From 2185bf47b6095d1f574c9542a71e99708b22a6d3 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Mon, 24 Jun 2024 13:00:23 -0700 Subject: [PATCH 1/6] Bump GitHub Actions (#260) * update actions steps * update actions steps * add failOnError * follow docs * new approach * cleanup * ci: Update SURGE_TOKEN to be accesed via env * ci: Change preview to use pull_request_target * use workflow_run for surge preview --------- Co-authored-by: Don Setiawan --- .github/dependabot.yml | 1 + .github/workflows/main.yaml | 44 ++++++++++++-------- .github/workflows/preview.yaml | 64 ----------------------------- .github/workflows/pull_request.yaml | 53 ++++++++++++++++++++++++ .github/workflows/qaqc.yaml | 2 +- .github/workflows/surge_preview.yml | 42 +++++++++++++++++++ 6 files changed, 124 insertions(+), 82 deletions(-) delete mode 100644 .github/workflows/preview.yaml create mode 100644 .github/workflows/pull_request.yaml create mode 100644 .github/workflows/surge_preview.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml index d3f21f7a..b117cbb3 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,3 +1,4 @@ +# Regularly update Docker tags and Actions steps version: 2 updates: - package-ecosystem: "docker" diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 466e782b..b2b827b6 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -1,10 +1,8 @@ -name: CI +name: Deploy Website to GitHub Pages on: push: branches: main - pull_request: - branches: main # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages permissions: @@ -18,19 +16,19 @@ concurrency: cancel-in-progress: true jobs: - build-and-deploy: + build: runs-on: ubuntu-latest steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Setup JupyterBook Cache - uses: actions/cache@v3 + uses: actions/cache@v4 with: path: _build # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20230707 + key: jupyterbook-20240517 - name: Install Conda environment with Micromamba uses: mamba-org/setup-micromamba@v1 @@ -50,19 +48,31 @@ jobs: run: | if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi - - name: Save Build + - name: Save Build Folder if: always() - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: build path: _build/ - - name: Publish to GitHub Pages - if: github.ref == 'refs/heads/main' - uses: peaceiris/actions-gh-pages@v3 + - name: Upload Pages Artifact + uses: actions/upload-pages-artifact@v3 with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: _build/html - publish_branch: gh-pages - cname: tutorial.xarray.dev - enable_jekyll: false + path: _build/html + + # Publish Website to GitHub Pages if built successfully + deploy: + needs: build + if: github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + + steps: + - name: Setup Pages + uses: actions/configure-pages@v5 + + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.github/workflows/preview.yaml b/.github/workflows/preview.yaml deleted file mode 100644 index 6a02c3bf..00000000 --- a/.github/workflows/preview.yaml +++ /dev/null @@ -1,64 +0,0 @@ -name: PR Preview - -on: [pull_request] - -permissions: - pull-requests: write # allow surge-preview to create/update PR comments - -concurrency: - group: ${{ github.workflow }}-${{ github.ref }} - cancel-in-progress: true - -jobs: - preview: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v3 - - - name: Setup JupyterBook Cache - uses: actions/cache@v3 - with: - path: _build - # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20230707 - - - name: Install Conda environment with Micromamba - uses: mamba-org/setup-micromamba@v1 - with: - environment-file: conda/conda-lock.yml - environment-name: xarray-tutorial - cache-environment: true - - - name: Build JupyterBook - # NOTE: login shell activates conda environment - shell: bash -l {0} - run: | - jupyter-book build ./ --warningiserror --keep-going - - - name: Dump Build Logs - if: always() - run: | - if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi - - - name: Publish to Surge - id: preview_step - uses: afc163/surge-preview@v1 - with: - surge_token: ${{ secrets.SURGE_TOKEN }} - github_token: ${{ secrets.GITHUB_TOKEN }} - dist: _build/html - build: | - npm install surge - - - name: Format preview url - id: format_url - run: | - echo "url_text=:rocket: Preview deployed at https://${{ steps.preview_step.outputs.preview_url }}" >> $GITHUB_OUTPUT - - - name: Get the preview_url - run: echo "${{ steps.format_url.outputs.url_text }}" - - - name: Generate summary - run: | - echo "#### ${{ steps.format_url.outputs.url_text }}" >> $GITHUB_STEP_SUMMARY diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml new file mode 100644 index 00000000..bb8218b0 --- /dev/null +++ b/.github/workflows/pull_request.yaml @@ -0,0 +1,53 @@ +name: Pull Request Build + +on: + pull_request: + types: [opened, synchronize, reopened, closed] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + preview: + runs-on: ubuntu-latest + defaults: + run: + shell: bash -el {0} + steps: + - name: Checkout repository + if: github.event.action != 'closed' + uses: actions/checkout@v4 + + - name: Setup JupyterBook Cache + if: github.event.action != 'closed' + uses: actions/cache@v4 + with: + path: _build + # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache + key: jupyterbook-20240517 + + - name: Install Conda environment with Micromamba + if: github.event.action != 'closed' + uses: mamba-org/setup-micromamba@v1 + with: + environment-file: conda/conda-lock.yml + environment-name: xarray-tutorial + cache-environment: true + + - name: Build JupyterBook + if: github.event.action != 'closed' + run: | + jupyter-book build ./ --warningiserror --keep-going + + - name: Dump Build Logs + if: github.event.action != 'closed' + run: | + if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi + + - name: Upload artifact + if: github.event.action != 'closed' + uses: actions/upload-artifact@v4 + with: + name: html + path: book/_build/html diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index 9bdbe69a..8686b7b4 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -17,7 +17,7 @@ jobs: shell: bash -el {0} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Install Conda environment with Micromamba uses: mamba-org/setup-micromamba@v1 diff --git a/.github/workflows/surge_preview.yml b/.github/workflows/surge_preview.yml new file mode 100644 index 00000000..69937ab7 --- /dev/null +++ b/.github/workflows/surge_preview.yml @@ -0,0 +1,42 @@ +name: Pull Request Preview + +on: + workflow_run: + workflows: ["Pull Request Build"] + types: + - completed + +permissions: + pull-requests: write # allow surge-preview to create/update PR comments + +concurrency: + group: ${{ github.workflow }}-${{ github.event.workflow_run.id }} + cancel-in-progress: true + +jobs: + # NOTE: match job name in pull_request.yaml + preview: + runs-on: ubuntu-latest + if: ${{ github.event.workflow_run.event == 'pull_request' && github.event.workflow_run.conclusion == 'success' }} + + steps: + # Ensure folder exists for PR 'closed' case + - run: mkdir html + + # Download built HTML from PR Build workflow + - uses: actions/download-artifact@v4 + continue-on-error: true + with: + github-token: ${{ github.token }} + run-id: ${{ github.event.workflow_run.id }} + + - name: Manage Surge.sh Deployment + id: preview_step + uses: afc163/surge-preview@v1 + with: + surge_token: ${{ secrets.SURGE_TOKEN }} + github_token: ${{ secrets.GITHUB_TOKEN }} + build: echo 'Uploading html/ folder contents to Surge.sh...' + dist: html # NOTE: match upload_artifact name in pull_request.yaml + failOnError: true + teardown: true From 20b2aa8e0b5109323b247079a35fa2b372714e1f Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Mon, 24 Jun 2024 15:54:36 -0700 Subject: [PATCH 2/6] fix jbook path, ignore devcontainer changes (#276) --- .github/workflows/main.yaml | 2 ++ .github/workflows/pull_request.yaml | 4 +++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index b2b827b6..4d386687 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -3,6 +3,8 @@ name: Deploy Website to GitHub Pages on: push: branches: main + paths-ignore: + - ".devcontainer/**" # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages permissions: diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml index bb8218b0..faf30561 100644 --- a/.github/workflows/pull_request.yaml +++ b/.github/workflows/pull_request.yaml @@ -3,6 +3,8 @@ name: Pull Request Build on: pull_request: types: [opened, synchronize, reopened, closed] + paths-ignore: + - ".devcontainer/**" concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -50,4 +52,4 @@ jobs: uses: actions/upload-artifact@v4 with: name: html - path: book/_build/html + path: _build/html From 208222383cc37b0334cdcb538fc88fd9b368fe33 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Mon, 24 Jun 2024 16:33:57 -0700 Subject: [PATCH 3/6] fix path-ignore indentation (#277) --- .github/workflows/main.yaml | 4 ++-- .github/workflows/pull_request.yaml | 4 ++-- .github/workflows/qaqc.yaml | 2 ++ 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 4d386687..2458f1d9 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -3,8 +3,8 @@ name: Deploy Website to GitHub Pages on: push: branches: main - paths-ignore: - - ".devcontainer/**" + paths-ignore: + - ".devcontainer/**" # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages permissions: diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml index faf30561..2e4a8f43 100644 --- a/.github/workflows/pull_request.yaml +++ b/.github/workflows/pull_request.yaml @@ -3,8 +3,8 @@ name: Pull Request Build on: pull_request: types: [opened, synchronize, reopened, closed] - paths-ignore: - - ".devcontainer/**" + paths-ignore: + - ".devcontainer/**" concurrency: group: ${{ github.workflow }}-${{ github.ref }} diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index 8686b7b4..ea4d17e0 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -4,6 +4,8 @@ on: pull_request: branches: - main + paths-ignore: + - ".devcontainer/**" concurrency: group: ${{ github.workflow }}-${{ github.ref }} From b8573b5405869f8185c6d24bf328dfcaf33a9f8b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 24 Jun 2024 16:37:08 -0700 Subject: [PATCH 4/6] Bump pangeo/base-image from 2024.06.02 to 2024.06.24 in /.devcontainer (#275) Bumps pangeo/base-image from 2024.06.02 to 2024.06.24. --- updated-dependencies: - dependency-name: pangeo/base-image dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .devcontainer/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index d2cb0ecb..bbb18631 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1 +1 @@ -FROM pangeo/base-image:2024.06.02 +FROM pangeo/base-image:2024.06.24 From da26b73e84714f834a913c7f441c3191ff912397 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Mon, 24 Jun 2024 16:40:11 -0700 Subject: [PATCH 5/6] Bump to Jupyter Book v1.0 (#270) * jbook1, drop sphinx-exercise * update exercise format * pin numpy<2 --- .prettierignore | 2 +- _config.yml | 1 - .../automatic-vectorizing-numpy.ipynb | 13 +- .../apply_ufunc/complex-output-numpy.ipynb | 23 +- advanced/apply_ufunc/core-dimensions.ipynb | 10 +- advanced/apply_ufunc/dask_apply_ufunc.ipynb | 27 +- .../apply_ufunc/numba-vectorization.ipynb | 15 +- conda/conda-lock.yml | 8099 +++++++++-------- conda/environment-unpinned.yml | 6 +- conda/environment.yml | 338 +- fundamentals/02.1_indexing_Basic.ipynb | 61 +- fundamentals/02.3_aligning_data_objects.ipynb | 21 +- .../03.1_computation_with_xarray.ipynb | 13 +- fundamentals/03.2_groupby_with_xarray.ipynb | 22 +- fundamentals/03.3_windowed.ipynb | 22 +- .../01-high-level-computation-patterns.ipynb | 241 +- .../indexing/boolean-masking-indexing.ipynb | 39 +- intermediate/xarray_and_dask.ipynb | 50 +- intermediate/xarray_ecosystem.ipynb | 16 +- 19 files changed, 4604 insertions(+), 4415 deletions(-) diff --git a/.prettierignore b/.prettierignore index 4c100aeb..9b89f28d 100644 --- a/.prettierignore +++ b/.prettierignore @@ -1 +1 @@ -conda/** +conda/ diff --git a/_config.yml b/_config.yml index a0ae745b..85a12b15 100644 --- a/_config.yml +++ b/_config.yml @@ -68,7 +68,6 @@ sphinx: # maintain old paths and redirect them (so google results dont go to 404) # https://github.com/wpilibsuite/sphinxext-rediraffe - sphinxext.rediraffe - - sphinx_exercise config: language: en # accessibility # application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb index 6a18888e..4fd42fdc 100644 --- a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb +++ b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb @@ -63,18 +63,17 @@ " out[index, :] = np.interp(..., array[index, :], ...)\n", "```\n", "\n", - "\n", - "```{exercise}\n", - ":label: coreloopdims\n", - "\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Consider the example problem of interpolating a 2D array with dimensions `space` and `time` along the `time` dimension.\n", "Which dimension is the core dimension, and which is the \"loop dimension\"?\n", - "```\n", - "```{solution} coreloopdims\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "`time` is the core dimension, and `space` is the loop dimension.\n", - "```\n", + ":::\n", + "::::\n", "\n", "## Vectorization\n", "\n", diff --git a/advanced/apply_ufunc/complex-output-numpy.ipynb b/advanced/apply_ufunc/complex-output-numpy.ipynb index d993fe0e..e7336a77 100644 --- a/advanced/apply_ufunc/complex-output-numpy.ipynb +++ b/advanced/apply_ufunc/complex-output-numpy.ipynb @@ -138,8 +138,8 @@ "tags": [] }, "source": [ - "```{exercise}\n", - ":label: newdim\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "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", "\n", @@ -147,10 +147,11 @@ "def add_new_dim(array):\n", " return np.expand_dims(array, axis=-1)\n", "```\n", - "````{solution} newdim\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", - "``` python\n", + "```python\n", "def add_new_dim(array):\n", " return np.expand_dims(array, axis=-1)\n", "\n", @@ -161,7 +162,8 @@ " output_core_dims=[[\"newdim\"]],\n", ")\n", "```\n", - "````" + ":::\n", + "::::" ] }, { @@ -327,8 +329,8 @@ "tags": [] }, "source": [ - "````{exercise}\n", - ":label: generalize\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "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", "\n", @@ -337,9 +339,8 @@ "air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n", "``` \n", "Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n", - "````\n", "\n", - "````{solution} generalize\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "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", @@ -352,8 +353,8 @@ " input_core_dims=[[\"lat\"]],\n", " output_core_dims=[[],[]],\n", ")\n", - "```\n", - "````" + ":::\n", + "::::" ] } ], diff --git a/advanced/apply_ufunc/core-dimensions.ipynb b/advanced/apply_ufunc/core-dimensions.ipynb index 82cb11ed..7a009782 100644 --- a/advanced/apply_ufunc/core-dimensions.ipynb +++ b/advanced/apply_ufunc/core-dimensions.ipynb @@ -335,13 +335,12 @@ "tags": [] }, "source": [ - "```{exercise}\n", - ":label: trapezoid\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Use `apply_ufunc` to apply `scipy.integrate.trapezoid` along the `time` axis.\n", - "```\n", "\n", - "````{solution} trapezoid\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", @@ -350,7 +349,8 @@ "\n", "xr.apply_ufunc(scipy.integrate.trapezoid, ds, input_core_dims=[[\"time\"]], kwargs={\"axis\": -1})\n", "```\n", - "````" + ":::\n", + "::::" ] } ], diff --git a/advanced/apply_ufunc/dask_apply_ufunc.ipynb b/advanced/apply_ufunc/dask_apply_ufunc.ipynb index c38f614e..768674c5 100644 --- a/advanced/apply_ufunc/dask_apply_ufunc.ipynb +++ b/advanced/apply_ufunc/dask_apply_ufunc.ipynb @@ -267,15 +267,15 @@ "Such functions involve the concept of \"core dimensions\". This concept is independent of the underlying array type, and is a property of the applied function. See the [core dimensions with NumPy](core-dimensions) tutorial for more.\n", "\n", "\n", - "```{exercise}\n", - ":label: daskmean\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Use `dask.array.mean` as an example of a function that can handle dask\n", "arrays and uses an `axis` kwarg. \n", - "```\n", "\n", - "````{solution} daskmean\n", + ":::{admonition} Solution\n", ":class: dropdown\n", + "\n", "```python\n", "def time_mean(da):\n", " return xr.apply_ufunc(\n", @@ -285,10 +285,11 @@ " dask=\"allowed\",\n", " kwargs={\"axis\": -1}, # core dimensions are moved to the end\n", " )\n", - "\n", - "\n", + " \n", "time_mean(ds.air)\n", - "````\n" + "```\n", + ":::\n", + "::::\n" ] }, { @@ -493,12 +494,11 @@ "tags": [] }, "source": [ - "```{exercise} \n", - ":label: rechunk\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Apply the integrate function to `ds` after rechunking to have a different chunksize along `lon` using `ds.chunk(lon=4)` (for example). What happens?\n", - "```\n", "\n", - "```{solution} rechunk\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "`apply_ufunc` complains that it cannot automatically parallelize because the dataset `ds` is now chunked along the core dimension `lon`. You should see the following error:\n", @@ -509,7 +509,8 @@ " ``.chunk(dict(lon=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \n", " but beware that this may significantly increase memory usage.\n", "\n", - "```" + ":::\n", + "::::" ] }, { @@ -652,7 +653,7 @@ "source": [ "### Adding new dimensions\n", "\n", - "We use the [expand_dims example](newdim) that changes the size of the input along a single dimension.\n", + "We use the `np.expand_dims` to change the size of the input along a single dimension.\n", "\n", "```python\n", "def add_new_dim(array):\n", diff --git a/advanced/apply_ufunc/numba-vectorization.ipynb b/advanced/apply_ufunc/numba-vectorization.ipynb index 91195214..6bcbf30f 100644 --- a/advanced/apply_ufunc/numba-vectorization.ipynb +++ b/advanced/apply_ufunc/numba-vectorization.ipynb @@ -7,9 +7,9 @@ "tags": [] }, "source": [ - "\n", + "# Fast vectorization with Numba\n", "\n", - "# Fast vectorization with Numba" + "" ] }, { @@ -241,12 +241,12 @@ "id": "18", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: g\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Apply `g` to `da_dask`\n", - "```\n", - "````{solution} g\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", @@ -259,7 +259,8 @@ " dask=\"parallelized\",\n", ")\n", "```\n", - "````" + ":::\n", + "::::" ] }, { diff --git a/conda/conda-lock.yml b/conda/conda-lock.yml index 9c74fa6f..d4ecf965 100644 --- a/conda/conda-lock.yml +++ b/conda/conda-lock.yml @@ -13,10 +13,10 @@ version: 1 metadata: content_hash: - osx-64: 66f5474d08c72922e1236c5da5c182fe36e41ba657a9a022a4b5bcbf96f4685c - linux-64: f6315ebbea8a6cc47a0d6f17dcd5f238a892cd373cd85cc60faf6c60871d8e02 - win-64: 4fe3b0846ccb6d9eb0e04a33e7607a353980c6563375db24b4c808039d5f2695 - osx-arm64: 244ff697d401ecf39ac7ac674af0ff84e6ab36d203d6d3c2916036feff5ea088 + osx-64: 04db034b972956d26ea5a2e216d203ad9ec50d1264db68fad8b8819687e2dbca + linux-64: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780 + win-64: d2979a92577f588632cc336a9c1d5ca088886463ed8fbb6f6fd560c83c08442f + osx-arm64: 4f169409756d1bd8b7c8088061b8a81352704e1992ecda0baaad820a2facc9df channels: - url: conda-forge used_env_vars: [] @@ -53,55 +53,55 @@ package: category: main optional: false - name: accessible-pygments - version: 0.0.4 + version: 0.0.5 manager: conda platform: linux-64 dependencies: pygments: '' - python: '>=3.6' - url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda hash: - md5: 46a2e6e3dfa718ce3492018d5a110dd6 - sha256: ce532e2da08fb2b77df281e3d42e2b2131641bdd6a6e8127f3718ae6860bd70d + md5: 1bb1ef9806a9a20872434f58b3e7fc1a + sha256: 712c1875bcd32674e8ce2f418f0b2875ecb98e6bcbb21ec7502dae8ff4b0f73c category: main optional: false - name: accessible-pygments - version: 0.0.4 + version: 0.0.5 manager: conda platform: osx-64 dependencies: pygments: '' - python: '>=3.6' - url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda hash: - 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name: zlib - version: 1.2.13 + version: 1.3.1 manager: conda platform: win-64 dependencies: - libzlib: 1.2.13 + libzlib: 1.3.1 ucrt: '>=10.0.20348.0' vc: '>=14.2,<15' vc14_runtime: '>=14.29.30139' - url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.2.13-hcfcfb64_5.conda + url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda hash: - md5: a318e8622e11663f645cc7fa3260f462 - sha256: 0f91b719c7558046bcd37fdc7ae4b9eb2b7a8e335beb8b59ae7ccb285a46aa46 + md5: f8e0a35bf6df768ad87ed7bbbc36ab04 + sha256: 76409556e6c7cb91991cd94d7fc853c9272c2872bd7e3573ff35eb33d6fca5be category: main optional: false - name: zstd @@ -25737,7 +25920,7 @@ package: dependencies: libgcc-ng: '>=12' libstdcxx-ng: '>=12' - libzlib: '>=1.2.13,<1.3.0a0' + libzlib: '>=1.2.13,<2.0.0a0' url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda hash: md5: 4d056880988120e29d75bfff282e0f45 @@ -25750,7 +25933,7 @@ package: platform: osx-64 dependencies: __osx: '>=10.9' - libzlib: '>=1.2.13,<1.3.0a0' + libzlib: '>=1.2.13,<2.0.0a0' url: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.6-h915ae27_0.conda hash: md5: 4cb2cd56f039b129bb0e491c1164167e @@ -25763,7 +25946,7 @@ package: platform: osx-arm64 dependencies: __osx: '>=11.0' - libzlib: '>=1.2.13,<1.3.0a0' + libzlib: '>=1.2.13,<2.0.0a0' url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda hash: md5: d96942c06c3e84bfcc5efb038724a7fd @@ -25775,7 +25958,7 @@ package: manager: conda platform: win-64 dependencies: - libzlib: '>=1.2.13,<1.3.0a0' + libzlib: '>=1.2.13,<2.0.0a0' ucrt: '>=10.0.20348.0' vc: '>=14.2,<15' vc14_runtime: '>=14.29.30139' diff --git a/conda/environment-unpinned.yml b/conda/environment-unpinned.yml index d8a8d458..26c60c46 100644 --- a/conda/environment-unpinned.yml +++ b/conda/environment-unpinned.yml @@ -2,9 +2,9 @@ name: xarray-tutorial channels: - conda-forge dependencies: - - python=3.11 + - python # for building website: - - jupyter-book<1 + - jupyter-book - pre-commit # for user interface - dask-labextension @@ -24,6 +24,7 @@ dependencies: - ipython - matplotlib-base - netcdf4 + - numpy<2 - pint-xarray - pip - pydap @@ -32,7 +33,6 @@ dependencies: - rioxarray - scipy - sphinx-codeautolink - - sphinx-exercise - sphinx-notfound-page - sphinxext-rediraffe - xarray diff --git a/conda/environment.yml b/conda/environment.yml index 298f46f9..154f15b2 100644 --- a/conda/environment.yml +++ b/conda/environment.yml @@ -1,39 +1,40 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: f6315ebbea8a6cc47a0d6f17dcd5f238a892cd373cd85cc60faf6c60871d8e02 +# input_hash: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780 channels: - conda-forge dependencies: - _libgcc_mutex=0.1=conda_forge - _openmp_mutex=4.5=2_gnu - - accessible-pygments=0.0.4=pyhd8ed1ab_0 + - accessible-pygments=0.0.5=pyhd8ed1ab_0 - affine=2.4.0=pyhd8ed1ab_0 - - aiohttp=3.9.5=py311h459d7ec_0 + - aiohttp=3.9.5=py312h98912ed_0 - aiosignal=1.3.1=pyhd8ed1ab_0 - alabaster=0.7.16=pyhd8ed1ab_0 - - anyio=4.3.0=pyhd8ed1ab_0 + - anyio=4.4.0=pyhd8ed1ab_0 + - appdirs=1.4.4=pyh9f0ad1d_0 - argon2-cffi=23.1.0=pyhd8ed1ab_0 - - argon2-cffi-bindings=21.2.0=py311h459d7ec_4 + - argon2-cffi-bindings=21.2.0=py312h98912ed_4 - arrow=1.3.0=pyhd8ed1ab_0 - asciitree=0.3.3=py_2 - asttokens=2.4.1=pyhd8ed1ab_0 - async-lru=2.0.4=pyhd8ed1ab_0 - atk-1.0=2.38.0=h04ea711_2 - attrs=23.2.0=pyh71513ae_0 - - aws-c-auth=0.7.20=h5f1c8d9_0 - - aws-c-cal=0.6.12=h2ba76a8_0 - - aws-c-common=0.9.17=h4ab18f5_0 - - aws-c-compression=0.2.18=h36a0aea_4 - - aws-c-event-stream=0.4.2=h161de36_10 - - aws-c-http=0.8.1=h63f54a0_13 - - aws-c-io=0.14.8=h96d4d28_0 - - aws-c-mqtt=0.10.4=hcc7299c_2 - - aws-c-s3=0.5.8=h10bd90f_3 - - aws-c-sdkutils=0.1.16=h36a0aea_0 - - aws-checksums=0.1.18=h36a0aea_4 - - aws-crt-cpp=0.26.8=h02fd9b4_10 - - aws-sdk-cpp=1.11.267=h51dfee4_8 + - aws-c-auth=0.7.22=h9137712_5 + - aws-c-cal=0.6.15=h88a6e22_0 + - aws-c-common=0.9.19=h4ab18f5_0 + - aws-c-compression=0.2.18=h83b837d_6 + - aws-c-event-stream=0.4.2=h0cbf018_13 + - aws-c-http=0.8.2=h360477d_2 + - aws-c-io=0.14.9=h2d549f9_2 + - aws-c-mqtt=0.10.4=hf85b563_6 + - aws-c-s3=0.5.10=h679ed35_3 + - aws-c-sdkutils=0.1.16=h83b837d_2 + - aws-checksums=0.1.18=h83b837d_6 + - aws-crt-cpp=0.26.12=h8bc9c4d_0 + - aws-sdk-cpp=1.11.329=hf74b5d1_5 - azure-core-cpp=1.11.1=h91d86a7_1 - azure-identity-cpp=1.6.0=hf1915f5_1 - azure-storage-blobs-cpp=12.10.0=h00ab1b0_1 @@ -46,21 +47,21 @@ dependencies: - bokeh=3.4.1=pyhd8ed1ab_0 - brotli=1.1.0=hd590300_1 - brotli-bin=1.1.0=hd590300_1 - - brotli-python=1.1.0=py311hb755f60_1 + - brotli-python=1.1.0=py312h30efb56_1 - bzip2=1.0.8=hd590300_5 - c-ares=1.28.1=hd590300_0 - - ca-certificates=2024.2.2=hbcca054_0 + - ca-certificates=2024.6.2=hbcca054_0 - cached-property=1.5.2=hd8ed1ab_1 - cached_property=1.5.2=pyha770c72_1 - cachetools=5.3.3=pyhd8ed1ab_0 - - cairo=1.18.0=h3faef2a_0 - - cartopy=0.23.0=py311h320fe9a_0 - - certifi=2024.2.2=pyhd8ed1ab_0 - - cf_xarray=0.9.0=pyhd8ed1ab_0 - - cffi=1.16.0=py311hb3a22ac_0 + - cairo=1.18.0=hbb29018_2 + - cartopy=0.23.0=py312h1d6d2e6_1 + - certifi=2024.6.2=pyhd8ed1ab_0 + - cf_xarray=0.9.1=pyhd8ed1ab_0 + - cffi=1.16.0=py312hf06ca03_0 - cfgv=3.3.1=pyhd8ed1ab_0 - cfitsio=4.4.0=hbdc6101_1 - - cftime=1.6.3=py311h1f0f07a_0 + - cftime=1.6.4=py312h085067d_0 - charset-normalizer=3.3.2=pyhd8ed1ab_0 - click=8.1.7=unix_pyh707e725_0 - click-plugins=1.1.1=py_0 @@ -69,29 +70,31 @@ dependencies: - colorama=0.4.6=pyhd8ed1ab_0 - colorcet=3.1.0=pyhd8ed1ab_0 - comm=0.2.2=pyhd8ed1ab_0 - - contourpy=1.2.1=py311h9547e67_0 - - cryptography=42.0.7=py311h4a61cc7_0 + - contourpy=1.2.1=py312h8572e83_0 + - cryptography=42.0.8=py312hbcc2302_0 - cycler=0.12.1=pyhd8ed1ab_0 - - cytoolz=0.12.3=py311h459d7ec_0 - - dask=2024.5.0=pyhd8ed1ab_0 - - dask-core=2024.5.0=pyhd8ed1ab_0 - - dask-expr=1.1.0=pyhd8ed1ab_0 + - cytoolz=0.12.3=py312h98912ed_0 + - dask=2024.6.2=pyhd8ed1ab_0 + - dask-core=2024.6.2=pyhd8ed1ab_0 + - dask-expr=1.1.6=pyhd8ed1ab_0 - dask-labextension=7.0.0=pyhd8ed1ab_0 - dataclasses=0.8=pyhc8e2a94_3 - - datashader=0.16.1=pyhd8ed1ab_0 - - debugpy=1.8.1=py311hb755f60_0 + - datashader=0.16.2=pyhd8ed1ab_0 + - debugpy=1.8.1=py312h30efb56_0 - decorator=5.1.1=pyhd8ed1ab_0 - defusedxml=0.7.1=pyhd8ed1ab_0 - distlib=0.3.8=pyhd8ed1ab_0 - - distributed=2024.5.0=pyhd8ed1ab_0 + - distributed=2024.6.2=pyhd8ed1ab_0 - docopt=0.6.2=py_1 - - docutils=0.17.1=py311h38be061_5 + - docutils=0.20.1=py312h7900ff3_3 - entrypoints=0.4=pyhd8ed1ab_0 - exceptiongroup=1.2.0=pyhd8ed1ab_2 - executing=2.0.1=pyhd8ed1ab_0 - expat=2.6.2=h59595ed_0 - fasteners=0.17.3=pyhd8ed1ab_0 - - filelock=3.14.0=pyhd8ed1ab_0 + - filelock=3.15.3=pyhd8ed1ab_0 + - flexcache=0.3=pyhd8ed1ab_0 + - flexparser=0.3.1=pyhd8ed1ab_0 - fmt=10.2.1=h00ab1b0_0 - font-ttf-dejavu-sans-mono=2.37=hab24e00_0 - font-ttf-inconsolata=3.000=h77eed37_0 @@ -100,42 +103,42 @@ dependencies: - fontconfig=2.14.2=h14ed4e7_0 - fonts-conda-ecosystem=1=0 - fonts-conda-forge=1=0 - - fonttools=4.51.0=py311h459d7ec_0 + - fonttools=4.53.0=py312h9a8786e_0 - fqdn=1.5.1=pyhd8ed1ab_0 - freetype=2.12.1=h267a509_2 - freexl=2.0.0=h743c826_0 - fribidi=1.0.10=h36c2ea0_0 - - frozenlist=1.4.1=py311h459d7ec_0 - - fsspec=2024.3.1=pyhca7485f_0 - - gcsfs=2024.3.1=pyhd8ed1ab_0 + - frozenlist=1.4.1=py312h98912ed_0 + - fsspec=2024.6.0=pyhff2d567_0 + - gcsfs=2024.6.0=pyhd8ed1ab_0 - gdk-pixbuf=2.42.12=hb9ae30d_0 - geos=3.12.1=h59595ed_0 - - geotiff=1.7.1=h6cf1f90_16 + - geotiff=1.7.3=hf7fa9e8_1 - geoviews-core=1.12.0=pyha770c72_0 - gflags=2.2.2=he1b5a44_1004 - giflib=5.2.2=hd590300_0 - - glog=0.7.0=hed5481d_0 + - glog=0.7.1=hbabe93e_0 - google-api-core=2.19.0=pyhd8ed1ab_0 - - google-auth=2.29.0=pyhca7485f_0 + - google-auth=2.30.0=pyhff2d567_0 - google-auth-oauthlib=1.2.0=pyhd8ed1ab_0 - google-cloud-core=2.4.1=pyhd8ed1ab_0 - - google-cloud-storage=2.16.0=pyhca7485f_0 - - google-crc32c=1.1.2=py311h9b08b9c_5 + - google-cloud-storage=2.17.0=pyhff2d567_0 + - google-crc32c=1.1.2=py312h775cd15_5 - google-resumable-media=2.7.0=pyhd8ed1ab_0 - - googleapis-common-protos=1.63.0=pyhd8ed1ab_0 + - googleapis-common-protos=1.63.1=pyhd8ed1ab_0 - graphite2=1.3.13=h59595ed_1003 - - graphviz=9.0.0=h78e8752_1 - - greenlet=3.0.3=py311hb755f60_0 - - grpcio=1.62.2=py311ha6695c7_0 - - gsw=3.6.17=py311h1f0f07a_1 + - graphviz=11.0.0=hc68bbd7_0 + - greenlet=3.0.3=py312h30efb56_0 + - grpcio=1.62.2=py312hb06c811_0 + - gsw=3.6.18=py312h085067d_0 - gtk2=2.24.33=h280cfa0_4 - gts=0.7.6=h977cf35_4 - h11=0.14.0=pyhd8ed1ab_0 - h2=4.1.0=pyhd8ed1ab_0 - harfbuzz=8.5.0=hfac3d4d_0 - hdf4=4.2.15=h2a13503_7 - - hdf5=1.14.3=nompi_h4f84152_101 - - holoviews=1.18.3=pyhd8ed1ab_0 + - hdf5=1.14.3=nompi_hdf9ad27_105 + - holoviews=1.19.0=pyhd8ed1ab_0 - hpack=4.0.0=pyh9f0ad1d_0 - httpcore=1.0.5=pyhd8ed1ab_0 - httpx=0.27.0=pyhd8ed1ab_0 @@ -145,222 +148,222 @@ dependencies: - identify=2.5.36=pyhd8ed1ab_0 - idna=3.7=pyhd8ed1ab_0 - imagesize=1.4.1=pyhd8ed1ab_0 - - importlib-metadata=7.1.0=pyha770c72_0 - - importlib_metadata=7.1.0=hd8ed1ab_0 + - importlib-metadata=7.2.0=pyha770c72_0 + - importlib_metadata=7.2.0=hd8ed1ab_0 - importlib_resources=6.4.0=pyhd8ed1ab_0 - - ipykernel=6.29.3=pyhd33586a_0 - - ipython=8.24.0=pyh707e725_0 + - ipykernel=6.29.4=pyh3099207_0 + - ipython=8.25.0=pyh707e725_0 - isoduration=20.11.0=pyhd8ed1ab_0 - jedi=0.19.1=pyhd8ed1ab_0 - jinja2=3.1.4=pyhd8ed1ab_0 - json-c=0.17=h7ab15ed_0 - json5=0.9.25=pyhd8ed1ab_0 - - jsonpointer=2.4=py311h38be061_3 + - jsonpointer=3.0.0=py312h7900ff3_0 - jsonschema=4.22.0=pyhd8ed1ab_0 - jsonschema-specifications=2023.12.1=pyhd8ed1ab_0 - jsonschema-with-format-nongpl=4.22.0=pyhd8ed1ab_0 - - jupyter-book=0.15.1=pyhd8ed1ab_0 - - jupyter-cache=0.6.1=pyhd8ed1ab_0 + - jupyter-book=1.0.0=pyhd8ed1ab_0 + - jupyter-cache=1.0.0=pyhd8ed1ab_0 - jupyter-lsp=2.2.5=pyhd8ed1ab_0 - jupyter-resource-usage=1.0.2=pyhd8ed1ab_0 - - jupyter-server-proxy=4.1.2=pyhd8ed1ab_0 - - jupyter_client=8.6.1=pyhd8ed1ab_0 - - jupyter_core=5.7.2=py311h38be061_0 + - jupyter-server-proxy=4.2.0=pyhd8ed1ab_0 + - jupyter_client=8.6.2=pyhd8ed1ab_0 + - jupyter_core=5.7.2=py312h7900ff3_0 - jupyter_events=0.10.0=pyhd8ed1ab_0 - - jupyter_server=2.14.0=pyhd8ed1ab_0 + - jupyter_server=2.14.1=pyhd8ed1ab_0 - jupyter_server_terminals=0.5.3=pyhd8ed1ab_0 - - jupyterlab=4.2.0=pyhd8ed1ab_1 + - jupyterlab=4.2.2=pyhd8ed1ab_0 - jupyterlab-myst=2.4.2=pyhd8ed1ab_0 - jupyterlab_pygments=0.3.0=pyhd8ed1ab_1 - - jupyterlab_server=2.27.1=pyhd8ed1ab_0 - - kealib=1.5.3=h2f55d51_0 + - jupyterlab_server=2.27.2=pyhd8ed1ab_0 + - kealib=1.5.3=hee9dde6_1 - keyutils=1.6.1=h166bdaf_0 - - kiwisolver=1.4.5=py311h9547e67_1 + - kiwisolver=1.4.5=py312h8572e83_1 - krb5=1.21.2=h659d440_0 - latexcodec=2.0.1=pyh9f0ad1d_0 - lcms2=2.16=hb7c19ff_0 - - ld_impl_linux-64=2.40=h55db66e_0 + - ld_impl_linux-64=2.40=hf3520f5_7 - lerc=4.0.0=h27087fc_0 - libabseil=20240116.2=cxx17_h59595ed_0 - libaec=1.1.3=h59595ed_0 - - libarchive=3.7.2=h2aa1ff5_1 - - libarrow=16.0.0=hefa796f_1_cpu - - libarrow-acero=16.0.0=hac33072_1_cpu - - libarrow-dataset=16.0.0=hac33072_1_cpu - - libarrow-substrait=16.0.0=h7e0c224_1_cpu + - libarchive=3.7.4=hfca40fe_0 + - libarrow=16.1.0=h9102155_9_cpu + - libarrow-acero=16.1.0=hac33072_9_cpu + - libarrow-dataset=16.1.0=hac33072_9_cpu + - libarrow-substrait=16.1.0=h7e0c224_9_cpu - libblas=3.9.0=22_linux64_openblas - - libboost-headers=1.85.0=ha770c72_0 + - libboost-headers=1.85.0=ha770c72_1 - libbrotlicommon=1.1.0=hd590300_1 - libbrotlidec=1.1.0=hd590300_1 - libbrotlienc=1.1.0=hd590300_1 - libcblas=3.9.0=22_linux64_openblas - libcrc32c=1.1.2=h9c3ff4c_0 - - libcurl=8.7.1=hca28451_0 + - libcurl=8.8.0=hca28451_0 - libdeflate=1.20=hd590300_0 - libedit=3.1.20191231=he28a2e2_2 - libev=4.33=hd590300_2 - libevent=2.1.12=hf998b51_1 - libexpat=2.6.2=h59595ed_0 - libffi=3.4.2=h7f98852_5 - - libgcc-ng=13.2.0=h77fa898_7 + - libgcc-ng=13.2.0=h77fa898_11 - libgd=2.3.3=h119a65a_9 - - libgdal=3.8.5=h77540a9_5 - - libgfortran-ng=13.2.0=h69a702a_7 - - libgfortran5=13.2.0=hca663fb_7 + - libgdal=3.9.0=h471f4ab_7 + - libgfortran-ng=13.2.0=h69a702a_11 + - libgfortran5=13.2.0=h3d2ce59_11 - libglib=2.80.2=hf974151_0 - - libgomp=13.2.0=h77fa898_7 - - libgoogle-cloud=2.23.0=h9be4e54_1 - - libgoogle-cloud-storage=2.23.0=hc7a4891_1 + - libgomp=13.2.0=h77fa898_11 + - libgoogle-cloud=2.25.0=h2736e30_0 + - libgoogle-cloud-storage=2.25.0=h3d9a0c8_0 - libgrpc=1.62.2=h15f2491_0 - libiconv=1.17=hd590300_2 - libjpeg-turbo=3.0.0=hd590300_1 - libkml=1.3.0=h01aab08_1018 - liblapack=3.9.0=22_linux64_openblas - libllvm14=14.0.6=hcd5def8_4 - - libnetcdf=4.9.2=nompi_h9612171_113 + - libnetcdf=4.9.2=nompi_h135f659_114 - libnghttp2=1.58.0=h47da74e_1 - libnsl=2.0.1=hd590300_0 - libopenblas=0.3.27=pthreads_h413a1c8_0 - - libparquet=16.0.0=h6a7eafb_1_cpu + - libparquet=16.1.0=h6a7eafb_9_cpu - libpng=1.6.43=h2797004_0 - libpq=16.3=ha72fbe1_0 - libprotobuf=4.25.3=h08a7969_0 - libre2-11=2023.09.01=h5a48ba9_2 - - librsvg=2.58.0=hadf69e7_1 + - librsvg=2.58.1=hadf69e7_0 - librttopo=1.1.0=h8917695_15 - libsodium=1.0.18=h36c2ea0_1 - - libspatialite=5.1.0=h6f065fc_5 - - libsqlite=3.45.3=h2797004_0 + - libspatialite=5.1.0=h6fbd9c4_7 + - libsqlite=3.46.0=hde9e2c9_0 - libssh2=1.11.0=h0841786_0 - - libstdcxx-ng=13.2.0=hc0a3c3a_7 + - libstdcxx-ng=13.2.0=hc0a3c3a_11 - libthrift=0.19.0=hb90f79a_1 - libtiff=4.6.0=h1dd3fc0_3 - libutf8proc=2.8.0=h166bdaf_0 - libuuid=2.38.1=h0b41bf4_0 - libwebp=1.4.0=h2c329e2_0 - libwebp-base=1.4.0=hd590300_0 - - libxcb=1.15=h0b41bf4_0 + - libxcb=1.16=hd590300_0 - libxcrypt=4.4.36=hd590300_1 - - libxml2=2.12.7=hc051c1a_0 + - libxml2=2.12.7=hc051c1a_1 - libzip=1.10.1=h2629f0a_3 - - libzlib=1.2.13=hd590300_5 + - libzlib=1.3.1=h4ab18f5_1 - linkify-it-py=2.0.3=pyhd8ed1ab_0 - - llvmlite=0.42.0=py311ha6695c7_1 + - llvmlite=0.43.0=py312h9c5d478_0 - locket=1.0.0=pyhd8ed1ab_0 - - lz4=4.3.3=py311h38e4bf4_0 + - lz4=4.3.3=py312h03f37cb_0 - lz4-c=1.9.4=hcb278e6_0 - lzo=2.10=hd590300_1001 - markdown=3.6=pyhd8ed1ab_0 - - markdown-it-py=2.2.0=pyhd8ed1ab_0 - - markupsafe=2.1.5=py311h459d7ec_0 - - matplotlib-base=3.8.4=py311h54ef318_0 + - markdown-it-py=3.0.0=pyhd8ed1ab_0 + - markupsafe=2.1.5=py312h98912ed_0 + - matplotlib-base=3.8.4=py312h20ab3a6_2 - matplotlib-inline=0.1.7=pyhd8ed1ab_0 - mdit-py-plugins=0.4.1=pyhd8ed1ab_0 - mdurl=0.1.2=pyhd8ed1ab_0 - - minizip=4.0.5=h0ab5242_0 + - minizip=4.0.7=h401b404_0 - mistune=3.0.2=pyhd8ed1ab_0 - - msgpack-python=1.0.8=py311h52f7536_0 - - multidict=6.0.5=py311h459d7ec_0 + - msgpack-python=1.0.8=py312h2492b07_0 + - multidict=6.0.5=py312h98912ed_0 - multipledispatch=0.6.0=py_0 - munkres=1.1.4=pyh9f0ad1d_0 - - myst-nb=0.17.2=pyhd8ed1ab_0 - - myst-parser=0.18.1=pyhd8ed1ab_0 - - nbclient=0.7.4=pyhd8ed1ab_0 - - nbconvert-core=7.16.4=pyhd8ed1ab_0 + - myst-nb=1.1.0=pyhd8ed1ab_0 + - myst-parser=2.0.0=pyhd8ed1ab_0 + - nbclient=0.10.0=pyhd8ed1ab_0 + - nbconvert-core=7.16.4=pyhd8ed1ab_1 - nbformat=5.10.4=pyhd8ed1ab_0 - ncurses=6.5=h59595ed_0 - nest-asyncio=1.6.0=pyhd8ed1ab_0 - - netcdf4=1.6.5=nompi_py311he8ad708_100 - - nodeenv=1.8.0=pyhd8ed1ab_0 + - netcdf4=1.7.1=nompi_py312h1ef7fb6_101 + - nodeenv=1.9.1=pyhd8ed1ab_0 - notebook-shim=0.2.4=pyhd8ed1ab_0 - nspr=4.35=h27087fc_0 - - nss=3.100=hca3bf56_0 - - numba=0.59.1=py311h96b013e_0 - - numcodecs=0.12.1=py311h4332511_1 - - numpy=1.26.4=py311h64a7726_0 + - nss=3.101=h593d115_0 + - numba=0.60.0=py312h83e6fd3_0 + - numcodecs=0.12.1=py312h7070661_1 + - numpy=1.26.4=py312heda63a1_0 - oauthlib=3.2.2=pyhd8ed1ab_0 - openjpeg=2.5.2=h488ebb8_0 - - openssl=3.3.0=hd590300_0 - - orc=2.0.0=h17fec99_1 + - openssl=3.3.1=h4ab18f5_0 + - orc=2.0.1=h17fec99_1 - overrides=7.7.0=pyhd8ed1ab_0 - - packaging=24.0=pyhd8ed1ab_0 - - pandas=2.2.2=py311h320fe9a_0 + - packaging=24.1=pyhd8ed1ab_0 + - pandas=2.2.2=py312h1d6d2e6_1 - pandocfilters=1.5.0=pyhd8ed1ab_0 - - panel=1.4.2=pyhd8ed1ab_0 - - pango=1.52.2=ha41ecd1_0 + - panel=1.4.4=pyhd8ed1ab_0 + - pango=1.54.0=h84a9a3c_0 - param=2.1.0=pyhca7485f_0 - parso=0.8.4=pyhd8ed1ab_0 - partd=1.4.2=pyhd8ed1ab_0 - pcre2=10.43=hcad00b1_0 - pexpect=4.9.0=pyhd8ed1ab_0 - pickleshare=0.7.5=py_1003 - - pillow=10.3.0=py311h18e6fac_0 - - pint=0.23=pyhd8ed1ab_0 + - pillow=10.3.0=py312h287a98d_1 + - pint=0.24=pyhd8ed1ab_2 - pint-xarray=0.3=pyhd8ed1ab_0 - pip=24.0=pyhd8ed1ab_0 - pixman=0.43.2=h59595ed_0 - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1 - platformdirs=4.2.2=pyhd8ed1ab_0 - - pooch=1.8.1=pyhd8ed1ab_0 + - pooch=1.8.2=pyhd8ed1ab_0 - poppler=24.04.0=hb6cd0d7_0 - poppler-data=0.4.12=hd8ed1ab_0 - postgresql=16.3=h8e811e2_0 - pre-commit=3.7.1=pyha770c72_0 - - proj=9.4.0=h1d62c97_1 + - proj=9.4.1=hb784bbd_0 - prometheus_client=0.20.0=pyhd8ed1ab_0 - - prompt-toolkit=3.0.42=pyha770c72_0 + - prompt-toolkit=3.0.47=pyha770c72_0 - proto-plus=1.23.0=pyhd8ed1ab_0 - - protobuf=4.25.3=py311h7b78aeb_0 - - psutil=5.9.8=py311h459d7ec_0 + - protobuf=4.25.3=py312h72fbbdf_0 + - psutil=5.9.8=py312h98912ed_0 - pthread-stubs=0.4=h36c2ea0_1001 - ptyprocess=0.7.0=pyhd3deb0d_0 - pure_eval=0.2.2=pyhd8ed1ab_0 - - pyarrow=16.0.0=py311h781c19f_0 - - pyarrow-core=16.0.0=py311h8e2c35d_0_cpu + - pyarrow=16.1.0=py312h9cebb41_3 + - pyarrow-core=16.1.0=py312h70856f0_3_cpu - pyarrow-hotfix=0.6=pyhd8ed1ab_0 - pyasn1=0.6.0=pyhd8ed1ab_0 - pyasn1-modules=0.4.0=pyhd8ed1ab_0 - pybtex=0.24.0=pyhd8ed1ab_2 - - pybtex-docutils=1.0.3=py311h38be061_1 + - pybtex-docutils=1.0.3=py312h7900ff3_1 - pycparser=2.22=pyhd8ed1ab_0 - pyct=0.5.0=pyhd8ed1ab_0 - pydap=3.4.0=pyhd8ed1ab_0 - - pydata-sphinx-theme=0.15.2=pyhd8ed1ab_0 + - pydata-sphinx-theme=0.15.3=pyhd8ed1ab_0 - pygments=2.18.0=pyhd8ed1ab_0 - pyjwt=2.8.0=pyhd8ed1ab_1 - pyopenssl=24.0.0=pyhd8ed1ab_0 - pyparsing=3.1.2=pyhd8ed1ab_0 - - pyproj=3.6.1=py311hb3a3e68_6 + - pyproj=3.6.1=py312h5d05ceb_7 - pyshp=2.3.1=pyhd8ed1ab_0 - pysocks=1.7.1=pyha2e5f31_6 - - python=3.11.9=hb806964_0_cpython + - python=3.12.4=h194c7f8_0_cpython - python-dateutil=2.9.0=pyhd8ed1ab_0 - - python-fastjsonschema=2.19.1=pyhd8ed1ab_0 + - python-fastjsonschema=2.20.0=pyhd8ed1ab_0 - python-graphviz=0.20.3=pyh717bed2_0 - python-json-logger=2.0.7=pyhd8ed1ab_0 - python-tzdata=2024.1=pyhd8ed1ab_0 - - python_abi=3.11=4_cp311 + - python_abi=3.12=4_cp312 - pytz=2024.1=pyhd8ed1ab_0 - pyu2f=0.1.5=pyhd8ed1ab_0 - pyviz_comms=3.0.2=pyhd8ed1ab_1 - - pyyaml=6.0.1=py311h459d7ec_1 - - pyzmq=26.0.3=py311h08a0b41_0 - - rasterio=1.3.10=py311h375a7ea_0 + - pyyaml=6.0.1=py312h98912ed_1 + - pyzmq=26.0.3=py312h8fd38d8_0 + - rasterio=1.3.10=py312hc022a17_4 - re2=2023.09.01=h7f4b329_2 - readline=8.2=h8228510_1 - referencing=0.35.1=pyhd8ed1ab_0 - - requests=2.31.0=pyhd8ed1ab_0 + - requests=2.32.3=pyhd8ed1ab_0 - requests-oauthlib=2.0.0=pyhd8ed1ab_0 - rfc3339-validator=0.1.4=pyhd8ed1ab_0 - rfc3986-validator=0.1.1=pyh9f0ad1d_0 - - rioxarray=0.15.5=pyhd8ed1ab_0 - - rpds-py=0.18.1=py311h5ecf98a_0 + - rioxarray=0.15.6=pyhd8ed1ab_0 + - rpds-py=0.18.1=py312h4413252_0 - rsa=4.9=pyhd8ed1ab_0 - - s2n=1.4.13=he19d79f_0 - - scipy=1.13.0=py311h517d4fd_1 + - s2n=1.4.16=he19d79f_0 + - scipy=1.13.1=py312hc2bc53b_0 - send2trash=1.8.3=pyh0d859eb_0 - - setuptools=69.5.1=pyhd8ed1ab_0 - - shapely=2.0.4=py311h2032efe_0 + - setuptools=70.1.0=pyhd8ed1ab_0 + - shapely=2.0.4=py312ha5b4d35_1 - simpervisor=1.0.0=pyhd8ed1ab_0 - six=1.16.0=pyh6c4a22f_0 - snappy=1.2.0=hdb0a2a9_1 @@ -370,65 +373,64 @@ dependencies: - sortedcontainers=2.4.0=pyhd8ed1ab_0 - soupsieve=2.5=pyhd8ed1ab_1 - spdlog=1.13.0=hd2e6256_0 - - sphinx=5.0.2=pyh6c4a22f_0 - - sphinx-book-theme=1.0.1=pyhd8ed1ab_0 - - sphinx-codeautolink=0.15.1=pyhd8ed1ab_0 + - sphinx=7.3.7=pyhd8ed1ab_0 + - sphinx-book-theme=1.1.3=pyhd8ed1ab_0 + - sphinx-codeautolink=0.15.2=pyhd8ed1ab_0 - sphinx-comments=0.0.3=pyh9f0ad1d_0 - sphinx-copybutton=0.5.2=pyhd8ed1ab_0 - - sphinx-design=0.3.0=pyhd8ed1ab_0 - - sphinx-exercise=0.4.0=pyhd8ed1ab_0 - - sphinx-external-toc=0.3.1=pyhd8ed1ab_1 - - sphinx-jupyterbook-latex=0.5.2=pyhd8ed1ab_0 + - sphinx-design=0.6.0=pyhd8ed1ab_0 + - sphinx-external-toc=1.0.1=pyhd8ed1ab_0 + - sphinx-jupyterbook-latex=1.0.0=pyhd8ed1ab_0 - sphinx-multitoc-numbering=0.1.3=pyhd8ed1ab_0 - - sphinx-notfound-page=1.0.0=pyhd8ed1ab_0 - - sphinx-thebe=0.2.1=pyhd8ed1ab_0 + - sphinx-notfound-page=1.0.2=pyhd8ed1ab_0 + - sphinx-thebe=0.3.1=pyhd8ed1ab_0 - sphinx-togglebutton=0.3.2=pyhd8ed1ab_0 - sphinxcontrib-applehelp=1.0.8=pyhd8ed1ab_0 - - sphinxcontrib-bibtex=2.5.0=pyhd8ed1ab_0 + - sphinxcontrib-bibtex=2.6.2=pyhd8ed1ab_0 - sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0 - sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0 - sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0 - sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0 - sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0 - - sphinxext-rediraffe=0.2.7=pyhd8ed1ab_0 - - sqlalchemy=2.0.30=py311h331c9d8_0 - - sqlite=3.45.3=h2c6b66d_0 + - sphinxext-rediraffe=0.2.7=pyhd8ed1ab_1 + - sqlalchemy=2.0.31=py312h9a8786e_0 + - sqlite=3.46.0=h6d4b2fc_0 - stack_data=0.6.2=pyhd8ed1ab_0 - tabulate=0.9.0=pyhd8ed1ab_1 - tblib=3.0.0=pyhd8ed1ab_0 - terminado=0.18.1=pyh0d859eb_0 - - tiledb=2.23.0=h27f064a_0 + - tiledb=2.24.1=h73c5a7c_0 - tinycss2=1.3.0=pyhd8ed1ab_0 - tk=8.6.13=noxft_h4845f30_101 - tomli=2.0.1=pyhd8ed1ab_0 - toolz=0.12.1=pyhd8ed1ab_0 - - tornado=6.4=py311h459d7ec_0 + - tornado=6.4.1=py312h9a8786e_0 - tqdm=4.66.4=pyhd8ed1ab_0 - traitlets=5.14.3=pyhd8ed1ab_0 - types-python-dateutil=2.9.0.20240316=pyhd8ed1ab_0 - - typing-extensions=4.11.0=hd8ed1ab_0 - - typing_extensions=4.11.0=pyha770c72_0 + - typing-extensions=4.12.2=hd8ed1ab_0 + - typing_extensions=4.12.2=pyha770c72_0 - typing_utils=0.1.0=pyhd8ed1ab_0 - tzcode=2024a=h3f72095_0 - tzdata=2024a=h0c530f3_0 - uc-micro-py=1.0.3=pyhd8ed1ab_0 - - ukkonen=1.0.1=py311h9547e67_4 + - ukkonen=1.0.1=py312h8572e83_4 - uri-template=1.3.0=pyhd8ed1ab_0 - uriparser=0.9.8=hac33072_0 - - urllib3=2.2.1=pyhd8ed1ab_0 + - urllib3=2.2.2=pyhd8ed1ab_0 - virtualenv=20.26.2=pyhd8ed1ab_0 - wcwidth=0.2.13=pyhd8ed1ab_0 - - webcolors=1.13=pyhd8ed1ab_0 + - webcolors=24.6.0=pyhd8ed1ab_0 - webencodings=0.5.1=pyhd8ed1ab_2 - webob=1.8.7=pyhd8ed1ab_0 - websocket-client=1.8.0=pyhd8ed1ab_0 - wheel=0.43.0=pyhd8ed1ab_1 - - xarray=2024.5.0=pyhd8ed1ab_0 + - xarray=2024.6.0=pyhd8ed1ab_1 - xerces-c=3.2.5=hac6953d_0 - xorg-kbproto=1.0.7=h7f98852_1002 - xorg-libice=1.1.1=hd590300_0 - xorg-libsm=1.2.4=h7391055_0 - - xorg-libx11=1.8.9=h8ee46fc_0 + - xorg-libx11=1.8.9=hb711507_1 - xorg-libxau=1.0.11=hd590300_0 - xorg-libxdmcp=1.1.3=h7f98852_0 - xorg-libxext=1.3.4=h0b41bf4_2 @@ -436,13 +438,13 @@ dependencies: - xorg-renderproto=0.11.1=h7f98852_1002 - xorg-xextproto=7.3.0=h0b41bf4_1003 - xorg-xproto=7.0.31=h7f98852_1007 - - xyzservices=2024.4.0=pyhd8ed1ab_0 + - xyzservices=2024.6.0=pyhd8ed1ab_0 - xz=5.2.6=h166bdaf_0 - yaml=0.2.5=h7f98852_2 - - yarl=1.9.4=py311h459d7ec_0 - - zarr=2.18.0=pyhd8ed1ab_0 + - yarl=1.9.4=py312h98912ed_0 + - zarr=2.18.2=pyhd8ed1ab_0 - zeromq=4.3.5=h75354e8_4 - zict=3.0.0=pyhd8ed1ab_0 - - zipp=3.17.0=pyhd8ed1ab_0 - - zlib=1.2.13=hd590300_5 + - zipp=3.19.2=pyhd8ed1ab_0 + - zlib=1.3.1=h4ab18f5_1 - zstd=1.5.6=ha6fb4c9_0 diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index 6b98ee39..ef4f4d39 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -204,11 +204,6 @@ "da[:, 20, 40]" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -581,6 +576,13 @@ "```" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -590,6 +592,13 @@ "Another technique is to use a list of datetime objects or date strings for indexing. For example, you could select data for specific, non-contiguous dates like this:" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -602,6 +611,13 @@ "ds.sel(time=dates)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -663,54 +679,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: indexing-1\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Select the first 30 entries of `latitude` and 30th to 40th entries of `longitude`:\n", - "```\n", "\n", - "````{solution} indexing-1\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "ds.isel(lat=slice(None, 30), lon=slice(30, 40))\n", "```\n", "\n", - "````" + ":::\n", + "::::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: indexing-2\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", - "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013 :\n", - "```\n", - "````{solution} indexing-2\n", + "Select all data at 75 degree north and between Jan 1, 2013 and Oct 15, 2013\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "ds.sel(lat=75, time=slice(\"2013-01-01\", \"2013-10-15\"))\n", "```\n", - "````" + ":::\n", + "::::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: indexing-3\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", - "Remove all entries at 260 and 270 degrees :\n", + "Remove all entries at 260 and 270 degrees\n", "\n", - "```\n", - "````{solution} indexing-3\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "ds.drop_sel(lon=[260, 270])\n", "```\n", - "````" + ":::\n", + "::::" ] }, { diff --git a/fundamentals/02.3_aligning_data_objects.ipynb b/fundamentals/02.3_aligning_data_objects.ipynb index e3d8deec..490fbc88 100644 --- a/fundamentals/02.3_aligning_data_objects.ipynb +++ b/fundamentals/02.3_aligning_data_objects.ipynb @@ -251,8 +251,8 @@ "tags": [] }, "source": [ - "````{exercise}\n", - ":label: ex1\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Consider the following 2D array. What are the dimensions of `array - array.mean(\"time\")`?\n", "```python\n", @@ -263,15 +263,16 @@ " name=\"array\",\n", ")\n", "```\n", - "````\n", "\n", - "````{solution} ex1\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", "(array - array.mean(\"time\")).dims\n", "```\n", - "````" + ":::\n", + "::::" ] }, { @@ -443,8 +444,8 @@ ] }, "source": [ - "````{exercise}\n", - ":label: dims\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Consider the following two arrays. Write down the `x` and `y` coordinate locations for `da1 - da2`\n", "```python\n", @@ -459,13 +460,13 @@ " coords={\"space\": [\"b\", \"d\"]},\n", ")\n", "```\n", - "````\n", "\n", - "```{solution} dims\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "`x = [\"b\"], y=[0, 1, 2, 3]` . `da2` has been broadcasted to 2D (so dimension \"y\" has been inserted) and the two arrays are aligned using `join=\"inner\"` prior to subtraction.\n", - "```" + ":::\n", + "::::" ] }, { diff --git a/fundamentals/03.1_computation_with_xarray.ipynb b/fundamentals/03.1_computation_with_xarray.ipynb index 2f5c5075..9dbaadd0 100644 --- a/fundamentals/03.1_computation_with_xarray.ipynb +++ b/fundamentals/03.1_computation_with_xarray.ipynb @@ -265,15 +265,18 @@ "tags": [] }, "source": [ - "```{exercise}\n", - ":label: sst-mean\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Take the mean of `sst` in both longitude and latitude. Make a simple timeseries plot.\n", - "```\n", - "````{solution} sst-mean\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", + "```python\n", "sst.mean([\"lat\", \"lon\"]).plot();\n", - "```\n" + "```\n", + ":::\n", + "::::\n" ] } ], diff --git a/fundamentals/03.2_groupby_with_xarray.ipynb b/fundamentals/03.2_groupby_with_xarray.ipynb index f16d61ef..cb1ba252 100644 --- a/fundamentals/03.2_groupby_with_xarray.ipynb +++ b/fundamentals/03.2_groupby_with_xarray.ipynb @@ -479,17 +479,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise} \n", - ":label: annual-mean\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Using `groupby`, plot the annual mean time series of SST at 300°E, 50°N\n", - "```\n", - "````{solution} annual-mean\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50).plot();\n", "```\n", - "````" + ":::\n", + "::::" ] }, { @@ -582,20 +583,21 @@ "tags": [] }, "source": [ - "```{exercise}\n", - ":label: resample-mean\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Using `resample`, plot the annual mean time series of SST at 300°E, 50°N.\n", "\n", "Compare this output to the groupby output. What differences do you see?\n", - "```\n", - "````{solution} resample-mean\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n", "resampled.plot();\n", "```\n", - "````" + ":::\n", + "::::" ] }, { diff --git a/fundamentals/03.3_windowed.ipynb b/fundamentals/03.3_windowed.ipynb index 504ee9b7..4454d50e 100644 --- a/fundamentals/03.3_windowed.ipynb +++ b/fundamentals/03.3_windowed.ipynb @@ -193,11 +193,11 @@ "id": "16", "metadata": {}, "source": [ - "```{exercise} \n", - ":label: rolling-center\n", + ":::{admonition} Exercise\n", + ":class: tip\n", "Illustrate the difference between `center=True` and `center=False` for rolling by looking at the `construct`-ed array.\n", - "```\n", - "````{solution} rolling-center\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "display(\"center=True\")\n", @@ -206,7 +206,8 @@ "display(\"center=False\")\n", "display(simple.rolling(time=5, center=False).construct(\"window\"))\n", "```\n", - "````" + ":::\n", + "::::" ] }, { @@ -336,8 +337,8 @@ "id": "27", "metadata": {}, "source": [ - "````{exercise} \n", - ":label: rolling-pad\n", + ":::{admonition} Exercise\n", + ":class: tip\n", "\n", "Imagine the array `months` was one element shorter. Use `boundary=\"pad\"` and the `side` kwarg to reshape `months.isel(time=slice(1, None))` to a 2D DataArray with the following values:\n", "\n", @@ -345,8 +346,8 @@ "array([[nan, 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.],\n", " [ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]])\n", "```\n", - "````\n", - "````{solution} rolling-pad\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", @@ -354,7 +355,8 @@ " time=(\"year\", \"month\")\n", ")\n", "```\n", - "````" + ":::\n", + "::::" ] }, { diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/01-high-level-computation-patterns.ipynb index 5aa3940f..e5f17c1c 100644 --- a/intermediate/01-high-level-computation-patterns.ipynb +++ b/intermediate/01-high-level-computation-patterns.ipynb @@ -457,39 +457,26 @@ "id": "26", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: rolling-reduce\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Calculate the rolling mean in 5 point bins along both latitude and longitude using\n", "[`rolling(...).reduce`](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DataArrayRolling.reduce.html)\n", "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "27", - "metadata": { - "slideshow": { - "slide_type": "subslide" - }, - "tags": [] - }, - "source": [ - "````{solution} rolling-reduce\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", "# exactly equivalent to data.rolling(...).mean()\n", "data.rolling(lat=5, lon=5, center=True).reduce(np.mean).plot();\n", "```\n", - "\n", - "````" + ":::\n", + "::::" ] }, { "cell_type": "markdown", - "id": "28", + "id": "27", "metadata": { "slideshow": { "slide_type": "subslide" @@ -505,7 +492,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29", + "id": "28", "metadata": {}, "outputs": [], "source": [ @@ -516,7 +503,7 @@ { "cell_type": "code", "execution_count": null, - "id": "30", + "id": "29", "metadata": {}, "outputs": [], "source": [ @@ -526,38 +513,36 @@ }, { "cell_type": "markdown", - "id": "31", + "id": "30", "metadata": {}, "source": [ "```{note}\n", - ":class: dropdown\n", "Because `.construct()` only returns a \"view\" (not a copy) of the original data object (i.e. it is not operating \"in-place\"), in order to \"save\" the results you would need to rewrite the original object: `simple = simple.rolling(time=5, center=True).construct(\"window\")`.\n", "```" ] }, { "cell_type": "markdown", - "id": "32", + "id": "31", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: rolling-construct\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Calculate the 5 point running mean in time using `rolling.construct`\n", - "```\n", "\n", - "````{solution} rolling-construct\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", "simple.rolling(time=5, center=True).construct(\"window\").mean(\"window\")\n", "```\n", - "\n", - "````\n" + ":::\n", + "::::" ] }, { "cell_type": "markdown", - "id": "33", + "id": "32", "metadata": {}, "source": [ "`construct` is clever.\n", @@ -569,7 +554,7 @@ }, { "cell_type": "markdown", - "id": "34", + "id": "33", "metadata": { "tags": [] }, @@ -582,7 +567,7 @@ { "cell_type": "code", "execution_count": null, - "id": "35", + "id": "34", "metadata": {}, "outputs": [], "source": [ @@ -591,7 +576,7 @@ }, { "cell_type": "markdown", - "id": "36", + "id": "35", "metadata": { "slideshow": { "slide_type": "subslide" @@ -615,7 +600,7 @@ { "cell_type": "code", "execution_count": null, - "id": "37", + "id": "36", "metadata": {}, "outputs": [], "source": [ @@ -625,7 +610,7 @@ { "cell_type": "code", "execution_count": null, - "id": "38", + "id": "37", "metadata": {}, "outputs": [], "source": [ @@ -635,7 +620,7 @@ { "cell_type": "code", "execution_count": null, - "id": "39", + "id": "38", "metadata": {}, "outputs": [], "source": [ @@ -645,7 +630,7 @@ { "cell_type": "code", "execution_count": null, - "id": "40", + "id": "39", "metadata": {}, "outputs": [], "source": [ @@ -654,7 +639,7 @@ }, { "cell_type": "markdown", - "id": "41", + "id": "40", "metadata": { "slideshow": { "slide_type": "subslide" @@ -664,30 +649,23 @@ "source": [ "#### Coarsen supports `reduce` for custom reductions\n", "\n", - "```{exercise}\n", - ":label: coarsen-reduce\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Use `coarsen.reduce` to apply `np.ptp` in 5x5 (lat x lon) point blocks to `data`\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "42", - "metadata": {}, - "source": [ - "````{solution} coarsen-reduce\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", "data.coarsen(lat=5, lon=5, boundary=\"trim\").reduce(np.mean).plot();\n", "```\n", - "\n", - "````\n" + ":::\n", + "::::" ] }, { "cell_type": "markdown", - "id": "43", + "id": "41", "metadata": { "slideshow": { "slide_type": "subslide" @@ -707,7 +685,7 @@ { "cell_type": "code", "execution_count": null, - "id": "44", + "id": "42", "metadata": {}, "outputs": [], "source": [ @@ -722,7 +700,7 @@ { "cell_type": "code", "execution_count": null, - "id": "45", + "id": "43", "metadata": {}, "outputs": [], "source": [ @@ -732,7 +710,7 @@ }, { "cell_type": "markdown", - "id": "46", + "id": "44", "metadata": {}, "source": [ "Note two things:\n", @@ -747,7 +725,7 @@ { "cell_type": "code", "execution_count": null, - "id": "47", + "id": "45", "metadata": {}, "outputs": [], "source": [ @@ -756,7 +734,7 @@ }, { "cell_type": "markdown", - "id": "48", + "id": "46", "metadata": {}, "source": [ "This adds values at the end of the array (see the 'nan' at the end of the time coordinate?), which is not so sensible for this\n", @@ -766,7 +744,7 @@ { "cell_type": "code", "execution_count": null, - "id": "49", + "id": "47", "metadata": {}, "outputs": [], "source": [ @@ -777,7 +755,7 @@ }, { "cell_type": "markdown", - "id": "50", + "id": "48", "metadata": {}, "source": [ "Note that `coarsen` pads with NaNs. For more control over padding, use\n", @@ -787,7 +765,7 @@ { "cell_type": "code", "execution_count": null, - "id": "51", + "id": "49", "metadata": {}, "outputs": [], "source": [ @@ -801,7 +779,7 @@ }, { "cell_type": "markdown", - "id": "52", + "id": "50", "metadata": {}, "source": [ "```{note}\n", @@ -812,22 +790,16 @@ }, { "cell_type": "markdown", - "id": "53", + "id": "51", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: reshape\n", + ":::{admonition} Exercise\n", + ":class: tip\n", "Reshape the `time` dimension of the DataArray `monthly` to year x\n", "month and visualize the seasonal cycle for two years at 250°E\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "54", - "metadata": {}, - "source": [ - "````{solution} reshape\n", + "\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", @@ -856,29 +828,20 @@ "# seasonal cycle for two years\n", "year_month.sel(lon=250).plot.contourf(col=\"year\", x=\"month\", y=\"lat\")\n", "```\n", - "\n", - "````\n" + ":::\n", + "::::" ] }, { "cell_type": "markdown", - "id": "55", + "id": "52", "metadata": {}, "source": [ - "\n", - "\n", - "```{exercise}\n", - ":label: rolling\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Calculate the rolling 4 month average, averaged across years. (This exercise came up during a live lecture).\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "56", - "metadata": {}, - "source": [ - "````{solution} rolling\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "1. We first reshape using `coarsen.construct` to add `year` as a new dimension.\n", @@ -890,13 +853,13 @@ "roll = reshaped.rolling(month=4, center=True)\n", "roll.construct(\"window\").mean([\"window\", \"year\"])\n", "```\n", - "\n", - "````" + ":::\n", + "::::" ] }, { "cell_type": "markdown", - "id": "57", + "id": "53", "metadata": {}, "source": [ "### Summary\n", @@ -912,7 +875,7 @@ }, { "cell_type": "markdown", - "id": "58", + "id": "54", "metadata": { "slideshow": { "slide_type": "slide" @@ -958,7 +921,7 @@ { "cell_type": "code", "execution_count": null, - "id": "59", + "id": "55", "metadata": {}, "outputs": [], "source": [ @@ -969,7 +932,7 @@ { "cell_type": "code", "execution_count": null, - "id": "60", + "id": "56", "metadata": {}, "outputs": [], "source": [ @@ -982,7 +945,7 @@ { "cell_type": "code", "execution_count": null, - "id": "61", + "id": "57", "metadata": {}, "outputs": [], "source": [ @@ -993,7 +956,7 @@ }, { "cell_type": "markdown", - "id": "62", + "id": "58", "metadata": {}, "source": [ "Notice that since we have averaged over all the years for each month, our resulting DataArray no longer has a \"year\" coordinate.\n", @@ -1004,7 +967,7 @@ { "cell_type": "code", "execution_count": null, - "id": "63", + "id": "59", "metadata": {}, "outputs": [], "source": [ @@ -1013,7 +976,7 @@ }, { "cell_type": "markdown", - "id": "64", + "id": "60", "metadata": {}, "source": [ "Similarly for binning (remember this is useful when the parameter you are binning over is not \"exact\", like a float),\n" @@ -1022,7 +985,7 @@ { "cell_type": "code", "execution_count": null, - "id": "65", + "id": "61", "metadata": {}, "outputs": [], "source": [ @@ -1031,7 +994,7 @@ }, { "cell_type": "markdown", - "id": "66", + "id": "62", "metadata": {}, "source": [ "and resampling...\n" @@ -1040,7 +1003,7 @@ { "cell_type": "code", "execution_count": null, - "id": "67", + "id": "63", "metadata": {}, "outputs": [], "source": [ @@ -1049,7 +1012,7 @@ }, { "cell_type": "markdown", - "id": "68", + "id": "64", "metadata": {}, "source": [ "```{note}\n", @@ -1061,7 +1024,7 @@ }, { "cell_type": "markdown", - "id": "69", + "id": "65", "metadata": { "slideshow": { "slide_type": "subslide" @@ -1095,7 +1058,7 @@ }, { "cell_type": "markdown", - "id": "70", + "id": "66", "metadata": { "slideshow": { "slide_type": "subslide" @@ -1114,7 +1077,7 @@ { "cell_type": "code", "execution_count": null, - "id": "71", + "id": "67", "metadata": { "tags": [] }, @@ -1126,7 +1089,7 @@ { "cell_type": "code", "execution_count": null, - "id": "72", + "id": "68", "metadata": {}, "outputs": [], "source": [ @@ -1136,7 +1099,7 @@ { "cell_type": "code", "execution_count": null, - "id": "73", + "id": "69", "metadata": {}, "outputs": [], "source": [ @@ -1146,7 +1109,7 @@ { "cell_type": "code", "execution_count": null, - "id": "74", + "id": "70", "metadata": {}, "outputs": [], "source": [ @@ -1155,7 +1118,7 @@ }, { "cell_type": "markdown", - "id": "75", + "id": "71", "metadata": {}, "source": [ "#### Construct and use custom labels\n", @@ -1170,7 +1133,7 @@ { "cell_type": "code", "execution_count": null, - "id": "76", + "id": "72", "metadata": {}, "outputs": [], "source": [ @@ -1180,7 +1143,7 @@ }, { "cell_type": "markdown", - "id": "77", + "id": "73", "metadata": {}, "source": [ "Create a new empty array\n" @@ -1189,7 +1152,7 @@ { "cell_type": "code", "execution_count": null, - "id": "78", + "id": "74", "metadata": {}, "outputs": [], "source": [ @@ -1199,7 +1162,7 @@ }, { "cell_type": "markdown", - "id": "79", + "id": "75", "metadata": {}, "source": [ "Use `isin` to assign custom seasons,\n" @@ -1208,7 +1171,7 @@ { "cell_type": "code", "execution_count": null, - "id": "80", + "id": "76", "metadata": {}, "outputs": [], "source": [ @@ -1220,7 +1183,7 @@ }, { "cell_type": "markdown", - "id": "81", + "id": "77", "metadata": {}, "source": [ "Turn our new seasonal group array into a DataArray." @@ -1229,7 +1192,7 @@ { "cell_type": "code", "execution_count": null, - "id": "82", + "id": "78", "metadata": {}, "outputs": [], "source": [ @@ -1240,7 +1203,7 @@ { "cell_type": "code", "execution_count": null, - "id": "83", + "id": "79", "metadata": {}, "outputs": [], "source": [ @@ -1256,7 +1219,7 @@ }, { "cell_type": "markdown", - "id": "84", + "id": "80", "metadata": { "slideshow": { "slide_type": "subslide" @@ -1272,7 +1235,7 @@ { "cell_type": "code", "execution_count": null, - "id": "85", + "id": "81", "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1245,7 @@ { "cell_type": "code", "execution_count": null, - "id": "86", + "id": "82", "metadata": {}, "outputs": [], "source": [ @@ -1293,7 +1256,7 @@ }, { "cell_type": "markdown", - "id": "87", + "id": "83", "metadata": { "slideshow": { "slide_type": "subslide" @@ -1311,7 +1274,7 @@ { "cell_type": "code", "execution_count": null, - "id": "88", + "id": "84", "metadata": {}, "outputs": [], "source": [ @@ -1320,7 +1283,7 @@ }, { "cell_type": "markdown", - "id": "89", + "id": "85", "metadata": { "tags": [] }, @@ -1333,7 +1296,7 @@ { "cell_type": "code", "execution_count": null, - "id": "90", + "id": "86", "metadata": {}, "outputs": [], "source": [ @@ -1342,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "91", + "id": "87", "metadata": {}, "source": [ "```{tip}\n", @@ -1352,7 +1315,7 @@ }, { "cell_type": "markdown", - "id": "92", + "id": "88", "metadata": { "tags": [] }, @@ -1371,7 +1334,7 @@ { "cell_type": "code", "execution_count": null, - "id": "93", + "id": "89", "metadata": {}, "outputs": [], "source": [ @@ -1381,7 +1344,7 @@ }, { "cell_type": "markdown", - "id": "94", + "id": "90", "metadata": {}, "source": [ "`group` is a DataArray containing data for all December days (because the last printed `label` value is `12`, so the last `group` value is for December)." @@ -1390,7 +1353,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95", + "id": "91", "metadata": { "tags": [] }, @@ -1401,7 +1364,7 @@ }, { "cell_type": "markdown", - "id": "96", + "id": "92", "metadata": {}, "source": [ "Maybe you want a histogram of December temperatures?\n" @@ -1410,7 +1373,7 @@ { "cell_type": "code", "execution_count": null, - "id": "97", + "id": "93", "metadata": {}, "outputs": [], "source": [ @@ -1419,7 +1382,7 @@ }, { "cell_type": "markdown", - "id": "98", + "id": "94", "metadata": {}, "source": [ "Remember, this example is just to show how you could operate on each group object in a groupby operation. If we wanted to just explore the December (or March) data, we should just filter for it directly:" @@ -1428,7 +1391,7 @@ { "cell_type": "code", "execution_count": null, - "id": "99", + "id": "95", "metadata": {}, "outputs": [], "source": [ @@ -1437,7 +1400,7 @@ }, { "cell_type": "markdown", - "id": "100", + "id": "96", "metadata": { "tags": [] }, @@ -1455,7 +1418,7 @@ { "cell_type": "code", "execution_count": null, - "id": "101", + "id": "97", "metadata": { "tags": [] }, @@ -1471,7 +1434,7 @@ }, { "cell_type": "markdown", - "id": "102", + "id": "98", "metadata": {}, "source": [ "***" @@ -1479,7 +1442,7 @@ }, { "cell_type": "markdown", - "id": "103", + "id": "99", "metadata": {}, "source": [ "## Summary\n", diff --git a/intermediate/indexing/boolean-masking-indexing.ipynb b/intermediate/indexing/boolean-masking-indexing.ipynb index 2ee0a6e5..67aeced7 100644 --- a/intermediate/indexing/boolean-masking-indexing.ipynb +++ b/intermediate/indexing/boolean-masking-indexing.ipynb @@ -160,11 +160,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: boolean-2\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Using the syntax you’ve learned so far, mask all the points with latitudes above 60° N.\n", - "```\n" + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "da_masked = da.where(da.yc >= 60)\n", + "da_masked[:, :].plot();\n", + "```\n", + ":::\n", + "::::" ] }, { @@ -176,19 +185,6 @@ "# write your answer here!" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "````{solution} boolean-2\n", - ":class: dropdown\n", - "```python\n", - "da_masked = da.where(da.yc >= 60)\n", - "da_masked[:, :].plot();\n", - "```\n", - "````" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -323,8 +319,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "````{exercise}\n", - ":label: boolean-1\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "If we load air temperature dataset from NCEP, we could use `sel` method for selecting a region:\n", "\n", @@ -336,9 +332,7 @@ "```\n", "Can you use a similar method as above using `sel` to crop a region using the RASM dataset? Why?\n", "\n", - "````\n", - "\n", - "````{solution} boolean-1\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "This method will not work here as the dimensions are different from coordinates here. Specifically, the variables xc (longitude) and yc (latitude) are two-dimensional scalar fields, which differ from the logical coordinates represented by x and y.\n", "\n", @@ -347,7 +341,8 @@ "cropped_ds = ds.sel(x=slice(min_lat,max_lat), y=slice(min_lon,max_lon))\n", "cropped_ds.Tair.plot()\n", "```\n", - "````\n" + ":::\n", + "::::" ] }, { diff --git a/intermediate/xarray_and_dask.ipynb b/intermediate/xarray_and_dask.ipynb index 446002c1..2f025b69 100644 --- a/intermediate/xarray_and_dask.ipynb +++ b/intermediate/xarray_and_dask.ipynb @@ -250,10 +250,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise} \n", - ":label: data-values\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?\n", - "```" + "::::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# use this cell for the exercise" ] }, { @@ -344,32 +353,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: compute\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Try running `mean.compute` and then examine `mean` after that. Is it still a dask array?\n", - "```\n", - "```{solution} compute\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "Computing returns a numpy array but does not modify in-place. So `mean` still contains a dask array.\n", - "```" + ":::\n", + "::::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: load\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "\n", "Now repeat that exercise with `mean.load`.\n", - "```\n", - "```{solution} load\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "`load` modifies an Xarray object in-place so `mean` now contains a numpy array.\n", - "```" + ":::\n", + "::::" ] }, { @@ -545,9 +556,18 @@ "tags": [] }, "source": [ - "```{exercise}\n", + "::::{admonition} Exercise\n", "Visualize the task graph for a few different computations on `ds.air`!\n", - "```" + "::::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use this cell for the exercise" ] }, { diff --git a/intermediate/xarray_ecosystem.ipynb b/intermediate/xarray_ecosystem.ipynb index a7b12b5a..017b9690 100644 --- a/intermediate/xarray_ecosystem.ipynb +++ b/intermediate/xarray_ecosystem.ipynb @@ -609,12 +609,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", - ":label: wrong-unit\n", + "::::{admonition} Exercise\n", + ":class: tip\n", "Write a function which will raise a `pint.DimensionalityError` if supplied with Xarray DataArray with the wrong units.\n", - "```\n", "\n", - "````{solution} wrong-unit\n", + "\n", + ":::{admonition} Solution\n", ":class: dropdown\n", "\n", "```python\n", @@ -627,21 +627,21 @@ " \"this function will only give the correct answer if the input is in units of miles\"\n", " )\n", "```\n", - "\n", - "````" + ":::\n", + "::::" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{exercise}\n", + "::::{admonition} Exercise\n", "Try this on some of your data!\n", "\n", "After you have imported pint-xarray (and maybe cf-xarray) as above, start with something like\n", "\n", "`ds = xr.open_dataset(my_data).pint.quantify()`\n", - "```" + "::::" ] }, { From e4061f2dcfcdca6c3e8ae9b9b1605954bcf047fa Mon Sep 17 00:00:00 2001 From: Jessica Scheick Date: Mon, 24 Jun 2024 22:11:12 -0400 Subject: [PATCH 6/6] update question card to link to help diagram in docs (#274) * update question card to link to help diagram in docs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Scott Henderson --- intro.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/intro.md b/intro.md index aec20724..2f0d5cb2 100644 --- a/intro.md +++ b/intro.md @@ -26,11 +26,11 @@ Live walk-throughs of this material and more on Xarray's YouTube Channel ``` ```{grid-item-card} -:link: https://github.com/pydata/xarray/discussions +:link: https://docs.xarray.dev/en/latest/help-diagram.html :class-header: bg-light Get involved 🙋 ^^^ -Ask questions on the Xarray Discussion Forum +Ask questions and participate in the Xarray community! ``` ````