Table of Contents
After you have cloned the Git repository, you will need to:
- create the Conda environment lock files for the dependencies
- create a virtual Conda environment for development, where to install the dependencies of the project
- execute the tests
- setup Git LFS if needed
- configure the pre-commit hooks for static code analysis and auto-formatting
- configure the Python IDE (PyCharm)
Install internal tool py-deps-lock to create the Conda environment lock files,
and the local development environment. py-deps-lock is a wrapper around conda-lock.
See details in the py-deps-lock documentation.
To install py-deps-lock with pipx, run the command:
$ pipx install git+https://github.com/MiraGeoscience/py-deps-lock
This expose commands to the execution PATH:
- create-dev-env
- deps-lock
First, create the Conda environment lock files (*.conda.lock.yml) for the dependencies defined
in pyproject.toml. From the root of the project, run the command:
$ deps-lock
It will create or replace the *.conda.lock.yml files in the environments folder:
one for runtime dependencies, and one for development dependencies (with the -dev suffix),
for each combinations of Python versions and platforms
(platforms are specified in conda-lock section of the pyproject.toml).
These files will be used by installation script and to create the development environment.
For development, you need a Conda environment. From the root of the project, run the command:
$ create-dev-env
This command install a local environment at the base of your repository: .conda-env.
This environment should automatically be recognized by the Conda installation.
To activate this environment, run the following command from the root of the project:
$ conda activate ./.conda-env
Dependencies are listed in pyproject.toml with version constraints.
Versions are then locked using deps-lock as previously described.
Anytime dependencies are added to or removed from the pyproject.toml file,
regenerate the Conda environment lock files, re-running deps-lock from command line.
Regenerate the Conda environment lock files as well when you want to fetch newly
available versions of the dependencies (typically patches, still in accordance with
the specifications expressed in pyproject.toml).
First install the dependency using conda:
$ conda install -p ./.conda-env my_new_dep
Then update the list of dependencies in pyproject.toml with a suited version constraint
(if for development only, place it under section [tool.poetry.group.dev.dependencies]).
For example, if conda installed version 1.5.2 of my_new_dep,
then add my_new_dep="^1.5.2".
Do not forget to regenerate the Conda environment lock files.
Poetry provides a command line interface to easily add or remove dependencies:
(path/to/.conda-env) $ poetry add another_package --lock
Note the --lock option, that simple creates or updates the lock file, without Poetry installing anything.
poetry would install the package through pip while we want dependencies to be installed through conda
so that they match the version pinned by conda-lock.
One limitation though: Poetry will look for packages in PiPY only and not in the Conda channels. The version selected by Poetry might thus not be available for Conda.
To install Poetry on your computer, refer to the Poetry documentation.
pre-commit is used to automatically run static code analysis upon commit. The list of tools to execute upon commit is configured in the file .pre-commit-config.yaml.
pre-commit can be installed using a Python installation on the system, or one from a Conda environment, or through pipx.
To install
pre-commitusingpipx(recommended):$ pipx install pre-commit
To install
pre-commitusing Python (and pip) in your system path:$ pip install --user pre-commit
Or to install from an activated Conda environment:
$ conda install -c conda-forge pre-commit
Then, in either way, install the pre-commit hooks as follow (current directory is the project folder):
$ pre-commit install
To prepare and check the commit messages, you can also use the following commands:
$ pre-commit install -t prepare-commit-msg -t commit-msg
It configures pre-commit to prepares and checks the commit ensuring it has a JIRA issue ID:
if no ID was provided, it extracts it from the branch name;
if one was provided, it checks it is the same one as in the branch name.
To run pre-commit manually, use the following command:
$ pre-commit run --all-files
To run only on changes staged for commit:
$ pre-commit run
If a tool fails running, it might be caused by an obsolete versions of the tools that pre-commit is trying to execute. Try the following command to update them:
$ pre-commit autoupdate
Upon every commit, all the pre-commit checks run automatically for you, and reformat files when required. Enjoy...
If you prefer to run pre-commit upon push, and not upon every commit, use the following commands:
$ pre-commit uninstall -t pre-commit $ pre-commit install -t pre-push
Test files are placed under the tests folder. Inside this folder and sub-folders,
Python test files are to be named with _test.py as a suffix.
The test function within this files must have a test_ prefix.
If you installed your environment through setup-dev.bat, pytest is already installed.
You can run it from the Conda command (in your project folder):
$ pytest tests
If you installed your environment through setup-dev.bat, pytest-cov is already installed.
It allows you to visualize the code coverage of your tests.
You can run the tests from the console with coverage:
$ pytest --cov-report html tests
The html report is generated in the folder htmlcov at the root of the project.
You can then explore the report by opening index.html in a browser.
In the case your package requires large files, git-lfs can be used to store those files. Copy it from the git-lfs website, and install it.
Then, in the project folder, run the following command to install git-lfs:
$ git lfs install
It will update the file .gitattributes with the list of files to track.
Then, add the files and the .gitattributes to the git repository, and commit.
Then, add the files to track with git-lfs:
$ git lfs track "*.desire_extension"
PyCharm, by JetBrains, is a very good IDE for developing with Python.
First, excluded the .conda-env folder from PyCharm.
Do so, in PyCharm, right-click on the .conda-env folder, and Mark Directory as > Excluded.
Then, you can add the Conda environment as a Python interpreter in PyCharm.
In PyCharm settings, open File > Settings, go to Python Interpreter,
and add click add interpreter (at the top left):
Select Conda Environment, Use existing environment,
and select the desired environment from the list (the one in the .conda-env folder):
Then you can check the list of installed packages in the Packages table. You should see
this source package and its dependencies. Make sure to turn off the Use Conda Package Manager
option to see also the packages installed through pip:
First, right click on the tests folder and select Mark Directory as > Test Sources Root:
You can now start tests with a right click on the tests folder and
select Run 'pytest in tests', or select the folder and just hit Ctrl+Shift+F10.
PyCharm will nicely present the test results and logs:
You can run the tests with a nice report of the code coverage, thanks to the pytest-cov plugin (already installed in the virtual environment as development dependency as per pyproject.toml).
To set up this option in PyCharm, right click on the tests folder and Modify Run Configuration...,
then add the following option in the Additional Arguments field:
select pytest in tests, and add the following option in the Additional Arguments field:
--cov-report html
Then, run the tests as usual, and you will get a nice report of the code coverage.
Here is a suggestion for some plugins you can install in PyCharm.
- Toml, to edit and validate
pyproject.tomlfile. - IdeaVim, for Vim lovers.
- GitHub Copilot, for AI assisted coding.
Copyright (c) 2023-2024 Mira Geoscience Ltd.