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Contributing to the Project Pythia Portal

This document contains information specific to contributing to the Project Pythia Portal. Please first refer to Guide for Contributing to Project Pythia for overall contribution guidelines (such as detailed description of Project Pythia structure, forking, repository cloning, branching, etc.).

Instructions for adding a blog post

Within the portal/posts/[YEAR] folder add your .md blog file with the following heading:

---

date: YYYY-MM-DD
author: github-handle
tags: [sample-tag]
---

To display the post, you need to add the file path in the myst.yml configuration file under toc: -> - title: Blog -> children:. Please add the newest blog post at the top of the blog file list. This is temporary until we have better blog infrastructure.

Instructions for building the portal site

The portal site is built with MyST-MD.

When testing new content it is important to build and view the site. Read the Docs automatically builds the site for you when each Pull Request is checked. However, you can also build it locally on your machine following the instructions below.

Building the site

After checking out a local copy of the site, in your favorite terminal, navigate to the portal directory of the source repository

cd projectpythia.github.io/portal

Use conda to set up a build environment:

conda env update -f ../environment.yml

This will create the dev environment (pythia). If you have previously created the environment, running this command will add any new packages that have since been added to the environment.yml file.

It's a good idea to also keep the versions of each package in the pythia environment up to date by doing:

conda activate pythia
conda update --all

Install pre-commit hooks

This repository includes pre-commit hooks (defined in .pre-commit-config.yaml). To activate/install these pre-commit hooks, run:

conda activate pythia
pre-commit install

Setting up the environment is typically a one-time step.

NOTE: The pre-commit package is already installed via the pythia conda environment.

Building the book locally

Build the site locally using Sphinx (which you just installed in the pythia environment, along with all necessary dependencies):

myst start --execute

If this step fails and you may not have updated your conda environment recently, try updating with conda env update -f ../environment.yml and conda update --all as described above.

The newly rendered site is now available at localhost:3000. A link should appear in your terminal.

More information on setting up a local test server is available from here

When you're done, you can deactivate the dedicated build environment with

conda deactivate

You can re-activate the pythia conda environment at any time with conda activate pythia. You may also want to update each package in the activated environment to their latest versions by doing

conda activate pythia
conda update --all

Instructions for intacting with the Google Analytics API

Setting up the Virtual Environment

Analytics must be run on a virtual environment. To create and activate this environment, with the necessary google-analytics-data package, the terminal commands are:

python -m venv analytics-api-env
source analytics-api-env/bin/activate
pip install google-analytics-data

Replace 'analytics-api-env' with any new environment name. Also, pip install any other packages you may want for your analytics work.

Setting up Credentials

To interact with the Google Analytics API locally you need to download the credentials file. This file has been uploaded to the ProjectPythia Google Drive and lives in the Analytics_API folder.

This credentials file needs to be kept secure, especially the private_key field. Do NOT share this file. If you do not have access to our Google Drive and need access to this file, please reach out to the team on discourse or in a community meeting.

The credentials file will have a name similar to cisl-vast-pythia-{letters and numbers}.json. This file may be replaced intermittently with a slightly different alphanumeric string for additional security.

One way to ensure that your Python script is using the correct credentials file is to read it as a dictionary and pass that into your API client at the begging of your script.

from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import DateRange, Dimension, Metric, RunReportRequest

with open('{credentials-file-path}') as json_file:
   credentials_dict = json.load(json_file)

client = BetaAnalyticsDataClient.from_service_account_info(credentials_dict)

Recommended and commonly needed import statements are also shown at the script beginning.

Making a request

Below is a sample function for calling an Analytics API request.

def _run_analytics_request(property_id):
    request = RunReportRequest(
        property=f'properties/{property_id}',
        dimensions=[Dimension(name='date')],
        metrics=[Metric(name='activeUsers')],
        date_ranges=[DateRange(start_date='2024-01-01', end_date='today')],
    )
    response = client.run_report(request)
    return response

This function demonstrates how to format your RunReportRequest() arguments, notably the dimensions and metrics fields, as well as the expected date formatting in date_ranges.

This Google Analytics API Schema documentation lists all of the available dimension and metric keys that can be passed into your request.

property_id is a 9-digit number associated with the project you are interested in. This number can be found on the Analytics project page. For Project Pythia, our three different property IDs are:

PORTAL_ID = '266784902'
FOUNDATIONS_ID = '281776420'
COOKBOOKS_ID = '324070631'

Working with your request output

Your Google Analytics response is formatted in a series of rows that each have the key dimension_value and metric_value. You may find it easier to work with your data in a dictionary or tuple. For the single dimension of "date" and metric of "activeUsers" as specified in our example function, here is what your data manipulation may look like before you can carry out additional analysis.

dates=[]
users=[]
for row in response.rows:
    date_str = row.dimension_values[0].value
    date = datetime.datetime.strptime(date_str, '%Y%m%d')
    dates.append(date)
    users.append(int(row.metric_values[0].value))

dates, users =  zip(*sorted(zip(dates, user_counts), key=lambda x: x[0]))

One thing to note is that your Analytics response rows are not automatically chronological, so in this example we zipped our sorted tuple to ensure that the dates are in the expected order.