Add asv benchmarks for edge_detection (#3672)

.claude/sweep-benchmarks-state.csv

@@ -1,3 +1,4 @@
 module,last_inspected,issue,severity_max,categories_found,notes
+edge_detection,2026-07-18,3672,MEDIUM,1,"No bench file existed; all 5 public funcs (sobel_x/y, prewitt_x/y, laplacian) uncovered. Compute delegates to convolve_2d (directly benchmarked, but only at 5x5/25x25 kernels, never 3x3, never via the wrappers) so MEDIUM not HIGH. Added benchmarks/benchmarks/edge_detection.py: EdgeDetection class, nx in [300,3000], numpy/cupy/dask, one timing method per public func. All 30 combos executed locally incl. cupy (GPU host). Cat 2/3/4 N/A (module implements no backends itself; no pre-existing bench to be broken)."
 geotiff,2026-07-02,3603,HIGH,1;2,"No benchmark existed for geotiff; open_geotiff/to_geotiff had zero asv coverage across numpy/dask/cupy. Added benchmarks/benchmarks/geotiff.py: WriteGeoTIFF (numpy/dask/cupy streaming), WriteCOG (numpy/cupy overview pyramid), ReadGeoTIFF (numpy/cupy decode), ReadGeoTIFFChunked (dask). All classes executed locally via direct call; cupy paths run on this GPU host. asv check discover fails suite-wide from an asv_runner + py3.14 metadata bug, unrelated to this file."
 pathfinding,2026-07-08,3645,HIGH,1;2;3,"Bench covered only numpy a_star_search at nx<=300; module also ships dask (separate sparse-Python A* + LRU chunk cache), cupy fallback, and public multi_stop_search with zero coverage. Extended AStarSearch to numpy/cupy/dask with nx up to 1000 (dask capped at 300, ~4s/call at 1000) and added MultiStopSearch (ordered + optimize_order). All combos executed locally incl. cupy (GPU host). LOW noted, not fixed: open-grid no-barrier/no-friction input is A* best case. dask+cupy not parameterized anywhere in suite (common.get_xr_dataarray has no such type). Existing bench imports/runs fine (Cat 4 clean)."

benchmarks/benchmarks/edge_detection.py

@@ -0,0 +1,34 @@
+from xrspatial.edge_detection import laplacian, prewitt_x, prewitt_y, sobel_x, sobel_y
+
+from .common import get_xr_dataarray
+
+
+class EdgeDetection:
+    params = ([300, 3000], ["numpy", "cupy", "dask"])
+    param_names = ("nx", "type")
+
+    def setup(self, nx, type):
+        ny = nx // 2
+        self.agg = get_xr_dataarray((ny, nx), type)
+
+    def _run(self, func):
+        result = func(self.agg)
+        # dask returns a lazy array; force the compute so the benchmark
+        # times the kernel, not just graph construction.
+        if hasattr(result.data, 'compute'):
+            result.data.compute()
+
+    def time_sobel_x(self, nx, type):
+        self._run(sobel_x)
+
+    def time_sobel_y(self, nx, type):
+        self._run(sobel_y)
+
+    def time_prewitt_x(self, nx, type):
+        self._run(prewitt_x)
+
+    def time_prewitt_y(self, nx, type):
+        self._run(prewitt_y)
+
+    def time_laplacian(self, nx, type):
+        self._run(laplacian)