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Merge pull request #4 from scottyhq/website
Add materials for creating a basic website
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‎.github/workflows/create_website.yml

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name: Deploy Website to GitHub Pages
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on:
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# Runs on pushes targeting the default branch
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push:
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branches: ["main"]
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# Allows you to run this workflow manually from the Actions tab
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workflow_dispatch:
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# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
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permissions:
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pages: write
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id-token: write
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jobs:
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# Build Website
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build:
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runs-on: ubuntu-latest
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defaults:
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run:
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shell: bash -l {0}
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Install Conda environment with Micromamba
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uses: mamba-org/setup-micromamba@v1
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with:
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environment-file: website_figure/environment.yml
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cache-environment: true
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- name: Build Website
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shell: bash -el {0}
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run: |
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jupyter nbconvert --execute --to html myfigure.ipynb
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: myfigure.html
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# Publish Website to GitHub Pages if built successfully
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deploy:
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needs: build
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if: github.ref == 'refs/heads/main'
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runs-on: ubuntu-latest
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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steps:
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- name: Setup Pages
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uses: actions/configure-pages@v5
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎.gitignore

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website_figure/*.html
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website_figure/_build
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*.bak
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.DS_Store
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.vscode
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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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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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cover/
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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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.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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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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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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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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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/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/

‎website_figure/README.md

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# Turn a Jupyter Notebook into a public website
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The basic idea is to *execute* a notebook and render both code and figures on a website
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(nbconvert)[https://nbconvert.readthedocs.io] is a tool in the Jupyter ecosystem to convert .ipynb to various formats including HTML
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We'll run this command
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`jupyter nbconvert --execute --to html myfigure.ipynb`
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Exercise 1: Explore the command line options to remove code cells https://nbconvert.readthedocs.io/en/latest/config_options.html#cli-flags-and-aliases
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Exercise 2: Hide specific cells using cell metadata https://nbconvert.readthedocs.io/en/latest/removing_cells.html#removing-cells-inputs-or-outputs
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## Other tools
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If you plan on building a more sophisticated website, the pattern is the same! But definitely check out these tools that add a lot of functionality such as contents navigation, search, image/output management, cross-referencing, extended markdown features and nice styling just to name a few!
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* https://mystmd.org
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* https://jupyterbook.org
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* https://quartopub.com

‎website_figure/environment.yml

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name: website
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channels:
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- conda-forge
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dependencies:
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- cartopy
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- geoviews
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- h5netcdf
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- hvplot
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- nbconvert
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- pooch
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- xarray

‎website_figure/myfigure.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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"# My scientific results!\n",
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"\n",
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"Below is a figure generated in GitHub Actions and made public on the web"
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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 cartopy.crs as ccrs\n",
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"import hvplot.xarray\n",
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"import xarray as xr\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')"
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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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"# https://tutorial.xarray.dev/fundamentals/04.3_geographic_plotting.html\n",
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"# https://tutorial.xarray.dev/intermediate/hvplot.html\n",
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"ds = xr.tutorial.open_dataset(\"air_temperature.nc\").rename({\"air\": \"Tair\"})"
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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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"ds.Tair.isel(time=1).hvplot(\n",
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" projection=ccrs.Orthographic(-90, 30),\n",
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" coastline=True,\n",
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")"
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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": "python3",
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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": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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