diff --git a/.binder/environment.yml b/.binder/environment.yml deleted file mode 120000 index faafc0cc..00000000 --- a/.binder/environment.yml +++ /dev/null @@ -1 +0,0 @@ -../conda/environment.yml \ No newline at end of file diff --git a/.binder/environment.yml b/.binder/environment.yml new file mode 100644 index 00000000..0e722a9a --- /dev/null +++ b/.binder/environment.yml @@ -0,0 +1,43 @@ +name: default +channels: + - conda-forge + - nodefaults +dependencies: + - jupyter-book >=1.0.4.post1,<2 + - pre-commit >=4.2.0,<5 + - dask-labextension >=7.0.0,<8 + - jupyterlab >=4.4.4,<5 + - jupyter_bokeh >=4.0.5,<5 + - jupyterlab-myst >=2.4.2,<3 + - jupyter-resource-usage >=1.1.1,<2 + - cartopy >=0.24.0,<0.25 + - cf_xarray >=0.10.6,<0.11 + - dask >=2025.5.1,<2026 + - datashader >=0.18.1,<0.19 + - distributed >=2025.5.1,<2026 + - gcsfs >=2025.5.1,<2026 + - geoviews-core >=1.14.0,<2 + - gsw >=3.6.19,<4 + - hvplot >=0.11.3,<0.12 + - h5netcdf >=1.6.3,<2 + - ipykernel >=6.29.5,<7 + - matplotlib-base >=3.10.3,<4 + - netcdf4 >=1.7.2,<2 + - numpy >=2.2.6,<3 + - pint-xarray >=0.5.0,<0.6 + - pydap >=3.5.5,<4 + - python-graphviz >=0.21,<0.22 + - pooch >=1.8.2,<2 + - rioxarray >=0.19.0,<0.20 + - scipy >=1.16.0,<2 + - sphinx-codeautolink >=0.17.4,<0.18 + - sphinxcontrib-mermaid >=1.0.0,<2 + - sphinx-notfound-page >=1.1.0,<2 + - sphinxext-rediraffe >=0.2.7,<0.3 + - s3fs >=2025.5.1,<2026 + - xarray >=2025.7.0,<2026 + - zarr >=3.0.10,<4 + - flox >=0.10.4,<0.11 + - numbagg >=0.9.0,<0.10 + - rich >=14.0.0,<15 + - python >=3.10 diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 70734b49..78ab0ed4 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1 +1,13 @@ -FROM pangeo/base-image:2024.09.11 +FROM mcr.microsoft.com/devcontainers/base:noble + +ARG PIXI_VERSION=v0.49.0 + +RUN curl -L -o /usr/local/bin/pixi -fsSL --compressed "https://github.com/prefix-dev/pixi/releases/download/${PIXI_VERSION}/pixi-$(uname -m)-unknown-linux-musl" \ + && chmod +x /usr/local/bin/pixi \ + && pixi info + +# set some user and workdir settings to work nicely with vscode +USER vscode +WORKDIR /home/vscode + +RUN echo 'eval "$(pixi completion -s bash)"' >> /home/vscode/.bashrc diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 68794f5b..d90ee6bd 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,20 +1,42 @@ +// https://pixi.sh/latest/integration/editor/vscode/#devcontainer-extension { + "name": "xarray-tutorial", "build": { - "dockerfile": "./Dockerfile", - "context": "../conda" - }, - "postCreateCommand": { - "vscode": "mkdir ${containerWorkspaceFolder}/.vscode && cp ${containerWorkspaceFolder}/.devcontainer/tasks.json ${containerWorkspaceFolder}/.vscode/tasks.json" - }, - "hostRequirements": { - "cpus": 2 + "dockerfile": "Dockerfile", + "context": ".." }, "customizations": { - "codespaces": { - "openFiles": ["README.md"] - }, "vscode": { - "extensions": ["ms-toolsai.jupyter", "ms-python.python"] + "settings": {}, + "extensions": [ + "ms-toolsai.jupyter", + "ms-python.python", + "executablebookproject.myst-highlight" + ] + } + }, + "features": { + // ensure GitHub Codespace 'Open with JupyterLab' works + // TODO: figure out why it doesn't work w/ jupyterlab in the pixi environment + "ghcr.io/devcontainers/features/python:1": { + "version": "3.12", + "installTools": false, + // NOTE: not working, so install with pip in postCreateCommand + // "toolsToInstall":"jupyterlab_myst,pixi-kernel", + "installJupyterlab": true } + }, + "mounts": [ + "source=${localWorkspaceFolderBasename}-pixi,target=${containerWorkspaceFolder}/.pixi,type=volume" + ], + // These should execute in order below + "onCreateCommand": { + "configure_jupyterlab": "pip install jupyterlab_myst pixi-kernel" + }, + "postCreateCommand": { + "set_pixi_permissions": "sudo chown vscode .pixi" + }, + "postStartCommand": { + "configure_jupyterlab": "pixi install" } } diff --git a/.devcontainer/scipy2023/devcontainer.json b/.devcontainer/scipy2023/devcontainer.json index 353cfda4..22f85b77 100644 --- a/.devcontainer/scipy2023/devcontainer.json +++ b/.devcontainer/scipy2023/devcontainer.json @@ -1,8 +1,5 @@ { - "build": { - "dockerfile": "../Dockerfile", - "context": "../../conda" - }, + "image": "quay.io/pangeo/pangeo-notebook:2023.07.05", "postCreateCommand": { "jupyterlab": "mkdir /home/jovyan/.jupyter && cp ${containerWorkspaceFolder}/.devcontainer/scipy2023/jupyter_lab_config.py /home/jovyan/.jupyter/jupyter_lab_config.py", "vscode": "mkdir ${containerWorkspaceFolder}/.vscode && cp ${containerWorkspaceFolder}/.devcontainer/scipy2023/tasks.json ${containerWorkspaceFolder}/.vscode/tasks.json" diff --git a/.devcontainer/scipy2023/jupyter_lab_config.py b/.devcontainer/scipy2023/jupyter_lab_config.py index 0bdc1e31..c8857f0d 100644 --- a/.devcontainer/scipy2023/jupyter_lab_config.py +++ b/.devcontainer/scipy2023/jupyter_lab_config.py @@ -1,15 +1,3 @@ -# Configuration file for lab. - c = get_config() # noqa - -## The default URL to redirect to from `/` -# Default: '/lab' -c.LabApp.default_url = '/lab/tree/workshops/scipy2023/index.ipynb' - -## Set the Access-Control-Allow-Origin header -# -# Use '*' to allow any origin to access your server. -# -# Takes precedence over allow_origin_pat. -# Default: '' -c.ServerApp.allow_origin = '*' +c.LabApp.default_url = "/lab/tree/workshops/scipy2023/index.ipynb" +c.ServerApp.allow_origin = "*" diff --git a/.devcontainer/scipy2024/devcontainer.json b/.devcontainer/scipy2024/devcontainer.json index 8c7934c0..83912331 100644 --- a/.devcontainer/scipy2024/devcontainer.json +++ b/.devcontainer/scipy2024/devcontainer.json @@ -1,8 +1,5 @@ { - "build": { - "dockerfile": "../Dockerfile", - "context": "../../conda" - }, + "image": "quay.io/pangeo/pangeo-notebook:2024.07.08", "postCreateCommand": { "jupyterlab": "mkdir /home/jovyan/.jupyter && cp ${containerWorkspaceFolder}/.devcontainer/scipy2024/jupyter_lab_config.py /home/jovyan/.jupyter/jupyter_lab_config.py", "vscode": "mkdir ${containerWorkspaceFolder}/.vscode && cp ${containerWorkspaceFolder}/.devcontainer/scipy2024/tasks.json ${containerWorkspaceFolder}/.vscode/tasks.json" diff --git a/.devcontainer/scipy2024/jupyter_lab_config.py b/.devcontainer/scipy2024/jupyter_lab_config.py index 9c612074..441e67d3 100644 --- a/.devcontainer/scipy2024/jupyter_lab_config.py +++ b/.devcontainer/scipy2024/jupyter_lab_config.py @@ -1,15 +1,3 @@ -# Configuration file for lab. - c = get_config() # noqa - -## The default URL to redirect to from `/` -# Default: '/lab' -c.LabApp.default_url = '/lab/tree/workshops/scipy2024/index.ipynb' - -## Set the Access-Control-Allow-Origin header -# -# Use '*' to allow any origin to access your server. -# -# Takes precedence over allow_origin_pat. -# Default: '' -c.ServerApp.allow_origin = '*' +c.LabApp.default_url = "/lab/tree/workshops/scipy2024/index.ipynb" +c.ServerApp.allow_origin = "*" diff --git a/.devcontainer/scipy2025/Dockerfile b/.devcontainer/scipy2025/Dockerfile new file mode 100644 index 00000000..78ab0ed4 --- /dev/null +++ b/.devcontainer/scipy2025/Dockerfile @@ -0,0 +1,13 @@ +FROM mcr.microsoft.com/devcontainers/base:noble + +ARG PIXI_VERSION=v0.49.0 + +RUN curl -L -o /usr/local/bin/pixi -fsSL --compressed "https://github.com/prefix-dev/pixi/releases/download/${PIXI_VERSION}/pixi-$(uname -m)-unknown-linux-musl" \ + && chmod +x /usr/local/bin/pixi \ + && pixi info + +# set some user and workdir settings to work nicely with vscode +USER vscode +WORKDIR /home/vscode + +RUN echo 'eval "$(pixi completion -s bash)"' >> /home/vscode/.bashrc diff --git a/.devcontainer/scipy2025/devcontainer.json b/.devcontainer/scipy2025/devcontainer.json new file mode 100644 index 00000000..39fc8725 --- /dev/null +++ b/.devcontainer/scipy2025/devcontainer.json @@ -0,0 +1,42 @@ +// https://pixi.sh/latest/integration/editor/vscode/#devcontainer-extension +{ + "name": "scipy2025-xarray-tutorial", + "build": { + "dockerfile": "Dockerfile", + "context": "../../" + }, + "customizations": { + "vscode": { + "settings": {}, + "extensions": [ + "ms-toolsai.jupyter", + "ms-python.python", + "executablebookproject.myst-highlight" + ] + } + }, + "features": { + // ensure GitHub Codespace 'Open with JupyterLab' works + // TODO: figure out why it doesn't work w/ jupyterlab in the pixi environment + "ghcr.io/devcontainers/features/python:1": { + "version": "3.12", + "installTools": false, + // NOTE: not working, so install with pip in postCreateCommand + // "toolsToInstall":"jupyterlab_myst,pixi-kernel", + "installJupyterlab": true + } + }, + "mounts": [ + "source=${localWorkspaceFolderBasename}-pixi,target=${containerWorkspaceFolder}/.pixi,type=volume" + ], + // These should execute in order below + "onCreateCommand": { + "configure_jupyterlab": "pip install jupyterlab_myst pixi-kernel" + }, + "postCreateCommand": { + "set_pixi_permissions": "sudo chown vscode .pixi" + }, + "postStartCommand": { + "configure_jupyterlab": "pixi install" + } +} diff --git a/.devcontainer/tasks.json b/.devcontainer/tasks.json deleted file mode 100644 index 3b034156..00000000 --- a/.devcontainer/tasks.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "version": "2.0.0", - "tasks": [ - { - "label": "jupyterlab", - "type": "shell", - "command": "/srv/conda/envs/notebook/bin/jupyter lab --LabApp.default_url='/lab/tree/overview/xarray-in-45-min.ipynb' --ServerApp.allow_origin='*' --no-browser", - "presentation": { - "reveal": "always" - }, - "runOptions": { - "runOn": "folderOpen" - } - } - ] -} diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..997504b4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting & preventing 3-way merges +pixi.lock merge=binary linguist-language=YAML linguist-generated=true -diff diff --git a/.github/actions/setup-pixi/action.yml b/.github/actions/setup-pixi/action.yml new file mode 100644 index 00000000..2d7902df --- /dev/null +++ b/.github/actions/setup-pixi/action.yml @@ -0,0 +1,11 @@ +name: 'Setup Pixi' +description: 'Create Python environment for GitHub Action Job' + +runs: + using: 'composite' + steps: + - uses: prefix-dev/setup-pixi@v0.8.10 + with: + manifest-path: pyproject.toml + cache: true + activate-environment: true diff --git a/.github/dependabot.yml b/.github/dependabot.yml index b117cbb3..482483ab 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,11 +1,7 @@ # Regularly update Docker tags and Actions steps version: 2 updates: - - package-ecosystem: "docker" - directory: "/.devcontainer" + - package-ecosystem: 'github-actions' + directory: '/.github' schedule: - interval: "daily" - - package-ecosystem: "github-actions" - directory: "/.github" - schedule: - interval: "monthly" + interval: 'monthly' diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml index 2458f1d9..915b0cbe 100644 --- a/.github/workflows/main.yaml +++ b/.github/workflows/main.yaml @@ -4,7 +4,7 @@ on: push: branches: main paths-ignore: - - ".devcontainer/**" + - '.devcontainer/**' # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages permissions: @@ -30,18 +30,11 @@ jobs: with: path: _build # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20240517 + key: jupyterbook-20250701 - - 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 + - uses: ./.github/actions/setup-pixi - name: Build JupyterBook - # NOTE: login shell activates conda environment - shell: bash -el {0} run: | jupyter-book build ./ --warningiserror --keep-going diff --git a/.github/workflows/nocache.yaml b/.github/workflows/nocache.yaml new file mode 100644 index 00000000..1fbc7ed4 --- /dev/null +++ b/.github/workflows/nocache.yaml @@ -0,0 +1,41 @@ +name: Rebuild Entire Jupyter Book on all Platforms + +on: + workflow_dispatch: + +# Allow one concurrent deployment +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + build: + name: Build on ${{ matrix.runs-on }} + runs-on: ${{ matrix.runs-on }} + strategy: + fail-fast: false + matrix: + runs-on: [ubuntu-latest, macos-latest, windows-latest] + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - uses: ./.github/actions/setup-pixi + + # https://github.com/xarray-contrib/xarray-tutorial/issues/311 + - name: Configure graphviz + if: matrix.runs-on == 'macos-latest' + run: | + dot -c + + - name: Build JupyterBook + id: jb-build + continue-on-error: true + run: | + jupyter-book build ./ --warningiserror --keep-going + + - name: Dump Build Logs + if: steps.jb-build.outcome == 'failure' + run: | + cat _build/html/reports/**/*.log diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml index 2e4a8f43..5cf4a6af 100644 --- a/.github/workflows/pull_request.yaml +++ b/.github/workflows/pull_request.yaml @@ -4,7 +4,7 @@ on: pull_request: types: [opened, synchronize, reopened, closed] paths-ignore: - - ".devcontainer/**" + - '.devcontainer/**' concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -13,9 +13,6 @@ concurrency: jobs: preview: runs-on: ubuntu-latest - defaults: - run: - shell: bash -el {0} steps: - name: Checkout repository if: github.event.action != 'closed' @@ -27,15 +24,9 @@ jobs: with: path: _build # NOTE: change key to "jupyterbook-DATE" to force rebuilding cache - key: jupyterbook-20240517 + key: jupyterbook-20250701 - - 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 + - uses: ./.github/actions/setup-pixi - name: Build JupyterBook if: github.event.action != 'closed' diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml index ea4d17e0..d29db2de 100644 --- a/.github/workflows/qaqc.yaml +++ b/.github/workflows/qaqc.yaml @@ -1,11 +1,12 @@ name: QualityContol on: + workflow_dispatch: pull_request: branches: - main paths-ignore: - - ".devcontainer/**" + - '.devcontainer/**' concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -14,29 +15,11 @@ concurrency: jobs: quality-control: runs-on: ubuntu-latest - defaults: - run: - shell: bash -el {0} steps: - uses: actions/checkout@v4 - - 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 - - # NOTE: this isn't a comprehensive spellcheck, just common typos - - name: Spellcheck - if: always() - uses: codespell-project/actions-codespell@v2 - with: - check_filenames: true - check_hidden: true - skip: ".git,*.js,qaqc.yml" - ignore_words_list: hist,nd + - uses: ./.github/actions/setup-pixi # borrowed from https://github.com/ProjectPythia/pythia-foundations/blob/main/.github/workflows/link-checker.yaml - name: Disable Notebook Execution Before Linkcheck @@ -50,8 +33,10 @@ jobs: with open('./_config.yml', 'w') as f: yaml.dump(data, f) + # Checking links is flaky, so continue-on-error: true - name: Check External Links timeout-minutes: 5 + continue-on-error: true if: always() run: | jupyter-book build ./ --builder linkcheck diff --git a/.github/workflows/surge_preview.yml b/.github/workflows/surge_preview.yml index 69937ab7..e9725198 100644 --- a/.github/workflows/surge_preview.yml +++ b/.github/workflows/surge_preview.yml @@ -2,7 +2,7 @@ name: Pull Request Preview on: workflow_run: - workflows: ["Pull Request Build"] + workflows: ['Pull Request Build'] types: - completed diff --git a/.gitignore b/.gitignore index 4cc41a3d..5d068dd6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ # project/repo specific +conf.py advanced/backends/*.bin scipy-tutorial/dask-report-large-chunk.html mydask.png @@ -9,6 +10,7 @@ _build/ *.tiff *.tif dask-worker-space/ +.jupyter_cache # Byte-compiled / optimized / DLL files __pycache__/ @@ -118,3 +120,9 @@ venv.bak/ # misc .DS_Store .vscode/ + +# pixi environments +.pixi +*.egg-info + +notes/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8bec18d3..7f5e07b3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer @@ -11,36 +11,48 @@ repos: - id: check-json exclude: ^.devcontainer/ - id: check-yaml - - id: double-quote-string-fixer - - repo: https://github.com/psf/black - rev: 24.4.2 + - repo: https://github.com/codespell-project/codespell + rev: v2.4.2 + hooks: + - id: codespell + + - repo: https://github.com/psf/black-pre-commit-mirror + rev: 26.5.1 hooks: - id: black - id: black-jupyter - repo: https://github.com/keewis/blackdoc - rev: v0.3.9 + rev: v0.4.6 hooks: - id: blackdoc + additional_dependencies: ['black==26.5.1'] + - id: blackdoc-autoupdate-black - - repo: https://github.com/PyCQA/flake8 - rev: 7.0.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.20 hooks: - - id: flake8 + - id: ruff-check + args: ['--fix', '--show-fixes'] - - repo: https://github.com/PyCQA/isort - rev: 5.13.2 + - repo: https://github.com/rbubley/mirrors-prettier + rev: v3.9.4 hooks: - - id: isort + - id: prettier + args: ['--cache-location=.prettier_cache/cache'] + exclude: 'pixi.lock' - - repo: https://github.com/pre-commit/mirrors-prettier - rev: v4.0.0-alpha.8 + - repo: https://github.com/ComPWA/taplo-pre-commit + rev: v0.9.3 hooks: - - id: prettier + - id: taplo-format + args: ['--option', 'array_auto_collapse=false'] + - id: taplo-lint + args: ['--no-schema'] - repo: https://github.com/kynan/nbstripout - rev: 0.7.1 + rev: 0.9.1 hooks: - id: nbstripout args: [--extra-keys=metadata.kernelspec metadata.language_info.version] diff --git a/.prettierignore b/.prettierignore deleted file mode 100644 index 9b89f28d..00000000 --- a/.prettierignore +++ /dev/null @@ -1 +0,0 @@ -conda/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 5d3ed768..f9940fab 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -22,7 +22,9 @@ guidelines: 1. Be concise and limited to one or two topics, such that a reader can get through the example within a few minutes of reading 1. Be of general relevance to Xarray users, and so not too specific on a particular problem or use case. -## Fork this repository +## Contribution process + +### Fork this repository We recommend first forking this repository and creating a local copy: @@ -31,24 +33,28 @@ git clone https://github.com/YOURACCOUNT/xarray-tutorial.git cd xarray-tutorial ``` -## Create a Python environment - -You'll need `conda` or `mamba`, which can be installed from https://github.com/conda-forge/miniforge +### Create a Python environment -We also use [conda-lock](https://conda.github.io/conda-lock/) to ensure we have reproducible environments across different operating systems +You'll need `pixi` or `conda` or `mamba`, which can be installed from https://github.com/conda-forge/miniforge We also use [pre-commit hooks](https://pre-commit.com) to run styling and other checks before committing code. +#### Using pixi (recommended) + +``` +pixi install +pixi shell # exit ``` -conda-lock install -f conda/conda-lock.yml -# Or latest package versions: `mamba env create -f conda/environment-unpinned.yml` -conda activate xarray-tutorial +#### Using conda +``` +mamba env create -f .binder/environment.yml -n xarray-tutorial +conda activate xarray-tutorial # conda deactivate pre-commit install ``` -## Add content +### Add content Develop your new content on a branch. See [JupyterBook Docs](https://jupyterbook.org/en/stable/intro.html) for guides on adding `.md`, `.ipynb` and other content. @@ -58,18 +64,45 @@ git add . git commit -m "added pages x,y and improved z" ``` -## Preview your changes +### Preview your changes Running jupyterbook will execute notebooks and render HTML pages for the website. Be sure to fix any execution errors and preview the website in your web browser to make sure everything looks good! ``` -jb build . +jupyter-book build ./ --warningiserror --keep-going +# Or "pixi run build" ``` -## Open a pull request +### Open a pull request ``` git push ``` Follow the link reported in a terminal to open a pull request! + +## Instructions for environment management + +[`pixi`](https://pixi.sh) can be used to create and update a multi-platform lockfile, so a reproducible set of package versions is installed across different operating systems. + +Dependencies (with optional pins) are specified in the `pyproject.toml` file, and specific locked versions for all platforms are kept in `pixi.lock`. + +Install environment from the lockfile + +``` +pixi install +pixi shell # activate environment, "exit" to deactivate +``` + +Upgrade all packages to latest versions: + +``` +pixi upgrade +``` + +## Render conda/mamba environment files + +``` +pixi project export conda-environment -p linux-64 .binder/environment.yml +pixi project export conda-explicit-spec -p linux-64 /tmp +``` diff --git a/README.md b/README.md index 859df993..bca652ed 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # Xarray Tutorial -[![CI](https://github.com/xarray-contrib/xarray-tutorial/workflows/CI/badge.svg?branch=main)](https://github.com/xarray-contrib/xarray-tutorial/actions?query=branch%3Amain) +[![Deploy Website to GitHub Pages](https://github.com/xarray-contrib/xarray-tutorial/actions/workflows/main.yaml/badge.svg)](https://github.com/xarray-contrib/xarray-tutorial/actions/workflows/main.yaml) [![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://tutorial.xarray.dev) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=workshops/scipy2024/index.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=workshops/scipy2025/index.ipynb) This is the repository for a Jupyter Book website with tutorial material for [Xarray](https://github.com/pydata/xarray), _an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!_ @@ -16,36 +16,47 @@ All notebooks can be run via the Mybinder.org 'Launch Binder' badge at the top o #### Github Codespaces -This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud" - with the conda environment specification in the [`conda-lock.yml`](conda/conda-lock.yml) file. +This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud" [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/xarray-contrib/xarray-tutorial/tree/main) ☝️ Click the button above to go to options window to launch a Github codespace. -A codespace is a development environment that's hosted in the cloud. GitHub currently gives every user [120 vCPU hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop or shut down your codespace when you are done by going to this page (https://github.com/codespaces).** -Once your codespace is launched, the following happens: +You can also use the GitHub CLI to launch a codespace -- [Visual Studio Code](https://code.visualstudio.com/) Interface will open up within your browser. -- A built in terminal will open and it will execute `jupyter lab` automatically. -- Once you see a url to click within the terminal, simply `cmd + click` the given url. -- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface. +``` +# This will output a URL to use VSCode in the browser +gh codespace create --repo xarray-contrib/xarray-tutorial +# Optionally launch JupyterLab instead of vscode (after codespace has been created) +gh codespace jupyter +``` #### Locally -You can also run these notebooks on your own computer! We recommend using [`micromamba`](https://mamba.readthedocs.io/en/latest/installation.html#micromamba) or [`conda-lock`](https://conda-incubator.github.io/conda-lock/) to ensure a fully reproducible Python environment: +You can also run these notebooks on your own computer! We recommend using [`pixi`](https://pixi.sh/latest/#installation) to ensure a fully reproducible Python environment: ```bash git clone https://github.com/xarray-contrib/xarray-tutorial.git cd xarray-tutorial +pixi run tutorial +``` + +### Building the Documentation Locally + +To build and serve the tutorial website locally with live reload: -conda-lock install conda/conda-lock.yml --name xarray-tutorial -# Or `micromamba create -n xarray-tutorial -f conda-lock.yml` -# Or latest package versions: `mamba env create -f conda/environment-unpinned.yml` +```bash +pixi run watch +``` + +This watches for changes, rebuilds, and serves at http://localhost:8000. -conda activate xarray-tutorial -jupyter lab +To build without serving: + +```bash +pixi run build ``` ## Contributing diff --git a/_config.yml b/_config.yml index e5301220..6ae842ed 100644 --- a/_config.yml +++ b/_config.yml @@ -1,21 +1,24 @@ # Learn more at https://jupyterbook.org/customize/config.html -title: "" +title: '' author: The Xarray Community -copyright: "2024" +copyright: '2025' logo: images/logo.png only_build_toc_files: true +exclude_patterns: [.github, .pixi] # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository html: # NOTE: this announcement shows up on all pages - announcement: 'The Xarray 2024 User Survey is live. Please take ~5 minutes to fill it out and help us improve Xarray.' - #announcement: 'ℹ️ SciPy 2024 Tutorial Attendees. Click here .' + #announcement: 'The Xarray 2024 User Survey is live. Please take ~5 minutes to fill it out and help us improve Xarray.' + #announcement: 'ℹ️ SciPy 2025 Tutorial Attendees. Click here! .' home_page_in_navbar: false use_edit_page_button: true use_issues_button: true use_repository_button: true - google_analytics_id: G-JRQHYVFQR7 extra_footer: '

Xarray is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community.
Theme by the Executable Book Project.

Content licensed under the terms of the Apache 2.0 License.' + analytics: + google_analytics_id: G-JRQHYVFQR7 + favicon: 'images/favicon.ico' parse: # https://jupyterbook.org/content/content-blocks.html?highlight=myst%20substitution#define-substitutions-for-your-whole-book @@ -37,7 +40,7 @@ parse: # Force re-execution of notebooks on each build. # See https://jupyterbook.org/content/execute.html execute: - execute_notebooks: "cache" + execute_notebooks: 'cache' allow_errors: false # Per-cell notebook execution limit (seconds) timeout: 300 @@ -50,11 +53,11 @@ latex: # Configure your Binder links, such as the URL of the BinderHub. launch_buttons: notebook_interface: jupyterlab - binderhub_url: "https://mybinder.org" + binderhub_url: 'https://mybinder.org' # Information about where the book exists on the web repository: - url: "https://github.com/xarray-contrib/xarray-tutorial" + url: 'https://github.com/xarray-contrib/xarray-tutorial' branch: main # Bibliography @@ -75,7 +78,7 @@ sphinx: config: language: en # accessibility # application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json - suppress_warnings: ["mystnb.unknown_mime_type", "misc.highlighting_failure"] + suppress_warnings: ['mystnb.unknown_mime_type', 'misc.highlighting_failure'] codeautolink_concat_default: True notfound_context: body: "

Whoops! 404 Page Not Found

\n\n

Sorry, this page doesn't exist. Many sections of this book have been updated recently.

Try the search box 🔎 to find what you're looking for!

" @@ -89,23 +92,23 @@ sphinx: intersphinx_mapping: xarray: - - https://docs.xarray.dev/en/latest/ + - https://docs.xarray.dev/en/stable - null numpy: - https://numpy.org/doc/stable - null + pandas: + - https://pandas.pydata.org/docs + - null scipy: - https://docs.scipy.org/doc/scipy - null - matplotlib: - - https://matplotlib.org/stable/ + python: + - https://docs.python.org/3 - null dask: - - https://docs.dask.org/en/latest + - https://docs.dask.org/en/stable - null - python: - - https://docs.python.org/3/ - - null - pandas: - - https://pandas.pydata.org/pandas-docs/stable + matplotlib: + - https://matplotlib.org/stable - null diff --git a/_static/dataarray.png b/_static/dataarray.png new file mode 100644 index 00000000..b8d6b364 Binary files /dev/null and b/_static/dataarray.png differ diff --git a/_static/dataset.png b/_static/dataset.png new file mode 100644 index 00000000..7e843144 Binary files /dev/null and b/_static/dataset.png differ diff --git a/_static/datatree1.png b/_static/datatree1.png new file mode 100644 index 00000000..0f806eca Binary files /dev/null and b/_static/datatree1.png differ diff --git a/_static/datatree2.png b/_static/datatree2.png new file mode 100644 index 00000000..463c4dd1 Binary files /dev/null and b/_static/datatree2.png differ diff --git a/_static/style.css b/_static/style.css index b1024666..842c6d9b 100644 --- a/_static/style.css +++ b/_static/style.css @@ -1,3 +1,11 @@ .bd-header-announcement { - background-color: var(--pst-color-info-bg); + background-color: var(--pst-color-accent); +} + +/* workaround Pydata Sphinx theme using light colors for widget cell outputs in dark-mode */ +/* works for many widgets but not for Xarray html reprs */ +/* https://github.com/pydata/pydata-sphinx-theme/issues/2189 */ +html[data-theme='dark'] div.cell_output .text_html:has(div.xr-wrap) { + background-color: var(--pst-color-on-background) !important; + color: var(--pst-color-text-base) !important; } diff --git a/_static/variable.png b/_static/variable.png new file mode 100644 index 00000000..b578a473 Binary files /dev/null and b/_static/variable.png differ diff --git a/_toc.yml b/_toc.yml index 6c0aeda8..dde8c61c 100644 --- a/_toc.yml +++ b/_toc.yml @@ -18,6 +18,7 @@ parts: - file: fundamentals/01_datastructures - file: fundamentals/01.1_creating_data_structures - file: fundamentals/01.1_io + - file: fundamentals/01_datatree_hierarchical_data.ipynb - file: fundamentals/02_labeled_data.md sections: - file: fundamentals/02.1_indexing_Basic.ipynb @@ -37,18 +38,28 @@ parts: - caption: Intermediate chapters: - - file: intermediate/01-high-level-computation-patterns + - file: intermediate/computation/index + sections: + - file: intermediate/computation/01-high-level-computation-patterns.ipynb + - file: intermediate/computation/hierarchical_computation.ipynb - file: intermediate/indexing/indexing sections: - file: intermediate/indexing/advanced-indexing.ipynb - file: intermediate/indexing/boolean-masking-indexing.ipynb + - file: intermediate/indexing/why-an-index.ipynb - file: intermediate/xarray_and_dask + - file: intermediate/intro-to-zarr.ipynb + - file: intermediate/storage_formats.ipynb - file: intermediate/xarray_ecosystem - file: intermediate/hvplot + - file: intermediate/datastructures-intermediate.ipynb + - file: intermediate/BiologyDataset.ipynb + - file: intermediate/hierarchical_zarr_store.ipynb - file: intermediate/remote_data/index sections: - file: intermediate/remote_data/cmip6-cloud.ipynb - file: intermediate/remote_data/remote-data.ipynb + - file: intermediate/cataloging.ipynb - file: intermediate/data_cleaning/05.1_intro.md sections: - file: intermediate/data_cleaning/05.2_examples.md @@ -58,6 +69,9 @@ parts: - caption: Advanced chapters: + - file: advanced/indexing/indexing.md + sections: + - file: advanced/indexing/why-trees.md - file: advanced/parallel-intro.md - file: advanced/apply_ufunc/apply_ufunc.md sections: @@ -73,14 +87,19 @@ parts: - file: advanced/map_blocks/simple_map_blocks - file: advanced/backends/backends.md sections: + - file: advanced/backends/intro-backends.ipynb + - file: advanced/backends/create_custom_backend.ipynb - file: advanced/backends/1.Backend_without_Lazy_Loading.ipynb - file: advanced/backends/2.Backend_with_Lazy_Loading.ipynb + - file: advanced/backends/rasterio_backend.ipynb - file: advanced/accessors/accessors.md sections: - file: advanced/accessors/01_accessor_examples.ipynb - caption: Workshops chapters: + - file: workshops/scipy2026/index.ipynb + - file: workshops/scipy2025/index.ipynb - file: workshops/scipy2024/index.ipynb - file: workshops/scipy2023/README - file: workshops/thinking-like-xarray/README diff --git a/advanced/accessors/01_accessor_examples.ipynb b/advanced/accessors/01_accessor_examples.ipynb index 8efce1c8..7cc557b2 100644 --- a/advanced/accessors/01_accessor_examples.ipynb +++ b/advanced/accessors/01_accessor_examples.ipynb @@ -98,7 +98,7 @@ "outputs": [], "source": [ "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", - "ds[\"skewair\"] = ds['air'].stats.skewness(dim=\"time\")\n", + "ds[\"skewair\"] = ds[\"air\"].stats.skewness(dim=\"time\")\n", "ds" ] }, @@ -126,8 +126,9 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import os\n", + "\n", + "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "\n", @@ -151,7 +152,7 @@ "\n", " @staticmethod\n", " def _validate(self, req_dim=None, req_vars=None):\n", - " '''\n", + " \"\"\"\n", " Make sure the xarray dataset has the correct dimensions and variables.\n", "\n", " Running this function will check that my dataset has all the needed dimensions and variables\n", @@ -164,7 +165,7 @@ " List of all required dimension names\n", " req_vars : list of str\n", " List of all required variable names\n", - " '''\n", + " \"\"\"\n", "\n", " if req_dim is not None:\n", " if all([dim not in list(self._xrds.dims) for dim in req_dim]):\n", @@ -179,8 +180,8 @@ "\n", " def change_vars_to_coords(\n", " self,\n", - " req_dim=['ny', 'nx'],\n", - " req_vars={'xaxis': ['nx'], 'yaxis': ['ny']},\n", + " req_dim=[\"ny\", \"nx\"],\n", + " req_vars={\"xaxis\": [\"nx\"], \"yaxis\": [\"ny\"]},\n", " ):\n", " \"\"\"\n", " Turn the xaxis and y axis variables into coordinates.\n", @@ -195,8 +196,8 @@ "\n", " self._validate(self, req_dim, req_vars)\n", "\n", - " self._xrds = self._xrds.swap_dims({'ny': 'yaxis', 'nx': 'xaxis'})\n", - " self._xrds = self._xrds.rename({'xaxis': 'x', 'yaxis': 'y'})\n", + " self._xrds = self._xrds.swap_dims({\"ny\": \"yaxis\", \"nx\": \"xaxis\"})\n", + " self._xrds = self._xrds.rename({\"xaxis\": \"x\", \"yaxis\": \"y\"})\n", "\n", " return self._xrds\n", "\n", @@ -207,7 +208,7 @@ " \"\"\"\n", "\n", " reorged = []\n", - " for reorg_var in ['vx', 'vy', 'err']:\n", + " for reorg_var in [\"vx\", \"vy\", \"err\"]:\n", " ds = self.reorg_var_time(reorg_var)\n", " reorged.append(ds)\n", "\n", @@ -232,12 +233,12 @@ " das_to_reorg = [self._xrds[var] for var in to_reorg]\n", "\n", " # add the time dimension\n", - " das_to_reorg = [das_to_reorg[var].expand_dims('time') for var in range(len(das_to_reorg))]\n", + " das_to_reorg = [das_to_reorg[var].expand_dims(\"time\") for var in range(len(das_to_reorg))]\n", "\n", " # update variable name to remove time\n", " das_to_reorg = [das_to_reorg[var].rename(reorg_var) for var in range(len(das_to_reorg))]\n", "\n", - " ds = xr.concat(das_to_reorg, dim='time')\n", + " ds = xr.concat(das_to_reorg, dim=\"time\")\n", "\n", " return ds" ] @@ -248,7 +249,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = xr.tutorial.open_dataset('ASE_ice_velocity.nc')" + "ds = xr.tutorial.open_dataset(\"ASE_ice_velocity.nc\")" ] }, { @@ -311,7 +312,7 @@ " # as specific to my workflow, saving time and headache later if they were missing and the computation fails\n", " # partway through.\n", " self._validate(\n", - " self, req_dim=['x', 'y', 'dtime'], req_vars={'elevation': ['x', 'y', 'dtime']}\n", + " self, req_dim=[\"x\", \"y\", \"dtime\"], req_vars={\"elevation\": [\"x\", \"y\", \"dtime\"]}\n", " )\n", "\n", " # ----------------------------------------------------------------------\n", @@ -319,7 +320,7 @@ "\n", " @staticmethod\n", " def _validate(self, req_dim=None, req_vars=None):\n", - " '''\n", + " \"\"\"\n", " Make sure the xarray dataset has the correct dimensions and variables\n", "\n", " Parameters\n", @@ -328,7 +329,7 @@ " List of all required dimension names\n", " req_vars : list of str\n", " List of all required variable names\n", - " '''\n", + " \"\"\"\n", "\n", " if req_dim is not None:\n", " if all([dim not in list(self._xrds.dims) for dim in req_dim]):\n", @@ -340,8 +341,8 @@ " # Notice that 'geoid' has been added to the req_vars list\n", " def to_geoid(\n", " self,\n", - " req_dim=['dtime', 'x', 'y'],\n", - " req_vars={'elevation': ['x', 'y', 'dtime', 'geoid']},\n", + " req_dim=[\"dtime\", \"x\", \"y\"],\n", + " req_vars={\"elevation\": [\"x\", \"y\", \"dtime\", \"geoid\"]},\n", " source=None,\n", " ):\n", " \"\"\"\n", @@ -361,11 +362,11 @@ "\n", " # check to make sure you haven't already run this function (and are thus applying the offset twice)\n", " try:\n", - " values = self._xrds.attrs['offset_names']\n", - " assert 'geoid_offset' not in values, \"You've already applied the geoid offset!\"\n", - " values = list([values]) + ['geoid_offset']\n", + " values = self._xrds.attrs[\"offset_names\"]\n", + " assert \"geoid_offset\" not in values, \"You've already applied the geoid offset!\"\n", + " values = list([values]) + [\"geoid_offset\"]\n", " except KeyError:\n", - " values = ['geoid_offset']\n", + " values = [\"geoid_offset\"]\n", "\n", " self._validate(self, req_dim, req_vars)\n", "\n", @@ -374,12 +375,12 @@ " # them to. If not, you will need to reproject and/or resample them to match the data to which you are applying them.\n", " # That step is not included here to emphasize the accessor aspect of the workflow.\n", " with rasterio.open(source) as src:\n", - " geoid = src['geoid_varname']\n", + " geoid = src[\"geoid_varname\"]\n", "\n", " # As noted above, this step will fail or produce unreliable results if your data is not properly gridded\n", - " self._xrds['elevation'] = self._xrds.elevation - geoid\n", + " self._xrds[\"elevation\"] = self._xrds.elevation - geoid\n", "\n", - " self._xrds.attrs['offset_names'] = values\n", + " self._xrds.attrs[\"offset_names\"] = values\n", "\n", " return self._xrds" ] @@ -401,7 +402,7 @@ }, "outputs": [], "source": [ - "ds = ds.geoidxr.to_geoid(source='/Path/to/Custom/source/file.nc')\n", + "ds = ds.geoidxr.to_geoid(source=\"/Path/to/Custom/source/file.nc\")\n", "ds" ] } diff --git a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb index 4fd42fdc..46c4180a 100644 --- a/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb +++ b/advanced/apply_ufunc/automatic-vectorizing-numpy.ipynb @@ -107,8 +107,8 @@ "source": [ "%xmode minimal\n", "\n", - "import xarray as xr\n", "import numpy as np\n", + "import xarray as xr\n", "\n", "xr.set_options(display_expand_data=False)\n", "\n", diff --git a/advanced/apply_ufunc/complex-output-numpy.ipynb b/advanced/apply_ufunc/complex-output-numpy.ipynb index 0ad4c244..e9e80fcf 100644 --- a/advanced/apply_ufunc/complex-output-numpy.ipynb +++ b/advanced/apply_ufunc/complex-output-numpy.ipynb @@ -49,8 +49,8 @@ "source": [ "%xmode minimal\n", "\n", - "import xarray as xr\n", "import numpy as np\n", + "import xarray as xr\n", "\n", "np.set_printoptions(threshold=10, edgeitems=2)\n", "xr.set_options(display_expand_data=False)\n", diff --git a/advanced/apply_ufunc/example-interp.ipynb b/advanced/apply_ufunc/example-interp.ipynb index 2cfe45d1..57318fd1 100644 --- a/advanced/apply_ufunc/example-interp.ipynb +++ b/advanced/apply_ufunc/example-interp.ipynb @@ -65,8 +65,8 @@ "source": [ "%xmode minimal\n", "\n", - "import xarray as xr\n", "import numpy as np\n", + "import xarray as xr\n", "\n", "# limit the amount of information printed to screen\n", "xr.set_options(display_expand_data=False)\n", @@ -562,7 +562,7 @@ "source": [ "`np.vectorize` is a very convenient function but is unfortunately slow. It is only marginally faster than writing a for loop in Python and looping. A common way to get around this is to write a base interpolation function that can handle nD arrays in a compiled language like Fortran and then pass that to `apply_ufunc`.\n", "\n", - "Another option is to use the numba package which provides a very [convenient `guvectorize` decorator](https://numba.pydata.org/numba-doc/latest/user/vectorize.html#the-guvectorize-decorator). Any decorated function gets compiled and will loop over any non-core dimension in parallel when necessary. \n", + "Another option is to use the numba package which provides a very [convenient `guvectorize` decorator](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator). Any decorated function gets compiled and will loop over any non-core dimension in parallel when necessary. \n", "\n", "We need to specify some extra information:\n", "\n", @@ -606,7 +606,7 @@ "tags": [] }, "source": [ - "The warnings are about [object-mode compilation](https://numba.pydata.org/numba-doc/latest/user/performance-tips.html#no-python-mode-vs-object-mode) relating to the `print` statement. This means we don't get much speed up. We'll keep the `print` statement temporarily to make sure that `guvectorize` acts like we want it to." + "The warnings are about [object-mode compilation](https://numba.readthedocs.io/en/stable/user/performance-tips.html) relating to the `print` statement. This means we don't get much speed up. We'll keep the `print` statement temporarily to make sure that `guvectorize` acts like we want it to." ] }, { diff --git a/advanced/apply_ufunc/numba-vectorization.ipynb b/advanced/apply_ufunc/numba-vectorization.ipynb index 6bcbf30f..3da709c5 100644 --- a/advanced/apply_ufunc/numba-vectorization.ipynb +++ b/advanced/apply_ufunc/numba-vectorization.ipynb @@ -25,7 +25,7 @@ "\n", "Another option is to use the [numba package](https://numba.pydata.org/) which provides two very convenient decorators to build [numpy universal functions or ufuncs](https://numba.readthedocs.io/en/stable/user/vectorize.html):\n", "1. [`vectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-vectorize-decorator) for functions that act on scalars, and \n", - "2. [`guvectorize`](https://numba.pydata.org/numba-doc/latest/user/vectorize.html#the-guvectorize-decorator) for functions that operates on subsets of the array along core-dimensions. Any decorated function gets compiled and will loop over the loop dimensions in parallel when necessary. \n", + "2. [`guvectorize`](https://numba.readthedocs.io/en/stable/user/vectorize.html#the-guvectorize-decorator) for functions that operates on subsets of the array along core-dimensions. Any decorated function gets compiled and will loop over the loop dimensions in parallel when necessary. \n", "\n", "For `apply_ufunc` the key concept is that we must provide `vectorize=False` (the default) when using Numba vectorized functions. \n", "Numba handles the vectorization (or looping) and `apply_ufunc` handles converting Xarray objects to bare arrays and handling metadata." @@ -81,7 +81,7 @@ }, "outputs": [], "source": [ - "from numba import vectorize, float64\n", + "from numba import float64, vectorize\n", "\n", "\n", "@vectorize([float64(float64, float64)])\n", @@ -138,7 +138,7 @@ "from numba import guvectorize, int64\n", "\n", "\n", - "@guvectorize([(int64[:], int64, int64[:])], '(n),()->(n)')\n", + "@guvectorize([(int64[:], int64, int64[:])], \"(n),()->(n)\")\n", "def g(x, y, res):\n", " for i in range(x.shape[0]):\n", " res[i] = x[i] + y\n", diff --git a/advanced/backends/backends.md b/advanced/backends/backends.md index 04f81265..d5b28a14 100644 --- a/advanced/backends/backends.md +++ b/advanced/backends/backends.md @@ -6,7 +6,8 @@ You can [read different types of files](https://docs.xarray.dev/en/stable/user-g ```python import xarray as xr -xr.open_dataset("my_file.grib" , engine="cfgrib") + +xr.open_dataset("my_file.grib", engine="cfgrib") ``` Navigating Xarray backends can be confusing, diff --git a/advanced/backends/create_custom_backend.ipynb b/advanced/backends/create_custom_backend.ipynb new file mode 100644 index 00000000..6cf850ec --- /dev/null +++ b/advanced/backends/create_custom_backend.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Create your own Xarray Backend\n", + "In this lesson, we will learn about and how to create your own custom xarray backend\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about xarray's support for custom backends\n", + "- Learn how to use a custom `imageio` backend to open and manipulate GIFs\n", + "- Learn how to extend the `imagegio` backend to write out a GIF\n", + ":::\n", + "\n", + "## Why should you create your own Xarray backend?\n", + "\n", + "- Allows you to use xarray's interface\n", + " - Attribute-like syntax, dict-like syntax and label based indexing\n", + "- You don’t need to integrate any code in Xarray\n", + "- Easy and fast!\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Setting up the BackendEntrypoint\n", + "\n", + "To set up a `BackendEntrypoint` we can implement a subclass of `BackendEntrypoint` and expose the `open_dataset` method to it. For this tutorial we have the `ImageIOBackend` already defined but we will extend the functionally to write GIFS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "image_path = \"io.gif\"" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "We can write a simple image reader function that we can then plug into our `MyBackendEntrypoint` class. For this example we are going to use `imageio` an image reader and writer library. With `imageio` you can read an image file with `iio.imopen`\n", + "\n", + ":::{note}\n", + "The `ImageIOBackend` also defines a `ImageIOBackendArray` with basic indexing.\n", + ":" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "import imageio as iio\n", + "from xarray.backends import BackendEntrypoint\n", + "\n", + "\n", + "def imageio_open(\n", + " filename_or_obj,\n", + "):\n", + " img = iio.imopen(filename_or_obj, io_mode=\"r\")\n", + " return img.read()\n", + "\n", + "\n", + "imageio_open(image_path)\n", + "\n", + "\n", + "class MyImageReader(BackendEntrypoint):\n", + " def open_dataset(\n", + " self,\n", + " filename_or_obj,\n", + " *,\n", + " drop_variables=None,\n", + " ):\n", + " return imageio_open(filename_or_obj)" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Reading image data\n", + "\n", + "Lets use our `ImageIOBackend` to open a GIF.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "from imageio_ import ImageIOBackend\n", + "\n", + "gif_ds = xr.open_dataset(image_path, engine=ImageIOBackend)\n", + "gif_ds" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "### Examining our image dataset\n", + "\n", + "Since our image is a `Dataset` object we can use xarray's interface for `Dataset` objects.\n", + "\n", + "Let's try listing all of the variables, dimensions and selecting data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.variables" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "We can list our dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.dims" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Let's try getting our `DataArray`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds[\"data\"]" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "## GIF metadata\n", + "We can examine, update and add metadata to our `Dataset` object. \n", + "\n", + "We can examine the attributes in our GIF \"data\" variable with the `.attrs` method" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "gif_ds.data.attrs" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you add a new attribute to our GIF \"data\" variable called \"fps\". This is the frames per second we can write our GIF to. You can set it to any value.\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "gif_ds.data.attrs['fps'] = 100\n", + "```\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### GIF writer\n", + "We can extend our backend with an GIF writer. Here we use `matplotlib`'s animation functions and `PillowWriter`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.animation as animation\n", + "import matplotlib.pyplot as plt\n", + "from imageio_ import ImageIOBackend\n", + "from matplotlib.animation import PillowWriter\n", + "\n", + "\n", + "class SimpleImageWriter(ImageIOBackend):\n", + " def to_giff(\n", + " self,\n", + " dataset,\n", + " variable,\n", + " time_dim,\n", + " out_filename,\n", + " **kwargs,\n", + " ):\n", + " fig, ax = plt.subplots()\n", + "\n", + " frames = []\n", + " variable_da = dataset[variable]\n", + "\n", + " for time in variable_da[time_dim]:\n", + " variable_da = variable_da.transpose(\"time\", \"width\", \"height\", \"color\")\n", + " to_plot = ax.pcolormesh(\n", + " variable_da.height,\n", + " variable_da.width,\n", + " variable_da.sel(time=time),\n", + " animated=True,\n", + " shading=\"auto\",\n", + " **kwargs,\n", + " )\n", + "\n", + " frames.append([to_plot])\n", + "\n", + " try:\n", + " writer = PillowWriter(fps=variable_da.attrs[\"fps\"], **kwargs)\n", + " except KeyError:\n", + " writer = PillowWriter(fps=50, **kwargs)\n", + "\n", + " ani = animation.ArtistAnimation(fig, frames, blit=True, repeat=True)\n", + " ani.save(filename=out_filename, writer=writer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "img_writer = SimpleImageWriter()\n", + "img_writer.to_giff(gif_ds.compute(), \"data\", \"time\", \"io_writer.gif\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/backends/imageio_.py b/advanced/backends/imageio_.py new file mode 100644 index 00000000..95c33e1f --- /dev/null +++ b/advanced/backends/imageio_.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +import xarray as xr + +if TYPE_CHECKING: + import os + from typing import Any, Literal, Protocol + + import numpy.typing as npt + + class FileLike(Protocol): ... + + class LockType(Protocol): + def __enter__(self) -> Any: ... + + def acquire(self, blocking: bool = True, timeout: int = -1) -> bool: ... + def release(self) -> None: ... + + IndexerType = int | slice | npt.NDArray[np.integer] + FilenameOrObjectType = str | os.PathLike | bytes | FileLike + + +@dataclass +class ImageIOBackendArray(xr.backends.BackendArray): + filename_or_obj: FilenameOrObjectType + + shape: tuple[int] + dtype: npt.DtypeLike + + lock: LockType + + def __getitem__(self, key: tuple[IndexerType]): + return xr.core.indexing.explicit_indexing_adapter( + key, + self.shape, + xr.core.indexing.IndexingSupport.BASIC, + self.basic_indexing, + ) + + def basic_indexing(self, key: tuple[IndexerType]) -> npt.NDArray: + import imageio.v3 as iio + + with self.lock, iio.imopen(self.filename_or_obj, io_mode="r") as f: + if key == (slice(None),) * len(self.shape): + return f.read() + + first_indexer = key[0] + if isinstance(first_indexer, int): + data = f.read(index=first_indexer, mode="P", writable=True) + + remaining_indexers = key[1:] + else: + if isinstance(first_indexer, slice): + indices = range(*first_indexer.indices(self.shape[0])) + else: + indices = first_indexer + + data = np.concatenate([f.read(index=index, mode="P") for index in indices], axis=0) + + remaining_indexers = (..., *key[1:]) + + return data[remaining_indexers] + + +class ImageIOBackend(xr.backends.BackendEntrypoint): + def open_dataset( + self, + filename_or_obj: FilenameOrObjectType, + *, + drop_variables: bool | None = None, + mode: Literal["grayscale", "color"] = "color", + ) -> xr.Dataset: + import imageio.v3 as iio + + with iio.imopen(filename_or_obj, io_mode="r") as f: + properties = f.properties() + metadata = f.metadata() + + dims = ["time", "height", "width", "color"] + + background = metadata["background"] + duration = metadata["duration"] + loop = metadata["loop"] + + shape = properties.shape + dtype = properties.dtype + + if isinstance(duration, (int, float)): + time_values = np.timedelta64(duration, "ms") * np.arange(shape[0]) + else: + time_values = np.array(duration, dtype="timedelta64[ms]") + + time = xr.indexes.PandasIndex(pd.Index(time_values), dim="time") + + backend_array = ImageIOBackendArray( + filename_or_obj=filename_or_obj, + shape=shape, + dtype=dtype, + lock=xr.backends.locks.SerializableLock(), + ) + data = xr.core.indexing.LazilyIndexedArray(backend_array) + + var = xr.Variable( + dims=dims, + data=data, + attrs={"loop": loop}, + encoding={ + "preferred_chunks": dict(zip(dims, (1, *shape[1:]))), + "fill_value": background, + }, + ) + coords = xr.Coordinates.from_xindex(time).assign(color=["red", "green", "blue"]) + + return xr.Dataset({"data": var}, coords=coords) diff --git a/advanced/backends/intro-backends.ipynb b/advanced/backends/intro-backends.ipynb new file mode 100644 index 00000000..d7002d09 --- /dev/null +++ b/advanced/backends/intro-backends.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Introduction to Xarray Backends\n", + "In this lesson, we will learn about and how to use xarray's backends\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about xarray's internal and external backends\n", + "- Learn how xarray selects backend engines\n", + "- Learn how to specify backend engines\n", + ":::\n", + "\n", + "## What are Xarray Backends\n", + "\n", + "Xarray supports direct serialization and IO to several file formats, through the use of internal and external backends. Xarray's internal backends cover many common data formats such as \"netCDF4\", \"h5netcdf\", and \"zarr\". These backends provide a set of instructions that tells xarray how read data and store it into a `Dataset`, `DataTree` or `DataTree` model and are stored in the underlying “backend”. \n", + "\n", + "Xarray also supports external backends such as \"rioxarray\" and \"cfgrib\" and even creating your own backend.\n", + "\n", + "\n", + ":::{admonition} See also\n", + "You can learn more about xarray's [internal](https://tutorial.xarray.dev/advanced/backends/backends.html#links-to-internal-backends) and [external](https://tutorial.xarray.dev/advanced/backends/backends.html#links-to-external-backends-not-comprehensive) with our [guide](https://docs.xarray.dev/en/latest/user-guide/io.html) to backends.\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Reading a dataset with the netCDF4 engine\n", + "\n", + "We can read in a dataset by selecting the \"netcdf4\" engine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "xr.tutorial.open_dataset(\"air_temperature\", engine=\"netcdf4\")" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## Internal Backend Selection\n", + "\n", + "When opening a file or URL without explicitly specifying the ``engine`` parameter,\n", + "xarray automatically selects an appropriate backend based on the file path or URL.\n", + "The backends are tried in order: **netcdf4 → h5netcdf → scipy → pydap → zarr**.\n" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Check for available backends\n", + "\n", + "We can get a list of our available engines with `list_engines`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "xr.backends.list_engines()" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Reading without specifying engine\n", + "\n", + "When opening a file or URL without explicitly specifying the ``engine`` parameter,\n", + "xarray automatically selects an appropriate backend based on the file path or URL.\n", + "The backends are tried in order: **netcdf4 → h5netcdf → scipy → pydap → zarr**.\n", + "\n", + "In the following example this dataset is read with the \"netcdf4\" engine without explicitly setting the `\"engine=netcdf4\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "xr.tutorial.open_dataset(\"air_temperature\")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Setting engine order\n", + "\n", + "You can customize the order in which netcdf4 backends are resolved using `xr.set_options(netcdf_engine_order=)`\n", + "\n", + "Let's update our engine order to have \"h5netcdf\" and the \"netCDF4\" and reopening the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "xr.set_options(netcdf_engine_order=[\"h5netcdf\", \"netcdf4\", \"scipy\"])" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "## Specifying the wrong backend" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "Lets trying specifying a bad engine for opening our dataset\n", + "\n", + ":::{warning}\n", + "This will fail because we cannot use the \"pydap\" engine to read this dataset\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "xr.tutorial.open_dataset(\"air_temperature\", engine=\"pydap\")" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/backends/io.gif b/advanced/backends/io.gif new file mode 100644 index 00000000..b404e3e5 Binary files /dev/null and b/advanced/backends/io.gif differ diff --git a/advanced/backends/ocean.gif b/advanced/backends/ocean.gif new file mode 100644 index 00000000..bfbe061a Binary files /dev/null and b/advanced/backends/ocean.gif differ diff --git a/advanced/backends/rasterio_backend.ipynb b/advanced/backends/rasterio_backend.ipynb new file mode 100644 index 00000000..2b3b9d49 --- /dev/null +++ b/advanced/backends/rasterio_backend.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Xarray's Rasterio backend\n", + "\n", + "In this lesson, we will learn how to use xarray's rasterio backend engine to open GeoTIFF rasters. By the end of the lesson, we will be able to:\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Learn about the GeoTIFF format\n", + "- Lean about xarray's \"rasterio\" backend and the \"rioxarray\" accessor\n", + "- Learn how to read and plot GeoTIFF files with xarray\n", + "- Explore how to perform reprojection operations on rasters\n", + ":::\n", + "\n", + "## What are GeoTIFFs?\n", + "\n", + "The TIFF (Tagged Image File Format) format is a metadata rich image format for raster data. GeoTIFF (Geographic Tagged Image File Format) files are TIFF files that use georeferencing information (such as map projection and coordinate systems) as metadata. A GeoTIFF file with a single band contains 2D raster data for a single characteristic (i.e., variable) and maps to a geographic region. A GeoTIFF file can have multiple bands that all map to the same geographic region.\n", + "\n", + "![Raster_Image](https://docs.qgis.org/3.44/en/_images/raster_dataset.png)\n", + "\n", + "## Rasterio and Rioxarray Backends\n", + "\n", + "[Rasterio](https://rasterio.readthedocs.io/en/stable/intro.html) is a geospatial raster library that expresses GDAL ([Geospatial Data Abstraction Library](http://gdal.org)) data model with a Python API and CLI. [Rioxarray](https://corteva.github.io/rioxarray/stable/readme.html) is a wrapper around the rasterio library, that also extends the xarray api with the *rio* accessor. When you open a GeoTIFF file with the \"rasterio\" engine it returns the data as an xarray object with access to methods from the *rio* accessor.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Reading a GeoTIFF\n", + "\n", + "Xarray's \"rasterio\" backend supports reading GeoTIFFs.\n", + "\n", + "Lets read a GeoTIFF file as an `xr.Dataset` by selecting `engine='rasterio'`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "rds = xr.tutorial.open_dataset(\"RGB.byte.tif\", engine=\"rasterio\")" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + ":::{note} We can also read GeoTIFFs with `rioxarray.open_rasterio`.\n", + "\n", + "The following code snippet opens a GeoTIFF and returns a `DataArray` object\n", + "```python\n", + "import rioxarray\n", + "rioxarray.open_rasterio(\"RGB.byte.tif\")\n", + "```\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Raster bands\n", + "\n", + "GeoTIFFS can have multiple bands each representing a range or band in the electromagnetic spectrum. Arrays stored within bands in xarray are data variables and `DataArray`'s objects in the the Xarray Data Model." + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "Lets get the `DataArray` object (or variable) from our dataset. The variable name is \"band_data\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "rda = rds[\"band_data\"]\n", + "rda" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "Lets try getting the total number of bands for this GeoTIFF. Since `rioxarray` extends xarray with the *rio* accessor we can this accessor be able use many of the builtin `rasterio` methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.count" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "### Selection by bands\n", + "We can also select by bands. Since there are 3 bands in this dataset we can return raster array for the first band of this `xr.DataArray` by with indexing\n", + "\n", + "Lets get the first band of this dataset and try plotting it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "rda[0].plot(cmap=\"pink\")" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## Bounds\n", + "With `.rio.bounds()` we can get the spatial bounding box of our `DataArray`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.bounds()" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "## Transformation\n", + "\n", + "With `.rio.transform()` we can get the affine transformation matrix that maps pixel locations in (col, row) coordinates to (x, y) spatial positions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.transform()" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### Coordinate Reference System (CRS)\n", + "We `rio.crs` we can get the CRS of our raster and reproject our raster." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17", + "metadata": {}, + "outputs": [], + "source": [ + "rda.rio.crs" + ] + }, + { + "cell_type": "markdown", + "id": "18", + "metadata": {}, + "source": [ + "### Reprojection\n", + "We can also reproject our raster from one CRS to another.\n", + "\n", + "Lets reproject our raster from \"EPSG:6326\" to \"EPSG:32612\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19", + "metadata": {}, + "outputs": [], + "source": [ + "rda_reproj = rda.rio.reproject(\"EPSG:32612\")" + ] + }, + { + "cell_type": "markdown", + "id": "20", + "metadata": {}, + "source": [ + ":::{note}\n", + "We have to update our CRS system to the new projection with `rio.write_crs`. We set `inplace=True` to write the CRS to the existing dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": {}, + "outputs": [], + "source": [ + "rda_reproj.rio.write_crs(\"EPSG:32612\", inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you reproject and update the CRS of second band of data to 'ESPG:3857' and plot your results?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "rda[1].rio.reproject(\"EPSG:3857\").rio.write_crs(\"EPSG:3857\", inplace=True).plot()\n", + "```\n", + ":::\n", + "::::\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/advanced/indexing/indexing.md b/advanced/indexing/indexing.md new file mode 100644 index 00000000..72dae2a4 --- /dev/null +++ b/advanced/indexing/indexing.md @@ -0,0 +1,5 @@ +# Indexing + +```{tableofcontents} + +``` diff --git a/advanced/indexing/why-trees.md b/advanced/indexing/why-trees.md new file mode 100644 index 00000000..18565713 --- /dev/null +++ b/advanced/indexing/why-trees.md @@ -0,0 +1,628 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.19.1 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Tree-Based Indexing + +```{seealso} +[NDPointIndex](https://xarray-indexes.readthedocs.io/blocks/ndpoint.html) — use KD-trees and Ball trees with xarray's indexing system for efficient nearest-neighbor lookups on real datasets. +``` + +Imagine you have measurements at irregular locations and want to find the **nearest** data point to your query location. + +**In this notebook you'll learn:** + +- Why naive nearest-neighbor search is slow (O(n) comparisons) +- How KD-trees speed this up dramatically (O(log n) comparisons) +- Why KD-trees can give wrong answers for geographic lat/lon data +- When to use a Ball tree instead + ++++ + +## The nearest neighbor problem in 1D + +Let's start with a simple 1D example: + +**The problem:** What temperature is it at 4.7 km? We need to find the nearest measurement. + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +import numpy as np +import matplotlib.pyplot as plt + +# Temperature measurements at 7 locations along a transect +locations = np.array([1, 3, 4, 7, 8, 9, 12]) +temperatures = np.array([15, 18, 17, 22, 24, 23, 19]) + +# Plot the data +fig, ax = plt.subplots(figsize=(10, 3)) +ax.scatter(locations, np.zeros_like(locations), s=100, c='blue', zorder=5) +for loc, temp in zip(locations, temperatures): + ax.annotate(f'{temp}°', (loc, 0.15), ha='center', fontsize=10) +ax.set_xlim(0, 14) +ax.set_ylim(-0.5, 0.8) +ax.set_xlabel('Location (km)') +ax.set_yticks([]) +ax.set_title('Temperature measurements at 7 irregular locations') +plt.tight_layout() +plt.show() +``` + +The naive approach checks the distance to every point: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# === Configuration: change this to explore different queries === +query = 4.7 + +# Naive approach: check distance to EVERY point +fig, ax = plt.subplots(figsize=(10, 4)) + +# Draw the data points on the number line +ax.scatter(locations, np.zeros_like(locations), s=100, c='blue', zorder=5) +ax.scatter(query, 0, s=150, c='red', marker='x', zorder=10, lw=3) +ax.axhline(0, color='black', lw=0.5, zorder=1) + +# Draw horizontal distance lines - stacked vertically for visibility +for i, loc in enumerate(locations): + y_offset = 0.12 * (i + 1) + # Horizontal line showing the distance + ax.plot([query, loc], [y_offset, y_offset], 'gray', alpha=0.7, lw=2) + # Vertical ticks at endpoints + ax.plot([query, query], [y_offset - 0.03, y_offset + 0.03], 'gray', alpha=0.7, lw=1) + ax.plot([loc, loc], [y_offset - 0.03, y_offset + 0.03], 'gray', alpha=0.7, lw=1) + # Label + ax.annotate(f'{abs(loc - query):.1f} km', ((query + loc)/2, y_offset + 0.04), + ha='center', fontsize=8, color='gray') + +ax.set_xlim(0, 14) +ax.set_ylim(-0.2, 1.1) +ax.set_xlabel('Location (km)') +ax.set_yticks([]) +ax.set_title(f'Naive search: compute distance to ALL {len(locations)} points (query={query})') +plt.tight_layout() +plt.show() + +print(f"Query: {query} km") +print(f"Nearest point: {locations[np.argmin(np.abs(locations - query))]} km (distance = {np.min(np.abs(locations - query)):.1f} km)") +print(f"Comparisons needed: {len(locations)}") +``` + +With 7 points this is fine, but with millions of points this becomes slow. + +**The solution:** Pre-compute a tree structure that partitions the space. In 1D, this is essentially a binary search tree - each split divides the remaining points in half: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +from scipy.spatial import KDTree +from matplotlib.patches import Rectangle + +# === Configuration === + +# Build the tree (this is the pre-computation step) +tree = KDTree(locations.reshape(-1, 1)) + +# Query the tree first to get the result +dist, idx = tree.query([[query]]) +nearest = locations[idx[0]] + +# Map from value to node name for finding the result node +value_to_node = {1: 'LL', 3: 'L1', 4: 'LR', 7: 'root', 8: 'RL', 9: 'R1', 12: 'RR'} +found_node = value_to_node[nearest] + +# Determine the search path based on query value +if query < 7: + if query < 3: + path_nodes = ['root', 'L1', 'LL'] + regions = [(0, 14), (0, 7), (0, 3)] + else: + path_nodes = ['root', 'L1', 'LR'] + regions = [(0, 14), (0, 7), (3, 7)] +else: + if query < 9: + path_nodes = ['root', 'R1', 'RL'] + regions = [(0, 14), (7, 14), (7, 9)] + else: + path_nodes = ['root', 'R1', 'RR'] + regions = [(0, 14), (7, 14), (9, 14)] + +# Create visualization: tree on left, 3 narrowing steps on right +fig = plt.figure(figsize=(16, 9)) + +# Left side: Tree diagram with spatial ranges +ax_tree = fig.add_subplot(1, 2, 1) +ax_tree.set_xlim(0, 16) +ax_tree.set_ylim(-0.5, 5.5) +ax_tree.axis('off') +ax_tree.set_title('KD-tree structure\n(each node shows the spatial range it covers)', fontsize=12, fontweight='bold') + +# Tree node positions - now includes spatial range for each node +nodes = { + 'root': {'pos': (8, 4.5), 'value': 7, 'color': 'steelblue', 'label': 'split=7', 'range': '[0, 14]'}, + 'L1': {'pos': (4, 2.6), 'value': 3, 'color': 'coral', 'label': 'split=3', 'range': '[0, 7)'}, + 'R1': {'pos': (12, 2.6), 'value': 9, 'color': 'seagreen', 'label': 'split=9', 'range': '[7, 14]'}, + 'LL': {'pos': (2, 0.8), 'value': 1, 'color': 'gray', 'label': '1', 'range': '[0, 3)'}, + 'LR': {'pos': (6, 0.8), 'value': 4, 'color': 'gray', 'label': '4', 'range': '[3, 7)'}, + 'RL': {'pos': (10, 0.8), 'value': 8, 'color': 'gray', 'label': '8', 'range': '[7, 9)'}, + 'RR': {'pos': (14, 0.8), 'value': 12, 'color': 'gray', 'label': '12', 'range': '[9, 14]'}, +} + +# Draw edges +edges = [('root', 'L1'), ('root', 'R1'), ('L1', 'LL'), ('L1', 'LR'), ('R1', 'RL'), ('R1', 'RR')] +for parent, child in edges: + px, py = nodes[parent]['pos'] + cx, cy = nodes[child]['pos'] + ax_tree.plot([px, cx], [py, cy], 'k-', lw=2, zorder=1) + +# Draw nodes with spatial range labels +for name, node in nodes.items(): + x, y = node['pos'] + is_split = 'split' in node['label'] + size = 2200 if is_split else 1500 + ax_tree.scatter(x, y, s=size, c=node['color'], zorder=5, edgecolors='black', linewidths=2) + ax_tree.annotate(node['label'], (x, y), ha='center', va='center', + fontsize=11 if is_split else 10, fontweight='bold', color='white') + # Add range label below each node + ax_tree.annotate(node['range'], (x, y - 0.55), ha='center', va='top', + fontsize=9, color='black', style='italic', + bbox=dict(boxstyle='round,pad=0.2', facecolor='white', edgecolor='gray', alpha=0.8)) + +# Highlight the path taken +for i in range(len(path_nodes) - 1): + px, py = nodes[path_nodes[i]]['pos'] + cx, cy = nodes[path_nodes[i+1]]['pos'] + ax_tree.plot([px, cx], [py, cy], 'r-', lw=5, alpha=0.4, zorder=2) + +# Add query annotation +ax_tree.annotate(f'query={query}', (8, 4.5), xytext=(11, 5.2), + fontsize=11, color='red', fontweight='bold', + arrowprops=dict(arrowstyle='->', color='red', lw=2)) + +# Mark the found node +found_x, found_y = nodes[found_node]['pos'] +ax_tree.annotate(f'found {nearest}!', (found_x + 1.2, found_y + 0.3), fontsize=11, ha='left', color='red', fontweight='bold') + +# Right side: 3 subplots showing narrowing search space +steps = [ + ("Step 1: Start with all points", regions[0], 'steelblue', f'{query} < 7? → go left' if query < 7 else f'{query} > 7? → go right'), + ("Step 2: After first split", regions[1], 'coral', f'{query} < 3? → go left' if query < 3 else f'{query} > 3? → go right' if query < 7 else f'{query} < 9? → go left' if query < 9 else f'{query} > 9? → go right'), + (f"Step 3: Found nearest = {nearest}", regions[2], 'gold', None), +] + +for i, (title, (region_start, region_end), color, annotation) in enumerate(steps): + ax = fig.add_subplot(3, 2, 2*(i+1)) + + # Draw all data points + for loc in locations: + in_region = region_start <= loc <= region_end + ax.scatter(loc, 0, s=100 if in_region else 60, + c='blue' if in_region else 'lightgray', + zorder=5, edgecolors='black' if in_region else 'gray', linewidths=1) + if in_region: + ax.annotate(f'{loc}', (loc, -0.25), ha='center', fontsize=9, fontweight='bold') + + # Draw query point + ax.scatter(query, 0, s=150, c='red', marker='x', zorder=10, lw=3) + + # Highlight the active region + rect = Rectangle((region_start, -0.15), region_end - region_start, 0.3, + fill=True, facecolor=color, alpha=0.2, edgecolor=color, lw=2, zorder=2) + ax.add_patch(rect) + + # Draw split lines + if i == 0: + ax.axvline(7, color='steelblue', lw=2, ls='--', alpha=0.8) + ax.annotate('split=7', (7, 0.25), ha='center', fontsize=9, color='steelblue', fontweight='bold') + elif i == 1: + if query < 7: + ax.axvline(3, color='coral', lw=2, ls='--', alpha=0.8) + ax.annotate('split=3', (3, 0.25), ha='center', fontsize=9, color='coral', fontweight='bold') + else: + ax.axvline(9, color='seagreen', lw=2, ls='--', alpha=0.8) + ax.annotate('split=9', (9, 0.25), ha='center', fontsize=9, color='seagreen', fontweight='bold') + + # Add decision annotation + if annotation: + ax.annotate(annotation, (0.98, 0.95), xycoords='axes fraction', ha='right', va='top', + fontsize=10, color='darkgreen', fontweight='bold', + bbox=dict(boxstyle='round', facecolor='lightyellow', edgecolor='green', alpha=0.8)) + + ax.set_xlim(-0.5, 14.5) + ax.set_ylim(-0.4, 0.45) + ax.set_title(title, fontsize=11, fontweight='bold') + ax.set_yticks([]) + if i == 2: + ax.set_xlabel('Location (km)', fontsize=10) + +plt.tight_layout() +plt.show() + +print(f"Nearest point: {nearest} km") +print(f"Comparisons needed: ~{len(path_nodes)} (log₂({len(locations)}) ≈ 3)") +``` + +## Extending to 2D + +The same idea works in higher dimensions. Now our measurements are scattered across a 2D area: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# 2D example: temperature measurements scattered across an area +from matplotlib.patches import Rectangle + +np.random.seed(42) +points_2d = np.random.rand(20, 2) * 10 # 20 points in a 10x10 area + +# === Configuration === +query_2d = np.array([6.5, 4.0]) # Change this to query a different location + +# Build tree - using leafsize=2 to demonstrate meaningful subdivision +# (default leafsize=10 would barely split with only 20 points!) +LEAFSIZE = 2 +tree_2d = KDTree(points_2d, leafsize=LEAFSIZE) +dist, idx = tree_2d.query([query_2d]) +nearest_2d = points_2d[idx[0]] + +# With leafsize=2, we get ~4 levels of splits (log2(20/2) ≈ 3-4) +# Let's show the first 2 splits conceptually, then the final leaf comparison + +# Approximate the splits (KD-tree alternates x, y, x, y...) +x_split = np.median(points_2d[:, 0]) # ~4.0 + +# Determine which half based on query x +if query_2d[0] >= x_split: + # Right half + half_points = points_2d[points_2d[:, 0] >= x_split] + x_decision = f"x={query_2d[0]} > {x_split:.1f}? → go right" + x_region = (x_split, 0, 10, 10) # (x_min, y_min, x_max, y_max) +else: + # Left half + half_points = points_2d[points_2d[:, 0] < x_split] + x_decision = f"x={query_2d[0]} < {x_split:.1f}? → go left" + x_region = (0, 0, x_split, 10) + +y_split = np.median(half_points[:, 1]) + +# Determine which quadrant based on query y +if query_2d[1] >= y_split: + # Upper region + y_decision = f"y={query_2d[1]} > {y_split:.1f}? → go up" + if query_2d[0] >= x_split: + final_region = (x_split, y_split, 10, 10) # top-right + else: + final_region = (0, y_split, x_split, 10) # top-left +else: + # Lower region + y_decision = f"y={query_2d[1]} < {y_split:.1f}? → go down" + if query_2d[0] >= x_split: + final_region = (x_split, 0, 10, y_split) # bottom-right + else: + final_region = (0, 0, x_split, y_split) # bottom-left + +# Define regions for visualization +regions = [ + (0, 0, 10, 10), # Step 1: all points + x_region, # Step 2: half based on x + final_region, # Step 3: quadrant based on y +] + +# Get actual points in final region (these are the leaf candidates) +x_min, y_min, x_max, y_max = final_region +final_candidates = [pt for pt in points_2d + if x_min <= pt[0] <= x_max and y_min <= pt[1] <= y_max] + +# Create figure +fig, axes = plt.subplots(2, 2, figsize=(12, 10)) +axes = axes.flatten() + +step_titles = [ + "Step 1: Start with all 20 points", + f"Step 2: Split on x ≈ {x_split:.1f}", + f"Step 3: Split on y ≈ {y_split:.1f}", + f"Step 4: Compare {len(final_candidates)} candidates in leaf" +] +step_colors = ['steelblue', 'coral', 'gold', 'limegreen'] +decisions = [x_decision, y_decision, None, None] + +for i, ax in enumerate(axes): + x_min, y_min, x_max, y_max = regions[min(i, 2)] + + # Get points in current region + points_in_region = [(pt, x_min <= pt[0] <= x_max and y_min <= pt[1] <= y_max) for pt in points_2d] + + # Draw all points + for pt, in_region in points_in_region: + ax.scatter(pt[0], pt[1], s=80 if in_region else 40, + c='blue' if in_region else 'lightgray', + edgecolors='black' if in_region else 'gray', + zorder=5, linewidths=1) + + # Draw query point + ax.scatter(*query_2d, s=150, c='red', marker='x', zorder=10, lw=3) + + # Draw the active region + rect = Rectangle((x_min, y_min), x_max - x_min, y_max - y_min, + fill=True, facecolor=step_colors[i], alpha=0.15, + edgecolor=step_colors[i], lw=2, zorder=2) + ax.add_patch(rect) + + # Draw split lines + if i >= 1: + ax.axvline(x_split, color='steelblue', lw=2, ls='--', alpha=0.8) + ax.annotate(f'x={x_split:.1f}', (x_split, 9.7), ha='center', fontsize=9, + color='steelblue', fontweight='bold') + if i >= 2: + # Only draw y split line in the relevant half + if query_2d[0] >= x_split: + ax.axhline(y_split, xmin=x_split/10, xmax=1, color='coral', lw=2, ls='--', alpha=0.8) + else: + ax.axhline(y_split, xmin=0, xmax=x_split/10, color='coral', lw=2, ls='--', alpha=0.8) + ax.annotate(f'y={y_split:.1f}', (9.7, y_split + 0.2), + ha='right', va='bottom', fontsize=9, color='coral', fontweight='bold') + + # Final step: draw lines to ALL candidates + if i == 3: + for pt, in_region in points_in_region: + if in_region: + is_nearest = np.allclose(pt, nearest_2d) + ax.plot([query_2d[0], pt[0]], [query_2d[1], pt[1]], + color='limegreen' if is_nearest else 'gray', + lw=3 if is_nearest else 1.5, + alpha=1.0 if is_nearest else 0.6, + zorder=6 if is_nearest else 4) + + ax.scatter(*nearest_2d, s=200, facecolors='none', edgecolors='limegreen', lw=3, zorder=15) + ax.annotate('nearest!', (nearest_2d[0] + 0.3, nearest_2d[1] + 0.3), + ha='left', fontsize=10, color='green', fontweight='bold') + + # Add decision annotation + if decisions[i]: + ax.annotate(decisions[i], (0.98, 0.98), xycoords='axes fraction', + ha='right', va='top', fontsize=10, color='darkgreen', fontweight='bold', + bbox=dict(boxstyle='round', facecolor='lightyellow', edgecolor='green', alpha=0.9)) + + ax.set_xlim(-0.5, 10.5) + ax.set_ylim(-0.5, 10.5) + ax.set_aspect('equal') + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_title(step_titles[i], fontsize=11, fontweight='bold') + +plt.tight_layout() +plt.show() + +print(f"Query: ({query_2d[0]}, {query_2d[1]})") +print(f"KDTree built with leafsize={LEAFSIZE}") +print(f"Started with {len(points_2d)} points") +print(f"After 2 tree splits: narrowed to {len(final_candidates)} candidates") +print(f"Final step: {len(final_candidates)} distance calculations") +print(f"Total: 2 splits + {len(final_candidates)} comparisons = {2 + len(final_candidates)} operations (vs 20 naive)") +``` + +## How it scales + +The same principle extends to 3D, 4D, and beyond. Here's how the number of comparisons scales: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# How comparisons scale with data size +data_sizes = np.array([10, 100, 1000, 10000, 100000, 1000000]) +naive_comparisons = data_sizes # O(n) +kdtree_comparisons = np.log2(data_sizes).astype(int) + 1 # O(log n) + +fig, ax = plt.subplots(figsize=(10, 5)) + +ax.plot(data_sizes, naive_comparisons, 'o-', color='gray', lw=2, markersize=8, label='Naive: O(n)') +ax.plot(data_sizes, kdtree_comparisons, 's-', color='steelblue', lw=2, markersize=8, label='KD-tree: O(log n)') + +ax.set_xscale('log') +ax.set_yscale('log') +ax.set_xlabel('Number of data points') +ax.set_ylabel('Comparisons per query') +ax.set_title('Finding nearest neighbor: Naive vs KD-tree') +ax.legend(fontsize=11) +ax.grid(True, alpha=0.3) + +# Annotate key points +for n, naive, kd in zip(data_sizes[::2], naive_comparisons[::2], kdtree_comparisons[::2]): + ax.annotate(f'{naive:,}', (n, naive), textcoords="offset points", xytext=(0,10), ha='center', fontsize=9, color='gray') + ax.annotate(f'{kd}', (n, kd), textcoords="offset points", xytext=(0,-15), ha='center', fontsize=9, color='steelblue') + +plt.tight_layout() +plt.show() + +print("With 1 million points: naive needs 1,000,000 comparisons, KD-tree needs ~20") +``` + +(the-problem-with-geographic-coordinates)= + +## The problem with geographic coordinates + +KD-trees use **Euclidean distance**—they measure straight-line distance in whatever coordinate system you give them. This works perfectly for x/y coordinates in meters or kilometers. + +But for **latitude/longitude coordinates**, Euclidean distance over degrees is wrong! Here's why: + +- **Latitude degrees are constant**: 1° latitude ≈ 111 km everywhere on Earth +- **Longitude degrees shrink toward the poles**: 1° longitude ≈ 111 km at the equator, but only ~19 km at 80°N + +This means a KD-tree treating lat/lon as flat coordinates will systematically pick the **wrong** nearest neighbor at high latitudes: + +```{code-cell} ipython3 +--- +tags: [hide-input] +--- +# Visualize haversine vs Euclidean - 2D circle diagram +from sklearn.neighbors import BallTree + +# At 80°N (near Arctic): longitude degrees are MUCH shorter! +lat = 80 +km_per_deg_lon = 111 * np.cos(np.radians(lat)) # ~19 km at 80°N! +km_per_deg_lat = 111 # always ~111 km + +# Query point and two candidates (in lat/lon degrees) +query_latlon = np.array([[lat, 0]]) +point_a_latlon = np.array([[lat, 2.0]]) +point_b_latlon = np.array([[lat + 0.5, 0]]) +points_latlon = np.vstack([point_a_latlon, point_b_latlon]) + +# Test both trees +kd_tree = KDTree(points_latlon) +kd_dist, kd_idx = kd_tree.query(query_latlon) +kd_picked = "A" if kd_idx[0] == 0 else "B" + +ball_tree = BallTree(np.radians(points_latlon), metric='haversine') +ball_dist, ball_idx = ball_tree.query(np.radians(query_latlon)) +ball_picked = "A" if ball_idx[0] == 0 else "B" + +km_to_a = 2.0 * km_per_deg_lon +km_to_b = 0.5 * km_per_deg_lat + +# Create figure +fig = plt.figure(figsize=(14, 10), constrained_layout=True) + +# === Top: Circle diagram showing arc vs chord === +ax_circle = fig.add_subplot(211) +ax_circle.set_aspect('equal') +ax_circle.axis('off') + +# Draw circle +radius = 1 +theta_full = np.linspace(0, 2*np.pi, 100) +ax_circle.plot(radius * np.cos(theta_full), radius * np.sin(theta_full), 'k-', lw=2.5) + +# Two points on the circle +theta_p = np.radians(120) # Point P +theta_q = np.radians(60) # Point Q + +p_x, p_y = radius * np.cos(theta_p), radius * np.sin(theta_p) +q_x, q_y = radius * np.cos(theta_q), radius * np.sin(theta_q) + +# Draw the ARC (haversine distance) - along the circle surface +arc_theta = np.linspace(theta_q, theta_p, 50) +arc_x = radius * np.cos(arc_theta) +arc_y = radius * np.sin(arc_theta) +ax_circle.plot(arc_x, arc_y, 'r-', lw=6, solid_capstyle='round', label='Arc length (haversine)', zorder=5) + +# Draw the CHORD (Euclidean distance) - straight line +ax_circle.plot([p_x, q_x], [p_y, q_y], 'b--', lw=4, label='Chord length (Euclidean)', zorder=4) + +# Draw points +ax_circle.scatter([p_x, q_x], [p_y, q_y], s=250, c='dodgerblue', edgecolors='black', lw=2, zorder=10) + +# Labels +ax_circle.annotate('P', (p_x - 0.18, p_y + 0.08), fontsize=24, fontweight='bold') +ax_circle.annotate('Q', (q_x + 0.1, q_y + 0.08), fontsize=24, fontweight='bold') + +# Distance annotations - positioned to avoid overlap +# Arc annotation (above the arc) +arc_mid_theta = (theta_p + theta_q) / 2 +arc_mid_x = 1.22 * np.cos(arc_mid_theta) +arc_mid_y = 1.22 * np.sin(arc_mid_theta) +ax_circle.annotate('arc length\n(along surface)', (arc_mid_x, arc_mid_y + 0.05), + fontsize=13, color='darkred', fontweight='bold', ha='center', + bbox=dict(boxstyle='round,pad=0.3', facecolor='mistyrose', edgecolor='red', alpha=0.9)) + +# Chord annotation (below the chord) +chord_mid_x = (p_x + q_x) / 2 +chord_mid_y = (p_y + q_y) / 2 +ax_circle.annotate('chord\n(straight line)', (chord_mid_x, chord_mid_y - 0.25), + fontsize=13, color='darkblue', fontweight='bold', ha='center', + bbox=dict(boxstyle='round,pad=0.3', facecolor='lightblue', edgecolor='blue', alpha=0.9)) + +# Calculate actual distances for display +arc_length = radius * abs(theta_p - theta_q) # s = r * theta +chord_length = np.sqrt((q_x - p_x)**2 + (q_y - p_y)**2) + +ax_circle.set_xlim(-1.5, 1.5) +ax_circle.set_ylim(-1.0, 1.7) +ax_circle.set_title('Haversine vs Euclidean: arc length ≠ chord length', + fontsize=16, fontweight='bold', pad=15) + +# Legend on the left side +ax_circle.legend(loc='center left', fontsize=11, bbox_to_anchor=(-0.15, 0.3)) + +# Formula box on the right side +formula_text = f"Arc length > Chord length\n(arc = {arc_length:.2f}, chord = {chord_length:.2f})" +ax_circle.text(1.15, 0.3, formula_text, fontsize=12, ha='center', + bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.9)) + +# === Bottom row: Side-by-side comparison === +# Bottom-left: KD-tree view (flat degrees) +ax_kd = fig.add_subplot(223) +ax_kd.scatter(0, 0, s=200, c='red', marker='x', zorder=10, lw=3, label='Query') +ax_kd.scatter(2.0, 0, s=140, c='green', zorder=5, label='Point A (2° east)') +ax_kd.scatter(0, 0.5, s=140, c='orange', zorder=5, label='Point B (0.5° north)') +ax_kd.plot([0, 2.0], [0, 0], 'g-', lw=2.5, alpha=0.7) +ax_kd.plot([0, 0], [0, 0.5], color='orange', lw=2.5, alpha=0.7) + +circle_kd = plt.Circle((0, 0.5), 0.15, fill=False, color='black', lw=3, linestyle='--', zorder=15) +ax_kd.add_patch(circle_kd) +ax_kd.annotate('KD-tree picks B\n(smaller in degrees)', (0.3, 0.75), fontsize=12, fontweight='bold') + +ax_kd.set_xlabel('Longitude offset (°)', fontsize=13) +ax_kd.set_ylabel('Latitude offset (°)', fontsize=13) +ax_kd.set_title(f'KD-tree: Euclidean on degrees\n(at {lat}°N latitude)', fontsize=13, fontweight='bold') +ax_kd.legend(loc='upper right', fontsize=10) +ax_kd.set_xlim(-0.5, 2.5) +ax_kd.set_ylim(-0.5, 1.2) +ax_kd.set_aspect('equal') +ax_kd.grid(True, alpha=0.3) +ax_kd.annotate('2.0°', (1.0, -0.15), ha='center', fontsize=14, color='green', fontweight='bold') +ax_kd.annotate('0.5°', (-0.25, 0.25), ha='center', fontsize=14, color='orange', fontweight='bold', rotation=90) + +# Bottom-right: Reality in kilometers +ax_real = fig.add_subplot(224) +ax_real.scatter(0, 0, s=200, c='red', marker='x', zorder=10, lw=3, label=f'Query ({lat}°N)') +ax_real.scatter(km_to_a, 0, s=140, c='green', zorder=5, label='Point A') +ax_real.scatter(0, km_to_b, s=140, c='orange', zorder=5, label='Point B') +ax_real.plot([0, km_to_a], [0, 0], 'g-', lw=2.5, alpha=0.7) +ax_real.plot([0, 0], [0, km_to_b], color='orange', lw=2.5, alpha=0.7) + +circle_ball = plt.Circle((km_to_a, 0), 6, fill=False, color='black', lw=3, linestyle='--', zorder=15) +ax_real.add_patch(circle_ball) +ax_real.annotate('Ball tree picks A\n(smaller in km)', (5, 55), fontsize=12, fontweight='bold') + +ax_real.set_xlabel('East-West distance (km)', fontsize=13) +ax_real.set_ylabel('North-South distance (km)', fontsize=13) +ax_real.set_title(f'Ball tree: haversine (true distance)\n1° longitude = only {km_per_deg_lon:.0f} km at {lat}°N!', + fontsize=13, fontweight='bold') +ax_real.legend(loc='upper right', fontsize=10) +ax_real.set_xlim(-10, 70) +ax_real.set_ylim(-10, 70) +ax_real.set_aspect('equal') +ax_real.grid(True, alpha=0.3) +ax_real.annotate(f'{km_to_a:.0f} km', (km_to_a/2, -6), ha='center', fontsize=14, color='green', fontweight='bold') +ax_real.annotate(f'{km_to_b:.0f} km', (-7, km_to_b/2), ha='center', fontsize=14, color='orange', fontweight='bold', rotation=90) + +plt.show() + +print(f"At {lat}°N: 1° longitude = {km_per_deg_lon:.0f} km, 1° latitude = {km_per_deg_lat} km") +print(f"\nPoint A: 2° east = {km_to_a:.0f} km (along surface)") +print(f"Point B: 0.5° north = {km_to_b:.0f} km") +print(f"\nKD-tree picked: Point {kd_picked} {'✗ WRONG!' if kd_picked == 'B' else '✓'}") +print(f"Ball tree picked: Point {ball_picked} {'✓ CORRECT!' if ball_picked == 'A' else '✗'}") +``` + +## Next steps + +Ready to use tree-based indexing with xarray? See [NDPointIndex](https://xarray-indexes.readthedocs.io/blocks/ndpoint.html) for how to integrate KD-trees and Ball trees with xarray's indexing system. diff --git a/advanced/map_blocks/simple_map_blocks.ipynb b/advanced/map_blocks/simple_map_blocks.ipynb index 0fe06b16..af46f405 100644 --- a/advanced/map_blocks/simple_map_blocks.ipynb +++ b/advanced/map_blocks/simple_map_blocks.ipynb @@ -12,7 +12,7 @@ "`map_blocks` is inspired by the `dask.array` function of the same name and lets\n", "you map a function on blocks of the xarray object (including Datasets!).\n", "\n", - "At _compute_ time, your function will receive an xarray object with concrete\n", + "At _compute_ time, your function will receive a chunk of an xarray object with concrete\n", "(computed) values along with appropriate metadata. This function should return\n", "an xarray object.\n" ] @@ -89,7 +89,9 @@ "id": "7", "metadata": {}, "source": [ - "Let's open a dataset. We specify `chunks` so that we create a dask arrays for the DataArrays" + "Let's open a dataset. We specify `chunks` so that we create a dask arrays for the DataArrays.\n", + "\n", + "Depending on the desired function to be applied on the chunks, it is vital to set the chunks correctly. Our goal is to compute the mean along the time dimension. Therefore we do not chunk the time dimension at all (indicated by `\"time\": -1`). We deliberately set `lat` and `lon` chunks to something smaller then the size of their respective dimension (otherwise we would potentially end up with a single big chunk for the entire `ds`)." ] }, { @@ -99,7 +101,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = xr.tutorial.open_dataset(\"air_temperature\", chunks={\"time\": 100})\n", + "ds = xr.tutorial.open_dataset(\"air_temperature\", chunks={\"time\": -1, \"lat\": 5, \"lon\": 10})\n", "ds" ] }, @@ -120,11 +122,11 @@ "metadata": {}, "outputs": [], "source": [ - "def time_mean(obj):\n", + "def time_mean(obj: xr.Dataset):\n", " # use xarray's convenient API here\n", " # you could convert to a pandas dataframe and use pandas' extensive API\n", " # or use .plot() and plt.savefig to save visualizations to disk in parallel.\n", - " return obj.mean(\"lat\")\n", + " return obj.mean(\"time\")\n", "\n", "\n", "ds.map_blocks(time_mean) # this is lazy!" @@ -136,17 +138,79 @@ "id": "11", "metadata": {}, "outputs": [], + "source": [ + "# this triggers the actual computation\n", + "ds.map_blocks(time_mean).compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], "source": [ "# this will calculate values and will return True if the computation works as expected\n", - "ds.map_blocks(time_mean).identical(ds.mean(\"lat\"))" + "ds.map_blocks(time_mean).equals(ds.mean(\"time\"))" ] }, { "cell_type": "markdown", - "id": "12", + "id": "13", + "metadata": {}, + "source": [ + "### Exercises\n" + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "::::{admonition} Exercise 1\n", + ":class: tip\n", + "\n", + "When opening the dataset, set the chunks for the dimension to anything smaller than the size of the time dimension (< 2920), e.g., `\"time\": 100`, and keep the size of the other chunks the same:\n", + "\n", + "```python\n", + "ds = xr.tutorial.open_dataset(\n", + " \"air_temperature\",\n", + " chunks={\"time\": 100, \"lat\": 5, \"lon\": 10},\n", + ")\n", + "```\n", + "\n", + "Now run the notebook again. The result of `ds.map_blocks(time_mean)` is no more equivalent to `ds.mean(\"time\")`. Why does `ds.map_blocks(time_mean)` return a different result this time?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "Quoting from the documentation of `map_blocks`: _The function will receive a subset or ‘block’ of obj (see below), corresponding to one chunk along each chunked dimension._\n", + "\n", + "`ds.mean(\"time\")` computes the mean over the entire time dimension. In our example `ds.map_blocks(time_mean)` passes individual chunks of `ds` to `time_mean`. Once the time dimension is chunked, `time_mean` receives more than a single chunk along the dimension, meaning `time_mean` computes the mean along the time dimension for a single chunk rather than along the entire time dimension. Therefore we do not receive an identical result.\n", + "\n", + "You can also modify the function to show the shape of the chunks passed to `time_mean`. Compare the output of the modified function with `ds.chunks` to find out how they relate to each other!\n", + "\n", + "```python\n", + "def time_mean(obj: xr.Dataset):\n", + " print(f\"received obj of type {type(obj)}\")\n", + " print(\"obj contains the following data variables:\")\n", + " for data_var in obj.data_vars:\n", + " print(f\"'{data_var}' with shape {obj[data_var].shape}\")\n", + "\n", + " return obj.mean(\"time\")\n", + "```\n", + "\n", + ":::\n", + "::::\n" + ] + }, + { + "cell_type": "markdown", + "id": "15", "metadata": {}, "source": [ - "### Exercise\n", + "::::{admonition} Exercise 2\n", + ":class: tip \n", "\n", "Try applying the following function with `map_blocks`. Specify `scale` as an\n", "argument and `offset` as a kwarg.\n", @@ -154,15 +218,17 @@ "The docstring should help:\n", "https://docs.xarray.dev/en/stable/generated/xarray.map_blocks.html\n", "\n", - "```\n", + "```python\n", "def time_mean_scaled(obj, scale, offset):\n", " return obj.mean(\"lat\") * scale + offset\n", - "```\n" + "```\n", + "\n", + "::::" ] }, { "cell_type": "markdown", - "id": "13", + "id": "16", "metadata": {}, "source": [ "### More advanced functions\n", @@ -177,7 +243,7 @@ { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "17", "metadata": {}, "outputs": [], "source": [ diff --git a/conda/README.md b/conda/README.md deleted file mode 100644 index 4ab90c46..00000000 --- a/conda/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Instructions for environment management - -## Install locked environment: - -`conda-lock` can be used to create a multi-platform lockfile, so a reproducible set of package versions is installed across different operating systems. https://conda-incubator.github.io/conda-lock/ - -``` -conda-lock install -``` - -\*Or on Linux: `mamba env create -f .binder/environment.yml` - -## Create/update multiplatform lockfile: - -``` -conda-lock lock --mamba -f environment-unpinned.yml -p osx-64 -p linux-64 -p win-64 -p osx-arm64 -``` - -## Render a mybinder.org compatible environment.yml (linux-64) - -``` -conda-lock render -k env -p linux-64 -mv conda-linux-64.lock.yml environment.yml -``` - -## Install unpinned environment: - -If you want to run tutorials with the latest versions of compatible packages - -If you need to install mamba see https://github.com/conda-forge/miniforge#mambaforge - -``` -mamba env create -f environment-unpinned.yml -mamba activate xarray-tutorial -``` diff --git a/conda/apt.txt b/conda/apt.txt deleted file mode 100644 index 82d0355f..00000000 --- a/conda/apt.txt +++ /dev/null @@ -1,2 +0,0 @@ -git -vim diff --git a/conda/conda-lock.yml b/conda/conda-lock.yml deleted file mode 100644 index 9c15d5a5..00000000 --- a/conda/conda-lock.yml +++ /dev/null @@ -1,26873 +0,0 @@ -# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). 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- 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 - - widgetsnbextension=4.0.11=pyhd8ed1ab_0 - - wrapt=1.16.0=py312h98912ed_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=hb711507_1 - - xorg-libxau=1.0.11=hd590300_0 - - xorg-libxdmcp=1.1.3=h7f98852_0 - - xorg-libxext=1.3.4=h0b41bf4_2 - - xorg-libxrender=0.9.11=hd590300_0 - - xorg-renderproto=0.11.1=h7f98852_1002 - - xorg-xextproto=7.3.0=h0b41bf4_1003 - - xorg-xproto=7.0.31=h7f98852_1007 - - xyzservices=2024.6.0=pyhd8ed1ab_0 - - xz=5.2.6=h166bdaf_0 - - yaml=0.2.5=h7f98852_2 - - 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=3.19.2=pyhd8ed1ab_0 - - zlib=1.3.1=h4ab18f5_1 - - zstandard=0.22.0=py312h5b18bf6_1 - - zstd=1.5.6=ha6fb4c9_0 diff --git a/fundamentals/01.1_creating_data_structures.ipynb b/fundamentals/01.1_creating_data_structures.ipynb index 826b9c35..d820cbd5 100644 --- a/fundamentals/01.1_creating_data_structures.ipynb +++ b/fundamentals/01.1_creating_data_structures.ipynb @@ -241,7 +241,7 @@ "\n", "Sometimes we want to attach coordinate variables along an existing dimension. Notice that \n", "1. `itime` is not bolded and \n", - "2. has a name \"time\" that is different from the dimension name \"time\"\n", + "2. has a name \"itime\" that is different from the dimension name \"time\"\n", "\n", "`itime` is an example of a non-dimension coordinate variable i.e. it is a coordinate variable that does not match a dimension name. Here we demonstrate the \"tuple\" form of assigninment: `(dims, data, attrs)`" ] diff --git a/fundamentals/01.1_io.ipynb b/fundamentals/01.1_io.ipynb index f5328c4c..0520f976 100644 --- a/fundamentals/01.1_io.ipynb +++ b/fundamentals/01.1_io.ipynb @@ -51,9 +51,27 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "id": "2", "metadata": {}, + "outputs": [], + "source": [ + "# Ensure we start with a clean directory for the tutorial\n", + "import pathlib\n", + "import shutil\n", + "\n", + "datadir = pathlib.Path(\"../data/io-tutorial\")\n", + "if datadir.exists():\n", + " shutil.rmtree(datadir)\n", + "else:\n", + " datadir.mkdir()" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, "source": [ "The constructor of `Dataset` takes three parameters:\n", "\n", @@ -66,7 +84,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -94,16 +112,16 @@ ")\n", "\n", "# write datasets\n", - "ds1.to_netcdf(\"ds1.nc\")\n", - "ds2.to_netcdf(\"ds2.nc\")\n", + "ds1.to_netcdf(datadir / \"ds1.nc\")\n", + "ds2.to_netcdf(datadir / \"ds2.nc\")\n", "\n", "# write dataarray\n", - "ds1.a.to_netcdf(\"da1.nc\")" + "ds1.a.to_netcdf(datadir / \"da1.nc\")" ] }, { "cell_type": "markdown", - "id": "4", + "id": "5", "metadata": {}, "source": [ "Reading those files is just as simple:\n" @@ -112,26 +130,26 @@ { "cell_type": "code", "execution_count": null, - "id": "5", + "id": "6", "metadata": {}, "outputs": [], "source": [ - "xr.open_dataset(\"ds1.nc\")" + "xr.open_dataset(datadir / \"ds1.nc\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "6", + "id": "7", "metadata": {}, "outputs": [], "source": [ - "xr.open_dataarray(\"da1.nc\")" + "xr.open_dataarray(datadir / \"da1.nc\")" ] }, { "cell_type": "markdown", - "id": "7", + "id": "8", "metadata": {}, "source": [ "\n", @@ -151,16 +169,16 @@ { "cell_type": "code", "execution_count": null, - "id": "8", + "id": "9", "metadata": {}, "outputs": [], "source": [ - "ds1.to_zarr(\"ds1.zarr\", mode=\"w\")" + "ds1.to_zarr(datadir / \"ds1.zarr\", mode=\"w\")" ] }, { "cell_type": "markdown", - "id": "9", + "id": "10", "metadata": {}, "source": [ "We can then read the created file with:\n" @@ -169,16 +187,16 @@ { "cell_type": "code", "execution_count": null, - "id": "10", + "id": "11", "metadata": {}, "outputs": [], "source": [ - "xr.open_zarr(\"ds1.zarr\", chunks=None)" + "xr.open_zarr(datadir / \"ds1.zarr\", chunks=None)" ] }, { "cell_type": "markdown", - "id": "11", + "id": "12", "metadata": {}, "source": [ "setting the `chunks` parameter to `None` avoids `dask` (more on that in a later\n", @@ -187,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "12", + "id": "13", "metadata": {}, "source": [ "**tip:** You can write to any dictionary-like (`MutableMapping`) interface:" @@ -196,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "14", "metadata": {}, "outputs": [], "source": [ @@ -207,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "14", + "id": "15", "metadata": {}, "source": [ "## Raster files using rioxarray\n", @@ -220,7 +238,7 @@ { "cell_type": "code", "execution_count": null, - "id": "15", + "id": "16", "metadata": {}, "outputs": [], "source": [ @@ -241,16 +259,16 @@ { "cell_type": "code", "execution_count": null, - "id": "16", + "id": "17", "metadata": {}, "outputs": [], "source": [ - "da.rio.to_raster('ds1_a.tiff')" + "da.rio.to_raster(datadir / \"ds1_a.tiff\")" ] }, { "cell_type": "markdown", - "id": "17", + "id": "18", "metadata": {}, "source": [ "NOTE: you can now load this file into GIS tools like [QGIS](https://www.qgis.org)! Or open back into Xarray:" @@ -259,11 +277,11 @@ { "cell_type": "code", "execution_count": null, - "id": "18", + "id": "19", "metadata": {}, "outputs": [], "source": [ - "DA = xr.open_dataarray('ds1_a.tiff', engine='rasterio')\n", + "DA = xr.open_dataarray(datadir / \"ds1_a.tiff\", engine=\"rasterio\")\n", "DA.rio.crs" ] } @@ -279,11 +297,6 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } } }, "nbformat": 4, diff --git a/fundamentals/01_datastructures.ipynb b/fundamentals/01_datastructures.ipynb index fa3875de..f9add737 100644 --- a/fundamentals/01_datastructures.ipynb +++ b/fundamentals/01_datastructures.ipynb @@ -36,8 +36,8 @@ "outputs": [], "source": [ "import numpy as np\n", - "import xarray as xr\n", "import pandas as pd\n", + "import xarray as xr\n", "\n", "# When working in a Jupyter Notebook you might want to customize Xarray display settings to your liking\n", "# The following settings reduce the amount of data displayed out by default\n", diff --git a/fundamentals/01_datatree_hierarchical_data.ipynb b/fundamentals/01_datatree_hierarchical_data.ipynb new file mode 100644 index 00000000..4c4d4142 --- /dev/null +++ b/fundamentals/01_datatree_hierarchical_data.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `xarray.DataTree` and hierarchical data\n", + "\n", + "In this lesson, we will learn how to use `xarray.DataTree` with hierarchical data. By the end of the lesson, we will be able to:\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Understand a basic `DataTree` structure (nodes, parents and children)\n", + "- Selecting `DataTree`\n", + "- Understand coordinate inheritance :::\n", + "\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## What is a DataTree\n", + "\n", + "### Example DataTree 1\n", + "\n", + "![image.png](../_static/datatree1.png)\n", + "\n", + "\n", + "\n", + "### Example DataTree 2\n", + "\n", + "![image.png](../_static/datatree2.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Opening a dataset with `open_datatree()`\n", + "\n", + "Let's open up a precipitation dataset. This dataset was derived from \"GPM_3IMERGHH_07\" and \"M2T1NXFLX_5.12.4\" products." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation = xr.tutorial.open_datatree(\"precipitation.nc4\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nodes\n", + "Groups in a netcdf4 or hdf5 file in the DataTree model are represented as \"nodes\" in the DataTree model.\n", + "We can list all of the groups with `.groups`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing variables in a nested groups\n", + "Nested variables and groups can be accessed with either dict-like syntax or method based syntax." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation[\"observed\"]\n", + "\n", + "# Returns a DataTree object, containing the variables, dimensions, and coordinates in the \"observed\" node" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation[\"/observed/precipitation\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.reanalysis.precipitation\n", + "\n", + "# Method based syntax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get the parent and child nodes from a group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation[\"reanalysis\"].parent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.children" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inheritance\n", + "DataTree implements a simple inheritance mechanism. Coordinates, dimensions and their associated indices are propagated downward from the root node to all descendent nodes.\n", + "\n", + "Let's take a look at some inherited coordinates with our precipitation dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `\"time\"` dimension is defined at the root node of our dataset and is propagated downward to the \"observed\" and \"reanalysis\" group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.observed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "precipitation.reanalysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Review\n", + "\n", + "\n", + "### Example DataTree 1\n", + "![image.png](../_static/datatree1.png)\n", + "\n", + "\n", + "### Example DataTree 2\n", + "![image.png](../_static/datatree2.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "\n", + "1. Make a plot of plot of the Oberserved and Predicted precipitation\n", + "2. Think through if there are datasets from your field" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/fundamentals/02.1_indexing_Basic.ipynb b/fundamentals/02.1_indexing_Basic.ipynb index ef4f4d39..217c21ab 100644 --- a/fundamentals/02.1_indexing_Basic.ipynb +++ b/fundamentals/02.1_indexing_Basic.ipynb @@ -481,7 +481,7 @@ }, "outputs": [], "source": [ - "ds.sel(time='2013-01-01 06:00')" + "ds.sel(time=\"2013-01-01 06:00\")" ] }, { @@ -499,7 +499,7 @@ }, "outputs": [], "source": [ - "ds.sel(time='2013-01-01')" + "ds.sel(time=\"2013-01-01\")" ] }, { @@ -560,7 +560,7 @@ "outputs": [], "source": [ "# This will return a subset of the dataset corresponding to the entire year of 2013.\n", - "ds.sel(time=slice('2013-01-01', '2013-12-31'))" + "ds.sel(time=slice(\"2013-01-01\", \"2013-12-31\"))" ] }, { @@ -607,7 +607,7 @@ }, "outputs": [], "source": [ - "dates = ['2013-07-09', '2013-10-11', '2013-12-24']\n", + "dates = [\"2013-07-09\", \"2013-10-11\", \"2013-12-24\"]\n", "ds.sel(time=dates)" ] }, diff --git a/fundamentals/02.2_manipulating_dimensions.ipynb b/fundamentals/02.2_manipulating_dimensions.ipynb index 9cf4bd15..f158badd 100644 --- a/fundamentals/02.2_manipulating_dimensions.ipynb +++ b/fundamentals/02.2_manipulating_dimensions.ipynb @@ -36,7 +36,7 @@ " dims=(\"x\", \"y\"),\n", " coords={\n", " \"x\": [-3.2, 2.1, 5.3, 6.5],\n", - " \"y\": pd.date_range(\"2009-01-05\", periods=6, freq=\"M\"),\n", + " \"y\": pd.date_range(\"2009-01-05\", periods=6, freq=\"ME\"),\n", " },\n", ")\n", "arr" diff --git a/fundamentals/03.1_computation_with_xarray.ipynb b/fundamentals/03.1_computation_with_xarray.ipynb index b72d93e9..8ef77a3e 100644 --- a/fundamentals/03.1_computation_with_xarray.ipynb +++ b/fundamentals/03.1_computation_with_xarray.ipynb @@ -4,6 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "(fundamentals/basic-computation)=\n", "# Basic Computation\n", "\n", "In this lesson, we discuss how to do scientific computations with xarray\n", @@ -23,9 +24,9 @@ "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", "\n", "# Ask Xarray to not show data values by default\n", "xr.set_options(display_expand_data=False)\n", diff --git a/fundamentals/03.2_groupby_with_xarray.ipynb b/fundamentals/03.2_groupby_with_xarray.ipynb index cb1ba252..5e4cc5cc 100644 --- a/fundamentals/03.2_groupby_with_xarray.ipynb +++ b/fundamentals/03.2_groupby_with_xarray.ipynb @@ -22,9 +22,9 @@ "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", "\n", "# don't expand data by default\n", "xr.set_options(display_expand_data=False, display_expand_attrs=False)\n", @@ -526,7 +526,7 @@ "metadata": {}, "outputs": [], "source": [ - "resample_obj = ds_anom.resample(time=\"5Y\")\n", + "resample_obj = ds_anom.resample(time=\"5YE\")\n", "resample_obj" ] }, @@ -593,7 +593,7 @@ ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", - "resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n", + "resampled = ds.resample(time='YE').mean().sst.sel(lon=300, lat=50)\n", "resampled.plot();\n", "```\n", ":::\n", @@ -629,7 +629,7 @@ "from IPython.display import display_html\n", "\n", "grouped = ds.groupby(\"time.year\").mean().sst.sel(lon=300, lat=50)\n", - "resampled = ds.resample(time='Y').mean().sst.sel(lon=300, lat=50)\n", + "resampled = ds.resample(time=\"YE\").mean().sst.sel(lon=300, lat=50)\n", "display_html(grouped)\n", "display_html(resampled)" ] diff --git a/fundamentals/03.3_windowed.ipynb b/fundamentals/03.3_windowed.ipynb index 4454d50e..9bdf9644 100644 --- a/fundamentals/03.3_windowed.ipynb +++ b/fundamentals/03.3_windowed.ipynb @@ -25,9 +25,9 @@ "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", "\n", "np.set_printoptions(threshold=10, edgeitems=2)\n", "xr.set_options(display_expand_data=False)\n", @@ -62,7 +62,7 @@ "Rolling window operations \n", "1. can be applied along any dimension, or along multiple dimensions.\n", "2. returns object of same shape as input\n", - "3. pads with NaNs to make (3) possible\n", + "3. pads with NaNs to make (2) possible\n", "\n", "Again, all common reduction operations are [available](https://docs.xarray.dev/en/stable/api.html#rolling-objects)" ] @@ -151,7 +151,7 @@ "source": [ "### Custom reductions\n", "\n", - "While common reductions are implemented by default, sometimes it is useful to apply our own windowed operations. For these uses, Xarray provides the `construct` methods for [DataArray.rolling](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DataArrayRolling.construct.html) and [Dataset.rolling](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DatasetRolling.construct.html).\n", + "While common reductions are implemented by default, sometimes it is useful to apply our own windowed operations. For these uses, Xarray provides the `construct` methods for [DataArray.rolling](https://docs.xarray.dev/en/stable/generated/xarray.computation.rolling.DataArrayRolling.construct.html#xarray.computation.rolling.DataArrayRolling.construct) and [Dataset.rolling](https://docs.xarray.dev/en/stable/generated/xarray.computation.rolling.DatasetRolling.construct.html#xarray.computation.rolling.DatasetRolling.construct).\n", "\n", "For rolling over a dimension `time` with a window size `N`, `construct` adds a new dimension (with user-provided name) of size `N`. \n", "\n", diff --git a/fundamentals/03.4_weighted.ipynb b/fundamentals/03.4_weighted.ipynb index 8f6807e1..c6d49756 100644 --- a/fundamentals/03.4_weighted.ipynb +++ b/fundamentals/03.4_weighted.ipynb @@ -22,9 +22,9 @@ "metadata": {}, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", "\n", "%config InlineBackend.figure_format='retina'" ] diff --git a/fundamentals/05_intro_to_dask.ipynb b/fundamentals/05_intro_to_dask.ipynb index d6877ad2..cf7065d5 100644 --- a/fundamentals/05_intro_to_dask.ipynb +++ b/fundamentals/05_intro_to_dask.ipynb @@ -160,7 +160,7 @@ "A crucal difference with Dask is that we must specify the `chunks` argument.\n", "\"Chunks\" describes how the array is split up over many sub-arrays.\n", "\n", - "![Dask Arrays](http://docs.dask.org/en/latest/_images/dask-array-black-text.svg)\n", + "![Dask Arrays](https://docs.dask.org/en/latest/_images/dask-array.svg)\n", "_source:\n", "[Dask Array Documentation](http://docs.dask.org/en/latest/array-overview.html)_\n", "\n", diff --git a/images/favicon.ico b/images/favicon.ico new file mode 100644 index 00000000..127feadc Binary files /dev/null and b/images/favicon.ico differ diff --git a/intermediate/BiologyDataset.ipynb b/intermediate/BiologyDataset.ipynb new file mode 100644 index 00000000..7d4d6ced --- /dev/null +++ b/intermediate/BiologyDataset.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Datasets in other fields\n", + "\n", + "\n", + "While there are many Xarray examples from geosciences the Xarray data model is not constrained to that field. Our examples so far solve problems that are highly analogous to the problems faced in biology. In this notebook we will load a dataset based on the [cells3d](https://scikit-image.org/docs/0.25.x/api/skimage.data.html#skimage.data.cells3d) dataset from scikit-image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "## Loading the Dataset\n", + "\n", + "This dataset follows a classic pattern in microscopy where we have a stack of images, with two fluoresence channels. In addition to that we have a mask (or labels layer) that identifies individual cells " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.load_dataset(\"cells3d\")\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "ds[\"images\"].max(\"z\").sel(c=\"nuclei\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "ds[\"images\"].sel(z=9, method=\"nearest\").sel(c=\"membrane\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], + "source": [ + "ds[\"images\"].max(\"z\").sel(c=\"nuclei\").plot()\n", + "ds[\"mask\"].plot.contour(colors=\"Red\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "ds[\"mask\"].plot()" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "## Allows high level computations.\n", + "\n", + "We won't get into here - but this structuring data allows you to leverage Xarray to do powerful operations such as computing per cell properties. See the [Computational Patterns Notebook](./computation/01-high-level-computation-patterns.ipynb) for more." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/cataloging.ipynb b/intermediate/cataloging.ipynb new file mode 100644 index 00000000..60c4a3af --- /dev/null +++ b/intermediate/cataloging.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "# Data cataloguing for Xarray\n", + "\n", + "**Goals:** At the end of this tutorial, you'll have an overview of what data cataloging is, why it is done, what tools are available. You'll also know how to open and browse some data catalogues.\n", + "\n", + "> A data catalog is a detailed inventory of data assets within an organization. It helps users easily discover, understand, manage, curate and access data. -[IBM](https://www.ibm.com/think/topics/data-catalog)\n", + "\n", + "Within an organisation, you may work with a lot of different datasets. As someone managing this data, you might want the following things:\n", + "\n", + "- **An organized, easily browsable collection**: By easing discovery and loading of your datasets you can reduce the friction for those analysing the data. Users can eaily get an overview of available datasets, discover datasets of interest, and load them for analysis.\n", + "- **Access control and logging**: You can limit who is able to access these datasets, and track when datasets are being accessed - keeping the data private, and providing metrics that can inform data management (e.g., by removing little-used datasets).\n", + "- **Combine individual data assets**: Combine individual data assets that are similar into a larger object (e.g., combining individual time snapshots into a larger data cube)\n", + "\n", + "There are a bunch of solutions available in Xarray that meet some of these needs - we'll broadly call these \"data catalogs\". These solutions differ in functionality, and the correct data cataloguing solution depends on your needs as an institute.\n", + "\n", + "If you feel there are data cataloguing tools missing from this page, please submit a PR.\n", + "\n", + "## intake v2\n", + "\n", + "Intake is a lightweight Python package which allows one to specify data catalogues (or a hierarchy of catalogues) via YAML files. These YAML files describe:\n", + "\n", + "- how to load the dataset. This is done via \"readers\", which support different file formats, and support lazily defining additional transformations to the dataset using Dask\n", + "- additional metadata for the dataset\n", + "\n", + "Uploading the catalogue file to a Git forge like GitHub allows for versioning of this file.\n", + "\n", + "This catalogue can be browsed in Python, or via third-party tools which parse the datalog, allowing the user to select and open a dataset. This tool has been adopted in the geosciences community for working with Xarray datasets, but is also flexible to work with other dataset types.\n", + "\n", + "There is no separate authentication layer in Intake, hence access control and logging depends entirely on the way the data is accessed. If the dataset is loaded from a file location on a mounted drive, then you need to have access to that mounted drive. If the dataset is located in an S3 bucket, you need to have the appropriate access credentials for the resource.\n", + "\n", + "Intake was originally developed within Anaconda by the team behind Dask.\n", + "\n", + "### Resources\n", + "\n", + "Project Pythia has a [cookbook on Intake](https://projectpythia.org/intake-cookbook/notebooks/creating-catalogs/).\n", + "\n", + "### Example code\n", + "\n", + "Here we work based off [an Intake tutorial on easy.gems!](https://easy.gems.dkrz.de/Processing/intake.html) from the DKRZ (German Climate Supercomputing Center). Note that the tutorial is based on v1 of Intake (which also uses `intake-xarray`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "# Browse an existing catalogue\n", + "\n", + "import intake\n", + "\n", + "cat = intake.open_catalog(\"https://data.nextgems-h2020.eu/online.yaml\")\n", + "# TIP: Its just a YAML file - open it in your browser and you'll see!\n", + "\n", + "# view the available subcatalogues or datasets\n", + "list(cat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "# Access catalogue elements using `[\"attr\"]` or `.attr` notation\n", + "# NOTE: The `.attr` notation only works if the entry is a valid Python attribute name - which the entry might not be\n", + "\n", + "# Let's see what the `tutorial` item is...\n", + "cat[\"tutorial\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "# it's a subcatalogue!\n", + "\n", + "print(list(cat[\"tutorial\"]))\n", + "\n", + "# Let's look at an entry within...\n", + "cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's open it as an xarray dataset\n", + "\n", + "ds = cat[\"tutorial\"][\"ICON.native.2d_PT6H_inst\"].to_dask()\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "That's it for the example! Feel free to poke around further to see what other datasets are available in this catalogue, or to see which other catalogues are available online." + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## STAC\n", + "\n", + "> SpatioTemporal Asset Catalogs\n", + "> The STAC specification is a common language to describe geospatial information, so it can more easily be worked with, indexed, and discovered.\n", + "> -stacspec.org\n", + "\n", + "Let's set the scene a bit: During satellite data processing, many image files (predominantly TIFF) get generated. Each of these files can have different spatial and temporal extents, and over time these individual files can number in the millions, containing terrabytes of data in total. When doing analyses on these files, the first thing one needs to know is whether the file is even relevant to the analysis at hand (i.e., at least \"what is the spatial and temporal extent of the file?\"), however downloading the file just to check this metadata within is very wasteful. This is the context from which the STAC project was born.\n", + "\n", + "The goal of the STAC project is to make geospatial data more accessible by providing a specification to describe assets and their metadata. Adding this metadata alongside the asset (or lifting it out above the asset itself) allows:\n", + "\n", + "- The efficient querying of assets based on properties of interest without downloading the entire asset.\n", + "- The combining of these assets into a datacube object (i.e., Xarray dataset) by reprojecting or interpolating the data to a common grid.\n", + "\n", + "\n", + "Within the STAC project is:\n", + "\n", + "- **The STAC Specification**: A set of specifications describing (from most atomic to least) STAC Items, STAC Catalogs, and STAC collections. There is also a spec for the STAC API.\n", + "- **Tooling for working with STAC**: More on this just now\n", + "- **The community**\n", + "\n", + "There are various tools in the STAC ecosystem. There are online STAC browsers such as the [Radiant Earth STAC browser](https://radiantearth.github.io/stac-browser/#/). There are Python packages for the creating of STAC items and searching of STAC catalogues (e.g., [`rustac-py`](https://stac-utils.github.io/rustac/) and [`pystac`](https://github.com/stac-utils/pystac)). There are also Python packages for combining STAC objects into larger datacubes (e.g., [`odc-stac`](https://github.com/opendatacube/odc-stac), [`stacstac`](https://github.com/gjoseph92/stackstac) and [`xpystac`](https://github.com/stac-utils/xpystac)).\n", + "\n", + "There are many packages with some overlapping functionality. An example workflow you might follow would be using an online STAC browser to get a better understanding of the STAC assets you're working with, and what properties are available to query on, using `rustac-py` to generate and run the query, and then (once you're happy with the result) using `stackstac` or `odc-stac` to open the result as an Xarray Dataset.\n", + "\n", + "### Resources\n", + "\n", + "- https://stacspec.org/en\n", + "- https://stacindex.org/\n", + "- Example using odc-stac: See example notebook from `odc-stac`docs [\"Access Sentinel 2 Data from AWS\"](https://odc-stac.readthedocs.io/en/latest/notebooks/stac-load-e84-aws.html).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "## Commercial offering - Arraylake\n", + "\n", + "There are also commercial offerings available that have data cataloguing functionality.\n", + "\n", + "Arraylake is Earthmover's managed cloud platform built on top of the open-source Icechunk data format\n", + "(where Icechunk is a project offering versioning of Zarr-like datasets).\n", + "\n", + "See [here](https://icechunk.io/en/latest/arraylake/) for a full comparison between Icechunk and Arraylake.\n", + "\n", + "A notable feature is that Arraylake integrates with [Earthmover's data marketplace](https://www.earthmover.io/marketplace/),\n", + "powering data discovery and allowing for the dissemination of datasets (or subsets of datasets) to other interested users.\n", + "\n", + "\n", + "\n", + "## More resources\n", + "\n", + "https://guide.cloudnativegeo.org/cookbooks/zarr-stac-report/data-consumers/" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/computation/01-high-level-computation-patterns.ipynb similarity index 86% rename from intermediate/01-high-level-computation-patterns.ipynb rename to intermediate/computation/01-high-level-computation-patterns.ipynb index 13c5f261..517fb604 100644 --- a/intermediate/01-high-level-computation-patterns.ipynb +++ b/intermediate/computation/01-high-level-computation-patterns.ipynb @@ -79,16 +79,16 @@ "\n", "Xarray provides methods for high-level analysis patterns:\n", "\n", - "1. `rolling` :\n", - " [Operate on rolling or sliding (fixed length, overlapping) windows of your data e.g. running mean.](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations)\n", - "1. `coarsen` :\n", - " [Operate on blocks (fixed length) of your data (downsample).](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays)\n", - "1. `groupby` :\n", - " [Parse data into groups (using an exact value) and operate on each one (reduce data).](https://docs.xarray.dev/en/stable/groupby.html)\n", - "1. `groupby_bins`: [GroupBy after discretizing a numeric (non-exact, e.g. float) variable.](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.groupby_bins.html)\n", - "1. `resample` :\n", - " [Groupby specialized for time axes. Either downsample or upsample your data.](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations)\n", - "1. `weighted`: [Weight your data before reducing.](https://docs.xarray.dev/en/stable/user-guide/computation.html#weighted-array-reductions) \n", + "1. [`rolling`]() :\n", + " Operate on rolling or sliding (fixed length, overlapping) windows of your data e.g. running mean.\n", + "1. [`coarsen`]() :\n", + " Operate on blocks (fixed length) of your data (downsample).\n", + "1. [`groupby`]() :\n", + " Parse data into groups (using an exact value) and operate on each one (reduce data).\n", + "1. [`groupby_bins`](): GroupBy after discretizing a numeric (non-exact, e.g. float) variable.\n", + "1. [`resample`]() :\n", + " [Groupby specialized for time axes. Either downsample or upsample your data.]\n", + "1. [`weighted`](): Weight your data before reducing.\n", "\n", "\n", "```{note}\n", @@ -118,9 +118,9 @@ }, "outputs": [], "source": [ + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", "\n", "# reduce figure size\n", "plt.rcParams[\"figure.dpi\"] = 90\n", @@ -352,21 +352,21 @@ "\n", "These are sequential windowed operations with a window of a fixed size.\n", "\n", - "1. `rolling` :\n", - " [Operate on rolling or sliding (fixed length, overlapping) windows of your data e.g. running mean.](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations)\n", - "1. `coarsen` :\n", - " [Operate on blocks (fixed length) of your data (downsample).](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays)\n", + "1. [`rolling`]() :\n", + " Operate on rolling or sliding (fixed length, overlapping) windows of your data e.g. running mean.\n", + "1. [`coarsen`]() :\n", + " Operate on blocks (fixed length) of your data (downsample).\n", "\n", "\n", "### Label space\n", "\n", "These are windowed operations with irregular windows based on your data. Members of a single group may be non-sequential and scattered through the dataset.\n", "\n", - "1. `groupby` :\n", - " [Parse data into groups (using an exact value) and operate on each one (reduce data).](https://docs.xarray.dev/en/stable/groupby.html)\n", - "1. `groupby_bins`: [GroupBy after discretizing a numeric (non-exact, e.g. float) variable.](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.groupby_bins.html)\n", - "1. `resample` :\n", - " [Groupby specialized for time axes. Either downsample or upsample your data.](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations)\n" + "1. [`groupby`]() :\n", + " Parse data into groups (using an exact value) and operate on each one (reduce data).\n", + "1. [`groupby_bins`]( ): GroupBy after discretizing a numeric (non-exact, e.g. float) variable.\n", + "1. [`resample`]() :\n", + " Groupby specialized for time axes. Either downsample or upsample your data.\n" ] }, { @@ -385,7 +385,7 @@ "\n", "## Index space: windows of fixed width\n", "\n", - "### Sliding windows of fixed length: [`rolling`](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.rolling.html)\n", + "### Sliding windows of fixed length: [`rolling`]()\n", "\n", "- Supports common reductions : `sum`, `mean`, `count`, `std`, `var` etc.\n", "- Returns object of same shape as input\n", @@ -435,11 +435,11 @@ "source": [ "#### Apply an existing numpy-only function with `reduce`\n", "\n", - "In some cases, we may want to apply a sliding window function using rolling that is not built in to Xarray. In these cases we can still leverage the sliding windows of rolling and apply our own function with [`reduce`](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DataArrayRolling.reduce.html).\n", + "In some cases, we may want to apply a sliding window function using rolling that is not built in to Xarray. In these cases we can still leverage the sliding windows of rolling and apply our own function with [`reduce`]().\n", "\n", "The `reduce` method on Xarray objects (e.g. DataArray, Dataset) expects a function that can *receive and return plain arrays (e.g. numpy)*, as in each of the \"windows\" provided by the rolling iterator. This is in contrast to the `map` method on DataArray and Dataset objects, which expects a function that can receive and return Xarray objects.\n", "\n", - "Here's an example function: [`np.ptp`](https://numpy.org/doc/stable/reference/generated/numpy.ptp.html).\n" + "Here's an example function: [`np.ptp`]().\n" ] }, { @@ -461,7 +461,7 @@ ":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", + "[`rolling(...).reduce`]()\n", "\n", ":::{admonition} Solution\n", ":class: dropdown\n", @@ -486,7 +486,7 @@ "source": [ "#### View the `rolling` operation as a Xarray object with `construct`\n", "\n", - "In the above examples, we plotted the outputs of our rolling operations. Xarray makes it easy to integrate the outputs from `rolling` directly into the DataArray using the [`construct`](https://docs.xarray.dev/en/stable/generated/xarray.core.rolling.DataArrayRolling.construct.html#xarray.core.rolling.DataArrayRolling.construct) method." + "In the above examples, we plotted the outputs of our rolling operations. Xarray makes it easy to integrate the outputs from `rolling` directly into the DataArray using the method.\n" ] }, { @@ -588,7 +588,7 @@ "\n", "### Block windows of fixed length: `coarsen`\n", "\n", - "For non-overlapping windows or \"blocks\" use [`coarsen`](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.coarsen.html). The syntax is very similar to `rolling`. You will need to specify how you want Xarray to handle the `boundary` if the length of the dimension is not a multiple of the block size.\n", + "For non-overlapping windows or \"blocks\" use [`coarsen`](). The syntax is very similar to `rolling`. You will need to specify how you want Xarray to handle the `boundary` if the length of the dimension is not a multiple of the block size.\n", "\n", "\n", "- Supports common reductions : `sum`, `mean`, `count`, `std`, `var` etc.\n", @@ -759,7 +759,7 @@ "metadata": {}, "source": [ "Note that `coarsen` pads with NaNs. For more control over padding, use\n", - "[DataArray.pad](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.pad.html) explicitly." + "[DataArray.pad]() explicitly." ] }, { @@ -910,8 +910,7 @@ "1. *Combine* : concatenate results from apply step along a new \"group\" dimension\n", "\n", "illustrated in this neat schematic from [Project Pythia](https://foundations.projectpythia.org/core/xarray/computation-masking.html#groupby-split-apply-combine):\n", - "\n", - "\n", + "\n", "\n", "But really there is a \"hidden\" first step: *identifying* groups (also called \"factorization\" or sometimes \"binning\"). Usually this is the hard part.\n", "\n", @@ -1036,23 +1035,22 @@ "\n", "If the automatic group detection doesn't work for your problem then these functions are useful for constructing specific \"group labels\" in many cases\n", "\n", - "1. [numpy.digitize](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html)\n", + "1. [numpy.digitize]()\n", " for binning\n", - "1. [numpy.searchsorted](https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html)\n", + "1. [numpy.searchsorted]()\n", " supports many other data types\n", - "1. [pandas.factorize](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html)\n", + "1. [pandas.factorize]()\n", " supports characters, strings etc.\n", - "1. [pandas.cut](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html)\n", + "1. [pandas.cut]()\n", " for binning\n", - "1. [\"Datetime components\"](https://docs.xarray.dev/en/stable/user-guide/time-series.html#datetime-components) of Xarray DataArrays\n", - "1. [DataArray.isin](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.isin.html)\n", - "1. [scipy.ndimage.label](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.label.html)\n", + "1. [\"Datetime components\"]() of Xarray DataArrays\n", + "1. [DataArray.isin]()\n", + "1. [scipy.ndimage.label]()\n", "1. Do you know of any more? (Send in a pull request to update this list!)\n", "\n", "\n", - "\n", "```{tip}\n", - "Xarray uses [`pandas.factorize`](https://pandas.pydata.org/docs/reference/api/pandas.factorize.html) for `groupby` and [`pandas.cut`](https://pandas.pydata.org/docs/reference/api/pandas.cut.html) for `groupby_bins`.\n", + "Xarray uses `pandas.factorize` for `groupby` and `pandas.cut` for `groupby_bins`.\n", "```" ] }, @@ -1066,10 +1064,9 @@ "tags": [] }, "source": [ - "#### [\"Datetime components\"](https://docs.xarray.dev/en/stable/user-guide/time-series.html#datetime-components) for creating groups\n", + "#### \"Datetime components\" for creating groups\n", "\n", - "See a full list\n", - "[here](https://docs.xarray.dev/en/stable/generated/xarray.core.accessor_dt.DatetimeAccessor.html?highlight=DatetimeAccessor)\n", + "See a full list [here]()\n", "\n", "These can be accessed in a few different ways as illustrated below.\n" ] @@ -1229,7 +1226,7 @@ "source": [ "##### `floor`, `ceil` and `round` on time\n", "\n", - "Additional functionality in the [datetime accessor](https://docs.xarray.dev/en/stable/generated/xarray.core.accessor_dt.DatetimeAccessor.html) allows us to effectively \"resample\" our time data to remove roundoff errors in timestamps.\n" + "Additional functionality in the [datetime accessor]() allows us to effectively \"resample\" our time data to remove roundoff errors in timestamps.\n" ] }, { @@ -1449,16 +1446,16 @@ "\n", "Xarray provides methods for high-level analysis patterns:\n", "\n", - "1. `rolling` :\n", - " [Operate on rolling (fixed length, overlapping) windows of your data e.g. running mean.](https://docs.xarray.dev/en/stable/user-guide/computation.html#rolling-window-operations)\n", - "1. `coarsen` :\n", - " [Operate on blocks (fixed length) of your data (downsample).](https://docs.xarray.dev/en/stable/user-guide/computation.html#coarsen-large-arrays)\n", - "1. `groupby` :\n", - " [Parse data into groups (using an exact value) and operate on each one (reduce data).](https://docs.xarray.dev/en/stable/groupby.html)\n", - "1. `groupby_bins`: [GroupBy after discretizing a numeric (non-exact, e.g. float) variable.](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.groupby_bins.html)\n", - "1. `resample` :\n", - " [Groupby specialized for time axes. Either downsample or upsample your data.](https://docs.xarray.dev/en/stable/user-guide/time-series.html#resampling-and-grouped-operations)\n", - "1. `weighted`: [Weight your data before reducing.](https://docs.xarray.dev/en/stable/user-guide/computation.html#weighted-array-reductions)\n", + "1. [`rolling`]() :\n", + " Operate on rolling or sliding (fixed length, overlapping) windows of your data e.g. running mean.\n", + "1. [`coarsen`]() :\n", + " Operate on blocks (fixed length) of your data (downsample).\n", + "1. [`groupby`]() :\n", + " Parse data into groups (using an exact value) and operate on each one (reduce data).\n", + "1. [`groupby_bins`](): GroupBy after discretizing a numeric (non-exact, e.g. float) variable.\n", + "1. [`resample`]() :\n", + " [Groupby specialized for time axes. Either downsample or upsample your data.]\n", + "1. [`weighted`](): Weight your data before reducing.\n", "\n", "Xarray also provides a consistent interface to make using those patterns easy:\n", "\n", diff --git a/intermediate/computation/hierarchical_computation.ipynb b/intermediate/computation/hierarchical_computation.ipynb new file mode 100644 index 00000000..1338be88 --- /dev/null +++ b/intermediate/computation/hierarchical_computation.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Hierarchical computations\n", + "\n", + "In this lesson, we extend what we learned about to hierarchical datasets. By the end of the lesson, we will be able to:\n", + "\n", + "- Apply basic arithmetic and label-aware reductions to xarray DataTree objects\n", + "- Apply arbitrary functions across all nodes across a tree" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "xr.set_options(keep_attrs=True, display_expand_attrs=False, display_expand_data=False)" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "## Example dataset\n", + "\n", + "First we load the NMC reanalysis air temperature dataset and arrange it to form a hierarchy of temporal resolutions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.open_dataset(\"air_temperature\")\n", + "\n", + "ds_daily = ds.resample(time=\"D\").mean(\"time\")\n", + "ds_weekly = ds.resample(time=\"W\").mean(\"time\")\n", + "ds_monthly = ds.resample(time=\"ME\").mean(\"time\")\n", + "\n", + "tree = xr.DataTree.from_dict(\n", + " {\n", + " \"daily\": ds_daily,\n", + " \"weekly\": ds_weekly,\n", + " \"monthly\": ds_monthly,\n", + " \"\": xr.Dataset(attrs={\"name\": \"NMC reanalysis temporal pyramid\"}),\n", + " }\n", + ")\n", + "tree" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Arithmetic\n", + "\n", + "As an extension to `Dataset`, `DataTree` objects automatically apply arithmetic to all variables within all nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "tree - 273.15" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "## Indexing\n", + "\n", + "Just like arithmetic, indexing is simply forwarded to the node datasets. The only difference is that nodes that don't have a certain coordinate / dimension are skipped instead of raising an error:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "tree.isel(lat=slice(None, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "tree.sel(time=\"2013-11\")" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "## Reductions\n", + "\n", + "In a similar way, we can reduce all nodes in the datatree at once:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "tree.mean(dim=[\"lat\", \"lon\"])" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "## Applying functions designed for `Dataset` with `map_over_datasets`\n", + "\n", + "What if we wanted to apply a element-wise function, for example to convert the data to log-space? For a `DataArray` we could just use {py:func}`numpy.log`, but this is not supported for `DataTree` objects:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "np.log(tree)" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "To map a function to all nodes, we can use {py:func}`xarray.map_over_datasets` and {py:meth}`xarray.DataTree.map_over_datasets`: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "tree.map_over_datasets(xr.ufuncs.log)" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "We can also use a custom function to perform more complex operations, like subtracting a group mean:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "def demean(ds):\n", + " return ds.groupby(\"time.day\") - ds.groupby(\"time.day\").mean()" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "Applying that to the dataset raises an error, though:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [], + "source": [ + "tree.map_over_datasets(demean)" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "The reason for this error is that the root node does not have any variables, and thus in particular no `\"time\"` coordinate. To avoid the error, we have to skip computing the function for that node:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "def demean(ds):\n", + " if \"time\" not in ds.coords:\n", + " return ds\n", + " return ds.groupby(\"time.day\") - ds.groupby(\"time.day\").mean()\n", + "\n", + "\n", + "tree.map_over_datasets(demean)" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "## Escape hatches\n", + "\n", + "For some more complex operations, it might make sense to work on {py:class}`xarray.Dataset` or {py:class}`xarray.DataArray` objects and reassemble the tree afterwards.\n", + "\n", + "Let's look at a new dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "precipitation = xr.tutorial.open_datatree(\"precipitation.nc4\").load()\n", + "precipitation" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "Suppose we wanted to interpolate the observed precipitation to the modelled precipitation. We could use `map_over_datasets` for this, but we can also have a bit more control:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "interpolated = xr.DataTree.from_dict(\n", + " {\n", + " \"/\": precipitation.ds,\n", + " \"/observed\": precipitation[\"/observed\"].ds.interp(\n", + " lat=precipitation[\"/reanalysis/lat\"],\n", + " lon=precipitation[\"/reanalysis/lon\"],\n", + " ),\n", + " \"/reanalysis\": precipitation[\"/reanalysis\"],\n", + " }\n", + ")\n", + "interpolated" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "Compute the difference between total observed and modelled precipitation, and plot the result.\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "total = precipitation.sum(dim=[\"lon\", \"lat\"])\n", + "difference = total[\"/observed/precipitation\"] - total[\"/reanalysis/precipitation\"]\n", + "difference.plot()\n", + "```\n", + ":::\n", + "::::\n" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/computation/index.md b/intermediate/computation/index.md new file mode 100644 index 00000000..2774c4ee --- /dev/null +++ b/intermediate/computation/index.md @@ -0,0 +1,5 @@ +# Computations + +```{tableofcontents} + +``` diff --git a/intermediate/data_cleaning/05.3_ice_velocity.ipynb b/intermediate/data_cleaning/05.3_ice_velocity.ipynb index e54ff918..ca29fc3d 100644 --- a/intermediate/data_cleaning/05.3_ice_velocity.ipynb +++ b/intermediate/data_cleaning/05.3_ice_velocity.ipynb @@ -28,10 +28,11 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", - "import pandas as pd\n", "import os\n", - "import numpy as np" + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr" ] }, { @@ -41,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = xr.tutorial.open_dataset('ASE_ice_velocity.nc')\n", + "ds = xr.tutorial.open_dataset(\"ASE_ice_velocity.nc\")\n", "ds" ] }, @@ -78,7 +79,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds.attrs['Projection']" + "ds.attrs[\"Projection\"]" ] }, { @@ -96,8 +97,8 @@ "metadata": {}, "outputs": [], "source": [ - "print(ds.dims['ny'])\n", - "print(ds.dims['nx'])" + "print(ds.dims[\"ny\"])\n", + "print(ds.dims[\"nx\"])" ] }, { @@ -126,7 +127,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = ds.swap_dims({'ny': 'yaxis', 'nx': 'xaxis'})" + "ds = ds.swap_dims({\"ny\": \"yaxis\", \"nx\": \"xaxis\"})" ] }, { @@ -154,7 +155,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = ds.rename({'xaxis': 'x', 'yaxis': 'y'})" + "ds = ds.rename({\"xaxis\": \"x\", \"yaxis\": \"y\"})" ] }, { @@ -193,9 +194,9 @@ "source": [ "var_ls = list(ds)\n", "\n", - "vx_ls = [var for var in var_ls if 'vx' in var]\n", - "vy_ls = [var for var in var_ls if 'vy' in var]\n", - "err_ls = [var for var in var_ls if 'err' in var]\n", + "vx_ls = [var for var in var_ls if \"vx\" in var]\n", + "vy_ls = [var for var in var_ls if \"vy\" in var]\n", + "err_ls = [var for var in var_ls if \"err\" in var]\n", "\n", "yr_ls = [int(var[-4:]) for var in vx_ls]" ] @@ -235,8 +236,8 @@ "metadata": {}, "outputs": [], "source": [ - "print('Object type: ', type(da_vx_ls))\n", - "print('Object length: ', len(da_vx_ls))\n", + "print(\"Object type: \", type(da_vx_ls))\n", + "print(\"Object length: \", len(da_vx_ls))\n", "da_vx_ls[0]" ] }, @@ -255,7 +256,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_vx_ls = [da_vx_ls[var].expand_dims('time') for var in range(len(da_vx_ls))]" + "da_vx_ls = [da_vx_ls[var].expand_dims(\"time\") for var in range(len(da_vx_ls))]" ] }, { @@ -312,7 +313,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_vx_ls = [da_vx_ls[var].rename('vx') for var in range(len(da_vx_ls))]" + "da_vx_ls = [da_vx_ls[var].rename(\"vx\") for var in range(len(da_vx_ls))]" ] }, { @@ -342,9 +343,9 @@ "metadata": {}, "outputs": [], "source": [ - "da_vy_ls = [ds[var].expand_dims(time=[int(var[-4:])]).rename('vy') for var in vy_ls]\n", + "da_vy_ls = [ds[var].expand_dims(time=[int(var[-4:])]).rename(\"vy\") for var in vy_ls]\n", "\n", - "da_err_ls = [ds[var].expand_dims(time=[int(var[-4:])]).rename('err') for var in err_ls]" + "da_err_ls = [ds[var].expand_dims(time=[int(var[-4:])]).rename(\"err\") for var in err_ls]" ] }, { @@ -362,9 +363,9 @@ "metadata": {}, "outputs": [], "source": [ - "vx_concat = xr.concat(da_vx_ls, dim='time')\n", - "vy_concat = xr.concat(da_vy_ls, dim='time')\n", - "err_concat = xr.concat(da_err_ls, dim='time')\n", + "vx_concat = xr.concat(da_vx_ls, dim=\"time\")\n", + "vy_concat = xr.concat(da_vy_ls, dim=\"time\")\n", + "err_concat = xr.concat(da_err_ls, dim=\"time\")\n", "\n", "ds_merge = xr.merge([vx_concat, vy_concat, err_concat])" ] @@ -394,7 +395,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds_merge['vv'] = np.sqrt((ds_merge.vx**2) + (ds_merge.vy**2))" + "ds_merge[\"vv\"] = np.sqrt((ds_merge.vx**2) + (ds_merge.vy**2))" ] }, { @@ -468,7 +469,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds_merge['vx'].sel(time=1996, drop=True).equals(ds.vx1996)" + "ds_merge[\"vx\"].sel(time=1996, drop=True).equals(ds.vx1996)" ] } ], diff --git a/intermediate/datastructures-intermediate.ipynb b/intermediate/datastructures-intermediate.ipynb new file mode 100644 index 00000000..bf58ee3b --- /dev/null +++ b/intermediate/datastructures-intermediate.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intermediate Xarray DataStructures\n", + "\n", + "\n", + "\n", + "In this lesson, we cover the basics of Xarray data structures. By the\n", + "end of the lesson, we will be able to:\n", + "\n", + "- Understand the basic data structures in Xarray\n", + "- Inspect `DataArray` and `Dataset` objects.\n", + "- Select Data using Dataset Coordinates\n", + "- Understand that these Datastructures generalize across fields\n", + "\n", + "\n", + "We'll start by reviewing the various components of the Xarray data model, represented here visually:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Variable\n", + "\n", + "At the the core of everything Xarray works with Variables:\n", + "\n", + "Variables hold arbitrary metadata, dimension names, and an Array of data\n", + "\n", + "![image.png](../_static/variable.png)\n", + "\n", + "## DataArray\n", + "\n", + "A DataArray is lowest level object you will regularly interact with. It is a collection of 1 variable containing the data, and optionally Variables associated with the dimensions (coords)\n", + "\n", + "![image.png](../_static/dataarray.png)\n", + "\n", + "\n", + "## Dataset\n", + "\n", + "A Dataset is also a collection of Variables. However it can contain multiple data variables. All dimensions MUST be compatible. When we access one of the data variables a DataArray will automatically created for us.\n", + "\n", + "![image.png](../_static/dataset.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Xarray has a few small real-world tutorial datasets hosted in the [xarray-data](https://github.com/pydata/xarray-data) GitHub repository.\n", + "\n", + "[xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) is a convenience function to download and open DataSets by name (listed at that link).\n", + "\n", + "Here we'll use `air temperature` from the [National Center for Environmental Prediction](https://www.weather.gov/ncep/). Xarray objects have convenient HTML representations to give an overview of what we're working with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that behind the scenes the `tutorial.open_dataset` downloads a file. It then uses [`xarray.open_dataset`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html#xarray-open-dataset) function to open that file (which for this datasets is a [netCDF](https://www.unidata.ucar.edu/software/netcdf/) file). \n", + "\n", + "\n", + "As a preview of the power of Xarray, using this data structure allows us to make very readable and powerful statements such as:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.air.mean(dim=\"time\").plot(x=\"lon\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's in a Dataset? \n", + "\n", + "*Many DataArrays!* \n", + "\n", + "Datasets are dictionary-like containers of \"DataArray\"s. They are a mapping of\n", + "variable name to DataArray:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out \"air\" dataarray with dictionary syntax\n", + "ds[\"air\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can save some typing by using the \"attribute\" or \"dot\" notation. This won't\n", + "work for variable names that clash with a built-in method name (like `mean` for\n", + "example).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out dataarray using dot notation\n", + "ds.air" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's in a DataArray? \n", + "\n", + "*data + (a lot of) metadata*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Name (optional)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da = ds.air\n", + "\n", + "da.name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Named dimensions \n", + "\n", + "`.dims` correspond to the axes of your data. \n", + "\n", + "In this case we have 2 spatial dimensions (`latitude` and `longitude` are stored with shorthand names `lat` and `lon`) and one temporal dimension (`time`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.dims" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Coordinate variables \n", + "\n", + "`.coords` is a simple [data container](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#coordinates)\n", + "for coordinate variables.\n", + "\n", + "Here we see the actual timestamps and spatial positions of our air temperature data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Coordinates objects support similar indexing notation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extracting coordinate variables\n", + "da.lon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# extracting coordinate variables from .coords\n", + "da.coords[\"lon\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is useful to think of the values in these coordinate variables as axis\n", + "\"labels\" such as \"tick labels\" in a figure. These are coordinate locations on a\n", + "grid at which you have data.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Arbitrary attributes \n", + "\n", + "`.attrs` is a dictionary that can contain arbitrary Python objects (strings, lists, integers, dictionaries, etc.) Your only\n", + "limitation is that some attributes may not be writeable to certain file formats." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.attrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# assign your own attributes!\n", + "da.attrs[\"who_is_awesome\"] = \"xarray\"\n", + "da.attrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Underlying data \n", + "\n", + "`.data` contains the [numpy array](https://numpy.org) storing air temperature values.\n", + "\n", + "\n", + "\n", + "Xarray structures wrap underlying simpler array-like data structures. This part of Xarray is quite extensible allowing for distributed array, GPU arrays, sparse arrays, arrays with units etc. We'll briefly look at this later in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da.data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# what is the type of the underlying data\n", + "type(da.data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Review\n", + "\n", + "Two of Xarray's main data structures are:\n", + "\n", + "1. [`DataArrays`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataarray) that wrap underlying data containers (e.g. numpy arrays) and contain associated metadata\n", + "1. [`Datasets`](https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataset) that are dictionary-like containers of DataArrays\n", + "\n", + "DataArrays contain underlying arrays and associated metadata:\n", + "1. Name\n", + "2. Dimension names\n", + "3. Coordinate variables\n", + "4. and arbitrary attributes.\n", + "\n", + "![image.png](../_static/variable.png)\n", + "\n", + "![image.png](../_static/dataarray.png)\n", + "\n", + "![image.png](../_static/dataset.png)\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "In this exercise the air dataset has been deconstructed into a dictionary of NumPy arrays. Please\n", + "\n", + "- Build a Dataset from scratch from this dictionary of NumPy arrays.\n", + "\n", + "\n", + "Hint: You will want to start by building DataArrays, then combining them into a Dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def air_as_dictionary():\n", + " ds = xr.tutorial.open_dataset(\"air_temperature\")\n", + " return {\n", + " \"lat\": ds[\"lat\"].values,\n", + " \"lon\": ds[\"lon\"].values,\n", + " \"attrs\": ds.attrs,\n", + " \"time\": ds[\"time\"].values,\n", + " \"air-temp\": ds[\"air\"].values,\n", + " }\n", + "\n", + "\n", + "data = air_as_dictionary()\n", + "\n", + "\n", + "# Your code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why Xarray? \n", + "\n", + "Metadata provides context and provides code that is more legible. This reduces the likelihood of errors from typos and makes analysis more intuitive and fun!\n", + "\n", + "### Analysis without xarray: `X(`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot the first timestep\n", + "lat = ds.air.lat.data # numpy array\n", + "lon = ds.air.lon.data # numpy array\n", + "temp = ds.air.data # numpy array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure()\n", + "plt.pcolormesh(lon, lat, temp[0, :, :]);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp.mean(axis=1) ## what did I just do? I can't tell by looking at this line." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Analysis with xarray `=)`\n", + "\n", + "How readable is this code?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.air.isel(time=0).plot(x=\"lon\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use dimension names instead of axis numbers\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.air.mean(dim=\"time\").plot(x=\"lon\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Extracting data or \"indexing\" \n", + "\n", + "Xarray supports\n", + "\n", + "- label-based indexing using `.sel`\n", + "- position-based indexing using `.isel`\n", + "\n", + "See the [user guide](https://docs.xarray.dev/en/stable/indexing.html) for more." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Label-based indexing\n", + "\n", + "Xarray inherits its label-based indexing rules from pandas; this means great\n", + "support for dates and times!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# here's what ds looks like\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out data for all of 2013-May\n", + "ds.sel(time=\"2013-05\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# demonstrate slicing\n", + "ds.sel(time=slice(\"2013-05\", \"2013-07\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.sel(time=\"2013\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# demonstrate \"nearest\" indexing\n", + "ds.sel(lon=240.2, method=\"nearest\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# \"nearest indexing at multiple points\"\n", + "ds.sel(lon=[240.125, 234], lat=[40.3, 50.3], method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Position-based indexing\n", + "\n", + "This is similar to your usual numpy `array[0, 2, 3]` but with the power of named\n", + "dimensions!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.air.data[0, 2, 3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pull out time index 0, lat index 2, and lon index 3\n", + "ds.air.isel(time=0, lat=2, lon=3) # much better than ds.air[0, 2, 3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# demonstrate slicing\n", + "ds.air.isel(lat=slice(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other field DataStructures\n", + "\n", + "\n", + "Link to next notebook: [Data structures In Biology](./BiologyDataset.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise\n", + "\n", + "Dataset of non-alignable data.\n", + "\n", + "For this exercise we will be using the `\"precipitation\"` example data. This data has two parts observed rainfall (`observed`), and predicted rainfall, on a different lat-lon grid. The next cell calls a function that will give you two different Datasets. Try to combine them using the tools we've discussed thus far and observe what goes wrong.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tree = xr.tutorial.open_datatree(\"precipitation.nc4\")\n", + "reanalysis = xr.Dataset(tree[\"observed\"])\n", + "observed = xr.Dataset(tree[\"reanalysis\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For more see the [user guide](https://docs.xarray.dev/en/stable/plotting.html), the [gallery](https://docs.xarray.dev/en/stable/examples/visualization_gallery.html), and [the tutorial material](https://tutorial.xarray.dev/fundamentals/04.0_plotting.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/intermediate/hierarchical_zarr_store.ipynb b/intermediate/hierarchical_zarr_store.ipynb new file mode 100644 index 00000000..6288cc03 --- /dev/null +++ b/intermediate/hierarchical_zarr_store.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Zarr Stores with Xarray\n", + "\n", + "## Learning Objectives:\n", + "- Learn about the Zarr data format and xarray's \"zarr\" backend engine\n", + "- Learn how to read a Zarr store with a hierarchical structure with `xr.DataTree`\n", + "- Learn how to select dask arrays from a Zarr store\n", + "- Explore how to use Zarr stores with xarray for computations and visualizations\n", + "\n", + "## What is Zarr?\n", + "\n", + "The Zarr data format is an open, community-maintained format designed for efficient, scalable storage of large N-dimensional arrays. It stores data as compressed and chunked arrays in a format well-suited to parallel processing and cloud-native workflows. Xarray’s Zarr backend allows xarray to leverage these capabilities, including the ability to store and analyze datasets far too large fit onto disk (particularly in combination with [dask](https://docs.xarray.dev/en/latest/user-guide/dask.html#dask)).\n", + "\n", + "### Zarr Data Organization:\n", + "- **Arrays**: N-dimensional arrays that can be chunked and compressed.\n", + "- **Groups**: A container for organizing multiple arrays and other groups with a hierarchical structure.\n", + "- **Metadata**: JSON-like metadata describing the arrays and groups, including information about data types, dimensions, chunking, compression, and user-defined key-value fields. \n", + "- **Dimensions and Shape**: Arrays can have any number of dimensions, and their shape is defined by the number of elements in each dimension.\n", + "- **Coordinates & Indexing**: Zarr supports coordinate arrays for each dimension, allowing for efficient indexing and slicing.\n", + "\n", + "The diagram below from [the Zarr v3 specification](https://wiki.earthdata.nasa.gov/display/ESO/Zarr+Format) showing the structure of a Zarr store:\n", + "\n", + "![ZarrSpec](https://zarr-specs.readthedocs.io/en/latest/_images/terminology-hierarchy.excalidraw.png)\n", + "\n", + "\n", + "NetCDF and Zarr share similar terminology and functionality, but the key difference is that NetCDF is a single file, while Zarr is a directory-based “store” composed of many chunked files, making it better suited for distributed and cloud-based workflows." + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Reading a zarr store\n", + "With xarray's \"zarr\" backend, we can read data remotely from cloud storage buckets. Since Zarr is a cloud-optimized format, our dataset is lazily-loaded as dask arrays.\n", + "The Zarr store in this tutorial is in a public storage bucket and is a precipitation dataset with a group hierarchical structure. The groups: \"observed\" and \"reanalysis\", each contain a \"precipitation\" data variable derived from *[GPM_3IMERGHH_07](https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary)* and *[M2T1NXFLX_5.12.4](https://disc.gsfc.nasa.gov/datasets/M2T1NXFLX_5.12.4/summary)* products, respectively. The precipitation data variables in each group have different spatial resolutions that cannot be represented in a single group, so we need to read them as `DataTree` objects.\n", + "\n", + "Let's read in our zarr store with `xr.open_datatree`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "precipitation_store = \"https://pub-45a1d62ac8d94c4c89f4dc63681a98ed.r2.dev/precipitation.zarr\"\n", + "\n", + "precip_dt = xr.open_datatree(precipitation_store, engine=\"zarr\", chunks={}, consolidated=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.observed.precipitation" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + ":::{note} We selected `\"zarr\"` backend engine, which tells xarray to load and decode a dataset from a Zarr store. The `chunks={}` parameter is used to load the data into a dask array. And `consolidated=True` enables zarr’s consolidated metadata capability. This lets us read all of the metadata from a single file which can improve performance.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Variable selection\n", + "With our `DataTree` object, we can select variables from our Zarr store with either dictionary and or attribute like syntax." + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "### Dictionary-like interface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt[\"observed/precipitation\"]" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "### Attribute-like access" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.observed.precipitation" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "### Dictionary and Attribute like access" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.observed[\"precipitation\"]" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + ":::{note} All of these variable selection options return the same \"precipitation\" `xr.DataArray` object, as a chunked `dask.Array`, from the \"observed\" group of our Zarr store.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "## Time slicing\n", + "\n", + "We can index and subset by time on our `xr.DataTree` object. Each time slice in our Zarr store represents one hour of data with a total of 10 hours of data.\n", + "\n", + "Let's explore the different ways we can get the first 5 hours of data. " + ] + }, + { + "cell_type": "markdown", + "id": "14", + "metadata": {}, + "source": [ + "### Label-based indexing" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "Let's try getting the first 5 hours of data with `.sel(time=)`. \n", + "\n", + "Since the time slices are ordered we can get a subset of the array of our time coordinate and pass it to the `.sel` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "time_index = precip_dt.time[0:5]\n", + "precip_dt.sel(time=time_index)" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### Datetime indexing\n", + "We can also subset by time with a `datetime` string." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.sel(time=slice(\"2021-08-29T07:30:00\", \"2021-08-29T16:30:00\"))" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "### Positional Indexing\n", + "Or by the index of our time dimension `.isel(time=slice())`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.isel(time=slice(0, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Chunking\n", + "Chunking is the process of dividing arrays into smaller pieces, which allows for parallel processing and efficient storage.\n", + "\n", + "To examine the chunks in our Zarr store, with `xarray` you can use the `chunks` attribute on the `xr.DataArray` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.observed[\"precipitation\"].data.chunks" + ] + }, + { + "cell_type": "markdown", + "id": "23", + "metadata": {}, + "source": [ + "### Selecting by chunks\n", + "\n", + "Since we loaded our data as a `dask.Array`, we can access data from each chunked array in our Zarr store. \n", + "\n", + "Let's get the first chunk of the \"observed/precipitation\" variable in our zarr store." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24", + "metadata": {}, + "outputs": [], + "source": [ + "precip_dt.observed[\"precipitation\"].data.blocks[0, 0, 0].compute()" + ] + }, + { + "cell_type": "markdown", + "id": "25", + "metadata": {}, + "source": [ + ":::{note}\n", + "We added `.data` to our `xr.DataArray` to access the `dask.Array`. The `.blocks[]` method allows you to index by chunk and `.compute()` returns the `np.ndarray`. \n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "## Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "27", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you calculate and plot the mean precipitation, starting at 09:55 for the reanalysis group in this zarr store?\n", + "\n", + ":::{admonition} Hint\n", + ":class: dropdown\n", + "This is how you could calculate mean \"precipitation\"\n", + "\n", + "```python\n", + "precip_dt.reanalysis['precipitation'].mean(dim='time')\n", + "```\n", + ":::\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "precip_dt.reanalysis['precipitation'].sel(time=slice('2021-08-29T09:55:00', '2021-08-29T16:30:00')).mean(dim='time').plot()\n", + "```\n", + ":::\n", + "::::" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index a5538151..b6ea47cb 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -98,7 +98,7 @@ "source": [ "**Pointwise** or **Vectorized indexing**, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions `(0, 0)`, `(2, 2)`, and `(4, 4)`, resulting in the values `[1, 13, 25]`. This is the default behavior of NumPy arrays.\n", " \n", - "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming a cross-product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n", + "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming the Cartesian product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n", " \n", "The output of vectorized indexing is a `1D array`, while the output of orthogonal indexing is a `3x3` array. \n", "\n", @@ -112,8 +112,8 @@ "\n", ":::\n", "\n", - ":::{note} Orthogonal indexing with NumPy\n", - ":class: dropdown\n", + ":::{admonition} Orthogonal indexing with NumPy\n", + ":class: note dropdown\n", "\n", "While pointwise indexing is the default behavior in NumPy, you can achieve orthogonal indexing by using the [`np.ix_` function](https://numpy.org/doc/stable/reference/generated/numpy.ix_.html). This function constructs an open mesh from multiple arrays, allowing you to index along each dimension independently similar to Xarray indexing behavior. For example: \n", "\n", @@ -147,7 +147,6 @@ "import numpy as np\n", "import xarray as xr\n", "\n", - "\n", "xr.set_options(display_expand_attrs=False)\n", "np.set_printoptions(threshold=10, edgeitems=2)\n", "%config InlineBackend.figure_format='retina'\n", @@ -268,7 +267,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_air.sel(lat=obs_lats, lon=obs_lats, method=\"nearest\") # -- orthogonal indexing" + "da_air.sel(lat=obs_lats, lon=obs_lons, method=\"nearest\") # -- orthogonal indexing" ] }, { @@ -354,7 +353,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x='time', hue='points');" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x=\"time\", hue=\"points\");" ] }, { @@ -366,18 +365,19 @@ "::::{admonition} Exercise\n", ":class: tip\n", "\n", - "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,0),(2,2),(4,4):\n", + "In the simple 2D 5x5 Xarray data array above, select the sub-array containing (0,4),(2,2),(4,0):\n", "\n", ":::{admonition} Solution\n", ":class: dropdown\n", "```python\n", "\n", - "indices = np.array([0, 2, 4])\n", + "x_indices = np.array([0, 2, 4])\n", + "y_indices = np.array([4, 2, 0])\n", "\n", - "xs_da = xr.DataArray(indices, dims=\"points\")\n", - "ys_da = xr.DataArray(indices, dims=\"points\")\n", + "da_x = xr.DataArray(x_indices, dims=\"points\")\n", + "da_y = xr.DataArray(y_indices, dims=\"points\")\n", "\n", - "subset_da = da.sel(x=xs_da, y=xs_da)\n", + "subset_da = da.sel(x=da_x, y=da_y)\n", "subset_da\n", "```\n", ":::\n", diff --git a/intermediate/indexing/boolean-masking-indexing.ipynb b/intermediate/indexing/boolean-masking-indexing.ipynb index 67aeced7..6432b86a 100644 --- a/intermediate/indexing/boolean-masking-indexing.ipynb +++ b/intermediate/indexing/boolean-masking-indexing.ipynb @@ -35,10 +35,10 @@ "outputs": [], "source": [ "import cartopy.crs as ccrs\n", + "import matplotlib as mpl\n", "import numpy as np\n", "import xarray as xr\n", "from matplotlib import pyplot as plt\n", - "import matplotlib as mpl\n", "\n", "xr.set_options(display_expand_attrs=False)\n", "np.set_printoptions(threshold=10, edgeitems=2)" diff --git a/intermediate/indexing/why-an-index.ipynb b/intermediate/indexing/why-an-index.ipynb new file mode 100644 index 00000000..3123f3d1 --- /dev/null +++ b/intermediate/indexing/why-an-index.ipynb @@ -0,0 +1,1009 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Why does xarray need an *index*? \n", + "\n", + "and when do you need **custom** one?\n", + "\n", + "Xarray `coordinates` allow you to `sel`. For simple 1D cases this is equivalent to calling `argmin` on your array. But that is just the start of what indexes allow you to do. In general an index lets you tell xarray about the *structure* in your coordinates. Such as\n", + "\n", + " - Complex Geographic Coordinate System\n", + " - Periodic Boundaries\n", + " - Lens Distortion\n", + "\n", + "And allows you to make ergonomic and efficient data selections by exploiting that knowledge of the structure\n", + "\n", + "## Learning objectives\n", + "\n", + "- See what a built-in `Index` lets you do\n", + "- Recognize when the *structure* of your coordinates outgrows the default index.\n", + "- Understand the basics of writing a custom `Index`\n", + "\n", + "## Outline\n", + "\n", + "**Built-in indexes** carry you a long way\n", + " - A video's **time axis** (`PandasIndex`)\n", + " - A **too-big-to-store** regular axis (`RangeIndex`)\n", + "\n", + "**A Simple Custom Index**\n", + " - Periodic on a ring\n", + "\n", + "**Non-linear coordinate transformations**\n", + "\n", + "\n", + "*See also: the [Xarray Indexes Gallery](https://xarray-indexes.readthedocs.io/) · [how to create a custom index](https://docs.xarray.dev/en/stable/internals/how-to-create-custom-index.html) · [indexing user guide](https://docs.xarray.dev/en/stable/user-guide/indexing.html).*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ.setdefault(\"KMP_WARNINGS\", \"0\") # silence a benign OpenMP info line in some envs\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "from xarray import Coordinates\n", + "from xarray.indexes import (\n", + " CoordinateTransform,\n", + " CoordinateTransformIndex,\n", + " NDPointIndex,\n", + " RangeIndex,\n", + ")\n", + "\n", + "\n", + "# helper reused later, when we get to the metal ring\n", + "def draw_circle(ax, r=1, lim=1.4):\n", + " circ = plt.Circle((0, 0), r, fill=False, ls=\":\", color=\"0.7\")\n", + " ax.add_patch(circ)\n", + " ax.set_aspect(\"equal\")\n", + " ax.set_xlim(-lim, lim)\n", + " ax.set_ylim(-lim, lim)\n", + " ax.set_xlabel(\"x\")\n", + " ax.set_ylabel(\"y\")" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "## 1. Built-in indexes \n", + "\n", + "For 1-D coordinates built in Indexes are excellent. Here we will \n", + "- `PandasIndex`\n", + "- `RangeIndex`\n" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "## 1.1 A coordinate is just an array of labels\n", + "\n", + "The most familiar index hides in plain sight. A video is just a **stack of frames**, and each frame happens at a **time**. That `time` axis is a coordinate — literally *an array of labels* — and it has exactly the structure the built-in [`PandasIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.PandasIndex.html) is built for: 1‑D and monotonic.\n", + "\n", + "We'll open a short clip with a tiny xarray backend that reads image formats (`ocean.gif`), so the whole video arrives as an ordinary `Dataset` with dims `(time, height, width, color)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "# A little xarray backend (from the \"Backends\" tutorial) that reads image formats via imageio.\n", + "# It turns a gif/mp4 into a Dataset with dims (time, height, width, color).\n", + "import sys\n", + "\n", + "sys.path.insert(0, \"../../advanced/backends\")\n", + "from imageio_ import ImageIOBackend\n", + "\n", + "# .load() pulls the whole (small) clip into memory up front\n", + "ocean = xr.open_dataset(\"../../advanced/backends/ocean.gif\", engine=ImageIOBackend).load()\n", + "ocean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "ocean.time" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "\n", + "### Finding a time point the naive way: `argmin`\n", + "\n", + "If we want the frame closest to 0.825 s we could write it using `np.argmin`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "target = np.timedelta64(825, \"ms\")\n", + "pos = np.abs(ocean.time.values - target).argmin() # a full O(n) scan\n", + "print(f\"nearest frame is index {pos}, at {ocean.time.values[pos] / np.timedelta64(1, 's'):g} s\")" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "### Let the index do it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "# select by timestamp (not by frame number!) — the index finds the nearest frame\n", + "frame = ocean.sel(time=\"0.825s\", method=\"nearest\")\n", + "\n", + "# Or a range of times\n", + "clip = ocean.sel(time=slice(\"0.5s\", \"1.2s\"))\n", + "\n", + "seconds = frame.time.values / np.timedelta64(1, \"s\")\n", + "fig, ax = plt.subplots(figsize=(5, 3))\n", + "ax.imshow(frame[\"data\"].values)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "# 📝 Play with it: change these timestamps (in seconds) and re-run — .sel picks the nearest frame each time\n", + "times = [\"0s\", \"0.4s\", \"0.8s\", \"1.2s\", \"1.6s\"]\n", + "\n", + "montage = ocean[\"data\"].sel(time=times, method=\"nearest\")\n", + "fig, axes = plt.subplots(1, len(times), figsize=(3 * len(times), 2))\n", + "for ax, t in zip(axes, montage.time.values):\n", + " ax.imshow(montage.sel(time=t).values)\n", + " ax.set(title=f\"{t / np.timedelta64(1, 's'):g} s\", xticks=[], yticks=[])\n", + "fig.suptitle(\"select frames by time — one .sel, no frame numbers\", y=1.05)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "### Recap: the 1‑D index\n", + "\n", + "On a 1‑D, monotonic coordinate, the default [`PandasIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.PandasIndex.html) gives you:\n", + "\n", + "- **ergonomics:** `nearest`, `tolerance`, and label `slice` as one-liners\n", + "- **alignment:** automatic label joins in `+`, `concat`, `merge`\n", + "- **performance:** a hash table for exact matches, binary search over the sorted labels for ranges and `nearest`\n", + "\n", + "The structure that this relies on is the index being monotonic, so it can create a precomputed look up structure, which lets use beat the performance of `argmin`." + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## 1.2 Generated, not stored: `RangeIndex`\n", + "\n", + "- A regular 1‑D axis can get *huge*:\n", + " - At 60 fps hour of video is ~216k frames\n", + " - A brain slice stack can be enormous (exabyte scale is the goal)\n", + "\n", + "Xarray coordinates are eagerly loaded into memory. So if we had an axis with 2 billion entries and we recorded a `float64` entry for each one we would use a ton of memory!\n", + "\n", + "\n", + "If we have regular spacing on the coordinate (structure!) we can encode that in the index and avoid the memory issues.\n", + "\n", + "[`RangeIndex`](https://docs.xarray.dev/en/stable/generated/xarray.indexes.RangeIndex.html) keeps three numbers (`start`, `stop`, `step`).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "n_z = 2_000_000_000\n", + "z = Coordinates.from_xindex(RangeIndex.arange(0.0, n_z * 4.0, 4.0, dim=\"z\"))[\"z\"]\n", + "z" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "type(z.variable._data).__name__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], + "source": [ + "# select by physical position (nm); only the requested points are ever computed\n", + "depth_nm = xr.DataArray([1e6, 5e6], dims=\"probe\") # 1 mm and 5 mm deep\n", + "z.sel(z=depth_nm, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "The labels never materialize unless we request them, so we didn't run out of memory." + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "## 1.3 Slicing in space *and* time\n", + "\n", + "- One `.sel` selects across *several* dimensions at once\n", + " - a time window\n", + " - a spatial crop.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "# give the pixel axes coordinates so BOTH space and time can be sliced by label\n", + "frames = ocean.assign_coords(\n", + " height=(\"height\", np.linspace(0, 1, ocean.sizes[\"height\"])),\n", + " width=(\"width\", np.linspace(0, 1, ocean.sizes[\"width\"])),\n", + ")\n", + "\n", + "# one .sel across time AND space: a temporal window + a spatial box\n", + "clip = frames.sel(time=slice(\"0.2s\", \"0.7s\"), height=slice(0.25, 0.75), width=slice(0.3, 0.7))\n", + "print(\"full:\", dict(frames.sizes))\n", + "print(\"clip:\", dict(clip.sizes))\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(7, 3))\n", + "axes[0].imshow(frames[\"data\"].isel(time=0).values)\n", + "axes[0].set(title=\"full frame\", xticks=[], yticks=[])\n", + "axes[1].imshow(clip[\"data\"].isel(time=0).values)\n", + "axes[1].set(title=\"sel(time, height, width)\", xticks=[], yticks=[])\n", + "plt.show()" + ] + }, + { + "attachments": { + "cccdc7e2-3388-44f0-8822-d62a07849acb.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "## 2. A metal ring: when the axis wraps around\n", + "\n", + "Not every 1-D coordinate lies on a straight line. Position around a **metal ring** is a *periodic* coordinate: `345°` and `0°` are neighbours, and `0°` **is** `360°`. Our hot spot sits at `0°`, right where the labels wrap — exactly where a linear index gets confused. The default `PandasIndex` treats `angle` as a straight axis `[0, 345]` and has no idea the ends meet.\n", + "\n", + "![Heating a metal ring with a torch, thermometers spaced around it — Fourier's experiment](attachment:cccdc7e2-3388-44f0-8822-d62a07849acb.png)\n", + "\n", + "```{admonition} This is a real physical problem\n", + ":class: note\n", + "Heat flow around a ring is the classic *periodic boundary* problem — the one Fourier introduced Fourier series to solve. Temperature at `360°` **is** the temperature at `0°`, so the model has to know the axis wraps.\n", + "\n", + "- 📺 [Fourier's experiments on the heating of a metal ring (video)](https://www.youtube.com/watch?v=O-zNVW7tFPQ&list=PLTGvK6jMx5QBhFJ27J4n7JGxHCnZ0USMf&index=6)\n", + "- 📄 [Reconstructing the early history of the theory of heat through Fourier's experiments (Eur. J. Phys.)](https://iopscience.iop.org/article/10.1088/1361-6404/acdf2e)\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "def ring_temperature(angle, hot_at=0.0, sigma=45.0):\n", + " d = (angle - hot_at + 180) % 360 - 180 # circular distance to the hot spot\n", + " return 15 + 20 * np.exp(-(d**2) / (2 * sigma**2))\n", + "\n", + "\n", + "angle = np.arange(0, 360, 15)\n", + "metal_ring = xr.DataArray(\n", + " ring_temperature(angle, hot_at=0),\n", + " dims=\"angle\",\n", + " coords={\n", + " \"angle\": angle,\n", + " \"x\": (\"angle\", np.cos(np.deg2rad(angle))),\n", + " \"y\": (\"angle\", np.sin(np.deg2rad(angle))),\n", + " },\n", + " name=\"temperature\",\n", + ")\n", + "metal_ring" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(5.2, 5.2))\n", + "\n", + "# the angle is the coordinate, so we drop the cartesian x/y frame\n", + "circ = plt.Circle((0, 0), 1, fill=False, ls=\":\", color=\"0.7\")\n", + "ax.add_patch(circ)\n", + "sc = ax.scatter(metal_ring.x, metal_ring.y, c=metal_ring, cmap=\"coolwarm\", s=140, zorder=3)\n", + "\n", + "# label the angle around the ring\n", + "for a in range(0, 360, 45):\n", + " t = np.deg2rad(a)\n", + " ax.text(\n", + " 1.28 * np.cos(t),\n", + " 1.28 * np.sin(t),\n", + " f\"{a}°\",\n", + " ha=\"center\",\n", + " va=\"center\",\n", + " color=\"0.4\",\n", + " fontsize=9,\n", + " )\n", + "\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_xlim(-1.5, 1.5)\n", + "ax.set_ylim(-1.5, 1.6)\n", + "ax.axis(\"off\")\n", + "fig.colorbar(sc, ax=ax, label=\"temperature (°C)\", shrink=0.8)\n", + "ax.set_title(\"A metal ring: a hot spot at 0°, where the labels wrap\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "22", + "metadata": {}, + "source": [ + "### The problem! \n", + "\n", + "What if we try to plot based on another valid way of describing the angles? (-180, 180) instead of (0, 360)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "q = np.linspace(-180, 180, 721)\n", + "default_only = metal_ring.sel(angle=q, method=\"nearest\")\n", + "\n", + "sensor_fold = ((metal_ring.angle.values + 180) % 360) - 180\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.scatter(sensor_fold, metal_ring.values, color=\"0.3\", s=45, zorder=5, label=\"the sensors\")\n", + "ax.plot(q, default_only, color=\"tab:red\", lw=2, label=\"default index: .sel(nearest)\")\n", + "ax.axvline(0, color=\"0.6\", ls=\":\", lw=1)\n", + "ax.set_xlabel(\"query angle (°)\")\n", + "ax.set_ylabel(\"temperature (°C)\")\n", + "ax.legend(loc=\"lower center\")\n", + "ax.set_xticks(np.arange(-180, 181, 45))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "## 2.1 📝 Write your own: a `PeriodicIndex`\n", + "\n", + "\n", + "For this we need an index that knows about the periodicity of the ring. \n", + "\n", + "A full custom `Index` can be tiny — the only method that carries the idea is **`sel`**. We've filled in the boilerplate: `__init__` wraps a plain `PandasIndex` (which holds the labels) plus the `period`, and `from_variables` is how `set_xindex` builds it.\n", + "\n", + "::::{admonition} Exercise — implement `sel`\n", + ":class: tip\n", + "\n", + "Selection should **wrap around** the period. \n", + "\n", + "```python\n", + "def sel(self, labels, method=None, tolerance=None):\n", + " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370} (one scalar)\n", + " # return : IndexSelResult({dim_name: positions}) positions = list of int rows, e.g. [3]\n", + " (name, label), = labels.items()\n", + " base = self._index.index.to_numpy() # the stored labels (one period)\n", + " q = float(label)\n", + " ... # circular distance -> nearest position -> pos\n", + " return IndexSelResult({self._index.dim: [pos]})\n", + "```\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "```python\n", + "def sel(self, labels, method=None, tolerance=None):\n", + " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370} (one scalar)\n", + " # return : IndexSelResult({dim_name: positions}) positions = list of int rows, e.g. [3]\n", + " (name, label), = labels.items()\n", + " base = self._index.index.to_numpy()\n", + " q = float(label)\n", + " d = np.abs((q - base + self.period / 2) % self.period - self.period / 2)\n", + " pos = int(d.argmin())\n", + " return IndexSelResult({self._index.dim: [pos]})\n", + "```\n", + ":::\n", + "::::\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "from xarray import Index\n", + "from xarray.core.indexing import IndexSelResult\n", + "from xarray.indexes import PandasIndex\n", + "\n", + "\n", + "class PeriodicIndex(Index):\n", + " \"\"\"A 1-D index whose axis wraps every ``period`` (e.g. angle, period=360).\"\"\"\n", + "\n", + " def __init__(self, pandas_index, period):\n", + " self._index = pandas_index # a plain PandasIndex holding the labels\n", + " self.period = period\n", + "\n", + " @classmethod\n", + " def from_variables(cls, variables, *, options):\n", + " # boilerplate: how set_xindex builds us from the coordinate\n", + " return cls(PandasIndex.from_variables(variables, options={}), options[\"period\"])\n", + "\n", + " def sel(self, labels, method=None, tolerance=None):\n", + " # labels : {coord_name: query} e.g. .sel(angle=370) -> {\"angle\": 370}\n", + " # return : IndexSelResult({dim_name: [position]}) position = int row into the axis\n", + " print(\"sel received:\", labels)\n", + " # the stored labels (one period) are self._index.index.to_numpy()\n", + " pos = 0 # <- replace: circular distance -> nearest position\n", + " return IndexSelResult({self._index.dim: [pos]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [], + "source": [ + "# attach OUR (unfinished) index to the metal ring, then select by angle\n", + "periodic = metal_ring.drop_indexes(\"angle\").set_xindex(\"angle\", PeriodicIndex, period=360.0)\n", + "\n", + "# .sel routes the query into PeriodicIndex.sel — run it to see what the index receives:\n", + "periodic.sel(angle=370)" + ] + }, + { + "cell_type": "markdown", + "id": "27", + "metadata": {}, + "source": [ + "### Does your index fix the -180, 180 issue?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# the same query, now resolved by BOTH indexes — the periodic one wraps\n", + "default_temp = metal_ring.sel(angle=q, method=\"nearest\")\n", + "periodic_temp = periodic.sel(angle=q)\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.scatter(sensor_fold, metal_ring.values, color=\"0.3\", s=45, zorder=5, label=\"the sensors\")\n", + "ax.plot(q, periodic_temp, color=\"tab:green\", lw=2.5, label=\"PeriodicIndex .sel\")\n", + "ax.plot(q, default_temp, color=\"tab:red\", lw=2, label=\"default index\")\n", + "ax.axvline(0, color=\"0.6\", ls=\":\", lw=1)\n", + "ax.set_xlabel(\"query angle (°)\")\n", + "ax.set_ylabel(\"temperature (°C)\")\n", + "ax.legend(loc=\"lower center\")\n", + "ax.set_xticks(np.arange(-180, 181, 45))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "29", + "metadata": {}, + "source": [ + "## 3. Build your own: a `CoordinateTransform` (step 1 of 2 — a 1-D ruler)\n", + "\n", + "We just subclassed `Index` and wrote `sel` by hand. A **`CoordinateTransform`** makes this much easier for many common cases. You just write **`forward`/`reverse`** and xarray does the rest.\n", + "\n", + "We'll build two, increasing in power:\n", + "\n", + "1. A **RangeIndex** (here)\n", + "2. A nonlinear **warp** — a fisheye lens\n", + "\n", + "\n", + "\n", + "A `CoordinateTransform` needs just two methods:\n", + "\n", + "- `forward` — dimension positions → coordinate labels\n", + "- `reverse` — coordinate labels → dimension positions\n", + "\n", + "::::{admonition} Exercise — implement the ruler\n", + ":class: tip\n", + "\n", + "Fill in `forward` and `reverse` so the transform maps slice index ↔ depth (`depth = index · thickness`). Each is handed a **dict** and returns a **dict** — run the cell after it to see what.\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "```python\n", + " def forward(self, dim_positions): # slice index -> depth\n", + " return {\"depth\": dim_positions[\"slice\"] * self.thickness}\n", + "\n", + " def reverse(self, coord_labels): # depth -> slice index\n", + " return {\"slice\": coord_labels[\"depth\"] / self.thickness}\n", + "```\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "class DIYRangeIndex(CoordinateTransform):\n", + " def __init__(self, n, thickness):\n", + " super().__init__((\"depth\",), {\"slice\": n})\n", + " self.n, self.thickness = n, thickness\n", + "\n", + " def forward(self, dim_positions): # index space -> world space\n", + " # given : {dim_name: positions} e.g. {\"slice\": array([0, 1, 2, ...])}\n", + " # return : {coord_name: labels} e.g. {\"depth\": }\n", + " print(\"forward received:\", dim_positions)\n", + " depth = dim_positions[\"slice\"] # <- replace: slice index -> depth\n", + " return {\"depth\": depth}\n", + "\n", + " def reverse(self, coord_labels): # world space -> index space\n", + " # given : {coord_name: labels} e.g. {\"depth\": array([10.0, 50.0])}\n", + " # return : {dim_name: positions} e.g. {\"slice\": }\n", + " print(\"reverse received:\", coord_labels)\n", + " slice_index = coord_labels[\"depth\"] # <- replace: depth -> slice index\n", + " return {\"slice\": slice_index}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31", + "metadata": {}, + "outputs": [], + "source": [ + "demo = xr.DataArray(\n", + " np.zeros((5, 2, 2)),\n", + " dims=(\"slice\", \"y\", \"x\"),\n", + " coords=Coordinates.from_xindex(CoordinateTransformIndex(DIYRangeIndex(5, 0.5))),\n", + ")\n", + "_ = demo.depth.values # forward runs: slice index -> depth\n", + "demo.sel(\n", + " depth=xr.DataArray([1.0], dims=\"q\"), method=\"nearest\"\n", + ") # reverse runs: depth -> slice index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32", + "metadata": {}, + "outputs": [], + "source": [ + "# an imaging stack: 200 slices, 0.5 µm apart.\n", + "\n", + "n_slices, thickness = 200, 0.5 # µm\n", + "depth_true = np.arange(n_slices) * thickness\n", + "structure = np.exp(-((depth_true - 50.0) ** 2) / (2 * 8.0**2)) # a bright band ~50 µm deep\n", + "rng = np.random.default_rng(0)\n", + "volume = xr.DataArray(\n", + " structure[:, None, None] * rng.random((n_slices, 32, 32)), # stand-in for the image data\n", + " dims=(\"slice\", \"y\", \"x\"),\n", + " coords=Coordinates.from_xindex(CoordinateTransformIndex(DIYRangeIndex(n_slices, thickness))),\n", + " name=\"intensity\",\n", + ")\n", + "volume # 'depth' is a lazy coordinate, generated by the rule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "# grab the slices nearest given physical depths (µm) in one call — only the rule (n, thickness) is stored\n", + "picked = volume.sel(depth=xr.DataArray([10.0, 50.0, 73.3], dims=\"d\"), method=\"nearest\")\n", + "picked" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34", + "metadata": {}, + "outputs": [], + "source": [ + "# mean intensity vs depth — the x-axis is the 'depth' coordinate, generated on demand by the rule\n", + "# xr.set_options(use_flox=False)\n", + "# volume.mean([\"y\", \"x\"]).plot(x=\"depth\")\n", + "# plt.gca().set(xlabel=\"depth (µm)\", title=\"brightness vs depth (structure ~50 µm deep)\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "35", + "metadata": {}, + "source": [ + "## 4. A nonlinear warp (a fisheye lens) 🔍\n", + "\n", + "\n", + "Here we will build an index that lets us select in world coordinates when our data is warped (e.g. by a fish eye lens). This sort of lens distortion is very common in science. Our data is on arranged on a regular pixel grid, but due to distortion selections in pixel space won't be \n", + "\n", + "https://www.microscopyu.com/tutorials/distortion\n", + "\n", + "\n", + "$$(x_\\text{world},\\, y_\\text{world}) = (x,\\, y)\\;\\cdot\\;\\frac{1}{1 - k\\,r^2}, \\qquad r = \\sqrt{x^2 + y^2}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36", + "metadata": {}, + "outputs": [], + "source": [ + "SQUARES = (\n", + " 1.5 # checkerboard squares per world unit (shared by the checker + the world-grid overlay)\n", + ")\n", + "\n", + "\n", + "def checker(x, y, squares=SQUARES):\n", + " return ((np.floor(x * squares) + np.floor(y * squares)) % 2).astype(float)\n", + "\n", + "\n", + "nx = ny = 1024\n", + "k = 0.35 # lens distortion strength\n", + "\n", + "# each sensor pixel looks out at a world point through the lens (radial model)\n", + "y_pixel, x_pixel = np.meshgrid(np.arange(ny), np.arange(nx), indexing=\"ij\")\n", + "x_norm = (x_pixel - (nx - 1) / 2) / ((nx - 1) / 2) # centered, normalized pixel coords\n", + "y_norm = (y_pixel - (ny - 1) / 2) / ((ny - 1) / 2)\n", + "factor = 1 / (1 - k * np.hypot(x_norm, y_norm) ** 2) # fisheye: the periphery expands (barrel)\n", + "x_world, y_world = x_norm * factor, y_norm * factor # the world position each pixel sees\n", + "\n", + "photo = checker(x_world, y_world) # a flat checkerboard, seen through the lens\n", + "photo_da = xr.DataArray(\n", + " photo,\n", + " dims=(\"y_pixel\", \"x_pixel\"),\n", + " coords={\"x_pixel\": np.arange(nx), \"y_pixel\": np.arange(ny)},\n", + " name=\"brightness\",\n", + ")\n", + "photo_da # our data: a plain, pixel-indexed image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# the flat world (the scene) vs the distorted photo (our data)\n", + "world_grid = np.linspace(-1.6, 1.6, 400)\n", + "flat = checker(*np.meshgrid(world_grid, world_grid))\n", + "\n", + "fig, (ax_world, ax_photo) = plt.subplots(1, 2, figsize=(10.5, 5.2))\n", + "ax_world.imshow(flat, cmap=\"gray\", origin=\"lower\", extent=[-1.6, 1.6, -1.6, 1.6])\n", + "ax_world.set_title(\"REAL WORLD — flat checkerboard\")\n", + "ax_world.set_xlabel(\"x_world\")\n", + "ax_world.set_ylabel(\"y_world\")\n", + "ax_photo.imshow(photo, cmap=\"gray\", origin=\"lower\")\n", + "ax_photo.set_title(\"STORED fisheye photo — DISTORTED (our data)\")\n", + "ax_photo.set_xlabel(\"x_pixel\")\n", + "ax_photo.set_ylabel(\"y_pixel\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "38", + "metadata": {}, + "source": [ + "### Can xarray handle the undistorted coordinates\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "distorted = xr.DataArray(\n", + " photo,\n", + " dims=(\"y_pixel\", \"x_pixel\"),\n", + " coords={\n", + " \"x_world\": ((\"y_pixel\", \"x_pixel\"), x_world),\n", + " \"y_world\": ((\"y_pixel\", \"x_pixel\"), y_world),\n", + " },\n", + " name=\"brightness\",\n", + ")\n", + "distorted" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "distorted.sel(x_world=0.3, y_world=0.15, method=\"nearest\")" + ] + }, + { + "cell_type": "markdown", + "id": "41", + "metadata": {}, + "source": [ + "**It breaks.** The standard index can't handle a 2-D coordinate.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "42", + "metadata": {}, + "source": [ + "### The better fix: A custom Index!\n", + "\n", + "\n", + "$$\\text{world } r = \\frac{\\text{pixel } r}{1 - k\\,(\\text{pixel } r)^2} \\qquad\\Longleftrightarrow\\qquad \\text{pixel } r = \\frac{-1 + \\sqrt{1 + 4k\\,(\\text{world } r)^2}}{2k\\,(\\text{world } r)}$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43", + "metadata": {}, + "outputs": [], + "source": [ + "class FisheyeTransform(CoordinateTransform):\n", + " \"\"\"Radial fisheye lens: pixel (x_pixel, y_pixel) <-> world (x_world, y_world).\n", + " world_r = pixel_r / (1 - k * pixel_r**2) — barrel, with a closed-form inverse.\"\"\"\n", + "\n", + " def __init__(self, nx, ny, k):\n", + " super().__init__((\"x_world\", \"y_world\"), {\"y_pixel\": ny, \"x_pixel\": nx})\n", + " self.nx, self.ny, self.k = nx, ny, k\n", + "\n", + " def _normalize(self, x_pixel, y_pixel): # pixel indices -> centered, normalized\n", + " return (\n", + " (np.asarray(x_pixel) - (self.nx - 1) / 2) / ((self.nx - 1) / 2),\n", + " (np.asarray(y_pixel) - (self.ny - 1) / 2) / ((self.ny - 1) / 2),\n", + " )\n", + "\n", + " def forward(self, dim_positions): # pixel -> world (undistort)\n", + " x_norm, y_norm = self._normalize(dim_positions[\"x_pixel\"], dim_positions[\"y_pixel\"])\n", + " factor = 1 / (1 - self.k * np.hypot(x_norm, y_norm) ** 2)\n", + " return {\"x_world\": x_norm * factor, \"y_world\": y_norm * factor}\n", + "\n", + " def reverse(self, coord_labels): # world -> pixel (distort, closed form)\n", + " x_world = np.asarray(coord_labels[\"x_world\"])\n", + " y_world = np.asarray(coord_labels[\"y_world\"])\n", + " world_r = np.hypot(x_world, y_world)\n", + " with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", + " pixel_r = (-1 + np.sqrt(1 + 4 * self.k * world_r**2)) / (2 * self.k * world_r)\n", + " scale = np.where(world_r > 0, pixel_r / world_r, 1.0)\n", + " return {\n", + " \"x_pixel\": (x_world * scale) * ((self.nx - 1) / 2) + (self.nx - 1) / 2,\n", + " \"y_pixel\": (y_world * scale) * ((self.ny - 1) / 2) + (self.ny - 1) / 2,\n", + " }\n", + "\n", + "\n", + "lens = FisheyeTransform(nx, ny, k)\n", + "\n", + "fish = xr.DataArray(\n", + " photo,\n", + " dims=(\"y_pixel\", \"x_pixel\"),\n", + " coords=Coordinates.from_xindex(CoordinateTransformIndex(lens)),\n", + " name=\"brightness\",\n", + ")\n", + "fish" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 6))\n", + "for c in np.arange(-4, 4.01, 1 / SQUARES): # spacing = one checkerboard square in world\n", + " ax.axhline(c, color=\"tab:red\", lw=0.6, alpha=0.7)\n", + " ax.axvline(c, color=\"tab:red\", lw=0.6, alpha=0.7)\n", + "fish.plot(x=\"x_world\", y=\"y_world\", ax=ax, cmap=\"gray\", add_colorbar=False)\n", + "ax.set_xlim(-4, 4)\n", + "ax.set_ylim(-4, 4)\n", + "ax.set_aspect(\"equal\")\n", + "ax.set_title(\"the fisheye captures a star-shaped patch of the regular world grid\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "# that same straight world line traces a CURVE across the pixels\n", + "# (by hand, via the lens' reverse() — exactly the work .sel just did for us)\n", + "line_x = np.linspace(-1.4, 1.4, 400) # a straight world line at y_world = 0.2\n", + "path = lens.reverse({\"x_world\": line_x, \"y_world\": np.full_like(line_x, 0.2)})\n", + "point = lens.reverse({\"x_world\": np.array([0.3]), \"y_world\": np.array([0.2])})\n", + "flat = checker(*np.meshgrid(np.linspace(-1.6, 1.6, 400), np.linspace(-1.6, 1.6, 400)))\n", + "\n", + "fig, (ax_world, ax_photo) = plt.subplots(1, 2, figsize=(11, 5.2))\n", + "ax_world.imshow(flat, cmap=\"gray\", origin=\"lower\", extent=[-1.6, 1.6, -1.6, 1.6])\n", + "ax_world.plot(line_x, np.full_like(line_x, 0.2), c=\"tab:red\", lw=2.5)\n", + "ax_world.set_title(\"WORLD — straight line: sel(x_world=.., y_world=0.2)\")\n", + "ax_photo.imshow(photo, cmap=\"gray\", origin=\"lower\")\n", + "ax_photo.plot(path[\"x_pixel\"], path[\"y_pixel\"], c=\"tab:red\", lw=2.5)\n", + "ax_photo.scatter(point[\"x_pixel\"], point[\"y_pixel\"], c=\"tab:orange\", s=160, marker=\"*\", zorder=5)\n", + "ax_photo.set_title(\"PHOTO — the same selection is a curved path\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46", + "metadata": {}, + "outputs": [], + "source": [ + "# read the brightness ALONG the line: world selection is a clean square wave; a raw pixel row is warped\n", + "world_line = fish.sel(\n", + " x_world=xr.DataArray(np.linspace(-1.4, 1.4, 400), dims=\"s\"),\n", + " y_world=xr.DataArray(np.zeros(400), dims=\"s\"),\n", + " method=\"nearest\",\n", + ")\n", + "pixel_row = fish.isel(y_pixel=ny // 2)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 4.2), sharex=True)\n", + "ax1.step(np.linspace(0, 1, world_line.sizes[\"s\"]), world_line.values, where=\"mid\", c=\"tab:red\")\n", + "ax1.set_title(\"along the WORLD line (.sel by x_world) — even bands\")\n", + "ax2.step(np.linspace(0, 1, pixel_row.sizes[\"x_pixel\"]), pixel_row.values, where=\"mid\", c=\"tab:blue\")\n", + "ax2.set_title(\"along a raw PIXEL row (.isel) — bands warp with the lens\")\n", + "for ax in (ax1, ax2):\n", + " ax.set_yticks([0, 1])\n", + " ax.set_yticklabels([\"black\", \"white\"])\n", + "ax2.set_xlabel(\"fraction along the line\")\n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "47", + "metadata": {}, + "source": [ + "https://xarray-indexes.readthedocs.io/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/intro-to-zarr.ipynb b/intermediate/intro-to-zarr.ipynb new file mode 100644 index 00000000..911ba391 --- /dev/null +++ b/intermediate/intro-to-zarr.ipynb @@ -0,0 +1,687 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Introduction to Zarr\n", + "\n", + "## Learning Objectives:\n", + "\n", + "- Understand the principles of the Zarr data format\n", + "- Learn how to read and write Zarr stores using the `zarr-python` library\n", + "- Explore how to use Zarr stores with `xarray` for data analysis and visualization\n", + "\n", + "This notebook provides a brief introduction to Zarr and how to\n", + "use it in cloud environments for scalable, chunked, and compressed data storage.\n", + "\n", + "Zarr is a data format with implementations in different languages. In this tutorial, we will look at an example of how to use the Zarr format by looking at some features of the `zarr-python` library and how Zarr files can be opened with `xarray`.\n", + "\n", + "## What is Zarr?\n", + "\n", + "The Zarr data format is an open, community-maintained format designed for efficient, scalable storage of large N-dimensional arrays. It stores data as compressed and chunked arrays in a format well-suited to parallel processing and cloud-native workflows. \n", + "\n", + "### Zarr Data Organization:\n", + "- **Arrays**: N-dimensional arrays that can be chunked and compressed.\n", + "- **Groups**: A container for organizing multiple arrays and other groups with a hierarchical structure.\n", + "- **Metadata**: JSON-like metadata describing the arrays and groups, including information about data types, dimensions, chunking, compression, and user-defined key-value fields. \n", + "- **Dimensions and Shape**: Arrays can have any number of dimensions, and their shape is defined by the number of elements in each dimension.\n", + "- **Coordinates & Indexing**: Zarr supports coordinate arrays for each dimension, allowing for efficient indexing and slicing.\n", + "\n", + "The diagram below from [the Zarr v3 specification](https://wiki.earthdata.nasa.gov/display/ESO/Zarr+Format) showing the structure of a Zarr store:\n", + "\n", + "![ZarrSpec](https://zarr-specs.readthedocs.io/en/latest/_images/terminology-hierarchy.excalidraw.png)\n", + "\n", + "\n", + "NetCDF and Zarr share similar terminology and functionality, but the key difference is that NetCDF is a single file, while Zarr is a directory-based “store” composed of many chunked files, making it better suited for distributed and cloud-based workflows." + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "### Zarr Fundamenals\n", + "A Zarr array has the following important properties:\n", + "- **Shape**: The dimensions of the array.\n", + "- **Dtype**: The data type of each element (e.g., float32).\n", + "- **Attributes**: Metadata stored as key-value pairs (e.g., units, description.\n", + "- **Compressors**: Algorithms used to compress each chunk (e.g., Zstd, Blosc, Zlib).\n", + "\n", + "\n", + "#### Example: Creating and Inspecting a Zarr Array\n", + "\n", + "Here we create a simple 2D array of shape `(40, 50)` with chunks of size `(10, 10)` ,write to the `LocalStore` in the `test.zarr` directory. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "import shutil\n", + "\n", + "import zarr\n", + "\n", + "# Ensure we start with a clean directory for the tutorial\n", + "datadir = pathlib.Path(\"../data/zarr-tutorial\")\n", + "if datadir.exists():\n", + " shutil.rmtree(datadir)\n", + "\n", + "output = datadir / \"test.zarr\"\n", + "z = zarr.create_array(shape=(40, 50), chunks=(10, 10), dtype=\"f8\", store=output)\n", + "z" + ] + }, + { + "cell_type": "markdown", + "id": "3", + "metadata": {}, + "source": [ + "`.info` provides a summary of the array's properties, including shape, data type, and compression settings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], + "source": [ + "z.info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "z.fill_value" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "No data has been written to the array yet. If we try to access the data, we will get a fill value: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], + "source": [ + "z[0, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "8", + "metadata": {}, + "source": [ + "This is how we assign data to the array. When we do this it gets written immediately." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "z[:] = 1\n", + "z[0, :] = np.arange(50)\n", + "z[:]" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "#### Attributes\n", + "\n", + "We can attach arbitrary metadata to our Array via attributes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "z.attrs[\"units\"] = \"m/s\"\n", + "z.attrs[\"standard_name\"] = \"wind_speed\"\n", + "print(dict(z.attrs))" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "### Zarr Data Storage\n", + "\n", + "Zarr can be stored in memory, on disk, or in cloud storage systems like Amazon S3.\n", + "\n", + "Let's look under the hood. _The ability to look inside a Zarr store and understand what is there is a deliberate design decision._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], + "source": [ + "z.store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "!tree -a {output}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "!cat {output}/zarr.json" + ] + }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "### Hierarchical Groups" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "Zarr allows you to create hierarchical groups, similar to directories. To create groups in your store, use the `create_group` method after creating a root group. Here, we’ll create two groups, `temp` and `precip`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "store = zarr.storage.MemoryStore()\n", + "root = zarr.create_group(store=store)\n", + "temp = root.create_group(\"temp\")\n", + "precip = root.create_group(\"precip\")\n", + "t2m = temp.create_array(\"t2m\", shape=(100, 100), chunks=(10, 10), dtype=\"i4\")\n", + "prcp = precip.create_array(\"prcp\", shape=(1000, 1000), chunks=(10, 10), dtype=\"i4\")\n", + "root.tree()" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "Groups can easily be accessed by name and index.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20", + "metadata": {}, + "outputs": [], + "source": [ + "display(root[\"temp\"])\n", + "root[\"temp/t2m\"][:, 3]" + ] + }, + { + "cell_type": "markdown", + "id": "21", + "metadata": {}, + "source": [ + "To get a look at your overall dataset, the `tree` and `info` methods are helpful.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22", + "metadata": {}, + "outputs": [], + "source": [ + "root.info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23", + "metadata": {}, + "outputs": [], + "source": [ + "root.tree()" + ] + }, + { + "cell_type": "markdown", + "id": "24", + "metadata": {}, + "source": [ + "#### Chunking\n", + "Chunking is the process of dividing Zarr arrays into smaller pieces. This allows for parallel processing and efficient storage.\n", + "\n", + "One of the important parameters in Zarr is the chunk shape, which determines how the data is divided into smaller, manageable pieces. This is crucial for performance, especially when working with large datasets.\n", + "\n", + "To examine the chunk shape of a Zarr array, you can use the `chunks` attribute. This will show you the size of each chunk in each dimension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25", + "metadata": {}, + "outputs": [], + "source": [ + "z.chunks" + ] + }, + { + "cell_type": "markdown", + "id": "26", + "metadata": {}, + "source": [ + "When selecting chunk shapes, we need to keep in mind two constraints:\n", + "\n", + "- Concurrent writes are possible as long as different processes write to separate chunks, enabling highly parallel data writing. \n", + "- When reading data, if any piece of the chunk is needed, the entire chunk has to be loaded. \n", + "\n", + "The optimal chunk shape will depend on how you want to access the data. E.g., for a 2-dimensional array, if you only ever take slices along the first dimension, then chunk across the second dimension.\n", + "\n", + "Here we will compare two different chunking strategies.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27", + "metadata": {}, + "outputs": [], + "source": [ + "output = datadir / \"c.zarr\"\n", + "c = zarr.create_array(shape=(200, 200, 200), chunks=(1, 200, 200), dtype=\"f8\", store=output)\n", + "c[:] = np.random.randn(*c.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28", + "metadata": {}, + "outputs": [], + "source": [ + "%time _ = c[:, 0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29", + "metadata": {}, + "outputs": [], + "source": [ + "output = datadir / \"d.zarr\"\n", + "d = zarr.create_array(shape=(200, 200, 200), chunks=(200, 200, 1), dtype=\"f8\", store=output)\n", + "d[:] = np.random.randn(*d.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30", + "metadata": {}, + "outputs": [], + "source": [ + "%time _ = d[:, 0, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "31", + "metadata": {}, + "source": [ + "### Sharding\n", + "When working with large arrays and small chunks, Zarr’s sharding feature can improve storage efficiency and performance. Instead of writing each chunk to a separate file—which can overwhelm file systems and cloud object stores—sharding groups multiple chunks into a single storage object.\n", + "\n", + "Why Use Sharding?\n", + "\n", + "- File systems struggle with too many small files.\n", + "- Small files (e.g., 1 MB or less) may waste space due to filesystem block size.\n", + "- Object storage systems (e.g., S3) can slow down with a high number of objects.\n", + "\n", + "With sharding, you choose:\n", + "- Shard size: the logical shape of each shard, which is expected to include one or more chunks\n", + "- Chunk size: the shape of each compressed chunk\n", + "\n", + "It is important to remember that the shard is the minimum unit of writing. This means that writers must be able to fit the entire shard (including all of the compressed chunks) in memory before writing a shard to a store.\n" + ] + }, + { + "cell_type": "markdown", + "id": "32", + "metadata": {}, + "source": [ + "This example shows how to create a sharded Zarr array with a chunk size of `(100, 100, 100)` and a shard size of `(1000, 1000, 1000)`. This means that each shard will contain 10 chunks, and each chunk will be of size `(100, 100, 100)`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33", + "metadata": {}, + "outputs": [], + "source": [ + "z6 = zarr.create_array(\n", + " store={},\n", + " shape=(10000, 10000, 1000),\n", + " chunks=(100, 100, 100),\n", + " shards=(1000, 1000, 1000),\n", + " dtype=\"uint8\",\n", + ")\n", + "\n", + "z6.info" + ] + }, + { + "cell_type": "markdown", + "id": "34", + "metadata": {}, + "source": [ + "\n", + "```{tip}\n", + "Choose shard and chunk sizes that balance I/O performance and manageability for your filesystem or cloud backend.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "35", + "metadata": {}, + "source": [ + "#### Compressors\n", + "A number of different compressors can be used with Zarr. The built-in options include Blosc, Zstandard, and Gzip. Additional compressors are available through the [NumCodecs](https://numcodecs.readthedocs.io) package, which supports LZ4, Zlib, BZ2, and LZMA. \n", + "\n", + "Let's check the compressor we used when creating the array:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36", + "metadata": {}, + "outputs": [], + "source": [ + "z.compressors" + ] + }, + { + "cell_type": "markdown", + "id": "37", + "metadata": {}, + "source": [ + "If you don’t specify a compressor, by default Zarr uses the Zstandard compressor." + ] + }, + { + "cell_type": "markdown", + "id": "38", + "metadata": {}, + "source": [ + "How much space was saved by compression?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "z.info_complete()" + ] + }, + { + "cell_type": "markdown", + "id": "40", + "metadata": {}, + "source": [ + "You can set `compression=None` when creating a Zarr array to turn off compression. This is useful for debugging or when you want to store data without compression." + ] + }, + { + "cell_type": "markdown", + "id": "41", + "metadata": {}, + "source": [ + "```{note}\n", + "`.info_complete()` provides a more detailed view of the Zarr array, including metadata about the chunks, compressors, and attributes, but will be slower for larger arrays. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "42", + "metadata": {}, + "source": [ + "#### Consolidated Metadata\n", + "Zarr supports consolidated metadata, which allows you to store all metadata in a single file. This can improve performance when reading metadata, especially for large datasets.\n", + "\n", + "So far we have only been dealing in single array Zarr data stores. In this next example, we will create a zarr store with multiple arrays and then consolidate metadata. The speed up is significant when dealing in remote storage options, which we will see in the following example on accessing cloud storage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43", + "metadata": {}, + "outputs": [], + "source": [ + "store = zarr.storage.MemoryStore()\n", + "group = zarr.create_group(store=store)\n", + "group.create_array(shape=(1,), name=\"a\", dtype=\"float64\")\n", + "group.create_array(shape=(2, 2), name=\"b\", dtype=\"float64\")\n", + "group.create_array(shape=(3, 3, 3), name=\"c\", dtype=\"float64\")\n", + "zarr.consolidate_metadata(store)" + ] + }, + { + "cell_type": "markdown", + "id": "44", + "metadata": {}, + "source": [ + "Now, if we open that group, the Group’s metadata has a zarr.core.group.ConsolidatedMetadata that can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45", + "metadata": { + "tags": [ + "hide-output" + ] + }, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "consolidated = zarr.open_group(store=store)\n", + "consolidated_metadata = consolidated.metadata.consolidated_metadata.metadata\n", + "\n", + "pprint(dict(sorted(consolidated_metadata.items())))" + ] + }, + { + "cell_type": "markdown", + "id": "46", + "metadata": {}, + "source": [ + "Note that while Zarr-Python supports consolidated metadata for v2 and v3 formatted Zarr stores, it is not technically part of the specification (hence the warning above). \n", + "\n", + "⚠️ Use Caution When ⚠️\n", + "- **Stale or incomplete consolidated metadata**: If the dataset is updated but the consolidated metadata entrypoint isn't re-consolidated, readers may miss chunks or metadata. Always run zarr.consolidate_metadata() after changes.\n", + "- **Concurrent writes or multi-writer pipelines**: Consolidated metadata can lead to inconsistent reads if multiple processes write without coordination. Use with caution in dynamic or shared write environments.\n", + "- **Local filesystems or mixed toolchains**: On local storage, consolidation offers little benefit as hierarchy discovery is generally quite cheap. " + ] + }, + { + "cell_type": "markdown", + "id": "47", + "metadata": {}, + "source": [ + "### Object Storage as a Zarr Store\n", + "\n", + "Zarr’s layout (many files/chunks per array) maps perfectly onto object storage, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage. Each chunk is stored as a separate object, enabling distributed reads/writes.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "48", + "metadata": {}, + "source": [ + "Here are some examples of Zarr stores on the cloud:\n", + "\n", + "* [Zarr data in Microsoft's Planetary Computer](https://planetarycomputer.microsoft.com/catalog?filter=zarr)\n", + "* [Zarr data from Google](https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset&_ga=2.226354714.1000882083.1692116148-1788942020.1692116148&pli=1&q=zarr)\n", + "* [Amazon Sustainability Data Initiative available from Registry of Open Data on AWS](https://registry.opendata.aws/collab/asdi/) - Enter \"Zarr\" in the Search input box.\n", + "* [Pangeo-Forge Data Catalog](https://pangeo-forge.org/catalog)\n" + ] + }, + { + "cell_type": "markdown", + "id": "49", + "metadata": {}, + "source": [ + "### Xarray and Zarr\n", + "\n", + "Xarray has built-in support for reading and writing Zarr data. You can use the `xarray.open_zarr()` function to open a Zarr store as an Xarray dataset.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "store = \"https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/gpcp-feedstock/gpcp.zarr\"\n", + "\n", + "ds = xr.open_dataset(store, engine=\"zarr\", chunks={}, consolidated=True)\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51", + "metadata": {}, + "outputs": [], + "source": [ + "ds.precip" + ] + }, + { + "cell_type": "markdown", + "id": "52", + "metadata": {}, + "source": [ + "::::{admonition} Exercise\n", + ":class: tip\n", + "\n", + "Can you calculate the mean precipitation for January 2020 in the GPCP dataset and plot it?\n", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "\n", + "```python\n", + "ds.precip.sel(time=slice('2020-01-01', '2020-01-31')).mean(dim='time').plot()\n", + "```\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "id": "53", + "metadata": {}, + "source": [ + "Check out our other [tutorial notebook]() that highlights the CMIP6 Zarr dataset stored in the Cloud\n", + "\n", + "## Additional Resources\n", + "\n", + "- [Zarr Documentation](https://zarr.readthedocs.io/en/stable/)\n", + "- [Cloud Optimized Geospatial Formats](https://guide.cloudnativegeo.org/zarr/zarr-in-practice.html)\n", + "- [Scalable and Computationally Reproducible Approaches to Arctic Research](https://learning.nceas.ucsb.edu/2025-04-arctic/sections/zarr.html)\n", + "- [Zarr Cloud Native Geospatial Tutorial](https://github.com/zarr-developers/tutorials/blob/main/zarr_cloud_native_geospatial_2022.ipynb)" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/intermediate/remote_data/cmip6-cloud.ipynb b/intermediate/remote_data/cmip6-cloud.ipynb index c9d05d9d..fa27ff7f 100644 --- a/intermediate/remote_data/cmip6-cloud.ipynb +++ b/intermediate/remote_data/cmip6-cloud.ipynb @@ -5,6 +5,7 @@ "id": "0", "metadata": {}, "source": [ + "(cmip6-cloud)=\n", "# Zarr in Cloud Object Storage\n", "\n", "In this tutorial, we'll cover the following:\n", @@ -20,7 +21,6 @@ "metadata": {}, "outputs": [], "source": [ - "import gcsfs\n", "import pandas as pd\n", "import xarray as xr" ] @@ -153,9 +153,7 @@ "id": "12", "metadata": {}, "source": [ - "In many cases, you'll need to first connect to the cloud provider.\n", - "The CMIP6 dataset allows anonymous access, but for some cases,\n", - "you may need to authenticate." + "If Zarr-python is version 3 or greater, we can simply pass URIs to Zarr Stores to `xr.open_zarr`. Note that anonymous access is used by default." ] }, { @@ -165,55 +163,16 @@ "metadata": {}, "outputs": [], "source": [ - "fs = gcsfs.GCSFileSystem(token=\"anon\")" - ] - }, - { - "cell_type": "markdown", - "id": "14", - "metadata": {}, - "source": [ - "Next, we'll need a mapping to the Google Storage object.\n", - "This can be done using `fs.get_mapper`.\n", - "\n", - "A more generic way (for other cloud providers) is to use\n", - "[`fsspec.get_mapper`](https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.get_mapper) instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "15", - "metadata": {}, - "outputs": [], - "source": [ - "store = fs.get_mapper(\n", - " \"gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ESM4/ssp585/r1i1p1f1/Omon/zos/gn/v20180701/\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "16", - "metadata": {}, - "source": [ - "With that, we can open the Zarr store like so." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17", - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.open_zarr(store=store, consolidated=True)\n", + "ds = xr.open_zarr(\n", + " \"gs://cmip6/CMIP6/ScenarioMIP/NOAA-GFDL/GFDL-ESM4/ssp585/r1i1p1f1/Omon/zos/gn/v20180701/\",\n", + " consolidated=True,\n", + ")\n", "ds" ] }, { "cell_type": "markdown", - "id": "18", + "id": "14", "metadata": {}, "source": [ "### Selecting time slices\n", @@ -226,7 +185,7 @@ { "cell_type": "code", "execution_count": null, - "id": "19", + "id": "15", "metadata": {}, "outputs": [], "source": [ @@ -236,7 +195,7 @@ }, { "cell_type": "markdown", - "id": "20", + "id": "16", "metadata": {}, "source": [ "Sea level change would just be 2100 minus 2015." @@ -245,7 +204,7 @@ { "cell_type": "code", "execution_count": null, - "id": "21", + "id": "17", "metadata": {}, "outputs": [], "source": [ @@ -254,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "22", + "id": "18", "metadata": {}, "source": [ "Note that up to this point, we have not actually downloaded any\n", @@ -268,7 +227,7 @@ { "cell_type": "code", "execution_count": null, - "id": "23", + "id": "19", "metadata": {}, "outputs": [], "source": [ @@ -277,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "24", + "id": "20", "metadata": {}, "source": [ "We can do a quick plot to show how Sea Level is predicted to change\n", @@ -287,7 +246,7 @@ { "cell_type": "code", "execution_count": null, - "id": "25", + "id": "21", "metadata": {}, "outputs": [], "source": [ @@ -296,7 +255,7 @@ }, { "cell_type": "markdown", - "id": "26", + "id": "22", "metadata": {}, "source": [ "Notice the blue parts between -40 and -60 South where sea level has dropped?\n", @@ -307,7 +266,7 @@ }, { "cell_type": "markdown", - "id": "27", + "id": "23", "metadata": {}, "source": [ "That's all! Hopefully this will get you started on accessing more cloud-native datasets!" diff --git a/intermediate/remote_data/remote-data.ipynb b/intermediate/remote_data/remote-data.ipynb index c0aa5d8e..df1f0952 100644 --- a/intermediate/remote_data/remote-data.ipynb +++ b/intermediate/remote_data/remote-data.ipynb @@ -55,15 +55,15 @@ "outputs": [], "source": [ "import sys\n", - "from IPython.display import Code\n", "\n", + "from IPython.display import Code\n", "\n", "tracing_output = []\n", "_match_pattern = \"xarray\"\n", "\n", "\n", "def trace_calls(frame, event, arg):\n", - " if event == 'call':\n", + " if event == \"call\":\n", " code = frame.f_code\n", " func_name = code.co_name\n", " func_file = code.co_filename.split(\"/site-packages/\")[-1]\n", @@ -79,7 +79,7 @@ "sys.settrace(None)\n", "\n", "# Print the trace with some syntax highlighting\n", - "Code(\" \\n\".join(tracing_output[0:10]), language='python')" + "Code(\" \\n\".join(tracing_output[0:10]), language=\"python\")" ] }, { @@ -111,7 +111,7 @@ "sys.settrace(None)\n", "\n", "# Print the top 10 calls to public methods\n", - "Code(\" \\n\".join(tracing_output[0:10]), language='python')" + "Code(\" \\n\".join(tracing_output[0:10]), language=\"python\")" ] }, { @@ -132,7 +132,7 @@ "The `open_dataset()` method is our entry point to n-dimensional data with xarray, the first argument we pass indicates what we want to open and is used by xarray to get the right backend and in turn is used by the backend to open the file locally or remote. The accepted types by xarray are:\n", "\n", "\n", - "* **str**: \"my-file.nc\" or \"s3:://my-zarr-store/data.zarr\"\n", + "* **str**: `my-file.nc` or `s3:://my-zarr-store/data.zarr`\n", "* **os.PathLike**: Posix compatible path, most of the times is a Pathlib cross-OS compatible path.\n", "* **BufferedIOBase**: some xarray backends can read data from a buffer, this is key for remote access.\n", "* **AbstractDataStore**: This one is the generic store and backends should subclass it, if we do we can pass a \"store\" to xarray like in the case of Opendap/Pydap\n", @@ -178,7 +178,7 @@ "id": "11", "metadata": {}, "source": [ - "xarray iterated through the registered backends and netcdf4 returned a `\"yes, I can open that extension\"` see: [netCDF4_.py#L618 ](https://github.com/pydata/xarray/blob/6c2d8c3389afe049ccbfd1393e9a81dd5c759f78/xarray/backends/netCDF4_.py#L618). However, **the backend doesn't know how to \"talk\" to a remote store** and thus it fails to open our file.\n", + "xarray iterated through the registered backends and netcdf4 returned a `\"yes, I can open that extension\"` see: [netCDF4_.py#L618](https://github.com/pydata/xarray/blob/6c2d8c3389afe049ccbfd1393e9a81dd5c759f78/xarray/backends/netCDF4_.py#L618). However, **the backend doesn't know how to \"talk\" to a remote store** and thus it fails to open our file.\n", "\n" ] }, @@ -250,7 +250,7 @@ "\n", "uri = \"https://its-live-data.s3-us-west-2.amazonaws.com/test-space/sample-data/sst.mnmean.nc\"\n", "# we prepend the cache type to the URI, this is called protocol chaining in fsspec-speak\n", - "file = fsspec.open_local(f\"simplecache::{uri}\", filecache={'cache_storage': '/tmp/fsspec_cache'})\n", + "file = fsspec.open_local(f\"simplecache::{uri}\", simplecache={\"cache_storage\": \"/tmp/fsspec_cache\"})\n", "\n", "ds = xr.open_dataset(file, engine=\"netcdf4\")\n", "ds" @@ -278,7 +278,7 @@ "%%time\n", "uri = \"https://its-live-data.s3-us-west-2.amazonaws.com/test-space/sample-data/sst.mnmean.nc\"\n", "\n", - "fs = fsspec.filesystem('http')\n", + "fs = fsspec.filesystem(\"http\")\n", "\n", "fsspec_caching = {\n", " \"cache_type\": \"blockcache\", # block cache stores blocks of fixed size and uses eviction using a LRU strategy.\n", @@ -317,7 +317,7 @@ "uri = \"s3://its-live-data/test-space/sample-data/sst.mnmean.nc\"\n", "\n", "# If we need to pass credentials to our remote storage we can do it here, in this case this is a public bucket\n", - "fs = fsspec.filesystem('s3', anon=True)\n", + "fs = fsspec.filesystem(\"s3\", anon=True)\n", "\n", "fsspec_caching = {\n", " \"cache_type\": \"blockcache\", # block cache stores blocks of fixed size and uses eviction using a LRU strategy.\n", diff --git a/intermediate/storage_formats.ipynb b/intermediate/storage_formats.ipynb new file mode 100644 index 00000000..33b556c4 --- /dev/null +++ b/intermediate/storage_formats.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hierarchical storage formats\n", + "\n", + "In the fundamentals section we saw that xarray can read and write to a variety of storage formats. We have also seen that xarray's data model can include a hierarchy of groups as part of an `xarray.DataTree` object.\n", + "\n", + "The design of each format makes certain choices, and here we will compare the structure of common data formats to xarray's full data model, concentrating on subtle differences." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Xarray is not a file format\n", + "\n", + "You sometimes hear people say things like \"save it as an xarray\". This does not make sense, because xarray is not a file-format, it is an in-memory data structure. It's analogous to the difference between a CSV file and a `pandas.DataFrame`.\n", + "\n", + "Xarray data structures were inspired by scientific file formats (particularly netCDF), but are not intended to be identical to any of them. This helps xarray be a completely domain-agnostic tool. \n", + "\n", + "Xarray supports reading from and writing to a range of file formats, but as common file formats have differences in their design, xarray data structures cannot be exactly equivalent to all of them.\n", + "\n", + "Overall though this makes sense because the use case is different: in-memory data is for analysis, on-disk data is for persistent storage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Groups everywhere\n", + "\n", + "Many storage formats for scientific data include some notion of \"groups\". \n", + "\n", + "The exact meaning of \"group\" differs between formats but are all ultimately motivated by a common recognition: that real scientific datasets often include related but otherwise heterogeneous data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zarr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll start with Zarr, because it has the most simple type of hierarchical structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Tree of groups** – Tree of arbitrary groups.\n", + "\n", + "* **Separate groups** – No relationship enforced between groups, and no references from one group to another.\n", + "\n", + "* **Separate arrays** – No relationship enforced between arrays within a group.\n", + "\n", + "* **Arbitrary JSON metadata** – Each holds arbitrary data in the form of arrays + metadata." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![ZarrSpec](https://zarr-specs.readthedocs.io/en/latest/_images/terminology-hierarchy.excalidraw.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does zarr relate to `xarray`?\n", + "\n", + "* **Arrays <-> `Variables`** - zarr arrays map well to `xarray.Variables`\n", + " - Especially as zarr v3 includes (optional) `dimension_names`\n", + "\n", + "* **Groups <-> `Datasets`** - zarr groups map reasonably well to `xarray.Dataset` objects\n", + " - Open a single zarr group in xarray via `xr.open_dataset(store, group='/path', engine='zarr')`\n", + "\n", + "* **Groups must be alignable** - But `xarray.Dataset`s require that all arrays in the Dataset have aligned dimensions\n", + " - so it is possible to create a zarr group that is not a valid `xarray.Dataset`, if the group contains arrays with non-aligning dimensions\n", + "\n", + "* **No \"coordinates\"** – No arrays are special, so Zarr has no intrinsic concept of \"coordinate\" vs \"data\" variables.\n", + " - So xarray has to save this piece of information as an additional piece of zarr metadata.\n", + "\n", + "* **Tree of groups <-> `DataTree`** - zarr store has a tree of groups\n", + " - maps to either a set of independent `xarray.Datasets`\n", + " - `xr.open_groups(store)`\n", + " - or to a single `xarray.DataTree`\n", + " - `xr.open_datatree(store)`\n", + " - `xarray.DataTree` enforces alignment between coordinates in parents and child groups\n", + " - means that you could write two `xarray.Datasets` as separate zarr groups that cannot be opened as one `xarray.DataTree`\n", + " - coordinate inheritance also means that inherited coordinates are implicitly present on child groups in the `DataTree`, but not saved explicitly into each zarr group." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## HDF5\n", + "\n", + "HDF5 (Hierarchical Data Format, version 5) is a **general-purpose container** for large, heterogeneous, hierarchical data. It includes these core components:\n", + "\n", + "* **Groups** \n", + "\n", + " *Nodes* in a directed graph that starts at the root /. \n", + "\n", + " They behave like folders in a UNIX filesystem (absolute paths, /sub/group/dataset), and *may* form cycles or self-links—although most scientific tools avoid that complexity.\n", + "\n", + "\n", + "* **Datasets** \n", + "\n", + " Rectangular N-dimensional arrays stored inside groups. \n", + "\n", + " Each dimension can optionally carry a **dimension scale**, an auxiliary dataset that describes the coordinate values along that axis.\n", + "\n", + "* **Attributes** \n", + "\n", + " Small pieces of metadata (strings, scalars, short arrays) attached to the file, any group, or any dataset.\n", + "\n", + "* **Storage features** \n", + "\n", + " Chunking, compression, checksums, parallel I/O via MPI-IO, and more. \n", + "\n", + " These are orthogonal to the logical data model.\n", + "\n", + "## NetCDF4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NetCDF4 builds upon HDF5 - it is really an opinionated subset of HDF5. From the [netCDF documentation](https://docs.unidata.ucar.edu/nug/current/file_structure_and_performance.html#:~:text=(Note%20that%20modifying%20these%20files%20with%20HDF5%20will%20almost%20certainly%20make%20them%20unreadable%20to%20netCDF%2D4.)):\n", + "\n", + "> (Note that modifying these files with HDF5 will almost certainly make them unreadable to netCDF-4.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does HDF5/netCDF4 relate to xarray?\n", + "\n", + "Generally very similar - not surprising as netCDF4 was the inspiration for xarray\n", + "\n", + "#### Similarities\n", + "- HDF groups can often be represented by groups in a `DataTree`\n", + "- Netcdf4 `Datasets` correspond to xarray `Datasets` (or to groups in the `DataTree`)\n", + "- NetCDF4 requires that every HDF5 dataset must have a dimension scale attached to each dimension.\n", + " - These end up working quite similarly to xarray's dimension coordinates\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Differences\n", + "- The group structure is not technically a tree - cycles and self-references are allowed in HDF5, unlike in `xarray.DataTree`.\n", + "HDF5 supports links between groups, but xarray does not\n", + " - (Note that this is consistent with UNIX filesystems, which support symbolic links between directories)\n", + "- NetCDF has an explicit concept of a dimension as a first-class object, which neither HDF5 nor xarray have\n", + " - means that e.g. a scalar variable can have a dimension in netCDF, but not in xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TIFF" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TIFF (Tag Image File Format) is a raster container widely used in biosciences, remote sensing and GIS. \n", + "\n", + "A **GeoTIFF** is simply a TIFF that stores additional additional georeferencing information tags (CRS, affine transform, etc.) so geospatial software knows where each pixel sits on Earth. \n", + "\n", + "* **Images (“IFDs”)** – each “page” in a TIFF holds a 2-D array of pixels. \n", + "\n", + " Multi-band rasters (e.g. RGB, multi-spectral) appear as *separate* IFDs or as extra samples within one IFD.\n", + "\n", + "* **Tags** – key–value metadata pairs (datatype, compression, nodata value, CRS, resolution, etc.). \n", + " \n", + " GeoTIFF adds standardised tags like ModelPixelScaleTag, ModelTiepointTag, GeoKeyDirectoryTag.\n", + "\n", + "* **Compression / tiling** – DEFLATE, LZW, etc. Tiling lets software fetch small windows efficiently.\n", + "\n", + "* **Cloud-optimized GeoTIFF (COG)** – same format, arranged so HTTP range requests can stream windows efficiently; xarray handles it transparently when rasterio is compiled with libcurl." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does TIFF relate to xarray?\n", + "\n", + "* **Dimensionality** – Each raster image maps well to a single `xarray.Variable`, but TIFF is inherently 2-D per band; no native time or vertical axis. If you need 4-D data, NetCDF or Zarr is usually a better fit.\n", + "\n", + "* **No named dimensions** - TIFFs don't have named dimensions for the two axes of the raster.\n", + "\n", + "* **IFDs as groups** - IFDs can be mapped to groups, which may be useful for multi-resolution TIFFs (also known as \"overviews\") and multi-page TIFFs.\n", + "\n", + "* **Metadata depth** – single-level tags only (no nested groups). For rich hierarchies, stick to HDF5 / NetCDF-4.\n", + "\n", + "* **Read** – use `rioxarray.open_rasterio()` (wraps rasterio) to get an immediate, Dask-chunked DataArray. However `rioxarray` is for interacting with GeoTIFFs, not general TIFFs.\n", + "\n", + "* **Write** – `DataArray.rio.to_raster(\"out.tif\")`; choose compression + tiling via driver_kwargs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mapping storage formats to one another\n", + "\n", + "Note that while we have discussed mapping various file formats to the xarray data model, it is also possible to map different file formats to one another.\n", + "\n", + "For example, the [VirtualiZarr](https://github.com/zarr-developers/VirtualiZarr) library maps a range of file formats (including HDF5 and TIFF) to the Zarr data model, to allow reading data from those formats via the zarr-python API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/intermediate/xarray_and_dask.ipynb b/intermediate/xarray_and_dask.ipynb index 2f025b69..aff1c790 100644 --- a/intermediate/xarray_and_dask.ipynb +++ b/intermediate/xarray_and_dask.ipynb @@ -412,11 +412,11 @@ "metadata": {}, "outputs": [], "source": [ - "from dask.distributed import Client\n", + "import os\n", "\n", "# This piece of code is just for a correct dashboard link mybinder.org or other JupyterHub demos\n", "import dask\n", - "import os\n", + "from dask.distributed import Client\n", "\n", "# if os.environ.get('JUPYTERHUB_USER'):\n", "# dask.config.set(**{\"distributed.dashboard.link\": \"/user/{JUPYTERHUB_USER}/proxy/{port}/status\"})\n", @@ -534,7 +534,7 @@ "metadata": {}, "source": [ "```{tip}\n", - "While these operations all work, not all of them are necessarily the optimal implementation for parallelism. Usually analysis pipelines need some tinkering and tweaking to get things to work. In particular read the user guidie recommendations for [chunking](https://docs.xarray.dev/en/stable/user-guide/dask.html#chunking-and-performance) and [performance](https://docs.xarray.dev/en/stable/user-guide/dask.html#optimization-tips)\n", + "While these operations all work, not all of them are necessarily the optimal implementation for parallelism. Usually analysis pipelines need some tinkering and tweaking to get things to work. In particular read the user guidie recommendations for [chunking](https://docs.xarray.dev/en/stable/user-guide/dask.html#chunking-and-performance).\n", "```" ] }, diff --git a/intermediate/xarray_ecosystem.ipynb b/intermediate/xarray_ecosystem.ipynb index 017b9690..d455b486 100644 --- a/intermediate/xarray_ecosystem.ipynb +++ b/intermediate/xarray_ecosystem.ipynb @@ -45,8 +45,8 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", - "import numpy as np" + "import numpy as np\n", + "import xarray as xr" ] }, { @@ -123,7 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds['air'].isel(time=1).hvplot(cmap=\"fire\")" + "ds[\"air\"].isel(time=1).hvplot(cmap=\"fire\")" ] }, { @@ -175,8 +175,8 @@ "metadata": {}, "outputs": [], "source": [ - "url = 'https://sentinel-s1-rtc-indigo.s3.us-west-2.amazonaws.com/tiles/RTC/1/IW/10/U/CU/2017/S1A_20170101_10UCU_ASC/Gamma0_VV.tif'\n", - "da = xr.open_dataarray(url, engine='rasterio', open_kwargs={\"overview_level\": 2})\n", + "url = \"https://sentinel-s1-rtc-indigo.s3.us-west-2.amazonaws.com/tiles/RTC/1/IW/10/U/CU/2017/S1A_20170101_10UCU_ASC/Gamma0_VV.tif\"\n", + "da = xr.open_dataarray(url, engine=\"rasterio\", open_kwargs={\"overview_level\": 2})\n", "da" ] }, @@ -211,7 +211,7 @@ "metadata": {}, "outputs": [], "source": [ - "da_lonlat = da.rio.reproject('epsg:4326')\n", + "da_lonlat = da.rio.reproject(\"epsg:4326\")\n", "da_lonlat" ] }, @@ -232,10 +232,10 @@ "import panel as pn\n", "\n", "img1 = da.sel(band=1).hvplot.image(\n", - " x='x', y='y', rasterize=True, cmap='gray', clim=(0, 0.5), title='UTM'\n", + " x=\"x\", y=\"y\", rasterize=True, cmap=\"gray\", clim=(0, 0.5), title=\"UTM\"\n", ")\n", "img2 = da_lonlat.sel(band=1).hvplot.image(\n", - " rasterize=True, cmap='gray', clim=(0, 0.5), title='LON/LAT'\n", + " rasterize=True, cmap=\"gray\", clim=(0, 0.5), title=\"LON/LAT\"\n", ")\n", "\n", "pn.Column(img1, img2)" @@ -252,7 +252,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Xarray objects can store [arbitrary metadata](https://docs.xarray.dev/en/stable/getting-started-guide/faq.html#what-is-your-approach-to-metadata) in the form of a `dict` attached to each `DataArray` and `Dataset` object, accessible via the `.attrs` property." + "Xarray objects can store [arbitrary metadata](https://docs.xarray.dev/en/stable/get-help/faq.html#what-is-your-approach-to-metadata) in the form of a `dict` attached to each `DataArray` and `Dataset` object, accessible via the `.attrs` property." ] }, { @@ -449,7 +449,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The reason this works is that a `pint.Quantity` array is what we call a \"duck array\", in that it behaves so similarly to a `numpy.ndarray` that xarray can treat them the same way. (See [python duck typing](https://towardsdatascience.com/duck-typing-python-7aeac97e11f8))" + "The reason this works is that a `pint.Quantity` array is what we call a \"duck array\", in that it behaves so similarly to a `numpy.ndarray` that xarray can treat them the same way. (See [python duck typing](https://realpython.com/duck-typing-python/))" ] }, { @@ -545,7 +545,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds.air.attrs['units']" + "ds.air.attrs[\"units\"]" ] }, { @@ -662,7 +662,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are many other libraries in the wider xarray ecosystem. For a list of a few packages we particularly like for geoscience work [see here](https://tutorial.xarray.dev/overview/xarray-in-45-min.html#other-cool-packages), and for a [more exhaustive list see here](https://docs.xarray.dev/en/stable/ecosystem.html)." + "There are many other libraries in the wider xarray ecosystem. For a list of a few packages we particularly like for geoscience work [see here](https://tutorial.xarray.dev/overview/xarray-in-45-min.html#other-cool-packages), and for a [more exhaustive list see here](https://docs.xarray.dev/en/stable/user-guide/ecosystem.html)." ] } ], diff --git a/intro.md b/intro.md index 2f0d5cb2..3ca09db0 100644 --- a/intro.md +++ b/intro.md @@ -11,7 +11,6 @@ ```{grid-item-card} :link: overview/get-started :link-type: doc -:class-header: bg-light Get started! 🚀 ^^^ Learn how to navigate the tutorials and run code examples interactively @@ -19,15 +18,13 @@ Learn how to navigate the tutorials and run code examples interactively ```{grid-item-card} :link: https://www.youtube.com/channel/UCBlxVSA6xQXeb-i4GgTlO7g -:class-header: bg-light Watch videos 🎬 ^^^ Live walk-throughs of this material and more on Xarray's YouTube Channel ``` ```{grid-item-card} -:link: https://docs.xarray.dev/en/latest/help-diagram.html -:class-header: bg-light +:link: https://docs.xarray.dev/en/stable/get-help/help-diagram.html#help-flowchart Get involved 🙋 ^^^ Ask questions and participate in the Xarray community! @@ -37,7 +34,7 @@ Ask questions and participate in the Xarray community! ## Citation -✏️ To cite this material, you can use the recommended [Xarray citation](https://docs.xarray.dev/en/stable/getting-started-guide/faq.html#how-should-i-cite-xarray): {cite:p}`hoyerhamman2017`, {cite:p}`xarray_v202230`. +✏️ To cite this material, you can use the recommended [Xarray citation](https://docs.xarray.dev/en/stable/get-help/faq.html#how-should-i-cite-xarray): {cite:p}`hoyerhamman2017`, {cite:p}`xarray_v202230`. ## Acknowledgements diff --git a/overview/get-started.md b/overview/get-started.md index ba33045d..b4e0192c 100644 --- a/overview/get-started.md +++ b/overview/get-started.md @@ -19,14 +19,14 @@ Many notebooks use special formatting ([Myst Markdown](https://mystmd.org/guide/ The easiest way to start modifying and experimenting with tutorial content is to launch a pre-configured server on the Cloud. This is easy thanks to several free resources which offer ephemeral computing instances (be aware you may loose your connection or work at any time) +```{warning} +Be patient, it can take a few minutes for a server to become available on the Cloud! +``` + #### Mybinder.org Clicking [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD) will load a pre-configured Jupyter Lab interface with _all_ tutorial notebooks for you to run. _You have minimal computing resources and any changes you make will not be saved._ Any page with executable content also has a {octicon}`rocket;2em` icon in the upper right that will launch an interactive session for that particular page. -```{warning} -Be patient, it can take a few minutes for a server to become available on the Cloud (Mybinder.org)! -``` - #### GitHub Codespaces This tutorial is available to run within [GitHub Codespaces](https://github.com/features/codespaces) - a preconfigured development environment running in Microsoft Azure. @@ -39,19 +39,37 @@ You can choose from a selection of virtual machine types: 2 cores - 8 GB RAM sho Additionally, you are able to chose from various configurations for specific workshops (such as Scipy2024). GitHub currently gives every user [120 vCPU hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop your codespace when you are done by going to this page (https://github.com/codespaces).** You can also chose to fully delete your codespace when you're done exploring tutorial content. +```{tip} +By default Codespaces open VSCode in a browser window. But you can also launch a JupyterLab interface. Once you've launched a codespace, use the auto-generated name in the following URL: `https://github.com/codespaces/CODESPACE-NAME?editor=jupyter` +``` + +Using the GitHub CLI is another very convenient way to start a codespace: + +```bash +gh codespace create -R xarray-contrib/xarray-tutorial +``` + +```bash +# Wait a few minutes for `gh codespace create` to finish, then run: +gh codespace jupyter +``` + ### On your computer Running tutorials on your computer requires some setup: -We recommend using [`conda-lock`](https://conda.github.io/conda-lock/) to ensure a fully reproducible Python environment +We recommend using [`pixi`](https://pixi.sh/latest/) to ensure a fully reproducible Python environment -``` +```bash git clone https://github.com/xarray-contrib/xarray-tutorial.git cd xarray-tutorial +pixi run tutorial +``` -conda-lock install conda/conda-lock.yml --name xarray-tutorial -# Or latest package versions: `mamba env create -f conda/environment-unpinned.yml` +If you prefer to use conda/mamba: +``` +mamba env create -f .binder/environment.yml -n xarray-tutorial conda activate xarray-tutorial jupyter lab ``` diff --git a/overview/intermediate-path/README.md b/overview/intermediate-path/README.md index 3fb0fc81..10e50f51 100644 --- a/overview/intermediate-path/README.md +++ b/overview/intermediate-path/README.md @@ -24,7 +24,7 @@ The tutorial was organized by: ``` ```{dropdown} Computational Patterns -{doc}`../../intermediate/01-high-level-computation-patterns` +{doc}`../../intermediate/computation/01-high-level-computation-patterns` ``` ```{dropdown} Wrapping other arrays: dask diff --git a/overview/xarray-in-45-min.ipynb b/overview/xarray-in-45-min.ipynb index 36667327..2566262d 100644 --- a/overview/xarray-in-45-min.ipynb +++ b/overview/xarray-in-45-min.ipynb @@ -593,7 +593,7 @@ "source": [ "### Broadcasting: expanding data\n", "\n", - "Our longitude and latitude length DataArrays are both 1D with different dimension names. If we multiple these DataArrays together the dimensionality is expanded to 2D by _broadcasting_:" + "Our longitude and latitude length DataArrays are both 1D with different dimension names. If we multiply these DataArrays together the dimensionality is expanded to 2D by _broadcasting_:" ] }, { @@ -822,7 +822,7 @@ "outputs": [], "source": [ "# resample to monthly frequency\n", - "ds.resample(time=\"M\").mean()" + "ds.resample(time=\"ME\").mean()" ] }, { @@ -922,7 +922,7 @@ "metadata": {}, "source": [ "```{note}\n", - "To avoid the `SerializationWarning` you can assign a _FillValue for any NaNs in 'air' array by adding the keyword argument encoding=dict(air={_FillValue=-9999})\n", + "To avoid the `SerializationWarning` you can assign a _FillValue for any NaNs in 'air' array by adding the keyword argument `encoding=dict(air=dict(_FillValue=-9999))`\n", "```" ] }, @@ -971,7 +971,7 @@ "\n", "Xarray ties in to the larger scientific python ecosystem and in turn many\n", "packages build on top of xarray. A long list of such packages is here:\n", - ".\n", + ".\n", "\n", "Now we will demonstrate some cool features.\n" ] @@ -1104,9 +1104,16 @@ "metadata": {}, "outputs": [], "source": [ - "import hvplot.xarray\n", - "\n", - "ds.air.hvplot(groupby=\"time\", clim=(270, 300), widget_location='bottom')" + "import hvplot.xarray" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.air.hvplot(groupby=\"time\", clim=(270, 300), widget_location=\"bottom\")" ] }, { @@ -1202,7 +1209,7 @@ "- [MetPy](https://unidata.github.io/MetPy/latest/index.html) : tools for working\n", " with weather data\n", "\n", - "Check the Xarray [Ecosystem](https://docs.xarray.dev/en/stable/ecosystem.html) page and [this tutorial](https://tutorial.xarray.dev/intermediate/xarray_ecosystem.html) for even more packages and demonstrations." + "Check the Xarray [Ecosystem](https://docs.xarray.dev/en/stable/user-guide/ecosystem.html) page and [this tutorial](https://tutorial.xarray.dev/intermediate/xarray_ecosystem.html) for even more packages and demonstrations." ] }, { @@ -1213,9 +1220,9 @@ "\n", "1. Read the [tutorial](https://tutorial.xarray.dev) material and [user guide](https://docs.xarray.dev/en/stable/user-guide/index.html)\n", "1. See the description of [common terms](https://docs.xarray.dev/en/stable/terminology.html) used in the xarray documentation: \n", - "1. Answers to common questions on \"how to do X\" with Xarray are [here](https://docs.xarray.dev/en/stable/howdoi.html)\n", + "1. Answers to common questions on \"how to do X\" with Xarray are [here](https://docs.xarray.dev/en/stable/get-help/howdoi.html)\n", "1. Ryan Abernathey has a book on data analysis with a [chapter on Xarray](https://earth-env-data-science.github.io/lectures/xarray/xarray_intro.html)\n", - "1. [Project Pythia](https://projectpythia.org/) has [foundational](https://foundations.projectpythia.org/landing-page.html) and more [advanced](https://cookbooks.projectpythia.org/) material on Xarray. Pythia also aggregates other [Python learning resources](https://projectpythia.org/resource-gallery.html).\n", + "1. [Project Pythia](https://projectpythia.org/) has [foundational](https://foundations.projectpythia.org) and more [advanced](https://cookbooks.projectpythia.org/) material on Xarray. Pythia also aggregates other [Python learning resources](https://projectpythia.org/resource-gallery).\n", "1. The [Xarray Github Discussions](https://github.com/pydata/xarray/discussions) and [Pangeo Discourse](https://discourse.pangeo.io/) are good places to ask questions.\n", "1. Tell your friends! Tweet!\n", "\n", diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 00000000..ce4b217d --- /dev/null +++ b/pixi.lock @@ -0,0 +1,15980 @@ +version: 7 +platforms: +- name: linux-64 +- name: osx-arm64 +- name: win-64 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.3-hdc83ae9_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.14-h6e18c7b_3.conda + - 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Planning", + "Intended Audience :: Science/Research", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering", + "Typing :: Typed", +] + [tool.black] line-length = 100 -target-version = ['py38'] -skip-string-normalization = true +target-version = ['py311'] + +[tool.ruff.lint] +select = [ + "I", # isort + "W", # pycodestyle warnings + "B", # flake8-bugbear + "F", # Pyflakes + "E", # Pycodestyle +] +ignore = [ + "E203", # whitespace before '{symbol}' + "E266", # too many leading '#' before block comment + "E501", # line too long + "E722", # do not use bare except + "E402", # module level import not at top of file + "E731", # do not assign a lambda expression, use a def + # to remove fix eventually + "F401", # unused import + "F811", # redefinition of name + "B006", # mutable argument defaults + "B007", # unused loop control variable + "B905", # zip without explicit strict parameter +] +fixable = ["I"] + +[tool.codespell] +ignore-words-list = "nd" +skip = "pixi.lock,.pixi,qaqc.yaml,intermediate/indexing/why-an-index.ipynb" + +[tool.pixi.workspace] +name = "xarray-tutorial" +channels = ["conda-forge"] +platforms = ["osx-arm64", "linux-64", "win-64"] + +[tool.pixi.pypi-dependencies] + +[tool.pixi.tasks] +tutorial = "jupyter lab --LabApp.default_url='/lab/tree/overview/xarray-in-45-min.ipynb'" +build = "jupyter-book build ./ --warningiserror --keep-going" +precommit = "pre-commit run --all" +checklinks = "jupyter-book build ./ --builder linkcheck" +clear-cache = "jupyter-book clean ." +# To clear a specific notebook pass ID number: jcache cache -p _build/.jupyter_cache remove 28 +# To clear all cached notebooks: jupyter-book clean . --all" +watch = { cmd = "jupyter-book config sphinx ./ && sphinx-autobuild ./ _build/html -b html --re-ignore '_build/.*'", description = "Build and serve with live reload" } +list-cache = "jcache cache -p _build/.jupyter_cache list" + +[tool.pixi.dependencies] +jupyter-book = ">=1.0.4.post1,<2" +pre-commit = ">=4.6.0,<5" +dask-labextension = ">=7.0.0,<8" +jupyterlab = ">=4.6.1,<5" +jupyter_bokeh = ">=4.1.0,<5" +jupyterlab-myst = ">=2.7.0,<3" +jupyter-resource-usage = ">=1.2.1,<2" +cartopy = ">=0.25.0,<0.26" +cf_xarray = ">=0.11.3,<0.12" +dask = ">=2026.7.0,<2027" +datashader = ">=0.19.1,<0.20" +distributed = ">=2026.7.0,<2027" +gcsfs = ">=2026.7.0,<2027" +geoviews-core = ">=1.15.1,<2" +gsw = ">=3.6.23,<4" +hvplot = ">=0.12.2,<0.13" +h5netcdf = ">=1.8.1,<2" +ipykernel = ">=7.3.0,<8" +matplotlib-base = ">=3.11.0,<4" +netcdf4 = ">=1.7.4,<2" +numpy = ">=2.4.6,<3" +pint-xarray = ">=0.6.1,<0.7" +pydap = ">=3.5.10,<4" +python-graphviz = ">=0.21,<0.22" +pooch = ">=1.9.0,<2" +rioxarray = ">=0.22.0,<0.23" +scikit-learn = ">=1.9.0,<2" +scipy = ">=1.18.0,<2" +sphinx-codeautolink = ">=0.18.1,<0.19" +sphinxcontrib-mermaid = ">=2.0.3,<3" +sphinx-notfound-page = ">=1.1.0,<2" +sphinx-autobuild = ">=2025.8.25,<2026" +sphinxext-rediraffe = ">=0.3.0,<0.4" +s3fs = ">=2026.6.0,<2027" +xarray = ">=2026.7.0,<2027" +zarr = ">=3.2.1,<4" +flox = ">=0.11.2,<0.12" +numbagg = ">=0.9.4,<0.10" +rich = ">=15.0.0,<16" +imageio = ">=2.37.0,<3" +intake = "<2" +intake-xarray = "<2" diff --git a/reference/resources.md b/reference/resources.md index ae61724e..854da382 100644 --- a/reference/resources.md +++ b/reference/resources.md @@ -38,4 +38,4 @@ material on Xarray and related packages. ## Electrophysiology -1. ["Analyzing intracranial electrophysiology data with xarray"](https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro/) +1. ["Analyzing intracranial electrophysiology data with xarray"](https://chrisholdgraf.com/blog/2019/2019-10-22-xarray-neuro) diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 2993a20a..00000000 --- a/setup.cfg +++ /dev/null @@ -1,16 +0,0 @@ -[flake8] -exclude = -ignore = E203,E266,E501,W503,E722,E402,C901 -max-line-length = 100 -max-complexity = 18 -select = B,C,E,F,W,T4,B9 - -[isort] -known_first_party= -known_third_party= -multi_line_output=3 -include_trailing_comma=True -force_grid_wrap=0 -combine_as_imports=True -line_length=100 -skip= diff --git a/workshops/online-tutorial-series/README.md b/workshops/online-tutorial-series/README.md index e777722c..6b68c89f 100644 --- a/workshops/online-tutorial-series/README.md +++ b/workshops/online-tutorial-series/README.md @@ -14,9 +14,10 @@ https://www.youtube.com/watch?v=a339Q5F48UQ&list=PLNemzZpJM7lUu_iGP_lA2m7SeSUwKS ## Schedule _These are the rough timings for the tutorial_: -| Topic | Time (min) | -| - | - | -| Xarray fundamentals | 30 | -| Indexing | 15 | -| Break | 5 | -| Computation & visualization | 40 | + +| Topic | Time (min) | +| --------------------------- | ---------- | +| Xarray fundamentals | 30 | +| Indexing | 15 | +| Break | 5 | +| Computation & visualization | 40 | diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index ea4009ee..2528e171 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -24,7 +24,7 @@ See instructions to set up the environment for running the tutorial material [he ### Github Codespaces -This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud" - with the conda environment specification in the [`conda-lock.yml`](../../conda/conda-lock.yml) file. +This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud" [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial/tree/main?devcontainer_path=.devcontainer%2Fscipy2023%2Fdevcontainer.json) @@ -57,7 +57,7 @@ Once your codespace is launched, the following happens: ``` ```{dropdown} Computational Patterns -{doc}`../../intermediate/01-high-level-computation-patterns` +{doc}`../../intermediate/computation/01-high-level-computation-patterns` ``` ```{dropdown} Wrapping other arrays: dask diff --git a/workshops/scipy2024/index.ipynb b/workshops/scipy2024/index.ipynb index 9cf8f355..8d58a17d 100644 --- a/workshops/scipy2024/index.ipynb +++ b/workshops/scipy2024/index.ipynb @@ -35,7 +35,7 @@ "| Introduction and Setup | 1:30 (10 min) | --- | \n", "| The Xarray Data Model | 1:40 (40 min) | [Data structures](../../fundamentals/01_data_structures.md)
[Basic Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) | \n", "| *10 minute Break* \n", - "| Indexing & Computational Patterns | 2:30 (50 min) | [Advanced Indexing](../../intermediate/indexing/indexing.md)
[Computational Patterns](../../intermediate/01-high-level-computation-patterns.ipynb)
| \n", + "| Indexing & Computational Patterns | 2:30 (50 min) | [Advanced Indexing](../../intermediate/indexing/indexing.md)
[Computational Patterns](../../intermediate/computation/01-high-level-computation-patterns.ipynb)
| \n", "| *10 minute Break* | \n", "| Xarray Integrations and Extensions | 3:30 (50 min) | [The Xarray Ecosystem](../../intermediate/xarray_ecosystem.ipynb) | \n", "| *10 minute Break* | \n", @@ -51,7 +51,7 @@ "If you prefer to work on your own computer, refer to instructions in the [Getting Started Section](../../overview/get-started.md)\n", ":::\n", "\n", - "This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - \"a development environment that's hosted in the cloud\" - with the conda environment specification in the [`conda-lock.yml`](../../conda/conda-lock.yml) file.\n", + "This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - \"a development environment that's hosted in the cloud\".\n", "\n", "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial/tree/main?devcontainer_path=.devcontainer%2Fscipy2024%2Fdevcontainer.json)\n", "\n", @@ -84,6 +84,12 @@ "- Max Jones (CarbonPlan)\n", "- Wietze Suijker (Space Intelligence)" ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/workshops/scipy2025/index.ipynb b/workshops/scipy2025/index.ipynb new file mode 100644 index 00000000..a9670341 --- /dev/null +++ b/workshops/scipy2025/index.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# SciPy 2025\n", + "\n", + "## Welcome to the Xarray SciPy 2025 Tutorial! \n", + "\n", + "\n", + "\n", + "**Hierarchical Data Analysis with Xarray DataTree & Zarr**\n", + "\n", + "July 8, 13:30–17:30 (US/Pacific), Tacoma Convention Center Room 315\n", + "\n", + "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Understand Xarray’s core data structures\n", + "- Understand how to map typical Xarray computations and workflows over hierarchical data\n", + "- Understand how common storage formats correspond to the DataTree model\n", + "- Open a public Zarr store in the cloud and manipulate the contents\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Schedule \n", + "\n", + "*Times in US/Pacific Timezone (Tacoma, WA)\n", + "\n", + "Use the links to navigate to the right notebooks.\n", + "\n", + "| Topic | Time | Notebook Links | \n", + "| :- | - | - | \n", + "| Introduction and Setup | 1:30 (10 min) | | \n", + "| The Xarray Data Model | 1:40 (50 min) | [Data structures Part 1](../../intermediate/datastructures-intermediate.ipynb)|\n", + "|||[Data structures In Biology](../../intermediate/BiologyDataset.ipynb)| \n", + "|||[Data structures Part 2](../../fundamentals/01_data_structures.md)| \n", + "| *10 minute Break* \n", + "| Computational Patterns | 2:30 (50 min) | [Computational Patterns](../../fundamentals/03.1_computation_with_xarray.ipynb)
[Hierarchical computation](../../intermediate/computation/hierarchical_computation.ipynb) | \n", + "| *10 minute Break* | \n", + "| Storage formats | 3:30 (50 min) | [Storage formats](../../fundamentals/01.1_io.ipynb) | \n", + "| *10 minute Break* | \n", + "| Zarr & the cloud | 4:30 (50 min) | [Intro to Zarr](../../intermediate/intro-to-zarr.ipynb) |\n", + "| | End 5:30 | |" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Tutorial Setup\n", + "\n", + "Refer to the [Getting Started Section](../../overview/get-started.md) for various options to run tutorial notebooks either on your personal computer or on a free Cloud-hosted server." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workshops/scipy2026/index.ipynb b/workshops/scipy2026/index.ipynb new file mode 100644 index 00000000..9ad3538c --- /dev/null +++ b/workshops/scipy2026/index.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# SciPy 2026\n", + "\n", + "## Welcome to the Xarray SciPy 2026 Tutorial! \n", + "\n", + "> 🚧👷‍♀️🏗️ This page is under construction. Please check back later.\n", + "\n", + "\n", + "\n", + "**Everything is an Xarray Dataset**\n", + "\n", + "July 14, 13:30–17:30 (CDT), University of Minnesota (526 Delaware Street SE), room HSEC 2-138\n", + "\n", + "This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n", + "\n", + ":::{admonition} Learning Goals\n", + "- Understand Xarray’s core data structures\n", + "- Understand how to use various backend engines, as well as create your own\n", + "- Understand what data cataloguing is, and how it applies to Xarray\n", + "- Understand how common storage formats correspond to the DataTree model\n", + "- Understand how to use Xarray's flexible indices\n", + ":::\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": {}, + "source": [ + "## Schedule\n", + "\n", + "*Times in local time\n", + "\n", + "Use the links to navigate to the right notebooks.\n", + "\n", + "**Everything is an Xarray dataset**\n", + "\n", + "| Topic | Time | Notebook Links | \n", + "| :- | - | - | \n", + "| Introduction and Setup | 1:30 (10 min) | | \n", + "| The Xarray Data Model | 1:40 (50 min) | [Data structures Part 1](../../intermediate/datastructures-intermediate.ipynb)
[Data structures Part 2](../../fundamentals/01_data_structures.md)
[Datatrees and Hierarchical data](../../fundamentals/01_datatree_hierarchical_data.ipynb)|\n", + "| *10 minute Break* \n", + "| Xarray backends | 2:40 (60 min) | [Intro to backends](../../advanced/backends/intro-backends.ipynb)
[Zarr stores in the cloud](../../intermediate/hierarchical_zarr_store.ipynb)
[Rioxarray](../../advanced/backends/rasterio_backend.ipynb)
[Creating custom backends](../../advanced/backends/create_custom_backend.ipynb) |\n", + "| *10 minute Break* |\n", + "| Data cataloguing | 3:50 (40 min) | [Data cataloging](../../intermediate/cataloging.ipynb) |\n", + "| *10 minute Break* \n", + "| Flexible indices | 4:40 (50 min) | [Why an index](../../intermediate/indexing/why-an-index.ipynb) |" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "### Tutorial Setup\n", + "\n", + "Refer to the [Getting Started Section](../../overview/get-started.md) for various options to run tutorial notebooks either on your personal computer or on a free Cloud-hosted server." + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}