Development

Intro …

General workflow

The development workflow can be followed in the following figure.

1. You write the Python source codes (.py) and the Jupyter notebooks (.ipynb) on your local machine. If you want, you can create the documentation locally using Sphinx.

2. When a logical unit has been finished, you commit the changes with git push. This will upload the new version of the modified files to a remote repository (currently GitHub). If you edit files in the remote repository and you want those changes to be present in your local copy, you can use git pull. For more details on Git, read the manual.

3. Several commit hooks are attached to the remote repository. When they detect a change, certain actions are activated. One the one hand, the online version of the documentation, hosted on Read the Docs will be updated. On the other hand, static analyzers will reanalyze the new version of the code. Some of them may create a pull request based on their recommendations.

Profiling

I use Pyinstrument for profiling the code. It comes with the CristalX installation.

Otherwise, you can install it with pip install pyinstrument or by conda after the conda-forge channel has been activated.

conda install -c conda-forge pyinstrument

As Pyinstrument has no dependencies, you can safely install it to your current environment. If you do not want to take a risk, create a new environment. With conda, you can do e.g.

conda create -n pyinstrument python=3.7 scipy matplotlib
conda activate pyinstrument
conda install -c conda-forge pyinstrument

The profiling module provides a wrapper around Pyinstrument. Put the code you want to profile in the profile context manager, e.g.

>>> import random
>>> from grains.profiling import profile
>>> with profile('html') as p:
...    for _ in range(1000000):
...        rand_num = random.uniform(1, 2.2)

For more details see the API reference.