jupyter-widgets/widget-ts-cookiecutter

Name: widget-ts-cookiecutter

Owner: Jupyter Widgets

Description: A highly opinionated cookiecutter template for ipywidget extensions.

Created: 2017-08-15 15:09:36.0

Updated: 2018-05-24 13:32:38.0

Pushed: 2018-05-24 13:32:37.0

Homepage: null

Size: 99

Language: Python

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README

widget-ts-cookiecutter

A cookiecutter template for creating a custom Jupyter widget project

A cookiecutter template for a custom Jupyter widget project.

What is widget-ts-cookiecutter?

With widget-ts-cookiecutter you can create a custom Jupyter interactive widget project with sensible defaults. widget-ts-cookiecutter helps custom widget authors get started with best practices for the packaging and distribution of a custom Jupyter interactive widget library.

Usage

Install cookiecutter:

$ pip install cookiecutter

After installing cookiecutter, use widget-ts-cookiecutter:

$ cookiecutter https://github.com/jupyter-widgets/widget-ts-cookiecutter.git

As widget-ts-cookiecutter runs, you will be asked for basic information about your custom Jupyter widget project. You will be prompted for the following information:

• author_name: your name or the name of your organization,
• author_email: your project's contact email,
• github_project_name: name of your custom Jupyter widget's GitHub repository,
• github_organization_name: name of your custom Jupyter widget's GitHub user or organization,
• python_package_name: name of the Python “back-end” package used in your custom widget.
• npm_package_name: name for the npm “front-end” package holding the JavaScript implementation used in your custom widget.
• npm_package_version: initial version of the npm package.
• jlab_extension_id: extension ID to supply to JupyterLab when registering the extension. The recommended format is “jupyter.extensions.“.
• project_short_description : a short description for your project that will be used for both the “back-end” and “front-end” packages.

After this, you will have a directory containing files used for creating a custom Jupyter widget. To check that eveything is set up as it should be, you should run the tests:

rst install the python package. This will also build the JS packages.
install -e ".[test, examples]".

n the python tests. This should not give you a few sucessful example tests
est

n the JS tests. This should again, only give TODO errors (Expected 'Value' to equal 'Expected value'):
test

When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For lab, this is done by the command:

ter labextension install .

For classic notebook, you can run:

ter nbextension install --sys-prefix --symlink --py <your python package name>
ter nbextension enable --sys-prefix --py <your python package name>

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

Releasing your initial packages:

• Add tests
• Ensure tests pass locally and on CI. Check that the coverage is reasonable.
• Make a release commit, where you remove the , 'dev' entry in _version.py.
• Relase the npm packages:

  login
  s
  publish
  w, you need to update the entry in /<your python package>/jlextension/package.json
  om "file:../../ts" to "^<the version of the NPM packge you just released>
  ter this, run:
  ./<your python package>/jlextension
  publish
  

• Bundle the python package: python setup.py sdist bdist_wheel
• Publish the package to PyPI:

  install twine
  e upload dist/*
  

• Tag the release commit (git tag <python package version identifier>)
• Update the version in _version.py, and put it back to dev (e.g. 0.1.0 -> 0.2.0.dev). Update the versions of the npm packages (without publishing).
• Commit the changes.
• git push and git push --tags.