For more detailed instructions, see our documentation.
To install expert_umbrella, we recommend using the mamba package manager:
mamba create -n expert_umbrella -c conda-forge -c divyasharma expert_umbrella
git clone [email protected]:divyasharma/expert_umbrella.git
cd expert_umbrella
mamba create -n expert_umbrella -c conda-forge --file requirements/base.txt --file requirements/dev.txt
mamba activate expert_umbrella
pip install --no-deps -e .For more detailed instructions, see our documentation.
There are many ways to contribute to expert_umbrella. Before making contributions to the expert_umbrella source code, see our contribution guidelines and follow the development install instructions.
If you plan to make changes to the code then please make regular use of the following tools to verify the codebase while you work:
pre-commit: runpre-commit installin your command line to load inbuilt checks that will run every time you commit your changes. The checks are: 1. check no large files have been staged, 2. lint python files for major errors, 3. format python files to conform with the pep8 standard. You can also run these checks yourself at any time to ensure staged changes are clean by simple callingpre-commit.pytest- run the unit test suite and check test coverage.pytest -p memray -m "high_mem" --no-cov(not available on Windows) - after installing memray (mamba install memray pytest-memray), test that memory and time performance does not exceed benchmarks.
For more information, see our documentation.
If you are unable to access the online documentation, you can build the documentation locally. First, install a development environment of expert_umbrella, then deploy the documentation using mike:
mike deploy develop
mike serve
Then you can view the documentation in a browser at http://localhost:8000/.
This package was created with Cookiecutter and the arup-group/cookiecutter-pypackage project template.