(Microsoft + DataKind AI in Cities Virtual Accelerator - DataDive) + (Bloc)
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Clone this repository to a directory on your local machine:
$ cd /path/to/your/preferred/directory $ git clone git@github.com:datakind/msvdd_Bloc.git $ cd msvdd_Bloc
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Create a virtual environment to isolate our project's dependencies from your other projects'. Use whichever tool you prefer (e.g.
virtualenv
,pyenv
,pipenv
). Here's an example usingpyenv
:msvdd_Bloc(master)$ pyenv virtualenv 3.7.4 bloc-env msvdd_Bloc(master)$ pyenv shell bloc-env
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Install the package in one of two ways.
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To use the
msvdd_bloc
code as-is without further development, installation is simple:(bloc-env) msvdd_Bloc(master)$ pip install .
-
To further develop the code, install the package in locally-editable (aka develop) mode, plus a few additional dependencies:
(bloc-env) msvdd_Bloc(master)$ pip install -e . (bloc-env) msvdd_Bloc(master)$ pip install -r requirements-dev.txt
-
-
Create a branch with a descriptive name for you to hack on, as needed:
(bloc-env) msvdd_Bloc(master)$ git pull (bloc-env) msvdd_Bloc(master)$ git checkout -b my-example-branch-name
Stand-alone doc files live under the top-level docs/
directory and are written in reStructured Text format. They are built using sphinx
:
$ cd docs
$ make html
As needed, commit the latest version of the built HTML docs to the project's master
branch:
$ git commit -am "Update built HTML docs"
$ git push origin master
These files are automatically published through GitHub Pages, and are accessible via web browser at https://datakind.github.io/msvdd_Bloc.
In-code docstrings follow Google style. These docstrings are automatically incorporated into the main docs via sphinx.ext.sphinx-autodoc
. Refer to the sphinx site for details.
Test modules live under the top-level tests/
directory. They are run using pytest
:
$ cd tests
$ pytest -vv .
A coverage report may additionally be generated using pytest-cov
:
$ pytest -vv --cov=msvdd_bloc --cov-report=term-missing .
Refer to the pytest site for details.