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‎.github/workflows/deploy.yml

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name: deploy
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on:
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# Trigger the workflow on push to main branch
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push:
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branches:
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- main
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# This job installs dependencies, build the book, and pushes it to `gh-pages`
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jobs:
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build-and-deploy-book:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [ubuntu-latest]
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python-version: [3.8]
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steps:
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- uses: actions/checkout@v2
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# Install dependencies
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v1
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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pip install -r requirements.txt
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# Build the book
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- name: Build the book
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run: |
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jupyter-book build notebooks
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# Deploy the book's HTML to gh-pages branch
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- name: GitHub Pages action
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uses: peaceiris/[email protected]
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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publish_dir: notebooks/_build/html

‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/

‎LICENSE

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MIT License
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Copyright (c) 2021 National Center for Atmospheric Research
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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# analysis_project_template
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This is a sample template meant to be used for workflows centered around notebooks
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## Why you should use this template
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This template is meant to give you the basic setup of an "analysis" repository, focused on storing notebooks or other pieces of scientific code/documentation. This repository is not meant to be a "one size fits all" repository, but rather, a starting point from which the user can build upon.
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## Essential checklist for your repository
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- [ ] Software license
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- [ ] Detailed readme
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- [ ] Update the environment.yml file in include the required libraries/packages
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- [ ] Your analysis code/notebooks!
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## Choosing a license for this repository
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There are a variety of software licenses to choose from. The default for this
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repository is the [MIT License](https://opensource.org/licenses/MIT) which is short
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and to the point. If you are interested in learning more about which license is
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best for you, check out [this resource](https://choosealicense.com/) to help you
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determine which is best for your specific needs, or consult [UCAR Legal](https://internal.ucar.edu/counsel/about)
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## What to include in your README (README.md)
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Once you copy over this template and choose your license, you should populate your README file. This file serves as a landing page for your repository and should provide the following:
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- Title of the project
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- Introduction and overview of what the analysis covers
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- How to contribute
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- How to reproduce the conda environment
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An example of this is given below
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```
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# Climate-Analysis
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This is a repository meant to show how to do climate analysis.
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## Contributing
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Clone this repository to your account, then create your own branch to work in
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"git checkout -b <nameofyourbranch>"
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## Reproducing the environment
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"conda env create -f envs/environment.yml"
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"conda activate environment_name"
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```
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## The importance of software citation and minting a DOI
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Once your repository is built and you have added your analysis, you may want to mint a DOI for your project, so you can share your work with the community AND receive credit for doing so.
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Directions on how to mint a DOI for your project can be found on the [NCAR Software Citation Documentation](https://ncar.github.io/software-citation/pages/recommendation/mint-doi.html)

‎envs/environment.yml

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name: analysis
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channels:
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- conda-forge
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- nodefaults
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dependencies:
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- numpy
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- xarray
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- matplotlib
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- cartopy
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- dask
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- jupyter
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- jupyterlab
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- netCDF4

‎notebooks/Notebook_Template.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sample Notebook\n",
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"\n",
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"Once you copy this repository, feel free to delete this notebook!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import something here\n",
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"import xarray as xr\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data Ingest"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read in the data using xarray or some other package\n",
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"ds = xr.open_dataset('some_file.nc')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data Operation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Do some sort of calculation on the data\n",
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"some_calc = ds.temp * some_equation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data Visualization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot the data\n",
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"plt.plot(x, y)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎notebooks/_config.yml

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title: Analysis Project Template
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author: NCAR ESDS Team
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execute:
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execute_notebooks: "off"

‎notebooks/_toc.yml

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format: jb-book
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root: intro
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chapters:
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- file: Notebook_Template

‎notebooks/intro.md

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# Introduction
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This is an overview of your project. Feel free to update accordingly!

‎requirements.txt

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jupyter-book
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matplotlib
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numpy
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ghp-import

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