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‎.devcontainer/Dockerfile

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FROM pangeo/base-image:2024.06.02
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FROM pangeo/base-image:2024.06.24

‎.github/dependabot.yml

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# Regularly update Docker tags and Actions steps
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version: 2
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updates:
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- package-ecosystem: "docker"

‎.github/workflows/main.yaml

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name: CI
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name: Deploy Website to GitHub Pages
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on:
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push:
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branches: main
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pull_request:
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branches: main
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paths-ignore:
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- ".devcontainer/**"
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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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cancel-in-progress: true
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jobs:
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build-and-deploy:
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build:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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- name: Setup JupyterBook Cache
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uses: actions/cache@v3
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uses: actions/cache@v4
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with:
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path: _build
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# NOTE: change key to "jupyterbook-DATE" to force rebuilding cache
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key: jupyterbook-20230707
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key: jupyterbook-20240517
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- name: Install Conda environment with Micromamba
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uses: mamba-org/setup-micromamba@v1
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run: |
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if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
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- name: Save Build
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- name: Save Build Folder
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if: always()
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uses: actions/upload-artifact@v3
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uses: actions/upload-artifact@v4
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with:
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name: build
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path: _build/
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- name: Publish to GitHub Pages
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if: github.ref == 'refs/heads/main'
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uses: peaceiris/actions-gh-pages@v3
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- name: Upload Pages Artifact
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uses: actions/upload-pages-artifact@v3
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}
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publish_dir: _build/html
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publish_branch: gh-pages
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cname: tutorial.xarray.dev
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enable_jekyll: false
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path: _build/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

‎.github/workflows/preview.yaml

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‎.github/workflows/pull_request.yaml

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name: Pull Request Build
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on:
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pull_request:
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types: [opened, synchronize, reopened, closed]
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paths-ignore:
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- ".devcontainer/**"
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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preview:
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runs-on: ubuntu-latest
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defaults:
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run:
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shell: bash -el {0}
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steps:
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- name: Checkout repository
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if: github.event.action != 'closed'
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uses: actions/checkout@v4
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- name: Setup JupyterBook Cache
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if: github.event.action != 'closed'
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uses: actions/cache@v4
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with:
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path: _build
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# NOTE: change key to "jupyterbook-DATE" to force rebuilding cache
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key: jupyterbook-20240517
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- name: Install Conda environment with Micromamba
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if: github.event.action != 'closed'
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uses: mamba-org/setup-micromamba@v1
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with:
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environment-file: conda/conda-lock.yml
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environment-name: xarray-tutorial
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cache-environment: true
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- name: Build JupyterBook
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if: github.event.action != 'closed'
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run: |
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jupyter-book build ./ --warningiserror --keep-going
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- name: Dump Build Logs
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if: github.event.action != 'closed'
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run: |
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if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
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- name: Upload artifact
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if: github.event.action != 'closed'
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uses: actions/upload-artifact@v4
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with:
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name: html
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path: _build/html

‎.github/workflows/qaqc.yaml

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pull_request:
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branches:
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- main
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paths-ignore:
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- ".devcontainer/**"
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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shell: bash -el {0}
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steps:
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- uses: actions/checkout@v3
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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

‎.github/workflows/surge_preview.yml

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name: Pull Request Preview
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on:
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workflow_run:
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workflows: ["Pull Request Build"]
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types:
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- completed
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permissions:
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pull-requests: write # allow surge-preview to create/update PR comments
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.workflow_run.id }}
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cancel-in-progress: true
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jobs:
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# NOTE: match job name in pull_request.yaml
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preview:
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runs-on: ubuntu-latest
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if: ${{ github.event.workflow_run.event == 'pull_request' && github.event.workflow_run.conclusion == 'success' }}
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steps:
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# Ensure folder exists for PR 'closed' case
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- run: mkdir html
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# Download built HTML from PR Build workflow
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- uses: actions/download-artifact@v4
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continue-on-error: true
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with:
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github-token: ${{ github.token }}
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run-id: ${{ github.event.workflow_run.id }}
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- name: Manage Surge.sh Deployment
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id: preview_step
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uses: afc163/surge-preview@v1
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with:
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surge_token: ${{ secrets.SURGE_TOKEN }}
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github_token: ${{ secrets.GITHUB_TOKEN }}
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build: echo 'Uploading html/ folder contents to Surge.sh...'
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dist: html # NOTE: match upload_artifact name in pull_request.yaml
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failOnError: true
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teardown: true

‎.prettierignore

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conda/**
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conda/

‎_config.yml

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# maintain old paths and redirect them (so google results dont go to 404)
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# https://github.com/wpilibsuite/sphinxext-rediraffe
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- sphinxext.rediraffe
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- sphinx_exercise
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- sphinx_codeautolink
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config:

‎advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb

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" out[index, :] = np.interp(..., array[index, :], ...)\n",
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"```\n",
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"\n",
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"\n",
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"```{exercise}\n",
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":label: coreloopdims\n",
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"\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"Consider the example problem of interpolating a 2D array with dimensions `space` and `time` along the `time` dimension.\n",
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"Which dimension is the core dimension, and which is the \"loop dimension\"?\n",
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"```\n",
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"```{solution} coreloopdims\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"`time` is the core dimension, and `space` is the loop dimension.\n",
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"```\n",
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":::\n",
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"::::\n",
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"\n",
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"## Vectorization\n",
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"\n",

‎advanced/apply_ufunc/complex-output-numpy.ipynb

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"tags": []
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},
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"source": [
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"```{exercise}\n",
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":label: newdim\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"Apply the following function using `apply_ufunc`. It adds a new dimension to the input array, let's call it `newdim`. Specify the new dimension using `output_core_dims`. Do you need any `input_core_dims`?\n",
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"\n",
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"```python\n",
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"def add_new_dim(array):\n",
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" return np.expand_dims(array, axis=-1)\n",
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"```\n",
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"````{solution} newdim\n",
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"\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"``` python\n",
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"```python\n",
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"def add_new_dim(array):\n",
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" return np.expand_dims(array, axis=-1)\n",
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"\n",
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" output_core_dims=[[\"newdim\"]],\n",
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")\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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},
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{
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"tags": []
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},
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"source": [
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"````{exercise}\n",
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":label: generalize\n",
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"::::{admonition} Exercise\n",
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":class: tip\n",
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"\n",
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"We presented the concept of \"core dimensions\" as the \"smallest unit of data the function could handle.\" Do you understand how the above use of `apply_ufunc` generalizes to an array with more than one dimension? \n",
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"\n",
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"air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n",
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"``` \n",
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"Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n",
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"````\n",
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"\n",
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"````{solution} generalize\n",
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":::{admonition} Solution\n",
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":class: dropdown\n",
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"\n",
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"We want to use `minmax` to compute the minimum and maximum along the \"lat\" dimension always, regardless of how many dimensions are on the input. So we specify `input_core_dims=[[\"lat\"]]`. The output does not contain the \"lat\" dimension, but we expect two returned variables. So we pass an empty list `[]` for each returned array, so `output_core_dims=[[], []]` just as before.\n",
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" input_core_dims=[[\"lat\"]],\n",
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" output_core_dims=[[],[]],\n",
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")\n",
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"```\n",
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"````"
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":::\n",
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"::::"
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]
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}
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],

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