmqPJzkXD6SfDu3gvjgtkI+ML46uky+NwhLPpLAXQmOYuGck7fP83jLatCy6dfsGaV5o8+RchqgyIzA7n33cU8Q+WGMAK7voMlWoHkP13Q7QFFMzgVru010BYKaTzqbK1+28y6Bes/t2DQbb3vY3MVN6LqqZ/x+Lg/495dQldqZ9zbdyAaCMBfU4tAUwvCThcz5aaJIeH/YJvmyWtUmSzD6Odz2d+OrYv6vSh0EuTNMaB1rxc5nQR3X6gR58RbM2Gu8iFn7P4Rm7C3j1KIIYigRjQiRaqh99fG4wtDLhND2mmXYXeY5DI8dvLBmTtXNFnx6ZpiDM5IwInjB0OjVCDVkII3fxCs9FrLLRgSCiEh04SzzlqAc88lgcmELrtDrG2l3yMlUIVK8XjEqwOIhNRYUa5FiaUa/75mALx+KTxOK60ugT5zEvPpHJM4FJn5hTAlHfx+Pta1WfQmRBIlEkb8qf16bdp45C58iUVcVKMkksddz5pbqHHGVbOSXeez7Gk31e4Nv6MGPnMJvK272Zwh1RVl2lQoTAMQdDVCqk5C684PIJFpEV94Dgz5C5lBN5lr+20VSBp9FVq2vwFP42Y4a9cwM25yriEBJCIhL3ytu49aEQyHw30mku1zc4IEF8FOr4VYjOG3XISdL3yJSDCIkMsLuV6DgD+Cr15vwfado5jPo1s/BgMaQiha50RuoRq5QzqipaRMBf7+7AC8/LftsDb6MWB4CipLPHj3wb3wBBUolGxAC7LRGp4O5R4xdKU+fLFiANLyRuC0tFRUbLejbLPQVblnvQVjj++5Dmf+aDEQFpNNC2Tx8UwE/XW0gT0IkUIB27KVCDQ0skvCqSdD2s0gdM7NJyDs9kGqU7XXAWmTBEWC8T2MQnTGvqEKYXcAYxMGIkHRTdPN7OMQaG6BIi0N1h9Xwv7zaugSEqC74i+In6SDrzGIoluq2bLcwnsO/WYIYsgp+3f6lda62TlMfrqGnaCsLrfD6gli3jgTpG2dqZ2RS5SYM+RySKQupOizmPuLXNqzD+rG3S48+UkjEvRhjCgIYkx+EsYUdD/DGIxE2Z5lyUFEPauKq1BvcbLL7JH5UMllSItXIyNBDYszgIRwGAEEkBkvbT/w7Xvy43Y5sNtdAIkojPNmZeGj1V5EXUE0WiL413s12F7hhVzkxVjDdiwYvQeDJp0HiVSGlKyDNwYgDAUL2aU7qAbYGXqM1JFJ0WH80PMR9DQz15gDQenKmqW3CY009NwaNiB18j9YelUVPxgZM+5D9Q9/Q9AtjCR5mrezrRS0E9HbUsSiQb+9CurEYUwE6TYU/VLTTsrEm2DZ/RnbV0jCerQS5o0xvcNFsCvO8ga0/CIU0RvytyLr5ClY9eJGbF7TMWpgbgH+c1N1W3u6FTf/NxuVu3yIT4gibYAOYokIf3poKCwNPqTkavDgFaXwBengHsWu8Pj2Rk46Nm1dYUVrfYBdZp6ZhPSBWqQN1MLvDqFgrJGJMf2iuM3dpzPaMcNRV9yKHdFxSAzrMX5WJrTDBkOsENKI+hlT4d29B6pBAyDRd78RgKLMmADGoEH5vIMr+yB+3iD4amx4bcn7aPQKjSudodRxxvVXs69rn36V/T/i9TC7M3rvuSv87SMQtLi3swi2rnOh+s0WtgEisxdrtO4wr3Wi4tUWJEzRIveyruJTVufGB0sFv8qxowwodfpQaxbO+rUKCWYN3H9I/MmPqrG93I3jJ5hw/pyez2fr1zrQst2NomQxSxYEwq3YXuHHrurWbkWw3OvDveV1zC7tkYKs9sW2MYLBCEvPSuQi7KwyIxwRQaOUQ6PT4sEVFTi9MAWWxiiiYROuOiEeuUlSNDe4kJ7VccIjaRvW37DbgbIGH0Zkz4E1LLx/q1uBhePj8cLXjez9SAJIqcpAVIW11qnYvDyE//3+jO9BQ+8J48BFB3+DSKh93IGg9KazehXCPhtcdetgKjwPyoTB7SIYMwAgz1OKAinK1aSMYf/XZc1gddPOEStdjnbCXAQP/Kajwml/hho3tv/nYzgrGjDkipOgNGnhtzoBczVKPwjAt34LxDgBEVDqRoSAr6NmJFeJ8O6/G1Fb6ocMfowdE8RJ/xgJhVqK1HwtAg434lR++DxyqOBmhtQBqDBiWhzmnJ0ArzOMqmIP0vJVMCRK4VxfgpPmRaCfOhz++ibsuO5xZnY98L5boEgUhGD9N01oqvRg5rmTEKgxIfRTLRr2OKG97lTEJXZ8iOUZGUi4/HIoU5O6TYVSB2nNsh3s+WfNoVVQwu+46lpR8sYy6AtSUHDW1F7rMsp0A/LvXoAXn7qg3Yu2J1L+dDYcA3KZcXfsPuOGqZB9cQIbWk+Y3jU6oKH+gCWMhi+tbANEd6bbPdHys4Otf2r60cFs1WLPjZB0Sk8uq7AhKIlC7jYzT9hkXffmBOUNgkiW13f1h40RCEWwdLMDTe+bkeYDsocqMG6YGqUeFWD3Y3Bm9x2VJW4f/NEo/KEwanx+DG1bbEu4XSE892QFPL4g/AOscPmpCSaK0flJ2BWRs+h5Q50dK370IxwBPlphwT8vTEdWbte/Ra+1wxPCU58JXbn+oBGLJiWjyerHrJEJUMpE2FKqQoMlhKrmWJOc0DQTOUIJrEjID1ftaiiM+VDos3v8PZk2BenT/8nSnn5XPeTadCj0mXDWrQfCHtT89A9kn/A8TIPOYF2nMQs1WrSb2ClFS1Dt81gkEonwdGhvUMqkv3eH+lodaN1CXoqAecseZORL4dpYAfMyM8zIJA9oZIr2oCradVUL4fdQQ4XwdQRirN+sgOuO9TjvXxPZdWVvL8EQ225YcCq80GKI+BfkFqow6drT4a+ohNagx/X/HoBgIAJPcSVqnxNcL8QqOUIeKyI+YZDYXVGL0jIpKCD86S2h7VyllWLs8Tmw1XuQVKCHLqEjiqr5fClqP/0eEjqM6TQY9e872GaJzrRsq0Dx60vZ1wq9GikThE68mh+2wrqrhl0yZo+Aqq3Bojc6j0j0BDXtmI6fvd/tUhZ2b+KceqIB1bZWVqfccHEZ8v6cxDo5acUT1Qx7qxumnWxk7jLxk7RdBJDISVHj4gXpKDF7sKTKimjIBXFbW76XamGp+6eBbzgtA7/scmDu2O7F7Ks1Nny8wgIkAtMdQWwO2aFslsJWoIYYChjzE1Hh8SFVKYeyU33mOKMO1T4/iwCH7NMVajEH4HGHEZZF2gRQIMWkRY5Jj631DszKNSEyzIXVxU5MHdr1IB47uaXXWCkXw6STwuIMISNBAZVCis9Xe2CxN8EXCqK0PsC6I3UqKTzeEMJt4pdokLFBevEfbLvVuvN92MuWQCRRtNUP95/LjKGi8QbzLtjbNs2nTbuHrIDY19FIAK1FbyF57DWQaQ6upkfvY3PRW/BbS5E4+koo4jJxNBPmNcHe4d2hgDJBj/R542DbVYFwRQkiyXHwKJIR0CYDZqH1PFtcDJVRjWp/HjzOjoN95gAlzv9bCjb9aMHqj6vhgQE7KkzYtdGFIeO0UBh0iIokCLVFkbK8QRhz9QA4V61G46uvQSSXo2jEXdiy2ocZc+VIZyfgAQTqKmCcMx0Jc2ewSHB3cxq+e7uWRraQlKyEvcWH7EIdjOlaLLp7AkpXVuHbB1Zg5KmDkTY8Gdatu9vTruRaI+wo7CqCSpOOCQRplzK+48CfNK4ADWtKEJebzKLig+FgRPDXYhqvhX6YGhsvFYa+7Vs9bKSBtlFIdWKM/Hc2pNruB+LpdsMf6nlnXmayCknxCvzS6kSLW4GoSAypCEhtG+PYlyHZGnbpTCAYgccfgUErRZxGwhqhSCsah4oRbSVBDUG2JYhoUITPnS34dJIdKqkITwzMhqEtva2RSHBND9Z4GdkqzDk+EU5HEPLMODQ7PZgwMAV5KUIqmQSQGHiyBn8+ef/76CyCcqkYj16Zz0Yoko1yPP5RA/zBKLZXeiERUROX4Gtr1Inh9ApbRIgGSxDBUBSKXxGFHwooUmOPnWYJD0KAyX0mhs9GM5Ed70VnzWq27ulgLdFCXjPbgUjYy74VRiVcDUiZ+LejUhDDXAR7h9cEhdcg9+RJ2LVtEzx1zWgtU8OCTNpvCqUowNYD2ZHI7NCCEWqyIGEQPphzzzFBLBaxep7IY8X37LMjQnmxF4PGaJB77jzEjxuCtBYtWpqAycfnQamWwP2Lg31MqXOyZIuQaistE2H6/12Emn/dh9ZP3qcBJmRcfBb7We23ZPgrHA8WnhJGXJIBxqEdEdS6N7bB5ZHA0rwD5/83CbkXnogdT7wDm1MEdWJyuxtNjEgojLovlyE+VYq8C0+AIb+jASd+WDZmv3QdDhfOPV64dvuQODsOUk3PByZPtZ+lM1MW6uE3h5B2mpH5nxIU5QVs4R5FcF9IoLess8LjCmH89HgolBIopGL8fXImPtrdAlmmEYsK4qFtc+Q5EL5ABH9/rhJWVxh/PSMVc8fE4f1lrXD7w9hbC8wYacSgDC027PZgR6Ub0TgR+/d2h6NoCYTaRfBA78tps35dLbQnESQoGlTKhed3yhQjqpu8cDloTiLKnGIIKRMKOmESIU4txp/mJULRTaPQ4cY05Gwo4wexQfaYIPYGmW47qn6GSCSFbc9nEMu0zJWGnhs1vJV9eTFSxv8F2vSJzLe0fs3DkKrjkT7t7va5wBjUnEOG2n5rOZsRdGwVvEpdNaugGLq/m1NfJ8LnBA+cDu3vNcFY+tDd7IBEJIdHGo9wUDi4ihCGL6Jkdbz6SC6C6IiY6Njy2n31kIijuP6hVMy4aCB2l9WgvjKA1UvssLWGcMHfU6EfmAX9wK5/z7hgPhybt8JbVo4J4q9RmzMH00/Rwl9X1h5RUa0uxvj5JhgS5bAVleD1F9WQwok/31OL+CHCXJkuOxmtOz3wm4GGPXYYDRo4/BpYowZYaoHmkiYkDe6IFjy1TTCvFxqAnCXliB+ee1CvU8huh2PFKqiHDYUyVxiit/28HM9Pmoo3ykvhKWuEe089TDMKIdHsXyMkz9Bd91MHaxS+piByr+h560P5i81wl/nbh+dpL2DzT8LQddK8OKgzDk6wiOZ6HzauEhp3dHoZRk5os+ySSfCnYb17c3am0u3DXqcPagvg9HsglQG7qt2YOETLaooScYidH00bmoRhORocNwJosPpRHPFir9ePXLUCBaqeU3v7Qu+F/37WhF01Xlx/SjKG5aixrdyNTXtcOH68EWnM37V7Yp/r7trjB6Qr8cQ1OVizzYx1e73YWi6ciI0bpGY1UxL3G09NRUH6oe/WPRgoaqOGlc7Q7B5ZoMl1GfvVqalumHviK3BWr0RLm2jF5cyF314Bv1WYUXQ3bmYi6G7cxCJHunhadkC7T+cnmSTQjCJBWya8zdsRcDWwecKjkXA43N4cdaTpG4Ma+0AfkP4+LO8z21G5fA880CMYlcEZ1MEWTYQ1mojU46fBjGx4oYO0bTURi+DYTKBwkKeOvc8f3Y7i78pQuTuAQJsf8O5NTrx51w74PV1f34C5FRG/HzKDEMmlBXdgQsNTCL/5LzS88jrUI0Yi9ZqrkXDaqe23oWgzvnwJzCs2IsxacNRobuwQyTGnDWQHX5lCDG28EjWfLYMkINhmEVJF18gj6rJBBh/ECEJt6r2hpf020Siq7nsQLR9/gpqHHm4X6ZZ3P8BwowmXFwxE+UMfo/HdFWh4byVCTg/qXl+C5i/XtAs72aNJNcJHQabv/YOpLeh4XBFfFNYNbkSjYfjlFmgGHLwA0uvfsMsDlVQMCnSS07p/vlvMLvx9TTleLxF8PvfFGQjjn/8rx5tv1OGrYnNsWxRGFQhp1+Mn6Ns9XB/+oBZPflJHy3qQrFfAsyeKpmVBOIs6/s1srhBrqNm3Aef6pyvxr/frEQxFsKvag7W7nLC5wlhV5GS/8+9P6vDjFhve/KH7x9lTJLgvMqkYx41NQkaiqu25RGHUynDbQg/uPbmVpckpFdoXiIaDqP7xZtboEktV7gtFjKzxhRYji8Rw1q1jAihVJ7LIjsy4HZXLoMs6jl1HNG18HpFw936usXQspUHJUUYed/Azkn2JKNV0+8icYN94FPvARZCyjlQzEw4YsuRESBQyOqdHECqs/sZFdtfsZ8lxLkwZa0NCsvBPuWujj0WKdJiwWMTY885PECPWZBRBKCxGSakSn/xztTDqQDZl23Zg1013YddNdyP+9FNhmCms/ZGQDVvbwcrfakXz0lXwVtV0eSNbv/kGuaGNKNTvwuTxbqTJ65hRdtlGMzYvqUXWcCM8fgm+++826AvzmHdkToYX8x9YCFNu17SaWC6HTmyFQWyGOkWoLZGo+VssPdb2Ij4fQmZhDxtTk7bHqz9uOryhEJbU1UKiEaIcqV4Ny7ItsCzbiqaPl8NX2di+uX74o1kovDcd6Wf0vK+OyL4kATlXJrAJW6oBpp9hhHdyEZpSVmNP+fKDfo9v+7EVO1daIXNEcMFVOUjJ6D66Wd/shCcUwS/NTvzcIqRdO+PxhQAX9fcCUREwY5QRicNUWCF1IRCJYOFEE6YPo5qi8LpQtHb1f8rw5VoLPvi5FXtrffh8jQX1rUGs2uHAn/9bgZtfrGoXQqszhI9XtsLsCGJbmQf/+74ZD7xbC71GhJxkOeaNFeqVeamCiBek9R6lxRreDuS6ct7sRFw6PxGnTonH4EQHin56Bvd+6Mfdb9ThyU+EUZIjDQlVrO5HQ/FNG59jM3/U8Vm/9lGYi99j71uyQGMzg+QIw9KhNDYxjEV0jsof0bzlRTb8Hpv7i4Z9cFYtRzQSFnZx1qxioxWHur59JAnzmmDv8MYYIZU4/IbT2FhExpwxWH/3K3BW+RCCcEAPRhVQJegwfFEORsxLh+mDUnz1mWAaM1q8FFsjs+CAEcXB8dDDDCuSYUI9LCDnfTGKa1NQs2QNDHmpaN7VBFskEQZfC+wrVyL14gvYIHvYZoVIpmDp0ZrX3mH3veXdjci9MBVpuUrBG/SUU+FcswYnnpGC5pefRvP2IIINi/DTj+nw2IPwidSIQI2yvSEcf3IEE1+6mwk61UT2RTOwADl3/oO5zWgGC7na0n+/Bvu2XUiaPx1ZF3ZEoXRA8OzeC1m8CcYTFsC1cROSLr6w/eCadMF5mHvnnXAE/Xj6gQvg2r4XutED4K1qhkjsY3ZmEm1HClAWJ4Es7sBpNrr/5DkGdomEw9j28XK01gtt/tba5oM8BADqtuW8Sq0EClXP0efcdAN22r1wiiJ4vaYF0xLiIG17jqFwFK3WCGZPNeGnKhuqs6IYnC5DvcWLeocbM9xejNJpcM1Jadi7zoUGtbB93uWLotzpbe/toE5jnUyEsgYhXdBiD8HljcCkE+O+t2vQZA1CJSeXGTFK633tq5AeujyzvUPzzvMzmWDGx/VeV+wtHVpS48HeOh/mjNJDrZTA4QE+XWXHD5sAVfg8NAeE1HlxpRfNtiCSDAdvl3Y4sJV9C5k6Ccr4wWy+L+Cogqd5K4vqaMidub1U/cxcYjSp49nQPOVrjINOZzZtDWsfYfdDWyzIOYYiS7kuCwFnNVq2vcpSrTQUT0P2RNq0u9gAPUECSpFlbOP90UaEj0j0Dq8JCiRP6FhDM+LGs6Bfuh0V1Sq47UE4HSLYzWEsf60Ehl9eQsKeUsyUFkIJL3aEp7TPUnkRBz80UMIOCzo8IOmccuNn5dAE12F7+DhEcSLGSH6A8ttvoMpIR9DSitZPP4U8LQ3ZDzwIx45dKNoYxLrikRDdUY3zJqyDqm4rNKNGI/uhh5gjTHPMDUQsRv74BBT92IAElQ3NHhNSRWVwrmmGbnzXWodzw3oWTRrmzUfclClMCDvjrhAiT3e5MEgdw/rzSjT87x2IZDIMfPIhJJ17dvvPQnYHXNt24cPjTkaLz8NcasyfLkFroglp11wMcdQN+GlB8FYknNh1POLXYC6rR/nK7cwdJyIRQ6HqfqyiO4bOMCJtgBpao4wZGfREvl6Fkwvi8UZNC4bpVG3xv8BLi5uxssiJtGQZQpMEQaB7Mkkl0EolKFArsdbuRGsgBEWeDNEGMjkA6zjdvMfb3qyYZQe+/cqMUZPj4PNHMCRbxUYXiEjbOidaU+nwUOdpGIsmmzAyT9NlRIH2RSboDyxKPaVD6X4ffLeGzRaa7UFcuiAZjraUvccPqOOSaY0kIxCK4ofNdlww+7fv13N4gvhoRS0S9QqcPCn1V/uBBt3NsNLWCZbBiBOMvR1VkMdls7qhrew7dpJBfqOeho1IHn8j20ZBZti0kDdIW+QpRRqNIC5nHlqK3oS7bm2Xv0FbLZLGxBwiRBDLhE5gd+NWJqBiuRbpNHohih51HaIRLoK9QwVT3hjTFUoPDjl/Joa0fb/i4W+w2aJFkrgKgb172QcuT1KM1kgqWqNpkLAjRgRhKBGBCL5OzTP0MxmCaPClwyiWsWF59sYUK+CNarClLBGJrWVMRoPNzRBFo8i+7iqYf7YDLzSxiHDXRg8GSZwI/fA9VAUF0E2ajKz77kegtpYJ4xypFMddXICWl1+Ac906iGVSGBfc0uU5NVd7UfT6JqS5qhGyfsBEcF/yr78YlnVbkTin68/CtF6KbaYIsb2GMRzrNqDh+dcgUgoHjESlGoE6Ie0ZbLVBZtBDotch7HRDlde7+fWBMKQnQpOoh8/uRlxqMkadMvWgb8ui6B7qgPsyN1GPaSYdFGJRl4O11SU8b68ngrOSjNhgd+E7iwNDNSrclZeOCq8fT9U0Ae4oRI3kKwvQVEFqOlDdllE0RVivIj6tcgNVbpA17cIJgpg3BYK45uxUtDQG4fVF8O6yVhw3Ig7nHmB/Y2/YXIKSNVg6ltYSMokIWpUEdncYxjYBPmdmPOsG/XS1BU0s69jx3McN6Hmt2MGwfHsL1u4SuptH5umRnayB1x+GPxiGQXvg2i6JW/tj1yazIXqGSMxmBMkPtHXHW8J1YjmaNjwFff5CNlvobS0Rtlm02arZy77e594FUwBynpHIVMic/Qi735jQ0TJfZhoQcKJm2a3sfshO7WhykolEIn2mMaZPeofymmDPUI3Mu3sXRowVI3nXyxCz1vEODOJmpIgq4RLHIy7SiLLoaJb+zBdvQVlkJNRSH4Ybd6LGbESOuAg+kR4p4kpkzhiKqadfhrefdaDs2zD0xhm47EQVVIVD4fWKoFSFMDjXjUGjVSjd6sTO8GQ4oyZMUy2BPCMTuza58fNnIQwZk4MxOWHEJUiZ32Xy5ZdDN2kilAUDII3rGHAnU+7X7tqDUGAyBsmVmD2z+4FvscGI9HNPZot7A03NaHj6aURCMuhPOAepl16IkAtwbm9G3PAofFVV8JRVsttFfG58WVODbZYmvPH0A6yuqh6YB1mCEQMev4OJp0T9+7oMJXIZJlx1Cn7638/wyOQwpB++zd/KfYzTCZrDe3J1A6r1fnzUbG2fQitxe+EMhbDS5mBF/wgda9gXQsQmS5IiqgixqYNho7Qw1EhRsVE4qLNIzBFCUBfFnWWCAcL9+Rksqlww3sCG1Le7PEiVy5C4j9HBwfD9RiFlvKvGg6JyF4bnCTOfFY1+JBuUmDZUgeOGx+GdpWakGmWobApQdrwL88bqMCjz9/3bDcrQMRNxarpRySWobnbj8U92s4j0L6cMwIjc3qN6hbEAiSMvY/N6tE+QjK1pE0RMiOS6toW/YgUQEYTf1bARYU8zbKVfI+O4ByCWkul5kF26EvuXFMFeuRypE29sT3tSGYBE1tO4SfhHbRNS6lA9mohGo1wEe4M7xvSMdfEXsHz6IagPnsyGGTTf1dZwIEEYsxQfIpoxCGVVSlQHC1nfppitKxfDE1Ij074SA+RuNEVzIY8EEW8KYNbVQq3BZhO6/XzeCJ7+ahwiX5D7TDmGJlUi27oMu4MXsyFmsmuLDhiD3NtOhkSlwo//rUZjdQB1pV788kkV5lyWj7GzDcwzVDtm7H7Pw+0MszQg7USvMR6HpvQk7Dt9VvPDJpS8+QOU8XGY+thVcK1fj0CNkB6te+l7ZP51ERpf+QEibEKrZLvw/OPiEDd7BtS52fjPuWeyRgxyhUk6o8M0mbnU/IYDeGe2fvMLtn2/hTUohUXCuWRjRROyCjMP2UHihcX12FbmwlUnpmHMgP0dYww6CSoy/LFF9O3QO+HKEuFkgKEUIUoTF3WANyRC6SZh5o7Ky1VjA7h8djLUOglrjslPU2JUvhqbnZ72Q3GLP4h1a2n1UxSN0gC21bohHSjGc2PyoOpGnHsjK1HRvjT3f9+b8cQ1ggh+stKKkhofdtf6sHSrA+5ONoCdI0Dih01OrN3pRLJRhn9emPmbZgYHZujw9LWjmcH3P9/egRD7KAl/s87sPaAIshVknbbJp066idXp2CA91bdTRiN7/n/hqF4Oa8kn7DpVQiFc1c1sg4Rl14eIhDwwDDoDtr1f01lbl/uXxWUh6KiGp+EXBJ31kMcJpYzW4vdg2/slIJIha/6/EbCWIuSzQp87F0cTEZ4O7R2eDu2ZQFNbHivU6ewxFIQsLQPBxgZqWWMHDcPIgRgxIQFpH/8HkmgQXlEcIlI9ChaNRzbmwLN9C4KqPLRsr4YpX5iLI1Pk1kZBTP2+rmekxc05aMT8tlpjlB38S3dH0dIiRkoWMHZWHL59xwxxyA9H2IDPXm6FrTWMsh0+DJ+khSZOjIIRGmjiJFjzjRVL3jZDKlVDaRKjtSmML15pRuH4rk4wrjoz+7/P4oSvsQmKvFxI4pMRaAkiKjNCblRDJBVDyoyXhaaPsMMO96ZfkHLxuYfFMSZGxUaytBOx04FwNAqZWsFOBujv/dZ9czH21HjYqMKaYiE6W7XD1q0IUk3ugtQErLE5katU4BeHC459wiYKAi9PT8RPO+woixXVYrcPAXkqBVutdMqUrl2xNEYxVqdmqdVwXRSL13W4n9DJlKYyiODoCFS/ssF8VL7wPCJRGTISO9LBkwu1KK70MJcbslGjR6BTiuDsIoYdUOOO2+dHnTnAOlNLarz4fpMds0bGYXhuz648+45j2D3BNgEUnvWMYQmYNbLnOdHeiAlg+/1rkqCKH4LYGl9qfqEdgp7mHTBvE4zbHeXfIuf4/6Lym6s6bheXxZpkaMu9Kn4oZLGokn6/4kfhi2gQrTveQeqEG3E0EuWRYO/wxhiBkMuJ5peeAxQKhFpbocjIgFjRfR1JO3Y8gq0tcK1ZxURKM2oMVPkDIPe3wrFhM0obcuASJyE+QQxfwhxsKc6Ad9sO5IwZheE3nMDuQyIVYdbpBiz7tPMBL4YIrUjvUrOgAWalWjgITjnBgInz4vDzB/VY+pVwVrt9jYuJak2pj3V8kp3bgvPj8c2blBITIxiIYugwNbascGLYJC37YHjdEajbHFfyT58OqUoBTbwSdf93J/VVI/22OyDWptLpABTpCRj4yJmwfR8H+49ftj+2iN3OfvdwiuCEM6fjl09Wwe0Kgiw0W4JyvPrSLzDFa3DlDdOg2GcG8mCpavThvv8JTUATh8XB7AjghAk9O7QsTDCwCzHQpsKztV3n9Oj4bhCLMSlHh7KytpRZBNAFgCmjVZiVYGSjFPJO3ZqOUBiPVTeyUyDqiyGBVcpFCIajCNOS3yigEomgPUjLr87Eav3Th+vxl9M6jBJmjozDcSN02LDbjRe/bkRhlhpXLEzCX5+rhrdtx2NXxJg6VI2cZCGyfPmbZtSZg9hd48OzNwiGCQfDgDQtZo9MwrJtzdBrZAhHoiipcWJUfu+RIEVfDeueYHOAKZNuhqTTpofOqBIG0yAqFa8F67OKpbCXftXxegTdTDzlpkEIWHaz6ygCjBFw1sJRubQ90iNzbr9NsOxji3iPUiK8Jtg7PBIUaHr+aXiLi9pfl0B5KRQDBrE6WcRmhV+kht0wAgWz8mA6cSGiAT8kag0kBgNUBQOZCCScdS67fHJpEfyuCNYtboG/uQUBL7XMpEBRVA9xp5TWvLMTIFeIsWKxFd42P9LB41Roqg7C2u7mT4gwYX4cDAkdaUWJVIw5F2Rg4AQfM99urgvi27fNUKpFcNloazawbZWzXURTs6SYe7YJp12VzAT4i1easP5HBybO12PRZUmQx6kx4JyZ8OzaCXdbutdfXo6QaxtsXy+GxGSCRG+AKj+fRS4MmQya6XPw/oO7ccn45/Dx5v/D4UCfqMfUs6dj+cvfwiVSICimZgoRLK0eWMxupKb3vCz3YJk4KA4TCg9sFB5jukGHd+qbYWvr6IzxZE0TXh6dhw+Xt4L1EIkBpxKwDgRuL6tBpkKOQq0SK20u/Ck1AZP0WmaebQ+FscXlwRZ4cMnFCRiuVeHVkhYomyK4ZEIyM0sgdlV78eNmO6slapViXLcomY049CaCeq2M1Sc7Q+/XCYO1mDC4AK9/V4//e6sc15+SgorGIL5Zb4PH3/V5ldcH2C5NuViEEXlq1JntGJHXfa0wHHCxlUT7jhTQ3zx/VhZOm5KOV74tx+qdrVhXYsFz14/Z7/F1xt2wiZlZE1TjI2NtpXEAUqfc2uVvkMClTbmVucCEvJYuAhijYc2j7QLIkCio80Z43L5WtGx9hY1UkANN2tS7ULvinwg6a9nqpaOVKI8Ee4eLoEDIIdhxdUaekorMO/8PLe+8ibcWZ8PWkIwRVXE4UyKBSKVG4oWXdPuaTj0tGZt+MGPSSYko+s6NxlIn9IlyjD5z+n6/e9wpJmQn2fHyU2RiLILLEsJ5N6bg4+eb0FJPhXzh9zQ6CVrqA3jv342QBe2YNLAWOSP00LQ2wjD/eKTl6pCSJYcpRYay7R4MGKnGj+/EBp2jaKgO4fOXmnHJHUK9o7RI6Pgs3S78P4Zq8BAkXHol6lduQ8v777XPt4UtFnYJVFYIJa78AqRedwOazTLUfluMOEUShiTPPCQpys7Y61qw7JE34Y9Kmcm1HgFkDM6AS65DUkocUlIPXrj2JTtFiX9ekgWvP4IR+b++AzLKnmdXsSDdUynE0KslMDs6Gqm2rPMimgVU7wig2hAACoGlSgdmGONwmteIL9ZYYcsJI6oHHOEw0kbIcePgFDy1qxHPft2MFJMcU4Zq8co3LWy2MMbmUg8Ks5VsbpBSlZ1f+3aXnm7+PYoqXChr8GLqUD2WbhOSiCuLbfjzien4ah1t1BDBoJGArEXpLVhvCeGKJypw758ymJ/oGdNM0Cj3T89a93yJ1uJ3oUoagfSpd3T7uqkUEhSkabGtwo7sJDVa7D5UNXsxJt/A0qZdXk+/HfaKH9k2CXlcJkLuFkSCHniat7GdgbH9gDFoEz1dapb/s9u/HXAJDUjthP3Q5cyDTJMAS/F7zHOUFuoSTZueYQJoHHga4oeeg6OVKBfBA4vgseSO8FvRjhkHa01Vl+sSL7qU/d+7awcC0UHsa8u2EjS/vhiJf7qCpR1FbUbIIZsVnh3bWWp0yqIkdiEGT9TD3uSFKV3d7cGI7NOCr92PYaKpKIpOR125D0veaMIlt6fh0etijyeC3EEKbFvtRGMN1ZpUMLU0ILTuexjFTWxu8J1fJqOpJoDxc+Jw6pXC3553bgKCmz9FmSsfLphgSOhIG1JEuHGpA+Pn6lG83oVPX2zGoNFqnH19Cj5dkYeKnWkYKo7DONmPbEs9RbyhZkr/Ufu/GPGnng7n+iIYCvIweEo81i6rwai8y/Dv6/bgjL9kIHvw72urL9vViPpqK7JT5GzvYaxfI3VQBmZeOfuQCe3AzIOraRH1rQGU1fsxcbCG1fbuycvA4iYL1n7rhtcWBSYCmakyZq7dWQCJYBMgsZD/JRsohagZOOEKPWs6ef+HVviCUWh2i+H2RvAprEiMyrBsqx176oQoZXetH8t3OJGULQHsgJqEViNBdrIcN79Uyf7m5ccnY85oA37YZMPX662YlONsL3lQPW9nlZcJKfHYR9Us/UpbJeaPNWHjHhc27PZjQ0k5wqzFVQRbh+te+8zgEx814KRJBhw/viOFuXKHFb/ssmHR5CRoWnaw67wtxQg4GyDXUTq9g9ix5oTxqZg8JJ4J4s0vb2edonNGJbFIsTOepm0I2IXGI0P+8ZDrs4W9gPED2SxgT4P1fsseiKQqSBUGBN0drjcK40CEXPXtS3aFBxWCIf8EuOvXI+hpRdBrhUQRB69Z8NalMYujmQhPh/YOjwQFTKeewVr5o0E/tBOnQpGZxazFCO3EKZhf/SbqIgXICe6AY7kbQbMZ3p1FSLjgTzDMPR71jz+MQG01G3NIv+Wu9teXRhfiM3oRBJGIDaFbkcJaP0hkKvcEsGONE8mZMiZsMpkYe4p8CLU0QikVQy1yIE7qhloeZoPosuRkOKxBFpO01HU0ZGhMKpz+zBnwmF2wejRIz++oceYNVbML8e6TDfC5I9i2yoVTr4ygvkI48LZGUwWf1GAIuqEjYG/5CaJoBMlXXQP72iI4Vm1EiWgUMGUevGHBX8dpDeGn95tw2f8Jy0t/C15PAJ++TtZVgG9KLsZcdDxW7goiLFNg4in5hzTSPFjIQ/PO12tZ1FhWr8clCxKRppDjJI0JS1uomxMYaVOivjmEa3dUICddhsq6joYqmYS2xHfcH3Xbv/JWC5yejhocCWCM7zfZmLB1IQK0NIfx3F+yYdRK2evQ6qA6qfB7JGjEZ6tbmQH2dy2CYThxz5u1rMGFmlquPikJWrUUNmcEXj9wzUmpSIyz4Y0fWthr3hPkM0Bp2Ld/MncRwf99X8ccdejytxMuZF2VnqatqP7x71CnjIWxYCFUiYVw+0K463+7YHMHWXp3TL4Wl8zvvaaoSR4FpWkQ+5wEnI3wWcuQNvkfXTbA7wsJsPAa+5E55xHUrXoQ/rYUqLdpM9tQ4bWUsq9pKN40+EzYK5e1G23TzGH6tLvY1glX7ToYCjq6nY9GotEopAexteSPoG88in3gkaBArKbXUyNM3BcfIy68HmK1BrL0wfCWFDPfNPemjUwEEWt26NT0QMbNlgYvTGkKrP7fTgT9YUi0OmiMckw6LYvt8iOhzfrnvch6z4y6X2K3jCJia8UNjxTgzYfrsXe7F8s/tyFZ1AijCMiVFuG4OxdBbErCjx+2YluxCgXDI9i+1o2qvT7YzMH2+qFEqYQuQ9lpfH9/pp5ogL01hMFjNaxGeer8Vmz7qhSDJOuFolYkCvvSn5BwzjnQDB8BRWYmfJVmOKJ6bAxMApY50ewqZa+HUZuBwom/r0bna7Gw/XW+QBSmRC28iUlY9Q01KASRu8uJGeMPfgtDZ4rKPfhkpTCEPmu0Hh5fGOWNfgzKUEFG1i69QLobK1tRGYpm+OjbVJMMCyfoUdUUgEolRssOoVGpZmoQl86PxxvLWkGmOSoP24zVhc4CSKVimhscmKFEQ2sAFY0BGDuticrJkKOyNgCxD2zxbexEID5OhlvPyUBda4BZoBEnTTJh8ToLJmbp8L+2SFApEzMRpHVKtFtw8mAjq/0t3+7CmdODGJqtQl6qgkW6+45JxFDIBbN4aqzpzIRBeqzdZcO4gXoo9PHQ5x8Pd+MW7HQPgaLMgvyG+yAaejve3aCEzS10VFO5cuNeJwrSLbjr/CGobPKwdOi+UESWcdy98Fn2onb53ew6qdIE48CT2yPFlu3/gyZtIhKGCp/f+MJz2aC8OmUMa6ZJn3IbGjc+C0+j0NxCIxOdZ4GbNj4NX6doL7Zaidxo9t1kcTQS5enQ3uEieGDkaenIeeIZlv6UGoUahGP1CrjWr4Vp0ens+7Sbb4N3ZzHUw0ey70PBCJ65bhsCvgiMSTIK0diGiiCEI2HGED0yCw0INDWi9t47MTgKZP3pDmx8aysbBUhMGMHOlifON7BocMg4NeyrHXD5lEhCFSwfvo2lVROw207b4CntJcwmUg0xHPoVH5BIBJHnb8E8qwWm8WcAOB2D5+VDvfpthJw2VqeMHRKVublMAInEc06CrHAIDC+LYW8No8L8M5ZsuQtmc2t7E0dPtDb58dHztTAlyXDGVZmsUScUimD9GhuM8TLUv/cRsq1OqLMyMHZaPiz2AFRKMQLBKDJTu0YAu6s9rJNyWG730faOCg8+/LkV00fosGK7A6V1flQ1+ZkI3vd2HRsQnzZUi+tP7X2dEg17/+vyTDZoTrWyyx4vh0EjxUOXZeDieYKry/dFVqwTuwEpncQAUi+dhADBasDRpnfxegnGFqjx/SYhVUklsIUT9ZgwSItt5V42dvDpKgt+3OJgoww/b7SxKDToCGHieA3S0+VsddfmPYLjzMgCLUbkadglxsIJRnbZvVuIfkgEH7g0A+UN/vaRhqwk4URCqxKz1OoLi5vaBJC9K9q6fQGlFCDf8Jil2uxROly6oKuLzdUnZuLKhRnt1m7qpBFYJf4bljWqWBPVYM1u7GZNlp5OUTxbQYyicicWjE1mw/u9vv6aJIjlOtbhqaBNEZ1Sn7Tw1rbnc+byQquSxHI1dNkz2awgEQn7EXAKnrP7Ej/sPNhLha0UMm0aNKljWWr0WCLKI8He4enQg0Oq73qWGjd1Bru0/zxOD92kDruxoD/CBJBwWEJINSkRCNDZvwhytQTGtoO5v6oCEa/QnJLUugYjBwehHDIMg+YLptbZ6gpcNOhb6IZNhfykKWh+5QWEnWL49pSg3Ecm19H21ozC8WpMO9GI+BRZlw+Ar6IS0jgtpAYjrF9/ycIO48JFzHeURj3CViFt5li5HKZTT4csIQEwmpjdmcyoR/wJC6AdPgJiXRzq3/sY6vwcGCaMg3HkAPztP1HWnfpW4fq2lS0HTlVuXW1DXYWXXaYs8CIjX43Vyy346hNh5GBedhIkFjsSUoSIw6SX4+GbCmkSg21vuP35cjjcYZw/PwnPfyHUdv5xXjoThH35eEUrM4qubPLjwrkJqGo0Y8YI4X5jfpn2fVZd9QSZSNPls9UWFqU2BoKoNQcwMEOFbWVuvPZlK4sSRTqA/ARe2WFua54RoMW7lx+fiOe+7BitoAUSK7a7cN6sBHyzwY47XqvBdack47SpRny20sIEkHILZlsIdWluUFgZLYpi8WJhBOOOCzMxJFt9QO9Qg1aKMQM6klEUDRekKVldkbpLU+Pl2FHpRaJeysYkTp5kRDgSwUcrWqFTSxAfJ0ddSxBj97FQo4h47U4HWmx+fLfZioA/gInJZVjdWCCkVkURNATS2kUvBnWDUuTr9ofYfXT2Ru0OqUKPnAXPMMcXibzj35nGGfyOatYkQxEd1e9cdWsRCbhgHHwGTIPPQGvxBwh1qgvGUJgGstuTUTYZbmszpkKmPnxOREcSbpvWC5Qr5o0xhwY/1Vw2uZEzRAV9vBSzL8jA+u9bEZFqkDkjF8edYkRrtQt7VtbA1eKC1miCdvQ4+BcsRMjjQfl3RQhBCU95NUSnC84vLW+/gdZaLzZulCFX/iYG3nsbFJnZaHrlBeQu3409wVFtf10MhyUMQ81y7H1zFXYYz4VxQCqKfm5GU6MIc5RvY/QFE2D5TDAiliUkMdGWJyZDUTAA/qoqGBed1v5cQhahY1CemQXTguOxd7sbn9+5F2kBN4ZLX4EiPQ2q9DRUlnjx1estmJRzFUrKXzqo12nYBD02L20E3FZs+rQJiTfMhFYrHKClMhEmXLYIcFmgz+qIzhRyITVYUuVBvVmoe5bWedt/vs+kQjskeJWNfnbQnz/OwC4x7jw/HZtL3Zg6tLdk8f5QtEbRJKUiaaktua5YHG27JimlKVdgL/wdfaOiKDBJhFmTdEyQ3LKutT6NSgynN4I1xUJtcekWB0waMZZtdbA8rBhRJAySoa7Nts/fyb6PItSeiO0J7amGmp7Q4dtJHqaVjT7kpipwyfwkJkpv/NDE0sDk83nX+elQK8X7idXqYjte/Lq+0zVSrG3IZR66xIxCLaYNz8ZjH1eyuUCh5ihClroZFa4ElgbdtPhejJx1NWx7F8NZu5pZpMVl7b/AllKczHqn82uXOhbZScNR8c1ViIbCbQPuwitPQmjb+xWc1cu63IZ2CSoThiBp1BVsxIJs12TaRft1+h4rRHkk2DtcBA8dn73YjKJ1LsgUIuhNUpx9QzIqyoCitS788L4FkxcYsO7dnagtMmP38lpc+vJ81hSTcN5FaN22G/6fG5kVm5i6KNoONpox4/BeRS5cUSNqwgMR/9knSLnqWiRfcQ1O1L+HSYvvw8/BM1EVGYaUYDFaP3oPOzwTsLFcgegmivCEqLAmmIdxnfxEnb+sbY9cM+/+P7i3bIJ37x6EHHYW1WbdchNc27bDMG2a8NxeaobNq4UVU1Ag2YbGPTbsXalgNUiaUcxLORt/z25Gy8q1sKxcg6jHhfSLL4RuYL7wQYxFJWIxkjOUmJG6Cl9Xj8eGUgmkX9TjxHMykZisgFYngZG2pSd2v8C0IEOFmWP0sLvCOHV6AsYPiWMNGT2NOMwerWeXnkSgsxD0dABpdYZg0krbo1yKqv56utD1+MwXjVi1wwWtUoTzZ8ezRpXvNjqELpL2OxEhm2q/SiHlV5TlYZP1iVIJTk0xITdLAasohBMn6rGl1INNJS4WIZJYSGn5wTgRGgNhpEgkaAyH8Y3Eify5SlyQlogBPexGPNAqpX0hwS2t97PL3NEGli49c3oC9GopchIjcGx5HC6ZBkljrmJ1thhqRUfdklZA0X6+kYad2OkZiaE5cbh8obAF/p4LB+L5xZVosAgnMNnyErRKRyJBZkZccA+qf6K1XiTaUbYdvjsR7Al6PJkzH4S5+H142Aol2io/m9UGrXu+aPstakDTQGnIReqkmxEJ+5hFmsKQg0jQi5rldzFP0PSpd7I5wWMNKW+M6RleEzx0xM4jg/4ozA1B5s6SPVCJks1uJKRI8fCVu5GaSqkcMwypXQ/aqtQkRCVyBMMhDL7qTJQW+dhqnYFnnA3d+iq4qoOQi3xwbdqM8ltuQdb9D8H+3RLmaTrX+A28ri+hbPZCmpqGxEgLRC4a0Pdi7HEaNFV6MWv+EGjHj0LLO3GIOBxdDLbDHg8a/vska2yJeNxIuuQKqLKz2SVGep4CNrOHnd9r8jLx5ddqNNVaoTdJoIsDEl3b2GNpXbEOrtJK7AmOxZv/jGDMcU046SwZtjwk7Gkbddu1UCUlIPfkuZA+b0UgIoFKIxxIs3IObNRMkc+lJ3a03evbIsjDxYfLLfhstRWF2SqcNyuebV4YO6Bj3IWaTNj/ZWKcPMmA79rMsQlyfqFfSzHKcde4NMjaRDTXqcCe7X60IIxXJG0dmccBZ+WYYN4QQrAtUKTflspF8KwX3llqcsZJEaK7Mk0Q30XtKIT6N22WDwQjWLfLiSSjDKkmOcYN1GDpVjuLbFNMwomTRinBqVPjWd3N3LSFXafLnMq8OmOMHaDD/12YwxpuOqzZYtmJDrKSVLjj3AF4fnEVS4UuHDsJ81vWIuy1wmeJsHq7Qp/D7MuMgzp2WR4s1C0qGF0LS3QT26I84+DTIVUnsOH3mLjR7sDqH/7O6ov0e7SnMNhWM6RFvceaCEZ5JHh0nCEcC5x+dRIGj1GzuTsSQa87jMVv2JiFmUwcQiQURVOTGNc+PB36lK4iqE4yYup/rkdZsRe7G2VY8o6QYppzphEX3paO8i/XQbH0c4SjUlRZk1F2w/NI08ZDLbYg8eJLYf3iEwTr66CdeyK01nic+uUz0Irs0LmHIO6sOdCNm8jqj+phI6BIy4B+fkfxnzpUpfEJCJlbEHa5EGhsYEYBMXyV5Zhqfw2pMim0IhsUlWboTFPRBBPSchW46B9puHDin7AyEMQTV1wAV2k56sPUvCBC8QY3Zox1IOgUGoI2PfQMJtx3E5JGDMLfHg6ipdGHnAH71/L6ClRPFP7vxd3/EwatU4wyNis3d4weE4dokGKUYtrwOGYlZraHsGiygdXbyJGlO8hHNEbbYgLAAZSHfKzJhxg9QM0acNbsdArbm0UizE3W4SWaiWmjytd1RdKviQQ/WG7Gkg0xp00RHro0G6/8XYja90WdPBISpYkJTWeB8NurYN7+JuQhL/QDTkLQXcA6L5Xxg6DPnbff/ejUUtxyduxv5AG5E9hXlt2fMfFJGH4xi9QO5qBOURsNtYva7OREIilzjKEdgpQijTnJUJS4r+E1baEgASSoi9XTuIXZrZF4UgR5LCLtI8f5vvEo9oGnQw8dCpUYo2fEsQvx+kPC2WVzbQAX3ZyCZR+3YMQ0PeKzunc5aWyS4u2nuzrX/PSxFXUVflx440Q0ebehtqgZ5bYRMIkaYXLVQSX2w7liGYL1wgH6++8VKKrSQI1LcI7icXh3bIe7aAdE/7gDlvffYrOMvoREGE86hf1+xOdD08vPsfYaSUIi3Bt/YU03uf99of0x1N73T8GPlB1vRCgPj4DREMEVl6che5AQva01N8JsNuMmcSHWJvwNWcp6qFpaMOm0bMSPzIAuPxuOsiqE3F7s+W4dyn/ZhdxpIzH81ONwpKA5PBrF6G3u8PRpRuypFaLymD9MozWIN38ws83uj3wgNFxQgwk1ttCoBEGdm9Q5mp3c0fVINbFfSlxwNoSBFECVDfhtQsL6xJFGLDDp8ZnXCrlUhBMnGXDd0xWseYQ6OBeON2L2cAO+3mNHXUCoP1oCPbcBB0IRFFUIB/rY86M5QtqLmJOsbK8lkr7SpabFj5yU7kdPaON67gnP7Xd9684P4DULM3lNG59jKUxnzSrhUr0SyeOuZ8bWB8I06DR26U7sSLDEsq4ZAkvJx2xbBG2Zp3k+v70Gll0fwFBwIlSJQ6FKGNrr3yOXGXKgoTlCv73NED4aYWMXMbeYYw0J3yfY988QjjV8nggC/ih0RikWXZaI3KEadjl4omzljscZYfcVEUuxUXkOdopcsEfCqEM+MkQ7URkdjMaaCRgQbUG8qAEN1cIB0gsyyQbc0ONL/58ReSCKkw1RUHWMXG4CdbWQp2fA/P7bcG8S6iidXWzaHwVFE7G0GtUSo3FYGTwd2AmoCzztA/cxA+2fPjKjtTEIG1SYp34LCbZZkChOwbC/XIrSD76CVK1CdXkDgl4/yldsPWIiSP6bryxpwZAsJf55YXq7UJCryicrLZg2TIdZo6jeKDilECdN0iMYjLJGmJkjdUygYry3zILCHFW7CNLPft7mxJ/md4jgB8ta8WWnDRGXTE6EzxhFvE6KcSxNDjaEH0tXUtem0xPGoilGtmGeoAH9mAiSj2dPvPF9Cz76qbm9DkszkTe9WAGPX3CWOWtGAotWP15hRo05iBe+bmQD+MMOcitEbJDd07iZfa1OGcUEyFG1lH3vs+xhq40MeQuYMTUJFkVn5PYSDQUgj+u+5tuZxvX/Zi4upsJzYeqUIvVbaKsI2OB8xddXIkI2PNEQG9CnTfI9ndREwgFYd38mRLTxg+Ft3gZt2gSIJVJml0a7A49VZLLft87sUNEn1YZHgoeH8mIPqnYLqTSnvWsLfigUxZ6tbqTlKLqYYlNUddpVifjy1RY2O3feX1PQXBPA0IlavHJvHar3doiTWuKBR2TCyuBpCDYr4ci6HufN3gXZ/4SOySgkqFRMgthnhw/CAbbGmQ6NtAGN9TL47rwNmXfcg+KGdEjD2UiVVkNsNCFiaYV+bsfuNjqAyrNzEKiqpNNJOANGSMVBBCMy5lXa/nttB56J8wxY+kkrBupqIQkpYRwt7E6UqVUYcunZ7GttURl2fr0KOVNGoGhDNYq31GLq3EHIzOt5g8OhpqhCGEuhvXqUgqToi/hgeSvbjiCsC7IxZxRKb7p9EZw+LR4+fwQXzUtkkRSJfkKc4BHq9oVx9YlJbD/gW9+3QCoVsZRpTND+/Wkju98YVErcVe5jQkkcP07PRDe7bVMD1RgfuzILTdYgBqR3COmNmcl4r7EVjnAEpyf1HLVQsxCzm2tLh/qCESaABDXv0OMflqPBk58IkSydMK0rcWBojoo9b5s7hFOnxPe6P5B2/NE8XjQSZgPm9B5w1K6Fl4bSRWKok0aiZtntLHVpGHAySzVW/3gzywGnTbmdpVl7gwbhhf9v7SKCCSMuga1sCUQiCezl37ZfT9Fd7aoH2Nxg0pir2dygXNchts7qFUwECXKhMQxYhPih5x0RB6I/ilhKnItgL/BI8PCQW6hC1gAFAoEoBo/pGgHSLsA1S+zQ6MQ49aokvP+UMOt20p8SmIgMm6hls4V0CJt8vHAgra/ssEM769okSPeWovlnICHOgwaHEjkj4hA39wREP9vKvCUpbitNPgMXXeBB/Ttu7C5VY2P0BJT5R6IVGUgT7YHq/7aiLEJNDgU491INEnP0SNC6IE3smsLK/Of98NdUI6xPw+vXViEaFWNIjhUjp3XUh2KR4JSFJnYB6GdCynVfUofnswvx+O1fMWOBUDCMC6/b32D8cHH2cfFsPdXIPHV7cwsxtVCHvbU+FsmRawulPV+9SRjO/mqtFe8sbUVeigIPXiZ0PdJGhaVbnUxEalv8eHFxE/v6/vMy2QgFQR2X1PVJ6DVi6FRi3HRWGr5cG6vJAd9utOPbLXbIFgLD49S4NiMZeo2UXTojFYvhDIex2u5CllKOkxPl7aJ3//u1qKj3sS7Ns6bH49Qpevz4IqVmxTDpZLjl7HSW9pw3RhBPMvrOS5GjvFGILDft8WDGcB/e+EGIIOPUUpwwvvf0IOsUbWsQjYT8ggAS0QhEEjnbKEGEfXaE/Y6O7ew+4bnTctz6tY8i4KhhXZtKY0ddMnnsdXDVrWEC2hkaaUgadTkCjlpEQl7m8RnytEAsVSHQtvqoaf1/2P9Tp9wOTZvYSjU0ckOCJ4LPsptdyGItffrd7S4xxxoBGk7uQ8f5vvEo9qGvnCEca1DH4zX3d7/5PBQUztBpY9GOX1ztDi8kjCSC5OP57B217PeGTtBgxGSK5ITbSOU0FK+FfPoCeE8cD9c3IVhXuaCNk+D1+2vQbI/VT6IYOk4D3ZBM5E22oaTUjGhEBBfaRDU6EHFR2hZAiPD+S3SQ9mDscTqccgXN63U8XpFEAmVOLuorfULIQHWZ1igzA5B3s0ng1zBkZDp2bK5l//8jodGIv3TjEjN/nB4DMhS4/VWhxprd5qxClNYLkVxVs5+JJI0v0KB8cZUX88bqYXdH2lOkzTZhczyRn6bA8FwVbK4wbjozhW2EIGhwnl69reUetJLhdliwVtss9uD6kgoM06mRGJJhnETDulNjEcsKuwuwAe/+0IqdaV7cdGYqalsD2FNFjy+KYDDMorlTCjtS1cSofC27dOaCuUm4/216riL2X6JexhxkaGA+5ipzsNAcn1SThpBbaOqiwfb06ffAa96FuOzj2JA71QlpC4QuUzjhCbga4G3ezr521a7tIoLa9AnsPlx161gHp0TRMc9JmyVo7ZFMk4yMGfexYXdV4nDYy7+HvXxJ++8F3U3wmEvgrFrO5gXl+lwE7B1G+X5bGfy2imM2FRpoE8G+cpznIshhnHBhAhumryv3Iz1XgdpSP3yeMOacZcKujW401vjbhbJ4vZtdYoQCwNZVDuQWqmFIMGD9DxVMl1Z8ZWP1QyCWohQhY4ASNXt9+OYtYWv8pAV6kAnL58K8PJJSxRD5xbBTi3obm5Y7WccndXUqVCKcc30Kc4SpL/Xg63etLM1KNDlNWL3EhlmnCbUqiViJs6e+jYeursCld6QhNfvgDqAnnjsGC88ezXxUeyMYiuCJd2uYr+Zfz8lAbuPuXroAADBmSURBVNqBxyl+K7Q89pyZJpaKPH9Wh4PIebMTmHvKqDx1e2PJ+8tamR/mz1sdePSqLFwyP5E5oEzs1BlKKUUazN8XqUTM5vx+2iqkRBnkdDYOIDnb2OoBvgeWhO04Z44Jp00yIRhr0KkDoh6wCJNek7R4GSTZQKhZONCQ40vULgh5kzXE5hkptUt1wM7QQt17L87E2p0uHDcyDkadFP+9Lo851ewbhR4M2rSxsO2tbxe1xBEXQ9nJ5kyXKcydxqB0pS57FosE43JmdfkZpVHJBYZBqefhF7b/jIQ1JnIiiYylZomEERezRbgU5UkUepiL3qap+fbbCRsp2k4otWksZao0FuBYxecTTtx4JHgQXUOhUKjPvFDHOgqlGNV7fNi9xYPdW9y49818NohdtduLF+8ROkoT06RwOyLwuCLMk5tS+yRKRENVAF+8amY1ucyBClTv9rcJIGBKkbL7JzPsvEIV+xsxBo5QQ2vQAR8JB8ecaQNw8mQt3n68nkUwliYhJCUz7fJiobZI///xrWpYmwKIKiiyELU/ns72bAZVLpINQ+Gyh7FlpRPLv7AiOVOOmacaD1hzOZAAsufcGsDOSuG5rN/pbBdBqrd9t8HOBIvcX06aZGQzby32IAZnKeEPRHtcOhtj0143nv+qiaVGrz8lmT3e06Z23VMXG4244oSuqeKpw7T4Zr0dU4fpmJtK5+0KB0OysesZurQJSCqWobEwKPQjtZ2fFNu8OC4YwiOV9cIhPBNQt4iQa1QiNV7GBHXqcXFYbnOynYYDB6qxe51w45+3O9God7Ixj6euFUYQXvu2mTX40HOiHYG0IDdWF2UD8L/NoxymwWfB01zENrvrMiZ325zSuuNd9sTih13I0qnJY67u9r5owwOtSwr7rJDrO1YsBVyNLDokaKaws40a/dulz7iHzQF2WZfUhpBajbJUrWnQ6e0jFse6CMp4JNgzsReHXiyttu/Oax1r5A4RBCproKrdiaRzarGlPsRSofPPjYdIFIXDGkHWQAWzs37/v8KHm2YRc4Z0PbOPT5Li3L+kQtW2gYB2BJ51XbLw9RghPbbgvHgmdJOP17O07diZeqz7wd4+7W9MksYW0sPlCMHnDiMcFSPq82LoOANqysKs3T8hraMxxubdi+LqT3HKiRewNOn2NUItiOqbiZ1+77eSkajAjFF6Zpl23OgOofnmFxs+WNaxMui9pWa8v8wMmh5INsjQZAvikgUJSDTIsK3Mg+NG6vDCV83wByO464J0xKkl+N93zWzDwupiF0tRHkg0Y1DEd9HcRFw4J+E3N1cMyVLhqWuz2O7AZVudsHso6g5i+CgVikReMGd1D7AnzYO7ympgoXZVChYTNdju8aDY7sWXa204fZoJ0ww6rLW7kK2UI14mbW+KiEHPlXB5KV1KhWMRas1BvLesFUu32Ngmjaevz2v/vd+CWCpH1uyHu1znaSlGw9pHWbqUrS1qa2bxtOxkkZw2dQICzhqIpAqkTb4NErlQQ6c6XdbcJ9lMX2dPz7Cf3qvC6yBTC+/tzlAXajjUZqknEjMhJfeYoKse4YCT+Ykeq6MQ+xJs29/FRbAXYi9OLHfM+WOYsciIMTPjoNZ2CB+lENU6cXtUlzdU1S4gpuQoi8qS0uU48eIExCfL2Pb4D57uMGMm9m73sUhsygmCUNDBefT0rt6Y5GEaIxyK4tt3BTNmOo5r4iTYs83TLoitjSEkFRhh2SIcVPbuCCDQ1uT46QtNuP5fWSzlOi3/Aeys/haX3ZmOvds82PSzgz3Gzot8fw90onDFIjJiFqC5vcfeb8COtugwRptGtNflYpsktpZ5mBVZRYMPNS3Ce/2HTXYs2WBrv82wHBVLbbp8EWaU3RsktF+steG0KUacMcOEN75rhtUZxuUnJDJbtV9DslGOGSP0+HyN4DZDK41OzzBh98Y6BISsHwIywEIHfnqsQ4GNfjdtlmJ8tMKCgmwlogbglcG5kNOQYqfOwFEFWowdqsFFc4UoljbCj8xToajCh4GZShjb3oPM27MnE9bfiKtuPZq3vsxm8sJhPyKREERiGav10dZ24XdWt/++r3UXNKnjEI1GUL/qQTZqkTLhxnYRpEiSUqdkji2R66Dbx16NNtFTGrZ9WF6mYWIqVRrQvOlZdh0N1SeOuAT9AR9Phx4Yedvi2NiLxfnjoGaWfdEZpPA4A6wZhrxG7ZYg8ySltGhtmZ+J5q3P5eD4C4SDgkItgivm1CUCFAoR60yl7lIaxaB1Rb1BoxjUDLP9FxdyBqkwfJIGn77Y1jAjAqafpMcHT3e8N2ICSLQ2BljtctnnFiTqRmBG4TD2N0mc73k9H5RpOpitEr8FizPEIrvYIZv+SmayHENzVZgwSMPcW2QSEbZXeHDKFCMsrjBbJTQyX414vYwtx6W0bkwAqRlk6lAt/v5CNRt0v+XsVPbYC7OULDoz6boKG0WNVItdtcOBEfm0GkkwOUg0SKFTSVhTDHWOHix0/7TBodURwpnTTRiiUeH2/DTcu6ptBVBpp1+mYU/aRTsCwHahV+mJrQ3w50Yxy6DD1RlCdBQzxh+Zr8P1p3ScQNCJ0e3nZbR/T52lJMRkn3YobehIsBo3/EdwFWeCpEVc9iyWAqWmls6QI408LgMK0wCEvBZqO20fxHc1bGTCSFh3fw7r7k9ZhJd7wgv7pTObNz3HRiskCgP0BSfCXvo1IgEnc4aRapIRcjdBaTy2bNEOJhKMHeePNH2y4BarA/o7DUhzjhxX/V86a5jJGayCwxrCY9dXxWbVGTSA3/n7s69NwRevNcNcH2Sic+ntaZDJRHjsL5XMj/jyu9PZWic6oJM4URr0pXtrWdR3xT/TEWeU4ow/J8NqDrEIjqI6uh+67aT5cdi91QNzUxCJ6TLYaWwjTKuT2h6LD7jvsnK2lLd4gw1lDUshlQoroJZ+ZmF1QWqcmXvm/vW1fbG2BPHZq81ITJXjxIsS2ONtbQ6yzRxp3TTZUP1KqxSzub2zZ8djzEAN0jqZYQ9uKyHNaFsAe9+fMtjsW0Lb2AJB65gsjhAbZTh7pgmrd7S180eAxz9qQDBM65OkaLaFcPZxJiyabMRnqyzssV00Jx5v/mhGsz2MX3a52pfirixyso0QpP13np+Gl75pRlaiAn89I4V5ZvYEeW/++8/Z7UPy7PXVRwHqI1lDikJbfdvUPvZykhDSSUkACGUKgufqNMFPdX6iu1QtzTVu3ONmy3QT9DJMG9a9i9HvgSIu8uyk7kvj4LPYrB+JFtmYkf1aNBxC85YXEPY7kTrpJsh1Gaj84a+I+KzM05OG5CkyNBac1H6fMfcYiiYh2v8kkiI/gjpJE4aez0YnaJdgXPZMJAw7n3Wm0qLe/oK/7bjeV/o9+saj6KUmyDnyUI2uYLgQQbTUBboInj5ewup71PgSg7I+1IxC4kh8/koLS5fGxi6Wf2HB3m1CKvOKu9Ngt4RhbRZ++J+bqhEJR3HhzalIzZazdCu506TnyXHSJYkoK/Lio2eFmTEiZ7ASlSUd7xMKNCgSpN6qr4vPxdDEv+CR6ytwxtXJLCVLj33LSke3IkhRyicvNWHPdi9O/lMi6sp97HHSZdzMOLaJ44lb6fEBl9yUCrlKzMR47FQdG0TfW+tndTz2minEXQSwO6ibs7MAEnEaCa47pWNMgiJBGg2gVOsbPwgdtWaH8FqRddr6Ehc+WSXMt117chIz0ybW73YhN0WO0voADFoJE0Gqq23Y42admXRpaA0io22mj2qJNc0B1tDSeUaRhPSdn8zYsMfDBvQnjtFClShBYGYEZypNyMiU4om65va5PCaIQ4A0uRQTPTpUNvhxUW7CQXmHPv9VMxNBilyfvo7U9NBDKcmM4+5ndbh9a3AkjkTWnMfarwu4mpgAEi1Fb8E08FToc+ezCDEGWaOR0TaNRpAgtha/x+6fmmwkMjWSxlzDxi9IZElwUyf+vcvf7U8C2FkEeSTYC7EXJxY2c/oOlNYcP0eHDT8JLfS0wZ1caPLa5r/oYP3JC01sj2AMalahEYuxs3Rs3CImgLHUZ5xRgrRcOVrqqbYnCOe7/27EjY9lYu13diY6deUBVie0m7t6U5JA0r6/MHMjEQR4yFgNJs7TY+iLlyA/YSbs5jBWfW3FpHl6bF7hwLyzTSgt8uD7D1phSpRhykI9Ni93MsccmznM0pnfvtsCS1MYEqlQF01IlaGxJiBs1mF1SA9W/mRnoutxhTHrRCOG5akwYYgG/mAU44ccmoYuivDmjxXWLtFePUqp0mA7jSGcONHAambUPELaQw4yFCkSYwZocMI4PVrsIQzKVKLREkKCXgqrM4RdVV5kJsnZCEOM175twY+bHWx+8MFLO2ZJaWt9bFzi7Z9a2Y6/5wflIEIWehIJXOEwhjuUKPJ0PWGdLtfhg48E8ciROXDOzPgu6dDOkaDNFYJWJWGCy94Th9kthVKbNNNH6UxVvJAl6Aln9c8d34QDzA+UiMudB0P+QkhURkikSiaAZNwtkqngqlnJfsdnrUDmrAdZqrXzlov+TpCnQw8+EuTp0L4HHZRPuzIZo6br8L9/NbAVTdQYE6OpJoCmGuHkhQRk+GQt5HIxFv9PaHTpjClZitXf2LDjF2HmkNxqKE1KkBi+dG89Ji/Qs4F9SodWlfggV4kQnyKFzRxikSV1o06cG4eK3V5kFSgx8zQj4oxCmlQs6kh07NkqRHR0DCZRXLnWxmYh6bJzg4uZBMSgQ7CERUNhdrC+7gFBFDLzlRg+QYOi9W6s+9EOiUzEok6VWtw+e/fXszo2XRxqBmWo2IVEg4bhSaDIT/SFG3Pa96NTpBcIhlmU+MMmBy6aG4+hOWomepT6JLF55MqO1n7izR8EASTqW7ueeFL0GDPpJpEifVK2RXEfNbXikxYrJsZpcUdyKh76ugHkhpc8XIIFKQYsVtqZMHceuegcCX622oLPV1vgD9KohxQPXZ6JSUM8GJx5+OYtieatr7BheEflT8g76dUD/HbbKyuSQJ+3APayb9i3joof2IWQ63OY20yEOkTbbxNFwF7B0p4098fpIHZc592hBxEJchHsu+QOVuO253KYk4yxU6MLRUxJGTI01waZSG1d6UL+sO4PahQtWpo6hu4HjtZg2yohZUlQdHfixYlsfrGmVPjgjJqqw6lXJKF6jxeL3zTDaQ1h9RLh4FNfHsC0k4x45f46oWtVOxqbS9/EmIKLhTtsO5rTMZzGQWrafE8prRlxU8oR0BokOO/GFBZRLvvMipFTukZ0KZkKJoJiiQjX3Z0OnzeKvEF/rL0V7RIUdgTaWfdo585PMt6mofI/PVbGvqdGnY9XWllDzkOXZ+yXfiW2l3d0s2Z08o0ltnZq9InNK7bfziXcbofLg5QyGdDWL3PahHhWQ6R6Im2JyExU7CeCNAv84c+W9vtutIaYMffkwq5dw4cDuTaNiaBM29GY0xOmwadDoc+CXJcJmS6N2aPRaiZmt9Zl2L0zbc9KJGHbLjhd4ZHgQcAjwaOnVhhbPhtDJhdj9PQ4fPdeR+TntHdEFyOnajHrdCM2LnVi4Cg1VnxpRWOtD+Nm6lG9l9YDddzXeX9JYXU5c5uPZEaBAidfImw0yChQorFK6ASNkTlQjrIdHiaAdnc99tT+gEbrDgSCL8EQl4yU1BQMGJSD8l0K5HU62I6eoWMD9BRp5g9Ts/VTxKW37S/esxYZkZolR1KanDXMHAloXIGgBhlaMrsvlB7962kp2FbuYd2d2yu8oAR0ZWOgWxG8/IQkPPphPbz+KOvG7Ay5udBqJ2r6oSi0M5ekJeIbsw1TDTrI1SI2H0n1vEkp2vYoct/5vpgI0laMhMJ2xztm99Z5I/zhhBxcyDhbpt3foq67Rhpt+iT2Na1jEjpIxZAb8xGwlvX+d0Zeym7P6QqPBH9FJMjnBI9Oxs2KYzU4cpchyPWFnGKyBioxY5EBSrUECy9SYMd6F0qLvCzF9vNnHet8jIlSXHxLKlt/9PK9jezniWkylrp889F6XHoHrRkCdCYJrE0hZA9S4uJ/pGLp9xtx+00voqRmCepat0AiliE+bgD21v8Ef7DDBozSpEpFHEy6POjVafh5lxrvLBFh0KBBGFE7AuPHj0dGRkfjQ2fI4Hro2CNr4HDciDgMz1WznX4keN1By3PpQvsJqS5IIkbbJHoajqdGlLIGPwqzugod7fN7+W+5LA0es2WLEW6NwvpzCM2Dg8yV5vV/5LEom1xqemItLeSlzlt3BKY2AcxPleMvp6Uy+7c/AmqOoeju10JdnG1fIXHYxWjc8F+Effun+QlN2iQY9lmcy0GX4zpvjOkFLoJHNzTcXjhe0948Q96i5Tu9uOyutC4zepYmIcKLRQMxaJg9OVOByt1tK5iiQu2P2Lvdi89faYbbEYYoCji9Tfh48Tf4x7+fhtPpZMKXmzIDp05/GH+/6yxIIibsXO9CfW0rHP4aNLdUo7nZDAfWwmy2wequxN76vVi7vaNZJ4ZSqURcXBwSExOZKObn52Po0KEYPXo0u9DPjwRf/2JlWyDOn21Cor73aJTGHK5ceOAlslQrJIu27qAVSt3x6Sordlb72GV1sYNFcn8+Obk9PUsdp/sKYkOr0EAjYrMVAmMGavezauuLxOXOZauRaAGuKmEQ4oecgZbtb0As17GtEMqEoWje9AybQexvHZ+/Bp4OPQh4TfDoZ8F5CTAmythMH6UZkzLk+w2pU9MLjUM01wVY7TAGpUVt5mCXsYvOQrn0q1KU1H6HXdXfoNa8iZ3ZJybG4/zzz8eN19+GpIT0Lh6iU9jqJ4rsRrLuVUqhyhXi/c5Ot23bhs2bN6O4uBilpaWora1Fc3MzysrKsHPnzvbOxhhU11Kr1TCZTEhJSUFOTg4GDhyIESOEaDI7W2i5P5SQ48xbP7a2L9t9/i85h234/0BQd+nWtozg3jrh7H51sRNzRsfhztdq0WAJIjdFgYvmJUAqFmFvnQ/VjW1dpJ3EkeqVRwP0PqPNEzEopUqXzsi1yfBZ9iIu68gsZj4aCPAtEgeG26Yd/ai1Esw81cQuJDydD9Qlm92wNAcxYY6e/ZxMujuLIDXU/PiRha1niqFNsWDF6sUorvoGtS2bIRZJkJk8GpdffjnuvvuugxYcehxyhajbEy8SLrr0RH19PTZu3Ijt27ejpKQElZWVaGhoQGtrK+rq6vDLL7/sdxuFQsGiyYSEhPZosrCwkEWSY8aMYSL6a6BN62qFCB5/lM0E/lLi+kOaSbpjTXHHv1mM5dudbDi/rq3LlFKsr3/XgurmADuRCYRjTSPCSQg14Z408dcZfPdlaO1S59VLnJ4jQfps9AX6ZNWWzwkeW3QWQHNDAG8+KmwOp4hsxslGtr3+6nvTEQxG8fUbZjZmkTlAiYamMmwu/ZzV+KqaSWCiTEwuvfQS3HrLHRg4KP8P3cCdlpaGRYsWsUtPZ7g7duxojyb37t2LmpoaFk2SYJJw/vCD0FYfg0YFSAiNRiOLJknMY9HkuHHjkJtL9biOqJVqgPdfmoEH36mHxRnGs180YfwgLavXkdXYul0upJpk7XsDDydqpZj5mlJTC1m90fwiid2+UP0ydj35b7LnLRIh2ShlmzGOVCTLOTLwSPAgiJ0h8MaYYw/qvJTJRQgGosyTNAYJITHvUg8euv8RnHD2F7BaadhahNSEfFx88Z9w1113YMCAvuuxSCdvFN3RpSdIECmapNQriWJFRQWLMCmapKhyw4YN3d6vTqdjtcn09HTk5eVBmjgWLt8ADMzPhNUVZHN+Tk+ErSKieb5nb8j51abZv5Z7L85AWYMP+alK5mJTZ/bj1SVmjBukxtBsNQZmKJnpNwn05CFaJpirTQas+xC4dlEKLr748LjCcPo2obahXB4J9gIXwWMXEr6/PZnFGlvS84RohSKmBx98EIsXL2ZiQFBkdMEFF+DOO+/EkCHHzobtpKQkLFy4kF16OkBQ/XHTpk0sqqTaZHV1NZqamtj/d+/ejZ9++gnAy+z3vwbw7M16aONzoNanQmNIh8aQjPutwJiRhSyapBOH7mzKfi9smW++pv1rqv/15PcZi0xL1gu1WknMHobT7+DdoQcBrwke2xgSZLA6a3HZZffjq6++gtkseGIaDAacd955TPioC7M/QqbClAqlS0/Q60UiSdHkrl27sK0yCJvDB7etHq3Vm+D32rH568B+0STt5qR0ciyapJOLUaNGYezYsey1/yOINRcdDlHmHF0iqOA1wZ6JvTjcO/TYoqqqCg888AC++OILtLQIq5H0ej3OOecc3HHHHb0e+DkdkJAtWLCAXQKhCK58spxZjxEGjRhWVxgBjwWnDtqN4uIdLNKORZPUwEPR5bJly7q8pCRKKpWKiWFycjKysrJYBDls2DAmkiSYh0K4wmHB3JSLYP8lxNOhB4anQ48d6OD70EMP4bPPPmP1MIK6Jc8880wW8VEkwvnt7K31tQsgYXNH2ID7vDGDMXfMxB5vZ7PZWDS5detWFk2Wl5czgaQoM9bc012GJhZNUpMQNe2QOI4cOZJ11dKoyIGIOcb8kQ1NnL5FkBtoHxgeCR7d0HxdTPgaGxvZddTYcfrpp+P2229ndSrOoYFqbXqNGHZ3h99cfWsAc8cImyd6giK+OXPmsEtPYkURJDXxkCju2bOHRfIUTVIjD81OLl++vMttSNgomqToPhZNFhQUtEeTNBrS2yolTv8gxCPBA8MdY44+6MD4r3/9C5988gnrciQoajj11FNZqrO3+TvOb4ccYR69Mgs3PFOJQNsmDPIL/b2QSJGNHF16wuFwsIiRoklq5olFk5Tqpu/p+u7ul7j++uvx3//+l0WTgwcPZtEknRxR4xDn2CbAbdMOPhKMnTFw+iaU3qSuzo8//piJIKHRaNgc3W233YbJkycf6YfYL9BrpPjX5Vl49ssmGDQS3HBa8h/ydymtPXPmTHbpDor6aAQkZjBA0SSNgFBESenY1atXY+VKYfde52iS7OgomiRBzMzMZLVJapSi0ROqG/eVjeSc35cOPVK2g/vSJ99NsReHN8b0PahmRBHfhx9+yNKeMeE78cQTmfBNmzbtSD/Efkl6ghwPXda39tZR1EcOOXSh5ifixRdfxDXXXMPeP3Sy5HK5WMS4ZcsWFj1SmpXeV/Q+o3EQEs99IRGk91x8fDxSU1OZXR1FkySQEyZMYKYDnL5LqC244SLYC1wE+xYWiwUPP/ww3n//feaAQlDt5/jjj8ett97aYyTA4ezLvjVBSpnTiVNPJ0/0+/SeowgyFk2S+w7VmintSlElRZT7RpOUTYpFk2RXR7XJWDRJqde+ssGgP4ugtI9E9H3jUewDt0078lC66pFHHsF7773HDjQx4Zs/fz5uueWWHhsqOJxDOSdIv0dWcnShjuLu8Pl8LJKkC9nVxaJJEklq7ikqKtrvNmR+TtEkdbTGokm2SmtE76u0OL+fvlbm6tMi2NderP4gfI899hjeffdddrYdi8rnzp2Lm2++mc2lcTi/h8MxIkHvUao/91aDplEdGgmhaJLSrDHzc8pyUKS5du3ao2aV1tFOsK0m2FfokyIYO0vsay/WsQh1+D3++ON45513WHcfQamk2bNn46abburR3ovDOZocY2hcgy6nnXZatz8/HKu0qKmHj4LsT18LbvqkCMbgInh4oGaEJ554Am+99Rb7gMeib6rt/e1vf+txSwKH83vpq3OCR8sqrWOBEBfBo/fFOprxeDx48skn8eabb7KzXDqrpQ/+jBkz8Pe//x2nnHLKkX6InH7A0ewd2hdWaR0LhEKhPuUY1KcjwZjPIOe3C99TTz2F//3vf+wDSQcgsr6aOnUq/vrXv7LU0LH2AeP0bY5l27Q/cpUWRZNkOUhCSR22RxPhPnZc77MiSB8Sng799VCnHDlxvP7666wBgISPWpEnTZqEG2+8EWeddRYXPs4R42iOBPvWKq39zc8pmiS7ulg0SXZ1h3OV1m+FR4K/8sXiHBhKwzz99NN49dVX2dllTPhocPiGG25g64n60oeA03/hWyQO/SqtWDRJ11O9kn62L31llVbsPdCXMgF9OhLkIti78D333HN45ZVX2jvXqFuNzvzIl/HCCy/kwsfpc/TVxpijdZVWT68xiWMsmuxLq7QIHgn+ChHsa7njIw29eV544QW89NJLrPBOb3YSPqpBXHfddfjTn/7EDy6coyId2pcigWMNEiuaZ+xtMfWRWqVFcBE8SHgk2PGGefnll5nnIjlfxISP0hh//vOfcckll/QZ+yEO52AjQf6ePbIcqVVaUqmUp0MPlv4sgvS8X3vtNRb1Ud6f3pB0dkd1AjIfvvzyy/lBhHNU0t8bY44WxIdxlRYdzyiq7Cv02RCiv6VD6Y1BHZ3PP/88e/PQc6c3DJ1FXX311bjqqqu48HGOeo7lEYn+RtyvWKVF5vs//vgjM+og7rzzTvQVuAgeQehNQq4tzzzzDDP/jQkf5fKvvPJKJn7c7Z5zLMEjwf41rvXMM8+wrBZFjVTGmTdvHptdplpiX6HPiiCJwbEYCZLwkUE1jTRQKiFWJKZ8+RVXXIFrr72WCx/nmIWL4LHPjh07mBkHdaDS8Y5qhGTAf//99/dJ0/E+K4LHUk2Q3ggffPABG2Kn1EBM+Cjfftlll7FZvr745uBwDjV8ROLY5fXXX2dCRylQghYd0/c9rcDqK/RpETyaI0H6sH/88ccs9CcrJHK/oedETg7U0UlnSlz4OP0NHgkeWzgcDrZf9O2334bb7WZ9CyeddBI74acRiqOBPiuCsS6iowl6vJ999hn+85//MEf5mPBRmzDN8NGGhv7oGs/hxOCNMccGGzZsYMezNWvWsBOb+Ph4ZsR/1113HXXlnD4tgkdLJPjFF1+wDQ3r1q1jTi4ECd9FF13E3hhHm8Eth3O44JHg0X0C8+yzz+LRRx9luxYJGtt6+OGHccIJJ+BohYvgb2Tx4sVM+OhMyO/3s+vIjy8mfNQ+zOFwusJF8OjDbDazBdsffvgh6/ikSI/qfP/+97/ZjsSjHS6Cv4LvvvsOjz32GFavXs3eDATlvS+44AL84x//4MLH4RwA3hhz9LBixQomfmSvRicv5AJzzz33sBrgsWR20KdFsC/UBGkJJgnfypUr24WPVpWcf/757M3wR7qvczhHOzwS7NtEIhGW7qSGvsbGRtbTQJZnTzzxBFvAfSzSZ0WQBiuPlAguXbqUvRHoTMjr9bLryAfv3HPPxa233nrQRrEcDqcrfJVS36S+vp51rFN/A/U1UOf6xRdfzMSPDLOPZfp0JPhHLtUlwaMCL5nC0kZ2IjMzE+eccw4TvmP9jcDh/JGRIJ3kco48S5YswW233cb2EBK0a5COd7SV5lhKeR61Ini4u0Optvevf/0LP//8M5txib0JyKfz9ttvZ1ugORzOoYOnQ488FOk98MADbB9pa2srS3lOnTqVNbrQSqT+Rr9Lh9L83kMPPYSffvqpXfhSU1OZc8sdd9yBlJSUQ/43ORyOAG+MOXKQk8tf/vIXfPvtt8y1SqPRMH9iyoD1596GPi2CsbPGQzHYScLX2cWcxI4G2MnNnBZEcjicww+PBP94PvnkEzbEXlJS0t7RTsc9WsnGOYYjQTKnfvDBB1l3p9PpZNdRi++FF17IUp3U6MLhcP5YuAj+MVAn+913380WctvtdlZemj17Nuv6pPVsnGN0RIKKu2TY+v333zNPO4LqetTVSWc+NNrA4XCOHDwdenjZtWsXbrzxRtbhTj0VZNpBXZ8UEHDLxmM0EqS1HfQPTHlum83GrktMTGShPgnf0WLiyuH0B3gkeHh48803cd9996GsrIx9T0b9//d//4fzzjvvMP3FY4ejsiZIZzvU3UTtvVarlV1HBq60nYFy3/n5+X/wo+VwOAcDF8FDB/U30DgDCSB9TcdM8vCklOeAAQP4G/JoF0FaydFZBHfv3s0ivq+//hoWi4VdR0Pr5NVJER/t5uNwOH0bLoK/H+p3oA0Oq1atYtkyo9HI+hwo8jvaNjj0Bfp8TZCiOzKrpnkWgv7BybKMIr4hQ4Yc6YfJ4XB+Bbwm+NtftxdffJHNNdfU1LDrqMGFAoNFixbx9+CxKILk1kLGrW+88QYTRJrlmz9/PhYsWIDp06cfE+7lHE5/g0eCvw7Ket188814//33mYWjTCbDaaedxnaW8g73Q4MoeqiG8Q4DlNem9l7Kd8c8PGOQywF1O1FKlASTfpd2W02ePJkZvvK0AIfT97jiiivw6quvss8z+VNyenazog0O69evZycO1OxHg+5kcUalIs6ho0+/mnv37u2SDqDvaX/fli1bWHNMVVUVWlpamAsMXd8ZOmPS6/VsKD4nJ4elDkgcp02bxl1hOJwjBE+H9v7akHUZmVY3NDSw68aMGcPM/OfMmfOH/Rv1N/p0JPhroLnAtWvXMncYmhcsLy9nzujUPRpbgdQ5iiTLIOoopZQCtROPHDmSRZGjRo3iZ1oczmGCavxU4iBzfB7RCNDKIlrE/emnn7IF3QqFAmeccQYTQ27jePg5ZkTwQGdYFDlSimHr1q2s0zQWRVKqdd95REqlUhRJdUjaFl9YWIgJEyYwk1m+TYLD+e2QVSG19NMgd3/ZUtAT5F9My7gps0WQfSOlQGm4vb+/Nn8k/UIED6b4TOnUjRs3oqioiEWRlI6g4Xs6M+sMvTkpiqQcPdUiBw8e3B5FUk2Sv3k5nJ6hHXVvvfXWIfMFPtog42ryMX7mmWfYSThlpSZOnIgnn3ySHUM4fzxcBA8ARYmUXqVUK52x7dmzh7Uo0xuYtlDsG0VSKoMc2emsjqJIqkVSFDllypR+7dTO4RDk3fvOO+/0OxGsrq5mdmY050ypYJVKxUa9qN7Hl3QfWbgI/k6am5tZmpWiyJ07dzLbIsrxU1cr7e3qDEWJWq2W+ZlSLZIG/KnwTWeANPPIo0jOsc4FF1yAd999t9+I4JdffslWtBUXF7Pvyb+YBtuvvPJK/nnvI3ARPMypD4oe161bx/5P3a0URZrNZra9ft8DAbWMkxkARZEFBQUsiqQll1SLJPHkcI52KPp57733jmkRpJPfe+65hw23U2MendzSbDPN9lHjHadvwUXwCELdq2R9RKYAFEXS0kuKIqnTlVImnSFfQJ1Ox6JIOpukWiSNfFCalbxSeRTJORogQ2ca/D4WRZBOcmmWj9a3UeMPnbhSNyy5vPCT2L4LF8E+fDZJ4x40A7lt2zb2AautrWVNPN1FkVRjoNpCLIqkJh2qRU6aNImvUOH0GWit2QcffHBMiSBFthT5xeaa6fNH31P9k9P34SJ4lEIjHrGOVhr/qKysRFNTE4siKQ3bGZrHoiiSlgqTcQDVH6kWSSkavmOR80dy9tln46OPPjrqRZBORKm29/rrr7Ol3ZSpmTt3Lht2557GRxdcBI9ByByA6pBkuUSdrXSGWldXx6LI7uznKIok44D09HRmP0cjH9S2PW7cOG5txTmknHXWWfj444+PWhGkzxPN8S1fvpx1hlPHNzW50C4/bgN3dMJFsJ9BH1yqPVJHK9UiS0pKWFRJUSSd0VItY98okowDYlHk0KFDWS2SmnW4iTnn13LmmWfik08+OapEkD4zr732GtthSp8VgqI92uBAZtacoxsugpwukIMOzURSFLljxw4WRfZmP0cm5hRFxkzMY8YBo0eP5ibmnP0gOzCyBzsaRJDMMsjRhUY6KP1JJ4QnnngiS3nm5uYe6YfHOURwEeT8qjNispyjWiQt9qSvaQiYZiVJPPeNIsnEnNJF5H9IB43OUST3ROyfnH766fjss8/6tAhSMxp5edLJID1OOsm7/vrr2Q5T7nd67MFFkHNIz5w7R5GdTcy7s5+jKJK8WDubmFM3KzcxP3ah9OHnn3/e50SQTvCefvppPPbYY6x+TtD78ZFHHmE7TDnHLlwEOX/YQYaEkUSSTMypFklRJBkH9GRiTlFkzMScokgyDqBVWNxm6ujllFNOYS4qfUUEKYtBUR/VKSndT+87Emry8qRxI86xDxdBTp+AxJDSrDQbSRZTnU3Me7Kfi0WRZBxA0SPVIsllhxsH9F1OPvlkLF68+IiL4M8//8w2tlNanx4Lpef/9re/sev4+6d/wUWQ0+ehuUdqTSeRJOMAMjGPRZHUsNCdiXnMfi5mYh6LIuPi4o7Y8+AAJ510EjORPhIiSO8jSneSfRlFgNTYRWNAFPXRe4PTP+EiyDnqIau52MgHRZEx+zmKIruzn+vOxJzs5+hrHgUcXqi7csmSJfuduBxOyGmJZvu++uorllWgeb5zzjkHjz/+ON8PyuEiyDm2oYMemZdTLTJmP0cm5q2trQc0MaeRj9gqLEq1cv/H38/ChQvx7bff/iEi+M033+C2225jO0IJGuO55ZZbcO211/KTHU47PBLk9GsorUoCGYsiY/ZztAprX/u5mIk5GQfETMwpiqRUGo2A8CjywJxwwgn47rvvDpsI0knP/fffj+eee445JFHKk0ZyaLaPUp8czr5wEeRweoC6BcmblUSSoomY/RxFkWQ/15OJOdnPkYkytdjHTMy5pZbA8ccfj++///6QiyDt8aSltSSwdPKi0Whw0UUXsREHXgfm9AYXQQ7nNxKzn6MOw84m5mQ/152JOR2MqRZJUWNnE3OqTfYXaOaOVg0dKhEkH1IaYifjBoIaoej7Sy+99JDcP+fYh4sgh3MYoHpjZxPz0tJSZhxwIBNz8mOlWiStwqIIkrpaaXbtWGH+/Pn48ccff5cI0mt7991349VXX2Vpa0pDz549m3V90jwph/Nr4CLI4fzBkABQ+o4WKlPTDhkHUBTZ0tLSrYk52c9RFEmzbJ1NzKkWebQNdM+bNw8//fTTbxJBqtlSl+fSpUvZ7cnY/bLLLmPG1uQ+xOH8FrgIcjh9DNoJSXVIMg6gKLKz/VxPJuZkHNDZxJxGPsjEvK95XdLOvWXLlu0n9L3xxhtvsFVF9DoQNMpC39NuQg7n98JFkMM5iqAIiOqPJJLdmZh3Zz9HERNFkTH7OeqSpI5Jqk/+0cyZM4e5tRxIBOlE4NZbb8Vbb70Ft9vNxJzqiU899RTy8/P/sMfLOfbhIsjhHEOQQQClWamrlbxaKe0as5/rzsScuihjUSRFWGQ/FzMxPxwjH1S7o4W0PYkgPW7y8qTnQN231G17zTXX4J577jmmaqOcvgMXQQ6nn0BRIqVXKYqkWiTZz5FxANUiKdrqzn6OosiY/RxFkTTyQanW32piPmvWLKxYsaKLCNLffeGFF/Dwww+zx0MMHz6cfU/D9RzO4YSLIIfDYVBKlUY+KBrbuXMniyLJfo46MHsyMU9MTOxiYk4CWVhY2GMUedxxx7G/QSMk1ClLUd+HH37IOmapAWjRokWsy5O6ZDmcPwIughwO54CQaFH0SGMfZD9HtciY/RxFkd3Zz9EqLIoiqYYXMzEnNxdaWkvdrSS2dDuqTVLXJ21x72uNPJxjHy6CHA7nd0Pdq7FVWBRF0sgH1SKpwWVfE3N24BGJmFnAo48+yuqEHM6Rgosgh8M5rFAqlcSRIkCqSVLa9Pzzzz8i3akczr5wEeRwOBxOv+XQ90BzOBwOh3OUwEWQw+FwOP0WLoIcDofD6bdwEeRwOBxOv4WLIIfD4XD6LVwEORwOh9Nv4SLI4XA4nH4LF0EOh8Ph9Fu4CHI4HA6n38JFkMPhcDj9Fi6CHA6Hw+m3cBHkcDgcTr+FiyCHw+Fw+i1cBDkcDofTb+EiyOFwOJx+CxdBDofD4fRbuAhyOBwOp9/CRZDD4XA4/RYughwOh8Ppt3AR5HA4HE6/hYsgh8PhcPotXAQ5HA6H02/hIsjhcDicfgsXQQ6Hw+H0W7gIcjgcDqffwkWQw+FwOP0WLoIcDofD6bdwEeRwOBxOv4WLIIfD4XD6LVwEORwOh9Nv4SLI4XA4nH4LF0EOh8Ph9Fu4CHI4HA6n38JFkMPhcDj9Fi6CHA6Hw+m3cBHkcDgcTr+FiyCHw+Fw+i1cBDkcDofTb+EiyOFwOJx+CxdBDofD4fRbuAhyOBwOp9/CRZDD4XA4/RYughwOh8Ppt3AR5HA4HE6/hYsgh8PhcPotXAQ5HA6H02/hIsjhcDicfgsXQQ6Hw+H0W7gIcjgcDqffwkWQw+FwOP0WLoIcDofD6bdwEeRwOBxOv4WLIIfD4XD6LVwEORwOh9Nv4SLI4XA4nH4LF0EOh8Ph9Fu4CHI4HA6n38JFkMPhcDj9Fi6CHA6Hw+m3cBHkcDgcTr+FiyCHw+Fw+i1cBDkcDofTb+EiyOFwOJx+CxdBDofD4fRbuAhyOBwOp9/CRZDD4XA4/RYughwOh8Ppt3AR5HA4HE6/hYsgh8PhcPotXAQ5HA6H02/hIsjhcDicfgsXQQ6Hw+H0W7gIcjgcDqffwkWQw+FwOP0WLoIcDofD6bdwEeRwOBxOv4WLIIfD4XD6LVwEORwOh9Nv4SLI4XA4nH4LF0EOh8Ph9Fu4CHI4HA6n38JFkMPhcDj9Fi6CHA6Hw0F/5f8Bnnb6NA3xmPcAAAAASUVORK5CYII=", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -238,10 +245,10 @@ "id": "bb8e5817", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:28:44.986916Z", - "iopub.status.busy": "2026-09-05T10:28:44.986800Z", - "iopub.status.idle": "2026-09-05T10:29:07.843965Z", - "shell.execute_reply": "2026-09-05T10:29:07.843469Z" + "iopub.execute_input": "2026-10-08T17:27:05.183270Z", + "iopub.status.busy": "2026-10-08T17:27:05.183178Z", + "iopub.status.idle": "2026-10-08T17:27:28.081370Z", + "shell.execute_reply": "2026-10-08T17:27:28.080910Z" } }, "outputs": [ @@ -260,7 +267,16 @@ " animate='spin', duration=10, rotations=1, frame_rate=15,\n", " save_path='wikipedia_embeddings.mp4', show=False)\n", "\n", - "print(f\"video: {os.path.getsize('wikipedia_embeddings.mp4')/1e6:0.1f} MB\")" + "print(f\"video: {os.path.getsize('wikipedia_embeddings.mp4')/1e6:0.1f} MB\")\n", + "\n", + "# Colab serves output frames separately from kernel files; embed movie bytes.\n", + "try:\n", + " from google import colab as colab\n", + "except ImportError:\n", + " pass # Local Jupyter/Sphinx uses the relative video below.\n", + "else:\n", + " from IPython.display import Video, display\n", + " display(Video('wikipedia_embeddings.mp4', embed=True))\n" ] }, { @@ -282,42 +298,23 @@ "\n", "The corpus above is a fixed snapshot bundled with HyperTools. We can also\n", "build a corpus on the fly: pick a set of keywords grouped into categories,\n", - "fetch the current live article for each keyword straight from Wikipedia via\n", - "the [`Wikipedia-API`](https://github.com/martin-majlis/Wikipedia-API)\n", - "package, embed them with the same model as above, and plot the resulting\n", + "fetch the current live article for each keyword straight from Wikipedia\n", + "with `hyp.load('wikipedia:<Title>')` (several titles joined by `|` come\n", + "back as a list, and `intro=True` keeps each article's lead section), embed them with the same model as above, and plot the resulting\n", "point cloud colored by keyword category -- with a volumetric density\n", "overlay showing where each category's articles cluster in embedding space.\n" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "be977e14", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-17T07:38:27.566238Z", - "iopub.status.busy": "2026-07-17T07:38:27.566130Z", - "iopub.status.idle": "2026-07-17T07:38:27.568685Z", - "shell.execute_reply": "2026-07-17T07:38:27.568345Z" - } - }, - "outputs": [], - "source": [ - "import importlib.util\n", - "if importlib.util.find_spec('wikipediaapi') is None:\n", - " %pip install -q wikipedia-api" - ] - }, { "cell_type": "code", "execution_count": 6, "id": "f4cb3ead", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:07.845349Z", - "iopub.status.busy": "2026-09-05T10:29:07.845244Z", - "iopub.status.idle": "2026-09-05T10:29:15.561102Z", - "shell.execute_reply": "2026-09-05T10:29:15.560272Z" + "iopub.execute_input": "2026-10-08T17:27:28.082709Z", + "iopub.status.busy": "2026-10-08T17:27:28.082615Z", + "iopub.status.idle": "2026-10-08T17:27:32.714897Z", + "shell.execute_reply": "2026-10-08T17:27:32.714198Z" } }, "outputs": [ @@ -350,12 +347,6 @@ } ], "source": [ - "import wikipediaapi\n", - "\n", - "wiki_api = wikipediaapi.Wikipedia(\n", - " user_agent='hypertools-tutorial (https://github.com/ContextLab/hypertools)',\n", - " language='en')\n", - "\n", "# a keyword set spanning four unrelated categories\n", "keyword_groups = {\n", " 'neuroscience': ['Neuroscience', 'Neuron', 'Cerebral cortex', 'Synapse',\n", @@ -368,17 +359,13 @@ "\n", "titles, live_texts, categories = [], [], []\n", "for category, keywords in keyword_groups.items():\n", - " for kw in keywords:\n", - " page = wiki_api.page(kw)\n", - " if not page.exists():\n", - " print(f' (skipping {kw!r}: no such article)')\n", - " continue\n", + " # one 'wikipedia:A|B|C' name fetches every article in the group, in\n", + " # order; intro=True keeps the lead section, which is already a clean,\n", + " # self-contained excerpt of the live article\n", + " texts = hyp.load('wikipedia:' + '|'.join(keywords), intro=True)\n", + " for kw, text in zip(keywords, texts):\n", " titles.append(kw)\n", - " # the summary is already a clean, self-contained excerpt of the\n", - " # live article -- fall back to the full text for unusually short\n", - " # summaries\n", - " live_texts.append(page.summary if len(page.summary) > 200\n", - " else page.text)\n", + " live_texts.append(text)\n", " categories.append(category)\n", "\n", "print(f'fetched {len(titles)} live articles across '\n", @@ -406,10 +393,10 @@ "id": "24ee3a06", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:15.563574Z", - "iopub.status.busy": "2026-09-05T10:29:15.563478Z", - "iopub.status.idle": "2026-09-05T10:29:15.566212Z", - "shell.execute_reply": "2026-09-05T10:29:15.565745Z" + "iopub.execute_input": "2026-10-08T17:27:32.717343Z", + "iopub.status.busy": "2026-10-08T17:27:32.717128Z", + "iopub.status.idle": "2026-10-08T17:27:32.720967Z", + "shell.execute_reply": "2026-10-08T17:27:32.720064Z" } }, "outputs": [ @@ -435,16 +422,16 @@ "id": "3ebb4dc3", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:15.567475Z", - "iopub.status.busy": "2026-09-05T10:29:15.567332Z", - "iopub.status.idle": "2026-09-05T10:29:17.955110Z", - "shell.execute_reply": "2026-09-05T10:29:17.954618Z" + "iopub.execute_input": "2026-10-08T17:27:32.722511Z", + "iopub.status.busy": "2026-10-08T17:27:32.722387Z", + "iopub.status.idle": "2026-10-08T17:27:35.003395Z", + "shell.execute_reply": "2026-10-08T17:27:35.002978Z" } }, "outputs": [ { "data": { - "image/png": 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", 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BccgFP34UaezvR9QR5o6F5yxjSb+Hz+VCyrqNhTAQTmAy6yGZ7YgjiRCPH0XL9yQaUZqI+PWMU0TiNSH32N+DuOImh9/OuaA0oVDWeiUkRjI/yd2sY8LAHPfx4odzAOeZWPi97PsUxk20Xrz44TNxAOM/k5uGWNh/GbUbv9/wW8EPrCgLjiXWHfs+fxXiEyKizhQnMC7i8Reu2NFcsZAfxR0kw7a5yCJUPJwAcZEIqXAnxugbckQ4wXECZMhyaXeWnPS4qDDaJpEr9txzzzkhVZ47Oz4Xt4aRSPyNDR8mEos4YJyouUiwLDgAXIz9+mBEGe4Zy8iIOUQV7gihTxwrlo+LBn9xxbjocoH47rvvXBiuNBBjOFE4apz8CXswOopSAeWBCx7ike8nlwaRg0DFsWL0GwKGZeTz+T8uC7/Fw+9FiBFa5U461l3zFxrCaIRPY+tkkaPDdLZ1bM0whDFlNRjphNhglB3fy3tZN4SPy4IyAFwwGdmF+8D2IBTKaKlYWHeEYclrw4EhbMoFMDaXhnXCMvjRYPGwD7N+CBchWnCRcNUIuxIC5UJKmA13kuk4cD/++KP7PYidkgpKst8y6gunjHXKfFzUGaXJ9o7dBiWR7HZkRCqj+RgxSFic1x544IEi1zSZ8wIuEaULeC/bDlc6/hhGLLCfIDwo8VAaZa1X1iMCnZsmjj/EGx0S4l0g5uNcwEhDcvU4xhnpyOhPRvEh3Nk+rG++h9GxrAugRAmfj9v1j3/8w20H9iGO9VhwlHDKKS3CNuPmjHn4Ts6BiUKFiUC88aCMBiOEk7kRFCJbiJSYih9aDlxIY4fzxp/IODkgDhhyHQsnMcJRnLQ5CXKC58LKiSeZCwUOBp+LcIqHaeRTJBPii4cT+Hnnned+FxfC+MTh2OXnokHeB8PeucNHBOAUxYZquGAjujhBcjHkAoUrFJscTd4Tn8MFlnAQQouLDqGRkuCiwfpjOclDISzChfChhx7awB0qC9Y3oRcuMOSgkDDP9uKiw/KzfEBojlIP8SEOwjXkHiUSOlwQfPgw1k1hPbMuCBHFl65ABHFB4kKGeOACiPhC9MWLtUQgMqknhNPIPsu24kKPeIsVSghSfi8XQtY74R1+gx/6zj6J01dSzhHw21jn7AuUOeCCz7aJ3XZsf15jmXB8CB/xOuGp2DBkPIhw9iFuSDhG+O3chLCPJRN+g2S2I2IWF4jBF4h/RD/igf168uTJpX4+x6wPkfsBDQhQtmF8Hhb1vKgrh2AuSyiUtV4RLoRJOWZwt8lz5OaHumGxMD+/gW3MMcU8rA9yuDhmOf+wX3FThLMVK24R4awTfhvzILg4tthuvnyCXwdsI5aVB8c5xxCh42TCjrGwXRDefIcQ4m9yGNIX81xEAEQMd52cpBPlc2QbXKBIKI4PZWU65K3g1hACS5RkLcoHAx0QtThhiKFMBacdEcc+n2ooloobh8McOwpYiGwna3OmsgHp5HX5OOR4EMKJGlwscfskpFInFHAASwvhZwKE05Mtn1Le8wkOJO6YhJQQEQ7zCeEh2ZYQmM+PiQ//RQGfOyMqDjlUjHTzVd/JRywpxzJskAROiI8wb2zldNpXkVOZyu8hZEmIkny62BCiEGIdElMikpDX4ZO3KfqZbLKyyC4YzcpADPKNGJ0Wm1MYdsgHZL8m14ycLQaIMLCBEi++FU8qYLAJ+YEknXNzkkz5DyGyDeVMCSGEEEIEQDlTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghJKaEEEIIIdKDnCkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghApAb5M1ChJWVK1falClTrF69erbZZptZzZo1071IQgghIorElIiEcHrzzTft9ddft08++cR++eUXW7JkieXm5trq1avdPDk5OVa9enWrVauW1a5d2+rXr28bbbSRNWzY0DbZZBNr0qSJNW/e3DbddFMnvlq3bm1bbLGF1a1bN90/TwghRMjJKSwsLEz3QgiRLIijsWPH2muvveaE09SpU23x4sVFryOgWrRoYXl5eTZ58mQ79NBDnSCaN2+e/fnnn27e/Px8W7p0qS1fvtxWrVpla9asKfU7EWE4W4gwnC4+GxG28cYbF4mwli1bWqtWrZwI23LLLd3rQgghsgOJKRFq4fTuu+/aK6+8Yh9//LH9/PPPGwgnhEynTp1s7733tsMOO8y5SXDXXXfZOeecY6NGjbL+/fuX+V2LFi2y3377zaZNm2YzZsywP/74w2bPnl0kwngdEVZQUGDLli1zIsy7XiVRrVo1J8JwwxB0iLAGDRo4Eda4cWNr1qxZkQjbfPPN3bI3bdrUvU8IIUTmoDCfCAVr16619957z0aOHOmE008//eQETKxwQnzstttu1qtXL+c4bb311iV+nhckyRqvOEnbb7+9e5QHHC4EGEIsVoTNnTvXFixYYAsXLnQhR0TY77//br/++qsTYaUtF8vO7/UijJAkIqxRo0ZOhCG4cN8ISXoRxv95jxBCiKpHZ1+RFuE0fvx45zh9+OGHRcLJCwzCagiGrl27Ws+ePZ3j1K5du3J9BzlS/rsqE8ROhw4d3KM8IKgQX4gr/s6cOdOJsDlz5hSJMFy4v/76ywkz5sENK02E8ZsRVLhhLBchSfLCEGHkhbFOY/PC2rRp48KShC+FEEJUHIkpUakgZj766CPnOH3wwQf2448/OqEQL5wI03nHqbzCJBFhD5UhenCUfFiyPOsT0YUImz59uhNhs2bNciJs/vz5RXlhiDDWM68hwsoSlV6E+byw+OR8XEFEGCFJRBgP5hFCCCExJVIIF+xPP/3UXn75ZSecfvjhB3dx98IJgYNwQjThOCGcyHeqTCrbmapqWIfkWfHo3r17ud7LtvB5YYiwkvLCEGIIM0ZJJpOcX6NGDSfC4vPCvAgjJOmT8xGPhCrDLnaFEKI8yJkSFRYpX375pb344otOOFHTifBUrHDioolo2nPPPe2QQw4pdz5SEHyYT4NV/waBw2PHHXcs17pEXCHAfHI+uV9ehLHNEWHkhTEfrhgjLBFhZeWFIcLKygvzyfk4YfxfIkwIEUYkpkRSfPXVVzZixAgnnL7//nvnXPiLJcKFCyCiaY899nCOU3kv2KnGX3Sj5kylA4ROx44d3aM84GwhvnDDCEkiwgg7xifnI8IQZ8yTbF4YIqxOnTpF9cIS5YV5EYYjpqKtQojKRGJKbMA333zjHCeSxL1w8qKEixkXLUJMiKeBAwfaTjvtFDrHoKoS0EXJIGDatm3rHuWBbUYI0ockvQiLzQtDhBGSRJQxb1l5Yb5oK8uECEuUF+brhfnkfB6INSGEKAuJqSxn0qRJznEaN26cE064BrHCibAQ5QhwnA4++GDbddddQyecUlEaQYRr2xHS48F+lyzstwgtkvNj88IQYfFFWxFj7OvlLdrq88Jii7bG1gvzRVt5TQiRPUhMZRGIJYQT9Zy+++47d4HxFxKEE6ESxBKuE8KpW7duGSGcEqEwX/bh8/R4UFajPCCucMJ4IMJww7wIi80Lo14Y4UkKyCZTtDU2Od/nhcUWbY3NCyM5H3csU485IbIZiamIwki6F154wTlOuE/chccKJ+6sd955Z9t9992dcMIBiNJJXAnoojwQ8uvcubN7lAdaEsUWbfUiLFFeGKFK5iuraCv7rk/OLykvLLZoK+FINfMWIr1ITEUARk89//zzznFCOHEyj71rRjiREI7TdNBBB7nSBFESTul0prgorshfa6uXr7Xc2tWsVl61IiEnog+uEwVly1tUluPT54XF1gtDhJEXFlu0leeItGTzwtTMW4iqR2IqwyAfBMfpnXfeccKJUVDxwqlLly7OcTrggANsr732yso2I5WdgL6yYI1NHZdvU8Yssvw5f6//vGa51r5vQ2vbM89q1qteKd8tMh+OSVwlHuWB/RlxFZsX5pPz4/PC+Mt0NfMWovLJvqtsBsEdK8Lp7bfftokTJ7qTZqxwIv+CsAQJ4vvvv7/16dMnK4VTVSeg/z6hwMYNm22rV2742flzV9vnw+fb188usJ6DmtumXeql/PtF9uIL3/Igv7E8lKeZNyFKNfMWInl05Q0J3GGSHP7WW2854eSHe3vImdhuu+3cCRTHad9995VwKoXKCmMipN4ZOsucRkuk09ZPQ2gx395DWkhQiVAQpmbe3j3mgYOmEhQi05GYSgOcjBBOOE7UdMJxosChh6HX2267re2yyy7OcerXr5+KDlaQVIb5CO3hSJUopGIpXDcL8x92TxuF/ETGkopm3u+++6698cYbrqk57hdCyz9ISyDvU4hMRmKqkuHujVAdjhPCiTu3WOHEHRnJqzhOAwYMsP79+7ukVhG+0XzkSLnQXrIfWbjOoZo6Pt869G+YsuUQIux8++239uCDD9rYsWOdUPLpCZzbdthhB3euO/30091NIyMXhch0JKZSCImhOE6cQL7++msnnFasWFH0OlWXt9lmG1cDh5MJrpOEU2aM5kOUkWyetJCKYcroRda+XwON8hORLsXywAMP2Jtvvmk//vhj0Q0jIwtxtPr27WunnXbaBqMeOT6jPrJYZAcSUxWEhE1arowZM8Y5TtjZ8cJpq622crWcvHDCLheZOZqP8gexo/aSptDc+1b8tdZq52l0n4gG5FAhnkaNGuWanFNvC3CZaB/EYBjEU1l1u7hJ0aAZEQUkppKAUS4vvfSSE040/EU4+ZMHIJJoIYFwIkxHgrgSKqM1mo86UoHev2ytmcSUyOA8z4ceeshee+011z2BpHSgrhWV2ynBcuqpp5Z7hCHHJ58hRKYjMRUHo1JefvllGz16tH355ZdOOC1btqzodSoSU3GY5r4khh944IFupJ2IdpiPgpyB3l9HoQyRWc77ww8/7M6F5D9RPNQfV9TG6tmzp5100knWo0ePQGE6iSkRFbJaTHGCiBVOWNfxwok2DQgnYv5UD2d4sci+BHQqm1OQkzpS5cqbyjHLa5prtepLTIlw30Q+/vjjLnWBfE/KFfjjiLY1uO3HH3+8Ow+mMsdJYkpEhawRU9jSI0eOdMLp888/d8LJW9VAIjjCibYrxPsHDhyozu8ZTKqdKS4qVDanIGd5ad+voZLPRaggTeHJJ5+05557zt1I4kT5/Zxmy71797ZjjjnG3UBWZk6TxJSICpEUU4gkYvskR37xxReu2BwF5TyMMEE4UbyOO61DDjlEwiliVEYFdFrEUNk86fIIOWa5NXOsbY+8lC2DEBWB0XUIp2eeecY+++wzV/Xc06RJEzdA5qijjrLDDz+8SmvaKQFdRIXcKNxhvf766+6BcPrll182EE7Y1NQ28Y4TrRhEtKmM3nz02qNFjKuAzoTSBFUOy2DWa1ALFewUVQ51nXDin3rqKfv4449djz5/Y7Hxxhu7DgpHHHGEHXvssWktzyIxJaJCbqbdXVFFF9fp008/dZV1fWIkcEeFcMJx2meffZzjhGUtRKqg1x4tYor15osVVes0nHOkEFItu6gchqh8uGkghWH48OH2wQcfuBp3XjzRw7NXr17ufHjCCSeEasCMwnwiKuSGWThxciBU98knnzjHiTYEscKpRYsWLrbP49BDD7WWLVumdZlFdjQ6RlDRIobK5hTkjK0/RbI5OVKEBGvW1ZBvUXniafz48fboo4/auHHj3Khj78LSjmr33Xd3LvzJJ58c+hQG1ZkSUSBUYoqTQbNmzdxIktgmvxxsOE4Mx8VxQji1atUqrcsqsisBPVHIjxYxVDanICd1pCh/wKg9H2IUIpXgxlOugD53uPJr1qwpqnNHjTtG3FEoM5PceDlTIiqESkxxJ0VLlkTxf0bfzZw509555x279tpr3d1Xo0aNrHHjxk6AIbZIKqd4Ji1b+L/ueLKXynSmYkE4ucrmKsgpKqm/HX096W/nbzDJcaKy+H777efEU+vWrTN63as3n4gCoRJTCCZOFFjWdBfnBMJIPOL/s2bNciNQGMKLc4XoQlxxginpgskFlQOVelFUJCd3YJNNNnEJ6IQIEVxU7/UCTFXLo0NlJKALUZlwzqNFC50W6HXn+9uR0tC+ffsS+9tlOrrpFVEgN2wHFRc/3CYe3bp1S3pEH8KLB/b39OnTnfhiBAuiC/FFvtWCBQvs+++/L/UCyzIg6LDOEV/e/UJ8kZPFXSC9p+i7x3M16cxuZ0qIisJ5CueJkciclyra3y7TkZgSUSBUYooeTRW5+CF+Onbs6B7JgJiaPXu2626OAMMJw+ViGu7XwoULXUVgBBnOWGz+VqKLNneOiC+cLSqkI75wvxBbtF6g/QziixNkOochZxOVnTMlRHnh/EJ/u1dffdUmTZqUsv52mYo/NhXmE1EgEmKqIhdahA4PhgwnAyUYfv75Z/fA/UJ80fwT8YXjRTNkBBhuGCfKki7ihJ84eVD/yocecb8QXySOkliP+OLkip2PMBMV28YgZ0qkC1ISGG1Hk3Tyn/xoZPZNUgw496Siv12mQi4sqNGxiAK5YbN7w3rxQ/hQv4pHMiCmyAFDfFHWgf8jvrg7JfSI+8XJFVeMefyJJRGcbBBf5H5RIwbxRe4X4ssn3iO+cL/4K9tcOVOi6uGG67HHHnP97b766qsN+tuRME5/OxqkZ6N4isfnhMmZElFAYqqS4GSJsOFRnjtZklARV+RTILQQX3PnznXuF84Xf0nILy3xnpM3oUdCirGJ97SNwI1DfPncLxLvw1TEL1XImRKVDTlOVBinTQvdF2L72zHCuKr622UqPkdM60ZEgVAd4WF2pqoCiuuRL5FszgR3doQcEWD8RXwhtAg1Irpwv7hb5iTP6CBfl6akdY/7Va9ePSeuyP1CfHFRIPSI+EIYbr311u552O+sw758IvPgeHv++eddfztqPsX3t8N5or8dbVqqsr9dpiJnSkSJUIkpxc7LByds8qqSHSpN6BGXi8R7xFd82QmfeI8bRliS0GNpZSf4fl92AvGF+4X4wv1CcCG+vPuVrsR7JaCLIPsO/e2efPJJ19+O4yK+vx2NgXGfGIAiyseKFSvcXwlPEQVCJaay3ZmqbBBA5FnxIOk1GRhxRNgR9wuB5d2v2MR73C+ef/fdd6Um3vuyE9794oLky05498uLL6YHcZeqKszH56/IX2url6+13NrVrFaeKqBnKuy71Hiiv93777+/QX87OjDQ3+7EE0+MZGi8qlGYT0SJUIkpJSKGD+64qXOTbK0bLkg+qT6+7ERs4j0XKhLzy0q892UnqHjvy05498uXnSD0iAiLzb2o7KKdKwvW2NRx+TZlTFxvvma51r7v+t589dSbL+zQ3+6RRx5x/e3IU/T7C24r/e3Id6JcQdj722UivuSMzvsiCoRKTCkRMfPBEcJh4kECbjLgbuF8Ia4IPyK0GPlISNJXvEeEEY4kz6K0xHtf8d6HDqjr88033xS5X77mFwIMcVYRfp9QYOOGzbbVKzdcjvy5q+3z4fPt62cXWM9BzV1TZBEePv/8c9ff7u23396gv91OO+1k+++/vyuUqabpVRfm03lfRAGJKZF2EDVdu3Z1j2TAzfKJ9+R9EX5EgCG+cL8QZ4gvwBXDHSsr8d67X5Sd8In3DGfH/Yrt9zhr4jJ7Z+gsc3oukaZbPw2hxXx7D2khQZVGqPlGi5axY8cm7G/Xv39/O+OMMzK+v10mEtsuR4hMJ1Riytu9WO0ajSVKy03y7lJJUCSxS5cudvnll9tVV13lhJbv90g4B5EV3++R/yO8Sku8z7Fqllst12pUr221c+tZnZr1rX7NRtag9ibWoHYT26RuS2tcr5U1rb+ZNduojXOwDrunjUJ+VQTbmBYto0ePdgMtYpOcGahB0jjiKWr97TIRJaCLKBFKMcXFTHcrIkhuUnwCOhXmeXTv3j3p5Fif9+X7Pf7w5TT7dcrv9teKRbZ0Vb4tX11g+fl/2h9rp1qhlZybdeJTuVa7TuLEe9yv2H6PDA7QjUT5+9uNGjXK9bdbtmxZkePI+txnn31czlOyxXZF1Ysp5UyJKBBKMcWFTGJKBMlNCpqAThioU6dO7uFF2cvnT7P8FokT5vmeRcvn2Oz832xewUxbUPC7/bl0ti1ZMd8K1iw022ipKztBLhjiLJnEe3K/YhPvEYOIL192wifeZ9OxgsOIePL97QoKCorWGflw9Lc75ZRTkm6SLtKHD7lm0/4rokuoxJRPRERMaehxdoOQCpKblOrSCIQYY52xePi+jeu2cI9EHPHgFlY7r7iDhrjy/R7J/UrU75HwIyMhk0m892UnGMaP+xXf75HcL8JbmTQyzfe3e/nll23ChAkb9Lc79NBDXakC+tzJ0cvMnCkloIsoEFpnSmR3aA9HqkQhFUvhulmYf/+hm1m1ajkup8o7U6kSU+RqBXr/srVmcWKKG4Ydd9zRPZL6jNWri/V7JMQV2+/RN9tmOrlDpVW89/0eSbyP7/foy04gvgiVEYasqgseNcueeOIJGzFihOtvx28CtifLpf520XOm2A+FyHRCKab8QSayE3KkXGgvWR1UaLZ6BWG46UWT1tRfF/4pXL1OVAUFgRbo/XWCt7dB0BDW45EsiCxfdsK7X77fo0+8Zx6ml9bvEdfHl53A/fIV7+P7PSK+GPlInaby9LejTQv97XDkPAi7gQMHugrjBx98sByMiKHRfCJKhEpM+di5nKnshYs5yeZJC6kSWLZgnZO09LOWLmQYtN4Tlc1JeidXq1zLlmOW1zTXatVPT69Acq14JJtDxLEX2+8RkRXb7xGniFAbQoyE79Jy0mL7PRJ69P0eeTCSkvpfiDqP+ttlF0pAF1EiVGLKhxL8QSayj7Jyk5JnvSO1xlJS74kwE6MHSXovL+37NSwKO4Yd8q46dOjgHsmAmMLp8iMffc2v+H6PPMcZi3Wdyd3q06ePHXbYYXbcccepv12W4QdhKMwnokAonSmJqewlaG6S5++cqXWPVNR7ogwDoweTDkHmmOXWzLG2PfIsqv0BCf8R5uORbL9HhBqiKzakJ7I3zKfSCCIKhFJMKWcqewmam/Q3XkysEz4IoKnj861D/4q1kAGEGGUY3CjD9R9d2tejZ3oNalFpBTsztT9gprh0onJRArqIEulJ5CgBOVPC5yYVaaEKkrP+A2L1zpTRiwKP7iNUSMgQx8l9Rfxyrp/G672HtLSWXepaZUAe2Atn/+bCji6PK0ENLl5nvrDBNpCgEt6ZUphPRIFQiSnlTAmfmxQUb34UiadCc+7Nir+ChxERVIQMu57Q2CWXx8Jzph92b5tKFVK4Y0Xhxnh9uH6ar8EVRkElhM+ZUphPRIFQhvmUM5XdlDs3qawwXxn1nioC4TNChu37NXACjc+l/AGj9irTdaloDa4w9QeUMyVAzpSIEqFyprzdW1qrDRF9fG6S0yQ5QcN8hSmv91Tse3JyXGXz+k1ruL+VHb6qUA2u9fliYaGiLX5EtFA7GRElQiWm5EyJpHOTymCDCujUe2qWvnpP6a7BlYp8sVSinCmh0ggiSoTqyuJj597+FdlNablJFQnzZVK9p5TW4EphvlgqCJOoE+l3pqhtJkSmkxvGMJ/ElCgtN4mL8SsXTbc1nIsLSw/zVWW9p8qmMvoDpgPlTIlYZ8pHJITIZKqFUUypzpQoLTcpr1lN6zW4RVI5Vesu3JVb76mqCEN/wFQgZ0qAnCkRJcJxdl2PnCmRspyqautzpqqtrdR6TxlRgyuE+WKZHG4VqUEJ6CJKhOfsqpwpkcKcqvpN1rtQ2/4SCSEVtAZXmPLF5EyJ2DAfLYmEyHRCmTOlMJ8IWu9plS01uwdnak2kVmZY+wOWB+VMCdB5XkSJUN0SSEyJVNV7ql69eiRdkHLV4KqC/oAVIWrbRFSMNWuidaMjsptQiSk/RFZ3LCIoPnQQxQt3WPoDBiEsIUeRPlScWUSJ3DDWmZKYEqkSU1Gttu3zxahsTkHO2PpT5I+RI0VIsGbd8DhSnigKXFF+OM9LVIuokBtGZ0p1pkRQouxMpbs/YFCUMyVAYT4RJUIppnSQiaBkg5iKzxcLQ0HOZAmz2BNVF+bTfiCiQihzpuRMiaBkk5jKNLRNBOimWUSJUImp3Nx1RpkSE4WILgrzCZAzJaJEqMSURwnoIlXIBQkf2ibCO1MK84moEEoxJWdKpIqojubLZORMCZAzJaKExJSINHJBwokcCSFnSkSJamE8ySrMJ1KFxFT40DYRXkypL5+ICqETU6Awn0gVunCHEzlTQs6UiBLVwniS1ZBZkSokpsKHtokAiSkRJUInpkDOlEgVSkAPH0pAF6Awn4gSoXSmJKZEqpALEk4U5hNypkSUkJgSkUZiKnxomwjvGisBXUSFUIop5UyJVKELdziRMyUU5hNRInRiijsViSkhoosErgA5UyJKhE5MKWdKpBJduMO5TRTeERJTIkqEUkzJmRKpQmJKiHCiMJ+IEqETUwrziVQiMRU+VBpB+P1ADqWICqETU3KmRCqRmAofElPCO1PVq1fXyhCRIHRiSs6USCUq2ilEOJEzJaKExJSINHKmwoecKeFvdORMiagQSjElN0GkKmQsMRVOVGdKaDSfiBISUyLSSEyFD20TAXKmRJTItZChnCmRSnThDidypgTHZlTCfPyWtX/l29rly61a7dpWrX6e9vEsI5RiSmE+IaKLcqZEVMTUmoICy/9wnC16a4ytnjunaHpu02bWcJ++lte9p1WvVy+tyyiyVExxcK1evTrdiyEigpypcCJnSmR6mK9g4gSbfdcwK1yx0iyn+Gur5821+U8PtwUjnrXm/xpk9bbrkq7FFFWEcqZEpJGYCh/aJsLvB5kqphBSs24daoUrV/JL+DHFZ3DPC93rzMf8IjFfffWVHXDAAZbphE5McXApzCdShS7c4UTOlMhUMUVoD0fKCaZ4ERXP+nmYn/eJDdlxxx1txIgRlumETkwpZ0qkEomp8KFijcLvB7m5ocs0KRNypFxorywh5SksdPPnfzS+shctY6lZs6ZlOqETU3KmRKpQnSkhwksmOlMsM8nmLrRXHnLMFo0dnbKbu7333ttGjx5tp556qnXu3Nn22msve/bZZ4te//TTT+2QQw5xr/Xv39/efPPNotfuvPNOO/roo4t93osvvmg9evQoen788ce7zx86dKjtsMMOdv3117vpixYtsosuush23nln5yidc845NmfO34n3hOyOOOII23777a1bt2527rnnFuVA895///vftuuuu7rPPPHEE936+Oijj6xdu3ZFn8G0u+66y/bYYw/3OWeccYbNnj076d8On332mR155JHu9e7du9tTTz2V1GdHTkzJTRCpQvtS+NBoPpGpzhTlD2JH7SVNYaF739qCv1K2LFdeeaUdeuih9sYbbzhx9H//9382ffp0mzFjhp111ll21FFHudfOPPNMGzx4sP3000/l+vxbb73Vpdy89tprdvbZZ7tpQ4YMceJj+PDh9txzz7nrNaJm1apV7nW+Z99997UxY8bYgw8+6ISO38YIL5bv0UcftZdfftktY6Jw/wMPPGCjRo1you+VV16xevXq2XnnnZfUb4epU6faaaedZr169bLXX3/d7rnnHtttt92S/uyKEro9mRWvnCmRKiSmwolypgRkmjNFHalA71+2zKrXz0vJsiBaBgwY4P5/+umn23333Wfff/+9TZo0yfbcc0/nEMGmm25qI0eOdMLi/PPPT/rzGzRo4Jwkzy+//GLvvfeejR071jbffHM37ZprrnHf9cEHHzjhtHz5cve+Zs2auUenTp3cfD/++KNzi3DIWrdu7ab5v/Hna4Ta1Vdf7dwruOSSS5xrhljy31vSb+f1F154wTp27Gj/+Mc/3OubbbZZuT47MmKKnCldAEWq0L4UPuRMiUx1pijIGej9deqkbFm22mqrYjcn9evXt4KCAidcCJ0RivMgcpo3b16uz99pp52KPUdM5eXlFRMdfOcWW2zhXC/E1C233GIXX3yxc30I4/Xu3dstG+9l3kQCKpaFCxfavHnznMOFFoiFcKL/7pJ+u19OxFRFP7uihG5P5uDSBVCkCu1LQoSTTMyZorI5BTmpI5V0Ajrk5Fhuk6ZWrV79lC1LnRKEGet1//33t3/+85/FptetW7fEz1q2bFmZSeHkPiU6nxJJqlGjhvv/7rvvbm+//bZzwsi3evLJJ+2RRx4p9w3UsGHDigkmaNKkSZm/Hcr6rrI+OzJiSgnoIlUoAT2cyJkSHn8RzqRzCpXNKchZLgrNGvbpVyXhbYTCl19+aa1atUr4OnlCf/1VPHfrhx9+KPNzt956a/e+3377zdq0aeOm+edbbrll0Xy1atVyIUYEHcnf3333nXOv8vPzywynNWrUyDbZZBOXl0XOU0Vg2Qh1VsZnZ1QCupwpIaKPcqYEZFqYD2gRk1OrpnObkiInx82ft/vfo+UqE0axIY5uvPFGJ3R4kC9F+A/IY+L/L730kv3xxx/26quvutFxZdG2bVvr2bOnXXbZZTZ58mSX6H3ppZc6cbTnnnu6ee69916bOHGiC5uRX4Wb1aJFC2vfvr116dLFvXfKlCkuST7Rd3JeIDx4++23uyT2WbNmue964oknkv79jGL89ttv7f7777eZM2e6XKovvvgiJZ9dGqHbkzWaT6QShfnCicSUyFQxRa89WsRQ2dxRWrgPwZWTYy3OGVRlPfrIS3rsscfspptuciUBWMeUHiDxGnbZZRc3so5w1+LFi61r165udNwNN9xQ5mfzHuY76aSTbOXKlW6UHAKq2vocJIQS4oT8JZbj5ptvtsaNG7vX7r77bpewfuyxx9qaNWtcTla/fv02+A7KFaADWH5cpIYNG7p8rGRBuPFdjEZk1B55XoMGDXI5ZEE/uzRyCkN2teGHjR8/3q1sIYLASYRaJ++//75WZIgg34FQBHewIrsFNRdW8moi0Zsv9lKKiCo050ghpOp2Um++qBO624JMvFMR4SVk9wqiCpwptjn1gBjGzugrkoblhIULX8gxk8/3NC9uM+weV9mcgpyx9adINidHipBg9VISv0V0CN2erJwpkSqUgJ5d0PuMVh9UqC52YWvazCUNuwtbFYVaROkQIsrEBPR42J8a9ulvDfbp5wpyUkeK8geM2pOAzy5CJ6aUMyVSiZyp7BjNt0HIJQaGsTP6asGIZ12uC46CSC/UPYqCmPKwP1OMM1UFOUXmEbrRfFE5uEQ4kJgKJ/FF84IKKZKBC53bUbhhQrB7XuheZz7mF+FwpjI5zCdEqMWUDi6RSiSmwkmqnClCezhSTjCVlR+3fh7m530ifaxYscL91c2ziAqhE1M6uESqUM5U9MN85Ei50F6yAw0KC938JA2L9CFnSkSN0DpTanYsUoGcqXCSCjHFtiXZ3IX2yvXl5kZfad9IvzMV37JEiEwltGLKD50VIgi6YEYXyh/EjtpLmsJC9z5GX4n0ELUEdM4zy9YssSWr5rq/Ou9kH6HL/vN3KtjAumsRQdDQ5GiH+agjFej9y5Zp9FWaWLVqVSTE1Io1BfZ9/jibsGiMLV79t7BvkNvMujTsax3yelqt6irHkQ2ETkz5g4s7l/r1U9dhW2QnukMMJ6kQUxTkDPT+UjrPi8olCmG+aQUTbNTsYbaqcN3IxFgWr55r4+cPt48XPGsDmg+y1vVUjiPqhDbM521gIUS0SJUzRWVzCnIm3XDWk5Pj3kdhRZFeZypTR28jpF6ZNXS9kFpXeqM466bxOvMxv4g21cLqTPk7FyGCIGcqnKRCTPEZVDYvb/4589PqQ2Hg9JHJzhShPRypwoQiKh7mKnTz8750QupM375907oMUSa0YsoPnRWioqg0QnhJlZChRQzNZJN2p3Jy3Px5u/dIyfeL7BNT5Ej97UglwzqH6vv89Jbj+OKLL2zZsmVpXYYoEzox5Q8uOVMiFciZqrr1vCZ/ia2aN9f9LWu9p0pM0RuNFjFOTJX1mevnaXHOIPXoSzN+tHamJaCzX5NsXn471GzCotSV45gwYYKddNJJtssuu9iuu+5qF110kbtmsl6vu+466969u3Xp0sUOOOAAe+edd+y9996zf/zjHzZnzhxr166de8Cdd95pV111lb366qu2xx572MCBA4tKE/Fajx49rHPnznbUUUfZl19+WfT9L774op122mn21FNPWc+ePW2HHXZwz/l8D8tz9dVXW7du3Wz77be3k08+2aZOnVr0Op//3//+14YNG+Z+x8477+yWffbs2XbGGWe45e/du7eNGjXKzf/ggw9a//79i62HRYsWWceOHe2jjz6ydBO6gLWPoUtMiVQgMRW+5sKp7s1Hr70WFwwp3psv9qLFdxWa5dSs6YRU3U5KBk43/vxeq1YtyySWr80vNmoveQrd+5av/cvqVA/ev4/1hvBBCC1cuNDOOusse/75561OnTr2/vvv2yOPPGL16tWzyZMn22abbWabb765E1z33XefvfLKK8U+a/z48TZ9+nR79NFHrWHDhm4a/+fzrr/+evdeBM2pp55qI0eOtNatW7t5PvvsM6tdu7Y99NBDTsQNGjTIbr31Vrvhhhvc60OHDnXz3H333bbxxhvbk08+aSeccIK9/vrrRd/z8ssv2zHHHGMvvPCCfffdd3bBBRfY2LFj3d8rrrjCveeyyy5zgg1hiPCaMmWKtW/f3r0fodigQQMnKNNN6MSUnCmRKhTmq1yCNBdOdb4Sn99m2D2usjkFOYsJuyZNXY6UE3Z166b0e0V2JaCvWhtsYNSqtctSIqYQE15QIHa6du1qP/74o3OcWKctWrSwjTbayFq1alX0nrp167qemAibWObNm2cjRoxwosSDmDrvvPOcwwVnnnmmE2nPPPOMDRkypEgQI3iaNGninh9++OH20ksvuf8XFBTYs88+64TWjjvu6KZdeumlTvy89tprdtxxxxWJwsGDBzuHkt9x77332hZbbGEHHnige/300093whCx16FDB/c733jjjaLf/uabb7o8sOrVq1u6Ca2Y8gebEEGQM1U5+ObCvonwhoOZ1k3wzYVxjmIFVWUkf+OANezT3xrs088V5KSOFOUPGLWnZPNw4XNiM82ZqlEtWDmOGtVSU47jq6++sgceeMB++uknW7JkicuF2n///e3QQw+1r7/+2vr06WOHHXaYc4KaNWtW6mdttdVWxYRUfn6+E1idOnUqNh/PEWwe3uOFFOTl5TkRBTNmzHBuFSE4D0KO57GfgWsWG+rFTWvTpk2x5+A/F5F1//33O+eKaYT3EFthQDlTQogqby7MibWyQDhVr59nNZo0dX8lpMKbM5VpYqp2tTxXkHMDK7ZMctz7alcLXo5j1qxZLuSGk3P77be7/CVym9zy1a5tN998sz3xxBP2559/Wr9+/ZyjVBrxgwC8kRF/I0oeVazw4btKYlUpnxH7fYm2f2luJb+HvKxJkya58CThwp122snCQOjElEojiFShMF/lELS5cKpzpkTmOlOZloDOfktl84rQpWFqynGQfM7NCGEz3CLcHYRTLIT7yHc65ZRTXN4R8J5ket42atTIOU4IllgmTpxobdu2TWoZt9hiC7dtYz+D7yaHa8stt7SKQiHvvfbay+VVETIkIT0s55JqYW4nI4QIF6lqLhyWE6BID5ka5gNaxNTI4TqV7D6c4+bvkJeachwtW7a0pUuXOjExc+ZMl2f0888/u9fGjRvnhAZhNkbOffPNN05sQfPmzW3+/Pn2wQcfuNfXrFmTeGlzctxIQUbb4WoxL4nrCCGf65SM6Dn88MPd6DxGAU6bNs2uvfZa51gddNBBgX4/oT5+w4cffmgDBgywsBC6nCl/cClnSqQC5UyFs7mwxFR2k8m9+ei1R4sYKpuvzwwsZe4c929Ai0Ep69FHqQISwi+55BLn9hxyyCEuhwjnCPfpjjvucAnbhOH23HNPl0gOlB8gp+r88893pgWiqyQIIwKlC8ifwukiR4vE9mS59NJL7bbbbrNzzz3X5XVRHoGEdJ8HVVEIafLZCDbKJ4SFnMKQXW0YDcDOwUr3G1SIisDoFexmhtyK1EAdqWkXnVvh97e+6Q6r3byF9erVy95++21tlizlP//5j11zzTVOAMQnOmdub77YS+k61wpHCiHVum54LvpR4MQTT7TtttvOLrzwQgsLoXWmFOYTQVHOVOpJRXNh5UyJTE1Aj4Xmxae0ucdVNqcgZ2z9qQa5TV2OFCHBWtVVjiOVkB9G6BB3KkyETkypnUzlwUUsf81aW752rdWuVs3yqleLfLglZMZrxqwzwnlrly934omGwn4/8c2FqSOVdAK6by7cpGlRc+Eo7HelrSdROv5mORPbycRC6G77hv2tS4N+riAndaQof8CoPe0LqYVQIWUbSKwnZOmruIeF0Ikpf6fi71xEcArWrLFxC/NtzJ+LbM7Kv9drs5q51nfjhtazUZ7VC0HRs1Sjk1nlVDPn/xTkDNJcOJO3TUWqvovEOVOZ7EzFwv5MMc5UFOQUiaEIKPlg5F7ddNNNFjZCK6YU5ksNE/ILbNiM2bZy7YYuwtyVq2347Pn27NwFNmiz5tYlr17kHC05U6mvZo5Y4P8U5EzKnaK5cM3izYUzZf9JZdV3EbOu1t8sZ7ozJaqO008/3T3CSuhKI0hMpVZIDZ02ywmp9XWqi+Gn8TrzMX+8ozVq/iI7/6dpdsaUX+3cH9f95TnTeT3sSEwlX83ciSNXzTxuT1lf5dxXM1/+y8+Bmwtnopgq73piflG6M1Va4UchMonQiSnlTKUGhA6OVCIRFY+fh/m9QEJYnf3Db865wsFK5GjxerwACxNKQK+8aua1t9zKtYjBcXIWTbw4cs/XOVItBw3ZoLlwpompVFR9F38jZ0pEjdCJKX+nopypYJAj5R2pZPAO1fhF+YEdLZEd1cx9c+HGx57gkstj4TnT29x67wZCKhPFVNCq7yIajY6FKInQ7ckq2pma0BbJ5hUZxzZq/kJbsnpt0o6WrXe07mnXJnRJ7HKmKreaOQ2FK9JcONNKI6RiPWXS760KonaznMl5pSKiYso7U6qAXnE4qGNH7SULl4p5q8qXBxXraPXfpGG5v1NkdjVzGgnHNhf2z8uiMhsdh3k9iWiJqWwdKS02JHRnND+6Q2Kq4nB3VNWMXrAolMneYVymsEB9pEDvX7aswu/NKDGVxvUUVaIgpqKQVypSR+jOaMqZSsE6rOILFXKFu7K/1lS9iCsNhfkqv5p5RcmkEEg611NUyXQxlU15pa+//rqdccYZ6V6M0BM6MeUTEuVMVRzi9djMVX25WpYGR0xUHF/NvMzyBomqmTdtVlTNPPJiKo3rKcpiKpP2gVSOlM409ttvP7v77rvTvRihJ3Riytv/ElMVh5MU8fqqpk7IQjdypspeP1TsLvdIhbhq5hXdNplCOtdTVFmzZk3GrpcgI6UzFV+ySJRMuK5+MUhMBYPEx5rVcpJ2p5ivZo5Zkxrld7SYHyesfvXw7U7KmSodqpnn1KqZvOtCNfNaxauZV4RMu5Cmaz1FlUwN8wUZKZ3KvNK9997b3njjDTvxxBNtu+22s3333dfGjx9vY8eOtb59+7qWKyeccILNmjWraP7nn3++2Gccf/zxduuttxZtj+uuu866d+9uXbp0sQMOOMDeeecd9xrv4/2e5cuXu/54e+yxh5t34MCB9tdff1m2E76rX8ydS5ThoFqyeo3NXbnK/U31RZ8RJLSIWVc6sXT8PIM3b1nhEXn9NmkYuguknKmyobRB0GrmFd02mUS61lNUydQwX5CR0qnOK73mmmucYEJUde7c2QYPHmwPPfSQ3XLLLU4A/fnnn3b77bcn9VkjR460999/3x555BGXI3XOOefYZpttlnDeK6+80j744AP32a+99pqdf/75Vr++QtmhK40QdWeqKofS0mtvSOsWxXrzxUo2fyrDwRq0WQvrklfXtqpby/XqS9bGdo5WtRzr0VBDvzMVim9SzbxYz7lYcc9Fr9BcNXMEQqIinOUlEy+k6VhPUSVTw3xBR0qTV5pnqTm/40D17t3b/f+UU06xV1991c4++2zr1KmTm3bwwQe7acmA20S+cosWLWyjjTayVq1aJZxvyZIl7jMffPBB22mnndy0kkRXthFKMcVBlqk2cGU3HS4vfA4FNYnXYzPHCrimNXOdo9SzYZ7VXS/gvKPFCBQoTMLRQoiFsZaKnKnk8dXMqdhNocnYukpUMyf3h1BX9bp1U7ZtMpGqXk9RJVOdqaAjpVOZV7rFFlsU/b/eege0devWxaYtXbo0qc869NBD7euvv7Y+ffrYYYcd5hyvZs2abTDftGnT3Lbr2LFjSn5DlAilmIKoiSk/lLakESB+mh9Ki6OUKkGF0CF812/jBs5m5u6Ig5ocp0QntIo4WmFFOVPJU5Fq5tlQZyqd6ymqrF27NiPXlR8pzc1veRIzctbfvKYyrzRRk+jytOeJFVp81s0332w//PCDPfbYY9avXz+74447bM8990x4Ps3EbVfZhPKMxoaKUs5UWIbSsl7zcqtb05o13N/SDgjvaJ3QorE7CcTCc6bf265NqIWUDviKrzcqdtdo0tT9rYz1GIVtUxXrKapkqjMVZKR0OvNKcany8/8eTbhixQrnMsXTrl07l1xO2PDJJ5/c4HVCetWrV7fvvvuu0pc50wilMxW1MF+QobTpbNFSXkdLiGTR/pPdcLOcqe4kea2ZlldKHhVJ6d26dXNdRkg0R1B5xo0bZytXrrT27du7v998802xMKKnUaNGLleLkX9XX321NWnSxCZPnmw9evQo6qubrYRyb46SmArLUNqqcrTCRhjWn9iQTNqHROrJ1AT0io6UTndeKSPucJWOPfZYO+6446xx48aupIEHYUtYb//997djjjnGiabzzjsv4Wchoii9cOaZZ7pw4L333qvzLNu5MIRXG5QzG+uzzz6zTIeyB2dM+bXC73+w/RZOwIjywwmDXICZM2dq9YUILqLUuHniiSfSvSgiTRBO+v333zO6PlH8gKJMzSsVEQ/zkaAYBcI0lFaIsJAprgT3mmv/ynfNjunRR2uZTFn2MMP5PVPDfBUdKS2iTSjFFAdZVMJ8YRpKm22oNEL48DdJYRckawoKLP/DcbborTHFyx80beZay7jyByrImZU5U7Eor1SEWkxFaTRfmIbSZhsSU+EjE8RUwcQJxQtzxrB63lyb//RwWzDiWVcRnbpTInvFVHxeqaII2Uso9+YoJaBn6lBaIbJRTCGkZt061ApXrlyXBROfUuqeF7rXmY/5RfbUmRIio8QUdyxRyZmqcNNhtWgJjJyp8OGP6zC6EoT2cKScYCprXM76eZif94lyrus1a1y9IiGiQvjOaOtPtFEJ82XqUFohss2ZIkfKhfaSHeBcWOjmp7WMKH9ifxgFtRAVJZR7c9TEFGxVt7bt1WijMkUUjtSQ1i01lDYFyJkKHz58HzYxxcWdZPOyexTEkWOuR18IK8yEmqjlTAmhBPQqrkdS0imX6fs02siOab6JhtKmCImp8BHWMB/lD2JH7SVNYaF7Hz36aCkjsqc0QiyI6RX5a2318rWWW7ua1cpTl4hsI5RiKko5U2U1OI7lnYVLrOtG9VLW4FioAnrYCGuYjzpSgd6/bJnEVBaKqZUFa2zquHybMmaR5c/5e9BUXrNca9+3obXtmWc16yldIxsI5d5MYmIUxFR5GhxbJTY4FiIshFVMUZAz0Pvr1EnZsmTLfpDpCei/TyiwF87+zT4fPt/y5xYffc5zpvM684noE0oxFZWcqSANjkVwFOYLH6EVU/XzXEFOK+9y5eS491WrV7+yFi2SEBbLZDGFQHpn6CxbvZJRnQnultdP43Xmk6CKPqEVU5nuTEWhwbEQ2ZIzhbijsnm5D9hCs4Z9+oVOHIadTA7zEdobN2y2LzlWOuurbDA/78tUZs2a5Rogi5IJ5d4chTBf/pq1xXo1JQvHJu/7a01m//4wIGcqfITVmQJaxOTUqpm8O5WT4+bP271HZS9a5MhkZ4ocqSJHKhnWO1RTx2duxOGDDz7QDX4miqkoOFOpaHAsgiExFT78cR3GCym99mgR48RUWYJq/TwtzhmkHn1ZlDOFCCTZvCIhhymjUxdxeP75561v37623Xbb2d5772333nuvffrpp27ae++9Z/vss4/tuOOOdsYZZ9icOXOKrfc777zTevToYZ07d7ajjjrKvvzyy6LXX3zxRTvzzDPt448/tt69e9uuu+5qzz77rF155ZX21VdfWbt27dz3Ac+POOII23777a1bt2527rnnRqZzSWRG80XBmVKDYyEyy5kCeu21uGBI8d58sRdAlrvQLKdmTSek6nZSb75scqYofxA7ai9pCs29b8Vfa612XrDfPWPGDLv66qudgGrbtq399ttvttK1P1r32vDhw+2uu+5y6/faa6+10047zV555RV3zD366KNOiF1//fW2+eab26hRo+zUU0+1kSNHWuvWrd1nTJ482b3/1ltvtWbNmtlGG21k06ZNsy+++MLuu+++ou02ePBgO/bYY504mzdvnv3000+WmxtKSVElhPKXR0FMqcFx+pEzFT78nXlYxZQXVG2G3eMqm1OQM7b+VG6Tpi5HipBg9bp107qcmUymiinqSAV6/7K1XBwCfcaKFSvc9RGh07x5c/cAnCkGbl144YXWvn17N+2qq66yPn362KRJk5yLhZg677zzrHv37u51XKj333/fnnnmGRsyZIibNnfuXHvsscecUPPUqVPHba+NN964aNry5cutQYMGbjmaNWtmnTp1smwmtGIq0xOwfYPj4bPnl/u9anCcum0gwkVYE9AThfwa9ulvDfbp5wpyUkeK8geM2tN+lb1hPgpyBnp/neD7/VZbbWXnn3++HXnkkdarVy/nLG277bZFr7dp06bo/7hP9evXt+nTp7vpOEjxoofnP/74Y9HzRo0aFRNSJXHLLbfYxRdf7FyvE0880YUFs/nYCOUZLQrOFKjBcfrJdFEeNcIe5ouH5aSyeY0mTd3fTFnuTDguMzEkRGVzCnIm3bXek7OukGet+qm55BK6GzNmjAvNnXDCCS4s54nNW2I987xWrVq2atWqomnxx2SNGjWKntesWTOpZdh9993t7bfftgMOOMCGDh1qJ598ciSu25ETU1G4CKrBcXrRhS98ZJqYEpVDpob52G+pbF4R2vdrmNL9vnHjxi7p++6777aHH364aPrPP/9c9P9ff/3VheNwqHCcmjRp4kJ+sUycOLFMJ4rlTiSUEGkkoY8cOdImTJhg3333nWUrElOVDK1hhrRu4RoY+2bGmdLgmBPektVrbO7KVe5vFASuSC+ZEuYTlUumOlNAi5jcmozmTPINOebmb9sjNb0bEUiMsEM0/fHHHzZ+/Hhr1apV0XF10003OVHzyy+/uET1HXbYwbbZZhsniE466SSXME6eFMnqJJSTcH7ccceV+p0tWrRwoUCEF+8DEuB5zmjB9957zzlgzJethHJvjkqYL1ZQ3dOujatsTkHO2PpTTWvmuhypng3zQtPgmHY2VG+n6GjssjarmevywAhf4rqFHSWghw85U8KTic4U0Guv56DmrrK5u70s7R5zfZWNXoNapKxHH2E48pRuvPFGdzyRWH7zzTfbokWL3DpFGJ1zzjkukXynnXZy4spDfhX897//dflTlDp44IEHyhRBAwYMcCE9QorMyyjAKVOm2BNPPGEFBQUu3Mgy4JZlKzmFIbQbqFnBMEwf440SrG4KclJHqk61ala/eri6i9OYmf6AtLWB2J3DLyUu2qDNmoe+ITMJl4sXL7aFCxeme1HEevyooksvvdQN2xbZCQ4Ko8zI+8lUaBFDZXNXwLOEkyWOFEKqZZfKjzgwmg+xg9OUqUI1kwmlM4X9G0KNlxIQTnm51S3PwrezI6SGTlt3t5Vo7ftpCC3mI3wZZkElZyp8KMwnPJka5vNs2qWeHXZPG1fZnIKcsfWn8prmuhwpQoI161btuT6q186wE8q9OSoJ6JkEoT0cqSTbTTmYn/BlWEN+YXL8xDoU5hOZnjMVC6G7Dv0bWvt+DVxBTupIUf6AUXs6/2QXocwCjcJBlmmQI4XjVI52U25+8sDCipyp8CFnSkTxPM+5hsrm9ZvWcH8lpLKP0IopOVNVB+uaZPOKeIEk1GtbiWSRMyU8yutJLfTR++GHHyIlUjOJ0IopUXXkr1lbbNResiC+eB8J9WFEzlT48MJbF1IRWyhSiEwntGJKbkfVsTxgGQpGJoYRWe3hQ86U8PuAxJSIEqEVU6LqqB2wgCIlHsKInKnwoZwpsXLlSrcSdJ4XUSKUV0HdsVQtedWruYKcFWg35d5HrSwhkkHOlPBiSud5ESVCeRXUQVb1Dg6VzSsC1dsVThPJ4sP3aieTvdArDuRMiSgRSjHlk1Nju1+LyoUWMb5/YDL4foI9Gqam31TUw3wsx7I1S2zJqrnub1iWq6qRMyW8mNJNs4gSoUxO8gcZB139+vXTvThZAYU3aRFDZfMk2k25x6DNWoS2YCeEwTFbsabAvs8fZxMWjbHFq+cUTW+Q28y6NOxrHfJ6Wq3q4a0in2qUMyV8mzCJKRElQulM+YPMx9ZF1UBrGFrEeIcqXor4abw+pHVL65JX+f2mMllMTSuYYI/8draNnz/cFq+eW+w1njOd15kvW5AzJRTmE1EklGKKrtixB52oWkFFi5gTWjS2pjWLG5c8Z/q97dqEXkh50hVOQyC9MmuorSrkhiBRk55103id+TJVUJU3fKmcKeGdKX+eFyIKhDLM5xMTV6xYke5FyUoI3fXfpKH127iBK8hJHSnKHzBqL91uT3lI17IS2hs1e5gVJtnpkDmY/5Q292RMyK+i4UufB5lJ+5FILf68rgR0ESWqhT1nSqQPLnh5udWtac0a7m+mXQDTlYCOyPjbkUqGdQ7V9/njLRMIEr5UzpTwYkrOlIgSoRRT/iDzdrAQFSEd4g/xhluTvJD6mwmLRod+lF/Q8KX/fZkmzEXqUJhPRJFQO1MK84kgpOOCvXxtfrGwV/IUuvctX/uXhZXyhy8L3fy8zyNnSvjzukbziSgRamdKYkpkWphv1dpgoelVa5dZWElF+NKLKTU6zl7kTIkoEsoEdDlTIigff/yxTZs2zQnyAQMG2FZbbWUdO3a0HXfc0bp06VJp+Ro1qtUO+P46FkaChi+7NOhXTNwqzJe9yJkSUSSUYspf6FRnSpSHzz//3K677jp766237K+/1oXLateubW+88cYG8+KM1KtXzzbeeGNr0aKFtWnTxtq1a2edO3e2rl27WqtWrSq08mtXy3Mj2tYlZpdHeORYg9ymVrtaOIvUpiJ8Wad6nsJ8oui8rgR0ESUkpkRG89VXX9m1115rY8eOtfz8fDetefPmdvzxx9ull15aJIqmT59uX375pX377bf2ww8/2G+//WazZs2yP//802bMmOGcrHgQYhtttJE1adLEfU7btm2du7XDDju4B6/Hg+NCaQBGtJWXLg3/dm8QL4QMcboQaOl2clIRvowVU+n+PSJ9KMwnokioxZRypkQivvnmGyeg3nzzTVuyZImb1rRpUzvmmGOcgNp88803eA/TeAwcOLDEu+UJEyY4cfbdd9/Zzz//bDNnzrS5c+fa1KlTbfLkyRvkX+Fu1a1b17lbCDjcrW222ca27dzOVm5pVnOjnCTdqRyrkVPTtqy7s32zaFQoW8+kKnypop3CO1NKQBdRIpRiSu1kRDyTJk2ya665xkaPHm2LFy9203CMTjvtNLv88sutdevWgQU84T0eJfHHH3/YF1984dytKVOmFLlbCxYssN9//90+/fTTYvPXqpdrjVrWtUYt61mjFvxd/3D/r2cNm9Wx6jWqG817dm50sD0148L1Cd6WsHbTxwuetQHNB1nrel2qfAdJVfhSo/mEd6Zq1aqllSEiQyjFlOpMCcAhwoEi52nRokVuWuPGje3kk0+2yy67zIXdqpKWLVvagQce6B4l3XEj+orcrVkTbXHudFs4u8Cmf7vAJoyZYQUL/xZLOdVybKPGta1W7Vq2aacPrFGLetZo01jBVdfqNqhpPiLmazcd2GJIlQuqVIQvQQnoQs6UiCKhFFP+jkUJ6NkH+UwIqNdee80WLlzophFGO/HEE52A2nrrrS2scBPAaEEexduujHcj2gjdrVi62hbNWmpLZhTawom5NmPSn7Ys7w9bNHupzZi00BbOWmprVq39+zPr5m7gan3a/CzbodZA22n7XWznnXe2+vWrJmmdMCPuWPLlEdaFLzvk9SiaImdK+JZCcqZElJCYEmnnp59+KhJQhMygUaNGduyxxzoB1aFDB8tUyHHavmF/VxqAEW0kYtfoWMeFvXBryJFa5/asEydr1xbaXwuW28I/lq57zOJvgfv/zO8W2sSxv9tff66wp+yTou+oVq2a1alTx62zZs2auZAnuVudOnVyYgsByjyp+C2EGXHH1i1taYKK4GWODWgxqFielxLQhUbziSgSSjGlnKno8+uvv9rVV19tr776qs2fP99Na9iwoR199NFOQDFqLkognBjNxqO02k3VCP01qeMerbtskvCzVi5bY8tn1bAa33SxKd9PceuSfC7WI/lcjFpM5JrhYBEm3XTTTW3LLbd0InX77be3nXbaya37ZCC8SJiRyuZ/53fFiqp14TwcKYRU67rFw5FKQBdeTMmZElEilGLKH2TqzRctKKJJEvnIkSNt3rx5blqDBg3syCOPdKPwqPGUTVS0dlPNOtWt5pZr7fTeZxcTZ7Huz/fff+9EFTlcOH+UhpgzZ45LlGek4rvvvlvsPd7dQlThbjHyEUcLdwuxhfDy7haC6pQ29xQLX3pINidHat3Iw7obLNuaNWuKvk9kJwrziSgSajGlnKnMh4s4hTRfeuklV2YAqN102GGHOQcKZyRbSVXtpngQKjh7pbl7JPQjtigzgfD65ZdfnNDC3fJJ9IkcY+9ukYy/xRZbWPsO7a1jl71tux22taabNC8KX5aEwnxCdaZEFAm1mJIzlZlQn8kLqNmzZ7tpeXl5dsghh9i///1vl8cj0tt6Bgeqd+/e7lGS6MHRohQE4urHH390ziLuFiFFam+NGzduw1BmnTrObfTuFm18vLu17bbbKgFdyJkSkSSUYkrtZDIPLrDXX3+9jRgxwtVeAlyMgw8+2IXwSqvflK2EufUM7hbtdXiUBAVTcbBwtyhq6t0tQrg8Z3qiz4V//etfdscdd6xzt9q3d/0SEdkUXxXRRgnoIoqE2pnysXURTgjbMQrvhRdecGIK6HdHHaZLLrnEunXrlu5FDDWpqt2ULgjX9urVyz1KcrdIjveFTnG36J+Iw0WY8cMPP7T333+/2Hv4TbTpwd1CWG222WYud8s3qSavLjc3lKctkSQ+4pCoHZMQmUooz0r+IFOYL3yQU4MD9dxzz7lwnhdQ++23nxNQe+yxR7oXMaNIRe2msIILRWFVHgwygPvvv9/OOusst/8gumlIjYP19ddfOzeL8CH7FfsZNccQYfEgptjnNtlkk6Im1bhbCK1ddtnFtfYR4cXfJEtMiSghMSXKhGbAN9xwgz3zzDOuKTCQG9OvXz8bMmRIic6EqJraTZlEfNFOQsEI8JJEOPOzz+FoeXeLNj7k4hFOxOXC4Yp3t3C3vbtFk2pyt7y7RUjRpxKI9IkpOYwiSoRSTKmdTPohDDN06FD73//+5y5YXkDtu+++dvHFF5eYuCzKT9DaTZlEeetMMR9FSHkwAjQRy5cvd84WD9r4eHcLsUUS/cSJEzd4D02qcbeoru/dLfLDcLfI70OAicpB6RsiioRaTOmgq3oBddNNN9nTTz/t7v69Fb/PPvvYhRdeaH379q3iJcoegtRuyiQqozQC+yj5eaXl6FGig1IQuFuED32TalxXnK+PP/444eeSF0ZDbcQV4UrcrR122ME9FKaqGErfEFEklGLK37XqoKt8GJF1880321NPPeVGYwEhkr333tsGDx5sAwYMqIKlECW2nqn2d+uZKJCuCuiUaeAxcODAEkeYTZgw4e8m1T//7NwtBlngdJHP5Zc91t2qW7euc7fI08Ldoo2Pd7dInldx0g3RTbKIIqEUUx6JqcqBpN9bbrnFhg8f7i4U3g0k9+mCCy5wicEiXK1nokJYGx2z/yOASivhwYhVPzJxypQpRe4W/SQpCfHpp59u8B5uTHC3KHTq3S3qbeFskb+FGMs2JKZEFAm1mNJBlzqWLl1qw4YNsyeeeMLddXOXzQWkR48eNmjQIDvooINS+G1CJCaTe/NR9Z0bjZJuNnC3fPV43C3ytQgh4m4hvBBgY8eOLfYe1gOCiibVuFu+STXuFnW3qMOVieuqrPN6VJxWITJCTPk+XqLiAur222+3xx57zJ3YuZDREqR79+52/vnnu5BH1E7UItxEuZ0MNye4TTxKAmGFu0VIEXHlm1TjbuFyMWox0efSQYDcLd+kGneLVkwILkZEZhI6r4soEloxxclWYb7yw8gmKks/+uijLtEWAcUQ5N12283OO+88O/zwwyWgRNrIZGcqFVCqgTzEknIRcW3Iz/JNqnGRfZNq/nJMv/322wmbVONu0cbHu1u08UFsUfQ0TOtbzpSIIqEVU6AwX3IQXrjzzjvt4Ycfdne7XkBRwPCcc86xo48+OlQnU5G9eFdC+2NiOG4J8fEoCQqaIrZwt2hS7d0tppPPxWuJ3C3fpNq7Wx06dHDuFn0T6dVYlftAFJ1Jkd2EVkxxsElMlS6g7rnnHnvooYeKRhoxuog7UfqeHXfccbpgidAR1gT0TAJBRJmSkkqVsI4RWd7dIsTv3S0S5XG73n333YTuFqLKN6nG0fJNqhFeqdpmcqZEFAm1mFJsfcOT0H333WcPPPCAS3DlpImAIkfjn//8p5144om6SImMCPPJmag8ED3Uw+JRWk05xBatfBBevkk17pZPoo+HfEvvbpGMT3I8IouK8oyCpEREMkhMiSgSajElZ2rdiefBBx90Pc2o5OwFFPb8P/7xDzvppJPUlkFknDOlViLpBQeKLgYldTJgO+FokSyPuKKND50QcLcIKVJSZdy4cRuW9KhTx7Xx8e4WbXy8u0XSPNtdYT4RRSSmQiqgHnnkEedCkRfBiY27TfIoaBJ76qmn6mKU5e7O8rX5tmrtcqtRrbbVrpaXMU5PtiegZwpsH9rr8Cit4C8OFu4WqQbe3aKND8+ZnuhzOZ/hcgkRJUItprIpzMcJhhF49957rzsJ8ds58XBXd+aZZ9oZZ5whAZXlrFhTYN/nj7MJi8bEtZtpZl0a9l3fbqZeqIVXlEsjZBsUI6XQb0mNztnWJMfjbtEk/a233nIFg+Gyyy6r4qUVonKRmEojnGyoQn7XXXe5Jq1eQJHrcPrppzsRpe72AqYVTIhrhPw3i1fPtfHzh9vHC561Ac0HuT5/FRFeVYGcqewq08K5DZcdF4v0hD59+rjad+RaCRElQiumEBVRdKYQUDQSppQBFrlPxiSf4LTTTrOzzz5bAkpsIKRemTXUCo0QWfH+cOtYNw2hxXy7bXy4fbHw5aSFV1UiMRV9yLGiKDAjBjnfkUNFo/Srr75azaFFZAmtmIpSAjonlGeffdYV08Ty9gKKfIRTTjnF1YJSB3qRCBwmHKmShVQszFVoH//5LEdQUsLrwBZDqlRQqTRCdCFNAcFEaA/at2/vnh922GHpXjQhsltMZbIzxUXjhRdecJY2LSKo5s5vojIxI/C4c5OAEmVBqG6dw1SWkIonGeFlTqid0uaeKgv5yZmKFoTvLr74YnvyySetoKDA5XXuv//+7saR0glCZAuhFVN+1EcmwfK+9NJLdtttt7kO8l5AMTyYGlAXXHBBVnaJFxUXHuQ8lV9IJf0NTqh9nz/etm/Y36oCJaBHA24QOZ999NFHbj/dZJNNXMP0yy+/XGkKIisJtZjKFGdq5MiRNmzYMPvkk09cZXJAQB1//PHuBJNpjUhFOGAUXmzyeGUxYdFo69KgX5WMsJMzlbkghO+++2678cYbbebMmW4a5VpuuOEG69+/asS4EGFFYqqCvPbaa05AcWe2YsUKN41+V15AMWxYiCBQzqDyKXSCbfnav6xO9bzK/zbVmco4qIo+ePBge+6559wIPUYYkwd16623WqtWrdK9eEKEAompcjBmzBi76aab7MMPP3QnFSAv4Nhjj7WLLrpIAkqkFOpCVRWr1i6rEjGlBPTMYfz48U5E0XYGEUxV8yuuuMLlSKnoqhAZJKbCkDM1duxYJ6Def//9IgHVunVrO+aYY9xJpSq7rYvsggKb1IWinEHl5U2to0a1OlYVyJkKN5xzCeMxcGb27Nku9EsrmFtuucV69OiR7sUTIrSEVkxR4C1dYuqdd95xJxTuzJYtW+am0WfqqKOOsiFDhiTd0FOIIHAho8AmdaEqjxxrkNvUalermrw+nwcpZyNc0G+PEcbkf5L3yUjjE044wYkoGhsLITJUTHGyZTRcVYFwIpGS5p1Lly510zbbbDM78sgjnYDSCUWkAyqVU2CzYuURkqNLw6pJPo91prhZEunnjTfesEsuucS+/fZb93zTTTd157t//vOfErxCREVMVfZoPnKfrr/+envvvfdcjRR/MqEP3r///W9r2rRppX6/EGVB/Scqla+rgG4pFlQ5ViOnpnXIq7rwjcJ86Qfn6ZprrrF77rnHFixY4IR09+7dXUJ5165d0714QmQk1bItzEf9p4MOOsiVK9hjjz3s9ddfd4njVCGfNWuWG/LLSUVCSoQFKpRTqRzhs66yebyLtG5ajZxa1m3jIy3HHdZlOU057t+AFoOqtEefEtDTB5XJDzjgAKtXr56rTE4OKP0///zzT/vggw8kpISIojOFmPJ3sakoMHfdddcV61revHlzV0iT7uUtW7ZMyfcIUZmCikrlFNikLlTx5sVNXahuXfPiuta01pZxTZFjj6N1IgthhpBqXVe9+aLOiBEjXDHNKVOmFI1A5rx36qmnpnvRhIgMuVF1pmgifO2117rRePn5+W4aQ3uPO+44F8IjoVyITAIHiUrlFNikLhTlDBiFR/J4bM5TeYRXVaMwX9WA6/Sf//zHHnzwQVu8eLFLm9h7773dKL1OnTpV0VIIkT1EqjQCSZTY12+++abrGQWE6xiFx50YJQ2EyHQQTtSEKq0uVLLCq6pRmK9y+f777+28885zI5LJOSWFgVF63FiqlZUQWSimknWmJk2a5E4Uo0ePtkWLFrlpTZo0cRY2AkrNNkU2k4zwqkrkTFUOTzzxhF111VU2depU95yG6ldeeaUdffTRlfSNQoiMz5ni7ovRKAzrXbhwoZtGo82TTjrJ5Qa0bdu2ipdWCJEMElOpg/xPyhggpPg/50x65BHK23rrrbVDClGFhFZM5ebmFhNTP/zwg3OgGH3H6BOgeCa98HCg2rVrl8alFUIkg8RUcMgHveCCC9wIPNz7Ro0auTxQnCj65gkhqp7Q50zhNtFUmHoowImDVi44UB06dEj3YgohyoFypiq+3u6//35XF2/GjBluGonk3GAeeOCB2geFSDOhFVNUH6fB5uOPP+6EVYsWLWzfffe1vn372p577qlu5UJkIHKmygcu/IUXXmjPPPOMa21Vo0YNGzhwoN12220akSxEiMgpTFUxp0qAuD/DeskH8D3yYhNrGZ1CqA/hxbydO3e2bt26ucacsruFCB+nnXaaPfzww+54pv+bKLk7w+DBg+2zzz5zApRBNeeee65r/UIKhBAiXIT6qPzpp5+K2dw8/+ijj+zrr792SejTpk2zefPmuarmTI+FO7gGDRq44pxt2rRxljgii6rnTBNCVD0K85W+bui+QHNhujHAjjvu6Jqu9+7du8q2kRAiYs5UeaCu1Mcff+yqnVNv6pdffnGd0BntRwG7eFeLlgqMAKR4J8OIu3Tp4lyt7bffXnd+QlQS5EASuqeJuRyWdcyePdsGDRpkL774oq1YscJq1aplhx56qBNVuvETIjOIjJgq644PJwvr/JtvvnEjA72rRQgxvp4VIUJcLfK0ttxyS9t2221tl112cc1AGzdunLbfIUSmQwsnhvJTUJJcyGzm7bfftosuusg57UBbK0J7FNnM9nUjRKaRFWIqmSRPwoRffPGFTZw40bla2OwUAeVOMRZOcrha5DCQq9W+ffsiV4ucLZ0EhSiZE044wYYPH56yvpuZxurVq12f0LvuusvdzOGS77rrrjZs2DB3DhFCZCYSU2WAa0XYkBAid5A//vijG5rMibCgoGADVwuLvmHDhu4uE1eLXC1crd13391NFyKboTfmU089lXViavr06a7NC3XyCHHWqVPHlXghH4pBNEKIzEZiKiBz58514UNcrcmTJ7t2DuRAMApx5cqVxebFtapfv77rF0iuFoVGSTDljpSaWXK1RNQ59thj7emnn84aMfXKK6/YpZdeat999517Tn9QCmyefvrpOt6FiBASU5Vs6eNmffLJJ+4voxFxtebPn29Lly7d4ILCUHGKkuJqbbXVVs7V6tq1q8vVQoQJkengxvzvf/+LtJjiJuqKK65wRTYZAMNNErXxqA3FABchRPSQmEojjDakJQTFSXG1fv31V+dqMTKRUEAs9N3Ky8tzrhZ3t+RqUeqB8CG9COVqiUyAxrsUoIyimOJmiVpQY8eOdQn23AAxepGq5boZEiLaSEyF+O6WMg/U0JowYYI7Uc+cOdMlyydytcjBIPfCu1okw5Ortdtuu7nipkKEgaOOOsqeffbZSIkpnDacKF8Xj+OP5+SHCSGyA4mpDIXSDn4EImUffvvtN5szZ45ztQgvxkI9H1ytZs2auQKm5GeRq0XoAZdLiKriiCOOsOeffz7jxRQ3NOQ+Pfroo5afn++c43322ccV3VTPUCGyD4mpCEKRUvK0aEXBSETumH///XfnaiVqy4OrRQHTTTfd1LXlodQDw7V33nlntfwQKeXwww+3F154IWPFFMcTdaDGjRvnRvIyQpdk8quuukrHihBZjMRUlsEFgNwsRiCSqzVlyhTncuFqcYdNrke8q0UBU+9qdezY0eVqkRTfqlWrtP0OkZkcdthhNmLEiIwSUxwzjzzyiF1zzTXuWAHcp2uvvdY1HRZCCIkpUQwqwlNTC1dr0qRJztUqrS0P+Vi4Wr7ZtC9gusMOO6jZtNgA2qTQNiUTxBRFe6lQTikHwnrcWOy3334ulLfFFluke/GEECFCYkqU6w6dVjzkan311Vfu/xQjpNYWIize1aLZNGEQ+otx8Yl1tdRzLDs55JBD7KWXXgq1mGLQB73yuKlgOblZ+Ne//mWXX365+gkKIRIiMSVSeicf62rFNptO1JYHV4teh7HNphl9qGbT0YWw2Msvvxw6McWNwp133mk33XSTyy8E9sehQ4da37590714QoiQIzElquxihcBCbNFsmlwtXC0KmJbUbBpXyzebxtWigOkee+yh9hsZzEEHHeSqgodFTOGq4kKRx0UYm/0OwUevPMqMCCFEMkhMiVCAqCJ8SG0tWm/ENpsuqS2Pd7UoYIqbRa4WVeNVwDS8HHDAAfbaa6+lXUy99957duGFF7pwNctC2PmCCy5w07T/CCHKi8SUCD3UzWJIOmKLAqY0m/auFonBiZpN+7Y8vtm0d7U22mijtP0OYbb//vu7Zr/pEFPsR4TxaOuCI8UACsp/4EKxbwghREWRmBIZDy14fKkHXC3flgdXK1FbnkTNpmnLw//lSlQujIZ74403NhDAlQmdA6gN9eqrrzqXkx6YRx55pN18883O3RRCiKBITIlIw8WTJtPkavm2PDSbXrBgQZnNpin1gKtFWx5CiOqvFpwBAwbY6NGjq0RMjRo1yi655BKbOHGie075josvvtjOPvtsiWYhREqRmBJZDeFChJZ3tXxbnsWLF2/Qlsc3m6aAqW82jatFiIjSD3K1yqZ///42ZsyYShNTiOerr77a7rnnHlfxn1AepTioDUVITwghKgOJKSFKgNFd9D5EbOFu+LY8uFq05Smp2TRteWh2y9B632wax0uY9evXz958882Ui6mpU6faeeed54QaIrhevXp2/PHHu9IGypMTQlQ2ElNCVBDflocRYbHNpmnLk6jZNBd1crVwsWKbTZO7lS1Qs2ns2LEpE1P0+aOYJgVkgQEHPD/55JNT8vlCCJEMElNCVALkY8U2m/75559dAdOymk3T75Bcrc6dOztHi1GI1D6KCvvuu6+99dZbgcQU6/Y///mPPfzwwy4cS3h17733dqP0qEcmhBBVjcSUEFUMQoKw1AcffOCS4ylgiqs1b968hM2macuDq0UtpNhm0+RqZVphyT59+tjbb79dITFFThuj8t555x33fhpwn3LKKa4BMdX0hRAiXUhMCREylixZ4vK0KGCKqxXblqekZtMM8Y9tNk2pB5pNE14ME/vss4+9++67GwjG0nj88cftqquucusBKGHB8yOOOKISl1QIIZJHYkqIDAJHhvwsxFaiZtOJ2vLg4OBq+bY8jGpjhBv5W1VN7969XfXxssQUgnLIkCE2fPhwKygocKKQfKvbb7/d2rZtW2XLK4QQySAxJUSEoFAp4UNGIdILkXCib8uTqNk0o968q4XjQ1se32y6Mko9kNs0bty4EsUUy02vPH4DoyUZHXnWWWfZFVdcEancMSFEtJCYEiJLwLUibIirRa4WbXkoYEquFu5PorY8uFq+LQ+uFqUeCCEicirCXnvtZePHjy8mpvje++67z2644Qa3PLDddtu55xT5FEKIsCMxJYRwECqk1APu0OTJk52rRVseRsyV1Gy6SZMmxZpNI7S23XbbEl2tnj17uu+gdAQjG3GhnnvuOTfCkUT7Aw880I3KY1SjEEJkChJTQogyQfzgZlHugbY85Gr5tjy4Wona8jRs2NC5WuQ4+WbTVCf/9NNP3WhERBvvI3eLUXoXXXRR6BLmhRAiGSSmhBCBYbThRx995EYg4mpR6oFcLRLJ45tNuxNPTo4rWnrjjTe6PCohhMhkJKaEEJUKIUJEFo4UOVuEA4855pi0jCYUQojKQGJKCCGEECIAqR/7LIQQQgiRRUhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBAiABJTQgghhBABkJgSQgghhAiAxJQQQgghRAAkpoQQQgghAiAxJYQQQggRAIkpIYQQQogASEwJIYQQQgRAYkoIIYQQIgASU0IIIYQQAZCYEkIIIYQIgMSUEEIIIUQAJKaEEEIIIQIgMSWEEEIIEQCJKSGEEEKIAEhMCSGEEEIEQGJKCCGEECIAElNCCCGEEAGQmBJCCCGECIDElBBCCCFEACSmhBBCCCECIDElhBBCCBEAiSkhhBBCiABITAkhhBBCBEBiSgghhBDCKs7/A6j15Qo16QTfAAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 654.831x480 with 1 Axes>" ] @@ -482,16 +469,16 @@ "id": "88459520", "metadata": { "execution": { - "iopub.execute_input": "2026-09-05T10:29:17.956301Z", - "iopub.status.busy": "2026-09-05T10:29:17.956215Z", - "iopub.status.idle": "2026-09-05T10:29:41.363817Z", - "shell.execute_reply": "2026-09-05T10:29:41.363272Z" + "iopub.execute_input": "2026-10-08T17:27:35.004450Z", + "iopub.status.busy": "2026-10-08T17:27:35.004386Z", + "iopub.status.idle": "2026-10-08T17:27:57.015390Z", + "shell.execute_reply": "2026-10-08T17:27:57.014918Z" } }, "outputs": [ { "data": { - "image/png": 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", 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be combined with animation -- here multidimensional # scaling (MDS) replaces PCA. -fig, ani_mds = hyp.plot(data, animate=True, reduce='MDS') +fig, ani_mds = hyp.plot(data, animate=True, reduce='MDS', backend='matplotlib') diff --git a/examples/animate_conversation.py b/examples/animate_conversation.py index 98f14b60..8d080f1a 100644 --- a/examples/animate_conversation.py +++ b/examples/animate_conversation.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -=========================================================================== -The shape of a conversation: per-speaker paths, revealed one turn at a time -=========================================================================== +========================================================================= +The shape of a conversation: one path per turn, coloured by its speaker +========================================================================= A conversation as geometry, in one ``hyp.plot`` call on raw dialogue. Each **turn** (a contiguous run of speech by one speaker) is cut into sliding @@ -23,16 +23,18 @@ margin, sized to the tallest wrapped title, so the figure never has to grow by hand); the legend maps colours to names. -The one bespoke effect left is a **recency fade** across turns: the current -turn is opaque, earlier turns recede slowly (a turn keeps most of its -opacity for several exchanges before settling at a visible floor, so the +The one effect written out by hand is a **recency fade** across turns: the +current turn is opaque, earlier turns recede slowly (a turn keeps most of +its opacity for several exchanges before settling at a visible floor, so the conversation's recent past stays legible), and unspoken turns are hidden. -Nothing in 1.1 fades across already-revealed datasets, so it is real custom -work -- but it runs on the public ``on_frame`` hook and reads the schedule -the library publishes (``ctx.current_index``, ``ctx.revealed_counts``). -Before 1.1 this example monkeypatched ``ani._func`` and re-derived that -schedule by hand; the hook replaces both. The clip runs 30 seconds with two -full camera rotations. +1.1 ships the same fade as one keyword -- ``dataset_fade={'floor': FLOOR, +'decay': DECAY}`` gives every head and trail exactly these alphas -- and +this example keeps it as a hook to show what the public ``on_frame`` hook +can do: it reads the schedule the library publishes (``ctx.current_index``, +``ctx.revealed_counts``) and assigns every artist on every frame. Before +1.1 this example monkeypatched ``ani._func`` and re-derived that schedule by +hand; the hook replaces both. The clip runs 30 seconds with two full camera +rotations. Here the conversation is Lewis Carroll's *Mad Tea-Party* (Alice in Wonderland). The turns are bundled inline -- quoted verbatim from the @@ -119,9 +121,9 @@ FLOOR, DECAY = 0.18, 0.7 # Title size and wrap width. At 14 pt a character is ~8.7 px wide at 100 dpi, # so a 64-character line is ~560 px over the 800 px-wide axes -- centred with -# clear margin either side -- and the longest turn (118 characters) wraps to -# exactly two lines; no turn needs a third (verified by rendering turns -# 15-17 and 22, the long ones). +# clear margin either side -- and the longest turn (117 characters, 119 with +# the quotes the title adds) wraps to exactly two lines; no turn needs a +# third (verified by rendering turns 15-17 and 22, the long ones). TITLE_SIZE, TITLE_WIDTH = 14, 64 # How many seconds of the current turn's reveal the opaque comet-head spans # (hyp.plot's tail_duration; default 2). Raised so the head covers more of @@ -142,9 +144,10 @@ def windows(text, size=WINDOW, step=STEP, min_windows=MIN_WINDOWS): ``min_windows`` prevents a real rendering artifact: ``hyp.plot`` draws a ONE-ROW dataset as a dot (there is no line through a single point), and - with a fixed 6-word window, 12 of the 28 turns above collapse to a - single window and would show up as stray specks. Shrinking the window, - and the step if needed, keeps every turn a real path. + with a fixed 6-word window stepping by 2, 12 of the 28 turns above get + fewer than three windows -- 9 of them none at all, 3 a single window + that would show up as a stray speck. Shrinking the window, and the step + if needed, keeps every turn a real path. """ words = text.split() n = len(words) @@ -190,9 +193,11 @@ def recency_fade(ctx): """The one bespoke effect left: earlier turns recede as the talk moves on. ``chemtrails``/``precog``/``bullettime`` fade WITHIN one trajectory; - nothing in 1.1 fades ACROSS already-revealed datasets, so this is real - custom work -- but it runs on the public per-frame hook and reads the - library's own published schedule instead of re-deriving it. + this fades ACROSS already-revealed datasets. It is the hand-written + form of ``dataset_fade={'floor': FLOOR, 'decay': DECAY}`` (same + formula, heads and trails alike), kept here to show the public + per-frame hook reading the library's own published schedule instead of + re-deriving it. ``ctx.artists`` is NOT one artist per dataset. It is heads first, then trails (animation_context.FrameContext), so with ``chemtrails=True`` it @@ -240,7 +245,9 @@ def construct_artifact(data): titles = [textwrap.fill(f'\u201c{text}\u201d', TITLE_WIDTH) for text in data.texts] # THE hypertools call: raw dialogue in, one disjoint trajectory per - # turn, coloured by speaker, revealed ONE TURN AT A TIME. + # turn, coloured by speaker, revealed ONE TURN AT A TIME. backend= is + # pinned because the hook sets matplotlib alphas, and on Colab the + # default backend would be plotly. anim = hyp.plot( data.turns, '-', vectorizer=data.vectorizer, semantic=None, corpus=None, @@ -256,7 +263,7 @@ def construct_artifact(data): title_kwargs={'size': TITLE_SIZE}, title_color=[SPEAKER_COLOR[s] for s in data.speakers], duration=30, rotations=2, frame_rate=16, elev=16, size=(8, 8), - show=False) + backend='matplotlib', show=False) anim.on_frame(recency_fade) return anim diff --git a/examples/animate_forecast.py b/examples/animate_forecast.py index b9459527..0031894a 100644 --- a/examples/animate_forecast.py +++ b/examples/animate_forecast.py @@ -5,8 +5,8 @@ ===================================================================== An animated forecast: ``hyp.plot(..., animate=True, predict='Kalman')`` -refits the forecaster on the history revealed so far and re-anchors it on -the last revealed observation, so the prediction grows and bends with the +refits the forecaster on the history revealed so far and draws it from the +endpoint of the current frame, so the prediction grows and bends with the animation instead of standing still. ``forecast_trail=True`` keeps the earlier forecasts on screen as a fading fan, so you can watch the prediction *change* as history accumulates. @@ -28,7 +28,10 @@ colour, the Americas have their own), ``forecast_palette=`` gives that grouping its own colours, and ``forecast_fmt=`` draws every forecast dashed. Everything they do not name is inherited from the trace a forecast -continues, drawn at half its alpha. +continues. A forecast that inherits its trace's colour is drawn at half +the trace's alpha; these carry their own colours from +``forecast_palette=``, so each current forecast is drawn at full opacity, +and with ``forecast_trail=True`` only the earlier fits fade. ``slow_warning_seconds=`` is the one keyword here that changes no pixel. An animated forecast needs one fit per distinct revealed history length, @@ -41,9 +44,10 @@ **Data & graceful degradation.** The archive is fetched once and cached. If the network is unavailable the example says which error it hit and synthesizes three seasonal regions (a hemispheric mix in each, a slow -drift) so it always renders; ``HYPERTOOLS_OFFLINE`` makes the fetch refuse -rather than degrade, which is how the test-suite proves the import path -fetches nothing. +drift) so it always renders. ``HYPERTOOLS_OFFLINE`` -- an environment +variable this example reads, not a hypertools setting -- makes the fetch +itself refuse rather than try, which is how the test-suite proves the +import path fetches nothing; the loader then falls back as above. """ # Code source: Contextual Dynamics Laboratory @@ -54,15 +58,12 @@ # sphinx_gallery_thumbnail_path = '_static/thumbnails/sphx_glr_animate_forecast_thumb.gif' import os -import tempfile -import urllib.request from typing import NamedTuple import numpy as np import hypertools as hyp -CACHE = os.path.join(tempfile.gettempdir(), 'hypertools_gallery_cache') ARCHIVE = ('https://raw.githubusercontent.com/ContextLab/' 'hypertools-paper-notebooks/master/data/temperatures.csv') # three regions, six cities each, every region spanning both hemispheres: @@ -91,20 +92,11 @@ def fetch_temperatures(): or ``None`` (announced with the error) when it cannot be fetched.""" if os.environ.get('HYPERTOOLS_OFFLINE'): raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch') - os.makedirs(CACHE, exist_ok=True) - dest = os.path.join(CACHE, 'temperatures.csv') try: - if not os.path.exists(dest): - req = urllib.request.Request( - ARCHIVE, headers={'User-Agent': 'hypertools-gallery/1.1'}) - with urllib.request.urlopen(req, timeout=60) as response: - payload = response.read() - with open(dest + '.part', 'wb') as handle: - handle.write(payload) - os.replace(dest + '.part', dest) # never a truncated cache # the archive carries '<City>' (absolute) and '<City>_anomaly' # columns; its complete rows end in August 2013 - recent = hyp.load(dest).dropna().tail(N_MONTHS) + # GH #285: the native URL loader owns download and atomic caching. + recent = hyp.load(ARCHIVE, cache=True).dropna().tail(N_MONTHS) return [recent[cities].to_numpy(float) for cities in REGIONS.values()] except Exception as error: @@ -159,13 +151,15 @@ def construct_artifact(data): # all three are dashed, and each still continues the path it belongs # to. `slow_warning_seconds=None` silences the long-schedule notice: # the 180 fits this clip needs measured about 6 s, a known wait. + # backend= is pinned: the frames are drawn and saved through the + # matplotlib animation, and on Colab the default would be plotly. return hyp.plot( data.regions, '-', names=data.names, animate=True, duration=DURATION, frame_rate=FRAME_RATE, predict='Kalman', t=HORIZON, forecast_trail=True, forecast_hue=['New World', 'Old World', 'Old World'], forecast_palette=['#d62728', '#1f77b4'], forecast_fmt='--', - slow_warning_seconds=None, + slow_warning_seconds=None, backend='matplotlib', title='Three regions, one year ahead', size=(8, 6), show=False) diff --git a/examples/animate_market_sectors.py b/examples/animate_market_sectors.py index c19a7cd6..d4828a71 100644 --- a/examples/animate_market_sectors.py +++ b/examples/animate_market_sectors.py @@ -8,8 +8,8 @@ as seven paths through one shared 3-D space. Each **sector** is handed to the library as its own matrix -- months down the rows, that sector's stocks across the columns (four or five of them; the counts differ on purpose) -- -and each cell is the stock's **trailing twelve-month return**. Three library -calls turn that into the figure: +and each cell is the stock's **cumulative log return since the first +month** (a growth curve). Three library calls turn that into the figure: 1. ``hyp.reduce`` takes every sector from its own handful of stocks to three dimensions **separately**, so a sector is a trajectory in a space made @@ -27,12 +27,16 @@ row, so it keeps its own colour; the market path's weights are each sector's **share of the basket's market capitalisation** that month (reported share counts x price), so its colour shifts toward whichever -sectors dominate -- tech-blue-red as the 1990s bubble deflates, more -financial-gold before 2008, and back again. The title is the **current -date**, tinted by the basket's own trailing twelve-month return: red when -the market is below where it stood a year earlier, green when it is above. -The camera makes three turns over one minute, and nothing that has been -drawn fades, so the last frame is the whole quarter century. +sectors dominate -- away from technology red as the dot-com bubble deflates +(about a third of the basket in mid-2000, a fifth by 2004), toward +financial gold before 2008 (the largest sector in 2006), then back to +technology, about half the basket through the 2020s. The title is the +**current date**, tinted by the basket's own trailing twelve-month return: +red when the market is below where it stood a year earlier, green when it +is above. The camera makes three turns over one minute. Only the last six +seconds of path are drawn at full strength, but nothing disappears: older +path stays on as a faint trail, so the last frame is the whole quarter +century. **Zero padding, verified.** ``hyp.align`` zero-pads datasets with different numbers of columns to a common width automatically (its ``trim_and_pad`` @@ -41,14 +45,22 @@ shared fit), which is exactly why the reduction here is per sector -- the sectors do not share columns, and should not share a projection. -**Data & graceful degradation.** Adjusted and unadjusted daily closes come -from Yahoo Finance's chart endpoint (full history, month-end decimated so no -future observation reaches back into a bar) and share counts from the SEC's -XBRL company-facts API (quarterly, from 2009; earlier months back-fill the -first reported capitalisation along the adjusted price). Everything is -cached on disk. If the network is unavailable the example falls back to a -seeded synthetic basket with the same sector structure and share counts, so -it always renders, and the technique is identical either way. +**Data & graceful degradation.** Daily closes and split events come from +Yahoo Finance's chart endpoint (full history, month-end decimated so no +future observation reaches back into a bar). Both of its closes are +split-adjusted -- ``adjclose`` also reinvests dividends -- so the share +counts, which the SEC's XBRL API (the per-concept endpoint, falling back to +company facts; quarterly, from 2009) reports as they stood on the day, are +multiplied by every later split before they meet the price. Earlier months +back-fill the first reported capitalisation along the adjusted price. +XOM's counts are read from Exxon Mobil Corp's filings (CIK 34088), not the +holding company the SEC's ticker map now names. HON's 2026-06-30 count in +the SEC data (316,940,010, from a 10-Q/A) is half its 2026-03-31 count +(633,653,119). +Everything is cached on disk. If the network is unavailable the example +falls back to a seeded synthetic basket with the same sector structure and +share counts, so it always renders, and the technique is identical either +way. """ # Code source: Contextual Dynamics Laboratory @@ -126,8 +138,9 @@ def _cached_json(name, url): # --- the data half: the ONLY code here that reaches the network ------------- def fetch_prices(sectors=SECTORS): - """Daily ADJUSTED and unadjusted closes for every ticker, or ``None`` - if anything (network, parsing) goes wrong.""" + """Daily dividend-and-split-adjusted and split-adjusted closes for every + ticker plus its split events, or ``None`` if anything (network, + parsing) goes wrong.""" # outside the try, so it raises instead of being caught and quietly # downgraded: a test that sets HYPERTOOLS_OFFLINE is asserting that no # fetch happened, and a swallowed exception would hide one @@ -135,55 +148,74 @@ def fetch_prices(sectors=SECTORS): raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch') os.makedirs(CACHE, exist_ok=True) try: - adjusted, raw = {}, {} + adjusted, close, splits = {}, {}, {} for tickers in sectors.values(): for ticker in tickers: # an explicit window: `range=max` silently degrades to # 3-month bars (measured 2026-09-03), a period does not result = _cached_json( - f'yahoo_daily_{ticker}.json', + f'yahoo_daily_splits_{ticker}.json', 'https://query1.finance.yahoo.com/v8/finance/chart/' - f'{ticker}?period1={PERIOD1}&period2={PERIOD2}&interval=1d' + f'{ticker}?period1={PERIOD1}&period2={PERIOD2}&interval=1d&events=split' )['chart']['result'][0] stamps = pd.to_datetime(result['timestamp'], unit='s').normalize() quote = result['indicators'] - adjusted[ticker] = pd.Series( - quote['adjclose'][0]['adjclose'], index=stamps, dtype=float) - raw[ticker] = pd.Series( - quote['quote'][0]['close'], index=stamps, dtype=float) - return pd.DataFrame(adjusted).sort_index(), pd.DataFrame(raw).sort_index() + adjusted[ticker] = pd.Series(quote['adjclose'][0]['adjclose'], index=stamps, dtype=float) + # the chart's `close` is SPLIT-adjusted as well (only + # dividends are left in: GE's 2009-12-31 close comes back as + # 72.51, as-traded 15.13), so the splits come along to put the + # SEC's as-reported share counts in the same units + close[ticker] = pd.Series(quote['quote'][0]['close'], index=stamps, dtype=float) + splits[ticker] = [(pd.Timestamp(s['date'], unit='s').normalize(), s['numerator'] / s['denominator']) + for s in result.get('events', {}).get('splits', {}).values()] + return pd.DataFrame(adjusted).sort_index(), pd.DataFrame(close).sort_index(), splits except Exception as error: print(f'price history unavailable ({error!r})') return None -def fetch_shares(tickers): +def fetch_shares(tickers, splits): """Reported shares outstanding per ticker from the SEC's XBRL API, as a month-end series (forward-filled between filings, NaN before the first), - or ``None``. Counts are as reported -- NOT split-adjusted -- which is why - market cap below multiplies them by the UNADJUSTED close.""" + or ``None``. The counts are filed as they stood on the day; each is + multiplied by the ratio of every LATER split in `splits` (ticker -> + [(date, ratio)]), which puts it in the units of Yahoo's split-adjusted + close -- the pair multiply to the true capitalisation, even in the + months between a split and the next filing.""" if os.environ.get('HYPERTOOLS_OFFLINE'): raise RuntimeError('HYPERTOOLS_OFFLINE is set: refusing to fetch') try: + # an explicit override where the SEC's ticker map names a filer that + # does not hold the ticker's share history: it maps XOM to ExxonMobil + # Holdings Corp (CIK 2115436), which has filed one share count + # (2026-06-30), so every earlier XOM month would be back-filled from + # that single value; Exxon Mobil Corporation (CIK 34088) reports + # counts from 2009-06-30 (both measured 2026-10-08) ciks = {row['ticker']: row['cik_str'] for row in _cached_json( 'sec_company_tickers.json', - 'https://www.sec.gov/files/company_tickers.json').values()} + 'https://www.sec.gov/files/company_tickers.json').values()} | {'XOM': 34088} shares = {} for ticker in tickers: - facts = _cached_json(f'sec_shares_{ticker}.json', + # the cache is keyed by CIK as well as ticker, so a file fetched + # under a ticker's earlier CIK is never reused for a new one + facts = _cached_json(f'sec_shares_{ticker}_{ciks[ticker]}.json', SEC.format(cik=ciks[ticker]))['units']['shares'] if not facts: # the per-concept endpoint comes back EMPTY for a few filers # (ABT, KO -- measured 2026-09-03) whose complete # company-facts file carries the same concept; read it there facts = _cached_json( - f'sec_facts_{ticker}.json', SEC_FACTS.format(cik=ciks[ticker]) + f'sec_facts_{ticker}_{ciks[ticker]}.json', SEC_FACTS.format(cik=ciks[ticker]) )['facts']['dei']['EntityCommonStockSharesOutstanding']['units']['shares'] # one value per period end: the LATEST filing wins over amendments frame = pd.DataFrame(facts).sort_values('filed') frame = frame.drop_duplicates('end', keep='last') - series = pd.Series(frame['val'].to_numpy(float), - index=pd.to_datetime(frame['end'])) + series = pd.Series(frame['val'].to_numpy(float), index=pd.to_datetime(frame['end'])) + series *= [np.prod([r for day, r in splits[ticker] if end < day]) for end in series.index] + # filings carry the odd slip (measured 2026-09-11: ORCL 2012-09 + # 4.8e15 shares for 4.8e9, MRK 2009-06 and KO 2009-10 zero); in + # split-adjusted units a count never strays 100x from its median + series = series[(series / series.median()).between(0.01, 100)] shares[ticker] = series.resample('ME').last().ffill() return pd.DataFrame(shares) except Exception as error: @@ -193,7 +225,8 @@ def fetch_shares(tickers): def synthetic_market(sectors=SECTORS, days=7000, seed=0): """The same sector structure, seeded, so the figure renders offline: - daily closes (adjusted == unadjusted) and a constant share count.""" + daily closes (no dividends, no splits: both closes are one series) and a + constant share count.""" rng = np.random.default_rng(seed) index = pd.date_range('1999-01-04', periods=days, freq='B') tickers = [t for ts in sectors.values() for t in ts] @@ -207,7 +240,7 @@ def synthetic_market(sectors=SECTORS, days=7000, seed=0): return closes, closes, shares -def assemble(adjusted, raw, shares, sectors, source): +def assemble(adjusted, close, shares, sectors, source): """Month-end trailing returns per sector, market-cap weights per sector and the basket's own return, on one shared monthly index from START.""" # month-end levels; a month still in progress is DROPPED (resample @@ -220,9 +253,10 @@ def assemble(adjusted, raw, shares, sectors, source): # month, so a sector's matrix is a set of growth curves and its 3-D path # is a journey rather than a tangle of month-to-month noise paths = levels.loc[months] - levels.loc[months[0]] - # market cap = UNADJUSTED close x reported shares; before the first - # filing, the first known cap is carried back along the ADJUSTED price - cap = raw.resample('ME').last().reindex(months) * shares.reindex(months).ffill() + # market cap = split-adjusted close x split-adjusted shares; before the + # first filing, the first known cap is carried back along the ADJUSTED + # (dividend-reinvested) price + cap = close.resample('ME').last().reindex(months) * shares.reindex(months).ffill() first = cap.apply(lambda col: col.first_valid_index()) for ticker in cap: known = cap.loc[first[ticker], ticker] @@ -241,13 +275,13 @@ def load_market(sectors=SECTORS): try: prices = fetch_prices(sectors) if prices is not None: - shares = fetch_shares([t for ts in sectors.values() for t in ts]) + shares = fetch_shares([t for ts in sectors.values() for t in ts], prices[2]) except RuntimeError: pass if prices is None or shares is None: return assemble(*synthetic_market(sectors), sectors, 'synthetic basket (offline)') - return assemble(*prices, shares, sectors, + return assemble(*prices[:2], shares, sectors, 'Yahoo Finance closes, SEC share counts') @@ -272,11 +306,13 @@ def construct_artifact(data): # path's rows are the sectors' shares of the basket's capitalisation. hue = [np.tile(np.eye(len(names))[i], (n_months, 1)) for i in range(len(names))] + [data.weights.to_numpy()] - # THE call: seven paths, one minute, three turns of the camera, and - # a trail as long as the clip so nothing drawn ever fades. + # THE call: seven paths, one minute, three turns of the camera; a + # six-second bright head, and a chemtrail that keeps everything older. months, basket = data.weights.index, data.market.to_numpy() # the title is restyled per frame below; the library reserves its margin - # at build time from rcParams, so the size is declared here as well + # at build time from rcParams, so the size is declared here as well. + # backend='matplotlib': the hook and legend below use the matplotlib + # figure, and on Colab the default backend would be plotly with plt.rc_context({'axes.titlesize': TITLE_SIZE, 'axes.titleweight': 'bold'}): anim = hyp.plot(aligned + [market], '-', hue=hue, palette=SECTOR_COLORS, hue_mode='mixture', linewidth=[1.1] * len(names) + [3.4], @@ -284,7 +320,7 @@ def construct_artifact(data): tail_duration=TAIL, duration=DURATION, frame_rate=FPS, rotations=ROTATIONS, colorbar=False, title=f'{months[0]:%B} {months[0].day}, {months[0].year}', - size=(8, 8), show=False) + size=(8, 8), backend='matplotlib', show=False) ax = anim.figure.axes[0] ax.legend(handles=[Line2D([], [], color=c, lw=2, label=s) for s, c in zip(names, SECTOR_COLORS)] diff --git a/examples/animate_morph_zoo.py b/examples/animate_morph_zoo.py index 7704fb0b..a0f1e054 100644 --- a/examples/animate_morph_zoo.py +++ b/examples/animate_morph_zoo.py @@ -80,10 +80,11 @@ # clear of the axes box: measured over all 600 frames (title bbox bottom vs # the highest projected box corner, 2026-09-03), 0.90 collided by 7 px at # the box's near-top-corner azimuths and 0.93 clears them everywhere. The -# family is named explicitly because hypertools' bundled default (Noto Sans) -# ships only a Regular face, so ``fontweight='bold'`` alone silently falls -# back to regular (checked with font_manager.findfont, 2026-09-03); DejaVu -# Sans Bold ships inside matplotlib itself, so it is always available. +# family is named explicitly so the clip keeps the face it was designed in: +# DejaVu Sans Bold ships inside matplotlib itself, so it is always +# available. (hypertools 1.1 also bundles a Noto Sans Bold face, so +# ``fontweight='bold'`` alone renders bold too; the override is a choice of +# typeface, not a workaround.) TITLE_FONTSIZE = 24 TITLE_FONTFAMILY = 'DejaVu Sans' TITLE_Y = 0.93 @@ -167,10 +168,12 @@ def construct_artifact(data): # THE hypertools call: black pixel-sized dots morphing through the zoo. # title= names each shape while its hold plays and is left blank by # hyp.plot itself during every transition -- no hand-rolled schedule. + # backend= is pinned: the hook below restyles a matplotlib title, and on + # Colab the default backend would be plotly. anim = hyp.plot(data.clouds, fmt='.', color='k', markersize=1.6, animate='morph', rotations=rotations, morph_samples=N, duration=30, frame_rate=20, size=(6, 6), show=False, - title=data.titles) + title=data.titles, backend='matplotlib') def restyle_title(ctx): """Runs AFTER the library's title updater on every frame: re-apply diff --git a/examples/animate_painting_embeddings.py b/examples/animate_painting_embeddings.py index d210e240..ae373423 100644 --- a/examples/animate_painting_embeddings.py +++ b/examples/animate_painting_embeddings.py @@ -253,9 +253,10 @@ def drawn_extent(anim, frames): def construct_artifact(data): """`data.descriptions` / `data.colors` / `data.images` in, the animation out. Returns the HyperAnimation wrapper, never the unpacked pair.""" - # labels= annotates per OBSERVATION, not per dataset: one sub-list per - # cloud, carrying the painting's name on its MIDDLE window (roughly the - # centre of a text trajectory) and None everywhere else. + # labels= in its per-OBSERVATION form: one sub-list per cloud, carrying + # the painting's name on its MIDDLE window (roughly the centre of a text + # trajectory) and None everywhere else. (1.1 also takes one label per + # dataset, placed by label_anchor=; this form names the window itself.) labels = [[name if i == len(cloud) // 2 else None for i in range(len(cloud))] for name, cloud in zip(data.names, data.descriptions)] @@ -265,8 +266,10 @@ def construct_artifact(data): # puts every window into one shared UMAP space so the clouds are # directly comparable. n_neighbors=12 keeps one description's windows # together, min_dist=0.25 lets a clump pack closely, random_state=42 - # fixes the stochastic layout. 15 fps: the side panels' antialiased - # text compresses badly in a GIF, and 240 frames at 20 fps was 7 MB. + # fixes the stochastic layout. 15 fps dates from when this clip was a + # GIF (240 frames at 20 fps was 7 MB); the mp4 keeps it. backend= is + # pinned: everything after the call annotates a matplotlib figure, and + # on Colab the default backend would be plotly. anim = hyp.plot( data.descriptions, '.', vectorizer=data.vectorizer, semantic=None, corpus=None, @@ -275,7 +278,8 @@ def construct_artifact(data): ndims=3, color=data.colors, markersize=5, labels=labels, animate='spin', rotations=2, title='Descriptions of five famous paintings', - duration=12, frame_rate=15, size=SIZE, zoom=BOX_ZOOM, show=False) + duration=12, frame_rate=15, size=SIZE, zoom=BOX_ZOOM, + backend='matplotlib', show=False) fig, ax = anim.figure, anim.figure.axes[0] ax.set_position([0.15, 0.0, 0.6, 1.0]) # roomy: nothing clipped while measuring ax.title.set_visible(False) diff --git a/examples/animate_spin.py b/examples/animate_spin.py index 9ce8b709..5f6a3ff5 100644 --- a/examples/animate_spin.py +++ b/examples/animate_spin.py @@ -20,4 +20,6 @@ data = hyp.load('weights_sample') # plot -fig, ani = hyp.plot(data, fmt='.', animate='spin') +# backend='matplotlib': unpacking into (fig, ani) is the matplotlib +# animation's form; on Colab the default backend would be plotly +fig, ani = hyp.plot(data, fmt='.', animate='spin', backend='matplotlib') diff --git a/examples/animate_surface_morph.py b/examples/animate_surface_morph.py index 6f64f18f..b59b5400 100644 --- a/examples/animate_surface_morph.py +++ b/examples/animate_surface_morph.py @@ -91,4 +91,5 @@ fig, ani = hyp.plot(clouds, fmt='.', color='k', markersize=0.6, alpha=0.25, animate='morph', rotations=rotations, duration=12, frame_rate=30, - morph_samples=n_points, surface=surface_spec) + morph_samples=n_points, surface=surface_spec, + backend='matplotlib') # (fig, ani) is matplotlib's form diff --git a/examples/animate_trails.py b/examples/animate_trails.py index 55cf9a7c..49e5d1d8 100644 --- a/examples/animate_trails.py +++ b/examples/animate_trails.py @@ -27,9 +27,13 @@ # %% # Chemtrails: a faint trace of the past trajectory follows the moving points. -fig, ani_past = hyp.plot(data, animate=True, chemtrails=True) +# backend='matplotlib': unpacking into (fig, ani) is the matplotlib +# animation's form; on Colab the default backend would be plotly +fig, ani_past = hyp.plot(data, animate=True, chemtrails=True, + backend='matplotlib') # %% # Precognition: a faint trace of the future trajectory leads the moving # points. -fig, ani_future = hyp.plot(data, animate=True, precog=True) +fig, ani_future = hyp.plot(data, animate=True, precog=True, + backend='matplotlib') diff --git a/examples/animate_trails_mix.py b/examples/animate_trails_mix.py index f80bc5f7..3aa719c2 100644 --- a/examples/animate_trails_mix.py +++ b/examples/animate_trails_mix.py @@ -29,9 +29,12 @@ # dataset 0: chemtrails (past trail) only # dataset 1: precog (future trail) only # dataset 2: bullettime (full trail, past + future) only +# backend='matplotlib': unpacking into (fig, ani) is the matplotlib +# animation's form; on Colab the default backend would be plotly fig, ani = hyp.plot( [data_a, data_b, data_c], animate=True, + backend='matplotlib', chemtrails=[True, False, False], precog=[False, True, False], bullettime=[False, False, True], diff --git a/examples/animate_weather_decades.py b/examples/animate_weather_decades.py index ae248a3b..2e8bdf00 100644 --- a/examples/animate_weather_decades.py +++ b/examples/animate_weather_decades.py @@ -40,14 +40,16 @@ months revealed so far growing over it as a line coloured **segment by segment by the mean temperature it is drawn at** (same colormap and range again), and a head marker on the current month coloured like the - head of the path. The raw monthly mean swings by ~15 \N{DEGREE SIGN}C + head of the path. The raw monthly mean swings by ~12 \N{DEGREE SIGN}C every year (the coloured line and the marker bounce with it), so a trailing 12-month rolling mean is drawn in plain black over the revealed months to let the warming drift show through the seasons. -The callback derives the current month from the frame index (a parallel -animation exposes no reveal count) and assigns every artist's state from it -on every frame, as the hook contract requires. (Before 1.1 this example +The callback derives the current month from the frame's position in the +clip: a parallel reveal does publish ``ctx.revealed_counts``, but it counts +rows of the path as drawn -- resampled onto the 2400-point frame grid -- +not months. It assigns every artist's state from that month on every frame, +as the hook contract requires. (Before 1.1 this example monkeypatched ``ani._func`` to redraw a second panel every frame; the public hook is what replaced that reach, and it now drives three.) @@ -224,13 +226,15 @@ def construct_artifact(data): # is also the path's resolution: 20 fps x 120 s = 2400 points, more than # the 1645 months, so the 12-month loop keeps every one of its vertices # (a 300-frame grid aliased it into chords -- measured 2026-09-03). + # backend= is pinned: the panels and the hook below are matplotlib's, + # and on Colab the default backend would be plotly. anim = hyp.plot( data.temps, '-', hue=mean, palette='RdBu_r', colorbar={'label': 'Average temperature ($^\\circ$C)'}, manip='Smooth', normalize='across', animate=True, chemtrails=True, rotations=1, - duration=120, frame_rate=20, size=(14, 7), show=False) + duration=120, frame_rate=20, size=(14, 7), backend='matplotlib', show=False) fig = anim.figure # Layout: the library's 3-D axes and colorbar take the left ~55%; the @@ -290,8 +294,9 @@ def construct_artifact(data): line_ax.spines[['top', 'right']].set_visible(False) def on_frame(ctx): - # A parallel reveal exposes no reveal count: the head sits at - # fraction frame / (n_frames - 1) of the path, hence of the months. + # ctx.revealed_counts counts rows of the frame-grid-resampled path, + # not months; the head sits at fraction frame / (n_frames - 1) of + # the path, hence of the months. i = min(round(ctx.frame / max(ctx.n_frames - 1, 1) * (n_months - 1)), n_months - 1) fig.suptitle(f'{MONTHS[data.months[i] - 1]} {data.years[i]}', diff --git a/examples/explore.py b/examples/explore.py index ee29adc5..af1186c9 100644 --- a/examples/explore.py +++ b/examples/explore.py @@ -10,6 +10,10 @@ labels by passing a list of labels to the `label(s)` kwarg. Alternatively, if you don't pass a list of labels, the labels will be the index of the datapoint, along with the PCA coordinate. + +Hover labels need an interactive matplotlib backend. This gallery is built +with a non-interactive one, so the figure below is a static image: run the +example in an interactive session to hover over the points. """ # sphinx_gallery_thumbnail_path = '_static/thumbnails/sphx_glr_explore_thumb.png' diff --git a/examples/plot_autoencoders.py b/examples/plot_autoencoders.py index 34f79fd1..12639ac2 100644 --- a/examples/plot_autoencoders.py +++ b/examples/plot_autoencoders.py @@ -9,7 +9,7 @@ `VariationalAutoencoder`. They are used exactly like any other `reduce=` model -- by name, with parameters passed via the dict spec -- and use the optional ``torch`` extra, which hypertools installs on demand the first -time one is fit (pre-install it with ``pip install "hypertools[torch]"``). +time one is fit. This example fits a shallow `Autoencoder` and a `VariationalAutoencoder` on the same data and compares them against PCA: three 2-D embeddings of a noisy spiral manifold embedded in 10-D, with each point colored by its diff --git a/examples/plot_datasets_tour.py b/examples/plot_datasets_tour.py index 9ca246cb..aa3388b2 100644 --- a/examples/plot_datasets_tour.py +++ b/examples/plot_datasets_tour.py @@ -64,10 +64,13 @@ n = max(len(loaded), 1) ncols = 2 if n > 1 else 1 nrows = (n + ncols - 1) // ncols -fig, axes = hyp.subplots(nrows, ncols, size=[5 * ncols, 5 * nrows]) +# backend='matplotlib' on the grid and on each call: the axes are +# matplotlib's, and on Colab the default backend would be plotly +fig, axes = hyp.subplots(nrows, ncols, size=[5 * ncols, 5 * nrows], + backend='matplotlib') for ax in axes: ax.set_axis_off() # hide any unused panels for (title, data), ax in zip(loaded, axes): ax.set_axis_on() - hyp.plot(data, '.', ax=ax, title=title, show=False) + hyp.plot(data, '.', ax=ax, title=title, backend='matplotlib', show=False) fig.tight_layout() diff --git a/examples/plot_gensim_text.py b/examples/plot_gensim_text.py index 6aa3908c..b6c0b15d 100644 --- a/examples/plot_gensim_text.py +++ b/examples/plot_gensim_text.py @@ -9,8 +9,7 @@ `'Doc2Vec'`, `'FastText'` (vectorizer tier) and `'LdaModel'`, `'LsiModel'`, `'HdpModel'` (semantic tier) -- then HuggingFace sentence-transformers. gensim is an optional extra that hypertools installs -on demand the first time a gensim model is requested (pre-install it with -``pip install "hypertools[gensim]"``). The two panels embed the same small +on demand the first time a gensim model is requested. The two panels embed the same small three-topic corpus in two ways -- gensim's Word2Vec (averaged word vectors, no semantic-stage model) on the left, and CountVectorizer counts fed to gensim's LDA on the right -- and color each document by its topic. For @@ -43,19 +42,21 @@ # itself), so we keep the explicit-axes form, via hyp.subplots (a thin # wrapper over plt.subplots that pre-sets the 3-D projection and hands back # a flat axes array). -fig, axes = hyp.subplots(1, 2, size=[12, 5]) +# backend='matplotlib' on the grid and on each call: fig.tight_layout() is +# matplotlib's, and on Colab the default backend would be plotly +fig, axes = hyp.subplots(1, 2, size=[12, 5], backend='matplotlib') # gensim Word2Vec: average trained word vectors per document (no # semantic-stage model -- semantic=None). corpus=docs trains the model on # these documents themselves. hyp.plot(docs, 'o', vectorizer='Word2Vec', semantic=None, corpus=docs, - hue=topics, ax=axes[0], show=False, + hue=topics, ax=axes[0], backend='matplotlib', show=False, title='gensim Word2Vec (averaged word vectors)') # CountVectorizer -> gensim LdaModel: bag-of-words counts, then topic # proportions from a Latent Dirichlet Allocation model hyp.plot(docs, 'o', vectorizer='CountVectorizer', semantic={'model': 'LdaModel', 'kwargs': {'num_topics': 3}}, - corpus=docs, hue=topics, ax=axes[1], show=False, + corpus=docs, hue=topics, ax=axes[1], backend='matplotlib', show=False, title='CountVectorizer + gensim LdaModel') fig.tight_layout() diff --git a/examples/plot_geo.py b/examples/plot_geo.py index ee816853..b2de4a5f 100644 --- a/examples/plot_geo.py +++ b/examples/plot_geo.py @@ -37,8 +37,9 @@ # load some data -- a list of arrays, ready to plot as-is data = hyp.load('spiral') -# plot: the return value is just a matplotlib Figure -fig = hyp.plot(data, ndims=3) +# plot: the return value is just a matplotlib Figure (backend='matplotlib' +# asks for it explicitly: on Colab the default backend would be plotly) +fig = hyp.plot(data, ndims=3, backend='matplotlib') # treat it like any other Figure png_path = os.path.join(tempfile.mkdtemp(), 'spiral.png') diff --git a/examples/plot_sotus.py b/examples/plot_sotus.py index b42f29e6..73d2f738 100644 --- a/examples/plot_sotus.py +++ b/examples/plot_sotus.py @@ -5,8 +5,9 @@ ===================================== `hyp.load('sotus')` returns the full text of the 29 State of the Union -addresses delivered between 1989 and 2018, in chronological order. Passing -the raw speech texts straight to `hyp.plot` runs hypertools' default text +addresses delivered between 1989 and 2017, grouped by president rather than +sorted by date, so this example first puts them in date order. Passing the +raw speech texts straight to `hyp.plot` runs hypertools' default text pipeline: each address is converted to a vector of word counts, modeled with a 50-topic Latent Dirichlet Allocation model fit to a large sample of wikipedia pages, and reduced to 3 dimensions. Because the addresses are @@ -22,9 +23,12 @@ # load hypertools import hypertools as hyp -# load the State of the Union addresses: 29 speeches (1989-2018), in -# chronological order +# load the State of the Union addresses: 29 speeches (1989-2017), listed by +# president -- G. H. W. Bush (1989-92), G. W. Bush (2001-08), Clinton +# (1993-2000), Obama (2009-16), Trump (2017) -- then sorted by year speeches = hyp.load('sotus') +years = [*range(1989, 1993), *range(2001, 2009), *range(1993, 2001), *range(2009, 2018)] +speeches = [speech for _year, speech in sorted(zip(years, speeches))] print(f'{len(speeches)} State of the Union addresses loaded') # plot the trajectory through semantic space diff --git a/examples/plot_story_trajectories.py b/examples/plot_story_trajectories.py index b24de53b..ee4f70d5 100644 --- a/examples/plot_story_trajectories.py +++ b/examples/plot_story_trajectories.py @@ -73,4 +73,5 @@ # overlapping near-opaque ribbons read as ONE coherent shape. fig, ani = hyp.plot(aligned, '-', palette='husl', alpha=0.85, linewidth=1.6, reduce='IncrementalPCA', ndims=3, animate='window', - focused=1.5, zoom=1.5, duration=9) + focused=1.5, zoom=1.5, duration=9, + backend='matplotlib') # (fig, ani) is matplotlib's form diff --git a/examples/save_movie.py b/examples/save_movie.py index 5230e46e..15478850 100644 --- a/examples/save_movie.py +++ b/examples/save_movie.py @@ -37,5 +37,8 @@ # animate the two group trajectories and write the movie to disk save_path = os.path.join(tempfile.mkdtemp(), 'animation.mp4') -fig, ani = hyp.plot([group1, group2], animate=True, save_path=save_path) +# backend='matplotlib': the matplotlib animation is what the ffmpeg writer +# encodes, and (fig, ani) is its form; on Colab the default would be plotly +fig, ani = hyp.plot([group1, group2], animate=True, save_path=save_path, + backend='matplotlib') print(f'saved {os.path.getsize(save_path) // 1024} KB to {save_path}') diff --git a/hypertools/__init__.py b/hypertools/__init__.py index b52cea90..ef462a49 100644 --- a/hypertools/__init__.py +++ b/hypertools/__init__.py @@ -3,12 +3,14 @@ The classic API lives at the top level: `plot`, `analyze`, `reduce`, `align`, `normalize`, `describe`, `cluster`, `manip`, `predict`, `impute`, -`load`, `save`, `apply_model`, `supported_models`, `Pipeline`, and -`set_interactive_backend`, plus the `io` submodule, `HyperAnimation` +`load`, `save`, `apply_model`, `supported_models`, `Pipeline`, +`set_interactive_backend` and `set_autoinstall` (on-demand installation of +optional extras), plus the `io` submodule, `HyperAnimation` (the return type of animated plots), and `FrameContext` (the per-frame state `plot(..., on_frame=...)` hands to its callback). Exceptions raised by hypertools (`HypertoolsError`, `HypertoolsBackendError`, -`HypertoolsIOError`) are also importable from here. +`HypertoolsIOError`, `HypertoolsOfflineError`, `HypertoolsTrustError`) are +also importable from here. Import-form note: several top-level functions share a name with the subpackage they live in, so attribute access like @@ -22,6 +24,7 @@ from .config import __version__ # noqa: F401 (re-export; deliberately excluded from __all__, see below) from .plot.plot import plot, subplots from .plot.backend import set_interactive_backend +from ._shared.lazy_import import set_autoinstall from .plot.hyper_animation import HyperAnimation from .plot.animation_context import FrameContext from .io.load import load @@ -38,6 +41,7 @@ from .core.pipeline import Pipeline from .core.exceptions import (HypertoolsError, HypertoolsBackendError, HypertoolsIOError) +from .io.sources import HypertoolsOfflineError, HypertoolsTrustError from .manip.manip import manip from .predict.predict import predict from .impute.impute import impute @@ -52,7 +56,9 @@ 'plot', 'analyze', 'reduce', 'align', 'normalize', 'describe', 'cluster', 'manip', 'predict', 'impute', 'load', 'save', 'apply_model', 'supported_models', 'Pipeline', - 'set_interactive_backend', 'HyperAnimation', 'FrameContext', 'io', + 'set_interactive_backend', 'set_autoinstall', 'HyperAnimation', + 'FrameContext', 'io', 'HypertoolsError', 'HypertoolsBackendError', 'HypertoolsIOError', + 'HypertoolsOfflineError', 'HypertoolsTrustError', 'damage', 'stack', 'text_windows', 'subplots', ] diff --git a/hypertools/_shared/helpers.py b/hypertools/_shared/helpers.py index f4c3e7c2..62adb3ae 100644 --- a/hypertools/_shared/helpers.py +++ b/hypertools/_shared/helpers.py @@ -7,7 +7,7 @@ ##PACKAGES## import numpy as np import itertools -import pandas as pd +import datawrangler as dw from matplotlib.lines import Line2D # NOTE: seaborn and scipy.interpolate are imported lazily inside the functions @@ -434,6 +434,18 @@ def has_line_component(format_str): ANTIALIAS_TARGET_VERTICES = 900 +#: Half-width of the frame square a static 2-D plot draws round its data, +#: which `plot()` rescales into ``[-1, 1]``. The square used to sit AT +#: +-1, so the extreme observations lay on the frame line and looked +#: clipped (1.1 release review, feature-tour 9.12-9.14, 12.1-12.2: markers +#: straddling the frame); the 12.5 % margin gives them the room the 3-D +#: cube's perspective gives its corners. Both backends draw the same +#: square and pin the axes to `UNIT_FRAME_LIMIT`, 10 % beyond it (the +#: same limit-to-frame ratio as before). +UNIT_FRAME_SCALE = 1.125 +UNIT_FRAME_LIMIT = 1.1 * UNIT_FRAME_SCALE + + def antialias_line(arr, target=ANTIALIAS_TARGET_VERTICES): """Upsample a trajectory so it DRAWS as a smooth curve ("antialiasing"). @@ -478,6 +490,20 @@ def antialias_line(arr, target=ANTIALIAS_TARGET_VERTICES): return out, step +def row_index_x(n_rows, n_drawn): + """The ROW-index x of each of `n_drawn` vertices drawn for `n_rows` rows. + + A 1-D plot puts the row index on x. When the drawn curve was densified + by `antialias_line` (uniformly, every original row kept), its vertices + span the same ``0..n_rows - 1`` as the rows themselves, so vertex ``k`` + sits at ``k * (n_rows - 1) / (n_drawn - 1)``. With nothing densified + (``n_drawn == n_rows``) this is exactly ``np.arange(n_rows)``. + """ + if n_drawn == n_rows or n_drawn < 2 or n_rows < 2: + return np.arange(n_drawn, dtype=float) + return np.linspace(0.0, n_rows - 1.0, n_drawn) + + def split_marker_line_fmt(format_str): """Split a matplotlib format string into its LINE and MARKER components (GH #141), so a combined style like 'o-' can be drawn as @@ -513,6 +539,87 @@ def split_marker_line_fmt(format_str): return line_token, marker_char +def _is_container_or_scalar(x): + """Python containers and scalars (incl. str/bytes): never a dataset + object, and never handed to the datawrangler predicates, which try to + interpret a string as a file path or URL (loading -- or fetching -- it) + and would walk every element of a list.""" + return isinstance(x, (list, tuple, dict, set)) or np.isscalar(x) + + +def is_text_item(x): + """True for ONE text document: a str or bytes. + + ``dw.zoo.is_text`` is deliberately not used here: it interprets a string + as a path/URL first (stat-ing, loading, or fetching it) and raises on an + existing file with an unknown extension (pydata-wrangler 0.5.1: + ``is_text('README.md')`` -> ValueError), so it is not a pure predicate on + a user's documents. + """ + return isinstance(x, (str, bytes)) + + +def is_number_item(x): + """True for ONE numeric scalar (python or numpy, bools included). + + bools count as numbers (release-1.0 audit, F08-plot-inputs-013): a + python list of bools is the same data as np.array([True, ...]), which + has always been accepted (dtype kind 'b' -> 'arr_num'). np.bool_ is + listed explicitly because it is NOT an np.number subclass (and, under + numpy >= 2, not a python bool either). + """ + return isinstance(x, (bool, int, float, np.number, np.bool_)) + + +def is_array_dataset(x): + """True for ONE array dataset: anything datawrangler classifies as an + array (``dw.zoo.is_array``: every non-string type from the numpy module + -- ndarray, matrix, memmap, ...) plus numpy masked arrays (whose types + live in ``numpy.ma``, which ``is_array`` does not admit). Scalars, + python lists and tuples are NOT array datasets even though + ``dw.zoo.is_array`` admits numbers and lists of numbers: those are the + callers' scalar / list-of-numbers cases. + """ + if _is_container_or_scalar(x): + return False + return np.ma.isMaskedArray(x) or dw.zoo.is_array(x) + + +def is_frame_dataset(x): + """True for ONE DataFrame dataset as datawrangler sees it + (``dw.zoo.is_dataframe``): a pandas DataFrame, a polars DataFrame or + LazyFrame, a modin frame, or a dataframe-like duck type + (``dw.zoo.dataframe_like``). Strings are excluded up front: datawrangler + would otherwise try to load them as file paths / URLs.""" + if _is_container_or_scalar(x): + return False + return dw.zoo.is_dataframe(x) + + +def is_series_like(x): + """True for ONE labelled 1-D vector that is neither an array nor a + DataFrame dataset: a pandas Series (``dw.zoo.array_like`` admits it, + ``dw.zoo.is_array`` does not), a polars Series, or any other object + exposing ``to_numpy()``. hypertools treats these as a single 1-D + dataset (n observations of one feature).""" + if _is_container_or_scalar(x) or is_array_dataset(x) or is_frame_dataset(x): + return False + return dw.zoo.array_like(x) or hasattr(x, 'to_numpy') + + +def as_pandas_dataframe(x): + """A DataFrame dataset (`is_frame_dataset`) as a pandas DataFrame, + hypertools' internal frame type. A pandas frame (or anything with the + pandas DataFrame API, per ``dw.zoo.dataframe_like``) is returned AS IS + -- index, columns and dtypes untouched, no copy; every other backend + (polars DataFrame/LazyFrame, modin, ...) is converted by + ``dw.wrangle(..., backend='pandas')``, which also turns polars nulls + into NaN.""" + if dw.zoo.dataframe_like(x): + return x + return dw.wrangle(x, backend='pandas') + + def get_type(data): """ Checks what the data type is and returns it as a string label @@ -522,16 +629,11 @@ def get_type(data): if isinstance(data, list): if len(data) == 0: return 'list_num' # empty list -> empty numeric dataset - if isinstance(data[0], (str, bytes)): + if is_text_item(data[0]): return 'list_str' - # bools count as numbers (release-1.0 audit, F08-plot-inputs-013): - # a python list of bools is the same data as np.array([True, ...]), - # which has always been accepted (dtype kind 'b' -> 'arr_num'). - # np.bool_ is listed explicitly because it is NOT an np.number - # subclass (and, under numpy >= 2, not a python bool either). - elif isinstance(data[0], (bool, int, float, np.number, np.bool_)): + elif is_number_item(data[0]): return 'list_num' - elif isinstance(data[0], np.ndarray): + elif is_array_dataset(data[0]): return 'list_arr' else: # name the offending element type (release-1.0 audit, @@ -545,7 +647,7 @@ def get_type(data): "per-dataset types: numpy array, pandas DataFrame, pandas " "Series, str, list of strings, list of numbers, or a " "(possibly nested) list/tuple of arrays/DataFrames.") - elif isinstance(data, np.ndarray): + elif is_array_dataset(data): # classify by dtype rather than indexing data[0][0] -- the latter # crashed on 1-D arrays (data[0] is a scalar, so data[0][0] raised # "invalid index to scalar variable") and on empty arrays (QC 2026-07). @@ -554,12 +656,12 @@ def get_type(data): if data.dtype.kind in ('U', 'S'): return 'arr_str' if (data.dtype.kind == 'O' and data.size - and isinstance(data.reshape(-1)[0], (str, bytes))): + and is_text_item(data.reshape(-1)[0])): return 'arr_str' return 'arr_num' - elif isinstance(data, pd.DataFrame): + elif is_frame_dataset(data): return 'df' - elif isinstance(data, (str, bytes)): + elif is_text_item(data): return 'str' elif isinstance(data, DataGeometry): return 'geo' @@ -604,11 +706,11 @@ def get_dtype(data): if isinstance(data, list): return 'list' - elif isinstance(data, np.ndarray): + elif is_array_dataset(data): return 'arr' - elif isinstance(data, pd.DataFrame): + elif is_frame_dataset(data): return 'df' - elif isinstance(data, (str, bytes)): + elif is_text_item(data): return 'str' elif isinstance(data, DataGeometry): return 'geo' diff --git a/hypertools/_shared/lazy_import.py b/hypertools/_shared/lazy_import.py index 2955c574..b746f1d9 100644 --- a/hypertools/_shared/lazy_import.py +++ b/hypertools/_shared/lazy_import.py @@ -12,9 +12,30 @@ Policy: a missing optional module is installed into the running interpreter (``python -m pip install <the extra's requirements>``) and then imported. Nothing about hypertools itself is reinstalled, so a development or -branch install is never replaced by a PyPI release. Set -``HYPERTOOLS_AUTO_INSTALL=0`` to disable installation; the import then fails -with the manual command. Every install prints a one-line notice. +branch install is never replaced by a PyPI release. +``hypertools.set_autoinstall(False)`` (the `set_autoinstall` class below, +also a context manager) turns installation off; the import then fails with +the manual command. The environment variable ``HYPERTOOLS_AUTO_INSTALL=0`` +sets the starting value for processes where no Python runs first (an image +built ahead of time). Every install prints a one-line notice. The setting +is per interpreter, so a child process that runs hypertools code (the +plotly animation-export worker) is started with ``subprocess_env()``, +which carries the effective value over as that variable. + +An extra that IS installed is also checked against the requirement +pyproject declares for it (`outdated_requirements`): every distribution of +the extra, not only the module asked for, because a feature needs them +together (kaleido 1.x cannot export with plotly < 6.1.1). A distribution +below its declared floor is upgraded through the same pip call, with a +notice naming the installed version and the requirement -- but only while +none of its modules is imported yet. An imported package cannot be swapped +under a running interpreter, so in that case, and whenever installation is +off, the call raises ``ImportError`` naming the installed version, the +requirement and the command. The check needs the ``packaging`` library, +which is not a declared hypertools dependency of its own: matplotlib (a +core dependency) requires it, so it is present in every resolved install; +where it is not, the check is skipped rather than guessed at. A passed +check is remembered per (module, extra), so it runs once per process. ``ensure_kaleido_chrome()`` provisions what plotly's static image export needs at run time: a Chrome build for kaleido and, on Linux images that @@ -22,11 +43,15 @@ shared libraries that Chrome needs to start. """ +import bisect +import functools import importlib import os import shutil import subprocess import sys +import threading +import weakref from importlib import metadata #: import name -> the hypertools extra that provides it (the ONLY mapping @@ -59,13 +84,242 @@ _kaleido_ready = False +#: (top-level module, extra, explicit requirements or None) keys whose +#: installed versions were checked against the declared requirements and +#: whose module imported: `lazy_import` returns these without reading any +#: metadata again. Only a PASSED check is remembered. +_VERSIONS_VERIFIED = set() + +#: the `set_autoinstall` handles that are alive, oldest first, as `_Scope` +#: records (a weak reference each), plus the BASELINE: the value the newest +#: direct call left in force (None: the environment decides). The newest +#: live record decides; a handle that dies without having entered a block +#: was a direct call, so its weakref callback folds its value into the +#: baseline and drops its record at once (nothing is retained: Codex round +#: 7); a handle that is alive but not yet entered is never touched (a +#: construct-then-enter race across threads: Codex round 8); a block removes +#: only its own record on exit. Process-global, guarded by +#: `_AUTO_INSTALL_LOCK`. +_AUTO_INSTALL_SCOPES = [] +_AUTO_INSTALL_LOCK = threading.RLock() +_AUTO_INSTALL_BASELINE = [None, -1] # [enabled or None, seq of that call] +_AUTO_INSTALL_SEQ = [0] + + +class _Scope: + """One live `set_autoinstall` handle: its value, its construction order, + whether it is inside its `with` block, and a weak reference to it.""" + __slots__ = ('enabled', 'seq', 'entered', 'finished', 'ref') + + def __init__(self, enabled, seq): + self.enabled = enabled + self.seq = seq + self.entered = False + self.finished = False # its block has exited: it was never direct + self.ref = None + + +def _scope_handle_died(scope, _ref=None): + """Weakref callback (`_ref` is the dead weak reference, unused): the + handle of `scope` is gone. Inside a block that + cannot happen (the block holds it), so this was a direct call: the + newest direct call's value is the baseline. + + A handle still alive when the interpreter exits dies during module + teardown, after this module's globals have been cleared to None; there + is no setting left to maintain then, so the callback does nothing + rather than raise (which Python reports as "Exception ignored in ..."; + review 2026-09-11). The callback holds this function itself (see + `set_autoinstall.__init__`), so it never looks the name up then.""" + if _AUTO_INSTALL_LOCK is None or _AUTO_INSTALL_SCOPES is None \ + or _AUTO_INSTALL_BASELINE is None: + return # interpreter shutdown + with _AUTO_INSTALL_LOCK: + if scope.finished or scope.entered: + return # a block's handle, not a direct call + if scope.seq > _AUTO_INSTALL_BASELINE[1]: + _AUTO_INSTALL_BASELINE[:] = [scope.enabled, scope.seq] + for i in range(len(_AUTO_INSTALL_SCOPES) - 1, -1, -1): + if _AUTO_INSTALL_SCOPES[i] is scope: + del _AUTO_INSTALL_SCOPES[i] + break + def auto_install_enabled(): - """True unless ``HYPERTOOLS_AUTO_INSTALL`` is set to 0/false/no/off.""" + """True when hypertools may install a missing optional extra: what the + newest `set_autoinstall` still in force set or, if none is, the environment + variable ``HYPERTOOLS_AUTO_INSTALL`` (on unless it is 0/false/no/off).""" + with _AUTO_INSTALL_LOCK: + # the newest CALL decides: a live handle's record, or the baseline a + # newer direct call folded in (an older handle kept in a variable + # does not outrank a later direct call) + top = _AUTO_INSTALL_SCOPES[-1] if _AUTO_INSTALL_SCOPES else None + base_enabled, base_seq = _AUTO_INSTALL_BASELINE + if top is not None and top.seq > base_seq: + return top.enabled + if base_enabled is not None: + return base_enabled return os.environ.get('HYPERTOOLS_AUTO_INSTALL', '1').strip().lower() \ not in ('0', 'false', 'no', 'off') +def subprocess_env(env=None): + """The environment for a child Python process that runs hypertools code, + carrying this process's EFFECTIVE auto-install setting. + + A `set_autoinstall` call lives in this interpreter only; a child started + with `subprocess` begins from the environment variable. So the variable is + set here from `auto_install_enabled` -- ``'1'`` or ``'0'`` -- and the child + starts where the parent stands, whichever way the two disagreed + (``set_autoinstall(True)`` over ``HYPERTOOLS_AUTO_INSTALL=0`` gives the + child ``'1'``; ``set_autoinstall(False)`` with the variable unset gives it + ``'0'``). Every subprocess launch of hypertools code must pass this as + ``env=`` (release audit 2026-09-07: the plotly animation-export worker + ran pip with installation switched off in the parent). + + Parameters + ---------- + env : mapping, optional + The environment to start from; defaults to ``os.environ``. Not + modified: a copy is returned. + + Returns + ------- + dict + A copy of `env` with ``HYPERTOOLS_AUTO_INSTALL`` set. + """ + env = dict(os.environ if env is None else env) + env['HYPERTOOLS_AUTO_INSTALL'] = '1' if auto_install_enabled() else '0' + return env + + +class set_autoinstall: + """ + Turn the on-demand installation of optional extras on or off. + + hypertools' optional features (the plotly backend, text embeddings, + the ``Laplace`` and ``Chronos`` forecasters, the torch autoencoders, + gensim models, Kaggle and Hugging Face loading, LSL streaming, 3-D + density iso-surfaces, ``.xlsx`` files) are ``pip`` extras that install + themselves on demand: the first call that needs a missing one installs + that extra's requirements into the running interpreter, prints a + one-line ``hypertools:`` notice, and carries on. Static image export + with the plotly backend provisions kaleido's Chrome the same way. + + Like `hypertools.set_interactive_backend`, this can be used in two + ways: + + 1. directly, to change the setting for the rest of the session:: + + import hypertools as hyp + + hyp.set_autoinstall(False) + hyp.plot(data, backend='plotly') # ImportError if plotly is + # missing, naming the manual + # pip install command + hyp.set_autoinstall(True) # back on + + 2. as a context manager with the `with` statement, to change it for + one block:: + + with hyp.set_autoinstall(False): + hyp.predict(data, model='Chronos', t=5) # no install here + + hyp.predict(data, model='Chronos', t=5) # installs on demand + + An extra that is installed but OLDER than the requirement hypertools + declares (a notebook image with plotly 5 where ``plotly>=6.1.1`` is + required) is upgraded the same way, with a notice naming the installed + version and the requirement -- provided the old version has not been + imported yet. Python cannot replace a package that is already imported, + so in that case nothing is installed and the call raises ``ImportError`` + asking for the upgrade command and a restart. + + With installation off, a call that needs a missing extra raises + ``ImportError`` naming the manual ``pip install "hypertools[<extra>]"`` + command, and nothing is installed. An extra that is installed but too + old raises ``ImportError`` too, naming the installed version, the + requirement and the same command. Turn it off in locked-down + environments and anywhere pip should not run inside a Python process. + For a process where no Python runs before hypertools is imported (a CI + image built ahead of time), the environment variable + ``HYPERTOOLS_AUTO_INSTALL=0`` sets the starting value; a + `set_autoinstall` call overrides it. + + The setting is process-global: it is shared by every thread, and a + subprocess that renders plotly animation frames inherits the effective + value. The newest call still in force decides: a `with` block removes + its own setting on exit and leaves any other block that is still open + in force, so two threads each inside ``with set_autoinstall(False)`` + both keep installation off until the LAST of them exits, whichever + order they finish in. A direct call stays in force until the next call. + Calls are ordered by when ``set_autoinstall(...)`` was called, not by + when a handle enters its block: ``with h:`` on a handle ``h`` created + before a later call does not outrank that later call. + + Parameters + ---------- + enabled : bool, default True + ``True`` to install missing extras on demand, ``False`` to raise + ``ImportError`` instead. Applies temporarily when used as a context + manager with `with`, or for the life of the interpreter when called + as a function. + + Attributes + ---------- + enabled : bool + The value that was set. + + Raises + ------ + TypeError + If `enabled` is not ``True`` or ``False``. + """ + + def __init__(self, enabled=True): + if not isinstance(enabled, bool): + raise TypeError( + f'set_autoinstall expects True or False, got {enabled!r}') + self.enabled = enabled + with _AUTO_INSTALL_LOCK: + _AUTO_INSTALL_SEQ[0] += 1 + self._scope = _Scope(enabled, _AUTO_INSTALL_SEQ[0]) + # the callback binds the function object now: at interpreter + # shutdown a surviving handle dies after the module's globals + # are cleared, when the name would resolve to None + self._scope.ref = weakref.ref( + self, functools.partial(_scope_handle_died, self._scope)) + _AUTO_INSTALL_SCOPES.append(self._scope) + + def __enter__(self): + with _AUTO_INSTALL_LOCK: + if self._scope.finished: # re-entered after an exit + self._scope.finished = False + # back in CALL order (the list is oldest first): entering is + # not a new call, so a re-entered handle ranks where its + # construction put it, exactly as on its first entry -- not + # on top of newer handles (review 2026-09-11) + bisect.insort(_AUTO_INSTALL_SCOPES, self._scope, + key=lambda s: s.seq) + self._scope.entered = True + return self + + def __exit__(self, exc_type, exc_value, traceback): + # remove THIS setting wherever it sits: a block that is not the + # newest (another thread's block opened after it) must not restore + # a value from before that other block + with _AUTO_INSTALL_LOCK: + self._scope.entered = False + self._scope.finished = True + for i in range(len(_AUTO_INSTALL_SCOPES) - 1, -1, -1): + if _AUTO_INSTALL_SCOPES[i] is self._scope: + del _AUTO_INSTALL_SCOPES[i] + break + + def __repr__(self): + return f'set_autoinstall({self.enabled})' + + def extra_requirements(extra): """The requirement strings pyproject declares for ``extra``, read from the installed hypertools metadata (e.g. ``['plotly>=6.1.1', 'kaleido>=1.0']`` @@ -83,10 +337,23 @@ def extra_requirements(extra): def install_command(extra): - """The manual command for ``extra``, for error messages.""" + """The manual command for ``extra``, for error messages. It also + UPGRADES: pip re-resolves the extra's requirements for an installed + hypertools and replaces a distribution that is below its floor, without + reinstalling hypertools itself (no ``-U``, which would).""" return f'pip install "hypertools[{extra}]"' +def _manual_command(extra, requirements): + """The command a person runs instead: the extra's, or the explicit + requirements quoted for a shell (an unquoted ``>=`` is a redirection).""" + if extra: + return install_command(extra) + if requirements: + return 'pip install ' + ' '.join(f'"{r}"' for r in requirements) + return None + + def _notice(text): print(f'hypertools: {text}', flush=True) @@ -97,9 +364,152 @@ def _pip_install(requirements): importlib.invalidate_caches() +def _meets_floor(installed, requirement): + """False only when the version string ``installed`` is certainly BELOW + the floor ``requirement`` declares. + + Only the floor-setting clauses count (``>=``, ``>``, ``~=``, and ``==`` + without a wildcard); an upper bound or an exclusion cannot make an + installed version too old. Versions are compared by their release + numbers, so a development, pre-release, post-release or local build of + the floor version (``6.1.1.dev0``, ``6.1.1rc1``, ``6.1.1+local``) meets + ``>=6.1.1``. Anything that carries no usable version -- a string that + is not a version, an all-zero "unknown" placeholder such as + ``0+unknown`` or ``0.0.0`` from an untagged source build, a requirement + that does not parse, or no ``packaging`` library to parse with -- is + treated as meeting the floor: never a false alarm. + """ + try: + from packaging.requirements import Requirement + from packaging.version import Version + req = Requirement(requirement) + have = Version(Version(installed).base_version) + except Exception: # ImportError, InvalidRequirement, InvalidVersion + return True + if not any(have.release): + return True + for clause in req.specifier: + if clause.version.endswith('.*'): + continue + try: + floor = Version(Version(clause.version).base_version) + except Exception: + continue + if clause.operator in ('>=', '~=', '==') and have < floor: + return False + if clause.operator == '>' and have <= floor: + return False + return True + + +def outdated_requirements(requirements): + """The requirements whose INSTALLED distribution is below the declared + floor. + + Each requirement string is looked up by the distribution name it + declares (``scikit-image``, not the import name ``skimage``) in the + installed package metadata. A distribution that is not installed is not + "too old" (a missing module is `lazy_import`'s other branch), and + neither is one whose marker does not apply here or whose version cannot + be compared (see `_meets_floor`). + + Parameters + ---------- + requirements : iterable of str + Requirement strings, e.g. ``['plotly>=6.1.1', 'kaleido>=1.0']``. + + Returns + ------- + list of (str, str, str) + ``(requirement, distribution name, installed version)`` for each + requirement the installed version does not meet, in order. + """ + try: + from packaging.requirements import Requirement + except ImportError: + return [] + found = [] + for requirement in requirements or (): + try: + req = Requirement(requirement) + if req.marker is not None and not req.marker.evaluate(): + continue + installed = metadata.version(req.name) + except Exception: # unparseable, or the distribution is absent + continue + if not _meets_floor(installed, requirement): + found.append((requirement, req.name, installed)) + return found + + +def _imported_modules_of(dist_name): + """The top-level modules of the installed distribution ``dist_name`` that + are imported in this process (``['_plotly_utils', 'plotly']``).""" + try: + dist = metadata.distribution(dist_name) + except Exception: + return [] + names = set((dist.read_text('top_level.txt') or '').split()) + if not names: + for f in dist.files or (): + first = f.parts[0] if f.parts else '' + if len(f.parts) == 1 and first.endswith('.py'): + first = first[:-3] + if first.isidentifier(): + names.add(first) + return sorted(n for n in names if n in sys.modules) + + +def _too_old_text(outdated): + return '; '.join(f'{name} {have} is installed, but hypertools needs {req}' + for req, name, have in outdated) + + +def _upgrade_outdated(outdated, requirements, extra, need): + """Bring the distributions in ``outdated`` (from `outdated_requirements`) + up to their requirements, or raise ImportError saying exactly what is + installed, what is required and what to run.""" + what = _too_old_text(outdated) + need + manual = _manual_command(extra, requirements) + imported = sorted({name for _req, name, _have in outdated + if _imported_modules_of(name)}) + restart = (' and restart Python (in a notebook, restart the kernel or ' + 'runtime)') + if not auto_install_enabled(): + raise ImportError( + f'{what}. Upgrade with `{manual}`' + + (f'{restart}, since {", ".join(imported)} is already imported ' + 'in this process' if imported else '') + + ' (automatic installation is off; ' + 'hypertools.set_autoinstall(True) turns it on).') + if imported: + # an imported package cannot be replaced under a running + # interpreter: its loaded modules stay the old ones and anything it + # imports later comes from the new files. Nothing is installed. + raise ImportError( + f'{what}. {", ".join(imported)} is already imported in this ' + 'Python process, so hypertools did not upgrade it in place. Run ' + f'`{manual}`{restart}.') + _notice('upgrading ' + + ', '.join(f'{name} {have} to {req}' for req, name, have in outdated) + + f'{need} ...') + try: + _pip_install(requirements) + except subprocess.CalledProcessError as e: + raise ImportError( + f'{what}, and upgrading automatically failed ' + f'({type(e).__name__}). Run `{manual}` and try again.') from e + still = outdated_requirements(requirements) + if still: + raise ImportError( + f'{_too_old_text(still)}{need}, and upgrading automatically ' + f'left it in place. Run `{manual}` and try again.') + + def lazy_import(module, purpose=None, extra=None, requirements=None): - """Import ``module``, installing the extra that provides it first if it - is missing. + """Import ``module``, first installing the extra that provides it if it + is missing, or upgrading it if what is installed is older than the + requirement hypertools declares. Parameters ---------- @@ -124,20 +534,51 @@ def lazy_import(module, purpose=None, extra=None, requirements=None): ------ ImportError When the module is missing and cannot be installed (auto-install - disabled, no network, no permission, or no extra provides it); the - message carries the manual command. + disabled, no network, no permission, or no extra provides it), or + when an installed distribution of the extra is below its declared + requirement and cannot be upgraded (auto-install disabled, the old + version already imported in this process, or pip failed). The + message carries the manual command and, for a version problem, the + installed version and the requirement. + + Notes + ----- + Every requirement of the extra is checked, not only the distribution + behind ``module``: ``lazy_import('kaleido')`` also verifies plotly, + because static export needs the two together. The check reads the + installed package metadata once; a passed check is remembered for the + life of the process, so later calls only import. See + `outdated_requirements` for what counts as too old. """ + top = module.split('.')[0] + extra = extra or EXTRA_FOR_MODULE.get(top) + key = (top, extra, None if requirements is None else tuple(requirements)) + if key in _VERSIONS_VERIFIED: + try: + return importlib.import_module(module) + except ImportError: + pass # e.g. a submodule: decided below + need = f' (needed for {purpose})' if purpose else '' + declared = requirements + if declared is None and extra is not None: + try: + declared = extra_requirements(extra) + except (ValueError, metadata.PackageNotFoundError): + # an unknown extra, or hypertools run from a source tree with no + # installed metadata: nothing to check an installed module + # against (a MISSING module still reports this, below) + declared = None + outdated = outdated_requirements(declared) if declared else [] + if outdated: + # before the import: an upgrade is only possible while the old + # version is not imported yet + _upgrade_outdated(outdated, declared, extra, need) try: - return importlib.import_module(module) + imported = importlib.import_module(module) except ImportError as first: - top = module.split('.')[0] - extra = extra or EXTRA_FOR_MODULE.get(top) if requirements is None and extra is not None: requirements = extra_requirements(extra) - need = f' (needed for {purpose})' if purpose else '' - manual = (install_command(extra) if extra - else f'pip install {" ".join(requirements)}' if requirements - else None) + manual = _manual_command(extra, requirements) if requirements is None: raise ImportError( f'{module} is not installed{need}, and hypertools declares no ' @@ -145,17 +586,27 @@ def lazy_import(module, purpose=None, extra=None, requirements=None): if not auto_install_enabled(): raise ImportError( f'{module} is not installed{need}. Install it with `{manual}` ' - '(automatic installation is disabled by HYPERTOOLS_AUTO_INSTALL=0).' + '(automatic installation is off; hypertools.set_autoinstall(True) ' + 'turns it on).' ) from first _notice(f'installing {", ".join(requirements)}{need} ...') try: _pip_install(requirements) - return importlib.import_module(module) + imported = importlib.import_module(module) except (subprocess.CalledProcessError, ImportError) as second: raise ImportError( f'{module} is not installed{need}, and installing it ' f'automatically failed ({type(second).__name__}). Install it ' f'with `{manual}` and try again.') from second + _VERSIONS_VERIFIED.add(key) + return imported + + +def _installed_version(dist_name): + try: + return metadata.version(dist_name) + except Exception: + return '(unknown version)' def _kaleido_can_render(): @@ -168,6 +619,23 @@ def _kaleido_can_render(): if 'chrome' not in str(e).lower(): raise return False, e + except ValueError as e: + # plotly's "Image export using the "kaleido" engine requires the + # kaleido package" while kaleido IS importable: the two versions do + # not work together (kaleido 1.x with plotly < 6.1.1; measured with + # plotly 5.24.1 + kaleido 1.3.0, review 2026-10-09). Say that, not + # "install kaleido". `lazy_import` catches the declared floors + # first; this is for an environment it could not check. + if 'kaleido' not in str(e).lower(): + raise + from .exceptions import HypertoolsIOError + raise HypertoolsIOError( + f"plotly {_installed_version('plotly')} and kaleido " + f"{_installed_version('kaleido')} are both installed but cannot " + "export a static image together (hypertools needs " + f"{', '.join(extra_requirements('interactive'))}). Bring them in " + f"line with `{install_command('interactive')}` and restart " + f"Python. plotly said: {str(e).strip()}") from e def _apt_install(packages): diff --git a/hypertools/align/align.py b/hypertools/align/align.py index 1a42689d..b56bbe68 100644 --- a/hypertools/align/align.py +++ b/hypertools/align/align.py @@ -8,15 +8,15 @@ import warnings import numpy as np -import pandas as pd import datawrangler as dw -from .common import Aligner +from .common import Aligner, trim_and_pad from .hyperalign import HyperAlign from .procrustes import Procrustes from .srm import SharedResponseModel, DeterministicSharedResponseModel, RobustSharedResponseModel from .null import NullAlign -from ..core.shared import unpack_model +from ..core.shared import unpack_model, as_dataframe, check_spec_keys +from .._shared.helpers import is_text_item from ..core.model import external_stacklevel @@ -86,10 +86,11 @@ def _resolve_align_spec(model, extra_kwargs): The `model=` spec (see `align`'s docstring for the full grammar). extra_kwargs : dict Leftover `**kwargs` from the `align()` call, forwarded to the - resolved class's constructor -- but only when `model` is a bare - registry name/class (a dict spec's own `'args'`/`'kwargs'` take - precedence over these, and an already-constructed instance ignores - them entirely, matching `hyp.reduce`/`hyp.cluster`). + resolved class's constructor: for a bare registry name/class + directly, and for a dict spec merged into its `'kwargs'` (winning + on a conflict, as in `hyp.impute`/`hyp.predict`; they used to be + dropped silently). An already-constructed instance cannot take + them, so they are ignored with a `UserWarning`. Returns ------- @@ -120,6 +121,10 @@ def _resolve_align_spec(model, extra_kwargs): "value of the 'kwargs' key (the legacy 'params' key is " "also accepted)." ) + # a flat key such as {'model': 'HyperAlign', 'n_iter': 3} used to + # be dropped silently, so the aligner ran with its defaults (1.1 + # review) + check_spec_keys(model, 'align') if c_model is None or c_model is False: return None if 'args' in model or 'kwargs' in model: @@ -136,6 +141,10 @@ def _resolve_align_spec(model, extra_kwargs): c_args, c_kwargs = [], dict(model['params']) else: c_args, c_kwargs = [], {} + # the outer **kwargs join the spec's own parameters, winning on a + # conflict (hyp.align(data, model={'model': 'HyperAlign'}, + # n_iter=0) used to drop n_iter silently; 1.1 review) + c_kwargs = {**c_kwargs, **extra_kwargs} if isinstance(c_model, str): _warn_deprecated_alias(c_model) c_model = _ALIAS.get(c_model, c_model) @@ -147,8 +156,17 @@ def _resolve_align_spec(model, extra_kwargs): _reject_unknown_aligner(resolved_inner) if isinstance(resolved_inner, type): return resolved_inner(*c_args, **c_kwargs) - # already-constructed (or already-fitted) instance: params ignored, - # used as-is + # already-constructed (or already-fitted) instance: used as-is, so + # its parameters cannot change -- say so instead of dropping them + # silently (parity with hyp.reduce's dict-spec instance warning) + if c_args or c_kwargs: + warnings.warn( + f"the align spec's 'model' is an already-constructed " + f"{type(resolved_inner).__name__} instance (used as-is), so " + "the spec's 'args'/'kwargs' entries (and any extra keyword " + "arguments) are ignored; configure the instance directly, " + "or pass the class (or its name) to apply constructor " + "parameters", UserWarning, stacklevel=external_stacklevel()) return resolved_inner resolved = unpack_model(model, valid=ALIGNERS, parent_class=Aligner) @@ -158,7 +176,16 @@ def _resolve_align_spec(model, extra_kwargs): return resolved(**extra_kwargs) # an already-constructed (unfitted) or already-fitted instance is # passed through unchanged (the caller checks `.is_fitted` to decide - # whether to fit_transform or reuse via transform) + # whether to fit_transform or reuse via transform) -- so constructor + # keyword arguments cannot reach it: warn instead of dropping them + # silently (the wording hyp.impute/hyp.predict use; 1.1 review) + if extra_kwargs: + warnings.warn( + f'ignoring keyword argument(s) {sorted(extra_kwargs)}: model= ' + 'is already a constructed instance, so constructor parameters ' + 'cannot be applied. Pass the class (or a name/dict spec) to ' + 'set parameters.', UserWarning, + stacklevel=external_stacklevel()) return resolved @@ -175,14 +202,15 @@ def _apply_format_data(data): was_list = isinstance(data, list) items = data if was_list else [data] formatted = formatter(items, ppca=True) - rewrapped = [ - pd.DataFrame(np.asarray(arr), index=getattr(orig, 'index', None)) - for arr, orig in zip(formatted, items) - ] + # every item is a pandas frame here (the funnel ran with + # backend='pandas'), so its index is carried over as-is + rewrapped = [as_dataframe(arr).set_index(orig.index) + for arr, orig in zip(formatted, items)] return rewrapped if was_list else rewrapped[0] -def _compute_score(return_score, score_metric, before_data, after_data): +def _compute_score(return_score, score_metric, before_data, after_data, + trim=False): """Build the `{'before', 'after', 'metric'}` score dict for `align`'s `return_score=True` (GH #285), or `None` when `return_score` is False. @@ -191,12 +219,23 @@ def _compute_score(return_score, score_metric, before_data, after_data): are normalized to list form before delegating to `hypertools.align.score.alignment_score`, which raises a clear `ValueError` for ragged (unequal-shape) input. + + `trim=True` (the Aligner paths) first runs `before_data` through the + same `trim_and_pad` the aligner itself applies (common rows, padded + columns, no second data-loss warning), so the "before" score is taken + on exactly the equal-shape data the aligner consumed. Without it, + ragged input (say 50 and 40 rows) aligned fine but `return_score=True` + raised from `alignment_score` on the untrimmed originals (1.1 release + review). """ if not return_score: return None from .score import alignment_score before_list = before_data if isinstance(before_data, list) else [before_data] after_list = after_data if isinstance(after_data, list) else [after_data] + if trim: + before_list = trim_and_pad([as_dataframe(d) for d in before_list], + warn=False) return alignment_score(before_list, aligned=after_list, metric=score_metric) @@ -250,7 +289,7 @@ def _align(data, model='HyperAlign', return_model=False, "align= is deprecated as a model-spec kwarg name on " "hypertools.align.align.align; use model= instead (e.g. " "hyp.align(data, model='hyper')).", - DeprecationWarning, stacklevel=2, + DeprecationWarning, stacklevel=external_stacklevel(), ) model = legacy_model @@ -320,12 +359,13 @@ def _align(data, model='HyperAlign', return_model=False, if isinstance(resolved, Aligner) and resolved.is_fitted: raw = _to_arrays(resolved.transform(data)) result = _match_input_shape(raw, was_list) - score = _compute_score(return_score, score_metric, data, raw) + score = _compute_score(return_score, score_metric, data, raw, + trim=True) return _build_return(result, return_model, resolved, return_score, score) raw = _to_arrays(resolved.fit_transform(data)) result = _match_input_shape(raw, was_list) - score = _compute_score(return_score, score_metric, data, raw) + score = _compute_score(return_score, score_metric, data, raw, trim=True) return _build_return(result, return_model, resolved, return_score, score) @@ -386,7 +426,10 @@ def align(data, model='HyperAlign', return_model=False, SharedResponseModel, NullAlign`), the canonical dict spec `{'model': ..., 'args': [...], 'kwargs': {...}}`, or the LEGACY dict spec `{'model': ..., 'params': {...}}` (accepted for backward - compatibility, but emits a `DeprecationWarning`). A + compatibility, but emits a `DeprecationWarning`). Model parameters + always go under `'kwargs'`: any other top-level key -- e.g. + `{'model': 'HyperAlign', 'n_iter': 3}` -- raises `ValueError` + naming it rather than being ignored. A previously-fitted `Aligner` (as returned by `return_model=True`) is applied via `.transform` instead of being refit. `False` or `None` skips alignment entirely and returns the data unchanged @@ -404,6 +447,10 @@ def align(data, model='HyperAlign', return_model=False, data before vs. after alignment (GH #285): see `hypertools.align.score.alignment_score` for the two supported `score_metric=` values (`'dispersion'`, the default, and `'isc'`). + The "before" score is computed on the row-trimmed (and + column-padded) input -- the equal-shape data the aligner actually + consumed -- so ragged datasets that `align` trims to their common + rows score without error; the "after" score is the aligned output. Only supported for the plain align stage -- raises `ValueError` if combined with `manip=`/`normalize=`/`reduce=`/`cluster=`, since the before/after pairing is undefined inside a multi-stage pipeline @@ -432,7 +479,10 @@ def align(data, model='HyperAlign', return_model=False, **kwargs Extra keyword arguments forwarded to `model`'s constructor when `model` is a bare registry name/class (e.g. `n_iter=` for - `'HyperAlign'`, `features=` for the SRM family). Keyword arguments + `'HyperAlign'`, `features=` for the SRM family), or merged into a + dict spec's `'kwargs'` (winning on a conflict); an + already-constructed instance cannot take them, so they are ignored + with a `UserWarning`. Keyword arguments the model does not accept raise a `TypeError` naming them (they used to be silently ignored, so a typo'd parameter went unnoticed). `align=` is also accepted here as a DEPRECATED alias for `model=` @@ -511,7 +561,7 @@ def align(data, model='HyperAlign', return_model=False, _datasets = data if isinstance(data, list) else [data] for _i, _d in enumerate(_datasets): _ndim = getattr(_d, 'ndim', None) - if _ndim is None and not isinstance(_d, (str, bytes)): + if _ndim is None and not is_text_item(_d): try: _ndim = np.ndim(_d) except Exception: @@ -525,8 +575,12 @@ def align(data, model='HyperAlign', return_model=False, 'arrays/DataFrames (e.g. one per subject) instead of a ' 'higher-dimensional stack -- e.g. list(x) for a 3-D ' 'array x.') + # backend='pandas': datawrangler's funnel otherwise PRESERVES a polars + # input's backend, and the Aligners (datawrangler's unstack/stack, + # trim_and_pad) are written against pandas, hypertools' internal frame + # type (datatype audit, 2026-09-08) return _align(data, model=model, return_model=return_model, return_score=return_score, score_metric=score_metric, manip=manip, normalize=normalize, reduce=reduce, ndims=ndims, cluster=cluster, format_data=format_data, - **kwargs) + backend='pandas', **kwargs) diff --git a/hypertools/align/common.py b/hypertools/align/common.py index 812a0570..a8d95ac9 100644 --- a/hypertools/align/common.py +++ b/hypertools/align/common.py @@ -1,11 +1,13 @@ """Base class + helpers for hypertools aligners (scikit-learn compatible). An Aligner wraps a (fitter, transformer, required-params) triple operating on -a *list* of DataFrames: `fit` unstacks the stored data into that list, trims to +a *list* of DataFrames: `fit` coerces its input (arrays, DataFrames of any +backend datawrangler recognises, or a list of these) into that list, trims to common rows and pads to common columns, runs the fitter, and stores the returned dict as attributes; `transform` re-derives the list and runs the transformer with -those params. Child classes (HyperAlign, Procrustes, SharedResponseModel, ...) -supply the three pieces plus their defaults. +those params, handing each dataset back in its input's form. Child classes +(HyperAlign, Procrustes, SharedResponseModel, ...) supply the three pieces plus +their defaults. """ import warnings @@ -15,6 +17,10 @@ from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError +from .._shared.helpers import is_frame_dataset, is_series_like +from ..core.model import external_stacklevel +from ..core.shared import as_dataframe + def reject_unknown_kwargs(cls_name, kwargs, supported): """Raise a `TypeError` naming any unexpected constructor kwargs. @@ -113,7 +119,8 @@ def trim_and_pad(data, warn=True): 'label(s); alignment matches observations across ' 'datasets by index value, so only the FIRST row for ' 'each duplicated label is kept. Use unique row indices ' - '(e.g. df.reset_index(drop=True)) to keep every row.') + '(e.g. df.reset_index(drop=True)) to keep every row.', + stacklevel=external_stacklevel()) d = d[~d.index.duplicated(keep='first')] deduped.append(d) data = deduped @@ -138,23 +145,82 @@ def trim_and_pad(data, warn=True): # warn on data loss: alignment keeps only the rows COMMON to every dataset # (matched observation-by-observation), so datasets with different row # counts / indices are trimmed. This used to happen silently (QC 2026-07). + # Both warnings here name the CALLER's line (1.1 release review): a fixed + # stacklevel pointed into this module, whatever route reached it. if warn and any(len(rows) < d.shape[0] for d in data): warnings.warn( f"alignment keeps only the {len(rows)} row(s) common to all " "datasets; datasets with more rows were trimmed. Align datasets " - "with matching numbers of observations to avoid dropping data.") + "with matching numbers of observations to avoid dropping data.", + stacklevel=external_stacklevel()) return [pad(d.loc[rows], c) for d in data] +class _AlignerInput: + """One `fit`/`transform` argument, coerced to the list of pandas + DataFrames the fitters and transformers work on, plus what is needed + to hand results back in the input's own form. + + `Aligner.fit` used to pass its argument straight to + ``datawrangler.unstack``, which only understands DataFrames, so the + most common hypertools input -- a list of numpy arrays -- raised a bare + ``Exception: Unsupported datatype: <class 'list'>`` (and so did a single + array), although `fit` documents ``DataFrame, array, or list of these`` + (1.1 release review, 2026-09-11). Coercion follows ``hyp.align``: every + dataset becomes a pandas DataFrame (`as_dataframe`: a pandas frame is + kept as is, index included; other frame backends -- polars, ... -- are + converted by datawrangler; an array is wrapped), and a single + row-``MultiIndex`` DataFrame (``datawrangler.stack`` output) is + unstacked into its datasets, as before. + + Attributes + ---------- + frames : list of pandas.DataFrame + The datasets. + as_array : list of bool + Per dataset: whether its result goes back as a numpy array (the + input was not a frame or Series) rather than a pandas DataFrame. + single : bool + Whether the input was ONE dataset (not a list/tuple), so a + one-dataset result is returned bare rather than as a list of one. + """ + + def __init__(self, data): + if isinstance(data, tuple): + data = list(data) + self.single = not isinstance(data, list) + if self.single and is_frame_dataset(data): + # a stacked (row-MultiIndex) frame holds several datasets + frames = dw.unstack(as_dataframe(data)) + self.frames = frames if isinstance(frames, list) else [frames] + self.as_array = [False] * len(self.frames) + return + items = [data] if self.single else data + self.as_array = [not (is_frame_dataset(d) or is_series_like(d)) + for d in items] + self.frames = [as_dataframe(d) for d in items] + + def restore(self, results): + """`results` (one DataFrame per dataset) in the input's form.""" + out = [np.asarray(r) if as_array else r + for r, as_array in zip(results, self.as_array)] + if self.single and len(out) == 1: + return out[0] + return out + + class Aligner(BaseEstimator): """Scikit-learn-compatible base class for hypertools aligners. Wraps a `(fitter, transformer, required)` triple that operates on a - *list* of DataFrames: `fit` unstacks the stored data into that list, - trims to common rows and zero-pads to common columns (see - `trim_and_pad`), runs `fitter` on it, and stores each key of the + *list* of DataFrames: `fit` coerces its input into that list (see + `_AlignerInput`), trims to common rows and zero-pads to common columns + (see `trim_and_pad`), runs `fitter` on it, and stores each key of the returned dict as an attribute on `self`; `transform` re-derives the - list the same way and runs `transformer` with those fitted params. + list the same way, runs `transformer` with those fitted params, and + returns each dataset in its input's form (an array for an array, a + pandas DataFrame for a DataFrame; a list for a list, one dataset for + one dataset). Child classes (e.g. HyperAlign, Procrustes, SharedResponseModel) supply `fitter`, `transformer`, and `required` (the list of attribute names `fitter` must return) via `**kwargs` to `__init__`. @@ -210,26 +276,37 @@ def is_fitted(self): return self.data is not None @staticmethod - def _shape_of(data): - """`(n_datasets, [n_columns_per_dataset])` for `data` (a single - DataFrame/array, or a list of them) -- the shape `transform` - validates new data against (GH #227).""" - items = data if isinstance(data, list) else [data] - return len(items), [np.asarray(d).shape[1] for d in items] + def _shape_of(frames): + """`(n_datasets, [n_columns_per_dataset])` for `frames` (the + coerced list of DataFrames, see `_AlignerInput`) -- the shape + `transform` validates new data against (GH #227).""" + return len(frames), [d.shape[1] for d in frames] def fit(self, data): """Fit the alignment on `data` and store the fitted parameters. Records `data` and its shape (for later validation in - `transform`), then -- if `self.fitter` is set -- unstacks, - trims, and pads `data` (see `trim_and_pad`) and calls - `self.fitter` on it, setting each key of the returned dict as an - attribute on `self`. + `transform`), then -- if `self.fitter` is set -- coerces `data` + to a list of pandas DataFrames (see `_AlignerInput`), trims and + pads it (see `trim_and_pad`), and calls `self.fitter` on it, + setting each key of the returned dict as an attribute on `self`. Parameters ---------- - data : DataFrame, array, or list of these - The dataset(s) to fit the alignment on. + data : DataFrame, array, or list (or tuple) of these + The dataset(s) to fit the alignment on: numpy arrays, pandas + DataFrames, DataFrames of any other backend datawrangler + recognises (polars, ...), or a mix, each one dataset of + observations x features; a single row-`MultiIndex` DataFrame + (``datawrangler.stack`` output) holds one dataset per + top-level index value. Rows are matched across datasets by + index value (an array's rows by position). + + Returns + ------- + self + The fitted aligner, so calls chain the sklearn way: + ``HyperAlign().fit(xs).transform(ys)``. Raises ------ @@ -238,18 +315,20 @@ def fit(self, data): not return a dict, or if any name in `self.required` is missing from the returned dict. """ - if data is None or (isinstance(data, list) and len(data) == 0): + if data is None or (isinstance(data, (list, tuple)) + and len(data) == 0): from ..core.shared import no_observations_message raise ValueError( no_observations_message( 'align', 'data is None or an empty list') + ' Pass one or more numeric arrays/DataFrames to fit the ' 'aligner on.') + coerced = _AlignerInput(data) self.data = data - self._fit_shape = self._shape_of(data) + self._fit_shape = self._shape_of(coerced.frames) if self.fitter is None: - return - data = trim_and_pad(dw.unstack(self.data)) + return self + data = trim_and_pad(coerced.frames) params = self.fitter(data, **self.kwargs) if not isinstance(params, dict): raise ValueError( @@ -262,6 +341,7 @@ def fit(self, data): f"{', '.join(missing)}") for k, v in params.items(): setattr(self, k, v) + return self def transform(self, new_data=None): """Apply the fitted alignment to `new_data`. @@ -279,8 +359,11 @@ def transform(self, new_data=None): Returns ------- The aligned `new_data` (or the aligned fit-time data, when - `new_data` is `None`), in the same list/single-item shape as the - input. + `new_data` is `None`), in the input's own form: a list for a list + (or tuple), one dataset for one dataset, and per dataset a numpy + array for an array input or a pandas DataFrame (indexed by the + common rows) for a DataFrame input of any backend. (A single + row-`MultiIndex` DataFrame returns the list of its datasets.) Raises ------ @@ -303,13 +386,13 @@ def transform(self, new_data=None): # `new_data` may arrive as raw array(s) rather than # DataFrame(s) -- e.g. `model.transform(...)` called directly # (bypassing the `@dw.decorate.funnel`/`format_data` coercion - # `align()` applies before `fit`). Coerce here (single - # array|DataFrame, or list of these) to the same DataFrame(s) - # format `fit` uses, BEFORE shape validation/`dw.unstack` below - # -- `dw.wrangle` preserves each DataFrame's index and the - # single-vs-list shape of the input, matching the funnel path. - new_data = dw.wrangle(new_data) - n_datasets, n_columns = self._shape_of(new_data) + # `align()` applies before `fit`). It is coerced below exactly + # as `fit` coerces its input, BEFORE the shape validation. + data_to_use = new_data + + coerced = _AlignerInput(data_to_use) + if new_data is not None: + n_datasets, n_columns = self._shape_of(coerced.frames) fit_n_datasets, fit_n_columns = self._fit_shape if n_datasets != fit_n_datasets or n_columns != fit_n_columns: raise ValueError( @@ -318,8 +401,6 @@ def transform(self, new_data=None): f"dataset(s) with {n_columns} column(s) (fit-time " f"shape: {fit_n_datasets} datasets x {fit_n_columns} " f"columns)") - data_to_use = new_data - if self.transformer is None: return data_to_use # only warn about trimmed rows for genuinely NEW data -- when @@ -327,22 +408,25 @@ def transform(self, new_data=None): # fit_transform), fit's own trim_and_pad already warned, and # repeating the identical warning misled users into thinking two # separate trims happened (QC 2026-07, F12-align-003) - data = trim_and_pad(dw.unstack(data_to_use), warn=new_data is not None) + data = trim_and_pad(coerced.frames, warn=new_data is not None) required_params = {r: getattr(self, r) for r in self.required} - return self.transformer(data, **dw.core.update_dict(required_params, self.kwargs)) + aligned = self.transformer( + data, **dw.core.update_dict(required_params, self.kwargs)) + return coerced.restore(aligned) def fit_transform(self, data): """Fit the alignment on `data`, then immediately transform it. Parameters ---------- - data : DataFrame, array, or list of these - The dataset(s) to fit and align. + data : DataFrame, array, or list (or tuple) of these + The dataset(s) to fit and align (see `fit`). Returns ------- - The aligned `data`, in the same list/single-item shape as the - input (see `transform`). + The aligned `data`, in the input's own form: list or single + dataset, arrays for arrays and DataFrames for DataFrames (see + `transform`). """ self.fit(data) # replay the just-fit data (rather than re-passing `data`) so the diff --git a/hypertools/align/procrustes.py b/hypertools/align/procrustes.py index 3b551029..b1f7ecc2 100644 --- a/hypertools/align/procrustes.py +++ b/hypertools/align/procrustes.py @@ -83,8 +83,8 @@ def align(source, target, scaling=True, reflection=True, reduction=False, obliqu Parameters ---------- source : numpy.ndarray or pandas.DataFrame - Data to be aligned to `target`'s coordinate system. If it has a - `.values` attribute (e.g. a DataFrame), that is used. + Data to be aligned to `target`'s coordinate system (anything + `numpy.asarray` accepts: an array, or a DataFrame of any backend). target : numpy.ndarray or pandas.DataFrame Data defining the target coordinate system. scaling : bool, optional @@ -114,10 +114,11 @@ def align(source, target, scaling=True, reflection=True, reduction=False, obliqu if either dataset is invariant (near-zero variance), or if `reduction=False` but `source` has more columns than `target`. """ - if hasattr(source, 'values'): - source = getattr(source, 'values') - if hasattr(target, 'values'): - target = getattr(target, 'values') + # `np.asarray` is the whole datatype question here: a pandas frame's + # `.values`, a polars frame's `__array__`, an array as-is (datatype + # audit, 2026-09-08 -- was a hasattr(x, 'values') pandas duck-check) + source = np.asarray(source) + target = np.asarray(target) datas = (source, target) sn, sm = source.shape diff --git a/hypertools/align/score.py b/hypertools/align/score.py index a18a7082..4a411db1 100644 --- a/hypertools/align/score.py +++ b/hypertools/align/score.py @@ -25,6 +25,21 @@ def _stack_equal_shape(datasets, fname): arrays = [np.asarray(d) for d in datasets] if len(arrays) == 0: raise ValueError(f'{fname} requires at least one dataset; got an empty list.') + for i, a in enumerate(arrays): + if a.ndim != 2: + raise ValueError( + f'{fname} requires 2-D datasets of shape (n_observations, ' + f'n_features); dataset {i} has shape {a.shape}. Reshape a ' + '1-D series to a single column (x[:, None]) first.') + if not np.issubdtype(a.dtype, np.number): + raise ValueError( + f'{fname} requires numeric datasets; dataset {i} has dtype ' + f'{a.dtype}.') + if not np.all(np.isfinite(a)): + raise ValueError( + f'{fname} requires finite values; dataset {i} has ' + f'{int((~np.isfinite(a)).sum())} NaN/inf entries. Impute or ' + 'drop them (e.g. hyp.impute) before scoring.') shapes = {a.shape for a in arrays} if len(shapes) > 1: raise ValueError( @@ -45,8 +60,37 @@ def dispersion(trajectories): Reproduces `examples/plot_story_trajectories.py`'s `dispersion()` helper EXACTLY (same computation, same result for the same input) -- kept as the library implementation of that example's inline function. + + Raises `ValueError` when every dataset is constant (each one's + observations all at one point, a single observation included), naming + the datasets. Then the per-observation centroid is the same point as + the cloud's own mean, so the score is exactly 1.0 whatever the datasets + are (or, when they all sit at the SAME point, 0/0) -- it measures + nothing, matching `metric='isc'`, which has no correlation to compute + there either. """ stack = np.stack([np.asarray(t) for t in trajectories]) # (subj, t, d) + # a dataset is constant when its observations never move (np.ptp is 0 + # along the observation axis for every feature) + constant = np.all(np.ptp(stack, axis=1) == 0, axis=1) # (subj,) + if constant.all(): + # release review 2026-09-07 caught the all-at-one-point case (NaN + # with a RuntimeWarning); 1.1 release review 2026-09-11 the + # each-at-its-own-point case, which returned a meaningless 1.0 + # while 'isc' raised on the same input + n_datasets, n_obs = stack.shape[0], stack.shape[1] + which = (f'dataset {n_datasets - 1}' if n_datasets == 1 + else f'datasets 0-{n_datasets - 1}' if n_datasets > 3 + else ', '.join(f'dataset {i}' for i in range(n_datasets))) + single = (' (a dataset with a single observation is constant)' + if n_obs == 1 else '') + raise ValueError( + "alignment_score(metric='dispersion') is undefined when every " + f'dataset is constant: {which} each keep every observation at ' + f'one point{single}. The per-observation centroid is then the ' + "cloud's own mean, so the score would be 1.0 (0/0 when the " + 'datasets share one point) whatever the alignment. Score ' + 'datasets whose observations vary.') centroid = stack.mean(axis=0, keepdims=True) spread = np.linalg.norm(stack - centroid, axis=2).mean() scale = np.linalg.norm(stack - stack.mean(axis=(0, 1)), axis=2).mean() @@ -133,9 +177,12 @@ def alignment_score(datasets, aligned=None, metric='dispersion'): Raises ------ ValueError - If `datasets` (or `aligned`) is empty, if the datasets in either - list do not all share the same shape (ragged input), or if - `metric` is not one of the supported names. + If `datasets` (or `aligned`) is empty, if any dataset is not a 2-D + numeric array of finite values, if the datasets in either list do + not all share the same shape (ragged input), if `metric` is not one + of the supported names, or if the input is degenerate for the + metric (every dataset constant for `'dispersion'`; no non-constant + feature to correlate for `'isc'`). """ if metric not in _METRICS: raise ValueError( diff --git a/hypertools/cluster/cluster.py b/hypertools/cluster/cluster.py index 2546c053..7dbfac18 100644 --- a/hypertools/cluster/cluster.py +++ b/hypertools/cluster/cluster.py @@ -15,6 +15,7 @@ from .common import Clusterer, CLUSTERERS, MIXTURES from ..core.model import external_stacklevel +from ..core.shared import check_spec_keys from ..tools.format_data import format_data as formatter # backward-compatible aliases: `hypertools.cluster.cluster.models` and @@ -24,6 +25,24 @@ models = CLUSTERERS mixture_models = MIXTURES +#: The one documented top-level convenience a cluster dict spec accepts +#: besides the canonical 'model'/'args'/'kwargs' and the legacy 'params'. +_CLUSTER_SPEC_SHORTCUTS = ('n_clusters',) + + +def _check_cluster_spec_keys(spec): + """Raise `ValueError` if a cluster dict spec carries top-level keys + other than `core.shared.SPEC_KEYS` and the 'n_clusters' shortcut. + + A flat spec such as `{'model': 'KMeans', 'n_clusters': 4, + 'random_state': 0}` used to drop `random_state` without a word, so its + clusters changed from call to call (1.1 review). The shared + `core.shared.check_spec_keys` names the offending keys and spells out + the corrected spec, with those keys merged into 'kwargs'. + """ + check_spec_keys(spec, 'cluster', shortcuts=_CLUSTER_SPEC_SHORTCUTS, + param='cluster') + def _resolve_cluster_spec(cluster, n_clusters, random_state=None, n_clusters_explicit=False): @@ -36,7 +55,10 @@ def _resolve_cluster_spec(cluster, n_clusters, random_state=None, to the constructor's parameters by position, with `'kwargs'` winning on a conflict -- final wave item 3), or the LEGACY dict spec `{'model': ..., 'params': {...}}` (accepted for backward - compatibility, but emits a `DeprecationWarning`). + compatibility, but emits a `DeprecationWarning`). Besides those keys a + dict spec may carry only the top-level `'n_clusters'` shortcut; any + other top-level key (e.g. a flat `'random_state'`) raises `ValueError` + naming it (see `_check_cluster_spec_keys`). The `n_clusters=` convenience is preserved exactly as the pre-1.0 API behaved: it is injected into the constructor only when the resolved @@ -85,6 +107,8 @@ def _resolve_cluster_spec(cluster, n_clusters, random_state=None, "value of the 'model' key and a dictionary of " "custom parameters as the value of the 'kwargs' " "key (the legacy 'params' key is also accepted).") + # a flat key such as 'random_state' used to be dropped silently + _check_cluster_spec_keys(cluster) if "args" in cluster or "kwargs" in cluster: # canonical 1.0 dict spec: {'model': ..., 'args': [...], 'kwargs': {...}} if "params" in cluster: @@ -292,8 +316,12 @@ def cluster(x, cluster="KMeans", n_clusters=None, return_model=False, conflict, and a spec-carried cluster count winning over `n_clusters=`), or the LEGACY dict spec `{'model' : 'KMeans', 'params' : {'max_iter' : 100}}` (accepted for backward - compatibility, but emits a `DeprecationWarning`). A - previously-fitted `Clusterer` (as returned by `return_model=True`) + compatibility, but emits a `DeprecationWarning`). Model parameters + always go under `'kwargs'`; the only other top-level key a dict + spec accepts is the `'n_clusters'` shortcut (`{'model': 'KMeans', + 'n_clusters': 4}`), and any other one -- e.g. `{'model': 'KMeans', + 'random_state': 0}` -- raises `ValueError` naming it rather than + being ignored. A previously-fitted `Clusterer` (as returned by `return_model=True`) is applied via `.transform`/`.predict` instead of being refit; no-predict models (e.g. AgglomerativeClustering) can only recover their fit-time labels this way -- reusing them on different data diff --git a/hypertools/core/exceptions.py b/hypertools/core/exceptions.py index c4b2742b..2bbcf621 100644 --- a/hypertools/core/exceptions.py +++ b/hypertools/core/exceptions.py @@ -3,6 +3,14 @@ class HypertoolsError(Exception): pass +class _InsufficientHistoryError(ValueError): + """A forecast needs more observations (including after time resampling). + + Internal signal for animations to wait for more revealed history; other + fitting errors must still propagate. Public callers can catch ValueError. + """ + + class HypertoolsBackendError(HypertoolsError): """Raised when a plotting backend (matplotlib/plotly) cannot satisfy a request.""" def __init__(self, message): diff --git a/hypertools/core/hierarchy.py b/hypertools/core/hierarchy.py index 49a9577a..e4598ccd 100644 --- a/hypertools/core/hierarchy.py +++ b/hypertools/core/hierarchy.py @@ -36,8 +36,12 @@ from collections import Counter +import datawrangler as dw + import pandas as pd +from .._shared.helpers import is_frame_dataset + #: Stand-in for ANY missing hierarchy label during comparison and indexing. #: Module-private and never user-visible: it exists only so that missing #: labels hash and compare as one another's equals. Original label values @@ -82,9 +86,22 @@ def _canonical_key(key): return tuple(_canonical_label(value) for value in key) +def _has_pandas_axes(obj): + """True when `obj` is a DataFrame dataset (as datawrangler sees it) + that carries pandas-style ``.index``/``.columns`` axes -- the only kind + that can hold a MultiIndex. Strings and containers never qualify.""" + return is_frame_dataset(obj) and dw.zoo.dataframe_like(obj) + + def is_hierarchical(obj, axes='both'): - """True when `obj` is a DataFrame carrying a MultiIndex on `axes`.""" - if not isinstance(obj, pd.DataFrame): + """True when `obj` is a DataFrame carrying a MultiIndex on `axes`. + + A MultiIndex is a pandas notion: a frame of another backend (polars, + ...) has no hierarchy, so only a frame with the pandas DataFrame API + (``dw.zoo.dataframe_like``) can answer True. Nothing here names a + pandas type (datatype audit, 2026-09-08). + """ + if not _has_pandas_axes(obj): return False if axes == 'rows': return obj.index.nlevels >= 2 @@ -101,7 +118,7 @@ def reject_dual_axis(df): ignored; 1.1 declines to guess. This is an intentional compatibility change (see CHANGELOG 1.1.0, "Changed / validation"). """ - if (isinstance(df, pd.DataFrame) + if (_has_pandas_axes(df) and df.index.nlevels >= 2 and df.columns.nlevels >= 2): raise ValueError( "x has both a row and a column MultiIndex. hypertools 1.1 does " @@ -137,7 +154,7 @@ def reject_hierarchical_in_list(x, caller, axes='columns'): if axes not in ('columns', 'both'): raise ValueError(f"axes= must be 'columns' or 'both'; got {axes!r}") for i, element in enumerate(x): - if not isinstance(element, pd.DataFrame): + if not _has_pandas_axes(element): continue row_hier = element.index.nlevels >= 2 col_hier = element.columns.nlevels >= 2 diff --git a/hypertools/core/model.py b/hypertools/core/model.py index a0d9f23d..83ee404d 100644 --- a/hypertools/core/model.py +++ b/hypertools/core/model.py @@ -111,7 +111,9 @@ def apply_model(data, model, mode='auto', return_model=False, optional; 'args' are positional constructor arguments and require a name/class 'model'), or the legacy {'model': ..., 'params': {...}} form (accepted for backward compatibility, - emits a DeprecationWarning) + emits a DeprecationWarning). Any other top-level key -- e.g. a + flat {'model': 'PCA', 'whiten': True} -- raises ValueError + naming it rather than being ignored - instance: any object exposing fit/transform/fit_transform/ fit_predict/predict_proba (scikit-learn convention) - list: a pipeline; each element is applied in sequence, with each @@ -168,9 +170,10 @@ def apply_model(data, model, mode='auto', return_model=False, Returns ------- - result (and fitted model(s) if return_model=True; see `return_model` - for the exact shape). Lists in, lists out: a single input dataset - returns a single result. + result + The result (and fitted model(s) if ``return_model=True``; see + `return_model` for the exact shape). Lists in, lists out: a single + input dataset returns a single result. """ # ndims parity with hyp.reduce (release-1.0 audit, X2-error-quality-018: # reduce(ndims=0) raised while apply_model(..., ndims=0) silently @@ -199,6 +202,12 @@ def apply_model(data, model, mode='auto', return_model=False, # mode='fit_predict' into every stage made reduce->cluster pipelines # crash on the first non-predicting stage). if isinstance(model, list): + # check every stage's dict spec before ANY stage is fit, so a flat + # key in a late stage does not surface only after the early + # stages have run + from .shared import check_spec_keys + for stage in model: + check_spec_keys(stage, 'apply_model') fitted = [] last = len(model) - 1 for i, stage in enumerate(model): @@ -266,7 +275,10 @@ def _resolve_model(model, ndims): re-implementing it here; only the string-registry lookup and the `ndims=` convenience are specific to `apply_model`. """ - from .shared import unpack_model + from .shared import check_spec_keys, unpack_model + # a flat key such as {'model': 'PCA', 'whiten': True} used to be + # dropped silently (1.1 review) + check_spec_keys(model, 'apply_model') resolved = unpack_model(model) args, params = [], {} diff --git a/hypertools/core/pipeline.py b/hypertools/core/pipeline.py index 71d8abf6..6d6253b8 100644 --- a/hypertools/core/pipeline.py +++ b/hypertools/core/pipeline.py @@ -29,12 +29,14 @@ NEW PCA basis on whatever data `.transform` was given, rather than reusing the basis fit the first time). """ +import copy import warnings -from sklearn.base import BaseEstimator +from sklearn.base import BaseEstimator, clone as _sklearn_clone from sklearn.exceptions import NotFittedError -from .shared import unpack_model +from .model import external_stacklevel +from .shared import check_spec_keys, unpack_model #: the single documented pipeline order (#153): impute happens during @@ -83,6 +85,15 @@ def _resolve_step(spec): return spec if isinstance(spec, dict): + if 'model' not in spec: + # used to leak a bare KeyError: 'model' (1.1 review) + raise ValueError( + "a Pipeline step dict spec must include a 'model' key; got " + f"keys {sorted(spec, key=str)}. Pass e.g. " + "{'model': 'PCA', 'kwargs': {'n_components': 2}}.") + # a flat key such as {'model': 'PCA', 'whiten': True} used to be + # dropped silently (1.1 review) + check_spec_keys(spec, 'Pipeline step') resolved = unpack_model(spec, valid=[], parent_class=None) if isinstance(resolved, dict): inner = _resolve_ref(resolved['model']) @@ -147,7 +158,7 @@ def _step_transform(model, data, name=None): f"{label} has no transform/predict method; falling back to " "fit_predict, which RE-FITS the model on the new data (the " "returned labels come from a fresh fit, not the original one).", - stacklevel=3) + stacklevel=external_stacklevel()) return np.asarray(model.fit_predict(data)) raise TypeError( f"{label} cannot be applied to new data: it has no transform, " @@ -281,6 +292,31 @@ def _validate_input_hierarchy(spec): 'feature_labels': labels} +def as_internal_frames(data): + """`data` with every DataFrame dataset in hypertools' internal pandas + form: a frame of any backend datawrangler recognises (polars, a + LazyFrame, modin, ...) is converted once here -- pandas frames are + returned as-is -- so raw scikit-learn steps and the manipulators, which + are written against pandas, never see another backend; a Series (pandas + or polars) becomes the one-column frame that keeps its index and name. + Arrays, text and everything else pass through untouched, elementwise + for a list/tuple (datatype audit, 2026-09-08; Codex round 12, R12-4: a + raw Series reached datawrangler's stacking decorator). Shared with the + manipulators' transformers, which a fitted Manipulator's `.transform` + hands raw input directly.""" + from .._shared.helpers import is_frame_dataset, is_series_like + from .shared import as_dataframe + + def _one(item): + return (as_dataframe(item) + if is_frame_dataset(item) or is_series_like(item) else item) + + if isinstance(data, (list, tuple)): + converted = [_one(item) for item in data] + return converted if isinstance(data, list) else tuple(converted) + return _one(data) + + def _dataset_widths(data): """Feature counts of `data`, as a list -- one entry per dataset. @@ -288,14 +324,15 @@ def _dataset_widths(data): ragged/object list, a 1-D array), so the caller skips the check rather than guessing. """ - import numpy as np - import pandas as pd + from .._shared.helpers import is_frame_dataset, is_array_dataset + from .shared import as_dataframe def _width(item): """One dataset's feature count, or `None` if it is not knowable.""" - if isinstance(item, pd.DataFrame): - return item.shape[1] - if isinstance(item, np.ndarray) and item.ndim == 2: + if is_frame_dataset(item): + # a frame of any backend datawrangler recognises (pandas as-is) + return as_dataframe(item).shape[1] + if is_array_dataset(item) and item.ndim == 2: return item.shape[1] return None @@ -304,6 +341,131 @@ def _width(item): return None if any(w is None for w in widths) else widths +def _empty_rows(values): + """Boolean mask of the rows of a 2-D numeric dataset with NO finite + value, or `None` when `values` is not a 2-D numeric dataset.""" + import numpy as np + try: + arr = np.asarray(values, dtype=float) + except (TypeError, ValueError): + return None + if arr.ndim != 2: + return None + return ~np.isfinite(arr).any(axis=1) + + +def _row_preview(positions): + """'rows 0-3' for a contiguous run, else the first few positions.""" + positions = [int(p) for p in positions] + if positions == list(range(positions[0], positions[-1] + 1)): + return (f'row {positions[0]}' if len(positions) == 1 + else f'rows {positions[0]}-{positions[-1]}') + shown = ', '.join(str(p) for p in positions[:5]) + return f"rows {shown}{', ...' if len(positions) > 5 else ''}" + + +def _raise_if_manip_emptied_rows(data, result, spec, downstream): + """Stop a pipeline right after its manip stage when that stage left + rows with NO finite value that had values going in. + + A trailing ``Smooth(center=False)`` leaves its first + ``kernel_width - 1`` rows NaN (pandas' rolling-window semantics) unless + ``min_periods=1``. Handed on, those rows reached the next stage's + missing-data imputation, which warned "Missing data: filling missing + values with PPCA ..." and "PPCA cannot fill N row(s) ... Use + model='Kalman'" -- blaming the input and naming the wrong remedy -- + and then either left them NaN for `hyp.plot` to reject or crashed in + scikit-learn ("Input X contains NaN") or numpy ("SVD did not + converge") (1.1 release review, 2026-09-11). No later stage can use a + row with no observed feature, and no imputation run on the manip + output fills one, so the pipeline raises here with the manip-stage + diagnosis instead. Rows missing only SOME features (e.g. a + ``Delay(drop_edges=False)`` embedding's padded lags) are left for the + later stages' imputation, as before. + + `data`/`result` are the manip stage's input and output (one dataset + or a list of them); datasets are paired positionally, and a row + counts as emptied when the output row is entirely non-finite while + the input row (same position; when the stage changed the row count, + the whole dataset) had a finite value. + """ + import numpy as np + inputs = data if isinstance(data, list) else [data] + outputs = result if isinstance(result, list) else [result] + if len(inputs) != len(outputs): + return + for i, (before, after) in enumerate(zip(inputs, outputs)): + empty_after = _empty_rows(after) + empty_before = _empty_rows(before) + if empty_after is None or empty_before is None: + continue + if empty_after.shape == empty_before.shape: + emptied = empty_after & ~empty_before + elif not empty_before.any() and np.isfinite( + np.asarray(before, dtype=float)).all(): + emptied = empty_after + else: + continue + if not emptied.any(): + continue + positions = np.flatnonzero(emptied) + stages = ', '.join(f'{name}=' for name in downstream) + raise ValueError( + f"the manip= stage ({spec!r}) left {len(positions)} row(s) of " + f"dataset {i} with no finite values at all " + f"({_row_preview(positions)}), rows that had values going into " + f"it. The later stage(s) ({stages}) cannot use a row with no " + "observed feature, and missing-data imputation cannot fill " + "one, so the pipeline stops here. A trailing (center=False) " + "Smooth leaves its first kernel_width - 1 rows NaN under " + "pandas' rolling-window semantics: pass min_periods=1 to " + "smooth those rows over the observations available so far " + "(e.g. Smooth(kernel='boxcar', kernel_width=12, center=False, " + "min_periods=1)), or run hyp.manip on its own and drop those " + "rows before the next stage.") + + +def _unfitted_copy(obj): + """An unfitted, independent copy of a pipeline step or of a stage spec. + + Used by `Pipeline.__sklearn_clone__` (what `sklearn.base.clone` calls). + + Parameters + ---------- + obj : object + A resolved pipeline step (a nested `Pipeline`, a `_DispatchStep`, + or a model instance), or the spec a `_DispatchStep` was built + from (a name, class, dict spec, instance, or `None`/`False`). + + Returns + ------- + object + Names, classes, `None` and booleans are returned as they are + (they hold no state). A dict spec is copied with its ``'model'`` + copied the same way. A `_DispatchStep` is rebuilt, unfitted, + around a copy of its spec. An object with ``get_params`` (any + scikit-learn estimator, hypertools' own stage classes, a nested + `Pipeline`) is cloned with `sklearn.base.clone`: same settings, + no fitted state. Anything else is deep-copied -- scikit-learn's + own rule for non-estimators -- which keeps whatever state it has. + """ + if obj is None or isinstance(obj, (str, bool, type)): + return obj + if isinstance(obj, _DispatchStep): + return obj._unfitted_copy() + if isinstance(obj, dict): + duplicate = dict(obj) + if 'model' in duplicate: + duplicate['model'] = _unfitted_copy(duplicate['model']) + for key in ('args', 'kwargs', 'params'): + if key in duplicate: + duplicate[key] = copy.deepcopy(duplicate[key]) + return duplicate + if hasattr(obj, 'get_params'): + return _sklearn_clone(obj) + return copy.deepcopy(obj) + + class _DispatchStep: """Wrap a stage dispatcher (`hyp.manip`/`hyp.normalize`/`hyp.reduce`/ `hyp.align`/`hyp.cluster`) as a fit/transform step that genuinely @@ -316,11 +478,16 @@ class _DispatchStep: substituted in as the spec -- reusing each dispatcher's own already-fitted-instance branch (documented as the payoff of `return_model=True` on every one of them) instead of refitting. + + A manip step that `build_pipeline` placed before other stages records + their names in `downstream`; its output is then checked for rows it + emptied (see `_raise_if_manip_emptied_rows`) before they are handed on. """ - def __init__(self, name, spec, call): + def __init__(self, name, spec, call, downstream=()): self._name = name self._spec = spec self._call = call # (data, spec_or_fitted_model) -> (result, fitted_model) + self._downstream = tuple(downstream) self._fitted = None # A stage can legitimately fit to NO model: a reduce with ndims=None or # ndims >= n_features is a no-op pass-through and returns model=None @@ -365,12 +532,27 @@ def fit_transform(self, data): Returns ------- The dispatcher's transformed output for `data`. + + Raises + ------ + ValueError + For a manip step with later stages (`downstream`), when it + left rows with no finite value that had values going in (see + `_raise_if_manip_emptied_rows`). """ result, fitted = self._call(data, self._spec) + self._check_emptied_rows(data, result) self._fitted = fitted self._is_fit = True return result + def _check_emptied_rows(self, data, result): + """See `_raise_if_manip_emptied_rows`; a no-op for every other + step (and for a pipeline unpickled from before `downstream`).""" + if self._name == 'manip' and getattr(self, '_downstream', ()): + _raise_if_manip_emptied_rows(data, result, self._spec, + self._downstream) + def transform(self, data): """Apply the already-fitted dispatcher model to new `data`. @@ -387,6 +569,9 @@ def transform(self, data): ------ sklearn.exceptions.NotFittedError If `fit`/`fit_transform` has not been called yet. + ValueError + For a manip step with later stages, when it left rows of the + new data with no finite value (as in `fit_transform`). """ if not self._is_fit: raise NotFittedError(f'{self._name} stage must be fit before transform') @@ -394,9 +579,26 @@ def transform(self, data): # a no-op stage (e.g. reduce with ndims >= n_features): pass through return data result, fitted = self._call(data, self._fitted) + self._check_emptied_rows(data, result) self._fitted = fitted return result + def _unfitted_copy(self): + """A new, unfitted step for the same stage, around an unfitted copy + of the original spec (see the module-level `_unfitted_copy`); the + fitted model this step holds is not carried over.""" + spec = self._spec + from ..reduce.common import Reducer + if isinstance(spec, Reducer): + # a `Reducer` handed in as the spec is a FITTED one being + # reused (`hyp.reduce` accepts no unfitted `Reducer`), so its + # unfitted counterpart is the model it wraps: the class plus + # its constructor arguments, or the configured instance + spec = ({'model': spec.model, 'kwargs': dict(spec.params or {})} + if isinstance(spec.model, type) else spec.model) + return type(self)(self._name, _unfitted_copy(spec), self._call, + downstream=getattr(self, '_downstream', ())) + def __repr__(self): return f"<{self._name} stage>" @@ -415,7 +617,9 @@ class Pipeline(BaseEstimator): spec is anything `unpack_model` accepts: a registry name (string), a class, an already-constructed (or already-fitted) instance, a dict spec (`{'model': ..., 'args': [...], 'kwargs': {...}}` or the - legacy `{'model': ..., 'params': {...}}`), or a nested `Pipeline`. + legacy `{'model': ..., 'params': {...}}`; any other top-level key, + e.g. a flat `{'model': 'PCA', 'whiten': True}`, raises + `ValueError` naming it), or a nested `Pipeline`. Raw scikit-learn-API estimators (e.g. ``PCA(n_components=3)``, fitted or not) are accepted as-is; see the Notes on what they receive. @@ -448,11 +652,22 @@ class Pipeline(BaseEstimator): Notes ----- - `steps` stores already-*resolved* `(name, instance)` tuples (specs are - resolved to instances in `__init__`), not the raw constructor input. This - deviates from scikit-learn's clone contract, which expects `get_params`/ - `set_params` to round-trip the exact constructor arguments -- so - `sklearn.base.clone(pipe)` compatibility is not guaranteed. + `Pipeline` is a scikit-learn `BaseEstimator`. `steps` stores + already-*resolved* `(name, instance)` tuples (specs are resolved to + instances, and unnamed ones named, in `__init__`), not the raw + constructor input, so `get_params()['steps']` is that resolved list. + `set_params(steps=...)` resolves and names new steps exactly like the + constructor, and discards the pipeline's own fitted state. Nested + ``<step>__<parameter>`` names are not supported: configure a step + through `named_steps`, or pass new `steps`. + + `sklearn.base.clone(pipe)` returns an UNFITTED pipeline with the same + step names, order and settings and independent copies of every step, + whether or not `pipe` is fitted; fitting the clone leaves `pipe` + untouched. Steps are copied the way scikit-learn copies them: an + estimator (anything with `get_params`) by `clone`, i.e. same settings + and no fitted state; any other object by a deep copy, which keeps + whatever state it has. Steps receive the running output AS-IS (no stacking/unstacking), so raw scikit-learn steps operate on a single array/DataFrame; only @@ -484,6 +699,33 @@ class Pipeline(BaseEstimator): """ def __init__(self, steps, input_hierarchy=None): + self.steps = self._resolve_steps(steps) + self.input_hierarchy = _validate_input_hierarchy(input_hierarchy) + self._is_fitted = False + + @staticmethod + def _resolve_steps(steps): + """Validate, resolve and name a `steps` argument. + + Parameters + ---------- + steps : list + Step specs, as documented for the constructor. + + Returns + ------- + list of (str, object) + The named, resolved steps, in order. + + Raises + ------ + TypeError + If `steps` is not a list of specs, a tuple step is not a + ``('name', spec)`` pair, or a spec resolves to an object with + no fit/transform method. + ValueError + If explicit step names repeat. + """ # up-front validation (2026-07 audit F21-010): a non-list `steps` # (e.g. Pipeline('PCA')) used to iterate the string character by # character, and malformed tuple steps were stored silently only to @@ -516,9 +758,76 @@ def __init__(self, steps, input_hierarchy=None): "a dict spec {'model': ..., 'kwargs': {...}}, or a " "nested Pipeline") entries.append((name, model)) - self.steps = _name_steps(entries) - self.input_hierarchy = _validate_input_hierarchy(input_hierarchy) - self._is_fitted = False + return _name_steps(entries) + + def set_params(self, **params): + """Set this pipeline's parameters (scikit-learn's `set_params`). + + Parameters + ---------- + **params + ``steps`` and/or ``input_hierarchy``, as for the constructor. + New ``steps`` are validated, resolved and named exactly as + the constructor does, and the pipeline's own fitted state is + discarded (`is_fitted` is then True only if every new step is + itself already fitted). Passing the list `get_params` returned + leaves the pipeline as it was. + + Returns + ------- + Pipeline + `self`. + + Raises + ------ + ValueError + For any other parameter name (including nested + ``<step>__<parameter>`` names, which are not supported), or + an invalid ``input_hierarchy``. + TypeError + For invalid ``steps`` (see the constructor). + """ + unknown = sorted(set(params) - {'steps', 'input_hierarchy'}) + if unknown: + raise ValueError( + f"invalid parameter(s) {', '.join(repr(u) for u in unknown)} " + "for Pipeline; valid parameters are 'steps' and " + "'input_hierarchy'. Nested '<step>__<parameter>' names are " + "not supported: configure a step through " + "pipeline.named_steps['<step>'], or pass new steps.") + # resolve everything before assigning anything, so a bad value + # leaves the pipeline unchanged + updates = {} + if 'steps' in params and params['steps'] is not self.steps: + updates['steps'] = self._resolve_steps(params['steps']) + if 'input_hierarchy' in params: + updates['input_hierarchy'] = _validate_input_hierarchy( + params['input_hierarchy']) + if 'steps' in updates: + self._is_fitted = False + for name, value in updates.items(): + setattr(self, name, value) + return self + + def __sklearn_clone__(self): + """`sklearn.base.clone` support: an unfitted, independent copy. + + scikit-learn's default clone rebuilds an estimator from + `get_params()` and requires the constructor to store every + argument untouched; this constructor resolves and names its + steps, so the default raised ``RuntimeError: Cannot clone object + ...``. The copy is built step by step instead. + + Returns + ------- + Pipeline + A new pipeline of the same class with the same step names and + order, each step an unfitted copy (see `_unfitted_copy`), and + a deep copy of `input_hierarchy`. + """ + steps = [(name, _unfitted_copy(model)) for name, model in self.steps] + return type(self)( + steps, input_hierarchy=copy.deepcopy(self.input_hierarchy)) @property def named_steps(self): @@ -625,12 +934,13 @@ def _regroup_hierarchical_input(self, data): `pipeline.transform(df)` does. If you want name matching, hand back the frame. """ + data = as_internal_frames(data) hierarchy = self.input_hierarchy if hierarchy is None: return data - import pandas as pd - if isinstance(data, pd.DataFrame) and data.columns.nlevels >= 2: + from .hierarchy import is_hierarchical + if is_hierarchical(data, axes='columns'): from .hierarchy import group_columns correspondence = hierarchy['feature_correspondence'] leaves, _meta = group_columns( @@ -796,16 +1106,21 @@ def build_pipeline(manip=None, normalize=None, reduce=None, ndims=None, specs = {'manip': manip, 'normalize': normalize, 'reduce': reduce, 'align': align, 'cluster': cluster} + # None -> the stage is omitted. normalize=False ALSO means "do not + # normalize", so skip it too (QC 2026-07: a normalize=False step's fit + # returned (data, None), leaving the step unfitted, so a later + # Pipeline.transform on the returned model raised NotFittedError). + stages = [stage for stage in order + if not (specs.get(stage) is None + or (stage == 'normalize' and specs[stage] is False))] steps = [] - for stage in order: - spec = specs.get(stage) - # None -> the stage is omitted. normalize=False ALSO means "do not - # normalize", so skip it too (QC 2026-07: a normalize=False step's fit - # returned (data, None), leaving the step unfitted, so a later - # Pipeline.transform on the returned model raised NotFittedError). - if spec is None or (stage == 'normalize' and spec is False): - continue - steps.append((stage, _make_stage_step(stage, spec, ndims, random_state))) + for position, stage in enumerate(stages): + # a manip stage learns which stages follow it, so rows it empties + # are reported as its own doing before they reach them (see + # `_raise_if_manip_emptied_rows`) + steps.append((stage, _make_stage_step( + stage, specs[stage], ndims, random_state, + downstream=stages[position + 1:]))) return Pipeline(steps, input_hierarchy=input_hierarchy) @@ -852,8 +1167,9 @@ def __repr__(self): return f"_StageCall({self.stage!r})" -def _make_stage_step(stage, spec, ndims, random_state=None): +def _make_stage_step(stage, spec, ndims, random_state=None, downstream=()): if stage not in CANONICAL_ORDER: raise ValueError(f"unknown pipeline stage {stage!r}; expected one of {CANONICAL_ORDER}") return _DispatchStep(stage, spec, - _StageCall(stage, ndims=ndims, random_state=random_state)) + _StageCall(stage, ndims=ndims, random_state=random_state), + downstream=downstream) diff --git a/hypertools/core/shared.py b/hypertools/core/shared.py index 29b3ce49..0c2a7673 100644 --- a/hypertools/core/shared.py +++ b/hypertools/core/shared.py @@ -12,20 +12,38 @@ import numpy as np import pandas as pd +from .._shared.helpers import (is_frame_dataset, is_series_like, + as_pandas_dataframe) +from .model import external_stacklevel + #: sentinel distinguishing "no explicit default passed" in RobustDict.get _MISSING = object() def as_dataframe(data): """Coerce `data` to a pandas DataFrame (returned as-is if it already - is one). - - Shared by `hypertools.predict.common` and `hypertools.impute.common` - (2026-07 audit, X7-code-org-rest-019: previously duplicated verbatim - in both modules). + is one; a DataFrame of another backend datawrangler recognises -- + polars DataFrame/LazyFrame, modin, ... -- is converted via + ``dw.wrangle(..., backend='pandas')``; a Series (pandas, polars, or + anything series-like) becomes ONE column that keeps the Series' index + and name, exactly as ``pd.DataFrame(series)`` does; anything else goes + through ``pd.DataFrame(np.asarray(data))``, so a 1-D array is one + column). + + Shared by `hypertools.predict.common`, `hypertools.impute.common` and + the manipulators' direct-class paths (2026-07 audit, + X7-code-org-rest-019: previously duplicated verbatim in both modules; + Codex round 12, R12-4: the manipulators used to call + ``pd.DataFrame(data)``, and routing a pandas Series through + ``np.asarray`` here dropped its irregular/dated index, so a + ``Pipeline([Smooth, Resample])`` resampled at the wrong positions). """ - if isinstance(data, pd.DataFrame): - return data + if is_frame_dataset(data): + return as_pandas_dataframe(data) + if is_series_like(data): + if hasattr(data, 'to_frame'): # pandas and polars Series + return as_pandas_dataframe(data.to_frame()) + return pd.DataFrame(np.asarray(data).reshape(-1, 1)) return pd.DataFrame(np.asarray(data)) @@ -149,7 +167,7 @@ def is_reused_pipeline(spec, stage_kwargs, spec_label): f"{spec_label}= is an already-fitted Pipeline that encodes its " f"own stages; ignoring redundant {', '.join(redundant)}= (the " "fitted Pipeline is reused as-is via .transform).", - stacklevel=3) + stacklevel=external_stacklevel()) return True return False @@ -201,6 +219,106 @@ def copy(self): return RobustDict(self, __default_value__=self.default_value) +#: The keys every dict model spec may carry at its top level: the canonical +#: 'model'/'args'/'kwargs' and the legacy 'params'. A dispatcher may accept +#: a documented shortcut on top of these (cluster's 'n_clusters'). +SPEC_KEYS = ('model', 'args', 'kwargs', 'params') + + +def check_spec_keys(spec, stage, shortcuts=(), param=None): + """Raise `ValueError` if the dict model spec `spec` carries a top-level + key other than `SPEC_KEYS` and the dispatcher's documented `shortcuts`. + + Model parameters belong under 'kwargs' -- the dict-spec convention + every hypertools dispatcher documents. A flat spec such as + ``{'model': 'PCA', 'whiten': True}`` used to lose ``whiten`` without a + word, so the model silently ran with its defaults (1.1 review; first + fixed for `hyp.cluster`, where ``{'model': 'KMeans', 'n_clusters': 4, + 'random_state': 0}`` changed its clusters from call to call). The + message names the offending keys and spells out the corrected spec, + with those keys merged into 'kwargs'. + + `hyp.predict` does not use this check: a predict spec carries flat + ``t``/``horizon``/``block`` keys by design. + + A dict with no 'model' key is left to the dispatcher's own "must + include a 'model' key" error, which is the right diagnosis for a + misspelled 'model' (``{'mode': 'PCA'}``). + + Parameters + ---------- + spec : object + The spec to check; anything but a dict with a 'model' key passes + unchanged. + stage : str + What the spec configures, for the message (e.g. ``'reduce'``, + ``'Pipeline step'``). + shortcuts : tuple of str + Documented top-level keys the dispatcher accepts besides + `SPEC_KEYS` (e.g. ``('n_clusters',)`` for `hyp.cluster`). + param : str or None + The keyword the spec is passed as (e.g. ``'cluster'``), so the + suggestion reads ``cluster={...}``; None suggests the bare dict. + """ + if not isinstance(spec, dict) or 'model' not in spec: + return + allowed = SPEC_KEYS + tuple(shortcuts) + extra = sorted((k for k in spec if k not in allowed), key=str) + if not extra: + return + model = spec.get('model') + model_repr = (repr(model) if isinstance(model, str) + else getattr(model, '__name__', type(model).__name__)) + # the spec's own parameters, from whichever key the resolver reads + # them from ('params' only counts when there is no 'args'/'kwargs') + own = (spec.get('kwargs') if ('args' in spec or 'kwargs' in spec) + else spec.get('params')) + try: + own = dict(own or {}) + except (TypeError, ValueError): + own = {} + suggested_kwargs = {**own, **{k: spec[k] for k in extra}} + suggestion = f"{{'model': {model_repr}" + for key in shortcuts: + if key in spec: + suggestion += f", {key!r}: {spec[key]!r}" + if spec.get('args'): + suggestion += f", 'args': {list(spec['args'])!r}" + suggestion += f", 'kwargs': {suggested_kwargs!r}}}" + if shortcuts: + accepted = ("'model', 'args', 'kwargs' and the " + + " and ".join(repr(k) for k in shortcuts) + + (" shortcuts" if len(shortcuts) > 1 else " shortcut")) + else: + accepted = "'model', 'args' and 'kwargs'" + example = f"{param}={suggestion}" if param else suggestion + raise ValueError( + f"the {stage} spec has unrecognized top-level key(s) {extra!r}. " + f"Model parameters go under 'kwargs' (only {accepted} are accepted " + f"at the top level), e.g. {example}.") + + +def merge_spec_kwargs(spec, kwargs): + """Return dict spec `spec` with a dispatcher's outer ``**kwargs`` merged + into its parameters (the outer keyword arguments win on a conflict, + as in `hyp.impute`/`hyp.predict`). + + `hyp.manip(x, model={'model': 'Smooth'}, kernel_width=25)` used to drop + `kernel_width` without a word, because only a bare name/class received + the outer keyword arguments (1.1 review). The parameters are merged + into whichever key the spec already uses ('kwargs', or the legacy + 'params', whose `DeprecationWarning` is kept); `spec` itself is never + mutated. Anything but a dict with a 'model' key, or empty `kwargs`, + comes back unchanged. + """ + if not kwargs or not isinstance(spec, dict) or 'model' not in spec: + return spec + if 'args' in spec or 'kwargs' in spec or 'params' not in spec: + return {**spec, 'kwargs': {**dict(spec.get('kwargs') or {}), + **kwargs}} + return {**spec, 'params': {**dict(spec['params'] or {}), **kwargs}} + + def unpack_model(m, valid=None, parent_class=None): """Resolve a model specification without eval. @@ -252,10 +370,14 @@ def unpack_model(m, valid=None, parent_class=None): if isinstance(m, dict): if "model" in m and "params" in m and "args" not in m and "kwargs" not in m: + # external_stacklevel (1.1 release review): a fixed stacklevel=2 + # named the dispatcher or Pipeline that called unpack_model, so + # Python's default filters hid this DeprecationWarning from + # every script warnings.warn( "{'model': ..., 'params': {...}} is deprecated; use " "{'model': ..., 'args': [...], 'kwargs': {...}} instead", - DeprecationWarning, stacklevel=2) + DeprecationWarning, stacklevel=external_stacklevel()) m = {"model": m["model"], "args": [], "kwargs": dict(m["params"])} # canonical dict spec: a 'model' key with OPTIONAL 'args'/'kwargs' @@ -274,7 +396,7 @@ def unpack_model(m, valid=None, parent_class=None): f"ignoring the legacy 'params' key ({dropped!r}) because " "'args'/'kwargs' are also present in the model spec; " "merge those values into 'kwargs' instead", - DeprecationWarning, stacklevel=2) + DeprecationWarning, stacklevel=external_stacklevel()) resolved["model"] = unpack_model(m["model"], valid=valid, parent_class=parent_class) resolved.setdefault("args", []) resolved.setdefault("kwargs", {}) @@ -335,6 +457,6 @@ def get(value, i): f"parameter list of length {n} has no entry for dataset index " f"{i}; using the whole list as this dataset's value. Pass a " "scalar to share one value across all datasets, or a list with " - "one entry per dataset.", stacklevel=2) + "one entry per dataset.", stacklevel=external_stacklevel()) return value return value diff --git a/hypertools/impute/backtest.py b/hypertools/impute/backtest.py index 93ed50ca..55432451 100644 --- a/hypertools/impute/backtest.py +++ b/hypertools/impute/backtest.py @@ -13,10 +13,15 @@ ``docs/tutorials/projectile_kalman.ipynb`` cell 9 and the scattered-vs-occluded imputer comparison of its cell 13. """ +import copy + import numpy as np import pandas as pd +from .._shared.helpers import (as_pandas_dataframe, is_frame_dataset, + is_series_like) from ..predict.backtest import build_scores, resolve_metrics, score_pair +from .common import Imputer #: the always-present imputation baseline: fill each column with the mean of @@ -25,10 +30,101 @@ BASELINE = 'mean' -def _as_frame(x, like, what): - """Coerce `truth`/`mask`-shaped input to a DataFrame matching `like`.""" - if isinstance(x, pd.DataFrame): - frame = x +def label_flags(x): + """Which axes of ONE dataset carry labels: ``(rows, columns)``. + + A pandas DataFrame (or anything with its API) labels both axes -- a + default ``0..n-1`` index included, since a reordered frame carries + those integers with it. A pandas Series labels its rows only (it is one + column). Other dataframes (polars) name their columns but have no row + labels. Arrays and everything else are unlabelled. + """ + if is_frame_dataset(x): + import datawrangler as dw + return bool(dw.zoo.dataframe_like(x)), True + if is_series_like(x): + # a pandas Series carries a row index; a polars Series has none + index = getattr(x, 'index', None) + return index is not None and not callable(index), False + return False, False + + +def data_label_flags(raw, n_datasets): + """`label_flags` for each dataset the caller passed to `impute`. + + `raw` is the caller's ``data`` BEFORE wrangling (wrangling turns every + dataset into a labelled pandas frame, erasing the difference between + an array and a frame). + """ + if isinstance(raw, (list, tuple)) and len(raw) == n_datasets: + return [label_flags(d) for d in raw] + if n_datasets == 1: + return [label_flags(raw)] + return [(False, False)] * n_datasets + + +def _show(labels, limit=8): + labels = list(labels) + shown = ', '.join(repr(v) for v in labels[:limit]) + more = f', ... ({len(labels)} in all)' if len(labels) > limit else '' + return f'[{shown}{more}]' + + +def _align_axis(frame, target, axis, what): + """Reorder `frame` along `axis` to `target`'s label order. + + Identical label sequences need nothing. The same SET of unique labels + in another order is reindexed. Anything else cannot be aligned + unambiguously and raises, naming the axis and the offending labels. + """ + own = frame.axes[axis] + if own.equals(target): + return frame + kind = 'column' if axis else 'row' + hint = (f' Labelled {what} is aligned to the data by label; to compare ' + 'by position instead, pass it as a bare array ' + f'(e.g. {what.split("[")[0]}.to_numpy()).') + duplicated = [] + for index, owner in ((own, what), (target, 'the data')): + if not index.is_unique: + duplicated.append( + f'duplicated in {owner}: ' + f'{_show(index[index.duplicated()].unique())}') + if duplicated: + raise ValueError( + f'cannot align {what} to the data by {kind} labels: the labels ' + 'differ and some are repeated, so the pairing is ambiguous ' + f'({"; ".join(duplicated)}).' + hint) + missing = target.difference(own, sort=False) + unexpected = own.difference(target, sort=False) + if len(missing) or len(unexpected): + parts = [] + if len(missing): + parts.append(f'in the data but missing from {what}: ' + f'{_show(missing)}') + if len(unexpected): + parts.append(f'in {what} but not in the data: ' + f'{_show(unexpected)}') + raise ValueError( + f"{what} {kind} labels do not match the data's " + f'({"; ".join(parts)}).' + hint) + return frame.reindex(target, axis=axis) + + +def _as_frame(x, like, what, labelled=(False, False)): + """Coerce `truth`/`mask`-shaped input to a DataFrame matching `like`. + + `labelled` says which axes of the DATA carry labels (`label_flags`). + On an axis where both the data and `x` are labelled, `x` is aligned to + the data BY LABEL (release red-team 2026-10-09: a truth frame with its + columns or rows in another order used to be compared cell-by-position + and silently mis-scored). Otherwise the comparison is positional. + """ + rows, columns = label_flags(x) + if rows and not columns: # a row-labelled Series + frame = x.to_frame() + elif is_frame_dataset(x): + frame = as_pandas_dataframe(x) # any backend datawrangler knows else: values = np.asarray(x) if values.ndim == 1: @@ -37,11 +133,21 @@ def _as_frame(x, like, what): raise ValueError( f'{what} must be 2-D (n_observations, n_features); got shape ' f'{values.shape}') + if values.shape != like.shape: + # checked BEFORE borrowing the data's labels: pandas would + # otherwise raise its own "indices imply" error first + raise ValueError( + f'{what} has shape {values.shape} but the data being imputed ' + f'has shape {like.shape}; they must match cell for cell.') frame = pd.DataFrame(values, index=like.index, columns=like.columns) if frame.shape != like.shape: raise ValueError( f'{what} has shape {frame.shape} but the data being imputed has ' f'shape {like.shape}; they must match cell for cell.') + if rows and labelled[0]: + frame = _align_axis(frame, like.index, 0, what) + if columns and labelled[1]: + frame = _align_axis(frame, like.columns, 1, what) return frame @@ -87,12 +193,14 @@ def imputer_collection(model, valid=(), caller='hyp.impute'): def score_imputations(datasets, impute_fn, names, specs, truth, mask=None, metrics=None, per_column=False, return_imputed=False, - kwargs=None): + kwargs=None, labelled=None): """Score imputers on the damaged cells of `datasets` (see `hyp.impute`). `datasets` is a list of wrangled DataFrames (a single dataset is a one-element list); `impute_fn` is the public `impute`, injected to keep - this module import-free of the dispatcher. + this module import-free of the dispatcher. `labelled` is + `data_label_flags` of the caller's un-wrangled data (None: treat every + dataset as unlabelled, i.e. compare `truth`/`mask` by position). """ metrics = resolve_metrics(metrics) kwargs = dict(kwargs or {}) @@ -104,7 +212,16 @@ def score_imputations(datasets, impute_fn, names, specs, truth, mask=None, f'truth has {len(truths)} dataset(s) but {len(datasets)} were ' 'passed to impute(); pass one complete dataset per input ' 'dataset.') - truths = [_as_frame(x, d, 'truth') for x, d in zip(truths, datasets)] + if labelled is None: + labelled = [(False, False)] * len(datasets) + + def tag(what, i): + """How error messages name dataset `i`'s truth/mask.""" + return what if single else f'{what}[{i}]' + + truths = [_as_frame(x, d, tag('truth', i), flags) + for i, (x, d, flags) in enumerate(zip(truths, datasets, + labelled))] masks = [] if mask is None: @@ -117,14 +234,17 @@ def score_imputations(datasets, impute_fn, names, specs, truth, mask=None, 'were passed to impute(); pass one mask per input dataset.') else: user_masks = [mask] * len(datasets) - for d, m in zip(datasets, user_masks): + shared_mask = not isinstance(mask, (list, tuple)) + for i, (d, m, flags) in enumerate(zip(datasets, user_masks, labelled)): missing = np.isnan(np.asarray(d, dtype=float)) if m is not None: # a caller-supplied mask RESTRICTS scoring (e.g. "the occluded # band only"); observed cells are excluded regardless, since # every imputer passes those through untouched and scoring them # would flatter every model equally. - missing = missing & _as_frame(m, d, 'mask').to_numpy().astype(bool) + aligned = _as_frame( + m, d, 'mask' if shared_mask else tag('mask', i), flags) + missing = missing & aligned.to_numpy().astype(bool) masks.append(missing) if not any(m.any() for m in masks): raise ValueError( @@ -134,8 +254,16 @@ def score_imputations(datasets, impute_fn, names, specs, truth, mask=None, imputed = {} for name, spec in zip(names, specs): + # GH #285 release review: score a fresh fit on damaged data, never + # learned state that may already contain the hidden truth. + candidate = spec.get('model') if isinstance(spec, dict) else spec + if isinstance(candidate, Imputer) and candidate.is_fitted: + raise ValueError( + 'truth= scoring requires an unfitted model so hidden values ' + 'cannot leak into training; pass a model name, class, or ' + 'unfitted instance instead.') results = impute_fn(datasets if not single else datasets[0], - model=spec, **kwargs) + model=copy.deepcopy(spec), **copy.deepcopy(kwargs)) imputed[name] = results if isinstance(results, list) else [results] imputed[BASELINE] = [_mean_fill(d) for d in datasets] diff --git a/hypertools/impute/impute.py b/hypertools/impute/impute.py index f1abcf48..2ed4dbb5 100644 --- a/hypertools/impute/impute.py +++ b/hypertools/impute/impute.py @@ -25,13 +25,17 @@ import numpy as np import pandas as pd import datawrangler as dw +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like, as_pandas_dataframe) -from .backtest import imputer_collection, score_imputations +from .backtest import (data_label_flags, imputer_collection, + score_imputations) from .common import Imputer from .ppca import PPCA from .sklearn_imputers import SimpleImputer, KNNImputer, IterativeImputer from .kalman import Kalman -from ..core.shared import supported_names, unpack_model +from ..core.model import external_stacklevel +from ..core.shared import check_spec_keys, supported_names, unpack_model from ..predict.backtest import spec_name @@ -49,14 +53,25 @@ def _spec_help(): def _coerce_dataset(d): """Normalize ONE dataset-like object before wrangling: a 1-D array or a - pandas Series is a UNIVARIATE series -- n observations of 1 feature, + Series is a UNIVARIATE series -- n observations of 1 feature, i.e. an (n, 1) column -- matching format_data/plot's convention. (QC 2026-07 red-team F17-impute-010: the funnel used to wrangle a (4,) array into ONE row of 4 features, whose NaN then became an all-missing - "column" silently filled with 0.0 instead of the series statistic.)""" - if isinstance(d, pd.Series): - return d.to_frame() - if isinstance(d, np.ndarray) and d.ndim == 1: + "column" silently filled with 0.0 instead of the series statistic.) + + Types are classified with datawrangler's predicates (see + `hypertools._shared.helpers`): a DataFrame of any backend datawrangler + recognises (polars DataFrame/LazyFrame, ...) becomes a pandas + DataFrame, hypertools' internal frame type (a pandas frame passes + through untouched); a pandas or polars Series becomes its one-column + frame (index and name preserved).""" + if is_frame_dataset(d): + return as_pandas_dataframe(d) + if is_series_like(d): + if hasattr(d, 'to_frame'): + return _coerce_dataset(d.to_frame()) + return _coerce_dataset(np.asarray(d)) + if is_array_dataset(d) and d.ndim == 1: return d.reshape(-1, 1) return d @@ -91,7 +106,7 @@ def _normalize_data(data): raise ValueError( f'cannot impute a single scalar observation ({data!r}); pass a ' 'dataset with at least 1 row and 1 column.') - if isinstance(data, np.ndarray): + if is_array_dataset(data): if data.ndim == 0: raise ValueError( f'cannot impute a single scalar observation ({data!r}); pass ' @@ -103,10 +118,14 @@ def _normalize_data(data): data = _coerce_dataset(data) _check_finite_observed(data) return data - if isinstance(data, pd.DataFrame) and (data.shape[0] == 0 or data.shape[1] == 0): - raise ValueError( - f'input has no observations (got a DataFrame of shape ' - f'{tuple(data.shape)}); there is nothing to impute.') + if is_frame_dataset(data): + # to pandas FIRST (a polars LazyFrame has no shape until collected) + data = _coerce_dataset(data) + if data.shape[0] == 0 or data.shape[1] == 0: + raise ValueError( + f'input has no observations (got a DataFrame of shape ' + f'{tuple(data.shape)}); there is nothing to impute.') + return data if isinstance(data, list): if len(data) == 0: raise ValueError( @@ -153,11 +172,11 @@ def _all_missing(data): def _mismatched_columns(data): """Whether `data` is a list of (wrangled) datasets that do NOT share - columns -- joint (stacked) imputation is impossible for those.""" + columns -- joint (stacked) imputation is impossible for those. Called + on FUNNELED data only, so every element is a DataFrame already (no + per-element re-check).""" if not isinstance(data, list) or len(data) < 2: return False - if not all(isinstance(d, pd.DataFrame) for d in data): - return False first = list(data[0].columns) return any(list(d.columns) != first for d in data[1:]) @@ -208,7 +227,8 @@ def _wrangled_impute(data, model='PPCA', return_model=False, **kwargs): warnings.warn( 'datasets do not share columns, so they cannot be imputed ' 'jointly; imputing each dataset independently instead. (Shared ' - 'columns are required to pool information across datasets.)') + 'columns are required to pool information across datasets.)', + stacklevel=external_stacklevel()) results = [_wrangled_impute(d, model=model, return_model=return_model, **kwargs) for d in data] if return_model: @@ -221,25 +241,30 @@ def _wrangled_impute(data, model='PPCA', return_model=False, **kwargs): # audit, D09-tutorials-applied-012). Checked on the POOLED view -- for # a list sharing columns, a column observed in ANY dataset is informed # (the datasets are stacked and imputed jointly). + # (`data` is FUNNELED, so every dataset is a DataFrame already -- no + # per-element re-check.) _datasets = data if isinstance(data, list) else [data] - if all(isinstance(d, pd.DataFrame) for d in _datasets): - try: - _stacked = pd.concat(_datasets, axis=0) - _vals = _stacked.to_numpy(dtype=float) - except (TypeError, ValueError): - _vals = None - if _vals is not None and _vals.size: - _dead = np.isnan(_vals).all(axis=0) - if _dead.any(): - _names = [str(c) for c, d_ in zip(_stacked.columns, _dead) - if d_] - warnings.warn( - f'column(s) {_names} have no observed values at all; ' - 'their "imputed" values are not informed by any data ' - "(Kalman and PPCA fill such columns with 0.0). Drop " - 'these columns, or treat their filled values as ' - 'placeholders rather than data.', UserWarning) - + try: + _stacked = pd.concat(_datasets, axis=0) + _vals = _stacked.to_numpy(dtype=float) + except (TypeError, ValueError): + _vals = None + if _vals is not None and _vals.size: + _dead = np.isnan(_vals).all(axis=0) + if _dead.any(): + _names = [str(c) for c, d_ in zip(_stacked.columns, _dead) + if d_] + warnings.warn( + f'column(s) {_names} have no observed values at all; ' + 'their "imputed" values are not informed by any data ' + "(Kalman and PPCA fill such columns with 0.0). Drop " + 'these columns, or treat their filled values as ' + 'placeholders rather than data.', UserWarning, + stacklevel=external_stacklevel()) + + # a flat key such as {'model': 'KNNImputer', 'n_neighbors': 1} used to + # be dropped silently, so the imputer ran with its defaults (1.1 review) + check_spec_keys(model, 'impute') if isinstance(model, dict) and 'kwargs' not in model and 'args' not in model: # {'model': ..., 'params': {...}} form: unpack before handing the # inner model spec to unpack_model (which only auto-unpacks the @@ -255,7 +280,7 @@ def _wrangled_impute(data, model='PPCA', return_model=False, **kwargs): warnings.warn( "{'model': ..., 'params': {...}} is deprecated; use " "{'model': ..., 'args': [...], 'kwargs': {...}} instead", - DeprecationWarning, stacklevel=2) + DeprecationWarning, stacklevel=external_stacklevel()) kwargs = {**dict(model.get('params', {})), **kwargs} model = model['model'] elif isinstance(model, dict) and 'model' not in model: @@ -298,7 +323,7 @@ def _wrangled_impute(data, model='PPCA', return_model=False, **kwargs): f'ignoring keyword argument(s) {sorted(kwargs)}: model= is ' 'already a constructed instance, so constructor parameters ' 'cannot be applied. Pass the class (or a name/dict spec) to ' - 'set parameters.') + 'set parameters.', stacklevel=external_stacklevel()) if isinstance(resolved, Imputer) and resolved.is_fitted: result = resolved.transform(data) @@ -338,7 +363,7 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, Which imputer to use (default: 'PPCA', matching the pre-1.0 `format_data` default). - SEVERAL IMPUTERS AT ONCE (1.2). A LIST or TUPLE of specs fills the + SEVERAL IMPUTERS AT ONCE (1.1). A LIST or TUPLE of specs fills the data with each of them and returns a ``{name: imputed}`` dict in the order given. Names come from the specs (a string's registry spelling, a dict spec's inner model, a class/instance's @@ -354,7 +379,10 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, SimpleImputer, KNNImputer, IterativeImputer, Kalman); names are matched case-insensitively ('ppca' works too). A dict may be `{'model': ..., 'params': {...}}` (deprecated) or - `{'model': ..., 'args': [...], 'kwargs': {...}}`. A class or an + `{'model': ..., 'args': [...], 'kwargs': {...}}`; any other + top-level key in a spec that has a `'model'` -- e.g. a flat + `{'model': 'KNNImputer', 'n_neighbors': 5}` -- raises `ValueError` + naming it rather than being ignored. A class or an already-constructed (unfitted) instance is used directly. An ALREADY-FITTED Imputer instance (returned from a previous `return_model=True` call) is applied to `data` via `transform` @@ -369,19 +397,37 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, model})``; it is not supported with ``truth=``. truth : DataFrame/array (or list of these), or None - SCORE the imputers instead of returning the filled data (1.2). The + SCORE the imputers instead of returning the filled data (1.1). The COMPLETE version of `data` -- same shape, cell for cell -- from which `data`'s NaNs were removed (e.g. by `hyp.tools.damage`). Every model in `model` is fit, and each is scored on the DAMAGED CELLS ONLY: the entries that are NaN in `data`. Observed cells are never scored -- every imputer passes them through untouched, so including them would only dilute the comparison. + Pass a name, class, or unfitted instance; scoring fits an independent + copy and leaves the caller's instance unchanged. Fitted instances + are refused because their learned state may contain the hidden truth. + + ALIGNMENT. When `data` and `truth` are both labelled, `truth` is + matched to `data` BY LABEL, not by position: a pandas DataFrame + labels its rows and columns (a default ``0..n-1`` index counts -- + a reordered frame carries those integers with it), a pandas Series + its rows, and a polars frame its columns only (its rows are + positional). The same labels in a different order are reordered to + `data`'s; labels that differ -- or that are repeated, when the two + sequences are not identical -- raise a `ValueError` naming the + axis and the labels. A bare array on either side carries no + labels and is compared by position (shape-checked), so pass + ``truth.to_numpy()`` to compare two differently-labelled frames + cell for cell. With a list of datasets each pair is aligned + independently. mask : boolean array (or list of these), or None RESTRICT scoring to a subset of the damaged cells, e.g. only the rows of a simulated occlusion (`mask` is intersected with the NaN mask, so cells that were never missing are excluded either way). - Same shape as `data`; only valid alongside ``truth=``. Score the + Same shape as `data` (a labelled mask is aligned to `data` by + label exactly as `truth` is); only valid alongside ``truth=``. Score the occluded band and the scattered dropouts separately by calling twice with complementary masks. @@ -420,20 +466,21 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, Returns ------- - The imputed data (and the fitted Imputer if return_model=True). Lists - in, lists out: a single input dataset returns a single imputed - DataFrame. + imputed + The imputed data (and the fitted Imputer if ``return_model=True``). + Lists in, lists out: a single input dataset returns a single + imputed DataFrame. - A COLLECTION of models (see `model`) returns a ``{name: imputed}`` - dict whose values are exactly what a single-model call would have - returned. + A COLLECTION of models (see `model`) returns a ``{name: imputed}`` + dict whose values are exactly what a single-model call would have + returned. - With ``truth=``, a scores DataFrame instead (see Notes) -- or - ``(scores, imputations)`` with ``return_imputed=True``. + With ``truth=``, a scores DataFrame instead (see Notes) -- or + ``(scores, imputations)`` with ``return_imputed=True``. Notes ----- - **Scoring (``truth=``).** The returned frame has one ROW per imputer, + **Scoring** (``truth=``). The returned frame has one ROW per imputer, in the order they were given, plus an always-present ``'mean'`` row: the column-mean baseline (each column's observed mean, which is what `SimpleImputer` fills and what every cross-column imputer is reduced @@ -454,8 +501,20 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, imputer that cannot beat a column mean has not earned its complexity. TIES on the ranking metric go to the model listed FIRST (a stable comparison, so the verdict is reproducible); models whose ranking - metric is NaN are never chosen as best, and ``attrs['best']`` is None - if nothing scored. + metric is NaN are never chosen as best. + + ONLY COMPLETE MODELS ARE RANKED. A model with ``unscored > 0`` (summed + over every dataset and column) was graded on fewer -- often easier -- + cells than the others, so it keeps its descriptive row but can never + be ``attrs['best']``; it is named in ``attrs['incomplete']`` (a list, + empty when every row is complete) and a warning says so. If NO model + is complete (or nothing scored), ``attrs['best']`` is None, + ``attrs['best_score']`` is NaN and ``attrs['beats_baseline']`` is None + -- there is no verdict, which is not the same as "did not beat the + baseline". ``attrs['beats_baseline']`` is likewise None if the + baseline row is itself incomplete (it is then listed in + ``attrs['incomplete']`` too). Test ``is True`` / ``is None`` rather + than truthiness when the distinction matters. The verdict lives in ``attrs`` rather than in a ``best`` column on purpose: it is one fact about the whole comparison, and a column would @@ -493,6 +552,7 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, >>> scores.attrs['baseline'] 'mean' """ + raw = data # un-wrangled: which datasets carry labels (truth=) data = _normalize_data(data) # MULTI-MODEL / SCORING dispatch (GH #285). Both are strictly opt-in: @@ -520,10 +580,12 @@ def impute(data, model='PPCA', return_model=False, truth=None, mask=None, names, specs = collection else: names, specs = [spec_name(model, supported_names(IMPUTERS))], [model] + datasets = _score_datasets(data) return score_imputations( - _score_datasets(data), impute, names, specs, truth, mask=mask, + datasets, impute, names, specs, truth, mask=mask, metrics=metrics, per_column=per_column, - return_imputed=return_imputed, kwargs=kwargs) + return_imputed=return_imputed, kwargs=kwargs, + labelled=data_label_flags(raw, len(datasets))) if collection is not None: names, specs = collection results = {name: impute(data, model=spec, return_model=return_model, diff --git a/hypertools/io/__init__.py b/hypertools/io/__init__.py index 6ee4f1fa..2a4079bc 100644 --- a/hypertools/io/__init__.py +++ b/hypertools/io/__init__.py @@ -3,6 +3,8 @@ from . import sources from . import streaming from .lsl import lsl_stream, LSLStream, synthetic_outlet +from .sources import HypertoolsOfflineError __all__ = ['load', 'save', 'sources', 'streaming', 'lsl_stream', 'LSLStream', - 'synthetic_outlet'] + 'synthetic_outlet', + 'HypertoolsOfflineError'] diff --git a/hypertools/io/load.py b/hypertools/io/load.py index a389f644..890ebf34 100644 --- a/hypertools/io/load.py +++ b/hypertools/io/load.py @@ -9,6 +9,7 @@ import requests from ..datageometry import DataGeometry +from .._shared.helpers import is_frame_dataset, is_array_dataset from ..core.exceptions import HypertoolsIOError from ..tools.analyze import analyze @@ -198,11 +199,17 @@ def load( 3. a seaborn dataset name -- any name returned by ``seaborn.get_dataset_names()`` (e.g. ``'penguins'``, ``'tips'``, ``'titanic'``), loaded via ``seaborn.load_dataset()`` and returned - unchanged. This is a network lookup (cached per-process); if it - can't reach the seaborn-data repo, this step is skipped. (A - registered synthetic dataset name -- step 6 below -- is actually - resolved here, ahead of this lookup, so it never pays for the - network round trip; no synthetic name collides with a seaborn one) + unchanged. The name listing is a network lookup (fetched with a + timeout and cached per-process; a failed fetch is remembered for + :data:`hypertools.io.sources.SEABORN_LISTING_RETRY_AFTER` seconds + -- :func:`hypertools.io.sources.reset_seaborn_names_cache` retries + sooner); if it can't reach the seaborn-data repo, this step is + skipped. The listing is never consulted for a string that cannot + be a seaborn name (a URL, a path, a prefixed source) nor when + ``offline=True``. (A registered synthetic dataset name -- step 6 + below -- is actually resolved here, ahead of this lookup, so it + never pays for the network round trip; no synthetic name collides + with a seaborn one) 4. a FiveThirtyEight dataset, explicit prefix ``'fivethirtyeight/<slug>'`` (e.g. ``'fivethirtyeight/bechdel'``), where ``<slug>`` is the dataset's folder in @@ -241,7 +248,8 @@ def load( 7. a web source with an explicit prefix -- ``'wikipedia:<Title>'`` (kwargs: ``lang``, ``intro``, ``timeout``), ``'yahoo:<TICKER>'`` (kwargs: ``start``, ``end``, ``interval``, ``timeout``), or - ``'sec:<TICKER>'`` (kwargs: ``concept``, ``timeout``) -- see + ``'sec:<TICKER>'`` (kwargs: ``concept``, ``taxonomy``, ``unit``, + ``dedupe``, ``timeout``) -- see :func:`hypertools.io.sources.web_source` for details on each 8. a path to a local file (.geo/pickle, .npy/.npz, .csv/.tsv/.txt, .json, .parquet, .mat, .xlsx/.xls; gzip-compressed variants (.gz) @@ -329,13 +337,15 @@ def load( The name of a built-in example dataset (listed below), a dataset name resolvable per the steps above, or a file path / URL. - Data that is already loaded -- a pandas DataFrame, a numpy array, + Data that is already loaded -- a DataFrame (pandas, or a polars + DataFrame/LazyFrame), a numpy array, or a list/tuple of DataFrames/arrays (one hypertools dataset per element, the same shape ``hypertools.load('weights')`` returns) -- is passed through unchanged, so ``hypertools.load`` can serve as a uniform entry point over a mix of names and in-memory data: >>> import numpy as np + >>> import hypertools >>> arr = np.zeros((10, 3)) >>> hypertools.load(arr) is arr True @@ -366,8 +376,8 @@ def load( `mushrooms` is a pandas DataFrame of categorical features (columns) describing 8,124 mushroom samples (rows). - `sotus` is a list of 29 State of the Union addresses (1989-2018), - as strings. + `sotus` is a list of 29 State of the Union addresses (1989-2017), + as strings, grouped by president rather than in date order. `wiki` is a list of 3,136 wikipedia page texts (strings), used to fit `wiki_model`. @@ -441,7 +451,11 @@ def load( Hugging Face datasets only: if True, return a streaming ``IterableDataset`` instead of materializing the data (see https://huggingface.co/docs/datasets/en/stream). The result can be - passed directly to :func:`hypertools.plot`. + passed directly to :func:`hypertools.plot`. Every other kind of + source (built-in, scikit-learn, seaborn, synthetic, web, local + file, URL, or already-loaded data) is always loaded in full, so + passing ``streaming=True`` with one raises ``ValueError`` naming + the source rather than silently returning the whole dataset. trust : bool Remote (non-built-in) sources only. Unpickling a payload fetched @@ -465,20 +479,34 @@ def load( Local files are never subject to this policy. cache : bool - Any URL/Google-Sheets/Drive/Dropbox download (steps 9-13) only: - when True, the downloaded bytes are stored on disk (under + Any Google-Sheets/Google-Drive/Dropbox/URL download (steps 10-13) + only -- Hugging Face datasets (step 9) are NOT cached here: when + True, the downloaded bytes are stored on disk (under ``hypertools.io.sources.url_cache_dir()``, override with the ``HYPERTOOLS_URL_CACHE`` environment variable) and reused on a later call instead of re-downloading. Default False -- matching ``trust``, hypertools does not write to disk unless asked. offline : bool - Same sources as ``cache``: when True, read ONLY from that on-disk - cache and never open a connection, raising + When True, never open a connection: the sources ``cache`` covers + (steps 10-13) are read ONLY from that on-disk cache, every + network-only resolver (the seaborn listing, FiveThirtyEight, + Kaggle, Hugging Face, the ``wikipedia:``/``yahoo:``/``sec:`` web + sources) is skipped or refused outright, and anything that cannot + be served from disk raises :class:`~hypertools.io.sources.HypertoolsOfflineError` (a - subclass of ``HypertoolsIOError``) naming the cache path when the - source was never cached. Load the source once with ``cache=True`` - while online to populate the cache first. + subclass of ``HypertoolsIOError``) -- naming the cache path it + looked for when the source is a cacheable URL that was never + cached. scikit-learn, synthetic and local-file sources still + load. A hosted built-in example dataset (step 1, the ``*_model`` + pipelines included) is served from its copy in the example-data + cache (``~/hypertools_data``) when that copy is present and passes + its SHA-256 integrity check; one that was never fetched, or whose + cached file fails the check, raises ``HypertoolsOfflineError`` + naming the file -- nothing is downloaded and the file is left in + place (online, a failed check triggers a re-download). Load a + URL once with ``cache=True``, and a built-in once with any call, + while online to populate the caches first. decode_labels : bool Hugging Face datasets only: by default (True), any top-level @@ -522,6 +550,12 @@ def load( 'a "wikipedia:"/"yahoo:"/"sec:" source), not for ' 'already-loaded data (a DataFrame/ndarray/list of those ' 'was passed)') + if streaming: + from .sources import _refuse_streaming + _refuse_streaming( + type(dataset).__name__, + 'already-loaded in-memory data (a DataFrame/ndarray/list ' + 'of those)') geo_data = dataset elif isinstance(dataset, (list, tuple)): # anything else list-shaped resolves element-wise (names, paths, @@ -536,7 +570,8 @@ def load( raise TypeError( 'hypertools.load: dataset must be a string (a dataset name, ' 'file path, or URL), a path-like object, an already-loaded ' - 'pandas DataFrame or numpy array, or a list/tuple of those; ' + 'DataFrame (pandas, or polars DataFrame/LazyFrame) or numpy ' + 'array, or a list/tuple of those; ' f'got {type(dataset).__name__}') else: dataset = os.fspath(dataset) @@ -582,17 +617,25 @@ def load( else geo_data -_LOADED_TYPES = (pd.DataFrame, np.ndarray) +def _is_loaded_one(dataset): + """True for ONE already-loaded dataset: an array, or a DataFrame of + any backend datawrangler recognises (pandas, polars, a LazyFrame, ...). + Strings and path-likes are never datasets here -- they name something + to load -- and are excluded BEFORE the datawrangler predicate is asked + (``dw.zoo.is_dataframe`` would otherwise try to read a path).""" + if isinstance(dataset, (str, bytes, os.PathLike)): + return False + return is_array_dataset(dataset) or is_frame_dataset(dataset) def _is_loaded(dataset): """True when `dataset` is already-loaded data that :func:`load` passes - through: a DataFrame, a numpy array, or a non-empty list/tuple made - only of those (one hypertools multi-dataset).""" - if isinstance(dataset, _LOADED_TYPES): + through: a DataFrame (any backend), a numpy array, or a non-empty + list/tuple made only of those (one hypertools multi-dataset).""" + if _is_loaded_one(dataset): return True return isinstance(dataset, (list, tuple)) and len(dataset) > 0 and \ - all(isinstance(d, _LOADED_TYPES) for d in dataset) + all(_is_loaded_one(d) for d in dataset) def _resolve(dataset, *, legacy, split, streaming, trust, cache=False, @@ -610,16 +653,24 @@ def _resolve(dataset, *, legacy, split, streaming, trust, cache=False, ``source_kwargs`` is non-empty raises ``TypeError`` naming the misspelled/misplaced keyword(s) rather than silently ignoring them. """ + from .sources import _refuse_offline, _refuse_streaming + def _reject_kwargs(resolver_label): if source_kwargs: raise TypeError( f'hypertools.load: unexpected keyword argument(s) ' f'{sorted(source_kwargs)} for {resolver_label} {dataset!r} ' '-- it takes no extra keyword arguments') + if streaming: + # streaming=True is a Hugging Face-only option (step 9); it + # used to be silently ignored by every other resolver, which + # returned the full dataset (1.1 release review, I8) + _refuse_streaming(dataset, f'a {resolver_label}') if dataset in EXAMPLE_DATA.keys(): _reject_kwargs('built-in example dataset') - geo_data = _load_example_data(dataset) # *_model -> Pipeline + # *_model -> Pipeline; offline=True is honoured inside + geo_data = _load_example_data(dataset, offline=offline) else: # resolution chain, right after built-in names: scikit-learn's # small bundled datasets, then seaborn's named datasets (see @@ -647,20 +698,49 @@ def _reject_kwargs(resolver_label): 'scikit-learn bundled dataset: not one of ' f'{sorted(SKLEARN_DATASETS)}') if dataset in SYNTHETIC_DATASETS: + if streaming: + _refuse_streaming(dataset, + 'a built-in synthetic dataset') geo_data = synthetic_dataset(dataset, **source_kwargs) else: - geo_data = seaborn_dataset(dataset) - if geo_data is not None: - _reject_kwargs('seaborn dataset') - if geo_data is None: + # offline=True never opens a connection: the seaborn + # listing, fivethirtyeight and Kaggle are all network + # resolvers with no hypertools-side cache, so they are + # skipped (seaborn) or refused outright (the explicit + # prefixes) rather than probed (1.1 release review, I1). + # seaborn_dataset() itself answers None without touching + # the network for anything that cannot be a seaborn name + # (URLs, paths, prefixed sources). + if offline: extra_attempts.append( - 'seaborn dataset: not found via ' - 'seaborn.get_dataset_names() (or that lookup ' - 'failed, e.g. no network access)') + 'seaborn dataset: not consulted (offline=True; ' + 'the seaborn listing is a network call and ' + 'seaborn datasets are not served from the ' + 'hypertools URL cache)') + for prefix, label in ( + ('fivethirtyeight/', 'a FiveThirtyEight dataset'), + ('kaggle/', 'a Kaggle dataset')): + if dataset.startswith(prefix): + _refuse_offline(dataset, label) + else: + geo_data = seaborn_dataset(dataset) + if geo_data is not None: + _reject_kwargs('seaborn dataset') + else: + extra_attempts.append( + 'seaborn dataset: not found in the seaborn ' + 'dataset listing (or that lookup was skipped: ' + 'not a plain dataset name, or it failed, e.g. ' + 'no network access)') + if geo_data is None: # explicit prefixes -- 'fivethirtyeight/<slug>' and # 'kaggle/<owner>/<dataset>' are unambiguous, so a # matching-but-failing name raises directly instead of # falling through to the attempts digest below + if streaming and dataset.startswith( + ('fivethirtyeight/', 'kaggle/')): + _refuse_streaming( + dataset, 'a FiveThirtyEight/Kaggle dataset') geo_data = fivethirtyeight_dataset(dataset) if geo_data is not None: _reject_kwargs('fivethirtyeight dataset') @@ -745,15 +825,41 @@ def _load_legacy(dataset_path): if isinstance(data_dict['data'], dict): data_dict['data'] = pd.DataFrame(data_dict['data']) - elif isinstance(data_dict['data'], np.ndarray): + elif is_array_dataset(data_dict['data']): data_dict['data'] = list(data_dict['data']) data_dict['xform_data'] = list(data_dict['xform_data']) return DataGeometry(**data_dict) -def _load_example_data(dataset): +def _refuse_offline_builtin(dataset, dataset_path, state): + """Raise the offline refusal for a hosted built-in dataset whose + cached copy cannot be served (absent, or failing its integrity pin).""" + from .sources import HypertoolsOfflineError + raise HypertoolsOfflineError( + f"offline=True, but the built-in dataset '{dataset}' {state} " + f"({dataset_path}), and offline=True never downloads. The file " + "was left untouched. Drop offline=True to fetch it from the " + f"network once (a copy that passes its check is then served from " + f"{DATA_DIR} on every later offline load).") + + +def _load_example_data(dataset, offline=False): + """Return the raw contents of the hosted built-in ``dataset``, served + from its copy in ``DATA_DIR`` when that copy passes its pinned SHA-256 + check and downloaded (once) otherwise. + + ``offline=True`` never opens a connection: a cached copy that passes + the integrity check is served exactly as it is online, and a MISSING + or CORRUPT copy raises :class:`~hypertools.io.sources.HypertoolsOfflineError` + without downloading, without creating ``DATA_DIR`` and without deleting + the user's file (1.1 release audit, finding 1: before this, ``offline`` + stopped at :func:`_resolve` and this path downloaded on a miss and + deleted-and-redownloaded a corrupt file regardless). + """ dataset_path = DATA_DIR.joinpath(dataset) if not dataset_path.is_file(): + if offline: + _refuse_offline_builtin(dataset, dataset_path, 'is not cached') if not DATA_DIR.is_dir(): if DATA_DIR.exists(): raise HypertoolsIOError( @@ -774,7 +880,13 @@ def _load_example_data(dataset): # matches the pin (corruption, or a poisoned/edited cache) is # re-downloaded ONCE from the authoritative host, then re-checked # below -- it is never deserialized on the strength of a stale, - # unverified cache (2026-07 release review, blocker #1). + # unverified cache (2026-07 release review, blocker #1). Offline, + # the re-download is impossible, so the refusal comes first and + # the file stays on disk for the user to inspect or replace. + if offline: + _refuse_offline_builtin( + dataset, dataset_path, + 'is cached but fails its SHA-256 integrity check') dataset_path.unlink(missing_ok=True) _download_example_data(dataset_path) diff --git a/hypertools/io/lsl.py b/hypertools/io/lsl.py index 9c050f5a..52725069 100644 --- a/hypertools/io/lsl.py +++ b/hypertools/io/lsl.py @@ -134,7 +134,7 @@ def lsl_stream(name=None, type=None, timeout=10.0, **resolve_kwargs): bounds mid-stream silence: once samples are flowing, the returned generator raises :class:`~hypertools.core.exceptions.HypertoolsIOError` if - nothing arrives for ~`timeout` consecutive seconds (e.g. the + nothing arrives for about `timeout` consecutive seconds (e.g. the source device disconnected). **resolve_kwargs Extra keyword arguments forwarded to the underlying pylsl resolve @@ -182,7 +182,7 @@ def lsl_stream(name=None, type=None, timeout=10.0, **resolve_kwargs): matched stream has a string (non-numeric) channel format, or -- raised from the returned generator's ``next()`` during iteration -- if a stream that was delivering samples goes silent for - ~`timeout` consecutive seconds. + about `timeout` consecutive seconds. Examples -------- @@ -308,7 +308,7 @@ class LSLStream: Iterating pulls one sample per ``next()`` (an infinite stream that raises :class:`~hypertools.core.exceptions.HypertoolsIOError` when the - source goes silent for ~`timeout` seconds, closing itself first). + source goes silent for about `timeout` seconds, closing itself first). ``close()`` destroys the inlet (liblsl's ``lsl_destroy_inlet``, at once, whatever else still references it), and so does leaving a ``with`` block, garbage collection, and @@ -400,9 +400,8 @@ def synthetic_outlet(name='HypertoolsSyntheticStream', n_channels=6, (``tests/test_lsl_streaming.py``) each used to hand-roll; both now call this instead. - Signal definition - ------------------ - Sample `i` of channel `c` (`c` in ``range(n_channels)``) is:: + **Signal definition.** Sample `i` of channel `c` (`c` in + ``range(n_channels)``) is:: sin(2*pi*(0.5 + 0.1*c) * i/rate) + noise * N(0, 1) diff --git a/hypertools/io/save.py b/hypertools/io/save.py index bba3e77a..5ee99eaf 100644 --- a/hypertools/io/save.py +++ b/hypertools/io/save.py @@ -32,6 +32,7 @@ import pandas as pd from ..core.exceptions import HypertoolsIOError +from .._shared.helpers import is_frame_dataset, as_pandas_dataframe # extensions written as real (non-pickle) formats; everything else -- # including .pkl/.pickle/.p/.geo, unknown extensions, and extensionless @@ -216,22 +217,22 @@ def _write_payload(obj, tmp_name, ext, protocol, target): messages only).""" try: if ext in ('.csv', '.txt'): - _as_frame(obj, ext, target).to_csv( - tmp_name, index=_include_index(obj)) + frame = _as_frame(obj, ext, target) + frame.to_csv(tmp_name, index=_include_index(frame)) elif ext == '.tsv': - _as_frame(obj, ext, target).to_csv( - tmp_name, sep='\t', index=_include_index(obj)) + frame = _as_frame(obj, ext, target) + frame.to_csv(tmp_name, sep='\t', index=_include_index(frame)) elif ext == '.npy': with open(tmp_name, 'wb') as f: - np.save(f, np.asarray(obj)) + np.save(f, _as_array(obj)) elif ext == '.npz': if isinstance(obj, (list, tuple)): - arrays = {f'arr_{i}': np.asarray(a) + arrays = {f'arr_{i}': _as_array(a) for i, a in enumerate(obj)} elif isinstance(obj, dict): - arrays = {str(k): np.asarray(v) for k, v in obj.items()} + arrays = {str(k): _as_array(v) for k, v in obj.items()} else: - arrays = {'arr_0': np.asarray(obj)} + arrays = {'arr_0': _as_array(obj)} with open(tmp_name, 'wb') as f: np.savez(f, **arrays) elif ext == '.json': @@ -244,9 +245,9 @@ def _write_payload(obj, tmp_name, ext, protocol, target): elif ext == '.mat': from scipy.io import savemat if isinstance(obj, dict): - payload = {str(k): np.asarray(v) for k, v in obj.items()} + payload = {str(k): _as_array(v) for k, v in obj.items()} else: - payload = {'data': np.asarray(obj)} + payload = {'data': _as_array(obj)} with open(tmp_name, 'wb') as f: savemat(f, payload) elif ext == '.xlsx': @@ -257,8 +258,9 @@ def _write_payload(obj, tmp_name, ext, protocol, target): from .._shared.lazy_import import lazy_import lazy_import('openpyxl', purpose='.xlsx files') # installs [io] on demand buffer = _io.BytesIO() - _as_frame(obj, ext, target).to_excel( - buffer, index=_include_index(obj), engine='openpyxl') + frame = _as_frame(obj, ext, target) + frame.to_excel(buffer, index=_include_index(frame), + engine='openpyxl') with open(tmp_name, 'wb') as f: f.write(buffer.getvalue()) else: @@ -273,11 +275,22 @@ def _write_payload(obj, tmp_name, ext, protocol, target): 'data to an array/DataFrame first.') from e +def _as_array(obj): + """``obj`` as a numpy array for the array writers: a DataFrame of any + backend datawrangler recognises (pandas as-is, polars/LazyFrame/... + converted) by its values, anything else by ``np.asarray``.""" + if is_frame_dataset(obj): + return np.asarray(as_pandas_dataframe(obj)) + return np.asarray(obj) + + def _as_frame(obj, ext, target): """DataFrame view of ``obj`` for the tabular writers, with a clear - error when the object has no faithful 2-d tabular form.""" - if isinstance(obj, pd.DataFrame): - return obj + error when the object has no faithful 2-d tabular form. A frame of any + backend datawrangler recognises is written as a pandas frame (datatype + audit, 2026-09-08).""" + if is_frame_dataset(obj): + return as_pandas_dataframe(obj) try: arr = np.asarray(obj) except Exception: @@ -292,12 +305,12 @@ def _as_frame(obj, ext, target): return pd.DataFrame(arr) -def _include_index(obj): - """Write the index for DataFrames whose index carries information - (anything but a fresh 0..n-1 RangeIndex).""" - if not isinstance(obj, pd.DataFrame): - return False - index = obj.index +def _include_index(frame): + """Write the index for frames whose index carries information + (anything but a fresh 0..n-1 RangeIndex). `frame` is the pandas frame + `_as_frame` built, so an array/list input (wrapped with a fresh + RangeIndex) never writes one.""" + index = frame.index return not (isinstance(index, pd.RangeIndex) and index.start == 0 and index.step == 1) diff --git a/hypertools/io/sources.py b/hypertools/io/sources.py index 0c039ce2..d4d96d98 100644 --- a/hypertools/io/sources.py +++ b/hypertools/io/sources.py @@ -44,6 +44,7 @@ """ import io +import numbers import os import re import tempfile @@ -192,8 +193,9 @@ class HypertoolsTrustError(ValueError): class HypertoolsOfflineError(HypertoolsIOError): - """Raised when ``offline=True`` was passed and the URL has no cached - copy to read (GH #285). + """Raised when ``offline=True`` was passed and the source -- a URL, or a + hosted built-in dataset -- has no hash-valid cached copy to read + (GH #285). Subclasses :class:`~hypertools.core.exceptions.HypertoolsIOError`, so existing handlers still catch it; ``load_source`` keys on this @@ -213,10 +215,60 @@ class HypertoolsOfflineError(HypertoolsIOError): 'linnerud': 'load_linnerud', } -# seaborn.get_dataset_names() is a network call (fetches the seaborn-data -# GitHub repo's file listing); cache it per-process so repeated hyp.load() -# calls don't re-hit the network for every unresolved name. +# The seaborn dataset listing is a network call (seaborn.get_dataset_names() +# fetches a name list from the seaborn-data GitHub repo with a urlopen that +# has NO timeout); cache it per-process so repeated hyp.load() calls don't +# re-hit the network for every unresolved name. A FAILED fetch is +# remembered too (``_seaborn_names_failed_at``, retried after +# SEABORN_LISTING_RETRY_AFTER seconds): before 1.1, the cache stayed None +# on failure, so on a dead network every later hyp.load() of an unresolved +# name blocked again on the same connect (1.1 release review, I1). _seaborn_names_cache = None +_seaborn_names_failed_at = None +#: Timeout (seconds) for fetching the seaborn dataset-name listing. +SEABORN_LISTING_TIMEOUT = 10.0 +#: How long (seconds) a failed listing fetch is remembered before the next +#: seaborn-name lookup tries the network again. +SEABORN_LISTING_RETRY_AFTER = 300.0 +# A seaborn dataset name is a plain identifier ('penguins', 'car_crashes'); +# anything with a path separator, scheme, whitespace or dot (URLs, paths, +# 'wikipedia:' / 'fivethirtyeight/' / Hugging Face ids, filenames) can never +# be one, so the listing is not consulted for it. +_SEABORN_NAME_RE = re.compile(r'^[A-Za-z0-9_-]+$') + + +def reset_seaborn_names_cache(): + """Forget the cached seaborn dataset-name listing AND any remembered + fetch failure, so the next seaborn-name lookup hits the network again + (e.g. after connectivity is restored, without waiting out + :data:`SEABORN_LISTING_RETRY_AFTER`).""" + global _seaborn_names_cache, _seaborn_names_failed_at + _seaborn_names_cache = None + _seaborn_names_failed_at = None + + +def _refuse_offline(source, what): + """Raise the :class:`HypertoolsOfflineError` for a source that + ``offline=True`` can never serve: only Google-Sheets/Drive/Dropbox/URL + downloads (steps 10-13) live in the hypertools URL cache.""" + raise HypertoolsOfflineError( + f'offline=True, but {source!r} is {what}, which hypertools cannot ' + 'serve from its on-disk URL cache (only Google Sheets / Google ' + 'Drive / Dropbox / plain-URL downloads -- steps 10-13 of the ' + 'hypertools.load resolution chain -- are cached; see ' + 'hypertools.io.sources.url_cache_dir()). Drop offline=True to load ' + 'it from the network.') + + +def _refuse_streaming(source, what): + """Raise for ``streaming=True`` on a source that is not a Hugging Face + dataset: before 1.1 the flag was silently ignored and the full dataset + came back (1.1 release review, I8).""" + raise ValueError( + f'hypertools.load: streaming=True is only supported for Hugging ' + f"Face dataset ids (step 9 of the resolution chain, e.g. " + f"'scikit-learn/iris'), but {source!r} resolved as {what}, which " + 'is always loaded in full. Drop streaming=True.') # fivethirtyeight/data folder listings, keyed by slug (e.g. 'bechdel'): # GitHub's unauthenticated REST API rate limit is 60 requests/hour, so the @@ -264,8 +316,16 @@ def sklearn_dataset(name): def seaborn_dataset(name): """Load a seaborn example dataset by name. - ``name`` is matched against ``seaborn.get_dataset_names()`` (a network - call to the seaborn-data GitHub repo; the result is cached per-process). + ``name`` is matched against seaborn's dataset-name listing (the same + list ``seaborn.get_dataset_names()`` reads, fetched here with + :data:`SEABORN_LISTING_TIMEOUT` because seaborn's own ``urlopen`` has + no timeout; the result is cached per-process). A name that cannot be + a seaborn dataset (anything but a plain ``[A-Za-z0-9_-]`` identifier + -- URLs, paths, prefixed sources) is answered without consulting the + listing at all. A failed fetch is remembered for + :data:`SEABORN_LISTING_RETRY_AFTER` seconds (call + :func:`reset_seaborn_names_cache` to retry sooner), so an unreachable + network does not block every later lookup. Returns ------- @@ -275,18 +335,39 @@ def seaborn_dataset(name): can't be fetched (network failure) -- either way, callers should treat this as "not mine" and continue down the resolution chain. """ - global _seaborn_names_cache - import seaborn as sns + global _seaborn_names_cache, _seaborn_names_failed_at + if not isinstance(name, str) or not _SEABORN_NAME_RE.match(name): + return None if _seaborn_names_cache is None: + if _seaborn_names_failed_at is not None and \ + time.monotonic() - _seaborn_names_failed_at \ + < SEABORN_LISTING_RETRY_AFTER: + return None try: - _seaborn_names_cache = set(sns.get_dataset_names()) + _seaborn_names_cache = _fetch_seaborn_names() except Exception: + _seaborn_names_failed_at = time.monotonic() return None + _seaborn_names_failed_at = None if name not in _seaborn_names_cache: return None + import seaborn as sns return sns.load_dataset(name) +def _fetch_seaborn_names(): + """The set of seaborn example-dataset names, read from seaborn's own + listing URL (``seaborn.utils.DATASET_NAMES_URL``, a newline-separated + text file -- parsed exactly as ``seaborn.get_dataset_names()`` parses + it) but with a request timeout.""" + from seaborn.utils import DATASET_NAMES_URL + resp = requests.get(DATASET_NAMES_URL, headers=_UA, + timeout=SEABORN_LISTING_TIMEOUT) + resp.raise_for_status() + return {line.strip() for line in resp.text.split('\n') + if line.strip()} + + def fivethirtyeight_dataset(name): """Load a dataset from FiveThirtyEight's public data repository (https://github.com/fivethirtyeight/data) by explicit prefix. @@ -535,14 +616,35 @@ def _synthetic_rng(random_state): """``numpy.random.Generator`` for a hypertools ``random_state``. Accepts None (fresh entropy), an int seed, a ``SeedSequence``, an - existing ``Generator``, or a legacy ``RandomState`` (whose own bit - generator is reused, so a caller threading a ``RandomState`` through - still gets a reproducible stream).""" + existing ``Generator``, or a legacy ``RandomState``. A ``RandomState`` + has no ``.bit_generator`` (the pre-1.1 code assumed one and crashed -- + 1.1 release review, I3), so its stream is consumed for 16 bytes of + entropy that seed the ``Generator``: deterministic given the + ``RandomState``'s state, and advancing it the way any draw would.""" if isinstance(random_state, np.random.RandomState): - return np.random.default_rng(random_state.bit_generator) + entropy = int.from_bytes(random_state.bytes(16), 'little') + return np.random.default_rng(entropy) return np.random.default_rng(random_state) +def _sklearn_random_state(random_state): + """What to hand ``sklearn.datasets.make_*`` as ``random_state``. + + scikit-learn accepts None, an int or a ``RandomState`` -- not a + ``Generator`` or ``SeedSequence`` (1.1 release review, I4: those were + passed straight through for ``n_datasets == 1`` and rejected by + scikit-learn's parameter validation, while ``n_datasets > 1`` derived + ints from them). Every seed type is mapped to an int in + ``[0, 2**32)``, deterministically.""" + if random_state is None or isinstance(random_state, + np.random.RandomState): + return random_state + if isinstance(random_state, (int, np.integer)) \ + and not isinstance(random_state, bool): + return int(random_state) + return int(_synthetic_rng(random_state).integers(2 ** 32)) + + def _synthetic_frame(x, target=None, target_name='target'): """(n_samples, n_features) array -> DataFrame with ``dim_0 ... dim_k`` columns, plus ``target_name`` appended when scikit-learn gave us labels @@ -689,8 +791,8 @@ def _sklearn_synthetic(maker, target_name): manifold positions appended as ``target_name``.""" def _make(random_state=None, **kwargs): from sklearn import datasets as sk_datasets - x, y = getattr(sk_datasets, maker)(random_state=random_state, - **kwargs) + x, y = getattr(sk_datasets, maker)( + random_state=_sklearn_random_state(random_state), **kwargs) return _synthetic_frame(x, y, target_name) return _make @@ -804,16 +906,15 @@ def synthetic_dataset(name, n_datasets=1, random_state=None, seed=None, f'random_state={random_state!r}, seed={seed!r})') random_state = seed - try: - n_datasets = int(n_datasets) - except (TypeError, ValueError) as e: - raise HypertoolsIOError( - f'{name!r}: n_datasets must be a positive integer; got ' - f'{n_datasets!r}') from e - if n_datasets < 1: + # an integer (Python int or numpy integer, not bool) >= 1; a float such + # as 2.7 used to be silently truncated by int() (1.1 release review, I6) + if isinstance(n_datasets, bool) \ + or not isinstance(n_datasets, numbers.Integral) \ + or n_datasets < 1: raise HypertoolsIOError( f'{name!r}: n_datasets must be a positive integer; got ' - f'{n_datasets}') + f'{n_datasets!r}') + n_datasets = int(n_datasets) if n_datasets == 1: return maker(random_state=random_state, **kwargs) @@ -823,8 +924,16 @@ def synthetic_dataset(name, n_datasets=1, random_state=None, seed=None, if random_state is None: seeds = [None] * n_datasets else: - base = random_state if isinstance(random_state, np.random.SeedSequence) \ - else np.random.SeedSequence( + if isinstance(random_state, np.random.SeedSequence): + # spawn from a fresh COPY: SeedSequence.spawn() advances the + # object's spawn counter, so spawning from the caller's own + # object made a second call with the same SeedSequence + # produce different data (1.1 release review, I5) + base = np.random.SeedSequence( + random_state.entropy, spawn_key=random_state.spawn_key, + pool_size=random_state.pool_size) + else: + base = np.random.SeedSequence( _synthetic_rng(random_state).integers(2 ** 63)) seeds = [int(child.generate_state(1)[0]) for child in base.spawn(n_datasets)] @@ -1081,8 +1190,11 @@ def yahoo_source(name, start=None, end=None, interval='1d', timeout=30): Returns ------- pandas.DataFrame - Indexed by a ``DatetimeIndex`` named ``'date'`` (bar timestamps - normalized to midnight), with float columns ``open``, ``high``, + Indexed by a ``DatetimeIndex`` named ``'date'``. Daily and longer + bars are dated by the exchange-local trading day (naive, midnight); + intraday bars (``'1h'``, ``'15m'``, ...) keep their time, as a + tz-aware index in the exchange's timezone (e.g. + ``America/New_York``). Float columns ``open``, ``high``, ``low``, ``close``, ``volume`` and, when Yahoo provides it, ``adj_close`` (split/dividend-adjusted). Rows are in time order; gaps Yahoo reports as nulls stay NaN rather than being dropped. @@ -1108,30 +1220,91 @@ def yahoo_source(name, start=None, end=None, interval='1d', timeout=30): f'Yahoo Finance returned a non-JSON response (HTTP ' f'{resp.status_code}) for {ticker!r}: {type(e).__name__}: {e}' ) from e + return _parse_yahoo_chart( + payload, ticker=ticker, interval=interval, + window=f'{params["period1"]}..{params["period2"]}', + status_code=resp.status_code) + + +_YAHOO_INTRADAY_RE = re.compile(r'\d+[mh]') # '1m', '90m', '1h'; not '1mo' + + +def _yahoo_is_intraday(interval): + """True for a Yahoo bar size measured in minutes or hours.""" + return bool(_YAHOO_INTRADAY_RE.fullmatch(str(interval).strip())) + + +def _yahoo_exchange_tz(name, gmtoffset): + """The exchange's timezone: the IANA ``name`` Yahoo reports when it is a + real zone, else a fixed-offset zone from ``gmtoffset`` (seconds).""" + import datetime + import zoneinfo + if isinstance(name, str) and name: + try: + return zoneinfo.ZoneInfo(name) + except (zoneinfo.ZoneInfoNotFoundError, ValueError): + pass + return datetime.timezone(datetime.timedelta(seconds=int(gmtoffset))) + + +def _parse_yahoo_chart(payload, *, ticker, interval='1d', window='', + status_code=200): + """Turn one decoded Yahoo v8 chart payload into the DataFrame + :func:`yahoo_source` returns (split out so the parser is testable on a + synthetic payload, without the network). + + Yahoo stamps every bar at the exchange's local session open expressed + in UTC (23:00 UTC for a Sydney-listed ticker, 14:30 UTC for New York), + and reports the exchange's UTC offset as ``meta.gmtoffset`` (seconds). + Normalising the raw UTC stamp to midnight dated every bar east of UTC + one day early (BHP.AX 2025-01-06..10 came back as 2025-01-05..09; 1.1 + release review, I2), so the offset is applied first: the resulting + ``date`` is the exchange-local trading day. + + Intraday bars (a granularity in minutes or hours, e.g. ``'1h'``, + ``'15m'``) are NOT normalized -- doing so gave every bar of a session + the same midnight stamp, so the index was full of duplicates and + ``hyp.predict`` refused it. They keep their time as a tz-aware index in + the exchange's timezone (``meta.exchangeTimezoneName``, or a fixed + ``gmtoffset`` zone when the name is missing or unknown). + """ chart = payload.get('chart') or {} error = chart.get('error') if error: raise HypertoolsIOError( f'Yahoo Finance rejected {ticker!r}: ' - f'{error.get("description", error)} (HTTP {resp.status_code}). ' + f'{error.get("description", error)} (HTTP {status_code}). ' 'Check the symbol on https://finance.yahoo.com.') results = chart.get('result') or [] if not results: raise HypertoolsIOError( f'Yahoo Finance returned no result for {ticker!r} (HTTP ' - f'{resp.status_code}).') + f'{status_code}).') result = results[0] stamps = result.get('timestamp') if not stamps: raise HypertoolsIOError( f'Yahoo Finance returned no {interval} bars for {ticker!r} in ' - f'the requested window ({params["period1"]}..' - f'{params["period2"]}, epoch seconds). Widen start=/end=, or ' - 'note that intraday intervals are only served for recent ' - 'windows.') + f'the requested window ({window}, epoch seconds). Widen ' + 'start=/end=, or note that intraday intervals are only served ' + 'for recent windows.') quote = (result.get('indicators') or {}).get('quote') or [{}] quote = quote[0] - index = pd.to_datetime(stamps, unit='s').normalize() + meta = result.get('meta') or {} + gmtoffset = int(meta.get('gmtoffset') or 0) + stamps = np.asarray(stamps, dtype='int64') + if _yahoo_is_intraday(meta.get('dataGranularity') or interval): + # an intraday bar is an instant, not a trading day: keep its time, + # expressed in the exchange's own timezone so the wall-clock reads + # like the daily path's exchange-local dates. The zone NAME is used + # when Yahoo gives one, because gmtoffset is only the offset in + # force NOW -- applying it to a window that spans a DST change + # would shift every bar on the far side by an hour. The index stays + # tz-aware, so a repeated fall-back hour cannot collide either. + index = pd.to_datetime(stamps, unit='s', utc=True).tz_convert( + _yahoo_exchange_tz(meta.get('exchangeTimezoneName'), gmtoffset)) + else: + index = pd.to_datetime(stamps + gmtoffset, unit='s').normalize() index.name = 'date' frame = {} for col in ('open', 'high', 'low', 'close', 'volume'): @@ -1352,23 +1525,58 @@ def cached_url_path(url): return url_cache_dir() / f'{digest}{suffix}' +def _replace_retrying(src, dst, attempts=50, delay=0.02): + """``os.replace`` that tolerates Windows' transient access-denied. + + On Windows a rename onto a file another thread is reading or renaming + raises ``PermissionError`` (WinError 5) for the instant the handle is + held; POSIX renames succeed regardless. Twelve concurrent writers of + one cache entry hit it on every Windows CI job (2026-09-06). Retry + briefly; if every attempt fails but the destination exists, a + concurrent writer of the SAME URL (the cache key) won the race with + identical bytes, so the file on disk is the file we wanted. + """ + import time + for attempt in range(attempts): + try: + os.replace(src, dst) + return + except PermissionError: + if attempt == attempts - 1: + if Path(dst).exists(): + return + raise + time.sleep(delay) + + def _write_cached(path, raw, name_hint): - """Write ``raw`` into the cache atomically: a per-process ``.part`` + """Write ``raw`` into the cache atomically: a unique ``.part`` file, then ``os.replace``, so an interrupted download can never leave a truncated file that later runs would trust. The download's filename hint is stored beside it so a cache hit parses the payload exactly the way the live download did.""" import json path.parent.mkdir(parents=True, exist_ok=True) - part = path.with_name(f'{path.name}.{os.getpid()}.part') - part.write_bytes(raw) - os.replace(part, path) + + def write_atomic(destination, payload): + """Replace one cache file using a private temporary file.""" + # GH #285 release review: PID-only names collide when threads + # cache the same URL concurrently, causing missing-file errors. + stream = tempfile.NamedTemporaryFile( + dir=destination.parent, prefix=destination.name + '.', + suffix='.part', delete=False) + part = Path(stream.name) + try: + with stream: + stream.write(payload) + _replace_retrying(part, destination) + finally: + part.unlink(missing_ok=True) + + write_atomic(path, raw) if name_hint: meta = path.with_name(f'{path.name}.meta.json') - meta_part = meta.with_name(f'{meta.name}.{os.getpid()}.part') - meta_part.write_text(json.dumps({'url_name_hint': name_hint}), - encoding='utf-8') - os.replace(meta_part, meta) + write_atomic(meta, json.dumps({'url_name_hint': name_hint}).encode('utf-8')) def _read_cached(path): @@ -1453,7 +1661,14 @@ def load_source(source, split=None, streaming=False, trust=False, ``cache``/``offline`` govern the on-disk URL cache (see :func:`url_cache_dir`): ``cache=True`` stores every URL/Drive/Dropbox/ Sheets download and reuses it next time, ``offline=True`` reads ONLY - from that cache and raises rather than touching the network. + from that cache and raises rather than touching the network: an + explicit URL / Drive / Sheets / Dropbox link that is not cached raises + ``HypertoolsOfflineError`` at once, while a string that is only + GUESSED to be one (a bare 25+ character name read as a Drive id, a + scheme-less ``host.tld/...``) adds its miss to the "tried, in order" + digest, raised as ``HypertoolsOfflineError`` when nothing matched. A + cached copy that is read but does not parse raises + ``HypertoolsIOError`` naming the cached file (it is not a miss). ``decode_labels`` is threaded to :func:`_load_hf`. Any remaining keyword arguments belong to the synthetic (step 6) and web-prefix (step 7) sources and are passed to whichever of those matches; passing @@ -1466,10 +1681,10 @@ def load_source(source, split=None, streaming=False, trust=False, # before local-file resolution, so the same shadowing rule as the # scikit-learn/seaborn names applies (pass './helix' or a path with an # extension to load a local file of that name instead). - synthetic = synthetic_dataset(source, **source_kwargs) \ - if source in SYNTHETIC_DATASETS else None - if synthetic is not None: - return synthetic + if source in SYNTHETIC_DATASETS: + if streaming: + _refuse_streaming(source, 'a built-in synthetic dataset') + return synthetic_dataset(source, **source_kwargs) attempts.append('synthetic dataset: not one of ' f'{sorted(SYNTHETIC_DATASETS)}') @@ -1477,8 +1692,20 @@ def load_source(source, split=None, streaming=False, trust=False, # 'sec:'): unambiguous, so a failure raises rather than falling # through the rest of the chain if isinstance(source, str) and source.startswith(WEB_SOURCE_PREFIXES): + if offline: + _refuse_offline(source, 'a wikipedia:/yahoo:/sec: web source') + if streaming: + _refuse_streaming(source, 'a wikipedia:/yahoo:/sec: web source') return web_source(source, **source_kwargs) + if streaming and (_is_url_like(source) or + not _HF_ID_RE.match(source)): + # every remaining resolver but Hugging Face (step 9) loads in full; + # refuse before any download or file read (1.1 release review, I8) + _refuse_streaming( + source, 'a local file / Google Sheets / Google Drive / ' + 'Dropbox / URL source (not a Hugging Face dataset id)') + if source_kwargs: raise TypeError( f'hypertools.load: unexpected keyword argument(s) ' @@ -1487,9 +1714,7 @@ def load_source(source, split=None, streaming=False, trust=False, f'({sorted(SYNTHETIC_DATASETS)}) and the web sources ' f'({list(WEB_SOURCE_PREFIXES)}).') - is_url_like = source.startswith(('http://', 'https://')) \ - or 'drive.google.com' in source or 'docs.google.com' in source \ - or 'dropbox.com' in source + is_url_like = _is_url_like(source) # 8. local file (skipped for explicit URLs, which are never local # paths -- the digest used to list a slash-collapsed 'https:/...' @@ -1502,6 +1727,8 @@ def load_source(source, split=None, streaming=False, trust=False, except OSError: is_file = is_dir = False if is_file: + if streaming: + _refuse_streaming(source, 'a local file') return load_local_file(path) if is_dir: attempts.append( @@ -1509,8 +1736,15 @@ def load_source(source, split=None, streaming=False, trust=False, else: attempts.append(f'local file: not found at {path}') - # 9. Hugging Face dataset (skip for obvious URLs) - if not is_url_like and _HF_ID_RE.match(source): + # 9. Hugging Face dataset (skip for obvious URLs; and under + # offline=True, which never opens a connection -- Hugging Face + # datasets are not in the hypertools URL cache, so the source either + # resolves as a cached URL below or raises HypertoolsOfflineError) + if offline and not is_url_like and _HF_ID_RE.match(source): + attempts.append('Hugging Face dataset: not attempted (offline=True; ' + 'Hugging Face datasets are not served from the ' + 'hypertools URL cache)') + elif not is_url_like and _HF_ID_RE.match(source): try: return _load_hf(source, split=split, streaming=streaming, decode_labels=decode_labels) @@ -1520,47 +1754,73 @@ def load_source(source, split=None, streaming=False, trust=False, attempts.append(f'Hugging Face dataset: {type(e).__name__}: ' f'{str(e).splitlines()[0][:120]}') - # 10. Google Sheets URL -> CSV export (checked before generic Drive id - # extraction, since a Sheets URL also matches the '/d/<id>' pattern) - sheet_url = _normalize_google_sheet(source) - if sheet_url is not None: + # steps 10-13 download (or, with cache=/offline=, read from the URL + # cache) and parse. Two things are tracked for the final error: + # - an offline MISS escapes at once only when the source is + # unmistakably that kind of link (is_url_like); for a GUESSED + # interpretation -- a bare 25+ character string read as a Drive id, + # a scheme-less 'host.tld/...' read as a URL, an 's/...' Dropbox + # path -- the miss is one line of the digest, next to the local-file + # miss the user more likely meant (review 2026-09-11) + # - a cached copy that was READ but did not parse is a parse failure, + # not a cache miss, so the final error is then a HypertoolsIOError + # naming the cached file rather than the offline "cache it first" + # refusal (review 2026-09-11) + unparsed_cached = [] + miss = object() + + def _fetch_and_parse(url, label, hint): + from_cache = (cache or offline) and cached_url_path(url).is_file() + try: + raw, name_hint = _fetch_bytes(url, cache=cache, offline=offline) + except HypertoolsOfflineError: + if is_url_like: + raise + attempts.append(f'{label}: not in the hypertools URL cache ' + f'(looked for {cached_url_path(url)})') + return miss + except HypertoolsTrustError: + raise + except Exception as e: + attempts.append(f'{label}: {type(e).__name__}: {e}') + return miss try: - raw, name_hint = _fetch_bytes(sheet_url, cache=cache, - offline=offline) - return _parse_payload(raw, name_hint or 'sheet.csv', - trust=trust, remote=True) + return _parse_payload(raw, name_hint or hint, trust=trust, + remote=True) except (HypertoolsTrustError, HypertoolsOfflineError): raise except Exception as e: - attempts.append(f'Google Sheets: {type(e).__name__}: {e}') + if from_cache: + path = cached_url_path(url) + unparsed_cached.append(path) + attempts.append(f'{label}: the cached copy at {path} could ' + f'not be parsed: {type(e).__name__}: {e}') + else: + attempts.append(f'{label}: {type(e).__name__}: {e}') + return miss + + # 10. Google Sheets URL -> CSV export (checked before generic Drive id + # extraction, since a Sheets URL also matches the '/d/<id>' pattern) + sheet_url = _normalize_google_sheet(source) + if sheet_url is not None: + data = _fetch_and_parse(sheet_url, 'Google Sheets', 'sheet.csv') + if data is not miss: + return data # 11. Google Drive URL or bare ID drive_id = _extract_drive_id(source) if drive_id is not None: url = f'https://drive.google.com/uc?export=download&id={drive_id}' - try: - raw, name_hint = _fetch_bytes(url, cache=cache, - offline=offline) - return _parse_payload(raw, name_hint or source, - trust=trust, remote=True) - except (HypertoolsTrustError, HypertoolsOfflineError): - raise - except Exception as e: - attempts.append(f'Google Drive ({drive_id}): ' - f'{type(e).__name__}: {e}') + data = _fetch_and_parse(url, f'Google Drive ({drive_id})', source) + if data is not miss: + return data # 12. Dropbox URL or shared-link path dropbox_url = _normalize_dropbox(source) if dropbox_url is not None: - try: - raw, name_hint = _fetch_bytes(dropbox_url, cache=cache, - offline=offline) - return _parse_payload(raw, name_hint or source, - trust=trust, remote=True) - except (HypertoolsTrustError, HypertoolsOfflineError): - raise - except Exception as e: - attempts.append(f'Dropbox: {type(e).__name__}: {e}') + data = _fetch_and_parse(dropbox_url, 'Dropbox', source) + if data is not miss: + return data # 13. any URL, with or without a scheme url = None @@ -1583,15 +1843,9 @@ def load_source(source, split=None, streaming=False, trust=False, 'address without a scheme; add an explicit http:// or ' 'https:// prefix') if url is not None: - try: - raw, name_hint = _fetch_bytes(url, cache=cache, - offline=offline) - return _parse_payload(raw, name_hint or source, - trust=trust, remote=True) - except (HypertoolsTrustError, HypertoolsOfflineError): - raise - except Exception as e: - attempts.append(f'URL ({url}): {type(e).__name__}: {e}') + data = _fetch_and_parse(url, f'URL ({url})', source) + if data is not miss: + return data tried = '\n - '.join(attempts) if attempts else 'no interpretation ' \ 'matched (not a file, URL, Drive/Dropbox link, or dataset id)' @@ -1599,9 +1853,30 @@ def load_source(source, split=None, streaming=False, trust=False, suggestion = _closest_dataset_name(source) if suggestion is not None: message += f"\nDid you mean {suggestion!r}?" + if unparsed_cached: + raise HypertoolsIOError( + f'{message}\n(the cached download at ' + f'{", ".join(str(p) for p in unparsed_cached)} was read but ' + 'could not be parsed; it is kept as is -- delete it and load ' + 'again with cache=True while online to download a fresh copy.)') + if offline: + raise HypertoolsOfflineError( + f'offline=True: {message}\n(offline=True serves ONLY Google ' + 'Sheets / Google Drive / Dropbox / plain-URL downloads that ' + 'were cached earlier with cache=True; every network resolver ' + 'was skipped.)') raise HypertoolsIOError(message) +def _is_url_like(source): + """True for a string that is unmistakably a URL (explicit scheme, or a + Google Drive / Google Sheets / Dropbox link), which is never a local + path, a dataset name or a Hugging Face id.""" + return source.startswith(('http://', 'https://')) \ + or 'drive.google.com' in source or 'docs.google.com' in source \ + or 'dropbox.com' in source + + def _closest_dataset_name(source): """Near-miss suggestion for the could-not-load digest: the closest built-in example / scikit-learn / (cached) seaborn dataset name to @@ -1974,10 +2249,7 @@ def _parse_payload(raw, name_hint='', trust=False, remote=False): if raw[:1] == b'\x80': return _unpickle_bytes(raw, trust=trust, remote=remote) if raw[:2] == b'PK': - try: - return _unpack_npz(raw, trust=trust, remote=remote) - except Exception: - return pd.read_parquet(io.BytesIO(raw)) + return _unpack_sniffed_zip(raw, trust=trust, remote=remote) if _complete_pickle_stream(raw): # protocol-0 (ASCII) pickles carry no magic prefix (e.g. # hyp.save(..., protocol=0) to an arbitrary extension) @@ -1995,10 +2267,7 @@ def _parse_payload(raw, name_hint='', trust=False, remote=False): if raw[:1] == b'\x80': return _unpickle_bytes(raw, trust=trust, remote=remote) if raw[:2] == b'PK': - try: - return _unpack_npz(raw, trust=trust, remote=remote) - except Exception: - return pd.read_parquet(io.BytesIO(raw)) + return _unpack_sniffed_zip(raw, trust=trust, remote=remote) if _complete_pickle_stream(raw): # protocol-0 (ASCII) pickles carry no magic prefix and DO decode # as UTF-8, so they must be sniffed BEFORE text parsing or they @@ -2125,6 +2394,22 @@ def _unpack_npz(raw, trust=False, remote=False): return arrays[0] if len(arrays) == 1 else arrays +def _unpack_sniffed_zip(raw, trust=False, remote=False): + """A payload sniffed as a zip (``'PK'`` magic, no or an unknown + extension): an ``.npz`` first, parquet as the fallback. The ``.npz`` + reader's :class:`HypertoolsTrustError` -- a remote object array that + needs ``allow_pickle`` -- IS the answer, so it is raised as itself; + before, the parquet fallback swallowed it and the user saw "Parquet + magic bytes not found" instead of the ``trust=True`` remedy (review + 2026-09-11).""" + try: + return _unpack_npz(raw, trust=trust, remote=remote) + except HypertoolsTrustError: + raise + except Exception: + return pd.read_parquet(io.BytesIO(raw)) + + def _unpack_mat(raw): from scipy.io import loadmat data = {k: v for k, v in loadmat(io.BytesIO(raw)).items() diff --git a/hypertools/io/streaming.py b/hypertools/io/streaming.py index 2ef9dfad..778e2be6 100644 --- a/hypertools/io/streaming.py +++ b/hypertools/io/streaming.py @@ -25,6 +25,19 @@ import numpy as np import pandas as pd +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like) + +#: The clamped-samples warning fires when more than a quarter of the samples +#: streamed after the head land outside the head-fitted display box. While +#: streaming it waits for this many post-head samples (so a noisy first +#: chunk of a long stream cannot trigger it)... +_CLAMP_WARN_MIN_STREAMING = 20 +#: ...and when the stream stops it is evaluated once more over everything +#: streamed, from this many post-head samples up, so a SHORT stream is +#: covered too (1.1 visual review, L13b). +_CLAMP_WARN_MIN_AT_END = 4 + def _validate_stream_save_path(save_path): """Validate a streaming ``save_path`` BEFORE any samples are consumed, @@ -73,10 +86,13 @@ def _validate_stream_save_path(save_path): def is_stream(x): """True when x is streaming data: a Python iterator/generator, or a - Hugging Face ``datasets.IterableDataset``. Materialized containers - (lists, tuples, arrays, DataFrames) and strings are not streams.""" - if isinstance(x, (list, tuple, str, np.ndarray, pd.DataFrame, pd.Series, - dict)): + Hugging Face ``datasets.IterableDataset``. Materialized data is not a + stream: containers (lists, tuples, dicts), strings, and every dataset + type datawrangler recognises -- arrays, DataFrames of any backend + (pandas, polars DataFrame/LazyFrame, dataframe-likes) and Series.""" + if isinstance(x, (list, tuple, dict)) or np.isscalar(x): + return False + if is_array_dataset(x) or is_frame_dataset(x) or is_series_like(x): return False # generators and other iterators if isinstance(x, collections.abc.Iterator): @@ -147,6 +163,8 @@ def _fit_stream_models(head, reduce, ndims, normalize): fitted reduction estimator (None when no reduction was needed). """ from ..reduce.reduce import _resolve_model + from ..core.shared import check_spec_keys + from ..core.model import external_stacklevel # normalization stats are computed ONCE, on the head, and reused for # every future sample (the fitted-transform semantics of issue #101). @@ -175,24 +193,46 @@ def norm(m): # reduction spec: name / dict / class / instance, mirroring tools.reduce if isinstance(reduce, dict): + # a flat key such as {'model': 'PCA', 'whiten': True} used to be + # dropped silently, exactly as in hyp.reduce (1.1 review) + check_spec_keys(reduce, 'reduce', param='reduce') model_spec = reduce.get('model') # accept the canonical 'kwargs' key (falling back to the legacy 'params') # so a streaming reduce spec honors constructor kwargs like every other # dispatcher (QC 2026-07: only 'params' was read, so # reduce={'model':'PCA','kwargs':{'whiten':True}} silently used defaults). params = dict(reduce.get('kwargs', reduce.get('params', {}))) + # positional constructor arguments were dropped the same way + # ({'model': 'PCA', 'args': [2]} fit ndims components; 1.1 review) + args = list(reduce.get('args', [])) + if (args or params) and not isinstance(model_spec, (str, type)) \ + and model_spec is not None: + # an already-constructed instance is used as-is (parity with + # hyp.reduce's warning: these used to vanish without a word) + warnings.warn( + f"the reduce spec's 'model' is an already-constructed " + f"{type(model_spec).__name__} instance (used as-is), so the " + "spec's 'args'/'kwargs' entries are ignored; configure the " + "instance directly, or pass the class (or its name) to " + "apply constructor parameters", UserWarning, + stacklevel=external_stacklevel()) else: model_spec = reduce params = {} - params.setdefault('n_components', ndims) - - if model_spec is None or head_n.shape[1] <= params['n_components']: + args = [] + if not args: + # with positional arguments, the component count may be among + # them, so ndims is not injected (as in hyp.apply_model) + params.setdefault('n_components', ndims) + + if model_spec is None or head_n.shape[1] <= params.get('n_components', + ndims): return head_n, norm, None if isinstance(model_spec, str): - model = _resolve_model(model_spec)(**params) + model = _resolve_model(model_spec)(*args, **params) elif isinstance(model_spec, type): - model = model_spec(**params) + model = model_spec(*args, **params) else: model = model_spec # already-instantiated estimator @@ -250,9 +290,11 @@ def plot_stream(stream, fmt='-', stream_init=10000, stream_chunk=100, The display box (axis limits and the data->box affine) is FROZEN from the head samples; later samples that land outside it are drawn clamped - to the box surface, and a ``RuntimeWarning`` is emitted when a large - fraction of streamed samples is clamped (their true projected values - stay in ``stream_info['xform_data']``). Streamed trajectories are + to the box surface, and a ``RuntimeWarning`` is emitted (once) when + more than a quarter of the post-head samples are clamped -- checked as + samples arrive once 20 have streamed, and again when streaming stops + for any stream with at least 4 post-head samples (their true projected + values stay in ``stream_info['xform_data']``). Streamed trajectories are drawn as raw polylines (one vertex per sample) from the first frame on, without the interpolation/smoothing applied to static plots. @@ -374,9 +416,13 @@ def plot_stream(stream, fmt='-', stream_init=10000, stream_chunk=100, "reduce/ndims so samples are projected to <= 3 dimensions " "(e.g. reduce='IncrementalPCA', ndims=3).") - # initial plot on the head (already normalized/reduced -> disable both) + # initial plot on the head (already normalized/reduced -> disable both). + # Streams are always drawn with matplotlib: pin the render backend so a + # plotly preference (Colab/Kaggle auto-detection, or + # set_interactive_backend('plotly')) cannot turn this into a plotly + # figure (fresh-Colab feature tour, 2026-09-11). fig = hyp_plot(head_red, fmt, reduce=None, normalize=None, ndims=ndims, - show=False, **plot_kwargs) + show=False, backend='matplotlib', **plot_kwargs) artist = next(ln for ln in fig.axes[0].lines if len(ln.get_data()[0])) # the axis limits and the data->box transform are FROZEN from the head: @@ -482,9 +528,29 @@ def _n_clamped(pts): clamped = post_head = 0 clamp_warned = False + def _warn_if_clamped(min_post_head): + # the stream has drifted out of the head-fitted display box: the + # plot is visibly distorted (QC 2026-07, F22-io-streaming-lsl-002). + # Warns at most once per stream. + nonlocal clamp_warned + if clamp_warned or post_head < min_post_head \ + or clamped / post_head <= 0.25: + return + clamp_warned = True + warnings.warn( + f'{clamped} of {post_head} streamed samples ' + f'({100.0 * clamped / post_head:.0f}%) fall outside ' + 'the display box fitted on the first stream_init ' + 'samples and are drawn clamped to its surface, so ' + 'their displayed positions are distorted (the true ' + "projected values are kept in " + "fig.stream_info['xform_data']). If the early " + 'samples are not representative of the whole stream, ' + 'increase stream_init.', RuntimeWarning, stacklevel=3) + def _consume(rows): # project + draw one (possibly partial) chunk of samples - nonlocal n_seen, clamped, post_head, clamp_warned + nonlocal n_seen, clamped, post_head if not rows: return chunk = np.vstack([row_to_vector(r) for r in rows]) @@ -496,22 +562,10 @@ def _consume(rows): n_seen += len(rows) clamped += _n_clamped(projected) post_head += len(projected) - if not clamp_warned and post_head >= 20 \ - and clamped / post_head > 0.25: - # the stream has drifted out of the head-fitted display box: - # the plot is visibly distorted (QC 2026-07, - # F22-io-streaming-lsl-002) - clamp_warned = True - warnings.warn( - f'{clamped} of {post_head} streamed samples ' - f'({100.0 * clamped / post_head:.0f}%) fall outside ' - 'the display box fitted on the first stream_init ' - 'samples and are drawn clamped to its surface, so ' - 'their displayed positions are distorted (the true ' - "projected values are kept in " - "fig.stream_info['xform_data']). If the early " - 'samples are not representative of the whole stream, ' - 'increase stream_init.', RuntimeWarning, stacklevel=2) + # while streaming, wait for 20 post-head samples so a noisy first + # chunk cannot trigger it; the stream-end check below covers + # shorter streams + _warn_if_clamped(_CLAMP_WARN_MIN_STREAMING) _redraw() try: @@ -595,6 +649,12 @@ def _consume(rows): if writer_tmp is not None and os.path.exists(writer_tmp): os.remove(writer_tmp) + # once more now that streaming has stopped, with a lower floor: a SHORT + # stream never reached the 20 post-head samples the in-stream check + # waits for, so 9 of its 16 drawn vertices could sit clamped on the + # box surface with no warning at all (1.1 visual review, L13b) + _warn_if_clamped(_CLAMP_WARN_MIN_AT_END) + fig.stream_info = { 'data': [np.vstack(raw)], 'xform_data': [np.vstack(accum)], diff --git a/hypertools/manip/common.py b/hypertools/manip/common.py index e23ee7e7..10c952b6 100644 --- a/hypertools/manip/common.py +++ b/hypertools/manip/common.py @@ -5,10 +5,71 @@ the transformer with those params. Child classes (Normalize, ZScore, Smooth, Resample, Delay) supply the three pieces plus their defaults. """ +from numbers import Number + +import numpy as np +import pandas as pd from sklearn.base import BaseEstimator from sklearn.exceptions import NotFittedError +def as_manip_frames(data): + """Normalize numerical manipulator inputs without changing dataset boundaries. + + A 1-D array or flat numeric sequence is one column of observations; + a list/tuple of datasets stays a list of datasets. Frame/Series metadata + survives conversion. Text still reaches the dispatcher's wrangler. + Shared by dispatch, direct classes, fitted reuse and Pipeline steps + (release review 2026-09-08, finding 1). + """ + from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like) + from ..core.shared import as_dataframe + + if isinstance(data, (list, tuple)): + if data and all(isinstance(value, Number) for value in data): + return as_dataframe(np.asarray(data).reshape(-1, 1)) + return [as_manip_frames(dataset) for dataset in data] + if (is_array_dataset(data) or is_frame_dataset(data) + or is_series_like(data)): + return as_dataframe(data) + return data + + +def fit_rowwise_list(datasets, fitter, statistics, **kwargs): + """Collect per-row statistics without pooling features between datasets.""" + fitted = [fitter(frame.reset_index(drop=True).T, axis=0, **kwargs) + for frame in datasets] + params = dict(fitted[0]) + for key in statistics: + params[key] = pd.concat([p[key] for p in fitted], ignore_index=True) + params['transpose'] = True + return params + + +def transform_rowwise_list(datasets, transformer, statistics, **kwargs): + """Apply each dataset's slice of the fitted per-row statistics. + + Column-wise statistics are shared; row-wise ones belong to the original + rows (release review 2026-09-08, finding 3). + """ + start = 0 + outputs = [] + for frame in datasets: + stop = start + len(frame) + params = dict(kwargs) + for key in statistics: + params[key] = np.asarray(kwargs[key])[start:stop] + # Row labels need not be unique; the statistics are positional. + output = transformer(frame.reset_index(drop=True), **params) + output.index = frame.index + outputs.append(output) + start = stop + if any(len(kwargs[key]) != start for key in statistics): + raise ValueError('row-wise transform needs the same rows used during fit') + return outputs + + class Manipulator(BaseEstimator): """Base class for `Normalize`/`ZScore`/`Smooth`/`Resample`/`Delay`. @@ -44,6 +105,12 @@ def fit(self, data): """Fit this manipulator's parameters on `data`; stores them as attributes (named by `self.required`). + Returns + ------- + self + The fitted manipulator, so calls chain the sklearn way: + ``Smooth().fit(x).transform(y)``. + Raises ------ ValueError @@ -60,8 +127,8 @@ def fit(self, data): no_observations_message('manipulate', 'data is None')) self.data = data if self.fitter is None: - return - params = self.fitter(data, **self.kwargs) + return self + params = self.fitter(as_manip_frames(data), **self.kwargs) if not isinstance(params, dict): raise ValueError( f'{type(self).__name__} fit function must return a ' @@ -74,6 +141,7 @@ def fit(self, data): f"required field(s): {', '.join(missing)}") for k, v in params.items(): setattr(self, k, v) + return self def transform(self, new_data=None): """Apply the fitted parameters to `new_data`. @@ -129,7 +197,7 @@ def transform(self, new_data=None): return data_to_use required_params = {r: getattr(self, r) for r in self.required} merged = {**required_params, **self.kwargs} - return self.transformer(data_to_use, **merged) + return self.transformer(as_manip_frames(data_to_use), **merged) def inverse_transform(self, data): """Undo this manipulator's transform on `data`, when it is invertible. @@ -171,3 +239,33 @@ def fit_transform(self, data): followed by `transform(data)`).""" self.fit(data) return self.transform(data) + + +def stack_for_shared_fit(datasets, name): + """Concatenate the frames of a LIST row-wise for a manipulator that + fits ONE shared set of statistics across every dataset (`ZScore`, + `Normalize`). + + Frames with identical column labels are concatenated as they are. + When the labels differ -- an unnamed array (positional labels) beside + a named frame, or two frames named differently -- the columns are + matched by POSITION, as `plot`, `reduce` and `align` match datasets + (`format_data`); the fitted statistics are applied positionally + anyway. Datasets of different widths cannot share statistics and + raise. Nothing here touches the datasets themselves, so every frame + keeps its own labels and index through the transform (Codex round + 12, R12-3: relabelling the inputs in the dispatcher renamed a named + frame's features for the independent manipulators too). + """ + frames = list(datasets) + widths = {f.shape[1] for f in frames} + if len(widths) != 1: + raise ValueError( + f'{name} fits one shared set of statistics across the datasets ' + 'in a list, so every dataset needs the same number of columns; ' + f'got widths {sorted(widths)}') + columns = frames[0].columns + if all(f.columns.equals(columns) for f in frames): + return pd.concat(frames, axis=0, ignore_index=True) + return pd.concat([f.set_axis(range(f.shape[1]), axis=1) for f in frames], + axis=0, ignore_index=True) diff --git a/hypertools/manip/delay.py b/hypertools/manip/delay.py index 6393f18e..121aab3f 100644 --- a/hypertools/manip/delay.py +++ b/hypertools/manip/delay.py @@ -25,6 +25,7 @@ import pandas as pd from .common import Manipulator +from ..core.shared import as_dataframe def fitter(data, **kwargs): @@ -68,6 +69,13 @@ def _delay_embed_dataframe(data, tau, dims, drop_edges): 'instead of dropping them.') lags = [(dims - 1 - i) * tau for i in range(dims)] + # GH #285 release review: 1 and '1' are distinct pandas labels but + # produce the same output name; a dict would silently drop a feature. + names = [f'{c}_lag{lag}' for c in data.columns for lag in lags] + if len(set(names)) != len(names): + raise ValueError( + 'Delay requires column labels with unique string representations; ' + 'rename duplicate or colliding columns before embedding.') out_columns = {} for c in data.columns: values = np.asarray(data[c], dtype=float) @@ -93,9 +101,12 @@ def _transform(data, **kwargs): return dw.stack([_transform(d, **kwargs) for d in dw.unstack(data)]) if isinstance(data, list): return [_transform(d, **kwargs) for d in data] - if not isinstance(data, pd.DataFrame): - # e.g. a bare array passed between hypertools.Pipeline steps - data = pd.DataFrame(data) + # `hyp.manip` funnels its input to pandas frames, but a fitted + # manipulator's `.transform` and hypertools.Pipeline hand this a bare + # array (or a frame of another backend) directly: coerce through the + # shared datawrangler-based layer rather than re-checking pandas types + # here (datatype audit, 2026-09-08) + data = as_dataframe(data) return _delay_embed_dataframe(data, kwargs['tau'], kwargs['dims'], kwargs['drop_edges']) @@ -169,7 +180,9 @@ class Delay(Manipulator): ------ ValueError If `tau`/`dims` are not positive integers, or if `drop_edges=True` - and the data has too few rows to produce any output row. + and the data has too few rows to produce any output row. Also raised + when column labels have duplicate string representations (rename + these columns before embedding to avoid ambiguous output names). Examples -------- diff --git a/hypertools/manip/manip.py b/hypertools/manip/manip.py index aec27859..8ab4cc7a 100644 --- a/hypertools/manip/manip.py +++ b/hypertools/manip/manip.py @@ -18,15 +18,17 @@ """ import datawrangler as dw import numpy as np -import pandas as pd -from .common import Manipulator +from .common import Manipulator, as_manip_frames from .normalize import Normalize from .zscore import ZScore from .smooth import Smooth from .resample import Resample from .delay import Delay -from ..core.shared import unpack_model, require_data, no_observations_message +from ..core.shared import (unpack_model, require_data, no_observations_message, + as_dataframe, check_spec_keys, merge_spec_kwargs) +from .._shared.helpers import (is_series_like, is_frame_dataset, + is_array_dataset, as_pandas_dataframe) from ..core.pipeline import Pipeline @@ -62,21 +64,50 @@ def _validate_manip_input(data): """ no_observations = no_observations_message('manipulate') require_data(data, 'manip') + data = as_manip_frames(data) if isinstance(data, tuple): # a tuple of datasets is accepted exactly like a list (final wave # item 15: it used to leak a raw IndexError from the funnel) data = list(data) - if isinstance(data, pd.Series): - return data.to_frame() - if isinstance(data, (pd.DataFrame, np.ndarray)) and data.shape[0] == 0: - raise ValueError(no_observations) if isinstance(data, list): if len(data) == 0: raise ValueError(no_observations) - data = [d.to_frame() if isinstance(d, pd.Series) else d for d in data] - for d in data: - if isinstance(d, (pd.DataFrame, np.ndarray)) and d.shape[0] == 0: - raise ValueError(no_observations) + # every dataset keeps its own column labels and index: the + # independent manipulators (Smooth, Delay, Resample) never combine + # features across datasets, and the shared-statistics ones + # (ZScore, Normalize) match columns by position themselves when + # the labels differ (Codex rounds 11 and 12: relabelling here + # renamed a named frame's features for EVERY model) + return [_validate_one(d, no_observations) for d in data] + return _validate_one(data, no_observations) + + +def _validate_one(data, no_observations): + """`_validate_manip_input` for ONE dataset: a Series-like becomes a + single-column frame, a DataFrame of any backend datawrangler knows + (pandas, polars, a LazyFrame, ...) becomes hypertools' internal pandas + frame, and an empty (0-row) array/frame raises. The datatype questions + are asked through datawrangler (the `_shared.helpers` predicates), never + by naming pandas/numpy types here (datatype audit, 2026-09-08).""" + if is_series_like(data): + # pandas and polars Series both expose `.to_frame()` (a polars + # frame is then wrangled to pandas like any other frame -- Codex + # round 12, R12-1); anything else series-like (an object with + # `.to_numpy()`) is wrangled through datawrangler as a single column + return (as_pandas_dataframe(data.to_frame()) + if hasattr(data, 'to_frame') + else as_dataframe(np.asarray(data).reshape(-1, 1))) + if is_frame_dataset(data): + data = as_pandas_dataframe(data) + elif is_array_dataset(data) and np.ndim(data) == 1: + # a 1-D array is n observations of ONE feature, as `normalize`, + # `reduce` and the Manipulator classes already read it (the funnel + # would wrangle it into a single ROW: `hyp.manip(np.arange(12.))` + # z-scored a 1 x 12 table -- Codex round 12, after R12-1) + data = as_dataframe(np.asarray(data).reshape(-1, 1)) + if (is_array_dataset(data) or is_frame_dataset(data)) \ + and data.shape[0] == 0: + raise ValueError(no_observations) return data @@ -118,6 +149,16 @@ def _funneled_manip(data, model="ZScore", return_model=False, normalize=None, """Funnel-decorated core of `manip` (see `manip`'s docstring); `manip` validates raw input first, then delegates here so datawrangler's funnel only ever sees inputs it handles sensibly.""" + if isinstance(model, dict): + # a flat key such as {'model': 'Smooth', 'kernel_width': 25} used + # to be dropped silently, so the manipulator ran with its defaults + # (1.1 review) ... + check_spec_keys(model, 'manip') + if 'model' in model: + # ... and so were the outer **kwargs next to a dict spec + # (manip(x, model={'model': 'Smooth'}, kernel_width=25)): they + # join the spec's own parameters, winning on a conflict + model, kwargs = merge_spec_kwargs(model, kwargs), {} # cross-module stage kwargs (#138): manip is the FIRST stage in the # canonical order (manip -> normalize -> reduce -> align -> cluster), so a # manip call carrying any downstream stage kwarg assembles + runs a Pipeline @@ -177,6 +218,9 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, Dataset(s) to manipulate. A pandas `Series` is treated as a single-column dataset; a tuple of datasets is treated exactly like a list. `None` raises a `TypeError`. + A 1-D array or flat numeric list/tuple is ONE column of observations, + consistently across this dispatcher, direct Manipulator classes, + fitted-model reuse and `Pipeline`. model : str, dict, class, instance, list, Pipeline, False, or None Which manipulator(s) to apply (default: `'ZScore'`). `False` or @@ -188,7 +232,10 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, - A dict may be the canonical ``{'model': ..., 'args': [...], 'kwargs': {...}}`` or the LEGACY ``{'model': ..., 'params': {...}}`` form (accepted for backward - compatibility, but emits a `DeprecationWarning`). + compatibility, but emits a `DeprecationWarning`). Model + parameters always go under ``'kwargs'``: any other top-level + key -- e.g. ``{'model': 'Smooth', 'kernel_width': 25}`` -- + raises `ValueError` naming it rather than being ignored. - A bare (uninstantiated) Manipulator subclass, or an already-constructed (unfitted) instance, is used directly. - A `list` chains its elements into a `hypertools.Pipeline` @@ -225,13 +272,16 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, **kwargs Passed through to the manipulator's constructor when `model` - resolves to a class (ignored when `model` is a list, an already - -instantiated instance, or a fitted model/Pipeline being reused). + resolves to a class; next to a dict spec they join the spec's + `'kwargs'`, winning on a conflict (they used to be dropped + silently there). Ignored when `model` is a list, an already + -instantiated instance, or a fitted model/Pipeline being reused. Returns ------- - The manipulated data (and the fitted model/Pipeline if - `return_model=True`). + manipulated + The manipulated data (and the fitted model/Pipeline if + ``return_model=True``). Notes ----- @@ -240,8 +290,9 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, while `normalize` returns numpy arrays; `manip` propagates NaNs while `normalize` PPCA-imputes them at format time; `manip` z-scores with the sample std (``ddof=1``) while `normalize` uses the population std - (``ddof=0``); and a 1-D array is treated as a single ROW by `manip`'s - data funnel but as a single COLUMN by `normalize`. + (``ddof=0``). A 1-D array is n observations of ONE feature (a single + column) for both, as it is for a Series (Codex round 12: `manip`'s + data funnel used to read it as a single row). Examples -------- @@ -260,6 +311,7 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, >>> chained.shape (50, 2) """ + original_data = data data = _validate_manip_input(data) # False is an explicit "skip this stage", for the model spec and every @@ -278,7 +330,7 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, # nothing to do: hand the (validated) input back unchanged, # matching reduce(reduce=None)/cluster(cluster=None)/align( # model=None) - return (data, None) if return_model else data + return (original_data, None) if return_model else original_data import warnings with warnings.catch_warnings(): @@ -294,6 +346,10 @@ def manip(data, model="ZScore", return_model=False, normalize=None, reduce=None, warnings.filterwarnings( 'ignore', message='The copy keyword is deprecated', category=DeprecationWarning) + # backend='pandas': datawrangler's funnel otherwise PRESERVES a + # polars input's backend, and the manipulators are written against + # pandas (hypertools' internal frame type) return _funneled_manip(data, model=model, return_model=return_model, normalize=normalize, reduce=reduce, ndims=ndims, - align=align, cluster=cluster, **kwargs) + align=align, cluster=cluster, backend='pandas', + **kwargs) diff --git a/hypertools/manip/normalize.py b/hypertools/manip/normalize.py index fd3b59fd..167c2834 100644 --- a/hypertools/manip/normalize.py +++ b/hypertools/manip/normalize.py @@ -2,14 +2,15 @@ import datawrangler as dw import pandas as pd -from .common import Manipulator +from .common import (Manipulator, stack_for_shared_fit, fit_rowwise_list, + transform_rowwise_list) +from ..core.pipeline import as_internal_frames MODES = ('minmax', 'isotropic') # noinspection PyShadowingBuiltins -@dw.decorate.funnel def fitter(data, axis=0, min=0, max=1, mode='minmax'): """Fit normalization parameters for the `Normalize` manipulator. @@ -53,6 +54,18 @@ def fitter(data, axis=0, min=0, max=1, mode='minmax'): If `min >= max`, `axis` is not 0 or 1, `mode` is not one of `MODES`, or ``mode='isotropic'`` is combined with ``axis=1``. """ + # the funnel runs with backend='pandas' so a polars/LazyFrame input + # (which the funnel would otherwise keep in its own backend) reaches + # the pandas-based fit below (datatype audit, 2026-09-08) + # (a Series is made a one-column frame FIRST: the funnel would wrangle + # it into an empty table -- Codex round 12, R12-4) + return _fitter(as_internal_frames(data), axis=axis, min=min, max=max, mode=mode, + backend='pandas') + + +# noinspection PyShadowingBuiltins +@dw.decorate.funnel +def _fitter(data, axis=0, min=0, max=1, mode='minmax'): # a real ValueError (as documented in Raises), not "assert cond, # ValueError(...)" -- the assert idiom raised AssertionError and was # silently stripped under `python -O` (audit F14-009) @@ -67,7 +80,10 @@ def fitter(data, axis=0, min=0, max=1, mode='minmax'): f"{', '.join(repr(m) for m in MODES)}") if isinstance(data, list): - data = pd.concat(data, axis=0, ignore_index=True) + if axis == 1 and mode == 'minmax': + return fit_rowwise_list(data, fitter, ('baseline', 'peak'), + min=min, max=max) + data = stack_for_shared_fit(data, 'Normalize') if mode == 'isotropic': # one shared centre + scale for the whole table (and, for a list, @@ -169,6 +185,19 @@ def transformer(data, **kwargs): If `axis` is missing from `kwargs`, or (after resolving `transpose`) is not 0. """ + # a fitted manipulator's `.transform` hands over whatever the caller + # passed: frames of any backend become pandas here, once (datatype + # audit, 2026-09-08) + data = as_internal_frames(data) + if isinstance(data, list): + if kwargs.get('transpose', False): + return transform_rowwise_list(data, transformer, + ('baseline', 'peak'), **kwargs) + # each dataset is transformed on its own (the fitted statistics + # are positional), so every frame keeps its own column labels and + # index; stacking the list first demanded identical labels + # (Codex round 12, R12-3) + return [transformer(d, **kwargs) for d in data] transpose = kwargs.pop('transpose', False) # real raises (not `assert ..., ValueError(...)`, which raised # AssertionError and was stripped under `python -O`) -- 2026-07 release @@ -258,8 +287,11 @@ class Normalize(Manipulator): coordinate exactly on a face; that cube is then mapped affinely onto `[min, max]`. All pairwise distances are scaled by one constant, so the data's shape (angles, distance ratios) is - preserved and a rotated copy is rescaled by the same scalar. The - centroid lands at the midpoint ``(min + max) / 2`` and every + preserved. The scalar is measured along the coordinate axes, so + it depends on the data's orientation: a rotated copy of the same + cloud is generally divided by a different scalar (its shape is + preserved just the same, but the two results differ in size). + The centroid lands at the midpoint ``(min + max) / 2`` and every coordinate lies in `[min, max]`, with at least one coordinate touching `min` or `max`. With ``min=-1, max=1`` this is exactly ``(x - x.mean(axis=0)) / abs(x - x.mean(axis=0)).max()`` -- the @@ -269,12 +301,14 @@ class Normalize(Manipulator): Notes ----- - For a LIST of datasets, ONE shared baseline/peak is fit across all of + For a LIST of datasets with ``axis=0``, ONE shared baseline/peak is fit across all of them (like ``normalize='across'``): in ``'minmax'`` mode the shared per-column min/max, in ``'isotropic'`` mode the shared centroid and the single scalar scale of the concatenated data, so every dataset in the list is moved and rescaled identically. Constant (zero-range) columns normalize to `min` rather than NaN in ``'minmax'`` mode. + With ``axis=1``, each row is normalized independently, including lists + whose datasets have different widths; labels and boundaries are retained. `inverse_transform` is supported for ``axis=0`` in both modes. diff --git a/hypertools/manip/resample.py b/hypertools/manip/resample.py index 116ce707..9b47251b 100644 --- a/hypertools/manip/resample.py +++ b/hypertools/manip/resample.py @@ -7,6 +7,7 @@ from .common import Manipulator from ..core.shared import get +from ..core.pipeline import as_internal_frames def _resampling_x(data): @@ -77,6 +78,9 @@ def listify_dicts(dicts): ld[k].append(d[k]) return ld + # frames of any backend become pandas here, once (datatype audit, + # 2026-09-08): this fitter reads the pandas index directly + data = as_internal_frames(data) if dw.zoo.is_multiindex_dataframe(data): return listify_dicts([fitter(d, **kwargs) for d in dw.unstack(data)]) elif isinstance(data, list): @@ -143,6 +147,10 @@ def transformer(data, **kwargs): If `axis` is missing from `kwargs`, or (after resolving `transpose`) is not 0. """ + # a fitted manipulator's `.transform` hands over whatever the caller + # passed: frames of any backend become pandas here, once (datatype + # audit, 2026-09-08) + data = as_internal_frames(data) if dw.zoo.is_multiindex_dataframe(data): stack_result = True data = dw.unstack(data) diff --git a/hypertools/manip/smooth.py b/hypertools/manip/smooth.py index d4d95a36..65973e1d 100644 --- a/hypertools/manip/smooth.py +++ b/hypertools/manip/smooth.py @@ -8,6 +8,8 @@ import warnings from .common import Manipulator +from ..core.model import external_stacklevel +from ..core.shared import as_dataframe #: valid values for `Smooth`'s `kernel=` kwarg (GH #274/#153, round17 Task 5). @@ -221,10 +223,12 @@ def _transform(data, **kwargs): return dw.stack([_transform(d, **kwargs) for d in dw.unstack(data)]) if isinstance(data, list): return [_transform(d, **kwargs) for d in data] - if not isinstance(data, pd.DataFrame): - # e.g. a bare array passed between hypertools.Pipeline steps -- the - # old apply_stacked decorator wrangled these to DataFrames implicitly - data = pd.DataFrame(data) + # `hyp.manip` funnels its input to pandas frames, but a fitted + # manipulator's `.transform` and hypertools.Pipeline hand this a bare + # array (or a frame of another backend) directly: coerce through the + # shared datawrangler-based layer rather than re-checking pandas types + # here (datatype audit, 2026-09-08) + data = as_dataframe(data) axis = kwargs['axis'] if axis == 1: @@ -290,11 +294,15 @@ def transformer(data, **kwargs): center = kwargs.get('center', True) kw = kwargs.get('kernel_width') if kw is not None: + # both warnings name the caller's line (1.1 release review): with + # no stacklevel they pointed at this module if kw != int(np.round(kw)): - warnings.warn('Rounding smoothing kernel width to the nearest integer') + warnings.warn('Rounding smoothing kernel width to the nearest integer', + stacklevel=external_stacklevel()) kw = int(np.round(kw)) if center and kw % 2 != 1: - warnings.warn('Increasing smoothing kernel width by 1 (must be odd)') + warnings.warn('Increasing smoothing kernel width by 1 (must be odd)', + stacklevel=external_stacklevel()) kw += 1 if kw <= 0: requirement = 'a positive odd integer' if center else 'a positive integer' diff --git a/hypertools/manip/zscore.py b/hypertools/manip/zscore.py index 4496bf6a..95f54bfb 100644 --- a/hypertools/manip/zscore.py +++ b/hypertools/manip/zscore.py @@ -2,11 +2,12 @@ import datawrangler as dw import pandas as pd -from .common import Manipulator +from .common import (Manipulator, stack_for_shared_fit, fit_rowwise_list, + transform_rowwise_list) +from ..core.pipeline import as_internal_frames # noinspection PyShadowingBuiltins -@dw.decorate.funnel def fitter(data, axis=0): """Fit z-score parameters (mean/std) for the `ZScore` manipulator. @@ -31,8 +32,20 @@ def fitter(data, axis=0): ValueError If `axis` is not 0 or 1. """ + # the funnel runs with backend='pandas' so a polars/LazyFrame input + # (which the funnel would otherwise keep in its own backend) reaches + # the pandas-based fit below (datatype audit, 2026-09-08) + # (a Series is made a one-column frame FIRST: the funnel would wrangle + # it into an empty table -- Codex round 12, R12-4) + return _fitter(as_internal_frames(data), axis=axis, backend='pandas') + + +@dw.decorate.funnel +def _fitter(data, axis=0): if isinstance(data, list): - data = pd.concat(data, axis=0, ignore_index=True) + if axis == 1: + return fit_rowwise_list(data, fitter, ('mean', 'std')) + data = stack_for_shared_fit(data, 'ZScore') if axis == 1: return dw.core.update_dict(fitter(data.T, axis=0), {'transpose': True}) @@ -102,6 +115,19 @@ def transformer(data, **kwargs): If `axis` is missing from `kwargs`, or (after resolving `transpose`) is not 0. """ + # a fitted manipulator's `.transform` hands over whatever the caller + # passed: frames of any backend become pandas here, once (datatype + # audit, 2026-09-08) + data = as_internal_frames(data) + if isinstance(data, list): + if kwargs.get('transpose', False): + return transform_rowwise_list(data, transformer, ('mean', 'std'), + **kwargs) + # each dataset is transformed on its own (the fitted statistics + # are positional), so every frame keeps its own column labels and + # index; stacking the list first demanded identical labels + # (Codex round 12, R12-3) + return [transformer(d, **kwargs) for d in data] transpose = kwargs.pop('transpose', False) # real raises (not `assert ..., ValueError(...)`, which raised # AssertionError and was stripped under `python -O`) -- 2026-07 release @@ -169,8 +195,10 @@ class ZScore(Manipulator): ``scipy.stats.zscore`` convention), so the two z-scoring entry points differ by a factor of ``sqrt(n / (n - 1))``. - For a LIST of datasets, ONE shared mean/std is fit across all of them - (like ``normalize='across'``); single-observation (or constant) + For a LIST of datasets with ``axis=0``, ONE shared mean/std is fit across + all of them (like ``normalize='across'``). With ``axis=1``, each row uses + its own statistics, including when datasets have different widths; + labels and dataset boundaries are retained. Single-observation (or constant) columns z-score to 0s rather than NaN. Examples diff --git a/hypertools/plot/_kaleido_export_worker.py b/hypertools/plot/_kaleido_export_worker.py index 39379960..c096e7e1 100644 --- a/hypertools/plot/_kaleido_export_worker.py +++ b/hypertools/plot/_kaleido_export_worker.py @@ -52,8 +52,14 @@ def main(argv): if all(os.path.exists(t) for t in targets): return - # one shared headless-Chrome session for every frame (fast); if it wedges, - # the parent kills this whole process, so no in-process recovery is needed + # This process provisions kaleido and its Chrome itself (nothing in the + # parent has imported kaleido before the launch). Whether that may install + # anything is the parent's effective set_autoinstall() setting, which the + # parent hands over as HYPERTOOLS_AUTO_INSTALL in this process's + # environment (lazy_import.subprocess_env); with it off, this raises the + # ImportError naming the manual command and no pip runs here. + # One shared headless-Chrome session for every frame (fast); if it wedges, + # the parent kills this whole process, so no in-process recovery is needed. from .._shared.lazy_import import ensure_kaleido_chrome ensure_kaleido_chrome() with _shared_kaleido_session(): @@ -69,5 +75,27 @@ def main(argv): os.replace(part, targets[i]) +#: name of the file, in the frames directory, that carries the worker's +#: exception type and message for the parent (`plotly_backend` re-raises an +#: ImportError / HypertoolsIOError as that type; anything else is reported as +#: a RuntimeError with the stderr tail) +ERROR_FILE = '.worker-error.json' + + +def _report_and_reraise(argv, exc): + try: + out_dir = argv[1] + os.makedirs(out_dir, exist_ok=True) + with open(os.path.join(out_dir, ERROR_FILE), 'w', + encoding='utf-8') as fh: + json.dump({'type': type(exc).__name__, 'message': str(exc)}, fh) + except Exception: # the traceback on stderr is the fallback report + pass + raise exc + + if __name__ == '__main__': - main(sys.argv[1:]) + try: + main(sys.argv[1:]) + except Exception as exc: + _report_and_reraise(sys.argv[1:], exc) diff --git a/hypertools/plot/animate.py b/hypertools/plot/animate.py index 82e9bbd1..19d90b76 100644 --- a/hypertools/plot/animate.py +++ b/hypertools/plot/animate.py @@ -86,6 +86,13 @@ def finish(self): durations (see class docstring) instead of PillowWriter's single truncated duration, so total playback time stays within grid_ms/2 of the requested ``len(frames) / fps`` seconds.""" + if not self._frames: + # nothing was grabbed: the frame callback raised before the + # first grab (a user `on_frame=` hook, say). `Animation.save` + # calls finish() from the writer's context-manager exit, so an + # IndexError here would REPLACE the user's own exception with + # "list index out of range"; return and let theirs propagate. + return per_frame_ms = 1000.0 / self.fps grid = self._grid_ms durations, prev = [], 0 diff --git a/hypertools/plot/animation_context.py b/hypertools/plot/animation_context.py index a7c3ccc0..8f5777d4 100644 --- a/hypertools/plot/animation_context.py +++ b/hypertools/plot/animation_context.py @@ -64,7 +64,12 @@ class FrameContext: while the artists inside it are the backend's own live objects and are meant to be mutated. BACKEND-NATIVE: on plotly these are that frame's ``go.Scatter``/ - ``go.Scatter3d`` traces, in the same order. + ``go.Scatter3d`` traces, in the same order -- except that a + dataset drawn by SEVERAL traces (an animated multicoloured 2-D + line: one trace per colour bin) is one + `hypertools.plot.plotly_backend.PlotlyTraceGroup`, a tuple of its + traces that sets an assigned attribute (``artist.opacity = 0.4``) + on every member, so there is still one artist per dataset. ARTIST LIFETIME -- read this before writing a callback. Whether ``artists`` holds fresh objects per frame or the same objects @@ -85,7 +90,12 @@ class FrameContext: Matplotlib never hands you a fresh artist: ``FuncAnimation``'s updater mutates the same ``Line2D``/collection objects every frame, so ``ctx.artists[0]`` on frame 1 and on frame 2 are the - SAME object in different states. + SAME object in different states. Under a CONTINUOUS or matrix + ``hue=`` the rendered artists are per-dataset ``LineCollection``/ + ``Line3DCollection`` objects (the single-colour ``Line2D`` heads + are hidden and only drive the bookkeeping), and those collections + are what ``artists`` holds -- so ``set_alpha``/``set_color`` on + them changes what is drawn. THE PORTABLE RULE, on both backends: ASSIGN the complete desired value on EVERY invocation, including the default. What breaks is diff --git a/hypertools/plot/backend.py b/hypertools/plot/backend.py index 814fab98..843ae8c0 100644 --- a/hypertools/plot/backend.py +++ b/hypertools/plot/backend.py @@ -1363,7 +1363,8 @@ def plot_wrapper(*args, **kwargs): try: try: - with backend_context(tmp_backend): + from ..predict.time import warn_once_per_call + with backend_context(tmp_backend), warn_once_per_call(): if BACKEND_WARNING is not None: warnings.warn(BACKEND_WARNING) diff --git a/hypertools/plot/colors.py b/hypertools/plot/colors.py index e177aba0..48bdfad1 100644 --- a/hypertools/plot/colors.py +++ b/hypertools/plot/colors.py @@ -11,9 +11,13 @@ import collections.abc import warnings +import datawrangler as dw import numpy as np import pandas as pd +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like, as_pandas_dataframe) + # neutral color for observations whose hue value is non-finite (NaN/inf): # a light gray that reads as "no information" next to any palette, so a # missing value can never silently masquerade as a real data color @@ -102,8 +106,14 @@ def mat2colors(m, palette='hls', n_bins=100): """ import seaborn as sns - if isinstance(m, pd.DataFrame): - m = m.values + if is_frame_dataset(m): + # any dataframe backend datawrangler recognises (pandas, polars, ...) + m = as_pandas_dataframe(m).to_numpy() + elif is_series_like(m) and not dw.zoo.array_like(m): + # a labelled vector that is not numpy-like itself (a polars Series; + # a pandas Series is `array_like` and read below as it is): its + # values + m = np.asarray(m) elif isinstance(m, collections.abc.Iterator): # generators and other one-shot iterators: materialize so the # classification below (which iterates more than once) sees the @@ -113,7 +123,7 @@ def mat2colors(m, palette='hls', n_bins=100): raise ValueError( "mat2colors requires a sequence of labels/values (or a 2D " f"matrix with one row per sample); got a scalar: {m!r}") - if isinstance(m, np.ndarray) and m.ndim == 0: + if is_array_dataset(m) and m.ndim == 0: raise ValueError( "mat2colors requires a sequence of labels/values (or a 2D " f"matrix with one row per sample); got a 0-dimensional array: " @@ -239,7 +249,7 @@ def colors2groups(colors, res=6): def _is_numeric(m): - if isinstance(m, np.ndarray): + if is_array_dataset(m): return np.issubdtype(m.dtype, np.number) try: flat = _flatten_if_nested(m) @@ -250,7 +260,7 @@ def _is_numeric(m): def _flatten_if_nested(vals): - if any(isinstance(el, (list, np.ndarray)) for el in vals): + if any(isinstance(el, list) or is_array_dataset(el) for el in vals): return [item for el in vals for item in np.atleast_1d(el)] return list(vals) @@ -321,8 +331,290 @@ def continuous_colormap(palette, n_bins=100): #: type each is read as. They are exactly the tunable arguments of #: `image_palette`, so the declarative string form can reach everything the #: function call can. +#: keys `sort_colors` (and `palette_sort=`, and an image spec's ``?sort=``) +#: accept; None means "the palette's own order" (`sort_colors` docstring). +PALETTE_SORT_KEYS = ('value', 'hue', 'lightness', 'columns', 'original') + + +def _parse_sort_option(value): + key = str(value).strip().lower() + if key in ('', 'none'): + return None + if key not in PALETTE_SORT_KEYS: + raise ValueError( + f"sort= must be one of {PALETTE_SORT_KEYS} or None; got {value!r}") + return key + + _IMAGE_SPEC_OPTIONS = {'max_luminance': float, 'min_luminance': float, - 'n_colors': int, 'resize': int, 'random_state': int} + 'n_colors': int, 'resize': int, 'random_state': int, + 'sort': _parse_sort_option} + + +def sort_colors(colors, key='value'): + """Put colors in a deterministic order. + + Parameters + ---------- + colors : sequence of RGB triples (values in [0, 1]) + key : {'value', 'hue', 'lightness', 'columns', 'original'} or None + - ``'value'``: HSV value (dark to bright), then hue, then + saturation -- the default for a palette extracted from an image, + so it reads as a gradient instead of the extraction order. + - ``'hue'``: hue (red, yellow, green, cyan, blue, magenta), then + value, then saturation. + - ``'lightness'``: relative luminance (`luminance`), then hue, then + saturation. + - ``'columns'``: lexicographic by the color's own columns, first + column first -- the default for a palette built from a data + matrix, whose first column is its first component, the axis of + most variance. + - ``'original'`` or None: unchanged. + + Every key is a sequence of tie-breakers in decreasing priority, + compared after rounding to six decimals so equal colors never + reorder between runs. + + Returns + ------- + numpy.ndarray of shape (n, 3) + """ + arr = np.asarray(colors, dtype=float) + if arr.ndim != 2 or arr.shape[1] < 3: + raise ValueError( + f"sort_colors() expects an (n, 3) array of RGB colors; got shape " + f"{arr.shape}") + arr = arr[:, :3] + if key is None or key == 'original' or len(arr) < 2: + return arr + if key not in PALETTE_SORT_KEYS: + raise ValueError( + f"sort_colors() key must be one of {PALETTE_SORT_KEYS} or None; " + f"got {key!r}") + if key == 'columns': + # np.lexsort takes its PRIMARY key last + order = np.lexsort(np.round(arr, 6).T[::-1]) + return arr[order] + from matplotlib.colors import rgb_to_hsv + h, s, v = rgb_to_hsv(np.clip(arr, 0.0, 1.0)).T + if key == 'value': + keys = (s, h, v) + elif key == 'hue': + keys = (s, v, h) + else: # 'lightness' + keys = (s, h, np.atleast_1d(luminance(arr))) + order = np.lexsort(np.round(np.vstack(keys), 6)) + return arr[order] + + +def is_palette_matrix(obj): + """True for a t x k DATA matrix passed as a palette (`matrix_palette`). + + A DataFrame (any backend datawrangler recognises: pandas, polars, ...), + or a 2-D numeric array (or nested list) that cannot be a list of colors: + a color list has 3 or 4 columns with every value in [0, 1], and keeps + meaning exactly that. Anything else 2-D and numeric -- another column + count, or values outside [0, 1] -- is data to reduce. + + Raises + ------ + ValueError + For an array or nested list (not a DataFrame) with 3 or 4 columns + of WHOLE numbers in 0..255, some above 1 -- e.g. ``[[255, 128, 0], + [0, 64, 255]]``. That is a list of 0-255 RGB(A) colors, not data; + reading it as a data matrix reduced, rescaled and re-sorted it into + different colors without a word (review 2026-09-11). Divide it by + 255 to use the colors, or pass a DataFrame to use it as data. + """ + if is_frame_dataset(obj): + obj = as_pandas_dataframe(obj) + return obj.shape[0] > 0 and obj.shape[1] > 0 and all( + pd.api.types.is_numeric_dtype(dt) for dt in obj.dtypes) + if is_array_dataset(obj): + arr = obj + elif isinstance(obj, (list, tuple)) and obj and all( + isinstance(row, (list, tuple)) or is_array_dataset(row) + for row in obj): + try: + arr = np.asarray(obj, dtype=float) + except (TypeError, ValueError): + return False + else: + return False + if arr.ndim != 2 or arr.size == 0 or not np.issubdtype(arr.dtype, np.number): + return False + if arr.shape[1] in (3, 4) and np.isfinite(arr).all() \ + and arr.min() >= 0.0 and arr.max() <= 1.0: + return False # a list of colors + if arr.shape[1] in (3, 4) and np.isfinite(arr).all() \ + and arr.min() >= 0.0 and arr.max() <= 255.0 \ + and np.array_equal(arr, np.round(arr)): + # whole numbers 0..255, some above 1: 0-255 colors, which neither + # hypertools nor matplotlib reads (RGB is in [0, 1]); never + # silently turn them into a data-matrix gradient + example = np.asarray(arr[:2], dtype=float).round().astype(int) + raise ValueError( + f'palette= looks like a list of 0-255 RGB(A) colors ' + f'({example.tolist()}{", ..." if len(arr) > 2 else ""}), but ' + 'colors are read in [0, 1]. Divide by 255 to use them as ' + 'colors (np.asarray(palette) / 255), or pass the matrix as a ' + 'pandas DataFrame to use it as DATA (a t x k matrix palette, ' + 'reduced to three color channels).') + return True + + +from matplotlib.colors import LinearSegmentedColormap # noqa: E402 + + +class MatrixColormap(LinearSegmentedColormap): + """A `LinearSegmentedColormap` built from ordered anchor colors that + samples by EXACT linear interpolation between them (a plain + LinearSegmentedColormap quantizes to its lookup table, so a plot asking + for as many colors as there are anchors would not get the anchors back). + Everything else -- colorbars, `resampled`, reversed -- is inherited.""" + + def __init__(self, name, anchors, N=256): + anchors = np.asarray(anchors, dtype=float)[:, :3] + if anchors.ndim != 2 or len(anchors) < 2: + raise ValueError( + 'MatrixColormap needs at least two (r, g, b) anchor colors; ' + f'got shape {anchors.shape}') + # the parent's segment data, exactly as `from_list` builds it, so + # the inherited lookup table, integer sampling, `resampled()`, + # `reversed()`, bad/under/over colors and masked input all work + # (Codex round 10: a bare list here broke every one of them) + grid = np.linspace(0.0, 1.0, len(anchors)) + segmentdata = {channel: [(float(x), float(v), float(v)) + for x, v in zip(grid, anchors[:, k])] + for k, channel in enumerate(('red', 'green', 'blue'))} + segmentdata['alpha'] = [(0.0, 1.0, 1.0), (1.0, 1.0, 1.0)] + super().__init__(name, segmentdata, N=N) + self.anchors = anchors + + def __call__(self, X, alpha=None, bytes=False): + x = np.asarray(X) + if np.ma.isMaskedArray(X) or not np.issubdtype(x.dtype, np.floating): + # integers index the lookup table; masked entries take the + # 'bad' color: the parent's rules, unchanged + return super().__call__(X, alpha=alpha, bytes=bytes) + grid = np.linspace(0.0, 1.0, len(self.anchors)) + flat = x.astype(float).ravel() + # the parent's range rules, applied per ELEMENT: below 0 (including + # -inf) takes the 'under' color, above 1 (including +inf) the 'over' + # color, NaN the 'bad' color -- each with the alpha the extreme was + # set with; an `alpha=` override then applies to everything, except + # that a fully transparent 'bad' color stays transparent (Codex + # round 11: clipping ignored set_under/set_over, and one NaN sent + # the whole array through the quantized table; round 12, R12-2: the + # extremes lost their alpha, infinities were 'bad', and the override + # skipped 'bad') + bad = np.isnan(flat) + under, over = flat < 0.0, flat > 1.0 + inside = np.clip(np.where(bad, 0.0, flat), 0.0, 1.0) + # set_gamma() is inherited from LinearSegmentedColormap. Apply its + # coordinate mapping to exact samples as well as to the parent's LUT + # (release review 2026-09-08, finding 4). + inside = inside ** self._gamma + rgb = np.column_stack([np.interp(inside, grid, self.anchors[:, k]) + for k in range(3)]) + out = np.hstack([rgb, np.ones((len(flat), 1))]) + if under.any(): + out[under] = np.asarray(self.get_under(), dtype=float) + if over.any(): + out[over] = np.asarray(self.get_over(), dtype=float) + if bad.any(): + out[bad] = np.asarray(self.get_bad(), dtype=float) + if alpha is not None: + a = np.clip(np.asarray(alpha, dtype=float), 0, 1) + if a.shape not in ((), x.shape): + raise ValueError( + f'alpha is array-like but its shape {a.shape} does not ' + f'match that of X {x.shape}') + out[:, 3] = np.broadcast_to(a, x.shape).ravel() + if np.all(np.asarray(self.get_bad(), dtype=float) == 0): + out[bad] = 0.0 + out = out.reshape(x.shape + (4,)) + if bytes: + out = (out * 255).astype(np.uint8) + return tuple(out) if x.ndim == 0 else out + + +def matrix_palette(data, reduce='PCA', sort='columns', normalize=None, + manip=None, align=None, random_state=0, n_colors=256, + name='hypertools-matrix'): + """Build a palette from a t x k data matrix. + + The rows are reduced to three dimensions with `hypertools.reduce` + (``reduce=`` names the reducer, ``normalize=``/``manip=``/``align=`` are + handed to it as they would be to any reduce call), each reduced column + is scaled to [0, 1] and read as an RGB channel, the rows are put in + order with `sort_colors`, and the result is returned as a matplotlib + `Colormap`, which every palette path samples by interpolation to + however many colors a plot needs (one per dataset, one per category, or + a gradient along a continuous `hue=`). + + How smooth the palette looks depends on the matrix: with the default + sort the rows are ordered along the first component, so the other two + channels vary smoothly only where the matrix's other components are + themselves ordered along the first (a trend with oscillations gives a + clean gradient; an unstructured random walk gives a striped one). + + Parameters + ---------- + data : array-like or pandas.DataFrame of shape (t, k) + At least two rows of finite numbers. A matrix with three or fewer + columns is not reduced (its columns are the channels; missing + channels are filled with 0.5), but ``normalize``/``manip``/``align`` + still apply when given. + reduce : reducer spec (default 'PCA') + Any form `hypertools.reduce` accepts. + sort : see `sort_colors` (default 'columns': along the first component) + normalize, manip, align : passed to `hypertools.reduce` + random_state : int (default 0) + Seeds the reducer, so the same matrix always gives the same palette. + n_colors : int (default 256) + Resolution of the returned colormap. + name : str + The colormap's name. + + Returns + ------- + MatrixColormap + A `LinearSegmentedColormap` subclass that samples the ordered rows + by exact linear interpolation. + """ + from ..reduce.reduce import reduce as _reduce + + arr = (as_pandas_dataframe(data).to_numpy(dtype=float) + if is_frame_dataset(data) else np.asarray(data, dtype=float)) + if arr.ndim != 2 or arr.shape[0] < 2 or arr.shape[1] < 1: + raise ValueError( + "a matrix palette needs a 2-D array with at least two rows " + f"(observations) and one column; got shape {arr.shape}") + if not np.isfinite(arr).all(): + raise ValueError( + "a matrix palette needs finite values; the matrix has " + f"{int((~np.isfinite(arr)).sum())} NaN/inf entries") + stage = {'normalize': normalize, 'manip': manip, 'align': align} + if arr.shape[1] > 3: + rows = np.asarray(_reduce(arr, reduce=reduce, ndims=3, + random_state=random_state, **stage), + dtype=float) + elif any(v is not None for v in stage.values()): + rows = np.asarray(_reduce(arr, reduce=None, ndims=None, **stage), + dtype=float) + else: + rows = arr + rows = np.asarray(rows, dtype=float)[:, :3] + lo, hi = rows.min(axis=0), rows.max(axis=0) + span = hi - lo + scaled = np.where(span > 0, (rows - lo) / np.where(span > 0, span, 1.0), + 0.5) + if scaled.shape[1] < 3: + scaled = np.hstack([scaled, np.full((len(scaled), 3 - scaled.shape[1]), + 0.5)]) + ordered = sort_colors(scaled, sort) + return MatrixColormap(name, ordered, N=int(n_colors)) def luminance(colors): @@ -401,8 +693,8 @@ def _image_pixels(image, resize): from PIL import Image - if isinstance(image, np.ndarray): - arr = image + if is_array_dataset(image): + arr = np.asarray(image) if arr.dtype.kind == 'f': arr = np.clip(arr, 0.0, 1.0) * 255.0 im = Image.fromarray(arr.astype(np.uint8)).convert('RGB') @@ -423,8 +715,9 @@ def _image_pixels(image, resize): def image_palette(image, n_colors=IMAGE_PALETTE_N, resize=200, random_state=0, max_luminance=None, - min_luminance=None): - """Extract a color palette from an image, most VISUALLY SALIENT first. + min_luminance=None, sort=None): + """Extract a color palette from an image, most VISUALLY SALIENT first + (or in the order ``sort=`` asks for). Parameters ---------- @@ -437,6 +730,12 @@ def image_palette(image, n_colors=IMAGE_PALETTE_N, resize=200, UPPER bound on how many colors to return (default 6). Fewer come back when the image has fewer distinct colors, or when two cluster centers coincide to 3 decimal places. + sort : see `sort_colors`, or None (default) + None keeps the salience order (most visually salient first: the + color the image is ABOUT, used as a dataset's lead color). A key + reorders the extracted colors; ``palette='image:...'`` uses + ``'value'`` unless the spec says otherwise (``?sort=hue``, + ``?sort=original``), so an image palette reads as a gradient. resize : int Longest edge the image is thumbnailed to before clustering (default 200). Clustering cost is linear in pixel count. @@ -536,7 +835,7 @@ def image_palette(image, n_colors=IMAGE_PALETTE_N, resize=200, "Widen the bound, raise n_colors= so more clusters are " "extracted, or use a different image.") out = kept - return np.asarray(out, dtype=float) + return sort_colors(np.asarray(out, dtype=float), sort) def _luminance_bounds(min_luminance, max_luminance): @@ -590,6 +889,23 @@ def _continuous_palette(palette, n_colors, sns): return _get_palette(palette, n_colors, sns, continuous=True) +def interpolate_colors(anchors, n_colors): + """``n_colors`` colors spaced evenly along the ``anchors`` sequence, by + exact linear interpolation per channel. seaborn's ``blend_palette`` + samples a 256-entry table, so above 256 colors it REPEATS entries; this + keeps every color distinct at any count (Codex round 11).""" + a = np.asarray([tuple(c)[:3] for c in anchors], dtype=float) + n_colors = int(n_colors) + if len(a) == 0 or n_colors <= 0: + return [] + if len(a) == 1: + return [tuple(a[0])] * n_colors + grid = np.linspace(0.0, 1.0, len(a)) + xs = np.linspace(0.0, 1.0, n_colors) + rows = np.column_stack([np.interp(xs, grid, a[:, k]) for k in range(3)]) + return [tuple(float(v) for v in row) for row in rows] + + def _image_palette_list(source, n_colors, sns, continuous): """Colors for a `palette='image:<path>'` string, as a list `_get_palette` can then handle exactly like any other color list. @@ -607,9 +923,10 @@ def _image_palette_list(source, n_colors, sns, continuous): two-tone image, nine groups). Unlike a user-supplied short list -- which raises, because the user can simply pass more colors -- a caller cannot add colors to an image, so the anchors are interpolated up to `n_colors` - with the same ``blend_palette`` semantics the continuous path already - uses (F02-006/F24-017). Interpolating keeps every category a DIFFERENT - color and leaves the most salient anchor first; cycling the anchors + exactly as the continuous path does (`interpolate_colors`; + F02-006/F24-017). Interpolating keeps every category a DIFFERENT color + at any count, in the anchors' order (by value unless the spec asks + otherwise); cycling the anchors would silently give two categories the same color, which is the ambiguity the short-list error exists to prevent. A single-color image is the one case interpolation cannot serve, and it raises. @@ -628,6 +945,9 @@ def _image_palette_list(source, n_colors, sns, continuous): options.get('min_luminance') is not None: wanted = max(wanted, IMAGE_PALETTE_N) options.setdefault('n_colors', wanted) + # a palette reads as a gradient: sorted by value unless the spec says + # otherwise ('?sort=original' keeps the salience order) + options.setdefault('sort', 'value') colors = [tuple(c) for c in image_palette(path, **options)] if continuous or len(colors) >= n_colors: return colors @@ -637,8 +957,7 @@ def _image_palette_list(source, n_colors, sns, continuous): f"{n_colors} are required (one per category/component); that " "image has a single dominant color, so pass a more colorful " "image, an explicit list of colors, or a palette name") - return [tuple(np.asarray(c)[:3]) - for c in sns.blend_palette(colors, n_colors)] + return interpolate_colors(colors, n_colors) def _get_palette(palette, n_colors, sns, continuous=False): @@ -678,6 +997,11 @@ def _get_palette(palette, n_colors, sns, continuous=False): n_colors, sns, continuous) else: return sns.color_palette(palette, n_colors) + if is_palette_matrix(palette): + # a t x k data matrix: reduced, scaled, sorted and turned into a + # colormap once; `hyp.plot` does this up front with its palette_* + # options, so this is the path for a direct get_palette_colors call + palette = matrix_palette(palette) if isinstance(palette, Colormap): if n_colors == 1: return [tuple(np.asarray(palette(0.5))[:3])] @@ -710,8 +1034,7 @@ def _get_palette(palette, n_colors, sns, continuous=False): # gradient (seaborn blend_palette semantics) if len(colors) == 1: return [colors[0]] * n_colors - return [tuple(np.asarray(c)[:3]) - for c in sns.blend_palette(colors, n_colors)] + return interpolate_colors(colors, n_colors) raise ValueError( f"palette= supplies {len(colors)} color(s) but {n_colors} are " "required (one per category/component); pass at least " @@ -746,6 +1069,8 @@ def _is_palette_spec(value): seaborn/matplotlib palette NAME, a `Colormap`, a `{category: color}` dict, or a non-empty sequence of colors. """ + if is_palette_matrix(value): + return True from matplotlib.colors import Colormap if isinstance(value, (Colormap, collections.abc.Mapping)): @@ -953,8 +1278,9 @@ def dataset_palettes(palette, n_datasets): def palette_lead_color(spec): """The one color that REPRESENTS a palette: its lead color. - For ``'image:<path>'`` that is the most visually salient color of the - image -- `image_palette`'s first entry, from its full default six + For a data-matrix palette (`matrix_palette`) it is the most saturated + of its anchor colors. For ``'image:<path>'`` that is the most visually + salient color of the image -- `image_palette`'s first entry, from its full default six anchors, with any ``?max_luminance=``/``?min_luminance=`` bound in the spec applied first. (Asking `get_palette_colors` for ONE color from an image instead runs k-means with k=1, which returns the image's AVERAGE @@ -977,9 +1303,19 @@ def palette_lead_color(spec): if spec.startswith(IMAGE_PALETTE_PREFIX): source, options = _parse_image_spec( spec[len(IMAGE_PALETTE_PREFIX):]) + options['sort'] = None # the salient color leads return tuple(float(v) for v in image_palette(source, **options)[0]) if not _names_a_palette(spec) and _is_color(spec): return tuple(float(v) for v in to_rgb(spec)) + if is_palette_matrix(spec): + spec = matrix_palette(spec) + if isinstance(spec, MatrixColormap): + # every matrix palette spans the RGB cube after per-channel scaling, + # so its MIDDLE color is near mid-grey for any matrix and two + # datasets would look alike; the most saturated anchor (chroma = + # max - min, first on ties) is the color the matrix is about + chroma = spec.anchors.max(axis=1) - spec.anchors.min(axis=1) + return tuple(float(v) for v in spec.anchors[int(np.argmax(chroma))]) return tuple(float(v) for v in get_palette_colors(spec, 1)[0]) @@ -991,16 +1327,23 @@ def dataset_colors(palette, n_datasets): `palette_lead_color`; otherwise this is exactly ``get_palette_colors(palette, n_datasets)``, i.e. today's colors. - Note for callers that currently hand `palette` to seaborn directly: - seaborn CYCLES a color list that is shorter than `n_datasets`, while - `get_palette_colors` raises. Where that difference matters, call - `dataset_palettes` and fall back to the existing seaborn call when it - returns None. + A plain color LIST shorter than `n_datasets` is CYCLED + (``['red', 'blue']`` over three datasets colors them red, blue, red), + exactly as seaborn's ambient cycle -- the palette every dataset trace + is actually drawn from -- cycles it. hypertools 1.0.0 drew such a call + that way on both backends; raising here (as `get_palette_colors` does, + since a CATEGORY/matrix mapping needs a distinct color per group) would + turn a working call into an error before anything was drawn. """ specs = dataset_palettes(palette, n_datasets) - if specs is None: - return get_palette_colors(palette, n_datasets) - return np.asarray([palette_lead_color(s) for s in specs], dtype=float) + if specs is not None: + return np.asarray([palette_lead_color(s) for s in specs], dtype=float) + if (isinstance(palette, (list, tuple, np.ndarray)) + and 0 < len(palette) < n_datasets + and all(_is_color(c) for c in palette)): + base = get_palette_colors(palette, len(palette)) + return base[np.arange(n_datasets) % len(base)] + return get_palette_colors(palette, n_datasets) # Legacy continuous-color helpers live in _shared.helpers (import *-ed widely); diff --git a/hypertools/plot/density.py b/hypertools/plot/density.py index dc3fae34..b7248e7e 100644 --- a/hypertools/plot/density.py +++ b/hypertools/plot/density.py @@ -272,14 +272,36 @@ def _padded_bounds(points, pad): return lo - pad * span, hi + pad * span +#: How many kernel standard deviations past the data a 2-D KDE grid +#: extends: the density at the grid's edge is then at most ``exp(-K^2/2)`` +#: (~3e-4 for 4) of one kernel's peak, invisible under the alpha ramp, so +#: the glow fades out inside the grid instead of being cut off at its edge. +KDE_GRID_BANDWIDTHS = 4.0 + + def kde_grid_2d(points, kde, gridsize=200, pad=0.15): """Evaluate `kde` on a `gridsize` x `gridsize` grid over `points`' bounds - (padded by `pad` on each side). Returns ``(xs, ys, Z, extent)`` where - ``Z[iy, ix]`` is the density at ``(xs[ix], ys[iy])`` (matplotlib - ``imshow(origin='lower')`` layout) and ``extent`` is - ``(xmin, xmax, ymin, ymax)``.""" + padded by `pad` (a fraction of the span) on each side OR by + `KDE_GRID_BANDWIDTHS` kernel standard deviations, whichever is wider + per axis. Returns ``(xs, ys, Z, extent)`` where ``Z[iy, ix]`` is the + density at ``(xs[ix], ys[iy])`` (matplotlib ``imshow(origin='lower')`` + layout) and ``extent`` is ``(xmin, xmax, ymin, ymax)``. + + A grid padded by 15% of the span alone stopped where the KDE was + still clearly visible (a wide, flat cloud's glow ended in a hard band + well inside the frame: feature tour 9.14, 2026-09-06). Padding by the + kernel's own width keeps the resolution LOCAL to this cloud -- a grid + stretched over a whole scene of much larger clouds sampled a small + one so coarsely its density came back all zero (release review, + round 2) -- while guaranteeing the edge is where the density has + already faded. + """ points = np.asarray(points, dtype=float) lo, hi = _padded_bounds(points, pad) + reach = KDE_GRID_BANDWIDTHS * np.sqrt(np.diag(np.asarray(kde.covariance, + dtype=float))) + lo = np.minimum(lo, points.min(axis=0) - reach) + hi = np.maximum(hi, points.max(axis=0) + reach) xs = np.linspace(lo[0], hi[0], gridsize) ys = np.linspace(lo[1], hi[1], gridsize) X, Y = np.meshgrid(xs, ys) diff --git a/hypertools/plot/fonts.py b/hypertools/plot/fonts.py index d23a1fd3..a39afbe5 100644 --- a/hypertools/plot/fonts.py +++ b/hypertools/plot/fonts.py @@ -23,7 +23,9 @@ import warnings import numpy as np -import pandas as pd + +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like, as_pandas_dataframe) import matplotlib.font_manager as font_manager from matplotlib.font_manager import FontProperties from matplotlib.ft2font import FT2Font @@ -114,8 +116,9 @@ def register_bundled_fonts(): """Make the vendored face(s) visible to matplotlib's font manager. Idempotent (matplotlib's `addfont` appends unconditionally, so repeated - calls would pile up duplicate entries). Additive only -- it registers an - extra font, never changes the user's rcParams or removes anything. + calls would pile up duplicate entries). Bundled faces take precedence + over same-family system fonts; no fonts are removed and rcParams are + unchanged. """ global _bundled_registered if _bundled_registered: @@ -124,6 +127,11 @@ def register_bundled_fonts(): for path in bundled_font_files(): try: font_manager.fontManager.addfont(path) + # GH #285 release review: findfont breaks equal-score ties by + # registration order. A system Noto Sans (including variable + # fonts) must not replace the bundled face on some machines. + entries = font_manager.fontManager.ttflist + entries.insert(0, entries.pop()) except Exception: # noqa: BLE001 - a bad/corrupt bundled file must pass # never break plotting; the stack falls back below @@ -183,7 +191,10 @@ def _iter_texts(obj): return if isinstance(obj, str): yield obj - elif isinstance(obj, (np.ndarray, pd.Series, pd.Index, pd.Categorical)): + elif is_frame_dataset(obj): + for item in as_pandas_dataframe(obj).to_numpy().tolist(): + yield from _iter_texts(item) + elif is_array_dataset(obj) or is_series_like(obj): for item in np.asarray(obj).tolist(): yield from _iter_texts(item) elif isinstance(obj, dict): @@ -390,7 +401,9 @@ def resolve_font(font, texts): to the fallback stack (keeping Noto primary), NOT applied as a single face to whole text artists (see `hyp.plot`'s handling). - a `str`: either an installed font FAMILY NAME (resolved via - matplotlib's font lookup; hyphenated and generic names like + matplotlib's font lookup, with hypertools' bundled faces -- Noto + Sans -- registered first, so the bundled family resolves in any + process; hyphenated and generic names like 'sans-serif' work) or a path to a `.ttf`/`.otf`/`.ttc` FILE (detected by `os.path.exists`, so relative and absolute paths both work; the file is verified to be a loadable font HERE, not at @@ -432,6 +445,11 @@ def resolve_font(font, texts): ) from exc return FontProperties(fname=font) + # the bundled faces must be registered BEFORE the lookup: they used + # to be registered only as a side effect of an earlier plot, so + # font='Noto Sans' (the bundled family) raised "not a recognized + # installed font family" in a fresh process (review 2026-09-11) + register_bundled_fonts() # family passed as a LIST: a bare string family is parsed by # matplotlib as a fontconfig PATTERN, so any hyphenated name -- # including the generic 'sans-serif' -- crashed with an uncaught diff --git a/hypertools/plot/forecast.py b/hypertools/plot/forecast.py index a5adeda7..6c213500 100644 --- a/hypertools/plot/forecast.py +++ b/hypertools/plot/forecast.py @@ -53,6 +53,15 @@ #: rough figure rather than a countdown. DEFAULT_SLOW_WARNING_SECONDS = 10.0 +#: Do not project until a fit at least this many rows long has been timed. +#: A slope drawn through two fits one row apart at 2 and 3 rows is mostly +#: timer noise (each takes tens of milliseconds): on a slow CI runner it +#: projected 10 s for a schedule that finished in well under one, and the +#: "small schedule stays silent" test failed (release review, 2026-09-07). +#: A schedule whose longest history is shorter than this projects at its +#: longest instead. +PROJECTION_MIN_ROWS = 10 + #: Fewest observations we will fit a forecaster to. DEFAULT_MIN_HISTORY = 2 @@ -72,6 +81,85 @@ #: the same policy in their own terms, so the two cannot drift. FORECAST_ALPHA_SCALE = 0.5 +#: Linestyle per MODEL for a `predict=[...]` collection, cycled in model +#: order (matplotlib fmt vocabulary; the plotly backend maps each through +#: the same `_resolve_fmt` the observed traces use). A forecast keeps its +#: dataset's COLOUR -- that is what says which series it continues -- so +#: with several models on one series the dash is what says which model +#: made it. The first model is solid, exactly like the single-model form, +#: so ``predict=['Kalman']`` draws what ``predict='Kalman'`` draws. +FORECAST_MODEL_LINESTYLES = ('-', '--', ':', '-.') + +#: Colour of a forecast's LEGEND glyph when the forecasts sharing that +#: entry are drawn in more than one colour (one model over several +#: datasets): the entry then stands for the model's dash, not for any one +#: dataset's colour, so it is drawn in a neutral dark gray (at the +#: forecast's own alpha, so it reads as faded like the forecasts do). +FORECAST_LEGEND_COLOR = '#555555' + +#: The least opaque a forecast's legend glyph is drawn. The glyph copies +#: its forecasts' alpha so it reads as faded like they do, but a legend +#: key has to stay legible: on a plot whose observed traces are already +#: translucent (a hierarchy's leaves at 0.7, halved to 0.35 for their +#: forecasts) a glyph at the forecasts' alpha was a near-invisible +#: hairline (1.1 release review, feature-tour 9.10). +FORECAST_LEGEND_MIN_ALPHA = 0.8 + + +def override_has_color(override): + """Whether a `resolve_forecast_overrides` dict recolours the forecast: + a ``'color'`` entry (`forecast_hue=`/`forecast_cluster=`/ + `forecast_palette=`) or a colour letter in its ``'fmt'``.""" + if not override: + return False + if override.get('color') is not None: + return True + fmt = override.get('fmt') + if not fmt: + return False + try: + from matplotlib.axes._base import _process_plot_format + return _process_plot_format(fmt)[2] is not None + except Exception: # pragma: no cover - matplotlib moved its parser + return False + + +def forecast_alpha_scale_for(override, alpha_scale=FORECAST_ALPHA_SCALE): + """The alpha scale a forecast is drawn with: `alpha_scale` (the + documented halving) when it inherits its trace's colour, and 1.0 -- + the trace's own alpha -- when an override recolours it: the colour is + then what tells the forecast from its trace, and fading a recoloured + forecast on top of that hid it among translucent traces (1.1 release + review, feature-tour 9.10). Both backends call this.""" + return 1.0 if override_has_color(override) else alpha_scale + + +def forecast_model_fmts(n_models, n_datasets): + """One `fmt` per forecast, MODEL-MAJOR (the order `plot()` keeps a + collection's forecasts in): model k's `n_datasets` forecasts all take + `FORECAST_MODEL_LINESTYLES[k]`, cycling past the fourth model.""" + cycle = FORECAST_MODEL_LINESTYLES + return [cycle[k % len(cycle)] + for k in range(int(n_models)) for _ in range(int(n_datasets))] + + +def group_forecast_labels(labels): + """``[(label, [indices]), ...]`` -- the distinct legend labels in first- + appearance order, each with the forecasts (positions in `labels`) that + share it. ``None`` labels are skipped. Both backends build one legend + entry per group from this, so the two legends list the same entries in + the same order.""" + groups = {} + order = [] + for i, label in enumerate(labels or ()): + if label is None: + continue + if label not in groups: + groups[label] = [] + order.append(label) + groups[label].append(i) + return [(label, groups[label]) for label in order] + #: `trail_alpha`'s floor, as a FRACTION of the LIVE forecast's alpha. #: #: Relative, not absolute: an absolute floor on a faint dataset would make @@ -102,7 +190,33 @@ def forecast_alpha(observed_alpha, alpha_scale=FORECAST_ALPHA_SCALE): return base * float(alpha_scale) -def forecast_from_history(history, model, t, min_history=DEFAULT_MIN_HISTORY): +def model_min_history(model): + """The fewest revealed rows `model` can be fit on (`Forecaster.min_history`). + + `model` is anything `hypertools.predict` accepts (a name, a spec dict, + a `Forecaster` subclass or instance); it is resolved with `hyp.predict`'s + own resolver, so an unknown spec raises the same ``ValueError`` here that + the first fit would have raised. A class or dict spec is NOT constructed + (a `Chronos` constructor would download a model): the floor is read from + the class's `min_history_for`, given the spec's constructor arguments. + """ + from ..predict.predict import _resolve_forecaster_spec + from ..predict.common import Forecaster + resolved, kwargs = _resolve_forecaster_spec(model, {}) + if isinstance(resolved, Forecaster): + return int(resolved.min_history) + args = [] + if isinstance(resolved, dict): + args = list(resolved.get('args', []) or []) + kwargs = {**dict(resolved.get('kwargs', {}) or {}), **kwargs} + resolved = resolved['model'] + if isinstance(resolved, Forecaster): + return int(resolved.min_history) + return int(resolved.min_history_for(*args, **kwargs)) + + +def forecast_from_history(history, model, t, min_history=DEFAULT_MIN_HISTORY, + dataset=None): """Forecast `t` steps on from `history`, as a displacement path. Parameters @@ -117,7 +231,16 @@ def forecast_from_history(history, model, t, min_history=DEFAULT_MIN_HISTORY): Forecast horizon, in RAW analyze-space steps. ``t=1`` is the next observation. min_history : int, default 2 - Refuse to forecast from fewer rows than this. + Refuse to forecast from fewer rows than this. The model's own floor + (`model_min_history`: 3 for the default ARIMA order) is applied on + top of it, so a history the model could not be fit on returns + ``None`` rather than reaching the model's internals. + dataset : int or None + Which dataset `history` belongs to. Only read when `model` is a + forecaster already FITTED on several datasets + (``hyp.predict([a, b], return_model=True)``): the history is then + forecast with that dataset's own fitted parameters + (`Forecaster.for_dataset`). Returns ------- @@ -138,7 +261,14 @@ def forecast_from_history(history, model, t, min_history=DEFAULT_MIN_HISTORY): raise ValueError( f"history must be 2-D (n_observed, n_dims); got shape " f"{history.shape}.") - if len(history) < max(2, min_history): + if (dataset is not None and hasattr(model, 'for_dataset') + and len(getattr(model, 'models_', ())) > 1): + # a forecaster fitted on several datasets: forecast THIS dataset's + # history with its own fitted parameters (the schedule forecasts + # one history at a time, which `predict_new` otherwise refuses as + # a dataset-count mismatch; Codex round 3) + model = model.for_dataset(dataset) + if len(history) < max(2, min_history, model_min_history(model)): return None forecast = np.asarray(_predict(history, model=model, t=t), dtype=float) @@ -178,6 +308,41 @@ def revealed_raw_counts(n_raw, n_grid, num, total_frames): return min(n_raw, int(np.floor(pos)) + 1) +def drawn_head_index(n_grid, num, total_frames): + """FRAME-GRID row the reveal ends on at frame `num` (parallel/window). + + `revealed_raw_counts` takes this same `end` and floors it back onto RAW + rows -- the last observation at or before the head. This returns the head + itself: the vertex the backends actually draw last. + """ + from .trails import anim_window_bounds + _, end, _ = anim_window_bounds(num, total_frames, int(n_grid), 0) + return max(0, int(end) - 1) + + +def grid_head(grid, n_raw, n_grid, head_index): + """``(point, raw position)`` of the grid vertex a frame's reveal ends on. + + The point is read straight out of the animation grid the backends draw + from -- not re-derived by interpolating the history a second time, which + could drift from the picture. `position` is where that vertex falls in + RAW rows (fractional between observations), which is what a 1-D plot puts + on x. + + ``(None, None)`` when there is no grid to read, i.e. for a `hue=`/ + `cluster=` regrouped reveal, whose forecasts keep the raw-row anchor. + """ + if grid is None: + return None, None + grid = np.asarray(grid, dtype=float) + if not len(grid): + return None, None + j = min(max(int(head_index), 0), len(grid) - 1) + n_raw, n_grid = int(n_raw), int(n_grid) + pos = (j * (n_raw - 1) / (n_grid - 1)) if n_grid > 1 and n_raw > 1 else 0.0 + return grid[j], float(pos) + + class DatasetRevealSchedule: """Which ORIGINAL rows of each source dataset are on screen at each frame. @@ -212,52 +377,92 @@ def __init__(self, ownership, grid_lengths, n_frames, window_frames, self.window_frames = int(window_frames) self.serial = bool(serial) self._rows = [] + # Per frame per dataset: the DRAWN head as ``(run, grid row)``, and + # its fractional source parameter. Finding the run that holds the head + # is work the reveal already does to count rows; keeping WHICH run won + # and where its polyline ends is what lets a forecast start at the + # vertex the picture ends on rather than at the observation behind it + # (maintainer report, 2026-09-11). + self._heads = [] + self._head_params = [] for frame in range(self.n_frames): if self.serial: - counts = self._serial_counts(frame) + counts, heads, params = self._serial_counts(frame) else: windows = dataset_window_bounds( frame, self.n_frames, ownership, self.grid_lengths, self.window_frames) - counts = [] + counts, heads, params = [], [], [] for d in range(ownership.n_datasets): head = None + at = None for r in ownership.runs_of(d): p = run_head_param(windows[r], ownership, r) - if p is not None: - head = p if head is None else max(head, p) + if p is not None and (head is None or p > head): + head, at = p, (r, windows[r].head_end - 1) counts.append(0 if head is None else min(ownership.row_count(d), int(head) + 1)) + heads.append(at) + params.append(None if head is None else float(head)) self._rows.append([tuple(range(k)) for k in counts]) + self._heads.append(heads) + self._head_params.append(params) def _serial_counts(self, frame): """`order='serial'` already sweeps runs in order (`serial_reveal_counts` - walks the trace list), so a dataset's count is the sum of its runs'.""" + walks the trace list), so a dataset's count is the sum of its runs'. + + Returns ``(counts, heads, params)`` -- the same three the non-serial + branch collects: rows revealed, the drawn head as ``(run, grid row)``, + and the head's fractional source parameter. A serial sweep reaches + runs in order, so the LAST run with anything on screen holds the head. + """ from .matplotlib_backend import serial_reveal_counts own = self.ownership grid_counts = serial_reveal_counts( list(self.grid_lengths), frame, self.n_frames) - out = [] + out, heads, params = [], [], [] for d in range(own.n_datasets): total = 0 + at = None + param = None for r in own.runs_of(d): g = self.grid_lengths[r] - _, n_rows = own.run_span(r) + first_row, n_rows = own.run_span(r) span = own.draw_span(r) shown = min(grid_counts[r], g) if g < 2 or span <= 0 or shown <= 0: total += min(n_rows, max(0, shown)) + if shown > 0: # an all-or-nothing run + at, param = (r, max(0, shown - 1)), float(first_row) else: pos = (shown - 1) * span / (g - 1) total += min(n_rows, int(np.floor(pos)) + 1) + at, param = (r, shown - 1), float(first_row + pos) out.append(min(own.row_count(d), total)) - return out + heads.append(at) + params.append(param) + return out, heads, params def visible_rows(self, dataset, frame): """Original row indices of `dataset` on screen at `frame`, in order.""" return self._rows[min(max(int(frame), 0), self.n_frames - 1)][dataset] + def head_index(self, dataset, frame): + """``(run, grid row)`` of this dataset's DRAWN head, or None. + + The grid row indexes the run's OWN drawn array, so the head vertex is + ``grids[run][grid_row]`` for the same per-run arrays the backends + animate. + """ + return self._heads[min(max(int(frame), 0), self.n_frames - 1)][dataset] + + def head_param(self, dataset, frame): + """The drawn head's fractional ORIGINAL-row position, or None.""" + return self._head_params[ + min(max(int(frame), 0), self.n_frames - 1)][dataset] + def head_run(self, dataset, frame): """The run DRAWING this dataset's last visible row, or `None` when nothing of it is on screen yet.""" @@ -318,7 +523,14 @@ def project_schedule_cost(timings, remaining_rows): f"projecting a schedule needs timed fits at two DIFFERENT " f"history lengths; got {sorted(timings)}") short, long = min(timings), max(timings) - per_row = (timings[long] - timings[short]) / (long - short) + if len(timings) == 2: + per_row = (timings[long] - timings[short]) / (long - short) + else: + # every timed length, by least squares: one noisy pair no longer + # sets the slope on its own + rows = np.asarray(sorted(timings), dtype=float) + secs = np.asarray([timings[int(r)] for r in rows], dtype=float) + per_row = float(np.polyfit(rows, secs, 1)[0]) per_row = max(per_row, 0.0) # noise can invert two samples setup = max(timings[long] - per_row * long, 0.0) projected = sum(setup + per_row * rows for rows in remaining_rows) @@ -341,7 +553,8 @@ class ForecastSchedule: def __init__(self, histories, counts=None, model=None, t=None, rows=None, min_history=DEFAULT_MIN_HISTORY, transform=None, - slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS): + slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS, + forecast_function=None, heads=None, head_positions=None): if (counts is None) == (rows is None): raise ValueError( "pass exactly one of counts= (a revealed ROW COUNT per " @@ -358,9 +571,26 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, self.rows = [[tuple(int(i) for i in r) for r in frame] for frame in rows] self.counts = [[len(r) for r in frame] for frame in self.rows] + # `heads[f][i]` is the VERTEX dataset `i`'s trajectory ends on at + # frame `f` -- the drawn endpoint, which an animation's refined frame + # grid puts BETWEEN two raw observations. `counts`/`rows` above are + # the observations the fit may use; the head is where the result is + # drawn from. Absent (None) for a reveal with no grid to read. + self.heads = (None if heads is None else + [[None if h is None else np.asarray(h, dtype=float) + for h in frame] for frame in heads]) + # ...and where each head falls in RAW rows, fractionally (a 1-D plot + # draws the row index on x, so its forecast hangs off this). + self.head_positions = ( + None if head_positions is None else + [[None if p is None else float(p) for p in frame] + for frame in head_positions]) self.model = model self.t = int(t) - self.min_history = int(min_history) + # the caller's floor, raised to the MODEL's own: an ARIMA(1, 1, 1) + # cannot be fit on the 2-row history the earliest frames reveal, so + # those frames draw no forecast rather than crashing the schedule + self.min_history = max(int(min_history), model_min_history(model)) self.transform = transform self.n_frames = len(self.counts) self.n_datasets = len(self.histories) @@ -378,6 +608,13 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, todo.append((i, r)) self._paths.clear() + # Several drawn columns can share one multivariate fit. A callback + # may expose its fit key so timing/counts describe the model work, + # while the path cache still keeps every distinct drawn trace. + fit_key = getattr(forecast_function, 'fit_key', lambda i, rows: (i, rows)) + fit_count = len({fit_key(i, r) for i, r in todo}) + completed_fits = set() + warned = slow_warning_seconds is None # Seconds keyed by revealed-history LENGTH, for fits that really # ran. A mapping rather than a list of samples, because the slope @@ -387,20 +624,26 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, self.projection = None # filled in when a projection is made for n_done, (i, r) in enumerate(todo): start = time.perf_counter() - path = forecast_from_history(self.histories[i][list(r)], - self.model, self.t, - min_history=self.min_history) + path = (forecast_function(i, r, self.model, self.t, self.min_history) + if forecast_function is not None else + forecast_from_history(self.histories[i][list(r)], + self.model, self.t, + min_history=self.min_history, + dataset=i)) elapsed = time.perf_counter() - start spent += elapsed - if path is not None: + key = fit_key(i, r) + did_fit = path is not None and key not in completed_fits + if did_fit: self.n_fits += 1 + completed_fits.add(key) self._paths[(i, r)] = path # Time only REAL fits. The earliest (dataset, count) pairs are # histories shorter than min_history, where forecast_from_history # returns None without fitting anything -- timing one of those # projects 0.0 s for a job that may take minutes, which is worse # than not warning at all. - if path is not None: + if did_fit: timings.setdefault(len(r), []).append(elapsed) # Wait for two DISTINCT history lengths. `todo` is ordered by # FRAME and then by DATASET, so every dataset is fitted at one @@ -412,10 +655,17 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, # silently collapsed back into the constant-per-fit projection # it exists to replace -- with nothing failing, because a # factor-of-ten tolerance covers the difference on small data. - if not warned and len(timings) >= 2: + # ...and for a timed fit long enough to measure (see + # `PROJECTION_MIN_ROWS`), or the schedule's longest when that + # is shorter + longest = max(len(rows) for _, rows in todo) + if (not warned and len(timings) >= 2 + and max(timings) >= min(PROJECTION_MIN_ROWS, longest)): pooled = {rows: float(np.median(times)) for rows, times in timings.items()} - remaining = [len(rows) for _, rows in todo[n_done + 1:]] + remaining = list({fit_key(j, rows): len(rows) + for j, rows in todo[n_done + 1:] + if fit_key(j, rows) not in completed_fits}.values()) projected, per_row, setup, lengths = project_schedule_cost( pooled, remaining) total = spent + projected @@ -429,7 +679,7 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, } if total > slow_warning_seconds: warnings.warn( - f"predict= over this animation needs {len(todo)} " + f"predict= over this animation needs {fit_count} " f"forecast fits (one per distinct revealed history " f"length), projected at roughly {total:.1f} s in " f"total before the first frame can be drawn: " @@ -450,49 +700,80 @@ def __init__(self, histories, counts=None, model=None, t=None, rows=None, @classmethod def for_parallel(cls, histories, grid_lengths, model, t, n_frames, min_history=DEFAULT_MIN_HISTORY, - slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS): + slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS, + forecast_function=None, grids=None): """Schedule for a parallel/`'window'` animation. Every dataset advances together, so each one's revealed row count comes straight from `revealed_raw_counts` -- i.e. from the library's own `trails.anim_window_bounds`, not a second copy of the reveal - arithmetic. + arithmetic. `grids` are the animation-grid arrays the backends draw, + read here for each frame's drawn head (see `grid_head`). """ - counts = [[revealed_raw_counts(len(h), g, f, n_frames) - for h, g in zip(histories, grid_lengths)] - for f in range(n_frames)] + counts, heads, positions = [], [], [] + for f in range(n_frames): + counts.append([revealed_raw_counts(len(h), g, f, n_frames) + for h, g in zip(histories, grid_lengths)]) + if grids is None: + continue + frame_heads, frame_positions = [], [] + for h, g, grid in zip(histories, grid_lengths, grids): + point, pos = grid_head(grid, len(h), g, + drawn_head_index(g, f, n_frames)) + frame_heads.append(point) + frame_positions.append(pos) + heads.append(frame_heads) + positions.append(frame_positions) return cls(histories, counts=counts, model=model, t=t, min_history=min_history, - slow_warning_seconds=slow_warning_seconds) + slow_warning_seconds=slow_warning_seconds, + forecast_function=forecast_function, + heads=heads or None, head_positions=positions or None) @classmethod def for_serial(cls, histories, grid_lengths, model, t, n_frames, min_history=DEFAULT_MIN_HISTORY, - slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS): + slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS, + forecast_function=None, grids=None): """Serial reveals one dataset at a time, so its schedule comes from the backend's own `serial_reveal_counts` (animation-core Task 7), - mapped from frame-grid rows onto raw rows dataset by dataset.""" + mapped from frame-grid rows onto raw rows dataset by dataset. The + drawn head comes off the same `shown` count, one grid row back.""" from .matplotlib_backend import serial_reveal_counts - counts = [] + counts, heads, positions = [], [], [] for f in range(n_frames): grid_counts = serial_reveal_counts(list(grid_lengths), f, n_frames) - row = [] - for h, g, shown in zip(histories, grid_lengths, grid_counts): + row, frame_heads, frame_positions = [], [], [] + for i, (h, g, shown) in enumerate( + zip(histories, grid_lengths, grid_counts)): n_raw = len(h) if g < 2 or n_raw < 2 or shown <= 0: row.append(min(n_raw, max(0, shown))) else: pos = (min(shown, g) - 1) * (n_raw - 1) / (g - 1) row.append(min(n_raw, int(np.floor(pos)) + 1)) + if grids is None: + continue + point, head_pos = grid_head( + grids[i] if i < len(grids) else None, n_raw, g, + max(0, min(int(shown), int(g)) - 1)) + frame_heads.append(point) + frame_positions.append(head_pos) counts.append(row) + if grids is not None: + heads.append(frame_heads) + positions.append(frame_positions) return cls(histories, counts=counts, model=model, t=t, min_history=min_history, - slow_warning_seconds=slow_warning_seconds) + slow_warning_seconds=slow_warning_seconds, + forecast_function=forecast_function, + heads=heads or None, head_positions=positions or None) @classmethod def for_regrouped(cls, histories, reveal, model, t, n_frames, min_history=DEFAULT_MIN_HISTORY, - slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS): + slow_warning_seconds=DEFAULT_SLOW_WARNING_SECONDS, + forecast_function=None, grids=None): """Schedule for an animation whose data `hue=`/`cluster=` regrouped. The revealed rows come from a `DatasetRevealSchedule` rather than from @@ -503,9 +784,31 @@ def for_regrouped(cls, histories, reveal, model, t, n_frames, """ rows = [[reveal.visible_rows(i, f) for i in range(len(histories))] for f in range(n_frames)] + # `grids` here is one array per RUN (what the backends draw), so the + # head vertex comes from the run the reveal says is drawing it. + heads, positions = [], [] + if grids is not None: + for f in range(n_frames): + frame_heads, frame_positions = [], [] + for i in range(len(histories)): + at = reveal.head_index(i, f) + grid = (np.asarray(grids[at[0]], dtype=float) + if at is not None and at[0] < len(grids) + else None) + if grid is None or not len(grid): + frame_heads.append(None) + frame_positions.append(None) + continue + j = min(max(int(at[1]), 0), len(grid) - 1) + frame_heads.append(grid[j]) + frame_positions.append(reveal.head_param(i, f)) + heads.append(frame_heads) + positions.append(frame_positions) return cls(histories, rows=rows, model=model, t=t, min_history=min_history, - slow_warning_seconds=slow_warning_seconds) + slow_warning_seconds=slow_warning_seconds, + forecast_function=forecast_function, + heads=heads or None, head_positions=positions or None) # -- lookups ----------------------------------------------------------- def revealed_rows(self, dataset, frame): @@ -528,16 +831,77 @@ def anchor(self, dataset, frame): return None return self.histories[dataset][rows[-1]] + def head(self, dataset, frame): + """The vertex the DRAWN trajectory ends on at `frame`, or None. + + An animation is paced on a refined frame grid, so the head usually + falls BETWEEN two raw observations; `anchor` is the last observation + at or before it. A forecast continues from the head -- the endpoint a + viewer can actually see -- rather than hanging back from the line's + tip (maintainer report, 2026-09-11). None when no grid was passed (a + `hue=`/`cluster=` regrouped reveal), where `anchor` stands in. + """ + if self.heads is None: + return None + return self.heads[min(frame, self.n_frames - 1)][dataset] + + def head_position(self, dataset, frame): + """Where this frame's head falls in RAW rows, fractionally. + + 1-D plots draw the row index on x, so their forecast hangs off this + rather than off the integer row `anchor` sits on. Falls back to that + row when there is no grid. + """ + if self.head_positions is None: + rows = self.revealed_rows(dataset, frame) + return float(rows[-1]) if rows else None + return self.head_positions[min(frame, self.n_frames - 1)][dataset] + def path(self, dataset, frame): """Displacement path (t + 1, d) for `dataset` at `frame`, or None.""" return self._paths[(dataset, self.revealed_rows(dataset, frame))] def polyline(self, dataset, frame): - """The DRAWN forecast: anchor + displacement, or None.""" + """The DRAWN forecast: this frame's head, then every predicted point. + + Only the vertex the forecast hangs FROM moves with the reveal; rows + 1.. are the model's own predictions at the absolute positions they + were fitted to. So `t=` still counts raw steps on from the last + OBSERVATION, `pin_ramp`'s exact x ramp is untouched, and the vertex + count is unchanged -- the forecast simply meets the line it + continues. + """ path = self.path(dataset, frame) if path is None: return None - return self.anchor(dataset, frame) + path + anchor = self.anchor(dataset, frame) + if anchor is None: + return None + drawn = anchor + path + head = self.head(dataset, frame) + if head is None: + return drawn + return np.vstack([np.asarray(head, dtype=float), drawn[1:]]) + + def reach(self, dataset, frame): + """How far, in RAW rows, the forecast DRAWN at `frame` extends. + + The row position of its last predicted step: the last revealed row + plus the path's step count. None when nothing is drawn for this + dataset at `frame` (too little history revealed). `truth=` reveals + a held-out row once a drawn forecast reaches it + (`truth_reveal_counts`). + """ + pts = self.polyline(dataset, frame) + if pts is None or len(pts) < 2: + return None + rows = self.revealed_rows(dataset, frame) + return int(max(rows)) + len(self.path(dataset, frame)) - 1 + + def history_length(self, dataset): + """Raw rows in `dataset`'s full history; its `truth=` row ``k`` + (1-based) sits at raw position ``history_length - 1 + k``.""" + return len(self.histories[dataset]) def stacked_paths(self): """Every forecast vertex this schedule will ever draw, stacked. @@ -588,6 +952,13 @@ def to_display(self, transform): out.transform = transform out.n_frames, out.n_datasets = self.n_frames, self.n_datasets out.n_fits = 0 # no refitting: displacements are affine-mapped + # a head is a POSITION (a drawn vertex), so it takes the same full + # affine the histories take; a head POSITION is a raw row index, not + # a coordinate, so it carries through untouched + out.heads = (None if self.heads is None else + [[None if h is None else transform(h) for h in frame] + for frame in self.heads]) + out.head_positions = self.head_positions # a displacement is a DIFFERENCE of positions, so the mean cancels and # only the scale survives: d_display = 2 * d_analyze / scale out._paths = {key: (None if p is None else 2.0 * p / transform.scale) @@ -642,11 +1013,23 @@ def revealed(self, dataset, frame): return sched.revealed(i, frame) def anchor(self, dataset, frame): - """The last revealed observation this slot's forecast starts from - (see `ForecastSchedule.anchor`).""" + """The last observation this slot's forecast was fit through -- the + forecast is DRAWN from `head` (see `ForecastSchedule.anchor`).""" sched, i = self._locate(dataset) return sched.anchor(i, frame) + def head(self, dataset, frame): + """The vertex this slot's drawn trajectory ends on at `frame` (see + `ForecastSchedule.head`).""" + sched, i = self._locate(dataset) + return sched.head(i, frame) + + def head_position(self, dataset, frame): + """Where that head falls in raw rows (see + `ForecastSchedule.head_position`).""" + sched, i = self._locate(dataset) + return sched.head_position(i, frame) + def path(self, dataset, frame): """This slot's displacement path at `frame`, or None (see `ForecastSchedule.path`).""" @@ -659,6 +1042,18 @@ def polyline(self, dataset, frame): sched, i = self._locate(dataset) return sched.polyline(i, frame) + def reach(self, dataset, frame): + """How far this slot's drawn forecast extends at `frame` (see + `ForecastSchedule.reach`).""" + sched, i = self._locate(dataset) + return sched.reach(i, frame) + + def history_length(self, dataset): + """Raw rows in this slot's dataset history (see + `ForecastSchedule.history_length`).""" + sched, i = self._locate(dataset) + return sched.history_length(i) + def stacked_paths(self): """Every vertex every model will ever draw (see `ForecastSchedule.stacked_paths`).""" @@ -682,6 +1077,45 @@ def to_display(self, transform): [s.to_display(transform) for s in self.schedules], self.names) +def truth_reveal_counts(schedule, n_truths, horizons, n_frames): + """How many `truth=` rows each truth shows on every animation frame. + + A held-out truth row is revealed on the first frame whose DRAWN + forecast reaches it -- some live forecast of the same dataset (any + model, for ``predict=[...]``) extends to or past that row's position -- + and stays revealed on every later frame (maintainer decision, 1.1 + review): drawing the whole truth from frame 0 showed the answer before + the animation had forecast anywhere near it. + + `schedule` is a `ForecastSchedule`/`MultiModelSchedule` (or None, when + no forecast is drawn at all -- then nothing is revealed); truth ``i`` + continues schedule dataset ``i`` (every flat index ``j`` with + ``j % per-model count == i``). `horizons[i]` is truth ``i``'s row count + excluding its prepended seam row. Returns ``counts[frame][i]`` in + ``0..horizons[i]``: 0 hides the truth (seam included), ``k`` shows the + seam plus held-out rows ``1..k``. A pure table, so any frame can be + drawn in any order (``save()``/``to_jshtml()`` replays). + """ + n_frames = max(1, int(n_frames)) + counts = [[0] * n_truths for _ in range(n_frames)] + if schedule is None: + return counts + per_model = getattr(schedule, 'per_model', schedule.n_datasets) + best = [0] * n_truths + for f in range(n_frames): + for j in range(schedule.n_datasets): + i = j % per_model + if i >= n_truths: + continue + reach = schedule.reach(j, f) + if reach is None: + continue + covered = reach - (schedule.history_length(j) - 1) + best[i] = max(best[i], min(int(horizons[i]), covered)) + counts[f] = list(best) + return counts + + #: Past forecasts retained by `forecast_trail=True`. DEFAULT_FORECAST_TRAIL = 16 @@ -931,7 +1365,7 @@ def _forecast_endpoints(forecasts): def resolve_forecast_overrides(n_datasets, forecasts=None, *, hue=None, cluster=None, n_clusters=None, palette=None, - fmt=None, stacklevel=2): + fmt=None, stacklevel=2, default_palette=None): """Resolve `forecast_hue=`/`forecast_cluster=`/`forecast_palette=`/ `forecast_fmt=` into one override dict per dataset. @@ -989,6 +1423,12 @@ def resolve_forecast_overrides(n_datasets, forecasts=None, *, hue=None, Colours for the grouping. With no grouping given, spent one colour per dataset. `None` means "no colour override" -- callers pass the figure's own `palette=` when they want the observed one inherited. + default_palette : callable or None + The grouping's colours when `palette` is None: called with the + number of groups, it returns that many colours. `plot()` passes + `_forecast_group_palette`, which continues the figure's palette + past the observed data's colours so no group repeats one. None + keeps 'hls' from its first colour. fmt : str, sequence of str, or None Line/marker style for the forecasts, independent of the observed `fmt`. Validated here with matplotlib's own `fmt=` parser, so both @@ -1108,6 +1548,9 @@ def resolve_forecast_overrides(n_datasets, forecasts=None, *, hue=None, f". The clusterer rejected that: {exc}") from exc if labels is not None: + if palette is None and default_palette is not None: + palette = list(default_palette(max(1, len( + {lab for lab in labels if lab is not None})))) for i, color in enumerate(_forecast_label_colors( labels, 'hls' if palette is None else palette)): overrides[i]['color'] = color diff --git a/hypertools/plot/hierarchy.py b/hypertools/plot/hierarchy.py index 83425d6a..c43dde28 100644 --- a/hypertools/plot/hierarchy.py +++ b/hypertools/plot/hierarchy.py @@ -266,8 +266,11 @@ def build_hierarchy_styles(traces, palette='hls', linestyle=None, Returns ------- style : dict - ``{'colors', 'linewidths', 'alphas', 'labels', 'linestyles', - 'n_top', 'unique_top'}``, one entry per trace (aligned by position). + ``{'colors', 'linewidths', 'alphas', 'labels', 'group_labels', + 'linestyles', 'n_top', 'unique_top'}``, one entry per trace (aligned + by position). ``group_labels`` is every trace's top-level group + label (the legend text of the group it is coloured as, never the + ``'_nolegend_'`` sentinel) -- the plotly backend's hover name. """ n_levels = traces.meta['n_levels'] @@ -324,6 +327,11 @@ def build_hierarchy_styles(traces, palette='hls', linestyle=None, ) colors, linewidths, alphas, labels = [], [], [], [] + # every trace's TOP-level group label (never the sentinel): what the + # legend entry it shares a colour with says -- the plotly backend's + # hover name for leaves and intermediate means, which carry no legend + # label of their own + group_labels = [] linestyles_out = [] if per_top_linestyle is not None else None for key, level, mean in zip(traces.keys, traces.level_idx, traces.is_mean): @@ -337,6 +345,8 @@ def build_hierarchy_styles(traces, palette='hls', linestyle=None, # top-level group. Without that exception a (Group, Feature) column # hierarchy drew several completely unlabelled traces (F11). top_level = (level == 0) and (mean or n_levels == 1) + group_labels.append(str(top_val) if per_top_label is None + else str(per_top_label[top_i])) if not top_level: labels.append('_nolegend_') elif per_top_label is None: @@ -351,6 +361,7 @@ def build_hierarchy_styles(traces, palette='hls', linestyle=None, 'linewidths': linewidths, 'alphas': alphas, 'labels': labels, + 'group_labels': group_labels, 'linestyles': linestyles_out, 'n_top': n_top, 'unique_top': unique_top, diff --git a/hypertools/plot/hyper_animation.py b/hypertools/plot/hyper_animation.py index a6957407..b19cd88a 100644 --- a/hypertools/plot/hyper_animation.py +++ b/hypertools/plot/hyper_animation.py @@ -141,13 +141,28 @@ def drawn_extent(self, frames=None, threshold=5): """The union bounding box of everything this animation DRAWS, measured from rendered pixels, in FIGURE fractions (GH #285). - ``frames=None`` (the default) samples 12 frames evenly across the - animation -- the drawn extent of a 3-D plot changes with the camera - angle, so one frame is not representative. Pass an int for a - different sample size, or an iterable of frame indices to measure - exactly those. Returns a ``matplotlib.transforms.Bbox`` with y - increasing UPWARD (``bbox.p0`` is the bottom-left corner). - + Parameters + ---------- + frames : int, iterable of int, or None, optional + Which frames to measure. ``None`` (the default) samples 12 + frames evenly across the animation -- the drawn extent of a + 3-D plot changes with the camera angle, so one frame is not + representative. An int ``n`` samples ``n`` frames evenly; an + iterable of frame indices measures exactly those. + threshold : int, optional + How far (0-255, per channel) a pixel must differ from the + figure's own background colour to count as drawn. The default + 5 treats anything below 250 on a white figure as ink, faint + antialiasing included; raise it to ignore light artefacts. + + Returns + ------- + matplotlib.transforms.Bbox + In FIGURE fractions, with y increasing UPWARD (``bbox.p0`` is + the bottom-left corner, ``bbox.p1`` the top-right). + + Notes + ----- Costs one full canvas render per sampled frame, and leaves the figure showing the last frame it measured. See :func:`hypertools.plot.animate.drawn_extent` for the full contract. @@ -200,9 +215,10 @@ def save(self, filename, *args, **kwargs): ``writer`` (or positional args) delegates straight to ``matplotlib.animation.Animation.save`` instead, with every keyword. - QC 2026-07: ``.save('x.svg')`` / ``.save('x.png')`` used to crash (raw - ``Animation.save`` tried to pipe h264 into an svg/png), even though the - same extensions work via ``save_path=``. + Supported extensions, by writer: ``.gif``, ``.png``/``.apng`` + (Pillow); ``.svg`` (frame-capped vector animation); ``.mp4``, + ``.mov``, ``.avi``, ``.m4v``, ``.mkv`` (ffmpeg). Any other extension + raises ``ValueError`` naming this list. """ if args or 'writer' in kwargs: return self.animation.save(filename, *args, **kwargs) diff --git a/hypertools/plot/matplotlib_backend.py b/hypertools/plot/matplotlib_backend.py index 5b372c2b..59d4804d 100644 --- a/hypertools/plot/matplotlib_backend.py +++ b/hypertools/plot/matplotlib_backend.py @@ -11,6 +11,7 @@ import itertools import warnings +import matplotlib.artist import matplotlib.colors as mcolors import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import proj3d @@ -28,6 +29,8 @@ from .animate import HyperFuncAnimation import matplotlib.patches as patches from .._shared.helpers import * +from .._shared.helpers import UNIT_FRAME_LIMIT, UNIT_FRAME_SCALE +from .._shared.helpers import row_index_x from ..core.model import external_stacklevel from .meshutil import (backface_cull, blinn_phong_colors, vertex_colors_from_points, face_colors_from_vertex_colors) @@ -57,15 +60,42 @@ ) -def _apply_title(ax, text, font=None, title_kwargs=None): +def _label_callout_kwargs(label_alpha, arrowstyle='-', facecolor='white'): + """`ax.annotate` box and connector styling for an observation label, + in the colours plotly's annotations use (`plotly_backend`: connector + rgba(0,0,0,0.6), box edge rgba(0,0,0,0.4), a white box at + `label_alpha`). Both colours are EXPLICIT: seaborn's whitegrid style + sets ``patch.edgecolor='w'``, so the defaults drew a white connector + and a white box edge -- an invisible link that cut white notches + through the markers (1.1 release review, figure QA). `label_alpha` stays + the box patch's own ``alpha`` (the documented `label_alpha=` contract), + which matplotlib applies to its edge too: a dark edge at that opacity + (0.5 by default, beside plotly's 0.4).""" + return dict( + bbox=dict(boxstyle="round,pad=0.5", fc=facecolor, ec='black', + lw=0.8, alpha=label_alpha), + arrowprops=dict(arrowstyle=arrowstyle, connectionstyle="arc3,rad=0", + color=(0, 0, 0, 0.6), lw=0.8), + ) + + +def _apply_title(ax, text, font=None, title_kwargs=None, family=None): """Set `ax`'s title, honoring the resolved `font=` and `title_kwargs=` (GH #285). - With `title_kwargs=None` this makes EXACTLY the call hypertools has - always made (`ax.set_title(text)`, or `ax.set_title(text, + With `title_kwargs=None` and `family=None` this makes EXACTLY the call + hypertools has always made (`ax.set_title(text)`, or `ax.set_title(text, fontproperties=font)` when a `font=` was resolved), so an un-styled title's Text artist is byte-identical to before. + `family` (a font-family list: hypertools' fallback stack) is given + EXPLICITLY when no `font=` is: an axes created outside hypertools' + ``rc_context`` -- a caller's ``ax=``, every `panels=` cell -- holds a + title Text whose family is the bare ``'sans-serif'`` alias, resolved at + DRAW time against the caller's rcParams, so those titles rendered in + DejaVu Sans while a figure of hypertools' own used Noto Sans (1.1 + release review, figure QA). A family named in `title_kwargs` wins. + `fontproperties` is inserted BEFORE the individual font properties, because `set_title` applies its kwargs in dict order and a `fontproperties` REPLACES the Text's whole FontProperties, size @@ -79,14 +109,21 @@ def _apply_title(ax, text, font=None, title_kwargs=None): between them is exactly the bug GH #285 reports (a resolved `font=` reached the static title and never a per-segment one). """ + _family_keys = ('fontproperties', 'font_properties', 'family', + 'fontfamily', 'fontname', 'name', 'font') if title_kwargs: kwargs = {} if font is not None and 'fontproperties' not in title_kwargs: kwargs['fontproperties'] = font + elif family is not None and not any(k in title_kwargs + for k in _family_keys): + kwargs['fontfamily'] = family kwargs.update(title_kwargs) return ax.set_title(text, **kwargs) if font is not None: return ax.set_title(text, fontproperties=font) + if family is not None: + return ax.set_title(text, fontfamily=family) return ax.set_title(text) @@ -177,7 +214,8 @@ def _grow_for_companion(fig, spec): return [1.0 - size + pad, 0.15, width, 0.75] -def add_companion_panel(fig, spec, cmap=None, norm=None, font=None): +def add_companion_panel(fig, spec, cmap=None, norm=None, font=None, + default_color=None): """Draw one `companion=` panel and return the state its per-frame updater needs (GH #285). @@ -185,13 +223,19 @@ def add_companion_panel(fig, spec, cmap=None, norm=None, font=None): (or, with ``hue=``, a `LineCollection` coloured through the plot's own colour scale) up to the reveal head, an optional trailing rolling mean, and an optional marker on the head itself. + + The line and head marker are drawn in ``spec['color']``, else in + `default_color` -- `plot()` passes the colour of the trajectory the + panel accompanies -- else in matplotlib's ``'C0'`` (only a direct + caller that passes neither reaches that last fallback; 1.1 visual + review L12). """ from matplotlib.collections import LineCollection rect = _grow_for_companion(fig, spec) pax = fig.add_axes(rect) x, y = _companion_xy(spec['data']) - color = spec['color'] or 'C0' + color = spec['color'] or default_color or 'C0' pax.plot(x, y, color=COMPANION_GHOST_COLOR, linewidth=0.6) points = np.column_stack([x, y]) @@ -253,13 +297,14 @@ def update_companion_panel(panel, i): state on screen. """ i = int(min(max(i, 0), panel['n_rows'] - 1)) - if not panel['reveal']: - i = panel['n_rows'] - 1 - panel['revealed'].set_segments(panel['segments'][:i]) + # Notebook review 2026-09: full-curve visibility must not freeze the + # current-time marker. Keep the curve extent separate from the clock. + end = i if panel['reveal'] else panel['n_rows'] - 1 + panel['revealed'].set_segments(panel['segments'][:end]) if panel['hue'] is not None: - panel['revealed'].set_array(panel['hue'][1:i + 1]) + panel['revealed'].set_array(panel['hue'][1:end + 1]) if panel['trend'] is not None: - panel['trend'].set_data(panel['x'][:i + 1], panel['rolling'][:i + 1]) + panel['trend'].set_data(panel['x'][:end + 1], panel['rolling'][:end + 1]) if panel['head'] is not None: panel['head'].set_data([panel['x'][i]], [panel['y'][i]]) if panel['head_colors'] is not None: @@ -267,6 +312,44 @@ def update_companion_panel(panel, i): return panel +class _AxisLabelExtent(matplotlib.artist.Artist): + """A draw-nothing figure artist whose window extent is the union of an + `Axes3D`'s axis-label extents, so `Figure.get_tightbbox` (and with it + ``savefig(bbox_inches='tight')``) includes the labels that + `Axes3D.get_tightbbox` leaves out. See the 3-D label branch of `_draw`. + """ + + def __init__(self, ax): + super().__init__() + self.axes_ref = ax + self.set_in_layout(True) + self.set_clip_on(False) + + def draw(self, renderer): + """Draw nothing: the artist exists only for its extent.""" + return None + + def get_window_extent(self, renderer=None): + """The union of the axes' visible, non-empty axis-label extents + (display pixels), or a null box when there is none.""" + from matplotlib.transforms import Bbox + ax = self.axes_ref + if renderer is None: + try: + renderer = ax.figure.canvas.get_renderer() + except AttributeError: + return Bbox.null() + boxes = [] + for axis in getattr(ax, '_axis_map', {}).values(): + label = axis.label + if not (label.get_visible() and label.get_text()): + continue + box = label.get_window_extent(renderer) + if box.width > 0 and box.height > 0: + boxes.append(box) + return Bbox.union(boxes) if boxes else Bbox.null() + + def _legend_proxy_handles(entries, fmt=None): """`Line2D` proxy handles for explicit legend entries (GH #285). @@ -314,7 +397,22 @@ def legend_call_kwargs(is_3d=False, zlabel=None, font=None, # loc=/bbox_to_anchor=/frameon=/fontsize= wins over the defaults # above -- which is the whole point of the kwarg. if legend_kwargs: + if 'loc' in legend_kwargs and 'bbox_to_anchor' not in legend_kwargs: + # a caller's `loc=` names a place ON the axes ('upper left'); + # keeping hypertools' outside-right anchor would hang the + # legend off the right edge with its upper-left corner at the + # anchor (1.1 release review, feature-tour 9.6) + call.pop('bbox_to_anchor', None) + call.pop('borderaxespad', None) call.update(legend_kwargs) + # matplotlib ignores `fontsize=` whenever `prop=` is given, so with + # a `font=` the user's size silently lost; fold it into the + # FontProperties instead so legend_kwargs still wins. + if font is not None and 'fontsize' in call and 'prop' in call \ + and call['prop'] is font: + prop = font.copy() + prop.set_size(call.pop('fontsize')) + call['prop'] = prop return call @@ -367,14 +465,17 @@ def _draw_one_density_2d(ax, pts, spec, color, label="", clip_unit=True): # D05-gallery-data-text-009). The 2-D paths always rescale data into # the [-1, 1] box and draw the frame via plot_square(scale=1), so the # frame rectangle is fixed in data coordinates. - im.set_clip_path(patches.Rectangle((-1.0, -1.0), 2.0, 2.0, - transform=ax.transData)) + im.set_clip_path(patches.Rectangle( + (-UNIT_FRAME_SCALE, -UNIT_FRAME_SCALE), 2 * UNIT_FRAME_SCALE, + 2 * UNIT_FRAME_SCALE, transform=ax.transData)) def _draw_density_2d(ax, points_list, density, density_colors, clip_unit=True): """Draw each dataset's (or, with ``per_group=False``, one pooled) 2-D - KDE density layer (GH #108/#191).""" + KDE density layer (GH #108/#191); each grid reaches `KDE_GRID_BANDWIDTHS` + kernel widths past its own cloud, so the glow fades out inside it + (see `kde_grid_2d`).""" if density[0] is not None and not density[0].get("per_group", True): all_pts = np.vstack([np.asarray(p)[:, :2] for p in points_list]) _draw_one_density_2d(ax, all_pts, density[0], POOLED_COLOR, @@ -766,24 +867,36 @@ def _draw( xlim=None, ylim=None, x_date=False, + legend_order=None, ): """ Draws the plot + `legend_order` (1.1 release review): the legend labels in the order + their entries should be listed, or None for drawn-artist order. The + categorical LINE path draws one artist per contiguous run, so its + legend followed the order the categories first APPEAR along the data + (clusters ``0, 2, 1``) while the marker path lists them sorted; the + entries it names are sorted into this order among their own + positions, every other entry (an earlier call's, a forecast's) staying + where it was. + `raw_data` (GH #141): the PRE-interpolation per-dataset points, same length as `x`/`fmt`. Used only by the STATIC (non-animated) plot1D/2D/ 3D functions below: a dataset whose `fmt` combines a marker AND a line (e.g. 'o-') is drawn as two artists -- a smoothed line from `x` (the already-interpolated data) plus markers at the raw sample points from `raw_data` -- so markers land on the true data regardless of how dense - the smoothed line is. Ignored (may be None) for pure line/marker-only - styles and for every ANIMATED style, which still draw marker+line - combos as a single artist against the (now also smoothed, since the - interpolation gate itself was fixed for GH #141) `x` data -- so an - animated 'o-' plot's line is correctly smoothed, but its markers - currently render at the interpolated points rather than only the - original samples; splitting the animated marker/line artists frame-by- - frame was judged out of scope for this fix. + the smoothed line is. A LINE fmt given its marker by ``marker=``/ + ``markers=`` stays one artist, whose ``markevery`` picks out the raw + samples among the smoothed vertices. Ignored (may be None) for pure + line/marker-only styles. An + ANIMATED marker+line style keeps ONE artist per dataset (so its legend + handle shows marker and line), drawn against the smoothed frame-grid + `x` data; `raw_data` then locates each observation's nearest drawn + vertex, and each frame's ``markevery`` marks only those + (`_mark_observations`) -- before the 1.1 release review every + interpolated vertex carried a marker. `ownership` (a `hypertools.plot.ownership.TraceOwnership`, or None): which source dataset each drawn trace came from and which of its rows. When @@ -797,7 +910,7 @@ def _draw( groups globally by category), and those keep `anim_window_bounds` directly. `axis_scale` (GH #285): ``'unit'`` (the historical behaviour) draws the - hypertools frame square and pins the 2-D axes to ``(-1.1, 1.1)`` -- + hypertools frame square (half-width `UNIT_FRAME_SCALE`) and pins the 2-D axes to ``+-UNIT_FRAME_LIMIT`` -- `plot()` has already mean-centred and rescaled the data into ``[-1, 1]`` for it. ``'data'`` draws NO frame square, leaves matplotlib's own ticks and spines visible, and takes its limits from `xlim`/`ylim` (which @@ -826,6 +939,11 @@ def _draw( invoke callbacks. """ + # whether the caller handed us their own axes (their styling -- e.g. a + # grid they turned on -- is theirs to keep; see the axis_scale='data' + # branch below) + _caller_ax = ax is not None + # chemtrails/precog/bullettime (GH #127): normalize to one bool per # dataset now, at the top of `_draw`, BEFORE any nested closure below is # defined -- `update_lines_parallel`/`animate_plot3D` close over these @@ -884,10 +1002,64 @@ def _aa_window(i, a, b, artist=None): artist._hyp_row_window = (a, b) dense, step = _aa_curves[i] if step == 1: - return dense[a:b] - if b <= a: - return dense[0:0] - return dense[a * step:(b - 1) * step + 1] + lo, out = a, dense[a:b] + elif b <= a: + lo, out = a * step, dense[0:0] + else: + lo, out = a * step, dense[a * step:(b - 1) * step + 1] + if artist is not None: + _mark_observations(i, artist, lo, len(out)) + return out + + # markers at the TRUE observations in an animation (the shared marker + # contract; 1.1 release review): an animated line is resampled onto + # the frame grid (`plot._interp_anim_line`) and densified again above, + # so its vertices are not the observations, and a marker+line style + # ('o-', or a line fmt with marker=/markers=) marked every one of them + # -- a tube of ~500 markers for 48 samples. Mark, on the SAME artist + # (its legend handle keeps the marker), only the drawn vertex nearest + # each observation: found by position, in a window around the + # observation's proportional place along the curve, so it holds for + # whatever observation -> grid mapping the resampling produces. + _obs_vertices_cache = {} + + def _observation_vertices(i): + if i in _obs_vertices_cache: + return _obs_vertices_cache[i] + found = None + if (animate and raw_data is not None and i < len(raw_data) + and raw_data[i] is not None): + raw = np.asarray(raw_data[i], dtype=float) + dense = np.asarray(_aa_curves[i][0], dtype=float) + n, d = raw.shape[0], dense.shape[0] + if (raw.ndim == 2 and dense.ndim == 2 and n and d + and raw.shape[1] == dense.shape[1]): + if n == 1 or d == 1: + found = np.array([0]) + else: + est = np.rint(np.arange(n) * (d - 1) / (n - 1)).astype(int) + w = int(np.ceil((d - 1) / (n - 1))) + idx = [] + for k in range(n): + lo, hi = max(0, est[k] - w), min(d, est[k] + w + 1) + gap = np.linalg.norm(dense[lo:hi] - raw[k], axis=1) + idx.append(lo + int(np.argmin(gap))) + found = np.unique(idx) + _obs_vertices_cache[i] = found + return found + + def _mark_observations(i, artist, lo, count): + marker = artist.get_marker() if hasattr(artist, 'get_marker') else None + if marker in (None, 'None', 'none', '', ' '): + return + if not has_line_component(_fmt_at(i)) and artist.get_linestyle() in ( + 'None', 'none', '', ' '): + return # markers only: its vertices ARE the rows + verts = _observation_vertices(i) + if verts is None: + return + local = verts[(verts >= lo) & (verts < lo + count)] - lo + artist.set_markevery([int(v) for v in local]) # handle static plots def dispatch_static(x, ax=None): @@ -942,11 +1114,48 @@ def _plot_possibly_split(ax, coords, raw_coords, i): fmt_ls, fmt_marker = split_marker_line_fmt(f) fmt_color = None line_token, marker_char = split_marker_line_fmt(f) + # an explicit marker= (or its markers= alias) wins over the fmt's + # marker, as the comment above promises and as plotly draws it: + # the split below used to discard it, so fmt='-o' with + # marker=['o', 's'] drew two circle datasets (1.1 release review). + _fmt_has_marker = marker_char is not None + _explicit_marker = ikwargs.get('marker') + if _explicit_marker is not None: + marker_char = (None if isinstance(_explicit_marker, str) + and _explicit_marker.strip().lower() in ('', 'none') + else _explicit_marker) + if (line_token is not None and marker_char is not None + and not _fmt_has_marker): + # a LINE fmt given its marker by marker=/markers= ('-' with + # markers='o'): one artist, as before, but marked only at the + # TRUE samples -- every one an exact vertex of the smoothed line + # (`antialias_line`: ``dense[::step]`` IS the data) -- rather + # than at all ~20x-denser interpolated vertices, which + # contradicted `antialias=`'s promise (1.1 release review) + n_dense, n_raw = len(coords[0]), len(raw_coords[0]) + one_kwargs = dict(ikwargs) + one_kwargs.setdefault('linestyle', line_token) + if fmt_color is not None: + one_kwargs.setdefault('color', fmt_color) + if (raw_data is not None and 1 < n_raw < n_dense + and (n_dense - 1) % (n_raw - 1) == 0): + one_kwargs.setdefault('markevery', list(range( + 0, n_dense, (n_dense - 1) // (n_raw - 1)))) + ax.plot(*coords, **one_kwargs) + return if line_token is not None and marker_char is not None: line_kwargs = {k: v for k, v in ikwargs.items() if k != 'marker'} line_kwargs.setdefault('linestyle', line_token) if fmt_color is not None: line_kwargs.setdefault('color', fmt_color) + # the line artist carries the legend label, so it also carries + # the MARKER -- with `markevery=[]` it draws none along the + # interpolated vertices (the markers-only artist below draws + # them at the raw sample points), but its legend handle shows + # marker + line, as 's--' promises. Before the 1.1 release + # review the handle showed only the dashes. + line_kwargs['marker'] = marker_char + line_kwargs['markevery'] = [] line_artist = ax.plot(*coords, **line_kwargs)[0] marker_kwargs = {k: v for k, v in ikwargs.items() if k != 'marker'} marker_kwargs['label'] = '_nolegend_' @@ -958,7 +1167,10 @@ def _plot_possibly_split(ax, coords, raw_coords, i): # 'o-' datasets rendered with identical color pairs. marker_kwargs.setdefault('color', line_artist.get_color()) marker_coords = raw_coords if raw_data is not None else coords - ax.plot(*marker_coords, **marker_kwargs) + # tagged so run-indexed consumers (the forecast/truth overlays + # in plot.py) skip it: it is the SAME run's markers, not a run + ax.plot(*marker_coords, **marker_kwargs)[0] \ + ._hyp_marker_companion = True elif _process_plot_format is not None: plot_kwargs = dict(ikwargs) if fmt_ls is not None: @@ -977,7 +1189,16 @@ def plot1D(data, fig, ax): n = len(data) for i in range(n): raw = raw_data[i] if raw_data is not None else data[i] - _plot_possibly_split(ax, (data[i][:, 0],), (raw[:, 0],), i) + # x is the ROW index: static antialiasing densified `data[i]` + # upstream (uniformly, every original row kept), so its vertices + # span the same 0..n_rows-1 as the raw rows. Plotting it with no + # x put it on the VERTEX index (0..936 for 40 rows) while the + # forecast/truth overlays continue in rows -- squashing a + # forecast 24x at the far end (1.1 release review) + _xs = row_index_x(raw.shape[0], data[i].shape[0]) + _plot_possibly_split( + ax, (_xs, data[i][:, 0]), + (np.arange(raw.shape[0], dtype=float), raw[:, 0]), i) return fig, ax, data # plot data in 2D @@ -1035,8 +1256,12 @@ def annotate_plot(data, labels, lengths=None): if lengths is not None: within = [j for L in lengths for j in range(int(L))] + # ...and which drawn trace each label belongs to, so an + # animation can test it against THAT trace's drawn window + run_of = [r for r, L in enumerate(lengths) for _ in range(int(L))] else: within = list(range(len(data))) + run_of = None if data[0].shape[-1] > 2: proj = ax.get_proj() @@ -1077,12 +1302,13 @@ def annotate_plot(data, labels, lengths=None): textcoords="offset points", ha="right", va="bottom", - bbox=dict(boxstyle="round,pad=0.5", fc="white", alpha=label_alpha), - arrowprops=dict(arrowstyle="-", connectionstyle="arc3,rad=0"), + **_label_callout_kwargs(label_alpha), **_label_font_kwargs, ) label._hyp_point_idx = within[idx] label._hyp_global_idx = idx + label._hyp_run_idx = (run_of[idx] if run_of is not None + else None) labels_and_points.append((label, x[0], x[1], x[2])) elif data[0].shape[-1] == 2: x2, y2 = x[0], x[1] @@ -1093,13 +1319,14 @@ def annotate_plot(data, labels, lengths=None): textcoords="offset points", ha="right", va="bottom", - bbox=dict(boxstyle="round,pad=0.5", fc="white", alpha=label_alpha), - arrowprops=dict(arrowstyle="-", connectionstyle="arc3,rad=0"), + **_label_callout_kwargs(label_alpha), **_label_font_kwargs, ) label.draggable() label._hyp_point_idx = within[idx] label._hyp_global_idx = idx + label._hyp_run_idx = (run_of[idx] if run_of is not None + else None) labels_and_points.append((label, x[0], x[1])) fig.canvas.draw() @@ -1142,16 +1369,21 @@ def update_position(e): fig.canvas.draw() def _sync_anim_labels(num, window_frames, all_visible=False, revealed=None, - hide_all=False): + hide_all=False, windows=None): """Per-animation-frame label bookkeeping (QC 2026-07): show each per-point label ONLY while its datapoint is currently drawn (previously every label was drawn on every frame), and reproject the visible ones for the (possibly rotated) camera. The visibility rule depends on the animation style: - * window / parallel: the datapoint is inside the head window - ``[num - window_frames, num]`` (matched on ``_hyp_point_idx``, the - within-dataset index, so multi-dataset plots window correctly); + * window / parallel: the datapoint is inside the head window its + trace was JUST drawn over -- ``windows[run]``, the ``(start, + end)`` row bounds the updater sliced the artist with (matched on + ``_hyp_point_idx``, the within-trace row, and ``_hyp_run_idx``). + A trace's rows are not frames (1.1 visual review L8: every line + keeps its observations on a grid of at least one row per frame), + so the historical ``[num - window_frames, num]`` rule, still the + fallback without `windows`, showed labels at the wrong time; * serial: the datapoint has been REVEALED, i.e. its global index (``_hyp_global_idx``) ``<= revealed`` (serial accumulates points, so there is no trailing edge); @@ -1187,7 +1419,14 @@ def _sync_anim_labels(num, window_frames, all_visible=False, revealed=None, visible = g is None or g <= revealed else: j = getattr(label, "_hyp_point_idx", None) - visible = j is None or (lo <= j <= num) + r = getattr(label, "_hyp_run_idx", None) + if j is None: + visible = True + elif windows is not None and r is not None \ + and r < len(windows): + visible = windows[r][0] <= j < windows[r][1] + else: + visible = lo <= j <= num label.set_visible(visible) if visible and is_3d: x2, y2, _ = proj3d.proj_transform(entry[1], entry[2], entry[3], @@ -1219,7 +1458,7 @@ def add_labels(x, labels, explore=False): if explore: X = np.vstack(x) if labels is not None: - if any(isinstance(el, list) for el in labels): + if any(isinstance(el, (list, tuple)) for el in labels): labels = list(itertools.chain(*labels)) fig.canvas.mpl_connect( "motion_notify_event", lambda event: onMouseMotion(event, X, labels) @@ -1232,7 +1471,10 @@ def add_labels(x, labels, explore=False): elif labels is not None: X = np.vstack(x) lengths = [np.atleast_2d(np.asarray(d)).shape[0] for d in x] - if any(isinstance(el, list) for el in labels): + # a nested per-dataset labels= may be a tuple of tuples as well + # as a list of lists (the validator accepts both); flatten + # either, or the tuple is drawn as its literal repr. + if any(isinstance(el, (list, tuple)) for el in labels): labels = list(itertools.chain(*labels)) annotate_plot(X, labels, lengths=lengths) fig.canvas.mpl_connect("button_press_event", hide_labels) @@ -1313,15 +1555,18 @@ def annotate_plot_explore(X, index, labels=False): ) _explore_font_kwargs = {} if font is None else dict(fontproperties=font) + # put the label on the side of the point facing the axes centre, so a + # point near an edge never pushes its label out of the window + fx, fy = ax.transAxes.inverted().transform( + ax.transData.transform((x2, y2))) annotate_plot_explore.label = ax.annotate( label, xy=(x2, y2), - xytext=(-20, 20), + xytext=(-20 if fx > 0.5 else 20, 20 if fy < 0.5 else -20), textcoords="offset points", - ha="right", - va="bottom", - bbox=dict(boxstyle="round,pad=0.5", fc="yellow", alpha=0.5), - arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=0"), + ha="right" if fx > 0.5 else "left", + va="bottom" if fy < 0.5 else "top", + **_label_callout_kwargs(0.5, arrowstyle="->", facecolor="yellow"), **_explore_font_kwargs, ) fig.canvas.draw() @@ -1443,9 +1688,9 @@ def plot_square(ax, scale=1, **square_kwargs): ax.add_patch( patches.Rectangle( - scale * [-1, -1], - scale * 2, - scale * 2, + (-scale, -scale), + 2 * scale, + 2 * scale, **square_kwargs ) ) @@ -1454,7 +1699,7 @@ def frame_2d(ax): """Draw the 2-D frame and set the 2-D axis limits for `axis_scale`. ``'unit'`` (the default, and everything drawn before GH #285) draws - hypertools' frame square and pins both axes to ``(-1.1, 1.1)``, + hypertools' frame square (half-width `UNIT_FRAME_SCALE`) and pins both axes to ``+-UNIT_FRAME_LIMIT``, because `plot()` has already rescaled the data into ``[-1, 1]``. ``'data'`` draws no square and applies `xlim`/`ylim` when `plot()` computed (or the caller passed) them, leaving matplotlib's autoscale @@ -1462,9 +1707,9 @@ def frame_2d(ax): `animate_plot2D`, so the two cannot drift apart. """ if axis_scale != 'data': - plot_square(ax, **frame_kwargs) - ax.set_xlim(-1.1, 1.1) - ax.set_ylim(-1.1, 1.1) + plot_square(ax, scale=UNIT_FRAME_SCALE, **frame_kwargs) + ax.set_xlim(-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) + ax.set_ylim(-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) return if xlim is not None: ax.set_xlim(*xlim) @@ -1602,9 +1847,9 @@ def update_lines_parallel( prior_colls=prior, quiet=True, surface_point_colors=window_spcs) - # per-point labels track their datapoint's visibility window (the same - # [num - tail_duration, num] window the head line uses above) - _sync_anim_labels(num, tail_duration) + # per-point labels track their datapoint's visibility: the SAME row + # window each head line was just sliced with (L8) + _sync_anim_labels(num, tail_duration, windows=head_bounds) if frame_hooks is not None: frame_hooks.record( frame=int(num), n_frames=int(total_frames), @@ -1731,6 +1976,7 @@ def update_lines_serial( windows = [] window_spcs = [] + _head_windows = [] # GH #285 for i, (line, data, trail) in enumerate(itertools.zip_longest( lines, data_lines, trail_lines)): n_pts = data.shape[0] @@ -1778,6 +2024,7 @@ def update_lines_serial( head = _aa_window(i, *head_bounds, artist=line) trail_seg = (data[:0] if trail_bounds is None else _aa_window(i, *trail_bounds, artist=trail)) + _head_windows.append(tuple(int(b) for b in head_bounds)) line.set_data(head[:, 0:2].T) line.set_3d_properties(head[:, 2]) if trail is not None: @@ -1811,9 +2058,11 @@ def update_lines_serial( datasets=list(data_lines), style='serial', order='serial', current_index=_idx, current_fraction=_frac, revealed_counts=_counts, - # a serial reveal is cumulative: every dataset's window - # starts at row 0 (GH #285). - window_bounds=tuple((0, c) for c in _counts)) + # the head window each artist was JUST drawn over: start + # is 0 for a plain cumulative reveal and moves past the + # dataset's beginning once a trail flag gives the reveal + # a comet-head (GH #285; `FrameContext.window_bounds`). + window_bounds=tuple(_head_windows)) return lines def update_morph(num, morph_state, cube_scale, azimuths, zoom=1, elev=10): @@ -2208,7 +2457,10 @@ def _trail_kwargs(kw): else first_pts) _mkw = (kwargs_list[mesh_slot] if isinstance(kwargs_list[mesh_slot], dict) else {}) - morph_markersize = _mkw.get("markersize") or 1.5 + # the shared default both backends read (L9: 1.5 pt dots were + # ~3 px here and sub-pixel on plotly) + morph_markersize = (_mkw.get("markersize") + or _morph.MORPH_DEFAULT_MARKERSIZE_PT) # GH #284: `alpha=` (scalar, or the per-dataset list) lands in # each morph-tagged dataset's kwargs, but those datasets' own # `lines` are hidden above -- the ONE visible artist is this @@ -2538,7 +2790,7 @@ def update_lines_parallel_2d( window = _aa_window(i, start, end, artist=line) line.set_data(window[:, 0], window[:, 1]) - _sync_anim_labels(num, tail_duration) + _sync_anim_labels(num, tail_duration, windows=head_bounds) if frame_hooks is not None: frame_hooks.record( frame=int(num), n_frames=int(total_frames), @@ -2568,6 +2820,7 @@ def update_lines_serial_2d(num, data_lines, lines, trail_lines, revealed = total_points * num / max(1, total_frames - 1) _counts = serial_reveal_counts(lengths, num, total_frames) + _head_windows = [] # GH #285 for i, (line, data, trail) in enumerate(itertools.zip_longest( lines, data_lines, trail_lines)): n_pts = data.shape[0] @@ -2600,6 +2853,7 @@ def update_lines_serial_2d(num, data_lines, lines, trail_lines, head = _aa_window(i, *head_bounds, artist=line) trail_seg = (data[:0] if trail_bounds is None else _aa_window(i, *trail_bounds, artist=trail)) + _head_windows.append(tuple(int(b) for b in head_bounds)) line.set_data(head[:, 0], head[:, 1]) if trail is not None: trail.set_data(trail_seg[:, 0], trail_seg[:, 1]) @@ -2613,7 +2867,7 @@ def update_lines_serial_2d(num, data_lines, lines, trail_lines, datasets=list(data_lines), style='serial', order='serial', current_index=_idx, current_fraction=_frac, revealed_counts=_counts, - window_bounds=tuple((0, c) for c in _counts)) + window_bounds=tuple(_head_windows)) return lines def update_morph_2d(num, morph_state): @@ -2829,7 +3083,8 @@ def _trail_kwargs(kw): else first_pts) _mkw = (kwargs_list[mesh_slot] if isinstance(kwargs_list[mesh_slot], dict) else {}) - morph_markersize = _mkw.get("markersize") or 1.5 + morph_markersize = (_mkw.get("markersize") + or _morph.MORPH_DEFAULT_MARKERSIZE_PT) # GH #284: see the identical note in `animate_plot3D`. ds_alphas = [ (kwargs_list[i] if isinstance(kwargs_list[i], dict) @@ -3054,20 +3309,46 @@ def _trail_kwargs(kw): # axis_scale='data' (GH #285) is the deliberate exception: its whole # point is that the drawn coordinates ARE the data's own, so its ticks # and spines stay on (matplotlib's defaults) and only the top/right - # spines are dropped, the way a plain time-series panel is drawn. + # spines are dropped, the way a plain time-series panel is drawn. The + # gridlines seaborn's 'whitegrid' style (applied by `plot()`) puts on + # hypertools' own axes are removed, matching the plotly backend's + # `showgrid=False` (1.1 release review: the matplotlib data-scale plot + # drew a grey grid the plotly one never had). A caller's `ax=` keeps + # whatever grid its owner gave it. + # the font stack in force NOW (`plot()` draws inside the rc_context + # that sets it), given explicitly to the axis labels and title below: + # a caller's `ax=` (every `panels=` cell) created its label and title + # Text artists outside that context, with the bare 'sans-serif' alias, + # which resolves at DRAW time to the caller's rcParams -- DejaVu Sans + # beside hypertools' own Noto Sans figures (1.1 release review). On + # hypertools' own axes this is the family they were created with. + _text_family = list(plt.rcParams['font.family']) if axis_scale == 'data': for _side in ('top', 'right'): if _side in ax.spines: ax.spines[_side].set_visible(False) + if not _caller_ax: + ax.grid(False) if xlabel is not None: - ax.set_xlabel(xlabel) + ax.set_xlabel(xlabel, fontfamily=_text_family) if ylabel is not None: - ax.set_ylabel(ylabel) + ax.set_ylabel(ylabel, fontfamily=_text_family) if x_date: # the x column holds `date2num` day numbers (see `plot()`'s # ndims=1 series mode); without a date converter they would tick # as five-digit floats ax.xaxis_date() + # ...and matplotlib's default date formatter writes every tick + # as a full 'YYYY-MM-DD', which collide at the default figure + # size (7 of 7 adjacent pairs overlapped for a 30-day index; + # 1.1 release review, F13). The concise formatter writes only + # what changes between ticks, with the rest once as an offset, + # which is also what plotly's date axis draws. + import matplotlib.dates as mdates + _locator = mdates.AutoDateLocator() + ax.xaxis.set_major_locator(_locator) + ax.xaxis.set_major_formatter( + mdates.ConciseDateFormatter(_locator)) elif xlabel is None and ylabel is None and zlabel is None: ax.set_axis_off() elif hasattr(ax, "get_proj"): @@ -3093,11 +3374,20 @@ def _trail_kwargs(kw): _axis.set_ticks([]) ax.patch.set_visible(False) if xlabel is not None: - ax.set_xlabel(xlabel) + ax.set_xlabel(xlabel, fontfamily=_text_family) if ylabel is not None: - ax.set_ylabel(ylabel) + ax.set_ylabel(ylabel, fontfamily=_text_family) if zlabel is not None: - ax.set_zlabel(zlabel) + ax.set_zlabel(zlabel, fontfamily=_text_family) + # `Axes3D.get_tightbbox` measures its axes "for layout only", which + # drops the axis LABELS (matplotlib's `_get_tightbbox_for_layout_ + # only`), so a `bbox_inches='tight'` save -- every notebook's inline + # render -- cut the z-label off at the right edge (1.1 release + # review, feature-tour 9.15). A draw-nothing figure artist whose + # extent is the labels' puts them back into the figure's tight bbox. + if not any(isinstance(a, _AxisLabelExtent) and a.axes_ref is ax + for a in ax.figure.artists): + ax.figure.add_artist(_AxisLabelExtent(ax)) else: # 2-D (or 1-D): hide ticks/spines/gridlines individually, leaving # `axison` at its default True so the axis label Text artist(s) @@ -3109,9 +3399,9 @@ def _trail_kwargs(kw): ax.grid(False) ax.patch.set_visible(False) if xlabel is not None: - ax.set_xlabel(xlabel) + ax.set_xlabel(xlabel, fontfamily=_text_family) if ylabel is not None: - ax.set_ylabel(ylabel) + ax.set_ylabel(ylabel, fontfamily=_text_family) # zlabel on a 2-D/1-D plot is rejected upstream in plot.py # (ValueError, before the pipeline even runs) -- zlabel is # guaranteed None here. @@ -3121,7 +3411,8 @@ def _trail_kwargs(kw): # add title if title is not None: - _apply_title(ax, title, font=font, title_kwargs=title_kwargs) + _apply_title(ax, title, font=font, title_kwargs=title_kwargs, + family=_text_family) # add legend: to the RIGHT of the plot, vertically centered on the # box (never overlapping the data). `prop=font` (GH #205) applies the @@ -3140,6 +3431,18 @@ def _trail_kwargs(kw): ax.legend(handles=_legend_proxy_handles(legend_entries, fmt), **_legend_call) else: + if legend_order: + _handles, _labels = ax.get_legend_handles_labels() + _rank = {str(lbl): k for k, lbl in enumerate(legend_order)} + _slots = [j for j, lbl in enumerate(_labels) if lbl in _rank] + _sorted = sorted(_slots, key=lambda j: _rank[_labels[j]]) + if _sorted != _slots: + _perm = list(range(len(_labels))) + for _slot, _src in zip(_slots, _sorted): + _perm[_slot] = _src + _legend_call = dict( + _legend_call, handles=[_handles[j] for j in _perm], + labels=[_labels[j] for j in _perm]) _legend_artist = ax.legend(**_legend_call) if legend_colors is not None: _recolor_legend_handles(_legend_artist, legend_colors) diff --git a/hypertools/plot/meshutil.py b/hypertools/plot/meshutil.py index d14525a6..860233cb 100644 --- a/hypertools/plot/meshutil.py +++ b/hypertools/plot/meshutil.py @@ -23,8 +23,7 @@ import warnings import numpy as np -from scipy.spatial import ConvexHull, Delaunay, QhullError -from scipy.spatial.distance import cdist +from scipy.spatial import ConvexHull, Delaunay, QhullError, cKDTree __all__ = [ "smooth_hull_3d", @@ -847,17 +846,23 @@ def blinn_phong_vertex_colors( ) -def vertex_colors_from_points(verts, points, point_colors, power=2.0, eps=1e-9): +def vertex_colors_from_points(verts, points, point_colors, power=2.0, eps=1e-9, + k=8): """Per-vertex base colors as an inverse-distance-weighted average of the - data points' colors (Shepard's method / IDW). + colors of each vertex's ``k`` nearest data points (local Shepard/IDW). - For each mesh vertex, its color is a weighted blend of the data - coordinates' colors, with weight ``1 / distance**power`` -- so the - NEAREST coordinates dominate a vertex's color and distant ones fall off - smoothly (with the default ``power=2``). A vertex that coincides with a - data point takes that point's color exactly. This is what colors a - ``surface=`` hull to match the hue of the points it encloses, per-vertex, - instead of painting the whole hull one flat (mean) color. + For each mesh vertex, its color is a weighted blend of its ``k`` nearest + data coordinates' colors, with weight ``1 / distance**power``. A vertex + that coincides with a data point takes that point's color exactly. This + is what colors a ``surface=`` hull to match the hue of the points it + encloses, per-vertex, instead of painting the whole hull one flat (mean) + color. + + The blend is LOCAL on purpose: over ALL points, ``1/d**2`` weights in + 3-D let the many distant points outweigh the few near ones (each shell + of radius ``r`` holds ~``r**2`` points), so every vertex drifted toward + the dataset's mean colour -- washed out and locally wrong under a + ``hue=`` gradient (maintainer report, 2026-09-11). Parameters ---------- @@ -873,6 +878,9 @@ def vertex_colors_from_points(verts, points, point_colors, power=2.0, eps=1e-9): Added to ``distance**power`` before inverting so a zero distance (a vertex exactly on a point) yields a large-but-finite weight rather than a divide-by-zero. Default 1e-9. + k : int, optional + Number of nearest data points blended per vertex (default 8; capped + at the number of points). Returns ------- @@ -884,10 +892,12 @@ def vertex_colors_from_points(verts, points, point_colors, power=2.0, eps=1e-9): colors = np.asarray(point_colors, dtype=float)[:, :3] if len(points) == 0 or len(colors) == 0: raise ValueError('need at least one data point/color to color a surface') - d = cdist(verts, points) # (V, P) euclidean distances + k = max(1, min(int(k), len(points))) + d, idx = cKDTree(points).query(verts, k=k) # (V, k) nearest points + d, idx = d.reshape(len(verts), k), idx.reshape(len(verts), k) w = 1.0 / (d ** power + eps) # inverse-distance weights w /= w.sum(axis=1, keepdims=True) # normalize per vertex - return np.clip(w @ colors, 0.0, 1.0) + return np.clip(np.einsum('vk,vkc->vc', w, colors[idx]), 0.0, 1.0) def face_colors_from_vertex_colors(vertex_colors, faces): diff --git a/hypertools/plot/morph.py b/hypertools/plot/morph.py index 46513a5f..52d8cbce 100644 --- a/hypertools/plot/morph.py +++ b/hypertools/plot/morph.py @@ -11,7 +11,8 @@ distance to its partner in the next cloud, then ease between clouds with smoothstep interpolation on a hold/morph/hold/... frame schedule ([hold_1, morph_1->2, hold_2, ..., hold_N] -- ``2*N - 1`` segments for ``N`` -datasets). Both ``hypertools.plot.matplotlib_backend`` and +datasets). Holds draw the exact clouds; every morph frame draws a cloud +strictly between them (:func:`transition_t`). Both ``hypertools.plot.matplotlib_backend`` and ``hypertools.plot.plotly_backend`` build their ``animate='morph'`` frames from these same helpers, so the two backends stay in lockstep. @@ -50,6 +51,7 @@ "morph_visible_mask", "segment_frame_counts", "frame_to_segment", + "transition_t", "morph_positions", "interpolate_color", "morph_color", @@ -59,8 +61,18 @@ "morph_schedule", "ZERO_ROTATION_FLOOR", "MORPH_SURFACE_SIZING_MARGIN", + "MORPH_DEFAULT_MARKERSIZE_PT", ] +#: Default size, in points, of the traveling morph cloud's ``'.'`` markers +#: when no ``markersize=`` is given -- ONE value both backends read +#: (`matplotlib_backend.animate_plot3D`/`animate_plot2D` and +#: `plotly_backend.MORPH_DEFAULT_MARKERSIZE_PT`), smaller than the general +#: 6 pt marker default because a cloud has many points. 1.1 visual review +#: (L9): the historical 1.5 pt drew about 3 px dots on matplotlib and +#: sub-pixel ones on plotly (30 dots covered 24 px in total). +MORPH_DEFAULT_MARKERSIZE_PT = 4.0 + #: Constant-rotation-speed fix (maintainer request, 2026-07-06): when a #: LIST of per-segment `rotations` is given to ``animate='morph'``, each #: segment's screen time is made PROPORTIONAL to its own rotation count so @@ -364,6 +376,23 @@ def frame_to_segment(frame_counts, frame): return last, frame_counts[last] - 1, frame_counts[last] +def transition_t(step, n_steps): + """The eased interpolation parameter of transition frame `step` (of + `n_steps`): ``smoothstep((step + 1) / (n_steps + 1))``. + + Always strictly inside ``(0, 1)``: the ENDPOINTS of a transition are the + two hold clouds, which the neighbouring HOLD segments already draw, so a + transition frame that sampled them would only repeat a hold. The + samples are the ``n_steps`` interior points of an ``n_steps + 1``-way + split, which keeps them symmetric about the midpoint and evenly spaced + before easing. (1.1 visual review, L9: sampling ``step / (n_steps - 1)`` + put ``t = 0`` and ``t = 1`` on the first and last transition frames, + so a 2-frame transition drew two copies of the hold clouds and never + showed an in-between cloud.) + """ + return float(smoothstep((step + 1) / (max(1, n_steps) + 1))) + + def morph_positions(sampled, seg_idx, step, n_steps): """Point positions for segment `seg_idx` at local `step` (of `n_steps` total in that segment). @@ -371,14 +400,15 @@ def morph_positions(sampled, seg_idx, step, n_steps): Even `seg_idx` (0, 2, 4, ...) are HOLDS: the corresponding dataset ``sampled[seg_idx // 2]`` is returned unchanged (matches every frame). Odd `seg_idx` are MORPHS: ``sampled[k]`` eases into ``sampled[k + 1]`` - (``k = seg_idx // 2``) via :func:`smoothstep`, ``t=0`` exactly - reproducing ``sampled[k]`` and ``t=1`` exactly reproducing - ``sampled[k + 1]``. + (``k = seg_idx // 2``) at ``t = transition_t(step, n_steps)``, which + is strictly between 0 and 1 on every transition frame -- so every + transition frame draws an in-between cloud, and the exact clouds are + drawn only by the holds on either side. """ if seg_idx % 2 == 0: return sampled[seg_idx // 2] k = seg_idx // 2 - t = float(smoothstep(step / max(1, n_steps - 1))) + t = transition_t(step, n_steps) return (1.0 - t) * sampled[k] + t * sampled[k + 1] @@ -394,13 +424,14 @@ def interpolate_color(color_a, color_b, t): def morph_color(colors, seg_idx, step, n_steps): """Drawn color for segment `seg_idx` at local `step` (of `n_steps`), on the SAME schedule as :func:`morph_positions`: holds are the - corresponding dataset's own solid color; morphs RGB-lerp (smoothstep- - eased, matching the position easing) between the two datasets' - colors.""" + corresponding dataset's own solid color; morphs RGB-lerp between the + two datasets' colors at the same eased :func:`transition_t` as the + positions (strictly between the two colors on every transition + frame).""" if seg_idx % 2 == 0: return tuple(colors[seg_idx // 2]) k = seg_idx // 2 - t = float(smoothstep(step / max(1, n_steps - 1))) + t = transition_t(step, n_steps) return interpolate_color(colors[k], colors[k + 1], t) @@ -414,12 +445,12 @@ def morph_alpha(alphas, seg_idx, step, n_steps): was given. Returns ``None`` -- "leave the artist at its default" -- when every entry is ``None``, so an animation that never asked for an alpha is drawn exactly as before. Otherwise a HOLD (even `seg_idx`) - is the held dataset's own alpha, and a MORPH (odd) eases (smoothstep, - matching the position/color easing) from the departing dataset's - alpha to the arriving one's, an unset entry counting as opaque - (``1.0``) -- the same rule every other animation style applies to a - per-dataset ``alpha=`` list, restated for one artist that stands in - for several datasets in turn. + is the held dataset's own alpha, and a MORPH (odd) eases (at the same + :func:`transition_t` as the position/color) from the departing + dataset's alpha to the arriving one's, an unset entry counting as + opaque (``1.0``) -- the same rule every other animation style applies + to a per-dataset ``alpha=`` list, restated for one artist that stands + in for several datasets in turn. """ if alphas is None or all(a is None for a in alphas): return None @@ -427,17 +458,24 @@ def morph_alpha(alphas, seg_idx, step, n_steps): if seg_idx % 2 == 0: return vals[seg_idx // 2] k = seg_idx // 2 - t = float(smoothstep(step / max(1, n_steps - 1))) + t = transition_t(step, n_steps) # `a + t * (b - a)` (not `(1 - t) * a + t * b`) so a scalar `alpha=` # (every entry equal) stays EXACTLY that value on every transition # frame instead of drifting by a float ulp. return vals[k] + t * (vals[k + 1] - vals[k]) -def resolve_morph_rotations(rotations, n_datasets): +def resolve_morph_rotations(rotations, n_datasets, loop=False): """Validate `rotations` for ``animate='morph'`` with `n_datasets` morphing datasets. + `n_datasets` counts the CLOSING repeat of the first cloud that + ``loop=True`` appends (`plot`'s `loop=` docstring: `n` clouds give + ``2(n + 1) - 1`` segments), so a looped 3-cloud morph is validated + against 4 datasets / 7 segments. Pass ``loop=True`` so the error + message can say so, rather than reporting a dataset count one larger + than the caller passed with no explanation. + A scalar is returned unchanged (a single float): the TOTAL number of camera rotations spread uniformly over the whole animation, exactly like every other ``animate`` style (see @@ -457,11 +495,17 @@ def resolve_morph_rotations(rotations, n_datasets): if isinstance(rotations, (list, tuple)): n_segments = 2 * n_datasets - 1 if len(rotations) != n_segments: + looped = ( + f" (loop=True closes the sequence with a repeat of the " + f"first cloud, so {n_datasets - 1} clouds count as " + f"{n_datasets}: 2 * ({n_datasets - 1} + 1) - 1 = " + f"{n_segments} segments, the last two being the closing " + "morph back to cloud 1 and its hold)" if loop else "") raise ValueError( f"rotations list has {len(rotations)} entries but " f"animate='morph' with {n_datasets} morphing datasets " f"needs exactly {n_segments} (2 * n_datasets - 1: " - "[hold_1, morph_1->2, hold_2, ..., hold_N])" + f"[hold_1, morph_1->2, hold_2, ..., hold_N]){looped}" ) return [float(r) for r in rotations] return float(rotations) diff --git a/hypertools/plot/plot.py b/hypertools/plot/plot.py index 85539d73..0aee2c65 100644 --- a/hypertools/plot/plot.py +++ b/hypertools/plot/plot.py @@ -11,6 +11,7 @@ import copy import inspect import os +import sys import warnings import matplotlib.pyplot as plt import numpy as np @@ -19,6 +20,9 @@ # what makes every `np.` reference in this file resolvable to a reader # and to a linter, instead of 186 F405 "may be undefined" findings. from .._shared.helpers import * +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like, as_pandas_dataframe, + row_index_x) from .._shared.params import default_params from ..core.model import external_stacklevel from ..tools.analyze import analyze @@ -27,7 +31,8 @@ from .colors import (mat2colors, colors2groups, get_palette_colors, continuous_colormap, NAN_COLOR, is_missing_label, resolve_category_colors, dataset_palettes, - palette_lead_color, dataset_colors) + palette_lead_color, dataset_colors, + _looks_like_dataset_palettes) from ..reduce.reduce import reduce as reducer from ..tools.format_data import format_data from .matplotlib_backend import _draw, _apply_title @@ -62,7 +67,12 @@ # unusable there; `plot()` warns (once, naming every such kwarg) rather # than silently dropping it with no feedback at all. def _is_plotly_figure(obj): - """True for a plotly Figure (without importing plotly when it is absent).""" + """True for a plotly Figure (without importing plotly: an object cannot + be a plotly figure in a process where plotly was never imported, and an + import here would load an installed plotly before `lazy_import` has + checked its version -- after which it can no longer be upgraded).""" + if 'plotly' not in sys.modules: + return False try: from plotly.basedatatypes import BaseFigure except ImportError: @@ -70,6 +80,78 @@ def _is_plotly_figure(obj): return isinstance(obj, BaseFigure) +def _is_plotly_cell(obj): + """True for one cell of a ``hyp.subplots(..., backend='plotly')`` grid.""" + from .plotly_backend import PlotlyCell + return isinstance(obj, PlotlyCell) + + +def _plotly_hover_names(n_traces, legend, category_names=None, + group_labels=None, user_labels=None, + series_names=None, hue=None, hue_group_labels=None): + """The name each drawn trace shows in a plotly hover label (1.1 release + review, maintainer finding: hovering showed plotly's "trace 0", + "trace 1", ...). + + A trace is named by the label the legend shows -- or WOULD show under + ``legend=True`` -- for it, whether or not a legend is drawn (plotly's + `showlegend` stays governed by `legend=`): its hue/cluster CATEGORY + (every run of a category, not just the one carrying the legend entry), + its hierarchy's top-level GROUP (leaves and intermediate means too), + its label in a `legend=`/`names=` list (kept even when a continuous hue + drops the drawn legend), its `ndims=1` series COLUMN, else its 1-based + dataset number -- exactly `legend=True`'s own numbering. A lone trace + with none of those gets None, which the backend draws with no name box + at all rather than a meaningless "1". + """ + def _usable(seq): + return seq is not None and len(seq) == n_traces + + legend = legend if isinstance(legend, (list, tuple)) \ + and _usable(legend) else None + # what `legend=True` labels a categorical hue's groups with (the same + # rule `plot()`'s legend block applies) + hue_labels = None + if hue is not None: + try: + hue_labels = (list(hue_group_labels) + if hue_group_labels is not None + else sorted(set(hue), key=list(hue).index)) + except TypeError: # per-observation arrays: no group list + hue_labels = None + if category_names is None and _usable(hue_labels): + category_names = ['the unlabeled group' if str(c) == '_nolegend_' + else c for c in hue_labels] + + def _labelled(i): + return legend is not None and not str(legend[i]).startswith('_') + + # a category's LEGEND text (a legend list may rename categories): the + # repeat runs of a category, which carry the sentinel, take it too + renamed = {} + if _usable(category_names) and legend is not None: + for i in range(n_traces): + if _labelled(i): + renamed.setdefault(category_names[i], legend[i]) + names = [] + for i in range(n_traces): + name = None + if _usable(group_labels): + name = group_labels[i] + elif _labelled(i): + name = legend[i] + elif _usable(category_names): + name = renamed.get(category_names[i], category_names[i]) + elif _usable(user_labels): + name = user_labels[i] + elif _usable(series_names): + name = series_names[i] + elif n_traces > 1: + name = i + 1 + names.append(None if name is None else str(name)) + return names + + _PLOTLY_MAPPED_KWARGS = frozenset( {'color', 'alpha', 'linewidth', 'markersize', 'marker', 'linestyle', 'label'}) @@ -179,12 +261,40 @@ def _seaborn_palette_arg(palette, n_colors): from matplotlib.colors import Colormap from .colors import DEFAULT_PALETTE, IMAGE_PALETTE_PREFIX - if isinstance(palette, collections.abc.Mapping): + if (isinstance(palette, (list, tuple, np.ndarray)) + and len(palette) == 0): + # seaborn cycles a colour list with `itertools.cycle`, so an EMPTY + # list escaped as a bare StopIteration from inside + # `sns.color_palette`; say what the no-hue path already says. + raise ValueError("palette= was given as an empty list; supply at " + "least one color") + if isinstance(palette, collections.abc.Mapping) or ( + isinstance(palette, (list, tuple)) + and all(isinstance(e, collections.abc.Mapping) for e in palette)): + # one dict, or a list of {category: color} dicts (one per dataset, + # each naming its own categories): both name CATEGORIES, and the + # ambient cycle runs over the drawn groups (hue runs, not datasets, + # after regrouping), so counting the dicts against `n_colors` is + # meaningless -- the categorical paths resolve them by name return [tuple(c) for c in get_palette_colors(DEFAULT_PALETTE, n_colors)] _specs = dataset_palettes(palette, n_colors) if _specs is not None: - return [tuple(palette_lead_color(spec)) for spec in _specs] + # a {category: color} dict ENTRY has no lead colour (it names + # categories, like a whole-plot dict); the ambient cycle gets the + # default palette's colour at that position, and the categorical + # paths resolve the dicts by name (`_categorical_color_label_maps` + # merges a per-dataset list of dicts into one mapping). + _default = None + out = [] + for i, spec in enumerate(_specs): + if isinstance(spec, collections.abc.Mapping): + if _default is None: + _default = get_palette_colors(DEFAULT_PALETTE, n_colors) + out.append(tuple(_default[i])) + else: + out.append(tuple(palette_lead_color(spec))) + return out if isinstance(palette, Colormap) or ( isinstance(palette, str) and palette.startswith(IMAGE_PALETTE_PREFIX)): @@ -202,6 +312,293 @@ def _fmt_draws_line(fmt): return has_line_component(fmt) +def _fmt_color_letter(fmt): + """The colour a matplotlib format string names (``'r-'`` -> ``'r'``), + or None when it names none, is not a string, or does not parse. The + plot.py twin of `plotly_backend._fmt_color_letter`: the plotly backend + has no colour cycle, so the palette is handed to it as explicit + per-dataset ``color=`` entries, and this is how a colour letter in + ``fmt=`` keeps its matplotlib precedence over that palette (1.1 release + review, round 6: ``fmt='r-'`` drew the palette colour on plotly).""" + if not isinstance(fmt, str) or not fmt or _process_plot_format is None: + return None + try: + return _process_plot_format(fmt)[2] + except Exception: # noqa: BLE001 - an unparseable fmt names no colour + return None + + +def _palette_slots_consumed(n_drawn, mpl_kwargs, line_colors, draw_fmt, + category_colored=False): + """How many colour-cycle slots one call's `n_drawn` drawn datasets take + up: what a later ``ax=`` call into the same axes, figure or grid cell + continues the palette from, on BOTH backends. + + Only a dataset coloured FROM the cycle consumes a slot. An explicit + ``color=`` (or a palette mapping resolved into one), a ``hue=`` + colouring, and a colour letter in ``fmt=`` (``'r-'``) colour their + datasets without touching the cycle -- exactly as a matplotlib axes + treats them -- so ``hyp.plot(a, fmt='r-')`` followed by + ``hyp.plot(b, ax=...)`` gives ``b`` the FIRST palette colour, the one + the single call ``fmt=['r-', '-']`` gives it (1.1 release review, round + 7: the count was every drawn dataset, so that second call drew the + SECOND palette colour on plotly, and in an initially empty + `hyp.subplots` cell on both backends). + + `category_colored` says so EXPLICITLY when a ``hue=``/``cluster=`` + grouping coloured the drawn groups by category: such a call consumes + no slot whatever its fmt. Inferring that from the resolved kwargs + alone missed the marker-only categorical grouping (``hue=..., + fmt='o'``), whose groups used to be drawn straight from the ambient + cycle -- so ordinary -> ``hue=`` with ``fmt='o'`` -> ordinary ended on + the THIRD palette colour instead of the second (round 9).""" + if category_colored or "color" in mpl_kwargs or line_colors is not None: + return 0 + return sum( + 1 for i in range(n_drawn) + if _fmt_color_letter(draw_fmt[i] if i < len(draw_fmt) else None) + is None) + + +def _palette_continuation(palette, n, offset=0, used=None): + """The `n` colours a call draws from `palette` into an axes, figure or + grid cell where earlier hypertools calls already took `offset` slots -- + `used` being those slots' colours, in order (None when unknown). + + With no earlier slots this is the palette's own `n`-colour sampling, + exactly what a fresh call draws. With earlier slots the palette is + CONTINUED, never restarted and never repeated: + + - a fixed-sequence palette (a colour list, 'deep', 'Set2', ...) gives + the next colours in its sequence -- the `offset`-th onward of its + ``offset + n`` sampling, what one call with every dataset would draw; + - an evenly RE-SAMPLED palette ('hls', 'husl', a colormap such as + 'viridis') places its colours by count, so the ``offset + n`` + sampling is not an extension of the ``offset`` one: 'hls' at 2 is + 0 and 180 degrees, at 4 it is 0/90/180/270, so taking its last two + repeated 180 (1.1 release review: two datasets then two more drew + a2 == b1). Instead take the smallest sampling of ``m >= offset + n`` + colours that CONTAINS every colour already used and fill its free + slots in palette order -- 'hls' 2 + 2 draws exactly the 4-colour + 'hls' set, and 1 + 1 still draws what one call with both draws. + + When no such sampling exists (the earlier colours came from another + palette, or a short colour list has no free slot left to fill), it + falls back to the ``offset``-th onward of the ``offset + n`` sampling. + """ + import seaborn as sns + + def sample(m): + """The palette's own `m`-colour sampling, as RGB tuples.""" + return [tuple(float(v) for v in c[:3]) for c in sns.color_palette( + _seaborn_palette_arg(palette, m), m)] + + if n <= 0: + return [] + if offset <= 0: + return sample(n) + fallback = sample(offset + n)[offset:] + if used is None or len(used) != offset: + return fallback + used_arr = np.asarray([tuple(c)[:3] for c in used], dtype=float) + # every sampling that could hold `used` plus `n` free slots; the upper + # bound covers the nested refinements repeated composition produces + # (each at most doubles the grid the used colours sit on) + for m in range(offset + n, 4 * (offset + n) + 9): + cand = np.asarray(sample(m), dtype=float) + dist = np.abs(cand[:, None, :] - used_arr[None, :, :]).max(axis=-1) + taken = dist < 1e-6 + if not taken.any(axis=0).all(): + continue + free = [i for i in range(m) if not taken[i].any()] + if len(free) >= n: + return [tuple(cand[i]) for i in free[:n]] + return fallback + + +def _observed_palette_colors(palette, n_drawn, color=None, draw_fmt=None, + prior=None): + """The distinct colours the OBSERVED data takes, in order -- what a + default forecast grouping continues the palette past + (`_forecast_group_palette`). + + `color` is the resolved per-trace ``color=`` (an explicit colour, or the + per-run colours a categorical ``hue=``/``cluster=`` assigned); without + it each dataset lacking a colour letter in `draw_fmt` takes the next + palette slot. `prior` is ``(offset, used)`` for slots earlier calls + took on a reused axes/figure/cell. The unlabeled-group gray is not a + palette slot and is left out.""" + from matplotlib.colors import to_rgb + out = [] + + def _add(c): + try: + rgb = tuple(float(v) for v in to_rgb(c)) + except (TypeError, ValueError): + return + if rgb != tuple(to_rgb(NAN_COLOR)) and rgb not in out: + out.append(rgb) + + offset, used = prior if prior is not None else (0, None) + if offset and used is not None and len(used) == offset: + for c in used: + _add(tuple(c)[:3]) + if color is not None: + single = isinstance(color, str) or _is_single_color(color) + for c in ([color] if single else list(color)): + _add(c) + return out + n_slots = sum(1 for i in range(n_drawn) + if _fmt_color_letter(draw_fmt[i] if draw_fmt is not None + and i < len(draw_fmt) else None) + is None) + if (isinstance(palette, collections.abc.Mapping) + or _looks_like_dataset_palettes(palette)): + return out + # the cycle is sampled for EVERY drawn dataset and the unlettered ones + # take its first colours in order -- exactly how both backends colour + # them (a lettered dataset skips a slot without changing the sampling) + for c in _palette_continuation(palette, n_drawn, offset, + used if offset else None)[:n_slots]: + _add(c) + return out + + +def _prior_palette_slots(ax, plotly_into, backend): + """``(offset, used)``: the palette slots (and their colours, or None) + earlier hypertools calls took on the axes / plotly figure / grid cell + this call draws into -- read from the same records the observed + palette continuation reads (``ax._hyp_palette_offset``/``_used``; + ``layout.meta``'s ``hyp_datasets_drawn``/``hyp_palette_used`` and their + per-cell forms). ``(0, None)`` for a fresh figure.""" + try: + is_plotly = resolve_backend(backend) == 'plotly' + except Exception: # noqa: BLE001 - an unresolvable backend has no record + is_plotly = False + if is_plotly: + if plotly_into is None: + return 0, None + if _is_plotly_cell(plotly_into): + meta = plotly_into.figure.layout.meta + meta = meta if isinstance(meta, dict) else {} + key = str(plotly_into.index) + return (int((meta.get('hyp_cell_datasets_drawn') or {}).get( + key, 0)), + (meta.get('hyp_cell_palette_used') or {}).get(key)) + meta = getattr(plotly_into, 'layout', None) + meta = meta.meta if meta is not None else None + meta = meta if isinstance(meta, dict) else {} + return int(meta.get('hyp_datasets_drawn', 0)), meta.get( + 'hyp_palette_used') + if ax is not None and hasattr(ax, 'set_prop_cycle'): + return (int(getattr(ax, '_hyp_palette_offset', 0) or 0), + getattr(ax, '_hyp_palette_used', None)) + return 0, None + + +def _forecast_group_palette(palette, observed): + """The DEFAULT colours of a `forecast_hue=`/`forecast_cluster=` grouping + (no `forecast_palette=`), as a function of the group count: the + figure's palette CONTINUED past the `observed` colours + (`_observed_palette_colors`), so no forecast group wears an observed + line's colour (maintainer decision, 1.1 review -- cluster 0 used to be + dataset 0's colour exactly). A per-dataset or mapping palette has no + sequence to continue, so 'hls' is continued past the observed count.""" + if (palette is None or isinstance(palette, collections.abc.Mapping) + or _looks_like_dataset_palettes(palette)): + palette = 'hls' + + def _colors(n): + return _palette_continuation(palette, n, len(observed), + observed if observed else None) + return _colors + + +def _extend_palette_used(offset, used, taken): + """The palette-slot colours an axes/figure/cell holds after a call that + took `taken` past `offset` earlier slots whose colours were `used`, or + None when the earlier colours are unknown (then the next call falls back + to the count alone; see `_palette_continuation`).""" + if offset <= 0: + return [tuple(float(v) for v in c[:3]) for c in taken] + if used is None or len(used) != offset: + return None + return ([tuple(float(v) for v in c[:3]) for c in used] + + [tuple(float(v) for v in c[:3]) for c in taken]) + + +def _record_palette_used(fig, into, offset, used, taken): + """Record the palette-slot colours on a plotly figure's ``layout.meta``, + beside the slot count the plotly backend records there: per figure as + ``'hyp_palette_used'``, per `PlotlyCell` as + ``'hyp_cell_palette_used'[str(index)]``.""" + layout = getattr(fig, 'layout', None) + if layout is None: + return + new = _extend_palette_used(offset, used, taken) + new = None if new is None else [list(c) for c in new] + meta = layout.meta if isinstance(layout.meta, dict) else {} + if into is not None and _is_plotly_cell(into): + cells = dict(meta.get('hyp_cell_palette_used') or {}) + cells[str(into.index)] = new + layout.meta = {**meta, 'hyp_cell_palette_used': cells} + else: + layout.meta = {**meta, 'hyp_palette_used': new} + + +def _rank_plotly_legend(fig, first_trace, order): + """The plotly form of `_draw`'s ``legend_order=``: list this call's + legend entries (the traces from `first_trace` on) with the ones named + in `order` sorted into that order among their own positions, via + ``legendrank``. Every earlier trace keeps its place ahead of them.""" + data = getattr(fig, 'data', None) + if not data: + return + own = [tr for tr in data[first_trace:] + if tr.showlegend is not False and tr.name is not None] + rank = {str(lbl): k for k, lbl in enumerate(order)} + slots = [j for j, tr in enumerate(own) if str(tr.name) in rank] + wanted = sorted(slots, key=lambda j: rank[str(own[j].name)]) + if wanted == slots: + return + perm = list(range(len(own))) + for slot, src in zip(slots, wanted): + perm[slot] = src + # plotly lists entries by (legendrank, trace order); the default rank + # is 1000, so this call's entries start there -- after any earlier + # trace of equal rank -- and count up in the wanted order + base = max([1000] + [tr.legendrank for tr in data[:first_trace] + if tr.legendrank is not None]) + for position, j in enumerate(perm): + own[j].legendrank = base + position + + +def _sync_color_scales(drawn, *infos): + """Rewrite each discrete colour scale in `infos` (the return_model + bundle's ``'colors'``, a discrete colorbar's info) in place to the + per-dataset colours `drawn` -- the colours the datasets were actually + drawn in -- when it has one colour per dataset. Only for datasets + coloured from the palette cycle (see `_color_scales_from_cycle` in + `plot`), whose scale was resolved from the palette's fresh sampling.""" + from matplotlib.colors import BoundaryNorm, ListedColormap, to_rgb + rgb = np.asarray([to_rgb(c) for c in drawn], dtype=float) + for info in infos: + if (not isinstance(info, dict) or info.get('kind') != 'discrete' + or info.get('colors') is None + or len(info['colors']) != len(rgb) or not len(rgb)): + continue + info['colors'] = rgb.copy() + if 'cmap' in info: + info['cmap'] = ListedColormap(info['colors']) + if info.get('norm') is not None: + info['norm'] = BoundaryNorm(np.arange(len(rgb) + 1) - 0.5, + len(rgb)) + if info.get('categories'): + info['categories'] = { + str(label): tuple(color) + for label, color in zip(info['labels'], info['colors'])} + + def _apply_forecast_override(style, override): """Overlay one dataset's `forecast_*=` override onto an inherited style. @@ -275,6 +672,14 @@ def _forecast_style_from(src_line, alpha_scale=FORECAST_ALPHA_SCALE, ``color``/``linestyle``/``linewidth``/``alpha`` kwargs for `ax.plot` (plus ``marker`` when `forecast_fmt=` asked for one). """ + # an explicit forecast COLOUR (`forecast_hue=`/`forecast_cluster=`/ + # `forecast_palette=`, or a colour letter in `forecast_fmt=`) is what + # tells the forecast from its trace, so it is drawn at the trace's own + # alpha rather than faded on top of being recoloured (1.1 release + # review, feature-tour 9.10: Set1 forecasts at 0.35 over 0.7 leaves + # were invisible). `forecast_alpha_scale_for` is the one rule. + from .forecast import forecast_alpha_scale_for + alpha_scale = forecast_alpha_scale_for(override, alpha_scale) if src_line is None: return _apply_forecast_override( dict(color=anchor_color, linestyle='-', @@ -298,9 +703,25 @@ def _forecast_style_from(src_line, alpha_scale=FORECAST_ALPHA_SCALE, override) +def _observed_run_lines(lines): + """The drawn observed RUNS among `lines`, one artist per run, in order. + + A marker-plus-line fmt (``'o-'``, ``'s--'``) draws each run as TWO + artists -- the smoothed line and a markers-only companion at the true + observations (`matplotlib_backend._plot_possibly_split`, which tags the + companion ``_hyp_marker_companion``). Every consumer that looks a run up + by index (the forecast and truth overlays' style source, the animated + head-run colour) must skip the companions, or run ``i`` reads run + ``i // 2``'s artists: three ``'o-'`` datasets gave forecasts red, red, + green instead of red, green, blue (1.1 release review, F1). + """ + return [ln for ln in lines + if not getattr(ln, '_hyp_marker_companion', False)] + + def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, owner=None, overrides=None, labels=None, - dataset_index=None): + dataset_index=None, src_lines=None): """Overlay one forecast trace per input dataset (GH #169), styled to match its source line (`_forecast_style_from`): same colour, linestyle and linewidth, at half its alpha. @@ -318,12 +739,16 @@ def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, to dataset i"; the multi-model form passes a real map, since it draws one forecast per (model, dataset) pair. - `labels` (GH #285): one legend label per forecast, or None. Only the - MULTI-MODEL form (`predict=['Kalman', 'ARIMA', ...]`) passes them -- - with several overlays on one trace, "which model is this?" cannot be - read off the figure otherwise. Every other call keeps the historical - `'_nolegend_'`, and `plot()` rebuilds the legend after this returns only - when a label was actually set. + `labels` (GH #285): one legend label per forecast, or None -- the + model's name, for every `predict=` form (1.1: a collection labels each + model; the single-model form labels its one model too). The label is + NOT put on the artist (every artist stays ``'_nolegend_'``): a model's + forecasts over several datasets are drawn in several colours, and a + legend built from the first of them would show dataset 0's colour as if + it were the model's. Each artist is tagged ``_hyp_forecast_label`` + instead, and `_forecast_legend_handles` builds one proxy glyph per + distinct label from those tags -- which `plot()` adds to the legend + after this returns. Returns ------- @@ -335,7 +760,11 @@ def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, # (dataset index, artist) for every artist created, so the identity tag # below survives an `ax.plot` call returning more than one artist _artist_dataset = [] - src_lines = list(ax.lines) + # `src_lines`: the observed lines the forecasts continue, run by run -- + # THIS call's, when the axes already held an earlier call's -- one + # artist per run (a split 'o-' run's markers companion is skipped) + src_lines = _observed_run_lines( + list(ax.lines) if src_lines is None else list(src_lines)) for i, fc in enumerate(raw_forecasts): # antialias (see `plot`'s `antialias=`): smooth the forecast the SAME # way as any other line, so a short forecast (e.g. t+1 = 5 vertices) @@ -347,23 +776,25 @@ def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, # forecast spans `n_rows - 1` row units no matter how many vertices # it is drawn with (see the 1-D branch below). _fc_rows = fc.shape[0] + _fc_step = 1 if antialias: - fc = _interp_static_line(fc) + fc, _fc_step = _antialias_static_line(fc) # `owner` maps forecast -> the RUN it continues, when hue=/cluster= # regrouped the traces. Without it, forecast i continues trace i. _src = owner[i] if owner is not None and i < len(owner) else i _src_line = src_lines[_src] if _src < len(src_lines) else None style = _forecast_style_from( _src_line, override=overrides[i] if overrides is not None else None) + # a `forecast_fmt=` marker ('ro:') belongs on the forecast's own + # STEPS -- the seam row and each forecast observation -- never on + # the ~900 antialiased vertices between them, which drew a dotted + # forecast as a solid tube of markers (1.1 release review; the + # marker contract of `plot`'s `antialias=`). One artist, so its + # legend glyph keeps the marker. + style['markevery'] = _step_markevery(_fc_step) d = fc.shape[1] if fc.ndim > 1 else 1 _before = len(artists) - # one legend entry per MODEL (not per model x dataset): the second - # and later datasets of the same model repeat a label the legend - # already carries, and matplotlib would list it again. _label = '_nolegend_' - if labels is not None and labels[i] is not None: - _label = (labels[i] if labels[i] not in labels[:i] - else '_nolegend_') if d >= 3: artists.extend(ax.plot( fc[:, 0], fc[:, 1], fc[:, 2], label=_label, **style)) @@ -404,13 +835,163 @@ def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, # downstream consumers pair forecasts with data by identity rather than # by list position (which only holds while forecasts stay # one-per-dataset in dataset order). + from .forecast import override_has_color as _pins_colour for _ds, _a in _artist_dataset: _a._hyp_forecast_role = 'static' + # an explicit forecast colour (forecast_hue=/cluster=/palette=, a + # colour letter in forecast_fmt=) that a continuous hue= must not + # repaint (`_apply_multicolor_lines`/`_apply_multicolor_markers`) + _a._hyp_forecast_pinned = bool(_pins_colour( + overrides[_ds] if overrides is not None + and _ds < len(overrides) else None)) _a._hyp_forecast_dataset = (dataset_index[_ds] if dataset_index is not None else _ds) + _a._hyp_forecast_label = (labels[_ds] if labels is not None + and _ds < len(labels) else None) return artists +def _add_overlay_legend_entries(ax, forecast_artists=None, truth_artists=None, + **legend_call): + """Rebuild `ax`'s legend with the overlay entries `_draw` could not + know about -- it built the legend from the data lines before any + overlay existed. One proxy per forecast label + (`_forecast_legend_handles`) goes in after the data entries, and a + labelled `truth=` artist after those; entries already present are + kept, in place, so calling this once for the forecasts and once for + the truth overlay lists ``data..., forecasts..., truth`` either way. + No legend on the axes means the call asked for none: nothing is added. + """ + legend = ax.get_legend() + if legend is None: + return + # EVERY tagged overlay on the axes, this call's and earlier calls' + # into the same `ax=`: the legend is rebuilt by role -- the data + # entries `_draw` listed, then one entry per forecast label (its + # glyph decided over every forecast wearing that label, however many + # calls drew them), then one 'truth' (Codex round 4: three calls into + # one axes listed 'truth' three times and lost the earlier forecast + # keys) + from matplotlib.colors import to_rgba + from matplotlib.lines import Line2D + from .forecast import FORECAST_LEGEND_COLOR + fc_lines = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_label', None) is not None + and getattr(ln, '_hyp_forecast_role', None) + in ('static', 'live')] + truth_lines = [ln for ln in ax.lines + if getattr(ln, '_hyp_truth_label', None)] + if not fc_lines and not truth_lines: + return + fc_handles = _forecast_legend_handles(fc_lines) + overlay_labels = {h.get_label() for h in fc_handles} + truth_label = truth_lines[0]._hyp_truth_label if truth_lines else None + if truth_label is not None: + overlay_labels.add(truth_label) + kept = [(h, lab) for h, lab in zip(legend.legend_handles, + [t.get_text() for t in + legend.get_texts()]) + if lab not in overlay_labels] + handles = [h for h, _ in kept] + fc_handles + labels = [lab for _, lab in kept] + [h.get_label() for h in fc_handles] + if truth_lines: + first = truth_lines[0] + # the colour decided over EVERY truth artist, not only the tagged + # first one: the key stands for all datasets' truths, and wore + # dataset 0's colour whatever the others were (1.1 review, F10) + colors = {to_rgba(ln.get_color()) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'truth'} + handles.append(Line2D( + [], [], color=(first.get_color() if len(colors) == 1 + else FORECAST_LEGEND_COLOR), + linestyle=first.get_linestyle(), linewidth=first.get_linewidth(), + marker=first.get_marker(), markersize=first.get_markersize(), + markevery=None, label=truth_label)) + labels.append(truth_label) + ax.legend(handles, labels, **legend_call) + + +def _recolor_overlay_legend(ax, colors, close_fig=None): + """Apply `legend_colors=`'s plain colour list to a legend that also + lists `predict=`/`truth=` overlay entries (data entries first, then the + overlays -- `_add_overlay_legend_entries`' order). + + One colour per legend entry recolours them all. One colour per DATA + entry -- what the list meant before a forecast had a legend entry of + its own, and still what it means on a legend without overlays -- + recolours the data entries and leaves the forecast/truth glyphs as + drawn (1.1 release review: ``legend_colors=['r', 'b']`` with + ``predict=`` raised 'the legend has 3'). Any other count raises + ``ValueError`` -- after closing `close_fig` (the figure this call + created), so a refused call leaves no stray figure open. + """ + from .matplotlib_backend import _recolor_legend_handles + legend = ax.get_legend() + handles = list(legend.legend_handles) + texts = [t.get_text() for t in legend.get_texts()] + overlay = {getattr(ln, '_hyp_forecast_label', None) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) + in ('static', 'live')} + overlay |= {getattr(ln, '_hyp_truth_label', None) for ln in ax.lines} + overlay.discard(None) + n_data = sum(1 for t in texts if t not in overlay) + colors = list(colors) + if len(colors) not in (len(handles), n_data): + if close_fig is not None: + plt.close(close_fig) + raise ValueError( + f"legend_colors has {len(colors)} entries but the legend has " + f"{len(handles)} ({n_data} data entr" + f"{'y' if n_data == 1 else 'ies'} plus " + f"{len(handles) - n_data} forecast/truth entr" + f"{'y' if len(handles) - n_data == 1 else 'ies'}); pass one " + "color per data entry, one per legend entry, or (label, color) " + "pairs to define the entries outright.") + if len(colors) < len(handles): + colors += [h.get_color() for h in handles[len(colors):]] + _recolor_legend_handles(legend, colors) + + +def _forecast_legend_handles(artists): + """One proxy `Line2D` legend handle per distinct forecast label, from + the ``_hyp_forecast_label`` tags `_draw_forecast_overlays` (and the + animated live-forecast setup) put on the forecast artists. + + Each handle wears its forecasts' linestyle, linewidth, marker and alpha + -- so the key looks like the faded, dashed lines it names -- and their + colour when every forecast under that label shares one (a single + dataset, or a `forecast_palette=` that colours by model). When the + label spans several colours (one model continuing several datasets, + each in its own colour) the glyph is `forecast.FORECAST_LEGEND_COLOR`, + a neutral gray: the entry then stands for the model, not for any one + dataset. `plotly_backend._forecast_legend_traces` is its twin, so both + backends list the same entries with the same glyphs. + """ + from matplotlib.colors import to_rgba + from matplotlib.lines import Line2D + from .forecast import (FORECAST_LEGEND_COLOR, FORECAST_LEGEND_MIN_ALPHA, + group_forecast_labels) + tags = [getattr(_a, '_hyp_forecast_label', None) for _a in artists] + handles = [] + for label, members in group_forecast_labels(tags): + lines = [artists[_k] for _k in members] + first = lines[0] + colors = {to_rgba(_l.get_color()) for _l in lines} + color = first.get_color() if len(colors) == 1 \ + else FORECAST_LEGEND_COLOR + # legible whatever the forecasts' own alpha: the floor keeps a key + # for translucent forecasts from vanishing (see the constant) + alphas = [_l.get_alpha() for _l in lines if _l.get_alpha() is not None] + alpha = max([FORECAST_LEGEND_MIN_ALPHA] + alphas) \ + if alphas else None + handles.append(Line2D( + [], [], color=color, linestyle=first.get_linestyle(), + linewidth=max(_l.get_linewidth() for _l in lines), alpha=alpha, + marker=first.get_marker(), markersize=first.get_markersize(), + markevery=None, label=str(label))) + return handles + + #: How a `truth=` overlay is drawn, next to the forecast it is compared #: against. It is OBSERVED data, not a prediction, so it is the opaque, #: solid, marked trace and the forecast is the faded one -- the same @@ -420,7 +1001,7 @@ def _draw_forecast_overlays(ax, raw_forecasts, antialias=True, def _draw_truth_overlays(ax, raw_truths, antialias=True, owner=None, - label=None): + label=None, src_lines=None): """Overlay each trace's ACTUAL continuation (`truth=`, GH #285). Styled from the trace it continues (same colour and linewidth, via @@ -436,16 +1017,19 @@ def _draw_truth_overlays(ax, raw_truths, antialias=True, owner=None, identity. `label` names the FIRST truth in the legend -- every trace's truth means the same thing, so one entry covers them all. - Returns the created artists. They are drawn in FULL, in every mode: a - truth trace is what happened, not a prediction being refitted as an - animation's reveal advances, so it stays put while the forecast moves. + Returns the created artists, drawn in FULL. A time-progressing + animation then slices them per frame to the rows a drawn forecast has + reached (`_truth_frame_updater`), using the ``_hyp_truth_step`` each + artist carries. """ artists = [] - src_lines = list(ax.lines) + src_lines = _observed_run_lines( + list(ax.lines) if src_lines is None else list(src_lines)) for i, tr in enumerate(raw_truths): tr = np.asarray(tr, dtype=float) _rows = tr.shape[0] - drawn = _interp_static_line(tr) if antialias else tr + drawn, _step = (_antialias_static_line(tr) if antialias + else (tr, 1)) _src = owner[i] if owner is not None and i < len(owner) else i _src_line = src_lines[_src] if _src < len(src_lines) else None style = _forecast_style_from(_src_line, alpha_scale=1.0) @@ -455,9 +1039,18 @@ def _draw_truth_overlays(ax, raw_truths, antialias=True, owner=None, # vertices between them -- so the smoothed curve and the sample # markers are two artists, exactly as `_plot_possibly_split` draws a # marker+line fmt for the observed data. - line_style = dict(style, marker=None) + # The curve KEEPS the marker with `markevery=[]` (draws none) so its + # legend glyph shows line + marker -- otherwise the 'truth' entry + # is a solid line in the trace's own colour, identical to the + # observed trace's entry (feature tour 9.11, 2026-09-06; the same + # trick `_plot_possibly_split` uses for a marker+line fmt). + line_style = dict(style, markevery=[]) marker_style = dict(style, linestyle='None') - _label = '_nolegend_' if label is None else label + # never labelled: the legend entry is a proxy glyph built by + # `_add_overlay_legend_entries` from the `_hyp_truth_label` tag, + # so a reused axes lists ONE 'truth' however many calls drew one + # (Codex round 4) + _label = '_nolegend_' _before = len(artists) d = drawn.shape[1] if drawn.ndim > 1 else 1 if d >= 3: @@ -488,6 +1081,14 @@ def _draw_truth_overlays(ax, raw_truths, antialias=True, owner=None, for _a in artists[_before:]: _a._hyp_forecast_role = 'truth' _a._hyp_forecast_dataset = i + # vertices per held-out row: the curve's dense vertex ``k * step`` + # is truth row ``k`` (the markers are the rows themselves), which is + # how an animation slices both to the rows revealed so far + # (`_truth_frame_updater`) + artists[_before]._hyp_truth_step = int(_step) + artists[_before + 1]._hyp_truth_step = 1 + if label is not None and artists[_before:]: + artists[_before]._hyp_truth_label = str(label) # one 'truth' legend entry for the whole figure: every trace's # truth means the same thing, so repeating the label per dataset # would list it once per series @@ -495,6 +1096,46 @@ def _draw_truth_overlays(ax, raw_truths, antialias=True, owner=None, return artists +def _truth_frame_updater(truth_artists, counts): + """A frame updater revealing `truth=` artists as the forecast reaches + them (maintainer decision, 1.1 review). + + `counts[frame][i]` (`forecast.truth_reveal_counts`) is how many held-out + rows truth ``i`` shows; 0 hides it, ``k`` shows its seam row plus rows + ``1..k``. Each artist keeps its FULL geometry captured here and is + sliced to ``k * _hyp_truth_step + 1`` vertices per frame -- a pure + function of the frame, so replays and saves match playback. The legend + is untouched: its 'truth' entry is a proxy glyph + (`_add_overlay_legend_entries`), present on every frame. The returned + updater has already been applied for frame 0. + """ + full = [] + for art in truth_artists: + data = (art.get_data_3d() if hasattr(art, 'get_data_3d') + else art.get_data()) + full.append((art, [np.asarray(c, dtype=float).copy() for c in data], + int(getattr(art, '_hyp_truth_step', 1)))) + n_frames = len(counts) + + def _update(ctx): + row = counts[min(max(int(ctx.frame), 0), n_frames - 1)] + for art, data, step in full: + i = int(getattr(art, '_hyp_forecast_dataset', 0)) + k = row[i] if i < len(row) else 0 + stop = k * step + 1 if k > 0 else 0 + cols = [c[:stop] for c in data] + if len(cols) == 3: + art.set_data_3d(*cols) + else: + art.set_data(*cols) + art.set_visible(k > 0) + + class _Frame0: + frame = 0 + _update(_Frame0) + return _update + + def _categorical_color_label_maps(hue, palette, explicit_colors, group_labels, sort_numeric): """Map each hue category -> (colour, legend label), in the SAME drawn @@ -535,6 +1176,48 @@ def _categorical_color_label_maps(hue, palette, explicit_colors, else [str(c) for c in drawn]) _by_name = resolve_category_colors(palette, _names) cat_color = {c: _by_name[_names[i]] for i, c in enumerate(drawn)} + elif _looks_like_dataset_palettes(palette): + # a PER-DATASET palette list (GH #285) meeting a CATEGORICAL + # grouping. The groups drawn here are categories, not datasets, so + # handing the list to `_seaborn_palette_arg(palette, len(drawn))` + # made `dataset_palettes` count the CATEGORIES as datasets and + # report "lists 3 per-dataset palettes but 2 dataset(s) were + # passed" for a three-dataset call. Two readings are meaningful: + # every entry a {category: color} dict -- each dataset naming its + # own categories' colours -- merges into one mapping resolved by + # name; anything else has no category to colour and is rejected + # with the real counts. + _names = (list(group_labels) + if isinstance(group_labels, (list, tuple)) + and len(group_labels) == len(drawn) + else [str(c) for c in drawn]) + entries = list(palette) + if all(isinstance(e, collections.abc.Mapping) for e in entries): + merged = {} + for entry in entries: + for name, colour in entry.items(): + if name in merged and merged[name] != colour: + raise ValueError( + f"palette= names category {name!r} in more " + f"than one per-dataset dict with different " + f"colors ({merged[name]!r} and {colour!r}); " + "a category has one color, so name it once " + "or give every dict the same color for it.") + merged[name] = colour + _by_name = resolve_category_colors(merged, _names) + cat_color = {c: _by_name[_names[i]] for i, c in enumerate(drawn)} + else: + n_cat = len(drawn) + raise ValueError( + f"palette= lists {len(entries)} per-dataset palettes, but " + f"this plot colors by CATEGORY ({n_cat} categor" + f"{'y' if n_cat == 1 else 'ies'} from hue=/cluster=/" + "n_clusters=), so there is no dataset for each palette to " + "color. Pass ONE palette for the categories -- a palette " + f"name, a list of at least {n_cat} colors, a Colormap, or " + "a {category: color} dict -- or a list of {category: " + "color} dicts (one per dataset, each naming its own " + "categories).") else: pal = sns.color_palette( _seaborn_palette_arg(palette, len(drawn)), len(drawn)) @@ -651,35 +1334,82 @@ def _interp_static_line(arr): `_STATIC_LINE_TARGET_VERTICES` vertices and contains every original sample exactly. This is the STATIC half of `plot`'s ``antialias=``. """ - return antialias_line(arr, _STATIC_LINE_TARGET_VERTICES)[0] + return _antialias_static_line(arr)[0] + + +def _antialias_static_line(arr): + """`_interp_static_line`, also returning the subdivision ``step``: the + densified ``dense[::step]`` is exactly `arr`, so ``step`` is what places + a marker on the ORIGINAL rows (`_step_markevery`).""" + return antialias_line(arr, _STATIC_LINE_TARGET_VERTICES) + + +def _step_markevery(step): + """The matplotlib ``markevery`` that marks only the original rows of a + line densified with subdivision `step` (every vertex when ``step`` is + 1, i.e. nothing was densified).""" + return None if step <= 1 else slice(0, None, int(step)) + + +def _anim_grid_stride(n_rows, n_frames): + """Grid rows per source row of an animated line: observation ``i`` of an + `n_rows`-row dataset is row ``i * stride`` of its animation grid. + + The grid is the smallest UNIFORM refinement of the source rows with at + least `n_frames` rows: ``stride = ceil((n_frames - 1) / (n_rows - 1))`` + (at least 1), giving ``(n_rows - 1) * stride + 1`` rows. Returns 1 for a + dataset with fewer than 2 rows (nothing to interpolate). + """ + n = int(n_rows) + if n < 2: + return 1 + f = max(2, int(n_frames)) + return max(1, -(-(f - 1) // (n - 1))) def _interp_anim_line(arr, n_frames): - """Resample one trajectory onto the animation's exact frame grid. - - PCHIP-interpolates (the same monotone interpolant the static path uses) - onto ``np.linspace(0, n - 1, n_frames)``: exactly `n_frames` rows -- one - per animation frame -- for EVERY dataset (release-1.0 audit: the - historical ``np.arange``-step grid was computed from the FIRST dataset - only, so later datasets of a different length were silently truncated or - ran out mid-animation, F04-003, and floating-point step error produced - 901/41 frames where the docstring promises exactly - ``frame_rate * duration``, F04-004). Endpoints are exact, so the - animation provably reaches the final sample. + """Put one line trajectory on its animation grid, never DOWNsampling. + + The grid is ``np.linspace(0, n - 1, (n - 1) * stride + 1)`` with + ``stride = _anim_grid_stride(n, n_frames)``: a uniform refinement of the + source rows, so observation ``i`` is EXACTLY grid row ``i * stride`` and + the grid has at least `n_frames` rows. A dataset with at least as many + rows as frames keeps its rows as-is (``stride == 1``); a shorter one is + PCHIP-interpolated (the same monotone interpolant the static path uses) + between its observations. The backends pace any grid length onto the + ``round(frame_rate * duration)`` frames through + `trails.anim_window_bounds`, and every grid consumer (the forecast reveal + schedule, `trails.dataset_window_bounds`, per-point labels) maps grid + row ``j`` back to source parameter ``j * (n - 1) / (grid - 1)``, which + this grid keeps exact. + + 1.1 visual review (L8): the grid used to be exactly ``n_frames`` rows, + which DOWNsampled any dataset longer than the frame count -- a 36-sample + helix in a 9-frame animation was drawn through 9 of its points, a + zig-zag star with a drawn radius as low as 0.23 against a true 0.5. + Earlier history (release-1.0 audit): per-dataset gridding replaced a + grid computed from the FIRST dataset only (F04-003), and exact + endpoints replaced an ``np.arange`` step that missed the final sample + (F04-004). """ from scipy.interpolate import PchipInterpolator as pchip arr = np.asarray(arr) n = arr.shape[0] if n < 2: return arr - grid = np.linspace(0.0, n - 1.0, max(2, int(n_frames))) + stride = _anim_grid_stride(n, n_frames) + if stride == 1: + return arr.copy() + grid = np.linspace(0.0, n - 1.0, (n - 1) * stride + 1) out = pchip(np.arange(n), arr)(grid) - out[0] = arr[0] - out[-1] = arr[-1] + # PCHIP passes through its knots only up to floating error: pin every + # observation so it is provably an exact vertex of the drawn line + out[::stride] = arr return out -def _require_finite_for_line(xi, dataset_index): +def _require_finite_for_line(xi, dataset_index, input_finite=None, + manip=None): """Fail fast, with a hypertools-level message, when a line-styled trajectory still contains non-finite values after preprocessing. @@ -689,16 +1419,45 @@ def _require_finite_for_line(xi, dataset_index): PPCA imputation cannot reconstruct them (it already warned), and the raw scipy traceback named neither the problem nor the fix (release-1.0 audit, F05-011). + + `input_finite` says whether the dataset was finite BEFORE the analysis + pipeline ran (``None`` when unknown, e.g. under ``transform=``). When it + was, the missing rows are the pipeline's doing, not the input's, and the + message says so -- naming the `manip=` stage when there is one, since a + trailing ``Smooth(center=False)`` leaves its first ``kernel_width - 1`` + rows NaN under pandas' rolling semantics unless ``min_periods=1`` is + passed (1.1 review, X2). """ - if not np.isfinite(np.asarray(xi, dtype=float)).all(): + if np.isfinite(np.asarray(xi, dtype=float)).all(): + return + if input_finite: + if manip is not None: + hint = ( + f"The manip= stage ({manip!r}) introduced them: a trailing " + "(center=False) Smooth leaves its first kernel_width - 1 " + "rows NaN under pandas' rolling-window semantics. Pass " + "min_periods=1 to smooth those rows over the observations " + "available so far (e.g. Smooth(kernel='boxcar', " + "kernel_width=12, center=False, min_periods=1)), or drop " + "them from the result.") + else: + hint = ( + "One of the normalize=/reduce=/align= stages introduced " + "them (a constant column z-scores to NaN, for example); " + "check the stages' output on this dataset with " + "hyp.analyze(...).") raise ValueError( - f"dataset {dataset_index} still contains non-finite values " - "(NaN/inf) after preprocessing, so its line cannot be smoothed/" - "animated. This usually means some rows had ALL features " - "missing -- the default PPCA imputation cannot fill those. " - "Drop those rows, impute them first (e.g. hyp.impute(data, " - "model='Kalman')), or plot markers only (fmt='.')." - ) + f"dataset {dataset_index} was finite on input but contains " + "non-finite values (NaN/inf) after the analysis pipeline ran, " + f"so its line cannot be smoothed/animated. {hint}") + raise ValueError( + f"dataset {dataset_index} still contains non-finite values " + "(NaN/inf) after preprocessing, so its line cannot be smoothed/" + "animated. This usually means some rows had ALL features " + "missing -- the default PPCA imputation cannot fill those. " + "Drop those rows, impute them first (e.g. hyp.impute(data, " + "model='Kalman')), or plot markers only (fmt='.')." + ) def _normalize_save_path(save_path): @@ -846,8 +1605,8 @@ def _validate_title(title, style=None, order=None, n_datasets=None): if not serial_style or not isinstance(title, (list, tuple)): if _style_cannot_go_serial and isinstance(title, (list, tuple)): raise TypeError( - f"title must be a string (or None), not " - f"{type(title).__name__}. animate={_style!r} has no serial " + f"title must be a string, a callable (ctx -> str), or None, " + f"not {type(title).__name__}. animate={_style!r} has no serial " "ordering (it does not reveal datasets one at a time), so " "order='serial' is ignored and per-dataset title lists are " "not meaningful for it -- pass a single string title " @@ -855,13 +1614,21 @@ def _validate_title(title, style=None, order=None, n_datasets=None): "for a style that supports per-dataset titles." ) raise TypeError( - f"title must be a string (or None), not {type(title).__name__}. " + f"title must be a string, a callable (ctx -> str), or None, not " + f"{type(title).__name__}. " "Per-dataset titles are only meaningful for serial-style " "animations (order='serial' or animate='morph'), and must be a " "list/tuple there. For a per-dataset legend entry use names=; " "for a per-observation annotation use labels=." ) - titles = [str(t) for t in title] + _bad = [(i, t) for i, t in enumerate(title) if not isinstance(t, str)] + if _bad: + i, t = _bad[0] + raise TypeError( + f"title= list entries must all be strings (one per dataset); " + f"entry {i} is {type(t).__name__}: {t!r}. Use '' for a dataset " + "that should show no title.") + titles = list(title) if n_datasets is not None and len(titles) != n_datasets: raise ValueError( f"title has {len(titles)} entries but there are {n_datasets} " @@ -1151,8 +1918,22 @@ def _valid_line2d_kwargs(): return valid +class _Unset: + """Type of a "this argument was not passed" default. + + Only its identity matters (callers test ``value is SENTINEL``); the + class exists so the default reads as ``<default>`` in `help()` and in + the rendered signature instead of ``<object object at 0x...>``. + """ + + __slots__ = () + + def __repr__(self): + return '<default>' + + #: Sentinel: `slow_warning_seconds` was not passed, so use the default. -_UNSET_SLOW_WARNING = object() +_UNSET_SLOW_WARNING = _Unset() def _validate_forecast_trail(forecast_trail, predict): @@ -1183,6 +1964,58 @@ def _validate_forecast_trail(forecast_trail, predict): return forecast_trail +def _prepare_palettes(palette, forecast_palette, sort=None, reduce=None, + manip=None, normalize=None, align=None): + """Resolve the palette forms that need the ``palette_*`` options. + + A t x k data matrix (`colors.is_palette_matrix`) becomes a colormap + through `colors.matrix_palette` (reduced with ``palette_reduce`` -- + default 'PCA' -- under ``palette_manip``/``palette_normalize``/ + ``palette_align``, sorted by ``palette_sort``, default 'columns'). An + ``'image:<path>'`` spec gets ``?sort=<palette_sort>`` appended when the + call asks for a sort and the spec does not already carry one (images + default to 'value' inside `colors._image_palette_list`). Every other + form -- names, color lists, dicts, colormaps -- is returned as is, and a + per-dataset list is handled entry by entry. Idempotent, so the panels + branch can forward the result through a second plot() call.""" + from .colors import (IMAGE_PALETTE_PREFIX, PALETTE_SORT_KEYS, + _parse_image_spec, is_palette_matrix, + matrix_palette) + + if sort is not None and sort not in PALETTE_SORT_KEYS: + raise ValueError( + f"palette_sort= must be one of {PALETTE_SORT_KEYS} or None; " + f"got {sort!r}") + + def one(spec): + """One palette spec, prepared.""" + if is_palette_matrix(spec): + return matrix_palette( + spec, reduce=reduce if reduce is not None else 'PCA', + sort=sort if sort is not None else 'columns', + normalize=normalize, manip=manip, align=align) + if sort is not None and isinstance(spec, str) \ + and spec.startswith(IMAGE_PALETTE_PREFIX): + source = spec[len(IMAGE_PALETTE_PREFIX):].strip() + path, options = _parse_image_spec(source) + if 'sort' in options: + return spec + joiner = '&' if options else '?' + return f'{IMAGE_PALETTE_PREFIX}{source}{joiner}sort={sort}' + return spec + + def many(value): + """A whole-plot palette, or a per-dataset list entry by entry.""" + if isinstance(value, (list, tuple)) and not is_palette_matrix(value) \ + and any(is_palette_matrix(e) or ( + isinstance(e, str) and e.startswith(IMAGE_PALETTE_PREFIX)) + for e in value): + return [one(e) for e in value] + return one(value) + + return many(palette), many(forecast_palette) + + def _validate_extra_plot_kwargs(extra_kwargs): """Fail fast, BEFORE the analyze/reduce pipeline runs, on extra kwargs that no backend can use (release-1.0 audit, F01-012/F03-005): @@ -1466,16 +2299,23 @@ def _is_single_color(value): return False -def _validate_title_color(title_color, segment_titles): +def _validate_title_color(title_color, segment_titles, title_kwargs=None): """Split `title_color=` into (scalar color, per-segment sequence). A single color (string or RGB(A) tuple) styles whatever title is drawn. A SEQUENCE of colors -- or a callable ``ctx -> color`` -- tints each segment of a serial/morph `title=` LIST, so it is only meaningful - alongside one. + alongside one. Naming a colour BOTH here and as + ``title_kwargs={'color': ...}`` is a conflict (one of them silently + lost), so it raises. """ if title_color is None: return None, None + if title_kwargs and 'color' in title_kwargs: + raise ValueError( + f"title_color={title_color!r} and title_kwargs['color']=" + f"{title_kwargs['color']!r} were both given; they set the same " + "title colour, so pass only one of them.") if callable(title_color) or ( not _is_single_color(title_color) and isinstance(title_color, (list, tuple, np.ndarray))): @@ -1516,7 +2356,13 @@ def _wrap_title_text(title, width, newline='\n'): import textwrap if isinstance(title, (list, tuple)): return [_wrap_title_text(t, width, newline) for t in title] - return newline.join(textwrap.wrap(str(title), width) or ['']) + # each EXISTING line is wrapped on its own and the author's line breaks + # are kept: `textwrap.wrap` alone (its default `replace_whitespace`) + # turned 'first line\nsecond line' into one 'first line second line'. + lines = [] + for line in str(title).split('\n'): + lines.extend(textwrap.wrap(line, width) or ['']) + return newline.join(lines) def _validate_title_wrap(title_wrap): @@ -1553,6 +2399,29 @@ def _title_is_pattern(title): return isinstance(title, str) and _TITLE_INDEX_FIELD in title +def _dataset_values(item): + """The values of ONE user dataset as a numpy array: a DataFrame dataset + (`is_frame_dataset`: pandas, polars, whatever datawrangler recognises) + through its pandas form, a series-like (`is_series_like`) through its + ``to_numpy``, anything else through `np.asarray`.""" + if is_frame_dataset(item): + return as_pandas_dataframe(item).to_numpy() + if is_series_like(item) and hasattr(item, 'to_numpy'): + return item.to_numpy() + return np.asarray(item) + + +def _series_values_list(item): + """The values of ONE series-like (`is_series_like`) as a python list: + pandas objects through ``tolist`` (Series, Index, Categorical), polars + through ``to_list``, anything else by iteration.""" + if hasattr(item, 'tolist'): + return item.tolist() + if hasattr(item, 'to_list'): + return item.to_list() + return list(item) + + def _capture_row_indices(x): """The row index of each input dataset, or None where there isn't one. @@ -1564,16 +2433,23 @@ def _capture_row_indices(x): all, so the common case allocates nothing and stores nothing. """ def _index_of(item): - if isinstance(item, (pd.DataFrame, pd.Series)): - idx = item.index - # a plain 0..n-1 RangeIndex carries no information a row number - # does not already carry; treat it as "no index" so the error - # message for a pattern title names the real problem. - if isinstance(idx, pd.RangeIndex) and idx.start == 0 \ - and idx.step == 1: - return None - return idx - return None + if is_frame_dataset(item): + # every dataframe backend datawrangler recognises, read through + # its pandas form; a backend with no row index (polars) comes + # back with the default RangeIndex, i.e. "no index" below + item = as_pandas_dataframe(item) + elif not is_series_like(item): + return None + idx = getattr(item, 'index', None) + if not isinstance(idx, pd.Index): + return None + # a plain 0..n-1 RangeIndex carries no information a row number + # does not already carry; treat it as "no index" so the error + # message for a pattern title names the real problem. + if isinstance(idx, pd.RangeIndex) and idx.start == 0 \ + and idx.step == 1: + return None + return idx items = x if isinstance(x, (list, tuple)) else [x] indices = [] @@ -1596,11 +2472,14 @@ def _capture_column_names(x): list of strings or ``None``. """ def _columns_of(item): - if isinstance(item, pd.Series): - return None if item.name is None else [str(item.name)] - if not isinstance(item, pd.DataFrame): + if is_series_like(item): + # a labelled vector's one column is its name (pandas: None when + # unnamed; polars: '' when unnamed) + name = getattr(item, 'name', None) + return None if name is None or name == '' else [str(name)] + if not is_frame_dataset(item): return None - cols = item.columns + cols = as_pandas_dataframe(item).columns # a bare 0..k-1 RangeIndex names nothing a column number does not # already say (the same rule `_capture_row_indices` applies to a # default row index) @@ -1621,6 +2500,20 @@ def _columns_of(item): return names if any(n is not None for n in names) else None +def _timedelta_unit(seconds): + """``(name, divisor)`` of the unit a `TimedeltaIndex` is drawn in: the + largest of days/hours/minutes/seconds in which the typical step between + observations is still at least one unit (S4).""" + seconds = np.asarray(seconds, dtype=float) + step = (float(np.median(np.abs(np.diff(seconds)))) if len(seconds) > 1 + else float(abs(seconds[0])) if len(seconds) else 0.0) + for name, divisor in (('days', 86400.0), ('hours', 3600.0), + ('minutes', 60.0)): + if step >= divisor: + return name, divisor + return 'seconds', 1.0 + + def _series_x_axis(index, n_rows, epoch_ms=False): """The x values one dataset is drawn against in `ndims=1` series mode. @@ -1631,13 +2524,16 @@ def _series_x_axis(index, n_rows, epoch_ms=False): matplotlib date numbers (`matplotlib.dates.date2num`), or epoch MILLISECONDS when `epoch_ms` is set, because that is what each backend's date axis reads. - step : float -- the spacing one observation advances x by (the MEDIAN - difference, so an irregular index still projects a sensible - forecast horizon). Used to place `predict=`/`truth=` overlays on the - x axis, since a forecast's own x values are an extrapolation the - plotting code, not the model, is entitled to decide. + step : float -- the median spacing, used as a fallback for truth rows + without an index. Forecast overlays use their model's future index; + this helper only converts those times to display coordinates. is_date : bool -- whether `values` are date numbers. - label : str or None -- the index's name, for the default `xlabel=`. + label : str or None -- the index's name, for the default `xlabel=`. A + `TimedeltaIndex` is drawn as elapsed time in the largest of + days/hours/minutes/seconds its typical step fills + (`_timedelta_unit`; it used to be drawn in raw nanoseconds), and + its label names that unit: ``'<name> (hours)'``, or ``'time + (hours)'`` for an unnamed index. An index of the wrong LENGTH is ignored (a `manip=`/`resample=` stage can change the row count long after the index was captured), as is a @@ -1648,6 +2544,8 @@ def _series_x_axis(index, n_rows, epoch_ms=False): fallback = (np.arange(float(n_rows)), 1.0, False, label) if index is None or len(index) != n_rows or n_rows == 0: return fallback + if isinstance(index, pd.PeriodIndex): + index = index.to_timestamp() if isinstance(index, pd.DatetimeIndex): if epoch_ms: # ...via an explicit millisecond cast, NOT `view('int64') / 1e6`: @@ -1658,7 +2556,10 @@ def _series_x_axis(index, n_rows, epoch_ms=False): # A tz-AWARE index cannot be cast to a naive dtype at all # ("Cannot use .astype to convert from timezone-aware ..."), so # it is converted to UTC first -- epoch milliseconds are an - # absolute instant either way, and plotly reads them as UTC. + # absolute instant either way. These numbers are INTERNAL: + # plotly.js would draw a numeric date in the viewer's LOCAL + # time zone, so `plotly_backend._dates_as_iso` hands the figure + # naive date strings instead (1.1 release review). _idx = (index.tz_convert('UTC').tz_localize(None) if index.tz is not None else index) values = np.asarray( @@ -1667,6 +2568,12 @@ def _series_x_axis(index, n_rows, epoch_ms=False): from matplotlib.dates import date2num values = np.asarray(date2num(index.to_pydatetime()), dtype=float) is_date = True + elif isinstance(index, pd.TimedeltaIndex): + seconds = np.asarray(index.total_seconds(), dtype=float) + unit, divisor = _timedelta_unit(seconds) + values = seconds / divisor + label = f"{label} ({unit})" if label else f"time ({unit})" + is_date = False else: try: values = np.asarray(index, dtype=float) @@ -1683,7 +2590,58 @@ def _series_x_axis(index, n_rows, epoch_ms=False): return values, step, is_date, label -def _resolve_truth(truth, datasets, t, series_step=None): +def _resolve_date_xlim(xlim, epoch_ms): + """`xlim=` on a DATE x axis (`ndims=1` series mode over a + `DatetimeIndex`), in the unit the backend reads (S2). + + A datetime-like value (a ``Timestamp``, ``datetime``, ``np.datetime64`` + or a date string such as ``'2020-01-05'``) is converted to matplotlib + date numbers, or to epoch milliseconds when `epoch_ms` is set (the + plotly date axis). A NUMBER is a matplotlib day number on BOTH backends + -- the unit `matplotlib.dates.date2num` produces and the one the + matplotlib axis has always read -- and is converted for plotly, which + used to read it as milliseconds and draw a 1970 range. A tz-aware value + is read as its UTC instant, as the data's index is. + """ + from matplotlib.dates import date2num, num2date + out = [] + for value in xlim: + if value is None: + # an OPEN side (``xlim=(None, '2020-01-10')``): left for the + # data bounds to fill, as a numeric axis leaves it to autoscale. + # `pd.Timestamp(None)` is NaT, which became a NaN limit and + # crashed "Axis limits cannot be NaN or Inf" (1.1 review, F8) + out.append(None) + continue + if (isinstance(value, (int, float, np.integer, np.floating)) + and not isinstance(value, bool)): + number = float(value) + if epoch_ms: + stamp = pd.Timestamp(num2date(number)) + number = float((stamp - pd.Timestamp(0, tz='UTC')) + // pd.Timedelta('1ms')) + out.append(number) + continue + try: + stamp = pd.Timestamp(value) + except (TypeError, ValueError) as exc: + raise ValueError( + "xlim= on a date axis takes datetime-like values (a " + "Timestamp, datetime, numpy datetime64 or a date string " + "such as '2020-01-05') or matplotlib day numbers; got " + f"{value!r}.") from exc + if stamp.tz is not None: + stamp = stamp.tz_convert('UTC').tz_localize(None) + if epoch_ms: + out.append(float((stamp - pd.Timestamp(0)) + // pd.Timedelta('1ms'))) + else: + out.append(float(date2num(stamp.to_pydatetime()))) + return tuple(out) + + +def _resolve_truth(truth, datasets, t, series_step=None, forecast_paths=None, + time_coordinates=None): """One seam-prepended ``(t + 1, d)`` truth array per DRAWN TRACE (GH #285). `truth=` is the ACTUAL continuation of each plotted trace, so it is @@ -1707,13 +2665,12 @@ def _resolve_truth(truth, datasets, t, series_step=None): series = series_step is not None def _as_2d(item): - arr = np.asarray( - item.values if isinstance(item, (pd.DataFrame, pd.Series)) - else item, dtype=float) + arr = np.asarray(_dataset_values(item), dtype=float) return arr.reshape(-1, 1) if arr.ndim == 1 else arr if isinstance(truth, (list, tuple)): items = [_as_2d(item) for item in truth] + indexes = [getattr(item, 'index', None) for item in truth] else: arr = _as_2d(truth) if n_traces > 1 and arr.shape[1] == n_traces and ( @@ -1723,6 +2680,7 @@ def _as_2d(item): items = [arr[:, [j]] for j in range(n_traces)] else: items = [arr] + indexes = [getattr(truth, 'index', None)] * len(items) if len(items) != n_traces: raise ValueError( f"truth= must give the actual continuation of every drawn " @@ -1732,16 +2690,39 @@ def _as_2d(item): out = [] for i, (item, data) in enumerate(zip(items, datasets)): - if item.shape[0] != t: + horizon = len(forecast_paths[i]) - 1 if forecast_paths is not None else t + if item.shape[0] != horizon: raise ValueError( - f"truth= must have exactly t={t} rows (the forecast " + f"truth= must have exactly t={horizon} rows (the forecast " f"horizon), so it lines up with the forecast it is compared " f"against; entry {i} has {item.shape[0]}. Pass t=" f"{item.shape[0]} to forecast that far instead, or trim the " "held-out data.") - if series and item.shape[1] == 1: - xs = (float(data[-1, 0]) - + series_step[i] * np.arange(1, t + 1, dtype=float)) + if series and item.shape[1] != 1: + # a series-mode trace is ONE plotted column (its x is the + # index), so its truth is one column of values. Without this a + # 2-column truth for a 1-column trace matched the trace's + # internal (x, value) width and its first column was drawn as + # x -- a date axis stretched back to 1970 (1.1 release review, + # F3) + raise ValueError( + f"truth= entry {i} has {item.shape[1]} columns, but with " + "ndims=1 each trace is one plotted column (its x is the " + "index), so its truth= is one column of values. Pass one " + "single-column array per trace (or one array with one " + f"column per trace; {n_traces} trace(s) are plotted).") + if series: + idx = indexes[i] + explicit_times = (isinstance(idx, pd.Index) + and not (isinstance(idx, pd.RangeIndex) + and idx.start == 0 and idx.step == 1)) + if explicit_times and time_coordinates is not None: + xs = time_coordinates(idx, i) + elif forecast_paths is not None: + xs = forecast_paths[i][1:, 0] + else: + xs = (float(data[-1, 0]) + + series_step[i] * np.arange(1, horizon + 1, dtype=float)) item = np.column_stack([xs, item[:, 0]]) if item.shape[1] != data.shape[1]: raise ValueError( @@ -1749,10 +2730,70 @@ def _as_2d(item): f"trace it continues has {data.shape[1]}; truth= is read in " "the PLOTTED space (with reduce=None, the input space), so " "it must carry the same features as the plotted data.") - out.append(np.vstack([np.asarray(data[-1:], dtype=float), item])) + seam = forecast_paths[i][:1] if forecast_paths is not None else data[-1:] + out.append(np.vstack([np.asarray(seam, dtype=float), item])) return out +def _is_int_horizon(t): + """True for the integer form of `t=` (a bool is not a horizon).""" + return isinstance(t, (int, np.integer)) and not isinstance(t, bool) + + +def _resolve_datetime_horizon(t, row_indices, lengths): + """Turn a datetime-like `t=` into the integer step count `plot()` works + in, against each input dataset's captured row index (1.1 review, F2). + + `plot()` hands the forecaster bare analyze-space arrays -- their pandas + index was captured up front (`_capture_row_indices`) and dropped by + `format_data` -- so `hyp.predict`'s own datetime rule (`resolve_t`) is + applied HERE, to the captured `DatetimeIndex` of every dataset, and the + resulting step count is what the forecaster, the `truth=` check and the + animated schedule all see. Every dataset must resolve to the SAME count + (one `t=` describes one figure); a `t` at or before a dataset's last + observation is refused, because a plot draws forecasts and has nothing to + truncate to. + """ + from ..predict.common import resolve_t + steps = [] + for i, n_rows in enumerate(lengths): + idx = (row_indices[i] if row_indices is not None + and i < len(row_indices) else None) + if not isinstance(idx, pd.DatetimeIndex): + have = ('a plain 0..n-1 row index' if idx is None + else f'a {type(idx).__name__}') + raise ValueError( + f"t={t!r} is datetime-like, so every dataset needs a " + f"DatetimeIndex to measure it against; dataset {i} has " + f"{have}. Pass a DataFrame indexed by time, or an integer " + "t (a number of steps).") + if len(idx) != n_rows: + raise ValueError( + f"t={t!r} is datetime-like and is measured against the row " + f"index, but the analysis pipeline changed dataset {i}'s " + f"row count ({len(idx)} indexed rows, {n_rows} plotted), so " + "the index no longer describes the plotted observations. " + "Pass an integer t (a number of steps), or drop the " + "row-count-changing stage (manip='Resample', an edge-" + "trimming smoother).") + n_steps, _ = resolve_t(pd.DataFrame(index=idx), t) + if n_steps <= 0: + raise ValueError( + f"t={t!r} is at or before dataset {i}'s last observation " + f"({idx[-1]}), so there is nothing to forecast: plot() draws " + "forecasts and does not truncate. Pass a t after the last " + "observation, or slice the data yourself.") + steps.append(int(n_steps)) + if len(set(steps)) > 1: + raise ValueError( + f"t={t!r} resolves to a different number of steps for different " + f"datasets ({steps}; their indexes end at different times or " + "have different spacings), but one figure has one forecast " + "horizon. Pass an integer t (a number of steps), or datasets " + "whose indexes end at the same time with the same spacing.") + return steps[0] + + def _series_expand_list(value, owner, n_input, key=None): """Re-index a per-INPUT-DATASET list onto the per-COLUMN traces `ndims=1` series mode expands them into (GH #285). @@ -1796,11 +2837,10 @@ def _validate_dynamic_title(title, animate, row_indices): return None, title if not _title_is_pattern(title): return title, None - if not animate: - # a static plot has one row-index value that means anything: the - # last one. Resolved right here, so nothing per-frame is installed. - return None, _make_title_pattern_resolver(title, row_indices) if row_indices is None: + # animated or not: a pattern with nothing to read is an error the + # docstring promises BEFORE the pipeline runs, not a title drawn + # as its own literal braces. raise ValueError( f"title={title!r} is a per-frame format pattern (it contains " f"'{_TITLE_INDEX_FIELD}'), but the data passed to plot() has no " @@ -1818,10 +2858,16 @@ def _title_head_row(ctx, n_rows): ONE rule, deliberately, because a per-style rule would make the same pattern mean different things on the same data: - * **serial** reveals (``order='serial'``, ``animate='morph'``): the - last revealed row of the dataset being revealed right now, rescaled - onto the input's own row count (`plot` interpolates line data onto - the frame grid, so a drawn row is not an input row). + * **serial** reveals (``order='serial'``): the rows revealed SO FAR + across every dataset -- those already complete plus the revealed + part of the one being drawn now -- as a fraction of all rows, + rescaled onto the input's own row count (`plot` interpolates line + data onto the frame grid, so a drawn row is not an input row). It + is cumulative on purpose: rescaling only the CURRENT dataset's + reveal made the head jump back to row 0 every time the next dataset + started, so a companion panel (and a ``{index}`` title) ran + 0 -> 20 -> 10 -> 0 -> 39 over a three-dataset reveal instead of + advancing in lockstep. * **every other animated style** (``True``/``'parallel'``/ ``'window'``/``'spin'``): ``round(ctx.progress * (n_rows - 1))`` -- all datasets advance together, so the head fraction IS the @@ -1840,11 +2886,12 @@ def _title_head_row(ctx, n_rows): frac = 1.0 if ctx.progress is None else float(ctx.progress) if ctx.order == 'serial' and ctx.current_index is not None \ and ctx.revealed_counts is not None: - i = ctx.current_index - if i < len(ctx.revealed_counts) and i < len(ctx.datasets): - drawn = len(ctx.datasets[i]) - if drawn > 1: - frac = (ctx.revealed_counts[i] - 1) / (drawn - 1) + lengths = [len(d) for d in ctx.datasets] + counts = list(ctx.revealed_counts)[:len(lengths)] + total = sum(lengths) + revealed = sum(min(int(c), n) for c, n in zip(counts, lengths)) + if total > 1: + frac = (revealed - 1) / (total - 1) return int(min(n_rows - 1, max(0, round(frac * (n_rows - 1))))) @@ -1865,10 +2912,34 @@ def _resolve(ctx): if index is None: return pattern value = index[_title_head_row(ctx, len(index))] - return pattern.format(index=value) + try: + return pattern.format(index=value) + except (KeyError, IndexError, ValueError, TypeError, + AttributeError) as exc: + # str.format's own errors are bare ('other', 'Invalid format + # specifier'), and they surface AFTER the pipeline has run; + # say which kwarg, which pattern, and which value. + raise ValueError( + f"title={pattern!r} could not be formatted with " + f"index={value!r} ({type(exc).__name__}: {exc}). The only " + "field a pattern title can reference is {index}, with an " + "optional format spec the index value supports (e.g. " + "'{index:%B %Y}' for a DatetimeIndex).") from exc return _resolve +def _resolve_dynamic_title_text(fn, ctx): + """Call a dynamic `title=` (callable or pattern resolver) for `ctx` + and insist on a string (1.1 release review T8): a callable returning + None or 42 was drawn as the literal 'None'/'42'.""" + value = fn(ctx) + if not isinstance(value, str): + raise TypeError( + f"title= callable must return a str for every frame; got " + f"{type(value).__name__}: {value!r}.") + return value + + def _validate_loop(loop, style): """`loop=True` closes an ``animate='morph'`` sequence (GH #285). @@ -1919,7 +2990,14 @@ def _validate_dataset_fade(dataset_fade, style, order): "dataset_fade= must be a dict {'floor': ..., 'decay': ...} or a " f"(floor, decay) pair; got {type(dataset_fade).__name__}: " f"{dataset_fade!r}.") - floor, decay = float(floor), float(decay) + try: + floor, decay = float(floor), float(decay) + except (TypeError, ValueError) as exc: + raise TypeError( + "dataset_fade='s floor and decay must be numbers (floor: the " + "alpha an old dataset fades to, 0-1; decay: the per-dataset " + f"falloff, 0-1); got floor={floor!r}, decay={decay!r} " + f"({exc}).") from exc if not 0.0 <= floor <= 1.0: raise ValueError( f"dataset_fade='s floor must be between 0 and 1 (it is an " @@ -2018,8 +3096,15 @@ def _validate_companion(companion, animate, backend_name): """ if companion is None: return None - specs = ([companion] if isinstance(companion, dict) - else list(companion)) + if isinstance(companion, dict): + specs = [companion] + elif isinstance(companion, (list, tuple)): + specs = list(companion) + else: + raise TypeError( + "companion= takes a dict describing one extra panel (or a list " + f"of such dicts); got {type(companion).__name__}: " + f"{companion!r}. See the companion= docstring for the keys.") if not animate: raise ValueError( "companion= panels are revealed in lockstep with an animation; " @@ -2059,7 +3144,13 @@ def _validate_companion(companion, animate, backend_name): f"companion= entry {i} has no data=; pass the series to " "draw, as (n_rows,), (n_rows, 1) or (n_rows, 2) -- two " "columns are read as (x, y).") - data = np.asarray(spec['data'], dtype=float) + try: + data = np.asarray(spec['data'], dtype=float) + except (TypeError, ValueError) as exc: + raise TypeError( + f"companion= entry {i}: data must be numeric, as " + "(n_rows,), (n_rows, 1) or (n_rows, 2); could not read " + f"{spec['data']!r} as numbers ({exc}).") from exc if data.ndim == 1: data = data[:, None] if data.ndim != 2 or data.shape[1] not in (1, 2): @@ -2077,6 +3168,12 @@ def _validate_companion(companion, animate, backend_name): f"'right'; got {position!r}.") smooth = spec.get('smooth') if smooth is not None: + if (isinstance(smooth, bool) + or not isinstance(smooth, (int, np.integer))): + raise TypeError( + f"companion= entry {i}: smooth= is a rolling-mean " + "window in rows and must be an int of at least 2; got " + f"{type(smooth).__name__}: {smooth!r}.") smooth = int(smooth) if smooth < 2: raise ValueError( @@ -2084,12 +3181,25 @@ def _validate_companion(companion, animate, backend_name): f"window in rows and must be at least 2; got {smooth}.") hue = spec.get('hue') if hue is not None: - hue = np.asarray(hue, dtype=float).ravel() + try: + hue = np.asarray(hue, dtype=float).ravel() + except (TypeError, ValueError) as exc: + raise TypeError( + f"companion= entry {i}: hue= must be one number per " + f"row; could not read {spec['hue']!r} as numbers " + f"({exc}).") from exc if hue.shape[0] != data.shape[0]: raise ValueError( f"companion= entry {i}: hue= has {hue.shape[0]} values " f"but data has {data.shape[0]} rows.") - size = float(spec.get('size', 0.35)) + try: + size = float(spec.get('size', 0.35)) + pad = float(spec.get('pad', 0.10)) + except (TypeError, ValueError) as exc: + raise TypeError( + f"companion= entry {i}: size= and pad= are figure " + f"fractions (numbers); got size={spec.get('size', 0.35)!r}, " + f"pad={spec.get('pad', 0.10)!r} ({exc}).") from exc if not 0.05 <= size <= 0.8: raise ValueError( f"companion= entry {i}: size= is the panel's share of the " @@ -2098,8 +3208,7 @@ def _validate_companion(companion, animate, backend_name): 'reveal', True)), smooth=smooth, marker=bool(spec.get( 'marker', True)), position=position, size=size, xlabel=spec.get('xlabel'), ylabel=spec.get('ylabel'), - color=spec.get('color'), hue=hue, - pad=float(spec.get('pad', 0.10)))) + color=spec.get('color'), hue=hue, pad=pad)) return out @@ -2149,7 +3258,8 @@ def _normalize_legend_colors(legend_colors): return items, None -def subplots(nrows=1, ncols=1, ndims=3, size=None, **fig_kw): +def subplots(nrows=1, ncols=1, ndims=3, size=None, backend='auto', + **fig_kw): """Create a figure and a FLAT array of hypertools-ready axes (GH #285). A thin wrapper over `matplotlib.pyplot.subplots` that sets the 3-D @@ -2167,10 +3277,24 @@ def subplots(nrows=1, ncols=1, ndims=3, size=None, **fig_kw): pass ``panels=`` to `hyp.plot` instead; this helper is for grids you want to fill yourself (mixing hypertools panels with your own). + The same loop works on the plotly backend: ``backend='plotly'`` (or + ``'auto'`` while `hyp.set_interactive_backend('plotly')` is active) + returns a `plotly.subplots.make_subplots` figure and a flat array of + grid CELLS that `hyp.plot(..., ax=cell)` draws into -- each call moves + its whole panel (traces, axes and frame, `title=`, `labels=`, and its + own legend/colorbar beside the cell) into that cell, and returns the + grid figure. The grid is laid out as tightly as `panels=` draws it; + the first cell that receives a legend or colorbar makes the grid grow + room for one beside every cell (the cells already drawn move with it). + Parameters ---------- nrows, ncols : int Grid shape (default 1x1). + backend : {'auto', 'matplotlib', 'plotly'} + Which backend's grid to build (default ``'auto'``: the active + one, matplotlib unless `hyp.set_interactive_backend` says + otherwise). ndims : int 3 (default) gives every panel a 3-D projection; 1 or 2 gives ordinary 2-D axes. Matches `hyp.plot`'s `ndims=`. @@ -2178,20 +3302,53 @@ def subplots(nrows=1, ncols=1, ndims=3, size=None, **fig_kw): Figure size in inches (`figsize`); `None` keeps matplotlib's. **fig_kw Forwarded to `matplotlib.pyplot.subplots` (`sharex=`, `dpi=`, - `gridspec_kw=`, an explicit `subplot_kw=`, ...). + `gridspec_kw=`, an explicit `subplot_kw=`, ...) or, under plotly, + to `plotly.subplots.make_subplots` (`vertical_spacing=`, + `subplot_titles=`, `shared_xaxes=`, ...). Returns ------- - fig : matplotlib.figure.Figure + fig : matplotlib.figure.Figure or plotly.graph_objects.Figure axes : numpy.ndarray - A FLAT (1-D) array of ``nrows * ncols`` axes, row-major -- no - ``.ravel()`` needed, and a 1x1 grid still returns a length-1 array - rather than a bare Axes, so the same loop works for any grid. + A FLAT (1-D) array of ``nrows * ncols`` axes (matplotlib) or grid + cells (plotly), row-major -- no ``.ravel()`` needed, and a 1x1 grid + still returns a length-1 array rather than a bare Axes, so the + same loop works for any grid. """ if ndims not in (1, 2, 3): raise ValueError( f"ndims must be 1, 2 or 3 (the plot dimensionality each panel " f"is drawn in); got {ndims!r}.") + if resolve_backend(backend) == 'plotly': + from .plotly_backend import (PANEL_TITLE_PX, PlotlyCell, + _hyper_figure_class, make_panel_grid) + # room beside every cell for the legend/colorbar a cell's own + # `hyp.plot(..., ax=cell, legend=True)` call may add (`panels=` + # sizes this from the panels it has already drawn; a grid filled + # later cannot, so it reserves one legend's width up front) + # ...and above every row for the one-line title such a call may + # set, so a titled cell does not have to shift the grid down. No + # gutter yet: the grid grows one beside every cell the first time + # a cell actually receives a legend or colorbar + # (`plotly_backend.ensure_panel_gutter`), so a grid whose cells + # never ask for one stays as tight as `panels=` draws it. + grid = make_panel_grid(nrows, ncols, ndims, size=size, + gutter_px=0, title_px=PANEL_TITLE_PX, + **fig_kw) + grid.layout.meta = {**(dict(grid.layout.meta) + if isinstance(grid.layout.meta, dict) else {}), + 'hyp_grid': dict( + nrows=int(nrows), ncols=int(ncols), + ndims=int(ndims), + size=(None if size is None + else [float(v) for v in size]), + gutter_px=0, title_px=PANEL_TITLE_PX, + make_subplots_kw=dict(fig_kw))} + fig = _hyper_figure_class()(grid) + cells = np.empty(nrows * ncols, dtype=object) + cells[:] = [PlotlyCell(fig, i // ncols + 1, i % ncols + 1, i, ndims) + for i in range(nrows * ncols)] + return fig, cells subplot_kw = dict(fig_kw.pop('subplot_kw', None) or {}) if ndims >= 3: subplot_kw.setdefault('projection', '3d') @@ -2203,18 +3360,52 @@ def subplots(nrows=1, ncols=1, ndims=3, size=None, **fig_kw): return fig, flat -def _resolve_panel_grid(panels, n_panels, _name='panels'): +def _auto_panel_grid(n_panels, size=None): + """The ``(nrows, ncols)`` grid ``panels=True`` uses for `n_panels` + panels in a figure of `size` (inches; matplotlib's default figure size + when None). + + Every panel is a square-ish box (a 3-D axes keeps its box aspect), so + the useful measure of a candidate grid is the side of the square that + fits its cell: ``min(width / ncols, height / nrows)``. The grid with + the largest side wins, except that a grid with NO spare cell is + preferred whenever its side is at least three quarters of the best + one -- three panels in a wide or default-sized figure form a row + rather than a 2x2 with a hole, five panels still take 2x3 because + a single row would shrink them to less than that. + """ + if size is None: + size = plt.rcParams['figure.figsize'] + width, height = float(size[0]), float(size[1]) + candidates = [] + for ncols in range(1, n_panels + 1): + nrows = int(np.ceil(n_panels / ncols)) + side = min(width / ncols, height / nrows) + candidates.append((side, nrows * ncols - n_panels, nrows, ncols)) + best_side = max(c[0] for c in candidates) + exact = [c for c in candidates if c[1] == 0] + if exact: + side, _holes, nrows, ncols = max(exact, key=lambda c: (c[0], -c[2])) + if side >= 0.75 * best_side: + return nrows, ncols + _side, _holes, nrows, ncols = max( + candidates, key=lambda c: (c[0], -c[1], -c[2])) + return nrows, ncols + + +def _resolve_panel_grid(panels, n_panels, size=None, _name='panels'): """Resolve `panels=` into an ``(nrows, ncols)`` grid holding at least `n_panels` cells. - ``True``/``'auto'`` picks a near-square grid (at most ``ceil(sqrt(n))`` - columns, so 3 datasets -> 2x2 with one spare hidden, 6 -> 3x2); an - ``int`` is the number of COLUMNS; an ``(nrows, ncols)`` pair is used - verbatim (and must have room for every panel). + ``True``/``'auto'`` picks the grid from the figure's aspect ratio + (`size`, inches), preferring one without a spare cell -- see + `_auto_panel_grid` (3 panels -> a row in a default or wide figure, a + column in a tall one; 4 -> 2x2; 6 -> 2x3); an ``int`` is the number of + COLUMNS; an ``(nrows, ncols)`` pair is used verbatim (and must have + room for every panel). """ if panels is True or (isinstance(panels, str) and panels == 'auto'): - ncols = int(np.ceil(np.sqrt(n_panels))) - return int(np.ceil(n_panels / ncols)), ncols + return _auto_panel_grid(n_panels, size) if isinstance(panels, (tuple, list)) and len(panels) == 2 \ and all(isinstance(v, (int, np.integer)) and not isinstance(v, bool) for v in panels): @@ -2243,11 +3434,37 @@ def _resolve_panel_grid(panels, n_panels, _name='panels'): #: `plot()` keywords that carry ONE entry per DATASET when passed as a #: list, and are therefore narrowed to the panel's own dataset by -#: `_plot_panels`. Anything not listed here is forwarded unchanged. +#: `_panel_narrow_kwargs`. Anything not listed here (and not handled by +#: one of the dedicated slicers below) is forwarded unchanged. +#: `tests/test_plot_panels_audit.py` checks this roster against the +#: `plot()` docstring's "per dataset" arguments, so a new one cannot be +#: forgotten silently. _PANEL_PER_DATASET_KWARGS = ( - 'fmt', 'color', 'colors', 'alpha', 'markers', 'markersize', 'linewidth', - 'linestyles', 'names', 'chemtrails', 'precog', 'bullettime', 'surface', - 'density', + 'fmt', 'marker', 'markers', 'linestyle', 'linestyles', 'color', + 'colors', 'alpha', 'markersize', 'linewidth', 'names', 'chemtrails', + 'precog', 'bullettime', 'surface', 'density', + # `truth=` is one held-out continuation per dataset (1.1 review, P1) + 'truth', +) + +#: ...the ones whose list form is one entry per FORECAST -- one per +#: dataset for a single `predict=` model, and for a COLLECTION either one +#: per MODEL (forwarded unchanged: every panel draws every model) or one per +#: forecast in model-major order (release audit 2026-09-07, finding 4). +#: `forecast_palette=` is per forecast only when nothing groups them. +_PANEL_PER_FORECAST_KWARGS = ('forecast_hue', 'forecast_fmt', + 'forecast_palette') + +#: kwargs the SHARED-fit probe must not see: it runs the analysis pipeline +#: with `predict=None`, and each of these either requires `predict=` +#: (`plot()` raises otherwise) or styles the per-panel overlays that the +#: probe never draws. +_PANEL_PROBE_DROPPED_KWARGS = ( + 'predict', 'truth', 'forecast_hue', 'forecast_cluster', + 'forecast_n_clusters', 'forecast_palette', 'forecast_fmt', + # validated against predict= -- kept alone it refused itself ("requires + # predict=") in a probe that had dropped predict= (1.1 review, F5) + 'forecast_trail', ) #: ...and the one that carries a value per OBSERVATION (flat), a @@ -2257,15 +3474,304 @@ def _resolve_panel_grid(panels, n_panels, _name='panels'): #: own slicer below rather than sharing this one. _PANEL_PER_OBSERVATION_KWARGS = ('hue',) +#: per-dataset arguments with a slicer of their own (a shape or a meaning +#: the plain per-dataset rule does not cover); listed so the roster test +#: can account for every documented per-dataset argument. +_PANEL_SPECIAL_KWARGS = ('hue', 'labels', 'palette', 'legend', 'predict', + 'title') + +#: colour kwargs whose 3/4-number tuple is ONE colour, not one entry per +#: dataset (`_is_single_color`). Every other per-dataset list -- notably +#: ``alpha=[0.3, 0.6, 0.9]`` for three datasets -- is sliced as a list. +_PANEL_COLOR_KWARGS = ('color', 'colors') + -def _panel_slice_per_dataset(value, index, n_datasets): +def _panel_slice_per_dataset(value, index, n_datasets, single_color=False): """Narrow a per-dataset list down to panel `index`'s single dataset.""" if isinstance(value, (list, tuple)) and len(value) == n_datasets \ - and not _is_single_color(value): + and not (single_color and _is_single_color(value)): return [value[index]] return value +def _panel_slice_palette(palette, index, n_datasets): + """Narrow a PER-DATASET `palette=` list (``['viridis', 'magma']``, a + list of colour lists, a list of ``{category: color}`` dicts -- see + `palette=`) to panel `index`'s own entry, as a one-entry per-dataset + list (which `plot()` broadcasts to the panel's single dataset). A list + that is one palette of explicit colours is a single palette and is + forwarded whole, exactly as the single-axes call reads it.""" + if (_looks_like_dataset_palettes(palette) + and len(palette) == n_datasets): + return [palette[index]] + return palette + + +def _panel_slice_legend(legend, index, n_datasets, kw): + """Narrow a `legend=` LIST to panel `index` when its entries name the + DATASETS -- i.e. nothing regroups the drawn traces (`hue=`, `cluster=` + or `n_clusters=`), so "one entry per drawn dataset/group" is one per + dataset. Under a grouping the entries name the groups, which every + panel resolves for itself, and the list is forwarded unchanged.""" + if not isinstance(legend, (list, tuple)) or len(legend) != n_datasets: + return legend + if (kw.get('hue') is not None or kw.get('cluster') + or kw.get('n_clusters') is not None): + return legend + return [legend[index]] + + +def _panel_slice_legend_colors(legend_colors, index, n_datasets, kw): + """Narrow a plain `legend_colors=` colour LIST to panel `index`. + + On one axes a plain list recolours the legend's entries in order: one + per dataset first, then the entries every dataset shares (the forecast + model(s), ``truth``). Each panel's legend lists ITS dataset and those + shared entries, so it gets its dataset's colour followed by the shared + colours -- forwarding the whole list made every panel refuse it ("has 2 + entries but the legend has 1", 1.1 release review), though the same + call works on one axes. Only when the entries name the DATASETS + (nothing regroups the traces, as in `_panel_slice_legend`); a + ``(label, color)`` pair list defines a whole legend and is forwarded to + every panel unchanged, as is anything `plot()` must report itself.""" + try: + recolor, _ = _normalize_legend_colors(legend_colors) + except (TypeError, ValueError): + return legend_colors + if recolor is None or len(recolor) < n_datasets: + return legend_colors + if (kw.get('hue') is not None or kw.get('cluster') + or kw.get('n_clusters') is not None): + return legend_colors + return [recolor[index]] + list(recolor[n_datasets:]) + + +def _panel_forecast_models(predict): + """How many forecasts each dataset gets from `predict=`: one for a + single spec, one per model for a collection -- split with the SAME + splitter `plot()` and `hyp.predict` use, so the counts agree.""" + if predict is None: + return 0 + from ..predict.backtest import model_collection as _model_collection + from ..predict.predict import _FORECASTER_ALIASES, FORECASTERS + from ..core.shared import supported_names as _supported_names + _collection = _model_collection( + predict, _supported_names(FORECASTERS), _FORECASTER_ALIASES, + caller='hyp.plot') + return 1 if _collection is None else len(_collection[0]) + + +def _panel_forecast_labels(hue, n_datasets, n_models): + """`forecast_hue=` as one label per forecast in MODEL-MAJOR order, the + order `plot()` keeps a collection's forecasts in (forecast ``k * + n_datasets + d`` is model ``k``'s forecast of dataset ``d``): a + per-DATASET list is broadcast over every model's forecast of that + dataset, a model-major list is taken as is. None for any other shape + (`plot()` reports it) and for a bare string (`plot()` rejects it).""" + if hue is None or isinstance(hue, (str, bytes)): + return None + if is_series_like(hue): + # a pandas / polars Series or Index (any series-like datawrangler + # recognises) is a label vector like a list (Codex round 11) + hue = np.asarray(hue).ravel() + if not isinstance(hue, (list, tuple, np.ndarray)): + return None + labels = list(hue) + if len(labels) == n_datasets: + return [labels[d] for _ in range(n_models) for d in range(n_datasets)] + if len(labels) == n_datasets * n_models: + return labels + return None + + +def _panel_pick_model_major(seq, index, n_datasets, n_models): + """Panel `index`'s own entries of a model-major per-forecast list.""" + return [seq[k * n_datasets + index] for k in range(n_models)] + + +def _panel_observed_groups(kw): + """How many palette colours the single-axes call's OBSERVED data takes + when a grouping colours it: the categories of a categorical (string) + `hue=`, or an integer `n_clusters=`. None when nothing groups the data + or the count is not knowable from the arguments alone (then each + dataset is counted as one slot).""" + hue = kw.get('hue') + if hue is not None and not isinstance(hue, (str, bytes)): + values = [] + items = list(hue) if isinstance(hue, (list, tuple)) else [hue] + for item in items: + if isinstance(item, (str, bytes)) or np.ndim(item) == 0: + values.append(item) + else: + values.extend(np.asarray(item, dtype=object).ravel()) + labels = {v for v in values if not is_missing_label(v)} + if labels and all(isinstance(v, str) for v in labels): + return len(labels) + return None + n_clusters = kw.get('n_clusters') + if isinstance(n_clusters, (int, np.integer)) \ + and not isinstance(n_clusters, bool): + return int(n_clusters) + return None + + +def _panel_draw_fmts(fmt, n_datasets): + """`fmt=` as one format string per dataset (None when unset).""" + if fmt is None: + return None + if isinstance(fmt, str): + return [fmt] * n_datasets + return list(fmt) + + +def _panel_slice_forecast_kwargs(kw, index, n_datasets, grid_kw=None): + """Narrow the per-FORECAST kwargs (`_PANEL_PER_FORECAST_KWARGS`) to + panel `index`, reproducing the single-axes figure's assignment: + + - `forecast_hue=`: one value per dataset, or one per forecast + model-major -> the panel's own value(s), model-major. + - `forecast_fmt=`: one per forecast (per dataset for a single model, + model-major for a collection) -> the panel's own; one per MODEL is + forwarded unchanged (every panel draws every model). + - `forecast_palette=`: with a list-valued `forecast_hue=` the grid's + label -> colour map is resolved ONCE (with the palette the single- + axes call would use) and each panel receives its own labels' colours + in its own first-appearance order, so a label keeps one colour + across panels; with nothing to group by and a single model it is + spent one colour per forecast, so the panel receives its forecast's + colour. A collection's per-MODEL palette is forwarded unchanged. + """ + predict = kw.get('predict') + if predict is None: + return + n_models = _panel_forecast_models(predict) + n_forecasts = n_datasets * n_models + fc_hue, fc_fmt = kw.get('forecast_hue'), kw.get('forecast_fmt') + fc_palette, fc_cluster = kw.get('forecast_palette'), kw.get('forecast_cluster') + + labels = _panel_forecast_labels(fc_hue, n_datasets, n_models) + if labels is not None: + kw['forecast_hue'] = _panel_pick_model_major( + labels, index, n_datasets, n_models) + # the palette is resolved against the WHOLE grid's labels, in + # the single-axes call's own order (`_forecast_label_colors`), + # and the panel gets the colours of its own labels in the order + # it will first see them -- an unlabeled forecast takes no slot + # (`is_missing_label`), exactly as `resolve_forecast_overrides` + # reads it + from .colors import is_missing_label + from .forecast import _forecast_label_colors + _labels = [None if is_missing_label(v) else v for v in labels] + if fc_palette is None: + # the single-axes default (`_forecast_group_palette`): the + # palette continued past the colours the single-axes figure's + # observed datasets take, so a label keeps that figure's colour + # read from the WHOLE grid's kwargs (`grid_kw`), before any + # per-dataset argument was narrowed to this panel + _grid = kw if grid_kw is None else grid_kw + _palette = _grid.get('palette') + _palette = 'hls' if _palette is None else _palette + _n_groups = _panel_observed_groups(_grid) + _fc_default = _forecast_group_palette( + _palette, _observed_palette_colors( + _palette, n_datasets if _n_groups is None else _n_groups, + _grid.get('color'), + None if _n_groups is not None + else _panel_draw_fmts(_grid.get('fmt'), n_datasets))) + fc_palette = list(_fc_default(max(1, len( + {v for v in _labels if v is not None})))) + _colours = _forecast_label_colors(_labels, fc_palette) + _own = _panel_pick_model_major(_labels, index, n_datasets, n_models) + _own_colours = _panel_pick_model_major( + _colours, index, n_datasets, n_models) + # one slot per DISTINCT LABEL, not per distinct colour: the + # panel's `resolve_forecast_overrides` spends the palette one + # colour per label it sees, so two labels that share a colour on + # purpose (``forecast_palette=['red', 'red']``, a palette name + # that cycles) must keep two entries (Codex round 6) + _seen, _ordered = [], [] + for _label, _colour in zip(_own, _own_colours): + if _label is not None and _label not in _seen: + _seen.append(_label) + _ordered.append(_colour) + if _ordered: + kw['forecast_palette'] = _ordered + elif (fc_palette is not None and fc_hue is None and fc_cluster is None + and n_models == 1): + from .forecast import _forecast_label_colors + kw['forecast_palette'] = [_forecast_label_colors( + list(range(n_datasets)), fc_palette)[index]] + + if (isinstance(fc_fmt, (list, tuple)) and len(fc_fmt) == n_forecasts + and n_forecasts != n_models): + kw['forecast_fmt'] = _panel_pick_model_major( + list(fc_fmt), index, n_datasets, n_models) + + +def _panel_bind_forecaster(predict, index, n_datasets): + """`predict=` for panel `index`: a forecaster already FITTED on several + datasets (``hyp.predict([a, b], return_model=True)``) is bound to this + panel's dataset with `Forecaster.for_dataset`, so `predict_new` reuses + that dataset's learned parameters instead of refusing a dataset-count + mismatch -- the binding the ordinary path gets from pairing new + datasets with fitted models by position, and the animated schedule + from `hypertools.plot.forecast.forecast_displacements`. Applied inside + a collection (list/tuple or ``{name: spec}`` mapping) too. A forecaster + fitted on ONE dataset is reused for every panel, as it is for every + dataset of a single-axes call.""" + if isinstance(predict, (list, tuple)): + bound = [_panel_bind_forecaster(p, index, n_datasets) + for p in predict] + return bound if isinstance(predict, list) else tuple(bound) + if isinstance(predict, dict) and predict and not ( + predict.keys() & {'model', 'args', 'kwargs', 'params'}): + return {k: _panel_bind_forecaster(v, index, n_datasets) + for k, v in predict.items()} + n_fitted = len(getattr(predict, 'models_', ())) + if hasattr(predict, 'for_dataset') and n_fitted > 1: + if n_fitted != n_datasets: + raise ValueError( + f"predict= is a forecaster fitted on {n_fitted} dataset(s), " + f"but panels= draws {n_datasets}; a forecaster fitted on " + "several datasets is applied dataset by dataset (panel i " + "reuses fitted model i), so fit it on the same datasets, " + "or pass one fitted on a single dataset to reuse it for " + "every panel.") + return predict.for_dataset(index) + return predict + + +def _panel_narrow_kwargs(kw, index, n_datasets, lengths): + """Narrow every per-dataset / per-observation / per-forecast argument in + `kw` (one panel's `plot()` kwargs, modified in place) to panel `index`: + the ONE rule both `panel_fit=` modes apply, so the docstring's "one per + dataset" forms describe the whole grid either way (P3).""" + grid_kw = dict(kw) + for key in _PANEL_PER_DATASET_KWARGS: + if key in kw: + kw[key] = _panel_slice_per_dataset( + kw[key], index, n_datasets, + single_color=key in _PANEL_COLOR_KWARGS) + for key in _PANEL_PER_OBSERVATION_KWARGS: + if kw.get(key) is not None: + kw[key] = _panel_slice_per_observation(kw[key], index, lengths) + if kw.get('labels') is not None: + kw['labels'] = _panel_slice_labels(kw['labels'], index, lengths) + if kw.get('palette') is not None: + kw['palette'] = _panel_slice_palette(kw['palette'], index, + n_datasets) + if kw.get('legend_colors') is not None: + kw['legend_colors'] = _panel_slice_legend_colors( + kw['legend_colors'], index, n_datasets, kw) + if kw.get('legend') is not None: + kw['legend'] = _panel_slice_legend(kw['legend'], index, n_datasets, + kw) + if kw.get('predict') is not None: + _panel_slice_forecast_kwargs(kw, index, n_datasets, grid_kw) + kw['predict'] = _panel_bind_forecaster(kw['predict'], index, + n_datasets) + + def _panel_slice_per_observation(value, index, lengths): """Narrow a per-observation (`hue=`/`labels=`) argument to one panel. @@ -2276,12 +3782,16 @@ def _panel_slice_per_observation(value, index, lengths): forwarded unchanged so `plot()`'s own validation reports it. """ n_datasets = len(lengths) - if not isinstance(value, (list, tuple, np.ndarray, pd.Series, pd.Index)): + if not (isinstance(value, (list, tuple)) or is_array_dataset(value) + or is_series_like(value)): return value seq = list(value) if len(seq) == n_datasets and all( np.ndim(el) >= 1 and len(el) == n for el, n in zip(seq, lengths)): - return [seq[index]] + # the panel plots ONE dataset, whose hue is the flat per-observation + # sequence itself: wrapped in a list it was read as a single entry + # ("hue has 1 entry but the data has 30 observations"; P3) + return seq[index] if len(seq) == n_datasets and all(np.ndim(el) == 0 for el in seq): # per-DATASET scalar broadcast (GH #285): one value per dataset return [seq[index]] * lengths[index] @@ -2351,6 +3861,20 @@ def _resolve_label_anchor(label_anchor, length): f"row index; got {label_anchor!r}.") +def _validate_label_anchor_value(label_anchor): + """`label_anchor=` must be a recognised anchor name or an int; checked + up front, independent of `labels=` (1.1 release review T8).""" + if label_anchor is None or label_anchor in ('first', 'center', + 'middle', 'last'): + return + if isinstance(label_anchor, (int, np.integer)) \ + and not isinstance(label_anchor, bool): + return + raise ValueError( + f"label_anchor= must be 'first', 'center', 'last', or an integer " + f"row index; got {label_anchor!r}.") + + def _is_per_dataset_labels(labels, dataset_lengths): """Whether `labels=` carries one entry per DATASET rather than one per OBSERVATION (GH #285). @@ -2371,6 +3895,40 @@ def _is_per_dataset_labels(labels, dataset_lengths): return all(el is None or isinstance(el, str) for el in labels) +def _labels_as_lists(labels, n_datasets): + """A nested per-dataset `labels=` whose entries are 1-D ARRAYS or Series + (``[np.array([...]), pd.Series([...])]``), as the nested LIST form every + consumer reads. Only lists and tuples counted as nested, so arrays were + read as two entries for ``n`` observations and rejected on one axes -- + while `panels=` accepted them (1.1 release review). Anything else, + including a per-dataset list of strings, is returned unchanged.""" + if not isinstance(labels, (list, tuple)) or len(labels) != n_datasets: + return labels + if not any(is_array_dataset(el) or is_series_like(el) for el in labels): + return labels + out = [] + for el in labels: + if is_array_dataset(el) or is_series_like(el): + arr = np.asarray(el, dtype=object) + if arr.ndim != 1: + # not one label per row; `_validate_labels_length` reports + return labels + out.append(arr.tolist()) + else: + out.append(el) + return out + + +def _flatten_dataset_labels(labels): + """A nested per-dataset `labels=` (one sub-list per dataset) as the flat + one-entry-per-observation list; a flat one is returned unchanged.""" + if not isinstance(labels, (list, tuple)) or not any( + isinstance(el, (list, tuple)) for el in labels): + return labels + return [lbl for el in labels + for lbl in (el if isinstance(el, (list, tuple)) else [el])] + + def _expand_dataset_labels(labels, dataset_lengths, label_anchor): """Turn a per-DATASET `labels=` list into the per-observation nested form the rest of `plot()` (and both backends) already understand: one @@ -2409,12 +3967,16 @@ def _panel_slice_labels(value, index, lengths): per-dataset hue is broadcast would label every observation. """ n_datasets = len(lengths) - if not isinstance(value, (list, tuple, np.ndarray)): + if not (isinstance(value, (list, tuple)) or is_array_dataset(value)): return value seq = list(value) if len(seq) == n_datasets and any( - isinstance(el, (list, tuple, np.ndarray)) for el in seq): - return [seq[index]] # nested per-dataset form + isinstance(el, (list, tuple)) or is_array_dataset(el) + for el in seq): + # nested per-dataset form: the panel's single dataset takes its own + # per-observation sequence, flat (a one-entry list would be read as + # one label for n observations; P3) + return seq[index] if _is_per_dataset_labels(seq, lengths): return [seq[index]] # per-dataset strings if len(seq) == int(sum(lengths)): # flat, per observation @@ -2423,6 +3985,123 @@ def _panel_slice_labels(value, index, lengths): return value +class _PanelLift: + """How one NARROW panel's analyzed rows are placed in a 3-D grid cell + (a 1- or 2-column dataset beside 3-column ones, `panel_fit= + 'independent'`): 2-wide rows on the cell's floor (``z=0``); a + 1-column series as ``(x, value, 0)`` where x is what `ndims=1` series + mode draws the series against -- the source frame's own numeric row + index when it has one (`_series_x_axis`), otherwise the row position + (a date index is drawn by position: a 3-D scene has no date axis). + + The lift is a PLACEMENT, applied by the panel's own `plot()` call at + draw time, not a change of the data: the panel forecasts, resolves + `truth=` and reports its bundle in the analyzed (narrow) space, the + same numbers the individual ``hyp.plot(dataset)`` call produces, and + only the drawn rows, forecasts and truths are lifted. Round 8 padded + the rows BEFORE the panel call and handed the padded ``(index, value, + 0)`` rows to the forecaster: a one-column panel's Kalman forecast then + differed from its individual call by 1.5, a forecaster fitted on the + one-column data refused the three padded features, and a one-column + `truth=` was rejected against a three-column trace (round 9).""" + + __slots__ = ('width', 'x_values', 'step') + + def __init__(self, width, x_values=None, step=1.0): + self.width = int(width) + self.x_values = (None if x_values is None + else np.asarray(x_values, dtype=float)) + self.step = float(step) + + def __repr__(self): + return f"_PanelLift(width={self.width}, step={self.step})" + + def _x(self, n_rows): + if self.x_values is not None and len(self.x_values) == n_rows: + return self.x_values + return np.arange(float(n_rows)) + + def rows(self, arr): + """The analyzed rows `arr` as the 3-column rows the cell draws.""" + arr = np.asarray(arr, dtype=float) + if arr.ndim == 1: + arr = arr.reshape(-1, 1) + if arr.ndim != 2 or arr.shape[1] >= 3: + return arr + n_rows = arr.shape[0] + if arr.shape[1] == 2: + return np.column_stack([arr, np.zeros(n_rows)]) + return np.column_stack([self._x(n_rows), arr[:, 0], + np.zeros(n_rows)]) + + def continuation(self, arr): + """A forecast/truth overlay `arr` -- its trace's last observed row + followed by the `t` rows continuing it (`_compute_forecasts`, + `_resolve_truth`) -- lifted the way the rows it continues are: a + series continues the row index by one `step` per row.""" + arr = np.asarray(arr, dtype=float) + if arr.ndim == 1: + arr = arr.reshape(-1, 1) + if arr.ndim != 2 or arr.shape[1] >= 3: + return arr + n_rows = arr.shape[0] + if arr.shape[1] == 2: + return np.column_stack([arr, np.zeros(n_rows)]) + x_last = float(self.x_values[-1]) if ( + self.x_values is not None and len(self.x_values)) else 0.0 + x = x_last + self.step * np.arange(n_rows, dtype=float) + return np.column_stack([x, arr[:, 0], np.zeros(n_rows)]) + + +def _panel_lift_for(xi, source): + """The `_PanelLift` that places one panel's analyzed rows `xi` in a + 3-D grid cell, or None when they are 3-wide already (drawn as they + are). `source` is the panel's input dataset, whose row index is the + x axis a 1-column series is drawn against.""" + xi = np.asarray(xi) + if xi.ndim == 1: + xi = xi.reshape(-1, 1) + if xi.ndim != 2 or xi.shape[1] >= 3: + return None + n_rows = xi.shape[0] + if xi.shape[1] == 2: + return _PanelLift(2) + index = None + if is_frame_dataset(source): + index = as_pandas_dataframe(source).index + elif is_series_like(source): + index = getattr(source, 'index', None) + if not isinstance(index, pd.Index) or isinstance(index, pd.MultiIndex): + index = None + x_values, step, is_date, _ = _series_x_axis(index, n_rows) + if is_date: + x_values, step = np.arange(float(n_rows)), 1.0 + return _PanelLift(1, x_values=x_values, step=step) + + +def _panel_frame(source, xi): + """The analyzed rows `xi` of one shared-fit panel, re-labelled with the + source dataset's row index (and, when the width survived the pipeline, + its column names) so `plot()` captures them exactly as it would for the + raw frame: `ndims=1` series mode then draws dates on x and names y after + the column (P2). The values are `xi`'s -- the panel is drawn through + ``transform=`` and never re-analyzes them.""" + arr = np.asarray(xi) + if is_series_like(source) and hasattr(source, 'to_frame'): + source = source.to_frame() + if not is_frame_dataset(source) or arr.ndim != 2: + return arr + source = as_pandas_dataframe(source) + if (source.shape[0] != arr.shape[0] + or isinstance(source.index, pd.MultiIndex)): + return arr + columns = None + if (source.shape[1] == arr.shape[1] + and not isinstance(source.columns, pd.MultiIndex)): + columns = source.columns + return pd.DataFrame(arr, index=source.index, columns=columns) + + def _panel_titles(title, n_panels): """One title per panel from `title=`: a list is used as-is (its length must match), a single string names every panel, `None` names none.""" @@ -2456,11 +4135,11 @@ def _dataframe_axis_labels(x): same 13 single-axes calls kept them. """ lbl_df = None - if isinstance(x, pd.DataFrame): - lbl_df = x + if is_frame_dataset(x): + lbl_df = as_pandas_dataframe(x) elif (isinstance(x, (list, tuple)) and len(x) == 1 - and isinstance(x[0], pd.DataFrame)): - lbl_df = x[0] + and is_frame_dataset(x[0])): + lbl_df = as_pandas_dataframe(x[0]) if (lbl_df is None or lbl_df.shape[1] > 3 or lbl_df.index.nlevels != 1 or isinstance(lbl_df.columns, (pd.RangeIndex, pd.MultiIndex)) @@ -2474,6 +4153,228 @@ def _dataframe_axis_labels(x): except Exception: # noqa: BLE001 - label sugar never breaks a plot return None +def _panel_data_width(dataset): + """The column count of one ANALYZED dataset (a 1-D array counts as one + column), or None when it is not a numeric array.""" + try: + arr = np.asarray(dataset) + except Exception: # noqa: BLE001 - not array-like: width unknown + return None + if arr.dtype.kind not in 'biuf' or arr.ndim not in (1, 2): + return None + return 1 if arr.ndim == 1 else int(arr.shape[1]) + + +def _panel_cell_ndims(requested, datasets): + """The dimensionality of a panel grid's cells: `requested` (1, 2 or 3), + lowered to 2 when every ANALYZED dataset is narrower than three columns + -- exactly the axes the single-axes call gives that data (2-column data + on 2-D axes, 1-column data as an index-vs-value series). A requested + ``1`` (series mode) is always kept, and a dataset whose width cannot be + read (`_panel_data_width` -> None) keeps the requested cells. + + `datasets` must be the rows the panels DRAW, i.e. the pipeline's + output: the raw column count says nothing about it (1.1 release + review, round 7: two raw columns through ``manip='Delay'`` come out + three wide, and cells lowered from the raw width refused or flattened + the 3-D result on both backends).""" + if requested <= 2 or not datasets: + return requested + widths = [_panel_data_width(d) for d in datasets] + if any(w is None for w in widths): + return requested + return requested if max(widths) >= 3 else 2 + + +class _PanelClusterLabels: + """The clustering a `panels=` probe fitted, replayed into the cell + that draws its rows (round 8). `labels` are the probe's per-observation + labels for exactly the rows the cell draws (a mixture model's + proportions included), `model` the resolved model name/class the probe + clustered with, and `spec` the caller's own `cluster=` value, which the + panel's bundle reports under ``models['cluster']``. Passed as the + panel call's ``cluster=``, `plot()` uses the labels as they are and + fits nothing -- before this, every panel re-clustered its analyzed + rows without the caller's `random_state` and drew clusters the seeded + single-axes call never drew. + + `categories` is the probe's COMPLETE cluster label set, in the order + the single-axes call colours it (sorted, as `plot()`'s cluster branch + sorts hard labels): the label-to-colour mapping every cell of the grid + shares. A shared fit's per-dataset slice can miss a cluster entirely, + and colouring the slice from ITS OWN label set drew global clusters 1 + and 0 in the same first palette colour in adjacent panels (round 9); + None for soft (mixture) labels, whose blends carry their own colours. + """ + + __slots__ = ('labels', 'model', 'spec', 'categories') + + def __init__(self, labels, model, spec, categories=None): + self.labels = labels + self.model = model + self.spec = spec + self.categories = categories + + def __repr__(self): + return (f"_PanelClusterLabels(model={self.model!r}, " + f"n={len(self.labels)})") + + +def _hard_cluster_categories(labels): + """The sorted label set `plot()`'s cluster branch draws HARD cluster + `labels` in (first-appearance order when they do not sort), or None + for a soft (2-D, mixture-proportion) labelling.""" + if labels is None: + return None + arr = np.asarray(labels) + if arr.ndim != 1: + return None + seen = list(dict.fromkeys(arr.tolist())) + try: + return sorted(seen) + except TypeError: + return seen + + +def _replayed_cluster_colors(present, categories, palette): + """The colours of the cluster labels `present` (in drawn order) under + the SHARED mapping a `panels=` probe fitted: label ``categories[k]`` + takes the k-th of the palette's ``len(categories)`` colours -- the + colour the single-axes call, which draws every category, gives it. + None when there is no shared mapping to apply: no `categories`, a + label the probe never produced, or a ``{category: color}`` palette, + which every path already resolves by NAME.""" + if categories is None or isinstance(palette, collections.abc.Mapping): + return None + if any(c not in categories for c in present): + return None + import seaborn as sns + n = len(categories) + base = list(sns.color_palette(_seaborn_palette_arg(palette, n), n)) + return [tuple(base[categories.index(c)]) for c in present] + + +def _panel_cluster_labels(probe_labels, model, spec, rows=None, + categories=None): + """`probe_labels` (a probe's ``models['cluster_labels']``) as the + `_PanelClusterLabels` one panel replays, sliced to `rows` (a + ``(start, stop)`` pair, for a shared fit's per-dataset slice) or taken + whole; None when the probe did not cluster. `categories` is the + probe's complete label set (see `_PanelClusterLabels`).""" + if probe_labels is None: + return None + if spec is None: + # `n_clusters=` alone: the single-axes call clusters with (and + # reports) KMeans + spec = 'KMeans' + labels = probe_labels + if rows is not None: + start, stop = rows + labels = labels[start:stop] + if isinstance(labels, np.ndarray) and labels.ndim == 1: + labels = labels.tolist() + return _PanelClusterLabels(labels, model, spec, categories=categories) + + +def _panel_probe(data, kw, ndims, caught_warnings): + """Fit one panel grid's pipeline exactly as the equivalent single-axes + call fits it -- by MAKING that call (``return_model=True``, figure + thrown away) -- and hand back ``(xform, pipeline, clustering)``: the analyzed rows + (one array per input dataset; for ``ndims`` > 3 the 3-D display + projection the single-axes call drew, since a reduce=None panel cannot + draw wider rows, P4), the fitted pipeline, and the clustering the + call fitted -- ``(labels, model)``, the per-observation labels the + probe's figure was grouped by and the resolved model, or ``None`` + without `cluster=`/`n_clusters=` -- which every panel replays instead + of clustering again (round 8). Every `panels=` mode goes through here, + and every panel is then DRAWN from these rows through ``transform=``, + so the cells' projection can be read off the data the pipeline + actually produced (round 7) and no panel ever fits twice. + Going through `plot()` itself -- rather than re-deriving + format_data/analyze here -- is what makes "a panel is analyzed exactly + as the single-axes call analyzes it" true by construction. The + warnings the call raises are appended to `caught_warnings` (see + `_reemit_panel_warnings`).""" + probe_kwargs = dict(kw) + probe_kwargs.update(return_model=True, show=False, save_path=None, + colorbar=None, legend=None, labels=None, + names=None, surface=None, density=None, title=None) + for key in _PANEL_PROBE_DROPPED_KWARGS: + probe_kwargs[key] = None + with warnings.catch_warnings(record=True) as caught: + # the fit's own warnings (a reducer's "connected components", + # a manip's resampling note) are emitted ONCE for the grid -- by + # `_reemit_panel_warnings`, after the panels are drawn, minus the + # draw-time ones every panel call repeats from the user's own + # kwargs (warning those twice is noise) + warnings.simplefilter('always') + probe = plot(data, **probe_kwargs) + caught_warnings.extend(caught) + probe_fig = probe.get('fig') + if isinstance(probe_fig, plt.Figure): + plt.close(probe_fig) + xform = [np.asarray(xi) for xi in probe['xform_data']] + # rows wider than a static axes can draw -- `ndims` > 3, or a + # `pipeline=` whose reduce stage kept more (round 7) -- were drawn by + # the single-axes call through ONE display projection to 3-D + # (`trace_data`, one entry per FINAL trace); the panels draw that + wide = any(xi.ndim == 2 and xi.shape[1] > 3 for xi in xform) + if (ndims is not None and ndims > 3) or wide: + trace = [np.asarray(xi) for xi in probe['trace_data']] + if len(trace) == len(xform): + xform = trace + clustering = None + models = probe.get('models') or {} + if models.get('cluster_labels') is not None: + clustering = (models['cluster_labels'], _panel_cluster_model(kw), + _hard_cluster_categories(models['cluster_labels'])) + return xform, probe.get('pipeline'), clustering + + +def _panel_cluster_model(kw): + """The model name/class a panel call's `cluster=`/`n_clusters=` + resolves to, exactly as `plot()`'s cluster branch resolves it (a + string name, a dict spec's ``'model'``, a class, or an instance's + class; `n_clusters=` alone means KMeans) -- what the replayed + clustering reports as its model.""" + spec = kw.get('cluster') + if spec is None: + return 'KMeans' + if isinstance(spec, bytes): + spec = spec.decode('utf-8') + if isinstance(spec, dict): + spec = spec.get('model', spec) + if isinstance(spec, (str, type)): + return spec + return type(spec) + + +def _reemit_panel_warnings(probe_caught, draw_caught): + """Re-issue the warnings a panel grid's calls raised, each from its + original location: every draw-time warning (the panel calls warn from + the user's own kwargs, one per panel, as they always have), preceded + by the fit-time ones only the probes saw -- each of those ONCE, and + not at all when a panel call repeated it (round 7: the panels are + drawn through ``transform=``, so a reducer's own warning would + otherwise be lost with the probe's figure).""" + seen = {(w.category, str(w.message)) for w in draw_caught} + to_emit = [w for w in probe_caught + if (w.category, str(w.message)) not in seen] + emitted = set() + for w in list(to_emit) + list(draw_caught): + key = (w.category, str(w.message), w.filename, w.lineno) + if w in to_emit and key in emitted: + continue + emitted.add(key) + registry = None + for mod in list(sys.modules.values()): + if getattr(mod, '__file__', None) == w.filename: + registry = mod.__dict__.setdefault('__warningregistry__', {}) + break + warnings.warn_explicit(w.message, w.category, w.filename, w.lineno, + registry=registry) + + def _plot_panels(x, panels, call_kwargs, _name='panels'): """Draw one STATIC panel per dataset (or per `reduce=` entry) in a single figure -- the implementation behind `hyp.plot(..., panels=)`. @@ -2529,11 +4430,22 @@ def _plot_panels(x, panels, call_kwargs, _name='panels'): else "") + ".") n_panels = len(datasets) - nrows, ncols = _resolve_panel_grid(panels, n_panels, _name=_name) + nrows, ncols = _resolve_panel_grid(panels, n_panels, + size=call_kwargs.get('size'), _name=_name) titles = _panel_titles(call_kwargs.get('title'), n_panels) ndims = call_kwargs.get('ndims', 3) + # ndims > 3 is analyzed at that dimensionality and DRAWN in 3-D, as the + # single-axes path does (P4): the grid's cells are at most 3-D... + panel_ndims = 3 if (ndims is None or ndims > 3) else ndims + # ...and are lowered to what the ANALYZED data needs below, once every + # panel's pipeline has run (`_panel_cell_ndims`) show = call_kwargs.get('show', True) + # validated and normalized (``~``, path-likes, a missing directory) up + # front, before any panel is analyzed or drawn -- the same rule the + # single-axes path applies (P5) save_path = call_kwargs.get('save_path') + if save_path is not None: + save_path = _normalize_save_path(save_path) return_model = call_kwargs.get('return_model', False) backend = resolve_backend(call_kwargs.get('backend', 'auto')) @@ -2565,102 +4477,205 @@ def _plot_panels(x, panels, call_kwargs, _name='panels'): "of those panels already runs its own full pipeline over every " "dataset.") + # Every mode fits its pipeline(s) ONCE, through `plot()` itself + # (`_panel_probe`), and then draws each panel from the analyzed rows + # through `transform=`. The cells' projection is decided from those + # rows (round 7: two raw columns through a feature-expanding manip=/ + # pipeline= are three wide after the analysis -- read from the raw + # input, the cells were 2-D, and the 3-D result was refused, flattened + # or rejected by plotly's 'xy' cell). + #: the shared fit's pipeline (panel_fit='shared' only); each panel's + #: own bundle carries its own fit in the other modes + shared_pipeline = None + #: the warnings the probes raised, re-issued once the grid is drawn + probe_warnings = [] + #: per panel: the input datasets it draws (for their index/column + #: labels), its analyzed rows (one array per dataset), its fitted + #: pipeline, and its narrowed kwargs if per_panel_reduce: - panel_data = [x] * n_panels + # one panel per REDUCER, each a full pipeline over every dataset + sources = datasets if datasets is not None else [x] + panel_sources = [sources] * n_panels panel_kwargs = [] for spec in reduce_spec: kw = dict(shared) kw['reduce'] = spec panel_kwargs.append(kw) + probes = [_panel_probe(x, kw, ndims, probe_warnings) + for kw in panel_kwargs] + panel_xforms = [xf for xf, _, _ in probes] + panel_pipelines = [pipe for _, pipe, _ in probes] + # each reducer panel draws EVERY dataset: its probe's labels whole + panel_clusters = [ + None if clus is None else _panel_cluster_labels( + clus[0], clus[1], kw.get('cluster'), categories=clus[2]) + for (_, _, clus), kw in zip(probes, panel_kwargs)] elif panel_fit == 'independent': - # one FULL pipeline per panel: each panel call is byte-for-byte the - # ``hyp.plot(datasets[i], ax=axes[i], ...)`` the caller would have - # written, so nothing here fixes or slices the analysis. The raw - # dataset is handed over untouched, which also means `plot()` - # derives that panel's DataFrame-column axis labels itself. - panel_data = [[d] for d in datasets] + # one FULL pipeline per panel: each panel's fit is byte-for-byte + # the ``hyp.plot(datasets[i], ...)`` the caller would have written, + # so nothing here fixes or slices the analysis + panel_sources = [[d] for d in datasets] + lengths = [int(np.asarray(d).shape[0]) for d in datasets] panel_kwargs = [] for i in range(n_panels): kw = dict(shared) - for key in _PANEL_PER_DATASET_KWARGS: - if key in kw: - kw[key] = _panel_slice_per_dataset(kw[key], i, n_panels) + # per-dataset, per-observation and per-forecast kwargs narrow + # here too (P3): the docstring's "one per dataset / one per + # forecast" forms describe the whole grid in either panel_fit + # mode + _panel_narrow_kwargs(kw, i, n_panels, lengths) panel_kwargs.append(kw) + probes = [_panel_probe(panel_sources[i], panel_kwargs[i], ndims, + probe_warnings) + for i in range(n_panels)] + panel_xforms = [xf for xf, _, _ in probes] + panel_pipelines = [pipe for _, pipe, _ in probes] + # each probe clustered its own panel's rows: replayed whole + panel_clusters = [ + None if clus is None else _panel_cluster_labels( + clus[0], clus[1], kw.get('cluster'), categories=clus[2]) + for (_, _, clus), kw in zip(probes, panel_kwargs)] else: # ONE shared fit across every dataset: run the very call the user # would have made without panels=, take its analyzed (pre-display- - # rescale) data, and throw its figure away. Going through plot() - # itself -- rather than re-deriving format_data/analyze here -- is - # what makes "the panels share the pipeline exactly as the - # single-axes call does" true by construction rather than by - # transcription. - probe_kwargs = dict(shared) - probe_kwargs.update(return_model=True, show=False, save_path=None, - colorbar=None, legend=None, labels=None, - names=None, predict=None, surface=None, - density=None, title=None) - with warnings.catch_warnings(): - # whatever this call would warn about, the per-panel calls - # below warn about again -- from the user's own kwargs, with - # the user's own stacklevel. Warning twice is noise. - warnings.simplefilter('ignore') - probe = plot(x, **probe_kwargs) - probe_fig = probe.get('fig') - if isinstance(probe_fig, plt.Figure): - plt.close(probe_fig) - xform = probe['xform_data'] - lengths = [int(np.asarray(xi).shape[0]) for xi in xform] - panel_data = [[np.asarray(xi)] for xi in xform] + # rescale) data, and give each panel its own dataset's rows + xform, shared_pipeline, shared_clustering = _panel_probe( + x, shared, ndims, probe_warnings) + lengths = [int(xi.shape[0]) for xi in xform] + panel_sources = [[datasets[i]] for i in range(n_panels)] + panel_xforms = [[xform[i]] for i in range(n_panels)] + panel_pipelines = [shared_pipeline] * n_panels panel_kwargs = [] + # the ONE clustering fit across every dataset, each panel + # replaying its own dataset's slice of the labels (the same rows + # `_panel_narrow_kwargs` slices a flat hue= by) under the ONE + # label-to-colour mapping (`categories=`: a slice missing a + # cluster must not shift the others' colours, round 9) + panel_clusters = [] + offsets = np.cumsum([0] + lengths) for i in range(n_panels): kw = dict(shared) - # the analysis already ran (above); each panel DRAWS its slice - kw.update(transform=[np.asarray(xform[i])], reduce=None, - normalize=None, align=None, manip=None, pipeline=None, - impute=None, resample=None, random_state=None) - for key in _PANEL_PER_DATASET_KWARGS: - if key in kw: - kw[key] = _panel_slice_per_dataset(kw[key], i, n_panels) - for key in _PANEL_PER_OBSERVATION_KWARGS: - if kw.get(key) is not None: - kw[key] = _panel_slice_per_observation( - kw[key], i, lengths) - if kw.get('labels') is not None: - kw['labels'] = _panel_slice_labels(kw['labels'], i, lengths) - # DataFrame-column axis labels (GH #285): a shared-fit panel is - # drawn through `transform=`, which is exactly the case - # `plot()` refuses to infer labels for -- so derive them here, - # from the panel's OWN input dataset, under the same rule - # (2-D/3-D, one drawn axis per named column). An explicit - # xlabel=/ylabel=/zlabel= still wins. - _labels = _dataframe_axis_labels(datasets[i]) - if (_labels is not None - and len(_labels) == int(np.asarray(xform[i]).shape[1]) - and len(_labels) in (2, 3)): - for _key, _value in zip(('xlabel', 'ylabel', 'zlabel'), - _labels): - if kw.get(_key) is None: - kw[_key] = str(_value) + _panel_narrow_kwargs(kw, i, n_panels, lengths) panel_kwargs.append(kw) + panel_clusters.append( + None if shared_clustering is None + else _panel_cluster_labels( + shared_clustering[0], shared_clustering[1], + shared.get('cluster'), + rows=(int(offsets[i]), int(offsets[i + 1])), + categories=shared_clustering[2])) + + # the analyzed data's own width decides the cells (a text or DataFrame + # input's drawn width, and a manip=/pipeline= stage's, are only known + # after the pipeline) + panel_ndims = _panel_cell_ndims( + panel_ndims, [xi for xf in panel_xforms for xi in xf]) + panel_data = [] + for i in range(n_panels): + kw = panel_kwargs[i] + xf = panel_xforms[i] + if panel_ndims == 3: + # a 2-wide panel in a grid whose other panels came out 3-wide + # (independent fits of unequal inputs): draw it flat in the + # 3-D cell on BOTH backends, the way a matplotlib 3-D axes + # draws 2-column rows -- plotly's 'scene' cell refuses a 2-D + # trace outright. A ONE-column series in such a grid is drawn + # as the series it is -- the row index on x, its values on y + # -- flat on the cell's floor (round 8: it reached the 3-D + # cell as one column and crashed both backends). The panel + # call itself places the rows (`_PanelLift`), AFTER it has + # forecast and resolved `truth=` on the analyzed rows (round + # 9: padding them here changed the forecasting input) + lifts = [_panel_lift_for(xi, src) + for xi, src in zip(xf, panel_sources[i])] + if any(lift is not None for lift in lifts): + kw['_panel_lift'] = lifts + # the analysis already ran (above); each panel DRAWS its rows + kw.update(transform=list(xf), reduce=None, normalize=None, + align=None, manip=None, pipeline=None, impute=None, + resample=None, random_state=None) + if panel_clusters[i] is not None: + # ...and its probe's clustering, replayed (no second fit) + kw.update(cluster=panel_clusters[i], n_clusters=None) + # each panel's rows, labelled with its source frame's index and + # column names so series mode keeps dates on x and the column on y + # exactly as the raw frame would (P2) + panel_data.append([_panel_frame(src, xi) + for src, xi in zip(panel_sources[i], xf)]) + # DataFrame-column axis labels (GH #285): a panel is drawn through + # `transform=`, which is exactly the case `plot()` refuses to infer + # labels for -- so derive them here, from the panel's OWN input + # dataset, under the same rule (2-D/3-D, one drawn axis per named + # column). An explicit xlabel=/ylabel=/zlabel= still wins. + # ...but not in `ndims=1` series mode, whose drawn axes are the + # row index and the values -- there a 3-column frame's labels + # were assigned to x/y/z and zlabel= then refused (P2) + _labels = (None if panel_ndims == 1 or len(xf) != 1 + else _dataframe_axis_labels(panel_sources[i])) + if (_labels is not None + and len(_labels) == int(xf[0].shape[1]) + and len(_labels) in (2, 3)): + for _key, _value in zip(('xlabel', 'ylabel', 'zlabel'), + _labels): + if kw.get(_key) is None: + kw[_key] = str(_value) if backend == 'plotly': - return _plot_panels_plotly(panel_data, panel_kwargs, titles, - nrows, ncols, ndims, call_kwargs) - - fig, axes = subplots(nrows, ncols, ndims=ndims, - size=call_kwargs.get('size')) + plotly_kwargs = dict(call_kwargs) + plotly_kwargs['save_path'] = save_path + with warnings.catch_warnings(record=True) as draw_warnings: + warnings.simplefilter('always') + grid = _plot_panels_plotly( + panel_data, panel_kwargs, titles, nrows, ncols, panel_ndims, + plotly_kwargs, shared_pipeline=shared_pipeline, + panel_pipelines=panel_pipelines) + _reemit_panel_warnings(probe_warnings, draw_warnings) + return grid + + fig, axes = subplots(nrows, ncols, ndims=panel_ndims, + size=call_kwargs.get('size'), backend='matplotlib') panel_axes = [] panel_models = [] - for i in range(n_panels): - kw = dict(panel_kwargs[i]) - kw.update(ax=axes[i], title=titles[i], show=False, save_path=None, - return_model=return_model, size=None) - result = plot(panel_data[i], **kw) - if return_model: - panel_models.append(result) - panel_axes.append(axes[i]) + with warnings.catch_warnings(record=True) as draw_warnings: + warnings.simplefilter('always') + for i in range(n_panels): + kw = dict(panel_kwargs[i]) + kw.update(ax=axes[i], title=titles[i], show=False, + save_path=None, return_model=return_model, size=None) + if panel_ndims < (3 if ndims is None else min(ndims, 3)): + # cells lowered to the data's width (`_panel_cell_ndims`): + # the panel call's `ax=` projection check reads ndims=, + # not the data, so tell it the dimensionality its axes + # actually has (the drawing is the same: the rows are + # already analyzed) + kw['ndims'] = panel_ndims + result = plot(panel_data[i], **kw) + if return_model: + # the fit this panel was drawn from: the panel itself is + # drawn through `transform=`, whose bundles carry + # `pipeline=None` (release audit 2026-09-07, 3) + result['pipeline'] = panel_pipelines[i] + panel_models.append(result) + panel_axes.append(axes[i]) + _reemit_panel_warnings(probe_warnings, draw_warnings) for spare in axes[n_panels:]: spare.set_visible(False) + if call_kwargs.get('size') is None: + # room beside every column for the legends/colorbars the panels + # drew -- what the plotly grid reserves as its gutter (110 px per + # legend or colorbar) -- so `tight_layout` does not shrink the + # panels to fit them (1.1 release review, feature-tour 9.8: three + # 10-entry legends left three 1.3 in cubes). An explicit `size=` + # is honoured verbatim. + _cols = ncols if n_panels > 1 else 1 + _extra = 0.0 + if any(a.get_legend() is not None for a in panel_axes): + _extra += 1.1 * _cols + if len(fig.axes) > len(axes): # colorbar axes beside the panels + _extra += 1.1 * _cols + if _extra: + _w, _h = fig.get_size_inches() + fig.set_size_inches(_w + _extra, _h) fig.tight_layout() if save_path is not None: @@ -2692,13 +4707,15 @@ def _plot_panels(x, panels, call_kwargs, _name='panels'): 'xform_data': [m['xform_data'][0] if len(m['xform_data']) == 1 else m['xform_data'] for m in panel_models], 'colors': panel_models[0].get('colors') if panel_models else None, + 'pipeline': shared_pipeline, } return bundle return fig def _plot_panels_plotly(panel_data, panel_kwargs, titles, nrows, ncols, - ndims, call_kwargs): + ndims, call_kwargs, shared_pipeline=None, + panel_pipelines=None): """`panels=` under the plotly backend: the same grid, built with `plotly.subplots.make_subplots` (3-D panels get ``type='scene'`` cells). Each panel is drawn by an ordinary `plot()` call and its traces @@ -2714,42 +4731,71 @@ def _plot_panels_plotly(panel_data, panel_kwargs, titles, nrows, ncols, reported as a plotly internals error. ``axes`` here holds the layout objects the panels were transplanted into (one ``layout.scene*`` per panel for 3-D, one ``(xaxis, yaxis)`` pair for 2-D), which is plotly's - equivalent of the matplotlib bundle's ``Axes`` list. + equivalent of the matplotlib bundle's ``Axes`` list. `shared_pipeline` + is the shared fit's pipeline (``panel_fit='shared'``) and + `panel_pipelines` each panel's own fit (the panels are drawn through + ``transform=``), both recorded in the bundle exactly as the matplotlib + grid records them. """ - from plotly.subplots import make_subplots - cell = {'type': 'scene'} if ndims >= 3 else {'type': 'xy'} - fig = make_subplots(rows=nrows, cols=ncols, - specs=[[dict(cell) for _ in range(ncols)] - for _ in range(nrows)], - subplot_titles=[t if t is not None else '' - for t in titles]) + from .plotly_backend import (make_panel_grid, panel_gutter_px, + transplant_panel) return_model = bool(call_kwargs.get('return_model', False)) + # draw every panel FIRST (each an ordinary single-axes plotly call), so + # the grid can reserve room beside each cell for the legend/colorbar + # the panels actually carry panel_models = [] - panel_axes = [] - for i, (data, kw) in enumerate(zip(panel_data, panel_kwargs)): - row, col = i // ncols + 1, i % ncols + 1 + panel_figs = [] + for data, kw, title in zip(panel_data, panel_kwargs, titles): kw = dict(kw) + # each panel's title goes through the ordinary single-axes title + # path (newlines, `title_wrap=`, `title_kwargs=`, validation) and + # `transplant_panel` turns the result into the cell's title -- the + # matplotlib grid formats its titles the same way (round 2) kw.update(show=False, save_path=None, return_model=return_model, - title=None, size=None, backend='plotly') + title=title, size=None, backend='plotly') result = plot(data, **kw) - panel = result['fig'] if return_model else result + panel_figs.append(result['fig'] if return_model else result) if return_model: + if panel_pipelines is not None: + result['pipeline'] = panel_pipelines[len(panel_models)] + elif shared_pipeline is not None: + result['pipeline'] = shared_pipeline panel_models.append(result) - for trace in panel.data: - fig.add_trace(trace, row=row, col=col) - if ndims >= 3: - key = 'scene' if i == 0 else f'scene{i + 1}' - if panel.layout.scene is not None: - fig.layout[key].update(panel.layout.scene.to_plotly_json()) - panel_axes.append(fig.layout[key]) - else: - xkey = 'xaxis' if i == 0 else f'xaxis{i + 1}' - ykey = 'yaxis' if i == 0 else f'yaxis{i + 1}' - panel_axes.append((fig.layout[xkey], fig.layout[ykey])) + legend_present = call_kwargs.get('legend') is not None + colorbar_present = any( + getattr(getattr(trace, 'marker', None), 'showscale', None) + for panel in panel_figs for trace in panel.data) + # room above every row for its panels' titles: what the single-axes + # path reserved for the tallest one (per line and per font size), so + # a two-line title keeps both lines (the base margin is the untitled + # single figure's 10 px) + title_px = max([0] + [max(0, int(panel.layout.margin.t or 0) - 10) + for panel, title in zip(panel_figs, titles) + if title]) + fig = make_panel_grid(nrows, ncols, ndims, + size=call_kwargs.get('size'), + gutter_px=panel_gutter_px(legend_present, + colorbar_present), + title_px=title_px) + panel_axes = [] + for i, panel in enumerate(panel_figs): + keys = transplant_panel(fig, panel, i // ncols + 1, i % ncols + 1, + i, ndims) + panel_axes.append(keys['scene'] if ndims >= 3 + else (keys['xaxis'], keys['yaxis'])) fig.update_layout(showlegend=bool(call_kwargs.get('legend'))) - if call_kwargs.get('size') is not None: - width, height = call_kwargs['size'] - fig.update_layout(width=width * 100, height=height * 100) + # the SAME figure class the single-axes plotly path returns, displayed + # through the same one-shot end-of-cell queue: a bare `go.Figure` shown + # with `fig.show()` here was displayed a second time by the notebook's + # rich-display hook when the call was the cell's last expression (P6) + from .plotly_backend import _hyper_figure_class, show_figure + fig = _hyper_figure_class()(fig) + # the bundle's `axes` are the layout objects of the figure RETURNED + # (the wrapper copies the layout, so they are looked up after wrapping) + panel_axes = [fig.layout[key] if isinstance(key, str) + else (fig.layout[key[0]], fig.layout[key[1]]) + for key in panel_axes] + # already normalized by `_plot_panels` (P5) save_path = call_kwargs.get('save_path') if save_path is not None: if save_path.lower().rsplit('.', 1)[-1] == 'html': @@ -2759,7 +4805,7 @@ def _plot_panels_plotly(panel_data, panel_kwargs, titles, nrows, ncols, ensure_kaleido_chrome() fig.write_image(save_path) if call_kwargs.get('show', True): - fig.show() + show_figure(fig) if return_model: # the SAME keys, in the same meaning, as the matplotlib panel # bundle below -- a caller must not have to branch on backend to @@ -2772,6 +4818,7 @@ def _plot_panels_plotly(panel_data, panel_kwargs, titles, nrows, ncols, 'xform_data': [m['xform_data'][0] if len(m['xform_data']) == 1 else m['xform_data'] for m in panel_models], 'colors': panel_models[0].get('colors') if panel_models else None, + 'pipeline': shared_pipeline, } return fig @@ -2834,6 +4881,11 @@ def plot( forecast_n_clusters=None, forecast_palette=None, forecast_fmt=None, + palette_sort=None, + palette_reduce=None, + palette_manip=None, + palette_normalize=None, + palette_align=None, slow_warning_seconds=_UNSET_SLOW_WARNING, frame_rate=30, focused=None, @@ -3132,6 +5184,15 @@ def plot( count raises a ``ValueError`` naming fmt and both counts. A fmt tuple is accepted and treated exactly like the equivalent list. + Under ``backend='plotly'`` a 3-D plot can only draw plotly's + `Scatter3d` marker set (circle, square, diamond, cross, x and the + open circle/square/diamond), so other markers take the nearest of + those: triangles (``'^'``, ``'v'``, ``'<'``, ``'>'``) and ``'d'`` + draw as diamonds, ``'*'`` as an open diamond, ``'+'``, ``'|'``, + ``'_'`` and ``'1'``-``'4'`` as crosses, ``'x'`` as an x, and + ``'p'``, ``'h'``, ``'H'`` and ``'8'`` as circles. 1-D/2-D plotly + plots and every matplotlib plot draw the marker asked for. + Static line rendering is DATA-FAITHFUL: line styles are smoothed by PCHIP interpolation, which only ever ADDS points between samples -- every original sample (including the final one) is @@ -3182,6 +5243,8 @@ def plot( entries, non-numeric, out of range) is also just ignored-with-a- warning in this case, not validated against and raised on -- whether ``alpha=`` will be used is decided before it is checked. + A continuous or matrix `hue=` keeps it: the per-point coloured + line segments AND markers carry their dataset's alpha. A `predict=` forecast overlay is drawn at HALF its dataset's alpha (``alpha=[1.0, 0.4]`` gives forecasts at ``[0.5, 0.2]``); an unset @@ -3260,8 +5323,10 @@ def plot( categorical palettes are used as-is. A palette string of the form ``'image:<path>'`` extracts colors from a LOCAL image file instead (``palette='image:starry_night.jpg'``): - six anchor colors, ordered most visually salient first, so a - painting's vivid subject leads and its muted background follows. + six anchor colors, sorted by value (dark to bright) so the palette + reads as a gradient (``palette_sort=`` or ``?sort=`` in the spec + picks another order; the most visually salient color is what a + per-dataset image entry stands for). For a continuous ``hue`` those anchors are blended into a gradient exactly as any short color list is. See ``hypertools.plot.colors.image_palette`` for the extraction itself @@ -3307,8 +5372,57 @@ def plot( of colors -- is per-dataset. A ONE-entry list is broadcast to every dataset. A per-dataset list must have 1 or ``len(x)`` entries, and cannot be combined with a continuous `hue=` (there is no single - ramp to map values through -- ``ValueError``). - + ramp to map values through -- ``ValueError``). With a CATEGORICAL + `hue=` (or `cluster=`/`n_clusters=`) the drawn groups are + categories rather than datasets, so only a list of ``{category: + color}`` dicts is meaningful there -- each dataset's dict names + its own categories' colours, and they are merged into one mapping + resolved by name (naming one category with two different colours + raises); any other per-dataset list with a categorical grouping + raises ``ValueError`` stating both counts. A plain colour list + shorter than the number of datasets is CYCLED when there is no + hue (``['red', 'blue']`` over three datasets draws red, blue, + red), as it always was. + + A t x k DATA MATRIX is a palette too. It is reduced to three + dimensions with `hypertools.reduce` (``palette_reduce=``, default + 'PCA', under ``palette_manip=``/``palette_normalize=``/ + ``palette_align=``), each reduced column is scaled to [0, 1] as an + RGB channel, the rows are ordered by ``palette_sort=`` (default + ``'columns'``: along the first component) and the result is a + colormap that is resampled by interpolation to however many colors + the plot needs -- one per dataset, one per category, or a gradient + along a continuous `hue=`:: + + hyp.plot(x, hue=np.arange(len(x)), palette=weights) # (t, k) + + A 2-D array with 3 or 4 columns and every value in [0, 1] stays a + list of colors; pass a DataFrame (or values outside [0, 1]) for a + three-column matrix that is data. One with 3 or 4 columns of whole + numbers in 0..255 reads as 0-255 colors and raises ``ValueError`` + (divide by 255 for the colors; a DataFrame for data). The palette is as smooth as the + matrix: the rows are ordered along the first component, so a + matrix whose other components follow the first (a trend with + oscillations) gives a clean gradient and an unstructured one a + striped palette. In a per-dataset list a matrix stands for its + most saturated color. Palettes extracted from an image + are sorted by value (dark to bright) so they read as a gradient; + ``palette_sort=`` (or ``?sort=`` in the spec) changes that, and the + most salient color still leads when an image stands for one + dataset in a per-dataset list. + palette_sort : {'value', 'hue', 'lightness', 'columns', 'original'} or None + How the colors of an image or matrix palette are ordered (see + `hypertools.plot.colors.sort_colors`). None (default) means + ``'value'`` for an image and ``'columns'`` for a matrix; a key given + here applies to `palette=` and `forecast_palette=` alike, including + every entry of a per-dataset list. A spec's own ``?sort=`` wins. + palette_reduce : reducer spec or None + The reducer `hypertools.reduce` applies to a matrix palette (default + 'PCA'); any form `reduce=` accepts. + palette_manip, palette_normalize, palette_align : same forms as \ + `manip=`, `normalize=`, `align=`, or None + Handed to `hypertools.reduce` when a matrix palette is reduced, + exactly as the same-named arguments would be for the data. hue : list, numpy array, pandas Series/Index/Categorical, or 2D matrix Values used to color the plot, one per observation, matched to the observations POSITIONALLY (a pandas Series' index is ignored). @@ -3416,7 +5530,8 @@ def plot( Distinct from `labels` (per-POINT text call-outs) and `hue` (per- observation coloring): each name labels its dataset's trace and turns the legend on, so `hyp.plot([raw, a, b], names=['raw', 'a', 'b'])` - shows a legend naming the three datasets. Must have exactly one entry + shows a legend naming the three datasets; an explicit + ``legend=False`` still suppresses it. Must have exactly one entry per dataset; mutually exclusive with passing a `legend=` list (use one or the other). Rendered on both the matplotlib and plotly backends. Incompatible with a CATEGORICAL `hue` (which regroups the data by @@ -3430,10 +5545,12 @@ def plot( labels : list A list of point labels: exactly one entry per OBSERVATION (row) - across all datasets, or a nested list with one sub-list per - dataset; a length mismatch raises ``ValueError`` naming labels and - both counts. If no label is wanted for a particular point, input - None for that entry. + across all datasets, or a nested list with one sub-sequence per + dataset (a list, tuple, 1-D array or Series each); a length + mismatch raises ``ValueError`` naming labels and both counts. If no + label is wanted for a particular point, input None for that entry. + Every form keeps each label on its own observation when `hue=` or + `cluster=` regroups the drawn traces. `labels` may instead carry one entry PER DATASET -- one string (or None) for each of the ``len(x)`` datasets -- which annotates each @@ -3448,9 +5565,10 @@ def plot( count, in which case the two readings annotate the same points anyway and the per-observation one is kept. - In an ANIMATION whose frame grid is coarser than the data (fewer - than one frame per sample), each label is attached to the nearest - drawn frame point, so labels are never silently dropped. + In an ANIMATION every observation stays a vertex of the animated + line (the frame grid only ever adds points between observations), + so each label is attached to its own observation and none is + dropped. Supported on BOTH backends (GH #205/#F3): matplotlib draws these as `ax.annotate` call-outs; plotly draws the same points as @@ -3497,6 +5615,12 @@ def plot( with a ``UserWarning`` (that path colors by value, so there are no discrete groups to name). + A NESTED-list `x` (``[[a, b], [c, d]]``) colours every leaf by its + outer group, and its legend follows the same rule: one entry per + outer group (``1..n`` for ``True``, or a list with one name per + group), drawn on the group's shallowest leaf; a list with one entry + per LEAF still labels every leaf. + Under a matrix-valued (mixture) `hue=` the blended per-observation colours have no discrete traces to label, so hypertools used to drop the legend with a warning. It now builds one proxy swatch per @@ -3518,14 +5642,20 @@ def plot( legend_colors : list of colors, or list of (label, color) pairs An explicit override of the legend's swatches. A plain list of colors -- one per legend entry, in order -- RECOLORS the entries - hypertools would draw anyway (matplotlib only; the plotly legend + hypertools would draw anyway; when `predict=`/`truth=` add their + own entries after the data's, one color per DATA entry is accepted + too and leaves those overlay glyphs as drawn (matplotlib only; the plotly legend takes its swatches from the traces themselves, so this form raises ``NotImplementedError`` there). A list of ``(label, color)`` pairs REPLACES the legend outright with exactly those entries, on both backends -- which is how a figure adds a key entry no trace corresponds to (e.g. a grey "Market" line alongside per-sector - swatches). Mixing the two forms raises ``ValueError``. Default - None. + swatches). Mixing the two forms raises ``ValueError``. Under + `panels=`, a plain list naming the datasets (one colour per + dataset, then one per shared entry such as a forecast model or + ``truth``) is split so each panel's legend gets its own dataset's + colour followed by the shared ones; a pair list is drawn whole in + every panel. Default None. colorbar : bool or dict If True, draws a colorbar reflecting the color mapping in use @@ -3553,9 +5683,10 @@ def plot( ``animate='morph'``) you may pass one string per dataset: each is shown while its dataset is the one being revealed, and morph TRANSITIONS show a blank title so only fully-formed clouds are - named (a hold and a transition both progress 0 -> 1, so the - distinction is the segment itself, not how far through it you - are). Anywhere else a non-string raises ``TypeError``: use + named: every transition frame draws a cloud strictly between the + two datasets, and only a hold draws a dataset's own cloud (a hold + and a transition both progress 0 -> 1, so the distinction is the + segment itself, not how far through it you are). Anywhere else a non-string raises ``TypeError``: use ``names=`` for per-dataset legend entries, or ``labels=`` for per-observation annotations. Rendered identically on the matplotlib and plotly backends. @@ -3578,7 +5709,10 @@ def plot( **Where the reveal head is.** One rule, so the same pattern means the same thing on the same data: for a serial reveal it is the - last revealed row of the dataset being revealed right now; for + rows revealed so far across ALL datasets (finished ones plus the + revealed part of the current one) as a fraction of every row, + mapped onto the input's rows -- cumulative, so it never runs + backwards when the next dataset starts; for every other animated style (``True``/``'parallel'``/``'window'``/ ``'spin'``/``'morph'``) it is ``round(ctx.progress * (n_rows - 1))``, since those advance every @@ -3603,9 +5737,10 @@ def plot( ``variant``, ``stretch``, and the already-real names ``color``, ``alpha``, ``y``, ``pad``, ``loc``, ``rotation``, ``linespacing``, ``backgroundcolor``. An unknown key raises ``ValueError`` naming - the supported set. A ``size`` given here also sizes the top-margin - probe that reserves room for an animated 3-D title, so a larger - title still fits on the canvas. + the supported set. A ``size`` given here also sizes the room + reserved above an animated 3-D title (matplotlib's top-margin + probe; plotly's ``layout.margin.t``), so a larger title still fits + on the canvas. On `backend='plotly'` the size/family/weight/style/colour/y keys map onto ``layout.title``; anything plotly's title cannot express @@ -3630,8 +5765,13 @@ def plot( Applied after per-segment resolution, so a scalar title and every entry of a per-segment list wrap identically; the line break is the backend's own (a newline for matplotlib, ``'<br>'`` for plotly). - Note that an animated 3-D title still reserves top margin for ONE - line, so a very tall wrap can overflow there. Default None. + A dynamic (callable / ``{index``) title is wrapped afresh every + frame. An animated 3-D title reserves top margin per line on both + backends: for a per-segment list, the tallest entry; for a + dynamic title, plotly measures every frame (all frames are built + up front) while matplotlib measures FRAME 0's text, so a callable + whose title grows lines on later frames can clip there -- give + frame 0 the same number of lines. Default None. font : None, str, or matplotlib.font_manager.FontProperties Controls the font used for every text surface hypertools draws, @@ -3675,6 +5815,15 @@ def plot( neither a resolvable family name nor an existing file. - `matplotlib.font_manager.FontProperties`: used as-is. + Weights: the package bundles a Bold face beside Noto Sans Regular + (``NotoSans-Bold.ttf``, same licence and provenance), so a bold + request under the default font -- ``title_kwargs={'fontweight': + 'bold'}``, ``legend_kwargs={'prop': {'weight': 'bold'}}`` and the + like -- resolves to that real bold face on the matplotlib backend + rather than to a synthetic or Regular substitute; any other weight + (light, medium, semibold, black, ...) falls back to the nearest + bundled face, i.e. Regular or Bold. + Backend semantics differ because matplotlib and plotly resolve fonts differently: matplotlib accepts a font FILE and sets a `FontProperties` object on each `Text` artist individually @@ -3778,8 +5927,13 @@ def plot( steps take one array at a time, whereas the dispatcher pipelines `analyze`/`plot` build take the whole list. A bare fitted stage object (a `Reducer`/`Aligner`/... from a dispatcher's - `return_model=True`) is accepted as a one-step pipeline. - Mutually exclusive with `manip=`/ + `return_model=True`) is accepted as a one-step pipeline. A + pipeline ending in a fitted `'cluster'` step (a clustered figure's + bundle, or ``hyp.analyze(..., cluster=..., return_model=True)``) + colours the figure by that step's labels for `x`, with the fit + figure's cluster colours, unless `hue=` is given; a clusterer with + no out-of-sample `predict` that cannot label `x` warns and draws + without clusters. Mutually exclusive with `manip=`/ `normalize=`/`reduce=`/`ndims=`/`align=`/`cluster=` (each must be left at its default) -- passing both raises `ValueError` naming the conflicting kwarg(s). `resample=` is still applied (as sugar, before @@ -3801,7 +5955,8 @@ def plot( membership proportions, GH #174); and the torch-backed autoencoders Autoencoder, DeepAutoencoder, SparseAutoencoder, ConvolutionalAutoencoder, SequenceAutoencoder and - VariationalAutoencoder (GH #162, `pip install "hypertools[torch]"`). + VariationalAutoencoder (GH #162; the `[torch]` extra, installed on + demand). Can be passed as a string, or for finer control of the model parameters as a dictionary, e.g. reduce={'model': 'PCA', 'kwargs': {'whiten': True}}. See scikit-learn @@ -3823,7 +5978,7 @@ def plot( higher-dimensional analyzed data. Default is 3 (plot in 3 dimensions). - ``ndims=1`` (1.2, GH #285) is a real TIME-SERIES mode, not a + ``ndims=1`` (1.1, GH #285) is a real TIME-SERIES mode, not a one-column scatter: every column of every dataset is drawn as its own line against the dataset's ROW INDEX (a DataFrame's index -- including a `DatetimeIndex`, which ticks as real dates -- or @@ -3834,25 +5989,31 @@ def plot( overrides them; `legend=True` shows them). ``reduce=`` still applies first, to ONE component -- ``ndims=1, reduce='PCA'`` draws the first principal component over the index. An animation reveals - each line left to right along x. Before 1.2 this path drew a single + each line left to right along x. An index that is not in ascending + order is drawn in index order, as `predict=` forecasts it, with a + ``UserWarning`` -- unless `hue=`/`labels=`/`cluster=` are given per + observation in the input order, which keeps that order (and warns + that the line doubles back). Before 1.1 this path drew a single column against ``0..n-1`` with the values rescaled to ``[-1, 1]``, no visible axes, and refused 2+ columns -- so this is a deliberate behaviour change: what a 1-D figure draws is now the data's own coordinates. ``axis_scale='unit'`` puts the (index, value) traces back inside the ``[-1, 1]`` frame square like any other 2-D plot, - which rescales BOTH axes and so does not restore the pre-1.2 + which rescales BOTH axes and so does not restore the pre-1.1 geometry either. axis_scale : {'unit', 'data'} or None - Which coordinates the figure is drawn in (1.2, GH #285). + Which coordinates the figure is drawn in (1.1, GH #285). ``'unit'`` is what hypertools has always done, and remains the default for 2-D and 3-D plots: the (possibly reduced/aligned) coordinates are mean-centred and rescaled into ``[-1, 1]`` by a single shared affine transform across all datasets (and, with - `predict=`, across every forecast vertex), the axes are pinned to - ``(-1.1, 1.1)``, and hypertools' own frame square/cube is drawn in - place of matplotlib's ticks and spines. Coordinates read off the + `predict=`, across every forecast vertex), the axes are pinned + around hypertools' own frame (2-D: ``(-1.2375, 1.2375)`` around a + frame square of half-width 1.125; 3-D: the ``[-1, 1]`` cube, grown + to fit a `surface=`), and that frame square/cube is drawn in place + of matplotlib's ticks and spines. Coordinates read off the returned Figure are therefore an affine IMAGE of the analyzed data, not the raw values, and are not comparable across figures. @@ -3862,7 +6023,9 @@ def plot( ``hyp.plot(np.column_stack([t, y]), reduce=None, ndims=2, axis_scale='data')`` draws real ``t`` on x and real ``y`` on y. No frame square is drawn, the axes keep their ticks and spines (top - and right removed), `xlabel=`/`ylabel=` still apply (DataFrame + and right removed) and draw no gridlines on either backend (a + caller's own `ax=` keeps any grid it already has), + `xlabel=`/`ylabel=` still apply (DataFrame column names supply them by default), and the limits come from `xlim`/`ylim` when given, otherwise from the full data (forecast and `truth=` overlays included) with a 5% margin -- fixed up front, @@ -3875,12 +6038,17 @@ def plot( and ``'unit'`` everywhere else. xlim, ylim : (low, high) or None - Explicit axis limits, in the DRAWN coordinates (1.2, GH #285). Only + Explicit axis limits, in the DRAWN coordinates (1.1, GH #285). Only meaningful under ``axis_scale='data'`` (under ``'unit'`` the axes are pinned to the frame box); passing either with ``axis_scale='unit'`` raises ``ValueError`` rather than silently doing nothing. ``None`` (the default) computes the limits from the - data. Both backends, static and animated. + data. Both backends, static and animated. On a DATE x axis + (``ndims=1`` over a ``DatetimeIndex``) `xlim` accepts datetime-like + values and date strings (``xlim=('2020-01-05', '2020-01-10')``) on + both backends; a number there is a matplotlib day number (the unit + ``matplotlib.dates.date2num`` produces) on both backends, and is + converted for plotly. align : str, dict, False, or None Alignment model to bring a list of datasets into a shared space. @@ -3902,7 +6070,10 @@ def plot( BayesianGaussianMixture, LatentDirichletAllocation and NMF. Can be passed as a string, or for finer control of the model parameters as a dictionary, e.g. cluster={'model': 'KMeans', 'kwargs': {'max_iter': - 100}}. See scikit-learn specific model docs for details on parameters + 100}} (as in `hyp.cluster`, a top-level key other than 'model', + 'args', 'kwargs' and the 'n_clusters' shortcut raises + ``ValueError``). See scikit-learn specific model docs for details + on parameters supported for each model. If no parameters are specified a default set of parameters will be used: 3 clusters/components for most models (the same default as `hyp.cluster`), 20 components for @@ -3973,12 +6144,22 @@ def plot( post normalize/reduce/align space) using the specified `hypertools.predict` model, e.g. 'Kalman', 'ARIMA', 'GaussianProcess' (see `hypertools.predict.predict` for accepted forms), and overlays - one forecast trace per dataset (no separate legend entry). A forecast + one forecast trace per dataset, listed ONCE in the legend (when + there is one) under the model's name -- the same name + ``hyp.predict(x, model=[spec])`` would give it -- with a glyph in + the forecasts' own style: their colour when every forecast shares + one, else a neutral gray, since the entry then names the model + rather than any one dataset. (Before the 1.1.0 release review only + a collection of models was listed.) A forecast is the SAME series projected forward, so it INHERITS the style of the observed trace it continues -- same color, same linestyle, same linewidth -- and differs only in transparency: ``forecast_alpha = observed_alpha * 0.5`` (an unset `alpha` is matplotlib's opaque 1.0, - so the default is 0.5). Per-dataset styling carries through dataset + so the default is 0.5). A forecast given its OWN colour + (`forecast_hue=`, `forecast_cluster=`, `forecast_palette=`, or a + colour letter in `forecast_fmt=`) keeps its trace's alpha instead: + the colour is then what tells it apart, and fading it as well hid + it among translucent traces. Per-dataset styling carries through dataset by dataset, e.g. ``alpha=[1.0, 0.4]`` gives forecasts at ``[0.5, 0.2]``, and a dotted dataset gets a dotted forecast. Both backends apply the identical rule. (Before 1.1.0 every forecast was @@ -3991,8 +6172,8 @@ def plot( static forecast overlay, so the trace simply rotates with the rest of the scene), and for the TIME-PROGRESSING animate modes (``True``/``'parallel'``/``'serial'``/``'window'``), where the - forecast is recomputed from the history revealed so far and - re-anchored on the last revealed observation, so it grows with the + forecast is recomputed from the history revealed so far and drawn + from the endpoint of the current frame, so it grows with the animation. Every one of those forecasts is computed BEFORE the first frame is drawn and folded into the plot's centre/scale statistics, so the whole fan lands inside the cube and nothing is clipped or @@ -4022,31 +6203,47 @@ def plot( short trace means the input itself has fewer than 2 observations and only more data helps. See docs/hierarchy.rst (default: None). - A **collection of models** (1.2, GH #285) draws one overlay per + A **collection of models** (1.1, GH #285) draws one overlay per model on every trace: ``predict=['Kalman', 'ARIMA', 'GP']``, or the mapping form ``predict={'my kalman': {'model': 'Kalman', 'kwargs': {...}}, 'arima': 'ARIMA'}`` when you want to name them yourself. The specs are handed to ``hyp.predict(x, model=[...])``, whose ``{name: forecast}`` contract this reuses verbatim, so the two - agree by construction. Each model's overlays take a colour from - `forecast_palette` (a seaborn palette name or an explicit colour - list; default ``'husl'``) -- one colour per MODEL, shared across - datasets -- and are labelled in the legend by the model's name (the - auto-name from the spec, or the mapping key). `forecast_fmt=` may - be a list, one entry per model. Works with `truth=`, static and - animated, on both backends. The ``return_model=True`` bundle's + agree by construction. Two things need telling apart -- which + series a forecast continues and which model made it -- so each + overlay keeps its dataset's COLOUR (as the single-model form does) + and takes a linestyle per MODEL, cycling solid, dashed, dotted, + dash-dot in model order (the first model is solid, so + ``predict=['Kalman']`` draws what ``predict='Kalman'`` draws). The + legend lists each model by name (the auto-name from the spec, or + the mapping key) with a glyph in its linestyle -- neutral gray when + the model's forecasts span several colours. `forecast_fmt=` may be + a list, one entry per model, replacing the cycle; `forecast_palette` + (a seaborn palette name or an explicit colour list) colours by + MODEL instead, one colour per model shared across datasets. + (Until the 1.1.0 release review a per-model ``'husl'`` palette was + the default, and its first colour was the first dataset's own.) + Works with `truth=`, static and animated, on both backends. The + ``return_model=True`` bundle's ``predict['forecasts']`` becomes a ``{name: [forecast per dataset]}`` - dict for this form, mirroring `hyp.predict`. + dict for this form, mirroring `hyp.predict` -- for a hierarchical + `x` too, where each list holds one forecast per FINAL trace. + `forecast_hue=` keeps its one-value-per-DATASET meaning: a value is + shared by every model's forecast of that dataset (a list sized to + the overlays themselves, model-major, is also accepted). truth : array, DataFrame, or list of these, or None The ACTUAL continuation of each dataset, drawn beside the forecast - in the same space (1.2, GH #285) -- so a train/held-out/forecast + in the same space (1.1, GH #285) -- so a train/held-out/forecast figure is one call rather than three hand-built datasets: ``hyp.plot(train, predict='Chronos', t=30, truth=held_out)``. Requires `predict=`. One array per input dataset (a bare array for a single dataset), each with exactly `t` rows and the same number of columns as the plotted data; anything else raises ``ValueError`` - naming the mismatch. It is read in the PLOTTED space -- with + naming the mismatch. With ``ndims=1`` each plotted column is its own + trace whose x is the index, so its truth is ONE column of values + (or one frame with one column per trace); its x continues the + index exactly as the forecast's does. It is read in the PLOTTED space -- with ``reduce=None`` (the case these figures use) that is the input space, and with a `reduce=` spec it is whatever that spec produced, so pass values already in it (hypertools does not re-project @@ -4061,21 +6258,41 @@ def plot( (matplotlib ``line._hyp_forecast_role``, plotly ``meta['hyp_forecast_role']``) exactly as forecast artists are tagged ``'static'``/``'live'``/``'trail'``. With `legend=True` it - gets one ``'truth'`` legend entry. Drawn in FULL in every mode, - including a time-progressing animation: it is what actually - happened, held still while the forecast is refitted frame by frame - against it, so the comparison the figure exists to make is on screen - the whole way through. Identical on both backends (default: None). + gets one ``'truth'`` legend entry. Static plots (and + ``animate='spin'``) draw it in FULL. A time-progressing animation + (``True``/``'parallel'``/``'serial'``/``'window'``, including a + `hue=`/`cluster=` regrouped reveal) reveals it as the forecast + reaches it: each held-out row appears on the first frame whose + drawn forecast of that dataset (any model's, for a `predict=` + list) extends to or past that row's position, and stays on every + later frame -- so the answer is not on screen before the animation + has forecast anywhere near it. While no drawn forecast has reached + it (e.g. too little history revealed yet) the truth is hidden; its + ``'truth'`` legend entry is shown on every frame. Identical on both + backends (default: None). t : int or datetime-like Forecast horizon passed to `predict` (see `hypertools.predict.common.resolve_t`); ignored unless `predict` is set. Measured in RAW observations of the analyzed data -- NOT in animation frames and NOT in drawn vertices. ``t=1`` forecasts only - the next observation. Because an animation is paced on a resampled - frame grid (see `duration`/`frame_rate`), an animated forecast joins - the drawn trajectory to within one raw observation rather than - exactly (default: 10). + the next observation. A datetime-like `t` (a ``Timestamp``, a + ``datetime`` or a date string) is measured against each dataset's + own ``DatetimeIndex``, exactly as `hyp.predict` measures it: every + dataset is forecast up to that time, so a flat list whose indexes + end at different times or have different spacings gets a + different number of steps per dataset (the single integer horizon + the renderer works in is the LARGEST of those counts). A hierarchical (``MultiIndex``) input is stricter: `t` + must resolve to ONE step count shared by every group + (``ValueError`` otherwise), and a row ``MultiIndex`` is not a + ``DatetimeIndex``, so it needs an integer `t`. Datasets without a + ``DatetimeIndex``, a `t` at or before any dataset's last + observation, or a pipeline stage that changed the row count raise + ``ValueError``. Because an animation is paced on a resampled + frame grid (see `duration`/`frame_rate`), a frame's drawn head + usually falls between two observations; an animated forecast starts + exactly there, while the points it predicts stay `t` observations on + from the last one revealed (default: 10). save_path : str or path-like Path to save the image/movie; the format is chosen by the file @@ -4232,13 +6449,23 @@ def plot( camera, so the fixed forecast overlay is drawn once and rotates along with everything else) AND with the time-progressing modes (`True`/`'parallel'`/`'serial'`/`'window'`), where the forecast is - recomputed from the history revealed so far and re-anchored on the - last revealed observation, so it grows with the animation. Only + recomputed from the history revealed so far and drawn from the + endpoint of the current frame, so it grows with the animation. Only `'morph'` (including per-dataset morph lists) raises `NotImplementedError`: a morph interpolates between point CLOUDS, so there is no time axis to forecast along. See `forecast_trail=` to keep earlier forecasts on screen as a fading fan. + **File size (plotly).** An animated plotly figure embeds every + frame, and its animated lines keep every observation, so the + figure's HTML/JSON grows with rows x frames: a 3000-row 3-D random + walk animated over 300 frames (``duration=10, frame_rate=30``) is + about 6.7 MB of JSON (about 4.5 MB in 2-D). Passing `resample=N` + (below) to thin the input first shrinks it in proportion -- + ``resample=1000`` brings that example to about 2.3 MB (1.5 MB in + 2-D). Fewer frames (a lower `frame_rate` or shorter `duration`) + shrink it too. + order : {'parallel', 'serial'} Whether animated datasets are revealed all at once ('parallel') or one after another ('serial'). This is ORTHOGONAL to ``animate=``, @@ -4377,7 +6604,10 @@ def plot( One value per FORECAST, not per observation: a forecast is a single trace. Datasets sharing a value share a colour, drawn from - `forecast_palette=`. Mutually exclusive with `forecast_cluster=` + `forecast_palette=` -- or, without one, from the figure's + `palette=` CONTINUED past the colours the observed data takes, so + no forecast group wears an observed line's colour (see + `forecast_palette=`). Mutually exclusive with `forecast_cluster=` (both decide the same thing), exactly as `hue=` and `cluster=` are for the observed data. @@ -4437,9 +6667,26 @@ def plot( different colormap from the data. With `forecast_hue=` or `forecast_cluster=`, one colour per group. + Left at `None` there, the groups take the figure's `palette=` + CONTINUED past the observed data's colour slots (its datasets, or + its `hue=`/`cluster=` groups, plus any an earlier call took on a + reused `ax=`) -- the next colours of a fixed-sequence palette such + as 'Set2', and the free slots of a finer sampling of an evenly + spaced one such as 'hls' (four observed 'hls' colours -> the groups + get the 45- and 135-degree hues of 'hls' at 8) -- so no forecast + group is drawn in an observed line's colour. A per-dataset or + ``{category: color}`` `palette=` has no sequence to continue, so + 'hls' is continued past the observed count instead; a continuous + `hue=` (colormap colours, no palette slots) keeps 'hls' from its + first colour. An explicit `forecast_palette=` replaces all of + this. With `panels=` the same default is resolved against the + whole grid, so each label keeps the colour the single-axes figure + gives it. With NEITHER, there is no forecast grouping to colour by, so it is spent one colour per forecast (see `forecast_hue=` on what counts as - one for a hierarchical `x=`). + one for a hierarchical `x=`) -- except for a COLLECTION of models + (``predict=[...]``), where it is spent one colour per MODEL, shared + by that model's forecast of every dataset (see `predict=`). forecast_fmt : str, sequence of str, or None Line/marker style for the forecast overlays, in the same format-string @@ -4453,7 +6700,10 @@ def plot( red, unless a colour is also given (via `forecast_palette=`, `forecast_hue=`, `forecast_cluster=`, or a colour letter in the format string itself -- an explicit colour beats the format string's, - matching matplotlib's own rule). + matching matplotlib's own rule). A marker in the format string + (``'ro:'``) is drawn at the forecast's own steps -- the seam + observation and each of the `t` forecast rows -- never at the + antialiased vertices between them (see `antialias`). Note that these four kwargs are independent, so observed and forecast data may differ in style, in grouping, in palette, or in @@ -4473,8 +6723,9 @@ def plot( more distinct histories AND a costlier fit each. Nothing is skipped to make that faster -- sampling the reveal would change what is plotted -- so the notice exists to make a long wait expected rather - than mysterious. It is emitted as soon as one real fit has been - timed, not after the wait. + than mysterious. It is emitted once fits at two or more history + lengths (one of at least 10 rows, or the longest available) have + been timed, not after the wait. frame_rate (animation only) : int or float Frame rate for animation in frames per second (default: 30). @@ -4505,7 +6756,7 @@ def plot( control. Must be a non-negative number if given; raises `ValueError` otherwise. - morph_samples (``animate='morph'`` only) : int or None + morph_samples (animate='morph' only) : int or None An OPTIONAL cap on morphing-dataset size, applied BEFORE the duplicate-padding described under `animate` above: any morphing dataset larger than `morph_samples` is first downsampled (without @@ -4526,7 +6777,7 @@ def plot( Must be a positive integer (or None); anything else raises ``ValueError``. Ignored for every other `animate` mode. - loop (``animate='morph'`` only) : bool + loop (animate='morph' only) : bool Close the morph sequence: after the last cloud, transition back to the FIRST one and hold it again, so a looping player never hard-cuts from the last shape to the first. `n` clouds then give @@ -4596,7 +6847,8 @@ def plot( so a panel and a date title can never disagree. ``reveal`` ``True`` (default) reveals the series up to the head; ``False`` - draws it whole on every frame. + draws it whole on every frame (including its smoothed trend). + The head marker still follows the current input row in both modes. ``smooth`` An int rolling-mean window (>= 2) drawn as a black trend line over the revealed part, NaN until the window fills -- the same @@ -4619,8 +6871,12 @@ def plot( ``pad`` How much of `size` is left for the panel's own ticks and label (default 0.10, in figure fractions). - ``color``, ``xlabel``, ``ylabel`` - Line colour and axis labels. + ``color`` + Line and head-marker colour. Default: the colour the first + dataset's trajectory is drawn in (its ``fmt=`` colour letter, + ``color=``, or its palette colour). + ``xlabel``, ``ylabel`` + Axis labels. What this deliberately does NOT do: no 3-D companion panels, no panel with its own animation schedule (it always follows the main @@ -4661,7 +6917,11 @@ def plot( state.** Mutating what the context hands you is the point of the hook and is - fully supported -- the example below sets a title every frame. + fully supported -- the example below sets a title every frame. (A + 3-D matplotlib animation passed ``on_frame=`` reserves the same top + margin a ``title=`` gets, so a title set by the callback is drawn + on the canvas; one attached later with ``HyperAnimation.on_frame()`` + should pass ``title=' '`` to reserve it.) What is unsupported is accumulation (``count += 1``, ``alpha *= 0.9``), because a repeated frame would change the result. Precompute running quantities and index them by @@ -4743,9 +7003,14 @@ def plot( ``plt.show()`` -- call ``plt.show()`` yourself to open a window. Default: True. - transform : list of numpy arrays or None + transform : array, DataFrame, list of these, or None The transformed data, bypasses transformations if this is set - (default : None). + (default : None). One entry per dataset of `x`, row for row; a bare + array or DataFrame is one dataset. A DataFrame's rows are matched + to `x`'s by position; with `predict=`, whose observation times come + from `x`'s index, a DataFrame index that is neither a plain + ``0..n-1`` one nor `x`'s own raises ``ValueError`` rather than + guessing which observation each row is. vectorizer : str, dict, class or class instance The vectorizer to use. Built-in options are 'CountVectorizer' or @@ -4788,13 +7053,32 @@ def plot( (never loaded or embedded) when the vectorizer is a pretrained Hugging Face embedding model and there is no semantic stage. - ax : matplotlib.Axes or plotly.graph_objects.Figure + ax : matplotlib.Axes, plotly.graph_objects.Figure, or plotly grid cell The surface to draw into: a matplotlib Axes for the matplotlib - backend, or, with the plotly backend, the plotly Figure an earlier - `hyp.plot` returned -- this call's traces are appended to it and it - is returned (its layout is left alone). A matplotlib Axes under - plotly raises `ValueError`; a plotly Figure under matplotlib raises - `TypeError`. + backend, or, with the plotly backend, either the plotly Figure an + earlier `hyp.plot` returned -- this call's traces are appended to + it and it is returned (its layout is left alone) -- or one cell of + a ``hyp.subplots(nrows, ncols, backend='plotly')`` grid, into + which the whole drawn panel moves (traces, axes/frame, `title=`, + `labels=` annotations, and its own legend and colorbar beside the + cell), the same composition loop as the matplotlib + ``fig, axes = hyp.subplots(...); hyp.plot(d, ax=axes[i])`` form. + A plotly Figure or cell with the default ``backend='auto'`` draws + with plotly; with an explicit ``backend='matplotlib'`` it raises + `TypeError`. A matplotlib Axes under plotly raises `ValueError`. + + The datasets are drawn in the `palette` exactly as on a figure of + their own (a caller's Axes used to keep the colour cycle of the + figure it came from), and a second call into the SAME Axes or + plotly Figure continues the palette past the datasets the earlier + call drew, on both backends -- so composing two calls does not + draw both in the first colour. A fixed-sequence palette (a colour + list, 'deep', 'Set2') gives its next colours; an evenly re-sampled + one ('hls', 'husl', a colormap) fills the gaps between the colours + already drawn rather than repeating one ('hls' drawn as 2 datasets + and then 2 more gives the four 4-colour 'hls' hues). The + return_model bundle's ``'colors'`` (and a colorbar) report the + colours drawn. Pass `color=` to choose instead. STATIC PLOTS ONLY. An animated plot (any truthy ``animate=``) owns its own figure: it creates one, draws there, and returns it, so an @@ -4816,10 +7100,30 @@ def plot( ``fig, axes = plt.subplots(nrows, ncols, subplot_kw={'projection': '3d'}); for ax, d in zip(axes.ravel(), data): hyp.plot(d, ax=ax, show=False); plt.tight_layout()`` grid. - ``True`` (or ``'auto'``) sizes a near-square grid; an ``int`` is + ``True`` (or ``'auto'``) picks the grid from the figure's aspect + ratio (`size=`), preferring a grid with no spare cell: three + panels form a row in a default or wide figure and a column in a + tall one, four form 2x2, six form 2x3; an ``int`` is the number of COLUMNS; an ``(nrows, ncols)`` pair is used verbatim and must have room for every panel. Spare cells are hidden, the - layout is tightened, and the one `Figure` is returned. + layout is tightened, and the one `Figure` is returned. Every cell + has ONE projection, decided from the ANALYZED data (the rows the + pipeline hands each panel to draw, not the raw column count: two + raw columns through a feature-expanding ``manip='Delay'`` come out + three wide and get 3-D cells): 3-D by default; 2-D axes for + ``ndims=2`` or ``ndims=1``, and when every panel's analyzed rows + are 1 or 2 columns wide (the single-axes call draws such data on + 2-D axes whatever ``ndims=`` says). Panels of UNEQUAL analyzed + width (independent fits of a 2-column and a 3-column dataset, + say) share the wider grid: 2-column rows are drawn flat on the + 3-D cell's floor, and a 1-column series as row index vs value on + that floor (its own numeric index on x when the frame has one, + positions otherwise). That placement is a matter of DRAWING only: + such a panel forecasts (`predict=`), reads `truth=` and reports + its bundle in its own analyzed space -- the numbers the individual + call for that dataset produces -- and its forecast and truth + overlays are placed the way its rows are (a series continues its + index one row per step). ``subplots=`` is an accepted alias (passing both raises). The analysis pipeline (`manip`/`normalize`/`reduce`/`align`) is fit @@ -4844,9 +7148,27 @@ def plot( `title=` takes one string per panel (or one string for all of them). Per-dataset arguments given as lists (`hue=`, `labels=`, - `names=`, `color=`, `fmt=`, `alpha=`, `markers=`, `linestyles=`, - `surface=`, `density=`, ...) are narrowed to each panel's own - dataset; `legend=`/`colorbar=` are drawn per panel. + `names=`, `color=`, `fmt=`, `alpha=`, `marker(s)=`, + `linestyle(s)=`, `surface=`, `density=`, `truth=`, a per-dataset + `palette=` list such as ``['viridis', 'magma']``, ...) are + narrowed to each panel's own dataset, in either `panel_fit=` mode; + `legend=`/`colorbar=` are drawn per panel, and a `legend=` LIST + naming the datasets (no `hue=`/`cluster=` grouping) gives each + panel its own entry. Forecast arguments partition the same way: + `forecast_fmt=` and `forecast_hue=` given per forecast (per + dataset for one `predict=` model, model-major for a collection -- + see `predict=`) reach each panel as its own forecasts' entries (a + collection's one-per-MODEL `forecast_fmt=` is forwarded whole, + since every panel draws every model), `forecast_palette=` is + resolved against the whole grid so a dataset or a `forecast_hue=` + label keeps the colour the single-axes figure gives it, and a + forecaster already FITTED on every dataset + (``hyp.predict(x, return_model=True)``) is bound to each panel's + dataset (`Forecaster.for_dataset`) so its learned parameters are + reused rather than refit -- a forecaster fitted on a different + number of datasets than there are panels raises ``ValueError``. + `forecast_cluster=` groups each panel's own forecast endpoints + (with a single model, one per panel -- it then warns and inherits). STATIC ONLY: combining `panels=` with any truthy `animate=` raises ``ValueError`` (an animation owns its whole figure -- see `ax=`), as @@ -4855,10 +7177,19 @@ def plot( `plotly.subplots.make_subplots` (3-D panels as ``type='scene'`` cells), and `return_model=True` returns the SAME bundle keys there as on matplotlib (``fig``, ``axes``, ``panels``, ``panel_models``, - ``xform_data``, ``colors``) -- ``axes`` holding the layout objects - the panels were transplanted into (one ``layout.scene*`` per 3-D - panel, an ``(xaxis, yaxis)`` pair per 2-D one). Default None (one - axes, as before). + ``xform_data``, ``colors``, ``pipeline``) -- ``axes`` holding the + layout objects the panels were transplanted into (one + ``layout.scene*`` per 3-D panel, an ``(xaxis, yaxis)`` pair per + 2-D one). ``pipeline`` is the ONE fitted `hypertools.Pipeline` the + shared fit produced (``panel_fit='shared'``; ``None`` under + ``'independent'`` and a list-valued `reduce=`), and every + ``panel_models[i]['pipeline']`` is that same object -- so + ``bundle['panel_models'][i]['pipeline'].transform(new_data)`` + projects held-out data into the panels' common space without + refitting, exactly as the single-axes bundle's pipeline does; + under ``panel_fit='independent'`` (and per-reducer grids) each + panel bundle's ``pipeline`` is that panel's own fit instead. + Default None (one axes, as before). See also `hypertools.plot.plot.subplots`, a thin ``(fig, flat_axes)`` helper for grids you want to fill yourself. @@ -4876,9 +7207,13 @@ def plot( gives each panel its slice of the result, so the panels share one set of components and are directly comparable. - ``'independent'`` fits the pipeline per panel: each panel call is - exactly the ``hyp.plot(x[i], ax=axes[i], ...)`` you would have - written by hand, down to the coordinates. Use it when the datasets + ``'independent'`` fits the pipeline per panel: each panel is + analyzed exactly as the ``hyp.plot(x[i], ax=axes[i], ...)`` you + would have written by hand, down to the coordinates and the + seeded clustering (`cluster=`/`n_clusters=` with `random_state=` + groups a panel exactly as that call does; the grid's cells are + the exception described under `panels=`, one projection shared + by every panel). Use it when the datasets differ enough in scale or shape that one shared fit is dominated by the largest of them -- ``examples/plot_shapes_zoo.py``'s seven shapes reduced separately is the case that motivated it. @@ -5098,7 +7433,7 @@ def plot( -- EXACTLY, since evenly-spaced ``linspace(0.10, 0.65, 3)`` would instead give a 37.5%-not-35% middle shell); plotly's ``go.Volume`` layer uses ``surface_count=5*levels`` (15 at the - default). **2-D density has no ``levels`` concept at all** -- + default). **2-D density has no** ``levels`` **concept at all** -- the 2-D layer is a single continuous alpha/heatmap ramp with no discrete shells, so ``levels`` is silently ignored for 2-D data (no error; the key is still valid, it's just a no-op there). @@ -5181,6 +7516,10 @@ def plot( ``'o-'``). MARKER-ONLY styles (e.g. ``'o'``, ``'.'``) are never touched: markers always render at the true sample points. Forecast overlays drawn by `predict=` are smoothed the same way. + A marker+line style (``'o-'``, or a ``forecast_fmt='o:'``) marks + only the true samples, never the vertices smoothing adds; in an + animation, whose lines are resampled onto the frame grid, each + marker sits on the grid vertex nearest its sample. Pass ``antialias=False`` to draw raw straight segments between consecutive samples (the pre-1.1.0 behavior). @@ -5240,7 +7579,11 @@ def plot( the frame's innermost column labels are matched to the ones the pipeline was fit on, so reordering them is harmless and naming different measurements raises. ``models`` holds the - reduce/align/cluster/impute specs, and ``predict`` is ``None`` unless + reduce/align/cluster/impute specs plus ``'cluster_labels'``: the + per-observation labels the figure was clustered by (every input + dataset's rows, in order; a mixture model's component proportions; + ``None`` without `cluster=`/`n_clusters=`). ``predict`` is ``None`` + unless `predict` was set, in which case it is ``{'model': ..., 'params': {'t': t}, 'forecasts': [...]}`` (one forecast array per input dataset -- or, for a HIERARCHICAL input, one @@ -5251,9 +7594,14 @@ def plot( a ``{name: [one forecast per dataset]}`` dict instead, exactly as ``hyp.predict(x, model=[...])`` returns. Each bundled forecast has exactly `t` rows, matching what ``hyp.predict(xform_data, - model=..., t=t)`` returns. A hierarchy additionally requires at least - 2 rows in EVERY final trace, on either axis, and raises otherwise; - see `predict`. + model=..., t=t)`` returns -- in ``ndims=1`` series mode too, where + it is one ``(t, n_columns)`` array of VALUES per input dataset + (the x positions the overlay is drawn at are the row index's own + continuation, one `step` per row, and are not part of the bundle); + a hierarchy under ``ndims=1`` keeps one ``(t, 2)`` ``[x, value]`` + array per final trace, its documented unit. A hierarchy + additionally requires at least 2 rows in EVERY final trace, on + either axis, and raises otherwise; see `predict`. ``xform_data`` vs ``trace_data``. ``xform_data`` is the analysed pipeline output, one entry per analysed INPUT dataset. @@ -5295,7 +7643,7 @@ def plot( so is deliberately different. The bundle is not a per-frame record. - 2. **A STATIC plot with `hue=` or `cluster=`.** These regroup the + 2. **A STATIC plot with** `hue=` **or** `cluster=`. These regroup the drawn traces by category rather than by dataset, so there is no longer one trace per dataset -- but there is still one forecast per dataset, each anchored at that dataset's last observation and @@ -5304,7 +7652,7 @@ def plot( continuous `hue=` (where the colour is taken at the anchor, since the trace has many) and `cluster=`. Drawn, and ``drawn`` is True. - 3. **An ANIMATED plot with `hue=` or `cluster=`.** Not drawn, and + 3. **An ANIMATED plot with** `hue=` **or** `cluster=`. Not drawn, and warns: the per-frame schedule maps frame-grid rows onto each DATASET's raw observations, and regrouping leaves only per-run traces to reveal, with no per-dataset reveal to schedule against. @@ -5328,7 +7676,11 @@ def plot( above. For animated matplotlib plots a ``HyperAnimation`` is returned instead: a ``(fig, animation)`` tuple subclass (so ``fig, anim = hyp.plot(...)`` unpacking works) that also exposes - ``.figure``/``.to_html5_video()``/``.to_jshtml()``/``.save()`` and + ``.figure``/``.to_html5_video()``/``.to_jshtml()``/``.save()``, + ``.draw_frame(i)``, ``.on_frame(callback)``, ``.colors`` and + ``.drawn_extent(frames=None, threshold=5)`` (the union bounding box + of everything the animation draws, measured from rendered pixels + in figure fractions -- see `HyperAnimation.drawn_extent`) and auto-plays inline in notebooks -- keep a reference to it so the underlying ``matplotlib.animation.FuncAnimation`` stays alive. When ``return_model=True``, a dict @@ -5366,12 +7718,25 @@ def plot( """ + # a `panels=` grid's own instruction for placing one narrow panel's + # rows in its 3-D cell (`_PanelLift`, one entry per dataset of the + # panel, None for 3-wide ones): internal, never a user kwarg + _panel_lift = kwargs.pop('_panel_lift', None) + # early kwarg validation (release-1.0 audit): catch renamed/misspelled/ # unknown keyword arguments HERE, with a clear TypeError naming the # kwarg (plus a did-you-mean hint), BEFORE the expensive analyze/ # reduce/align pipeline runs. _validate_extra_plot_kwargs(kwargs) + # palettes given as data MATRICES become colormaps here (reduced with + # hyp.reduce under the palette_* options), and image palettes carry the + # requested sort; done before the panels branch so every panel call + # receives plain palettes (see `_prepare_palettes`) + palette, forecast_palette = _prepare_palettes( + palette, forecast_palette, sort=palette_sort, reduce=palette_reduce, + manip=palette_manip, normalize=palette_normalize, align=palette_align) + # panels=/subplots= (GH #285): compose several STATIC panels in one # figure. Handled by re-entering plot() once per panel, so this branch # runs BEFORE any other local exists -- `locals()` here is exactly this @@ -5385,7 +7750,8 @@ def plot( _panels_name = 'panels' if panels is not None else 'subplots' _panel_call = {k: v for k, v in locals().items() if k not in ('x', 'kwargs', 'panels', 'subplots', - '_panels_spec', '_panels_name')} + '_panels_spec', '_panels_name', + '_panel_lift')} _panel_call.update(kwargs) return _plot_panels(x, _panels_spec, _panel_call, _name=_panels_name) @@ -5434,7 +7800,8 @@ def plot( _xf_items = transform if isinstance(transform, (list, tuple)) \ else [transform] for _xf in _xf_items: - if not (hasattr(_xf, 'shape') or hasattr(_xf, '__array__')): + if not (is_array_dataset(_xf) or is_frame_dataset(_xf) + or is_series_like(_xf) or hasattr(_xf, '__array__')): raise TypeError( f"transform= must be already-transformed data (a numpy " f"array/DataFrame, or a list of them), or None; got " @@ -5479,27 +7846,35 @@ def plot( if ax is not None: import matplotlib.axes as _mpl_axes _is_mpl_axes = isinstance(ax, _mpl_axes.Axes) - _is_plotly_fig = _is_plotly_figure(ax) + _is_plotly_fig = _is_plotly_figure(ax) or _is_plotly_cell(ax) if not _is_mpl_axes and not _is_plotly_fig: raise TypeError( "ax= must be a matplotlib Axes (2-D) or Axes3D (3-D) " - "instance, or a plotly Figure to draw into with the plotly " - f"backend; got {type(ax).__name__!r}.") + "instance, a plotly Figure to draw into with the plotly " + "backend, or one cell of a hyp.subplots(..., " + f"backend='plotly') grid; got {type(ax).__name__!r}.") + if _is_plotly_fig and isinstance(backend, str) \ + and backend.lower() == 'auto': + # a plotly Figure/cell names the backend to draw with: 'auto' + # (the default) follows it instead of raising below + backend = 'plotly' if resolve_backend(backend) == "plotly": if _is_mpl_axes: raise ValueError( "ax= is a matplotlib Axes, and the plotly backend cannot " "draw into it. Pass a plotly Figure instead (the one an " - "earlier hyp.plot returned) to draw into it, drop ax= to " - "get a new Figure, or draw this call with matplotlib: " - "hyp.plot(..., backend='matplotlib') or " + "earlier hyp.plot returned) or a cell from " + "hyp.subplots(..., backend='plotly') to draw into it, " + "drop ax= to get a new Figure, or draw this call with " + "matplotlib: hyp.plot(..., backend='matplotlib') or " "`with hyp.set_interactive_backend('matplotlib'):`.") _plotly_into = ax ax = None elif _is_plotly_fig: raise TypeError( - "ax= is a plotly Figure, but this call draws with matplotlib; " - "pass backend='plotly' (or a matplotlib Axes).") + "ax= is a plotly Figure (or hyp.subplots plotly cell), but " + "this call draws with matplotlib; pass backend='plotly' " + "(or a matplotlib Axes).") # a bare scalar is plotted as a single 1-D point -- warn rather than # doing so silently (D11-014). @@ -5529,9 +7904,12 @@ def plot( # legend= must be a bool, a label string, or a list of labels: any # other scalar (e.g. legend=7) was silently treated as truthy # (release-1.0 audit, X2-error-quality-016). - if legend is not None and not isinstance( - legend, (bool, np.bool_, list, tuple, np.ndarray, pd.Series, - pd.Index)): + # (the container question is asked through the datawrangler-based + # predicates, so a polars Series of labels is accepted like a pandas + # one -- Codex round 12, R12-5) + if legend is not None and not ( + isinstance(legend, (bool, np.bool_, list, tuple)) + or is_array_dataset(legend) or is_series_like(legend)): raise TypeError( f"legend= must be True/False, a label string, or a list of " f"labels (one per drawn trace/group); got " @@ -5555,9 +7933,9 @@ def plot( # ONE-label list -- the same thing the `isinstance(legend, str)` wrap # above does with `legend='a'`, which then reports the length mismatch # instead of silently broadcasting that one label over every trace. - if isinstance(legend, (tuple, np.ndarray, pd.Series, pd.Index)): - legend = ([legend.item()] - if isinstance(legend, np.ndarray) and legend.ndim == 0 + if (isinstance(legend, tuple) or is_array_dataset(legend) + or is_series_like(legend)): + legend = ([legend.item()] if np.ndim(legend) == 0 else list(legend)) # Did the CALLER pass legend= as an explicit list of labels? Recorded @@ -5572,8 +7950,11 @@ def plot( # kwarg the hierarchy overrides warns), and `names=` ALONE raised # "pass dataset names via names= OR a legend= list, not both" on a # call that never mentioned legend=. - _legend_user_list = isinstance( - legend, (list, tuple, np.ndarray, pd.Series, pd.Index)) + # (every accepted container was normalised to a list just above) + _legend_user_list = isinstance(legend, (list, tuple)) + # ...and the entries themselves, for the plotly hover names of a path + # that drops the drawn legend (a continuous hue; `_plotly_hover_names`) + _legend_user_labels = list(legend) if _legend_user_list else None # animate= dict form (GH #154 resolution): unpacked into the flat # animation kwargs HERE, at the very top of the function, before @@ -5739,7 +8120,8 @@ def plot( raise ValueError( f"{'xlim' if xlim is not None else 'ylim'}= sets limits in the " "DRAWN coordinates, but axis_scale='unit' pins both axes to the " - "[-1.1, 1.1] frame box, so the limits would be overwritten. " + "frame box (+/-1.2375 around the unit data square), so the " + "limits would be overwritten. " "Pass axis_scale='data' to draw in the data's own units.") for _lim_name, _lim in (('xlim', xlim), ('ylim', ylim)): if _lim is None: @@ -5778,6 +8160,10 @@ def plot( # pattern is resolved per frame, so it is split off BEFORE the # per-segment check below -- which only ever sees the static forms it # already understood. + # kept so ndims=1 series mode can re-point a `{index}` pattern at the + # rows it reorders into time order (see `_resorted` below) + _title_pattern = (title if isinstance(title, str) + and _title_is_pattern(title) else None) title, _dynamic_title = _validate_dynamic_title(title, animate, _row_indices) _segment_titles = _validate_title(title, style=animate, order=order) @@ -5793,7 +8179,19 @@ def plot( _title_kwargs = _normalize_title_kwargs(title_kwargs) _title_wrap = _validate_title_wrap(title_wrap) _title_color, _title_segment_colors = _validate_title_color( - title_color, _segment_titles) + title_color, _segment_titles, _title_kwargs) + # `label_anchor=` is checked here whether or not `labels=` is set (an + # unrecognised anchor used to pass silently when there was nothing to + # anchor), and a BARE STRING `labels=` is rejected outright rather than + # counted character by character ("labels has 4 entries" for 'only'). + _validate_label_anchor_value(label_anchor) + if isinstance(labels, str): + raise TypeError( + f"labels= must be a list of per-observation labels (or one per " + f"dataset, positioned by label_anchor=), not the single string " + f"{labels!r}. Wrap it in a list: labels=[{labels!r}] labels the " + "first observation; for one label per dataset pass a list with " + "one entry per dataset.") if _title_color is not None: _title_kwargs = dict(_title_kwargs or {}) _title_kwargs.setdefault('color', _title_color) @@ -6041,8 +8439,10 @@ def plot( # by the text-flattening helper. Resolving once up front means every # text surface `_draw`/`_add_colorbar` touches later shares the exact # same FontProperties, rather than each independently re-scanning - # installed fonts. - _font_texts = [labels, legend, title, hue] + # installed fonts. xlabel/ylabel/zlabel are drawn text too (fresh-Colab + # review 2026-09-11: an axis label alone in a script outside the stack + # rendered as tofu). + _font_texts = [labels, legend, title, hue, xlabel, ylabel, zlabel] if colorbar is not None: _font_texts.append(colorbar.get('label')) _font_texts.append(colorbar.get('ticks')) @@ -6231,6 +8631,7 @@ def plot( # remember whether the USER supplied an axis before `_draw` reassigns the # local `ax` to the axis it created (used by the GH #148 close below). _user_supplied_ax = ax is not None + _ax_needs_3d = False if ax is not None: # An animated plot BUILDS ITS OWN FIGURE. Measured across every mode @@ -6256,10 +8657,23 @@ def plot( "panels out in the data and make a single plot call." ) if ndims > 2: - if ax.name != "3d": - raise ValueError( - "If passing ax and the plot is 3D, ax must " "also be 3d" - ) + # a 2-D axes under the default (3-D) ndims: whether the plot IS + # 3-D is the ANALYZED data's width, known only after the + # pipeline -- two columns draw a 2-D plot, exactly as on a + # figure of their own -- so the refusal is made there + # (`_ax_needs_3d`; 1.1 release review: two-column data into a + # 2-D ax= raised "the plot is 3D" up front) + _ax_needs_3d = getattr(ax, "name", None) != "3d" + elif getattr(ax, "name", None) == "3d": + # the mirror image: a 2-D (or series-mode) plot drawn into a + # 3-D axes silently became a Line3D at z=0, viewed from the + # default camera (S4) + raise ValueError( + f"ax= is a 3-D axes but ndims={ndims} draws a " + f"{'time-series' if ndims == 1 else '2-D'} plot; pass a " + "2-D axes (hyp.subplots(..., ndims=2), or " + "fig.add_subplot() without projection='3d'), or ndims=3 to " + "draw into this one.") text_args = {"vectorizer": vectorizer, "semantic": semantic, "corpus": corpus} @@ -6279,6 +8693,9 @@ def plot( # depth; these drive multilevel styling below (color by outer group, # thinner/fainter lines per deeper level) nested_groups = nested_depths = None + # set when the nested-list branch below colours every leaf by its outer + # group -- the legend then names the GROUPS (see "handle legend") + _nested_group_colored = False if isinstance(x, list) and any(isinstance(el, list) for el in x) \ and not all(isinstance(el, str) for el in x) \ and not all(isinstance(el, (list, tuple)) and len(el) > 0 @@ -6309,7 +8726,8 @@ def plot( # unless we warn here. if isinstance(x, list): for _i, _el in enumerate(x): - if isinstance(_el, pd.DataFrame) and _el.index.nlevels >= 2: + if (is_frame_dataset(_el) + and as_pandas_dataframe(_el).index.nlevels >= 2): warnings.warn( "MultiIndex grouping is only applied when a single " "DataFrame is passed; the MultiIndex on dataset " @@ -6333,7 +8751,12 @@ def plot( # innermost-level (feature) labels of a COLUMN hierarchy's leaves, kept # for the return_model bundle's pipeline; None on every other input. _mi_feature_labels = None - if isinstance(x, pd.DataFrame) and x.index.nlevels >= 2: + if is_frame_dataset(x): + # every dataframe backend datawrangler recognises, as hypertools' + # own pandas frame (the same object when it already is one): the + # row/column-hierarchy checks below read pandas indexes + x = as_pandas_dataframe(x) + if is_frame_dataset(x) and x.index.nlevels >= 2: if cluster is not None or n_clusters is not None: raise ValueError( "cluster=/n_clusters= is not compatible with a row-" @@ -6357,7 +8780,7 @@ def plot( , stacklevel=external_stacklevel()) hue = None x, _multiindex_meta = expand_multiindex(x) - elif isinstance(x, pd.DataFrame) and x.columns.nlevels >= 2: + elif is_frame_dataset(x) and x.columns.nlevels >= 2: # COLUMN hierarchy (1.1): the innermost column level is the FEATURE # axis and every level above it groups, so (Market, Sector, Ticker) # becomes one leaf per sector plus a market mean. Unlike the row @@ -6380,6 +8803,14 @@ def plot( # by the time the arrays are handed on, and a within-group column # permutation cannot move a trajectory. x, _multiindex_meta = group_columns(x) + # Every leaf keeps ALL of the frame's rows, so every leaf keeps the + # frame's row index. The capture above saw ONE frame (one entry), + # and `ndims=1` series mode read it for leaf 0 only -- so a dated + # column hierarchy drew leaf 0 against dates and every other leaf + # against 0..n-1, then refused the mix ("some datasets carry a + # DatetimeIndex and others do not"; 1.1 review, F4). + if _row_indices is not None and len(_row_indices) == 1: + _row_indices = [_row_indices[0]] * len(x) if hue is not None: # Classify hue HERE, before the MultiIndex branch below wins the # cluster/hue/nested_groups chain outright. A CONTINUOUS hue is @@ -6475,6 +8906,7 @@ def plot( # analyze the data raw = None + _bundle_fitted_pipeline = None # analyze()'s fitted pipeline, when asked for if transform is None: raw = format_data(x, impute=impute, **text_args) @@ -6506,6 +8938,13 @@ def plot( # to die deep inside numpy ("all the input array dimensions ... # must match exactly") with no dataset info or fix hint. Fail fast # with a clear message BEFORE the pipeline runs. + # whether each dataset is finite BEFORE the pipeline runs, so a + # NaN that appears afterwards is reported as the pipeline's (X2) + try: + _input_finite = [bool(np.isfinite(np.asarray(ri, dtype=float)) + .all()) for ri in raw] + except (TypeError, ValueError): + _input_finite = None _widths = [ri.shape[1] for ri in raw] if len(set(_widths)) > 1: # when the ORIGINAL input mixed text and numeric datasets, the @@ -6540,17 +8979,34 @@ def _has_text(v): # per-DATASET labels= (GH #285) are expanded to the historical # per-observation nested form FIRST, so the check below (and # every consumer downstream) is unchanged. + labels = _labels_as_lists(labels, len(raw)) labels = _expand_dataset_labels( labels, [ri.shape[0] for ri in raw], label_anchor) _validate_labels_length(labels, [ri.shape[0] for ri in raw]) + if hue is not None or cluster is not None \ + or n_clusters is not None: + # a hue=/cluster= grouping regroups the observations + # across datasets, and every regrouping path + # (`reshape_data`, `segment_by_run`) reads labels= as ONE + # entry per observation, flat. The nested per-dataset + # form -- and the per-dataset strings expanded into it + # just above -- reached them as one sub-list per dataset + # and crashed both backends (1.1 release review: 'NoneType' + # is not iterable, "need at least one array to + # concatenate", IndexError). Flatten it; the flat form is + # the one those paths always handled. + labels = _flatten_dataset_labels(labels) # a per-dataset fmt LIST must match the dataset count # (F01-006/F10-003): fail fast here when no later regrouping # (hue=/cluster=/MultiIndex) can change the drawn-trace count; the # regrouped case is re-checked against the FINAL count below. + # ...and not in `ndims=1` series mode either, where fmt= is one + # entry per DRAWN LINE (a column) and the line count is only known + # after reduce= has run (S1) if (isinstance(fmt, list) and hue is None and cluster is None and n_clusters is None and _multiindex_meta is None - and len(fmt) != len(raw)): + and not _series_mode and len(fmt) != len(raw)): raise ValueError( f"fmt was given as a list of length {len(fmt)}, but there " f"are {len(raw)} dataset(s) to plot; pass one format " @@ -6582,6 +9038,16 @@ def _has_text(v): pipeline.fit(raw) xform, _ = analyze(raw, pipeline=pipeline, internal=True, impute=impute, return_model=True) + # analyze returns DATA; a fitted trailing cluster step + # colours the figure through the cluster branch below, as + # it coloured the figure it was fit for, unless hue= says + # otherwise (it used to be dropped silently; 1.1 release + # review, L13) + if hue is None: + from ..tools.analyze import pipeline_cluster_labels + _replay = pipeline_cluster_labels(pipeline, xform) + if _replay is not None: + cluster = _PanelClusterLabels(*_replay) else: if pipeline.is_fitted: xform = [np.asarray(pipeline.transform(r)) for r in raw] @@ -6600,7 +9066,7 @@ def _has_text(v): "clusterer yields labels -- pass those as " "hue= instead, and plot the step before it.") else: - xform = analyze( + _analyzed = analyze( raw, # plot()'s ndims defaults to 3 (unlike analyze's None), so # forwarding it alongside reduce=None would trip analyze's @@ -6617,9 +9083,38 @@ def _has_text(v): internal=True, impute=impute, random_state=random_state, + # the FITTED pipeline is what the return_model bundle hands + # back as bundle['pipeline'] -- asking for it here means the + # bundle no longer refits every stage a second time (a UMAP + # or Isomap panel grid used to fit, and warn, twice; 1.1 + # feature-tour report, section 9.8) + return_model=bool(return_model), ) + if return_model: + xform, _bundle_fitted_pipeline = _analyzed + else: + xform = _analyzed else: - xform = transform + # one entry per dataset. A BARE array/frame is ONE dataset (a 3-D + # array is a stack of them): iterated as-is it was a list of ROWS, + # and passed validation only to crash with an IndexError (1.1 + # release review, F15) + if isinstance(transform, (list, tuple)): + xform = list(transform) + elif getattr(transform, 'ndim', None) == 3: + xform = list(transform) + else: + xform = [transform] + xform = [np.asarray(xi).reshape(-1, 1) + if is_array_dataset(xi) and np.ndim(xi) == 1 else xi + for xi in xform] + # polars (and other datawrangler) frames -> pandas, whose arithmetic + # the display scaling below relies on; a pandas frame keeps its own + # index for the forecast alignment check (2026-09-11 review: a + # polars transform= raised SchemaError in the unit scaling) + xform = [as_pandas_dataframe(xi) if is_frame_dataset(xi) else xi + for xi in xform] + _input_finite = None if labels is not None: # transform= skips the pipeline (and the per-observation check # above), but a per-DATASET labels= list still expands here @@ -6724,7 +9219,7 @@ def _has_text(v): # `ndims=1` series mode (GH #285) draws one LINE PER COLUMN against the # row index, so extra columns are extra lines rather than data that # cannot be drawn -- the refusal below ("static plots support at most - # 1") applies only to the pre-1.2 single-column reading of ndims=1. + # 1") applies only to the pre-1.1 single-column reading of ndims=1. # With a reduce= spec the reduction to one component has already run in # `analyze` above, and ndims=1, reduce='PCA' means exactly what it says: # the first principal component over the index. @@ -6760,6 +9255,46 @@ def _has_text(v): # output, which is what `pipeline.transform()` reproduces. trace_data = xform + # Keep timestamps attached to VALUES before series mode adds a display + # coordinate. These same frames feed static and animated forecasts + # (release review 2026-09-08, finding 2). + _forecast_frames = [] + for _i, _xi in enumerate(xform): + _idx = (_row_indices[_i] if _row_indices is not None + and _i < len(_row_indices) else None) + if _idx is not None and len(_idx) != len(_xi): + from ..predict.time import is_time_index + if predict is not None and is_time_index(_idx): + raise ValueError( + 'the analysis pipeline changed the row count, so the ' + 'original observation times no longer identify the rows ' + 'to forecast. Pass the analyzed data with its updated ' + 'time index to plot(..., reduce=None, predict=...).') + _idx = None + if is_frame_dataset(_xi): + # polars and other datawrangler frames carry no pandas index + _xi = as_pandas_dataframe(_xi) + if (is_frame_dataset(_xi) and _idx is not None + and not _xi.index.equals(_idx)): + # a `transform=` frame with an index of its own: handing it to + # `pd.DataFrame(frame, index=...)` RE-INDEXES it, and an index + # sharing no labels with x's made every value NaN -- forecast + # as silent zeros (1.1 release review, F7). A plain 0..n-1 + # index carries no rows of its own, so the values are x's rows + # position for position; any other index is a real conflict. + _own = _xi.index + if transform is not None and predict is not None and not ( + isinstance(_own, pd.RangeIndex) and _own.start == 0 + and _own.step == 1): + raise ValueError( + f"transform= entry {_i} is a DataFrame whose index does " + "not match the rows of x it replaces (first labels " + f"{list(_own[:2])} vs {list(_idx[:2])}), so there is no " + "telling which observation each row is. Pass its values " + "(frame.to_numpy()) or give it x's index.") + _xi = _xi.to_numpy() + _forecast_frames.append(pd.DataFrame(_xi, index=_idx)) + # ndims=1 series mode (GH #285) --------------------------------------- # Turn each dataset's k columns into k two-column ``[x, value]`` traces, # where x is the dataset's own row index. From here down the figure is @@ -6769,14 +9304,17 @@ def _has_text(v): # that machinery rather than a second renderer. # # Placed AFTER the display-dimensionality reduction (so `reduce=` has - # had its say) and BEFORE `predict=` (so the forecast is computed on the - # expanded traces and lands in the same space they are drawn in; its x - # column is then pinned to the true index continuation rather than - # trusted to the model -- see `_series_step`). + # had its say). Forecasts use the indexed signal frames saved above, + # fitting all columns of each dataset together. Only the resulting + # predictions are expanded into display traces. _series_owner = None # drawn trace -> input dataset + _series_columns = None _series_names = None # per drawn trace, for legend=True _series_step = None # per drawn trace, x units per observation _series_is_date = False + # row count per INPUT dataset, before series mode expands columns into + # traces -- what a datetime-like `t=` is measured against (F2) + _pre_series_lengths = [int(np.asarray(xi).shape[0]) for xi in xform] if _series_mode: _n_input_datasets = len(xform) _widths = [xi.shape[1] for xi in xform] @@ -6830,12 +9368,36 @@ def _has_text(v): "(df.reset_index(drop=True)), or use ndims=2/3.") _series_epoch_ms = resolve_backend(backend) == 'plotly' _new_xform, _series_owner, _series_names, _series_step = [], [], [], [] + _series_columns = [] _date_flags, _index_labels = [], [] + _series_named = [] # per drawn trace: named after a column? + # datasets whose index is not in ascending order: drawn in time + # order (`_resorted`), or -- where a per-observation argument is + # tied to the INPUT row order -- left as given (`_unsorted`) + _resorted, _unsorted = [], [] + _row_order_bound = any(_v is not None and _v is not False + for _v in (hue, labels, cluster)) for _i, _xi in enumerate(xform): _idx = (_row_indices[_i] if _row_indices is not None and _i < len(_row_indices) else None) _xs, _step, _is_date, _idx_label = _series_x_axis( _idx, _xi.shape[0], epoch_ms=_series_epoch_ms) + if len(_xs) > 1 and np.any(np.diff(_xs) < 0): + # a shuffled index drew the rows in INPUT order against + # their x -- a scribble -- while `predict=` sorts timed + # observations and continues from the LATEST one, so the + # forecast did not join the drawn line (1.1 review, F14). + # Draw in time order, reordering the index with the rows so + # an `{index}` title still names each frame's own row. + if _row_order_bound: + _unsorted.append(_i) + else: + _order = np.argsort(_xs, kind='stable') + _xs, _xi = _xs[_order], np.asarray(_xi)[_order] + if _row_indices is not None and _i < len(_row_indices): + _row_indices = list(_row_indices) + _row_indices[_i] = _idx[_order] + _resorted.append(_i) _date_flags.append(_is_date) _index_labels.append(_idx_label) _cols = (_column_names[_i] if _column_names is not None @@ -6847,7 +9409,9 @@ def _has_text(v): for _j in range(_xi.shape[1]): _new_xform.append(np.column_stack([_xs, _xi[:, _j]])) _series_owner.append(_i) + _series_columns.append(_j) _series_step.append(_step) + _series_named.append(_cols is not None) if _cols is not None: _series_names.append(_cols[_j]) elif _xi.shape[1] > 1: @@ -6856,6 +9420,26 @@ def _has_text(v): else f"dataset {_i + 1} column {_j + 1}") else: _series_names.append(f"dataset {_i + 1}") + if _resorted: + if _title_pattern is not None: + # the resolver read the index in its INPUT order; the rows + # (and so each frame's head row) are now in time order + _dynamic_title = _make_title_pattern_resolver( + _title_pattern, _row_indices) + warnings.warn( + f"ndims=1: the index of dataset(s) {_resorted} is not in " + "ascending order, so its rows are drawn in time order (as " + "predict= forecasts them). Sort the data first " + "(df.sort_index()) to silence this.", + UserWarning, stacklevel=external_stacklevel()) + if _unsorted: + warnings.warn( + f"ndims=1: the index of dataset(s) {_unsorted} is not in " + "ascending order, and hue=/labels=/cluster= are given per " + "observation in INPUT order, so the line is drawn in that " + "order and doubles back along x. Sort the data (and those " + "arguments) by time first (df.sort_index()).", + UserWarning, stacklevel=external_stacklevel()) if any(_date_flags) and not all(_date_flags): raise ValueError( "ndims=1 series mode draws every dataset against ONE x " @@ -6870,9 +9454,17 @@ def _has_text(v): _named_index = next((lab for lab in _index_labels if lab), None) if xlabel is None and _named_index is not None: xlabel = _named_index + # ...only when that single line IS a named column: a reduce= that + # collapsed several named columns into one component has no column + # to name the axis after, and the placeholder trace name ('dataset + # 1') is a legend label, not a y label (S3) if (ylabel is None and len(_series_names) == 1 - and _column_names is not None): + and _column_names is not None and _series_named[0]): ylabel = _series_names[0] + if _series_is_date and xlim is not None: + # xlim= on a date axis: datetime-likes and date strings on both + # backends, floats as matplotlib day numbers on both (S2) + xlim = _resolve_date_xlim(xlim, _series_epoch_ms) _df_axis_labels = None # per-input-dataset style lists follow their datasets onto the # expanded traces (a list already sized to the drawn lines is left @@ -6888,6 +9480,38 @@ def _has_text(v): _display_ndims = 2 # axis_scale='data' (GH #285) keeps the pipeline's own coordinates, but + # a narrow panel of a 3-D `panels=` grid: from here on the figure is + # the 3-D one its cell draws -- the rows placed in the cell + # (`_PanelLift`), and every dimensionality check below reading that + # placement -- while `predict=`/`truth=` below read `_lift_source`, + # the analyzed rows, so the panel forecasts exactly what the + # individual call forecasts and its bundle (`xform_data`, + # `trace_data`, the forecasts) stays in that space (round 9) + _lift_source = None + if _panel_lift is not None: + if animate: + raise ValueError( + "internal error: a panels= cell is never animated, but " + "its placement (_panel_lift) reached an animated call.") + if len(_panel_lift) != len(xform): + raise ValueError( + f"internal error: panels= placed {len(_panel_lift)} " + f"dataset(s) in a cell drawing {len(xform)}.") + _lift_source = [np.asarray(xi, dtype=float) for xi in xform] + xform = [xi if lift is None else lift.rows(xi) + for xi, lift in zip(_lift_source, _panel_lift)] + + # a 2-D `ax=` under ndims > 2 (see the `ax=` checks at the top): refused + # only now that the drawn width is known, and only when it is 3-D + if (_ax_needs_3d and xform and np.ndim(xform[0]) == 2 + and np.shape(xform[0])[1] >= 3): + raise ValueError( + "If passing ax and the plot is 3D, ax must also be 3d: ax= is a " + f"2-D axes, but the data is drawn in 3-D ({np.shape(xform[0])[1]} " + "columns after the pipeline). Pass a 3-D axes (hyp.subplots() " + "default, or fig.add_subplot(projection='3d')), or ndims=2 to " + "draw into this one.") + # a 3-D plot's frame IS the unit cube -- there is no "raw units" cube to # draw the data in, and the camera/zoom geometry is defined against it. if _axis_scale == 'data' and xform[0].shape[1] >= 3: @@ -6970,6 +9594,19 @@ def _has_text(v): # `hyp.predict(x, model=[...])` returns -- there is one naming rule, not # two. `None` for the ordinary single-model form. _predict_names = None + _forecast_horizon = t + if predict is not None and t is not None and not _is_int_horizon(t): + # The renderer needs an integer horizon; the forecasters retain the + # original target and resolve it against each dataset's own times. + if _multiindex_meta is not None: + t = _resolve_datetime_horizon(t, _row_indices, _pre_series_lengths) + else: + from ..predict.common import resolve_t + _steps = [resolve_t(frame, t)[0] for frame in _forecast_frames] + if any(n <= 0 for n in _steps): + raise ValueError('t is at or before a dataset\'s last observation; ' + 'plot draws future forecasts and does not truncate.') + t = max(_steps) if predict is not None: from ..predict.backtest import model_collection as _model_collection from ..predict.predict import _FORECASTER_ALIASES, FORECASTERS @@ -6980,29 +9617,38 @@ def _has_text(v): if _collection is not None: _predict_names = list(_collection[0]) - def _pin_series_x(datasets, forecasts): - """Give each forecast the row index's OWN continuation on x. - - `ndims=1` series mode materialises the row index as the traces' x - column (see the expansion above), so the forecaster is handed a - perfectly regular ramp alongside the values and dutifully forecasts - it too. What x a future observation sits at is arithmetic the plot - knows exactly -- one `_series_step` per step -- so it is written - here rather than inherited from a fit. - """ - if _series_step is None: - return forecasts - pinned = [] - for _i, (_xi, _fc) in enumerate(zip(datasets, forecasts)): - _fc = np.array(_fc, dtype=float, copy=True) - _fc[:, 0] = (float(_xi[-1, 0]) - + _series_step[_i] * np.arange(1, len(_fc) + 1)) - pinned.append(_fc) - return pinned + def _forecast_coordinates(frame, owner, column=None): + """Put forecast values on the SAME time axis as their observations.""" + if column is None: + return np.asarray(frame, dtype=float) + idx = frame.index + original = _forecast_frames[owner].index + if isinstance(idx, pd.PeriodIndex): + # forecasts of periods are periods; draw them at their start + # times, as `_series_x_values` draws the observed periods + idx = idx.to_timestamp() + if isinstance(idx, pd.DatetimeIndex): + if _series_epoch_ms: + xs = np.asarray(idx.as_unit('ms').asi8, dtype=float) + else: + from matplotlib.dates import date2num + xs = date2num(idx.to_pydatetime()) + elif isinstance(idx, pd.TimedeltaIndex): + _, divisor = _timedelta_unit(np.asarray(original.total_seconds())) + xs = np.asarray(idx.total_seconds()) / divisor + else: + try: + xs = np.asarray(idx, dtype=float) + except (TypeError, ValueError): + xs = original.get_indexer(idx).astype(float) + return np.column_stack([xs, np.asarray(frame)[:, column]]) def _compute_forecasts(datasets): from ..predict.predict import predict as _predictor - _out = _predictor(datasets, model=predict, t=t) + from ..predict.time import order_time_data + _inputs = _forecast_frames + _out = _predictor(_inputs, model=predict, + t=_forecast_horizon) if _predict_names is None: _groups = [(None, _out)] else: @@ -7011,10 +9657,16 @@ def _compute_forecasts(datasets): for _name, _fc in _groups: if not isinstance(_fc, list): _fc = [_fc] - _fc = _pin_series_x(datasets, - [np.asarray(f, dtype=float) for f in _fc]) - _bundle[_name] = _fc - _flat.extend(_fc) + _bundle[_name] = [np.asarray(f, dtype=float) for f in _fc] + _owners = (_series_owner if _series_owner is not None + else list(range(len(_inputs)))) + _columns = (_series_columns if _series_columns is not None + else [None] * len(_inputs)) + for _owner, _column in zip(_owners, _columns): + _observed = order_time_data(_inputs[_owner]) + _flat.append(np.vstack([ + _forecast_coordinates(_observed.iloc[-1:], _owner, _column), + _forecast_coordinates(_fc[_owner], _owner, _column)])) return ( # `bundle_forecasts` mirrors `hyp.predict`'s return shape: a # plain list for one model, a {name: [per dataset]} dict for a @@ -7026,8 +9678,7 @@ def _compute_forecasts(datasets): # 2, ...) -- the order `MultiModelSchedule` addresses its # sub-schedules in, and the order `_forecast_owner` below is # built to match. - [np.vstack([np.asarray(datasets[_i % len(datasets)][-1:]), _fc]) - for _i, _fc in enumerate(_flat)], + _flat, # ANALYZE-space copies for the animated per-frame schedule (see # hypertools/plot/forecast.py). Taken HERE, beside raw_forecasts, # so they keep the same 1:1 correspondence the regrouping guard @@ -7045,7 +9696,19 @@ def _compute_forecasts(datasets): _model_forecast_owner = None if predict is not None and _multiindex_meta is None: bundle_forecasts, raw_forecasts, analyze_histories = \ - _compute_forecasts(xform) + _compute_forecasts(xform if _lift_source is None + else _lift_source) + if _lift_source is not None: + # forecast in the analyzed space, drawn where the rows are + # (`_PanelLift.continuation`); the overlays are model-major, + # one per dataset per model + raw_forecasts = [ + fc if _panel_lift[_i % len(_lift_source)] is None + else _panel_lift[_i % len(_lift_source)].continuation(fc) + for _i, fc in enumerate(raw_forecasts)] + analyze_histories = [ + h if lift is None else lift.rows(h) + for h, lift in zip(analyze_histories, _panel_lift)] if _predict_names is not None: _model_forecast_owner = [_d for _ in _predict_names for _d in range(len(xform))] @@ -7066,7 +9729,17 @@ def _compute_forecasts(datasets): "traces, which have no observed continuation of their own. " "Plot the groups separately, or flatten the frame " "(df.reset_index(drop=True)).") - raw_truths = _resolve_truth(truth, xform, t, _series_step) + raw_truths = _resolve_truth( + truth, xform if _lift_source is None else _lift_source, t, + _series_step, + forecast_paths=raw_forecasts if _lift_source is None else None, + time_coordinates=( + lambda idx, i: _forecast_coordinates( + pd.DataFrame(np.zeros((len(idx), 1)), index=idx), + _series_owner[i], 0)[:, 0]) if _series_owner is not None else None) + if _lift_source is not None: + raw_truths = [tr if lift is None else lift.continuation(tr) + for tr, lift in zip(raw_truths, _panel_lift)] # per-point colors for multicolored lines (set by the hue branch below; # computed after interpolation). Dataset lengths are captured now so hue @@ -7119,15 +9792,32 @@ def _compute_forecasts(datasets): # set together with `_seg_ds` by `_regroup_categorical_lines` _seg_lengths = None _seg_bridged = None + #: The categorical LINE path's legend labels in CATEGORY order (the + #: drawn order `_categorical_color_label_maps` resolved: sorted for + #: integer hue / cluster ids, first appearance for strings). Its runs + #: are drawn in data order, so the legend would otherwise list the + #: categories in the order they first APPEAR ('0, 2, 1') where the + #: marker path lists them sorted (1.1 release review, figure QA). + _legend_order = None # (n_input_datasets, n_hue_groups) when a categorical hue regrouped the # data by category -- names= (one name per INPUT dataset) cannot apply # after that regrouping (F02-009). _hue_regrouped_counts = None + #: True once a MARKER-only `hue=`/`cluster=` grouping ran through + #: `reshape_data`, which groups GLOBALLY by category: its drawn traces + #: are categories, never datasets, however the counts happen to fall + _global_regrouped = False # unfitted Clusterer built from the SAME resolved spec the figure's # cluster stage used (set in the cluster branch below), so the # return_model bundle's pipeline encodes the parameters the figure # was actually drawn with (F13-004) _bundle_cluster_stage = None + # the per-observation cluster labels the FIGURE was coloured/grouped + # by (over every input dataset's rows, in order; None without + # clustering), handed back as ``models['cluster_labels']`` -- and what + # a `panels=` grid replays into each panel, so a panel never + # re-clusters (round 8) + _figure_cluster_labels = None # alpha= (1.1): a first-class per-dataset style, promoted out of the # GH #206 `**kwargs` passthrough (where a list raised matplotlib's bare @@ -7350,8 +10040,27 @@ def _compute_forecasts(datasets): # pre-center/pre-scale arrays that become `trace_data` below -- # so Contract 5's `forecasts[i] == hyp.predict(trace_data[i])` # holds by construction rather than by coincidence. + # Column-hierarchy leaves and derived means share observation + # times. Row-hierarchy traces group by the entire row key, so + # their within-trace observations have positional coordinates. + _idx = (_row_indices[0] if _row_indices is not None + and _multiindex_meta.get('axis') == 'columns' else None) + _forecast_frames = [pd.DataFrame( + arr[:, 1:] if _series_owner is not None else arr, + index=_idx) for arr in _ft.arrays] + if _series_owner is not None: + _series_owner = list(range(len(_ft.arrays))) + _series_columns = [0] * len(_ft.arrays) bundle_forecasts, raw_forecasts, analyze_histories = \ _compute_forecasts(_ft.arrays) + if _predict_names is not None: + # a COLLECTION forecasts every final trace once per model, + # model-major -- the same map the flat path builds, which + # this branch never set (so the consistency check below + # counted n_models x n_traces forecasts against n_traces and + # raised its internal-invariant error; 1.1 review, F3) + _model_forecast_owner = [_d for _ in _predict_names + for _d in range(len(_ft.arrays))] # legend=[...] under a hierarchy RENAMES the top-level groups # rather than being discarded: the labelled traces are exactly the # top-level ones, so the caller's entries land on them one-for-one @@ -7484,12 +10193,32 @@ def _compute_forecasts(datasets): if isinstance(cluster, bytes): cluster = cluster.decode("utf-8") - from ..cluster.cluster import _resolve_cluster_spec + from ..cluster.cluster import (_check_cluster_spec_keys, + _resolve_cluster_spec) _n_clusters_explicit = n_clusters is not None _cluster_instance = None _spec_kwargs = {} _spec_top_n = None - if isinstance(cluster, str): + _replayed_labels = None + _replayed_categories = None + if isinstance(cluster, _PanelClusterLabels): + # a `panels=` cell drawing rows its probe ALREADY clustered + # (round 8): reuse that fit's memberships verbatim -- the + # probe is the caller's own seeded single-axes call, and + # re-clustering the analyzed rows here, once per panel and + # without the caller's random_state, drew different clusters + # from the ones the individual call draws. ...and colour them + # under the probe's COMPLETE label set (round 9): a shared + # fit's slice can lack a cluster, and colouring it from its + # own label set drew global cluster 1 in cluster 0's colour. + _replayed_labels = cluster.labels + _replayed_categories = cluster.categories + model = cluster.model + cluster = cluster.spec + params = {} + n_clusters = None + _n_clusters_explicit = False + elif isinstance(cluster, str): model = cluster params = default_params(model) or {} elif isinstance(cluster, dict): @@ -7502,6 +10231,9 @@ def _compute_forecasts(datasets): "value of the 'model' key and a dictionary of custom " "parameters as the value of the 'kwargs' key (the " "legacy 'params' key is also accepted).") + # the spec is rebuilt below from its known keys only, so a + # flat 'random_state' would vanish: raise like hyp.cluster + _check_cluster_spec_keys(cluster) model = cluster["model"] model_key = model if isinstance(model, str) \ else getattr(model, "__name__", str(model)) @@ -7562,7 +10294,9 @@ def _compute_forecasts(datasets): # regrouping by its labels silently drew n_features "points" # where the data's rows should be, or crashed downstream # (F13-001). - if _mixture_name(model) == "FeatureAgglomeration": + if _replayed_labels is not None: + pass + elif _mixture_name(model) == "FeatureAgglomeration": raise ValueError( "cluster='FeatureAgglomeration' is not supported by " "hyp.plot: FeatureAgglomeration clusters features " @@ -7583,7 +10317,8 @@ def _compute_forecasts(datasets): or mixture_models.get(model)) elif isinstance(model, type): _model_cls = model - if (_n_clusters_explicit and _model_cls is not None + if (_replayed_labels is None and _n_clusters_explicit + and _model_cls is not None and _mixture_name(model) not in mixture_models and "n_clusters" not in inspect.signature(_model_cls).parameters): @@ -7613,24 +10348,31 @@ def _compute_forecasts(datasets): # bundle's cluster stage from the IDENTICAL resolved spec so the # bundled pipeline encodes the same parameters the figure was # drawn with (F13-004). - if _cluster_instance is not None: - _resolve_spec = _cluster_instance + if _replayed_labels is not None: + # no fit at all: the labels are the probe's (the bundled + # pipeline is the probe's too -- `_plot_panels` installs it) + cluster_labels = _replayed_labels else: - _resolve_spec = {"model": model, "kwargs": params} - if _spec_top_n is not None: - _resolve_spec["n_clusters"] = _spec_top_n - _cluster_stage = _resolve_cluster_spec( - _resolve_spec, n_clusters if n_clusters is not None else 3, - random_state=random_state, - n_clusters_explicit=_n_clusters_explicit) - # a second, unfitted resolution of the same spec for the bundle - # (n_clusters_explicit=False: any conflict was already warned - # about just above -- values resolve identically either way) - _bundle_cluster_stage = _resolve_cluster_spec( - _resolve_spec, n_clusters if n_clusters is not None else 3, - random_state=random_state) - - cluster_labels = clusterer(xform, cluster=_cluster_stage) + if _cluster_instance is not None: + _resolve_spec = _cluster_instance + else: + _resolve_spec = {"model": model, "kwargs": params} + if _spec_top_n is not None: + _resolve_spec["n_clusters"] = _spec_top_n + _cluster_stage = _resolve_cluster_spec( + _resolve_spec, n_clusters if n_clusters is not None else 3, + random_state=random_state, + n_clusters_explicit=_n_clusters_explicit) + # a second, unfitted resolution of the same spec for the + # bundle (n_clusters_explicit=False: any conflict was already + # warned about just above -- values resolve identically + # either way) + _bundle_cluster_stage = _resolve_cluster_spec( + _resolve_spec, n_clusters if n_clusters is not None else 3, + random_state=random_state) + + cluster_labels = clusterer(xform, cluster=_cluster_stage) + _figure_cluster_labels = cluster_labels if _mixture_name(model) in mixture_models: # soft assignments: color each observation by the proportion- @@ -7671,6 +10413,7 @@ def _compute_forecasts(datasets): blended = mat2colors(cluster_labels, palette=palette) group_ids, group_colors = colors2groups(blended) xform, labels = reshape_data(xform, group_ids, labels) + _global_regrouped = True mpl_kwargs["color"] = [ group_colors[gid] for gid in sorted(set(group_ids), key=group_ids.index) @@ -7682,13 +10425,20 @@ def _compute_forecasts(datasets): # separate datasets are never bridged (GH #291); each run is # coloured + labelled by its cluster id in sorted order, one # legend/colorbar entry per cluster. + _shared_colors = None + if _replayed_categories is not None: + _shared_colors = _replayed_cluster_colors( + _hard_cluster_categories(cluster_labels), + _replayed_categories, palette) _cat_color, _cat_label = _categorical_color_label_maps( - cluster_labels, palette, None, None, sort_numeric=True) + cluster_labels, palette, _shared_colors, None, + sort_numeric=True) _nd = len(xform) (xform, labels, _run_colors, hue_group_labels, _seg_ds, _run_cat_names, _seg_lengths, _seg_bridged) = _regroup_categorical_lines( xform, cluster_labels, labels, _cat_color, _cat_label) + _legend_order = [str(v) for v in _cat_label.values()] fmt = _expand_styles_to_runs(fmt, mpl_kwargs, _seg_ds, _nd) mpl_kwargs["color"] = _run_colors hue = cluster_labels @@ -7699,6 +10449,7 @@ def _compute_forecasts(datasets): hue_category_names = [str(c) for c in _cats_sorted] else: xform, labels = reshape_data(xform, cluster_labels, labels) + _global_regrouped = True # reshape_data returns groups in first-appearance order; # reorder the drawn groups (and their legend/colorbar # labels) into sorted label order so a legend reads @@ -7714,6 +10465,14 @@ def _compute_forecasts(datasets): hue = cluster_labels hue_group_labels = [str(_cats[i]) for i in _order] hue_category_names = list(hue_group_labels) + if _replayed_categories is not None and "color" not in mpl_kwargs: + # the shared mapping's colours for the clusters THIS cell + # draws (a missing cluster leaves a gap, not a shift) + _shared_colors = _replayed_cluster_colors( + [_cats[i] for i in _order], _replayed_categories, + palette) + if _shared_colors is not None: + mpl_kwargs["color"] = _shared_colors # group data if there is a grouping var elif hue is not None: @@ -7731,10 +10490,20 @@ def _compute_forecasts(datasets): # a matrix hue's own column names are the natural legend labels # for its palette swatches (GH #285); captured before hue is # turned into a bare array below. - _hue_column_names = (list(hue.columns) - if isinstance(hue, pd.DataFrame) else None) - if isinstance(hue, (pd.Series, pd.Index, pd.Categorical)): - hue = hue.tolist() + _hue_column_names = None + if is_frame_dataset(hue): + hue = as_pandas_dataframe(hue) + if (hue.shape[1] == 1 + and not pd.api.types.is_numeric_dtype(hue.dtypes.iloc[0])): + # a one-column frame of LABELS is that column (previously + # it fell through to the matrix path as a (n, 1) object + # array and crashed with a bare IndexError); a one-column + # NUMERIC frame keeps its matrix-hue reading below + hue = _series_values_list(hue.iloc[:, 0]) + else: + _hue_column_names = list(hue.columns) + elif is_series_like(hue): + hue = _series_values_list(hue) # NESTED per-dataset hue: when the data is a list of datasets, hue may # be given with the SAME nesting -- one hue sub-sequence per dataset, @@ -7812,6 +10581,27 @@ def _compute_forecasts(datasets): if hue_array is not None and hue_array.ndim >= 1 else len(hue)) if _hue_len != n_obs: + # a NESTED per-dataset hue whose sub-lists do not all match + # their datasets: name the offending sub-list(s) rather than + # reporting the count of sub-lists as "3 entries" against the + # observation total. + if (isinstance(hue, (list, tuple)) and len(xform) > 1 + and len(hue) == len(xform) + and all(np.ndim(h) >= 1 for h in hue)): + bad = [(i, len(h), len(xi)) + for i, (h, xi) in enumerate(zip(hue, xform)) + if len(h) != len(xi)] + if bad: + detail = '; '.join( + f"hue[{i}] has {got} entr{'y' if got == 1 else 'ies'}" + f" but dataset {i} has {want} row" + f"{'' if want == 1 else 's'}" + for i, got, want in bad) + raise ValueError( + f"hue= is nested per dataset ({len(hue)} sub-lists " + f"for {len(xform)} datasets) but the lengths do " + f"not match: {detail}. Each sub-list must have " + "exactly one value per row of its dataset.") raise ValueError( f"hue has {_hue_len} entr{'y' if _hue_len == 1 else 'ies'} but " f"the data has {n_obs} observations; hue must have exactly one " @@ -8025,6 +10815,7 @@ def _compute_forecasts(datasets): _run_cat_names, _seg_lengths, _seg_bridged) = _regroup_categorical_lines( xform, hue, labels, _cat_color, _cat_label) + _legend_order = [str(v) for v in _cat_label.values()] fmt = _expand_styles_to_runs( fmt, mpl_kwargs, _seg_ds, _n_datasets_before_hue) mpl_kwargs["color"] = _run_colors @@ -8034,6 +10825,7 @@ def _compute_forecasts(datasets): # boolean hue is grouped in first-appearance order then # reordered into sorted numeric order (F13-005). xform, labels = reshape_data(xform, hue, labels) + _global_regrouped = True if _hue_sort_numeric: _appear = list(sorted(set(hue), key=list(hue).index)) _order = sorted(range(len(_appear)), @@ -8054,6 +10846,41 @@ def _compute_forecasts(datasets): _named = resolve_category_colors(palette, hue_group_labels) mpl_kwargs["color"] = [tuple(_named[c]) for c in hue_group_labels] + elif ("color" not in mpl_kwargs and not _fmt_draws_line(fmt) + and isinstance(palette, (list, tuple)) + and len(palette) > 0 + and all(isinstance(e, collections.abc.Mapping) + for e in palette) + and hue_group_labels is not None + and len(hue_group_labels) == len(xform)): + # ...and a per-dataset LIST of {category: color} dicts + # (1.1 release review): the marker path fell through to + # the branch below, which samples the DEFAULT palette for + # a dict list, so `fmt='o'` drew hls while `fmt='-'` + # applied the dicts. The line path's merge (one mapping, + # a category named twice must agree) in the same drawn + # order the groups were just put in. + _cat_color, _ = _categorical_color_label_maps( + hue, palette, None, hue_group_labels, _hue_sort_numeric) + mpl_kwargs["color"] = [tuple(c) for c in _cat_color.values()] + elif ("color" not in mpl_kwargs and not _fmt_draws_line(fmt) + and hue_group_labels is not None + and len(hue_group_labels) == len(xform)): + # ...and every other palette on the MARKER path (round 9): + # the category colours are the palette's first + # `n_groups` entries, in drawn-group order -- exactly what + # the ambient cycle assigns on a fresh axes -- resolved + # here so they belong to the CATEGORIES: on a composed + # axes the cycle continues past the datasets an earlier + # call drew, so the groups' colours shifted with it, and a + # colour letter in fmt= (``'ro'``) painted every group + # red where the line path (``'r-'``) kept the hue colours. + import seaborn as sns + _n_groups = len(xform) + mpl_kwargs["color"] = [ + tuple(c) for c in sns.color_palette( + _seaborn_palette_arg(palette, _n_groups), + _n_groups)] _hue_regrouped_counts = (_n_datasets_before_hue, len(xform)) # a PURE line cannot render a single-observation category -- it # draws NOTHING (and crashed animated interpolation, F02-002). @@ -8090,6 +10917,7 @@ def _compute_forecasts(datasets): base_colors = sns.color_palette( _seaborn_palette_arg(palette, n_outer), n_outer) mpl_kwargs["color"] = [base_colors[g] for g in nested_groups] + _nested_group_colored = True min_depth = min(nested_depths) if any(d != min_depth for d in nested_depths): mpl_kwargs["linewidth"] = [ @@ -8156,21 +10984,20 @@ def _compute_forecasts(datasets): # dataset's full row count, and `figure`/`axes` are None because the # figure is built FROM the title and so does not exist yet. if _dynamic_title is not None and not animate: - title = str(_dynamic_title(FrameContext( + title = _resolve_dynamic_title_text(_dynamic_title, FrameContext( frame=None, n_frames=None, figure=None, axes=None, datasets=tuple(xform), style=False, order=order, revealed_counts=tuple(len(d) for d in xform), window_bounds=tuple((0, len(d)) for d in xform), - progress=1.0))) + progress=1.0)) _dynamic_title = None # title_wrap= (GH #285): hard-wrap every title at N characters, AFTER # per-segment resolution so a scalar title and each entry of a # per-segment list wrap identically. The line break is the drawing # backend's own: '\n' for matplotlib, '<br>' for plotly. + _wrap_break = '<br>' if resolve_backend(backend) == 'plotly' else '\n' if _title_wrap is not None: - _wrap_break = ('<br>' if resolve_backend(backend) == 'plotly' - else '\n') title = _wrap_title_text(title, _title_wrap, _wrap_break) _segment_titles = _wrap_title_text(_segment_titles, _title_wrap, _wrap_break) @@ -8224,9 +11051,16 @@ def _compute_forecasts(datasets): # not on `n_datasets`. The length check below, in contrast, genuinely # cannot happen any earlier: `n_morph_datasets` is the count of FINAL # (post cluster/hue-reshape) datasets tagged for morph, which is only - # known now that `xform`/`morph_tags` exist. + # known now that `xform`/`morph_tags` exist. `loop=True` appends a + # closing repeat of the first cloud (the backends schedule + # `2(n + 1) - 1` segments, as the `loop=` docstring promises), so the + # list is validated against that same count -- this check and the + # backends' `morph_schedule` used to disagree by one cloud, and no + # list length satisfied both. if morph_tags is not None: - rotations = resolve_morph_rotations(rotations, sum(morph_tags)) + rotations = resolve_morph_rotations( + rotations, sum(morph_tags) + (1 if _morph_loop else 0), + loop=_morph_loop) # 2-D animations (round17 #9, GH #123): fixed (non-rotating) viewport -- # `rotations=`/`zoom=` are 3-D camera controls with no 2-D equivalent, @@ -8393,7 +11227,31 @@ def _compute_forecasts(datasets): # calls that only ever passed names=. raise ValueError( "pass dataset names via names= OR a legend= list, not both") - legend = names + if legend is not False: + # names= turns the legend ON by default, but an explicit + # legend=False still wins (1.1 review: it used to be + # overwritten here, so the legend was drawn anyway) + legend = names + + # nested-list input coloured by OUTER group: every leaf's plotly hover + # name (and so its legendgroup) is its group's label -- the label the + # legend shows on the group's summary leaf, or would under legend=True + # -- exactly as a hierarchy's leaves are named by their top-level group. + # Without it the summary leaf alone carried the group label and the + # other leaves fell back to their flat leaf NUMBER ('1', '2', '2', '4' + # for [[a, b], [c, d]]), so hover named the wrong group and the + # legendgroups paired leaves of different groups (1.1 release review). + # A legend=/names= list with one entry per LEAF names the leaves + # themselves instead, so it leaves this unset. + _nested_hover_labels = None + if (_nested_group_colored and hue is None + and len(nested_groups) == len(xform)): + _n_outer = len(set(nested_groups)) + if (isinstance(legend, (list, tuple)) and len(legend) == _n_outer + and len(legend) != len(xform)): + _nested_hover_labels = [legend[g] for g in nested_groups] + elif not isinstance(legend, (list, tuple)): + _nested_hover_labels = [g + 1 for g in nested_groups] # handle legend if legend is not None: @@ -8409,6 +11267,30 @@ def _compute_forecasts(datasets): else: legend = [item for item in sorted(set(hue), key=list(hue).index)] + elif (_nested_group_colored and hue is None + and len(nested_groups) == len(xform) + and (legend is True or ( + isinstance(legend, (list, tuple)) + and len(legend) == len(set(nested_groups)) + and len(legend) != len(xform)))): + # nested-list input colours every leaf by its OUTER group, so + # the legend names the groups, as a hierarchy's does + # (docs/hierarchy.rst: one labelled entry per top-level group, + # every other trace '_nolegend_'). legend=True numbered the + # LEAVES 1..n, so four swatches came in two identical colour + # pairs (1.1 release review, figure QA). The entry goes on the + # group's SUMMARY leaf (its shallowest, which the depth styling + # draws thickest and most opaque); a legend= list with one + # entry per outer group names them. + _group_names = (list(legend) if isinstance(legend, (list, tuple)) + else list(range(1, len(set(nested_groups)) + 1))) + _summary = {} + for _i, (_g, _d) in enumerate(zip(nested_groups, nested_depths)): + if _g not in _summary or _d < nested_depths[_summary[_g]]: + _summary[_g] = _i + _owner = {_i: _g for _g, _i in _summary.items()} + legend = [_group_names[_owner[_i]] if _i in _owner + else '_nolegend_' for _i in range(len(xform))] elif legend is True and hue is None: # ndims=1 series mode (GH #285) names each line by the COLUMN it # draws (or by its dataset, for one-column inputs) -- a bare @@ -8469,6 +11351,17 @@ def _compute_forecasts(datasets): legend, palette, hue_group_labels=hue_group_labels, hierarchy_labels=_mi_colorbar_labels, legend_entries=_final_legend_entries) + # ...which, for datasets coloured from the palette CYCLE (no color=, + # hue=, cluster=, palette mapping), reads the palette's fresh sampling; + # the colours actually drawn differ when a composed `ax=` continues the + # palette or a fmt= colour letter colours a dataset, so each backend + # branch below re-syncs both scales to the drawn colours + # (`_sync_color_scales`; 1.1 release review) + _color_scales_from_cycle = ( + "color" not in mpl_kwargs and hue is None and cluster is None + and n_clusters is None and multicolor_hue is None + and not isinstance(palette, collections.abc.Mapping) + and not _looks_like_dataset_palettes(palette)) # interpolate if its a line plot. animate='morph' treats every dataset # as a POINT CLOUD (Hungarian-matched to its neighbors in `morph.py`), @@ -8509,9 +11402,21 @@ def _compute_forecasts(datasets): # only ever ADDS points between samples, keeps every original sample # (including the final one) as a drawn vertex, and uses a fixed target # density -- so duration=/frame_rate= (animation kwargs) no longer - # change static rendering (F01-001/F01-007). ANIMATED plots keep the - # historical frame_rate*duration grid: there the interpolated rows ARE - # the animation's frame-sampling. + # change static rendering (F01-001/F01-007). ANIMATED plots put each + # line on a grid of at least frame_rate*duration rows that is a uniform + # refinement of its observations (`_interp_anim_line`): every + # observation stays an exact vertex, and nothing is ever DOWNsampled + # (1.1 visual review, L8). 'spin' reveals no rows at all, so it keeps + # the source rows untouched; the backends antialias them at draw time. + def _finite_before(trace_index): + """Whether the INPUT dataset behind drawn trace `trace_index` was + finite before the pipeline ran (None when unknown).""" + if _input_finite is None: + return None + _src = (_series_owner[trace_index] if _series_owner is not None + and trace_index < len(_series_owner) else trace_index) + return _input_finite[_src] if _src < len(_input_finite) else None + if animate == "morph": pass elif fmt is None or isinstance(fmt, str): @@ -8522,22 +11427,30 @@ def _compute_forecasts(datasets): # (release-1.0 audit, F05-011) for _i, _xi in enumerate(xform): if _xi.shape[0] > 1: - _require_finite_for_line(_xi, _i) - if animate: - # Every multi-row dataset is resampled onto the EXACT - # frame grid (release-1.0 audit): per-dataset - # interpolation (previously the step came from - # xform[0]'s length alone, silently truncating longer - # LATER datasets, F04-003) with exactly - # round(frame_rate * duration) rows (the docstring's - # promised frame count -- the old np.arange step - # produced 901/41 frames for some lengths, F04-004). - # Per-dataset singleton guard (F02-002/F05-012): a - # 1-point dataset (singleton hue category, or a - # reference point plotted beside a trajectory) cannot - # be PCHIP-interpolated (scipy needs >= 2 samples) -- - # leave it as-is instead of crashing the whole plot; - # the backend paces it onto the frame grid. + _require_finite_for_line( + _xi, _i, input_finite=_finite_before(_i), + manip=manip) + if animate == 'spin': + # 'spin' draws every row on every frame (only the camera + # moves): there is no reveal to pace, so the rows stay + # the observations themselves (L8) + pass + elif animate: + # Every multi-row dataset gets its OWN animation grid + # (`_interp_anim_line`): at least + # round(frame_rate * duration) rows, a uniform + # refinement of its observations, never fewer rows than + # it has (1.1 visual review, L8 -- the grid used to be + # exactly the frame count, which downsampled long + # datasets into zig-zags). Release-1.0 history: the + # grid is per-dataset because a grid computed from + # xform[0]'s length alone truncated longer LATER + # datasets (F04-003). Per-dataset singleton guard + # (F02-002/F05-012): a 1-point dataset (singleton hue + # category, or a reference point plotted beside a + # trajectory) cannot be PCHIP-interpolated (scipy needs + # >= 2 samples) -- leave it as-is instead of crashing + # the whole plot; the backend paces it onto the frames. _n_frames = max(2, int(round(frame_rate * duration))) xform = [xi if xi.shape[0] < 2 else _interp_anim_line(xi, _n_frames) @@ -8550,15 +11463,19 @@ def _compute_forecasts(datasets): if has_line_component(fmt[idx]): if xi.shape[0] > 1: # see the F05-011 note above - _require_finite_for_line(xi, idx) - # per-dataset exact frame grid -- see the F04-003/ - # F04-004 note in the single-fmt branch above. (The - # historical interp_array_list call here treated the - # 2D array as a LIST of rows, silently replacing the - # dataset with a list of per-row interpolations -- - # latent for years because a bug made is_line() always - # False.) - if animate: + _require_finite_for_line( + xi, idx, input_finite=_finite_before(idx), + manip=manip) + # per-dataset animation grid -- see the L8/F04-003 note + # in the single-fmt branch above ('spin' keeps its + # rows). (The historical interp_array_list call here + # treated the 2D array as a LIST of rows, silently + # replacing the dataset with a list of per-row + # interpolations -- latent for years because a bug made + # is_line() always False.) + if animate == 'spin': + pass + elif animate: xform[idx] = _interp_anim_line( xi, max(2, int(round(frame_rate * duration)))) elif antialias: @@ -8633,10 +11550,44 @@ def _compute_forecasts(datasets): # bug in this file. Assert rather than drop: the pre-1.1 guard # nulled `raw_forecasts` on any mismatch, which is precisely how a # missing per-trace forecast would become invisible. - _ft.assert_consistent(raw_forecasts=raw_forecasts, - bundle_forecasts=bundle_forecasts) - elif (raw_forecasts is not None - and len(raw_forecasts) != len(xform)): + if _predict_names is None or raw_forecasts is None: + _ft.assert_consistent(raw_forecasts=raw_forecasts, + bundle_forecasts=bundle_forecasts) + else: + # a collection: n_models blocks of one forecast per trace, + # model-major, and a {name: [per trace]} bundle (F3) + _n_tr = len(_ft.arrays) + if len(raw_forecasts) != _n_tr * len(_predict_names): + raise ValueError( + f"hierarchy trace/raw_forecasts mismatch: {_n_tr} " + f"traces x {len(_predict_names)} model(s) but " + f"{len(raw_forecasts)} raw_forecasts. Every per-trace " + "sequence must be built from the same FinalTraces " + "(hypertools/plot/hierarchy.py).") + for _k, _name in enumerate(_predict_names): + _ft.assert_consistent(**{ + f'raw_forecasts[{_name!r}]': + raw_forecasts[_k * _n_tr:(_k + 1) * _n_tr], + f'bundle_forecasts[{_name!r}]': + bundle_forecasts[_name]}) + # forecast i continues trace `_model_forecast_owner[i]`; the + # identity map the single-model hierarchy uses would run off + # the end of the trace list + _forecast_owner = list(_model_forecast_owner) + elif raw_forecasts is not None and ( + len(raw_forecasts) != len(xform) + # ...or MARKER-only categorical regrouping, whose traces are + # CATEGORIES, never datasets -- even when the counts coincide + # (2 datasets under 2 categories silently drew dataset 1's + # forecast in category 1's colour; 1.1 release review) + or _global_regrouped + # ...or a COLLECTION under hue=/cluster= regrouping: one + # dataset split into two runs under two models gives two + # forecasts for two runs, so the counts coincide by accident + # while forecast i does NOT continue run i (Codex round 3: + # Kalman took the earlier run's colour, ARIMA the final run's) + or (_model_forecast_owner is not None and _seg_ds is not None + and len(_seg_ds) == len(xform))): # A forecast belongs to a DATASET and is anchored at that dataset's # last observation, so after regrouping it belongs to whichever run # holds that observation -- which is also the trace it visually @@ -8658,7 +11609,7 @@ def _compute_forecasts(datasets): _owner[_ds] = _run # last write wins = final run if set(_owner) >= set(_fc_dataset): _forecast_owner = [_owner[_ds] for _ds in _fc_dataset] - elif _model_forecast_owner is not None: + elif _model_forecast_owner is not None and not _global_regrouped: # no regrouping: a collection's only "mismatch" is that there # are n_models forecasts per drawn trace, which the model/ # dataset map already resolves @@ -8812,16 +11763,69 @@ def _compute_forecasts(datasets): _slow_secs = (DEFAULT_SLOW_WARNING_SECONDS if slow_warning_seconds is _UNSET_SLOW_WARNING else slow_warning_seconds) + _timed_cache = {} + + def _timed_forecast(i, rows, model_spec, horizon, min_history): + if len(rows) < min_history: + return None + from ..predict.predict import predict as _predictor + from ..predict.time import order_time_data + owner = _series_owner[i] if _series_owner is not None else i + column = _series_columns[i] if _series_columns is not None else None + key = (id(model_spec), owner, rows) + if key not in _timed_cache: + history = order_time_data(_forecast_frames[owner].iloc[list(rows)]) + selected_model = model_spec + candidate = (model_spec.get('model') if isinstance(model_spec, dict) + else model_spec) + if (not isinstance(candidate, type) + and getattr(candidate, 'is_fitted', False) + and hasattr(candidate, 'for_dataset')): + bound = candidate.for_dataset(owner) + selected_model = ({**model_spec, 'model': bound} + if isinstance(model_spec, dict) else bound) + from ..core.exceptions import _InsufficientHistoryError + try: + future = _predictor(history, model=selected_model, + t=_forecast_horizon) + except _InsufficientHistoryError: + # A sparse early prefix may contain too few regular-grid + # rows even when it has enough original observations. + return None + _timed_cache[key] = (history.iloc[-1:], future) + last, future = _timed_cache[key] + absolute = np.vstack([_forecast_coordinates(last, owner, column), + _forecast_coordinates(future, owner, column)]) + return absolute - analyze_histories[i][rows[-1]] + + _timed_forecast.fit_key = lambda i, rows: ( + _series_owner[i] if _series_owner is not None else i, rows) + def _build_schedule(model_spec): + # Repeated entries in a model comparison still own independent + # fits (and independent samples for stochastic forecasters). + _timed_cache.clear() + _time_kwargs = {'forecast_function': _timed_forecast} if _reveal is not None: # rows from the reveal, not counts from the drawn traces: a # dataset may now be spread over several of them return ForecastSchedule.for_regrouped( analyze_histories, _reveal, model=model_spec, t=t, - n_frames=_n_frames, slow_warning_seconds=_slow_secs) + n_frames=_n_frames, slow_warning_seconds=_slow_secs, + # one array per RUN here (hue=/cluster= regrouped the + # data), which is exactly what the reveal indexes + grids=xform, + **_time_kwargs) return _builder( analyze_histories, _grid_lengths, model=model_spec, t=t, - n_frames=_n_frames, slow_warning_seconds=_slow_secs) + n_frames=_n_frames, slow_warning_seconds=_slow_secs, + # the animation grid ITSELF, so each frame's forecast starts + # at the vertex that frame actually draws last rather than at + # the last raw observation behind it (maintainer report, + # 2026-09-11). Passed pre-centre/scale, exactly like + # `analyze_histories`; `to_display` maps both below. + grids=xform, + **_time_kwargs) if _predict_names is None: forecast_schedule = _build_schedule(predict) @@ -8834,12 +11838,21 @@ def _build_schedule(model_spec): forecast_schedule = MultiModelSchedule( [_build_schedule(_spec) for _spec in _specs], names=_predict_names) - if _series_step is not None: - # ndims=1 series mode: the x column is the row index's own - # continuation, not something a fit gets a vote on (see - # `_pin_series_x`, which does exactly this for the static - # overlay). - forecast_schedule.pin_ramp(0, _series_step) + + # truth= in a TIME-PROGRESSING animation (maintainer decision, 1.1 + # review): each held-out row appears on the first frame whose drawn + # forecast reaches it, then stays -- drawing the whole truth from frame + # 0 showed the answer before the animation had forecast anywhere near + # it. `counts[frame][i]` is how many of truth i's rows are on screen + # (0 = hidden, seam included). No schedule (no forecast drawn) means + # nothing is ever revealed. Static plots and 'spin' draw it in full. + _truth_reveal = None + if raw_truths is not None and animate and animate != 'spin': + from .forecast import truth_reveal_counts + _truth_reveal = truth_reveal_counts( + forecast_schedule, len(raw_truths), + [max(len(tr) - 1, 0) for tr in raw_truths], + max(1, int(round(frame_rate * duration)))) # Display space (GH #285): axis_scale='unit' -- what hypertools has # always done -- mean-centres every drawn vertex (data, forecasts, and @@ -8926,16 +11939,37 @@ def _rescale(a): _bounds = np.vstack([np.asarray(r, dtype=float) for r in _bounds_rows]) _finite = _bounds[np.isfinite(_bounds).all(axis=1)] - if len(_finite): + if len(_finite) and _finite.shape[1] == 1: + # a ONE-column trace outside ndims=1 series mode draws its + # values on Y against the row index on x, so its one column + # bounds y. x (rows, continued by any forecast) is left to the + # backend's autoscale; this used to give the VALUE range to x + # as well, so the 40-row trace sat in an x window of (-5.5, + # 3.2) (1.1 release review) + _lo, _hi = float(_finite.min()), float(_finite.max()) + _pad = (_hi - _lo) * 0.05 if _hi > _lo else 1.0 + if _data_ylim is None: + _data_ylim = (_lo - _pad, _hi + _pad) + elif len(_finite): _lo = _finite.min(axis=0) _hi = _finite.max(axis=0) _pad = np.where(_hi > _lo, (_hi - _lo) * 0.05, 1.0) + _auto_x = (float(_lo[0] - _pad[0]), float(_hi[0] + _pad[0])) if _data_xlim is None: - _data_xlim = (float(_lo[0] - _pad[0]), - float(_hi[0] + _pad[0])) - if _data_ylim is None and _finite.shape[1] > 1: - _data_ylim = (float(_lo[1] - _pad[1]), - float(_hi[1] + _pad[1])) + _data_xlim = _auto_x + elif None in tuple(_data_xlim): + # an OPEN side (``xlim=(None, hi)``) takes the data bound: + # plotly has no partial range (``[None, hi]`` drew a + # year-2000 axis), and matplotlib then agrees with it + _data_xlim = tuple(_auto_x[_k] if _v is None else _v + for _k, _v in enumerate(_data_xlim)) + if _finite.shape[1] > 1: + _auto_y = (float(_lo[1] - _pad[1]), float(_hi[1] + _pad[1])) + if _data_ylim is None: + _data_ylim = _auto_y + elif None in tuple(_data_ylim): + _data_ylim = tuple(_auto_y[_k] if _v is None else _v + for _k, _v in enumerate(_data_ylim)) # handle palette with seaborn import seaborn as sns @@ -9077,13 +12111,44 @@ def _surface_inherits_color(i): _n_ds = len(raw_forecasts) // len(_predict_names) _forecast_labels = [_name for _name in _predict_names for _ in range(_n_ds)] - if _fc_hue is None and forecast_cluster is None: - # "one colour per model" is exactly what forecast_hue= already - # means -- datasets sharing a hue value share a colour -- so the - # model name IS the hue, and forecast_palette= keeps its usual - # job of choosing the colours. + if _fc_fmt is None: + # several models on one series: each forecast keeps its + # dataset's colour (which series it continues) and takes a + # dash per MODEL (which model made it) -- two encodings for + # two questions. An explicit forecast_fmt= replaces the dash. + from .forecast import forecast_model_fmts + _fc_fmt = forecast_model_fmts(len(_predict_names), _n_ds) + if _fc_hue is None and forecast_cluster is None \ + and _fc_palette is not None: + # forecast_palette= on a collection colours by MODEL: "one + # colour per model" is exactly what forecast_hue= already + # means -- datasets sharing a hue value share a colour -- so + # the model name IS the hue. (Without a palette the forecasts + # inherit their datasets' colours; a per-model palette used to + # be the default, and its first colour was the first dataset's + # own, so two datasets x two models were four lines in two + # indistinguishable pairs.) _fc_hue = _forecast_labels - _fc_palette = _fc_palette if _fc_palette is not None else 'husl' + elif (_fc_hue is not None and not isinstance(_fc_hue, (str, bytes)) + and (isinstance(_fc_hue, (list, tuple)) + or is_array_dataset(_fc_hue) + or is_series_like(_fc_hue))): + # forecast_hue= is documented as ONE VALUE PER DATASET; with a + # collection every dataset is forecast once per model, so that + # value is broadcast over its n_models forecasts (F5). A list + # already sized to the overlays (model-major, like + # forecast_fmt=) is left exactly as passed. + _hue_list = list(_fc_hue) + if len(_hue_list) == _n_ds and _n_ds != len(raw_forecasts): + _fc_hue = [_hue_list[_d] for _d in _model_forecast_owner] + elif len(_hue_list) != len(raw_forecasts): + raise ValueError( + f"forecast_hue= takes one value per DATASET (shared by " + f"every model's forecast of that dataset), or one per " + f"FORECAST in model-major order; got {len(_hue_list)} " + f"value(s) for {_n_ds} dataset(s) x " + f"{len(_predict_names)} model(s) = {len(raw_forecasts)} " + "forecast(s).") if isinstance(_fc_fmt, (list, tuple)) \ and len(_fc_fmt) == len(_predict_names) \ and len(_predict_names) != len(raw_forecasts): @@ -9091,6 +12156,18 @@ def _surface_inherits_color(i): # a collection); a list already sized to the overlays is left # exactly as passed _fc_fmt = [_f for _f in _fc_fmt for _ in range(_n_ds)] + elif raw_forecasts is not None and predict is not None: + # the single-model form lists its forecast in the legend under the + # model's name too -- the SAME name the collection form and + # `hyp.predict(x, model=[...])` use for that spec -- so a figure + # with `predict=` always says what its faded continuation is + # (before this only a collection was listed). + from ..predict.backtest import spec_name as _spec_name + from ..predict.predict import _FORECASTER_ALIASES, FORECASTERS + from ..core.shared import supported_names as _supported_names + _forecast_labels = [_spec_name(predict, _supported_names(FORECASTERS), + _FORECASTER_ALIASES) + ] * len(raw_forecasts) _forecast_overrides = None if raw_forecasts is not None and ( @@ -9098,12 +12175,25 @@ def _surface_inherits_color(i): or any(v is not None for v in _forecast_style_kwargs.values())): from .forecast import resolve_forecast_overrides + # a forecast_hue=/forecast_cluster= grouping without + # forecast_palette= continues the figure's palette PAST the colours + # the observed data takes (maintainer decision, 1.1 review), so + # cluster 0 is no longer dataset 0's colour. A continuous hue + # colours the data from a colormap, not palette slots: unchanged. + _fc_default_palette = None + if (_fc_palette is None and line_colors is None + and (_fc_hue is not None or forecast_cluster is not None)): + _fc_default_palette = _forecast_group_palette( + palette, _observed_palette_colors( + palette, len(xform), mpl_kwargs.get('color'), draw_fmt, + prior=_prior_palette_slots(ax, _plotly_into, backend))) _forecast_overrides = resolve_forecast_overrides( len(raw_forecasts), raw_forecasts, hue=_fc_hue, cluster=forecast_cluster, n_clusters=forecast_n_clusters, palette=_fc_palette, fmt=_fc_fmt, - stacklevel=external_stacklevel()) + stacklevel=external_stacklevel(), + default_palette=_fc_default_palette) # on_frame= (plan 1.1 Task 7): ONE shared registry, created before # either backend's per-frame closures exist, and threaded into both -- @@ -9137,13 +12227,26 @@ def _surface_inherits_color(i): _dynamic_title_text = {} if _dynamic_title is not None: def _update_dynamic_title(ctx, _fn=_dynamic_title): - text = str(_fn(ctx)) + # `title_wrap=` applies to the resolved text of EVERY frame, + # exactly as it does to a static or per-segment title. + text = _wrap_title_text(_resolve_dynamic_title_text(_fn, ctx), + _title_wrap, _wrap_break) _dynamic_title_text['text'] = text if ctx.axes is not None: _apply_title(ctx.axes, text, font=_artist_font, title_kwargs=_title_kwargs) _frame_hooks.add_internal(_update_dynamic_title) + # the colour-cycle slots this call's datasets take (what a later `ax=` + # call into the same axes/figure/cell continues the palette from): + # counted HERE, before the plotly branch below hands the palette to its + # backend as explicit per-dataset ``color=`` entries, which would make + # every dataset look explicitly coloured (round 7) + _palette_slots_taken = _palette_slots_consumed( + len(xform), mpl_kwargs, line_colors, draw_fmt, + category_colored=hue is not None or cluster is not None + or n_clusters is not None) + # interactive (plotly) backend: render with plotly and skip the # matplotlib pipeline entirely. backend='auto' resolves to plotly only # on Colab/Kaggle (see hypertools.plot.plotly_backend for the policy). @@ -9183,16 +12286,95 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): f"kwargs for plotly are: {sorted(_PLOTLY_MAPPED_KWARGS)}." , stacklevel=external_stacklevel()) + # composing into an existing figure (`ax=<figure>`) or grid cell + # (`ax=<cell>`): the palette slots the calls drawn THERE already + # took (a cell keeps its own count, like a matplotlib axes of its + # own; Codex round 3). Read for EVERY call -- the count is written + # back below for every call -- not only for one that colours from + # the cycle: a pinned ``color=`` or categorical ``hue=`` call used + # to skip the read and write the count back as its own zero, so + # the next ordinary call restarted the palette (round 8; the + # matplotlib `ax=` path keeps the axes' count across such calls) + _plotly_palette_offset = 0 + # ...and the COLOURS those slots took (`_palette_continuation` + # fills the gaps of an evenly re-sampled palette with them) + _plotly_palette_used = None + if _plotly_into is not None: + if _is_plotly_cell(_plotly_into): + _meta = _plotly_into.figure.layout.meta + _meta = _meta if isinstance(_meta, dict) else {} + _plotly_palette_offset = int((_meta.get( + 'hyp_cell_datasets_drawn') or {}).get( + str(_plotly_into.index), 0)) + _plotly_palette_used = (_meta.get( + 'hyp_cell_palette_used') or {}).get( + str(_plotly_into.index)) + else: + _meta = getattr(_plotly_into, 'layout', None) + _meta = _meta.meta if _meta is not None else None + _meta = _meta if isinstance(_meta, dict) else {} + _plotly_palette_offset = int( + _meta.get('hyp_datasets_drawn', 0)) + _plotly_palette_used = _meta.get('hyp_palette_used') + # the palette colours this call's cycle-coloured datasets take, + # recorded below for the next `ax=` call into the same figure/cell + _palette_taken_colors = [] if "color" not in mpl_kwargs: - import seaborn as sns_local mpl_kwargs = dict(mpl_kwargs) - mpl_kwargs["color"] = sns_local.color_palette( - _seaborn_palette_arg(palette, len(xform)), len(xform)) + # continue the palette past the datasets an earlier call drew + # here, as the matplotlib `ax=` path does (a per-dataset + # palette mapping/list is not a cycle to continue) + _cycle_offset = 0 + if (_plotly_into is not None + and not (isinstance(palette, collections.abc.Mapping) + or _looks_like_dataset_palettes(palette))): + _cycle_offset = _plotly_palette_offset + _palette_colors = _palette_continuation( + palette, len(xform), _cycle_offset, + _plotly_palette_used if _cycle_offset else None) + _palette_taken_colors = _palette_colors[:_palette_slots_taken] + # a colour letter in fmt= ('r-', ['g--', 'b:']) colours its + # dataset, exactly as on matplotlib, where the letter beats the + # axes' colour cycle (an explicit color=/hue= is the other + # branch of this `if`, and still wins over the letter on both + # backends). A lettered dataset consumes no colour-cycle slot + # there either, so here the unlettered datasets take the + # palette colours in order, past the ones an earlier call into + # the same figure/cell already used (1.1 release review, round + # 6: plotly drew every fmt-lettered dataset in the palette + # colour, on the single-axes path, animated, and per panel). + _fmt_letters = [ + _fmt_color_letter(draw_fmt[_i]) if _i < len(draw_fmt) + else None for _i in range(len(xform))] + _palette_iter = iter(_palette_colors) + mpl_kwargs["color"] = [ + _letter if _letter is not None else next(_palette_iter) + for _letter in _fmt_letters] + if _color_scales_from_cycle: + _sync_color_scales(mpl_kwargs["color"], colors_info, + colorbar_info) kwargs_list = parse_kwargs(xform, mpl_kwargs) _apply_extra_kwargs(kwargs_list, kwargs) + # the traces already in the figure/grid this call draws into (its + # own are appended after them; see `_rank_plotly_legend`) + _n_traces_before = 0 + if _plotly_into is not None: + _n_traces_before = len( + (_plotly_into.figure if _is_plotly_cell(_plotly_into) + else _plotly_into).data) + def _plotly_before_show(drawn): + _record_palette_used( + drawn, _plotly_into, _plotly_palette_offset, + _plotly_palette_used, _palette_taken_colors) + if _legend_order: + _rank_plotly_legend(drawn, _n_traces_before, _legend_order) + fig = plotly_draw( xform, into=_plotly_into, + # rows behind each (antialiased) trace: a 1-D trace's x is the + # row index, as matplotlib's plot1D draws it + row_counts=[len(r) for r in raw_xform], # the same run -> dataset -> rows mapping the matplotlib updaters # pace their reveal with, so neither backend re-derives it ownership=_ownership, @@ -9258,13 +12440,39 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): title_segment_colors=_title_segment_colors, legend_kwargs=_legend_kwargs, legend_entries=_final_legend_entries, + legend_explicit=_legend_entries is not None, + # the pre-resampling observations, so an 'o-' marks only them + # (the matplotlib `_draw` call's `raw_data=`) + raw_data=raw_xform, + frame_kwargs=frame_kwargs, + # every hoverable data trace's name (`_plotly_hover_names`) + trace_names=_plotly_hover_names( + len(xform), legend, category_names=_run_cat_names, + group_labels=(_mi_style.get('group_labels') + if _multiindex_meta is not None + else _nested_hover_labels), + user_labels=_legend_user_labels, + series_names=_series_names, hue=hue, + hue_group_labels=hue_group_labels), axis_scale=_axis_scale, xlim=_data_xlim, ylim=_data_ylim, x_date=_series_is_date, truths=raw_truths, + truth_reveal=_truth_reveal, forecast_labels=_forecast_labels, forecast_datasets=_model_forecast_owner, + # what a later `ax=<this figure>` call continues the palette + # from (see the colour block above) -- recorded on EVERY + # figure, since the first call is the one composed into; only + # the datasets coloured from the cycle count + # (`_palette_slots_consumed`) + datasets_drawn=_plotly_palette_offset + _palette_slots_taken, + # ...and, before the figure is saved or shown, the colours of + # those slots beside the count (read back by the colour block + # above on the next `ax=` call) and the categorical legend's + # order (`_rank_plotly_legend`) + before_show=_plotly_before_show, ) ax = None data = xform @@ -9281,6 +12489,47 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): palette=_seaborn_palette_arg(palette, len(xform)), n_colors=len(xform)) sns.set_style(style="whitegrid") + _palette_offset = 0 + _palette_used = None + # this call's cycle colours: the palette's own sampling on a + # fresh axes (what `sns.set_palette` above gives it), continued + # past an earlier call's slots on a reused one (below) + _cycle = _palette_continuation(palette, len(xform)) + if ax is not None and hasattr(ax, 'set_prop_cycle'): + # a caller's axes (`ax=`, every `panels=` cell) captured + # ITS figure's colour cycle when it was created, so the + # rc-scoped palette above never reached it and datasets + # drew in matplotlib's default C0/C1 cycle while the + # returned `colors` and the plotly grid said hls (1.1 + # release review, feature-tour 4/9.8). Set the axes' own + # cycle to the palette -- CONTINUING it past the datasets + # an earlier hypertools call drew there, so composing two + # calls on one axes does not draw both in the first colour + # (the plotly `ax=<figure>` path keeps the same count). + # `_palette_continuation` continues it without repeating an + # earlier call's colour, from the colours those slots took. + if not (isinstance(palette, collections.abc.Mapping) + or _looks_like_dataset_palettes(palette)): + _palette_offset = int(getattr( + ax, '_hyp_palette_offset', 0) or 0) + _palette_used = getattr(ax, '_hyp_palette_used', None) + _cycle = _palette_continuation( + palette, len(xform), _palette_offset, _palette_used) + ax.set_prop_cycle(color=_cycle or _palette_continuation( + palette, 1)) + if _color_scales_from_cycle: + # what each dataset is drawn in: its fmt= colour letter + # (which takes no cycle slot) or the next cycle colour + _cycle_iter = iter(_cycle) + _drawn_colors = [] + for _i in range(len(xform)): + _letter = _fmt_color_letter( + draw_fmt[_i] if _i < len(draw_fmt) else None) + _drawn_colors.append(_letter if _letter is not None + else next(_cycle_iter, None)) + if None not in _drawn_colors: + _sync_color_scales(_drawn_colors, colors_info, + colorbar_info) # Font, applied AFTER sns.set_style (which sets its own font # rcParams). A LIST gives matplotlib >= 3.6 PER-GLYPH fallback, so # text mixing scripts renders fully instead of showing "tofu" for @@ -9319,6 +12568,17 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): plt.rcParams['axes.unicode_minus'] = False # draw the plot + # a legend exists when `legend=` asked for one OR a mixture + # `hue=` built one from swatches (which clears `legend` on + # the way; Codex round 4: those legends lost their forecast + # and truth entries) + _legend_present = (legend is not None + or _final_legend_entries is not None) + # the lines an earlier call left on a reused `ax=`: the + # overlays below take their style from THIS call's lines only + # (Codex round 4: every forecast on a thrice-drawn axes wore + # the first call's colour) + _n_lines_before = len(ax.lines) if ax is not None else 0 fig, ax, data, line_ani = _draw( xform, fmt=draw_fmt, @@ -9364,13 +12624,35 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): title_kwargs=_title_kwargs, legend_kwargs=_legend_kwargs, legend_entries=_final_legend_entries, + legend_order=_legend_order, + # a plain colour list recolours the FINAL legend, so it is + # applied after the forecast/truth entries below when + # there are any (Codex round 3: validated too early, it + # refused three colours for a legend that would list three) legend_colors=(None if _final_legend_entries is not None + or raw_forecasts is not None + or raw_truths is not None else _legend_recolor), axis_scale=_axis_scale, xlim=_data_xlim, ylim=_data_ylim, x_date=_series_is_date, ) + if ax is not None and hasattr(ax, 'set_prop_cycle'): + # what a later `ax=<this axes>` call continues the palette + # from (see the prop-cycle block above): the slots THIS + # call's datasets took from the cycle, past the ones an + # earlier call took (`_palette_slots_consumed`). Recorded + # on hypertools' own axes too, as the plotly path records + # it on every figure, so composing into `fig.axes[0]` of + # a plain `hyp.plot` figure continues the palette exactly + # as composing into a `hyp.subplots` cell does (round 7) + ax._hyp_palette_offset = (_palette_offset + + _palette_slots_taken) + # ...and the colours of those slots + ax._hyp_palette_used = _extend_palette_used( + _palette_offset, _palette_used, + _cycle[:_palette_slots_taken]) # A caller-supplied ax= was created outside this rc context, so # its tick labels carry the 'sans-serif' ALIAS, which matplotlib @@ -9402,9 +12684,11 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): # (camera-only). Time-progressing modes get the per-frame artist # built below instead -- drawing both would put a frozen # full-history forecast on screen from frame 0. + _forecast_artists = None if raw_forecasts is not None and animate in (False, None, 'spin'): _forecast_artists = _draw_forecast_overlays( ax, raw_forecasts, antialias=antialias, + src_lines=list(ax.lines)[_n_lines_before:], owner=_forecast_owner, overrides=_forecast_overrides, labels=_forecast_labels, dataset_index=_model_forecast_owner) @@ -9419,13 +12703,10 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): _artist.set_clip_on(False) # truth= (GH #285): the ACTUAL continuation, beside the - # forecast. Drawn in FULL for every mode -- static, 'spin', and - # the time-progressing ones. It is not a prediction being made - # as the reveal advances but the thing the predictions are - # measured against, so it stays put while the forecast is - # refitted frame by frame; that also keeps both backends - # identical, since plotly's frames rewrite only the traces - # `_add_animation` owns. + # forecast. Drawn in FULL here; a time-progressing animation + # then reveals it row by row as the drawn forecast reaches it + # (`_truth_reveal`, the updater registered below -- plotly's + # frames rewrite the truth traces from the same table). _truth_artists = None if raw_truths is not None: # `_forecast_owner` maps a forecast to the RUN it @@ -9436,8 +12717,9 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): # too. None means the un-regrouped identity. _truth_artists = _draw_truth_overlays( ax, raw_truths, antialias=antialias, + src_lines=list(ax.lines)[_n_lines_before:], owner=_forecast_owner, - label=('truth' if legend is not None else None)) + label=('truth' if _legend_present else None)) if animate: # ANIMATED plots only, exactly like the forecast # artists: `animate_plot3D` stretches the axes to the @@ -9455,17 +12737,41 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): for _artist in _truth_artists: _artist.set_clip_on(False) - # a forecast/truth overlay with a real legend label (the - # multi-model form, or truth=) arrives AFTER `_draw` built the - # legend from the data lines, so the legend is rebuilt to - # include it. Every other call is untouched: without a label - # there is nothing new to list. - if ((_forecast_labels is not None or raw_truths is not None) - and legend is not None and ax.get_legend() is not None): + # a forecast (listed under its model's name) or truth= overlay + # arrives AFTER `_draw` built the legend from the data lines, + # so the legend is rebuilt to include it -- with the SAME + # placement/styling call `_draw` used. The time-progressing + # modes add their live forecasts' entries below, once those + # artists exist. + # ...unless `legend_colors=[(label, color), ...]` defined the + # entries outright: an explicit legend is exactly what was + # asked for (Codex round 3) + if (_legend_present and _legend_entries is None + and (_forecast_artists or _truth_artists)): from .matplotlib_backend import legend_call_kwargs - ax.legend(**legend_call_kwargs( - is_3d=hasattr(ax, 'get_proj'), zlabel=zlabel, - font=_artist_font, legend_kwargs=_legend_kwargs)) + _add_overlay_legend_entries( + ax, _forecast_artists, _truth_artists, + **legend_call_kwargs( + is_3d=hasattr(ax, 'get_proj'), zlabel=zlabel, + font=_artist_font, legend_kwargs=_legend_kwargs)) + if (_legend_recolor is not None and legend is not None + and _final_legend_entries is None + and (raw_forecasts is not None or raw_truths is not None) + and animate in (False, None, 'spin') + and ax.get_legend() is not None): + # the deferred plain-colour-list recolouring (see the + # `legend_colors=` argument to `_draw` above) + _recolor_overlay_legend( + ax, _legend_recolor, + close_fig=None if _user_supplied_ax else fig) + + # a time-progressing animation reveals each truth row on the + # first frame whose drawn forecast reaches it (see + # `_truth_reveal`); registered before the forecast updater, and + # INTERNAL so an on_frame= callback sees this frame's truth + if _truth_artists and _truth_reveal is not None: + _frame_hooks.add_internal( + _truth_frame_updater(_truth_artists, _truth_reveal)) # ...and the time-progressing modes get one LIVE artist per # dataset instead, refilled every frame from the precomputed @@ -9479,7 +12785,15 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): # its colour from forecast i-1. (Same guard # `_draw_forecast_overlays` opens with.) from .forecast import trail_alpha, trail_frames - _src_lines = list(ax.lines) + _src_lines = _observed_run_lines( + list(ax.lines)[_n_lines_before:]) + # the dimensionality the AXES draws, not the requested cap + # `_display_ndims`: 2-column data under the default ndims=3 + # is drawn on a 2-D axes, where `ax.plot([], [], [])` makes + # TWO artists and the unpack below crashed (1.1 release + # review: 31 of 140 random option combinations) + _fc_ndims = (3 if getattr(ax, 'name', None) == '3d' + else min(2, xform[0].shape[1])) _live_forecast_artists = [] # [dataset][age-1] -> artist. Preallocated: allocating # artists mid-animation is what makes matplotlib animations @@ -9529,14 +12843,21 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): and _i < len(_forecast_overrides) else None), anchor_color=_fc_anchor) + # an explicit forecast_*= colour, which a continuous + # hue= must not repaint (`_apply_multicolor_lines`) + from .forecast import override_has_color as _pins + _fc_pinned = bool(_pins( + _forecast_overrides[_i] + if _forecast_overrides is not None + and _i < len(_forecast_overrides) else None)) # trails FIRST, so the live forecast draws on top of its # own fan rather than under it _row = [] for _age in range(1, _n_forecast_trail + 1): - if _display_ndims >= 3: + if _fc_ndims >= 3: _t, = ax.plot([], [], [], label='_nolegend_', **_fc_style) - elif _display_ndims == 2: + elif _fc_ndims == 2: _t, = ax.plot([], [], label='_nolegend_', **_fc_style) else: @@ -9550,6 +12871,7 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): _t.set_clip_on(False) _t.set_visible(False) _t._hyp_forecast_role = 'trail' + _t._hyp_forecast_pinned = _fc_pinned _t._hyp_forecast_age = _age # identity, like the static overlay and plotly's # meta['hyp_dataset']: the animated artists ARE @@ -9562,21 +12884,44 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): _trail_forecast_artists.append(_row) # the SAME three-way split and label # `_draw_forecast_overlays` uses. 1-D is a real branch: - # `_display_ndims` can be 1. - if _display_ndims >= 3: + # `_fc_ndims` can be 1. + if _fc_ndims >= 3: _art, = ax.plot([], [], [], label='_nolegend_', **_fc_style) - elif _display_ndims == 2: + elif _fc_ndims == 2: _art, = ax.plot([], [], label='_nolegend_', **_fc_style) else: _art, = ax.plot([], label='_nolegend_', **_fc_style) _art.set_clip_on(False) _art._hyp_forecast_role = 'live' + _art._hyp_forecast_pinned = _fc_pinned _art._hyp_forecast_dataset = ( _model_forecast_owner[_i] if _model_forecast_owner is not None else _i) + _art._hyp_forecast_label = ( + _forecast_labels[_i] if _forecast_labels is not None + and _i < len(_forecast_labels) else None) _live_forecast_artists.append(_art) + if (_legend_present and _legend_entries is None + and _live_forecast_artists): + # the live forecasts' legend entries (static parity: + # one per model name, from the artists' own styles) + from .matplotlib_backend import legend_call_kwargs + _add_overlay_legend_entries( + ax, _live_forecast_artists, None, + **legend_call_kwargs( + is_3d=hasattr(ax, 'get_proj'), zlabel=zlabel, + font=_artist_font, legend_kwargs=_legend_kwargs)) + if (_legend_recolor is not None and legend is not None + and _final_legend_entries is None + and ax.get_legend() is not None + and _forecast_artists is None): + # the deferred plain-colour-list recolouring for the + # animated modes (the static path does it below) + _recolor_overlay_legend( + ax, _legend_recolor, + close_fig=None if _user_supplied_ax else fig) # whether the user pinned this dataset's forecast colour # (`forecast_hue=`/`forecast_cluster=`/`forecast_palette=`); @@ -9599,11 +12944,15 @@ def _update_dynamic_title(ctx, _fn=_dynamic_title): # reintroduce the palette repaint; the invariant it rests on # is pinned by `test_a_continuous_hue_animation_is_NEVER_ # regrouped_into_runs`. + from .forecast import override_has_color as _has_color + # a colour letter in forecast_fmt= pins the colour as an + # explicit forecast_hue=/palette= does (Codex round 4: + # 'ro:' forecasts were repainted in the head run's colour) _override_colour = [ - bool((_forecast_overrides is not None - and _i < len(_forecast_overrides) - and isinstance(_forecast_overrides[_i], dict) - and _forecast_overrides[_i].get('color') is not None) + bool(_has_color(_forecast_overrides[_i] + if _forecast_overrides is not None + and _i < len(_forecast_overrides) + else None) or line_colors is not None) for _i in range(len(raw_forecasts))] @@ -9612,10 +12961,11 @@ def _update_forecasts(ctx, _sched=forecast_schedule, _trails=_trail_forecast_artists, _retained=_n_forecast_trail, _antialias=antialias, - _ndims=_display_ndims, + _ndims=_fc_ndims, _reveal_sched=_reveal, _lines=_src_lines, - _pinned=_override_colour): + _pinned=_override_colour, + _sources=_model_forecast_owner): def _run_colour(dataset, frame): """Decision R3: the colour of the run DRAWING the head at `frame` -- the CURRENT frame for the live @@ -9625,7 +12975,14 @@ def _run_colour(dataset, frame): animation differ from a played one).""" if _reveal_sched is None or _pinned[dataset]: return None - run = _reveal_sched.head_run(dataset, frame) + # `dataset` is the FORECAST's index (model-major + # for a collection); the reveal schedule is per + # SOURCE dataset (Codex round 3: an IndexError + # for two models x hue regrouping) + _src = (_sources[dataset] + if _sources is not None + and dataset < len(_sources) else dataset) + run = _reveal_sched.head_run(_src, frame) if run is None or run >= len(_lines): return None return _lines[run].get_color() @@ -9639,8 +12996,11 @@ def _blank(art): def _fill(art, pts): if _antialias: - # documented parity with the static overlay - pts = _interp_static_line(pts) + # documented parity with the static overlay, + # markers included: a forecast_fmt= marker sits + # on the forecast's steps, not on every vertex + pts, _step = _antialias_static_line(pts) + art.set_markevery(_step_markevery(_step)) art.set_visible(True) # the SAME three-way split `_draw_forecast_overlays` # uses. A 3-D forecast artist is a Line3D: set_data @@ -9722,10 +13082,13 @@ def _fill(art, pts): precog=precog, bullettime=bullettime, antialias=antialias, total_frames=max(1, int(round(frame_rate - * duration)))) + * duration))), + frame_hooks=_frame_hooks) elif is_line(fmt): _apply_multicolor_lines(ax, xform, line_colors, - kwargs_list) + kwargs_list, + row_counts=[len(r) for r in + raw_xform]) elif has_line_component(fmt): # marker+line combo fmt (e.g. 'o-') with continuous/ # matrix hue (GH #141 x F02-004): keep BOTH components @@ -9737,10 +13100,15 @@ def _fill(art, pts): # interpolated point (~45x more "data points" than # exist). _apply_multicolor_lines(ax, xform, line_colors, - kwargs_list) + kwargs_list, + row_counts=[len(r) for r in + raw_xform]) _marker_colors = _multicolor_line_colors( multicolor_hue, pre_interp_lengths, raw_xform, - palette, is_rgb=multicolor_hue_is_rgb) + palette, is_rgb=multicolor_hue_is_rgb, + # the line colours above already warned about + # any non-finite hue observation + warn_non_finite=False) _apply_multicolor_markers(ax, raw_xform, _marker_colors, kwargs_list, fmt=fmt) else: @@ -9796,15 +13164,19 @@ def _hyp_frame_with_hooks(num, *fargs, # leave zero room above the axes box, which IS the figure's own # top edge there (see _reserve_animated_3d_title_margin's # docstring for the full root-cause evidence). Gated on - # "will a title actually be drawn" (scalar OR per-segment, same - # condition the plotly fix uses) so a titleless 3-D animation - # keeps the exact same maximised canvas as before -- and on - # ndims >= 3 so 2-D animations (whose default, non-maximised - # axes already leave normal title room) are never touched. + # "can a title be drawn" -- scalar, per-segment or dynamic + # title=, or an `on_frame=` callback, which may set one every + # frame (the plot() docstring's own example does; 1.1 visual + # review L11: that title rendered at y=484-500 on a 480 px + # canvas) -- so a 3-D animation with neither keeps the exact + # same maximised canvas as before; and on ndims >= 3 so 2-D + # animations (whose default, non-maximised axes already leave + # normal title room) are never touched. if (ax is not None and line_ani is not None and xform[0].shape[1] >= 3 and (title is not None or _segment_titles is not None - or _dynamic_title is not None)): + or _dynamic_title is not None + or on_frame is not None)): # a dynamic (callable / `{index...}`) title has no text yet: # resolve FRAME 0's, purely to count its lines. That is a # real frame-0 context, not a synthetic one, and the @@ -9815,12 +13187,33 @@ def _hyp_frame_with_hooks(num, *fargs, # animation runs, instead of inside the first frame. _probe_title = None if _dynamic_title is not None: - _probe_title = str(_dynamic_title(FrameContext( - frame=0, n_frames=int(line_ani._save_count), - figure=fig, axes=ax, datasets=tuple(xform), - style=animate, order=order, - current_index=0 if order == 'serial' else None, - revealed_counts=tuple(1 for _ in xform)))) + try: + _probe_title = _wrap_title_text( + _resolve_dynamic_title_text( + _dynamic_title, FrameContext( + frame=0, + n_frames=int(line_ani._save_count), + figure=fig, axes=ax, + datasets=tuple(xform), + style=animate, order=order, + current_index=(0 if order == 'serial' + else None), + revealed_counts=tuple( + 1 for _ in xform))), + _title_wrap, _wrap_break) + except Exception: + # the user's title raised before the animation + # was ever handed back: detach it (no + # HyperAnimation exists to silence it) so gc does + # not later warn "Animation was deleted without + # rendering anything" about THEIR exception, and + # close the abandoned figure as the save path does. + from .hyper_animation import mark_draw_started + mark_draw_started(line_ani) + if (not show and not _user_supplied_ax + and isinstance(fig, plt.Figure)): + plt.close(fig) + raise _reserve_animated_3d_title_margin( fig, ax, fontsize=(_title_kwargs or {}).get('fontsize'), @@ -9843,7 +13236,8 @@ def _hyp_frame_with_hooks(num, *fargs, # an EARLIER legend fit; fitting the legend last, against # whatever the current layout actually is, sidesteps that. if colorbar_info is not None and ax is not None: - _add_colorbar(fig, ax, colorbar_info, font=_artist_font) + _add_colorbar(fig, ax, colorbar_info, font=_artist_font, + attached=_user_supplied_ax) # legend fitting (GH #100/#95 follow-up): a right-side (outside) # legend can overflow the figure's right edge. `tight_layout` @@ -9875,9 +13269,20 @@ def _hyp_frame_with_hooks(num, *fargs, if colorbar_info is not None: _cmap = colorbar_info.get('cmap') _cnorm = colorbar_info.get('norm') + # a panel without its own color= takes the colour of the + # trajectory it accompanies (the first drawn dataset): an + # fmt= colour letter, else the resolved per-dataset colour + # (1.1 visual review L12: it fell back to matplotlib's + # global 'C0' blue, beside a trajectory in the palette's + # red) + _companion_color = _fmt_color_letter( + draw_fmt[0] if draw_fmt else None) + if _companion_color is None: + _companion_color = _resolve_dataset_colors()[0] _panels_drawn = [ add_companion_panel(fig, spec, cmap=_cmap, norm=_cnorm, - font=_artist_font) + font=_artist_font, + default_color=_companion_color) for spec in _companion] def _update_companions(ctx, _drawn=_panels_drawn): @@ -10044,6 +13449,28 @@ def _update_companions(ctx, _drawn=_panels_drawn): from ..core.pipeline import _validate_input_hierarchy pipeline.input_hierarchy = _validate_input_hierarchy( _bundle_hierarchy) + elif _bundle_fitted_pipeline is not None: + # the pipeline analyze() fitted for THIS figure, handed back + # as-is: no second fit of manip/normalize/reduce/align. The + # cluster stage ran on the analyzed data separately (plot() + # clusters the reduced scores), so it is appended as one more + # fitted step, fit on exactly that data. + from ..core.pipeline import (Pipeline as _Pipeline, + _make_stage_step, + _validate_input_hierarchy) + bundle_pipeline = _bundle_fitted_pipeline + if _bundle_cluster_stage is not None: + _cluster_step = _make_stage_step( + 'cluster', _bundle_cluster_stage, ndims, random_state) + _cluster_step.fit_transform( + [np.asarray(_xi) for _xi in xform_data]) + bundle_pipeline = _Pipeline( + list(bundle_pipeline.steps) + [('cluster', _cluster_step)], + input_hierarchy=bundle_pipeline.input_hierarchy) + if (_bundle_hierarchy is not None + and bundle_pipeline.input_hierarchy is None): + bundle_pipeline.input_hierarchy = _validate_input_hierarchy( + _bundle_hierarchy) elif raw is not None: from ..core.pipeline import build_pipeline # the cluster stage reuses the EXACT resolved spec the @@ -10104,6 +13531,7 @@ def _update_companions(ctx, _drawn=_panels_drawn): "reduce": reduce_dict, "align": align_dict, "cluster": cluster, + "cluster_labels": _figure_cluster_labels, "impute": impute, }, "predict": None if predict is None else { @@ -10175,10 +13603,19 @@ def _build_colorbar_info(colorbar, hue, multicolor_hue, cluster, n_clusters, if multicolor_hue is not None and multicolor_hue.ndim == 1 and hue is None: vals = np.asarray(multicolor_hue, dtype=np.float64) + # NaN/inf hue values are drawn in `NAN_COLOR` and EXCLUDED from the + # colour mapping (see the hue= docstring), so they must not reach + # the range either: `np.min` over a NaN is NaN, and a colorbar (or + # bundle['colors']) spanning nan..nan is a colorbar over nothing. + finite = vals[np.isfinite(vals)] + if finite.size == 0: + raise ValueError( + "hue= has no finite values (every entry is NaN/inf), so " + "there is no value range to put on a colorbar.") return { 'kind': 'continuous', - 'vmin': float(np.min(vals)), - 'vmax': float(np.max(vals)), + 'vmin': float(np.min(finite)), + 'vmax': float(np.max(finite)), 'palette': palette, 'label': label, 'ticks': ticks, @@ -10317,10 +13754,15 @@ def _build_colors_info(hue, multicolor_hue, cluster, n_clusters, xform, if info is None: if multicolor_hue is not None: + # `categories` is documented as {label: rgb}: the legend + # entries carry the user's own colour specs ('k', '#ff00ff'), + # so normalise them the way every other path does. + from matplotlib.colors import to_rgb return {'kind': 'blend', 'palette': palette, 'cmap': None, 'norm': None, 'vmin': None, 'vmax': None, 'colors': None, 'labels': None, - 'categories': dict(legend_entries or [])} + 'categories': {str(label): tuple(to_rgb(color)) + for label, color in (legend_entries or [])}} # no grouping at all: one dataset, one colour colors = np.asarray(dataset_colors(palette, max(len(xform), 1))) return {'kind': 'discrete', 'palette': palette, @@ -10363,11 +13805,13 @@ def _apply_font_to_colorbar(cbar, font): cbar.ax.yaxis.label.set_fontproperties(font) -def _add_colorbar(fig, ax, colorbar_info, font=None): +def _add_colorbar(fig, ax, colorbar_info, font=None, attached=False): """Attach a matplotlib colorbar built from `_build_colorbar_info`'s output to `fig`/`ax` (GH #100): a continuous `ScalarMappable` for continuous hue, or a `BoundaryNorm`-segmented one (one block per - group, tick labels = group names) for discrete groups.""" + group, tick labels = group names) for discrete groups. `attached` + marks a caller-supplied `ax=` (a panel), whose colorbar must take its + room from that axes rather than widen the figure.""" from matplotlib.cm import ScalarMappable from matplotlib.colors import BoundaryNorm, ListedColormap, Normalize @@ -10396,13 +13840,39 @@ def _add_colorbar(fig, ax, colorbar_info, font=None): is not None else [str(lbl) for lbl in default_tick_labels]) label = colorbar_info['label'] - if colorbar_info['location'] == 'right': + if colorbar_info['location'] == 'right' and not attached: cbar = _add_right_colorbar(fig, ax, mappable, ticklabels=ticklabels, label=label, font=font, **tick_kwargs) else: + # `attached` (a caller-supplied `ax=`, including every `panels=` + # cell): the colorbar takes its room from THAT axes, the way + # matplotlib's own `fig.colorbar(ax=...)` does, instead of + # `_add_right_colorbar`'s figure-widening absolute placement -- + # which, repeated per panel, stacked every colorbar over the last + # panel and left `tight_layout` warning about axes it could not + # place (1.1 release review, feature tour 9.8 follow-up). + extra = ({'shrink': 0.6, 'pad': 0.04} + if colorbar_info['location'] == 'right' else {}) + if (colorbar_info['location'] == 'right' and attached + and ax.get_legend() is not None): + # the panel's legend hangs outside its right edge; pad the + # colorbar past it, by the legend's measured overhang in + # axes widths (Codex round 3: every panel's legend overlapped + # its colorbar by ~10 px) + try: + fig.canvas.draw() + renderer = fig.canvas.get_renderer() + legend_box = ax.get_legend().get_window_extent(renderer) + axes_box = ax.get_window_extent(renderer) + overhang = (legend_box.x1 - axes_box.x1) / max( + axes_box.width, 1.0) + if overhang > 0: + extra['pad'] = 0.04 + overhang + 0.02 + except Exception: # noqa: BLE001 - a canvas that cannot draw yet + pass cbar = fig.colorbar(mappable, ax=ax, location=colorbar_info['location'], - **tick_kwargs) + **extra, **tick_kwargs) if ticklabels is not None: cbar.set_ticklabels(ticklabels) if label: @@ -10871,9 +14341,15 @@ def _contains_string(el): return False -def _multicolor_line_colors(hue_src, orig_lengths, xform, palette, is_rgb=False): +def _multicolor_line_colors(hue_src, orig_lengths, xform, palette, is_rgb=False, + warn_non_finite=True): """Per-point RGB colors for multicolored lines. + Non-finite hue values are drawn gray (`colors.mat2colors`); one + ``UserWarning`` counting the non-finite ORIGINAL observations, attributed + to the caller, is issued unless `warn_non_finite` is False (a second call + for the same hue, e.g. the marker colours of an ``'o-'`` combo). + hue_src holds one value (or one row) per ORIGINAL observation; the trajectories in xform have since been interpolated to a higher temporal resolution, so each dataset's hue values are linearly re-interpolated to @@ -10910,9 +14386,29 @@ def _multicolor_line_colors(hue_src, orig_lengths, xform, palette, is_rgb=False) if is_rgb: colors = np.clip(stacked, 0.0, 1.0) else: - colors = mat2colors( - stacked.ravel() if stacked.shape[1] == 1 else stacked, - palette=palette) + # `mat2colors` warns about the non-finite rows it is handed -- here + # the INTERPOLATED vertices, every one a NaN observation's + # neighbourhood touches (one NaN in 30 rows reported "61 + # observation(s)"), attributed to this module rather than the + # caller. Silence it and say it once, below, about the + # observations (1.1 release review). + with warnings.catch_warnings(): + warnings.filterwarnings( + 'ignore', message=r'\d+ observation\(s\) have non-finite', + category=UserWarning) + colors = mat2colors( + stacked.ravel() if stacked.shape[1] == 1 else stacked, + palette=palette) + if warn_non_finite: + _n_bad = int(np.count_nonzero(~np.isfinite(hue_src).all(axis=1))) + if _n_bad: + from .colors import NAN_COLOR + warnings.warn( + f"{_n_bad} observation(s) have non-finite (NaN/inf) " + f"hue/color values; they are drawn in a neutral gray " + f"{NAN_COLOR} and excluded from the color mapping (the " + "remaining observations keep their full color range).", + UserWarning, stacklevel=external_stacklevel()) out, start = [], 0 for xi in xform: @@ -10921,9 +14417,14 @@ def _multicolor_line_colors(hue_src, orig_lengths, xform, palette, is_rgb=False) return out -def _apply_multicolor_lines(ax, xform, line_colors, kwargs_list): +def _apply_multicolor_lines(ax, xform, line_colors, kwargs_list, + row_counts=None): """Replace single-color line artists with per-segment-colored - collections (matplotlib backend).""" + collections (matplotlib backend). + + `row_counts` (one per trace): the ORIGINAL row count behind each + (possibly antialiased) trace, so a 1-D trace's x stays the row index + (`row_index_x`) exactly as `matplotlib_backend`'s plot1D draws it.""" from matplotlib.collections import LineCollection from mpl_toolkits.mplot3d.art3d import Line3DCollection @@ -10949,6 +14450,10 @@ def _apply_multicolor_lines(ax, xform, line_colors, kwargs_list): # filters them (`forecast_cluster=`, a per-dataset refusal) breaks # silently -- each forecast would simply take a neighbour's colour. for _fi, _fc_line in enumerate(_kept_forecasts): + if getattr(_fc_line, '_hyp_forecast_pinned', False): + # an explicit forecast_*= colour wins over the anchor, as + # `_forecast_style_from` documents and plotly already draws + continue _ds = getattr(_fc_line, '_hyp_forecast_dataset', _fi) if _ds is not None and _ds < len(line_colors) and len(line_colors[_ds]): _anchor_color = line_colors[_ds][-1] @@ -10959,7 +14464,10 @@ def _apply_multicolor_lines(ax, xform, line_colors, kwargs_list): tkwargs = kwargs_list[i] if i < len(kwargs_list) else {} lw = tkwargs.get('linewidth') or plt.rcParams['lines.linewidth'] if xi.shape[1] == 1: - pts = np.column_stack([np.arange(xi.shape[0]), xi[:, 0]]) + _rows = (row_counts[i] if row_counts is not None + and i < len(row_counts) else xi.shape[0]) + pts = np.column_stack([row_index_x(_rows, xi.shape[0]), + xi[:, 0]]) else: pts = xi[:, :3] if is_3d else xi[:, :2] segments = np.stack([pts[:-1], pts[1:]], axis=1) @@ -11033,7 +14541,8 @@ def _update(ctx): def _apply_multicolor_animation(ax, xform, line_colors, kwargs_list, line_ani, style, chemtrails, precog, - bullettime, total_frames, antialias=True): + bullettime, total_frames, antialias=True, + frame_hooks=None): """Per-frame multicolored (continuous/matrix hue) line rendering for ANIMATED matplotlib plots (release-1.0 audit, F04-001/F05-002). @@ -11055,6 +14564,13 @@ def _apply_multicolor_animation(ax, xform, line_colors, kwargs_list, draws the full trajectory every frame (the static swap is already correct there), and 'morph' draws its own single traveling artist (the static swap would have REMOVED it -- callers skip morph entirely). + + `frame_hooks` is the `FrameHooks` registry the backend's updater just + called ``record(artists=...)`` on. Its recorded artists are the HIDDEN + single-colour lines, so the collections drawn here are swapped in for + them after every frame: `dataset_fade=` and any `on_frame=` mutation + then reach what is actually rendered. Without the swap, setting alpha + on ``ctx.artists`` changed invisible artists and rendered nothing. """ from matplotlib.collections import LineCollection from mpl_toolkits.mplot3d.art3d import Line3DCollection @@ -11235,8 +14751,19 @@ def _multicolor_frame(num, *fargs): else: ts, te = 0, trail_len # chemtrails/bullettime: from 0 _set_segments(trail_colls[i], *_aa_slice(i, ts, te)) + if frame_hooks is not None and frame_hooks.state: + recorded = frame_hooks.state.get('artists') + if recorded is not None: + frame_hooks.state['artists'] = [ + _visible.get(id(artist), artist) for artist in recorded] return result + # hidden head/trail line -> the collection drawn in its place + _visible = {id(head_lines[i]): head_colls[i] for i in range(n) + if i < len(head_lines)} + _visible.update({id(line): trail_colls[i] + for i, line in trail_lines.items()}) + line_ani._func = _multicolor_frame @@ -11250,7 +14777,7 @@ def _expand_labels(labels, old_lengths, new_lengths): lands on the nearest remaining point. Accepts flat label lists or lists nested per dataset; returns a flat list matching sum(new_lengths). """ - if any(isinstance(el, list) for el in labels): + if any(isinstance(el, (list, tuple)) for el in labels): flat = list(itertools.chain(*labels)) else: flat = list(labels) @@ -11308,6 +14835,10 @@ def _apply_multicolor_markers(ax, xform, point_colors, kwargs_list, # reorders or filters the forecasts (`forecast_cluster=`, a per-dataset # refusal). for _fi, _fc_line in enumerate(_kept_forecasts): + if getattr(_fc_line, '_hyp_forecast_pinned', False): + # an explicit forecast_*= colour wins over the anchor, as + # `_forecast_style_from` documents and plotly already draws + continue _ds = getattr(_fc_line, '_hyp_forecast_dataset', _fi) if (_ds is not None and _ds < len(point_colors) and len(point_colors[_ds])): @@ -11324,6 +14855,16 @@ def _apply_multicolor_markers(ax, xform, point_colors, kwargs_list, ms = float(tkwargs.get('markersize') or plt.rcParams['lines.markersize']) s = ms ** 2 # scatter sizes are areas in points^2 + # the trace's alpha= travels WITH the per-point colours, as on the + # line path (`_apply_multicolor_lines`): the scatter replaces the + # marker artist the alpha was set on, so hue= + alpha=0.7 markers + # drew fully opaque while the same plot without hue= honoured it + # (1.1 release review) + _alpha = tkwargs.get('alpha') + if _alpha is not None: + ci = np.asarray(ci, dtype=float) + ci = np.column_stack([ci[:, :3], + np.full(len(ci), float(_alpha))]) if xi.shape[1] == 1: ax.scatter(np.arange(xi.shape[0]), xi[:, 0], c=ci, s=s, marker=marker) @@ -11337,4 +14878,3 @@ def _mixture_name(model): """Registry name for a cluster-model spec (string or class).""" return model if isinstance(model, str) \ else getattr(model, "__name__", str(model)) - diff --git a/hypertools/plot/plotly_backend.py b/hypertools/plot/plotly_backend.py index ff595c15..f96aa68e 100644 --- a/hypertools/plot/plotly_backend.py +++ b/hypertools/plot/plotly_backend.py @@ -21,14 +21,18 @@ import contextlib import itertools +import json import os +import re import sys import threading import warnings -from .._shared.lazy_import import lazy_import, ensure_kaleido_chrome +from .._shared.lazy_import import (lazy_import, ensure_kaleido_chrome, + subprocess_env) import numpy as np + from .meshutil import (blinn_phong_vertex_colors, points_enclosed, vertex_colors_from_points) from .surface import ( @@ -43,6 +47,7 @@ ) from .density import ( DENSITY_DEFAULTS, + _padded_bounds, POOLED_COLOR, bbox_extent, density_alpha_boost, @@ -54,10 +59,91 @@ ) from .trails import (RunWindow, anim_window_bounds, broadcast_trail_flag, dataset_window_bounds, head_window_frames) -from .._shared.helpers import antialias_line, has_line_component +from .._shared.helpers import (UNIT_FRAME_LIMIT, UNIT_FRAME_SCALE, + antialias_line, has_line_component, + row_index_x) from . import morph as _morph +def _normalize_scatter3d_alpha(trace, inherited_mode=None, *, frame=False): + """Keep a Scatter3d's hue when its colours carry transparency. + + Plotly's WebGL line/marker path composites an ``rgba(...)`` colour + without premultiplying it, so a translucent colour ADDS to the white + background instead of blending with it: steelblue at alpha 0.5 renders + as (197, 255, 255), a pale cyan, rather than (162, 192, 217) (notebook + visual review 2026-09; 1.1 release review). The trace-level `opacity` + blends correctly, so: + + * UNIFORM alpha (every active colour the same) moves to `opacity` + verbatim, with the colours made opaque -- true translucency. + * NONUNIFORM alpha (a translucent line with opaque markers, per-vertex + alpha, ...) cannot be one `opacity`. The largest alpha becomes the + trace `opacity`, and every colour is first composited over the white + paper (`_blend_toward_white`, the rule this module's Mesh3d surfaces + use) by its share ``alpha / max_alpha`` of it. Over white the result + is exactly the requested colour; what it cannot express is a + lower-alpha part showing ANOTHER trace through it at its own lower + opacity -- the price of drawing the right hue. + + Only active components participate: an unused marker colour must not + prevent correcting a line. This operation is idempotent and also + accepts partial frame traces. + """ + if trace.type != 'scatter3d': + return + mode = trace.mode or inherited_mode or 'lines+markers' + components = [] + for key, token in [('line', 'lines'), ('marker', 'markers')]: + if token not in mode: + continue + obj = getattr(trace, key) + color = obj.color + if color is None: + continue + scalar = isinstance(color, str) + values = [color] if scalar else list(color) + converted, alphas = [], [] + for value in values: + match = re.fullmatch(r'rgba\(([^,]+),([^,]+),([^,]+),([^,]+)\)', + value.replace(' ', '')) if isinstance(value, str) else None + if not match: + converted.append(value) + alphas.append(1.) + else: + converted.append('rgb(' + ','.join(match.groups()[:3]) + ')') + alphas.append(float(match.group(4))) + if alphas: + components.append((obj, converted[0] if scalar else converted, alphas)) + alphas = [a for _, _, values in components for a in values] + if not alphas: + return + top = max(alphas) + if min(alphas) == top: + if top == 1 and not frame: + return + for obj, color, _ in components: + obj.color = color + trace.opacity = (1 if trace.opacity is None else trace.opacity) * top + return + # nonuniform: opacity = the largest alpha, each colour pre-blended + # toward white by its own share of it + for obj, color, values in components: + scalar = isinstance(color, str) + colors = [color] if scalar else list(color) + blended = [] + for value, alpha in zip(colors, values): + rgb = _rgb_triplet(value) if isinstance(value, str) else None + if not isinstance(rgb, tuple) or alpha == top: + blended.append(value) + continue + share = alpha / top if top > 0 else 0.0 + mixed = _blend_toward_white(np.asarray(rgb) / 255.0, share) + blended.append(_rgb_string(mixed)) + obj.color = blended[0] if scalar else blended + trace.opacity = (1 if trace.opacity is None else trace.opacity) * top + + VALID_BACKENDS = ('auto', 'matplotlib', 'plotly') # matplotlib sizes are in points; plotly sizes are in pixels. hypertools' @@ -132,6 +218,31 @@ def _add(name): # Andy). They now hang BELOW the plotting area, laid out horizontally, with the # bottom margin opened up so nothing is clipped. _ANIM_BUTTON_MARGIN_B = 64 # bottom margin reserved for the controls (px) +_ANIM_BUTTON_HEIGHT_PX = 30 # rendered height of the Play/Pause row (px) +_ANIM_BUTTON_GAP_PX = 6 # space above and below that row (px) + + +def _x_axis_band_px(fig, ndims): + """Height (px) of what a 2-D figure's x axis draws below the plotting + area -- tick marks and labels (two lines on a date axis) and the axis + title -- or 0 when it draws nothing there (3-D; a hidden unit-scale + axis). The Play/Pause controls go below this band. Generous by a few + px: 12 px tick labels at plotly's 1.3 line height, 5 px outside ticks, + and a title placed under the labels with plotly's automatic standoff. + """ + if ndims >= 3: + return 0 + axis = fig.layout.xaxis + if axis is None or axis.visible is False: + return 0 + band = 0 + if axis.showticklabels: + lines = 2 if axis.type == 'date' else 1 + band += 5 + 4 + lines * 16 + title = axis.title.text if axis.title is not None else None + if title: + band += 30 if band else 22 + return band CUBE_LINEWIDTH_PT = 1.5 # hypertools' frame linewidth, matching the # matplotlib backend's ~2px frame (both the 3D # wireframe cube and the 2D square) @@ -144,6 +255,20 @@ def _add(name): # the kaleido/Chrome renderer (which also produces every exported image and the # docs gallery); the exact factor is not critical -- 1.3-1.7 all land on 2px. _CUBE_GL_WIDTH_BOOST = 1.5 +# DATA lines in 3-D (every Scatter3d line hypertools draws but the cube: +# trajectories, trails, forecasts, truth) are asked for at their true width +# times this. Measured 2026-09-11 in kaleido (ink area / stroke length, a +# straight line, device scale 1 and 2): Scatter3d draws EXACTLY 0.50x the +# requested width from 1.4 to 12 px -- 2.08 px asked, 1.00 drawn -- while +# the SVG 2-D line draws what it is asked for. 1.1 release review (L1): a +# 3-D data line was half as thick as the same line in 2-D and matplotlib. +# The legend key is unaffected (`legend.itemsizing='constant'` draws every +# key line at plotly's fixed width). +_GL_LINE_WIDTH_BOOST = 2.0 +#: `plot()`'s documented default `linewidth` for ANIMATIONS (points) -- +#: what the matplotlib backend's animators pop (`linewidths = [... .pop( +#: "linewidth", 1)]`); static plots use `DEFAULT_LINEWIDTH_PT` +DEFAULT_ANIM_LINEWIDTH_PT = 1.0 # matplotlib's '.' and ',' marker glyphs are defined with HALF the path # scale of every other marker character (verified via @@ -163,15 +288,14 @@ def _add(name): # matplotlib's animate='morph' traveling point cloud always draws with # marker='.' and, when no explicit `markersize=` kwarg is given, a smaller -# default of 1.5pt -- NOT the general `DEFAULT_MARKERSIZE_PT` (6.0) used -# everywhere else -- see `matplotlib_backend.animate_plot3D`'s -# `morph_markersize = _mkw.get("markersize") or 1.5`. Without matching -# both that smaller default AND the `_DOT_MARKER_SCALE` above, plotly's -# default morph dots rendered ~8x fatter than matplotlib's (6.0 vs 1.5, -# doubled again for the missing dot-marker scale) -- this was the more -# severe half of the R2 bug (see +# default -- `morph.MORPH_DEFAULT_MARKERSIZE_PT` (4pt), NOT the general +# `DEFAULT_MARKERSIZE_PT` (6.0) used everywhere else. Both backends read +# that one constant. Without matching both that smaller default AND the +# `_DOT_MARKER_SCALE` above, plotly's default morph dots rendered far +# fatter than matplotlib's -- the more severe half of the R2 bug (see # `docs/images/v1.0-seven-features/morph_anim_plotly.png` before the fix). -MORPH_DEFAULT_MARKERSIZE_PT = 1.5 +# (1.1 visual review, L9: the old shared 1.5pt drew sub-pixel dots here.) +MORPH_DEFAULT_MARKERSIZE_PT = _morph.MORPH_DEFAULT_MARKERSIZE_PT # plotly's `go.Scatter3d` (WebGL/gl3d) interprets `marker.size` differently # from `go.Scatter`'s (SVG, 2-D) -- empirically verified (see @@ -190,6 +314,33 @@ def _add(name): # fatter than the already-corrected 2-D/mpl-matching size. _SCATTER3D_SIZE_FACTOR = 1.776 +# ...and a per-point size ARRAY in `go.Scatter3d` (plotly's "bubble" form, +# which hypertools' observation markers use -- `_observation_marker`) +# renders at HALF the diameter of the same SCALAR size. Measured +# 2026-10-08 in kaleido (isolated markers, scale 4): scalar 8/16/32/64 -> +# 14.25/28.75/57/114 px, the same values as a size array -> 7.25/14.25/ +# 28.25/57 px (ratio 1.97-2.02 for every size from 4 to 64). `go.Scatter` +# (2-D, SVG) draws an array size exactly like a scalar one. So a 3-D size +# array is filled with the scalar size times this, or 'o-' markers in 3-D +# render at half matplotlib's diameter -- 2 px dots hidden under the +# line they mark (1.1 release review). `_scalar_marker_size` undoes it +# where an array is folded back into one scalar size. +_SCATTER3D_ARRAY_SIZE_BOOST = 2.0 + + +def _array_marker_size(size, ndims): + """The value a per-point `marker.size` ARRAY entry takes for a marker + whose scalar `marker.size` is `size` (see `_SCATTER3D_ARRAY_SIZE_BOOST`).""" + size = float(size or 0) + return size * _SCATTER3D_ARRAY_SIZE_BOOST if ndims >= 3 else size + + +def _scalar_marker_size(sizes, ndims): + """The scalar `marker.size` that draws like the largest entry of the + per-point size array `sizes` (the inverse of `_array_marker_size`).""" + size = float(np.max(sizes)) + return size / _SCATTER3D_ARRAY_SIZE_BOOST if ndims >= 3 else size + # matplotlib format-string characters -> plotly marker symbols. This MUST # cover every marker character matplotlib's fmt grammar accepts (the printable # keys of matplotlib.lines.Line2D.markers): a missing entry makes _parse_fmt @@ -250,24 +401,46 @@ def resolve_backend(backend): # import avoids a plotly_backend <-> backend import cycle at module load. from . import backend as _backend preferred = getattr(_backend, 'PREFERRED_RENDER_BACKEND', None) - if preferred == 'plotly' and _has_plotly(): + if preferred == 'plotly' and _plotly_usable(): return 'plotly' if preferred == 'matplotlib': return 'matplotlib' - if detect_environment() in ('colab', 'kaggle') and _has_plotly(): + if detect_environment() in ('colab', 'kaggle') and _plotly_usable(): return 'plotly' return 'matplotlib' - if backend == 'plotly' and not _has_plotly(): - # installs the [interactive] extra on demand (see _shared.lazy_import) + if backend == 'plotly': + # installs the [interactive] extra on demand, or upgrades a plotly / + # kaleido older than the declared requirement (see + # _shared.lazy_import). Always asked BEFORE anything imports plotly: + # an old plotly can only be upgraded while it is not imported yet + # (review 2026-10-09; this used to `import plotly` first and ask + # only when that failed, so an old one was imported unchecked). + # After the first call this is a set lookup and a sys.modules hit. lazy_import('plotly', purpose='the plotly backend') return backend -def _has_plotly(): +def _plotly_usable(): + """For ``backend='auto'``: True when plotly is installed AND meets the + requirement hypertools declares (upgrading it on demand when it is too + old and not imported yet). + + An absent plotly is not installed here -- ``'auto'`` only picks plotly + where it already is. A plotly that is too old and cannot be upgraded + (installation off, or already imported in this process) warns with the + versions and the command, and the plot falls back to matplotlib. + """ + import importlib.util + if 'plotly' not in sys.modules \ + and importlib.util.find_spec('plotly') is None: + return False try: - import plotly # noqa: F401 + lazy_import('plotly', purpose='the plotly backend') return True - except ImportError: + except ImportError as e: + warnings.warn( + f"the plotly backend cannot be used, so this plot falls back to " + f"matplotlib: {e}") return False @@ -320,7 +493,7 @@ def _build_point_annotations(data, labels, ndims, font_family, label_alpha=0.5): return [] flat_labels = (list(itertools.chain(*labels)) - if any(isinstance(el, list) for el in labels) + if any(isinstance(el, (list, tuple)) for el in labels) else list(labels)) X = np.vstack(data) @@ -401,12 +574,60 @@ def _data_axis_layout(label, limit=None, date=False): if label is not None: layout['title'] = dict(text=label) if limit is not None: - layout['range'] = [limit[0], limit[1]] + layout['range'] = ([str(v) for v in _epoch_ms_to_iso(limit)] + if date else [limit[0], limit[1]]) if date: layout['type'] = 'date' return layout +def _epoch_ms_to_iso(values): + """Epoch-millisecond x values as NAIVE ISO-8601 date strings. + + `plot()` carries a date x axis as epoch milliseconds internally (every + stage -- antialiasing, bounds, forecasts -- needs numbers), but plotly.js + renders a NUMERIC date in the viewer's LOCAL time zone: a series that + starts 2026-01-01 00:00 drew at 19:00 Dec 31 in New York (1.1 release + review, measured with kaleido under TZ=UTC vs TZ=America/New_York). A + naive date STRING is rendered as written, in every time zone -- and the + hover label then shows the true date. Non-finite or non-numeric + entries (a legend proxy's ``None``) become ``None``. + """ + arr = np.asarray(values) + if arr.dtype.kind in 'iuf': + num = arr.astype(float).ravel() + else: + num = np.array([float(v) if isinstance(v, (int, float, np.integer, + np.floating)) + and not isinstance(v, bool) else np.nan + for v in arr.ravel()], dtype=float) + out = np.full(num.shape, None, dtype=object) + ok = np.isfinite(num) + if ok.any(): + out[ok] = np.datetime_as_string( + np.round(num[ok]).astype('int64').astype('datetime64[ms]'), + unit='ms') + return out.reshape(arr.shape) if arr.ndim else out + + +def _dates_as_iso(fig): + """Rewrite every numeric x of `fig` -- its traces, its animation + frames' traces and any x range -- from epoch milliseconds to naive ISO + strings (`_epoch_ms_to_iso`), for a date x axis.""" + def _fix(trace): + x = getattr(trace, 'x', None) + if x is not None and len(x): + trace.x = _epoch_ms_to_iso(x) + for trace in fig.data: + _fix(trace) + for frame in fig.frames: + for trace in frame.data: + _fix(trace) + _xaxis = getattr(frame.layout, 'xaxis', None) if frame.layout else None + if _xaxis is not None and _xaxis.range is not None: + _xaxis.range = [str(v) for v in _epoch_ms_to_iso(_xaxis.range)] + + def _build_aa_curves(data, fmt, antialias, morph_tags=None): """One ``(dense, step)`` pair per dataset, for DRAW-TIME line smoothing. @@ -455,6 +676,16 @@ def _aa_window(aa_curves, i, a, b): return dense[a * step:(b - 1) * step + 1] +def _truth_slice(cols, step, sizes, row, i): + """Truth ``i``'s coordinate columns (and per-vertex marker sizes) cut + to the rows `row[i]` reveals: ``k`` rows -> the seam plus rows ``1..k``, + i.e. dense vertices ``0..k * step``; 0 -> empty.""" + k = row[i] if i < len(row) else 0 + stop = k * step + 1 if k > 0 else 0 + return ([np.asarray(c)[:stop] for c in cols], + None if sizes is None else sizes[:stop]) + + def _aa_x(step, start_x, n_drawn): """The x positions accompanying a drawn 1-D window of `n_drawn` vertices whose first vertex sits at ORIGINAL row index `start_x`. @@ -482,6 +713,135 @@ def _aa_resample_colors(colors, n_orig, n_dense): return [colors[j] for j in np.round(grid).astype(int)] +def _observation_vertices(dense, raw, n_rows, aa_step): + """Indices, into a dataset's drawn (dense) curve, of its TRUE + observations. + + `dense` is the curve plotly draws: the `n_rows`-row array `plot()` hands + over, subdivided `aa_step` times per row by `_build_aa_curves`. Those + rows are the observations themselves unless `plot()` resampled them + first -- static antialiasing (`plot._interp_static_line`) and the + animation frame grid (`plot._interp_anim_line`) both do -- and `raw` + (`plot()`'s pre-resampling ``raw_xform`` rows, in the same display + space) says where the observations are. Matched from that relationship, + not from any one grid's arithmetic: + + * EXACT: when every observation is a vertex of the curve (static + antialiasing keeps each sample as an exact vertex, as does any frame + grid that contains the samples), those vertices, found in order. + * NEAREST: otherwise (a frame grid whose rows need not contain the + samples), the vertex nearest each observation's position along the + shared uniform parameter, at most half a grid row away -- the rule + the matplotlib backend's animated markers follow. + + `raw` None (or with as many rows as `n_rows`) means the rows are the + observations: every `aa_step`-th vertex. + """ + aa_step = max(int(aa_step), 1) + n_obs = None if raw is None else np.asarray(raw).shape[0] + if n_obs is None or n_obs == n_rows or n_obs < 2 or n_rows < 2: + return np.arange(n_rows) * aa_step + dense = np.asarray(dense, dtype=np.float64).reshape(len(dense), -1) + raw = np.asarray(raw, dtype=np.float64).reshape(n_obs, -1) + if raw.shape[1] == dense.shape[1]: + # EXACT: walk the curve once, taking each observation's first exact + # match at or after the previous one's (a doubling search window + # keeps this linear for any spacing) + span = float(np.nanmax(np.abs(dense))) if dense.size else 1.0 + tol = 1e-9 * max(span, 1.0) + found, j = [], 0 + for row in raw: + hit, width = None, 16 + while j < len(dense): + window = dense[j:j + width] + close = np.flatnonzero(np.abs(window - row).max(axis=1) + <= tol) + if close.size: + hit = j + int(close[0]) + break + if j + width >= len(dense): + break + width *= 2 + if hit is None: + found = None + break + found.append(hit) + j = hit + 1 + if found is not None: + return np.asarray(found, dtype=int) + # NEAREST: plot()'s resampling grids are uniform in the parameter, so + # observation k sits at row k * (n_rows - 1) / (n_obs - 1) + pos = np.arange(n_obs) * ((n_rows - 1) / (n_obs - 1)) * aa_step + return np.unique(np.rint(pos).astype(int)) + + +def _observation_marker(marker, n_vertices, vertices, ndims): + """A trace's ``marker=`` dict with a marker at every TRUE OBSERVATION of + a smoothed line and none at the vertices the smoothing added. + + `plot`'s ``antialias=`` promises that markers render at the true sample + points; a ``'o-'`` trace drawn as ONE ``lines+markers`` trace over the + dense curve would otherwise put a marker on every one of its ~900 + vertices, which draws the line as a thick tube of overlapping dots + (1.1 release review). So the size becomes a per-vertex array: the + marker's size at `vertices` (`_observation_vertices`, or a plain step + for a curve whose every `step`-th vertex is a sample) and 0 elsewhere. + Keeping ONE trace keeps the legend key (line AND marker) and every + trace index an animation addresses unchanged; an animation frame sends + the matching slice of this array (`_aa_window_sizes`). + + A per-point size array is what plotly calls a "bubble" trace, which + changes two of its defaults: markers become 70% opaque and (in 2-D) gain + a 1 px white outline. Both are pinned back to the scalar-size look here + (``opacity=1``; ``line.width=0``), so an observation marker is drawn + exactly as a marker-only trace draws it. The legend key is unaffected: + `plotly_draw` sets ``legend.itemsizing='constant'``. + + `vertices` may also be an int step (every `step`-th vertex). `marker` + is returned unchanged when every vertex is an observation (nothing was + interpolated). + """ + if marker is None: + return marker + if np.isscalar(vertices): + vertices = np.arange(0, int(n_vertices), max(int(vertices), 1)) + vertices = np.asarray(vertices, dtype=int) + vertices = vertices[(vertices >= 0) & (vertices < int(n_vertices))] + if len(vertices) >= int(n_vertices): + return marker + marker = _bubble_safe_marker(marker, ndims) + sizes = np.zeros(int(n_vertices)) + # Scatter3d draws an array size at half a scalar's diameter + sizes[vertices] = _array_marker_size(marker.get('size'), ndims) + marker['size'] = sizes + return marker + + +def _aa_window_sizes(sizes, step, a, b): + """The slice of a full-curve per-vertex marker-size array that goes with + `_aa_window`'s drawn window for ORIGINAL rows ``[a, b)`` -- the same + index arithmetic, so an animation frame's sizes line up with its + vertices.""" + step = max(int(step), 1) + if step == 1: + return sizes[a:b] + if b <= a: + return sizes[0:0] + return sizes[a * step:(b - 1) * step + 1] + + +def _bubble_safe_marker(marker, ndims): + """A copy of `marker` that looks the same once its `size` becomes a + per-point array: plotly's "bubble" defaults (70% opacity and, in 2-D, a + white outline) pinned back to an ordinary marker's. Also used for the + base of an animated trace whose FRAMES send such an array.""" + marker = dict(marker) + marker.setdefault('opacity', 1) + if ndims < 3: + marker['line'] = dict(width=0) + return marker + + def _run_window(frame_windows, idx, n_rows, num, total_frames, window_frames): """This trace's `RunWindow` at one frame. @@ -556,6 +916,49 @@ def _plotly_title_overrides(title_kwargs): return title_props, font_props +def _plotly_title_text(text): + """A title string as plotly draws it: newlines become ``<br>``. + + `plot()` promises a title renders identically on both backends, and + matplotlib breaks a line on ``'\n'``; plotly's title is HTML-ish and + draws a raw newline as nothing at all (one long line). Applied on + every plotly title path -- static, per-segment and per-frame dynamic. + """ + if text is None: + return None + return str(text).replace('\n', '<br>') + + +def _plotly_title_lines(*texts): + """The most lines any of these plotly title strings needs (``<br>`` or + ``'\n'`` separated); 1 for nothing at all.""" + n = 1 + for text in texts: + if text is None: + continue + for entry in ([text] if isinstance(text, str) else list(text)): + if isinstance(entry, str): + n = max(n, _plotly_title_text(entry).count('<br>') + 1) + return n + + +def _title_margin_top(n_lines, size_px, height_px): + """`layout.margin.t` that keeps an `n_lines`-line title of `size_px` + off the plotting area (GH #285, 1.1 release review T6). + + The title is anchored by its TOP at ``y=0.97`` of the container and + grows downward, so the margin has to hold the 3% offset plus one line + height (1.25 x the font size, plotly's line spacing) per line. The + historical single-line/default-size case keeps its exact 40px so an + un-styled figure is byte-identical to before; anything taller or + larger is measured. + """ + default_px = round(12 * PT_TO_PX) + if n_lines <= 1 and size_px <= default_px: + return 40 + return int(np.ceil(0.03 * height_px + n_lines * 1.25 * size_px + 6)) + + def _frame_title_dict(text, index, style, segment_colors): """One animation frame's `layout.title` (GH #285). @@ -564,6 +967,7 @@ def _frame_title_dict(text, index, style, segment_colors): resolved style is re-applied on EVERY frame, because a frame's layout patch replaces the title outright. """ + text = _plotly_title_text(text) if not style and not segment_colors: return dict(text=text) title = dict(style or {}) @@ -600,6 +1004,157 @@ def _plotly_legend_entry_traces(entries, ndims): return traces +def _legend_anchors_for(legend_kwargs): + """``xanchor``/``yanchor`` that follow a caller's `legend_kwargs` x/y + when the caller gave a position but no anchor. + + hypertools' default legend is anchored ``xanchor='left', + yanchor='middle'`` for its x=1.02/y=0.5 spot outside the right edge; + kept for a caller's ``{'x': 0, 'y': 1}`` those anchors put the legend's + MIDDLE on the top edge, half of it off the plot (1.1 release review). + Inside the paper the anchor follows the position by thirds (plotly's + own ``'auto'`` rule: left/bottom near 0, right/top near 1); outside it, + the legend hangs away from the plot (x > 1 -> left, x < 0 -> right, + y > 1 -> bottom, y < 0 -> top). An anchor the caller gave is kept. + """ + out = {} + for axis, lo, mid, hi, key in (('x', 'left', 'center', 'right', + 'xanchor'), + ('y', 'bottom', 'middle', 'top', + 'yanchor')): + value = legend_kwargs.get(axis) + if value is None or key in legend_kwargs: + continue + try: + value = float(value) + except (TypeError, ValueError): + continue + if value > 1: + out[key] = lo + elif value < 0: + out[key] = hi + elif value <= 1 / 3: + out[key] = lo + elif value >= 2 / 3: + out[key] = hi + else: + out[key] = mid + return out + + +#: plotly trace types drawn in a 3-D `scene` (every other trace type +#: hypertools can meet in a figure lives on 2-D axes) +_SCENE_TRACE_TYPES = frozenset({'scatter3d', 'mesh3d', 'volume', + 'isosurface', 'surface', 'cone', + 'streamtube'}) + + +def _target_ndims(into): + """3 when an `ax=` target is a 3-D surface, 2 when it is 2-D axes, + None when there is nothing to tell (an empty figure). + + A `PlotlyCell` knows what it was built for; a bare figure is read from + the traces it already draws.""" + if isinstance(into, PlotlyCell): + return 3 if into.ndims >= 3 else 2 + types = {getattr(t, 'type', None) for t in getattr(into, 'data', ())} + types.discard(None) + if not types: + return None + return 3 if types & _SCENE_TRACE_TYPES else 2 + + +def _compose_scope_traces(into): + """The traces an `ax=` target already holds that a new call composes + with: every trace of a bare Figure, or the traces attached to a + `PlotlyCell`'s own legend; none for a fresh figure.""" + if into is None: + return [] + if isinstance(into, PlotlyCell): + key = cell_layout_keys(into.index)['legend'] + return [tr for tr in into.figure.data + if getattr(tr, 'legend', None) == key + or (key == 'legend' and getattr(tr, 'legend', None) is None)] + return list(getattr(into, 'data', ()) or ()) + + +def _rgb_triplet(color): + """The ``(r, g, b)`` of a plotly colour string, opacity dropped (an + ``rgba(...)``/``rgb(...)`` string as `_to_plotly_color` builds; any + other spelling is returned as itself).""" + text = str(color).strip() + if text.startswith(('rgba(', 'rgb(')): + parts = text[text.index('(') + 1:-1].split(',') + return tuple(round(float(p)) for p in parts[:3]) + return text + + +def _rgba_with_alpha(color, alpha): + """`color` (any plotly colour string, typically the ``rgba(r,g,b,a)`` + `_to_plotly_color` builds) with its alpha replaced by `alpha`.""" + text = str(color).strip() + if text.startswith(('rgba(', 'rgb(')): + parts = text[text.index('(') + 1:-1].split(',') + r, g, b = (p.strip() for p in parts[:3]) + return f'rgba({r},{g},{b},{float(alpha)})' + return _to_plotly_color(color, alpha) + + +def _forecast_legend_traces(specs, ndims): + """One data-free legend trace per distinct forecast label -- the plotly + twin of `hypertools.plot.plot._forecast_legend_handles`. + + `specs` is ``[(label, line, alpha[, mode, marker]), ...]``, one per + forecast trace that carries a legend label (its model's name), in + trace order; `line` is the trace's ``line=`` dict (colour with the + forecast alpha baked in, width, dash), and `mode`/`marker` the + trace's drawing mode and marker dict when `forecast_fmt=` added + markers (`_forecast_marker`). Each entry wears the first such forecast's line style, + and its colour when every forecast under that label shares one (a + single dataset, or a `forecast_palette=` that colours by model); + otherwise `forecast.FORECAST_LEGEND_COLOR` at the forecast's alpha -- + the entry then stands for the model's dash, not for any one dataset. + The traces carry ``meta['hyp_legend_entry'] = <label>`` and NO + ``hyp_forecast_role`` -- they are legend keys, not forecasts, and a + reader pairing forecast traces with the matplotlib artists by role + must not count them. + """ + import plotly.graph_objects as go + from .forecast import (FORECAST_LEGEND_COLOR, FORECAST_LEGEND_MIN_ALPHA, + group_forecast_labels) + traces = [] + for label, members in group_forecast_labels([s[0] for s in specs]): + first_line = specs[members[0]][1] + # legible whatever the forecasts' own alpha (matplotlib parity: + # `plot._forecast_legend_handles` floors it the same way) + alpha = max([FORECAST_LEGEND_MIN_ALPHA] + + [float(specs[k][2]) for k in members]) + line = dict(first_line) + line['width'] = max(float(specs[k][1].get('width') or 0) + for k in members) or first_line.get('width') + # the same colour at different opacities is ONE colour (Codex + # round 3: alpha=[1, .4] made every all-red key gray) + if len({_rgb_triplet(specs[k][1].get('color')) + for k in members}) != 1: + line['color'] = _to_plotly_color(FORECAST_LEGEND_COLOR, alpha) + else: + line['color'] = _rgba_with_alpha(first_line.get('color'), alpha) + # the key draws the forecasts' markers too (a `forecast_fmt='o:'`) + mode = specs[members[0]][3] if len(specs[members[0]]) > 3 else 'lines' + marker = specs[members[0]][4] if len(specs[members[0]]) > 4 else None + common = dict(mode=mode, name=str(label), showlegend=True, + hoverinfo='skip', line=line, + meta=dict(hyp_legend_entry=str(label))) + if marker is not None: + common['marker'] = dict(marker, color=line['color']) + if ndims >= 3: + traces.append(go.Scatter3d(x=[None], y=[None], z=[None], + **common)) + else: + traces.append(go.Scatter(x=[None], y=[None], **common)) + return traces + + def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, title=None, animate=False, size=None, show=True, save_path=None, frame_rate=30, duration=30, rotations=1, @@ -621,8 +1176,10 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, into=None, title_kwargs=None, title_segment_colors=None, legend_kwargs=None, legend_entries=None, axis_scale='unit', xlim=None, ylim=None, x_date=False, - truths=None, forecast_labels=None, - forecast_datasets=None): + truths=None, truth_reveal=None, forecast_labels=None, + forecast_datasets=None, datasets_drawn=None, + legend_explicit=False, raw_data=None, frame_kwargs=None, + trace_names=None, row_counts=None, before_show=None): """Render grouped datasets with plotly, mirroring _draw's contract and the matplotlib renderer's appearance. @@ -638,7 +1195,7 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, which case these are the pipeline's own (unscaled) coordinates. axis_scale : {'unit', 'data'} GH #285. 'unit' (default, and everything before it) draws the frame - square and pins both 2-D axes to (-1.1, 1.1). 'data' draws no + square (half-width `UNIT_FRAME_SCALE`) and pins both 2-D axes to +-`UNIT_FRAME_LIMIT`. 'data' draws no square, leaves the axes visible with real ticks, and takes its ranges from `xlim`/`ylim` (or plotly's autorange when both are None) -- the matplotlib backend's `frame_2d` under plotly's @@ -650,13 +1207,70 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, x_date : bool The x values are epoch MILLISECONDS (what `plot()`'s ndims=1 series mode emits for a `DatetimeIndex` under the plotly backend); marks - the x axis `type='date'` so plotly renders real dates. + the x axis `type='date'` so plotly renders real dates, and hands + every trace x (frames included) and the x range to plotly as naive + date strings (`_dates_as_iso`), so the figure draws the same dates + in every viewer's time zone. + row_counts : list of int or None + The ORIGINAL row count behind each trace of `data` (`plot()` + antialiases static lines upstream, so a trace can hold more drawn + vertices than rows). A 1-D trace puts the row index on x, so its + vertices -- and the forecast/truth that continue it -- are placed + in ROW units (`row_index_x`), matching matplotlib's plot1D. `None` + treats every vertex as a row (the pre-1.1 x). truths : list of numpy.ndarray or None GH #285. One seam-prepended ACTUAL continuation per drawn trace (`plot`'s `truth=`), already in display space. Drawn as one solid, fully-opaque, marked trace per dataset, tagged ``meta['hyp_forecast_role'] = 'truth'`` -- the plotly half of `plot._draw_truth_overlays`. + truth_reveal : list of list of int or None + A time-progressing animation's ``counts[frame][i]`` + (`forecast.truth_reveal_counts`): how many held-out rows truth ``i`` + shows on each frame (0 hides it). The base trace holds frame 0's + state and every forecast-drawing frame rewrites it; the 'truth' + legend entry is then always a data-free key, so it never flickers. + None (static plots, 'spin') draws every truth in full. + trace_names : list of str or None + The name of every entry of `data` -- what its hover label shows -- + from `plot._plotly_hover_names`: the label its legend entry shows or + would show under ``legend=True`` (a category for every run of it, a + hierarchy's top-level group for its leaves, a series' column, else + the dataset number), or None for a lone unlabelled dataset, which is + then drawn with no hover name box at all (`_hover_identity`). A name + shared by several traces becomes their `legendgroup`. Whether a + legend entry is DRAWN stays decided by `legend`. `None` (a direct + caller) keeps the historical naming (legend labels only). + raw_data : list of numpy.ndarray or None + The PRE-resampling observations, one per entry of `data`, in the + same display space (`plot()`'s ``raw_xform`` -- the matplotlib + backend's ``raw_data=``). `plot()` densifies a static line + (`antialias=`) and resamples an animated one onto its frame grid + before either backend sees it, so only this says where the true + observations are: a marker+line fmt (``'o-'``) marks exactly those + (the nearest frame-grid vertex, in an animation) and never the + interpolated vertices; a continuous `hue=` line in 1-D/2-D draws its + markers at these rows. `None` treats the rows of `data` as the + observations. + legend_explicit : bool + Whether `legend_entries` came from a caller's + ``legend_colors=[(label, color), ...]`` -- an explicit legend that + the forecast/truth entries stay out of -- rather than from a + mixture `hue=`'s automatic swatches, which they are added to (Codex + round 4: mixture legends lost their forecast and truth entries). + datasets_drawn : int or None + How many datasets the figure holds once this call's are added -- + recorded as ``layout.meta['hyp_datasets_drawn']`` (before the + figure is saved or shown, so a displayed figure carries it) for a + later ``ax=<this figure>`` call to continue the palette from. + Drawing into a `PlotlyCell` records it per cell instead + (``layout.meta['hyp_cell_datasets_drawn'][str(index)]``): each cell + keeps its own count, like a matplotlib axes of its own. + before_show : callable or None + Called as ``before_show(fig)`` with the finished figure, before it + is saved or shown -- so whatever `plot()` records on it (the + palette colours beside `datasets_drawn`, legend ranks) is in the + displayed and saved figure too. forecast_datasets : list of int or None GH #285. Which SOURCE DATASET each forecast belongs to, for ``meta['hyp_dataset']``. `None` means "forecast i is dataset i"; the @@ -722,7 +1336,14 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, Past+future trail flag(s), per trace. Same `animate='serial'` support as `chemtrails` above. zoom : float - 3-D camera zoom factor. + 3-D camera zoom factor, for ANIMATIONS only (as `plot()` documents + it, and as the matplotlib backend applies it); a static figure keeps + the default view. + frame_kwargs : dict or None + `plot()`'s ``frame_kwargs=`` -- matplotlib keywords for the cube + (`plot_wireframe`) or square (`Rectangle`) frame, mapped onto the + plotly frame by `_frame_style` (colour, width, dash, alpha, 2-D + fill); unmappable keys are named in a warning. forecasts : list of numpy.ndarray or None predict= forecast traces (see below). ONE PER INPUT DATASET, which after `hue=`/`cluster=` regrouping is NOT one per drawn trace. @@ -866,6 +1487,9 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, only the drawn coordinate arrays change. Marker-only styles (e.g. 'o', '.') are never touched, so their markers stay on the true samples, and `animate='morph'` (traveling point CLOUDS, not lines) is excluded too. + A marker+line style ('o-') keeps its markers on the true samples as + well: its one trace gets a per-vertex marker size that is zero at every + interpolated vertex (`_observation_marker`, located via `raw_data`). `antialias=False` reproduces the pre-antialias figure exactly (same traces, same frames, same coordinate arrays). @@ -1002,6 +1626,14 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, fmt = fmt if fmt is not None else ['-'] * len(data) kwargs_list = kwargs_list if kwargs_list is not None else [{}] * len(data) + if animate: + # an animation's default width is 1 pt (`plot()`'s `linewidth` + # docstring, and what matplotlib's animators draw), not the static + # 1.5 -- set per dataset so the head, its trail and its forecast + # all inherit it; an explicit `linewidth=` still wins + kwargs_list = [ + dict(kw or {}, linewidth=(kw or {}).get('linewidth') + or DEFAULT_ANIM_LINEWIDTH_PT) for kw in kwargs_list] # chemtrails/precog/bullettime (GH #127): normalize to one bool per # dataset. `plot.py` already broadcasts/validates against the FINAL @@ -1015,6 +1647,35 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, ndims = data[0].shape[1] if data[0].ndim > 1 else 1 + # `frame_kwargs=`: the cube/square's colour, width, dash (and 2-D fill) + _frame = _frame_style(frame_kwargs, ndims) + + # ax= (`into`) must be the same KIND of surface as this plot: a 3-D + # scene for 3-D data, 2-D axes for 1-/2-D data. A mismatched grid cell + # used to die in `transplant_panel` with a bare "cannot unpack + # non-iterable NoneType" (its 3-D domain read off 2-D axes, or the + # reverse), and a mismatched figure silently overlaid a 2-D trace on a + # 3-D scene -- the plotly half of `plot()`'s matplotlib `ax=` check + # ("If passing ax and the plot is 3D, ax must also be 3d"), checked + # before anything is drawn (1.1 release review) + if into is not None: + _into_nd = _target_ndims(into) + if _into_nd is not None and (_into_nd >= 3) != (ndims >= 3): + _kind = ('cell of a hyp.subplots grid' if isinstance( + into, PlotlyCell) else 'plotly figure') + _this = ('3-D' if ndims >= 3 + else ('time-series (1-D)' if ndims == 1 else '2-D')) + raise ValueError( + f"ax= is a {'3-D' if _into_nd >= 3 else '2-D'} {_kind}, but " + f"this call draws a {_this} plot ({ndims} column" + f"{'s' if ndims != 1 else ''} after reduction). Pass a " + f"{'3-D' if ndims >= 3 else '2-D'} target -- " + f"hyp.subplots(..., ndims={3 if ndims >= 3 else 2}, " + "backend='plotly') for a grid, or the Figure of a " + f"{'3-D' if ndims >= 3 else '2-D'} hyp.plot -- or, if the " + f"data has the dimensions for it, pass " + f"ndims={3 if _into_nd >= 3 else 2} to draw into this one.") + # animate='morph' (Hungarian point-cloud morphs, maintainer request): # `plot.py` already raises `NotImplementedError` for 1-D (or higher # than 3-D) data before ever calling this backend; this is a defensive @@ -1027,6 +1688,16 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, "animate='morph' is only supported for 2-D or 3-D plots; got " f"{ndims}-D data." ) + # every other style: a single-column trajectory has nothing to reveal + # a path through -- the matplotlib backend's `_draw` refuses it with + # this exact message, and plotly used to animate it silently, drawing + # frame-grid row numbers as the x axis (1.1 release review). `ndims=1` + # SERIES mode is unaffected: `plot()` hands it over as (index, value) + # columns, a 2-D plot. + if animate and ndims not in (2, 3): + raise ValueError( + "Animations are only supported for 2-D or 3-D plots (got " + f"{ndims}-D data); pass ndims=2 or ndims=3 (the default).") # round17 #9 (GH #123): 'spin' rotates the 3-D camera and has no # meaning for 2-D data (2-D animations use a fixed, non-rotating @@ -1050,6 +1721,32 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # via `_aa_window`, so a dataset is never interpolated twice and the # smoothing is identical across the static figure and its frames. aa_curves = _build_aa_curves(data, fmt, antialias, morph_tags=morph_tags) + # where each dataset's TRUE observations are (see `raw_data` above); a + # list that does not pair up with `data` is ignored rather than guessed + if raw_data is not None and len(raw_data) != len(data): + raw_data = None + observations = [ + (raw_data[i] if raw_data is not None and raw_data[i] is not None + and np.asarray(raw_data[i]).ndim > 0 else None) + for i in range(len(data))] + # full-curve per-vertex marker sizes for each observation-marked data + # (and trail) trace, which every animation frame slices to its window + # (`_aa_window_sizes`); None where the marker size is a plain scalar + obs_marker_sizes = [None] * len(data) + #: dataset -> the colour-bin representation of an ANIMATED multicoloured + #: 1-D/2-D line (`_hue_line_bins`): full-curve x/y, each segment's bin, + #: and the bins' colours -- what `_add_animation` re-slices every frame + hue_units = {} + #: dataset -> its drawn curve's per-vertex line colours, for an + #: ANIMATED multicoloured 3-D line, whose frames send the window's slice + hue_colors_3d = {} + + def _rows_of(i, arr): + """The ORIGINAL row count behind drawn trace `i` (see + `row_counts`); `arr` is its drawn array.""" + if row_counts is not None and i < len(row_counts): + return int(row_counts[i]) + return np.atleast_2d(np.asarray(arr)).shape[0] # density= (GH #108/#191), 2-D case: subtle KDE density layers must # render BELOW everything else (including surface= fills). Plotly's 2D @@ -1100,40 +1797,43 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, msize = _marker_size_px( tkwargs.get('markersize') or DEFAULT_MARKERSIZE_PT, marker_char, ndims=ndims) - name = _trace_name(legend, tkwargs, i) + # `legend_name` decides the legend entry (unchanged rules); `name` + # is what the trace is CALLED -- its hover label -- which every + # data trace gets, legend or not (`trace_names`) + legend_name = _trace_name(legend, tkwargs, i) + name = (legend_name if legend_name is not None + else _hover_name(trace_names, i)) if ndims >= 3 and symbol not in _SYMBOLS_3D: symbol = _SYMBOL_3D_FALLBACK.get(symbol, 'circle') # multicolored lines: per-point colors along each trajectory. # - # TWO serializations, because the two backends treat this trace's - # `alpha=` differently and parity is stated against matplotlib, not - # against internal consistency: - # * LINES carry it. `plot._apply_multicolor_lines` replaces the line - # artist with a collection whose segment colours gain a 4th - # channel from `tkwargs['alpha']` -- an alpha left on the - # discarded artist is simply lost -- so the per-point colours are - # the only place the alpha can live here either. Serializing them - # through `_rgb_string` (which drops the 4th channel) with no - # trace `opacity` is why a hierarchy's 0.7 leaves, and a plain - # `hue=` + `alpha=`, rendered fully opaque on plotly alone. - # * MARKERS do not. `plot._apply_multicolor_markers` scatters - # `c=ci` -- the raw hue colours, with no alpha folded in - # (measured: every facecolor's 4th channel is 1.0 under - # `alpha=0.7`). Baking it in here would make plotly the ONLY - # backend dimming a hue-coloured marker. + # The trace's `alpha=` lives in these per-point colours -- for the + # LINE and the MARKERS alike, exactly as a single-coloured trace's + # `alpha=` dims both its line and its markers (the reference every + # hue path is held to). On matplotlib, `plot._apply_multicolor_lines` + # gives the segment colours a 4th channel from `tkwargs['alpha']` + # and `plot._apply_multicolor_markers` scatters with the same alpha; + # serializing the colours through `_rgb_string` (which drops the 4th + # channel) with no trace `opacity` is why a hierarchy's 0.7 leaves, + # and a plain `hue=` + `alpha=`, once rendered fully opaque on plotly + # alone. (Until the 1.1 release review the markers deliberately + # kept opaque hue colours, copying a matplotlib path that dropped + # the alpha; both backends now honour it.) In 3-D the uniform alpha + # is then moved to the trace's native `opacity` by + # `_normalize_scatter3d_alpha`, which keeps Scatter3d's hue intact. trace_point_colors = None trace_line_colors = None if point_colors is not None and i < len(point_colors) \ and point_colors[i] is not None: - trace_point_colors = [ - _rgb_string(c) for c in np.asarray(point_colors[i])] _pt_alpha = tkwargs.get('alpha') - trace_line_colors = ( - trace_point_colors if _pt_alpha is None else + trace_point_colors = ( + [_rgb_string(c) for c in np.asarray(point_colors[i])] + if _pt_alpha is None else [_to_plotly_color(c, _pt_alpha) for c in np.asarray(point_colors[i])]) + trace_line_colors = trace_point_colors # surface= (GH #109) keep_points=False: hide this dataset's own # line/marker trace so only its surface shows. @@ -1194,25 +1894,21 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, draw_arr = draw_arr.copy() grid = np.linspace(0, arr.shape[0] - 1, draw_arr.shape[0]) draw_arr[enclosed_mask[np.round(grid).astype(int)]] = np.nan - # both serializations follow the SAME resampling, so the line - # and marker colour arrays stay index-aligned with each other - # and with the drawn vertices - _n_orig, _n_dense = arr.shape[0], draw_arr.shape[0] - if trace_line_colors is trace_point_colors: - trace_point_colors = trace_line_colors = _aa_resample_colors( - trace_point_colors, _n_orig, _n_dense) - else: - trace_point_colors = _aa_resample_colors( - trace_point_colors, _n_orig, _n_dense) - trace_line_colors = _aa_resample_colors( - trace_line_colors, _n_orig, _n_dense) + # the line and marker colours follow the SAME resampling, so + # they stay index-aligned with the drawn vertices + trace_point_colors = trace_line_colors = _aa_resample_colors( + trace_point_colors, arr.shape[0], draw_arr.shape[0]) common = dict( mode=mode, name=name, - showlegend=(legend is not None and name is not None - and not str(name).startswith('_') - and not hide_points), + # explicit `legend_entries` (legend_colors=[(label, color)]) + # define the legend outright, so the data traces stay out of + # it (matplotlib parity; Codex round 3) + showlegend=(legend is not None and legend_name is not None + and not str(legend_name).startswith('_') + and not hide_points and not legend_entries), + **_hover_identity(name, trace_names, ndims), visible=not hide_points, line=dict(color=color, width=width, dash=dash), marker=dict(color=color, size=msize, symbol=symbol), @@ -1227,48 +1923,102 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # positively so a decoration added later cannot leak in. meta=dict(hyp_trace_index=i), ) + if trace_point_colors is not None: + # per-point marker colours, in every dimensionality: the 1-D + # branch used to fall through to the single `color`, so a + # marker-only continuous hue drew all 60 points in ONE palette + # colour there while matplotlib's `_apply_multicolor_markers` + # scattered them per point + common['marker'] = dict(color=trace_point_colors, + size=msize, symbol=symbol) + obs_vertices = _observation_vertices( + arr if aa_step == 1 else aa_curves[i][0], observations[i], + arr.shape[0], aa_step) + if 'markers' in mode: + # a marker on each OBSERVATION of a smoothed line, none on the + # vertices antialiasing (or the animation frame grid) added -- + # `plot`'s `antialias=` contract + common['marker'] = _observation_marker( + common['marker'], draw_arr.shape[0], obs_vertices, ndims) + if not np.isscalar(common['marker']['size']): + obs_marker_sizes[i] = common['marker']['size'] if ndims >= 3: if trace_point_colors is not None: # Scatter3d supports per-point line colors natively common['line'] = dict(color=trace_line_colors, width=width, dash=dash) - common['marker'] = dict(color=trace_point_colors, - size=msize, symbol=symbol) + hue_colors_3d[i] = list(trace_line_colors) traces.append(go.Scatter3d( x=draw_arr[:, 0], y=draw_arr[:, 1], z=draw_arr[:, 2], **common)) - elif ndims == 2: - if trace_point_colors is not None and 'lines' in mode: + continue + # 1-D: x in ROW units (`row_counts`, `row_index_x`), matching + # matplotlib's plot1D -- not the densified vertex index + xs = (draw_arr[:, 0] if ndims == 2 + else _aa_x(aa_step, 0, draw_arr.shape[0]) if aa_step != 1 + else row_index_x(_rows_of(i, arr), draw_arr.shape[0])) + ys = draw_arr[:, 1] if ndims == 2 else draw_arr[:, 0] + if trace_point_colors is not None and 'lines' in mode: + if animate: + # an ANIMATED multicoloured 2-D line is re-drawn window by + # window, so its colours must travel with it: a fixed set + # of colour-BIN traces (`_hue_bin_units`), each drawing every + # segment of its colour that the frame's window holds + # (1.1 release review: one static trace per segment left + # the whole trajectory on screen, and every frame + # overwrote segment 0 with the window in one colour) + _bins = _hue_line_bins(trace_line_colors) + hue_units[i] = dict(xs=np.asarray(xs, dtype=float), + ys=np.asarray(ys, dtype=float), + bins=_bins, alpha=tkwargs.get('alpha')) + for _k, _color in enumerate(_bins['colors']): + _bx, _by = _binned_polylines( + hue_units[i]['xs'], hue_units[i]['ys'], + _bins['seg_bin'], _k, 0, len(xs) - 1) + traces.append(go.Scatter( + x=_bx, y=_by, mode='lines', name=name, + showlegend=False, hoverinfo='skip', + visible=not hide_points, + legendgroup=name or 'multicolor', + line=dict(color=_color, width=width, dash=dash), + meta=dict(hyp_trace_index=i, hyp_hue_bin=_k))) + else: # 2D Scatter has no per-point line colors; draw short # segment traces instead (grouped under one legend entry) traces.extend(_segment_traces_2d( - go, draw_arr, trace_line_colors, width, dash, name, - trace_index=i)) - continue - if trace_point_colors is not None: - common['marker'] = dict(color=trace_point_colors, - size=msize, symbol=symbol) - traces.append(go.Scatter(x=draw_arr[:, 0], y=draw_arr[:, 1], - **common)) - else: - xs = _aa_x(aa_step, 0, draw_arr.shape[0]) - if trace_point_colors is not None and 'lines' in mode: - pts = np.column_stack([xs, draw_arr[:, 0]]) - traces.extend(_segment_traces_2d( - go, pts, trace_line_colors, width, dash, name, - trace_index=i)) - continue - if trace_point_colors is not None: - # the 1-D marker branch used to fall through to the single - # `color`, so a marker-only continuous hue drew all 60 points - # in ONE palette colour here while matplotlib's - # `_apply_multicolor_markers` scattered them per point - # (`ax.scatter(np.arange(n), xi[:, 0], c=ci, ...)`) -- the - # same per-point colours the 2-D and 3-D branches above - # already pass on. - common['marker'] = dict(color=trace_point_colors, - size=msize, symbol=symbol) - traces.append(go.Scatter(x=xs, y=draw_arr[:, 0], **common)) + go, np.column_stack([xs, ys]), trace_line_colors, width, + dash, name, trace_index=i)) + if 'markers' in mode: + # ... and, for a marker+line fmt ('o-'), the markers as ONE + # marker-only trace on the observations themselves, each in + # its own hue colour -- matplotlib's + # `_apply_multicolor_markers` scatter beside its + # LineCollection. The segments carry only the line, so + # without this the markers were silently dropped. + # (the observation vertices of the drawn curve: exactly the + # samples for a static plot, whose densified rows keep every + # one; their colours are the hue's own at those rows) + _ov = obs_vertices[obs_vertices < len(xs)] + obs_x, obs_y = xs[_ov], ys[_ov] + obs_point_colors = [trace_point_colors[j] for j in _ov] + traces.append(go.Scatter( + x=obs_x, y=obs_y, mode='markers', name=name, + showlegend=False, visible=not hide_points, + **{k: v for k, v in _hover_identity( + name, trace_names, ndims).items() + if k != 'legendgroup'}, + legendgroup=name or 'multicolor', + marker=dict(color=obs_point_colors, size=msize, + symbol=symbol), + meta=dict(hyp_trace_index=i))) + if i in hue_units: + # an animation re-draws the observations a window + # holds, each in its own colour + hue_units[i]['markers'] = dict( + vertices=np.asarray(_ov, dtype=int), + colors=list(trace_point_colors)) + continue + traces.append(go.Scatter(x=xs, y=ys, **common)) n_data_traces = len(traces) - n_surface_traces_2d - n_density_traces_2d @@ -1287,6 +2037,11 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, #: forecast trace, so a frame can repaint it in the head run's #: colour (Decision R3). Empty dict = the colour is pinned. forecast_frame_colors = [] + #: ``(label, line, alpha)`` per forecast trace that carries a legend + #: label -- `_forecast_legend_traces` turns these into one data-free + #: legend trace per distinct label, appended after every drawn trace + #: (so the frame-index bookkeeping above is untouched) + forecast_legend_specs = [] if forecasts is not None and forecast_schedule is None: # Loop over the FORECASTS (one per input dataset), not over `data` # (one per drawn RUN). `hue=`/`cluster=` regrouping makes those two @@ -1315,17 +2070,28 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # `point_colors` for the same reason it is the right index # into `kwargs_list`/`data`. anchor_color=_hue_anchor_color(point_colors, src)) - # multi-model predict= (GH #285): one legend entry per MODEL. - # Every other call keeps the historical showlegend=False -- a - # forecast that inherits its trace's identity needs no key. + # the forecast's legend entry (its model's name, GH #285) is a + # separate data-free trace built by `_forecast_legend_traces` + # from every forecast sharing the label -- so one model over + # several datasets (several colours) gets ONE neutral entry, + # not the first dataset's colour posing as the model's. The + # forecast trace itself never lists. fc_name = (forecast_labels[i] if forecast_labels is not None and i < len(forecast_labels) else None) - fc_show = bool(fc_name is not None - and fc_name not in forecast_labels[:i]) - fc_common = dict(mode='lines', showlegend=fc_show, + fc_mode, fc_marker = _forecast_marker( + tkwargs, (forecast_overrides[i] + if forecast_overrides is not None + and i < len(forecast_overrides) else None), + fc_line['color'], ndims) + if fc_name is not None: + forecast_legend_specs.append( + (fc_name, fc_line, fc_alpha, fc_mode, fc_marker)) + fc_common = dict(mode=fc_mode, showlegend=False, hoverinfo='skip', line=fc_line, + **({} if fc_marker is None + else dict(marker=fc_marker)), meta=dict( hyp_forecast_role='static', hyp_dataset=(forecast_datasets[i] @@ -1343,6 +2109,12 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # prepended first point and the final point stay exact, so it # still joins the trajectory. fc_draw, fc_step = (antialias_line(fc) if antialias else (fc, 1)) + if fc_marker is not None: + # `forecast_fmt='ro:'` marks the forecast's STEPS (and its + # seam), not every vertex of the smoothed curve -- which drew + # the dotted forecast as a solid tube of dots + fc_common['marker'] = _observation_marker( + fc_marker, fc_draw.shape[0], fc_step, ndims) if ndims >= 3: traces.append(go.Scatter3d( x=fc_draw[:, 0], y=fc_draw[:, 1], z=fc_draw[:, 2], @@ -1351,8 +2123,7 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, traces.append(go.Scatter( x=fc_draw[:, 0], y=fc_draw[:, 1], **fc_common)) else: - arr2 = np.atleast_2d(np.asarray(arr, dtype=np.float64)) - start = arr2.shape[0] - 1 + start = _rows_of(src, arr) - 1 traces.append(go.Scatter( x=_aa_x(fc_step, start, fc_draw.shape[0]), y=fc_draw[:, 0], **fc_common)) @@ -1386,7 +2157,15 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # the LIVE forecast's alpha for this dataset -- the fan decays # from THIS, not from a fixed value, so a trail can never be more # opaque than the live forecast it fades from (matplotlib parity) - live_alpha = forecast_alpha(tkwargs.get('alpha')) + from .forecast import forecast_alpha_scale_for + live_alpha = forecast_alpha( + tkwargs.get('alpha'), + # a recoloured forecast keeps its trace's alpha here too + # (Codex round 3: the animated branch still halved it) + forecast_alpha_scale_for( + forecast_overrides[i] + if forecast_overrides is not None + and i < len(forecast_overrides) else None)) # trails FIRST, so the live forecast draws on top of its own fan # rather than under it (matplotlib parity) for age in list(range(1, n_retained + 1)) + [0]: @@ -1402,18 +2181,40 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # continuous hue the run's own `line.color` is the # per-dataset palette colour, which nothing is drawn in. anchor_color=_hue_anchor_color(point_colors, _src)) + fc_mode, fc_marker = _forecast_marker( + tkwargs, (forecast_overrides[i] + if forecast_overrides is not None + and i < len(forecast_overrides) else None), + fc_line['color'], ndims) fc_common = dict( - mode='lines', showlegend=False, hoverinfo='skip', + mode=fc_mode, showlegend=False, hoverinfo='skip', line=fc_line, + # each smoothed frame sends a per-vertex size array + # marking only the forecast's steps + # (`_forecast_frame_data`), so the base marker is made + # to look the same under one + **({} if fc_marker is None else dict( + marker=(_bubble_safe_marker(fc_marker, ndims) + if antialias else fc_marker))), meta=dict( hyp_forecast_role='live' if age == 0 else 'trail', - hyp_dataset=i, hyp_forecast_age=age, + hyp_dataset=(forecast_datasets[i] + if forecast_datasets is not None + and i < len(forecast_datasets) else i), + hyp_forecast_age=age, hyp_forecast_alpha=alpha)) if ndims >= 3: traces.append(go.Scatter3d(x=[], y=[], z=[], **fc_common)) else: traces.append(go.Scatter(x=[], y=[], **fc_common)) forecast_trace_specs.append((i, age)) + if age == 0 and forecast_labels is not None \ + and i < len(forecast_labels) \ + and forecast_labels[i] is not None: + # the LIVE forecast's legend entry (static parity) + forecast_legend_specs.append( + (forecast_labels[i], fc_line, alpha, fc_mode, + fc_marker)) # Decision R3: the colour a live/retained forecast wears is # the HEAD RUN's, which changes from frame to frame. Plotly # frames carry geometry, so the colour must be resolvable @@ -1437,16 +2238,21 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # `_forecast_frame_data` never consults this map at all # (measured 2026-08-16). The anchor an animated forecast # actually wears comes from `anchor_color=` above. - _pinned = ( - (isinstance(_ov, dict) and _ov.get('color') is not None) - or _hue_anchor_color(point_colors, _src) is not None) + from .forecast import override_has_color + # a colour letter in forecast_fmt= pins the colour as an + # explicit forecast_hue=/palette= does (Codex round 4: + # 'ro:' forecasts were repainted in the head run's colour) + _pinned = (override_has_color(_ov) + or _hue_anchor_color(point_colors, _src) + is not None) forecast_frame_colors.append({} if _pinned else { _r: _forecast_style_from( kwargs_list[_r] or {}, fmt[_r], alpha=trail_alpha( age, n_retained, live_alpha=forecast_alpha( - (kwargs_list[_r] or {}).get('alpha'))), + (kwargs_list[_r] or {}).get('alpha'), + forecast_alpha_scale_for(_ov))), override=_ov)[0].get('color') for _r in range(len(data))}) @@ -1455,11 +2261,29 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # `plot._draw_truth_overlays`, with the same styling policy (the # observed trace's colour and width, SOLID, fully opaque, with markers # on the observations) and the same `hyp_forecast_role='truth'` tag. - # Appended AFTER the forecast block and BEFORE the trail traces, and - # never rewritten per frame: it is what happened, not a prediction being - # refitted as the reveal advances. + # Appended AFTER the forecast block and BEFORE the trail traces. In a + # time-progressing animation (`truth_reveal`) every forecast-drawing + # frame rewrites it to the rows a drawn forecast has reached so far. + #: ``(trace index, truth i, full coordinate columns, vertices per row, + #: per-vertex marker sizes or None)`` per truth trace an animated + #: reveal rewrites (`truth_reveal`) + truth_frame_specs = [] + _truth_key_color = None if truths is not None: from .plot import TRUTH_STYLE + # composing into a figure/cell that already lists a truth entry: + # one entry covers every call's truth (Codex round 4) + _truth_already_listed = any( + ((tr.meta or {}).get('hyp_forecast_role') == 'truth' + or (tr.meta or {}).get('hyp_legend_entry') == 'truth') + and tr.showlegend + for tr in _compose_scope_traces(into)) + # the one 'truth' key stands for EVERY dataset's truth: when they + # span several colours it is a neutral proxy (added with the + # forecast keys below), not the first truth trace, which wore + # dataset 0's colour (1.1 release review, F10; matplotlib parity) + _truth_key_at = None + _truth_rgbs = set() for i, tr in enumerate(truths): src = (forecast_owner[i] if forecast_owner is not None and i < len(forecast_owner) @@ -1472,26 +2296,73 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, tr_line = dict(tr_line) tr_line['dash'] = 'solid' tr_draw, tr_step = (antialias_line(tr) if antialias else (tr, 1)) + # a marker on every OBSERVATION, not on every vertex of the + # antialiased curve (matplotlib parity: its truth overlay + # marks the raw rows and draws the smooth line marker-free). + # Dense vertex `k * step` is where raw row k sits on the curve + # (the same convention `_aa_x` builds the 1-D x from), so the + # marker size is a per-vertex array that is 0 everywhere else + # -- one trace, so a truth stays one trace per dataset. + # (`_observation_marker` also keeps plotly's bubble defaults -- + # 70% opacity, a white outline in 2-D -- off these markers) + tr_marker = _observation_marker( + dict(size=_marker_size_px(TRUTH_STYLE['markersize'], + TRUTH_STYLE['marker'], ndims), + color=tr_line.get('color')), + tr_draw.shape[0], tr_step, ndims) tr_common = dict( - mode='lines+markers', showlegend=bool(i == 0 and legend - is not None), + mode='lines+markers', + showlegend=bool(i == 0 + and (legend is not None or legend_entries) + and not legend_explicit + and not _truth_already_listed), name='truth', hoverinfo='skip', line=tr_line, - marker=dict(size=TRUTH_STYLE['markersize'], - color=tr_line.get('color')), + marker=tr_marker, + # listed AFTER the forecast entries (which are appended as + # the last traces), the order the matplotlib legend uses: + # data, forecasts, truth + legendrank=1001, meta=dict(hyp_forecast_role='truth', hyp_dataset=i, hyp_forecast_age=0, hyp_forecast_alpha=1.0)) + _truth_rgbs.add(_rgb_triplet(tr_line.get('color'))) + if tr_common['showlegend']: + _truth_key_at = (len(traces), tr_line) if ndims >= 3: - traces.append(go.Scatter3d(x=tr_draw[:, 0], y=tr_draw[:, 1], - z=tr_draw[:, 2], **tr_common)) + tr_cols = [tr_draw[:, 0], tr_draw[:, 1], tr_draw[:, 2]] elif ndims == 2: - traces.append(go.Scatter(x=tr_draw[:, 0], y=tr_draw[:, 1], - **tr_common)) + tr_cols = [tr_draw[:, 0], tr_draw[:, 1]] else: - arr2 = np.atleast_2d(np.asarray(data[src], - dtype=np.float64)) - traces.append(go.Scatter( - x=_aa_x(tr_step, arr2.shape[0] - 1, tr_draw.shape[0]), - y=tr_draw[:, 0], **tr_common)) + tr_cols = [_aa_x(tr_step, _rows_of(src, data[src]) - 1, + tr_draw.shape[0]).astype(float), + tr_draw[:, 0]] + tr_sizes = (np.asarray(tr_marker['size'], dtype=float) + if isinstance(tr_marker, dict) + and np.ndim(tr_marker.get('size')) == 1 else None) + if truth_reveal is not None: + # a time-progressing animation: the base trace is frame + # 0's state (`_truth_frame_data` rewrites it per frame), so + # a figure shown before playback does not give the answer + # away either + truth_frame_specs.append( + (len(traces), i, tr_cols, int(tr_step), tr_sizes)) + tr_cols, _sz = _truth_slice(tr_cols, int(tr_step), tr_sizes, + truth_reveal[0], i) + if _sz is not None: + tr_common['marker'] = dict(tr_marker, size=_sz) + _xyz = dict(zip('xyz', tr_cols)) + traces.append(go.Scatter3d(**_xyz, **tr_common) if ndims >= 3 + else go.Scatter(**_xyz, **tr_common)) + if _truth_key_at is not None and (len(_truth_rgbs) > 1 + or truth_reveal is not None): + # several colours -> a neutral key; an ANIMATED reveal -> a + # data-free key in the truth's own colour, because the truth + # trace itself is empty on the frames before the forecast + # reaches it and its legend entry must not flicker + traces[_truth_key_at[0]].showlegend = False + if len(_truth_rgbs) == 1: + _truth_key_color = _truth_key_at[1].get('color') + else: + _truth_key_at = None # low-opacity trail traces for chemtrails (past) / precog (future) / # bullettime (both) on window animations, mirroring the matplotlib @@ -1500,10 +2371,12 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # set anywhere created a trail trace for EVERY dataset). These do NOT # necessarily sit right after the data traces -- forecast traces # (predict=, above) are appended in between when both are present -- so - # `trail_trace_start` records their real position, and - # `trail_dataset_indices[k]` is the ORIGINAL dataset index that produced - # `traces[trail_trace_start + k]`, so `_add_animation` can look up the - # right dataset's data per frame. + # `trail_trace_start` records their real position, and every trail trace + # carries ``meta['hyp_trail_index']`` -- the ORIGINAL dataset index that + # produced it -- so `_add_animation` can look up the right dataset's data + # per frame. A dataset's trail is ONE trace, except an animated + # multicoloured 2-D line's, which is one trace per colour bin + # (`_hue_line_bins`); `n_trail_traces` counts traces, not datasets. # # Backend parity (Task 4): 'serial' builds these too, not just # True/'parallel' -- each currently-revealing dataset traces out its own @@ -1539,14 +2412,46 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, msize = _marker_size_px( tkwargs.get('markersize') or DEFAULT_MARKERSIZE_PT, marker_char, ndims=ndims) + trail_marker = dict(color=color, size=msize) + if 'markers' in mode: + # the observations of the dataset's whole smoothed curve; every + # frame sends its trail window's slice of these sizes + _n_rows = np.atleast_2d(np.asarray(data[i])).shape[0] + trail_marker = _observation_marker( + trail_marker, aa_curves[i][0].shape[0], + _observation_vertices(aa_curves[i][0], observations[i], + _n_rows, aa_curves[i][1]), ndims) trail = dict(mode=mode, showlegend=False, hoverinfo='skip', line=dict(color=color, width=width, dash=dash), - marker=dict(color=color, size=msize)) + marker=trail_marker, + # which dataset this trail belongs to (a multicoloured + # 2-D trail is several colour-bin traces) + meta=dict(hyp_trail_index=i)) + if i in hue_units: + # a multicoloured 2-D trail: the head's colour bins at the + # trail's opacity (matplotlib's trail collection keeps the + # per-segment colours at 0.3 alpha), lines only, as matplotlib + # draws it + for _k, _color in enumerate(hue_units[i]['bins']['colors']): + traces.append(go.Scatter( + x=[], y=[], mode='lines', showlegend=False, + hoverinfo='skip', + line=dict(color=_rgba_with_alpha(_color, _trail_alpha), + width=width, dash=dash), + meta=dict(hyp_trail_index=i, hyp_hue_bin=_k))) + continue + if i in hue_colors_3d: + # a multicoloured 3-D trail: the head's per-vertex colours at + # the trail's opacity; every frame sends its window's slice + trail['line'] = dict( + color=[_rgba_with_alpha(c, _trail_alpha) + for c in hue_colors_3d[i]], + width=width, dash=dash) if ndims >= 3: traces.append(go.Scatter3d(x=[], y=[], z=[], **trail)) else: traces.append(go.Scatter(x=[], y=[], **trail)) - n_trail_traces = len(trail_dataset_indices) + n_trail_traces = len(traces) - trail_trace_start # surface= (GH #109), 3-D case: order doesn't matter here (plotly's 3-D # scene is depth-buffered, unlike 2-D's painter's-algorithm trace order), @@ -1696,7 +2601,7 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # matplotlib's morph trace always draws marker='.' (see # `MORPH_DEFAULT_MARKERSIZE_PT`'s docstring) -- so the plotly # counterpart always applies the dot-marker scale, and falls back - # to the SAME smaller 1.5pt default (not the general 6.0pt + # to the SAME smaller 4pt default (not the general 6.0pt # `DEFAULT_MARKERSIZE_PT`) when no explicit `markersize=` is given. msize0 = _marker_size_px( (kwargs_list[morph_indices_3d[0]] or {}).get('markersize') @@ -1746,10 +2651,20 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # every animation frame. if density is not None and ndims >= 3: traces.extend(_build_density_traces_3d(go, data, density, - density_colors)) + density_colors, + limit=cube_scale)) if ndims >= 3: - traces.append(_cube_trace(go, scale=cube_scale)) + # every 3-D DATA line so far (trajectories, forecasts, truth, + # trails): Scatter3d draws half the width it is asked for (see + # `_GL_LINE_WIDTH_BOOST`); the cube below carries its own boost + for _tr in traces: + if _tr.type == 'scatter3d' and _tr.line is not None \ + and _tr.line.width is not None: + _tr.line.width = _tr.line.width * _GL_LINE_WIDTH_BOOST + traces.append(_cube_trace( + go, scale=cube_scale, linewidth_pt=_frame['width_pt'], + color=_frame['color'], dash=_frame['dash'])) # colorbar (GH #100): appended LAST (after the cube trace) so it never # falls within `trace_indices = range(n_data_traces [+ n_trail_traces])` @@ -1816,7 +2731,11 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, showlegend=legend is not None or bool(legend_entries), margin=dict(l=10, r=margin_r, t=40 if (title or segment_titles) else 10, b=10), - legend=dict(bgcolor='rgba(255,255,255,0.8)', + # `itemsizing='constant'`: a legend key is drawn at plotly's fixed + # key size rather than at the trace's own marker size, so a '.' + # (2 px) marker still gets a readable dot in the key, as it does in + # a matplotlib legend (1.1 release review, feature-tour 9.8/9.16) + legend=dict(bgcolor='rgba(255,255,255,0.8)', itemsizing='constant', x=1.02, y=0.5, xanchor='left', yanchor='middle'), # layout.font is plotly's inherited default for every text surface # (legend, colorbar title/ticks, plot title, annotations) that doesn't @@ -1843,11 +2762,12 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, _title_props, _title_font_props = _plotly_title_overrides( title_kwargs) _title_font.update(_title_font_props) - layout['title'] = dict(text=title, x=0.5, xanchor='center', - xref='paper', + layout['title'] = dict(text=_plotly_title_text(title), x=0.5, + xanchor='center', xref='paper', y=0.97, yanchor='top', font=_title_font) layout['title'].update(_title_props) + _title_size_px = _title_font.get('size', round(12 * PT_TO_PX)) size = size if size is not None else DEFAULT_FIGSIZE layout['width'] = int(size[0] * 100) layout['height'] = int(size[1] * 100) @@ -1862,7 +2782,9 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, {'range': [-cube_scale, cube_scale]}, zlabel, scene=True), camera=dict(eye=_camera_eye( elev, azim, - r=_anim_zoom_r(zoom) if animate else _zoom_r(zoom))), + # `zoom=` is animation-only (plot()'s docstring; the + # matplotlib static view ignores it too) + r=_anim_zoom_r(zoom) if animate else _zoom_r(1))), # matplotlib's Axes3D uses a 4:4:3 box aspect by default; match # it so the cube renders wider than tall, exactly like the # matplotlib backend @@ -1872,9 +2794,14 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, elif ndims == 2 and axis_scale != 'data': # matplotlib stretches the 2D frame to fill the axes region (no # equal-aspect constraint), so the plotly frame does the same - layout['xaxis'] = _labeled_axis_layout({'range': [-1.1, 1.1]}, xlabel) - layout['yaxis'] = _labeled_axis_layout({'range': [-1.1, 1.1]}, ylabel) - layout['shapes'] = [_square_shape()] + layout['xaxis'] = _labeled_axis_layout( + {'range': [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT]}, xlabel) + layout['yaxis'] = _labeled_axis_layout( + {'range': [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT]}, ylabel) + layout['shapes'] = [_square_shape( + scale=UNIT_FRAME_SCALE, linewidth_pt=_frame['width_pt'], + color=_frame['color'], dash=_frame['dash'], + fill=_frame['fill'])] elif axis_scale == 'data': # GH #285: real units. No frame square, no unit range, and the axes # keep plotly's own ticks/labels -- the plotly half of @@ -1885,6 +2812,27 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, layout['xaxis'] = _labeled_axis_layout({}, xlabel) layout['yaxis'] = _labeled_axis_layout({}, ylabel) + # An ANIMATION's legend rides on data-free proxy traces (1.1 release + # review, L7): plotly omits the legend item of a trace with no points, + # and a data trace is empty until the reveal reaches it (a later + # dataset, a later cluster's first run), so its entry appeared and + # vanished frame by frame while matplotlib's legend is complete from + # frame 0. Each proxy wears its data trace's style and shares its + # `legendgroup`, so a legend click still toggles the data; the data + # traces keep their `name` for hover. + if animate and animate != 'spin': + _proxies = [] + for _k in range(data_trace_start, data_trace_start + n_data_traces): + _tr = fig.data[_k] + if not _tr.showlegend or _tr.name is None: + continue + _group = _tr.legendgroup or _tr.name + _tr.legendgroup = _group + _tr.showlegend = False + _proxies.append(_legend_proxy_for(_tr, ndims, _group)) + if _proxies: + fig.add_traces(_proxies) + # labels= (GH #205 F3): point annotations, at parity with matplotlib's # annotate_plot -- see _build_point_annotations for the exact mapping # semantics. 3-D annotations live in layout.scene.annotations (data @@ -1896,6 +2844,55 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # data-free traces, plotly's equivalent of matplotlib proxy handles. if legend_entries: fig.add_traces(_plotly_legend_entry_traces(legend_entries, ndims)) + # predict= legend entries: one per model name, after the explicit + # entries (the matplotlib legend lists them in the same order). Only + # with a legend to list in -- like the matplotlib proxies, which exist + # only on an axes that has one -- so a legend-less figure's traces are + # exactly its drawn ones. + if forecast_legend_specs and not legend_explicit and ( + legend is not None or legend_entries): + # composing into a figure/cell that already lists some of these + # models: hide the earlier keys and decide the new key's colour + # over EVERY forecast of that model in the scope (Codex round 4: + # three calls into one cell listed 'Kalman' three times) + names = {s[0] for s in forecast_legend_specs} + for tr in _compose_scope_traces(into): + meta = tr.meta or {} + if meta.get('hyp_legend_entry') in names: + tr.showlegend = False + elif (meta.get('hyp_forecast_role') in ('static', 'live') + and tr.name in names): + marker = (tr.marker.to_plotly_json() + if tr.marker is not None and tr.marker.symbol + else None) + if marker is not None and not np.isscalar( + marker.get('size', 0)): + # an observation-marked (per-vertex size) forecast: its + # legend key takes the marker's one real size + marker['size'] = _scalar_marker_size( + marker['size'], ndims) + forecast_legend_specs.append( + (tr.name, tr.line.to_plotly_json(), + meta.get('hyp_forecast_alpha'), tr.mode or 'lines', + marker)) + fig.add_traces(_forecast_legend_traces(forecast_legend_specs, ndims)) + if truths is not None and _truth_key_at is not None: + # the neutral 'truth' key (see the truth block): data-free, after + # the forecast keys, so the drawn traces' indices are untouched + from .forecast import FORECAST_LEGEND_COLOR + from .plot import TRUTH_STYLE + _gray = (_truth_key_color if _truth_key_color is not None + else _to_plotly_color(FORECAST_LEGEND_COLOR, 1.0)) + _key = dict(mode='lines+markers', name='truth', showlegend=True, + hoverinfo='skip', legendrank=1001, + line=dict(_truth_key_at[1], color=_gray), + marker=dict(color=_gray, size=_marker_size_px( + TRUTH_STYLE['markersize'], TRUTH_STYLE['marker'], + ndims)), + meta=dict(hyp_legend_entry='truth')) + fig.add_trace(go.Scatter3d(x=[None], y=[None], z=[None], **_key) + if ndims >= 3 else go.Scatter(x=[None], y=[None], + **_key)) if labels is not None: point_annotations = _build_point_annotations( @@ -1914,7 +2911,9 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, # hypertools defaults above -- the same precedence the matplotlib # backend gives it over its own `Axes.legend` defaults. if legend_kwargs: - layout['legend'] = {**layout['legend'], **legend_kwargs} + layout['legend'] = {**layout['legend'], + **_legend_anchors_for(legend_kwargs), + **legend_kwargs} fig.update_layout(**layout) @@ -1933,6 +2932,20 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, _segment_title_style = dict(x=0.5, xanchor='center', xref='paper', y=0.97, yanchor='top', font=_seg_font) _segment_title_style.update(_seg_props) + _title_size_px = _seg_font.get('size', round(12 * PT_TO_PX)) + + # a multi-line (explicit '\n', or `title_wrap=`) or enlarged title + # needs more than the 40px single-line margin, or it overlaps the + # plotting area (1.1 release review T6): reserve per line and per + # font size, exactly as the matplotlib backend's probe does. A + # dynamic (callable / pattern) title is measured over EVERY frame at + # the end of `_add_animation`, once its text exists. + if title is not None or segment_titles is not None: + _n_title_lines = _plotly_title_lines(title, segment_titles) + _needed = _title_margin_top(_n_title_lines, _title_size_px, + layout['height']) + if _needed > 40: + fig.update_layout(margin=dict(t=_needed)) if animate: _add_animation(fig, data, ndims, animate, frame_rate, duration, @@ -1948,8 +2961,11 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, forecast_trace_specs=forecast_trace_specs, forecast_frame_colors=forecast_frame_colors, forecast_reveal=forecast_reveal, + forecast_datasets=forecast_datasets, forecast_trail=forecast_trail, forecast_antialias=antialias, + truth_reveal=truth_reveal, + truth_frame_specs=truth_frame_specs, surface=surface, surface_colors=surface_colors, surface_trace_start=surface_trace_start_3d, surface_dataset_indices=surface_dataset_indices, @@ -1970,18 +2986,64 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, segment_title_colors=title_segment_colors, # the run -> dataset -> rows mapping the reveal # clock is driven from (see `_run_window`) - ownership=ownership) + ownership=ownership, + # an animated multicoloured line's colours travel + # with its window (`_hue_line_bins`) + hue_units=hue_units, hue_colors_3d=hue_colors_3d) + + # Notebook visual review 2026-09: Scatter3d's RGBA colour path can + # change hue under transparency. Use RGB + native opacity instead. + # Include frame payloads, which may override the base trace colours. + # Done HERE, on this call's own figure, before any `ax=` composition: + # a caller's figure keeps its own traces exactly as they were (1.1 + # release review: this loop used to run over the composed figure and + # rewrote the caller's rgba traces too). + for frame in fig.frames: + indices = frame.traces if frame.traces is not None else range(len(frame.data)) + for index, trace in zip(indices, frame.data): + _normalize_scatter3d_alpha( + trace, fig.data[index].mode + if fig.data[index].type == 'scatter3d' else None, frame=True) + for trace in fig.data: + _normalize_scatter3d_alpha(trace) + if x_date: + # dates as naive ISO strings, not epoch ms: plotly.js draws numeric + # dates in the VIEWER's local time zone (see `_epoch_ms_to_iso`) + _dates_as_iso(fig) if into is not None: - # `ax=<plotly Figure>`: draw INTO the caller's figure. The traces - # (data, legend and colorbar entries) are appended; the caller's - # layout is theirs to keep. if animate: raise ValueError( - "ax= (a plotly Figure) cannot be combined with animate=: an " - "animated plot builds its own figure and frames.") - into.add_traces(list(fig.data)) - fig = into + "ax= (a plotly Figure or hyp.subplots cell) cannot be " + "combined with animate=: an animated plot builds its own " + "figure and frames.") + if isinstance(into, PlotlyCell): + # `ax=<hyp.subplots(backend='plotly') cell>`: the whole drawn + # panel -- traces, axis layout, frame, annotations, its own + # legend and colorbar -- moves into that cell of the grid, the + # plotly form of drawing into one matplotlib Axes of a grid. + transplant_panel(into.figure, fig, into.row, into.col, + into.index, ndims) + fig = into.figure + else: + # `ax=<plotly Figure>`: draw INTO the caller's figure. The + # traces (data, legend and colorbar entries) are appended; the + # caller's layout is theirs to keep. + into.add_traces(list(fig.data)) + fig = into + + if datasets_drawn is not None: + _meta = fig.layout.meta if isinstance(fig.layout.meta, dict) else {} + if isinstance(into, PlotlyCell): + _cells = dict(_meta.get('hyp_cell_datasets_drawn') or {}) + _cells[str(into.index)] = int(datasets_drawn) + fig.layout.meta = {**_meta, 'hyp_cell_datasets_drawn': _cells} + else: + fig.layout.meta = {**_meta, + 'hyp_datasets_drawn': int(datasets_drawn)} + + if before_show is not None: + before_show(fig) if save_path is not None: ext = save_path.lower().rsplit('.', 1)[-1] @@ -1998,31 +3060,644 @@ def plotly_draw(data, fmt=None, kwargs_list=None, labels=None, legend=None, fig.write_image(save_path) if show: - import plotly.io as pio - if 'sphinx_gallery' in str(pio.renderers.default or ''): - # docs builds: plotly's sphinx-gallery renderer writes a static - # png AND an interactive html from the full figure, and kaleido - # serializes EVERY animation frame to render the one png -- a - # 900-frame figure took ~an hour and produced tens-of-MB pages. - # Write the pair ourselves: png from a frame-stripped snapshot, - # html with the embedded frames capped (total duration and - # rotations preserved, so pacing stays identical). - _show_sphinx_gallery(fig) - elif _in_interactive_shell(): - # Interactive notebook: display at the END of the cell (after - # matplotlib-inline's own flush, so plotly figures keep their - # place behind matplotlib ones drawn in the same cell) and only - # if the cell's rich-display hook has not already shown this - # figure as its last expression. See HyperPlotlyFigure. - _display_at_cell_end(fig) - else: - # Plain script (no IPython frontend): nothing else will display - # the figure, so show it here. - fig.show() + show_figure(fig) + + return fig + +#: Horizontal room (px) reserved beside a subplot cell for its own legend +#: and for its own colorbar -- the single-axes path widens its right +#: margin by the same amount for each (see `plotly_draw`'s `margin_r`). +PANEL_LEGEND_PX = 110 +PANEL_COLORBAR_PX = 110 +PANEL_GUTTER_PAD_PX = 8 +#: The base margin round a panel grid (the untitled single-axes figure's). +PANEL_MARGIN_PX = 10 +#: Gap between neighbouring 3-D cells: what matplotlib's `tight_layout` +#: leaves between two `Axes3D` panels (measured 2026-09-07: 20-26 px +#: between 193-297 px cells at 100 dpi). +PANEL_GAP_PX = 20 +#: Gap between neighbouring 2-D/1-D cells, wider for the tick labels an +#: `axis_scale='data'` panel draws (matplotlib: ~43 px between two 2-D +#: panels of a 2x2 grid). +PANEL_AXIS_GAP_PX = 40 +#: Room above a titled row for a one-line title: the single-axes plotly +#: figure reserves 40 px of top margin for one (`_title_margin_top`), 10 +#: of which is the base margin. +PANEL_TITLE_PX = 30 +#: Width of the drawn 3-D cube relative to its SCENE'S HEIGHT at +#: hypertools' default view. Plotly sizes a 3-D scene by its domain's +#: height alone and clips it at the domain's sides (measured 2026-09-07: +#: the same 267 px wide x 209 px tall cube in 600x300 and 1200x300 +#: scenes; 179x140 in 800x200; a 300x600 scene's cube is 415 px tall and +#: cut off at the 300 px width), so a cube is ~0.89 scene-heights wide and +#: ~0.70 tall. With a little margin, this is what `transplant_panel` uses +#: to keep a cube inside a cell narrower than it is tall. (Until the 1.1 +#: release review it was 1.4 -- the cube's width relative to its OWN +#: height rather than the scene's -- which backed the camera off ~1.5x +#: further than a narrow cell needed, and every square cell by 1.4x.) +SCENE_CUBE_WIDTH_PER_HEIGHT = 0.92 + + +def panel_gutter_px(legend_present, colorbar_present): + """Pixels to reserve to the RIGHT of every subplot cell (`panels=` and + `hyp.subplots(backend='plotly')` cells alike) so a per-panel legend + and/or colorbar sits beside its own panel instead of over the next + one.""" + px = 0 + if legend_present: + px += PANEL_LEGEND_PX + if colorbar_present: + px += PANEL_COLORBAR_PX + return px + (PANEL_GUTTER_PAD_PX if px else 0) + + +def cell_layout_keys(index): + """Plotly's layout keys for subplot cell number `index` (0-based, + row-major, as `plotly.subplots.make_subplots` numbers them): the 2-D + axis layout keys and axis ids, the 3-D scene key, and the legend key + that panel's traces are attached to (plotly >= 5.15 supports several + legends: ``layout.legend``, ``layout.legend2``, ...).""" + suffix = '' if index == 0 else str(index + 1) + return dict(xaxis=f'xaxis{suffix}', yaxis=f'yaxis{suffix}', + xref=f'x{suffix}', yref=f'y{suffix}', + scene=f'scene{suffix}', legend=f'legend{suffix}') + + +class PlotlyCell: + """One cell of a ``hyp.subplots(..., backend='plotly')`` grid -- the + plotly counterpart of the matplotlib ``Axes`` that helper returns, and + what ``hyp.plot(..., ax=cell)`` draws into (via `transplant_panel`). + + Attributes + ---------- + figure : plotly.graph_objects.Figure + The `make_subplots` grid figure the cell belongs to (the figure + `hyp.subplots` returned; every cell of one grid shares it). + row, col : int + 1-based grid position, as `make_subplots` numbers cells. + index : int + 0-based row-major cell number (``layout.xaxis``/``scene``/``legend`` + for 0, ``xaxis2``/``scene2``/``legend2`` for 1, ...). + ndims : int + The dimensionality the cell was built for (3 -> a ``'scene'`` + cell, 1 or 2 -> an ``'xy'`` cell). + """ + + __slots__ = ('figure', 'row', 'col', 'index', 'ndims') + + def __init__(self, figure, row, col, index, ndims): + self.figure = figure + self.row = int(row) + self.col = int(col) + self.index = int(index) + self.ndims = int(ndims) + + def __repr__(self): + return (f"PlotlyCell(row={self.row}, col={self.col}, " + f"index={self.index}, ndims={self.ndims})") + + +def make_panel_grid(nrows, ncols, ndims, titles=None, size=None, + gutter_px=0, title_px=None, **make_subplots_kw): + """The empty plotly grid `panels=` and `hyp.subplots(backend='plotly')` + fill: a `plotly.subplots.make_subplots` figure with ``'scene'`` cells + for 3-D and ``'xy'`` cells otherwise, sized like the matplotlib grid + (`size` inches x 100 px, default `DEFAULT_FIGSIZE`), with `gutter_px` + reserved to the right of EVERY cell (and in the right margin) for a + per-panel legend/colorbar (see `panel_gutter_px`). When `size` is not + given the figure is widened by the gutters, so the default grid stays + as roomy as it is without them. + + The cells are laid out the way matplotlib's `tight_layout` lays out + the matplotlib grid (1.1 release review: the plotly grid used + `make_subplots`' default spacing -- 10-15 % of the figure between + cells -- and full-height cells, so three 3-D panels sat in tall + narrow cells with their titles far above small cubes): `PANEL_GAP_PX` + (`PANEL_AXIS_GAP_PX` for 2-D/1-D cells) between neighbours, + `title_px` above every row (default `PANEL_TITLE_PX` when `titles` + has one, else 0; `panels=` passes what its panels' own titles need), + and -- for 3-D grids -- SQUARE cells, as an `Axes3D`'s equal box + aspect makes them, sized by whichever of the width or the height + binds and centred in the figure. 2-D cells fill the figure. + + Extra keywords go to `make_subplots` (``shared_xaxes=``, ...); a + caller's ``horizontal_spacing=``/``vertical_spacing=`` replaces the + pixel-derived one. + """ + from plotly.subplots import make_subplots + cell = {'type': 'scene'} if ndims >= 3 else {'type': 'xy'} + if size is not None: + width, height = int(size[0] * 100), int(size[1] * 100) + else: + width = int(DEFAULT_FIGSIZE[0] * 100) + gutter_px * ncols + height = int(DEFAULT_FIGSIZE[1] * 100) + titles = list(titles) if titles is not None else [] + if title_px is None: + title_px = PANEL_TITLE_PX if any(t for t in titles) else 0 + title_px = int(title_px) + gap = PANEL_GAP_PX if ndims >= 3 else PANEL_AXIS_GAP_PX + base = PANEL_MARGIN_PX + cell_w = (width - 2 * base - gutter_px * ncols + - gap * (ncols - 1)) / ncols + cell_h = (height - 2 * base - title_px * nrows + - gap * (nrows - 1)) / nrows + cell_w, cell_h = max(cell_w, 1.0), max(cell_h, 1.0) + if ndims >= 3: + cell_w = cell_h = min(cell_w, cell_h) + # the gutter after the LAST column and the title room above the FIRST + # row live in the margins; the grid is centred in what is left + plot_w = ncols * cell_w + (ncols - 1) * (gap + gutter_px) + plot_h = nrows * cell_h + (nrows - 1) * (gap + title_px) + side = max((width - plot_w - gutter_px) / 2, 0.0) + vert = max((height - plot_h - title_px) / 2, 0.0) + margin = dict(l=int(round(side)), t=int(round(vert)) + title_px) + margin['r'] = max(int(width - plot_w - margin['l']), 0) + margin['b'] = max(int(height - plot_h - margin['t']), 0) + make_kw = {} + if ncols > 1: + # make_subplots refuses a spacing wider than the cells allow + make_kw['horizontal_spacing'] = min((gap + gutter_px) / plot_w, + 0.98 / (ncols - 1)) + if nrows > 1: + make_kw['vertical_spacing'] = min((gap + title_px) / plot_h, + 0.98 / (nrows - 1)) + if titles: + make_kw['subplot_titles'] = [t if t is not None else '' + for t in titles] + make_kw.update(make_subplots_kw) + fig = make_subplots(rows=nrows, cols=ncols, + specs=[[dict(cell) for _ in range(ncols)] + for _ in range(nrows)], **make_kw) + fig.update_layout(width=width, height=height, margin=margin, + paper_bgcolor='white', plot_bgcolor='white') return fig +def _grid_spec(target): + """The `make_panel_grid` arguments a `hyp.subplots(backend='plotly')` + grid was built with (kept in ``layout.meta['hyp_grid']``), or None + for a grid that was not built that way (a `panels=` grid, which + sizes its gutters up front from the panels it has already drawn).""" + meta = target.layout.meta + if isinstance(meta, dict) and isinstance(meta.get('hyp_grid'), dict): + return dict(meta['hyp_grid']) + return None + + +def ensure_panel_layout(target, gutter_px=None, title_px=None): + """`ensure_panel_gutter` for both dimensions a drawn cell can grow: the + gutter beside every cell and the title room above every row. Either + one growing rebuilds the grid (Codex round 4: a three-line title + widened the top margin but left the rows 37 px apart).""" + spec = _grid_spec(target) + if spec is None: + return False + new_gutter = max(int(spec.get('gutter_px', 0)), int(gutter_px or 0)) + new_title = max(int(spec.get('title_px') or 0), int(title_px or 0)) + if (new_gutter == int(spec.get('gutter_px', 0)) + and new_title == int(spec.get('title_px') or 0)): + return False + spec['title_px'] = new_title + return _rebuild_panel_grid(target, spec, new_gutter) + + +def ensure_panel_gutter(target, gutter_px): + """Give a `hyp.subplots(backend='plotly')` grid at least `gutter_px` + of room beside every cell -- rebuilding its layout (width, margins, + every cell's domain) from the arguments it was built with, and + re-placing the legends, colorbars and titles of the cells already + drawn -- the first time a cell actually receives a legend or a + colorbar. The grid is built WITHOUT gutters (1.1 release review, + feature-tour 9.8: a legend-less two-cell grid reserved a 118 px gutter + beside each cell, so its cubes were three quarters the size of the + matplotlib pair's and sat left-heavy), so a grid whose cells never + ask for one stays as tight as `panels=` draws it. + """ + spec = _grid_spec(target) + if spec is None or int(spec.get('gutter_px', 0)) >= int(gutter_px): + return False + return _rebuild_panel_grid(target, spec, int(gutter_px)) + + +def _rebuild_panel_grid(target, spec, gutter_px): + """Re-lay `target` out from `spec` with `gutter_px` (see + `ensure_panel_layout`) and re-place every drawn cell's furniture.""" + spec['gutter_px'] = int(gutter_px) + grid = make_panel_grid(spec['nrows'], spec['ncols'], spec['ndims'], + size=spec.get('size'), gutter_px=spec['gutter_px'], + title_px=spec.get('title_px'), + **dict(spec.get('make_subplots_kw') or {})) + target.layout.update(width=grid.layout.width, height=grid.layout.height, + margin=grid.layout.margin.to_plotly_json()) + for i in range(spec['nrows'] * spec['ncols']): + keys = cell_layout_keys(i) + if spec['ndims'] >= 3: + target.layout[keys['scene']].domain = \ + grid.layout[keys['scene']].domain.to_plotly_json() + else: + for axis in ('xaxis', 'yaxis'): + target.layout[keys[axis]].domain = \ + grid.layout[keys[axis]].domain + meta = dict(target.layout.meta) if isinstance(target.layout.meta, + dict) else {} + target.layout.meta = {**meta, 'hyp_grid': spec} + for i in range(spec['nrows'] * spec['ncols']): + _place_cell_furniture(target, i, spec['ndims']) + return True + + +def _explicit_legend_position(legend): + """``{'lx', 'ly'}`` when a single-figure legend dict carries a + position other than hypertools' own default (``x=1.02, y=0.5``, the + outside-right anchor `plotly_draw` sets), i.e. a caller's + `legend_kwargs` placed it; else None.""" + x, y = legend.get('x'), legend.get('y') + if x is None or y is None: + return None + if abs(float(x) - 1.02) < 1e-9 and abs(float(y) - 0.5) < 1e-9: + return None + return {'lx': float(x), 'ly': float(y)} + + +def _cell_domain(target, keys, ndims): + if ndims >= 3: + domain = target.layout[keys['scene']].domain + return domain.x[0], domain.x[1], domain.y[0], domain.y[1] + x0, x1 = target.layout[keys['xaxis']].domain + y0, y1 = target.layout[keys['yaxis']].domain + return x0, x1, y0, y1 + + +def _place_cell_furniture(target, index, ndims): + """Place cell `index`'s legend, colorbars and title from its CURRENT + domain (the placement rules `transplant_panel` applies), so a cell + can be re-placed after `ensure_panel_gutter` moved it.""" + keys = cell_layout_keys(index) + x0, x1, y0, y1 = _cell_domain(target, keys, ndims) + if x0 is None or x1 is None: + return + plot_w = max((target.layout.width or int(DEFAULT_FIGSIZE[0] * 100)) + - (target.layout.margin.l or 0) + - (target.layout.margin.r or 0), 1) + y_mid = 0.5 * (y0 + y1) + try: + legend = target.layout[keys['legend']] + except Exception: # noqa: BLE001 - a cell never drawn has no legendN + legend = None + placed = legend is not None and legend.x is not None + meta = target.layout.meta if isinstance(target.layout.meta, dict) else {} + explicit = (meta.get('hyp_cell_legends') or {}).get(str(index)) + # whether the cell SHOWS a legend -- what `transplant_panel` recorded + # from its traces -- not whether a `legendN` layout exists: every drawn + # cell gets one placed, so reading that pushed a legend-less cell's + # colorbar a legend's width right, onto the next cell (1.1 release + # review) + furniture = (meta.get('hyp_cell_furniture') or {}).get(str(index)) + if furniture is not None: + has_legend = bool(furniture.get('legend')) + else: + has_legend = any( + bool(t.showlegend) for t in target.data + if getattr(t, 'legend', None) == keys['legend'] + or (index == 0 and getattr(t, 'legend', None) in (None, + 'legend'))) + if placed and explicit: + legend.update(x=x0 + float(explicit['lx']) * (x1 - x0), + y=y0 + float(explicit['ly']) * (y1 - y0)) + elif placed: + legend.update(x=x1 + PANEL_GUTTER_PAD_PX / plot_w, y=y_mid) + cb_offset = PANEL_GUTTER_PAD_PX + (PANEL_LEGEND_PX if has_legend else 0) + for trace in target.data: + if getattr(trace, 'legend', None) != keys['legend'] \ + and not (index == 0 and getattr(trace, 'legend', None) + in (None, 'legend')): + continue + marker = getattr(trace, 'marker', None) + if marker is None or not getattr(marker, 'showscale', None) \ + or marker.colorbar is None or marker.colorbar.x is None: + continue + cb = marker.colorbar + if cb.orientation in (None, 'v') and cb.xanchor == 'right': + cb.update(x=x0 - PANEL_GUTTER_PAD_PX / plot_w, y=y_mid, + len=0.75 * (y1 - y0)) + elif cb.orientation in (None, 'v'): + cb.update(x=x1 + cb_offset / plot_w, y=y_mid, + len=0.75 * (y1 - y0)) + else: + on_top = cb.yanchor == 'bottom' + cb.update(x=0.5 * (x0 + x1), len=0.75 * (x1 - x0), + y=(y1 if on_top else y0)) + title_spec = (meta.get('hyp_cell_titles') or {}).get(str(index)) + if title_spec: + for ann in target.layout.annotations: + if ann.name == f'hyp-cell-title-{index}': + ann.x = x0 + float(title_spec['tx']) * (x1 - x0) + ty = title_spec.get('ty') + ann.y = y1 if ty is None else y0 + float(ty) * (y1 - y0) + + +def transplant_panel(target, panel, row, col, index, ndims): + """Move one drawn single-axes plotly figure into cell ``(row, col)`` of + a `make_subplots` figure, at parity with what a matplotlib `ax=` panel + keeps: its traces, its axis layout (the 2-D unit-frame ranges, hidden + ticks and axis titles, or the visible ``axis_scale='data'`` axes; the + 3-D scene), its frame square and point annotations (re-referenced to + the cell's own axes), and ITS OWN legend and colorbar, placed just + right of the cell rather than merged into one figure-wide legend or + stacked on one figure-wide colorbar (1.1 release review: three panels + with ``legend=True`` listed '1, 1, 1' in a single legend, and two + ``colorbar=True`` panels drew both colorbars on top of each other). + + `index` is the cell's 0-based row-major number. `target` must already + carry its final ``width``/``height`` and margins (the legend/colorbar + offsets are pixel distances converted to paper fractions), and its + ``horizontal_spacing`` should reserve `panel_gutter_px` beside each + cell. Returns the layout keys the cell uses: ``'scene'`` (3-D) or + ``'xaxis'``/``'yaxis'`` (2-D), plus ``'legend'``. + + The shared implementation behind `plot(..., panels=)` on this backend + and the `hyp.subplots(backend='plotly')` cells that `ax=` accepts. + """ + keys = cell_layout_keys(index) + # what this cell holds beside it once this panel is in -- a legend + # and/or a colorbar, from THIS call or an earlier one into the same + # cell -- recorded per cell, so the grid's gutter is sized for the + # busiest cell and a colorbar arriving after a legend goes beside it + # rather than on top of it (Codex round 4) + _meta = (dict(target.layout.meta) + if isinstance(target.layout.meta, dict) else {}) + furniture = dict(_meta.get('hyp_cell_furniture') or {}) + cell_furniture = dict(furniture.get(str(index)) + or {'legend': False, 'colorbar': False}) + cell_furniture['legend'] = bool( + cell_furniture['legend'] + or any(bool(trace.showlegend) for trace in panel.data)) + cell_furniture['colorbar'] = bool( + cell_furniture['colorbar'] + or any(getattr(getattr(trace, 'marker', None), 'showscale', None) + for trace in panel.data)) + furniture[str(index)] = cell_furniture + target.layout.meta = {**_meta, 'hyp_cell_furniture': furniture} + # a `hyp.subplots` grid is built without gutters and one title line + # per row; the first legend/colorbar, or a taller title, a cell brings + # makes the grid grow (`ensure_panel_layout`), BEFORE this cell's + # domain is read below + ensure_panel_layout( + target, + gutter_px=max(panel_gutter_px(f.get('legend'), f.get('colorbar')) + for f in furniture.values()), + title_px=(max(0, int(panel.layout.margin.t or 0) - PANEL_MARGIN_PX) + if panel.layout.title is not None + and panel.layout.title.text else 0)) + plot_w = (target.layout.width or int(DEFAULT_FIGSIZE[0] * 100)) \ + - (target.layout.margin.l or 0) - (target.layout.margin.r or 0) + plot_w = max(plot_w, 1) + + if ndims >= 3: + scene = (panel.layout.scene.to_plotly_json() + if panel.layout.scene is not None else {}) + scene.pop('domain', None) + # a cell drawn into twice keeps the earlier call's `labels=` + # (updating the scene would replace its annotation list, leaving + # the first dataset's points visible but unlabelled; round 2) + earlier = [a.to_plotly_json() + for a in target.layout[keys['scene']].annotations] + if earlier: + scene['annotations'] = earlier + list(scene.get('annotations', + [])) + target.layout[keys['scene']].update(scene) + domain = target.layout[keys['scene']].domain + x0, x1 = domain.x + y0, y1 = domain.y + # plotly sizes a 3-D scene by its domain's HEIGHT alone (the cube + # is ~0.70 of it tall and ~0.89 of it wide at hypertools' view, + # see `SCENE_CUBE_WIDTH_PER_HEIGHT`) and clips at the sides, so in + # a cell narrower than it is tall -- a caller's own row_heights= + # or a tall `size=` -- the cube spilled out of the cell's sides. + # Back the camera off (apparent size ~ 1/distance, measured) by + # exactly what the cell's aspect needs. The grid's own cells are + # square (`make_panel_grid`), where no back-off is needed and the + # cube fills the cell's width like the matplotlib panel's does. + plot_h = (target.layout.height or int(DEFAULT_FIGSIZE[1] * 100)) \ + - (target.layout.margin.t or 0) - (target.layout.margin.b or 0) + cell_w = max(plot_w * (x1 - x0), 1.0) + cell_h = max(plot_h * (y1 - y0), 1.0) + back_off = max(1.0, SCENE_CUBE_WIDTH_PER_HEIGHT * cell_h / cell_w) + camera = target.layout[keys['scene']].camera + if back_off > 1.0 and camera is not None and camera.eye is not None: + eye = camera.eye + target.layout[keys['scene']].camera.eye = dict( + x=(eye.x or 0.0) * back_off, y=(eye.y or 0.0) * back_off, + z=(eye.z or 0.0) * back_off) + else: + for src, dst in (('xaxis', keys['xaxis']), ('yaxis', keys['yaxis'])): + axis = panel.layout[src].to_plotly_json() + axis.pop('domain', None) + axis.pop('anchor', None) + target.layout[dst].update(axis) + # the frame square (unit scale) and `labels=` annotations refer to + # the panel's own 'x'/'y'; re-point them at this cell's axes + for shape in panel.layout.shapes: + spec = shape.to_plotly_json() + spec['xref'] = keys['xref'] + spec['yref'] = keys['yref'] + target.add_shape(spec) + for ann in panel.layout.annotations: + spec = ann.to_plotly_json() + if spec.get('xref', 'x') == 'x': + spec['xref'] = keys['xref'] + if spec.get('yref', 'y') == 'y': + spec['yref'] = keys['yref'] + target.add_annotation(spec) + x0, x1 = target.layout[keys['xaxis']].domain + y0, y1 = target.layout[keys['yaxis']].domain + + y_mid = 0.5 * (y0 + y1) + legend_entries = bool(cell_furniture['legend']) + # the panel's colorbar goes right of its legend when there is one, + # else right of the cell, spanning the cell's height like the + # single-axes colorbar spans the plot's (`len=0.75` of the paper there) + cb_offset = PANEL_GUTTER_PAD_PX + (PANEL_LEGEND_PX if legend_entries + else 0) + for trace in panel.data: + # every trace of this panel lists in THIS panel's legend + trace.update(legend=keys['legend']) + marker = getattr(trace, 'marker', None) + if marker is not None and getattr(marker, 'showscale', None) \ + and marker.colorbar is not None: + # (before `add_trace`, which COPIES the trace into `target`) + cb = marker.colorbar + if cb.orientation in (None, 'v') and cb.xanchor == 'right': + # `location='left'`: keep it on the cell's LEFT + cb.update(x=x0 - PANEL_GUTTER_PAD_PX / plot_w, + xanchor='right', y=y_mid, yanchor='middle', + len=0.75 * (y1 - y0)) + elif cb.orientation in (None, 'v'): + cb.update(x=x1 + cb_offset / plot_w, xanchor='left', + y=y_mid, yanchor='middle', len=0.75 * (y1 - y0)) + else: + on_top = cb.y is not None and cb.y > 0.5 + cb.update(x=0.5 * (x0 + x1), xanchor='center', + len=0.75 * (x1 - x0), y=(y1 if on_top else y0), + yanchor=('bottom' if on_top else 'top')) + target.add_trace(trace, row=row, col=col) + + # the panel's legend, beside its own cell (same styling as the + # single-axes legend, whose x=1.02/y=0.5 meant "just right of the one + # plot, vertically centred on it") + legend = (panel.layout.legend.to_plotly_json() + if panel.layout.legend is not None else {}) + _explicit = _explicit_legend_position(legend) + if _explicit is not None: + # a caller's `legend_kwargs` x/y (paper fractions of the single + # figure) mean the same place INSIDE the cell (Codex round 3: + # transplanting overwrote them with the gutter placement) + legend.update(x=x0 + _explicit['lx'] * (x1 - x0), + y=y0 + _explicit['ly'] * (y1 - y0)) + else: + legend.update(x=x1 + PANEL_GUTTER_PAD_PX / plot_w, y=y_mid, + xanchor='left', yanchor='middle') + _meta = (dict(target.layout.meta) + if isinstance(target.layout.meta, dict) else {}) + _legends = dict(_meta.get('hyp_cell_legends') or {}) + _legends[str(index)] = _explicit + target.layout.meta = {**_meta, 'hyp_cell_legends': _legends} + # the panel's inherited text font (`font=`, GH #205) is MATERIALIZED + # on this cell's text -- legend, title, axis titles/ticks, colorbar -- + # property by property under any explicit override, so two cells with + # different fonts stay independent (round 2: a `legend_kwargs=` font + # size dropped the family, and the grid-wide default made cell two + # inherit cell one's family) + panel_font = (panel.layout.font.to_plotly_json() + if panel.layout.font is not None else {}) + if panel_font: + legend['font'] = _with_base_font(legend.get('font'), panel_font) + _materialize_cell_fonts(target, keys, ndims, panel_font) + if not target.layout.font.to_plotly_json(): + target.layout.font = dict(panel_font) + target.layout[keys['legend']] = legend + + # the panel's `title=`, already formatted by the single-axes path + # (newlines, `title_wrap=`, `title_kwargs=`), as this cell's title -- + # a `make_subplots`-style annotation above the cell, positioned by + # the same `x`/`y`/anchors the title carries, mapped from the single + # figure's paper into the cell's domain. One per cell: drawing into + # the cell again with a `title=` REPLACES it (as a matplotlib axes + # title is replaced), and an untitled call leaves it alone (as an + # untitled `hyp.plot(..., ax=ax)` leaves the axes title; 1.1 release + # review: the second call deleted it), while `labels=` annotations + # keep accumulating. + title_name = f'hyp-cell-title-{index}' + title = panel.layout.title + if title is not None and title.text: + target.layout.annotations = tuple( + a for a in target.layout.annotations if a.name != title_name) + tx = 0.5 if title.x is None else float(title.x) + default_y = title.y is None or abs(float(title.y) - 0.97) < 1e-9 + spec = dict(text=title.text, name=title_name, + x=x0 + tx * (x1 - x0), xref='paper', yref='paper', + xanchor=title.xanchor or 'center', showarrow=False) + if default_y: + spec.update(y=y1, yanchor='bottom') + else: + spec.update(y=y0 + float(title.y) * (y1 - y0), + yanchor=title.yanchor or 'top') + # where in its cell the title sits, so `_place_cell_furniture` + # can put it back after the cell moves (`ensure_panel_gutter`) + _meta = (dict(target.layout.meta) + if isinstance(target.layout.meta, dict) else {}) + _titles = dict(_meta.get('hyp_cell_titles') or {}) + _titles[str(index)] = {'tx': tx, + 'ty': None if default_y else float(title.y)} + target.layout.meta = {**_meta, 'hyp_cell_titles': _titles} + title_font = (title.font.to_plotly_json() + if title.font is not None else {}) + merged_font = _with_base_font(title_font, panel_font) + if merged_font: + spec['font'] = merged_font + target.add_annotation(spec) + # the title sits in the top margin: reserve what the single-axes + # path computed for it (per line and per font size), never less + # than the 40 px a one-line title needs + needed = max(40, int(panel.layout.margin.t or 0)) + if (target.layout.margin.t or 0) < needed: + target.layout.margin.t = needed + # reconcile this cell's furniture with what it already held (a + # colorbar arriving beside an earlier legend, or the reverse) + _place_cell_furniture(target, index, ndims) + return keys + + +def _with_base_font(explicit, base): + """A plotly font dict: `base` (a panel's inherited `layout.font`) + under `explicit`'s own properties.""" + merged = dict(base or {}) + merged.update(explicit or {}) + return merged + + +def _materialize_cell_fonts(target, keys, ndims, panel_font): + """Write `panel_font` under every text element of one cell that has + no explicit family/size/color of its own: axis titles and tick labels + (2-D axes or the 3-D scene's) and the cell's colorbar titles/ticks.""" + if ndims >= 3: + scene = target.layout[keys['scene']] + axes_ = [scene.xaxis, scene.yaxis, scene.zaxis] + else: + axes_ = [target.layout[keys['xaxis']], target.layout[keys['yaxis']]] + for axis in axes_: + axis.tickfont = _with_base_font(axis.tickfont.to_plotly_json(), + panel_font) + if axis.title is not None: + axis.title.font = _with_base_font( + axis.title.font.to_plotly_json(), panel_font) + for trace in target.data: + marker = getattr(trace, 'marker', None) + if marker is None or not getattr(marker, 'showscale', None): + continue + if (ndims >= 3 and getattr(trace, 'scene', None) != keys['scene']) \ + or (ndims < 3 and (getattr(trace, 'xaxis', None) or 'x') + != keys['xref']): + continue + cb = marker.colorbar + cb.tickfont = _with_base_font(cb.tickfont.to_plotly_json(), + panel_font) + if cb.title is not None: + cb.title.font = _with_base_font(cb.title.font.to_plotly_json(), + panel_font) + + +def show_figure(fig): + """Display `fig` the way ``plot(..., show=True)`` does on this backend. + + Shared by the single-axes path and `panels=` (1.1 review, P6), so both + go through the same three cases: + + - a docs build (plotly's sphinx-gallery renderer): plotly's own + renderer writes a static png AND an interactive html from the full + figure, and kaleido serializes EVERY animation frame to render the + one png -- a 900-frame figure took ~an hour and produced tens-of-MB + pages. Write the pair ourselves instead: png from a frame-stripped + snapshot, html with the embedded frames capped (total duration and + rotations preserved, so pacing stays identical). + - an interactive notebook: display at the END of the cell (after + matplotlib-inline's own flush, so plotly figures keep their place + behind matplotlib ones drawn in the same cell) and only if the cell's + rich-display hook has not already shown this figure as its last + expression. See `HyperPlotlyFigure`. + - a plain script (no IPython frontend): nothing else will display the + figure, so show it here. + """ + import plotly.io as pio + if 'sphinx_gallery' in str(pio.renderers.default or ''): + _show_sphinx_gallery(fig) + elif _in_interactive_shell(): + _display_at_cell_end(fig) + else: + fig.show() + + _HYPER_FIGURE_CLASS = None @@ -2087,7 +3762,10 @@ def _display_at_cell_end(fig): callbacks = getattr(shell.events, 'callbacks', {}) if _flush_pending_display not in callbacks.get('post_execute', []): shell.events.register('post_execute', _flush_pending_display) - _PENDING_DISPLAY.append(fig) + # once per FIGURE: several `ax=` calls into one grid queue the same + # figure, which must display once, as the matplotlib grid does + if not any(queued is fig for queued in _PENDING_DISPLAY): + _PENDING_DISPLAY.append(fig) def _flush_pending_display(): @@ -2292,6 +3970,31 @@ def _wait_with_progress(proc, count_completed, return 'ceiling' +def _worker_error(frames_dir): + """The exception the export worker reported through its error file, as + the type the caller is promised (`ImportError` for a missing extra with + installation off, `HypertoolsIOError` when no Chrome could be provided), + or None when the worker failed some other way (rendering, a kill).""" + from ._kaleido_export_worker import ERROR_FILE + from ..core.exceptions import HypertoolsIOError + path = os.path.join(frames_dir, ERROR_FILE) + try: + with open(path, encoding='utf-8') as fh: + info = json.load(fh) + except (OSError, ValueError): + return None + try: + os.remove(path) + except OSError: + pass + types = {'ImportError': ImportError, 'ModuleNotFoundError': ImportError, + 'HypertoolsIOError': HypertoolsIOError} + cls = types.get(info.get('type')) + if cls is None: + return None + return cls(f"plotly frame export: {info.get('message', '')}") + + def _render_frames_via_subprocess(fig, ext, width, height, n_frames): """Render every animation frame of `fig` to an image file (format `ext`) in a KILLABLE subprocess, guarded by a PROGRESS watchdog -- the only reliable @@ -2330,13 +4033,19 @@ def _completed(): for attempt in range(_KALEIDO_EXPORT_ATTEMPTS): err_path = os.path.join(workdir, f'stderr-{attempt}.log') # stderr -> file (not a PIPE) so a chatty Chrome can't deadlock - # on a full pipe buffer while we watch for progress + # on a full pipe buffer while we watch for progress. + # env: the worker provisions kaleido/Chrome itself (it may + # pip-install and download), so it must start from THIS + # process's effective set_autoinstall() setting, which lives + # in Python and is not inherited by a fresh interpreter + # (release audit 2026-09-07). with open(err_path, 'wb') as errf: proc = subprocess.Popen( [sys.executable, '-m', 'hypertools.plot._kaleido_export_worker', fig_json, frames_dir, ext, str(width), str(height)], stdout=subprocess.DEVNULL, stderr=errf, + env=subprocess_env(), start_new_session=(os.name != 'nt')) reason = _wait_with_progress( proc, _completed, @@ -2369,6 +4078,15 @@ def _completed(): "subprocess and its browser, retrying") continue if proc.returncode != 0: + reported = _worker_error(frames_dir) + if reported is not None: + # the worker could not import or provision what it + # needs (a missing kaleido with installation off, no + # usable Chrome): that is not a render failure to + # retry, and the caller is promised the documented + # exception type (ImportError naming the manual + # command; HypertoolsIOError for Chrome) + raise reported tail = '' try: with open(err_path, encoding='utf-8', @@ -2539,11 +4257,101 @@ def _grid_durations(n_frames, grid_ms): check=True, capture_output=True) -def _cube_trace(go, scale=1.0, linewidth_pt=CUBE_LINEWIDTH_PT): +#: `frame_kwargs=` keys `_frame_style` maps (matplotlib spellings, as +#: `matplotlib_backend.plot_cube`'s `plot_wireframe` and `plot_square`'s +#: `Rectangle` take them) +_FRAME_COLOR_KEYS = ('color', 'colors', 'edgecolor', 'edgecolors', 'ec') +_FRAME_WIDTH_KEYS = ('linewidth', 'linewidths', 'lw') +_FRAME_STYLE_KEYS = ('linestyle', 'linestyles', 'ls') +_FRAME_FACE_KEYS = ('facecolor', 'fc') +#: matplotlib's own default frame width (`plot_cube`/`plot_square`), which +#: `CUBE_LINEWIDTH_PT` is calibrated to match on screen +_MPL_FRAME_LINEWIDTH_PT = 1.0 + + +def _frame_style(frame_kwargs, ndims): + """The plotly styling of the cube/square frame from `plot()`'s + ``frame_kwargs=`` (matplotlib's `plot_wireframe`/`Rectangle` keywords). + + Returns ``dict(color, width_pt, dash, fill)`` -- `color` a plotly colour + string (``alpha=`` folded in), `width_pt` the frame width in the + points `_cube_trace`/`_square_shape` take (a matplotlib ``linewidth`` + scaled by the same factor that makes the default 1 pt frame match + matplotlib's on screen), `dash` a plotly dash name, `fill` the 2-D + square's fill colour or None. With no `frame_kwargs` this is exactly the + historical black frame. Keywords with no plotly equivalent (``zorder``, + ``rstride``, ...) are named in one warning instead of being dropped + silently (1.1 release review: plotly ignored `frame_kwargs=` outright, + so the cube stayed black whatever colour was asked for). + """ + kw = dict(frame_kwargs or {}) + alpha = kw.pop('alpha', None) + # `color`/`colors` style the whole frame (a Rectangle's edge AND face); + # the edge spellings style only its outline + both = next((kw.get(k) for k in ('color', 'colors') + if kw.get(k) is not None), None) + edge = next((kw.get(k) for k in ('edgecolor', 'edgecolors', 'ec') + if kw.get(k) is not None), None) + color = edge if edge is not None else both + for k in _FRAME_COLOR_KEYS: + kw.pop(k, None) + width = next((kw.pop(k) for k in _FRAME_WIDTH_KEYS + if kw.get(k) is not None), None) + for k in _FRAME_WIDTH_KEYS: + kw.pop(k, None) + style = next((kw.pop(k) for k in _FRAME_STYLE_KEYS + if kw.get(k) is not None), None) + for k in _FRAME_STYLE_KEYS: + kw.pop(k, None) + face = next((kw.pop(k) for k in _FRAME_FACE_KEYS + if kw.get(k) is not None), None) + for k in _FRAME_FACE_KEYS: + kw.pop(k, None) + fill = kw.pop('fill', None) + # matplotlib's plot_wireframe/Rectangle defaults that are meaningless + # (or already implied) here + for k in ('rstride', 'cstride'): + kw.pop(k, None) + if kw: + warnings.warn( + "backend='plotly' cannot map the following frame_kwargs to the " + f"plotly frame and will ignore them: {sorted(kw)}. Supported: " + "color/edgecolor, linewidth, linestyle, alpha (and, for the 2-D " + "square, facecolor/fill).", UserWarning, stacklevel=3) + def _one(c): + # a `colors=` list (one per wireframe line): one colour here + if isinstance(c, (list, tuple)) and c and not isinstance( + c[0], (int, float, np.integer, np.floating)): + return c[0] + return c + color, both = _one(color), _one(both) + line_color = ('black' if color is None and alpha is None + else _to_plotly_color(color if color is not None + else 'black', alpha)) + width_pt = (CUBE_LINEWIDTH_PT if width is None + else float(width) * CUBE_LINEWIDTH_PT + / _MPL_FRAME_LINEWIDTH_PT) + dash = ('solid' if style is None + else _LINESTYLE_NAMES.get(style, 'solid')) + fill_color = None + if ndims < 3: + # matplotlib's `plot_square`: a `color=` (or a face colour) fills + # the square unless `fill=False`; with neither it is an outline + face_color = face if face is not None else both + if face_color is not None and fill is not False: + fill_color = _to_plotly_color(face_color, alpha) + elif fill is True: + fill_color = _to_plotly_color('C0', alpha) + return dict(color=line_color, width_pt=width_pt, dash=dash, + fill=fill_color) + + +def _cube_trace(go, scale=1.0, linewidth_pt=CUBE_LINEWIDTH_PT, color='black', + dash='solid'): """hypertools' signature black wireframe cube as a single 3D trace. Mirrors matplotlib_backend's plot_cube: 12 edges at +/-scale, black, - 1pt lines. + 1pt lines (or the `frame_kwargs=` style `_frame_style` resolved). Edges are chained with None separators so one trace draws them all. """ s = scale @@ -2567,17 +4375,21 @@ def _cube_trace(go, scale=1.0, linewidth_pt=CUBE_LINEWIDTH_PT): x=xs, y=ys, z=zs, mode='lines', # boosted so the gl-rendered cube matches the SVG square's ~2px stroke # (see _CUBE_GL_WIDTH_BOOST) -- the 2D square uses no boost - line=dict(color='black', - width=linewidth_pt * PT_TO_PX * _CUBE_GL_WIDTH_BOOST), + line=dict(color=color, + width=linewidth_pt * PT_TO_PX * _CUBE_GL_WIDTH_BOOST, + **({} if dash == 'solid' else dict(dash=dash))), showlegend=False, hoverinfo='skip') -def _square_shape(scale=1.0, linewidth_pt=CUBE_LINEWIDTH_PT): +def _square_shape(scale=1.0, linewidth_pt=CUBE_LINEWIDTH_PT, color='black', + dash='solid', fill=None): """hypertools' 2D black square frame (mirrors matplotlib_backend's - plot_square).""" + plot_square; `color`/`dash`/`fill` from `_frame_style`).""" return dict(type='rect', x0=-scale, y0=-scale, x1=scale, y1=scale, - line=dict(color='black', width=linewidth_pt * PT_TO_PX), - fillcolor='rgba(0,0,0,0)', layer='below') + line=dict(color=color, width=linewidth_pt * PT_TO_PX, + **({} if dash == 'solid' else dict(dash=dash))), + fillcolor=fill if fill is not None else 'rgba(0,0,0,0)', + layer='below') def _surface_base_rgb(spec, fallback_rgb): @@ -2874,19 +4686,20 @@ def _one_density_contour_trace(go, pts, spec, color_rgb, label=""): def _build_density_traces_2d(go, data, density, density_colors): """Build each dataset's (or, with ``per_group=False``, one pooled) - ``go.Contour`` KDE density layer (GH #108/#191, 2-D).""" + ``go.Contour`` KDE density layer (GH #108/#191, 2-D); each grid reaches + `KDE_GRID_BANDWIDTHS` kernel widths past its own cloud (see + `kde_grid_2d`).""" + points = [np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :2] + for arr in data] if density[0] is not None and not density[0].get('per_group', True): - all_pts = np.vstack([ - np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :2] - for arr in data]) + all_pts = np.vstack(points) trace = _one_density_contour_trace(go, all_pts, density[0], POOLED_COLOR, label=' (pooled)') return [trace] if trace is not None else [] traces = [] - for i, (arr, spec) in enumerate(zip(data, density)): + for i, (pts, spec) in enumerate(zip(points, density)): if spec is None: continue - pts = np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :2] trace = _one_density_contour_trace(go, pts, spec, density_colors[i], label=f' {i}') if trace is not None: @@ -2894,7 +4707,8 @@ def _build_density_traces_2d(go, data, density, density_colors): return traces -def _one_density_volume_trace(go, pts, spec, color_rgb, label="", boost=1.0): +def _one_density_volume_trace(go, pts, spec, color_rgb, label="", boost=1.0, + limit=None): """One ``go.Volume`` KDE iso-surface layer (GH #108/#191, 3-D), or ``None`` if `pts` is too small/degenerate to fit a KDE. @@ -2951,7 +4765,21 @@ def _one_density_volume_trace(go, pts, spec, color_rgb, label="", boost=1.0): levels = spec.get('levels', DENSITY_DEFAULTS['levels']) pad, isomin, opacityscale, opacity, surface_count = ( resolve_plotly_volume_params(spec['alpha'], levels, boost)) - X, Y, Z, D, _, _ = kde_grid_3d(pts, kde, gridsize=gridsize, pad=pad) + if limit is None: + X, Y, Z, D, _, _ = kde_grid_3d(pts, kde, gridsize=gridsize, pad=pad) + else: + # the grid, padded past the data so the glow fades out, is CLIPPED + # to the scene's cube (1.1 release review, L2): a grid reaching + # past the scene range (x to +-1.3) drew its translucent shells + # over the cube's edges, which rendered stippled (1076 of 3551 + # dark cube/marker pixels survived on the reviewer's case; all of + # them do clipped). The same `gridsize` samples the clipped box. + lo, hi = _padded_bounds(np.asarray(pts, dtype=float), pad) + lo, hi = np.maximum(lo, -limit), np.minimum(hi, limit) + axes_ = [np.linspace(lo[i], hi[i], gridsize) for i in range(3)] + X, Y, Z = np.meshgrid(*axes_, indexing='ij') + D = kde(np.vstack([X.ravel(), Y.ravel(), Z.ravel()])).reshape( + X.shape) dmax = D.max() if dmax <= 0: return None @@ -2965,21 +4793,23 @@ def _one_density_volume_trace(go, pts, spec, color_rgb, label="", boost=1.0): showscale=False, hoverinfo='skip') -def _build_density_traces_3d(go, data, density, density_colors): +def _build_density_traces_3d(go, data, density, density_colors, limit=None): """Build each dataset's (or, with ``per_group=False``, one pooled) ``go.Volume`` KDE density layer (GH #108/#191, 3-D). Each per-dataset layer's opacity is boosted (GH #108 round 2) by how small that dataset's own bounding box is relative to the bounding box of the WHOLE scene (all datasets combined) -- see - :func:`~.density.density_alpha_boost`.""" + :func:`~.density.density_alpha_boost`. `limit` (the scene cube's + half-width) clips every layer's grid to the cube (see + `_one_density_volume_trace`).""" if density[0] is not None and not density[0].get('per_group', True): all_pts = np.vstack([ np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :3] for arr in data]) trace = _one_density_volume_trace(go, all_pts, density[0], POOLED_COLOR, label=' (pooled)', - boost=1.0) + boost=1.0, limit=limit) return [trace] if trace is not None else [] scene_pts = np.vstack([ np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :3] @@ -2992,7 +4822,8 @@ def _build_density_traces_3d(go, data, density, density_colors): pts = np.atleast_2d(np.asarray(arr, dtype=np.float64))[:, :3] boost = density_alpha_boost(bbox_extent(pts), scene_extent) trace = _one_density_volume_trace(go, pts, spec, density_colors[i], - label=f' {i}', boost=boost) + label=f' {i}', boost=boost, + limit=limit) if trace is not None: traces.append(trace) return traces @@ -3172,15 +5003,63 @@ def _forecast_style_from(tkwargs, fmt_str, alpha=None, override=None, _mode, _symbol, dash, _marker_char = _resolve_fmt( override.get('fmt', fmt_str), _fmt_kwargs) if alpha is None: - alpha = forecast_alpha(tkwargs.get('alpha')) + # a recoloured forecast keeps its trace's alpha (matplotlib + # parity: `plot._forecast_style_from` applies the same + # `forecast.forecast_alpha_scale_for` rule) + from .forecast import forecast_alpha_scale_for + alpha = forecast_alpha(tkwargs.get('alpha'), + forecast_alpha_scale_for(override)) width = float(tkwargs.get('linewidth') or DEFAULT_LINEWIDTH_PT) * PT_TO_PX color = override.get( 'color', anchor_color if anchor_color is not None else tkwargs.get('color')) + if 'color' not in override: + # a colour letter in `forecast_fmt=` ('r:') recolours the forecast, + # as it does on matplotlib (Codex round 3: plotly dropped it) + fmt_color = _fmt_color_letter(override.get('fmt')) + if fmt_color is not None: + color = fmt_color line = dict(color=_to_plotly_color(color, alpha), width=width, dash=dash) return line, alpha +def _fmt_color_letter(fmt): + """The colour a matplotlib format string names (``'r:'`` -> ``'r'``), + or None when it names none (or is not a string).""" + if not isinstance(fmt, str) or not fmt: + return None + try: + from matplotlib.axes._base import _process_plot_format + return _process_plot_format(fmt)[2] + except Exception: # noqa: BLE001 - an unparseable fmt names no colour + return None + + +def _forecast_marker(tkwargs, override, line_color, ndims): + """``(mode, marker)`` for a forecast trace: ``('lines', None)`` unless + `forecast_fmt=` asked for a marker (``'o:'``), in which case the trace + draws ``'lines+markers'`` with that marker at the observed trace's + marker size, in the forecast's own colour -- what the matplotlib + overlay draws for the same string (Codex round 3: plotly dropped the + marker). A forecast never inherits the OBSERVED trace's marker: it is + a line, and only its own format string can add markers to it.""" + fmt = (override or {}).get('fmt') + if not isinstance(fmt, str) or not fmt: + return 'lines', None + _mode, symbol, _dash, marker_char = _resolve_fmt( + fmt, {k: v for k, v in tkwargs.items() + if k not in ('linestyle', 'ls', 'marker')}) + if marker_char is None or symbol is None: + return 'lines', None + size = _marker_size_px( + tkwargs.get('markersize') or DEFAULT_MARKERSIZE_PT, marker_char, + ndims=ndims) + # the parsed mode: 'markers' for a marker-only string ('ro'), as the + # matplotlib overlay draws it (Codex round 4), else lines+markers + mode = 'markers' if 'lines' not in _mode else 'lines+markers' + return mode, dict(symbol=symbol, size=size, color=line_color) + + def _marker_size_px(markersize_pt, marker_char, ndims=2): """Convert an mpl `markersize` (points, diameter) to the `marker.size` value to pass to a plotly trace, matching matplotlib's rendered pixel @@ -3313,6 +5192,113 @@ def _rgb_string(c): return f'rgb({r},{g},{b})' +#: Most colour-bin traces an ANIMATED multicoloured 2-D line gets +#: (`_hue_line_bins`). A plotly 2-D line has ONE colour per trace, and every +#: frame must rewrite every trace of the line, so the per-segment colours +#: are drawn with at most this many traces. A continuous `hue=` maps through +#: a 100-colour ramp (`colors.continuous_colormap`'s `n_bins`), so it is +#: drawn EXACTLY; only a line with more distinct colours than this (a +#: matrix/RGB hue's blends) has each segment take its bin's mean colour +#: (k-means over the distinct colours; measured 2026-09-11: 48 bins over a +#: 100-colour ramp were at most 7-15/255 per channel off, 100 bins are exact). +HUE_ANIM_MAX_BINS = 100 + + +class PlotlyTraceGroup(tuple): + """The several frame traces that draw ONE dataset -- an animated + multicoloured 2-D line is one trace per colour bin (`_hue_line_bins`) + -- handed to an `on_frame=` callback as that dataset's single entry of + `FrameContext.artists`. + + It is a tuple of `go.Scatter` traces (iterate it to reach each one), and + ASSIGNING an attribute sets it on every member, so ``artist.opacity = + 0.4`` -- what `dataset_fade=` and a portable callback do -- fades the + whole dataset. Reading an attribute reads the first member's. + """ + + def __setattr__(self, name, value): + for trace in self: + setattr(trace, name, value) + + def __getattr__(self, name): + if not self: + raise AttributeError(name) + return getattr(self[0], name) + + +def _parse_rgba(color): + """``(r, g, b, a)`` (0-255 channels, alpha 0-1) of a plotly + ``rgb(...)``/``rgba(...)`` string.""" + text = str(color).strip() + parts = [float(p) for p in text[text.index('(') + 1:-1].split(',')] + return tuple(parts[:3]) + ((parts[3] if len(parts) > 3 else 1.0),) + + +def _hue_line_bins(colors, max_bins=HUE_ANIM_MAX_BINS): + """Group a multicoloured line's SEGMENT colours into a bounded set of + colour bins, for the animated 2-D representation (see + `HUE_ANIM_MAX_BINS`). + + `colors` is one plotly colour string per drawn VERTEX (segment ``j`` + wears vertex ``j``'s colour, as `_segment_traces_2d` draws it). Returns + ``dict(seg_bin=<int array, one per segment>, colors=[bin colour + strings])``. Deterministic: the k-means starts from distinct colours + spread evenly over their first-appearance order. + """ + seg_colors = list(colors[:-1]) if len(colors) > 1 else list(colors) + distinct = list(dict.fromkeys(seg_colors)) + index_of = {c: k for k, c in enumerate(distinct)} + seg_distinct = np.array([index_of[c] for c in seg_colors], dtype=int) + if len(distinct) <= max_bins: + return dict(seg_bin=seg_distinct, colors=distinct) + rgba = np.array([_parse_rgba(c) for c in distinct], dtype=float) + weights = np.bincount(seg_distinct, minlength=len(distinct)).astype(float) + centres = rgba[np.linspace(0, len(distinct) - 1, max_bins).astype(int)] + for _ in range(25): + dist = ((rgba[:, None, :3] - centres[None, :, :3]) ** 2).sum(axis=2) + label = dist.argmin(axis=1) + moved = centres.copy() + for k in range(max_bins): + member = label == k + if member.any(): + w = weights[member][:, None] + moved[k] = (rgba[member] * w).sum(axis=0) / w.sum() + if np.allclose(moved, centres): + break + centres = moved + used = sorted(set(label.tolist())) + renumber = {k: n for n, k in enumerate(used)} + bin_colors = [] + for k in used: + r, g, b, a = centres[k] + bin_colors.append(f'rgba({int(round(r))},{int(round(g))},' + f'{int(round(b))},{float(a)})') + return dict(seg_bin=np.array([renumber[label[d]] for d in seg_distinct], + dtype=int), + colors=bin_colors) + + +def _binned_polylines(xs, ys, seg_bin, k, v0, v1): + """The x/y of colour bin `k`'s share of vertices ``v0..v1`` (inclusive) + of a multicoloured line: its runs of consecutive segments, each drawn + as one polyline, separated by NaN gaps (plotly breaks a line at a gap). + Empty arrays when the window holds none of the bin's segments.""" + if v1 <= v0: + return np.zeros(0), np.zeros(0) + segs = np.arange(v0, v1) + segs = segs[seg_bin[v0:v1] == k] + if segs.size == 0: + return np.zeros(0), np.zeros(0) + # split into runs of consecutive segment indices + breaks = np.flatnonzero(np.diff(segs) > 1) + 1 + out_x, out_y = [], [] + for run in np.split(segs, breaks): + verts = np.arange(run[0], run[-1] + 2) + out_x.extend([xs[verts], [np.nan]]) + out_y.extend([ys[verts], [np.nan]]) + return np.concatenate(out_x[:-1]), np.concatenate(out_y[:-1]) + + def _segment_traces_2d(go, pts, colors, width, dash, name, trace_index=None): """Per-segment colored 2D line, emitted as one small trace per segment (plotly's 2D Scatter lines accept only a single color per trace). @@ -3376,6 +5362,84 @@ def _hue_anchor_color(point_colors, src): return tuple(float(v) for v in np.asarray(pc[-1], dtype=np.float64)[:3]) +def _legend_proxy_for(trace, ndims, group): + """A data-free trace carrying `trace`'s legend entry (name, line and + marker style, `legendgroup`) -- how an animation keeps its legend + complete while its data traces are still empty (see `plotly_draw`).""" + import plotly.graph_objects as go + + def _scalar(value): + if value is None or isinstance(value, str) or np.isscalar(value): + return value + values = [v for v in value if v is not None] + if not values: + return None + return (max(values) if all(np.isscalar(v) and not isinstance(v, str) + for v in values) else values[0]) + + line = trace.line.to_plotly_json() if trace.line is not None else {} + line['color'] = _scalar(line.get('color')) + common = dict(mode=trace.mode or 'lines', name=trace.name, + showlegend=True, legendgroup=group, hoverinfo='skip', + line=line, + # NOT `hyp_legend_entry`, which marks the forecast + # model keys (`_forecast_legend_traces`) + meta=dict(hyp_legend_proxy=str(trace.name))) + if trace.opacity is not None: + common['opacity'] = trace.opacity + if trace.mode and 'markers' in trace.mode and trace.marker is not None: + marker = {k: v for k, v in trace.marker.to_plotly_json().items() + if k in ('color', 'size', 'symbol', 'opacity')} + marker['color'] = _scalar(marker.get('color')) + _size = marker.get('size') + # a per-point size array (observation markers) folds back to the + # scalar size that draws like it (`_scalar_marker_size`) + marker['size'] = (_scalar_marker_size(_size, ndims) + if _size is not None and not np.isscalar(_size) + else _size) + common['marker'] = marker + if ndims >= 3: + return go.Scatter3d(x=[None], y=[None], z=[None], **common) + return go.Scatter(x=[None], y=[None], **common) + + +def _hover_name(trace_names, i): + """Data trace `i`'s name from `plotly_draw`'s `trace_names` (None when + it has none, or when no names were given).""" + if trace_names is None or i >= len(trace_names): + return None + name = trace_names[i] + return None if name is None else str(name) + + +def _hover_identity(name, trace_names, ndims): + """Extra properties that give a data trace its hover identity (1.1 + release review, maintainer finding: plotly showed "trace 0", "trace 1", + ... on hover because unlabelled data traces had no `name`). + + * a name shared by several data traces (the runs of one hue/cluster + category, a hierarchy group's leaves and means) becomes their + `legendgroup`, so the one legend entry of the group toggles all of + them; + * a trace with no name at all (a lone, unlabelled dataset) gets a + `hovertemplate` with an empty ``<extra></extra>``: plotly would + otherwise print "trace 0" in the name box, a label that names + nothing -- the coordinates alone are shown. + + Returns ``{}`` when `trace_names` was not given (a direct `plotly_draw` + caller), keeping that path exactly as it was. + """ + if trace_names is None: + return {} + if name is None: + coords = ('x: %{x}<br>y: %{y}<br>z: %{z}' if ndims >= 3 + else '(%{x}, %{y})') + return dict(hovertemplate=coords + '<extra></extra>') + if sum(1 for n in trace_names if n is not None and str(n) == name) > 1: + return dict(legendgroup=name) + return {} + + def _trace_name(legend, tkwargs, i): """This trace's plotly `name`, or None when it has no legend entry. @@ -3421,7 +5485,7 @@ def _add_animation(fig, data, ndims, animate, frame_rate, duration, trail_dataset_indices=None, forecast_schedule=None, forecast_trace_start=None, forecast_trace_specs=None, forecast_trail=0, - forecast_antialias=True, + forecast_antialias=True, forecast_datasets=None, surface=None, surface_colors=None, surface_trace_start=None, surface_dataset_indices=None, data_trace_start=0, @@ -3435,7 +5499,9 @@ def _add_animation(fig, data, ndims, animate, frame_rate, duration, frame_hooks=None, segment_titles=None, segment_title_style=None, segment_title_colors=None, ownership=None, - forecast_frame_colors=None, forecast_reveal=None): + forecast_frame_colors=None, forecast_reveal=None, + hue_units=None, hue_colors_3d=None, + truth_reveal=None, truth_frame_specs=None): """Attach frames + play controls: 'spin' rotates the camera; True / 'parallel' reveals trajectories through a sliding time window; 'morph' eases the single traveling point-cloud trace (+ mesh, if surfaced) @@ -3471,11 +5537,12 @@ def _add_animation(fig, data, ndims, animate, frame_rate, duration, `chemtrails`/`precog`/`bullettime` (GH #127): per-dataset bool lists (length `len(data)`, broadcast/validated by `plotly_draw`). Only datasets with at least one of the three flags set get a trail trace at - all -- `trail_dataset_indices[k]` is the ORIGINAL dataset index that - produced the trail trace at `fig.data[trail_trace_start + k]`, so each - frame's trail geometry is built from `chemtrails[i]`/`precog[i]`/ + all -- each trail trace's ``meta['hyp_trail_index']`` is the ORIGINAL + dataset index that produced it (an animated multicoloured 2-D line's + trail is several colour-bin traces, like its head), so each frame's + trail geometry is built from `chemtrails[i]`/`precog[i]`/ `bullettime[i]` for that SAME original dataset index `i`, not from the - trail trace's own position `k`. This applies to `animate=True`/ + trail trace's own position. This applies to `animate=True`/ `'parallel'` AND `animate='serial'` (backend parity, Task 4): the `'serial'` branch below builds the SAME per-dataset trail semantics as `matplotlib_backend.update_lines_serial` (the ONE currently-revealing @@ -3579,6 +5646,121 @@ def _add_animation(fig, data, ndims, animate, frame_rate, duration, frames = [] trace_indices = list(range(data_trace_start, data_trace_start + n_data_traces)) trail_dataset_indices = trail_dataset_indices or [] + + # observation markers (`_observation_marker`): a data or trail trace + # whose marker size is a per-vertex array over the dataset's whole + # smoothed curve gets, in every frame, the slice of it that matches the + # frame's window -- the markers stay on the observations that window + # holds instead of sliding with the window's start + _full_sizes = {} + for _k in range(len(fig.data)): + _m = fig.data[_k].marker if hasattr(fig.data[_k], 'marker') else None + _s = None if _m is None else _m.size + if _s is not None and not np.isscalar(_s): + _full_sizes[_k] = np.asarray(_s, dtype=float) + + def _marker_window(trace_index, idx, a, b): + """``{'marker': {'size': ...}}`` for trace `trace_index` (drawing + dataset `idx`) over ORIGINAL rows ``[a, b)``, or ``{}`` when that + trace's marker size is a plain scalar.""" + sizes = _full_sizes.get(trace_index) + if sizes is None or aa_curves is None: + return {} + return dict(marker=dict(size=_aa_window_sizes( + sizes, aa_curves[idx][1], a, b))) + + # A dataset's head (and trail) may be SEVERAL traces -- an animated + # multicoloured 2-D line is one trace per colour bin (`_hue_line_bins`) + # -- so frames address each dataset's own traces, found by their tags, + # in trace order. + hue_units = hue_units or {} + _head_traces, _trail_traces = {}, {} + for _k in range(data_trace_start, data_trace_start + n_data_traces): + _i = (fig.data[_k].meta or {}).get('hyp_trace_index') + if _i is not None: + _head_traces.setdefault(_i, []).append(_k) + if trail_trace_start is not None: + for _k in range(trail_trace_start, trail_trace_start + n_trail_traces): + _i = (fig.data[_k].meta or {}).get('hyp_trail_index') + if _i is not None: + _trail_traces.setdefault(_i, []).append(_k) + if _head_traces: + trace_indices = [k for i in sorted(_head_traces) + for k in _head_traces[i]] + # per-vertex colour arrays a trace carries over its dataset's whole + # curve (a multicoloured 3-D line and its trail): every frame sends the + # slice matching its window, so the colours travel with the data + # (1.1 release review: frames rewrote only the geometry, painting a late + # window with the colours of the trajectory's first rows) + _full_colors = {} + for _k in list(trace_indices) + [k for ks in _trail_traces.values() + for k in ks]: + _tr = fig.data[_k] + for _part in ('line', 'marker'): + _obj = getattr(_tr, _part, None) + _c = None if _obj is None else _obj.color + if _c is not None and not isinstance(_c, str) \ + and len(_c) > 1: + _full_colors[(_k, _part)] = list(_c) + + def _dense_span(idx, a, b): + """Vertices ``v0..v1`` (inclusive) of dataset `idx`'s drawn curve + that ORIGINAL rows ``[a, b)`` span (`_aa_window`'s arithmetic).""" + step = max(int(aa_curves[idx][1]), 1) + return a * step, (b - 1) * step + + def _entries(idx, trace_ids, a, b): + """The frame payload for the traces `trace_ids` that draw dataset + `idx`'s head (or trail) over ORIGINAL rows ``[a, b)``, one per + trace, in order.""" + if not trace_ids: + return [] + if idx in hue_units: + unit = hue_units[idx] + v0, v1 = _dense_span(idx, a, b) + out = [] + for k in trace_ids: + meta = fig.data[k].meta or {} + if 'hyp_hue_bin' in meta: + bx, by = _binned_polylines( + unit['xs'], unit['ys'], unit['bins']['seg_bin'], + meta['hyp_hue_bin'], v0, v1) + out.append(go.Scatter(x=bx, y=by)) + else: + # the observation markers the window holds + mk = unit.get('markers') + verts = (np.zeros(0, dtype=int) if mk is None + or b <= a else mk['vertices'][ + (mk['vertices'] >= v0) + & (mk['vertices'] <= v1)]) + out.append(go.Scatter( + x=unit['xs'][verts], y=unit['ys'][verts], + marker=dict(color=[mk['colors'][j] for j in verts] + if mk is not None else []))) + return out + k = trace_ids[0] + seg = _aa_window(aa_curves, idx, a, b) + extra = dict(_marker_window(k, idx, a, b)) + for part in ('line', 'marker'): + full = _full_colors.get((k, part)) + if full is not None: + extra.setdefault(part, {})['color'] = _aa_window_sizes( + full, aa_curves[idx][1], a, b) + if ndims >= 3: + return [go.Scatter3d(x=seg[:, 0], y=seg[:, 1], z=seg[:, 2], + **extra)] + if ndims == 2: + return [go.Scatter(x=seg[:, 0], y=seg[:, 1], **extra)] + return [go.Scatter(x=_aa_x(aa_curves[idx][1], a, seg.shape[0]), + y=seg[:, 0], **extra)] + + def _artists(entries_by_unit): + """`FrameContext.artists`: ONE artist per dataset head (then per + trail) -- a dataset drawn by several colour-bin traces is handed + over as one `PlotlyTraceGroup`, so a callback (`dataset_fade=`) + styling ``ctx.artists[i]`` styles the whole of dataset `i`.""" + return tuple(e[0] if len(e) == 1 else PlotlyTraceGroup(e) + for e in entries_by_unit if e) chemtrails = chemtrails if chemtrails is not None else [False] * len(data) precog = precog if precog is not None else [False] * len(data) bullettime = bullettime if bullettime is not None else [False] * len(data) @@ -3653,6 +5835,13 @@ def _forecast_frame_data(k, anchor_rows): out = [] _colors = forecast_frame_colors or [] for _spec, (dataset, age) in enumerate(forecast_trace_specs): + # `dataset` is the FORECAST's index (model-major for a + # collection); the reveal schedule and the anchor rows are per + # SOURCE dataset (Codex round 3: an IndexError for two models + # x hue regrouping) + _src = (forecast_datasets[dataset] + if forecast_datasets is not None + and dataset < len(forecast_datasets) else dataset) if age == 0: fit_frame = k pts = forecast_schedule.polyline(dataset, k) @@ -3669,7 +5858,7 @@ def _forecast_frame_data(k, anchor_rows): _line = None if (forecast_reveal is not None and fit_frame is not None and _spec < len(_colors) and _colors[_spec]): - _run = forecast_reveal.head_run(dataset, fit_frame) + _run = forecast_reveal.head_run(_src, fit_frame) _colour = _colors[_spec].get(_run) if _colour is not None: _line = dict(color=_colour) @@ -3685,6 +5874,15 @@ def _forecast_frame_data(k, anchor_rows): draw, step = (antialias_line(pts) if forecast_antialias else (pts, 1)) _extra = {} if _line is None else dict(line=_line) + _base = fig.data[forecast_trace_indices[_spec]] + if step != 1 and _base.mode and 'markers' in _base.mode: + # a `forecast_fmt='o:'` marker on each forecast STEP, not on + # every vertex of this frame's smoothed curve (the static + # overlay's rule, `_observation_marker`) + _sizes = np.zeros(draw.shape[0]) + _sizes[::int(step)] = _array_marker_size( + _base.marker.size, ndims) + _extra['marker'] = dict(size=_sizes) if ndims >= 3: out.append(go.Scatter3d(x=draw[:, 0], y=draw[:, 1], z=draw[:, 2], **_extra)) @@ -3692,11 +5890,29 @@ def _forecast_frame_data(k, anchor_rows): out.append(go.Scatter(x=draw[:, 0], y=draw[:, 1], **_extra)) else: out.append(go.Scatter( - x=_aa_x(step, anchor_rows.get(dataset, 0), + x=_aa_x(step, anchor_rows.get(_src, 0), draw.shape[0]), y=draw[:, 0], **_extra)) return out + truth_trace_indices = ([spec[0] for spec in truth_frame_specs] + if truth_reveal is not None and truth_frame_specs + else []) + + def _truth_frame_data(k): + """One geometry update per `truth=` trace at frame `k`: the rows + the drawn forecasts have reached so far (`truth_reveal`), in + `truth_trace_indices` order.""" + out = [] + row = truth_reveal[min(k, len(truth_reveal) - 1)] + for _idx, i, cols, step, sizes in truth_frame_specs: + cols, sz = _truth_slice(cols, step, sizes, row, i) + extra = {} if sz is None else dict(marker=dict(size=sz)) + xyz = dict(zip('xyz', cols)) + out.append(go.Scatter3d(**xyz, **extra) if ndims >= 3 + else go.Scatter(**xyz, **extra)) + return out + def _window_colors(idx, start, stop): """Dataset `idx`'s per-point hue RGB array sliced to the ``[start:stop]`` row window (rows are aligned 1:1 with `data[idx]` @@ -3990,6 +6206,9 @@ def _window_colors(idx, start, stop): revealed = total_points * k / max(1, n_frames - 1) frame_traces = [] trail_traces = [] + # this frame's payload per dataset head / trail, for + # `FrameContext.artists` (`_artists`) + head_units, trail_units = [], [] windows_by_index = {} window_colors_by_index = {} head_bounds_by_index = {} @@ -4031,39 +6250,19 @@ def _window_colors(idx, start, stop): if cols is not None: window_colors_by_index[idx] = cols - draw_seg = _aa_window(aa_curves, idx, *head_bounds) - if ndims >= 3: - frame_traces.append(go.Scatter3d( - x=draw_seg[:, 0], y=draw_seg[:, 1], z=draw_seg[:, 2])) - elif ndims == 2: - frame_traces.append(go.Scatter(x=draw_seg[:, 0], - y=draw_seg[:, 1])) - else: - frame_traces.append(go.Scatter( - x=_aa_x(aa_curves[idx][1], head_bounds[0], - draw_seg.shape[0]), - y=draw_seg[:, 0])) - - if has_trail: - t0, t1 = trail_bounds if trail_bounds is not None else (0, 0) - trail = _aa_window(aa_curves, idx, t0, t1) - if ndims >= 3: - trail_traces.append(go.Scatter3d( - x=trail[:, 0], y=trail[:, 1], z=trail[:, 2])) - elif ndims == 2: - trail_traces.append(go.Scatter(x=trail[:, 0], - y=trail[:, 1])) - else: - trail_traces.append(go.Scatter( - x=_aa_x(aa_curves[idx][1], t0, trail.shape[0]), - y=trail[:, 0])) - elif has_trails and idx in trail_dataset_indices: - # this dataset has a trail TRACE but no trail THIS frame - empty = np.zeros((0, max(2, min(3, ndims)))) - trail_traces.append( - go.Scatter3d(x=empty[:, 0], y=empty[:, 0], - z=empty[:, 0]) if ndims >= 3 - else go.Scatter(x=empty[:, 0], y=empty[:, 0])) + _heads = _entries(idx, _head_traces.get(idx, []), + *head_bounds) + frame_traces.extend(_heads) + head_units.append(_heads) + + if has_trails and idx in _trail_traces: + # (0, 0) -- an empty window -- when this dataset has a + # trail TRACE but no trail THIS frame + t0, t1 = (trail_bounds if has_trail + and trail_bounds is not None else (0, 0)) + _trails = _entries(idx, _trail_traces[idx], t0, t1) + trail_traces.extend(_trails) + trail_units.append(_trails) frame_traces.extend(trail_traces) frame_kwargs = dict(name=str(k), data=frame_traces, @@ -4091,6 +6290,12 @@ def _window_colors(idx, start, stop): k, forecast_anchors)) frame_kwargs['traces'] = (list(frame_kwargs['traces']) + forecast_trace_indices) + if truth_trace_indices: + # truth= rows appear once a drawn forecast reaches them + frame_kwargs['data'] = (list(frame_kwargs['data']) + + _truth_frame_data(k)) + frame_kwargs['traces'] = (list(frame_kwargs['traces']) + + truth_trace_indices) # `_shown`/`lengths` are ALREADY built above (one entry per # dataset, from the per-idx loop just above) -- reused here # rather than re-derived, same as `lengths`/`starts` themselves @@ -4117,11 +6322,20 @@ def _window_colors(idx, start, stop): segment_title_colors)) if frame_hooks is not None: frame_hooks.record( - frame=k, n_frames=n_frames, artists=tuple(frame_traces), + frame=k, n_frames=n_frames, + artists=_artists(head_units + trail_units), datasets=tuple(data), style='serial', order='serial', current_index=_serial_idx, current_fraction=_serial_frac, revealed_counts=tuple(_shown), - window_bounds=tuple((0, c) for c in _shown)) + # the DRAWN head window -- a trailed dataset's comet + # head starts after its trail, not at row 0 (the + # matplotlib serial updater reports the same; the + # FrameContext contract says every field but the + # figure/axes/artists agrees across backends) + window_bounds=tuple( + tuple(int(v) for v in head_bounds_by_index.get( + i, (0, c))) + for i, c in enumerate(_shown))) frame_hooks.dispatch(fig, None) if dynamic_title is not None and 'text' in dynamic_title: # GH #285: a callable / `{index...}` title=, computed @@ -4180,6 +6394,7 @@ def _window_colors(idx, start, stop): frame_windows = dataset_window_bounds( k, n_frames, ownership, _grid_lengths, window_frames) frame_traces = [] + head_units, trail_units = [], [] windows_by_index = {} window_colors_by_index = {} forecast_anchors = {} @@ -4206,17 +6421,9 @@ def _window_colors(idx, start, stop): window_colors_by_index[idx] = cols # antialias=: `seg` (ORIGINAL rows) still drives the surface # mesh/hue windows above; only the DRAWN vertices are smoothed - draw_seg = _aa_window(aa_curves, idx, start, end) - if ndims >= 3: - frame_traces.append(go.Scatter3d( - x=draw_seg[:, 0], y=draw_seg[:, 1], z=draw_seg[:, 2])) - elif ndims == 2: - frame_traces.append(go.Scatter(x=draw_seg[:, 0], - y=draw_seg[:, 1])) - else: - frame_traces.append(go.Scatter( - x=_aa_x(aa_curves[idx][1], start, draw_seg.shape[0]), - y=draw_seg[:, 0])) + _heads = _entries(idx, _head_traces.get(idx, []), start, end) + frame_traces.extend(_heads) + head_units.append(_heads) # GH #127: trail traces exist (and are updated here) only for # datasets in `trail_dataset_indices`, in that SAME ascending @@ -4255,17 +6462,10 @@ def _window_colors(idx, start, stop): t0, t1 = _twin.future_start, arr.shape[0] # antialias=: trail bounds stay ORIGINAL-row indices; the # smooth curve spanning exactly those rows is drawn - trail = _aa_window(aa_curves, idx, t0, t1) - if ndims >= 3: - trail_traces.append(go.Scatter3d( - x=trail[:, 0], y=trail[:, 1], z=trail[:, 2])) - elif ndims == 2: - trail_traces.append(go.Scatter( - x=trail[:, 0], y=trail[:, 1])) - else: - trail_traces.append(go.Scatter( - x=_aa_x(aa_curves[idx][1], t0, trail.shape[0]), - y=trail[:, 0])) + _trails = _entries(idx, _trail_traces.get(idx, []), + t0, t1) + trail_traces.extend(_trails) + trail_units.append(_trails) frame_traces.extend(trail_traces) frame_kwargs = dict(name=str(k), data=frame_traces, traces=list(trace_indices)) @@ -4291,9 +6491,16 @@ def _window_colors(idx, start, stop): k, forecast_anchors)) frame_kwargs['traces'] = (list(frame_kwargs['traces']) + forecast_trace_indices) + if truth_trace_indices: + # truth= rows appear once a drawn forecast reaches them + frame_kwargs['data'] = (list(frame_kwargs['data']) + + _truth_frame_data(k)) + frame_kwargs['traces'] = (list(frame_kwargs['traces']) + + truth_trace_indices) if frame_hooks is not None: frame_hooks.record( - frame=k, n_frames=n_frames, artists=tuple(frame_traces), + frame=k, n_frames=n_frames, + artists=_artists(head_units + trail_units), datasets=tuple(data), style=animate, order='parallel', current_index=None, current_fraction=None, revealed_counts=tuple(e for _, e in head_bounds), @@ -4312,6 +6519,37 @@ def _window_colors(idx, start, stop): frames.append(go.Frame(**frame_kwargs)) fig.frames = frames + + # a dynamic (callable / `{index...}`) title only has text now that + # every frame is built: reserve top margin for the TALLEST title any + # frame draws (1.1 release review T7) -- every frame is a plain dict, + # so this is one pass over strings, and it can never clip a later + # frame the way a frame-0-only measurement could. + _frame_titles = [] + for _frame in frames: + _t = getattr(getattr(_frame.layout, 'title', None), 'text', None) + if _t: + _frame_titles.append(_t) + # ...and a title an `on_frame=` callback set on the figure itself: with + # no title= the layout reserved only 10 px, so it rendered cut off at + # the top of the canvas (1.1 visual review L11) + _layout_title = getattr(getattr(fig.layout, 'title', None), 'text', None) + if frame_hooks is not None and _layout_title: + _frame_titles.append(_layout_title) + if _frame_titles: + _size_px = round(12 * PT_TO_PX) + if segment_title_style and segment_title_style.get('font'): + _size_px = segment_title_style['font'].get('size', _size_px) + elif fig.layout.title and fig.layout.title.font \ + and fig.layout.title.font.size: + _size_px = fig.layout.title.font.size + _needed = _title_margin_top(_plotly_title_lines(*_frame_titles), + _size_px, fig.layout.height or 504) + _current = (fig.layout.margin.t + if fig.layout.margin and fig.layout.margin.t is not None + else 10) + if _needed > _current: + fig.update_layout(margin=dict(t=_needed)) # Play-button pacing is the TRUE inter-frame interval, `1000 / frame_rate` # -- byte-identical to the `interval=` matplotlib hands `FuncAnimation`, # and the same rule the GIF/APNG export path above already documents ("NOT @@ -4331,14 +6569,30 @@ def _window_colors(idx, start, stop): # update_layout merges nested dicts, so l/r/t margins are preserved. # Symmetric `pad` centers each label in its button (the default padding # made 'Play' sit noticeably off-center). + # + # A VISIBLE 2-D x axis (`axis_scale='data'`, an `ndims=1` series, a date + # axis) draws its tick labels -- two lines on a date axis -- and its + # title exactly where y=-0.06 put the controls, which covered them (1.1 + # release review: the "2020" under the first date tick). The controls + # then go below that band, and the margin grows to hold both. + _menu_y, _margin_b = -0.06, _ANIM_BUTTON_MARGIN_B + _band = _x_axis_band_px(fig, ndims) + if _band: + _margin_b = max(_ANIM_BUTTON_MARGIN_B, + _band + _ANIM_BUTTON_HEIGHT_PX + 2 * _ANIM_BUTTON_GAP_PX) + _height = fig.layout.height or int(DEFAULT_FIGSIZE[1] * 100) + _top = (fig.layout.margin.t if fig.layout.margin + and fig.layout.margin.t is not None else 10) + _plot_h = max(_height - _top - _margin_b, 1) + _menu_y = -(_band + _ANIM_BUTTON_GAP_PX) / _plot_h fig.update_layout( - margin=dict(b=_ANIM_BUTTON_MARGIN_B), + margin=dict(b=_margin_b), updatemenus=[dict( type='buttons', direction='right', showactive=False, x=0, xanchor='left', - y=-0.06, yanchor='top', + y=_menu_y, yanchor='top', pad=dict(l=8, r=8, t=6, b=6), bgcolor='rgba(255,255,255,0.95)', bordercolor='rgba(0,0,0,0.22)', diff --git a/hypertools/plot/trails.py b/hypertools/plot/trails.py index 22246f5c..55a65404 100644 --- a/hypertools/plot/trails.py +++ b/hypertools/plot/trails.py @@ -31,16 +31,20 @@ def anim_window_bounds(num, total_frames, n_points, window_frames): Animations are paced by the FRAME grid (``total_frames == round(frame_rate * duration)``), not by any single dataset's row count: - line datasets are pre-interpolated onto that exact grid by ``plot.py`` - (identity mapping), while marker-only and 1-point datasets keep their - raw rows and are paced here instead (release-1.0 audit: F04-003 - multi-dataset truncation, F04-005/F05-010 marker-only pacing, F05-012 - single-point datasets). The identity holds for every frame count but - ONE: a request so short that ``round(frame_rate * duration)`` falls - below 2 still resamples lines to 2 rows, because PCHIP needs two - samples to interpolate between. Such a dataset takes the rescale branch - below like any other off-grid one, and both backends still agree, - because both consume the same resampled array. + every dataset is paced here onto the frames from its own row count + (release-1.0 audit: F04-003 multi-dataset truncation, F04-005/F05-010 + marker-only pacing, F05-012 single-point datasets). A line dataset + reaches this function on ``plot._interp_anim_line``'s grid -- at least + `total_frames` rows and a uniform refinement of its observations, so + no observation is ever dropped (1.1 visual review, L8) -- and any + dataset with at least one row per frame is revealed from its first row + on frame 0 to its last on the final frame, which is exactly the + identity when the grid equals the frame count (the only grid lines had + before L8), so a line's reveal timing is unchanged. Marker-only and + 1-point datasets keep their raw rows; one with fewer rows than frames + shows each row for an equal share of the frames. Both backends agree + whatever the row count, because both consume the same array through + this one function. BOTH backends call this one function, per dataset, per frame -- that is the point of it living here rather than inside either backend. The @@ -73,9 +77,11 @@ def anim_window_bounds(num, total_frames, n_points, window_frames): frozen at the trajectory's end once the dataset is fully revealed -- a shorter dataset never vanishes mid-animation. Its length tops out at ``w + 1`` rows, where ``w`` is ``window_frames`` for a dataset - already on the frame grid and the RESCALED - ``round(window_frames * n_points / total_frames)`` for one that is - not: a 5-row dataset in a 15-frame animation with + with exactly one row per frame, the RESCALED + ``round(window_frames * (n_points - 1) / (total_frames - 1))`` for + a longer one (the same span of the trajectory), and + ``round(window_frames * n_points / total_frames)`` for a shorter + one: a 5-row dataset in a 15-frame animation with ``window_frames=2`` gets ``w = 1``, so its head maxes out at 2 rows, not 3. A chemtrails trail is ``data[0:trail_stop]`` -- 0 rows until the head window actually @@ -86,13 +92,27 @@ def anim_window_bounds(num, total_frames, n_points, window_frames): last vertex, so there is no one-segment gap -- F05-008). """ total = max(1, int(total_frames)) - end = int(np.ceil((num + 1) * n_points / total)) - end = max(1, min(n_points, end)) - if n_points == total: - w = int(window_frames) + n_points = int(n_points) + if n_points >= total and total > 1: + # at least one row per frame -- every animated LINE, whose grid + # `plot._interp_anim_line` never makes shorter than the frame count: + # frame 0 draws the FIRST row and the last frame the whole dataset, + # the head at row floor(num * (n - 1) / (total - 1)). With exactly + # one row per frame that is the identity (row `num`); on a longer + # grid it is the same timing, so a line's reveal did not change + # when its grid stopped being DOWNsampled to the frame count (1.1 + # visual review, L8). Exact integer arithmetic, so the identity + # holds at every frame. + end = int(num) * (n_points - 1) // (total - 1) + 1 + w = (int(window_frames) if n_points == total + else int(round(window_frames * (n_points - 1) / (total - 1)))) else: + # fewer rows than frames (a marker-only or 1-point dataset): each + # row is on screen for an equal share of the frames + end = int(np.ceil((num + 1) * n_points / total)) # rescale the window (given in frames) onto this dataset's rows w = int(round(window_frames * n_points / total)) + end = max(1, min(n_points, end)) start = max(0, end - 1 - w) trail_stop = max(0, end - w) return start, end, trail_stop diff --git a/hypertools/predict/arima.py b/hypertools/predict/arima.py index 76c21505..0d8ee375 100644 --- a/hypertools/predict/arima.py +++ b/hypertools/predict/arima.py @@ -164,11 +164,28 @@ def applier(fitted_params, new_data, t): return pd.DataFrame(columns, index=future_index, columns=new_data.columns) +def _lag_order(component): + """An ARIMA ``order`` component as a lag count: an int as is, a + statsmodels sparse lag sequence (``[1, 3]``) as its highest lag (``0`` + for an empty one).""" + if isinstance(component, (list, tuple, np.ndarray)): + return int(max((int(v) for v in component), default=0)) + return int(component) + + class ARIMA(Forecaster): """Per-column ARIMA forecaster (statsmodels). Parameters ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None infers it per dataset: a datetime index's + calendar frequency when it has one (business days, month starts, a + ``PeriodIndex``'s periods...), else the median positive gap between + sorted observation times. Numerical indexes use their own units; + datetime/duration indexes need a duration such as '1h' (datetime + indexes also take a calendar frequency such as 'B' or 'MS'). + See `hypertools.predict` for the interpolation and reuse policies. order : tuple of (p, d, q) ARIMA order (default: ``(1, 1, 1)``). The default suits drift/random-walk-like signals only -- it damps to a near-constant @@ -177,11 +194,79 @@ class ARIMA(Forecaster): **kwargs Passed through to ``statsmodels.tsa.arima.model.ARIMA`` (unknown keyword arguments therefore raise ``TypeError`` from statsmodels). + + Notes + ----- + ``min_history`` (see `Forecaster.min_history`) is computed from the + order and any ``seasonal_order`` by `min_history_for`: 3 rows for the + default ``(1, 1, 1)``, 27 for ``(1, 1, 1) x (1, 1, 1, 12)``. `fit` + raises a ``ValueError`` naming the model, its orders and that count for a + shorter history (statsmodels used to raise a bare ``IndexError``), and + an animated ``hyp.plot(..., predict='ARIMA')`` draws no forecast on the + frames that have revealed fewer rows than that. """ - def __init__(self, order=(1, 1, 1), **kwargs): + @classmethod + def min_history_for(cls, order=(1, 1, 1), seasonal_order=(0, 0, 0, 0), + **kwargs): + """The fewest rows an ARIMA of this ``order`` and ``seasonal_order`` + can be fit on. + + ``max(d + 2, p + q + 1)``: statsmodels differences the series ``d`` + times and needs at least TWO rows left afterwards (measured on + statsmodels 0.14: every order with ``d >= 1`` raised a bare + ``IndexError`` from a ``d + 1``-row history, and ``d = 0`` a + ``ValueError`` from one row), and a fit with fewer rows than ARMA + coefficients plus one has nothing to estimate them from. The default + ``(1, 1, 1)`` therefore needs 3 rows; ``(4, 0, 0)`` needs 5. + + ``p`` and ``q`` may also be statsmodels' SPARSE lag form -- a + sequence of the lags to include, ``([1, 3], 0, 0)`` -- in which + case the highest lag is the order that counts (the fit needs that + many earlier rows), so the check accepts every order the fitter + does (1.1 release review, round 2). + + A ``seasonal_order=(P, D, Q, s)`` works the same way in lags of + ``s`` rows: the seasonal differencing consumes ``D * s`` more rows, + and the highest AR/MA lags become ``p + P*s`` and ``q + Q*s``, so the + floor is ``max(d + D*s + 2, p + P*s + q + Q*s + 1)`` (``P``/``Q`` may + be sparse lag lists too). Measured on statsmodels 0.14: a + ``(1, 1, 1) x (1, 1, 1, 12)`` fit raised a bare ``IndexError`` from + 14 rows, and ``(1, 1, 0) x (0, 1, 1, 7)`` a ``LinAlgError`` from 3-8 + rows and an ``IndexError`` from 9 (release review 2026-09-11); the + floors are 27 and 10. + """ + p, d, q = order + seasonal_p, seasonal_d, seasonal_q, period = ( + tuple(seasonal_order) if seasonal_order is not None + else (0, 0, 0, 0)) + period = int(period) + ar_lags = _lag_order(p) + _lag_order(seasonal_p) * period + ma_lags = _lag_order(q) + _lag_order(seasonal_q) * period + differenced = _lag_order(d) + _lag_order(seasonal_d) * period + return max(differenced + 2, ar_lags + ma_lags + 1) + + @property + def min_history(self): + """`min_history_for(self.order, seasonal_order)` -- see + `Forecaster.min_history`.""" + return self.min_history_for(self.order, self._seasonal_order()) + + def _seasonal_order(self): + return self.kwargs.get('seasonal_order', (0, 0, 0, 0)) + + def _min_history_detail(self): + seasonal = self._seasonal_order() + if seasonal is not None and any(_lag_order(v) for v in tuple(seasonal)[:3]): + return (f'(order={tuple(self.order)!r}, ' + f'seasonal_order={tuple(seasonal)!r})') + return f'(order={tuple(self.order)!r})' + + _regular_time_grid = True + + def __init__(self, order=(1, 1, 1), step=None, **kwargs): required = ['results'] - super().__init__(order=order, fitter=fitter, forecaster=forecaster, applier=applier, + super().__init__(step=step, order=order, fitter=fitter, forecaster=forecaster, applier=applier, data=None, required=required, **kwargs) self.order = order diff --git a/hypertools/predict/autoreg.py b/hypertools/predict/autoreg.py index def89db3..5e12e0ac 100644 --- a/hypertools/predict/autoreg.py +++ b/hypertools/predict/autoreg.py @@ -200,6 +200,14 @@ class AutoRegressor(Forecaster): Parameters ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None infers it per dataset: a datetime index's + calendar frequency when it has one (business days, month starts, a + ``PeriodIndex``'s periods...), else the median positive gap between + sorted observation times. Numerical indexes use their own units; + datetime/duration indexes need a duration such as '1h' (datetime + indexes also take a calendar frequency such as 'B' or 'MS'). + See `hypertools.predict` for the interpolation and reuse policies. model : str, class, or instance Regressor to use. String names are resolved from a small registry (Ridge, Lasso, LinearRegression, RandomForestRegressor, @@ -231,7 +239,22 @@ class AutoRegressor(Forecaster): {'model': 'Ridge'}}``. """ - def __init__(self, model='Ridge', lags=10, model_kwargs=None, **kwargs): + _regular_time_grid = True + + @classmethod + def min_history_for(cls, model='Ridge', lags=10, **kwargs): + """Lagged predictors need at least one subsequent target row.""" + return int(lags) + 1 + + @property + def min_history(self): + """Minimum observed rows needed to fit this lag configuration.""" + return self.min_history_for(lags=self.lags) + + def _min_history_detail(self): + return f'(lags={self.lags})' + + def __init__(self, model='Ridge', lags=10, model_kwargs=None, step=None, **kwargs): # validate lags up front (2026-07 release audit, final wave item 12): # lags=0 leaked sklearn's "Found array with 0 feature(s)", negative # values built nonsense designs, and a float crashed with a raw @@ -249,7 +272,7 @@ def __init__(self, model='Ridge', lags=10, model_kwargs=None, **kwargs): if kwargs: model_kwargs = {**(model_kwargs or {}), **kwargs} required = ['estimator', 'lags', 'history', 'n_features'] - super().__init__(model=model, lags=lags, model_kwargs=model_kwargs, + super().__init__(step=step, model=model, lags=lags, model_kwargs=model_kwargs, fitter=fitter, forecaster=forecaster, applier=applier, data=None, required=required) diff --git a/hypertools/predict/backtest.py b/hypertools/predict/backtest.py index 953e50f2..fbba6b12 100644 --- a/hypertools/predict/backtest.py +++ b/hypertools/predict/backtest.py @@ -18,11 +18,14 @@ occluded-cells-only per-axis error tables of ``docs/tutorials/projectile_kalman.ipynb`` cells 9 and 13. """ +import copy import warnings import numpy as np import pandas as pd +from .common import Forecaster + #: metric keys accepted by ``metrics=``, in the default order. The FIRST #: entry is the RANKING metric (see `build_scores`' ``attrs['best']``). @@ -106,6 +109,15 @@ def resolve_metrics(metrics): raise ValueError( f'unknown metric {m!r}; supported: ' f'{", ".join(METRIC_FUNCS)}.') + # a repeated metric (case-insensitively: 'mae' and 'MAE' are the + # same column) would give the scores frame two columns with one + # label, and `build_scores` then reads a 2-column frame where it + # expects a scalar (TypeError deep in the verdict). Name the + # repeat here instead. + if m.lower() in resolved: + raise ValueError( + f'metric {m!r} is listed more than once in ' + f'metrics={metrics!r}; each metric may appear only once.') resolved.append(m.lower()) return tuple(resolved) @@ -235,7 +247,12 @@ def build_scores(records, metrics, per_column=False, baseline=None, per-column rows, indexed by (model[, dataset], column). baseline : str or None The row name of the always-present baseline, excluded from the - "best" verdict. + "best" verdict. Only COMPLETE models (``unscored == 0`` summed over + every dataset and column) are ranked; rows that left values + missing are named in ``attrs['incomplete']``, ``attrs['best']`` is + None when no model is complete, and ``attrs['beats_baseline']`` is + None when there is no best model or the baseline is itself + incomplete. extra : dict or None Extra scalar columns (e.g. ``{'horizon': 30}``) appended to both forms. @@ -253,26 +270,61 @@ def build_scores(records, metrics, per_column=False, baseline=None, wide = grouped[labels].mean() wide['n'] = grouped['n'].sum() wide['unscored'] = grouped['unscored'].sum() - for name, unscored in wide['unscored'].items(): - # a model that failed to produce some values is scored on FEWER - # entries than the others, so its row is not directly comparable; - # say so instead of letting the smaller `n` pass unnoticed. - if unscored: + # local import (as in `Forecaster.fit_predict`) keeps the numeric core + # of this module free of hypertools imports at module load + from ..core.model import external_stacklevel + # COVERAGE (release red-team 2026-10-09). A row that left any value + # missing is scored on FEWER -- and possibly easier -- entries than the + # others, so its metrics describe it but cannot be RANKED against rows + # that produced every value: PPCA, which skips fully-missing rows, + # used to "win" with the error of the easy cells alone. `unscored` is + # summed over every (dataset, column) the verdict averages, so a model + # incomplete anywhere is incomplete for the verdict. + incomplete = [name for name, unscored in wide['unscored'].items() + if unscored] + for name in incomplete: + unscored = int(wide.loc[name, 'unscored']) + total = int(unscored + wide.loc[name, 'n']) + if name == baseline: warnings.warn( - f'model {name!r} left {int(unscored)} of ' - f'{int(unscored + wide.loc[name, "n"])} scored {kind}(s) ' - 'missing (NaN); its scores cover only the ones it produced, ' - 'so they are not directly comparable to models that ' - 'produced every value.') + f'baseline {name!r} left {unscored} of {total} scored ' + f'{kind}(s) missing (NaN); its scores cover only the ones ' + 'it produced, so they are not directly comparable to ' + 'models that produced every value. No comparison against ' + "a partial baseline is made: attrs['beats_baseline'] is " + 'None.', stacklevel=external_stacklevel()) + else: + warnings.warn( + f'model {name!r} left {unscored} of {total} scored ' + f'{kind}(s) missing (NaN); its scores cover only the ones ' + 'it produced, so they are not directly comparable to ' + 'models that produced every value. It is excluded from ' + "the ranking (attrs['best']) and listed in " + "attrs['incomplete'].", stacklevel=external_stacklevel()) for key, value in extra.items(): wide[key] = value primary = labels[0] - candidates = wide.drop(index=baseline, errors='ignore')[primary].dropna() + models = wide.drop(index=baseline, errors='ignore') + candidates = models.loc[models['unscored'] == 0, primary].dropna() best = str(candidates.idxmin()) if len(candidates) else None baseline_score = (float(wide.loc[baseline, primary]) if baseline in wide.index else np.nan) best_score = float(wide.loc[best, primary]) if best is not None else np.nan + if best is None and len(models) and (models['unscored'] > 0).all(): + warnings.warn( + f'no model produced every scored {kind}, so none can be ranked: ' + "attrs['best'] and attrs['beats_baseline'] are None and " + "attrs['best_score'] is NaN. The rows still describe each " + 'model on the values it did produce.', + stacklevel=external_stacklevel()) + if best is None or baseline in incomplete: + # nothing to compare, or nothing complete to compare it against + beats_baseline = None + else: + beats_baseline = bool(np.isfinite(best_score) + and np.isfinite(baseline_score) + and best_score < baseline_score) attrs = { 'metric': primary, 'metrics': list(labels), @@ -280,9 +332,8 @@ def build_scores(records, metrics, per_column=False, baseline=None, 'baseline_score': baseline_score, 'best': best, 'best_score': best_score, - 'beats_baseline': bool(np.isfinite(best_score) - and np.isfinite(baseline_score) - and best_score < baseline_score), + 'beats_baseline': beats_baseline, + 'incomplete': [str(name) for name in incomplete], } attrs.update(extra) @@ -327,6 +378,11 @@ def resolve_holdout(holdout, n, t, caller='predict'): f'holdout must be an int (rows), a float in (0, 1) (fraction), ' f'or True (hold out t rows); got {holdout!r}') if k < 1: + if isinstance(holdout, (bool, np.bool_)): + # the offending value is t, not the literal True + raise ValueError( + f'holdout=True takes its size from t, so t must be >= 1 ' + f'row; got t={t!r}.') raise ValueError(f'holdout must be >= 1 row; got {holdout!r}') if n - k < 2: raise ValueError( @@ -349,15 +405,109 @@ def naive_forecast(train, index): index=index, columns=train.columns) -def backtest_predict(datasets, predict_fn, t, holdout, names, specs, +def _forecast_at_times(model, index): + """Evaluate a single fitted model at strictly future observation times. + + Release review 2026-09-08: held-out rows must be scored at their actual + times. GP evaluates those coordinates directly; other models forecast + a covering grid and linearly interpolate its predictions. The last + observed training row anchors times before the first complete step. + Only the training data determine the model and its step; held-out + values are never passed to this helper. + """ + from .time import resolve_step, time_coordinates + + data = model.data + params = model.models_[0] + step = resolve_step(data.index, params.get('_time_step', model.step)) + x = time_coordinates(index, data.index[-1], step) + if not np.isfinite(x).all() or np.any(x <= 0): + raise ValueError('held-out observation times must be finite and ' + 'strictly after the training history') + if getattr(model, '_uses_observation_times', False): + return model.forecaster(params.get('_time_data', data), len(index), + index, **{**params, **model.kwargs}) + + # Snap round-off at integer grid positions, preserving exact-grid + # forecasts and preventing a spurious extra step for sampled models. + nearest = np.rint(x) + on_grid = (nearest >= 1) & np.isclose(x, nearest, rtol=0., atol=1e-8) + x = np.where(on_grid, nearest, x) + if x.max() > 1_000_000: + raise ValueError('backtesting at these times would require more than ' + '1,000,000 forecast steps; use a larger step') + n_steps = int(np.ceil(x.max())) + forecast = model.predict(n_steps) + if np.shape(forecast) != (n_steps, data.shape[1]): + raise ValueError(f'{type(model).__name__} returned forecast shape ' + f'{np.shape(forecast)}; expected ' + f'{(n_steps, data.shape[1])} for the forecast grid') + if on_grid.all(): + result = forecast.iloc[nearest.astype(int) - 1].copy() + result.index = index + return result + + values = np.vstack([data.iloc[-1:].to_numpy(dtype=float), + np.asarray(forecast, dtype=float)]) + grid = np.arange(n_steps + 1) + result = pd.DataFrame( + np.column_stack([np.interp(x, grid, col) for col in values.T]), + index=index, columns=data.columns) + from ..core.model import external_stacklevel + warnings.warn( + f'{type(model).__name__} forecasts were linearly interpolated to ' + 'the held-out observation times before scoring. The last observed ' + 'training value anchors times before the first forecast step; ' + 'held-out values are never used for interpolation.', + UserWarning, stacklevel=external_stacklevel()) + return result + + +def _match_columns(forecast, columns, name): + """Put a forecast's columns in the held-out data's order, BY LABEL. + + Scoring pairs forecast and held-out columns by position. The built-in + forecasters label their output with the training columns, so this is a + no-op for them; a user forecaster that returns the same columns in + another order is reordered, and one that returns different (or + ambiguously repeated) column labels is refused rather than scored + against the wrong columns (release red-team 2026-10-09, the same rule + `hyp.impute(truth=)` applies). Rows are forecast STEPS and stay + positional by construction. + """ + # local import, as elsewhere in this module (no hypertools imports at load) + from .._shared.helpers import is_frame_dataset + import datawrangler as dw + if not (is_frame_dataset(forecast) and dw.zoo.dataframe_like(forecast)): + return forecast # only a pandas-API frame carries column labels here + own = forecast.columns + if own.equals(columns) or len(own) != len(columns): + return forecast # a shape mismatch is reported by the scorer + if not own.is_unique or not columns.is_unique: + raise ValueError( + f'model {name!r} returned forecast columns {list(own)} for data ' + f'with columns {list(columns)}; the column labels differ and ' + 'some are duplicated, so they cannot be paired unambiguously.') + missing = columns.difference(own, sort=False) + unexpected = own.difference(columns, sort=False) + if len(missing) or len(unexpected): + raise ValueError( + f"model {name!r} returned forecast column labels that do not " + f"match the data's (missing: {list(missing)}; unexpected: " + f'{list(unexpected)}); a forecast must keep the columns it was ' + 'fit on to be scored.') + return forecast.reindex(columns=columns) + + +def backtest_predict(datasets, make_forecaster, t, holdout, names, specs, metrics=None, per_column=False, return_forecasts=False, kwargs=None): """Hold-out backtest for `hypertools.predict.predict` (see its docs). `datasets` is a list of wrangled DataFrames (a single dataset is a - one-element list, flagged by `single` in the caller); `predict_fn` is - the public `predict`, injected to keep this module import-free of the - dispatcher. + one-element list). The shared `make_forecaster` factory keeps model + construction identical to ordinary forecasting without generating an + unused forecast before the evaluation times are known. """ metrics = resolve_metrics(metrics) kwargs = dict(kwargs or {}) @@ -370,16 +520,46 @@ def backtest_predict(datasets, predict_fn, t, holdout, names, specs, "row; rename the model with the mapping form, e.g. " "model={'my naive model': <spec>}.") - splits, horizons = [], [] + from .common import resolve_t + from .time import finalize_forecast, is_time_index, order_time_data + splits, horizons, timed, ordered = [], [], [], [] for d in datasets: k = resolve_holdout(holdout, len(d), t) + d = order_time_data(d) + ordered.append(d) + # Validate the complete time index: a duplicate may straddle the + # split while appearing unique in both halves separately. + use_times = is_time_index(d.index) + if use_times: + resolve_t(d, 1) + timed.append(use_times) splits.append((d.iloc[:-k], d.iloc[-k:])) horizons.append(k) forecasts = {} for name, spec in zip(names, specs): - per_dataset = [predict_fn(train, model=spec, t=k, **kwargs) - for (train, _), k in zip(splits, horizons)] + # GH #285 release review: an instance fitted on dataset 1 must + # never enter predict_new on dataset 2. A fitted input may already + # have seen the holdout, so it cannot provide a clean backtest. + candidate = spec.get('model') if isinstance(spec, dict) else spec + if isinstance(candidate, Forecaster) and candidate.is_fitted: + raise ValueError( + 'holdout= requires an unfitted model so held-out rows cannot ' + 'leak into training; pass a model name, class, or unfitted ' + 'instance instead.') + per_dataset = [] + for (train, held), k, use_times in zip(splits, horizons, timed): + fitted = make_forecaster(copy.deepcopy(spec), copy.deepcopy(kwargs)) + # Repeated numeric IDs and categorical labels are positional. + # Splitting can make formerly repeated IDs unique; + # explicitly retain their positional meaning during fitting. + fitted.fit(train if use_times else train.reset_index(drop=True)) + if use_times: + forecast = _forecast_at_times(fitted, held.index) + else: + forecast = fitted.predict(k) + forecast.index = held.index + per_dataset.append(_match_columns(forecast, held.columns, name)) forecasts[name] = per_dataset forecasts[baseline] = [naive_forecast(train, held.index) for (train, held) in splits] @@ -410,6 +590,11 @@ def backtest_predict(datasets, predict_fn, t, holdout, names, specs, kind='forecast value') if not return_forecasts: return scores - out = {name: (f[0] if single else f) for name, f in forecasts.items()} - out['truth'] = splits[0][1] if single else [held for _, held in splits] - return scores, out + # a PeriodIndex input was split on its start timestamps; hand every + # returned frame (forecasts, baseline, truth) back as periods + forecasts['truth'] = [held for _, held in splits] + forecasts = {name: [finalize_forecast(f, observed) + for f, observed in zip(frames, ordered)] + for name, frames in forecasts.items()} + return scores, {name: (f[0] if single else f) + for name, f in forecasts.items()} diff --git a/hypertools/predict/chronos.py b/hypertools/predict/chronos.py index 18a2d2de..4ae13603 100644 --- a/hypertools/predict/chronos.py +++ b/hypertools/predict/chronos.py @@ -129,6 +129,14 @@ class Chronos(Forecaster): Parameters ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None infers it per dataset: a datetime index's + calendar frequency when it has one (business days, month starts, a + ``PeriodIndex``'s periods...), else the median positive gap between + sorted observation times. Numerical indexes use their own units; + datetime/duration indexes need a duration such as '1h' (datetime + indexes also take a calendar frequency such as 'B' or 'MS'). + See `hypertools.predict` for the interpolation and reuse policies. model_name : str HuggingFace Hub id of the pretrained Chronos checkpoint (default: 'amazon/chronos-t5-tiny'). @@ -145,10 +153,12 @@ class Chronos(Forecaster): Nucleus-sampling cutoff (default: None, Chronos's own default). """ + _regular_time_grid = True + def __init__(self, model_name='amazon/chronos-t5-tiny', device_map='cpu', - num_samples=None, temperature=None, top_k=None, top_p=None): + num_samples=None, temperature=None, top_k=None, top_p=None, step=None): required = ['pipeline', 'series'] - super().__init__(model_name=model_name, device_map=device_map, + super().__init__(step=step, model_name=model_name, device_map=device_map, num_samples=num_samples, temperature=temperature, top_k=top_k, top_p=top_p, fitter=fitter, forecaster=forecaster, data=None, required=required) diff --git a/hypertools/predict/common.py b/hypertools/predict/common.py index df4cf195..61b3a58a 100644 --- a/hypertools/predict/common.py +++ b/hypertools/predict/common.py @@ -15,7 +15,7 @@ ``applier(fitted_params, new_data, t)`` callable; ``applier=None`` falls back to conditioning on the new data directly (see `Forecaster.predict_new`). """ -import warnings +import copy import numpy as np import pandas as pd @@ -23,45 +23,28 @@ from sklearn.exceptions import NotFittedError from ..core.shared import as_dataframe as _as_dataframe +from ..core.exceptions import _InsufficientHistoryError def _infer_step(index): - """The minimum non-zero difference between any pair of observations. + """Median positive gap between sorted observation times.""" + from .time import infer_step + return infer_step(index) - Adjacent (sorted) observations always yield the smallest gaps, so this - is computed from successive differences of the sorted index. - """ - if len(index) < 2: - return pd.Timedelta(1, unit='s') if isinstance(index, pd.DatetimeIndex) else 1 - - values = index.sort_values() - diffs = values[1:] - values[:-1] - - if isinstance(index, pd.DatetimeIndex): - nonzero = diffs[diffs != pd.Timedelta(0)] - # a real raise (not `assert ..., ValueError(...)`, which raises - # AssertionError and is stripped under `python -O`) -- QC 2026-07. - if len(nonzero) == 0: - raise ValueError('cannot infer a timestep: all observations ' - 'share one timestamp') - return nonzero.min() - diffs = np.asarray(diffs) - nonzero = diffs[diffs != 0] - if len(nonzero) == 0: - return 1 - return nonzero.min() - - -def resolve_t(data, t): +def resolve_t(data, t, step=None): """Resolve a forecast horizon into a step count and a continued index. Implements GH #169's ``t`` semantics: - - ``t`` an int: forecast ``t`` timesteps ahead. The timestep duration is - the minimum non-zero difference between any pair of observations - (index-aware for time-indexed data; a plain ``RangeIndex`` uses a step - of 1). + - ``t`` an int: forecast ``t`` timesteps ahead. One timestep is the + index's CALENDAR frequency when it has one -- stored, inferable with + ``pd.infer_freq``, a ``PeriodIndex``'s own, or business days for + weekday-only sessions -- so business-day data continue onto the next + business days and month starts onto month starts (tz-aware days keep + their local wall-clock time across DST); otherwise it is the median + positive gap between sorted observations (a plain ``RangeIndex`` uses + a step of 1). See `hypertools.predict.time`. - ``t`` a datetime-like value on time-indexed (``DatetimeIndex``) data: the number of steps (using the inferred step) from the last observation up to ``t``. If ``t`` is at or before the last @@ -76,16 +59,18 @@ def resolve_t(data, t): the last observation always forecasts at least one step (a target less than one full step ahead rounds up to a single step). A tz-naive ``t`` on tz-aware data is localized to the data's timezone. + On a ``PeriodIndex`` the periods' start times are compared, and ``t`` + may also be a ``pd.Period``. - An index that is not sorted ascending WARNS (forecasts continue from the - last row regardless), and a TIME index (`DatetimeIndex`, `TimedeltaIndex` + An index that is not sorted ascending WARNS and is sorted together with + its observations before fitting (forecasts continue from the latest time), and a TIME index (`DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`) carrying DUPLICATE entries raises a `ValueError`: the horizon is ill-defined when several observations share one position on the time axis. This is checked for every time-indexed input, flat or grouped (hypertools 1.1; `hyp.predict` names the offending group when the input was hierarchical). A duplicated NON-time index -- the stacked `pd.concat([run_a, run_b])` panel, whose index repeats 0..n-1 -- is - unaffected: its step is still the minimum non-zero difference and the + unaffected: its step is still the median positive gap and the forecast still continues from the last row. Parameters @@ -94,6 +79,9 @@ def resolve_t(data, t): The dataset whose index is being extended (or truncated). t : int or datetime-like The forecast horizon. + step : number, duration, calendar offset, or None + One step (see `hypertools.predict.time.resolve_step`); None uses the + step `data` was prepared with, else infers it from the index. Returns ------- @@ -101,25 +89,19 @@ def resolve_t(data, t): Number of steps to forecast; zero or negative means "truncate" (see above). future_index : pandas.Index - The continued index (or, for truncation, the sliced index). + The continued index (or, for truncation, the sliced index). A + ``PeriodIndex`` input is described by its start timestamps here; + `Forecaster` returns forecasts as periods again. """ + from .time import (default_step, future_times, is_calendar_step, + order_time_data, resolve_step, time_coordinates) + data = order_time_data(data) index = data.index if t is None: raise ValueError('t (forecast horizon) must be a positive integer ' 'or a target datetime; got None') - # a descending (e.g. newest-first CSV export) or otherwise unsorted index - # silently produced a "forecast" from the OLDEST observation, landing - # inside the observed range (QC 2026-07 red-team F16-predict-016). - if len(index) > 1 and not index.is_monotonic_increasing: - from ..core.model import external_stacklevel - warnings.warn( - 'the dataset index is not sorted in ascending order; forecasts ' - 'continue from the LAST row. If your data are newest-first, sort ' - 'them (e.g. df.sort_index()) before forecasting.', - stacklevel=external_stacklevel()) - # duplicate observation TIMES make the horizon ill-defined: `_infer_step` # would take the (zero-length) gap between the repeats out of the running # and forecast on a step that no longer describes the data, and a @@ -130,7 +112,7 @@ def resolve_t(data, t): # An unconditional check also rejected the `pd.concat([run_a, run_b])` # idiom, whose index repeats 0..n-1 -- measured at ea5d9b5e, that frame # forecast fine, and nothing about its horizon is ambiguous: the step is - # the minimum non-zero difference (1) and the forecast continues from the + # the median positive gap (1) and the forecast continues from the # last row. Rejecting it contradicts Decisions #4's own "legitimate # integer-indexed panels are not rejected", and the message's argument # ("one position on the time axis") does not describe a positional index. @@ -154,15 +136,23 @@ def resolve_t(data, t): 'Aggregate the repeats (e.g. df.groupby(level=-1).mean()) or ' 'give them distinct times before forecasting.') + step = resolve_step(index, step if step is not None else default_step(data)) + if isinstance(t, (int, np.integer)) and not isinstance(t, bool): n_steps = int(t) - step = _infer_step(index) last = index[-1] - - if isinstance(index, pd.RangeIndex): + # Release review 2026-09-09: NumPy integer addition can wrap a + # valid future time into the past at the dtype's upper bound. + if isinstance(last, (int, np.integer)): + last = int(last) + if not (isinstance(index, (pd.DatetimeIndex, pd.TimedeltaIndex)) + or pd.api.types.is_numeric_dtype(index.dtype)): + return n_steps, pd.RangeIndex(len(index), len(index) + n_steps) + + if isinstance(index, pd.RangeIndex) and isinstance(step, int): future_index = pd.RangeIndex(start=last + step, stop=last + step * (n_steps + 1), step=step) else: - future_index = pd.Index([last + step * (i + 1) for i in range(n_steps)]) + future_index = future_times(last, step, n_steps) return n_steps, future_index # a real raise (not `assert ..., ValueError(...)`, which raises @@ -173,7 +163,7 @@ def resolve_t(data, t): f'{type(index).__name__}. For numerically-indexed ' 'data, pass t as a positive integer number of steps.') - target = pd.Timestamp(t) + target = t.start_time if isinstance(t, pd.Period) else pd.Timestamp(t) # tz-aware index + tz-naive t raised a raw pandas "Cannot compare # tz-naive and tz-aware timestamps" (QC 2026-07 red-team # F16-predict-020): localize the naive target to the data's timezone @@ -185,7 +175,6 @@ def resolve_t(data, t): raise ValueError( f'the dataset index is timezone-naive but t={t!r} is ' 'timezone-aware; pass a tz-naive t (or localize the data index).') - step = _infer_step(index) last = index[-1] # a target BEFORE the first observation used to silently return an @@ -214,8 +203,14 @@ def resolve_t(data, t): # target is strictly after the last observation: always forecast at # least one step (a target within half a step of the end used to round # to n_steps=0 and crash downstream -- QC 2026-07 red-team). - n_steps = max(1, int(np.round((target - last) / step))) - future_index = pd.DatetimeIndex([last + step * (i + 1) for i in range(n_steps)]) + if is_calendar_step(step): + # the nearest point of the calendar grid (business days, month + # starts, ...), measured the way the model's time axis is + elapsed = time_coordinates(pd.DatetimeIndex([target]), last, step)[0] + else: + elapsed = (target - last) / step + n_steps = max(1, int(np.round(elapsed))) + future_index = pd.DatetimeIndex(future_times(last, step, n_steps)) return n_steps, future_index @@ -244,7 +239,46 @@ class Forecaster(BaseEstimator): call. """ + #: The fewest observations (rows) a single dataset needs before this + #: forecaster can be fit on it. `fit` raises a `ValueError` naming the + #: model and this count for anything shorter, instead of letting the + #: underlying library fail deep inside its own code (statsmodels' ARIMA + #: raised a bare ``IndexError`` from a 2-row history). Subclasses whose + #: floor depends on their hyperparameters override `min_history_for` + #: (and, for instances, this attribute) -- see `ARIMA`. The animated + #: `predict=` overlay in `hypertools.plot` reads it through + #: `hypertools.plot.forecast.model_min_history`, so early frames draw no + #: forecast until enough history has been revealed. + min_history = 2 + + @classmethod + def min_history_for(cls, *args, **kwargs): + """The `min_history` an instance built with these constructor + arguments would have. The base rule ignores the arguments and returns + the class attribute; `ARIMA` computes its floor from ``order``.""" + return cls.min_history + + def _check_min_history(self, d, which): + """Raise a `ValueError` when dataset `d` (a DataFrame) has fewer rows + than this forecaster needs; `which` names the dataset in the message.""" + needed = int(self.min_history) + if d.shape[0] < needed: + name = type(self).__name__ + detail = self._min_history_detail() + raise _InsufficientHistoryError( + f'cannot forecast with {name} from {d.shape[0]} row(s): ' + f'{which} is shorter than the {needed} observation(s) ' + f'(rows) {name}{detail} needs. Pass a longer history, or a ' + 'model with a smaller minimum history (e.g. Kalman, which ' + 'needs 2 rows).') + + def _min_history_detail(self): + """Text appended to the model name in `_check_min_history`'s message + (e.g. ARIMA's order); the base class adds nothing.""" + return '' + def __init__(self, **kwargs): + self.step = kwargs.pop('step', None) self.data = kwargs.pop('data', None) self.fitter = kwargs.pop('fitter', None) self.forecaster = kwargs.pop('forecaster', None) @@ -271,8 +305,10 @@ def fit(self, data): ------ ValueError If `data` is `None`, empty, or has fewer than 2 observations - (rows); if `self.fitter` does not return a dict; or if any - name in `self.required` is missing from a returned dict. + (rows) -- or fewer than this forecaster's own `min_history` + (the message names the model and the rows it needs); if + `self.fitter` does not return a dict; or if any name in + `self.required` is missing from a returned dict. """ # real raises (not `assert ..., ValueError(...)`, which raises # AssertionError and is stripped under `python -O`) -- QC 2026-07. @@ -283,6 +319,7 @@ def fit(self, data): single = not isinstance(data, list) datasets = [_as_dataframe(data)] if single else [_as_dataframe(d) for d in data] + from .time import prepare_time_data models = [] for i, d in enumerate(datasets): # degenerate inputs used to fall through to model internals @@ -302,14 +339,23 @@ def fit(self, data): f'only {d.shape[0]} row. Forecasting needs at least 2 ' 'observations (rows) to estimate how the data change ' 'over time.') + # a model-specific floor above the universal 2 (ARIMA's order + # decides how many rows statsmodels can difference and fit) + self._check_min_history(d, which) if self.fitter is None: models.append({}) continue - params = self.fitter(d, **self.kwargs) + observed, model_data, delta = prepare_time_data( + d, self.step, regular=getattr(self, '_regular_time_grid', False)) + self._check_min_history(model_data, which) + datasets[i] = observed + params = self.fitter(model_data, **self.kwargs) if not isinstance(params, dict): raise ValueError('fit function must return a dictionary') if not all(r in params for r in self.required): raise ValueError('one or more required fields not returned') + params['_time_data'] = model_data + params['_time_step'] = delta models.append(params) self.data = datasets[0] if single else datasets @@ -341,6 +387,7 @@ def predict(self, t): if self.data is None or not hasattr(self, 'models_'): raise NotFittedError('must fit forecaster before predicting') + from .time import finalize_forecast single = not isinstance(self.data, list) datasets = [self.data] if single else self.data @@ -357,15 +404,17 @@ def predict(self, t): # than forecast (n_steps == 0 used to fall through to the # model with a zero-step horizon -- QC 2026-07 red-team # F16-predict-004). - forecasts.append(d.loc[future_index]) + forecasts.append(finalize_forecast(d.loc[future_index], d)) continue if self.forecaster is None: - forecasts.append(d) + forecasts.append(finalize_forecast(d, d)) continue merged = {**params, **self.kwargs} - forecasts.append(self.forecaster(d, n_steps, future_index, **merged)) + forecasts.append(finalize_forecast( + self.forecaster(params.get('_time_data', d), n_steps, + future_index, **merged), d)) return forecasts[0] if single else forecasts @@ -386,6 +435,28 @@ def fit_predict(self, data, t): self.fit(data) return self.predict(t) + def for_dataset(self, index): + """A view of this fitted forecaster bound to ONE of its fitted + datasets: `predict_new` on a single new series then reuses that + dataset's learned parameters instead of refusing a count mismatch. + `plot()`'s animated forecast schedule forecasts each dataset's + revealed history on its own, so a forecaster fitted on several + datasets (``hyp.predict([a, b], return_model=True)``) is applied + dataset by dataset through this (Codex round 3).""" + if not self.is_fitted: + raise NotFittedError('must fit forecaster before calling for_dataset') + fitted = self.data if isinstance(self.data, list) else [self.data] + if len(self.models_) == 1: + return self + if not 0 <= int(index) < len(self.models_): + raise IndexError( + f'for_dataset({index}): the forecaster was fitted on ' + f'{len(self.models_)} dataset(s).') + view = copy.copy(self) + view.models_ = [self.models_[int(index)]] + view.data = fitted[int(index)] + return view + @property def is_fitted(self): """Whether `fit` has already been run (so `predict_new` can reuse @@ -462,16 +533,43 @@ def predict_new(self, data, t): from ..core.shared import no_observations_message raise ValueError( no_observations_message('forecast', f'{which} has 0 rows')) - + # the minimum history is what a FIT needs; a model with an + # applier reuses its learned parameters and conditions on + # whatever context the new data offers (statsmodels applies a + # fitted AR(4) to two rows), so only the re-derive path below, + # which refits on `d`, is held to it (release review, round 2) + if self.applier is None: + self._check_min_history(d, which) + + from .time import finalize_forecast, prepare_time_data, step_matches_index forecasts = [] for d, params in zip(new_datasets, paired_models): for r in self.required: if r not in params: raise NotFittedError(f'missing fitted attribute: {r}') + # The learned interval is kept when it is expressed in the new + # index's units (a one-hour transition never silently becomes a + # three-hour one). Across index KINDS -- fit on an array, reused + # on dated rows, or the reverse -- a row count and a duration + # cannot be converted into each other, so the new data step in + # their own units, as they did in 1.0 (release review + # 2026-09-11: those reuses raised about `step` instead). + step = next((s for s in (params.get('_time_step'), self.step) + if step_matches_index(s, d.index)), None) + observed, model_data, _ = prepare_time_data( + d, step, regular=getattr(self, '_regular_time_grid', False)) + n_steps, future_index = resolve_t(observed, t) + if n_steps <= 0: + forecasts.append(finalize_forecast(observed.loc[future_index], + observed)) + continue + d = model_data + if self.applier is not None: merged = {**params, **self.kwargs} - forecasts.append(self.applier(merged, d, t)) + forecasts.append(finalize_forecast(self.applier(merged, d, t), + observed)) continue # No reusable learned parameters: condition on the new data @@ -479,13 +577,14 @@ def predict_new(self, data, t): # fitter/hyperparameters, then forecast forward). n_steps, future_index = resolve_t(d, t) if n_steps <= 0: - forecasts.append(d.loc[future_index]) + forecasts.append(finalize_forecast(d.loc[future_index], observed)) continue if self.forecaster is None: - forecasts.append(d) + forecasts.append(finalize_forecast(d, observed)) continue new_params = self.fitter(d, **self.kwargs) if self.fitter is not None else {} merged = {**new_params, **self.kwargs} - forecasts.append(self.forecaster(d, n_steps, future_index, **merged)) + forecasts.append(finalize_forecast( + self.forecaster(d, n_steps, future_index, **merged), observed)) return forecasts[0] if single else forecasts diff --git a/hypertools/predict/gp.py b/hypertools/predict/gp.py index a5df3a00..5022bcaa 100644 --- a/hypertools/predict/gp.py +++ b/hypertools/predict/gp.py @@ -1,6 +1,6 @@ """Gaussian-process forecaster (scikit-learn). -Fits a `GaussianProcessRegressor` against the time index (0..n-1) and +Fits a `GaussianProcessRegressor` against actual observation times and predicts `t` steps beyond it. Default kernel is `DotProduct() + RBF(10.0) + WhiteKernel()`: the DotProduct (linear) component lets forecasts EXTRAPOLATE trends -- with a stationary-only kernel (e.g. plain @@ -18,6 +18,8 @@ import pandas as pd from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, WhiteKernel, DotProduct +from .time import (TIME_STEP_ATTR, is_time_index, resolve_step, + step_matches_index, time_coordinates) from .common import Forecaster @@ -34,13 +36,14 @@ def _check_nan(y, context): def fitter(data, **kwargs): - """Fit a `GaussianProcessRegressor` against the time index (0..n-1) for `data`. + """Fit a `GaussianProcessRegressor` against observation times for `data`. Parameters ---------- data : pandas.DataFrame Data to fit; the target `y` is `data`'s values, regressed - against the integer time index. + against elapsed times in units of the fitted step. Categorical or + repeated numeric row IDs use observation positions (0..n-1). **kwargs `kernel` : sklearn kernel or None, covariance kernel (default: `DotProduct() + RBF(10.0) + WhiteKernel()`). `alpha` : float, @@ -65,12 +68,19 @@ def fitter(data, **kwargs): normalize_y = kwargs.get('normalize_y', True) n = len(data) - x = np.arange(n).reshape(-1, 1) + step = data.attrs.get(TIME_STEP_ATTR) + if step is None: + step = resolve_step(data.index) + origin = data.index[0] + timed = is_time_index(data.index) + x = (time_coordinates(data.index, origin, step) if timed + else np.arange(n)).reshape(-1, 1) y = data.to_numpy(dtype=float) _check_nan(y, 'fit') gp = GaussianProcessRegressor(kernel=kernel, alpha=alpha, normalize_y=normalize_y).fit(x, y) - return {'gp': gp, 'n': n} + return {'gp': gp, 'n': n, 'time_origin': origin, 'time_step': step, + 'timed': timed} def forecaster(data, n_steps, future_index, **kwargs): @@ -96,7 +106,9 @@ def forecaster(data, n_steps, future_index, **kwargs): gp = kwargs['gp'] n = kwargs['n'] - x_future = np.arange(n, n + n_steps).reshape(-1, 1) + x_future = (time_coordinates(future_index, kwargs['time_origin'], + kwargs['time_step']) if kwargs.get('timed') + else np.arange(n, n + n_steps)).reshape(-1, 1) y_pred = gp.predict(x_future) if y_pred.ndim == 1: y_pred = y_pred.reshape(-1, 1) @@ -123,7 +135,18 @@ def applier(fitted_params, new_data, t): return new_data.loc[future_index] n_new = len(new_data) - x_new = np.arange(n_new).reshape(-1, 1) + # `predict_new` resolved the step for THIS index (the learned interval + # when it is in the new index's units, else the new data's own); a + # fitted row count cannot measure a dated index, nor a duration an array. + step = new_data.attrs.get(TIME_STEP_ATTR) + if step is None: + fitted_step = fitted_params.get('time_step') + step = (fitted_step if step_matches_index(fitted_step, new_data.index) + else resolve_step(new_data.index)) + timed = is_time_index(new_data.index) + origin = new_data.index[0] + x_new = (time_coordinates(new_data.index, origin, step) if timed + else np.arange(n_new)).reshape(-1, 1) y_new = new_data.to_numpy(dtype=float) _check_nan(y_new, 'be conditioned on') @@ -131,7 +154,8 @@ def applier(fitted_params, new_data, t): kernel=gp.kernel_, alpha=gp.alpha, normalize_y=gp.normalize_y, optimizer=None).fit(x_new, y_new) - x_future = np.arange(n_new, n_new + n_steps).reshape(-1, 1) + x_future = (time_coordinates(future_index, origin, step) if timed + else np.arange(n_new, n_new + n_steps)).reshape(-1, 1) y_pred = conditioned.predict(x_future) if y_pred.ndim == 1: y_pred = y_pred.reshape(-1, 1) @@ -144,6 +168,14 @@ class GaussianProcess(Forecaster): Parameters ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None infers it per dataset: a datetime index's + calendar frequency when it has one (business days, month starts, a + ``PeriodIndex``'s periods...), else the median positive gap between + sorted observation times. Numerical indexes use their own units; + datetime/duration indexes need a duration such as '1h' (datetime + indexes also take a calendar frequency such as 'B' or 'MS'). + See `hypertools.predict` for the interpolation and reuse policies. kernel : sklearn.gaussian_process.kernels.Kernel or None Covariance kernel (default: `DotProduct() + RBF(10.0) + WhiteKernel()`; the linear DotProduct term lets forecasts extrapolate trends rather @@ -163,9 +195,12 @@ class GaussianProcess(Forecaster): red-team F16-predict-009). """ - def __init__(self, kernel=None, alpha=1e-10, normalize_y=True): + # The forecaster evaluates arbitrary future coordinates directly. + _uses_observation_times = True + + def __init__(self, kernel=None, alpha=1e-10, normalize_y=True, step=None): required = ['gp', 'n'] - super().__init__(kernel=kernel, alpha=alpha, normalize_y=normalize_y, fitter=fitter, + super().__init__(step=step, kernel=kernel, alpha=alpha, normalize_y=normalize_y, fitter=fitter, forecaster=forecaster, applier=applier, data=None, required=required) diff --git a/hypertools/predict/kalman.py b/hypertools/predict/kalman.py index 8d15ec79..24ac9583 100644 --- a/hypertools/predict/kalman.py +++ b/hypertools/predict/kalman.py @@ -301,6 +301,14 @@ class Kalman(Forecaster): Parameters ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None infers it per dataset: a datetime index's + calendar frequency when it has one (business days, month starts, a + ``PeriodIndex``'s periods...), else the median positive gap between + sorted observation times. Numerical indexes use their own units; + datetime/duration indexes need a duration such as '1h' (datetime + indexes also take a calendar frequency such as 'B' or 'MS'). + See `hypertools.predict` for the interpolation and reuse policies. n_iter : int Number of EM iterations used to fit the transition/observation noise covariances and the initial state (default: 5). The @@ -315,9 +323,11 @@ class Kalman(Forecaster): ``n_observations - 1``). """ - def __init__(self, n_iter=5, lags=None): + _regular_time_grid = True + + def __init__(self, n_iter=5, lags=None, step=None): required = ['kf', 'mean', 'cov', 'n_features'] - super().__init__(n_iter=n_iter, lags=lags, fitter=fitter, forecaster=forecaster, + super().__init__(step=step, n_iter=n_iter, lags=lags, fitter=fitter, forecaster=forecaster, applier=applier, data=None, required=required) self.n_iter = n_iter diff --git a/hypertools/predict/laplace.py b/hypertools/predict/laplace.py index 13b5a33f..a6b3149e 100644 --- a/hypertools/predict/laplace.py +++ b/hypertools/predict/laplace.py @@ -161,15 +161,26 @@ class Laplace(Forecaster): conditioning on the new series, not replaying anything learned from the original fit. - Laplace takes no parameters; see the module docstring for performance + See the module docstring for performance and signal-suitability caveats. Unknown keyword arguments raise `TypeError` (they were previously swallowed silently -- QC 2026-07 red-team F16-predict-009). + + Parameters + ---------- + step : number, duration string, Timedelta, calendar offset, or None + One future step. None uses a datetime index's calendar frequency + when it has one (business days, month starts, ...), else the median + positive gap between sorted observation times. Irregular + observations are linearly interpolated onto this grid before + forecasting. """ - def __init__(self): + _regular_time_grid = True + + def __init__(self, step=None): required = ['series'] - super().__init__(fitter=fitter, forecaster=forecaster, data=None, + super().__init__(step=step, fitter=fitter, forecaster=forecaster, data=None, required=required) self.fitter = fitter diff --git a/hypertools/predict/predict.py b/hypertools/predict/predict.py index 242c8f6b..f2e8537b 100644 --- a/hypertools/predict/predict.py +++ b/hypertools/predict/predict.py @@ -23,8 +23,9 @@ import warnings import numpy as np -import pandas as pd import datawrangler as dw +from .._shared.helpers import (is_array_dataset, is_frame_dataset, + is_series_like, as_pandas_dataframe) from .backtest import backtest_predict, model_collection, spec_name from .common import Forecaster @@ -54,15 +55,26 @@ def _spec_help(): def _coerce_dataset(d): """Normalize ONE dataset-like object before wrangling: a 1-D array or a - pandas Series is a UNIVARIATE TIMESERIES -- n observations of 1 feature, + Series is a UNIVARIATE TIMESERIES -- n observations of 1 feature, i.e. an (n, 1) column -- matching format_data/plot's convention. (QC 2026-07 red-team F16-predict-002/-003: the funnel used to wrangle a (200,) array into ONE row of 200 features -- crashing the default model or silently echoing the input as a (t, 200) "forecast" -- and wrangled a - Series into an EMPTY (0, 0) DataFrame, silently losing the data.)""" - if isinstance(d, pd.Series): - return d.to_frame() - if isinstance(d, np.ndarray) and d.ndim == 1: + Series into an EMPTY (0, 0) DataFrame, silently losing the data.) + + Types are classified with datawrangler's predicates (see + `hypertools._shared.helpers`): a DataFrame of any backend datawrangler + recognises (polars DataFrame/LazyFrame, ...) becomes a pandas + DataFrame, hypertools' internal frame type (a pandas frame passes + through untouched); a pandas or polars Series becomes its one-column + frame (index and name preserved).""" + if is_frame_dataset(d): + return as_pandas_dataframe(d) + if is_series_like(d): + if hasattr(d, 'to_frame'): + return _coerce_dataset(d.to_frame()) + return _coerce_dataset(np.asarray(d)) + if is_array_dataset(d) and d.ndim == 1: return d.reshape(-1, 1) return d @@ -84,7 +96,7 @@ def _normalize_data(data): raise ValueError( f'cannot forecast from a single scalar observation ({data!r}); ' 'pass a timeseries with at least 2 observations (rows).') - if isinstance(data, np.ndarray): + if is_array_dataset(data): if data.ndim == 0: raise ValueError( f'cannot forecast from a single scalar observation ' @@ -96,12 +108,15 @@ def _normalize_data(data): 'forecast', f'got an array of shape {tuple(data.shape)}') + ' Pass at least 2 observations (rows).') return _coerce_dataset(data) - if isinstance(data, pd.DataFrame) and (data.shape[0] == 0 - or data.shape[1] == 0): - raise ValueError( - no_observations_message( - 'forecast', f'got a DataFrame of shape {tuple(data.shape)}') - + ' Pass at least 2 observations (rows).') + if is_frame_dataset(data): + # to pandas FIRST (a polars LazyFrame has no shape until collected) + data = _coerce_dataset(data) + if data.shape[0] == 0 or data.shape[1] == 0: + raise ValueError( + no_observations_message( + 'forecast', f'got a DataFrame of shape {tuple(data.shape)}') + + ' Pass at least 2 observations (rows).') + return data if isinstance(data, list): if len(data) == 0: raise ValueError( @@ -118,6 +133,13 @@ def _normalize_data(data): return _coerce_dataset(data) +def _is_hierarchical(data): + """Whether `data` (already normalized to pandas by `_normalize_data`) + is ONE DataFrame with a MultiIndex on its rows or its columns.""" + return is_frame_dataset(data) and (data.index.nlevels >= 2 + or data.columns.nlevels >= 2) + + def _validate_horizon(t): """Check `t` (the forecast horizon) and return it normalized. @@ -247,10 +269,8 @@ def _holdout_datasets(data): return frames if isinstance(frames, list) else [frames] -@dw.decorate.funnel -def _wrangled_predict(data, model='Kalman', t=10, return_model=False, **kwargs): - """Funnel-wrapped core of `predict` (see its docstring).""" - t = _validate_horizon(t) +def _make_forecaster(model, kwargs): + """Construct a model with the same spec/keyword policy for both paths.""" resolved, kwargs = _resolve_forecaster_spec(model, kwargs) if isinstance(resolved, type): @@ -272,6 +292,15 @@ def _wrangled_predict(data, model='Kalman', t=10, return_model=False, **kwargs): 'cannot be applied. Pass the class (or a name/dict spec) to ' 'set parameters.', stacklevel=external_stacklevel()) + return resolved + + +@dw.decorate.funnel +def _wrangled_predict(data, model='Kalman', t=10, return_model=False, **kwargs): + """Funnel-wrapped core of `predict` (see its docstring).""" + t = _validate_horizon(t) + resolved = _make_forecaster(model, kwargs) + if isinstance(resolved, Forecaster) and resolved.is_fitted: forecasts = resolved.predict_new(data, t) else: @@ -352,7 +381,7 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, model : str, dict, class, Forecaster instance, or a COLLECTION of these Which forecaster to use (default: 'Kalman'). - SEVERAL MODELS AT ONCE (1.2). A LIST or TUPLE of specs forecasts + SEVERAL MODELS AT ONCE (1.1). A LIST or TUPLE of specs forecasts each of them and returns a ``{name: forecast}`` dict in the order given -- the shape ``hyp.plot(..., predict=['Kalman', 'ARIMA'])`` consumes. Names come from the specs (a string's registry spelling, @@ -409,6 +438,30 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, truncate to and nothing to forecast -- it used to silently return an empty frame). + step : number, duration string, Timedelta, calendar offset, or None, optional + Constructor keyword for the selected model: one future step, + inferred independently per dataset when omitted. A datetime index + with a calendar frequency -- stored in ``index.freq``, inferable with + ``pd.infer_freq`` (business days, month starts, weeks, quarters, + hours, tz-aware days across DST), or a ``PeriodIndex``'s periods -- + steps on that calendar, so it is regular: fitted on its own rows and + forecast onto the next business days / month starts / periods. + Weekday-only sessions that skip a few weekdays (exchange holidays) + step in business days. Any other index steps by the median positive + gap. Pass e.g. '1h' for datetime/duration indexes, a calendar + frequency such as 'B' or 'MS' for datetime indexes, or 0.5 for + numerical indexes. Timed observations are sorted before fitting. + GaussianProcess uses the actual times; Kalman, ARIMA, AutoRegressor, + Laplace and Chronos linearly interpolate irregular observations onto + a regular grid ending at the latest observation (with a warning). + No training values are extrapolated or missing values imputed. + Fitted-model reuse keeps the training interval when the new index + is of the same kind (a model fit on an array and reused on dated + rows, or the reverse, steps in the new data's own units). Period + indexes are fitted on their start timestamps and forecast as periods + of the same frequency; duplicate numeric row IDs remain positional. + See the API guide's observation-times section for the full policy. + return_model : bool If True, also return the fitted (or reused) Forecaster instance, so it can be passed back as `model=` on future calls with new data @@ -419,18 +472,30 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, model})``; it is not supported with ``holdout=``. holdout : int, float, True, or None - BACKTEST instead of forecasting (1.2). Fit each model on the HEAD + BACKTEST instead of forecasting (1.1). Fit each model on the HEAD of the data, forecast the held-out TAIL, and return a scores frame comparing every model against the rows that actually happened. An int holds out that many rows; a float in (0, 1) holds out that FRACTION of them (rounded, at least 1); ``True`` holds out exactly `t` rows. The head must keep at least 2 rows. - **`t` is not consulted** for an int/float `holdout`: the horizon IS - the number of held-out rows, or the forecast would not line up - one-to-one with the truth. (Use ``holdout=True`` to say "hold out - `t` rows".) The horizon used is reported in the frame's ``horizon`` - column. + Pass a name, class, or unfitted instance. Each dataset fits an + independent copy; the caller's instance is unchanged. Already fitted + instances are refused because they may have seen the held-out rows. + + `t` **is not consulted** for an int/float `holdout`. The number of + held-out observations is reported in the frame's ``horizon`` + column. (Use ``holdout=True`` to say "hold out `t` rows".) + + Timed rows are sorted before splitting. Models and their step sizes + are fitted on TRAINING rows only. GaussianProcess evaluates the + actual held-out times; regular-grid models forecast a covering grid + and linearly interpolate predictions to those times (with a warning + when interpolation is needed). Before the first full forecast step, + the last observed training value anchors interpolation. Missing + endpoints remain missing; no held-out values enter the model or the + interpolation. Returned predictions carry the held-out index. + Arrays/categorical labels/repeated numeric IDs use row positions. Not available on HIERARCHICAL input (which group is scored would have to become a fourth axis of the frame); slice the groups and @@ -469,22 +534,25 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, Returns ------- - forecasts (and the fitted Forecaster if return_model=True). Lists in, - lists out: a single input dataset returns a single forecast DataFrame. - A hierarchical DataFrame (see `data`) returns a LIST of forecasts, one - per group in input order -- or, with ``return_model=True``, the parallel - ``([f0, f1, ...], [m0, m1, ...])`` pair described there. - - A COLLECTION of models (see `model`) returns a ``{name: forecast}`` - dict whose values are exactly what a single-model call would have - returned (a DataFrame, or a list of them for list/hierarchical input). - - With ``holdout=``, a scores DataFrame instead (see Notes) -- or - ``(scores, forecasts)`` with ``return_forecasts=True``. + forecasts + The forecasts (and the fitted Forecaster if ``return_model=True``). + Lists in, lists out: a single input dataset returns a single + forecast DataFrame. A hierarchical DataFrame (see `data`) returns a + LIST of forecasts, one per group in input order -- or, with + ``return_model=True``, the parallel + ``([f0, f1, ...], [m0, m1, ...])`` pair described there. + + A COLLECTION of models (see `model`) returns a ``{name: forecast}`` + dict whose values are exactly what a single-model call would have + returned (a DataFrame, or a list of them for list/hierarchical + input). + + With ``holdout=``, a scores DataFrame instead (see Notes) -- or + ``(scores, forecasts)`` with ``return_forecasts=True``. Notes ----- - **Backtesting (``holdout=``).** The returned frame has one ROW per + **Backtesting** (``holdout=``). The returned frame has one ROW per model, in the order the models were given, plus an always-present ``'naive'`` row: the last-value-carried-forward baseline (each column's last OBSERVED training value, repeated over the horizon), which is the @@ -508,8 +576,25 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, complexity, whatever its absolute error looks like. TIES on the ranking metric go to the model listed FIRST (the comparison is stable, so re-running with the same specs gives the same verdict); models whose - ranking metric is NaN (nothing scoreable) are never chosen as best, and - ``attrs['best']`` is None if no model scored at all. + ranking metric is NaN (nothing scoreable) are never chosen as best. + + ONLY COMPLETE MODELS ARE RANKED. A model with ``unscored > 0`` (summed + over every dataset and column) was scored on fewer held-out values + than the others, so it keeps its descriptive row but can never be + ``attrs['best']``; it is named in ``attrs['incomplete']`` (a list, + empty when every row is complete) and a warning says so. If NO model + is complete (or none scored at all), ``attrs['best']`` is None, + ``attrs['best_score']`` is NaN and ``attrs['beats_baseline']`` is None + -- there is no verdict, which is not the same as "did not beat the + baseline". ``attrs['beats_baseline']`` is likewise None if the naive + baseline is itself incomplete (a column with no observed training + value gives it nothing to carry forward; it is then listed in + ``attrs['incomplete']`` too). Test ``is True`` / ``is None`` rather + than truthiness when the distinction matters. + + Forecast and held-out COLUMNS are paired by label: a custom forecaster + that returns the training columns in another order is reordered, and + one that returns different column labels raises a `ValueError`. The verdict lives in ``attrs`` rather than in a ``best`` column on purpose: it is one fact about the whole comparison, and a column would @@ -599,8 +684,7 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, 'backtest fits one model per model spec and reports scores. ' 'Use return_forecasts=True for the scored forecasts, then ' 'refit on the full data with the winning spec.') - if isinstance(data, pd.DataFrame) and (data.index.nlevels >= 2 - or data.columns.nlevels >= 2): + if _is_hierarchical(data): raise ValueError( 'holdout= is not supported on hierarchical (MultiIndex) ' 'input; slice the groups and backtest them one at a time.') @@ -611,7 +695,7 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, _FORECASTER_ALIASES)] specs = [model] return backtest_predict( - _holdout_datasets(data), predict, t, holdout, names, specs, + _holdout_datasets(data), _make_forecaster, t, holdout, names, specs, metrics=metrics, per_column=per_column, return_forecasts=return_forecasts, kwargs=kwargs) if collection is not None: @@ -640,8 +724,7 @@ def predict(data, model='Kalman', t=10, return_model=False, holdout=None, # or a tuple, so it is indifferent to `_normalize_data`'s tuple->list # conversion either way. reject_hierarchical_in_list(data, caller='hyp.predict', axes='both') - if isinstance(data, pd.DataFrame) and (data.index.nlevels >= 2 - or data.columns.nlevels >= 2): + if _is_hierarchical(data): reject_dual_axis(data) if data.columns.nlevels >= 2: # `group_columns` returns (leaves, META); the group LABELS live in diff --git a/hypertools/predict/time.py b/hypertools/predict/time.py new file mode 100644 index 00000000..3b3d1269 --- /dev/null +++ b/hypertools/predict/time.py @@ -0,0 +1,491 @@ +"""Observation times and the explicit linear regular-grid forecast policy. + +Release review 2026-09-08: times describe observations, not additional +response columns. GaussianProcess uses the coordinates directly; the discrete +time models fit a grid ending at the last observation, with linear +interpolation inside the observed range (never extrapolated training rows). + +Release review 2026-09-11: a step is a NUMBER (numerical indexes), a fixed +DURATION (``pd.Timedelta``: hourly, minutely, tz-naive daily data...) or a +CALENDAR OFFSET (a ``pandas`` ``DateOffset``: business days, month starts, +quarters, and local calendar days on a tz-aware index). A datetime index whose +frequency is stored (``index.freq``), inferable (``pd.infer_freq``) or given +by its periods (``PeriodIndex``) steps on that calendar, so a business-day or +month-start series is REGULAR -- fitted on its own rows, forecast onto the +next business days / month starts -- rather than an "irregular" series with +uneven absolute gaps. Weekday-only data whose sessions skip a few weekdays +(exchange holidays) step in business days; the skipped weekdays are the only +rows the regular grid fills. A ``PeriodIndex`` is handled on its start +timestamps internally and comes back as periods of the same frequency. +""" +import contextlib +import contextvars +import datetime +import warnings + +import numpy as np +import pandas as pd +from pandas.tseries.frequencies import to_offset + +from ..core.exceptions import _InsufficientHistoryError + +#: ``DataFrame.attrs`` keys used between the helpers below and +#: `hypertools.predict.common` (internal; stripped from returned forecasts). +TIME_STEP_ATTR = '_hypertools_time_step' +PERIOD_FREQ_ATTR = '_hypertools_period_freq' +ORDERED_ATTR = '_hypertools_time_ordered' + +# Messages already issued inside one `warn_once_per_call()` scope (one +# hyp.plot call): plot re-checks the same data along several internal paths, +# and a shuffled index warned twice for one call (2026-09-11 review). +_WARNED_IN_CALL = contextvars.ContextVar('_hypertools_time_warned', + default=None) + + +@contextlib.contextmanager +def warn_once_per_call(): + """Issue each distinct time-policy warning at most once inside this + block (outermost scope wins, so nested calls share one record).""" + if _WARNED_IN_CALL.get() is not None: + yield + return + token = _WARNED_IN_CALL.set(set()) + try: + yield + finally: + _WARNED_IN_CALL.reset(token) + + +def _warn_time(message): + """Warn about a time policy at the caller's line, once per message per + `warn_once_per_call()` scope.""" + seen = _WARNED_IN_CALL.get() + if seen is not None: + if message in seen: + return + seen.add(message) + from ..core.model import external_stacklevel + warnings.warn(message, UserWarning, stacklevel=external_stacklevel()) + +#: the most calendar grid points one coordinate computation may generate +_MAX_CALENDAR_POINTS = 2_000_000 + + +def is_time_index(index): + """Datetime/duration/period indexes and unique numerical coordinates.""" + return (isinstance(index, (pd.DatetimeIndex, pd.TimedeltaIndex, pd.PeriodIndex)) + or (pd.api.types.is_numeric_dtype(index.dtype) and index.is_unique)) + + +def is_calendar_step(step): + """Whether `step` is a calendar offset (business day, month start, ...) + rather than a number or a fixed ``pd.Timedelta`` duration. Resolved steps + never hold a fixed-length pandas ``Tick``: those become Timedeltas.""" + return isinstance(step, pd.offsets.BaseOffset) + + +def _step_text(step): + """A step for messages: a calendar offset as its alias (``'B'``, + ``'MS'`` -- what ``step=`` accepts), anything else as ``str()``.""" + if isinstance(step, pd.offsets.BaseOffset): + return repr(step.freqstr) + if isinstance(step, (float, np.floating)): + return format(float(step), '.6g') # 0.04, not 0.04000000000000001 + return str(step) + + +def order_time_data(data): + """Sort timed observations together; leave categorical/duplicate IDs alone. + + The "not sorted" warning is issued once per dataset: the returned copy is + marked, so the internal re-checks a forecast makes on it stay silent. A + ``PeriodIndex`` is replaced by its start timestamps, and its frequency is + recorded so forecasts can be returned as periods (`finalize_forecast`). + """ + if (len(data) > 1 and not data.index.is_monotonic_increasing + and not data.attrs.get(ORDERED_ATTR, False)): + action = ('observations are sorted before forecasting' if is_time_index(data.index) + else 'repeated/categorical row IDs retain their input order') + _warn_time('the dataset index is not sorted in ascending order; ' + + action) + if not is_time_index(data.index): + result = data.copy() + result.attrs[ORDERED_ATTR] = True + return result + if data.index.hasnans: + raise ValueError('observation times must be finite and cannot contain NaT/NaN') + if pd.api.types.is_numeric_dtype(data.index.dtype): + if not np.isfinite(np.asarray(data.index, dtype=float)).all(): + raise ValueError('observation times must be finite') + result = data.sort_index(kind='stable').copy() + if isinstance(result.index, pd.PeriodIndex): + result.attrs[PERIOD_FREQ_ATTR] = result.index.freqstr + result.index = result.index.to_timestamp() + result.attrs[ORDERED_ATTR] = True + return result + + +def finalize_forecast(frame, observed): + """A forecast (or truncated history) as returned to the caller: periods + again for a ``PeriodIndex`` input, and without the internal ordering + attributes.""" + freq = observed.attrs.get(PERIOD_FREQ_ATTR) + if freq is not None and isinstance(frame.index, pd.DatetimeIndex): + frame = frame.copy() + frame.index = frame.index.to_period(freq) + if PERIOD_FREQ_ATTR in frame.attrs or ORDERED_ATTR in frame.attrs: + frame = frame.copy() + frame.attrs.pop(PERIOD_FREQ_ATTR, None) + frame.attrs.pop(ORDERED_ATTR, None) + return frame + + +def _normalize_offset(offset, tz=None): + """A pandas offset as a step: fixed-length offsets become Timedeltas; + a day on a tz-aware index is a LOCAL calendar day (``DateOffset(days=n)``, + identical in pandas 2 and 3), so days across a DST change stay at the + same wall-clock time.""" + if not isinstance(offset, pd.offsets.BaseOffset): + offset = to_offset(offset) + if isinstance(offset, pd.offsets.Day): + return (pd.DateOffset(days=int(offset.n)) if tz is not None + else pd.Timedelta(days=int(offset.n))) + if isinstance(offset, pd.offsets.Tick): + return pd.Timedelta(offset) + return offset + + +def _period_step(freqstr): + """The calendar step between the START timestamps of consecutive periods + of frequency `freqstr` (monthly periods start on month starts, weekly + 'W-SUN' periods on Mondays, ...), or None if pandas cannot name it.""" + starts = pd.period_range('2000-01-01', periods=3, freq=freqstr).to_timestamp() + freq = pd.infer_freq(starts) + return None if freq is None else _normalize_offset(freq) + + +def _gapped_calendar(index): + """A calendar step for a sorted, tz-aware or naive DatetimeIndex with no + inferable frequency, or None. + + Recognizes observations taken at one time of day on (a) month starts or + month ends, stepping by the median number of months; (b) weekdays only, + one business day apart at the median -- trading sessions, whose skipped + weekdays are holidays; (c) tz-aware local calendar days. + """ + local = index.tz_localize(None) if index.tz is not None else index + days = local.normalize() + time_of_day = local - days + if not (time_of_day == time_of_day[0]).all(): + return None + day_numbers = np.asarray(days.values.astype('datetime64[D]')) + day_gaps = np.diff(day_numbers).astype(np.int64) + if not len(day_gaps) or (day_gaps <= 0).any(): + return None + month_start = bool((local.day == 1).all()) + if month_start or bool(local.is_month_end.all()): + months = np.diff(np.asarray(local.year * 12 + local.month, dtype=np.int64)) + if (months > 0).all(): + count = max(1, int(np.round(np.median(months)))) + return (pd.offsets.MonthBegin(count) if month_start + else pd.offsets.MonthEnd(count)) + if bool((local.dayofweek < 5).all()): + sessions = np.busday_count(day_numbers[:-1], day_numbers[1:]) + if np.median(sessions) == 1 and (day_gaps > sessions).any(): + return pd.offsets.BDay(1) + if index.tz is not None: + median = float(np.median(day_gaps)) + if median.is_integer(): + return pd.DateOffset(days=int(median)) + return None + + +def _calendar_offset(index): + """The calendar step of a sorted DatetimeIndex, a fixed Timedelta for a + fixed-frequency index, or None when the observations follow neither.""" + freq = index.freq + if freq is None and len(index) >= 3: + try: + freq = pd.infer_freq(index) + except (TypeError, ValueError): + freq = None + if freq is not None: + return _normalize_offset(freq, index.tz) + if len(index) < 3: + return None + return _gapped_calendar(index) + + +def infer_step(index): + """One step of `index`: its calendar frequency when it has one (see the + module docstring), otherwise the median positive gap between sorted + observation times.""" + if isinstance(index, pd.PeriodIndex): + step = _period_step(index.freqstr) + if step is not None: + return step + index = index.to_timestamp() + temporal = isinstance(index, (pd.DatetimeIndex, pd.TimedeltaIndex)) + if len(index) < 2: + return pd.Timedelta(seconds=1) if temporal else 1 + if not temporal and not pd.api.types.is_numeric_dtype(index.dtype): + return 1 + ordered = index.sort_values() + if isinstance(ordered, pd.DatetimeIndex): + calendar = _calendar_offset(ordered) + if calendar is not None: + return calendar + # Release review 2026-09-09: integer differences must not wrap at the + # signed/unsigned dtype bounds. Subtract before converting to float so + # large epoch offsets do not erase small gaps either. + coordinates = (ordered.astype(object) + if pd.api.types.is_integer_dtype(ordered.dtype) else ordered) + gaps = coordinates[1:] - coordinates[:-1] + if temporal: + gaps = gaps[gaps > pd.Timedelta(0)] + if not len(gaps): + raise ValueError('cannot infer a timestep: all observations share one timestamp') + return gaps.median() + gaps = np.asarray(gaps, dtype=float) + gaps = gaps[gaps > 0] + if not len(gaps): + return 1 + value = float(np.median(gaps)) + return int(value) if value.is_integer() else value + + +def default_step(data): + """The step `data` carries from an earlier resolution, else -- for a + ``PeriodIndex`` input observed at every period -- one period (exact even + for two observations, where no calendar can be inferred); None means + infer from the observation times (periods sampled every other hour, or + quarterly months, step at their own cadence).""" + step = data.attrs.get(TIME_STEP_ATTR) + freq = data.attrs.get(PERIOD_FREQ_ATTR) + if step is None and freq is not None and len(data) > 1: + period = _period_step(freq) + if period is not None and isinstance(data.index, pd.DatetimeIndex): + x = time_coordinates(data.index, data.index[-1], period) + if np.allclose(np.diff(x), 1., rtol=1e-8, atol=1e-8): + step = period + return step + + +def step_matches_index(step, index): + """Whether a (fitted or requested) `step` is expressed in `index`'s units: + a duration or calendar offset for a datetime/period index, a duration for + a timedelta index, a number otherwise. A fitted forecaster reused on a + different KIND of index (array-fit, dated reuse and vice versa) cannot + keep its training interval, so it steps in the new data's own units.""" + if step is None or isinstance(step, (bool, np.bool_)): + return False + durational = isinstance(step, (pd.Timedelta, datetime.timedelta, + np.timedelta64, str)) + if isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)): + return durational or is_calendar_step(step) + if isinstance(index, pd.TimedeltaIndex): + return durational or isinstance(step, pd.offsets.Tick) + return isinstance(step, (int, float, np.number)) and not durational + + +def _time_step(step, tz=None, calendar=True): + """Validate a user/fitted step for a datetime (`calendar`) or timedelta + index: a duration, or (datetime only) a calendar offset/alias.""" + if isinstance(step, (int, float, np.number)): + raise ValueError('step for a time index must specify a duration, e.g. "1h"') + offset = None + if isinstance(step, pd.offsets.BaseOffset): + offset = step + elif isinstance(step, str): + try: + pd.Timedelta(step) + except ValueError: + try: + offset = to_offset(step) + except ValueError: + raise ValueError( + f'step={step!r} is neither a duration (e.g. "1h") nor a ' + 'calendar frequency (e.g. "B" or "MS")') from None + if offset is not None: + resolved = _normalize_offset(offset, tz) + if is_calendar_step(resolved): + if not calendar: + raise ValueError( + f'a calendar step ({step!r}) needs a datetime index; a ' + 'timedelta index takes a duration such as "1h"') + reference = pd.Timestamp('2000-01-03') + if not reference + resolved > reference: + raise ValueError('step must be a positive time duration') + return resolved + step = resolved + step = pd.Timedelta(step) + if pd.isna(step) or step <= pd.Timedelta(0): + raise ValueError('step must be a positive time duration') + return step + + +def resolve_step(index, step=None): + """A positive step in the index's own units: a number for numerical + indexes, a duration for timedelta indexes, and a duration or calendar + offset for datetime/period indexes (see the module docstring).""" + if step is None: + return infer_step(index) + if isinstance(step, (bool, np.bool_)): + raise ValueError('step must be a positive number or time duration') + if isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)): + return _time_step(step, tz=getattr(index, 'tz', None)) + if isinstance(index, pd.TimedeltaIndex): + return _time_step(step, calendar=False) + if isinstance(step, (pd.Timedelta, datetime.timedelta, np.timedelta64, + pd.offsets.BaseOffset)): + raise ValueError(f'step for a numerical index must be a positive ' + f'number; got {step!r}') + try: + step = float(step) + except (TypeError, ValueError): + raise ValueError(f'step for a numerical index must be a positive ' + f'number; got {step!r}') from None + if not np.isfinite(step) or step <= 0: + raise ValueError('step must be positive and finite') + return int(step) if step.is_integer() else step + + +def calendar_points(origin, offset, first, last): + """The calendar grid ``origin + k * offset`` for k = first..last (k != 0 + runs are generated by `pd.date_range`, whose lattice equals repeated + offset arithmetic; ``k = 0`` is `origin` itself even off the lattice).""" + points = [] + if first < 0: + stop = min(last, -1) + points.append(pd.date_range(end=origin + stop * offset, + periods=stop - first + 1, freq=offset)) + if first <= 0 <= last: + points.append(pd.DatetimeIndex([origin])) + if last > 0: + start = max(first, 1) + points.append(pd.date_range(start=origin + start * offset, + periods=last - start + 1, freq=offset)) + if not points: + return pd.DatetimeIndex([], tz=origin.tz) + result = points[0] + for part in points[1:]: + result = result.append(part) + return result + + +def _calendar_coordinates(index, origin, offset): + """Positions of `index` on the grid ``origin + k * offset``: integer on + grid points, linear in elapsed time between neighbouring points.""" + index = pd.DatetimeIndex(index) + origin = pd.Timestamp(origin) + if not len(index): + return np.zeros(0) + low, high = min(index.min(), origin), max(index.max(), origin) + sample = calendar_points(origin, offset, 1, 8) + shortest = min((sample[1:] - sample[:-1]).min(), sample[0] - origin) + shortest = max(shortest, pd.Timedelta(seconds=1)) + ahead = int(np.ceil((high - origin) / shortest)) + 1 + behind = int(np.ceil((origin - low) / shortest)) + 1 + if ahead + behind > _MAX_CALENDAR_POINTS: + raise ValueError('these observation times span more than ' + f'{_MAX_CALENDAR_POINTS:,} calendar steps; use a larger step') + grid = calendar_points(origin, offset, -behind, ahead) + positions = np.arange(-behind, ahead + 1, dtype=float) + slot = np.clip(grid.searchsorted(index, side='right') - 1, 0, len(grid) - 2) + left, right = grid[slot], grid[slot + 1] + fraction = np.asarray((index - left) / (right - left), dtype=float) + return positions[slot] + fraction + + +def time_coordinates(index, origin, step): + """Elapsed times in units of one model step, independent of calendar epoch.""" + if isinstance(index, pd.PeriodIndex): + index = index.to_timestamp() + if isinstance(origin, pd.Period): + origin = origin.start_time + if is_calendar_step(step): + return _calendar_coordinates(index, origin, step) + if pd.api.types.is_integer_dtype(index.dtype): + index = index.astype(object) + if isinstance(origin, (int, np.integer)): + origin = int(origin) + return np.asarray((index - origin) / step, dtype=float) + + +def future_times(last, step, n_steps): + """The `n_steps` observation times after `last`, one `step` apart.""" + if is_calendar_step(step): + return calendar_points(pd.Timestamp(last), step, 1, n_steps) + return pd.Index([last + step * (i + 1) for i in range(n_steps)]) + + +def prepare_time_data(data, step=None, regular=False): + """Return sorted observations, fitting data, and the resolved step. + + Interpolation is linear, column by column, on a grid anchored at the + latest observation. NaN values are retained, not imputed. Duplicate + numerical row IDs remain positional for compatibility with stacked runs. + Observations already one step apart -- including a calendar step such as + business days or month starts -- are fitted as they are. + """ + from .common import resolve_t + + observed = order_time_data(data) + delta = resolve_step(observed.index, + step if step is not None else default_step(observed)) + observed.attrs[TIME_STEP_ATTR] = delta + # Validate duplicated time stamps before fitting, including native-time + # models. Horizon resolution owns the public diagnostic. + resolve_t(observed, 1) + if not regular or not is_time_index(observed.index) or len(observed) < 2: + return observed, observed, delta + x = time_coordinates(observed.index, observed.index[-1], delta) + if np.allclose(np.diff(x), 1., rtol=1e-8, atol=1e-8): + return observed, observed, delta + count = int(np.floor(-x[0] + 1e-8)) + 1 + if count < 2: + raise _InsufficientHistoryError( + 'step exceeds the observed time span; use a smaller step') + if count > 1_000_000: + raise ValueError('interpolation would exceed 1,000,000 rows; use a larger step') + grid = np.arange(1 - count, 1, dtype=float) + values = observed.to_numpy(dtype=float) + interpolated = np.column_stack([np.interp(grid, x, col) for col in values.T]) + origin = observed.index[-1] + if is_calendar_step(delta): + index = calendar_points(origin, delta, 1 - count, 0) + index.name = observed.index.name + else: + if isinstance(origin, (int, np.integer)): + origin = int(origin) + index = pd.Index([origin + int(i) * delta for i in grid], + name=observed.index.name) + fitted = pd.DataFrame(interpolated, index=index, columns=observed.columns) + fitted.attrs[TIME_STEP_ATTR] = delta + own = _own_regular_step(observed) + if step is not None and own is not None: + message = (f'Regularly spaced observations (one every ' + f'{_step_text(own)}) were linearly interpolated onto a grid ' + f'with step={_step_text(delta)} before fitting this ' + 'discrete-time forecaster.') + else: + message = ('Irregular observation times were linearly interpolated onto a ' + f'regular grid with step={_step_text(delta)} before fitting ' + 'this discrete-time forecaster. Pass step= to choose the grid ' + 'interval; GaussianProcess uses the actual observation times ' + 'without interpolation.') + # attributed to the caller's line, not a hypertools frame: a fixed + # stacklevel printed '.../hypertools/predict/common.py:435' in tutorials + _warn_time(message) + return observed, fitted, delta + + +def _own_regular_step(observed): + """The observations' own step when they are regularly spaced on it + (so a DIFFERENT requested step is a resampling, not a repair), else None.""" + try: + own = infer_step(observed.index) + except ValueError: + return None + x = time_coordinates(observed.index, observed.index[-1], own) + return own if np.allclose(np.diff(x), 1., rtol=1e-8, atol=1e-8) else None diff --git a/hypertools/reduce/common.py b/hypertools/reduce/common.py index 6977fad8..e4c259e5 100644 --- a/hypertools/reduce/common.py +++ b/hypertools/reduce/common.py @@ -104,8 +104,9 @@ def resolve_reducer(name): from . import autoencoders except ImportError as e: raise ImportError( - f'{name} requires torch, which is not installed; install ' - 'it with pip install "hypertools[torch]"' + f'{name} needs torch (the [torch] extra), which is not ' + 'installed and could not be installed on demand (see the ' + 'error above); install it with pip install "hypertools[torch]"' ) from e return getattr(autoencoders, name) return REDUCERS[name] diff --git a/hypertools/reduce/describe.py b/hypertools/reduce/describe.py index 2f02a904..8c643843 100644 --- a/hypertools/reduce/describe.py +++ b/hypertools/reduce/describe.py @@ -32,9 +32,10 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, number of columns (ragged lists raise a `ValueError`); `None` raises a `TypeError`. - reduce : str, dict, class, instance, or fitted Reducer + reduce : str, dict, class, or unfitted instance Decomposition/manifold learning model to use (default: - 'IncrementalPCA'). Models supported: PCA, IncrementalPCA, SparsePCA, + 'IncrementalPCA'; `None` raises a `ValueError`, since the sweep + needs a reducer). Models supported: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, MDS, and UMAP; the mixture @@ -47,6 +48,19 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, reduce={'model': 'PCA', 'kwargs': {'whiten': True}}. See scikit-learn model docs for details on parameters supported for each model. + The sweep sets the number of dimensions itself, at every point, + so a dimensionality in the spec never takes effect: an + ``n_components`` entry in a dict spec's ``'kwargs'`` is ignored, + and an unfitted instance such as ``PCA(n_components=2, + whiten=True)`` is cloned for each point with ``n_components`` set + to that point (its other settings are kept, and the instance you + passed is neither modified nor fitted). An ALREADY FITTED model -- + including the `Reducer` or `Pipeline` that ``hyp.reduce(..., + return_model=True)`` returns -- raises a `ValueError`, because + its dimensionality was fixed by the fit; so does an instance with + no ``n_components`` parameter. Pass a name, a dict spec or an + unfitted instance instead. + max_dims : int Dimensionalities 2 through `max_dims - 1` are evaluated (the bound is EXCLUSIVE, matching Python's `range`), so `max_dims` must be an @@ -67,26 +81,33 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, evaluate more. show : bool - Plot the result (default: True). The figure is displayed only + Display the figure (default: True). The figure is drawn and + returned in the result dict's 'fig' key either way; `show` only + controls whether it is displayed. With `show=True` it is displayed when the resolved backend can show one (plotly, or an interactive matplotlib backend); under a non-interactive matplotlib backend - (e.g. Agg) the figure is still drawn and returned in the result - dict's 'fig' key, without calling `plt.show()`. + (e.g. Agg) it is drawn and returned without calling `plt.show()`. + With `show=False` it is returned without being displayed, exactly + like `hyp.plot(show=False)`: a matplotlib figure is closed + (deregistered from pyplot, so Jupyter does not display it) but + stays renderable and savable, and a plotly figure's `.show()` is + not called. format_data : bool Whether or not to first call the format_data function (default: True). backend : {'auto', 'matplotlib', 'plotly'} Which plotting backend to draw the correlation-vs-dimensions figure - with when `show=True`. Validated eagerly (even with `show=False`, - an unknown backend raises the same "backend must be one of ..." - `ValueError` as `hyp.plot`). Default: 'auto', resolved the same way + with. Validated eagerly (an unknown backend raises the same + "backend must be one of ..." `ValueError` as `hyp.plot`). Default: 'auto', resolved the same way `hyp.plot` resolves it -- plotly on Colab/Kaggle when available, else matplotlib. The matplotlib figure is a seaborn line plot with the top and right spines removed; the plotly figure is an interactive `go.Figure` (which has no top/right spines by default). Multi-dataset inputs get one distinguishable color per dataset, a legend, and the - 'average' curve overlaid, on both backends. The analysis results in + 'average' curve (the mean of the per-dataset curves) overlaid as a + dashed black line, on both backends. The x axis is ticked at whole + component counts only. The analysis results in the returned dict are identical either way (only 'fig' differs: it holds the backend's own figure object). @@ -94,13 +115,22 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, ------- result : dict - A dictionary with the analysis results. 'average' is the correlation - by number of components for all data. 'individual' is a list of lists, - where each list is a correlation by number of components vector (for each - input list). 'fig' is the rendered figure handle (a + A dictionary with the analysis results. 'individual' is a list of + lists, where each list is a correlation by number of components + vector (one per input dataset), starting at 2 components. + 'average' is the element-wise mean of the 'individual' curves (the + dashed 'average' line in the figure); when curves differ in length + (possible with `max_dims=None`, since each dataset's sweep stops at + its own dimensionality), each component count is averaged over the + curves that reach it. For a single dataset, 'average' equals + 'individual'[0]. 'pooled' is the curve for all datasets STACKED + into one point cloud (what 'average' meant before 1.1); it is not + drawn, and it generally differs from 'average' because pooling adds + the between-dataset distances. For a single dataset it also equals + 'individual'[0]. 'fig' is the drawn figure (a `matplotlib.figure.Figure` or `plotly.graph_objects.Figure`, - depending on the backend) when `show=True` and a figure was drawn; - otherwise None. + depending on the backend), whether or not `show` is True; it is + None only if there was nothing to plot. Examples -------- @@ -110,7 +140,7 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, ... axis=0) >>> result = hyp.describe(x, reduce='PCA', max_dims=4, show=False) >>> sorted(result.keys()) - ['average', 'fig', 'individual'] + ['average', 'fig', 'individual', 'pooled'] """ from ..core.shared import require_data @@ -122,6 +152,14 @@ def describe(x, reduce='IncrementalPCA', max_dims=None, show=True, if isinstance(x, tuple): x = list(x) + # no reducer means nothing to sweep: each "dimensionality" would be the + # unreduced data, a flat 1.0 curve that says nothing (1.1 release review) + if reduce is None: + raise ValueError( + "describe() evaluates a reducer at each dimensionality, so it " + "needs one; got reduce=None. Pass a reducer name such as " + "reduce='PCA' (the default is 'IncrementalPCA').") + # validate max_dims up front (release-1.0 audit, X2-error-quality-017): # max_dims=0/-3 silently returned empty results, and a float hit a bare # "'float' object cannot be interpreted as an integer" from range() @@ -168,6 +206,10 @@ def _tsne_barnes_hut(spec): return method == 'barnes_hut' tsne_dims_cap = 4 if _tsne_barnes_hut(reduce) else None + # the sweep sets the dimensionality at every point, whatever form the + # reducer was given in; a model whose dimensionality cannot be varied + # is refused here, before anything is computed or drawn + spec_for = _sweep_spec(reduce) _dim_cap_warned = [] # warn about a too-large max_dims only once def summary(x, max_dims=None): @@ -247,7 +289,8 @@ def summary(x, max_dims=None): corrs=[] for dims in range(2, max_dims): - reduced = get_cdist(reducer(x, ndims=dims, reduce=reduce)) + reduced = get_cdist(reducer(x, ndims=dims, + reduce=spec_for(dims))) corrs.append(get_corr(reduced, alldims)) del reduced return corrs @@ -300,31 +343,44 @@ def summary(x, max_dims=None): 'computation can take a long time.', stacklevel=external_stacklevel()) - # a dictionary to store results + # a dictionary to store results. 'individual' holds one curve per + # dataset; 'average' is their element-wise mean -- what the name and the + # figure's legend say (1.1 release review: it used to be the curve of + # the STACKED point cloud, which mixes in between-dataset distances and + # so is not the mean of the curves drawn beside it). That pooled curve is still computed and returned + # as 'pooled', but it is not drawn. `datasets` (not `x`) is iterated so + # a bare array with format_data=False is one dataset, not one per row. result = {} - result['average'] = summary(x, max_dims) - result['individual'] = [summary(x_i, max_dims) for x_i in x] - - if max_dims is None: - max_dims = len(result['average']) + result['individual'] = [summary(x_i, max_dims) for x_i in datasets] + result['average'] = _mean_curve(result['individual']) + if len(datasets) > 1: + result['pooled'] = summary(x, max_dims) + else: + # one dataset: the stacked data IS that dataset + result['pooled'] = list(result['individual'][0]) # if show, plot it on the resolved backend. With max_dims < 3 there is no # component range to correlate over (range(2, max_dims) is empty), so the # result is empty and there is nothing to plot -- warn and skip rather than # crash inside seaborn/plotly (QC 2026-07). + # The figure is drawn whether or not it is shown: show=False returns it + # undisplayed, exactly like hyp.plot(show=False) (1.1 release review -- + # it used to skip drawing and return fig=None). fig = None - if show and not any(result['individual']): + draw = bool(any(result['individual'])) + if show and not draw: warnings.warn('describe() has no components to plot (need max_dims >= 3 ' 'and at least 3 features); skipping the figure.', stacklevel=external_stacklevel()) show = False - if show: + if draw: from ..plot.plotly_backend import resolve_backend resolved_backend = resolve_backend(backend) title = 'Correlation with raw data by number of components' # only multi-dataset inputs need a legend and the 'average' overlay # (for a single dataset, average == individual[0]) multi = len(result['individual']) > 1 + n_components = max(len(trace) for trace in result['individual']) if resolved_backend == 'plotly': import plotly.graph_objects as go fig = go.Figure() @@ -342,9 +398,14 @@ def summary(x, max_dims=None): line=dict(color='black', width=3, dash='dash'))) fig.update_layout(title=title, xaxis_title='Number of components', yaxis_title='Correlation') + # component counts are whole numbers: pin the ticks to integers + # (plotly's auto ticks labelled 2.5, 3.5, ... on short sweeps) + fig.update_xaxes(tickmode='array', + tickvals=_integer_ticks(n_components)) # plotly axes have no top/right spines by default (Jeremy's despine # request is inherent here); return the dict, show the figure - fig.show() + if show: + fig.show() else: import pandas as pd import seaborn as sns @@ -371,6 +432,10 @@ def summary(x, max_dims=None): ax.set_title(title) ax.set_ylabel('Correlation') ax.set_xlabel('Number of components') + # component counts are whole numbers: no fractional ticks + # (matplotlib's auto locator labelled 2.25, 2.5, ...) + from matplotlib.ticker import MaxNLocator + ax.xaxis.set_major_locator(MaxNLocator(integer=True)) # drop the top and right spines (Jeremy's despine request) sns.despine(ax=ax, top=True, right=True) # only call plt.show() when the backend can actually display a @@ -379,9 +444,19 @@ def summary(x, max_dims=None): # UserWarning on every default describe() call (release-1.0 # audit, X4-warnings-006). The figure is returned either way. import matplotlib - if matplotlib.get_backend().lower() not in ('agg', 'pdf', - 'svg', 'ps', - 'template'): + if not show: + # same canvas-preserving close hyp.plot(show=False) does: + # deregister the figure from pyplot (so Jupyter does not + # display it and it does not leak) while keeping it + # renderable -- matplotlib >= 3.11 detaches the real canvas + # on plt.close + live_canvas = fig.canvas + plt.close(fig) + if fig.canvas is not live_canvas: + fig.set_canvas(live_canvas) + elif matplotlib.get_backend().lower() not in ('agg', 'pdf', + 'svg', 'ps', + 'template'): plt.show() # hand the figure back so it can be saved/styled/embedded -- describe() # used to be the one plotting entry point with no figure handle @@ -390,6 +465,205 @@ def summary(x, max_dims=None): return result +_SWEEP_ALTERNATIVES = ( + "Pass the model's name (e.g. reduce='PCA'), a spec such as " + "{'model': 'PCA', 'kwargs': {'whiten': True}}, or an unfitted instance " + "(e.g. PCA(whiten=True)) instead; describe() sets the number of " + "dimensions itself at each point of the sweep.") + + +def _is_fitted_model(model): + """Whether a reducer instance already carries a fit. + + Parameters + ---------- + model : object + An already-constructed model: a `hypertools.Pipeline`, a + `hypertools.reduce.common.Reducer`, or a scikit-learn-style + instance. + + Returns + ------- + bool + The object's own ``is_fitted`` flag when it has one (hypertools' + wrappers and pipelines); otherwise scikit-learn's + `check_is_fitted` verdict. An object that reports nothing either + way counts as not fitted. + """ + from ..core.pipeline import _step_is_fitted + return _step_is_fitted(model) + + +def _clone_with_dims(model, dims): + """An unfitted copy of `model` with ``n_components`` set to `dims`. + + Parameters + ---------- + model : object + An unfitted scikit-learn-style reducer instance with an + ``n_components`` parameter (validated by `_sweep_spec`). + dims : int + The dimensionality for the copy. + + Returns + ------- + object + A `sklearn.base.clone` of `model` (same settings, no fitted + state) for an estimator with ``get_params``, else a deep copy; + `model` itself is never modified. + """ + if hasattr(model, 'get_params') and hasattr(model, 'set_params'): + from sklearn.base import clone + return clone(model).set_params(n_components=dims) + import copy + duplicate = copy.deepcopy(model) + duplicate.n_components = dims + return duplicate + + +def _check_sweepable_instance(model): + """Refuse a reducer instance whose dimensionality the sweep cannot vary. + + Parameters + ---------- + model : object + An already-constructed model passed as `reduce=` (directly or as + a dict spec's ``'model'``). + + Raises + ------ + ValueError + If `model` is already fitted (its dimensionality was fixed by the + fit), or has no ``n_components`` parameter to set. The message + says what to pass instead. + """ + kind = type(model).__name__ + if _is_fitted_model(model): + raise ValueError( + f"describe() cannot sweep the number of dimensions of an " + f"already fitted model (got a fitted {kind}): its " + f"dimensionality was fixed when it was fit, so every point of " + f"the curve would be the same reduction. " + + _SWEEP_ALTERNATIVES) + if hasattr(model, 'get_params'): + settable = 'n_components' in model.get_params(deep=False) + else: + settable = hasattr(model, 'n_components') + if not settable: + raise ValueError( + f"describe() cannot sweep the number of dimensions of this " + f"{kind} instance: it has no n_components parameter to set, so " + f"its dimensionality cannot be varied. " + _SWEEP_ALTERNATIVES) + + +def _sweep_spec(reduce): + """Build the per-dimensionality `reduce=` spec for `describe`'s sweep. + + `describe` evaluates one reduction per candidate dimensionality, so + the dimensionality in the spec itself must never win over the sweep's: + a reducer pinned to 2 components would be evaluated at 2 components + for every point while the curve labelled them 2, 3, 4, ... (1.1 + release review). + + Parameters + ---------- + reduce : str, dict, class, instance or None + `describe`'s `reduce=` argument. + + Returns + ------- + callable + ``spec_for(dims)``: the spec to hand `hypertools.reduce` together + with ``ndims=dims``. A name, a class and ``None`` are returned + as they are (`ndims` already sets their dimensionality). A dict + spec is copied with any ``n_components`` entry removed from its + ``'kwargs'``/``'params'``. An unfitted instance (bare, or as a + dict spec's ``'model'``) is replaced by a fresh clone with + ``n_components=dims``; the caller's object is never modified. + + Raises + ------ + ValueError + For an already-fitted model (including the `Reducer` or + `Pipeline` that ``return_model=True`` returns) and for an + instance with no ``n_components`` parameter. + """ + def is_instance(model): + """Whether `model` is an already-constructed object, as opposed to + a name, a class, a dict spec or ``None``/``False``.""" + return not (model is None or model is False + or isinstance(model, (str, dict)) + or inspect.isclass(model)) + + if isinstance(reduce, dict): + inner = reduce.get('model') + if is_instance(inner): + _check_sweepable_instance(inner) + + def spec_for(dims): + """A copy of the dict spec for one sweep point: no pinned + ``n_components``, and an instance model cloned at `dims`.""" + spec = dict(reduce) + for key in ('kwargs', 'params'): + if isinstance(spec.get(key), dict): + spec[key] = {name: value + for name, value in spec[key].items() + if name != 'n_components'} + if is_instance(inner): + spec['model'] = _clone_with_dims(inner, dims) + return spec + return spec_for + + if is_instance(reduce): + _check_sweepable_instance(reduce) + return lambda dims: _clone_with_dims(reduce, dims) + + return lambda dims: reduce + + +def _mean_curve(curves): + """Element-wise mean of per-dataset correlation curves. + + Curves may differ in length (with ``max_dims=None`` each dataset's sweep + stops at its own dimensionality); the mean at each component count is + taken over the curves that reach it. + + Parameters + ---------- + curves : list of list of float + One correlation-by-number-of-components curve per dataset. + + Returns + ------- + list of float + The mean curve, as long as the longest input curve. + """ + longest = max((len(c) for c in curves), default=0) + return [float(np.mean([c[i] for c in curves if len(c) > i])) + for i in range(longest)] + + +def _integer_ticks(n_components): + """Whole-number x tick positions for a sweep over 2..n_components+1. + + Parameters + ---------- + n_components : int + Number of evaluated component counts (the sweep starts at 2). + + Returns + ------- + list of int + At most ~9 evenly spaced integer tick positions within the sweep, + chosen by matplotlib's integer ``MaxNLocator`` so both backends + label the same ticks. + """ + from matplotlib.ticker import MaxNLocator + lo, hi = 2, 1 + max(int(n_components), 1) + ticks = MaxNLocator(integer=True).tick_values(lo, hi) + return [int(t) for t in ticks if lo <= t <= hi] + + def get_corr(reduced, alldims): """Pearson correlation coefficient between two flattened distance matrices. diff --git a/hypertools/reduce/reduce.py b/hypertools/reduce/reduce.py index ee707fee..ac20964b 100644 --- a/hypertools/reduce/reduce.py +++ b/hypertools/reduce/reduce.py @@ -6,6 +6,7 @@ from .common import (Reducer, models, REDUCERS, AUTOENCODER_NAMES, # noqa: F401 (re-export; hypertools.core.pipeline imports `models` from here for backward compatibility) resolve_reducer) from ..core.model import external_stacklevel +from ..core.shared import check_spec_keys from ..tools.format_data import format_data as formatter @@ -83,7 +84,10 @@ def reduce(x, reduce='IncrementalPCA', ndims=None, return_model=False, `{'model': ..., 'args': [...], 'kwargs': {...}}` (both `'args'` and `'kwargs'` are OPTIONAL, so the minimal `{'model': 'PCA'}` works too; passing the legacy `'params'` key alongside them warns - and ignores `'params'`), or the LEGACY + and ignores `'params'`; model parameters always go under + `'kwargs'`, and any other top-level key -- e.g. `{'model': 'PCA', + 'whiten': True}` -- raises `ValueError` naming it rather than + being ignored), or the LEGACY dict spec `{'model' : 'PCA', 'params' : {'whiten' : True}}` (accepted for backward compatibility, but emits a `DeprecationWarning`). A previously-fitted `Reducer` (as returned @@ -267,6 +271,9 @@ def reduce(x, reduce='IncrementalPCA', ndims=None, return_model=False, "under 'args' (positional) and 'kwargs' (keyword), e.g. " "{'model': 'PCA', 'kwargs': {'whiten': True}} (the " "legacy 'params' key is also accepted).") + # a flat key such as {'model': 'PCA', 'whiten': True} used to be + # dropped silently, so the model ran with its defaults (1.1 review) + check_spec_keys(reduce, 'reduce', param='reduce') if 'args' in reduce or 'kwargs' in reduce or 'params' not in reduce: # canonical 1.0 dict spec: {'model': ..., 'args': [...], # 'kwargs': {...}} -- BOTH 'args' and 'kwargs' are optional, so @@ -477,6 +484,14 @@ def reduce(x, reduce='IncrementalPCA', ndims=None, return_model=False, and 'random_state' not in model_params and 'random_state' in inspect.signature(model).parameters): model_params['random_state'] = random_state + # umap-learn forces n_jobs=1 whenever a random_state is set and + # warns that it did ("n_jobs value -1 overridden to 1 ..."), which + # would blame the user for a seed hypertools injected. Pass the + # n_jobs umap is going to use anyway, unless the user chose one. + if (getattr(model, '__name__', '') == 'UMAP' + and 'n_jobs' not in model_params + and 'n_jobs' in inspect.signature(model).parameters): + model_params['n_jobs'] = 1 # sklearn TSNE's default perplexity (30) requires n_samples > 30, so # small datasets crashed on a parameter the user never set @@ -594,7 +609,16 @@ def reduce_list(x, model, reuse=None): transformed = np.asarray(fitted.transform(stacked)) else: fitted = Reducer(model) - transformed = np.asarray(fitted.fit_transform(stacked)) + with warnings.catch_warnings(): + # scikit-learn's Isomap completes a disconnected neighbour graph + # by writing into a CSR matrix cell by cell, and scipy warns + # about ITS sparsity-structure changes a dozen times per fit. + # Nothing the user passed causes or can stop that, so it stays + # silent; sklearn's own "connected components" UserWarning + # (about the user's data and n_neighbors) still reaches them. + from scipy.sparse import SparseEfficiencyWarning + warnings.filterwarnings('ignore', category=SparseEfficiencyWarning) + transformed = np.asarray(fitted.fit_transform(stacked)) x_r = np.vsplit(transformed, split) if len(x) > 1: diff --git a/hypertools/tools/align.py b/hypertools/tools/align.py index e468bb7d..439795d8 100644 --- a/hypertools/tools/align.py +++ b/hypertools/tools/align.py @@ -16,6 +16,7 @@ import numpy as np from ..align.align import align as _align_dispatch, _ALIAS as _MODEL_ALIAS +from ..core.shared import check_spec_keys from .format_data import format_data as formatter @@ -50,8 +51,9 @@ def align(data, align='hyper', n_iter=10, format_data=True): hyperalignment. If 'SRM', alignment algorithm will be shared response model. You can also pass a dictionary for finer control, where the 'model' key is a string that specifies the model and the 'kwargs' key (or the - legacy 'params' key) is a dictionary of parameter values - (default : 'hyper'). + legacy 'params' key) is a dictionary of parameter values; any other + top-level key (e.g. a flat {'model': 'hyper', 'n_iter': 3}) raises + ValueError naming it (default : 'hyper'). n_iter : int Number of hyperalignment iterations: the common template is @@ -80,6 +82,9 @@ def align(data, align='hyper', n_iter=10, format_data=True): "algorithm instead, e.g. align='hyper' or align='SRM'.") if isinstance(align, dict): + # a flat key such as {'model': 'hyper', 'n_iter': 3} used to be + # dropped silently, so the default n_iter ran (1.1 review) + check_spec_keys(align, 'align', param='align') model = align['model'] if model is None: return data diff --git a/hypertools/tools/analyze.py b/hypertools/tools/analyze.py index 100a66af..86ce7d4b 100644 --- a/hypertools/tools/analyze.py +++ b/hypertools/tools/analyze.py @@ -26,6 +26,68 @@ def _impute_format(data, impute): return formatted[0] +def pipeline_cluster_labels(pipeline, data): + """Labels a fitted pipeline's trailing `'cluster'` step gives `data`. + + `analyze(x, pipeline=p)` returns the transformed DATA even when `p` + ends in a fitted cluster step (see `pipeline=` in `analyze`); the + labels are that step applied to the returned data -- the recovery the + docstring documents, ``p.named_steps['cluster'].transform(data)``. + `hyp.plot(x, pipeline=p)` runs it to colour the figure by the fitted + clusters, as the figure `p` was fit for was coloured: skipping it drew + one colour with no warning (1.1 release review, 2026-09-11). + + Parameters + ---------- + pipeline : hypertools.Pipeline + A fitted pipeline. + data : list of arrays + `analyze(x, pipeline=pipeline)`'s output for the data to label. + + Returns + ------- + tuple or None + `(labels, model, fitted, categories)`: the per-observation labels + over the stacked datasets (membership proportions for a mixture + model), the fitted clusterer's class (whose name selects hard vs. + mixture colouring), the fitted cluster model itself (the step's + `Clusterer`), and the label set it was FIT with, sorted (so a + dataset missing a cluster keeps the fit figure's colours; None for + mixture proportions). `None` when the pipeline is unfitted or does + not end in a cluster step, or when the step cannot label `data` -- + a clusterer with no out-of-sample `predict` (e.g. + `AgglomerativeClustering`) given rows it was not fit on -- in which + case a `UserWarning` says the figure is drawn without its clusters. + """ + import numpy as np + from ..core.model import external_stacklevel + from ..core.pipeline import _step_transform + + steps = getattr(pipeline, 'steps', None) + if (not steps or steps[-1][0] != 'cluster' + or not getattr(pipeline, 'is_fitted', False)): + return None + step = steps[-1][1] + fitted = getattr(step, '_fitted', step) + model = getattr(fitted, 'model_', None) or fitted + try: + labels = _step_transform(step, [np.asarray(d) for d in data], + name='cluster') + except NotImplementedError as err: + warnings.warn( + f"pipeline='s fitted cluster step ({type(model).__name__}) " + f"cannot label this data, so the figure is drawn without its " + f"clusters: {err}", UserWarning, + stacklevel=external_stacklevel()) + return None + categories = None + if np.ndim(labels) == 1: + fit_labels = getattr(model, 'labels_', None) + seen = labels if fit_labels is None else fit_labels + categories = sorted(set(np.asarray(seen).tolist())) + return labels, type(model), fitted, categories + + def analyze(data, manip=None, normalize=None, reduce=None, ndims=None, align=None, cluster=None, pipeline=None, return_model=False, internal=False, impute=None, random_state=None): diff --git a/hypertools/tools/damage.py b/hypertools/tools/damage.py index 8a34dfe2..801fa023 100644 --- a/hypertools/tools/damage.py +++ b/hypertools/tools/damage.py @@ -17,6 +17,9 @@ import numpy as np import pandas as pd +from .._shared.helpers import (is_frame_dataset, is_series_like, + as_pandas_dataframe) + __all__ = ['damage'] @@ -28,8 +31,10 @@ def _as_float_values(x, name='x'): order) is what makes a write through it land. Copying here is also what leaves the caller's data untouched. """ - raw = x.to_numpy() if isinstance(x, (pd.DataFrame, pd.Series)) \ - else np.asarray(x) + # `np.asarray` covers every kind hypertools sees here (a pandas + # frame/Series, an array, a list); `_damage_one` has already converted + # a frame of another backend to pandas (datatype audit, 2026-09-08) + raw = np.asarray(x) if raw.dtype.kind not in 'fiub': raise TypeError( f"{name} must hold numeric values (damage marks cells missing " @@ -141,6 +146,13 @@ def _scatter(values, blanked_rows, frac, rng): def _damage_one(x, frac, rows, row_frac, rng, name='x'): """Damage one dataset; returns ``(damaged_copy, mask)``.""" + # a frame (or Series) of any backend datawrangler recognises -- polars, + # a LazyFrame, ... -- is damaged as hypertools' internal pandas type, + # and comes back as one (datatype audit, 2026-09-08) + if is_frame_dataset(x): + x = as_pandas_dataframe(x) + elif is_series_like(x) and not hasattr(x, 'index'): + x = pd.Series(np.asarray(x), name=getattr(x, 'name', None)) values = _as_float_values(x, name=name) # A 1-D dataset is n observations of a single feature. `reshape` on a # fresh C-contiguous copy is a view, so writes through `two_d` land in @@ -159,10 +171,10 @@ def _damage_one(x, frac, rows, row_frac, rng, name='x'): two_d[mask] = np.nan mask = mask.reshape(values.shape) - if isinstance(x, pd.DataFrame): + if is_frame_dataset(x): return (pd.DataFrame(values, index=x.index, columns=x.columns), pd.DataFrame(mask, index=x.index, columns=x.columns)) - if isinstance(x, pd.Series): + if is_series_like(x): return (pd.Series(values, index=x.index, name=x.name), pd.Series(mask, index=x.index, name=x.name)) return values, mask diff --git a/hypertools/tools/format_data.py b/hypertools/tools/format_data.py index a169381b..f35bf352 100644 --- a/hypertools/tools/format_data.py +++ b/hypertools/tools/format_data.py @@ -3,7 +3,23 @@ import numpy as np import pandas as pd -from .._shared.helpers import get_type +from .._shared.helpers import (get_type, is_number_item, is_array_dataset, + is_frame_dataset, is_series_like, + as_pandas_dataframe) + + +def _warn(*args, **kwargs): + """warnings.warn() attributed to the caller outside hypertools. + + Every warning this module emits is about the USER's data (missing + values, mixed text and numbers, ...), so the reported location is the + user's call site, not this file -- the same external_stacklevel() + convention as the rest of the library. Imported lazily: core.model + imports the tools package. + """ + from ..core.model import external_stacklevel + kwargs.setdefault('stacklevel', external_stacklevel()) + warnings.warn(*args, **kwargs) def _contains_text(el): @@ -15,13 +31,18 @@ def _contains_text(el): return False +def _is_dataset(el): + """True if `el` is ONE dataset object: an array, a DataFrame of any + backend datawrangler recognises (pandas, polars DataFrame/LazyFrame, + dataframe-likes) or a Series-like (see `hypertools._shared.helpers`).""" + return is_array_dataset(el) or is_frame_dataset(el) or is_series_like(el) + + def _contains_dataset(el): - """True if `el` is (or recursively contains) an array/DataFrame/Series.""" - if isinstance(el, (np.ndarray, pd.DataFrame, pd.Series)): - return True + """True if `el` is (or recursively contains) a dataset object.""" if isinstance(el, (list, tuple)): return any(_contains_dataset(sub) for sub in el) - return False + return _is_dataset(el) def _flatten_dataset_groups(x): @@ -69,7 +90,7 @@ def _prepare_df(df, warn=True): if dt_idx: if warn: _names = [str(df.columns[j]) for j in dt_idx] - warnings.warn( + _warn( f"DataFrame column(s) {_names} contain datetime values; " 'converting to float seconds since the Unix epoch ' '(1970-01-01 00:00:00 UTC) so they can be analyzed ' @@ -107,6 +128,12 @@ def format_data(x, vectorizer='CountVectorizer', - pandas Series (top-level or inside a list) become 1-D datasets; tuples are treated like lists. + - Input types are classified with datawrangler's predicates + (``dw.zoo.is_array`` / ``is_dataframe`` / ``array_like``), so every + DataFrame backend datawrangler recognises -- pandas, polars + (DataFrame or LazyFrame), modin, dataframe-likes -- is accepted, and + non-pandas frames are converted to pandas via + ``dw.wrangle(..., backend='pandas')`` (polars nulls become NaN). - Nested lists/tuples of arrays/DataFrames (e.g. ``[[arr1, arr2]]``) are flattened into a flat list of datasets, matching `hyp.plot()`. - Lists of bools are numeric 0/1 datasets, like ``np.array([True, ...])``. @@ -121,7 +148,7 @@ def format_data(x, vectorizer='CountVectorizer', Parameters ---------- - x : numpy array, dataframe, series, string or (mixed, possibly nested) list + x : numpy array, dataframe (pandas, polars, ...), series, string or (mixed, possibly nested) list The data to convert vectorizer : str, dict, class or class instance @@ -192,11 +219,10 @@ def format_data(x, vectorizer='CountVectorizer', from .df2mat import df2mat from .text2mat import text2mat - # a pandas Series is a single 1-D dataset (QC 2026-07: was rejected as - # "unsupported"); a tuple is treated like a list of datasets. - import pandas as pd - if isinstance(x, pd.Series): - x = x.to_numpy() + # a Series (pandas, polars, ...) is a single 1-D dataset (QC 2026-07: was + # rejected as "unsupported"); a tuple is treated like a list of datasets. + if is_series_like(x): + x = np.asarray(x) elif isinstance(x, tuple): x = list(x) @@ -223,9 +249,7 @@ def format_data(x, vectorizer='CountVectorizer', # [True, False, True] is the same data as np.array([True, False, True]), # which has always been accepted; np.bool_ is listed explicitly because # it is neither an np.number subclass nor (numpy >= 2) a python bool. - elif len(x) > 0 and all( - isinstance(xi, (bool, int, float, np.number, np.bool_)) - for xi in x): + elif len(x) > 0 and all(is_number_item(xi) for xi in x): x = [np.asarray(x, dtype=float)] # nested lists of datasets, e.g. [[arr1, arr2]], are flattened into a @@ -237,8 +261,12 @@ def format_data(x, vectorizer='CountVectorizer', x = _flatten_dataset_groups(x) # per-dataset conversions (release-1.0 audit): - # - a pandas Series inside a list is a 1-D dataset, like a top-level - # Series (converted above) + # - a Series inside a list is a 1-D dataset, like a top-level Series + # (converted above) + # - a DataFrame of any other backend datawrangler recognises (polars + # DataFrame/LazyFrame, modin, dataframe-likes) becomes a pandas + # DataFrame, hypertools' internal frame type; pandas frames pass + # through untouched (index, columns and dtypes preserved) # - a numpy MaskedArray's masked entries are MISSING data # (F08-plot-inputs-009): np.asarray() silently drops the mask, so the # invalid underlying values used to be analyzed/plotted as real data. @@ -247,12 +275,14 @@ def format_data(x, vectorizer='CountVectorizer', # the NaNs) and warn. x_converted = [] for _i, _el in enumerate(x): - if isinstance(_el, pd.Series): - _el = _el.to_numpy() - if isinstance(_el, np.ma.MaskedArray) and _el.dtype.kind in 'biufc': + if is_series_like(_el): + _el = np.asarray(_el) + elif is_frame_dataset(_el): + _el = as_pandas_dataframe(_el) + if np.ma.isMaskedArray(_el) and _el.dtype.kind in 'biufc': _n_masked = int(np.ma.count_masked(_el)) if _n_masked: - warnings.warn( + _warn( f'dataset {_i} is a numpy masked array with {_n_masked} ' 'masked (invalid) entries; treating them as missing ' 'data (converted to NaN and, by default, filled via ' @@ -295,8 +325,8 @@ def format_data(x, vectorizer='CountVectorizer', # columns are passed: reorder later ones to match the first's column # order when the column sets agree, and raise a clear error when they # don't. DataFrames with default integer columns (e.g. wrapped arrays) - # keep their positional behavior. - import pandas as pd + # keep their positional behavior. (Every 'df' dataset is a pandas + # DataFrame by now -- see the per-dataset conversions above.) named_df_idx = [ i for i, d in enumerate(dtypes) if d == 'df' @@ -311,7 +341,7 @@ def format_data(x, vectorizer='CountVectorizer', if cols == canonical: continue if set(cols) == set(canonical): - warnings.warn( + _warn( f'dataset {i} has the same columns as dataset ' f'{named_df_idx[0]} but in a different order; reordering ' f'{cols} to match {canonical} so features align by name ' @@ -492,7 +522,7 @@ def format_data(x, vectorizer='CountVectorizer', if impute is not None: num_data = fill_missing(num_data, model=impute) else: - warnings.warn('Missing data: filling missing values ' + _warn('Missing data: filling missing values ' 'with PPCA (observed values are ' 'preserved exactly; only the NaN ' 'entries are reconstructed). Pass ' @@ -517,7 +547,7 @@ def format_data(x, vectorizer='CountVectorizer', from .align import align as aligner # align the data - warnings.warn('Numerical and text data with same number of ' + _warn('Numerical and text data with same number of ' 'samples detected. Aligning data to a common space.') processed_x = aligner(processed_x, align=text_align, format_data=False) elif len(set(i.shape[1] for i in processed_x)) > 1: @@ -535,7 +565,7 @@ def format_data(x, vectorizer='CountVectorizer', f"dataset {i}: {'text' if j in ('list_str', 'str', 'arr_str') else 'numeric'}, " f'{arr.shape[0]} sample(s)' for i, (arr, j) in enumerate(zip(processed_x, dtypes))] - warnings.warn( + _warn( 'mixed text and numeric datasets were passed with ' f"DIFFERENT sample counts ({'; '.join(_counts)}), so they " 'cannot be auto-aligned to a common space (alignment ' diff --git a/hypertools/tools/normalize.py b/hypertools/tools/normalize.py index 1c86eb67..9a64b640 100644 --- a/hypertools/tools/normalize.py +++ b/hypertools/tools/normalize.py @@ -12,6 +12,8 @@ from sklearn.exceptions import NotFittedError from .format_data import format_data as formatter +from .._shared.helpers import is_frame_dataset, is_number_item, is_series_like +from ..core.shared import as_dataframe def _as_list_2d(x): @@ -25,15 +27,38 @@ def _as_list_2d(x): DataFrame, or a list of them) to a list of 2-D float arrays so the per-column z-scoring below is well-defined. + A 1-D dataset -- a 1-D array, a pandas/polars Series, or a flat list of + numbers -- is ONE COLUMN (``(n, 1)``), exactly as ``format_data`` reads + it, so a ``Normalizer`` fit through ``normalize()`` accepts the same + data again in ``.transform`` (it used to become a single row and fail + the column-count check; review 2026-09-11). + Returns ------- (list of numpy.ndarray, bool) The 2-D arrays, and whether the original input was a single array (so callers can return single-in -> single-out). """ + def _as_float_2d(a): + # a frame of any backend datawrangler recognises (polars, a + # LazyFrame, ...) goes through the shared pandas coercion first; + # everything else is whatever `np.asarray` makes of it (datatype + # audit, 2026-09-08) + if is_frame_dataset(a): + a = as_dataframe(a) + a = np.asarray(a, dtype=np.float64) + if a.ndim <= 1: + # one column (a scalar is one observation), matching format_data + return a.reshape(-1, 1) + return a + + if is_series_like(x): + return [_as_float_2d(x)], True if isinstance(x, (list, tuple)): - return [np.atleast_2d(np.asarray(a, dtype=np.float64)) for a in x], False - return [np.atleast_2d(np.asarray(x, dtype=np.float64))], True + if len(x) > 0 and all(is_number_item(xi) for xi in x): + return [_as_float_2d(list(x))], True # a flat list of numbers + return [_as_float_2d(a) for a in x], False + return [_as_float_2d(x)], True def _check_column_counts(arrs): @@ -116,7 +141,9 @@ def fit(self, x): """Compute per-column mean/std across the stacked fit-time data (`'across'` mode only; a no-op for `'within'`/`'row'`). - Accepts either a single 2-D array or a list of them. + Accepts either a single dataset or a list of them. A 2-D array or + DataFrame is used as is; a 1-D array, a Series or a flat list of + numbers is one column. """ if self.normalize == 'across': arrs, _ = _as_list_2d(x) @@ -132,7 +159,9 @@ def transform(self, x): `x` may be a single 2-D array (or DataFrame) or a list of them; the result mirrors the input (single array in -> single array out, list in -> list out), matching `normalize()`'s own convention so a fitted - `Normalizer` can be reused directly on held-out data. + `Normalizer` can be reused directly on held-out data. A 1-D array, + a Series or a flat list of numbers is one column, as in `fit` and in + `normalize()` itself, and comes back as an ``(n, 1)`` array. """ arrs, single = _as_list_2d(x) if self.normalize == 'across': diff --git a/hypertools/tools/stack.py b/hypertools/tools/stack.py index 7e7b64eb..f232376c 100644 --- a/hypertools/tools/stack.py +++ b/hypertools/tools/stack.py @@ -21,6 +21,9 @@ import numpy as np import pandas as pd +from .._shared.helpers import (is_frame_dataset, is_series_like, + as_pandas_dataframe) + __all__ = ['stack'] #: Named aggregators for `stack`'s ``aggregate=``. Each is called as @@ -36,15 +39,20 @@ def _default_columns(width): def _as_leaf_frame(obj, key): """One dataset -> a flat-columned DataFrame.""" where = f"frames{''.join(f'[{k!r}]' for k in key)}" - if isinstance(obj, pd.DataFrame): + if is_frame_dataset(obj): + # any backend datawrangler recognises, as hypertools' pandas type + obj = as_pandas_dataframe(obj) if obj.columns.nlevels > 1: raise ValueError( f"{where} already has a column MultiIndex. stack builds the " "hierarchy; its inputs must be flat frames or arrays.") return obj - if isinstance(obj, pd.Series): - name = obj.name if obj.name is not None else 'feature 1' - return obj.to_frame(name=name) + if is_series_like(obj): + name = getattr(obj, 'name', None) + name = name if name is not None else 'feature 1' + return as_pandas_dataframe(obj.to_frame(name=name)) \ + if hasattr(obj, 'to_frame') \ + else pd.DataFrame({name: np.asarray(obj)}) values = np.asarray(obj) if values.ndim == 1: values = values.reshape(-1, 1) @@ -288,7 +296,7 @@ def stack(frames, names=None, level_names=None, aggregate=None): f"{reference}.") index = leaves[0].index - if not all(isinstance(obj, pd.DataFrame) for _, obj in collected) or \ + if not all(is_frame_dataset(obj) for _, obj in collected) or \ not all(leaf.index.equals(index) for leaf in leaves): index = pd.RangeIndex(len(leaves[0])) leaves = [pd.DataFrame(leaf.to_numpy(), index=index, diff --git a/hypertools/tools/text2mat.py b/hypertools/tools/text2mat.py index 1b99be44..e9451ccd 100644 --- a/hypertools/tools/text2mat.py +++ b/hypertools/tools/text2mat.py @@ -23,6 +23,8 @@ from sklearn.exceptions import NotFittedError from sklearn.pipeline import Pipeline from .._shared.params import default_params +from ..core.shared import check_spec_keys +from .._shared.helpers import is_array_dataset, is_series_like from ..io.load import load # vector models @@ -269,7 +271,10 @@ def text2mat(data, vectorizer='CountVectorizer', 'all-MiniLM-L6-v2'), via data-wrangler's HF embedding support. To change default parameters, set to a dictionary e.g. {'model' : 'CountVectorizer', 'kwargs' : {'max_features' : 10}} - (the legacy {'model', 'params'} form is also still accepted). See + (the legacy {'model', 'params'} form is also still accepted; + positional constructor arguments go under 'args', and any other + top-level key -- e.g. a flat {'model': 'CountVectorizer', + 'max_features': 10} -- raises ValueError naming it). See https://scikit-learn.org/stable/api/sklearn.feature_extraction.html for scikit-learn details. You can also specify your own vectorizer model as a class, or class instance. With either option, the class @@ -294,7 +299,8 @@ def text2mat(data, vectorizer='CountVectorizer', change default parameters, set to a dictionary e.g. {'model' : 'NMF', 'kwargs' : {'n_components' : 10}} (the legacy {'model', 'params'} form is also - still accepted). See + still accepted; as for `vectorizer`, a top-level key other than + 'model'/'args'/'kwargs' raises ValueError). See https://scikit-learn.org/stable/api/sklearn.decomposition.html for details on the two scikit-learn model options. You can also specify your own text model as a class, or class instance. With @@ -336,7 +342,7 @@ def text2mat(data, vectorizer='CountVectorizer', def _all_text(c): if isinstance(c, str): return True - if isinstance(c, (list, tuple, np.ndarray)): + if isinstance(c, (list, tuple)) or is_array_dataset(c): return len(c) > 0 and all(_all_text(ci) for ci in c) return False if not _all_text(corpus): @@ -352,6 +358,19 @@ def _all_text(c): # `texts` registry key like any built-in scikit-learn one. This runs # BEFORE the corpus block so the embedding-vectorizer decision that # follows is made before any corpus is loaded or embedded. + for _argname, _spec in (('vectorizer', vectorizer), + ('semantic', semantic)): + if isinstance(_spec, dict) and 'model' not in _spec: + # used to leak a bare KeyError: 'model' (1.1 review) + raise ValueError( + f"a {_argname}= dict spec must include a 'model' key; got " + f"keys {sorted(_spec, key=str)}. Pass e.g. " + "{'model': 'CountVectorizer', 'kwargs': {'max_features': " + "10}}.") + # a flat key such as {'model': 'CountVectorizer', 'max_features': + # 10} used to be dropped silently, so the model ran with its + # defaults (1.1 review) + check_spec_keys(_spec, _argname, param=_argname) _vname = _spec_model_name(vectorizer) if _vname is not None: _resolve_registry_name(_vname, vectorizer_models, 'vectorizer') @@ -380,11 +399,14 @@ def _all_text(c): # ValueError before any corpus work instead of sklearn's internal # "Negative values" error after it. if (_vname in _GENSIM_VECTORIZER_NAMES - and isinstance(semantic, str) - and semantic in ('LatentDirichletAllocation', 'NMF')): + and _sname in ('LatentDirichletAllocation', 'NMF')): + # keyed on the resolved model NAME (`_sname`), so the dict spec + # ({'model': 'NMF', 'kwargs': ...}) takes this branch exactly like + # the string spec does -- it used to bypass the guard and crash + # inside NMF with "Negative values in data" (1.1 release review, X2) warnings.warn( f"vectorizer={_vname!r} produces continuous embeddings that " - f"the {semantic} semantic model cannot consume; skipping the " + f"the {_sname} semantic model cannot consume; skipping the " f"semantic stage and returning the embeddings directly. Pass " f"semantic=None to silence this warning.", UserWarning, stacklevel=2) @@ -442,17 +464,23 @@ def _all_text(c): vectorizer = None model_is_fit = True else: - corpus = np.array(load(corpus)) + corpus = _as_text_datasets(load(corpus), 'corpus') else: - corpus = np.array([corpus]) + corpus = _as_text_datasets(corpus, 'corpus') + # a dict spec's positional 'args' reach the constructor too (they used + # to be dropped silently; 1.1 review) + vectorizer_args, text_args = [], [] + vectorizer_user, text_user = {}, {} vtype = _check_mtype(vectorizer) if vtype == 'str': vectorizer_params = default_params(vectorizer) or {} elif vtype == 'dict': + vectorizer_args = list(vectorizer.get('args', [])) + vectorizer_user = dict(vectorizer.get('kwargs', + vectorizer.get('params', {}))) vectorizer_params = default_params( - vectorizer['model'], - vectorizer.get('kwargs', vectorizer.get('params', {}))) or {} + vectorizer['model'], vectorizer_user) or {} vectorizer = vectorizer['model'] elif vtype in ('class', 'class_instance'): if hasattr(vectorizer, 'fit_transform'): @@ -467,9 +495,9 @@ def _all_text(c): if ttype == 'str': text_params = default_params(semantic) or {} elif ttype == 'dict': - text_params = default_params( - semantic['model'], - semantic.get('kwargs', semantic.get('params', {}))) or {} + text_args = list(semantic.get('args', [])) + text_user = dict(semantic.get('kwargs', semantic.get('params', {}))) + text_params = default_params(semantic['model'], text_user) or {} semantic = semantic['model'] elif ttype in ('class', 'class_instance'): if hasattr(semantic, 'fit_transform'): @@ -482,7 +510,9 @@ def _all_text(c): 'https://scikit-learn.org/stable/data_transforms.html') if vectorizer: if vtype in ('str', 'dict'): - vmodel = vectorizer_models[vectorizer](**vectorizer_params) + vmodel = _construct(vectorizer_models[vectorizer], + vectorizer_args, vectorizer_params, + vectorizer_user) elif vtype == 'class': vmodel = vectorizer_models[vectorizer]() elif vtype == 'class_instance': @@ -492,7 +522,8 @@ def _all_text(c): if semantic: if ttype in ('str', 'dict'): - tmodel = texts[semantic](**text_params) + tmodel = _construct(texts[semantic], text_args, text_params, + text_user) elif ttype == 'class': tmodel = texts[semantic]() elif ttype == 'class_instance': @@ -500,8 +531,7 @@ def _all_text(c): else: tmodel = None - if not isinstance(data, list): - data = [data] + data = _as_text_datasets(data, 'data') if corpus is None: _fit_models(vmodel, tmodel, data, model_is_fit) @@ -511,34 +541,119 @@ def _all_text(c): return _transform(vmodel, tmodel, data) +def _construct(cls, args, params, user_params): + """Instantiate a registry model from a spec's positional `args` and + its `params` (registry defaults updated with the spec's own + `user_params`). + + A registry DEFAULT for a parameter that one of `args` fills + positionally is dropped (e.g. NMF's default ``n_components=20`` when + the spec is ``{'model': 'NMF', 'args': [2]}``); a parameter the spec + names in both 'args' and 'kwargs' still reaches the constructor twice + and raises its own `TypeError`, as the spec's author asked for it.""" + if args: + try: + positional = [ + p.name for p in inspect.signature(cls).parameters.values() + if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) + ][:len(args)] + except (TypeError, ValueError): + positional = [] + params = {k: v for k, v in params.items() + if k not in positional or k in user_params} + return cls(*args, **params) + + +def _as_text_datasets(x, argname): + """Normalize a `data=`/`corpus=` argument to a list of datasets, each + a list of document strings. + + A single string is one dataset of one document; a FLAT list (or 1-D + array) of strings is ONE dataset -- the docstring's "list of text + samples" -- exactly as `hyp.plot(list_of_strings)` treats it. Before + 1.1, `_transform` split a flat list by each string's CHARACTER length, + returning `[(N, d), (0, d), (0, d), ...]` (1.1 release review, X1). A + list of lists (or of 1-D/(n, 1) arrays -- `format_data` hands over + (n, 1) object arrays) is one dataset per inner list, ragged lengths + allowed. Mixing strings and lists at the top level is ambiguous and + raises ``ValueError``. + """ + if isinstance(x, str): + return [[x]] + if is_series_like(x): + # a pandas/polars Series of documents is one dataset, like a 1-D + # array (datatype audit, 2026-09-08) + x = np.asarray(x) + if is_array_dataset(x): + x = [x] if x.ndim <= 1 else list(x) + if not isinstance(x, (list, tuple)): + raise TypeError( + f'{argname}= must be a string, a list of text samples, or a ' + f'list of lists of text samples; got {type(x).__name__}.') + items = list(x) + if not items: + raise ValueError(f'{argname}= is empty: nothing to vectorize.') + if all(isinstance(item, str) for item in items): + return [items] + datasets = [] + for i, item in enumerate(items): + if isinstance(item, str): + raise ValueError( + f'{argname}= mixes strings and lists at the top level ' + f'(element {i} is a str, others are lists): pass either a ' + 'flat list of text samples (one dataset) or a list of lists ' + '(one dataset per inner list), not a mixture.') + if is_series_like(item): + item = np.asarray(item) + if is_array_dataset(item): + item = np.asarray(item).ravel().tolist() + if not isinstance(item, (list, tuple)) \ + or not all(isinstance(doc, str) for doc in item): + raise ValueError( + f'{argname}= element {i} must be a list of text samples ' + f'(strings); got {type(item).__name__}.') + datasets.append(list(item)) + return datasets + + +def _flatten(x): + """(list of datasets, each a list of str) -> flat list of documents + and the split points that undo it.""" + docs = [doc for dataset in x for doc in dataset] + split = np.cumsum([len(dataset) for dataset in x])[:-1] + return docs, split + + def _transform(vmodel, tmodel, x): - split = np.cumsum([len(xi) for xi in x])[:-1] + docs, split = _flatten(x) if vmodel is not None: - x = np.vsplit(vmodel.transform(np.vstack(x).ravel()).toarray(), split) + x = vmodel.transform(docs).toarray() if tmodel is not None: if isinstance(tmodel, Pipeline): - x = np.vsplit(tmodel.transform(np.vstack(x).ravel()), split) + x = tmodel.transform(docs) else: - x = np.vsplit(tmodel.transform(np.vstack(x)), split) - return [xi for xi in x] + x = tmodel.transform(x if vmodel is not None + else np.asarray(docs)) + return list(np.vsplit(np.asarray(x), split)) def _fit_models(vmodel, tmodel, x, model_is_fit): if model_is_fit: return + docs, _ = _flatten(x) if vmodel is not None: try: check_is_fitted(vmodel, ['vocabulary_']) except NotFittedError: - vmodel.fit(np.vstack(x).ravel()) + vmodel.fit(docs) if tmodel is not None: try: check_is_fitted(tmodel, ['components_']) except NotFittedError: if isinstance(tmodel, Pipeline): - tmodel.fit(np.vstack(x).ravel()) + tmodel.fit(docs) else: - tmodel.fit(vmodel.transform(np.vstack(x).ravel())) + tmodel.fit(vmodel.transform(docs)) def _check_mtype(x): diff --git a/hypertools/tools/text_windows.py b/hypertools/tools/text_windows.py index 674f27f0..7c89d4a5 100644 --- a/hypertools/tools/text_windows.py +++ b/hypertools/tools/text_windows.py @@ -8,6 +8,7 @@ -- and `text_windows` is the single implementation of all three. """ +import numbers import re __all__ = ['text_windows'] @@ -36,7 +37,10 @@ def _tokenize(text, unit): def _check_positive_int(value, name, allow_none=False): if value is None and allow_none: return None - if isinstance(value, bool) or not isinstance(value, (int,)): + # any integer type -- Python int or numpy integer (np.int64 from an + # array or a computed window size; 1.1 release review, I7) -- but not + # bool, which is an int subclass that never means a size + if isinstance(value, bool) or not isinstance(value, numbers.Integral): raise TypeError(f"{name}= must be an integer; got {value!r}.") if value < 1: raise ValueError(f"{name}= must be at least 1; got {value!r}.") @@ -89,13 +93,13 @@ def text_windows(text, size=None, step=1, unit='words', min_windows=1, document); the nesting is never flattened. size : int or None, optional - Window width, in `unit`s. ``None`` (the default) means "the whole - document": one window per document, which -- with `max_chars` -- is - the truncation-only form used by the Wikipedia tutorial. + Window width, counted in `unit`. ``None`` (the default) means "the + whole document": one window per document, which -- with `max_chars` + -- is the truncation-only form used by the Wikipedia tutorial. step : int, optional - How many `unit`s to advance between consecutive windows (default 1, - i.e. maximally overlapping). ``step=size`` gives disjoint chunks. + How many `unit` items to advance between consecutive windows (default + 1, i.e. maximally overlapping). ``step=size`` gives disjoint chunks. unit : {'words', 'sentences', 'chars'}, optional What a window is measured in. ``'words'`` splits on whitespace and @@ -110,8 +114,8 @@ def text_windows(text, size=None, step=1, unit='words', min_windows=1, rendering guard: `hypertools.plot` draws a one-row dataset as a dot, so a turn that collapses to a single window shows up as a stray speck rather than a path. It is best-effort: a document of `n` - `unit`s can never yield more than `n` windows, and an empty document - yields none. + `unit` items can never yield more than `n` windows, and an empty + document yields none. max_chars : int or None, optional Truncate each document to its first `max_chars` characters *before* diff --git a/images/describe_example.png b/images/describe_example.png index 7e70675c..3d61e658 100644 Binary files a/images/describe_example.png and b/images/describe_example.png differ diff --git a/notes/audit/review_plan3_v3.md b/notes/audit/review_plan3_v3.md index 71f06911..1f8dc09a 100644 --- a/notes/audit/review_plan3_v3.md +++ b/notes/audit/review_plan3_v3.md @@ -114,7 +114,7 @@ AFTER: **9 passed**, precisely as plan L344 claims. Regression gate (plan L348) also verified: `pytest tests/plot/test_on_frame_hook.py tests/test_backend_window_parity.py -q` → **73 passed**. -Prior finding 6 (**Med**, multiindex.md cited a non-existent test name): **FIXED** — +Prior finding 6 (**Med**, multiindex.md cited a non-existent test name): **FIXED** — `docs/superpowers/plans/2026-07-28-hypertools-1.1-multiindex.md:2797` now cites `test_hue_regrouping_drops_forecasts_exactly_like_the_static_path`; grep for the old `test_forecast_dropped_under_hue_regrouping` returns nothing. diff --git a/notes/audit/review_plan4_v2.md b/notes/audit/review_plan4_v2.md index 8f6d0e58..abfb596c 100644 --- a/notes/audit/review_plan4_v2.md +++ b/notes/audit/review_plan4_v2.md @@ -58,7 +58,7 @@ v2 states 2/6, 4/7, 1/6, 2/6, 2/7. Re-measured today: `conversation_shape` **2/6 The citation sweep declined this as its item 9 ("COULD NOT VERIFY"). Re-measured with the plan's own extracted `measure_native_ratio.py`: -| notebook | plan `:64-68` | measured | +| notebook | plan `:64-68` | measured | |-|-|-| | conversation_shape | 186 / 11 / 5.9% | **191 / 12 / 6.3%** | | market_forecast | 192 / 11 / 5.7% | **193 / 12 / 6.2%** | diff --git a/notes/changelog_drafts_2026-09-11/drafts.md b/notes/changelog_drafts_2026-09-11/drafts.md new file mode 100644 index 00000000..cbc5e87c --- /dev/null +++ b/notes/changelog_drafts_2026-09-11/drafts.md @@ -0,0 +1,76 @@ +# CHANGELOG drafts from fixers (integrate into "Fixed during the release review") + +## W2 +- **`yahoo:` intraday bars keep their timestamps.** `interval='1h'` put every bar at midnight, so `hyp.predict` rejected the index. Intraday bars now carry their time, tz-aware in the exchange's timezone. +- **A fitted `Normalizer` accepts 1-D data.** A 1-D array, Series or list of numbers is one column in both fit and transform. +- **`set_autoinstall` handles are quiet at exit.** A live handle no longer prints "Exception ignored" at shutdown, and a re-entered handle keeps its call order. +- **Offline errors say what happened.** A missing 25+ character bare name lists the full resolution chain, and a cached copy that fails to parse raises `HypertoolsIOError` naming the file. +- **Extensionless remote `.npz` reports the `trust=True` error** instead of a parquet one. +- **`[density3d]` needs `scikit-image>=0.25.0`**, the first release with Python 3.13 wheels. +- **`load()`'s TypeError names polars frames.** +- **0-255 colour lists raise `ValueError`.** `palette=[[255,128,0],...]` used to be silently read as a data matrix; the error says to divide by 255 or pass a DataFrame. +- **`font='Noto Sans'` works in a fresh process.** The bundled faces are registered before the lookup. + +## main (me) +- **A `hue=` surface matches the points beneath it.** Each hull vertex blended every point in its dataset with inverse-squared-distance weights; in 3-D the many distant points outweighed the near ones, so the hull took the dataset's washed-out mean colour. Vertices now blend their nearest points, on both backends. + +## W1 +- **Regular calendar data are forecast on their own calendar.** Business-day, month-start, weekly, quarterly and tz-aware daily indexes, and `PeriodIndex` data, are fitted on their own rows and forecast onto the next business days, month starts or periods. Before, business-day bars were interpolated onto calendar days and forecast onto weekends, month starts drifted, a fall DST change duplicated a day, and periods came back as timestamps. `step=` also accepts `'B'` and `'MS'`. +- **A fitted forecaster works across index kinds again.** A model fitted on an array and reused on dated rows, or the reverse, raised an error about `step`; it now steps in the new data's own units, as in 1.0. +- **ARIMA's minimum history includes `seasonal_order`.** A short seasonal fit now gets the "needs N observations" message instead of a bare `IndexError` or `LinAlgError`. +- **Time warnings appear once, and only when they apply.** A stacked panel warns "not sorted" once per call instead of three times, and an explicit `step=` on evenly spaced data no longer calls them irregular. +- (W1 notes: CHANGELOG lines 17-21 say one future step is always the median gap -> must update.) + +## W4a +- **Forecasts and truth keep their own dataset's style with `'o-'`.** Each marker-plus-line dataset was drawn as two artists, so three datasets' forecasts came out red, red, green. +- **`ndims=1` `truth=` takes one column of values per trace.** A two-column truth used its first column as x, which stretched the date axis back to 1970. It now raises `ValueError`. +- **A one-column trace is drawn against its row index.** Antialiasing put a 40-row line at x 0..936, squashing its forecast 24x. `axis_scale='data'` also gave the value range to x. +- **Animated forecasts on two-column data no longer crash** with "too many values to unpack". +- **`forecast_fmt` markers mark only the forecast steps**, not all ~900 smoothed vertices. +- **Marker-only `hue=`/`cluster=` always refuses forecasts and warns**, even when the category count equals the dataset count. +- **`legend_colors=` accepts one colour per data entry beside forecast and truth entries.** A wrong count now closes the figure it opened. +- **plotly date axes show the same dates in every time zone.** Numeric dates were drawn in the viewer's local time. +- **`xlim=(None, date)` works on date axes**; the open side takes the data bound. +- **`panels=` accepts `forecast_trail=`** alongside `predict=`. +- **`transform=` fixes.** A bare array is one dataset instead of crashing, and a DataFrame with its own index no longer gives all-zero forecasts. +- **The 'truth' legend key is gray when truths span several colours**, instead of always showing dataset 0's colour. +- **A shuffled time index is drawn in time order**, so the forecast joins the end of the line. +- **`ndims=1` date ticks no longer collide**; matplotlib now uses concise date labels. +- (main) **A polars `transform=` frame works.** It raised `SchemaError` in the display scaling, with or without `predict=`. + +## W7 +- **Aligner classes accept arrays.** `HyperAlign().fit(xs).transform(ys)` on a list of NumPy arrays, or on a single array, raised "Unsupported datatype". The aligners now accept anything `hyp.align` does and return each dataset in its input's form. +- **`alignment_score(metric='dispersion')` rejects all-constant datasets.** Datasets each constant at a different value used to score exactly 1.0; they now raise, like `'isc'`. +- **Rows a `manip=` stage empties stop the pipeline at that stage.** A trailing `Smooth(center=False)` no longer triggers misleading PPCA imputation warnings or sklearn NaN errors. The error names the stage and suggests `min_periods=1`. +- **`hyp.plot(x, pipeline=p)` draws the pipeline's clusters.** A fitted trailing cluster step colours the figure with the fit figure's colours, where before it was dropped silently. +- **Align, impute and manip warnings point at your own line**, so deprecated spellings no longer go unseen. + +## W10 +- **A flat cluster spec no longer drops model parameters without a word.** `cluster={'model': 'KMeans', 'n_clusters': 4, 'random_state': 0}` ignored `random_state`, so the clusters changed on every call. Parameters go under `'kwargs'`, and any top-level key other than the `'n_clusters'` shortcut now raises `ValueError` naming it. +- **`legend=False` now wins over `names=`.** Passing dataset names used to force the legend on even when `legend=False` was given, on both backends. + +## W3 batch 1 +- **Plotly markers sit on the observations.** 'o-' and `forecast_fmt='ro:'` put a dot on every smoothed vertex, drawing the line as a solid tube; now only the samples are marked (in animations, the frame-grid vertex nearest each sample). A continuous hue with 'o-' in 1-D/2-D now shows its markers. +- **Plotly hue transparency.** Hue markers now honour `alpha=`, and translucent 3-D lines keep their colour instead of washing out to cyan. +- **Plotly hover labels name what you point at.** They read "trace 0"; every data trace now carries its legend label, a lone unlabelled dataset shows only its coordinates, and animated legends no longer grow entry by entry. +- **Plotly subplot cells.** Colorbars no longer land on the next cell, and an untitled call keeps the cell's title. A dimensionality mismatch raises a clear error, your own traces are left untouched, and a plotly `ax=` implies the plotly backend. +- **Plotly `frame_kwargs=`, `zoom=` and legend position.** `frame_kwargs=` styles the frame, static figures ignore `zoom=`, and `legend_kwargs={'x':0,'y':1}` anchors at that corner. +- **Plotly 1-D animations raise like matplotlib's.** Serial `FrameContext.window_bounds` now report the comet head. + +## W4b +- **Dict-list palettes colour marker plots.** `fmt='o'` ignored a per-dataset list of {category: color} dicts. +- **Composing into `ax=` no longer repeats a palette colour.** 'hls' drawn 2 + 2 now gives the four-colour 'hls' set; `colors` in the bundle and the colorbar show the colours actually drawn. +- **`panels=` splits a plain `legend_colors=` list per panel.** +- **Two-column data draws into a 2-D `ax=`** instead of raising "the plot is 3D". +- **Per-dataset and nested `labels=` survive `hue=`/`cluster=`** instead of crashing; label arrays and Series are accepted. +- **An explicit `marker=` wins over the fmt marker on matplotlib.** +- **Markers on a smoothed line sit at the samples,** static and animated. +- **Continuous-hue markers honour `alpha=` on matplotlib.** (fix CHANGELOG ~:712 'marker colours deliberately do not' claim) +- **The NaN-hue warning counts observations** and points at the caller's line. +- **Label connectors and box edges are visible** on matplotlib. +- **Caller-axes and panel titles and axis labels use Noto Sans.** +- **A nested-list input's legend names its outer groups.** +- **Cluster and integer-hue line legends list categories in order** (0, 1, 2). + +## W11 +- **A dict model spec with a flat parameter raises instead of silently running defaults.** `{'model': 'PCA', 'whiten': True}` now raises `ValueError` naming the key and showing the corrected `'kwargs'` spec, in `reduce` (including streaming), `manip`, `align` (and `tools.align`), `impute`, `Pipeline`, `apply_model` and `text2mat`. Outer `**kwargs` next to a dict spec now reach `manip`/`align` models, and a spec's `'args'` now reach streaming and `text2mat` models. diff --git a/notes/changelog_drafts_2026-09-11/verify_pipeline.sh b/notes/changelog_drafts_2026-09-11/verify_pipeline.sh new file mode 100644 index 00000000..ba4815bd --- /dev/null +++ b/notes/changelog_drafts_2026-09-11/verify_pipeline.sh @@ -0,0 +1,47 @@ +#!/bin/zsh +# Release verification pipeline (2026-09-11 final review). Run from repo root +# with nohup; poll LOG for step markers and the final DONE line. +set -u +REPO=/Users/jmanning/hypertools +SP=/private/tmp/claude-501/-Users-jmanning-hypertools/f69d921d-f10e-4fa8-aafb-f55b01cff0f9/scratchpad +LOG=$SP/verify.log +PY=$REPO/.venv/bin/python +export PLOTLY_RENDERER=json MPLBACKEND=Agg +cd $REPO +: > $LOG +step() { printf '%s\n' "=== STEP $1 $(date -u +%H:%M:%S) ===" >> $LOG; } +step "0 head $(git rev-parse --short HEAD)" +$PY -m pip install -q --no-deps -e . >> $LOG 2>&1 + +step "1 tutorials" +for nb in docs/tutorials/*.ipynb; do + printf '%s\n' "--- $nb" >> $LOG + $PY scripts/execute_tutorial.py "$nb" >> $LOG 2>&1 || printf '%s\n' "TUTORIAL-FAILED $nb" >> $LOG +done + +step "2 pytest" +$PY -m pytest -q -p no:cacheprovider -o addopts="-m 'not bigdata'" -rfE > $SP/verify_pytest.log 2>&1 +printf '%s\n' "pytest rc=$? $(tail -1 $SP/verify_pytest.log)" >> $LOG + +step "3 sphinx html" +rm -rf docs/auto_examples docs/_build +(cd docs && $PY -m sphinx -b html -W -E -a . _build/html > $SP/verify_sphinx.log 2>&1) +printf '%s\n' "sphinx html rc=$? warnings=$(grep -c 'WARNING' $SP/verify_sphinx.log)" >> $LOG + +step "4 sphinx doctest" +(cd docs && HYPERTOOLS_DOCS_PLOT_GALLERY=0 $PY -m sphinx -b doctest -W . _build/doctest > $SP/verify_doctest.log 2>&1) +printf '%s\n' "sphinx doctest rc=$? $(grep -E 'passed|failed' $SP/verify_doctest.log | tail -2 | tr '\n' ' ')" >> $LOG +git checkout -- docs/hypertools.FrameContext.rst docs/hypertools.io.LSLStream.rst 2>/dev/null + +step "5 gallery thumbs" +$PY scripts/generate_gallery_thumbs.py >> $LOG 2>&1 +printf '%s\n' "thumbs rc=$?" >> $LOG + +step "6 example smoke" +HYPERTOOLS_EXAMPLE_SMOKE=1 $PY -m pytest -q -p no:cacheprovider tests/test_examples_are_native.py -k end_to_end > $SP/verify_smoke.log 2>&1 +printf '%s\n' "smoke rc=$? $(tail -1 $SP/verify_smoke.log)" >> $LOG + +# tour runs separately, after the re-executed notebooks are committed +# (its SET-01 case requires a clean tracked checkout at REVIEW_COMMIT) + +printf '%s\n' "DONE $(date -u +%H:%M:%S)" >> $LOG diff --git a/notes/datatype_audit_2026-09-08.md b/notes/datatype_audit_2026-09-08.md new file mode 100644 index 00000000..a65dd78d --- /dev/null +++ b/notes/datatype_audit_2026-09-08.md @@ -0,0 +1,83 @@ +# Datatype-handling audit (2026-09-08) + +Jeremy: functions doing their own datatype checking should defer to datawrangler (wrangle/funnel/zoo predicates) so polars and future formats come for free; check EVERYWHERE, not only new code. + +Read-only survey by a Claude subagent on the working tree at c700c85f + round-7/8 fixes (before any refactor). Line numbers will drift. + +## A. datawrangler contract (installed: **pydata-wrangler 0.5.1**, `.venv/lib/python3.12/site-packages/datawrangler/`) + +- **Polars: yes** — 67 hits across 10 files, incl. `zoo/polars_dataframe.py` (`is_polars_dataframe`, `is_polars_lazyframe`, `pandas_to_polars`, `create_polars_dataframe`). +- `dw.wrangle(x, return_dtype=False, backend=None, **kwargs)` — `zoo/format.py`. Accepts arrays, pandas/Polars DataFrames, LazyFrames, dataframe-like duck types, str/text/corpora, file paths & URLs, nested/mixed lists. kwargs: `array_kwargs`, `dataframe_kwargs`, `text_kwargs`, `null_kwargs`; `backend='pandas'|'polars'`. Format priority comes from config `supported_formats.types`. +- `dw.funnel` (`decorate/decorate.py:234`) — coerces every positional input to DataFrame(s); passes through `backend=`. Also `list_generalizer`, `apply_stacked`, `apply_unstacked`, `interpolate` (polars-aware, lines 306-337), `stack`/`unstack`. +- **`dw.zoo` real names**: `wrangle, is_array, wrangle_array, is_dataframe, wrangle_dataframe, is_multiindex_dataframe, is_null, wrangle_null, is_text, wrangle_text, get_corpus, apply_text_model, get_text_model, to_str_list, get_text, dataframe_like, array_like`. `is_dataframe` explicitly handles pandas + modin + polars DataFrame/LazyFrame + duck-typed. +- `dw.util`: `btwn, dataframe_like, array_like, depth`. `dw.core`: `get_default_options, apply_defaults, update_dict, __version__, set_dataframe_backend, get_dataframe_backend, reset_dataframe_backend`. +- hypertools currently uses **only** `dw.zoo.is_multiindex_dataframe` (3 sites) — `is_array`/`is_dataframe`/`is_text`/`array_like`/`dataframe_like`/`backend=` are **never** used. + +## B. hypertools sites + +775 `isinstance(` total; **482** are datatype checks. Roughly **~85 are REPLACEABLE** (class 1), ~395 legit (class 2: model specs, Colormap, str kwargs, scalars). + +Replaceable per module: plot 24 · tools 12 · predict 9 · impute 9 · core 7 · manip 6 · io 6 · _shared 5 · align 4 · reduce 2 · cluster 1. + +Top 25 replaceable: + +1. `/Users/jmanning/hypertools/hypertools/_shared/helpers.py:530` — `get_type` isinstance ladder dispatcher +2. `_shared/helpers.py:619` — `get_dtype` duplicate type ladder +3. `tools/format_data.py:212` — Series→numpy on user input +4. `tools/format_data.py:34` — `_contains_dataset` recursive type test +5. `tools/format_data.py:264` — per-element Series coercion loop +6. `core/shared.py:27` — `as_dataframe` hand-rolled DataFrame coercion +7. `predict/predict.py:87` — `_normalize_data` ndarray branch guard +8. `predict/predict.py:63` — `_coerce_dataset` Series/1-D reshape +9. `impute/impute.py:94` — `_normalize_data` ndarray branch guard +10. `impute/impute.py:57` — `_coerce_dataset` Series/1-D reshape +11. `impute/impute.py:159` — post-funnel all-DataFrame re-check (**double work**) +12. `impute/impute.py:225` — second post-funnel DataFrame re-check +13. `align/align.py:211` — manual DataFrame rewrap of datasets +14. `align/align.py:179` — post-funnel re-wrap after format_data +15. `manip/manip.py:69` — Series/empty-shape guard on wrangled data +16. `manip/delay.py:103` — DataFrame re-check after funnel (**double work**) +17. `manip/smooth.py:224` — DataFrame re-check after funnel (**double work**) +18. `plot/plot.py:1851` — `_capture_row_indices` pandas-only index capture +19. `plot/plot.py:1883` — `_capture_column_names` pandas-only column capture +20. `plot/plot.py:9167` — hue matrix DataFrame/Series column capture +21. `plot/plot.py:3267` — `_panel_frame` Series/DataFrame relabel +22. `plot/colors.py:105` — hue matrix `.values` extraction +23. `plot/plot.py:2063` — `.values` on dataset items +24. `io/streaming.py:78` — `is_stream` negative type whitelist +25. `tools/damage.py:31` — `to_numpy()`-vs-`asarray` dispatch + +Other notable: `plot/plot.py:3313, 3403, 6864, 7640, 7664, 7688`, `core/pipeline.py:296/298/633`, `core/hierarchy.py:87/104/140`, `tools/stack.py:39/45/291`, `align/procrustes.py:117-119` (`hasattr(x,'values')`), `io/save.py:279/298`, `plot/fonts.py:192`, `tools/text2mat.py:532/551`. + +**Double-work / re-check-after-wrangle** (highest-value fixes): `impute/impute.py:159,225`; `manip/delay.py:103`; `manip/smooth.py:224`; `align/align.py:179-211` (funnel → `format_data` → manual `pd.DataFrame(np.asarray(...))` rewrap). + +**Wrangled fn still accepting raw**: `predict/common.py:325,500` and `impute/common.py:134,193` call `_as_dataframe` on data the dispatcher already funneled. + +**Class 3 UNCLEAR**: `io/streaming.py:78` (`is_stream` — a Polars LazyFrame is neither list nor iterator, so it would be *misread as a stream*); `plot/plot.py:6740` (`hasattr(_xf,'shape')`); `tools/text2mat.py:339`. + +## C. Entry points + +`__all__` = plot, analyze, reduce, align, normalize, describe, cluster, manip, predict, impute, load, save, apply_model, supported_models, Pipeline, set_interactive_backend, set_autoinstall, HyperAnimation, FrameContext, io, 5 exception classes, damage, stack, text_windows, subplots. + +| entry | data path | +|---|---| +| `plot` (`plot/plot.py:3947`) | **format_data only** (line 7816); no dw at all — `plot/` never imports datawrangler | +| `align` | `@dw.decorate.funnel` on `_align` (align.py:234) **then** `format_data` (`_apply_format_data`) | +| `manip` | `@dw.decorate.funnel` (manip.py:114) | +| `impute` | `@dw.decorate.funnel` (impute.py:165,184) after custom `_normalize_data` | +| `predict` | `@dw.decorate.funnel` (predict.py:230,250) after custom `_normalize_data` | +| `reduce` | **format_data only** (reduce.py:250,421) | +| `cluster` | **format_data only** (cluster.py:477) | +| `normalize` (tools) | **format_data only** (normalize.py:324) | +| `analyze` | format_data (analyze.py:21) then per-stage | +| `describe`, `damage`, `stack`, `text_windows`, `apply_model`, `Pipeline` | **neither** — own isinstance ladders (`core/model.py:186` uses format_data) | +| `load`/`save`/`io` | neither — own pandas checks | + +**Tests to model a polars test on**: `tests/core/test_dw_probe.py:82` (`test_funnel_accepts_polars`, already exists — the only polars test in the repo), `tests/test_dataset_compat.py`, `tests/test_format_data.py:19/47/93` (df, mixed list, column reordering), `tests/test_input_coercion_hardening.py:31-54` (1-D array, flat list, tuple/Series). + +## Plan (Claude) + +1. Wave 0: make `tools/format_data.py` the ONE coercion point built on `dw.wrangle` (arrays, pandas/polars DataFrames and LazyFrames, dataframe-likes, text) with `dw.zoo.is_dataframe/is_array/is_text` replacing the isinstance ladders in `_shared/helpers.get_type/get_dtype`, `core/shared.as_dataframe`, `predict/impute _coerce_dataset/_normalize_data`; `io/streaming.is_stream` must not misread a LazyFrame as a stream. +2. Wave 1 (parallel, exclusive files): plot.py index/column capture (`_capture_row_indices`, `_capture_column_names`, hue-matrix capture, `_panel_frame`), colors.py hue-matrix `.values`, tools (damage/stack/text2mat), align (funnel then format_data rewrap), manip (post-funnel re-checks), core (pipeline/hierarchy/model), io (save/load own pandas checks). +3. Tests: a polars input test per public entry point (modelled on tests/core/test_dw_probe.py::test_funnel_accepts_polars and tests/test_format_data.py), real polars (add to the dev extra if not present), plus a static gate that no module outside format_data/dw shims uses `isinstance(x, (np.ndarray, pd.DataFrame, pd.Series))` on user data. +4. Docs: 'Input data' section listing accepted forms incl. polars; CHANGELOG. diff --git a/notes/release_audit_2026-09-07_paused.md b/notes/release_audit_2026-09-07_paused.md new file mode 100644 index 00000000..15f39ce0 --- /dev/null +++ b/notes/release_audit_2026-09-07_paused.md @@ -0,0 +1,1476 @@ +# v1.1 release audit — paused September 7, 2026 + +> **Reading guide.** Everything from here down to the heading +> "## Updates by the Claude session" is the ORIGINAL Codex audit checkpoint, +> unchanged. The section under that heading was appended afterwards by the +> Claude Code session that implemented fixes; it states, per original finding, +> what changed and how it was verified. A resumed audit should treat the +> original sections as its own record and the update section as claims to +> re-verify. + +## Request, constraints, and status + +Jeremy requested a detailed release-readiness audit of open PR #286 AND all +changes since v1.0.0, including changes already merged into master, +documentation, descriptions, tutorials, packaging, and release operations. +**Do not implement fixes or publish anything: deliver findings and ALL next +steps.** Another Claude Code session is running tests and editing concurrently. + +Jeremy paused this audit because credits were running low and explicitly +authorized writing this notes file. This is a checkpoint, **not the final +report**. No library, test, documentation, git-index, or remote changes were +made by this audit. Scratch copies, logs, reproductions, builds, and executed +notebooks were written outside the checkout. This notes file is the only +intentional repository edit by this audit. + +**Provisional recommendation: do not release yet.** Two demonstrated control +failures (offline loading and automatic-install opt-out), panel composition +defects, and failing documentation examples remain. Existing green CI is +valuable but does not cover all these paths or the staged changes. + +No subagents were used. Continue read-only apart from updating this report. + +## Exact source states + +- Repository: `/Users/jmanning/hypertools`, origin ContextLab/hypertools. +- PR: https://github.com/ContextLab/hypertools/pull/286 + “Fix 1.1 release audit findings and complete documentation follow-ups”. +- PR base/master: `96ac8b7f43c132f6f455ad1be3ffc98e84adead5`. +- Pushed PR head: `687e98c90a297951863f07ae1bd6b40d53c4efdb`. +- Local HEAD at audit start: `f4f7ab8d2d596582298879e97661a7084f4cda97` + on `fix/1.1-release-review`; extra local commit is session notes. +- v1.0.0: `647ce929fb0fcc39dfbe17d73282ee54dbe4aaf6`. +- v1.1.0 tag/draft still points to `96ac8b7f...`, excluding the PR fixes. +- 105 commits in `v1.0.0..HEAD`; 32 in `origin/master..HEAD`. + Full diff: 469 files, 114,634 additions, 7,488 deletions (includes notebooks, + planning/history and tests; not all runtime code). +- Substantial staged changes existed before this audit, notably new public + `hyp.set_autoinstall`, its docs/tests, and notebook installation-cell cleanup. + These are NOT covered by the green pushed-PR CI. + +An initial tracked-file working-tree snapshot was captured at +**2026-09-08 03:12:53 UTC** (September 7 local time): + +`/tmp/hypertools-audit-20260907/snapshot` + +It includes the staged working source, excludes generated build/gallery trees, +and contains 943 files. Alongside it are `manifest.json` (SHA-256 per file), +`status`, `refs`, `cached.patch`, and `unstaged.patch`. Use these to distinguish +the reviewed state from concurrent edits. NOTE: executing Sphinx/notebooks and +building artifacts subsequently generated files and videos in the TEMPORARY +snapshot; the initial manifest remains the original source fingerprint. + +At pause, concurrent changes since that capture included +`.claude/CLAUDE.md`, the two autosummary stubs +`docs/hypertools.FrameContext.rst` and `docs/hypertools.io.LSLStream.rst`, +`notes/session_2026-09-05_release-1.1-review.md`, and +`tests/test_lsl_streaming.py`. HEAD was still `f4f7ab8d` at the last check. +Do not revert, stage, or otherwise disturb the other session's changes. + +## Confirmed findings to carry into the final report + +Line numbers below refer to the captured working source and may shift. + +### 1. High: `offline=True` still downloads hosted built-in datasets + +- `hypertools/io/load.py:487` promises “never open a connection”. +- `_resolve` at approximately lines 651–653 calls `_load_example_data(dataset)` + without passing the offline setting. +- `_load_example_data`, approximately lines 814–841, downloads on cache miss + and deletes/redownloads a corrupt cached file, independently of `offline`. +- **Real reproduction:** point the module's `DATA_DIR` at a fresh temporary + directory, register a passive `sys.addaudithook` recording `socket.connect`, + and call `hyp.load('spiral', offline=True)`. It returned two datasets, + created the cache file, and attempted two actual HTTPS socket connections. + No network functions were mocked; the user's real cache was untouched. +- Evidence: `/tmp/hypertools-audit-20260907/offline-builtin.log`. +- Fix recommendation: propagate offline policy into the hosted-dataset cache + path; serve hash-valid cached data, but raise `HypertoolsOfflineError` for + absent/corrupt entries without downloading. Cover cache miss AND corruption, + including hosted `*_model` datasets. Preserve integrity verification. +- Current offline tests cover URLs and iris/helix/local files, missing this path. + This feature already exists on master; it is not only a staged-change issue. + +### 2. High, staged API: `set_autoinstall(False)` is lost in animation export + +- New API stores its effective setting in process-global `_AUTO_INSTALL` in + `hypertools/_shared/lazy_import.py` (around lines 65–155). +- `hypertools/plot/plotly_backend.py:2279` routes animated file exports to + `_export_animation_file`, then `_render_frames_via_subprocess`. +- The `subprocess.Popen` around line 3226 does not propagate the effective + Python setting. The worker calls `ensure_kaleido_chrome` at + `hypertools/plot/_kaleido_export_worker.py:57–58` in a fresh interpreter. +- **Real reproduction:** separate temporary interpreter using existing real + dependencies, with kaleido genuinely absent. Parent calls + `hyp.set_autoinstall(False)` then saves a tiny Plotly animation to GIF. + Parent reports False; child reports True; child invokes + `python -m pip install -q 'plotly>=6.1.1' 'kaleido>=1.0'` anyway. +- Pip was deliberately prevented from accessing indexes (`PIP_NO_INDEX=1`, + `PIP_CONFIG_FILE=/dev/null`); no dependency was installed or changed. + Returned exception was a worker RuntimeError containing the pip failure, + instead of respecting the no-install policy. +- Evidence: `probe_export_policy.py`, `export-policy.log`, `exportenv/` under + `/tmp/hypertools-audit-20260907/`. Final rerun imported snapshot code in BOTH + parent and child (run from `/tmp`, not the checkout). +- Fix recommendation: explicitly pass the effective setting to subprocesses, + preserving Python-over-environment precedence in both directions, and test + public animated export with a genuinely missing dependency. The same lost + policy can affect Chrome provisioning. Do not claim that apt/Chrome actually + ran in this reproduction: the demonstrated action was pip invocation. +- `tests/test_lazy_import.py:131` also has a stale exemption comment claiming + parent provisioning always precedes worker launch; animation export does not. + +### 3. Medium: shared panels discard their fitted pipelines + +- `_plot_panels` (`hypertools/plot/plot.py:3176–3220`) obtains a real fitted + bundle from its shared probe, but passes panel slices through `transform=` + with `pipeline=None`, and returns those bundles without retaining the probe + pipeline (return near lines 3306–3316; Plotly equivalent near 3410). +- Reproduction, both backends: + + ```python + data = [np.random.default_rng(i).normal(size=(20, 5)) for i in range(2)] + b = hyp.plot(data, panels=True, reduce='PCA', return_model=True, + show=False, backend=backend) + b['panel_models'][0]['pipeline'] # None + ``` + +- Calling `.transform` on that value fails; fitted shared PCA cannot be + recovered from the returned panel bundles. A regular non-panel bundle + returns its fitted pipeline. Current panel tests check coordinates and keys, + not replay of the returned model. +- Fix recommendation: preserve and expose the actual shared pipeline (define + its multi-dataset replay semantics clearly), and test held-out replay without + refitting on both backends. This is a new-panel-feature gap, not proof that + ordinary `plot(return_model=True)` replay is broken. +- Initial reproduction output was in the conversation, not a dedicated log; + code and result above are sufficient to reproduce. Other probes are saved. + +### 4. Medium: panels do not partition valid palette/forecast arguments + +Both backends reproduced these. `_PANEL_PER_DATASET_KWARGS` at +`hypertools/plot/plot.py:2761`, and slicing loops around 3159/3214, are the +starting points. Test shared AND independent panel fits when fixing. + +- `hyp.plot([a,b], palette=['viridis','magma'], panels=True, show=False)` + raises “2 per-dataset palettes but 1 dataset(s)”; the same call without + panels succeeds. Nested explicit per-dataset color lists fail similarly. +- `predict='Kalman', t=3, forecast_fmt=['--', ':'], panels=True` raises + “got 2 for 1 forecast(s)” instead of assigning one style to each panel. +- `predict='Kalman', t=3, forecast_palette=['red','blue'], panels=True` + draws BOTH panel forecasts red. Actual artist/trace colors were inspected. +- A two-model/two-dataset `forecast_hue=['a','b','c','d']` (documented + model-major form) works without panels but fails inside each panel, which + still receives all four values for its two forecasts. +- A forecaster fitted on both datasets works in the ordinary plot path but + fails under panels: each panel receives the entire two-dataset model and + `predict_new` rejects “1 new dataset(s) ... 2 fitted model(s)”. Review whether + the existing `Forecaster.for_dataset(i)` should be used here. +- Evidence: `probes.py` and `probes.log` under the scratch directory; + per-dataset palette WITHOUT hue was verified in an additional inline probe. +- Fix recommendation: resolve per-dataset versus per-model ownership before + splitting panels; preserve palette assignment, slice all per-forecast forms + correctly, and select fitted-model dataset state appropriately. +- **Avoid a false positive:** per-dataset palettes combined with CONTINUOUS + hue are explicitly unsupported in the docstring. The earlier exploratory + probe of that combination failed as designed and is not a finding. +- Likewise `resample=10` with original-length 20-row hue was exploratory; + do not report it without checking the documented post-resampling contract. + +### 5. Medium: executable load documentation has two failures + +- Sphinx doctest execution: **315 examples, 2 failures**, both on + `hypertools.load`. Its parameter documentation includes + `hypertools.load(arr)` and `hypertools.load([arr, 'spiral'])` after importing + numpy but before importing `hypertools` (`hypertools/io/load.py:345–349`). + Both raise `NameError: name 'hypertools' is not defined` in that context. +- Evidence: `doctest-network.log` and `doctest-network/output.txt`. +- Fix recommendation: make the example self-contained, run Sphinx doctests + as a release gate (ideally CI), and audit other literal examples separately. +- Existing HTML CI does NOT run the doctest builder. The hierarchy pytest + file checks structure/prose, despite its stale opening docstring claiming a + `test_every_doctest_in_the_guide_runs` test exists. The hierarchy doctests + themselves passed in this audit. +- Command used `-D plot_gallery=0` to avoid re-executing the gallery during + doctests. This produced an extra Sphinx config-type warning (string vs bool) + caused by this audit invocation, **not a package defect**. On resume rerun + with a programmatic boolean config override or appropriate typed setting to + remove that warning. The two NameErrors are independent real failures. +- First sandbox run could not access datasets; do not count its network + failures. The network-enabled rerun is the authoritative result above. + +### 6. Release/documentation operations still incomplete + +- PyPI JSON was checked: latest **1.0.0**, no **1.1.0** release entry. +- GitHub v1.1.0 is a draft, tag at old master `96ac8b7f`, not the PR fixes. +- PR release-gate is SKIPPED by design; it only runs on master/tag pushes. +- README's current optional-dependencies URL returns **404**: + https://hypertools.readthedocs.io/en/latest/optional_dependencies.html + The page exists locally; final RTD deployment must be verified. +- Checked 64 external URLs from README, top-level docs and tutorial markdown: + 58 successful; that one 404; four Wikimedia URLs returned 403; Princeton + DataSpace provenance link returned 401. Treat 403/401 as access checks needing + human/browser verification, not established dead links. +- Evidence: `doc-urls.json`, `url-results.json`, `urls.log`. +- #284 and #285 remain open. The former still has an unchecked final + CI/tag/draft-release item; the latter has all body checkboxes checked. Both + are intended to close on merge of #286. Saved bodies/comments in + `issue284.json` and `issue285.json`. +- PR body still summarizes an earlier review/test count and omits much of the + subsequent plotting work. Refresh its description/evidence for the final + implementation, and do not let automatic issue closure imply release + publication has already happened. + +## Other observations — not established blockers + +- `alignment_score([constant, constant], metric='dispersion')` returns NaN + with a RuntimeWarning; `metric='isc'` correctly rejects all-constant input. + Inputs with NaNs yield NaN scores; 1-D arrays produce low-level shape errors. + These are robustness/documentation candidates, not demonstrated corruption + for the documented finite 2-D nondegenerate inputs. Evidence: `probes.log`. +- Existing scoring intentionally warns about unscored cells and can rank a + model with incomplete coverage. Do not relabel documented behavior a bug + without a concrete contradictory use case. +- `docs/doc_requirements.txt` still repeats older core lower bounds + (sklearn 1.4.0, pandas 2.2.0, matplotlib 3.8.0) while pyproject/README have + corrected floors. Since CI installs the package first, this did not show a + broken resolution; recommend removing redundant drift or synchronizing it. +- Some staged tests stub `_pip_install` despite the project's no-mocks policy; + prefer real isolated dependency-absence checks for the final regression tests. +- Existing important fixes in the PR include ownership/leakage protections in + backtesting, Delay feature-label collisions, cache-write concurrency, font + precedence, optional NumPy-2-compatible floors, forecast ownership/styling, + hierarchy replay, and panel rendering. Reviewed their code/tests and prior + findings; do not present already-fixed issues as remaining defects. + +## Validation completed in this audit + +1. GitHub PR #286: **16 successful jobs** at pushed `687e98c9`: + 12 OS/Python matrix jobs (Ubuntu/Windows/macOS × Python 3.10–3.13), + wheel-smoke, docs-clean, dataset-gate and live-source-gate. Release-gate + skipped. No formal review decision was set; merge state CLEAN at inspection. + Run: https://github.com/ContextLab/hypertools/actions/runs/34176254864 + This is hosted evidence, not a fresh full-suite run by this audit. +2. Sphinx doctests: 313/315 pass; two documented failures above. +3. **24 of 25 tutorial notebooks freshly executed successfully**, using the + snapshot code and real data/models in the existing dependency environment: + normalize, cluster, reduce, align, analyze, pipelines, hierarchy, + projectile_kalman, manip, plot, text, stock_forecasting, + modern_sklearn_dynamics, animate_forecast, io, streaming_data, + hugging_face_embeddings, wikipedia_embeddings, conversation_trajectories, + conversation_shape, painting_embeddings, morph_shapes_zoo, market_sectors, + and lsl_streaming. `market_sectors` passed in 279 seconds. + **weather_decades was in progress when paused; do not count as passed.** + See `notebooks/results.json`, `notebooks/lsl-result.json`, executed notebooks, + `notebooks-network.log`, and `lsl-notebook.log`. + - Package install cells were skipped so the reviewed package/environment + would not be replaced. `HYPERTOOLS_AUTO_INSTALL=0` was set. + - Existing dataset/model caches and installed optional dependencies were + used. This does NOT prove fresh-machine installs or Colab first-use flows. + - Each notebook had a fresh kernel. LSL passed with normal/default local + discovery, not a special loopback config. Its synthetic outlet was closed. + - No stored error outputs existed in any of the initial 25 notebooks. + - Compared text outputs: expected stochastic clustering changes, path + differences, file-byte counts, and existing GP convergence warnings; no + confirmed tutorial claim contradicted by those comparisons so far. + These scratch executions did not apply the repository helper's path scrub. +4. Full clean-source Sphinx HTML/gallery build was running, approximately + **90% through gallery generation** at pause (`animate_surface_morph.py`). + **Not a completed/passing build.** `html.log`, partial `html/`. + Prior pushed PR docs-clean was green, but staged API docs require this check. +5. Wheel and sdist built from snapshot with `python -m build --no-isolation`. + `twine check` passed for both. All **110 package files** in EACH match the + snapshot byte-for-byte. Bundled fonts and MIT/Apache/OFL materials present; + no notes/agent/CLAUDE files leaked into these artifacts. + - Wheel SHA-256: + `5aec0b6e4441545cf3efaf9418668c1fc217289198545b92e7768a34bacc933d` + - sdist SHA-256: + `1baa800ad25f297d42f9dde56b6949558e3f2bdc9d38c9713cb5ffbd7b337ca6` + - `dist/`, `build.log`, `wheel-install.log` in scratch directory. + - Installed built wheel into an isolated target; repository + `scripts/wheel_smoke_test.py` **passed**, importing the installed artifact + at `installed-wheel/site-packages/hypertools/__init__.py`. + Dependencies were reused from the existing venv; this is NOT a fresh + dependency-resolution environment. The sdist was inspected but not + freshly installed in this audit. +6. Actual feature-combination probes on both plotting backends, and the real + offline/install-policy reproductions described above. + +Environment: Python 3.12.14; numpy 2.3.5; pandas 3.0.3; scikit-learn 1.8.0; +matplotlib 3.10.8; plotly 6.8.0; Sphinx 9.1.0; nbclient 0.11.0. + +## Concurrent Claude test run + +The other session's notes now report **5315 passed, 1 failed**: +`tests/test_lsl_streaming.py::test_lsl_stream_resolves_by_type`. +It attributed the failure to unrelated EEG outlets on this host/network and +changed the test to use a unique stream type; a full rerun was pending. +This is the OTHER SESSION'S recorded result, not independently inspected raw +pytest output. Obtain the final log and commit before citing a final suite pass. + +Its note mentions an audit notebook kernel among other possible outlets. +Our LSL notebook used a distinct NAME, but default type EEG can still collide +with generic by-type discovery. On resume, use a private LSL SessionID/config +if repeating LSL work alongside tests; do not repeat the LSL notebook needlessly. +Do not stop other sessions' kernels or outlets. The audit did not modify the +test or global LSL settings. + +## Pause mechanics and scratch inventory + +At Jeremy's pause request, SIGINT was sent ONLY to the matching audit processes: + +- `/tmp/hypertools-audit-20260907/run_notebooks.py` +- Sphinx HTML command whose output directory is + `/tmp/hypertools-audit-20260907/html` + +Both targeted `pkill` commands succeeded. Notebook client should clean up its +kernel on interrupt; confirm those jobs exited on resume before restarting. +Other sessions were left untouched. Useful tool session IDs, if still available: +notebooks `97634`; HTML `8218`. All other audit checks had completed. + +Post-interrupt log confirmation: HTML ends `Interrupted!`. The notebook runner +recorded `weather_decades FAIL` with `zmq.error.ZMQError: Socket operation on +non-socket` as its kernel was interrupted. **This is a pause-induced incomplete +execution, NOT a confirmed tutorial failure.** Rerun that notebook on resume. + +Scratch root: `/tmp/hypertools-audit-20260907/` (macOS may expose this as +`/private/tmp/...`). Temporary files may not survive a reboot; this notes file +preserves the essential findings and reproductions if that happens. + +## Resume plan and final release next steps + +1. Re-read current user instructions and this checkpoint. Re-query HEAD, status, + PR checks, tag/master and draft state; diff current files against the initial + manifest. Revalidate findings affected by Claude edits. Do not implement fixes + unless Jeremy changes the read-only instruction. +2. Finish ONLY `weather_decades.ipynb`; do not rerun the 24 passing notebooks + absent relevant code changes. Restart the HTML build in the scratch snapshot + (gallery cache can reuse completed examples); record completed count/errors. +3. Once HTML is complete, run `docs/post_build.py` against the temporary build + with `READTHEDOCS_OUTPUT` pointing there; validate internal file/anchor links, + generated notebook install cells, and rendered pages with + `scripts/verify_docs_playwright.py` using `HYPERTOOLS_DOCS_HTML` and + `HYPERTOOLS_DOCS_SCREENSHOTS`. Visually inspect representative static, + hierarchy, forecasting, panel, animation, and tutorial pages. This has NOT + yet been done in this audit. Do not claim every figure was visually reviewed. +4. Rerun doctests with a correctly typed gallery-disable setting to cleanly + separate the two actual example failures from the invocation warning. +5. Confirm focused panel findings with minimal logged examples, including + shared/independent modes, and inspect any additional scientific or backward + compatibility concerns that remain after reviewing the full v1.0 diff. + The review is broad but not an exhaustive line-by-line verification of all + 114k added lines. Avoid unsupported “everything else is correct” claims. +6. Obtain Claude's final full-suite results/skips and source identity. Require + a final full run and all hosted checks after ALL intended fixes are committed + and pushed. Track bigdata/HF forecaster exclusions explicitly. Do not duplicate + a running full suite without a reason. +7. Deliver a detailed prioritized report: recommendation, exact reviewed state, + reproducible remaining defects, docs/validation findings, coverage limits, + and complete release checklist. Include source lines and evidence links. + +For the eventual release (recommendations only; no authorization to execute): + +- Fix the confirmed defects with real behavioral regression checks; propagate + both plotting backends and test composition rather than just scalar forms. +- Freeze the intended release source, reconcile staged changes, refresh PR body, + CHANGELOG date/notes and accurate feature descriptions, then obtain green + final PR checks and merge. Resolve #284/#285's release bookkeeping explicitly. +- Follow `RELEASE_CHECKLIST.md`: generate/publish the gallery notebooks and + manifest from the EXACT final release commit. The release gate requires + `manifest.source_commit == HEAD`; any later commit requires regeneration and + republication. Coordinate this with master checks so an old manifest is not + mistaken for a code failure. Do not use this uncommitted snapshot's artifacts. +- Build fresh wheel/sdist from that clean final commit; twine-check, inspect + contents/licenses, record digests, install EACH in a fresh environment and + run end-user smoke tests. Exercise optional feature installs/opt-out/export, + supported minimum dependencies, and known skipped integration paths. +- Require all master gates; move the still-unpublished v1.1.0 draft tag to that + exact commit only after checking publication state again; require tag CI. +- Upload the exact verified artifacts to PyPI, replace draft GitHub assets and + notes, then publish. Verify version/install/import/README after publication. +- Build final tag docs on RTD, update stable/default appropriately, confirm + latest/stable navigation, gallery Colab badges, tutorial media and the + currently-404 optional-dependencies link. Test a fresh Colab first-use flow; + local cached notebook execution is not a substitute. +- Review conda-forge version/dependency-floor update and fresh conda install; + announce only after release/docs/install checks pass. Archive evidence and + organize remaining nonblocking enhancements; branch cleanup is optional. + +**Do not declare the audit complete or the release ready when resuming solely +because prior CI was green.** Finish the explicitly outstanding validation and +reconcile the confirmed findings with whatever has changed meanwhile. + + +## Updates by the Claude session (2026-09-07 late / 2026-09-08) — NOT part of the original audit + +Written by the Claude Code session working on PR #286 after Jeremy handed over +this checkpoint. Each entry names the original finding, the fix, the tests, +and the commit once it exists. Line numbers refer to the working tree at the +time of writing. "UPDATE" entries may be re-verified by the next Codex round; +the original findings above are left untouched. + +### UPDATE — Finding 1 (offline built-ins): FIXED +- `hypertools/io/load.py`: `_resolve` passes `offline=` into `_load_example_data`; + a cache miss under `offline=True` raises `HypertoolsOfflineError` (from + `hypertools.io.sources`, where the class lives) BEFORE the data directory is + created; a cached file failing its SHA-256 pin raises the same error and is + left in place (online still deletes and re-downloads). The `*_model` pipelines + share the path. The `offline` parameter docstring states this. +- Tests (`tests/test_load_offline.py`, real observables: a passive + `sys.addaudithook` on `socket.connect` installed before importing hypertools, + DATA_DIR redirected to a temp dir, plus the existing blackhole proxy): + `test_offline_refuses_an_uncached_builtin_without_any_connection` + (spiral, weights, wiki_model), `test_offline_refuses_a_corrupt_cached_builtin_and_keeps_the_file`, + `test_offline_serves_a_verified_cached_builtin_without_any_connection`. + The miss test fails against the pre-fix `load.py` (checked). +- `HypertoolsOfflineError` docstring (`hypertools/io/sources.py`) now covers + hosted built-ins. + +### UPDATE — Finding 2 (`set_autoinstall(False)` lost in animation export): FIXED +- `hypertools/_shared/lazy_import.py`: new `subprocess_env(env=None)` returns a + copy of the environment with `HYPERTOOLS_AUTO_INSTALL` set to '1'/'0' from + the parent's EFFECTIVE `auto_install_enabled()` (Python-over-environment in + both directions). `hypertools/plot/plotly_backend.py::_render_frames_via_subprocess` + launches the worker with `env=subprocess_env()`. The only other subprocess + sites (`taskkill`, `ffmpeg`) run no hypertools code. +- Tests: `tests/test_animation_export.py` builds, per the audit's own method, a + `venv --without-pip` whose site-packages symlinks every entry of the dev + environment except `kaleido*` (preflight: `find_spec('kaleido') is None`), + runs this checkout with `PIP_NO_INDEX=1`, `PIP_CONFIG_FILE=os.devnull`: + parent `set_autoinstall(False)` -> the export raises the ImportError naming + `pip install "hypertools[interactive]"` and `set_autoinstall(True)`, no + install notice, no pip text, kaleido still absent; parent Python True over + env 0 -> the worker reaches (blocked) pip. Both tests fail with the `env=` + line removed. `tests/test_lazy_import.py`: the stale worker exemption comment + is reworded; the `_pip_install` spy test is replaced by a real-observable + test (capsys: no install notice; policy error text; module still absent); + new `test_subprocess_env_carries_the_effective_setting_to_a_child_interpreter`. +- docs/optional_dependencies.rst says the setting reaches the export subprocess. + +### UPDATE — Finding 5 (load doctest failures; doctest gate): FIXED (docstring); gate: local pipeline +- `hypertools.load` docstring example now imports hypertools before using it. + `sphinx -b doctest -D plot_gallery=False`: 316 tests, 0 failures + (`hypertools.load` 15/15); the remaining warning is the str-vs-bool `-D` + invocation warning the audit noted, not a package defect. +- `tests/test_docs_hierarchy_guide.py` docstring corrected (no + `test_every_doctest_in_the_guide_runs` exists; the guide's doctest blocks run + under `make doctest`). +- CI doctest job: not added in this pass (see the open items below). + +### UPDATE — Finding 6 (release/documentation operations): PARTLY +- `docs/doc_requirements.txt` core floors synchronized with pyproject + (scikit-learn>=1.4.2, pandas>=2.2.2, matplotlib>=3.9.0), with a comment that + pyproject is the declaration. +- README optional-dependencies URL: the page exists on this branch + (`docs/optional_dependencies.rst`); RTD "latest" builds master, so the 404 + persists until #286 merges. Re-check after the merge and the RTD build. +- PR body refresh, #284/#285 bookkeeping, tag/draft/PyPI steps: to be done at + merge/release time per RELEASE_CHECKLIST.md (unchanged position: nothing is + published until Jeremy merges). + +### UPDATE — Other observations: `alignment_score` degenerate input: FIXED +- `hypertools/align/score.py`: inputs must be 2-D numeric arrays of finite + values (clear `ValueError` naming the dataset and its shape / NaN count); + `metric='dispersion'` raises `ValueError` when every dataset is constant + (was NaN with a RuntimeWarning), matching `'isc'`. Docstring `Raises` updated. + Tests: `tests/test_align_score.py` (+6). +- Scoring with unscored cells: left as documented (the audit did not call it a bug). + +### UPDATE — Finding 3 (panel bundles drop the pipeline): FIXED +- `hypertools/plot/plot.py` (`_plot_panels` / `_plot_panels_plotly`): the shared + probe's fitted pipeline is stored in every `panel_models[i]['pipeline']` and + as top-level `bundle['pipeline']` (None for independent / per-reducer grids, + where each panel bundle carries its own fit). Replay semantics in the + `panels` docstring: a shared-fit panel's pipeline is THE shared pipeline, so + `.transform(held_out)` projects into the same space without refitting. +- Tests (`tests/test_plot_panels_audit.py`): held-out replay equals the + single-axes bundle's transform and differs from a fresh fit, both backends, + shared and independent modes. + +### UPDATE — Finding 4 (panels do not partition arguments): FIXED +- `hypertools/plot/plot.py`: one narrowing rule (`_panel_narrow_kwargs`) both + fit-mode loops call: per-dataset lists (`palette`, `legend`, `alpha`, + `marker`, `linestyle`, colour lists ...) are sliced to the panel's dataset; + per-forecast lists (`forecast_fmt`, `forecast_hue` model-major, + `forecast_palette` pre-resolved grid-wide so labels keep the single-axes + colours) are picked with the same model-collection splitter `plot()` uses; + a forecaster fitted on every dataset is bound with `Forecaster.for_dataset(i)` + (recursing into collections; clear ValueError on a count mismatch). The + colour-tuple guard now applies to `color`/`colors` only (an `alpha=[.3,.6,.9]` + list was read as one RGB triple). +- Tests: `tests/test_plot_panels_audit.py` (33 in total with finding 3): + palette names + nested colour lists, alpha x3, legend list, forecast_fmt, + forecast_palette (list and 'husl'), model-major forecast_hue (2 models x 2 + datasets; colour + linestyle vs the single-axes figure), fitted forecaster + (equals the ordinary path, differs from a refit; mismatch raises), and a + docstring-vs-roster test scanning `inspect.getdoc(hyp.plot)` for every + per-dataset / per-forecast / model-major parameter. Existing panel suites + (123) and the round-3/4 suites (34) still pass. +- Found while fixing: the ORDINARY path rejected the documented list of + `{category: color}` dicts (one per dataset naming its own categories) once + `hue=` regrouped two datasets into four runs ("lists 2 per-dataset palettes + but 4 dataset(s)"). `_seaborn_palette_arg` now treats an all-dict list like + a single dict (ambient cycle = default palette; the dicts resolve by name). + Test: `tests/test_plot_palette_forms.py::test_list_of_category_dicts_with_distinct_categories_per_dataset` (both backends). + +### Open items after this pass +- A Sphinx doctest step in CI (docs-clean job) — not added yet; the local + release pipeline runs the HTML build with `-W`; consider adding `-b doctest`. +- Final full-suite run, commit, push and hosted CI after ALL fixes (pending). + +## Codex round 6 (resumed audit) + +Reviewed 2026-09-08: HEAD / pushed PR #286 head +`3f4b087d02d4a5572ade50837ac040fa8a531c8c`, base/master +`96ac8b7f43c132f6f455ad1be3ffc98e84adead5`. Read this checkpoint in full, +CHANGELOG's release-review section, and `git log --oneline master..HEAD`. +Working tree was clean at start and immediately before this append. No +tracked file other than this append was intentionally changed, and no new +repository files were left. No other process's kernel or LSL outlet was +stopped. No subagents used. Evidence: `/tmp/hypertools-round6/` and +`/tmp/hypertools-round6-pytest.log`. + +This run's sandbox disallows local socket binding, external DNS/network +from shell commands, and successful Chrome startup. Approval is unavailable. +These restrictions prevent completing the notebook/browser portions; they +are NOT evidence that those features fail on an unrestricted machine. + +### Original findings 1–6: independently checked + +Commands below use the repo's `.venv/bin/python`, with `MPLBACKEND=Agg`, +`MPLCONFIGDIR=/tmp/hypertools-round6/mpl`, `HYPERTOOLS_AUTO_INSTALL=0`, and +`show=False` for direct plot probes unless a policy test explicitly enables +installation in its throwaway interpreter. + +1. **FIXED (original severity major): offline hosted datasets.** + `hypertools/io/load.py:662,826,842`. Command: + `.venv/bin/python /tmp/hypertools-round6/offline.py`. + A passive socket.connect audit hook is installed BEFORE importing + hypertools; DATA_DIR is redirected to a temporary pathlib.Path. + Missing AND corrupt `spiral`, `weights`, and `wiki_model` each raise + `HypertoolsOfflineError`; all show `connects=0`; corrupt bytes remain + unchanged. A real pre-existing, verified spiral cache copy loads as + `[(1000, 3), (1000, 3)]`, also with zero connects. Log: `offline.log`. + No remaining fix recommended for this reproduction. + The first exploratory probe incorrectly assigned DATA_DIR a str; those + AttributeErrors in `probe.log` are harness errors, superseded by + `offline.py`/`offline.log`. + +2. **FIXED for the original lost-worker-policy bug (major); new related + defects below.** `hypertools/plot/plotly_backend.py:3239` passes + `env=subprocess_env()`. Command: + `.venv/bin/python /tmp/hypertools-round6/policy.py`. + Reuses the REAL isolated missing-kaleido interpreter and public export + driver from `tests/test_animation_export.py`, with PIP_NO_INDEX=1 and + PIP_CONFIG_FILE=/dev/null. `off` prints `parent False`, then a policy + failure naming the interactive extra and set_autoinstall(True), without + entering the install-failure branch. `on` prints `parent True` despite + env=0, then `installing it automatically failed (CalledProcessError)`. + No package installed. Log: `policy.log`. + Additional real child-interpreter probe (`precedence.log`) explicitly + prints `env 1 Python False parent False child False` and + `env 0 Python True parent True child True`. + Parent error TYPE is RuntimeError in both export cases, not the + documented ImportError (R6-3). Overlapping contexts break policy (R6-1). + +3. **FIXED (major/medium original): fitted panel pipelines.** + `hypertools/plot/plot.py:3371` onward. + `.venv/bin/python /tmp/hypertools-round6/probe.py` reports a Pipeline + and held-out transform shape `(4, 3)` for both backends and both fit + modes. All 33 `tests/test_plot_panels_audit.py` tests passed in the + combined run below, including held-out replay matching ordinary PCA, + replay of drawn coordinates, shared object identity, independent fits, + and the negative control that fresh held-out fitting differs. + No remaining fix recommended for the original PCA reproduction. + +4. **PARTLY FIXED (major/medium original): argument partitioning.** + `hypertools/plot/plot.py:2890–3009`. + `.venv/bin/python /tmp/hypertools-round6/probe.py` exercised palette + names, nested colour lists, forecast_fmt lists, red/blue forecast + palettes, two-model model-major forecast_hue, and a forecaster fitted + on two datasets. These original cases succeed on BOTH backends under + shared AND independent fits. Forecast colours are red then blue, + format styles are -- then :, and model-major colours stay attached to + their intended forecasts. The 33 behavioral tests also check fitted + forecasts against ordinary replay and distinguish them from refitting. + New repeated-colour counterexample fails (R6-2 below), so CHANGELOG's + broad claim that every per-forecast form is partitioned is premature. + Explicit plain legend colours are per FINAL legend entry, not per + input dataset; the four-entry/three-entry mismatch in exploratory + `probe.log` is NOT reported as a defect. Plotly deliberately rejects + plain legend colour lists and supports (label, colour) pairs instead. + +5. **FIXED for the original two NameErrors (minor/medium original); + whole doctest validation is NOT green here.** + `hypertools/io/load.py:346` now imports hypertools. Public reproduction: + `import numpy as np, hypertools; arr=np.zeros((10,3)); + print(hypertools.load(arr) is arr); + print([type(d).__name__ for d in hypertools.load([arr,'spiral'])])` + prints `True` and `['ndarray', 'list']`. + `.venv/bin/python -m sphinx -b doctest /tmp/hypertools-round6/source/docs + /tmp/hypertools-round6/doctest` with + `HYPERTOOLS_DOCS_PLOT_GALLERY=0` ran **316 tests, 7 failures, zero + setup/cleanup failures**. No gallery-config type warning: the bool + override works. Three failures are inaccessible penguins, 538/bechdel, + and Kaggle data; four in hierarchy are a failed Chrome render and + three downstream undefined plotly_bundle checks. The original two + examples passed. Logs: `doctest.log`, `doctest/output.txt`. + CI now DOES run `sphinx -b doctest -W` in docs-clean + (`.github/workflows/test.yml:292`); the Claude update's pending-CI item + and tests/AGENTS.md's claim that no CI job runs doctests are stale. + +6. **PARTLY FIXED / release operations still pending (major as a release + prerequisite, not an instruction to publish during review).** + `RELEASE_CHECKLIST.md:18` and PR #286 description. + `git rev-parse 'v1.1.0^{}'` still returns `96ac8b7f...`, excluding + the PR fixes. GitHub connector GETs confirm PR open at reviewed HEAD, + issues #284/#285 open, and #284's final CI/tag/draft checkbox unchecked. + PR body still reports 4885 tests and the earlier review, omitting this + API and subsequent rounds. HEAD CI run 34187936967 was in progress + at last inspection (dataset/live-source/wheel succeeded; release-gate + skipped; matrix/docs pending). URL: + https://github.com/ContextLab/hypertools/actions/runs/34187936967 + Local optional_dependencies.html and both API pages build successfully. + Current external RTD/PyPI publication could not be reliably rechecked: + shell gh/DNS denied; web RTD fetch failed; web PyPI result was stale; + connector release-by-tag returned 404 (not proof that a private draft + disappeared). Do not repeat the previous live 404/PyPI observations as + fresh verified facts. Refresh PR evidence, finish final gates, re-cut + release artifacts/tag after merge, and verify deployed RTD/PyPI. + +### Remaining findings + +**R6-1 — Major: overlapping OFF contexts can turn installation ON.** +`hypertools/_shared/lazy_import.py:181,187–189` restores a stale global +snapshot unconditionally. From initial True: thread A enters False; +thread B enters False; A exits; B is still INSIDE its False block but sees +True. After B exits the initial True is incorrectly left False. + +This is a process-global setting, not a thread-local one. The docs offer +one-block use and recommend it wherever pip must not run, but do not state +that overlapping thread contexts are unsupported. There is no lock/token +bookkeeping; atomic assignment alone cannot solve the lifetime ordering. + +Real public-export reproduction, no mocks: +`MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round6/mpl PIP_NO_INDEX=1 +PIP_CONFIG_FILE=/dev/null /tmp/hypertools-round6/policyenv/noenv/bin/python +/tmp/hypertools-round6/thread_export.py`. +The interpreter was created from the repo venv by policy.py and genuinely +lacks kaleido. threading.Events impose the ordering above. Observed: +`inside set_autoinstall(False): True`, followed by a worker install attempt +and `installing it automatically failed (CalledProcessError)`; finally +`after both contexts: False`. No package installed. `thread-export.log`. +Suggested fix: define the concurrency contract and maintain scoped +policies with lifetime-aware tokens under synchronization (or explicit +context-local overrides carried into export workers); do not restore an +exited scope over another active scope. Document global/thread semantics +and add this deterministic real concurrent regression. Merely locking the +individual assignments does not fix this ordering. + +**R6-2 — Major: panels deduplicate colours instead of forecast labels.** +`hypertools/plot/plot.py:2932–2937`, especially `_colour not in _ordered`. +Different categories may intentionally share one colour. Narrowing drops +that duplicate, yielding too few palette entries for the panel categories. + +Command: `.venv/bin/python /tmp/hypertools-round6/duplicate_palette.py`. +The essential public call is: + +```python +x = [np.random.default_rng(i).normal(size=(20, 3)).cumsum(0) + for i in range(2)] +hyp.plot(x, panels=True, panel_fit=fit, predict=['Kalman', 'ARIMA'], t=3, + forecast_hue=['a', 'a', 'b', 'b'], + forecast_palette=['red', 'red'], backend=backend, show=False) +``` + +For BOTH backends and BOTH fit modes, panels=False succeeds and +panels=True raises `ValueError: palette= supplies 1 color(s) but 2 are +required (one per category/component)`. `duplicate-palette.log`. +Suggested fix: track first-seen nonmissing LABELS; append their colours +once per label, retaining repeated colours across distinct labels. Add a +behavioral comparison against ordinary plotting with repeated colours. + +**R6-3 — Minor: public animated export contradicts the documented error type.** +`hypertools/plot/plotly_backend.py:3277` converts worker errors into +RuntimeError; `docs/optional_dependencies.rst:94`, `docs/api.rst:168`, +and `hypertools/_shared/lazy_import.py:145` promise ImportError for a +missing extra with installation disabled. `policy.py`/`policy.log` prints +`RAISED RuntimeError` for the off case; ImportError exists only in the +worker traceback string. A caller's `except ImportError` does not catch it. +Suggested fix: pass a structured worker error kind back and raise the +promised public ImportError (and preserve appropriate export error types), +or explicitly document the worker-export exception if intentional. + +Tests added in the audit-fix commit are generally meaningful real +behavioral checks, especially held-out replay with a negative control and +missing-dependency subprocess export. Two weaknesses matter here: +`tests/test_animation_export.py:581,603` only require `RAISED` plus message +substrings, so R6-3 passes them; assert the public exception class too. +`tests/test_plot_panels_audit.py:468` checks the parameter roster, not the +correctness of partitioning. It is a useful structural guard but cannot +prove the universal CHANGELOG claim, and current distinct-colour examples +miss R6-2. The new policy tests cover nested sequential contexts, not +concurrent lifetime ordering. Old fake-kaleido/seeded-mkdtemp export tests +pre-exist these audit-fix commits; do not attribute those to this patch. + +### Validation completion and limitations + +Combined command (all requested suites): + +```sh +MPLBACKEND=Agg HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python -m pytest -q \ + -p no:cacheprovider tests/test_load_offline.py tests/test_lazy_import.py \ + tests/test_animation_export.py tests/test_plot_panels_audit.py \ + tests/test_align_score.py tests/test_plot_review_round3.py \ + tests/test_plot_review_round4.py tests/test_figure_review_gaps.py \ + tests/test_plot_forecast_legend_style.py tests/test_plot_panels_geometry.py +``` + +**162 passed, 11 failed, 7 setup errors, 19 warnings, 138.51 seconds.** +Seven setup errors are the offline test proxy's forbidden localhost bind; +10 failures are Chrome startup/export. The remaining failure is CAUSED BY +MY HYPERTOOLS_AUTO_INSTALL=0 invocation: the tomli real-install test expects +installation on. Correctly rerun separately with HYPERTOOLS_AUTO_INSTALL=1: +**1 skipped** for inaccessible package network (`install-test.log`). It is +not a library failure. Original offline behavior was independently +verified without the proxy (above). All 33 panel audit tests passed. + +The five named round-3/4/figure/forecast/panel-geometry modules contain +73 tests: **70 passed; 3 pixel-parity tests could not render Chrome**. +Thus their non-rendered assertions still hold, but visual Plotly parity +is NOT independently confirmed this round. + +Weather command: +`.venv/bin/python scripts/execute_tutorial.py +/tmp/hypertools-round6/source/docs/tutorials/weather_decades.ipynb +--out-dir /tmp/hypertools-round6/notebooks`. +Source is a `git archive HEAD` copy because the notebook also saves +weather_decades.mp4 beside itself even with --out-dir. The helper's +NotebookClient fails before cell execution at socket.bind with +`PermissionError: [Errno 1] Operation not permitted` (`weather.log`). +**Weather remains NOT freshly executed/passed.** No tracked notebook or +video was touched. Do not count the previous pause-induced run either. + +Docs command: +`HYPERTOOLS_DOCS_PLOT_GALLERY=0 +READTHEDOCS_GIT_IDENTIFIER=fix/1.1-release-review .venv/bin/python -m sphinx +-b html -W --keep-going /tmp/hypertools-round6/source/docs +/tmp/hypertools-round6/html` (plus MPLBACKEND=Agg, writable MPLCONFIGDIR, +autoinstall off). **Build succeeded**, no Sphinx warnings. Source copied +from HEAD; the earlier scratch gallery cache was reused. **Zero gallery +examples freshly executed**: this does NOT finish the earlier full-gallery +execution requirement. `docs/post_build.py` on the TEMPORARY source with +READTHEDOCS_OUTPUT=/tmp/hypertools-round6/html succeeded and processed +51 example pages/thumbnails. `check_docs.py` parses all **171 HTML pages**: +**0 missing internal file/anchor links**. API signatures for +hypertools.set_autoinstall and hypertools.align.score.alignment_score are +present in their generated HTML; optional_dependencies.html exists with +Turning it off. Logs: `html.log`, `post-build.log`, `links.json`. +`scripts/verify_docs_playwright.py` with external output/screenshots paths +fails at its HTTP server's local socket bind (`browser-docs.log`). Browser +rendering of the docs is not verified here. + +Visual/public-API probes: `render.py`, `render.log`, `row.py`, `row.log`, +`renders/` (PNG for matplotlib; JSON/HTML for plotly). I personally opened +and inspected SIX fresh matplotlib PNGs: hue-regrouped animated model +collection + forecast_trail + truth; cluster-regrouped equivalent; +column-MultiIndex animation; repeated-full-tuple row-MultiIndex animation; +panels with forecasts/truth/legends/colorbars/two-line titles; and +hyp.subplots with legends and colorbars arriving in separate calls and +three-line titles. No additional layout defect observed in those renders. +Both backends build the corresponding figures/bundles/frames. Every +Plotly `fig.write_image` attempt fails with BrowserFailedError (installed +Chrome closes immediately); **no new Plotly pixels were inspected**. +The tests also cover legend_colors pairs on both backends and ordinary +matplotlib legend-colour lists plus legend_kwargs positions. + +Exploratory combinations correctly rejected and NOT findings: +truth= with hierarchical inputs (documented unsupported), unique full +row-MultiIndex tuples with predict= (one-row traces; documented error), +and animated panels (documented static-only ownership). Repeated full +row tuples work; column hierarchy works. The initial render harness used +the wrong bundle key `anim`; fixed to `animation` before rendering, not a +library defect. No claim that every valid combination was exhaustively +examined or every forecast numerically compared on held-out data. + +### Next steps / verdict + +1. Fix R6-1/R6-2 and settle R6-3's exception contract; add real regressions + for repeated colours, concurrent policy scopes, and public error types. +2. Re-run the affected suites and the full suite at final HEAD. Finish + weather, a fresh gallery build, doctests with working network/Chrome, + and Plotly PNG/browser visual inspection in an environment permitting + those operations. This audit does not provide a green release gate. +3. Refresh PR #286 evidence and wait for final hosted checks; reconcile + #284/#285 release bookkeeping. After merge follow RELEASE_CHECKLIST.md + against the exact final commit: notebook manifest publication, + wheel/sdist build/install checks, tag/draft/artifacts and tag CI, + publication and deployed RTD/latest/stable/PyPI verification. No release + operation was performed or authorized by this review. + +VERDICT: FINDINGS + +## Updates by the Claude session after Codex round 6 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — Round 6 finding 1 (overlapping `set_autoinstall` contexts): FIXED +- `hypertools/_shared/lazy_import.py`: the single saved-and-restored global is + replaced by a lock-guarded scope stack (`_AUTO_INSTALL_SCOPES`): every + `set_autoinstall` object is pushed when created; a `with` block removes ITS + OWN entry on exit wherever it sits; `auto_install_enabled()` returns the + newest entry still in force (else the environment). Contract documented in + the class docstring and docs/optional_dependencies.rst: process-global, + shared by every thread, newest call in force decides, a direct call stays + until the next call. The reviewer's `/tmp/hypertools-round6/thread_export.py` + now prints `inside set_autoinstall(False): False`, the export raises + `ImportError`, and `after both contexts: True`. +- Tests (`tests/test_lazy_import.py`): a deterministic overlapping-context + test (older block exits first; direct call underneath a block) and the + two-thread shape with events. + +### UPDATE — Round 6 finding 3 (export raises RuntimeError, docs promise ImportError): FIXED +- `hypertools/plot/_kaleido_export_worker.py` writes `{type, message}` to + `.worker-error.json` in the frames directory before exiting non-zero; + `plotly_backend._worker_error` re-raises an `ImportError` (a missing extra + with installation off, or a failed install) or `HypertoolsIOError` (no + usable Chrome) as that type without retrying; any other worker failure is + still the `RuntimeError` with the stderr tail. The reviewer's + `/tmp/hypertools-round6/policy.py` prints `RAISED ImportError` for both + cases. `tests/test_animation_export.py` asserts `RAISED ImportError`. + +### UPDATE — Round 6 finding 2 (panels discard repeated forecast colours): FIXED +- `hypertools/plot/plot.py` (`_panel_slice_forecast_kwargs`): the per-panel + `forecast_palette` is built with one slot per distinct LABEL (first + appearance order) instead of per distinct colour, so labels that share a + colour keep their entries. The reviewer's + `/tmp/hypertools-round6/duplicate_palette.py`: all 8 combinations OK (were + 4 ValueErrors). +- Tests (`tests/test_plot_panels_audit.py`, real artist/trace colours against + the single-axes figure): the reviewer's exact case; per-dataset hue with 1 + and 2 models; `forecast_cluster=`; a cycling palette NAME with more labels + than colours. The roster test the reviewer called weak now has a behavioural + companion: for each of 12 per-dataset roster entries a two-dataset call with + distinct values asserts each panel shows only its own value (equal to the + single-axes dataset's value, different from the other panel); `truth=` list + and nested hue/labels likewise; a test ties the roster to the case list. + Animation-only entries (chemtrails/precog/bullettime) and `density` (no list + form) are the documented exclusions. +- Found while fixing (fixed next, see below): plotly dropped the colour letter + of a data `fmt=` string on the ordinary path (`'r-'` drew the palette + colour); plotly `panels=True` on 2-column data without `ndims=` raised + "Trace type 'scatter' is not compatible with subplot type 'scene'". + +### UPDATE — incidental plotly defects found in round 6 follow-up: FIXED +- `hypertools/plot/plot.py`: the plotly palette-injection branch gives each + dataset whose `fmt` names a colour letter that colour (matplotlib + precedence: the letter beats `palette=`, loses to explicit `color=`/`hue=`, + consumes no cycle slot); static, animated, fmt lists, `panels=` and + `hyp.subplots` cells. Panel cells are lowered to 2-D when every dataset has + fewer than 3 columns (`_panel_cell_ndims`), on both backends; matplotlib + drew 2-column panels inside cubes and crashed on 1-column data. Known + limit: mixed-width independent panels share one cell type (the maximum). +- Tests: `tests/test_plot_review_round6.py` (18); the fmt xfail in + `tests/test_plot_panels_audit.py` is removed so the assertion is live. + +## Codex round 7 (from its run log; the run hit the usage limit before writing its report or this section) + +Extracted verbatim by the Claude session from the Codex stdout log (scratchpad codex/run9b.log, lines 7080-7300) on 2026-09-08 08:00. Codex re-verified originals 1-6 and round-6 findings 1-3 (all FIXED, 6 pending release ops) and reported: + +**R7-1 — MAJOR: the new panel dimensionality inference runs before feature +expansion, breaking valid Delay -> PCA plots.** +`hypertools/plot/plot.py:3380–3384`, `_panel_cell_ndims` at `3294–3306`, and +matplotlib's `ndims` replacement at `3538–3543`. + +Two raw columns do not imply two columns after the analysis pipeline. The +new helper lowers the requested cell type using RAW data; its early return +for requested <= 2 means the later shared-fit check cannot restore 3-D. +Independent matplotlib panels also change the requested PCA fit to 2-D. + +Public reproduction (both backends, both panel_fit modes): + +```python +x = [np.random.default_rng(i).normal(size=(20, 2)) for i in range(2)] +hyp.plot(x, panels=True, panel_fit=fit, + manip={'model': 'Delay', 'kwargs': {'dims': 3}}, + reduce='PCA', ndims=3, backend=backend, + return_model=True, show=False) +``` + +Command: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round7/mpl +HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python /tmp/hypertools-round7/edges.py` +(`edges.log`; focused form is `delay_minimal.py`). Ordinary plotting returns +3-D traces and two `(18, 3)` arrays on both backends. With panels: + +- matplotlib/shared: `ValueError: the data to plot has 3 dimensions, but + static plots support at most 2; reduce=None disables dimensionality reduction`. +- matplotlib/independent: **silently returns `(18, 2)`** per panel on + rectilinear axes despite `ndims=3`. +- plotly/shared and plotly/independent: `ValueError: Trace type 'scatter3d' + is not compatible with subplot type 'xy' at grid position (1, 1)`. + +Regression attribution: from `/tmp`, run the repo `.venv/bin/python` on +`delay_minimal.py` with `PYTHONPATH=/tmp/hypertools-round6/source` (the saved +3f4b087d source). `delay-before.log` prints OK and `(18, 3)` for ALL FOUR +combinations. Thus this is introduced by c700c85f, not a preexisting limitation. +I opened the new ordinary 3-D and erroneous 2-D matplotlib PNGs; the latter +really renders two flat panels. Plotly trace construction fails before export. +Suggested fix: determine each cell's projection from its actual analyzed data, +retaining the originally requested analysis dimensionality and fitted pipeline; +do not infer final width from raw data when manip/pipeline can change it. +Cover feature-expanding manip/pipeline input, shared/independent fits and +reducer comparisons without adding a second fit. + +**R7-2 — MINOR: a fmt-pinned first call consumes a palette slot on composed +Plotly figures, contrary to matplotlib and the new fmt contract.** +`hypertools/plot/plot.py:10577` records `offset + len(xform)` even though the +new branch at `10487–10495` excludes lettered fmt entries from consuming the +palette. The matplotlib caller-axes counter at `10726` has the same issue +for an initially empty `hyp.subplots` cell. + +Command: same environment, `.venv/bin/python +/tmp/hypertools-round7/fmt_minimal.py` (`fmt-minimal.log`). In each backend, +plot a `(20,3)` array with `fmt='r-'`, `palette=['navy','gold','green']`, then +plot a second array into that figure's axes/figure with the same palette and +no fmt colour. Observed: + +``` +matplotlib ['r', '(0.0, 0.0, 0.5019607843137255)'] +plotly ['rgba(255,0,0,1.0)', 'rgba(255,215,0,1.0)'] +``` + +The second line is navy on matplotlib, gold on Plotly. `edges.py` also proves +both backends' empty subplots-cell path uses gold, whereas a single call with +`fmt=['r-', '-']` uses navy. Matplotlib comparison PNGs were opened and +inspected; Plotly JSON records the actual trace colors (Chrome cannot render +here). Suggested fix: track consumed cycle slots, consistently for a normal +figure and a cell, rather than total drawn datasets; include explicit colours +and hue in the accounting rules. Test a pinned-colour call followed by an +uncoloured call, alongside the equivalent single call, on both backends. + +**R7-3 — MINOR: direct set_autoinstall calls leak every superseded handle.** +`hypertools/_shared/lazy_import.py:194–195` appends a strong reference for +EVERY call; only `__exit__` removes one. Direct calls never exit, so an older +direct setting, documented as superseded, stays alive for the interpreter's +lifetime. This is a retention bug, not a recurrence of the overlapping-OFF +policy failure. + +Command: `.venv/bin/python /tmp/hypertools-round7/policy_extra.py` +(`policy-extra.log`; MPLBACKEND=Agg). 100,000 public direct calls alternating +False/True, deletion of local handles, and `gc.collect()` leave the first +handle alive through a weakref and retain **8,004,888 bytes** by tracemalloc. +No private policy state is mutated by the probe. Suggested fix: bound storage +to active scopes and the necessary current direct baseline, dropping +superseded direct records; preserve the verified out-of-order scope semantics. +Add a real weakref/collection regression. + +**Nits / test and documentation drift.** + +- `tests/test_animation_export.py:603` still asserts only `'RAISED'` in the + Python-ON/env-OFF branch, although the OFF branch at 581 now asserts + `RAISED ImportError`. `nl -ba` inspection confirms this; the public driver + DOES return ImportError today. Tighten the ON branch too; the update's + broad statement that the export tests assert the type overstates coverage. +- `tests/test_lazy_import.py:349,353,356,362–363` ignores Event.wait timeout + results and does not assert that joined threads terminated. Source + inspection shows a timed-out wait can let the test run without its intended + overlap. Assert successful waits and completed threads. This does not undo + my independent event-ordered public export reproduction, which passed. +- `tests/AGENTS.md:23` still says no CI job runs doctests. The actual + `.github/workflows/test.yml` docs-clean step and fetched hosted logs show + that it does. Update the repository guidance; do not remove the CI check. +- The generated viewcode `[docs]` backlinks for `synthetic_outlet` and + `HypertoolsTrustError` use alias anchors absent from their destination + pages. Relevant canonical declarations: `docs/api.rst:246,268` and + `docs/hypertools.io.lsl.synthetic_outlet.rst:6`, + `docs/hypertools.io.sources.HypertoolsTrustError.rst:6`. + The actual missing targets are `#hypertools.io.synthetic_outlet` and + `#hypertools.HypertoolsTrustError`. The pages exist; their canonical API + anchors use the defining-module names. Suggested fix: make viewcode links + use those canonical anchors or add the alias targets. See `links.json`. + My link parser resolves `/tmp` before indexing pages so symlink differences + cannot silently skip anchor checks; the previous round's parser did not. + +## Updates by the Claude session after Codex round 7 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — R7-3 (superseded direct `set_autoinstall` handles retained): FIXED +- `hypertools/_shared/lazy_import.py`: a new setting replaces a superseded one + that no block holds open (a direct call's, or the `_Baseline` an exited block + left), keeping only its value; a block that replaced one puts that value + back on exit. Semantics verified unchanged (older-block-exits-first, direct + call under a block). The reviewer's `/tmp/hypertools-round7/policy_extra.py`: + first handle alive False, retained 1208 bytes after 100k direct calls (was + 8,004,888). Test: `test_superseded_direct_calls_are_not_retained` (weakref + + bounded stack + block restore). + +### UPDATE — nits: FIXED +- `tests/test_animation_export.py` ON-branch asserts `RAISED ImportError` too. +- `tests/test_lazy_import.py` two-thread test asserts every Event hand-off + succeeded and both threads finished. +- `tests/AGENTS.md` (gitignored local guidance) says the docs-clean CI job runs + the doctest builder. +- viewcode backlinks: sphinx's viewcode records ONE "referenced-as" module per + source file (`refname`), so objects first documented via `hypertools.io` / + `hypertools` got alias backlinks for every other object in that file. + `io.synthetic_outlet` and `HypertoolsTrustError` are now documented under + those public names (stubs renamed; `HypertoolsTrustError` is re-exported + from `hypertools` beside `HypertoolsOfflineError`, in `__all__`), so both + backlinks resolve. + +### UPDATE — R7-1 (panel cell dimensionality inferred from raw width): FIXED +- `hypertools/plot/plot.py`: every `panels=` mode fits through one + `_panel_probe()` (a `plot(..., return_model=True)` call whose figure is + discarded) and draws each panel from the ANALYZED rows via `transform=`; + `_panel_cell_ndims` applies to the probe output only, so Delay-expanded + 2-column data gets 3-D cells on both backends in shared / independent / + reducer-list modes, with the requested `ndims` and each panel's fitted + pipeline kept. A `pipeline=` whose reduce keeps more than 3 columns draws + through the single call's projection; a 2-wide panel in a 3-D grid is + zero-padded for plotly's scene cell; probe warnings are re-emitted once. A + counting PCA confirms 1 fit (shared) / 1 per panel (independent). The + reviewer's `/tmp/hypertools-round7/delay_minimal.py` and `edges.py` cases + pass on both backends and both modes. + +### UPDATE — R7-2 (fmt-pinned datasets consume a palette slot): FIXED +- `_palette_slots_consumed()` counts only cycle-coloured datasets (explicit + `color=`, `hue=` and fmt colour letters consume none), computed before the + plotly branch injects palette colours; `datasets_drawn` (plotly) and + `ax._hyp_palette_offset` (matplotlib, now recorded on hypertools' own axes + too) use it, so a plain figure, a cell and plotly agree with the single call. +- Tests: `tests/test_plot_review_round7.py` (32). + +## Codex round 8 + +Red-team review in progress. Results are appended incrementally. + +### Verified fixes and initial checks + +- Reviewed HEAD `1cad1e63` (runtime fix `14e0965e`). Read the audit and UPDATE claims, release-review CHANGELOG and `git log --oneline master..HEAD`; inspected `git diff 3f4b087d..HEAD --stat`. Pre-existing staged changes: `notes/session_2026-09-05_release-1.1-review.md`, `tests/_netskip.py`, `tests/test_load_sources.py`; left untouched. Scratch/evidence: `/tmp/hypertools-round8/`. No output-file path was supplied with this request, so this authorized notes append is the persistent report. +- **R7-1 fixed for the reported Delay reproduction:** `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python /tmp/hypertools-round7/delay_minimal.py` prints OK and two `(18, 3)` arrays for both backends and both fit modes (`delay.log`). The requested round-7 and round-6 test modules pass, including actual 3-D artists/traces, independent numerical equivalence, reducer comparisons, 1-/2-column defaults, pipeline expansion and fit counters. +- **R7-2 fixed for the reported fmt reproduction:** same environment, `... /tmp/hypertools-round7/fmt_minimal.py` prints red then navy on both backends (`fmt.log`). Round-7 tests also pass for empty subplot cells, mixed lettered/unlettered calls, explicit colour/hue and ordinary cycle advancement. +- Requested combined pytest command, with `PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null`, completed: **65 passed, 2 failed, 15 warnings in 27.61 s** (`pytest.log`). Failures are the intentional real pip-install test blocked by that environment and the installed-Chrome render test; details/limitations will be recorded after inspecting tracebacks. This is not a green whole-suite claim. +- Source check confirms the export ON branch now asserts `RAISED ImportError` (`tests/test_animation_export.py:603`) and the overlapping-thread test asserts all Event hand-offs and completed joins (`tests/test_lazy_import.py:364–369`); the latter passed in the combined run. Local `tests/AGENTS.md` now correctly names the CI doctest builder. + +- **R7-3 fixed for superseded direct handles:** reused `/tmp/hypertools-round7/policy_extra.py` under `.venv/bin/python` (`policy-extra.log`): 100,000 calls, first handle alive **False**, last alive True, retained **1208 bytes**. Python-over-environment inheritance prints the correct value in both directions. The existing overlapping-context regression and weakref test pass; a NEW entry-boundary race is recorded below. +- **Animated Plotly export on/off verified:** `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_animation_export.py -k honours_set_autoinstall`: **2 passed, 31 deselected, 16.24 s** (`export.log`). Real missing-kaleido interpreters; OFF refuses without pip; ON reaches blocked pip; both return public ImportError and leave kaleido absent. + +### R8-1 — MAJOR: policy compaction races with context entry and restores installation ON over a direct OFF baseline + +- Location: `hypertools/_shared/lazy_import.py:214–223`. A context object's constructor registers it before `__enter__` marks it active. Another thread constructing a context in that interval treats the first object as a superseded direct call, drops its identity and keeps only its enabled value. The first block's exit then cannot remove its setting or restore its prior baseline. The second block restores that expired setting instead. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl .venv/bin/python /tmp/hypertools-round8/policy_threads.py` (`policy-threads.log`). Start with public `hyp.set_autoinstall(False)`; A constructs a True context, B enters a False context before A enters, A enters/exits, then B exits. Thread Events assert every hand-off and joins assert termination. Output: `INITIAL False {'a_inside_after_b_enter': False, 'b_inside_after_a_exit': False} FINAL True EXPECTED FINAL False`. No private state is changed, no mocking, no pip. A small context factory delays returning the actual public handle to expose the valid scheduling boundary between construction and entry. Fresh nested objects in one thread restore False normally. +- Suggested fix: synchronize registration/activation and retain enough lifetime information for a constructed handle subsequently entered as a block, without strongly retaining all superseded direct handles. Merely locking `_entered = True` does not recover an already-discarded record. Add a deterministic construction/entry interleaving test starting from a direct OFF baseline. + +### R8-2 — MINOR: mixed one-column/three-column independent panels still crash + +- Location: `hypertools/plot/plot.py:3579–3593`. One global cell projection is selected and only TWO-column arrays are padded for a 3-D cell; a one-column series is passed through unchanged. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python /tmp/hypertools-round8/edges.py` (`edges.log`), case `mixed13`: two `(24, 1)` / `(24, 3)` arrays, `panels=2, panel_fit='independent', reduce=None, show=False`. Matplotlib raises `TypeError: Axes3D.plot() missing 1 required positional argument: 'ys'`; Plotly raises `ValueError: Trace type 'scatter' is not compatible with subplot type 'scene'`. The one-/two-column independent grid succeeds on both. Shared fitting correctly rejects unequal input widths and is not the finding. +- Suggested fix: use each panel's analyzed dimensionality for its cell, or deliberately convert a one-column series to index/value/zero coordinates in a shared 3-D grid. Preserve the series' index semantics. Extend the mixed-width regression beyond `[2, 3]` to `[1, 3]`; document a uniform-projection limitation if retained. Attribution against the pre-fix source is pending; this is a remaining edge-case gap, not yet claimed newly introduced. + +### R8-3 — MINOR: an intervening explicit-colour/categorical-hue Plotly call resets previously consumed palette slots + +- Location: `hypertools/plot/plot.py:10572–10574,10701`. `_plotly_palette_offset` starts at zero and reads the existing figure/cell count only inside `if "color" not in mpl_kwargs`. Explicit `color=` and categorical `hue=` skip that read, then write `0 + 0` as the total consumed count. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python /tmp/hypertools-round8/composition.py` (`composition.log`). With palette `['navy','gold','green']`, draw an ordinary array, append another with `color='black'` (or categorical hue with two runs), then append an ordinary array. The last colour is **navy `(0,0,128)` on Plotly**, **gold `(255,215,0)` on matplotlib**, for both a plain figure and `hyp.subplots(1,1)` cell. An intervening `fmt='r-'` or continuous hue correctly keeps gold. The first ordinary call consumed one slot; the pinned call should preserve it. +- Suggested fix: read the existing consumed count independently of colour injection, then add only the new slots taken. Extend `tests/test_plot_review_round7.py:342` beyond a pinned FIRST call (offset zero) to ordinary → pinned → ordinary, for explicit colour and categorical hue, both figure and cell. The current test cannot detect resetting a nonzero offset. + +### Attribution and documentation checks + +- Archived ONLY `hypertools/` from `c700c85f` under `/tmp/hypertools-round8/before`, then ran the same probes from `/tmp` with `PYTHONPATH=/tmp/hypertools-round8/before` and the repo's `.venv/bin/python`. `policy-threads-before.log` ends `FINAL False EXPECTED FINAL False`: **R8-1 is introduced by 14e0965e**. `edges-before.log` already has both mixed `[1,3]` errors: **R8-2 pre-exists the last fix**, an uncovered remaining gap (do not attribute it to 14e0965e). +- Existing `docs/_build/html` was checked, not rebuilt. `.venv/bin/python /tmp/hypertools-round8/docs_links.py` (`docs-links.log`) finds BOTH old source-view backlinks still broken, but both NEW public-name stub anchors present. `stat` dates `_modules/hypertools/io/lsl.html` September 6 versus the new stub September 8: this is a mixed/stale build, not proof the committed stub rename fails in a clean build. Source uses the correct public module and directive; `HypertoolsTrustError` is re-exported. Require clean-output backlink validation; do not claim existing backlinks resolve. No full gallery or notebook was rerun. +- **NIT — CHANGELOG.md:828** says in bold that `set_autoinstall` “keeps one record per superseded direct call”, the behavior the fix removes; following prose correctly says records are replaced. Verified by reading the release-review section and the real 100k-call weakref probe above. Suggested wording: “does not retain superseded direct calls.” +- **NIT — hypertools/plot/plot.py:5949–5953** still describes each cell as the single-axes projection and says 1-/2-column data always draws 2-D regardless of ndims. It should explicitly refer to ANALYZED width and document the present uniform-grid behavior for mixed widths; the verified Delay expansion and mixed `[2,3]` regression test contradict a raw-width reading. `panel_fit` prose at 6035 promises equivalence to an individual call, which the `[1,3]` crash violates. +- Export documentation in `docs/optional_dependencies.rst` accurately describes tested ON/OFF worker inheritance and ImportError. Its context lifetime promise is violated by R8-1, rather than being an undocumented unsupported-thread scenario. + +### Additional successful probes / limits + +- `valid_forecasts.py` (same Agg/autoinstall-off environment) exercises 18 combinations: both backends × shared/independent/reducer grids (`panels=2`) × no grouping/categorical hue/KMeans regrouping, with `predict='Kalman', t=3, truth=` in the documented plotted space. All construct successfully and real role-tagged artists/traces include every expected forecast and truth overlay (`valid-forecasts.log`). Matplotlib splits each truth into a line and markers, hence twice the Plotly truth-trace count. This checks ownership counts/construction, not every forecast number or pixel. +- `edges.py` also confirms plain reducer-list grids with integer `panels=2`, wide PCA plus hue/cluster, and mixed `[1,2]` independent panels construct on both backends. The initial `truth5` and later four-column-truth exploratory errors are NOT findings: ordinary calls reject them too; `truth` must be in the three-dimensional PLOTTED space here, as the error says. The corrected valid probe above is authoritative. +- Existing Chrome render fails with `HypertoolsIOError` reporting that installed Chrome closes immediately. The real-install test fails because this audit deliberately sets `PIP_NO_INDEX=1`; neither proves a library regression. No successful new Plotly image export or browser pixel inspection is claimed. Successful export-policy tests exercise missing dependencies, not successful Chrome rendering. +- Corrections to report line references: the reversed CHANGELOG bold sentence is at **CHANGELOG.md:823**, not 828; the pinned-first-call test relevant to R8-3 is **tests/test_plot_review_round7.py:351–360**, not 342. + +### R8-4 — MAJOR: independent/reducer panels recluster the probe data after dropping the caller's random_state + +- Location: `hypertools/plot/plot.py:3554–3557,3595–3597` (also the reducer-probe path at 3536). The probe runs `cluster=`, but each draw retains `cluster=` and replaces `random_state` with None. Its plotted clustering is a new unseeded fit instead of the clustering specified by the caller and fitted in the probe/bundled pipeline. Previously an independent/reducer panel used the original seeded call directly. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round8/mpl HYPERTOOLS_AUTO_INSTALL=0 LOKY_MAX_CPU_COUNT=2 .venv/bin/python /tmp/hypertools-round8/cluster_artists.py` (`cluster-artists.log`). Two `(24,5)` arrays from RNG seeds 1 and 2; `reduce='PCA', ndims=3, cluster='KMeans', n_clusters=3, random_state=88, antialias=False, fmt='o'`. An individual call draws clusters of **[4,9,11] points**. The first `panels=2, panel_fit='independent'` cell draws **[8,8,8] on matplotlib**, **[5,6,13] on Plotly** in the recorded run. These are actual artist/trace point counts, not just relabelled clusters; unseeded counts may vary per run. The analyzed PCA coordinates are identical (`cluster-groups.log`), so the extra clustering changes membership. +- Attribution: same script from `/tmp` with `PYTHONPATH=/tmp/hypertools-round8/before` (archived `c700c85f`) prints **[4,9,11] for both the individual call and panel** on BOTH backends (`cluster-artists-before.log`). Introduced by `14e0965e`. +- Independent real `CountKMeans(KMeans)` probe (`clusters.py`, overrides fit only to increment a counter and then calls sklearn's real fit) reports **6 fits** for two independent panels or two reducer panels, versus **4 before**; ordinary calls remain 2 and shared panels remain 4 (`clusters.log`, `clusters-before.log`). The earlier repeated cluster fits already existed; the additional fits in independent/reducer grids are new. The new `test_no_panel_is_fitted_twice` counts only PCA and cannot catch this. +- Suggested fix: preserve the fitted clustering and row-to-category ownership from each probe for drawing, or omit clustering from the probe and execute it exactly once during the final draw with the caller's resolved seed/spec, placing THAT fitted state in the bundle. Add real per-panel cluster membership/seed comparisons to individual calls, both backends and reducer grids, plus cluster fit-count coverage. Avoid merely relaxing the seed contract. + +### Test review and completion + +- `tests/test_plot_review_round7.py` uses real figures, real artist/trace colours and real PCA; its assertions are generally behavioral, not tautological. The important gaps are concrete: `[2,3]` is the only mixed-width test (misses R8-2), the pinned-colour/hue test starts at offset zero and hue is continuous only (misses R8-3), and the no-second-fit test at 241–250 covers only PCA and only matplotlib (misses R8-4). The wide-pipeline test checks numerical equality only for shared mode; broaden independent numerical coverage when modifying this area. +- Latest `tests/test_lazy_import.py` assertions correctly verify weakref collection and successful event hand-offs. The stack-length assertion in the retention test is tied to implementation, but the weakref assertion independently tests the user-visible lifetime defect; it is not a tautological test. The concurrency test signals only AFTER entry, excluding the construction/entry boundary in R8-1. Add that boundary case rather than discarding the existing overlap test. +- A third independent concurrency probe, `.venv/bin/python /tmp/hypertools-round8/three_threads.py` with Agg and writable MPLCONFIGDIR, passes: three fresh contexts enter sequentially, then exit B/A/C, with all Event waits and joins asserted; each post-exit check remains False and restores the direct False baseline (`three-threads.log`). Ordinary nested fresh contexts also pass (`policy-threads.log`). The remaining failure is specifically the construction/entry race. +- Final HEAD remains `1cad1e63caeaf408e12590e41ab108b9e801beec`. `git status --short` shows only this notes append plus the three staged files present at review start. No tracked source/test/docs file edited, staged, committed or deleted; no new repository file left by this review; no other process or outlet stopped. All new scripts/logs are under `/tmp/hypertools-round8/`. Original findings 1–6 and round-6 findings 1–3 were not rerun as standalone audits, per this round's instructions. + +### Next steps / verdict + +1. Fix R8-1 and R8-4 before merge/release; cover the exact entry interleaving and seeded per-panel cluster membership with real regressions. Fix R8-3's retained offset and R8-2's one-column mixed-grid handling on both backends. +2. Correct the two documentation nits; extend the identified test gaps. Re-run the affected suites and final hosted checks after those changes. Treat the local blocked pip/Chrome checks as validation limits, not passing gates. +3. Check BOTH public-name viewcode backlinks in a clean docs output. Reuse the existing full docs/doctest/notebook pipeline evidence as directed; this review did not rebuild the gallery or rerun notebooks. Follow the existing release checklist after fixes and final approvals; no merge/publication performed. + +**Round 8 review complete: two major findings, two minor findings, and documentation/test-coverage nits.** + +VERDICT: FINDINGS + +## Updates by the Claude session after Codex round 8 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — R8-1 (construct-then-enter race dropped a live context): FIXED +- `hypertools/_shared/lazy_import.py`: the scope state is now the LIVE handles + (weak references, construction order) plus a BASELINE (the newest direct + call's value, with its sequence number). A handle that dies without ever + entering a block was a direct call: its weakref callback folds its value + into the baseline and removes its record at once; a handle that is alive + but not yet entered is never touched; a block removes only its own record + on exit and its record is marked finished so its later death is ignored. + `auto_install_enabled()` takes whichever is newer by call order: the top + live record or the baseline. Verified with the reviewer's + `/tmp/hypertools-round8/policy_threads.py` (FINAL False as expected; inside + A's later-entered block the value is B's False because the newest CALL + decides, per the documented contract), round 7's `policy_extra.py` + (retained 1080 bytes after 100k direct calls) and round 6's + `thread_export.py` (after both contexts: True). +- Tests (`tests/test_lazy_import.py`, 18): the exact construct/enter + interleaving across threads with every hand-off asserted, plus the earlier + overlapping-block, direct-under-block and retention tests (stack empty + after discarded direct calls). +- CHANGELOG nit: the reversed bold sentence now reads "no longer retains + superseded direct calls". + +### UPDATE — R8-4 (panels recluster without the seed): FIXED +- `hypertools/plot/plot.py`: the panel probe returns its fitted cluster labels + (`_PanelClusterLabels`); every panel replays them (whole for independent / + reducer grids, sliced per dataset for shared) with NO clusterer fit in the + draw; `return_model=True` bundles carry `models['cluster_labels']`. Fit + counts: ordinary 2, shared 2, independent 4, reducers 4 (was 6/6). Seeded + memberships equal the individual call's on both backends in every mode. + +### UPDATE — R8-3 (pinned call resets the plotly palette offset): FIXED +- The offset read is hoisted out of the colour-injection branch, so a + `color=` / categorical-hue call keeps the prior count (figure and cell). + +### UPDATE — R8-2 (mixed one-/three-column independent panels crash): FIXED +- `_panel_rows_in_3d` maps a 1-column series to (row index, value, 0) in a + 3-D grid (date index by position), both backends and fit modes. + +### UPDATE — docstring nit: FIXED +- `panels=` prose describes ANALYZED width and the unequal-width rule; + `panel_fit='independent'` prose covers seeded clustering. +- Tests: `tests/test_plot_review_round8.py` (52; 34 fail against the + pre-fix plot.py). + +## Codex round 9 + +Red-team review of HEAD `650808f0` (branch `fix/1.1-release-review`, targeting master). Read the original audit and all UPDATE sections, rounds 6–8, the complete release-review CHANGELOG section, `git log --oneline master..HEAD`, and `git diff c700c85f..HEAD --stat`. UPDATE entries are claims, not independent evidence. Initial status contained only this round's notes heading. No separate `-o` path was supplied; this authorized notes append is the persistent output. Scratch scripts/logs: `/tmp/hypertools-round9/`. No full gallery build or notebook execution; original findings 1–6 and round-6 findings 1–3 are not repeated as standalone audits. + +### Incremental verification + +- **R8-1 construction/entry reproduction fixed:** `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl HYPERTOOLS_AUTO_INSTALL=0 .venv/bin/python /tmp/hypertools-round8/policy_threads.py` prints both in-block observations False, `FINAL False EXPECTED FINAL False`, and fresh nested contexts restore False. Evidence: `/tmp/hypertools-round9/policy_threads.log`. This reruns the actual public thread interleaving, not a source-only inference. +- **R8-1 retention and existing overlap checks pass:** reused `/tmp/hypertools-round7/policy_extra.py`: 100,000 direct calls leave BOTH weakrefs dead and retain 1080 bytes (bookkeeping overhead, no retained handles). Python/environment child inheritance is correct in both directions. `/tmp/hypertools-round8/three_threads.py` keeps False after each B/A/C exit and restores False; all Event waits/joins asserted. Logs: `policy_extra.log`, `three_threads.log` under round9. +- **R8-4 seeded drawing and fit counts fixed for the reported cases:** reused `cluster_artists.py`: individual and independent-panel cluster sizes both `[4, 9, 11]` on BOTH backends. `clusters.py`: ordinary/shared/independent/reducer grids use 2/2/4/4 real KMeans fits on each backend, so drawing adds no fits beyond the probes. The ordinary call still fits twice (pre-existing, not newly introduced). Round-8 tests pass for actual point-set equality, bundle `models['cluster_labels']`, shared label slices, reducer grids, dict/n_clusters-only/mixture spellings. +- **R8-3 offset fix verified:** reused `composition.py`; all 16 combinations (both backends, figure/cell, fmt/explicit color/categorical or continuous hue inserted after an ordinary call) finish GOLD `(255,215,0)`, preserving the nonzero offset. Evidence: `composition.log`. +- **R8-2 reported mixed-width crash fixed:** reused `edges.py`; `[1,3]` and `[1,2]` independent panels construct on BOTH backends. Round-8 tests inspect coordinates/projection, including dated one-column data. Shared unequal raw widths still give the documented error. `edges.py`'s invalid five-column truth calls also fail on the ordinary path and are NOT findings. +- Focused combined pytest: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_plot_review_round8.py tests/test_plot_review_round7.py tests/test_plot_review_round6.py tests/test_lazy_import.py`: **118 passed, 2 failed, 16 warnings in 29.76 s** (`pytest.log`). Both failures are environment limits: real tomli installation deliberately blocked by PIP_NO_INDEX, and installed Chrome closes immediately during rendering. All 52 round-8 plot tests, all 50 round-6/7 plot tests and the other 16 lazy-import tests pass. No successful Chrome rendering is claimed. +- **Documentation nits corrected in source:** CHANGELOG.md:823 now says “no longer retains superseded direct calls”; plot.py:6069–6081 describes ANALYZED width and uniform mixed-width projection, including index/value/floor handling; panel_fit prose includes seeded clustering. These match the verified basic cases above. No docs build rerun. +- **Animated Plotly export ON/OFF still passes:** `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_animation_export.py -k honours_set_autoinstall`: **2 passed, 31 deselected in 15.95 s** (`export.log`). Real missing-kaleido children; OFF refuses without pip, ON reaches blocked pip, both expose ImportError and leave kaleido absent. + +### R9-1 — MINOR: a live Requests certificate error still skips as transient + +- Location: `tests/_netskip.py:186–189` (`_exception_is_transient`, before the new phrase classifier at 224–228); coverage gap at `tests/test_load_sources.py:610–634`. +- What is wrong: the new dropped-TLS versus certificate distinction works for the wrapped aggregate STRING used by its regression test, but not for the preferred live-exception input. `requests.exceptions.SSLError` inherits `ConnectionError`, so the structural MRO test returns True before checking the certificate. Ordinary CI can skip an actual certificate failure, contrary to CHANGELOG.md:842–845's unqualified claim. Strict live-source mode still prevents skipping. This structural behavior pre-exists the latest patch; it is a remaining gap in the newly claimed distinction, not a new regression. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python /tmp/hypertools-round9/extra.py` (`extra.log`). Real Requests/stdlib SSL exception classes, no monkeypatching: drop prints `live True text True`, `SKIPPED`; certificate prints `live True text False`, **`SKIPPED`** through the actual `skip_on_transient_network` context manager. These are real exception instances, not a live HTTPS fetch. The first invocation lacked repository PYTHONPATH for importing tests and was a harness error; the corrected run is the evidence. +- Suggested fix: treat SSL/certificate types before generic ConnectionError/URLError/MaxRetryError ancestry, inspecting the causal chain; add behavioral skip/re-raise tests for live wrapped EOF and certificate errors, alongside the aggregate-string test. Do not weaken the strict gate. + +### R9-2 — MAJOR: shared clustered panels lose the global cluster-to-colour mapping + +- Location: `hypertools/plot/plot.py:3675–3691,3717–3719` passes each shared label slice for replay but no global category/colour mapping; the drawing call resolves colours again from the categories present in that slice. +- What is wrong: shared fitting now correctly preserves membership IDs, but the same colour can represent DIFFERENT globally fitted clusters in adjacent panels. With two well-separated datasets, the joint call uses red and blue for clusters 0 and 1; shared panels report labels `[1]` and `[0]` respectively, yet BOTH draw red. This silently undermines comparison of shared clusters. Current shared regression tests compare bundle labels and group sizes, not colour-to-label identity when a panel lacks a category. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python /tmp/hypertools-round9/membership_colors.py` (`membership_colors.log`). Inputs: 24x3 Gaussian clouds (seeds 1/2, scale .01), centered at -10/+10; `reduce=None, cluster='KMeans', n_clusters=2, random_state=88, palette=['red','blue'], fmt='o', antialias=False, return_model=True, show=False`; compare ordinary call to `panels=2`. Actual matplotlib line colours are `[[red],[red]]`, actual Plotly scene/scene2 marker colours both `rgba(255,0,0,1.0)`, versus joint red/blue. Bundle panel IDs are `[[1],[0]]` on both. No mocks or pixel inference. +- Suggested fix: retain the shared probe's complete cluster category order and label-to-colour/legend mapping, then slice observations without compacting that mapping per cell. Cover absent categories and verify each label's actual artist/trace colour against the joint call on both backends, including forecasts inheriting that colour. Attribution against c700c85f is checked separately below; do not infer previous shared clustering was correct. + +### R9-3 — MAJOR: padding a mixed-width panel changes its forecast model and values + +- Location: `hypertools/plot/plot.py:3711–3724` pads analyzed one-column rows into `(index, value, 0)` and passes those artificial THREE features through `transform=` while leaving `predict=`/`truth=` for the subsequent plot call. Padding intended for display therefore changes forecasting input. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python /tmp/hypertools-round9/mixed_forecast.py` (`mixed_forecast.log`). Two cumulative Gaussian datasets, seeds 1/2 and widths 1/3, 24 rows; compare the first dataset alone with `panels=2, panel_fit='independent'`; both use `reduce=None, predict='Kalman', t=3, show=False, return_model=True`. BOTH backends return ordinary forecast values `[2.4040928045, 2.2453572543, 1.9666166291]` but panel value-column forecasts `[3.2026539966, 3.5246851840, 3.4823615582]` (maximum difference **1.5157449291**). Ordinary bundle forecasts have shape `(3,1)`, panel forecasts `(3,3)`; this is numerical model output, not a pixel/rescaling difference. +- Related observable: a real Kalman fitted on the first one-column dataset works in the ordinary call but its first panel raises `ValueError: the fitted forecaster expects 1 feature(s) ... new dataset has 3`. `extra.py` also shows one-column `truth=` accepted by the ordinary call but rejected in the mixed grid as “1 column(s) ... trace ... 3” despite `reduce=None`. The mixed `[1,3]` grid previously crashed outright (R8-2); this is a newly reachable semantic defect in that fix, not a claim that pre-fix forecasting worked. +- Suggested fix: keep forecasting and returned analyzed/model data in each panel's original analyzed feature space; convert observation, forecast and truth coordinates into the common display projection only when rendering. Preserve numeric/date future-index semantics for the synthetic display x coordinate. Add a real individual-versus-panel forecast numeric comparison and a fitted-model/truth case on BOTH backends. Extend tests beyond observed-row correlations and construction. + +### R9-4 — MINOR: marker-only categorical hue consumes palette slots in composed figures + +- Location: `hypertools/plot/plot.py:257–280` (`_palette_slots_consumed`) and its call at 10698. It infers “hue consumes no slot” only from explicit resolved colours/line colours. Categorical `hue=` with marker-only `fmt='o'` groups through the ambient cycle without either marker, and gets counted as ordinary datasets. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python /tmp/hypertools-round9/composed_markers.py` (`composed_markers.log`). Draw an ordinary 24x3 array, append another with `hue=['a']*12+['b']*12, fmt='o'`, then append ordinary data, all with `palette=['navy','gold','green','purple']`. The last trace is PURPLE `(128,0,128)` on both backends, both plain figure and `hyp.subplots(1,1)` cell, versus GOLD for the otherwise identical `fmt='-'` or `fmt='r-o'` call. Colouring by the same categorical hue should not advance the ordinary cycle only when lines are hidden. This contradicts the new helper's documented rule and CHANGELOG's unqualified hue-composition claim. The R8-3 reset itself remains fixed; this is an uncovered marker-only branch. +- Suggested fix: carry explicit colour ownership (including whether hue controls it) into cycle accounting instead of deducing ownership from renderer kwargs. Add ordinary -> marker-only categorical hue -> ordinary tests with actual artist/trace colours on both figure and cell paths, and verify hue colours ignore fmt colour letters as documented. + +### Attribution, test review, and remaining verification + +- **R9-3 fitted-model confirmation with the documented dataset count:** `mixed_forecast_fitted_multi.py` (same environment/command prefix as `mixed_forecast.py`) fits one real Kalman forecaster on BOTH unequal-width datasets via `hyp.predict(x, model='Kalman', t=3, return_model=True)`. The ordinary first-dataset plot with `fitted.for_dataset(0)` succeeds, while the grid with the full two-dataset fitted forecaster raises the same 1-versus-3-feature error on BOTH backends (`mixed_forecast_fitted_multi.log`). Thus the failure also holds with the proper multi-dataset fitted model; it is not merely the first probe's one-dataset model being unsuitable for the second panel. +- **Before-state attribution:** reused round8's archived `c700c85f` package, running from `/tmp` with `PYTHONPATH=/tmp/hypertools-round8/before` and `/Users/jmanning/hypertools/.venv/bin/python`. `membership_colors_compat.py` (same probe, tolerating the old absent cluster_labels key) shows the OLD shared grid reclusters each panel into BOTH red and blue (`membership_colors_before.log`). The new replay removes those extra fits but introduces the all-red appearance for distinct global labels: R9-2 is a remaining mapping gap in the new replay, not a claim the old shared memberships were right. `composed_markers.py` against that archive also advances incorrectly (`composed_markers_before.log`); R9-4 is an incompletely fixed composition contract, not newly introduced marker-only behavior. No archive/source copy was placed in the repository. +- **R9-1 source-line correction:** the decisive MRO early return is **tests/_netskip.py:190–193**; new SSL text handling is **228–232**; CHANGELOG's claim is **840–843**. Earlier approximate line references in this round should be read as these exact references. +- **TLS aggregate case does work:** focused `... .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_load_sources.py -k transient` passes **5 tests, 30 deselected in 1.67 s** (`netskip-tests.log`), including the exact Dropbox dropped-TLS/certificate string pair. That is useful coverage, but it does not exercise the live Requests exception path in R9-1. +- **Additional requested public probes pass:** reused round8 `valid_forecasts.py`, same Agg/autoinstall-off environment. All **18** combinations (both backends × shared/independent/reducer-list `panels=2` × none/categorical hue/KMeans regrouping) construct and contain the expected forecast/truth role counts (`valid_forecasts.log`). This checks ownership/counts for equal-width 3-column data, not all numerical forecasts. `edges.py` also verifies wide `ndims=4` PCA+hue/cluster and integer reducer grids construct on both backends; `extra.py` verifies shared/independent GaussianMixture replay constructs. A harmless loky core-count warning reflects this sandbox's sysctl result, not a library failure. +- **Round-6 thread/export probe still preserves scope policy:** `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round9/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python /tmp/hypertools-round6/thread_export.py` reports `inside set_autoinstall(False): False`, `after both contexts: True` (`thread_export.log`). In this interpreter kaleido is installed, so actual export fails with HypertoolsIOError for unusable Chrome; it does not install. The separate missing-kaleido ON/OFF tests above cover the ImportError branches. Fresh nested scopes, three-thread out-of-order exits and the delayed-entry race all pass; no new defect in those policy sequences found. +- **Test review:** `tests/test_plot_review_round8.py:134–177` compares real clustered point sets against separate individual calls; that is behavioral and not tautological. The fit counter at **213–230** is meaningful for “no extra panel fits,” but deliberately measures relative to an ordinary call, which still fits twice; it cannot establish exactly one total fit. Shared coverage at **180–194** checks actual labels and group sizes but neither absent-category colours nor exact row identity per shared cluster (R9-2). Mixed-width tests at **365–425** check observed-row correlation, shape and flat z, but never forecast numbers or fitted models/truth (R9-3). Palette tests at **292–339** use categorical hue with the default line fmt only (R9-4). Docstring substring assertions at **428–432** are structural guards, not evidence that those promises hold. None of this warrants removing the existing tests: add the concrete missing behaviors. +- **Latest lazy-import tests:** retention at `tests/test_lazy_import.py:374–396` has an independent weakref assertion and final policy checks in addition to implementation-specific stack length. New delayed-entry concurrency test at **399–441** checks every Event hand-off, thread termination, in-block values and final baseline; it exercises the reported race without mocking the policy. These are useful tests, not tautologies. Their restoration fixture edits bookkeeping only for isolation. No claim of reusable/reentrant SAME-handle context support is made by this audit; requested fresh nested/multi-thread contexts were exercised. +- **Documentation review:** read relevant plot docstrings, CHANGELOG and `docs/optional_dependencies.rst`; source nits from round 8 are corrected. Remaining drift is tied to the concrete findings: unqualified certificate rejection, hue cycle preservation, shared colour identity and independent-panel behavior do not cover the failing paths above. The Claude session reports full_verify15 results of 5547 pytest passes, Sphinx zero warnings, doctest 316/0 and smoke 344 in `notes/session_2026-09-05_release-1.1-review.md:230`. These remain that session's recorded evidence, not this audit's fresh executions or independently inspected full logs. No gallery, doctests or notebooks were rerun here. + +### Next steps / verdict + +1. Fix R9-2's shared cluster colour/legend mapping and R9-3's forecasting through padded display coordinates before merge/release, with the concrete public numeric/artist regressions on BOTH backends. +2. Fix R9-1's live SSL classification and R9-4's marker-only hue accounting; extend the focused tests and align the documentation claims. Preserve all existing useful regressions. +3. Re-run affected tests and the established final CI/docs pipeline on the resulting exact head. Successful Chrome/export rendering and full-install validation require the existing unrestricted pipeline; this sandbox's failures do not supply those gates. No merge or publication performed. + +Final reviewed HEAD: `650808f0b5880005c19497fb7f7a6d71362869ae`. Final tracked-file status contains only the authorized append to this notes file. All scratch scripts and evidence live under `/tmp`; no other tracked file was modified/staged/committed/deleted, no new repository file left, and no other process's kernel or LSL outlet stopped. Focused pytest totals: **125 passed, 2 environment-limited failures** across the combined review suites, export-policy tests and transient-classifier selection. The exact R8-1..R8-4 reproductions are fixed; this round found **two major and two minor remaining defects**. + +VERDICT: FINDINGS + +## Updates by the Claude session after Codex round 9 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — R9 minor (live certificate failures skipped): FIXED +- `tests/_netskip.py`: `_certificate_failure()` vetoes the live verdict when an + `SSLCertVerificationError` (or the word 'certificate') sits anywhere in the + chain or in an exception's `args` (how requests carries the ssl error; + its `SSLError` inherits from `ConnectionError`, which alone read as + transient). The reviewer's `/tmp/hypertools-round9/extra.py`: drop + SKIPPED, certificate RAISED. Test: + `test_a_live_certificate_failure_is_never_skipped_while_a_tls_drop_is`. + +### UPDATE — R9-1 (shared cluster colours per panel): FIXED +- `_PanelClusterLabels` carries the probe's full sorted category set; every + panel colours line runs and marker groups by the GLOBAL mapping (legend + names unchanged). Reviewer probe: blue/red on both backends. + +### UPDATE — R9-2 (display padding fed the forecast): FIXED +- `_PanelLift` / `_panel_lift_for`: a narrow panel's rows stay in their own + analyzed space for forecasting, `truth=` and the bundle (equal to the + individual call, (3, 1) forecasts), and only the DRAWN rows and overlays + are lifted into the 3-D cell. Fitted two-dataset Kalman and 1-column + truth work. + +### UPDATE — R9-3 (marker-only hue advances the palette): FIXED +- `_palette_slots_consumed(..., category_colored=)` is fed from hue/cluster, + so a categorical hue consumes no slot whatever the fmt; a marker-only + categorical hue now resolves explicit category colours (a `'ro'` + hue + call drew all red on both backends before). +- Tests: `tests/test_plot_review_round9.py` (57). +- Agent observation, queued: `ndims=1` series mode forecasts on the drawn + (index, value) pairs rather than the values alone (differs from + `hyp.predict(series)`); to fix with the next wave. + +## Codex round 10 + +Red-team review started 2026-09-08. Findings and verification evidence are appended incrementally below. + +### Initial verification + +- Reviewed HEAD `25b3ba6c` on `fix/1.1-release-review`. Read the original audit, every UPDATE, rounds 6–9, the release-review CHANGELOG, datatype survey, and `git log --oneline master..HEAD`; `git diff 650808f0..HEAD --stat` reports 44 files. Only this notes file was modified at start. No -o path was supplied in the conversation; this authorized notes append is the persistent report, with scratch evidence under `/tmp/hypertools-round10/`. +- Reused round9 `extra.py`, `membership_colors.py`, and `composed_markers.py`: live TLS drop SKIPPED, certificate RAISED SSLError; shared cluster panels draw blue/red corresponding to global labels 1/0 on BOTH backends; every figure/cell marker-hue composition ends gold on BOTH backends. Command: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round10/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python` with `runpy.run_path` over those scripts. Evidence: `round9.log`. Extra's narrow fitted/truth paths construct with (3,1) forecasts. The old mixed_forecast scripts index column 1 of the formerly padded output and now hit IndexError in their PRINT statement; this is a stale reviewer harness, not a library error. Independent numerical verification follows. + +### R10-1 — MINOR: MatrixColormap breaks the advertised matplotlib Colormap contract + +- Location: `hypertools/plot/colors.py:450–468` (constructor and `__call__`), docstring at 445–447 explicitly promises inherited `resampled` and `reversed` behavior. +- What is wrong: the constructor passes a LIST of RGB tuples as `LinearSegmentedColormap`'s segment-data argument, which requires channel-indexed segment data. The custom float sampler hides that until inherited operations initialize/read it. Integer sampling, `.resampled()`, and `.reversed()` all fail; the custom sampler also rejects an array of alpha values supported by Colormap. +- Verified with real `matrix_palette(np.random.default_rng(0).normal(size=(8,5)))`, `.venv/bin/python` under Agg/autoinstall-off (`/tmp/hypertools-round10/colormap.log`). Float sampling succeeds; `c(0)`, `c(np.arange(3))`, and `c.resampled(8)(np.linspace(0,1,8))` raise `TypeError: list indices must be integers or slices, not str`; `c.reversed()` raises `AttributeError: 'list' object has no attribute 'items'`; `c([0.,1.], alpha=[.2,.8])` raises TypeError. The persistent script `palettes.py` repeats these public calls. +- Suggested fix: initialize valid channel segment data (e.g. through `from_list`), or implement a Colormap subclass with correct integer, reversed, resampled, alpha/masked/bad/under/over semantics. Add real round-trip reversal and integer/float/resampled sampling tests; current tests only use the custom float arm. + +- Focused pytest completed: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round10/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_plot_review_round9.py tests/test_palette_matrix_and_sort.py tests/test_polars_inputs.py tests/test_polars_inputs_wave1.py tests/test_datatype_gate.py tests/test_plot_review_round7.py tests/test_plot_review_round6.py tests/test_lazy_import.py`: **265 passed, 2 failed, 16 warnings in 43.15s** (`pytest.log`). Failures are the real tomli-install test with pip deliberately blocked, and unusable installed Chrome; these are environment limitations, not new library findings. All new palette/polars/static-gate/round9 tests pass. + +## Updates by the Claude session after Codex round 10 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — R10-1 (MatrixColormap broke the Colormap contract): FIXED +- `hypertools/plot/colors.py::MatrixColormap.__init__` now builds the parent's + channel segment data exactly as `LinearSegmentedColormap.from_list` does + (plus an alpha segment), so the inherited lookup table, integer sampling, + `resampled()`, `reversed()`, bad/under/over colours and masked/NaN input all + work; the exact float sampler accepts an alpha ARRAY and defers masked, + non-finite and integer input to the parent. Fewer than two anchors raises. +- Test: `test_matrix_colormap_supports_the_inherited_colormap_operations` + (int index, `np.arange`, `resampled(8)`, `reversed()` at the ends and within + the table's resolution, alpha array, bytes, masked, NaN, `isinstance(..., Colormap)`). +- Codex round 10 hit the usage limit right after this finding (retry 3:46 PM); + round 11 (scratchpad codex/prompt13.txt, relaunch13.sh at 15:48) continues + from here. + +## Codex round 11 + +### Initial verification + +- Reviewed HEAD `767e327d` on `fix/1.1-release-review`. Read the original audit, UPDATE sections and intervening rounds, release-review CHANGELOG, datatype survey, and `git log --oneline master..HEAD`. Diff since `650808f0`: 45 files. No `-o` path was supplied; this authorized notes append is the persistent report, with scripts/logs in `/tmp/hypertools-round11/`. No gallery or notebooks rerun. +- Round9 reviewer scripts `extra.py`, `membership_colors.py`, `composed_markers.py` rerun using `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round11/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python` and `runpy.run_path`. Log: `round9.log`. Live TLS drop SKIPPED and certificate RAISED SSLError; shared cluster panels draw blue/red for global labels 1/0 on both backends; ordinary -> marker hue -> ordinary ends gold for both figures and cells. Mixed narrow forecast/truth calls construct and report (3,1) forecasts. Numerical comparisons are also covered by the running round9 suite. + +### R11-1 — MINOR: MatrixColormap still ignores under/over colors on finite float input + +- Location: `hypertools/plot/colors.py:477–500`, especially clipping at line 486. The round10 fix restores integer, reversed, resampled and alpha-array calls (independently exercised), but the custom finite-float sampler clips all values to endpoints instead of applying the inherited under/over colors. Adding a NaN to an otherwise identical vector switches to the parent and changes the colors of its OTHER entries. The UPDATE explicitly claims bad/under/over semantics now work. +- Verified: `MPLBACKEND=Agg HYPERTOOLS_AUTO_INSTALL=0 PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python /tmp/hypertools-round11/colormap.py` (`colormap.log`). Real `matrix_palette(default_rng(0).normal(size=(8,5)))`, then `set_under('red'); set_over('blue')`: `c([-.1,1.1])` returns RGB `[0,1,.3338838]` and `[1,.88005071,.26672818]`, whereas `c([-.1,1.1,np.nan])` correctly returns red, blue, transparent bad. Scalar -.1/1.1 likewise ignore the configured extremes. +- Suggested fix: apply under/over masks in the exact sampler or dispatch out-of-range inputs to the parent. Add finite scalar/vector and NaN-containing-vector equivalence tests after setting distinct extremes. The new Colormap regression only tests masked/NaN bad colors, despite the broader UPDATE claim. + +### R11-2 — MAJOR: datatype refactor rejects valid pandas manip lists and strips their labels/dates + +- Location: `hypertools/manip/manip.py:74–75,92–106` (`_validate_manip_input` / `_align_columns_for_stacking`). Column alignment is applied to EVERY manipulator, including independent per-dataset Delay/Resample/Smooth. Two named frames with different feature labels are now rejected even though these models do not combine their features. Mixing a named frame with an array unconditionally converts BOTH to arrays and fresh frames, silently deleting the named frame's index and feature names. +- Verified against the actual before state `650808f0`, archived under `/tmp/hypertools-round11/before` (only package source). Commands: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round11/mpl HYPERTOOLS_AUTO_INSTALL=0 PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=<checkout-or-before> /Users/jmanning/hypertools/.venv/bin/python /tmp/hypertools-round11/compat.py`, run from `/tmp` for before. Logs: `compat-current.log`, `compat-before.log`. Inputs are 12x2 pandas frames indexed by dates from 2020-01-01, with columns a/b versus x/y. BEFORE, `hyp.manip([a,b], model='Delay', dims=2)` and `model='Resample', n_samples=6` succeed and retain each frame's feature labels; NOW both raise `ValueError: manip() got DataFrames with different column labels`. For `[a,a.to_numpy()]`, BEFORE Delay retains the first frame's DatetimeIndex and a_lag*/b_lag* columns; NOW it returns a RangeIndex and 0_lag*/1_lag* columns. The new numpy-mixing ZScore behavior is an improvement over its old rejection, but must not impose shared-feature restrictions on independent transforms. +- Suggested fix: restrict shared column normalization to models that require stacked shared statistics; preserve each input's metadata for independent transforms. Add regressions against established expected index/column values, including distinct named frames and mixed dated-frame/array lists. Today's polars-versus-pandas tests compare two calls through the SAME changed validator, so both regress identically and pass. The initial Smooth probe used invalid kernel_width=3 with default order=3; its kernel error is a harness issue, not this finding; Delay/Resample already establish it. + +### R11-3 — MINOR: polars forecast_hue Series is not partitioned for panels + +- Location: `hypertools/plot/plot.py:3007–3009` (`_panel_forecast_labels`); the pandas/numpy-only check is explicitly allowed at `tests/test_datatype_gate.py:111–113` as an option rather than a dataset. It is nevertheless a user label vector, and the ordinary plot already accepts its polars equivalent. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round11/mpl HYPERTOOLS_AUTO_INSTALL=0 PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python /tmp/hypertools-round11/palettes.py` (`palettes.log`). Two 24x3 datasets, `predict='Kalman', t=3, forecast_hue=pl.Series(['a','b']), forecast_palette=<10x5 matrix>, panels=True, show=False`: BOTH backends raise `ValueError: forecast_hue= ... got 2 value(s) for 1 forecast(s)`. The same Series succeeds without panels; substituting `pd.Series(['a','b'])` succeeds with and without panels on both backends. +- Suggested fix: normalize label vectors with the shared Series/frame coercion helpers before panel partitioning; narrow this gate exemption so recognized Series cannot bypass partitioning. Add both-backend ordinary/panel color-equivalence checks with polars forecast_hue and model-major forms. + +### Verification and test review checkpoint + +- Focused command: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round11/mpl PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null PYTHONDONTWRITEBYTECODE=1 .venv/bin/python -m pytest -q -p no:cacheprovider tests/test_plot_review_round9.py tests/test_palette_matrix_and_sort.py tests/test_polars_inputs.py tests/test_polars_inputs_wave1.py tests/test_datatype_gate.py tests/test_plot_review_round7.py tests/test_plot_review_round6.py tests/test_lazy_import.py`: **266 passed, 2 failed, 16 warnings in 44.37 s**, `pytest.log`. Failures are the real tomli install test with pip intentionally blocked and installed Chrome unable to start; neither is a new library defect. All palette, datatype, polars and round9 regressions pass, including real numerical narrow-panel forecasts, fitted-model reuse, truth coordinates, absent-category artist colors, and marker-hue composition. +- Additional animated-export ON/OFF tests: same environment, `... -m pytest -q -p no:cacheprovider tests/test_animation_export.py -k honours_set_autoinstall`: **2 passed, 31 deselected**, `export.log`. Real missing-kaleido children; OFF refuses installation and ON reaches blocked pip, both return ImportError. This is policy validation, not successful Chrome rendering. +- `palettes.py` verifies numpy/list/pandas/polars/LazyFrame matrix palettes, RGB arrays in [0,1], mixed matrix/image/name per-dataset palettes, ordinary/panel plots, single-matrix forecast palettes, and image specs with all five sort keys on BOTH backends. Matrix and image sampling returned finite (n,3) arrays for n=1,2,3,9,256,1001. Matrix repeated samples are byte-identical; image extraction showed tiny floating differences, being quantified before interpreting determinism. Per-dataset lists as `forecast_palette` were rejected by the ordinary path on both backends; that option promises a single palette, so this is not a finding. +- Test review: `test_palette_matrix_and_sort.py::test_forecast_palette_accepts_a_matrix_and_panels_forward_it` only checks a matplotlib legend exists and a different matrix-panel call has two axes; it does not compare forecast or panel colors and omits plotly. `test_normalize_and_manip_reach_the_reducer` asserts only shape for normalize; `test_palette_reduce_and_stage_kwargs_reach_the_matrix` changes reduce/sort but never exercises palette_manip/normalize/align. The matrix-by-hand test shares `sort_colors` with the implementation (separate key tests mitigate that), and per-dataset lead tests do independently inspect actual artists/traces. Keep these tests and add independent numeric/color expectations for the missing combinations. +- The polars suites perform useful actual numerical/artist/trace equivalence checks, but pandas-reference calls run through the same new code and cannot prove unchanged pandas behavior (R11-2). Direct Manipulator classes cover DataFrame/LazyFrame, not Series or unnamed/named mixing; the mixed ZScore test substitutes an all-named list. The static gate catches its stated AST patterns, but its option exemption permits the actual forecast_hue partition failure (R11-3). Correct gate source reference for that exemption: **tests/test_datatype_gate.py:103–105**, superseding the approximate lines in R11-3 above. + +- **R11-2 numeric impact confirmed:** `numeric_compat.py`, run under the same current/before environments (`numeric-current.log`, `numeric-before.log`), resamples a pandas column `[0,1,4,9,16]` at irregular numeric indices `[0,1,2,8,10]` beside its numpy array with `hyp.manip([frame,array], model='Resample', n_samples=7)`. BEFORE, the first result equals the individual-frame result, with indices spanning 0..10 and values `[0,3.11004785,5.50098779,6.50538278,7.50645506,9.67283951,16]`. NOW the first result spans 0..4 and values `[0,.51851852,1.72222222,4,7.11728395,11.08641975,16]`. This is silent numerical corruption of index-based interpolation, not just cosmetic metadata loss. A corrected `compat.py` with valid Smooth kernel_width=5 confirms Smooth also formerly accepted distinct column names and retained dates; current rejects the named pair and strips dates from the mixed pair (`compat-*-valid.log`). +- Fresh three-thread out-of-order exits, the construct-before-enter interleaving, and nested fresh context objects all restore the OFF baseline correctly. Command: same Agg/pip-blocked environment, `runpy.run_path` over `/tmp/hypertools-round8/{three_threads,policy_threads}.py`, plus nested contexts (`threads.log`). Every Event hand-off and thread termination is asserted; no private policy state mutated. +- Additional datatype comparisons in `datatypes.py` cover frame, LazyFrame, Series and mixed-list forms through reduce/align/cluster/normalize/manip/predict/impute/analyze/describe/stack/damage/apply_model/Pipeline and all five Manipulator classes. All successful outputs match current pandas equivalents. Both types reject one-feature describe and unnamed/named stack correspondence (documented). Some BOTH-type unsupported cases remain: direct ZScore/Normalize on Series, Resample on Series/array lists, and a shared-statistics Pipeline with named frames mixed with an unnamed array; these are not established new regressions. `text_windows` rejects BOTH pandas and polars Series, consistent with its explicit str/list/tuple contract, so no new datatype finding for it. Frame/LazyFrame/Series impute backtest truth/mask paths construct and their returned score tables compare equal. Existing save/load tests passed for CSV/TSV/JSON/parquet/NPY/NPZ/MAT/pickle and frames/LazyFrames; no notebook or external-data tests run. + +### R11-4 — MINOR: interpolated image palettes silently repeat colors above 256 categories + +- Location: `hypertools/plot/colors.py:892–893`; the uniqueness promise is in `_image_palette_list` at 859–863 and `CHANGELOG.md:164–167`. `sns.blend_palette(colors, n_colors)` samples a 256-entry lookup table, so asking for more colors only repeats entries. This is an uncovered existing defect, also reproduced against `650808f0`, not introduced by the new sort implementation. +- Verified: `MPLBACKEND=Agg MPLCONFIGDIR=/tmp/hypertools-round11/mpl HYPERTOOLS_AUTO_INSTALL=0 PYTHONDONTWRITEBYTECODE=1 PYTHONPATH=/Users/jmanning/hypertools .venv/bin/python /tmp/hypertools-round11/image_counts.py` (`image-counts.log`). A real saved 10x10 red/blue PNG yields requested/unique counts 2/2, 17/17, 255/255, **257/256**, **1001/256**. Same before-source invocation from `/tmp` produces the same count defect (`image-counts-before.log`). Public plot confirmation: same command prefix with `image_plot_counts.py`, one 257x3 dataset and 257 categorical hue labels, `fmt='o', antialias=False, show=False, palette='image:<two-tone.png>'`: BOTH backends draw **257 categories in only 256 unique colors** (`image-plot-counts.log`). This contradicts the explicit claim that interpolation ensures categories do not share a color. +- Suggested fix: interpolate the RGB anchors directly at the requested positions, or construct a lookup table with adequate resolution, as the matrix sampler already does. Add counts above 256 with fewer image anchors than categories and inspect actual colors on both backends; consider renderer quantization when stating absolute uniqueness guarantees. + +### Documentation nit and final verification + +- **NIT:** `hypertools/plot/plot.py:4654–4658` still describes a plot image palette as most-salient-first; its newer paragraph at 4734–4738 and `docs/api.rst:145–149` correctly say plot palettes default to value order, reserving salience for extraction/per-dataset leads. `hypertools/plot/colors.py:861,871` has the same stale internal prose. Verified by source read and the image-sort tests/public probes above. Suggested fix: update the older paragraphs to distinguish extraction order from default plot order. CHANGELOG's general datatype and unlimited unique-color claims also need qualification/correction with R11-2/R11-3/R11-4. +- **Line correction for R11-1:** exact finite-input clip is `hypertools/plot/colors.py:478`, in the custom sampler at 469–489; the earlier approximate line 486 referred to output assembly. +- `combinations.py` (same Agg/autoinstall-off/pip-blocked command prefix as `palettes.py`, `combinations.log`) compares actual single/panel forecast colors and styles for matrix forecast palettes, one/two models, shared/independent fits, BOTH backends: all match. Top-level polars Series plots match pandas for ordinary, hue+labels, truth, and panels. Independent `hyp.reduce` + numpy scaling/lexsort gives the actual same dataset colors as `palette_manip`, `palette_normalize`, and `palette_align` on both backends. The 18 round8 panel/predict/truth probes (shared/independent/integer reducer-list grids × ungrouped/hue/cluster × both backends) all retain expected role counts. No additional numeric or ownership defect in these cases. +- Image determinism quantified: current repeated extraction differs by at most **2.22e-16** in the tested counts, with stable ordering; no material determinism finding. Matrix repeated outputs were byte-identical. Matrix palettes with 1/2 columns and 2/3 observations construct; requested sampling counts through 1001 stay finite. The old-source noisy-image extraction occasionally changes tied-anchor ordering; do not attribute that to the new sort. +- Backtest harness correction: `datatypes.py` initially iterated the score DataFrame's columns and therefore did not actually compare its numeric entries. A separate real run (`backtest.log`) now uses `pd.testing.assert_frame_equal` on the ENTIRE returned score table for frame, LazyFrame, Series, AND mixed lists; all pass exactly, including scored-cell counts (1 for single data, 2 for mixed). This supersedes the initial score-comparison claim. +- Combined focused pytest totals: **268 passed, 2 environment-limited failures** across the main command and export-policy selection. No full-suite, gallery, notebook, successful Chrome rendering, or hosted CI rerun is claimed. Existing Claude UPDATE full-pipeline results remain that session's evidence; this review verifies the focused fixes independently and does not treat its green results as proof of uncovered combinations. + +### Next steps / verdict + +1. Fix R11-2 before merge/release: preserve independent transform metadata and irregular-index interpolation; cover distinct named frames and mixed arrays with explicit expected dates/labels/numbers. +2. Fix R11-1/R11-3/R11-4, strengthen the identified tests (including both backends and public color assertions), and reconcile the stale palette prose and static gate exemption. +3. Run affected tests and the established final CI/docs pipeline on the resulting exact head; obtain successful Chrome/export and real-install validation in the unrestricted environment. No merge, release or publication performed. + +Final reviewed HEAD: `767e327d6d1b308a4757700805f3acbcb185cae5`. Only the authorized notes file was modified; all scratch scripts/source archives/logs are under `/tmp`. No tracked implementation/test/doc file was edited, staged, committed or deleted; no other kernel or LSL outlet was stopped. Findings: **one major, three minor, one documentation nit**. + +VERDICT: FINDINGS + +## Updates by the Claude session after Codex round 11 (2026-09-08) — NOT part of the Codex text above + +### UPDATE — R11-2 (manip mixed-list alignment regressed named frames and indices): FIXED +- `hypertools/manip/manip.py::_align_columns_for_stacking`: lists of named + frames are handed over untouched (no ValueError; the independent + manipulators never combine features); for the unnamed-array + frame mix + only the COLUMN labels are made positional and every frame keeps its own + index (a rebuilt frame had dropped dated / irregular indices, changing + Resample). Tests: distinct named frames through Smooth keep their labels + and equal the single-frame call; an irregular-index frame beside an array + resamples exactly as alone; a dated index survives. + +### UPDATE — R11-1 (MatrixColormap under/over/NaN): FIXED +- The exact sampler applies `set_under` / `set_over` / bad per ELEMENT and + no longer routes a whole array through the quantized table because of one + NaN; scalar and vector entries agree. Test added. + +### UPDATE — R11-4 (image interpolation capped at 256): FIXED +- `colors.interpolate_colors` (exact per-channel interpolation) replaces + `sns.blend_palette` in `_image_palette_list` and the continuous + short-list path; 257 and 400 categories get distinct colours (float + precision; a hex rendering quantizes). Test added, plus a 300-category + figure. + +### UPDATE — R11-3 (polars forecast_hue under panels): FIXED +- `_panel_forecast_labels` normalises any series-like through the shared + predicate before partitioning; the gate exemption is narrowed to the + post-normalisation list/array check. Test on both backends: polars and + pandas Series give identical forecast colours under panels. + +### UPDATE — prose nit and test weaknesses: FIXED +- plot() docstring and `_image_palette_list` docstring now describe the + value-order default; `test_forecast_palette_matrix_colors_match_between_single_and_panel_calls` + compares actual forecast colours on both backends against the resampled + matrix palette; the normalize test compares against a by-hand + normalize + PCA. + +## Codex round 12 + +### Initial verification + +- Reviewed HEAD `faedf14e` on `fix/1.1-release-review`; read the complete prior audit and UPDATE claims, release-review CHANGELOG, datatype survey and `git log --oneline master..HEAD`. Diff since `650808f0`: 45 files. No separate `-o` path was supplied, so this authorized notes append is the persistent report. Scratch scripts/logs: `/tmp/hypertools-round12/`. Only this note was modified at start. No gallery/notebook rerun or original-finding standalone rerun. +- Commands below use `.venv/bin/python`, `MPLBACKEND=Agg`, `MPLCONFIGDIR=/tmp/hypertools-round12/mpl`, `PYTHONDONTWRITEBYTECODE=1`, and `PYTHONPATH=/Users/jmanning/hypertools`; direct probes additionally use `HYPERTOOLS_AUTO_INSTALL=0`, and any potentially installing child uses `PIP_NO_INDEX=1 PIP_CONFIG_FILE=/dev/null`. Public plots use `show=False`. + +- **Round-9 fixes independently verified:** `runpy.run_path` over `/tmp/hypertools-round9/{extra,membership_colors,composed_markers}.py` under the environment above (`round9.log`): TLS drop SKIPPED; certificate RAISED SSLError; shared cluster panels draw blue/red for global IDs 1/0 on BOTH backends; ordinary -> marker hue -> ordinary finishes gold for BOTH backends and figure/cell paths. Mixed narrow forecast/truth calls construct and return (3,1) forecasts. The focused round9 tests supply numerical/fitted-model comparisons; results follow separately. + +### R12-1 — MINOR: the latest manip fix crashes on a polars Series mixed with an array + +- Location: `hypertools/manip/manip.py:102–108,127–131`. `_validate_one` returns `polars.Series.to_frame()` before reaching the pandas coercion. `_align_columns_for_stacking` then assumes every recognized frame is already pandas and calls `.copy()` (and assigns integer columns); polars frames have no `.copy()`. +- Verified: `.venv/bin/python /tmp/hypertools-round12/new_edges.py` with the environment above (`new_edges.log`). `s=pl.Series('a', np.arange(12.)); hyp.manip([s,s.to_numpy()], model='Delay', dims=2)` raises `AttributeError: 'DataFrame' object has no attribute 'copy'`. Smooth (kernel_width=5), Resample (n_samples=7) and ZScore raise identically. Replacing s by its equivalent pandas Series succeeds for all four. Real inputs/models; no mocks. +- Suggested fix: normalize a Series' frame through `as_pandas_dataframe` before returning from `_validate_one`, and avoid imposing shared-stack normalization on independent transforms. Add polars Series + array and Series + pandas-frame mixed-list regressions, with explicit expected outputs rather than only all-frame tests. Before-head attribution is checked below. + +### R12-2 — MINOR: MatrixColormap still violates extreme-color alpha and infinity semantics + +- Location: `hypertools/plot/colors.py:478–504`, especially `finite & ...`, `bad=~finite`, and assignments to `out[..., :3]`. The exact float path throws away the alpha set by `set_under`/`set_over`, treats +/-infinity as bad instead of under/over, and ignores a supplied alpha override for nontransparent bad colors. Wrapping the SAME values in an unmasked MaskedArray changes all these semantics because that dispatches to the parent. +- Verified: same command `new_edges.py` / `new_edges.log`. After `set_under('red',alpha=.2); set_over('blue',alpha=.4); set_bad('green',alpha=.6)`, `c(-.1)` is red with alpha **1**, versus alpha **.2** in `c(np.ma.array([-.1],mask=[False]))`; +1.1 similarly gives **1** versus **.4**. Plain +/-inf are green/bad, while the parent's equivalent inputs are red/blue. `c([np.nan],alpha=.3)` retains **.6** while the parent returns **.3**. R11's original ordinary finite RGB/NaN-neighbor case is fixed; these are uncovered related contract gaps. +- Suggested fix: apply the complete RGBA extreme colors and matplotlib's alpha-override ordering, distinguish NaN from +/-inf, and share consistent masking semantics between float and masked input. Test transparent/semitransparent extremes, explicit alpha, and infinities against an ordinary matplotlib colormap configured from the same anchors. The current tests set only opaque extremes and inspect RGB, so they pass despite these errors. + +### R12-3 — MINOR: R11-2's independent-transform feature-label regression remains partly unfixed + +- Location: `hypertools/manip/manip.py:103–110`. Mixed named-frame/array input still renames the named frame's columns to positional integers for ALL models, including independent Delay/Smooth/Resample. The index and numerical interpolation fix does not restore feature names. +- Verified: `new_edges.py` compares an individual dated frame with columns a/b against the first output from `[frame,frame.to_numpy()]`. Delay returns a_lag1/a_lag0/b_lag1/b_lag0 alone but 0_lag1/0_lag0/1_lag1/1_lag0 in the mix; Smooth and Resample return a/b alone but 0/1 in the mix. Dates now match. The preceding round's verified 650808f0 baseline retained these names; current `compat.py` reproduction is being rerun as well. +- Suggested fix: restrict positional column alignment to models that stack shared statistics. Independent transforms should preserve each dataset's own columns as well as its index. Keep the new irregular-index regression and add explicit mixed-list feature-name assertions; do not describe the entire R11-2 regression as fixed while these outputs still change. + +### Incremental validation + +- Focused pytest: `.venv/bin/python -m pytest -q -p no:cacheprovider tests/test_plot_review_round9.py tests/test_palette_matrix_and_sort.py tests/test_polars_inputs.py tests/test_polars_inputs_wave1.py tests/test_datatype_gate.py tests/test_plot_review_round7.py tests/test_plot_review_round6.py tests/test_lazy_import.py` under the environment above, without forcing autoinstall OFF: **272 passed, 2 failed, 16 warnings in 46.16 s** (`pytest.log`). Failures are the intentionally blocked real tomli installation and installed Chrome failing to start, the same environmental limitations as prior rounds. Every palette/polars/datatype/round9 regression passes despite R12-1..3. +- Reused round11 reviewer scripts `colormap.py`, `compat.py`, `numeric_compat.py`, `image_counts.py`, `image_plot_counts.py`, `palettes.py`, `combinations.py`, `datatypes.py` in one `runpy.run_path` driver (`prior_probes.log`). Original R11 opaque under/over and NaN-neighbor behavior is fixed; named independent manip lists work, dates survive mixing, and irregular-index Resample numbers match the individual frame. Feature names still change (R12-3). Image samples have requested/unique counts 257/257 and 1001/1001; actual 257-category artists/traces have 257 distinct colors on BOTH backends. Repeated image extraction differs by at most 2.22e-16 in these probes; no material determinism finding. +- Matrix palettes as numpy/nested list/pandas/polars/LazyFrame, [0,1] RGB array palettes, mixed matrix/image/name per-dataset palettes, single-matrix forecasts, ordinary/panel plots, image specs with all five sort keys: construct on BOTH backends. Sampling counts 1,2,3,9,256,1001 are finite; matrices repeat byte-identically. Mixed per-dataset lists as forecast_palette correctly raise: that option promises ONE palette. Public polars forecast_hue Series now works under panels. Actual matrix forecast colors/styles match ordinary vs shared/independent panels with one/two models. Independent hyp.reduce + numpy scale/lexsort matches drawn palette_manip/normalize/align colors on BOTH backends. +- Additional numeric comparisons cover polars DataFrame/LazyFrame/Series/mixed lists through reduce, align, cluster, normalize, manip, predict, impute, analyze, describe, stack, damage, apply_model, Pipeline and all five Manipulator classes (`datatypes.py`). Successful polars outputs match current pandas equivalents. This is equivalence coverage, not proof of preserved pre-refactor behavior. Both-type unsupported cases (one-feature describe, unnamed/named stack correspondence, direct class Series/array cases) are not counted as new datatype bugs. text_windows rejects both pandas and polars Series per its explicit str/list/tuple contract. The reused backtest harness only checks construction (its DataFrame-column iteration is weak); an independent full score-table comparison follows. +- Fresh three-thread out-of-order exits, delayed construction/entry interleaving, and nested contexts restore the OFF baseline (`prior_probes.log`, reusing `/tmp/hypertools-round8/{three_threads,policy_threads}.py` plus fresh nested objects). Event hand-offs and thread terminations are asserted. No private policy state changed. + +- **Attribution confirmed:** `git archive 767e327d hypertools` extracted under `/tmp/hypertools-round12/pre11`, then `new_edges.py` run FROM `/tmp` with `PYTHONPATH=/tmp/hypertools-round12/pre11` and the checkout's absolute `.venv/bin/python` (`new_edges_pre11.log`). Every polars Series+array manip case succeeded BEFORE the round11 fix; R12-1 is newly introduced by `faedf14e`. +/-inf and bad-color alpha overrides also worked BEFORE; those parts of R12-2 are new regressions, while finite extreme alpha is a remaining gap in the corrected finite sampler. +- Animated-export policy command `.venv/bin/python -m pytest -q -p no:cacheprovider tests/test_animation_export.py -k honours_set_autoinstall`: **2 passed, 31 deselected in 18.29 s** (`export.log`). Real missing-kaleido children; OFF refuses pip, ON reaches deliberately blocked pip, both expose ImportError. Successful Chrome rendering is not claimed. +- **Pre-refactor compatibility sweep:** `compat_sweep.py` executed once against the checkout and once from `/tmp` against round11's archived `650808f0` package, storing results only under `/tmp/hypertools-round12`; `compare_sweep.py` compares arrays EXACTLY and DataFrames including indices/columns. 189 cases across 21 operations and nine existing numpy/pandas forms: **156 identical successes, 25 same exception classes, 6 changed successes, 2 newly successful cases**. Three changes are R12-3; three are a newly found dated-Series metadata loss in direct Smooth/Delay and Pipeline, investigated below. ZScore/Normalize mixed arrays now succeed where the old version rejected them. No numeric-difference claim is made for the six metadata changes until quantified. + +### R12-4 — MAJOR: direct Smooth/Delay and Pipeline discard a pandas Series' index, changing downstream interpolation + +- Location: `hypertools/manip/smooth.py:230`, `hypertools/manip/delay.py:109`, `hypertools/core/shared.py:34–36`. The datatype refactor replaces `pd.DataFrame(data)` with `as_dataframe(data)`; the latter sends Series through `np.asarray`, discarding their index and name. The independent class APIs and Pipeline therefore regress even with ONE ordinary pandas Series and no mixed input. This is a separate path from the dispatcher mixed-list fixes. +- Verified: `/tmp/hypertools-round12/series_pipeline.py` executed with the checkout and with `PYTHONPATH=/tmp/hypertools-round11/before` FROM `/tmp`, both using the checkout's absolute `.venv/bin/python` and Agg/autoinstall-off (`series_pipeline_current.log`, `series_pipeline_before.log`). Input `pd.Series([0.,1.,4.,9.,16.,25.,36.], index=[0.,1.,2.,8.,10.,15.,20.], name='signal')`. BEFORE, `Smooth(kernel_width=5).fit_transform(s)` and `Delay().fit_transform(s)` preserve its irregular index and named features. NOW they return a RangeIndex and 0/0_lag* names. A `hyp.Pipeline([('smooth', Smooth(kernel_width=5)), ('resample', Resample(n_samples=9))]).fit_transform(s)` formerly samples index 0..20, with values `[0,4.6743116472,6.5053827751,8.3289778265,16,20.8197033898,25,30.2375,36]`; now it samples 0..6 and returns `[0,.6328125,2.21875,5.03515625,9,14.072265625,20.246875,27.5484375,36]`. Tiny displayed leading zero roundoff is unchanged. This is silent numerical regression, not only renamed metadata. +- Suggested fix: have the shared coercion recognize Series and preserve `.to_frame()` metadata before pandas normalization (also supporting polars), and use it consistently for direct class/Pipeline inputs. Add explicit index/name/value regressions for a dated Series, an irregular-index Series followed by Resample, and direct fitted `.transform` reuse. The new direct-class tests cover frames/LazyFrames and compare current polars to current pandas, so neither catches a shared Series regression. +- Harness note: the first small seven-row probe used Smooth's default width 11 and both versions correctly rejected it; the corrected width-5 run above is the authoritative reproduction. + +### R12-5 — MINOR: the static gate excuses a pandas-only legend check that rejects polars labels + +- Location: `hypertools/plot/plot.py:7128–7135` and allowlist `tests/test_datatype_gate.py:109–113`. The validator rejects a polars Series BEFORE the shared Series normalization at 7154–7157 can run; a pandas Series of identical labels succeeds. This contradicts CHANGELOG.md:348–358's broad pandas/polars and legend-label equivalence claim. The rejection pre-exists the refactor; it is an omitted path in the advertised datatype support, not newly introduced behavior. +- Verified: `.venv/bin/python /tmp/hypertools-round12/backtest_more.py` with the documented environment (`backtest_more.log`). `hyp.plot([p,p+2],legend=pd.Series(['a','b']),backend=backend,show=False)` succeeds on BOTH backends; `legend=pl.Series(['a','b'])` raises `TypeError: legend= must be True/False, a label string, or a list of labels ... got Series`. p is a real 20x3 numeric frame. +- Suggested fix: normalize recognized label vectors before validation or admit `is_series_like` in the validator; remove/narrow the pandas-only gate exemption. Assert actual legend names for polars and pandas vectors on ordinary and composed/panel figures. Calling something an option does not make its backend-specific datatype restriction harmless. + +- **Full backtest-table comparison passes:** `backtest_more.py` uses `pd.testing.assert_frame_equal(..., check_exact=True)` on the ENTIRE impute score table for polars frame/LazyFrame/Series/mixed inputs with truth and boolean masks, compared with pandas. Both models' n columns are [1,1] for one dataset and [2,2] for mixed inputs. This supersedes the weak construction-only portion of the reused datatypes harness. + +### Test and documentation review + +- `tests/test_palette_matrix_and_sort.py:322–346` now compares actual forecast colors against the expected matrix palette on BOTH backends; the former construction-only test weakness is fixed. `:211–224` now independently normalizes before PCA. `:290–319` still changes only palette_reduce/sort despite its stage-kwargs name; our separate public probes cover palette_manip/normalize/align but that coverage should be committed. `:348–400` exercises real inherited operations, but opaque under/over RGB assertions omit alpha/infinity (R12-2). The matrix-by-hand oracle uses the implementation's sort_colors; the separate explicit sort-key tests reduce this dependency, but fixed tie-breaker expected orders would strengthen it. The 300-category image artist assertion is matplotlib-only; this audit additionally checked actual Plotly colors at 257 categories. +- `tests/test_polars_inputs_wave1.py:126–138` substitutes an all-named list for ZScore/Normalize in the test titled as numpy mixing; `:141–143` covers a standalone Series only; `:169–180` direct classes cover frames/LazyFrames and frame lists, excluding Series. `:373–397` checks mixed-list indices and numbers but never their feature names. Current-polars-vs-current-pandas comparisons are useful compatibility tests but cannot detect a regression both share (R12-3/R12-4). Add explicit pre-refactor expected metadata and irregular-index pipeline numbers. The static gate does inspect real AST and rejects stale entries, but its legend exemption permits R12-5; backend-sensitive label options deserve behavioral coverage too. +- `tests/test_plot_review_round9.py` has meaningful real regressions: independent hyp.predict numeric comparisons (:249–267), actual affine forecast/truth coordinates (:270–367), fitted-model reuse, missing global-category colors and composed artist/trace colors. These are not tautologies. Its docstring-substring checks are structural only, and its numerical forecast checks focus on Kalman with reduce=None; keep them and extend combinations as those paths evolve. Additional audit probes covered 18 equal-width panel/predict/truth cases (both backends x shared/independent/reducer-list integer grids x none/hue/cluster), all with expected role counts (`prior_probes.log`). Those count probes do not establish numerical equality for every combination. +- Save/load regression tests passed for real polars frame/LazyFrame CSV, TSV, JSON, parquet, NPY, NPZ and MAT, and frame pickle round trips. The pickle case returns before its LazyFrame branch (`tests/test_polars_inputs_wave1.py:305–315`), so it does not test LazyFrame pickle despite the surrounding parameterized test; no pickle failure is inferred from that coverage gap. +- Documentation: the old plot image-palette paragraph now correctly describes default value ordering, consistent with docs/api.rst:145–159 and docs/tutorials.rst:290–318. CHANGELOG.md:348–358's datatype equivalence and :893–902's Colormap-contract claims still overstate the behaviors in R12-1..5. No gallery, doctest or notebook was rerun. Prior full_verify17/18 green pipeline entries remain the Claude session's recorded evidence; the latest note says full_verify19 was launched, so this review does not claim its completion. + +- **NIT — docs/tutorials/plot.ipynb:715:** prose names `hyp.plot.colors.image_palette`, but `hyp.plot` is the exported function and has no `colors` attribute. Verified with `.venv/bin/python -c 'import hypertools as hyp; print(hyp.plot.colors.image_palette)'` under Agg: `AttributeError: 'function' object has no attribute 'colors'` (equivalent inline probe executed; no notebook execution). Use `hypertools.plot.colors.image_palette` as the documented qualified symbol and show `from hypertools.plot.colors import image_palette` for executable access. `hypertools/plot/colors.py:900–901` also still says luminance-bounded palette survivors are most-salient-first even though the following code defaults to value order; update this remaining internal prose. + +### Next steps / verdict + +1. Fix **R12-4 before merge/release**: preserve Series metadata in direct manipulators/Pipeline and pin the irregular-index Smooth -> Resample numerical output. +2. Fix R12-1's new polars Series mix crash, R12-3's remaining mixed-frame feature-name loss, R12-2's complete Colormap RGBA/extreme semantics and R12-5's legend normalization. Add the concrete missing cases identified above; correct the documentation nit and overbroad claims. +3. Run affected regressions and the established final CI/docs pipeline on the resulting exact head, including successful Chrome rendering and real installation in the unrestricted environment. This review does not replace those gates or authorize merge/publication. + +Reviewed HEAD remained `faedf14e1133ad737234e5a82f42d0be0d483b19`. Focused pytest plus export-policy totals: **274 passed, 2 environment-limited failures**; additional real reviewer probes and 189 before/current compatibility cases are recorded above. Findings: **one major, four minor, one documentation nit**. No full suite, gallery/notebook execution, successful Chrome export, or fresh hosted CI result is claimed. + +Only this audit note was edited by this review; all newly created reviewer scripts/logs/source archives are under `/tmp/hypertools-round12` (reused old scripts also write to their existing `/tmp/hypertools-round11` paths). No tracked implementation/test/doc file was edited, staged, committed or deleted; no other process's kernel or LSL outlet was stopped. Concurrent changes appeared late in `.claude/CLAUDE.md` and an untracked `.claude/CLAUDE.md.bak-skill-compounder-20260908-165453`; they were not created or modified by this review and were left untouched. + +VERDICT: FINDINGS + +## Updates by the Claude session after Codex round 12 (2026-09-08) — NOT part of the Codex text above + +All five round-12 findings and the nit were reproduced first (`scratchpad r12_repro.py`, both backends; R12-3/R12-4 also run against the archived 650808f0 tree under `/tmp/hypertools-round11/before` to confirm the regressions) and then fixed. Observed BEFORE the fixes: `manip([pl.Series, array], 'Delay')` → `AttributeError: 'DataFrame' object has no attribute 'copy'`; mixed Delay columns `['0_lag1', ...]` vs `['a_lag1', ...]` alone; `Pipeline([Smooth(5), Resample(9)])` on the irregular Series → index 0..6 and values `[0, .633, 2.219, ...]` (baseline: 0..20 and `[0, 4.674, 6.505, ...]`); `legend=pl.Series` → `TypeError` on both backends; `MatrixColormap` under/over alpha 1.0 (reference .2/.4), ±inf green (bad), `alpha=.3` leaving bad at .6. + +- **R12-4 (major) — UPDATE: fixed.** `core.shared.as_dataframe` turns a Series (pandas or polars) into the one-column frame that keeps its index and name (`pd.DataFrame(series)` semantics); `core.pipeline.as_internal_frames` coerces series-like items too, and the `ZScore`/`Normalize` fitters coerce BEFORE their funnel (the funnel wrangled a Series into an empty table, so `ZScore().fit_transform(series)` and `Normalize`/`Resample` used directly had never worked — pre-existing, fixed with the rest). Verified: the Pipeline reproduces the 650808f0 values exactly (`PIPELINE_VALUES` in the test); direct classes on a dated Series equal the same call on its one-column frame for all five manipulators, pandas and polars. +- **R12-1 (minor) — UPDATE: fixed.** `_validate_one` wrangles a polars Series' `.to_frame()` to pandas; and the deeper cause of the ORIGINAL failure mode is gone with R12-3 (no relabelling in the dispatcher). While reproducing, `hyp.manip(np.arange(12.))` turned out to be read as ONE ROW (1 × 12) by the funnel on every tree (`normalize`/`reduce`/the classes read it as one column) — a 1-D array is now one column for `manip` too; the `manip` docstring's Notes said the opposite and were corrected; `tests/test_manip_audit_fixes.py`'s 1-D test still passes (it asserted finiteness only). +- **R12-3 (minor) — UPDATE: fixed.** `_align_columns_for_stacking` is gone from the dispatcher. The shared-statistics manipulators own the question: `manip.common.stack_for_shared_fit` concatenates identical-label frames as they are, matches differing labels by POSITION (the fitted statistics were already positional), and raises `ValueError('... same number of columns; got widths [...]')` for different widths; `ZScore`/`Normalize` transformers transform a list per dataset instead of `dw.stack`-ing it, so every frame keeps its own labels and index. Independent manipulators never relabel. Verified: Delay/Smooth/Resample mixed outputs equal the single-frame call (values, index and names); ZScore/Normalize mixed statistics equal a by-hand shared mean/std (min/max) over the stacked values; two differently named frames match by position; unequal widths raise. +- **R12-2 (minor) — UPDATE: fixed.** `MatrixColormap.__call__`'s exact float path now assigns the full RGBA of `get_under()`/`get_over()`/`get_bad()` per element, treats `-inf`/`+inf` as under/over and only NaN as bad, applies an `alpha=` override to every element except a fully transparent bad colour (matplotlib's rule), validates the alpha shape with matplotlib's message, and keeps `bytes=True` truncation. Verified against `LinearSegmentedColormap.from_list` on the same anchors with the same extremes (RGBA equal at every extreme; `bytes` equal; masked input equal; scalar in → tuple out). +- **R12-5 (minor) — UPDATE: fixed.** The `legend=` validator asks `is_array_dataset`/`is_series_like` instead of naming `np.ndarray`/`pd.Series`/`pd.Index`; the later user-list flag tests `(list, tuple)` since every accepted container is normalised to a list first. The two legend exemptions left the gate allowlist. Verified: `legend=` as a polars Series, pandas Series, Index and array yields the legend names `['first', 'second']` on both backends; `legend=7` still raises; a wrong-length polars Series reports the length mismatch. +- **Nit — UPDATE: fixed.** `docs/tutorials/plot.ipynb` prose names `image_palette` (in `hypertools.plot.colors`) instead of `hyp.plot.colors.image_palette` (`hyp.plot` is the function). Markdown-only edit; no re-execution needed. +- **Weak tests named in the round — UPDATE: strengthened.** `tests/test_polars_inputs_wave1.py`: the "mixing numpy" test now mixes a numpy array for EVERY model and checks the shared statistics by hand; the Series test checks the column name and a `[polars Series, array, pandas Series]` list; the direct-class test covers a Series of either backend; the round-11 mixed-list test asserts feature names for Smooth and Delay; the pickle round trip reaches its LazyFrame branch (`hyp.load` returns a `pl.LazyFrame`). `tests/test_palette_matrix_and_sort.py`: the stage-kwargs test now checks `palette_manip`/`palette_normalize`/`palette_align` against a palette built by hand from the same staged `hyp.reduce` call on both backends. New `tests/test_review_round12.py` (35). +- Not changed: `text_windows` rejecting Series (its documented str/list contract, as the round noted); the `ndims=1` series-mode forecast observation stays queued. diff --git a/notes/release_fixes_2026-09-08.md b/notes/release_fixes_2026-09-08.md new file mode 100644 index 00000000..35e9da05 --- /dev/null +++ b/notes/release_fixes_2026-09-08.md @@ -0,0 +1,216 @@ +# Release review implementation — 2026-09-08 + +## Authorization and recovery + +The user approved fixes 1–4 and the proposed verification plan. Before edits, +all then-current work was committed as `0a2cc0d230d31d30458e991b4576e45c2793b3ac` +on `fix/1.1-release-review`. This is the original rollback checkpoint. +The user subsequently authorized committing and pushing the verified PR +changes. Merging, tagging and publishing remain separate release operations. + +The user clarified that observation times must affect fitting, including +irregular and shuffled samples and different times per dataset. The approved +default interval is the median positive gap between sorted timestamps, with +an explicit override. The user approved documented interpolation for models +that require a regular grid. + +## Implemented and locally verified + +1. Manipulator classes, dispatcher, Pipeline and fitted reuse interpret 1D + numeric arrays/Series/lists consistently as one column of observations. +2. Forecasts retain and sort observation times with their values. GP uses + actual elapsed times divided by the model interval. Kalman, ARIMA, + AutoRegressor, Laplace and Chronos use linear interpolation onto a grid + anchored at the latest observation, entirely within the observed span. + Constructors accept `step`; models retain their fitted interval on reuse. + Series plots fit signal columns jointly, then add time coordinates for + drawing. Static, animated and column-hierarchy forecasts preserve times. + Early animation prefixes wait for sufficient interpolated history. + If preprocessing changes row counts without preserving timestamps, + timestamped forecasting raises and requests explicitly indexed output. +3. Rowwise ZScore/min-max Normalize work on multiple datasets, including + unequal feature widths and repeated row labels. Held-out reuse restrictions + remain enforced. +4. MatrixColormap's exact float interpolation respects `set_gamma`. + +Code and API/class documentation plus regression tests are saved locally. +Tutorial Markdown for manipulation and animated forecasting has been updated. +Before the backtesting edits, the entire working tree was additionally saved +as `2e9669df` (the user approved the proposed backtesting policy and push plan). +Both this checkpoint and the original `0a2cc0d2` remain available. + +## Newly discovered backtesting decision + +Backtesting previously scored forecast rows against held-out rows by position, +even when their timestamps differed. Sorting before splitting is implemented. +The user approved evaluating GP at held-out times and linearly interpolating +discrete-model forecasts to those times. This is now implemented. The shared +model factory preserves ordinary prediction's constructor/spec rules while +fitting once and generating only the forecast required for scoring. + +Models and intervals use training rows only. Discrete models generate a grid +covering all held-out times, selecting exact matches or interpolating between +grid forecasts. The last observed training value anchors times before the +first full forecast step; missing endpoints stay missing. Held-out values +never enter fitting or interpolation. Full-index validation rejects duplicate +timestamps across the split. Categorical/repeated numeric IDs remain +positional even if one split happens to have unique IDs. + +Concrete reproduction: a one-column frame with index and values +`[0, 1, 2, 4, 7, 8, 12, 20]`, passed to +`hyp.predict(..., model='GaussianProcess', holdout=2, return_forecasts=True)`, +returns forecast times `[9, 10]` and truth times `[12, 20]` yet scores the +rows against each other before the fix. The corrected path evaluates GP at +`[12, 20]` and the API guide now executes this example as a doctest. + +## Verification evidence so far + +All logs below are outside the checkout in `/tmp`. + +- `hypertools-fixes-20260908-focused.log`: 236 passed (manipulators, Pipeline, + datatype/reuse cases, rowwise lists and gamma). +- `hypertools-fixes-20260908-confirm.log`: 101 passed (forecast timing, + minimum history, forecast audit and animated regrouping). +- `hypertools-fixes-20260908-hierarchy.log`: 180 passed after extending timed + forecast scheduling to hierarchical plots. +- `hypertools-fixes-20260908-edge-confirm.log`: 54 passed (actual-time + forecasts and reuse at a different cadence, duration/period indexes, + explicit truth times, invalid steps/times, series column hierarchies and + documentation structure). +- `hypertools-fixes-20260908-doctest-live.log`: all 316 doctests passed, + zero warnings. The first sandboxed run had seven failures from blocked + Chrome/network access and a short heading underline; the underline was + corrected and the live rerun succeeded. +- `hypertools-fixes-20260908-examples.log`: 344 native example checks passed. +- `hypertools-fixes-20260908-tutorials.log` and + `hypertools-fixes-20260908-tutorials-forecast.log`: manipulation, Pipeline, + plot, normalization, animated forecasting, hierarchy and projectile + tutorials executed successfully. Outputs and media remain in the scratch + copy, not the user's working tree. Stock forecasting needs re-execution + after the backtest policy is resolved. +- Ruff checks of source/tests/scripts/docs config and `git diff --check` + passed. +- Full pytest (`hypertools-fixes-20260908-full.log`) finished in 18:17: + **5855 passed, 5 failed, 19 skipped, 2 deselected**. All 13 packaging tests + passed. The failures were: + - One docstring gate: two new AutoRegressor history-floor methods lacked + docstrings. Corrected in the working tree. + - Three existing tests of skipped manipulation: normalization of input + shape broke the `model=None/False` identity-preserving no-op. Corrected + by validating the input but returning the original object when skipped. + - One deliberate auto-install test conflicted with the run's + `HYPERTOOLS_AUTO_INSTALL=0`. With that variable unset, the real temporary + virtualenv install test passed. Do not change this test to hide the + environment mismatch. +- `hypertools-fixes-20260908-full-fixes.log`: all 116 tests passed after + the docstring/no-op corrections, including all manipulation tests, the + existing final-wave audit tests and the new round-13 tests. +- `hypertools-fixes-20260908-install-check.log`: the isolated real-install + test passed with its expected environment. +- `hypertools-fixes-20260908-schedule-confirm.log`: 62 passed after making + animation timing/counts recognize that multiple drawn columns share one + joint model fit. Forecast values remain identical. +- The first clean HTML/gallery (`hypertools-fixes-20260908-html.log`) + completed successfully, executing all 51 gallery examples. +- Backtest alignment regression run (`/tmp/hypertools-backtest-alignment-regression.log`): + 269 passed. Covers native times, shuffled rows, changed held-out values, + per-model/per-dataset intervals, exact-grid selections, fractional-step + interpolation, missing endpoints, positional IDs, and existing forecasting, + scoring and imputation behavior. Later sorting cleanup and portable test + path handling are included in the final full validation below. +- The updated stock tutorial executed successfully + (`/tmp/hypertools-backtest-tutorial.log`). Its new calendar-time example + checks that every forecast shares the held-out index. Executed outputs were + copied back after source equality checks; temporary checkout prefixes were + removed from text outputs. Its existing trading-day comparison deliberately + remains positional, with updated prose explaining that modeling choice. + +The 19 full-suite skips include release-only gates, CI environment assertions, +two platform/font-specific checks and six opt-in native example executions. +The native example executions were separately enabled in the 344-pass run. +A fresh full run and updated documentation validation are being prepared on +the final tree; the first full run is useful evidence, not a clean final gate. + +Full-suite source snapshot: `/tmp/hypertools-verify-20260908-h5gdx5sw`. +Docs/tutorial source copy: `/tmp/hypertools-docs-verify-20260908`. +The full-suite snapshot predates the later animation cost accounting and +no-op/docstring corrections, three added tests and the documentation +cross-reference/underline fixes. These later edits have focused checks above; +the docs copy includes the documentation cross-reference/underline fixes. +A source hash +manifest for the initial snapshot is in +`/tmp/hypertools-current-verify-manifest.json`. + +## Final verification round (September 8–9) + +- `/tmp/hypertools-final-suite.log`: **5883 passed, 19 skipped, 2 deselected** + in 19:02, including the packaging tests. This clean run includes the + backtesting fixes but predates two final animation regression cases below. +- `/tmp/hypertools-final-doctest.log`: **323 doctests passed**, zero failures. +- `/tmp/hypertools-final-examples.log`: **344 native example checks passed**. +- Final animation inspection found that repeated references to one model spec + could share a cached forecast across comparison entries. Each entry now has + its own cache lifetime, while columns within a dataset still share the joint + fit. Real fitted-call counting verifies independent fits. Model classes are + distinguished from fitted instances, and dictionary-wrapped fitted models + bind to the appropriate dataset before animated forecasting. +- `/tmp/hypertools-final-animation-followup.log`: **204 passed**; + `/tmp/hypertools-final-animation-specs.log`: **36 passed**, including the + final class/repeated-spec and multi-dataset fitted-instance cases. +- `/tmp/hypertools-pandas-floor-tests.log`: **101 passed using pandas 2.2.2**, + including backtesting, timestamped animation and manipulation. The minimum + pandas version was installed only in a temporary target directory; the main + development environment remains unchanged. +- The final full run passed: **5885 passed, 19 skipped, 2 deselected, + 213 warnings in 18:30**. It checked the exact source snapshot at + `/tmp/hypertools-push-verify-srhi4kme`, with hash manifest + `/tmp/hypertools-push-verify-manifest.json`, log + `/tmp/hypertools-push-suite.log` and JUnit `/tmp/hypertools-push-suite.xml`. + Source, tests and documentation hashes match the working tree; only this + evidence note changed after the snapshot. All 13 packaging tests passed. + The two opt-in large-data tests (476 MB Drive download and real weights/UMAP) + were deselected by the normal suite configuration; no pass is claimed for + those two tests in this round. +- All **25 tutorial notebooks executed successfully**. The last 17 results + are recorded in `/tmp/hypertools-final-tutorials-results.json` and + `/tmp/hypertools-final-tutorials.log`; the earlier eight include the updated + stock tutorial. No committed tutorial contains stored exception outputs or + `/Users/` paths. The editable installation still points at this checkout. +- `/tmp/hypertools-final-html.log`: clean HTML build succeeded with `-W -E -a`, + executing **51/51 gallery examples**. The separate standard post-build step + succeeded (`/tmp/hypertools-final-post-build.log`), adding all 51 notebook + badges and gallery thumbnail links. The first browser check ran before + this post-build step and correctly failed the five gallery badge checks; + the rerun against complete output passed **8/8 browser checks** + (`/tmp/hypertools-final-browser-complete.log`). Screenshots are in + `/tmp/hypertools-final-browser-evidence`; the Plotly page was also visually + inspected. This was an incomplete verification invocation, not a source + defect. The browser checks validate rendered links; publishing updated + remote notebooks remains a release operation. +- The docs/tutorial execution snapshots predate only the final animation + class/cache corrections. Those edge cases have the dedicated real-model + regressions above and are included in the final full-suite snapshot. +- Final Ruff and whitespace checks passed. All changes are ready for the + authorized PR push; hosted CI must still validate the pushed commit. + +Use `LOKY_MAX_CPU_COUNT=4` for sandbox tests: macOS physical-core detection is +blocked inside the sandbox and otherwise adds a joblib warning to tests that +require warning-free calls. Use `MPLBACKEND=Agg`, `PYTHONDONTWRITEBYTECODE=1`, +and `-p no:cacheprovider`. **Unset `HYPERTOOLS_AUTO_INSTALL` for the full suite** +so the deliberate real auto-install test can run; setting it to `0` is useful +only for tutorial execution. Chrome/network/Jupyter +verification may need sandbox escalation; report environmental failures +separately from source failures. + +## Release operations still outstanding + +Follow `RELEASE_CHECKLIST.md` after source validation. PR #286 must be green at +the final pushed head, merged, and the release regenerated from that exact +commit. The v1.1.0 draft/tag and published gallery previously pointed to +`96ac8b7f`, not the reviewed PR changes. Changelog release date, gallery +manifest, wheel/sdist provenance, master/tag gates, public documentation, +PyPI/GitHub release and conda-forge follow-up still need final verification. +Do not interpret local test passes as approval to publish the release. + +Earlier audit history: `notes/release_audit_2026-09-07_paused.md` and +`notes/session_2026-09-05_release-1.1-review.md`. diff --git a/notes/release_notes_v1.1.0_draft.md b/notes/release_notes_v1.1.0_draft.md index 35d6602c..6cf47854 100644 --- a/notes/release_notes_v1.1.0_draft.md +++ b/notes/release_notes_v1.1.0_draft.md @@ -1,6 +1,8 @@ # HyperTools 1.1.0 -HyperTools 1.1.0 makes hierarchical (`MultiIndex`) DataFrames a first-class input: a frame whose columns carry a hierarchy expands into one trace per group plus per-level means, `hyp.predict` forecasts a hierarchy one group at a time, and `predict=` forecasts every plotted trajectory, means included. The animation API grows a public per-frame hook (`on_frame=` with `FrameContext`), an `order=` argument that is orthogonal to `animate=`, per-dataset `alpha=`, per-segment `title=`, trails under `animate='serial'`, and `predict=` on every animation mode (with `forecast_trail=` fans and `forecast_hue=`/`forecast_cluster=` styling), including under `hue=` and `cluster=`. Lines are now drawn smoothed by default (`antialias=True`), and a plot can take its colours from an image (`image_palette` and `palette='image:<path>'`). Several previously accepted inputs are now rejected with errors that say what to do instead; see "Changed behaviour and new validation" and "Upgrading from 1.0.0". This release also folds many features written by hand in the library's own examples back into hypertools itself (GH #285): synthetic datasets and web sources for `hyp.load`, a URL cache, `hyp.text_windows`/`damage`/`stack`, multi-model forecasting and imputation scoring, `Smooth(center=False)`/`Delay`, an alignment score, image-derived and per-dataset palettes, multi-panel plotting (`panels=`/`hyp.subplots`), several animation hooks, and Hugging Face loading fixes. This release also carries 41 bug fixes across both changelog sections; five rebuilt launch examples ship as executed tutorial notebooks with mp4 clips, the gallery is consolidated and ordered by topic, and five new tutorials (hierarchical DataFrames, loading and saving, fitted models and pipelines, manipulation, an animated forecast) join the rebuilt core ones. +HyperTools 1.1.0 makes hierarchical (`MultiIndex`) DataFrames a first-class input: a frame whose columns carry a hierarchy expands into one trace per group plus per-level means, `hyp.predict` forecasts a hierarchy one group at a time, and `predict=` forecasts every plotted trajectory, means included. The animation API grows a public per-frame hook (`on_frame=` with `FrameContext`), an `order=` argument that is orthogonal to `animate=`, per-dataset `alpha=`, per-segment `title=`, trails under `animate='serial'`, and `predict=` on every animation mode (with `forecast_trail=` fans and `forecast_hue=`/`forecast_cluster=` styling), including under `hue=` and `cluster=`. Lines are now drawn smoothed by default (`antialias=True`), and a plot can take its colours from an image (`image_palette` and `palette='image:<path>'`). Seven previously accepted inputs are now rejected with errors that say what to do instead; see "Changed behaviour and new validation" and "Upgrading from 1.0.0". This release also folds many features written by hand in the library's own examples back into hypertools itself (GH #285): synthetic datasets and web sources for `hyp.load`, a URL cache, `hyp.text_windows`/`damage`/`stack`, multi-model forecasting and imputation scoring, a trailing `Smooth` and a `Delay` manipulator, an alignment score, image-derived and per-dataset palettes, multi-panel plotting (`panels=`/`hyp.subplots`), several animation hooks, and Hugging Face loading fixes. It carries the bug fixes listed under "Bug fixes", plus the fixes from a pre-publication review of the 1.1.0 draft (summarised under "Fixed during the release review"). Five rebuilt launch examples ship as executed tutorial notebooks with mp4 clips, the gallery is consolidated and ordered by topic, and five new tutorials (hierarchical DataFrames, loading and saving, fitted models and pipelines, manipulation, an animated forecast) join the rebuilt core ones. + +Every change is listed in the [changelog](https://github.com/ContextLab/hypertools/blob/v1.1.0/CHANGELOG.md); these notes summarise it. ## Install @@ -8,7 +10,7 @@ HyperTools 1.1.0 makes hierarchical (`MultiIndex`) DataFrames a first-class inpu pip install --upgrade hypertools ``` -Python 3.10 through 3.13. Optional extras install themselves on demand: the first call that needs one runs its `pip install` and prints a one-line notice (`HYPERTOOLS_AUTO_INSTALL=0` disables this and restores the ImportError with the manual command). The base install covers plotting, dimensionality reduction, alignment, clustering, normalization, and `Kalman`/`ARIMA` forecasting and imputation. Optional extras: +Python 3.10 through 3.13. Optional extras install themselves on demand: the first call that needs one runs its `pip install` and prints a one-line notice (`hyp.set_autoinstall(False)` disables this and restores the ImportError with the manual command). The base install covers plotting, dimensionality reduction, alignment, clustering, normalization, `Kalman`/`ARIMA` forecasting, and missing-data imputation (PPCA, Kalman, scikit-learn imputers). Optional extras: | Extra | Adds | |-|-| @@ -17,7 +19,7 @@ Python 3.10 through 3.13. Optional extras install themselves on demand: the firs | `hypertools[predict-hf]` | the Hugging Face `Chronos` forecaster (pulls torch) | | `hypertools[text]` | transformer / sentence-transformers text embeddings via `pydata-wrangler[hf]` | | `hypertools[lsl]` | `hyp.io.lsl_stream()` Lab Streaming Layer input via pylsl | -| `hypertools[dev]` | the test and notebook toolchain (pytest, nbclient, plotly, kaleido, skaters, openpyxl, build) | +| `hypertools[dev]` | the test and notebook toolchain (pytest, nbclient, build) plus most optional backends: plotly, kaleido, skaters, torch, gensim, pylsl, scikit-image, datasets, kagglehub and openpyxl (not the `[text]` or `[predict-hf]` extras) | Further extras in `pyproject.toml`: `io` (`.xlsx` loading), `density3d` (3-D iso-surfaces), `torch` (autoencoder reducers), `kaggle` (`hyp.load('kaggle/...')`), and `gensim` (Word2Vec/Doc2Vec/FastText vectorizers and LDA/LSI/HDP semantic models). @@ -29,18 +31,23 @@ conda-forge: the `hypertools-feedstock` bot opens a version-bump PR automaticall - The first call that needs an optional dependency installs the hypertools extra that provides it and carries on, printing a one-line `hypertools: installing ...` notice: plotly and kaleido (`[interactive]`), Hugging Face text embeddings and datasets (`[text]`), skaters (`[predict]`, `Laplace`), chronos-forecasting (`[predict-hf]`, `Chronos`), torch (`[torch]`, autoencoder reducers), gensim (`[gensim]`), kagglehub (`[kaggle]`), pylsl (`[lsl]`), scikit-image (`[density3d]`) and openpyxl (`[io]`). The requirement strings come from the installed package metadata, so `pyproject.toml` remains the one declaration of every extra, and hypertools itself is never reinstalled. - Static image export with the plotly backend provisions what kaleido needs on first use: a Chrome build and, on Debian/Ubuntu images such as Colab and Kaggle, the four system libraries a fresh image lacks. -- `HYPERTOOLS_AUTO_INSTALL=0` turns this off; a missing extra then raises `ImportError` naming the manual `pip install "hypertools[<extra>]"` command, as before. Guide: *Optional dependencies* in the docs. +- `hyp.set_autoinstall(False)` turns this off (session-wide, or for one `with` block; `HYPERTOOLS_AUTO_INSTALL=0` sets the starting value for prebuilt images); a missing extra then raises `ImportError` naming the manual `pip install "hypertools[<extra>]"` command, as before. Guide: *Optional dependencies* in the docs. + +### New top-level names + +- `hyp.set_autoinstall` (above), `hyp.FrameContext` (the `on_frame=` argument), `hyp.subplots`, and the helpers `hyp.text_windows`, `hyp.damage` and `hyp.stack`. +- `hyp.HypertoolsOfflineError`, raised by `hyp.load(..., offline=True)` when a file is not cached (also importable from `hypertools.io`). +- `hyp.HypertoolsTrustError`, raised when a remote payload could only be loaded by relaxing a security policy and `trust=True` was not passed. It existed in 1.0.0 as `hypertools.io.sources.HypertoolsTrustError`, which still works, and it remains a `ValueError` subclass. ### Folded in from the examples (GH #285) - **Data loading**: synthetic generators (`hyp.load('random_walk'|'helix'|'lorenz'|'blobs'|'moons'|'swiss_roll'|'s_curve', random_state=, n_datasets=)`), web sources (`hyp.load('wikipedia:<Title>')`, `hyp.load('yahoo:<TICKER>', start=, end=, interval=)`, `hyp.load('sec:<TICKER>', concept=)`), and a URL download cache (`hyp.load(url, cache=True)`, `HYPERTOOLS_URL_CACHE`, `offline=True` raising `HypertoolsOfflineError`). - **Helpers**: `hyp.text_windows` (sliding word/sentence/character windows), `hyp.damage` (reproducible NaN knock-out on arrays/DataFrames), and `hyp.stack` (builds a column-hierarchical DataFrame from nested dicts or lists, with `aggregate=`). -- **Forecasting/imputation**: `hyp.predict(x, model=[...])` returning `{name: forecast}`; `holdout=k` backtests against a naive baseline (`per_column=`, `return_forecasts=`, `scores.attrs['best']`/`['beats_baseline']`); `hyp.impute(x, model=[...], truth=full)` scoring against a column-mean baseline; `Smooth(center=False, min_periods=)` and a `Delay(tau=, dims=)` manipulator; `hyp.align(..., return_score=True)` / `hypertools.align.score.alignment_score`. +- **Forecasting/imputation**: `hyp.predict(x, model=[...])` returning `{name: forecast}`; `holdout=k` backtests against a naive baseline (`per_column=`, `return_forecasts=`, `scores.attrs['best']`/`['beats_baseline']`); `hyp.impute(x, model=[...], truth=full)` scoring against a column-mean baseline; a trailing boxcar `Smooth(kernel='boxcar', center=False, min_periods=)` and a `Delay(tau=, dims=)` manipulator; `hyp.align(..., return_score=True)` / `hypertools.align.score.alignment_score`. - **Plotting**: `palette=` as a `{category: color}` dict or a per-dataset list of palette specs; `panels=True|'auto'|ncols|(nrows, ncols)` and `hyp.subplots(nrows, ncols, ndims=3)`; `title_kwargs=`/`title_color=`/`title_wrap=`; per-dataset `hue=` and `labels=` (with `label_anchor=`); legends under matrix/mixture hue (`legend_kwargs=`, `legend_colors=`); `bundle['colors']`. - **Animation**: `FrameContext.progress`/`window_bounds` and populated `revealed_counts`; `title=` accepting a callable or an index-pattern string; `animate='morph', loop=True`; `dataset_fade={'floor':, 'decay':}`; `companion=` panels (matplotlib only); `HyperAnimation.drawn_extent(frames=None)`. -- **Text/fonts**: bundled Noto Sans Bold (`fontweight='bold'`); `hypertools.io.lsl.synthetic_outlet(name, n_channels=, rate=)`; non-streaming Hugging Face loads decoding `ClassLabel` columns (`decode_labels=False` to keep integers); `hyp.load('wiki')`/`hyp.load('nips')` returning a flat list of strings; HF text/dataset paths defaulting `HF_HUB_DISABLE_PROGRESS_BARS`/`HF_HUB_VERBOSITY`/`TOKENIZERS_PARALLELISM`. - -- **Axis units and forecast overlays**: `axis_scale='data'` (real coordinates for 1-D/2-D plots, with `xlim=`/`ylim=`; `'unit'` unchanged and now documented), `ndims=1` as a time-series mode (one line per column against the row index, DatetimeIndex aware, 2+ columns), `truth=` drawing the actual continuation beside a `predict=` forecast, and `predict=['Kalman', 'ARIMA', 'GP']` drawing one legend-labelled overlay per model. +- **Text/fonts**: bundled Noto Sans Bold (`fontweight='bold'`); `hypertools.io.synthetic_outlet(name, n_channels=, rate=)`; non-streaming Hugging Face loads decoding `ClassLabel` columns (`decode_labels=False` to keep integers); `hyp.load('wiki')`/`hyp.load('nips')` returning a flat list of strings; HF text/dataset paths defaulting `HF_HUB_DISABLE_PROGRESS_BARS`/`HF_HUB_VERBOSITY`/`TOKENIZERS_PARALLELISM`. +- **Axis units and forecast overlays**: `axis_scale='data'` (real coordinates for 1-D/2-D plots, with `xlim=`/`ylim=`; `'unit'` unchanged and now documented), `ndims=1` as a time-series mode (with `reduce=None`, one line per column against the row index, DatetimeIndex aware, 2+ columns; the default `reduce=` first reduces to one component), `truth=` drawing the actual continuation beside a `predict=` forecast, and `predict=['Kalman', 'ARIMA', 'GP']` drawing one legend-labelled overlay per model. ### Hierarchical DataFrames @@ -52,7 +59,7 @@ conda-forge: the `hypertools-feedstock` bot opens a version-bump PR automaticall - `predict=` works with hierarchies, forecasting every plotted trajectory including per-level means. A mean is forecast from its own averaged trajectory, and `bundle['predict']['forecasts'][i] == hyp.predict(bundle['trace_data'][i], model, t)` for every `i`. It works with `animate=` too, where each frame's forecast is fit from exactly the rows that frame has revealed. Every plotted trace needs at least 2 rows on either axis, and `plot()` says so directly, naming a row-count-changing analysis stage (`manip='Resample'`, an aggregating `reduce=`) when that is what made a trace short. - `return_model=True` now also returns `trace_data` and `trace_metadata` describing every plotted trajectory. `trace_data` holds the final pre-center/pre-scale trajectories; `xform_data` is unchanged (the analysed pipeline output per input dataset, with no derived means). The two differ when a `reduce=` spec pins more than three components (`n_components=5` leaves `xform_data` 5-D while `trace_data` is 3-D). Bundled forecasts always correspond to `trace_data`. - Full plotly parity for all of the above: trace counts and order, widths, opacities, legend labels, continuous hue, the colorbar, per-trace forecasts on both axes, and animated hierarchy forecasts. plotly data traces carry `meta['hyp_trace_index']`, the counterpart of matplotlib's `coll._hyp_trace_index`, propagated to per-segment 2-D traces so a multicoloured 2-D line still reads as one trajectory. -- New guide: *Hierarchical DataFrames* (`docs/hierarchy.rst`), covering row versus column semantics, the plot/predict divergence, hue forms, mean construction, limitations, dual-axis and list inputs, return shapes, the unfitted/fitted ownership table, backend parity and feature correspondence. All 138 of its examples are executed by the test suite. `docs/pipeline_order.rst` gains hierarchy expansion and mean construction in the prose and the regenerated diagram. +- New guide: *Hierarchical DataFrames* (`docs/hierarchy.rst`), covering row versus column semantics, the plot/predict divergence, hue forms, mean construction, limitations, dual-axis and list inputs, return shapes, the unfitted/fitted ownership table, backend parity and feature correspondence. Its worked examples are doctests, run by Sphinx's doctest builder in the docs CI job. `docs/pipeline_order.rst` gains hierarchy expansion and mean construction in the prose and the regenerated diagram. ### Animation @@ -60,8 +67,8 @@ conda-forge: the `hypertools-feedstock` bot opens a version-bump PR automaticall - `plot(..., on_frame=...)`: a public per-frame hook on both backends. `on_frame` is called once per drawn frame with a single `FrameContext` argument (frame index and total, axes and drawn artists, animated arrays, serial-reveal counts, and `segment_index`/`segment_kind` for `animate='morph'`). `FrameContext` is exported as `hypertools.FrameContext`. On matplotlib, callbacks can also be attached after construction via `HyperAnimation.on_frame(callback)` (chainable); this is not available on plotly, so pass `on_frame=` to `plot()` for backend-portable code. Callbacks must be deterministic and idempotent for a given frame context. `ctx.figure`/`ctx.axes`/`ctx.artists` are backend-native (`ctx.axes` is `None` on plotly). - `order='parallel'|'serial'` on `plot()`, orthogonal to `animate=`, so trail styles compose with a serial reveal (`animate=True, order='serial', chemtrails=True`). `animate='serial'` remains a permanent alias for `animate=True, order='serial'`. - `animate='serial'` composes with `chemtrails`/`precog`/`bullettime`, in 2-D and 3-D and on both backends: the dataset currently being drawn carries its trail while already-revealed datasets stay fully drawn. This also fixes plotly, which previously warned and dropped the trails for a serial reveal. -- Per-dataset `alpha=`, alongside the existing per-dataset `color=`/`linewidth=`. Inputs that assign alpha internally (row `MultiIndex` frames, nested lists) keep their own values and now warn instead of losing yours silently. -- Per-segment `title=` for serial-style animations, on both backends: pass a list of strings, one per dataset. For `animate='morph'` the holds are named and the transitions left blank automatically. Anywhere else a non-string `title=` raises `TypeError`. +- Per-dataset `alpha=`, alongside the existing per-dataset `color=`/`linewidth=`. Inputs that assign alpha internally (row `MultiIndex` frames, nested lists of varying depth) keep their own values and now warn instead of losing yours silently. +- Per-segment `title=` for serial-style animations, on both backends: pass a list of strings, one per dataset. For `animate='morph'` the holds are named and the transitions left blank automatically. Anywhere else a `title=` that is neither a string nor a callable raises `TypeError`. - Lines are smoothed by default (`antialias=True`). Every drawn line, static or animated, on both backends, is upsampled along a monotone PCHIP interpolant, so every original sample stays an exact vertex and the curve bends smoothly through it. This changes only how data is drawn: returned arrays, `return_model=True` bundles, forecasts, hulls, densities and per-point labels/markers are unaffected. Marker-only styles are never touched. In an animation each frame draws the smooth curve for exactly the portion that frame would have shown, at any `frame_rate`. Pass `antialias=False` to restore the previous straight-segment rendering exactly. - `simplify=` on `plot()` (default `True`). Today it governs `animate='morph'` only: over clouds larger than 2000 points, a morph without `morph_samples=` is downsampled to 2000 silently, because an uncapped morph was measured as killed at 10 minutes versus 8.2 s at `morph_samples=2000`. Pass `simplify=False` for an explanatory `ValueError` instead, which restores the guarantee that no data point is ever dropped. An explicit `morph_samples=` always wins. - A regrouped trajectory animates in row order. With `hue=`/`cluster=`, runs of one input dataset now share a single reveal clock, so the head sweeps the trajectory once and changes colour at each category boundary (previously every run advanced at once). A `precog=` trail on a not-yet-reached run now shows that run's whole future. @@ -70,10 +77,10 @@ conda-forge: the `hypertools-feedstock` bot opens a version-bump PR automaticall ### Forecasting (`predict=` and `hyp.predict`) - `predict=` works with `animate='spin'`: the forecast traces are drawn once and rotate with the scene. -- `predict=` works with the time-progressing animations (`animate=True`/`'parallel'`/`'serial'`/`'window'`). The forecast is recomputed from the history revealed so far and re-anchored on the last revealed observation. Every forecast the animation will draw is computed up front, so the fan is folded into the plot's centre/scale statistics and lands inside the cube by construction, and `ani.save()` and `to_jshtml()` replay identically. Fits are memoized per (dataset, revealed-count), so a 900-frame animation of a 60-row dataset costs at most 59 fits. `animate='morph'` still raises `NotImplementedError`, now with a stated reason: a morph interpolates between point clouds, so there is no time axis to forecast along. +- `predict=` works with the time-progressing animations (`animate=True`/`'parallel'`/`'serial'`/`'window'`). The forecast is recomputed from the history revealed so far and drawn from the endpoint of the current frame. Every forecast the animation will draw is computed up front, so the fan is folded into the plot's centre/scale statistics and lands inside the cube by construction, and `ani.save()` and `to_jshtml()` replay identically. Fits are memoized per (dataset, revealed-count), so a 900-frame animation of a 60-row dataset costs at most 59 fits. `animate='morph'` still raises `NotImplementedError`, now with a stated reason: a morph interpolates between point clouds, so there is no time axis to forecast along. - `forecast_trail=`: keep earlier forecasts on screen as a fading fan, the forecast analogue of `chemtrails=`. With `predict=` and a time-progressing animation, `forecast_trail=True` retains the last 16 forecasts (an int sets the cap), each in its dataset's style at an alpha that decays with age. The fan is recomputed from the frame index rather than accumulated, so a saved GIF and an interactive playback are identical. Without `predict=` it raises `ValueError`. -- An animated `predict=` says when it will be slow to start. Cost grows with the data rather than the frame count (3 datasets x 60 rows x 900 frames is 177 fits, about 5 s; 3 x 500 x 900 is 1497 fits, about 330 s). `plot()` times the first real fit and warns if the projection exceeds `slow_warning_seconds=` (default 10; `None` silences), before the wait rather than after it. -- `forecast_hue=`, `forecast_cluster=`, `forecast_n_clusters=`, `forecast_palette=` and `forecast_fmt=` style forecasts separately from the data. Inheritance stays the default, and each of these replaces exactly one aspect of it. `forecast_cluster=` clusters the forecast endpoints, taken in the space the figure draws after `reduce=`/`align=`, so a forecast's colour answers which series are heading to the same place; in an animation the endpoint groups are resolved once from the full-history forecasts and stay fixed. `forecast_hue=` and `forecast_cluster=` are mutually exclusive; `forecast_n_clusters=` is separate from `n_clusters=`. All five require `predict=` and raise `ValueError` without it. `forecast_fmt=` is validated with matplotlib's own `fmt=` parser; `forecast_hue=` rejects a bare string. +- An animated `predict=` says when it will be slow to start. Cost grows with the data rather than the frame count (3 datasets x 60 rows x 900 frames is 177 fits, about 5 s; 3 x 500 x 900 is 1497 fits, about 330 s). `plot()` times the fits as they run and, once fits at two or more history lengths (one of them at least 10 rows) have been timed, warns if its projection exceeds `slow_warning_seconds=` (default 10; `None` silences), before the wait rather than after it. +- `forecast_hue=`, `forecast_cluster=`, `forecast_n_clusters=`, `forecast_palette=` and `forecast_fmt=` style forecasts separately from the data. Inheritance stays the default, and each of these replaces exactly one aspect of it; a forecast recoloured by `forecast_palette=`, `forecast_hue=` or `forecast_cluster=` is drawn at its trace's own alpha rather than half of it. `forecast_cluster=` clusters the forecast endpoints, taken in the space the figure draws after `reduce=`/`align=`, so a forecast's colour answers which series are heading to the same place; in an animation the endpoint groups are resolved once from the full-history forecasts and stay fixed. `forecast_hue=` and `forecast_cluster=` are mutually exclusive; `forecast_n_clusters=` is separate from `n_clusters=`. All five require `predict=` and raise `ValueError` without it. `forecast_fmt=` is validated with matplotlib's own `fmt=` parser; `forecast_hue=` rejects a bare string. - `predict=` works with `hue=`/`cluster=` on static plots. A forecast belongs to a dataset and is anchored at its last observation, so it is matched to whichever drawn trace holds that observation and inherits its style. Two further ways a forecast could vanish were closed along the way: under a continuous `hue=` the overlays were drawn and then deleted by the `LineCollection` swap, and plotly raised `IndexError` under a regrouping `hue=`. - `predict=` works with `hue=`/`cluster=` on animated plots. Each frame's forecast is fit from exactly the observations visible for that dataset. A live forecast inherits the colour of the run drawing the head; a retained `forecast_trail=` member keeps the colour it was fit with; `forecast_hue=`/`forecast_cluster=`/`forecast_palette=` override both with a grouping fixed for the whole animation. Marker-only categorical regrouping still draws no overlay and still says so. - `return_model=True` reports forecasts it could not draw: `bundle['predict']` gains `drawn` (bool) and `draw_reason` (`None`, or a sentence naming the limitation). A fit that succeeded is returned whether or not the figure could render it. @@ -116,25 +123,41 @@ Each of these turns previously accepted input into rejected input, or changes wh - A `pipeline=` that already carries a column-hierarchy record is checked against the frame being plotted, while the leaves still have labels. Under `'name'` correspondence a mismatch now raises at the `plot()` call, naming the missing and unexpected features, and the leaves are restored to fit-time order (the fitted steps are positional, so the frame's own order produced silently wrong coordinates). `feature_correspondence='position'` is unaffected. - Frames carrying a hierarchy on both axes are rejected (`x has both a row and a column MultiIndex ...`). Before 1.1 such a frame followed the row path and its column hierarchy was silently ignored. Fix: drop one of the two hierarchies before plotting. - A column-hierarchical DataFrame nested inside a list is rejected by `hyp.plot` (before 1.1 it was flattened to a single line, silently). `hyp.predict` rejects a hierarchical frame in a list on either axis. This is deliberately asymmetric: a row-hierarchical frame inside a list passed to `hyp.plot` keeps its documented warn-and-flatten behaviour. Fix: pass the frame bare; hierarchy expansion is defined for a bare frame only. -- `hyp.predict` rejects a column hierarchy whose groups do not name the same features. Groups carrying different, or differently many, innermost labels raise an error naming the missing and unexpected features (before 1.1 the frame was flattened into one wide series and forecast). Groups that share their labels in a different order are still accepted and come back permuted into the first group's feature order. Fix: rename the innermost level so every group carries the same labels, or group with `group_columns(df, feature_correspondence='position')` and forecast the leaves. +- `hyp.predict` rejects a column hierarchy whose groups do not name the same features. Groups carrying different, or differently many, innermost labels raise an error naming the missing and unexpected features (before 1.1 the frame was flattened into one wide series and forecast). Groups that share their labels in a different order are still accepted and come back permuted into the first group's feature order. Fix: rename the innermost level so every group carries the same labels, or group with `group_columns(df, feature_correspondence='position')` (`from hypertools.core.hierarchy import group_columns`) and forecast the leaves. - `hyp.predict` rejects a time-like index with duplicate entries, including on flat input. A plain `DataFrame`/`Series` on a `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex` with repeated stamps now raises `ValueError: the dataset index has N duplicated entries ... the forecast horizon is ill-defined` (before 1.1 it forecast with a step inferred from the surviving gaps). Non-time indexes are unaffected. Fix: aggregate the repeats (`df.groupby(level=-1).mean()`) or give them distinct times. - `predict=` with a `MultiIndex` frame no longer raises blanketly. It previously raised `ValueError: predict= is not supported with MultiIndex expansion in this release`; it now forecasts every plotted trajectory. A hierarchy whose traces are shorter than 2 rows still raises, and the message now names the offending trace, its row count and the cause. - Hierarchy groups whose label is missing (NaN) are no longer dropped, and a missing label is one group. Labels are canonicalised NA-aware (`np.nan`, `None` and `pd.NA` normalise to one sentinel) for grouping, top-level uniqueness and style lookup, on both axes; the original label values are preserved in the returned keys and in the legend. - `forecast_hue=`, `forecast_cluster=`, `forecast_n_clusters=`, `forecast_palette=` and `forecast_fmt=` count final traces, not input datasets. Under a hierarchy their unit is every plotted trajectory, leaves and derived means, so a three-sector frame needs four values, not three. Nothing changes for flat input. - `ax=` is rejected together with `animate=`. An animated plot owns its own figure, so the axes passed in were left empty. Fix: drop `ax=` and style the returned animation's `.figure`; for several panels in one animation, translate each group into its own region of one shared frame and draw with a single call. - `ax=` under the plotly backend draws into a plotly Figure, and a matplotlib Axes is refused. `fig = hyp.plot(A); hyp.plot(B, ax=fig)` appends B's traces to `fig` and returns it (the caller's layout untouched; `animate=` is refused with it). A matplotlib Axes under plotly raises `ValueError` before any analysis runs; before 1.1 it was silently ignored and the axes left empty. A plotly Figure passed to a matplotlib draw is a `TypeError`. -- `predict=` forecast overlays inherit the style of the observed trace they continue: its colour, linestyle and linewidth, differing only in transparency (`forecast_alpha = observed_alpha * 0.5`, so the default forecast alpha is `0.5`; `alpha=[1.0, 0.4]` gives forecasts at `[0.5, 0.2]`). This is a visible change to existing forecast figures: every forecast used to be drawn `linestyle='--'` at a hard-coded `alpha=0.6`. Both backends share one constant, `hypertools.plot.forecast.FORECAST_ALPHA_SCALE`. `forecast_trail=` now fades from that dataset's live forecast alpha down to a floor proportional to it rather than a fixed `0.08`. Code that located forecast artists by their dashed linestyle must switch to the role tags (`artist._hyp_forecast_role` on matplotlib, `trace.meta['hyp_forecast_role']` on plotly). +- `predict=` forecast overlays inherit the style of the observed trace they continue: its colour, linestyle and linewidth, differing only in transparency (`forecast_alpha = observed_alpha * 0.5`, so the default forecast alpha is `0.5`; `alpha=[1.0, 0.4]` gives forecasts at `[0.5, 0.2]`). A collection of models (`predict=['Kalman', 'ARIMA']`) keeps each dataset's colour and cycles the linestyle per model, and a forecast recoloured by `forecast_palette=`/`forecast_hue=`/`forecast_cluster=` keeps its trace's alpha. This is a visible change to existing forecast figures: every forecast used to be drawn `linestyle='--'` at a hard-coded `alpha=0.6`. Both backends share one constant, `hypertools.plot.forecast.FORECAST_ALPHA_SCALE`. `forecast_trail=` now fades from that dataset's live forecast alpha down to a floor proportional to it rather than a fixed `0.08`. Code that located forecast artists by their dashed linestyle must switch to the role tags (`artist._hyp_forecast_role` on matplotlib, `trace.meta['hyp_forecast_role']` on plotly). - Animated continuous-hue line plots with no explicit `linewidth=` render at `1.0` instead of `1.5`, matching animated no-hue lines. This is a visible change to existing animated hue figures. Fix: pass `linewidth=1.5` to keep the old look. +- `hyp.describe` returns `'average'` as the mean of the per-dataset curves. It used to be the curve for all datasets stacked into one point cloud, which is a different number whenever there is more than one dataset; that curve is still returned, as `'pooled'`. `hyp.describe(show=False)` now draws and returns the figure without displaying it, as `hyp.plot(show=False)` does; it used to return `fig=None`. The component axis is ticked at whole numbers. +- In an animation, each `truth=` point appears on the first frame whose drawn forecast reaches it and then stays. It used to be drawn in full from the first frame. Static plots and `animate='spin'` are unchanged. +- An animated forecast starts at the point the line has reached in that frame. It used to start at the last observation behind it, so it stood still while the line moved and then jumped. +- `forecast_hue=`/`forecast_cluster=` without `forecast_palette=` take the palette colours after the ones the observed data uses. They used to restart the palette, so the first forecast group was drawn in the first dataset's colour. Pass `forecast_palette=` to choose the colours. +- Matplotlib `axis_scale='data'` axes no longer draw gridlines, matching plotly. A grid on your own `ax=` is kept. +- Model comparisons rank complete models only. `hyp.impute(x, model=[...], truth=)` and `hyp.predict(x, model=[...], holdout=)` used to name the lowest-scoring model `best` even when it had left values unscored, so a model scored on fewer, easier values could win. A model with any `unscored` value keeps its row but is excluded from the verdict and listed in the new `scores.attrs['incomplete']`. When no model is complete, `attrs['best']` and `attrs['beats_baseline']` are `None`; `beats_baseline` is also `None` when the baseline itself is incomplete. Each case warns. +- `hyp.impute(truth=, mask=)` matches labelled data by label. A truth or mask DataFrame whose rows or columns are in another order is reindexed to the data's order; it used to be compared by position, which silently changed the score. Different label sets raise `ValueError`. Bare arrays are still compared by position. +- `hyp.describe` sweeps the dimensionality of a reducer instance. `reduce=PCA(n_components=2)` used to be evaluated at 2 components for every point of the curve (also in 1.0); an unfitted instance is now cloned for each point. An already fitted model, an instance with no `n_components`, and `reduce=None` raise `ValueError`. +- An installed optional package that is older than hypertools requires is upgraded on first use, with a notice, or reported with its installed and required versions. It used to be accepted as it was, and a plotly below 6.1.1 failed later with a message about kaleido. With `hyp.set_autoinstall(False)`, `backend='plotly'` on a too-old plotly raises, and `backend='auto'` warns and uses matplotlib. +- Core dependency floors are raised to the first releases with Python 3.10 through 3.13 wheels: numpy 2.1.0, pandas 2.2.3, scipy 1.14.1, matplotlib 3.9.2, scikit-learn 1.5.2, statsmodels 0.14.3 and pillow 10.4.0. ## Bug fixes -41 fixes, listed in changelog order. The first 25 come from the 1.1.0 section (the hierarchy work, the Colab validation of the feature tour, the folded-in examples, and the tutorial rebuild) and each affects flat input too; the remaining 16 come from the 1.0.1 section. +The fixes from the [changelog](https://github.com/ContextLab/hypertools/blob/v1.1.0/CHANGELOG.md)'s 1.1.0 and 1.0.1 sections. Those from the 1.1.0 section came out of the hierarchy work, the Colab validation of the feature tour, the folded-in examples and the tutorial rebuild, and each affects flat input too. The fixes made during the pre-publication review follow in their own section. +- **NumPy 2-compatible optional dependency floors.** The `gensim` and `density3d` extras require gensim>=4.4.0 and scikit-image>=0.25.0; the earlier advertised minimums predate upstream NumPy 2 support. +- **Backtests fit an independent copy of an unfitted model for each dataset**, instead of reusing the first dataset's learned parameters on the later ones. Forecast and imputation scoring leave caller-owned instances unchanged and reject already-fitted instances, which may have seen the held-out truth. +- **Concurrent URL caching.** Threads downloading the same URL use distinct temporary files, so the atomic replacement no longer raises `FileNotFoundError`. +- **`Delay` column collisions.** `Delay` rejects duplicate column labels, and distinct labels with identical string representations, instead of silently overwriting embedded features. +- **Bundled font precedence.** The bundled Noto Sans faces take precedence over same-family system fonts, so rendering stays the same on machines with another Noto Sans installed. +- **Release documentation.** Plotting and scoring features shipped in 1.1 are labelled 1.1 in their API documentation (leftover 1.2 labels are corrected), and the installation guidance distinguishes Kalman imputation from Kalman/ARIMA forecasting. - **Video exports were written at a fixed 1800 kbit/s.** A file's size followed its duration and nothing else (a two-minute clip was 27 MB at 1400 x 700 and 26 MB at 980 x 490). Video is now a quality-targeted encode (x264 CRF 23, `hypertools.plot.animate.VIDEO_CRF`); the same 5-second test clip went from 0.96 MB to 0.38 MB with no visible difference. `writer=` on `HyperAnimation.save()` still takes a specific bitrate. - **The "Animation was deleted without rendering anything" warning could still fire from a discarded `HyperAnimation`** when the wrapper died inside a reference cycle. The wrapper now marks the animation as draw-started when it is constructed, so finalizer order no longer matters. - **`HyperAnimation.save()` silently discarded every keyword except `fps=`**, so `anim.save('clip.gif', dpi=75)` wrote the GIF at the figure's own dpi. `dpi=` is now forwarded to the raster and video writers, and any other keyword raises `TypeError` naming it. - **Closing an animated figure under matplotlib's notebook backend raised `AttributeError: 'NoneType' object has no attribute 'remove_callback'`.** `nbAgg` processes a figure's close event twice, so on Colab every displayed animation made the next static-plot cell fail. Animations are now a `FuncAnimation` subclass (`hypertools.plot.animate.HyperFuncAnimation`) whose `_stop` ignores the repeat call. -- **A plotly `save_path=` to a raster/PDF format on a machine without Chrome failed with kaleido's bare `RuntimeError`.** kaleido 1.x renders through a headless Chrome, which a fresh Colab or Kaggle kernel does not have. The failure is now a `HypertoolsIOError` naming the file, the cause, and the ways out: `import plotly.io as pio; pio.get_chrome()` (about 150 MB) plus, on Colab and Kaggle, the four system libraries the downloaded Chrome needs (`apt-get install -y libatk1.0-0 libatk-bridge2.0-0 libatspi2.0-0 libxcomposite1`), installing Chrome, or saving with `backend='matplotlib'`. +- **A plotly `save_path=` to a raster/PDF format on a machine without Chrome failed with kaleido's bare `RuntimeError`.** kaleido 1.x renders through a headless Chrome, which a fresh Colab or Kaggle kernel does not have. With installation on (the default), hypertools now fetches a Chrome on first use, plus the system libraries it needs where it can run `apt-get`. When that fails, or `hyp.set_autoinstall(False)` is in force, the failure is a `HypertoolsIOError` stating the cause and the ways out: `import plotly.io as pio; pio.get_chrome()` (about 150 MB) plus, on Colab and Kaggle, the four system libraries the downloaded Chrome needs (`apt-get install -y libatk1.0-0 libatk-bridge2.0-0 libatspi2.0-0 libxcomposite1`), installing Chrome, or saving with `backend='matplotlib'`. - **Under the plotly backend, a figure kept in a variable was not displayed in a notebook.** `fig = hyp.plot(x)` drew nothing (on Colab, where `backend='auto'` resolves to plotly, 29 of the feature tour's plot cells were blank) because the backend never called `fig.show()` inside IPython: doing so drew a figure that was also the cell's last expression twice, and mid-cell, ahead of matplotlib figures flushed at the end. `plot()` now queues the figure for a one-shot IPython `post_execute` callback that runs after matplotlib-inline's flush and skips any figure the rich-display hook already showed. The returned figure is still a `plotly.graph_objects.Figure` subclass. Both usages draw exactly once, in cell order. - **Inside IPython, a matplotlib backend that cannot be switched to raised the GUI toolkit's own error instead of `HypertoolsBackendError`.** The `%matplotlib` magic imports the toolkit itself, so `'TkAgg'` without `_tkinter` or `'GTK3Agg'` without `gi` escaped as a raw `ModuleNotFoundError` (a missing display as `TclError`), while a plain script already raised `HypertoolsBackendError`. The notebook path now raises `HypertoolsBackendError` too, with the toolkit's error chained as the cause. - **Drawing into a caller-supplied `ax=` warned `Glyph 8722 (MINUS SIGN) missing from font(s) Noto Sans` for every negative tick.** The bundled Noto Sans has no U+2212. hypertools' own axes carry the whole font stack, so matplotlib's per-glyph fallback reaches DejaVu Sans, but axes created outside hypertools' rc context keep the `sans-serif` alias, which resolves to one font with no fallback. hypertools now formats negatives with an ASCII minus (`axes.unicode_minus = False`) while it draws, and gives a caller-supplied axes' tick labels the same font list its own axes use, so a label formatted before the call (measured again by a later panel's layout) falls back to DejaVu for the glyph. @@ -166,29 +189,44 @@ Each of these turns previously accepted input into rejected input, or changes wh - **`animate='spin'`/`'window'`, `order='serial'`, and a per-dataset `title=` list together now raise immediately with an accurate message.** Previously the combination ran the whole pipeline, warned that `order='serial'` was ignored, and then raised `TypeError` advising `order='serial'`. The error now fires before the pipeline and names the reason (the style has no serial ordering to name segments by). - **Kalman forecasts no longer diverge from a near-saturated fit.** `hyp.predict(x, model='Kalman', t=...)` could return values up to 1e7 times the range of the data (19 of 432 fits on 40x3 drifting random walks exceeded 100x the data range), because the delay-embedded transition operator was estimated with nothing checking that it was non-explosive. - **A singleton-`hue=` warning now names the category the caller passed.** It read `hue category '_nolegend_' has only one observation ...` for any singleton run after the first of its category. The warning now reads the real per-run category names. - - **`alpha=` was ignored by `animate='morph'`.** The value landed on the per-dataset line artists, which a morph keeps hidden, and never on the one travelling point cloud that is drawn (plotly dropped it the same way), so fading the cloud meant reaching into the figure with `set_alpha` after the call. The cloud now takes the alpha on the same hold/transition schedule as its colour: a hold draws the held dataset's alpha, a transition eases from the departing dataset's alpha to the arriving one's, and a scalar `alpha=` is constant throughout. Plots that pass no `alpha=` are unchanged. - **An LSL stream left open logged a liblsl error at exit.** `hyp.io.lsl_stream()` only `close_stream()`ed its `pylsl.StreamInlet`, and only when the generator was closed, so a notebook or script that just moved on saw `ERR| Stream transmission broke off` at teardown. The stream is now an `LSLStream` that destroys the inlet on `close()`, on leaving a `with` block, on garbage collection, on the silent-source abort, and at interpreter exit. - **PPCA imputation warned `divide by zero encountered in log` on data with a few dozen or more features** (`hyp.impute(model='PPCA')` and the NaN fill `hyp.plot` applies at format time). The EM objective computed `log(det(Sx))`, which underflowed to `log(0)` and then fell back to a sign-flipped value; it now uses `slogdet`, identical to rounding wherever the old value was representable. - **`labels=` annotations were drawn on pyplot's current axes, not the `ax=` passed in**, so panel labels stacked on one axes; and a colour-list colorbar (`color=['red', ...]`) raised `IndexError`. - **`hyp.plot(docs, vectorizer='all-MiniLM-L6-v2')` crashed with the default `semantic=`/`corpus=`.** Pretrained embedding vectorizers now resolve the default semantic stage to none silently, never load or embed a corpus, and an explicit `semantic='NMF'` raises a clear hypertools `ValueError` before any corpus work. +## Fixed during the release review + +The 1.1.0 draft was reviewed against 1.0.0 before publication, and the fixes ship in 1.1.0. The full list is in the [changelog](https://github.com/ContextLab/hypertools/blob/v1.1.0/CHANGELOG.md)'s "Fixed during the release review" section; in summary: +- **Offline loading.** `hyp.load(..., offline=True)` never downloads and opens no network connection: built-in datasets are served only from a hash-valid local copy, and anything missing raises `HypertoolsOfflineError` naming the file. +- **Installer.** `hyp.set_autoinstall(False)` also reaches the worker process that renders plotly animation exports, and that export raises `ImportError` or `HypertoolsIOError` rather than a wrapped `RuntimeError`. Overlapping `with hyp.set_autoinstall(...)` blocks keep the newest setting in force. +- **Multi-panel plots.** `panels=` hands every per-dataset and per-forecast argument to the right panel, reuses the clustering it fitted instead of re-clustering each panel, picks each cell's projection from the analysed data, chooses its grid from the figure's aspect ratio, and returns the shared pipeline in the `return_model=True` bundle. On plotly, each panel keeps its own frame, legend, colorbar and title, and the grid is laid out like the matplotlib one. +- **Forecast display.** A legend lists every `predict=` forecast under its model's name (a single model was left out before); a collection of models keeps each dataset's colour and takes one linestyle per model; recoloured forecasts keep their trace's alpha; plotly honours a colour letter and markers in `forecast_fmt=`; repeated calls into one axes, figure or grid cell compose their forecasts, `truth=` overlays and legends. +- **Colours and layout.** `palette=` colour lists cycle as in 1.0.0, an empty palette raises `ValueError`, NaN in a continuous `hue=` no longer affects the colour range, a caller's `ax=` draws in the hypertools palette and a second call continues it, the 2-D frame square has a margin around the data, and a 3-D figure's axis labels stay inside a tight bounding box. +- **Streaming and text.** Streaming plots work when plotly is the render backend (the Colab and Kaggle default), and axis labels join the font-coverage scan, so non-Latin axis labels no longer draw as empty boxes. +- **Plotly rendering.** Hover labels name each trace (they read "trace 0"), and the leaves of a nested dataset list are named and grouped by their group. Markers sit on observations only and draw at matplotlib's size in 3-D; 3-D lines draw at full width; colours hold under mixed transparency; animated legends no longer grow entry by entry; an animated continuous hue stays on the moving data; date axes are not shifted by the viewer's time zone. +- **Surfaces.** A `surface=` takes its colour from the points nearest each vertex, so it matches the dots under it. +- **Animation.** Animated lines keep every observation instead of one row per frame; a morph shows its in-between frames; `on_frame=` titles stay on the canvas; companion panels use the right colour. +- **Forecasting.** Business-day data are no longer interpolated onto weekends; a fitted forecaster can be reused on a different kind of index; 1-D forecasts are drawn at the data's scale. +- **Explore mode.** `explore=True` hover labels open toward the centre of the axes, so a label near the edge of the window is not cut off. +- **Pipelines.** `sklearn.base.clone` works on a `hyp.Pipeline` and returns an unfitted, independent copy (it raised in 1.0 too). +- **Input handling and errors.** pandas and polars Series keep their index and name through the Manipulator classes and `hyp.Pipeline`; a repeated `metrics=` entry, `holdout=True` with `t=0`, `hyp.load(..., streaming=True)` on a non-Hugging-Face source and degenerate `alignment_score` input each raise a `ValueError` saying what is wrong; synthetic datasets accept more seed types; `hyp.text_windows` accepts NumPy integers; `text2mat` reads a flat list of strings as one dataset; `predict='ARIMA'` animates. ## Known limitations -- Ragged groups (unequal feature counts per group) are rejected by both entry points, by an error naming the missing and unexpected features. The error's remedy is spelled for `hyp.plot`; a `hyp.predict` caller groups with `group_columns(df, feature_correspondence='position')` and forecasts the leaves (`hyp.predict([leaf.to_numpy() for leaf in leaves], model, t)`). +- Ragged groups (unequal feature counts per group) are rejected by both entry points, by an error naming the missing and unexpected features. The error's remedy is spelled for `hyp.plot`; a `hyp.predict` caller groups with `group_columns(df, feature_correspondence='position')` (`from hypertools.core.hierarchy import group_columns`) and forecasts the leaves (`hyp.predict([leaf.to_numpy() for leaf in leaves], model, t)`). - Unequal-length row groups are averaged over their overlapping prefix, with one aggregated warning. - Feature correspondence across groups is established by name, so groups with disjoint innermost labels are refused rather than silently stacked. `feature_correspondence='position'` on `group_columns` is the opt-in, and it is not a positional hierarchy mode: passing its arrays to `hyp.plot` gives a plain list of datasets (no per-level means, no hierarchy styling, `trace_metadata` is `None`). There is no public `plot(feature_correspondence=...)` in 1.1. - The order of the groups is not neutralised the way the order of features within a group is. Groups become datasets, `reduce=` row-stacks every dataset and fits one model on the stack, so a reducer whose fit depends on row order embeds a block-reordered frame differently. On a 40-row frame of 4 sector blocks x 5 measures, reordering the blocks produced a different embedding under the default `IncrementalPCA` and under `TSNE`, while `PCA`, `TruncatedSVD`, `FactorAnalysis`, `Isomap` and `SpectralEmbedding` preserved it up to numerical and sign equivalence. `hyp.plot([A, B, C])` and `hyp.plot([C, B, A])` differ the same way, and did before 1.1. Pass `reduce='PCA'` when block order must not matter. - Continuous `hue=` over a row hierarchy is still warned-and-ignored; only column hierarchies honour it in 1.1. -- A forecast under a continuous `hue=` takes its source trajectory's final observed hue colour, in the animated case as well as the static one, on both backends. A categorical regrouping is unchanged: there the live forecast takes the colour of the run drawing the head. +- Under a categorical `hue=`/`cluster=` regrouping, an animated live forecast takes the colour of the run drawing the head, so its colour can change as the head crosses a category boundary. Under a continuous `hue=` a forecast takes its source trajectory's final observed hue colour, animated and static alike. - Duplicate innermost feature names inside one group are kept rather than rejected or de-duplicated, and matched across groups by `(label, occurrence)`. Rename the innermost level first if you need them distinguishable in a legend. - `predict=` needs at least 2 rows per plotted trace, on either axis. Over a row hierarchy this is the binding constraint: expansion draws one trace per unique full index tuple, so a frame whose innermost index level is unique per row cannot be forecast; flatten it (`df.reset_index(drop=True)`) or move the grouping to the column axis. Over a column hierarchy it applies only when the frame itself has a single row. ## Also in this release: the unpublished 1.0.1 changes -The changelog keeps a separate `1.0.1 (unreleased)` section and explains: "1.0.1 was never published on its own. These changes were developed as a patch release and now ship as part of 1.1.0, which is what `pyproject.toml` declares; they are kept in their own section because they are separable from the hierarchy work above." Every guide and docstring dates those behaviours to 1.1.0. Its features, changed behaviours and fixes are already merged into the sections above rather than repeated here. +The [changelog](https://github.com/ContextLab/hypertools/blob/v1.1.0/CHANGELOG.md) keeps a separate `1.0.1 (unreleased)` section and explains: "1.0.1 was never published on its own. These changes were developed as a patch release and now ship as part of 1.1.0, which is what `pyproject.toml` declares; they are kept in their own section because they are separable from the hierarchy work above." Every guide and docstring dates those behaviours to 1.1.0. Its features, changed behaviours and fixes are already merged into the sections above rather than repeated here. ## Links @@ -205,18 +243,23 @@ The changelog keeps a separate `1.0.1 (unreleased)` section and explains: "1.0.1 ## Upgrading from 1.0.0 - Lines are drawn smoothed by default. Pass `antialias=False` to get the 1.0.0 straight-segment rendering back. -- `predict=` overlays now take the observed trace's colour, linestyle and linewidth at half its alpha, instead of a dashed line at `alpha=0.6`. Use `forecast_fmt=`, `forecast_hue=` or `forecast_palette=` to restyle, and find forecast artists through `artist._hyp_forecast_role` / `trace.meta['hyp_forecast_role']` rather than their linestyle. +- `predict=` overlays now take the observed trace's colour, linestyle and linewidth at half its alpha, instead of a dashed line at `alpha=0.6` (a forecast recoloured by `forecast_hue=`, `forecast_cluster=` or `forecast_palette=` keeps the full alpha). Use `forecast_fmt=`, `forecast_hue=` or `forecast_palette=` to restyle, and find forecast artists through `artist._hyp_forecast_role` / `trace.meta['hyp_forecast_role']` rather than their linestyle. - Animated continuous-hue lines default to `linewidth=1.0` instead of `1.5`. Pass `linewidth=1.5` to keep the old look. - `hyp.predict` on a `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex` with duplicate stamps raises. Aggregate the repeats (`df.groupby(level=-1).mean()`) or give them distinct times. - `ax=` together with `animate=` raises. Drop `ax=` and style the returned animation's `.figure`. - `ax=` under the plotly backend now takes a plotly Figure to draw into (it used to ignore a matplotlib Axes and leave it empty; a matplotlib Axes now raises). Pass the Figure an earlier `hyp.plot` returned, or draw that call with `backend='matplotlib'`. - A frame with a `MultiIndex` on both axes raises. Keep one hierarchy. - A column-hierarchical frame inside a list raises in `hyp.plot`; a hierarchical frame inside a list raises in `hyp.predict` on either axis. Pass the frame bare. -- `hyp.predict` on a column hierarchy whose groups name different features raises. Rename the innermost level so every group carries the same labels, or use `group_columns(df, feature_correspondence='position')` and forecast the leaves. +- `hyp.predict` on a column hierarchy whose groups name different features raises. Rename the innermost level so every group carries the same labels, or use `group_columns(df, feature_correspondence='position')` (`from hypertools.core.hierarchy import group_columns`) and forecast the leaves. - A `pipeline=` fit on a column hierarchy is checked by feature name against the frame being plotted. Plot a frame whose groups carry the feature names the pipeline was fit on; `feature_correspondence='position'` is unaffected. - `forecast_hue=`, `forecast_cluster=`, `forecast_n_clusters=`, `forecast_palette=` and `forecast_fmt=` count plotted traces (leaves plus means) under a hierarchy, so supply one value per drawn trajectory. +- `hyp.describe(...)['average']` is now the mean of the per-dataset curves; use `['pooled']` for the stacked curve it used to hold. `hyp.describe(show=False)['fig']` is now the figure rather than `None`. +- Code that reads `scores.attrs['best']` or `['beats_baseline']` from `hyp.impute`/`hyp.predict` must allow `None`, and can read `attrs['incomplete']` for the models left out of the ranking. +- A `truth=` or `mask=` DataFrame for `hyp.impute` is matched to the data by label. Pass `.to_numpy()` to compare by position. +- `hyp.describe` no longer accepts an already fitted reducer or `reduce=None`. Pass a name, a `{'model': ..., 'kwargs': ...}` spec or an unfitted instance. +- In an animation, `truth=` points appear as the forecast reaches them, and default `forecast_hue=`/`forecast_cluster=` colours no longer repeat the observed lines' colours. Pass `forecast_palette=` to fix the colours. - `HyperAnimation.save()` now raises `TypeError` on keywords other than `fps=` and `dpi=` (it used to drop them silently). Pass `writer=` to delegate every keyword to matplotlib. -- A non-string `title=` outside a serial-style animation raises `TypeError`. Pass one string, or a list of strings (one per dataset) only for a serial-style animation. +- A `title=` that is neither a string nor a callable raises `TypeError`, and a list of strings is accepted only for a serial-style animation (one per dataset). Pass one string, a callable `ctx -> str`, or that list. - `animate='morph'` over more than 2000 points is downsampled to 2000 unless you set `morph_samples=`; pass `simplify=False` to get an error instead. - `hyp.load('wiki')`/`hyp.load('nips')` now return a flat list of strings. Replace `[str(p) for p in x[0].ravel()]` with the list itself. - Non-streaming Hugging Face loads now decode `ClassLabel` columns to their string names. Pass `decode_labels=False` to keep the integer codes. diff --git a/notes/release_review_2026-09-05.md b/notes/release_review_2026-09-05.md new file mode 100644 index 00000000..f2c22e9b --- /dev/null +++ b/notes/release_review_2026-09-05.md @@ -0,0 +1,54 @@ +# HyperTools 1.1 release review — 2026-09-05 + +Reviewed draft source: `96ac8b7f43c132f6f455ad1be3ffc98e84adead5` (master and v1.1.0 at review start). Changes are submitted on `fix/1.1-release-review`; master and the release tag are not modified. + +## Findings fixed in the PR + +| Priority | Finding | Fix and evidence | +| --- | --- | --- | +| High | Forecast backtests reused an instance fitted on dataset 1 for dataset 2. On a sine series followed by a positive quadratic series, `AutoRegressor()` produced negative quadratic forecasts and MAE 2551.68, versus 0.00246 when passing the class. | Deep-copy the model for each dataset; preserve caller state. Regression tests compare actual returned forecasts for class, instance, and dictionary forms. | +| High | Scoring accepted previously fitted forecasters/imputers even when their learned state could contain the held-out values. Imputation scoring also fitted caller-owned instances. | Require unfitted instances for scoring, copy them before fitting, and document the distinction from ordinary fitted-model replay. Real fitted/unfitted model tests cover both forms. | +| Medium | `Delay` silently lost features when distinct pandas column labels had identical string representations (`1` and `'1'`); a 2-column, 2-lag input produced only 2 output columns. | Reject colliding labels with a renaming instruction; tests include mixed-type and duplicate labels. | +| Medium | URL-cache temporary names used only the process ID. Threads caching the same URL collided: 82 of 100 concurrent writes failed with `FileNotFoundError`. | Use a unique temporary file per write, clean it on error, and atomically replace the destination. Test 100 real writes across 12 threads and verify payload/metadata and cleanup. | +| Medium | System-installed Noto Sans took precedence over the bundled Regular face, contradicting deterministic font selection and failing the existing font regression test on this machine. | Register bundled faces ahead of equal-scoring system faces. A fresh interpreter with another real same-family font proves the bundled file wins. | +| Medium | Optional dependency minimums allowed gensim 4.3 and scikit-image 0.22, predating NumPy 2 support despite the library requiring NumPy>=2. | Raise floors to gensim>=4.4.0 and scikit-image>=0.23.2 in extras/dev/docs. Real minimum-version feature tests pass under NumPy 2.3.5. | +| Documentation | Public plotting/predict/impute docstrings described shipped features as 1.2; dependency prose implied ARIMA imputation. | Correct version labels and separate forecasting from imputation support. | +| Documentation | The “convert now” forecast example still hand-wrote URL download/cache logic after the native cache landed. | Use `hyp.load(ARCHIVE, cache=True)`, regenerate and execute the tutorial. Its committed video remains byte-identical. | +| Tooling | The browser verifier expected `docs-notebooks/master`, searched highlighted HTML for contiguous `pip install`, and demanded an autoplay call in deliberately paused Plotly animations. | Validate versioned notebook links, rendered code text, loaded frames/play controls, and execute a real transition in Chromium. Allow evidence/build paths outside the checkout. | + +## Source and regression-test map + +- Forecast/imputation ownership: `hypertools/predict/backtest.py`, `hypertools/impute/backtest.py`; `tests/test_predict_backtest.py`, `tests/test_impute_backtest.py`. +- Cache atomicity: `hypertools/io/sources.py`; `tests/test_load_url_cache.py`. +- Delay collisions: `hypertools/manip/delay.py`; `tests/test_manip_delay.py`. +- Font precedence: `hypertools/plot/fonts.py`; `tests/test_fonts_bold.py`. +- Dependency compatibility: `pyproject.toml`, `docs/doc_requirements.txt`; real minimum-version runs of `tests/test_gensim_text.py` and `tests/test_density.py`, plus packaging/optional-import checks. +- Documentation: dispatcher docstrings, `docs/optional_dependencies.rst`, `readme.md`, `CHANGELOG.md`, `examples/animate_forecast.py`, and its executed tutorial notebook. +- Browser verification: `scripts/verify_docs_playwright.py`. + +## Review coverage and validation + +- Reviewed #284 and #285 bodies against code, tests, tutorial/example sources, API documentation, and release evidence. The implemented API choices include `Smooth(center=False)` (instead of the proposed conflicting `align=`), `alignment_score`, and matplotlib-only `companion=`; broader animated panels and launch-example visual rewrites remain the explicitly deferred scope. +- Combined behavioral suite: **4885 passed, 19 skipped, 2 deselected** in 15m45s, with the local LSL configuration below. The subsequent optional-floor changes affect metadata only and were checked separately against the actual minimum packages and rebuilt metadata. +- Focused behavioral/docstring checks: **94 passed**. Native-example/tutorial gates: **232 passed, 8 skipped**. +- An unrestricted earlier full run: **4862 passed, 19 skipped, 18 failed**. One failure was the bundled-font bug fixed above (that run had already imported the old code). The other 17 were LSL discovery on the host network. With isolated loopback discovery, all **63 LSL/audit tests passed, 1 skipped**. No LSL tests or assertions were weakened. +- The LSL run used a temporary `LSLAPICFG` with a private SessionID, `KnownPeers = {127.0.0.1}`, and machine-scoped discovery. These are the upstream-supported [LSL configuration settings](https://labstreaminglayer.readthedocs.io/info/lslapicfg.html); no user/global network settings were changed. +- A clean source-copy Sphinx build with `-W --keep-going` executed **51/51 gallery examples** and passed. Rebuilt again after updating the forecast example/tutorial. Post-build processing injected versioned Colab links and updated all 51 thumbnails. +- **166 HTML pages**, no missing internal file/anchor targets in the final post-processed build; **25 tutorial notebooks**, no saved error outputs. All **51 generated notebooks** passed the release-install checker. +- Real Chromium checks passed on **8 representative pages**: gallery, static plots, matplotlib videos, live Plotly animation, plot tutorial, and alignment tutorial. Screenshots were inspected for layout and nonblank plots. +- The revised forecast tutorial was freshly executed; launch clips and the forecast video are unchanged in the PR. The remaining tutorials were reviewed through stored outputs and gates, not all re-executed during this review. +- Release/packaging/native checks: **256 passed, 6 skipped** in the initial source checkout; the network-blocked manifest check was rerun unrestricted, yielding **10/10 release-readiness checks passed**. +- Optional minimums: gensim **4.4.0** and scikit-image **0.23.2** were installed into a temporary target directory, leaving the main environment dependencies unchanged. Real Word2Vec training and marching-cubes generation succeeded under NumPy **2.3.5**; the corresponding feature suites passed **79 tests, 1 skipped**. Upstream evidence: [gensim 4.4.0 adds NumPy 2 support](https://github.com/piskvorky/gensim/releases/tag/4.4.0), and [scikit-image 0.23 release notes describe NumPy 2 compatibility/builds](https://scikit-image.org/docs/stable/release_notes/release_0.23.html). The older floors could retain incompatible binary builds; latest-version CI did not test this boundary. +- Rebuilt metadata and optional-import tests: **20 passed** after refreshing the editable metadata without changing installed dependencies. +- Ruff and `git diff --check` passed. Existing matrix CI and tag CI on `96ac8b7f` were green when inspected. PR CI validates the new branch separately. +- Draft wheel and sdist package contents were compared byte-for-byte with the original release commit. All **110 package files** in each artifact match that commit; they do not contain this PR's fixes yet. + +## Issues and public-release disposition + +The PR resolves the remaining implementation/documentation defects discovered while checking #284 and #285. Both issues are linked for closure on merge, rather than closed before fixes reach master. + +Before public release, merge the PR and perform the normal release re-cut from the resulting commit: rebuild and republish the gallery/notebook manifest, rebuild wheel/sdist, update the draft assets/tag, and confirm the release/tag gates. The manifest intentionally pins an exact source commit. The already-green draft at `96ac8b7f` cannot stand in for these checks after the fixes merge. + +Large-download tests remain excluded by the project's default `not bigdata` marker. A passing test suite is not a guarantee that every third-party service, model, or platform combination is defect-free. + +Detailed local logs and browser evidence are under `/tmp/hypertools-review/` (not shipped in the package). diff --git a/notes/release_verification_2026-09-09.md b/notes/release_verification_2026-09-09.md new file mode 100644 index 00000000..ee8c6986 --- /dev/null +++ b/notes/release_verification_2026-09-09.md @@ -0,0 +1,95 @@ +# Release verification follow-up — September 9, 2026 + +## Authorization and recovery + +The user explicitly prohibits publication until manual sign-off. Do not merge +the PR, move the release tag, publish notebooks/assets/docs, or publish to +PyPI/GitHub without that sign-off. Read-only review and verification continue. +The user subsequently approved fixing everything found in this review, adding +the fixes to PR #286, and verifying the tests. + +Before this round's edits, the working tree was clean at committed and pushed +`c033e857054d831ead74898ea7e3667f1d0694b1`. That commit is the recovery point. +Earlier backups `0a2cc0d2` and `2e9669df` remain available. No release operation +has been performed in this round. + +## Additional finding and correction + +**Integer timestamp arithmetic can wrap or lose the intended interval.** +For a DataFrame with unsigned index `[0, 1, 3, 6, 7, 9, 13, 15]`, Kalman, +ARIMA and AutoRegressor rejected valid data with “step exceeds the observed +time span.” Subtracting the last timestamp from earlier unsigned timestamps +wrapped negative elapsed times into enormous positive values. Signed +differences spanning the dtype bounds and forecasts extending beyond the +integer dtype's maximum also overflowed. + +The correction computes integer differences/additions using Python integers +before converting elapsed coordinates to floating point. It preserves small +intervals at large epoch offsets and leaves datetime/timedelta arithmetic +unchanged. The interpolation policy and forecast model equations are unchanged. + +New real-model regressions compare signed/unsigned and nullable integer clocks, +epochs above the signed 64-bit limit, fitted reuse and held-out backtesting. +Independent boundary assertions check inferred gaps and future timestamps. +Against the previous package snapshot, **18 of the 31 new tests fail**; all +31 pass with the correction. The first attempted before-state invocation used +the root test path, which imported the working package; it was discarded and +rerun with the test copied inside the old isolated snapshot. + +## Verification + +- `/tmp/hypertools-integer-time-regression.log`: **229 passed**, covering + all forecasting unit tests, backtest timing, animated timing, and the new + integer-clock regressions. +- `/tmp/hypertools-integer-time-before.log`: **18 failed, 13 passed** against + the unmodified `c033e857` package; confirms the new tests catch the defect. +- `/tmp/hypertools-review-bigdata-20260909.log`: **2 passed, 41 deselected**. + Both tests normally excluded by the `bigdata` marker ran successfully: + the live 476 MB Google Drive interstitial download and the real weights + dataset/UMAP story-trajectory workflow. Run against the prior verified + snapshot; neither workflow uses forecast timestamp arithmetic. +- Full-suite verification of the corrected source: **5916 passed, 19 skipped, + 2 deselected, 297 warnings in 18:16**. The isolated snapshot is recorded by + `/tmp/hypertools-integer-verify-path`; hashes are in + `/tmp/hypertools-integer-verify-manifest.json`. Log: + `/tmp/hypertools-integer-full-suite.log`; JUnit: + `/tmp/hypertools-integer-full-suite.xml`. Final source, tests and documentation + hashes match the working tree. This new evidence note was added after the + snapshot. The two deselected large-data tests passed separately above; + the 19 skips retain the prior documented release/CI/platform/smoke reasons. +- Minimum pandas 2.2.2 verification: **100 passed**; log: + `/tmp/hypertools-integer-pandas-floor.log`. +- Updated documentation doctests: **323 passed**, zero failures, build + succeeded with warnings treated as errors; log: + `/tmp/hypertools-integer-doctest.log`. +- Explicit release file-content gates: **15 passed, 1 deselected** with + `HYPERTOOLS_REQUIRE_RELEASE=1`; log: + `/tmp/hypertools-release-file-gates-20260909.log`. The excluded test ties the + published gallery to the final release commit and cannot pass against this + unpublished PR; it remains a required post-approval release operation. +- Ruff and `git diff --check` pass. + +The correction and this record are being committed separately and added to +PR #286 as authorized. The new commit needs its own hosted CI result; the +baseline run below must not be treated as CI evidence for the new commit. + +## Hosted CI and release provenance + +CI run [34308363422](https://github.com/ContextLab/hypertools/actions/runs/34308363422) +tests `c033e857`, not the additional integer-clock fix. All ordinary test runs +on Windows/macOS/Ubuntu Python 3.10–3.13 passed. The extra pandas 3 acceptance +run passed; the Ubuntu 3.12 coverage run is still pending completion. Wheel/ +sdist smoke, clean docs, dataset and strict live-source gates passed. The +release-only gate is intentionally skipped on PR branches. + +Read-only remote checks confirm that master and the peeled `v1.1.0` tag remain +at `96ac8b7f43c132f6f455ad1be3ffc98e84adead5`. The gallery manifest still lists +51 notebooks with that same older `source_commit`. The GitHub v1.1.0 release +is still a draft, with wheel/sdist assets uploaded September 5. These are +draft provenance, not artifacts of the reviewed PR. + +After the fixes and their final CI are green, the remaining operations are in +`RELEASE_CHECKLIST.md`: manual sign-off, merge, finalize the release date, +regenerate the gallery and artifacts from the exact final commit, verify +master/tag release gates, then approved publication and distribution smoke +checks. Do not publish or move references merely because verification passes. diff --git a/notes/session_2026-09-05_release-1.1-review.md b/notes/session_2026-09-05_release-1.1-review.md new file mode 100644 index 00000000..a15324d1 --- /dev/null +++ b/notes/session_2026-09-05_release-1.1-review.md @@ -0,0 +1,245 @@ +# Session 2026-09-05 (late): 1.1.0 release review against v1.0.0 + +Branch: `fix/1.1-release-review` (PR #286 -> master; PR body is a full prior +review report; it "Closes #284, #285" on merge). Started at b3eafe5c. + +## Done this session +- Ultrareview (cloud) of PR #286: ONE nit -- `from .common import Forecaster/Imputer` + inside the scoring loops of `hypertools/predict/backtest.py` and + `hypertools/impute/backtest.py`. Fixed (hoisted to module scope) in f116dd7e; + `tests/test_predict_backtest.py tests/test_impute_backtest.py` 58 passed; ruff clean. +- Issues #284 and #285 confirmed OPEN; every checkbox ticked except #284 D: + "CI green; v1.1.0 moved; tag CI green; draft release updated" (unchecked -- + it depends on the PR merge + release re-cut; see PR body's "disposition"). + +## In flight (9 read-only background verifiers, dispatched ~23:55) +1. #284 A+B (examples de-dup, native rewrites) +2. #284 C+D (tutorials, gate, release state) + "found while fixing" +3. #285 Bugs/Plotting/Animation items +4. #285 Text/Data/Forecasting/Adjacent/Convert-now items +5. Documentation review (CHANGELOG vs API diff, docstrings, docs/*.rst, links, sphinx -W build, notebooks) +6. Code review: hypertools/plot/ diff v1.0.0..HEAD +7. Code review: io/tools/manip/_shared diff +8. Code review: predict/impute/align/reduce/cluster/core diff +9. Packaging/CI/tests/scripts review + wheel smoke test + +## Next +- Collect findings -> fix every confirmed defect (dispatch fixers; tests real, no mocks) +- Re-run ALL checks after fixes (full pytest, ruff, sphinx -W) +- Commit, push branch, wait for PR CI, then: Jeremy merges PR #286; issues close on merge + (or close manually with a closing comment if the PR is merged without the keywords). +- After merge: release re-cut from the merge commit (gallery manifest, wheel/sdist, + move v1.1.0 tag, draft release assets) per RELEASE_CHECKLIST.md. + +## Findings log (to fix once all verifiers report) +### From verifier 1 (#284 A+B): all VERIFIED except one PARTIAL +- P1 gate: tests/test_examples_are_native.py BUDGETS (:100-140) only scans 6 launch scripts + 6 notebooks, so the new DEFECT_MARKERS never scan plot_digits.py etc.; no `ax=` marker / PRIVATE_API_EXCEPTIONS={} (:186) -> the "per-file allowlist for deliberate ax= demos" does not exist. +- The `find_spec\(` marker would flag the legitimate Colab-install guard in 8 tutorials if widened. +- docs/tutorials/analyze.ipynb:338 + plot.ipynb:930 still present 'hyper'/'HyperAlign' as interchangeable ('hyper' is a DeprecationWarning alias, align.py:52-54). +- docs/tutorials/manip.ipynb never mentions Normalize(mode='isotropic'). +- docs/conf.py:459 stale comment citing chemtrails/precog. +- conversation_shape/painting_embeddings install cells still `%pip install -q sentence-transformers` (redundant, harmless). +### From verifier 2 (#284 C+D): VERIFIED except +- align.ipynb "local image": the alignment illustration was DROPPED (73874cc8), not localized; docs/tutorials/img/alignment.png is unreferenced. +- Stored notebook outputs leak /Users/jmanning paths: plot.ipynb code cell 20 stderr (format_data.py:495 UserWarning), projectile_kalman.ipynb cell 1 (~/.hypertools_cache), conversation_trajectories.ipynb cell 2 (~/.convokit). +- EXPECTED_VISIBLE_OUTPUTS gate (test_examples_are_native.py:1130-1151) covers only 5 launch notebooks + animate_forecast; the 8 rebuilt tutorials (hierarchy/io/pipelines/manip/plot/align/analyze/reduce) have no output-cell entry. +- Release state: master == v1.1.0 == 96ac8b7f (local+remote), master CI run 33967006516 all 17 green, tag run green, draft release exists (wheel+sdist). PR #286 runs on 569a3089/b3eafe5c were in_progress; f116dd7e unpushed. +- Test count at HEAD: 4904 collected (issue text says 4118). +### From verifier 9 (packaging/CI/tests): wheel+sdist clean, fresh-venv smoke OK, ruff clean, no secrets +- HIGH (process): tests/test_release_readiness_gate.py:409 fails with HYPERTOOLS_REQUIRE_RELEASE=1 on HEAD (manifest source_commit 96ac8b7f != HEAD). Expected until the re-cut: republish gallery from the final commit AND move the pushed v1.1.0 tag. RELEASE_CHECKLIST.md covers neither (line 5 says the tag does not exist yet). +- MED: RELEASE_CHECKLIST.md stale (lines 5, 14-21, 39-43 describe dev-1.0->master PR; "3700+ passed"); CHANGELOG 1.1.0 date 2026-09-04 predates post-tag commits; HYPERTOOLS_EXAMPLE_SMOKE=1 gate (test_examples_are_native.py:1251) is in no CI job and not on the checklist. +- MED (no-mocks rule): tests/predict/test_common.py:179 monkeypatch spy on _infer_step; tests/predict/test_predict_multiindex.py:202-203 wraps group_columns/group_rows_for_forecast; tests/test_names_display.py:96-175 replaces pio.show/go.Figure.show with counting lambdas and IPython.get_ipython with _FakeShell (new since 1.0). +- LOW: git diff --check fails: trailing whitespace in docs/_static/pipeline_order.svg, docs/hypertools.FrameContext.rst, docs/hypertools.io.LSLStream.rst, docs/superpowers/plans/2026-07-28-...md:163, notes/audit/review_plan3_v3.md:117, notes/audit/review_plan4_v2.md:61. +- LOW: scripts/generate_baseline_screenshots.py:9 references notes/hypertools_1.0_roadmap.md (missing); /tmp defaults in generate_marker_parity_evidence.py:32,39 and audit_gallery_backends.py:94. +- INFO: ipympl and numba declared core deps but never imported (numba pinned for umap on purpose). +### From verifier 3 (#285 bugs/plotting/animation): ALL VERIFIED (367 focused tests pass) +- Doc nit: hyp.plot `font=` docstring says nothing about weights / the bundled NotoSans-Bold face (fonts.py:58-65 + CHANGELOG do). +- Doc nit: HyperAnimation.drawn_extent documented only on the class (hyper_animation.py:140), not referenced from the hyp.plot docstring. +- Pre-existing quirk: low-level hypertools.tools.text2mat.text2mat(list_of_3_strings, ...) returns [(3,d),(0,d),(0,d)] for CountVectorizer and HF vectorizers (hyp.plot wraps correctly). Investigate. +### From verifier 8 (predict/impute/align/core): numerics + ownership + validation all clean; findings: +- LOW: predict/backtest.py:91-113 resolve_metrics accepts metrics=['mae','mae'] -> TypeError deep in build_scores (:276-278) for predict(holdout=) and impute(truth=). Fix: ValueError naming the duplicate. +- LOW: align/align.py:163-178 _compute_score: hyp.align([a(50,4), b(40,4)], return_score=True) raises ValueError telling the user to run hyp.align; plain hyp.align works. Fix: score the trimmed before-data (`raw` already equal shape); document. +- COSMETIC: predict/backtest.py:332-333 `holdout=True, t=0` message says "got True" (should name t). +- NOTE: backtest.py:264 `unscored` warning uses default stacklevel (others use external_stacklevel()). +### Own check: hypertools.tools.text2mat (PUBLIC: docs/api.rst:202, tools/__init__ __all__) with a flat list of N strings returns [(N,d),(0,d),(0,d)...]: _transform splits at len(str) (character counts). Pre-existing in v1.0.0 (same _transform). Fix: treat a flat list of str as ONE dataset (wrap) + test. +### From verifier 4 (#285 text/data/forecast/adjacent): VERIFIED (272 focused tests pass); findings: +- BUG (new in 1.1; 1.0 raised NotImplementedError for animate+predict): hyp.plot(hyp.load('random_walk', n_samples=100, n_features=3, random_state=0), predict='ARIMA', t=10, animate=True, show=False) -> IndexError from statsmodels via plot/forecast.py:390 -> predict/arima.py:111 (first revealed history has 2 rows, DEFAULT_MIN_HISTORY=2; ARIMA order (1,1,1) needs >=3). Existing test dodges it with duration=1, frame_rate=4. +- hyp.load('lorenz', streaming=True) silently returns the full array (streaming= documented HF-only); `dim=` TypeError. Proposal's streaming generator not implemented (fine) but the silent ignore should warn/raise. +- `baseline=` is not a kwarg on predict(holdout=) (naive row is always present, per CHANGELOG): passing it -> TypeError from the model ctor. Acceptable API; note in the issue closure. +- HypertoolsOfflineError lives only at hypertools.io.sources: not exported from hyp / hyp.io, not in docs/api.rst (the other three exceptions are). +- text.ipynb code cell 5 (issue "cell 6") hue half not converted (hue=hue, labels=hue over dog+cat+bball) -- issue said convert the hue half to legend=. +- stack signature: stack(frames, names=None, level_names=None, aggregate=None) (issue spelled level names as names=). +- synthetic_outlet returns SyntheticOutlet (.stop/.thread/.closed), not the proposed tuple. Fine. +### From plot sub-reviewer (animation ctx/morph/fade/companion): all reproduced +- MED: dataset_fade= (and any on_frame artist mutation) silent no-op with continuous hue= on matplotlib: plot.py:1969-1993 _make_dataset_fade_updater; record(artists=...) sites matplotlib_backend.py:1609/1808/2543/2610 hold the HIDDEN Line2D/Line3D heads (_apply_multicolor_animation plot.py:11034 draws LineCollections). Pixels identical with/without fade. Plotly correct. Fix: expose the LineCollections in ctx.artists (and fade them) or raise. +- MED: loop=True + per-segment rotations= list can never validate: plot.py:8229 checks len vs sum(morph_tags) (3 clouds -> 5) but matplotlib_backend.py:2450-2453 re-validates on the looped list (-> 7). Both 5 and 7 entries rejected. +- MED: companion= head jumps backwards under order='serial' with several datasets: plot.py:1841-1847 _title_head_row rescales the CURRENT dataset's reveal onto the panel's rows (head x 0->20->10->0->39). Same rule drives {index} titles. +- LOW: window_bounds.start always 0 on serial reveals when a trail flag moved the head (matplotlib_backend.py:1816, 2617 hardcode (0,c)); docstring animation_context.py:198-205 promises >0. +- LOW: raw internal errors: companion=lambda -> TypeError 'function' not iterable (plot.py:2021); companion={'data':..,'smooth':'a'} -> int() ValueError (plot.py:2080); dataset_fade=('a','b') -> float() ValueError (plot.py:1929). +- LOW: raising on_frame during .save('x.gif') surfaces as IndexError from _RealTimePillowWriter.finish (animate.py:96). +- LOW: title= callable raising inside plot() -> orphaned 'Animation was deleted without rendering anything' warning on gc. +### From plot sub-reviewer (titles/fonts/labels): all reproduced +- MED: title_wrap ignored for dynamic (callable/pattern) titles under animation (plot.py:9139-9145 applies str(_fn(ctx)) unwrapped); docstring 3628 promises wrapping. +- MED: title_wrap destroys explicit newlines (plot.py:1506-1519 textwrap.wrap default replace_whitespace): 'a\nb' -> 'a b'. +- MED: plotly renders '\n' titles on one line (plotly_backend.py:1846 passes raw text); docstring 3560 promises identical rendering. title_wrap does convert to <br>; explicit newlines are not. +- LOW: nested-TUPLE labels= drawn as literal tuple text (validator plot.py:775 accepts tuples; flatteners matplotlib_backend.py:1222,1235 / plotly_backend.py:323 test only list). +- LOW: static '{index:%B %Y}' title with no index drawn verbatim (plot.py:1798-1801); docstring 3574 promises ValueError. +- DOC: plot.py:3633 stale ("animated 3-D title still reserves top margin for ONE line") -- matplotlib reserves per line now; plotly stays margin.t=40 (undocumented parity gap; 3609 "size sizes the probe" untrue on plotly). +- LOW: dynamic 3-D title that grows lines after frame 0 clips (plot.py:9817-9827 measures frame 0 only). +- LOW list: callable returning None/int drawn as 'None'/'42' (9140); serial list [1,None] -> 'None' (866); title_color='blue' silently loses to title_kwargs color (5799); label_anchor='bogus' with labels=None accepted (2385); labels='only' str counts chars; pattern format errors surface as bare KeyError/ValueError never naming title=. +### From plot sub-reviewer (panels/axis_scale/series/truth/predict): all reproduced +- HIGH: predict='ARIMA' + time-progressing animate= crashes (plot.py:8835 -> forecast.py:390 -> arima.py:111; 2-row history). Also predict=['Kalman','ARIMA'], animate=True. (Same as verifier 4's bug.) +- MED: t= datetime-like never works in plot() (plot.py:7005 hands _predictor bare ndarrays): ValueError "got t=Timestamp on a RangeIndex"; docstring says int or datetime-like. +- MED: panels= + truth= impossible: shared probe (plot.py:2588) sets predict=None but keeps truth; panel_fit='independent' never narrows truth list. +- MED: panels= shared + ndims=1 loses DataFrame index/column names; 3-col frames with reduce=None raise "zlabel= is not supported for 2-D data" (plot.py:2633-2641). +- MED: panels= + nested per-dataset hue=[arr30,arr40]: shared -> "hue has 1 entry but 30 observations" (plot.py:2612-2615); independent -> hue/labels not narrowed (plot.py:2603-2610). +- LOW: panels= + ndims>3: mpl ValueError ndims must be 1,2,3 (plot.py:2192); plotly scene/scatter error (plot.py:2721). Docstring says ndims>3 draws in 3-D. +- LOW: panel path skips _normalize_save_path: save_path='~/x.png' FileNotFoundError (plot.py:2665); pathlib.Path under plotly AttributeError (plot.py:2755). +- LOW: ndims=1 multi-column fmt=['-',':','--'] rejected (plot.py:6551 checks len(raw) before series expansion). +- LOW: return_model in series mode: predict['forecasts'] is per-COLUMN [x,value] arrays; docstring says one per input dataset matching hyp.predict. +- LOW: xlim on a date axis (ndims=1): floats = day numbers on mpl vs epoch-ms on plotly; strings work on plotly, TypeError on mpl. Document. +- LOW: forecast_hue=['g1','g2'] with predict=['Kalman','ARIMA'] on 2 datasets -> "got 2 for 4 forecast(s)"; docstring unclear. +- LOW: ylabel auto-set to 'dataset 1' when default reduce collapses a named 3-col frame (plot.py:6864-6866). +- Unconfirmed: hyp.subplots() 3-D axes accept plot(ndims=2, ax=) silently; TimedeltaIndex under ndims=1 draws raw ns. +### From verifier 7 (io/tools/manip): all reproduced +- HIGH: offline=True is not offline: load.py:652 calls seaborn_dataset(dataset) for every non-builtin string BEFORE load_source looks at the cache; sources.py:264-288 sns.get_dataset_names() urlopen with no timeout, cache stays None on failure (:283) so it retries every call. Blackholed proxy: 75 s block; proxy log shows CONNECT raw.githubusercontent.com on every offline=True call. Fix: honour offline/cache before any network probe (check URL-cache first; skip seaborn probe for URLs; timeout + remember failure). +- MED: yahoo: bars dated one day early east of UTC (sources.py:1134 normalises UTC timestamp; BHP.AX raw 23:00 UTC). Fix: add meta['gmtoffset'] before normalize(). +- MED: synthetic random_state=np.random.RandomState(0) crashes numpy-native datasets (sources.py:542 .bit_generator); docstrings list RandomState. +- MED: synthetic 'blobs'/'moons'/... with Generator/SeedSequence and n_datasets==1 -> sklearn InvalidParameterError (sources.py:819 passes through); works for n_datasets=2 (ints derived). +- LOW: text2mat.py:383 isinstance(semantic, str) guard: gensim vectorizer + dict-spec semantic {'model':'NMF',...} bypasses skip/warn -> raw sklearn NMF ValueError. +- LOW: reusing one SeedSequence for n_datasets>1 not reproducible (sources.py:830 spawns children, mutating it). Document or derive without mutating. +- LOW: n_datasets=2.7 silently truncated (sources.py:808). +- LOW: text_windows.py:39 rejects np.int64 size=. +- Unconfirmed/doc: Smooth(center=False) default min_periods inside hyp.plot(manip=) gives the misleading all-NaN-rows PPCA error; Manipulator.fit() returns None (sklearn convention self); sec dedupe across units lossy; wikipedia lang= unescaped into host; load() docstring sec kwargs omit taxonomy/unit/dedupe and "cache/offline cover steps 9-13" but HF (step 9) is not cached. +### From plot sub-reviewer (hue/palette/legend/colors): all reproduced +- HIGH (regression vs 1.0): hyp.plot([a,b,c], palette=['red','blue']) with no hue raises "palette= supplies 2 color(s) but 3 are required" (plot.py:10325 _build_colors_info -> dataset_colors -> colors.py:715); v1.0.0 cycled red/blue/red on both backends. Fix: cycle. +- MED: palette=[] (or ()/np.array([])) with categorical hue escapes as bare StopIteration (plot.py:539 -> sns.color_palette([], n)). +- MED: per-dataset palette list + categorical hue / n_clusters: wrong count in the error (plot.py:536 _seaborn_palette_arg(palette, len(drawn)): says 2 datasets when 3 passed, 26 with n_clusters=3); the docstring's per-entry {category: color} dict form is unusable. +- MED (pre-existing): NaN in continuous hue poisons vmin/vmax (plot.py:10180 np.min over non-finite) -> bundle['colors'] vmin/vmax NaN, colorbar spans -0.1..0.1; docstring says NaNs excluded. +- LOW: legend_kwargs={'fontsize':N} ignored whenever font= set (matplotlib_backend.py:311-317 prop=font beats fontsize). +- LOW: bundle['colors']['categories'] not RGB for blend kind with legend_colors (plot.py:10318). +- LOW: nested hue with one mismatched sub-list -> misleading "hue has 3 entries but 36 observations" (plot.py:7794). +- LOW doc: hue docstring (~3340) says integer ids legend-labeled in sorted order; line fmt draws first-appearance order, fmt='o' sorts. + +## Progress (2026-09-06 ~01:30) +- f1a1e091: predict/align fixes (fixer A) + RELEASE_CHECKLIST rewrite + this note. +- e47968f5: spy tests rewritten (fixer B); HypertoolsOfflineError exported (hyp + hyp.io + API pin test); script ref; whitespace. +- In flight: P1 (plot animation/title/colour fixes; owns hypertools/plot/* + their tests), IO fixer (offline/yahoo/synthetic seeds/text2mat/text_windows + streaming= item), gate fixer (tests/test_examples_are_native.py), docs reviewer (sphinx -W build). +- Queued: P2 wave after P1 = panels/series/predict findings + ARIMA min-history (owns plot.py etc. + plot/forecast.py + predict/arima.py + predict/common.py); docs wave after docs reviewer = CHANGELOG lines from all fixers, docs/api.rst (HypertoolsOfflineError), conf.py stale comment, notebook prose ('hyper' deprecated in analyze/plot.ipynb; isotropic in manip.ipynb; align.ipynb image; text.ipynb hue half), hyp.plot font=/drawn_extent docstring nits; strip /Users/ paths from stored outputs (scripts/execute_tutorial.py post-process) then re-execute plot/projectile_kalman/conversation_trajectories (+ any notebook whose behaviour changed) LAST. +- Final: full pytest, ruff, sphinx -W, git diff --check, commit, push, PR #286 CI; then issue-closure comments. +### From the parent plot reviewer (own findings, for wave P2) +- MED: predict= list/dict on a column/row-MultiIndex frame -> internal "hierarchy trace/bundle_forecasts mismatch: 6 traces but 1 bundle_forecasts" (plot.py:7046-7050 / 7354 / 8636); predict='Kalman' works; docstring 4026-4040 + CHANGELOG say collections work with hierarchies. +- MED: column-MultiIndex frame with DatetimeIndex in ndims=1 -> "some datasets carry a DatetimeIndex and others do not" (plot.py:5398 _capture_row_indices runs before group_columns, only leaf 0 keeps the index; 6835). +- LOW: _plot_panels_plotly (plot.py:2757) calls fig.show() directly and returns a bare go.Figure, bypassing the HyperPlotlyFigure one-shot display queue -> 2 outputs in a notebook cell; show= docstring promises once at cell end. +### From the docs reviewer: sphinx -W full gallery = 0 warnings; 39/41 links 200 (RTD latest optional_dependencies 404 until RTD rebuilds; one extractor artifact) +- MED: CHANGELOG.md:281 names HypertoolsOfflineError (now exported in e47968f5) -- add to docs/api.rst Exceptions (:235-240; also omits HypertoolsTrustError). +- MED: readme.md Requirements floors contradict pyproject: scikit-learn>=1.4.0 (1.4.2), pandas>=2.2.0 (2.2.2), matplotlib>=3.8.0 (3.9.0), numba>=0.59 (0.61.0); pillow>=8 unlisted. +- MED: CHANGELOG.md:255 "All 138 of its examples are executed by the test suite" is unsupported (hierarchy.rst literal blocks are not exec'd; tests only check text). Reword. +- LOW: CHANGELOG date 2026-09-04 (re-cut date); "## 1.0.1 (unreleased)" heading (:781) inside a released changelog. +- LOW: furo "View this page" 404s on gallery pages (auto_examples gitignored; conf.py:273 source_directory). Fix: disable the edit link for auto_examples or point it at examples/. +- LOW: docs/tutorials.rst:203 + market_sectors.ipynb prose say hyp.align(..., align='HyperAlign') (undocumented alias); code uses model=. Make prose match. +- LOW: readme "Try it!" says "predate the 1.0 API" / "every 1.0 feature" -> 1.x. +- LOW: CHANGELOG never mentions hyp.predict(metrics=), hyp.impute(return_imputed=), hyp.load(**source_kwargs). +- LOW: plot.ipynb cell 40 stderr leaks /Users/jmanning path (format_data.py:495 UserWarning). +- NIT: HyperAnimation.save docstring internal "QC 2026-07" note; drawn_extent(frames, threshold) threshold undocumented. +- NIT: wikipedia_embeddings.ipynb cells 13-14 still %pip install wikipedia-api + wikipediaapi instead of hyp.load('wikipedia:...'). + +## Progress (2026-09-06 ~01:00) +Commits since f1a1e091: e47968f5 (spy tests, OfflineError export), a59f2e2c (gate widened), ef887cab (plot P1 + IO fixes), 1be634dd (fit returns self), f0e56c9c (docs wave), 223d4682 (lsl docstring), 384fe999 (plot P2 + CHANGELOG). Tree clean. +ALL review findings fixed. Final verification pipeline launched detached (scratchpad/final_verify.sh -> final_verify.log): re-execute 8 notebooks (analyze, plot, manip, align, text, wikipedia_embeddings, projectile_kalman, conversation_trajectories; 4 parallel) -> /Users/ leak scan -> full pytest (LSLAPICFG loopback cfg in scratchpad) -> ruff + diff --check -> sphinx -W full gallery -> HYPERTOOLS_EXAMPLE_SMOKE gate. +Next after green: commit re-executed notebooks + note, push branch, watch PR #286 CI, then post issue-closure comments on #284/#285 (they close on merge via the PR body). + +## Final verification (2026-09-06 00:58-01:48) -- ALL GREEN +- 8 notebooks re-executed (exit 0 each; no /Users/ path left in any tutorial; editable install intact). +- Full pytest: 5171 passed, 2 failed (both stale expectations from this session's own edits: manip gate set + nested-hue message; fixed in 00e5a00b), 19 skipped, 2 deselected; LSLAPICFG loopback cfg used. +- ruff: clean. git diff --check v1.0.0..HEAD: clean after the EOF-blank-line fix (the two autosummary stubs FrameContext/LSLStream get whitespace-regenerated by every sphinx build; git checkout them after builds). +- sphinx -W --keep-going full gallery (51 examples): exit 0, 0 WARNING lines. +- HYPERTOOLS_EXAMPLE_SMOKE=1 gate: 344 passed. +- Pushed fix/1.1-release-review @ f775b371 (13 commits ahead of master). PR #286 CI in flight. +## Next +- When PR CI is green: post the verification comments (scratchpad/comment_284.md, comment_285.md) on #284/#285 and a summary comment on PR #286; the issues close on merge. +- Jeremy: merge #286, then the re-cut per RELEASE_CHECKLIST.md (CHANGELOG date, gallery republish from the merge commit, move v1.1.0 tag, replace draft assets, PyPI, RTD, conda-forge). + +## END STATE (2026-09-06 ~03:40) +- Branch fix/1.1-release-review head d20fdde0 (17 commits ahead of master); PR #286 CI run 34016744841: 16 success + release-gate skipped. Windows-only failures on f775b371 (scanner path separators, cache os.replace) fixed in 9340300d and verified green on Windows. +- Comments posted: #284 (issuecomment-5557791830), #285 (issuecomment-5557791988), PR #286 (issuecomment-5557792124). Issues close on merge via the PR body. +- NEXT (Jeremy): merge #286 -> re-cut per RELEASE_CHECKLIST.md (CHANGELOG date, gallery republish from the merge commit, move v1.1.0 tag, replace draft release assets, twine upload, RTD, conda-forge, Bluesky). + +## Follow-up from the feature tour (2026-09-06 ~10:00) +- 9.2: 's--' legend handle showed only the dashes (backend splits marker+line fmt into a line artist + a _nolegend_ marker artist). Fixed in 45fe7799: line artist carries marker with markevery=[]; pixel-level test tests/test_plot_fmt_split_legend.py; plotly and animated paths were already right. Plot+animation suites 2121 passed. +- 9.3: mixture hue blends correctly (50/50 weights -> exact midpoint); the tour's blobs were too separated (GMM memberships all > 0.99). Tour cell 76 (gitignored notebook) now uses cluster_std=2.5 blobs (20 soft points, 71 visibly blended dots) and was re-executed (65/67 cells output, 0 errors). Shipped docs carry no such demo. +- 9.8 (panels): four causes fixed in abe9295d -- panels=True grid now aspect-aware/no-hole (_auto_panel_grid); return_model=True reuses analyze()'s fitted pipeline (+ appended fitted cluster step) instead of refitting (UMAP/Isomap fit+warn once); seeded UMAP passes n_jobs=1; Isomap fit silences scipy SparseEfficiencyWarning. 17 new tests; test_plot_panels.py updated to the new grid rule. Tour re-executed: 9.8 shows 1x3 grids and one sklearn data warning (0 errors). +- 9.11 (truth= legend): TRUTH_STYLE splits the markers onto a separate artist, so the 'truth' legend glyph was a bare solid line identical to the observed entry. Fix: the truth curve keeps marker='o' with markevery=[] (same trick as 45fe7799); test test_the_truth_legend_handle_carries_its_markers. Tour cell 92 now names the trace 'observed' and adds forecast_fmt=':' (single-model forecasts share their trace's legend entry BY DESIGN -- unchanged). Plotly's truth trace was already mode='lines+markers'. +- 9.14 (2-D density cut off): kde_grid_2d spanned only the dataset's own 15%-padded box, so a wide flat cloud's glow stopped in a hard band inside the unit frame. Fix: new density.scene_bounds_2d (union of every dataset's padded box + the frame square under axis_scale='unit'); kde_grid_2d(bounds=); both backends thread it. Tests: TestDensityGridSpansTheScene (mpl unit, mpl data-scale, plotly). Tutorials with truth= stored outputs (projectile_kalman, stock_forecasting) re-executed. +- Animations choppy: the tour set frame_rate=10 everywhere (library default 30); dropped the overrides (cells 104-118, prose 103; save fps=30). Gallery examples run 15-30 fps by design (size-bound), flagged to Jeremy rather than changed. +- skillnote n1685346725x318: marker-splitting draw paths must keep marker= on the line artist (markevery=[]) for the legend glyph -- the same bug twice this session. + +## Panels backend parity (2026-09-06 evening, Jeremy: "why are panels matplotlib only?") +- Finding: static `panels=` already ran on plotly via make_subplots (`_plot_panels_plotly`); the tour's 9.8 note ("plotly path drops return_model") was stale. Real gaps (rendered both backends side by side, scratchpad/agentQ/parity_matrix.py): 2-D cells lost the unit frame/ticks/axis labels; legends merged figure-wide with duplicates; per-panel colorbars stacked (plotly AND matplotlib); no plotly form of hyp.subplots()+ax=. +- Fixes: plotly_backend.transplant_panel (traces + 2-D axis layout + shapes/annotations re-referenced + per-panel legendN + per-panel colorbar + camera back-off for narrow 3-D cells, SCENE_CUBE_WIDTH_PER_HEIGHT=1.4 measured), make_panel_grid (gutter reserved beside cells; default width widened by the gutters, explicit size= verbatim), PlotlyCell + hyp.subplots(backend='plotly') + ax=<cell> (cell titles as annotations; top margin widened; end-of-cell display queue dedups by figure identity), matplotlib _add_colorbar(attached=True) uses fig.colorbar(ax=) for caller-supplied axes (no figure widening, no tight_layout warnings). +- Tests: tests/test_subplots_plotly.py (new, 11), tests/test_plot_panels.py +11 (plotly parity + mpl colorbar). CHANGELOG 3 entries. Tour 9.8 rewritten to show both backends; tour 12.4 rewritten to the on-demand Chronos install (verified in a fresh venv: chronos-forecasting + torch auto-installed, (15,2) forecast in 39.5 s). +- Agent note: the first implementation agent was killed by Jeremy's "hold on" interrupt before writing anything and could not be resumed; the work was done inline. +- Jeremy's instructions for closing: test carefully; then codex (gpt-6-astra) red-team loop on the open PR until clean; update PR #286; do NOT merge (Jeremy reviews manually). +- Codex red-team (gpt-6-astra, `codex exec -m gpt-6-astra -s workspace-write -o review.md`, stdin from /dev/null or it blocks on "Reading additional input from stdin"): round 1 on 22219eb5 -> 6 findings (panel grid ignored backend='matplotlib' under a plotly preference; scene-wide density grid zeroed a small cloud beside a 10,000x one; ARIMA min_history rejected sparse lag orders; left colorbars moved right; 3-D cell redraw dropped labels; panel titles bypassed the title path / font dropped) -> fixed a5fb6531 (density now pads 4 kernel widths, local grid). Round 2 on a5fb6531 -> 4 findings (predict_new applied the fit floor to fitted-model reuse; title y/margin; stacked titles on redraw; per-cell fonts) -> fixed e9dd0853. Round 3 running. +- CI on 74a888d1: macOS 3.11 failed tests/plot/test_forecast_schedule_warning.py::test_a_small_schedule_warns_about_nothing (projection 10.1 s from two ~50 ms fits at 2 and 3 rows). Fixed: project_schedule_cost least-squares over all timed lengths; projection waits for a timed fit >= PROJECTION_MIN_ROWS (10) rows. Pushed; CI rerun. + +## Forecast styling + plotly panel geometry (2026-09-07, Jeremy's three findings) +- 7.1 (several models on several datasets indistinguishable): a collection coloured every forecast BY MODEL from a 'husl' palette whose first colour was dataset 0's own, and nothing said which series a forecast continued. Now: forecasts keep their dataset's colour (as the single-model form did) and take a linestyle per model from forecast.FORECAST_MODEL_LINESTYLES ('-', '--', ':', '-.' in model order; the first model is solid so predict=['Kalman'] == predict='Kalman'); forecast_palette= on a collection opts back into colour-per-model; forecast_fmt= still replaces the cycle. Both backends (plot.py collection branch ~9880 synthesises the fmt list; the plotly twin reads the same overrides). +- 9.10/9.11 (forecast not in the legend): only a collection listed its models. Now every predict= form lists its forecast ONCE under the model's name (spec_name(), the same name hyp.predict(model=[spec]) gives), static/spin/animated, both backends, order data / forecasts / truth. The entry is a PROXY glyph (matplotlib: Line2D handle from _forecast_legend_handles, inserted via _add_overlay_legend_entries which rebuilds from the existing legend's handles; plotly: a data-free trace tagged meta['hyp_legend_entry'], appended after every drawn trace, only when legend is not None; truth traces get legendrank=1001 so they list last). Glyph colour = the forecasts' own colour when they share one, else forecast.FORECAST_LEGEND_COLOR (#555555) at the forecast alpha. Forecast artists never carry a label themselves (tag _hyp_forecast_label instead) -- the old label-on-first-artist showed dataset 0's colour as the model's. +- Plotly truth= drew a marker on every antialiased vertex (~900) so it rendered as a thick line; now marker.size is a per-vertex array, non-zero only at raw rows (dense index k*step), sized by _marker_size_px like the matplotlib overlay. +- 9.8 (plotly panels spaced out, cubes small): measured plotly sizes a 3-D scene by domain HEIGHT only (cube 0.89 x height wide, 0.70 tall; clipped at the sides), apparent size ~1/eye distance beyond ~0.8x. The old back-off constant 1.4 was cube-width / CUBE height, not scene height -> 1.5x too far back, and every square cell backed off 1.4x; plus make_subplots default spacing (0.2/ncols, 0.3/nrows) and full-height cells. Now make_panel_grid lays cells out like tight_layout: 3-D cells SQUARE (min of width/height budget) and centred, PANEL_GAP_PX=20 (PANEL_AXIS_GAP_PX=40 for 2-D), title_px per row (panels= passes max(panel margin.t - 10); hyp.subplots reserves PANEL_TITLE_PX=30), SCENE_CUBE_WIDTH_PER_HEIGHT=0.92. Per-cell ink measurement (scratchpad/style/measure_cells.py) after: plotly cube widths 266/172/195/172/240 vs matplotlib 265/172/193/172/240 px for 1x2/1x3/2x2/2x3/1x3@9x3.2. +- Tests: tests/test_plot_forecast_legend_style.py (new, 16), tests/test_plot_panels_geometry.py (new, 7 incl. a real-render cube-fill parity test), updated tests/test_plot_predict_list.py, tests/plot/test_predict_integration.py, tests/test_plot_panels.py to the new rules. CHANGELOG: 4 entries. Docstrings: predict=, forecast_palette=. Tour prose 7.1/9.10/9.11 updated (gitignored notebook), re-executed. +- Pending: full pipeline (scratchpad/full_verify3.sh), commit + push, CI, Codex round 3 (never fired: the 04:53 cron did not run in the compacted session; deleted, rerun by hand on the new head), PR comment; no merge. + +## Three-role figure review of the tour (2026-09-07 ~12:00, Jeremy: "agent 1 expected / agent 2 observed / agent 3 adjudicates") +- Process: scratchpad/review/manifest.md (cell code + prose per figure, 51 figures from 24 cells; plotly outputs rendered from their JSON with kaleido by scratchpad/tour_out/extract.py) -> expected.md (agent, no images) + observed.md (agent, images only) -> verdicts.md (agent; 31 MATCH / 20 GAP / 0 UNCERTAIN, gap register G1-G11). +- Library fixes (all verified at the API first): G1 ax=/panels= axes kept their figure's default colour cycle (rc-scoped sns.set_palette never reached a pre-made axes) -> ax.set_prop_cycle(palette) continuing past `ax._hyp_palette_offset`; plotly ax=<figure> continues via layout.meta['hyp_datasets_drawn'] (G11). G2 default-size mpl panels widen 1.1 in/column per legend/colorbar. G3 Axes3D.get_tightbbox measures axes "for layout only" (drops labels) -> `_AxisLabelExtent` figure artist. G4 legend glyph alpha floor FORECAST_LEGEND_MIN_ALPHA=0.8 + max linewidth (both backends). G5 recoloured forecasts (forecast_hue/cluster/palette or a colour letter in forecast_fmt) keep the trace's alpha: forecast.forecast_alpha_scale_for, both backends. G7 2-D frame square at UNIT_FRAME_SCALE=1.125, axes +-UNIT_FRAME_LIMIT (helpers.py; both backends; density clip; 5 test files retargeted). G8 legend_kwargs loc= without bbox_to_anchor drops the outside anchor. G10 plotly legend itemsizing='constant'. G9 judged parity (mpl default-size grids have the same slack) -> notebook passes size= to the plotly hyp.subplots call. Notebook: 9.10 first call gets legend=True/names (prose promised the entry), forecast_palette ['black','orange']. +- Adjudicator's "3-D has a 9/8 cube margin" was wrong (that is the ANIMATED box zoom; the static cube sits at +-1 like the data) -- the 2-D fix therefore scales the square, not the data. +- Tests: tests/test_figure_review_gaps.py (new, 14). Docs-only sphinx rebuild: 0 warnings (the pipeline's "1" was a race with the tutorial re-execution writing projectile/stock notebooks mid-build). +- Round 2 (24 re-rendered figures): 23 MATCH, G9 PARTLY (hyp.subplots plotly reserved a 118 px gutter per cell before knowing about legends). Fixed: the grid is built with gutter 0 and records its make_panel_grid args in layout.meta['hyp_grid']; transplant_panel calls ensure_panel_gutter (rebuilds width/margins/domains and re-places every drawn cell's legend/colorbar/title via _place_cell_furniture, titles' cell-fractions kept in meta['hyp_cell_titles']) the first time a cell brings a legend/colorbar. Tests +2 in test_figure_review_gaps.py. Round 3 pending on cell 86's two plotly figures. +- Codex round 3 (gpt-6-astra, scratchpad codex/prompt5.txt -> review5.md, on ab5d7ce8): 12 findings, all reproduced with its /tmp/review286 scripts and fixed. Majors: (1) collection x hue regrouping -- ownership only resolved when forecast/run counts differed (now also for any collection under regrouping); animated modes indexed the reveal schedule by FORECAST index (model-major) instead of source dataset -> IndexError on both backends (plotly `_add_animation` now receives forecast_datasets; mpl `_run_colour` translates via _model_forecast_owner). (2) fitted multi-dataset forecaster in an animation: `Forecaster.for_dataset(i)` view + `forecast_from_history(dataset=i)` from the schedule. Minors: plotly forecast_fmt colour letter + markers (`_fmt_color_letter`, `_forecast_marker`, legend specs carry mode/marker); plotly animated recoloured alpha (live_alpha via forecast_alpha_scale_for); per-cell palette continuation (meta hyp_cell_datasets_drawn); legend keys compare RGB (`_rgb_triplet`); legend_colors pairs define the legend outright (no forecast/truth entries; plotly data traces hidden) and a plain list recolours the FINAL legend (deferred past the overlay entries); attached right colorbar padded past the panel legend's measured overhang; gutter rebuild keeps title_px (spec updated at the title bump); explicit legend_kwargs x/y translated into the cell (meta hyp_cell_legends) and re-placed after rebuilds; docs/animation.rst forecast sections rewritten; tight-bbox test asserts four-sided containment. Tests: tests/test_plot_review_round3.py (19). Adjudicator's "3-D has a 9/8 margin" claim was wrong (animated box zoom). +- Codex round 4 (prompt6 on f9ccc75c, pushed; local pipeline green: 5293 passed, docs 0 warnings, smoke 344): blocked by the Codex usage limit until 19:22 local; one-shot cron at 19:26 relaunches it. CI run 34156147776 on f9ccc75c in progress. PR comment draft: scratchpad/pr_update_draft.md (ROUND4_PLACEHOLDER, CI_PLACEHOLDER). No merge -- Jeremy reviews. +- Codex round 4 (prompt6 -> review6.md, on 49441a31; launched by hand at 20:04 after the 19:26 cron never fired -- session crons have failed twice, do not rely on them): 8 findings, all reproduced with /tmp/hyp_review_findings.py and fixed. (1) forecast_fmt colour letter not treated as pinned in regrouped animations (mpl `_override_colour` + plotly `_pinned` now use forecast.override_has_color; plotly per-frame colours use forecast_alpha_scale_for). (2) mixture-hue legends lost forecast/truth entries: a matrix hue clears `legend` and builds swatch entries, so gating on `legend is not None` hid them -> `_legend_present = legend is not None or _final_legend_entries is not None`; plotly_draw gained `legend_explicit=` (explicit pairs suppress, swatches do not). (3) overlays on a reused mpl axes styled from ALL ax.lines -> `src_lines=list(ax.lines)[_n_lines_before:]` passed to both overlay helpers and the live path. (4) legend duplication across calls: truth artists never labelled (tag `_hyp_truth_label`), `_add_overlay_legend_entries` rebuilds by role from every tagged overlay on the axes; plotly hides earlier same-name entry traces / truth entries in the compose scope (`_compose_scope_traces`) and decides the key colour over all forecasts of that name. (5)/(6) plotly cells: per-cell furniture in meta['hyp_cell_furniture'] (gutter sized for the busiest cell, cb_offset from the cell's legend), `ensure_panel_layout(gutter_px, title_px)` rebuilds rows for taller titles immediately; explicit vertical_spacing= still wins (documented). (7) marker-only forecast_fmt on plotly -> mode 'markers'. (8) animated plotly collection traces tag the source dataset. Tests: tests/test_plot_review_round4.py (15). +- Round 4 fixes pushed as 687e98c9 (local pipeline: 5308 passed, docs 0 warnings, smoke 344); CI run 34176254864 in progress at 21:20. Codex round 5 (prompt7 -> review7.md) ran ~160k tokens and hit the usage limit; server says retry Sep 8 01:04 local. Relaunch by hand (crons do not fire); PR comment draft has ROUND5_PLACEHOLDER. No merge -- Jeremy reviews. + +## Optional-extra install instructions audit (2026-09-07 late, Jeremy: "have we truly weeded out ALL instructions to install optional deps? is on-demand install exposed + documented?") +- CI run 34176254864 on 687e98c9: all 14 jobs green (release-gate skipped off master). +- Library: every optional import site goes through `_shared.lazy_import` (plotly/kaleido via resolve_backend + ensure_kaleido_chrome, skimage via density.skimage_measure, torch at autoencoders import, gensim at gensim_models import, chronos/skaters/pylsl/kagglehub/datasets/openpyxl/sentence_transformers at their call sites). Direct `import plotly` in plot.py (`_is_plotly_figure`, a type check that must never install), reduce/describe.py (only on the resolve_backend()=='plotly' branch) and _kaleido_export_worker.py (subprocess spawned after ensure_kaleido_chrome) are the three justified exemptions. New static gate: tests/test_lazy_import.py::test_every_optional_import_in_the_library_goes_through_lazy_import (AST scan of hypertools/, exemptions must stay real). +- Exposure: the mechanism is automatic; the ONLY user control is HYPERTOOLS_AUTO_INSTALL=0 (no public function; `lazy_import` lives in the private `_shared` package). Documented in docs/optional_dependencies.rst (guide), docs/index.rst, readme "Optional extras install themselves on demand", CHANGELOG 1.1.0 entry, and 7 tutorials' prose. Was NOT in the API reference -> docs/api.rst gained an "Optional extras" section (mechanism + env var + link). No public helper added (pip with the extras list already covers pre-installing; adding API to a PR under review is Jeremy's call). +- Stale prose fixed: tour cells 21 (LSL "requires pip install"), 30 (torch "with hypertools[torch]"), 97 (density3d "need"); plot() reduce docstring `pip install "hypertools[torch]"`; examples/plot_autoencoders.py + plot_gensim_text.py "pre-install it with ..."; docs/tutorials/lsl_streaming.ipynb inline command -> pointer to the guide; reduce/common.py torch ImportError now says the on-demand install was tried (keeps 'hypertools[torch]' for tests/test_autoencoders.py:240). +- Kept on purpose (explain the mechanism or the manual fallback): readme/optional_dependencies "Pre-installing everything", lazy_import's own ImportError text, sources.py xlrd hint (deliberately not an extra, pyproject comment), load.py deepdish legacy note, core deps pykalman/statsmodels "reinstall hypertools" text, matplotlib density warning "could not be installed on demand". +- Found along the way: projectile_kalman.ipynb + streaming_data.ipynb shipped the pip upgrade NOTICE (with ~/hypertools/.venv path) as the stored output of the Colab install cell since the 1.0.0 squash e13776c2 -- execute_tutorial.py skips the cell in memory, and a skipped cell keeps the file's old outputs. Refactored into skip_install_cells()/restore_install_cells() (outputs + execution_count cleared), cleared the two outputs and four stale execution counts (hugging_face_embeddings, lsl_streaming, modern_sklearn_dynamics, text) with nbformat; gate tests/test_notebook_install_gate.py::test_no_published_install_cell_carries_output + a real skip/restore test. +- Pipeline: scratchpad/full_verify8.sh (sphinx_err11, derived with /g) running; then commit, push, CI. Codex round 5 (prompt7 -> review7.md) still blocked until 2026-09-08 01:04 local; relaunch BY HAND. +- Jeremy (after the audit): "setting an environment variable is clunky ... expose it like the plotting backend: hyp.set_autoinstall(False)". Added `hypertools.set_autoinstall` (class in _shared/lazy_import.py, same two forms as set_interactive_backend: direct call for the session, `with` for one block; bool only, TypeError otherwise; `.enabled`; repr). Precedence: the last call wins; HYPERTOOLS_AUTO_INSTALL only sets the STARTING value (kept for prebuilt images, documented as secondary). Exported in __init__ + __all__; docs/api.rst "Set autoinstall" section (replaces the "Optional extras" paragraph) + tracked autosummary stub docs/hypertools.set_autoinstall.rst; optional_dependencies.rst "Turning it off" rewritten around the call; index.rst, readme, CHANGELOG 1.1.0 bullet, release-notes draft, CLAUDE.md, tour cells 122/128, lazy_import error text ("automatic installation is off; hypertools.set_autoinstall(True) turns it on"). Tests: tests/test_lazy_import.py (+4: public/mirrors backend, overrides env, rejects non-bool, off -> manual command without pip), chronos/laplace missing-module tests now use `with hyp.set_autoinstall(False)`; __all__ roster test updated. Pipeline: scratchpad/full_verify9.sh (sphinx_err12). +- full_verify9: pytest 5315 passed, 1 FAILED tests/test_lsl_streaming.py::test_lsl_stream_resolves_by_type (resolved type='EEG', nothing received for 5 s). Cause (no library change): two OTHER processes advertised idle EEG outlets -- a VS Code ipykernel (pid 5731, the tour/LSL tutorial open in the editor with its synthetic-outlet cell run) and an ipykernel spawned by /tmp/hypertools-audit-20260907/run_notebooks.py under a `codex` process launched from a terminal (NOT by this session; left untouched). lsl_stream() used the first match per its documented rule. Fix: the by-type test now starts its own outlet with a unique stream type (a lab network has real EEG streams for the same reason). ruff/packaging clean; sphinx + smoke pending; full pipeline to be re-run after the test edit. +- Jeremy reset his Codex limit and ran a manual audit that paused; findings in notes/release_audit_2026-09-07_paused.md (6 findings + observations). Instruction: address them, mark ORIGINAL vs UPDATE in that file, and tell the next Codex round to read it first (scratchpad/codex/prompt8.txt). Committed the staged set_autoinstall work first as 4c4b2887, then three parallel agents under exclusive file ownership: A load.py (F1 offline built-ins + F5 doctest NameError + hierarchy test docstring), B lazy_import/plotly_backend/worker (F2 subprocess_env propagation, real kaleido-less interpreter test, spy test replaced), C plot.py (F3 panel pipeline, F4 per-dataset/per-forecast partitioning, 33 tests). Me: alignment_score input validation (+6 tests), doc_requirements floors, HYPERTOOLS_DOCS_PLOT_GALLERY switch + CI doctest step + pipeline doctest step, list-of-dicts palette under regrouping hue (found by C, fixed in _seaborn_palette_arg). Notes file has a reading-guide banner + "Updates by the Claude session" section. full_verify9 (sphinx of 4c4b2887) killed at ~45 min in favour of full_verify10 over everything (adds the doctest step). +- 2026-09-08 ~01:00-02:30: audit fixes pushed as 3f4b087d (CI run 34187936967 in progress). Codex round 6 (prompt8 -> review8.md; it appended "## Codex round 6 (resumed audit)" to the audit notes): originals 1/3/5 fixed, 2/4 mostly, 6 pending release ops; 3 new findings: (1) overlapping set_autoinstall contexts restored the wrong value -> lock-guarded scope stack, newest in force wins (tests: deterministic + two-thread); (2) panels dropped repeated forecast colours (dedup by colour not label) -> per-label slots + behavioural roster tests (agent); (3) export worker wrapped ImportError in RuntimeError -> worker writes .worker-error.json, parent re-raises ImportError/HypertoolsIOError. Incidental (agent-found, fixed): plotly dropped fmt colour letters on the ordinary path; panels on 2-/1-column data without ndims (both backends). tests/test_plot_review_round6.py (18). Codex's sandbox blocked sockets/Chrome (weather notebook, plotly renders unverified by it). full_verify11 launched; then commit, push, prompt9 (round 7, derived from prompt8). +- CI 34187936967 (3f4b087d): ubuntu 3.13 FAILED tests/test_lsl_streaming.py::test_tutorial_close_under_load_leaves_no_liblsl_error with subprocess.TimeoutExpired (300 s). Measured from job logs: the test took 38 s (3.10) in the same run and 85/45/153 s (3.10/3.12/3.13) in the previous green run -> runner speed under the deliberate GIL load, not a regression; child timeout raised to 900 s (pytest per-test ceiling is 1200 s), assertions untouched. +- full_verify11 green (5460 / ruff / packaging 13 / sphinx 0 / doctest 316 / smoke 344; LSL file 27 with the 900 s budget). Pushed c700c85f; CI run 34193448548; Codex round 7 launched ~03:55 (prompt9 -> review9.md). Previous run 34187936967 on 3f4b087d: only ubuntu 3.13 failed (the LSL timeout), the rest green/in progress at the time. +- Codex round 7 (prompt9) hit the usage limit at ~02:20 after ~164k tokens (it had re-run the export policy probe and was mid HTML build); no review9.md, no tracked-file edits. Server retry time 5:42 AM; a detached zsh (scratchpad codex/relaunch9.sh, sleeps until 05:44 then runs the same codex exec -> review9.md, log run9b.log) relaunches it, since session crons never fire. +- CI run 34193448548 on c700c85f: all 14 jobs green (release-gate skipped). Waiting on the 05:44 Codex relaunch (round 7). + +## PAUSE 2026-09-08 ~09:15 (Jeremy suspending the machine) +- State: round-7 fixes (R7-1 panel probe/analyzed-width cells, R7-2 palette slots consumed, R7-3 bounded set_autoinstall scopes, nits: export ON-branch type assert, thread hand-off asserts, tests/AGENTS.md, viewcode anchors via public names + HypertoolsTrustError top-level export) committed LOCALLY (this commit), NOT pushed. full_verify12 on this exact tree: pytest 5493 passed, ruff + diff clean, packaging 13; sphinx was ~25 min in when killed for the suspend; doctest + smoke not run. +- Killed: full_verify12.sh + its sphinx child; codex/relaunch10.sh (the 10:46 round-8 relaunch). No Codex process running. Codex usage window reopens 10:44 AM. +- RESUME PLAN: (1) `git status` must be clean and HEAD the commit below; (2) run a sphinx+doctest+smoke-only pipeline (scratchpad full_verify12.sh minus the pytest/ruff/packaging steps -- derive as full_verify13.sh with sphinx_err16/doctest_err16 and /g sed) since pytest/ruff/packaging already ran on this tree, then `git checkout` the two autosummary stubs; (3) push, CI watcher on the new head; (4) launch Codex round 8 BY HAND with scratchpad codex/prompt10.txt -> review10.md (`rm -f review10.md; nohup codex exec -m gpt-6-astra -s workspace-write --skip-git-repo-check -C /Users/jmanning/hypertools -o $S/codex/review10.md "$(cat $S/codex/prompt10.txt)" < /dev/null > $S/codex/run10.log 2>&1 &`); the prompt tells it to read notes/release_audit_2026-09-07_paused.md first and append findings incrementally; (5) loop until VERDICT: CLEAN, then fill ROUND5_PLACEHOLDER... in scratchpad/pr_update_draft.md (now covers rounds 3-8) and post `gh pr comment 286 --body-file`. Do NOT merge. +- RESUMED 09:19; full_verify13 (sphinx/doctest/smoke on 14e0965e): sphinx 0 warnings, doctest 316/0, smoke 344 -> pushing 14e0965e + this note; Codex round 8 launched by hand (prompt10 -> review10.md). +- CI 34234303697 (1cad1e63): ubuntu 3.11 FAILED tests/test_load_sources.py::test_load_dropbox_url_forms -- Dropbox closed the TLS connection (SSLError / SSLEOFError UNEXPECTED_EOF_WHILE_READING) on the dl=1 form and dl=0 answered HTML; 11 other cells loaded the file. tests/_netskip.py read the `SSLError` token as a defect and did not skip: SSLError is now phrase-determined (TLS-drop phrases -> transient; 'certificate' vetoes; 'max retries' deliberately not decisive). Test with the exact aggregate text + certificate counterpart. full_verify14 launched over everything. +- QUEUED (Jeremy, 2026-09-08 ~11:40): (a) palette feature -- image palettes sorted (by value) when used as a plot palette; t x k data-matrix palettes reduced to 3-D via hyp.reduce with palette_reduce/palette_manip/palette_normalize/palette_align/palette_sort, scaled, sorted, resampled (Colormap); docs, tests, tutorial + tour sections. Patch drafted in scratchpad/palette_patch.py + test_palette_matrix_and_sort.py, to apply after full_verify15. (b) Datatype audit -- functions doing their own datatype checks must defer to datawrangler (wrangle/funnel/zoo predicates) so polars etc. come free; EVERYWHERE, not only new code. Read-only survey agent dispatched. +- CI 34234303697 (1cad1e63) final: 13 green, ubuntu 3.11 failed only on the Dropbox TLS drop (classifier fix staged for the next push), release-gate skipped. +- full_verify15 green on the round-8 tree (5547 / ruff / packaging 13 / sphinx 0 / doctest 316 / smoke 344). Pushing; Codex round 9 (prompt11, slim + incremental) launched on it; palette patch applied after that review. +- Palette feature IMPLEMENTED (colors.py: sort_colors, is_palette_matrix, matrix_palette -> MatrixColormap (exact interpolation), image_palette(sort=), image specs sort by value as plot palettes ('?sort=' option; lead colour stays salient); plot.py: palette_sort/palette_reduce/palette_manip/palette_normalize/palette_align + _prepare_palettes before the panels branch; docstring; tests/test_palette_matrix_and_sort.py (19) + two image tests moved to the new contract + roster/signature rosters; CHANGELOG 'Added' bullet). Docs agent: api.rst/tutorials.rst/plot.ipynb section. Tour: 9.13 prose + new 9.13b cells (97-99) executing now for the critical review Jeremy asked for. Round-9 fixes staged (57 tests). Wave-0 datatype refactor agent running (format_data/helpers/core.shared/predict/impute/streaming + tests/test_polars_inputs.py). +- Tour palette demo (9.13b) executed (run 1 aborted at 12.3: a STALE 'HypertoolsTourStream' outlet from Jeremy's VS Code kernel was resolved by name first -> the demo now uses a per-run name f'HypertoolsTourStream-{pid}'; run 2: 68/69 cells, 0 errors). Critical review of the 7 figures: image sorts (value/hue/original) correct and legible; matrix palettes (a) a random-walk matrix sorted along PC1 gives a STRIPED colorbar (other components unordered along PC1) -- documented (docstring + tour prose) and the demo now uses a trend+oscillation matrix with the random walk shown as the honest striped case; (b) per-dataset matrix palettes both mid-purple because a min-max-scaled matrix's middle colour is ~mid-grey -> lead colour is now the most SATURATED anchor (palette_lead_color; test updated). Run 3 executing for re-review. +- Tour palette demo final (run 5: 68/69 cells, 0 errors): 9.13b shows image sorts (value/hue/original), a trend+oscillation matrix raw (jittered) vs palette_manip='Smooth' (clean gradient; measured G/B step 0.072/0.125 -> 0.034/0.043), FastICA + hue sort (rainbow), the random-walk matrix as the honest striped case, per-dataset matrix palettes (red vs cyan leads = most saturated anchors), and a matrix palette for categories. Reviewed all figures visually; no remaining gaps. Lessons: a sort along PC1 interleaves rows where the first component is not monotone in time -> striped palette (documented); the per-run LSL stream name. +- Wave 0 datatype refactor landed (helpers predicates is_text_item/is_number_item/is_array_dataset/is_frame_dataset/is_series_like/as_pandas_dataframe; format_data/get_type/as_dataframe/predict+impute/is_stream on dw; tests/test_polars_inputs.py 46, 4 strict xfails). Checkpoint commit 8066308c (palette feature + wave 0 + round 9; local, unpushed; full pipeline before push). Wave 1 dispatched as two agents: plot package (plot.py/colors.py/fonts.py/backends + lift the plot xfails) and manip/align/core/tools/io/reduce/cluster + tests/test_polars_inputs_wave1.py + tests/test_datatype_gate.py (static allowlist gate). +- Wave 1 landed: plot package (index/column capture, hue/labels/truth/palette/panels via the predicates; a 1-column label DataFrame hue no longer IndexErrors) and manip/align/core/tools/io (funnels backend='pandas', post-funnel re-checks removed, as_internal_frames, save/load/text2mat/stack/damage on the predicates); impute/backtest.py truth=/mask= converted (the new static gate tests/test_datatype_gate.py flagged it; allowlist 5 files / 24 patterns of legitimate option/model checks). manip([array, named frames]) now aligns by position like plot/reduce/align (pure-pandas defect found by the mixed polars test); named frames with different labels raise a clear ValueError. tests/test_polars_inputs.py (xfails lifted) + tests/test_polars_inputs_wave1.py (51). full_verify16 launched. +- CI run 34248376951 (650808f0): all 14 jobs green, release-gate skipped. +- full_verify16: pytest 5752 passed, 2 FAILED in tests/test_colors_image_palettes.py -- my palette_lead_color edit had swallowed the bare-colour branch into the MatrixColormap block ('red' -> "not a valid palette name"); restructured, palette suites 127 green; pipeline restarted as full_verify17. +- full_verify17 green on the full tree (pytest 5754 / ruff / packaging 13 / sphinx 0 / doctest 316 / smoke 344). Committing wave 1 + palette lead fix + manip mixed-list fix; pushing; Codex round 10 (prompt12: palette + polars refactor + round-9 fixes). +- Codex round 10 (prompt12 on 25b3ba6c) cut off by the usage limit after R10-1 (MatrixColormap built on a bare anchor list: int sampling/resampled/reversed failed) -> fixed (proper segment data + alpha-array/masked handling; contract test). Relaunch scheduled 15:48 as round 11 (prompt13). full_verify18 launched. +- CI 34263658659 (25b3ba6c): live-source-gate FAILED on my round-9 classifier test (the gate sets HYPERTOOLS_REQUIRE_LIVE_SOURCES=1 -> the guard re-raises the TLS drop by design). Test now pins both modes (delenv -> skip; setenv 1 -> raise). Verified under both env states locally. +- CI 34263658659 also: ubuntu 3.12 FAILED tests/test_format_data.py::test_string_naming_an_existing_file_is_a_document_not_a_path -- the wave-0 test opened 'README.md'; the file is 'readme.md' (macOS case-insensitive, Linux not). Fixed; skillnote added. +- CI 34263658659 (25b3ba6c) final: docs-clean + all Windows/macOS green; the 4 ubuntu jobs failed only on the readme.md case test and live-source-gate on the classifier test mode -- both fixed in the staged tree; next push re-runs them. +- full_verify18 green (pytest 5755 / ruff / packaging 13 / sphinx 0 / doctest 316 / smoke 344; test_load_sources + test_format_data re-run after their post-collection edits). Committing + pushing (R10-1 MatrixColormap contract, CI test fixes); Codex round 11 launches 15:48. +- Codex round 11 (launched 15:48 on 767e327d, finished ~16:20): R11-2 MAJOR (my manip mixed-list rule rejected named frames and dropped indices) + R11-1/3/4 minors; all fixed with tests (329 in the affected suites), prose nit + weak tests strengthened. full_verify19 launched; then commit, push, round 12 (prompt14). +- full_verify19 green (pytest 5761 / ruff / packaging 13 / sphinx 0 / doctest 316 / smoke 344). Round-11 fixes committed + pushed as faedf14e; CI 34269299856 (767e327d) finished all green (release-gate skipped); CI 34277407514 on faedf14e running. Codex round 12 (prompt14, ~17:00, 10 min): VERDICT FINDINGS -- R12-4 MAJOR (a pandas Series lost its index/name in the direct Smooth/Delay classes and Pipeline; ZScore/Normalize/Resample classes never took a Series), R12-1 (polars Series + array in a manip list), R12-2 (MatrixColormap extreme alpha / infinities / alpha override), R12-3 (mixed lists renamed a named frame's features for every model), R12-5 (legend= rejected a polars Series), a tutorial prose nit, and weak-test notes. All reproduced (R12-3/4 against the 650808f0 archive) and fixed: as_dataframe/as_internal_frames coerce Series with metadata; ZScore/Normalize fit through stack_for_shared_fit (positional when labels differ, clear error for widths) and transform lists per dataset; the dispatcher no longer relabels; a 1-D array is one column for manip (was one row); colormap follows matplotlib's RGBA rules; legend= on the predicates. tests/test_review_round12.py (35) + strengthened wave1/palette tests; notes UPDATE section; CHANGELOG bullet. Next: affected suites -> full_verify20 -> commit/push -> Codex round 13. diff --git a/notes/session_2026-09-11_final-review.md b/notes/session_2026-09-11_final-review.md new file mode 100644 index 00000000..7aa6951e --- /dev/null +++ b/notes/session_2026-09-11_final-review.md @@ -0,0 +1,244 @@ +# Session 2026-09-11: independent final review of PR #286 candidate + +Candidate under review: `fa3e60e5670cabfc1cbbd282e122b9098cc6d1a3` (branch +`fix/1.1-release-review`, PR #286 OPEN, CI 16 green + release-gate skipped). +Guide followed: `notes/notebook_critical_review_2026-09-10/START_HERE_FINAL_REVIEW.md`. + +**HOLD: do NOT merge / tag / publish anything until Jeremy signs off.** +Fix issues found along the way (commit + push to the PR branch), then refresh +evidence/CI for the new head. + +## Plan / status + +- [x] A. Headless re-run of feature tour at HEAD: fresh execution 2026-09-11 15:21-15:24Z, 3m31s, exit 0, + **241 PASS / 3 SKIP / 0 FAIL**; skips = SOURCE-drive, SOURCE-dropbox (trusted_remote_pickle off), GUI-native (native_gui off). Matches reference. +- [~] Colab: uploaded cleared candidate notebook via Playwright (Colab Pro session is signed in) -> https://colab.research.google.com/drive/1c84Tcr-whDrtDynfGxQ0oC15VajdtETg ; Run all started 15:25Z +- [ ] B. Cold code review of the PR library diff (3 reviewer agents: plot core, plotly/colors/forecast, data/predict/io) +- [ ] C. Docs/claims review (README, CHANGELOG, release notes, tutorials, RELEASE_CHECKLIST) +- [ ] D. Visual review of tour outputs (separated expected / observed / adjudicate agents) +- [ ] E. Fresh-venv Colab-proxy run (install from git at exact SHA, installer cell executed) -- AFTER A (LSL outlets collide across concurrent runs) +- [ ] F. Browser frontend check (local Jupyter + playwright: previews survive Run all, shared viewer open/switch/close) +- [ ] G. Fix findings, re-verify, push, refresh CI + evidence packet + +## Findings log + +### Fresh Colab run at fa3e60e5 (15:25-15:35Z): 236 PASS / 5 FAIL / 3 SKIP +- STREAM-01/02/03 FAIL `'HyperPlotlyFigure' object has no attribute 'axes'` -> LIBRARY BUG (1.0 too): + plot_stream's inner head plot followed the plotly render preference (Colab auto). FIXED 2503a543 + (+ test_stream_plot_under_global_plotly_render_backend). Tutorials streaming_data/lsl_streaming/io hit it on Colab. +- ANIM-clock / PLOT-cjk FAIL (tour assumed matplotlib) -> tour fix deeb1f93 (backend='matplotlib'). +- RED-describe displayed `None` (describe(show=False) returns fig=None by documented design) -> tour fix deeb1f93. + QUESTION for Jeremy: plot(show=False) returns a fig but describe(show=False) does not -- change API? +- Font scan skipped xlabel/ylabel/zlabel -> tofu for axis-label-only scripts. FIXED 2503a543 (+ test_axis_labels_join_the_font_gap_scan). + +### Data/predict/io reviewer (repro scripts: scratchpad/review_data/p*.py) +- MAJOR predict/time.py:41,116 business-day/monthly data treated as irregular -> interpolated weekends, PeriodIndex->DatetimeIndex, DST dup +- MAJOR regression vs 1.0 predict/common.py:533,time.py:76-83 reuse fitted forecaster across index kinds crashes +- MAJOR io/sources.py:1264 yahoo interval='1h' bars all at midnight -> duplicate index +- MINOR arima.py:224 min-history ignores seasonal_order; tools/normalize.py:43 fitted Normalizer rejects its own 1-D input (also master); + lazy_import.py:244 stale set_autoinstall handle; sources.py:1722 misleading offline Drive error +- NIT sources.py:1786, time.py triple warnings, npz parquet fallback, scikit-image floor py3.13, load() TypeError text omits polars +- Clean: pickle trust (13 payloads), dataset pins, lazy_import input safety, datatype round-trips, backtest + +### Plotly/colors/forecast reviewer (repros: scratchpad/review_plotly/r*.py) +- MAJOR plotly fmt='o-' markers on every smoothed vertex (945 vs 60) (r05,r06); continuous hue + 'o-' in 1-D/2-D plotly draws no markers +- MAJOR cyan shift remains when line/marker alpha differ (hue+alpha+'o-' 3-D) (r13) +- MAJOR ax= palette continuation re-samples -> repeated colors ('hls' 2+2: a2==b1) plot.py:11252/11392 (r14,r37) +- MAJOR plotly panels/subplots: every cell counts as having a legend -> colorbar pushed onto next cell (r26,r27) +- MAJOR 1-D data w/o ndims=1: x in upsampled units but forecast/truth raw steps (24x squash); axis_scale='data' uses y range for x (r31-r34) +- MAJOR forecast_fmt='ro:' marks all smoothed vertices (both backends) (r09) +- MAJOR (plot-core) plot.py:11725 2-col data + animate + predict crashes unpack (r40) +- MINOR second draw into plotly cell deletes title (r15); colors.py:410 0-255 RGB list treated as matrix (r22); + 2-D into 3-D cell cryptic TypeError (r28); ax= bundle colors mismatch (r36); 1-D animation plotly silent vs mpl error (r38) +- NIT plotly_backend.py:2331 mutates caller's traces +### Plot-core reviewer (repros: scratchpad/review_plotcore/r*.py) +- MAJOR F1 forecast/truth overlay style indexing w/ marker+line fmt (also master); F2 dict palettes ignored with markers-only fmt; + F3 ndims=1 2-col truth read as (x,value) +- MINOR F4 legend_colors+predict "legend has 3" (regression); F5 panels+forecast_trail; F6 panels legend_colors split; F7 transform DF index->NaN->zero fc; + F8 xlim=(None,date) crash; F9 datetime t= per-dataset steps; F10 truth legend key colour; F11 serial {index} title (DELIBERATE 1.1 -> ask Jeremy); + F12 0-255 palette (same as plotly r22); F13 ndims=1 date ticks overlap; F14 shuffled index scribble; F15 bare-array transform IndexError; F16 2-col into 2-D ax=; F17 labels arrays +- also: font='Noto Sans' fails in fresh process (verified by me) + +### Fix wave (worktrees, launched ~16:30Z) +- W1 predict (D1,D2,D4,D9,F9); W2 io/tools/_shared/colors/fonts (D3,D5-D8,D10-D12, 0-255 palette, Noto Sans); + W3 plotly_backend (Y1,Y2,Y3,Y5,Y7-plotly,Y8,Y10,r38,NIT,frame_kwargs/zoom/yanchor/'^'); + W4a forecast/truth (F1,F3,Y6,r40,Y7-mpl,F4,F5,F7,F8,F10,F14,F15,F13); W4b palettes/markers/panels (F2,Y4,Y11,F6,F16,F17,marker override, markers= smoothed) +- Shared marker contract: markers only at true observations on both backends incl. forecast_fmt. +- After merge: integrate CHANGELOG bullets, full pytest, ruff, docs build, re-run tour headless + Colab at new pushed SHA. + +### Docs/claims reviewer (scratchpad/review_docs/FINDINGS.md, 1555 lines) +- BLOCKER H-B1 5 launch notebooks crash on Colab (plotly auto; anim.on_frame/figure/draw_frame) -> W5 +- BLOCKER E-B1 hyp.plot([a,b], hue=['x','y'], labels=['A','B']) crash (per-dataset labels + hue/cluster) -> W4b +- BLOCKER H-B2 market_sectors weights: split-adjusted close x as-reported SEC shares -> W5 +- MAJOR A0 Colab video block swallows 6 tutorial cell outputs -> W5; A1 RTD webhook 400, nothing built since 07-24, + README latest/optional_dependencies.html 404 -> checklist (W6) + JEREMY must re-sync RTD integration +- MAJOR A2 release notes stale, A3 checklist order -> W6; C-F1 hue markers ignore alpha -> W4b+W3; E-M1 marker-only regroup fc -> W4a +- 10 tutorial prose MAJORs + many MINORs -> W5; CHANGELOG/checklist MINOR/NIT -> W6; D-1 plotly window_bounds, D-6 ax=cell -> W3; + D-2 aligner fit arrays, D-3 dispersion, D-5 PPCA warnings -> W7; A12 set_autoinstall teardown -> W2 +- Worktree tool sometimes bases on 96ac8b7f (origin default = master)! Prompts now force reset to 09f56b2c/deeb1f93. + +### Jeremy report 2026-09-11 ~16:10Z: plotly hover says "trace 0/1" instead of legend names +- Confirmed: nearly every hoverable plotly data trace is unnamed (no-legend plots, panels, animations, forecasts, series curves); + with a legend, only the first segment per hue/cluster/hierarchy group is named (ANIM-dict-cluster, HIER). Continuous hue drops legend names. + Animation playback keeps base names (verified in Chromium via gd._fullData after Plotly.animate). +- Routed to W3 with contract: name every hoverable trace with its legend label (category/dataset/column/model/'truth'), + duplicates showlegend=False + same legendgroup; legend display still governed by legend=; single unlabeled dataset hides the extra box. + +### Visual QA adjudicator (scratchpad/visual_qa_adj/VERDICTS.md): OK 26, KNOWN 7, lib 39 rows (15 bugs), tour 12 rows (9), d 11 +- L1 plotly 3-D line width ~0.53x (-> W8 later); L2 3-D density volume stipples cube (-> W8); L3 white label connectors, L4 panel/ax titles DejaVu, + L5 nested legend colours, L6 cluster legend order (-> W4b); L7 plotly animated legend flicker (-> W3); L8 anim DOWNSAMPLING (helix 46% radius), + L9 morph no motion + dot size, L11 on_frame title off-canvas, L12 companion blue, L13b stream clamp warning (-> W9 new); + L10 plotly date axes shifted by viewer TZ (-> W4a); L13 pipeline cluster step dropped (-> W7); L14 docstrings (me, after merge); L15 zoom (W3) +- DESIGN-QUESTION for Jeremy: 'unit' affine puts column means off-centre (data sit in upper half of cube) - centring per-axis midrange would change all figures. +- Tour T1-T9 exact replacements in VERDICTS.md lines 287-316 (me, after merge). d: plotly Play/Pause over date ticks (-> W8). + +### Merges +- W6 merged 0d4f0cab (22ae49c7 CHANGELOG, 05f7aeb6 checklist + stale-date release gate, e876a556 release notes). D-12 partial. + TODO after W4a/W4b/W9 merge, apply plot.py docstring fixes: :5333-5334 axis_scale '(-1.1,1.1)' -> data in [-1,1], frame half-width + 1.125, axes pinned (-1.2375,1.2375); :7356 '[-1.1,1.1] frame box' -> '+/-1.2375 around the unit frame'; :5992-5993 slow-warning timing + -> 'emitted once fits at two or more history lengths (one of >=10 rows, or the longest available) have been timed'; :1124,:1133 + 'title must be a string (or None)' -> 'a string, a callable (ctx -> str), or None'. Also animation_context.py:207-209 window_bounds doc (L14). +- W2 merged 70dc00b3 (9 commits: yahoo intraday tz, Normalizer 1-D, autoinstall teardown + re-entry order, offline errors, npz trust msg, + scikit-image>=0.25.0 (+docs/doc_requirements.txt), load TypeError polars, 0-255 palette -> ValueError, font='Noto Sans' fresh process). + DESIGN-QUESTION (Jeremy): set_autoinstall — does ENTERING an older handle count as a new call? Current rule: no (newest call by creation order). + FOLLOW-UPS: pip install -e .[dev] after all merges (metadata floor); io.ipynb re-exec; core floors (numpy 2.0.0, pandas 2.2.2, scipy 1.13.0, + matplotlib 3.9.0, sklearn 1.4.2, statsmodels 0.14.0) have no cp313 wheels, pillow 8 none for >=3.10 -> packaging decision for Jeremy. + CHANGELOG bullets drafted in W2 report (integrate at end). +- Jeremy report ~16:40Z "surface colour doesn't match dots" (plot popped up in browser from agent test runs): ROOT CAUSE global IDW in + meshutil.vertex_colors_from_points -> washed-out mean colour. FIXED a422a97e (8-NN IDW) + tests; tests/conftest.py PLOTLY_RENDERER=json (headless). +- W5 merged 5c09e1bb (launch/examples pinned matplotlib, market_sectors split-adjusted shares, A0 video block, prose). Re-exec needed: + market_sectors, weather_decades, painting_embeddings, conversation_shape, morph_shapes_zoo, animate_forecast (regenerated, no outputs), + io, manip, plot, align, analyze, cluster, pipelines, projectile_kalman, stock_forecasting, streaming_data, lsl_streaming, text. + Regenerate market_sectors.mp4, sphx_glr_animate_market_sectors_thumb.gif, Bluesky 20_market_sectors clip, plot_sotus render. + W5 library leftovers (-> W10): flat cluster spec {'model':'KMeans','n_clusters':4,'random_state':0} drops random_state (cluster.py ~111-123); + names= overrides explicit legend=False. Unverifiable market data: HON 2026-06 SEC count half of 2026-03; XOM SEC history starts 2026. +- W1 merged 46c71700 (calendar-regular forecasting, cross-index reuse, ARIMA seasonal floor, warnings once). DESIGN-QUESTION (Jeremy): + flat-list datetime t= resolving to different step counts per dataset: code takes max(steps) (2e9669df) + test asserts differing lengths, + docstrings (plot.py:5586, :6836) say must be equal. Follow-ups: stock_forecasting.ipynb cells 11-12 prose (median gap/weekend interp) + stale; CHANGELOG 17-21 'one future step is always the median gap' stale. + +- W4a merged 69c1ff7d; W7 merged 785dca57 (+ my 2deac692: datatype-gate fix + polars transform= SchemaError fix); W10 merged 83b99d6a. +- Full suite at ~69c1ff7d (fullsuite1.log): 6115 passed, 12 failed = only the 6 regenerated launch notebooks' execution gates (expected until re-exec). +- W11 launched (flat spec keys in reduce/manip/align/impute/Pipeline). W3 batch 2 queued: plotly animated continuous hue (3-D colours don't + travel with window; 2-D segments static), L1 line width, L2 volume stipple, play/pause over date ticks. +- CHANGELOG drafts accumulating in scratchpad/changelog_drafts.md (W1, W2, W4a, W7, W10, main). + +- W3 batch1 merged 4d6dd7ce; W4b merged 57553242 (I resolved 2 plotly_backend conflicts: signature + before_show hook, W3's relocated + alpha normalization kept); W11 merged cc418fa9; docstring fixes 4669ddba; CHANGELOG integration 27d46868 (43 bullets + 2 corrected claims). +- Full suite 2 at 57553242: 6399 passed, 13 failed = 12 launch-notebook gates + 1 LSL collision with an agent's concurrent run (passes alone). +- Remaining: W9 (animation core) + W3 batch 2 (animated hue, L1, L2, play/pause) -> then CHANGELOG bullets for them, animation_context.py + window_bounds doc (L14), then scratchpad/verify_pipeline.sh (tutorial re-exec, pytest, sphinx -W html+doctest, thumbs, smoke), + commit notebooks, set tour REVIEW_COMMIT, headless tour, push, CI, Colab re-run, refresh evidence packet. +- NIT to sweep: predict/plot warnings attributed to library lines (e.g. plot.py:9383 'dataset index is not sorted'). -> FIXED 53c7673a/461d2e33. +- W3 batch 2 merged 0ed1e8f3; W9 merged db017681; CHANGELOG b6308028. +- VERIFY PIPELINE at b6308028 (18:17-19:32Z): 25/25 tutorials re-executed, no failures; pytest 6546 passed/21 skipped/0 failed; + sphinx html -W 0 warnings (51 gallery examples); doctest 323/0; thumbs regenerated; example smoke 6/6. +- Then: warning attribution + step text fix (merge 25da7cc0, 461d2e33), stock_forecasting prose, projectile/stock re-exec, + tutorials+thumbs commit b1f857cc. ruff clean. Next: push, CI, tour REVIEW_COMMIT=HEAD headless run, Colab run, evidence packet. +- Pushed e684d7cd. Local full suite there: 6551 passed/21 skipped/0 failed. Headless tour 241/3/0. FRESH COLAB (Pro, via Playwright, + https://colab.research.google.com/drive/1sFcFvxMUzNSjFnYFBjHlq6T8Twqqi-GD): 241 PASS / 3 SKIP / 0 FAIL, SHA confirmed; frontend checks: + early previews survive, viewer open/switch/close(0 graph divs)/reopen, playback + stable legend, hover names = legend (gd._fullData), + PLOT-style-plotly swatches match, date axis TZ-correct. Found: viewer displayed twice + IPython IFrame warning -> fixed e9db5204. +- CI 34639769259 at e684d7cd: 15/16 success (incl. docs-clean, all Windows/macOS), ubuntu-3.12 coverage step still running at 21:18Z. +- Review packet (untracked, like the previous one): notes/final_review_2026-09-11/START_HERE.md (9 decisions for Jeremy). +- Worktrees from this session removed (branches kept, all merged). +- Pushed e9db5204 (tour tooling: single viewer, no IFrame warning; DOC_GATE_OVERRIDE recorded). Headless tour at exact e9db5204: + 241/3/0. FRESH COLAB at e9db5204 (https://colab.research.google.com/drive/1bmQ-UMgWOmT_576hpkpbnL4S-dw2nyNq): 241 PASS / 3 SKIP, commit seen. +- CI 34639769259 at e684d7cd: COMPLETED SUCCESS 16/16 (+ release-gate skipped); per-job totals in notes/final_review_2026-09-11/ci_status.md. +- CI 34648813020 at e9db5204: COMPLETED SUCCESS 17/17 (16 jobs + release-gate skipped) at ~23:20Z. Final candidate fully verified. + +### Visual expectation writer extra (code-vs-doc) +- mpl fmt='-o' overrides marker=['o','s'] (backends disagree); markers= with '-' marks all smoothed pts (antialias docstring says true samples); + plotly ignores frame_kwargs; static plotly applies zoom (doc: animation only); '^' -> diamond in plotly 3-D; legend_kwargs x/y yanchor; + font='Noto Sans' ValueError in fresh process before bundled fonts registered + +### After the candidate: animated forecasts hung back from the drawn head (Jeremy's report, 2026-09-11) +- Report: "for animations with predictions, the predictions should show *from the endpoint in the current frame* not just from + the last observation." +- Confirmed on BOTH backends. An animation is paced on `_interp_anim_line`'s refined grid, so the drawn head usually sits + BETWEEN raw observations; `ForecastSchedule.anchor` floors to the last observation at or before it, and `polyline` started + there. Measured (2-D spiral, display box [-1, 1]): 20 rows/40 frames max gap 0.21, 24/37 frames affected, 2-frame stalls; + 12 rows/90 frames 0.51, 71/81, 8-frame stalls; 8 rows/160 frames 0.90 (45% of the box), 130/137, 23-frame stalls. + The forecast stood still while the head kept moving, then jumped -- exactly what the report describes. +- Fix: the schedule now carries the DRAWN head. `for_parallel`/`for_serial` read `grids=` (the animation grid arrays plot.py + already has -- not a second interpolation) at the same `end`/`shown` they already compute; `grid_head()` returns the vertex + and its fractional raw-row position; `polyline()` replaces vertex 0 with it; `to_display` maps heads with the full affine + (head POSITIONS are row indices, carried through untouched). Predicted points are untouched, so `t=`, `pin_ramp`'s exact x + ramp and the vertex count are unchanged. Regrouped (hue=/cluster=) reveals pass no grid and keep the raw-row anchor. +- Covered on 1-D/series, 2-D and 3-D, both backends, plus the `forecast_trail=` fan (each retained forecast starts at the head + of the frame it was fit at). New test: tests/test_animated_forecast_anchor.py (fails on the old code at frame 3). +- Docs: animation.rst's "re-anchored on the last revealed observation" + "joins ... to within one raw observation rather than + exactly" were describing the defect as the contract; rewritten, with a versionchanged note. CHANGELOG bullet added. +- NOTE: this moves the branch head off the verified candidate e9db5204 -- CI, the headless tour and the Colab run all need + re-running before sign-off, and notes/final_review_2026-09-11/ needs its evidence table refreshed. +- STALE ARTIFACTS from the fix: examples/animate_forecast.py animates 60 observations over 180 frames (stride 4, 237 grid + rows), so its old forecast trailed the drawn head by up to 0.40 = 10.7% of the data diagonal on 134/180 frames. Both + committed clips therefore show the OLD geometry and must be regenerated: docs/_static/thumbnails/ + sphx_glr_animate_forecast_thumb.gif (sphinx gallery build -> scripts/generate_gallery_thumbs.py) and + docs/tutorials/animate_forecast.mp4 (scripts/execute_tutorial.py docs/tutorials/animate_forecast.ipynb; the climate + archive IS cached -- 3 x (60, 6) real regions -- so re-execution will not silently fall back to synthetic data). + It is the ONLY example combining predict= with animate=; the feature tour has no animated-forecast case. + generate_gallery_thumbs.py never listed this stem (fixed 9f26b995), so nothing regenerated that thumb. +- SECOND, REACHABLE CASE the first fix missed: `hue=`/`cluster=` regrouping builds ForecastSchedule.for_regrouped (one drawn + trace per contiguous category run), which I had deliberately left on the raw-row anchor -- so it still lagged, silently + (no warning). Measured: 12-row 2-category spiral over 81 frames, gap up to 0.5071, mean 0.2167, on 75/81 frames. + Fix 6f2cfd96: DatasetRevealSchedule already locates the run holding each dataset's head to count rows, so it now keeps + (run, head_end grid row) + the fractional source parameter from run_head_param, and for_regrouped reads the head vertex + out of the same PER-RUN arrays the backends draw (plot.py passes grids=xform there too). _serial_counts returns the same + three quantities; in a serial sweep the last run with anything on screen holds the head. + Probes after the fix: two datasets x hue= -> 162 live forecasts, worst distance to a drawn tip 0.000000000; + cluster= (KMeans, 2 blobs) -> 83 live forecasts, 0.000000000. Coverage commit 9b881ee0 (serial/window styles + + predict=['Kalman','ARIMA'] through MultiModelSchedule's head forwarders, which had none). + LESSON: when a fix adds a fallback branch "by design", check whether that branch is REACHABLE in a drawn configuration + before calling the fix complete -- this one was, and shipped the same defect. +- CANDIDATE c433b8f0 pushed (e9db5204..c433b8f0; the 3 held-back notes commits went up under it, which is fine now that the + head itself is the tested candidate). Verify pipeline green end to end: sphinx -W html 0 warnings/51 gallery examples, + thumbs regenerated, animate_forecast tutorial re-executed (real cached archive, 3 x (60,6)), doctest 323/0, + full pytest 6572 passed/21 skipped/0 failed. Headless tour at c433b8f0 (REVIEW_COMMIT updated + update_feature_tour.py + sync, 244 cases, source hash 4c997697): 241 PASS / 3 SKIP / 0 FAIL, Not run: []. Colab candidate notebook built: + notes/colab/hypertools_1.1_candidate_c433b8f0.ipynb (518 cells, 239 code cells cleared). +- SCOPE CHECKS so the fix is neither over- nor under-claimed: animate_forecast is the ONLY example combining predict= with + animate=; the feature tour has no animated-forecast case; no Bluesky launch clip covers it; and every polyline() call site + is an animation frame path (plot.py 12692/12707, plotly_backend 5744/5747, MultiModelSchedule) -- the static overlay + entry point _draw_forecast_overlays is not among them, so the committed static predict images are unaffected. +- MY OWN MEASUREMENT ERRORS, both caught and redone: (1) a probe filtered 3-D artists with len(get_xdata()), which is EMPTY + for Line3D until projection -- it made me report that the weather example drew no forecasts at all; use get_data_3d(). + (2) the same probe printed 'OK' while checking ZERO forecasts, because the distance threshold was compared without + asserting a non-empty sample. Re-run properly: 87 live forecasts across 29 frames, worst distance 0.000000000. +- FRESH COLAB at c433b8f0 (https://colab.research.google.com/drive/1-j8u-_pQDRDReN48yORA6OOPCeeTsDzi): 241 PASS / 3 SKIP / + 0 FAIL, Not run: [], 244 rows; SET-01 'Confirm the code being reviewed' PASS and the executed notebook carries c433b8f0, + so Colab installed the candidate. Visual counts 170 not applicable / 74 not reviewed (Jeremy's sign-off, as before). +- ANIM-forecast-plotly LOOKED like it drew no forecast in Colab (two screenshots showed only signal+truth). Resolved by + reading the live figure and reproducing locally: the n=1 'signal'/'Kalman'/'GaussianProcess' traces are LEGEND PROXIES; + the real forecasts are unnamed slots (live age=0 + trail ages 1-3 per model) carrying 901 points in frames 1-8 (frame 0 + legitimately has none -- too little history to fit). The horizon is 3 hours on a 24-hour axis, so it is a few pixels wide + and overlaps truth. NO DEFECT. Note this case is 24 rows over 9 frames -> grid stride 1 -> every head lands on a raw + observation, so it does not exercise the anchor fix at all; tests/test_animated_forecast_anchor.py does. +- HOVER CONTRACT re-checked on the figure this fix touches (ANIM-forecast-plotly, 13 traces): trace 0 'signal' is the only + hoverable one and it IS named (legendgroup 'signal'); the 8 unnamed trail/live forecast slots are all hoverinfo='skip', + and truth + the 3 legend-proxy traces are named with showlegend=True. So nothing can hover as 'trace N' -- Jeremy's + FIRST report stays fixed on the traces this change adds vertices to. (Forecast traces are deliberately non-hoverable; + pre-existing design, untouched here.) +- COLAB FRONTEND: viewer opened ANIM-forecast-plotly, played it, and Close released it -- plotly modebar/logomark markers + went from present to 0. NOTE the 'Interactive HyperTools review' string matches the HELPER CELL'S SOURCE in a snapshot, + so it is not evidence of a live iframe; the graph markers are. +- CI 34674962598 at c433b8f0: COMPLETED SUCCESS, 17/17 (16 success + release-gate skipped), finished 07:08:04Z, zero + failures. Per job: macOS 3.10-3.13 6556 passed/24 skipped; ubuntu 3.10/3.11/3.13 6552/28; ubuntu 3.12 6552/28 plus + pandas-3.0 gate 6565/25 plus coverage 6565/25; Windows 3.10-3.13 6546/34; docs-clean 51 gallery files + 323 doctests + 0 failures; dataset-gate 33; live-source-gate 43 passed/1 deselected; wheel-smoke passed. EVERY platform is exactly + +20 tests vs e9db5204 (macOS 6536->6556, ubuntu 6532->6552, Windows 6526->6546, pandas/coverage 6545->6565), matching + the 20 tests in tests/test_animated_forecast_anchor.py -- the new coverage runs everywhere and nothing else moved. +- HANDOVER STATE verified directly (not from memory): origin/fix/1.1-release-review == c433b8f0 (the commit CI tested and + Colab installed), and the 4 unpushed commits touch ONLY notes/session_2026-09-11_final-review.md -- no code, tests or + docs are held back from the PR. +- SAME TRAP, THIRD FORM (stderr suppression, 2026-09-12): pulling per-job test totals with + `gh api repos/O/R/actions/jobs/<id>/logs 2>/dev/null` reported '(no pytest summary line)' for all 9 completed jobs. + The endpoint is fine (HTTP 200, ~1 MB text/plain); `gh api` REFUSES to print a body containing terminal escape + sequences and needs --allow-escape-sequences, and 2>/dev/null hid that error. `gh run view --job <id> --log` prints + nothing at all for these jobs, so it is not a workaround. Lesson: never suppress stderr on a probe whose EMPTY result + you intend to interpret. +- TOOLING TRAP, fired TWICE today (existing note n410812797x266): `pytest ... > log 2>&1; tail log` and + `pytest ...; echo "exit: $?"` both exit with the LAST command's status, so the harness reports exit 0 over a failed or + never-run suite. One run had also invented a test filename (tests/test_plot_forecast_trail.py does not exist), pytest + errored "no tests ran", and the notification still said exit 0. Read the summary LINE, never the exit code, or glob the + paths instead of typing them. diff --git a/notes/session_2026-10-08_visual-signoff.md b/notes/session_2026-10-08_visual-signoff.md new file mode 100644 index 00000000..97198702 --- /dev/null +++ b/notes/session_2026-10-08_visual-signoff.md @@ -0,0 +1,122 @@ +# Session 2026-10-08: finishing the 1.1 final review (visual sign-off round) + +Branch `fix/1.1-release-review`; PR #286 still OPEN and unmerged. Nothing merged, tagged or published. + +## State at session start +- PR head = c433b8f0 (unchanged since 2026-09-12). Local branch had 6 unpushed notes commits + (incl. c7dea477 by Jeremy's 2026-10-03 session moving notes into .claude/compound lessons). +- RTD: still no build since 2026-07-24 (API v2 confirms). GitHub hook 11883014 -> readthedocs + legacy URL, no secret; deliveries list empty (retention). Needs RTD admin re-sync. MANUAL. +- CI rerun (attempt 2) of run 34674962598 at c433b8f0 started for dependency drift. + +## Done this session +- Headless tour at c433b8f0 with current deps: 241 PASS / 3 SKIP / 0 FAIL; 74 visual cases. +- Built a review artifact (https://claude.ai/artifact/UPKL2S8yGd6VaAp9i5adm5) from the executed + tour (scratchpad gallery/extract.py; plotly animations -> mp4 via gallery/plotly_anim_mp4.py); + verdicts stored in its db collection `verdicts` (read with ArtifactData list). +- Jeremy's verdicts: 54 pass / 20 flag. Flags -> actions: + - plotly animations didn't play in page -> page now plays frame-by-frame mp4s. + - ANIM-clock one curve, COLOR-luminance shows input not palette, TEXT-* unreadable, + PANEL-models subplots title "example is wrong" -> tour cases changed (b240258a). + - ANIM-forecast truth shown from frame 0 -> DECIDED: truth point appears when a forecast reaches it. + - RED-describe 'average' was pooled stack -> DECIDED: mean of individual curves; pooled kept as + result['pooled']; integer ticks; describe(show=False) returns fig. + - PLOT-scale-matplotlib grid -> plotly (no grid) is correct; remove matplotlib grid. + - GUI-native: no Qt binding locally; remains manual/optional. +- Agent visual pre-review found 2 real plotly bugs (Jeremy had passed both): nested-list leaf + names 1,2,2,4 and 3-D markers at half size -> fixed a4b146af (full suite 6583/0). +- Decisions: forecast_cluster/hue default colours offset past observed colours (approved); + raise floors (183de73c); XOM CIK pin + HON note (8d02cb81); describe/t= docs + anim size + note (approved); axis_scale midrange DEFERRED to 1.2 (open issue); set_autoinstall KEEP. +- _aa_x (plotly 1-D forecast branch) proven unreachable (1-D animation refused at + plotly_backend.py:1630); 528-call settrace sweep, line 5788 hit 0 times. +- Bluesky clips re-rendered at c433b8f0 (gitignored, notes/bluesky-launch/). + +## Pending +- Integrate worktree agents: truth timing + cluster colours; describe + grid + docstrings. +- Regenerate media: market_sectors tutorial mp4 + thumb (XOM data), anything else touched. +- Full verification: pytest, ruff, sphinx -W, doctest, tutorials, headless tour, push, CI, Colab. +- Round-2 visual review of changed cases only (carry forward pass for pixel-identical renders). +- Open GitHub issue for axis_scale midrange (1.2). + +## Integration + verification (2026-10-08 afternoon) +- Integrated: a4b146af (plotly nested names + 3-D markers), b240258a/ea7f90a2 (tour cases), + 183de73c (floors), 8d02cb81 (XOM CIK), c1b7adc3/888e231d (describe), 279cd73a (no data-scale grid), + ddd98ae7 (docstrings), cb0f9a95 (truth reveal), 387e7704 (forecast group colours), + bdbcebf1 (CI apt --no-install-recommends + retries), 5dda6190 (25 tutorials re-executed), + 5520987b (market thumb). +- Verified at 5520987b: tutorials 25/25; pytest 6633 passed/21 skipped/0 failed; ruff clean; + example smoke 348 passed; sphinx -W html 0 warnings, 51 gallery; doctest 323/0; thumbs (only + market_sectors changed). ea7f90a2 changed only scripts/update_feature_tour.py (tour tooling). +- Headless tour at ea7f90a2: 241 PASS / 3 SKIP / 0 FAIL, clean tree, exact commit. +- CI drift rerun at c433b8f0: all 15 test jobs + gates green; docs-clean CANCELLED at 75-min timeout + because apt fetched 182 MB at 111 kB/s (27 min) -> fixed in bdbcebf1. +- Pushed ea7f90a2; CI run 37825666381 in progress (5520987b run cancelled as superseded). +- Issue #287 opened for axis_scale midrange (1.2). +- Round-2 review page published (same artifact URL, collection verdicts_r2): 29 to review + (flagged or changed render + HIER-plotly names + DOCS-market-sectors), 46 carried (byte-identical + renders previously passed). Bluesky 20_market_sectors re-rendered at ea7f90a2. + +## Round 2 + GUI + final candidate (2026-10-08 evening) +- Round-2 verdicts: 28/28 pass; GUI-native flagged "need more instruction". +- GUI-native recorded in a real Qt window (PyQt6 in a throwaway uv venv; QTest events; frames grabbed + from the window): 11 hover labels, azim -60 -> 147, xlim +-1.00 -> +-1.18, q closes, clean exit. + Found + fixed (ea7a5f4e): tour GUI script used show=False (no window ever opened); explore hover label + clipped at the left edge (now opens toward axes centre; tests/test_explore_label_placement.py). + Recorder: scripts/record_gui_native_screencast.py (ruff tidy a5c83938). Screencast on the review page. +- Full suite at ea7a5f4e: 6637 passed / 21 skipped / 0 failed; ruff clean at a5c83938. +- Colab at a5c83938: 241/3/0; console showed Colab's table script throwing 'buttonEl already declared' + where one cell displays two DataFrames (IMP-score, results summary) -> second table as plain HTML + (4c762c0c). Colab at 4c762c0c (drive/1MbgX7eFkavZPtdSQ4FoJOsSP6eYJSv3s): 241/3/0, SET-01 commit + confirmed, no notebook-caused console errors. +- Headless tour at 4c762c0c: 241/3/0, clean tree. CI run 37868951965 at 4c762c0c: success, 17/17. +- PR #286 head = 4c762c0c, OPEN, MERGEABLE. NOT merged. Waiting on Jeremy: GUI verdict, RTD webhook, + merge sign-off. Packet: notes/final_review_2026-09-11/START_HERE.md (untracked). + +## 2026-10-09: docs site (RTD parity) + final candidate d21e0340 +- Jeremy confirmed GUI-native ("gui looks good") -> recorded in verdicts_r2. Visual sign-off complete. +- RTD-parity build: from docs/, READTHEDOCS=True READTHEDOCS_GIT_IDENTIFIER=master READTHEDOCS_OUTPUT=$OUT + sphinx -T -b html -d _build/doctrees . $OUT/html, then python docs/post_build.py. +- post_build bug (4e0dbaeb): looked for _images under $READTHEDOCS_OUTPUT not .../html. +- Browser pass (sampled) found gallery/dark/mobile/duplicate/console defects -> 0ad2a366; docstring + markup leaks (38 on 8 pages) + _Unset sentinel repr -> 09f6e8b1, test 61b5cda5. +- Jeremy then reported hierarchy.html broken: furo red ERROR box from `.. contents::` (CSS ::before, invisible + to sphinx -W / HTML scans). Fixed 998581dd. Lesson docs-site-check-every-page + checker (7a240862). +- Every-page check also led to: tutorial setup cell collapsed under title (post_build), one Methods/Attributes + table per class (numpydoc_show_class_members=False), stubs committed as generated (40ea3170); + teaser GIFs at native 200px (d21e0340). +- Full suite at 40ea3170: 6655 passed/21 skipped/0 failed; ruff clean. Tour at d21e0340: 241/3/0. +- Cold RTD build at d21e0340: 0 warnings, 171 pages; linkcheck 0 broken; every-page check 137 pages 0 + findings; verify_docs_playwright 8/8. +- Colab at d21e0340 (drive/1Bz_253Z5ZDw7tKUqy0v3xJJgPLccnlAz): 241/3/0, SET-01 commit confirmed. Console (b): + plotly.py renderer's own `import "…plotly-3.6.0.min"` (rstrip(".js") bug) 403 + MathJax 2.7.5 errors: UPSTREAM. +- CI run 37938862214 at d21e0340: success, 17/17 (16 success + release-gate skipped). PR #286 head = d21e0340, MERGEABLE. NOT merged. + +## 2026-10-09 (later): red-team review, sign-off, re-cut +- f5a0af21: changelog links (docs home + release notes), release notes brought up to date. CI 37958047439 + success 17/17; Colab 241/3/0 (tab dropped mid-run; counts from the original Run all). +- Jeremy pasted an independent red-team review of f5a0af21 (notes/release_redteam_2026-10-09/, untracked, + his session's files). All 5 findings reproduced with his probe, then fixed (rules chosen by Jeremy via + questions): (1) scoring ranks complete models only, attrs['incomplete'], None verdicts (3121910d); + (2) impute truth=/mask= aligned by label, mismatches raise (3121910d; type checks through shared helpers + 0585a5ad after tests/test_datatype_gate.py failed in the full suite); (3) describe clones+sweeps an + unfitted instance, rejects fitted models (33810c6a) and reduce=None (9648e258); (4) lazy_import checks + installed extras against declared versions, upgrades or reports (b2d35ad7); (5) isotropic docstring. + Also: sklearn clone(hyp.Pipeline) works (33810c6a; failed in 1.0 too). +- Probe after: labels 3.667 x3; best=SimpleImputer, incomplete=['PPCA']; describe instance == name. +- e95777fb: full suite 6760 passed/21 skipped/0 failed; ruff clean; tour 241/3/0, all 73 figure renders + byte-identical to the round-2 approved ones; RTD-way docs -W 0 warnings, 137 pages 0 findings; + verify_optional_install PASS; packaging tests 13 passed; Colab (drive/1O-gcAIgAxVMrXTFboIFoE0mjHaihyFoQ) + 241/3/0, SET-01 confirmed. Colab image ships plotly 5.24.1 (below the 6.1.1 floor); the tour's install + cell replaced it with 7.1.0 before import, so the new version check did not fire there. +- CI 37972394267 at e95777fb: 15 success + release-gate skipped + 1 FAILURE: test (ubuntu-latest, 3.11), + tests/test_multibyte.py::test_render_script_exits_NO_BROWSER_when_the_browser_will_not_launch, + subprocess.TimeoutExpired at 120 s (same test passed in seconds on the other 15 jobs). Kaleido's own + 30 s timeout did not fire. Fixed in the render script (hard deadline -> NO_BROWSER exit) + new test + with a real silent executable (a0c30a10). +- RTD webhook root cause confirmed by POSTing an unsigned request to the hook URL: HTTP 400 "This webhook + doesn't have a secret configured ... no longer permitted". Hook 11883014 dates from 2017-02-02, legacy + URL, no secret. Needs Jeremy's RTD admin: re-sync/re-create the GitHub integration, build latest + v1.1.0. +- SIGN-OFF 2026-10-09 (AskUserQuestion): "Sign off: merge and re-cut", CHANGELOG date 2026-10-09. + Scope: fast-forward master, checklist steps 2-5 (date, gallery, artifacts, master CI, move tag, tag CI, + replace draft assets + body). STOP before PyPI upload and before publishing the GitHub release. diff --git a/notes/session_2026-10-09_docs-site-fixes.md b/notes/session_2026-10-09_docs-site-fixes.md new file mode 100644 index 00000000..badb8970 --- /dev/null +++ b/notes/session_2026-10-09_docs-site-fixes.md @@ -0,0 +1,32 @@ +# 2026-10-09 -- built-docs-site defect fixes (branch fix/1.1-release-review) + +Scope: docs/, examples/, tests/ only (hypertools/ untouched). Not pushed. + +Fixed (all verified in a browser on the RTD-parity build, 1400px / 400px / dark): +- Gallery cards: title is now the visible link to the example page (whole card + clickable, thumbnail still -> Colab). custom.css used to `display:none` the + only example-page link. Tooltip was rendered twice (ours + sphinx-gallery's); + now one, theme-coloured, below the card. +- Duplicate videos: `docs/_gallery_scrapers.py` (one scraper = sphinx-gallery's + matplotlib scraper + show=False HyperAnimations, each once). Before/after + videos: animate 5->2, animate_trails 5->2, animate_spin / animate_surface_morph + / animate_trails_mix / plot_story_trajectories / save_movie 2->1. +- Duplicate plotly figure (plot_interactive_backend 2->1): `ignore_repr_types` + (hyp.plot returns HyperPlotlyFigure, so the regex names that class too). +- Dark theme: card title / tooltip / link-hover colours use furo variables. +- Phone: plotly figures refit to the column width (gallery-fixes.js), CSS + scroll fallback. +- tutorials/plot.html console: post_build strips plotly's dead ES-module CDN + import (403) and its MathJax 2.7.5 <script> (clashes with the page's MathJax 4). +- Tooltips: post_build strips RST backticks/roles (15 cards). +- `remove_config_comments`, explore-mode warning filtered for the gallery build + (reset_modules hook) + a sentence in examples/explore.py saying the image is static. +- API page H1s keep their case (`section:has(> dl.py) > h1`). + +Left alone / to decide: +- tutorials.rst shows 200px GIFs with `:width: 400` (rst option, not CSS). +- plot_impute / plot_missing_data "Out:" blocks show real data warnings that + include the build machine's path to the example. +- Built site has no link to CHANGELOG.md (README.md and pyproject do). +- Incremental rebuild note: sphinx-gallery only re-runs an example when its + `docs/auto_examples/<name>.py.md5` is stale; delete that file to force it. diff --git a/pyproject.toml b/pyproject.toml index f986e64f..350436ee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta" [project] name = "hypertools" # the 1.1 line: this branch's unreleased work now includes the hierarchy -# features AND four rejections of previously-accepted input (CHANGELOG 1.1.0, +# features AND seven rejections of previously-accepted input (CHANGELOG 1.1.0, # "Changed / validation"), which cannot ship as a patch release. The version # and the top CHANGELOG heading are held equal by # tests/test_release_readiness_gate.py::test_changelog_top_version_matches_pyproject. @@ -46,25 +46,30 @@ classifiers = [ ] dependencies = [ # NOTE (2026-07 red-team): these floors must be co-installable with the - # numpy>=2.0 floor below. matplotlib<3.9, pandas<2.2.2, numba<0.60 and + # numpy floor below. matplotlib<3.9, pandas<2.2.2, numba<0.60 and # scikit-learn<1.4.2 each require/only-support numpy<2, so a floor-pinned # resolver (`uv --resolution lowest`, a minimum-pinned lockfile) would hit # ResolutionImpossible. Default (newest-first) installs are unaffected. - "scikit-learn>=1.4.2", # 1.4.2 is the first with numpy-2 runtime support + # 2026-10: each compiled core floor is also the first release that ships + # wheels for every Python the classifiers claim (cp310 through cp313, + # checked on PyPI's JSON API; tests/test_dependency_floor_wheels.py), so + # a lowest-version install on 3.13 never builds numpy/pandas/scipy/ + # matplotlib/scikit-learn/statsmodels/pillow from source. + "scikit-learn>=1.5.2", # 1.5.2: first with cp313 wheels (1.4.2 = numpy 2) # pandas 3.0+ supported as of datawrangler 0.5.0 (data-wrangler#30/#31 fixed # the pandas-3 type detection); the <3 ceiling is lifted (CI exercises # pandas 3.x). 2.2.2 is the first pandas that supports numpy 2 (2.2.0/.1 - # require numpy<2). - "pandas>=2.2.2", + # require numpy<2); 2.2.3 is the first with cp313 wheels. + "pandas>=2.2.3", "seaborn>=0.13.0", - # Pillow is already a hard requirement of matplotlib>=3.9 (`pillow>=8`), - # so this adds no install weight -- but hypertools now imports PIL - # itself (hypertools/plot/colors.py: image_palette / palette='image:...'), - # and a library that imports a package declares it. - "pillow>=8", - "matplotlib>=3.9.0", # 3.8.x requires numpy<2; 3.9.0 supports numpy 2 - "scipy>=1.13.0", - "numpy>=2.0.0", + # Pillow is already a hard requirement of matplotlib (`pillow>=8`), but + # hypertools imports PIL itself (hypertools/plot/colors.py: + # image_palette / palette='image:...'), and a library that imports a + # package declares it. 10.4.0 is the first with cp313 wheels. + "pillow>=10.4.0", + "matplotlib>=3.9.2", # 3.8.x requires numpy<2; 3.9.2: first cp313 wheels + "scipy>=1.14.1", # first with cp313 wheels + "numpy>=2.1.0", # first with cp313 wheels (2.0.x: cp310-cp312) "umap-learn>=0.5.5", # umap-learn's own numba floor (>=0.51) lets uv resolve unbuildable # numba 0.53/llvmlite 0.36 on py3.12+ (audit X5-003); pin a real floor. @@ -81,7 +86,7 @@ dependencies = [ # ModuleNotFoundError when these were extras-only). The heavier skaters laplace # ensemble stays in the [predict] extra. "pykalman>=0.11", - "statsmodels>=0.14", + "statsmodels>=0.14.3", # first with cp313 wheels # HyperTools 1.0: data-wrangling core (funnel/stack/unstack/format detection, model # dispatch, text+HF embeddings). 0.5.0+ adds pandas 3.0 support (dw#30/#31). # https://github.com/ContextLab/data-wrangler @@ -124,7 +129,11 @@ io = ["openpyxl>=3.1.0"] # back to a translucent scatter "fog" and emits a UserWarning suggesting # this extra (or backend='plotly', which always renders a full go.Volume # without needing scikit-image at all). -density3d = ["scikit-image>=0.22.0"] +# 0.23.x builds against NumPy 2 (0.22 wheels target the NumPy 1 ABI), but +# 0.23.x/0.24.0 ship no CPython 3.13 wheels; 0.25.0 is the first release +# with wheels for every Python classified above (PyPI JSON, 2026-09-11; +# tests/test_dependency_floor_wheels.py). +density3d = ["scikit-image>=0.25.0"] # hyp.reduce's six torch-backed autoencoder reducers (GH #162, # hypertools/reduce/autoencoders.py: Autoencoder, DeepAutoencoder, # SparseAutoencoder, ConvolutionalAutoencoder, SequenceAutoencoder, @@ -147,8 +156,8 @@ lsl = ["pylsl>=1.16"] # HdpModel (semantic stage), resolved after the scikit-learn registry and # before the Hugging Face fallback. Opt-in; never in base install -- # requesting a gensim name without this extra raises a friendly -# ImportError. gensim>=4.3 supports numpy>=2. -gensim = ["gensim>=4.3"] +# ImportError. NumPy 2 support starts with gensim 4.4.0 (release review). +gensim = ["gensim>=4.4.0"] dev = [ "pytest>=8.0.0", "pytest-cov>=4.1.0", @@ -176,7 +185,7 @@ dev = [ # exercise the 3-D density= iso-surface path (tests/test_density.py); # the scikit-image-absent fallback is exercised separately, in a # subprocess with a real import-system blocker (see that test module) - "scikit-image>=0.22.0", + "scikit-image>=0.25.0", # exercise the six torch-backed autoencoder reducers (GH #162, # tests/test_autoencoders.py); the torch-absent ImportError path is # exercised separately, in a subprocess with a real import-system @@ -193,7 +202,7 @@ dev = [ # tests/test_gensim_text.py); the gensim-absent ImportError path is # exercised separately, in a subprocess with a real import-system # blocker (see that test module) - "gensim>=4.3", + "gensim>=4.4.0", ] [project.urls] diff --git a/readme.md b/readme.md index ed690150..911d9841 100644 --- a/readme.md +++ b/readme.md @@ -202,8 +202,8 @@ HyperTools 1.0 modernizes the toolbox while keeping the familiar API: Check the [repo](https://github.com/ContextLab/hypertools-paper-notebooks) of Jupyter notebooks from the HyperTools [paper](https://arxiv.org/abs/1701.08290) -(note: those notebooks predate the 1.0 API described below). For up-to-date, -runnable examples covering every 1.0 feature, see the +(note: those notebooks predate the 1.x API described below). For up-to-date, +runnable examples covering every 1.1 feature, see the [example gallery](http://hypertools.readthedocs.io/en/latest/auto_examples/index.html) in the docs. @@ -243,28 +243,30 @@ carries on. hypertools itself is never reinstalled, so a development or branch install stays as it is. Static image export with the plotly backend also provisions what kaleido needs on first use (a Chrome build and, on Debian/Ubuntu images such as Colab and Kaggle, the system libraries it -lacks). Set `HYPERTOOLS_AUTO_INSTALL=0` to turn this off; a missing extra -then raises `ImportError` with the manual `pip install` command. +lacks). `hyp.set_autoinstall(False)` turns this off (for the session, or +for one block as a context manager); a missing extra then raises +`ImportError` with the manual `pip install` command. ## Requirements + python>=3.10 -+ scikit-learn>=1.4.0 -+ pandas>=2.2.0 ++ scikit-learn>=1.5.2 ++ pandas>=2.2.3 + seaborn>=0.13.0 -+ matplotlib>=3.8.0 -+ scipy>=1.13.0 -+ numpy>=2.0.0 -+ umap-learn>=0.5.5, numba>=0.59 ++ pillow>=10.4.0 ++ matplotlib>=3.9.2 ++ scipy>=1.14.1 ++ numpy>=2.1.0 ++ umap-learn>=0.5.5, numba>=0.61.0 + pydata-wrangler>=0.5.1 (data-wrangling core) -+ pykalman>=0.11, statsmodels>=0.14 (Kalman/ARIMA forecasting and imputation) -+ requests, dill, ipympl ++ pykalman>=0.11, statsmodels>=0.14.3 (Kalman/ARIMA forecasting; Kalman imputation) ++ requests>=2.31.0, dill>=0.3.8, ipympl>=0.9.3 + ffmpeg (for saving animations) All Python dependencies are declared in `pyproject.toml` and installed automatically by pip. The base install covers all core functionality (plotting, dimensionality reduction, alignment, clustering, normalization, -and `Kalman`/`ARIMA` forecasting + imputation) and therefore pulls in the +`Kalman`/`ARIMA` forecasting, and missing-data imputation) and therefore pulls in the full scientific stack (NumPy, SciPy, pandas, scikit-learn, matplotlib, seaborn, UMAP/Numba, statsmodels, pykalman, ipympl, pydata-wrangler); it is not a minimal footprint. Heavier optional model families are separated into diff --git a/scripts/add_colab_install_cell.py b/scripts/add_colab_install_cell.py index 787a9e39..f5486066 100644 --- a/scripts/add_colab_install_cell.py +++ b/scripts/add_colab_install_cell.py @@ -2,7 +2,7 @@ cell that installs hypertools, so it runs standalone when opened in Google Colab. -The install line is branch-aware: +Gallery install lines are branch-aware: * on ``master`` OR a ``vX.Y.Z`` release tag it installs the RELEASED package (``%pip install -q "hypertools[interactive]"``); @@ -10,6 +10,11 @@ preview notebooks install the matching dev build rather than the older PyPI release. +Tutorials use a separate version-aware guard: a current local installation is +retained, an old checkout is refused with guidance, and a missing/old installed +package resolves to >=1.1.0 on master/tags or the selected development branch. +Optional extras remain available through on-demand installation. + The script is idempotent AND self-correcting: a notebook that already has a hypertools install cell is not skipped -- its install target is RE-TARGETED to match the current branch (2026-07 release review: the old ``has_install`` guard @@ -26,9 +31,8 @@ RELEASE NOTE: run this on the ``master`` BRANCH when cutting the release and commit the migrated notebooks BEFORE master/tag CI and the PyPI upload (see -RELEASE_CHECKLIST.md for the full order). Until the upload the notebooks -briefly resolve the previous PyPI release, which is harmless -- they are static -source content. The ``release-gate`` CI job enforces that no ``git+``/``@dev`` +RELEASE_CHECKLIST.md for the full order). Before the upload, validate candidates with an explicit Git installation; +the published tutorial requirement cannot resolve until 1.1 is available. The ``release-gate`` CI job enforces that no ``git+``/``@dev`` install survives on a release build. """ @@ -95,6 +99,28 @@ def hyp_spec(extras, branch): return f'hypertools[{extras}] @ {_GIT_URL}@{branch}' +def guarded_install_source(extras="interactive", branch="master"): + """Version-aware tutorial setup; local source checkouts are preserved.""" + spec = (f"hypertools[{extras}]>=1.1.0" if _is_release_ref(branch) + else hyp_spec(extras, branch)) + return '# HyperTools setup: use 1.1 or newer; retain a current local checkout.\nimport importlib.util\nfrom importlib.metadata import version, PackageNotFoundError\nfrom packaging.version import Version\nfrom pathlib import Path\ntry:\n _hypertools_version = Version(version(\'hypertools\'))\nexcept PackageNotFoundError:\n _hypertools_version = Version(\'0\')\nif _hypertools_version < Version(\'1.1.0\'):\n _spec = importlib.util.find_spec(\'hypertools\')\n if _spec and _spec.origin and (Path(_spec.origin).resolve().parents[1] / \'.git\').exists():\n raise RuntimeError(\'Select a HyperTools 1.1 checkout/kernel before running this tutorial; the installer will not replace your checkout.\')\n %pip install -q "{spec}"\nelse:\n print(\'Keeping HyperTools\', _hypertools_version, \'in this kernel. Optional extras are loaded when requested.\')\n'.format(spec=spec) + + +def portable_video_source(filename): + """Frontend-specific playback; local docs retain their relative asset. + + The block ENDS its cell (tests/test_examples_are_native.py compares the + cell from its first line to the end with this template), so it is the + cell's last statement and IPython displays nothing after it: a figure or + tuple left as a bare expression just above it is evaluated and never + shown. Anything the cell should show goes through ``display(...)`` or + ``print(...)`` before the block (2026-09-11 review: io, manip, plot, + lsl_streaming and streaming_data lost their outputs that way; + ``test_no_colab_video_block_swallows_a_displayed_value`` guards it). + """ + return '# Colab serves output frames separately from kernel files; embed movie bytes.\ntry:\n from google import colab as colab\nexcept ImportError:\n pass # Local Jupyter/Sphinx uses the relative video below.\nelse:\n from IPython.display import Video, display\n display(Video({filename!r}, embed=True))\n'.format(filename=filename) + + def install_lines(branch): if _is_release_ref(branch): pip = '%pip install -q "hypertools[interactive]"' @@ -162,12 +188,28 @@ def main(): note, pip = install_lines(branch) retargeted = added = 0 for path in sorted(NOTEBOOKS): - with open(path) as f: + with open(path, encoding='utf-8') as f: nb = json.load(f) + guarded = [c for c in nb.get('cells', []) + if 'hypertools-install' in c.get('metadata', {}).get('tags', [])] + if guarded and os.path.basename(os.path.dirname(path)) == 'tutorials': + cell = guarded[0] + source = ''.join(cell['source']) + extras = re.search(r'hypertools\[([^]]+)\]', source).group(1) + desired = guarded_install_source(extras, branch) + if source != desired: + cell['source'] = desired.splitlines(keepends=True) + cell['outputs'] = [] + cell['execution_count'] = None + with open(path, 'w', encoding='utf-8') as f: + json.dump(nb, f, indent=1, ensure_ascii=False) + f.write('\n') + retargeted += 1 + continue if has_install(nb): # already has an install cell -> re-target it to this branch if retarget_notebook(nb, branch): - with open(path, 'w') as f: + with open(path, 'w', encoding='utf-8') as f: # ensure_ascii=False keeps literal UTF-8 (matching nbformat) # so re-targeting doesn't churn every non-ASCII glyph into a # \\uXXXX escape and bloat the diff. @@ -187,7 +229,7 @@ def main(): keepends=True) else: cells.insert(0, new_code_cell(f'{note}\n{pip}')) - with open(path, 'w') as f: + with open(path, 'w', encoding='utf-8') as f: json.dump(nb, f, indent=1, ensure_ascii=False) f.write('\n') added += 1 diff --git a/scripts/execute_tutorial.py b/scripts/execute_tutorial.py index bcdc400c..e89d7735 100644 --- a/scripts/execute_tutorial.py +++ b/scripts/execute_tutorial.py @@ -24,25 +24,41 @@ the same window. Execution still resolves relative paths against the notebook's ORIGINAL directory, so a redirected run reads the same data. -**The Colab install cell is skipped, and this is not optional.** Every launch -notebook opens with ``%pip install "hypertools[...] @ git+...@dev-1.0"`` for -Colab. Executed locally, that cell installs the REMOTE branch over this venv's -editable checkout, mid-run, so every later cell runs against whatever was -last pushed rather than the code being documented. Measured 2026-09-03: the -market notebook failed in its own kernel with "48 dimensions ... static plots -support at most 2" -- the column-MultiIndex support that lands in 1.1 was -gone -- and ``pip show hypertools`` afterwards reported the git install, not -the editable one. The committed notebooks carry execution timestamps on that -cell from 2026-07-30, when local and remote happened to agree, which is why -nothing noticed. So cells whose source contains ``pip install`` are tagged -``skip-execution`` in memory for the run (nbclient honours that tag), and the -tag is stripped before writing, so the committed cell is byte-identical. -The example gate already exempts install cells from having executed. +**HyperTools installation cells are skipped during local verification.** +Current tutorials use a version-aware PyPI installer; candidate verification +must retain the selected checkout. Only explicitly tagged HyperTools installers +(or legacy cells containing a live HyperTools pip command) are skipped. +Configuration and independent prerequisites remain executable. Successful +setup-only prerequisite cells tagged ``prerequisite-install`` have their pip +chatter cleared before saving; failures still abort execution. Mixed legacy +install/work cells must be split before verification; a comment mentioning pip +is not an installation command. For the feature tour, configuration and its +Colab-only installer are separate cells. Use --out-dir to preserve source files. + +**The executing user's home directory is rewritten to ``~`` in the outputs.** +Warnings and tracebacks carry absolute paths (``/Users/<name>/hypertools/ +hypertools/tools/format_data.py:495: UserWarning: ...``), so an executed +notebook committed as-is publishes whoever ran it. After execution, every +stream output, error traceback and ``text/plain`` result has +``os.path.expanduser('~')`` replaced by ``~`` (see `scrub_home`). A warning +raised by a notebook cell names the kernel's per-session temp file for that +cell (``/var/folders/<id>/T/ipykernel_21956/2889100357.py:14``); that path is +rewritten to ``<cell>`` (``<cell>:14: UserWarning: ...``). Nothing else in +an output is touched. + +**liblsl's INFO log lines are kept out of the outputs.** liblsl logs +``api_config.cpp ... INFO| Loaded default config`` and a build line to +stderr the first time it loads (seen stored in lsl_streaming.ipynb, +2026-09-10). Unless ``LSLAPICFG`` is already set, the kernel is pointed at a +config file whose only setting is ``[log] level = -1`` (warnings and +errors still print; INFO does not) -- see `quiet_liblsl_config`. """ import json import os +import re import sys +import tempfile import nbformat from nbclient import NotebookClient @@ -59,6 +75,97 @@ os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1') SKIP_TAG = 'skip-execution' # what nbclient honours TIMEOUT = 1800 +#: a cell's code as the kernel names it in a warning: a per-session temp file +#: (``.../T/ipykernel_21956/2889100357.py`` on macOS, ``/tmp/ipykernel_...`` +#: on Linux, backslashes on Windows) +_CELL_FILE_RE = re.compile(r'[^\s"\'<>]*ipykernel_\d+[/\\]\d+\.py') +#: the liblsl configuration `quiet_liblsl_config` writes: INFO off, warnings on +LSL_QUIET_CONFIG = '[log]\nlevel = -1\n' + + +def _scrub_text(text, home): + return _CELL_FILE_RE.sub('<cell>', text.replace(home, '~')) + + +def scrub_home(nb, home=None): + """Rewrite `home` (default: this user's home directory) to ``~``, and a + kernel cell's temp-file path to ``<cell>``, in every text output of + `nb`, in place: stream text, error tracebacks and ``evalue``, and + ``text/plain`` display/execute-result data. Returns the number of + outputs changed.""" + home = home or os.path.expanduser('~') + changed = 0 + for cell in nb.cells: + for output in cell.get('outputs', []): + before = json.dumps(output, sort_keys=True) + kind = output.get('output_type') + if kind == 'stream': + output['text'] = _scrub_text(output['text'], home) + elif kind == 'error': + output['traceback'] = [_scrub_text(line, home) + for line in output['traceback']] + output['evalue'] = _scrub_text(output['evalue'], home) + elif kind in ('display_data', 'execute_result'): + text = output.get('data', {}).get('text/plain') + if isinstance(text, str): + output['data']['text/plain'] = _scrub_text(text, home) + changed += json.dumps(output, sort_keys=True) != before + return changed + + +def quiet_liblsl_config(directory, environ=None): + """Point ``LSLAPICFG`` in `environ` (default ``os.environ``, which the + kernel inherits) at a config in `directory` that turns liblsl's INFO + logging off, unless the caller already set ``LSLAPICFG``. Returns the + config path in use.""" + environ = os.environ if environ is None else environ + if environ.get('LSLAPICFG'): + return environ['LSLAPICFG'] + path = os.path.join(directory, 'lsl_api.cfg') + with open(path, 'w', encoding='utf-8') as handle: + handle.write(LSL_QUIET_CONFIG) + environ['LSLAPICFG'] = path + return path + + +def skip_install_cells(nb): + """Tag HyperTools installation cells of `nb` skip-execution (in memory) + and drop the outputs it carried; return those cells for + `restore_install_cells`. + + nbclient leaves a skipped cell exactly as the file had it, outputs + included: two 1.0.0 tutorials shipped a pip upgrade notice naming a + local interpreter path, from a run that DID execute the install cell. + A cell that did not run here has no output. + """ + installs = [c for c in nb.cells if c.cell_type == 'code' and ( + 'hypertools-install' in c.metadata.get('tags', []) or + re.search(r'^\s*[%!]pip\s+install[^\n]*hypertools', c.source, re.M))] + for cell in installs: + cell.metadata.setdefault('tags', []).append(SKIP_TAG) + cell.outputs = [] + cell.execution_count = None + return installs + + +def restore_install_cells(installs): + """Remove the in-memory skip tag `skip_install_cells` added.""" + for cell in installs: + cell.metadata['tags'].remove(SKIP_TAG) + if not cell.metadata['tags']: + del cell.metadata['tags'] + + +def clear_prerequisite_install_outputs(nb): + """Execute prerequisites normally, then omit successful pip chatter from docs. + + Called only after NotebookClient succeeds, so installation failures still + propagate with their traceback. These tagged cells contain setup only. + """ + for cell in nb.cells: + if 'prerequisite-install' in cell.metadata.get('tags', []): + cell.outputs = [] + cell.execution_count = None def execute(path, out=None): @@ -66,22 +173,27 @@ def execute(path, out=None): nb = nbformat.read(path, as_version=4) original = json.loads(json.dumps(nb.metadata.get('kernelspec', NEUTRAL_KERNELSPEC))) - installs = [c for c in nb.cells - if c.cell_type == 'code' and 'pip install' in c.source] - for cell in installs: - cell.metadata.setdefault('tags', []).append(SKIP_TAG) + installs = skip_install_cells(nb) # the notebook's OWN directory is the cwd it runs in, so its relative # data paths resolve -- `or '.'` because a bare filename has no dirname # (`'reduce.ipynb'.rsplit('/', 1)[0]` is the filename itself, which would # make the kernel's cwd a nonexistent directory) - NotebookClient(nb, timeout=TIMEOUT, kernel_name=KERNEL, - resources={'metadata': {'path': os.path.dirname(path) - or '.'}}).execute() + saved_lsl = os.environ.get('LSLAPICFG') + with tempfile.TemporaryDirectory() as scratch: + quiet_liblsl_config(scratch) # the kernel inherits os.environ + try: + NotebookClient(nb, timeout=TIMEOUT, kernel_name=KERNEL, + resources={'metadata': {'path': os.path.dirname(path) + or '.'}}).execute() + finally: + if saved_lsl is None: + os.environ.pop('LSLAPICFG', None) + else: + os.environ['LSLAPICFG'] = saved_lsl nb.metadata['kernelspec'] = original - for cell in installs: - cell.metadata['tags'].remove(SKIP_TAG) - if not cell.metadata['tags']: - del cell.metadata['tags'] + scrub_home(nb) + restore_install_cells(installs) + clear_prerequisite_install_outputs(nb) nbformat.write(nb, out or path) executed = sum(1 for c in nb.cells if c.cell_type == 'code' and c.get('outputs')) diff --git a/scripts/feature_tour_support.py b/scripts/feature_tour_support.py new file mode 100644 index 00000000..d94b3ddc --- /dev/null +++ b/scripts/feature_tour_support.py @@ -0,0 +1,448 @@ +"""Embedded, self-contained review helpers for the local/Colab feature tour. + +The notebook setup supplies hyp, np, pd, plt, SETTINGS, CASES, SCRATCH and the +standard-library/display imports. Keep its helper cell synchronized using +scripts/update_feature_tour.py. This file is not part of the installed package. +""" + +# ruff: noqa: F821 +# Run state is injected by the notebook setup; its interface is annotated below. +import base64 +import uuid +from collections import Counter +from IPython.display import Image, clear_output +import hashlib +import html +import importlib.util +import inspect +import json +from pathlib import Path +import shutil +import subprocess +import time +import traceback +import warnings +import zipfile +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import hypertools as hyp +from IPython.display import display, HTML + +# Supplied by the notebook setup; annotations document the embedded interface +# without overwriting the run's dictionaries when this cell is executed. +RESULTS: dict +MANUAL: dict +ENVIRONMENT: dict +CASES: list +SETTINGS: dict +BACKENDS: list +SCRATCH: Path +IN_COLAB: bool +NOTEBOOK_SOURCE_SHA256: str + +INTERACTIVE_PLOTS = {} +CURRENT_CASE = None + + +def source_hash(source): + return hashlib.sha256(source.strip().encode()).hexdigest() + + +def report_rows(payload=None): + rows = list(RESULTS.values()) if payload is None else payload["cases"] + manual = MANUAL if payload is None else payload.get("manual_review", {}) + return [ + dict( + row, + visual=manual.get(row["id"], {}).get("verdict", "not applicable"), + visual_notes=manual.get(row["id"], {}).get("notes", ""), + ) + for row in rows + ] + + +def save_report(): + payload = { + "environment": ENVIRONMENT, + "cases": list(RESULTS.values()), + "manual_review": MANUAL, + "inventory": CASES, + "notebook_source_sha256": NOTEBOOK_SOURCE_SHA256, + "inventory_sha256": source_hash(json.dumps(CASES, sort_keys=True)), + "interactive_plots": list(INTERACTIVE_PLOTS), + } + install_log = globals().get("INSTALL_LOG") + if install_log is not None and Path(install_log).exists(): + shutil.copyfile(install_log, SCRATCH / "install.log") + (SCRATCH / "results.json").write_text( + json.dumps(payload, indent=2, default=str), encoding="utf-8" + ) + if RESULTS: + pd.DataFrame(report_rows()).drop(columns=["traceback"], errors="ignore").to_csv( + SCRATCH / "results.csv", index=False + ) + with zipfile.ZipFile( + SCRATCH / "hypertools-feature-review.zip", "w", zipfile.ZIP_DEFLATED + ) as archive: + for name in ["results.json", "results.csv", "install.log"]: + if (SCRATCH / name).exists(): + archive.write(SCRATCH / name, arcname=name) + + +def run_case(case_id, fn): + global CURRENT_CASE + CURRENT_CASE = case_id + for key in list(INTERACTIVE_PLOTS): + if key.startswith(case_id + "-"): + del INTERACTIVE_PLOTS[key] + if "_LIVE_OUTPUT" in globals(): + with _LIVE_OUTPUT: + clear_output(wait=False) + entry = next(c for c in CASES if c["id"] == case_id) + # Approval belongs to one execution. Even a failed/blocked rerun invalidates it. + execution_id = uuid.uuid4().hex + if entry["visual"]: + MANUAL[case_id] = { + "verdict": "not reviewed", + "notes": "", + "inspect": entry["visual"], + "execution_id": execution_id, + } + row = dict( + id=case_id, + title=entry["title"], + covers=entry["covers"], + status="PASS", + seconds=0.0, + warnings=[], + detail="", + traceback="", + execution_id=execution_id, + source_sha256=source_hash(inspect.getsource(fn)), + ) + start = time.perf_counter() + caught = [] + try: + missing = [] + for name in entry["requires"]: + try: + available = importlib.util.find_spec(name) is not None + except ModuleNotFoundError: + available = False + if not available: + missing.append(name) + if entry["gate"] and not SETTINGS[entry["gate"]]: + row.update(status="SKIP", detail=f"Disabled setting: {entry['gate']}") + elif entry.get("backend") and entry["backend"] not in BACKENDS: + row.update(status="SKIP", detail="Backend disabled in BACKENDS") + elif missing: + row.update( + status="BLOCKED", + detail="Missing prerequisite modules: " + ", ".join(missing), + ) + else: + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + fn() + except KeyboardInterrupt: + row.update( + status="INTERRUPTED", + detail="Execution interrupted; this check did not pass.", + ) + raise + except Exception as exc: + missing_binary = [ + name for name in entry.get("binaries", []) if shutil.which(name) is None + ] + status = ( + "BLOCKED" + if missing_binary or type(exc).__name__ == "ChromeNotFoundError" + else "FAIL" + ) + row.update( + status=status, + detail=f"{type(exc).__name__}: {exc}", + traceback=traceback.format_exc(), + ) + if missing_binary: + row["detail"] += "; missing binaries: " + ", ".join(missing_binary) + print(row["traceback"]) + finally: + counts = Counter( + (w.category.__name__, str(w.message), w.filename, w.lineno) for w in caught + ) + row["warnings"] = [ + dict(category=c, message=m, filename=f, line=line_number, count=n) + for (c, m, f, line_number), n in counts.items() + ] + row["seconds"] = round(time.perf_counter() - start, 3) + RESULTS[case_id] = row + if case_id != "GUI-native": + plt.close("all") + save_report() + CURRENT_CASE = None + tint = {"PASS": "#137333", "FAIL": "#b31412", "SKIP": "#666", "BLOCKED": "#975600"}[ + row["status"] + ] + display( + HTML( + f'<p style="border-left:5px solid {tint};padding:8px"><b>{html.escape(case_id)}: ' + f"{row['status']}</b> · {row['seconds']} s<br>{html.escape(row['detail'])}</p>" + ) + ) + if row["warnings"]: + display( + HTML( + "<details><summary>Warnings (retained in report)</summary><pre>" + + html.escape(json.dumps(row["warnings"], indent=2)) + + "</pre></details>" + ) + ) + + +def expect_error(kind, fn, contains=None): + try: + fn() + except kind as exc: + if contains is not None: + assert contains.lower() in str(exc).lower(), str(exc) + print(f"Expected {type(exc).__name__}: {exc}") + return exc + raise AssertionError(f"Expected {kind}, but the call succeeded") + + +def finite(value, shape=None): + array = np.asarray(value) + if shape is not None: + assert array.shape == shape, (array.shape, shape) + assert np.isfinite(array).all(), "Result contains NaN or infinity" + return array + + +def download_artifact(path): + """Prepare a frontend-safe download on demand, without bloating Run all.""" + import ipywidgets as widgets + + path = Path(path) + button = widgets.Button( + description=("Download " if IN_COLAB else "Prepare download: ") + path.name, + layout={"width": "auto"}, + ) + output = widgets.Output() + + def prepare(_): + global _DOWNLOAD_OUTPUT + if "_DOWNLOAD_OUTPUT" in globals(): + with _DOWNLOAD_OUTPUT: + clear_output(wait=False) + _DOWNLOAD_OUTPUT = output + with output: + if IN_COLAB: + from google.colab import files + + files.download(str(path)) + else: + data = base64.b64encode(path.read_bytes()).decode() + display( + HTML( + f'<a download="{html.escape(path.name, quote=True)}" ' + f'href="data:application/octet-stream;base64,{data}">Save {html.escape(path.name)}</a>' + ) + ) + + button.on_click(prepare) + display(widgets.VBox([button, output])) + + +def show_result(obj): + if isinstance(obj, dict) and "fig" in obj: + obj = obj.get("animation") or obj["fig"] + if isinstance(obj, hyp.HyperAnimation): + assert obj.n_frames > 0 + display( + HTML(obj.to_html5_video() if shutil.which("ffmpeg") else obj.to_jshtml()) + ) + elif hasattr(obj, "to_plotly_json") and hasattr(obj, "write_image"): + from hypertools._shared.lazy_import import ensure_kaleido_chrome + import plotly.graph_objects as go + + ensure_kaleido_chrome() + key = f"{CURRENT_CASE or 'plot'}-{uuid.uuid4().hex[:8]}" + path = SCRATCH / (key + ".html") + obj.write_html(str(path), include_plotlyjs=True, auto_play=False) + INTERACTIVE_PLOTS[key] = str(path) + preview = go.Figure(obj) + # A still preview of the final frame, with no browser WebGL context. + if obj.frames: + last = obj.frames[-1] + for index, trace in zip( + last.traces if last.traces is not None else range(len(last.data)), + last.data, + ): + preview.data[index].update(trace.to_plotly_json()) + preview.update_layout(last.layout) + preview.frames = [] + preview.update_layout(updatemenus=[], sliders=[]) + image_path = SCRATCH / (key + ".png") + preview.write_image(str(image_path)) + display(Image(filename=str(image_path))) + print( + "Interactive controls/playback: select", + key, + "in the single viewer at the end of this notebook.", + ) + download_artifact(path) + else: + display(obj) + + +def interactive_viewer(): + """Only one live iframe: replacing/removing it disposes its WebGL contexts.""" + global _LIVE_OUTPUT + if "_LIVE_OUTPUT" in globals(): + with _LIVE_OUTPUT: + clear_output(wait=False) + import ipywidgets as widgets + + picker = widgets.Dropdown(options=list(INTERACTIVE_PLOTS), layout={"width": "95%"}) + open_button = widgets.Button(description="Open selected plot") + close_button = widgets.Button(description="Close interactive plot") + output = widgets.Output() + _LIVE_OUTPUT = output + + def close(_): + with output: + clear_output(wait=False) + + def show(_): + close(None) + source = Path(INTERACTIVE_PLOTS[picker.value]).read_text(encoding="utf-8") + with output: + display( + # wrapped in a div: IPython warns "Consider using + # IPython.display.IFrame" for HTML that starts with <iframe + HTML( + '<div><iframe title="Interactive HyperTools review" style="width:100%;height:750px;border:0" ' + 'srcdoc="' + html.escape(source, quote=True) + '"></iframe></div>' + ) + ) + + open_button.on_click(show) + close_button.on_click(close) + viewer = widgets.VBox([picker, widgets.HBox([open_button, close_button]), output]) + display(viewer) + return viewer + + +def verify_export(path, animated=False): + """Decode fresh artifacts; require distinct frames, not just nonempty files.""" + from PIL import Image as PILImage, ImageSequence + + path = Path(path) + assert path.is_file() and path.stat().st_size > 100 + suffix = path.suffix.lower() + if suffix in [".png", ".gif", ".apng"]: + with PILImage.open(path) as im: + decoded = [ + (frame.convert("RGB").tobytes(), frame.info.get("duration", 0)) + for frame in ImageSequence.Iterator(im) + ] + frames = [frame for frame, _ in decoded] + if animated: + assert len(frames) >= 4, f"{path.name}: only {len(frames)} frames" + assert len(set(frames)) >= 3, "Frames do not show distinct motion" + assert sum(duration for _, duration in decoded) > 0, ( + "Animation has no positive playback duration" + ) + else: + im.load() + elif suffix in [".mp4", ".mov", ".avi", ".webm", ".m4v", ".mkv"]: + assert shutil.which("ffmpeg"), "ffmpeg is required to decode this movie" + raw = subprocess.check_output( + [ + "ffmpeg", + "-v", + "error", + "-i", + str(path), + "-vf", + "scale=64:64", + "-f", + "rawvideo", + "-pix_fmt", + "rgb24", + "-", + ] + ) + size = 64 * 64 * 3 + frames = [raw[i : i + size] for i in range(0, len(raw), size)] + assert len(frames) >= 4 and len(set(frames)) >= 3, ( + "Movie lacks distinct decodable frames" + ) + elif suffix == ".svg": + import xml.etree.ElementTree as ET + + assert ET.parse(path).getroot().tag.endswith("svg") + elif suffix == ".pdf": + assert path.read_bytes().startswith(b"%PDF-") + elif suffix == ".html": + text = path.read_text(encoding="utf-8") + assert "<html" in text.lower() or "<div" in text.lower() + if animated: + assert "Plotly.addFrames(" in text or "new Animation(" in text, ( + "HTML has no animation payload" + ) + download_artifact(path) + + +def view(data, backend, **kwargs): + result = hyp.plot(data, backend=backend, show=False, **kwargs) + show_result(result) + return result + + +def fixtures(): + a = hyp.load("random_walk", n_samples=48, n_features=6, random_state=41) + b = hyp.load("random_walk", n_samples=48, n_features=6, random_state=42) + return np.asarray(a), np.asarray(b) + + +TEXTS = [ + "the dog plays with a ball", + "a puppy runs in the park", + "the cat sleeps beside the dog", + "kittens sleep in the sun", + "stocks rise after the report", + "bond prices change with rates", + "investors buy stocks and bonds", + "markets react to economic news", +] +# Short point labels so text plots can be checked by eye against TEXTS. +DOC_LABELS = [f"{i}: {' '.join(doc.split()[:3])}" for i, doc in enumerate(TEXTS)] + + +def assert_hosted_contract(name, data): + """Assert documented user-facing containers and dimensions.""" + assert len(data) > 0 + if name in ("wiki", "nips", "sotus"): + assert isinstance(data, list) and all(isinstance(v, str) for v in data) + if name == "wiki": + assert len(data) == 3136 + if name == "sotus": + assert len(data) == 29 + elif name == "datasaurus": + assert isinstance(data, list) and len(data) == 13 + assert all(isinstance(v, pd.DataFrame) and v.shape == (142, 2) for v in data) + assert all(list(v.columns) == ["x", "y"] and v.index.is_unique for v in data) + elif name in ("weights", "weights_avg", "weights_sample", "spiral"): + assert isinstance(data, list) and all( + isinstance(v, np.ndarray) and v.ndim == 2 for v in data + ) + if name == "spiral": + assert len(data) == 2 and all(v.shape == (1000, 3) for v in data) + elif name == "mushrooms": + assert isinstance(data, pd.DataFrame) and len(data) == 8124 + else: + assert np.asarray(data).ndim == 2 and np.asarray(data).shape[1] == 3 + finite(data) diff --git a/scripts/generate_baseline_screenshots.py b/scripts/generate_baseline_screenshots.py index cde33cbf..03a3fce0 100644 --- a/scripts/generate_baseline_screenshots.py +++ b/scripts/generate_baseline_screenshots.py @@ -6,7 +6,7 @@ These baselines document CURRENT behavior on master/v0.8.2 across the core use-case matrix, so the dev-1.0 modernization can be visually diffed against them (aesthetic parity was the main unresolved gap in the earlier -matplotlib-backend attempt — see notes/hypertools_1.0_roadmap.md). +matplotlib-backend attempt — see notes/hypertools_2.0_roadmap.md). Outputs: tests/screenshots/baseline_v0.8.2/<function>/<case>.png (directory is gitignored; screenshots are reviewed locally / uploaded as CI diff --git a/scripts/generate_gallery_thumbs.py b/scripts/generate_gallery_thumbs.py index 9c667449..68e00099 100644 --- a/scripts/generate_gallery_thumbs.py +++ b/scripts/generate_gallery_thumbs.py @@ -28,7 +28,11 @@ # the five 1.1 launch examples (Plan 4 Task 8 Step 6) 'animate_conversation', 'animate_market_sectors', 'animate_morph_zoo', 'animate_painting_embeddings', - 'animate_weather_decades'] + 'animate_weather_decades', + # ships a thumb and is registered in `post_build.GIF_REPLACEMENTS`, + # but was missing here, so nothing regenerated it -- found when the + # animated-forecast anchor fix made its clip stale (2026-09-11) + 'animate_forecast'] N_FRAMES = 40 FRAME_MS = 100 # 10 fps thumbs: light files, clearly animated diff --git a/scripts/generate_tutorial_notebook.py b/scripts/generate_tutorial_notebook.py index d0db48c9..33f42189 100644 --- a/scripts/generate_tutorial_notebook.py +++ b/scripts/generate_tutorial_notebook.py @@ -36,9 +36,10 @@ DRAW_LAST = '_ = anim.draw_frame(anim.n_frames - 1) # the fully revealed frame\n' #: notebook stem -> (example module, mp4 dpi, [(heading, prose, first symbol)]) -#: The dpi is the docs artefact's resolution: 70 for the two long clips -#: (market 1200 frames, weather 2400 frames -- at 100 dpi they were 15 and -#: 27 MB, too heavy for an autoplaying tutorial page), 100 for the rest. +#: The dpi is the docs artefact's resolution, 100 for every clip. (The two +#: long clips -- market 1200 frames, weather 2400 -- were once saved at 70, +#: when a fixed bitrate made them 15 and 27 MB at 100 dpi; the encoder is +#: CRF-bound now, so dpi no longer trades against size.) #: The first section always starts right after the docstring; its symbol is #: therefore ignored and given as None. `__main__` names the guard block. SPECS = { @@ -49,14 +50,18 @@ "a sector's common growth does not flatten its path onto one line, " 'and the colormap that tints the date red or green.', None), ('## 2. Prices and share counts, with a synthetic fallback', - 'Yahoo Finance supplies adjusted AND unadjusted daily closes (an ' - 'explicit date window: `range=max` silently degrades to quarterly ' - "bars); the SEC's XBRL API supplies reported shares outstanding, " - 'which are not split-adjusted, so market cap multiplies them by the ' - 'unadjusted close. Both are cached on disk. These are the only ' - 'functions that touch the network; `HYPERTOOLS_OFFLINE` makes them ' - 'refuse rather than degrade, which is how the test-suite proves the ' - 'import path fetches nothing.', 'Market'), + 'Yahoo Finance supplies daily closes and split events (an explicit ' + 'date window: `range=max` silently degrades to quarterly bars). ' + 'Both of its closes are split-adjusted -- `adjclose` also ' + "reinvests dividends -- while the SEC's XBRL API reports shares " + 'outstanding as they stood on the day, so every count is multiplied ' + 'by the splits that came after it before it meets the price (and ' + 'the odd filing slip, a count 100x off its median, is dropped). ' + 'Both sources are cached on disk. These are the only functions ' + 'that touch the network; `HYPERTOOLS_OFFLINE` (an environment ' + 'variable this notebook reads, not a hypertools setting) makes ' + 'them refuse rather than fetch, which is how the test-suite proves ' + 'the import path fetches nothing.', 'Market'), ('## 3. Growth curves per sector, market-cap weights, the basket\'s ' 'return', 'What is plotted is each stock\'s cumulative log return since the ' @@ -68,7 +73,7 @@ 'assemble'), ('## 4. Reduce per sector, hyperalign, draw seven paths', 'Three library calls: `hyp.reduce` per sector (its own stocks, its ' - 'own space), `hyp.align(..., align=\'HyperAlign\')` into one shared ' + 'own space), `hyp.align(..., model=\'HyperAlign\')` into one shared ' 'space, and `hyp.plot` on the six aligned paths plus their mean, ' 'coloured through the mixture hue. The `on_frame` hook only sets ' 'the title: the date under the head, tinted by the basket\'s ' @@ -104,8 +109,10 @@ 'Each description is cut into overlapping ten-word windows. The ' 'canvas is downloaded once (the only network access here) and ' '`image_palette` picks its most salient legible colour; offline, ' - 'the fallback colour and a flat swatch stand in. `fixture_data` ' - 'takes every colour from the one committed thumbnail and embeds ' + 'the fallback colour and a flat swatch stand in. `fixture_data` is ' + "the test-suite's path: it takes every colour from a thumbnail " + 'committed next to the example script (`examples/data/`, not ' + 'shipped with this notebook, so it is not called here) and embeds ' 'with TF-IDF, so no test fetches a canvas or a model.', 'Paintings'), ('## 3. One call, plus the annotated column', 'Raw text in, five clouds out, spun by `animate=\'spin\'`. ' @@ -123,11 +130,14 @@ 'carries the windows, the speakers, the spoken lines (for the ' 'titles) and the vectorizer. `fixture_data` embeds with TF-IDF so ' 'no test downloads a model.', 'Conversation'), - ('## 3. The recency fade and the title hooks', - '`recency_fade` fades earlier turns on the public `on_frame` hook; ' - '`speaker_title` tints the title with the current speaker\'s colour ' - 'on every frame; `make_room_for_title` grows the figure so a ' - 'two-line title clears the box.', 'turn_alpha'), + ('## 3. The recency fade, on the `on_frame` hook', + '`turn_alpha` is the fade\'s formula and `recency_fade` assigns it ' + 'to every head and trail on the public `on_frame` hook. It is the ' + 'hand-written form of `dataset_fade={\'floor\': FLOOR, ' + '\'decay\': DECAY}`, which gives exactly these alphas in one ' + 'keyword. The title needs no hook: `title_color=` tints each ' + 'turn\'s title with its speaker\'s colour, and the library reserves ' + 'the wrapped title\'s margin.', 'turn_alpha'), ('## 4. One call', 'Raw dialogue in, one disjoint trajectory per turn, coloured by ' 'speaker, revealed one turn at a time over thirty seconds and two ' @@ -163,8 +173,8 @@ 'fetches anything.', 'Climate'), ('## 3. One call: an animated forecast with a fading fan', '`predict=\'Kalman\'` with `animate=True` refits on every distinct ' - 'revealed history and re-anchors the forecast on the last revealed ' - 'month; `forecast_trail=True` keeps the earlier fits as a fading ' + 'revealed history and draws the forecast from the current frame\'s ' + 'endpoint; `forecast_trail=True` keeps the earlier fits as a fading ' 'fan. `forecast_hue=`, `forecast_palette=` and `forecast_fmt=` ' 'restyle only the forecasts, and `slow_warning_seconds=None` ' 'silences the long-schedule notice for a wait that is known.', @@ -230,15 +240,19 @@ def cell(kind, text, execution_count=None): def build(stem): + try: + from scripts.add_colab_install_cell import portable_video_source + except ModuleNotFoundError: + from add_colab_install_cell import portable_video_source module, dpi, spec = SPECS[stem] path = os.path.join(TUTORIALS, stem + '.ipynb') - with open(path if os.path.exists(path) else TEMPLATE) as handle: + with open(path if os.path.exists(path) else TEMPLATE, encoding='utf-8') as handle: old = json.load(handle) install = old['cells'][0] assert 'pip install' in ''.join(install['source']), path - install = {'cell_type': 'code', 'metadata': {}, 'execution_count': None, + install = {'cell_type': 'code', 'metadata': {'tags': ['hypertools-install']}, 'execution_count': None, 'outputs': [], 'source': install['source']} - with open(os.path.join(EXAMPLES, module + '.py')) as handle: + with open(os.path.join(EXAMPLES, module + '.py'), encoding='utf-8') as handle: source = handle.read() doc, sections, main_body = split_sections(source, spec) cells = [install, cell('markdown', docstring_to_markdown(doc))] @@ -251,7 +265,7 @@ def build(stem): cells.append(cell('code', main_body + DRAW_LAST)) cells.append(cell('markdown', f'## {n + 1}. Save the animation\n')) cells.append(cell('code', f"anim.save('{stem}.mp4', dpi={dpi})\n" - f"print('saved {stem}.mp4')\n")) + f"print('saved {stem}.mp4')\n\n" + portable_video_source(stem+'.mp4'))) title = docstring_to_markdown(doc).splitlines()[0][2:] # an mp4, not a GIF: the rebuilt clips run one to two minutes (1200-2400 # frames), which a GIF cannot carry at any useful size. The <video> tag @@ -265,7 +279,7 @@ def build(stem): f'[Download the clip]({stem}.mp4)\n')) notebook = {'cells': cells, 'metadata': old['metadata'], 'nbformat': old['nbformat'], 'nbformat_minor': old['nbformat_minor']} - with open(path, 'w') as handle: + with open(path, 'w', encoding='utf-8') as handle: json.dump(notebook, handle, indent=1, ensure_ascii=False) handle.write('\n') print(f'{stem}: {len(cells)} cells from examples/{module}.py') diff --git a/scripts/measure_marker_parity.py b/scripts/measure_marker_parity.py index ee99d89f..0adaac7e 100644 --- a/scripts/measure_marker_parity.py +++ b/scripts/measure_marker_parity.py @@ -99,15 +99,23 @@ def render_plotly_marker(size_px, path, color='red'): fig.write_image(path, width=CANVAS_SIZE[0], height=CANVAS_SIZE[1]) -def render_plotly_marker_3d(size_px, path, color='red'): +def render_plotly_marker_3d(size_px, path, color='red', marker=None): """Render one isolated plotly `go.Scatter3d` marker (`marker.size= size_px`), centered at the scene origin, on the same `CANVAS_SIZE` canvas -- `go.Scatter3d` needs `_SCATTER3D_SIZE_FACTOR` correction on - top of the 2-D conversion (see this module's docstring).""" + top of the 2-D conversion (see this module's docstring). + + `marker` (a full plotly marker dict) replaces the scalar marker: its + `size` may be a per-point ARRAY, drawn over that many points at the + origin -- the "bubble" form hypertools' observation markers use + (`plotly_backend._observation_marker`), which Scatter3d renders at + half the diameter of the same scalar size.""" import plotly.graph_objects as go + if marker is None: + marker = dict(color=color, size=size_px, symbol='circle') + n = 1 if np.isscalar(marker.get('size')) else len(marker['size']) fig = go.Figure(data=[go.Scatter3d( - x=[0], y=[0], z=[0], mode='markers', - marker=dict(color=color, size=size_px, symbol='circle'))]) + x=[0] * n, y=[0] * n, z=[0] * n, mode='markers', marker=marker)]) fig.update_layout( width=CANVAS_SIZE[0], height=CANVAS_SIZE[1], scene=dict(xaxis=dict(visible=False, range=[-1, 1]), diff --git a/scripts/record_gui_native_screencast.py b/scripts/record_gui_native_screencast.py new file mode 100644 index 00000000..3fb48e22 --- /dev/null +++ b/scripts/record_gui_native_screencast.py @@ -0,0 +1,138 @@ +"""Drive the GUI-native tour case in a real Qt window and record it. + +Usage (needs a Qt binding, e.g. in a throwaway env with `pip install -e . PyQt6`): + + python scripts/record_gui_native_screencast.py OUT_DIR + ffmpeg -framerate 6 -i OUT_DIR/f%04d.png -pix_fmt yuv420p screencast.mp4 + + +Same call as the feature tour's GUI-native case (QtAgg, interactive=True, +explore=True). Input is real Qt events (QTest), so it travels the same path +as a mouse. Each step grabs the window to a PNG +checks go to checks.json. +""" +import json +import sys +import time +from pathlib import Path +import numpy as np +import matplotlib +matplotlib.use('QtAgg') +import matplotlib.pyplot as plt +import hypertools as hyp +from mpl_toolkits.mplot3d import proj3d +from PyQt6.QtCore import Qt, QPoint, QTimer +from PyQt6.QtTest import QTest +from PyQt6.QtWidgets import QApplication + +OUT = Path(sys.argv[1]) +OUT.mkdir(parents=True, exist_ok=True) +hyp.set_interactive_backend('QtAgg') +hyp.plot(hyp.load('helix'), backend='matplotlib', interactive=True, explore=True, show=True) +fig = plt.gcf() +ax = fig.axes[0] +canvas = fig.canvas +window = canvas.window() +checks = {'qt_backend': matplotlib.get_backend(), 'window_class': type(window).__name__} +frames = [] + +def grab(tag, at=None): + """Save the window; `at` is the pointer position in canvas coordinates.""" + QApplication.processEvents() + path = OUT / f'f{len(frames):04d}.png' + window.grab().save(str(path)) + entry = {'tag': tag} + if at is not None: + pos = canvas.mapTo(window, at) + dpr = window.devicePixelRatioF() + entry['pointer'] = [pos.x() * dpr, pos.y() * dpr] + frames.append(entry) + + +def point_to_widget(i): + data = np.asarray(ax.lines[0].get_data_3d()).T if ax.lines else None + x, y, z = data[i] + xs, ys, _ = proj3d.proj_transform(x, y, z, ax.get_proj()) + dx, dy = ax.transData.transform((xs, ys)) + dpr = canvas.device_pixel_ratio + return QPoint(int(dx / dpr), int((canvas.height() * dpr - dy) / dpr)) + +def annotation_texts(): + return [t.get_text() for t in ax.texts if t.get_visible() and t.get_text()] + +steps = [] + + +def step(fn): + steps.append(fn) + return fn + +@step +def start(): + for _ in range(4): + grab('opened') + +@step +def hover(): + n = len(ax.lines[0].get_data_3d()[0]) + seen = [] + for i in np.linspace(0, n - 1, 12).astype(int): + where = point_to_widget(i) + QTest.mouseMove(canvas, where) + QTest.qWait(120) + seen.append(annotation_texts()) + for _ in range(3): + grab('hover', where) + checks['hover_annotations'] = [s for s in seen if s] + checks['hover_distinct'] = len({tuple(s) for s in seen if s}) + +@step +def rotate(): + before = (ax.azim, ax.elev) + c = QPoint(canvas.width() // 2, canvas.height() // 2) + QTest.mousePress(canvas, Qt.MouseButton.LeftButton, Qt.KeyboardModifier.NoModifier, c) + for k in range(1, 25): + QTest.mouseMove(canvas, c + QPoint(6 * k, 2 * k)) + QTest.qWait(40) + grab('rotate', c + QPoint(6 * k, 2 * k)) + QTest.mouseRelease(canvas, Qt.MouseButton.LeftButton, Qt.KeyboardModifier.NoModifier, c + QPoint(144, 48)) + checks['rotate_azim_elev'] = {'before': before, 'after': (ax.azim, ax.elev)} + +@step +def zoom(): + before = ax.get_xlim3d() + c = QPoint(canvas.width() // 2, canvas.height() // 2) + QTest.mousePress(canvas, Qt.MouseButton.RightButton, Qt.KeyboardModifier.NoModifier, c) + for k in range(1, 16): + QTest.mouseMove(canvas, c + QPoint(0, -5 * k)) + QTest.qWait(40) + grab('zoom', c + QPoint(0, -5 * k)) + QTest.mouseRelease(canvas, Qt.MouseButton.RightButton, Qt.KeyboardModifier.NoModifier, c + QPoint(0, -75)) + checks['zoom_xlim'] = {'before': list(before), 'after': list(ax.get_xlim3d())} + for _ in range(4): + grab('zoomed') + +@step +def close(): + QTest.keyClick(canvas, Qt.Key.Key_Q) # matplotlib's default close-figure key + QTest.qWait(300) + checks['figures_open_after_close'] = plt.get_fignums() + checks['window_visible_after_close'] = window.isVisible() + +def run(i=0): + if i < len(steps): + steps[i]() + QTimer.singleShot(150, lambda: run(i + 1)) + else: + (OUT / 'checks.json').write_text(json.dumps(checks, indent=1, default=str)) + (OUT / 'frames.json').write_text(json.dumps(frames)) + QApplication.instance().quit() + +QTimer.singleShot(800, run) +t0 = time.time() +plt.show(block=True) +checks['event_loop_seconds'] = round(time.time() - t0, 1) +# closing the last window can end the loop before run() reaches its else branch +(OUT / 'frames.json').write_text(json.dumps(frames)) +(OUT / 'checks.json').write_text(json.dumps(checks, indent=1, default=str)) +print('clean exit') diff --git a/scripts/render_multibyte_plotly.py b/scripts/render_multibyte_plotly.py index 4fccaa21..f7ad274e 100644 --- a/scripts/render_multibyte_plotly.py +++ b/scripts/render_multibyte_plotly.py @@ -21,12 +21,17 @@ that dataset's 15 points -- see `hypertools.plot.plotly_backend. _build_point_annotations`), or 'null'/omitted for no point labels. """ +import faulthandler import json import os import sys +import threading +import time import numpy as np +_T0 = time.monotonic() + sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import hypertools as hyp # noqa: E402 @@ -42,6 +47,15 @@ #: subprocess and a real `BrowserFailedError` rather than a stubbed one. BROWSER_PATH_ENV = 'HYPERTOOLS_RENDER_BROWSER_PATH' +#: Seconds the image export may take before the script gives up on the +#: browser. Kaleido's own `timeout` does not always fire: a browser that +#: starts and then never answers left a hosted CI job waiting for the caller's +#: whole 120 s (2026-10-09). A browser that never answers is "no usable +#: browser HERE", so the deadline ends with NO_BROWSER_EXIT, and it must stay +#: below the caller's subprocess timeout. +DEADLINE_ENV = 'HYPERTOOLS_RENDER_DEADLINE_S' +DEFAULT_DEADLINE_S = 75.0 + def is_browser_lifecycle_error(err): """Does `err` mean "no usable browser HERE" (-> skip) rather than "the @@ -82,7 +96,37 @@ def _write_image(fig, out_png): kopts={'path': override, 'timeout': 30}) +def _exit_now(code): + """Leave with `code` WITHOUT joining other threads. + + After a failed browser launch choreographer leaves non-daemon worker + threads blocked on a queue and a pipe read, and a normal exit waits for + them at interpreter shutdown -- forever, on Linux with Python 3.11 + (2026-10-09: the script had already reported NO_BROWSER and still ran + into its caller's 120 s timeout; every thread's stack showed the main + thread in `threading._shutdown`). The verdict is already decided, so + flush what was written and go. + """ + for stream in (sys.stdout, sys.stderr): + try: + stream.flush() + except Exception: + pass + os._exit(code) + + +def _stage(name): + """Timestamped progress on stderr, so a caller that times this script out + can see how far it got.""" + print(f'render stage [{time.monotonic() - _T0:6.1f} s]: {name}', + file=sys.stderr, flush=True) + + def main(): + # if anything below stalls, say WHERE: every thread's stack goes to stderr + # every 40 s (a caller's TimeoutExpired carries the captured stderr) + faulthandler.dump_traceback_later(40, repeat=True, file=sys.stderr) + _stage('started') legend_json, title, out_png = sys.argv[1], sys.argv[2], sys.argv[3] labels_json = sys.argv[4] if len(sys.argv) > 4 else 'null' legend = json.loads(legend_json) @@ -91,13 +135,30 @@ def main(): for i in range(len(legend))] fig = hyp.plot(data, legend=legend, title=title or None, labels=labels, backend='plotly', show=False) + _stage('figure built') + deadline = float(os.environ.get(DEADLINE_ENV) or DEFAULT_DEADLINE_S) + + def _give_up(): + # os.write, not print: another thread may hold sys.stderr's lock + os.write(2, (f'NO_BROWSER: TimeoutError: the browser did not answer ' + f'within {deadline:g} s\n').encode()) + os._exit(NO_BROWSER_EXIT) # the export thread cannot be interrupted + + watchdog = threading.Timer(deadline, _give_up) + watchdog.daemon = True + watchdog.start() try: + _stage('export started') _write_image(fig, out_png) + _stage('export finished') except Exception as err: if not is_browser_lifecycle_error(err): raise # a real failure: let the traceback through print(f'NO_BROWSER: {type(err).__name__}: {err}', file=sys.stderr) - sys.exit(NO_BROWSER_EXIT) + _exit_now(NO_BROWSER_EXIT) + finally: + watchdog.cancel() + faulthandler.cancel_dump_traceback_later() if __name__ == '__main__': diff --git a/scripts/scan_api_markup_leaks.py b/scripts/scan_api_markup_leaks.py new file mode 100644 index 00000000..66ff8374 --- /dev/null +++ b/scripts/scan_api_markup_leaks.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python +"""Find reStructuredText markup that leaked into the built API pages. + +Usage:: + + python scripts/scan_api_markup_leaks.py BUILD_HTML_DIR [--all] + +Reads the built ``hypertools.*.html`` and ``api.html`` pages (every +``*.html`` at the top of the build directory with ``--all``) and reports + +* text outside ``<code>``/``<pre>``/``<script>``/``<style>`` that contains + a backtick, ``:role:`` syntax, or ``**`` -- markup docutils did not + recognise and so printed literally; +* definition-list terms (``<dt>``) inside a numpydoc field list + (Parameters, Returns, ...) that read like prose rather than a + ``name : type`` item: long, or ending in sentence punctuation. That is + what a wrapped paragraph looks like after numpydoc has parsed each of + its lines as an item name; +* signatures showing a default whose repr says nothing + (``<object object at 0x...>``, ``<function ...>``). + +Exits 1 when anything is reported, 0 when the pages are clean. +""" +import glob +import os +import re +import sys +from html.parser import HTMLParser + +SKIP_TAGS = {'code', 'pre', 'script', 'style', 'title'} +VOID_TAGS = {'br', 'hr', 'img', 'input', 'link', 'meta', 'area', 'base', + 'col', 'embed', 'source', 'track', 'wbr'} + +LEAK_PATTERNS = [ + ('backtick', re.compile(r'`')), + ('role', re.compile(r':(?:py:)?(?:func|class|meth|mod|attr|obj|data|exc|' + r'ref|doc|term|math|any|const|file|envvar|option)' + r':')), + # a PAIR of markers around text; a lone `**kwargs` or `grid**3` is fine + ('strong-markers', re.compile(r'\*\*\S(?:.*?\S)?\*\*')), +] + +#: Default values whose repr says nothing to a reader of the signature. +OPAQUE_DEFAULT = re.compile(r'<object object|<function |<class |\bat 0x') + +#: Marks the element carrying ``role="main"`` on the scanner's tag stack. +MAIN_MARK = '<role-main>' + +#: A field-list term longer than this many words is prose, not `name : type`. +MAX_TERM_WORDS = 14 +#: ... and so is a term this long that has no description under it. +MAX_BARE_TERM_WORDS = 6 +TERM_END_PUNCTUATION = ('.', ',', ';', ':') + + +class _PageScanner(HTMLParser): + """Collect leak candidates from one built page.""" + + def __init__(self): + super().__init__(convert_charrefs=True) + self.stack = [] + self.hits = [] + self._dt_depth = None + self._dt_text = [] + self._dt_line = 0 + self._dt_name_parts = [] + self._pending_term = None + self._dd_depth = None + self._dd_text = [] + self._sig_depth = None + self._sig_text = [] + self._sig_line = 0 + + # -- helpers ----------------------------------------------------- + def _inside(self, names): + return any(tag in names for tag, _ in self.stack) + + def _in_field_list(self): + return any(tag == 'dl' and 'field-list' in cls + for tag, cls in self.stack) + + # -- parser callbacks ---------------------------------------------- + def handle_starttag(self, tag, attrs): + if tag in VOID_TAGS: + return + attrs = dict(attrs) + cls = attrs.get('class') or '' + if attrs.get('role') == 'main': + # the page body, in furo (<article>) and the basic theme (<div>) + cls += ' ' + MAIN_MARK + if (tag == 'dt' and self._dt_depth is None and self._in_field_list() + and 'field-' not in cls and 'sig' not in cls.split()): + # a numpydoc item term (not the "Parameters:" field name, not + # a nested autodoc signature) + self._dt_depth = len(self.stack) + self._dt_text = [] + self._dt_name_parts = [] + self._dt_line = self.getpos()[0] + elif (tag == 'dd' and self._pending_term is not None + and self._dd_depth is None): + self._dd_depth = len(self.stack) + self._dd_text = [] + elif tag == 'dt' and self._pending_term is not None: + # the next term began with no <dd> at all in between + self._judge_term('') + if tag == 'dt' and 'sig' in cls.split() and self._sig_depth is None: + self._sig_depth = len(self.stack) + self._sig_text = [] + self._sig_line = self.getpos()[0] + self.stack.append((tag, cls)) + + def handle_endtag(self, tag): + if tag in VOID_TAGS: + return + # tolerate unclosed <p>/<li>: pop back to the matching tag + for i in range(len(self.stack) - 1, -1, -1): + if self.stack[i][0] == tag: + del self.stack[i:] + break + if self._dt_depth is not None and len(self.stack) <= self._dt_depth: + self._finish_term() + elif self._dd_depth is not None and len(self.stack) <= self._dd_depth: + self._dd_depth = None + self._judge_term(''.join(self._dd_text)) + if self._sig_depth is not None and len(self.stack) <= self._sig_depth: + self._sig_depth = None + text = ' '.join(''.join(self._sig_text).split()) + match = OPAQUE_DEFAULT.search(text) + if match: + start = max(0, match.start() - 60) + self.hits.append((self._sig_line, 'opaque-default', + text[start:match.end() + 30])) + + def handle_data(self, data): + if self._dt_depth is not None: + self._dt_text.append(data) + if not any(tag == 'span' and 'classifier' in cls + for tag, cls in self.stack[self._dt_depth:]): + self._dt_name_parts.append(data) + if not any(MAIN_MARK in cls.split() for _, cls in self.stack): + return + if self._dd_depth is not None: + self._dd_text.append(data) + if self._sig_depth is not None: + self._sig_text.append(data) + return + if self._inside(SKIP_TAGS): + return + for kind, pattern in LEAK_PATTERNS: + if pattern.search(data): + self.hits.append((self.getpos()[0], kind, + ' '.join(data.split())[:160])) + + def _finish_term(self): + text = ' '.join(''.join(self._dt_text).split()) + name = ' '.join(''.join(self._dt_name_parts).split()) + self._dt_depth = None + if text: + self._pending_term = (self._dt_line, name, text) + + def _judge_term(self, description): + line, name, text = self._pending_term + self._pending_term = None + words = len(name.split()) + if words > MAX_TERM_WORDS: + self.hits.append((line, 'prose-term (long)', text[:160])) + elif name.endswith(TERM_END_PUNCTUATION): + self.hits.append((line, 'prose-term (punctuation)', text[:160])) + elif not description.strip() and words >= MAX_BARE_TERM_WORDS: + self.hits.append((line, 'prose-term (no description)', + text[:160])) + + +def scan_file(path): + """Return ``[(line, kind, text), ...]`` for one built HTML page.""" + scanner = _PageScanner() + with open(path, encoding='utf-8') as handle: + scanner.feed(handle.read()) + scanner.close() + if scanner._pending_term is not None: + scanner._judge_term('') + return sorted(scanner.hits) + + +def scan_directory(build_dir, all_pages=False): + """Return ``{page: hits}`` for the API pages under ``build_dir``.""" + if all_pages: + pages = sorted(glob.glob(os.path.join(build_dir, '*.html'))) + else: + pages = sorted(glob.glob(os.path.join(build_dir, 'hypertools.*.html'))) + api = os.path.join(build_dir, 'api.html') + if os.path.exists(api): + pages.append(api) + if not pages: + raise SystemExit(f'no built API pages under {build_dir!r}') + return {os.path.basename(p): scan_file(p) for p in pages} + + +def main(argv=None): + argv = list(sys.argv[1:] if argv is None else argv) + all_pages = '--all' in argv + argv = [a for a in argv if a != '--all'] + if len(argv) != 1: + raise SystemExit(__doc__) + results = scan_directory(argv[0], all_pages=all_pages) + total = 0 + for page, hits in results.items(): + if not hits: + continue + total += len(hits) + print(f'{page}: {len(hits)} hit(s)') + for line, kind, text in hits: + print(f' line {line}: [{kind}] {text}') + print(f'{len(results)} page(s) scanned, {total} hit(s)') + return 1 if total else 0 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/scripts/update_feature_tour.py b/scripts/update_feature_tour.py new file mode 100644 index 00000000..9dc7c5a4 --- /dev/null +++ b/scripts/update_feature_tour.py @@ -0,0 +1,806 @@ +"""Synchronize the local review notebook's helpers and content after review. + +This is a development utility, never a publisher. The tour lives in notes/colab. +Run from the repository root. Executed outputs are invalidated on source edits. +""" + +import ast +import hashlib +import json +import pprint +import re +from pathlib import Path + +PATH = Path("notes/colab/hypertools_1.1_feature_tour.ipynb") +nb = json.loads(PATH.read_text(encoding="utf-8")) + + +def text(cell): + return "".join(cell["source"]) + + +def set_source(cell, source): + if text(cell) != source: + cell["source"] = source.splitlines(keepends=True) + if cell["cell_type"] == "code": + cell["outputs"] = [] + cell["execution_count"] = None + + +def code_cell(source): + return dict( + cell_type="code", + metadata={}, + source=source.splitlines(keepends=True), + outputs=[], + execution_count=None, + ) + + +def markdown(source): + return dict( + cell_type="markdown", metadata={}, source=source.splitlines(keepends=True) + ) + + +def case_cell(case_id): + return next( + c + for c in nb["cells"] + if c["cell_type"] == "code" and f"run_case('{case_id}', demo)" in text(c) + ) + + +config = next(c for c in nb["cells"] if "REVIEW_COMMIT = " in text(c)) +s = text(config) +if "if IN_COLAB:\n spec =" in s: + start = s.index("if IN_COLAB:\n spec =") + end = s.index("os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS'", start) + install = s[start:end] + set_source(config, s[:start] + s[end:]) + cell = code_cell(install) + cell["metadata"]["tags"] = ["hypertools-install"] + nb["cells"].insert(nb["cells"].index(config) + 1, cell) + +setup = next(c for c in nb["cells"] if "STARTED = datetime.datetime" in text(c)) +set_source( + setup, + re.sub( + r"'status','--porcelain'(?:,'--untracked-files=no')*", + "'status','--porcelain','--untracked-files=no'", + text(setup), + ), +) +s = text(setup) +if "'optional_versions'" not in s: + s += """\noptional_packages = ['plotly','kaleido','Pillow','scipy','statsmodels','torch', + 'sentence-transformers','transformers','chronos-forecasting','gensim','skaters', + 'polars','pyarrow','ipywidgets','ipympl','pylsl','kagglehub','scikit-image','openpyxl'] +ENVIRONMENT['optional_versions'] = {} +for package in optional_packages: + try: ENVIRONMENT['optional_versions'][package] = metadata.version(package) + except metadata.PackageNotFoundError: ENVIRONMENT['optional_versions'][package] = None +ENVIRONMENT['binaries'] = {name:shutil.which(name) for name in ['ffmpeg','ffprobe','google-chrome','chromium']} +ENVIRONMENT['run_id'] = hashlib.sha256(STARTED.encode()).hexdigest()[:16] +""" + set_source(setup, s) +for install_cell in nb["cells"]: + if "hypertools-install" in install_cell.get("metadata", {}).get("tags", []): + set_source( + install_cell, + """# Preserve the full installer transcript even if setup fails. +from pathlib import Path +import tempfile +INSTALL_LOG = Path(tempfile.mkdtemp(prefix='hypertools-install-')) / 'install.log' +if IN_COLAB: + spec = f'hypertools[{EXTRAS}] @ git+https://github.com/ContextLab/hypertools.git@{REVIEW_COMMIT}' + command = [sys.executable, '-m', 'pip', 'install', spec, + 'pyarrow', 'polars', 'ipywidgets', 'xlrd', 'xlwt'] + with INSTALL_LOG.open('w', encoding='utf-8') as log: + process = subprocess.Popen(command, stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, text=True) + for line in process.stdout: + print(line, end='') + log.write(line) + status = process.wait() + print('Retained installation log:', INSTALL_LOG) + if status: + from google.colab import files + files.download(str(INSTALL_LOG)) + raise RuntimeError(f'Candidate installation failed ({status}); see {INSTALL_LOG}') +else: + INSTALL_LOG.write_text('Local candidate verification: installer intentionally not run.\\n') + print('Local mode: retaining the installed package / editable checkout.') + print('Optional full environment:', f'python -m pip install -e ".[{EXTRAS}]" pyarrow polars ipywidgets xlrd xlwt') +""", + ) +helper = next(c for c in nb["cells"] if "def run_case(" in text(c)) +set_source(helper, Path("scripts/feature_tour_support.py").read_text(encoding="utf-8")) + +VISUAL_FIXES = { + "COLOR-helpers": "The strip is the data-matrix colormap after gamma=2: continuous between anchors but visibly banded, because a random-walk matrix gives a striped palette (see matrix_palette). The three extracted image colors are checked by assertion only and are not drawn.", + "PANEL-models-matplotlib": "First figure: each panel is titled with its reducer (PCA, FactorAnalysis). Second figure: the subplots= alias gives one panel per dataset, titled Dataset A and Dataset B.", + "PANEL-models-plotly": "First figure: each panel is titled with its reducer (PCA, FactorAnalysis). Second figure: the subplots= alias gives one panel per dataset, titled Dataset A and Dataset B.", + "ANIM-fc-colors-matplotlib": "Each forecast takes the colour of its forecast_hue group from forecast_palette (four values, four viridis colours, no colorbar); clustered forecasts share one colour per endpoint cluster; the observed trajectories keep their own colours.", + "ANIM-fc-colors-plotly": "Each forecast takes the colour of its forecast_hue group from forecast_palette (four values, four viridis colours, no colorbar); clustered forecasts share one colour per endpoint cluster; the observed trajectories keep their own colours.", + "TEXT-plot-matplotlib": "Eight documents, animals in one colour and markets in the other. A 3-topic LDA model on eight short documents places several at the same spot, so fewer than eight markers are visible; the table gives each document's plotted position, and the markers should sit at those positions.", + "TEXT-plot-plotly": "Eight documents, animals in one colour and markets in the other. A 3-topic LDA model on eight short documents places several at the same spot, so fewer than eight markers are visible; the table gives each document's plotted position, and the markers should sit at those positions.", + "TEXT-transformer": "Each point is labelled with its document (the table lists them in full); animal and market documents form two separate groups. Compare with the topic model above.", + "ANIM-clock": "Each title runs from 0% to 100%. Parallel reveals both helices together, cumulatively; window shows only a sliding 0.5 s segment of each; spin rotates both complete paths.", + "PLOT-hierarchy-metadata": "The displayed trace keys list the two leaves and the derived mean in draw order; all share the single top-level group's colour, so left and right are identified by the keys rather than by colour.", +} +for _backend in ("matplotlib", "plotly"): + VISUAL_FIXES["COLOR-plot-" + _backend] = ( + "The image supplies one categorical color per dataset; the matrix palette supplies a continuous but banded scale shown by the colorbar." + ) + +inventory = next( + c for c in nb["cells"] if text(c).startswith("# Explicit coverage inventory") +) +cases = ast.literal_eval(ast.parse(text(inventory)).body[0].value) +for entry in cases: + if entry["id"].startswith("EXPORT-anim-") and entry["id"].split("-")[2] in ( + "mp4", + "mov", + "avi", + "webm", + "m4v", + "mkv", + ): + entry["binaries"] = ["ffmpeg"] + aliases = { + "AL-SRM": "SharedResponseModel", + "AL-DetSRM": "DeterministicSharedResponseModel", + "AL-RSRM": "RobustSharedResponseModel", + } + if ( + entry["id"] in aliases + and "align:" + aliases[entry["id"]] not in entry["covers"] + ): + entry["covers"].append("align:" + aliases[entry["id"]]) + if entry["id"].startswith("PLOT-1d") or "separate feature lines" in entry["visual"]: + entry["visual"] = entry["visual"].replace( + "separate feature lines", "one reduced signal" + ) + # Figure-QA adjudication 2026-09-11: Inspect text that the figures cannot meet. + if entry["id"] in VISUAL_FIXES: + entry["visual"] = VISUAL_FIXES[entry["id"]] + if entry["id"] == "ANIM-companion": + entry["visual"] = ( + "Both markers and the date advance together. The right curve stays fully visible; black is a trailing three-sample mean." + ) + +# Real export demonstrations: at least twelve frames, unique destination on rerun. +for cell in nb["cells"]: + if cell["cell_type"] != "code": + continue + s = text(cell) + s = s.replace("display(FileLink(str(path)))", "download_artifact(path)") + if "run_case('EXPORT-" in s and "path=SCRATCH/" in s: + s = re.sub( + r"path=SCRATCH/'([^']+)'", r"path=SCRATCH/(uuid.uuid4().hex+'-\1')", s + ) + if "run_case('EXPORT-anim-" in s: + s = s.replace( + "duration=.5,frame_rate=4", "duration=2,frame_rate=6" + ).replace("fps=4", "fps=6") + # Resolve generated literal branches while retaining the real backend path. + s = re.sub( + r" if 'matplotlib'=='matplotlib':(.*?)\n else:.*?\n show=False,save_path=str\(path\)\)", + r" \1", + s, + flags=re.S, + ) + s = re.sub( + r" if 'plotly'=='matplotlib':.*?\n else:", r" ", s, flags=re.S + ) + s = s.replace( + " assert path.is_file() and path.stat().st_size>100\n download_artifact(path);print(path.stat().st_size,'bytes')", + " verify_export(path, animated=True)", + ) + else: + s = s.replace( + " assert path.is_file() and path.stat().st_size>100\n download_artifact(path);print(path.stat().st_size,'bytes')", + " verify_export(path)", + ) + s = re.sub( + r"HashingVectorizer\(n_features=128,alternate_sign=False\) if '([^']+)'=='HashingVectorizer' else '[^']+'", + lambda m: ( + "HashingVectorizer(n_features=128,alternate_sign=False)" + if m[1] == "HashingVectorizer" + else repr(m[1]) + ), + s, + ) + set_source(cell, s) + +set_source( + case_cell("ANIM-companion"), + """def demo(): + a=hyp.load('helix',n_samples=30,random_state=0) + data=pd.DataFrame(a,index=pd.date_range('2026-01-01',periods=30)) + contexts=[] + obj=hyp.plot(data,backend='matplotlib',animate=True,duration=5,frame_rate=6,fmt='o-',markersize=4, + companion=[{'data':a[:,0],'smooth':3,'position':'bottom','xlabel':'Sample','ylabel':'Helix x; black: trailing mean'}, + {'data':a[:,1],'position':'right','reveal':False,'xlabel':'Sample','ylabel':'Helix y'}], + title='{index:%Y-%m-%d}',on_frame=contexts.append,show=False) + assert len(obj.figure.axes)==3 + for i in range(obj.n_frames): + obj.draw_frame(i) + for ax in obj.figure.axes[1:]: + assert list(ax.lines[-1].get_xdata())==[i] + assert obj.figure.axes[0].get_title()==data.index[i].strftime('%Y-%m-%d') + assert contexts[-1].revealed_counts==(i+1,) + show_result(obj) + +run_case('ANIM-companion', demo)""", +) + +# Native GUI must live in its own process, outside the inline notebook backend. +set_source( + case_cell("GUI-native"), + """def demo(): + if IN_COLAB: raise RuntimeError('Native desktop GUI is unavailable in Colab.') + script = SCRATCH/'native_gui_check.py' + script.write_text("import matplotlib\\nmatplotlib.use('QtAgg')\\nimport hypertools as hyp\\nimport matplotlib.pyplot as plt\\nhyp.set_interactive_backend('QtAgg')\\nhyp.plot(hyp.load('helix'), backend='matplotlib', interactive=True, explore=True, show=True)\\nplt.show(block=True)\\n") + process = subprocess.Popen([sys.executable,str(script)]) + time.sleep(2) + assert process.poll() is None, 'Native window process failed; install a Qt binding and inspect its error.' + print('Separate native window remains open for inspection. Close it manually after testing hover/rotation/zoom.') + +run_case('GUI-native', demo)""", +) + +# No implicit trust of ad-hoc remote pickles in Run all. Require an explicitly +# vetted content digest; raw bytes are verified before any deserialization. +for case_id, source in [ + ("SOURCE-drive", "1nHAusn2VsQinJk35xvJSd7CtWPC1uOwK"), + ("SOURCE-dropbox", "https://www.dropbox.com/s/7d9vo9idqk1hn31/bunny.pkl?dl=0"), +]: + cell = case_cell(case_id) + # Existing vetted built-in bunny has the same resolver routes; preserve + # actual connector coverage via a safe CSV URL for Dropbox separately. + # Until a digest is vetted, the dangerous calls must be explicit skips. + for e in cases: + if e["id"] == case_id: + e["gate"] = "trusted_remote_pickle" + s = text(cell) + if "Explicitly authorized" not in s: + s = s.replace( + "def demo():", + 'def demo():\n print("Explicitly authorized remote pickle: loading can execute code from the source owner.")', + ) + set_source(cell, s) +if "'trusted_remote_pickle'" not in text(config): + set_source( + config, + text(config).replace( + "'native_gui': False,", + "'trusted_remote_pickle': False, # opt in only after vetting Drive/Dropbox pickle owners\n 'native_gui': False,", + ), + ) + + +# Additional behavior demos identified by the reviewers. +def add( + case_id, title, source, covers, visual="", gate=None, requires=(), backend=None +): + if any(c["id"] == case_id for c in cases): + set_source( + case_cell(case_id), source.strip() + f"\n\nrun_case('{case_id}', demo)" + ) + return + cases.insert( + -1, + dict( + id=case_id, + title=title, + covers=covers, + gate=gate, + backend=backend, + requires=list(requires), + visual=visual, + ), + ) + anchor = nb["cells"].index(case_cell("COVERAGE")) - 1 + nb["cells"][anchor:anchor] = [ + markdown( + f'<a id="case-{case_id}"></a>\n\n### {case_id} · {title}\n\n' + + ("**Inspect:** " + visual + "\n" if visual else "") + ), + code_cell(source.strip() + f"\n\nrun_case('{case_id}', demo)"), + ] + + +for backend in ("matplotlib", "plotly"): + add( + "PLOT-series-" + backend, + "All input columns as time series and datetime limits", + f"""def demo(): + a,_=fixtures() + data=pd.DataFrame(a[:20,:3],index=pd.date_range('2026-01-01',periods=20)) + obj=hyp.plot(data,ndims=1,reduce=None,backend='{backend}',xlim=(data.index[2],data.index[-3]),show=False,return_model=True) + assert len(obj['trace_data'])==3 + show_result(obj)""", + ["plot:ndims", "plot:xlim", "behavior:multicolumn-series"], + "Three feature curves, with the displayed date range limited at both ends.", + requires=["plotly"] if backend == "plotly" else (), + backend=backend, + ) + add( + "PLOT-bundle-" + backend, + "Returned coordinates, colors and observation ownership", + f"""def demo(): + a,b=fixtures() + bundle=hyp.plot([a,b],backend='{backend}',hue=['A']*len(a)+['B']*len(b),return_model=True,show=False) + assert set(bundle)>={{'fig','xform_data','trace_data','trace_metadata','colors','pipeline'}} + assert len(bundle['trace_data'])==2 + display({{key:type(value).__name__ for key,value in bundle.items()}}) + print('Colors:',bundle['colors']);print('Trace metadata:',bundle['trace_metadata']) + show_result(bundle)""", + ["behavior:trace-bundle", "plot:return_model"], + "Returned color mapping agrees with the two visible groups.", + requires=["plotly"] if backend == "plotly" else (), + backend=backend, + ) + +add( + "PIPE-raw-fitted", + "Reuse a fitted raw sklearn stage without refitting", + """def demo(): + from sklearn.decomposition import PCA + a,b=fixtures(); pca=PCA(n_components=3).fit(a); before=pca.components_.copy() + pipe=hyp.Pipeline([('reduce',pca)]) + bundle=hyp.plot(b,pipeline=pipe,show=False,return_model=True) + np.testing.assert_allclose(bundle['xform_data'][0],pca.transform(b)) + np.testing.assert_array_equal(pca.components_,before) + show_result(bundle)""", + ["plot:pipeline", "behavior:fitted-sklearn"], + visual="Held-out data projected through the fitted training transform.", +) + +add( + "ANIM-clock", + "Actual reveal bounds and callable titles across animation modes", + """def demo(): + # Two helices (the second reversed and shifted) so parallel reveal is visible. + helix=np.asarray(hyp.load('helix',n_samples=12)) + a=[helix,helix[::-1]+np.array([2.,0.,0.])] + for mode in ['parallel','window','spin']: + contexts=[] + # draw_frame/n_frames are the matplotlib HyperAnimation API; Colab + # would otherwise auto-select plotly. + # focused= makes the window a 0.5 s segment; otherwise it spans the clip. + obj=hyp.plot(a,animate=mode,duration=2,frame_rate=6,show=False,backend='matplotlib', + **({'focused':.5} if mode=='window' else {}), + title=lambda ctx:f"{ctx.style}: {ctx.progress:.0%}",on_frame=contexts.append) + for frame in range(obj.n_frames): + obj.draw_frame(frame);ctx=contexts[-1] + assert ctx.progress==frame/(obj.n_frames-1) + if mode!='spin': + for artist,(start,end),data in zip(ctx.artists,ctx.window_bounds,ctx.datasets): + xyz=np.column_stack(artist.get_data_3d()) + if len(xyz):np.testing.assert_allclose(xyz[-1],data[end-1]) + show_result(obj)""", + ["behavior:frame-context", "plot:title", "plot:on_frame"], + visual="Each title runs from 0% to 100%. Parallel reveals both helices together, cumulatively; window shows only a sliding 0.5 s segment of each; spin rotates both complete paths.", +) + +add( + "IO-passthrough", + "Already-loaded inputs and unknown-argument rejection", + """def demo(): + a,_=fixtures();df=pd.DataFrame(a) + np.testing.assert_allclose(hyp.load(df),df) + np.testing.assert_allclose(hyp.load(a),a) + expect_error(TypeError,lambda:hyp.load('helix',definitely_not_an_option=True),contains='definitely_not_an_option')""", + ["behavior:load-passthrough", "behavior:load-invalid-kwargs"], +) + +for model in ("wiki_model", "nips_model", "sotus_model"): + add( + "IO-model-" + model, + "Hosted fitted topic pipeline: " + model, + f"""def demo(): + from sklearn.pipeline import Pipeline + model=hyp.load('{model}') + assert isinstance(model,Pipeline) + output=finite(model.transform(TEXTS[:2]),shape=(2,50)) + assert 'steps' in model.get_params() + print(model);display(pd.DataFrame(output))""", + ["io:hosted-models"], + gate="large_data", + ) + +add( + "MAN-warmup", + "Trailing smoothing warm-up versus partial windows", + """def demo(): + data=pd.DataFrame({'signal':np.arange(8,dtype=float)}) + full=hyp.manip(data,model='Smooth',kernel='boxcar',kernel_width=3,center=False,maintain_bounds=False) + partial=hyp.manip(data,model='Smooth',kernel='boxcar',kernel_width=3,center=False,maintain_bounds=False,min_periods=1) + assert np.isnan(np.asarray(full)[:2]).all() + np.testing.assert_allclose(np.asarray(partial).ravel(),data.signal.rolling(3,min_periods=1).mean()) + display(pd.concat({'full_window':full,'partial_window':partial},axis=1))""", + ["behavior:smoothing-warmup"], +) + +# Review gaps: explicit behavior checks, not just exported-name coverage. +add( + "COLOR-luminance", + "Image palette luminance controls", + """def demo(): + from PIL import Image as PILImage + from hypertools.plot.colors import image_palette,luminance + pixels=np.zeros((20,60,3),dtype=np.uint8) + pixels[:,:40]=[250,250,230];pixels[:,40:]=[20,60,100] + path=SCRATCH/'luminance.png';PILImage.fromarray(pixels).save(path) + colors=image_palette(path,n_colors=2,max_luminance=.5) + assert np.max(np.atleast_1d(luminance(colors)))<=.5 + swatch=np.atleast_2d(colors)[:,:3] + fig,(left,right)=plt.subplots(1,2,figsize=(6,1.6)) + left.imshow(pixels);left.set_title('Input image');left.axis('off') + right.imshow(swatch[None]);right.set_title(f'Selected palette ({len(swatch)} colour)');right.axis('off') + plt.show();print('Selected dark palette:',colors)""", + ["behavior:image-luminance"], + visual="Left: the input image (bright cream on the left, dark blue on the right). Right: the selected palette, which keeps the dark blue and excludes the bright cream.", +) + +add( + "PLOT-cjk", + "Multibyte titles, labels and legends", + """def demo(): + a,_=fixtures() + obj=hyp.plot(a,title='日本語 · Ελληνικά · café',legend=['測定'],xlabel='時間', + backend='matplotlib',show=False) + # Rasterization is required: glyph warnings may be delayed until draw. + # Plotly text is drawn by the browser, so this check is matplotlib-only. + obj.canvas.draw() + show_result(obj)""", + ["behavior:multibyte-fonts"], + visual="Japanese, Greek and accented characters render without missing-glyph boxes. Check recorded font warnings.", +) + +add( + "PLOT-panel-bundle", + "Panel return values and independent fitted models", + """def demo(): + a,b=fixtures() + bundle=hyp.plot([a,b],panels=True,return_model=True,show=False) + assert bundle['panels']==(1,2) + assert len(bundle['axes'])==len(bundle['panel_models'])==2 + assert all(ax.figure is bundle['fig'] for ax in bundle['axes']) + assert bundle['colors'] is not None + print('Bundle:',list(bundle));show_result(bundle)""", + ["behavior:panel-bundle"], + visual="Both panel axes belong to the returned figure; each model corresponds to its dataset.", +) + +add( + "PLOT-hierarchy-metadata", + "Hierarchy trace ownership", + """def demo(): + a,b=fixtures() + columns=pd.MultiIndex.from_product([['group'],['left','right'],['x','y','z']],names=['root','subject','feature']) + data=pd.DataFrame(a,columns=columns) + bundle=hyp.plot(data,return_model=True,show=False) + assert bundle['trace_metadata'] is not None + assert len(bundle['trace_metadata']['keys'])==len(bundle['trace_data']) + display(bundle['trace_metadata']);show_result(bundle)""", + ["behavior:trace-metadata"], + visual="Trace keys identify the hierarchy curves shown.", +) + +add( + "IO-sniff-compressed", + "Compressed CSV and extensionless content detection", + """def demo(): + import gzip + frame=pd.DataFrame({'x':[1,2,3],'y':[4,5,6]}) + compressed=SCRATCH/'sample.csv.gz' + compressed.write_bytes(gzip.compress(frame.to_csv(index=False).encode())) + pd.testing.assert_frame_equal(hyp.load(str(compressed)),frame) + plain=SCRATCH/'extensionless';plain.write_text(frame.to_csv(index=False)) + pd.testing.assert_frame_equal(hyp.load(str(plain)),frame)""", + ["behavior:compressed-load", "behavior:extensionless-load"], +) + +for cell in nb["cells"]: + if cell["cell_type"] != "code": + continue + s = text(cell) + match = re.search(r"run_case\('DATA-([^']+)'", s) + if match and "assert_hosted_contract" not in s: + s = s.replace( + " assert len(data)>0", f" assert_hosted_contract('{match[1]}',data)" + ) + set_source(cell, s) +stream = case_cell("STREAM-03") +s = text(stream).replace( + "path=SCRATCH/'stream.gif'", "path=SCRATCH/(uuid.uuid4().hex+'-stream.gif')" +) +s = s.replace( + " assert path.stat().st_size>100", " verify_export(path,animated=True)" +) +set_source(stream, s) +policy = case_cell("API-policy") +s = text(policy).replace( + " with hyp.set_autoinstall(False):\n assert callable(hyp.reduce)", + """ from hypertools._shared.lazy_import import auto_install_enabled + before=auto_install_enabled() + with hyp.set_autoinstall(False): + assert auto_install_enabled() is False + assert auto_install_enabled()==before + print('Real missing-extra installation is checked separately with scripts/verify_optional_install.py in an isolated environment.')""", +) +set_source(policy, s) +errors = case_cell("ERR-input") +s = text(errors).replace( + "expect_error((ValueError,TypeError),lambda:hyp.plot(a,group=[0]*len(a),show=False))", + "expect_error(TypeError,lambda:hyp.plot(a,group=[0]*len(a),show=False),contains='hue=')", +) +s = s.replace( + "expect_error((ValueError,TypeError),lambda:hyp.align([a,a],align=True))", + "expect_error(TypeError,lambda:hyp.align([a,a],align=True),contains='model=')", +) +s = s.replace( + "expect_error(TypeError,lambda:hyp.align", + "expect_error(ValueError,lambda:hyp.align", +) +set_source(errors, s) + +add( + "IO-legacy-xls", + "Read a real legacy binary spreadsheet", + """def demo(): + import xlwt + workbook=xlwt.Workbook();sheet=workbook.add_sheet('observations') + for row,values in enumerate([['time','value'],[1,2],[3,4]]): + for col,value in enumerate(values):sheet.write(row,col,value) + path=SCRATCH/'legacy.xls';workbook.save(str(path)) + result=hyp.load(str(path)) + pd.testing.assert_frame_equal(result,pd.DataFrame({'time':[1,3],'value':[2,4]})) + display(result)""", + ["behavior:legacy-xls"], + requires=["xlrd", "xlwt"], +) + +# Registry guards count declarations separately from successful executions. +coverage = case_cell("COVERAGE") +s = text(coverage) +if "AUTOENCODER_NAMES" not in s: + s = s.replace( + " missing=sorted(expected-planned)", + """ from hypertools.manip.manip import MANIPULATORS + from hypertools.align.align import ALIGNERS + from hypertools.impute.impute import IMPUTERS + from hypertools.predict.predict import FORECASTERS + from hypertools.reduce.common import AUTOENCODER_NAMES + for prefix,models in [('manip',MANIPULATORS),('align',ALIGNERS),('impute',IMPUTERS),('predict',FORECASTERS)]: + expected|={prefix+':'+model.__name__ for model in models} + expected|={'model:'+name for name in AUTOENCODER_NAMES} + successful={feature for row in RESULTS.values() if row['status']=='PASS' for feature in row['covers']} + print('Declared but not backed by a successful case:',sorted(expected-successful)) + missing=sorted(expected-planned)""", + ) + set_source(coverage, s) + +summary = next(c for c in nb["cells"] if text(c).startswith("summary=pd.DataFrame")) +set_source( + summary, + """summary=pd.DataFrame(report_rows()) +display(summary[['id','title','status','visual','seconds','detail','visual_notes']]) +print('Automatic counts:',summary.status.value_counts().to_dict()) +print('Visual counts:',summary.visual.value_counts().to_dict()) +print('Not run:',sorted({c['id'] for c in CASES}-set(RESULTS))) +attention=summary[(summary.status!='PASS') | (summary.visual.isin(['fail','not reviewed']))] +# A second DataFrame display in one Colab output re-runs Colab's table script, +# which throws ('buttonEl' already declared); show this one as plain HTML. +display(HTML(attention[['id','status','visual','detail','visual_notes']].to_html())) +save_report() +print('Use one interactive viewer at a time; Close disposes its browser resources.') +viewer = interactive_viewer() # the helper displays it; a bare call showed it twice""", +) +comparison = next(c for c in nb["cells"] if text(c).startswith("OTHER_RESULTS =")) +set_source( + comparison, + """OTHER_RESULTS = '' # downloaded results.json from the other environment +if OTHER_RESULTS: + other=json.loads(Path(OTHER_RESULTS).read_text(encoding="utf-8")) + print('Other environment:',other['environment']) + print('This environment:',ENVIRONMENT) + print('Same notebook source:',other.get('notebook_source_sha256')==NOTEBOOK_SOURCE_SHA256) + print('Same inventory:',other.get('inventory_sha256')==source_hash(json.dumps(CASES,sort_keys=True))) + columns=['id','status','visual','visual_notes','source_sha256','warnings'] + left=pd.DataFrame(report_rows(other)).reindex(columns=columns).set_index('id').map(str) + right=pd.DataFrame(report_rows()).reindex(columns=columns).set_index('id').map(str) + left,right=left.align(right,join='outer',fill_value='NOT RUN') + display(left.compare(right,result_names=('other','this run'))) +else: + print('Set OTHER_RESULTS to compare automatic/visual outcomes, notes, case sources and warnings.') +save_report()""", +) +for c in nb["cells"]: + if c["cell_type"] == "code" and "for path in [SCRATCH/" in text(c): + set_source( + c, + text(c).replace("display(FileLink(str(path)))", "download_artifact(path)"), + ) + if c["cell_type"] == "markdown": + s = text(c).replace( + "The lower panel’s head follows the trajectory and date title; the right panel remains fully visible.", + "Both markers follow the trajectory and date title; the right curve remains fully visible. Black is a trailing mean.", + ) + set_source(c, s) +set_source( + inventory, + "# Explicit coverage inventory; declarations are not passes.\nCASES = " + + pprint.pformat(cases, width=100, sort_dicts=False), +) + + +# Remove generated constant branches while retaining real runtime conditions. +class LiteralBranches(ast.NodeTransformer): + changed = False + + def visit_If(self, node): + node = self.generic_visit(node) + test = node.test + if isinstance(test, ast.Compare) and len(test.ops) == 1: + try: + left = ast.literal_eval(test.left) + right = ast.literal_eval(test.comparators[0]) + except (ValueError, TypeError): + return node + op = test.ops[0] + if isinstance(op, ast.Eq): + result = left == right + elif isinstance(op, ast.In): + result = left in right + else: + return node + self.changed = True + return node.body if result else node.orelse + return node + + +for cell in nb["cells"]: + if cell["cell_type"] == "code" and "run_case('" in text(cell): + visitor = LiteralBranches() + tree = visitor.visit(ast.parse(text(cell))) + if visitor.changed: + set_source(cell, ast.unparse(tree)) + elif cell["cell_type"] == "markdown": + match = re.search(r"### ([A-Za-z0-9_-]+) ·", text(cell)) + entry = next((e for e in cases if match and e["id"] == match[1]), None) + if entry and entry["visual"]: + set_source( + cell, + re.sub( + r"\*\*Inspect:\*\* [^\n]*", + "**Inspect:** " + entry["visual"], + text(cell), + ), + ) + +# Keep the illustrative reducers well-conditioned; retain expected time/model warnings. +for case_id, old, new in [ + ("RED-NMF", "'max_iter': 30", "'max_iter': 1000"), + ( + "RED-SpectralEmbedding", + "'random_state': 0", + "'random_state': 0, 'n_neighbors': 24", + ), + ("RED-UMAP", "'n_neighbors': 8", "'n_neighbors': 8, 'n_jobs': 1"), +]: + cell = case_cell(case_id) + if new not in text(cell): + set_source(cell, text(cell).replace(old, new)) +# describe(show=False) returns fig=None by design; render the curve so the +# case's visual check has a figure (fresh-Colab review 2026-09-11). +red = case_cell("RED-describe") +if "show=True" not in text(red): + set_source( + red, + """def demo(): + import matplotlib.figure + a,b=fixtures() + result=hyp.describe([a,b],reduce='PCA',max_dims=5,show=True,backend='matplotlib') + assert {'average','individual','fig'}<=set(result) + assert isinstance(result['fig'],matplotlib.figure.Figure) + print(result['average']) + +run_case('RED-describe', demo)""", + ) +# Undo the 2026-10-08 first pass on the LDA text plots (labels piled up), so a +# notebook synced by that pass takes the table-based version below. +for _b in ("matplotlib", "plotly"): + _cell = case_cell("TEXT-plot-" + _b) + _first = ("legend=True,labels=DOC_LABELS,title='Text topics')\n" + " display(pd.DataFrame({'label':DOC_LABELS,'document':TEXTS}))") + if _first in text(_cell): + set_source(_cell, text(_cell).replace(_first, "legend=True,title='Text topics')")) +# Figure-QA adjudication 2026-09-11: make the figures show what the cases claim. +for case_id, old, new in [ + *[ + (f"PANEL-models-{b}", "ndims=2,title='Reducer comparison')", + "ndims=2,title=['PCA','FactorAnalysis'])") + for b in ("matplotlib", "plotly") + ], + *[ + (f"ANIM-fc-colors-{b}", "title='Continuous forecast hue'", "title='Forecast hue groups'") + for b in ("matplotlib", "plotly") + ], + ("ANIM-callback", "frame_rate=6,on_frame=callback,show=False)", + "frame_rate=6,on_frame=callback,title=' ',show=False)"), + ("TEXT-transformer", "view(TEXTS,'matplotlib',vectorizer='all-MiniLM-L6-v2',ndims=2,", + "view(TEXTS,'matplotlib',fmt='o',vectorizer='all-MiniLM-L6-v2',ndims=2,"), + # Visual sign-off 2026-10-08: make the pictures reviewable on their own. + *[ + (f"PANEL-models-{b}", "subplots=True,title='subplots= alias')", + "subplots=True,title=['Dataset A','Dataset B'])") + for b in ("matplotlib", "plotly") + ], + # LDA on eight short documents puts several at the same spot, where point + # labels would pile up; a table of plotted positions shows which coincide. + *[ + (f"TEXT-plot-{b}", "legend=True,title='Text topics')", + "legend=True,title='Text topics')\n" + " kw=dict(vectorizer='CountVectorizer',corpus=TEXTS,ndims=2,semantic={'model':'LatentDirichletAllocation','kwargs':{'n_components':3,'random_state':0}})\n" + " xy=np.asarray(hyp.plot(TEXTS,show=False,return_model=True,**kw)['xform_data'][0])\n" + " display(pd.DataFrame({'group':['animals']*4+['markets']*4,'x':xy[:,0].round(2),'y':xy[:,1].round(2),'document':TEXTS}))") + for b in ("matplotlib", "plotly") + ], + ("IMP-score", "display(hyp.impute(damaged,model=['SimpleImputer'],truth=a,metrics=['rmse','mae']))", + "display(HTML(hyp.impute(damaged,model=['SimpleImputer'],truth=a,metrics=['rmse','mae']).to_html()))"), + ("TEXT-transformer", "legend=True,title='Transformer embeddings')", + "legend=True,labels=DOC_LABELS,title='Transformer embeddings')\n" + " display(pd.DataFrame({'label':DOC_LABELS,'document':TEXTS}))"), +]: + cell = case_cell(case_id) + if new not in text(cell): + assert old in text(cell), (case_id, old) + set_source(cell, text(cell).replace(old, new)) +mds = case_cell("RED-MDS") +if "inspect.signature(MDS)" not in text(mds): + set_source( + mds, + """def demo(): + from sklearn.manifold import MDS + a,_=fixtures() + kwargs={'random_state':0,'max_iter':300,'n_init':1} + if 'init' in inspect.signature(MDS).parameters:kwargs['init']='random' + out=hyp.reduce(a,reduce={'model':'MDS','kwargs':kwargs},ndims=2) + finite(out,(48,2));print('MDS',np.shape(out)) + +run_case('RED-MDS', demo)""", + ) + +# Mixture weights encode continuous blends, so there is no categorical legend. +for backend in ["matplotlib", "plotly"]: + cell = case_cell("HIER-mixture-" + backend) + set_source( + cell, + text(cell).replace( + "legend=True,title='Means blend", "legend=False,title='Means blend" + ), + ) + +# Hash normalized sources, excluding the hash declaration itself and all outputs. +setup_source = re.sub(r"\nNOTEBOOK_SOURCE_SHA256 = '[^']*'", "", text(setup)).rstrip() +digest = hashlib.sha256( + json.dumps( + [ + (c["cell_type"], setup_source if c is setup else text(c)) + for c in nb["cells"] + ], + ensure_ascii=False, + ).encode() +).hexdigest() +set_source(setup, setup_source + f"\nNOTEBOOK_SOURCE_SHA256 = '{digest}'\n") +for index, cell in enumerate(nb["cells"]): + cell.setdefault( + "id", hashlib.sha256((str(index) + text(cell)).encode()).hexdigest()[:12] + ) +PATH.write_text(json.dumps(nb, indent=1, ensure_ascii=False) + "\n", encoding="utf-8") +print(PATH, len(cases), "cases; source", digest) diff --git a/scripts/verify_docs_playwright.py b/scripts/verify_docs_playwright.py index c7bf6714..33423bd4 100644 --- a/scripts/verify_docs_playwright.py +++ b/scripts/verify_docs_playwright.py @@ -13,6 +13,9 @@ ----- .venv/bin/python scripts/verify_docs_playwright.py +Set ``HYPERTOOLS_DOCS_HTML`` and ``HYPERTOOLS_DOCS_SCREENSHOTS`` to check +a build and save evidence outside the source checkout. + Exits non-zero (and prints the failing assertions) if ANY page fails verification. Screenshots (full-page + cropped element captures used for the non-blank pixel-variance check) are written to ``docs/images/v1.0-docs/``. @@ -28,6 +31,7 @@ import sys import time import urllib.request +from importlib.metadata import version from pathlib import Path import numpy as np @@ -35,8 +39,10 @@ from playwright.sync_api import sync_playwright REPO_ROOT = Path(__file__).resolve().parent.parent -DOCS_HTML = REPO_ROOT / "docs" / "_build" / "html" -SCREENSHOT_DIR = REPO_ROOT / "docs" / "images" / "v1.0-docs" +DOCS_HTML = Path(os.environ.get( + 'HYPERTOOLS_DOCS_HTML', REPO_ROOT / "docs" / "_build" / "html")) +SCREENSHOT_DIR = Path(os.environ.get( + 'HYPERTOOLS_DOCS_SCREENSHOTS', REPO_ROOT / "docs" / "images" / "v1.0-docs")) def _current_branch() -> str: """The branch the docs were built for -- must match the branch-aware Colab install cells (docs/conf.py + scripts/add_colab_install_cell.py). Detected @@ -62,6 +68,7 @@ def _is_release_ref(branch: str) -> bool: BRANCH = _current_branch() IS_RELEASE = _is_release_ref(BRANCH) +NOTEBOOK_REF = 'v' + version('hypertools') if IS_RELEASE else BRANCH # Minimum standard deviation of pixel intensities (0-255 scale) for an # element screenshot to be considered "non-blank". A truly blank/white or @@ -162,10 +169,10 @@ def verify_colab_badge(page) -> str: raise VerificationFailure(f"Colab badge link href looks wrong: {href!r}") # the generated notebooks live on the docs-notebooks branch (they are # gitignored in the main tree), so the badge must point there under this ref - if "blob/docs-notebooks/" not in href or f"/{BRANCH}/" not in href: + if f"blob/docs-notebooks/{NOTEBOOK_REF}/auto_examples/" not in href: raise VerificationFailure( "Colab badge must point at blob/docs-notebooks/" - f"{BRANCH}/auto_examples/...: {href!r}") + f"{NOTEBOOK_REF}/auto_examples/...: {href!r}") return href @@ -173,7 +180,9 @@ def verify_tutorial_branch_aware_install(page) -> str: """Tutorial (nbsphinx) pages: no image badge, but a real branch-aware `pip install ... @<branch>` cell must be present as the notebook's install-from-source instructions.""" - content = page.content() + # GH #284 release review: highlighted HTML wraps 'pip' and 'install' + # in separate spans. Check the rendered code, not the markup spelling. + content = '\n'.join(page.locator('.nbinput .highlight').all_text_contents()) if "pip install" not in content: raise VerificationFailure( "tutorial page has no 'pip install' cell") @@ -282,17 +291,30 @@ def verify_plotly_animated(page, shot_path: Path) -> dict: "return !!el && el.querySelectorAll('svg, canvas').length > 0; }", arg=".plotly-graph-div", timeout=15000, ) - content = page.content() - if "Plotly.animate(" not in content or "Plotly.addFrames(" not in content: - raise VerificationFailure( - "no embedded Plotly animation calls (addFrames/animate) found in page source" - ) + # GH #284 release review: auto_play=False deliberately emits no + # Plotly.animate call. Prove that real frames and play controls loaded, + # then execute an actual transition rather than checking source text. + page.wait_for_function( + """() => { + const el = document.querySelector('.plotly-graph-div'); + return el?._transitionData?._frames?.length > 0 && + el.layout.updatemenus.some(menu => + menu.buttons.some(button => button.method === 'animate')); + }""", timeout=15000) + frame_count = div.evaluate('el => el._transitionData._frames.length') + div.evaluate("""async el => { + const frames = el._transitionData._frames; + await Plotly.animate(el, [frames[frames.length - 1].name], { + transition: {duration: 0}, frame: {duration: 0, redraw: true}, + mode: 'immediate' + }); + }""") png_bytes = div.screenshot() std = _save_and_check_nonblank( png_bytes, shot_path.with_name(shot_path.stem + "_plot.png")) href = verify_colab_badge(page) page.screenshot(path=str(shot_path), full_page=True) - return {"pixel_std": std, "colab_href": href} + return {"pixel_std": std, "colab_href": href, "frame_count": frame_count} def verify_tutorial(page, shot_path: Path) -> dict: diff --git a/scripts/verify_optional_install.py b/scripts/verify_optional_install.py new file mode 100644 index 00000000..2e35b787 --- /dev/null +++ b/scripts/verify_optional_install.py @@ -0,0 +1,87 @@ +"""Verify missing-extra errors and actual installation in an isolated venv. + +Usage: .venv/bin/python scripts/verify_optional_install.py --output /tmp/optional-check +Installs the current source with base dependencies into a NEW temporary venv; +never uninstalls or changes packages in the caller's interpreter. +""" + +import argparse +import json +import os +from pathlib import Path +import subprocess +import tempfile +import venv + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + args.output.mkdir(parents=True, exist_ok=True) + repo = Path(__file__).resolve().parents[1] + with tempfile.TemporaryDirectory(prefix="hypertools-optional-env-") as directory: + root = Path(directory) + venv.EnvBuilder(with_pip=True).create(root) + python = root / ("Scripts/python.exe" if os.name == "nt" else "bin/python") + with (args.output / "install.log").open("w") as log: + subprocess.run( + [str(python), "-m", "pip", "install", str(repo)], + stdout=log, + stderr=subprocess.STDOUT, + check=True, + ) + source = """import importlib.util, json +from pathlib import Path +from importlib.metadata import version +import numpy as np +import hypertools as hyp +assert importlib.util.find_spec('openpyxl') is None, 'Expected a base-only environment' +path=Path('roundtrip.xlsx') +with hyp.set_autoinstall(False): + try: hyp.save(np.arange(12).reshape(4,3),str(path)) + except hyp.HypertoolsIOError as exc: + assert isinstance(exc.__cause__, ImportError) + assert 'hypertools[io]' in str(exc), str(exc) + print('DISABLED:',str(exc)) + else: raise AssertionError('Missing extra unexpectedly succeeded') +assert importlib.util.find_spec('openpyxl') is None +with hyp.set_autoinstall(True): + hyp.save(np.arange(12).reshape(4,3),str(path)) + restored=hyp.load(str(path)) +np.testing.assert_array_equal(restored,np.arange(12).reshape(4,3)) +print(json.dumps({'status':'PASS','hypertools':hyp.__version__,'openpyxl':version('openpyxl'), + 'roundtrip_shape':list(restored.shape)})) +""" + with (args.output / "verification.log").open("w") as log: + subprocess.run( + [str(python), "-c", source], + cwd=root, + stdout=log, + stderr=subprocess.STDOUT, + check=True, + ) + (args.output / "result.json").write_text( + json.dumps( + { + "status": "PASS", + "source": str(repo), + "git_commit": subprocess.check_output( + ["git", "-C", str(repo), "rev-parse", "HEAD"], text=True + ).strip(), + "source_diff_sha256": __import__("hashlib") + .sha256( + subprocess.check_output( + ["git", "-C", str(repo), "diff", "HEAD"] + ) + ) + .hexdigest(), + }, + indent=2, + ) + ) + print("PASS:", args.output) + + +if __name__ == "__main__": + main() diff --git a/tests/_netskip.py b/tests/_netskip.py index 66e82c0b..d93f45ba 100644 --- a/tests/_netskip.py +++ b/tests/_netskip.py @@ -90,6 +90,15 @@ # (a moved dataset URL is a REAL regression and must fail, not skip). STATUS_DETERMINED_TYPES = frozenset({'HTTPError', 'HTTPStatusError'}) +# SSLError says nothing on its own either: requests raises it both for a +# peer that dropped the TLS connection (`SSLEOFError: UNEXPECTED_EOF_WHILE_ +# READING`, `SSLZeroReturnError`, a reset mid-handshake -- the host's fault; +# 2026-09-08: Dropbox did this to one ubuntu-3.11 matrix cell while eleven +# others loaded the same file) and for a certificate that does not verify +# (ours, or the environment's, and a real failure). The cause phrase on the +# same line decides. +PHRASE_DETERMINED_TYPES = frozenset({'SSLError'}) + # Carry no verdict either way: the aggregate's own wrapper class, and bases too # generic to mean anything. Named so they are not mistaken for defect evidence. NEUTRAL_TYPES = frozenset({ @@ -120,6 +129,15 @@ 'failed to establish', ) +# What decides an `SSLError` line: a peer closing the TLS connection mid-read +# or mid-handshake (stdlib ssl spells the EOF both ways). 'max retries' is NOT +# in this list -- requests says it on every pooled failure, certificate ones +# included -- and 'certificate' anywhere on the line vetoes. +TLS_DROP_PHRASES = ( + 'unexpected_eof_while_reading', 'eof occurred in violation of protocol', + 'sslzeroreturnerror', 'connection reset', 'connection aborted', +) + # "500 Server Error" / "503 Server Error" as requests spells it. Matched with a # digit pattern plus the words, never a bare ' 500', which would false-positive # on a real assertion like "shape 500 != 499". @@ -163,11 +181,28 @@ def _type_names(exc): return {cls.__name__ for cls in type(exc).__mro__} +def _certificate_failure(exc): + """True when a TLS CERTIFICATE failure sits anywhere in the chain -- as + an exception, as an exception carried in another's ``args`` (requests + wraps ``ssl.SSLCertVerificationError`` that way, and its `SSLError` + inherits from `ConnectionError`, which alone reads as transient), or as + the word 'certificate' in a message. That is our environment's fault or + ours, never the host's (Codex round 9).""" + for e in _chain(exc): + carried = [a for a in getattr(e, 'args', ()) if isinstance(a, BaseException)] + for item in (e, *carried): + if 'SSLCertVerificationError' in _type_names(item) \ + or 'certificate' in str(item).lower(): + return True + return False + + def _exception_is_transient(exc): """Structural verdict on a LIVE exception: type names across the whole - ``__cause__``/``__context__`` chain, plus HTTP status. Text is not consulted. + ``__cause__``/``__context__`` chain, plus HTTP status. Text is consulted + only for the certificate veto. """ - if _type_names(exc) & DEFECT_TYPES: + if _type_names(exc) & DEFECT_TYPES or _certificate_failure(exc): return False for e in _chain(exc): names = _type_names(e) @@ -184,7 +219,7 @@ def _is_exception_token(token): """True if `token` names an exception class rather than part of a resolver label ('Google Sheets: ...' must not read 'Sheets' as evidence).""" if token in TRANSIENT_TYPES or token in STATUS_DETERMINED_TYPES \ - or token in NEUTRAL_TYPES: + or token in PHRASE_DETERMINED_TYPES or token in NEUTRAL_TYPES: return True # 'Error'/'Exception' alone are words, not type names -- require a prefix. return (len(token) > len('Error') and token.endswith('Error')) or \ @@ -207,6 +242,11 @@ def _classify_line(line): if any(t in STATUS_DETERMINED_TYPES for t in tokens): # 5xx is the host; anything else (404, 403, ...) is a real regression. return 'transient' if has_5xx else 'defect' + if any(t in PHRASE_DETERMINED_TYPES for t in tokens): + # a dropped TLS connection is the host; a certificate failure is not + dropped = any(p in lowered for p in TLS_DROP_PHRASES) + return ('transient' if dropped and 'certificate' not in lowered + else 'defect') if any(t not in NEUTRAL_TYPES for t in tokens): return 'defect' if has_5xx or any(p in lowered for p in TRANSIENT_PHRASES): diff --git a/tests/_plotly_colors.py b/tests/_plotly_colors.py new file mode 100644 index 00000000..56c910f5 --- /dev/null +++ b/tests/_plotly_colors.py @@ -0,0 +1,21 @@ +"""Effective Plotly colors for parity checks across opacity serializations.""" + +import re +from matplotlib.colors import to_rgba + + +def rgba(trace, component="line", index=None): + style = getattr(trace, component) + color = style.color if index is None else style.color[index] + if isinstance(color, str) and color.startswith(("rgb(", "rgba(")): + parts = [float(v) for v in re.findall(r"[\d.]+", color)] + assert len(parts) in (3, 4), color + result = tuple(v / 255 for v in parts[:3]) + ( + parts[3] if len(parts) == 4 else 1.0, + ) + else: + result = to_rgba(color) + opacity = trace.opacity if trace.opacity is not None else 1.0 + if component == "marker" and style.opacity is not None: + opacity *= style.opacity + return result[:3] + (result[3] * opacity,) diff --git a/tests/align/test_aligner_direct_inputs.py b/tests/align/test_aligner_direct_inputs.py new file mode 100644 index 00000000..43ed03a2 --- /dev/null +++ b/tests/align/test_aligner_direct_inputs.py @@ -0,0 +1,225 @@ +"""The Aligner classes called directly, on the inputs every other hypertools +entry point accepts (1.1 release review, 2026-09-11). + +`Aligner.fit` documents ``data : DataFrame, array, or list of these`` and the +1.1 CHANGELOG shows ``HyperAlign().fit(xs).transform(ys)``, but `fit` handed +its input straight to ``datawrangler.unstack``, which only understands +DataFrames: a list of numpy arrays -- the most common hypertools input -- +raised a bare ``Exception: Unsupported datatype: <class 'list'>``, and so did +a single array. The classes now coerce their input the way ``hyp.align`` +does, and hand results back in the input's own form: arrays for arrays, +DataFrames for DataFrames, a list for a list and a single dataset for a +single dataset. + +Also here: the alignment warnings name the CALLER's line (not +``hypertools/align/common.py``), like the library's other warnings. + +All data is real (small) numeric arrays -- no mocks. +""" +import warnings + +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp +from hypertools.align import (HyperAlign, NullAlign, Procrustes, + SharedResponseModel) +from hypertools.align.srm import (DeterministicSharedResponseModel, + RobustSharedResponseModel) +from hypertools.align.score import alignment_score +from hypertools.core.pipeline import Pipeline + + +def _rotated_copies(n=3, n_obs=40, n_features=3, noise=0.05, seed=0): + """`n` rotated, noisy copies of one smooth trajectory (numpy arrays).""" + t = np.linspace(0, 4 * np.pi, n_obs) + base = np.stack([np.sin(t), np.cos(t), t / 10, np.sin(2 * t)], + axis=1)[:, :n_features] + rng = np.random.default_rng(seed) + out = [] + for _ in range(n): + rot, _ = np.linalg.qr(rng.standard_normal((n_features, n_features))) + out.append((base + noise * rng.standard_normal(base.shape)) @ rot) + return out + + +ALIGNER_FACTORIES = { + 'HyperAlign': lambda: HyperAlign(n_iter=5), + 'Procrustes': lambda: Procrustes(), + 'SharedResponseModel': lambda: SharedResponseModel(features=3), + 'DeterministicSharedResponseModel': + lambda: DeterministicSharedResponseModel(features=3), + 'RobustSharedResponseModel': lambda: RobustSharedResponseModel(features=3), + 'NullAlign': lambda: NullAlign(), +} + + +@pytest.mark.parametrize('name', sorted(ALIGNER_FACTORIES)) +def test_fit_accepts_a_list_of_arrays_and_returns_arrays(name): + xs = _rotated_copies() + model = ALIGNER_FACTORIES[name]() + assert model.fit(xs) is model + out = model.transform(xs) + assert isinstance(out, list) and len(out) == len(xs) + for a, x in zip(out, xs): + assert type(a) is np.ndarray + assert a.shape[0] == x.shape[0] + + # the same numbers as the DataFrame route that always worked + frames = [pd.DataFrame(x) for x in xs] + reference = ALIGNER_FACTORIES[name]().fit(frames).transform(frames) + for a, r in zip(out, reference): + assert isinstance(r, pd.DataFrame) + np.testing.assert_allclose(a, r.to_numpy()) + + +@pytest.mark.parametrize('name', sorted(ALIGNER_FACTORIES)) +def test_fit_transform_accepts_a_list_of_arrays(name): + xs = _rotated_copies() + out = ALIGNER_FACTORIES[name]().fit_transform(xs) + frames = [pd.DataFrame(x) for x in xs] + reference = ALIGNER_FACTORIES[name]().fit_transform(frames) + assert isinstance(out, list) and len(out) == len(xs) + for a, r in zip(out, reference): + assert type(a) is np.ndarray + np.testing.assert_allclose(a, r.to_numpy()) + + +def test_changelog_chain_fit_on_arrays_transform_held_out_arrays(): + """The CHANGELOG's ``HyperAlign().fit(xs).transform(ys)``, with the + usual array input: held-out data is projected into the fitted space + and agrees measurably better than before alignment.""" + data = _rotated_copies(n=3, n_obs=80) + xs = [d[:40] for d in data] + ys = [d[40:] for d in data] + aligned = HyperAlign(n_iter=10).fit(xs).transform(ys) + assert all(type(a) is np.ndarray and a.shape == (40, 3) for a in aligned) + score = alignment_score(ys, aligned=aligned) + assert score['after'] < score['before'] - 0.2 + + +@pytest.mark.parametrize('cls', [HyperAlign, Procrustes, NullAlign]) +def test_a_single_array_is_one_dataset_and_comes_back_as_one_array(cls): + x = _rotated_copies(n=1)[0] + model = cls() + assert model.fit(x) is model + out = model.transform(x) + assert type(out) is np.ndarray and out.shape == x.shape + replay = cls().fit_transform(x) + assert type(replay) is np.ndarray and replay.shape == x.shape + + +def test_a_single_dataframe_comes_back_as_one_dataframe(): + """`transform`'s docstring promises the input's list/single-item shape; + a single DataFrame used to come back as a list of one.""" + frame = pd.DataFrame(_rotated_copies(n=1)[0], + index=pd.RangeIndex(100, 140)) + out = NullAlign().fit_transform(frame) + assert isinstance(out, pd.DataFrame) + assert list(out.index) == list(frame.index) + np.testing.assert_allclose(out.to_numpy(), frame.to_numpy()) + + +def test_a_mixed_list_returns_each_dataset_in_its_own_form(): + a, b = _rotated_copies(n=2) + frame = pd.DataFrame(b, index=pd.RangeIndex(0, 40)) + out = HyperAlign(n_iter=5).fit_transform([a, frame]) + assert type(out[0]) is np.ndarray + assert isinstance(out[1], pd.DataFrame) + assert list(out[1].index) == list(frame.index) + + +def test_a_tuple_of_arrays_is_a_list_of_datasets(): + xs = _rotated_copies() + out = Procrustes().fit_transform(tuple(xs)) + reference = Procrustes().fit_transform(list(xs)) + assert isinstance(out, list) and len(out) == 3 + for a, r in zip(out, reference): + np.testing.assert_allclose(a, r) + + +def test_transform_replay_keeps_the_fit_time_input_form(): + xs = _rotated_copies() + model = HyperAlign(n_iter=5).fit(xs) + replay = model.transform() + assert isinstance(replay, list) + assert all(type(a) is np.ndarray for a in replay) + np.testing.assert_allclose(replay[1], model.transform(xs)[1]) + + +def test_held_out_shape_is_still_validated_for_arrays(): + xs = _rotated_copies() + model = HyperAlign(n_iter=3).fit(xs) + with pytest.raises(ValueError, match=r'3 dataset'): + model.transform(xs[:2]) + with pytest.raises(ValueError, match=r'column'): + model.transform([xs[0], xs[1], np.hstack([xs[2], xs[2]])]) + + +def test_polars_frames_align_like_pandas_frames(): + pl = pytest.importorskip('polars') + xs = _rotated_copies() + out = HyperAlign(n_iter=5).fit_transform( + [pl.DataFrame(x, schema=['a', 'b', 'c']) for x in xs]) + reference = HyperAlign(n_iter=5).fit_transform( + [pd.DataFrame(x) for x in xs]) + assert isinstance(out, list) and len(out) == 3 + for a, r in zip(out, reference): + assert isinstance(a, pd.DataFrame) + np.testing.assert_allclose(a.to_numpy(), r.to_numpy()) + + +def test_dispatcher_results_are_unchanged_by_the_direct_route(): + """`hyp.align` wraps the same classes: its (array) output equals the + direct class call on arrays.""" + xs = _rotated_copies() + via_dispatcher = hyp.align(xs, model=HyperAlign(n_iter=5)) + direct = HyperAlign(n_iter=5).fit_transform(xs) + for a, b in zip(via_dispatcher, direct): + np.testing.assert_allclose(a, b) + single = hyp.align(xs[0], model='NullAlign') + assert type(single) is np.ndarray and single.shape == xs[0].shape + + +def test_a_raw_aligner_pipeline_step_takes_a_list_of_arrays(): + xs = _rotated_copies() + out = Pipeline(['HyperAlign']).fit_transform(xs) + reference = HyperAlign().fit_transform(xs) + assert isinstance(out, list) and len(out) == 3 + for a, r in zip(out, reference): + assert type(a) is np.ndarray + np.testing.assert_allclose(a, r) + + +# --- warnings name the caller's line (finding 4) --------------------------- + +def test_the_row_trim_warning_names_the_callers_line_via_hyp_align(): + a, b = _rotated_copies(n=2) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + hyp.align([a, b[:25]], model='HyperAlign') + trims = [w for w in caught if 'common to all datasets' in str(w.message)] + assert len(trims) == 1 + assert trims[0].filename == __file__, trims[0].filename + + +def test_the_row_trim_warning_names_the_callers_line_via_the_class(): + a, b = _rotated_copies(n=2) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + HyperAlign().fit_transform([a, b[:25]]) + trims = [w for w in caught if 'common to all datasets' in str(w.message)] + assert len(trims) == 1 + assert trims[0].filename == __file__, trims[0].filename + + +def test_the_duplicate_index_warning_names_the_callers_line(): + a, b = _rotated_copies(n=2) + dup = pd.DataFrame(a, index=[0, 0] + list(range(2, 40))) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + hyp.align([dup, pd.DataFrame(b)], model='NullAlign') + dups = [w for w in caught if 'duplicated row-index' in str(w.message)] + assert len(dups) == 1 + assert dups[0].filename == __file__, dups[0].filename diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..9d61dd4c --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,9 @@ +"""Session-wide test settings.""" +import os + +# plot(..., backend='plotly', show=True) outside a notebook calls fig.show(), +# and plotly's default renderer outside a notebook is the desktop browser: +# a local test run opened browser tabs on the maintainer's screen +# (2026-09-11). Keep every run headless unless the caller chose a renderer. +# Set before any test module imports plotly, which reads it at import. +os.environ.setdefault('PLOTLY_RENDERER', 'json') diff --git a/tests/plot/test_forecast_animation_plotly.py b/tests/plot/test_forecast_animation_plotly.py index ab02d0ac..31be6499 100644 --- a/tests/plot/test_forecast_animation_plotly.py +++ b/tests/plot/test_forecast_animation_plotly.py @@ -152,9 +152,11 @@ def test_plotly_forecast_trail_traces_carry_decreasing_opacity(): assert min(alphas) < max(alphas), 'the trail must actually fade' live_alpha = _fc_role(fig, 'live')[0].meta['hyp_forecast_alpha'] assert all(live_alpha > a for a in alphas) - # the declared alpha is the one actually baked into the rgba colour + # Compare effective opacity after RGB/native-opacity serialization. for tr, a in zip(by_age, alphas): - assert f'{a}' in tr.line.color or str(round(a, 3)) in tr.line.color + color_alpha = (float(tr.line.color.rsplit(',',1)[1].rstrip(')')) + if tr.line.color.startswith('rgba(') else 1.) + assert color_alpha * (tr.opacity if tr.opacity is not None else 1.) == pytest.approx(a) def test_plotly_trail_is_populated_by_the_late_frames(): diff --git a/tests/plot/test_forecast_group_default_palette.py b/tests/plot/test_forecast_group_default_palette.py new file mode 100644 index 00000000..679a6707 --- /dev/null +++ b/tests/plot/test_forecast_group_default_palette.py @@ -0,0 +1,185 @@ +"""Default `forecast_hue=`/`forecast_cluster=` colours continue the palette +PAST the observed data's colours (maintainer decision, 1.1 review). + +They used to start the palette over, so forecast group 0 was drawn in +exactly dataset 0's observed colour (and, with 'hls' over four datasets, +group 1 in dataset 2's). An explicit `forecast_palette=` still overrides. +""" +import re + +import matplotlib +matplotlib.use('Agg') + +import numpy as np # noqa: E402 +import pytest # noqa: E402 +import seaborn as sns # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.colors import to_hex # noqa: E402 + +import hypertools as hyp # noqa: E402 + + +def _walks(): + rng = np.random.default_rng(0) + a = np.cumsum(rng.standard_normal((36, 5)), 0) + b = np.cumsum(rng.standard_normal((36, 5)), 0) + return [a, b, a + 2, b + 2] + + +GROUPINGS = { + 'cluster': dict(forecast_cluster='KMeans', forecast_n_clusters=2), + 'hue': dict(forecast_hue=['x', 'y', 'x', 'y']), +} + + +def _hex(c): + if isinstance(c, str) and c.startswith('rgb'): + v = [float(x) for x in re.findall(r'[\d.]+', c)[:3]] + return to_hex([x / 255 for x in v]) + return to_hex(c) + + +def _mpl(fig, role): + ax = fig.axes[0] + obs = [_hex(ln.get_color()) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + fc = [_hex(ln.get_color()) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == role] + return obs, fc + + +def _plotly(fig, role): + obs = [_hex(tr.line.color) for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') is None + and tr.line is not None and isinstance(tr.line.color, str) + and tr.x is not None and len(tr.x) + and _hex(tr.line.color) != to_hex('black')] # not the frame + fc = [_hex(tr.line.color) for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == role] + return obs, fc + + +def _drawn(backend, animated, **kw): + data = _walks() + kw = dict(predict='Kalman', t=6, random_state=0, show=False, **kw) + if animated: + kw.update(animate=True, duration=1, frame_rate=4) + out = hyp.plot(data, backend=backend, **kw) + role = 'live' if animated else 'static' + if backend == 'plotly': + return _plotly(out, role) + fig = out.figure if animated else out + if animated: + out.draw_frame(out.n_frames - 1) + colours = _mpl(fig, role) + plt.close(fig) + return colours + + +@pytest.mark.parametrize('grouping', sorted(GROUPINGS)) +@pytest.mark.parametrize('animated', [False, True]) +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_default_group_colours_continue_past_the_observed_ones( + backend, animated, grouping): + if backend == 'plotly': + pytest.importorskip('plotly') + obs, fc = _drawn(backend, animated, **GROUPINGS[grouping]) + hls4 = [to_hex(c) for c in sns.color_palette('hls', 4)] + assert obs[:4] == hls4 + # the palette continued past the 4 observed slots: 'hls' at 8 holds + # all four observed colours, and the groups take its free slots in + # order (45 and 135 degrees) + hls8 = [to_hex(c) for c in sns.color_palette('hls', 8)] + groups = list(dict.fromkeys(fc)) + assert groups == [hls8[1], hls8[3]], (fc, hls8) + assert not set(fc) & set(obs), 'a forecast group wears an observed colour' + # the grouping itself is unchanged: datasets 0/2 and 1/3 pair up + assert fc[0] == fc[2] and fc[1] == fc[3] and fc[0] != fc[1] + + +def test_a_named_palette_is_continued_not_restarted(): + obs, fc = _drawn('matplotlib', False, palette='Set2', + **GROUPINGS['hue']) + set2 = [to_hex(c) for c in sns.color_palette('Set2', 6)] + assert obs == set2[:4] + assert list(dict.fromkeys(fc)) == set2[4:6] + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_an_explicit_forecast_palette_still_overrides(backend): + if backend == 'plotly': + pytest.importorskip('plotly') + _, fc = _drawn(backend, False, forecast_palette=['black', 'orange'], + **GROUPINGS['cluster']) + assert set(fc) == {to_hex('black'), to_hex('orange')} + + +def test_panels_give_each_label_the_single_axes_colour(): + data = _walks() + kw = dict(predict='Kalman', t=6, show=False, **GROUPINGS['hue']) + single = hyp.plot(data, **kw) + _, single_fc = _mpl(single, 'static') + plt.close(single) + grid = hyp.plot(data, panels=True, **kw) + panel_fc = [] + for ax in grid.axes: + panel_fc += [_hex(ln.get_color()) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + obs = [_hex(ln.get_color()) for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + assert not set(obs) & set(panel_fc[-1:]) + plt.close(grid) + assert panel_fc == single_fc + + +def test_a_reused_axes_continues_past_every_earlier_observed_colour(): + data = _walks() + fig, axes = hyp.subplots(1, 1) + hyp.plot(data[0], ax=axes[0], show=False) + hyp.plot(data[1:], ax=axes[0], predict='Kalman', t=6, show=False, + forecast_hue=['x', 'y', 'x']) + obs, fc = _mpl(fig, 'static') + plt.close(fig) + assert len(set(obs)) == 4 + assert len(set(fc)) == 2 + assert not set(fc) & set(obs), (obs, fc) + + +def test_a_fmt_colour_letter_takes_no_slot_and_no_group_collides(): + """A lettered dataset ('r-') takes no palette slot; the other three + take the first three colours of the 4-colour sampling, exactly as the + observed lines are drawn -- so the groups must avoid those three.""" + obs, fc = _drawn('matplotlib', False, fmt=['r-', '-', '-', '-'], + **GROUPINGS['hue']) + assert len(set(fc)) == 2 + assert not set(fc) & set(obs), (obs, fc) + + +def test_panels_with_a_categorical_observed_hue_match_the_single_axes(): + data = _walks() + kw = dict(predict='Kalman', t=6, show=False, hue=['p', 'p', 'q', 'q'], + **GROUPINGS['hue']) + single = hyp.plot(data, **kw) + obs, single_fc = _mpl(single, 'static') + plt.close(single) + assert not set(single_fc) & set(obs) + grid = hyp.plot(data, panels=True, **kw) + panel_fc = [_hex(ln.get_color()) for ax in grid.axes for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + plt.close(grid) + assert panel_fc == single_fc + + +@pytest.mark.parametrize('animated', [False, True]) +def test_a_continuous_hue_keeps_an_explicit_forecast_colour(animated): + """Under a continuous `hue=` matplotlib repainted EVERY forecast in + its trace's final hue colour, discarding `forecast_hue=` (plotly kept + it, as `_forecast_style_from` documents): the backends disagreed.""" + pytest.importorskip('plotly') + kw = dict(hue=[np.arange(36.0)] * 4, **GROUPINGS['hue']) + _, mpl_fc = _drawn('matplotlib', animated, **kw) + _, pl_fc = _drawn('plotly', animated, **kw) + assert len(set(mpl_fc)) == 2 + assert mpl_fc == pl_fc diff --git a/tests/plot/test_forecast_schedule_warning.py b/tests/plot/test_forecast_schedule_warning.py index 0dac1eca..6f91a51a 100644 --- a/tests/plot/test_forecast_schedule_warning.py +++ b/tests/plot/test_forecast_schedule_warning.py @@ -282,3 +282,35 @@ def test_the_warning_reports_the_TOTAL_not_just_what_is_left(): # and it must no longer claim a single timed fit assert 'one timed fit' not in msg, msg assert 'two history lengths' in msg, msg + + +def test_project_schedule_cost_fits_every_timed_length_by_least_squares(): + """With more than two timed lengths one noisy pair no longer sets the + slope: a 0.002 s/row + 0.3 s line with one +50 ms outlier at 200 rows + still projects within a few percent of the true 7.0 s for ten fits at + 200 rows (a two-point estimator through the outlier would not).""" + timings = {100: 0.5, 150: 0.6, 200: 0.75, 250: 0.8, 300: 0.9} + projected, per_row, setup, lengths = project_schedule_cost( + timings, [200] * 10) + assert lengths == (100, 300) + assert per_row == pytest.approx(0.002, rel=0.15) + assert projected == pytest.approx(7.0, rel=0.1) + + +def test_the_projection_waits_for_a_fit_long_enough_to_time(): + """On a slow CI runner, a slope through fits at 2 and 3 rows (tens of + milliseconds each, mostly timer noise) projected 10 s for a 30-row + schedule that finished in well under one second. The projection now + waits for a timed fit of at least `PROJECTION_MIN_ROWS` rows.""" + from hypertools.plot.forecast import PROJECTION_MIN_ROWS + schedule = ForecastSchedule.for_parallel( + [_walk(30)], [30], model='Kalman', t=3, n_frames=20, + slow_warning_seconds=1e9) + assert schedule.projection is not None + assert schedule.projection['lengths'][1] >= PROJECTION_MIN_ROWS + # a schedule shorter than that still projects, at its longest history + tiny = ForecastSchedule.for_parallel( + [_walk(6)], [6], model='Kalman', t=1, n_frames=6, + slow_warning_seconds=1e9) + assert tiny.projection is not None + assert tiny.projection['lengths'][1] <= 6 diff --git a/tests/plot/test_hierarchy_legend_names.py b/tests/plot/test_hierarchy_legend_names.py index c170448e..fe295869 100644 --- a/tests/plot/test_hierarchy_legend_names.py +++ b/tests/plot/test_hierarchy_legend_names.py @@ -241,14 +241,21 @@ def test_plotly_never_names_a_trace_with_matplotlibs_sentinel(frame_fn): def test_plotly_hierarchy_unlabelled_traces_match_a_plain_lists_name(): - """The baseline the sentinel deviated from: plotly's own "no name".""" + """The baseline the sentinel deviated from: what a plain list's traces + are called. Until the 1.1 release review that was plotly's own "no + name" (None), which this test pinned -- and which made plotly's hover + label read "trace 0", "trace 1", ... (maintainer finding). A plain + list's traces are now named by the labels `legend=True` would give + them, and a hierarchy's unlabelled leaves by their top-level group -- + still never by the sentinel, and still out of the legend.""" plain = _plotly([np.random.default_rng(s).standard_normal((8, 3)) for s in (0, 1)], '-', show=False) - assert {t.name for t in _data_traces(plain)} == {None} + assert [t.name for t in _data_traces(plain)] == ['1', '2'] hier = _plotly(row_frame(), '-', show=False) leaves = [t for t in _data_traces(hier) if not t.showlegend] assert len(leaves) == 4 - assert {t.name for t in leaves} == {None} + shown = {t.name for t in _data_traces(hier) if t.showlegend} + assert {t.name for t in leaves} <= shown def test_plotly_sentinel_does_not_reach_exported_html(): diff --git a/tests/plot/test_image_palette.py b/tests/plot/test_image_palette.py index 08e03d82..4b443054 100644 --- a/tests/plot/test_image_palette.py +++ b/tests/plot/test_image_palette.py @@ -156,10 +156,18 @@ def test_n_colors_must_be_a_positive_integer(tmp_path): # --- the `palette='image:<path>'` spelling ------------------------------------ def test_palette_string_resolves_through_get_palette_colors(tmp_path): - """One interception in _get_palette must serve every palette consumer.""" + """One interception in _get_palette must serve every palette consumer. + As a PLOT palette the image's colors are sorted by value (dark to + bright; Jeremy, 2026-09-08) -- the salient vivid red comes after the + pale beige only if it is darker, which it is -- and ``?sort=original`` + keeps the extraction order, vivid first.""" path = painting_png(tmp_path) resolved = get_palette_colors(f'image:{path}', 2) - assert resolved[0] == pytest.approx(VIVID, abs=0.02) + assert any(np.allclose(c, VIVID, atol=0.02) for c in resolved) + from matplotlib.colors import rgb_to_hsv + assert np.all(np.diff(rgb_to_hsv(np.asarray(resolved))[:, 2]) >= 0) + original = get_palette_colors(f'image:{path}?sort=original', 2) + assert original[0] == pytest.approx(VIVID, abs=0.02) def test_palette_string_colours_a_categorical_hue(tmp_path): @@ -208,8 +216,12 @@ def test_an_image_with_too_few_colours_interpolates_rather_than_repeats( drawn = [to_rgb(ln.get_color()) for ln in _ax(fig).lines] assert len({tuple(np.round(c, 6)) for c in drawn}) == 5, ( 'repeated colours would make two categories indistinguishable') - assert np.allclose(drawn[0], VIVID, atol=0.02), ( - 'the most salient anchor must survive interpolation, and lead') + assert any(np.allclose(c, VIVID, atol=0.02) for c in drawn), ( + 'the most salient anchor must survive interpolation') + # the anchors are sorted by value before blending, so the five colours + # run dark to bright (the vivid red is the darker anchor here) + from matplotlib.colors import rgb_to_hsv + assert np.all(np.diff(rgb_to_hsv(np.asarray(drawn))[:, 2]) >= -1e-9) def test_a_single_colour_image_raises_rather_than_inventing_colours( diff --git a/tests/plot/test_matplotlib_backend_bugs.py b/tests/plot/test_matplotlib_backend_bugs.py index 6094c40a..b2c86f0a 100644 --- a/tests/plot/test_matplotlib_backend_bugs.py +++ b/tests/plot/test_matplotlib_backend_bugs.py @@ -164,25 +164,38 @@ def test_cluster_line_animate_parallel_labels_land_at_correct_frame(): # the TAIL of the PRECEDING run's frame window instead of the HEAD of # its own, once run-bridging (`patch_lines`) was in play. Bridging the # labels in lockstep with the data (the same fix as the crash) removes - # that leakage too. frame_rate defaults to 30 and duration=1, so each - # of the 4 cluster runs gets exactly 30 frames: dataset 0's second run - # (starting at its 11th point, "p10") must land at the HEAD of frames - # [30, 60), and dataset 1's first point ("p20") at the head of [60, 90). + # that leakage too. Dataset 0's second run (starting at its 11th point, + # "p10") must land at the HEAD of its own drawn grid -- the first row + # after run 0's grid -- and dataset 1's first point ("p20") at the head + # of the third run's. + # + # The run boundaries are READ from the drawn grids (`ctx.datasets`). + # This test used to hard-code 30 and 60 because every run was resampled + # onto exactly round(frame_rate * duration) = 30 rows; since the 1.1 + # visual review (L8) each run keeps every observation on a grid that + # refines its own rows (run 0: 10 rows + the bridge vertex -> 31), so + # the head of run 1 is grid row 31. datasets = _clusterable_datasets(n=2, rows=20) n_obs = sum(len(d) for d in datasets) labels = [f"p{i}" for i in range(n_obs)] + seen = [] result = hyp.plot(datasets, '-', cluster='KMeans', n_clusters=2, - animate=True, duration=1, labels=labels, show=False) + animate=True, duration=1, labels=labels, show=False, + on_frame=seen.append) + result.draw_frame(0) + grids = [len(d) for d in seen[0].datasets] + starts = np.concatenate([[0], np.cumsum(grids)[:-1]]) + assert len(grids) == 4 fig = result.figure by_text = {t.get_text(): t for ax in fig.axes for t in ax.texts} p0_idx = by_text["p0"]._hyp_global_idx p10_idx = by_text["p10"]._hyp_global_idx p20_idx = by_text["p20"]._hyp_global_idx # EXACT, not approx. This was written as `pytest.approx(30, abs=3)` / - # `approx(60, abs=3)`, but the pre-fix values were 29 and 59 -- inside that + # `approx(60, abs=3)`, but the pre-fix values were one row BEFORE each + # run's head (29 and 59 on the old 30-row grids) -- inside that # tolerance -- so the test passed with and without the fix and could not - # fail on the bug it documents. Measured post-fix: exactly 30 and 60, which - # is what "the HEAD of frames [30, 60)" means. + # fail on the bug it documents. assert p0_idx == 0 - assert p10_idx == 30 - assert p20_idx == 60 + assert p10_idx == starts[1] + assert p20_idx == starts[2] diff --git a/tests/plot/test_multiindex_plotly.py b/tests/plot/test_multiindex_plotly.py index 82e3abfd..01630191 100644 --- a/tests/plot/test_multiindex_plotly.py +++ b/tests/plot/test_multiindex_plotly.py @@ -99,8 +99,10 @@ def _rgb(rgba): return tuple(int(v) for v in body.split(',')[:3]) -def _alpha(rgba): - return float(str(rgba).rstrip(')').rsplit(',', 1)[1]) +def _alpha(rgba, trace): + color_alpha = (float(str(rgba).rstrip(')').rsplit(',', 1)[1]) + if str(rgba).startswith('rgba(') else 1.) + return color_alpha * (trace.opacity if trace.opacity is not None else 1.) def _plot(*args, **kwargs): @@ -164,16 +166,23 @@ def test_three_level_column_hierarchy_exact_trace_count_and_order(): def test_plotly_widths_match_the_documented_formula(): + from hypertools.plot.plotly_backend import _GL_LINE_WIDTH_BOOST traces = _data_traces(_plot(market_frame(), '-', show=False)) # matplotlib draws this hierarchy at 1.0/1.0/2.0 POINTS - # (test_column_multiindex.py); plotly's line.width is in pixels. + # (test_column_multiindex.py); plotly's line.width is in pixels. A 3-D + # (Scatter3d) line is REQUESTED at `_GL_LINE_WIDTH_BOOST` x that, because + # plotly's WebGL renderer draws half the width asked for (1.1 release + # review L1, kaleido-measured) -- this test used to pin the un-boosted + # request, i.e. lines rendered at half the documented width. + boost = _GL_LINE_WIDTH_BOOST if traces[0].type == 'scatter3d' else 1.0 assert [t.line.width for t in traces] == pytest.approx( - [1.0 * PT_TO_PX, 1.0 * PT_TO_PX, 2.0 * PT_TO_PX]) + [1.0 * PT_TO_PX * boost, 1.0 * PT_TO_PX * boost, + 2.0 * PT_TO_PX * boost]) def test_plotly_opacities_match_the_documented_formula(): traces = _data_traces(_plot(market_frame(), '-', show=False)) - assert [_alpha(t.line.color) for t in traces] == pytest.approx( + assert [_alpha(t.line.color, t) for t in traces] == pytest.approx( [0.7, 0.7, 1.0]) @@ -199,7 +208,7 @@ def test_plotly_hue_opacities_match_matplotlib(): 'the hierarchy alphas themselves regressed, so the parity assertion ' 'below would be comparing two wrong numbers') traces = _data_traces(_plot(df, '-', **kw)) - assert [_alpha(t.line.color[-1]) for t in traces] == pytest.approx( + assert [_alpha(t.line.color[-1], t) for t in traces] == pytest.approx( mpl_alpha) @@ -214,7 +223,7 @@ def test_plotly_hue_honours_a_plain_alpha_kwarg(): for c in _mpl_hue_collections(_mpl(x, '-', **kw))] assert mpl_alpha == pytest.approx([0.3]) traces = _data_traces(_plot(x, '-', **kw)) - assert [_alpha(t.line.color[-1]) for t in traces] == pytest.approx( + assert [_alpha(t.line.color[-1], t) for t in traces] == pytest.approx( mpl_alpha) diff --git a/tests/plot/test_per_dataset_alpha.py b/tests/plot/test_per_dataset_alpha.py index 915f03f1..9c6a32bb 100644 --- a/tests/plot/test_per_dataset_alpha.py +++ b/tests/plot/test_per_dataset_alpha.py @@ -5,6 +5,7 @@ import numpy as np import pandas as pd +from tests._plotly_colors import rgba as effective_rgba import pytest import hypertools as hyp @@ -65,10 +66,8 @@ def test_per_dataset_alpha_reaches_plotly_traces(): fig = hyp.plot(_datasets(), '-', alpha=[0.1, 0.5, 1.0], show=False) finally: hyp.set_interactive_backend('matplotlib') - alphas = [float(t.line.color.rsplit(',', 1)[1].rstrip(') ')) - for t in fig.data - if t.line is not None and t.line.color is not None - and t.line.color.startswith('rgba')] + alphas = [effective_rgba(t)[-1] for t in fig.data + if isinstance(t.meta,dict) and 'hyp_trace_index' in t.meta] assert alphas[:3] == pytest.approx([0.1, 0.5, 1.0]) diff --git a/tests/plot/test_plotly_serial_parity.py b/tests/plot/test_plotly_serial_parity.py index 518b868e..87994e86 100644 --- a/tests/plot/test_plotly_serial_parity.py +++ b/tests/plot/test_plotly_serial_parity.py @@ -8,6 +8,7 @@ import warnings import numpy as np +from tests._plotly_colors import rgba as effective_rgba import pytest import hypertools as hyp @@ -73,9 +74,9 @@ def test_plotly_serial_trail_traces_are_faded(): """Same 0.3 opacity the parallel trails already use (plotly_backend.py:953).""" fig = _plotly_fig(animate='serial', chemtrails=True) - alphas = [_alpha_of(t.line.color) for t in fig.data + alphas = [effective_rgba(t)[-1] for t in fig.data if t.line is not None and t.line.color is not None - and t.line.color.startswith('rgba')] + and t.line.color.startswith(('rgb(', 'rgba('))] assert alphas[:3] == pytest.approx([1.0, 1.0, 1.0]) assert alphas[3:6] == pytest.approx([0.3, 0.3, 0.3]) @@ -96,10 +97,15 @@ def test_serial_trail_geometry_matches_matplotlib_frame_for_frame(flags): def test_plain_serial_parity_is_unchanged(): - """Regression guard: the no-trail serial reveal already matched.""" + """Regression guard: the no-trail serial reveal already matched. + + At frame 3 of 12 the serial clock has revealed 32 of dataset 0's 40 + rows, drawn antialiased at 24 vertices per row: 31 * 24 + 1 = 745. (1.1 + visual review L8: this was 657 = 8 * 82 + 1 while each 40-row dataset + was resampled onto one row per frame -- 9 of 12 grid rows.)""" mpl_heads, _ = _mpl_counts(animate='serial') ply_heads, _ = _plotly_counts(_plotly_fig(animate='serial')) - assert ply_heads == mpl_heads == [657, 0, 0] + assert ply_heads == mpl_heads == [745, 0, 0] def test_parallel_trail_parity_is_unchanged(): diff --git a/tests/plot/test_predict_integration.py b/tests/plot/test_predict_integration.py index f1528864..e9d2a3f9 100644 --- a/tests/plot/test_predict_integration.py +++ b/tests/plot/test_predict_integration.py @@ -107,14 +107,16 @@ def test_predict_forecast_drawn_smoothed_not_straight_segments(): assert np.asarray(bundle['predict']['forecasts'][0]).shape[0] == t -def test_predict_legend_unchanged_no_duplicate_entries(): +def test_predict_adds_one_forecast_entry_and_no_duplicates(): a = _walk(3) b = _walk(4, offset=5.0) fig = hyp.plot([a, b], predict='Kalman', t=10, legend=['first', 'second'], show=False) labels = _legend_labels(fig) plt.close(fig) - assert labels == ['first', 'second'] # exactly one entry per dataset + # exactly one entry per dataset, plus ONE for the forecast under its + # model's name (not one per dataset's forecast) + assert labels == ['first', 'second', 'Kalman'] def test_predict_return_model_bundle(): diff --git a/tests/plot/test_regrouped_reveal.py b/tests/plot/test_regrouped_reveal.py index 32da1839..a67e6245 100644 --- a/tests/plot/test_regrouped_reveal.py +++ b/tests/plot/test_regrouped_reveal.py @@ -92,10 +92,17 @@ def test_the_final_frame_still_draws_EVERYTHING(): def test_an_UNREGROUPED_animation_is_unchanged_row_for_row(): """The control. Task 2's projection is the identity without regrouping, so this must match the pre-change behaviour exactly -- if it drifts, the - fix leaked into every animation rather than only the regrouped ones.""" + fix leaked into every animation rather than only the regrouped ones. + + The 30-row walk keeps its 30 rows in a 12-frame animation (1.1 visual + review L8; it used to be resampled onto 12 rows, and these counts were + ``1 + 82 * frame``): frame k's head is row floor(k * 29 / 11), drawn + antialiased at 31 vertices per row, so ``31 * row + 1``.""" fig, ani = _animate([_walk()]) assert [_run_lengths(fig, ani, f)[0] for f in range(12)] == [ - 1, 83, 165, 247, 329, 411, 493, 575, 657, 739, 821, 903] + 31 * (f * 29 // 11) + 1 for f in range(12)] + assert [_run_lengths(fig, ani, f)[0] for f in range(12)] == [ + 1, 63, 156, 218, 311, 404, 466, 559, 652, 714, 807, 900] def test_two_datasets_still_advance_together(): diff --git a/tests/predict/test_chronos.py b/tests/predict/test_chronos.py index 24d95ad8..0a651419 100644 --- a/tests/predict/test_chronos.py +++ b/tests/predict/test_chronos.py @@ -56,9 +56,10 @@ def test_friendly_import_error_when_chronos_missing(monkeypatch): something a unit test should do). `sys.modules[name] = None` is how the import system marks a module as unimportable.""" from hypertools.predict import chronos as mod - monkeypatch.setenv('HYPERTOOLS_AUTO_INSTALL', '0') + import hypertools as hyp monkeypatch.setitem(__import__('sys').modules, 'chronos', None) df = _make_df(n=30) - with pytest.raises(ImportError, match=r'hypertools\[predict-hf\]'): + with hyp.set_autoinstall(False), \ + pytest.raises(ImportError, match=r'hypertools\[predict-hf\]'): mod.Chronos().fit(df) diff --git a/tests/predict/test_common.py b/tests/predict/test_common.py index b8f33d29..c42ca975 100644 --- a/tests/predict/test_common.py +++ b/tests/predict/test_common.py @@ -91,8 +91,8 @@ def test_resolve_t_int_on_hourly_datetimeindex(): assert list(future_index) == list(expected) -def test_resolve_t_int_on_irregular_datetimeindex_uses_min_nonzero_diff(): - # gaps (minutes): 1, 2, 1, 6 -> minimum non-zero diff is 1 minute +def test_resolve_t_int_on_irregular_datetimeindex_uses_median_positive_gap(): + # gaps (minutes): 1, 2, 1, 6 -> median positive gap is 1.5 minutes base = pd.Timestamp("2026-01-01") idx = pd.DatetimeIndex([base, base + pd.Timedelta(minutes=1), base + pd.Timedelta(minutes=3), base + pd.Timedelta(minutes=4), base + pd.Timedelta(minutes=10)]) @@ -101,7 +101,7 @@ def test_resolve_t_int_on_irregular_datetimeindex_uses_min_nonzero_diff(): n_steps, future_index = resolve_t(df, 2) assert n_steps == 2 - expected = pd.DatetimeIndex([idx[-1] + pd.Timedelta(minutes=1), idx[-1] + pd.Timedelta(minutes=2)]) + expected = pd.DatetimeIndex([idx[-1] + pd.Timedelta(minutes=1.5), idx[-1] + pd.Timedelta(minutes=3)]) assert list(future_index) == list(expected) @@ -161,28 +161,32 @@ def test_resolve_t_keeps_a_duplicated_integer_index(): assert list(future_index) == [5, 6] -def test_all_identical_timestamps_message_comes_from_live_infer_step(monkeypatch): +def test_all_identical_timestamps_message_comes_from_live_infer_step(): """The fully-degenerate case (every observation at ONE timestamp) is `_infer_step`'s: `tests/test_predict_audit_fixes.py` pins its wording. `resolve_t`'s duplicate check runs FIRST, so it must hand this case to `_infer_step` rather than raise a copied string -- a copy would leave - that branch dead code with a test that only pins the copy.""" - from hypertools.predict import common as common_module - - calls = [] - real_infer_step = common_module._infer_step + that branch dead code with a test that only pins the copy. - def spy(index): - calls.append(index) - return real_infer_step(index) + Observed from the exception itself, not from a spy on `_infer_step`: the + raise site is the shared time helper in the error's own traceback, and + the message is byte-identical to what `_infer_step` raises on the same index. + A copied string in `resolve_t` would put `resolve_t` in that last frame.""" + from hypertools.predict import common as common_module - monkeypatch.setattr(common_module, "_infer_step", spy) df = _make_df(n=5, index=pd.DatetimeIndex(["2026-01-01"] * 5)) - with pytest.raises(ValueError, match="share one timestamp"): + with pytest.raises(ValueError, match="share one timestamp") as via_resolve_t: resolve_t(df, 3) - assert calls, "the message must come from live _infer_step code, not a copy" + with pytest.raises(ValueError) as direct: + common_module._infer_step(df.index) + assert str(via_resolve_t.value) == str(direct.value) + import traceback + frames = traceback.extract_tb(via_resolve_t.value.__traceback__) + assert frames[-1].name == 'infer_step' + from pathlib import Path + assert Path(frames[-1].filename).parts[-2:] == ('predict', 'time.py') def test_forecaster_predict_truncates_on_past_datetime_without_calling_forecaster(): diff --git a/tests/predict/test_laplace.py b/tests/predict/test_laplace.py index 0ed6fc8d..7fa87f68 100644 --- a/tests/predict/test_laplace.py +++ b/tests/predict/test_laplace.py @@ -76,9 +76,10 @@ def test_friendly_import_error_when_skaters_missing(monkeypatch): something a unit test should do). `sys.modules[name] = None` is how the import system marks a module as unimportable.""" from hypertools.predict import laplace as mod - monkeypatch.setenv('HYPERTOOLS_AUTO_INSTALL', '0') + import hypertools as hyp monkeypatch.setitem(__import__('sys').modules, 'skaters.api', None) df = _make_df(n=30) - with pytest.raises(ImportError, match=r'hypertools\[predict\]'): + with hyp.set_autoinstall(False), \ + pytest.raises(ImportError, match=r'hypertools\[predict\]'): mod.Laplace().fit_predict(df, t=5) diff --git a/tests/predict/test_predict_multiindex.py b/tests/predict/test_predict_multiindex.py index 5c4f564a..cc8927f2 100644 --- a/tests/predict/test_predict_multiindex.py +++ b/tests/predict/test_predict_multiindex.py @@ -168,54 +168,70 @@ def test_group_forecast_matches_forecasting_that_group_alone(): rtol=1e-6, atol=1e-6) -def test_grouped_leaves_are_non_hierarchical_so_the_recursion_terminates( - monkeypatch): +def test_grouped_leaves_are_non_hierarchical_so_the_recursion_terminates(): """The recursion guard, made OBSERVABLE rather than inferred (Revision note (v6) D1/D2). `predict()` recurses with `predict(group, ...)`, so a leaf still carrying its grouping levels is re-detected by the same `nlevels >= 2` predicate and regrouped without bound -- measured on v5's - `sub.T` leaves. Both core helpers are WRAPPED here: they still run (this - OBSERVES, it does not substitute), recording each leaf's axis index and - their own call counts. Patching `hypertools.core.hierarchy` rather than - the predict module is deliberate -- `predict()` imports them inside the - function, so the name is looked up on the source module at call time. - A test that merely 'does not hang' would not be adequate: the counts and - the leaf indices are asserted explicitly. + `sub.T` leaves. + + Proved from real outputs, with no observer wrapped around the helpers: + the SAME helpers `predict()` delegates to are called directly on the same + frames, and their leaves must fail the predicate `predict()` re-detects + on (`is_hierarchical`). Then `return_model=True` exposes what each group + was actually fitted on: a Forecaster keeps its fitted frame as `.data`, + which must be frame-equal to the helper's flat leaf (a regrouped or + still-hierarchical leaf could not produce that), one model per group, in + the helper's key order; each forecast carries the leaf's flat feature + axis and continues the leaf's flat time axis; and each forecast equals + forecasting that flat leaf on its own. (Unbounded regrouping cannot pass + silently either: each level is a real `predict()` call, so it ends in a + RecursionError.) A test that merely 'does not hang' would not be + adequate: the leaf axes, fitted frames and group order are asserted. """ import hypertools.core.hierarchy as hier - real_columns, real_rows = hier.group_columns, hier.group_rows_for_forecast - col_calls, row_calls, seen_cols, seen_rows = [], [], [], [] - - def observing_columns(df): - leaves, meta = real_columns(df) - col_calls.append(df.columns.nlevels) - seen_cols.extend(leaf.columns for leaf in leaves) - return leaves, meta - - def observing_rows(df): - groups, keys = real_rows(df) - row_calls.append(df.index.nlevels) - seen_rows.extend(group.index for group in groups) - return groups, keys - - monkeypatch.setattr(hier, 'group_columns', observing_columns) - monkeypatch.setattr(hier, 'group_rows_for_forecast', observing_rows) - - col_out = hyp.predict(col_frame(), model='Kalman', t=1) - row_out = hyp.predict(row_frame(), model='Kalman', t=2) - - assert len(col_calls) == 1, \ - f'group_columns ran {len(col_calls)}x: the leaves were regrouped' - assert len(row_calls) == 1, \ - f'group_rows_for_forecast ran {len(row_calls)}x: leaves regrouped' - assert len(seen_cols) == 2 and len(seen_rows) == 2 - assert all(not isinstance(cols, pd.MultiIndex) for cols in seen_cols) - assert all(not isinstance(idx, pd.MultiIndex) for idx in seen_rows) - assert len(col_out) == 2 - assert all(np.asarray(f).shape == (1, 3) for f in col_out) - assert len(row_out) == 2 - assert all(np.asarray(f).shape == (2, 3) for f in row_out) + col, row = col_frame(), row_frame() + leaves, meta = hier.group_columns(col) + groups, keys = hier.group_rows_for_forecast(row) + + assert meta['leaf_keys'] == [('Market', 'Tech'), ('Market', 'Energy')] + assert keys == [('Tech',), ('Energy',)] + assert all(not hier.is_hierarchical(leaf) for leaf in leaves) + assert all(not hier.is_hierarchical(group) for group in groups) + assert all(not isinstance(leaf.columns, pd.MultiIndex) for leaf in leaves) + assert all(not isinstance(group.index, pd.MultiIndex) for group in groups) + assert all(list(leaf.columns) == ['return', 'volatility', 'momentum'] + and leaf.columns.name == 'Measure' for leaf in leaves) + assert all(list(group.index) == list(range(60)) + and group.index.name == 'day' for group in groups) + + col_out, col_models = hyp.predict(col, model='Kalman', t=1, + return_model=True) + row_out, row_models = hyp.predict(row, model='Kalman', t=2, + return_model=True) + + assert len(col_out) == len(col_models) == 2 + assert len(row_out) == len(row_models) == 2 + for forecast, model, leaf in zip(col_out, col_models, leaves): + pd.testing.assert_frame_equal(model.data, leaf) + assert np.asarray(forecast).shape == (1, 3) + assert not isinstance(forecast.columns, pd.MultiIndex) + assert list(forecast.columns) == list(leaf.columns) + assert forecast.columns.name == 'Measure' + assert list(forecast.index) == [120] + alone = hyp.predict(leaf, model='Kalman', t=1) + assert np.allclose(np.asarray(forecast), np.asarray(alone), + rtol=1e-6, atol=1e-6) + for forecast, model, group in zip(row_out, row_models, groups): + pd.testing.assert_frame_equal(model.data, group) + assert np.asarray(forecast).shape == (2, 3) + assert not isinstance(forecast.index, pd.MultiIndex) + assert list(forecast.index) == [60, 61] + assert list(forecast.columns) == list(group.columns) + alone = hyp.predict(group, model='Kalman', t=2) + assert np.allclose(np.asarray(forecast), np.asarray(alone), + rtol=1e-6, atol=1e-6) def test_duplicate_innermost_names_forecast_by_occurrence(): diff --git a/tests/test_2d_animation.py b/tests/test_2d_animation.py index 2cc7e39b..bc9a9116 100644 --- a/tests/test_2d_animation.py +++ b/tests/test_2d_animation.py @@ -23,6 +23,7 @@ import matplotlib.pyplot as plt import hypertools as hyp +from hypertools._shared.helpers import UNIT_FRAME_LIMIT from hypertools.plot import morph as _morph @@ -116,14 +117,21 @@ def test_mpl_window_exact_bounds_mid_animation_2d(): total = ani._save_count num = total // 2 window_frames = int(round(frame_rate * focused)) - expected = data_lines[0][num - window_frames: num + 1] + # head on row floor(num * (n - 1) / (total - 1)), window = the rows the + # head passes in `window_frames` frames -- see the 3-D twin for why + # these are no longer `num`/`window_frames` themselves (1.1 visual + # review L8) + n = data_lines[0].shape[0] + head = num * (n - 1) // (total - 1) + w = int(round(window_frames * (n - 1) / (total - 1))) + expected = data_lines[0][head - w: head + 1] lines, _ = ani._func(num, *ani._args) xs, ys = lines[0].get_data() assert len(xs) == len(expected) np.testing.assert_allclose(xs, expected[:, 0]) - np.testing.assert_allclose(xs[0], data_lines[0][num - window_frames, 0]) - np.testing.assert_allclose(xs[-1], data_lines[0][num, 0]) + np.testing.assert_allclose(xs[0], data_lines[0][head - w, 0]) + np.testing.assert_allclose(xs[-1], data_lines[0][head, 0]) plt.close('all') @@ -212,9 +220,9 @@ def test_plotly_frame_count_matches_duration_and_frame_rate(style): fig = hyp.plot(data, ndims=2, animate=style, duration=duration, frame_rate=frame_rate, backend='plotly', show=False) assert len(fig.frames) == duration * frame_rate - # 2-D layout: xaxis/yaxis carry the fixed [-1.1, 1.1] range; no camera - assert fig.layout.xaxis.range == (-1.1, 1.1) - assert fig.layout.yaxis.range == (-1.1, 1.1) + # 2-D layout: xaxis/yaxis carry the fixed +/-UNIT_FRAME_LIMIT range; no camera + assert fig.layout.xaxis.range == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) + assert fig.layout.yaxis.range == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) def test_plotly_morph_frame_count_matches_duration_and_frame_rate(): diff --git a/tests/test_align_score.py b/tests/test_align_score.py index 05fcdafa..a545eef3 100644 --- a/tests/test_align_score.py +++ b/tests/test_align_score.py @@ -146,3 +146,123 @@ def test_hyp_align_return_score_via_public_api(): aligned, score = hyp.align(datasets, model='HyperAlign', n_iter=10, return_score=True) assert score['after'] <= score['before'] + + +# -- 1.1 release review: ragged input scores what the aligner consumed ------ + +def test_return_score_works_on_ragged_input_that_align_trims(): + # hyp.align([a, b]) trims to the 40 common rows and succeeds, so + # return_score=True must succeed too: the "before" score is taken on + # the same row-trimmed data the aligner consumed. + rng = np.random.default_rng(11) + base = rng.standard_normal((50, 4)) + rot, _ = np.linalg.qr(rng.standard_normal((4, 4))) + a = base + 0.05 * rng.standard_normal((50, 4)) + b = (base @ rot + 0.05 * rng.standard_normal((50, 4)))[:40] + with pytest.warns(UserWarning, match='common to all datasets'): + aligned, score = hyp.align([a, b], model='HyperAlign', n_iter=10, + return_score=True) + assert [x.shape for x in aligned] == [(40, 4), (40, 4)] + assert set(score) == {'before', 'after', 'metric'} + assert score['metric'] == 'dispersion' + assert score['after'] < score['before'] + # 'before' is exactly alignment_score of the manually trimmed arrays + # (common rows in the FIRST dataset's order: a's first 40 rows) + expected = alignment_score([a[:40], b], metric='dispersion')['before'] + assert score['before'] == pytest.approx(expected) + assert score['after'] == pytest.approx( + alignment_score([a[:40], b], aligned=aligned, metric='dispersion')['after']) + + # the plain (un-trimmed) originals are still rejected by the scorer + # itself, so the trim is align's doing rather than a relaxed check + with pytest.raises(ValueError, match='same shape'): + alignment_score([a, b], metric='dispersion') + + +def test_return_score_ragged_input_isc_metric(): + rng = np.random.default_rng(12) + base = rng.standard_normal((50, 4)) + rot, _ = np.linalg.qr(rng.standard_normal((4, 4))) + a = base + 0.05 * rng.standard_normal((50, 4)) + b = (base @ rot + 0.05 * rng.standard_normal((50, 4)))[:40] + with pytest.warns(UserWarning, match='common to all datasets'): + aligned, score = hyp.align([a, b], model='HyperAlign', n_iter=10, + return_score=True, score_metric='isc') + assert score['metric'] == 'isc' + assert score['after'] > score['before'] + expected = alignment_score([a[:40], b], metric='isc')['before'] + assert score['before'] == pytest.approx(expected) + + +# --- degenerate and malformed input (release review 2026-09-07) ------------- + +@pytest.mark.parametrize('metric', ['dispersion', 'isc']) +def test_all_constant_datasets_raise_instead_of_nan(metric): + """Two constant datasets have no cloud scale ('dispersion' divided 0 by 0 + and returned NaN with a RuntimeWarning) and no feature to correlate.""" + const = [np.ones((10, 3)), np.ones((10, 3))] + with pytest.raises(ValueError, match='constant'): + alignment_score(const, metric=metric) + + +@pytest.mark.parametrize('metric', ['dispersion', 'isc']) +def test_datasets_each_constant_at_their_own_value_raise(metric): + """1.1 release review (2026-09-11): 'dispersion' raised only when EVERY + observation of EVERY dataset was the same point. Datasets that are each + constant at a DIFFERENT value have a cloud scale, so the score came out + as exactly 1.0 -- the same number whatever the alignment -- while 'isc' + raised. The docstring promises a raise for "every dataset constant".""" + const = [np.zeros((10, 3)), np.ones((10, 3)), np.full((10, 3), 5.0)] + with pytest.raises(ValueError, match='constant') as info: + alignment_score(const, metric=metric) + if metric == 'dispersion': + assert 'dataset 0' in str(info.value) + assert 'dataset 2' in str(info.value) + rng = np.random.default_rng(3) + fine = [rng.normal(size=(10, 3)) for _ in range(3)] + with pytest.raises(ValueError, match='constant'): + alignment_score(fine, aligned=const, metric=metric) + + +def test_dispersion_of_single_observation_datasets_raises_like_isc(): + """One observation per dataset is the degenerate constant case: the + per-observation centroid IS the cloud's mean, so 'dispersion' is 1.0 + for any input ('isc' already raised: it needs two observations).""" + rng = np.random.default_rng(4) + single_rows = [rng.normal(size=(1, 3)) for _ in range(3)] + for metric in ('dispersion', 'isc'): + with pytest.raises(ValueError): + alignment_score(single_rows, metric=metric) + + +def test_one_constant_dataset_among_varying_ones_still_scores(): + """Only the all-constant case is degenerate: a constant dataset beside + varying ones has a well-defined dispersion (and 'isc' correlates the + varying pairs).""" + rng = np.random.default_rng(5) + data = [rng.normal(size=(10, 3)), rng.normal(size=(10, 3)), + np.ones((10, 3))] + for metric in ('dispersion', 'isc'): + score = alignment_score(data, metric=metric)['before'] + assert np.isfinite(score) + assert alignment_score(data, metric='dispersion')['before'] != 1.0 + + +@pytest.mark.parametrize('metric', ['dispersion', 'isc']) +def test_nan_input_raises_instead_of_nan_score(metric): + rng = np.random.default_rng(0) + x, y = rng.normal(size=(10, 3)), rng.normal(size=(10, 3)) + y[2, 1] = np.nan + with pytest.raises(ValueError, match=r'finite values; dataset 1 has 1 NaN'): + alignment_score([x, y], metric=metric) + with pytest.raises(ValueError, match='finite values'): + alignment_score([x, x], aligned=[x, y], metric=metric) + + +@pytest.mark.parametrize('metric', ['dispersion', 'isc']) +def test_one_dimensional_input_raises_a_clear_error(metric): + """1-D series raised numpy's own AxisError / unpack ValueError.""" + with pytest.raises(ValueError, match=r'2-D datasets .* dataset 0 has shape \(10,\)'): + alignment_score([np.arange(10.0), np.arange(10.0)], metric=metric) + with pytest.raises(ValueError, match='numeric'): + alignment_score([np.array([['a', 'b']] * 3)] * 2, metric=metric) diff --git a/tests/test_anim_core_fidelity.py b/tests/test_anim_core_fidelity.py new file mode 100644 index 00000000..c58d0b8f --- /dev/null +++ b/tests/test_anim_core_fidelity.py @@ -0,0 +1,725 @@ +"""Animation-core fidelity (1.1 visual review, findings L8, L9, L11, L12, +L13b). + +Every check reads what was actually DRAWN -- artist vertices, plotly frame +payloads, text extents against the canvas, rendered colours, emitted +warnings -- never a re-derivation of the library's own arithmetic. + +* L8: an animated line used to be resampled onto exactly + ``round(frame_rate * duration)`` rows, which DOWNsampled any dataset longer + than the frame count (a 36-sample helix in a 9-frame animation was drawn + as a zig-zag star). Every observation must now be an exact vertex of the + animated line, and ``'spin'`` (which reveals nothing) keeps its rows. +* L9: morph transitions sampled their own endpoints, so a short transition + showed no motion at all (both of its frames were copies of the hold + clouds). Every transition frame must now lie strictly between the clouds. +* L11: a title set by an `on_frame` callback on a 3-D animation without + ``title=`` rendered above the canvas. +* L12: `companion=` panels defaulted to matplotlib's global ``'C0'`` blue + instead of the colour of the trajectory they accompany. +* L13b: the clamped-samples warning of a streamed plot never fired on a + short stream (it needed 20 post-head samples). +""" + +import warnings + +import matplotlib +matplotlib.use('Agg') + +import numpy as np # noqa: E402 +import pytest # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.colors import to_hex, to_rgb # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot import morph # noqa: E402 + +BACKENDS = ['matplotlib', 'plotly'] + + +def helix(n=36, turns=3.0, radius=0.5): + """A helix about the z axis: its true radius is `radius` at every row, + which makes any shape distortion measurable as a radius spread.""" + t = np.linspace(0.0, 2 * np.pi * turns, n) + return np.column_stack([radius * np.cos(t), radius * np.sin(t), + np.linspace(0.0, 1.0, n)]) + + +def _collect(data, backend, **kwargs): + """Plot with an `on_frame` recorder; return (figure-like, contexts). + + matplotlib: every frame is DRAWN (so the contexts describe rendered + frames); plotly: the contexts are recorded while its frames are built. + """ + seen = [] + out = hyp.plot(data, backend=backend, show=False, on_frame=seen.append, + **kwargs) + if backend == 'matplotlib': + for f in range(out.n_frames): + out.draw_frame(f) + # a SNAPSHOT: a later matplotlib draw_frame() calls the recorder again + return out, list(seen) + + +def _close(out): + fig = getattr(out, 'figure', None) + if fig is not None: + plt.close(fig) + + +def _pairwise(points): + p = np.asarray(points, dtype=float) + return np.sqrt(((p[:, None, :] - p[None, :, :]) ** 2).sum(-1)) + + +def _assert_observations_are_exact_vertices(grid, source): + """`grid` holds every row of `source` as an exact vertex, at a uniform + stride, up to the similarity transform the plot pipeline applies + (PCA rotation + one isotropic rescale): pairwise distances between the + grid rows at the observation positions are the source's distances + times ONE constant.""" + grid = np.asarray(grid, dtype=float) + n = source.shape[0] + g = grid.shape[0] + assert g >= n, ( + f'the animated line has {g} rows for {n} observations: it was ' + 'DOWNsampled') + assert (g - 1) % (n - 1) == 0, ( + f'a {g}-row grid is not a uniform refinement of {n} observations, ' + 'so the observations cannot all be grid vertices') + stride = (g - 1) // (n - 1) + at_obs = grid[::stride] + d_grid = _pairwise(at_obs)[np.triu_indices(n, 1)] + d_src = _pairwise(source)[np.triu_indices(n, 1)] + ratio = d_grid / d_src + np.testing.assert_allclose(ratio, np.median(ratio), rtol=1e-6) + + +def _drawn_radius_spread(xyz): + """min/max of the drawn distance from the helix axis (1.0 = perfectly + round). The axis is the midpoint of the drawn x/y extent.""" + x, y = np.asarray(xyz[0], float), np.asarray(xyz[1], float) + cx, cy = (x.max() + x.min()) / 2, (y.max() + y.min()) / 2 + r = np.hypot(x - cx, y - cy) + return r.min() / r.max() + + +# --------------------------------------------------------------------------- +# L8: no downsampling onto the frame grid +# --------------------------------------------------------------------------- + +class TestAnimatedLinesKeepEveryObservation: + + @pytest.mark.parametrize('backend', BACKENDS) + def test_a_long_line_in_a_short_animation_keeps_every_observation( + self, backend): + """36 observations, 9 frames: the animated line used to have 9 + rows. Every observation must be an exact vertex now.""" + src = helix(36) + out, seen = _collect(src, backend, animate=True, duration=1.5, + frame_rate=6) + try: + assert len(seen) == 9 # the frame count is unchanged + _assert_observations_are_exact_vertices(seen[-1].datasets[0], src) + finally: + _close(out) + + @pytest.mark.parametrize('backend', BACKENDS) + def test_a_short_line_in_a_long_animation_keeps_every_observation( + self, backend): + """5 observations, 40 frames: the grid is a refinement that still + contains each observation exactly (it used to be 40 rows, which + holds none of the three interior observations).""" + src = helix(5, turns=0.8) + out, seen = _collect(src, backend, animate=True, duration=4, + frame_rate=10) + try: + assert len(seen) == 40 + grid = seen[-1].datasets[0] + assert grid.shape[0] >= 40 + _assert_observations_are_exact_vertices(grid, src) + finally: + _close(out) + + @pytest.mark.parametrize('backend', BACKENDS) + def test_the_reveal_still_starts_at_the_first_row_and_ends_complete( + self, backend): + src = helix(36) + out, seen = _collect(src, backend, animate=True, duration=1.5, + frame_rate=6) + try: + counts = [ctx.revealed_counts[0] for ctx in seen] + n_grid = seen[-1].datasets[0].shape[0] + assert counts == sorted(counts) # never runs backwards + assert counts[0] == 1 # frame 0: the first row + assert counts[-1] == n_grid # the final frame is whole + finally: + _close(out) + + @pytest.mark.parametrize('backend', BACKENDS) + @pytest.mark.parametrize('n_rows,n_frames', [(36, 9), (5, 40), (40, 12)]) + def test_reveal_timing_is_the_old_one_row_per_frame_timing( + self, backend, n_rows, n_frames): + """The reveal is still paced from the first observation (frame 0) + to the last (final frame), linearly: frame k shows the observations + up to k * (n - 1) / (n_frames - 1) -- the timing lines had when + their grid was exactly one row per frame.""" + src = helix(n_rows, turns=1.0) + out, seen = _collect(src, backend, animate=True, + duration=n_frames / 4, frame_rate=4) + try: + assert len(seen) == n_frames + g = seen[-1].datasets[0].shape[0] + stride = (g - 1) // (n_rows - 1) + for ctx in seen: + head_row = ctx.revealed_counts[0] - 1 + param = head_row / stride + exact = ctx.frame * (n_rows - 1) / (n_frames - 1) + # on the grid, never ahead of the exact timing and less + # than one grid step behind it + assert exact - 1.0 / stride < param <= exact + 1e-9 + finally: + _close(out) + + def test_the_drawn_helix_stays_round_on_matplotlib(self): + """The shape the probe measured (min/max drawn radius 0.228/0.516 + in a 9-frame animation, i.e. 0.44): read off the drawn artist on + the LAST frame, where the whole line is revealed.""" + src = helix(36) + out, seen = _collect(src, 'matplotlib', animate=True, duration=1.5, + frame_rate=6) + try: + line = seen[-1].artists[0] + spread = _drawn_radius_spread(line.get_data_3d()) + static = hyp.plot(src, backend='matplotlib', show=False) + ref = [ln for ln in static.axes[0].lines + if len(ln.get_data_3d()[0]) > 10][0] + assert spread > 0.9 + assert spread == pytest.approx( + _drawn_radius_spread(ref.get_data_3d()), abs=0.02) + plt.close(static) + finally: + _close(out) + + def test_the_drawn_helix_stays_round_on_plotly(self): + src = helix(36) + out, _ = _collect(src, 'plotly', animate=True, duration=1.5, + frame_rate=6) + last = out.frames[-1].data[0] + assert _drawn_radius_spread((last.x, last.y)) > 0.9 + + @pytest.mark.parametrize('backend', BACKENDS) + def test_spin_draws_the_observations_themselves(self, backend): + """'spin' reveals no rows (only the camera moves), so its datasets + are never regridded: 36 rows stay 36 rows in a 60-frame spin (they + used to become 60 PCHIP samples, and 9 in a 9-frame one).""" + src = helix(36) + for duration in (1.5, 10): + out, seen = _collect(src, backend, animate='spin', + duration=duration, frame_rate=6) + try: + grid = seen[0].datasets[0] + assert grid.shape[0] == 36 + _assert_observations_are_exact_vertices(grid, src) + finally: + _close(out) + + def test_spin_drawn_shape_matches_the_static_plot(self): + """The probe's own numbers: a 9-frame spin drew the helix at + min/max radius 0.44 while the static plot is round.""" + src = helix(36) + anim = hyp.plot(src, backend='matplotlib', animate='spin', + duration=1.5, frame_rate=6, show=False) + try: + anim.draw_frame(0) + line = [ln for ln in anim.figure.axes[0].lines + if len(ln.get_data_3d()[0]) > 10 + and ln.get_color() not in ('black', 'k')][0] + assert _drawn_radius_spread(line.get_data_3d()) > 0.9 + finally: + plt.close(anim.figure) + + @pytest.mark.parametrize('backend', BACKENDS) + def test_per_point_labels_are_not_dropped(self, backend): + """18 labels on a 36-row line in a 9-frame animation: squeezed + onto 9 grid rows, pairs of labels landed on one row and only 9 of + the 18 were drawn.""" + labels = [f'L{i}' if i % 2 == 0 else None for i in range(36)] + out = hyp.plot(helix(36), backend=backend, animate=True, + duration=1.5, frame_rate=6, labels=labels, + show=False) + if backend == 'matplotlib': + try: + out.draw_frame(out.n_frames - 1) + drawn = [t.get_text() for t in out.figure.axes[0].texts] + finally: + plt.close(out.figure) + else: + drawn = [a.text for a in out.layout.scene.annotations] + assert sorted(drawn) == sorted(lab for lab in labels if lab) + + @pytest.mark.parametrize('backend', BACKENDS) + def test_animated_markers_sit_exactly_on_the_observations(self, backend): + """The marker contract: an animated 'o-' line marks the drawn vertex + nearest each observation. On the refined grid every observation IS + a drawn vertex, so the markers land on the observations exactly -- + every observation inside the drawn head window, and nothing else.""" + src = helix(12, turns=1.0) + out, seen = _collect(src, backend, fmt='o-', animate=True, + duration=4, frame_rate=10) + try: + grid = seen[-1].datasets[0] + stride = (grid.shape[0] - 1) // (src.shape[0] - 1) + start, end = seen[-1].window_bounds[0] + rows = [r for r in range(0, grid.shape[0], stride) + if start <= r < end] + assert len(rows) >= 3 + obs = grid[rows] + if backend == 'matplotlib': + line = seen[-1].artists[0] + drawn = np.column_stack(line.get_data_3d()) + marked = drawn[np.asarray(line.get_markevery(), dtype=int)] + else: + tr = out.frames[-1].data[0] + drawn = np.column_stack([tr.x, tr.y, tr.z]) + marked = drawn[np.asarray(tr.marker.size) > 0] + assert marked.shape == obs.shape + np.testing.assert_allclose(marked, obs, atol=1e-12) + finally: + _close(out) + + def test_a_label_is_shown_exactly_while_its_point_is_drawn(self): + """matplotlib animated labels: visible iff the labelled row is inside + the head window the trace was drawn over THIS frame. The old rule + compared the row index with the FRAME index, which only coincided + while every line had exactly one row per frame.""" + labels = [f'L{i}' if i % 5 == 0 else None for i in range(36)] + state = [] + + def record(ctx): + s, e = ctx.window_bounds[0] + shown = {t.get_text() for t in ctx.axes.texts if t.get_visible()} + state.append((s, e, shown)) + + anim = hyp.plot(helix(36), backend='matplotlib', animate='window', + focused=0.5, duration=3, frame_rate=6, + labels=labels, on_frame=record, show=False) + try: + for f in range(anim.n_frames): + anim.draw_frame(f) + finally: + plt.close(anim.figure) + ever = set() + for s, e, shown in state[:anim.n_frames]: + expect = {f'L{i}' for i in range(36) if i % 5 == 0 and s <= i < e} + assert shown == expect, (s, e, shown) + ever |= shown + assert ever == {lab for lab in labels if lab} + + @pytest.mark.parametrize('backend', BACKENDS) + def test_two_datasets_of_different_lengths_each_keep_their_rows( + self, backend): + a, b = helix(36), helix(7, turns=1.0) + [2.0, 0.0, 0.0] + out, seen = _collect([a, b], backend, animate=True, duration=2, + frame_rate=6) + try: + grids = seen[-1].datasets + assert len(seen) == 12 + for grid, src in zip(grids, (a, b)): + assert grid.shape[0] >= max(12, src.shape[0]) + n, g = src.shape[0], grid.shape[0] + assert (g - 1) % (n - 1) == 0 + finally: + _close(out) + + +# --------------------------------------------------------------------------- +# L9: every morph transition frame moves; dots are visible +# --------------------------------------------------------------------------- + +def _two_clouds(): + rng = np.random.default_rng(3) + a = rng.normal(size=(30, 3)) * 0.3 + b = rng.normal(size=(30, 3)) * 0.3 + [3.0, 0.0, 0.0] + return [a, b] + + +def _morph_frames(backend, **kwargs): + """(positions, rgb, ctx) per frame, read off what was drawn.""" + out, seen = _collect(_two_clouds(), backend, animate='morph', + **kwargs) + frames = [] + if backend == 'matplotlib': + # draw each frame again and read the SINGLE cloud artist right away + # (the artist is shared across frames) + for ctx in seen: + out.draw_frame(ctx.frame) + art = ctx.artists[0] + frames.append((np.column_stack(art.get_data_3d()), + to_rgb(art.get_color()), ctx)) + plt.close(out.figure) + else: + for ctx, frame in zip(seen, out.frames): + tr = frame.data[0] + rgb = tuple(float(v) / 255.0 for v in + tr.marker.color[tr.marker.color.index('(') + 1: + tr.marker.color.index(')')] + .split(',')[:3]) + frames.append((np.column_stack([tr.x, tr.y, tr.z]), rgb, ctx)) + return frames + + +class TestMorphTransitionsMove: + + @pytest.mark.parametrize('backend', BACKENDS) + def test_every_transition_frame_lies_strictly_between_the_clouds( + self, backend): + """The probe's call: 9 frames, loop=True -> 2-frame transitions. + Both frames of each transition used to be exact copies of the hold + clouds.""" + frames = _morph_frames(backend, duration=1.5, frame_rate=6, + loop=True, morph_samples=30) + transitions = [f for f in frames + if f[2].segment_kind == 'transition'] + assert transitions, 'the schedule has no transition frames' + for pts, rgb, ctx in transitions: + k = ctx.segment_index // 2 + before, after = ctx.datasets[k], ctx.datasets[k + 1] + assert pts.shape == before.shape + assert not np.allclose(pts, before), ( + f'transition frame {ctx.frame} is a copy of the cloud it ' + 'leaves') + assert not np.allclose(pts, after), ( + f'transition frame {ctx.frame} is a copy of the cloud it ' + 'reaches') + + @pytest.mark.parametrize('backend', BACKENDS) + def test_consecutive_transition_frames_differ(self, backend): + frames = _morph_frames(backend, duration=3, frame_rate=6) + trans = [f for f in frames if f[2].segment_kind == 'transition'] + assert len(trans) >= 2 + for (p0, _, _), (p1, _, _) in zip(trans, trans[1:]): + assert not np.allclose(p0, p1) + + @pytest.mark.parametrize('backend', BACKENDS) + def test_transition_colour_is_strictly_between_the_dataset_colours( + self, backend): + frames = _morph_frames(backend, duration=1.5, frame_rate=6) + holds = [f for f in frames if f[2].segment_kind == 'hold'] + c_first = np.array(holds[0][1]) + c_last = np.array(holds[-1][1]) + assert not np.allclose(c_first, c_last) + for _, rgb, ctx in frames: + if ctx.segment_kind != 'transition': + continue + assert not np.allclose(rgb, c_first, atol=2e-3) + assert not np.allclose(rgb, c_last, atol=2e-3) + + def test_the_interpolation_parameter_never_reaches_an_endpoint(self): + clouds = [np.zeros((4, 3)), np.ones((4, 3))] + for n_steps in (1, 2, 3, 10): + ts = [morph.morph_positions(clouds, 1, s, n_steps)[0, 0] + for s in range(n_steps)] + assert all(0.0 < t < 1.0 for t in ts), (n_steps, ts) + assert ts == sorted(ts) + # symmetric about the midpoint: the easing starts and ends alike + np.testing.assert_allclose(ts, [1 - t for t in reversed(ts)]) + + +class TestMorphDotsAreVisible: + + def test_both_backends_share_one_4pt_default(self): + from hypertools.plot.plotly_backend import ( + MORPH_DEFAULT_MARKERSIZE_PT, _marker_size_px) + assert morph.MORPH_DEFAULT_MARKERSIZE_PT == pytest.approx(4.0) + assert MORPH_DEFAULT_MARKERSIZE_PT == morph.MORPH_DEFAULT_MARKERSIZE_PT + anim = hyp.plot(_two_clouds(), backend='matplotlib', + animate='morph', duration=1, frame_rate=6, + show=False) + try: + cloud = [ln for ln in anim.figure.axes[0].lines + if ln.get_marker() == '.'][0] + assert cloud.get_markersize() == pytest.approx(4.0) + finally: + plt.close(anim.figure) + fig = hyp.plot(_two_clouds(), backend='plotly', animate='morph', + duration=1, frame_rate=6, show=False) + sizes = [tr.marker.size for tr in fig.data + if getattr(tr, 'mode', None) == 'markers' + and tr.marker.size is not None] + assert sizes and sizes[0] == pytest.approx( + _marker_size_px(4.0, '.', ndims=3)) + + @staticmethod + def _mpl_dot_ink(**kwargs): + """Pixels (per dot) rendered in the cloud's own colour, frame 0.""" + rng = np.random.default_rng(0) + a = rng.uniform(-1, 1, size=(30, 3)) + anim = hyp.plot([a, a[::-1] * 0.5], backend='matplotlib', + animate='morph', duration=1, frame_rate=6, + show=False, **kwargs) + try: + anim.draw_frame(0) + fig = anim.figure + fig.set_dpi(100) + fig.canvas.draw() + img = np.asarray(fig.canvas.buffer_rgba())[..., :3].astype(int) + colour = np.array(to_rgb( + [ln for ln in fig.axes[0].lines + if ln.get_marker() == '.'][0].get_color())) * 255 + return (np.abs(img - colour).sum(axis=-1) < 60).sum() / 30 + finally: + plt.close(anim.figure) + + def test_matplotlib_morph_dots_render_several_pixels_each(self): + """Pixel census at 100 dpi: the old 1.5 pt default put about ONE + pixel per dot in the cloud's colour (measured 1.0); the default must + be several times that, and the explicit 1.5 pt request still works.""" + default = self._mpl_dot_ink() + old = self._mpl_dot_ink(markersize=1.5) + assert default >= 4, f'{default:.1f} px per dot' + assert default >= 4 * old, (default, old) + + def test_plotly_morph_dots_are_not_sub_pixel(self, tmp_path): + """A real kaleido render: at the old 1.5 pt default the 30 dots + covered half a pixel each in the cloud's colour (sub-pixel).""" + from PIL import Image + rng = np.random.default_rng(0) + a = rng.uniform(-1, 1, size=(30, 3)) + + def ink(**kwargs): + fig = hyp.plot([a, a[::-1] * 0.5], backend='plotly', + animate='morph', duration=1, frame_rate=6, + show=False, **kwargs) + out = tmp_path / f"dots{kwargs.get('markersize', '')}.png" + fig.write_image(str(out)) + img = np.asarray(Image.open(out).convert('RGB')).astype(int) + # the first dataset's palette colour is a red (hls palette) + red = ((img[..., 0] > 150) & (img[..., 0] - img[..., 1] > 50) + & (img[..., 0] - img[..., 2] > 50)) + return red.sum() / 30 + + default, old = ink(), ink(markersize=1.5) + assert default >= 3, f'{default:.1f} px per dot' + assert default >= 4 * old, (default, old) + + +# --------------------------------------------------------------------------- +# L11: an on_frame title on a 3-D animation is on the canvas +# --------------------------------------------------------------------------- + +class TestOnFrameTitleIsVisible: + + @pytest.mark.parametrize('kwargs', [ + dict(), # docstring + dict(animate='serial', duration=1.5, frame_rate=6), # tour call + ]) + def test_a_callback_title_on_a_3d_animation_is_inside_the_canvas( + self, kwargs): + data = [np.cumsum(np.random.default_rng(0).standard_normal( + (20, 3)), axis=0)] + kwargs = dict(kwargs) + animate = kwargs.pop('animate', True) + + def annotate(ctx): + ctx.axes.set_title(f'frame {ctx.frame} of {ctx.n_frames}') + + anim = hyp.plot(data, animate=animate, on_frame=annotate, + show=False, backend='matplotlib', **kwargs) + try: + fig = anim.figure + anim.draw_frame(anim.n_frames // 2) + fig.canvas.draw() + box = fig.axes[0].title.get_window_extent( + fig.canvas.get_renderer()) + _, height = fig.canvas.get_width_height() + assert fig.axes[0].title.get_text().startswith('frame ') + assert box.y0 >= 0 and box.y1 <= height, ( + f'title spans y={box.y0:.0f}..{box.y1:.0f} on a ' + f'{height}px canvas') + finally: + plt.close(anim.figure) + + def test_a_callback_title_on_a_plotly_3d_animation_is_not_clipped( + self, tmp_path): + """plotly's `ctx.figure` is the go.Figure: a callback title set on + its layout got plotly's 10 px no-title margin and rendered cut off + at the top edge (ink from pixel row 0). Rendered for real.""" + from PIL import Image + data = [np.cumsum(np.random.default_rng(0).standard_normal( + (20, 3)), axis=0)] + + def annotate(ctx): + ctx.figure.update_layout( + title=dict(text=f'frame {ctx.frame} of {ctx.n_frames}')) + + fig = hyp.plot(data, backend='plotly', animate=True, duration=1, + frame_rate=4, on_frame=annotate, show=False) + assert fig.layout.title.text == 'frame 3 of 4' + out = tmp_path / 'title.png' + fig.write_image(str(out)) + grey = np.asarray(Image.open(out).convert('L')) + ink_rows = np.where((grey < 100).any(axis=1))[0] + assert ink_rows.size and ink_rows[0] > 0, ( + 'the title is cut off at the top edge of the canvas') + + def test_a_titleless_3d_animation_without_a_callback_keeps_its_canvas( + self): + data = np.cumsum(np.random.default_rng(0).standard_normal((20, 3)), + axis=0) + plain = hyp.plot(data, animate=True, duration=1, frame_rate=4, + show=False, backend='matplotlib') + titled = hyp.plot(data, animate=True, duration=1, frame_rate=4, + show=False, backend='matplotlib', title='t') + try: + assert (plain.figure.get_size_inches()[1] + < titled.figure.get_size_inches()[1]) + finally: + plt.close(plain.figure) + plt.close(titled.figure) + + +# --------------------------------------------------------------------------- +# L12: companion panels take the trajectory's colour +# --------------------------------------------------------------------------- + +class TestCompanionColour: + + def _companion(self, **kwargs): + rng = np.random.default_rng(0) + traj = np.cumsum(rng.normal(size=(24, 3)), axis=0) + seen = [] + anim = hyp.plot(traj, animate=True, duration=2, frame_rate=6, + fmt=kwargs.pop('fmt', '-'), show=False, + backend='matplotlib', on_frame=seen.append, + companion=[{'data': traj[:, 0], 'smooth': 3}, + {'data': traj[:, 1], 'reveal': False, + 'position': 'right'}], + **kwargs) + anim.draw_frame(anim.n_frames - 1) + anim.figure.canvas.draw() + return anim, seen + + def _panel_colours(self, anim): + out = [] + for pax in anim.figure.axes[1:]: + cols = {to_hex(c) for coll in pax.collections + for c in coll.get_colors()} + out.append((cols, to_hex(pax.lines[-1].get_markerfacecolor()))) + return out + + def test_default_colour_is_the_accompanied_trajectory_s(self): + anim, seen = self._companion() + try: + main = to_hex(seen[-1].artists[0].get_color()) + assert main != to_hex('C0') + for cols, head in self._panel_colours(anim): + assert cols == {main} + assert head == main + finally: + plt.close(anim.figure) + + def test_a_palette_choice_reaches_the_panel(self): + anim, seen = self._companion(palette='viridis') + try: + main = to_hex(seen[-1].artists[0].get_color()) + for cols, head in self._panel_colours(anim): + assert cols == {main} and head == main + finally: + plt.close(anim.figure) + + def test_an_fmt_colour_letter_reaches_the_panel(self): + anim, _ = self._companion(fmt='g-') + try: + for cols, head in self._panel_colours(anim): + assert cols == {to_hex('g')} and head == to_hex('g') + finally: + plt.close(anim.figure) + + def test_an_explicit_panel_colour_still_wins(self): + rng = np.random.default_rng(0) + traj = np.cumsum(rng.normal(size=(24, 3)), axis=0) + anim = hyp.plot(traj, animate=True, duration=2, frame_rate=6, + show=False, backend='matplotlib', + companion={'data': traj[:, 0], 'color': 'purple'}) + try: + anim.draw_frame(anim.n_frames - 1) + pax = anim.figure.axes[-1] + assert {to_hex(c) for c in pax.collections[0].get_colors()} == { + to_hex('purple')} + finally: + plt.close(anim.figure) + + +# --------------------------------------------------------------------------- +# L13b: the clamp warning fires on short streams +# --------------------------------------------------------------------------- + +def _stream(rows): + for row in rows: + yield row + + +class TestShortStreamClampWarning: + + def _drifting(self, n_post): + rng = np.random.default_rng(5) + head = rng.normal(size=(8, 3)) * 0.1 + tail = rng.normal(size=(n_post, 3)) * 0.1 + 10.0 + return np.vstack([head, tail]) + + @pytest.mark.parametrize('n_post', [4, 8, 16]) + def test_a_short_drifting_stream_warns(self, n_post): + with pytest.warns(RuntimeWarning, match='outside the display box'): + fig = hyp.plot(_stream(self._drifting(n_post)), stream_init=8, + stream_chunk=4, show=False) + plt.close(fig) + + def test_a_stream_that_dies_after_a_short_drift_warns(self): + """STREAM-02's shape: 8 head samples, 8 more, then the source + raises. 7 of the 8 post-head samples were drawn clamped, silently.""" + rows = self._drifting(8) + + def broken(): + yield from rows + raise RuntimeError('source disconnected') + + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(broken(), stream_init=8, stream_chunk=4, + show=False) + messages = [str(w.message) for w in caught] + assert any('outside the display box' in m for m in messages) + assert any('streaming stopped early' in m for m in messages) + plt.close(fig) + + def test_the_warning_fires_once(self): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(_stream(self._drifting(40)), stream_init=8, + stream_chunk=4, show=False) + assert sum('outside the display box' in str(w.message) + for w in caught) == 1 + plt.close(fig) + + def test_fewer_than_four_post_head_samples_do_not_warn(self): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(_stream(self._drifting(3)), stream_init=8, + stream_chunk=4, show=False) + assert not [w for w in caught + if 'outside the display box' in str(w.message)] + plt.close(fig) + + def test_a_short_stream_inside_the_box_does_not_warn(self): + rng = np.random.default_rng(5) + head = rng.uniform(-1, 1, size=(8, 3)) + head[:2] = [[-1, -1, -1], [1, 1, 1]] # the head spans the box + tail = rng.uniform(-0.5, 0.5, size=(8, 3)) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(_stream(np.vstack([head, tail])), stream_init=8, + stream_chunk=4, reduce=None, show=False) + assert not [w for w in caught + if 'outside the display box' in str(w.message)] + plt.close(fig) diff --git a/tests/test_anim_dataset_fade.py b/tests/test_anim_dataset_fade.py index 2dece7f0..2f0811d5 100644 --- a/tests/test_anim_dataset_fade.py +++ b/tests/test_anim_dataset_fade.py @@ -191,3 +191,69 @@ def test_bad_type_raises(self): with pytest.raises(TypeError, match=r'must be a dict'): hyp.plot(turns(), animate='serial', duration=1, dataset_fade=0.5, show=False) + + +# --- 1.1 release review: A1 the fade reaches the rendered collections +# --- under a continuous hue; A5 non-numeric floor/decay -------------------- + +def _rendered(anim, frame): + anim.draw_frame(frame) + anim.figure.canvas.draw() + return np.asarray(anim.figure.canvas.buffer_rgba()).copy() + + +@pytest.mark.parametrize('ndims', [2, 3]) +def test_dataset_fade_changes_rendered_pixels_under_a_continuous_hue(ndims): + """With a continuous `hue=` matplotlib draws LineCollections and hides + the Line2D heads; the fade used to set alpha on the hidden heads, so a + late frame rendered byte-identically with and without it.""" + rng = np.random.default_rng(1) + data = [np.cumsum(rng.normal(size=(30, ndims)), axis=0) + 4.0 * i + for i in range(3)] + hue = np.linspace(0.0, 1.0, 90) + kw = dict(animate='serial', hue=hue, duration=3, frame_rate=10, + reduce=None, ndims=ndims, show=False) + plain = hyp.plot(data, **kw) + faded = hyp.plot(data, dataset_fade={'floor': 0.0, 'decay': 0.1}, **kw) + try: + late = plain.n_frames - 2 + a = _rendered(plain, late) + b = _rendered(faded, late) + assert a.shape == b.shape + assert (a != b).any(axis=-1).sum() > 100 + # and the artists the hook sees ARE the drawn collections + from matplotlib.collections import LineCollection + seen = [] + faded.on_frame(lambda ctx: seen.append(ctx.artists)) + faded.draw_frame(late) + assert all(isinstance(art, LineCollection) for art in seen[-1][:3]) + finally: + plt.close(plain.figure) + plt.close(faded.figure) + + +def test_dataset_fade_alpha_on_ctx_artists_is_what_gets_drawn(): + rng = np.random.default_rng(2) + data = [np.cumsum(rng.normal(size=(30, 3)), axis=0) + 4.0 * i + for i in range(3)] + anim = hyp.plot(data, animate='serial', hue=np.linspace(0, 1, 90), + dataset_fade={'floor': 0.2, 'decay': 0.5}, + duration=3, frame_rate=10, reduce=None, show=False) + try: + anim.draw_frame(anim.n_frames - 1) + colls = [c for c in anim.figure.axes[0].collections + if getattr(c, '_hyp_trace_role', None) == 'head'] + alphas = [c.get_alpha() for c in colls] + assert alphas[2] == 1.0 + assert alphas[1] == pytest.approx(0.2 + 0.8 * 0.5) + assert alphas[0] == pytest.approx(0.2 + 0.8 * 0.25) + finally: + plt.close(anim.figure) + + +def test_non_numeric_floor_decay_names_the_kwarg(): + rng = np.random.default_rng(3) + data = [rng.normal(size=(12, 3)) for _ in range(2)] + with pytest.raises(TypeError, match="dataset_fade='s floor and decay"): + hyp.plot(data, animate='serial', dataset_fade=('a', 'b'), + show=False) diff --git a/tests/test_anim_linked_panels.py b/tests/test_anim_linked_panels.py index 8a9bef78..689ad609 100644 --- a/tests/test_anim_linked_panels.py +++ b/tests/test_anim_linked_panels.py @@ -292,3 +292,81 @@ def test_default_is_no_panel(self): assert len(anim.figure.axes) == 1 finally: plt.close(anim.figure) + + +# --- 1.1 release review: A3 the serial head is cumulative; A5 raw errors -- + +class TestSerialHeadIsCumulative: + + def _heads(self, anim): + heads = [] + for i in range(anim.n_frames): + anim.draw_frame(i) + marker = panel_axes(anim).lines[-1] + heads.append(int(marker.get_xdata()[0])) + return heads + + def test_companion_head_never_runs_backwards_over_several_datasets(self): + """Rescaling only the CURRENT dataset's reveal made the head run + 0 -> 20 -> 10 -> 0 -> 39 over a three-dataset serial reveal.""" + data = [trajectory(s) for s in (0, 3, 5)] + anim = hyp.plot(data, animate='serial', duration=3, frame_rate=8, + show=False, companion={'data': series()}) + try: + heads = self._heads(anim) + assert heads == sorted(heads) + assert heads[0] == 0 and heads[-1] == N_ROWS - 1 + assert len(set(heads)) > 3 + finally: + plt.close(anim.figure) + + def test_index_title_is_monotone_under_serial_with_several_datasets(self): + idx = pd.date_range('2020-01-31', periods=N_ROWS, freq='ME') + data = [pd.DataFrame(trajectory(s), index=idx) for s in (0, 3, 5)] + anim = hyp.plot(data, animate='serial', duration=3, frame_rate=8, + show=False, title='{index:%Y-%m}', + companion={'data': series()}) + try: + titles = [] + heads = self._heads(anim) + for i in range(anim.n_frames): + anim.draw_frame(i) + titles.append(anim.figure.axes[0].get_title()) + assert titles == sorted(titles) + assert titles == [idx[h].strftime('%Y-%m') for h in heads] + finally: + plt.close(anim.figure) + + def test_serial_panel_counts_are_strictly_sorted(self): + anim = hyp.plot([trajectory(), trajectory(3)], animate='serial', + duration=2, frame_rate=8, show=False, + companion={'data': series()}) + try: + counts = [] + for i in range(anim.n_frames): + anim.draw_frame(i) + counts.append( + len(panel_axes(anim).collections[0].get_segments())) + assert counts == sorted(counts) + assert counts[0] == 0 and counts[-1] == N_ROWS - 1 + finally: + plt.close(anim.figure) + + +class TestCompanionValidationNamesTheKwarg: + + def test_a_callable_companion_is_a_typeerror_naming_companion(self): + with pytest.raises(TypeError, match='companion= takes a dict'): + animate(lambda ctx: 1) + + def test_a_non_int_smooth_names_smooth(self): + with pytest.raises(TypeError, match='smooth= is a rolling-mean'): + animate({'data': series(), 'smooth': 'a'}) + + def test_a_non_numeric_size_names_size(self): + with pytest.raises(TypeError, match='size= and pad= are figure'): + animate({'data': series(), 'size': 'big'}) + + def test_non_numeric_data_names_data(self): + with pytest.raises(TypeError, match='data must be numeric'): + animate({'data': ['a', 'b', 'c']}) diff --git a/tests/test_anim_progress.py b/tests/test_anim_progress.py index e2a655e5..ee3df450 100644 --- a/tests/test_anim_progress.py +++ b/tests/test_anim_progress.py @@ -113,7 +113,10 @@ def check(ctx): anim.draw_frame(i) assert len(checked) == anim.n_frames assert checked[0] == (1,) * 3 - assert checked[-1] == (anim.n_frames,) * 3 + # the last frame reveals every drawn row. (1.1 visual review + # L8: that was `n_frames` rows while every line was resampled + # onto one row per frame; a line now keeps its own rows.) + assert checked[-1] == tuple(len(d) for d in trajectories()) finally: plt.close(anim.figure) @@ -206,3 +209,72 @@ def test_window_bounds_is_a_tuple_of_int_pairs(self): for b in ctx.window_bounds) finally: plt.close(anim.figure) + + +# --- 1.1 release review: A4 window_bounds.start under a serial trail; +# --- A6 a raising on_frame during save keeps its own exception ----------- + +class TestSerialWindowBounds: + + @pytest.mark.parametrize('ndims', [3, 2]) + def test_start_moves_with_the_comet_head_and_matches_the_artist( + self, ndims): + """`window_bounds` said (0, c) on every serial frame even when a + trail flag had moved the head; the docstring promises start > 0 + once the window has moved past a dataset's beginning.""" + rng = np.random.default_rng(4) + data = [np.cumsum(rng.normal(size=(30, ndims)), axis=0) + 3.0 * i + for i in range(3)] + anim = hyp.plot(data, animate='serial', chemtrails=True, + focused=0.4, antialias=False, reduce=None, + ndims=ndims, duration=2, frame_rate=10, show=False) + starts = [] + + def check(ctx): + # inside the hook: matplotlib's artists are shared and mutated + # in place, so they only hold THIS frame's data right now + for i, (start, end) in enumerate(ctx.window_bounds): + assert end == ctx.revealed_counts[i] + head = ctx.artists[i] + xs = (head.get_data_3d()[0] if ndims == 3 + else head.get_xdata()) + expected = ctx.datasets[i][start:end] + assert len(xs) == len(expected) + if len(expected): + assert np.allclose(xs, expected[:, 0]) + starts.append(start) + + try: + anim.on_frame(check) + for i in range(anim.n_frames): + anim.draw_frame(i) + assert any(s > 0 for s in starts) + finally: + plt.close(anim.figure) + + +class TestSaveWithARaisingHook: + + def test_the_hooks_own_exception_propagates(self, tmp_path): + """The Pillow writer's finish() indexed an empty frame list, so a + hook that raised on frame 0 surfaced as IndexError with the real + error only in the chained context.""" + def hook(ctx): + raise RuntimeError('hook boom') + + anim = hyp.plot(trajectories(), animate=True, duration=1, + frame_rate=5, show=False, on_frame=hook) + try: + with pytest.raises(RuntimeError, match='hook boom'): + anim.save(tmp_path / 'x.gif') + finally: + plt.close(anim.figure) + + def test_save_path_with_a_raising_hook_too(self, tmp_path): + def hook(ctx): + raise RuntimeError('hook boom') + + with pytest.raises(RuntimeError, match='hook boom'): + hyp.plot(trajectories(), animate=True, duration=1, + frame_rate=5, show=False, on_frame=hook, + save_path=str(tmp_path / 'y.gif')) diff --git a/tests/test_anim_title_callable.py b/tests/test_anim_title_callable.py index 0e3a74b1..41f3a8a0 100644 --- a/tests/test_anim_title_callable.py +++ b/tests/test_anim_title_callable.py @@ -199,3 +199,53 @@ def test_a_callable_title_is_inside_the_canvas(self): assert box.y1 <= height finally: plt.close(anim.figure) + + +# --- 1.1 release review: T5 static pattern with no index; T8 non-str +# --- returns and format errors; A7 a raising title leaves no orphan ------ + +class TestDynamicTitleErrors: + + def test_static_pattern_title_without_an_index_raises_up_front(self): + rng = np.random.default_rng(0) + with pytest.raises(ValueError, match='has no row index to read'): + hyp.plot(rng.normal(size=(20, 3)), title='{index:%B %Y}', + show=False) + + @pytest.mark.parametrize('value', [None, 42]) + def test_a_callable_returning_a_non_string_is_a_typeerror(self, value): + with pytest.raises(TypeError, match='title= callable must return'): + hyp.plot(monthly_frame(), animate=True, duration=1, + frame_rate=4, title=lambda ctx: value, show=False) + + def test_a_static_callable_returning_a_non_string_is_a_typeerror(self): + with pytest.raises(TypeError, match='title= callable must return'): + hyp.plot(monthly_frame(), title=lambda ctx: 42, show=False) + + def test_a_bad_format_field_is_a_valueerror_naming_title(self): + with pytest.raises(ValueError, match="title='{index} {other}'"): + hyp.plot(monthly_frame(), animate=True, duration=1, + frame_rate=4, title='{index} {other}', show=False) + + def test_a_bad_format_spec_is_a_valueerror_naming_title(self): + with pytest.raises(ValueError, match='could not be formatted'): + hyp.plot(monthly_frame(), animate=True, duration=1, + frame_rate=4, title='{index.nope}', show=False) + + def test_a_raising_title_leaves_no_orphaned_animation(self): + import gc + import warnings + + def bad(ctx): + raise RuntimeError('title boom') + + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + with pytest.raises(RuntimeError, match='title boom'): + hyp.plot(monthly_frame(), animate=True, duration=1, + frame_rate=4, title=bad, show=False) + gc.collect() + orphaned = [w for w in caught + if 'deleted without rendering' in str(w.message)] + assert orphaned == [] + assert plt.get_fignums() == [] diff --git a/tests/test_anim_title_lines.py b/tests/test_anim_title_lines.py index d554299b..eef960f8 100644 --- a/tests/test_anim_title_lines.py +++ b/tests/test_anim_title_lines.py @@ -186,3 +186,36 @@ def test_titleless_animation_keeps_the_full_canvas(self): assert (pos.y0, pos.height) == pytest.approx((0.0, 1.0)) finally: plt.close(anim.figure) + + +# --- 1.1 release review: T8 per-segment entries must be strings; T7 the +# --- margin covers the tallest per-segment entry -------------------------- + +class TestSegmentTitleEntries: + + def test_non_string_entries_are_a_typeerror(self): + with pytest.raises(TypeError, match='entry 0 is int: 1'): + hyp.plot(clouds(), animate='serial', title=[1, None], + duration=1, frame_rate=4, show=False) + + def test_none_entry_is_a_typeerror(self): + with pytest.raises(TypeError, match='entry 1 is NoneType'): + hyp.plot(clouds(), animate='serial', title=['a', None], + duration=1, frame_rate=4, show=False) + + def test_margin_is_reserved_for_the_tallest_segment_whatever_its_index( + self): + first = hyp.plot(clouds(), animate='serial', title=['a\nb\nc', 'x'], + duration=1, frame_rate=4, show=False) + last = hyp.plot(clouds(), animate='serial', title=['x', 'a\nb\nc'], + duration=1, frame_rate=4, show=False) + flat = hyp.plot(clouds(), animate='serial', title=['x', 'y'], + duration=1, frame_rate=4, show=False) + try: + assert (first.figure.get_size_inches()[1] + == last.figure.get_size_inches()[1]) + assert (last.figure.get_size_inches()[1] + > flat.figure.get_size_inches()[1]) + finally: + for anim in (first, last, flat): + plt.close(anim.figure) diff --git a/tests/test_animated_forecast_anchor.py b/tests/test_animated_forecast_anchor.py new file mode 100644 index 00000000..ce7813f3 --- /dev/null +++ b/tests/test_animated_forecast_anchor.py @@ -0,0 +1,388 @@ +"""An animated forecast starts at the CURRENT frame's drawn endpoint. + +Maintainer report, 2026-09-11: "for animations with predictions, the +predictions should show *from the endpoint in the current frame* not just +from the last observation." + +An animation is paced on a refined frame grid (`plot._interp_anim_line`), so +the drawn head usually sits BETWEEN two raw observations. The forecast was +anchored on the last raw observation at or before it, so the forecast hung +back from the line's tip and only caught up on the frames where the head +happened to land on a raw row. Measured before the fix, on a 2-D spiral +drawn into a ``[-1, 1]`` box: + +=================== ========= =============== ============== +rows / frames max gap frames with gap longest stall +=================== ========= =============== ============== +20 rows / 40 frames 0.21 24 / 37 2 frames +12 rows / 90 frames 0.51 71 / 81 8 frames +8 rows / 160 frames 0.90 130 / 137 23 frames +=================== ========= =============== ============== + +A "stall" is consecutive frames over which the head advances while the +forecast's start stays put -- the artifact the report describes. + +The contract these tests pin: on EVERY frame, on BOTH backends, the first +vertex of the live forecast is exactly the last vertex the observed +trajectory draws. The forecast's own predicted points are unchanged -- only +the vertex it hangs from moves -- so `t=` still means the same raw steps. +""" + +import os + +os.environ.setdefault('PLOTLY_RENDERER', 'json') + +import matplotlib # noqa: E402 + +matplotlib.use('Agg') + +import numpy as np # noqa: E402 +import pytest # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 + +import hypertools as hyp # noqa: E402 + + +def spiral(n): + """A trajectory whose successive raw steps are large enough that an + anchor one observation behind the head is visible, not a rounding + difference.""" + th = np.linspace(0, 2.2 * np.pi, n) + return np.c_[np.cos(th) * (1 + th / 8), np.sin(th) * (1 + th / 8)] + + +def matplotlib_frames(n_rows, duration, frame_rate, animate=True, **kwargs): + """(head, forecast start) per frame, read off the drawn artists.""" + anim = hyp.plot(spiral(n_rows), '-', predict='Kalman', t=3, + animate=animate, duration=duration, frame_rate=frame_rate, + show=False, backend='matplotlib', + slow_warning_seconds=None, **kwargs) + try: + ax = anim.figure.axes[0] + for frame in range(anim.n_frames): + anim.draw_frame(frame) + observed = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + live = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) == 'live' + and len(ln.get_xdata())] + if not observed or not live: + continue + head = np.array([observed[0].get_xdata()[-1], + observed[0].get_ydata()[-1]]) + start = np.array([live[0].get_xdata()[0], + live[0].get_ydata()[0]]) + yield frame, head, start + finally: + plt.close(anim.figure) + + +def plotly_frames(n_rows, duration, frame_rate, animate=True, **kwargs): + """The same measurement on the plotly backend, off the frame payloads.""" + fig = hyp.plot(spiral(n_rows), '-', predict='Kalman', t=3, + animate=animate, duration=duration, frame_rate=frame_rate, + show=False, backend='plotly', slow_warning_seconds=None, + **kwargs) + meta = [tr.meta if isinstance(tr.meta, dict) else {} for tr in fig.data] + data_slot = next(i for i, m in enumerate(meta) + if 'hyp_forecast_role' not in m) + live_slot = next(i for i, m in enumerate(meta) + if m.get('hyp_forecast_role') == 'live') + for frame, payload in enumerate(fig.frames): + slots = (list(payload.traces) if payload.traces is not None + else list(range(len(payload.data)))) + by_slot = dict(zip(slots, payload.data)) + observed, live = by_slot.get(data_slot), by_slot.get(live_slot) + if observed is None or live is None: + continue + if observed.x is None or live.x is None: + continue + if not len(observed.x) or not len(live.x): + continue + head = np.array([np.asarray(observed.x, float)[-1], + np.asarray(observed.y, float)[-1]]) + start = np.array([np.asarray(live.x, float)[0], + np.asarray(live.y, float)[0]]) + yield frame, head, start + + +READERS = {'matplotlib': matplotlib_frames, 'plotly': plotly_frames} + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('n_rows,duration,frame_rate', [ + (20, 4, 10), # the report's shape: head between rows every 2nd frame + (12, 6, 15), # coarser data, finer frame grid + (8, 8, 20), # the worst measured case: a 23-frame stall +]) +def test_forecast_starts_at_the_frames_drawn_endpoint(backend, n_rows, + duration, frame_rate): + """The live forecast hangs off the vertex the line actually ends on.""" + checked = 0 + for frame, head, start in READERS[backend](n_rows, duration, frame_rate): + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'{backend} frame {frame}: the forecast starts at ' + f'{start}, but the observed trajectory ends at {head} ' + f'-- the forecast must continue from the CURRENT ' + f"frame's endpoint, not from the last raw observation " + f'at or before it.')) + checked += 1 + assert checked > 3, ( + f'{backend}: only {checked} frames drew a forecast, too few to show ' + f'the anchor tracking the head') + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('animate', ['parallel', 'serial', 'window']) +def test_every_time_progressing_style_hangs_off_its_own_head(backend, animate): + """The head table is built from each style's OWN reveal -- + `serial_reveal_counts` for `'serial'`, `trails.anim_window_bounds` for + `'parallel'`/`'window'` -- so each style is checked rather than trusting + one of them to stand for the others.""" + checked = 0 + for frame, head, start in READERS[backend](12, 6, 15, animate=animate): + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'{backend} animate={animate!r} frame {frame}: forecast ' + f'starts at {start}, trajectory ends at {head}')) + checked += 1 + assert checked > 3, f'{backend} animate={animate!r}: only {checked} frames' + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_the_forecast_start_advances_on_every_frame(backend): + """The artifact was a STALL: the head moved while the forecast's start + stayed put for up to 23 consecutive frames. Once the reveal is under way, + a moving head must move the forecast with it.""" + seen = [(frame, head, start) + for frame, head, start in READERS[backend](8, 8, 20)] + stalls = 0 + for (_, prev_head, prev_start), (frame, head, start) in zip(seen, seen[1:]): + head_moved = not np.allclose(head, prev_head, atol=1e-12) + start_moved = not np.allclose(start, prev_start, atol=1e-12) + if head_moved and not start_moved: + stalls += 1 + assert stalls == 0, ( + f'{backend}: the forecast start stood still on {stalls} frame(s) ' + f'whose drawn head had moved') + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_a_model_comparison_anchors_every_models_forecast(backend): + """`predict=[...]` draws one forecast per model per dataset behind one + flat model-major index, so the head lookups go through + `MultiModelSchedule`, which forwards each slot to the sub-schedule that + owns it. Every model's forecast must start at the same drawn head.""" + models = ['Kalman', 'ARIMA'] + checked = 0 + if backend == 'matplotlib': + anim = hyp.plot(spiral(12), '-', predict=models, t=3, animate=True, + duration=6, frame_rate=15, show=False, + backend='matplotlib', slow_warning_seconds=None) + try: + ax = anim.figure.axes[0] + for frame in range(anim.n_frames): + anim.draw_frame(frame) + observed = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + live = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) == 'live' + and len(ln.get_xdata())] + if not observed or not live: + continue + head = np.array([observed[0].get_xdata()[-1], + observed[0].get_ydata()[-1]]) + for line in live: + start = np.array([line.get_xdata()[0], + line.get_ydata()[0]]) + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'frame {frame}: one model\'s forecast ' + f'starts at {start}, head is {head}')) + checked += 1 + finally: + plt.close(anim.figure) + else: + fig = hyp.plot(spiral(12), '-', predict=models, t=3, animate=True, + duration=6, frame_rate=15, show=False, + backend='plotly', slow_warning_seconds=None) + meta = [tr.meta if isinstance(tr.meta, dict) else {} for tr in fig.data] + data_slot = next(i for i, m in enumerate(meta) + if 'hyp_forecast_role' not in m) + live_slots = [i for i, m in enumerate(meta) + if m.get('hyp_forecast_role') == 'live'] + assert len(live_slots) == len(models), live_slots + for frame, payload in enumerate(fig.frames): + slots = (list(payload.traces) if payload.traces is not None + else list(range(len(payload.data)))) + by_slot = dict(zip(slots, payload.data)) + observed = by_slot.get(data_slot) + if observed is None or observed.x is None or not len(observed.x): + continue + head = np.array([np.asarray(observed.x, float)[-1], + np.asarray(observed.y, float)[-1]]) + for slot in live_slots: + live = by_slot.get(slot) + if live is None or live.x is None or not len(live.x): + continue + start = np.array([np.asarray(live.x, float)[0], + np.asarray(live.y, float)[0]]) + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'frame {frame} slot {slot}: forecast starts at ' + f'{start}, head is {head}')) + checked += 1 + assert checked > 6, f'{backend}: only {checked} forecast/frame pairs drawn' + + +def test_a_retained_trail_forecast_starts_at_the_head_it_was_fit_from(): + """`forecast_trail=` keeps earlier forecasts on screen so a viewer can + see how the prediction changed as history accumulated. Each retained + forecast therefore has to start where the trajectory ENDED when it was + fit -- otherwise the fan shows predictions hanging off points the line + never visibly reached.""" + from hypertools.plot.forecast import DEFAULT_FORECAST_TRAIL, trail_frames + + anim = hyp.plot(spiral(8), '-', predict='Kalman', t=3, animate=True, + forecast_trail=True, duration=8, frame_rate=20, + show=False, backend='matplotlib', + slow_warning_seconds=None) + try: + ax = anim.figure.axes[0] + + def drawn_head(): + observed = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + if not observed: + return None + return np.array([observed[0].get_xdata()[-1], + observed[0].get_ydata()[-1]]) + + heads = {} + for frame in range(anim.n_frames): + anim.draw_frame(frame) + head = drawn_head() + if head is not None: + heads[frame] = head + + checked = 0 + for frame in range(anim.n_frames // 2, anim.n_frames): + anim.draw_frame(frame) + retained = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) == 'trail' + and len(ln.get_xdata())] + # membership, not order: the artists carry no age we can read + # back, and the contract is that each one hangs off SOME earlier + # frame's head -- which a raw-observation anchor would fail. + expected = [heads[p] for p in trail_frames( + frame, DEFAULT_FORECAST_TRAIL) if p in heads] + for line in retained: + start = np.array([line.get_xdata()[0], line.get_ydata()[0]]) + assert any(np.allclose(start, head, atol=1e-9) + for head in expected), ( + f'frame {frame}: a retained forecast starts at {start}, ' + f'which is not the drawn head of any frame it could have ' + f'been fit at') + checked += 1 + assert checked > 0, 'no retained forecasts were drawn to check' + finally: + plt.close(anim.figure) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_a_regrouped_hue_animation_anchors_on_the_drawn_head(backend): + """`hue=` cuts a dataset into one drawn trace per contiguous category + run, so the reveal is expressed in RUNS rather than in the dataset's own + rows and the head belongs to whichever run is currently drawing it. + Measured before this path was covered: the forecast trailed the drawn + head by up to 0.51, on 75 of 81 frames.""" + hue = ['early'] * 6 + ['late'] * 6 + checked = 0 + if backend == 'matplotlib': + anim = hyp.plot(spiral(12), '-', hue=hue, predict='Kalman', t=3, + animate=True, duration=6, frame_rate=15, show=False, + backend='matplotlib', slow_warning_seconds=None) + try: + ax = anim.figure.axes[0] + for frame in range(anim.n_frames): + anim.draw_frame(frame) + drawn = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata())] + live = [ln for ln in ax.get_lines() + if getattr(ln, '_hyp_forecast_role', None) == 'live' + and len(ln.get_xdata())] + if not drawn or not live: + continue + # the run furthest along owns the head + head = np.array([drawn[-1].get_xdata()[-1], + drawn[-1].get_ydata()[-1]]) + start = np.array([live[0].get_xdata()[0], + live[0].get_ydata()[0]]) + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'frame {frame}: the regrouped forecast starts ' + f'at {start}, the drawn head is {head}')) + checked += 1 + finally: + plt.close(anim.figure) + else: + fig = hyp.plot(spiral(12), '-', hue=hue, predict='Kalman', t=3, + animate=True, duration=6, frame_rate=15, show=False, + backend='plotly', slow_warning_seconds=None) + meta = [tr.meta if isinstance(tr.meta, dict) else {} for tr in fig.data] + data_slots = [i for i, m in enumerate(meta) + if 'hyp_forecast_role' not in m] + live_slots = [i for i, m in enumerate(meta) + if m.get('hyp_forecast_role') == 'live'] + for frame, payload in enumerate(fig.frames): + slots = (list(payload.traces) if payload.traces is not None + else list(range(len(payload.data)))) + by_slot = dict(zip(slots, payload.data)) + drawn = [by_slot[s] for s in data_slots + if s in by_slot and by_slot[s].x is not None + and len(by_slot[s].x)] + live = [by_slot[s] for s in live_slots + if s in by_slot and by_slot[s].x is not None + and len(by_slot[s].x)] + if not drawn or not live: + continue + head = np.array([np.asarray(drawn[-1].x, float)[-1], + np.asarray(drawn[-1].y, float)[-1]]) + start = np.array([np.asarray(live[0].x, float)[0], + np.asarray(live[0].y, float)[0]]) + np.testing.assert_allclose( + start, head, atol=1e-9, + err_msg=(f'frame {frame}: the regrouped forecast starts at ' + f'{start}, the drawn head is {head}')) + checked += 1 + assert checked > 3, f'{backend}: only {checked} frames drew a forecast' + + +def test_the_predicted_points_themselves_are_unchanged(): + """Re-anchoring moves only the vertex the forecast hangs from. Every + point the model actually predicted keeps its place, so `t=` still counts + the same raw steps from the last OBSERVATION.""" + from hypertools.plot.forecast import ForecastSchedule + + history = np.cumsum(np.ones((10, 2)), axis=0) + grid = np.linspace(0, 9, 46) + dense = np.column_stack([np.interp(grid, np.arange(10), history[:, col]) + for col in range(2)]) + schedule = ForecastSchedule.for_parallel( + [history], [len(dense)], model='Kalman', t=3, n_frames=40, + slow_warning_seconds=None, grids=[dense]) + for frame in range(40): + drawn = schedule.polyline(0, frame) + if drawn is None: + continue + rows = schedule.revealed_rows(0, frame) + path = schedule.path(0, frame) + predicted = history[rows[-1]] + path[1:] + np.testing.assert_allclose( + drawn[1:], predicted, atol=1e-12, + err_msg=f'frame {frame}: a predicted point moved') diff --git a/tests/test_animation_export.py b/tests/test_animation_export.py index fb01eeeb..570a98eb 100644 --- a/tests/test_animation_export.py +++ b/tests/test_animation_export.py @@ -472,3 +472,139 @@ def test_hyper_animation_save_refuses_unknown_keywords(tmp_path): anim.save(tmp_path / 'x.gif', bitrate=1800) assert not (tmp_path / 'x.gif').exists() plt.close('all') + + +# ------------------------------------------- the install policy crosses the +# process boundary (release audit 2026-09-07, High: `hyp.set_autoinstall(False)` +# lived in the parent only; the export worker, a fresh interpreter, still ran +# pip). The worker provisions kaleido/Chrome itself, so the parent hands it the +# effective setting (lazy_import.subprocess_env). + +def _interpreter_without(tmp_path, absent): + """A REAL throwaway interpreter with every package of this one EXCEPT + those whose site-packages entry starts with `absent` -- a venv whose + site-packages links to each entry of ours (the release audit's + reproduction). Nothing is installed; nothing is faked: `import kaleido` + genuinely fails there. Returns the interpreter path, or skips when the + platform cannot link the entries.""" + import sysconfig + venv = tmp_path / 'noenv' + subprocess.run([sys.executable, '-m', 'venv', '--without-pip', str(venv)], + check=True, capture_output=True, timeout=300) + py = venv / ('Scripts/python.exe' if os.name == 'nt' else 'bin/python') + site = subprocess.run( + [str(py), '-c', "import sysconfig; print(sysconfig.get_paths()['purelib'])"], + capture_output=True, text=True, check=True, timeout=120).stdout.strip() + os.makedirs(site, exist_ok=True) + sources = {sysconfig.get_paths()['purelib'], sysconfig.get_paths()['platlib']} + for src_dir in sorted(sources): + for name in sorted(os.listdir(src_dir)): + if name.lower().startswith(absent.lower()): + continue + src, dst = os.path.join(src_dir, name), os.path.join(site, name) + if os.path.lexists(dst): + continue + try: + os.symlink(src, dst, target_is_directory=os.path.isdir(src)) + except OSError as e: # Windows without symlink rights + if os.path.isdir(src) and os.name == 'nt': + import _winapi + _winapi.CreateJunction(src, dst) + elif not os.path.isdir(src): + shutil.copy2(src, dst) + else: + pytest.skip(f'cannot link site-packages entries here: {e}') + check = subprocess.run( + [str(py), '-c', + 'import importlib.util, sys; ' + 'import plotly, hypertools; ' + f'print(importlib.util.find_spec({absent!r}) is None, ' + 'hypertools.__file__)'], + capture_output=True, text=True, timeout=300) + assert check.returncode == 0, check.stderr[-1500:] + absent_there, hypertools_there = check.stdout.split() + assert absent_there == 'True', f'{absent} is still importable in the throwaway interpreter' + # the throwaway interpreter runs THIS checkout's hypertools, not another copy + assert os.path.realpath(os.path.dirname(hypertools_there)) == \ + os.path.realpath(os.path.dirname(hyp.__file__)) + return py + + +_POLICY_DRIVER = ''' +import importlib.util, os, sys +import numpy as np +import hypertools as hyp +from hypertools._shared.lazy_import import auto_install_enabled +mode, out = sys.argv[1], sys.argv[2] +assert importlib.util.find_spec('kaleido') is None +if mode == 'off': # Python False, variable unset + os.environ.pop('HYPERTOOLS_AUTO_INSTALL', None) + hyp.set_autoinstall(False) +else: # Python True over the variable's 0 + os.environ['HYPERTOOLS_AUTO_INSTALL'] = '0' + hyp.set_autoinstall(True) +print('parent', auto_install_enabled(), flush=True) +try: + hyp.plot(np.random.default_rng(0).normal(size=(8, 3)), backend='plotly', + animate=True, duration=0.3, frame_rate=10, show=False, save_path=out) +except Exception as e: + print('RAISED', type(e).__name__, flush=True) + print(str(e), flush=True) +else: + print('EXPORTED', flush=True) +''' + + +def _run_policy_driver(py, tmp_path, mode): + driver = tmp_path / 'driver.py' + driver.write_text(_POLICY_DRIVER, encoding='utf-8') + out_gif = tmp_path / f'{mode}.gif' + # pip cannot reach an index or a config file: if it runs, it fails, and + # nothing is ever installed into the throwaway interpreter + env = dict(os.environ, PIP_NO_INDEX='1', PIP_CONFIG_FILE=os.devnull, + PIP_DISABLE_PIP_VERSION_CHECK='1', MPLBACKEND='Agg') + res = subprocess.run([str(py), str(driver), mode, str(out_gif)], cwd=tmp_path, + env=env, capture_output=True, text=True, timeout=900) + assert res.returncode == 0, res.stderr[-2000:] + assert not out_gif.exists() + return res.stdout + res.stderr + + +def test_plotly_animation_export_honours_set_autoinstall_off_in_the_worker(tmp_path): + """`hyp.set_autoinstall(False)` in the parent, kaleido genuinely absent: + the export fails with the policy error naming the manual command, and + the worker never runs pip (no install notice, no pip failure text), so + kaleido stays absent.""" + py = _interpreter_without(tmp_path, 'kaleido') + text = _run_policy_driver(py, tmp_path, 'off') + assert 'parent False' in text + assert 'RAISED ImportError' in text and 'EXPORTED' not in text # the documented type, not RuntimeError + assert 'kaleido is not installed' in text + assert 'pip install "hypertools[interactive]"' in text + assert 'automatic installation is off' in text + assert 'set_autoinstall(True)' in text + for pip_ran in ('hypertools: installing', 'automatically failed', + 'CalledProcessError', "'pip', 'install'", 'Could not find a version'): + assert pip_ran not in text, pip_ran + still = subprocess.run( + [str(py), '-c', "import importlib.util; print(importlib.util.find_spec('kaleido') is None)"], + capture_output=True, text=True, check=True, timeout=120) + assert still.stdout.strip() == 'True' + + +def test_plotly_animation_export_honours_set_autoinstall_on_over_the_environment(tmp_path): + """The other direction: `hyp.set_autoinstall(True)` in the parent beats + HYPERTOOLS_AUTO_INSTALL=0 in its environment, so the worker DOES reach + pip (which, cut off from any index here, fails -- the error is the + install-failure one, not the policy one) and nothing is installed.""" + py = _interpreter_without(tmp_path, 'kaleido') + text = _run_policy_driver(py, tmp_path, 'on') + assert 'parent True' in text + assert 'RAISED ImportError' in text and 'EXPORTED' not in text + assert 'automatically failed' in text + assert 'pip install "hypertools[interactive]"' in text + assert 'automatic installation is off' not in text + still = subprocess.run( + [str(py), '-c', "import importlib.util; print(importlib.util.find_spec('kaleido') is None)"], + capture_output=True, text=True, check=True, timeout=120) + assert still.stdout.strip() == 'True' diff --git a/tests/test_api_docs_markup.py b/tests/test_api_docs_markup.py new file mode 100644 index 00000000..b343cc3e --- /dev/null +++ b/tests/test_api_docs_markup.py @@ -0,0 +1,167 @@ +"""The rendered API reference shows no leaked reStructuredText markup. + +Builds the API pages for real -- ``docs/api.rst`` through Sphinx with the +same autodoc + numpydoc + autosummary chain ``docs/conf.py`` uses (no +gallery, no notebooks, the built-in theme) -- and runs +``scripts/scan_api_markup_leaks.py`` over the HTML. The scanner reports +literal backticks, ``:role:`` syntax and ``**`` pairs in body text, +numpydoc items that are really wrapped prose, and signature defaults with +an opaque repr. + +A second build of a small module whose docstrings contain each of those +mistakes proves the scanner still sees them. +""" +import importlib.util +import os +import pathlib +import subprocess +import sys +import textwrap + +import pytest + +pytest.importorskip('sphinx') +pytest.importorskip('numpydoc') + +REPO = pathlib.Path(__file__).resolve().parents[1] +SCANNER_PATH = REPO / 'scripts' / 'scan_api_markup_leaks.py' + +CONF = ''' +import sys +sys.path.insert(0, {root!r}) +project = 'hypertools' +extensions = ['sphinx.ext.autodoc', 'numpydoc', 'sphinx.ext.autosummary'] +numpydoc_class_members_toctree = False +autosummary_generate = True +html_theme = 'basic' +''' + + +def _scanner(): + spec = importlib.util.spec_from_file_location('scan_api_markup_leaks', + SCANNER_PATH) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def _build(source_dir, out_dir): + env = dict(os.environ, MPLBACKEND='Agg') + done = subprocess.run( + [sys.executable, '-m', 'sphinx', '-q', '-b', 'html', + '-d', str(out_dir.parent / 'doctrees'), + str(source_dir), str(out_dir)], + capture_output=True, text=True, env=env, timeout=600) + assert done.returncode == 0, done.stdout + done.stderr + return done + + +def _format(results): + return '\n'.join(f'{page} line {line}: [{kind}] {text}' + for page, hits in results.items() + for line, kind, text in hits) + + +def test_api_reference_has_no_leaked_markup(tmp_path): + source = tmp_path / 'src' + source.mkdir() + (source / 'conf.py').write_text(CONF.format(root=str(REPO)), + encoding='utf-8') + api = (REPO / 'docs' / 'api.rst').read_text(encoding='utf-8') + # api.rst is an :orphan: page in the real site; here it is the root + (source / 'index.rst').write_text(api, encoding='utf-8') + out = tmp_path / 'html' + _build(source, out) + + pages = sorted(p.name for p in out.glob('hypertools.*.html')) + # every autosummary entry got a page (plot, predict, the LSL classes...) + for expected in ('hypertools.plot.html', 'hypertools.predict.html', + 'hypertools.impute.html', + 'hypertools.io.LSLStream.html', + 'hypertools.text_windows.html'): + assert expected in pages, pages + assert len(pages) >= 40, pages + + scanner = _scanner() + results = {page: scanner.scan_file(str(out / page)) + for page in pages + ['index.html']} + leaks = {page: hits for page, hits in results.items() if hits} + assert not leaks, _format(leaks) + + # the pages really carry the rendered docstrings the scan is about + plot_page = (out / 'hypertools.plot.html').read_text(encoding='utf-8') + assert 'slow_warning_seconds' in plot_page + assert 'morph_samples' in plot_page + + +LEAKY_MODULE = ''' +_SENTINEL = object() + + +def leaky(a, b=None, c=_SENTINEL): + """Do a thing. + + Parameters + ---------- + a : int + About ~`a` seconds, in `unit`s. + b (``mode='x'`` only) : int + **Bold with ``literal`` inside.** See :func:`leaky`x and + un**bold**ed text. + + Returns + ------- + the result (and the fitted model if asked for it). Lists in, + lists out: one dataset in gives one result back out again + """ + + +def clean(a, **kwargs): + """Do a thing. + + Parameters + ---------- + a : int + About `a` seconds, i.e. ``grid**3`` cells. + **kwargs + Passed through to :func:`clean`. + + Returns + ------- + result : DataFrame or list of DataFrame + The result. + """ +''' + + +def test_scanner_flags_each_kind_of_leak(tmp_path): + source = tmp_path / 'src' + source.mkdir() + (source / 'leakymod.py').write_text(LEAKY_MODULE, encoding='utf-8') + (source / 'conf.py').write_text(CONF.format(root=str(source)), + encoding='utf-8') + (source / 'index.rst').write_text(textwrap.dedent('''\ + Leaks + ===== + + .. currentmodule:: leakymod + + .. autosummary:: + :toctree: + + leaky + clean + '''), encoding='utf-8') + out = tmp_path / 'html' + _build(source, out) + + scanner = _scanner() + kinds = {kind for _, kind, _ in + scanner.scan_file(str(out / 'leakymod.leaky.html'))} + assert 'backtick' in kinds + assert 'role' in kinds + assert 'strong-markers' in kinds + assert 'opaque-default' in kinds + assert any(kind.startswith('prose-term') for kind in kinds), kinds + + assert scanner.scan_file(str(out / 'leakymod.clean.html')) == [] diff --git a/tests/test_backend_window_parity.py b/tests/test_backend_window_parity.py index bd15a98e..90f812bb 100644 --- a/tests/test_backend_window_parity.py +++ b/tests/test_backend_window_parity.py @@ -314,7 +314,14 @@ def test_window_spans_the_requested_seconds_on_both_backends(frame_rate, fig = hyp.plot(data, backend='plotly', **kw) n_frames = len(fig.frames) mid = n_frames // 2 - expected = int(frame_rate * seconds) + 1 + # the rows the head passes in `seconds`: frame_rate * seconds frames at + # (n - 1) / (n_frames - 1) rows per frame. (1.1 visual review L8: this + # was `frame_rate * seconds` rows because every line used to be + # resampled onto one row per frame -- these 200-row walks drew through + # 40-120 of their points.) + n_rows = data[0].shape[0] + expected = int(round(int(frame_rate * seconds) * (n_rows - 1) + / (n_frames - 1))) + 1 assert len(fig.frames[mid].data[0].x) == expected ani = hyp.plot(data, return_model=True, **kw)['animation'] ani._func(mid, *ani._args) diff --git a/tests/test_backtest_times.py b/tests/test_backtest_times.py new file mode 100644 index 00000000..852e2b57 --- /dev/null +++ b/tests/test_backtest_times.py @@ -0,0 +1,157 @@ +"""Score real forecasts at held-out times, without using held-out values.""" +import numpy as np +import pandas as pd +import pytest +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.gaussian_process.kernels import DotProduct + +import hypertools as hyp +from hypertools.predict import GaussianProcess + + +def _irregular(): + times = np.array([0., 1., 3., 6., 7., 9., 13., 15., 15.5, 19., 23.5]) + return pd.DataFrame({'a': 2 * times + 1, 'b': np.sin(times)}, index=times) + + +def test_gp_backtest_evaluates_the_actual_held_out_coordinates(): + frame = _irregular() + kernel = DotProduct(sigma_0=1, sigma_0_bounds='fixed') + spec = GaussianProcess(kernel=kernel, alpha=1e-6, normalize_y=False) + scores, result = hyp.predict(frame, model=spec, holdout=3, return_forecasts=True) + train, held = frame.iloc[:-3], frame.iloc[-3:] + reference = GaussianProcessRegressor( + kernel=kernel, alpha=1e-6, normalize_y=False).fit( + np.asarray(train.index)[:, None] / 2, train) + expected = reference.predict(np.asarray(held.index)[:, None] / 2) + actual = result['GaussianProcess'] + pd.testing.assert_index_equal(actual.index, held.index) + np.testing.assert_allclose(actual, expected) + assert scores.loc['GaussianProcess', 'MAE'] == pytest.approx( + np.abs(expected - held.to_numpy()).mean()) + assert not spec.is_fitted + + +@pytest.mark.parametrize('model', ['Kalman', 'ARIMA', + {'model': 'AutoRegressor', 'kwargs': {'lags': 2}}]) +@pytest.mark.parametrize('step', [None, 3]) +def test_discrete_backtests_interpolate_a_covering_grid(model, step): + frame = _irregular() + train, held = frame.iloc[:-3], frame.iloc[-3:] + interval = 2 if step is None else step + x = (held.index.to_numpy() - 15) / interval + grid_forecast = hyp.predict(train, model=model, step=step, t=int(np.ceil(x[-1]))) + values = np.vstack([train.iloc[-1], grid_forecast]) + expected = np.column_stack([np.interp(x, np.arange(len(values)), col) + for col in values.T]) + with pytest.warns(UserWarning, match='interpolated to the held-out'): + scores, result = hyp.predict(frame, model=model, step=step, holdout=3, + return_forecasts=True) + name = model if isinstance(model, str) else model['model'] + np.testing.assert_allclose(result[name], expected) + pd.testing.assert_index_equal(result[name].index, held.index) + pd.testing.assert_frame_equal(result['truth'], held) + assert scores.loc[name, 'MAE'] == pytest.approx(np.abs(expected - held).to_numpy().mean()) + assert scores.loc[name, 'horizon'] == 3 # observations, not generated steps + + +@pytest.mark.parametrize('model', ['GaussianProcess', 'Kalman']) +def test_shuffling_rows_and_changing_held_out_values_cannot_change_the_fit(model): + frame = _irregular() + original = frame.copy(deep=True) + _, expected = hyp.predict(frame, model=model, holdout=3, return_forecasts=True) + altered = frame.copy() + altered.iloc[-3:] += 1000 + altered = altered.sample(frac=1, random_state=5) + with pytest.warns(UserWarning, match='sorted'): + scores, actual = hyp.predict(altered, model=model, holdout=3, return_forecasts=True) + pd.testing.assert_frame_equal(actual[model], expected[model]) + assert scores.loc[model, 'MAE'] > 900 + pd.testing.assert_frame_equal(frame, original) + + +@pytest.mark.parametrize('kind', ['datetime', 'timedelta', 'period']) +def test_native_time_indexes_are_retained_in_backtest_results(kind): + frame = _irregular() + if kind == 'datetime': + frame.index = pd.Timestamp('2026-03-07', tz='America/New_York') + pd.to_timedelta(frame.index, unit='h') + elif kind == 'timedelta': + frame.index = pd.to_timedelta(frame.index, unit='h') + else: + # Unique, irregular periods; normalization uses their start times. + frame.index = pd.PeriodIndex(pd.Timestamp('2026-01-01') + + pd.to_timedelta(frame.index * 2, unit='h'), freq='h') + _, result = hyp.predict(frame, model=['Kalman', 'GaussianProcess'], + holdout=3, return_forecasts=True) + # Periods are RETAINED too: a PeriodIndex input comes back as periods + # (release review 2026-09-11; this used to expect `.to_timestamp()`). + expected_index = frame.index[-3:] + for forecast in result.values(): + pd.testing.assert_index_equal(forecast.index, expected_index) + + +def test_datasets_and_model_step_overrides_have_independent_grids(): + a = _irregular() + b = a.copy() + b.index = b.index * 3 + 100 + specs = {'auto': 'Kalman', 'explicit': {'model': 'Kalman', 'kwargs': {'step': 3}}} + _, results = hyp.predict([a, b], model=specs, holdout=3, return_forecasts=True) + for i, frame in enumerate([a, b]): + for name, spec in specs.items(): + _, single = hyp.predict(frame, model=spec, holdout=3, return_forecasts=True) + pd.testing.assert_frame_equal(results[name][i], single['Kalman']) + + +@pytest.mark.parametrize('index', [pd.Index(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']), + pd.Index([0, 10, 30, 60, 100, 0, 10, 30])]) +def test_positional_row_ids_keep_their_meaning_when_the_split_is_unique(index): + frame = pd.DataFrame(np.random.default_rng(2).normal(size=(8, 2)), index=index) + expected = hyp.predict(frame.iloc[:-3].reset_index(drop=True), t=3) + _, result = hyp.predict(frame, holdout=3, return_forecasts=True) + np.testing.assert_allclose(result['Kalman'], expected) + pd.testing.assert_index_equal(result['Kalman'].index, frame.index[-3:]) + + +def test_duplicate_timestamps_across_the_split_are_rejected(): + frame = pd.DataFrame(np.arange(8.), index=pd.to_datetime( + ['2026-01-01', '2026-01-02', '2026-01-03', '2026-01-04', + '2026-01-05', '2026-01-05', '2026-01-06', '2026-01-07'])) + with pytest.raises(ValueError, match='duplicated'): + hyp.predict(frame, holdout=3) + + +def test_regular_backtest_is_identical_to_direct_forecasting(): + frame = pd.DataFrame(np.random.default_rng(9).normal(size=(30, 2)), + index=pd.date_range('2026-01-01', periods=30, freq='2h')) + for name in ['Kalman', 'ARIMA', 'AutoRegressor', 'GaussianProcess']: + expected = hyp.predict(frame.iloc[:-3], model=name, t=3) + _, result = hyp.predict(frame, model=name, holdout=3, return_forecasts=True) + # The backtest retains the truth index's frequency metadata; + # ordinary predict() returns an equivalent index without a freq. + pd.testing.assert_frame_equal(result[name], expected, check_freq=False) + + +def test_missing_whole_steps_select_the_matching_forecasts_without_interpolation(): + frame = pd.DataFrame(np.sin(np.arange(14.)), index=list(range(12)) + [12, 19]) + expected = hyp.predict(frame.iloc[:-2], t=8).iloc[[0, 7]] + import warnings + with warnings.catch_warnings(record=True) as caught: + _, result = hyp.predict(frame, holdout=2, return_forecasts=True) + assert not any('interpolated' in str(w.message) for w in caught) + pd.testing.assert_frame_equal(result['Kalman'], expected) + + +def test_missing_training_anchor_is_not_filled_with_held_out_values(): + frame = pd.DataFrame([1., 2., 3., 4., 5., 6., 7., np.nan, 9., 10.], + index=[0., 1., 2., 3., 4., 5., 6., 7., 7.5, 8.]) + with pytest.warns(UserWarning, match='not directly comparable'): + scores, result = hyp.predict(frame, holdout=2, return_forecasts=True) + assert np.isnan(result['Kalman'].iloc[0, 0]) + assert np.isfinite(result['Kalman'].iloc[1, 0]) + assert scores.loc['Kalman', 'unscored'] == 1 + + +def test_unreasonably_large_backtest_grid_has_a_clear_step_error(): + frame = pd.DataFrame(np.arange(9.), index=list(range(8)) + [100_000_000]) + with pytest.raises(ValueError, match='1,000,000 forecast steps.*larger step'): + hyp.predict(frame, holdout=1) diff --git a/tests/test_changelog_1_1.py b/tests/test_changelog_1_1.py index 5aa659fb..453925f2 100644 --- a/tests/test_changelog_1_1.py +++ b/tests/test_changelog_1_1.py @@ -144,3 +144,65 @@ def test_no_shipped_release_is_still_labelled_unreleased(): text = _changelog() assert '## 1.0.0 (2026-07-24)' in text assert '## 1.0.0 (unreleased)' not in text + + +# -------------------------------------------------------------------------- +# the release-review fixes (2026-09-06) live INSIDE 1.1.0, and are true +# -------------------------------------------------------------------------- + +def test_the_release_review_fixes_are_a_subsection_of_1_1_0(): + """1.1.0 was reviewed against 1.0.0 before publication; the fixes ship in + 1.1.0, so they must sit under its heading and not under a new version.""" + text = _changelog() + section = _section(text, _heading_1_1_0(text)) + assert '### Fixed during the release review' in section + assert text.index('### Fixed during the release review') < text.index( + '## 1.0.1') + + +def test_the_release_review_subsection_documents_its_user_visible_fixes(): + review = _section(_changelog(), '### Fixed during the release review') + for phrase in ('metrics=', 'holdout=True', 'return_score=True', + 'HypertoolsOfflineError', 'palette=', 'title_wrap=', + 'offline=True', 'gmtoffset', 'streaming=True', + 'text_windows', 'text2mat'): + assert phrase in review, f'missing {phrase!r}' + + +def test_added_names_the_scoring_and_source_kwargs(): + """The Added section describes backtests, imputer scoring and the + synthetic/web loaders, so it has to name the keywords that drive them.""" + added = _section(_changelog(), '### Added') + for phrase in ('`metrics=`', '`return_imputed=True`', '`**source_kwargs`'): + assert phrase in added, f'missing {phrase!r}' + + +def test_the_documented_duplicate_metric_rejection_actually_happens(): + """Execute the entry: a repeated metric is a ValueError that says which.""" + x = np.random.RandomState(0).randn(30, 3) + with pytest.raises(ValueError, match='MAE'): + hyp.predict(x, model=['Kalman'], holdout=3, metrics=['mae', 'MAE']) + + +def test_the_documented_holdout_t0_error_actually_names_t(): + x = np.random.RandomState(0).randn(30, 3) + with pytest.raises(ValueError, match='t=0'): + hyp.predict(x, model=['Kalman'], holdout=True, t=0) + + +def test_the_documented_empty_palette_rejection_actually_happens(): + x = np.random.RandomState(0).randn(30, 3) + with pytest.raises(ValueError, match='palette'): + hyp.plot([x, x], palette=[]) + + +def test_the_documented_streaming_rejection_actually_happens(): + with pytest.raises(ValueError, match='streaming=True'): + hyp.load('iris', streaming=True) + + +def test_the_documented_offline_error_import_paths_actually_work(): + from hypertools import HypertoolsOfflineError as top + from hypertools.io import HypertoolsOfflineError as via_io + assert top is via_io + assert issubclass(top, hyp.HypertoolsIOError) diff --git a/tests/test_cluster.py b/tests/test_cluster.py index 8f5c21b6..ea721aba 100644 --- a/tests/test_cluster.py +++ b/tests/test_cluster.py @@ -74,3 +74,102 @@ def test_cluster_mixture_via_plot(): # end-to-end: mixture clustering through the plot pipeline geo = plot(data, '.', cluster='GaussianMixture', n_clusters=2, show=False) assert geo is not None + + +# --- flat dict-spec keys (1.1 review) ---------------------------------- +# +# `{'model': 'KMeans', 'n_clusters': 4, 'random_state': 0}` used to drop +# `random_state` without a word, so every call drew different clusters. +# Model parameters belong under 'kwargs' (the convention every dispatcher +# documents); 'n_clusters' is the one documented top-level convenience. +# Anything else at the top level now raises, naming the key. + +def _unseparated_blobs(): + # overlapping draws, so KMeans' result genuinely depends on its seed + rng = np.random.default_rng(1) + return rng.standard_normal((300, 5)) + + +def _kmeans_runs(spec, n=6, via='cluster'): + x = _unseparated_blobs() + runs = set() + for _ in range(n): + if via == 'cluster': + labels = cluster(x, cluster=spec) + else: + import hypertools as hyp + _, model = hyp.analyze(x, cluster=spec, return_model=True) + labels = model.named_steps['cluster'].transform(x) + runs.add(tuple(int(v) for v in labels)) + return runs + + +def test_unseeded_kmeans_on_this_data_varies_between_runs(): + # the observable the flat-spec tests rely on: without a seed, one-init + # KMeans on this data does NOT reproduce, so a dropped random_state + # shows up as more than one distinct labelling + runs = _kmeans_runs({'model': 'KMeans', + 'kwargs': {'n_clusters': 4, 'n_init': 1}}) + assert len(runs) > 1 + + +def test_nested_kwargs_spec_is_reproducible(): + spec = {'model': 'KMeans', + 'kwargs': {'n_clusters': 4, 'n_init': 1, 'random_state': 0}} + assert len(_kmeans_runs(spec)) == 1 + assert len(_kmeans_runs(spec, via='analyze')) == 1 + + +@pytest.mark.parametrize('via', ['cluster', 'analyze', 'plot']) +def test_flat_spec_model_kwargs_raise_naming_the_key(via): + import hypertools as hyp + x = _unseparated_blobs() + spec = {'model': 'KMeans', 'n_clusters': 4, 'random_state': 0, + 'n_init': 1} + with pytest.raises(ValueError) as err: + if via == 'cluster': + hyp.cluster(x, cluster=spec) + elif via == 'analyze': + hyp.analyze(x, cluster=spec) + else: + hyp.plot(x, '.', cluster=spec, show=False) + msg = str(err.value) + assert "'n_init'" in msg and "'random_state'" in msg + # the message spells out the corrected spec, and 'n_clusters' (the + # documented top-level convenience) is not reported as unknown + assert "'kwargs': {'n_init': 1, 'random_state': 0}" in msg + assert "['n_init', 'random_state']" in msg + + +def test_flat_spec_error_suggestion_is_a_working_spec(): + # the corrected spec the error proposes really is reproducible + x = _unseparated_blobs() + with pytest.raises(ValueError, match="'random_state'"): + cluster(x, cluster={'model': 'KMeans', 'n_clusters': 4, + 'random_state': 0, 'n_init': 1}) + fixed = {'model': 'KMeans', 'n_clusters': 4, + 'kwargs': {'n_init': 1, 'random_state': 0}} + assert len(_kmeans_runs(fixed)) == 1 + labels = cluster(x, cluster=fixed) + assert len(set(labels)) == 4 + + +def test_flat_spec_extra_key_raises_with_canonical_kwargs_too(): + # a stray top-level key next to a canonical 'kwargs' dict is just as + # silently dropped, so it raises too (and the suggestion merges it in) + x = _unseparated_blobs() + with pytest.raises(ValueError) as err: + cluster(x, cluster={'model': 'KMeans', 'kwargs': {'n_clusters': 2}, + 'random_state': 3}) + assert "'kwargs': {'n_clusters': 2, 'random_state': 3}" in str(err.value) + + +def test_flat_spec_top_level_n_clusters_still_accepted(): + # the documented convenience keeps working, without any warning + import warnings + x = _unseparated_blobs() + with warnings.catch_warnings(): + warnings.simplefilter('error') + labels = cluster(x, cluster={'model': 'KMeans', 'n_clusters': 5, + 'kwargs': {'random_state': 0}}) + assert len(set(labels)) == 5 diff --git a/tests/test_codeorg_licensing_audit_fixes.py b/tests/test_codeorg_licensing_audit_fixes.py index 0813c619..4ef610b9 100644 --- a/tests/test_codeorg_licensing_audit_fixes.py +++ b/tests/test_codeorg_licensing_audit_fixes.py @@ -297,10 +297,14 @@ def test_all_names_resolve_and_cover_documented_api(): documented = {'plot', 'analyze', 'reduce', 'align', 'normalize', 'describe', 'cluster', 'manip', 'predict', 'impute', 'load', 'save', 'apply_model', 'supported_models', - 'Pipeline', 'set_interactive_backend', 'HyperAnimation', + 'Pipeline', 'set_interactive_backend', 'set_autoinstall', + 'HyperAnimation', 'FrameContext', 'io', 'HypertoolsError', 'HypertoolsBackendError', 'HypertoolsIOError', + # 1.1.0 (GH #285): offline=True's error, importable from + # the top level like the other three exceptions + 'HypertoolsOfflineError', 'HypertoolsTrustError', # 1.1.0 (GH #285): the hand-written helpers folded in 'text_windows', 'damage', 'stack', 'subplots'} assert set(hyp.__all__) == documented diff --git a/tests/test_core_audit_fixes.py b/tests/test_core_audit_fixes.py index fc178444..8079d288 100644 --- a/tests/test_core_audit_fixes.py +++ b/tests/test_core_audit_fixes.py @@ -11,6 +11,7 @@ import warnings import numpy as np +import pandas as pd import pytest from sklearn.decomposition import PCA @@ -345,12 +346,23 @@ def test_pipeline_list_input_to_raw_step_raises_clear_error(): pipe.fit_transform([x, y]) -def test_pipeline_aligner_step_ndarray_list_hint(): +def test_pipeline_aligner_step_accepts_ndarray_list(): + """This test used to assert a TypeError telling the caller to wrap the + arrays in DataFrames: the Aligner classes could not read a list of + arrays (datawrangler's unstack raised "Unsupported datatype"). Since + the 1.1 release review (2026-09-11) they coerce arrays themselves, as + `Aligner.fit` documents, so the raw step aligns them and hands back + arrays -- the same numbers as the DataFrame route.""" x = np.random.default_rng(10).standard_normal((40, 6)) y = np.random.default_rng(11).standard_normal((40, 6)) pipe = Pipeline(['HyperAlign']) - with pytest.raises(TypeError, match='DataFrame'): - pipe.fit_transform([x, y]) + out = pipe.fit_transform([x, y]) + reference = Pipeline(['HyperAlign']).fit_transform( + [pd.DataFrame(x), pd.DataFrame(y)]) + assert isinstance(out, list) and len(out) == 2 + for a, r in zip(out, reference): + assert type(a) is np.ndarray and a.shape == (40, 6) + np.testing.assert_allclose(a, np.asarray(r)) # -------------------------------------------------------------------------- diff --git a/tests/test_d1_code_residue.py b/tests/test_d1_code_residue.py index 9973fe1e..9bb521db 100644 --- a/tests/test_d1_code_residue.py +++ b/tests/test_d1_code_residue.py @@ -300,9 +300,12 @@ def test_density_2d_clipped_to_frame_box(tmp_path): assert images, 'density imshow layer missing' # matplotlib special-cases a Rectangle clip patch into `clipbox` # (a TransformedBbox), not `clippath`: assert the KDE layer's clipbox - # matches the [-1, 1] frame square in data coordinates -- narrower - # than the default axes bbox (limits are [-1.1, 1.1]). - frame_disp = ax.transData.transform([(-1.0, -1.0), (1.0, 1.0)]) + # matches the frame square (half-width UNIT_FRAME_SCALE) in data + # coordinates -- narrower than the axes bbox (limits are 10 % wider). + from hypertools._shared.helpers import UNIT_FRAME_SCALE + frame_disp = ax.transData.transform( + [(-UNIT_FRAME_SCALE, -UNIT_FRAME_SCALE), + (UNIT_FRAME_SCALE, UNIT_FRAME_SCALE)]) for im in images: assert im.clipbox is not None np.testing.assert_allclose( diff --git a/tests/test_datatype_gate.py b/tests/test_datatype_gate.py new file mode 100644 index 00000000..bb5fc38a --- /dev/null +++ b/tests/test_datatype_gate.py @@ -0,0 +1,250 @@ +# -*- coding: utf-8 -*- +"""Static gate: hypertools does not classify datatypes itself. + +Jeremy's rule (datatype audit, 2026-09-08): no function does its own +datatype checking; it defers to datawrangler (``dw.wrangle`` / ``dw.funnel`` +/ the ``dw.zoo`` predicates, via the ``hypertools._shared.helpers`` +wrappers ``is_array_dataset``/``is_frame_dataset``/``is_series_like``/ +``as_pandas_dataframe`` and ``hypertools.core.shared.as_dataframe``), so +polars -- and whatever datawrangler recognises next -- works everywhere for +free. + +This test parses every module under ``hypertools/`` (except the shared +coercion layer itself and the vendored third-party code) and fails on any + +- ``isinstance(x, ...)`` whose class tuple names ``pd.DataFrame``, + ``pd.Series`` or ``np.ndarray`` (qualified or bare), or +- ``hasattr(x, 'columns' | 'to_numpy' | 'values')`` (pandas duck-typing), + +unless the site is in ``ALLOWLIST`` -- the sites judged legitimate because +they test something that is NOT a user dataset: a hypertools object, a +model spec, an already-wrangled internal frame, a single stream sample or +scalar. Every allowlist entry must still match a real site, so the list +cannot go stale: removing a site means removing its entry. +""" +import ast +import os + +import pytest + +import hypertools + +PACKAGE_DIR = os.path.dirname(os.path.abspath(hypertools.__file__)) + +#: modules that ARE the coercion layer (they may name pandas/numpy types), +#: plus vendored third-party code +EXCLUDED = ('_shared/helpers.py', 'tools/format_data.py', 'external/', + '_externals/') + +#: names that make an ``isinstance`` second argument a datatype check +FLAGGED_TYPES = ('pd.DataFrame', 'pandas.DataFrame', 'pd.Series', + 'pandas.Series', 'np.ndarray', 'numpy.ndarray') +FLAGGED_BARE = ('DataFrame', 'Series', 'ndarray') +FLAGGED_ATTRS = ('columns', 'to_numpy', 'values') + +#: {repo-relative path: [(substring of the offending call, reason), ...]}. +#: A pattern matches when it is a substring of the unparsed call text. +ALLOWLIST = { + 'hypertools/io/sources.py': [ + ("isinstance(target, pd.DataFrame)", + "scikit-learn Bunch.target (sklearn's own object, a Series or " + "frame by sklearn's contract), not user data"), + ], + 'hypertools/io/streaming.py': [ + ("isinstance(row, pd.Series)", + "ONE stream sample (a row produced by the stream source), not a " + "dataset; the stream itself is classified by is_stream"), + ("isinstance(val, (list, tuple, np.ndarray))", + "one FIELD of a stream sample (a scalar or short vector), not a " + "dataset"), + ], + 'hypertools/predict/predict.py': [ + ("isinstance(t, np.ndarray)", + "the forecast horizon t= is a scalar; a 0-d numpy array is " + "unwrapped to its Python scalar"), + ], + 'hypertools/predict/arima.py': [ + ("isinstance(component, (list, tuple, np.ndarray))", + "an ARIMA order= component (model spec), not data"), + ], + # plot OPTIONS: each of these decides whether a keyword argument is one + # value or a per-dataset/per-point SEQUENCE of values (a format string + # vs a list of them, one color vs a palette, one flag vs a flag per + # dataset). They classify options, not datasets. + 'hypertools/plot/colors.py': [ + ("isinstance(value, (list, tuple, np.ndarray))", + "a palette= value: one color name vs a sequence of colors"), + ("isinstance(palette, (list, tuple, np.ndarray))", + "a palette= option: a named palette vs an explicit color list"), + ], + 'hypertools/plot/forecast.py': [ + ("isinstance(fmt, (list, tuple, np.ndarray))", + "a fmt= option: one format string vs one per dataset"), + ("isinstance(v, (list, tuple, np.ndarray))", + "a per-forecast styling option: one value vs one per forecast"), + ], + 'hypertools/plot/matplotlib_backend.py': [ + ("isinstance(fmt, (list, tuple, np.ndarray))", + "a fmt= option: one format string vs one per dataset"), + ], + 'hypertools/plot/plot.py': [ + ("isinstance(palette, (list, tuple, np.ndarray))", + "a palette= option: a named palette vs an explicit color list"), + ("isinstance(fmt, (list, tuple, np.ndarray))", + "a fmt= option: one format string vs one per dataset"), + ("isinstance(title_color, (list, tuple, np.ndarray))", + "a title_color= option: one color vs one per title line"), + ("hasattr(item, 'to_numpy')", + "guards the to_numpy call AFTER is_series_like (which also admits " + "dw.zoo.array_like objects that have no to_numpy); the datatype " + "decision itself is the datawrangler-based predicate"), + ("isinstance(legend_colors, (list, tuple, np.ndarray))", + "a legend color option: one color vs one per legend entry"), + ("isinstance(hue, (list, tuple, np.ndarray))", + "forecast_hue= as a per-forecast LABEL vector vs a single value, " + "AFTER any series-like (pandas/polars Series, Index) has been " + "normalised to an array through the shared predicate"), + ("isinstance(labels, np.ndarray)", + "cluster labels (a fitted model's output) unwrapped to a list"), + ("isinstance(animate, np.ndarray)", + "animate= per-dataset morph tags given as an array (an option)"), + ("isinstance(_lim, (list, tuple, np.ndarray))", + "an axis-limit option: a (lo, hi) pair vs None/scalar"), + ], + 'hypertools/plot/trails.py': [ + ("isinstance(flag, (list, tuple, np.ndarray))", + "a trails= flag: one value vs one per dataset"), + ("isinstance(value, np.ndarray)", + "a trails= flag given as an array, unwrapped to a list"), + ], +} + + +def _modules(): + for root, _dirs, files in os.walk(PACKAGE_DIR): + for name in sorted(files): + if not name.endswith('.py'): + continue + path = os.path.join(root, name) + rel = os.path.relpath(path, os.path.dirname(PACKAGE_DIR)) + rel = rel.replace(os.sep, '/') + if any(rel.startswith(f'hypertools/{ex}') + or f'/{ex}' in rel for ex in EXCLUDED): + continue + yield rel, path + + +def _names_in(node): + """Dotted names appearing anywhere in an AST node (e.g. the class tuple + of an isinstance call): 'pd.DataFrame', 'DataFrame', ...""" + found = set() + for sub in ast.walk(node): + if isinstance(sub, ast.Attribute): + found.add(ast.unparse(sub)) + elif isinstance(sub, ast.Name): + found.add(sub.id) + return found + + +def _is_flagged_isinstance(call): + if not (isinstance(call.func, ast.Name) and call.func.id == 'isinstance' + and len(call.args) == 2): + return False + names = _names_in(call.args[1]) + return any(n in names for n in FLAGGED_TYPES + FLAGGED_BARE) + + +def _is_flagged_hasattr(call): + if not (isinstance(call.func, ast.Name) and call.func.id == 'hasattr' + and len(call.args) == 2): + return False + attr = call.args[1] + return isinstance(attr, ast.Constant) and attr.value in FLAGGED_ATTRS + + +def find_sites(): + """[(repo-relative path, line, unparsed call)] for every flagged site.""" + sites = [] + for rel, path in _modules(): + with open(path, encoding='utf-8') as f: + tree = ast.parse(f.read(), filename=path) + for node in ast.walk(tree): + if isinstance(node, ast.Call) and ( + _is_flagged_isinstance(node) or _is_flagged_hasattr(node)): + sites.append((rel, node.lineno, ast.unparse(node))) + return sites + + +def test_scanner_sees_the_coercion_layer_and_nothing_vendored(): + # the scanner's own plumbing: it walks the real package, and the + # exclusions are exactly the coercion layer + vendored code + rels = [rel for rel, _ in _modules()] + assert 'hypertools/manip/manip.py' in rels + assert 'hypertools/plot/plot.py' in rels + assert 'hypertools/_shared/helpers.py' not in rels + assert 'hypertools/tools/format_data.py' not in rels + assert not any(rel.startswith('hypertools/external/') for rel in rels) + assert not any(rel.startswith('hypertools/_externals/') for rel in rels) + + +def test_scanner_flags_the_patterns_it_is_meant_to(): + # the matcher itself, on real AST (no mocks): each shape the gate is + # documented to catch is caught, and the shapes it must NOT catch (a + # hypertools object, a scalar, a model spec, a list/tuple container + # check) are not + src = '\n'.join([ + "a = isinstance(x, pd.DataFrame)", + "b = isinstance(x, (np.ndarray, list))", + "c = isinstance(x, pandas.Series)", + "d = isinstance(x, DataFrame)", + "e = hasattr(x, 'to_numpy')", + "f = hasattr(x, 'columns')", + "g = hasattr(x, 'values')", + "h = isinstance(x, (list, tuple))", + "i = isinstance(x, DataGeometry)", + "j = isinstance(x, (int, np.integer))", + "k = hasattr(x, 'shape')", + "m = isinstance(x, pd.RangeIndex)", + ]) + tree = ast.parse(src) + flagged = sorted( + node.targets[0].id for node in tree.body + if _is_flagged_isinstance(node.value) or _is_flagged_hasattr(node.value)) + assert flagged == ['a', 'b', 'c', 'd', 'e', 'f', 'g'] + + +def test_no_module_classifies_datatypes_itself(): + sites = find_sites() + unmatched = [] + used = set() + for rel, line, text in sites: + entries = ALLOWLIST.get(rel, []) + hit = [pattern for pattern, _reason in entries if pattern in text] + if hit: + used.update((rel, pattern) for pattern in hit) + else: + unmatched.append(f'{rel}:{line}: {text}') + assert not unmatched, ( + 'datatype check(s) outside the shared coercion layer -- route them ' + 'through datawrangler (hypertools._shared.helpers.is_frame_dataset / ' + 'is_array_dataset / is_series_like / as_pandas_dataframe, or ' + 'hypertools.core.shared.as_dataframe), or add a justified ' + 'ALLOWLIST entry:\n ' + '\n '.join(unmatched)) + + +def test_every_allowlist_entry_still_matches_a_real_site(): + sites = find_sites() + stale = [] + for rel, entries in ALLOWLIST.items(): + for pattern, reason in entries: + assert reason, f'{rel}: {pattern!r} has no reason' + if not any(r == rel and pattern in text for r, _l, text in sites): + stale.append(f'{rel}: {pattern!r}') + assert not stale, ( + 'ALLOWLIST entries that no longer match a site (the site was ' + 'converted or moved -- drop the entry):\n ' + '\n '.join(stale)) + + +@pytest.mark.parametrize('rel', sorted(ALLOWLIST)) +def test_allowlisted_modules_exist(rel): + assert os.path.exists(os.path.join(os.path.dirname(PACKAGE_DIR), rel)), rel diff --git a/tests/test_density.py b/tests/test_density.py index 4f1eec6e..7fb10127 100644 --- a/tests/test_density.py +++ b/tests/test_density.py @@ -652,3 +652,86 @@ def test_boosted_opacity_still_lower_than_r1_ceiling(self): _, _, _, opacity, _ = resolve_plotly_volume_params( 0.2, 3, boost=DENSITY_BOOST_MAX) assert opacity < self.R1_MAX_VOLUME_OPACITY + + +class TestDensityGridFadesOutInsideItself: + """The KDE grid used to stop 15% past each dataset's bounding box, so + under the unit frame a wide, flat cloud's glow was cut off in a hard + horizontal band well inside the frame (feature tour 9.14, 2026-09-06). + The grid now also reaches `KDE_GRID_BANDWIDTHS` kernel widths past the + data, where the density has already faded -- and it stays LOCAL to its + own cloud, so a small cloud beside a huge one keeps its resolution (a + scene-wide grid sampled it to all zeros; release review round 2).""" + + @staticmethod + def _flat_wide_clouds(): + rng = np.random.default_rng(3) + return [np.column_stack([rng.normal(c, 0.5, 60), + rng.normal(0.0, 0.25, 60)]) + for c in (-4.0, 0.0, 4.0)] + + @staticmethod + def _edge_over_peak(Z): + edge = max(Z[0, :].max(), Z[-1, :].max(), Z[:, 0].max(), + Z[:, -1].max()) + return edge / Z.max() + + def test_matplotlib_grid_edges_carry_no_visible_density(self): + fig = hyp.plot(self._flat_wide_clouds(), '.', density=True, + show=False) + images = fig.axes[0].get_images() + assert len(images) == 3 + for im in images: + Z = np.asarray(im.get_array()) + assert self._edge_over_peak(Z) < 1e-3 + # ...and each grid reaches past its own data on every side + for im, line in zip(images, fig.axes[0].lines): + xmin, xmax, ymin, ymax = im.get_extent() + drawn = np.column_stack(line.get_data()) + assert xmin < drawn[:, 0].min() and xmax > drawn[:, 0].max() + assert ymin < drawn[:, 1].min() and ymax > drawn[:, 1].max() + mpl.pyplot.close(fig) + + def test_matplotlib_grid_reaches_four_bandwidths_past_the_data(self): + from hypertools.plot.density import KDE_GRID_BANDWIDTHS, fit_kde + cloud = self._flat_wide_clouds()[1] + fig = hyp.plot([cloud], '.', density=True, axis_scale='data', + reduce=None, ndims=2, show=False) + im = fig.axes[0].get_images()[0] + xmin, xmax, ymin, ymax = im.get_extent() + drawn = np.column_stack(fig.axes[0].lines[0].get_data()) + kde = fit_kde(drawn) + sx, sy = np.sqrt(np.diag(kde.covariance)) + assert xmin <= drawn[:, 0].min() - KDE_GRID_BANDWIDTHS * sx + 1e-9 + assert xmax >= drawn[:, 0].max() + KDE_GRID_BANDWIDTHS * sx - 1e-9 + assert ymin <= drawn[:, 1].min() - KDE_GRID_BANDWIDTHS * sy + 1e-9 + assert ymax >= drawn[:, 1].max() + KDE_GRID_BANDWIDTHS * sy - 1e-9 + mpl.pyplot.close(fig) + + def test_a_small_cloud_beside_a_huge_one_keeps_its_density(self): + rng = np.random.default_rng(0) + small = rng.normal(size=(200, 2)) + huge = rng.normal(size=(200, 2)) * 10000 + fig = hyp.plot([small, huge], '.', density=True, axis_scale='data', + reduce=None, ndims=2, show=False) + images = fig.axes[0].get_images() + assert len(images) == 2 + Z = np.asarray(images[0].get_array()) + assert Z.max() > 0 and np.count_nonzero(Z) > 0.5 * Z.size + xmin, xmax, ymin, ymax = images[0].get_extent() + iy, ix = np.unravel_index(np.argmax(Z), Z.shape) + peak = (np.linspace(xmin, xmax, Z.shape[1])[ix], + np.linspace(ymin, ymax, Z.shape[0])[iy]) + assert np.allclose(peak, small.mean(axis=0), atol=0.5) + # its grid is its own neighbourhood, not the huge cloud's + assert xmax - xmin < 50 + mpl.pyplot.close(fig) + + def test_plotly_contour_grid_edges_carry_no_visible_density(self): + fig = hyp.plot(self._flat_wide_clouds(), '.', density=True, + backend='plotly', show=False) + contours = [t for t in fig.data if t.type == 'contour'] + assert len(contours) == 3 + for c in contours: + Z = np.asarray(c.z) + assert self._edge_over_peak(Z) < 1e-3 diff --git a/tests/test_dependency_floor_wheels.py b/tests/test_dependency_floor_wheels.py new file mode 100644 index 00000000..f6a214d4 --- /dev/null +++ b/tests/test_dependency_floor_wheels.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- +"""A declared dependency floor must be installable on every Python the +package claims (review 2026-09-11). + +``density3d = ["scikit-image>=0.23.2"]`` (and the same pin in ``[dev]`` and +``docs/doc_requirements.txt``) named a release with no CPython 3.13 wheel, +while the classifiers list 3.13: a lowest-version resolve on 3.13 (``uv pip +install --resolution lowest``) had to build scikit-image from source. The +first release with cp313 wheels is 0.25.0 (measured on PyPI's JSON API, +2026-09-11: 0.23.x and 0.24.0 ship cp310-cp312 only). + +This is a REAL query of PyPI's JSON API for the exact floor version, wrapped +in ``skip_on_transient_network`` so an outage skips while a wrong floor +fails. +""" +import re +from pathlib import Path + +import pytest +import requests + +from tests._netskip import skip_on_transient_network + +REPO = Path(__file__).resolve().parents[1] +PYPROJECT = (REPO / 'pyproject.toml').read_text(encoding='utf-8') +DOC_REQUIREMENTS = (REPO / 'docs' / 'doc_requirements.txt').read_text( + encoding='utf-8') + + +def _classifier_pythons(): + found = re.findall(r'Programming Language :: Python :: (3\.\d+)"', + PYPROJECT) + assert found, 'no Python version classifiers found in pyproject.toml' + return found + + +def _declared_floors(package): + """Every ``<package>>=X`` floor pyproject and the docs requirements + declare (quoted in pyproject, bare in the requirements file).""" + name = re.escape(package) + floors = re.findall(rf'"{name}>=([0-9][0-9.]*)', PYPROJECT) + floors += re.findall(rf'^{name}>=([0-9][0-9.]*)', DOC_REQUIREMENTS, re.M) + assert floors, f'{package} is not declared with a >= floor' + return sorted(set(floors)) + + +def _wheel_filenames(package, version): + resp = requests.get(f'https://pypi.org/pypi/{package}/{version}/json', + timeout=30) + resp.raise_for_status() + return [f['filename'] for f in resp.json()['urls'] + if f['packagetype'] == 'bdist_wheel'] + + +def test_scikit_image_floor_has_a_wheel_for_every_claimed_python(): + pythons = _classifier_pythons() + assert '3.13' in pythons + floors = _declared_floors('scikit-image') + # pyproject's [density3d] and [dev] and the docs requirements agree + assert len(floors) == 1, floors + with skip_on_transient_network('querying PyPI for scikit-image wheels'): + wheels = _wheel_filenames('scikit-image', floors[0]) + for py in pythons: + tag = f'-cp{py.replace(".", "")}-' + assert any(tag in w for w in wheels), ( + f'scikit-image {floors[0]} (the declared floor) has no {py} ' + f'wheel; wheels: {sorted(wheels)}') + + +# Core runtime floors (review 2026-10): each was raised to the first release +# with cp313 wheels (numpy 2.0.x, pandas 2.2.2, scipy 1.13/1.14.0, matplotlib +# 3.9.0/3.9.1, scikit-learn <=1.5.1, statsmodels 0.14.2 and pillow 10.3.0 +# ship cp310-cp312 only; measured on PyPI's JSON API, 2026-10-08). +CORE_COMPILED = ['numpy', 'pandas', 'scipy', 'matplotlib', 'scikit-learn', + 'statsmodels', 'pillow'] + + +@pytest.mark.parametrize('package', CORE_COMPILED) +def test_core_floor_has_a_wheel_for_every_claimed_python(package): + pythons = _classifier_pythons() + assert '3.13' in pythons + floors = _declared_floors(package) + # pyproject and the docs requirements (where listed) agree + assert len(floors) == 1, floors + with skip_on_transient_network(f'querying PyPI for {package} wheels'): + wheels = _wheel_filenames(package, floors[0]) + for py in pythons: + tag = f'-cp{py.replace(".", "")}-' + assert any(tag in w for w in wheels), ( + f'{package} {floors[0]} (the declared floor) has no {py} ' + f'wheel; wheels: {sorted(wheels)}') + + +def test_readme_requirements_match_pyproject_core_floors(): + readme = (REPO / 'readme.md').read_text(encoding='utf-8') + for package in CORE_COMPILED: + (floor,) = _declared_floors(package) + assert f'{package}>={floor}' in readme, ( + f'readme.md Requirements does not list {package}>={floor}') diff --git a/tests/test_describe.py b/tests/test_describe.py index 729a7df6..f473799f 100644 --- a/tests/test_describe.py +++ b/tests/test_describe.py @@ -54,7 +54,7 @@ def test_describe_plotly_backend_runs_and_returns_dict(monkeypatch): assert type(result) is dict # 'fig' added by the 2026-07 release audit (F11-reduce-describe-015): # describe() now hands back its figure so it can be saved/embedded - assert set(result.keys()) == {'average', 'individual', 'fig'} + assert set(result.keys()) == {'average', 'individual', 'pooled', 'fig'} assert isinstance(result['fig'], go.Figure) @@ -109,3 +109,165 @@ def test_get_corr_perfect_and_known_correlations(): reduced = hyp.reduce(embedded, reduce='PCA', ndims=2) r = get_corr(get_cdist(np.asarray(reduced)), get_cdist(embedded)) assert r > 0.95 + + +# --------------------------------------------------------------------------- +# 'average' is the element-wise mean of the per-dataset curves; the old +# pooled (stacked) curve moved to 'pooled' (1.1 release review). The pooled +# curve mixes in between-dataset distances, so the dashed line labelled +# 'average' was not the mean of the curves drawn beside it. +# --------------------------------------------------------------------------- + +def _walks(n=3, rows=40, cols=8, seed=0): + rng = np.random.default_rng(seed) + return [np.cumsum(rng.standard_normal((rows, cols)), axis=0) + 5 * i + for i in range(n)] + + +def test_describe_average_is_mean_of_individual_curves(): + walks = _walks() + result = describe(walks, reduce='PCA', max_dims=6, show=False) + plt.close('all') + individual = np.asarray(result['individual'], dtype=float) + assert individual.shape == (3, 4) + np.testing.assert_allclose(result['average'], individual.mean(axis=0)) + # the average lies within the individual curves at every component count + avg = np.asarray(result['average']) + assert (avg >= individual.min(axis=0) - 1e-12).all() + assert (avg <= individual.max(axis=0) + 1e-12).all() + + +def test_describe_pooled_is_the_stacked_curve(): + walks = _walks() + result = describe(walks, reduce='PCA', max_dims=6, show=False) + stacked = describe(np.vstack(walks), reduce='PCA', max_dims=6, + show=False) + plt.close('all') + np.testing.assert_allclose(result['pooled'], stacked['individual'][0]) + # the pooled curve is a different quantity from the average here + assert not np.allclose(result['pooled'], result['average']) + + +def test_describe_single_dataset_average_and_pooled_equal_individual(): + x = _walks(n=1)[0] + result = describe(x, reduce='PCA', max_dims=6, show=False) + plt.close('all') + assert len(result['individual']) == 1 + np.testing.assert_allclose(result['average'], result['individual'][0]) + np.testing.assert_allclose(result['pooled'], result['individual'][0]) + + +def test_describe_average_with_unequal_curve_lengths(): + # with max_dims=None each dataset's sweep is capped at its own + # dimensionality, so curves can differ in length; the average at each + # component count is the mean over the datasets that reach it + rng = np.random.default_rng(1) + short = rng.standard_normal((4, 6)) # min(shape) = 4 -> dims 2..3 + long = rng.standard_normal((30, 6)) # min(shape) = 6 -> dims 2..5 + result = describe([short, long], reduce='PCA', show=False) + plt.close('all') + a, b = result['individual'] + assert len(a) < len(b) + expected = [np.mean([a[i], b[i]]) if i < len(a) else b[i] + for i in range(len(b))] + np.testing.assert_allclose(result['average'], expected) + + +def test_describe_matplotlib_average_line_draws_the_average(): + walks = _walks() + result = describe(walks, reduce='PCA', max_dims=6, show=False, + backend='matplotlib') + ax = result['fig'].axes[0] + avg_lines = [ln for ln in ax.get_lines() if ln.get_label() == 'average'] + assert len(avg_lines) == 1 + np.testing.assert_allclose(avg_lines[0].get_ydata(), result['average']) + np.testing.assert_array_equal(avg_lines[0].get_xdata(), [2, 3, 4, 5]) + + +def test_describe_plotly_average_trace_draws_the_average(): + pytest.importorskip('plotly') + walks = _walks() + result = describe(walks, reduce='PCA', max_dims=6, show=False, + backend='plotly') + avg = [tr for tr in result['fig'].data if tr.name == 'average'] + assert len(avg) == 1 + np.testing.assert_allclose(avg[0].y, result['average']) + assert list(avg[0].x) == [2, 3, 4, 5] + + +def test_describe_matplotlib_x_ticks_are_whole_component_counts(): + walks = _walks() + for max_dims in (4, 5, 6, 8): + result = describe(walks, reduce='PCA', max_dims=max_dims, + show=False, backend='matplotlib') + fig = result['fig'] + fig.canvas.draw() + ax = fig.axes[0] + lo, hi = ax.get_xlim() + ticks = [t for t in ax.get_xticks() if lo - 1e-9 <= t <= hi + 1e-9] + assert ticks, max_dims + assert all(float(t).is_integer() for t in ticks), (max_dims, ticks) + labels = [lbl.get_text() for lbl in ax.get_xticklabels() + if lbl.get_text()] + assert all('.' not in lbl for lbl in labels), (max_dims, labels) + + +def test_describe_plotly_x_ticks_are_whole_component_counts(): + pytest.importorskip('plotly') + walks = _walks() + for max_dims in (4, 5, 6, 8): + result = describe(walks, reduce='PCA', max_dims=max_dims, + show=False, backend='plotly') + xaxis = result['fig'].layout.xaxis + assert xaxis.tickmode == 'array' + vals = list(xaxis.tickvals) + assert vals and all(float(v).is_integer() for v in vals), vals + assert min(vals) >= 2 and max(vals) <= max_dims - 1 + + +# --------------------------------------------------------------------------- +# describe(show=False) returns the drawn figure without displaying it, like +# hyp.plot(show=False) (1.1 release review; it used to return fig=None) +# --------------------------------------------------------------------------- + +def test_describe_show_false_returns_matplotlib_figure_without_registering(): + import io + plt.close('all') + before = set(plt.get_fignums()) + result = describe(_walks(), reduce='PCA', max_dims=6, show=False, + backend='matplotlib') + fig = result['fig'] + assert isinstance(fig, plt.Figure) + # closed like plot(show=False): not left registered with pyplot + assert set(plt.get_fignums()) == before + # ... but still renderable and savable + buf = io.BytesIO() + fig.savefig(buf, format='png') + assert buf.getvalue()[:8] == b'\x89PNG\r\n\x1a\n' + assert len(fig.axes[0].get_lines()) >= 4 # 3 datasets + average + + +def test_describe_show_false_returns_plotly_figure_without_showing( + monkeypatch): + pytest.importorskip('plotly') + import plotly.graph_objects as go + shown = [] + monkeypatch.setattr(go.Figure, 'show', + lambda self, *a, **k: shown.append(self)) + result = describe(_walks(), reduce='PCA', max_dims=6, show=False, + backend='plotly') + assert isinstance(result['fig'], go.Figure) + assert shown == [] + assert len(result['fig'].data) == 4 # 3 datasets + average + + +def test_describe_bare_array_without_format_data_is_one_dataset(): + # format_data=False with a bare array used to iterate its ROWS as + # datasets and crash with "IndexError: tuple index out of range" + x = _walks(n=1)[0] + result = describe(x, reduce='PCA', max_dims=5, format_data=False, + show=False) + plt.close('all') + assert len(result['individual']) == 1 + assert len(result['individual'][0]) == 3 + np.testing.assert_allclose(result['average'], result['individual'][0]) diff --git a/tests/test_describe_reducer_sweep.py b/tests/test_describe_reducer_sweep.py new file mode 100644 index 00000000..36bc1918 --- /dev/null +++ b/tests/test_describe_reducer_sweep.py @@ -0,0 +1,202 @@ +# -*- coding: utf-8 -*- +"""`hyp.describe` sweeps the DIMENSIONALITY of whatever reducer it is given. + +1.1 release red-team: a configured reducer instance (``PCA(n_components=2)``), +a fitted reducer from ``hyp.reduce(..., return_model=True)``, and a dict spec +whose kwargs pin ``n_components`` were all reduced to that ONE dimensionality +at every sweep point, while the curve labelled the points 2..max_dims-1 -- a +flat, false sweep. + +Contract under test: + +- an UNFITTED instance is cloned for each sweep point with ``n_components`` + set to that point (other settings kept; the caller's instance untouched); +- a dict spec's pinned ``n_components`` is overridden by the sweep; +- a FITTED model, and an instance with no ``n_components`` parameter, raise + a ``ValueError`` that says what to pass instead; +- no "Unequal values passed to dims and n_components" warnings. + +Real scikit-learn models and real `hyp.describe` calls throughout. +""" +import warnings + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pytest # noqa: E402 +from sklearn.decomposition import PCA, FastICA # noqa: E402 +from sklearn.preprocessing import StandardScaler # noqa: E402 + +import hypertools as hyp # noqa: E402 + + +def _x(): + return np.random.default_rng(8).normal(size=(60, 6)) + + +def _curve(reduce, **kwargs): + kwargs.setdefault('max_dims', 6) + return hyp.describe(_x(), reduce=reduce, show=False, **kwargs)['average'] + + +def _strict(reduce, **kwargs): + """The curve, with every warning turned into an error.""" + with warnings.catch_warnings(): + warnings.simplefilter('error') + return _curve(reduce, **kwargs) + + +def test_reference_curve_by_name(): + curve = _curve('PCA') + assert np.allclose(curve, [0.739, 0.852, 0.930, 0.974], atol=5e-4) + assert np.all(np.diff(curve) > 0) + + +def test_unfitted_instance_sweeps_like_the_name(): + assert np.allclose(_strict(PCA(n_components=2)), _curve('PCA')) + + +def test_unfitted_instance_without_a_dimensionality_sweeps_like_the_name(): + assert np.allclose(_strict(PCA()), _curve('PCA')) + + +def test_caller_instance_is_not_mutated_or_fitted(): + model = PCA(n_components=2, whiten=True) + _curve(model) + assert model.n_components == 2 + assert model.whiten is True + assert not hasattr(model, 'components_') + + +def test_instance_keeps_its_other_settings(): + """The sweep changes ONLY the dimensionality: a whitened PCA gives a + different curve from the default one, and the same curve as the dict + spec that asks for whitening.""" + whitened = _strict(PCA(n_components=2, whiten=True)) + spec = _curve({'model': 'PCA', 'kwargs': {'whiten': True}}) + assert np.allclose(whitened, spec) + assert not np.allclose(whitened, _curve('PCA')) + + +def test_bare_class_sweeps_like_the_name(): + assert np.allclose(_strict(PCA), _curve('PCA')) + + +@pytest.mark.parametrize('spec', [ + {'model': 'PCA', 'kwargs': {'n_components': 2}}, + {'model': PCA, 'kwargs': {'n_components': 2}}, + {'model': PCA(n_components=2)}, +], ids=['name', 'class', 'instance']) +def test_dict_spec_pinned_dimensionality_is_overridden_by_the_sweep(spec): + assert np.allclose(_strict(spec), _curve('PCA')) + + +def test_dict_spec_is_not_mutated(): + spec = {'model': 'PCA', 'kwargs': {'n_components': 2, 'whiten': True}} + _curve(spec) + assert spec == {'model': 'PCA', + 'kwargs': {'n_components': 2, 'whiten': True}} + + +def test_legacy_params_spec_pinned_dimensionality_is_overridden(): + with warnings.catch_warnings(): + warnings.simplefilter('error') + # the legacy form's own DeprecationWarning is unrelated to the sweep + warnings.simplefilter('ignore', DeprecationWarning) + curve = _curve({'model': 'PCA', 'params': {'n_components': 2}}) + assert np.allclose(curve, _curve('PCA')) + + +def test_seeded_instance_sweeps_reproducibly(): + a = _curve(FastICA(n_components=2, random_state=0, max_iter=1000)) + b = _curve(FastICA(n_components=2, random_state=0, max_iter=1000)) + assert a == b + assert len(set(np.round(a, 6))) == len(a) + + +def test_fitted_reducer_from_return_model_is_rejected(): + x = _x() + _, fitted = hyp.reduce(x, reduce='PCA', ndims=2, return_model=True) + with pytest.raises(ValueError, match='already fitted') as err: + hyp.describe(x, reduce=fitted, max_dims=6, show=False) + message = str(err.value) + assert "'kwargs'" in message and 'unfitted' in message + assert 'Reducer' in message + + +def test_fitted_sklearn_instance_is_rejected(): + x = _x() + model = PCA(n_components=2).fit(x) + with pytest.raises(ValueError, match='already fitted'): + hyp.describe(x, reduce=model, max_dims=6, show=False) + with pytest.raises(ValueError, match='already fitted'): + hyp.describe(x, reduce={'model': model}, max_dims=6, show=False) + + +def test_fitted_pipeline_from_return_model_is_rejected(): + x = _x() + _, pipeline = hyp.reduce(x, reduce='PCA', ndims=2, normalize='across', + return_model=True) + assert isinstance(pipeline, hyp.Pipeline) + with pytest.raises(ValueError, match='already fitted'): + hyp.describe(x, reduce=pipeline, max_dims=6, show=False) + + +@pytest.mark.parametrize('make', [ + StandardScaler, + lambda: hyp.Pipeline([('pca', PCA(n_components=2))]), +], ids=['StandardScaler', 'unfitted-Pipeline']) +def test_instance_whose_dimensionality_cannot_be_set_is_rejected(make): + with pytest.raises(ValueError, match='n_components') as err: + hyp.describe(_x(), reduce=make(), max_dims=6, show=False) + message = str(err.value) + assert 'cannot' in message and "'kwargs'" in message + + +def test_rejection_happens_before_any_figure_is_drawn(): + before = plt.get_fignums() + with pytest.raises(ValueError, match='already fitted'): + hyp.describe(_x(), reduce=PCA(n_components=2).fit(_x()), max_dims=6) + assert plt.get_fignums() == before + + +def test_multiple_datasets_sweep_with_an_instance(): + rng = np.random.default_rng(3) + data = [rng.normal(size=(40, 6)), rng.normal(size=(40, 6))] + by_instance = hyp.describe(data, reduce=PCA(n_components=2), max_dims=6, + show=False) + by_name = hyp.describe(data, reduce='PCA', max_dims=6, show=False) + for key in ('average', 'pooled'): + assert np.allclose(by_instance[key], by_name[key]) + assert len(set(np.round(by_instance[key], 6))) == 4 + assert np.allclose(by_instance['individual'], by_name['individual']) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_instance_curve_is_what_both_backends_draw(backend): + result = hyp.describe(_x(), reduce=PCA(n_components=2), max_dims=6, + show=False, backend=backend) + expected = _curve('PCA') + if backend == 'plotly': + import plotly.graph_objects as go + fig = result['fig'] + assert isinstance(fig, go.Figure) + xs, ys = list(fig.data[0].x), list(fig.data[0].y) + else: + assert isinstance(result['fig'], plt.Figure) + line = result['fig'].axes[0].lines[0] + xs, ys = list(line.get_xdata()), list(line.get_ydata()) + assert xs == [2, 3, 4, 5] + assert np.allclose(ys, expected) + + +def test_reduce_none_is_refused(): + """With no reducer there is nothing to sweep: every "dimensionality" was + the unreduced data, so the curve was a flat 1.0 at every point.""" + import numpy as np + import pytest + import hypertools as hyp + x = np.random.default_rng(8).normal(size=(60, 6)) + with pytest.raises(ValueError, match='reduce=None'): + hyp.describe(x, reduce=None, max_dims=6, show=False) diff --git a/tests/test_docs_hierarchy_guide.py b/tests/test_docs_hierarchy_guide.py index 0ba7ccbf..bd37bf79 100644 --- a/tests/test_docs_hierarchy_guide.py +++ b/tests/test_docs_hierarchy_guide.py @@ -1,19 +1,22 @@ -"""The hierarchy guide exists, is reachable, and is TRUE. - -Two kinds of assertion live here. - -Structural ones pin the sections the maintainer review named (F22) and the -links that make the page reachable -- a guide that is written but never -linked is not documentation, and an .rst in no toctree is also a Sphinx -warning against a zero-warning build standard. - -The one that matters most is `test_every_doctest_in_the_guide_runs`. Every -example on the page is a real, executed `hyp.plot`/`hyp.predict` call whose -printed output -- trace counts, hierarchy keys, forecast shapes, and the -verbatim text of six error/warning messages -- is compared against what the -library actually produces. A guide is the first place a user meets this -feature, so a stale example is worse than no example: this test is what -makes the page fail loudly instead of aging quietly. +"""The hierarchy guide exists, is reachable, and describes what it links. + +Every assertion here is structural or prose-level: the guide page exists +and sits in the index toctree (an .rst in no toctree is both unreachable +and a Sphinx warning against a zero-warning build standard); it carries +every section the maintainer review named (F22); its "How each axis is +read" comparison table shows the row-plot vs row-forecast divergence and +the `plot(..., predict=)` shape rule on all three rows; `api.rst` links +it from BOTH the Plot and Predict sections and `tutorials.rst` links it +too; and the market-sectors section of `tutorials.rst` is checked +against what `market_sectors.ipynb` actually does (hierarchy framing +only if the notebook builds a MultiIndex, otherwise the hyperalignment +and market-mean framing). + +The guide's worked examples are `.. doctest::` blocks; this file does +NOT execute them. They run under Sphinx's doctest builder +(`cd docs && make doctest`, see the `sphinx.ext.doctest` note in +docs/conf.py), which is where a stale printed output or quoted message +fails. """ import json import os diff --git a/tests/test_docs_site_fixes.py b/tests/test_docs_site_fixes.py new file mode 100644 index 00000000..9aad67fc --- /dev/null +++ b/tests/test_docs_site_fixes.py @@ -0,0 +1,305 @@ +"""Guards for defects found by browsing the BUILT documentation site (2026-10). + +Each test exercises the real helper on real inputs: the gallery scraper renders +real ``hyp.plot(..., animate=True)`` animations through sphinx-gallery's own +writer, and the HTML post-processors run on real files written to ``tmp_path``. +""" +import importlib.util +import os +import re + +import pytest + +DOCS_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'docs')) + + +def _load(name): + path = os.path.join(DOCS_DIR, name + '.py') + if not os.path.exists(path): + pytest.skip(f'docs/{name}.py not present') + spec = importlib.util.spec_from_file_location('_hyp_docs_' + name, path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + + +def _custom_css_rules(): + """(selector, body) pairs of docs/_static/custom.css, comments removed.""" + path = os.path.join(DOCS_DIR, '_static', 'custom.css') + if not os.path.exists(path): + pytest.skip('docs/_static/custom.css not present') + with open(path, encoding='utf-8') as f: + css = re.sub(r'/\*.*?\*/', '', f.read(), flags=re.S) + return [(sel.strip(), body) for sel, body in + re.findall(r'([^{}]+)\{([^{}]*)\}', css)] + + +# -- 1. gallery cards: the example page must be reachable from its card ----- + +def test_gallery_card_link_to_the_example_page_is_not_hidden(): + """sphinx-gallery's card markup carries exactly one link to the example + page: ``<p><a class="reference internal" href="X.html">``. custom.css + used to ``display: none`` that paragraph, which left the card with only + the thumbnail's Colab link and no way to reach the example page.""" + for selector, body in _custom_css_rules(): + for sel in selector.split(','): + if re.fullmatch(r'\.sphx-glr-thumbcontainer\s+p(\s+a)?', sel.strip()): + assert not re.search(r'display\s*:\s*none', body), ( + f'{sel.strip()!r} hides the only link to the example page') + + +def test_gallery_card_colours_follow_the_theme(): + """A hard-coded colour on the card title / tooltip is illegible in one of + furo's two themes (``#333`` on the dark background); they must use the + theme's CSS variables.""" + offenders = [] + for selector, body in _custom_css_rules(): + if 'sphx-glr-thumb' not in selector: + continue + for prop, value in re.findall(r'([\w-]+)\s*:\s*([^;]+);', body): + if prop in ('color', 'background', 'background-color', + 'border', 'border-color'): + if re.search(r'#[0-9a-fA-F]{3,8}\b|rgba?\(|\b(white|black)\b', + value) and 'var(' not in value: + offenders.append((selector, prop, value.strip())) + assert not offenders, offenders + + +def test_api_page_titles_keep_their_case(): + """The sitewide ``text-transform: lowercase`` heading style must not + apply to the autosummary pages' titles: ``hypertools.io.LSLStream`` + rendered as ``hypertools.io.lslstream``, a name that does not exist.""" + rules = _custom_css_rules() + assert any('text-transform' in body and 'lowercase' in body + for _, body in rules), 'the lowercase heading style is gone' + exempt = [sel for sel, body in rules + if re.search(r'text-transform\s*:\s*none', body)] + assert any('dl.py' in sel and 'h1' in sel for sel in exempt), exempt + + +# -- 2. each animation is rendered exactly once ------------------------------ + +def _scrape_blocks(tmp_path, make_blocks): + """Run ``make_blocks`` (a generator yielding after each example "block" + has bound its names into the shared namespace it is handed) through the + gallery's real scraper, the way sphinx-gallery calls it once per block. + Returns the number of ``.. video::`` directives emitted per block.""" + pytest.importorskip('sphinx_gallery') + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + from matplotlib.animation import FFMpegWriter + if not FFMpegWriter.isAvailable(): + pytest.skip('ffmpeg not available') + from sphinx_gallery.gen_gallery import DEFAULT_GALLERY_CONF + from sphinx_gallery.scrapers import ImagePathIterator + + scrapers = _load('_gallery_scrapers') + gallery_conf = dict(DEFAULT_GALLERY_CONF) + gallery_conf.update(src_dir=str(tmp_path), builder_name='html', + matplotlib_animations=(True, 'mp4')) + (tmp_path / 'images').mkdir() + block_vars = { + 'example_globals': {}, + 'src_file': str(tmp_path / 'example.py'), + 'image_path_iterator': ImagePathIterator( + str(tmp_path / 'images' / 'sphx_glr_example_{0:03}.png')), + } + counts = [] + plt.close('all') + try: + for _ in make_blocks(block_vars['example_globals']): + rst = scrapers.matplotlib_and_hyperanimation_scraper( + ('code', '', 1), block_vars, gallery_conf) + counts.append(rst.count('.. video::')) + # sphinx-gallery closes every pyplot figure after each block + assert plt.get_fignums() == [] + finally: + plt.close('all') + return counts + + +def _data(): + import numpy as np + return np.cumsum(np.random.default_rng(0).standard_normal((12, 4)), axis=0) + + +def test_managed_animations_are_scraped_once_per_block(tmp_path): + """examples/animate.py: two blocks, each ``fig, ani = hyp.plot(..., + animate=True)`` on a pyplot-managed figure. The built page showed FIVE + videos (2 + 3): the matplotlib scraper rendered the block's animation and + closed its figure, so the HyperAnimation scraper then judged the same + animation "unmanaged" and rendered it again -- and re-rendered every + earlier block's animation still alive in the example's namespace.""" + import hypertools as hyp + + def blocks(ns): + ns['fig'], ns['ani'] = hyp.plot(_data(), animate=True, + backend='matplotlib', duration=1) + yield + ns['fig'], ns['ani_mds'] = hyp.plot(_data(), animate=True, + backend='matplotlib', duration=1) + yield + yield # a later block that plots nothing + + assert _scrape_blocks(tmp_path, blocks) == [1, 1, 0] + + +def test_unmanaged_show_false_animation_is_still_scraped_once(tmp_path): + """The reason the HyperAnimation scraper exists: ``show=False`` leaves the + figure unmanaged by pyplot, so the matplotlib scraper never sees it. It + must still be rendered -- once, in the block that created it.""" + import hypertools as hyp + + def blocks(ns): + ns['ani'] = hyp.plot(_data(), animate=True, backend='matplotlib', + duration=1, show=False) + yield + yield + + assert _scrape_blocks(tmp_path, blocks) == [1, 0] + + +# -- 5. tutorial pages: no dead plotly import, no second MathJax ------------- + +NOTEBOOK_PLOTLY_OUTPUT = '''<html><head> +<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@4/tex-mml-chtml.js"></script> +</head><body> +<div class="output_area rendered_html docutils container"> + <script type="text/javascript"> + window.PlotlyConfig = {MathJaxConfig: 'local'}; + if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: "STIX-Web"}});} + </script> + <script type="module">import "https://cdn.plot.ly/plotly-3.6.0.min"</script> + </div> +<div class="output_area rendered_html docutils container"> +<div style="height:480px; width:640px;"> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: "STIX-Web"}});}</script> <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script> + <script charset="utf-8" src="https://cdn.plot.ly/plotly-3.6.0.min.js" integrity="sha256-abc" crossorigin="anonymous"></script> <div id="f1" class="plotly-graph-div" style="height:100%; width:100%;"></div> <script>window.PLOTLYENV=window.PLOTLYENV || {};</script></div> +</div></body></html> +''' + + +def test_notebook_plotly_scripts_are_cleaned(tmp_path): + """tutorials/plot.html logged a 403 and a MathJax TypeError: plotly's + notebook renderer stores (a) a bare ES-module import of + ``cdn.plot.ly/plotly-X.min`` (no ``.js``; the CDN answers 403) and (b) a + MathJax 2.7.5 <script> in every figure, which collides with the MathJax 4 + the page itself loads. The figure's own ``plotly-X.min.js`` <script> must + survive -- it is what actually draws the figure.""" + mod = _load('post_build') + page = tmp_path / 'tutorials' / 'plot.html' + page.parent.mkdir() + page.write_text(NOTEBOOK_PLOTLY_OUTPUT, encoding='utf-8') + untouched = tmp_path / 'tutorials' / 'other.html' + untouched.write_text('<html><body><p>no plotly</p></body></html>', + encoding='utf-8') + + assert mod.clean_notebook_plotly_scripts(str(tmp_path)) == 1 + html = page.read_text(encoding='utf-8') + assert 'plotly-3.6.0.min"' not in html + assert 'mathjax/2.7.5' not in html + assert html.count('src="https://cdn.plot.ly/plotly-3.6.0.min.js"') == 1 + assert 'class="plotly-graph-div"' in html + assert 'mathjax@4/tex-mml-chtml.js' in html # the page's own MathJax + assert untouched.read_text(encoding='utf-8') == ( + '<html><body><p>no plotly</p></body></html>') + # idempotent + assert mod.clean_notebook_plotly_scripts(str(tmp_path)) == 0 + + +# -- 6. gallery tooltips: no literal RST markup ------------------------------ + +@pytest.mark.parametrize('raw, clean', [ + ('Anything (``fig.savefig(...)``, grabbing fig.axes[0]) works.', + 'Anything (fig.savefig(...), grabbing fig.axes[0]) works.'), + ('Every stage dispatcher (`hyp.manip`, hyp.normalize) accepts it.', + 'Every stage dispatcher (hyp.manip, hyp.normalize) accepts it.'), + ('Its vectorizer=/``semantic=`` specs; .mov/``.avi`` too.', + 'Its vectorizer=/semantic= specs; .mov/.avi too.'), + ('See :func:`hypertools.plot` and :class:`~hypertools.Pipeline`.', + 'See hypertools.plot and Pipeline.'), + ('(``hyp.load('digits')``) holds 8x8 images', + '(hyp.load('digits')) holds 8x8 images'), + ('No markup at all: 5 < 6 & a - b.', + 'No markup at all: 5 < 6 & a - b.'), +]) +def test_tooltip_markup_is_stripped(raw, clean): + assert _load('post_build').strip_rst_markup(raw) == clean + + +def test_gallery_tooltips_are_cleaned_in_the_built_index(tmp_path): + mod = _load('post_build') + index = tmp_path / 'index.html' + card = ('<div class="sphx-glr-thumbcontainer" tooltip="{tip}">' + '<img alt="" src="../_images/sphx_glr_x_thumb.png" />\n' + '<p><a class="reference internal" href="x.html">' + '<span class="doc">X</span></a></p>\n' + ' <div class="sphx-glr-thumbnail-title">X</div>\n</div>') + body = '<p>Prose keeps its <code>``literal``</code> text.</p>' + index.write_text( + card.format(tip='Pass (``animate=True``) to `hyp.plot`.') + + card.format(tip='Nothing to clean here.') + body, encoding='utf-8') + + assert mod.clean_gallery_tooltips(str(index)) == 1 + html = index.read_text(encoding='utf-8') + assert 'tooltip="Pass (animate=True) to hyp.plot."' in html + assert 'tooltip="Nothing to clean here."' in html + assert body in html # only tooltip attributes are touched + assert mod.clean_gallery_tooltips(str(index)) == 0 + + +def test_no_page_adds_its_own_contents_directive(): + """furo draws every page's table of contents in the right-hand column and + renders a red ERROR box over a page that adds a ``.. contents::`` of its + own (docs/hierarchy.rst did, 2026-10-09).""" + import pathlib + docs = pathlib.Path(__file__).resolve().parents[1] / 'docs' + offenders = [str(p.relative_to(docs)) for p in docs.rglob('*.rst') + if '_build' not in p.parts and 'auto_examples' not in p.parts + and any(line.lstrip().startswith('.. contents::') + for line in p.read_text(encoding='utf-8').splitlines())] + assert not offenders, offenders + + +_TUTORIAL_PAGE = '''<html><body><article role="main" id="furo-main-content"> + <div class="nbinput nblast docutils container"> +<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: +</pre></div> +</div> +<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># HyperTools setup: use 1.1 or newer; retain a current local checkout.</span> +<span class="o">%</span><span class="k">pip</span> install -q "hypertools[interactive]>=1.1.0" +</pre></div> +</div> +</div> +<section id="Alignment"> +<h1>Alignment<a class="headerlink" href="#Alignment">¶</a></h1> +<p>The align function.</p> +</section></article></body></html>''' + + +def test_tutorial_setup_cell_is_collapsed_below_the_title(tmp_path): + """Every tutorial notebook starts with its install cell, which nbsphinx + renders ABOVE the page title: 15 lines of setup code before the heading. + post_build moves it under the title, collapsed (the notebook is untouched).""" + import importlib.util + import pathlib + spec = importlib.util.spec_from_file_location( + '_hyp_post_build_setup', + pathlib.Path(__file__).resolve().parents[1] / 'docs' / 'post_build.py') + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + (tmp_path / 'tutorials').mkdir() + page = tmp_path / 'tutorials' / 'align.html' + page.write_text(_TUTORIAL_PAGE, encoding='utf-8') + plain = tmp_path / 'tutorials' / 'noinstall.html' + plain.write_text(_TUTORIAL_PAGE.replace('HyperTools setup', 'First cell') + .replace('pip</span> install', 'x</span> y'), encoding='utf-8') + assert mod.collapse_tutorial_setup_cells(str(tmp_path)) == 1 + html = page.read_text(encoding='utf-8') + title, details, cell = (html.index('<h1>'), html.index('<details'), + html.index('HyperTools setup')) + assert title < details < cell < html.index('</details>') + assert html.index('</details>') < html.index('The align function') + assert html.count('class="nbinput') == 1 # moved, not copied + assert mod.collapse_tutorial_setup_cells(str(tmp_path)) == 0 # idempotent + assert plain.read_text(encoding='utf-8').count('<details') == 0 # not an install cell diff --git a/tests/test_docs_thumbnails.py b/tests/test_docs_thumbnails.py index 19d5989f..005e749f 100644 --- a/tests/test_docs_thumbnails.py +++ b/tests/test_docs_thumbnails.py @@ -80,3 +80,19 @@ def test_publish_ref_matches_conf_release_rule(): # non-release refs -> literal namespace for ref in ('dev-1.0', 'feature-x', 'v1.0', 'v1.0.0rc1', '1.0.0'): assert mod._publish_ref(ref, v) == ref + + +def test_post_build_finds_html_under_readthedocs_output(tmp_path, monkeypatch): + """Read the Docs writes the site to ``$READTHEDOCS_OUTPUT/html``; post_build + used to look for ``_images`` directly under ``$READTHEDOCS_OUTPUT``, so the + variable never matched and only a path guess made the RTD build work.""" + html = tmp_path / 'html' + (html / '_images').mkdir(parents=True) + (html / 'auto_examples').mkdir() + (html / 'auto_examples' / 'index.html').write_text('<html></html>') + monkeypatch.setenv('READTHEDOCS_OUTPUT', str(tmp_path)) + mod = _load_post_build() + images_dir, gallery_html = mod.find_build_dirs() + assert images_dir is not None, 'READTHEDOCS_OUTPUT/html was not found' + assert os.path.samefile(images_dir, html / '_images') + assert os.path.samefile(gallery_html, html / 'auto_examples' / 'index.html') diff --git a/tests/test_examples_are_native.py b/tests/test_examples_are_native.py index 97a113e9..fc9c1507 100644 --- a/tests/test_examples_are_native.py +++ b/tests/test_examples_are_native.py @@ -188,6 +188,22 @@ def test_notebook_budgets_are_derived_not_written_down(): #: and is its to-do, not something to be silenced by re-adding a dead entry. PRIVATE_API_EXCEPTIONS = {} +#: The `ax=` defect (issue #284, G2): a figure built by hand and handed to +#: `hyp.plot(..., ax=...)`, or raw matplotlib drawing next to it. The four +#: alternatives, in order: the kwarg pass itself (`ax=`, the PEP 8 kwarg +#: spelling; `ax = fig.axes[0]` is a READ of plot's own axes and is not +#: matched), raw `ax.plot`/`ax.scatter` (2-D or 3-D), pyplot's axes +#: constructors, and `.add_subplot(`. `hyp.subplots(...)` is the library's +#: own helper and does not match -- it is the documented way to build a +#: panel grid -- but the `ax=` that draws into it still does, so EVERY +#: panel demo is counted and pinned in `DEFECT_ALLOWLIST` below. Known +#: gap, stated rather than hidden: `import matplotlib.pyplot as mpl; +#: mpl.subplots(` evades the third alternative. +AX_MARKER = (r'\bax=(?!=)' + r'|\bax\.(?:plot|scatter|plot3D|scatter3D)\(' + r'|\bplt\.(?:subplots|figure|axes|gca)\(' + r'|\.add_subplot\(') + #: Every one of these was found in the launch examples or the older #: tutorials and removed. Each maps to the native API that replaced it. DEFECT_MARKERS = { @@ -210,15 +226,89 @@ def test_notebook_budgets_are_derived_not_written_down(): r'import sentence_transformers': ("delete the guard: vectorizer='<hf-model-id>' " "installs the text extra on demand"), r'find_spec\(': 'delete the guard: optional extras install themselves on demand', + AX_MARKER: ("hyp.plot() draws its own figure; for a panel grid use " + "`fig, axes = hyp.subplots(...)` and pass ax=axes[i], then " + "record the deliberate demo (with its match COUNT) in " + "DEFECT_ALLOWLIST"), +} + +#: (file stem, marker) -> (expected match count, reason). The per-file +#: allowlist of DELIBERATE demos for the widened scan (issue #284, G2). +#: +#: The stem is the path under the repo root without its extension, so +#: `examples/animate_forecast` and `docs/tutorials/animate_forecast` cannot +#: collide. The COUNT is what stops the allowlist from being a blanket +#: exemption: a NEW `ax=` use in an allowlisted file changes the count and +#: fails, and a demo that was removed leaves the entry stale and fails the +#: other way (`test_every_defect_allowlist_entry_still_matches`). +#: +#: Every entry was derived by running the scanner over the repo on +#: 2026-09-06 and reading each hit: all six are the documented multi-panel +#: form (`hyp.subplots` + `ax=`), or plot.ipynb's deliberate demonstration +#: of the ax=/animate= refusal. No hit was judged a defect. +DEFECT_ALLOWLIST = { + ('examples/plot_datasets_tour', AX_MARKER): ( + 1, 'one hyp.subplots() grid; every dataset is drawn into its own ' + 'panel by the same hyp.plot(..., ax=ax) call'), + ('examples/plot_gensim_text', AX_MARKER): ( + 2, 'two gensim vectorizers side by side on hyp.subplots(1, 2)'), + ('docs/tutorials/align', AX_MARKER): ( + 6, 'three before/after alignment pairs, each on hyp.subplots(1, 2)'), + ('docs/tutorials/plot', AX_MARKER): ( + 4, 'resample= before/after on hyp.subplots(1, 2) (2 hits), and the ' + 'error-reporting section builds `fig, ax = plt.subplots()` and ' + 'passes ax=ax with animate=True to SHOW the ValueError the ' + 'library raises for that combination (2 hits)'), + ('docs/tutorials/projectile_kalman', AX_MARKER): ( + 1, 'one forecast panel per coordinate on hyp.subplots(1, 3, ndims=1)'), + ('docs/tutorials/stock_forecasting', AX_MARKER): ( + 1, 'one forecast panel per ticker on hyp.subplots(2, 2, ndims=1)'), } + +def _review_setup_overhead(path): + """Keep the existing example-code budget and strictly validate new setup. + + The release review requires a version-aware installer and a portable Colab + movie display. Count their EXACT shared templates separately, rather than + enlarging the algorithm/example budget or exempting arbitrary tagged code. + """ + if not path.endswith('.ipynb'): + return 0 + import json + from scripts.add_colab_install_cell import guarded_install_source, portable_video_source + from scripts.measure_native_ratio import strip_docstrings + nb=json.loads(_read(path)) + overhead=0 + installers=[c for c in nb['cells'] if 'hypertools-install' in c.get('metadata',{}).get('tags',[])] + assert len(installers)<=1 + for c in installers: + source=''.join(c['source']) + extras=re.search(r'hypertools\[([^]]+)\]',source).group(1) + assert source==guarded_install_source(extras), 'Noncanonical setup must not bypass the example budget' + # The prior one-line package installation already belonged to the budget. + overhead+=len(list(strip_docstrings(source.splitlines())))-1 + videos=0 + for c in nb['cells']: + source=''.join(c['source']) + marker='# Colab serves output frames separately' + if c['cell_type']=='code' and marker in source: + snippet=source[source.index(marker):] + filename=re.search(r"display\(Video\('([^']+)'",snippet).group(1) + assert snippet==portable_video_source(filename) + overhead+=len(list(strip_docstrings(snippet.splitlines()))) + videos+=1 + assert videos<=1 + return overhead + + def _read(path): full = os.path.join(REPO, path) with open(full, encoding='utf-8') as handle: return handle.read() -def _code_text(path): +def _code_text(path, exclude_install=False): """Code only -- and DOCSTRINGS ARE NOT CODE here. Two reasons, both load-bearing: @@ -250,6 +340,9 @@ def _code_text(path): # to eliminate it. kept = [] for cell in nb['cells']: + if exclude_install and 'hypertools-install' in cell.get('metadata',{}).get('tags',[]): + _review_setup_overhead(path) # exact canonical source required + continue if cell.get('cell_type') != 'code': continue kept.extend(strip_docstrings( @@ -336,8 +429,9 @@ def _parsable_code(path): @pytest.mark.parametrize('path,max_code', BUDGETS) def test_file_is_within_its_size_budget(path, max_code): code, _native = measure(os.path.join(REPO, path)) - assert code <= max_code, ( - f'{path}: {code} code lines exceeds the {max_code}-line budget') + example_code = code - _review_setup_overhead(path) + assert example_code <= max_code, ( + f'{path}: {example_code} example code lines exceeds the {max_code}-line budget') def test_native_ratio_is_reported(capsys): @@ -365,11 +459,297 @@ def test_native_ratio_is_reported(capsys): def test_no_defect_marker_in_the_launch_examples(path, _max, marker, fix): if (path, marker) in PRIVATE_API_EXCEPTIONS: pytest.skip(f'allowlisted: {PRIVATE_API_EXCEPTIONS[(path, marker)]}') - text = _code_text(path) + text = _code_text(path, exclude_install=True) assert not re.search(marker, text), ( f'{path} contains {marker!r} again -- {fix}') +# --------------------------------------------------------------------------- +# The WIDENED scan (issue #284, G1/G3): every example and every tutorial. +# +# The launch test above iterates `BUDGETS`, which is the twelve launch files +# and nothing else -- so `plot_digits.py`, `plot_procrustes.py`, +# `plot_gensim_text.py`, `streaming_data.ipynb` and the rest were never +# scanned, and a `load_digits(` planted in `examples/plot_basic.py` left the +# gate green (the release review's repro). The scanner below takes a ROOT +# rather than reading `REPO` so `test_the_widened_scanner_detects_a_planted_marker` +# can prove that on a scratch tree instead of asserting it. +# --------------------------------------------------------------------------- + +def _scan_inputs(root): + """Every `examples/*.py` and `docs/tutorials/*.ipynb` under `root`, + as paths RELATIVE to `root`, sorted.""" + import glob + found = (glob.glob(os.path.join(root, 'examples', '*.py')) + + glob.glob(os.path.join(root, 'docs', 'tutorials', '*.ipynb'))) + # POSIX separators whatever the host: the allowlist, the roster and the + # findings are compared as strings, and Windows CI produced + # 'docs\\tutorials\\align.ipynb' against 'docs/tutorials/align' + # (2026-09-06, every Windows job red). + return sorted(os.path.relpath(p, root).replace(os.sep, '/') for p in found) + + +def _numbered_code_lines(lines): + """[(1-based line number, line)] for the CODE lines of `lines`. + + `strip_docstrings` yields the kept lines in order but not their + positions; this walks the original alongside it to recover them, so a + finding can name the real line rather than an index into the stripped + text. + """ + from scripts.measure_native_ratio import strip_docstrings + kept = list(strip_docstrings(lines)) + out, k = [], 0 + for n, line in enumerate(lines, 1): + if k < len(kept) and line == kept[k]: + out.append((n, line)) + k += 1 + assert k == len(kept), 'strip_docstrings returned a line not in its input' + return out + + +def _code_units(full_path): + """[(unit label, [(lineno, line), ...])] -- one unit per .py file, one + per NON-INSTALL code cell of a notebook. Docstrings, comments and blanks + are dropped by `strip_docstrings`; markdown cells never enter. + + The install cell is skipped by CONTENT (`_is_install_cell`, the same + `'pip install'` test `scripts/execute_tutorial.py` uses to tag it + skip-execution). That is what keeps the `find_spec\\(` marker honest + (G3): eight tutorials open with `if importlib.util.find_spec('hypertools') + is None: %pip install ...`, which is a Colab bootstrap, not the + "optional extra guarded by hand" the marker exists to catch -- the + marker's fix ('delete the guard') would be wrong advice there. Scope + handles it; the marker is NOT weakened, so a `find_spec(` in any other + cell still fails. + """ + with open(full_path, encoding='utf-8') as handle: + raw = handle.read() + if full_path.endswith('.ipynb'): + import json + nb = json.loads(raw) + units = [] + for i, cell in enumerate(nb['cells']): + if cell.get('cell_type') != 'code': + continue + source = ''.join(cell['source']) + if _is_install_cell(source): + continue + units.append((f'cell {i}', _numbered_code_lines(source.split('\n')))) + return units + return [('', _numbered_code_lines(raw.split('\n')))] + + +def scan_for_defects(root, markers=None, allowlist=None): + """Scan every example and tutorial under `root` for the defect markers. + + Returns a list of human-readable findings (empty means clean). Each hit + of a marker not covered by `allowlist` is one finding, naming the file, + the cell (for a notebook), the line and the fix. For an allowlisted + `(stem, marker)` the total match COUNT across the file is compared with + the recorded count, and a mismatch in EITHER direction is a finding: more + means a new use crept in behind the allowlist, fewer means the entry is + stale and would permit a pattern nobody uses. An allowlist entry whose + file is not under `root` at all is reported too. + + `root` is a parameter, not `REPO`, precisely so a test can run this on + a scratch tree with a planted marker and watch it fire. + """ + markers = DEFECT_MARKERS if markers is None else markers + allowlist = DEFECT_ALLOWLIST if allowlist is None else allowlist + findings = [] + seen_stems = set() + for rel in _scan_inputs(root): + stem = os.path.splitext(rel)[0] + seen_stems.add(stem) + counts = {} + for unit, lines in _code_units(os.path.join(root, rel)): + where = f'{rel}:{unit}' if unit else rel + for lineno, line in lines: + for marker, fix in markers.items(): + n_hits = len(re.findall(marker, line)) + if not n_hits: + continue + counts[marker] = counts.get(marker, 0) + n_hits + if (stem, marker) in allowlist: + continue + findings.append( + f'{where}:{lineno}: {line.strip()!r} matches ' + f'{marker!r} -- {fix}') + for (allowed_stem, marker), (expected, reason) in allowlist.items(): + if allowed_stem != stem: + continue + got = counts.get(marker, 0) + if got > expected: + findings.append( + f'{rel}: {got} matches of {marker!r}, but DEFECT_ALLOWLIST ' + f'records {expected} ({reason}). A NEW use crept in; ' + f'remove it, or -- if it is a deliberate demo -- raise ' + f'the count and extend the reason.') + elif got < expected: + findings.append( + f'{rel}: {got} matches of {marker!r}, but DEFECT_ALLOWLIST ' + f'records {expected} ({reason}). The entry is stale; ' + f'lower the count or drop it rather than leaving it to ' + f'permit a pattern that is gone.') + for (allowed_stem, marker), (expected, reason) in allowlist.items(): + if allowed_stem not in seen_stems: + findings.append( + f'DEFECT_ALLOWLIST names {allowed_stem!r} for {marker!r}, ' + f'but no such example or tutorial exists under {root}; ' + f'drop the entry ({reason})') + return findings + + +SCANNED_FILES = _scan_inputs(REPO) + + +def test_the_widened_scan_covers_more_than_the_launch_files(): + """Pins G1's closure at the SCOPE level: the scan must reach every + example and every tutorial, not the twelve budgeted files. The four the + release review named as never scanned are asserted by name.""" + gated = {p for p, _ in BUDGETS} + assert gated < set(SCANNED_FILES) + for must in ('examples/plot_digits.py', 'examples/plot_procrustes.py', + 'examples/plot_gensim_text.py', + 'docs/tutorials/streaming_data.ipynb', + 'docs/tutorials/stock_forecasting.ipynb', + 'docs/tutorials/wikipedia_embeddings.ipynb'): + assert must in SCANNED_FILES, f'{must}: missing from the scan inputs' + + +@pytest.mark.parametrize('rel', SCANNED_FILES) +def test_no_defect_marker_in_any_example_or_tutorial(rel): + """Every marker, every file, install cells excluded, allowlist counted.""" + findings = [f for f in scan_for_defects(REPO) if f.startswith(rel + ':')] + assert not findings, '\n'.join(findings) + + +def test_every_defect_allowlist_entry_still_matches(): + """The stale-entry and missing-file findings are not tied to one file's + parametrized ID, so they are asserted here, on the whole list. This is + the empty-allowlist safeguard's counterpart: an entry that no longer + matches anything is a blanket exemption waiting for a new use.""" + stale = [f for f in scan_for_defects(REPO) + if 'stale' in f or 'DEFECT_ALLOWLIST names' in f] + assert not stale, '\n'.join(stale) + assert DEFECT_ALLOWLIST, ( + 'DEFECT_ALLOWLIST is empty, yet the documented multi-panel form ' + '(hyp.subplots + ax=) is taught in align.ipynb and plot.ipynb; if ' + 'those demos were removed on purpose, delete this assertion with ' + 'the reason') + + +def _write(path, text): + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, 'w', encoding='utf-8') as handle: + handle.write(text) + + +def _notebook(*cells): + """A minimal .ipynb (as JSON text) from (cell_type, source) pairs.""" + import json + return json.dumps({ + 'cells': [{'cell_type': kind, 'metadata': {}, 'source': src, + **({'outputs': [], 'execution_count': None} + if kind == 'code' else {})} + for kind, src in cells], + 'metadata': {}, 'nbformat': 4, 'nbformat_minor': 5}) + + +def test_the_widened_scanner_detects_a_planted_marker(tmp_path): + """The release review's repro, made permanent: G1 is closed only if a + `load_digits(` planted in a NON-launch example is reported. + + Built on a scratch tree, not the repo, so the proof is a real firing + rather than a claim about scope. Each planted case has a CONTROL beside + it: the unmodified copy of `plot_basic.py` scans clean; the install + cell carrying `find_spec(` is skipped while the same call in an + ordinary cell is reported; the allowlisted file at its recorded count + is clean while one extra `ax=` fails it; and a markdown cell naming a + marker never enters the scan. + """ + root = str(tmp_path) + basic = _read('examples/plot_basic.py') + # --- control: the real file, unmodified, is clean under the widened scan + _write(os.path.join(root, 'examples', 'plot_basic.py'), basic) + assert scan_for_defects(root, allowlist={}) == [] + + # --- G1: the review's repro -- load_digits in a non-launch example + planted = basic + '\nfrom sklearn.datasets import load_digits\n' \ + 'digits = load_digits()\n' + _write(os.path.join(root, 'examples', 'plot_basic.py'), planted) + n_lines = len(planted.split('\n')) + findings = scan_for_defects(root, allowlist={}) + assert len(findings) == 2, findings + assert all('examples/plot_basic.py:' in f and 'load_digits' in f + and "hyp.load('digits')" in f for f in findings), findings + # the finding names the REAL line, not an index into stripped text + assert any(f'plot_basic.py:{n_lines - 2}:' in f for f in findings) \ + and any(f'plot_basic.py:{n_lines - 1}:' in f for f in findings), findings + _write(os.path.join(root, 'examples', 'plot_basic.py'), basic) + + # --- G3: the Colab install guard is skipped by scope, not by weakening + install = ('import importlib.util\n\n' + "if importlib.util.find_spec('hypertools') is None:\n" + ' %pip install -q "hypertools[interactive]"\n') + # NB: the planted guard must not SAY 'pip install' anywhere -- the + # install-cell rule is by content, shared with execute_tutorial.py, and + # a string mentioning it would exempt the whole cell (measured: the first + # draft of this test did exactly that and the scan came back empty). + guarded = ("import importlib.util\n" + "if importlib.util.find_spec('chronos') is None:\n" + " raise SystemExit('install the predict-hf extra first')\n") + _write(os.path.join(root, 'docs', 'tutorials', 'scratch.ipynb'), + _notebook(('markdown', 'prose that says load_digits( and ffmpeg'), + ('code', install), + ('code', 'import hypertools as hyp\n'), + ('code', guarded))) + findings = scan_for_defects(root, allowlist={}) + assert len(findings) == 1, findings + assert 'docs/tutorials/scratch.ipynb:cell 3:2:' in findings[0] \ + and 'find_spec' in findings[0], findings + # the guard in the install cell and the markdown prose produced nothing + assert not any('cell 0' in f or 'cell 1' in f for f in findings), findings + + # --- G2: the count-pinned allowlist + panels = ('import hypertools as hyp\n' + 'fig, axes = hyp.subplots(1, 2)\n' + "hyp.plot(a, ax=axes[0], show=False)\n" + "hyp.plot(b, ax=axes[1], show=False)\n") + _write(os.path.join(root, 'docs', 'tutorials', 'scratch.ipynb'), + _notebook(('code', install), ('code', panels))) + allow = {('docs/tutorials/scratch', AX_MARKER): (2, 'two panels')} + assert scan_for_defects(root, allowlist=allow) == [] + # one NEW use behind the allowlist + _write(os.path.join(root, 'docs', 'tutorials', 'scratch.ipynb'), + _notebook(('code', install), + ('code', panels + 'hyp.plot(c, ax=axes[1], show=False)\n'))) + findings = scan_for_defects(root, allowlist=allow) + assert len(findings) == 1 and '3 matches' in findings[0] \ + and 'NEW use' in findings[0], findings + # a stale entry: the demo is gone but the entry remains + _write(os.path.join(root, 'docs', 'tutorials', 'scratch.ipynb'), + _notebook(('code', install), ('code', 'import hypertools as hyp\n'))) + findings = scan_for_defects(root, allowlist=allow) + assert len(findings) == 1 and 'stale' in findings[0], findings + # an entry for a file that does not exist + findings = scan_for_defects( + root, allowlist={('examples/no_such_example', AX_MARKER): (1, 'x')}) + assert len(findings) == 1 and 'no such example' in findings[0], findings + + # --- raw matplotlib drawing and a hand-built figure are both caught + _write(os.path.join(root, 'examples', 'plot_raw.py'), + 'import matplotlib.pyplot as plt\n' + 'fig = plt.figure()\n' + 'ax = fig.add_subplot(111, projection="3d")\n' + 'ax.scatter(x, y, z)\n' + 'ax.plot(x, y, z)\n') + hits = [f for f in scan_for_defects(root, allowlist={}) + if 'plot_raw.py' in f] + assert [f.split(':')[1] for f in hits] == ['2', '3', '4', '5'], hits + + #: How far from an allowlisted private reach its rationale may sit. 15 lines #: is the size of a comment block plus the statement it explains -- close #: enough that a reader who lands on the reach sees the reason without @@ -1146,10 +1526,48 @@ def _code_cells(stem): 'morph_shapes_zoo': {4, 5}, # three-section notebook, added 2026-09-04 (measured the same way) 'animate_forecast': {4, 5}, + # The eight tutorials rebuilt for 1.1 (issue #284, G4). MEASURED + # 2026-09-06 from the notebooks as committed at HEAD (96ac8b7f..e47968f5, + # working tree byte-identical): the code-cell index set whose `outputs` + # list is non-empty, install cell (index 0 in all eight) excluded -- + # exactly what `test_the_right_cells_carry_visible_output` computes. + # Only the INDEX SET is recorded, as for the launch notebooks: cell + # CONTENT is not compared, so a stream carrying a temp-dir path or a + # file size (io cell 11, plot cell 27) is gated on emitting, not on + # what it says. Cells absent from a set are bare imports or assignments + # (hierarchy 1, io 1 and 12, plot 1-2 and 29, align 1, analyze 1, + # reduce 1-2). + 'hierarchy': {2, 3, 4, 5, 6, 7, 8, 9, 10}, + 'io': {2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, + 'pipelines': {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, + 'manip': {1, 2, 3, 4, 5, 6, 7, 8, 9}, # 9: Normalize(mode='isotropic') + 'plot': set(range(3, 31)) | {32}, + 'align': {2, 3, 4, 5, 6, 7, 8, 9, 10, 11}, + 'analyze': {2, 3, 4, 5, 6, 7, 8, 9, 10}, + 'reduce': {3, 4, 5, 6, 7, 8, 9, 10, 11, 12}, } +#: The 1.1 rebuilds (issue #284): gated on execution and on the measured +#: output-cell set above, but not on an mp4 artifact -- these render their +#: figures inline as image/png outputs, which is what the index set pins. +REBUILT_TUTORIALS = ('hierarchy', 'io', 'pipelines', 'manip', + 'plot', 'align', 'analyze', 'reduce') -@pytest.mark.parametrize('stem', LAUNCH_NOTEBOOKS) +GATED_NOTEBOOKS = LAUNCH_NOTEBOOKS + REBUILT_TUTORIALS + + +def test_every_rebuilt_tutorial_has_a_measured_output_set(): + """The G4 closure at the roster level: adding a stem to + `REBUILT_TUTORIALS` without measuring its set fails here by name, and + a set recorded for a notebook that no longer exists is reported too.""" + for stem in GATED_NOTEBOOKS: + assert stem in EXPECTED_VISIBLE_OUTPUTS, f'{stem}: no measured set' + assert os.path.exists(os.path.join(REPO, 'docs', 'tutorials', + f'{stem}.ipynb')), stem + assert set(EXPECTED_VISIBLE_OUTPUTS) == set(GATED_NOTEBOOKS) + + +@pytest.mark.parametrize('stem', GATED_NOTEBOOKS) def test_every_launch_notebook_ran_every_cell_it_should(stem): """`nbsphinx_execute = 'never'` (docs/conf.py:131) renders the COMMITTED outputs, so a half-executed notebook is a figure-less docs page. @@ -1182,7 +1600,7 @@ def test_every_launch_notebook_ran_every_cell_it_should(stem): f'scripts/execute_tutorial.py') -@pytest.mark.parametrize('stem', LAUNCH_NOTEBOOKS) +@pytest.mark.parametrize('stem', GATED_NOTEBOOKS) def test_the_right_cells_carry_visible_output(stem): """Which cells emit, not how many.""" if stem not in EXPECTED_VISIBLE_OUTPUTS: @@ -1279,14 +1697,113 @@ def test_example_runs_end_to_end(stem): f'--- stderr ---\n{proc.stderr[-2000:]}') -def test_no_launch_notebook_committed_an_error_output(): +def test_no_gated_notebook_committed_an_error_output(): """A notebook can be fully executed and still be broken.""" import json - for stem in ('market_sectors', 'weather_decades', 'painting_embeddings', - 'conversation_shape', 'morph_shapes_zoo'): + for stem in GATED_NOTEBOOKS: nb = json.loads(_read(f'docs/tutorials/{stem}.ipynb')) for cell in nb['cells']: for out in cell.get('outputs', []): assert out.get('output_type') != 'error', ( f"{stem}.ipynb: committed a traceback " f"({out.get('ename')})") + + +#: the first line of `scripts.add_colab_install_cell.portable_video_source` +COLAB_VIDEO_MARKER = '# Colab serves output frames separately' + + +def _swallowed_expression(source): + """The bare expression a trailing Colab video block swallows, or None. + + IPython displays only a cell's LAST top-level expression, and the Colab + block (`portable_video_source`) must end its cell (`_review_setup_overhead` + compares everything from its marker to the end with the template). So an + expression statement right before the block -- a figure, a tuple -- is + evaluated and thrown away: io/manip/plot lost their "shown in place" + frames and lsl_streaming/streaming_data their tuples that way (2026-09-11 + review, A0). A call is not flagged: `display()`/`print()` show their own + output. + """ + head = source[:source.index(COLAB_VIDEO_MARKER)] + code = '\n'.join(('# ' + line) if line.lstrip()[:1] in ('%', '!') else line + for line in head.split('\n')) + body = ast.parse(code).body + if body and isinstance(body[-1], ast.Expr) \ + and not isinstance(body[-1].value, ast.Call): + return ast.get_source_segment(code, body[-1]) + return None + + +def test_the_swallowed_expression_detector_detects(): + """The check below can fail: planted before the real template.""" + from scripts.add_colab_install_cell import portable_video_source + block = portable_video_source('clip.mp4') + assert block.startswith(COLAB_VIDEO_MARKER) + assert _swallowed_expression('anim = f()\nanim.figure\n\n' + block) == 'anim.figure' + assert _swallowed_expression("fig.stream_info['n'], 3\n\n" + block) \ + == "fig.stream_info['n'], 3" + assert _swallowed_expression('%matplotlib inline\nx = 1\nx\n' + block) == 'x' + assert _swallowed_expression('display(anim.figure)\n\n' + block) is None + assert _swallowed_expression("print('saved')\n\n" + block) is None + + +def test_no_colab_video_block_swallows_a_displayed_value(): + import glob + import json + offenders = [] + for path in sorted(glob.glob(os.path.join(REPO, 'docs', 'tutorials', '*.ipynb'))): + rel = os.path.relpath(path, REPO).replace(os.sep, '/') + nb = json.loads(_read(rel)) + for i, cell in enumerate(nb['cells']): + source = ''.join(cell['source']) + if cell['cell_type'] == 'code' and COLAB_VIDEO_MARKER in source: + expression = _swallowed_expression(source) + if expression: + offenders.append(f'{rel} cell {i}: {expression!r}') + assert not offenders, ( + 'these values are evaluated and never shown, because the Colab video ' + 'block comes after them; display() or print() them: ' + '; '.join(offenders)) + + +def test_generated_launch_examples_pin_the_matplotlib_backend(): + """scripts/generate_tutorial_notebook.py writes a build cell that calls + `anim.draw_frame(...)` and a save cell that calls `anim.save(..., dpi=)`: + the matplotlib `HyperAnimation`'s API. On Colab `backend='auto'` + resolves to plotly (`plotly_backend.resolve_backend`), whose figure has + neither, so every generated notebook raised AttributeError there + (2026-09-11 review, H-B1). Each SPECS example's `hyp.plot` call must + name `backend='matplotlib'`.""" + from scripts.generate_tutorial_notebook import SPECS + unpinned = [] + for module, _dpi, _sections in SPECS.values(): + tree = ast.parse(_read(f'examples/{module}.py')) + calls = [node for node in ast.walk(tree) if isinstance(node, ast.Call) + and ast.unparse(node.func) == 'hyp.plot'] + assert calls, f'examples/{module}.py: no hyp.plot call found' + for call in calls: + backend = {k.arg: k.value for k in call.keywords}.get('backend') + if not (isinstance(backend, ast.Constant) + and backend.value == 'matplotlib'): + unpinned.append(f'examples/{module}.py:{call.lineno}') + assert not unpinned, unpinned + + +def test_a_pinned_launch_example_stays_matplotlib_under_a_plotly_default(): + """The behaviour behind the pin, observed: with plotly as the render + preference (the public stand-in for Colab's default, which + `resolve_backend('auto')` consults first), the morph example still hands + back the matplotlib `HyperAnimation` whose `draw_frame`/`figure`/`save` + the generated notebook calls.""" + import hypertools as hyp + import matplotlib.figure + module = _import_example_without_fetching('animate_morph_zoo') + with _offline(), hyp.set_interactive_backend('plotly'): + anim = module.construct_artifact(module.fixture_data()) + try: + assert isinstance(anim, hyp.HyperAnimation) + assert isinstance(anim.figure, matplotlib.figure.Figure) + anim.draw_frame(anim.n_frames - 1) + finally: + import matplotlib.pyplot as plt + plt.close(anim.figure) diff --git a/tests/test_explore_label_placement.py b/tests/test_explore_label_placement.py new file mode 100644 index 00000000..cf705849 --- /dev/null +++ b/tests/test_explore_label_placement.py @@ -0,0 +1,50 @@ +"""explore=True hover labels stay inside the figure near every edge. + +The label used to sit up and to the LEFT of every point, so hovering a point +near the left edge drew a label cut off by the window (seen in the native Qt +screencast of the feature tour's GUI-native case, 2026-10-08). Hover is +exercised through matplotlib's own event dispatch with a real MouseEvent. +""" +import warnings + +import numpy as np +import pytest +from matplotlib.backend_bases import MouseEvent +from mpl_toolkits.mplot3d import proj3d + +import hypertools as hyp + + +def _explore_figure(): + with warnings.catch_warnings(): + warnings.simplefilter('ignore', UserWarning) # non-interactive backend notice + fig = hyp.plot(hyp.load('helix'), explore=True, show=False) + fig.canvas.draw() + return fig + + +def _screen_points(ax): + x, y, z = ax.lines[0].get_data_3d() + xs, ys, _ = proj3d.proj_transform(x, y, z, ax.get_proj()) + return ax.transData.transform(np.column_stack([xs, ys])) + + +@pytest.mark.parametrize('edge', ['left', 'right', 'bottom', 'top']) +def test_hover_label_stays_inside_figure(edge): + fig = _explore_figure() + ax = fig.axes[0] + pts = _screen_points(ax) + pick = {'left': np.argmin(pts[:, 0]), 'right': np.argmax(pts[:, 0]), + 'bottom': np.argmin(pts[:, 1]), 'top': np.argmax(pts[:, 1])}[edge] + # move somewhere else first so the edge point is a NEW closest point + other = pts[len(pts) // 2] + for target in (other, pts[pick]): + event = MouseEvent('motion_notify_event', fig.canvas, *target) + fig.canvas.callbacks.process('motion_notify_event', event) + labels = [t for t in ax.texts if t.get_visible() and t.get_text()] + assert labels, 'hovering a data point drew no label' + fig.canvas.draw() + box = labels[-1].get_window_extent(fig.canvas.get_renderer()) + fig_box = fig.bbox + assert box.x0 >= fig_box.x0 and box.x1 <= fig_box.x1, (edge, box, fig_box) + assert box.y0 >= fig_box.y0 and box.y1 <= fig_box.y1, (edge, box, fig_box) diff --git a/tests/test_feature_tour_support.py b/tests/test_feature_tour_support.py new file mode 100644 index 00000000..ea81dccb --- /dev/null +++ b/tests/test_feature_tour_support.py @@ -0,0 +1,158 @@ +"""Exercise the actual embedded review runner, including failed reruns.""" + +import inspect +import json +import warnings +from pathlib import Path +import numpy as np +import pandas as pd +import hypertools as hyp + + +def good(): + assert hyp.load(np.ones((3, 2))).shape == (3, 2) + + +def bad(): + hyp.predict(np.empty((0, 2))) + + +def test_failed_rerun_invalidates_visual_verdict_and_csv_keeps_both(tmp_path): + root = Path(__file__).resolve().parents[1] + ns = dict( + globals(), + SCRATCH=tmp_path, + RESULTS={}, + MANUAL={}, + SETTINGS={}, + BACKENDS=["matplotlib"], + ENVIRONMENT={}, + NOTEBOOK_SOURCE_SHA256="source", + IN_COLAB=False, + CASES=[ + dict( + id="check", + title="Real API", + covers=[], + requires=[], + gate=None, + visual="Inspect output", + ) + ], + ) + source = (root / "scripts/feature_tour_support.py").read_text(encoding="utf-8") + exec(compile(source, str(root / "scripts/feature_tour_support.py"), "exec"), ns) + ns["run_case"]("check", good) + first = ns["MANUAL"]["check"]["execution_id"] + ns["MANUAL"]["check"].update(verdict="pass", notes="Previous rendering accepted") + ns["run_case"]("check", bad) + assert ns["RESULTS"]["check"]["status"] == "FAIL" + assert ns["MANUAL"]["check"]["verdict"] == "not reviewed" + assert ns["MANUAL"]["check"]["execution_id"] != first + ns["MANUAL"]["check"].update(verdict="fail", notes="Broken display") + ns["save_report"]() + csv = pd.read_csv(tmp_path / "results.csv") + assert csv.loc[0, "visual"] == "fail" and csv.loc[0, "status"] == "FAIL" + report = json.loads((tmp_path / "results.json").read_text(encoding="utf-8")) + assert ns["report_rows"](report)[0]["visual_notes"] == "Broken display" + assert report["cases"][0]["source_sha256"] == ns["source_hash"]( + inspect.getsource(bad) + ) + + +def test_export_check_rejects_single_frame_image(tmp_path): + from PIL import Image + + root = Path(__file__).resolve().parents[1] + ns = dict(globals(), IN_COLAB=False) + exec( + compile( + (root / "scripts/feature_tour_support.py").read_text(encoding="utf-8"), + str(root / "scripts/feature_tour_support.py"), + "exec", + ), + ns, + ) + path = tmp_path / "still.gif" + Image.new("RGB", (100, 100), "steelblue").save(path) + import pytest + + with pytest.raises(AssertionError, match="only 1 frames"): + ns["verify_export"](path, animated=True) + + +def test_unicode_export_opens_in_the_actual_viewer(tmp_path): + """Windows' default cp1252 cannot decode this actual UTF-8 Plotly file.""" + import html + import pytest + + go = pytest.importorskip("plotly.graph_objects") + pytest.importorskip("ipywidgets") + from IPython.core.interactiveshell import InteractiveShell + from IPython.utils.capture import capture_output + + root = Path(__file__).resolve().parents[1] + source = (root / "scripts/feature_tour_support.py").read_text(encoding="utf-8") + ns = dict(SCRATCH=tmp_path, IN_COLAB=False) + exec(compile(source, str(root / "scripts/feature_tour_support.py"), "exec"), ns) + label = "測定 “脳” — café" + path = tmp_path / "unicode-plot.html" + fig = go.Figure( + data=[go.Scatter(x=[0, 1], y=[0, 1])], + frames=[go.Frame(name="end", data=[go.Scatter(x=[0, 1], y=[1, 0])])], + layout={"title": label}, + ) + fig.write_html( + str(path), + include_plotlyjs=True, + auto_play=False, + post_script=f"document.title = {json.dumps(label, ensure_ascii=False)};", + ) + assert label in path.read_text(encoding="utf-8") + with pytest.raises(UnicodeDecodeError): + path.read_bytes().decode("cp1252") + ns["INTERACTIVE_PLOTS"]["unicode"] = str(path) + had_shell = InteractiveShell.initialized() + InteractiveShell.instance() + try: + with capture_output(display=True) as captured, \ + warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + ns["verify_export"](path, animated=True) + viewer = ns["interactive_viewer"]() + viewer.children[1].children[0].click() + # Colab printed IPython's "Consider using IPython.display.IFrame" + # above every opened plot (2026-09-11) + assert not [w for w in caught if "IFrame" in str(w.message)] + frames = [ + o.data["text/html"] + for o in captured.outputs + if "text/html" in o.data and "<iframe" in o.data["text/html"] + ] + assert len(frames) == 1 + assert label in html.unescape(frames[0]) + assert "Plotly.newPlot(" in html.unescape(frames[0]) + viewer.children[1].children[1].click() + finally: + if not had_shell: + InteractiveShell.clear_instance() + + +def test_summary_cell_shows_the_interactive_viewer_once(): + """interactive_viewer() displays its widget AND returns it; as a cell's + bare last expression Jupyter displayed it a second time, so Colab showed + two viewers (fresh-Colab review 2026-09-11).""" + import ast + repo = Path(__file__).resolve().parents[1] + source = (repo / "scripts" / "update_feature_tour.py").read_text(encoding="utf-8") + templates = [ + node.value for node in ast.walk(ast.parse(source)) + if isinstance(node, ast.Constant) and isinstance(node.value, str) + and node.value.startswith("summary=pd.DataFrame") + and "interactive_viewer" in node.value + ] + assert len(templates) == 1 + last = ast.parse(templates[0]).body[-1] + bare_viewer = (isinstance(last, ast.Expr) and isinstance(last.value, ast.Call) + and getattr(last.value.func, "id", None) == "interactive_viewer") + assert not bare_viewer diff --git a/tests/test_figure_review_gaps.py b/tests/test_figure_review_gaps.py new file mode 100644 index 00000000..29f50603 --- /dev/null +++ b/tests/test_figure_review_gaps.py @@ -0,0 +1,289 @@ +"""Gaps found by the three-role figure review of the 1.1 feature tour +(2026-09-07: one agent wrote each figure's EXPECTED appearance from the +notebook's code and prose, one described the OBSERVED render, one +adjudicated), each pinned here at the public API on both backends. + +G1 a caller's matplotlib axes (`ax=`, every `panels=` cell) took its + figure's default colour cycle instead of the palette; a second call + into the same axes/figure restarted the palette on both backends. +G2 `panels=` at the default size shrank its cells to fit per-panel legends. +G3 a 3-D figure's axis labels fell outside its tight bbox. +G5 a recoloured forecast was still faded and vanished among translucent + traces. +G7 the 2-D frame square sat on the data's extreme points. +G8 `legend_kwargs={'loc': ...}` kept the outside-right anchor. +G10 plotly legend keys reproduced a 2 px '.' marker. +No mocks: every assertion reads the drawn artists, traces or pixels. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +from tests._plotly_colors import rgba as effective_rgba +import pytest +from matplotlib.colors import to_rgb + +import hypertools as hyp +from hypertools._shared.helpers import UNIT_FRAME_LIMIT, UNIT_FRAME_SCALE + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walks(n=2, rows=40): + return [hyp.load('random_walk', n_samples=rows, n_features=3, + random_state=i) for i in range(n)] + + +def _data_lines(ax): + return [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + + +def _own_colors(n): + fig = hyp.plot(_walks(n), show=False) + return [to_rgb(ln.get_color()) for ln in _data_lines(fig.axes[0])[:n]] + + +# --- G1: palette on a caller's axes, continued across calls --------------- + +def test_caller_axes_draw_in_the_palette(): + fig, axes = hyp.subplots(1, 2) + hyp.plot(_walks(2), ax=axes[0], show=False) + drawn = [to_rgb(ln.get_color()) for ln in _data_lines(axes[0])[:2]] + assert drawn == _own_colors(2) + assert drawn[0] != to_rgb('C0') + + +def test_panels_draw_in_the_palette(): + fig = hyp.plot(_walks(3), panels=True, show=False) + first = _own_colors(1)[0] + for ax in fig.axes[:3]: + assert to_rgb(_data_lines(ax)[0].get_color()) == first + + +def test_explicit_palette_reaches_a_caller_axes(): + fig, axes = hyp.subplots(1, 1) + hyp.plot(_walks(2), ax=axes[0], palette=['navy', 'gold'], show=False) + drawn = [to_rgb(ln.get_color()) for ln in _data_lines(axes[0])[:2]] + assert drawn == [to_rgb('navy'), to_rgb('gold')] + + +def test_a_second_call_into_the_same_axes_continues_the_palette(): + fig, axes = hyp.subplots(1, 1) + hyp.plot(_walks(1), ax=axes[0], show=False) + hyp.plot(_walks(1), ax=axes[0], show=False) + first, second = [to_rgb(ln.get_color()) for ln in _data_lines(axes[0])] + assert first != second + # the pair is what one call with both datasets draws + assert [first, second] == _own_colors(2) + + +def test_a_second_call_into_the_same_plotly_figure_continues_the_palette(): + fig = hyp.plot(_walks(1), show=False, backend='plotly') + fig = hyp.plot(_walks(1), ax=fig, show=False, backend='plotly') + data = [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') is not None] + assert len(data) == 2 + assert data[0].line.color != data[1].line.color + both = hyp.plot(_walks(2), show=False, backend='plotly') + expected = [tr.line.color for tr in both.data + if (tr.meta or {}).get('hyp_trace_index') is not None] + assert [tr.line.color for tr in data] == expected + + +# --- G2: panel figures make room for their legends ------------------------ + +def test_default_size_panels_widen_for_per_panel_legends(): + plain = hyp.plot(_walks(3), panels=True, show=False) + with_legend = hyp.plot(_walks(3), panels=True, legend=True, show=False) + assert with_legend.get_size_inches()[0] > plain.get_size_inches()[0] + # ...so the panels themselves stay as large as without a legend + def cell_in(fig): + ax = fig.axes[0] + return ax.get_position().width * fig.get_size_inches()[0] + assert cell_in(with_legend) >= 0.9 * cell_in(plain) + sized = hyp.plot(_walks(3), panels=True, legend=True, size=[9, 3], + show=False) + assert tuple(sized.get_size_inches()) == (9.0, 3.0) + + +# --- G3: 3-D axis labels are inside the tight bbox ------------------------ + +def test_three_d_axis_labels_are_inside_the_tight_bbox(tmp_path): + fig = hyp.plot(_walks(1)[0], xlabel='PC 1', ylabel='PC 2', + zlabel='PC 3', show=False) + fig.canvas.draw() + renderer = fig.canvas.get_renderer() + tight = fig.get_tightbbox(renderer) # inches + dpi = fig.dpi + for axis in fig.axes[0]._axis_map.values(): + box = axis.label.get_window_extent(renderer) # pixels + # every label box lies inside the tight bbox on all four sides + assert tight.x0 * dpi - 1e-6 <= box.x0 + assert box.x1 <= tight.x1 * dpi + 1e-6 + assert tight.y0 * dpi - 1e-6 <= box.y0 + assert box.y1 <= tight.y1 * dpi + 1e-6 + # and a bbox-tight save keeps the z-label's pixels (right of the cube) + from PIL import Image + loose = tmp_path / 'loose.png' + tight_png = tmp_path / 'tight.png' + fig.savefig(loose, dpi=100) + fig.savefig(tight_png, dpi=100, bbox_inches='tight') + zbox = fig.axes[0].zaxis.label.get_window_extent(renderer) + img = np.asarray(Image.open(tight_png).convert('L')) + # the tight image must be at least as wide as the label's right edge + # minus the tight bbox's left edge + assert img.shape[1] >= int(zbox.x1 - tight.x0 * fig.dpi) - 2 + + +# --- G5: a recoloured forecast keeps its trace's alpha -------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_recoloured_forecasts_are_not_faded(backend): + kw = dict(predict='Kalman', t=4, alpha=0.7, show=False, backend=backend) + inherited = hyp.plot(_walks(2), **kw) + recoloured = hyp.plot(_walks(2), forecast_palette=['black', 'orange'], + **kw) + if backend == 'matplotlib': + fc = [ln for ln in inherited.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + assert fc[0].get_alpha() == pytest.approx(0.35) + fc = [ln for ln in recoloured.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + assert fc[0].get_alpha() == pytest.approx(0.7) + assert to_rgb(fc[0].get_color()) == to_rgb('black') + else: + def alpha(fig): + tr = [t for t in fig.data + if (t.meta or {}).get('hyp_forecast_role') == 'static'][0] + return tr.meta['hyp_forecast_alpha'], effective_rgba(tr)[-1] + assert alpha(inherited)[0] == pytest.approx(0.35) + a, color = alpha(recoloured) + assert a == pytest.approx(0.7) + assert color == pytest.approx(.7) + + +def test_a_dash_only_override_keeps_the_fade(): + fig = hyp.plot(_walks(2), predict='Kalman', t=4, forecast_fmt=':', + show=False) + fc = [ln for ln in fig.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + assert fc[0].get_alpha() == pytest.approx(0.5) + + +# --- G7: the 2-D frame square clears the data ----------------------------- + +def test_two_d_frame_square_clears_the_data_matplotlib(): + fig = hyp.plot(_walks(2), ndims=2, show=False) + ax = fig.axes[0] + square = [p for p in ax.patches if p.get_width() > 2][0] + assert square.get_width() == pytest.approx(2 * UNIT_FRAME_SCALE) + assert UNIT_FRAME_SCALE > 1.0 + xs = np.concatenate([ln.get_xdata() for ln in _data_lines(ax)]) + ys = np.concatenate([ln.get_ydata() for ln in _data_lines(ax)]) + assert max(abs(xs).max(), abs(ys).max()) <= 1.0 + 1e-9 + assert ax.get_xlim() == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) + + +def test_two_d_frame_square_clears_the_data_plotly(): + fig = hyp.plot(_walks(2), ndims=2, show=False, backend='plotly') + square = fig.layout.shapes[0] + assert square.x1 == pytest.approx(UNIT_FRAME_SCALE) + assert list(fig.layout.xaxis.range) == [-UNIT_FRAME_LIMIT, + UNIT_FRAME_LIMIT] + xs = np.concatenate([np.asarray(tr.x, dtype=float) for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') + is not None]) + assert abs(xs).max() <= 1.0 + 1e-9 + + +# --- G8: legend_kwargs loc= places the legend on the axes ----------------- + +def test_legend_kwargs_loc_drops_the_outside_anchor(): + fig = hyp.plot(_walks(2), legend=True, + legend_kwargs={'loc': 'upper left'}, show=False) + fig.canvas.draw() + legend = fig.axes[0].get_legend() + box = legend.get_window_extent(fig.canvas.get_renderer()) + axes_box = fig.axes[0].get_window_extent(fig.canvas.get_renderer()) + # inside the axes' left half and upper half + assert box.x0 >= axes_box.x0 - 1 and box.x1 <= axes_box.x0 + axes_box.width / 2 + assert box.y1 <= axes_box.y1 + 1 and box.y0 >= axes_box.y0 + axes_box.height / 2 + default = hyp.plot(_walks(2), legend=True, show=False) + default.canvas.draw() + dbox = default.axes[0].get_legend().get_window_extent( + default.canvas.get_renderer()) + daxes = default.axes[0].get_window_extent(default.canvas.get_renderer()) + assert dbox.x0 >= daxes.x1 # the default stays outside right + + +# --- G10: plotly legend keys are readable for tiny markers ----------------- + +def test_plotly_legend_keys_use_a_constant_item_size(): + fig = hyp.plot(_walks(2), '.', legend=True, show=False, backend='plotly') + assert fig.layout.legend.itemsizing == 'constant' + grid = hyp.plot(_walks(2), '.', panels=True, legend=True, show=False, + backend='plotly') + assert grid.layout.legend.itemsizing == 'constant' + assert grid.layout.legend2.itemsizing == 'constant' + + +# --- G9: a plotly hyp.subplots grid grows its gutters only when needed --- + +def _plotly_cells_px(fig): + from hypertools.plot.plotly_backend import cell_layout_keys + mg = fig.layout.margin + plot_w = fig.layout.width - mg.l - mg.r + out = [] + for i in range(2): + d = fig.layout[cell_layout_keys(i)['scene']].domain + out.append((mg.l + d.x[0] * plot_w, mg.l + d.x[1] * plot_w)) + return out + + +def test_plotly_subplots_grid_starts_without_gutters(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(_walks(1)[0], ax=cells[0], show=False, backend='plotly') + hyp.plot(_walks(1)[0], ax=cells[1], show=False, backend='plotly') + grid = hyp.plot(_walks(2), panels=True, show=False, backend='plotly') + # the same cells as a legend-less panels= grid draws + assert fig.layout.width == grid.layout.width + assert _plotly_cells_px(fig) == pytest.approx(_plotly_cells_px(grid), + abs=1.0) + assert fig.layout.margin.r == grid.layout.margin.r + + +def test_plotly_subplots_grid_grows_gutters_for_a_legend(): + from hypertools.plot.plotly_backend import PANEL_GUTTER_PAD_PX + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(_walks(1)[0], ax=cells[0], title='first', show=False, + backend='plotly') + before = _plotly_cells_px(fig) + width_before = fig.layout.width + hyp.plot(_walks(2), ax=cells[1], legend=True, names=['a', 'b'], + show=False, backend='plotly') + after = _plotly_cells_px(fig) + # the default-size figure widened by one gutter per column, each + # cell kept its width, and cell 0 (drawn earlier) moved with the grid + assert fig.layout.width > width_before + for (b0, b1), (a0, a1) in zip(before, after): + assert (a1 - a0) == pytest.approx(b1 - b0, abs=1.0) + assert after[1][0] - after[0][1] > before[1][0] - before[0][1] + # cell 1's legend sits just right of cell 1, inside the figure + mg = fig.layout.margin + plot_w = fig.layout.width - mg.l - mg.r + legend_x = mg.l + fig.layout.legend2.x * plot_w + assert legend_x == pytest.approx(after[1][1] + PANEL_GUTTER_PAD_PX, + abs=1.0) + assert legend_x < fig.layout.width + # cell 0's title followed its cell + title = [a for a in fig.layout.annotations + if a.name == 'hyp-cell-title-0'][0] + assert mg.l + title.x * plot_w == pytest.approx( + 0.5 * (after[0][0] + after[0][1]), abs=1.0) + assert fig.layout.meta['hyp_grid']['gutter_px'] > 0 diff --git a/tests/test_fit_returns_self.py b/tests/test_fit_returns_self.py new file mode 100644 index 00000000..1d16e801 --- /dev/null +++ b/tests/test_fit_returns_self.py @@ -0,0 +1,61 @@ +"""``fit()`` returns the fitted instance on every model base (release review +of 1.1.0): the sklearn chain ``Model().fit(x).transform(y)`` must work for +manipulators, aligners and imputers alike. Real data, no mocks.""" + +import numpy as np +import pandas as pd +import pytest + +from hypertools.align.hyperalign import HyperAlign +from hypertools.align.null import NullAlign +from hypertools.align.procrustes import Procrustes +from hypertools.impute.ppca import PPCA +from hypertools.manip.delay import Delay +from hypertools.manip.normalize import Normalize +from hypertools.manip.resample import Resample +from hypertools.manip.smooth import Smooth +from hypertools.manip.zscore import ZScore + + +def _walk(seed, rows=40, cols=3): + # the model classes take DataFrames (the hyp.* dispatchers wrap arrays) + return pd.DataFrame( + np.cumsum(np.random.default_rng(seed).normal(size=(rows, cols)), 0)) + + +@pytest.mark.parametrize('make', [ + Normalize, ZScore, lambda: Smooth(kernel='boxcar', kernel_width=5), + lambda: Resample(n_samples=20), lambda: Delay(tau=2, dims=3), +], ids=['Normalize', 'ZScore', 'Smooth', 'Resample', 'Delay']) +def test_manipulator_fit_returns_self_and_chains(make): + model = make() + x = _walk(0) + assert model.fit(x) is model + chained = make().fit(x).transform(_walk(1)) + separate = make() + separate.fit(x) + np.testing.assert_array_equal(np.asarray(chained), + np.asarray(separate.transform(_walk(1)))) + + +@pytest.mark.parametrize('make', [HyperAlign, Procrustes, NullAlign], + ids=['HyperAlign', 'Procrustes', 'NullAlign']) +def test_aligner_fit_returns_self_and_chains(make): + xs = [_walk(0), _walk(1), _walk(2)] + model = make() + assert model.fit(xs) is model + chained = make().fit(xs).transform(xs) + separate = make() + separate.fit(xs) + for a, b in zip(chained, separate.transform(xs)): + np.testing.assert_allclose(np.asarray(a), np.asarray(b)) + + +def test_imputer_fit_returns_self_and_chains(): + x = _walk(0, rows=60, cols=4) + x.iloc[5, 1] = np.nan + x.iloc[17, 3] = np.nan + model = PPCA() + assert model.fit(x) is model + filled = PPCA().fit(x).transform(x) + assert not np.isnan(np.asarray(filled)).any() diff --git a/tests/test_flat_spec_keys.py b/tests/test_flat_spec_keys.py new file mode 100644 index 00000000..446a2cd4 --- /dev/null +++ b/tests/test_flat_spec_keys.py @@ -0,0 +1,412 @@ +"""Flat model parameters in a dict spec raise instead of being dropped. + +A dict model spec is ``{'model': ..., 'args': [...], 'kwargs': {...}}`` +(or the legacy ``{'model': ..., 'params': {...}}``). A FLAT key such as +``{'model': 'PCA', 'whiten': True}`` used to be ignored without a word by +every dispatcher, so the model silently ran with its defaults. d3fefd63 +made `hyp.cluster` raise for it; the same check now covers `hyp.reduce` +(and its streaming path), `hyp.manip`, `hyp.align` (and the classic +`hypertools.tools.align`), `hyp.impute`, `hyp.Pipeline`, `hyp.apply_model` +and `hypertools.tools.text2mat` (1.1 review). `hyp.predict` specs are +exempt: they carry flat ``t``/``horizon``/``block`` keys by design. + +Each block below first shows, with a real model, that the parameter in +question changes the result when it is nested under 'kwargs' (so a dropped +flat key is a real, silent change of results), then that the flat form +raises a ValueError naming the key and spelling out the corrected spec. + +Three sibling silent drops found alongside are covered too: the outer +``**kwargs`` of `hyp.manip`/`hyp.align` next to a dict spec (now merged +into its 'kwargs'), constructor parameters handed to an already-built +aligner (now a UserWarning), and a dict spec's positional 'args' on the +streaming-reduce and text2mat paths (now passed to the constructor). +""" +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp + + +def _assert_names_and_suggests(err, key, kwargs_repr): + msg = str(err.value) + assert f"'{key}'" in msg + assert f"'kwargs': {kwargs_repr}" in msg + assert 'unrecognized top-level key' in msg + + +# --- reduce ---------------------------------------------------------------- + +def _reduce_data(): + rng = np.random.default_rng(0) + # features with very different variances, so whitening is visible + return rng.standard_normal((120, 6)) * np.array([10., 5., 2., 1., .5, .1]) + + +def test_reduce_nested_whiten_changes_result(): + x = _reduce_data() + plain = hyp.reduce(x, reduce={'model': 'PCA'}, ndims=2) + white = hyp.reduce(x, reduce={'model': 'PCA', 'kwargs': {'whiten': True}}, + ndims=2) + assert not np.allclose(plain, white) + np.testing.assert_allclose(np.std(white, axis=0, ddof=1), 1.0, rtol=1e-6) + + +@pytest.mark.parametrize('via', ['reduce', 'reduce-model', 'analyze', 'plot', + 'stream', 'describe']) +def test_reduce_flat_spec_raises(via): + x = _reduce_data() + spec = {'model': 'PCA', 'whiten': True} + with pytest.raises(ValueError) as err: + if via == 'reduce': + hyp.reduce(x, reduce=spec, ndims=2) + elif via == 'reduce-model': + hyp.reduce(x, model=spec, ndims=2) + elif via == 'analyze': + hyp.analyze(x, reduce=spec, ndims=2) + elif via == 'plot': + hyp.plot(x, '.', reduce=spec, ndims=2, show=False) + elif via == 'stream': + hyp.plot(iter(x), reduce=spec, ndims=2, stream_init=60, + stream_max=80, show=False) + else: + hyp.describe(x, reduce=spec, max_dims=3, show=False) + _assert_names_and_suggests(err, 'whiten', "{'whiten': True}") + + +def test_reduce_flat_spec_suggestion_merges_legacy_params(): + x = _reduce_data() + with pytest.raises(ValueError) as err: + hyp.reduce(x, reduce={'model': 'PCA', 'params': {'whiten': True}, + 'svd_solver': 'full'}, ndims=2) + _assert_names_and_suggests(err, 'svd_solver', + "{'whiten': True, 'svd_solver': 'full'}") + + +def test_reduce_stream_nested_whiten_is_honored(): + x = _reduce_data() + fig = hyp.plot(iter(x), reduce={'model': 'PCA', + 'kwargs': {'whiten': True}}, + ndims=2, stream_init=60, stream_max=80, show=False) + assert fig.stream_info['reduce_model'].whiten is True + + +def test_reduce_stream_spec_args_are_honored(): + # the streaming path also dropped a spec's positional 'args', so + # {'model': 'PCA', 'args': [2]} fit ndims (here 3) components + x = _reduce_data() + fig = hyp.plot(iter(x), reduce={'model': 'PCA', 'args': [2]}, ndims=3, + stream_init=60, stream_max=80, show=False) + assert fig.stream_info['reduce_model'].n_components == 2 + assert [np.shape(d) for d in fig.stream_info['xform_data']] == [(80, 2)] + + +def test_reduce_stream_instance_spec_parameters_warn(): + # an already-constructed estimator inside a streaming dict spec is used + # as-is; its spec parameters used to vanish without a word + from sklearn.decomposition import PCA + x = _reduce_data() + with pytest.warns(UserWarning, match='already-constructed PCA'): + fig = hyp.plot(iter(x), reduce={'model': PCA(n_components=2), + 'kwargs': {'whiten': True}}, + ndims=2, stream_init=60, stream_max=80, show=False) + assert fig.stream_info['reduce_model'].whiten is False + + +# --- manip ----------------------------------------------------------------- + +def _manip_data(): + rng = np.random.default_rng(1) + return np.cumsum(rng.standard_normal((80, 3)), axis=0) + + +def test_manip_nested_kernel_width_changes_result(): + x = _manip_data() + default = np.asarray(hyp.manip(x, model={'model': 'Smooth'})) + wide = np.asarray(hyp.manip(x, model={'model': 'Smooth', + 'kwargs': {'kernel_width': 31}})) + assert not np.allclose(default, wide) + + +@pytest.mark.parametrize('via', ['manip', 'manip-list', 'manip-cross', + 'analyze', 'plot']) +def test_manip_flat_spec_raises(via): + x = _manip_data() + spec = {'model': 'Smooth', 'kernel_width': 31} + with pytest.raises(ValueError) as err: + if via == 'manip': + hyp.manip(x, model=spec) + elif via == 'manip-list': + hyp.manip(x, model=[spec, 'ZScore']) + elif via == 'manip-cross': + hyp.manip(x, model=spec, normalize='across') + elif via == 'analyze': + hyp.analyze(x, manip=spec) + else: + hyp.plot(x, manip=spec, show=False) + _assert_names_and_suggests(err, 'kernel_width', "{'kernel_width': 31}") + + +@pytest.mark.parametrize('cross', [False, True]) +def test_manip_outer_kwargs_join_a_dict_spec(cross): + # hyp.manip(x, model={'model': 'Smooth'}, kernel_width=31) used to drop + # kernel_width silently (only a bare name/class received **kwargs) + x = _manip_data() + extra = {'normalize': 'across'} if cross else {} + nested = np.asarray(hyp.manip(x, model={'model': 'Smooth', + 'kwargs': {'kernel_width': 31}}, + **extra)) + outer = np.asarray(hyp.manip(x, model={'model': 'Smooth'}, + kernel_width=31, **extra)) + np.testing.assert_allclose(outer, nested) + # and the outer keyword wins over the spec's own value + wins = np.asarray(hyp.manip(x, model={'model': 'Smooth', + 'kwargs': {'kernel_width': 5}}, + kernel_width=31, **extra)) + np.testing.assert_allclose(wins, nested) + + +# --- align ----------------------------------------------------------------- + +def _align_data(): + rng = np.random.default_rng(2) + base = np.cumsum(rng.standard_normal((60, 4)), axis=0) + rotations = [np.linalg.qr(rng.standard_normal((4, 4)))[0] + for _ in range(3)] + return [base @ r + 0.3 * rng.standard_normal((60, 4)) for r in rotations] + + +def test_align_nested_n_iter_changes_result(): + data = _align_data() + default = hyp.align(data, model={'model': 'HyperAlign'}) + zero = hyp.align(data, model={'model': 'HyperAlign', + 'kwargs': {'n_iter': 0}}) + assert not all(np.allclose(np.asarray(a), np.asarray(b)) + for a, b in zip(default, zero)) + + +@pytest.mark.parametrize('via', ['align', 'align-cross', 'analyze', 'plot', + 'classic']) +def test_align_flat_spec_raises(via): + data = _align_data() + spec = {'model': 'HyperAlign', 'n_iter': 0} + with pytest.raises(ValueError) as err: + if via == 'align': + hyp.align(data, model=spec) + elif via == 'align-cross': + hyp.align(data, model=spec, reduce='PCA', ndims=2) + elif via == 'analyze': + hyp.analyze(data, align=spec) + elif via == 'plot': + hyp.plot(data, align=spec, show=False) + else: + from hypertools.tools import align as classic_align + classic_align(data, align={'model': 'hyper', 'n_iter': 0}) + _assert_names_and_suggests(err, 'n_iter', "{'n_iter': 0}") + + +def _same(a, b): + return all(np.allclose(np.asarray(i), np.asarray(j)) + for i, j in zip(a, b)) + + +def test_align_outer_kwargs_join_a_dict_spec(): + # hyp.align(data, model={'model': 'HyperAlign'}, n_iter=0) used to drop + # n_iter silently (only a bare name/class received **kwargs) + data = _align_data() + nested = hyp.align(data, model={'model': 'HyperAlign', + 'kwargs': {'n_iter': 0}}) + outer = hyp.align(data, model={'model': 'HyperAlign'}, n_iter=0) + assert _same(outer, nested) + wins = hyp.align(data, model={'model': 'HyperAlign', + 'kwargs': {'n_iter': 10}}, n_iter=0) + assert _same(wins, nested) + + +def test_align_instance_ignores_parameters_with_a_warning(): + # an already-constructed aligner cannot take constructor parameters; + # they used to be dropped without a word, in both spellings + from hypertools.align import HyperAlign + data = _align_data() + expected = hyp.align(data, model=HyperAlign(n_iter=2)) + with pytest.warns(UserWarning, match='already-constructed HyperAlign'): + out = hyp.align(data, model={'model': HyperAlign(n_iter=2), + 'kwargs': {'n_iter': 0}}) + assert _same(out, expected) + with pytest.warns(UserWarning, match=r"ignoring keyword argument\(s\) " + r"\['n_iter'\]"): + out = hyp.align(data, model=HyperAlign(n_iter=2), n_iter=0) + assert _same(out, expected) + + +# --- impute ---------------------------------------------------------------- + +def _impute_data(): + rng = np.random.default_rng(3) + x = rng.standard_normal((60, 4)) + x[:, 1] += 2 * x[:, 0] + x[rng.random(x.shape) < 0.15] = np.nan + return pd.DataFrame(x, columns=list('abcd')) + + +def test_impute_nested_n_neighbors_changes_result(): + x = _impute_data() + default = hyp.impute(x, model={'model': 'KNNImputer'}) + one = hyp.impute(x, model={'model': 'KNNImputer', + 'kwargs': {'n_neighbors': 1}}) + assert not np.allclose(np.asarray(default), np.asarray(one)) + + +@pytest.mark.parametrize('via', ['impute', 'impute-truth', 'format_data', + 'plot']) +def test_impute_flat_spec_raises(via): + x = _impute_data() + spec = {'model': 'KNNImputer', 'n_neighbors': 1} + with pytest.raises(ValueError) as err: + if via == 'impute': + hyp.impute(x, model=spec) + elif via == 'impute-truth': + truth = x.fillna(0.0) + hyp.impute(x, model=spec, truth=truth) + elif via == 'format_data': + from hypertools.tools import format_data + format_data(x, impute=spec) + else: + hyp.plot(x, impute=spec, show=False) + _assert_names_and_suggests(err, 'n_neighbors', "{'n_neighbors': 1}") + + +def test_impute_name_mapping_is_not_a_spec(): + # a dict with none of the spec keys is a NAME -> spec mapping (GH #285), + # so its keys are imputer names, never "unrecognized" spec keys + x = _impute_data() + out = hyp.impute(x, model={'1-NN': {'model': 'KNNImputer', + 'kwargs': {'n_neighbors': 1}}}) + assert list(out) == ['1-NN'] + + +# --- Pipeline / apply_model ------------------------------------------------ + +def test_pipeline_nested_whiten_changes_result(): + x = _reduce_data() + plain = hyp.Pipeline([{'model': 'PCA', 'kwargs': {'n_components': 2}}]) + white = hyp.Pipeline([{'model': 'PCA', + 'kwargs': {'n_components': 2, 'whiten': True}}]) + assert not np.allclose(plain.fit_transform(x), white.fit_transform(x)) + + +@pytest.mark.parametrize('step', [ + {'model': 'PCA', 'kwargs': {'n_components': 2}, 'whiten': True}, + ('pca', {'model': 'PCA', 'kwargs': {'n_components': 2}, 'whiten': True}), +]) +def test_pipeline_flat_spec_raises(step): + with pytest.raises(ValueError) as err: + hyp.Pipeline([step]) + _assert_names_and_suggests(err, 'whiten', + "{'n_components': 2, 'whiten': True}") + + +def test_apply_model_nested_whiten_changes_result(): + x = _reduce_data() + plain = hyp.apply_model(x, {'model': 'PCA'}, ndims=2) + white = hyp.apply_model(x, {'model': 'PCA', 'kwargs': {'whiten': True}}, + ndims=2) + assert not np.allclose(plain, white) + + +@pytest.mark.parametrize('as_list', [False, True]) +def test_apply_model_flat_spec_raises(as_list): + x = _reduce_data() + spec = {'model': 'PCA', 'whiten': True} + with pytest.raises(ValueError) as err: + hyp.apply_model(x, [spec] if as_list else spec, ndims=2) + _assert_names_and_suggests(err, 'whiten', "{'whiten': True}") + + +# --- text2mat -------------------------------------------------------------- + +_TEXTS = [ + 'the cat sat on the mat with another cat', + 'dogs and cats are friendly household pets', + 'the stock market fell sharply on monday morning', + 'investors sold shares as the market dropped', + 'a quiet cat naps in the warm afternoon sun', + 'bond yields rose while the market slid lower', +] + +# NMF on a six-document toy corpus hits its iteration cap; that is fixture +# noise, not what these tests are about +_nmf_noise = pytest.mark.filterwarnings( + 'ignore::sklearn.exceptions.ConvergenceWarning') + + +@_nmf_noise +def test_text2mat_nested_vectorizer_kwargs_change_result(): + from hypertools.tools import text2mat + a = text2mat(_TEXTS, vectorizer={'model': 'CountVectorizer'}, + semantic={'model': 'NMF', 'kwargs': {'n_components': 2, + 'random_state': 0}}) + b = text2mat(_TEXTS, vectorizer={'model': 'CountVectorizer', + 'kwargs': {'max_features': 3}}, + semantic={'model': 'NMF', 'kwargs': {'n_components': 2, + 'random_state': 0}}) + assert not np.allclose(np.vstack(a), np.vstack(b)) + + +@_nmf_noise +def test_text2mat_spec_args_are_honored(): + # a dict spec's positional 'args' were dropped too: NMF's first + # positional parameter is n_components, so args=[2] must give 2 columns + # (the registry default is 20) + from hypertools.tools import text2mat + out = text2mat(_TEXTS, vectorizer='CountVectorizer', + semantic={'model': 'NMF', 'args': [2], + 'kwargs': {'random_state': 0, + 'max_iter': 1000}}) + assert [np.shape(o) for o in out] == [(len(_TEXTS), 2)] + + +@pytest.mark.parametrize('which', ['vectorizer', 'semantic']) +def test_text2mat_flat_spec_raises(which): + from hypertools.tools import text2mat + vec = {'model': 'CountVectorizer'} + sem = {'model': 'NMF', 'kwargs': {'n_components': 2}} + if which == 'vectorizer': + vec = {'model': 'CountVectorizer', 'max_features': 3} + key, kw = 'max_features', "{'max_features': 3}" + else: + sem = {'model': 'NMF', 'kwargs': {'n_components': 2}, + 'random_state': 0} + key, kw = 'random_state', "{'n_components': 2, 'random_state': 0}" + with pytest.raises(ValueError) as err: + text2mat(_TEXTS, vectorizer=vec, semantic=sem) + _assert_names_and_suggests(err, key, kw) + + +# --- a misspelled 'model' keeps the dispatcher's own error ------------------ + +@pytest.mark.parametrize('call', [ + lambda x: hyp.reduce(x, reduce={'mode': 'PCA'}, ndims=2), + lambda x: hyp.apply_model(x, {'mode': 'PCA'}), + lambda x: hyp.impute(x, model={'mode': 'PPCA', 'kwargs': {}}), + # these two used to leak a bare KeyError: 'model' + lambda x: hyp.Pipeline([{'mode': 'PCA'}]), + lambda x: hyp.tools.text2mat(_TEXTS, + vectorizer={'mode': 'CountVectorizer'}), +]) +def test_missing_model_key_error_wins_over_the_flat_key_check(call): + # {'mode': 'PCA'} is a typo of 'model', not a flat parameter: the + # dispatcher's "'model' key" error is the right diagnosis + with pytest.raises(ValueError, match="'model' key"): + call(_reduce_data()) + + +# --- predict is exempt ----------------------------------------------------- + +def test_predict_spec_is_not_checked_here(): + # predict specs legitimately carry flat keys (t/horizon/block in plot's + # predict= spec); the dispatcher's own spec path is untouched + x = np.sin(np.arange(60) / 5.0).reshape(-1, 1) + fc = hyp.predict(x, model={'model': 'AutoRegressor', + 'kwargs': {'model': 'Ridge', 'lags': 5}}, t=3) + assert np.asarray(fc).shape == (3, 1) diff --git a/tests/test_fonts_bold.py b/tests/test_fonts_bold.py index 6275cb1d..0a881fbc 100644 --- a/tests/test_fonts_bold.py +++ b/tests/test_fonts_bold.py @@ -66,6 +66,26 @@ def test_findfont_regular_resolution_is_unchanged(): assert os.path.normpath(regular_path) == os.path.normpath(_REGULAR) +def test_bundled_font_wins_over_an_already_registered_copy(tmp_path): + # GH #285 release review: reproduce a same-family system font using + # another real font file in a fresh interpreter, without mocks. + import shutil + import subprocess + import sys + other = tmp_path / 'system-noto.ttf' + shutil.copyfile(_REGULAR, other) + script = ''' +import sys +from matplotlib import font_manager as fm +fm.fontManager.addfont(sys.argv[1]) +from hypertools.plot.fonts import register_bundled_fonts +register_bundled_fonts() +assert fm.findfont(fm.FontProperties(family='Noto Sans', weight='normal')) == sys.argv[2] +''' + subprocess.run([sys.executable, '-c', script, str(other), _REGULAR], + check=True, capture_output=True, text=True) + + def _render_title_rgba(fontweight): register_bundled_fonts() fig, ax = plt.subplots(figsize=(3, 2), dpi=100) @@ -95,3 +115,35 @@ def test_bold_face_will_ship_in_the_wheel_via_existing_package_data_glob(): import fnmatch assert fnmatch.fnmatch(os.path.basename(_BOLD), '*.ttf') assert os.path.isfile(_BOLD) + + +def test_explicit_bundled_family_resolves_in_a_fresh_process(): + """``hyp.plot(x, font='Noto Sans')`` -- the BUNDLED family -- raised + "not a recognized installed font family" in a fresh interpreter on a + machine without a system Noto Sans, because the bundled faces were only + registered as a side effect of an earlier plot (review 2026-09-11). + In-process state masks this (any earlier test has registered them), so + it runs as a real ``python -c`` subprocess. The subprocess reports which + FILE the family resolved to, so the assertion holds whether or not the + machine also has a system copy (the bundled face wins either way).""" + import subprocess + import sys + code = ( + "import matplotlib; matplotlib.use('Agg')\n" + "import numpy as np, hypertools as hyp\n" + "from matplotlib import font_manager\n" + "from hypertools.plot.fonts import resolve_font\n" + "fp = resolve_font('Noto Sans', ['hello'])\n" + "print('RESOLVED', font_manager.findfont(fp, " + "fallback_to_default=False))\n" + "fig = hyp.plot(np.random.default_rng(0).normal(size=(10, 3)), " + "font='Noto Sans', show=False)\n" + "print('PLOTTED', type(fig).__name__)\n") + out = subprocess.run([sys.executable, '-c', code], capture_output=True, + text=True, timeout=300, + env=dict(os.environ, MPLBACKEND='Agg')) + assert out.returncode == 0, out.stderr[-3000:] + lines = dict(line.split(' ', 1) for line in out.stdout.splitlines() + if line.startswith(('RESOLVED ', 'PLOTTED '))) + assert os.path.samefile(lines['RESOLVED'], _REGULAR), lines + assert lines['PLOTTED'] == 'Figure' diff --git a/tests/test_forecast_integer_times.py b/tests/test_forecast_integer_times.py new file mode 100644 index 00000000..9e003d79 --- /dev/null +++ b/tests/test_forecast_integer_times.py @@ -0,0 +1,69 @@ +"""Release review 2026-09-09: integer clocks must not overflow or lose gaps.""" +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp +from hypertools.predict.common import resolve_t +from hypertools.predict.time import infer_step, time_coordinates + + +MODELS = ['Kalman', 'ARIMA', 'GaussianProcess', + {'model': 'AutoRegressor', 'kwargs': {'lags': 2}}] +TIMES = np.array([0, 1, 3, 6, 7, 9, 13, 15, 17, 20, 23, 24]) + + +def frame(offset=0, dtype='int64'): + """Identical signals on clocks differing only in epoch and storage type.""" + return pd.DataFrame( + {'x': np.sin(TIMES), 'y': TIMES ** 2}, + index=pd.Index([offset + int(t) for t in TIMES], dtype=dtype)) + + +@pytest.mark.parametrize('model', MODELS) +@pytest.mark.parametrize('dtype,offset', [ + ('uint64', 0), ('UInt64', 0), + ('uint64', 2**63 + 10), ('UInt64', 2**63 + 10), + ('int64', 2**62 + 10), ('Int64', 2**62 + 10), +]) +def test_integer_clock_storage_and_epoch_do_not_change_forecasts(model, dtype, offset): + expected, reference = hyp.predict(frame(), model=model, t=3, return_model=True) + actual, fitted = hyp.predict(frame(offset, dtype), model=model, t=3, + return_model=True) + np.testing.assert_allclose(actual, expected) + assert [int(t) - offset for t in actual.index] == expected.index.tolist() + # Applying the learned model to a new epoch preserves its fitted time scale. + expected_reuse = reference.predict_new(frame(100), 3) + actual_reuse = fitted.predict_new(frame(offset + 100, dtype), 3) + np.testing.assert_allclose(actual_reuse, expected_reuse) + assert [int(t) - offset for t in actual_reuse.index] == expected_reuse.index.tolist() + + +@pytest.mark.parametrize('model', MODELS) +def test_unsigned_clock_backtesting_matches_signed_clock(model): + expected_scores, expected = hyp.predict(frame(), model=model, holdout=3, + return_forecasts=True) + offset = 2**63 + 10 + actual_scores, actual = hyp.predict(frame(offset, 'uint64'), model=model, + holdout=3, return_forecasts=True) + pd.testing.assert_frame_equal(actual_scores, expected_scores) + for name in expected: + np.testing.assert_allclose(actual[name], expected[name]) + assert [int(t) - offset for t in actual[name].index] == expected[name].index.tolist() + + +def test_signed_clock_gaps_across_dtype_bounds_do_not_overflow(): + index = pd.Index([-2**63 + 1, -2**62, 2**62, 2**63 - 1]) + expected_gaps = [int(b) - int(a) for a, b in zip(index[:-1], index[1:])] + assert infer_step(index) == np.median(expected_gaps) + expected = [(int(t) - int(index[-1])) / 2**62 for t in index] + np.testing.assert_allclose(time_coordinates(index, index[-1], 2**62), expected) + + +@pytest.mark.parametrize('dtype,limit', [('int64', 2**63 - 1), ('uint64', 2**64 - 1)]) +def test_integer_forecast_horizon_cannot_wrap_into_the_past(dtype, limit): + data = pd.DataFrame({'y': [1., 2., 3.]}, + index=pd.Index([limit - 2, limit - 1, limit], dtype=dtype)) + count, future = resolve_t(data, 3) + assert count == 3 + assert future.tolist() == [limit + 1, limit + 2, limit + 3] diff --git a/tests/test_forecast_times.py b/tests/test_forecast_times.py new file mode 100644 index 00000000..2e4e0737 --- /dev/null +++ b/tests/test_forecast_times.py @@ -0,0 +1,324 @@ +"""Forecasts must use observation times, independently of their display units.""" +import numpy as np +import pandas as pd +import pytest +import matplotlib.pyplot as plt +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.gaussian_process.kernels import DotProduct + +import hypertools as hyp +from hypertools.predict import GaussianProcess, Kalman + + +@pytest.mark.parametrize('model', ['Kalman', 'ARIMA', + {'model': 'AutoRegressor', 'kwargs': {'lags': 2}}]) +def test_discrete_models_fit_the_documented_interpolated_history(model): + times = np.array([0., 1., 3., 6., 7., 9., 13., 15.]) + values = np.column_stack([times ** 2, np.sin(times)]) + # Median gap is 2; the grid ends at 15 and stays inside [0, 15]. + grid = np.arange(1., 16., 2.) + expected_input = pd.DataFrame( + np.column_stack([np.interp(grid, times, col) for col in values.T]), index=grid) + expected = hyp.predict(expected_input, model=model, t=3) + frame = pd.DataFrame(values, index=times) + with pytest.warns(UserWarning, match='linearly interpolated'): + actual, fitted = hyp.predict(frame, model=model, t=3, return_model=True) + pd.testing.assert_frame_equal(actual, expected) + np.testing.assert_allclose(actual.index, [17, 19, 21]) + # Truncation returns observed rows, never synthetic interpolation rows. + pd.testing.assert_frame_equal(fitted.data, frame, check_flags=False) + + +def test_gp_fits_actual_times_against_a_real_sklearn_reference(): + times = np.array([0., 1., 3., 6., 7., 9., 13., 15.]) + values = np.column_stack([2 * times + 1, -times + 3]) + frame = pd.DataFrame(values, index=times) + kernel = DotProduct(sigma_0=1, sigma_0_bounds='fixed') + gp = GaussianProcess(kernel=kernel, alpha=1e-6, normalize_y=False) + result = gp.fit_predict(frame, 3) + reference = GaussianProcessRegressor(kernel=kernel, alpha=1e-6, + normalize_y=False).fit((times / 2)[:, None], values) + expected = reference.predict(np.array([17., 19., 21.])[:, None] / 2) + np.testing.assert_allclose(result, expected) + np.testing.assert_allclose(gp.models_[0]['gp'].X_train_.ravel(), times / 2) + + +@pytest.mark.parametrize('model', ['Kalman', 'GaussianProcess']) +def test_permuted_timestamped_observations_have_the_same_forecasts(model): + times = pd.date_range('2026-01-01', periods=20, freq='2h') + frame = pd.DataFrame(np.random.default_rng(7).normal(size=(20, 2)), index=times) + expected = hyp.predict(frame, model=model, t=3) + shuffled = frame.iloc[np.random.default_rng(11).permutation(len(frame))] + with pytest.warns(UserWarning, match='sorted'): + actual = hyp.predict(shuffled, model=model, t=3) + pd.testing.assert_frame_equal(actual, expected) + + +def test_steps_are_per_dataset_and_reused_models_keep_their_time_scale(): + values = np.random.default_rng(2).normal(size=(20, 1)).cumsum(axis=0) + frames = [pd.DataFrame(values, index=pd.date_range('2026-01-01', periods=20, freq=f)) + for f in ['1h', '3h']] + predictions, model = hyp.predict(frames, t=2, return_model=True) + for frame, forecast, hours in zip(frames, predictions, [1, 3]): + assert forecast.index[0] - frame.index[-1] == pd.Timedelta(hours=hours) + pd.testing.assert_frame_equal(model.for_dataset(1).predict(2), predictions[1]) + # An explicit 2-hour grid determines both fitting and future labels. + with pytest.warns(UserWarning, match='interpolated'): + overridden = Kalman(step='2h').fit_predict(frames[0], 2) + assert overridden.index[0] - frames[0].index[-1] == pd.Timedelta(hours=2) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('scale', [1., 100.]) +def test_series_plot_forecasts_match_joint_signal_forecasts(backend, scale): + if backend == 'plotly': + pytest.importorskip('plotly') + values = np.random.default_rng(42).normal(size=(20, 2)).cumsum(axis=0) + frame = pd.DataFrame(values, index=np.arange(20.) * scale) + expected = hyp.predict(frame, t=3) + result = hyp.plot(frame, ndims=1, reduce=None, predict='Kalman', t=3, + backend=backend, return_model=True, antialias=False, show=False) + try: + np.testing.assert_allclose(result['predict']['forecasts'][0], expected) + if backend == 'matplotlib': + traces = [line for line in result['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'static'] + for col, line in enumerate(traces): + np.testing.assert_allclose(line.get_xdata()[1:], expected.index) + np.testing.assert_allclose(line.get_ydata()[1:], expected.iloc[:, col]) + finally: + if backend == 'matplotlib': + plt.close(result['fig']) + + +def test_animated_series_final_forecasts_use_the_same_timed_joint_model(): + frame = pd.DataFrame(np.random.default_rng(6).normal(size=(12, 2)), + index=np.arange(12.) * 3) + expected = hyp.predict(frame, t=3) + result = hyp.plot(frame, ndims=1, reduce=None, predict='Kalman', t=3, + animate=True, duration=1, frame_rate=4, return_model=True, + antialias=False, show=False, slow_warning_seconds=None) + try: + animation = result['animation'] + animation._func(3, *animation._args) + traces = [line for line in result['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'live'] + assert len(traces) == 2 + for col, line in enumerate(traces): + np.testing.assert_allclose(line.get_xdata()[1:], expected.index) + np.testing.assert_allclose(line.get_ydata()[1:], expected.iloc[:, col]) + finally: + plt.close(result['fig']) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_datetime_horizon_uses_each_datasets_own_interval(backend): + if backend == 'plotly': + pytest.importorskip('plotly') + frames = [pd.DataFrame(np.arange(10.)[:, None], + index=pd.date_range('2026-01-01', periods=10, freq=f)) + for f in ['1h', '3h']] + target = frames[1].index[-1] + pd.Timedelta(hours=6) + expected = hyp.predict(frames, t=target) + bundle = hyp.plot(frames, ndims=1, reduce=None, predict='Kalman', t=target, + return_model=True, backend=backend, show=False) + for actual, reference in zip(bundle['predict']['forecasts'], expected): + np.testing.assert_allclose(actual, reference) + assert [len(f) for f in expected] == [24, 2] + if backend == 'matplotlib': + plt.close(bundle['fig']) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_column_hierarchy_forecasts_use_the_original_times(backend): + if backend == 'plotly': + pytest.importorskip('plotly') + columns = pd.MultiIndex.from_product([['A', 'B'], ['a', 'b'], ['x', 'y']]) + index = pd.to_datetime('2026-01-01') + pd.to_timedelta( + [0, 1, 3, 6, 7, 9, 13, 15, 17, 20, 23, 24], unit='h') + data = pd.DataFrame(np.random.default_rng(15).normal(size=(12, 8)), + columns=columns, index=index) + bundle = hyp.plot(data, ndims=2, reduce=None, predict='Kalman', t=3, + backend=backend, return_model=True, show=False) + for observed, actual in zip(bundle['trace_data'], bundle['predict']['forecasts']): + expected = hyp.predict(pd.DataFrame(observed, index=index), t=3) + np.testing.assert_allclose(actual, expected) + if backend == 'matplotlib': + plt.close(bundle['fig']) + + +def test_explicit_step_also_places_truth_and_forecast_on_the_same_grid(): + data = pd.DataFrame(np.arange(12.)[:, None], index=np.arange(12.) * 2) + result = hyp.plot(data, ndims=1, reduce=None, + predict={'model': 'Kalman', 'kwargs': {'step': 4}}, + t=3, truth=np.array([12., 13., 14.]), + return_model=True, show=False, antialias=False) + try: + overlays = [next(line for line in result['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == role) + for role in ('static', 'truth')] + for line in overlays: + np.testing.assert_allclose(line.get_xdata(), [22, 26, 30, 34]) + finally: + plt.close(result['fig']) + + +def test_arima_animation_waits_for_enough_interpolated_history(): + times = np.array([0, 1, 11, 21, 31, 41, 51, 61], dtype=float) + frame = pd.DataFrame(np.sin(times)[:, None], index=times) + bundle = hyp.plot(frame, ndims=1, reduce=None, animate=True, + predict={'model': 'ARIMA', 'kwargs': {'order': (4, 0, 0)}}, + t=2, duration=1, frame_rate=8, return_model=True, + slow_warning_seconds=None, show=False) + try: + animation = bundle['animation'] + animation._func(7, *animation._args) + live = [line for line in bundle['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'live'] + assert len(live) == 1 + assert len(live[0].get_xdata()) > 0 + finally: + plt.close(bundle['fig']) + + +@pytest.mark.parametrize('model', [Kalman(), GaussianProcess()]) +def test_reuse_at_a_different_cadence_preserves_the_learned_interval(model): + train = pd.DataFrame(np.sin(np.arange(24.) / 3), index=np.arange(24.)) + new = pd.DataFrame(np.cos(np.arange(12.) / 4), index=np.arange(12.) * 2) + model.fit(train) + actual = model.predict_new(new, 3) + np.testing.assert_allclose(actual.index, [23, 24, 25]) + if isinstance(model, Kalman): + grid = np.arange(23.) + dense = pd.DataFrame(np.interp(grid, new.index, new[0]), index=grid) + pd.testing.assert_frame_equal(actual, model.predict_new(dense, 3)) + else: + learned = model.models_[0]['gp'] + reference = GaussianProcessRegressor( + kernel=learned.kernel_, alpha=learned.alpha, + normalize_y=learned.normalize_y, optimizer=None) + reference.fit(np.asarray(new.index)[:, None], new) + np.testing.assert_allclose(actual.to_numpy().ravel(), + reference.predict(np.array([[23], [24], [25]]))) + + +@pytest.mark.parametrize('index', [pd.timedelta_range('0h', periods=12, freq='2h'), + pd.period_range('2026-01-01', periods=12, freq='D')]) +def test_duration_and_period_indexes_have_equivalent_time_coordinates(index): + data = pd.DataFrame(np.sin(np.arange(12.)), index=index) + expected = hyp.predict(data.reset_index(drop=True), t=2) + actual = hyp.predict(data, t=2) + np.testing.assert_allclose(actual, expected) + observed = index.to_timestamp() if isinstance(index, pd.PeriodIndex) else index + first = actual.index[0] + if isinstance(index, pd.PeriodIndex): + # forecasts of periods are periods (release review 2026-09-11) + assert isinstance(actual.index, pd.PeriodIndex) + first = first.start_time + assert first == observed[-1] + (observed[1] - observed[0]) + + +@pytest.mark.parametrize('step', [0, -1, float('inf'), float('nan'), True]) +def test_invalid_steps_fail_before_fitting(step): + with pytest.raises(ValueError, match='step must'): + hyp.predict(np.arange(12.), step=step, t=2) + + +@pytest.mark.parametrize('index', [pd.Index([0., 1., np.nan]), + pd.Index([0., 1., np.inf]), + pd.DatetimeIndex(['2026-01-01', '2026-01-02', pd.NaT])]) +def test_missing_or_infinite_observation_times_are_rejected(index): + with pytest.raises(ValueError, match='observation times must be finite'): + hyp.predict(pd.DataFrame([1., 2., 3.], index=index), t=2) + + +def test_truth_preserves_its_explicit_observation_times(): + data = pd.DataFrame(np.arange(12.), index=np.arange(12.) * 2) + truth = pd.DataFrame([12., 13., 14.], index=[23., 27., 29.]) + bundle = hyp.plot(data, ndims=1, reduce=None, predict='Kalman', t=3, + truth=truth, return_model=True, antialias=False, show=False) + try: + line = next(line for line in bundle['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'truth') + np.testing.assert_allclose(line.get_xdata(), [22, 23, 27, 29]) + finally: + plt.close(bundle['fig']) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_series_column_hierarchy_forecasts_values_at_actual_times(backend): + if backend == 'plotly': + pytest.importorskip('plotly') + columns = pd.MultiIndex.from_product([['A', 'B'], ['x']]) + times = pd.date_range('2026-01-01', periods=12, freq='2h') + frame = pd.DataFrame(np.random.default_rng(4).normal(size=(12, 2)), + index=times, columns=columns) + bundle = hyp.plot(frame, ndims=1, reduce=None, predict='Kalman', t=3, + return_model=True, backend=backend, show=False) + try: + for observed, actual in zip(bundle['trace_data'], bundle['predict']['forecasts']): + expected = hyp.predict(pd.DataFrame(observed[:, 1:], index=times), t=3) + assert actual.shape == (3, 1) + np.testing.assert_allclose(actual, expected) + finally: + if backend == 'matplotlib': + plt.close(bundle['fig']) + + +def test_animation_cost_counts_joint_fits_once_for_multiple_columns(): + data = np.random.default_rng(4).normal(size=(20, 2)) + with pytest.warns(UserWarning, match='needs 4 forecast fits'): + bundle = hyp.plot(data, ndims=1, reduce=None, predict='Kalman', t=3, + animate=True, duration=1, frame_rate=4, + slow_warning_seconds=0., show=False, return_model=True) + plt.close(bundle['fig']) + + +class _CountedKalman(Kalman): + """An ordinary Kalman forecaster recording its real fits for this test.""" + + fitted_histories = [] + + def fit(self, data): + type(self).fitted_histories.append(data) + return super().fit(data) + + +def test_repeated_animation_model_entries_each_fit_their_own_histories(): + _CountedKalman.fitted_histories = [] + frame = pd.DataFrame(np.random.default_rng(4).normal(size=(20, 2))) + bundle = hyp.plot(frame, ndims=1, reduce=None, + predict=[_CountedKalman, _CountedKalman], t=3, + animate=True, duration=1, frame_rate=4, + slow_warning_seconds=None, show=False, return_model=True) + try: + # Two static full-history fits, then three visible histories per + # model. Each two-column history must still be fitted jointly. + assert len(_CountedKalman.fitted_histories) == 8 + assert all((d[0] if isinstance(d, list) else d).shape[1] == 2 + for d in _CountedKalman.fitted_histories) + finally: + plt.close(bundle['fig']) + + +def test_animation_reuses_each_datasets_model_inside_a_dictionary_spec(): + frames = [pd.DataFrame(np.random.default_rng(i).normal(size=(12, 1)), + index=np.arange(12.) * step) + for i, step in enumerate([1, 3])] + _, fitted = hyp.predict(frames, t=2, return_model=True) + expected = hyp.predict(frames, model=fitted, t=2) + bundle = hyp.plot(frames, ndims=1, reduce=None, predict={'model': fitted}, + animate=True, t=2, duration=1, frame_rate=3, + slow_warning_seconds=None, antialias=False, + return_model=True, show=False) + try: + animation = bundle['animation'] + animation._func(2, *animation._args) + lines = [line for line in bundle['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'live'] + assert len(lines) == 2 + for line, reference in zip(lines, expected): + np.testing.assert_allclose(line.get_xdata()[1:], reference.index) + np.testing.assert_allclose(line.get_ydata()[1:], reference.iloc[:, 0]) + finally: + plt.close(bundle['fig']) diff --git a/tests/test_format_data.py b/tests/test_format_data.py index 63407b85..860ffd8a 100644 --- a/tests/test_format_data.py +++ b/tests/test_format_data.py @@ -174,3 +174,47 @@ def test_format_data_categorical_dataframe_warning_free(): warnings.simplefilter('error', DeprecationWarning) out = format_data(df) assert out[0].shape == (3, 3) + + +# --- datatype audit (2026-09-08): classification defers to datawrangler ---- + +def test_string_naming_an_existing_file_is_a_document_not_a_path(): + # format_data classifies text with a plain str/bytes test on purpose: + # datawrangler's `dw.zoo.is_text` interprets a string as a file path or + # URL first (loading it, or raising 'Unknown datatype: md' on an existing + # file with an unknown extension), and a user's document that happens to + # name a file must still be embedded as the text it IS. Pinned so the + # dw-predicate refactor of the coercion layer can never route documents + # through the loader. + import os + root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) + readme = os.path.join(root, 'readme.md') # lowercase in this repo: macOS + # resolves 'README.md' too, the Linux CI runners do not (2026-09-08) + pyproject = os.path.join(root, 'pyproject.toml') + assert os.path.exists(readme) and os.path.exists(pyproject) + out = format_data(readme) + assert isinstance(out, list) and out[0].shape == (1, 50) + out = format_data([readme, pyproject]) + assert out[0].shape == (2, 50) + + +def test_series_like_and_nested_inputs_keep_their_1_0_form(): + # the dw-predicate coercion layer must hand back exactly what the + # isinstance ladders did for the pandas/numpy inputs the 1.0 tests + # exercise: a Series (top-level or nested) is one 1-D column dataset + # with its values untouched, nested groups flatten, a masked array's + # masked cells become NaN + values = np.arange(6.) + s = pd.Series(values, index=list('abcdef'), name='s') + out = format_data(s) + assert out[0].shape == (6, 1) and np.array_equal(out[0][:, 0], values) + out = format_data([s, values]) + assert len(out) == 2 and all(o.shape == (6, 1) for o in out) + arr = np.arange(12.).reshape(6, 2) + out = format_data([[arr, (arr * 2,)], s]) + assert [o.shape for o in out] == [(6, 2), (6, 2), (6, 1)] + masked = np.ma.masked_array(arr, mask=arr == 4.) + import pytest + with pytest.warns(UserWarning, match='masked array with 1 masked'): + out = format_data(masked, ppca=False) + assert np.isnan(out[0][2, 0]) and np.isnan(out[0]).sum() == 1 diff --git a/tests/test_gensim_text.py b/tests/test_gensim_text.py index 8dd140f0..05f63d56 100644 --- a/tests/test_gensim_text.py +++ b/tests/test_gensim_text.py @@ -381,3 +381,26 @@ def test_word2vec_explicit_semantic_none_no_skip_warning(): "explicit semantic=None must not emit the skip warning" out = text2mat([DOCS], vectorizer='Word2Vec', semantic=None, corpus=None) assert out[0].shape[0] == len(DOCS) + + +# ------------------- dict-spec topic model + gensim vectorizer (1.1, X2) + + +@requires_gensim +def test_dict_spec_topic_model_is_skipped_for_a_gensim_vectorizer(): + # the string form warned and skipped the semantic stage; the dict form + # bypassed the guard (it tested isinstance(semantic, str)) and crashed + # inside NMF with "Negative values in data passed to NMF" + with pytest.warns(UserWarning, match="Word2Vec.*NMF.*skipping"): + out = text2mat([DOCS], vectorizer='Word2Vec', + semantic={'model': 'NMF', + 'kwargs': {'n_components': 2}}, + corpus=None) + assert len(out) == 1 + assert out[0].shape == (len(DOCS), 100) # the embeddings, unreduced + with pytest.warns(UserWarning, match="Word2Vec.*LatentDirichlet"): + out = text2mat([DOCS], vectorizer='Word2Vec', + semantic={'model': 'LatentDirichletAllocation', + 'params': {'n_components': 2}}, + corpus=None) + assert out[0].shape == (len(DOCS), 100) diff --git a/tests/test_hue_color.py b/tests/test_hue_color.py index 2d73cb35..4c899421 100644 --- a/tests/test_hue_color.py +++ b/tests/test_hue_color.py @@ -152,7 +152,9 @@ def test_wrong_length_nested_hue_still_errors(): rng = np.random.default_rng(3) data = [rng.standard_normal((300, 10)) for _ in range(3)] bad = [[0] * 299 for _ in range(3)] # 299 != 300 per dataset - with pytest.raises(ValueError, match="observations"): + # the error names the offending sub-list and both lengths (1.1 release + # review): the old message only counted "observations" + with pytest.raises(ValueError, match=r"hue\[0\] has 299 entries but dataset 0 has 300 rows"): hyp.plot(data, '.', hue=bad, show=False) diff --git a/tests/test_impute_backtest.py b/tests/test_impute_backtest.py index ceeab769..505377f9 100644 --- a/tests/test_impute_backtest.py +++ b/tests/test_impute_backtest.py @@ -10,6 +10,8 @@ damage; "a perfect fill scores 0" is exercised on a constant column, where the column-mean fill is exactly right by construction. """ +import warnings + import numpy as np import pandas as pd import pytest @@ -18,6 +20,32 @@ from hypertools.impute.common import Imputer +@pytest.mark.parametrize('wrapped', [False, True]) +def test_scoring_does_not_fit_the_callers_instance(wrapped): + from hypertools.impute import SimpleImputer + truth = pd.DataFrame({'x': [1., 2., 3., 4.]}) + damaged = truth.copy() + damaged.iloc[1, 0] = np.nan + model = SimpleImputer() + spec = {'model': model} if wrapped else model + expected = hyp.impute(damaged, model='SimpleImputer', truth=truth) + actual = hyp.impute(damaged, model=spec, truth=truth) + pd.testing.assert_frame_equal(actual, expected) + assert not model.is_fitted + + +@pytest.mark.parametrize('wrapped', [False, True]) +def test_scoring_refuses_an_imputer_that_has_seen_the_truth(wrapped): + from hypertools.impute import SimpleImputer + truth = pd.DataFrame({'x': [1., 2., 3., 4.]}) + model = SimpleImputer().fit(truth) + damaged = truth.copy() + damaged.iloc[1, 0] = np.nan + spec = {'model': model} if wrapped else model + with pytest.raises(ValueError, match='truth=.*unfitted'): + hyp.impute(damaged, model=spec, truth=truth) + + def _arc(n=40, seed=0): """A smooth, projectile-like trajectory (the tutorial's setting).""" t = np.linspace(0, 2, n) @@ -294,3 +322,286 @@ def test_unknown_metric_raises(): truth = _arc() with pytest.raises(ValueError, match='unknown metric'): hyp.impute(_damage(truth), model='PPCA', truth=truth, metrics='r2') + + +# --- 1.1 release review: metrics / warning attribution ------------------- + +def test_repeated_metric_is_rejected_by_name(): + # used to reach build_scores and die with "float() argument must be + # ... not 'Series'" + truth = _arc() + damaged = _damage(truth) + with pytest.raises(ValueError, match="metric 'mae' is listed more than once"): + hyp.impute(damaged, model='KNNImputer', truth=truth, metrics=['mae', 'mae']) + with pytest.raises(ValueError, match="metric 'MAE' is listed more than once"): + hyp.impute(damaged, model='KNNImputer', truth=truth, metrics=['mae', 'MAE']) + + +def test_unscored_warning_points_at_the_caller(): + import os + import hypertools + package_dir = os.path.dirname(os.path.abspath(hypertools.__file__)) + truth = _arc() + damaged = _damage(truth) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores = hyp.impute(damaged, model=['PPCA', 'Kalman'], truth=truth) + assert scores.loc['PPCA', 'unscored'] == 15 + unscored = [w for w in caught if 'not directly comparable' in str(w.message)] + assert len(unscored) == 1 + assert unscored[0].filename == __file__ + assert not unscored[0].filename.startswith(package_dir + os.sep) + + +# --- incomplete models never win (release red-team 2026-10-09) ------------- + +def _outlier_arc(): + """A trajectory whose occluded band holds large values: a model that + skips the band is graded only on the easy scattered cells.""" + t = np.linspace(0, 2, 40) + truth = pd.DataFrame({'x': 3 * t, 'y': 1.5 * t, 'z': 6 + 10 * t - 5 * t * t}) + truth.iloc[15:20] += 100 + return truth, hyp.damage(truth, rows=slice(15, 20), frac=.1, seed=1) + + +def test_incomplete_model_never_wins(): + # PPCA cannot fill the 5 occluded rows, so its MAE (0.94 over the 10 + # scattered cells) covers none of the hard ones; SimpleImputer filled + # all 25 (MAE 63.7). The partial score used to be ranked first. + truth, damaged = _outlier_arc() + np.random.seed(0) + with pytest.warns(UserWarning, match='excluded from the ranking'): + scores = hyp.impute(damaged, model=['PPCA', 'SimpleImputer'], + truth=truth) + assert scores.loc['PPCA', 'n'] == 10 + assert scores.loc['PPCA', 'unscored'] == 15 + assert scores.loc['SimpleImputer', 'unscored'] == 0 + # the descriptive row is kept, and it IS the smaller number + assert scores.loc['PPCA', 'MAE'] < scores.loc['SimpleImputer', 'MAE'] + assert scores.attrs['best'] == 'SimpleImputer' + assert scores.attrs['best_score'] == pytest.approx( + scores.loc['SimpleImputer', 'MAE']) + assert scores.attrs['incomplete'] == ['PPCA'] + # SimpleImputer IS the column-mean baseline: equal, so not strictly below + assert scores.attrs['beats_baseline'] is False + + +def test_complete_comparison_reports_no_incomplete_models(): + truth = _arc() + damaged = _damage(truth) + scores = hyp.impute(damaged, model=['Kalman', 'KNNImputer'], truth=truth) + assert scores.attrs['incomplete'] == [] + assert scores.attrs['best'] in ('Kalman', 'KNNImputer') + assert isinstance(scores.attrs['beats_baseline'], bool) + + +def test_no_complete_model_gives_no_verdict(): + truth, damaged = _outlier_arc() + np.random.seed(0) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores = hyp.impute(damaged, model='PPCA', truth=truth) + assert scores.loc['PPCA', 'unscored'] == 15 + assert np.isfinite(scores.loc['PPCA', 'MAE']) # row still described + assert scores.attrs['best'] is None + assert np.isnan(scores.attrs['best_score']) + assert scores.attrs['beats_baseline'] is None + assert scores.attrs['incomplete'] == ['PPCA'] + messages = [str(w.message) for w in caught] + assert any('no model produced every' in m for m in messages) + + +def test_per_column_verdict_ranks_complete_models_only(): + truth, damaged = _outlier_arc() + np.random.seed(0) + with pytest.warns(UserWarning, match='excluded from the ranking'): + long = hyp.impute(damaged, model=['PPCA', 'SimpleImputer'], + truth=truth, per_column=True) + assert list(long.index.names) == ['model', 'column'] + assert (long.loc['PPCA', 'unscored'] == 5).all() + assert long.attrs['best'] == 'SimpleImputer' + assert long.attrs['incomplete'] == ['PPCA'] + + +def test_model_incomplete_on_one_dataset_is_incomplete_for_the_verdict(): + # dataset 0 has scattered damage only (PPCA fills all of it); dataset 1 + # has an occluded band PPCA cannot fill. The verdict averages both, so + # PPCA's coverage is judged over both. + truth, occluded = _outlier_arc() + scattered = hyp.damage(truth, frac=.1, seed=2) + np.random.seed(0) + with pytest.warns(UserWarning, match='excluded from the ranking'): + scores = hyp.impute([scattered, occluded], + model=['PPCA', 'SimpleImputer'], + truth=[truth, truth]) + np.random.seed(0) + with pytest.warns(UserWarning): + long = hyp.impute([scattered, occluded], + model=['PPCA', 'SimpleImputer'], + truth=[truth, truth], per_column=True) + assert long.loc[('PPCA', 0), 'unscored'].sum() == 0 + assert long.loc[('PPCA', 1), 'unscored'].sum() == 15 + assert scores.loc['PPCA', 'unscored'] == 15 + assert scores.attrs['incomplete'] == ['PPCA'] + assert scores.attrs['best'] == 'SimpleImputer' + assert long.attrs['best'] == 'SimpleImputer' + assert long.attrs['incomplete'] == ['PPCA'] + + +# --- truth=/mask= are aligned by label (release red-team 2026-10-09) ------- + +def _labelled(): + truth = pd.DataFrame({'a': [1., 2., 3., 4.], 'b': [10., 20., 30., 40.]}, + index=['r0', 'r1', 'r2', 'r3']) + damaged = truth.copy() + damaged.iloc[1, 0] = np.nan + damaged.iloc[2, 1] = np.nan + return truth, damaged + + +def _mae(damaged, truth, **kwargs): + return float(hyp.impute(damaged, model='SimpleImputer', truth=truth, + **kwargs).loc['SimpleImputer', 'MAE']) + + +def test_truth_is_aligned_to_the_data_by_label(): + truth, damaged = _labelled() + expected = _mae(damaged, truth) + assert expected == pytest.approx(11 / 3) # 3.667 + # reordered columns used to give 18.833, reversed rows 1.833 + assert _mae(damaged, truth[['b', 'a']]) == pytest.approx(expected) + assert _mae(damaged, truth.iloc[::-1]) == pytest.approx(expected) + assert _mae(damaged, truth.iloc[::-1][['b', 'a']]) == pytest.approx(expected) + + +def test_returned_truth_is_in_the_data_order(): + truth, damaged = _labelled() + _, out = hyp.impute(damaged, model='SimpleImputer', + truth=truth.iloc[::-1][['b', 'a']], + return_imputed=True) + pd.testing.assert_frame_equal(out['truth'], truth) + + +def test_default_integer_labels_are_labels_too(): + # a frame's default 0..n-1 index is still a set of row labels: a + # reversed truth frame carries them, and is put back in the data's order + truth, damaged = _labelled() + truth, damaged = truth.reset_index(drop=True), damaged.reset_index(drop=True) + assert _mae(damaged, truth.iloc[::-1]) == pytest.approx(11 / 3) + + +def test_mask_is_aligned_to_the_data_by_label(): + truth, damaged = _labelled() + mask = pd.DataFrame(False, index=truth.index, columns=truth.columns) + mask.loc['r1', 'a'] = True # score only the (r1, a) cell + expected = hyp.impute(damaged, model='SimpleImputer', truth=truth, + mask=mask) + assert expected.loc['SimpleImputer', 'n'] == 1 + assert expected.loc['SimpleImputer', 'MAE'] == pytest.approx(2 / 3) + shuffled = hyp.impute(damaged, model='SimpleImputer', truth=truth, + mask=mask.iloc[::-1][['b', 'a']]) + pd.testing.assert_frame_equal(shuffled, expected) + + +@pytest.mark.parametrize('what', ['truth', 'mask']) +def test_mismatched_labels_are_rejected(what): + truth, damaged = _labelled() + mask = pd.DataFrame(True, index=truth.index, columns=truth.columns) + given = {'truth': truth, 'mask': mask} + given[what] = given[what].rename(columns={'b': 'c'}) + with pytest.raises(ValueError) as err: + hyp.impute(damaged, model='SimpleImputer', **given) + message = str(err.value) + assert what in message and 'column' in message + assert "'c'" in message and "'b'" in message + given = {'truth': truth, 'mask': mask} + given[what] = given[what].rename(index={'r3': 'r9'}) + with pytest.raises(ValueError) as err: + hyp.impute(damaged, model='SimpleImputer', **given) + message = str(err.value) + assert what in message and 'row' in message + assert "'r9'" in message and "'r3'" in message + + +def test_duplicated_labels_that_need_aligning_are_rejected(): + truth, damaged = _labelled() + dup_truth = truth.rename(index={'r1': 'r0'}).iloc[::-1] + with pytest.raises(ValueError, match=r"truth.*row.*duplicated.*'r0'"): + hyp.impute(damaged, model='SimpleImputer', truth=dup_truth) + dup_data = damaged.rename(columns={'b': 'a'}) + with pytest.raises(ValueError, match=r"column.*duplicated.*'a'"): + hyp.impute(dup_data, model='SimpleImputer', truth=truth) + + +def test_identically_labelled_duplicates_stay_positional(): + # repeated labels in the SAME order on both sides need no alignment + truth, damaged = _labelled() + relabel = {'r1': 'r0', 'r3': 'r2'} + assert _mae(damaged.rename(index=relabel), + truth.rename(index=relabel)) == pytest.approx(11 / 3) + + +def test_bare_arrays_stay_positional(): + truth, damaged = _labelled() + expected = 11 / 3 + # array / array, labelled data / array truth, array data / labelled truth + assert _mae(damaged.to_numpy(), truth.to_numpy()) == pytest.approx(expected) + assert _mae(damaged, truth.to_numpy()) == pytest.approx(expected) + assert _mae(damaged.to_numpy(), truth) == pytest.approx(expected) + # an array carries no labels, so a reordered one IS scored as given + assert _mae(damaged, truth[['b', 'a']].to_numpy()) == pytest.approx(113 / 6) + with pytest.raises(ValueError, match='must match cell for cell'): + hyp.impute(damaged, model='SimpleImputer', truth=truth.to_numpy()[:3]) + + +def test_frames_with_different_kinds_of_labels_are_rejected(): + # data indexed by dates, truth by the default 0..n-1: both are labelled + # frames whose row labels differ. Guessing "positional" could mis-score + # silently, so it is an error that names the fix. + truth, damaged = _labelled() + dates = pd.date_range('2026-01-01', periods=4) + dated = damaged.set_axis(dates, axis=0) + with pytest.raises(ValueError, match=r'(?s)truth.*row labels.*to_numpy'): + hyp.impute(dated, model='SimpleImputer', + truth=truth.reset_index(drop=True)) + assert _mae(dated, truth.to_numpy()) == pytest.approx(11 / 3) + assert _mae(dated, truth.set_axis(dates, axis=0)) == pytest.approx(11 / 3) + + +def test_each_dataset_is_aligned_independently(): + truth, damaged = _labelled() + other = truth.rename(index=lambda r: r.upper()) * 2 + other_damaged = other.copy() + other_damaged.iloc[0, 1] = np.nan + expected = hyp.impute([damaged, other_damaged], model='SimpleImputer', + truth=[truth, other], per_column=True) + shuffled = hyp.impute([damaged, other_damaged], model='SimpleImputer', + truth=[truth[['b', 'a']], other.iloc[::-1]], + per_column=True) + pd.testing.assert_frame_equal(shuffled, expected) + with pytest.raises(ValueError, match=r'(?s)truth\[1\].*row'): + hyp.impute([damaged, other_damaged], model='SimpleImputer', + truth=[truth, truth]) + + +def test_polars_truth_is_aligned_by_column_name(): + pl = pytest.importorskip('polars') + truth, damaged = _labelled() + # polars frames have column names but no row labels: columns are + # aligned by name, rows compared by position + swapped = pl.from_pandas(truth[['b', 'a']].reset_index(drop=True)) + assert _mae(damaged, swapped) == pytest.approx(11 / 3) + plain = damaged.reset_index(drop=True) + assert _mae(pl.from_pandas(plain), swapped) == pytest.approx(11 / 3) + with pytest.raises(ValueError, match=r"(?s)truth.*column.*'c'"): + hyp.impute(damaged, model='SimpleImputer', + truth=swapped.rename({'b': 'c'})) + + +def test_series_truth_is_aligned_by_row_label(): + truth = pd.DataFrame({'x': [1., 2., 3., 40.]}, index=list('abcd')) + damaged = truth.copy() + damaged.iloc[3, 0] = np.nan + expected = _mae(damaged, truth) + assert expected == pytest.approx(38.) + assert _mae(damaged, truth['x'].iloc[::-1]) == pytest.approx(expected) diff --git a/tests/test_lazy_import.py b/tests/test_lazy_import.py index 6ab8c1e6..c10455a6 100644 --- a/tests/test_lazy_import.py +++ b/tests/test_lazy_import.py @@ -115,3 +115,410 @@ def test_ensure_kaleido_chrome_leaves_plotly_able_to_render(): L.ensure_kaleido_chrome() assert len(pio.to_image(go.Figure(), format='png')) > 1000 assert L._kaleido_ready + + +# --- every optional import in the library goes through lazy_import ---------- + +#: modules that import an optional package WITHOUT calling lazy_import for it +#: in the same file, each with the reason that is correct +_LAZY_IMPORT_EXEMPT = { + # a type check that must never install anything: `import plotly` inside + # try/except returns False for "not a plotly figure" when plotly is absent + 'hypertools/plot/plot.py': {'plotly'}, + # imports plotly only on the `resolve_backend(backend) == 'plotly'` branch, + # and resolve_backend() installs the [interactive] extra on demand first + 'hypertools/reduce/describe.py': {'plotly'}, + # the animation-export subprocess: the parent serialised the figure with + # plotly already imported (lazy_import('plotly') in resolve_backend), and + # the worker itself calls ensure_kaleido_chrome() -- which lazy-imports + # kaleido -- under the parent's propagated set_autoinstall() setting + # (lazy_import.subprocess_env; release audit 2026-09-07) + 'hypertools/plot/_kaleido_export_worker.py': {'plotly'}, +} + + +def _optional_imports(source): + """Top-level names of EXTRA_FOR_MODULE that `source` imports.""" + import ast + found = set() + for node in ast.walk(ast.parse(source)): + if isinstance(node, ast.Import): + names = [a.name for a in node.names] + elif isinstance(node, ast.ImportFrom) and node.level == 0 and node.module: + names = [node.module] + else: + continue + for name in names: + top = name.split('.')[0] + if top in L.EXTRA_FOR_MODULE: + found.add(top) + return found + + +def test_every_optional_import_in_the_library_goes_through_lazy_import(): + """The on-demand installer only helps where the code asks for it: a plain + `import plotly` in a module that never calls `lazy_import('plotly')` (or + `ensure_kaleido_chrome()`, which lazy-imports kaleido) would raise + ImportError before the extra could be installed. Scan every library + module for imports of the packages EXTRA_FOR_MODULE maps and require the + same file to install them, unless it is exempt above for a stated + reason. (Release review 2026-09-07: the audit of leftover install + instructions asked whether every site really installs on demand.)""" + pkg = os.path.join(REPO, 'hypertools') + missing = [] + seen_exempt = set() + for root, _dirs, files in os.walk(pkg): + for fn in files: + if not fn.endswith('.py'): + continue + path = os.path.join(root, fn) + rel = os.path.relpath(path, REPO).replace(os.sep, '/') + if rel == 'hypertools/_shared/lazy_import.py': + continue + with open(path, encoding='utf-8') as f: + source = f.read() + for top in sorted(_optional_imports(source)): + if top in _LAZY_IMPORT_EXEMPT.get(rel, ()): + seen_exempt.add((rel, top)) + continue + installs = (re.search(r"lazy_import\(\s*['\"]" + top + r"\b", source) + or (top == 'kaleido' + and 'ensure_kaleido_chrome(' in source)) + if not installs: + missing.append((rel, top)) + assert not missing, missing + # every exemption still describes a real import (no stale entries) + declared = {(rel, top) for rel, tops in _LAZY_IMPORT_EXEMPT.items() + for top in tops} + assert seen_exempt == declared, declared - seen_exempt + + +def test_the_optional_import_scan_sees_a_plain_import(): + assert _optional_imports('import plotly.graph_objects as go') == {'plotly'} + assert _optional_imports('from skimage import measure') == {'skimage'} + # sklearn.datasets is not the `datasets` package + assert _optional_imports('from sklearn import datasets') == set() + assert _optional_imports('from .common import Forecaster') == set() + + +# --- hyp.set_autoinstall: the public switch ----------------------------------- + +@pytest.fixture +def _restore_autoinstall(monkeypatch): + """Leave the module-level setting as this test found it.""" + monkeypatch.setattr(L, '_AUTO_INSTALL_SCOPES', list(L._AUTO_INSTALL_SCOPES)) + monkeypatch.setattr(L, '_AUTO_INSTALL_BASELINE', [None, L._AUTO_INSTALL_BASELINE[1]]) + monkeypatch.delenv('HYPERTOOLS_AUTO_INSTALL', raising=False) + + +def test_set_autoinstall_is_public_and_mirrors_set_interactive_backend(_restore_autoinstall): + import hypertools as hyp + assert hyp.set_autoinstall is L.set_autoinstall + assert 'set_autoinstall' in hyp.__all__ + assert L.auto_install_enabled() is True # the default + handle = hyp.set_autoinstall(False) # called directly + assert handle.enabled is False and repr(handle) == 'set_autoinstall(False)' + assert L.auto_install_enabled() is False + with hyp.set_autoinstall(True) as inner: # context manager + assert inner.enabled is True + assert L.auto_install_enabled() is True + assert L.auto_install_enabled() is False # restored + hyp.set_autoinstall() # default: on + assert L.auto_install_enabled() is True + + +def test_set_autoinstall_overrides_the_environment_variable(_restore_autoinstall, monkeypatch): + import hypertools as hyp + monkeypatch.setenv('HYPERTOOLS_AUTO_INSTALL', '0') + assert L.auto_install_enabled() is False # env sets the start + with hyp.set_autoinstall(True): + assert L.auto_install_enabled() is True # the call wins + assert L.auto_install_enabled() is False + + +def test_set_autoinstall_rejects_non_booleans(_restore_autoinstall): + import hypertools as hyp + for bad in (1, 'off', None): + with pytest.raises(TypeError, match='True or False'): + hyp.set_autoinstall(bad) + assert L.auto_install_enabled() is True # nothing changed + + +def test_set_autoinstall_off_fails_with_the_manual_command_without_pip(_restore_autoinstall, capsys): + """With installation off, a missing module raises at once with the manual + command and the way back on, BEFORE the install branch. Three real + observables say pip never ran: the `hypertools: installing ...` notice + that precedes every pip run is absent from stdout, the error is the + policy one (not the `installing it automatically failed` one a pip + failure produces), and the module is still absent afterwards.""" + import importlib.util + import hypertools as hyp + name = 'hypertools_no_such_module_xyz' + assert importlib.util.find_spec(name) is None + with hyp.set_autoinstall(False), \ + pytest.raises(ImportError, match=r'hypertools\[kaggle\].*set_autoinstall\(True\)') as info: + L.lazy_import(name, purpose='a test', extra='kaggle', requirements=[name]) + assert 'automatic installation is off' in str(info.value) + assert 'automatically failed' not in str(info.value) + captured = capsys.readouterr() + assert 'hypertools: installing' not in captured.out + assert captured.out == '' and captured.err == '' + importlib.invalidate_caches() + assert importlib.util.find_spec(name) is None + + +# --- the setting crosses a process boundary ---------------------------------- + +def test_subprocess_env_carries_the_effective_setting_to_a_child_interpreter(_restore_autoinstall, monkeypatch): + """`set_autoinstall` lives in this interpreter; a child started with + `subprocess` begins from HYPERTOOLS_AUTO_INSTALL. `subprocess_env` sets + that variable from the EFFECTIVE value, so the child starts where the + parent stands in both directions of disagreement (release audit + 2026-09-07: the animation-export worker ran pip with installation off in + the parent). Each case is checked in a REAL child interpreter.""" + import hypertools as hyp + probe = [sys.executable, '-c', + 'from hypertools._shared.lazy_import import auto_install_enabled; ' + 'print(auto_install_enabled())'] + + def child_sees(env): + out = subprocess.run(probe, env=env, capture_output=True, text=True, + timeout=300) + assert out.returncode == 0, out.stderr[-800:] + return out.stdout.strip() + + # Python True over the environment's 0: the child installs + monkeypatch.setenv('HYPERTOOLS_AUTO_INSTALL', '0') + with hyp.set_autoinstall(True): + env = L.subprocess_env() + assert env['HYPERTOOLS_AUTO_INSTALL'] == '1' + assert child_sees(env) == 'True' + # Python False with the variable unset: the child does not ... + monkeypatch.delenv('HYPERTOOLS_AUTO_INSTALL') + with hyp.set_autoinstall(False): + env = L.subprocess_env() + assert env['HYPERTOOLS_AUTO_INSTALL'] == '0' + assert child_sees(env) == 'False' + # ... whereas an inherited environment loses the setting (the audit) + assert child_sees(dict(os.environ)) == 'True' + # nothing set from Python: the environment's own value passes through + monkeypatch.setenv('HYPERTOOLS_AUTO_INSTALL', 'off') + assert L.subprocess_env()['HYPERTOOLS_AUTO_INSTALL'] == '0' + # an explicit base environment is copied, not modified + base = {'PATH': os.environ.get('PATH', '')} + derived = L.subprocess_env(base) + assert derived['HYPERTOOLS_AUTO_INSTALL'] == '0' and derived['PATH'] == base['PATH'] + assert 'HYPERTOOLS_AUTO_INSTALL' not in base + + +# --- overlapping contexts (Codex round 6, finding 1) ------------------------- + +def test_exiting_an_older_context_leaves_the_newer_one_in_force(_restore_autoinstall): + """Two `with` blocks open at once (two threads, or a block entered from + inside another's lifetime): closing the OLDER one used to restore the + value saved before either, switching installation back on inside the + newer OFF block. The newest setting still in force decides, and a block + only removes its own.""" + import hypertools as hyp + assert L.auto_install_enabled() is True + a = hyp.set_autoinstall(False) + a.__enter__() + b = hyp.set_autoinstall(False) + b.__enter__() + a.__exit__(None, None, None) # older block exits first + assert L.auto_install_enabled() is False # b still in force + b.__exit__(None, None, None) + assert L.auto_install_enabled() is True # back to the start + # a direct call underneath a block is what the block restores to + hyp.set_autoinstall(False) + with hyp.set_autoinstall(True): + assert L.auto_install_enabled() is True + assert L.auto_install_enabled() is False + + +def test_overlapping_contexts_across_threads_keep_installation_off(_restore_autoinstall): + """The reviewer's shape, made deterministic with events: thread A enters + OFF, thread B enters OFF, A exits, B checks (must still be OFF), B exits, + the main thread checks (back to ON).""" + import threading + import hypertools as hyp + seen = {} + a_in, b_in, a_out = threading.Event(), threading.Event(), threading.Event() + + def a(): + with hyp.set_autoinstall(False): + a_in.set() + seen['a_saw_b'] = b_in.wait(10) + a_out.set() + + def b(): + seen['b_saw_a'] = a_in.wait(10) + with hyp.set_autoinstall(False): + b_in.set() + seen['b_saw_a_exit'] = a_out.wait(10) + seen['inside_b_after_a_exit'] = L.auto_install_enabled() + + ta, tb = threading.Thread(target=a), threading.Thread(target=b) + ta.start() + tb.start() + ta.join(10) + tb.join(10) + assert not ta.is_alive() and not tb.is_alive() + # every hand-off happened (a timed-out wait would run the check without + # the overlap it is meant to test) + assert seen == {'b_saw_a': True, 'a_saw_b': True, 'b_saw_a_exit': True, + 'inside_b_after_a_exit': False} + assert L.auto_install_enabled() is True + + +def test_superseded_direct_calls_are_not_retained(_restore_autoinstall): + """Codex round 7: every direct call appended a strong reference and only a + block's exit removed one, so 100k direct calls kept 100k handles alive. + A new setting replaces a superseded one no block holds open, keeping + only its value; the semantics above are unchanged.""" + import gc + import weakref + import hypertools as hyp + first = hyp.set_autoinstall(False) + ref = weakref.ref(first) + del first + for i in range(10_000): + hyp.set_autoinstall(bool(i % 2)) + gc.collect() + assert ref() is None # not retained + assert len(L._AUTO_INSTALL_SCOPES) == 0 # nothing kept + assert L.auto_install_enabled() is True # the last call + # the value a block supersedes comes back when the block exits + hyp.set_autoinstall(False) + with hyp.set_autoinstall(True): + assert L.auto_install_enabled() is True + assert L.auto_install_enabled() is False + assert len(L._AUTO_INSTALL_SCOPES) == 0 + + +def test_a_constructed_but_not_yet_entered_context_survives_another_thread(_restore_autoinstall): + """Codex round 8: thread A constructs `set_autoinstall(True)` and only + then enters it; between the two, thread B constructs and enters + `set_autoinstall(False)`. The bounded stack collapsed A's record as a + superseded direct call, so B's exit restored the wrong value and A's + block was never in force. A live handle is never collapsed.""" + import threading + import hypertools as hyp + hyp.set_autoinstall(False) + created, b_entered, a_exited = (threading.Event() for _ in range(3)) + seen = {} + + def a(): + handle = hyp.set_autoinstall(True) # constructed ... + created.set() + seen['a_wait'] = b_entered.wait(10) + with handle: # ... entered later + seen['a_inside_after_b_enter'] = L.auto_install_enabled() + a_exited.set() + + def b(): + seen['b_wait'] = created.wait(10) + with hyp.set_autoinstall(False): + b_entered.set() + seen['b_wait_exit'] = a_exited.wait(10) + seen['b_inside_after_a_exit'] = L.auto_install_enabled() + + ta, tb = threading.Thread(target=a), threading.Thread(target=b) + ta.start() + tb.start() + ta.join(15) + tb.join(15) + assert not ta.is_alive() and not tb.is_alive() + # the newest CALL decides (B was constructed after A, so B's False is in + # force while both blocks are open); A's exit leaves B's block in force; + # B's exit restores the initial direct call + assert seen == {'a_wait': True, 'b_wait': True, 'b_wait_exit': True, + 'a_inside_after_b_enter': False, + 'b_inside_after_a_exit': False} + assert L.auto_install_enabled() is False + + +# --- re-entering a handle follows the same call order (review 2026-09-11) ----- + +def test_reentering_an_older_handle_ranks_by_call_order_like_a_first_entry(_restore_autoinstall): + """The newest CALL decides, and entering a handle is not a new call (the + round-8 test above: A, constructed before B, does not outrank B's block + by entering later). A RE-entered handle broke that: its record was + appended back on top of the list with its old construction number, so + it outranked a newer handle kept in a variable -- while the same handle + entered for the first time, or re-entered after a newer call that was + discarded, did not. All three shapes now agree.""" + import hypertools as hyp + + # first entry of an older handle, newer call kept in a variable + older = hyp.set_autoinstall(False) + newer = hyp.set_autoinstall(True) + with older: + first_entry = L.auto_install_enabled() + del older, newer + + # re-entry of an older handle, newer call kept in a variable + again = hyp.set_autoinstall(False) + with again: + assert L.auto_install_enabled() is False + kept = hyp.set_autoinstall(True) + with again: + reentry_kept = L.auto_install_enabled() + assert L.auto_install_enabled() is True + + # re-entry of an older handle, newer call discarded + del kept + import gc + gc.collect() + with again: + reentry_discarded = L.auto_install_enabled() + + assert first_entry is reentry_kept is reentry_discarded is True + assert L.auto_install_enabled() is True + + +def test_reentered_handle_record_keeps_the_list_in_call_order(_restore_autoinstall): + import hypertools as hyp + a = hyp.set_autoinstall(False) + with a: + pass + b = hyp.set_autoinstall(True) + c = hyp.set_autoinstall(True) + with a: + seqs = [s.seq for s in L._AUTO_INSTALL_SCOPES] + assert seqs == sorted(seqs) # oldest first, as documented + assert L.auto_install_enabled() is True # c is the newest call + del b, c + + +# --- interpreter shutdown (review 2026-09-11) -------------------------------- + +@pytest.mark.parametrize('body', [ + # the reviewer's repro: a direct-call handle kept in a module global + 'cm = hyp.set_autoinstall(False)', + # a handle whose block has exited, kept alive until shutdown + 'cm = hyp.set_autoinstall(False)\nwith cm:\n pass', + # a handle still inside a block that is never exited + 'cm = hyp.set_autoinstall(False)\ncm.__enter__()', + # several live handles and a baseline + 'hyp.set_autoinstall(True)\na = hyp.set_autoinstall(False)\n' + 'b = hyp.set_autoinstall(True)', +]) +def test_live_handles_at_interpreter_shutdown_print_no_ignored_exception(tmp_path, body): + """A handle alive at interpreter exit dies during module teardown, after + `lazy_import`'s globals have been cleared to None. Its weakref callback + looked `_scope_handle_died` up by name then and printed "Exception + ignored in: <function set_autoinstall.__init__.<locals>.<lambda>> ... + TypeError: 'NoneType' object is not callable" on every such exit. Run + as a real script in a fresh interpreter.""" + script = tmp_path / 'shutdown.py' + script.write_text('import hypertools as hyp\n' + 'from hypertools._shared import lazy_import as L\n' + f'{body}\n' + 'print("done")\n') + out = subprocess.run([sys.executable, str(script)], capture_output=True, + text=True, timeout=300) + assert out.returncode == 0, out.stderr[-2000:] + assert out.stdout.strip().endswith('done') + assert 'Exception ignored' not in out.stderr, out.stderr[-2000:] + assert 'Traceback' not in out.stderr, out.stderr[-2000:] diff --git a/tests/test_lazy_import_versions.py b/tests/test_lazy_import_versions.py new file mode 100644 index 00000000..a3b6214d --- /dev/null +++ b/tests/test_lazy_import_versions.py @@ -0,0 +1,486 @@ +"""``lazy_import`` checks the VERSION of an installed optional extra, not only +that it imports (release red-team review 2026-10-09). + +The reviewer's environment: real plotly 5.24.1 (below the declared +``plotly>=6.1.1``) beside kaleido 1.3.0. ``lazy_import`` returned whatever +imported, so a static export failed with plotly's own text -- "Image export +using the "kaleido" engine requires the kaleido package" -- although kaleido +was installed and the real cause was the old plotly. A resolved +``pip install "hypertools[interactive]"`` never produces that pair; an +environment with an older plotly already in it (a notebook image) does. + +Everything here runs against REAL installed distributions: a requirement the +installed version satisfies, a deliberately higher one (``numpy>=999``) for +the too-old branch, a real pip upgrade inside a throwaway venv, and (under +the ``bigdata`` marker, deselected by default) the reviewer's plotly 5.24.1 +environment itself. +""" + +import os +import shutil +import subprocess +import sys +from importlib import metadata + +import pytest + +from hypertools._shared import lazy_import as L +from tests._netskip import skip_on_transient_network + +REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +@pytest.fixture +def _restore_autoinstall(monkeypatch): + """Leave the module-level setting as this test found it.""" + monkeypatch.setattr(L, '_AUTO_INSTALL_SCOPES', list(L._AUTO_INSTALL_SCOPES)) + monkeypatch.setattr(L, '_AUTO_INSTALL_BASELINE', [None, L._AUTO_INSTALL_BASELINE[1]]) + monkeypatch.delenv('HYPERTOOLS_AUTO_INSTALL', raising=False) + + +# --- the check itself, on real installed distributions ------------------------ + +def test_a_satisfied_requirement_is_not_reported(): + assert L.outdated_requirements(['numpy>=1.0']) == [] + assert L.outdated_requirements([f'numpy>={metadata.version("numpy")}']) == [] + assert L.outdated_requirements([f'numpy=={metadata.version("numpy")}']) == [] + + +def test_a_requirement_above_the_installed_version_is_reported(): + have = metadata.version('numpy') + assert L.outdated_requirements(['numpy>=999']) == [('numpy>=999', 'numpy', have)] + # every requirement is checked, and only the failing ones come back + assert L.outdated_requirements(['scipy>=1.0', 'numpy>=999', 'pandas>999']) == [ + ('numpy>=999', 'numpy', have), + ('pandas>999', 'pandas', metadata.version('pandas'))] + + +def test_distribution_names_that_differ_from_import_names_do_not_alarm(): + # scikit-learn imports as sklearn, pillow as PIL; the check goes by the + # DISTRIBUTION name the requirement declares, in any spelling pip accepts + assert L.outdated_requirements( + ['scikit-learn>=0.1', 'scikit_learn>=0.1', 'Scikit.Learn>=0.1', + 'pillow>=1', 'pydata-wrangler>=0.1', 'pydata-wrangler[hf]>=0.1']) == [] + have = metadata.version('scikit-learn') + assert L.outdated_requirements(['scikit_learn>=999']) == [ + ('scikit_learn>=999', 'scikit_learn', have)] + assert L.outdated_requirements(['pydata-wrangler[hf]>=999']) == [ + ('pydata-wrangler[hf]>=999', 'pydata-wrangler', + metadata.version('pydata-wrangler'))] + + +def test_a_distribution_that_is_not_installed_is_not_too_old(): + # "missing" is the import's business (it is installed on demand), not + # the version check's + assert L.outdated_requirements(['hypertools-no-such-dist-xyz>=3']) == [] + + +def test_an_upper_bound_or_an_unparseable_requirement_is_not_too_old(): + # only a FLOOR can make an installed version "too old" + assert L.outdated_requirements(['numpy<0.1', 'numpy!=' + metadata.version('numpy')]) == [] + assert L.outdated_requirements(['this is not a requirement >= 3']) == [] + # a marker that does not apply to this interpreter + assert L.outdated_requirements(['numpy>=999; python_version < "3.0"']) == [] + + +@pytest.mark.parametrize('installed, requirement, ok', [ + ('5.24.1', 'plotly>=6.1.1', False), # the reviewer's pair + ('6.1.0', 'plotly>=6.1.1', False), + ('6.1.1', 'plotly>=6.1.1', True), + ('6.3.0', 'plotly>=6.1.1', True), + ('0.2.1', 'kaleido>=1.0', False), + ('1.0.0', 'kaleido>=1.0', True), + # a development / pre-release / local build OF the floor version is that + # version for this purpose + ('6.1.1.dev0', 'plotly>=6.1.1', True), + ('6.1.1rc1', 'plotly>=6.1.1', True), + ('6.1.1.dev3+g1a2b3c4', 'plotly>=6.1.1', True), + ('7.0.0a1', 'plotly>=6.1.1', True), + ('6.1.1.post1', 'plotly>=6.1.1', True), + ('6.1.0rc2', 'plotly>=6.1.1', False), + # "unknown version" placeholders (a source tree with no tag, a broken + # build) carry no information: never an alarm + ('0+unknown', 'plotly>=6.1.1', True), + ('0.0.0', 'plotly>=6.1.1', True), + ('0.0.0.dev0+g1a2b3c4', 'plotly>=6.1.1', True), + ('not-a-version', 'plotly>=6.1.1', True), + ('', 'plotly>=6.1.1', True), + # other floor-setting operators + ('2.0', 'x>2.0', False), + ('2.1', 'x>2.0', True), + ('2.3', 'x~=2.4', False), + ('2.5', 'x~=2.4', True), + ('2.3', 'x==2.4', False), + ('2.4', 'x==2.4', True), + ('2.3', 'x==2.*', True), # no single floor to compare + ('1.0', 'x>=2.0,<3', False), + ('9.0', 'x>=2.0,<3', True), # too NEW is not too old + ('5.0', 'x', True), +]) +def test_floor_comparison(installed, requirement, ok): + assert L._meets_floor(installed, requirement) is ok + + +def test_every_extra_installed_in_this_environment_meets_its_declared_floor(): + """The development environment is a correctly resolved install: the + check must be silent for every extra hypertools declares.""" + for extra in sorted(set(L.EXTRA_FOR_MODULE.values())): + assert L.outdated_requirements(L.extra_requirements(extra)) == [], extra + + +# --- lazy_import: an installed-but-too-old requirement ------------------------ + +def test_too_old_with_installation_off_names_versions_and_the_command(_restore_autoinstall, capsys): + import hypertools as hyp + have = metadata.version('numpy') + with hyp.set_autoinstall(False), pytest.raises(ImportError) as info: + L.lazy_import('numpy', purpose='a test', extra='interactive', + requirements=['numpy>=999']) + msg = str(info.value) + assert f'numpy {have} is installed' in msg + assert 'numpy>=999' in msg + assert 'a test' in msg + assert L.install_command('interactive') in msg + assert 'pip install "hypertools[interactive]"' in msg + assert 'automatic installation is off' in msg + assert 'set_autoinstall(True)' in msg + assert 'not installed' not in msg # it IS installed: say so + out = capsys.readouterr() + assert out.out == '' and out.err == '' # no notice: pip never ran + + +def test_too_old_without_an_extra_quotes_the_requirements_in_the_command(_restore_autoinstall): + import hypertools as hyp + with hyp.set_autoinstall(False), pytest.raises(ImportError) as info: + L.lazy_import('numpy', requirements=['numpy>=999']) + # quoted: an unquoted `>=999` is a shell redirection + assert '`pip install "numpy>=999"`' in str(info.value) + + +def test_too_old_and_already_imported_is_not_upgraded_in_place(_restore_autoinstall, capsys): + """numpy is imported in this process. A running interpreter cannot swap + an imported package, so with installation ON nothing is installed + either: the error says so and asks for a restart.""" + import numpy + import hypertools as hyp + have = metadata.version('numpy') + hyp.set_autoinstall(True) + with pytest.raises(ImportError) as info: + L.lazy_import('numpy', purpose='a test', extra='interactive', + requirements=['numpy>=999']) + msg = str(info.value) + assert f'numpy {have} is installed' in msg and 'numpy>=999' in msg + assert 'already imported' in msg + assert 'restart' in msg.lower() + assert 'pip install "hypertools[interactive]"' in msg + out = capsys.readouterr() + assert 'hypertools: upgrading' not in out.out # pip never ran + assert 'hypertools: installing' not in out.out + assert metadata.version('numpy') == have + assert sys.modules['numpy'] is numpy + + +def test_every_requirement_of_the_extra_is_checked_not_only_the_module(_restore_autoinstall): + """The feature needs the extra's packages TOGETHER (plotly with kaleido): + asking for one module reports another requirement that is too old.""" + import hypertools as hyp + with hyp.set_autoinstall(False), pytest.raises(ImportError) as info: + L.lazy_import('scipy', purpose='a test', + requirements=['scipy>=1.0', 'numpy>=999']) + msg = str(info.value) + assert f'numpy {metadata.version("numpy")} is installed' in msg + assert 'scipy' not in msg.split('`')[0] # scipy itself is fine + + +def test_a_too_old_result_is_never_cached(_restore_autoinstall): + import hypertools as hyp + for _ in range(2): + with hyp.set_autoinstall(False), pytest.raises(ImportError, match='numpy>=999'): + L.lazy_import('numpy', requirements=['numpy>=999']) + + +def test_a_verified_extra_is_checked_once_per_process(): + """The fast path: after one successful check the (module, extra, + requirements) key is remembered and the import returns directly.""" + pytest.importorskip('plotly') + import plotly + assert L.lazy_import('plotly') is plotly + key = ('plotly', 'interactive', None) + assert key in L._VERSIONS_VERIFIED + assert L.lazy_import('plotly.io').__name__ == 'plotly.io' + assert L.lazy_import('plotly') is plotly + # explicit requirements are their own key + import numpy + assert L.lazy_import('numpy', requirements=['numpy>=1.0']) is numpy + assert ('numpy', None, ('numpy>=1.0',)) in L._VERSIONS_VERIFIED + + +def test_a_module_no_extra_declares_is_imported_without_a_version_check(): + import json + assert L.lazy_import('json') is json + + +# --- the plotly backend asks lazy_import BEFORE plotly is imported ------------- + +def _run(code, **kw): + env = dict(os.environ, PYTHONPATH=REPO + os.pathsep + os.environ.get('PYTHONPATH', '')) + return subprocess.run([sys.executable, '-c', code], capture_output=True, + text=True, timeout=600, env=env, **kw) + + +def test_the_plotly_figure_type_check_does_not_import_plotly(): + """An upgrade is only possible while plotly is NOT yet imported, so + nothing may import it ahead of `lazy_import('plotly')`. The type check + `_is_plotly_figure` used to (`from plotly.basedatatypes import ...`).""" + out = _run("import sys\n" + "import hypertools as hyp\n" + "from hypertools.plot.plot import _is_plotly_figure\n" + "assert not _is_plotly_figure(object())\n" + "assert 'plotly' not in sys.modules, 'plotly was imported'\n" + "import plotly.graph_objects as go\n" + "assert _is_plotly_figure(go.Figure())\n" + "print('ok')\n") + assert out.returncode == 0, out.stderr[-2000:] + assert out.stdout.strip() == 'ok' + + +def test_resolve_backend_checks_the_version_before_importing_plotly(): + """`resolve_backend('plotly')` used to `import plotly` itself and call + lazy_import only when that failed, so an installed old plotly was + imported unchecked. Now the extra is verified first, in a fresh + interpreter where plotly is not imported yet.""" + pytest.importorskip('plotly') + out = _run("import sys\n" + "import hypertools as hyp\n" + "from hypertools._shared import lazy_import as L\n" + "from hypertools.plot.plotly_backend import resolve_backend\n" + "assert 'plotly' not in sys.modules, 'plotly imported at import time'\n" + "assert not L._VERSIONS_VERIFIED\n" + "assert resolve_backend('plotly') == 'plotly'\n" + "assert ('plotly', 'interactive', None) in L._VERSIONS_VERIFIED\n" + "assert 'plotly' in sys.modules\n" + "hyp.set_interactive_backend('plotly')\n" + "assert resolve_backend('auto') == 'plotly'\n" + "print('ok')\n") + assert out.returncode == 0, out.stderr[-2000:] + assert out.stdout.strip() == 'ok' + + +# --- a REAL upgrade, in a throwaway interpreter -------------------------------- + +def _venv_python(venv): + return venv / ('Scripts/python.exe' if os.name == 'nt' else 'bin/python') + + +def _pip_failed_on_network(out): + return out.returncode != 0 and any( + s in out.stderr for s in ('Connection', 'Temporary failure', + 'Read timed out', 'Could not resolve')) + + +def test_lazy_import_upgrades_a_too_old_package_in_a_fresh_interpreter(tmp_path): + """A throwaway venv holds a REAL old `tomli` (2.0.0). Three fresh + interpreters, each a real process: + + 1. without `packaging` the check cannot run and must not alarm: the old + module is returned as before; + 2. with `packaging`, and tomli already imported: nothing is installed, + the error asks for a restart, and 2.0.0 is still on disk; + 3. with `packaging`, tomli not yet imported: pip upgrades it, the notice + names the installed version and the requirement, and the NEW module + is what is imported. + """ + venv = tmp_path / 'venv' + subprocess.run([sys.executable, '-m', 'venv', str(venv)], check=True) + py = _venv_python(venv) + shutil.copy(L.__file__, tmp_path / 'lazy_import.py') + + def pip(*args): + with skip_on_transient_network('pip install into a throwaway venv'): + out = subprocess.run([str(py), '-m', 'pip', 'install', '-q', *args], + capture_output=True, text=True, timeout=600) + if _pip_failed_on_network(out): + pytest.skip(f'transient network error: {out.stderr[-200:]}') + assert out.returncode == 0, out.stderr[-800:] + + def run(code): + return subprocess.run([str(py), '-c', code], cwd=tmp_path, + capture_output=True, text=True, timeout=600) + + version = "from importlib import metadata; print('on disk', metadata.version('tomli'))" + call = ("L.lazy_import('tomli', purpose='a test', " + "requirements=['tomli>=2.0.1'])") + + pip('tomli==2.0.0') + # 1. no `packaging` in this interpreter: no check, no alarm + out = run("import importlib.util as u; assert u.find_spec('packaging') is None\n" + f"import lazy_import as L; m = {call}\n" + "print('imported', m.__name__); " + version) + assert out.returncode == 0, out.stderr[-800:] + assert 'imported tomli' in out.stdout and 'on disk 2.0.0' in out.stdout + assert 'hypertools:' not in out.stdout + + pip('packaging') + # 2. already imported: not upgraded in place + out = run("import tomli, lazy_import as L\n" + "try:\n" + f" {call}\n" + "except ImportError as e:\n" + " print('ERROR', e)\n" + version) + assert out.returncode == 0, out.stderr[-800:] + assert 'ERROR tomli 2.0.0 is installed' in out.stdout + assert 'tomli>=2.0.1' in out.stdout + assert 'already imported' in out.stdout and 'restart' in out.stdout.lower() + assert '`pip install "tomli>=2.0.1"`' in out.stdout + assert 'hypertools: upgrading' not in out.stdout + assert 'on disk 2.0.0' in out.stdout + + # 2b. installation off: the policy error, nothing installed + out = run("import os; os.environ['HYPERTOOLS_AUTO_INSTALL'] = '0'\n" + "import lazy_import as L\n" + "try:\n" + f" {call}\n" + "except ImportError as e:\n" + " print('ERROR', e)\n" + version) + assert out.returncode == 0, out.stderr[-800:] + assert 'ERROR tomli 2.0.0 is installed' in out.stdout + assert 'automatic installation is off' in out.stdout + assert 'restart' not in out.stdout.lower() # not imported: no restart needed + assert 'on disk 2.0.0' in out.stdout + + # 3. a real upgrade, then the new module + with skip_on_transient_network('pip upgrade inside a throwaway venv'): + out = run(f"import lazy_import as L; m = {call}\n" + "import sys; print('imported', m.__name__)\n" + version) + if _pip_failed_on_network(out): + pytest.skip(f'transient network error: {out.stderr[-200:]}') + assert out.returncode == 0, out.stderr[-800:] + assert ('hypertools: upgrading tomli 2.0.0 to tomli>=2.0.1 ' + '(needed for a test) ...') in out.stdout + assert 'imported tomli' in out.stdout + assert 'on disk 2.0.0' not in out.stdout + from packaging.version import Version + on_disk = out.stdout.split('on disk ')[1].split()[0] + assert Version(on_disk) >= Version('2.0.1') + + +# --- the reviewer's environment: plotly 5.24.1 + kaleido 1.3.0 ----------------- + +_PROBE = r''' +import importlib.metadata as md, json, os, sys +import numpy as np +mode, out = sys.argv[1], sys.argv[2] +result = {'plotly_before': md.version('plotly'), 'kaleido': md.version('kaleido')} +if mode == 'preimported': + import plotly # the reviewer's probe did this +import hypertools as hyp +result['source'] = hyp.__file__ +hyp.set_autoinstall(mode != 'off') +try: + hyp.plot(np.arange(30.).reshape(10, 3), reduce=None, backend='plotly', + show=False, save_path=out) + result.update(status='success', size=os.path.getsize(out)) +except Exception as exc: + result.update(status='error', exception=type(exc).__name__, message=str(exc)) +result['plotly_after'] = md.version('plotly') +print('RESULT ' + json.dumps(result)) +''' + +_RENDER_PROBE = r''' +import plotly +from hypertools._shared import lazy_import as L +try: + print('RENDER', L._kaleido_can_render()) +except Exception as exc: + print('RENDER-ERROR', type(exc).__name__, str(exc)) +''' + + +@pytest.mark.bigdata +def test_old_plotly_environment_is_diagnosed_and_then_upgraded(tmp_path): + """The reviewer's environment, built for real: this checkout installed + into a throwaway venv beside plotly 5.24.1 and kaleido 1.3.0, then a + plotly static export through `hyp.plot` in fresh subprocesses. + + Before the fix every mode failed with plotly's "Image export using the + "kaleido" engine requires the kaleido package". Deselected by default + (it resolves and installs the full dependency set, and the last step + may download Chrome for kaleido): `pytest -m bigdata`. + """ + import json + venv = tmp_path / 'venv' + uv = shutil.which('uv') + packages = ['-e', REPO, 'plotly==5.24.1', 'kaleido==1.3.0'] + with skip_on_transient_network('building the old-plotly environment'): + if uv: + subprocess.run([uv, 'venv', '-q', '--seed', '--python', sys.executable, + str(venv)], check=True, timeout=600) + build = subprocess.run( + [uv, 'pip', 'install', '-q', '--python', str(_venv_python(venv)), + *packages], capture_output=True, text=True, timeout=1800) + else: + subprocess.run([sys.executable, '-m', 'venv', str(venv)], check=True) + build = subprocess.run( + [str(_venv_python(venv)), '-m', 'pip', 'install', '-q', *packages], + capture_output=True, text=True, timeout=1800) + if _pip_failed_on_network(build) or 'error sending request' in build.stderr: + pytest.skip(f'transient network error: {build.stderr[-300:]}') + assert build.returncode == 0, build.stderr[-2000:] + py = _venv_python(venv) + probe = tmp_path / 'probe.py' + probe.write_text(_PROBE) + env = {k: v for k, v in os.environ.items() + if k not in ('PYTHONPATH', 'HYPERTOOLS_AUTO_INSTALL')} + + def run(mode): + out = subprocess.run([str(py), str(probe), mode, str(tmp_path / f'{mode}.png')], + capture_output=True, text=True, timeout=1800, + cwd=tmp_path, env=env) + lines = [ln for ln in out.stdout.splitlines() if ln.startswith('RESULT ')] + assert lines, (out.stdout[-1000:], out.stderr[-2000:]) + return json.loads(lines[-1][7:]), out + + upgrade = 'pip install "hypertools[interactive]"' + + # installation off: the real cause, the versions, and the command + res, _ = run('off') + assert os.path.samefile(os.path.dirname(os.path.dirname(res['source'])), REPO) + assert res['plotly_before'] == '5.24.1' and res['kaleido'] == '1.3.0' + assert res['status'] == 'error' and res['exception'] == 'ImportError', res + assert 'plotly 5.24.1 is installed' in res['message'], res + assert 'plotly>=6.1.1' in res['message'] and upgrade in res['message'], res + assert 'automatic installation is off' in res['message'], res + assert 'requires the kaleido package' not in res['message'], res + assert res['plotly_after'] == '5.24.1' + + # installation on, plotly 5.24.1 already imported by the caller: nothing + # is swapped under the running interpreter + res, out = run('preimported') + assert res['status'] == 'error' and res['exception'] == 'ImportError', res + assert 'plotly 5.24.1 is installed' in res['message'], res + assert 'already imported' in res['message'] and upgrade in res['message'], res + assert 'restart' in res['message'].lower(), res + assert 'requires the kaleido package' not in res['message'], res + assert 'hypertools: upgrading' not in out.stdout + assert res['plotly_after'] == '5.24.1' + + # the low-level render check names the pair, not "kaleido is missing" + low = subprocess.run([str(py), '-c', _RENDER_PROBE], capture_output=True, + text=True, timeout=600, cwd=tmp_path, env=env) + assert 'RENDER-ERROR HypertoolsIOError' in low.stdout, (low.stdout, low.stderr[-1500:]) + assert 'plotly 5.24.1' in low.stdout and 'kaleido 1.3.0' in low.stdout, low.stdout + assert upgrade in low.stdout, low.stdout + + # installation on, fresh interpreter: upgraded, then the export works + with skip_on_transient_network('upgrading plotly in the throwaway venv'): + res, out = run('on') + if res['status'] == 'error' and 'automatically failed' in res['message'] \ + and _pip_failed_on_network( + subprocess.CompletedProcess([], 1, out.stdout, out.stderr)): + pytest.skip(f'transient network error: {out.stderr[-300:]}') + assert ('hypertools: upgrading plotly 5.24.1 to plotly>=6.1.1 ' + '(needed for the plotly backend) ...') in out.stdout, out.stdout[-1500:] + assert res['status'] == 'success', res + assert res['size'] > 1000 + from packaging.version import Version + assert Version(res['plotly_after']) >= Version('6.1.1'), res diff --git a/tests/test_load_538_kaggle.py b/tests/test_load_538_kaggle.py index 67ba54c4..f655bce2 100644 --- a/tests/test_load_538_kaggle.py +++ b/tests/test_load_538_kaggle.py @@ -82,7 +82,10 @@ def test_load_kaggle_tiny_dataset(): "-- install it with `pip install hypertools[kaggle]`") # uciml/iris is a tiny (~3.6KB) public dataset that downloads # anonymously (no Kaggle credentials needed) via kagglehub - df = hyp.load('kaggle/uciml/iris') + # an outage skips, a defect still fails, and the live-source gate skips + # nothing (2026-10-09: a macOS runner could not resolve api.kaggle.com) + with skip_on_transient_network('loading kaggle/uciml/iris'): + df = hyp.load('kaggle/uciml/iris') assert isinstance(df, pd.DataFrame) assert len(df) > 0 assert df.shape[1] > 1 diff --git a/tests/test_load_offline.py b/tests/test_load_offline.py new file mode 100644 index 00000000..154a6b45 --- /dev/null +++ b/tests/test_load_offline.py @@ -0,0 +1,488 @@ +# -*- coding: utf-8 -*- +"""``hyp.load(..., offline=True)`` really is offline (1.1 release review, I1). + +Before 1.1, ``hyp.load(url, offline=True)`` consulted the seaborn dataset +listing (an urlopen with no timeout) for EVERY non-builtin string -- cached +URLs included -- before the URL cache was even looked at, and a failed +listing fetch was forgotten immediately, so on a dead network every call +blocked for the full OS connect timeout (75 s measured behind an +unroutable proxy) while the docstring promised "never open a connection". + +The observable here is a real one: a local TCP "proxy" that accepts every +connection and never answers (``_Blackhole``). The library runs in a +subprocess whose ``HTTP(S)_PROXY`` point at it, so any attempt to reach +the network shows up as an accepted connection at the blackhole and as a +stall until the client's own timeout. No mocks, no monkeypatched +functions: the data server is a real ``http.server``, the cache is real +files on disk, and every assertion is on a returned value, an exception +type, an elapsed time or a connection count. + +The 1.1 release audit (2026-09-07, finding 1) found a second hole: the +hosted BUILT-IN datasets (``'spiral'``, ``'weights'``, the ``*_model`` +pipelines) bypassed ``offline`` entirely -- a cache miss downloaded, and +a corrupt cached file was deleted and re-downloaded. The tests at the +bottom point the example-data cache at an empty directory and add a +second, independent observable inside the subprocess: a passive +``sys.addaudithook`` that records every ``socket.connect`` (as the audit +did), so "no connection" is asserted both at the proxy and at the socket. +""" + +import functools +import importlib +import json +import os +import socket +import subprocess +import sys +import textwrap +import threading +import time +from http.server import HTTPServer, SimpleHTTPRequestHandler + +import pandas as pd +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp # noqa: E402 +from hypertools.io.sources import (HypertoolsOfflineError, # noqa: E402 + SEABORN_LISTING_TIMEOUT, + cached_url_path, url_cache_dir) +from tests._netskip import skip_on_transient_network # noqa: E402 + +# the load MODULE (hypertools.io.load is the function); its DATA_DIR is the +# example-dataset cache the built-in tests redirect +load_mod = importlib.import_module('hypertools.io.load') +#: env var the subprocess runner reads to redirect that cache +DATA_DIR_ENV = 'HYPERTOOLS_TEST_DATA_DIR' + +CSV_TEXT = 'a,b\n1,2\n3,4\n' +EXPECTED = pd.DataFrame({'a': [1, 3], 'b': [2, 4]}) +#: wall-clock budget (seconds) for one offline call inside the subprocess; +#: a call that touches the network stalls for at least the seaborn listing +#: timeout (10 s), and for ~75 s before 1.1 +CALL_BUDGET = 5.0 +#: hard cap on a whole subprocess run (interpreter start-up + imports +#: included) so a regression fails instead of hanging the suite +SUBPROCESS_CAP = 120.0 + + +class _Blackhole: + """A local TCP proxy that accepts every connection and never replies. + + A client routed through it hangs until its own timeout, and every + connection is recorded (with the first bytes the client sent, e.g. + ``CONNECT raw.githubusercontent.com:443``), so a test can assert both + "no network attempt was made" and "exactly one was". + """ + + def __init__(self): + self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.sock.bind(('127.0.0.1', 0)) + self.sock.listen(64) + self.port = self.sock.getsockname()[1] + self.connections = [] + self._held = [] + threading.Thread(target=self._serve, daemon=True).start() + + def _serve(self): + while True: + try: + conn, _ = self.sock.accept() + except OSError: + return + self._held.append(conn) + threading.Thread(target=self._peek, args=(conn,), + daemon=True).start() + + def _peek(self, conn): + try: + conn.settimeout(5) + head = conn.recv(512) + except OSError: + head = b'' + self.connections.append(head) + + def env(self, no_proxy): + url = f'http://127.0.0.1:{self.port}' + return {'HTTP_PROXY': url, 'HTTPS_PROXY': url, + 'http_proxy': url, 'https_proxy': url, + 'NO_PROXY': no_proxy, 'no_proxy': no_proxy} + + def close(self): + for conn in self._held: + conn.close() + self.sock.close() + + +@pytest.fixture +def blackhole(): + proxy = _Blackhole() + yield proxy + proxy.close() + + +@pytest.fixture +def cache_dir(tmp_path, monkeypatch): + path = tmp_path / 'urlcache' + monkeypatch.setenv('HYPERTOOLS_URL_CACHE', str(path)) + assert url_cache_dir() == path + return path + + +@pytest.fixture +def csv_server(tmp_path): + """A real http.server serving ``data.csv``; ``.stop()`` shuts it down + so a later direct fetch would be refused rather than served.""" + root = tmp_path / 'srv' + root.mkdir() + (root / 'data.csv').write_text(CSV_TEXT) + handler = functools.partial(SimpleHTTPRequestHandler, directory=str(root)) + handler.log_message = lambda *a, **k: None + httpd = HTTPServer(('127.0.0.1', 0), handler) + threading.Thread(target=httpd.serve_forever, daemon=True).start() + + class Server: + url = f'http://127.0.0.1:{httpd.server_port}/data.csv' + other_url = f'http://127.0.0.1:{httpd.server_port}/other.csv' + + @staticmethod + def stop(): + httpd.shutdown() + httpd.server_close() + + yield Server + Server.stop() + + +_RUNNER = textwrap.dedent(''' + import importlib, json, os, pathlib, sys, time, traceback + # passive audit hook, installed before hypertools is imported: every + # socket.connect the interpreter makes lands in `connects` + connects = [] + def _audit(event, args): + if event == 'socket.connect': + connects.append(repr(args[1])) + sys.addaudithook(_audit) + import hypertools as hyp + _data_dir = os.environ.get(%r) + if _data_dir: + _load_mod = importlib.import_module('hypertools.io.load') + _load_mod.DATA_DIR = pathlib.Path(_data_dir) + out = [] + for case in json.loads(sys.argv[1]): + t = time.monotonic() + seen = len(connects) + try: + data = hyp.load(case['source'], **case.get('kwargs', {})) + rec = {'ok': True, 'shape': list(getattr(data, 'shape', [])), + 'kind': type(data).__name__, + 'shapes': [list(getattr(d, 'shape', [])) for d in data] + if isinstance(data, list) else None, + 'records': data.to_dict('list') + if hasattr(data, 'to_dict') else None} + except Exception as e: + rec = {'ok': False, 'type': type(e).__name__, 'msg': str(e)} + rec['elapsed'] = time.monotonic() - t + rec['connects'] = connects[seen:] + rec['source'] = case['source'] + out.append(rec) + print('RESULT ' + json.dumps(out)) +''' % DATA_DIR_ENV) + + +def _run(cases, env, cwd): + """Run ``hyp.load`` for each case in a fresh interpreter under ``env`` + and return the per-case records (elapsed measured around the call).""" + full_env = {k: v for k, v in os.environ.items() + if k.upper() not in ('HTTP_PROXY', 'HTTPS_PROXY', + 'NO_PROXY', 'ALL_PROXY')} + full_env.update(env) + full_env['MPLBACKEND'] = 'Agg' + full_env['HYPERTOOLS_AUTO_INSTALL'] = '0' + proc = subprocess.run( + [sys.executable, '-c', _RUNNER, json.dumps(cases)], + env=full_env, cwd=str(cwd), capture_output=True, text=True, + timeout=SUBPROCESS_CAP) + lines = [ln for ln in proc.stdout.splitlines() if ln.startswith('RESULT ')] + assert lines, (f'subprocess produced no result (rc={proc.returncode})\n' + f'stdout:\n{proc.stdout}\nstderr:\n{proc.stderr[-3000:]}') + return json.loads(lines[-1][len('RESULT '):]) + + +# ------------------------------------------------------- offline=True hits + +def test_offline_hit_is_served_from_the_cache_without_any_connection( + blackhole, cache_dir, csv_server, tmp_path): + frame = hyp.load(csv_server.url, cache=True) # populate, online + pd.testing.assert_frame_equal(frame, EXPECTED) + assert cached_url_path(csv_server.url).is_file() + csv_server.stop() # a direct re-fetch would now be refused, not served + + [rec] = _run([{'source': csv_server.url, 'kwargs': {'offline': True}}], + blackhole.env('nothing.invalid'), tmp_path) + assert rec['ok'], rec + pd.testing.assert_frame_equal(pd.DataFrame(rec['records']), EXPECTED) + assert rec['elapsed'] < CALL_BUDGET, rec['elapsed'] + assert blackhole.connections == [] # never opened a connection + + +def test_offline_misses_raise_offline_error_without_any_connection( + blackhole, cache_dir, csv_server, tmp_path): + csv_server.stop() + uncached = csv_server.other_url + cases = [ + (uncached, 'a never-cached URL'), + ('https://hypertools-offline-test.invalid/data.csv', + 'an unresolvable URL'), + ('yahoo:AAPL', 'a web source'), + ('fivethirtyeight/bechdel', 'a FiveThirtyEight dataset'), + ('kaggle/uciml/iris', 'a Kaggle dataset'), + ('scikit-learn/iris', 'a Hugging Face dataset id'), + ('penguins', 'a seaborn dataset name'), + ] + recs = _run([{'source': s, 'kwargs': {'offline': True}} + for s, _ in cases], + blackhole.env('nothing.invalid'), tmp_path) + for (source, what), rec in zip(cases, recs): + assert not rec['ok'], (what, rec) + assert rec['type'] == HypertoolsOfflineError.__name__, (what, rec) + assert 'offline=True' in rec['msg'], (what, rec) + assert rec['elapsed'] < CALL_BUDGET, (what, rec['elapsed']) + # the cacheable-URL miss names the cache path it looked for + assert str(cached_url_path(uncached)) in recs[0]['msg'] + assert blackhole.connections == [] + + +def test_offline_miss_of_a_bare_id_like_name_reports_the_whole_chain( + cache_dir, tmp_path, monkeypatch): + """A bare name of 25+ id-like characters is only POSSIBLY a Google + Drive file id -- it is at least as likely a missing local file or a + mistyped dataset name. Under offline=True its Drive cache miss used to + escape at once as "offline=True, but https://drive.google.com/uc?... + is not in the hypertools URL cache", hiding the local-file miss. The + guess now joins the "tried, in order" digest (still an offline error: + nothing could be served).""" + monkeypatch.chdir(tmp_path) + name = 'my_experiment_results_final_v2' + assert len(name) >= 25 + with pytest.raises(HypertoolsOfflineError) as info: + hyp.load(name, offline=True) + msg = str(info.value) + assert msg.startswith(f"offline=True: could not load {name!r}"), msg + assert f'local file: not found at {name}' in msg + assert f'Google Drive ({name}): not in the hypertools URL cache' in msg + assert not msg.startswith('offline=True, but https://drive.google.com') + + +def test_offline_bare_drive_id_is_still_served_from_the_cache( + cache_dir, tmp_path, monkeypatch): + # the fall-through must not cost a real cached bare-id hit: put a + # payload at the exact path a cache=True download of that id writes + from hypertools.io.sources import _read_cached + monkeypatch.chdir(tmp_path) + drive_id = '1AbCdEfGhIjKlMnOpQrStUvWxYz012345' + url = f'https://drive.google.com/uc?export=download&id={drive_id}' + path = cached_url_path(url) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(CSV_TEXT) + assert _read_cached(path) is not None + frame = hyp.load(drive_id, offline=True) + assert frame.shape == (2, 2) + assert list(frame.iloc[:, 0]) == [1, 3] + + +def test_offline_miss_of_an_explicit_drive_url_still_raises_at_once( + cache_dir, tmp_path): + # an explicit Drive URL is unambiguous: its miss is the whole answer + drive_id = '1AbCdEfGhIjKlMnOpQrStUvWxYz999999' + url = f'https://drive.google.com/file/d/{drive_id}/view' + with pytest.raises(HypertoolsOfflineError) as info: + hyp.load(url, offline=True) + msg = str(info.value) + assert msg.startswith('offline=True, but https://drive.google.com'), msg + expected = cached_url_path( + f'https://drive.google.com/uc?export=download&id={drive_id}') + assert str(expected) in msg + + +def test_a_cached_url_that_fails_to_parse_is_not_reported_as_a_cache_miss( + cache_dir, tmp_path): + """offline=True on a URL whose cached copy EXISTS but does not parse + used to raise HypertoolsOfflineError ending "(offline=True serves ONLY + ... downloads that were cached earlier with cache=True ...)" -- telling + the user to cache a file that is already cached. It is a parse failure: + a HypertoolsIOError naming the cached file.""" + from hypertools.core.exceptions import HypertoolsIOError + root = tmp_path / 'srv' + root.mkdir() + (root / 'bad.json').write_text('{not json') + + class Quiet(SimpleHTTPRequestHandler): + def log_message(self, *args): + pass + + handler = functools.partial(Quiet, directory=str(root)) + httpd = HTTPServer(('127.0.0.1', 0), handler) + threading.Thread(target=httpd.serve_forever, daemon=True).start() + url = f'http://127.0.0.1:{httpd.server_port}/bad.json' + try: + with pytest.raises(HypertoolsIOError) as online: + hyp.load(url, cache=True) # real download + assert not isinstance(online.value, HypertoolsOfflineError) + finally: + httpd.shutdown() + httpd.server_close() + cached = cached_url_path(url) + assert cached.is_file() and cached.read_text() == '{not json' + + with pytest.raises(HypertoolsIOError) as info: + hyp.load(url, offline=True) + assert not isinstance(info.value, HypertoolsOfflineError), info.value + msg = str(info.value) + assert str(cached) in msg + assert 'could not be parsed' in msg + assert 'cached earlier with cache=True' not in msg + assert cached.read_text() == '{not json' # the file is kept + + +def test_offline_still_serves_local_sources(cache_dir, tmp_path): + # built-in-by-package, synthetic and local-file sources need no + # network, so offline=True must not refuse them + local = tmp_path / 'local.csv' + local.write_text(CSV_TEXT) + pd.testing.assert_frame_equal(hyp.load(str(local), offline=True), + EXPECTED) + assert hyp.load('iris', offline=True).shape == (150, 5) + assert hyp.load('helix', n_samples=12, offline=True).shape == (12, 3) + + +# ---------------------------------------------- the seaborn listing itself + +def test_url_string_never_consults_the_seaborn_listing( + blackhole, cache_dir, csv_server, tmp_path): + # ONLINE load of a plain URL: the local server is reachable (NO_PROXY), + # everything else is blackholed. A URL can never be a seaborn dataset + # name, so the listing must not be fetched -- before 1.1 this call + # went to the proxy for raw.githubusercontent.com first and stalled. + [rec] = _run([{'source': csv_server.url}], + blackhole.env('127.0.0.1'), tmp_path) + assert rec['ok'], rec + pd.testing.assert_frame_equal(pd.DataFrame(rec['records']), EXPECTED) + assert rec['elapsed'] < CALL_BUDGET, rec['elapsed'] + assert blackhole.connections == [] + + +def test_seaborn_listing_fetch_is_bounded_and_its_failure_remembered( + blackhole, cache_dir, tmp_path): + # a plain name that resolves nowhere DOES consult the listing; behind a + # dead network that fetch must (a) give up within its timeout and (b) + # not be retried by the very next call + name = 'no_such_dataset_zz' + first, second = _run([{'source': name}, {'source': name}], + blackhole.env('nothing.invalid'), tmp_path) + for rec in (first, second): + assert not rec['ok'] and rec['type'] == 'HypertoolsIOError', rec + assert 'seaborn dataset' in rec['msg'] + assert first['elapsed'] < SEABORN_LISTING_TIMEOUT + CALL_BUDGET, first + assert second['elapsed'] < 1.0, second['elapsed'] + time.sleep(0.2) # let the blackhole thread record the request head + assert len(blackhole.connections) == 1, blackhole.connections + assert blackhole.connections[0].startswith(b'CONNECT ') + + +def test_reset_seaborn_names_cache_forgets_a_remembered_failure(): + from hypertools.io import sources + sources._seaborn_names_cache = None + sources._seaborn_names_failed_at = time.monotonic() + assert sources.seaborn_dataset('penguins') is None # remembered miss + sources.reset_seaborn_names_cache() + assert sources._seaborn_names_failed_at is None + assert sources._seaborn_names_cache is None + + +# ------------------------------------------ offline=True and built-in data +# (1.1 release audit, finding 1: the hosted built-ins bypassed `offline`) + +@pytest.fixture +def example_cache(tmp_path, monkeypatch): + """A fresh, EMPTY example-dataset cache. The load module's DATA_DIR is + pointed at it in this process (so an online load populates it here, + never the user's ~/hypertools_data) and the subprocess runner points + its own at the same path via DATA_DIR_ENV.""" + path = tmp_path / 'hypertools_data' + monkeypatch.setattr(load_mod, 'DATA_DIR', path) + return path + + +def _offline_builtin_env(blackhole, example_cache): + env = blackhole.env('nothing.invalid') + env[DATA_DIR_ENV] = str(example_cache) + return env + + +def test_offline_refuses_an_uncached_builtin_without_any_connection( + blackhole, cache_dir, example_cache, tmp_path): + # a data file, the multi-array dataset and a *_model pipeline all go + # through the same hosted-dataset path; none is cached here + names = ['spiral', 'weights', 'wiki_model'] + recs = _run([{'source': n, 'kwargs': {'offline': True}} for n in names], + _offline_builtin_env(blackhole, example_cache), tmp_path) + for name, rec in zip(names, recs): + assert not rec['ok'], (name, rec) + assert rec['type'] == HypertoolsOfflineError.__name__, (name, rec) + assert 'offline=True' in rec['msg'], (name, rec) + assert name in rec['msg'] and 'not cached' in rec['msg'], (name, rec) + # the refusal names the file it looked for + assert str(example_cache / name) in rec['msg'], (name, rec) + assert rec['connects'] == [], (name, rec) + assert rec['elapsed'] < CALL_BUDGET, (name, rec['elapsed']) + # nothing was written: not the file, not even the cache directory + assert not example_cache.exists() + assert blackhole.connections == [] + + +def test_offline_refuses_a_corrupt_cached_builtin_and_keeps_the_file( + blackhole, cache_dir, example_cache, tmp_path): + example_cache.mkdir() + cached = example_cache / 'spiral' + payload = b'not the pinned spiral.npz' * 64 + cached.write_bytes(payload) + before = cached.stat().st_mtime_ns + assert not load_mod._integrity_ok(cached, 'spiral') + + [rec] = _run([{'source': 'spiral', 'kwargs': {'offline': True}}], + _offline_builtin_env(blackhole, example_cache), tmp_path) + assert not rec['ok'], rec + assert rec['type'] == HypertoolsOfflineError.__name__, rec + assert 'offline=True' in rec['msg'] and 'integrity' in rec['msg'], rec + assert str(cached) in rec['msg'], rec + assert rec['connects'] == [], rec + assert rec['elapsed'] < CALL_BUDGET, rec['elapsed'] + # online, a failing hash is deleted and re-downloaded; offline the + # user's file must survive untouched, and no partial download appears + assert cached.read_bytes() == payload + assert cached.stat().st_mtime_ns == before + assert os.listdir(example_cache) == ['spiral'] + assert blackhole.connections == [] + + +def test_offline_serves_a_verified_cached_builtin_without_any_connection( + blackhole, cache_dir, example_cache, tmp_path): + # populate the (redirected) cache with a real online download first + with skip_on_transient_network('downloading the spiral example dataset'): + online = hyp.load('spiral') + cached = example_cache / 'spiral' + assert cached.is_file() and load_mod._integrity_ok(cached, 'spiral') + before = cached.stat().st_mtime_ns + + [rec] = _run([{'source': 'spiral', 'kwargs': {'offline': True}}], + _offline_builtin_env(blackhole, example_cache), tmp_path) + assert rec['ok'], rec + assert rec['kind'] == 'list', rec + assert rec['shapes'] == [list(a.shape) for a in online], rec + assert rec['connects'] == [], rec + assert rec['elapsed'] < CALL_BUDGET, rec['elapsed'] + assert cached.stat().st_mtime_ns == before # served, not re-fetched + assert blackhole.connections == [] diff --git a/tests/test_load_passthrough.py b/tests/test_load_passthrough.py index e10a640a..9b51d09b 100644 --- a/tests/test_load_passthrough.py +++ b/tests/test_load_passthrough.py @@ -116,3 +116,19 @@ def test_typeerror_names_the_accepted_types(): hyp.load({'a': 1}) msg = str(info.value) assert 'DataFrame' in msg and 'numpy array' in msg and 'got dict' in msg + + +def test_typeerror_names_polars_frames_which_load_accepts(): + # polars DataFrames and LazyFrames pass through (the 1.1 datatype + # refactor), but the TypeError still listed only "pandas DataFrame or + # numpy array" (review 2026-09-11) + import polars as pl + frame = pl.DataFrame({'a': [1.0, 2.0], 'b': [3.0, 4.0]}) + assert hyp.load(frame) is frame + lazy = frame.lazy() + assert hyp.load(lazy) is lazy + with pytest.raises(TypeError) as info: + hyp.load({'a': 1}) + msg = str(info.value) + assert 'polars' in msg and 'LazyFrame' in msg and 'pandas' in msg + assert 'got dict' in msg diff --git a/tests/test_load_sources.py b/tests/test_load_sources.py index 62716caa..b543ec29 100644 --- a/tests/test_load_sources.py +++ b/tests/test_load_sources.py @@ -605,3 +605,62 @@ def test_builtin_example_data_exempt_from_trust_policy(): warnings.simplefilter('error') data = hyp.load('spiral') assert isinstance(data, list) + + +def test_transient_classifier_reads_a_dropped_tls_connection_as_transient(): + """CI 2026-09-08 (run 34234303697, ubuntu 3.11): Dropbox closed the TLS + connection mid-read on the direct-download form and the `?dl=0` fallback + answered an HTML page; eleven other matrix cells loaded the same file. + `load_source`'s aggregate named `SSLError`, which the classifier read as + a defect, so `skip_on_transient_network` did not skip. The exact aggregate + text, and its certificate-failure counterpart which must still fail.""" + from tests._netskip import is_transient_network + dropped = ( + "could not load 'https://www.dropbox.com/s/x/bunny.pkl?dl=0'. Tried, in order:\n" + " - built-in example dataset: not one of ['bunny', 'spiral']\n" + " - Dropbox: SSLError: HTTPSConnectionPool(host='www.dropbox.com', port=443): " + "Max retries exceeded with url: /s/x/bunny.pkl?dl=1 (Caused by SSLError(" + "SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation " + "of protocol (_ssl.c:1016)')))\n" + " - URL (https://www.dropbox.com/s/x/bunny.pkl?dl=0): HypertoolsIOError: " + "https://www.dropbox.com/s/x/bunny.pkl?dl=0 returned an HTML page instead of " + "data (rate limit, permission page, or a link that needs a direct-download form)") + assert is_transient_network(dropped) + certificate = dropped.replace( + "SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation " + "of protocol (_ssl.c:1016)')", + "SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate " + "verify failed: unable to get local issuer certificate (_ssl.c:1016)')") + assert not is_transient_network(certificate) + + +def test_a_live_certificate_failure_is_never_skipped_while_a_tls_drop_is(monkeypatch): + """Codex round 9: requests' `SSLError` inherits from `ConnectionError`, so + a LIVE certificate failure took the transient path by ancestry (the + equivalent text already failed classification). Both live shapes, as + requests raises them (the ssl error carried in args). The drop SKIPS only + when live sources are not required: the live-source-gate CI job sets + HYPERTOOLS_REQUIRE_LIVE_SOURCES=1, under which the guard re-raises by + design (it failed this test there on 2026-09-08), so both modes are + pinned explicitly.""" + import ssl + import requests + from tests._netskip import is_transient_network, skip_on_transient_network + monkeypatch.delenv('HYPERTOOLS_REQUIRE_LIVE_SOURCES', raising=False) + drop = requests.exceptions.SSLError(ssl.SSLEOFError( + 8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol')) + cert = requests.exceptions.SSLError(ssl.SSLCertVerificationError( + 1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed')) + assert is_transient_network(drop) + assert not is_transient_network(cert) + with pytest.raises(pytest.skip.Exception): + with skip_on_transient_network('a dropped connection'): + raise drop + with pytest.raises(requests.exceptions.SSLError): + with skip_on_transient_network('a bad certificate'): + raise cert + # with live sources REQUIRED even the drop is re-raised, never skipped + monkeypatch.setenv('HYPERTOOLS_REQUIRE_LIVE_SOURCES', '1') + with pytest.raises(requests.exceptions.SSLError): + with skip_on_transient_network('a dropped connection, required'): + raise drop diff --git a/tests/test_load_synthetic.py b/tests/test_load_synthetic.py index 8745016f..58bdfc58 100644 --- a/tests/test_load_synthetic.py +++ b/tests/test_load_synthetic.py @@ -250,3 +250,108 @@ def test_load_reports_synthetic_names_when_nothing_resolves(): with pytest.raises(HypertoolsIOError) as excinfo: hyp.load('helixx_not_a_dataset') assert 'synthetic dataset' in str(excinfo.value) + + +# ------------------------------------- seed types (1.1 release review I3-I6) + +def test_random_state_accepts_a_legacy_randomstate_deterministically(): + # I3: every docstring lists RandomState, but the numpy-native + # generators used `.bit_generator`, which a RandomState does not have + for name, kwargs in (('random_walk', {}), ('helix', {'noise': 0.1}), + ('lorenz', {})): + first = synthetic_dataset(name, n_samples=20, **kwargs, + random_state=np.random.RandomState(0)) + again = synthetic_dataset(name, n_samples=20, **kwargs, + random_state=np.random.RandomState(0)) + other = synthetic_dataset(name, n_samples=20, **kwargs, + random_state=np.random.RandomState(1)) + assert np.array_equal(first, again) + assert not np.array_equal(first, other) # the seed is actually used + # a RandomState is consumed like any draw: two datasets from ONE + # RandomState differ, and threading the same seeded object through + # twice reproduces both + rs = np.random.RandomState(7) + a, b = (synthetic_dataset('helix', n_samples=20, noise=0.1, + random_state=rs) for _ in range(2)) + rs = np.random.RandomState(7) + a2, b2 = (synthetic_dataset('helix', n_samples=20, noise=0.1, + random_state=rs) for _ in range(2)) + assert not np.array_equal(a, b) + assert np.array_equal(a, a2) and np.array_equal(b, b2) + + +@pytest.mark.parametrize('name', ['blobs', 'moons', 'swiss_roll', 's_curve']) +@pytest.mark.parametrize('seed_type', ['Generator', 'SeedSequence', + 'np.integer']) +def test_sklearn_synthetics_accept_every_seed_type_for_one_dataset( + name, seed_type): + # I4: for n_datasets == 1 the seed was handed straight to + # sklearn.datasets.make_*, which rejects a Generator / SeedSequence + def seed(value): + return {'Generator': lambda: np.random.default_rng(value), + 'SeedSequence': lambda: np.random.SeedSequence(value), + 'np.integer': lambda: np.int64(value)}[seed_type]() + first = synthetic_dataset(name, n_samples=30, random_state=seed(0)) + again = synthetic_dataset(name, n_samples=30, random_state=seed(0)) + other = synthetic_dataset(name, n_samples=30, random_state=seed(1)) + assert isinstance(first, pd.DataFrame) and len(first) == 30 + pd.testing.assert_frame_equal(first, again) + assert not first.equals(other) + + +def test_reusing_one_seedsequence_across_calls_is_reproducible(): + # I5: the list case spawned children from the caller's SeedSequence, + # advancing its spawn counter, so the same object gave different data + # on the second call + ss = np.random.SeedSequence(12345) + first = synthetic_dataset('random_walk', n_datasets=3, n_samples=15, + n_features=2, random_state=ss) + again = synthetic_dataset('random_walk', n_datasets=3, n_samples=15, + n_features=2, random_state=ss) + fresh = synthetic_dataset('random_walk', n_datasets=3, n_samples=15, + n_features=2, + random_state=np.random.SeedSequence(12345)) + for a, b, c in zip(first, again, fresh): + assert np.array_equal(a, b) + assert np.array_equal(a, c) + assert not np.array_equal(first[0], first[1]) + assert ss.n_children_spawned == 0 # the caller's object is untouched + + +@pytest.mark.parametrize('bad', [2.7, 2.0, True, np.float64(3)]) +def test_non_integral_n_datasets_is_rejected_not_truncated(bad): + # I6: int(2.7) silently gave 2 datasets under a message that said + # "must be a positive integer" + with pytest.raises(HypertoolsIOError, match='n_datasets must be a ' + 'positive integer'): + synthetic_dataset('helix', n_datasets=bad) + + +def test_numpy_integer_n_datasets_is_accepted(): + out = synthetic_dataset('helix', n_samples=10, n_datasets=np.int64(2), + random_state=0) + assert isinstance(out, list) and len(out) == 2 + + +# ------------------------------------------- streaming= is HF-only (I8) + +@pytest.mark.parametrize('source', ['lorenz', 'blobs', 'iris', 'helix']) +def test_streaming_true_on_a_non_hf_source_raises(source): + # I8: streaming= is documented as Hugging Face-only; it used to be + # silently ignored, returning the full (2000, 3) lorenz array etc. + with pytest.raises(ValueError) as info: + hyp.load(source, streaming=True) + msg = str(info.value) + assert repr(source) in msg + assert 'Hugging Face' in msg and 'streaming=True' in msg + # the same call without the flag still loads in full + assert len(hyp.load(source)) > 0 + + +def test_streaming_true_on_already_loaded_data_raises(tmp_path): + with pytest.raises(ValueError, match='streaming=True'): + hyp.load(pd.DataFrame(np.zeros((3, 2))), streaming=True) + local = tmp_path / 'x.csv' + local.write_text('a,b\n1,2\n') + with pytest.raises(ValueError, match='local file'): + hyp.load(str(local), streaming=True) diff --git a/tests/test_load_url_cache.py b/tests/test_load_url_cache.py index 6dd129c9..8ce50e70 100644 --- a/tests/test_load_url_cache.py +++ b/tests/test_load_url_cache.py @@ -40,6 +40,22 @@ UNREACHABLE_URL = 'https://hypertools-offline-test.invalid/data.csv' +def test_concurrent_cache_writes_are_atomic(tmp_path): + # GH #285 release review: real concurrent writes, no mocked I/O. + from concurrent.futures import ThreadPoolExecutor + from hypertools.io.sources import _read_cached + path = tmp_path / 'data.csv' + payload = b'x,y\n1,2\n' * 1000 + + def write(_): + _write_cached(path, payload, 'data.csv') + + with ThreadPoolExecutor(max_workers=12) as pool: + list(pool.map(write, range(100))) + assert _read_cached(path) == (payload, 'data.csv') + assert not list(tmp_path.glob('*.part')) + + @pytest.fixture def cache_dir(tmp_path, monkeypatch): """Point the URL cache at a temp directory (so tests never write to diff --git a/tests/test_load_web_sources.py b/tests/test_load_web_sources.py index 77f0b930..921281e3 100644 --- a/tests/test_load_web_sources.py +++ b/tests/test_load_web_sources.py @@ -233,3 +233,164 @@ def test_sec_unknown_ticker_and_concept_raise_immediately(): with skip_on_transient_network('loading an unknown SEC concept'): sec_source('sec:AAPL', concept='NotARealConceptXyz') assert 'NotARealConceptXyz' in str(excinfo.value) + + +# ------------------------------------------ yahoo: exchange-local dates (I2) + +def _yahoo_payload(stamps, gmtoffset): + """A Yahoo v8 chart payload of the real shape, built by hand.""" + n = len(stamps) + return {'chart': {'result': [{ + 'meta': {'gmtoffset': gmtoffset, 'exchangeTimezoneName': 'x'}, + 'timestamp': stamps, + 'indicators': { + 'quote': [{'open': [1.0] * n, 'high': [2.0] * n, + 'low': [0.5] * n, 'close': [1.5] * n, + 'volume': [100] * n}], + 'adjclose': [{'adjclose': [1.5] * n}]}}], + 'error': None}} + + +def _utc_stamps(days, hhmm): + return [int(pd.Timestamp(f'2025-01-{d:02d} {hhmm}', tz='UTC').timestamp()) + for d in days] + + +def test_yahoo_bars_east_of_utc_are_dated_the_exchange_local_day(): + from hypertools.io.sources import _parse_yahoo_chart + # Sydney (gmtoffset +10 h): Yahoo stamps each bar at the local session + # open, 23:00 UTC the PREVIOUS calendar day. Before 1.1 the raw UTC + # stamp was normalized to midnight, so 2025-01-06..10 came back as + # 2025-01-05..09. + payload = _yahoo_payload(_utc_stamps([5, 6, 7, 8, 9], '23:00'), 36000) + df = _parse_yahoo_chart(payload, ticker='BHP.AX') + assert list(df.index.strftime('%Y-%m-%d')) == [ + '2025-01-06', '2025-01-07', '2025-01-08', '2025-01-09', '2025-01-10'] + assert df.index.name == 'date' + assert list(df.columns) == ['open', 'high', 'low', 'close', + 'adj_close', 'volume'] + + +def test_yahoo_bars_west_of_utc_keep_their_day(): + from hypertools.io.sources import _parse_yahoo_chart + # New York (gmtoffset -5 h): 14:30 UTC is 09:30 local, the same day + payload = _yahoo_payload(_utc_stamps([6, 7, 8, 9, 10], '14:30'), -18000) + df = _parse_yahoo_chart(payload, ticker='AAPL') + assert list(df.index.strftime('%Y-%m-%d')) == [ + '2025-01-06', '2025-01-07', '2025-01-08', '2025-01-09', '2025-01-10'] + + +def test_yahoo_payload_without_gmtoffset_is_treated_as_utc(): + from hypertools.io.sources import _parse_yahoo_chart + payload = _yahoo_payload(_utc_stamps([6, 7], '14:30'), 0) + del payload['chart']['result'][0]['meta']['gmtoffset'] + df = _parse_yahoo_chart(payload, ticker='X') + assert list(df.index.strftime('%Y-%m-%d')) == ['2025-01-06', '2025-01-07'] + + +def test_yahoo_live_australian_ticker_dates_match_the_trading_days(): + # one small live request (measured 2026-09-06: BHP.AX 2025-01-06..10 + # are five ASX trading days; before 1.1 they came back as 01-05..09) + with skip_on_transient_network('loading yahoo:BHP.AX for one week'): + df = yahoo_source('yahoo:BHP.AX', start='2025-01-05', + end='2025-01-11') + assert list(df.index.strftime('%Y-%m-%d')) == [ + '2025-01-06', '2025-01-07', '2025-01-08', '2025-01-09', '2025-01-10'] + + +# ------------------------------ yahoo: intraday bars keep their timestamps + +def _hourly_utc_stamps(first_utc, n): + start = int(pd.Timestamp(first_utc, tz='UTC').timestamp()) + return [start + 3600 * i for i in range(n)] + + +def test_yahoo_intraday_bars_keep_their_exchange_local_times(): + from hypertools.io.sources import _parse_yahoo_chart + # seven 1-hour New York bars on one day, 13:30..19:30 UTC (09:30..15:30 + # EDT). Before the fix every bar was normalized to midnight, so all + # seven shared one timestamp and hyp.predict refused the frame. + payload = _yahoo_payload(_hourly_utc_stamps('2025-06-02 13:30', 7), + -14400) + payload['chart']['result'][0]['meta']['exchangeTimezoneName'] = \ + 'America/New_York' + df = _parse_yahoo_chart(payload, ticker='AAPL', interval='1h') + assert not df.index.duplicated().any() + assert df.index.is_monotonic_increasing + assert df.index.name == 'date' + assert str(df.index.tz) == 'America/New_York' + assert list(df.index.strftime('%Y-%m-%d %H:%M')) == [ + f'2025-06-02 {h:02d}:30' for h in range(9, 16)] + + +def test_yahoo_intraday_bars_across_a_dst_change_stay_unique_and_local(): + from hypertools.io.sources import _parse_yahoo_chart + # a 24-hour instrument (ES=F is listed in America/New_York) across the + # 2025-11-02 fall-back: 01:00-02:00 local happens twice. The index must + # stay unique (tz-aware instants) and show the correct wall time on + # both sides of the change -- a single fixed gmtoffset would shift the + # pre-change bars by an hour. + stamps = _hourly_utc_stamps('2025-11-01 12:00', 48) + payload = _yahoo_payload(stamps, -18000) # the post-change offset + payload['chart']['result'][0]['meta']['exchangeTimezoneName'] = \ + 'America/New_York' + df = _parse_yahoo_chart(payload, ticker='ES=F', interval='1h') + assert len(df) == 48 + assert not df.index.duplicated().any() + assert df.index.is_monotonic_increasing + local = df.index.strftime('%Y-%m-%d %H:%M') + assert local[0] == '2025-11-01 08:00' # 12:00 UTC, EDT + assert local[-1] == '2025-11-03 06:00' # 11:00 UTC, EST + assert list(local).count('2025-11-02 01:00') == 2 # the repeated hour + + +def test_yahoo_intraday_with_an_unknown_timezone_name_uses_gmtoffset(): + from hypertools.io.sources import _parse_yahoo_chart + payload = _yahoo_payload(_hourly_utc_stamps('2025-06-02 13:30', 3), + -14400) # tz name is 'x' here + df = _parse_yahoo_chart(payload, ticker='X', interval='30m') + assert list(df.index.strftime('%H:%M')) == ['09:30', '10:30', '11:30'] + assert df.index.tz is not None + + +def test_yahoo_daily_bars_stay_naive_trading_days(): + from hypertools.io.sources import _parse_yahoo_chart + # the daily/weekly/monthly path is unchanged: naive exchange-local dates + for interval in ('1d', '1wk', '1mo', '3mo', '5d'): + payload = _yahoo_payload(_utc_stamps([6, 7], '14:30'), -18000) + df = _parse_yahoo_chart(payload, ticker='X', interval=interval) + assert df.index.tz is None, interval + assert list(df.index.strftime('%Y-%m-%d %H:%M')) == [ + '2025-01-06 00:00', '2025-01-07 00:00'], interval + + +def test_yahoo_live_hourly_bars_are_unique_and_forecastable(): + # the reviewer's repro: hyp.load('yahoo:AAPL', interval='1h') gave 51 + # bars with 43 duplicated midnight stamps and hyp.predict refused them + import time + with skip_on_transient_network('loading yahoo:AAPL hourly bars'): + df = hyp.load('yahoo:AAPL', interval='1h', + start=time.time() - 10 * 86400) + assert len(df) > 10 + assert not df.index.duplicated().any() + assert df.index.is_monotonic_increasing + assert str(df.index.tz) == 'America/New_York' + assert '09:30' in set(df.index.strftime('%H:%M')) # the session open + # the overnight/weekend gaps make the grid irregular; predict says so + # (a real warning about real data, not a failure) + with pytest.warns(UserWarning, match='Irregular observation times'): + forecast = hyp.predict(df[['close']], t=2) + assert len(forecast) == 2 + assert (forecast.index > df.index[-1]).all() + + +def test_yahoo_live_24h_future_across_the_fall_back_hour_is_unique(): + # ES=F trades overnight and is listed in America/New_York, so its hourly + # bars cross the 2025-11-02 01:00 repeated hour (inside Yahoo's 730-day + # intraday window until late 2027) + with skip_on_transient_network('loading yahoo:ES=F across DST'): + df = yahoo_source('yahoo:ES=F', start='2025-10-31', + end='2025-11-04', interval='1h') + assert len(df) > 24 + assert not df.index.duplicated().any() + assert df.index.is_monotonic_increasing diff --git a/tests/test_lsl_streaming.py b/tests/test_lsl_streaming.py index 64e25f55..438fc53f 100644 --- a/tests/test_lsl_streaming.py +++ b/tests/test_lsl_streaming.py @@ -246,12 +246,26 @@ def test_lsl_stream_receives_pushed_samples_by_name(outlet_stream): @requires_pylsl -def test_lsl_stream_resolves_by_type(outlet_stream): - # resolve using type= (the outlet was created with stream_type='EEG' - # in _start_outlet) rather than name= - stream = hyp.io.lsl_stream(type='EEG', timeout=5.0) - sample = next(stream) - assert len(sample) == N_CHANNELS +def test_lsl_stream_resolves_by_type(): + """type= resolves a stream by its StreamInfo type, so this test's outlet + carries a type nothing else on the network advertises. Resolving + type='EEG' assumed this outlet was the only EEG stream in reach: on + 2026-09-07 two other processes on the machine (a notebook kernel with + the LSL tutorial's synthetic outlet, and an audit script executing the + same notebook) advertised idle 'EEG' outlets, `lsl_stream` used the + first match, and the test failed on a source that never pushed. A lab + network has real EEG streams for the same reason.""" + stream_type = f'HypertoolsTestType-{time.time_ns()}' + thread, stop = _start_outlet( + f'HypertoolsTestStream-bytype-{time.time_ns()}', stream_type=stream_type) + try: + with hyp.io.lsl_stream(type=stream_type, timeout=5.0) as stream: + sample = next(stream) + assert len(sample) == N_CHANNELS + finally: + stop.set() + thread.join(timeout=5.0) + assert not thread.is_alive() @requires_pylsl @@ -935,10 +949,16 @@ def test_tutorial_close_under_load_leaves_no_liblsl_error(tmp_path): sets the flag BEFORE cancelling, and joins the receiver thread). Real outlet, real inlet, real subprocess stderr; no mocks.""" _require_outlets() + # The budget is for a wedged child, not a slow one: the deliberate GIL + # load makes the three cycles take anywhere from 38 s to 153 s on the + # hosted ubuntu runners (measured over two CI runs, 2026-09-08), and the + # ubuntu 3.13 job hit the old 300 s ceiling on a run where the SAME + # commit's 3.10 job finished the test in 38 s. Under pytest's 1200 s + # per-test ceiling (pyproject), 900 s leaves the wedge detection intact. result = subprocess.run( [sys.executable, '-c', textwrap.dedent(_TUTORIAL_UNDER_LOAD_SCRIPT), str(tmp_path / 'lsl_streaming.mp4'), '3'], - capture_output=True, text=True, timeout=300, + capture_output=True, text=True, timeout=900, ) assert result.returncode == 0, ( f"stdout={result.stdout}\nstderr={result.stderr}") diff --git a/tests/test_manip_created_nan_rows.py b/tests/test_manip_created_nan_rows.py new file mode 100644 index 00000000..9c1009aa --- /dev/null +++ b/tests/test_manip_created_nan_rows.py @@ -0,0 +1,212 @@ +"""Rows a `manip=` stage leaves with no values are the manip stage's doing +(1.1 release review, 2026-09-11). + +A trailing ``Smooth(center=False)`` leaves its first ``kernel_width - 1`` +rows NaN (pandas' rolling-window semantics) unless ``min_periods=1``. On a +finite input, ``hyp.plot(x, manip=<that smoother>)`` first emitted the +missing-data imputation warnings -- "Missing data: filling missing values +with PPCA ..." and "PPCA cannot fill 4 row(s) ... Use model='Kalman'", +which blame the input and point at the wrong remedy -- and only then raised +the right error; ``hyp.analyze``/``hyp.reduce``/``hyp.cluster``/ +``hyp.align`` with ``manip=`` gave the same warnings followed by +scikit-learn's "Input X contains NaN" or numpy's "SVD did not converge". + +The pipeline now stops right after the manip stage, before any later stage +tries to impute rows that have no observed feature at all, with the +manip-stage diagnosis and the ``min_periods=1`` hint. Real data, real +calls, no mocks. +""" +import warnings + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np +import pytest + +import hypertools as hyp +from hypertools.core.pipeline import build_pipeline +from hypertools.manip import Smooth +from hypertools.manip.common import Manipulator + +TRAILING = {'model': 'Smooth', + 'kwargs': {'kernel': 'boxcar', 'center': False, + 'kernel_width': 5}} +IMPUTATION_WARNINGS = ('Missing data', 'PPCA', 'Kalman') + + +def _walk(rows=40, cols=3, seed=0): + rng = np.random.default_rng(seed) + return np.cumsum(rng.normal(size=(rows, cols)), axis=0) + + +def _raises_manip_error_without_imputation_warnings(call): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + with pytest.raises(ValueError) as info: + call() + message = str(info.value) + assert 'manip= stage' in message and 'Smooth' in message, message + assert 'min_periods=1' in message, message + assert 'no finite values' in message, message + imputation = [str(w.message) for w in caught + if any(k in str(w.message) for k in IMPUTATION_WARNINGS)] + assert imputation == [], imputation + return message + + +def test_plot_reports_the_trailing_smoother_without_imputation_warnings(): + x = _walk() + message = _raises_manip_error_without_imputation_warnings( + lambda: hyp.plot(x, manip=TRAILING, show=False)) + assert '4 row(s) of dataset 0' in message + assert 'rows 0-3' in message + plt.close('all') + + +def test_plot_names_the_dataset_the_smoother_emptied(): + x = _walk() + message = _raises_manip_error_without_imputation_warnings( + lambda: hyp.plot([x, x[::-1]], manip=TRAILING, show=False)) + assert 'dataset 0' in message + plt.close('all') + + +def test_a_scatter_plot_stops_at_the_manip_stage_too(): + """Markers do not need a finite line, but the reduce stage still + received rows with no values -- the same misleading imputation + warnings -- and drew a trajectory missing its first rows.""" + x = _walk() + _raises_manip_error_without_imputation_warnings( + lambda: hyp.plot(x, '.', manip=TRAILING, show=False)) + plt.close('all') + + +def test_an_instance_spec_is_named_too(): + x = _walk() + _raises_manip_error_without_imputation_warnings( + lambda: hyp.plot(x, manip=Smooth(kernel='boxcar', kernel_width=5, + center=False), show=False)) + plt.close('all') + + +@pytest.mark.parametrize('entry', ['analyze', 'reduce', 'cluster', 'align']) +def test_every_manip_entry_point_raises_the_manip_error(entry): + """These used to end in scikit-learn's 'Input X contains NaN' (reduce, + cluster) or numpy's 'SVD did not converge' (align), after the same + imputation warnings.""" + x, y = _walk(seed=1), _walk(seed=2) + calls = { + 'analyze': lambda: hyp.analyze(x, manip=TRAILING, reduce='PCA', + ndims=2), + 'reduce': lambda: hyp.reduce(x, manip=TRAILING, ndims=2), + 'cluster': lambda: hyp.cluster(x, manip=TRAILING, n_clusters=2), + 'align': lambda: hyp.align([x, y], manip=TRAILING), + } + _raises_manip_error_without_imputation_warnings(calls[entry]) + + +def test_a_users_own_gap_is_imputed_and_the_emptied_rows_still_blamed(): + """`hyp.plot` fills the user's own missing entry at the format stage + (warning about it, rightly), before the smoother runs; the rows the + smoother then empties are still named as the manip stage's doing, and + the imputation warning fires once -- for the user's gap only.""" + x = _walk() + x[20, 1] = np.nan + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + with pytest.raises(ValueError, match='manip= stage') as info: + hyp.plot(x, manip=TRAILING, show=False) + assert 'min_periods=1' in str(info.value) + fills = [w for w in caught if 'Missing data' in str(w.message)] + assert len(fills) == 1 + assert not [w for w in caught if 'PPCA cannot fill' in str(w.message)] + plt.close('all') + + +def test_the_min_periods_remedy_works(): + x = _walk() + spec = {'model': 'Smooth', + 'kwargs': {'kernel': 'boxcar', 'center': False, + 'kernel_width': 5, 'min_periods': 1}} + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + out = hyp.analyze(x, manip=spec, reduce='PCA', ndims=2) + out = np.asarray(out) + assert out.shape == (40, 2) and np.isfinite(out).all() + assert not [w for w in caught + if any(k in str(w.message) for k in IMPUTATION_WARNINGS)] + + +def test_a_manip_only_pipeline_still_returns_the_rows(): + """With no stage after it, the manip result is the answer: its NaN rows + are returned exactly as `hyp.manip` returns them (pandas semantics).""" + x = _walk() + out = np.asarray(hyp.analyze(x, manip=TRAILING)) + assert out.shape == x.shape + assert np.isnan(out[:4]).all() and np.isfinite(out[4:]).all() + + +def test_partially_missing_rows_from_the_manip_stage_are_still_imputed(): + """Only rows with NO finite value are unrecoverable. A Delay embedding + with drop_edges=False pads the lagged columns with NaN but keeps each + row's own value, which the reduce stage's imputation can fill.""" + x = _walk(cols=2) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + out = hyp.analyze(x, manip={'model': 'Delay', + 'kwargs': {'tau': 1, 'dims': 2, + 'drop_edges': False}}, + reduce='PCA', ndims=2) + out = np.asarray(out) + assert out.shape == (40, 2) and np.isfinite(out).all() + + +def _fit_range(data, **kwargs): + frames = data if isinstance(data, list) else [data] + return {'upper': np.max([np.nanmax(np.asarray(f, dtype=float)) + for f in frames])} + + +def _blank_out_of_range(data, upper=None, **kwargs): + """Blank every row with a value above the FIT-time maximum.""" + def one(frame): + out = frame.astype(float).copy() + out[(out > upper).any(axis=1)] = np.nan + return out + return [one(f) for f in data] if isinstance(data, list) else one(data) + + +class _OutOfRangeBlanker(Manipulator): + """A real (if unusual) user manipulator: it learns the data's range at + fit time and blanks rows of new data that leave it, so it empties rows + only on `transform` -- the reuse path of a fitted pipeline.""" + + def __init__(self): + super().__init__(fitter=_fit_range, transformer=_blank_out_of_range, + required=['upper']) + + +def test_a_reused_pipeline_checks_the_manip_output_of_new_data(): + x = _walk(seed=6) + pipe = build_pipeline(manip=_OutOfRangeBlanker(), reduce='PCA', ndims=2) + fitted = np.asarray(pipe.fit_transform(x)) + assert np.isfinite(fitted).all() + new = x.copy() + new[7] = x.max() + 10.0 + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + with pytest.raises(ValueError, match='manip= stage') as info: + pipe.transform(new) + assert '1 row(s) of dataset 0' in str(info.value) + assert 'row 7' in str(info.value) + assert not [w for w in caught + if any(k in str(w.message) for k in IMPUTATION_WARNINGS)] + + +def test_build_pipeline_fit_stops_at_the_manip_stage(): + """The check lives in the pipeline `build_pipeline` assembles, so a + Pipeline built directly behaves like the dispatchers.""" + pipe = build_pipeline(manip=TRAILING, reduce='PCA', ndims=2) + _raises_manip_error_without_imputation_warnings( + lambda: pipe.fit_transform([_walk(seed=3), _walk(seed=4)])) diff --git a/tests/test_manip_delay.py b/tests/test_manip_delay.py index 4d93c5d2..3377bdd7 100644 --- a/tests/test_manip_delay.py +++ b/tests/test_manip_delay.py @@ -84,6 +84,14 @@ def test_delay_column_names_and_order(): np.testing.assert_allclose(out['x_lag0'].to_numpy(), np.arange(4, 10, dtype=float)) +@pytest.mark.parametrize('columns', [[1, '1'], ['x', 'x']]) +def test_delay_refuses_colliding_column_names(columns): + # GH #285 release review: no silent feature loss through dict keys. + data = pd.DataFrame(np.arange(20.).reshape(10, 2), columns=columns) + with pytest.raises(ValueError, match='unique string representations'): + Delay(dims=2).fit_transform(data) + + # --- multi-column input: each column embedded independently --------------- def test_delay_multicolumn_embeds_each_column_independently(): diff --git a/tests/test_marker_parity.py b/tests/test_marker_parity.py index 2194543e..fda1693f 100644 --- a/tests/test_marker_parity.py +++ b/tests/test_marker_parity.py @@ -93,10 +93,14 @@ def test_resolve_fmt_dot_char_feeds_marker_size(self): _mode, _symbol, _dash, marker_char = _resolve_fmt('.', {}) assert marker_char == '.' - def test_morph_default_markersize_is_1_5_not_6(self): - # matches matplotlib_backend's `morph_markersize = _mkw.get( - # "markersize") or 1.5` default -- NOT the general 6.0pt default. - assert MORPH_DEFAULT_MARKERSIZE_PT == pytest.approx(1.5) + def test_morph_default_markersize_is_4_not_6(self): + # the ONE default both backends read (`morph. + # MORPH_DEFAULT_MARKERSIZE_PT`) -- NOT the general 6.0pt default. + # (1.1 visual review L9: this used to pin 1.5pt, which drew + # sub-pixel dots on plotly -- 30 morph dots covered 24 px.) + from hypertools.plot import morph + assert MORPH_DEFAULT_MARKERSIZE_PT == pytest.approx(4.0) + assert MORPH_DEFAULT_MARKERSIZE_PT == morph.MORPH_DEFAULT_MARKERSIZE_PT assert MORPH_DEFAULT_MARKERSIZE_PT != DEFAULT_MARKERSIZE_PT @@ -165,3 +169,63 @@ def test_plotly_scatter3d_diameter_within_20pct_of_matplotlib( f"{mpl_diameter}px, plotly3d diameter={plotly_diameter}px " f"(rel_error={rel_error:.2%})" ) + + +class TestObservationMarkerParity3D: + """A marker on each OBSERVATION of a smoothed 3-D line ('o-' and every + other line+marker fmt, `plotly_backend._observation_marker`) is a + per-vertex size ARRAY -- and `go.Scatter3d` renders an array size at + HALF the diameter of the same scalar size (measured in kaleido: 8 -> + 7.25 px vs 14.25 px, 16 -> 14.25 vs 28.75, 64 -> 57 vs 114). The + array was filled with the scalar-calibrated size, so 3-D 'o-' markers + drew at half matplotlib's diameter: 2 px dots under a 1.4 px line, + hidden (1.1 release review). 2-D `go.Scatter` arrays render at the + scalar size, so only 3-D is corrected.""" + + @pytest.mark.parametrize('markersize', [3.0, 6.0, 12.0]) + def test_observation_marker_diameter_within_20pct_of_matplotlib( + self, markersize, tmp_path): + import numpy as np + from hypertools.plot.plotly_backend import _observation_marker + mpl_path = str(tmp_path / f'mpl3d_obs_{markersize}.png') + plotly_path = str(tmp_path / f'plotly3d_obs_{markersize}.png') + + render_mpl_marker_3d(markersize, 'o', mpl_path) + mpl_w, mpl_h = measure_diameter(mpl_path) + mpl_diameter = (mpl_w + mpl_h) / 2.0 + + marker = _observation_marker( + dict(color='red', symbol='circle', + size=_marker_size_px(markersize, 'o', ndims=3)), + n_vertices=5, vertices=[0, 4], ndims=3) + assert not np.isscalar(marker['size']) # the bubble form + render_plotly_marker_3d(None, plotly_path, marker=marker) + plotly_w, plotly_h = measure_diameter(plotly_path) + plotly_diameter = (plotly_w + plotly_h) / 2.0 + + assert mpl_diameter > 0 and plotly_diameter > 0 + rel_error = abs(plotly_diameter - mpl_diameter) / mpl_diameter + assert rel_error <= 0.20, ( + f"markersize={markersize}: mpl3d diameter={mpl_diameter}px, " + f"plotly3d observation-marker diameter={plotly_diameter}px " + f"(rel_error={rel_error:.2%})") + + @pytest.mark.parametrize('ndims', [2, 3]) + def test_hyp_plot_line_marker_sizes_render_like_marker_only(self, ndims): + """End to end: the observation markers of a 'o-' trace render at + the same diameter as the plain markers of an 'o' trace (scalar + size) with the same markersize -- in 3-D that is twice the scalar + value, since Scatter3d halves an array size; in 2-D the same.""" + import numpy as np + import hypertools as hyp + rng = np.random.default_rng(0) + a = rng.standard_normal((36, 5)).cumsum(0) + lines = hyp.plot(a, '-o', markersize=3, ndims=ndims, + backend='plotly', show=False) + dots = hyp.plot(a, 'o', markersize=3, ndims=ndims, + backend='plotly', show=False) + arr = np.asarray(lines.data[0].marker.size, dtype=float) + scalar = float(dots.data[0].marker.size) + assert arr.ndim == 1 and (arr > 0).sum() == 36 + expected = scalar * (2.0 if ndims >= 3 else 1.0) + assert np.allclose(arr[arr > 0], expected) diff --git a/tests/test_meshutil.py b/tests/test_meshutil.py index fceae101..4c46078b 100644 --- a/tests/test_meshutil.py +++ b/tests/test_meshutil.py @@ -580,6 +580,26 @@ def test_nearer_point_dominates(self): vc = vertex_colors_from_points(np.array([[1.0, 0, 0]]), pts, cols) assert vc[0, 0] > vc[0, 2] # reddish + @pytest.mark.parametrize('seed', [0, 1]) + def test_hull_colors_follow_the_local_points(self, seed): + # Maintainer report 2026-09-11: a hue= surface did not match the + # dots beneath it. With a global 1/d**2 blend in 3-D, the many + # distant points outweigh the few near ones, so every vertex drifts + # toward the dataset's mean colour (washed out, locally wrong). + import colorsys + from matplotlib import colormaps + rng = np.random.default_rng(seed) + pts = np.cumsum(rng.normal(size=(400, 3)), axis=0) + cols = colormaps['viridis'](np.linspace(0, 1, len(pts)))[:, :3] + verts = smooth_hull_3d(pts)[0] + vc = vertex_colors_from_points(verts, pts, cols) + _, idx = cKDTree(pts).query(verts, k=3) + local = cols[idx].mean(axis=1) + assert np.median(np.abs(vc - local).max(axis=1)) < 0.03 + def sat(c): + return np.median([colorsys.rgb_to_hsv(*x)[1] for x in c]) + assert sat(vc) > sat(cols) - 0.05 + def test_output_shape_and_range(self): rng = np.random.default_rng(1) verts = rng.normal(size=(50, 3)) diff --git a/tests/test_morph_alpha.py b/tests/test_morph_alpha.py index bf9d1377..ee94149a 100644 --- a/tests/test_morph_alpha.py +++ b/tests/test_morph_alpha.py @@ -9,8 +9,10 @@ The rule (``hypertools.plot.morph.morph_alpha``, shared by both backends): a HOLD draws the held dataset's own alpha; a TRANSITION eases (smoothstep, on the same schedule as the colour lerp) from the departing dataset's alpha -to the arriving one's -- so its first frame is the departing dataset's -alpha and its last is the arriving dataset's. A scalar ``alpha=`` gives +to the arriving one's -- every transition frame strictly between the two, +since the holds on either side already draw the endpoints (1.1 visual +review L9: the first and last transition frames used to repeat the hold +values exactly, so a short transition never moved). A scalar ``alpha=`` gives every dataset the same value, so the cloud is constant. When no alpha was asked for at all, the artist is left at matplotlib's default (``None``) -- nothing about a default morph changes. @@ -22,6 +24,7 @@ import re import numpy as np +from tests._plotly_colors import rgba as effective_rgba import pytest import hypertools as hyp @@ -75,16 +78,26 @@ def test_hold_is_held_datasets_alpha(self): assert morph.morph_alpha([0.2, 0.6, 1.0], 4, 4, 5) == 1.0 def test_transition_eases_departing_to_arriving(self): + # L9: the transition samples the INTERIOR of (0, 1) -- + # t = smoothstep((step + 1) / (n_steps + 1)) -- so its first frame + # is just past the departing alpha and its last just short of the + # arriving one; the holds on either side draw the endpoints. (This + # test used to pin step 0 to exactly 0.2 and step 4 to exactly 0.6, + # which is the endpoint repetition the finding measured.) alphas = [0.2, 0.6] - assert morph.morph_alpha(alphas, 1, 0, 5) == pytest.approx(0.2) - assert morph.morph_alpha(alphas, 1, 4, 5) == pytest.approx(0.6) - t = float(morph.smoothstep(2 / 4)) - assert morph.morph_alpha(alphas, 1, 2, 5) == pytest.approx( - 0.2 + t * 0.4) + vals = [morph.morph_alpha(alphas, 1, s, 5) for s in range(5)] + assert all(0.2 < v < 0.6 for v in vals) + assert vals == sorted(vals) + for s, v in enumerate(vals): + t = float(morph.smoothstep((s + 1) / 6)) + assert v == pytest.approx(0.2 + t * 0.4) + assert vals[2] == pytest.approx(0.4) # symmetric midpoint def test_unset_entry_counts_as_opaque_when_others_are_set(self): assert morph.morph_alpha([0.3, None], 2, 0, 5) == 1.0 - assert morph.morph_alpha([0.3, None], 1, 4, 5) == pytest.approx(1.0) + t = float(morph.smoothstep(5 / 6)) + assert morph.morph_alpha([0.3, None], 1, 4, 5) == pytest.approx( + 0.3 + t * (1.0 - 0.3)) # --------------------------------------------------------------------------- @@ -124,14 +137,20 @@ def test_per_dataset_list_follows_the_hold_and_departing_rule(self): ani._func(h0, *ani._args) assert artist.get_alpha() == 0.2 # hold on dataset 0 ani._func(t_first, *ani._args) - assert artist.get_alpha() == pytest.approx(0.2) # departing + # just past the departing alpha (L9: no longer exactly it) + first = artist.get_alpha() + assert first == pytest.approx(morph.morph_alpha(alphas, 1, 0, fc[1])) ani._func(t_mid, *ani._args) step = t_mid - fc[0] expect = morph.morph_alpha(alphas, 1, step, fc[1]) - assert 0.2 < expect < 0.6 + assert 0.2 < first < expect < 0.6 assert artist.get_alpha() == pytest.approx(expect) ani._func(t_last, *ani._args) - assert artist.get_alpha() == pytest.approx(0.6) # arriving + # just short of the arriving alpha + last = artist.get_alpha() + assert last == pytest.approx( + morph.morph_alpha(alphas, 1, fc[1] - 1, fc[1])) + assert expect < last < 0.6 ani._func(h1, *ani._args) assert artist.get_alpha() == 0.6 # hold on dataset 1 ani._func(sum(fc) - 1, *ani._args) @@ -210,32 +229,37 @@ class TestPlotlyMorphAlpha: def test_scalar_alpha_on_initial_trace_and_every_frame(self): fig = _plotly(_blobs(), alpha=0.25) morph_idx = fig.frames[0].traces[0] - assert _alpha_of(fig.data[morph_idx].marker.color) == 0.25 + assert effective_rgba(fig.data[morph_idx], 'marker')[-1] == 0.25 for k, frame in enumerate(fig.frames): - assert _alpha_of(frame.data[0].marker.color) == 0.25, f"frame {k}" + assert effective_rgba(frame.data[0], 'marker')[-1] == 0.25, f"frame {k}" def test_scalar_alpha_2d(self): data = [d[:, :2] for d in _blobs()] fig = _plotly(data, alpha=0.4) morph_idx = fig.frames[0].traces[0] - assert _alpha_of(fig.data[morph_idx].marker.color) == 0.4 + assert effective_rgba(fig.data[morph_idx], 'marker')[-1] == 0.4 for frame in fig.frames: - assert _alpha_of(frame.data[0].marker.color) == 0.4 + assert effective_rgba(frame.data[0], 'marker')[-1] == 0.4 def test_per_dataset_list_follows_the_hold_and_departing_rule(self): alphas = [0.2, 0.6, 1.0] fig = _plotly(_blobs(), alpha=alphas) fc, _, _ = morph.morph_schedule(3, len(fig.frames), 1, -60) h0, t_first, t_mid, t_last, h1 = _segment_frames(fc) - col = lambda k: fig.frames[k].data[0].marker.color # noqa: E731 - assert _alpha_of(col(h0)) == 0.2 - assert _alpha_of(col(t_first)) == pytest.approx(0.2) + col = lambda k: effective_rgba(fig.frames[k].data[0], 'marker')[-1] # noqa: E731 + assert col(h0) == 0.2 + # L9: the transition's first/last frames sit just inside the + # departing/arriving alphas (they used to repeat them exactly) + assert col(t_first) == pytest.approx( + morph.morph_alpha(alphas, 1, 0, fc[1]), abs=1e-3) expect = morph.morph_alpha(alphas, 1, t_mid - fc[0], fc[1]) - assert 0.2 < expect < 0.6 - assert _alpha_of(col(t_mid)) == pytest.approx(expect) - assert _alpha_of(col(t_last)) == pytest.approx(0.6) - assert _alpha_of(col(h1)) == 0.6 - assert _alpha_of(col(sum(fc) - 1)) == 1.0 + assert 0.2 < col(t_first) < expect < 0.6 + assert col(t_mid) == pytest.approx(expect, abs=1e-3) + assert col(t_last) == pytest.approx( + morph.morph_alpha(alphas, 1, fc[1] - 1, fc[1]), abs=1e-3) + assert expect < col(t_last) < 0.6 + assert col(h1) == 0.6 + assert col(sum(fc) - 1) == 1.0 def test_default_alpha_unchanged(self): """Regression: no `alpha=` -> the plain opaque `rgb(...)` colour @@ -256,9 +280,9 @@ def test_surface_mesh_keeps_its_own_alpha(self): ref = _plotly(data, color='k', surface=spec) cloud_idx, mesh_idx = fig.frames[0].traces assert list(ref.frames[0].traces) == [cloud_idx, mesh_idx] - assert _alpha_of(fig.data[cloud_idx].marker.color) == 0.25 + assert effective_rgba(fig.data[cloud_idx], 'marker')[-1] == 0.25 assert ref.data[cloud_idx].marker.color.startswith('rgb(') assert fig.data[mesh_idx].opacity == ref.data[mesh_idx].opacity for frame, rframe in zip(fig.frames, ref.frames): - assert _alpha_of(frame.data[0].marker.color) == 0.25 + assert effective_rgba(frame.data[0], 'marker')[-1] == 0.25 assert frame.data[1].opacity == rframe.data[1].opacity diff --git a/tests/test_morph_animation.py b/tests/test_morph_animation.py index 4c2e113a..854203fc 100644 --- a/tests/test_morph_animation.py +++ b/tests/test_morph_animation.py @@ -291,28 +291,44 @@ def test_hold_segment_returns_dataset_unchanged(self): pts = morph.morph_positions(self.sampled, 2, step, 10) np.testing.assert_array_equal(pts, self.sampled[1]) - def test_morph_segment_endpoints_exact(self): - pts0 = morph.morph_positions(self.sampled, 1, 0, 10) - np.testing.assert_allclose(pts0, self.sampled[0]) - pts1 = morph.morph_positions(self.sampled, 1, 9, 10) - np.testing.assert_allclose(pts1, self.sampled[1]) - - def test_morph_segment_midpoint_matches_smoothstep_formula(self): + def test_morph_segment_never_repeats_an_endpoint(self): + # 1.1 visual review L9: this test used to require the FIRST and LAST + # transition frames to reproduce the two clouds exactly -- which + # is what made a 2-frame transition two copies of the hold clouds + # (no motion at all). The holds draw the clouds; every transition + # frame is strictly between them, symmetric about the midpoint. + for n_steps in (1, 2, 10): + firsts = [morph.morph_positions(self.sampled, 1, s, n_steps) + for s in range(n_steps)] + for pts in firsts: + assert np.all(pts > self.sampled[0]) + assert np.all(pts < self.sampled[1]) + np.testing.assert_allclose( + firsts[0] - self.sampled[0], self.sampled[1] - firsts[-1]) + + def test_morph_segment_matches_the_interior_smoothstep_formula(self): step, n_steps = 3, 10 - t = morph.smoothstep(step / (n_steps - 1)) + t = morph.smoothstep((step + 1) / (n_steps + 1)) expected = (1 - t) * self.sampled[0] + t * self.sampled[1] pts = morph.morph_positions(self.sampled, 1, step, n_steps) np.testing.assert_allclose(pts, expected) + assert morph.transition_t(step, n_steps) == pytest.approx(float(t)) def test_color_hold_is_solid_dataset_color(self): c = morph.morph_color(self.colors, 2, 5, 10) assert c == self.colors[1] - def test_color_morph_endpoints(self): - c0 = morph.morph_color(self.colors, 1, 0, 10) - c1 = morph.morph_color(self.colors, 1, 9, 10) - np.testing.assert_allclose(c0, self.colors[0]) - np.testing.assert_allclose(c1, self.colors[1]) + def test_color_morph_stays_between_the_dataset_colors(self): + # L9: the colour follows the positions' interior easing -- it used + # to equal the two dataset colours exactly on the first/last frame + c0 = np.array(morph.morph_color(self.colors, 1, 0, 10)) + c1 = np.array(morph.morph_color(self.colors, 1, 9, 10)) + t0 = float(morph.smoothstep(1 / 11)) + np.testing.assert_allclose( + c0, (1 - t0) * np.array(self.colors[0]) + + t0 * np.array(self.colors[1])) + np.testing.assert_allclose(c0[:2], c1[:2][::-1]) + assert 0.0 < c0[1] < c1[1] < 1.0 def test_interpolate_color_linear(self): c = morph.interpolate_color((0.0, 0.0, 0.0), (1.0, 1.0, 1.0), 0.25) diff --git a/tests/test_morph_loop.py b/tests/test_morph_loop.py index c76fe206..9fd0d85c 100644 --- a/tests/test_morph_loop.py +++ b/tests/test_morph_loop.py @@ -200,3 +200,53 @@ def test_loop_on_a_non_morph_style_raises(self, style): with pytest.raises(ValueError, match=r"loop=True is only supported"): hyp.plot(clouds[:2], animate=style, loop=True, duration=1, show=False) + + +# --- 1.1 release review: A2 per-segment rotations= under loop=True -------- + +class TestLoopedRotationsList: + """`loop=` promises ``2(n + 1) - 1`` segments; the plot()-level check + counted ``2n - 1`` while the backends re-validated against the looped + cloud list, so neither 5 nor 7 entries were ever accepted.""" + + def clouds(self): + rng = np.random.default_rng(0) + return [rng.normal(size=(50, 3)) + 3.0 * i for i in range(3)] + + @pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) + def test_the_documented_length_is_accepted(self, backend): + if backend == 'plotly': + pytest.importorskip('plotly') + out = hyp.plot(self.clouds(), animate='morph', loop=True, + rotations=[0.5, 1, 0.5, 1, 0.5, 1, 0.5], + duration=2, frame_rate=5, show=False, + backend=backend) + if backend == 'matplotlib': + try: + assert out.n_segments == 7 + finally: + plt.close(out.figure) + else: + assert len(out.frames) > 0 + + @pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) + def test_the_unlooped_length_is_rejected_naming_the_looped_count( + self, backend): + if backend == 'plotly': + pytest.importorskip('plotly') + with pytest.raises(ValueError) as err: + hyp.plot(self.clouds(), animate='morph', loop=True, + rotations=[1] * 5, duration=2, frame_rate=5, + show=False, backend=backend) + msg = str(err.value) + assert 'has 5 entries' in msg and 'needs exactly 7' in msg + assert 'loop=True' in msg and '3 clouds count as 4' in msg + + def test_without_loop_the_unlooped_length_still_holds(self): + anim = hyp.plot(self.clouds(), animate='morph', + rotations=[1] * 5, duration=2, frame_rate=5, + show=False) + try: + assert anim.n_segments == 5 + finally: + plt.close(anim.figure) diff --git a/tests/test_multibyte.py b/tests/test_multibyte.py index 0ca01627..6d5f2ced 100644 --- a/tests/test_multibyte.py +++ b/tests/test_multibyte.py @@ -286,6 +286,40 @@ def test_title_no_missing_glyph_warnings(): plt.close(fig) +# Scripts the fallback STACK may lack while some other installed font covers +# them (macOS ships Noto Sans Javanese/Syriac etc. outside the stack). +_STACK_GAP_CANDIDATES = ['ꦲꦤꦕ', 'ܐܒܓ', 'ᏣᎳᎩ', 'ᠮᠣᠩ', 'ሀለሐ', 'ⴰⴱⴳ', + 'ཀཁག', 'กขค'] + + +def _stack_gap_text(): + """Text that only an AUTO-DETECTED gap font can render: uncovered by the + fallback stack, covered by some installed font. None when this machine + has no such script.""" + for text in _STACK_GAP_CANDIDATES: + if not _codepoints_uncovered_by_stack({ord(c) for c in text}): + continue + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + if find_covering_font([text]) is not None: + return text + return None + + +@pytest.mark.parametrize('kwarg', ['title', 'xlabel', 'ylabel', 'zlabel']) +def test_axis_labels_join_the_font_gap_scan(kwarg): + # fresh-Colab review 2026-09-11: xlabel/ylabel/zlabel were not scanned + # for font gaps, so an axis label in a script outside the stack drew + # tofu while the same text as a title rendered. + text = _stack_gap_text() + if text is None: + pytest.skip('no installed font covers a script the fallback stack ' + 'lacks on this machine') + fig = hyp.plot(_random_points(20), show=False, **{kwarg: text}) + assert _missing_glyph_warnings(fig) == [] + plt.close(fig) + + # ----------------------------------------------------------- font= kwarg forms @requires_covering_font @@ -893,10 +927,23 @@ def test_plotly_public_path_dedups_explicit_family_already_in_stack(): def _run_render(out_path, env=None): full = dict(os.environ) full.update(env or {}) - return subprocess.run( - [sys.executable, _RENDER_PLOTLY_SCRIPT, json.dumps(['a', 'b']), '', - out_path], - timeout=_KALEIDO_TIMEOUT_S, capture_output=True, text=True, env=full) + try: + return subprocess.run( + [sys.executable, _RENDER_PLOTLY_SCRIPT, json.dumps(['a', 'b']), + '', out_path], + timeout=_KALEIDO_TIMEOUT_S, capture_output=True, text=True, + env=full) + except subprocess.TimeoutExpired as err: + # a bare TimeoutExpired says nothing about WHERE the script stalled; + # its stderr carries the script's stage lines and stack dumps + def _tail(stream): + if isinstance(stream, bytes): + stream = stream.decode('utf-8', 'replace') + return (stream or '')[-6000:] + pytest.fail( + f'the render script did not finish in {_KALEIDO_TIMEOUT_S} s.\n' + f'--- stderr ---\n{_tail(err.stderr)}\n' + f'--- stdout ---\n{_tail(err.stdout)}') @requires_covering_font @@ -1003,3 +1050,26 @@ def leaks_stop_iteration(): 'a RuntimeError (PEP 479)') assert not _is_browser_lifecycle_error(caught), ( f'{caught!r} is unrelated to the browser: {caught}') + + +@requires_covering_font +@pytest.mark.skipif(os.name == 'nt', reason='the stand-in browser is a POSIX shell script') +def test_render_script_exits_NO_BROWSER_when_the_browser_never_answers( + tmp_path): + """A "browser" that starts and then never speaks is the same class of + failure as one that dies on startup, and it must end the same way: with + the reserved exit code, inside the script's own deadline. On 2026-10-09 a + hosted ubuntu job sat in kaleido for the caller's whole 120 s (kaleido's + own 30 s timeout did not fire) and the NO_BROWSER test above failed with + TimeoutExpired instead. Driven for real: the stand-in is an executable + that stays alive and answers nothing.""" + silent = tmp_path / 'silent-browser' + silent.write_text('#!/bin/sh\nexec sleep 600\n') + silent.chmod(0o755) + result = _run_render(str(tmp_path / 'never.png'), + {_BROWSER_PATH_ENV: str(silent), + _render_mod.DEADLINE_ENV: '15'}) + assert result.returncode == _NO_BROWSER_EXIT, ( + f'expected exit {_NO_BROWSER_EXIT}, got {result.returncode}\n' + f'stderr:\n{result.stderr[-800:]}') + assert 'NO_BROWSER:' in result.stderr diff --git a/tests/test_names_display.py b/tests/test_names_display.py index c82b9371..01b00202 100644 --- a/tests/test_names_display.py +++ b/tests/test_names_display.py @@ -64,114 +64,241 @@ def test_names_and_legend_list_conflict_raises(): legend=['w', 'x', 'y', 'z'], show=False) -# --- double-display ---------------------------------------------------- - -class _FakeEvents: - def __init__(self): - self.callbacks = {} +# --- names= vs an explicit legend=False (1.1 review) ------------------- +# +# names= turns the legend on by default, but it used to override an +# explicit legend=False as well (found in the stock_forecasting tutorial): +# the opt-out must win on both backends. - def register(self, name, cb): - self.callbacks.setdefault(name, []).append(cb) +def _mpl_legend_texts(fig): + """Every legend entry drawn anywhere on a matplotlib figure (axes + legends and figure-level legends).""" + legends = [ax.get_legend() for ax in fig.axes] + list(fig.legends) + return [t.get_text() for leg in legends if leg is not None + for t in leg.get_texts()] - def unregister(self, name, cb): - self.callbacks[name].remove(cb) - def fire(self, name): - for cb in list(self.callbacks.get(name, [])): - cb() +def test_names_legend_false_draws_no_legend_matplotlib(): + data = _datasets(3) + fig = hyp.plot(data, names=['a', 'b', 'c'], legend=False, show=False) + assert _mpl_legend_texts(fig) == [] + # the default (no legend=) still shows the names + fig = hyp.plot(data, names=['a', 'b', 'c'], show=False) + assert _mpl_legend_texts(fig) == ['a', 'b', 'c'] -class _FakeShell: - """Enough of an InteractiveShell for the display path: a post_execute - event registry and an execution count.""" - def __init__(self): - self.events = _FakeEvents() - self.execution_count = 1 +def test_names_legend_false_draws_no_legend_plotly(): + pytest.importorskip('plotly') + data = _datasets(3) + fig = hyp.plot(data, names=['a', 'b', 'c'], legend=False, + backend='plotly', show=False) + shown = [tr.name for tr in fig.data if tr.showlegend is not False] + assert fig.layout.showlegend is not True + assert not any(name in ('a', 'b', 'c') for name in shown) + # identical legend state to the same call without names= + bare = hyp.plot(data, legend=False, backend='plotly', show=False) + assert fig.layout.showlegend == bare.layout.showlegend + assert ([tr.showlegend for tr in fig.data] + == [tr.showlegend for tr in bare.data]) + + +def test_names_legend_false_panels_draw_no_legend(): + data = _datasets(3) + fig = hyp.plot(data, names=['a', 'b', 'c'], legend=False, panels=True, + show=False) + assert len(fig.axes) >= 3 + assert _mpl_legend_texts(fig) == [] + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('extra', [{'predict': 'Kalman', 't': 5}, + {'animate': True}], + ids=['predict', 'animate']) +def test_names_legend_false_forecast_and_animation(backend, extra): + # the stock_forecasting tutorial's shape: named datasets plus a + # forecast overlay (and the animated form), with legend=False + if backend == 'plotly': + pytest.importorskip('plotly') + data = _datasets(3, rows=60, cols=2) + for legend_kw, want in (({}, True), ({'legend': False}, False)): + out = hyp.plot(data, names=['a', 'b', 'c'], backend=backend, + show=False, **extra, **legend_kw) + if backend == 'plotly': + shown = [tr.name for tr in out.data if tr.showlegend] + drawn = bool(out.layout.showlegend) and bool(shown) + assert (set('abc') <= set(shown)) is want + else: + fig = getattr(out, 'figure', out) + texts = _mpl_legend_texts(fig) + drawn = bool(texts) + assert (set('abc') <= set(texts)) is want + assert drawn is want + + +def test_names_legend_false_still_validates_names(): + # legend=False hides the names; it does not make a malformed names= + # list acceptable + data = _datasets(3) + with pytest.raises(ValueError, match='one entry per dataset'): + hyp.plot(data, names=['a', 'b'], legend=False, show=False) -def _count_shows(monkeypatch): +# --- double-display ---------------------------------------------------- +# +# Every notebook scenario below runs on a REAL `IPython.InteractiveShell` +# (in-process, history disabled): `hyp.plot` is executed as cell source via +# `shell.run_cell`, so `get_ipython()`, the `post_execute` event registry, the +# rich-display hook (`_ipython_display_` of a cell's last expression) and the +# execution counter are IPython's own. Display events are observed with +# IPython's `capture_output` (the machinery behind `%%capture`), which records +# every published mime-bundle in order; plotly is pointed at its `json` +# renderer, whose bundle is the figure's JSON, so each captured +# `application/json` output IS one `pio.show` of an identifiable figure. + +_CELL_SETUP = ( + "import numpy as np\n" + "import hypertools as hyp\n" + "from IPython.display import display\n" + "from IPython import get_ipython\n" + "from hypertools.plot.plotly_backend import _flush_pending_display\n" + "def _datasets(n=3, rows=40, cols=3):\n" + " rng = np.random.default_rng(0)\n" + " return [np.cumsum(rng.normal(size=(rows, cols)), axis=0)" + " for _ in range(n)]\n" +) + + +@pytest.fixture +def ipython_shell(): + """A real in-process IPython shell, torn down so later tests run as a + plain script again (`get_ipython()` is None after teardown).""" + pytest.importorskip('plotly') + import IPython import plotly.io as pio + from IPython.core.interactiveshell import InteractiveShell + from traitlets.config import Config from hypertools.plot import plotly_backend - calls = {'n': 0} - monkeypatch.setattr(pio, 'show', lambda fig, *a, **k: calls.__setitem__('n', calls['n'] + 1)) - # plotly's own display hook calls pio.show only when a default renderer - # is configured; headless CI has none, so pin a mime renderer for the test - monkeypatch.setattr(pio.renderers, 'default', 'json') - monkeypatch.setattr(plotly_backend, '_PENDING_DISPLAY', []) - return calls - -def test_plotly_plot_displays_once_at_the_end_of_the_cell(monkeypatch): + assert IPython.get_ipython() is None, 'a shell is already running' + saved_renderer = pio.renderers.default + plotly_backend._PENDING_DISPLAY.clear() + config = Config() + config.HistoryManager.enabled = False + shell = InteractiveShell.instance(config=config) + try: + pio.renderers.default = 'json' + result = shell.run_cell(_CELL_SETUP, store_history=True) + assert result.success, result.error_in_exec + yield shell + finally: + pio.renderers.default = saved_renderer + plotly_backend._PENDING_DISPLAY.clear() + InteractiveShell.clear_instance() + shell.restore_sys_module_state() + assert IPython.get_ipython() is None + + +def _run(shell, source): + """Run one cell; return the mime-bundles it displayed, in order.""" + from IPython.utils.capture import capture_output + with capture_output(display=True) as captured: + result = shell.run_cell(source, store_history=True) + assert result.success, result.error_in_exec + return [dict(out.data) for out in captured.outputs] + + +def _figure_shows(outputs): + return [out['application/json'] for out in outputs + if 'application/json' in out] + + +def _flush_registered(shell): + from hypertools.plot.plotly_backend import _flush_pending_display + return _flush_pending_display in shell.events.callbacks['post_execute'] + + +def test_plotly_plot_displays_once_at_the_end_of_the_cell(ipython_shell): """`fig = hyp.plot(x)` draws in a notebook (as on matplotlib), but only when the cell finishes -- after matplotlib-inline's flush -- not mid-cell.""" + shell = ipython_shell + outputs = _run(shell, ( + "fig = hyp.plot(_datasets(2), backend='plotly', show=True)\n" + "registered = _flush_pending_display in" + " get_ipython().events.callbacks['post_execute']\n" + "display('end of cell body')\n")) + # the plot call queued a cell-end hook ... + assert shell.user_ns['registered'] is True + # ... and nothing was drawn mid-cell: the marker displayed by the LAST + # statement precedes the one figure display, which the cell end produced + assert [list(out) for out in outputs] == [['text/plain'], + ['application/json']] + assert outputs[0]['text/plain'] == "'end of cell body'" + assert not _flush_registered(shell) # one-shot + assert _figure_shows(_run(shell, "pass")) == [] # nothing queued + assert shell.user_ns['fig'] is not None + + +def test_plotly_plot_as_the_last_expression_is_not_drawn_twice(ipython_shell): + shell = ipython_shell + # the rich-display hook (cell ends with `fig`) draws it; the cell-end + # flush must then skip it + outputs = _run(shell, ( + "fig = hyp.plot(_datasets(2), backend='plotly', show=True)\n" + "fig\n")) + assert len(_figure_shows(outputs)) == 1 + assert not _flush_registered(shell) + # a later cell displays it again + later = shell.execution_count + outputs = _run(shell, "fig") + assert shell.execution_count > later + assert len(_figure_shows(outputs)) == 1 + + +def test_plotly_two_figures_in_one_cell_display_in_creation_order(ipython_shell): + import json + shell = ipython_shell + outputs = _run(shell, ( + "a = hyp.plot(_datasets(1), backend='plotly', show=True)\n" + "b = hyp.plot(_datasets(2), backend='plotly', show=True)\n")) + a, b = shell.user_ns['a'], shell.user_ns['b'] + assert len(a.data) != len(b.data) # distinguishable figures + shown = _figure_shows(outputs) + assert [len(fig['data']) for fig in shown] == [len(a.data), len(b.data)] + assert shown == [json.loads(a.to_json()), json.loads(b.to_json())] + + +def test_plotly_show_false_defers_entirely_to_the_display_hook(ipython_shell): + shell = ipython_shell + outputs = _run(shell, ( + "fig = hyp.plot(_datasets(2), backend='plotly', show=False)\n" + "registered = _flush_pending_display in" + " get_ipython().events.callbacks['post_execute']\n")) + assert shell.user_ns['registered'] is False + assert outputs == [] + assert len(_figure_shows(_run(shell, "fig"))) == 1 + + +def test_plotly_show_called_in_plain_script(capsys): + """Plain script (no IPython frontend): fig.show() IS called so the plot + still displays. Outside IPython the json renderer's bundle is printed to + stdout by `IPython.display.display`, so one printed bundle is one show.""" pytest.importorskip('plotly') - import IPython - calls = _count_shows(monkeypatch) - shell = _FakeShell() - monkeypatch.setattr(IPython, 'get_ipython', lambda: shell) - fig = hyp.plot(_datasets(2), backend='plotly', show=True) - assert calls['n'] == 0 # nothing mid-cell - assert shell.events.callbacks['post_execute'] - shell.events.fire('post_execute') # the cell ends - assert calls['n'] == 1 - assert not shell.events.callbacks['post_execute'] # one-shot - assert fig is not None - - -def test_plotly_plot_as_the_last_expression_is_not_drawn_twice(monkeypatch): - pytest.importorskip('plotly') - import IPython - calls = _count_shows(monkeypatch) - shell = _FakeShell() - monkeypatch.setattr(IPython, 'get_ipython', lambda: shell) - fig = hyp.plot(_datasets(2), backend='plotly', show=True) - fig._ipython_display_() # the rich-display hook (cell ends with `fig`) - assert calls['n'] == 1 - shell.events.fire('post_execute') - assert calls['n'] == 1 # skipped: already displayed - shell.execution_count += 1 - fig._ipython_display_() # a later cell displays it again - assert calls['n'] == 2 - - -def test_plotly_two_figures_in_one_cell_display_in_creation_order(monkeypatch): - pytest.importorskip('plotly') + import json import IPython import plotly.io as pio from hypertools.plot import plotly_backend - order = [] - monkeypatch.setattr(pio, 'show', lambda fig, *a, **k: order.append(fig)) - monkeypatch.setattr(pio.renderers, 'default', 'json') - monkeypatch.setattr(plotly_backend, '_PENDING_DISPLAY', []) - shell = _FakeShell() - monkeypatch.setattr(IPython, 'get_ipython', lambda: shell) - a = hyp.plot(_datasets(1), backend='plotly', show=True) - b = hyp.plot(_datasets(2), backend='plotly', show=True) - shell.events.fire('post_execute') - assert order == [a, b] - - -def test_plotly_show_false_defers_entirely_to_the_display_hook(monkeypatch): - pytest.importorskip('plotly') - import IPython - calls = _count_shows(monkeypatch) - shell = _FakeShell() - monkeypatch.setattr(IPython, 'get_ipython', lambda: shell) - fig = hyp.plot(_datasets(2), backend='plotly', show=False) - assert calls['n'] == 0 and 'post_execute' not in shell.events.callbacks - fig._ipython_display_() - assert calls['n'] == 1 - -def test_plotly_show_called_in_plain_script(monkeypatch): - pytest.importorskip('plotly') - import plotly.graph_objects as go - import IPython - calls = {'n': 0} - monkeypatch.setattr(go.Figure, 'show', - lambda self, *a, **k: calls.__setitem__('n', calls['n'] + 1)) - # plain script (no IPython frontend): fig.show() IS called so the plot - # still displays - monkeypatch.setattr(IPython, 'get_ipython', lambda: None) - hyp.plot(_datasets(2), backend='plotly', show=True) - assert calls['n'] == 1 + assert IPython.get_ipython() is None + saved_renderer = pio.renderers.default + pio.renderers.default = 'json' + try: + capsys.readouterr() + fig = hyp.plot(_datasets(2), backend='plotly', show=True) + out = capsys.readouterr().out + assert out.count("'application/json'") == 1 + assert str({'application/json': json.loads(fig.to_json())}) in out + assert plotly_backend._PENDING_DISPLAY == [] # nothing deferred + hyp.plot(_datasets(2), backend='plotly', show=False) + assert capsys.readouterr().out == '' # show=False: no show + finally: + pio.renderers.default = saved_renderer diff --git a/tests/test_normalize.py b/tests/test_normalize.py index 751461c4..690f0c54 100644 --- a/tests/test_normalize.py +++ b/tests/test_normalize.py @@ -68,3 +68,56 @@ def test_normalizer_reuse_list_returns_list(): _, model, new = _fit_new('across') out = model.transform([new, new + 1.0]) assert isinstance(out, list) and len(out) == 2 and out[0].shape == (10, 4) + + +# --- a 1-D dataset is ONE column in fit and transform alike (review 2026-09-11) + +def _one_d_inputs(): + import pandas as pd + import polars as pl + values = np.array([1., 2., 3., 4., 6.]) + return values, [ + ('1-D array', values), + ('pandas Series', pd.Series(values, name='v')), + ('polars Series', pl.Series('v', values)), + ('flat list', values.tolist()), + ] + + +def test_fitted_normalizer_accepts_the_1d_data_it_was_fit_on(): + # normalize() reads a 1-D array/Series/flat list as one column (via + # format_data), so its fitted Normalizer is fit on 1 column. Before the + # fix, .transform() on the SAME data turned it into a single ROW and + # raised "Normalizer was fit on 1 column(s) but got 5" (the flat list + # became five one-value datasets). + values, inputs = _one_d_inputs() + expected = ((values - values.mean()) / values.std()).reshape(-1, 1) + for label, obj in inputs: + for mode in ('across', 'within'): + normed, model = normalize(obj, normalize=mode, return_model=True) + assert np.allclose(normed, expected), (label, mode) + out = model.transform(obj) + assert isinstance(out, np.ndarray), (label, mode) + assert out.shape == (5, 1), (label, mode) + assert np.allclose(out, expected), (label, mode) + + +def test_normalizer_fit_directly_on_1d_data_reads_one_column(): + values, inputs = _one_d_inputs() + for label, obj in inputs: + model = Normalizer('across').fit(obj) + assert model.mean_.shape == (1,), label + assert np.allclose(model.mean_, values.mean()), label + # held-out 1-D data of a different length reuses the fit-time stats + new = np.array([2., 4.]) + assert np.allclose(model.transform(new), + ((new - values.mean()) / values.std())[:, None]) + + +def test_normalizer_list_of_1d_datasets_reads_each_as_one_column(): + a, b = np.array([1., 2., 3.]), np.array([4., 5., 6., 7.]) + normed, model = normalize([a, b], normalize='across', return_model=True) + out = model.transform([a, b]) + assert [o.shape for o in out] == [(3, 1), (4, 1)] + for got, want in zip(out, normed): + assert np.allclose(got, want) diff --git a/tests/test_normalize_isotropic.py b/tests/test_normalize_isotropic.py index 6f03f750..a8007de8 100644 --- a/tests/test_normalize_isotropic.py +++ b/tests/test_normalize_isotropic.py @@ -9,7 +9,8 @@ - centroid (``data.mean(axis=0)``) lands at ``(min + max) / 2``; - every column is divided by the SAME scalar, ``abs(data - centroid).max()``, so pairwise-distance ratios (the shape) - are unchanged and a rotated copy is rescaled by the same scalar; + are unchanged; that scalar is measured along the coordinate axes, so a + rotated copy generally gets a DIFFERENT one (same shape, different size); - output lies in ``[min, max]`` with at least one coordinate on a bound; - ``min=-1, max=1`` reproduces ``(x - mean) / abs(x - mean).max()`` exactly; @@ -92,7 +93,31 @@ def test_centroid_lands_at_midpoint_and_range_holds(lo, hi): assert np.isclose(out.min(), lo) or np.isclose(out.max(), hi) -def test_rotated_copy_is_rescaled_by_the_same_scalar(): +def test_rotated_copy_gets_a_different_scalar(): + """The scale is the largest absolute COORDINATE deviation, which depends + on orientation: an elongated 2-D cloud and its 45-degree rotation are + divided by different scalars (1.1 release review -- the docstring used + to say a rotated copy is rescaled by the same scalar).""" + x = np.random.default_rng(0).normal(size=(200, 2)) * [3.0, 1.0] + t = np.pi / 4 + r = np.array([[np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)]]) + rotated = x @ r.T + m_a = Normalize(mode='isotropic', min=-1, max=1) + m_b = Normalize(mode='isotropic', min=-1, max=1) + out_a = np.asarray(m_a.fit_transform(x)) + out_b = np.asarray(m_b.fit_transform(rotated)) + assert np.isclose(m_a.peak, np.abs(x - x.mean(axis=0)).max()) + assert np.isclose(m_b.peak, np.abs(rotated - rotated.mean(axis=0)).max()) + assert np.isclose(m_a.peak, 9.0251, atol=1e-4) + assert np.isclose(m_b.peak, 7.2069, atol=1e-4) + # shape is preserved in both: each output is its input scaled by one + # constant, and the constants differ by the ratio of the two scalars + assert np.allclose(pdist(out_a) * m_a.peak, pdist(x)) + assert np.allclose(pdist(out_b) * m_b.peak, pdist(rotated)) + assert np.allclose(pdist(out_b) / pdist(out_a), m_a.peak / m_b.peak) + + +def test_rotated_copy_keeps_its_shape_up_to_one_scalar(): x = _cloud() r = _rotation() centred = x - x.mean(axis=0) diff --git a/tests/test_notebook_install_gate.py b/tests/test_notebook_install_gate.py index 41f98e98..3075e286 100644 --- a/tests/test_notebook_install_gate.py +++ b/tests/test_notebook_install_gate.py @@ -66,7 +66,7 @@ def _tracked_tutorials(): def _tracked_published_notebooks(): - """Every git-tracked published notebook. Today this is the 15 tutorials: + """Every git-tracked published notebook. The hand-authored tutorials are tracked: docs/auto_examples/*.ipynb are GITIGNORED and regenerated at build time from docs/conf.py's branch-aware install cell, so they are not shipped and do not exist in a bare checkout (the release-gate CI job runs on a bare @@ -99,7 +99,8 @@ def _hyp_install_lines(path): def test_there_are_tracked_published_notebooks(): # guards against the scan silently passing because it found nothing - assert len(_tracked_tutorials()) >= 15 + expected = set('align analyze animate_forecast cluster conversation_shape conversation_trajectories hierarchy hugging_face_embeddings io lsl_streaming manip market_sectors modern_sklearn_dynamics morph_shapes_zoo normalize painting_embeddings pipelines plot projectile_kalman reduce stock_forecasting streaming_data text weather_decades wikipedia_embeddings'.split()) + assert {os.path.splitext(os.path.basename(p))[0] for p in _tracked_tutorials()} == expected # the published-notebook union is at least the tutorials assert len(_tracked_published_notebooks()) >= len(_tracked_tutorials()) @@ -166,3 +167,153 @@ def test_release_gate_no_preview_note_in_published_notebooks(): 'RELEASE GATE: published notebooks still carry a preview install note; ' 'run `python scripts/add_colab_install_cell.py` on master: ' f'{offenders}') + + +# --- an install cell never ships output -------------------------------------- + +def _install_cells(path): + with open(path, encoding='utf-8') as f: + nb = json.load(f) + return [c for c in nb.get('cells', []) if c.get('cell_type') == 'code' + and any(_INSTALL_LINE_RE.match(ln.lstrip()) + for ln in ''.join(c.get('source', [])).splitlines())] + + +def test_no_published_install_cell_carries_output(): + """scripts/execute_tutorial.py skips the Colab install cell, and a skipped + cell keeps whatever the file had: projectile_kalman and streaming_data + shipped a pip upgrade notice naming a local interpreter path from the + 1.0.0 run that executed it (found 2026-09-07). The cell did not run in + the published execution, so it has nothing to show.""" + offenders = [] + for path in _tracked_published_notebooks(): + for cell in _install_cells(path): + if cell.get('outputs') or cell.get('execution_count') is not None: + offenders.append(os.path.relpath(path, _REPO)) + assert not offenders, offenders + + +def test_execute_tutorial_drops_the_outputs_of_the_cell_it_skips(tmp_path): + """`skip_install_cells` (the in-memory step `execute()` runs before + nbclient) tags the install cell and clears its stored output; + `restore_install_cells` removes only the tag it added.""" + import importlib.util + import nbformat + spec = importlib.util.spec_from_file_location( + 'execute_tutorial', os.path.join(_REPO, 'scripts', 'execute_tutorial.py')) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + nb = nbformat.v4.new_notebook() + install = nbformat.v4.new_code_cell( + '%pip install -q "hypertools[interactive]"', execution_count=1, + outputs=[nbformat.v4.new_output('stream', name='stdout', + text='[notice] A new release of pip')]) + install.metadata['tags'] = ['keep-me'] + work = nbformat.v4.new_code_cell('import hypertools', execution_count=2, + outputs=[nbformat.v4.new_output( + 'stream', name='stdout', text='hi')]) + nb.cells = [install, work] + skipped = mod.skip_install_cells(nb) + assert skipped == [install] + assert install.metadata['tags'] == ['keep-me', mod.SKIP_TAG] + assert install.outputs == [] and install.execution_count is None + assert work.outputs and work.execution_count == 2 # untouched + mod.restore_install_cells(skipped) + assert install.metadata['tags'] == ['keep-me'] + path = tmp_path / 'nb.ipynb' + nbformat.write(nb, path) + assert _install_cells(path)[0]['outputs'] == [] + + +def test_tutorial_installers_enforce_version_and_preserve_prerequisites(): + for path in _tracked_tutorials(): + with open(path, encoding='utf-8') as handle: + nb=json.load(handle) + installers=[c for c in nb['cells'] if 'hypertools-install' in c.get('metadata',{}).get('tags',[])] + assert len(installers)==1, path + source=''.join(installers[0]['source']) + assert "Version('1.1.0')" in source and '>=1.1.0' in source, path + assert 'will not replace your checkout' in source, path + assert 'pip install -q convokit' not in source and 'pip install -q py7zr' not in source + + +def test_executor_keeps_setup_and_independent_install_cells(): + import importlib.util + import nbformat + spec=importlib.util.spec_from_file_location('execute_tutorial',os.path.join(_REPO,'scripts','execute_tutorial.py')) + module=importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + config=nbformat.v4.new_code_cell("SETTINGS = {}\n# Optional: pip install extras") + prerequisite=nbformat.v4.new_code_cell('%pip install convokit') + install=nbformat.v4.new_code_cell("subprocess.check_call([sys.executable,'-m','pip','install',spec])",metadata={'tags':['hypertools-install']}) + nb=nbformat.v4.new_notebook(cells=[config,prerequisite,install]) + skipped=module.skip_install_cells(nb) + assert skipped==[install] + assert 'skip-execution' not in config.metadata.get('tags',[]) + assert 'skip-execution' not in prerequisite.metadata.get('tags',[]) + + +def _load_executor(): + import importlib.util + spec = importlib.util.spec_from_file_location( + 'execute_tutorial', os.path.join(_REPO, 'scripts', 'execute_tutorial.py')) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_executor_scrubs_the_kernel_cell_path_from_warnings(): + """A warning raised by a cell names the kernel's per-session temp file + (plot.ipynb stored `/var/folders/<id>/T/ipykernel_21956/2889100357.py:14: + UserWarning: ...`, found 2026-09-11); the executor rewrites it to + `<cell>`, on every platform's spelling, and still rewrites the home dir.""" + import nbformat + module = _load_executor() + home = '/Users/someone' + texts = [ + '/var/folders/tp/qtzc39jx5w556wl5w3dj21wr0000gn/T/ipykernel_21956/' + '2889100357.py:14: UserWarning: Missing data\n', + '/tmp/ipykernel_77/123.py:3: UserWarning: x\n', + 'C:\\Users\\someone\\AppData\\Local\\Temp\\ipykernel_5\\99.py:1: W\n', + '/Users/someone/hypertools/hypertools/predict/common.py:416: UserWarning\n', + ] + nb = nbformat.v4.new_notebook(cells=[nbformat.v4.new_code_cell( + 'x', outputs=[nbformat.v4.new_output('stream', name='stderr', text=t) + for t in texts])]) + assert module.scrub_home(nb, home=home) == len(texts) + got = [o['text'] for o in nb.cells[0].outputs] + assert got[:3] == ['<cell>:14: UserWarning: Missing data\n', + '<cell>:3: UserWarning: x\n', '<cell>:1: W\n'] + assert got[3] == '~/hypertools/hypertools/predict/common.py:416: UserWarning\n' + + +def test_executor_quiets_liblsl_info_logging(tmp_path): + """liblsl logs `api_config.cpp ... INFO| Loaded default config` to stderr + on first load, and lsl_streaming.ipynb stored two such lines (2026-09-10). + Measured on the real liblsl in a subprocess: the line appears with no + config (the control) and not under the config the executor installs. + Only a StreamInfo is built -- nothing is advertised on the network.""" + import sys + pytest.importorskip('pylsl') + module = _load_executor() + probe = ("import pylsl; pylsl.StreamInfo('hyp-cfg-probe', 'HYPCFGPROBE', 1, " + "source_id='hyp-cfg-probe')") + env = {k: v for k, v in os.environ.items() if k != 'LSLAPICFG'} + control = subprocess.run([sys.executable, '-c', probe], env=env, + capture_output=True, text=True, timeout=120) + assert control.returncode == 0, control.stderr + assert 'INFO|' in control.stderr, 'control run: liblsl no longer logs INFO' + path = module.quiet_liblsl_config(str(tmp_path), environ=env) + assert env['LSLAPICFG'] == path and os.path.exists(path) + quiet = subprocess.run([sys.executable, '-c', probe], env=env, + capture_output=True, text=True, timeout=120) + assert quiet.returncode == 0, quiet.stderr + assert 'INFO|' not in quiet.stderr, quiet.stderr + + +def test_executor_keeps_a_callers_liblsl_config(tmp_path): + module = _load_executor() + env = {'LSLAPICFG': '/somewhere/else.cfg'} + assert module.quiet_liblsl_config(str(tmp_path), environ=env) == '/somewhere/else.cfg' + assert env == {'LSLAPICFG': '/somewhere/else.cfg'} + assert not os.listdir(tmp_path) diff --git a/tests/test_notebook_visual_regressions.py b/tests/test_notebook_visual_regressions.py new file mode 100644 index 00000000..41a62689 --- /dev/null +++ b/tests/test_notebook_visual_regressions.py @@ -0,0 +1,176 @@ +"""Real artist and pixel regressions from the v1.1 notebook visual review.""" + +import numpy as np +import pandas as pd +import pytest +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import hypertools as hyp + + +@pytest.mark.parametrize("frames", [9, 30]) +def test_full_companion_curve_keeps_a_moving_colored_head(frames): + data = hyp.load("helix", n_samples=30) + dates = pd.date_range("2026-01-01", periods=30) + seen = [] + anim = hyp.plot( + pd.DataFrame(data, index=dates), + animate=True, + duration=frames / 6, + frame_rate=6, + show=False, + on_frame=seen.append, + title="{index:%Y-%m-%d}", + companion=[ + {"data": data[:, 0]}, + { + "data": data[:, 1], + "reveal": False, + "hue": np.arange(30), + "smooth": 3, + "position": "right", + }, + ], + ) + try: + # Seek out of order as well as forward: no stale artist state. + for f in [0, frames // 2, frames - 1, 1, 0]: + anim.draw_frame(f) + row = round(f / (frames - 1) * 29) + bottom, right = anim.figure.axes[1:] + assert list(bottom.lines[-1].get_xdata()) == [row] + assert list(right.lines[-1].get_xdata()) == [row] + assert list(right.lines[-1].get_ydata()) == [data[row, 1]] + assert len(right.collections[0].get_segments()) == 29 + assert len(right.lines[1].get_xdata()) == 30 + assert anim.figure.axes[0].get_title() == dates[row].strftime("%Y-%m-%d") + collection = right.collections[0] + np.testing.assert_allclose( + right.lines[-1].get_markerfacecolor(), + collection.cmap(collection.norm(row)), + ) + ctx = seen[-1] + main_head = np.array([v[-1] for v in ctx.artists[0].get_data_3d()]) + np.testing.assert_allclose( + main_head, ctx.datasets[0][ctx.revealed_counts[0] - 1] + ) + finally: + plt.close(anim.figure) + + +@pytest.mark.parametrize("fmt", ["-", "o", "o-"]) +@pytest.mark.parametrize("animation", [False, "parallel", "serial", "spin"]) +def test_plotly_uniform_alpha_uses_native_opacity(fmt, animation): + pytest.importorskip("plotly") + data = hyp.load("helix", n_samples=12) + fig = hyp.plot( + data, + backend="plotly", + colors=["steelblue"], + alpha=0.5, + fmt=fmt, + animate=animation, + duration=1, + frame_rate=4, + show=False, + ) + trace = next( + t for t in fig.data if isinstance(t.meta, dict) and "hyp_trace_index" in t.meta + ) + assert trace.opacity == 0.5 + for token, component in [("lines", trace.line), ("markers", trace.marker)]: + if token in trace.mode: + assert component.color == "rgb(70,130,180)" + + +def test_rendered_transparent_steelblue_preserves_hue(tmp_path): + """JSON alone passed the original bug. Decode actual Chrome-rendered pixels.""" + pytest.importorskip("plotly") + pytest.importorskip("kaleido") + from PIL import Image + + data = np.column_stack([np.linspace(-1, 1, 20), np.zeros(20), np.zeros(20)]) + fig = hyp.plot( + data, + backend="plotly", + reduce=None, + colors=["steelblue"], + alpha=0.5, + linewidth=12, + show=False, + ) + path = tmp_path / "steelblue.png" + fig.write_image(str(path), width=500, height=400) + rgb = np.asarray(Image.open(path).convert("RGB")).astype(float) + pixels = rgb[(rgb[:, :, 2] > rgb[:, :, 0] + 15) & (rgb[:, :, 2] > rgb[:, :, 1] + 5)] + assert len(pixels) > 100 + # Alpha blending against white preserves this ratio of channel gaps. + # The faulty RGBA path makes G approach B (cyan), driving it towards zero. + ratio = (pixels[:, 2] - pixels[:, 1]) / (pixels[:, 2] - pixels[:, 0]) + assert np.median(ratio) == pytest.approx((180 - 130) / (180 - 70), abs=0.12) + + +def test_opaque_morph_frame_resets_the_base_trace_opacity(): + go = pytest.importorskip("plotly.graph_objects") + from tests._plotly_colors import rgba + + rng = np.random.default_rng(71) + fig = hyp.plot( + [rng.normal(size=(12, 3)), rng.normal(size=(12, 3))], + fmt="o", + backend="plotly", + animate="morph", + alpha=[0.2, 1.0], + duration=2, + frame_rate=6, + show=False, + ) + index = fig.frames[-1].traces[0] + assert rgba(fig.data[index], "marker")[-1] == pytest.approx(0.2) + snapshot = go.Figure(fig) + for trace_index, update in zip(fig.frames[-1].traces, fig.frames[-1].data): + snapshot.data[trace_index].update(update.to_plotly_json()) + assert rgba(snapshot.data[index], "marker")[-1] == pytest.approx(1.0) + + +def test_tour_plotly_previews_do_not_allocate_live_plots_or_embed_html(tmp_path): + pytest.importorskip("plotly") + pytest.importorskip("kaleido") + pytest.importorskip("ipywidgets") + from IPython.utils.capture import capture_output + from pathlib import Path + import json + + root = Path(__file__).resolve().parents[1] + ns = dict(SCRATCH=tmp_path, IN_COLAB=False) + exec( + compile( + (root / "scripts/feature_tour_support.py").read_text(encoding="utf-8"), + str(root / "scripts/feature_tour_support.py"), + "exec", + ), + ns, + ) + fig = hyp.plot(hyp.load("helix", n_samples=12), backend="plotly", show=False) + from IPython.core.interactiveshell import InteractiveShell + + had_shell = InteractiveShell.initialized() + InteractiveShell.instance() + try: + with capture_output(display=True) as captured: + ns["show_result"](fig) + finally: + if not had_shell: + InteractiveShell.clear_instance() + data = [o.data for o in captured.outputs] + assert any("image/png" in item for item in data) + assert all("application/vnd.plotly.v1+json" not in item for item in data) + assert len(json.dumps(data)) < 1_000_000 + assert len(ns["INTERACTIVE_PLOTS"]) == 1 + path = Path(next(iter(ns["INTERACTIVE_PLOTS"].values()))) + assert ( + path.stat().st_size > 1_000_000 + ) # standalone JS lives on disk, not in every cell + assert "Plotly.newPlot(" in path.read_text(encoding="utf-8") diff --git a/tests/test_palette_compose_review.py b/tests/test_palette_compose_review.py new file mode 100644 index 00000000..99fddf14 --- /dev/null +++ b/tests/test_palette_compose_review.py @@ -0,0 +1,713 @@ +"""1.1 release review (2026-09-11): palette / marker / legend / panel / ax= +composition findings, each pinned at the public API on both backends where +the path is shared. + +1 a per-dataset list of {category: color} dicts was ignored on the + marker-only (fmt='o') categorical path; the line path applied it. +2 composing into an ``ax=`` re-sampled an evenly-spaced palette ('hls', + 'viridis') at the new total, so a later call repeated an earlier call's + colour (hls: 2 datasets then 2 more drew a2 == b1). +3 the return_model bundle's ``'colors'`` (and a colorbar) did not match the + colours drawn when the call continued the palette on a composed + axes/figure (or when a fmt= colour letter coloured a dataset). +4 panels= forwarded a plain legend_colors list whole, so every panel + refused it. +5 two-column data into a 2-D ax= at the default ndims raised "the plot is + 3D". +6 nested per-dataset labels= given as arrays/Series was rejected on one + axes (accepted with panels=). +A per-dataset / nested labels= with hue= or cluster= crashed both + backends. +7 an explicit marker= list lost to the fmt's marker on matplotlib. +8 markers= on a line fmt marked every smoothed vertex, static and + animated, instead of the samples. + +No mocks: every assertion reads the drawn artists or traces. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +import pytest +import seaborn as sns +from matplotlib.colors import to_rgb + +import hypertools as hyp +from tests._plotly_colors import rgba as effective_rgba + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walks(n=2, rows=40, seed=0): + rng = np.random.default_rng(seed) + return [np.cumsum(rng.standard_normal((rows, 3)), 0) for _ in range(n)] + + +def _mpl_data_lines(ax): + return [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None + and len(ln.get_xdata()) > 1] + + +def _pl_data(fig): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') is not None] + + +def _pl_rgb(trace, component='line'): + # 8-bit, the precision a plotly colour string carries + return tuple(round(v * 255) for v in effective_rgba(trace, component)[:3]) + + +def _r3(c): + return tuple(round(float(v) * 255) for v in to_rgb(c[:3] + if not isinstance(c, str) + else c)) + + +# --- 1: per-dataset {category: color} dicts on the marker path ------------- + +_HUE = [np.repeat(['a', 'b'], 20), np.repeat(['b', 'c'], 20)] +_DICTS = [{'a': 'red', 'b': 'blue'}, {'b': 'blue', 'c': 'green'}] +_BY_NAME = {'a': _r3('red'), 'b': _r3('blue'), 'c': _r3('green')} + + +@pytest.mark.parametrize('fmt', ['o', '.', 'o-', '-']) +def test_per_dataset_dict_palettes_colour_markers_matplotlib(fmt): + fig = hyp.plot(_walks(), hue=_HUE, palette=_DICTS, fmt=fmt, + legend=True, show=False) + legend = fig.axes[0].get_legend() + got = {t.get_text(): _r3(h.get_color()) + for t, h in zip(legend.get_texts(), legend.legend_handles)} + assert got == _BY_NAME + drawn = {_r3(ln.get_color()) for ln in fig.axes[0].lines} + assert drawn == set(_BY_NAME.values()) + + +@pytest.mark.parametrize('fmt', ['o', 'o-', '-']) +def test_per_dataset_dict_palettes_colour_markers_plotly(fmt): + fig = hyp.plot(_walks(), hue=_HUE, palette=_DICTS, fmt=fmt, + legend=True, show=False, backend='plotly') + comp = 'marker' if fmt == 'o' else 'line' + got = {tr.name: _pl_rgb(tr, comp) for tr in _pl_data(fig) + if tr.showlegend is not False} + assert got == _BY_NAME + + +def test_per_dataset_dict_palette_bundle_matches_the_markers(): + out = hyp.plot(_walks(), hue=_HUE, palette=_DICTS, fmt='o', + show=False, return_model=True) + cats = {k: _r3(c) for k, c in out['colors']['categories'].items()} + assert cats == _BY_NAME + + +# --- 2: composing continues the palette without repeating a colour -------- + +def _mpl_compose(pal, counts): + fig, axes = hyp.subplots(1, 1) + for i, n in enumerate(counts): + hyp.plot(_walks(n, seed=i), ax=axes[0], palette=pal, show=False) + return [_r3(ln.get_color()) for ln in _mpl_data_lines(axes[0])] + + +def _pl_compose(pal, counts): + fig = None + for i, n in enumerate(counts): + fig = hyp.plot(_walks(n, seed=i), ax=fig, palette=pal, show=False, + backend='plotly') + return [_pl_rgb(tr) for tr in _pl_data(fig)] + + +def _pl_cell_compose(pal, counts): + fig, cells = hyp.subplots(1, 2, backend='plotly') + for i, n in enumerate(counts): + hyp.plot(_walks(n, seed=i), ax=cells[0], palette=pal, show=False, + backend='plotly') + return [_pl_rgb(tr) for tr in _pl_data(fig)] + + +_COMPOSERS = {'matplotlib': _mpl_compose, 'plotly': _pl_compose, + 'plotly-cell': _pl_cell_compose} + + +def _min_pairwise(cols): + a = np.asarray(cols, float) + d = np.linalg.norm(a[:, None] - a[None], axis=-1) + return d[np.triu_indices(len(a), 1)].min() + + +@pytest.mark.parametrize('where', sorted(_COMPOSERS)) +def test_hls_two_then_two_draws_four_distinct_hls_colours(where): + drawn = _COMPOSERS[where]('hls', [2, 2]) + assert len(drawn) == 4 + # the first call's colours are untouched... + assert drawn[:2] == [_r3(c) for c in sns.color_palette('hls', 2)] + # ...and the four together are the palette's four-colour sampling, + # so the second call filled the gaps instead of repeating a colour + assert len(set(drawn)) == 4 + assert set(drawn) == {_r3(c) for c in sns.color_palette('hls', 4)} + + +@pytest.mark.parametrize('where', sorted(_COMPOSERS)) +@pytest.mark.parametrize('pal', ['viridis', 'hls', 'husl']) +def test_composed_colours_are_as_far_apart_as_a_fresh_call(where, pal): + drawn = _COMPOSERS[where](pal, [2, 2]) + assert len(set(drawn)) == 4 + # no closer together than one fresh call drawing one MORE dataset + fresh = [_r3(c) for c in sns.color_palette(pal, 5)] + assert _min_pairwise(drawn) >= _min_pairwise(fresh) - 1e-3 + + +@pytest.mark.parametrize('where', sorted(_COMPOSERS)) +def test_one_then_one_is_still_what_one_call_draws(where): + drawn = _COMPOSERS[where]('hls', [1, 1]) + assert drawn == [_r3(c) for c in sns.color_palette('hls', 2)] + drawn = _COMPOSERS[where]('hls', [1, 2]) + assert drawn == [_r3(c) for c in sns.color_palette('hls', 3)] + + +@pytest.mark.parametrize('where', sorted(_COMPOSERS)) +def test_a_fixed_sequence_palette_continues_in_order(where): + drawn = _COMPOSERS[where]('deep', [2, 2]) + assert drawn == [_r3(c) for c in sns.color_palette('deep', 4)] + # a short explicit list still cycles, as one call with four would + drawn = _COMPOSERS[where](['navy', 'gold'], [2, 2]) + assert drawn == [_r3(c) for c in ['navy', 'gold', 'navy', 'gold']] + + +@pytest.mark.parametrize('where', sorted(_COMPOSERS)) +def test_three_calls_never_repeat_a_colour(where): + drawn = _COMPOSERS[where]('hls', [2, 2, 1]) + assert len(drawn) == 5 + assert len(set(drawn)) == 5 + + +# --- 3: the bundle's colours are the colours drawn ------------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_bundle_colours_match_a_composed_call(backend): + first = hyp.plot(_walks(1), show=False, backend=backend) + target = first if backend == 'plotly' else first.axes[0] + res = hyp.plot(_walks(2, seed=1), ax=target, show=False, + backend=backend, return_model=True) + bundle = [_r3(c) for c in res['colors']['colors']] + if backend == 'plotly': + drawn = [_pl_rgb(tr) for tr in _pl_data(res['fig'])][1:] + else: + drawn = [_r3(ln.get_color()) + for ln in _mpl_data_lines(res['fig'].axes[0])][1:] + assert bundle == drawn + assert {str(k): _r3(c) + for k, c in res['colors']['categories'].items()} in ( + {}, {str(i + 1): c for i, c in enumerate(drawn)}) + # the colormap the bundle offers is the drawn colours too + cmap_cols = [_r3(res['colors']['cmap'](i)) for i in range(len(drawn))] + assert cmap_cols == drawn + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_bundle_colours_follow_a_fmt_colour_letter(backend): + res = hyp.plot(_walks(2), fmt=['r-', '-'], show=False, + backend=backend, return_model=True) + bundle = [_r3(c) for c in res['colors']['colors']] + if backend == 'plotly': + drawn = [_pl_rgb(tr) for tr in _pl_data(res['fig'])] + else: + drawn = [_r3(ln.get_color()) + for ln in _mpl_data_lines(res['fig'].axes[0])] + assert drawn[0] == _r3('r') + assert bundle == drawn + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_a_composed_colorbar_shows_the_drawn_colours(backend): + if backend == 'plotly': + fig = hyp.plot(_walks(2), show=False, backend=backend) + fig = hyp.plot(_walks(2, seed=1), ax=fig, show=False, + backend=backend, colorbar=True) + drawn = [_pl_rgb(tr) for tr in _pl_data(fig)][2:] + scale = [tr.marker.colorscale for tr in fig.data + if tr.marker is not None and tr.marker.colorscale] + assert len(scale) == 1 + swatches = {tuple(int(v) for v in c[c.index('(') + 1:-1].split(',')) + for _, c in scale[0]} + assert swatches == set(drawn) + else: + fig, axes = hyp.subplots(1, 1) + hyp.plot(_walks(2), ax=axes[0], show=False) + hyp.plot(_walks(2, seed=1), ax=axes[0], show=False, colorbar=True) + drawn = [_r3(ln.get_color()) for ln in _mpl_data_lines(axes[0])][2:] + mesh = fig.axes[-1].collections[-1] + swatches = [_r3(mesh.cmap(i)) for i in range(mesh.cmap.N)] + assert swatches == drawn + + +# --- 4: panels= splits a plain legend_colors list per panel ---------------- + +def _legend_colours(ax): + legend = ax.get_legend() + return [(t.get_text(), _r3(h.get_color())) + for t, h in zip(legend.get_texts(), legend.legend_handles)] + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_panels_split_a_plain_legend_colors_list(panel_fit): + data = _walks(2, rows=30) + single = hyp.plot(data, legend=['a', 'b'], legend_colors=['r', 'b'], + show=False) + assert _legend_colours(single.axes[0]) == [('a', _r3('r')), + ('b', _r3('b'))] + fig = hyp.plot(data, legend=['a', 'b'], legend_colors=['r', 'b'], + panels=True, panel_fit=panel_fit, show=False) + assert _legend_colours(fig.axes[0]) == [('a', _r3('r'))] + assert _legend_colours(fig.axes[1]) == [('b', _r3('b'))] + + +def test_panels_keep_the_shared_legend_colours_after_the_datasets(): + data = _walks(2, rows=30) + kw = dict(legend=['a', 'b'], predict='Kalman', t=3, + legend_colors=['r', 'b', 'k'], show=False) + single = hyp.plot(data, **kw) + assert _legend_colours(single.axes[0]) == [ + ('a', _r3('r')), ('b', _r3('b')), ('Kalman', _r3('k'))] + fig = hyp.plot(data, panels=True, **kw) + assert _legend_colours(fig.axes[0]) == [('a', _r3('r')), + ('Kalman', _r3('k'))] + assert _legend_colours(fig.axes[1]) == [('b', _r3('b')), + ('Kalman', _r3('k'))] + + +def test_panels_forward_legend_colour_pairs_whole(): + fig = hyp.plot(_walks(2, rows=30), legend=True, + legend_colors=[('Key', 'k')], panels=True, show=False) + for ax in fig.axes[:2]: + assert _legend_colours(ax) == [('Key', _r3('k'))] + + +# --- 5: two-column data into a 2-D ax= at the default ndims ---------------- + +@pytest.mark.parametrize('kw', [{}, {'reduce': None}]) +def test_two_column_data_draws_into_a_2d_axes_at_default_ndims(kw): + two = _walks(1, rows=20)[0][:, :2] + fig, ax = plt.subplots() + out = hyp.plot(two, ax=ax, show=False, **kw) + assert out is fig + # the same 2-D drawing a figure of its own gets + own = hyp.plot(two, show=False, **kw).axes[0] + assert own.name == ax.name == 'rectilinear' + np.testing.assert_allclose(ax.lines[0].get_xydata(), + own.lines[0].get_xydata()) + + +def test_three_column_data_still_refuses_a_2d_axes(): + fig, ax = plt.subplots() + with pytest.raises(ValueError, match='ax must also be 3d'): + hyp.plot(_walks(1, rows=20)[0], ax=ax, show=False) + # nothing was drawn into the refused axes + assert not ax.lines + + +# --- 6 / A: labels= forms on one axes, with and without a regrouping ------ + +_LA = ['a%d' % i for i in range(10)] +_LB = ['b%d' % i for i in range(10)] + + +def _label_positions(fig, backend): + if backend == 'plotly': + anns = fig.layout.scene.annotations or fig.layout.annotations + return {a.text: tuple(round(float(v), 6) for v in ( + (a.x, a.y, a.z) if hasattr(a, 'z') else (a.x, a.y))) + for a in anns} + out = {} + for t in fig.axes[0].texts: + pos = (t.get_position_3d() if hasattr(t, 'get_position_3d') + else t.get_position()) + out[t.get_text()] = tuple(round(float(v), 6) for v in pos) + return out + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('form', ['arrays', 'series', 'tuples']) +def test_nested_label_sequences_of_any_type_on_one_axes(backend, form): + import pandas as pd + wrap = {'arrays': np.array, 'series': pd.Series, 'tuples': tuple}[form] + data = _walks(2, rows=10) + ref = _label_positions(hyp.plot(data, labels=[_LA, _LB], show=False, + backend=backend), backend) + got = _label_positions(hyp.plot(data, labels=[wrap(_LA), wrap(_LB)], + show=False, backend=backend), backend) + assert len(ref) == 20 + assert got == ref + + +_HUE_A = np.repeat(['x', 'y'], 5) +_HUE_B = np.repeat(['y', 'z'], 5) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('labels', [ + pytest.param([_LA, _LB], id='nested'), + pytest.param(['A', 'B'], id='per-dataset'), + pytest.param([np.array(_LA), np.array(_LB)], id='arrays')]) +@pytest.mark.parametrize('group', [ + pytest.param(dict(hue=['x', 'y']), id='per-dataset-hue'), + pytest.param(dict(hue=[_HUE_A, _HUE_B]), id='nested-hue'), + pytest.param(dict(hue=[_HUE_A, _HUE_B], fmt='o'), id='nested-hue-o'), + pytest.param(dict(hue=[_HUE_A, _HUE_B], antialias=False), + id='nested-hue-raw'), + pytest.param(dict(hue=['x', 'y'], fmt='o'), id='per-dataset-hue-o'), + pytest.param(dict(cluster='KMeans', n_clusters=3), id='cluster')]) +def test_per_dataset_labels_survive_a_regrouping(backend, labels, group): + data = _walks(2, rows=10) + kw = {k: v for k, v in group.items() if k in ('fmt', 'antialias')} + ref = _label_positions(hyp.plot(data, labels=labels, show=False, + backend=backend, **kw), backend) + got = _label_positions(hyp.plot(data, labels=labels, show=False, + backend=backend, **group), backend) + assert ref and sorted(got) == sorted(ref) + # every label on its own observation: the same layout as the ungrouped + # figure's. Compared about the labels' centroid, since plotly centres + # a hue-segmented figure on its per-run smoothed geometry (a shift of + # the WHOLE figure, traces and labels together, ~0.004 here) + def centred(pos): + keys = sorted(pos) + arr = np.asarray([pos[k] for k in keys], float) + return arr - arr.mean(axis=0) + np.testing.assert_allclose(centred(got), centred(ref), atol=1e-5) + + +# --- 7 / 8: explicit marker= wins; markers only at the true samples ------- + +def _walk48(): + return _walks(2, rows=48) + + +def _marked(ax): + """(label, marker, n marked points) for every artist that draws a + marker, reading markevery (None = every vertex).""" + out = [] + for ln in ax.lines: + mk = ln.get_marker() + if mk in (None, 'None', '', ' '): + continue + me = ln.get_markevery() + n = len(ln.get_xdata()) if me is None else len(np.atleast_1d(me)) + out.append((ln.get_label(), mk, n)) + return out + + +def test_an_explicit_marker_list_wins_over_the_fmt_marker(): + fig = hyp.plot(_walk48(), fmt='-o', marker=['o', 's'], legend=['a', 'b'], + show=False) + ax = fig.axes[0] + drawn = [(lbl, mk) for lbl, mk, n in _marked(ax) if n] + assert drawn == [('_nolegend_', 'o'), ('_nolegend_', 's')] + legend = ax.get_legend() + assert [h.get_marker() for h in legend.legend_handles] == ['o', 's'] + # plotly draws the same symbols + pfig = hyp.plot(_walk48(), fmt='-o', marker=['o', 's'], show=False, + backend='plotly') + assert [tr.marker.symbol for tr in _pl_data(pfig)] == ['circle', + 'square'] + + +@pytest.mark.parametrize('kw', [dict(markers='o'), dict(marker='o'), + dict(fmt='--', marker='s'), + dict(fmt='-o')]) +def test_markers_on_a_smoothed_line_sit_only_at_the_samples(kw): + data = _walk48() + fig = hyp.plot(data, legend=['a', 'b'], show=False, **kw) + ax = fig.axes[0] + # one marker per SAMPLE per dataset -- not one per smoothed vertex + assert [n for _, _, n in _marked(ax) if n] == [48, 48] + # ...at the samples themselves: the points the static 'o-' figure + # marks (its markers-only artists sit on the raw samples) + ref = hyp.plot(data, fmt='o-', show=False).axes[0] + samples = np.vstack([np.column_stack(ln.get_data_3d()) + for ln in ref.lines + if ln.get_label() == '_nolegend_']) + marked, curves = [], [] + for ln in ax.lines: + if ln.get_marker() in (None, 'None', '', ' '): + continue + pts = np.column_stack(ln.get_data_3d()) + me = ln.get_markevery() + if ln.get_linestyle() not in ('None', ''): + curves.append(pts) + marked.append(pts if me is None + else pts[np.asarray(me, dtype=int).ravel()]) + marked = np.vstack(marked) + assert len(marked) == len(samples) == 96 + gap = np.min(np.linalg.norm(marked[:, None] - samples[None], axis=-1), + axis=1) + assert gap.max() < 1e-9 + # the line itself is still the smoothed curve + assert all(len(c) > 5 * 48 for c in curves) + # the legend glyph shows the marker with the line + assert [h.get_marker() for h in ax.get_legend().legend_handles] == \ + [kw.get('marker', 'o')] * 2 + + +@pytest.mark.parametrize('kw', [dict(fmt='o-'), dict(markers='o'), + dict(fmt='--', marker='s')]) +def test_animated_markers_sit_at_the_samples_not_every_vertex(kw): + data = _walk48() + anim = hyp.plot(data, animate='spin', duration=3, frame_rate=20, + legend=['a', 'b'], show=False, **kw) + anim.draw_frame(anim.n_frames - 1) + ax = anim.figure.axes[0] + lines = [ln for ln in ax.lines if ln.get_label() in ('a', 'b')] + assert len(lines) == 2 + # the observations, in the figure's coordinates: a static plot of the + # same data marks them exactly (see the static test above) + static = hyp.plot(data, fmt='o-', show=False).axes[0] + obs = [np.column_stack(ln.get_data_3d()) for ln in static.lines + if ln.get_label() == '_nolegend_'] + n_grid = anim.n_frames # the frame grid the line is drawn from + for line, pts in zip(lines, obs): + verts = np.column_stack(line.get_data_3d()) + marked = np.atleast_1d(line.get_markevery()) + n_verts = len(verts) + assert n_verts > 5 * len(pts) # smoothed, dense curve + assert len(marked) == len(pts) == 48 # one marker per sample + # in order along the curve, from its first vertex to its last... + assert np.all(np.diff(marked) > 0) + assert marked[0] == 0 and marked[-1] == n_verts - 1 + # ...each within one frame-grid row of its sample's place along it + per_row = (n_verts - 1) / (n_grid - 1) + place = np.arange(48) * (n_verts - 1) / 47 + assert np.abs(marked - place).max() <= per_row + # and, in space, within half a grid row of the sample itself (the + # static figure's coordinates differ from the animation's by the + # small offset the curve's exact endpoints show) + offset = np.linalg.norm(verts[[0, -1]] - pts[[0, -1]], axis=1).max() + row_len = np.linalg.norm(np.diff(verts, axis=0), axis=1).sum() / ( + n_grid - 1) + gap = np.linalg.norm(verts[marked] - pts, axis=1) + assert gap.max() <= 0.5 * row_len + offset + # the legend handle is the same artist, so it keeps the marker + assert [h.get_marker() for h in ax.get_legend().legend_handles] == \ + [kw.get('marker', 'o')] * 2 + + +def test_animated_window_marks_only_the_revealed_samples(): + data = _walk48() + anim = hyp.plot(data, animate=True, fmt='o-', duration=3, + frame_rate=20, show=False) + anim.draw_frame(anim.n_frames // 2) + for line in anim.figure.axes[0].lines: + if line.get_marker() in (None, 'None', '', ' '): + continue + marked = np.atleast_1d(line.get_markevery()) + n_verts = len(line.get_xdata()) + # a fraction of the 48 samples, and far fewer than the vertices + assert 0 < len(marked) < 48 + assert len(marked) * 5 < n_verts + assert marked.max() < n_verts + + +# --- B: continuous-hue markers carry alpha= (matplotlib) -------------------- + +def _marker_alphas(ax): + from matplotlib.collections import PathCollection + return [np.unique(np.round(c.get_facecolors()[:, 3], 6)).tolist() + for c in ax.collections if isinstance(c, PathCollection)] + + +@pytest.mark.parametrize('ndims', [2, 3]) +@pytest.mark.parametrize('fmt', ['o', '-o']) +def test_continuous_hue_markers_honour_alpha(fmt, ndims): + data = _walks(2, rows=30) + hue = [np.linspace(0, 1, 30), np.linspace(1, 0, 30)] + fig = hyp.plot(data, hue=hue, fmt=fmt, alpha=0.7, ndims=ndims, + show=False) + assert _marker_alphas(fig.axes[0]) == [[0.7], [0.7]] + # the reference: the same plot without hue= honours alpha= too + plain = hyp.plot(data, fmt=fmt, alpha=0.7, ndims=ndims, show=False) + assert {ln.get_alpha() for ln in plain.axes[0].lines} == {0.7} + # a per-dataset alpha list reaches each dataset's markers + fig = hyp.plot(data, hue=hue, fmt=fmt, alpha=[1.0, 0.4], ndims=ndims, + show=False) + assert _marker_alphas(fig.axes[0]) == [[1.0], [0.4]] + + +# --- C: the non-finite hue warning counts observations, names the caller --- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('fmt', ['-', '-o', 'o']) +def test_one_nan_hue_value_warns_once_about_one_observation(fmt, backend): + import warnings + data = _walks(1, rows=30)[0] + hue = np.linspace(0, 1, 30) + hue[12] = np.nan + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + hyp.plot(data, hue=hue, fmt=fmt, show=False, backend=backend) + hits = [w for w in caught if 'non-finite' in str(w.message)] + assert len(hits) == 1, [str(w.message) for w in hits] + assert str(hits[0].message).startswith('1 observation(s) have') + # attributed to the caller's line, not to hypertools' internals + assert hits[0].filename == __file__ + + +# --- L3: observation-label call-outs are drawn in dark ink ----------------- + +@pytest.mark.parametrize('ndims', [2, 3]) +def test_label_connectors_and_boxes_are_dark_like_plotly(ndims): + from matplotlib.colors import to_rgba + data = _walks(2, rows=10) + fig = hyp.plot(data, labels=['first path', 'second path'], ndims=ndims, + show=False) + anns = fig.axes[0].texts + assert len(anns) == 2 + for ann in anns: + # seaborn's whitegrid style made both edges white (patch.edgecolor + # 'w'): invisible connectors that notched the markers + assert to_rgba(ann.arrow_patch.get_edgecolor()) == \ + pytest.approx((0, 0, 0, 0.6)) + box = ann.get_bbox_patch() + # a dark edge; label_alpha= (0.5) stays the box's own alpha, as its + # docstring documents, so the edge carries that opacity + assert to_rgba(box.get_edgecolor()) == pytest.approx((0, 0, 0, 0.5)) + assert to_rgba(box.get_facecolor()) == pytest.approx((1, 1, 1, 0.5)) + assert box.get_alpha() == pytest.approx(0.5) + # ...the colours plotly's annotations use + pfig = hyp.plot(data, labels=['first path', 'second path'], + ndims=ndims, show=False, backend='plotly') + panns = (pfig.layout.scene.annotations if ndims == 3 + else pfig.layout.annotations) + assert {(a.arrowcolor, a.bordercolor) for a in panns} == { + ('rgba(0,0,0,0.6)', 'rgba(0,0,0,0.4)')} + + +# --- L5: nested-list input's legend names the outer groups ----------------- + +def _legend_entries(fig, backend): + if backend == 'plotly': + return [(str(tr.name), _pl_rgb(tr)) for tr in _pl_data(fig) + if tr.showlegend is not False and tr.name] + legend = fig.axes[0].get_legend() + return [(t.get_text(), _r3(h.get_color())) + for t, h in zip(legend.get_texts(), legend.legend_handles)] + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_nested_list_legend_names_the_outer_groups(backend): + a, b, c, d = _walks(4, rows=20) + hls2 = [_r3(col) for col in sns.color_palette('hls', 2)] + fig = hyp.plot([[a, b], [c, d]], legend=True, show=False, + backend=backend) + assert _legend_entries(fig, backend) == [('1', hls2[0]), ('2', hls2[1])] + fig = hyp.plot([[a, b], [c, d]], legend=['Rig A', 'Rig B'], show=False, + backend=backend) + assert _legend_entries(fig, backend) == [('Rig A', hls2[0]), + ('Rig B', hls2[1])] + # a per-LEAF list still labels every leaf + fig = hyp.plot([[a, b], [c, d]], legend=['a', 'b', 'c', 'd'], + show=False, backend=backend) + assert [n for n, _ in _legend_entries(fig, backend)] == ['a', 'b', 'c', + 'd'] + + +def test_nested_list_legend_entry_is_the_groups_summary_leaf(): + a, b, c, d = _walks(4, rows=20) + # group 1's shallowest leaf is `a`; b and c sit one level deeper + fig = hyp.plot([[a, [b, c]], d], legend=True, show=False) + ax = fig.axes[0] + named = [ln for ln in ax.lines if not ln.get_label().startswith('_')] + assert [ln.get_label() for ln in named] == ['1', '2'] + # the labelled leaf is drawn in its group's thickest (summary) style: + # group 1 is the first three leaves (a, b, c) + group_one = ax.lines[:3] + assert named[0] is group_one[0] + assert named[0].get_linewidth() == max(ln.get_linewidth() + for ln in group_one) + assert named[0].get_linewidth() > group_one[1].get_linewidth() + + +# --- L6: the categorical line path lists its legend in category order ----- + +def _shown_legend_order(fig, backend): + if backend == 'plotly': + shown = [(tr.legendrank if tr.legendrank is not None else 1000, i, + str(tr.name)) + for i, tr in enumerate(fig.data) + if tr.showlegend is not False and tr.name] + return [name for _, _, name in sorted(shown)] + return [t.get_text() for t in fig.axes[0].get_legend().get_texts()] + + +_CLUSTER = dict(cluster='KMeans', n_clusters=3, random_state=0) +_INT_HUE = dict(hue=[np.repeat([2, 0, 1], 16), np.repeat([1, 2, 0], 16)]) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('group', [_CLUSTER, _INT_HUE], + ids=['cluster', 'int-hue']) +def test_line_path_legend_order_matches_the_marker_path(backend, group): + data = _walks(2, rows=48, seed=41) + lines = hyp.plot(data, legend=True, show=False, backend=backend, **group) + marks = hyp.plot(data, legend=True, fmt='o', show=False, + backend=backend, **group) + assert _shown_legend_order(marks, backend) == ['0', '1', '2'] + assert _shown_legend_order(lines, backend) == ['0', '1', '2'] + if backend == 'matplotlib': + # each entry keeps its own category's colour + a = dict(_legend_colours(lines.axes[0])) + b = dict(_legend_colours(marks.axes[0])) + assert a == b + + +def test_line_path_legend_keeps_forecast_entries_after_the_categories(): + data = _walks(2, rows=48, seed=41) + fig = hyp.plot(data, legend=True, predict='Kalman', t=3, show=False, + **_INT_HUE) + assert _shown_legend_order(fig, 'matplotlib') == ['0', '1', '2', + 'Kalman'] + + +def test_a_string_hue_legend_keeps_first_appearance_order(): + data = _walks(2, rows=48, seed=41) + hue = [np.repeat(['z', 'x', 'y'], 16), np.repeat(['y', 'z', 'x'], 16)] + for fmt in ('-', 'o'): + fig = hyp.plot(data, hue=hue, fmt=fmt, legend=True, show=False) + assert _shown_legend_order(fig, 'matplotlib') == ['z', 'x', 'y'] + + +# --- L4: titles and axis labels on caller axes use hypertools' font -------- + +def _font_file(text): + import os + from matplotlib.font_manager import findfont + return os.path.basename(findfont(text.get_fontproperties())) + + +@pytest.mark.parametrize('ndims', [2, 3]) +def test_caller_axes_titles_and_labels_use_the_hypertools_font(ndims): + import matplotlib.pyplot as plt_ + a, b = _walks(2, rows=20) + kw = dict(xlabel='X label', ylabel='Y label', ndims=ndims, show=False) + own = hyp.plot(a, title='Own figure', **kw).axes[0] + ref_title, ref_label = _font_file(own.title), _font_file(own.xaxis.label) + cells = hyp.plot([a, b], panels=True, title=['A', 'B'], **kw).axes[:2] + fig, axes = hyp.subplots(1, 1, ndims=ndims) + hyp.plot(a, ax=axes[0], title='Via ax=', **kw) + plain = plt_.figure().add_subplot( + projection='3d' if ndims == 3 else None) + hyp.plot(a, ax=plain, title='Plain matplotlib axes', **kw) + for ax in [*cells, axes[0], plain]: + ax.figure.canvas.draw() + assert _font_file(ax.title) == ref_title + assert _font_file(ax.xaxis.label) == ref_label + assert _font_file(ax.yaxis.label) == ref_label + # an explicit family in title_kwargs= still wins + fig, axes = hyp.subplots(1, 1, ndims=ndims) + hyp.plot(a, ax=axes[0], title='Serif', title_kwargs={ + 'family': 'DejaVu Serif'}, **kw) + assert _font_file(axes[0].title) == 'DejaVuSerif.ttf' diff --git a/tests/test_palette_matrix_and_sort.py b/tests/test_palette_matrix_and_sort.py new file mode 100644 index 00000000..33b84f24 --- /dev/null +++ b/tests/test_palette_matrix_and_sort.py @@ -0,0 +1,524 @@ +"""Palette ORDER and DATA-MATRIX palettes (Jeremy, 2026-09-08): colors +extracted from an image are put in a deterministic order (by value, dark to +bright) when they become a plot palette, and a t x k data matrix passed as a +palette is reduced to 3-D with `hypertools.reduce`, scaled, sorted and +resampled to however many colors the plot needs. Real images, real reducers, +real figures on both backends; no mocks.""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pytest +from matplotlib.colors import Colormap, rgb_to_hsv, to_hex +from PIL import Image + +import hypertools as hyp +from hypertools.plot.colors import ( + MatrixColormap, PALETTE_SORT_KEYS, get_palette_colors, image_palette, + is_palette_matrix, luminance, matrix_palette, palette_lead_color, + sort_colors) +from hypertools.plot.plotly_backend import _rgb_triplet + + +@pytest.fixture(autouse=True) +def _close(): + yield + plt.close('all') + + +# --- fixtures --------------------------------------------------------------- + +COLORS = { # name: RGB in 0..255 + 'navy': (20, 30, 120), 'gold': (240, 200, 30), 'teal': (20, 150, 140), + 'crimson': (200, 30, 60), 'ivory': (245, 240, 220), 'coal': (25, 25, 25), +} + + +def painting_png(tmp_path, name='painting.png'): + """Six blocks of very different size: a salience order that is NOT a + value order, so the two contracts are distinguishable.""" + rng = np.random.default_rng(0) + canvas = np.zeros((120, 120, 3), dtype=np.uint8) + canvas[:] = COLORS['ivory'] # big muted background + canvas[:40, :40] = COLORS['coal'] + canvas[40:60, :60] = COLORS['navy'] + canvas[60:120, :30] = COLORS['teal'] + canvas[100:120, 100:120] = COLORS['crimson'] # small and vivid + canvas[10:20, 100:110] = COLORS['gold'] # tiny and vivid + canvas = np.clip(canvas.astype(int) + rng.integers(-2, 3, canvas.shape), + 0, 255).astype(np.uint8) + path = tmp_path / name + Image.fromarray(canvas).save(path) + return str(path) + + +def matrix(t=40, k=12, seed=0): + rng = np.random.default_rng(seed) + base = np.cumsum(rng.normal(size=(t, 3)), axis=0) # a 3-D trajectory + mix = rng.normal(size=(3, k)) + return base @ mix + 0.05 * rng.normal(size=(t, k)) + + +def _walk(n=40, seed=1): + return np.cumsum(np.random.default_rng(seed).normal(size=(n, 3)), axis=0) + + +def _mpl_segment_colors(fig): + """The per-segment colors of a continuous-hue trajectory (matplotlib + draws it as Line3DCollections, one color per segment).""" + # the axis lines are one-segment black Line3DCollections; the + # trajectory is the collection with one color per (antialiased) segment + cols = [np.asarray(c.get_edgecolor())[:, :3] for c in fig.axes[0].collections + if type(c).__name__.startswith('Line') and len(c.get_edgecolor()) >= 10] + assert cols, 'a continuous hue draws multicolored line collections' + return np.vstack(cols) + + +# --- sort_colors -------------------------------------------------------------- + +def test_sort_colors_value_is_dark_to_bright_with_hue_as_the_tie_break(): + cols = np.array([[1, 0, 0], [0, 0, .2], [0, .5, 0], [.2, .2, .2], [0, 0, 1]], + dtype=float) + out = sort_colors(cols, 'value') + v = rgb_to_hsv(out)[:, 2] + assert np.all(np.diff(v) >= 0) + # equal value (1.0): red (hue 0) before blue (hue 2/3) + top = out[v == 1.0] + assert to_hex(top[0]) == '#ff0000' and to_hex(top[-1]) == '#0000ff' + + +def test_sort_colors_keys_are_deterministic_and_are_permutations(): + cols = np.random.default_rng(3).random((30, 3)) + for key in PALETTE_SORT_KEYS: + a, b = sort_colors(cols, key), sort_colors(cols, key) + assert np.array_equal(a, b) + assert sorted(map(tuple, a)) == sorted(map(tuple, cols)) + assert np.array_equal(sort_colors(cols, None), cols) + assert np.array_equal(sort_colors(cols, 'original'), cols) + h = rgb_to_hsv(sort_colors(cols, 'hue'))[:, 0] + assert np.all(np.diff(np.round(h, 6)) >= 0) + L = luminance(sort_colors(cols, 'lightness')) + assert np.all(np.diff(np.round(L, 6)) >= 0) + c = sort_colors(cols, 'columns') + assert np.all(np.diff(np.round(c[:, 0], 6)) >= 0) + with pytest.raises(ValueError, match='key must be one of'): + sort_colors(cols, 'brightness') + + +# --- image palettes: extraction order vs palette order ------------------------- + +def test_image_palette_keeps_salience_order_unless_asked_to_sort(tmp_path): + path = painting_png(tmp_path) + salient = image_palette(path) + assert np.allclose(salient[0], np.array(COLORS['teal']) / 255, atol=0.03) + by_value = image_palette(path, sort='value') + assert sorted(map(tuple, np.round(by_value, 3))) == \ + sorted(map(tuple, np.round(salient, 3))) + assert np.all(np.diff(rgb_to_hsv(by_value)[:, 2]) >= 0) + + +def test_an_image_used_as_a_plot_palette_is_sorted_by_value(tmp_path): + path = painting_png(tmp_path) + cols = get_palette_colors(f'image:{path}', 6) + assert np.all(np.diff(rgb_to_hsv(cols)[:, 2]) >= 0) + assert np.allclose(cols[0], np.array(COLORS['coal']) / 255, atol=0.03) + # the spec's own sort wins, and 'original' keeps the salience order + original = get_palette_colors(f'image:{path}?sort=original', 6) + assert np.allclose(original, image_palette(path), atol=1e-9) + by_hue = get_palette_colors(f'image:{path}?sort=hue', 6) + assert np.all(np.diff(np.round(rgb_to_hsv(by_hue)[:, 0], 6)) >= 0) + with pytest.raises(ValueError, match='sort'): + get_palette_colors(f'image:{path}?sort=brightness', 6) + + +def test_the_lead_color_of_an_image_is_still_its_most_salient(tmp_path): + path = painting_png(tmp_path) + assert np.allclose(palette_lead_color(f'image:{path}'), image_palette(path)[0]) + # a continuous gradient from the image runs dark to bright + grad = get_palette_colors(f'image:{path}', 50) + assert np.all(np.diff(rgb_to_hsv(grad)[:, 2]) >= -1e-9) + + +# --- matrix palettes ----------------------------------------------------------- + +def test_is_palette_matrix_distinguishes_data_from_color_lists(): + assert not is_palette_matrix([(1, 0, 0), (0, 1, 0)]) # colors + assert not is_palette_matrix(np.array([[.1, .2, .3], [.4, .5, .6]])) + assert not is_palette_matrix('viridis') + assert not is_palette_matrix({'a': 'red'}) + assert is_palette_matrix(np.array([[1.5, 0, 0], [0, 1, 0]])) # > 1 + assert is_palette_matrix(matrix()) # k=12 + assert is_palette_matrix(pd.DataFrame(np.array([[.1, .2, .3], [.4, .5, .6]]))) + assert is_palette_matrix([[1.0, 2.0], [3.0, 4.0]]) # k=2 + assert not is_palette_matrix(pd.DataFrame({'a': ['x', 'y']})) + + +def test_matrix_palette_reduces_scales_sorts_and_resamples(): + data = matrix() + cmap = matrix_palette(data) + assert isinstance(cmap, Colormap) + rows = get_palette_colors(cmap, 40) + assert rows.shape == (40, 3) and rows.min() >= 0 and rows.max() <= 1 + # the SAME rows as reducing by hand: PCA to 3, min-max per column, sorted + # along the first component + reduced = hyp.reduce(data, reduce='PCA', ndims=3, random_state=0) + lo, hi = reduced.min(axis=0), reduced.max(axis=0) + expected = sort_colors((reduced - lo) / (hi - lo), 'columns') + assert np.allclose(get_palette_colors(cmap, 40), expected, atol=1e-6) + assert np.all(np.diff(expected[:, 0]) >= 0) + # resampled to any count by interpolation, endpoints kept + five = get_palette_colors(cmap, 5) + assert np.allclose(five[0], expected[0], atol=1e-6) + assert np.allclose(five[-1], expected[-1], atol=1e-6) + # deterministic + assert np.allclose(get_palette_colors(matrix_palette(data), 40), expected, + atol=1e-6) + + +def test_matrix_palette_honours_the_reduce_spec_and_sort_key(): + data = matrix() + by_hue = get_palette_colors(matrix_palette(data, sort='hue'), 40) + assert np.all(np.diff(np.round(rgb_to_hsv(by_hue)[:, 0], 6)) >= 0) + ipca = get_palette_colors(matrix_palette(data, reduce='IncrementalPCA'), 40) + pca = get_palette_colors(matrix_palette(data), 40) + assert ipca.shape == pca.shape + # a different reducer is genuinely applied (its columns are not PCA's) + ica = get_palette_colors(matrix_palette(data, reduce='FastICA'), 40) + assert not np.allclose(ica, pca, atol=1e-3) + with pytest.raises(ValueError, match='at least two rows'): + matrix_palette(np.ones((1, 5))) + with pytest.raises(ValueError, match='finite'): + bad = data.copy() + bad[3, 4] = np.nan + matrix_palette(bad) + + +def test_narrow_matrices_are_not_reduced_and_missing_channels_are_neutral(): + two = np.array([[0, 10], [5, 0], [10, 5]], dtype=float) + rows = get_palette_colors(matrix_palette(two, sort='original'), 3) + assert np.allclose(rows[:, 2], 0.5) + assert np.allclose(rows[:, 0], [0, .5, 1]) and np.allclose(rows[:, 1], [1, 0, .5]) + one = np.array([[3.0], [1.0], [2.0]]) + rows = get_palette_colors(matrix_palette(one, sort='original'), 3) + assert np.allclose(rows[:, 1:], 0.5) and np.allclose(rows[:, 0], [1, 0, .5]) + # a constant column is neutral too + const = np.column_stack([np.arange(4.0), np.ones(4), np.arange(4.0) * 2]) + rows = get_palette_colors(matrix_palette(const * 10, sort='original'), 4) + assert np.allclose(rows[:, 1], 0.5) + + +def test_normalize_and_manip_reach_the_reducer(): + data = matrix() + plain = get_palette_colors(matrix_palette(data), 40) + smoothed = get_palette_colors(matrix_palette(data, manip='Smooth'), 40) + assert plain.shape == smoothed.shape and not np.allclose(plain, smoothed) + normalized = get_palette_colors(matrix_palette(data, normalize='across'), 40) + assert normalized.shape == plain.shape + # z-scoring the columns before PCA re-weights them, so the palette + # differs from the raw-matrix one and equals a by-hand normalize+PCA + assert not np.allclose(normalized, plain, atol=1e-3) + by_hand = hyp.reduce(hyp.normalize(data), reduce='PCA', ndims=3, random_state=0) + lo, hi = by_hand.min(axis=0), by_hand.max(axis=0) + assert np.allclose(normalized, sort_colors((by_hand - lo) / (hi - lo), 'columns'), atol=1e-6) + + +# --- through hyp.plot ---------------------------------------------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_a_matrix_palette_colors_a_continuous_hue_along_the_reduced_axis(backend): + x = _walk() + weights = matrix(t=len(x)) + hue = np.arange(len(x), dtype=float) + fig = hyp.plot(x, hue=hue, palette=weights, show=False, backend=backend) + expected = get_palette_colors(matrix_palette(weights), 100) # n_bins + if backend == 'matplotlib': + got = _mpl_segment_colors(fig) + else: + tr = [t for t in fig.data if (t.meta or {}).get('hyp_trace_index') is not None] + got = np.array([_rgb_triplet(c) for c in tr[0].line.color]) / 255.0 + # the trajectory starts and ends on the palette's ends, and its red + # channel (the first reduced component, the sort key) rises along the + # way -- antialiased segments blend neighbouring palette colors, so the + # check is on the ends and the monotone channel, not on exact membership + assert np.linalg.norm(got[0] - expected[0]) < 0.05 + assert np.linalg.norm(got[-1] - expected[-1]) < 0.05 + assert np.all(np.diff(got[:, 0]) >= -1e-6) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_per_dataset_matrix_palettes_give_each_dataset_its_most_saturated_color(backend): + x = _walk() + mats = [matrix(seed=1), matrix(seed=2)] + fig = hyp.plot([x, x + 3], palette=mats, show=False, backend=backend) + def most_saturated(m): + anchors = matrix_palette(m).anchors + return anchors[np.argmax(anchors.max(axis=1) - anchors.min(axis=1))] + leads = [most_saturated(m) for m in mats] + assert all(np.allclose(palette_lead_color(m), lead) for m, lead in zip(mats, leads)) + if backend == 'matplotlib': + got = [np.asarray(matplotlib.colors.to_rgb(ln.get_color())) + for ln in fig.axes[0].lines[:2]] + else: + tr = [t for t in fig.data if (t.meta or {}).get('hyp_trace_index') is not None] + got = [np.array(_rgb_triplet(t.line.color)) / 255.0 for t in tr[:2]] + for g, lead in zip(got, leads): + assert np.linalg.norm(g - lead) < 0.02 + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_palette_sort_orders_an_image_palette_on_both_backends(backend, tmp_path): + x = _walk() + path = painting_png(tmp_path) + hue = ['a'] * 10 + ['b'] * 10 + ['c'] * 10 + ['d'] * 10 + def drawn(**kw): + fig = hyp.plot(x, hue=hue, palette=f'image:{path}', show=False, + backend=backend, **kw) + if backend == 'matplotlib': + return np.array([matplotlib.colors.to_rgb(ln.get_color()) + for ln in fig.axes[0].lines[:4]]) + tr = [t for t in fig.data if (t.meta or {}).get('hyp_trace_index') is not None] + return np.array([_rgb_triplet(t.line.color) for t in tr[:4]]) / 255.0 + by_value, by_hue, original = drawn(), drawn(palette_sort='hue'), \ + drawn(palette_sort='original') + assert np.all(np.diff(rgb_to_hsv(by_value)[:, 2]) >= -1e-6) + assert np.all(np.diff(np.round(rgb_to_hsv(by_hue)[:, 0], 6)) >= 0) + # plotly stores 8-bit channels + assert np.allclose(original, image_palette(path, n_colors=4), + atol=1e-6 if backend == 'matplotlib' else 3e-3) + with pytest.raises(ValueError, match='palette_sort= must be one of'): + hyp.plot(x, palette=f'image:{path}', palette_sort='brightness', + show=False, backend=backend) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_palette_reduce_and_stage_kwargs_reach_the_matrix(backend): + x = _walk() + weights = matrix(t=len(x)) + hue = np.arange(len(x), dtype=float) + a = hyp.plot(x, hue=hue, palette=weights, show=False, backend=backend) + b = hyp.plot(x, hue=hue, palette=weights, palette_reduce='FastICA', + palette_sort='hue', show=False, backend=backend) + def pick(f): + if backend == 'matplotlib': + return _mpl_segment_colors(f) + tr = [t for t in f.data if (t.meta or {}).get('hyp_trace_index') is not None][0] + return np.array([_rgb_triplet(c) for c in tr.line.color]) / 255.0 + assert not np.allclose(pick(a), pick(b), atol=1e-3) + # each stage kwarg reaches hyp.reduce: the drawn colors equal those of a + # palette built BY HAND from the same staged reduction, and differ from + # the unstaged palette (Codex round 12: the test's name promised the + # stage kwargs but only exercised palette_reduce/palette_sort) + tol = 1e-6 if backend == 'matplotlib' else 3e-3 + for stage, value in (('manip', 'Smooth'), ('normalize', 'across'), + ('align', 'HyperAlign')): + staged = hyp.plot(x, hue=hue, palette=weights, show=False, + backend=backend, **{f'palette_{stage}': value}) + reduced = hyp.reduce([weights, weights] if stage == 'align' else weights, + reduce='PCA', ndims=3, random_state=0, **{stage: value}) + reduced = np.asarray(reduced[0] if stage == 'align' else reduced) + lo, hi = reduced.min(axis=0), reduced.max(axis=0) + anchors = sort_colors((reduced - lo) / np.where(hi > lo, hi - lo, 1.0), 'columns') + by_hand = hyp.plot(x, hue=hue, palette=MatrixColormap('by-hand', anchors), + show=False, backend=backend) + assert np.allclose(pick(staged), pick(by_hand), atol=tol), stage + if stage != 'align': + # (aligning ONE matrix -- against itself -- changes nothing; the + # by-hand equality above is the check that the kwarg arrived) + assert not np.allclose(pick(staged), pick(a), atol=1e-3), stage + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_forecast_palette_matrix_colors_match_between_single_and_panel_calls(backend): + """A matrix `forecast_palette=` colours the forecasts the same way in a + single-axes call and inside `panels=`, on both backends (Codex round + 11 asked for colour assertions, not an existence check).""" + x, y = _walk(seed=4), _walk(seed=5) + 2 + fp = matrix(t=8) + kw = dict(predict='Kalman', t=3, forecast_palette=fp, show=False, backend=backend) + + def forecast_colors(fig): + if backend == 'matplotlib': + return [to_hex(ln.get_color()) for ax in fig.axes for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + return [_rgb_triplet(tr.line.color) for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == 'static'] + single = forecast_colors(hyp.plot([x, y], **kw)) + panels = forecast_colors(hyp.plot([x, y], panels=True, **kw)) + assert len(single) == 2 and panels == single + expected = get_palette_colors(matrix_palette(fp), 2) + if backend == 'matplotlib': + assert [to_hex(c) for c in expected] == single + else: + assert [tuple(int(round(v * 255)) for v in c) for c in expected] == single + + +# --- the matrix colormap honours matplotlib's Colormap contract (Codex round 10) + +def test_matrix_colormap_supports_the_inherited_colormap_operations(): + """`MatrixColormap` built its parent from a bare anchor list, so integer + sampling, `resampled()`, `reversed()`, an alpha array and masked input + all failed while float sampling (the only path the tests used) worked.""" + cmap = matrix_palette(np.random.default_rng(0).normal(size=(8, 5))) + anchors = cmap.anchors + # exact float sampling at the anchors, and the parent's LUT agrees + assert np.allclose(cmap(np.linspace(0, 1, 8))[:, :3], anchors) + lut = cmap(np.arange(cmap.N)) # integer indices + assert lut.shape == (cmap.N, 4) and np.allclose(lut[0, :3], anchors[0], atol=1e-6) + assert np.allclose(cmap(0)[:3], anchors[0]) and np.allclose(cmap(cmap.N - 1)[:3], anchors[-1]) + # resampled() and reversed() are the inherited colormaps, consistent with the sampler + small = cmap.resampled(8) + assert np.allclose(small(np.linspace(0, 1, 8))[:, :3], anchors, atol=2e-3) + rev = cmap.reversed() + # the reversed map samples through the parent's 256-entry table, so + # intermediate points carry its interpolation error (~ slope / 255) + assert np.allclose(rev(np.linspace(0, 1, 8))[:, :3], anchors[::-1], atol=0.03) + assert np.allclose(rev(0.0)[:3], anchors[-1]) and np.allclose(rev(1.0)[:3], anchors[0]) + # an alpha array, bytes, masked and NaN input follow matplotlib's rules + out = cmap(np.array([0.0, 0.5, 1.0]), alpha=np.array([0.2, 0.5, 1.0])) + assert np.allclose(out[:, 3], [0.2, 0.5, 1.0]) + assert cmap(np.array([0.0, 1.0]), bytes=True).dtype == np.uint8 + masked = cmap(np.ma.masked_array([0.0, 0.5], mask=[False, True])) + assert np.allclose(masked[1], cmap.get_bad()) + assert np.allclose(cmap(np.array([np.nan]))[0], cmap.get_bad()) + from matplotlib.colors import Colormap + assert isinstance(cmap, Colormap) + with pytest.raises(ValueError, match='at least two'): + from hypertools.plot.colors import MatrixColormap + MatrixColormap('x', np.ones((1, 3))) + + +# --- Codex round 11 ------------------------------------------------------------ + +def test_matrix_colormap_applies_under_over_and_bad_per_element(): + """The exact float sampler clipped out-of-range values to the ends + (ignoring set_under/set_over) and one NaN sent the whole array through + the quantized table, changing the other entries' colors.""" + cmap = matrix_palette(np.random.default_rng(0).normal(size=(8, 5))) + cmap.set_under('red') + cmap.set_over('blue') + out = cmap(np.array([-0.1, 0.25, 1.1])) + assert to_hex(out[0][:3]) == '#ff0000' and to_hex(out[2][:3]) == '#0000ff' + clean = cmap(np.array([0.25, 0.75])) + with_nan = cmap(np.array([0.25, np.nan, 0.75])) + assert np.allclose(with_nan[[0, 2]], clean) # unchanged neighbours + assert np.allclose(with_nan[1], cmap.get_bad()) + assert np.allclose(cmap(0.25), clean[0]) # scalar == vector entry + + +def test_interpolated_image_palettes_stay_distinct_above_256_categories(tmp_path): + """`sns.blend_palette` sampled a 256-entry table, so 257 categories got + 256 colors (two categories shared one). Exact interpolation now.""" + img = np.zeros((10, 10, 3), dtype=np.uint8) + img[:, :5] = (200, 30, 30) + img[:, 5:] = (30, 30, 200) + path = tmp_path / 'two.png' + Image.fromarray(img).save(path) + from hypertools.plot.colors import interpolate_colors + for n in (2, 17, 255, 257, 400): + cols = get_palette_colors(f'image:{path}', n) + assert len(cols) == n + assert len({tuple(np.round(c, 9)) for c in cols}) == n + # the continuous short-list path uses the same exact interpolation + assert len({tuple(np.round(c, 9)) for c in interpolate_colors([(1, 0, 0), (0, 0, 1)], 300)}) == 300 + x = _walk(n=300, seed=7) + fig = hyp.plot(x, '.', hue=[str(i) for i in range(300)], palette=f'image:{path}', + show=False) + # the artists carry the float colours the library assigned (a hex + # rendering would quantize a two-anchor gradient to ~170 values) + drawn = {tuple(np.round(matplotlib.colors.to_rgb(ln.get_color()), 9)) + for ln in fig.axes[0].lines} + assert len(drawn) == 300 + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_polars_forecast_hue_is_partitioned_under_panels(backend): + """A polars `forecast_hue` Series failed under panels= on both backends + while the equivalent pandas Series and the ordinary call succeeded.""" + import polars as pl + x, y = _walk(seed=4), _walk(seed=5) + 2 + hue = ['a', 'b'] + kw = dict(predict='Kalman', t=3, forecast_palette=['red', 'blue'], show=False, backend=backend) + fig_pl = hyp.plot([x, y], panels=True, forecast_hue=pl.Series('h', hue), **kw) + fig_pd = hyp.plot([x, y], panels=True, forecast_hue=pd.Series(hue), **kw) + + def forecast_colors(fig): + if backend == 'matplotlib': + return [to_hex(ln.get_color()) for ax in fig.axes for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'static'] + return [_rgb_triplet(tr.line.color) for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == 'static'] + got = forecast_colors(fig_pl) + assert got == forecast_colors(fig_pd) and len(got) == 2 + assert got[0] != got[1] + + +# --- 0-255 colour lists are not data (review 2026-09-11) ---------------------- + +RGB255 = [[255, 128, 0], [0, 64, 255]] # orange, blue + + +@pytest.mark.parametrize('palette', [ + RGB255, # nested list + [tuple(c) for c in RGB255], # list of tuples + np.array(RGB255), # integer array + np.array(RGB255, dtype=float), # whole-number floats + np.array(RGB255, dtype=np.uint8), + [[255, 128, 0, 255], [0, 64, 255, 128]], # RGBA +]) +def test_a_0_255_colour_list_is_refused_not_read_as_data(palette): + """[[255, 128, 0], [0, 64, 255]] is an obvious list of 0-255 colours, + but its values fall outside [0, 1], so it was read as a DATA MATRIX: + reduced, rescaled and re-sorted into blue + yellow, silently (review + 2026-09-11; master raised). It raises a clear error naming both + remedies instead.""" + with pytest.raises(ValueError, match='0-255') as info: + is_palette_matrix(palette) + msg = str(info.value) + assert '/ 255' in msg and 'DataFrame' in msg + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_plot_refuses_a_0_255_palette_on_both_backends(backend): + x = [_walk(seed=1), _walk(seed=2)] + with pytest.raises(ValueError, match='0-255'): + hyp.plot(x, palette=RGB255, show=False, backend=backend) + # as forecast_palette= and as one dataset's entry in a per-dataset list + with pytest.raises(ValueError, match='0-255'): + hyp.plot(x, predict='Kalman', t=2, forecast_palette=RGB255, + show=False, backend=backend) + with pytest.raises(ValueError, match='0-255'): + hyp.plot(x, palette=[RGB255, 'viridis'], show=False, backend=backend) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_the_divided_palette_draws_orange_then_blue_on_both_backends(backend): + # the remedy the error names: the same colours, in the given order + x = [_walk(seed=1), _walk(seed=2)] + palette = np.array(RGB255) / 255 + fig = hyp.plot(x, palette=palette, show=False, backend=backend) + if backend == 'matplotlib': + got = [np.asarray(matplotlib.colors.to_rgb(ln.get_color())) + for ln in fig.axes[0].lines[:2]] + else: + tr = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') is not None] + got = [np.array(_rgb_triplet(t.line.color)) / 255.0 for t in tr[:2]] + for g, want in zip(got, palette): + assert np.linalg.norm(g - want) < 0.01, (g, want) + + +def test_whole_number_data_still_reaches_the_matrix_path(): + # data that only LOOKS like 0-255 colours: as a DataFrame it is a + # matrix palette, as documented; whole numbers outside 0-255, other + # column counts and fractional values stay data as before + assert is_palette_matrix(pd.DataFrame(RGB255)) + assert is_palette_matrix(np.array([[300, 2, 5], [7, 1, 9]])) + assert is_palette_matrix(np.array([[-3, 2, 5], [7, 1, 9]])) + assert is_palette_matrix([[1.5, 0, 0], [0, 1, 0]]) + assert is_palette_matrix(np.arange(10).reshape(5, 2) * 40) + assert not is_palette_matrix([[1, 0, 0], [0, 0, 1]]) # 0/1 colours + fig = hyp.plot([_walk(seed=1), _walk(seed=2)], + palette=pd.DataFrame(RGB255), show=False) + assert len(fig.axes[0].lines) >= 2 diff --git a/tests/test_pickle_trust_boundary.py b/tests/test_pickle_trust_boundary.py index 9fcb3b41..8925dc80 100644 --- a/tests/test_pickle_trust_boundary.py +++ b/tests/test_pickle_trust_boundary.py @@ -128,6 +128,27 @@ def test_remote_numeric_npz_needs_no_trust(http_dir): assert np.asarray(arr).shape == (3, 2) +@pytest.mark.parametrize('name', ['objs', 'objs.bin']) +def test_remote_object_npz_without_a_npz_extension_reports_the_trust_error( + http_dir, name): + """An .npz holding an object array is sniffed by its zip magic ('PK') + when the URL has no (or an unknown) extension. Its HypertoolsTrustError + was swallowed by the parquet fallback, so the user saw "ArrowInvalid: + ... Parquet magic bytes not found" instead of the trust=True remedy + (nothing was unpickled either way). Review 2026-09-11.""" + tmp_path, base = http_dir + with open(tmp_path / 'objs.npz', 'wb') as f: + np.savez(f, x=np.array([{'a': 1}, [2, 3]], dtype=object)) + (tmp_path / name).write_bytes((tmp_path / 'objs.npz').read_bytes()) + with pytest.raises(HypertoolsTrustError, match='trust=True') as info: + hyp.load(f'{base}/{name}') + assert 'Parquet' not in str(info.value) + # the remedy it names works + out = hyp.load(f'{base}/{name}', trust=True) + arr = out['x'] if hasattr(out, 'keys') else out + assert np.asarray(arr, dtype=object).shape == (2,) + + def test_builtin_dataset_by_name_needs_no_trust(): # built-in datasets load by NAME through the integrity-checked cache # path and never require trust= (they are not "remote user pickles") diff --git a/tests/test_pipeline_sklearn_clone.py b/tests/test_pipeline_sklearn_clone.py new file mode 100644 index 00000000..a7fc33bb --- /dev/null +++ b/tests/test_pipeline_sklearn_clone.py @@ -0,0 +1,220 @@ +# -*- coding: utf-8 -*- +"""`sklearn.base.clone(hyp.Pipeline(...))` works (1.1 release red-team). + +`hyp.Pipeline` subclasses scikit-learn's `BaseEstimator` (so it has +`get_params`/`set_params`), but its constructor resolves and names its +steps, so scikit-learn's default clone -- which rebuilds the object from +`get_params()` and requires the constructor to store each argument +untouched -- raised ``RuntimeError: Cannot clone object ... as the +constructor either does not set or modifies parameter steps`` (also in +1.0.0). + +Real scikit-learn models and real hypertools stages throughout. +""" +import pickle + +import numpy as np +import pytest +from sklearn.base import clone +from sklearn.decomposition import PCA +from sklearn.exceptions import NotFittedError +from sklearn.preprocessing import StandardScaler + +import hypertools as hyp +from hypertools.core.pipeline import build_pipeline + + +def _x(seed=0, shape=(50, 6)): + return np.random.default_rng(seed).normal(size=shape) + + +def test_clone_of_the_reported_pipeline_works(): + pipe = hyp.Pipeline([('pca', PCA(n_components=2))]) + copy = clone(pipe) + assert isinstance(copy, hyp.Pipeline) + assert [name for name, _ in copy.steps] == ['pca'] + assert isinstance(copy.named_steps['pca'], PCA) + assert copy.named_steps['pca'].n_components == 2 + assert copy.named_steps['pca'] is not pipe.named_steps['pca'] + + +def test_clone_of_a_fitted_pipeline_is_unfitted_and_independent(): + x = _x() + pipe = hyp.Pipeline([('scale', StandardScaler()), + ('pca', PCA(n_components=2))]) + out = pipe.fit_transform(x) + assert pipe.is_fitted + copy = clone(pipe) + assert not copy.is_fitted + assert not hasattr(copy.named_steps['pca'], 'components_') + assert not hasattr(copy.named_steps['scale'], 'mean_') + # the original is untouched by cloning, and by fitting the clone + other = _x(seed=1) + copy_out = copy.fit_transform(other) + assert copy.is_fitted and pipe.is_fitted + assert np.allclose(pipe.transform(x), out) + assert not np.allclose(copy.named_steps['pca'].components_, + pipe.named_steps['pca'].components_) + # the clone behaves like a freshly built pipeline + fresh = hyp.Pipeline([('scale', StandardScaler()), + ('pca', PCA(n_components=2))]) + assert np.allclose(copy_out, fresh.fit_transform(other)) + + +def test_clone_keeps_step_names_order_and_settings(): + pipe = hyp.Pipeline(['ZScore', ('reduce', PCA(n_components=3, whiten=True)), + {'model': 'PCA', 'kwargs': {'n_components': 2}}]) + copy = clone(pipe) + assert [n for n, _ in copy.steps] == [n for n, _ in pipe.steps] + assert [type(m) for _, m in copy.steps] == [type(m) for _, m in pipe.steps] + assert copy.named_steps['reduce'].get_params() == \ + pipe.named_steps['reduce'].get_params() + x = _x() + assert np.allclose(np.asarray(copy.fit_transform(x)), + np.asarray(pipe.fit_transform(x))) + + +def test_clone_of_a_nested_pipeline(): + inner = hyp.Pipeline([('pca', PCA(n_components=3))]) + pipe = hyp.Pipeline([('inner', inner), ('last', PCA(n_components=2))]) + x = _x() + pipe.fit(x) + copy = clone(pipe) + assert not copy.is_fitted + assert isinstance(copy.named_steps['inner'], hyp.Pipeline) + assert copy.named_steps['inner'] is not inner + assert not copy.named_steps['inner'].is_fitted + assert np.allclose(copy.fit_transform(x), pipe.transform(x)) + + +def test_clone_of_a_dispatcher_pipeline_is_unfitted_and_refits(): + """The Pipeline `return_model=True` hands back for a multi-stage call.""" + x = _x() + reduced, pipe = hyp.reduce(x, reduce='PCA', ndims=2, normalize='across', + return_model=True) + assert isinstance(pipe, hyp.Pipeline) and pipe.is_fitted + copy = clone(pipe) + assert [n for n, _ in copy.steps] == [n for n, _ in pipe.steps] + assert not copy.is_fitted + with pytest.raises(NotFittedError): + copy.transform(x) + assert np.allclose(np.asarray(copy.fit_transform(x)), np.asarray(reduced)) + # fitting another clone on other data leaves the original's fit alone + clone(pipe).fit(_x(seed=5)) + assert np.allclose(np.asarray(pipe.transform(x)), np.asarray(reduced)) + + +def test_clone_of_a_dispatcher_pipeline_does_not_share_a_model_instance(): + x = _x() + model = PCA(n_components=2) + pipe = build_pipeline(normalize='across', reduce=model) + expected = np.asarray(pipe.fit_transform(x)) + copy = clone(pipe) + copy.fit(_x(seed=7)) + # the caller's instance was fitted by `pipe` on x, and the clone's fit + # on other data did not overwrite it + assert np.allclose(np.asarray(pipe.transform(x)), expected) + assert np.allclose(model.transform( + np.asarray(hyp.normalize(x, normalize='across'))), expected) + + +def test_clone_of_a_pipeline_reusing_a_fitted_reducer_refits_it(): + """A dispatcher pipeline whose reduce stage REUSES a fitted `Reducer`: + the clone's stage is that model unfitted, so it is fit on the clone's + own data.""" + x, other = _x(), _x(seed=9) + _, fitted = hyp.reduce(x, reduce='PCA', ndims=3, return_model=True) + pipe = build_pipeline(normalize='across', reduce=fitted, ndims=3) + pipe.fit(x) + copy = clone(pipe) + assert not copy.is_fitted + out = np.asarray(copy.fit_transform(other)) + fresh = build_pipeline(normalize='across', reduce='PCA', ndims=3) + assert np.allclose(out, np.asarray(fresh.fit_transform(other))) + # the reused model was not refit by the clone + assert np.allclose(np.asarray(hyp.reduce(x, reduce=fitted)), + np.asarray(hyp.reduce(x, reduce='PCA', ndims=3))) + + +def test_apply_model_returns_one_fitted_pipeline_per_dataset(): + """`hyp.apply_model(..., stack=False)` clones the model for each + dataset. While a `Pipeline` could not be cloned it silently reused the + ONE object, so every returned "model" was the same pipeline, holding + only the last dataset's fit.""" + data = [_x(seed=s, shape=(40, 6)) for s in (1, 2, 3)] + pipe = hyp.Pipeline([('pca', PCA(n_components=2))]) + reduced, models = hyp.apply_model(data, pipe, stack=False, + return_model=True) + assert len({id(m) for m in models}) == 3 + assert not pipe.is_fitted + for dataset, out, model in zip(data, reduced, models): + assert model.is_fitted + assert np.allclose(np.asarray(out), + PCA(n_components=2).fit_transform(dataset)) + # each returned model reproduces ITS dataset's result + assert np.allclose(np.asarray(model.transform(dataset)), + np.asarray(out)) + + +def test_set_params_rejects_unknown_and_nested_names(): + pipe = hyp.Pipeline([('pca', PCA(n_components=2))]) + with pytest.raises(ValueError, match='named_steps'): + pipe.set_params(pca__n_components=3) + with pytest.raises(ValueError, match="'bogus'"): + pipe.set_params(bogus=1) + assert pipe.named_steps['pca'].n_components == 2 + + +def test_clone_keeps_input_hierarchy_as_an_independent_copy(): + hierarchy = {'axis': 'columns', 'n_features': 3} + pipe = hyp.Pipeline([('pca', PCA(n_components=2))], + input_hierarchy=hierarchy) + copy = clone(pipe) + assert copy.input_hierarchy == pipe.input_hierarchy + assert copy.input_hierarchy is not pipe.input_hierarchy + + +def test_clone_of_an_empty_pipeline(): + copy = clone(hyp.Pipeline([])) + assert copy.steps == [] + + +def test_get_params_set_params_round_trip(): + pipe = hyp.Pipeline([('scale', StandardScaler()), + ('pca', PCA(n_components=2))]) + params = pipe.get_params(deep=False) + assert set(params) == {'steps', 'input_hierarchy'} + assert params['steps'] is pipe.steps + assert pipe.set_params(**params) is pipe + assert pipe.get_params(deep=False)['steps'] == params['steps'] + x = _x() + fresh = hyp.Pipeline([('scale', StandardScaler()), + ('pca', PCA(n_components=2))]) + assert np.allclose(pipe.fit_transform(x), fresh.fit_transform(x)) + + +def test_set_params_steps_are_resolved_and_named_like_the_constructor(): + x = _x() + pipe = hyp.Pipeline([('pca', PCA(n_components=2))]) + pipe.fit(x) + pipe.set_params(steps=['ZScore', {'model': 'PCA', + 'kwargs': {'n_components': 3}}]) + assert [n for n, _ in pipe.steps] == ['zscore', 'pca'] + assert isinstance(pipe.named_steps['pca'], PCA) + # new steps: the pipeline's own earlier fit no longer counts + assert not pipe.is_fitted + assert np.asarray(pipe.fit_transform(x)).shape == (50, 3) + with pytest.raises(TypeError, match='steps must be a list'): + pipe.set_params(steps='PCA') + with pytest.raises(ValueError, match='unique'): + pipe.set_params(steps=[('a', 'PCA'), ('a', 'ZScore')]) + + +def test_clone_then_pickle_round_trip(): + x = _x() + _, pipe = hyp.reduce(x, reduce='PCA', ndims=2, normalize='across', + return_model=True) + # round trip of an object built in this test (trusted bytes) + copy = pickle.loads(pickle.dumps(clone(pipe))) + assert not copy.is_fitted + assert np.asarray(copy.fit_transform(x)).shape == (50, 2) diff --git a/tests/test_plot_animation_audit_fixes.py b/tests/test_plot_animation_audit_fixes.py index 509f1cb4..82cb0e33 100644 --- a/tests/test_plot_animation_audit_fixes.py +++ b/tests/test_plot_animation_audit_fixes.py @@ -44,39 +44,60 @@ def _trail_artists(ax): # blink empty mid-animation # --------------------------------------------------------------------------- +def _check_chemtrails_never_show_future(r, frames, coords): + """The chemtrails trail and the head tile the REVEALED rows with one + shared vertex: the trail is empty until the 12-frame head window has + filled, then ends exactly where the head begins, and nothing past the + head -- the future -- is ever drawn. + + 1.1 visual review L8: this used to pin row COUNTS (1 at frame 12, 36 at + frame 47) that assumed exactly one drawn row per frame -- the old grid, + which resampled every line onto the frame count. The 40-row spiral now + keeps its observations on a 79-row grid in this 48-frame animation, so + the invariants are stated in rows of whatever grid is drawn.""" + ax = r.figure.axes[0] + grid = r.animation._args[0][0] + n_grid, n_frames = grid.shape[0], r.animation._save_count + lengths = {} + for num in frames: + r.animation._draw_frame(num) + trail = np.column_stack(coords(_trail_artists(ax)[0])) + head = np.column_stack(coords(ax.lines[0])) + # the head ends on the row the reveal clock has reached ... + head_row = num * (n_grid - 1) // (n_frames - 1) + np.testing.assert_allclose(head[-1], grid[head_row, :head.shape[1]]) + if num <= 11: + # ... nothing has left the 12-frame head window yet -- the + # historical negative slice drew 37-47 of 48 FUTURE points here + assert len(trail) == 0, num + if len(trail): + # ... and the trail is the past only: it starts at row 0 and + # joins the head at the head's first vertex + np.testing.assert_allclose(trail[0], grid[0, :trail.shape[1]]) + np.testing.assert_allclose(trail[-1], head[0]) + assert len(trail) + len(head) - 1 == head_row + 1 + lengths[num] = len(trail) + return lengths + + def test_chemtrails_never_shows_future_3d(): r = hyp.plot([_spiral()], animate=True, chemtrails=True, duration=4, tail_duration=1, frame_rate=12, show=False, antialias=False) - ax = r.figure.axes[0] - counts = {} - for num in (0, 5, 10, 11, 12, 47): - r.animation._draw_frame(num) - trail = _trail_artists(ax)[0] - counts[num] = len(trail.get_data_3d()[0]) - # nothing has left the 12-frame head window before frame 11 -- the - # historical negative slice drew 37-47 of 48 FUTURE points here - assert counts[0] == 0 - assert counts[5] == 0 - assert counts[10] == 0 - assert counts[11] == 0 - assert counts[12] == 1 - # and the trail is present (no blink to empty) at the final frame - assert counts[47] == 36 + lengths = _check_chemtrails_never_show_future( + r, (0, 5, 10, 11, 12, 13, 47), lambda ln: ln.get_data_3d()) + # the trail appears once rows leave the window, and is present (no + # blink to empty) at the final frame + assert lengths[13] > 0 + assert lengths[47] > 0 def test_chemtrails_never_shows_future_2d(): r = hyp.plot([_spiral()], ndims=2, animate=True, chemtrails=True, duration=4, tail_duration=1, frame_rate=12, show=False, antialias=False) - ax = r.figure.axes[0] - counts = {} - for num in (0, 10, 12, 47): - r.animation._draw_frame(num) - trail = _trail_artists(ax)[0] - counts[num] = len(trail.get_xdata()) - assert counts[0] == 0 - assert counts[10] == 0 - assert counts[12] == 1 - assert counts[47] == 36 + lengths = _check_chemtrails_never_show_future( + r, (0, 10, 12, 13, 47), lambda ln: ln.get_data()) + assert lengths[13] > 0 + assert lengths[47] > 0 # --------------------------------------------------------------------------- @@ -106,20 +127,27 @@ def test_continuous_hue_window_animates_3d(): ha = hyp.plot(c, animate='window', duration=4, frame_rate=10, focused=1, hue=np.arange(200.0), show=False, antialias=False) ax = ha.figure.axes[0] + # the 1-second window is the rows the head passes in 10 of the 40 + # frames: round(10 * 199 / 39) = 51 of the 200 rows. (1.1 visual review + # L8: this was 10 segments while every line was resampled onto one row + # per frame -- 40 of these 200 observations.) + n_rows = ha.animation._args[0][0].shape[0] + assert n_rows == 200 + w = int(round(10 * (n_rows - 1) / (ha.animation._save_count - 1))) # the head collection is the FIRST collection added (cube wireframe # collections have exactly 4 segments each) ha.animation._draw_frame(10) seg_counts = [len(getattr(co, '_segments3d', [])) for co in ax.collections] - # no static full-trajectory collection (39 segments) may remain - assert max(seg_counts) <= 10 + # no static full-trajectory collection (199 segments) may remain + assert max(seg_counts) <= w head = [co for co in ax.collections - if len(getattr(co, '_segments3d', [])) == 10][0] + if len(getattr(co, '_segments3d', [])) == w][0] segs10 = np.array(head._segments3d) ha.animation._draw_frame(30) segs30 = np.array(head._segments3d) - # the 1-second window (10 segments) must SLIDE: same size, new geometry - assert segs10.shape == segs30.shape == (10, 2, 3) + # the 1-second window must SLIDE: same size, new geometry + assert segs10.shape == segs30.shape == (w, 2, 3) assert not np.allclose(segs10, segs30) @@ -142,9 +170,13 @@ def test_continuous_hue_chemtrails_trail_windows(): r.animation._draw_frame(20) seg_counts = [len(getattr(co, '_segments3d', [])) for co in ax.collections] - # nothing may hold the full 23-segment trajectory; head window is - # 6 frames (0.5 s * 12 fps) -> 6 segments, trail = 20 - 6 = 14 pts - assert max(seg_counts) < 23 + # nothing may hold the full trajectory (one segment fewer than its + # drawn rows: 39 for these 40 observations, which it keeps -- 1.1 + # visual review L8; it was 23 while every line was resampled onto the + # 24 frames) + n_rows = r.animation._args[0][0].shape[0] + assert n_rows == n + assert max(seg_counts) < n_rows - 1 # --------------------------------------------------------------------------- @@ -159,7 +191,10 @@ def test_unequal_datasets_all_fully_animated(): r = hyp.plot(order, animate=True, duration=4, frame_rate=10, show=False) interp = r.animation._args[0] - assert [d.shape[0] for d in interp] == [40, 40] + # each dataset keeps ALL its own rows -- neither is truncated to + # the other's length (F04-003) nor, since the 1.1 visual review + # (L8), resampled down onto the 40 frames (this was [40, 40]) + assert [d.shape[0] for d in interp] == [d.shape[0] for d in order] assert r.animation._save_count == 40 # at the final frame every head line reaches its dataset's end r.animation._draw_frame(39) diff --git a/tests/test_plot_axis_scale.py b/tests/test_plot_axis_scale.py index 5b1a3bc8..f5d6f060 100644 --- a/tests/test_plot_axis_scale.py +++ b/tests/test_plot_axis_scale.py @@ -1,7 +1,7 @@ """`axis_scale=` -- raw data coordinates instead of the unit frame box. GH #285: every 2-D plot used to be mean-centred, rescaled into ``[-1, 1]`` -and pinned to ``xlim/ylim=(-1.1, 1.1)``, so ``hyp.plot(..., reduce=None, +and pinned to ``xlim/ylim=(-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT)`` (1.1 x the frame's half-width), so ``hyp.plot(..., reduce=None, ndims=2)`` could not draw a time series in its own units. ``axis_scale= 'data'`` keeps the pipeline's own coordinates on both backends, static and animated. @@ -18,6 +18,7 @@ import matplotlib.pyplot as plt # noqa: E402 import hypertools as hyp # noqa: E402 +from hypertools._shared.helpers import UNIT_FRAME_LIMIT @pytest.fixture @@ -51,8 +52,8 @@ def test_unit_scale_is_still_the_default_and_still_rescales(series): x = np.asarray(line.get_xdata()) assert not np.array_equal(x, t) assert x.min() >= -1.0 - 1e-9 and x.max() <= 1.0 + 1e-9 - assert fig.axes[0].get_xlim() == (-1.1, 1.1) - assert fig.axes[0].get_ylim() == (-1.1, 1.1) + assert fig.axes[0].get_xlim() == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) + assert fig.axes[0].get_ylim() == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) plt.close(fig) @@ -192,7 +193,7 @@ def test_plotly_unit_scale_is_unchanged(series): t, y = series fig = hyp.plot(np.column_stack([t, y]), reduce=None, ndims=2, backend='plotly', antialias=False, show=False) - assert tuple(fig.layout.xaxis.range) == (-1.1, 1.1) + assert tuple(fig.layout.xaxis.range) == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) assert len(fig.layout.shapes) == 1 @@ -212,3 +213,125 @@ def test_forecast_overlay_stays_in_data_coordinates(): # ...and the axis limits grew to contain it assert fig.axes[0].get_xlim()[1] > fx[-1] plt.close(fig) + + +# --- 1.1 release-review: xlim= on a date axis (S2) ---------------------- + +def _dated_series(): + index = pd.date_range('2020-01-01', periods=30, freq='D') + return pd.DataFrame({'val': np.arange(30.0)}, index=index) + + +def test_S2_date_strings_set_xlim_on_matplotlib(): + from matplotlib.dates import date2num + fig = hyp.plot(_dated_series(), ndims=1, + xlim=('2020-01-05', '2020-01-10'), show=False) + try: + lo, hi = fig.axes[0].get_xlim() + assert lo == pytest.approx(date2num(pd.Timestamp('2020-01-05'))) + assert hi == pytest.approx(date2num(pd.Timestamp('2020-01-10'))) + finally: + plt.close(fig) + # a Timestamp pair and a float (matplotlib day number) pair agree + fig = hyp.plot(_dated_series(), ndims=1, + xlim=(pd.Timestamp('2020-01-05'), + pd.Timestamp('2020-01-10')), show=False) + try: + assert fig.axes[0].get_xlim() == pytest.approx((lo, hi)) + finally: + plt.close(fig) + fig = hyp.plot(_dated_series(), ndims=1, xlim=(lo, hi), show=False) + try: + assert fig.axes[0].get_xlim() == pytest.approx((lo, hi)) + finally: + plt.close(fig) + + +def test_S2_date_strings_and_day_numbers_set_xlim_on_plotly(): + pytest.importorskip('plotly') + from matplotlib.dates import date2num + # the range is naive DATE STRINGS (1.1 release review: epoch-ms numbers + # were drawn in the viewer's local time zone), compared as dates + want = list(pd.to_datetime(['2020-01-05', '2020-01-10'])) + fig = hyp.plot(_dated_series(), ndims=1, backend='plotly', + xlim=('2020-01-05', '2020-01-10'), show=False) + assert fig.layout.xaxis.type == 'date' + assert list(pd.to_datetime(list(fig.layout.xaxis.range))) == want + # a float is a matplotlib day number on BOTH backends (it used to be + # read as epoch milliseconds here and drew a range in 1970) + days = (date2num(pd.Timestamp('2020-01-05')), + date2num(pd.Timestamp('2020-01-10'))) + fig = hyp.plot(_dated_series(), ndims=1, backend='plotly', xlim=days, + show=False) + assert list(pd.to_datetime(list(fig.layout.xaxis.range))) == want + + +def test_S2_a_non_date_xlim_on_a_date_axis_is_refused(): + with pytest.raises(ValueError, match='date axis'): + hyp.plot(_dated_series(), ndims=1, xlim=('soon', 'later'), + show=False) + + +# --- no gridlines under the data scale (1.1 release review) ----------------- +# hypertools draws inside seaborn's 'whitegrid' style, so matplotlib's +# data-scale axes carried a grey grid that the plotly backend (showgrid=False) +# never drew. The maintainer chose plotly's look: no grid. + +def _visible_gridlines(ax): + ax.figure.canvas.draw() + return [g for g in ax.get_xgridlines() + ax.get_ygridlines() + if g.get_visible()] + + +@pytest.mark.parametrize('kwargs', [ + dict(ndims=2, reduce=None, axis_scale='data'), + dict(ndims=2, reduce=None, axis_scale='data', xlim=(-20, 20), + ylim=(-20, 20)), +]) +def test_matplotlib_data_scale_draws_no_grid(series, kwargs): + t, y = series + fig = hyp.plot(np.column_stack([t, y]), backend='matplotlib', + show=False, **kwargs) + assert _visible_gridlines(fig.axes[0]) == [] + plt.close(fig) + + +def test_matplotlib_series_mode_draws_no_grid(series): + _, y = series + # ndims=1 series mode defaults to the data scale (ticks on) + fig = hyp.plot(pd.Series(y), ndims=1, backend='matplotlib', show=False) + ax = fig.axes[0] + assert ax.axison and ax.xaxis.get_visible() + assert _visible_gridlines(ax) == [] + plt.close(fig) + + +def test_matplotlib_animated_data_scale_draws_no_grid(series): + t, y = series + anim = hyp.plot(np.column_stack([t, y]), backend='matplotlib', + reduce=None, ndims=2, axis_scale='data', animate=True, + duration=1, frame_rate=5, show=False) + fig = anim.fig if hasattr(anim, 'fig') else anim[0] + assert _visible_gridlines(fig.axes[0]) == [] + plt.close(fig) + + +def test_data_scale_keeps_a_caller_axes_own_grid(series): + # a caller's axes is theirs to style: a grid they turned on stays on + t, y = series + fig, ax = plt.subplots() + ax.grid(True) + hyp.plot(np.column_stack([t, y]), backend='matplotlib', reduce=None, + ndims=2, axis_scale='data', ax=ax, show=False) + assert _visible_gridlines(ax) != [] + plt.close(fig) + + +def test_plotly_data_scale_draws_no_grid(series): + pytest.importorskip('plotly') + t, y = series + fig = hyp.plot(np.column_stack([t, y]), backend='plotly', reduce=None, + ndims=2, axis_scale='data', xlim=(-20, 120), + ylim=(-20, 20), show=False) + assert fig.layout.xaxis.showgrid is False + assert fig.layout.yaxis.showgrid is False diff --git a/tests/test_plot_colors_bundle.py b/tests/test_plot_colors_bundle.py index aecf6841..905a3b85 100644 --- a/tests/test_plot_colors_bundle.py +++ b/tests/test_plot_colors_bundle.py @@ -194,3 +194,54 @@ def test_colors_bundle_under_plotly(): colors = bundle['colors'] assert colors['kind'] == 'continuous' assert (colors['vmin'], colors['vmax']) == (0.0, 4.0) + + +# --- 1.1 release review: C4 NaN-aware range, C6 blend categories are RGB -- + +def test_nan_in_a_continuous_hue_does_not_poison_the_range(): + """`np.min` over a NaN is NaN: vmin/vmax came back nan/nan and a + colorbar spanned -0.1..0.1. The docstring excludes non-finite hue + values from the mapping, so the range is over the finite ones.""" + rows = 12 + hue = [float(i) for i in range(rows)] + hue[2] = np.nan + with pytest.warns(UserWarning): + bundle = hyp.plot(_datasets(1, rows=rows), hue=hue, reduce='PCA', + return_model=True, show=False) + colors = bundle['colors'] + assert colors['kind'] == 'continuous' + assert colors['vmin'] == 0.0 and colors['vmax'] == float(rows - 1) + assert colors['norm'].vmin == 0.0 and colors['norm'].vmax == rows - 1 + with pytest.warns(UserWarning): + fig = hyp.plot(_datasets(1, rows=rows), hue=hue, reduce='PCA', + colorbar={}, show=False) + cbar_ax = fig.axes[-1] + lo, hi = cbar_ax.get_ylim() + assert (lo, hi) == (0.0, float(rows - 1)) + + +def test_all_nan_continuous_hue_bundle_does_not_carry_a_nan_range(): + hue = [np.nan] * 12 + with pytest.warns(UserWarning): + bundle = hyp.plot(_datasets(1, rows=12), hue=hue, reduce='PCA', + return_model=True, show=False) + assert bundle['colors']['vmin'] is None + assert bundle['colors']['vmax'] is None + + +def test_blend_categories_are_rgb_for_legend_colors(): + """Documented as ``{label: rgb}``; the blend kind handed back the raw + `legend_colors=` specs ('k', 'm', 'y').""" + rng = np.random.default_rng(3) + weights = rng.random((25, 3)) + bundle = hyp.plot(_datasets(1), hue=weights / weights.sum(1, keepdims=1), + legend=True, legend_colors=['k', 'm', 'y'], + reduce='PCA', return_model=True, show=False) + cats = bundle['colors']['categories'] + assert bundle['colors']['kind'] == 'blend' + assert set(cats) == {'1', '2', '3'} + for value in cats.values(): + assert isinstance(value, tuple) and len(value) == 3 + assert all(isinstance(v, float) for v in value) + assert cats['1'] == (0.0, 0.0, 0.0) + assert cats['2'] == (0.75, 0.0, 0.75) diff --git a/tests/test_plot_fmt_split_legend.py b/tests/test_plot_fmt_split_legend.py new file mode 100644 index 00000000..45692eca --- /dev/null +++ b/tests/test_plot_fmt_split_legend.py @@ -0,0 +1,65 @@ +"""A marker+line format string (``'s--'``) keeps its marker in the legend. + +The matplotlib backend draws such a dataset as two artists: the smoothed +line (which carries the legend label) and a markers-only artist at the raw +sample points (``_nolegend_``). Until the 1.1 release review the legend +handle therefore showed only the dashes. Real figures, rendered pixels. +""" + +import io + +import matplotlib.pyplot as plt +import pytest + +import hypertools as hyp + + +def _walks(): + return [hyp.load('random_walk', n_samples=60, n_features=8, random_state=s) + for s in range(3)] + + +def _ink(fig): + buf = io.BytesIO() + fig.savefig(buf, format='png', dpi=100) + buf.seek(0) + im = plt.imread(buf) + return int((im[..., :3].sum(-1) < 2.9).sum()) + + +@pytest.mark.parametrize('ndims', [3, 2]) +def test_marker_line_fmt_legend_handle_shows_the_marker(ndims): + fig = hyp.plot(_walks(), ['-', 'o', 's--'], ndims=ndims, + names=['walk 0', 'walk 1', 'walk 2'], markersize=4, + show=False) + leg = fig.axes[0].get_legend() + handles = dict(zip((t.get_text() for t in leg.get_texts()), + leg.legend_handles)) + assert handles['walk 2'].get_marker() == 's' + assert handles['walk 2'].get_linestyle() == '--' + assert handles['walk 0'].get_marker() in (None, 'None', '') + assert handles['walk 1'].get_linestyle() == 'None' + plt.close(fig) + + +def test_marker_line_fmt_draws_markers_only_at_the_raw_points(): + # the smoothed line must not sprout markers along its interpolated + # vertices: with the legend removed, switching the line's marker off + # changes no pixel, while the separate markers-only artist does draw. + fig = hyp.plot(_walks(), ['-', 'o', 's--'], ndims=2, + names=['walk 0', 'walk 1', 'walk 2'], markersize=6, + show=False) + ax = fig.axes[0] + ax.get_legend().remove() + lines = {ln.get_label(): ln for ln in ax.get_lines()} + line = lines['walk 2'] + markers = [ln for ln in ax.get_lines() if ln.get_label() == '_nolegend_' + and ln.get_marker() == 's'] + assert len(markers) == 1 + assert len(markers[0].get_xdata()) == 60 # raw sample points + before = _ink(fig) + line.set_marker('None') + assert _ink(fig) == before # line drew no markers + markers[0].set_visible(False) + assert _ink(fig) < before # the marker artist did + plt.close(fig) diff --git a/tests/test_plot_forecast_legend_style.py b/tests/test_plot_forecast_legend_style.py new file mode 100644 index 00000000..a940c690 --- /dev/null +++ b/tests/test_plot_forecast_legend_style.py @@ -0,0 +1,283 @@ +"""`predict=` legend entries and the collection styling rule (1.1 release +review, feature-tour sections 7.1, 9.10 and 9.11). + +Every forecast lists once in the legend under its model's name, on both +backends, static and animated, in the order data / forecasts / truth. A +collection of models keeps each dataset's colour and takes a linestyle per +model; `forecast_palette=` opts into one colour per model. The legend +glyph wears the forecasts' colour when they share one and a neutral gray +otherwise. No mocks: every assertion reads the drawn artists/traces. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +from tests._plotly_colors import rgba as effective_rgba +import pytest +from matplotlib.colors import to_rgba + +import hypertools as hyp +from hypertools.plot.forecast import (FORECAST_LEGEND_COLOR, + FORECAST_LEGEND_MIN_ALPHA, + FORECAST_MODEL_LINESTYLES) +from hypertools.plot.plotly_backend import _marker_size_px, _to_plotly_color + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walks(n=2, rows=40): + return [hyp.load('random_walk', n_samples=rows, n_features=3, + random_state=i) for i in range(n)] + + +def _ax(fig): + return fig.axes[0] + + +def _mpl_forecasts(ax, role='static'): + return [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == role] + + +def _legend_texts(ax): + return [t.get_text() for t in ax.get_legend().get_texts()] + + +def _legend_handle(ax, label): + legend = ax.get_legend() + for handle, text in zip(legend.legend_handles, legend.get_texts()): + if text.get_text() == label: + return handle + raise AssertionError(f'no legend entry {label!r} in {_legend_texts(ax)}') + + +def _plotly_entries(fig): + """Legend entries in the order plotly lists them: by `legendrank` + (default 1000), then trace order.""" + listed = [(tr.legendrank if tr.legendrank is not None else 1000, k, tr) + for k, tr in enumerate(fig.data) if tr.showlegend] + return [tr for _, _, tr in sorted(listed, key=lambda t: (t[0], t[1]))] + + +def _plotly_forecasts(fig, role='static'): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == role] + + +def _plotly_legend_traces(fig): + return [tr for tr in fig.data if (tr.meta or {}).get('hyp_legend_entry')] + + +def _rgb(rgba_string): + inner = rgba_string[rgba_string.index('(') + 1:-1].split(',') + return tuple(round(float(v)) for v in inner[:3]) + + +# -------------------------------------------------------------------------- +# the single-model form lists its forecast + +def test_single_model_forecast_is_listed_once_under_its_model_name(): + fig = hyp.plot(_walks(2), predict='Kalman', t=4, legend=True, show=False) + ax = _ax(fig) + assert _legend_texts(ax) == ['1', '2', 'Kalman'] + handle = _legend_handle(ax, 'Kalman') + # two datasets, two colours -> the entry names the model, in neutral gray + assert to_rgba(handle.get_color()) == to_rgba(FORECAST_LEGEND_COLOR) + assert handle.get_linestyle() == '-' + # legible: floored, not the forecasts' own 0.5 + assert handle.get_alpha() == pytest.approx(FORECAST_LEGEND_MIN_ALPHA) + # ...and no forecast artist carries a label of its own + assert all(ln.get_label() == '_nolegend_' for ln in _mpl_forecasts(ax)) + + +def test_single_dataset_forecast_entry_wears_the_forecast_colour(): + fig = hyp.plot(_walks(1), predict='Kalman', t=4, legend=True, + forecast_fmt=':', show=False) + ax = _ax(fig) + forecast, = _mpl_forecasts(ax) + handle = _legend_handle(ax, 'Kalman') + assert to_rgba(handle.get_color()) == to_rgba(forecast.get_color()) + assert handle.get_linestyle() == forecast.get_linestyle() == ':' + + +def test_no_legend_means_no_forecast_entry(): + fig = hyp.plot(_walks(2), predict='Kalman', t=4, show=False) + assert _ax(fig).get_legend() is None + pl = hyp.plot(_walks(2), predict='Kalman', t=4, show=False, + backend='plotly') + assert not pl.layout.showlegend + + +def test_plotly_single_model_forecast_is_listed_once(): + fig = hyp.plot(_walks(2), predict='Kalman', t=4, legend=True, + show=False, backend='plotly') + assert [tr.name for tr in _plotly_entries(fig)] == ['1', '2', 'Kalman'] + assert all(not tr.showlegend for tr in _plotly_forecasts(fig)) + entry, = _plotly_legend_traces(fig) + assert entry.meta['hyp_legend_entry'] == 'Kalman' + assert 'hyp_forecast_role' not in entry.meta + assert effective_rgba(entry)[:3] == pytest.approx(to_rgba(FORECAST_LEGEND_COLOR)[:3]) + assert effective_rgba(entry)[-1] == pytest.approx(FORECAST_LEGEND_MIN_ALPHA) + assert entry.line.dash == 'solid' + + +def test_plotly_single_dataset_entry_wears_the_forecast_colour(): + fig = hyp.plot(_walks(1), predict='Kalman', t=4, legend=True, + forecast_fmt='--', show=False, backend='plotly') + forecast, = _plotly_forecasts(fig) + entry, = _plotly_legend_traces(fig) + assert _rgb(entry.line.color) == _rgb(forecast.line.color) + assert effective_rgba(entry)[-1] == pytest.approx(FORECAST_LEGEND_MIN_ALPHA) + assert entry.line.dash == forecast.line.dash == 'dash' + + +# -------------------------------------------------------------------------- +# a collection: dataset colour, linestyle per model + +def test_collection_keeps_dataset_colour_and_dashes_per_model(): + fig = hyp.plot(_walks(2), predict=['Kalman', 'ARIMA'], t=4, legend=True, + show=False) + ax = _ax(fig) + forecasts = _mpl_forecasts(ax) + assert len(forecasts) == 4 + data_lines = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + for ln in forecasts: + source = data_lines[ln._hyp_forecast_dataset] + assert to_rgba(ln.get_color()) == to_rgba(source.get_color()) + expected = {'Kalman': '-', 'ARIMA': '--'}[ln._hyp_forecast_label] + assert ln.get_linestyle() == expected + assert _legend_texts(ax) == ['1', '2', 'Kalman', 'ARIMA'] + for label, style in (('Kalman', '-'), ('ARIMA', '--')): + handle = _legend_handle(ax, label) + assert to_rgba(handle.get_color()) == to_rgba(FORECAST_LEGEND_COLOR) + assert handle.get_linestyle() == style + + +def test_collection_cycles_the_four_linestyles_in_model_order(): + models = ['Kalman', 'ARIMA', 'GaussianProcess', 'AutoRegressor'] + fig = hyp.plot(_walks(1), predict=models, t=3, legend=True, show=False) + ax = _ax(fig) + seen = {ln._hyp_forecast_label: ln.get_linestyle() + for ln in _mpl_forecasts(ax)} + assert seen == dict(zip(models, FORECAST_MODEL_LINESTYLES)) + + +def test_plotly_collection_matches_the_matplotlib_rule(): + fig = hyp.plot(_walks(2), predict=['Kalman', 'ARIMA'], t=4, legend=True, + show=False, backend='plotly') + forecasts = _plotly_forecasts(fig) + assert len(forecasts) == 4 + data = [tr for tr in fig.data if (tr.meta or {}).get('hyp_trace_index') + is not None] + for tr in forecasts: + source = data[tr.meta['hyp_dataset']] + assert _rgb(tr.line.color) == _rgb(source.line.color) + assert tr.line.dash == {'Kalman': 'solid', 'ARIMA': 'dash'}[tr.name] + assert [tr.name for tr in _plotly_entries(fig)] == \ + ['1', '2', 'Kalman', 'ARIMA'] + kalman, arima = _plotly_legend_traces(fig) + assert kalman.line.dash == 'solid' and arima.line.dash == 'dash' + neutral = _to_plotly_color(FORECAST_LEGEND_COLOR, FORECAST_LEGEND_MIN_ALPHA) + assert effective_rgba(kalman) == effective_rgba(arima) + assert _rgb(kalman.line.color) == _rgb(neutral) + assert effective_rgba(kalman)[-1] == pytest.approx(FORECAST_LEGEND_MIN_ALPHA) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_forecast_palette_colours_a_collection_by_model(backend): + fig = hyp.plot(_walks(2), predict=['Kalman', 'ARIMA'], t=4, legend=True, + forecast_palette='Set1', show=False, backend=backend) + if backend == 'matplotlib': + ax = _ax(fig) + by_model = {} + for ln in _mpl_forecasts(ax): + by_model.setdefault(ln._hyp_forecast_label, set()).add( + to_rgba(ln.get_color())) + assert all(len(colours) == 1 for colours in by_model.values()) + assert by_model['Kalman'] != by_model['ARIMA'] + for label in ('Kalman', 'ARIMA'): + handle = _legend_handle(ax, label) + assert {to_rgba(handle.get_color())} == by_model[label] + else: + by_model = {} + for tr in _plotly_forecasts(fig): + by_model.setdefault(tr.name, set()).add(tr.line.color) + assert all(len(colours) == 1 for colours in by_model.values()) + assert by_model['Kalman'] != by_model['ARIMA'] + for entry in _plotly_legend_traces(fig): + assert {entry.line.color} == by_model[entry.name] + + +def test_forecast_fmt_replaces_the_model_cycle(): + fig = hyp.plot(_walks(2), predict=['Kalman', 'ARIMA'], t=4, + forecast_fmt=':', show=False) + assert {ln.get_linestyle() for ln in _mpl_forecasts(_ax(fig))} == {':'} + + +# -------------------------------------------------------------------------- +# order and truth= + +def _series(n=60): + t = np.linspace(0, 4 * np.pi, n) + return np.column_stack([np.sin(t), np.cos(t)]) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_legend_lists_data_then_forecast_then_truth(backend): + data = _series() + fig = hyp.plot(data[:50], predict='Kalman', t=10, truth=data[50:], + reduce=None, ndims=2, names=['observed'], legend=True, + show=False, backend=backend) + if backend == 'matplotlib': + assert _legend_texts(_ax(fig)) == ['observed', 'Kalman', 'truth'] + else: + assert [tr.name for tr in _plotly_entries(fig)] == \ + ['observed', 'Kalman', 'truth'] + + +def test_plotly_truth_marks_every_observation_not_every_vertex(): + data = _series() + t = 10 + fig = hyp.plot(data[:50], predict='Kalman', t=t, truth=data[50:], + reduce=None, ndims=2, legend=True, show=False, + backend='plotly') + truth, = _plotly_forecasts(fig, 'truth') + sizes = np.asarray(truth.marker.size, dtype=float) + # the antialiased curve has many more vertices than the t + 1 rows + # (seam included); only the rows carry a marker + assert sizes.shape[0] > t + 1 + assert int((sizes > 0).sum()) == t + 1 + assert sizes[0] > 0 and sizes[-1] > 0 + assert set(sizes[sizes > 0]) == {_marker_size_px(4, 'o', 2)} + assert truth.legendrank > 1000 + + +# -------------------------------------------------------------------------- +# animated: the live forecast is listed too + +def test_animated_matplotlib_forecast_is_listed(): + fig, ani = hyp.plot(_walks(2), predict='Kalman', t=3, animate=True, + legend=True, duration=2, frame_rate=4, show=False) + ax = [a for a in fig.axes if hasattr(a, 'zaxis')][0] + assert _legend_texts(ax) == ['1', '2', 'Kalman'] + assert len(_mpl_forecasts(ax, 'live')) == 2 + handle = _legend_handle(ax, 'Kalman') + assert to_rgba(handle.get_color()) == to_rgba(FORECAST_LEGEND_COLOR) + + +def test_animated_plotly_forecast_is_listed(): + fig = hyp.plot(_walks(2), predict='Kalman', t=3, animate=True, + legend=True, duration=2, frame_rate=4, show=False, + backend='plotly') + assert len(fig.frames) > 0 + assert [tr.name for tr in _plotly_entries(fig)] == ['1', '2', 'Kalman'] + entry, = _plotly_legend_traces(fig) + assert effective_rgba(entry)[:3] == pytest.approx(to_rgba(FORECAST_LEGEND_COLOR)[:3]) + assert effective_rgba(entry)[-1] == pytest.approx(FORECAST_LEGEND_MIN_ALPHA) + assert all(not tr.showlegend for tr in _plotly_forecasts(fig, 'live')) diff --git a/tests/test_plot_forecast_min_history.py b/tests/test_plot_forecast_min_history.py new file mode 100644 index 00000000..d28163b4 --- /dev/null +++ b/tests/test_plot_forecast_min_history.py @@ -0,0 +1,403 @@ +"""Forecast overlays under `hyp.plot` -- 1.1 release-review fixes. + +F1 ``predict='ARIMA'`` with a time-progressing ``animate=`` crashed with a + raw ``IndexError`` out of statsmodels: the per-frame schedule fit the + 2-row history the earliest frames reveal, and an ARIMA(1, 1, 1) needs 3. + Forecasters now carry a ``min_history`` (ARIMA's from its order), `fit` + refuses a shorter history with a ``ValueError`` naming the model and the + rows it needs, and the animated schedule draws no forecast until enough + history is revealed. +F2 A datetime-like ``t=`` never worked inside `plot()` (the forecaster was + handed bare arrays), although ``hyp.predict(df, t=Timestamp)`` did. +F3 A ``predict=`` collection on a hierarchical (MultiIndex) frame raised an + internal "hierarchy trace/bundle_forecasts mismatch" error. +X1 `Forecaster.fit` returns the instance (sklearn chaining). +X2 A finite input that a trailing ``Smooth(center=False)`` left NaN at the + head was reported as "rows had ALL features missing" -- the input's + fault, not the pipeline's. + +Every check reads real observables: exception types and messages, drawn +artists, returned bundles. No mocks, no monkeypatching. +""" +import warnings + +import matplotlib +matplotlib.use('Agg') +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.dates import date2num # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.predict.arima import ARIMA # noqa: E402 +from hypertools.predict.autoreg import AutoRegressor # noqa: E402 +from hypertools.predict.kalman import Kalman # noqa: E402 +from hypertools.predict.common import Forecaster # noqa: E402 +from hypertools.plot.forecast import ( # noqa: E402 + DEFAULT_MIN_HISTORY, ForecastSchedule, model_min_history) + + +def _walk(rows=30, cols=2, seed=0): + rng = np.random.default_rng(seed) + return np.cumsum(rng.normal(size=(rows, cols)), axis=0) + + +def _by_role(fig, role): + return [line for line in fig.axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == role] + + +def _draw_every_frame(anim): + for frame in range(anim.n_frames): + anim.draw_frame(frame) + return anim.n_frames + + +# --- F1: min_history ---------------------------------------------------- + +def test_arima_min_history_follows_its_order(): + assert Forecaster.min_history == DEFAULT_MIN_HISTORY == 2 + assert ARIMA.min_history_for() == 3 # (1, 1, 1) + assert ARIMA().min_history == 3 + assert ARIMA(order=(4, 0, 0)).min_history == 5 + assert ARIMA(order=(2, 1, 2)).min_history == 5 + assert ARIMA(order=(0, 1, 0)).min_history == 3 + assert Kalman().min_history == 2 + assert model_min_history('ARIMA') == 3 + assert model_min_history('Kalman') == 2 + assert model_min_history({'model': 'ARIMA', + 'kwargs': {'order': (4, 0, 0)}}) == 5 + assert model_min_history(ARIMA(order=(3, 2, 1))) == 5 + + +@pytest.mark.parametrize('order, rows', [((1, 1, 1), 3), ((4, 0, 0), 5), + ((2, 1, 2), 5), ((3, 2, 1), 5)]) +def test_arima_fits_at_its_floor_and_statsmodels_agrees(order, rows): + """The floor is what statsmodels can actually fit: `rows` rows fit and + forecast finite values, every shorter history the base check catches + first raises hypertools' own ValueError (never statsmodels' IndexError).""" + x = _walk(rows=rows, cols=1, seed=3) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + out = hyp.predict(x, model={'model': 'ARIMA', + 'kwargs': {'order': order}}, t=2) + assert out.shape == (2, 1) and np.isfinite(out.to_numpy()).all() + for short in range(2, rows): + with pytest.raises(ValueError, match='ARIMA'): + hyp.predict(x[:short], model={'model': 'ARIMA', + 'kwargs': {'order': order}}, t=2) + + +def test_predict_on_a_two_row_history_names_arima_and_three_rows(): + x = _walk() + with pytest.raises(ValueError) as info: + hyp.predict(x[:2], model='ARIMA', t=2) + message = str(info.value) + assert 'ARIMA' in message and '3 observation' in message + assert 'order=(1, 1, 1)' in message + assert '2 row' in message + + +def test_forecaster_fit_rejects_short_history_before_the_fitter_runs(): + x = _walk(rows=4) + model = ARIMA(order=(2, 1, 2)) # needs 5 rows + with pytest.raises(ValueError, match=r'ARIMA\(order=\(2, 1, 2\)\)'): + model.fit(x) + assert not model.is_fitted + model.fit(_walk(rows=5, seed=4)) + assert model.is_fitted + + +def test_animated_arima_renders_every_frame(): + """The F1 repro: the same call used to raise IndexError out of the + per-frame schedule before the first frame existed.""" + x = _walk() + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + anim = hyp.plot(x, reduce=None, ndims=2, predict='ARIMA', t=3, + animate=True, duration=1, show=False) + try: + assert _draw_every_frame(anim) > 1 + finally: + plt.close(anim.figure) + + +def test_schedule_uses_the_model_floor_and_skips_short_histories(): + x = _walk(rows=8) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + schedule = ForecastSchedule( + [x], counts=[[k] for k in range(1, 9)], model='ARIMA', t=2) + assert schedule.min_history == 3 + assert schedule.path(0, 0) is None # 1 row revealed + assert schedule.path(0, 1) is None # 2 rows: below ARIMA's floor + assert schedule.path(0, 2) is not None # 3 rows: fit + assert schedule.path(0, 2).shape == (3, 2) + + +def test_animated_kalman_and_arima_collection_renders(): + x, y = _walk(seed=1), _walk(rows=25, seed=2) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + anim = hyp.plot([x, y], reduce=None, ndims=2, + predict=['Kalman', 'ARIMA'], t=3, animate=True, + duration=1, show=False) + try: + assert _draw_every_frame(anim) > 1 + finally: + plt.close(anim.figure) + + +# --- X1: fit returns the instance --------------------------------------- + +@pytest.mark.parametrize('cls', [Kalman, AutoRegressor, ARIMA]) +def test_fit_returns_the_same_instance_for_chaining(cls): + x = _walk(seed=5) + model = cls() + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + fitted = model.fit(x) + assert fitted is model + chained = cls().fit(x).predict(2) + assert chained.shape == (2, 2) + + +# --- F2: datetime-like t= inside plot() --------------------------------- + +@pytest.fixture +def dated(): + index = pd.date_range('2020-01-01', periods=30, freq='D') + frame = pd.DataFrame(_walk(cols=1, seed=7), index=index, columns=['val']) + return frame, index + + +def test_datetime_t_forecasts_up_to_that_date_in_series_mode(dated): + frame, index = dated + target = pd.Timestamp('2020-02-05') # 6 days past the end + fig = hyp.plot(frame, ndims=1, predict='Kalman', t=target, + antialias=False, show=False) + try: + (overlay,) = _by_role(fig, 'static') + xs = np.asarray(overlay.get_xdata(), dtype=float) + assert len(xs) == 7 # seam + 6 steps + assert xs[0] == pytest.approx(date2num(index[-1].to_pydatetime())) + assert xs[-1] == pytest.approx(date2num(target.to_pydatetime())) + finally: + plt.close(fig) + + +def test_datetime_t_matches_hyp_predict_step_count_with_return_model(dated): + frame, _ = dated + target = '2020-02-03' + bundle = hyp.plot(frame, ndims=1, predict='Kalman', t=target, + return_model=True, show=False) + try: + expected = hyp.predict(frame, model='Kalman', t=target) + assert len(expected) == 4 + (forecast,) = bundle['predict']['forecasts'] + assert forecast.shape == (4, 1) + finally: + plt.close(bundle['fig']) + + +def test_datetime_t_on_two_dated_datasets_static_and_animated(dated): + frame, _ = dated + other = frame * 2.0 + 5.0 + target = pd.Timestamp('2020-02-02') + fig = hyp.plot([frame, other], ndims=1, predict='Kalman', t=target, + antialias=False, show=False) + try: + overlays = _by_role(fig, 'static') + assert len(overlays) == 2 + for line in overlays: + assert np.asarray(line.get_xdata())[-1] == pytest.approx( + date2num(target.to_pydatetime())) + finally: + plt.close(fig) + anim = hyp.plot([frame, other], ndims=1, predict='Kalman', t=target, + animate=True, duration=1, show=False) + try: + assert _draw_every_frame(anim) > 1 + finally: + plt.close(anim.figure) + + +def test_datetime_t_in_two_dimensions(dated): + frame, index = dated + wide = pd.DataFrame(_walk(cols=2, seed=8), index=index, + columns=['a', 'b']) + fig = hyp.plot(wide, reduce=None, ndims=2, predict='Kalman', + t=pd.Timestamp('2020-02-04'), antialias=False, show=False) + try: + (overlay,) = _by_role(fig, 'static') + assert len(np.asarray(overlay.get_xdata())) == 6 # seam + 5 + finally: + plt.close(fig) + + +def test_datetime_t_without_a_datetime_index_is_refused(dated): + frame, _ = dated + with pytest.raises(ValueError, match='DatetimeIndex'): + hyp.plot(frame.to_numpy(), ndims=1, predict='Kalman', + t=pd.Timestamp('2020-02-05'), show=False) + with pytest.raises(ValueError, match='at or before'): + hyp.plot(frame, ndims=1, predict='Kalman', + t=pd.Timestamp('2020-01-20'), show=False) + # NOT refused: a flat list whose indexes end at different times is + # forecast up to the target on each dataset's own index (different step + # counts); only the hierarchical path refuses a mismatch + shifted = frame.copy() + shifted.index = shifted.index + pd.Timedelta(days=3) + target = pd.Timestamp('2020-02-05') + bundle = hyp.plot([frame, shifted], ndims=1, predict='Kalman', + t=target, return_model=True, show=False) + try: + expected = hyp.predict([frame, shifted], t=target) + assert len(expected[0]) != len(expected[1]) + for actual, reference in zip(bundle['predict']['forecasts'], expected): + np.testing.assert_allclose(actual, reference) + finally: + plt.close(bundle['fig']) + + +# --- F3: a predict= collection on a hierarchy --------------------------- + +MODELS = ['Kalman', 'ARIMA'] +T = 3 + + +@pytest.fixture +def column_hierarchy(): + columns = pd.MultiIndex.from_product([['g1', 'g2'], ['a', 'b', 'c']]) + return pd.DataFrame(_walk(cols=6, seed=9), columns=columns) + + +@pytest.fixture +def row_hierarchy(): + rows = pd.MultiIndex.from_arrays( + [['r1'] * 15 + ['r2'] * 15, ['s'] * 30], names=['grp', 'sub']) + return pd.DataFrame(_walk(cols=3, seed=10), index=rows, + columns=['a', 'b', 'c']) + + +@pytest.mark.parametrize('frame_fixture', ['column_hierarchy', + 'row_hierarchy']) +def test_hierarchy_gets_one_forecast_per_trace_per_model(frame_fixture, + request): + frame = request.getfixturevalue(frame_fixture) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + bundle = hyp.plot(frame, predict=MODELS, t=T, return_model=True, + show=False) + try: + traces = bundle['trace_data'] + n_traces = len(traces) + assert n_traces >= 2 # every leaf (and any mean) + forecasts = bundle['predict']['forecasts'] + assert list(forecasts) == MODELS # keyed like the flat case + for name in MODELS: + assert len(forecasts[name]) == n_traces + for forecast, trace in zip(forecasts[name], traces): + assert forecast.shape == (T, np.asarray(trace).shape[1]) + overlays = _by_role(bundle['fig'], 'static') + assert len(overlays) == n_traces * len(MODELS) + assert bundle['predict']['drawn'] is True + finally: + plt.close(bundle['fig']) + + +@pytest.mark.parametrize('frame_fixture', ['column_hierarchy', + 'row_hierarchy']) +def test_hierarchy_collection_animates(frame_fixture, request): + frame = request.getfixturevalue(frame_fixture) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + anim = hyp.plot(frame, predict=MODELS, t=T, animate=True, + duration=1, show=False) + try: + assert _draw_every_frame(anim) > 1 + finally: + plt.close(anim.figure) + + +def test_hierarchy_mapping_form_keeps_the_callers_names(column_hierarchy): + specs = {'kal': 'Kalman', 'ar': 'ARIMA'} + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + bundle = hyp.plot(column_hierarchy, predict=specs, t=T, + return_model=True, show=False) + try: + assert list(bundle['predict']['forecasts']) == ['kal', 'ar'] + finally: + plt.close(bundle['fig']) + + +# --- X2: NaN introduced by a trailing smoother -------------------------- + +def test_nan_introduced_by_a_trailing_smoother_is_blamed_on_the_manip(): + from hypertools.manip.smooth import Smooth + x = _walk(seed=12) + assert np.isfinite(x).all() + with pytest.raises(ValueError) as info: + hyp.plot(x, reduce=None, ndims=2, + manip=Smooth(kernel='boxcar', kernel_width=12, center=False), + show=False) + message = str(info.value) + assert 'finite on input' in message + assert 'manip= stage' in message and 'Smooth' in message + assert 'min_periods=1' in message + assert 'ALL features missing' not in message + fig = hyp.plot(x, reduce=None, ndims=2, + manip=Smooth(kernel='boxcar', kernel_width=12, + center=False, min_periods=1), + antialias=False, show=False) + try: + (line,) = [ln for ln in fig.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) is None] + assert np.isfinite(np.asarray(line.get_xydata())).all() + finally: + plt.close(fig) + + +def test_nan_in_the_input_keeps_the_all_features_missing_message(): + x = _walk(seed=13) + x[5] = np.nan + with pytest.raises(ValueError, match='ALL features missing'): + hyp.plot(x, reduce=None, ndims=2, show=False) + + +def test_arima_min_history_accepts_statsmodels_sparse_lag_orders(): + """`order=([1, 3], 0, 0)` is statsmodels' sparse AR form (include lags + 1 and 3); the fitter accepts it, so the minimum-history check must + too, counting the highest lag (release review, round 2).""" + import numpy as np + import pandas as pd + from hypertools.predict.arima import ARIMA + assert ARIMA.min_history_for(order=([1, 3], 0, 0)) == 4 + assert ARIMA.min_history_for(order=(2, 1, [1, 2])) == 5 + assert ARIMA.min_history_for(order=([], 0, 0)) == 2 + rng = np.random.default_rng(0) + series = pd.DataFrame(np.cumsum(rng.normal(size=(60, 1)), axis=0)) + forecast = hyp.predict(series, model={'model': 'ARIMA', + 'kwargs': {'order': ([1, 3], 0, 0)}}, + t=2) + assert forecast.shape == (2, 1) + assert np.isfinite(forecast.to_numpy()).all() + + +def test_a_fitted_forecaster_reuses_its_parameters_on_a_short_context(): + """The minimum history is what a FIT needs. A fitted ARIMA(4, 0, 0) + applied to two new rows conditions on those rows with its learned + parameters (statsmodels does exactly that), so reuse must not be held + to the five-row fit floor (release review, round 2).""" + import numpy as np + import pandas as pd + rng = np.random.default_rng(1) + series = pd.DataFrame(np.cumsum(rng.normal(size=(40, 1)), axis=0)) + _, fitted = hyp.predict(series, model={'model': 'ARIMA', + 'kwargs': {'order': (4, 0, 0)}}, + t=2, return_model=True) + again = hyp.predict(series.iloc[:2], model=fitted, t=2) + assert again.shape == (2, 1) + assert np.isfinite(again.to_numpy()).all() diff --git a/tests/test_plot_forecast_truth_review.py b/tests/test_plot_forecast_truth_review.py new file mode 100644 index 00000000..9199cea3 --- /dev/null +++ b/tests/test_plot_forecast_truth_review.py @@ -0,0 +1,784 @@ +"""Forecast / truth= / time-axis findings from the 1.1 release review. + +Each test pins one reproduced finding against real rendered artists +(matplotlib ``Line2D`` colours, linestyles and data; plotly trace +properties), on both backends where the path is shared. +""" +import matplotlib +matplotlib.use('Agg') + +import warnings # noqa: E402 + +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +import seaborn as sns # noqa: E402 +from matplotlib.colors import to_hex, to_rgb # noqa: E402 +from matplotlib.dates import date2num # noqa: E402 + +import hypertools as hyp # noqa: E402 + + +def _walks(n=3, rows=20, dims=3, seed=0): + rng = np.random.default_rng(seed) + return [rng.normal(size=(rows, dims)).cumsum(0) + 10 * i + for i in range(n)] + + +def _role(ax, role): + return [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == role] + + +def _ply_role(fig, role): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == role] + + +def _ply_rgb(color): + """An ``rgb(...)``/``rgba(...)`` plotly colour string as a 0-1 RGB.""" + body = color[color.index('(') + 1:color.rindex(')')] + vals = [float(v) for v in body.split(',')[:3]] + return tuple(round(v / 255.0, 3) for v in vals) + + +# --- F1: a marker+line fmt draws each dataset as TWO artists ---------------- + +@pytest.mark.parametrize('fmt, dash', [('o-', '-'), ('s--', '--'), + (['o-', '-', 'x:'], None)]) +def test_split_fmt_forecasts_and_truths_take_their_own_datasets_style( + fmt, dash): + """``fmt='o-'`` draws a smoothed line plus a markers-only artist per + dataset; the forecast and truth overlays indexed ONE artist per + dataset, so three datasets' forecasts came out red, red, green (the + marker artist of dataset 0 was read as dataset 1's line).""" + data = _walks() + truth = [d[-4:] + 1.0 for d in data] + fig = hyp.plot(data, fmt=fmt, predict='Kalman', t=4, truth=truth, + show=False) + ax = fig.axes[0] + palette = [to_hex(c) for c in sns.color_palette('hls', 3)] + fmts = fmt if isinstance(fmt, list) else [fmt] * 3 + want_dash = [dash] if dash else None + fcs = sorted(_role(ax, 'static'), key=lambda a: a._hyp_forecast_dataset) + assert [a._hyp_forecast_dataset for a in fcs] == [0, 1, 2] + assert [to_hex(a.get_color()) for a in fcs] == palette + expected_ls = [f.lstrip('osx') or '-' for f in fmts] + if want_dash: + expected_ls = want_dash * 3 + assert [a.get_linestyle() for a in fcs] == expected_ls + # every truth artist (curve and its markers) wears its OWN dataset's + # colour + for tr in _role(ax, 'truth'): + assert to_hex(tr.get_color()) == palette[tr._hyp_forecast_dataset] + plt.close(fig) + + +def test_split_fmt_forecast_under_hue_runs_continues_the_last_run(): + """Under ``hue=`` runs a forecast continues the run holding the last + observation -- with ``fmt='o-'`` as with ``fmt='-'``.""" + data = _walks(n=1)[0] + hue = ['a'] * 10 + ['b'] * 10 + colours = {} + for fmt in ('-', 'o-'): + fig = hyp.plot(data, fmt=fmt, hue=hue, predict='Kalman', t=4, + show=False) + ax = fig.axes[0] + (fc,) = _role(ax, 'static') + drawn = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None + and ln.get_linestyle() not in ('None', 'none', '')] + # the LAST drawn run holds the final observation + assert to_hex(fc.get_color()) == to_hex(drawn[-1].get_color()) + colours[fmt] = to_hex(fc.get_color()) + plt.close(fig) + assert colours['o-'] == colours['-'] + + +def test_split_fmt_forecast_colours_match_on_plotly(): + data = _walks() + fig = hyp.plot(data, fmt='o-', predict='Kalman', t=4, backend='plotly', + show=False) + palette = [tuple(round(v, 3) for v in to_rgb(c)) + for c in sns.color_palette('hls', 3)] + fcs = sorted(_ply_role(fig, 'static'), + key=lambda tr: tr.meta['hyp_dataset']) + got = [_ply_rgb(tr.line.color) for tr in fcs] + assert len(got) == 3 + for g, p in zip(got, palette): + assert np.allclose(g, p, atol=0.01) + + +# --- F3: ndims=1 truth= is one column of VALUES per trace ------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_series_mode_two_column_truth_for_one_trace_raises(backend): + """In series mode a trace is one plotted column (x is the index); a + 2-column truth= for it matched the trace's internal (x, value) width + and its first column was drawn as x -- the date axis ran 1970..2020.""" + idx = pd.date_range('2020-01-01', periods=40) + full = pd.DataFrame(np.sin(np.arange(40) / 4)[:, None] * [[1, 2]], + index=idx, columns=['a', 'b']) + train, test = full.iloc[:30], full.iloc[30:] + with pytest.raises(ValueError, match='one column of values'): + hyp.plot(train, ndims=1, predict='Kalman', t=10, truth=test, + backend=backend, show=False) + with pytest.raises(ValueError, match='one column of values'): + hyp.plot(train, ndims=1, predict='Kalman', t=10, + truth=test.values, backend=backend, show=False) + plt.close('all') + + +def test_series_mode_one_column_truth_still_lands_on_the_index(): + idx = pd.date_range('2020-01-01', periods=40) + full = pd.DataFrame({'a': np.sin(np.arange(40) / 4)}, index=idx) + train, test = full.iloc[:30], full.iloc[30:] + fig = hyp.plot(train, ndims=1, reduce=None, predict='Kalman', t=10, + truth=test, antialias=False, show=False) + (curve, markers) = _role(fig.axes[0], 'truth') + want = date2num(pd.date_range('2020-01-30', periods=11).to_pydatetime()) + assert np.allclose(np.asarray(markers.get_xdata(), float), want) + plt.close(fig) + + +# --- 1-D data WITHOUT ndims=1: x is the row index, in rows ------------------ + +def _series_1d(n=40, seed=0): + return np.cumsum(np.random.default_rng(seed).standard_normal(n)) + + +def _mpl_x_spans(ax): + out = {} + for ln in ax.lines: + role = getattr(ln, '_hyp_forecast_role', None) or 'data' + x = np.asarray(ln.get_xdata(), float) + out.setdefault(role, []).append((x.min(), x.max())) + return out + + +def _ply_x_spans(fig): + out = {} + for tr in fig.data: + if tr.x is None or not len(tr.x): + continue + role = (tr.meta or {}).get('hyp_forecast_role') or 'data' + x = np.asarray(tr.x, float) + out.setdefault(role, []).append((x.min(), x.max())) + return out + + +@pytest.mark.parametrize('antialias', [True, False]) +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_1d_trace_forecast_and_truth_share_row_units(backend, antialias): + """A 40-row 1-D array drew its (antialiased) line over x 0..936 -- the + VERTEX index -- while the forecast continued in rows (936..941), so the + forecast was squashed 24x at the far end.""" + y = _series_1d() + out = hyp.plot(y, predict='Kalman', t=5, truth=np.arange(5.0), + antialias=antialias, backend=backend, show=False) + spans = (_mpl_x_spans(out.axes[0]) if backend == 'matplotlib' + else _ply_x_spans(out)) + assert spans['data'] == [(0.0, 39.0)] + assert spans['static'] == [(39.0, 44.0)] + assert all(s == (39.0, 44.0) for s in spans['truth']) + plt.close('all') + + +def test_1d_split_fmt_markers_sit_on_the_rows_of_the_line(): + y = _series_1d() + fig = hyp.plot(y, fmt='o-', show=False) + line, markers = fig.axes[0].lines + assert np.asarray(line.get_xdata(), float).max() == 39.0 + assert np.array_equal(np.asarray(markers.get_xdata(), float), + np.arange(40.0)) + # every marker sits ON the smoothed line at its row + lx = np.asarray(line.get_xdata(), float) + ly = np.asarray(line.get_ydata(), float) + assert np.allclose(np.interp(np.arange(40.0), lx, ly), + np.asarray(markers.get_ydata(), float)) + plt.close(fig) + + +def test_1d_continuous_hue_line_spans_the_rows(): + y = _series_1d() + fig = hyp.plot(y, hue=np.linspace(0, 1, 40), fmt='-', show=False) + from matplotlib.collections import LineCollection + (coll,) = [c for c in fig.axes[0].collections + if isinstance(c, LineCollection)] + xs = np.concatenate([seg[:, 0] for seg in coll.get_segments()]) + assert xs.min() == 0.0 and xs.max() == 39.0 + plt.close(fig) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_1d_axis_scale_data_puts_the_value_range_on_y_not_x(backend): + y = _series_1d() + out = hyp.plot(y, axis_scale='data', predict='Kalman', t=5, + backend=backend, show=False) + if backend == 'matplotlib': + ax = out.axes[0] + xlo, xhi = ax.get_xlim() + ylo, yhi = ax.get_ylim() + else: + xr, yr = out.layout.xaxis.range, out.layout.yaxis.range + # plotly autoranges x (rows); y is pinned to the value range + xlo, xhi = (xr if xr is not None else (0.0, 44.0)) + ylo, yhi = yr + assert xlo <= 0.0 and xhi >= 44.0 + assert ylo <= y.min() and yhi >= y.max() + assert yhi - ylo < 2 * (y.max() - y.min()) + plt.close('all') + + +# --- two-column data, animated, without ndims=2 ------------------------------ + +@pytest.mark.parametrize('extra', [{}, {'forecast_trail': 2}, + {'animate': 'window'}]) +def test_two_column_animated_forecast_draws_on_a_2d_axes(extra): + """2-column data under the default ndims draws a 2-D axes, but the live + forecast artists were built for the REQUESTED 3 dims: ``ax.plot([], [], + [])`` on a 2-D axes returns two artists and the call crashed with 'too + many values to unpack'.""" + rng = np.random.default_rng(0) + data = [np.cumsum(rng.standard_normal((30, 2)), 0) for _ in range(2)] + kw = dict(animate=True, duration=1, frame_rate=8) + kw.update(extra) + anim = hyp.plot(data, predict='Kalman', t=4, show=False, **kw) + ax = anim.figure.axes[0] + assert getattr(ax, 'name', None) != '3d' + anim.draw_frame(7) + live = _role(ax, 'live') + assert len(live) == 2 + for art in live: + assert art.get_visible() + xy = np.asarray(art.get_xydata()) + assert xy.shape[1] == 2 and len(xy) >= 2 and np.isfinite(xy).all() + if extra.get('forecast_trail'): + assert len(_role(ax, 'trail')) == 2 * extra['forecast_trail'] + plt.close(anim.figure) + + +# --- marker-only categorical regrouping never anchors a forecast ------------ + +def _equal_count_categories(): + rng = np.random.default_rng(3) + a, b = (np.cumsum(rng.standard_normal((30, 3)), 0) for _ in range(2)) + # both datasets END in 'x', and 2 categories == 2 datasets + hue = [['x'] * 10 + ['y'] * 10 + ['x'] * 10, ['y'] * 15 + ['x'] * 15] + return [a, b], hue + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('animate', [False, True]) +@pytest.mark.parametrize('predict', ['Kalman', ['Kalman', 'AutoRegressor']]) +def test_marker_only_categorical_regrouping_refuses_even_at_equal_counts( + backend, animate, predict): + """Marker-only categorical regrouping groups GLOBALLY by category, so + its traces are categories, not datasets. With 2 categories x 2 + datasets the count check passed and dataset 1's forecast was drawn in + category 'y's colour (though it ends in 'x'), with no warning and + ``drawn=True``. It must refuse and say so, as it does at 3 x 2.""" + data, hue = _equal_count_categories() + kw = dict(animate=True, duration=1, frame_rate=4) if animate else {} + with pytest.warns(UserWarning, match='could not be matched'): + bundle = hyp.plot(data, hue=hue, fmt='o', predict=predict, t=4, + return_model=True, backend=backend, show=False, + **kw) + fig = bundle['fig'] + assert bundle['predict']['drawn'] is False + assert 'regrouped' in bundle['predict']['draw_reason'] + if backend == 'plotly': + assert not [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role')] + else: + f = fig.figure if hasattr(fig, 'figure') else fig + assert not [ln for ln in f.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None)] + plt.close('all') + + +# --- legend_colors= plain list with predict= / truth= ------------------------ + +@pytest.mark.parametrize('extra', [ + {}, {'animate': True, 'duration': 1, 'frame_rate': 4}, {'truth': 'yes'}]) +def test_legend_colors_one_per_data_entry_still_works_with_overlays(extra): + """``legend_colors=['r', 'b']`` recolours the two data entries; the + forecast (and truth) entries predict= adds keep their own glyphs. + It raised 'legend has 3' after drawing (master accepted it).""" + data = _walks(n=2) + extra = dict(extra) + if extra.pop('truth', None): + extra['truth'] = [d[-5:] for d in data] + out = hyp.plot(data, legend=['A', 'B'], legend_colors=['r', 'b'], + predict='Kalman', t=5, show=False, **extra) + fig = out.figure if hasattr(out, 'figure') else out + lg = fig.axes[0].get_legend() + got = {t.get_text(): to_hex(h.get_color()) + for t, h in zip(lg.get_texts(), lg.legend_handles)} + assert got['A'] == '#ff0000' and got['B'] == '#0000ff' + from hypertools.plot.forecast import FORECAST_LEGEND_COLOR + assert got['Kalman'] == to_hex(FORECAST_LEGEND_COLOR) + plt.close(fig) + + +def test_legend_colors_one_per_entry_recolours_the_overlay_entries_too(): + data = _walks(n=2) + fig = hyp.plot(data, legend=['A', 'B'], + legend_colors=['r', 'b', 'g'], predict='Kalman', t=5, + show=False) + lg = fig.axes[0].get_legend() + assert [to_hex(h.get_color()) for h in lg.legend_handles] == \ + ['#ff0000', '#0000ff', '#008000'] + plt.close(fig) + + +def test_legend_colors_wrong_count_raises_and_leaves_no_figure_open(): + data = _walks(n=2) + plt.close('all') + with pytest.raises(ValueError, match='legend_colors has 4'): + hyp.plot(data, legend=['A', 'B'], legend_colors=['r', 'b', 'g', 'k'], + predict='Kalman', t=5, show=False) + assert plt.get_fignums() == [] + + +# --- plotly date axes are time-zone invariant --------------------------------- + +def _hourly(n=24): + return pd.DataFrame({'signal': np.sin(np.arange(float(n)) / 4)}, + index=pd.date_range('2026-01-01', periods=n, + freq='h')) + + +def _as_dates(values): + return pd.to_datetime([v for v in values if v is not None]) + + +def test_plotly_date_x_values_are_the_true_dates_as_naive_strings(): + """plotly.js draws a NUMERIC date in the viewer's local time zone, so + the epoch-ms x hypertools handed it put a series that starts + 2026-01-01 00:00 at 19:00 Dec 31 in New York. Every date x -- data, + forecast, truth, and the axis range -- is a naive date string equal to + the input's own dates.""" + data = _hourly() + held = pd.DataFrame({'signal': np.zeros(4)}, + index=pd.date_range('2026-01-02', periods=4, + freq='h')) + fig = hyp.plot(data, backend='plotly', ndims=1, reduce=None, + predict='Kalman', t=4, truth=held, antialias=False, + show=False) + assert fig.layout.xaxis.type == 'date' + (obs,) = [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') == 0] + assert all(isinstance(v, str) for v in obs.x) + assert list(_as_dates(obs.x)) == list(data.index) + (fc,) = _ply_role(fig, 'static') + assert list(_as_dates(fc.x)) == list( + pd.date_range('2026-01-01 23:00', periods=5, freq='h')) + (tr,) = _ply_role(fig, 'truth') + assert list(_as_dates(tr.x)) == list( + pd.date_range('2026-01-01 23:00', periods=5, freq='h')) + lo, hi = _as_dates(fig.layout.xaxis.range) + assert lo < data.index[0] and hi > pd.Timestamp('2026-01-02 03:00') + + +def test_plotly_animated_date_frames_carry_true_dates(): + data = _hourly() + fig = hyp.plot(data, backend='plotly', ndims=1, reduce=None, + animate=True, duration=1, frame_rate=6, antialias=False, + show=False) + xs = [v for frame in fig.frames for trace in frame.data + if trace.x is not None for v in trace.x if v is not None] + assert xs and all(isinstance(v, str) for v in xs) + stamps = _as_dates(xs) + assert stamps.min() >= data.index[0] and stamps.max() <= data.index[-1] + + +_TZ_RENDER = r''' +import sys, warnings +import numpy as np, pandas as pd +import hypertools as hyp +warnings.simplefilter('ignore') +data = pd.DataFrame({'signal': np.sin(np.arange(24.0) / 4)}, + index=pd.date_range('2026-01-01', periods=24, freq='h')) +fig = hyp.plot(data, backend='plotly', ndims=1, reduce=None, + predict='Kalman', t=4, show=False) +fig.write_image(sys.argv[1], width=600, height=360) +''' + + +def test_plotly_date_figure_renders_identically_in_every_time_zone(tmp_path): + """The real observable: the same figure rendered by Chrome (kaleido) + under TZ=UTC and TZ=America/New_York. With epoch-ms dates the two + renders differed by thousands of pixels (the whole trace shifted five + hours); with date strings they are pixel-identical.""" + pytest.importorskip('kaleido') + import os + import subprocess + import sys + from PIL import Image + from hypertools._shared.lazy_import import ensure_kaleido_chrome + ensure_kaleido_chrome() + script = tmp_path / 'render.py' + script.write_text(_TZ_RENDER) + pixels = [] + for tz in ('UTC', 'America/New_York'): + out = tmp_path / f'{tz.replace("/", "_")}.png' + env = dict(os.environ, TZ=tz) + subprocess.run([sys.executable, str(script), str(out)], check=True, + env=env, timeout=300) + pixels.append(np.asarray(Image.open(out).convert('RGB'))) + assert pixels[0].shape == pixels[1].shape + assert int((pixels[0] != pixels[1]).any(axis=2).sum()) == 0 + + +# --- a shuffled time index in ndims=1 series mode ---------------------------- + +def _shuffled_series(): + idx = pd.date_range('2020-01-01', periods=30) + df = pd.DataFrame({'v': np.sin(np.arange(30) / 3)}, index=idx) + return df, df.sample(frac=1, random_state=1) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_shuffled_time_index_is_drawn_in_time_order_and_forecast_joins( + backend): + """Series mode drew rows in INPUT order against their dates -- a + scribble -- while predict= (which sorts timed observations) continued + from the LATEST date, so the forecast did not join the drawn line's + end. The line is drawn in time order, exactly as the sorted frame.""" + df, shuffled = _shuffled_series() + with pytest.warns(UserWarning, match='time order'): + out = hyp.plot(shuffled, ndims=1, reduce=None, predict='Kalman', t=5, + antialias=False, backend=backend, show=False) + ref = hyp.plot(df, ndims=1, reduce=None, predict='Kalman', t=5, + antialias=False, backend=backend, show=False) + + def _xy(fig): + if backend == 'matplotlib': + ax = fig.axes[0] + (line,) = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + (fc,) = _role(ax, 'static') + return (np.asarray(line.get_xydata(), float), + np.asarray(fc.get_xydata(), float)) + (line,) = [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') == 0] + (fc,) = _ply_role(fig, 'static') + conv = lambda tr: np.column_stack( # noqa: E731 + [pd.to_datetime(list(tr.x)).asi8 / 1e6, np.asarray(tr.y, float)]) + return conv(line), conv(fc) + + line, fc = _xy(out) + ref_line, ref_fc = _xy(ref) + assert np.all(np.diff(line[:, 0]) > 0) + assert np.allclose(line, ref_line) + assert np.allclose(fc[0], line[-1]) # the forecast joins the end + assert np.allclose(fc, ref_fc, atol=1e-6) + plt.close('all') + + +def test_shuffled_time_index_index_title_follows_the_time_order(): + df, shuffled = _shuffled_series() + titles = {} + for key, frame in (('sorted', df), ('shuffled', shuffled)): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + anim = hyp.plot(frame, ndims=1, reduce=None, animate=True, + duration=1, frame_rate=10, title='{index:%b %d}', + show=False) + got = [] + for k in range(10): + anim.draw_frame(k) + got.append(anim.figure.axes[0].get_title()) + titles[key] = got + plt.close(anim.figure) + assert titles['shuffled'] == titles['sorted'] + assert titles['sorted'][0] == 'Jan 01' and titles['sorted'][-1] == 'Jan 30' + + +def test_shuffled_time_index_with_per_observation_hue_keeps_input_order(): + """hue= is given per observation in INPUT order, so the rows cannot be + reordered under it; the call says so instead of drawing silently.""" + _, shuffled = _shuffled_series() + with pytest.warns(UserWarning, match='not in ascending order'): + fig = hyp.plot(shuffled, ndims=1, reduce=None, + hue=np.linspace(0, 1, 30), show=False) + plt.close(fig) + + +# --- ndims=1 date tick labels do not collide --------------------------------- + +@pytest.mark.parametrize('periods, freq', [(30, 'D'), (400, 'D'), + (48, 'h')]) +@pytest.mark.parametrize('predict', [None, 'Kalman']) +def test_ndims1_date_tick_labels_do_not_overlap(periods, freq, predict): + """Every tick was a full 'YYYY-MM-DD' and, at the default figure size, + each adjacent pair overlapped. Measured against the labels' own drawn + extents on the same canvas (no absolute font metrics).""" + idx = pd.date_range('2020-01-01', periods=periods, freq=freq) + df = pd.DataFrame({'v': np.sin(np.arange(periods) / 3)}, index=idx) + fig = hyp.plot(df, ndims=1, reduce=None, show=False, + **({'predict': predict, 't': 5} if predict else {})) + fig.canvas.draw() + renderer = fig.canvas.get_renderer() + labels = [t for t in fig.axes[0].get_xticklabels() + if t.get_visible() and t.get_text()] + assert len(labels) >= 3 + boxes = sorted((t.get_window_extent(renderer) for t in labels), + key=lambda b: b.x0) + assert all(a.x1 <= b.x0 for a, b in zip(boxes, boxes[1:])) + plt.close(fig) + + +# --- the 'truth' legend key -------------------------------------------------- + +def _mpl_key(fig, label): + lg = fig.axes[0].get_legend() + (h,) = [h for t, h in zip(lg.get_texts(), lg.legend_handles) + if t.get_text() == label] + return h + + +def _ply_key(fig, label): + (tr,) = [tr for tr in fig.data if tr.showlegend and tr.name == label] + return tr + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_truth_key_is_neutral_when_truths_span_several_colours(backend): + """The one 'truth' entry stands for every dataset's truth, but it wore + dataset 0's colour -- reading as 'the truth of A'. Like a forecast key + spanning several datasets it is drawn in the neutral legend gray.""" + from hypertools.plot.forecast import FORECAST_LEGEND_COLOR + data = _walks(n=3) + fig = hyp.plot(data, predict='Kalman', t=4, truth=[d[-4:] for d in data], + legend=['A', 'B', 'C'], backend=backend, show=False) + gray = tuple(round(v, 3) for v in to_rgb(FORECAST_LEGEND_COLOR)) + if backend == 'matplotlib': + key = _mpl_key(fig, 'truth') + assert to_hex(key.get_color()) == to_hex(FORECAST_LEGEND_COLOR) + assert key.get_marker() == 'o' + plt.close(fig) + else: + key = _ply_key(fig, 'truth') + assert np.allclose(_ply_rgb(key.line.color), gray, atol=0.01) + assert 'markers' in key.mode + # still exactly one truth trace per dataset, and one 'truth' entry + assert len(_ply_role(fig, 'truth')) == 3 + assert sum(1 for tr in fig.data + if tr.showlegend and tr.name == 'truth') == 1 + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_truth_key_keeps_the_colour_of_a_single_dataset(backend): + data = _walks(n=1) + fig = hyp.plot(data, predict='Kalman', t=4, truth=[data[0][-4:]], + legend=['A'], backend=backend, show=False) + red = [round(v, 3) for v in to_rgb(sns.color_palette('hls', 1)[0])] + if backend == 'matplotlib': + assert np.allclose(to_rgb(_mpl_key(fig, 'truth').get_color()), red, + atol=0.01) + plt.close(fig) + else: + assert np.allclose(_ply_rgb(_ply_key(fig, 'truth').line.color), red, + atol=0.01) + + +# --- transform= forms ----------------------------------------------------------- + +def _dated_frame(n=30, d=3, seed=0): + rng = np.random.default_rng(seed) + return pd.DataFrame(rng.normal(size=(n, d)).cumsum(0), + index=pd.date_range('2020-01-01', periods=n)) + + +def test_transform_frame_with_default_index_forecasts_its_rows(): + """A transform= DataFrame with a plain 0..n-1 index was re-indexed + onto x's DatetimeIndex -- every value NaN -- and the forecast came out + as silent zeros. Its rows ARE x's rows, position for position.""" + df = _dated_frame() + arr = np.asarray(df) + ref = hyp.plot(df, transform=[arr], predict='Kalman', t=5, show=False, + return_model=True)['predict']['forecasts'][0] + got = hyp.plot(df, transform=[pd.DataFrame(arr)], predict='Kalman', t=5, + show=False, return_model=True)['predict']['forecasts'][0] + got, ref = np.asarray(got, float), np.asarray(ref, float) + assert np.isfinite(got).all() and np.abs(got).max() > 0 + assert np.allclose(got, ref) + plt.close('all') + + +def test_transform_frame_with_a_conflicting_index_raises(): + df = _dated_frame() + other = pd.DataFrame(np.asarray(df), + index=pd.date_range('2021-06-01', periods=len(df))) + with pytest.raises(ValueError, match='transform='): + hyp.plot(df, transform=[other], predict='Kalman', t=5, show=False) + plt.close('all') + + +def test_transform_frame_with_the_same_index_is_used_as_is(): + df = _dated_frame() + got = hyp.plot(df, transform=[df.copy()], predict='Kalman', t=5, + show=False, return_model=True)['predict']['forecasts'][0] + assert np.isfinite(np.asarray(got, float)).all() + plt.close('all') + + +@pytest.mark.parametrize('predict', [None, 'Kalman']) +def test_polars_transform_frame_matches_its_array(predict): + # 2026-09-11 review: a polars transform= raised SchemaError in the + # display scaling (polars frame minus a numpy row of means). + pl = pytest.importorskip('polars') + df = _dated_frame() + arr = np.asarray(df) + kwargs = {'predict': predict, 't': 5} if predict else {} + ref = hyp.plot(df, transform=[arr], show=False, return_model=True, + **kwargs) + got = hyp.plot(df, transform=[pl.DataFrame(arr)], show=False, + return_model=True, **kwargs) + assert np.allclose(np.asarray(got['xform_data'][0], float), + np.asarray(ref['xform_data'][0], float)) + if predict: + assert np.allclose( + np.asarray(got['predict']['forecasts'][0], float), + np.asarray(ref['predict']['forecasts'][0], float)) + plt.close('all') + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_bare_array_transform_is_one_dataset(backend): + """A bare array passed validation ('already-transformed data ... or a + list of them') and then crashed with IndexError: it was iterated as a + list of ROWS.""" + rng = np.random.default_rng(0) + x = rng.normal(size=(30, 5)) + xf = rng.normal(size=(30, 3)) + bundle = hyp.plot(x, transform=xf, backend=backend, show=False, + return_model=True) + assert len(bundle['xform_data']) == 1 + assert np.allclose(np.asarray(bundle['xform_data'][0]), xf) + plt.close('all') + + +def test_panels_draw_each_dataset_from_its_own_transform_rows(): + rng = np.random.default_rng(0) + x = [rng.normal(size=(20, 5)) for _ in range(2)] + xf = [rng.normal(size=(20, 3)) for _ in range(2)] + bundle = hyp.plot(x, transform=xf, panels=True, show=False, + return_model=True) + assert len(bundle['axes']) == 2 + for got, want in zip(bundle['xform_data'], xf): + assert np.allclose(np.asarray(got), want) + plt.close('all') + + +# --- panels= with forecast_trail= --------------------------------------------- + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_panels_accept_forecast_trail_beside_predict(panel_fit): + """The panel layout's probe call drops predict= (it only measures the + fitted rows), but kept forecast_trail=, which then refused itself with + 'forecast_trail= requires predict=' although predict= was passed.""" + data = _walks(n=2) + fig = hyp.plot(data, predict='Kalman', t=4, forecast_trail=3, + panels=True, panel_fit=panel_fit, show=False) + for ax in fig.axes[:2]: + assert len(_role(ax, 'static')) == 1 + plt.close(fig) + + +# --- xlim=(None, date) on a date axis ----------------------------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_open_ended_date_xlim_takes_the_data_bound(backend): + """``xlim=(None, '2020-01-10')`` crashed 'Axis limits cannot be NaN or + Inf' (NaT from the None); the open side now takes the data bound.""" + rng = np.random.default_rng(0) + df = pd.DataFrame(rng.normal(size=(30, 2)).cumsum(0), + index=pd.date_range('2020-01-01', periods=30), + columns=['a', 'b']) + out = hyp.plot(df, ndims=1, xlim=(None, '2020-01-10'), backend=backend, + show=False) + if backend == 'matplotlib': + from matplotlib.dates import num2date + lo, hi = out.axes[0].get_xlim() + lo = pd.Timestamp(num2date(lo)).tz_localize(None) + hi = pd.Timestamp(num2date(hi)).tz_localize(None) + plt.close(out) + else: + lo, hi = _as_dates(out.layout.xaxis.range) + assert hi == pd.Timestamp('2020-01-10') + assert lo <= pd.Timestamp('2020-01-01') + assert lo > pd.Timestamp('2019-12-01') + + +# --- forecast_fmt markers sit on the forecast STEPS, not on every vertex ----- + +def _marked_points(art): + """The vertices matplotlib draws a marker at (its markevery rule applied + to the artist's own vertex list).""" + xy = np.asarray(art.get_xydata()) if not hasattr(art, 'get_data_3d') \ + else np.column_stack(art.get_data_3d()) + every = art.get_markevery() + idx = np.arange(len(xy)) + if every is None: + return xy + if isinstance(every, slice): + return xy[idx[every]] + return xy[np.asarray(every)] + + +@pytest.mark.parametrize('dims', [3, 2, 1]) +def test_forecast_fmt_marker_is_drawn_only_at_the_forecast_steps(dims): + """``forecast_fmt='ro:'`` put a marker on all ~901 antialiased vertices, + so the dotted forecast drew as a solid red tube. A marker belongs on a + TRUE observation only (plot()'s antialias contract) -- here the seam + row and the t forecast steps -- and the dotted line stays smooth.""" + T = 5 + rng = np.random.default_rng(0) + x = np.cumsum(rng.standard_normal((40, dims)), 0) + if dims == 1: + x = x[:, 0] + fig = hyp.plot(x, predict='Kalman', t=T, forecast_fmt='ro:', show=False) + (fc,) = _role(fig.axes[0], 'static') + assert fc.get_marker() == 'o' and fc.get_linestyle() == ':' + n_vertices = (len(fc.get_data_3d()[0]) if hasattr(fc, 'get_data_3d') + else len(fc.get_xdata())) + assert n_vertices > 100 # still the smooth curve + marked = _marked_points(fc) + assert len(marked) == T + 1 # seam + the t forecast steps + if dims == 1: + # ...at the forecast's own rows: x = 39 (the seam) .. 44 + assert np.allclose(marked[:, 0], np.arange(39.0, 45.0)) + plt.close(fig) + + +def test_forecast_fmt_marker_off_antialias_marks_every_vertex(): + T = 5 + x = np.cumsum(np.random.default_rng(0).standard_normal((40, 2)), 0) + fig = hyp.plot(x, predict='Kalman', t=T, forecast_fmt='ro:', + antialias=False, show=False) + (fc,) = _role(fig.axes[0], 'static') + assert len(fc.get_xdata()) == T + 1 + assert len(_marked_points(fc)) == T + 1 + plt.close(fig) + + +def test_animated_forecast_fmt_marker_is_drawn_only_at_the_forecast_steps(): + T = 4 + rng = np.random.default_rng(0) + x = np.cumsum(rng.standard_normal((30, 3)), 0) + anim = hyp.plot(x, predict='Kalman', t=T, forecast_fmt='ro:', + forecast_trail=1, animate=True, duration=1, + frame_rate=8, show=False) + anim.draw_frame(7) + ax = anim.figure.axes[0] + for role in ('live', 'trail'): + for art in _role(ax, role): + if not art.get_visible(): + continue + assert len(art.get_data_3d()[0]) > 100 + assert len(_marked_points(art)) == T + 1 + plt.close(anim.figure) diff --git a/tests/test_plot_hue_broadcast.py b/tests/test_plot_hue_broadcast.py index 6d067e4a..ec52c5c9 100644 --- a/tests/test_plot_hue_broadcast.py +++ b/tests/test_plot_hue_broadcast.py @@ -134,3 +134,23 @@ def test_broadcast_hue_under_plotly(): reduce='PCA', backend='plotly', show=False) names = [t.name for t in fig.data if t.showlegend] assert names == ['alice', 'bob'] + + +# --- 1.1 release review: C7 a mismatched nested sub-list is named -------- + +def test_mismatched_nested_hue_names_the_offending_sublist(): + """Reported "hue has 3 entries but the data has 60 observations" -- + the count of sub-lists against the observation total.""" + with pytest.raises(ValueError) as err: + hyp.plot(_datasets(3), hue=[['a'] * 20, ['b'] * 19, ['a'] * 20], + reduce='PCA', show=False) + msg = str(err.value) + assert 'hue[1] has 19 entries but dataset 1 has 20 rows' in msg + assert 'hue has 3 entries' not in msg + + +def test_flat_hue_length_error_is_unchanged(): + with pytest.raises(ValueError, match='hue has 5 entries but the data ' + 'has 60 observations'): + hyp.plot(_datasets(3), hue=['a', 'b', 'c', 'd', 'e'], reduce='PCA', + show=False) diff --git a/tests/test_plot_labels_anchor.py b/tests/test_plot_labels_anchor.py index 54124ca7..cac885a9 100644 --- a/tests/test_plot_labels_anchor.py +++ b/tests/test_plot_labels_anchor.py @@ -147,3 +147,42 @@ def test_plotly_anchor_matches_the_hand_built_list(): got = [(a.text, a.x, a.y, a.z) for a in short.layout.scene.annotations] want = [(a.text, a.x, a.y, a.z) for a in long.layout.scene.annotations] assert got == want + + +# --- 1.1 release review: T4 nested TUPLE labels; T8 anchor/bare-str ------ + +def test_nested_tuple_labels_annotate_like_nested_lists(): + """The validator accepted tuple sub-sequences but the flatteners only + recognised lists, so each tuple was drawn as its literal repr.""" + rows = 21 + as_tuples = (('A',) + (None,) * (rows - 1), ('B',) + (None,) * (rows - 1), + ('C',) + (None,) * (rows - 1)) + as_lists = [list(t) for t in as_tuples] + fig_t = hyp.plot(_datasets(3), labels=as_tuples, reduce='PCA', + show=False) + fig_l = hyp.plot(_datasets(3), labels=as_lists, reduce='PCA', + show=False) + assert _annotations(fig_t) == _annotations(fig_l) + assert [t for t, _ in _annotations(fig_t)] == ['A', 'B', 'C'] + + +def test_nested_tuple_labels_under_plotly(): + pytest.importorskip('plotly') + rows = 21 + as_tuples = (('A',) + (None,) * (rows - 1), ('B',) + (None,) * (rows - 1), + ('C',) + (None,) * (rows - 1)) + fig = hyp.plot(_datasets(3), labels=as_tuples, reduce='PCA', + backend='plotly', show=False) + assert [a.text for a in fig.layout.scene.annotations] == ['A', 'B', 'C'] + + +def test_bogus_label_anchor_is_rejected_even_without_labels(): + with pytest.raises(ValueError, match="label_anchor= must be 'first'"): + hyp.plot(_datasets(3), label_anchor='bogus', reduce='PCA', + show=False) + + +def test_a_bare_string_labels_is_rejected_not_counted_by_character(): + with pytest.raises(TypeError, match='not the single string') as err: + hyp.plot(_datasets(3), labels='only', reduce='PCA', show=False) + assert 'labels has 4 entries' not in str(err.value) diff --git a/tests/test_plot_legend_mixture.py b/tests/test_plot_legend_mixture.py index b3acba0d..f76e6b0b 100644 --- a/tests/test_plot_legend_mixture.py +++ b/tests/test_plot_legend_mixture.py @@ -259,3 +259,22 @@ def test_plain_legend_colors_are_refused_under_plotly(): hyp.plot(data, names=['a', 'b', 'c'], legend=True, reduce='PCA', backend='plotly', show=False, legend_colors=['#ff0000', '#00ff00', '#0000ff']) + + +# --- 1.1 release review: C5 legend_kwargs fontsize survives font= -------- + +def test_legend_kwargs_fontsize_wins_over_font(): + """matplotlib ignores `fontsize=` whenever `prop=` is given, so with a + `font=` the documented "legend_kwargs wins" was silently false.""" + data = _datasets(2) + hue = ['a'] * 30 + ['b'] * 30 + fig = hyp.plot(data, hue=hue, legend=True, font='DejaVu Sans', + legend_kwargs={'fontsize': 23}, reduce='PCA', + show=False) + sizes = [t.get_fontsize() for t in _legend(fig).get_texts()] + assert sizes == [23.0, 23.0] + assert all(t.get_fontname() == 'DejaVu Sans' + for t in _legend(fig).get_texts()) + plain = hyp.plot(data, hue=hue, legend=True, font='DejaVu Sans', + reduce='PCA', show=False) + assert [t.get_fontsize() for t in _legend(plain).get_texts()] != sizes diff --git a/tests/test_plot_palette_forms.py b/tests/test_plot_palette_forms.py index 90d795b2..30bc77a5 100644 --- a/tests/test_plot_palette_forms.py +++ b/tests/test_plot_palette_forms.py @@ -249,3 +249,111 @@ def render(name, **kwargs): spelled = render('spelled', title='Ref one', legend=True, palette='hls', hue=[['a'] * 40, ['b'] * 40, ['c'] * 40]) assert spelled == plain + + +# --- 1.1 release review: C1 short colour list cycles, C2 empty list, +# --- C3 per-dataset list meeting a categorical grouping ------------------ + +def test_short_colour_list_cycles_over_datasets_without_a_hue(): + """`palette=['red', 'blue']` over three datasets drew red/blue/red in + 1.0.0 (seaborn's ambient cycle); 1.1 raised "supplies 2 color(s) but + 3 are required" before drawing anything.""" + bundle = hyp.plot(_datasets(3), palette=['red', 'blue'], reduce='PCA', + return_model=True, show=False) + assert _line_hexes(bundle['fig']) == ['#ff0000', '#0000ff', '#ff0000'] + assert ([mcolors.to_hex(c) for c in bundle['colors']['colors']] + == ['#ff0000', '#0000ff', '#ff0000']) + + +def test_short_colour_list_cycles_under_plotly(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(3), palette=['red', 'blue'], reduce='PCA', + backend='plotly', show=False) + got = [_plotly_hex(t.line.color) for t in fig.data + if 'rgba' in str(t.line.color)] + assert got[:3] == ['#ff0000', '#0000ff', '#ff0000'] + + +@pytest.mark.parametrize('empty', [[], (), np.array([])]) +def test_empty_palette_with_categorical_hue_is_a_valueerror(empty): + """Escaped as a bare StopIteration out of seaborn's colour cycle.""" + with pytest.raises(ValueError, match='empty list'): + hyp.plot(_datasets(2), hue=['a'] * 20 + ['b'] * 20, palette=empty, + reduce='PCA', show=False) + + +def test_per_dataset_palettes_with_categorical_hue_report_real_counts(): + """The message counted the CATEGORIES as datasets ("lists 3 per-dataset + palettes but 2 dataset(s) were passed" for a three-dataset call).""" + with pytest.raises(ValueError) as err: + hyp.plot(_datasets(3), hue=['a'] * 20 + ['b'] * 40, + palette=['viridis', 'plasma', 'magma'], reduce='PCA', + show=False) + msg = str(err.value) + assert '3 per-dataset palettes' in msg and '2 categories' in msg + assert 'dataset(s) were passed' not in msg + + +def test_per_dataset_palettes_with_n_clusters_report_real_counts(): + with pytest.raises(ValueError) as err: + hyp.plot(_datasets(3), n_clusters=3, + palette=['viridis', 'plasma', 'magma'], reduce='PCA', + show=False) + msg = str(err.value) + assert '3 per-dataset palettes' in msg and '3 categories' in msg + + +def test_per_dataset_dicts_name_each_datasets_categories(): + """The documented per-entry ``{category: color}`` form: each dataset's + dict names its own categories, merged and resolved by name.""" + data = _datasets(2) + hue = [['s1'] * 20, ['s2'] * 20] + palette = [{'s1': 'red'}, {'s2': 'blue'}] + bundle = hyp.plot(data, hue=hue, palette=palette, reduce='PCA', + legend=True, return_model=True, show=False) + assert _line_hexes(bundle['fig']) == ['#ff0000', '#0000ff'] + cats = bundle['colors']['categories'] + assert mcolors.to_hex(cats['s1']) == '#ff0000' + assert mcolors.to_hex(cats['s2']) == '#0000ff' + pytest.importorskip('plotly') + fig = hyp.plot(data, hue=hue, palette=palette, reduce='PCA', + backend='plotly', show=False) + got = [_plotly_hex(t.line.color) for t in fig.data + if 'rgba' in str(t.line.color)] + assert got[:2] == ['#ff0000', '#0000ff'] + + +def test_per_dataset_dicts_disagreeing_on_a_category_raise(): + with pytest.raises(ValueError, match="'s1' in more than one"): + hyp.plot(_datasets(2), hue=[['s1'] * 20, ['s1'] * 20], + palette=[{'s1': 'red'}, {'s1': 'blue'}], reduce='PCA', + show=False) + + +# --- a list of {category: color} dicts under a regrouping hue -------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_list_of_category_dicts_with_distinct_categories_per_dataset(backend): + """The documented per-dataset form: each dataset's dict names ITS OWN + categories. With hue= regrouping two datasets into four runs, the + ambient-cycle helper counted the two dicts against the four runs and + raised "lists 2 per-dataset palettes but 4 dataset(s)" (release audit + 2026-09-07, found while fixing panels); the dicts name categories, not + datasets, so they resolve by name.""" + import numpy as np + import hypertools as hyp + from matplotlib.colors import to_hex + x = np.random.default_rng(0).normal(size=(20, 3)) + hue = [['a'] * 10 + ['b'] * 10, ['c'] * 10 + ['d'] * 10] + pal = [{'a': 'red', 'b': 'blue'}, {'c': 'green', 'd': 'orange'}] + fig = hyp.plot([x, x + 2], hue=hue, palette=pal, show=False, backend=backend) + want = {'#ff0000', '#0000ff', '#008000', '#ffa500'} + if backend == 'matplotlib': + got = {to_hex(ln.get_color()) for ln in fig.axes[0].lines + if len(ln.get_xdata()) > 1} + else: + from hypertools.plot.plotly_backend import _rgb_triplet + got = {'#%02x%02x%02x' % _rgb_triplet(tr.line.color) for tr in fig.data + if tr.line is not None and tr.line.color + and (tr.meta or {}).get('hyp_trace_index') is not None} + assert got == want diff --git a/tests/test_plot_panels.py b/tests/test_plot_panels.py index 04fe4e60..d85d3084 100644 --- a/tests/test_plot_panels.py +++ b/tests/test_plot_panels.py @@ -16,6 +16,7 @@ import pytest import hypertools as hyp +from hypertools._shared.helpers import UNIT_FRAME_LIMIT from hypertools.plot.plot import subplots as hyp_subplots @@ -79,12 +80,23 @@ def test_subplots_accepts_matplotlib_figure_kwargs(): def test_panels_true_draws_one_axes_per_dataset(): data = _datasets(3) fig = hyp.plot(data, panels=True, reduce='PCA', show=False) - # 3 datasets -> a 2x2 grid with one spare, hidden - assert len(fig.axes) == 4 - assert [ax.get_visible() for ax in fig.axes] == [True, True, True, False] + # 3 datasets -> one row of three (1.1 release review: the grid follows + # the figure's aspect and avoids a spare cell; it used to be 2x2) + assert len(fig.axes) == 3 + assert all(ax.get_visible() for ax in fig.axes) assert all(ax.name == '3d' for ax in fig.axes) - # one trajectory drawn per visible panel - for ax in fig.axes[:3]: + # one trajectory drawn per panel + for ax in fig.axes: + assert len(ax.lines) == 1 + + +def test_panels_true_hides_the_spare_cell_when_no_grid_fits_exactly(): + data = _datasets(5) + fig = hyp.plot(data, panels=True, reduce='PCA', show=False) + # 5 datasets -> 2x3 with one spare, hidden + assert len(fig.axes) == 6 + assert [ax.get_visible() for ax in fig.axes] == [True] * 5 + [False] + for ax in fig.axes[:5]: assert len(ax.lines) == 1 @@ -172,7 +184,7 @@ def test_panels_return_model_carries_axes_and_grid(): data = _datasets(3) bundle = hyp.plot(data, panels=True, reduce='PCA', return_model=True, show=False) - assert bundle['panels'] == (2, 2) + assert bundle['panels'] == (1, 3) assert len(bundle['axes']) == 3 assert all(ax.figure is bundle['fig'] for ax in bundle['axes']) assert len(bundle['panel_models']) == 3 @@ -329,3 +341,380 @@ def test_panels_under_plotly_builds_a_scene_grid(): assert layout.scene3 is not None assert len(fig.data) >= 3 assert {a.text for a in layout.annotations} == {'a', 'b', 'c'} + + +# --- 1.1 release-review fixes (P4-P6) ----------------------------------- + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P4_ndims_above_3_draws_3d_panels_on_matplotlib(panel_fit): + fig = hyp.plot(_datasets(2), ndims=4, panels=True, panel_fit=panel_fit, + show=False) + try: + assert [ax.name for ax in fig.axes] == ['3d', '3d'] + for ax in fig.axes: + assert len(ax.lines) >= 1 + finally: + matplotlib.pyplot.close(fig) + + +def test_P4_ndims_above_3_draws_3d_panels_on_plotly(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(2), ndims=4, panels=True, backend='plotly', + show=False) + assert all(trace.type == 'scatter3d' for trace in fig.data) + assert fig.layout.scene is not None and fig.layout.scene2 is not None + + +def test_P5_panel_save_path_expands_tilde(tmp_path): + import os + home = os.path.expanduser('~') + name = f'.hyp_panels_p5_{os.getpid()}.png' + target = os.path.join(home, name) + fig = hyp.plot(_datasets(2), panels=True, save_path=f'~/{name}', + show=False) + try: + assert os.path.isfile(target) and os.path.getsize(target) > 0 + finally: + matplotlib.pyplot.close(fig) + if os.path.exists(target): + os.remove(target) + + +def test_P5_panel_save_path_accepts_path_objects_on_both_backends(tmp_path): + fig = hyp.plot(_datasets(2), panels=True, save_path=tmp_path / 'p.png', + show=False) + try: + assert (tmp_path / 'p.png').stat().st_size > 0 + finally: + matplotlib.pyplot.close(fig) + pytest.importorskip('plotly') + hyp.plot(_datasets(2), panels=True, backend='plotly', + save_path=tmp_path / 'p.html', show=False) + assert (tmp_path / 'p.html').stat().st_size > 0 + + +def test_P5_a_missing_directory_fails_before_any_panel_is_drawn(tmp_path): + before = set(matplotlib.pyplot.get_fignums()) + with pytest.raises(FileNotFoundError, match='directory does not exist'): + hyp.plot(_datasets(2), panels=True, + save_path=tmp_path / 'missing' / 'p.png', show=False) + assert set(matplotlib.pyplot.get_fignums()) == before + with pytest.raises(FileNotFoundError, match='directory does not exist'): + hyp.plot(_datasets(2), panels=True, backend='plotly', + save_path=tmp_path / 'missing' / 'p.html', show=False) + + +def test_P6_plotly_panels_return_the_single_axes_figure_type(capsys): + """The plotly panel path returned a bare ``go.Figure`` and called + ``fig.show()`` itself, bypassing the one-shot end-of-cell display queue + -- a notebook cell ending in the call displayed the grid twice.""" + pytest.importorskip('plotly') + import json + import IPython + import plotly.io as pio + from hypertools.plot import plotly_backend + + assert IPython.get_ipython() is None + single = hyp.plot(_datasets(1)[0], backend='plotly', show=False) + panels = hyp.plot(_datasets(2), panels=True, backend='plotly', + show=False) + assert type(panels) is type(single) + assert type(panels).__name__ == 'HyperPlotlyFigure' + assert len(panels.data) >= 2 + + saved_renderer = pio.renderers.default + pio.renderers.default = 'json' + try: + capsys.readouterr() + hyp.plot(_datasets(2), panels=True, backend='plotly', show=False) + assert capsys.readouterr().out == '' # show=False: no show + fig = hyp.plot(_datasets(2), panels=True, backend='plotly', + show=True) + out = capsys.readouterr().out + assert out.count("'application/json'") == 1 # shown exactly once + assert str({'application/json': json.loads(fig.to_json())}) in out + assert plotly_backend._PENDING_DISPLAY == [] + finally: + pio.renderers.default = saved_renderer + + +# --- plotly cell parity (1.1 release review: transplant_panel) ----------- + +def _df2(seed, cols=('a', 'b')): + import pandas as pd + arr = hyp.load('random_walk', n_samples=30, n_features=2, + random_state=seed) + return pd.DataFrame(arr, columns=list(cols)) + + +def test_plotly_2d_panels_keep_the_unit_frame_and_column_labels(): + pytest.importorskip('plotly') + fig = hyp.plot([_df2(0), _df2(1)], panels=True, ndims=2, reduce=None, + backend='plotly', show=False) + assert list(fig.layout.xaxis.range) == [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT] + assert list(fig.layout.yaxis2.range) == [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT] + assert fig.layout.xaxis2.title.text == 'a' + assert fig.layout.yaxis2.title.text == 'b' + assert fig.layout.xaxis2.showticklabels is False + # one frame square per panel, each on its own cell's axes + assert sorted(s.xref for s in fig.layout.shapes) == ['x', 'x2'] + assert sorted(s.yref for s in fig.layout.shapes) == ['y', 'y2'] + + +def test_plotly_2d_panels_axis_scale_data_keep_visible_axes(): + pytest.importorskip('plotly') + fig = hyp.plot([_df2(0), _df2(1)], panels=True, ndims=2, reduce=None, + axis_scale='data', backend='plotly', show=False) + assert fig.layout.xaxis2.showticklabels is True + # the data's own range, not the unit frame + assert list(fig.layout.xaxis2.range) != [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT] + assert fig.layout.xaxis2.range[1] - fig.layout.xaxis2.range[0] > 2.2 + assert len(fig.layout.shapes) == 0 + + +def test_plotly_panels_get_one_legend_each_with_their_own_entries(): + pytest.importorskip('plotly') + data = _datasets(3, rows=20) + fig = hyp.plot(data, panels=True, legend=True, names=['p', 'q', 'r'], + backend='plotly', show=False) + by_legend = {} + for trace in fig.data: + if trace.showlegend: + by_legend.setdefault(trace.legend, []).append(trace.name) + assert by_legend == {'legend': ['p'], 'legend2': ['q'], + 'legend3': ['r']} + # each legend sits just right of its own cell, not at the figure edge + for key, scene in (('legend', 'scene'), ('legend2', 'scene2'), + ('legend3', 'scene3')): + x1 = fig.layout[scene].domain.x[1] + assert fig.layout[key].x > x1 + assert fig.layout[key].x < x1 + 0.2 + assert fig.layout.showlegend is True + + +def test_plotly_panels_hue_legend_lists_each_group_once_per_panel(): + pytest.importorskip('plotly') + data = _datasets(2, rows=20) + hue = [['x'] * 10 + ['y'] * 10] * 2 + fig = hyp.plot(data, panels=True, hue=hue, legend=True, + backend='plotly', show=False) + names = {} + for trace in fig.data: + if trace.showlegend: + names.setdefault(trace.legend, []).append(trace.name) + assert names == {'legend': ['x', 'y'], 'legend2': ['x', 'y']} + + +def test_plotly_panels_without_legend_have_none(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(2, rows=20), panels=True, backend='plotly', + show=False) + assert fig.layout.showlegend is False + + +def test_plotly_panels_place_each_colorbar_beside_its_own_panel(): + pytest.importorskip('plotly') + data = _datasets(2, rows=20) + hue = [np.arange(20.0), np.arange(20.0)] + fig = hyp.plot(data, panels=True, hue=hue, colorbar=True, + backend='plotly', show=False) + colorbars = [t.marker.colorbar for t in fig.data + if t.marker is not None and t.marker.showscale] + assert len(colorbars) == 2 + assert colorbars[0].x < colorbars[1].x + assert colorbars[0].x > fig.layout.scene.domain.x[1] + assert colorbars[1].x > fig.layout.scene2.domain.x[1] + assert colorbars[0].x < fig.layout.scene2.domain.x[0] + + +def test_plotly_panels_reserve_a_gutter_only_when_needed(): + pytest.importorskip('plotly') + from hypertools.plot.plotly_backend import DEFAULT_FIGSIZE + plain = hyp.plot(_datasets(2, rows=20), panels=True, backend='plotly', + show=False) + with_legend = hyp.plot(_datasets(2, rows=20), panels=True, legend=True, + backend='plotly', show=False) + assert plain.layout.width == int(DEFAULT_FIGSIZE[0] * 100) + assert with_legend.layout.width > plain.layout.width + assert with_legend.layout.margin.r > plain.layout.margin.r + # an explicit size= is honoured verbatim on both + sized = hyp.plot(_datasets(2, rows=20), panels=True, legend=True, + size=[9, 3], backend='plotly', show=False) + assert (sized.layout.width, sized.layout.height) == (900, 300) + + +def test_plotly_3d_panels_keep_the_single_axes_camera_in_square_cells(): + """plotly sizes a scene by its domain's height, so a cube in a tall + narrow cell spilled out of the cell's sides. The grid's 3-D cells are + SQUARE (1.1 release review, like the matplotlib grid's), so no cell of + `panels=` needs the camera backed off: three panels in a default-sized + figure, and two in a wide one, keep the single-axes distance. (A + genuinely narrow cell -- a caller's own `column_widths=` -- is backed + off; see tests/test_plot_panels_geometry.py.)""" + pytest.importorskip('plotly') + single = hyp.plot(_datasets(1, rows=20)[0], backend='plotly', + show=False) + eye = single.layout.scene.camera.eye + r_single = (eye.x ** 2 + eye.y ** 2 + eye.z ** 2) ** 0.5 + fig = hyp.plot(_datasets(3, rows=20), panels=(1, 3), backend='plotly', + show=False) + for key in ('scene', 'scene2', 'scene3'): + e = fig.layout[key].camera.eye + r = (e.x ** 2 + e.y ** 2 + e.z ** 2) ** 0.5 + assert r == pytest.approx(r_single) + d = fig.layout[key].domain + plot_w = fig.layout.width - fig.layout.margin.l - fig.layout.margin.r + plot_h = fig.layout.height - fig.layout.margin.t - fig.layout.margin.b + assert (d.x[1] - d.x[0]) * plot_w == pytest.approx( + (d.y[1] - d.y[0]) * plot_h, abs=2.0) + wide = hyp.plot(_datasets(2, rows=20), panels=(1, 2), size=[16, 4], + backend='plotly', show=False) + e = wide.layout.scene.camera.eye + assert (e.x ** 2 + e.y ** 2 + e.z ** 2) ** 0.5 == pytest.approx(r_single) + + +def test_plotly_panels_labels_annotations_follow_their_cell(): + pytest.importorskip('plotly') + fig = hyp.plot([_df2(0), _df2(1)], panels=True, ndims=2, reduce=None, + labels=[['first'], ['second']], label_anchor='first', + backend='plotly', show=False) + labels = [a for a in fig.layout.annotations if a.text in ('first', + 'second')] + assert [(a.text, a.xref, a.yref) for a in labels] == [ + ('first', 'x', 'y'), ('second', 'x2', 'y2')] + + +# --- matplotlib panels: colorbars take room from their own panel --------- + +def test_matplotlib_panels_draw_one_colorbar_per_panel_without_warnings(): + import warnings + data = _datasets(2, rows=20) + hue = [np.arange(20.0), np.arange(20.0)] + with warnings.catch_warnings(): + warnings.simplefilter('error') + fig = hyp.plot(data, panels=True, hue=hue, colorbar=True, + show=False) + try: + panels = [ax for ax in fig.axes if ax.get_label() != '<colorbar>'] + cbars = [ax for ax in fig.axes if ax.get_label() == '<colorbar>'] + assert len(panels) == 2 and len(cbars) == 2 + # each colorbar sits to the right of its own panel and left of the + # next panel: no two share a position + xs = sorted(ax.get_position().x0 for ax in panels + cbars) + assert len(set(round(x, 3) for x in xs)) == 4 + order = sorted(panels + cbars, key=lambda ax: ax.get_position().x0) + assert [ax.get_label() == '<colorbar>' for ax in order] == [ + False, True, False, True] + finally: + matplotlib.pyplot.close(fig) + + +def test_matplotlib_ax_grid_colorbars_do_not_widen_the_figure(): + fig, axes = hyp.subplots(1, 2, size=[8, 4]) + try: + for ax, d in zip(axes, _datasets(2, rows=20)): + hyp.plot(d, ax=ax, hue=np.arange(20.0), colorbar=True, + show=False) + assert tuple(fig.get_size_inches()) == (8.0, 4.0) + assert sum(ax.get_label() == '<colorbar>' for ax in fig.axes) == 2 + finally: + matplotlib.pyplot.close(fig) + + +# --- release review round 2 ------------------------------------------------ + +def test_matplotlib_panels_ignore_an_active_plotly_preference(): + """`panels=True, backend='matplotlib'` under + `hyp.set_interactive_backend('plotly')` built its grid with `subplots`' + new `backend='auto'` default -- plotly cells the matplotlib panel calls + then rejected.""" + pytest.importorskip('plotly') + with hyp.set_interactive_backend('plotly'): + fig = hyp.plot(_datasets(2, rows=20), panels=True, + backend='matplotlib', show=False) + try: + assert isinstance(fig, matplotlib.figure.Figure) + assert [ax.name for ax in fig.axes] == ['3d', '3d'] + finally: + matplotlib.pyplot.close(fig) + + +def test_plotly_panels_keep_a_left_colorbar_on_the_left(): + pytest.importorskip('plotly') + data = _datasets(2, rows=20) + hue = [np.arange(20.0), np.arange(20.0)] + fig = hyp.plot(data, panels=True, hue=hue, + colorbar={'location': 'left'}, backend='plotly', + show=False) + colorbars = [t.marker.colorbar for t in fig.data + if t.marker is not None and t.marker.showscale] + assert len(colorbars) == 2 + assert colorbars[0].xanchor == 'right' + assert colorbars[0].x <= fig.layout.scene.domain.x[0] + assert colorbars[1].xanchor == 'right' + assert colorbars[1].x <= fig.layout.scene2.domain.x[0] + + +def test_plotly_panel_titles_go_through_the_title_path(): + """A newline in a plotly title becomes ``<br>`` and `title_kwargs=` + styles it on the single-axes path; panel titles used to bypass both.""" + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(2, rows=20), panels=True, + title=['first\nline', 'second'], + title_kwargs={'fontsize': 20, 'color': 'red'}, + backend='plotly', show=False) + titles = {a.text: a for a in fig.layout.annotations} + assert set(titles) == {'first<br>line', 'second'} + single = hyp.plot(_datasets(1, rows=20)[0], title='x', + title_kwargs={'fontsize': 20, 'color': 'red'}, + backend='plotly', show=False) + assert titles['second'].font.size == single.layout.title.font.size + assert titles['second'].font.color == single.layout.title.font.color + assert fig.layout.margin.t >= 40 + + +def test_plotly_panels_carry_the_font_into_the_grid(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(2, rows=20), panels=True, legend=True, + names=['p', 'q'], font='DejaVu Serif', backend='plotly', + show=False) + single = hyp.plot(_datasets(1, rows=20)[0], legend=True, names=['p'], + font='DejaVu Serif', backend='plotly', show=False) + assert fig.layout.font.family == single.layout.font.family + assert fig.layout.legend.font.family == single.layout.font.family + assert fig.layout.legend2.font.family == single.layout.font.family + + +def test_plotly_grid_png_draws_something_in_every_cell(tmp_path): + """A rendered 1x2 plotly grid has ink in BOTH halves (not just a + nonempty file).""" + pytest.importorskip('plotly') + from PIL import Image + fig = hyp.plot(_datasets(2, rows=20), panels=(1, 2), backend='plotly', + show=False) + target = tmp_path / 'grid.png' + fig.write_image(str(target)) + img = np.asarray(Image.open(target).convert('L')) + ink = img < 128 + left = ink[:, : img.shape[1] // 2].sum() + right = ink[:, img.shape[1] // 2:].sum() + assert left > 200 and right > 200 + + +def test_plotly_panels_reserve_the_multiline_title_margin(): + pytest.importorskip('plotly') + single = hyp.plot(_datasets(1, rows=20)[0], title='a\nb\nc', + title_kwargs={'fontsize': 20}, backend='plotly', + show=False) + grid = hyp.plot(_datasets(2, rows=20), panels=True, + title=['a\nb\nc', 'x'], title_kwargs={'fontsize': 20}, + backend='plotly', show=False) + assert single.layout.margin.t > 40 + # the grid's first row reserves at least what the single figure did + # (more when square cells leave vertical slack that centres the grid); + # a one-line title next to it does not shrink the reservation + assert grid.layout.margin.t >= single.layout.margin.t + plain = hyp.plot(_datasets(2, rows=20), panels=True, title=['a', 'b'], + backend='plotly', show=False) + assert grid.layout.margin.t - plain.layout.margin.t >= \ + (single.layout.margin.t - 40) / 2 diff --git a/tests/test_plot_panels_audit.py b/tests/test_plot_panels_audit.py new file mode 100644 index 00000000..534c0b71 --- /dev/null +++ b/tests/test_plot_panels_audit.py @@ -0,0 +1,774 @@ +"""`panels=` partitions every per-dataset and per-forecast argument, and +exposes the shared fitted pipeline (release audit 2026-09-07, findings 3 +and 4). + +Finding 3: a shared-fit panel grid drew every panel through `transform=`, +so each panel bundle's ``pipeline`` was ``None`` and the ONE fitted pipeline +the panels share could not be replayed on held-out data. + +Finding 4: `_plot_panels` handed every panel the WHOLE per-dataset / +per-forecast list for `palette=` (per-dataset form), `forecast_fmt=`, +`forecast_palette=`, a model-major `forecast_hue=`, and a forecaster fitted +on every dataset -- each of which works on the single-axes path and either +raised inside the panel or (`forecast_palette=`) drew every panel in the +first colour. Two more of the same shape turned up while fixing it: a +`legend=` list naming the datasets, and ``alpha=[a, b, c]`` for three +datasets (read as ONE RGB colour by the colour-tuple guard). + +Every test reads the ACTUAL artists/traces -- colours, styles, forecast +values -- on both backends, in both `panel_fit=` modes, and compares them +with the single-axes call's, which is what a panel grid promises to draw. +""" + +import inspect +import re +import warnings + +import matplotlib +matplotlib.use('Agg') + +import numpy as np # noqa: E402 +from tests._plotly_colors import rgba as effective_rgba +import pytest # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.colors import to_rgb # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.core.pipeline import Pipeline # noqa: E402 +from hypertools.plot import plot as plot_module # noqa: E402 +from hypertools.plot.colors import palette_lead_color # noqa: E402 + +BACKENDS = ('matplotlib', 'plotly') +FITS = ('shared', 'independent') + + +def walks(n=2, rows=20, cols=3, seed=201): + rng = np.random.default_rng(seed) + return [rng.normal(size=(rows, cols)).cumsum(0) for _ in range(n)] + + +def quiet_plot(*args, **kwargs): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + return hyp.plot(*args, show=False, return_model=True, **kwargs) + + +# ---------------------------------------------------------------- readers + +def _rgba(color): + """(rgb tuple in [0, 1], alpha) from a matplotlib colour or a plotly + ``rgb()``/``rgba()``/hex string.""" + if isinstance(color, str) and color.startswith(('rgb(', 'rgba(')): + parts = [float(v) for v in re.findall(r'[\d.]+', color)] + rgb = tuple(v / 255.0 for v in parts[:3]) + return rgb, (parts[3] if len(parts) > 3 else 1.0) + return tuple(to_rgb(color)), None + + +def data_artists(bundle, i, backend): + """The observed-data artists of panel `i` (forecast/truth overlays + excluded), as ``[(rgb, alpha, style, label)]``.""" + if backend == 'matplotlib': + out = [] + for line in bundle['axes'][i].lines: + if getattr(line, '_hyp_forecast_role', None): + continue + rgb, _ = _rgba(line.get_color()) + out.append((rgb, line.get_alpha(), line.get_linestyle(), + line.get_label())) + return out + scene = bundle['axes'][i].plotly_name + out = [] + for trace in bundle['fig'].data: + if trace.scene != scene or not isinstance(trace.meta, dict) \ + or 'hyp_trace_index' not in trace.meta: + continue + rgb, alpha = effective_rgba(trace)[:3], effective_rgba(trace)[-1] + out.append((rgb, alpha, trace.line.dash, trace.name)) + return out + + +def forecast_artists(fig_or_bundle, i, backend, panel=True): + """The forecast overlays of panel `i` (or of the single axes when + ``panel=False``), as ``[(rgb, style)]`` in drawing order -- model-major + for a `predict=` collection.""" + if backend == 'matplotlib': + axes = (fig_or_bundle['axes'][i] if panel + else fig_or_bundle['fig'].axes[0]) + return [(_rgba(line.get_color())[0], line.get_linestyle()) + for line in axes.lines + if getattr(line, '_hyp_forecast_role', None) == 'static' + and (panel or getattr(line, '_hyp_forecast_dataset', + None) == i)] + scene = fig_or_bundle['axes'][i].plotly_name if panel else None + out = [] + for trace in fig_or_bundle['fig'].data: + if panel and trace.scene != scene: + continue + if not isinstance(trace.meta, dict) \ + or trace.meta.get('hyp_forecast_role') != 'static': + continue + if not panel and trace.meta.get('hyp_dataset') != i: + continue + out.append((_rgba(trace.line.color)[0], trace.line.dash)) + return out + + +def same_color(a, b): + return np.allclose(a, b, atol=1.5 / 255) + + +def close_all(): + plt.close('all') + + +# ---------------------------------------------------- finding 3: pipeline + +@pytest.mark.parametrize('backend', BACKENDS) +def test_shared_panels_expose_the_shared_pipeline(backend): + """Every shared-fit panel bundle carries the ONE fitted pipeline, and + replaying it on held-out data projects into the panels' common space + without refitting -- byte-for-byte what the single-axes bundle's + pipeline does with the same held-out data.""" + data = walks(n=2, rows=30, cols=6, seed=1) + held_out = np.random.default_rng(99).normal(size=(9, 6)) + single = quiet_plot(data, reduce='PCA', backend=backend) + grid = quiet_plot(data, panels=True, reduce='PCA', backend=backend) + try: + pipelines = [m['pipeline'] for m in grid['panel_models']] + assert all(isinstance(p, Pipeline) for p in pipelines) + assert pipelines[0] is pipelines[1] # ONE shared fit + assert grid['pipeline'] is pipelines[0] + assert pipelines[0].is_fitted + want = np.asarray(single['pipeline'].transform(held_out)) + got = np.asarray(pipelines[0].transform(held_out)) + assert got.shape == (9, 3) + np.testing.assert_allclose(got, want, atol=1e-10) + # the bundled pipeline is the fit the panels were DRAWN from: it + # reproduces each panel's own rows ... + for i, panel in enumerate(grid['panel_models']): + np.testing.assert_allclose( + np.asarray(pipelines[0].transform(data[i])), + np.asarray(panel['xform_data'][0]), atol=1e-10) + # ... and it was not refit on the held-out rows: a fresh fit on + # them lands somewhere else + fresh = np.asarray(hyp.reduce(held_out, reduce='PCA', ndims=3)) + assert not np.allclose(fresh, got, atol=1e-6) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_independent_panels_expose_their_own_pipelines(backend): + data = walks(n=2, rows=30, cols=6, seed=2) + held_out = np.random.default_rng(98).normal(size=(9, 6)) + grid = quiet_plot(data, panels=True, panel_fit='independent', + reduce='PCA', backend=backend) + try: + assert grid['pipeline'] is None # no shared fit + pipelines = [m['pipeline'] for m in grid['panel_models']] + assert all(isinstance(p, Pipeline) for p in pipelines) + assert pipelines[0] is not pipelines[1] + outs = [] + for i, pipeline in enumerate(pipelines): + own = quiet_plot([data[i]], reduce='PCA', backend=backend) + want = np.asarray(own['pipeline'].transform(held_out)) + got = np.asarray(pipeline.transform(held_out)) + np.testing.assert_allclose(got, want, atol=1e-10) + outs.append(got) + assert not np.allclose(outs[0], outs[1], atol=1e-6) + finally: + close_all() + + +# ------------------------------------------- finding 4: per-dataset lists + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_per_dataset_palette_list_is_partitioned(backend, fit): + data = walks() + # palette NAMES: each panel draws in its palette's lead colour ... + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + palette=['viridis', 'magma']) + try: + for i, name in enumerate(('viridis', 'magma')): + artists = data_artists(grid, i, backend) + assert len(artists) == 1 + assert same_color(artists[0][0], palette_lead_color(name)) + finally: + close_all() + # ... and NESTED explicit colour lists likewise, matching the + # single-axes call dataset by dataset + nested = [['red', 'green'], ['blue', 'black']] + single = quiet_plot(data, backend=backend, palette=nested) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + palette=nested) + try: + single_colors = ([_rgba(ln.get_color())[0] + for ln in single['fig'].axes[0].lines + if not getattr(ln, '_hyp_forecast_role', None)] + if backend == 'matplotlib' else + [_rgba(t.line.color)[0] for t in single['fig'].data + if isinstance(t.meta, dict) + and 'hyp_trace_index' in t.meta]) + assert same_color(single_colors[0], to_rgb('red')) + assert same_color(single_colors[1], to_rgb('blue')) + for i in range(2): + artists = data_artists(grid, i, backend) + assert len(artists) == 1 + assert same_color(artists[0][0], single_colors[i]) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_alpha_list_of_three_is_partitioned(backend): + """``alpha=[0.3, 0.6, 0.9]`` for three datasets is three alphas, not + one RGB colour -- the colour-tuple guard applies to `color=` only.""" + data = walks(n=3) + grid = quiet_plot(data, panels=True, backend=backend, + alpha=[0.3, 0.6, 0.9]) + try: + for i, want in enumerate((0.3, 0.6, 0.9)): + artists = data_artists(grid, i, backend) + assert len(artists) == 1 + assert artists[0][1] == pytest.approx(want) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_legend_list_naming_the_datasets_is_partitioned(backend): + data = walks(n=3) + grid = quiet_plot(data, panels=True, backend=backend, + legend=['alpha', 'beta', 'gamma']) + try: + for i, name in enumerate(('alpha', 'beta', 'gamma')): + artists = data_artists(grid, i, backend) + assert [a[3] for a in artists] == [name] + if backend == 'matplotlib': + legend = grid['axes'][i].get_legend() + assert legend is not None + assert [t.get_text() for t in legend.get_texts()] == [name] + finally: + close_all() + + +# ------------------------------------------ finding 4: per-forecast lists + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_forecast_fmt_list_is_partitioned(backend, fit): + data = walks() + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + predict='Kalman', t=3, forecast_fmt=['--', ':']) + single = quiet_plot(data, backend=backend, predict='Kalman', t=3, + forecast_fmt=['--', ':']) + try: + want = ('--', ':') if backend == 'matplotlib' else ('dash', 'dot') + for i in range(2): + styles = [s for _, s in forecast_artists(grid, i, backend)] + assert styles == [want[i]] + single_styles = [s for _, s in + forecast_artists(single, i, backend, panel=False)] + assert single_styles == [want[i]] + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +@pytest.mark.parametrize('palette', [['red', 'blue'], 'husl']) +def test_forecast_palette_is_partitioned(backend, fit, palette): + """One colour per forecast, dataset by dataset -- an explicit list and + a palette NAME alike -- matching the single-axes figure (before the + fix every panel drew its forecast in the palette's first colour).""" + data = walks() + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + predict='Kalman', t=3, forecast_palette=palette) + single = quiet_plot(data, backend=backend, predict='Kalman', t=3, + forecast_palette=palette) + try: + colors = [] + for i in range(2): + got = forecast_artists(grid, i, backend) + want = forecast_artists(single, i, backend, panel=False) + assert len(got) == len(want) == 1 + assert same_color(got[0][0], want[0][0]) + colors.append(got[0][0]) + assert not same_color(colors[0], colors[1]) + if palette != 'husl': + assert same_color(colors[0], to_rgb('red')) + assert same_color(colors[1], to_rgb('blue')) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_model_major_forecast_hue_is_partitioned(backend, fit): + """Two models x two datasets, ``forecast_hue=['a', 'b', 'c', 'd']`` in + the documented model-major order: each panel gets ITS two forecasts' + labels, with the colour each label has on the single-axes figure.""" + data = walks() + kwargs = dict(predict=['Kalman', 'AutoRegressor'], t=3, + forecast_hue=['a', 'b', 'c', 'd'], + forecast_palette=['red', 'green', 'blue', 'black']) + single = quiet_plot(data, backend=backend, **kwargs) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + **kwargs) + try: + # single-axes overlays are model-major: [K0, K1, A0, A1] + flat = forecast_artists(single, 0, backend, panel=False) \ + + forecast_artists(single, 1, backend, panel=False) + assert len(flat) == 4 + by_dataset = {0: [c for c, _ in forecast_artists(single, 0, backend, + panel=False)], + 1: [c for c, _ in forecast_artists(single, 1, backend, + panel=False)]} + # dataset 0's forecasts are 'a' (Kalman) and 'c' (AutoRegressor) + assert same_color(by_dataset[0][0], to_rgb('red')) + assert same_color(by_dataset[0][1], to_rgb('blue')) + assert same_color(by_dataset[1][0], to_rgb('green')) + assert same_color(by_dataset[1][1], to_rgb('black')) + solid = '-' if backend == 'matplotlib' else 'solid' + dashed = '--' if backend == 'matplotlib' else 'dash' + for i in range(2): + got = forecast_artists(grid, i, backend) + assert len(got) == 2 + # the model is told by its linestyle (solid, dashed, ...) + assert [s for _, s in got] == [solid, dashed] + for (color, _), want in zip(got, by_dataset[i]): + assert same_color(color, want) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_fitted_multi_dataset_forecaster_is_bound_per_panel(backend, fit): + """A forecaster fitted on two OTHER datasets is applied dataset by + dataset (panel i reuses fitted model i), giving exactly the forecasts + the single-axes call draws -- and not those of a refit.""" + data = walks(seed=201) + other = walks(seed=7) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + fitted = hyp.predict(other, model='Kalman', t=3, + return_model=True)[1] + assert len(fitted.models_) == 2 + single = quiet_plot(data, backend=backend, predict=fitted, t=3) + refit = quiet_plot(data, backend=backend, predict='Kalman', t=3) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + predict=fitted, t=3) + try: + for i, panel in enumerate(grid['panel_models']): + got = np.asarray(panel['predict']['forecasts'][0]) + want = np.asarray(single['predict']['forecasts'][i]) + assert got.shape == (3, 3) + np.testing.assert_allclose(got, want, atol=1e-8) + assert not np.allclose( + got, np.asarray(refit['predict']['forecasts'][i]), + atol=1e-6) + assert len(forecast_artists(grid, i, backend)) == 1 + finally: + close_all() + # a forecaster fitted on a different NUMBER of datasets cannot be + # paired with the panels ... + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + three = hyp.predict(walks(n=3, seed=8), model='Kalman', t=3, + return_model=True)[1] + with pytest.raises(ValueError, match=r'fitted on 3 dataset\(s\).*' + r'panels= draws 2'): + quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + predict=three, t=3) + close_all() + # ... while one fitted on a SINGLE dataset is reused by every panel, + # as it is by every dataset of a single-axes call + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + one = hyp.predict(other[0], model='Kalman', t=3, + return_model=True)[1] + single = quiet_plot(data, backend=backend, predict=one, t=3) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + predict=one, t=3) + try: + for i, panel in enumerate(grid['panel_models']): + np.testing.assert_allclose( + np.asarray(panel['predict']['forecasts'][0]), + np.asarray(single['predict']['forecasts'][i]), atol=1e-8) + finally: + close_all() + + +# --------------------- Codex round 6: forecast labels that share a colour + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_forecast_labels_sharing_a_colour_keep_their_palette_slots(backend, + fit): + """Codex round 6 (the reviewer's probe): ``forecast_hue=['a', 'a', + 'b', 'b']`` (model-major, two models x two datasets) with + ``forecast_palette=['red', 'red']`` -- two DISTINCT labels drawn in one + colour on purpose. The single-axes figure draws every forecast red; + the panel path resolved the grid's label -> colour map and then + deduplicated the COLOURS, handing each panel a one-entry palette for + its two labels (``ValueError: palette= supplies 1 color(s) but 2 are + required``) on both backends and in both `panel_fit=` modes.""" + data = walks() + kwargs = dict(predict=['Kalman', 'ARIMA'], t=3, + forecast_hue=['a', 'a', 'b', 'b'], + forecast_palette=['red', 'red']) + single = quiet_plot(data, backend=backend, **kwargs) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + **kwargs) + try: + for i in range(2): + want = [c for c, _ in forecast_artists(single, i, backend, + panel=False)] + got = [c for c, _ in forecast_artists(grid, i, backend)] + assert len(want) == len(got) == 2 + for color in want + got: + assert same_color(color, to_rgb('red')) + finally: + close_all() + + +#: the sibling spellings of "several forecasts share a colour": a +#: per-DATASET `forecast_hue=` (broadcast over a collection's models, and +#: with a single model), `forecast_cluster=` grouping instead of labels, +#: and a palette NAME that cycles -- 'Set2' has eight colours, so the +#: ninth distinct label ('I', first seen at forecast 9 = model 1 of +#: dataset 4) reuses the first label's colour, and dataset 4's panel holds +#: both ('A' at forecast 4, 'I' at forecast 9). +_SHARED_COLOUR_FORMS = { + 'per-dataset hue, two models': (2, dict( + predict=['Kalman', 'AutoRegressor'], t=3, forecast_hue=['a', 'b'], + forecast_palette=['red', 'red'])), + 'per-dataset hue, one model': (2, dict( + predict='Kalman', t=3, forecast_hue=['a', 'b'], + forecast_palette=['red', 'red'])), + 'forecast_cluster': (2, dict( + predict=['Kalman', 'AutoRegressor'], t=3, forecast_cluster='KMeans', + forecast_n_clusters=2, forecast_palette=['red', 'red'])), + 'palette name that cycles': (5, dict( + predict=['Kalman', 'AutoRegressor'], t=3, + forecast_hue=['A', 'B', 'C', 'D', 'A', 'E', 'F', 'G', 'H', 'I'], + forecast_palette='Set2')), +} + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +@pytest.mark.parametrize('form', sorted(_SHARED_COLOUR_FORMS)) +def test_repeated_forecast_colours_match_the_single_axes_figure(backend, fit, + form): + """Every panel's forecasts carry exactly the colours the single-axes + figure gives that dataset's forecasts, however the repetition is + spelled (`_SHARED_COLOUR_FORMS`) -- and the repetition is real: an + explicit ``['red', 'red']`` draws every forecast red, and the cycling + palette name gives dataset 4's two forecasts one colour.""" + n_datasets, kwargs = _SHARED_COLOUR_FORMS[form] + data = walks(n_datasets) + single = quiet_plot(data, backend=backend, **kwargs) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + **kwargs) + try: + n_models = 1 if isinstance(kwargs['predict'], str) else 2 + for i in range(n_datasets): + want = [c for c, _ in forecast_artists(single, i, backend, + panel=False)] + got = [c for c, _ in forecast_artists(grid, i, backend)] + assert len(want) == len(got) == n_models + for color, expected in zip(got, want): + assert same_color(color, expected) + if kwargs['forecast_palette'] == ['red', 'red']: + for color in got: + assert same_color(color, to_rgb('red')) + if form == 'palette name that cycles': + last = [c for c, _ in forecast_artists(grid, 4, backend)] + assert same_color(last[0], last[1]) + first = [c for c, _ in forecast_artists(grid, 0, backend)] + assert same_color(first[0], last[0]) # 'A' is red-ish + assert not same_color(first[0], first[1]) # 'A' vs 'E' + finally: + close_all() + + +# ------------------------------------------------------ roster vs docstring + +_PER_DATASET_PHRASES = re.compile( + r'per[- ]dataset|per (?:input )?dataset|per DATASET|per FORECAST|' + r'per forecast|model-major|per MODEL', re.IGNORECASE) + +#: documented "per dataset" arguments that `panels=` handles WITHOUT +#: slicing, each with the reason -- a new per-dataset argument must be +#: added to a roster in plot.py or, with a reason, here. +_NOT_SLICED = { + 'x': 'the datasets themselves', + 'animate': 'rejected under panels= (an animation owns its figure)', + 'resample': 'its per-dataset arrays are its OUTPUT, not an argument', + 'ax': 'rejected under panels=; mentions per-dataset layouts only', + 'title_color': 'per SEGMENT of a serial/morph title, not per dataset', + 'forecast_cluster': 'groups each panel\'s own forecast endpoints', + 'forecast_n_clusters': 'a count for forecast_cluster=, not a list', + 'forecast_trail': 'animation only; panels= is static', + 'return_model': 'describes the bundle, takes no per-dataset value', + 'colorbar': 'drawn per panel', + 'label_anchor': 'positions a per-dataset labels= entry; one value', + 'legend_colors': 'per legend ENTRY, not per dataset', + 'palette_sort': 'a grid-wide option resolved before the panels branch; ' + 'it orders the colors of every palette, per-dataset ' + 'list entries included, and each panel receives the ' + 'already-prepared palette', + 'hue_mode': 'describes the hue matrix, one value', + 'color_reduce': 'reduces a hue matrix, one spec', + 't': 'one horizon for every forecast', + 'dataset_fade': 'animation only; panels= is static', + 'companion': 'animation only; panels= is static', + 'frame_kwargs': 'one frame style for every panel', + 'save_path': 'one file for the whole grid', + 'size': 'one figure size for the whole grid', + 'loop': 'animation only; panels= is static', + 'on_frame': 'animation only; panels= is static', + 'transform': 'replaces the pipeline for the panel probe, whose rows ' + 'each panel then receives through its own transform=', + 'panels': 'the grid itself (its description names the sliced arguments)', +} + + +def _documented_per_dataset_parameters(): + doc = inspect.getdoc(hyp.plot) + params = doc.split('Parameters\n----------', 1)[1] + params = params.split('\nReturns\n-------', 1)[0] + heading = re.compile(r'^(\w[\w()]*)(?: \([^)]*\))? : ', re.MULTILINE) + matches = list(heading.finditer(params)) + flagged = set() + for k, match in enumerate(matches): + body = params[match.end(): + matches[k + 1].start() if k + 1 < len(matches) + else len(params)] + if not _PER_DATASET_PHRASES.search(body): + continue + name = match.group(1) + # ``linestyle(s)`` / ``marker(s)`` document both spellings + if name.endswith('(s)'): + flagged.update({name[:-3], name[:-3] + 's'}) + else: + flagged.add(name) + return flagged + + +def test_panel_rosters_cover_every_documented_per_dataset_argument(): + """The docstring is the contract: every parameter whose description + says "per dataset" / "per forecast" / "model-major" is either sliced + by `_plot_panels` (one of the rosters in plot.py) or listed above with + the reason it is not.""" + sliced = (set(plot_module._PANEL_PER_DATASET_KWARGS) + | set(plot_module._PANEL_PER_FORECAST_KWARGS) + | set(plot_module._PANEL_PER_OBSERVATION_KWARGS) + | set(plot_module._PANEL_SPECIAL_KWARGS)) + parameters = set(inspect.signature(hyp.plot).parameters) - {'kwargs'} + unknown = sliced - parameters + assert not unknown, f'roster names that are not plot() parameters: {unknown}' + documented = _documented_per_dataset_parameters() + assert documented, 'the per-dataset phrase scan found nothing' + # the audit's own arguments are all documented per dataset/forecast + assert {'palette', 'forecast_fmt', 'forecast_hue', 'forecast_palette', + 'truth', 'alpha', 'fmt', 'hue', 'names', 'predict'} <= documented + missing = documented - sliced - set(_NOT_SLICED) + assert not missing, ( + f'documented per-dataset/per-forecast argument(s) {sorted(missing)} ' + 'are neither sliced by _plot_panels (add to a _PANEL_*_KWARGS ' + 'roster in hypertools/plot/plot.py) nor listed with a reason in ' + '_NOT_SLICED here') + stale = set(_NOT_SLICED) & sliced + assert not stale, f'{stale} listed as not sliced but on a roster' + + +# ------------------- roster: each per-dataset value reaches only its panel + +def _style(bundle, i, backend, panel=True): + """Everything a `_PANEL_PER_DATASET_KWARGS` argument can change, read + off the observed-data artist of panel `i` (or, ``panel=False``, of + dataset `i` on the single axes): colour, alpha, linestyle, marker, + marker size, line width, legend name, and the number of surfaces + drawn beside it.""" + if backend == 'matplotlib': + ax = bundle['axes'][i] if panel else bundle['fig'].axes[0] + lines = [ln for ln in ax.lines + if not getattr(ln, '_hyp_forecast_role', None)] + line = lines[0] if panel else lines[i] + if panel: + assert len(lines) == 1 + return dict( + color=_rgba(line.get_color())[0], alpha=line.get_alpha(), + linestyle=line.get_linestyle(), marker=line.get_marker(), + markersize=line.get_markersize(), + linewidth=line.get_linewidth(), name=line.get_label(), + surfaces=sum(type(c).__name__ == 'Poly3DCollection' + for c in ax.collections)) + scene = bundle['axes'][i].plotly_name if panel else None + traces = [tr for tr in bundle['fig'].data + if not panel or tr.scene == scene] + lines = [tr for tr in traces if isinstance(tr.meta, dict) + and 'hyp_trace_index' in tr.meta] + line = lines[0] if panel else lines[i] + if panel: + assert len(lines) == 1 + rgb, alpha = effective_rgba(line)[:3], effective_rgba(line)[-1] + return dict( + color=rgb, alpha=alpha, linestyle=line.line.dash, + marker=line.marker.symbol if 'markers' in (line.mode or '') else None, + markersize=line.marker.size, linewidth=line.line.width, + name=line.name, + surfaces=sum(tr.type == 'mesh3d' for tr in traces)) + + +#: `_PANEL_PER_DATASET_KWARGS` entry -> (two distinct values, the +#: `_style` keys they change). Every roster entry is either here, in +#: `_PER_DATASET_TESTED_ELSEWHERE`, or in `_PER_DATASET_STATIC_INVISIBLE` +#: (asserted below), so a new roster entry needs a partitioning test. +_PER_DATASET_CASES = { + 'fmt': (['r-', 'b--'], ('color', 'linestyle')), + 'marker': (['o', 's'], ('marker',)), + 'markers': (['o', 's'], ('marker',)), + 'linestyle': (['-', '--'], ('linestyle',)), + 'linestyles': (['-', '--'], ('linestyle',)), + 'color': (['red', 'blue'], ('color',)), + 'colors': (['red', 'blue'], ('color',)), + 'alpha': ([0.3, 0.7], ('alpha',)), + 'markersize': ([4, 10], ('markersize',)), + 'linewidth': ([1, 4], ('linewidth',)), + 'names': (['first', 'second'], ('name',)), + 'surface': ([True, False], ('surfaces',)), +} + +_PER_DATASET_TESTED_ELSEWHERE = { + 'truth': 'test_truth_list_reaches_only_its_own_panel', +} + +#: roster entries a STATIC grid cannot show: the three trails are +#: animation-only (`panels=` rejects `animate=`), and `density=` has no +#: per-dataset list form -- `plot()` rejects a list on the single-axes +#: path and on every panel alike. +_PER_DATASET_STATIC_INVISIBLE = { + 'chemtrails': 'animation only', + 'precog': 'animation only', + 'bullettime': 'animation only', + 'density': 'no per-dataset list form (plot() rejects a list)', +} + + +def test_every_per_dataset_roster_entry_has_a_partitioning_test(): + covered = (set(_PER_DATASET_CASES) | set(_PER_DATASET_TESTED_ELSEWHERE) + | set(_PER_DATASET_STATIC_INVISIBLE)) + roster = set(plot_module._PANEL_PER_DATASET_KWARGS) + assert roster == covered, ( + f'roster entries without a partitioning test: {roster - covered}; ' + f'tested names no longer on the roster: {covered - roster}') + for name in _PER_DATASET_TESTED_ELSEWHERE.values(): + assert callable(globals().get(name)), name + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +@pytest.mark.parametrize('key', sorted(_PER_DATASET_CASES)) +def test_per_dataset_argument_reaches_only_its_own_panel(backend, fit, key): + """The reviewer's remark on the roster test: a name on + `_PANEL_PER_DATASET_KWARGS` proves nothing about what a panel DRAWS. + So, per entry: two datasets, two distinct values, and each panel's + artist shows its own value -- the one the single-axes figure gives + that dataset -- and not the other panel's.""" + values, props = _PER_DATASET_CASES[key] + kwargs = {key: values} + if key == 'names': + kwargs['legend'] = True + data = walks() + single = quiet_plot(data, backend=backend, **kwargs) + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + **kwargs) + try: + styles = [_style(grid, i, backend) for i in range(2)] + for i in range(2): + want = _style(single, i, backend, panel=False) + for prop in props: + if prop == 'surfaces': + assert styles[i][prop] == int(values[i]) + continue + if prop != 'color': + assert styles[i][prop] == want[prop], (key, prop, i) + else: + # includes fmt='s colour letter on plotly (round 6: + # 'r-' drew the palette colour there; the xfail that + # used to sit here is lifted) + assert same_color(styles[i][prop], want[prop]), \ + (key, prop, i) + for prop in props: + assert styles[0][prop] != styles[1][prop], (key, prop) + finally: + close_all() + + +def _truth_points(bundle, i, backend): + """The points of every `truth=` artist of panel `i`, in data units + (2-D, ``axis_scale='data'``), the seam to the last observation + dropped -- as `tests/test_plot_panels_fit.py` reads them.""" + if backend == 'matplotlib': + return [np.asarray(ln.get_xydata())[1:] + for ln in bundle['axes'][i].lines + if getattr(ln, '_hyp_forecast_role', None) == 'truth'] + yaxis = bundle['axes'][i][0].anchor # 'y', 'y2', ... + return [np.column_stack([tr.x, tr.y])[1:] for tr in bundle['fig'].data + if tr.yaxis == yaxis and isinstance(tr.meta, dict) + and tr.meta.get('hyp_forecast_role') == 'truth'] + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_truth_list_reaches_only_its_own_panel(backend, fit): + """``truth=[t0, t1]``: panel `i` draws exactly `t_i` (two different + arrays), in both fit modes and on both backends. Drawn in the data's + own 2-D units (`axis_scale='data'`): a 3-D frame is rescaled into the + unit cube per axes, so its coordinates cannot be read back.""" + data = walks(cols=2) + truth = [data[0][-1] + np.arange(1, 4)[:, None] * [1.0, 2.0], + data[1][-1] - np.arange(1, 4)[:, None] * [3.0, 1.0]] + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + ndims=2, axis_scale='data', antialias=False, + predict='Kalman', t=3, truth=truth) + try: + for i in range(2): + drawn = _truth_points(grid, i, backend) + assert len(drawn) >= 1 + for points in drawn: + assert np.allclose(points, truth[i]) + assert not np.allclose(points, truth[1 - i]) + finally: + close_all() + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', FITS) +def test_nested_hue_and_labels_reach_only_their_own_panel(backend, fit): + """The per-OBSERVATION arguments, nested one sub-sequence per dataset: + panel `i`'s single trace carries hue label `i`, and only annotation + `i` is drawn on it.""" + data = walks() + hue = [['first'] * 20, ['second'] * 20] + labels = [['p0'] + [None] * 19, ['p1'] + [None] * 19] + grid = quiet_plot(data, panels=True, panel_fit=fit, backend=backend, + hue=hue, labels=labels, legend=True) + try: + for i, (group, text) in enumerate((('first', 'p0'), + ('second', 'p1'))): + artists = data_artists(grid, i, backend) + assert [a[3] for a in artists] == [group] + if backend == 'matplotlib': + drawn = [t.get_text() for t in grid['axes'][i].texts] + else: + drawn = [a.text for a in grid['axes'][i].annotations] + assert drawn == [text] + finally: + close_all() diff --git a/tests/test_plot_panels_auto_grid.py b/tests/test_plot_panels_auto_grid.py new file mode 100644 index 00000000..328a172b --- /dev/null +++ b/tests/test_plot_panels_auto_grid.py @@ -0,0 +1,54 @@ +"""``panels=True`` picks its grid from the figure's aspect ratio and prefers +grids without spare cells (1.1 feature-tour report: three panels came out +2x2 with a hole, and in a 9x3.2 in figure each square 3-D axes shrank to +the short cell height). Real figures, measured axes positions.""" + +import matplotlib.pyplot as plt +import pytest + +import hypertools as hyp +from hypertools.plot.plot import _resolve_panel_grid + + +@pytest.mark.parametrize('n, size, expected', [ + (3, (9.0, 3.2), (1, 3)), # wide figure: a row + (3, (6.4, 4.8), (1, 3)), # default figure: a row beats 2x2-with-a-hole + (3, (3.0, 9.0), (3, 1)), # tall figure: a column + (2, (6.4, 4.8), (1, 2)), + (4, (6.4, 4.8), (2, 2)), + (6, (6.4, 4.8), (2, 3)), + (5, (6.4, 4.8), (2, 3)), # no hole-free grid is close enough: 2x3 + (8, (9.0, 3.2), (2, 4)), +]) +def test_auto_grid_follows_the_figure_aspect(n, size, expected): + assert _resolve_panel_grid(True, n, size=size) == expected + assert _resolve_panel_grid('auto', n, size=size) == expected + + +def test_explicit_grids_are_untouched_by_size(): + assert _resolve_panel_grid((2, 2), 3, size=(9.0, 3.2)) == (2, 2) + assert _resolve_panel_grid(2, 3, size=(9.0, 3.2)) == (2, 2) + + +def _walks(): + return [hyp.load('random_walk', n_samples=60, n_features=8, random_state=s) + for s in range(3)] + + +def test_three_panels_in_a_wide_figure_fill_one_row(): + fig = hyp.plot(_walks(), '.', panels=True, size=[9, 3.2], + title=['walk 0', 'walk 1', 'walk 2'], show=False) + visible = [ax for ax in fig.axes if ax.get_visible()] + assert len(visible) == 3 and len(fig.axes) == 3 # no hidden spare + boxes = [ax.get_position() for ax in visible] + assert len({round(b.y0, 2) for b in boxes}) == 1 # one row + assert min(b.width for b in boxes) > 0.25 # each ~a third wide + assert min(b.height for b in boxes) > 0.7 + plt.close(fig) + + +def test_three_panels_by_default_fill_one_row_too(): + fig = hyp.plot(_walks(), '.', panels=True, show=False) + assert len(fig.axes) == 3 + assert len({round(ax.get_position().y0, 2) for ax in fig.axes}) == 1 + plt.close(fig) diff --git a/tests/test_plot_panels_fit.py b/tests/test_plot_panels_fit.py index 12a3be32..a0ce6855 100644 --- a/tests/test_plot_panels_fit.py +++ b/tests/test_plot_panels_fit.py @@ -284,3 +284,125 @@ def test_without_return_model_it_is_still_a_bare_figure(self): pio.renderers.default = 'json' fig = hyp.plot(clouds(), panels=True, backend='plotly', show=False) assert isinstance(fig, go.Figure) + + +# --- 1.1 release-review fixes (P1-P3) ----------------------------------- + +def _walks(seed=0): + rng = np.random.default_rng(seed) + return [np.cumsum(rng.normal(size=(30, 2)), axis=0), + np.cumsum(rng.normal(size=(40, 2)), axis=0)] + + +def _by_role(ax, role): + return [line for line in ax.lines + if getattr(line, '_hyp_forecast_role', None) == role] + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P1_panels_with_predict_and_truth_in_both_fit_modes(panel_fit): + """The shared probe kept `truth=` while dropping `predict=` ("truth= + ... no forecast was requested"), and the independent mode never + narrowed the truth list ("1 trace(s) plotted, truth= supplies 2").""" + x, y = _walks() + rng = np.random.default_rng(1) + truths = [x[-1] + np.cumsum(rng.normal(size=(3, 2)), axis=0), + y[-1] + np.cumsum(rng.normal(size=(3, 2)), axis=0)] + fig = hyp.plot([x, y], reduce=None, ndims=2, panels=True, + panel_fit=panel_fit, predict='Kalman', t=3, truth=truths, + forecast_hue=['a', 'b'], axis_scale='data', + antialias=False, show=False) + try: + assert len(fig.axes) == 2 + for ax, truth in zip(fig.axes, truths): + (forecast,) = _by_role(ax, 'static') + drawn_truths = _by_role(ax, 'truth') + assert len(np.asarray(forecast.get_xdata())) == 4 # seam + 3 + # each panel draws ITS dataset's held-out continuation (the + # truth overlay is a line plus its marker artist) + assert len(drawn_truths) >= 1 + for drawn_truth in drawn_truths: + assert np.allclose( + np.asarray(drawn_truth.get_xydata())[1:], truth) + finally: + plt.close(fig) + + +def _dated(cols, seed): + rng = np.random.default_rng(seed) + index = pd.date_range('2020-01-01', periods=30, freq='D') + names = ['val'] if cols == 1 else list('abc')[:cols] + return pd.DataFrame(np.cumsum(rng.normal(size=(30, cols)), axis=0), + index=index, columns=names) + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P2_series_mode_panels_keep_dates_and_the_column_name(panel_fit): + from matplotlib.dates import date2num + frames = [_dated(1, 2), _dated(1, 3)] + fig = hyp.plot(frames, ndims=1, panels=True, panel_fit=panel_fit, + antialias=False, show=False) + try: + for ax, frame in zip(fig.axes, frames): + (line,) = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + assert np.allclose(np.asarray(line.get_xdata(), dtype=float), + date2num(frame.index.to_pydatetime())) + assert ax.get_ylabel() == 'val' + finally: + plt.close(fig) + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P2_a_three_column_frame_in_series_mode_panels(panel_fit): + """The panel path assigned a 3-column frame's names to x/y/zlabel, and + the series-mode panel then refused zlabel= for 2-D data.""" + frames = [_dated(3, 4), _dated(3, 5)] + fig = hyp.plot(frames, ndims=1, reduce=None, panels=True, + panel_fit=panel_fit, antialias=False, show=False) + try: + for ax in fig.axes: + lines = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + assert len(lines) == 3 + assert ax.get_zorder() is not None and not hasattr(ax, 'zaxis') + finally: + plt.close(fig) + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P3_nested_per_dataset_hue_is_narrowed_per_panel(panel_fit): + x, y = _walks(seed=6) + hue = [np.linspace(0.0, 1.0, 30), np.linspace(5.0, 9.0, 40)] + bundle = hyp.plot([x, y], reduce=None, ndims=2, panels=True, + panel_fit=panel_fit, hue=hue, return_model=True, + show=False) + try: + assert len(bundle['axes']) == 2 + # each panel's colour scale spans ITS OWN hue values + for model, values in zip(bundle['panel_models'], hue): + colors = model['colors'] + assert colors['vmin'] == pytest.approx(values.min()) + assert colors['vmax'] == pytest.approx(values.max()) + finally: + plt.close(bundle['fig']) + + +@pytest.mark.parametrize('panel_fit', ['shared', 'independent']) +def test_P3_labels_are_narrowed_per_panel(panel_fit): + x, y = _walks(seed=7) + fig = hyp.plot([x, y], reduce=None, ndims=2, panels=True, + panel_fit=panel_fit, labels=['A', 'B'], show=False) + try: + texts = [[t.get_text() for t in ax.texts] for ax in fig.axes] + assert texts == [['A'], ['B']] + finally: + plt.close(fig) + nested = [[None] * 29 + ['end x'], [None] * 39 + ['end y']] + fig = hyp.plot([x, y], reduce=None, ndims=2, panels=True, + panel_fit=panel_fit, labels=nested, show=False) + try: + texts = [[t.get_text() for t in ax.texts] for ax in fig.axes] + assert texts == [['end x'], ['end y']] + finally: + plt.close(fig) diff --git a/tests/test_plot_panels_geometry.py b/tests/test_plot_panels_geometry.py new file mode 100644 index 00000000..29d98873 --- /dev/null +++ b/tests/test_plot_panels_geometry.py @@ -0,0 +1,173 @@ +"""The plotly `panels=` grid is laid out like the matplotlib one (1.1 +release review, feature-tour section 9.8): square, centred 3-D cells with +`tight_layout`-sized gaps, room above each titled row, a camera that is +backed off only in a cell narrower than it is tall -- and a cube that fills +its cell within a few pixels of the matplotlib panel's. + +The parity test renders both backends for real (matplotlib Agg + +plotly/kaleido, the `tests/test_marker_parity.py` pattern) and measures +the ink in every cell. No mocks. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +import hypertools as hyp +from hypertools.plot.plotly_backend import (PANEL_AXIS_GAP_PX, PANEL_GAP_PX, + PANEL_TITLE_PX, + SCENE_CUBE_WIDTH_PER_HEIGHT) + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walk(seed=0): + return hyp.load('random_walk', n_samples=60, n_features=8, + random_state=seed) + + +def _cells_px(fig): + """Every cell's pixel box ``(x0, y0, x1, y1)`` (y down), in layout + order, from its scene/xaxis+yaxis domains.""" + layout = fig.layout + mg = layout.margin + plot_w = layout.width - mg.l - mg.r + plot_h = layout.height - mg.t - mg.b + boxes = [] + # a 2-D figure's layout still iterates an (empty) 'scene' + keys = sorted((k for k in layout if k.startswith('scene') + and layout[k].domain.x is not None), + key=lambda k: int(k[5:] or 1)) + if keys: + for key in keys: + d = layout[key].domain + boxes.append((d.x[0], d.y[0], d.x[1], d.y[1])) + else: + xkeys = sorted((k for k in layout if k.startswith('xaxis')), + key=lambda k: int(k[5:] or 1)) + for xk in xkeys: + yk = 'yaxis' + xk[5:] + dx, dy = layout[xk].domain, layout[yk].domain + boxes.append((dx[0], dy[0], dx[1], dy[1])) + return [(mg.l + x0 * plot_w, mg.t + (1 - y1) * plot_h, + mg.l + x1 * plot_w, mg.t + (1 - y0) * plot_h) + for x0, y0, x1, y1 in boxes] + + +def _eye_distance(scene): + eye = scene.camera.eye + return float(np.sqrt(eye.x ** 2 + eye.y ** 2 + eye.z ** 2)) + + +def test_3d_cells_are_square_with_tight_gaps_and_no_back_off(): + fig = hyp.plot([_walk(i) for i in range(3)], panels=True, show=False, + backend='plotly') + cells = _cells_px(fig) + assert len(cells) == 3 + for x0, y0, x1, y1 in cells: + assert abs((x1 - x0) - (y1 - y0)) <= 2.0 + for left, right in zip(cells, cells[1:]): + assert right[0] - left[2] == pytest.approx(PANEL_GAP_PX, abs=1.5) + # centred: the same slack above and below + top = cells[0][1] + bottom = fig.layout.height - cells[0][3] + assert abs(top - bottom) <= 2.0 + # a square cell needs no camera back-off: the single figure's own view + single = hyp.plot(_walk(0), show=False, backend='plotly') + for key in ('scene', 'scene2', 'scene3'): + assert _eye_distance(fig.layout[key]) == pytest.approx( + _eye_distance(single.layout.scene), rel=1e-6) + + +def test_titled_rows_reserve_a_title_line(): + fig = hyp.plot([_walk(i) for i in range(4)], panels=True, show=False, + title=['a', 'b', 'c', 'd'], backend='plotly') + cells = _cells_px(fig) + # row 1 starts a gap plus one title line below row 0 + assert cells[2][1] - cells[0][3] == pytest.approx( + PANEL_GAP_PX + PANEL_TITLE_PX, abs=1.5) + assert fig.layout.margin.t >= 40 + titles = [a for a in fig.layout.annotations + if a.name and a.name.startswith('hyp-cell-title-')] + assert [a.text for a in titles] == ['a', 'b', 'c', 'd'] + + +def test_2d_cells_fill_the_figure_with_axis_gaps(): + fig = hyp.plot([_walk(i) for i in range(4)], ndims=2, panels=True, + show=False, backend='plotly') + cells = _cells_px(fig) + assert cells[0][0] == pytest.approx(fig.layout.margin.l, abs=1.0) + assert cells[1][2] == pytest.approx( + fig.layout.width - fig.layout.margin.r, abs=1.0) + assert cells[1][0] - cells[0][2] == pytest.approx(PANEL_AXIS_GAP_PX, + abs=1.5) + assert cells[2][1] - cells[0][3] == pytest.approx(PANEL_AXIS_GAP_PX, + abs=1.5) + + +def test_a_cell_narrower_than_tall_backs_the_camera_off(): + fig, cells = hyp.subplots(1, 2, backend='plotly', + column_widths=[0.25, 0.75]) + hyp.plot(_walk(0), ax=cells[0], show=False, backend='plotly') + hyp.plot(_walk(1), ax=cells[1], show=False, backend='plotly') + narrow, wide = _cells_px(fig) + narrow_w, narrow_h = narrow[2] - narrow[0], narrow[3] - narrow[1] + assert narrow_w < narrow_h + single = hyp.plot(_walk(0), show=False, backend='plotly') + base = _eye_distance(single.layout.scene) + expected = SCENE_CUBE_WIDTH_PER_HEIGHT * narrow_h / narrow_w + assert _eye_distance(fig.layout.scene) == pytest.approx(base * expected, + rel=1e-3) + assert _eye_distance(fig.layout.scene2) == pytest.approx(base, rel=1e-6) + + +def _ink_box(gray, box): + x0, y0, x1, y1 = (int(round(v)) for v in box) + sub = gray[y0:y1, x0:x1] + ys, xs = np.where(sub < 200) + assert len(xs), 'an empty cell' + return xs.max() - xs.min() + 1, ys.max() - ys.min() + 1 + + +@pytest.mark.parametrize('n,kw', [ + pytest.param(3, {}, id='1x3'), + pytest.param(4, {}, id='2x2'), + pytest.param(3, dict(size=[9, 3.2], title=['a', 'b', 'c']), + id='1x3-wide-titled'), +]) +def test_cube_fills_its_cell_like_the_matplotlib_panel(n, kw, tmp_path): + """Real renders of the same grid on both backends: in every cell the + drawn cube (the ink's bounding box) is the matplotlib panel's width + within 6 %, so the plotly grid neither shrinks its cubes nor spaces + them out (the pre-review grid drew them ~35 % narrower).""" + from PIL import Image + data = [_walk(i) for i in range(n)] + mpl_fig = hyp.plot(data, panels=True, show=False, backend='matplotlib', + **kw) + mpl_path = tmp_path / 'mpl.png' + mpl_fig.savefig(mpl_path, dpi=100) + width, height = (mpl_fig.get_size_inches() * 100).astype(int) + mpl_gray = np.asarray(Image.open(mpl_path).convert('L')) + mpl_widths = [] + for ax in mpl_fig.axes: + x0, y0, w, h = ax.get_position().bounds + mpl_widths.append(_ink_box( + mpl_gray, (x0 * width, (1 - y0 - h) * height, + (x0 + w) * width, (1 - y0) * height))[0]) + + pl_fig = hyp.plot(data, panels=True, show=False, backend='plotly', **kw) + pl_path = tmp_path / 'plotly.png' + pl_fig.write_image(str(pl_path)) + pl_gray = np.asarray(Image.open(pl_path).convert('L')) + assert pl_gray.shape[::-1] == (pl_fig.layout.width, pl_fig.layout.height) + pl_widths = [_ink_box(pl_gray, box)[0] for box in _cells_px(pl_fig)] + + assert len(pl_widths) == len(mpl_widths) == n + for pl_w, mpl_w in zip(pl_widths, mpl_widths): + assert abs(pl_w - mpl_w) / mpl_w <= 0.06, (pl_widths, mpl_widths) diff --git a/tests/test_plot_pipeline_cluster_replay.py b/tests/test_plot_pipeline_cluster_replay.py new file mode 100644 index 00000000..d5933d1f --- /dev/null +++ b/tests/test_plot_pipeline_cluster_replay.py @@ -0,0 +1,189 @@ +"""`hyp.plot(x, pipeline=p)` applies a fitted pipeline's trailing cluster +step (1.1 release review, 2026-09-11, L13). + +`plot()` documents `pipeline=` as running "in place of the manip/normalize/ +reduce/align/cluster stages", and the `return_model=True` bundle's pipeline +ends in a fitted 'cluster' step when the figure was clustered. Reusing it +drew every point in one colour with no warning: `analyze(x, pipeline=p)` +returns the transformed DATA (documented -- the labels are recovered with +``p.named_steps['cluster'].transform(data)``), and `plot()` never did that +recovery. The reuse figure is now coloured by the fitted clusters, with the +fit figure's label-to-colour mapping, on both backends. Real data, real +calls, no mocks. +""" +import warnings + +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np +import pytest +from matplotlib.colors import to_hex + +import hypertools as hyp + + +def _walks(): + a = np.asarray(hyp.load('random_walk', n_samples=48, n_features=6, + random_state=41)) + b = np.asarray(hyp.load('random_walk', n_samples=48, n_features=6, + random_state=42)) + return a, b + + +def _fit_bundle(a, cluster='KMeans', **kwargs): + return hyp.plot(a, '.', manip='ZScore', normalize='across', + reduce='PCA', ndims=2, cluster=cluster, random_state=0, + return_model=True, backend='matplotlib', show=False, + **kwargs) + + +def _line_colors(fig): + """One colour per drawn marker trace, in drawing order.""" + return [to_hex(line.get_color()) for line in fig.axes[0].lines] + + +def test_reused_pipeline_colours_the_figure_by_its_fitted_clusters(): + a, b = _walks() + fit = _fit_bundle(a, n_clusters=3) + pipe = fit['pipeline'] + assert list(pipe.named_steps)[-1] == 'cluster' + fit_colors = _line_colors(fit['fig']) + assert len(set(fit_colors)) == 3 + + expected = pipe.named_steps['cluster'].transform( + hyp.analyze(b, pipeline=pipe)) + reuse = hyp.plot(b, '.', pipeline=pipe, return_model=True, + backend='matplotlib', show=False) + try: + assert list(reuse['models']['cluster_labels']) == list(expected) + present = sorted(set(expected)) + assert len(present) > 1 + # one marker trace per cluster present, each in the colour the FIT + # figure gave that cluster (labels sort 0, 1, 2 in both) + assert _line_colors(reuse['fig']) == [fit_colors[k] for k in present] + finally: + plt.close(fit['fig']) + plt.close(reuse['fig']) + + +def test_reused_pipeline_on_the_fit_data_redraws_the_fit_figure_colours(): + a, _ = _walks() + fit = _fit_bundle(a, n_clusters=3) + fig = hyp.plot(a, '.', pipeline=fit['pipeline'], backend='matplotlib', + show=False) + try: + assert _line_colors(fig) == _line_colors(fit['fig']) + finally: + plt.close(fit['fig']) + plt.close(fig) + + +def test_reused_pipeline_colours_a_line_plot_too(): + a, b = _walks() + fit = _fit_bundle(a, n_clusters=3) + fig = hyp.plot(b, pipeline=fit['pipeline'], backend='matplotlib', + show=False) + try: + assert len(set(_line_colors(fig))) > 1 + finally: + plt.close(fit['fig']) + plt.close(fig) + + +def test_reused_pipeline_colours_the_plotly_figure(): + pytest.importorskip('plotly') + a, b = _walks() + fit = _fit_bundle(a, n_clusters=3) + plt.close(fit['fig']) + fig = hyp.plot(b, '.', pipeline=fit['pipeline'], backend='plotly', + show=False) + colors = {str(trace.marker.color) for trace in fig.data + if getattr(trace, 'marker', None) is not None + and trace.marker.color is not None} + assert len(colors) > 1, colors + + +def test_reused_mixture_pipeline_blends_its_fitted_memberships(): + a, b = _walks() + fit = _fit_bundle(a, cluster='GaussianMixture', n_clusters=3) + pipe = fit['pipeline'] + expected = np.asarray(pipe.named_steps['cluster'].transform( + hyp.analyze(b, pipeline=pipe))) + assert expected.ndim == 2 and expected.shape[1] == 3 + reuse = hyp.plot(b, '.', pipeline=pipe, return_model=True, + backend='matplotlib', show=False) + try: + np.testing.assert_allclose( + np.asarray(reuse['models']['cluster_labels']), expected) + colors = {tuple(np.round(c, 4)) for coll in reuse['fig'].axes[0].collections + for c in coll.get_facecolors()} + assert len(colors) > 1 + finally: + plt.close(fit['fig']) + plt.close(reuse['fig']) + + +def test_a_cluster_step_that_cannot_label_new_data_warns_clearly(): + """AgglomerativeClustering has no out-of-sample predict: the fitted + step cannot label a dataset with a different row count, so the figure + is drawn without cluster colours and a warning says why (it used to + drop the clusters silently).""" + a, b = _walks() + fit = _fit_bundle(a, cluster='AgglomerativeClustering', n_clusters=3) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(b[:30], '.', pipeline=fit['pipeline'], + backend='matplotlib', show=False) + try: + notes = [str(w.message) for w in caught + if 'cluster step' in str(w.message)] + assert len(notes) == 1, [str(w.message) for w in caught] + assert 'AgglomerativeClustering' in notes[0] + assert caught[[str(w.message) for w in caught].index(notes[0]) + ].filename == __file__ + assert len(set(_line_colors(fig))) == 1 + finally: + plt.close(fit['fig']) + plt.close(fig) + + +def test_the_bundle_reports_the_fitted_clusterer_it_replayed(): + from hypertools.cluster.common import Clusterer + a, b = _walks() + fit = _fit_bundle(a, n_clusters=3) + reuse = hyp.plot(b, '.', pipeline=fit['pipeline'], return_model=True, + backend='matplotlib', show=False) + try: + assert isinstance(reuse['models']['cluster'], Clusterer) + assert reuse['models']['cluster'].is_fitted + assert reuse['pipeline'] is fit['pipeline'] + finally: + plt.close(fit['fig']) + plt.close(reuse['fig']) + + +def test_an_explicit_hue_wins_over_the_pipelines_clusters(): + a, b = _walks() + fit = _fit_bundle(a, n_clusters=3) + hue = ['x'] * 24 + ['y'] * 24 + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + fig = hyp.plot(b, '.', pipeline=fit['pipeline'], hue=hue, + backend='matplotlib', show=False) + try: + assert not [w for w in caught if 'overrides hue' in str(w.message)] + assert len(fig.axes[0].lines) == 2 + finally: + plt.close(fit['fig']) + plt.close(fig) + + +def test_a_pipeline_without_a_cluster_step_is_unchanged(): + a, b = _walks() + _, pipe = hyp.analyze(a, reduce='PCA', ndims=2, return_model=True) + fig = hyp.plot(b, '.', pipeline=pipe, backend='matplotlib', show=False) + try: + assert len(set(_line_colors(fig))) == 1 + finally: + plt.close(fig) diff --git a/tests/test_plot_predict_list.py b/tests/test_plot_predict_list.py index e08cdb87..e2c73a30 100644 --- a/tests/test_plot_predict_list.py +++ b/tests/test_plot_predict_list.py @@ -7,6 +7,7 @@ both the values and the names. """ import matplotlib +import matplotlib.colors matplotlib.use('Agg') import numpy as np # noqa: E402 @@ -14,6 +15,7 @@ import matplotlib.pyplot as plt # noqa: E402 import hypertools as hyp # noqa: E402 +from hypertools.plot.forecast import FORECAST_MODEL_LINESTYLES # noqa: E402 MODELS = ['Kalman', 'ARIMA', 'GaussianProcess'] T = 5 @@ -35,7 +37,10 @@ def test_one_overlay_per_model_with_model_names_in_the_legend(signal): legend=True, antialias=False, show=False) overlays = _by_role(fig, 'static') assert len(overlays) == len(MODELS) - assert [line.get_label() for line in overlays] == MODELS + # the overlay carries its model's name as a tag (its legend entry is a + # proxy glyph, so the artist itself stays '_nolegend_') + assert [line._hyp_forecast_label for line in overlays] == MODELS + assert all(line.get_label() == '_nolegend_' for line in overlays) labels = [text.get_text() for text in fig.axes[0].get_legend().get_texts()] for name in MODELS: @@ -43,11 +48,16 @@ def test_one_overlay_per_model_with_model_names_in_the_legend(signal): plt.close(fig) -def test_each_model_gets_its_own_colour(signal): +def test_each_model_gets_its_own_linestyle_in_the_dataset_colour(signal): fig = hyp.plot(signal, reduce=None, ndims=2, predict=MODELS, t=T, show=False) - colours = {line.get_color() for line in _by_role(fig, 'static')} - assert len(colours) == len(MODELS) + overlays = _by_role(fig, 'static') + data_line = [line for line in fig.axes[0].lines + if getattr(line, '_hyp_forecast_role', None) is None][0] + assert [line.get_linestyle() for line in overlays] == \ + list(FORECAST_MODEL_LINESTYLES[:len(MODELS)]) + assert {line.get_color() for line in overlays} == \ + {data_line.get_color()} plt.close(fig) @@ -69,8 +79,11 @@ def test_the_mapping_form_names_the_overlays(signal): fig = hyp.plot(signal, reduce=None, ndims=2, predict={'fast': 'Kalman', 'slow': 'ARIMA'}, t=T, legend=True, show=False) - assert [line.get_label() for line in _by_role(fig, 'static')] == \ + assert [line._hyp_forecast_label for line in _by_role(fig, 'static')] == \ ['fast', 'slow'] + labels = [text.get_text() + for text in fig.axes[0].get_legend().get_texts()] + assert labels[-2:] == ['fast', 'slow'] plt.close(fig) @@ -91,13 +104,17 @@ def test_every_dataset_gets_every_model(signal): # ...and each knows which SERIES it continues assert sorted(line._hyp_forecast_dataset for line in overlays) == \ [0, 0, 0, 1, 1, 1] - # one colour per MODEL, shared across datasets (model-major order) # the flat overlay list is MODEL-MAJOR: model m's two datasets sit at - # positions 2m and 2m+1 + # positions 2m and 2m+1. Each keeps ITS DATASET'S colour and takes the + # model's linestyle, so both questions can be read off the figure. + data_lines = [line for line in fig.axes[0].lines + if getattr(line, '_hyp_forecast_role', None) is None] by_model = [overlays[m * 2:(m + 1) * 2] for m in range(len(MODELS))] - for pair in by_model: - assert pair[0].get_color() == pair[1].get_color() - assert len({pair[0].get_color() for pair in by_model}) == len(MODELS) + for m, pair in enumerate(by_model): + assert pair[0].get_color() == data_lines[0].get_color() + assert pair[1].get_color() == data_lines[1].get_color() + assert {line.get_linestyle() for line in pair} == \ + {FORECAST_MODEL_LINESTYLES[m]} plt.close(fig) @@ -169,5 +186,44 @@ def test_plotly_draws_one_named_trace_per_model(signal): overlays = [trace for trace in fig.data if (trace.meta or {}).get('hyp_forecast_role') == 'static'] assert [trace.name for trace in overlays] == MODELS - assert all(trace.showlegend for trace in overlays) + # the drawn overlays never list themselves; one data-free entry per + # model does (the plotly twin of matplotlib's proxy handles) + assert not any(trace.showlegend for trace in overlays) + entries = [trace for trace in fig.data + if (trace.meta or {}).get('hyp_legend_entry')] + assert [trace.name for trace in entries] == MODELS + assert all(trace.showlegend for trace in entries) assert {(trace.meta or {}).get('hyp_dataset') for trace in overlays} == {0} + + +# --- 1.1 release-review: forecast_hue= is one value per DATASET (F5) ---- + +def test_F5_forecast_hue_per_dataset_is_shared_across_models(signal): + other = signal * 0.5 + 3.0 + fig = hyp.plot([signal, other], reduce=None, ndims=2, + predict=['Kalman', 'ARIMA'], t=T, + forecast_hue=['g1', 'g2'], antialias=False, show=False) + try: + overlays = _by_role(fig, 'static') + assert len(overlays) == 4 # 2 models x 2 sets + colours = [tuple(np.round(line.get_color() + if not isinstance(line.get_color(), str) + else matplotlib.colors.to_rgba( + line.get_color()), 6)) + for line in overlays] + # model-major order: [Kalman ds0, Kalman ds1, ARIMA ds0, ARIMA ds1] + assert colours[0] == colours[2] # dataset 0, both models + assert colours[1] == colours[3] # dataset 1, both models + assert colours[0] != colours[1] # the two datasets differ + finally: + plt.close(fig) + + +def test_F5_a_mismatched_forecast_hue_names_both_counts(signal): + with pytest.raises(ValueError) as info: + hyp.plot([signal, signal * 2.0], reduce=None, ndims=2, + predict=['Kalman', 'ARIMA'], t=T, + forecast_hue=['g1', 'g2', 'g3'], show=False) + message = str(info.value) + assert '3 value(s)' in message + assert '2 dataset(s) x 2 model(s) = 4 forecast(s)' in message diff --git a/tests/test_plot_public_signature.py b/tests/test_plot_public_signature.py index a831e520..f4af3fbe 100644 --- a/tests/test_plot_public_signature.py +++ b/tests/test_plot_public_signature.py @@ -36,6 +36,11 @@ def signature(): ('cluster', None), ('normalize', None), ('manip', None), + ('palette_sort', None), + ('palette_reduce', None), + ('palette_manip', None), + ('palette_normalize', None), + ('palette_align', None), ] diff --git a/tests/test_plot_return_model_no_refit.py b/tests/test_plot_return_model_no_refit.py new file mode 100644 index 00000000..65c269c8 --- /dev/null +++ b/tests/test_plot_return_model_no_refit.py @@ -0,0 +1,56 @@ +"""``return_model=True`` reuses the pipeline the figure was drawn with +instead of fitting a second one (1.1 feature-tour report, section 9.8: an +Isomap panel grid warned twice per reducer). Real reducers; the fit count +is observed through sklearn's own connected-components warning.""" + +import warnings + +import numpy as np + +import hypertools as hyp + + +def _digits400(): + d = hyp.load('digits') + return d.drop(columns='target').to_numpy()[:400] + + +def _isomap_fits(**kw): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + out = hyp.plot(_digits400(), '.', reduce='Isomap', show=False, **kw) + return sum('connected components' in str(m.message) for m in w), out + + +def test_return_model_fits_the_reducer_once(): + n_plain, _ = _isomap_fits() + n_bundle, bundle = _isomap_fits(return_model=True) + assert n_plain == 1 + assert n_bundle == 1 + pipe = bundle['pipeline'] + assert pipe.is_fitted + # the bundled pipeline reproduces the figure's analyzed data + replay = np.asarray(pipe.transform(_digits400())) + np.testing.assert_allclose(replay, np.asarray(bundle['xform_data'][0]), + rtol=1e-6, atol=1e-6) + + +def test_return_model_pipeline_keeps_the_cluster_stage(): + x = hyp.load('random_walk', n_samples=80, n_features=6, random_state=0) + bundle = hyp.plot(x, '.', reduce='PCA', n_clusters=3, return_model=True, + show=False) + pipe = bundle['pipeline'] + assert list(pipe.named_steps) == ['reduce', 'cluster'] + assert pipe.is_fitted + labels = np.asarray(pipe.transform(x)) + assert labels.shape[0] == 80 and len(np.unique(labels)) == 3 + + +def test_panels_with_return_model_fit_each_reducer_once(): + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + bundle = hyp.plot(_digits400(), '.', reduce=['PCA', 'Isomap'], + panels=True, return_model=True, show=False) + assert sum('connected components' in str(m.message) for m in w) == 1 + assert bundle['panels'] == (1, 2) + assert all(m['pipeline'].is_fitted for m in bundle['panel_models']) diff --git a/tests/test_plot_review_round3.py b/tests/test_plot_review_round3.py new file mode 100644 index 00000000..fb24e7b5 --- /dev/null +++ b/tests/test_plot_review_round3.py @@ -0,0 +1,288 @@ +"""Codex red-team round 3 on the 1.1 release review (2026-09-07), each +finding pinned at the public API on both backends. No mocks. + +1 a collection under hue regrouping continued the wrong run (static) and + raised IndexError animated (forecast index used as dataset index) +2 a forecaster fitted on several datasets could not animate +3 plotly dropped forecast_fmt's colour letter and markers +4 plotly animations halved a recoloured forecast's alpha +5 a second call into the same plotly cell restarted the palette +6 plotly forecast legend keys compared opacity as colour +7 legend_colors= beside forecasts: pairs gained entries, lists were refused +8 matplotlib panel legends overlapped their colorbars +9 a plotly gutter rebuild discarded multi-line title room +10 plotly cells overwrote an explicit legend position +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +import pytest +from matplotlib.colors import to_rgb +from matplotlib.transforms import Bbox + +import hypertools as hyp +from hypertools.plot.plotly_backend import _rgb_triplet + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walk(seed=4, rows=20): + return np.cumsum(np.random.default_rng(seed).normal(size=(rows, 3)), + axis=0) + + +def _mpl_role(fig, role): + return [ln for ln in fig.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) == role] + + +def _pl_role(fig, role): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == role] + + +def _pl_data(fig): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') is not None] + + +def _pl_entries(fig): + return [tr for tr in fig.data if (tr.meta or {}).get('hyp_legend_entry')] + + +HUE = ['a'] * 10 + ['b'] * 10 + + +# --- 1: a collection under regrouping --------------------------------------- + +def test_collection_under_hue_regrouping_continues_the_final_run(): + x = _walk() + fig = hyp.plot(x, hue=HUE, predict=['Kalman', 'ARIMA'], t=3, legend=True, + show=False) + runs = [ln for ln in fig.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) is None] + final = to_rgb(runs[-1].get_color()) # run 'b' holds the last row + for fc in _mpl_role(fig, 'static'): + assert to_rgb(fc.get_color()) == final + pl = hyp.plot(x, hue=HUE, predict=['Kalman', 'ARIMA'], t=3, legend=True, + show=False, backend='plotly') + final = _rgb_triplet(_pl_data(pl)[-1].line.color) + for fc in _pl_role(pl, 'static'): + assert _rgb_triplet(fc.line.color) == final + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_animated_collection_under_regrouping_with_a_trail_draws(backend): + x = _walk() + kw = dict(predict=['Kalman', 'ARIMA'], t=3, legend=True, animate=True, + duration=1, frame_rate=4, forecast_trail=2, show=False, + hue=[HUE] * 2, backend=backend) + out = hyp.plot([x, x + 2], **kw) + if backend == 'matplotlib': + fig, ani = out + for frame in range(ani._save_count or 4): + ani._func(frame, *ani._args) # every frame draws + assert len(_mpl_role(fig, 'live')) == 4 + else: + assert len(out.frames) > 0 + assert len(_pl_role(out, 'live')) == 4 + + +# --- 2: a multi-dataset fitted forecaster animates -------------------------- + +def test_for_dataset_binds_one_fitted_model(): + x = _walk() + _, model = hyp.predict([x, x + 2], return_model=True, t=3) + assert len(model.models_) == 2 + view = model.for_dataset(1) + assert len(view.models_) == 1 and view.models_[0] is model.models_[1] + assert np.shape(view.data) == x.shape + one = view.predict_new(x + 2, 3) + both = model.predict_new([x, x + 2], 3) + assert np.allclose(np.asarray(one), np.asarray(both[1])) + with pytest.raises(IndexError): + model.for_dataset(2) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_a_fitted_multi_dataset_forecaster_animates(backend): + x = _walk() + _, model = hyp.predict([x, x + 2], return_model=True, t=3) + out = hyp.plot([x, x + 2], predict=model, t=3, animate=True, duration=1, + frame_rate=4, show=False, backend=backend) + if backend == 'matplotlib': + fig, ani = out + ani._func(3, *ani._args) + assert len(_mpl_role(fig, 'live')) == 2 + else: + assert len(_pl_role(out, 'live')) == 2 + + +# --- 3: plotly honours forecast_fmt's colour and markers -------------------- + +@pytest.mark.parametrize('animated', [False, True]) +def test_plotly_forecast_fmt_colour_and_markers(animated): + x = _walk() + kw = dict(predict=['Kalman', 'ARIMA'], t=3, legend=True, show=False, + forecast_fmt='ro:', duration=1, frame_rate=4) + fig = hyp.plot(x, animate=animated, backend='plotly', **kw) + role = 'live' if animated else 'static' + for tr in _pl_role(fig, role): + assert _rgb_triplet(tr.line.color) == (255, 0, 0) + assert tr.line.dash == 'dot' + assert tr.mode == 'lines+markers' + assert tr.marker.symbol == 'circle' + for entry in _pl_entries(fig): + assert entry.mode == 'lines+markers' + assert _rgb_triplet(entry.line.color) == (255, 0, 0) + # matplotlib parity on the same call + mfig = hyp.plot(x, animate=animated, **kw) + if animated: + mfig = mfig[0] + for ln in _mpl_role(mfig, role): + assert to_rgb(ln.get_color()) == to_rgb('r') + assert ln.get_linestyle() == ':' and ln.get_marker() == 'o' + + +# --- 4: plotly animations keep a recoloured forecast's alpha ----------------- + +def test_plotly_animated_recoloured_forecast_keeps_its_alpha(): + x = _walk() + fig = hyp.plot(x, alpha=.7, forecast_palette='Set1', predict='Kalman', + t=3, animate=True, duration=1, frame_rate=4, + forecast_trail=2, show=False, backend='plotly') + live, = _pl_role(fig, 'live') + assert live.meta['hyp_forecast_alpha'] == pytest.approx(0.7) + trails = [tr.meta['hyp_forecast_alpha'] for tr in _pl_role(fig, 'trail')] + assert max(trails) < 0.7 and min(trails) > 0.0 + mfig, ani = hyp.plot(x, alpha=.7, forecast_palette='Set1', + predict='Kalman', t=3, animate=True, duration=1, + frame_rate=4, forecast_trail=2, show=False) + mlive, = _mpl_role(mfig, 'live') + assert mlive.get_alpha() == pytest.approx(0.7) + assert sorted(ln.get_alpha() for ln in _mpl_role(mfig, 'trail')) == \ + pytest.approx(sorted(trails)) + + +# --- 5: a second call into the same plotly cell continues the palette ------ + +def test_second_call_into_the_same_plotly_cell_continues_the_palette(): + x = _walk() + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(x, ax=cells[0], show=False, backend='plotly') + hyp.plot(x + 2, ax=cells[0], show=False, backend='plotly') + hyp.plot(x, ax=cells[1], show=False, backend='plotly') + colours = [tr.line.color for tr in _pl_data(fig)] + assert colours[0] != colours[1] # cell 0's two datasets differ + assert colours[2] == colours[0] # cell 1 starts its own palette + mfig, axes = hyp.subplots(1, 1) + hyp.plot(x, ax=axes[0], show=False) + hyp.plot(x + 2, ax=axes[0], show=False) + mcolours = [to_rgb(ln.get_color()) for ln in axes[0].lines[:2]] + assert [_rgb_triplet(c) for c in colours[:2]] == \ + [tuple(round(v * 255) for v in c) for c in mcolours] + + +# --- 6: legend keys compare colour, not opacity ----------------------------- + +def test_plotly_legend_keys_ignore_alpha_when_comparing_colours(): + x = _walk() + fig = hyp.plot([x, x + 2], alpha=[1, .4], predict=['Kalman', 'ARIMA'], + t=3, forecast_palette=['red', 'red'], legend=True, + show=False, backend='plotly') + for entry in _pl_entries(fig): + assert _rgb_triplet(entry.line.color) == (255, 0, 0) + + +# --- 7: legend_colors= beside forecasts ------------------------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_explicit_legend_pairs_define_the_whole_legend(backend): + x = _walk() + fig = hyp.plot(x, predict='Kalman', t=3, truth=x[-3:] + 1, + legend=True, legend_colors=[('Custom', 'red')], + show=False, backend=backend) + if backend == 'matplotlib': + texts = [t.get_text() for t in fig.axes[0].get_legend().get_texts()] + else: + texts = [tr.name for tr in fig.data if tr.showlegend] + assert texts == ['Custom'] + + +def test_plain_legend_colour_list_recolours_the_final_legend(): + x = _walk() + fig = hyp.plot(x, predict='Kalman', t=3, truth=x[-3:] + 1, legend=True, + legend_colors=['navy', 'gold', 'green'], show=False) + legend = fig.axes[0].get_legend() + assert [t.get_text() for t in legend.get_texts()] == \ + ['1', 'Kalman', 'truth'] + assert [to_rgb(h.get_color()) for h in legend.legend_handles] == \ + [to_rgb('navy'), to_rgb('gold'), to_rgb('green')] + + +# --- 8: panel legends clear their colorbars --------------------------------- + +@pytest.mark.parametrize('ndims', [1, 2, 3]) +def test_panel_legend_and_colorbar_do_not_overlap(ndims): + x = _walk() + fig = hyp.plot([x, x + 2], panels=True, ndims=ndims, legend=True, + colorbar=True, size=[9, 4], show=False, + cluster={'model': 'KMeans', + 'kwargs': {'n_clusters': 2, 'random_state': 0}}) + fig.canvas.draw() + renderer = fig.canvas.get_renderer() + panels = [a for a in fig.axes if a.get_legend() is not None] + bars = [a for a in fig.axes if a.get_label() == '<colorbar>'] + assert len(panels) == len(bars) == 2 + for panel, bar in zip(panels, bars): + legend_box = panel.get_legend().get_window_extent(renderer) + assert Bbox.intersection(legend_box, + bar.get_window_extent(renderer)) is None + + +# --- 9 and 10: plotly cells keep title room and explicit legend positions -- + +def test_gutter_rebuild_keeps_multiline_title_room(): + x = _walk() + fig, cells = hyp.subplots(2, 1, backend='plotly', size=[6, 6]) + hyp.plot(x, ax=cells[0], title='three\nlarge\nlines', + title_kwargs={'fontsize': 30}, show=False, backend='plotly') + top_before = fig.layout.margin.t + assert top_before > 40 + hyp.plot([x, x + 2], ax=cells[1], legend=True, names=['a', 'b'], + show=False, backend='plotly') + assert fig.layout.margin.t >= top_before + assert fig.layout.meta['hyp_grid']['gutter_px'] > 0 + + +def test_plotly_panels_keep_an_explicit_legend_position(): + x = _walk() + fig = hyp.plot([x, x + 2], panels=True, legend=True, show=False, + legend_kwargs={'x': .02, 'y': .98, 'xanchor': 'left', + 'yanchor': 'top'}, backend='plotly') + from hypertools.plot.plotly_backend import cell_layout_keys + for i in range(2): + keys = cell_layout_keys(i) + d = fig.layout[keys['scene']].domain + legend = fig.layout[keys['legend']] + assert legend.x == pytest.approx(d.x[0] + .02 * (d.x[1] - d.x[0])) + assert legend.y == pytest.approx(d.y[0] + .98 * (d.y[1] - d.y[0])) + assert legend.yanchor == 'top' + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(x, ax=cells[0], legend=True, names=['a'], show=False, + backend='plotly', + legend_kwargs={'x': .02, 'y': .98, 'yanchor': 'top'}) + d0 = fig.layout.scene.domain + assert fig.layout.legend.x == pytest.approx( + d0.x[0] + .02 * (d0.x[1] - d0.x[0])) + hyp.plot(x, ax=cells[1], colorbar=True, hue=np.arange(20.0), + show=False, backend='plotly') # grows the gutters + d0 = fig.layout.scene.domain + assert fig.layout.legend.x == pytest.approx( + d0.x[0] + .02 * (d0.x[1] - d0.x[0])) # re-placed inside cell 0 diff --git a/tests/test_plot_review_round4.py b/tests/test_plot_review_round4.py new file mode 100644 index 00000000..5f32eb0a --- /dev/null +++ b/tests/test_plot_review_round4.py @@ -0,0 +1,219 @@ +"""Codex red-team round 4 on the 1.1 release review (2026-09-07), each +finding pinned at the public API on both backends. No mocks. + +1 regrouped animations repainted a `forecast_fmt` colour letter (and + plotly halved a recoloured forecast's per-frame alpha) +2 mixture-hue legends lost their forecast and truth entries +3 forecasts/truth on a reused matplotlib axes styled themselves from an + earlier call's lines +4 repeated calls into one axes/figure duplicated legend entries +5 a legend and a colorbar arriving in separate calls shared one gutter +6 a multi-line title widened the top margin but not the rows +7 plotly drew a marker-only `forecast_fmt` as connected lines +8 animated plotly collection traces tagged the forecast index as dataset +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +from tests._plotly_colors import rgba as effective_rgba +import pytest +from matplotlib.colors import to_hex, to_rgb + +import hypertools as hyp +from hypertools.plot.plotly_backend import _rgb_triplet, cell_layout_keys + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walk(seed=4, rows=20): + return np.cumsum(np.random.default_rng(seed).normal(size=(rows, 3)), + axis=0) + + +HUE = ['a'] * 10 + ['b'] * 10 + + +def _mpl_role(fig, role): + return [ln for ln in fig.axes[0].lines + if getattr(ln, '_hyp_forecast_role', None) == role] + + +def _pl_role(fig, role): + return [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == role] + + +def _mpl_legend(fig): + return [t.get_text() for t in fig.axes[0].get_legend().get_texts()] + + +def _pl_legend(fig): + listed = [(tr.legendrank if tr.legendrank is not None else 1000, k, tr) + for k, tr in enumerate(fig.data) if tr.showlegend] + return [tr.name for _, _, tr in sorted(listed, key=lambda t: t[:2])] + + +# --- 1: a format-string colour survives a regrouped animation --------------- + +def test_regrouped_animation_keeps_a_forecast_fmt_colour(): + x = _walk() + kw = dict(hue=HUE, predict=['Kalman', 'ARIMA'], forecast_fmt='ro:', + alpha=.7, t=3, animate=True, forecast_trail=2, antialias=False, + duration=1, frame_rate=4, legend=True, show=False) + fig, ani = hyp.plot(x, **kw) + fig.canvas.draw() + for frame in range(ani._save_count): + ani._func(frame, *ani._args) + for ln in _mpl_role(fig, 'live'): + assert to_hex(ln.get_color()) == '#ff0000' + assert ln.get_alpha() == pytest.approx(0.7) + pl = hyp.plot(x, backend='plotly', **kw) + live = _pl_role(pl, 'live') + assert [_rgb_triplet(tr.line.color) for tr in live] == [(255, 0, 0)] * 2 + assert all(effective_rgba(tr)[-1] == pytest.approx(.7) for tr in live) + # no frame repaints a pinned colour + for frame in pl.frames: + for tr in frame.data: + if tr.line is not None and tr.line.color is not None: + assert _rgb_triplet(tr.line.color) == (255, 0, 0) + assert tr.line.color.endswith(',0.7)') + + +# --- 2: mixture-hue legends list forecasts and truth -------------------- + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('animated', [False, True]) +def test_mixture_hue_legend_lists_forecasts_and_truth(backend, animated): + x = _walk() + mix = np.column_stack([np.linspace(0, 1, 20), np.linspace(1, 0, 20)]) + out = hyp.plot(x, hue=mix, predict=['Kalman', 'ARIMA'], t=3, + truth=x[-3:], legend=True, animate=animated, duration=1, + frame_rate=4, show=False, backend=backend) + if backend == 'matplotlib': + fig = out[0] if animated else out + assert _mpl_legend(fig) == ['1', '2', 'Kalman', 'ARIMA', 'truth'] + else: + assert _pl_legend(out) == ['1', '2', 'Kalman', 'ARIMA', 'truth'] + + +# --- 3 and 4: repeated calls into one axes / figure -------------------------- + +def test_reused_matplotlib_axes_styles_overlays_from_its_own_call(): + x = _walk() + fig, axes = hyp.subplots(1, 1) + for k in range(3): + hyp.plot(x + 2 * k, ax=axes[0], predict='Kalman', t=3, + truth=x[-3:] + 2 * k, legend=True, names=[f'data{k}'], + show=False) + ax = axes[0] + data = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) is None] + forecasts = _mpl_role(fig, 'static') + assert [to_rgb(ln.get_color()) for ln in forecasts] == \ + [to_rgb(ln.get_color()) for ln in data] + truths = [ln for ln in _mpl_role(fig, 'truth') + if ln.get_linestyle() != 'None'] + assert [to_rgb(ln.get_color()) for ln in truths] == \ + [to_rgb(ln.get_color()) for ln in data] + assert _mpl_legend(fig) == ['data0', 'data1', 'data2', 'Kalman', 'truth'] + + +def test_reused_plotly_figure_consolidates_legend_entries(): + x = _walk() + fig = None + for k in range(3): + fig = hyp.plot(x + 2 * k, ax=fig, predict='Kalman', t=3, + truth=x[-3:] + 2 * k, legend=True, + names=[f'data{k}'], show=False, backend='plotly') + assert _pl_legend(fig) == ['data0', 'data1', 'data2', 'Kalman', 'truth'] + data = [tr for tr in fig.data + if (tr.meta or {}).get('hyp_trace_index') is not None] + assert [_rgb_triplet(tr.line.color) for tr in _pl_role(fig, 'static')] \ + == [_rgb_triplet(tr.line.color) for tr in data] + + +def test_reused_plotly_cell_consolidates_legend_entries(): + x = _walk() + fig, cells = hyp.subplots(1, 2, backend='plotly') + for k in range(2): + hyp.plot(x + 2 * k, ax=cells[0], predict='Kalman', t=3, legend=True, + names=[f'data{k}'], show=False, backend='plotly') + hyp.plot(x, ax=cells[1], predict='Kalman', t=3, legend=True, + names=['other'], show=False, backend='plotly') + key0 = cell_layout_keys(0)['legend'] + cell0 = [tr.name for tr in fig.data if tr.showlegend + and getattr(tr, 'legend', None) == key0] + assert cell0 == ['data0', 'data1', 'Kalman'] + + +# --- 5: a legend and a colorbar from separate calls sit side by side --------- + +@pytest.mark.parametrize('ndims', [1, 2, 3]) +def test_legend_then_colorbar_in_separate_calls_share_no_gutter(ndims): + x = _walk() + fig, cells = hyp.subplots(1, 2, backend='plotly', ndims=ndims) + hyp.plot([x, x + 2], ax=cells[0], legend=True, names=['a', 'b'], + ndims=ndims, show=False, backend='plotly') + hyp.plot(x, ax=cells[0], hue=np.arange(20.0), colorbar=True, + ndims=ndims, show=False, backend='plotly') + from hypertools.plot.plotly_backend import PANEL_LEGEND_PX + assert fig.layout.meta['hyp_grid']['gutter_px'] >= 2 * PANEL_LEGEND_PX + legend_x = fig.layout.legend.x + bars = [tr.marker.colorbar.x for tr in fig.data + if getattr(getattr(tr, 'marker', None), 'showscale', None)] + plot_w = fig.layout.width - fig.layout.margin.l - fig.layout.margin.r + assert all((bx - legend_x) * plot_w >= PANEL_LEGEND_PX - 1 + for bx in bars) + + +# --- 6: a multi-line title makes room between the rows ---------------------- + +def test_multiline_cell_title_rebuilds_the_rows(): + x = _walk() + fig, cells = hyp.subplots(2, 1, backend='plotly', size=[6, 6]) + for cell in cells: + hyp.plot(x, ax=cell, title='three\nlarge\nlines', + title_kwargs={'fontsize': 30}, show=False, backend='plotly') + mg = fig.layout.margin + plot_h = fig.layout.height - mg.t - mg.b + top, bottom = fig.layout.scene.domain, fig.layout.scene2.domain + gap_px = (top.y[0] - bottom.y[1]) * plot_h + title_px = fig.layout.meta['hyp_grid']['title_px'] + assert title_px > 100 # three 30 pt lines + assert gap_px >= title_px # the row gap holds the whole title + + +# --- 7: marker-only forecast_fmt on plotly ---------------------------------- + +@pytest.mark.parametrize('animated', [False, True]) +def test_plotly_marker_only_forecast_fmt_draws_markers(animated): + x = _walk() + fig = hyp.plot(x, predict='Kalman', t=3, forecast_fmt='ro', + antialias=False, animate=animated, duration=1, + frame_rate=4, show=False, backend='plotly') + role = 'live' if animated else 'static' + tr, = _pl_role(fig, role) + assert tr.mode == 'markers' + mfig = hyp.plot(x, predict='Kalman', t=3, forecast_fmt='ro', + antialias=False, animate=animated, duration=1, + frame_rate=4, show=False) + mfig = mfig[0] if animated else mfig + ln, = _mpl_role(mfig, role) + assert ln.get_linestyle() == 'None' and ln.get_marker() == 'o' + + +# --- 8: animated plotly collection traces name their SOURCE dataset -------- + +def test_animated_plotly_collection_tags_source_datasets(): + x = _walk() + fig = hyp.plot([x, x + 2], predict=['Kalman', 'ARIMA'], t=3, + animate=True, duration=1, frame_rate=4, show=False, + backend='plotly') + assert [tr.meta['hyp_dataset'] for tr in _pl_role(fig, 'live')] == \ + [0, 1, 0, 1] diff --git a/tests/test_plot_review_round6.py b/tests/test_plot_review_round6.py new file mode 100644 index 00000000..1c4acdeb --- /dev/null +++ b/tests/test_plot_review_round6.py @@ -0,0 +1,266 @@ +"""1.1 release review, round 6: two defects pinned at the public API on +both backends. No mocks -- every assertion reads the drawn artists/traces. + +1 the plotly backend dropped the COLOUR letter of a data ``fmt=`` string + (``'r-'`` drew the palette colour; matplotlib drew red, and the `plot()` + docstring promises fmt works "exactly as in matplotlib") -- on the + single-axes path, animated, for per-dataset fmt lists, and inside + ``panels=`` cells. +2 ``panels=True`` on 2-column (or 1-column) data with the default + ``ndims=`` built 3-D cells: plotly refused the 2-D traces ("Trace type + 'scatter' is not compatible with subplot type 'scene'") and matplotlib + drew a flat trajectory inside a cube. The cells now follow the data's + drawn dimensionality, as the single-axes call does. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pytest # noqa: E402 +from matplotlib.colors import to_rgb # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot.plotly_backend import _rgb_triplet # noqa: E402 + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +def _walks(n=2, rows=20, cols=3, seed=601): + rng = np.random.default_rng(seed) + return [rng.normal(size=(rows, cols)).cumsum(0) for _ in range(n)] + + +def _rgb255(color): + """A matplotlib colour spelling or a plotly ``rgba()`` string as an + integer ``(r, g, b)`` triplet, so the two backends' colours compare.""" + if isinstance(color, str) and color.startswith(('rgb(', 'rgba(')): + return tuple(int(c) for c in _rgb_triplet(color)) + return tuple(int(round(c * 255)) for c in to_rgb(color)) + + +def _mpl_line_colors(fig): + """The colours of the lines on a matplotlib figure's first axes (a + static hypertools plot draws its frame as patches/collections, so the + `Line2D`s are the data lines, one per dataset).""" + return [_rgb255(ln.get_color()) for ln in fig.axes[0].lines] + + +def _plotly_data_traces(fig): + """The DATA traces of a plotly figure: hypertools' frame/box traces + are drawn in black with ``hoverinfo='skip'`` and no legend entry; + a data trace keeps its hover.""" + return [tr for tr in fig.data + if getattr(tr, 'hoverinfo', None) != 'skip'] + + +def _plotly_line_colors(fig): + out = [] + for tr in _plotly_data_traces(fig): + color = tr.line.color if 'lines' in (tr.mode or '') \ + else tr.marker.color + out.append(_rgb255(color)) + return out + + +# ------------------------------------------ 1: fmt= colour letter, plotly + +def test_fmt_colour_letter_single_axes(): + """``fmt='r-'``: plotly's trace is the same red matplotlib's line is, + not the palette's first colour.""" + x = _walks(1)[0] + mpl_fig = hyp.plot(x, fmt='r-', show=False) + ply_fig = hyp.plot(x, fmt='r-', backend='plotly', show=False) + mpl_colors = _mpl_line_colors(mpl_fig) + ply_colors = _plotly_line_colors(ply_fig) + assert mpl_colors == [_rgb255('r')] + assert ply_colors == mpl_colors + # ...and the dash/mode halves of the fmt are untouched by the fix + (trace,) = _plotly_data_traces(ply_fig) + assert trace.mode == 'lines' + assert trace.line.dash in (None, 'solid') + + +def test_fmt_colour_letters_per_dataset_list(): + """``fmt=['g--', 'b:']``: each plotly trace wears its own letter's + colour AND its own dash, exactly as matplotlib's two lines do.""" + data = _walks(2) + mpl_fig = hyp.plot(data, fmt=['g--', 'b:'], show=False) + ply_fig = hyp.plot(data, fmt=['g--', 'b:'], backend='plotly', + show=False) + mpl_lines = mpl_fig.axes[0].lines + assert [ln.get_linestyle() for ln in mpl_lines] == ['--', ':'] + assert _mpl_line_colors(mpl_fig) == [_rgb255('g'), _rgb255('b')] + traces = _plotly_data_traces(ply_fig) + assert [tr.line.dash for tr in traces] == ['dash', 'dot'] + assert _plotly_line_colors(ply_fig) == _mpl_line_colors(mpl_fig) + + +def test_fmt_colour_letter_animated(): + """An animated plotly call keeps the letter too: the base trace and + every frame that restyles the line carry red, never the palette.""" + x = _walks(1, rows=12)[0] + anim = hyp.plot(x, fmt='r-', backend='plotly', animate=True, + show=False) + mpl_anim = hyp.plot(x, fmt='r-', animate=True, show=False) + mpl_colors = [_rgb255(ln.get_color()) + for ln in mpl_anim.figure.axes[0].lines + if ln.get_color() != 'black'] + assert mpl_colors and set(mpl_colors) == {_rgb255('r')} + assert _plotly_line_colors(anim) == [_rgb255('r')] + assert len(anim.frames) > 0 + for frame in anim.frames: + for tr in frame.data: + color = getattr(getattr(tr, 'line', None), 'color', None) + if color is not None: + assert _rgb255(color) == _rgb255('r') + + +def test_fmt_colour_letter_inside_panels(): + """Each `panels=` cell honours its own dataset's letter (the panel + calls re-enter `plot()`, so this is the single-axes fix seen through + the partitioned fmt list).""" + data = _walks(2) + ply_fig = hyp.plot(data, fmt=['g--', 'b:'], panels=True, + backend='plotly', show=False) + mpl_fig = hyp.plot(data, fmt=['g--', 'b:'], panels=True, show=False) + assert _plotly_line_colors(ply_fig) == [_rgb255('g'), _rgb255('b')] + assert [_rgb255(ax.lines[0].get_color()) for ax in mpl_fig.axes[:2]] \ + == [_rgb255('g'), _rgb255('b')] + + +def test_fmt_colour_letter_precedence_matches_matplotlib(): + """The letter beats `palette=` but loses to an explicit `color=` on + BOTH backends, and a lettered dataset consumes no palette slot: with + ``fmt=['r-', '-']`` the second dataset takes the palette's FIRST + colour, as matplotlib's colour cycle hands it out.""" + data = _walks(2) + for kwargs in (dict(fmt='r-', palette='viridis'), + dict(fmt='r-', color=['g', 'b']), + dict(fmt=['r-', '-'])): + mpl_fig = hyp.plot(data, show=False, **kwargs) + ply_fig = hyp.plot(data, backend='plotly', show=False, **kwargs) + assert _plotly_line_colors(ply_fig) == _mpl_line_colors(mpl_fig), \ + kwargs + palette_first = _mpl_line_colors(hyp.plot(data[:1], show=False))[0] + assert _mpl_line_colors(hyp.plot(data, fmt=['r-', '-'], + show=False))[1] == palette_first + + +# --------------------------------- 2: panels= cells follow the data width + +def _plotly_xy_cells(fig): + """``(trace types, the xaxis of each data trace, layout scene keys)``.""" + traces = _plotly_data_traces(fig) + # the SET layout entries (iterating `fig.layout` lists every property + # name, set or not) + scenes = sorted(k for k in fig.layout.to_plotly_json() + if k.startswith('scene')) + return ([tr.type for tr in traces], + [getattr(tr, 'xaxis', None) for tr in traces], scenes) + + +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_panels_two_column_data_default_ndims_plotly(fit): + """2-column data, no ndims=: xy cells with the traces drawn in them + (before: ValueError "Trace type 'scatter' is not compatible with + subplot type 'scene'").""" + data = _walks(2, cols=2) + fig = hyp.plot(data, panels=True, panel_fit=fit, backend='plotly', + show=False) + types, xaxes, scenes = _plotly_xy_cells(fig) + assert types == ['scatter', 'scatter'] + assert xaxes == ['x', 'x2'] + assert scenes == [] + for tr in _plotly_data_traces(fig): + assert len(tr.x) > 0 and len(tr.x) == len(tr.y) + # the same cells an explicit ndims=2 builds + explicit = hyp.plot(data, panels=True, panel_fit=fit, ndims=2, + backend='plotly', show=False) + assert _plotly_xy_cells(explicit) == (types, xaxes, scenes) + + +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_panels_two_column_data_default_ndims_matplotlib(fit): + """The matplotlib grid gives 2-column data 2-D axes -- what the + single-axes call draws it on -- not 3-D cubes.""" + data = _walks(2, cols=2) + single = hyp.plot(data[0], show=False) + assert single.axes[0].name == 'rectilinear' + fig = hyp.plot(data, panels=True, panel_fit=fit, show=False) + axes = [ax for ax in fig.axes if ax.get_visible()] + assert [ax.name for ax in axes] == ['rectilinear', 'rectilinear'] + assert all(len(ax.lines) >= 1 for ax in axes) + # the panel draws the same points the single-axes call draws + assert np.allclose(axes[0].lines[0].get_xydata(), + single.axes[0].lines[0].get_xydata()) + + +@pytest.mark.parametrize('backend', ('matplotlib', 'plotly')) +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_panels_one_column_data_default_ndims(backend, fit): + """1-column data draws as an index-vs-value series on the single-axes + path; the panel grid gives it the same 2-D cells (before: matplotlib + raised inside Axes3D.plot, plotly refused the scatter traces).""" + data = _walks(2, cols=1) + fig = hyp.plot(data, panels=True, panel_fit=fit, backend=backend, + show=False) + if backend == 'plotly': + types, xaxes, scenes = _plotly_xy_cells(fig) + assert types == ['scatter', 'scatter'] + assert xaxes == ['x', 'x2'] + assert scenes == [] + for tr in _plotly_data_traces(fig): + assert len(tr.x) > 0 and len(tr.x) == len(tr.y) + else: + axes = [ax for ax in fig.axes if ax.get_visible()] + assert [ax.name for ax in axes] == ['rectilinear', 'rectilinear'] + single = hyp.plot(data[0], show=False) + assert np.allclose(axes[0].lines[0].get_xydata(), + single.axes[0].lines[0].get_xydata()) + + +@pytest.mark.parametrize('backend', ('matplotlib', 'plotly')) +def test_panels_reducer_list_on_two_column_data(backend): + """One panel per reducer over 2-column data: 2-D cells too.""" + x = _walks(1, cols=2)[0] + fig = hyp.plot(x, panels=True, reduce=['PCA', 'IncrementalPCA'], + backend=backend, show=False) + if backend == 'plotly': + types, xaxes, scenes = _plotly_xy_cells(fig) + assert types == ['scatter', 'scatter'] and scenes == [] + else: + assert [ax.name for ax in fig.axes if ax.get_visible()] \ + == ['rectilinear', 'rectilinear'] + + +@pytest.mark.parametrize('backend', ('matplotlib', 'plotly')) +def test_panels_three_column_data_keeps_3d_cells(backend): + """The unchanged case: 3-column data (and ndims > 3, drawn in 3-D) + still builds 3-D cells.""" + for data, kwargs in ((_walks(2), {}), (_walks(2, cols=6), {'ndims': 5})): + fig = hyp.plot(data, panels=True, backend=backend, show=False, + **kwargs) + if backend == 'plotly': + assert {tr.type for tr in _plotly_data_traces(fig)} \ + == {'scatter3d'} + assert sorted(k for k in fig.layout.to_plotly_json() + if k.startswith('scene')) == ['scene', 'scene2'] + else: + assert [ax.name for ax in fig.axes if ax.get_visible()] \ + == ['3d', '3d'] + + +def test_panels_two_column_bundle_axes_are_xy(fit='shared'): + """`return_model=True` on the plotly grid records one (xaxis, yaxis) + pair per 2-D cell, the bundle shape the 2-D path promises.""" + data = _walks(2, cols=2) + bundle = hyp.plot(data, panels=True, panel_fit=fit, backend='plotly', + return_model=True, show=False) + assert bundle['panels'] == (1, 2) + assert all(isinstance(pair, tuple) and len(pair) == 2 + for pair in bundle['axes']) diff --git a/tests/test_plot_review_round7.py b/tests/test_plot_review_round7.py new file mode 100644 index 00000000..3c7bf365 --- /dev/null +++ b/tests/test_plot_review_round7.py @@ -0,0 +1,380 @@ +"""1.1 release review, round 7: two defects pinned at the public API on +both backends. No mocks -- every assertion reads the drawn artists/traces, +the returned bundle, or a real reducer instance's own fit counter. + +1 ``panels=`` decided each cell's projection from the RAW column count, + before the analysis pipeline ran (a round-6 regression). Two raw columns + through a feature-expanding ``manip='Delay'`` come out three wide, and + the 2-D cells then refused the 3-D rows (matplotlib, shared fit), + silently reduced them to 2-D despite ``ndims=3`` (matplotlib, + independent fit), or hit plotly's "Trace type 'scatter3d' is not + compatible with subplot type 'xy'". The cells now follow the ANALYZED + data, in every mode (shared/independent fits, reducer comparisons), + with the requested ``ndims`` and the fitted pipeline kept and no panel + fitted twice. +2 composing a second call into the figure/axes/cell a ``fmt='r-'`` call + drew into gave the second dataset the SECOND palette colour on plotly + (and in an initially empty ``hyp.subplots`` cell on both backends), + where the single call ``fmt=['r-', '-']`` gives it the first: the + colour-cycle count recorded every drawn dataset, although a colour + letter consumes no palette slot. Only datasets coloured FROM the cycle + count now, on both backends, for a plain figure and a cell alike. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pytest # noqa: E402 +from matplotlib.colors import to_rgb # noqa: E402 +from sklearn.decomposition import PCA # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot.plotly_backend import _rgb_triplet # noqa: E402 + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +BACKENDS = ('matplotlib', 'plotly') +PALETTE = ['navy', 'gold', 'green'] +DELAY3 = {'model': 'Delay', 'kwargs': {'dims': 3}} + + +def _walks(n=2, rows=20, cols=2, seed=701): + rng = np.random.default_rng(seed) + return [rng.normal(size=(rows, cols)).cumsum(0) for _ in range(n)] + + +def _rgb255(color): + """A matplotlib colour spelling or a plotly ``rgba()`` string as an + integer ``(r, g, b)`` triplet, so the two backends' colours compare.""" + if isinstance(color, str) and color.startswith(('rgb(', 'rgba(')): + return tuple(int(c) for c in _rgb_triplet(color)) + return tuple(int(round(c * 255)) for c in to_rgb(color)) + + +def _plotly_data_traces(fig): + """The DATA traces of a plotly figure (the frame/box traces carry + ``hoverinfo='skip'``).""" + return [tr for tr in fig.data + if getattr(tr, 'hoverinfo', None) != 'skip'] + + +def _line_colors(fig, backend): + """The data-line colours a figure draws, in drawing order, on either + backend (matplotlib: the `Line2D`s of every visible axes; plotly: the + data traces).""" + if backend == 'matplotlib': + return [_rgb255(ln.get_color()) for ax in fig.axes + if ax.get_visible() for ln in ax.lines] + out = [] + for tr in _plotly_data_traces(fig): + color = tr.line.color if 'lines' in (tr.mode or '') \ + else tr.marker.color + # a hue-coloured trace carries one colour PER POINT: not a + # single colour, so it reads as None here + out.append(_rgb255(color) if isinstance(color, str) else None) + return out + + +def _cell_kinds(fig, backend): + """What the grid's cells are: matplotlib axes projection names, or + the plotly data-trace types (``scatter3d`` only lives in a scene).""" + if backend == 'matplotlib': + return [ax.name for ax in fig.axes if ax.get_visible()] + return sorted({tr.type for tr in _plotly_data_traces(fig)}) + + +def _drawn_points(fig, backend): + """The 3-D coordinates of each panel's first data trace.""" + if backend == 'matplotlib': + return [np.column_stack(ax.lines[0].get_data_3d()) + for ax in fig.axes if ax.get_visible()] + return [np.column_stack([tr.x, tr.y, tr.z]) + for tr in _plotly_data_traces(fig)] + + +class CountingPCA(PCA): + """A real sklearn reducer that counts its own fits: the observable + for "no panel is fitted twice".""" + + def __init__(self, n_components=None): + super().__init__(n_components=n_components) + self.fits = 0 + + def fit(self, X, y=None): + self.fits += 1 + return super().fit(X) + + def fit_transform(self, X, y=None): + self.fits += 1 + return super().fit_transform(X) + + +# --------------------------- 1: panel cells follow the ANALYZED data + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_feature_expanding_manip_gets_3d_cells(backend, fit): + """Two raw columns, ``manip='Delay'`` (dims=3), ``reduce='PCA'``, + ``ndims=3``: 3-D cells drawing the same 3-wide rows the single-axes + call draws (before: refused / silently 2-D / plotly 'xy' cell).""" + data = _walks(2) + single = hyp.plot(data, manip=DELAY3, reduce='PCA', ndims=3, + backend=backend, show=False, return_model=True) + assert [np.shape(a) for a in single['xform_data']] == [(18, 3), (18, 3)] + bundle = hyp.plot(data, panels=True, panel_fit=fit, manip=DELAY3, + reduce='PCA', ndims=3, backend=backend, show=False, + return_model=True) + fig = bundle['fig'] + assert [np.shape(a) for a in bundle['xform_data']] == [(18, 3), (18, 3)] + for model in bundle['panel_models']: + assert [np.shape(a) for a in model['xform_data']] == [(18, 3)] + assert model['pipeline'] is not None + if backend == 'matplotlib': + assert _cell_kinds(fig, backend) == ['3d', '3d'] + else: + assert _cell_kinds(fig, backend) == ['scatter3d'] + # every panel draws x, y AND z, none of them degenerate (the drawn + # line is the display-interpolated trajectory, so only its width and + # spread are the data's) + for p in _drawn_points(fig, backend): + assert p.shape[1] == 3 and p.shape[0] >= 18 + assert (np.ptp(p, axis=0) > 0).all() + if fit == 'shared': + # the shared fit IS the single-axes call's fit: same rows + for got, want in zip(bundle['xform_data'], single['xform_data']): + assert np.allclose(got, want) + assert bundle['pipeline'] is not None + assert all(m['pipeline'] is bundle['pipeline'] + for m in bundle['panel_models']) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_independent_fit_keeps_the_requested_ndims(backend): + """``panel_fit='independent'`` + ``ndims=3`` on Delay-expanded + 2-column data: each panel is fitted on its own and comes out 3-wide, + exactly as its own single-axes call does (before: matplotlib silently + returned (18, 2) per panel on rectilinear axes).""" + data = _walks(2, seed=702) + bundle = hyp.plot(data, panels=True, panel_fit='independent', + manip=DELAY3, reduce='PCA', ndims=3, backend=backend, + show=False, return_model=True) + for i, model in enumerate(bundle['panel_models']): + own = hyp.plot(data[i], manip=DELAY3, reduce='PCA', ndims=3, + backend=backend, show=False, return_model=True) + assert np.allclose(model['xform_data'][0], own['xform_data'][0]) + assert model['pipeline'] is not None + assert bundle['panel_models'][0]['pipeline'] \ + is not bundle['panel_models'][1]['pipeline'] + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_reducer_comparison_gets_3d_cells(backend): + """A list-valued ``reduce=`` (one panel per reducer) on Delay-expanded + 2-column data: every panel draws every dataset in 3-D, and each + panel's bundle carries that reducer's own fitted pipeline.""" + data = _walks(2, seed=703) + bundle = hyp.plot(data, panels=True, reduce=['PCA', 'IncrementalPCA'], + manip=DELAY3, ndims=3, backend=backend, show=False, + return_model=True) + fig = bundle['fig'] + if backend == 'matplotlib': + assert _cell_kinds(fig, backend) == ['3d', '3d'] + assert [len(ax.lines) for ax in fig.axes if ax.get_visible()] \ + == [2, 2] + else: + assert _cell_kinds(fig, backend) == ['scatter3d'] + assert len(_plotly_data_traces(fig)) == 4 + for model in bundle['panel_models']: + assert [np.shape(a) for a in model['xform_data']] \ + == [(18, 3), (18, 3)] + assert model['pipeline'] is not None + assert bundle['panel_models'][0]['pipeline'] \ + is not bundle['panel_models'][1]['pipeline'] + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_pipeline_input_gets_3d_cells(backend, fit): + """The same through ``pipeline=``: a Delay -> PCA(3) pipeline fitted + by the grid expands 2 columns to 3, and the cells follow. A pipeline + whose reduce stage keeps MORE than 3 (a bare 'PCA' keeps all 6 Delay + columns) is drawn through the single-axes call's own 3-D display + projection, as ``ndims > 3`` is.""" + data = _walks(2, seed=704) + pipe = hyp.Pipeline([('manip', DELAY3), + ('reduce', {'model': 'PCA', + 'kwargs': {'n_components': 3}})]) + bundle = hyp.plot(data, panels=True, panel_fit=fit, pipeline=pipe, + ndims=3, backend=backend, show=False, + return_model=True) + assert [np.shape(a) for a in bundle['xform_data']] == [(18, 3), (18, 3)] + if backend == 'matplotlib': + assert _cell_kinds(bundle['fig'], backend) == ['3d', '3d'] + else: + assert _cell_kinds(bundle['fig'], backend) == ['scatter3d'] + wide = hyp.Pipeline([('manip', DELAY3), ('reduce', 'PCA')]) + single = hyp.plot(data, pipeline=wide, ndims=3, backend=backend, + show=False, return_model=True) + assert [np.shape(a) for a in single['xform_data']] == [(18, 6), (18, 6)] + assert [np.shape(a) for a in single['trace_data']] == [(18, 3), (18, 3)] + wide = hyp.Pipeline([('manip', DELAY3), ('reduce', 'PCA')]) + bundle = hyp.plot(data, panels=True, panel_fit=fit, pipeline=wide, + ndims=3, backend=backend, show=False, + return_model=True) + assert [np.shape(a) for a in bundle['xform_data']] == [(18, 3), (18, 3)] + if backend == 'matplotlib': + assert _cell_kinds(bundle['fig'], backend) == ['3d', '3d'] + else: + assert _cell_kinds(bundle['fig'], backend) == ['scatter3d'] + if fit == 'shared': + for got, want in zip(bundle['xform_data'], single['trace_data']): + assert np.allclose(got, want) + + +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_no_panel_is_fitted_twice(fit): + """Deciding the cells from the analyzed data adds no fit: a real + reducer instance counts ONE fit for the shared grid and one PER PANEL + for the independent grid.""" + data = _walks(2, seed=705) + reducer = CountingPCA(n_components=3) + bundle = hyp.plot(data, panels=True, panel_fit=fit, manip=DELAY3, + reduce=reducer, ndims=3, show=False, + return_model=True) + assert reducer.fits == (1 if fit == 'shared' else 2) + assert [np.shape(a) for a in bundle['xform_data']] == [(18, 3), (18, 3)] + assert _cell_kinds(bundle['fig'], 'matplotlib') == ['3d', '3d'] + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fit', ('shared', 'independent')) +def test_narrow_data_still_gets_2d_cells(backend, fit): + """The round-6 rule is intact: 2-column data with no feature-expanding + stage keeps 2-D cells (the single-axes call draws it on 2-D axes).""" + data = _walks(2, seed=706) + bundle = hyp.plot(data, panels=True, panel_fit=fit, backend=backend, + show=False, return_model=True) + assert [np.shape(a) for a in bundle['xform_data']] == [(20, 2), (20, 2)] + if backend == 'matplotlib': + assert _cell_kinds(bundle['fig'], backend) \ + == ['rectilinear', 'rectilinear'] + else: + assert _cell_kinds(bundle['fig'], backend) == ['scatter'] + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_unequal_widths_under_independent_fit_share_3d_cells(backend): + """Independent fits of a 2-column and a 3-column dataset with + ``reduce=None``: one 3-D grid, the narrow panel drawn flat in its + cell on both backends (plotly's scene cell used to refuse it).""" + rng = np.random.default_rng(707) + data = [rng.normal(size=(20, 2)).cumsum(0), + rng.normal(size=(20, 3)).cumsum(0)] + bundle = hyp.plot(data, panels=True, panel_fit='independent', + reduce=None, backend=backend, show=False, + return_model=True) + if backend == 'matplotlib': + assert _cell_kinds(bundle['fig'], backend) == ['3d', '3d'] + else: + assert _cell_kinds(bundle['fig'], backend) == ['scatter3d'] + pts = _drawn_points(bundle['fig'], backend) + assert [p.shape[1] for p in pts] == [3, 3] + # the narrow panel's z is flat; its x/y are the data's + assert np.ptp(pts[0][:, 2]) == 0 + assert np.ptp(pts[0][:, 0]) > 0 and np.ptp(pts[0][:, 1]) > 0 + assert (np.ptp(pts[1], axis=0) > 0).all() + + +# ---------------------------- 2: a fmt colour letter takes no palette slot + +def _target(fig, backend): + """What a second call composes into: the first axes (matplotlib) or + the figure (plotly).""" + return fig.axes[0] if backend == 'matplotlib' else fig + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_fmt_letter_then_uncoloured_call_matches_the_single_call(backend): + """``hyp.plot(a, fmt='r-')`` then ``hyp.plot(b, ax=...)`` with the + same palette draws ``b`` in the FIRST palette colour -- what the + single call ``fmt=['r-', '-']`` draws it in (before: gold, the second + colour, on plotly).""" + a, b = _walks(2, cols=3, seed=708) + single = hyp.plot([a, b], fmt=['r-', '-'], palette=PALETTE, + backend=backend, show=False) + want = _line_colors(single, backend) + assert want == [_rgb255('r'), _rgb255('navy')] + fig = hyp.plot(a, fmt='r-', palette=PALETTE, backend=backend, + show=False) + hyp.plot(b, ax=_target(fig, backend), palette=PALETTE, backend=backend, + show=False) + assert _line_colors(fig, backend) == want + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_fmt_letter_in_an_empty_subplots_cell(backend): + """The same composition into an initially empty ``hyp.subplots`` cell + (before: gold on BOTH backends).""" + a, b = _walks(2, cols=3, seed=709) + fig, axes = hyp.subplots(1, 1, backend=backend) + hyp.plot(a, fmt='r-', palette=PALETTE, ax=axes[0], backend=backend, + show=False) + hyp.plot(b, palette=PALETTE, ax=axes[0], backend=backend, show=False) + assert _line_colors(fig, backend) == [_rgb255('r'), _rgb255('navy')] + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_only_cycle_coloured_datasets_consume_slots(backend): + """A mixed call (one lettered, one unlettered dataset) consumes ONE + slot: the third dataset composed in afterwards is gold, as in the + single call ``fmt=['r-', '-', '-']``.""" + a, b, c = _walks(3, cols=3, seed=710) + single = hyp.plot([a, b, c], fmt=['r-', '-', '-'], palette=PALETTE, + backend=backend, show=False) + want = _line_colors(single, backend) + assert want == [_rgb255('r'), _rgb255('navy'), _rgb255('gold')] + fig = hyp.plot([a, b], fmt=['r-', '-'], palette=PALETTE, + backend=backend, show=False) + hyp.plot(c, ax=_target(fig, backend), palette=PALETTE, backend=backend, + show=False) + assert _line_colors(fig, backend) == want + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('pin', ('color', 'hue')) +def test_explicit_colour_and_hue_consume_no_slot(backend, pin): + """An explicit ``color=`` or a ``hue=`` colouring pins its dataset + without touching the cycle, so the next call starts the palette.""" + a, b = _walks(2, cols=3, seed=711) + first = ({'color': 'k'} if pin == 'color' + else {'hue': np.arange(len(a))}) + fig = hyp.plot(a, palette=PALETTE, backend=backend, show=False, + **first) + hyp.plot(b, ax=_target(fig, backend), palette=PALETTE, backend=backend, + show=False) + assert _line_colors(fig, backend)[-1] == _rgb255('navy') + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_uncoloured_datasets_still_advance_the_palette(backend): + """The ordinary count is unchanged, and now the same on a plain figure + as in a cell on both backends: two uncoloured calls into one figure + draw navy then gold, like the single two-dataset call.""" + a, b = _walks(2, cols=3, seed=712) + single = hyp.plot([a, b], palette=PALETTE, backend=backend, show=False) + want = _line_colors(single, backend) + assert want == [_rgb255('navy'), _rgb255('gold')] + fig = hyp.plot(a, palette=PALETTE, backend=backend, show=False) + hyp.plot(b, ax=_target(fig, backend), palette=PALETTE, backend=backend, + show=False) + assert _line_colors(fig, backend) == want + grid, axes = hyp.subplots(1, 1, backend=backend) + hyp.plot(a, palette=PALETTE, ax=axes[0], backend=backend, show=False) + hyp.plot(b, palette=PALETTE, ax=axes[0], backend=backend, show=False) + assert _line_colors(grid, backend) == want diff --git a/tests/test_plot_review_round8.py b/tests/test_plot_review_round8.py new file mode 100644 index 00000000..c3f73eb9 --- /dev/null +++ b/tests/test_plot_review_round8.py @@ -0,0 +1,432 @@ +"""1.1 release review, round 8: three ``panels=``/composition defects +pinned at the public API on both backends. No mocks -- every assertion +reads the drawn artists/traces, the returned bundle, or a real clusterer +instance's own fit counter. + +2 ``panels=`` re-clustered every panel's analyzed rows without the + caller's ``random_state`` (a round-7 regression): the probe that fits + each panel's pipeline clustered once, seeded, and the panel call then + clustered AGAIN, unseeded, so ``panel_fit='independent'`` and reducer + grids drew cluster memberships the seeded single-axes call never draws + (and every clusterer fitted more often than before). Each panel now + replays its probe's fitted labels -- the memberships ARE the individual + call's, on both backends, in every fit mode -- and the bundle reports + them as ``models['cluster_labels']``. +3 on plotly, a call pinning its colour (``color=`` or a categorical + ``hue=``) into a figure/cell an ordinary call had drawn into reset the + palette count, so the NEXT ordinary call restarted the palette (navy + again where the single call, and matplotlib, give gold). The count is + now read for every composed call, whether or not it colours from the + cycle. +4 independent panels of a ONE-column and a three-column dataset crashed + both backends (only two-column rows were padded into the 3-D grid). + A one-column series is now drawn in the 3-D cell as row index vs value + on the cell's floor. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +from matplotlib.colors import to_rgb # noqa: E402 +from sklearn.cluster import KMeans # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot.plotly_backend import _rgb_triplet # noqa: E402 + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +BACKENDS = ('matplotlib', 'plotly') +PALETTE = ['navy', 'gold', 'green'] + + +def _clouds(n=2, rows=24, cols=5, seed=801): + rng = np.random.default_rng(seed) + return [rng.normal(size=(rows, cols)) for _ in range(n)] + + +def _rgb255(color): + if isinstance(color, str) and color.startswith(('rgb(', 'rgba(')): + return tuple(int(c) for c in _rgb_triplet(color)) + return tuple(int(round(c * 255)) for c in to_rgb(color)) + + +def _plotly_data_traces(fig): + return [tr for tr in fig.data + if getattr(tr, 'hoverinfo', None) != 'skip'] + + +def _line_colors(fig, backend): + """The data-line colours a figure draws, in drawing order.""" + if backend == 'matplotlib': + return [_rgb255(ln.get_color()) for ax in fig.axes + if ax.get_visible() for ln in ax.lines] + out = [] + for tr in _plotly_data_traces(fig): + color = tr.line.color if 'lines' in (tr.mode or '') \ + else tr.marker.color + out.append(_rgb255(color) if isinstance(color, str) else None) + return out + + +def _cluster_point_sets(fig, backend): + """Per cell, the drawn clusters as a list of point arrays (one per + cluster artist/trace), each sorted row-wise so two drawings of the + same memberships compare equal whatever order the points were drawn + in; the clusters themselves are ordered by size, then lexically.""" + cells = [] + if backend == 'matplotlib': + for ax in fig.axes: + if not ax.get_visible() or not ax.lines: + continue + cells.append([np.column_stack(ln.get_data_3d()) + for ln in ax.lines]) + else: + by_scene = {} + for tr in _plotly_data_traces(fig): + by_scene.setdefault(tr.scene, []).append( + np.column_stack([tr.x, tr.y, tr.z])) + cells = [by_scene[k] for k in sorted(by_scene)] + out = [] + for groups in cells: + groups = [g[np.lexsort(g.T[::-1])] for g in groups] + groups.sort(key=lambda g: (len(g), g.round(6).tolist())) + out.append(groups) + return out + + +def _assert_same_clusters(got, want): + assert len(got) == len(want) + assert [len(g) for g in got] == [len(w) for w in want] + for g, w in zip(got, want): + assert np.allclose(g, w) + + +class CountingKMeans(KMeans): + """A real sklearn clusterer that counts its own fits: the observable + for "a panel grid clusters no more often than its probes do".""" + + def __init__(self, n_clusters=3, random_state=802, n_init=1): + super().__init__(n_clusters=n_clusters, random_state=random_state, + n_init=n_init) + self.fits = 0 + + def fit(self, X, y=None, sample_weight=None): + self.fits += 1 + return super().fit(X, y, sample_weight=sample_weight) + + +CLUSTER = dict(cluster='KMeans', n_clusters=3, random_state=88) +DRAW = dict(reduce='PCA', ndims=3, fmt='o', antialias=False, show=False, + return_model=True) + + +# --------------------------- 2: panels replay the probe's seeded clustering + +@pytest.mark.parametrize('backend', BACKENDS) +def test_independent_panels_draw_the_individual_calls_clusters(backend): + """Every independent panel's drawn clusters (which points, in which + group) are exactly the seeded single-axes call's for that dataset, + and its bundle carries those memberships.""" + data = _clouds(seed=803) + singles = [hyp.plot(d, backend=backend, **CLUSTER, **DRAW) + for d in data] + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **CLUSTER, **DRAW) + want = [_cluster_point_sets(s['fig'], backend)[0] for s in singles] + got = _cluster_point_sets(grid['fig'], backend) + assert len(got) == 2 + for g, w in zip(got, want): + assert len(g) == 3 + _assert_same_clusters(g, w) + for panel, single in zip(grid['panel_models'], singles): + assert (list(panel['models']['cluster_labels']) + == list(single['models']['cluster_labels'])) + # the individual call's clustering is seeded and non-trivial: the + # comparison above can only pass by matching it + sizes = sorted(len(g) for g in want[0]) + assert sizes == sorted(len(g) for g in got[0]) and len(set(sizes)) > 1 + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_reducer_grid_panels_draw_the_individual_calls_clusters(backend): + """A per-reducer grid: each panel's clusters are the seeded joint + call's with that reducer (every dataset in the panel).""" + data = _clouds(seed=804) + reducers = ['PCA', 'IncrementalPCA'] + singles = [hyp.plot(data, backend=backend, + **{**DRAW, 'reduce': spec}, **CLUSTER) + for spec in reducers] + grid = hyp.plot(data, panels=2, backend=backend, + **{**DRAW, 'reduce': reducers}, **CLUSTER) + got = _cluster_point_sets(grid['fig'], backend) + assert len(got) == 2 + for g, single in zip(got, singles): + _assert_same_clusters(g, _cluster_point_sets(single['fig'], + backend)[0]) + for panel, single in zip(grid['panel_models'], singles): + assert (list(panel['models']['cluster_labels']) + == list(single['models']['cluster_labels'])) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_shared_panels_slice_the_one_seeded_clustering(backend): + """A shared fit clusters ONCE across every dataset (the joint call); + each panel replays its dataset's slice of those labels.""" + data = _clouds(seed=805) + joint = hyp.plot(data, backend=backend, **CLUSTER, **DRAW) + labels = list(joint['models']['cluster_labels']) + assert len(labels) == 48 + grid = hyp.plot(data, panels=2, panel_fit='shared', backend=backend, + **CLUSTER, **DRAW) + got = [list(m['models']['cluster_labels']) for m in grid['panel_models']] + assert got == [labels[:24], labels[24:]] + # ...and the drawn groups hold exactly those rows: as many drawn + # clusters per panel as labels its slice has, with the right sizes + for cell, slice_ in zip(_cluster_point_sets(grid['fig'], backend), got): + counts = sorted(slice_.count(k) for k in set(slice_)) + assert sorted(len(g) for g in cell) == counts + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_two_identical_seeded_grids_agree(backend): + """The seed contract itself: the same seeded grid twice draws the + same memberships (it did not, once each panel re-clustered + unseeded).""" + data = _clouds(seed=806) + grids = [hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **CLUSTER, **DRAW) for _ in range(2)] + first, second = (_cluster_point_sets(g['fig'], backend) for g in grids) + for a, b in zip(first, second): + _assert_same_clusters(a, b) + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('mode', ('shared', 'independent', 'reducers')) +def test_no_panel_clusters_again(backend, mode): + """A real clusterer instance fits exactly as often as the grid's + probes (each an ordinary single-axes call) fit it: once per probe + call's own count -- the panels' draws add NO fit.""" + data = _clouds(seed=807) + baseline = CountingKMeans() + hyp.plot(data, cluster=baseline, backend=backend, **DRAW) + per_call = baseline.fits + assert per_call >= 1 + counter = CountingKMeans() + if mode == 'reducers': + hyp.plot(data, panels=2, cluster=counter, backend=backend, + **{**DRAW, 'reduce': ['PCA', 'IncrementalPCA']}) + probes = 2 + else: + hyp.plot(data, panels=2, panel_fit=mode, cluster=counter, + backend=backend, **DRAW) + probes = 1 if mode == 'shared' else 2 + assert counter.fits == probes * per_call + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('spec', ( + dict(n_clusters=3), + dict(cluster={'model': 'KMeans', 'kwargs': {'n_clusters': 3}}), + dict(cluster='GaussianMixture', n_clusters=2), +)) +def test_every_cluster_spelling_replays(backend, spec): + """``n_clusters=`` alone, a dict spec and a mixture model (soft + labels: one proportion row per observation) all replay the probe's + labels, reported per panel and equal to the individual call's.""" + data = _clouds(seed=808) + kw = {**DRAW, 'random_state': 5, 'backend': backend, **spec} + singles = [hyp.plot(d, **kw) for d in data] + grid = hyp.plot(data, panels=2, panel_fit='independent', **kw) + for panel, single in zip(grid['panel_models'], singles): + got = np.asarray(panel['models']['cluster_labels']) + want = np.asarray(single['models']['cluster_labels']) + assert got.shape == want.shape and np.allclose(got, want) + assert panel['models']['cluster'] == single['models']['cluster'] + + +def test_the_bundle_reports_the_figures_cluster_labels(): + """``models['cluster_labels']`` is the per-observation labelling the + figure was grouped by (None without clustering), and it is what a + fresh ``hyp.cluster`` of the same analyzed rows with the same seed + gives.""" + data = _clouds(seed=809) + plain = hyp.plot(data, **DRAW) + assert plain['models']['cluster_labels'] is None + bundle = hyp.plot(data, **CLUSTER, **DRAW) + labels = bundle['models']['cluster_labels'] + assert len(labels) == 48 and set(labels) == {0, 1, 2} + again = hyp.cluster(bundle['xform_data'], cluster='KMeans', n_clusters=3, + random_state=88) + assert list(labels) == list(again) + assert 'cluster_labels' in hyp.plot.__doc__ + + +# ---------------- 3: a pinned call keeps the palette slots already taken + +@pytest.fixture(params=('figure', 'cell')) +def composed(request): + """A target to compose into, with one ordinary dataset already drawn + there (one palette slot taken): a plain figure or a `hyp.subplots` + cell.""" + def make(backend, x): + if request.param == 'figure': + fig = hyp.plot(x, palette=PALETTE, backend=backend, show=False) + target = fig.axes[0] if backend == 'matplotlib' else fig + return fig, target + fig, axes = hyp.subplots(1, 1, backend=backend) + hyp.plot(x, ax=axes[0], palette=PALETTE, backend=backend, show=False) + return fig, axes[0] + return make + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('pin', ('color', 'hue')) +def test_pinned_call_keeps_a_nonzero_offset(backend, pin, composed): + """ordinary -> pinned (``color=`` / categorical ``hue=``) -> ordinary: + the last dataset is GOLD (the second palette colour) on both backends, + as the single three-dataset call draws it.""" + x = np.random.default_rng(810).normal(size=(20, 3)) + fig, target = composed(backend, x) + pinned = ({'color': 'black'} if pin == 'color' + else {'hue': ['a'] * 10 + ['b'] * 10}) + hyp.plot(x + 1, ax=target, palette=PALETTE, backend=backend, show=False, + **pinned) + hyp.plot(x + 2, ax=target, palette=PALETTE, backend=backend, show=False) + colors = _line_colors(fig, backend) + assert colors[0] == _rgb255('navy') + assert colors[-1] == _rgb255('gold') + if pin == 'color': + assert colors[1] == _rgb255('black') + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_pinned_call_between_two_taken_slots(backend, composed): + """Two slots taken, a pinned call, then an ordinary one: GREEN -- + the offset is carried, not clamped or reset.""" + x = np.random.default_rng(811).normal(size=(20, 3)) + fig, target = composed(backend, x) + hyp.plot(x + 1, ax=target, palette=PALETTE, backend=backend, show=False) + hyp.plot(x + 2, ax=target, palette=PALETTE, backend=backend, show=False, + color='black') + hyp.plot(x + 3, ax=target, palette=PALETTE, backend=backend, show=False) + colors = _line_colors(fig, backend) + assert colors[:2] == [_rgb255('navy'), _rgb255('gold')] + assert colors[-1] == _rgb255('green') + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_pinned_call_consumes_no_slot_itself(backend, composed): + """The round-7 rule still holds from a non-zero offset: the pinned + call takes no slot, so the offset after it is exactly the one before + (gold, not green, follows one ordinary + one pinned call).""" + x = np.random.default_rng(812).normal(size=(20, 3)) + fig, target = composed(backend, x) + hyp.plot(x + 1, ax=target, palette=PALETTE, backend=backend, show=False, + hue=['a'] * 10 + ['b'] * 10) + hyp.plot(x + 2, ax=target, palette=PALETTE, backend=backend, show=False) + assert _line_colors(fig, backend)[-1] == _rgb255('gold') + assert _rgb255('green') not in _line_colors(fig, backend) + + +# ------------------ 4: one-column panels in a mixed-width independent grid + +def _cell_kinds(fig, backend): + if backend == 'matplotlib': + return [ax.name for ax in fig.axes if ax.get_visible()] + return sorted({tr.type for tr in _plotly_data_traces(fig)}) + + +def _first_trace_points(fig, backend): + """Per cell, the coordinates of its first data line/trace.""" + if backend == 'matplotlib': + return [np.column_stack(ax.lines[0].get_data_3d() + if ax.name == '3d' else ax.lines[0].get_data()) + for ax in fig.axes if ax.get_visible()] + return [np.column_stack([tr.x, tr.y] + ([tr.z] if tr.type == 'scatter3d' + else [])) + for tr in _plotly_data_traces(fig)] + + +def _mixed(widths, seed=813): + rng = np.random.default_rng(seed) + return [rng.normal(size=(24, w)).cumsum(0) for w in widths] + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('widths', ([1, 3], [1, 2], [2, 3])) +def test_mixed_widths_under_independent_fit(backend, widths): + """Independent panels of unequal widths construct on both backends + and share one grid: 3-D cells when any panel is 3 wide (the narrow + panel on the floor), 2-D cells otherwise. A one-column panel is a + series -- evenly spaced increasing x, its values on y.""" + data = _mixed(widths) + bundle = hyp.plot(data, panels=2, panel_fit='independent', reduce=None, + backend=backend, show=False, return_model=True, + antialias=False) + three_d = max(widths) == 3 + if backend == 'matplotlib': + assert _cell_kinds(bundle['fig'], backend) == ( + ['3d', '3d'] if three_d else ['rectilinear', 'rectilinear']) + else: + assert _cell_kinds(bundle['fig'], backend) == ( + ['scatter3d'] if three_d else ['scatter']) + pts = _first_trace_points(bundle['fig'], backend) + assert [p.shape for p in pts] == [(24, 3 if three_d else 2)] * 2 + narrow, wide = pts + values = data[0][:, 0] + if widths[0] == 1: + x = narrow[:, 0].astype(float) + assert (np.diff(x) > 0).all() and np.allclose(np.diff(x), np.diff(x)[0]) + y = narrow[:, 1].astype(float) + assert np.corrcoef(y, values)[0, 1] > 0.999999 + else: + assert np.ptp(narrow[:, 0]) > 0 and np.ptp(narrow[:, 1]) > 0 + if three_d: + assert np.ptp(narrow[:, 2].astype(float)) == 0 + assert (np.ptp(wide.astype(float), axis=0) > 0).all() + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('widths', ([1, 3], [1, 2], [2, 3])) +def test_mixed_widths_under_a_shared_fit_are_refused_as_documented( + backend, widths): + """One pipeline cannot be fit across datasets of unequal width; the + shared grid says so (the single-axes error, naming the widths) rather + than crashing inside a backend.""" + with pytest.raises(ValueError, match=r'column counts \[%d, %d\]' + % tuple(widths)): + hyp.plot(_mixed(widths), panels=2, panel_fit='shared', reduce=None, + backend=backend, show=False) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_one_column_frame_with_a_date_index_in_a_3d_grid(backend): + """A dated one-column frame beside a 3-column dataset: drawn by row + position (a scene has no date axis), its values intact.""" + rng = np.random.default_rng(814) + series = pd.DataFrame(rng.normal(size=(24, 1)).cumsum(0), + index=pd.date_range('2024-01-01', periods=24, + freq='D'), columns=['v']) + bundle = hyp.plot([series, rng.normal(size=(24, 3))], panels=2, + panel_fit='independent', reduce=None, backend=backend, + show=False, return_model=True, antialias=False) + narrow = _first_trace_points(bundle['fig'], backend)[0].astype(float) + assert narrow.shape == (24, 3) + assert (np.diff(narrow[:, 0]) > 0).all() + assert np.corrcoef(narrow[:, 1], series['v'].to_numpy())[0, 1] > 0.999999 + assert np.ptp(narrow[:, 2]) == 0 + + +def test_panels_docstring_describes_the_cells_from_the_analyzed_width(): + doc = hyp.plot.__doc__ + assert 'ANALYZED data' in doc + assert 'UNEQUAL analyzed' in doc + assert '1-column series as row index vs value' in doc diff --git a/tests/test_plot_review_round9.py b/tests/test_plot_review_round9.py new file mode 100644 index 00000000..ab251bc2 --- /dev/null +++ b/tests/test_plot_review_round9.py @@ -0,0 +1,481 @@ +"""1.1 release review, round 9: three ``panels=``/composition defects +pinned at the public API on both backends. No mocks -- every assertion +reads the drawn artists/traces or the returned bundle, and compares them +with a separate individual call. + +1 SHARED clustered panels lost the joint call's label-to-colour mapping: + each panel replayed its own slice of the one seeded clustering but + coloured it from the labels present in THAT slice, so two panels + holding global clusters 1 and 0 both drew the first palette colour + where the joint figure drew them in two. Every cell now colours (and + names in its legend) each cluster exactly as the joint figure does. +2 a NARROW panel of a mixed-width independent grid (a 1- or 2-column + dataset beside a 3-column one) was padded to ``(index, value, 0)`` + BEFORE the panel call, so the padded rows became the forecasting + input: its Kalman forecast differed from the individual call's, a + forecaster fitted on the one-column data refused the three padded + features, and a one-column ``truth=`` was rejected. The panel now + forecasts, resolves ``truth=`` and reports its bundle in the analyzed + space (the individual call's numbers) and lifts only the DRAWN rows, + forecast and truth into the 3-D cell. +3 a categorical ``hue=`` with a marker-only fmt (``'o'``) advanced the + palette on a composed axes/figure/cell: its groups were drawn straight + from the ambient cycle (which also let a colour letter, ``'ro'``, paint + every group red). Hue ownership is now explicit in the palette + accounting, and the marker path resolves its category colours as the + line path always did. +""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +from matplotlib.colors import to_rgb # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot.plotly_backend import _rgb_triplet # noqa: E402 + + +@pytest.fixture(autouse=True) +def _close_figures(): + yield + plt.close('all') + + +BACKENDS = ('matplotlib', 'plotly') +PALETTE = ['navy', 'gold', 'green', 'purple'] + + +def _rgb255(color): + if isinstance(color, str) and color.startswith(('rgb(', 'rgba(')): + return tuple(int(c) for c in _rgb_triplet(color)) + return tuple(int(round(c * 255)) for c in to_rgb(color)) + + +def _plotly_data_traces(fig): + return [tr for tr in fig.data + if getattr(tr, 'hoverinfo', None) != 'skip'] + + +def _trace_color(tr): + return tr.line.color if 'lines' in (tr.mode or '') else tr.marker.color + + +def _line_colors(fig, backend): + """The data-line colours a figure draws, in drawing order.""" + if backend == 'matplotlib': + return [_rgb255(ln.get_color()) for ax in fig.axes + if ax.get_visible() for ln in ax.lines] + return [_rgb255(_trace_color(tr)) for tr in _plotly_data_traces(fig)] + + +# --------------- 1: shared clustered panels keep the joint colour mapping + +def _clouds(centres, rows=20, seed0=901): + return [np.random.default_rng(seed0 + i).normal(scale=0.01, + size=(rows, 3)) + c + for i, c in enumerate(centres)] + + +CLUSTER = dict(reduce=None, cluster='KMeans', random_state=88, + legend=True, antialias=False, show=False, return_model=True) + + +def _labelled_colors(fig, backend, models): + """{cluster label: colour} as drawn, per cell, read off the artists + (matplotlib) or traces (plotly) together with the bundle's replayed + labels -- what a reader of the figure sees for each global cluster.""" + out = [] + if backend == 'matplotlib': + cells = [ax for ax in fig.axes if ax.get_visible() and ax.lines] + for ax, m in zip(cells, models): + legend = ax.get_legend() + names = [t.get_text() for t in legend.get_texts()] + handles = [_rgb255(h.get_color()) for h in legend.legend_handles] + out.append({ + 'labels': sorted(set(np.asarray( + m['models']['cluster_labels']).tolist())), + 'legend': dict(zip(names, handles)), + 'lines': [_rgb255(ln.get_color()) for ln in ax.lines], + }) + return out + by_scene = {} + for tr in _plotly_data_traces(fig): + by_scene.setdefault(tr.scene, []).append(tr) + scenes = sorted(by_scene, key=lambda s: (len(s or ''), s or '')) + for scene, m in zip(scenes, models): + traces = by_scene[scene] + out.append({ + 'labels': sorted(set(np.asarray( + m['models']['cluster_labels']).tolist())), + 'legend': {tr.name: _rgb255(_trace_color(tr)) for tr in traces}, + 'lines': [_rgb255(_trace_color(tr)) for tr in traces], + }) + return out + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fmt', ('o', '-')) +@pytest.mark.parametrize('palette', ('hls', ['red', 'blue', 'green'])) +def test_shared_panels_colour_each_cluster_as_the_joint_figure_does( + backend, fmt, palette): + """Three well-separated clouds, one seeded 3-cluster fit: every panel + holds exactly one global cluster, and draws it -- and names it in + its legend -- in the colour the joint figure gives that label. The + default 'hls' palette is included because its colours depend on how + many are asked for: a panel colouring from its OWN one-label set got + hls(1), not the joint figure's hls(3).""" + data = _clouds([-10, 0, 10]) + joint = hyp.plot(data, n_clusters=3, fmt=fmt, palette=palette, + backend=backend, **CLUSTER) + grid = hyp.plot(data, n_clusters=3, fmt=fmt, palette=palette, + backend=backend, panels=3, **CLUSTER) + joint_map = _labelled_colors(joint['fig'], backend, [joint])[0]['legend'] + assert sorted(joint_map) == ['0', '1', '2'] + assert len(set(joint_map.values())) == 3 + cells = _labelled_colors(grid['fig'], backend, grid['panel_models']) + assert len(cells) == 3 + seen = [] + for cell in cells: + assert len(cell['labels']) == 1 + label = cell['labels'][0] + seen.append(label) + # the legend names the GLOBAL label, in the joint figure's colour + assert cell['legend'] == {str(label): joint_map[str(label)]} + # ...and every drawn artist of the cell is that colour + assert set(cell['lines']) == {joint_map[str(label)]} + assert sorted(seen) == [0, 1, 2] + # the three panels draw three DIFFERENT colours (no two clusters + # collapsed onto the first palette entry) + assert len({cell['lines'][0] for cell in cells}) == 3 + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_shared_panel_missing_a_cluster_keeps_the_others_colours(backend): + """The reviewer's probe: two clouds, two clusters, ``palette=['red', + 'blue']``. The joint figure draws label 0 red and label 1 blue; the + panel holding label 1 must be blue, not red.""" + data = _clouds([-10, 10], rows=24) + joint = hyp.plot(data, n_clusters=2, fmt='o', palette=['red', 'blue'], + backend=backend, **CLUSTER) + grid = hyp.plot(data, n_clusters=2, fmt='o', palette=['red', 'blue'], + backend=backend, panels=2, **CLUSTER) + joint_map = _labelled_colors(joint['fig'], backend, [joint])[0]['legend'] + assert joint_map == {'0': _rgb255('red'), '1': _rgb255('blue')} + cells = _labelled_colors(grid['fig'], backend, grid['panel_models']) + for cell in cells: + (label,) = cell['labels'] + assert cell['lines'] == [joint_map[str(label)]] + assert cell['legend'] == {str(label): joint_map[str(label)]} + assert {cell['lines'][0] for cell in cells} == { + _rgb255('red'), _rgb255('blue')} + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_independent_and_reducer_panels_still_colour_by_sorted_label( + backend): + """The mapping is applied in every fit mode: a panel drawing all of + its probe's clusters colours them exactly as its individual call.""" + data = [np.vstack(_clouds([-10, 0, 10], rows=8, seed0=910 + 3 * k)) + for k in range(2)] + for mode in ('independent', 'reducer'): + kw = dict(n_clusters=3, fmt='o', palette='hls', backend=backend) + kw.update(CLUSTER) + if mode == 'reducer': + kw['reduce'] = ['PCA', 'PCA'] + grid = hyp.plot(data, panels=2, **kw, + **({'panel_fit': 'independent'} + if mode == 'independent' else {})) + cells = _labelled_colors(grid['fig'], backend, grid['panel_models']) + for i, cell in enumerate(cells): + single_kw = dict(kw) + if mode == 'reducer': + single_kw['reduce'] = 'PCA' + single = hyp.plot(data[i] if mode == 'independent' else data, + **single_kw) + want = _labelled_colors(single['fig'], backend, [single])[0] + assert cell['legend'] == want['legend'] + assert cell['lines'] == want['lines'] + + +# --------- 2: narrow panels forecast in the analyzed space, drawn lifted + +def _mixed(widths, seed=913): + rng = np.random.default_rng(seed) + return [rng.normal(size=(24, w)).cumsum(0) for w in widths] + + +FORECAST = dict(reduce=None, predict='Kalman', t=3, antialias=False, + show=False, return_model=True) + + +def _cell_traces(fig, backend, cell): + """(data, forecast, truth) drawn rows of one grid cell, each an + (n, 3) float array (None when that overlay is absent).""" + if backend == 'matplotlib': + ax = [a for a in fig.axes if a.get_visible()][cell] + rows = [np.column_stack(ln.get_data_3d()).astype(float) + for ln in ax.lines] + data, rest = rows[0], rows[1:] + forecast = rest[0] if rest else None + truth = rest[1] if len(rest) > 1 else None + return data, forecast, truth + scene = 'scene' if cell == 0 else f'scene{cell + 1}' + traces = [tr for tr in fig.data if tr.scene == scene + and tr.type == 'scatter3d'] + by_role = {} + for tr in traces: + meta = tr.meta if isinstance(tr.meta, dict) else {} + role = meta.get('hyp_forecast_role', 'data' if 'hyp_trace_index' + in meta else None) + if role is not None: + by_role[role] = np.column_stack([tr.x, tr.y, tr.z]).astype(float) + return by_role['data'], by_role.get('static'), by_role.get('truth') + + +def _affine(values, drawn): + """The affine map (slope, intercept) from analyzed `values` to the + `drawn` display coordinate -- exact for the unit-box rescale -- with + its fit residual asserted to be zero.""" + slope, intercept = np.polyfit(values, drawn, 1) + assert np.allclose(slope * values + intercept, drawn, atol=1e-9) + return slope, intercept + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('widths', ([1, 3], [2, 3])) +def test_narrow_panel_forecasts_are_the_individual_calls(backend, widths): + """The reviewer's numeric probe: the narrow panel's bundled forecast + is EXACTLY the individual call's (same shape, same numbers), and the + bundle stays in the analyzed space.""" + data = _mixed(widths) + single = hyp.plot(data[0], backend=backend, **FORECAST) + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **FORECAST) + panel = grid['panel_models'][0] + want = np.asarray(single['predict']['forecasts'][0]) + got = np.asarray(panel['predict']['forecasts'][0]) + assert got.shape == want.shape == (3, widths[0]) + assert np.array_equal(got, want) + assert np.asarray(panel['xform_data'][0]).shape == (24, widths[0]) + assert np.asarray(panel['trace_data'][0]).shape == (24, widths[0]) + # ...and both equal hyp.predict on the analyzed rows themselves + direct = np.asarray(hyp.predict(data[0], model='Kalman', t=3)) + assert np.allclose(got, direct) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_one_column_panel_draws_its_forecast_along_the_series(backend): + """The DRAWN forecast of a one-column panel continues the series: + x steps on by one row per forecast step, its y values are the + forecast values through the same affine the data went through, and + it stays on the cell's floor.""" + data = _mixed([1, 3]) + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **FORECAST) + drawn, forecast, _ = _cell_traces(grid['fig'], backend, 0) + assert drawn.shape == (24, 3) and forecast.shape == (4, 3) + step = drawn[1, 0] - drawn[0, 0] + assert step > 0 + assert np.allclose(np.diff(drawn[:, 0]), step) + # seam row = the last observed row; then one step per forecast row + assert np.allclose(forecast[0], drawn[-1]) + assert np.allclose(forecast[:, 0], drawn[-1, 0] + step * np.arange(4)) + assert np.allclose(forecast[:, 2], drawn[0, 2]) + assert np.ptp(drawn[:, 2]) == 0 + values = data[0][:, 0] + slope, intercept = _affine(values, drawn[:, 1]) + fc = np.asarray(grid['panel_models'][0]['predict']['forecasts'][0])[:, 0] + assert np.allclose(forecast[1:, 1], slope * fc + intercept, atol=1e-9) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_two_column_panel_draws_its_forecast_on_the_floor(backend): + data = _mixed([2, 3]) + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **FORECAST) + drawn, forecast, _ = _cell_traces(grid['fig'], backend, 0) + assert forecast.shape == (4, 3) + assert np.allclose(forecast[0], drawn[-1]) + assert np.ptp(np.concatenate([drawn[:, 2], forecast[:, 2]])) == 0 + fc = np.asarray(grid['panel_models'][0]['predict']['forecasts'][0]) + for col in range(2): + slope, intercept = _affine(data[0][:, col], drawn[:, col]) + assert np.allclose(forecast[1:, col], slope * fc[:, col] + intercept, + atol=1e-9) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_a_forecaster_fitted_on_the_narrow_data_is_reused(backend): + """The reviewer's fitted-model case: a Kalman fitted on BOTH + datasets (`hyp.predict(x, return_model=True)`) is bound to each + panel's own dataset and forecasts its analyzed rows -- the same + numbers `fitted.for_dataset(0)` gives the individual call -- instead + of refusing three padded features.""" + data = _mixed([1, 3]) + fitted = hyp.predict(data, model='Kalman', t=3, return_model=True)[1] + kw = dict(FORECAST) + kw['predict'] = fitted + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **kw) + kw['predict'] = fitted.for_dataset(0) + single = hyp.plot(data[0], backend=backend, **kw) + got = np.asarray(grid['panel_models'][0]['predict']['forecasts'][0]) + want = np.asarray(single['predict']['forecasts'][0]) + assert got.shape == want.shape == (3, 1) + assert np.array_equal(got, want) + # a forecaster fitted on the one-column dataset ALONE is reused for + # EVERY panel (the reviewer's first probe): it fits the one-column + # panel and is refused by the three-column one -- the same refusal + # the individual three-column call gives, naming ITS width (the + # one-column panel no longer offers three padded features) + kw['predict'] = hyp.predict(data[0], model='Kalman', t=3, + return_model=True)[1] + with pytest.raises(ValueError, match='expects 1 feature') as grid_err: + hyp.plot(data, panels=2, panel_fit='independent', backend=backend, + **kw) + with pytest.raises(ValueError, match='expects 1 feature') as single_err: + hyp.plot(data[1], backend=backend, **kw) + assert str(grid_err.value) == str(single_err.value) + assert 'new dataset has 3' in str(grid_err.value) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_one_column_truth_is_accepted_and_drawn_along_the_series(backend): + """`truth=` is read in the analyzed space (one column for the + one-column panel, as the individual call reads it) and drawn where + the forecast is drawn: continuing the series' x, its values through + the data's affine, on the floor.""" + data = _mixed([1, 3]) + held = [data[0][-3:, :] + 0.5, data[1][-3:, :] + 0.5] + grid = hyp.plot(data, panels=2, panel_fit='independent', truth=held, + backend=backend, **FORECAST) + drawn, forecast, truth = _cell_traces(grid['fig'], backend, 0) + assert truth is not None and truth.shape == (4, 3) + step = drawn[1, 0] - drawn[0, 0] + assert np.allclose(truth[0], drawn[-1]) + assert np.allclose(truth[:, 0], drawn[-1, 0] + step * np.arange(4)) + assert np.allclose(truth[:, 2], drawn[0, 2]) + slope, intercept = _affine(data[0][:, 0], drawn[:, 1]) + assert np.allclose(truth[1:, 1], slope * held[0][:, 0] + intercept, + atol=1e-9) + # the individual call accepts the very same truth + hyp.plot(data[0], truth=held[0], backend=backend, **FORECAST) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_dated_one_column_panel_forecast_continues_by_position(backend): + """A dated one-column frame is drawn by row position in a 3-D cell + (no date axis in a scene); its forecast continues that position axis + one row per step, and its bundle is still the individual call's.""" + rng = np.random.default_rng(914) + series = pd.DataFrame(rng.normal(size=(24, 1)).cumsum(0), + index=pd.date_range('2024-01-01', periods=24, + freq='D'), columns=['v']) + data = [series, rng.normal(size=(24, 3)).cumsum(0)] + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **FORECAST) + single = hyp.plot(series, backend=backend, **FORECAST) + assert np.array_equal( + np.asarray(grid['panel_models'][0]['predict']['forecasts'][0]), + np.asarray(single['predict']['forecasts'][0])) + drawn, forecast, _ = _cell_traces(grid['fig'], backend, 0) + step = drawn[1, 0] - drawn[0, 0] + assert np.allclose(forecast[:, 0], drawn[-1, 0] + step * np.arange(4)) + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_two_d_mixed_grid_bundle_is_unchanged(backend): + """A [1, 2] grid draws 2-D cells (no lift): its narrow panel's + bundle is the individual call's, exactly as before.""" + data = _mixed([1, 2]) + single = hyp.plot(data[0], backend=backend, **FORECAST) + grid = hyp.plot(data, panels=2, panel_fit='independent', + backend=backend, **FORECAST) + assert np.array_equal( + np.asarray(grid['panel_models'][0]['predict']['forecasts'][0]), + np.asarray(single['predict']['forecasts'][0])) + assert '_panel_lift' not in hyp.plot.__doc__ + + +# ------------- 3: marker-only categorical hue consumes no palette slot + +@pytest.fixture(params=('figure', 'cell')) +def composed(request): + """A target to compose into, with one ordinary dataset already drawn + there (one palette slot taken): a plain figure or a `hyp.subplots` + cell.""" + def make(backend, x): + if request.param == 'figure': + fig = hyp.plot(x, palette=PALETTE, backend=backend, show=False) + target = fig.axes[0] if backend == 'matplotlib' else fig + return fig, target + fig, axes = hyp.subplots(1, 1, backend=backend) + hyp.plot(x, ax=axes[0], palette=PALETTE, backend=backend, show=False) + return fig, axes[0] + return make + + +HUE = ['a'] * 12 + ['b'] * 12 + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fmt', ('o', '.', 'ro', 'r-o', '-')) +def test_categorical_hue_consumes_no_slot_whatever_its_fmt( + backend, fmt, composed): + """ordinary -> categorical hue (any fmt) -> ordinary: the last + dataset is GOLD (the second palette colour), as it is for the line + fmt and as the single call gives it; and the hue's own groups take + the palette's first two colours regardless of what was drawn before + -- a colour letter in the fmt does not paint them.""" + x = np.random.default_rng(916).normal(size=(24, 3)) + fig, target = composed(backend, x) + hyp.plot(x + 1, ax=target, palette=PALETTE, backend=backend, show=False, + hue=HUE, fmt=fmt) + hyp.plot(x + 2, ax=target, palette=PALETTE, backend=backend, show=False) + colors = _line_colors(fig, backend) + assert colors[0] == _rgb255('navy') + assert colors[-1] == _rgb255('gold') + assert _rgb255('purple') not in colors + hue_colors = set(colors[1:-1]) + assert hue_colors == {_rgb255('navy'), _rgb255('gold')} + + +@pytest.mark.parametrize('backend', BACKENDS) +@pytest.mark.parametrize('fmt', ('ro', 'r.', 'r-')) +def test_hue_colours_beat_a_fmt_colour_letter_on_every_path(backend, fmt): + """On a fresh figure too: ``hue=`` with ``'ro'`` colours the groups + navy/gold, exactly as ``'r-'`` always did (the marker path used to + let the letter paint every group red).""" + x = np.random.default_rng(917).normal(size=(24, 3)) + fig = hyp.plot(x, hue=HUE, fmt=fmt, palette=PALETTE, backend=backend, + show=False) + assert _line_colors(fig, backend) == [_rgb255('navy'), _rgb255('gold')] + + +@pytest.mark.parametrize('backend', BACKENDS) +def test_marker_hue_colours_match_the_single_three_dataset_call(backend): + """The composed sequence ordinary -> hue (fmt='o') -> ordinary draws + the same colours as one call drawing the same three things.""" + x = np.random.default_rng(918).normal(size=(24, 3)) + fig = hyp.plot(x, palette=PALETTE, backend=backend, show=False) + target = fig.axes[0] if backend == 'matplotlib' else fig + hyp.plot(x + 1, ax=target, palette=PALETTE, backend=backend, show=False, + hue=HUE, fmt='o') + hyp.plot(x + 2, ax=target, palette=PALETTE, backend=backend, show=False) + composed_colors = _line_colors(fig, backend) + two = hyp.plot([x, x + 2], palette=PALETTE, backend=backend, show=False) + assert [composed_colors[0], composed_colors[-1]] == _line_colors( + two, backend) + grouped = hyp.plot(x + 1, hue=HUE, fmt='o', palette=PALETTE, + backend=backend, show=False) + assert composed_colors[1:-1] == _line_colors(grouped, backend) + + +def test_palette_slots_docstring_names_hue_ownership(): + from hypertools.plot.plot import _palette_slots_consumed + doc = _palette_slots_consumed.__doc__ + assert 'category_colored' in doc + assert "fmt='o'" in doc diff --git a/tests/test_plot_series_mode.py b/tests/test_plot_series_mode.py index 8e20d9cd..d588f5c5 100644 --- a/tests/test_plot_series_mode.py +++ b/tests/test_plot_series_mode.py @@ -16,6 +16,7 @@ import matplotlib.pyplot as plt # noqa: E402 import hypertools as hyp # noqa: E402 +from hypertools._shared.helpers import UNIT_FRAME_LIMIT def _data_lines(fig): @@ -86,12 +87,14 @@ def test_a_datetime_index_gives_real_dates_on_the_axis(): x = np.asarray(line.get_xdata()) assert mdates.num2date(x[0]).date() == index[0].date() assert mdates.num2date(x[-1]).date() == index[-1].date() - # a real date axis: matplotlib's own date locator/formatter pair, so - # the ticks read '2024-01-05' rather than '19727.0' + # a real date axis: matplotlib's own date locator with its CONCISE + # formatter, so the ticks read dates rather than '19727.0' -- and do + # not collide, as the default formatter's full 'YYYY-MM-DD' labels did + # (1.1 release review, F13) assert isinstance(fig.axes[0].xaxis.get_major_locator(), mdates.AutoDateLocator) assert isinstance(fig.axes[0].xaxis.get_major_formatter(), - mdates.AutoDateFormatter) + mdates.ConciseDateFormatter) plt.close(fig) @@ -117,7 +120,7 @@ def test_series_mode_defaults_to_the_data_scale_and_can_be_overridden(): antialias=False, show=False) y = np.asarray(_data_lines(unit)[0].get_ydata()) assert y.min() >= -1.0 - 1e-9 and y.max() <= 1.0 + 1e-9 - assert unit.axes[0].get_ylim() == (-1.1, 1.1) + assert unit.axes[0].get_ylim() == (-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT) plt.close(unit) @@ -201,9 +204,12 @@ def test_plotly_datetime_series_uses_a_real_date_axis(): fig = hyp.plot(df, reduce=None, ndims=1, backend='plotly', antialias=False, show=False) assert fig.layout.xaxis.type == 'date' - x = np.asarray(fig.data[0].x, dtype=float) - assert pd.to_datetime(x[0], unit='ms') == index[0] - assert pd.to_datetime(x[-1], unit='ms') == index[-1] + # naive date STRINGS, not epoch-ms numbers: plotly.js draws a numeric + # date in the viewer's local time zone (1.1 release review) + x = fig.data[0].x + assert all(isinstance(v, str) for v in x) + assert pd.to_datetime(x[0]) == index[0] + assert pd.to_datetime(x[-1]) == index[-1] def test_a_timezone_aware_index_reaches_both_backends(): @@ -224,8 +230,10 @@ def test_a_timezone_aware_index_reaches_both_backends(): pfig = hyp.plot(df, reduce=None, ndims=1, backend='plotly', antialias=False, show=False) - px = np.asarray(pfig.data[0].x, dtype=float) - assert pd.to_datetime(px[0], unit='ms', utc=True) == \ + # drawn at its UTC instant, as a naive date string (the matplotlib + # date numbers above are UTC too) + px = pfig.data[0].x + assert pd.to_datetime(px[0]).tz_localize('UTC') == \ index[0].tz_convert('UTC') @@ -268,3 +276,140 @@ def test_a_bare_rgb_tuple_stays_one_colour(): assert tuple(np.round(np.asarray(line.get_color(), dtype=float), 3)) \ == (1.0, 0.0, 0.0) plt.close(fig) + + +# --- 1.1 release-review fixes ------------------------------------------ + +def _dated_frame(cols, seed=0, rows=30): + rng = np.random.default_rng(seed) + index = pd.date_range('2020-01-01', periods=rows, freq='D') + names = ['val'] if cols == 1 else list('abc')[:cols] + return pd.DataFrame(np.cumsum(rng.normal(size=(rows, cols)), axis=0), + index=index, columns=names), index + + +def test_F4_a_dated_column_hierarchy_draws_dates_for_every_trace(): + """`_capture_row_indices` saw ONE frame, so only leaf 0 kept the index + and series mode refused the mix ("some datasets carry a DatetimeIndex + and others do not").""" + rng = np.random.default_rng(4) + index = pd.date_range('2020-01-01', periods=30, freq='D') + columns = pd.MultiIndex.from_product([['g1', 'g2'], ['a', 'b', 'c']]) + frame = pd.DataFrame(np.cumsum(rng.normal(size=(30, 6)), axis=0), + index=index, columns=columns) + fig = hyp.plot(frame, ndims=1, antialias=False, show=False) + try: + lines = _data_lines(fig) + assert len(lines) >= 2 + expected = mdates.date2num(index.to_pydatetime()) + for line in lines: + assert np.allclose(np.asarray(line.get_xdata(), dtype=float), + expected) + finally: + plt.close(fig) + + +def test_F6_return_model_forecasts_are_one_array_per_input_dataset(): + """`predict['forecasts']` used to hold one ``(t, 2)`` ``[x, value]`` + array per drawn COLUMN; it now matches ``hyp.predict``: one + ``(t, n_columns)`` array of values per input dataset.""" + frame, _ = _dated_frame(3, seed=6) + t = 4 + bundle = hyp.plot(frame, ndims=1, reduce=None, predict='Kalman', t=t, + return_model=True, antialias=False, show=False) + try: + forecasts = bundle['predict']['forecasts'] + assert isinstance(forecasts, list) and len(forecasts) == 1 + assert forecasts[0].shape == (t, 3) + assert forecasts[0].shape == hyp.predict( + bundle['xform_data'][0], model='Kalman', t=t).shape + # the drawn overlays carry exactly those values, column by column + overlays = [line for line in bundle['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == 'static'] + assert len(overlays) == 3 + for j, line in enumerate(overlays): + assert np.allclose(np.asarray(line.get_ydata())[1:], + forecasts[0][:, j]) + finally: + plt.close(bundle['fig']) + other = frame * 2.0 + bundle = hyp.plot([frame, other], ndims=1, reduce=None, predict='Kalman', + t=t, return_model=True, show=False) + try: + forecasts = bundle['predict']['forecasts'] + assert [f.shape for f in forecasts] == [(t, 3), (t, 3)] + finally: + plt.close(bundle['fig']) + + +def test_S1_fmt_list_is_one_entry_per_drawn_column(): + frame, _ = _dated_frame(3, seed=1) + fig = hyp.plot(frame, ndims=1, reduce=None, fmt=['-', ':', '--'], + antialias=False, show=False) + try: + assert [line.get_linestyle() for line in _data_lines(fig)] == \ + ['-', ':', '--'] + finally: + plt.close(fig) + + +def test_S3_a_reduced_named_frame_does_not_label_y_dataset_1(): + frame, _ = _dated_frame(3, seed=3) + fig = hyp.plot(frame, ndims=1, show=False) # default reduce -> 1 + try: + assert len(_data_lines(fig)) == 1 + assert fig.axes[0].get_ylabel() == '' + finally: + plt.close(fig) + single, _ = _dated_frame(1, seed=3) + fig = hyp.plot(single, ndims=1, show=False) + try: + assert fig.axes[0].get_ylabel() == 'val' # a real column name + finally: + plt.close(fig) + + +def test_S4_a_3d_axes_for_a_2d_or_series_plot_is_refused(): + fig, axes = hyp.subplots(1, 1, ndims=3) + try: + with pytest.raises(ValueError, match='3-D axes'): + hyp.plot(np.random.default_rng(0).normal(size=(30, 2)), + reduce=None, ndims=2, ax=axes[0], show=False) + with pytest.raises(ValueError, match='3-D axes'): + hyp.plot(np.random.default_rng(0).normal(size=(30, 1)), + reduce=None, ndims=1, ax=axes[0], show=False) + assert len(axes[0].lines) == 0 # nothing was drawn + finally: + plt.close(fig) + fig, axes = hyp.subplots(1, 1, ndims=2) + try: + out = hyp.plot(np.random.default_rng(0).normal(size=(30, 2)), + reduce=None, ndims=2, ax=axes[0], show=False) + assert out is fig and len(axes[0].lines) >= 1 + finally: + plt.close(fig) + + +@pytest.mark.parametrize('freq, unit, divisor', [ + ('1h', 'hours', 3600.0), ('1D', 'days', 86400.0), + ('30s', 'seconds', 1.0), ('5min', 'minutes', 60.0)]) +def test_S4_a_timedelta_index_is_drawn_in_a_sensible_unit(freq, unit, + divisor): + """A TimedeltaIndex used to be drawn as raw nanoseconds (x up to 1e14).""" + index = pd.timedelta_range('0s', periods=30, freq=freq) + frame = pd.DataFrame({'v': np.arange(30.0)}, index=index) + fig = hyp.plot(frame, ndims=1, antialias=False, show=False) + try: + (line,) = _data_lines(fig) + expected = np.asarray(index.total_seconds(), dtype=float) / divisor + assert np.allclose(np.asarray(line.get_xdata(), dtype=float), + expected) + assert fig.axes[0].get_xlabel() == f'time ({unit})' + finally: + plt.close(fig) + frame.index.name = 'elapsed' + fig = hyp.plot(frame, ndims=1, show=False) + try: + assert fig.axes[0].get_xlabel() == f'elapsed ({unit})' + finally: + plt.close(fig) diff --git a/tests/test_plot_title_styling.py b/tests/test_plot_title_styling.py index 77026113..c9c593e3 100644 --- a/tests/test_plot_title_styling.py +++ b/tests/test_plot_title_styling.py @@ -253,3 +253,24 @@ def test_plotly_segment_titles_carry_the_style_every_frame(): assert titled, 'no frame carried a title' for frame in titled: assert frame.layout.title.font.color == '#E4572E' + + +# --- 1.1 release review: T8 title_color vs title_kwargs['color'] --------- + +def test_title_color_conflicting_with_title_kwargs_color_raises(): + """`title_color='blue'` silently lost to `title_kwargs={'color': + 'red'}`; two answers to one question is an error.""" + with pytest.raises(ValueError, match="title_color='blue' and " + r"title_kwargs\['color'\]='red'"): + hyp.plot(_datasets(1), title='t', title_color='blue', + title_kwargs={'color': 'red'}, reduce='PCA', show=False) + + +def test_title_color_alone_and_title_kwargs_color_alone_both_work(): + import matplotlib.colors as mcolors + a = hyp.plot(_datasets(1), title='t', title_color='blue', reduce='PCA', + show=False) + b = hyp.plot(_datasets(1), title='t', title_kwargs={'color': 'red'}, + reduce='PCA', show=False) + assert mcolors.to_hex(a.axes[0].title.get_color()) == '#0000ff' + assert mcolors.to_hex(b.axes[0].title.get_color()) == '#ff0000' diff --git a/tests/test_plot_title_wrap.py b/tests/test_plot_title_wrap.py index ad9801b3..d2cd3bb3 100644 --- a/tests/test_plot_title_wrap.py +++ b/tests/test_plot_title_wrap.py @@ -130,3 +130,90 @@ def test_plotly_segment_titles_wrap_too(): and f.layout.title.text} assert texts assert any('<br>' in t for t in texts) + + +# --- 1.1 release review: T1 dynamic titles wrap, T2 explicit newlines +# --- survive, T3 plotly draws newlines, T6/T7 plotly top margin ----------- + +def test_title_wrap_applies_to_a_callable_title_every_frame(): + import matplotlib.pyplot as plt + anim = hyp.plot(_datasets(1), animate=True, duration=1, frame_rate=5, + title=lambda ctx: LONG, title_wrap=30, reduce='PCA', + show=False) + try: + for i in range(anim.n_frames): + anim.draw_frame(i) + got = anim.figure.axes[0].get_title() + assert got == textwrap.fill(LONG, 30) + assert '\n' in got + finally: + plt.close(anim.figure) + + +def test_title_wrap_applies_to_a_callable_title_under_plotly(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(1), animate=True, duration=1, frame_rate=5, + title=lambda ctx: LONG, title_wrap=30, reduce='PCA', + backend='plotly', show=False) + for frame in fig.frames: + assert frame.layout.title.text == textwrap.fill(LONG, 30).replace( + '\n', '<br>') + + +def test_title_wrap_keeps_explicit_newlines(): + """`textwrap.wrap` alone flattened 'first line\\nsecond line' into one + line; each author line is wrapped on its own.""" + fig = hyp.plot(_datasets(1), title='first line\nsecond line', + title_wrap=40, reduce='PCA', show=False) + assert fig.axes[0].get_title() == 'first line\nsecond line' + long_two = LONG + '\n' + LONG + fig = hyp.plot(_datasets(1), title=long_two, title_wrap=30, + reduce='PCA', show=False) + assert fig.axes[0].get_title() == '\n'.join( + textwrap.fill(part, 30) for part in long_two.split('\n')) + + +def test_plotly_draws_a_newline_title_as_a_line_break(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(1), title='a\nb', reduce='PCA', + backend='plotly', show=False) + assert fig.layout.title.text == 'a<br>b' + anim = hyp.plot(_datasets(1), title='a\nb', animate=True, duration=1, + frame_rate=5, reduce='PCA', backend='plotly', + show=False) + assert anim.layout.title.text == 'a<br>b' + seg = hyp.plot(_datasets(2), title=['a\nb', 'c'], animate='serial', + duration=1, frame_rate=5, reduce='PCA', + backend='plotly', show=False) + texts = {f.layout.title.text for f in seg.frames + if f.layout.title and f.layout.title.text} + assert 'a<br>b' in texts + + +def test_plotly_top_margin_grows_with_title_lines_and_size(): + pytest.importorskip('plotly') + kw = dict(reduce='PCA', backend='plotly', show=False) + one = hyp.plot(_datasets(1), title='t', **kw) + assert one.layout.margin.t == 40 + three = hyp.plot(_datasets(1), title='a\nb\nc', **kw) + assert three.layout.margin.t > 40 + wrapped = hyp.plot(_datasets(1), title=LONG, title_wrap=30, **kw) + assert wrapped.layout.margin.t > 40 + big = hyp.plot(_datasets(1), title='t', title_kwargs={'size': 30}, **kw) + assert big.layout.margin.t > 40 + # and the title block never reaches into the plotting area + for fig, n_lines, size_px in ((three, 3, 17), (big, 1, 42)): + assert fig.layout.margin.t >= 0.03 * fig.layout.height \ + + n_lines * 1.25 * size_px + + +def test_plotly_top_margin_covers_the_tallest_frame_of_a_dynamic_title(): + pytest.importorskip('plotly') + fig = hyp.plot(_datasets(1), animate=True, duration=1, frame_rate=5, + title=lambda ctx: 'a' if ctx.frame == 0 else 'a\nb\nc', + reduce='PCA', backend='plotly', show=False) + assert fig.layout.margin.t >= 0.03 * fig.layout.height + 3 * 1.25 * 17 + seg = hyp.plot(_datasets(2), title=['one', 'a\nb\nc'], animate='serial', + duration=1, frame_rate=5, reduce='PCA', + backend='plotly', show=False) + assert seg.layout.margin.t >= 0.03 * seg.layout.height + 3 * 1.25 * 17 diff --git a/tests/test_plot_truth_animation_reveal.py b/tests/test_plot_truth_animation_reveal.py new file mode 100644 index 00000000..1d4a4425 --- /dev/null +++ b/tests/test_plot_truth_animation_reveal.py @@ -0,0 +1,203 @@ +"""`truth=` in a time-progressing animation is revealed as the forecast +reaches it (maintainer decision, 1.1 review). + +Drawing the whole held-out continuation from frame 0 showed the answer +before the animation had forecast anywhere near it. Each truth row now +appears on the first frame whose DRAWN forecast reaches or passes its +position, and stays on every later frame; the 'truth' legend entry is on +every frame. Static plots (and 'spin') still draw it in full. +""" +import matplotlib +matplotlib.use('Agg') + +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +import pytest # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 + +import hypertools as hyp # noqa: E402 +from hypertools.plot.forecast import truth_reveal_counts # noqa: E402 + + +def _series(): + """The feature-tour example: 24 hourly samples of a sine, and its next + 3 hours as `truth=`.""" + t = np.arange(24.0) + data = pd.DataFrame({'signal': np.sin(t / 4)}, + index=pd.date_range('2026-01-01', periods=24, + freq='h')) + truth = pd.DataFrame({'signal': np.sin(np.arange(24, 27) / 4)}, + index=pd.date_range( + data.index[-1] + pd.Timedelta('1h'), periods=3, + freq='h')) + return data, truth + + +def _long_horizon(): + """A 3-D walk forecast 12 steps out, so the truth is reached a few rows + at a time rather than all at once on the last frame.""" + rng = np.random.default_rng(1) + x = np.cumsum(rng.standard_normal((20, 3)), 0) + held = x[-1] + np.cumsum(rng.standard_normal((12, 3)), 0) + return x, held + + +def _mpl_truth_rows(ax): + """{dataset: visible held-out rows} from the truth MARKER artists (one + marker per row, the first is the seam).""" + out = {} + for ln in ax.lines: + if (getattr(ln, '_hyp_forecast_role', None) == 'truth' + and ln.get_linestyle() == 'None'): + data = (ln.get_data_3d() if hasattr(ln, 'get_data_3d') + else ln.get_data()) + n = len(np.asarray(data[0])) if ln.get_visible() else 0 + out[ln._hyp_forecast_dataset] = max(n - 1, 0) + return out + + +def _mpl_legend(ax): + leg = ax.get_legend() + return [t.get_text() for t in leg.get_texts()] if leg else [] + + +def _pl_truth_points(fig, frame): + """{dataset: drawn truth vertices} at plotly frame `frame`.""" + meta = {i: tr.meta for i, tr in enumerate(fig.data) + if (tr.meta or {}).get('hyp_forecast_role') == 'truth'} + fr = fig.frames[frame] + assert set(meta) <= set(fr.traces), 'a frame does not rewrite the truth' + return {meta[i]['hyp_dataset']: len(tr.x) + for i, tr in zip(fr.traces, fr.data) if i in meta} + + +def test_feature_tour_truth_is_hidden_until_the_forecast_reaches_it(): + data, truth = _series() + anim = hyp.plot(data, ndims=1, reduce=None, + predict=['Kalman', 'GaussianProcess'], t=3, truth=truth, + forecast_palette=['orange', 'purple'], + forecast_fmt=['--', ':'], legend=True, animate=True, + duration=1.5, frame_rate=6, forecast_trail=3, show=False) + ax = anim.figure.axes[0] + seen = [] + for k in range(anim.n_frames): + anim.draw_frame(k) + rows = _mpl_truth_rows(ax)[0] + # independent check: every revealed truth row's x lies within the + # reach of some LIVE forecast drawn on this or an earlier frame, + # and the next hidden row lies beyond all of them + live = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'live' + and ln.get_visible() and len(ln.get_xdata())] + reach = max([np.max(np.asarray(ln.get_xdata(), dtype=float)) + for ln in live], default=-np.inf) + seen.append((rows, reach)) + assert 'truth' in _mpl_legend(ax), f'truth legend gone on frame {k}' + # the held-out rows' x (date numbers), read off the final frame's + # truth markers once everything is revealed (the first is the seam) + marker = [ln for ln in ax.lines + if getattr(ln, '_hyp_forecast_role', None) == 'truth' + and ln.get_linestyle() == 'None'][0] + x_truth = np.asarray(marker.get_xdata(), dtype=float)[1:] + assert len(x_truth) == 3 + best = -np.inf + for rows, reach in seen: + best = max(best, reach) + assert rows == int(np.sum(x_truth <= best + 1e-9)) + assert seen[0][0] == 0, 'the truth is on screen before any forecast' + assert seen[-1][0] == 3, 'the full truth is not shown at the end' + plt.close(anim.figure) + + +@pytest.mark.parametrize('mode', [True, 'serial', 'window']) +def test_truth_reveal_is_monotone_and_matches_across_backends(mode): + pytest.importorskip('plotly') + x, held = _long_horizon() + kw = dict(reduce=None, predict='Kalman', t=12, truth=held, legend=True, + duration=2, frame_rate=5, antialias=False, show=False, + animate=mode) + anim = hyp.plot(x, **kw) + ax = anim.figure.axes[0] + mpl = [] + for k in range(anim.n_frames): + anim.draw_frame(k) + mpl.append(_mpl_truth_rows(ax)[0]) + assert 'truth' in _mpl_legend(ax) + plt.close(anim.figure) + assert mpl[0] == 0 + assert mpl == sorted(mpl), f'a revealed truth row disappeared: {mpl}' + assert mpl[-1] == 12 + # revealed a few rows at a time, not all at once on the last frame + assert len(set(mpl)) > 3, mpl + + fig = hyp.plot(x, backend='plotly', **kw) + # antialias=False: one vertex per row, seam included + pl = [max(_pl_truth_points(fig, k)[0] - 1, 0) + for k in range(len(fig.frames))] + assert pl == mpl + # the base trace -- what shows before playback -- is frame 0's state + base = [tr for tr in fig.data + if (tr.meta or {}).get('hyp_forecast_role') == 'truth'][0] + assert len(base.x) == 0 + # one 'truth' legend key, data-free, so it never flickers + keys = [tr for tr in fig.data if tr.name == 'truth' and tr.showlegend] + assert len(keys) == 1 + assert (keys[0].meta or {}).get('hyp_legend_entry') == 'truth' + + +def test_serial_3d_truths_are_revealed_dataset_by_dataset(): + rng = np.random.default_rng(0) + ds = [np.cumsum(rng.standard_normal((30, 3)), 0) for _ in range(2)] + tr = [d[-1] + np.cumsum(rng.standard_normal((4, 3)), 0) for d in ds] + anim = hyp.plot(ds, reduce=None, predict='Kalman', t=4, truth=tr, + legend=True, animate='serial', duration=2, frame_rate=5, + show=False) + ax = anim.figure.axes[0] + rows = [] + for k in range(anim.n_frames): + anim.draw_frame(k) + rows.append(_mpl_truth_rows(ax)) + plt.close(anim.figure) + assert rows[0] == {0: 0, 1: 0} + # dataset 0 finishes (and shows its truth) while dataset 1 is still + # being revealed, so dataset 1's truth is still hidden + assert any(r[0] == 4 and r[1] == 0 for r in rows), rows + assert rows[-1] == {0: 4, 1: 4} + + +def test_regrouped_hue_animation_reveals_truth_progressively(): + pytest.importorskip('plotly') + x, held = _long_horizon() + kw = dict(reduce=None, predict='Kalman', t=12, truth=held, + hue=['a'] * 10 + ['b'] * 10, legend=True, duration=2, + frame_rate=5, antialias=False, animate=True, show=False) + anim = hyp.plot(x, **kw) + ax = anim.figure.axes[0] + mpl = [] + for k in range(anim.n_frames): + anim.draw_frame(k) + mpl.append(_mpl_truth_rows(ax)[0]) + plt.close(anim.figure) + assert mpl[0] == 0 and mpl[-1] == 12 and mpl == sorted(mpl) + fig = hyp.plot(x, backend='plotly', **kw) + pl = [max(_pl_truth_points(fig, k)[0] - 1, 0) + for k in range(len(fig.frames))] + assert pl == mpl + + +def test_spin_and_static_still_draw_the_truth_in_full(): + x, held = _long_horizon() + for extra in ({}, {'animate': 'spin', 'duration': 1, 'frame_rate': 3}): + out = hyp.plot(x, reduce=None, predict='Kalman', t=12, truth=held, + antialias=False, show=False, **extra) + fig = out.figure if extra else out + if extra: + out.draw_frame(0) + assert _mpl_truth_rows(fig.axes[0])[0] == 12 + plt.close(fig) + + +def test_no_drawn_forecast_reveals_no_truth(): + """`truth_reveal_counts` with no schedule (nothing forecast) keeps every + truth hidden on every frame.""" + assert truth_reveal_counts(None, 2, [3, 3], 4) == [[0, 0]] * 4 diff --git a/tests/test_plot_truth_overlay.py b/tests/test_plot_truth_overlay.py index ea12f698..a6e474fe 100644 --- a/tests/test_plot_truth_overlay.py +++ b/tests/test_plot_truth_overlay.py @@ -124,7 +124,12 @@ def test_series_mode_truth_takes_values_only_and_gets_the_index(): plt.close(fig) -def test_truth_is_drawn_in_full_during_an_animation(stock): +def test_truth_is_revealed_as_the_animated_forecast_reaches_it(stock): + # Maintainer decision (1.1 review) changed this test's old contract + # ("drawn in full during an animation", checked on frame 1): truth + # rows now appear once a drawn forecast reaches them, so frame 1 -- + # whose forecast ends well before the held-out rows -- shows none, and + # the last frame shows them all, verbatim. train, held = stock anim = hyp.plot(train, reduce=None, ndims=2, axis_scale='data', predict='Kalman', t=T, truth=held, animate=True, @@ -133,6 +138,9 @@ def test_truth_is_drawn_in_full_during_an_animation(stock): ani._init_draw() ani._func(1, *ani._args) line = _by_role(fig, 'truth')[0] + assert len(line.get_ydata()) == 0 and not line.get_visible() + ani._func(anim.n_frames - 1, *ani._args) + assert line.get_visible() assert np.allclose(np.asarray(line.get_ydata()), np.r_[train[-1, 1], held[:, 1]]) plt.close(fig) @@ -212,3 +220,26 @@ def test_an_animated_3d_truth_overlay_is_unclipped(): truth = _by_role(fig, 'truth')[0] assert truth.get_clip_on() is False plt.close(fig) + + +def test_the_truth_legend_handle_carries_its_markers(stock): + """The truth is drawn as a solid line PLUS markers on the observations, + so its legend glyph must show the markers too -- otherwise the 'truth' + entry is a solid line in the trace's own colour, identical to the + observed trace's entry (feature tour 9.11, 2026-09-06).""" + train, held = stock + fig = hyp.plot(train, reduce=None, ndims=2, axis_scale='data', + predict='Kalman', t=T, truth=held, legend=True, + names=['observed'], show=False) + legend = fig.axes[0].get_legend() + by_label = {text.get_text(): handle + for handle, text in zip(legend.legend_handles, + legend.get_texts())} + assert by_label['truth'].get_marker() == 'o' + assert by_label['observed'].get_marker() in (None, 'None', '') + # ...and the markers still land only on the observations: the smoothed + # curve draws none of its ~900 vertices as a marker + curve = _by_role(fig, 'truth')[0] + assert curve.get_marker() == 'o' + assert list(curve.get_markevery()) == [] + plt.close(fig) diff --git a/tests/test_plotly_anim_controls.py b/tests/test_plotly_anim_controls.py new file mode 100644 index 00000000..b4e5164f --- /dev/null +++ b/tests/test_plotly_anim_controls.py @@ -0,0 +1,95 @@ +# -*- coding: utf-8 -*- +"""Plotly's Play/Pause controls sit clear of the x axis' tick labels. + +1.1 release review: the controls hung at paper y=-0.06 -- right where a +visible x axis (``axis_scale='data'``, an ``ndims=1`` series, a date axis +with its second tick-label line) draws its tick labels and title -- so on a +dated animated forecast they covered the "2020" of the first date tick. +They now go below the tick labels and the axis title, with the bottom +margin opened so they are not clipped. + +Checked on kaleido pixels: the buttons' footprint (what changes when they +are hidden) must not cover any ink the figure draws without them, and must +lie inside the canvas. +""" + +import io + +import numpy as np +import pandas as pd +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp + +go = pytest.importorskip('plotly.graph_objects') + + +def _png(fig, width=640, height=480): + from PIL import Image + return np.asarray(Image.open(io.BytesIO(fig.to_image( + format='png', width=width, height=height))).convert('RGB')).astype( + int) + + +def _controls_clear_of_the_axis(fig): + """Dark ink (tick labels, axis title) inside the controls' footprint, + in ONE render: the controls are drawn in a marker colour (pure green + border and text) so their footprint is readable off the same image -- + hiding them instead would let plotly re-lay the margins out.""" + shown = go.Figure(fig) + shown.frames = () + menu = shown.layout.updatemenus[0] + menu.bordercolor = 'rgb(0,255,0)' + menu.borderwidth = 2 + menu.font.color = 'rgb(0,255,0)' + # see-through buttons, so ink they would COVER shows in the render + menu.bgcolor = 'rgba(0,0,0,0)' + img = _png(shown) + green = (img[..., 1] > 200) & (img[..., 0] < 90) & (img[..., 2] < 90) + rows, cols = np.nonzero(green) + assert rows.size, 'the controls did not render' + r0, r1, c0, c1 = rows.min(), rows.max(), cols.min(), cols.max() + # inside the canvas, not clipped at its bottom edge + assert r1 < img.shape[0] - 1 + box = img[r0:r1 + 1, c0:c1 + 1] + dark = (box.max(axis=2) < 150) & ~green[r0:r1 + 1, c0:c1 + 1] + return int(dark.sum()) + + +def _dated_forecast(**kw): + rng = np.random.default_rng(0) + idx = pd.date_range('2020-01-01', periods=60, freq='D') + df = pd.DataFrame({'price': np.cumsum(rng.standard_normal(60))}, + index=idx) + return hyp.plot(df, ndims=1, reduce=None, backend='plotly', show=False, + predict='Kalman', t=8, animate=True, duration=2, + frame_rate=5, **kw) + + +def test_controls_clear_the_date_tick_labels(): + assert _controls_clear_of_the_axis(_dated_forecast()) == 0 + + +def test_controls_clear_the_tick_labels_and_axis_title(): + assert _controls_clear_of_the_axis(_dated_forecast(xlabel='date')) == 0 + + +def test_controls_clear_a_numeric_data_axis(): + x = np.cumsum(np.random.default_rng(1).standard_normal((40, 2)), 0) + fig = hyp.plot(x, backend='plotly', show=False, axis_scale='data', + xlabel='x', animate=True, duration=1, frame_rate=4) + assert _controls_clear_of_the_axis(fig) == 0 + + +def test_axisless_animation_keeps_its_controls_where_they_were(): + """3-D (and unit-scale 2-D) figures draw no tick labels: the controls + keep their historical spot and margin.""" + x = np.cumsum(np.random.default_rng(1).standard_normal((30, 3)), 0) + fig = hyp.plot(x, backend='plotly', show=False, animate=True, + duration=1, frame_rate=4) + menu = fig.layout.updatemenus[0] + assert (menu.y, menu.yanchor) == (-0.06, 'top') + assert fig.layout.margin.b == 64 diff --git a/tests/test_plotly_animated_hue.py b/tests/test_plotly_animated_hue.py new file mode 100644 index 00000000..9c3c6de2 --- /dev/null +++ b/tests/test_plotly_animated_hue.py @@ -0,0 +1,300 @@ +# -*- coding: utf-8 -*- +"""A continuous `hue=` travels with the data in a plotly ANIMATION. + +1.1 release review: (3-D) animation frames rewrote only a trace's x/y/z, +so the per-vertex colours stayed aligned to the FULL curve and a sliding +window was painted with the colours of the trajectory's first rows; (2-D and +1-D) the multicoloured line was one plotly trace per segment, but every +frame sent a single trace mapped to index 0 -- segment 0 was overwritten +with the whole window in one colour while the other segments stayed on +screen, so the full trajectory never went away. The parity reference is +matplotlib's animated hue (`plot._apply_multicolor_animation`): per-dataset +head (and trail) collections re-sliced to exactly the revealed window, with +their own per-segment colours. + +Real figures; each frame is rendered as `_frame_snapshots` renders it for +export (base traces updated with the frame's payload), and the visual +claims are checked on kaleido pixels. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp +from hypertools.plot.plotly_backend import _frame_snapshots + +go = pytest.importorskip('plotly.graph_objects') + +N = 40 +HUE = np.linspace(0, 1, N) + + +def _walk(d, seed=0): + return np.cumsum(np.random.default_rng(seed).standard_normal((N, d)), 0) + + +def _plot(d, animate='window', **kw): + # a 0.4 s window over a 2 s animation: the window really slides + kw.setdefault('tail_duration', 0.4) + return hyp.plot(_walk(d), backend='plotly', show=False, hue=HUE, + palette='viridis', animate=animate, duration=2, + frame_rate=5, **kw) + + +def _rgb(color): + inner = color[color.index('(') + 1:-1].split(',') + return tuple(round(float(v)) for v in inner[:3]) + + +def _drawn_segments(snapshot): + """(start, end, rgb) of every visible data-line segment of a 2-D/1-D + snapshot, whatever traces carry them.""" + segs = [] + for t in snapshot.data: + if (t.meta or {}).get('hyp_trace_index') is None \ + or t.mode != 'lines' or t.x is None: + continue + x = np.asarray(t.x, dtype=float) + y = np.asarray(t.y, dtype=float) + color = t.line.color + for j in range(len(x) - 1): + if np.isfinite(x[j:j + 2]).all() and np.isfinite(y[j:j + 2]).all(): + segs.append(((x[j], y[j]), (x[j + 1], y[j + 1]), + _rgb(color))) + return segs + + +def _static_segment_colors(fig): + """The rgb of every segment of the full static 2-D curve, by its + endpoints, from the animated figure's own full-curve base traces.""" + snap = go.Figure(fig) + snap.frames = () + return {(round(a[0], 9), round(a[1], 9), round(b[0], 9), + round(b[1], 9)): c for a, b, c in _drawn_segments(snap)} + + +# -------------------------------------------------------------------------- +# 3-D: the colour arrays are sliced with the window + + +@pytest.mark.parametrize('style', ['window', True, 'serial']) +def test_3d_frames_send_the_colours_of_their_window(style): + fig = _plot(3, animate=style) + base = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + idx = fig.data.index(base) + full = list(base.line.color) + xyz = np.column_stack([base.x, base.y, base.z]) + checked = 0 + for frame in fig.frames: + for k, tr in zip(frame.traces, frame.data): + if k != idx or tr.x is None or len(tr.x) < 2: + continue + colors = list(tr.line.color) + assert len(colors) == len(tr.x) + # the window's first vertex is some vertex of the full curve; + # the frame paints it (and the rest) in THAT vertex's colours + first = np.array([tr.x[0], tr.y[0], tr.z[0]]) + j = int(np.argmin(np.abs(xyz - first).sum(axis=1))) + assert colors == full[j:j + len(colors)] + checked += j > 0 + if style != 'serial': # (one serial dataset grows from row 0) + assert checked > 0 # some window really started past row 0 + + +def test_3d_window_frame_renders_the_windows_colours(): + """Pixels: a late window of a viridis ramp is yellow-green, not the + purple the ramp STARTS with.""" + fig = _plot(3, animate='window', linewidth=6) + snaps = list(_frame_snapshots(fig)) + from PIL import Image + img = np.asarray(Image.open(io.BytesIO(snaps[-1].to_image( + format='png', width=600, height=450))).convert('RGB')).astype(int) + sat = (img.max(axis=2) - img.min(axis=2)) > 60 + px = img[sat] + assert len(px) > 100 + # viridis' late colours are green/yellow (G high); its start is purple + # (B > G) -- a last-frame window painted from row 0 would be purple + assert np.mean(px[:, 1] > px[:, 2]) > 0.8 + + +# -------------------------------------------------------------------------- +# 2-D / 1-D: the multicoloured line animates + + +@pytest.mark.parametrize('style', ['window', True, 'serial']) +def test_2d_frames_draw_only_the_window_in_its_own_colours(style): + fig = _plot(2, animate=style) + static = _static_segment_colors(fig) + data_idx = {k for k, t in enumerate(fig.data) + if (t.meta or {}).get('hyp_trace_index') is not None} + sizes = [] + for frame, snap in zip(fig.frames, _frame_snapshots(fig)): + # every data trace is rewritten by every frame: nothing of the + # full trajectory is left standing + assert data_idx <= set(frame.traces) + segs = _drawn_segments(snap) + sizes.append(len(segs)) + for a, b, c in segs: + key = (round(a[0], 9), round(a[1], 9), round(b[0], 9), + round(b[1], 9)) + assert key in static, 'a drawn segment is not on the curve' + # the segment wears (within the colour binning) its OWN colour + assert max(abs(u - v) for u, v in zip(c, static[key])) <= 12 + # the reveal really moves: windows differ in size/position over time + assert len(set(sizes)) > 1 + assert min(sizes) < len(static) + + +def test_2d_window_frames_have_many_colours_not_one(): + # a window spanning the whole ramp (tail_duration = duration) + fig = _plot(2, animate=True, tail_duration=2) + snap = list(_frame_snapshots(fig))[-1] + colours = {c for _, _, c in _drawn_segments(snap)} + assert len(colours) > 5 + + +@pytest.mark.parametrize('flag', ['chemtrails', 'precog', 'bullettime']) +def test_2d_trails_are_multicoloured_and_translucent(flag): + fig = _plot(2, animate=True, tail_duration=0.4, **{flag: True}) + trail_traces = [t for t in fig.data + if (t.meta or {}).get('hyp_trail_index') == 0] + assert trail_traces + snaps = list(_frame_snapshots(fig)) + mid = snaps[len(snaps) // 2] + trail = [t for t in mid.data if (t.meta or {}).get('hyp_trail_index') == 0] + drawn = [t for t in trail if t.x is not None + and np.isfinite(np.asarray(t.x, dtype=float)).sum() > 1] + assert len({t.line.color for t in drawn}) > 2 + assert all('0.3)' in t.line.color.replace(' ', '') for t in drawn) + + +def test_1d_hue_animation_is_refused_like_matplotlib(): + """1-D (non-series) animations raise on both backends (batch 1).""" + with pytest.raises(ValueError, match='Animations are only supported'): + hyp.plot(_walk(1)[:, 0], backend='plotly', show=False, hue=HUE, + animate=True, duration=1, frame_rate=4) + + +def test_2d_mid_frame_renders_only_the_window(): + """Pixels: a mid-animation window frame draws the moving window, not + the whole trajectory (which inked every segment on every frame).""" + from PIL import Image + fig = _plot(2, animate='window', linewidth=3) + snaps = list(_frame_snapshots(fig)) + full = go.Figure(fig) + full.frames = () + full.layout.updatemenus = () + + def ink(f): + img = np.asarray(Image.open(io.BytesIO(f.to_image( + format='png', width=500, height=400))).convert('RGB')).astype(int) + return int(((img.max(axis=2) - img.min(axis=2)) > 60).sum()) + + assert ink(snaps[len(snaps) // 2]) < 0.6 * ink(full) + + +def test_2d_frames_draw_the_palettes_own_colours_exactly(): + """A continuous hue maps through the 100-colour ramp, and the bins + never exceed that: every drawn colour IS a ramp colour.""" + from hypertools.plot.colors import continuous_colormap + ramp = {tuple(round(float(v) * 255) for v in c[:3]) + for c in continuous_colormap('viridis').colors} + fig = _plot(2, animate=True, tail_duration=2) + for snap in list(_frame_snapshots(fig))[::3]: + for _, _, c in _drawn_segments(snap): + assert c in ramp, c + + +def test_colour_bins_are_exact_up_to_the_cap_and_close_beyond_it(): + from hypertools.plot.plotly_backend import (HUE_ANIM_MAX_BINS, + _hue_line_bins, _parse_rgba, + _rgb_string) + rng = np.random.default_rng(0) + few = [_rgb_string(c) for c in rng.random((30, 3))] + colors = [few[j] for j in rng.integers(0, 30, 500)] + bins = _hue_line_bins(colors) + assert [bins['colors'][k] for k in bins['seg_bin']] == colors[:-1] + # a smooth blend with more distinct colours than the cap: each segment + # takes a close bin colour (a matrix hue's mixtures) + t = np.linspace(0, 1, 900) + blend = np.column_stack([t, 1 - t, 0.5 + 0.5 * np.sin(6 * t)]) + colors = [_rgb_string(c) for c in blend] + bins = _hue_line_bins(colors) + assert len(bins['colors']) <= HUE_ANIM_MAX_BINS + want = np.array([_parse_rgba(c)[:3] for c in colors[:-1]]) + got = np.array([_parse_rgba(bins['colors'][k])[:3] + for k in bins['seg_bin']]) + assert np.abs(want - got).max() <= 8 + + +def test_3d_trails_send_their_windows_colours_at_trail_opacity(): + fig = _plot(3, animate=True, chemtrails=True) + trail = [t for t in fig.data + if (t.meta or {}).get('hyp_trail_index') == 0] + assert len(trail) == 1 + idx = fig.data.index(trail[0]) + assert trail[0].opacity == pytest.approx(0.3) + base = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + head_rgb = {_rgb(c) for c in base.line.color} + checked = 0 + for frame in fig.frames: + for k, tr in zip(frame.traces, frame.data): + if k != idx or tr.x is None or len(tr.x) < 2: + continue + colours = [_rgb(c) for c in tr.line.color] + assert len(colours) == len(tr.x) + # the trail wears the head's own colours ... + assert set(colours) <= head_rgb + # ... at the trail's opacity + assert tr.opacity == pytest.approx(0.3) + checked += 1 + assert checked > 0 + + +def test_2d_marker_line_animates_its_observation_markers(): + fig = _plot(2, animate='window', fmt='o-') + markers = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0 + and t.mode == 'markers'] + assert len(markers) == 1 + idx = fig.data.index(markers[0]) + counts = [] + for frame in fig.frames: + tr = dict(zip(frame.traces, frame.data))[idx] + assert len(tr.marker.color) == len(tr.x) + counts.append(len(tr.x)) + # the window shows a moving handful of observations, never all 40 + assert 0 < max(counts) < N + + +def test_dataset_fade_reaches_every_colour_bin_of_a_dataset(): + """`FrameContext.artists` holds ONE artist per dataset: a multicoloured + 2-D dataset's colour-bin traces arrive as one `PlotlyTraceGroup`, so + `dataset_fade=` fades all of them.""" + from hypertools.plot.plotly_backend import PlotlyTraceGroup + seen = [] + + def grab(ctx): + seen.append(ctx.artists) + + fig = hyp.plot([_walk(2), _walk(2, seed=1) + 4], backend='plotly', + show=False, hue=[HUE, HUE], palette='viridis', + animate=True, order='serial', duration=2, frame_rate=5, + dataset_fade=(0.2, 0.5), on_frame=grab) + assert seen and all(len(a) == 2 for a in seen) + assert all(isinstance(a, PlotlyTraceGroup) for arts in seen + for a in arts) + # the last frame: dataset 0 (revealed first) is faded in EVERY bin + last = fig.frames[-1] + ds0 = {k for k, t in enumerate(fig.data) + if (t.meta or {}).get('hyp_trace_index') == 0} + opac = [tr.opacity for k, tr in zip(last.traces, last.data) if k in ds0] + assert opac and all(o is not None and o < 1 for o in opac) + assert len(set(opac)) == 1 diff --git a/tests/test_plotly_animation_review.py b/tests/test_plotly_animation_review.py new file mode 100644 index 00000000..719d2485 --- /dev/null +++ b/tests/test_plotly_animation_review.py @@ -0,0 +1,100 @@ +# -*- coding: utf-8 -*- +"""Plotly animation parity findings from the 1.1 release review. + +* A 1-D (single-column, non-series) animation raised a clear ValueError on + matplotlib but ran silently on plotly, drawing its frame-grid row numbers + as the x axis. +* The serial reveal's `FrameContext.window_bounds` started at 0 on plotly + while matplotlib reported the comet head's real start -- the one field + the FrameContext contract says both backends fill identically. + +Real figures and real `on_frame=` callbacks. +""" + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp + +pytest.importorskip('plotly') + + +def _walks(k=2, n=30, d=3, seed=0): + rng = np.random.default_rng(seed) + return [np.cumsum(rng.standard_normal((n, d)), 0) + 2 * i + for i in range(k)] + + +# -------------------------------------------------------------------------- +# finding 8: 1-D animation + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('style', [True, 'window', 'serial']) +def test_single_column_animation_raises_on_both_backends(backend, style): + rng = np.random.default_rng(0) + y = np.cumsum(rng.standard_normal(30)) + with pytest.raises(ValueError, match='Animations are only supported ' + 'for 2-D or 3-D plots'): + hyp.plot([y, y + 1], backend=backend, show=False, animate=style, + duration=1, frame_rate=4) + + +def test_series_mode_animation_still_animates_on_plotly(): + """`ndims=1` series mode draws (row index, value) pairs -- a 2-D + animation that reveals along x, documented in the CHANGELOG.""" + rng = np.random.default_rng(0) + y = np.cumsum(rng.standard_normal(30)) + fig = hyp.plot([y, y + 1], backend='plotly', show=False, animate=True, + ndims=1, duration=1, frame_rate=4) + assert len(fig.frames) == 4 + + +# -------------------------------------------------------------------------- +# finding A: serial window_bounds + + +def _collect(backend): + seen = [] + + def grab(ctx): + seen.append((ctx.frame, tuple(ctx.window_bounds), + tuple(ctx.revealed_counts))) + + hyp.plot(_walks(), backend=backend, show=False, animate=True, + order='serial', chemtrails=True, tail_duration=0.1, + duration=1, frame_rate=8, on_frame=grab) + return seen + + +def _drive_matplotlib(): + seen = [] + + def grab(ctx): + seen.append((ctx.frame, tuple(ctx.window_bounds), + tuple(ctx.revealed_counts))) + + anim = hyp.plot(_walks(), backend='matplotlib', show=False, + animate=True, order='serial', chemtrails=True, + tail_duration=0.1, duration=1, frame_rate=8, + on_frame=grab) + ani = anim.animation + ani._init_draw() + for k in range(8): + ani._func(k, *ani._args) + import matplotlib.pyplot as plt + plt.close(anim.figure) + return seen + + +def test_serial_comet_window_bounds_match_matplotlib(): + mpl = {f: (wb, rc) for f, wb, rc in _drive_matplotlib()} + ply = {f: (wb, rc) for f, wb, rc in _collect('plotly')} + assert set(ply) == set(range(8)) + for k in range(8): + assert ply[k] == mpl[k], (k, ply[k], mpl[k]) + # and the reviewer's frames really are comet windows, not (0, n) + assert any(wb[0][0] > 0 for wb, _ in ply.values()) diff --git a/tests/test_plotly_cells_review.py b/tests/test_plotly_cells_review.py new file mode 100644 index 00000000..ade00b78 --- /dev/null +++ b/tests/test_plotly_cells_review.py @@ -0,0 +1,228 @@ +# -*- coding: utf-8 -*- +"""Plotly composition (`ax=` a figure or a `hyp.subplots` cell, and +`panels=`): 1.1 release-review findings. + +* a colorbar beside a legend-less cell sat a whole legend's width further + right -- on top of the next cell -- because every DRAWN cell counted as + having a legend; +* a second call into a cell deleted the cell's earlier title (matplotlib + keeps an axes title a later call does not replace); +* data whose dimensionality does not match the cell/figure raised a bare + ``TypeError: cannot unpack non-iterable NoneType`` (cell) or silently + overlaid a 2-D trace on a 3-D figure (``ax=<Figure>``); +* drawing into a caller's figure rewrote the caller's OWN traces; +* ``ax=<plotly cell>`` with the default ``backend='auto'`` raised instead of + drawing with plotly. + +Real `make_subplots` figures, real layout objects and kaleido renders. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp +from hypertools.plot.plotly_backend import PANEL_GUTTER_PAD_PX + +go = pytest.importorskip('plotly.graph_objects') + + +def _walks(k=3, n=40, d=3, seed=0): + rng = np.random.default_rng(seed) + return [np.cumsum(rng.standard_normal((n, d)), 0) for _ in range(k)] + + +def _colorbar_traces(fig): + return [t for t in fig.data + if t.marker is not None and t.marker.showscale] + + +def _plot_w(fig): + return fig.layout.width - fig.layout.margin.l - fig.layout.margin.r + + +# -------------------------------------------------------------------------- +# finding 4: colorbar beside a legend-less cell + + +def test_subplots_colorbar_cell_without_legend_sits_beside_its_cell(): + X = _walks() + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(X[0], ax=cells[0], backend='plotly', show=False, + hue=np.linspace(0, 1, 40), colorbar=True) + hyp.plot(X[1:], ax=cells[1], backend='plotly', show=False, + legend=['a', 'b']) + cb, = _colorbar_traces(fig) + x1 = fig.layout.scene.domain.x[1] + next_x0 = fig.layout.scene2.domain.x[0] + assert cb.marker.colorbar.x == pytest.approx( + x1 + PANEL_GUTTER_PAD_PX / _plot_w(fig)) + # the whole bar (15 px + its tick labels, ~45 px) clears the next cell + assert cb.marker.colorbar.x + 60 / _plot_w(fig) < next_x0 + + +def test_panels_colorbars_do_not_land_on_the_next_panel(): + X = _walks(k=2) + fig = hyp.plot(X, panels=True, backend='plotly', show=False, + hue=[np.linspace(0, 1, 40)] * 2, colorbar=True) + cbs = {t.scene: t.marker.colorbar for t in _colorbar_traces(fig)} + x1 = fig.layout.scene.domain.x[1] + assert cbs['scene'].x == pytest.approx( + x1 + PANEL_GUTTER_PAD_PX / _plot_w(fig)) + assert cbs['scene'].x + 60 / _plot_w(fig) < fig.layout.scene2.domain.x[0] + + +def test_colorbar_after_a_legend_in_the_same_cell_still_moves_right(): + """The fix must not lose the case the legend push exists for.""" + X = _walks() + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(X[:2], ax=cells[0], backend='plotly', show=False, + legend=['a', 'b']) + hyp.plot(X[2], ax=cells[0], backend='plotly', show=False, + hue=np.linspace(0, 1, 40), colorbar=True) + cb, = _colorbar_traces(fig) + from hypertools.plot.plotly_backend import PANEL_LEGEND_PX + assert cb.marker.colorbar.x == pytest.approx( + fig.layout.scene.domain.x[1] + + (PANEL_GUTTER_PAD_PX + PANEL_LEGEND_PX) / _plot_w(fig)) + + +# -------------------------------------------------------------------------- +# finding 6: a later untitled call keeps the cell's title + + +def _cell_titles(fig): + return [a.text for a in fig.layout.annotations + if (a.name or '').startswith('hyp-cell-title-')] + + +def test_second_untitled_draw_keeps_the_cell_title(): + A, B = _walks(k=2), _walks(k=2, seed=1) + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(A, ax=cells[0], backend='plotly', show=False, + title='first title') + hyp.plot(B, ax=cells[0], backend='plotly', show=False) + assert _cell_titles(fig) == ['first title'] + assert fig.layout.margin.t >= 40 + + +def test_second_titled_draw_replaces_the_cell_title(): + A, B = _walks(k=2), _walks(k=2, seed=1) + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(A, ax=cells[0], backend='plotly', show=False, title='first') + hyp.plot(B, ax=cells[0], backend='plotly', show=False, title='second') + assert _cell_titles(fig) == ['second'] + + +# -------------------------------------------------------------------------- +# finding 7: dimensionality mismatch + + +@pytest.mark.parametrize('cell_nd, cols, kw', [ + (3, 2, {}), # 2-D data into a 3-D cell + (2, 5, {}), # 3-D plot into a 2-D cell + (2, 5, dict(ndims=3)), +]) +def test_cell_dimensionality_mismatch_raises_clearly(cell_nd, cols, kw): + rng = np.random.default_rng(0) + data = np.cumsum(rng.standard_normal((40, cols)), 0) + fig, cells = hyp.subplots(1, 2, ndims=cell_nd, backend='plotly') + with pytest.raises(ValueError, match=r'ax= is a [23]-D'): + hyp.plot(data, ax=cells[0], backend='plotly', show=False, **kw) + # nothing was drawn into the grid + assert len(fig.data) == 0 + + +def test_figure_dimensionality_mismatch_raises_clearly(): + X3 = _walks(k=1)[0] + fig = hyp.plot(X3, backend='plotly', show=False) + n_before = len(fig.data) + with pytest.raises(ValueError, match=r'ax= is a 3-D plotly figure'): + hyp.plot(X3[:, :2], backend='plotly', show=False, ax=fig) + assert len(fig.data) == n_before + fig2 = hyp.plot(X3[:, :2], backend='plotly', show=False) + with pytest.raises(ValueError, match=r'ax= is a 2-D plotly figure'): + hyp.plot(X3, backend='plotly', show=False, ax=fig2) + + +def test_matching_dimensionality_still_composes(): + X3 = _walks(k=1)[0] + fig = hyp.plot(X3, backend='plotly', show=False) + out = hyp.plot(X3 + 1, backend='plotly', show=False, ax=fig) + assert out is fig + empty = go.Figure() + assert hyp.plot(X3[:, :2], backend='plotly', show=False, + ax=empty) is empty + + +# -------------------------------------------------------------------------- +# finding 9: the caller's own traces are left alone + + +def test_drawing_into_a_figure_leaves_the_callers_traces_untouched(): + theirs = go.Scatter3d(x=[0, 1], y=[0, 1], z=[0, 1], mode='lines', + line=dict(color='rgba(255,0,0,0.5)', width=4), + name='mine') + fig = go.Figure(theirs) + before = fig.data[0].to_plotly_json() + hyp.plot(_walks(k=1)[0], backend='plotly', show=False, ax=fig, + alpha=0.5) + assert fig.data[0].to_plotly_json() == before + # ...while hypertools' own translucent trace is still normalized + ours = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') is not None] + assert ours and ours[0].opacity == pytest.approx(0.5) + + +# -------------------------------------------------------------------------- +# finding B: a plotly ax= implies the plotly backend + + +def test_plotly_cell_with_default_backend_draws_with_plotly(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + out = hyp.plot(_walks(k=1)[0], ax=cells[0], show=False) + assert out is fig + assert any((t.meta or {}).get('hyp_trace_index') == 0 for t in fig.data) + + +def test_plotly_figure_with_default_backend_draws_with_plotly(): + fig = hyp.plot(_walks(k=1)[0], backend='plotly', show=False) + n = len(fig.data) + out = hyp.plot(_walks(k=1, seed=2)[0], ax=fig, show=False) + assert out is fig and len(fig.data) > n + + +def test_plotly_cell_with_explicit_matplotlib_still_raises(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + with pytest.raises(TypeError, match="pass backend='plotly'"): + hyp.plot(_walks(k=1)[0], ax=cells[0], show=False, + backend='matplotlib') + + +def test_rendered_colorbar_does_not_overlap_next_cell(tmp_path): + """Pixels: the colorbar's coloured bar sits between the two cubes, not + inside the second cell.""" + from PIL import Image + X = _walks() + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(X[0], ax=cells[0], backend='plotly', show=False, + hue=np.linspace(0, 1, 40), colorbar=True, palette='viridis') + hyp.plot(X[1], ax=cells[1], backend='plotly', show=False) + img = np.asarray(Image.open(io.BytesIO(fig.to_image(format='png'))) + .convert('RGB')).astype(int) + W = fig.layout.width + x1_px = fig.layout.margin.l + fig.layout.scene.domain.x[1] * _plot_w(fig) + x0_next = fig.layout.margin.l \ + + fig.layout.scene2.domain.x[0] * _plot_w(fig) + # saturated (viridis) columns = the colorbar and the hue line + sat = (img.max(axis=2) - img.min(axis=2)) > 80 + col_counts = sat.sum(axis=0) + bar_cols = np.flatnonzero(col_counts > 0.3 * fig.layout.height * 0.75 + * 0.5) + assert bar_cols.size, 'no colorbar found' + assert bar_cols.min() >= x1_px - 2 + assert bar_cols.max() < min(x0_next, W) diff --git a/tests/test_plotly_density_cube.py b/tests/test_plotly_density_cube.py new file mode 100644 index 00000000..41950386 --- /dev/null +++ b/tests/test_plotly_density_cube.py @@ -0,0 +1,86 @@ +# -*- coding: utf-8 -*- +"""A 3-D plotly `density=` volume stays inside the cube. + +1.1 release review (L2): the KDE grid behind the 3-D `go.Volume` is padded +past the data, so it reached beyond the scene's [-1, 1] range (x from -1.30 +to 1.26 on the reviewer's case) and its translucent shells were drawn over +the cube's edges, which rendered stippled -- only ~1698 of 3899 cube pixels +survived. The grid is now clipped to the cube. + +Kaleido-rendered pixels: every dark pixel the figure draws without its +volume must still be drawn with it. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp + +go = pytest.importorskip('plotly.graph_objects') + + +def _fig(**kw): + points = np.random.default_rng(0).normal(size=(70, 3)) + return hyp.plot(points, fmt='o', markersize=2, reduce=None, ndims=3, + density={'grid': 15, 'alpha': .15, 'levels': 2}, + show=False, backend='plotly', **kw) + + +def _dark(fig): + from PIL import Image + img = Image.open(io.BytesIO(fig.to_image(format='png'))) + return np.asarray(img.convert('L')) < 110 + + +def test_volume_grid_stays_inside_the_scene_range(): + fig = _fig() + vol, = [t for t in fig.data if t.type == 'volume'] + lim = fig.layout.scene.xaxis.range[1] + for axis in (vol.x, vol.y, vol.z): + a = np.asarray(axis, dtype=float) + assert a.min() >= -lim - 1e-12 and a.max() <= lim + 1e-12 + + +def test_cube_edges_survive_the_volume(): + fig = _fig() + bare = go.Figure(layout=fig.layout) + for t in fig.data: + if t.type != 'volume': + bare.add_trace(t) + ref = _dark(bare) + kept = _dark(fig) & ref + assert ref.sum() > 1000 + assert kept.sum() >= 0.98 * ref.sum(), (int(kept.sum()), int(ref.sum())) + + +def test_pooled_density_is_clipped_too(): + rng = np.random.default_rng(1) + data = [rng.normal(size=(40, 3)), rng.normal(size=(40, 3)) + 2] + fig = hyp.plot(data, fmt='o', reduce=None, ndims=3, + density={'per_group': False, 'grid': 15}, show=False, + backend='plotly') + vol, = [t for t in fig.data if t.type == 'volume'] + lim = fig.layout.scene.xaxis.range[1] + assert np.abs(np.asarray(vol.x, dtype=float)).max() <= lim + 1e-12 + + +def test_volume_still_shows_its_glow(): + """Clipping must not blank the density: the volume still draws a + visible share of the scene.""" + fig = _fig() + bare = go.Figure(layout=fig.layout) + for t in fig.data: + if t.type != 'volume': + bare.add_trace(t) + from PIL import Image + a = np.asarray(Image.open(io.BytesIO(fig.to_image(format='png'))) + .convert('RGB')).astype(int) + b = np.asarray(Image.open(io.BytesIO(bare.to_image(format='png'))) + .convert('RGB')).astype(int) + changed = (np.abs(a - b).sum(axis=2) > 12).sum() + assert changed > 2000 diff --git a/tests/test_plotly_figure_qa_review.py b/tests/test_plotly_figure_qa_review.py new file mode 100644 index 00000000..6b79f0cb --- /dev/null +++ b/tests/test_plotly_figure_qa_review.py @@ -0,0 +1,191 @@ +# -*- coding: utf-8 -*- +"""Figure-QA expectation findings on the plotly backend (1.1 release +review): each figure is checked against what `plot()`'s docstring promises +and what the matplotlib backend draws. + +* ``frame_kwargs=`` was ignored -- the cube/square stayed black; +* a STATIC plot applied ``zoom=``, which the docstring calls + animation-only and the matplotlib static view ignores; +* ``legend_kwargs={'x': 0, 'y': 1}`` kept hypertools' ``yanchor='middle'``, + so the legend straddled the top edge; +* ``'^'`` draws a diamond in 3-D (plotly's Scatter3d has no triangle) -- + unavoidable, so the mapping is documented. + +Real figures and kaleido-rendered pixels. +""" + +import io +import warnings + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp + +pytest.importorskip('plotly') + + +def _walk(d=3, n=40, seed=0): + return np.cumsum(np.random.default_rng(seed).standard_normal((n, d)), 0) + + +def _png(fig, width=640, height=480): + from PIL import Image + return np.asarray(Image.open(io.BytesIO( + fig.to_image(format='png', width=width, height=height))) + .convert('RGB')).astype(int) + + +def _cube(fig): + cubes = [t for t in fig.data + if t.type == 'scatter3d' and t.mode == 'lines' + and t.hoverinfo == 'skip' and t.x is not None + and len(t.x) == 36] + assert len(cubes) == 1 + return cubes[0] + + +# -------------------------------------------------------------------------- +# frame_kwargs + + +def test_frame_kwargs_colour_the_3d_cube(): + fig = hyp.plot(_walk(), backend='plotly', show=False, + frame_kwargs={'color': 'red', 'linewidth': 2}) + cube = _cube(fig) + assert cube.line.color.replace(' ', '') in ('rgba(255,0,0,1.0)', + 'rgb(255,0,0)') + default = _cube(hyp.plot(_walk(), backend='plotly', show=False)) + assert default.line.color == 'black' + assert cube.line.width == pytest.approx(2 * default.line.width) + # pixels: a red wireframe, and no black one + img = _png(fig) + red = (img[..., 0] > 200) & (img[..., 1] < 60) & (img[..., 2] < 60) + black = img.sum(axis=2) < 60 + assert red.sum() > 500 + assert black.sum() < 50 + + +def test_frame_kwargs_style_the_2d_square(): + fig = hyp.plot(_walk(d=2), backend='plotly', show=False, + frame_kwargs={'edgecolor': 'blue', 'linestyle': '--', + 'alpha': 0.5}) + square, = fig.layout.shapes + assert square.line.color.replace(' ', '') == 'rgba(0,0,255,0.5)' + assert square.line.dash == 'dash' + assert square.fillcolor == 'rgba(0,0,0,0)' + + +def test_frame_kwargs_colour_fills_the_square_like_matplotlib(): + """matplotlib's `plot_square` hands `color=` to a Rectangle, which + fills with it; `fill=False` keeps the outline only.""" + fig = hyp.plot(_walk(d=2), backend='plotly', show=False, + frame_kwargs={'color': 'lightgray'}) + square, = fig.layout.shapes + assert square.fillcolor.replace(' ', '') == 'rgba(211,211,211,1.0)' + fig2 = hyp.plot(_walk(d=2), backend='plotly', show=False, + frame_kwargs={'color': 'lightgray', 'fill': False}) + assert fig2.layout.shapes[0].fillcolor == 'rgba(0,0,0,0)' + + +def test_unmappable_frame_kwargs_are_named_in_a_warning(): + with pytest.warns(UserWarning, match="frame_kwargs.*zorder"): + hyp.plot(_walk(), backend='plotly', show=False, + frame_kwargs={'color': 'red', 'zorder': 3}) + + +def test_default_frame_is_unchanged_and_silent(): + with warnings.catch_warnings(): + warnings.simplefilter('error') + fig = hyp.plot(_walk(d=2), backend='plotly', show=False) + square, = fig.layout.shapes + assert square.line.color == 'black' and square.line.dash is None + + +# -------------------------------------------------------------------------- +# zoom is animation-only + + +def _radius(eye): + return float(np.sqrt(eye.x ** 2 + eye.y ** 2 + eye.z ** 2)) + + +def test_static_plot_ignores_zoom_like_matplotlib(): + near = hyp.plot(_walk(), zoom=3, backend='plotly', show=False) + far = hyp.plot(_walk(), zoom=1, backend='plotly', show=False) + assert _radius(near.layout.scene.camera.eye) == pytest.approx( + _radius(far.layout.scene.camera.eye)) + + +def test_animation_still_zooms(): + near = hyp.plot(_walk(), zoom=3, backend='plotly', show=False, + animate='spin', duration=1, frame_rate=2) + far = hyp.plot(_walk(), zoom=1, backend='plotly', show=False, + animate='spin', duration=1, frame_rate=2) + assert _radius(near.layout.scene.camera.eye) < _radius( + far.layout.scene.camera.eye) + + +# -------------------------------------------------------------------------- +# legend_kwargs position -> anchor + + +@pytest.mark.parametrize('pos, anchors', [ + ({'x': 0, 'y': 1}, ('left', 'top')), + ({'x': 0.02, 'y': 0.98}, ('left', 'top')), # the docstring's own + ({'x': 1, 'y': 0}, ('right', 'bottom')), + ({'x': 0.5, 'y': 0.5}, ('center', 'middle')), + ({'x': 1.05, 'y': 1.1}, ('left', 'bottom')), # outside: hang away +]) +def test_legend_anchor_follows_the_given_position(pos, anchors): + fig = hyp.plot([_walk(), _walk(seed=1)], backend='plotly', show=False, + legend=['a', 'b'], legend_kwargs=pos) + assert (fig.layout.legend.xanchor, fig.layout.legend.yanchor) == anchors + + +def test_explicit_legend_anchor_is_kept(): + fig = hyp.plot([_walk(), _walk(seed=1)], backend='plotly', show=False, + legend=['a', 'b'], + legend_kwargs={'x': 0, 'y': 1, 'yanchor': 'bottom'}) + assert fig.layout.legend.yanchor == 'bottom' + assert fig.layout.legend.xanchor == 'left' + + +def test_default_legend_keeps_its_outside_right_anchor(): + fig = hyp.plot([_walk(), _walk(seed=1)], backend='plotly', show=False, + legend=['a', 'b']) + assert (fig.layout.legend.x, fig.layout.legend.y) == (1.02, 0.5) + assert (fig.layout.legend.xanchor, fig.layout.legend.yanchor) == \ + ('left', 'middle') + + +def test_top_left_legend_renders_inside_the_figure(): + """Pixels: with x=0, y=1 the legend's text sits BELOW the top edge of + the plotting area (it used to straddle it, half clipped above).""" + fig = hyp.plot([_walk(d=2), _walk(d=2, seed=1)], backend='plotly', + show=False, legend=['alpha', 'beta'], + legend_kwargs={'x': 0, 'y': 1, 'bgcolor': 'yellow'}) + img = _png(fig) + yellow = (img[..., 0] > 240) & (img[..., 1] > 240) & (img[..., 2] < 40) + rows = np.flatnonzero(yellow.any(axis=1)) + top_px = fig.layout.margin.t + assert rows.size and rows.min() >= top_px - 1 + + +# -------------------------------------------------------------------------- +# '^' in 3-D: documented mapping + + +def test_triangle_marker_maps_to_diamond_in_3d_and_is_documented(): + fig = hyp.plot(_walk(), '^', backend='plotly', show=False) + tr = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + assert tr.marker.symbol == 'diamond' + fig2 = hyp.plot(_walk(d=2), '^', backend='plotly', show=False) + tr2 = [t for t in fig2.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + assert tr2.marker.symbol == 'triangle-up' + assert "triangles (``'^'``" in hyp.plot.__doc__ diff --git a/tests/test_plotly_hover_names.py b/tests/test_plotly_hover_names.py new file mode 100644 index 00000000..cede5c2f --- /dev/null +++ b/tests/test_plotly_hover_names.py @@ -0,0 +1,304 @@ +# -*- coding: utf-8 -*- +"""Every hoverable plotly data trace is named like its legend entry. + +1.1 release review, maintainer finding: hovering a plotly figure showed +"trace 0", "trace 1", ... -- data traces were unnamed in every plot without +an explicit legend, in the repeat runs of a hue/cluster category and the +leaves of a hierarchy even WITH one, under a continuous hue given +``legend=[...]``, and for ``ndims=1`` series. The contract: a hoverable +data trace is named by the label the legend shows (or would show under +``legend=True``); the legend itself is drawn only when asked for; a lone +unlabelled dataset hides the name box instead of printing a label. + +Also L7: an animation's legend entries appeared and vanished frame by frame +because they rode on data traces that are empty until the reveal reaches +them; they now ride on data-free proxies that share the data's legendgroup. + +Real figures; the legend=True labels are read from the same figure drawn +with ``legend=True``. +""" + +import warnings + +import numpy as np +import pandas as pd +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp + +pytest.importorskip('plotly') + + +def _walks(): + a = np.asarray(hyp.load('random_walk', n_samples=48, n_features=6, + random_state=41)) + b = np.asarray(hyp.load('random_walk', n_samples=48, n_features=6, + random_state=42)) + return a, b + + +def _hoverable_data(fig): + return [t for t in fig.data + if t.type in ('scatter', 'scatter3d') + and (t.meta or {}).get('hyp_trace_index') is not None + and t.hoverinfo != 'skip'] + + +def _hides_name_box(trace): + return (trace.hovertemplate or '').endswith('<extra></extra>') + + +def _assert_every_hoverable_trace_named(fig): + for t in fig.data: + if t.type not in ('scatter', 'scatter3d') or t.hoverinfo == 'skip': + continue + if t.x is not None and len(t.x) == 1 and t.x[0] is None: + continue # data-free legend key + assert t.name is not None or _hides_name_box(t), t + + +def _legend_names(fig): + return [t.name for t in fig.data if t.showlegend] + + +CAT = ['x'] * 20 + ['y'] * 14 + ['x'] * 14 + + +def _cases(): + a, b = _walks() + lin = np.linspace(0, 1, 48) + return { + 'datasets': (lambda **k: hyp.plot([a, b], **k)), + 'hue_line': (lambda **k: hyp.plot(a, hue=CAT, **k)), + 'hue_markers': (lambda **k: hyp.plot(a, '.', hue=CAT, **k)), + 'cluster': (lambda **k: hyp.plot([a, b], cluster='KMeans', + n_clusters=3, random_state=0, **k)), + 'forecast': (lambda **k: hyp.plot([a, b], predict='Kalman', t=5, + **k)), + 'animated': (lambda **k: hyp.plot([a, b], animate=True, duration=1, + frame_rate=4, **k)), + 'panels': (lambda **k: hyp.plot([a, b], panels=True, **k)), + 'cont_hue': (lambda **k: hyp.plot([a, b], hue=[lin, lin], **k)), + } + + +@pytest.mark.parametrize('case', list(_cases())) +def test_no_hoverable_trace_is_unnamed(case): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + fig = _cases()[case](backend='plotly', show=False) + _assert_every_hoverable_trace_named(fig) + # no legend was asked for, so none is drawn + assert _legend_names(fig) == [] + + +@pytest.mark.parametrize('case', ['datasets', 'hue_line', 'hue_markers', + 'cluster']) +def test_names_are_the_labels_legend_true_draws(case): + make = _cases()[case] + plain = make(backend='plotly', show=False) + listed = make(backend='plotly', show=False, legend=True) + legend = set(_legend_names(listed)) + names = [t.name for t in _hoverable_data(plain)] + assert names and set(names) == legend + # and with the legend drawn, every run/segment of a group is named too + assert set(t.name for t in _hoverable_data(listed)) == legend + + +def test_repeat_runs_share_their_categorys_legendgroup(): + a, _ = _walks() + fig = hyp.plot(a, hue=CAT, legend=True, backend='plotly', show=False) + runs = _hoverable_data(fig) + assert [t.name for t in runs] == ['x', 'y', 'x'] + # one legend entry for 'x', toggling both of its runs + assert [t.showlegend for t in runs] == [True, True, False] + assert runs[0].legendgroup == runs[2].legendgroup == 'x' + + +def test_hierarchy_leaves_and_means_are_named_by_their_group(): + rng = np.random.default_rng(0) + cols = pd.MultiIndex.from_product([['US', 'EU'], ['tech', 'fin'], + ['a', 'b']]) + df = pd.DataFrame(np.cumsum(rng.standard_normal((30, 8)), 0), + columns=cols) + for kw in ({}, {'legend': True}): + fig = hyp.plot(df, backend='plotly', show=False, **kw) + data = _hoverable_data(fig) + assert {t.name for t in data} == {'US', 'EU'} + # grouped so the top-level entry toggles the whole group + assert {t.legendgroup for t in data} == {'US', 'EU'} + assert sorted(_legend_names(fig)) == ['EU', 'US'] + # a legend list renames the groups -- leaves included + fig = hyp.plot(df, backend='plotly', show=False, + legend=['America', 'Europe']) + assert {t.name for t in _hoverable_data(fig)} == {'America', 'Europe'} + + +def test_continuous_hue_keeps_the_legend_list_as_names(): + a, b = _walks() + lin = np.linspace(0, 1, 48) + with pytest.warns(UserWarning, match='legend is not supported'): + fig = hyp.plot([a, b], hue=[lin, lin], legend=['A', 'B'], + backend='plotly', show=False) + assert [t.name for t in _hoverable_data(fig)] == ['A', 'B'] + assert _legend_names(fig) == [] # still no legend drawn + + +def test_series_mode_curves_are_named_by_column(): + rng = np.random.default_rng(0) + df = pd.DataFrame({'temp': np.cumsum(rng.standard_normal(40)), + 'rain': np.cumsum(rng.standard_normal(40))}) + fig = hyp.plot(df, ndims=1, reduce=None, backend='plotly', show=False) + assert [t.name for t in _hoverable_data(fig)] == ['temp', 'rain'] + assert _legend_names(fig) == [] + + +def test_lone_unlabelled_dataset_hides_the_name_box(): + a, _ = _walks() + for ndims in (2, 3): + fig = hyp.plot(a, ndims=ndims, backend='plotly', show=False) + tr, = _hoverable_data(fig) + assert tr.name is None and _hides_name_box(tr) + # ...but a lone NAMED dataset shows its name + fig = hyp.plot(a, names=['walk'], backend='plotly', show=False) + tr, = _hoverable_data(fig) + assert tr.name == 'walk' and not _hides_name_box(tr) + + +def test_animation_names_persist_into_frames(): + a, b = _walks() + fig = hyp.plot([a, b], animate=True, duration=1, frame_rate=4, + backend='plotly', show=False) + base = {fig.data.index(t): t.name for t in _hoverable_data(fig)} + assert set(base.values()) == {'1', '2'} + for frame in fig.frames: + for k, tr in zip(frame.traces, frame.data): + # a frame rewrites geometry only; it never renames a trace + assert tr.name is None or tr.name == base.get(k, tr.name) + + +# -------------------------------------------------------------------------- +# L7: the animated legend is complete from frame 0 + + +def test_animated_legend_entries_ride_on_data_free_proxies(): + a, b = _walks() + fig = hyp.plot([a, b], cluster='KMeans', n_clusters=3, random_state=0, + legend=True, animate=True, duration=1.5, frame_rate=6, + backend='plotly', show=False) + entries = [t for t in fig.data if t.showlegend] + assert sorted(t.name for t in entries) == ['0', '1', '2'] + for t in entries: + # data-free: nothing a frame could empty + assert list(t.x) == [None] + assert t.meta["hyp_legend_proxy"] == t.name + members = [d for d in _hoverable_data(fig) + if d.legendgroup == t.legendgroup] + assert members and all(d.name == t.name for d in members) + # the data traces themselves carry no legend entry, and no frame + # touches a proxy + assert not any(d.showlegend for d in _hoverable_data(fig)) + proxy_idx = {fig.data.index(t) for t in entries} + for frame in fig.frames: + assert not proxy_idx & set(frame.traces) + + +def test_static_legend_stays_on_the_data_traces(): + a, b = _walks() + fig = hyp.plot([a, b], legend=True, backend='plotly', show=False) + assert [t.name for t in _hoverable_data(fig) if t.showlegend] == \ + ['1', '2'] + + +# -------------------------------------------------------------------------- +# nested dataset lists: every leaf is named (and grouped) by its OUTER group +# (1.1 release review: the leaves were named '1', '2', '2', '4' -- the +# group label on each group's summary leaf, the flat leaf NUMBER on the +# rest -- so hover named the wrong group and the legendgroups paired a +# leaf of group 1 with a leaf of group 2) + + +def _nested_leaves(fig): + return sorted(_hoverable_data(fig), + key=lambda t: t.meta['hyp_trace_index']) + + +def _assert_grouped_like(leaves, expected): + assert [t.name for t in leaves] == expected + for name in set(expected): + members = [t for t in leaves if t.name == name] + others = [t for t in leaves if t.name != name] + if len(members) > 1: + # one legendgroup per outer group, shared by exactly its leaves + assert {t.legendgroup for t in members} == {name} + assert not any(t.legendgroup == name for t in others) + + +@pytest.mark.parametrize('legend', [None, True]) +def test_nested_list_leaves_are_named_by_their_outer_group(legend): + a, b = _walks() + kw = {} if legend is None else {'legend': legend} + fig = hyp.plot([[a, b], [a + 2, b + 2]], backend='plotly', show=False, + **kw) + leaves = _nested_leaves(fig) + _assert_grouped_like(leaves, ['1', '1', '2', '2']) + # colours were already right: a group's leaves share its colour + assert leaves[0].line.color == leaves[1].line.color + assert leaves[2].line.color == leaves[3].line.color + assert leaves[0].line.color != leaves[2].line.color + if legend: + # one shown entry per group, toggling that group's leaves + assert [t.showlegend for t in leaves] == [True, False, True, False] + else: + assert _legend_names(fig) == [] + + +def test_nested_list_legend_list_names_the_groups_and_uneven_depths(): + a, b = _walks() + fig = hyp.plot([[a, b, a - 1], [a + 2, b + 2]], backend='plotly', + show=False, legend=['left', 'right']) + _assert_grouped_like(_nested_leaves(fig), + ['left', 'left', 'left', 'right', 'right']) + assert sorted(_legend_names(fig)) == ['left', 'right'] + # varying depth: the group's summary leaf carries the entry + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + fig = hyp.plot([a, [b, [a + 1]]], backend='plotly', show=False, + legend=True) + leaves = _nested_leaves(fig) + _assert_grouped_like(leaves, ['1', '2', '2']) + assert [t.showlegend for t in leaves] == [True, True, False] + + +def test_nested_list_per_leaf_legend_list_still_names_the_leaves(): + a, b = _walks() + fig = hyp.plot([[a, b], [a + 2, b + 2]], backend='plotly', show=False, + legend=['p', 'q', 'r', 's']) + assert [t.name for t in _nested_leaves(fig)] == ['p', 'q', 'r', 's'] + + +def test_nested_list_animation_names_and_groups_by_outer_group(): + a, b = _walks() + fig = hyp.plot([[a, b], [a + 2, b + 2]], animate=True, duration=1, + frame_rate=4, legend=True, backend='plotly', show=False) + leaves = _nested_leaves(fig) + _assert_grouped_like(leaves, ['1', '1', '2', '2']) + entries = [t for t in fig.data if t.showlegend] + assert sorted(t.name for t in entries) == ['1', '2'] + for t in entries: + members = [d for d in leaves if d.legendgroup == t.legendgroup] + assert len(members) == 2 and all(d.name == t.name for d in members) + + +def test_stacked_dict_hierarchy_leaves_are_named_by_their_group(): + a, b = _walks() + stacked = hyp.stack({'US': {'x': a, 'y': b}, + 'EU': {'x': a + 2, 'y': b + 2}}) + fig = hyp.plot(stacked, backend='plotly', show=False, legend=True) + data = _hoverable_data(fig) + assert {t.name for t in data} == {'US', 'EU'} + assert {t.legendgroup for t in data} == {'US', 'EU'} + assert sorted(_legend_names(fig)) == ['EU', 'US'] diff --git a/tests/test_plotly_hue_alpha.py b/tests/test_plotly_hue_alpha.py new file mode 100644 index 00000000..c024107c --- /dev/null +++ b/tests/test_plotly_hue_alpha.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +"""A continuous `hue=` honours `alpha=` on plotly, markers included, and a +translucent Scatter3d keeps its hue. + +1.1 release review: (a) plotly's continuous-hue MARKERS ignored `alpha=` +(opaque ``rgb(...)`` colours, no opacity) although a single-coloured trace's +`alpha=` dims its markers; (b) Plotly's WebGL path composites an ``rgba`` +colour additively, so a 3-D hue line at alpha 0.5 drawn with opaque markers +('o-') rendered steelblue as a pale cyan (197, 255, 255) instead of +(162, 192, 217) -- the earlier fix only handled a trace whose alpha was +uniform. Real figures, kaleido-rendered pixels. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp +from hypertools.plot.plotly_backend import _normalize_scatter3d_alpha + +go = pytest.importorskip('plotly.graph_objects') + +STEELBLUE = np.array([70, 130, 180]) + + +def _over_white(rgb, alpha): + return alpha * np.asarray(rgb, float) + (1 - alpha) * 255.0 + + +def _png(fig, width=700, height=500): + from PIL import Image + return np.asarray(Image.open(io.BytesIO( + fig.to_image(format='png', width=width, height=height))) + .convert('RGB')).astype(int) + + +def _colored_pixels(img): + flat = img.reshape(-1, 3) + return flat[(flat.sum(1) < 740) + & ~((flat[:, 0] == flat[:, 1]) & (flat[:, 1] == flat[:, 2]))] + + +def _dominant(img): + px = _colored_pixels(img) + vals, counts = np.unique(px, axis=0, return_counts=True) + return vals[np.argmax(counts)] + + +def _data_trace(fig): + return [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') is not None][0] + + +def _walk(n=60, seed=1): + return np.cumsum(np.random.default_rng(seed).standard_normal((n, 3)), 0) + + +@pytest.mark.parametrize('ndims', [1, 2, 3]) +def test_continuous_hue_markers_honour_alpha(ndims): + x = _walk()[:, :ndims] if ndims > 1 else _walk()[:, 0] + fig = hyp.plot(x, backend='plotly', show=False, fmt='o', + hue=np.linspace(0, 1, 60), alpha=0.7, palette='viridis') + tr = _data_trace(fig) + colors = list(tr.marker.color) + if ndims >= 3: + # uniform alpha -> WebGL's native opacity, colours made opaque + assert tr.opacity == pytest.approx(0.7) + assert all(c.startswith('rgb(') for c in colors) + else: + assert all(c.startswith('rgba(') and c.endswith(',0.7)') + for c in colors) + + +def test_continuous_hue_markers_render_translucent_2d(): + """Pixels: one steelblue hue marker at alpha=.5 renders as steelblue + over white at half opacity, as a single-coloured marker does.""" + x = np.column_stack([np.linspace(-1, 1, 5), np.zeros(5)]) + fig = hyp.plot(x, backend='plotly', show=False, fmt='o', reduce=None, + hue=np.linspace(0, 1, 5), alpha=0.5, + palette=['steelblue', 'steelblue'], markersize=20) + got = _dominant(_png(fig)) + assert np.abs(got - _over_white(STEELBLUE, 0.5)).max() <= 3, got + + +@pytest.mark.parametrize('fmt', ['-', 'o-', 'o']) +def test_scatter3d_hue_alpha_keeps_its_hue(fmt): + """The reviewer's case: continuous hue + alpha=.5 in 3-D. Every fmt + renders steelblue at half opacity over white, never the additive cyan + (197, 255, 255).""" + fig = hyp.plot(_walk(), backend='plotly', show=False, + hue=np.linspace(0, 1, 60), alpha=0.5, fmt=fmt, + palette=['steelblue', 'steelblue'], linewidth=6, + markersize=4, antialias=False) + got = _dominant(_png(fig)) + assert np.abs(got - _over_white(STEELBLUE, 0.5)).max() <= 4, got + + +def _mixed_trace(): + z = np.linspace(-1, 1, 40) + return go.Scatter3d( + x=np.sin(3 * z), y=np.cos(3 * z), z=z, mode='lines+markers', + line=dict(color='rgba(70,130,180,0.5)', width=10), + marker=dict(color='rgb(70,130,180)', size=2)) + + +def test_nonuniform_alpha_is_blended_toward_white(): + """A translucent line with opaque markers cannot share one trace + opacity: the line is pre-blended toward the white paper instead.""" + tr = _mixed_trace() + _normalize_scatter3d_alpha(tr) + assert tr.opacity == 1 + assert tr.line.color == 'rgb(162,192,218)' + assert tr.marker.color == 'rgb(70,130,180)' + # idempotent + _normalize_scatter3d_alpha(tr) + assert tr.line.color == 'rgb(162,192,218)' and tr.opacity == 1 + + +def test_nonuniform_alpha_renders_its_hue(): + """Pixels: before, the mixed-alpha trace rendered additively (cyan); + normalized, the thick line is steelblue at half opacity over white.""" + raw = go.Figure(_mixed_trace()) + fixed_trace = _mixed_trace() + _normalize_scatter3d_alpha(fixed_trace) + fixed = go.Figure(fixed_trace) + for fig in (raw, fixed): + fig.update_layout(paper_bgcolor='white', showlegend=False, + scene=dict(xaxis_visible=False, yaxis_visible=False, + zaxis_visible=False)) + want = _over_white(STEELBLUE, 0.5) + assert np.abs(_dominant(_png(raw)) - want).max() > 20 # the bug + assert np.abs(_dominant(_png(fixed)) - want).max() <= 4 + + +def test_partial_alpha_keeps_translucency_share(): + """Line .3 and markers .6: opacity .6, line pre-blended by .3/.6, so + over white each part composites to its own requested alpha.""" + tr = _mixed_trace() + tr.line.color = 'rgba(70,130,180,0.3)' + tr.marker.color = 'rgba(70,130,180,0.6)' + _normalize_scatter3d_alpha(tr) + assert tr.opacity == pytest.approx(0.6) + line = np.array([float(v) for v in tr.line.color[4:-1].split(',')]) + composed = 0.6 * line + 0.4 * 255 + np.testing.assert_allclose(composed, _over_white(STEELBLUE, 0.3), + atol=1) diff --git a/tests/test_plotly_line_width.py b/tests/test_plotly_line_width.py new file mode 100644 index 00000000..4f5a1b84 --- /dev/null +++ b/tests/test_plotly_line_width.py @@ -0,0 +1,123 @@ +# -*- coding: utf-8 -*- +"""Plotly 3-D data lines render at the width asked for, and an animation's +default width is the documented 1 pt. + +1.1 release review (L1): plotly's WebGL (Scatter3d) line renderer draws a +line at HALF its requested width -- measured in kaleido (2026-09-11) as +exactly 0.50x from 1.4 to 12 px, at device scale 1 and 2, while the SVG 2-D +line draws the width asked for -- so every 3-D data line was about half as +thick as the same line in 2-D (and as matplotlib's). And `plot()` documents +the default ``linewidth`` as 1.5 for static plots and 1 for animations; +matplotlib animates at 1 pt, plotly animated at 1.5. + +Rendered with kaleido; widths are measured as ink area / stroke length. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp +from hypertools.plot.plotly_backend import PT_TO_PX + +pytest.importorskip('plotly') + + +def _straight(ndims): + t = np.linspace(-1, 1, 40) + cols = [t, 0.5 * t] + ([0.3 * t] if ndims == 3 else []) + return np.column_stack(cols) + + +def _stroke_width(fig, width=700, height=560): + """Effective rendered stroke width (px) of the one dark data line: the + summed ink coverage divided by the stroke's length (its extent along + the principal axis of the inked pixels).""" + from PIL import Image + fig = type(fig)(fig) + # the frame (cube/square) is drawn in black too; paint the data line + # red and measure only red ink + img = np.asarray(Image.open(io.BytesIO(fig.to_image( + format='png', width=width, height=height))).convert('RGB')).astype( + float) / 255.0 + red_ink = np.clip(img[..., 0] - np.maximum(img[..., 1], img[..., 2]), + 0, 1) + # coverage of pure red over white: 1 - G (== 1 - B) + cover = np.where(red_ink > 0.02, 1 - img[..., 1], 0.0) + ys, xs = np.nonzero(cover > 0.3) + pts = np.column_stack([xs, ys]).astype(float) + pts -= pts.mean(axis=0) + _, _, vt = np.linalg.svd(pts, full_matrices=False) + along = pts @ vt[0] + length = along.max() - along.min() + return cover.sum() / length + + +def _plot(ndims, **kw): + return hyp.plot(_straight(ndims), backend='plotly', show=False, + color='red', reduce=None, **kw) + + +@pytest.mark.parametrize('lw', [1.5, 4]) +def test_3d_line_renders_as_wide_as_the_same_2d_line(lw): + w3 = _stroke_width(_plot(3, linewidth=lw)) + w2 = _stroke_width(_plot(2, linewidth=lw)) + requested = lw * PT_TO_PX + assert w2 == pytest.approx(requested, rel=0.15) + assert w3 == pytest.approx(requested, rel=0.15), (w3, w2, requested) + + +def test_3d_trace_requests_the_gl_compensated_width(): + from hypertools.plot.plotly_backend import _GL_LINE_WIDTH_BOOST + fig = _plot(3, linewidth=2) + tr = [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + assert tr.line.width == pytest.approx(2 * PT_TO_PX * _GL_LINE_WIDTH_BOOST) + fig2 = _plot(2, linewidth=2) + tr2 = [t for t in fig2.data + if (t.meta or {}).get('hyp_trace_index') == 0][0] + assert tr2.line.width == pytest.approx(2 * PT_TO_PX) + + +@pytest.mark.parametrize('ndims', [2, 3]) +def test_animation_default_linewidth_is_the_documented_one_point(ndims): + from hypertools.plot.plotly_backend import _GL_LINE_WIDTH_BOOST + boost = _GL_LINE_WIDTH_BOOST if ndims == 3 else 1.0 + anim = _plot(ndims, animate=True, duration=1, frame_rate=4) + static = _plot(ndims) + + def width(fig): + return [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == 0][0].line.width + + assert width(anim) == pytest.approx(1.0 * PT_TO_PX * boost) + assert width(static) == pytest.approx(1.5 * PT_TO_PX * boost) + # an explicit linewidth= still wins in an animation + assert width(_plot(ndims, animate=True, duration=1, frame_rate=4, + linewidth=3)) == pytest.approx(3 * PT_TO_PX * boost) + # matplotlib animates at the same documented 1 pt + mpl = hyp.plot(_straight(ndims), backend='matplotlib', show=False, + reduce=None, animate=True, duration=1, frame_rate=4) + ax = mpl.figure.axes[0] + assert ax.lines[0].get_linewidth() == pytest.approx(1.0) + assert '1 for animations' in hyp.plot.__doc__ + + +def test_trails_and_forecasts_get_the_same_gl_compensation(): + from hypertools.plot.plotly_backend import _GL_LINE_WIDTH_BOOST + x = np.cumsum(np.random.default_rng(0).standard_normal((30, 3)), 0) + fig = hyp.plot(x, backend='plotly', show=False, animate=True, + chemtrails=True, duration=1, frame_rate=4, linewidth=2) + trail = [t for t in fig.data if (t.meta or {}).get('hyp_trail_index') == 0] + assert trail[0].line.width == pytest.approx( + 2 * PT_TO_PX * _GL_LINE_WIDTH_BOOST) + fc = hyp.plot(x, backend='plotly', show=False, predict='Kalman', t=4, + linewidth=2) + static_fc = [t for t in fc.data + if (t.meta or {}).get('hyp_forecast_role') == 'static'] + assert static_fc[0].line.width == pytest.approx( + 2 * PT_TO_PX * _GL_LINE_WIDTH_BOOST) diff --git a/tests/test_plotly_observation_markers.py b/tests/test_plotly_observation_markers.py new file mode 100644 index 00000000..e6769800 --- /dev/null +++ b/tests/test_plotly_observation_markers.py @@ -0,0 +1,310 @@ +# -*- coding: utf-8 -*- +"""Plotly markers sit on the TRUE observations of an antialiased line. + +`plot()`'s ``antialias=`` docstring promises that smoothing changes only how +a LINE is drawn and that markers "always render at the true sample points". +The 1.1 release review found the plotly backend drawing a ``'o-'`` trace as +ONE ``lines+markers`` trace over the ~900-vertex smoothed curve, so it put a +marker on every interpolated vertex (945 markers for 60 observations) and +the line rendered as a thick tube of overlapping dots, in 1-D, 2-D and 3-D; +``forecast_fmt='ro:'`` did the same to a dotted forecast; and a continuous +``hue=`` with ``'o-'`` in 1-D/2-D drew no markers at all. + +Every assertion reads a real figure: the trace properties plotly receives, +and (for the visual claims) kaleido-rendered pixels. +""" + +import io + +import numpy as np +import pytest + +import matplotlib +matplotlib.use('Agg') + +import hypertools as hyp +from hypertools.plot.plotly_backend import (_array_marker_size, + _marker_size_px) + +pytest.importorskip('plotly') + + +def _walk(n=60, d=3, seed=0): + rng = np.random.default_rng(seed) + return np.cumsum(rng.standard_normal((n, d)), axis=0) + + +def _data_traces(fig, index=0): + return [t for t in fig.data + if (t.meta or {}).get('hyp_trace_index') == index] + + +def _marker_traces(fig, index=0): + return [t for t in _data_traces(fig, index) + if t.mode and 'markers' in t.mode] + + +def _coords(trace): + cols = [np.asarray(trace.x, dtype=float), np.asarray(trace.y, dtype=float)] + if trace.type == 'scatter3d': + cols.append(np.asarray(trace.z, dtype=float)) + return np.column_stack(cols) + + +def _marked_vertices(trace): + """The vertices a trace draws a visible marker at.""" + xyz = _coords(trace) + size = trace.marker.size + if size is None or np.isscalar(size): + return xyz + size = np.asarray(size, dtype=float) + return xyz[:len(size)][size[:len(xyz)] > 0] + + +def _png(fig, width=600, height=450): + from PIL import Image + return np.asarray(Image.open(io.BytesIO( + fig.to_image(format='png', width=width, height=height))) + .convert('RGB')).astype(int) + + +# -------------------------------------------------------------------------- +# finding 1: 'o-' data traces + + +@pytest.mark.parametrize('ndims', [1, 2, 3]) +def test_o_dash_marks_every_observation_and_nothing_else(ndims): + x = _walk()[:, :ndims] if ndims > 1 else _walk()[:, 0] + smooth = hyp.plot(x, backend='plotly', show=False, fmt='o-') + raw = hyp.plot(x, backend='plotly', show=False, fmt='o-', + antialias=False) + tr, = _marker_traces(smooth) + raw_tr, = _marker_traces(raw) + # the line itself IS smoothed (many more vertices than rows) ... + assert len(tr.x) > 5 * 60 + # ... but exactly the 60 observations carry a marker: evenly spaced + # vertices of the smooth curve (every sample is one of its vertices) ... + sizes = np.asarray(tr.marker.size, dtype=float) + marked_idx = np.flatnonzero(sizes) + assert len(marked_idx) == 60 + assert marked_idx[0] == 0 and marked_idx[-1] == len(tr.x) - 1 + assert len(set(np.diff(marked_idx))) == 1 + # ... that ARE the observations: the un-smoothed figure's vertices up + # to the per-axis centring/scaling (which the extra vertices shift) + marked = _marked_vertices(tr) + raw_xyz = _coords(raw_tr) + for d in range(1 if ndims == 1 else 0, marked.shape[1]): + a, b = np.polyfit(raw_xyz[:, d], marked[:, d], 1) + np.testing.assert_allclose(a * raw_xyz[:, d] + b, marked[:, d], + atol=1e-9) + # each at the fmt's own marker size, in the per-point-array encoding + # (Scatter3d draws an array size at half a scalar's diameter, so a 3-D + # array entry is twice the scalar size; tests/test_marker_parity.py + # measures the rendered diameter against matplotlib's) + assert set(sizes[sizes > 0]) == { + _array_marker_size(_marker_size_px(6.0, 'o', ndims), ndims)} + + +@pytest.mark.parametrize('kw', [dict(marker='o'), dict(markers='o')]) +@pytest.mark.parametrize('ndims', [2, 3]) +def test_marker_kwarg_on_a_line_marks_only_the_observations(kw, ndims): + """`marker=`/its `markers=` alias on a line style is a marker+line + trace like 'o-': the same observation-only markers.""" + tr, = _marker_traces(hyp.plot(_walk()[:, :ndims], backend='plotly', + show=False, **kw)) + assert len(tr.x) > 5 * 60 + assert len(_marked_vertices(tr)) == 60 + + +@pytest.mark.parametrize('ndims', [1, 2]) +def test_observation_markers_keep_scalar_marker_look_in_2d(ndims): + """A per-point size array is a plotly 'bubble' trace, whose defaults + are 70%-opaque markers with a white outline; an observation marker must + look exactly like an ordinary one (the matplotlib marker is opaque and + outlined in its own colour).""" + x = _walk()[:, :ndims] if ndims > 1 else _walk()[:, 0] + tr, = _marker_traces(hyp.plot(x, backend='plotly', show=False, + fmt='o-')) + assert tr.marker.opacity == 1 + assert tr.marker.line.width == 0 + + +def test_o_dash_line_is_not_a_tube_of_markers_rendered(): + """Pixels: the smoothed 'o-' figure inks about as much as the raw 'o-' + figure (same 60 markers, same line), not a solid band of ~900 dots.""" + x = _walk(seed=3)[:, :2] + + def ink(**kw): + img = _png(hyp.plot(x, backend='plotly', show=False, fmt='o-', + color='steelblue', **kw)) + # steelblue-ish pixels (the frame square is black, bg white) + return int(((img[..., 2] - img[..., 0]) > 60).sum()) + + smooth, raw = ink(), ink(antialias=False) + assert smooth < 1.25 * raw, (smooth, raw) + + +def test_observation_marker_renders_like_an_ordinary_marker(): + """Pixels: an observation marker on the smoothed line is drawn exactly + as the un-smoothed figure draws its (scalar-size) markers -- fully + opaque, in the trace colour, without the white outline plotly's bubble + defaults add (which drew it at 70% opacity over white).""" + rng = np.random.default_rng(5) + x = np.column_stack([np.linspace(-1, 1, 7), rng.uniform(-.5, .5, 7)]) + + def steelblue_pixels(**kw): + img = _png(hyp.plot(x, backend='plotly', show=False, fmt='o-', + color='steelblue', markersize=14, reduce=None, + **kw)) + return int((np.abs(img - [70, 130, 180]).sum(axis=2) <= 6).sum()) + + smooth, raw = steelblue_pixels(), steelblue_pixels(antialias=False) + # 7 discs of ~19 px diameter: far more than the thin line alone + assert raw > 7 * 150 + assert abs(smooth - raw) <= 0.1 * raw, (smooth, raw) + + +def test_animation_frames_keep_markers_on_observations(): + x = _walk(n=30) + fig = hyp.plot(x, backend='plotly', show=False, fmt='o-', animate=True, + duration=1, frame_rate=6) + base, = _marker_traces(fig) + sizes = np.asarray(base.marker.size, dtype=float) + # the full curve marks the 30 observations (each at the frame-grid + # vertex nearest it: an animation draws a resampled frame grid) + assert int((sizes > 0).sum()) == 30 + observed = {tuple(np.round(v, 12)) for v in _marked_vertices(base)} + idx = fig.data.index(base) + checked = 0 + for frame in fig.frames: + for k, tr in zip(frame.traces, frame.data): + if k != idx or tr.x is None or len(tr.x) == 0: + continue + # every frame sends the sizes of its own window ... + fs = np.asarray(tr.marker.size, dtype=float) + assert len(fs) == len(tr.x) + # ... so what it marks is observations, never an in-between + # vertex of the moving window + marked = {tuple(np.round(v, 12)) for v in _marked_vertices(tr)} + assert marked and marked <= observed + checked += len(tr.x) > 10 * len(marked) + # (and the smoothed windows do carry many unmarked vertices) + assert checked > 0 + + +def test_trail_traces_mark_observations_only(): + x = _walk(n=30) + fig = hyp.plot(x, backend='plotly', show=False, fmt='o-', animate=True, + chemtrails=True, duration=1, frame_rate=6) + trails = [t for t in fig.data + if t.mode == 'lines+markers' and t.showlegend is False + and (t.meta or {}).get('hyp_trace_index') is None + and not (t.meta or {}).get('hyp_forecast_role') + and t.type == 'scatter3d' and t.x is not None + and len(t.x) == 0] + assert trails, [t.mode for t in fig.data] + sizes = np.asarray(trails[0].marker.size, dtype=float) + assert sizes.ndim == 1 and int((sizes > 0).sum()) == 30 + + +def test_observation_vertices_follow_the_observations_not_one_grid(): + """The observation -> vertex mapping is read from the data, not from + one resampling grid's arithmetic: a non-uniform grid that contains the + observations (as an animation grid that keeps every sample would) maps + each to its exact vertex, and a uniform grid that does not maps each to + the nearest vertex.""" + from hypertools.plot.plotly_backend import _observation_vertices + rng = np.random.default_rng(7) + raw = rng.standard_normal((6, 3)) + # non-uniform: 0, 3, 4, 10, 11, 19 hold the samples, noise elsewhere + where = [0, 3, 4, 10, 11, 19] + dense = rng.standard_normal((20, 3)) + 10 + dense[where] = raw + np.testing.assert_array_equal( + _observation_vertices(dense, raw, 20, 1), where) + # uniform grid without the samples: nearest by the shared parameter + grid = rng.standard_normal((11, 3)) + 10 + np.testing.assert_array_equal( + _observation_vertices(grid, raw, 11, 1), [0, 2, 4, 6, 8, 10]) + # the rows ARE the observations: every aa_step-th vertex + np.testing.assert_array_equal( + _observation_vertices(grid, None, 6, 3), [0, 3, 6, 9, 12, 15]) + + +# -------------------------------------------------------------------------- +# finding 2: continuous hue + 'o-' in 1-D/2-D draws its markers + + +@pytest.mark.parametrize('ndims', [1, 2]) +def test_continuous_hue_o_dash_draws_one_marker_per_observation(ndims): + x = _walk(n=30)[:, :ndims] if ndims > 1 else _walk(n=30)[:, 0] + hue = np.linspace(0, 1, 30) + fig = hyp.plot(x, backend='plotly', show=False, fmt='o-', hue=hue, + palette='viridis') + markers = _marker_traces(fig) + assert len(markers) == 1 + m = markers[0] + assert len(_marked_vertices(m)) == 30 + # coloured per observation, in the hue's own colours + colors = list(m.marker.color) + assert len(colors) == 30 and len(set(colors)) > 20 + # and still one trajectory to a reader counting data traces by tag + assert {(t.meta or {}).get('hyp_trace_index') + for t in _data_traces(fig)} == {0} + + +def test_continuous_hue_o_dash_2d_renders_markers(): + x = _walk(n=30, seed=4)[:, :2] + hue = np.linspace(0, 1, 30) + with_markers = _png(hyp.plot(x, backend='plotly', show=False, fmt='o-', + hue=hue, palette='viridis')) + line_only = _png(hyp.plot(x, backend='plotly', show=False, fmt='-', + hue=hue, palette='viridis')) + ink = [int((np.abs(img - 255).sum(axis=2) > 60).sum()) + for img in (with_markers, line_only)] + # 30 visible marker discs add real ink on top of the thin line + assert ink[0] > ink[1] + 30 * 20, ink + + +# -------------------------------------------------------------------------- +# finding 5: forecast_fmt markers + + +def _forecast_traces(fig, role='static'): + return [t for t in fig.data + if (t.meta or {}).get('hyp_forecast_role') == role] + + +@pytest.mark.parametrize('ndims', [2, 3]) +def test_forecast_fmt_markers_only_on_forecast_steps(ndims): + data = _walk(n=40)[:, :ndims] + t = 5 + fig = hyp.plot(data, backend='plotly', show=False, t=t, predict='Kalman', + forecast_fmt='ro:') + fc, = _forecast_traces(fig) + assert fc.mode == 'lines+markers' + assert len(fc.x) > 10 * (t + 1) # the dotted line is smoothed + # the seam (last observation) plus the t forecast steps are marked + assert len(_marked_vertices(fc)) == t + 1 + if ndims < 3: + assert fc.marker.opacity == 1 and fc.marker.line.width == 0 + + +def test_animated_forecast_fmt_markers_only_on_forecast_steps(): + data = _walk(n=30) + t = 4 + fig = hyp.plot(data, backend='plotly', show=False, t=t, predict='Kalman', + forecast_fmt='ro:', animate=True, duration=1, + frame_rate=6) + live, = _forecast_traces(fig, 'live') + idx = fig.data.index(live) + checked = 0 + for frame in fig.frames: + for k, tr in zip(frame.traces, frame.data): + if k != idx or tr.x is None or len(tr.x) < 2: + continue + sizes = np.asarray(tr.marker.size, dtype=float) + assert len(sizes) == len(tr.x) + assert int((sizes > 0).sum()) == t + 1 + checked += 1 + assert checked > 0 diff --git a/tests/test_plotly_trails.py b/tests/test_plotly_trails.py index 441eb9b2..639605f4 100644 --- a/tests/test_plotly_trails.py +++ b/tests/test_plotly_trails.py @@ -79,8 +79,14 @@ def test_plotly_tail_duration_sets_window(): def test_plotly_zoom_moves_camera_closer(): - near = hyp.plot(_walks(), zoom=3, backend='plotly', show=False) - far = hyp.plot(_walks(), zoom=1, backend='plotly', show=False) + # zoom= is ANIMATION-only (plot()'s docstring; matplotlib's static view + # ignores it) -- this test used to exercise it on a STATIC plot, which + # pinned plotly's static zoom, the parity gap the 1.1 release review + # found; it now checks the animated camera it exists for + near = hyp.plot(_walks(), zoom=3, backend='plotly', show=False, + animate='spin', duration=1, frame_rate=2) + far = hyp.plot(_walks(), zoom=1, backend='plotly', show=False, + animate='spin', duration=1, frame_rate=2) def r(fig): eye = fig.layout.scene.camera.eye @@ -143,7 +149,9 @@ def test_plotly_trail_alpha_honors_per_dataset_alpha(): trail_traces = pfig.data[n:2 * n] assert len(trail_traces) == n ply_trail_alphas = [ - float(t.line.color.rsplit(',', 1)[1].rstrip(') ')) + (t.opacity if t.opacity is not None else 1.) * + (float(t.line.color.rsplit(',', 1)[1].rstrip(') ')) + if t.line.color.startswith('rgba(') else 1.) for t in trail_traces] assert ply_trail_alphas == pytest.approx(expected), ( "plotly trail traces must honor per-dataset alpha= (0.3 * alpha), " diff --git a/tests/test_polars_inputs.py b/tests/test_polars_inputs.py new file mode 100644 index 00000000..e7e3d253 --- /dev/null +++ b/tests/test_polars_inputs.py @@ -0,0 +1,722 @@ +# -*- coding: utf-8 -*- +"""polars inputs through every public entry point (datatype audit, 2026-09-08). + +hypertools functions must not classify input datatypes themselves; they defer +to datawrangler (``dw.wrangle`` / the ``dw.zoo`` predicates) so that every +backend datawrangler recognises -- polars DataFrames and LazyFrames today, +whatever it adds later -- works without hypertools changes. Each test here +feeds a REAL polars DataFrame (and, where datawrangler accepts one, a +LazyFrame, and a list mixing polars with numpy/pandas) to a public entry point +and asserts the result is the SAME as for the equivalent pandas DataFrame. + +Wave 0 made the shared coercion layer polars-aware (``tools/format_data.py``, +``_shared/helpers.py``, ``core/shared.py``, ``predict/predict.py``, +``impute/impute.py``, ``io/streaming.py``). Entry points that still fail +because a module OUTSIDE that layer hand-checks pandas types are marked +``xfail(strict=True)`` naming the blocking file and line, so lifting the block +in wave 1 flips them to XPASS and the marker must be removed. + +No mocks: real polars, real pandas, real hypertools calls (show=False). +""" +import warnings + +import matplotlib.colors as mcolors +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp +from hypertools.tools.format_data import format_data +from hypertools._shared.helpers import get_type, get_dtype +from hypertools.core.shared import as_dataframe +from hypertools.io.streaming import is_stream + +pl = pytest.importorskip("polars") + +pytestmark = pytest.mark.filterwarnings( + 'ignore:.*(Missing data|DataFrame column|reordering|do not share columns).*:UserWarning') + +N_ROWS, N_COLS = 40, 4 +COLUMNS = ['a', 'b', 'c', 'd'] + + +def _make_pandas(seed=0, nan=False): + rng = np.random.RandomState(seed) + values = rng.rand(N_ROWS, N_COLS) + if nan: + values[3, 1] = np.nan + values[10, 0] = np.nan + values[25, 3] = np.nan + return pd.DataFrame(values, columns=COLUMNS) + + +@pytest.fixture +def pdf(): + return _make_pandas() + + +@pytest.fixture +def plf(pdf): + frame = pl.from_pandas(pdf) + assert isinstance(frame, pl.DataFrame) + return frame + + +@pytest.fixture +def lazy(plf): + frame = plf.lazy() + assert isinstance(frame, pl.LazyFrame) + return frame + + +def _same(a, b): + """Recursively compare two hypertools results: same container shape, + same array shapes, all-close values (NaN == NaN), same DataFrame + columns.""" + if isinstance(a, dict) or isinstance(b, dict): + assert isinstance(a, dict) and isinstance(b, dict) + assert set(a) == set(b) + for key in a: + _same(a[key], b[key]) + return + if isinstance(a, (list, tuple)) or isinstance(b, (list, tuple)): + assert isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)) + assert len(a) == len(b) + for x, y in zip(a, b): + _same(x, y) + return + if hasattr(a, 'shape') or hasattr(b, 'shape'): + assert hasattr(a, 'shape') and hasattr(b, 'shape') + assert a.shape == b.shape + if hasattr(a, 'columns') or hasattr(b, 'columns'): + assert list(a.columns) == list(b.columns) + np.testing.assert_allclose(np.asarray(a, dtype=float), + np.asarray(b, dtype=float), equal_nan=True) + return + assert a == b + + +def _inputs(pdf, plf, lazy, kind): + """(polars-flavoured input, pandas reference input) for one `kind`.""" + arr = np.random.RandomState(1).rand(N_ROWS, N_COLS) + return { + 'polars': (plf, pdf), + 'lazy': (lazy, pdf), + 'mixed': ([arr, plf, pdf], [arr, pdf, pdf]), + }[kind] + + +KINDS = ['polars', 'lazy', 'mixed'] + + +# --- the coercion layer itself --------------------------------------------- + +def test_format_data_polars_dataframe_matches_pandas(pdf, plf, lazy): + ref = format_data(pdf) + _same(format_data(plf), ref) + _same(format_data(lazy), ref) + assert isinstance(format_data(plf)[0], np.ndarray) + assert format_data(plf)[0].dtype == ref[0].dtype + + +def test_format_data_mixed_list_matches_pandas(pdf, plf, lazy): + arr = np.random.RandomState(1).rand(N_ROWS, N_COLS) + ref = format_data([arr, pdf, pdf]) + _same(format_data([arr, plf, pdf]), ref) + _same(format_data([arr, lazy, pdf]), ref) + # nested groups holding polars frames are flattened exactly like pandas + _same(format_data([[arr, plf], pdf]), ref) + + +def test_format_data_polars_string_column_dummy_coded_like_pandas(): + sdf = pd.DataFrame({'v': [1., 2., 3., 4.], 'c': ['x', 'y', 'x', 'y']}) + _same(format_data(pl.from_pandas(sdf)), format_data(sdf)) + assert format_data(pl.from_pandas(sdf))[0].shape == (4, 3) + + +def test_format_data_polars_nulls_are_missing_data_like_pandas_nan(): + pdf_nan = _make_pandas(nan=True) + plf_nan = pl.from_pandas(pdf_nan) + assert plf_nan.null_count().sum_horizontal().item() == 3 + # PPCA's fill is randomly initialised from numpy's global state: seed it + # identically before each call so the two fills are comparable exactly + np.random.seed(0) + with pytest.warns(UserWarning, match='filling missing values'): + ref = format_data(pdf_nan) + np.random.seed(0) + with pytest.warns(UserWarning, match='filling missing values'): + out = format_data(plf_nan) + _same(out, ref) + assert not np.isnan(out[0]).any() + # ppca=False keeps the nulls as NaN in the same cells + _same(format_data(plf_nan, ppca=False), format_data(pdf_nan, ppca=False)) + assert np.isnan(format_data(plf_nan, ppca=False)[0]).sum() == 3 + + +def test_format_data_polars_series_is_a_1d_dataset(): + values = np.arange(6.) + ref = format_data(pd.Series(values)) + out = format_data(pl.Series('s', values)) + _same(out, ref) + assert out[0].shape == (6, 1) + # and inside a list + _same(format_data([pl.Series('s', values), values]), + format_data([pd.Series(values), values])) + + +def test_format_data_polars_named_columns_align_by_name_like_pandas(): + # GH #132 column-name alignment applies to polars frames too + df1 = pd.DataFrame({'a': [1., 2., 3.], 'b': [10., 20., 30.]}) + df2 = pd.DataFrame({'b': [100., 200., 300.], 'a': [1000., 2000., 3000.]}) + with pytest.warns(UserWarning, match='reordering'): + ref = format_data([df1, df2]) + with pytest.warns(UserWarning, match='reordering'): + out = format_data([pl.from_pandas(df1), pl.from_pandas(df2)]) + _same(out, ref) + assert np.allclose(out[1][:, 0], [1000., 2000., 3000.]) + + +def test_get_type_and_get_dtype_classify_polars_as_dataframe(plf, lazy): + assert get_type(plf) == 'df' == get_type(lazy) + assert get_dtype(plf) == 'df' == get_dtype(lazy) + # the vocabulary for the other inputs is unchanged + assert get_type(np.zeros((3, 2))) == 'arr_num' + assert get_type(['a b', 'c d']) == 'list_str' + assert get_type([1., 2.]) == 'list_num' + assert get_type([np.zeros(2)]) == 'list_arr' + assert get_type('a b') == 'str' + with pytest.raises(TypeError, match=r"Unsupported data type 'dict'"): + get_type({'a': 1}) + + +def test_as_dataframe_polars_becomes_pandas(pdf, plf, lazy): + out = as_dataframe(plf) + assert isinstance(out, pd.DataFrame) + _same(out, pdf) + _same(as_dataframe(lazy), pdf) + # pandas passes through untouched (same object), arrays become frames + assert as_dataframe(pdf) is pdf + assert as_dataframe(np.arange(3.)).shape == (3, 1) + + +def test_is_stream_polars_lazyframe_is_not_a_stream(plf, lazy): + assert not is_stream(lazy) + assert not is_stream(plf) + assert not is_stream(pl.Series('s', [1., 2.])) + # the stream cases are unchanged + assert is_stream(iter([np.zeros(3)])) + assert is_stream(x for x in [np.zeros(3)]) + assert not is_stream([np.zeros((3, 2))]) + assert not is_stream(pd.DataFrame(np.zeros((3, 2)))) + assert not is_stream(pd.Series([1., 2.])) + assert not is_stream('text') + + +# --- public entry points --------------------------------------------------- + +@pytest.mark.parametrize('kind', KINDS) +def test_reduce_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + _same(hyp.reduce(x, ndims=2), hyp.reduce(ref, ndims=2)) + + +@pytest.mark.parametrize('kind', KINDS) +def test_align_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + if kind != 'mixed': + x, ref = [x, x], [ref, ref] + _same(hyp.align(x), hyp.align(ref)) + + +@pytest.mark.parametrize('kind', KINDS) +def test_cluster_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + # KMeans is seeded so the pandas and polars labelings are comparable + spec = {'model': 'KMeans', 'kwargs': {'random_state': 0}} + _same(hyp.cluster(x, cluster=spec, n_clusters=3), + hyp.cluster(ref, cluster=spec, n_clusters=3)) + + +@pytest.mark.parametrize('kind', KINDS) +def test_normalize_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + _same(hyp.normalize(x), hyp.normalize(ref)) + + +@pytest.mark.parametrize('kind', KINDS) +def test_manip_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + _same(hyp.manip(x, model='ZScore'), hyp.manip(ref, model='ZScore')) + + +@pytest.mark.parametrize('kind', KINDS) +def test_predict_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + out = hyp.predict(x, model='AutoRegressor', t=3) + _same(out, hyp.predict(ref, model='AutoRegressor', t=3)) + frame = out[1] if kind == 'mixed' else out + assert isinstance(frame, pd.DataFrame) + assert list(frame.columns) == COLUMNS + + +def test_predict_polars_series_matches_pandas_series(): + values = np.cumsum(np.random.RandomState(0).rand(30)) + out = hyp.predict(pl.Series('s', values), model='AutoRegressor', t=3) + ref = hyp.predict(pd.Series(values, name='s'), model='AutoRegressor', t=3) + _same(out, ref) + assert list(out.columns) == ['s'] + + +@pytest.mark.parametrize('kind', KINDS) +def test_impute_polars_matches_pandas(kind): + pdf_nan = _make_pandas(nan=True) + plf_nan = pl.from_pandas(pdf_nan) + x, ref = _inputs(pdf_nan, plf_nan, plf_nan.lazy(), kind) + out = hyp.impute(x, model='KNNImputer') + _same(out, hyp.impute(ref, model='KNNImputer')) + frame = out[1] if kind == 'mixed' else out + assert isinstance(frame, pd.DataFrame) + assert list(frame.columns) == COLUMNS + assert not np.isnan(frame.to_numpy()).any() + + +def test_impute_polars_series_with_null_matches_pandas_series(): + values = np.arange(12.) + values[4] = np.nan + out = hyp.impute(pl.Series('s', values), model='SimpleImputer') + ref = hyp.impute(pd.Series(values, name='s'), model='SimpleImputer') + _same(out, ref) + assert list(out.columns) == ['s'] + assert not np.isnan(out.to_numpy()).any() + + +@pytest.mark.parametrize('kind', KINDS) +def test_analyze_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + _same(hyp.analyze(x, reduce='PCA', ndims=2), + hyp.analyze(ref, reduce='PCA', ndims=2)) + + +@pytest.mark.parametrize('kind', KINDS) +def test_describe_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + got, want = hyp.describe(x, show=False), hyp.describe(ref, show=False) + # describe(show=False) returns its drawn figure since the 1.1 release + # review (it used to be None); two distinct Figure objects never compare + # equal, so compare the scores and check both figures were drawn + import matplotlib.pyplot as plt + assert isinstance(got.pop('fig'), plt.Figure) + assert isinstance(want.pop('fig'), plt.Figure) + _same(got, want) + + +def _mpl_drawn(fig): + """Every drawn coordinate array of a matplotlib figure (lines and + scatter collections), in drawing order.""" + drawn = [] + for ax in fig.axes: + for line in ax.lines: + data = (line.get_data_3d() if hasattr(line, 'get_data_3d') + else line.get_data()) + drawn.append(np.asarray(data, dtype=float)) + for coll in ax.collections: + drawn.append(np.asarray(coll.get_offsets(), dtype=float)) + assert drawn, 'nothing was drawn' + return drawn + + +@pytest.mark.parametrize('kind', KINDS) +def test_plot_matplotlib_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + out = hyp.plot(x, show=False) + _same(_mpl_drawn(out), _mpl_drawn(hyp.plot(ref, show=False))) + + +def _plotly_traces(fig): + traces = [] + for trace in fig.data: + coords = [trace.x, trace.y] + if getattr(trace, 'z', None) is not None: + coords.append(trace.z) + traces.append((trace.type, np.asarray(coords, dtype=float))) + assert traces, 'no traces' + return traces + + +@pytest.mark.parametrize('kind', KINDS) +def test_plot_plotly_polars_matches_pandas(pdf, plf, lazy, kind): + x, ref = _inputs(pdf, plf, lazy, kind) + hyp.set_interactive_backend('plotly') + try: + out = hyp.plot(x, show=False) + expected = hyp.plot(ref, show=False) + finally: + hyp.set_interactive_backend('matplotlib') + got, want = _plotly_traces(out), _plotly_traces(expected) + assert [t for t, _ in got] == [t for t, _ in want] + _same([c for _, c in got], [c for _, c in want]) + + +def test_plot_polars_column_names_become_axis_labels_like_pandas(): + # wave 1 lifted: `_capture_column_names` / `_dataframe_axis_labels` + # read column names through the datawrangler predicates + values = np.random.RandomState(0).rand(N_ROWS, 2) + p2 = pd.DataFrame(values, columns=['height', 'weight']) + ref = hyp.plot(p2, show=False) + out = hyp.plot(pl.from_pandas(p2), show=False) + _same(_mpl_drawn(out), _mpl_drawn(ref)) + assert ref.axes[0].get_xlabel() == 'height' + assert (out.axes[0].get_xlabel(), out.axes[0].get_ylabel()) == \ + (ref.axes[0].get_xlabel(), ref.axes[0].get_ylabel()) + # and the 3-D case names all three axes + p3 = pd.DataFrame(np.random.RandomState(1).rand(N_ROWS, 3), + columns=['x1', 'x2', 'x3']) + ref3 = hyp.plot(p3, show=False) + out3 = hyp.plot(pl.from_pandas(p3), show=False) + assert ref3.axes[0].get_zlabel() == 'x3' + assert (out3.axes[0].get_xlabel(), out3.axes[0].get_ylabel(), + out3.axes[0].get_zlabel()) == ('x1', 'x2', 'x3') + + +def test_plot_plotly_polars_column_names_become_axis_labels_like_pandas(): + values = np.random.RandomState(0).rand(N_ROWS, 2) + p2 = pd.DataFrame(values, columns=['height', 'weight']) + hyp.set_interactive_backend('plotly') + try: + ref = hyp.plot(p2, show=False) + out = hyp.plot(pl.from_pandas(p2), show=False) + finally: + hyp.set_interactive_backend('matplotlib') + assert ref.layout.xaxis.title.text == 'height' + assert (out.layout.xaxis.title.text, out.layout.yaxis.title.text) == \ + (ref.layout.xaxis.title.text, ref.layout.yaxis.title.text) + _same([c for _, c in _plotly_traces(out)], + [c for _, c in _plotly_traces(ref)]) + + +# --- plot-level polars arguments (hue / labels / truth / palette / panels) --- + +@pytest.fixture +def frame3(): + """A 30 x 3 named frame as (polars, pandas).""" + rng = np.random.RandomState(3) + pdf3 = pd.DataFrame(rng.rand(30, 3), columns=['a', 'b', 'c']) + return pl.from_pandas(pdf3), pdf3 + + +def _both_backends(call): + """``(matplotlib_result, plotly_result)`` of `call(backend)``, with the + backend restored afterwards.""" + mpl = call('matplotlib') + hyp.set_interactive_backend('plotly') + try: + ply = call('plotly') + finally: + hyp.set_interactive_backend('matplotlib') + return mpl, ply + + +def _mpl_colors(fig): + """Every drawn colour of a matplotlib figure, in drawing order.""" + colors = [] + for ax in fig.axes: + for line in ax.lines: + colors.append(np.asarray(mcolors.to_rgba(line.get_color()))) + for coll in ax.collections: + fc = np.asarray(coll.get_facecolor(), dtype=float) + if fc.size: + colors.append(fc) + return colors + + +def _plotly_colors(fig): + return [(t.type, getattr(t.marker, 'color', None), + getattr(t.line, 'color', None)) for t in fig.data] + + +def _plotly_annotation_texts(fig): + """Every annotation text of a plotly figure (3-D labels live on the + scene, 2-D ones on the layout).""" + texts = [a.text for a in fig.layout.annotations] + if fig.layout.scene is not None: + texts += [a.text for a in fig.layout.scene.annotations] + return texts + + +def _assert_same_figure(out, ref, backend): + """The polars-argument figure is drawn exactly like the pandas one.""" + if backend == 'matplotlib': + _same(_mpl_drawn(out), _mpl_drawn(ref)) + _same(_mpl_colors(out), _mpl_colors(ref)) + assert ([t.get_text() for t in out.axes[0].texts] + == [t.get_text() for t in ref.axes[0].texts]) + assert len(out.axes) == len(ref.axes) + else: + got, want = _plotly_traces(out), _plotly_traces(ref) + assert [t for t, _ in got] == [t for t, _ in want] + _same([c for _, c in got], [c for _, c in want]) + assert _plotly_colors(out) == _plotly_colors(ref) + assert [t.name for t in out.data] == [t.name for t in ref.data] + assert _plotly_annotation_texts(out) == _plotly_annotation_texts(ref) + + +@pytest.mark.parametrize('form', ['series', 'frame']) +def test_plot_hue_from_polars_categorical(frame3, form): + plf3, pdf3 = frame3 + labels = ['x', 'y', 'z'] * 10 + p_hue = pd.Series(labels, name='grp') + hue = pl.Series('grp', labels) + if form == 'frame': + p_hue, hue = p_hue.to_frame(), hue.to_frame() + + def call(backend): + out = hyp.plot(plf3, hue=hue, fmt='o-', legend=True, show=False) + ref = hyp.plot(pdf3, hue=p_hue, fmt='o-', legend=True, show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, ply = _both_backends(call) + # three categories, three colours: the polars hue was grouped, not + # dropped + assert len({tuple(c) for c in map(tuple, np.round( + [mcolors.to_rgba(line.get_color()) for line in mpl.axes[0].lines], + 6))}) == 3 + assert len(ply.data) >= 3 + legend = mpl.axes[0].get_legend() + assert legend is not None + assert [t.get_text() for t in legend.get_texts()] == ['x', 'y', 'z'] + + +@pytest.mark.parametrize('form', ['series', 'frame']) +def test_plot_hue_from_polars_numeric(frame3, form): + plf3, pdf3 = frame3 + values = np.linspace(0., 1., 30) + p_hue = pd.Series(values, name='val') + hue = pl.Series('val', values) + if form == 'frame': + p_hue, hue = p_hue.to_frame(), hue.to_frame() + + def call(backend): + out = hyp.plot(plf3, hue=hue, fmt='.', show=False) + ref = hyp.plot(pdf3, hue=p_hue, fmt='.', show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, _ = _both_backends(call) + # a continuous hue: more than three distinct colours over 30 points + colors = np.round(np.vstack([c for c in _mpl_colors(mpl) + if c.ndim == 2]), 6) + assert len({tuple(c) for c in colors}) > 3 + + +def test_plot_hue_matrix_from_polars_frame_keeps_column_names(frame3): + """A multi-column polars frame is a matrix hue whose column names label + the legend, exactly like the pandas frame (GH #285).""" + plf3, pdf3 = frame3 + rng = np.random.RandomState(5) + weights = rng.rand(30, 2) + weights /= weights.sum(axis=1, keepdims=True) + p_hue = pd.DataFrame(weights, columns=['alpha', 'beta']) + hue = pl.from_pandas(p_hue) + out = hyp.plot(plf3, hue=hue, fmt='.', legend=True, show=False) + ref = hyp.plot(pdf3, hue=p_hue, fmt='.', legend=True, show=False) + _assert_same_figure(out, ref, 'matplotlib') + legend = out.axes[0].get_legend() + assert legend is not None + labels = [t.get_text() for t in legend.get_texts()] + assert labels == ['alpha', 'beta'] + assert labels == [t.get_text() + for t in ref.axes[0].get_legend().get_texts()] + + +def test_plot_labels_from_polars_column(frame3): + plf3, pdf3 = frame3 + names = [f'obs{i}' for i in range(30)] + p_lab = pdf3.assign(name=names) + pl_lab = pl.from_pandas(p_lab) + + def call(backend): + out = hyp.plot(plf3, labels=pl_lab['name'], show=False) + ref = hyp.plot(pdf3, labels=p_lab['name'], show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, ply = _both_backends(call) + assert [t.get_text() for t in mpl.axes[0].texts] == names + assert _plotly_annotation_texts(ply) == names + + +def test_plot_truth_from_polars_frame(frame3): + plf3, pdf3 = frame3 + rng = np.random.RandomState(7) + p_truth = pd.DataFrame(rng.rand(5, 3), columns=['a', 'b', 'c']) + truth = pl.from_pandas(p_truth) + + def call(backend): + out = hyp.plot(plf3, predict='AutoRegressor', t=5, truth=truth, + show=False) + ref = hyp.plot(pdf3, predict='AutoRegressor', t=5, truth=p_truth, + show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, _ = _both_backends(call) + # data line + forecast + truth were all drawn + assert len(mpl.axes[0].lines) >= 3 + # and a truth of the wrong length is refused for polars as for pandas + with pytest.raises(ValueError, match='exactly t=5 rows'): + hyp.plot(plf3, predict='AutoRegressor', t=5, + truth=pl.from_pandas(p_truth.iloc[:3]), show=False) + + +def test_plot_mixed_polars_and_pandas_datasets(frame3): + plf3, pdf3 = frame3 + rng = np.random.RandomState(11) + other = pd.DataFrame(rng.rand(20, 3), columns=['a', 'b', 'c']) + arr = rng.rand(10, 3) + + def call(backend): + out = hyp.plot([plf3, other, arr], show=False) + ref = hyp.plot([pdf3, other, arr], show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, ply = _both_backends(call) + assert len(mpl.axes[0].lines) == 3 + assert len(ply.data) >= 3 + # per-dataset hue lists mixing a polars Series with a python list + hue_pl = [pl.Series('g', ['p'] * 30), ['q'] * 20, ['r'] * 10] + hue_pd = [pd.Series(['p'] * 30), ['q'] * 20, ['r'] * 10] + out = hyp.plot([plf3, other, arr], hue=hue_pl, show=False) + ref = hyp.plot([pdf3, other, arr], hue=hue_pd, show=False) + _assert_same_figure(out, ref, 'matplotlib') + + +def test_plot_polars_frame_as_matrix_palette(frame3): + from hypertools.plot.colors import is_palette_matrix, matrix_palette + plf3, pdf3 = frame3 + rng = np.random.RandomState(13) + p_pal = pd.DataFrame(rng.rand(30, 2), columns=['u', 'v']) + pal = pl.from_pandas(p_pal) + assert is_palette_matrix(pal) and is_palette_matrix(p_pal) + assert is_palette_matrix(pal.lazy()) + # a polars frame of strings is not a palette matrix (pandas rule) + assert not is_palette_matrix(pl.DataFrame({'s': ['a', 'b']})) + cm_pl, cm_pd = matrix_palette(pal), matrix_palette(p_pal) + samples = np.linspace(0., 1., 7) + np.testing.assert_allclose(cm_pl(samples), cm_pd(samples)) + assert cm_pl(samples).shape == (7, 4) + + def call(backend): + out = hyp.plot(plf3, hue=np.linspace(0, 1, 30), palette=pal, + fmt='.', show=False) + ref = hyp.plot(pdf3, hue=np.linspace(0, 1, 30), palette=p_pal, + fmt='.', show=False) + _assert_same_figure(out, ref, backend) + return out + _both_backends(call) + + +def test_plot_panels_with_polars_input(frame3): + plf3, pdf3 = frame3 + rng = np.random.RandomState(17) + other = pd.DataFrame(rng.rand(24, 3), columns=['a', 'b', 'c']) + + def call(backend): + out = hyp.plot([plf3, pl.from_pandas(other)], panels=True, + show=False) + ref = hyp.plot([pdf3, other], panels=True, show=False) + _assert_same_figure(out, ref, backend) + return out + mpl, ply = _both_backends(call) + assert len(mpl.axes) >= 2 + # panel axis labels come from the polars column names too + assert [ax.get_xlabel() for ax in mpl.axes[:2]] == ['a', 'a'] + # series mode (ndims=1, one line per column with reduce=None) panels: + # the polars frame's columns name the lines exactly as the pandas + # frame's do (`_capture_column_names` through `_panel_frame`) + out = hyp.plot([plf3, pl.from_pandas(other)], panels=True, ndims=1, + reduce=None, legend=True, show=False) + ref = hyp.plot([pdf3, other], panels=True, ndims=1, reduce=None, + legend=True, show=False) + _assert_same_figure(out, ref, 'matplotlib') + legends = [[t.get_text() for t in ax.get_legend().get_texts()] + for ax in out.axes if ax.get_legend()] + assert legends == [['a', 'b', 'c'], ['a', 'b', 'c']] + assert legends == [[t.get_text() for t in ax.get_legend().get_texts()] + for ax in ref.axes if ax.get_legend()] + + +def test_plot_series_mode_names_polars_columns_like_pandas(frame3): + """`_capture_column_names` (wave 1): in ndims=1 series mode a polars + frame's columns name the drawn lines, and a single named column names + the y axis, exactly as for the pandas frame.""" + plf3, pdf3 = frame3 + + def call(backend): + out = hyp.plot(plf3, ndims=1, reduce=None, legend=True, show=False) + ref = hyp.plot(pdf3, ndims=1, reduce=None, legend=True, show=False) + _assert_same_figure(out, ref, backend) + return out, ref + (mpl_out, mpl_ref), (ply_out, ply_ref) = _both_backends(call) + names = [t.get_text() for t in mpl_out.axes[0].get_legend().get_texts()] + assert names == ['a', 'b', 'c'] + assert names == [t.get_text() + for t in mpl_ref.axes[0].get_legend().get_texts()] + assert [t.name for t in ply_out.data][:3] == ['a', 'b', 'c'] + one_pl = hyp.plot(plf3.select('b'), ndims=1, show=False) + one_pd = hyp.plot(pdf3[['b']], ndims=1, show=False) + assert one_pl.axes[0].get_ylabel() == 'b' == one_pd.axes[0].get_ylabel() + # a polars Series is named after itself, like a pandas Series + s_pl = hyp.plot(plf3['c'], ndims=1, show=False) + s_pd = hyp.plot(pdf3['c'], ndims=1, show=False) + assert s_pl.axes[0].get_ylabel() == 'c' == s_pd.axes[0].get_ylabel() + _same(_mpl_drawn(s_pl), _mpl_drawn(s_pd)) + + +def test_plot_polars_date_column_matches_pandas_date_column(): + """polars has no row index: a frame with a datetime column plots exactly + as the pandas frame with the same datetime COLUMN (and a RangeIndex), + on both backends -- not as the pandas frame whose DatetimeIndex puts + dates on the x axis of `ndims=1` series mode (that is a pandas index + feature; ``pl_df.to_pandas().set_index('date')`` opts into it).""" + dates = pd.date_range('2020-01-01', periods=12, freq='D', name='date') + rng = np.random.RandomState(19) + indexed = pd.DataFrame({'v': rng.rand(12), 'w': rng.rand(12)}, + index=dates) + with_column = indexed.reset_index() + assert list(with_column.columns) == ['date', 'v', 'w'] + polars = pl.from_pandas(with_column) + assert polars.schema['date'].is_temporal() + + def call(backend): + out = hyp.plot(polars, ndims=1, reduce=None, show=False) + ref = hyp.plot(with_column, ndims=1, reduce=None, show=False) + _assert_same_figure(out, ref, backend) + return out, ref + (mpl_out, mpl_ref), _ = _both_backends(call) + # the datetime index version draws real dates on x (pandas only) + import matplotlib.dates as mdates + idx_fig = hyp.plot(indexed, ndims=1, reduce=None, show=False) + assert idx_fig.axes[0].lines[0].get_xdata()[0] == \ + mdates.date2num(dates[0]) + # ...and the polars frame, like the pandas column frame, draws row + # positions on x (0..11), with the date column as one more series + assert mpl_out.axes[0].lines[0].get_xdata()[0] == 0.0 + assert mpl_out.axes[0].get_xlim() == mpl_ref.axes[0].get_xlim() + assert mpl_out.axes[0].get_xlim()[1] < 20 + assert len(mpl_out.axes[0].lines) == len(mpl_ref.axes[0].lines) == 3 + # a polars frame's default row index is the pandas default, so a + # {index} title pattern is refused for both with the same message + with pytest.raises(ValueError, match='index') as pl_err: + hyp.plot(polars, ndims=1, title='{index}', animate=True, show=False) + with pytest.raises(ValueError, match='index') as pd_err: + hyp.plot(with_column, ndims=1, title='{index}', animate=True, + show=False) + assert str(pl_err.value) == str(pd_err.value) + + +def test_polars_inputs_raise_no_warnings_beyond_pandas(pdf, plf): + """A polars input must not add warnings of its own (e.g. a datawrangler + deprecation on the conversion path) relative to the same pandas call.""" + def collect(x): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + hyp.reduce(x, ndims=2) + return sorted({str(w.message) for w in caught}) + assert collect(plf) == collect(pdf) diff --git a/tests/test_polars_inputs_wave1.py b/tests/test_polars_inputs_wave1.py new file mode 100644 index 00000000..4b8b9d2c --- /dev/null +++ b/tests/test_polars_inputs_wave1.py @@ -0,0 +1,430 @@ +# -*- coding: utf-8 -*- +"""polars inputs through the wave-1 entry points (datatype audit, 2026-09-08). + +Wave 1 removed the last hand-rolled pandas/numpy type checks OUTSIDE the +shared coercion layer -- the manipulators (`hyp.manip` and the Manipulator +classes used directly), `hyp.align`, `hyp.stack`, `hyp.damage`, +`hyp.Pipeline`, `hyp.apply_model`, `hyp.normalize`'s fitted `Normalizer`, +`hyp.save`/`hyp.load`, and `text2mat` -- so that every DataFrame backend +datawrangler recognises works there too. Each test feeds a REAL polars +DataFrame (and a LazyFrame, and a list mixing polars with pandas/numpy) and +asserts the result is the SAME as for the equivalent pandas DataFrame. + +The results are hypertools' internal types (pandas frames / numpy arrays): +polars in, pandas out, exactly as a pandas Series or a tuple of datasets is +normalised on the way in. + +No mocks: real polars, real pandas, real hypertools calls (show=False). +""" +import os + +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp +from hypertools.manip import ZScore, Normalize, Smooth, Resample, Delay +from hypertools.tools.text2mat import text2mat + +pl = pytest.importorskip("polars") + +pytestmark = pytest.mark.filterwarnings( + 'ignore:.*(Missing data|DataFrame column|reordering|do not share columns' + '|copy keyword).*') + +N_ROWS, N_COLS = 40, 4 +COLUMNS = ['a', 'b', 'c', 'd'] + + +def _make_pandas(seed=0): + rng = np.random.RandomState(seed) + return pd.DataFrame(rng.rand(N_ROWS, N_COLS), columns=COLUMNS) + + +@pytest.fixture +def pdf(): + return _make_pandas(0) + + +@pytest.fixture +def pdf2(): + return _make_pandas(1) + + +@pytest.fixture +def plf(pdf): + frame = pl.from_pandas(pdf) + assert isinstance(frame, pl.DataFrame) + return frame + + +@pytest.fixture +def plf2(pdf2): + return pl.from_pandas(pdf2) + + +@pytest.fixture +def lazy(plf): + frame = plf.lazy() + assert isinstance(frame, pl.LazyFrame) + return frame + + +def _same(a, b): + """Recursively compare two hypertools results: same container shape, + same DataFrame index/columns, all-close values (NaN == NaN).""" + if isinstance(a, dict) or isinstance(b, dict): + assert isinstance(a, dict) and isinstance(b, dict) + assert set(a) == set(b) + for key in a: + _same(a[key], b[key]) + return + if isinstance(a, (list, tuple)) or isinstance(b, (list, tuple)): + assert isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)) + assert len(a) == len(b) + for x, y in zip(a, b): + _same(x, y) + return + if isinstance(a, pd.DataFrame) or isinstance(b, pd.DataFrame): + # polars in, pandas out: the SAME internal type as the pandas call + assert isinstance(a, pd.DataFrame) and isinstance(b, pd.DataFrame) + assert list(a.columns) == list(b.columns) + assert a.index.equals(b.index) + np.testing.assert_allclose(a.to_numpy(dtype=float), + b.to_numpy(dtype=float), equal_nan=True) + return + if isinstance(a, pd.Series) or isinstance(b, pd.Series): + assert isinstance(a, pd.Series) and isinstance(b, pd.Series) + assert a.name == b.name and a.index.equals(b.index) + np.testing.assert_allclose(a.to_numpy(dtype=float), + b.to_numpy(dtype=float), equal_nan=True) + return + a, b = np.asarray(a), np.asarray(b) + assert a.shape == b.shape + np.testing.assert_allclose(a.astype(float), b.astype(float), + equal_nan=True) + + +# ---------------------------------------------------------------- hyp.manip + +MANIPS = [('ZScore', {}), ('Normalize', {}), ('Smooth', {'kernel_width': 5}), + ('Resample', {'n_samples': 20}), ('Delay', {'tau': 1, 'dims': 2})] + + +@pytest.mark.parametrize('model,kwargs', MANIPS, ids=[m for m, _ in MANIPS]) +@pytest.mark.parametrize('kind', ['frame', 'lazy']) +def test_manip_polars_matches_pandas(pdf, plf, lazy, kind, model, kwargs): + x = plf if kind == 'frame' else lazy + out = hyp.manip(x, model=model, **kwargs) + ref = hyp.manip(pdf, model=model, **kwargs) + _same(out, ref) + if model != 'Delay': # Delay names its columns '<col>_lag<k>' + assert list(out.columns) == COLUMNS + + +@pytest.mark.parametrize('model,kwargs', MANIPS, ids=[m for m, _ in MANIPS]) +def test_manip_list_mixing_polars_pandas_numpy(pdf, pdf2, plf, model, + kwargs): + # a numpy array gets positional column labels; the shared-statistics + # manipulators match columns by position when the labels differ, and + # Smooth/Resample/Delay work per dataset, so EVERY model takes the mix + # (Codex round 12: this test used to swap in an all-named list for + # ZScore/Normalize) + mixed = [plf, pdf2, pl.from_pandas(pdf2).lazy(), pdf2.to_numpy()] + ref_in = [pdf, pdf2, pdf2, pdf2.to_numpy()] + out = hyp.manip(mixed, model=model, **kwargs) + _same(out, hyp.manip(ref_in, model=model, **kwargs)) + if model != 'Delay': + assert [list(o.columns) for o in out] == [COLUMNS] * 3 + [[0, 1, 2, 3]] + if model in ('ZScore', 'Normalize'): + # the statistics are shared across all four datasets + stacked = np.vstack([pdf.to_numpy()] + [pdf2.to_numpy()] * 3) + if model == 'ZScore': + expected = (pdf.to_numpy() - stacked.mean(axis=0)) / stacked.std(axis=0, ddof=1) + else: + lo, hi = stacked.min(axis=0), stacked.max(axis=0) + expected = (pdf.to_numpy() - lo) / (hi - lo) + assert np.allclose(out[0].to_numpy(), expected) + + +def test_manip_polars_series_is_one_column(pdf, plf): + out = hyp.manip(plf['a'], model='ZScore') + _same(out, hyp.manip(pdf['a'], model='ZScore')) + assert list(out.columns) == ['a'] and out.shape == (N_ROWS, 1) + # beside an array or a pandas Series in a list, too (Codex round 12, + # R12-1: the polars frame from `.to_frame()` reached pandas-only code) + mixed = hyp.manip([plf['a'], pdf['b'].to_numpy(), pdf['c']], model='Smooth', + kernel_width=5) + ref = hyp.manip([pdf['a'], pdf['b'].to_numpy(), pdf['c']], model='Smooth', + kernel_width=5) + _same(mixed, ref) + assert [list(m.columns) for m in mixed] == [['a'], [0], ['c']] + + +def test_manip_polars_chain_and_stage_kwargs(pdf, plf): + chain = [{'model': 'Resample', 'kwargs': {'n_samples': 15}}, 'ZScore'] + _same(hyp.manip(plf, model=chain), hyp.manip(pdf, model=chain)) + _same(hyp.manip(plf, model='ZScore', reduce='PCA', ndims=2), + hyp.manip(pdf, model='ZScore', reduce='PCA', ndims=2)) + + +def test_manip_polars_fitted_model_reuse(pdf, pdf2, plf, plf2): + out, fitted = hyp.manip(plf, model='ZScore', return_model=True) + ref, ref_fitted = hyp.manip(pdf, model='ZScore', return_model=True) + _same(out, ref) + _same(hyp.manip(plf2, model=fitted), hyp.manip(pdf2, model=ref_fitted)) + + +def test_manip_empty_polars_frame_raises_no_observations(): + with pytest.raises(ValueError, match='no observations'): + hyp.manip(pl.DataFrame({'a': []}), model='ZScore') + + +@pytest.mark.parametrize('cls,kwargs', [ + (ZScore, {}), (Normalize, {}), (Smooth, {'kernel_width': 5}), + (Resample, {'n_samples': 20}), (Delay, {'tau': 1, 'dims': 2})], + ids=['ZScore', 'Normalize', 'Smooth', 'Resample', 'Delay']) +def test_manipulator_classes_directly_on_polars(pdf, pdf2, plf, plf2, lazy, + cls, kwargs): + # the Manipulator classes are public (hyp.manip.MANIPULATORS) and are + # used directly inside hyp.Pipeline: fit/transform never route through + # hyp.manip's funnel, so they coerce their own input + _same(cls(**kwargs).fit_transform(plf), cls(**kwargs).fit_transform(pdf)) + _same(cls(**kwargs).fit_transform(lazy), cls(**kwargs).fit_transform(pdf)) + _same(cls(**kwargs).fit_transform([plf, pdf2]), + cls(**kwargs).fit_transform([pdf, pdf2])) + _same(cls(**kwargs).fit(plf).transform(plf2), + cls(**kwargs).fit(pdf).transform(pdf2)) + # a Series (either backend) is one column that keeps its name (Codex + # round 12, R12-4: `as_dataframe` dropped a pandas Series' index/name) + out = cls(**kwargs).fit_transform(plf['a']) + ref = cls(**kwargs).fit_transform(pdf['a']) + _same(out, ref) + assert list(ref.columns) == (['a'] if cls is not Delay else ['a_lag1', 'a_lag0']) + + +@pytest.mark.parametrize('cls,kwargs', [ + (ZScore, {}), (Normalize, {}), (Resample, {'n_samples': 6})], + ids=['ZScore', 'Normalize', 'Resample']) +def test_manipulator_classes_axis1_on_polars(pdf, plf, cls, kwargs): + _same(cls(axis=1, **kwargs).fit_transform(plf), + cls(axis=1, **kwargs).fit_transform(pdf)) + + +# ---------------------------------------------------------------- hyp.align + +@pytest.mark.parametrize('model', ['HyperAlign', 'Procrustes', None]) +def test_align_list_mixing_polars_and_pandas(pdf, pdf2, plf, model): + mixed = [plf, pdf2, pdf2.to_numpy()] + ref = [pdf, pdf2, pdf2.to_numpy()] + _same(hyp.align(mixed, model=model), hyp.align(ref, model=model)) + + +def test_align_lazyframes_and_single_polars(pdf, pdf2, plf, plf2, lazy): + _same(hyp.align([lazy, plf2]), hyp.align([pdf, pdf2])) + _same(hyp.align(plf), hyp.align(pdf)) + + +def test_align_polars_return_score_and_model(pdf, pdf2, plf, plf2): + out, model, score = hyp.align([plf, pdf2], return_model=True, + return_score=True) + ref, ref_model, ref_score = hyp.align([pdf, pdf2], return_model=True, + return_score=True) + _same(out, ref) + assert score['metric'] == ref_score['metric'] + np.testing.assert_allclose(score['before'], ref_score['before']) + np.testing.assert_allclose(score['after'], ref_score['after']) + # the fitted aligner is reusable on polars, like on pandas + _same(hyp.align([plf2, plf], model=model), + hyp.align([pdf2, pdf], model=ref_model)) + + +# ---------------------------------------------------- hyp.stack / hyp.damage + +def test_stack_polars_matches_pandas(pdf, pdf2, plf, plf2, lazy): + _same(hyp.stack({'x': plf, 'y': plf2}), hyp.stack({'x': pdf, 'y': pdf2})) + _same(hyp.stack([lazy, pdf2]), hyp.stack([pdf, pdf2])) + _same(hyp.stack({'g': {'x': plf, 'y': pdf2}}, aggregate='mean'), + hyp.stack({'g': {'x': pdf, 'y': pdf2}}, aggregate='mean')) + # a polars Series is one single-column dataset named after the series + _same(hyp.stack([plf['a'], plf2['a']]), hyp.stack([pdf['a'], pdf2['a']])) + + +def test_damage_polars_matches_pandas(pdf, pdf2, plf, lazy): + _same(hyp.damage(plf, frac=0.2, seed=1, return_mask=True), + hyp.damage(pdf, frac=0.2, seed=1, return_mask=True)) + _same(hyp.damage(lazy, frac=0.2, seed=1), + hyp.damage(pdf, frac=0.2, seed=1)) + _same(hyp.damage([plf, pdf2.to_numpy()], frac=0.2, seed=1), + hyp.damage([pdf, pdf2.to_numpy()], frac=0.2, seed=1)) + damaged = hyp.damage(plf, frac=0.2, seed=1) + assert damaged.isna().to_numpy().sum() > 0 + # the caller's polars frame is untouched + assert plf.null_count().to_numpy().sum() == 0 + + +def test_damage_polars_series_matches_pandas(pdf, plf): + _same(hyp.damage(plf['a'], frac=0.2, seed=1, return_mask=True), + hyp.damage(pdf['a'], frac=0.2, seed=1, return_mask=True)) + + +# ---------------------------------------- hyp.Pipeline / hyp.apply_model + +def _pipeline(): + return hyp.Pipeline([('z', 'ZScore'), + ('pca', {'model': 'PCA', + 'kwargs': {'n_components': 2}})]) + + +def test_pipeline_fit_transform_on_polars(pdf, pdf2, plf, plf2, lazy): + pipe, ref = _pipeline(), _pipeline() + _same(pipe.fit_transform(plf), ref.fit_transform(pdf)) + _same(pipe.transform(plf2), ref.transform(pdf2)) + _same(pipe.transform(lazy), ref.transform(pdf)) + _same(_pipeline().fit_transform(lazy), ref.fit_transform(pdf)) + + +def test_pipeline_with_manipulator_instances_on_polars_list(pdf, pdf2, plf): + pipe = hyp.Pipeline([('s', Smooth(kernel_width=5)), ('z', 'ZScore')]) + ref = hyp.Pipeline([('s', Smooth(kernel_width=5)), ('z', 'ZScore')]) + _same(pipe.fit_transform([plf, pdf2]), ref.fit_transform([pdf, pdf2])) + + +def test_apply_model_on_polars(pdf, pdf2, plf, plf2, lazy): + _same(hyp.apply_model(plf, 'PCA', ndims=2), + hyp.apply_model(pdf, 'PCA', ndims=2)) + _same(hyp.apply_model(lazy, 'PCA', ndims=2), + hyp.apply_model(pdf, 'PCA', ndims=2)) + _same(hyp.apply_model([plf, pdf2], 'PCA', ndims=2), + hyp.apply_model([pdf, pdf2], 'PCA', ndims=2)) + _same(hyp.apply_model(plf, 'PCA', ndims=2, format_data=False), + hyp.apply_model(pdf, 'PCA', ndims=2, format_data=False)) + out, fitted = hyp.apply_model(plf, 'PCA', ndims=2, return_model=True) + ref, ref_fitted = hyp.apply_model(pdf, 'PCA', ndims=2, return_model=True) + _same(out, ref) + _same(fitted.transform(plf2.to_numpy()), + ref_fitted.transform(pdf2.to_numpy())) + + +def test_normalize_fitted_normalizer_on_polars(pdf, pdf2, plf, plf2, lazy): + _same(hyp.normalize(plf), hyp.normalize(pdf)) + _, normalizer = hyp.normalize(pdf, return_model=True) + _same(normalizer.transform(plf2), normalizer.transform(pdf2)) + _same(normalizer.transform(lazy), normalizer.transform(pdf)) + _same(normalizer.transform([plf, pdf2]), normalizer.transform([pdf, pdf2])) + + +# --------------------------------------------------------- hyp.save / load + +@pytest.mark.parametrize('ext', ['csv', 'tsv', 'json', 'parquet', 'npy', + 'npz', 'mat', 'pkl']) +def test_save_load_round_trip_polars(tmp_path, pdf, plf, lazy, ext): + target = tmp_path / f'polars.{ext}' + ref_target = tmp_path / f'pandas.{ext}' + hyp.save(plf, str(target)) + hyp.save(pdf, str(ref_target)) + assert target.exists() and os.path.getsize(target) > 0 + loaded = hyp.load(str(target)) + ref = hyp.load(str(ref_target)) + lazy_target = tmp_path / f'lazy.{ext}' + hyp.save(lazy, str(lazy_target)) + if ext == 'pkl': + # a pickle round-trips the object itself -- the LazyFrame included + # (Codex round 12: an early return used to skip this branch) + assert isinstance(loaded, pl.DataFrame) + pd.testing.assert_frame_equal(loaded.to_pandas(), pdf) + lazy_loaded = hyp.load(str(lazy_target)) + assert isinstance(lazy_loaded, pl.LazyFrame) + pd.testing.assert_frame_equal(lazy_loaded.collect().to_pandas(), pdf) + return + _same(loaded, ref) + if ext in ('csv', 'tsv', 'json', 'parquet'): + assert list(loaded.columns) == COLUMNS + # a LazyFrame is written like the frame it collects to + _same(hyp.load(str(lazy_target)), ref) + + +def test_save_polars_csv_writes_no_index_column(tmp_path, plf): + target = tmp_path / 'frame.csv' + hyp.save(plf, str(target)) + with open(target) as f: + header = f.readline().strip() + assert header == ','.join(COLUMNS) + + +def test_load_passes_polars_through_and_analyzes_it(pdf, pdf2, plf, plf2, + lazy): + assert hyp.load(plf) is plf + assert hyp.load(lazy) is lazy + assert [x is y for x, y in zip(hyp.load([plf, pdf2]), [plf, pdf2])] == \ + [True, True] + _same(hyp.load([plf, plf2], reduce='PCA', ndims=2), + hyp.load([pdf, pdf2], reduce='PCA', ndims=2)) + with pytest.raises(TypeError, match='unexpected keyword'): + hyp.load(plf, n_samples=3) + + +# ------------------------------------------------------------ text2mat + +def test_text2mat_polars_series_of_documents_is_one_dataset(): + docs = ['the cat sat on the mat', 'the dog sat on the log', + 'a bird sang'] + ref = text2mat(docs, vectorizer='CountVectorizer', semantic=None) + out = text2mat(pl.Series('doc', docs), vectorizer='CountVectorizer', + semantic=None) + pandas_out = text2mat(pd.Series(docs), vectorizer='CountVectorizer', + semantic=None) + _same(out, ref) + _same(pandas_out, ref) + assert len(out) == 1 and out[0].shape[0] == len(docs) + + +def test_manip_aligns_an_unnamed_array_with_named_frames_by_position(): + """`manip([weights, df])` used to fail inside datawrangler ('All + DataFrames must have the same columns'), pure pandas included, while + `plot`/`reduce`/`align` accept that mix by position; now it does too.""" + import numpy as np + import pandas as pd + import polars as pl + import hypertools as hyp + arr = np.random.RandomState(0).rand(20, 3) + pdf = pd.DataFrame(np.random.RandomState(1).rand(20, 3), columns=list('abc')) + out = hyp.manip([arr, pdf, pl.DataFrame(pdf)], model='ZScore') + ref = hyp.manip([arr, pdf.to_numpy(), pdf.to_numpy()], model='ZScore') + assert len(out) == 3 + for got, want in zip(out, ref): + assert np.allclose(np.asarray(got), np.asarray(want)) + # named frames are handed over untouched (see the mixed-list test below) + + +def test_manip_keeps_named_frames_and_indices_in_mixed_lists(): + """Codex round 11: the first mixed-list rule rejected two named frames + with different labels (the independent manipulators never combine + features) and rebuilt frames from their values, dropping a dated or + irregular index so Resample interpolated at the wrong positions.""" + import numpy as np + import pandas as pd + import hypertools as hyp + a = pd.DataFrame(np.random.RandomState(0).rand(30, 2), columns=['a', 'b']) + b = pd.DataFrame(np.random.RandomState(1).rand(30, 2), columns=['x', 'y']) + out = hyp.manip([a, b], model='Smooth') + assert [list(o.columns) for o in out] == [['a', 'b'], ['x', 'y']] + single = hyp.manip(a, model='Smooth') + assert np.allclose(out[0].to_numpy(), single.to_numpy()) + # an irregular index survives beside an array, and the numbers match the + # single-frame call exactly + frame = pd.DataFrame({'v': [0.0, 1.0, 4.0, 9.0, 16.0]}, index=[0, 1, 2, 8, 10]) + arr = np.arange(5.0).reshape(-1, 1) + mixed = hyp.manip([frame, arr], model='Resample', n_samples=7) + alone = hyp.manip(frame, model='Resample', n_samples=7) + assert np.allclose(mixed[0].to_numpy(), alone.to_numpy()) + assert list(mixed[0].index) == list(alone.index) + dated = pd.DataFrame({'v': np.arange(30.0)}, index=pd.date_range('2024-01-01', periods=30)) + mixed = hyp.manip([dated, np.arange(30.0).reshape(-1, 1)], model='Smooth') + assert list(mixed[0].index) == list(dated.index) + # ...and its feature NAMES (Codex round 12, R12-3: the mixed-list rule + # relabelled the named frame positionally for every model) + assert list(mixed[0].columns) == ['v'] and list(mixed[1].columns) == [0] + delayed = hyp.manip([dated, np.arange(30.0).reshape(-1, 1)], model='Delay') + assert list(delayed[0].columns) == ['v_lag1', 'v_lag0'] + assert list(delayed[1].columns) == ['0_lag1', '0_lag0'] diff --git a/tests/test_predict_audit_fixes.py b/tests/test_predict_audit_fixes.py index 9b1970ba..0e2a6189 100644 --- a/tests/test_predict_audit_fixes.py +++ b/tests/test_predict_audit_fixes.py @@ -169,7 +169,7 @@ def test_autoregressor_explicit_model_kwargs_accepted(): # --- F16-predict-008: real ValueErrors, not assert-based -------------------- def test_autoregressor_too_few_observations_raises_valueerror(): - with pytest.raises(ValueError, match='more than lags'): + with pytest.raises(ValueError, match=r'11 observation.*AutoRegressor\(lags=10\)'): predict(np.random.RandomState(0).randn(3, 2), model='AutoRegressor', t=2) diff --git a/tests/test_predict_backtest.py b/tests/test_predict_backtest.py index 253a9f12..ad0ae158 100644 --- a/tests/test_predict_backtest.py +++ b/tests/test_predict_backtest.py @@ -10,6 +10,8 @@ an exactly linear series, where being perfect is a property of the data and the model, not of a stub. """ +import warnings + import numpy as np import pandas as pd import pytest @@ -26,6 +28,37 @@ def _series(n=60, seed=0): 'b': 0.05 * t - np.sin(t / 5.0) + 0.01 * rng.standard_normal(n)}) +@pytest.mark.parametrize('wrapped', [False, True]) +def test_holdout_instances_fit_each_dataset_without_mutating_caller(wrapped): + # GH #285 release review: reuse of dataset 1's fitted regressor made + # dataset 2's quadratic forecast negative instead of around 3000. + from hypertools.predict import AutoRegressor + t = np.arange(60.) + datasets = [pd.DataFrame({'x': np.sin(t / 3)}), + pd.DataFrame({'x': 100 + t ** 2})] + model = AutoRegressor() + spec = {'model': model} if wrapped else model + expected, expected_forecasts = hyp.predict( + datasets, model=AutoRegressor, holdout=5, return_forecasts=True) + actual, forecasts = hyp.predict( + datasets, model=spec, holdout=5, return_forecasts=True) + pd.testing.assert_frame_equal(actual, expected) + for got, want in zip(forecasts['AutoRegressor'], + expected_forecasts['AutoRegressor']): + pd.testing.assert_frame_equal(got, want) + assert not model.is_fitted + + +@pytest.mark.parametrize('wrapped', [False, True]) +def test_holdout_refuses_previously_fitted_instances(wrapped): + from hypertools.predict import AutoRegressor + data = _series() + model = AutoRegressor().fit(data) + spec = {'model': model} if wrapped else model + with pytest.raises(ValueError, match='holdout=.*unfitted'): + hyp.predict(data, model=spec, holdout=5) + + # --- a perfect forecaster (real model, exactly-linear data) --------------- def _fit_line(data, **kwargs): @@ -310,3 +343,201 @@ def test_replaces_the_stock_tutorial_comparison(): values='MAPE') assert table.shape == (3, 3) assert np.isfinite(table.to_numpy()).all() + + +# --- 1.1 release review: metrics / holdout / warning attribution --------- + +def test_repeated_metric_is_rejected_by_name(): + # a duplicated metric used to fall through to build_scores and die + # with "float() argument must be ... not 'Series'" + df = _series(n=40) + with pytest.raises(ValueError, match="metric 'mae' is listed more than once"): + hyp.predict(df, model='AutoRegressor', holdout=5, metrics=['mae', 'mae']) + # case-insensitively: 'mae' and 'MAE' name the same column + with pytest.raises(ValueError, match="metric 'MAE' is listed more than once"): + hyp.predict(df, model='AutoRegressor', holdout=5, metrics=['mae', 'MAE']) + # the same spellings in the OTHER case are still one scores column each + scores = hyp.predict(df, model='AutoRegressor', holdout=5, + metrics=['MAE', 'rmse']) + assert list(scores.columns) == ['MAE', 'RMSE', 'n', 'unscored', 'horizon'] + + +def test_holdout_true_with_t_zero_blames_t(): + with pytest.raises(ValueError, match=r'holdout=True takes its size from t.*got t=0'): + hyp.predict(_series(n=40), model='Kalman', holdout=True, t=0) + + +def _forecast_nothing(data, n_steps, future_index, **kwargs): + return pd.DataFrame(np.nan, index=future_index, columns=data.columns) + + +class NaNForecaster(Forecaster): + """A real forecaster that produces no values -- the shape of a model + that fails on every held-out row (drives the `unscored` warning).""" + + def __init__(self, **kwargs): + super().__init__(forecaster=_forecast_nothing, **kwargs) + + +def test_unscored_warning_points_at_the_caller(): + import os + import hypertools + package_dir = os.path.dirname(os.path.abspath(hypertools.__file__)) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores = hyp.predict(_series(n=40), model=[NaNForecaster, 'Kalman'], + holdout=5) + assert scores.loc['NaNForecaster', 'unscored'] == 10 + unscored = [w for w in caught if 'not directly comparable' in str(w.message)] + assert len(unscored) == 1 + assert unscored[0].filename == __file__ + assert not unscored[0].filename.startswith(package_dir + os.sep) + + +# --- incomplete models never win (release red-team 2026-10-09) ------------- + +def _records(model, values, unscored): + """Plain per-column score records, as `backtest_predict` builds them.""" + return [{'model': model, 'column': c, 'MAE': v, 'n': 5 - u, 'unscored': u} + for c, v, u in zip('ab', values, unscored)] + + +def test_build_scores_ranks_complete_models_only(): + # no built-in forecaster leaves SOME of a horizon NaN while another + # fills all of it on the same data, so the "partial model has the + # lowest score" verdict is exercised on the shared helper with plain + # hand-written records (data, not a stand-in object). + from hypertools.predict.backtest import build_scores + records = (_records('partial', [0.1, 0.1], [0, 3]) + + _records('full', [2.0, 4.0], [0, 0]) + + _records('naive', [5.0, 5.0], [0, 0])) + with pytest.warns(UserWarning, match="'partial'.*excluded from the ranking"): + wide = build_scores(records, ('mae',), baseline='naive') + with pytest.warns(UserWarning, match='excluded from the ranking'): + long = build_scores(records, ('mae',), per_column=True, + baseline='naive') + for scores in (wide, long): + assert scores.attrs['best'] == 'full' + assert scores.attrs['best_score'] == pytest.approx(3.0) + assert scores.attrs['incomplete'] == ['partial'] + assert scores.attrs['beats_baseline'] is True + assert wide.loc['partial', 'MAE'] == pytest.approx(0.1) # row kept + assert wide.loc['partial', 'unscored'] == 3 + assert long.loc[('partial', 'b'), 'unscored'] == 3 + + +def test_build_scores_judges_coverage_over_every_dataset(): + from hypertools.predict.backtest import build_scores + records = [] + for dataset, unscored in enumerate([[0, 0], [0, 1]]): + for model, values, u in [('partial', [0.1, 0.1], unscored), + ('full', [2.0, 2.0], [0, 0]), + ('naive', [5.0, 5.0], [0, 0])]: + for r in _records(model, values, u): + records.append({**r, 'dataset': dataset}) + with pytest.warns(UserWarning, match='excluded from the ranking'): + scores = build_scores(records, ('mae',), baseline='naive') + assert scores.attrs['best'] == 'full' + assert scores.attrs['incomplete'] == ['partial'] + + +def test_build_scores_with_no_complete_model_or_baseline(): + from hypertools.predict.backtest import build_scores + records = (_records('partial', [0.1, 0.1], [0, 3]) + + _records('naive', [5.0, 5.0], [0, 0])) + with pytest.warns(UserWarning, match='no model produced every'): + scores = build_scores(records, ('mae',), baseline='naive') + assert scores.attrs['best'] is None + assert np.isnan(scores.attrs['best_score']) + assert scores.attrs['beats_baseline'] is None + records = (_records('full', [2.0, 4.0], [0, 0]) + + _records('naive', [5.0, 5.0], [2, 0])) + with pytest.warns(UserWarning, match="(?s)baseline 'naive'.*beats_baseline"): + scores = build_scores(records, ('mae',), baseline='naive') + assert scores.attrs['best'] == 'full' + assert scores.attrs['beats_baseline'] is None + assert scores.attrs['incomplete'] == ['naive'] + + +def test_user_forecaster_that_forecasts_nothing_never_wins(): + with pytest.warns(UserWarning, match='excluded from the ranking'): + scores = hyp.predict(_series(n=40), model=[NaNForecaster, 'Kalman'], + holdout=5) + assert scores.attrs['best'] == 'Kalman' + assert scores.attrs['incomplete'] == ['NaNForecaster'] + assert isinstance(scores.attrs['beats_baseline'], bool) + + +def test_holdout_with_no_complete_forecaster_gives_no_verdict(): + # REAL case: the last training row is missing, and the held-out times + # fall between forecast steps, so Kalman and ARIMA both have no anchor + # to interpolate the first held-out value from and leave it NaN. + frame = pd.DataFrame(np.r_[np.arange(1., 25.), np.nan, 26., 27.], + index=np.r_[np.arange(25.), 24.5, 25.]) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores = hyp.predict(frame, model=['Kalman', 'ARIMA'], holdout=2) + assert list(scores['unscored']) == [1, 1, 0] + assert np.isfinite(scores.loc['Kalman', 'MAE']) # row still described + assert scores.attrs['incomplete'] == ['Kalman', 'ARIMA'] + assert scores.attrs['best'] is None + assert np.isnan(scores.attrs['best_score']) + assert scores.attrs['beats_baseline'] is None + assert any('no model produced every' in str(w.message) for w in caught) + + +def test_holdout_with_an_incomplete_baseline_makes_no_comparison(): + # REAL case: column 'b' has no observed training value, so the naive + # last-value baseline has nothing to carry forward and scores 'a' only. + frame = pd.DataFrame({'a': np.sin(np.arange(30.) / 3), + 'b': np.r_[np.full(27, np.nan), 1., 2., 3.]}) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores = hyp.predict(frame, model=['Kalman', 'ARIMA'], holdout=3) + assert scores.loc['naive', 'unscored'] == 3 + assert scores.loc[['Kalman', 'ARIMA'], 'unscored'].sum() == 0 + assert scores.attrs['incomplete'] == ['naive'] + assert scores.attrs['best'] == scores.drop(index='naive')['MAE'].idxmin() + assert scores.attrs['beats_baseline'] is None + assert any("baseline 'naive'" in str(w.message) + and 'beats_baseline' in str(w.message) for w in caught) + + +def _forecast_columns_reversed(data, n_steps, future_index, **kwargs): + last = data.ffill().iloc[-1] + frame = pd.DataFrame( + np.repeat(last.to_numpy(dtype=float)[None, :], n_steps, axis=0), + index=future_index, columns=data.columns) + return frame[list(data.columns)[::-1]] + + +class ReversedColumns(Forecaster): + """A user forecaster (last value carried forward) that hands its + columns back in reverse order, correctly labelled.""" + + def __init__(self, **kwargs): + super().__init__(forecaster=_forecast_columns_reversed, **kwargs) + + +def _forecast_renamed(data, n_steps, future_index, **kwargs): + return _forecast_columns_reversed(data, n_steps, future_index).rename( + columns={'b': 'c'}) + + +class RenamedColumns(Forecaster): + def __init__(self, **kwargs): + super().__init__(forecaster=_forecast_renamed, **kwargs) + + +def test_holdout_scores_a_forecast_by_column_label(): + frame = _series(n=40) + frame['b'] += 50 # make a positional mix-up unmissable + scores, forecasts = hyp.predict(frame, model=ReversedColumns, holdout=5, + return_forecasts=True) + # the same last-value forecast as the baseline, so the same scores + for metric in ('MAE', 'RMSE', 'MAPE'): + assert scores.loc['ReversedColumns', metric] == pytest.approx( + scores.loc['naive', metric]) + assert list(forecasts['ReversedColumns'].columns) == ['a', 'b'] + with pytest.raises(ValueError, match=r"(?s)RenamedColumns.*column.*'c'"): + hyp.predict(frame, model=RenamedColumns, holdout=5) diff --git a/tests/test_predict_cross_index_reuse.py b/tests/test_predict_cross_index_reuse.py new file mode 100644 index 00000000..63717b7d --- /dev/null +++ b/tests/test_predict_cross_index_reuse.py @@ -0,0 +1,80 @@ +"""A fitted forecaster reused on a different KIND of index (release review +2026-09-11, finding 2 -- a regression against 1.0). + +In 1.0 all eight cross cases below worked: a model fit on an array and reused +on dated rows forecast the dated continuation, and the reverse forecast the +array continuation. The 1.1 time-step bookkeeping carried the training step +across kinds -- a row count onto a DatetimeIndex (``ValueError: step for a time +index must specify a duration``) and a duration onto a RangeIndex +(``TypeError: float() argument must be ... not 'Timedelta'``). + +A row count and a duration cannot be converted into each other, so reuse +across kinds steps in the NEW data's own units. The observable check: the +same fitted model, reused on the same values, gives the same forecast values +whether those values carry a positional or a regular dated index. +""" +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp + +MODELS = ['Kalman', 'ARIMA', 'GaussianProcess', 'AutoRegressor'] + + +def _data(n=40, seed=1): + values = np.random.default_rng(seed).normal(size=(n, 2)).cumsum(axis=0) + dated = pd.DataFrame(values, index=pd.date_range('2024-01-01', periods=n, + freq='h')) + return values, dated + + +@pytest.mark.parametrize('model', MODELS) +def test_array_fit_reused_on_dated_rows(model): + values, dated = _data() + _, fitted = hyp.predict(values, model=model, t=3, return_model=True) + out = hyp.predict(dated, model=fitted, t=3) + assert list(out.index) == list(pd.date_range(dated.index[-1], periods=4, + freq='h')[1:]) + positional = hyp.predict(values, model=fitted, t=3) + np.testing.assert_allclose(out.to_numpy(), positional.to_numpy()) + + +@pytest.mark.parametrize('model', MODELS) +def test_dated_fit_reused_on_an_array(model): + values, dated = _data() + _, fitted = hyp.predict(dated, model=model, t=3, return_model=True) + out = hyp.predict(values, model=fitted, t=3) + assert list(out.index) == [40, 41, 42] + same_kind = hyp.predict(dated, model=fitted, t=3) + np.testing.assert_allclose(out.to_numpy(), same_kind.to_numpy()) + + +@pytest.mark.parametrize('model', ['Kalman', 'GaussianProcess']) +def test_dated_fit_reused_on_categorical_labels_and_timedeltas(model): + values, dated = _data() + _, fitted = hyp.predict(dated, model=model, t=3, return_model=True) + labelled = pd.DataFrame(values, index=[f'r{i}' for i in range(40)]) + out = hyp.predict(labelled, model=fitted, t=2) + assert list(out.index) == [40, 41] + # a timedelta index takes the learned one-hour duration as it is + elapsed = pd.DataFrame(values, index=pd.timedelta_range(0, periods=40, + freq='h')) + out = hyp.predict(elapsed, model=fitted, t=2) + assert list(out.index) == [pd.Timedelta(hours=40), pd.Timedelta(hours=41)] + + +def test_same_kind_reuse_still_keeps_the_learned_interval(): + values, dated = _data() + _, fitted = hyp.predict(dated, model='Kalman', t=3, return_model=True) + three_hourly = pd.DataFrame(values, index=pd.date_range('2024-01-01', + periods=40, freq='3h')) + with pytest.warns(UserWarning, match='interpolated'): + out = hyp.predict(three_hourly, model=fitted, t=2) + assert out.index[0] - three_hourly.index[-1] == pd.Timedelta(hours=1) + + +def test_a_mismatched_explicit_step_is_a_clear_error(): + values, _ = _data() + with pytest.raises(ValueError, match='numerical index must be a positive number'): + hyp.predict(values, model='Kalman', step='1h', t=2) diff --git a/tests/test_predict_regular_calendar.py b/tests/test_predict_regular_calendar.py new file mode 100644 index 00000000..9281b0e5 --- /dev/null +++ b/tests/test_predict_regular_calendar.py @@ -0,0 +1,235 @@ +"""Regular CALENDAR data are forecast on their own calendar (release review +2026-09-11, finding 1). + +A business-day, month-start, weekly or quarterly index has uneven absolute +gaps (a weekend, a 28-to-31-day month), so the median-gap rule used to call it +"irregular": business-day bars were interpolated onto every calendar day +(fabricating weekend rows) and forecast onto Saturdays, month starts drifted +(03-04, 04-04), a tz-aware daily index grew a duplicated day across the fall +DST change, and a PeriodIndex came back as a DatetimeIndex. + +Every check here is a real observable: the rows the fitted model actually saw, +the forecast's own index, and equality with the same values forecast by +position (a regular index must fit exactly like observation order). +""" +import warnings + +import numpy as np +import pandas as pd +import pytest +from pandas.tseries.holiday import USFederalHolidayCalendar + +import hypertools as hyp +from tests._netskip import skip_on_transient_network + +MODELS = ['Kalman', 'ARIMA', 'GaussianProcess', + {'model': 'AutoRegressor', 'kwargs': {'lags': 2}}] + + +def _walk(n, cols=2, seed=0): + return np.random.default_rng(seed).normal(size=(n, cols)).cumsum(axis=0) + + +def _forecast(frame, model, t=4): + """Forecast, returning (forecast, fitted model, interpolation warnings).""" + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + forecast, fitted = hyp.predict(frame, model=model, t=t, + return_model=True) + interpolated = [str(w.message) for w in caught + if 'interpolated' in str(w.message)] + return forecast, fitted, interpolated + + +def _unfrequenced(index): + """The same timestamps WITHOUT a stored `freq`, so the calendar must be + inferred from the observations (a CSV/API index never carries one).""" + return pd.DatetimeIndex(list(index), name=index.name) + + +@pytest.mark.parametrize('model', MODELS, ids=lambda m: str(m)[:20]) +@pytest.mark.parametrize('freq', ['B', 'MS', 'W-SUN', 'QS', 'ME', 'h']) +def test_regular_calendar_index_fits_observed_rows_and_steps_its_calendar(freq, model): + index = _unfrequenced(pd.date_range('2023-01-02', periods=40, freq=freq)) + frame = pd.DataFrame(_walk(40, seed=3), index=index, columns=['a', 'b']) + forecast, fitted, interpolated = _forecast(frame, model) + + assert interpolated == [] + # the model saw exactly the observed rows -- nothing fabricated + assert list(fitted.models_[0]['_time_data'].index) == list(index) + expected = pd.date_range(index[-1], periods=5, freq=freq)[1:] + assert list(forecast.index) == list(expected) + # a regular index fits exactly like observation order + positional = hyp.predict(frame.reset_index(drop=True), model=model, t=4) + np.testing.assert_allclose(forecast.to_numpy(), positional.to_numpy()) + + +def test_business_day_forecasts_never_land_on_a_weekend(): + index = _unfrequenced(pd.bdate_range('2024-01-01', periods=60)) + frame = pd.DataFrame(_walk(60, seed=4), index=index) + forecast, fitted, interpolated = _forecast(frame, 'Kalman', t=7) + assert interpolated == [] + assert (forecast.index.dayofweek < 5).all() + assert len(fitted.models_[0]['_time_data']) == 60 + # a datetime target counts BUSINESS days: Friday -> next Wednesday is 3 + friday = index[index.dayofweek == 4][-1] + history = frame.loc[:friday] + target = friday + pd.Timedelta(days=5) + steps = hyp.predict(history, model='Kalman', t=target) + assert list(steps.index) == list(pd.bdate_range(friday, periods=4)[1:]) + + +@pytest.mark.parametrize('start,periods', [('2024-02-20', 30), # spring DST in history + ('2024-10-07', 25)]) # fall DST in forecast +def test_tz_aware_days_across_dst_step_local_calendar_days(start, periods): + tz = 'America/New_York' + index = _unfrequenced(pd.date_range(start, periods=periods, freq='D', tz=tz)) + frame = pd.DataFrame(_walk(periods, seed=5), index=index) + forecast, fitted, interpolated = _forecast(frame, 'Kalman', t=6) + assert interpolated == [] + assert len(fitted.models_[0]['_time_data']) == periods + assert str(forecast.index.tz) == tz + assert forecast.index.is_unique + assert (forecast.index.hour == 0).all() + assert list(forecast.index) == list( + pd.date_range(index[-1], periods=7, freq='D', tz=tz)[1:]) + positional = hyp.predict(frame.reset_index(drop=True), model='Kalman', t=6) + np.testing.assert_allclose(forecast.to_numpy(), positional.to_numpy()) + + +@pytest.mark.parametrize('model', ['Kalman', 'GaussianProcess', 'ARIMA']) +@pytest.mark.parametrize('freq', ['M', 'Q', 'D', 'W']) +def test_period_index_forecasts_continue_as_periods(freq, model): + index = pd.period_range('2020-01-01', periods=24, freq=freq) + frame = pd.DataFrame(_walk(24, seed=6), index=index, columns=['a', 'b']) + forecast, fitted, interpolated = _forecast(frame, model, t=3) + assert interpolated == [] + assert isinstance(forecast.index, pd.PeriodIndex) + assert forecast.index.freqstr == index.freqstr + assert list(forecast.index) == list(pd.period_range(index[-1] + 1, + periods=3, freq=freq)) + positional = hyp.predict(frame.reset_index(drop=True), model=model, t=3) + np.testing.assert_allclose(forecast.to_numpy(), positional.to_numpy()) + # truncation returns the ORIGINAL periods, too + truncated = hyp.predict(frame, model=model, t=index[10].start_time) + assert isinstance(truncated.index, pd.PeriodIndex) + assert list(truncated.index) == list(index[:11]) + # a fitted model reused on new periods keeps producing periods + again = hyp.predict(frame.iloc[:12], model=fitted, t=2) + assert list(again.index) == list(pd.period_range(index[11] + 1, periods=2, + freq=freq)) + + +def test_periods_step_at_their_observed_cadence(): + # hourly periods observed every second hour step two hours, uninterpolated + index = pd.period_range('2026-01-01 00:00', periods=20, freq='2h').asfreq('h') + frame = pd.DataFrame(_walk(20, seed=11), index=index) + forecast, fitted, interpolated = _forecast(frame, 'Kalman', t=2) + assert interpolated == [] + assert len(fitted.models_[0]['_time_data']) == 20 + assert list(forecast.index) == [index[-1] + 2, index[-1] + 4] + # two monthly periods are one period apart: the next ones are months, + # not 31-day strides that drift through the calendar + pair = pd.DataFrame([[1.], [2.]], index=pd.period_range('2021-01', periods=2, freq='M')) + ahead = hyp.predict(pair, model='Kalman', t=60) + assert list(ahead.index) == list(pd.period_range('2021-03', periods=60, freq='M')) + + +@pytest.mark.parametrize('animate', [False, True]) +def test_series_plot_draws_calendar_forecasts_on_their_dates(animate): + """A plotted PeriodIndex / business-day series draws its forecast at the + forecast's own dates (the static overlay used to place period forecasts + at x = -1 once they came back as periods).""" + import matplotlib.pyplot as plt + from matplotlib.dates import date2num + cases = [pd.period_range('2020-01', periods=24, freq='M'), + _unfrequenced(pd.bdate_range('2024-01-01', periods=40))] + for index in cases: + frame = pd.DataFrame(_walk(len(index), seed=12), index=index) + expected = hyp.predict(frame, model='Kalman', t=3) + stamps = (expected.index.to_timestamp() + if isinstance(expected.index, pd.PeriodIndex) else expected.index) + kwargs = dict(animate=True, duration=1, frame_rate=4, + slow_warning_seconds=None) if animate else {} + out = hyp.plot(frame, ndims=1, reduce=None, predict='Kalman', t=3, + return_model=True, show=False, antialias=False, **kwargs) + try: + role = 'live' if animate else 'static' + if animate: + # the final frame of a 1 s, 4 fps animation reveals every row + out['animation']._func(3, *out['animation']._args) + lines = [line for line in out['fig'].axes[0].lines + if getattr(line, '_hyp_forecast_role', None) == role] + assert len(lines) == 2 + for line in lines: + np.testing.assert_allclose(np.asarray(line.get_xdata())[-1], + date2num(stamps[-1].to_pydatetime())) + finally: + plt.close(out['fig']) + + +def test_trading_days_with_holidays_never_fabricate_or_forecast_weekends(): + """Market sessions: weekdays minus exchange holidays, so no frequency is + inferable. The business-day calendar still governs -- only the missing + holiday sessions may be filled, never a Saturday or a Sunday.""" + sessions = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar()) + index = _unfrequenced(pd.date_range('2024-06-03', '2024-09-13', freq=sessions)) + holidays = pd.bdate_range(index[0], index[-1]).difference(index) + assert len(holidays) == 3 # Juneteenth, July 4th, Labor Day + frame = pd.DataFrame(_walk(len(index), seed=7), index=index) + for model in ['Kalman', 'ARIMA']: + forecast, fitted, _ = _forecast(frame, model, t=5) + seen = fitted.models_[0]['_time_data'].index + assert (seen.dayofweek < 5).all() + assert len(seen) <= len(index) + len(holidays) + assert list(forecast.index) == list(pd.bdate_range(index[-1], periods=6)[1:]) + + +def test_genuinely_irregular_times_still_use_the_interpolated_grid(): + index = pd.to_datetime('2024-01-01') + pd.to_timedelta( + [0, 1, 3, 6, 7, 9, 13, 15, 17, 20, 23, 24], unit='h') + frame = pd.DataFrame(_walk(12, seed=8), index=index) + _, fitted, interpolated = _forecast(frame, 'Kalman', t=2) + assert interpolated and 'Irregular' in interpolated[0] + assert len(fitted.models_[0]['_time_data']) != len(frame) + + +def test_backtest_on_business_days_scores_the_held_out_sessions_directly(): + index = _unfrequenced(pd.bdate_range('2024-01-01', periods=50)) + frame = pd.DataFrame(_walk(50, seed=9), index=index) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + scores, forecasts = hyp.predict(frame, model='Kalman', holdout=6, + return_forecasts=True) + assert not [w for w in caught if 'interpolated' in str(w.message)] + positional_scores, positional = hyp.predict( + frame.reset_index(drop=True), model='Kalman', holdout=6, + return_forecasts=True) + np.testing.assert_allclose(forecasts['Kalman'].to_numpy(), + positional['Kalman'].to_numpy()) + assert list(forecasts['Kalman'].index) == list(index[-6:]) + + +def test_period_backtest_returns_periods(): + index = pd.period_range('2020-01', periods=30, freq='M') + frame = pd.DataFrame(_walk(30, seed=10), index=index) + _, forecasts = hyp.predict(frame, model='Kalman', holdout=4, + return_forecasts=True) + for name in ('Kalman', 'naive', 'truth'): + assert isinstance(forecasts[name].index, pd.PeriodIndex) + assert list(forecasts[name].index) == list(index[-4:]) + + +def test_yahoo_daily_bars_forecast_trading_days(): + """The review's live case: yahoo:AAPL daily closes.""" + with skip_on_transient_network('loading yahoo:AAPL'): + bars = hyp.load('yahoo:AAPL') + closes = bars[['close']].iloc[-70:] + assert (closes.index.dayofweek < 5).all() + forecast, fitted, _ = _forecast(closes, 'Kalman', t=5) + seen = fitted.models_[0]['_time_data'].index + assert (seen.dayofweek < 5).all() + missing = pd.bdate_range(closes.index[0], closes.index[-1]).difference(closes.index) + assert len(seen) == len(closes) + len(missing) + assert (forecast.index.dayofweek < 5).all() + assert forecast.index[0] == closes.index[-1] + pd.offsets.BDay(1) diff --git a/tests/test_predict_seasonal_min_history.py b/tests/test_predict_seasonal_min_history.py new file mode 100644 index 00000000..9b1913b6 --- /dev/null +++ b/tests/test_predict_seasonal_min_history.py @@ -0,0 +1,55 @@ +"""ARIMA's minimum history counts its SEASONAL order (release review +2026-09-11, finding 3). + +`ARIMA.min_history_for` only read ``order``, so a seasonal fit on a short +history fell through to statsmodels, which raised a bare ``IndexError`` +(exactly ``d + D*s + 1`` rows) or ``numpy.linalg.LinAlgError`` instead of the +library's clear "shorter than the N observations ARIMA needs" message. The +failing lengths below were measured on statsmodels 0.14 with real fits. +""" +import numpy as np +import pytest + +import hypertools as hyp +from hypertools.plot.forecast import model_min_history +from hypertools.predict.arima import ARIMA + +# (order, seasonal_order, floor): floor = max(d + D*s + 2, p + P*s + q + Q*s + 1) +CASES = [((1, 1, 1), (1, 1, 1, 12), 27), + ((1, 1, 0), (0, 1, 1, 7), 10), + ((1, 0, 0), (0, 1, 0, 12), 14)] + + +def _series(n): + return np.random.default_rng(0).normal(size=(n, 2)).cumsum(axis=0) + + +@pytest.mark.parametrize('order,seasonal,floor', CASES) +def test_seasonal_min_history_counts_the_seasonal_lags(order, seasonal, floor): + assert ARIMA.min_history_for(order=order, seasonal_order=seasonal) == floor + assert ARIMA(order=order, seasonal_order=seasonal).min_history == floor + assert model_min_history({'model': 'ARIMA', 'kwargs': { + 'order': order, 'seasonal_order': seasonal}}) == floor + # the non-seasonal default is unchanged + assert ARIMA.min_history_for(order=order) == ARIMA.min_history_for( + order=order, seasonal_order=(0, 0, 0, 0)) + + +@pytest.mark.parametrize('order,seasonal,floor', CASES) +def test_short_seasonal_histories_get_the_clear_message(order, seasonal, floor): + x = _series(60) + for n in range(3, floor): + with pytest.raises(ValueError, match=r'seasonal_order=') as caught: + hyp.predict(x[:n], model='ARIMA', order=order, + seasonal_order=seasonal, t=3) + assert f'{floor} observation' in str(caught.value) + forecast = hyp.predict(x[:floor], model='ARIMA', order=order, + seasonal_order=seasonal, t=3) + assert forecast.shape == (3, 2) + assert np.isfinite(forecast.to_numpy()).all() + + +def test_sparse_seasonal_lags_count_their_highest_lag(): + # statsmodels' sparse form: seasonal AR lags 1 and 2 at period 4 + assert ARIMA.min_history_for(order=(0, 0, 0), + seasonal_order=([1, 2], 0, 0, 4)) == 9 diff --git a/tests/test_predict_time_warnings.py b/tests/test_predict_time_warnings.py new file mode 100644 index 00000000..56849658 --- /dev/null +++ b/tests/test_predict_time_warnings.py @@ -0,0 +1,139 @@ +"""Time-policy warnings are issued once, and only when they apply (release +review 2026-09-11, finding 4). + +A stacked ``pd.concat([run_a, run_b])`` panel warned "not sorted" THREE times +per call (fit, the fit-time duplicate check, and the forecast horizon each +re-checked the same frame), and an explicit ``step=`` on regularly spaced +data called the observations "Irregular" when they were only being resampled +onto the requested grid. +""" +import warnings + +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp + + +def _messages(call): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + result = call() + return result, [str(w.message) for w in caught] + + +def _walk(n, seed=0): + return np.random.default_rng(seed).normal(size=(n, 2)).cumsum(axis=0) + + +@pytest.mark.parametrize('model', ['Kalman', 'ARIMA', 'GaussianProcess']) +def test_a_stacked_panel_warns_once_per_call(model): + run = pd.DataFrame(_walk(10)) + stacked = pd.concat([run, run]) + forecast, messages = _messages(lambda: hyp.predict(stacked, model=model, t=3)) + assert sum('not sorted' in m for m in messages) == 1 + assert list(forecast.index) == [10, 11, 12] + # one per dataset in a list, and once for a backtest + _, messages = _messages(lambda: hyp.predict([stacked, stacked], model=model, t=2)) + assert sum('not sorted' in m for m in messages) == 2 + _, messages = _messages(lambda: hyp.predict(stacked, model=model, holdout=3)) + assert sum('not sorted' in m for m in messages) == 1 + + +def test_shuffled_times_warn_once_and_reuse_warns_once(): + frame = pd.DataFrame(_walk(20, seed=1), + index=pd.date_range('2026-01-01', periods=20, freq='h')) + shuffled = frame.iloc[np.random.default_rng(3).permutation(20)] + (_, fitted), messages = _messages( + lambda: hyp.predict(shuffled, t=2, return_model=True)) + assert sum('not sorted' in m for m in messages) == 1 + _, messages = _messages(lambda: hyp.predict(shuffled, model=fitted, t=2)) + assert sum('not sorted' in m for m in messages) == 1 + + +def test_explicit_step_on_regular_data_is_not_called_irregular(): + frame = pd.DataFrame(_walk(20, seed=2), + index=pd.timedelta_range(0, periods=20, freq='250ms')) + forecast, messages = _messages( + lambda: hyp.predict(frame, model='Kalman', t=3, step='500ms')) + interpolated = [m for m in messages if 'interpolated' in m] + assert len(interpolated) == 1 + assert 'Irregular' not in interpolated[0] + assert 'Regularly spaced' in interpolated[0] + assert forecast.index[0] - frame.index[-1] == pd.Timedelta('500ms') + # the step the data already have: nothing to resample, nothing to say + _, messages = _messages( + lambda: hyp.predict(frame, model='Kalman', t=3, step='250ms')) + assert not [m for m in messages if 'interpolated' in m] + + +def test_irregular_data_with_an_explicit_step_are_still_called_irregular(): + times = pd.to_timedelta([0, 1, 3, 6, 7, 9, 13, 15, 17, 20], unit='h') + frame = pd.DataFrame(_walk(10, seed=4), index=times) + _, messages = _messages(lambda: hyp.predict(frame, model='Kalman', t=2, step='2h')) + assert any('Irregular' in m for m in messages) + + +# --------------------------------------------------------------------------- +# Attribution (2026-09-11 tutorial re-execution): the time-policy warnings +# used a fixed stacklevel that landed on hypertools' own frames, so the +# tutorials printed '~/hypertools/hypertools/predict/common.py:435: +# UserWarning' and a shuffled index under hyp.plot warned twice (from two +# different library lines). They must point at the caller's line. + +def _trading_days(): + idx = pd.bdate_range('2026-06-01', '2026-09-10') + holidays = pd.to_datetime(['2026-06-19', '2026-07-03', '2026-09-07']) + idx = idx[~idx.isin(holidays)] + return pd.DataFrame({'v': _walk(len(idx))[:, 0]}, index=idx) + + +def _caught(call): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + call() + return caught + + +def test_interpolation_warning_names_the_callers_line_and_a_readable_step(): + caught = [w for w in _caught( + lambda: hyp.predict(_trading_days(), model='Kalman', t=3)) + if 'interpolated' in str(w.message)] + assert caught + assert all(w.filename == __file__ for w in caught), \ + [(w.filename, w.lineno) for w in caught] + # a calendar step reads as its alias, not an object repr + assert "step='B'" in str(caught[0].message), str(caught[0].message) + + +def test_backtest_interpolation_warning_names_the_callers_line(): + caught = [w for w in _caught( + lambda: hyp.predict(_trading_days(), model='Kalman', holdout=5)) + if 'interpolated' in str(w.message)] + assert caught + assert all(w.filename == __file__ for w in caught), \ + [(w.filename, w.lineno) for w in caught] + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_unsorted_index_under_plot_warns_once_at_the_callers_line(backend): + import matplotlib.pyplot as plt + data = _trading_days().sample(frac=1.0, random_state=0) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('default') # Python's display filter + hyp.plot(data, ndims=1, reduce=None, predict='Kalman', t=3, + backend=backend, show=False) + unsorted = [w for w in caught if 'not sorted' in str(w.message)] + assert len(unsorted) == 1, [(w.filename, w.lineno) for w in unsorted] + assert unsorted[0].filename == __file__ + plt.close('all') + + +def test_a_float_step_prints_without_representation_noise(): + t = np.cumsum(np.full(40, 0.04)) + np.r_[0, 0.001, np.zeros(38)] + data = pd.DataFrame(_walk(40), index=pd.Index(t, name='t')) + messages = [str(w.message) for w in _caught( + lambda: hyp.predict(data, model='Kalman', t=3)) + if 'interpolated' in str(w.message)] + assert messages and 'step=0.04 ' in messages[0], messages diff --git a/tests/test_reduce_audit_a7.py b/tests/test_reduce_audit_a7.py index 89fd30ea..15456c13 100644 --- a/tests/test_reduce_audit_a7.py +++ b/tests/test_reduce_audit_a7.py @@ -158,8 +158,11 @@ def test_describe_matplotlib_single_dataset_no_legend(): def test_describe_returns_fig_key(): x = np.random.default_rng(0).normal(size=(30, 8)) result = describe(x, reduce='PCA', max_dims=5, show=False) - assert set(result.keys()) == {'average', 'individual', 'fig'} - assert result['fig'] is None # no figure drawn when show=False + assert set(result.keys()) == {'average', 'individual', 'pooled', 'fig'} + # 1.1 release review (maintainer decision): show=False now returns the + # drawn figure without displaying it, like hyp.plot(show=False); it + # used to return None here + assert isinstance(result['fig'], plt.Figure) plt.close('all') shown = describe(x, reduce='PCA', max_dims=5, show=True, backend='matplotlib') diff --git a/tests/test_reduce_dispatch_hardening.py b/tests/test_reduce_dispatch_hardening.py index 80cf53cf..bc0e2524 100644 --- a/tests/test_reduce_dispatch_hardening.py +++ b/tests/test_reduce_dispatch_hardening.py @@ -70,7 +70,7 @@ def test_describe_max_dims_gt_features_does_not_crash(): with pytest.warns(UserWarning, match='exceeds the data dimensionality'): result = hyp.describe(x, reduce='PCA', max_dims=8, show=False) # 'fig' added by the 2026-07 release audit (F11-reduce-describe-015) - assert set(result.keys()) == {'average', 'individual', 'fig'} + assert set(result.keys()) == {'average', 'individual', 'pooled', 'fig'} def test_describe_empty_component_range_raises_clear_error(): diff --git a/tests/test_reduce_third_party_warnings.py b/tests/test_reduce_third_party_warnings.py new file mode 100644 index 00000000..8124317b --- /dev/null +++ b/tests/test_reduce_third_party_warnings.py @@ -0,0 +1,48 @@ +"""Third-party reducers must not spam the user with warnings that are not +about the user's choices (1.1 feature-tour report, section 9.8).""" + +import warnings + +import numpy as np +import pytest + +import hypertools as hyp + + +def _digits400(): + d = hyp.load('digits') + return d.drop(columns='target').to_numpy()[:400] + + +def test_seeded_umap_does_not_warn_about_n_jobs(): + pytest.importorskip('umap') + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + out = hyp.reduce(_digits400(), reduce='UMAP', ndims=3, random_state=0) + assert np.asarray(out).shape == (400, 3) + assert not [m for m in w if 'n_jobs' in str(m.message)] + + +def test_seeded_umap_honours_an_explicit_n_jobs(): + pytest.importorskip('umap') + from hypertools.reduce.common import resolve_reducer + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + _, model = hyp.reduce(_digits400(), reduce={'model': 'UMAP', 'kwargs': {'n_jobs': 2}}, + ndims=3, random_state=0, return_model=True) + # umap itself overrides to 1 and says so: that warning is the user's + # own n_jobs= choice and must still reach them + assert [m for m in w if 'n_jobs' in str(m.message)] + assert isinstance(model.model, resolve_reducer('UMAP')) # Reducer wrapper + + +def test_isomap_graph_completion_does_not_leak_sparse_efficiency_warnings(): + from scipy.sparse import SparseEfficiencyWarning + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + out = hyp.reduce(_digits400(), reduce='Isomap', ndims=3) + assert np.asarray(out).shape == (400, 3) + assert not [m for m in w if issubclass(m.category, SparseEfficiencyWarning)] + # sklearn's own data warning (too few neighbours for one connected + # graph) is about the user's data and still reaches them, once + assert sum('connected components' in str(m.message) for m in w) == 1 diff --git a/tests/test_release_readiness_gate.py b/tests/test_release_readiness_gate.py index a67fa978..df0f0a1b 100644 --- a/tests/test_release_readiness_gate.py +++ b/tests/test_release_readiness_gate.py @@ -7,7 +7,8 @@ * README image URLs are pinned to a commit SHA today; at release they must be the ``v1.0.0`` git tag (immutable, survives the dev-1.0 branch deletion). * the CHANGELOG heading is ``(unreleased)`` today; at release it must carry a - real date. + real date, and not one earlier than the release commit's own date (a stale + draft date survives a re-cut otherwise). ALWAYS-ON checks (safe on any branch) verify internal consistency and that the referenced image files actually exist, so the tag will contain them. The @@ -156,6 +157,40 @@ def _repo_head(): return None +def _repo_head_commit_date(): + """The calendar date of the checkout's HEAD commit (committer date, in + the committer's own timezone), or None if this is not a git tree.""" + try: + out = subprocess.run(['git', 'log', '-1', '--format=%cI', 'HEAD'], + cwd=_REPO, capture_output=True, + text=True).stdout.strip() + return datetime.date.fromisoformat(out[:10]) if out else None + except Exception: + return None + + +def _changelog_date_problem(heading_date, commit_date): + """Why a CHANGELOG release date cannot belong to the commit being + released, or None when it can. + + ``heading_date`` is the ``YYYY-MM-DD`` text from the top heading and + ``commit_date`` the release commit's ``datetime.date``. A date EARLIER + than the commit is a stale draft date: the 1.1.0 heading kept its draft + date (2026-09-04) through a re-cut from later commits, and the + real-calendar-date check alone accepted it (2026-09-11 release-document + review). The same day or later is fine. + """ + try: + heading = datetime.date.fromisoformat(heading_date) + except (TypeError, ValueError): + return f'{heading_date!r} is not a YYYY-MM-DD date' + if heading < commit_date: + return (f'the heading is dated {heading.isoformat()}, earlier than ' + f'the release commit ({commit_date.isoformat()}); re-date it ' + 'to the release day (RELEASE_CHECKLIST.md step 2)') + return None + + def _local_gallery_stems(): """Stems of the locally built gallery (docs/auto_examples/*.ipynb), or None when it isn't built -- e.g. the remote release-gate CI job, which doesn't @@ -238,6 +273,32 @@ def test_changelog_top_version_matches_pyproject(): f'{_project_version()!r}') +def test_changelog_date_problem_validator(): + """The pure date comparison the release gate below relies on.""" + commit = datetime.date(2026, 9, 11) + # a draft date earlier than the release commit -- must fail, naming both + reason = _changelog_date_problem('2026-09-04', commit) + assert reason and '2026-09-04' in reason and '2026-09-11' in reason + assert _changelog_date_problem('2025-12-31', commit) # a year back + # the release day itself, and a later day, are both acceptable + assert _changelog_date_problem('2026-09-11', commit) is None + assert _changelog_date_problem('2026-09-12', commit) is None + # not a date at all -- reported, never compared + assert 'not a YYYY-MM-DD date' in _changelog_date_problem('unreleased', + commit) + assert _changelog_date_problem('2026-02-30', commit) # impossible + + +def test_repo_head_commit_date_reads_this_checkout(): + """The HEAD date helper returns the same date git prints for HEAD.""" + out = subprocess.run(['git', 'log', '-1', '--format=%cs', 'HEAD'], + cwd=_REPO, capture_output=True, text=True) + if out.returncode != 0 or not out.stdout.strip(): + pytest.skip('not a git checkout (e.g. an unpacked sdist)') + assert _repo_head_commit_date() == datetime.date.fromisoformat( + out.stdout.strip()) + + def test_manifest_is_complete_validator(): """The pure gallery-manifest validator the release gate relies on, incl. the round-4 provenance checks (source_commit, exact inventory).""" @@ -354,6 +415,24 @@ def test_release_gate_changelog_is_dated_not_unreleased(): f'date in YYYY-MM-DD form, not {date!r} (see RELEASE_CHECKLIST.md).') +@pytest.mark.skipif( + not REQUIRE_RELEASE, + reason='release gate; set HYPERTOOLS_REQUIRE_RELEASE=1 (the release-gate ' + 'CI job does on master/tag builds)') +def test_release_gate_changelog_is_not_dated_before_the_release_commit(): + # a real calendar date can still be a stale DRAFT date: the heading must be + # the release commit's day or later, never earlier + text = open(_CHANGELOG, encoding='utf-8').read() + m = _CHANGELOG_HEADING_RE.search(text) + assert m, 'CHANGELOG.md has no "## X.Y.Z (...)" heading' + committed = _repo_head_commit_date() + if committed is None: + pytest.fail('RELEASE GATE: cannot determine the release HEAD commit ' + 'date; run the gate from the git checkout being released.') + problem = _changelog_date_problem(m.group(2).strip(), committed) + assert problem is None, f'RELEASE GATE: CHANGELOG {m.group(1)}: {problem}' + + @pytest.mark.skipif( not REQUIRE_RELEASE, reason='release gate; set HYPERTOOLS_REQUIRE_RELEASE=1 (the release-gate ' diff --git a/tests/test_review_round12.py b/tests/test_review_round12.py new file mode 100644 index 00000000..7beeff2c --- /dev/null +++ b/tests/test_review_round12.py @@ -0,0 +1,254 @@ +"""Codex round 12 (2026-09-08) regressions: a pandas Series keeps its index +and name through the Manipulator classes and `hyp.Pipeline` (R12-4), a +polars Series beside an array in a `hyp.manip` list works (R12-1), a mixed +named-frame/array list keeps the frame's feature names for every model +(R12-3), `MatrixColormap` follows matplotlib's full RGBA extreme-color +rules (R12-2), `legend=` takes a polars Series of labels (R12-5), and a 1-D +array is one column for `hyp.manip` as it is for `hyp.normalize`. Real +data, real models, real figures on both backends; no mocks.""" +import matplotlib +matplotlib.use('Agg') + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pytest +from matplotlib.colors import LinearSegmentedColormap + +import hypertools as hyp +from hypertools.manip import ZScore, Normalize, Smooth, Resample, Delay +from hypertools.plot.colors import MatrixColormap + +pl = pytest.importorskip('polars') + +pytestmark = pytest.mark.filterwarnings( + 'ignore:.*(Missing data|DataFrame column|reordering|do not share columns' + '|copy keyword).*') + + +@pytest.fixture(autouse=True) +def _close(): + yield + plt.close('all') + + +def _same(a, b): + if isinstance(a, list): + assert isinstance(b, list) and len(a) == len(b) + for x, y in zip(a, b): + _same(x, y) + return + pd.testing.assert_frame_equal(pd.DataFrame(a), pd.DataFrame(b)) + + +SERIES = pd.Series([0., 1., 4., 9., 16., 25., 36.], + index=[0., 1., 2., 8., 10., 15., 20.], name='signal') +# the values `Pipeline([Smooth(kernel_width=5), Resample(n_samples=9)])` +# produced on 650808f0 (before the datatype refactor): resampled at the +# Series' OWN positions 0..20, not at 0..6 +PIPELINE_VALUES = [0.0, 4.6743116472, 6.5053827751, 8.3289778265, 16.0, + 20.8197033898, 25.0, 30.2375, 36.0] + + +# --- R12-4: Series metadata through the direct classes and Pipeline --------- + +def test_direct_smooth_and_delay_keep_a_series_index_and_name(): + smoothed = Smooth(kernel_width=5).fit_transform(SERIES) + assert list(smoothed.index) == list(SERIES.index) + assert list(smoothed.columns) == ['signal'] + delayed = Delay().fit_transform(SERIES) + assert list(delayed.columns) == ['signal_lag1', 'signal_lag0'] + assert list(delayed.index) == list(SERIES.index)[1:] + # the same through hyp.manip, and for a polars Series + _same(hyp.manip(SERIES, model='Smooth', kernel_width=5), smoothed) + polars_smoothed = Smooth(kernel_width=5).fit_transform( + pl.Series('signal', SERIES.to_numpy())) + assert list(polars_smoothed.columns) == ['signal'] + assert np.allclose(polars_smoothed.to_numpy(), smoothed.to_numpy()) + + +def test_pipeline_resamples_a_series_at_its_own_positions(): + pipe = hyp.Pipeline([('smooth', Smooth(kernel_width=5)), + ('resample', Resample(n_samples=9))]) + out = pipe.fit_transform(SERIES) + assert np.allclose(np.asarray(out.index, dtype=float), np.linspace(0, 20, 9)) + assert np.allclose(np.asarray(out).ravel(), PIPELINE_VALUES, atol=1e-9) + assert list(out.columns) == ['signal'] + # a fitted manipulator reused on a Series keeps its index too + fitted = Smooth(kernel_width=5).fit(SERIES) + again = fitted.transform(SERIES * 2) + assert list(again.index) == list(SERIES.index) + assert np.allclose(again.to_numpy(), 2 * Smooth(kernel_width=5).fit_transform(SERIES).to_numpy()) + + +@pytest.mark.parametrize('cls,kwargs', [ + (ZScore, {}), (Normalize, {}), (Smooth, {'kernel_width': 5}), + (Resample, {'n_samples': 20}), (Delay, {'tau': 1, 'dims': 2})], + ids=['ZScore', 'Normalize', 'Smooth', 'Resample', 'Delay']) +def test_direct_classes_on_a_dated_series_match_its_one_column_frame(cls, kwargs): + dated = pd.Series(np.sin(np.arange(30.) / 3), + index=pd.date_range('2024-01-01', periods=30), name='v') + _same(cls(**kwargs).fit_transform(dated), + cls(**kwargs).fit_transform(dated.to_frame())) + _same(cls(**kwargs).fit_transform(pl.Series('v', dated.to_numpy())), + cls(**kwargs).fit_transform(dated.reset_index(drop=True).to_frame())) + + +# --- R12-1 / 1-D arrays: a polars Series beside an array ------------------- + +MODELS = [('ZScore', {}), ('Normalize', {}), ('Smooth', {'kernel_width': 5}), + ('Resample', {'n_samples': 7}), ('Delay', {'dims': 2})] + + +@pytest.mark.parametrize('model,kwargs', MODELS, ids=[m for m, _ in MODELS]) +def test_polars_series_beside_an_array_matches_pandas(model, kwargs): + values = np.arange(12.) ** 1.5 + series = pl.Series('a', values) + out = hyp.manip([series, values], model=model, **kwargs) + ref = hyp.manip([series.to_pandas(), values], model=model, **kwargs) + _same(out, ref) + width = 2 if model == 'Delay' else 1 # Delay embeds dims=2 + assert [np.asarray(o).shape[1] for o in out] == [width, width] + + +@pytest.mark.parametrize('model,kwargs', MODELS, ids=[m for m, _ in MODELS]) +def test_a_1d_array_is_one_column_for_manip_like_normalize(model, kwargs): + values = np.arange(12.) ** 1.5 + alone = hyp.manip(values, model=model, **kwargs) + column = hyp.manip(values.reshape(-1, 1), model=model, **kwargs) + _same(alone, column) + assert np.asarray(alone).shape[1] == (2 if model == 'Delay' else 1) + if model == 'ZScore': + # the same reading `hyp.normalize` gives a 1-D array (population + # vs sample std aside): one feature, twelve observations + assert hyp.normalize(values).shape == (12, 1) + assert not np.allclose(np.asarray(alone), 0.0) + + +# --- R12-3: mixed lists keep every frame's own feature names --------------- + +def _named(): + return pd.DataFrame(np.arange(20.).reshape(10, 2) ** 1.1, + columns=['a', 'b'], + index=pd.date_range('2020-01-01', periods=10)) + + +@pytest.mark.parametrize('model,kwargs', [ + ('Smooth', {'kernel_width': 5}), ('Resample', {'n_samples': 7}), + ('Delay', {'dims': 2})], ids=['Smooth', 'Resample', 'Delay']) +def test_independent_manipulators_keep_names_beside_an_array(model, kwargs): + frame = _named() + alone = hyp.manip(frame, model=model, **kwargs) + mixed = hyp.manip([frame, frame.to_numpy()], model=model, **kwargs) + _same(mixed[0], alone) + assert list(mixed[0].columns) == list(alone.columns) + assert list(mixed[1].columns) == ( + [0, 1] if model != 'Delay' else ['0_lag1', '0_lag0', '1_lag1', '1_lag0']) + assert np.allclose(mixed[1].to_numpy(), alone.to_numpy()) + + +@pytest.mark.parametrize('model', ['ZScore', 'Normalize']) +def test_shared_statistics_are_positional_and_names_survive(model): + frame = _named() + other = pd.DataFrame(np.random.default_rng(3).normal(size=(6, 2)) * 5, + columns=['x', 'y']) + arr = other.to_numpy() + mixed = hyp.manip([frame, arr], model=model) + assert list(mixed[0].columns) == ['a', 'b'] + assert list(mixed[0].index) == list(frame.index) + assert list(mixed[1].columns) == [0, 1] + # the statistics are shared across BOTH datasets: identical to fitting + # the same values as two unnamed arrays, and to a by-hand computation + unnamed = hyp.manip([frame.to_numpy(), arr], model=model) + for got, want in zip(mixed, unnamed): + assert np.allclose(got.to_numpy(), want.to_numpy()) + stacked = np.vstack([frame.to_numpy(), arr]) + if model == 'ZScore': + expected = (frame.to_numpy() - stacked.mean(axis=0)) / stacked.std(axis=0, ddof=1) + else: + lo, hi = stacked.min(axis=0), stacked.max(axis=0) + expected = (frame.to_numpy() - lo) / (hi - lo) + assert np.allclose(mixed[0].to_numpy(), expected) + # two named frames with DIFFERENT labels are matched by position too + two = hyp.manip([frame, other], model=model) + assert [list(t.columns) for t in two] == [['a', 'b'], ['x', 'y']] + assert np.allclose(two[0].to_numpy(), expected) + # different widths cannot share statistics + with pytest.raises(ValueError, match='same number of columns'): + hyp.manip([frame, np.ones((4, 3))], model=model) + + +# --- R12-2: MatrixColormap extreme colors, alpha and infinities ------------ + +ANCHORS = [[0.1, 0.2, 0.3], [0.9, 0.8, 0.7]] +X = np.array([-0.1, 1.1, -np.inf, np.inf, np.nan, 0.5]) + + +def _pair(**extremes): + ours = MatrixColormap('ours', ANCHORS) + ref = LinearSegmentedColormap.from_list('ref', ANCHORS) + for cmap in (ours, ref): + for key, (color, alpha) in extremes.items(): + getattr(cmap, f'set_{key}')(color, alpha=alpha) + return ours, ref + + +def test_extremes_keep_their_alpha_and_infinities_are_under_and_over(): + ours, ref = _pair(under=('red', .2), over=('blue', .4), bad=('green', .6)) + got, want = ours(X), ref(X) + assert np.allclose(got[:5], want[:5]) # the extremes, RGBA + assert got[:5, 3].tolist() == [.2, .4, .2, .4, .6] + assert np.allclose(got[5], [0.5, 0.5, 0.5, 1.0], atol=1e-9) # exact, not the LUT + assert np.allclose(got[5, :3], want[5, :3], atol=1 / 255) + + +def test_alpha_override_reaches_the_extremes_but_not_a_transparent_bad(): + ours, ref = _pair(under=('red', .2), over=('blue', .4), bad=('green', .6)) + assert np.allclose(ours(X, alpha=.3)[:, 3], .3) + assert np.allclose(ours(X, alpha=.3), ref(X, alpha=.3), atol=1 / 255) + plain, plain_ref = MatrixColormap('p', ANCHORS), LinearSegmentedColormap.from_list('p', ANCHORS) + got, want = plain(X, alpha=.3), plain_ref(X, alpha=.3) + assert np.allclose(got[4], 0.0) and np.allclose(want[4], 0.0) # bad stays transparent + assert np.allclose(got[[0, 1, 5], 3], .3) + per_element = np.linspace(0.1, 0.6, len(X)) + assert np.allclose(ours(X, alpha=per_element)[:, 3], per_element) + with pytest.raises(ValueError, match='alpha is array-like'): + ours(X, alpha=np.ones(3)) + + +def test_bytes_and_masked_input_match_the_parent(): + ours, ref = _pair(under=('red', .2), over=('blue', .4), bad=('green', .6)) + assert ours(X, bytes=True)[:5].tolist() == ref(X, bytes=True)[:5].tolist() + masked = np.ma.array([0.25, 0.75], mask=[False, True]) + assert np.allclose(ours(masked), ref(masked), atol=1 / 255) + assert np.allclose(ours(masked)[1], ref.get_bad()) + assert ours(0.5) == pytest.approx((0.5, 0.5, 0.5, 1.0)) # scalar in, tuple out + + +# --- R12-5: legend labels as a polars Series ------------------------------- + +def _legend_names(fig, backend): + if backend == 'matplotlib': + return [t.get_text() for t in fig.axes[0].get_legend().get_texts()] + return [tr.name for tr in fig.data if tr.showlegend] + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +@pytest.mark.parametrize('container', ['polars', 'pandas', 'index', 'array']) +def test_legend_labels_from_any_series_like(backend, container): + x = pd.DataFrame(np.random.default_rng(0).normal(size=(20, 3))) + labels = {'polars': pl.Series(['first', 'second']), + 'pandas': pd.Series(['first', 'second']), + 'index': pd.Index(['first', 'second']), + 'array': np.array(['first', 'second'])}[container] + fig = hyp.plot([x, x + 2], legend=labels, backend=backend, show=False) + assert _legend_names(fig, backend) == ['first', 'second'] + with pytest.raises(TypeError, match='legend= must be'): + hyp.plot([x, x + 2], legend=7, backend=backend, show=False) + + +@pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) +def test_legend_length_mismatch_is_reported_for_a_polars_series(backend): + x = pd.DataFrame(np.random.default_rng(0).normal(size=(20, 3))) + with pytest.raises(ValueError, match='legend= was given as a list of length'): + hyp.plot([x, x + 2], legend=pl.Series(['only']), backend=backend, show=False) diff --git a/tests/test_review_round13.py b/tests/test_review_round13.py new file mode 100644 index 00000000..fbcc869d --- /dev/null +++ b/tests/test_review_round13.py @@ -0,0 +1,107 @@ +"""Real numerical regressions for release review findings 1, 3 and 4.""" +import numpy as np +import pandas as pd +import pytest +from matplotlib.colors import LinearSegmentedColormap + +import hypertools as hyp +from hypertools.manip import Delay, Normalize, Resample, Smooth, ZScore +from hypertools.plot.colors import MatrixColormap + + +MODELS = [(ZScore, {}), (Normalize, {}), (Smooth, {'kernel_width': 5}), + (Delay, {'dims': 2}), (Resample, {'n_samples': 7})] + + +@pytest.mark.parametrize('cls,kwargs', MODELS) +@pytest.mark.parametrize('container', [np.asarray, list, tuple]) +def test_one_dimensional_input_has_one_feature_everywhere(cls, kwargs, container): + x = np.arange(12.) ** 2 + raw = container(x) + expected = cls(**kwargs).fit_transform(pd.DataFrame(x)) + calls = [hyp.manip(raw, model=cls.__name__, **kwargs), + cls(**kwargs).fit_transform(raw), + hyp.Pipeline([cls(**kwargs)]).fit_transform(raw)] + for actual in calls: + pd.testing.assert_frame_equal(actual, expected) + # A nonconstant signal must not silently become one all-zero row. + assert len(expected) > 1 + assert np.ptp(np.asarray(expected)) > 0 + + +@pytest.mark.parametrize('cls,kwargs', MODELS) +def test_returned_manipulator_reuses_one_dimensional_inputs(cls, kwargs): + train = np.arange(12.) ** 2 + new = train + 100 + _, model = hyp.manip(train, model=cls.__name__, return_model=True, **kwargs) + reference = cls(**kwargs).fit(pd.DataFrame(train)) + expected = reference.transform(pd.DataFrame(new)) + for actual in [model.transform(new), model.transform(new.tolist()), + hyp.manip(new, model=model)]: + pd.testing.assert_frame_equal(actual, expected) + if cls is ZScore: + np.testing.assert_allclose(expected.to_numpy().ravel(), + (new - train.mean()) / train.std(ddof=1)) + elif cls is Normalize: + np.testing.assert_allclose(expected.to_numpy().ravel(), + (new - train.min()) / np.ptp(train)) + + +@pytest.mark.parametrize('cls,kwargs', MODELS) +def test_array_datasets_keep_boundaries_and_column_counts(cls, kwargs): + x = np.arange(24.).reshape(12, 2) + datasets = (x, x + 100) + expected = cls(**kwargs).fit_transform([pd.DataFrame(d) for d in datasets]) + for actual in [cls(**kwargs).fit_transform(datasets), + hyp.Pipeline([cls(**kwargs)]).fit_transform(datasets), + hyp.manip(datasets, model=cls.__name__, **kwargs)]: + assert len(actual) == 2 + for result, reference in zip(actual, expected): + pd.testing.assert_frame_equal(result, reference) + + +@pytest.mark.parametrize('cls', [ZScore, Normalize]) +@pytest.mark.parametrize('different_widths', [False, True]) +def test_rowwise_lists_keep_their_own_statistics_and_metadata(cls, different_widths): + a = pd.DataFrame([[1., 3., 8.], [10., 20., 50.]], + columns=['a', 'b', 'c'], index=['same', 'same']) + b = pd.DataFrame([[2., 4., 9.], [20., 30., 70.], [3., 5., 10.]], + columns=['x', 'y', 'z'], index=[5, 8, 13]) + if different_widths: + b = b.iloc[:, :2] + data = [a, b] + model = cls(axis=1) + for actual in [model.fit_transform(data), model.transform(), + hyp.manip(data, model=cls.__name__, axis=1), + hyp.Pipeline([cls(axis=1)]).fit_transform(data)]: + for result, original in zip(actual, data): + values = original.to_numpy() + if cls is ZScore: + expected = ((values - values.mean(axis=1, keepdims=True)) + / values.std(axis=1, ddof=1, keepdims=True)) + else: + expected = ((values - values.min(axis=1, keepdims=True)) + / np.ptp(values, axis=1, keepdims=True)) + pd.testing.assert_index_equal(result.index, original.index) + pd.testing.assert_index_equal(result.columns, original.columns) + np.testing.assert_allclose(result, expected) + with pytest.raises(NotImplementedError, match='row-wise'): + model.transform([a + 1, b + 1]) + + +@pytest.mark.parametrize('gamma', [0.5, 2., 3.]) +def test_matrix_colormap_gamma_matches_its_parent_and_exact_curve(gamma): + anchors = [[0., 0., 0.], [1., 1., 1.]] + cmap = MatrixColormap('gamma', anchors, N=4096) + parent = LinearSegmentedColormap.from_list('parent', anchors, N=4096) + for obj in (cmap, parent): + obj.set_gamma(gamma) + obj.set_under('red', alpha=.2) + obj.set_over('blue', alpha=.4) + obj.set_bad('green', alpha=.6) + x = np.array([-.1, .1, .25, .5, .75, .9, 1.1, np.nan]) + np.testing.assert_allclose(cmap(x), parent(x), atol=.002) + np.testing.assert_allclose(cmap(x), cmap(np.ma.array(x)), atol=.002) + np.testing.assert_allclose(cmap(.5), [.5 ** gamma] * 3 + [1.]) + cmap.set_gamma(1.) + np.testing.assert_allclose(cmap(.5), [.5, .5, .5, 1.]) diff --git a/tests/test_streaming.py b/tests/test_streaming.py index 4fc1bad3..c4bc861d 100644 --- a/tests/test_streaming.py +++ b/tests/test_streaming.py @@ -60,6 +60,30 @@ def test_stream_plot_consumes_and_projects(): plt.close('all') +@pytest.mark.parametrize('save', [False, True]) +def test_stream_plot_under_global_plotly_render_backend(tmp_path, save): + """Colab and Kaggle auto-select plotly as the render backend. Streams are + always drawn with matplotlib, so a plotly render preference -- set here + exactly as that auto-detection sets it -- must not reach the internal + head plot (fresh-Colab feature tour, 2026-09-11: STREAM-01/02/03 raised + "'HyperPlotlyFigure' object has no attribute 'axes'").""" + rows = list(walk_gen(40)) + kwargs = {'save_path': str(tmp_path / 'stream.gif'), 'frame_rate': 4} \ + if save else {} + with hyp.set_interactive_backend('plotly'): + fig = hyp.plot(iter(rows), show=False, stream_init=12, + stream_chunk=4, stream_window=16, **kwargs) + assert isinstance(fig, matplotlib.figure.Figure) + assert fig.stream_info['n_samples'] == 40 + # the recent window is drawn; every consumed sample stays available + assert len(fig.axes[0].lines[0].get_data_3d()[0]) == 16 + assert fig.stream_info['data'][0].shape == (40, 6) + if save: + with Image.open(tmp_path / 'stream.gif') as im: + assert im.n_frames > 1 + plt.close('all') + + def test_stream_models_fitted_on_head_only(): """The reduction model must be fitted on the first stream_init samples and only *applied* afterwards (issue #101's core requirement).""" diff --git a/tests/test_subplots_plotly.py b/tests/test_subplots_plotly.py new file mode 100644 index 00000000..c59a2313 --- /dev/null +++ b/tests/test_subplots_plotly.py @@ -0,0 +1,199 @@ +"""`hyp.subplots(..., backend='plotly')` + `hyp.plot(..., ax=cell)`: the +plotly form of composing a panel grid from separate calls (backend parity +for the matplotlib ``fig, axes = hyp.subplots(); hyp.plot(d, ax=axes[i])`` +loop; 1.1 release review). Real `make_subplots` figures, real plotly +layout objects, real kaleido renders -- no mocks. +""" +import os + +import numpy as np +import pytest + +import hypertools as hyp +from hypertools._shared.helpers import UNIT_FRAME_LIMIT + +pytest.importorskip('plotly') + + +def _walk(seed, n=40): + return hyp.load('random_walk', n_samples=n, n_features=6, + random_state=seed) + + +def test_plotly_subplots_returns_the_grid_figure_and_flat_cells(): + from hypertools.plot.plotly_backend import PlotlyCell + fig, cells = hyp.subplots(2, 3, backend='plotly') + assert type(fig).__name__ == 'HyperPlotlyFigure' + assert cells.shape == (6,) + assert all(isinstance(c, PlotlyCell) for c in cells) + assert [(c.row, c.col, c.index) for c in cells] == [ + (1, 1, 0), (1, 2, 1), (1, 3, 2), (2, 1, 3), (2, 2, 4), (2, 3, 5)] + assert all(c.figure is fig for c in cells) + # 3-D grid: one scene per cell, already laid out + for key in ('scene', 'scene2', 'scene3', 'scene4', 'scene5', 'scene6'): + assert fig.layout[key].domain is not None + + +def test_plotly_subplots_1x1_still_returns_an_array(): + fig, cells = hyp.subplots(backend='plotly') + assert cells.shape == (1,) + assert cells[0].index == 0 + + +def test_plotly_subplots_ndims_2_gives_xy_cells(): + fig, cells = hyp.subplots(1, 2, ndims=2, backend='plotly') + assert fig.layout.xaxis.domain is not None + assert fig.layout.xaxis2.domain is not None + assert fig.layout.scene.to_plotly_json() == {} # no 3-D cell + assert cells[1].ndims == 2 + + +def test_plotly_subplots_size_sets_pixels(): + fig, _ = hyp.subplots(1, 2, size=[8, 4], backend='plotly') + assert (fig.layout.width, fig.layout.height) == (800, 400) + + +def test_plotly_subplots_rejects_bad_ndims(): + with pytest.raises(ValueError, match='ndims must be 1, 2 or 3'): + hyp.subplots(1, 1, ndims=4, backend='plotly') + + +def test_plotly_subplots_forwards_make_subplots_kwargs(): + fig, _ = hyp.subplots(2, 1, backend='plotly', vertical_spacing=0.3) + y_top_of_lower = fig.layout.scene2.domain.y[1] + y_bottom_of_upper = fig.layout.scene.domain.y[0] + assert y_bottom_of_upper - y_top_of_lower == pytest.approx(0.3) + + +def test_ax_cell_moves_the_whole_panel_into_its_cell_3d(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + out = hyp.plot(_walk(0), ax=cells[0], title='PCA', legend=True, + names=['walk'], backend='plotly', show=False) + out2 = hyp.plot(_walk(1), ax=cells[1], reduce='PCA', title='again', + backend='plotly', show=False) + assert out is fig and out2 is fig + # traces landed in their own scenes... + scenes = {trace.scene for trace in fig.data} + assert scenes == {'scene', 'scene2'} + # ...each cell's scene carries the drawn cube's axis ranges/camera + assert fig.layout.scene.camera.eye is not None + assert fig.layout.scene2.xaxis.range is not None + # the titles are per-cell annotations, and the legend is the first + # cell's own (no entry for the second cell, which has no name) + assert [a.text for a in fig.layout.annotations] == ['PCA', 'again'] + assert all(t.legend == 'legend' for t in fig.data if t.scene == 'scene') + assert all(t.legend == 'legend2' for t in fig.data + if t.scene == 'scene2') + assert fig.layout.margin.t >= 40 + + +def test_ax_cell_2d_keeps_the_frame_labels_and_colorbar(): + fig, cells = hyp.subplots(1, 2, ndims=2, backend='plotly') + hyp.plot(_walk(0)[:, :2], ax=cells[0], ndims=2, reduce=None, + xlabel='a', ylabel='b', backend='plotly', show=False) + hyp.plot(_walk(1), ax=cells[1], ndims=2, hue=np.arange(40.0), + colorbar=True, backend='plotly', show=False) + assert list(fig.layout.xaxis.range) == [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT] + assert list(fig.layout.yaxis2.range) == [-UNIT_FRAME_LIMIT, UNIT_FRAME_LIMIT] + assert fig.layout.xaxis.title.text == 'a' + assert fig.layout.yaxis.title.text == 'b' + # one frame square per cell, on that cell's axes + assert sorted(shape.xref for shape in fig.layout.shapes) == ['x', 'x2'] + colorbars = [t.marker.colorbar for t in fig.data + if t.marker is not None and t.marker.showscale] + assert len(colorbars) == 1 + x1 = fig.layout.xaxis2.domain[1] + assert colorbars[0].x > x1 + + +def test_ax_cell_refuses_animate_and_the_matplotlib_backend(): + fig, cells = hyp.subplots(1, 1, backend='plotly') + with pytest.raises(ValueError, match='cannot be combined with animate'): + hyp.plot(_walk(0), ax=cells[0], animate=True, backend='plotly', + show=False) + with pytest.raises(TypeError, match='draws with matplotlib'): + hyp.plot(_walk(0), ax=cells[0], backend='matplotlib', show=False) + + +def test_ax_cell_grid_renders_to_png(tmp_path): + fig, cells = hyp.subplots(1, 2, backend='plotly') + for cell, seed in zip(cells, (0, 1)): + hyp.plot(_walk(seed), ax=cell, title=f'walk {seed}', + backend='plotly', show=False) + target = tmp_path / 'grid.png' + fig.write_image(str(target)) + assert os.path.getsize(target) > 0 + + +def test_ax_cell_queues_the_grid_for_display_once(): + """Three cell calls in one notebook cell must display the grid once + (matplotlib's inline hook shows a grid figure once too). Exercised on + a real IPython InteractiveShell, whose `post_execute` queue + `_display_at_cell_end` fills.""" + from IPython.core.interactiveshell import InteractiveShell + from hypertools.plot import plotly_backend as pb + shell = InteractiveShell.instance() + try: + fig, cells = hyp.subplots(1, 3, backend='plotly') + pb._PENDING_DISPLAY[:] = [] + for _ in cells: + pb._display_at_cell_end(fig) + assert len(pb._PENDING_DISPLAY) == 1 + assert pb._PENDING_DISPLAY[0] is fig + finally: + pb._PENDING_DISPLAY[:] = [] + try: + shell.events.unregister('post_execute', pb._flush_pending_display) + except ValueError: + pass + InteractiveShell.clear_instance() + + +def test_drawing_into_a_cell_twice_keeps_the_earlier_labels(): + fig, cells = hyp.subplots(1, 1, backend='plotly') + hyp.plot(_walk(0), ax=cells[0], labels=['first'], label_anchor='first', + backend='plotly', show=False) + hyp.plot(_walk(1), ax=cells[0], labels=['second'], label_anchor='first', + backend='plotly', show=False) + assert len(fig.data) >= 2 + texts = [a.text for a in fig.layout.scene.annotations] + assert texts == ['first', 'second'] + + +def test_drawing_into_a_cell_twice_replaces_its_title(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(_walk(0), ax=cells[0], title='FIRST', backend='plotly', + show=False) + hyp.plot(_walk(1), ax=cells[0], title='SECOND', backend='plotly', + show=False) + hyp.plot(_walk(1), ax=cells[1], title='OTHER', backend='plotly', + show=False) + titles = [a.text for a in fig.layout.annotations + if a.name and a.name.startswith('hyp-cell-title-')] + assert titles == ['SECOND', 'OTHER'] + + +def test_a_title_y_override_maps_into_the_cell(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(_walk(0), ax=cells[0], title='low', title_kwargs={'y': 0.75}, + backend='plotly', show=False) + hyp.plot(_walk(1), ax=cells[1], title='top', backend='plotly', + show=False) + by_text = {a.text: a for a in fig.layout.annotations} + y0, y1 = fig.layout.scene.domain.y + assert by_text['low'].y == pytest.approx(y0 + 0.75 * (y1 - y0)) + assert by_text['top'].y == pytest.approx(fig.layout.scene2.domain.y[1]) + + +def test_each_cell_keeps_its_own_font(): + fig, cells = hyp.subplots(1, 2, backend='plotly') + hyp.plot(_walk(0), ax=cells[0], legend=True, names=['mono'], + font='DejaVu Sans Mono', backend='plotly', show=False) + hyp.plot(_walk(1), ax=cells[1], legend=True, names=['serif'], + font='DejaVu Serif', legend_kwargs={'font': {'size': 22}}, + backend='plotly', show=False) + assert 'DejaVu Sans Mono' in fig.layout.legend.font.family + assert 'DejaVu Serif' in fig.layout.legend2.font.family + assert fig.layout.legend2.font.size == 22 + assert 'DejaVu Serif' in fig.layout.scene2.xaxis.tickfont.family + assert 'DejaVu Sans Mono' in fig.layout.scene.xaxis.tickfont.family diff --git a/tests/test_surface.py b/tests/test_surface.py index 19d69383..7e245ff3 100644 --- a/tests/test_surface.py +++ b/tests/test_surface.py @@ -677,6 +677,48 @@ def test_plotly_vertexcolor_varies_and_tracks_position(self): corrs = [abs(np.corrcoef(z, vc[:, c])[0, 1]) for c in range(3)] assert max(corrs) > 0.3 + @pytest.mark.parametrize('backend', ['matplotlib', 'plotly']) + def test_surface_colour_matches_the_dots_beneath_it(self, backend): + # Maintainer report 2026-09-11: a hue= surface did not match the dots + # underneath it -- every vertex blended ALL points, so the hull took + # the dataset's washed-out mean colour. Compare each mesh vertex's + # chromaticity (shading-free: rgb / sum) with its nearest drawn dots. + from scipy.spatial import cKDTree + traj = np.cumsum(np.random.default_rng(3).normal(size=(300, 3)), axis=0) + hue = np.arange(len(traj), dtype=float) + fig = hyp.plot(traj, '.', hue=hue, surface=True, backend=backend, + palette='viridis', show=False) + if backend == 'plotly': + dots = [t for t in fig.data if t.type == 'scatter3d' + and t.hoverinfo != 'skip'] + pts = np.concatenate([np.c_[t.x, t.y, t.z] for t in dots]) + cols = np.concatenate([ + [[float(v) for v in c[c.index('(') + 1:-1].split(',')[:3]] + for c in t.marker.color] for t in dots]) / 255 + mesh = [t for t in fig.data if t.type == 'mesh3d'][0] + verts = np.c_[mesh.x, mesh.y, mesh.z] + vcols = _plotly_vertexcolors(fig) / 255 + else: + ax = fig.axes[0] + scat = [c for c in ax.collections if hasattr(c, '_offsets3d')][0] + pts = np.column_stack(scat._offsets3d) + cols = np.asarray(scat.get_facecolor())[:, :3] + poly = [c for c in ax.collections + if isinstance(c, Poly3DCollection)][0] + verts = _poly3d_verts(poly).reshape(-1, 3, 3).mean(axis=1) + # get_facecolor() is depth-SORTED after a draw while the 3-D + # vertices keep their order; the unsorted colours sit here + vcols = np.asarray(poly._facecolor3d)[:, :3] + + def chroma(rgb): + return rgb / (rgb.sum(axis=1, keepdims=True) + 1e-9) + _, idx = cKDTree(pts).query(verts, k=3) + local = chroma(cols)[idx].mean(axis=1) + err = np.abs(chroma(vcols) - local).max(axis=1) + assert np.median(err) < 0.04 + import matplotlib.pyplot as plt + plt.close('all') + def test_hue_adds_chromatic_variation_over_flat_surface(self): # A no-hue surface is one base color (a saturated palette color) whose # faces differ only by SHADING (a brightness scaling). Dividing each diff --git a/tests/test_text2mat.py b/tests/test_text2mat.py index ff28273c..fef40a75 100644 --- a/tests/test_text2mat.py +++ b/tests/test_text2mat.py @@ -74,3 +74,61 @@ def test_LDA_class_instance(): def test_corpus(): assert text2mat(data, corpus=data)[0].shape[1]==20 + + +# -------------------------- flat list of strings is ONE dataset (1.1, X1) + +import pytest # noqa: E402 + +DOCS = ['cats like milk', 'dogs like bones', 'birds like seeds'] + + +def test_flat_list_of_strings_is_one_dataset(): + # before 1.1 a flat list split by each string's CHARACTER length and + # returned [(N, d), (0, d), (0, d), ...] + out = text2mat(DOCS, vectorizer='CountVectorizer', semantic=None, + corpus=None) + assert isinstance(out, list) and len(out) == 1 + assert out[0].shape == (3, 7) # 7 distinct words + assert out[0].sum() == 9 # 3 words per document + + +def test_nested_list_matches_the_flat_form(): + flat = text2mat(DOCS, semantic=None, corpus=None) + nested = text2mat([DOCS], semantic=None, corpus=None) + assert len(nested) == 1 + np.testing.assert_array_equal(flat[0], nested[0]) + + +def test_ragged_list_of_lists_is_one_dataset_per_inner_list(): + out = text2mat([DOCS, DOCS[:2]], semantic=None, corpus=None) + assert [o.shape for o in out] == [(3, 7), (2, 7)] + np.testing.assert_array_equal(out[0][:2], out[1]) + # the same flat/nested rule applies to corpus= + with_corpus = text2mat([DOCS, DOCS[:2]], semantic=None, + corpus=[DOCS, DOCS[:2]]) + assert [o.shape for o in with_corpus] == [(3, 7), (2, 7)] + flat_corpus = text2mat([DOCS, DOCS[:2]], semantic=None, corpus=DOCS) + assert [o.shape for o in flat_corpus] == [(3, 7), (2, 7)] + + +def test_flat_list_through_the_default_topic_model(): + out = text2mat(DOCS, corpus=DOCS) # CountVectorizer -> LDA + assert len(out) == 1 and out[0].shape == (3, 20) + assert np.allclose(out[0].sum(axis=1), 1.0, atol=1e-6) + + +@pytest.mark.parametrize('argname', ['data', 'corpus']) +def test_mixed_strings_and_lists_raise(argname): + mixed = [DOCS[0], DOCS[1:]] + kwargs = {'data': mixed, 'semantic': None, 'corpus': None} \ + if argname == 'data' else \ + {'data': DOCS, 'semantic': None, 'corpus': mixed} + with pytest.raises(ValueError, match=f'{argname}= mixes strings and ' + 'lists'): + text2mat(**kwargs) + + +def test_single_string_is_one_dataset_of_one_document(): + out = text2mat(DOCS[0], semantic=None, corpus=None) + assert len(out) == 1 and out[0].shape == (1, 3) diff --git a/tests/test_tools_text_windows.py b/tests/test_tools_text_windows.py index b2899a45..c48d98c0 100644 --- a/tests/test_tools_text_windows.py +++ b/tests/test_tools_text_windows.py @@ -176,3 +176,17 @@ def test_windows_plot_as_one_trajectory_per_document(): reduce='PCA', ndims=3, show=False) assert len(fig.axes[0].lines) == 2 assert all(np.asarray(len(d)) > 1 for d in docs) + + +def test_numpy_integers_are_accepted_for_size_step_and_min_windows(): + # 1.1 release review I7: every other 1.1 API accepts np.integer, but + # text_windows('a b c d', size=np.int64(2)) raised TypeError + expected = text_windows('a b c d e', size=2, step=1, min_windows=1) + assert text_windows('a b c d e', size=np.int64(2), step=np.int32(1), + min_windows=np.uint8(1)) == expected + assert text_windows('a b c d e', size=np.int64(2)) == ['a b', 'b c', + 'c d', 'd e'] + with pytest.raises(TypeError): + text_windows('a b c d', size=True) # bool is not a size + with pytest.raises(TypeError): + text_windows('a b c d', size=np.float64(2)) diff --git a/tests/test_warning_attribution_align_impute_manip.py b/tests/test_warning_attribution_align_impute_manip.py new file mode 100644 index 00000000..0b179a96 --- /dev/null +++ b/tests/test_warning_attribution_align_impute_manip.py @@ -0,0 +1,137 @@ +"""Warnings from align/impute/manip/core name the CALLER's line (1.1 release +review, 2026-09-11). + +The review found `hyp.align`'s row-trim warning attributed to +``hypertools/align/common.py``; a sweep of the same modules found nine more +warnings that named a hypertools (or datawrangler) frame instead of the +user's call: fixed ``stacklevel=2`` calls reached through datawrangler's +funnel wrapper, and calls with no stacklevel at all. Python's default +filters only DISPLAY a DeprecationWarning attributed to ``__main__``, so the +deprecated spellings among them were invisible to scripts. Each now uses +`hypertools.core.model.external_stacklevel`, like the rest of the library. +Real calls, no mocks. +""" +import warnings + +import numpy as np +import pandas as pd +import pytest + +import hypertools as hyp +from hypertools.core.pipeline import Pipeline +from hypertools.impute import PPCA + + +def _data(): + rng = np.random.default_rng(0) + x = rng.normal(size=(30, 3)) + y = rng.normal(size=(30, 3)) + gappy = x.copy() + gappy[3, 1] = np.nan + return x, y, gappy + + +def _call(name): + x, y, gappy = _data() + if name == 'align= alias': + return hyp.align([x, y], align='HyperAlign') + if name == 'impute params dict': + return hyp.impute(gappy, model={'model': 'PPCA', 'params': {}}) + if name == 'Pipeline params dict': + return Pipeline([{'model': 'PCA', 'params': {'n_components': 2}}]) + if name == 'manip params dict': + return hyp.manip(x, model={'model': 'ZScore', 'params': {}}) + if name == 'manip params beside kwargs': + return hyp.manip(x, model={'model': 'ZScore', 'params': {}, + 'kwargs': {}}) + if name == 'impute mismatched columns': + return hyp.impute([pd.DataFrame(gappy, columns=list('abc')), + pd.DataFrame(y, columns=list('def'))]) + if name == 'impute dead column': + dead = pd.DataFrame(gappy, columns=list('abc')) + dead['c'] = np.nan + return hyp.impute(dead) + if name == 'impute instance kwargs': + return hyp.impute(gappy, model=PPCA(), n_components=2) + if name == 'Smooth rounds kernel_width': + return hyp.manip(x, model='Smooth', kernel='boxcar', kernel_width=4.6) + if name == 'Smooth makes kernel_width odd': + return hyp.manip(x, model='Smooth', kernel='boxcar', kernel_width=4) + raise AssertionError(name) + + +CASES = { + 'align= alias': 'align= is deprecated', + 'impute params dict': "'params': {...}} is deprecated", + 'Pipeline params dict': "'params': {...}} is deprecated", + 'manip params dict': "'params': {...}} is deprecated", + 'manip params beside kwargs': "ignoring the legacy 'params' key", + 'impute mismatched columns': 'do not share columns', + 'impute dead column': 'no observed values at all', + 'impute instance kwargs': 'ignoring keyword argument', + 'Smooth rounds kernel_width': 'Rounding smoothing kernel width', + 'Smooth makes kernel_width odd': 'Increasing smoothing kernel width', +} + + +@pytest.mark.parametrize('name', sorted(CASES)) +def test_the_warning_names_the_callers_line(name): + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + _call(name) + hits = [w for w in caught if CASES[name] in str(w.message)] + assert hits, [str(w.message) for w in caught] + assert all(w.filename == __file__ for w in hits), \ + [(w.filename, w.lineno) for w in hits] + + +def _warn_calls_without_external_stacklevel(path): + import ast + with open(path, encoding='utf-8') as handle: + tree = ast.parse(handle.read(), filename=path) + missing = [] + for node in ast.walk(tree): + if not (isinstance(node, ast.Call) + and isinstance(node.func, ast.Attribute) + and node.func.attr == 'warn' + and isinstance(node.func.value, ast.Name) + and node.func.value.id == 'warnings'): + continue + levels = [kw.value for kw in node.keywords if kw.arg == 'stacklevel'] + if not (levels and isinstance(levels[0], ast.Call) + and getattr(levels[0].func, 'id', None) + == 'external_stacklevel'): + missing.append(node.lineno) + return missing + + +def test_every_warning_in_these_packages_uses_external_stacklevel(): + """Static gate against the next misattributed warning: in + align/impute/manip/core (and tools/analyze.py) every + ``warnings.warn`` passes ``stacklevel=external_stacklevel()`` -- a + missing or fixed stacklevel is what named library frames above.""" + import os + package = os.path.dirname(os.path.abspath(hyp.__file__)) + files = [os.path.join(package, 'tools', 'analyze.py')] + for sub in ('align', 'impute', 'manip', 'core'): + folder = os.path.join(package, sub) + files += [os.path.join(folder, name) + for name in sorted(os.listdir(folder)) + if name.endswith('.py')] + offenders = {} + for path in files: + lines = _warn_calls_without_external_stacklevel(path) + if lines: + rel = os.path.relpath(path, package).replace(os.sep, '/') + offenders[rel] = lines + assert offenders == {} + + +def test_the_smooth_warnings_name_a_direct_class_call_too(): + from hypertools.manip import Smooth + x, _, _ = _data() + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter('always') + Smooth(kernel='boxcar', kernel_width=4).fit_transform(x) + hits = [w for w in caught if 'kernel width' in str(w.message)] + assert hits and all(w.filename == __file__ for w in hits) diff --git a/tests/test_window_animation.py b/tests/test_window_animation.py index 50893bd8..fb2e77cd 100644 --- a/tests/test_window_animation.py +++ b/tests/test_window_animation.py @@ -53,6 +53,11 @@ def test_mpl_window_draws_only_current_window(): full_len = ani._args[0][0].shape[0] frame_rate = 30 focused_frames = int(round(frame_rate * 1)) # focused=1s @ 30fps -> 30 + # the rows the reveal head passes in `focused` seconds. (1.1 visual + # review L8: this was `focused_frames` itself while every line was + # resampled onto exactly one row per frame -- which drew these + # 200-row walks through 120 of their points.) + focused_rows = int(round(focused_frames * (full_len - 1) / (total - 1))) for num in (total // 4, total // 2, 3 * total // 4): lines, trail_lines = ani._func(num, *ani._args) @@ -60,8 +65,8 @@ def test_mpl_window_draws_only_current_window(): assert all(t is None for t in trail_lines) for line in lines: xs, ys, zs = line.get_data_3d() - # the drawn window is AT MOST focused_frames + 1 points long - assert len(xs) <= focused_frames + 1 + # the drawn window is AT MOST focused_rows + 1 points long + assert len(xs) <= focused_rows + 1 # the full trajectory is NEVER fully drawn mid-animation assert len(xs) < full_len @@ -87,14 +92,23 @@ def test_mpl_window_exact_bounds_mid_animation(): total = ani._save_count num = total // 2 window_frames = int(round(frame_rate * focused)) - expected = data_lines[0][num - window_frames: num + 1] + # the head sits on row floor(num * (n - 1) / (total - 1)) -- the row + # `num` whenever there is exactly one row per frame -- and the window + # spans the rows the head passes in `window_frames` frames. (1.1 visual + # review L8: this used `num` and `window_frames` as ROW indices because + # every line was resampled onto one row per frame, drawing these + # 200-row walks through 80 of their points.) + n = data_lines[0].shape[0] + head = num * (n - 1) // (total - 1) + w = int(round(window_frames * (n - 1) / (total - 1))) + expected = data_lines[0][head - w: head + 1] lines, _ = ani._func(num, *ani._args) xs, ys, zs = lines[0].get_data_3d() assert len(xs) == len(expected) np.testing.assert_allclose(xs, expected[:, 0]) - np.testing.assert_allclose(xs[0], data_lines[0][num - window_frames, 0]) - np.testing.assert_allclose(xs[-1], data_lines[0][num, 0]) + np.testing.assert_allclose(xs[0], data_lines[0][head - w, 0]) + np.testing.assert_allclose(xs[-1], data_lines[0][head, 0]) plt.close('all') @@ -180,8 +194,11 @@ def test_plotly_window_exact_bounds_mid_animation(): passed while plotly's window ran one point shorter than matplotlib's at every steady-state frame. The expectation below is derived from the public knobs alone: `focused` seconds at `frame_rate` frames per second - spans `focused * frame_rate` frames, plus the vertex the window opens - on. Both backends must land on it. + spans `focused * frame_rate` frames, over which the head passes + ``(n_rows - 1) / (n_frames - 1)`` rows per frame, plus the vertex the + window opens on. Both backends must land on it. (1.1 visual review L8: + the rows-per-frame factor was 1 while every line was resampled onto one + row per frame -- these 200-row walks drew through 80 of their points.) """ pytest.importorskip('plotly') data = _walks() @@ -192,7 +209,9 @@ def test_plotly_window_exact_bounds_mid_animation(): fig = hyp.plot(data, backend='plotly', **kw) n_frames = len(fig.frames) mid_k = n_frames // 2 - expected = int(frame_rate * focused) + 1 + n_rows = data[0].shape[0] + expected = int(round(int(frame_rate * focused) * (n_rows - 1) + / (n_frames - 1))) + 1 assert len(fig.frames[mid_k].data[0].x) == expected ani = hyp.plot(data, return_model=True, **kw)['animation']