diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index bbb18631..4b6dcb02 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1 +1 @@ -FROM pangeo/base-image:2024.06.24 +FROM pangeo/base-image:2024.06.28 diff --git a/.devcontainer/scipy2024/devcontainer.json b/.devcontainer/scipy2024/devcontainer.json index 1abbd53e..8c7934c0 100644 --- a/.devcontainer/scipy2024/devcontainer.json +++ b/.devcontainer/scipy2024/devcontainer.json @@ -12,7 +12,7 @@ }, "customizations": { "codespaces": { - "openFiles": ["workshops/scipy2024/README.md"] + "openFiles": ["workshops/scipy2024/index.ipynb"] }, "vscode": { "extensions": ["ms-toolsai.jupyter", "ms-python.python"] diff --git a/README.md b/README.md index fdc1b5c1..859df993 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![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) [![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=overview/fundamental-path/index.ipynb) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=workshops/scipy2024/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!_ diff --git a/_config.yml b/_config.yml index 85a12b15..e5301220 100644 --- a/_config.yml +++ b/_config.yml @@ -8,7 +8,8 @@ only_build_toc_files: true # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository html: # NOTE: this announcement shows up on all pages - #announcement: 'ℹ️ SciPy 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 2024 Tutorial Attendees. Click here .' home_page_in_navbar: false use_edit_page_button: true use_issues_button: true @@ -68,10 +69,14 @@ sphinx: # maintain old paths and redirect them (so google results dont go to 404) # https://github.com/wpilibsuite/sphinxext-rediraffe - sphinxext.rediraffe + - sphinx_codeautolink + - sphinxcontrib.mermaid + 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"] + 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!

" notfound_urls_prefix: / @@ -81,3 +86,26 @@ sphinx: fundamentals/02.1_working_with_labeled_data.ipynb: fundamentals/02.1_indexing_Basic.ipynb bibtex_reference_style: author_year # or label, super, \supercite + + intersphinx_mapping: + xarray: + - https://docs.xarray.dev/en/latest/ + - null + numpy: + - https://numpy.org/doc/stable + - null + scipy: + - https://docs.scipy.org/doc/scipy + - null + matplotlib: + - https://matplotlib.org/stable/ + - null + dask: + - https://docs.dask.org/en/latest + - null + python: + - https://docs.python.org/3/ + - null + pandas: + - https://pandas.pydata.org/pandas-docs/stable + - null diff --git a/_static/announcement.css b/_static/announcement.css deleted file mode 100644 index 5331eb00..00000000 --- a/_static/announcement.css +++ /dev/null @@ -1,4 +0,0 @@ -div.header-item.announcement { - background-color: lightblue; - color: #000; -} diff --git a/_static/style.css b/_static/style.css new file mode 100644 index 00000000..b1024666 --- /dev/null +++ b/_static/style.css @@ -0,0 +1,3 @@ +.bd-header-announcement { + background-color: var(--pst-color-info-bg); +} diff --git a/_toc.yml b/_toc.yml index aa80237a..6c0aeda8 100644 --- a/_toc.yml +++ b/_toc.yml @@ -45,7 +45,10 @@ parts: - file: intermediate/xarray_and_dask - file: intermediate/xarray_ecosystem - file: intermediate/hvplot - - file: intermediate/cmip6-cloud + - file: intermediate/remote_data/index + sections: + - file: intermediate/remote_data/cmip6-cloud.ipynb + - file: intermediate/remote_data/remote-data.ipynb - file: intermediate/data_cleaning/05.1_intro.md sections: - file: intermediate/data_cleaning/05.2_examples.md @@ -78,15 +81,16 @@ parts: - caption: Workshops chapters: + - file: workshops/scipy2024/index.ipynb - file: workshops/scipy2023/README - - file: workshops/oceanhackweek2020/README - sections: - - url: https://tutorial.xarray.dev/overview/xarray-in-45-min - title: Xarray in 45 minutes - file: workshops/thinking-like-xarray/README sections: - url: https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns title: High-level computation patterns + - file: workshops/oceanhackweek2020/README + sections: + - url: https://tutorial.xarray.dev/overview/xarray-in-45-min + title: Xarray in 45 minutes - file: workshops/online-tutorial-series/README sections: - file: workshops/online-tutorial-series/01_xarray_fundamentals diff --git a/advanced/apply_ufunc/complex-output-numpy.ipynb b/advanced/apply_ufunc/complex-output-numpy.ipynb index e7336a77..0ad4c244 100644 --- a/advanced/apply_ufunc/complex-output-numpy.ipynb +++ b/advanced/apply_ufunc/complex-output-numpy.ipynb @@ -336,7 +336,7 @@ "\n", "Try applying the minmax function to a 3d air temperature dataset \n", "```python\n", - "air3d = xr.tutorial.load_dataset(\"air_temperature\").air)\n", + "air3d = xr.tutorial.load_dataset(\"air_temperature\").air\n", "``` \n", "Your goal is to have a minimum and maximum value of temperature across all latitudes for a given time and longitude.\n", "\n", diff --git a/advanced/backends/1.Backend_without_Lazy_Loading.ipynb b/advanced/backends/1.Backend_without_Lazy_Loading.ipynb index 4519950e..e396d869 100644 --- a/advanced/backends/1.Backend_without_Lazy_Loading.ipynb +++ b/advanced/backends/1.Backend_without_Lazy_Loading.ipynb @@ -42,7 +42,6 @@ "\n", "```python\n", "setuptools.setup(\n", - " ...\n", " entry_points={\n", " 'xarray.backends': ['engine_name=package.module:my_backendentrypoint'],\n", " },\n", @@ -51,7 +50,7 @@ "or pass it in `xr.open_dataset`:\n", "\n", "```python\n", - "xr.open_dataset(..., engine=MyBackendEntrypoint)\n", + "xr.open_dataset(filename, engine=MyBackendEntrypoint)\n", "```" ] }, diff --git a/conda/conda-lock.yml b/conda/conda-lock.yml index d4ecf965..9c15d5a5 100644 --- a/conda/conda-lock.yml +++ b/conda/conda-lock.yml @@ -13,10 +13,10 @@ version: 1 metadata: content_hash: - osx-64: 04db034b972956d26ea5a2e216d203ad9ec50d1264db68fad8b8819687e2dbca - linux-64: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780 - win-64: d2979a92577f588632cc336a9c1d5ca088886463ed8fbb6f6fd560c83c08442f - osx-arm64: 4f169409756d1bd8b7c8088061b8a81352704e1992ecda0baaad820a2facc9df + osx-64: 49571e2dd9e6d47ce6aa3d5bbdc919081985983797be17081f68f976779c036e + linux-64: 4daa19bce4836dfdde9cc57bf64660e6a0a4f9cd3aec6d3431e4bed60e747064 + win-64: 6748589622af59ba72a8bf19ac474488b96e495deb23fbd7fe5599db2c6fbf0b + osx-arm64: c99d388694ed3922bfb2c9f3f7467188f2730fb6a376021cd10f2c3468b709c5 channels: - 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name: urllib3 @@ -24555,13 +25286,15 @@ package: manager: conda platform: win-64 dependencies: - python: '>=3.7' + python: '>=3.8' brotli-python: '>=1.0.9' pysocks: '>=1.5.6,<2.0,!=1.5.7' - url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_0.conda + h2: '>=4,<5' + zstandard: '>=0.18.0' + url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda hash: - md5: 92cdb6fe54b78739ad70637e4f0deb07 - sha256: 8cd972048f297b8e0601158ce352f5ca9510dda9f2706a46560220aa58b9f038 + md5: e804c43f58255e977093a2298e442bb8 + sha256: 00c47c602c03137e7396f904eccede8cc64cc6bad63ce1fc355125df8882a748 category: main optional: false - name: vc @@ -24589,7 +25322,7 @@ package: category: main optional: false - name: virtualenv - version: 20.26.2 + version: 20.26.3 manager: conda platform: linux-64 dependencies: @@ -24597,14 +25330,14 @@ package: filelock: <4,>=3.12.2 platformdirs: <5,>=3.9.1 python: '>=3.8' - url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.2-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.3-pyhd8ed1ab_0.conda hash: - 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url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.2-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.3-pyhd8ed1ab_0.conda hash: - md5: 7d36e7a485ea2f5829408813bdbbfb38 - sha256: 1eefd180723fb2fd295352323b53777eeae5765b24d62ae75fc9f1e71b455f11 + md5: 284008712816c64c85bf2b7fa9f3b264 + sha256: f78961b194e33eed5fdccb668774651ec9423a043069fa7a4e3e2f853b08aa0c category: main optional: false - name: virtualenv - version: 20.26.2 + version: 20.26.3 manager: conda platform: win-64 dependencies: @@ -24642,10 +25375,10 @@ package: distlib: <1,>=0.3.7 filelock: <4,>=3.12.2 platformdirs: <5,>=3.9.1 - url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.2-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.3-pyhd8ed1ab_0.conda hash: - md5: 7d36e7a485ea2f5829408813bdbbfb38 - sha256: 1eefd180723fb2fd295352323b53777eeae5765b24d62ae75fc9f1e71b455f11 + md5: 284008712816c64c85bf2b7fa9f3b264 + sha256: f78961b194e33eed5fdccb668774651ec9423a043069fa7a4e3e2f853b08aa0c category: main optional: false - name: vs2015_runtime @@ -24948,6 +25681,54 @@ package: sha256: cb318f066afd6fd64619f14c030569faf3f53e6f50abf743b4c865e7d95b96bc category: main optional: false +- name: widgetsnbextension + version: 4.0.11 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.11-pyhd8ed1ab_0.conda + hash: + md5: 95ba42a349c9d8eac28e30d0b637401f + sha256: 240582f3aff18f28b3500e76f727e1c58048bfc1a445c71b7087907a0a85a5e6 + category: main + optional: false +- name: widgetsnbextension + version: 4.0.11 + manager: conda + platform: osx-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.11-pyhd8ed1ab_0.conda + hash: + md5: 95ba42a349c9d8eac28e30d0b637401f + sha256: 240582f3aff18f28b3500e76f727e1c58048bfc1a445c71b7087907a0a85a5e6 + category: main + optional: false +- name: widgetsnbextension + version: 4.0.11 + manager: conda + platform: osx-arm64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.11-pyhd8ed1ab_0.conda + hash: + md5: 95ba42a349c9d8eac28e30d0b637401f + sha256: 240582f3aff18f28b3500e76f727e1c58048bfc1a445c71b7087907a0a85a5e6 + category: main + optional: false +- name: widgetsnbextension + version: 4.0.11 + manager: conda + platform: win-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.11-pyhd8ed1ab_0.conda + hash: + md5: 95ba42a349c9d8eac28e30d0b637401f + sha256: 240582f3aff18f28b3500e76f727e1c58048bfc1a445c71b7087907a0a85a5e6 + category: main + optional: false - name: win_inet_pton version: 1.1.0 manager: conda @@ -24972,6 +25753,62 @@ package: sha256: 9df10c5b607dd30e05ba08cbd940009305c75db242476f4e845ea06008b0a283 category: main optional: false +- name: wrapt + version: 1.16.0 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.16.0-py312h98912ed_0.conda + hash: + md5: fa957a1c7bee7e47ad44633caf7be8bc + sha256: dc8431b343961347ad93b33d2d8270e8c15d8825382f4f2540835c94aba2de05 + category: main + optional: false +- name: wrapt + version: 1.16.0 + manager: conda + platform: osx-64 + dependencies: + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/osx-64/wrapt-1.16.0-py312h41838bb_0.conda + hash: + md5: d87798aa7210da2c5eaf96c0346dca00 + sha256: 9ed208c4c844c50f161764df7ed7a226c42822917c892ab7c8f67eec6ca96dff + category: main + optional: false +- name: wrapt + version: 1.16.0 + manager: conda + platform: osx-arm64 + dependencies: + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.16.0-py312he37b823_0.conda + hash: + md5: 86726ebb1f6da39c68f306ae624ee4ed + sha256: 25824dd9a22f2c1e8f205eb55c906b28b2f4748a68cb8e3d95ffdf73f08cbac9 + category: main + optional: false +- name: wrapt + version: 1.16.0 + manager: conda + platform: win-64 + dependencies: + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + ucrt: '>=10.0.20348.0' + vc: '>=14.2,<15' + vc14_runtime: '>=14.29.30139' + url: https://conda.anaconda.org/conda-forge/win-64/wrapt-1.16.0-py312he70551f_0.conda + hash: + md5: cea7b1aa961de6a8ac90584b5968a01d + sha256: e4b5ac6c897e68a798dfe13a1499dc9b555c48b468aa477d456807f2a7366c30 + category: main + optional: false - name: xarray version: 2024.6.0 manager: conda @@ -25913,6 +26750,72 @@ package: sha256: 76409556e6c7cb91991cd94d7fc853c9272c2872bd7e3573ff35eb33d6fca5be category: main optional: false +- name: zstandard + version: 0.22.0 + manager: conda + platform: linux-64 + dependencies: + cffi: '>=1.11' + libgcc-ng: '>=12' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + zstd: '>=1.5.6,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.22.0-py312h5b18bf6_1.conda + hash: + md5: 27fe79bbc4dd3767be554fb171df362c + sha256: 3bd22e769ea6bf2c9f59cc9905b9b43058208bde1ecca9d9f656ecd834c137d0 + category: main + optional: false +- name: zstandard + version: 0.22.0 + manager: conda + platform: osx-64 + dependencies: + __osx: '>=10.13' + cffi: '>=1.11' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + zstd: '>=1.5.6,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.22.0-py312h331e495_1.conda + hash: + md5: b355647d5ee25f78565028ace80844d1 + sha256: ad6c48685ef9ac57a452cfdd107da7cd2dad01972502b192ba5e7eff9ebf5aab + category: main + optional: false +- name: zstandard + version: 0.22.0 + manager: conda + platform: osx-arm64 + dependencies: + __osx: '>=11.0' + cffi: '>=1.11' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + zstd: '>=1.5.6,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.22.0-py312h721a963_1.conda + hash: + md5: 13b5cc78a710f6f13ff3c5bee14355d2 + sha256: 3aea4c16de85cfe932ba523dc1bdec3d267e06ee5a8528e478e6258b2f419ea5 + category: main + optional: false +- name: zstandard + version: 0.22.0 + manager: conda + platform: win-64 + dependencies: + cffi: '>=1.11' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + ucrt: '>=10.0.20348.0' + vc: '>=14.2,<15' + vc14_runtime: '>=14.29.30139' + zstd: '>=1.5.6,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.22.0-py312h7606c53_1.conda + hash: + md5: 786be87a7cee3e81dea86dd2783ce06c + sha256: 8e7a8188c0bea8b60d2010f2640e8d3ef0b8e63e4c3c0e1cedb73b9048eaeeab + category: main + optional: false - name: zstd version: 1.5.6 manager: conda diff --git a/conda/environment-unpinned.yml b/conda/environment-unpinned.yml index 26c60c46..77650fe8 100644 --- a/conda/environment-unpinned.yml +++ b/conda/environment-unpinned.yml @@ -9,6 +9,7 @@ dependencies: # for user interface - dask-labextension - jupyterlab + - jupyter_bokeh - jupyterlab-myst - jupyter-resource-usage # for default notebook kernel: @@ -21,6 +22,7 @@ dependencies: - geoviews-core - gsw - hvplot + - h5netcdf - ipython - matplotlib-base - netcdf4 @@ -33,7 +35,9 @@ dependencies: - rioxarray - scipy - sphinx-codeautolink + - sphinxcontrib-mermaid - sphinx-notfound-page - sphinxext-rediraffe + - s3fs - xarray - zarr diff --git a/conda/environment.yml b/conda/environment.yml index 154f15b2..98a4a3b6 100644 --- a/conda/environment.yml +++ b/conda/environment.yml @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 1bf1f9f5d61f47892c7aa44e23f0d353c0b37071f3dfd42e1a88a79e0bf96780 +# input_hash: 4daa19bce4836dfdde9cc57bf64660e6a0a4f9cd3aec6d3431e4bed60e747064 channels: - conda-forge @@ -9,7 +9,9 @@ dependencies: - _openmp_mutex=4.5=2_gnu - accessible-pygments=0.0.5=pyhd8ed1ab_0 - affine=2.4.0=pyhd8ed1ab_0 + - aiobotocore=2.13.1=pyhd8ed1ab_0 - aiohttp=3.9.5=py312h98912ed_0 + - aioitertools=0.11.0=pyhd8ed1ab_0 - aiosignal=1.3.1=pyhd8ed1ab_0 - alabaster=0.7.16=pyhd8ed1ab_0 - anyio=4.4.0=pyhd8ed1ab_0 @@ -22,45 +24,47 @@ dependencies: - async-lru=2.0.4=pyhd8ed1ab_0 - atk-1.0=2.38.0=h04ea711_2 - attrs=23.2.0=pyh71513ae_0 - - aws-c-auth=0.7.22=h9137712_5 - - aws-c-cal=0.6.15=h88a6e22_0 - - aws-c-common=0.9.19=h4ab18f5_0 - - aws-c-compression=0.2.18=h83b837d_6 - - aws-c-event-stream=0.4.2=h0cbf018_13 - - aws-c-http=0.8.2=h360477d_2 - - aws-c-io=0.14.9=h2d549f9_2 - - aws-c-mqtt=0.10.4=hf85b563_6 - - aws-c-s3=0.5.10=h679ed35_3 - - aws-c-sdkutils=0.1.16=h83b837d_2 - - aws-checksums=0.1.18=h83b837d_6 - - aws-crt-cpp=0.26.12=h8bc9c4d_0 - - aws-sdk-cpp=1.11.329=hf74b5d1_5 - - azure-core-cpp=1.11.1=h91d86a7_1 - - azure-identity-cpp=1.6.0=hf1915f5_1 - - azure-storage-blobs-cpp=12.10.0=h00ab1b0_1 - - azure-storage-common-cpp=12.5.0=h94269e2_4 + - aws-c-auth=0.7.22=hf36ad8f_6 + - aws-c-cal=0.6.15=h816f305_1 + - aws-c-common=0.9.23=h4ab18f5_0 + - aws-c-compression=0.2.18=he027950_7 + - aws-c-event-stream=0.4.2=hb72ac1a_14 + - aws-c-http=0.8.2=h75ac8c9_3 + - aws-c-io=0.14.9=hd3d3696_3 + - aws-c-mqtt=0.10.4=hb0abfc5_7 + - aws-c-s3=0.5.10=h44b787d_4 + - aws-c-sdkutils=0.1.16=he027950_3 + - aws-checksums=0.1.18=he027950_7 + - aws-crt-cpp=0.26.12=he940a02_1 + - aws-sdk-cpp=1.11.329=h0f5bab0_6 + - azure-core-cpp=1.12.0=h830ed8b_0 + - azure-identity-cpp=1.8.0=hdb0d106_1 + - azure-storage-blobs-cpp=12.11.0=ha67cba7_1 + - azure-storage-common-cpp=12.6.0=he3f277c_1 + - azure-storage-files-datalake-cpp=12.10.0=h29b5301_1 - babel=2.14.0=pyhd8ed1ab_0 - beautifulsoup4=4.12.3=pyha770c72_0 - bleach=6.1.0=pyhd8ed1ab_0 - blinker=1.8.2=pyhd8ed1ab_0 - - blosc=1.21.5=hc2324a3_1 - - bokeh=3.4.1=pyhd8ed1ab_0 + - blosc=1.21.6=hef167b5_0 + - bokeh=3.4.2=pyhd8ed1ab_0 + - botocore=1.34.131=pyge310_1234567_0 - brotli=1.1.0=hd590300_1 - brotli-bin=1.1.0=hd590300_1 - brotli-python=1.1.0=py312h30efb56_1 - bzip2=1.0.8=hd590300_5 - c-ares=1.28.1=hd590300_0 - - ca-certificates=2024.6.2=hbcca054_0 + - ca-certificates=2024.7.4=hbcca054_0 - cached-property=1.5.2=hd8ed1ab_1 - cached_property=1.5.2=pyha770c72_1 - cachetools=5.3.3=pyhd8ed1ab_0 - cairo=1.18.0=hbb29018_2 - cartopy=0.23.0=py312h1d6d2e6_1 - certifi=2024.6.2=pyhd8ed1ab_0 - - cf_xarray=0.9.1=pyhd8ed1ab_0 + - cf_xarray=0.9.3=pyhd8ed1ab_0 - cffi=1.16.0=py312hf06ca03_0 - cfgv=3.3.1=pyhd8ed1ab_0 - - cfitsio=4.4.0=hbdc6101_1 + - cfitsio=4.4.1=hf8ad068_0 - cftime=1.6.4=py312h085067d_0 - charset-normalizer=3.3.2=pyhd8ed1ab_0 - click=8.1.7=unix_pyh707e725_0 @@ -74,17 +78,17 @@ dependencies: - cryptography=42.0.8=py312hbcc2302_0 - cycler=0.12.1=pyhd8ed1ab_0 - cytoolz=0.12.3=py312h98912ed_0 - - dask=2024.6.2=pyhd8ed1ab_0 - - dask-core=2024.6.2=pyhd8ed1ab_0 - - dask-expr=1.1.6=pyhd8ed1ab_0 + - dask=2024.7.0=pyhd8ed1ab_0 + - dask-core=2024.7.0=pyhd8ed1ab_0 + - dask-expr=1.1.7=pyhd8ed1ab_0 - dask-labextension=7.0.0=pyhd8ed1ab_0 - dataclasses=0.8=pyhc8e2a94_3 - datashader=0.16.2=pyhd8ed1ab_0 - - debugpy=1.8.1=py312h30efb56_0 + - debugpy=1.8.2=py312h7070661_0 - decorator=5.1.1=pyhd8ed1ab_0 - defusedxml=0.7.1=pyhd8ed1ab_0 - distlib=0.3.8=pyhd8ed1ab_0 - - distributed=2024.6.2=pyhd8ed1ab_0 + - distributed=2024.7.0=pyhd8ed1ab_0 - docopt=0.6.2=py_1 - docutils=0.20.1=py312h7900ff3_3 - entrypoints=0.4=pyhd8ed1ab_0 @@ -92,7 +96,7 @@ dependencies: - executing=2.0.1=pyhd8ed1ab_0 - expat=2.6.2=h59595ed_0 - fasteners=0.17.3=pyhd8ed1ab_0 - - filelock=3.15.3=pyhd8ed1ab_0 + - filelock=3.15.4=pyhd8ed1ab_0 - flexcache=0.3=pyhd8ed1ab_0 - flexparser=0.3.1=pyhd8ed1ab_0 - fmt=10.2.1=h00ab1b0_0 @@ -103,14 +107,14 @@ dependencies: - fontconfig=2.14.2=h14ed4e7_0 - fonts-conda-ecosystem=1=0 - fonts-conda-forge=1=0 - - fonttools=4.53.0=py312h9a8786e_0 + - fonttools=4.53.1=py312h41a817b_0 - fqdn=1.5.1=pyhd8ed1ab_0 - freetype=2.12.1=h267a509_2 - freexl=2.0.0=h743c826_0 - fribidi=1.0.10=h36c2ea0_0 - frozenlist=1.4.1=py312h98912ed_0 - - fsspec=2024.6.0=pyhff2d567_0 - - gcsfs=2024.6.0=pyhd8ed1ab_0 + - fsspec=2024.6.1=pyhff2d567_0 + - gcsfs=2024.6.1=pyhd8ed1ab_0 - gdk-pixbuf=2.42.12=hb9ae30d_0 - geos=3.12.1=h59595ed_0 - geotiff=1.7.3=hf7fa9e8_1 @@ -118,14 +122,14 @@ dependencies: - gflags=2.2.2=he1b5a44_1004 - giflib=5.2.2=hd590300_0 - glog=0.7.1=hbabe93e_0 - - google-api-core=2.19.0=pyhd8ed1ab_0 - - google-auth=2.30.0=pyhff2d567_0 + - google-api-core=2.19.1=pyhd8ed1ab_0 + - google-auth=2.31.0=pyhff2d567_0 - google-auth-oauthlib=1.2.0=pyhd8ed1ab_0 - google-cloud-core=2.4.1=pyhd8ed1ab_0 - google-cloud-storage=2.17.0=pyhff2d567_0 - google-crc32c=1.1.2=py312h775cd15_5 - google-resumable-media=2.7.0=pyhd8ed1ab_0 - - googleapis-common-protos=1.63.1=pyhd8ed1ab_0 + - googleapis-common-protos=1.63.2=pyhd8ed1ab_0 - graphite2=1.3.13=h59595ed_1003 - graphviz=11.0.0=hc68bbd7_0 - greenlet=3.0.3=py312h30efb56_0 @@ -135,51 +139,57 @@ dependencies: - gts=0.7.6=h977cf35_4 - h11=0.14.0=pyhd8ed1ab_0 - h2=4.1.0=pyhd8ed1ab_0 + - h5netcdf=1.3.0=pyhd8ed1ab_0 + - h5py=3.11.0=nompi_py312hb7ab980_102 - harfbuzz=8.5.0=hfac3d4d_0 - hdf4=4.2.15=h2a13503_7 - hdf5=1.14.3=nompi_hdf9ad27_105 - - holoviews=1.19.0=pyhd8ed1ab_0 + - holoviews=1.19.1=pyhd8ed1ab_0 - hpack=4.0.0=pyh9f0ad1d_0 - httpcore=1.0.5=pyhd8ed1ab_0 - httpx=0.27.0=pyhd8ed1ab_0 - hvplot=0.10.0=pyhd8ed1ab_0 - hyperframe=6.0.1=pyhd8ed1ab_0 - icu=73.2=h59595ed_0 - - identify=2.5.36=pyhd8ed1ab_0 + - identify=2.6.0=pyhd8ed1ab_0 - idna=3.7=pyhd8ed1ab_0 - imagesize=1.4.1=pyhd8ed1ab_0 - - importlib-metadata=7.2.0=pyha770c72_0 - - importlib_metadata=7.2.0=hd8ed1ab_0 + - importlib-metadata=8.0.0=pyha770c72_0 + - importlib_metadata=8.0.0=hd8ed1ab_0 - importlib_resources=6.4.0=pyhd8ed1ab_0 - - ipykernel=6.29.4=pyh3099207_0 - - ipython=8.25.0=pyh707e725_0 + - ipykernel=6.29.5=pyh3099207_0 + - ipython=8.26.0=pyh707e725_0 + - ipywidgets=8.1.3=pyhd8ed1ab_0 - isoduration=20.11.0=pyhd8ed1ab_0 - jedi=0.19.1=pyhd8ed1ab_0 - jinja2=3.1.4=pyhd8ed1ab_0 + - jmespath=1.0.1=pyhd8ed1ab_0 - json-c=0.17=h7ab15ed_0 - json5=0.9.25=pyhd8ed1ab_0 - jsonpointer=3.0.0=py312h7900ff3_0 - jsonschema=4.22.0=pyhd8ed1ab_0 - jsonschema-specifications=2023.12.1=pyhd8ed1ab_0 - jsonschema-with-format-nongpl=4.22.0=pyhd8ed1ab_0 - - jupyter-book=1.0.0=pyhd8ed1ab_0 + - jupyter-book=1.0.2=pyhd8ed1ab_0 - jupyter-cache=1.0.0=pyhd8ed1ab_0 - jupyter-lsp=2.2.5=pyhd8ed1ab_0 - jupyter-resource-usage=1.0.2=pyhd8ed1ab_0 - - jupyter-server-proxy=4.2.0=pyhd8ed1ab_0 + - jupyter-server-proxy=4.3.0=pyhd8ed1ab_0 + - jupyter_bokeh=4.0.5=pyhd8ed1ab_0 - jupyter_client=8.6.2=pyhd8ed1ab_0 - jupyter_core=5.7.2=py312h7900ff3_0 - jupyter_events=0.10.0=pyhd8ed1ab_0 - jupyter_server=2.14.1=pyhd8ed1ab_0 - jupyter_server_terminals=0.5.3=pyhd8ed1ab_0 - - jupyterlab=4.2.2=pyhd8ed1ab_0 + - jupyterlab=4.2.3=pyhd8ed1ab_0 - jupyterlab-myst=2.4.2=pyhd8ed1ab_0 - jupyterlab_pygments=0.3.0=pyhd8ed1ab_1 - jupyterlab_server=2.27.2=pyhd8ed1ab_0 + - jupyterlab_widgets=3.0.11=pyhd8ed1ab_0 - kealib=1.5.3=hee9dde6_1 - keyutils=1.6.1=h166bdaf_0 - kiwisolver=1.4.5=py312h8572e83_1 - - krb5=1.21.2=h659d440_0 + - krb5=1.21.3=h659f571_0 - latexcodec=2.0.1=pyh9f0ad1d_0 - lcms2=2.16=hb7c19ff_0 - ld_impl_linux-64=2.40=hf3520f5_7 @@ -187,33 +197,33 @@ dependencies: - libabseil=20240116.2=cxx17_h59595ed_0 - libaec=1.1.3=h59595ed_0 - libarchive=3.7.4=hfca40fe_0 - - libarrow=16.1.0=h9102155_9_cpu - - libarrow-acero=16.1.0=hac33072_9_cpu - - libarrow-dataset=16.1.0=hac33072_9_cpu - - libarrow-substrait=16.1.0=h7e0c224_9_cpu + - libarrow=16.1.0=he3e46ce_11_cpu + - libarrow-acero=16.1.0=he02047a_11_cpu + - libarrow-dataset=16.1.0=he02047a_11_cpu + - libarrow-substrait=16.1.0=hc9a23c6_11_cpu - libblas=3.9.0=22_linux64_openblas - - libboost-headers=1.85.0=ha770c72_1 + - libboost-headers=1.85.0=ha770c72_2 - libbrotlicommon=1.1.0=hd590300_1 - libbrotlidec=1.1.0=hd590300_1 - libbrotlienc=1.1.0=hd590300_1 - libcblas=3.9.0=22_linux64_openblas - libcrc32c=1.1.2=h9c3ff4c_0 - - libcurl=8.8.0=hca28451_0 + - libcurl=8.8.0=hca28451_1 - libdeflate=1.20=hd590300_0 - libedit=3.1.20191231=he28a2e2_2 - libev=4.33=hd590300_2 - libevent=2.1.12=hf998b51_1 - libexpat=2.6.2=h59595ed_0 - libffi=3.4.2=h7f98852_5 - - libgcc-ng=13.2.0=h77fa898_11 + - libgcc-ng=14.1.0=h77fa898_0 - libgd=2.3.3=h119a65a_9 - - libgdal=3.9.0=h471f4ab_7 - - libgfortran-ng=13.2.0=h69a702a_11 - - libgfortran5=13.2.0=h3d2ce59_11 - - libglib=2.80.2=hf974151_0 - - libgomp=13.2.0=h77fa898_11 - - libgoogle-cloud=2.25.0=h2736e30_0 - - libgoogle-cloud-storage=2.25.0=h3d9a0c8_0 + - libgdal=3.9.1=h086a8f6_3 + - libgfortran-ng=14.1.0=h69a702a_0 + - libgfortran5=14.1.0=hc5f4f2c_0 + - libglib=2.80.3=h8a4344b_1 + - libgomp=14.1.0=h77fa898_0 + - libgoogle-cloud=2.26.0=h26d7fe4_0 + - libgoogle-cloud-storage=2.26.0=ha262f82_0 - libgrpc=1.62.2=h15f2491_0 - libiconv=1.17=hd590300_2 - libjpeg-turbo=3.0.0=hd590300_1 @@ -224,7 +234,7 @@ dependencies: - libnghttp2=1.58.0=h47da74e_1 - libnsl=2.0.1=hd590300_0 - libopenblas=0.3.27=pthreads_h413a1c8_0 - - libparquet=16.1.0=h6a7eafb_9_cpu + - libparquet=16.1.0=h9e5060d_11_cpu - libpng=1.6.43=h2797004_0 - libpq=16.3=ha72fbe1_0 - libprotobuf=4.25.3=h08a7969_0 @@ -235,7 +245,7 @@ dependencies: - libspatialite=5.1.0=h6fbd9c4_7 - libsqlite=3.46.0=hde9e2c9_0 - libssh2=1.11.0=h0841786_0 - - libstdcxx-ng=13.2.0=hc0a3c3a_11 + - libstdcxx-ng=14.1.0=hc0a3c3a_0 - libthrift=0.19.0=hb90f79a_1 - libtiff=4.6.0=h1dd3fc0_3 - libutf8proc=2.8.0=h166bdaf_0 @@ -266,7 +276,7 @@ dependencies: - multidict=6.0.5=py312h98912ed_0 - multipledispatch=0.6.0=py_0 - munkres=1.1.4=pyh9f0ad1d_0 - - myst-nb=1.1.0=pyhd8ed1ab_0 + - myst-nb=1.1.1=pyhd8ed1ab_0 - myst-parser=2.0.0=pyhd8ed1ab_0 - nbclient=0.10.0=pyhd8ed1ab_0 - nbconvert-core=7.16.4=pyhd8ed1ab_1 @@ -277,13 +287,13 @@ dependencies: - nodeenv=1.9.1=pyhd8ed1ab_0 - notebook-shim=0.2.4=pyhd8ed1ab_0 - nspr=4.35=h27087fc_0 - - nss=3.101=h593d115_0 + - nss=3.102=h593d115_0 - numba=0.60.0=py312h83e6fd3_0 - numcodecs=0.12.1=py312h7070661_1 - numpy=1.26.4=py312heda63a1_0 - oauthlib=3.2.2=pyhd8ed1ab_0 - openjpeg=2.5.2=h488ebb8_0 - - openssl=3.3.1=h4ab18f5_0 + - openssl=3.3.1=h4ab18f5_1 - orc=2.0.1=h17fec99_1 - overrides=7.7.0=pyhd8ed1ab_0 - packaging=24.1=pyhd8ed1ab_0 @@ -291,15 +301,15 @@ dependencies: - pandocfilters=1.5.0=pyhd8ed1ab_0 - panel=1.4.4=pyhd8ed1ab_0 - pango=1.54.0=h84a9a3c_0 - - param=2.1.0=pyhca7485f_0 + - param=2.1.1=pyhff2d567_0 - parso=0.8.4=pyhd8ed1ab_0 - partd=1.4.2=pyhd8ed1ab_0 - - pcre2=10.43=hcad00b1_0 + - pcre2=10.44=h0f59acf_0 - pexpect=4.9.0=pyhd8ed1ab_0 - pickleshare=0.7.5=py_1003 - - pillow=10.3.0=py312h287a98d_1 - - pint=0.24=pyhd8ed1ab_2 - - pint-xarray=0.3=pyhd8ed1ab_0 + - pillow=10.4.0=py312h287a98d_0 + - pint=0.24.1=pyhd8ed1ab_1 + - pint-xarray=0.4=pyhd8ed1ab_0 - pip=24.0=pyhd8ed1ab_0 - pixman=0.43.2=h59595ed_0 - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1 @@ -318,8 +328,8 @@ dependencies: - pthread-stubs=0.4=h36c2ea0_1001 - ptyprocess=0.7.0=pyhd3deb0d_0 - pure_eval=0.2.2=pyhd8ed1ab_0 - - pyarrow=16.1.0=py312h9cebb41_3 - - pyarrow-core=16.1.0=py312h70856f0_3_cpu + - pyarrow=16.1.0=py312h9cebb41_4 + - pyarrow-core=16.1.0=py312h70856f0_4_cpu - pyarrow-hotfix=0.6=pyhd8ed1ab_0 - pyasn1=0.6.0=pyhd8ed1ab_0 - pyasn1-modules=0.4.0=pyhd8ed1ab_0 @@ -328,7 +338,7 @@ dependencies: - pycparser=2.22=pyhd8ed1ab_0 - pyct=0.5.0=pyhd8ed1ab_0 - pydap=3.4.0=pyhd8ed1ab_0 - - pydata-sphinx-theme=0.15.3=pyhd8ed1ab_0 + - pydata-sphinx-theme=0.15.4=pyhd8ed1ab_0 - pygments=2.18.0=pyhd8ed1ab_0 - pyjwt=2.8.0=pyhd8ed1ab_1 - pyopenssl=24.0.0=pyhd8ed1ab_0 @@ -356,17 +366,18 @@ dependencies: - requests-oauthlib=2.0.0=pyhd8ed1ab_0 - rfc3339-validator=0.1.4=pyhd8ed1ab_0 - rfc3986-validator=0.1.1=pyh9f0ad1d_0 - - rioxarray=0.15.6=pyhd8ed1ab_0 + - rioxarray=0.15.7=pyhd8ed1ab_0 - rpds-py=0.18.1=py312h4413252_0 - rsa=4.9=pyhd8ed1ab_0 - s2n=1.4.16=he19d79f_0 - - scipy=1.13.1=py312hc2bc53b_0 + - s3fs=2024.6.1=pyhd8ed1ab_0 + - scipy=1.14.0=py312hc2bc53b_1 - send2trash=1.8.3=pyh0d859eb_0 - - setuptools=70.1.0=pyhd8ed1ab_0 + - setuptools=70.1.1=pyhd8ed1ab_0 - shapely=2.0.4=py312ha5b4d35_1 - simpervisor=1.0.0=pyhd8ed1ab_0 - six=1.16.0=pyh6c4a22f_0 - - snappy=1.2.0=hdb0a2a9_1 + - snappy=1.2.1=ha2e4443_0 - sniffio=1.3.1=pyhd8ed1ab_0 - snowballstemmer=2.2.0=pyhd8ed1ab_0 - snuggs=1.4.7=py_0 @@ -390,6 +401,7 @@ dependencies: - sphinxcontrib-devhelp=1.0.6=pyhd8ed1ab_0 - sphinxcontrib-htmlhelp=2.0.5=pyhd8ed1ab_0 - sphinxcontrib-jsmath=1.0.1=pyhd8ed1ab_0 + - sphinxcontrib-mermaid=0.9.2=pyhd8ed1ab_0 - sphinxcontrib-qthelp=1.0.7=pyhd8ed1ab_0 - sphinxcontrib-serializinghtml=1.1.10=pyhd8ed1ab_0 - sphinxext-rediraffe=0.2.7=pyhd8ed1ab_1 @@ -399,7 +411,7 @@ dependencies: - tabulate=0.9.0=pyhd8ed1ab_1 - tblib=3.0.0=pyhd8ed1ab_0 - terminado=0.18.1=pyh0d859eb_0 - - tiledb=2.24.1=h73c5a7c_0 + - tiledb=2.24.1=haf8a068_2 - tinycss2=1.3.0=pyhd8ed1ab_0 - tk=8.6.13=noxft_h4845f30_101 - tomli=2.0.1=pyhd8ed1ab_0 @@ -417,14 +429,16 @@ dependencies: - ukkonen=1.0.1=py312h8572e83_4 - uri-template=1.3.0=pyhd8ed1ab_0 - uriparser=0.9.8=hac33072_0 - - urllib3=2.2.2=pyhd8ed1ab_0 - - virtualenv=20.26.2=pyhd8ed1ab_0 + - urllib3=2.2.2=pyhd8ed1ab_1 + - virtualenv=20.26.3=pyhd8ed1ab_0 - wcwidth=0.2.13=pyhd8ed1ab_0 - webcolors=24.6.0=pyhd8ed1ab_0 - webencodings=0.5.1=pyhd8ed1ab_2 - webob=1.8.7=pyhd8ed1ab_0 - websocket-client=1.8.0=pyhd8ed1ab_0 - wheel=0.43.0=pyhd8ed1ab_1 + - 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 @@ -447,4 +461,5 @@ dependencies: - 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_data_structures.md b/fundamentals/01_data_structures.md index 5add1dab..389f1f4d 100644 --- a/fundamentals/01_data_structures.md +++ b/fundamentals/01_data_structures.md @@ -1,5 +1,69 @@ # Data Structures +Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”) +are an essential part of computational science. They are encountered in a wide +range of fields, including physics, astronomy, geoscience, bioinformatics, +engineering, finance, and deep learning. In Python, [NumPy](https://numpy.org/) +provides the fundamental data structure and API for working with raw ND arrays. +However, real-world datasets are usually more than just raw numbers; they have +labels which encode information about how the array values map to locations in +space, time, etc. + +The N-dimensional nature of Xarray’s data structures makes it suitable for +dealing with multi-dimensional scientific data, and its use of dimension names +instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much +more manageable than the raw NumPy ndarray: with Xarray, you don’t need to keep +track of the order of an array’s dimensions or insert dummy dimensions of size 1 +to align arrays (e.g., using np.newaxis). + +The immediate payoff of using Xarray is that you’ll write less code. The +long-term payoff is that you’ll understand what you were thinking when you come +back to look at it weeks or months later. + +## Example: Weather forecast + +Here is an example of how we might structure a dataset for a weather forecast: + + + +You'll notice multiple data variables (temperature, precipitation), coordinate +variables (latitude, longitude), and dimensions (x, y, t). We'll cover how these +fit into Xarray's data structures below. + +Xarray doesn’t just keep track of labels on arrays – it uses them to provide a +powerful and concise interface. For example: + +- Apply operations over dimensions by name: `x.sum('time')`. + +- Select values by label (or logical location) instead of integer location: + `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`. + +- Mathematical operations (e.g., `x - y`) vectorize across multiple dimensions + (array broadcasting) based on dimension names, not shape. + +- Easily use the split-apply-combine paradigm with groupby: + `x.groupby('time.dayofyear').mean()`. + +- Database-like alignment based on coordinate labels that smoothly handles + missing values: `x, y = xr.align(x, y, join='outer')`. + +- Keep track of arbitrary metadata in the form of a Python dictionary: + `x.attrs`. + +## Example: Mosquito genetics + +Although the Xarray library was originally developed with Earth Science datasets in mind, the datastructures work well across many other domains! For example, below is a side-by-side view of a data schematic on the left and Xarray Dataset representation on the right taken from a mosquito genetics analysis: + +![malaria_dataset](../images/malaria_dataset.png) + +The data can be stored as a 3-dimensional array, where one dimension of the array corresponds to positions (**variants**) within a reference genome, another dimension corresponds to the individual mosquitoes that were sequenced (**samples**), and a third dimension corresponds to the number of genomes within each individual (**ploidy**)." + +You can explore this dataset in detail via the [training course in data analysis for genomic surveillance of African malaria vectors](https://anopheles-genomic-surveillance.github.io/workshop-5/module-1-xarray.html)! + +## Explore on your own + +The following collection of notebooks provide interactive code examples for working with example datasets and constructing Xarray data structures manually. + ```{tableofcontents} ``` diff --git a/fundamentals/01_datastructures.ipynb b/fundamentals/01_datastructures.ipynb index 655a1795..fa3875de 100644 --- a/fundamentals/01_datastructures.ipynb +++ b/fundamentals/01_datastructures.ipynb @@ -6,62 +6,13 @@ "source": [ "# Xarray's Data structures\n", "\n", - "In this lesson, we cover the basics of Xarray data structures. Our\n", - "learning goals are as follows. By the end of the lesson, we will be able to:\n", + "In this lesson, we cover the basics of Xarray data structures. By the end of the lesson, we will be able to:\n", "\n", - "- Understand the basic data structures (`DataArray` and `Dataset` objects) in Xarray\n", - "\n", - "---\n", - "\n", - "## Introduction\n", - "\n", - "Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called “tensors”)\n", - "are an essential part of computational science. They are encountered in a wide\n", - "range of fields, including physics, astronomy, geoscience, bioinformatics,\n", - "engineering, finance, and deep learning. In Python, [NumPy](https://numpy.org/)\n", - "provides the fundamental data structure and API for working with raw ND arrays.\n", - "However, real-world datasets are usually more than just raw numbers; they have\n", - "labels which encode information about how the array values map to locations in\n", - "space, time, etc.\n", - "\n", - "Here is an example of how we might structure a dataset for a weather forecast:\n", - "\n", - "\n", - "\n", - "You'll notice multiple data variables (temperature, precipitation), coordinate\n", - "variables (latitude, longitude), and dimensions (x, y, t). We'll cover how these\n", - "fit into Xarray's data structures below.\n", - "\n", - "Xarray doesn’t just keep track of labels on arrays – it uses them to provide a\n", - "powerful and concise interface. For example:\n", - "\n", - "- Apply operations over dimensions by name: `x.sum('time')`.\n", - "\n", - "- Select values by label (or logical location) instead of integer location:\n", - " `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`.\n", - "\n", - "- Mathematical operations (e.g., `x - y`) vectorize across multiple dimensions\n", - " (array broadcasting) based on dimension names, not shape.\n", - "\n", - "- Easily use the split-apply-combine paradigm with groupby:\n", - " `x.groupby('time.dayofyear').mean()`.\n", - "\n", - "- Database-like alignment based on coordinate labels that smoothly handles\n", - " missing values: `x, y = xr.align(x, y, join='outer')`.\n", - "\n", - "- Keep track of arbitrary metadata in the form of a Python dictionary:\n", - " `x.attrs`.\n", - "\n", - "The N-dimensional nature of xarray’s data structures makes it suitable for\n", - "dealing with multi-dimensional scientific data, and its use of dimension names\n", - "instead of axis labels (`dim='time'` instead of `axis=0`) makes such arrays much\n", - "more manageable than the raw numpy ndarray: with xarray, you don’t need to keep\n", - "track of the order of an array’s dimensions or insert dummy dimensions of size 1\n", - "to align arrays (e.g., using np.newaxis).\n", - "\n", - "The immediate payoff of using xarray is that you’ll write less code. The\n", - "long-term payoff is that you’ll understand what you were thinking when you come\n", - "back to look at it weeks or months later.\n" + ":::{admonition} Learning Goals\n", + "- Understand the basic Xarray data structures `DataArray` and `Dataset` \n", + "- Customize the display of Xarray data structures\n", + "- The connection between Pandas and Xarray data structures\n", + ":::" ] }, { @@ -72,13 +23,10 @@ "\n", "Xarray provides two data structures: the `DataArray` and `Dataset`. The\n", "`DataArray` class attaches dimension names, coordinates and attributes to\n", - "multi-dimensional arrays while `Dataset` combines multiple arrays.\n", + "multi-dimensional arrays while `Dataset` combines multiple DataArrays.\n", "\n", "Both classes are most commonly created by reading data.\n", - "To learn how to create a DataArray or Dataset manually, see the [Creating Data Structures](01.1_creating_data_structures.ipynb) tutorial.\n", - "\n", - "Xarray has a few small real-world tutorial datasets hosted in this GitHub repository https://github.com/pydata/xarray-data.\n", - "We'll use the [xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) convenience function to download and open the `air_temperature` (National Centers for Environmental Prediction) Dataset by name." + "To learn how to create a DataArray or Dataset manually, see the [Creating Data Structures](01.1_creating_data_structures.ipynb) tutorial." ] }, { @@ -88,7 +36,13 @@ "outputs": [], "source": [ "import numpy as np\n", - "import xarray as xr" + "import xarray as xr\n", + "import pandas as pd\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", + "xr.set_options(display_expand_attrs=False, display_expand_data=False)\n", + "np.set_printoptions(threshold=10, edgeitems=2)" ] }, { @@ -97,7 +51,10 @@ "source": [ "### Dataset\n", "\n", - "`Dataset` objects are dictionary-like containers of DataArrays, mapping a variable name to each DataArray.\n" + "`Dataset` objects are dictionary-like containers of DataArrays, mapping a variable name to each DataArray.\n", + "\n", + "Xarray has a few small real-world tutorial datasets hosted in this GitHub repository https://github.com/pydata/xarray-data.\n", + "We'll use the [xarray.tutorial.load_dataset](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html#xarray.tutorial.open_dataset) convenience function to download and open the `air_temperature` (National Centers for Environmental Prediction) Dataset by name." ] }, { @@ -147,14 +104,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is all this anyway? (String representations)\n", + "#### HTML vs text representations\n", "\n", "Xarray has two representation types: `\"html\"` (which is only available in\n", "notebooks) and `\"text\"`. To choose between them, use the `display_style` option.\n", "\n", "So far, our notebook has automatically displayed the `\"html\"` representation (which we will continue using).\n", - "The `\"html\"` representation is interactive, allowing you to collapse sections (left arrows) and\n", - "view attributes and values for each value (right hand sheet icon and data symbol)." + "The `\"html\"` representation is interactive, allowing you to collapse sections (▶) and\n", + "view attributes and values for each value (📄 and ≡)." ] }, { @@ -171,18 +128,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The output consists of:\n", + "☝️ From top to bottom the output consists of:\n", "\n", - "- a summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first\n", - " dimension is named `lat` and has a size of `25`, the second dimension is named\n", - " `time` and has a size of `2920`, and the third dimension is named `lon` and has a size\n", - " of `53`. Because we will access the dimensions by name, the order doesn't matter.\n", - "- an unordered list of *coordinates* or dimensions with coordinates with one item\n", - " per line. Each item has a name, one or more dimensions in parentheses, a dtype\n", - " and a preview of the values. Also, if it is a dimension coordinate, it will be\n", - " marked with a `*`.\n", - "- an alphabetically sorted list of *dimensions without coordinates* (if there are any)\n", - "- an unordered list of *attributes*, or metadata" + "- **Dimensions**: summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first dimension is named `lat` and has a size of `25`, the second dimension is named `time` and has a size of `2920`, and the third dimension is named `lon` and has a size of `53`. Because we will access the dimensions by name, the order doesn't matter.\n", + "- **Coordinates**: an unordered list of *coordinates* or dimensions with coordinates with one item per line. Each item has a name, one or more dimensions in parentheses, a dtype and a preview of the values. Also, if it is a dimension coordinate, it will be printed in **bold** font. *dimensions without coordinates* appear in plain font (there are none in this example, but you might imagine a 'mask' coordinate that has a value assigned at every point).\n", + "- **Data variables**: names of each nD *measurement* in the dataset, followed by its dimensions `(time, lat, lon)`, dtype, and a preview of values.\n", + "- **Indexes**: Each dimension with coordinates is backed by an \"Index\". In this example, each dimension is backed by a `PandasIndex`\n", + "- **Attributes**: an unordered list of metadata (for example, a paragraph describing the dataset)" ] }, { @@ -379,15 +331,6 @@ "methods on `xarray` objects:\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, { "cell_type": "code", "execution_count": null, @@ -429,8 +372,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**to_series**: This will always convert `DataArray` objects to\n", - "`pandas.Series`, using a `MultiIndex` for higher dimensions\n" + "### to_series\n", + "This will always convert `DataArray` objects to `pandas.Series`, using a `MultiIndex` for higher dimensions\n" ] }, { @@ -446,9 +389,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**to_dataframe**: This will always convert `DataArray` or `Dataset`\n", - "objects to a `pandas.DataFrame`. Note that `DataArray` objects have to be named\n", - "for this.\n" + "### to_dataframe\n", + "\n", + "This will always convert `DataArray` or `Dataset` objects to a `pandas.DataFrame`. Note that `DataArray` objects have to be named for this. Since columns in a `DataFrame` need to have the same index, they are\n", + "broadcasted." ] }, { @@ -459,23 +403,6 @@ "source": [ "ds.air.to_dataframe()" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since columns in a `DataFrame` need to have the same index, they are\n", - "broadcasted.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.to_dataframe()" - ] } ], "metadata": { diff --git a/images/malaria_dataset.png b/images/malaria_dataset.png new file mode 100644 index 00000000..b360a198 Binary files /dev/null and b/images/malaria_dataset.png differ diff --git a/images/orthogonal_vs_vectorized.png b/images/orthogonal_vs_vectorized.png new file mode 100644 index 00000000..e2033b87 Binary files /dev/null and b/images/orthogonal_vs_vectorized.png differ diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/01-high-level-computation-patterns.ipynb index e5f17c1c..13c5f261 100644 --- a/intermediate/01-high-level-computation-patterns.ipynb +++ b/intermediate/01-high-level-computation-patterns.ipynb @@ -657,7 +657,7 @@ ":class: dropdown\n", "\n", "```python\n", - "data.coarsen(lat=5, lon=5, boundary=\"trim\").reduce(np.mean).plot();\n", + "data.coarsen(lat=5, lon=5, boundary=\"trim\").reduce(np.ptp).plot();\n", "```\n", ":::\n", "::::" diff --git a/intermediate/indexing/advanced-indexing.ipynb b/intermediate/indexing/advanced-indexing.ipynb index 2665b5dd..a5538151 100644 --- a/intermediate/indexing/advanced-indexing.ipynb +++ b/intermediate/indexing/advanced-indexing.ipynb @@ -8,7 +8,9 @@ "\n", "## Learning Objectives\n", "\n", - "* Orthogonal vs. Vectorized and Pointwise Indexing" + "* Orthogonal vs. Pointwise (Vectorized) Indexing.\n", + "* Pointwise indexing in Xarray to extract data at a collection of points.\n", + "* Understand the difference between NumPy and Xarray indexing behavior." ] }, { @@ -17,60 +19,121 @@ "source": [ "## Overview\n", "\n", - "In the previous notebooks, we learned basic forms of indexing with xarray (positional and name based dimensions, integer and label based indexing), Datetime Indexing, and nearest neighbor lookups. In this tutorial, we will learn how Xarray indexing is different from Numpy and how to do vectorized/pointwise indexing using Xarray. \n", - "First, let's import packages needed for this repository: " + "In the previous notebooks, we learned basic forms of indexing with Xarray, including positional and label-based indexing, datetime indexing, and nearest neighbor lookups. We also learned that indexing an Xarray DataArray directly works (mostly) like it does for NumPy arrays; however, Xarray indexing behavior deviates from NumPy when using multiple arrays for indexing, like `arr[[0, 1], [0, 1]]`.\n", + "\n", + "To better understand this difference, let's take a look at an example of 2D 5x5 array:" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", "\n", + "# Create a 5x5 array with values from 1 to 25\n", + "np_array = np.arange(1, 26).reshape(5, 5)\n", + "np_array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a Xarray DataArray from this NumPy array: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", "\n", - "xr.set_options(display_expand_attrs=False)\n", - "np.set_printoptions(threshold=10, edgeitems=2)" + "da = xr.DataArray(np_array, dims=[\"x\", \"y\"])\n", + "da" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we’ll use air temperature tutorial dataset from the National Center for Environmental Prediction. " + "Now, let's see how the indexing behavior is different between NumPy array and Xarray DataArray when indexing with multiple arrays:" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ - "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", - "da = ds.air\n", - "ds" + "np_array[[0, 2, 4], [0, 2, 4]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da[[0, 2, 4], [0, 2, 4]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Orthogonal Indexing \n", + "The image below summarizes the difference between vectorized and orthogonal indexing for a 2D 5x5 NumPy array and Xarray DataArray:\n", "\n", - "As we learned in the previous tutorial, positional indexing deviates from the behavior exhibited by NumPy when indexing with multiple arrays. However, Xarray pointwise indexing supports the indexing along multiple labeled dimensions using list-like objects similar to NumPy indexing behavior.\n", "\n", - "If you only provide integers, slices, or unlabeled arrays (array without dimension names, such as `np.ndarray`, `list`, but not `DataArray()`) indexing can be understood as orthogonally (i.e. along independent axes, instead of using NumPy’s broadcasting rules to vectorize indexers). \n", "\n", - "*Orthogonal* or *outer* indexing considers one-dimensional arrays in the same way as slices when deciding the output shapes. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component *independently* selects along its corresponding dimension. \n", + "![Orthogonal vs. Vectorized Indexing](../../images/orthogonal_vs_vectorized.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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", + " \n", + "The output of vectorized indexing is a `1D array`, while the output of orthogonal indexing is a `3x3` array. \n", + "\n", + "\n", + ":::{tip} To Summarize: \n", + "\n", + "- *Pointwise* or *vectorized* indexing is a more general form of indexing that allows for arbitrary combinations of indexing arrays. This method of indexing is analogous to the broadcasting rules in NumPy, where the dimensions of the indexers are aligned and the result is determined by the shape of the indexers. This is the default behavior in NumPy.\n", + "\n", + "- *Orthogonal* or *outer* indexing allows for indexing along each dimension independently, treating the indexers as one-dimensional arrays. The principle of outer or orthogonal indexing is that the result mirrors the effect of independently indexing along each dimension with integer or boolean arrays, treating both the indexed and indexing arrays as one-dimensional. This method of indexing is analogous to vector indexing in programming languages like MATLAB, Fortran, and R, where each indexer component independently selects along its corresponding dimension. This is the default behavior in Xarray.\n", "\n", - "For example : " + "\n", + ":::\n", + "\n", + ":::{note} Orthogonal indexing with NumPy\n", + ":class: 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", + "```python\n", + "ixgrid = np.ix_([0, 2, 4], [0, 2, 4])\n", + "np_array[ixgrid]\n", + "```\n", + "\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Orthogonal Indexing in Xarray\n", + "\n", + "As explained earlier, when you use only integers, slices, or unlabeled arrays (arrays without dimension names, such as `np.ndarray` or `list`, but not `DataArray`) to index an `Xarray DataArray`, Xarray interprets these indexers orthogonally. This means it indexes along independent axes, rather than using NumPy's broadcasting rules to vectorize the indexers. \n", + "\n", + "In the example above we saw this behavior, but let's see this behavior in action with a real dataset. Here we’ll use `air temperature` data from the National Center for Environmental Prediction:" ] }, { @@ -81,14 +144,41 @@ }, "outputs": [], "source": [ - "da.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]).plot(); # -- orthogonal indexing" + "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", + "\n", + "ds = xr.tutorial.load_dataset(\"air_temperature\")\n", + "da_air = ds.air\n", + "da_air" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selected_da = da_air.isel(time=0, lat=[2, 4, 10, 13], lon=[1, 6, 7]) # -- orthogonal indexing\n", + "selected_da" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "👆 Please note that the output shape in the example above is `4x3` because the latitude indexer selects 4 rows, and the longitude indexer selects 3 columns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For more flexibility, you can supply `DataArray()` objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions:\n", + "For more flexibility, you can supply `DataArray()` objects as indexers. Dimensions on resultant arrays are given by the ordered union of the indexers’ dimensions.\n", "\n", "For example, in the example below we do orthogonal indexing using `DataArray()` objects. " ] @@ -104,14 +194,30 @@ "target_lat = xr.DataArray([31, 41, 42, 42], dims=\"degrees_north\")\n", "target_lon = xr.DataArray([200, 201, 202, 205], dims=\"degrees_east\")\n", "\n", - "da.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" + "da_air.sel(lat=target_lat, lon=target_lon, method=\"nearest\") # -- orthogonal indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In the above example, you can see how the output shape is `time` x `lats` x `lons`. " + "In the above example, you can see how the output shape is `time` x `lats` x `lons`. Please note that there are no shared dimensions between the indexers, so the output shape is the union of the dimensions of the indexers.\n", + "\n", + "```{attention}\n", + "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", + "```\n", + "\n", + "For example:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_air.sel(lat=[20, 30, 40], lon=target_lon, method=\"nearest\")" ] }, { @@ -121,20 +227,56 @@ }, "source": [ "\n", - "But what if we would like to find the information from the nearest grid cell to a collection of specified points (for example, weather stations or tower data)?\n", + "But what if we'd like to find the nearest climate model grid cell to a collection of specified points (for example observation sites, or weather stations)?\n", "\n", - "## Vectorized or Pointwise Indexing\n", + "## Vectorized or Pointwise Indexing in Xarray\n", "\n", - "Like NumPy and pandas, Xarray supports indexing many array elements at once in a\n", - "*vectorized* manner. \n", + "Like NumPy and pandas, Xarray supports indexing many array elements at once in a *vectorized* manner. \n", "\n", - "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified weather station latitudes and longitudes.\n", + "**Vectorized indexing** or **Pointwise Indexing** using `DataArrays()` can be used to extract information from the nearest grid cells of interest, for example, the nearest climate model grid cells to a collection of specified observation tower data latitudes and longitudes.\n", "\n", "```{hint}\n", - "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a new shared output dimension name. \n", + "To trigger vectorized indexing behavior, you will need to provide the selection dimensions with a new **shared** output dimension name. This means that the dimensions of both indexers must be the same, and the output will have the same dimension name as the indexers.\n", "```\n", "\n", - "In the example below, the selections of the closest latitude and longitude are renamed to an output dimension named `points`:" + "Let's see how this works with an example:\n", + "\n", + "A researcher wants to find the nearest climate model grid cell to a collection of observation sites. They have the latitude and longitude of the observation sites as following:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "obs_lats = [31.81, 41.26, 22.59, 44.47, 28.57]\n", + "\n", + "obs_lons = [200.16, 201.57, 305.54, 210.56, 226.59]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the researcher use the lists to index the DataArray, they will get the orthogonal indexing behavior, which is not what they want." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "da_air.sel(lat=obs_lats, lon=obs_lats, method=\"nearest\") # -- orthogonal indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To trigger the pointwise indexing, they need to create DataArray objects with the same dimension name, and then use them to index the DataArray. \n", + "For example, the code below first create DataArray objects for the latitude and longitude of the observation sites using a shared dimension name `points`, and then use them to index the DataArray `air_temperature`:" ] }, { @@ -145,9 +287,8 @@ }, "outputs": [], "source": [ - "# Define target latitude and longitude (where weather stations might be)\n", - "lat_points = xr.DataArray([31, 41, 42, 42], dims=\"points\")\n", - "lon_points = xr.DataArray([200, 201, 202, 205], dims=\"points\")\n", + "## latitudes of weather stations with a dimension of \"points\"\n", + "lat_points = xr.DataArray(obs_lats, dims=\"points\")\n", "lat_points" ] }, @@ -159,6 +300,8 @@ }, "outputs": [], "source": [ + "## longitudes of weather stations with a dimension of \"points\"\n", + "lon_points = xr.DataArray(obs_lons, dims=\"points\")\n", "lon_points" ] }, @@ -177,7 +320,7 @@ }, "outputs": [], "source": [ - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\") # -- pointwise indexing" ] }, { @@ -195,38 +338,50 @@ }, "outputs": [], "source": [ - "da.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").dims" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{attention}\n", - "Please note that slices or sequences/arrays without named-dimensions are treated as if they have the same dimension which is indexed along.\n", - "```\n", - "\n", - "For example:" + "Now, let's plot the data for all stations." ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], "source": [ - "da.sel(lat=[20, 30, 40], lon=lon_points, method=\"nearest\")" + "da_air.sel(lat=lat_points, lon=lon_points, method=\"nearest\").plot(x='time', hue='points');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "```{warning}\n", - "If an indexer is a `DataArray()`, its coordinates should not conflict with the selected subpart of the target array (except for the explicitly indexed dimensions with `.loc`/`.sel`). Otherwise, `IndexError` will be raised!\n", - "```" + "## Exercises\n", + "\n", + "::::{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", + "\n", + ":::{admonition} Solution\n", + ":class: dropdown\n", + "```python\n", + "\n", + "indices = np.array([0, 2, 4])\n", + "\n", + "xs_da = xr.DataArray(indices, dims=\"points\")\n", + "ys_da = xr.DataArray(indices, dims=\"points\")\n", + "\n", + "subset_da = da.sel(x=xs_da, y=xs_da)\n", + "subset_da\n", + "```\n", + ":::\n", + "::::" ] }, { @@ -235,7 +390,14 @@ "source": [ "## Additional Resources\n", "\n", - "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n" + "- [Xarray Docs - Indexing and Selecting Data](https://docs.xarray.dev/en/stable/indexing.html)\n", + "\n", + "\n", + ":::{seealso}\n", + "- [Introductions to Fancy Indexing](https://jakevdp.github.io/PythonDataScienceHandbook/02.07-fancy-indexing.html)\n", + "- [NumPy Docs - Advanced Indexing](https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing)\n", + "\n", + ":::\n" ] } ], diff --git a/intermediate/cmip6-cloud.ipynb b/intermediate/remote_data/cmip6-cloud.ipynb similarity index 99% rename from intermediate/cmip6-cloud.ipynb rename to intermediate/remote_data/cmip6-cloud.ipynb index 31b45166..c9d05d9d 100644 --- a/intermediate/cmip6-cloud.ipynb +++ b/intermediate/remote_data/cmip6-cloud.ipynb @@ -5,7 +5,7 @@ "id": "0", "metadata": {}, "source": [ - "# Accessing remote data stored on the cloud\n", + "# Zarr in Cloud Object Storage\n", "\n", "In this tutorial, we'll cover the following:\n", "- Finding a cloud hosted Zarr archive of CMIP6 dataset(s)\n", diff --git a/intermediate/remote_data/index.md b/intermediate/remote_data/index.md new file mode 100644 index 00000000..a6110848 --- /dev/null +++ b/intermediate/remote_data/index.md @@ -0,0 +1,5 @@ +# Remote Data + +```{tableofcontents} + +``` diff --git a/intermediate/remote_data/remote-data.ipynb b/intermediate/remote_data/remote-data.ipynb new file mode 100644 index 00000000..c0aa5d8e --- /dev/null +++ b/intermediate/remote_data/remote-data.ipynb @@ -0,0 +1,377 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Access Patterns to Remote Data with *fsspec*\n", + "\n", + "Accessing remote data with xarray usually means working with cloud-optimized formats like Zarr or COGs, the [CMIP6 tutorial](remote-data.ipynb) shows this pattern in detail. These formats were designed to be efficiently accessed over the internet, however in many cases we might need to access data that is not available in such formats.\n", + "\n", + "This notebook will explore how we can leverage xarray's backends to access remote files. For this we will make use of [`fsspec`](https://github.com/fsspec/filesystem_spec), a powerful Python library that abstracts the internal implementation of remote storage systems into a uniform API that can be used by many file-format specific libraries.\n", + "\n", + "Before starting with remote data, it may be helpful to understand how xarray handles local files and how xarray backends work. The following diagram shows the different components involved in accessing data either locally or remote using the `h5netcdf` backend which uses a format specific library to access HDF5 files.\n", + "\n", + "![xarray-access(3)](https://gist.github.com/assets/717735/3c3c6801-11ed-43a4-98ea-636b7dd612d8)\n", + "\n", + "Let's consider a scenario where we have a local NetCDF4 file containing gridded data. NetCDF is a common file format used in scientific research for storing array-like data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "localPath = \"../../data/sst.mnmean.nc\"\n", + "\n", + "ds = xr.open_dataset(localPath)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "## xarray backends under the hood\n", + "\n", + "* What happened when we ran `xr.open_dataset(\"path-to-file\")`?\n", + "\n", + "As we know xarray is a very flexible and modular library. When we open a file, we are asking xarray to use one of its format specific engines to get the actual array data from the file into memory. File formats come in different flavors, from general purpose HDF5 to the very domain-specific ones like GRIB2. When we call `open_dataset()` the first thing xarray does is try to guess which of the preinstalled backends can handle this file, in this case we pass a string with a valid local path.\n", + "\n", + "We'll use a helper function to print a simplified call stack and see what's going on under the hood.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from IPython.display import Code\n", + "\n", + "\n", + "tracing_output = []\n", + "_match_pattern = \"xarray\"\n", + "\n", + "\n", + "def trace_calls(frame, event, arg):\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", + " func_line = code.co_firstlineno\n", + " if not func_name.startswith(\"_\") and _match_pattern in func_file:\n", + " tracing_output.append(f\"def {func_name}() at {func_file}:{func_line}\")\n", + " return trace_calls\n", + "\n", + "\n", + "# we enable tracing and call open_dataset()\n", + "sys.settrace(trace_calls)\n", + "ds = xr.open_dataset(localPath)\n", + "sys.settrace(None)\n", + "\n", + "# Print the trace with some syntax highlighting\n", + "Code(\" \\n\".join(tracing_output[0:10]), language='python')" + ] + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "### **What are we seeing?** \n", + "\n", + "* xarray uses `guess_engine()` to identify which backend can open the file.\n", + "* `guess_engine()` will loop through the preinstalled backends and will run `guess_can_open()`.\n", + "* if an engine can handle the file type it will verify that we are working with a local file.\n", + "* Once that we know which backend we'll use we invoke that backend implementation of `open_dataset()`.\n", + "\n", + "Let's tell xarray which backend we need for our local file. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], + "source": [ + "tracing_output = []\n", + "\n", + "sys.settrace(trace_calls)\n", + "ds = xr.open_dataset(localPath, engine=\"h5netcdf\")\n", + "sys.settrace(None)\n", + "\n", + "# Print the top 10 calls to public methods\n", + "Code(\" \\n\".join(tracing_output[0:10]), language='python')" + ] + }, + { + "cell_type": "markdown", + "id": "6", + "metadata": {}, + "source": [ + "> It is important to note that there are overlaps between the pre-installed backends in xarray. Many of these backends support the same formats (e.g., NetCDF-4), and xarray uses them in a specific order unless a particular backend is specified. For example, when we request the h5netcdf engine, xarray will not attempt to guess the backend. However, it will still check if the URI is remote, which will involve some calls to a context manager. By examining the call stack, we can observe the use of a file handler and a cache, which are crucial for efficiently accessing remote files." + ] + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": [ + "### Supported file formats by backend\n", + "\n", + "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", + "* **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", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], + "source": [ + "# Listing which backends we have available, if we install more they should show up here.\n", + "xr.backends.list_engines()" + ] + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": [ + "### Trying to access a file on cloud storage (AWS S3)\n", + "\n", + "Now let's try to open a file on a remote file system, this will fail and we'll take a look into why it failed and how we'll use fsspec to overcome this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " ds = xr.open_dataset(\"s3://its-live-data/test-space/sample-data/sst.mnmean.nc\")\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "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", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "## Supported format + Read from Buffers = Remote access \n", + "\n", + "Some of xarray's backends can read and write data to memory, this coupled with fsspec's ability to abstract remote files allows us to **access remote files as if they were local**. The following table helps us to identify if a backend can be used to access remote files with fsspec.\n", + "\n", + "\n", + "| Backend | HDF/NetCDF Support | Can Read from Buffer | Handles Own I/O |\n", + "|-----------------|--------------------|----------------------|-----------------|\n", + "| netCDF4 | Yes | No | Yes |\n", + "| scipy | Limited | Yes | Yes |\n", + "| pydap | Yes | No | No |\n", + "| h5netcdf | Yes | Yes | Yes |\n", + "| zarr | No | Yes | Yes |\n", + "| cfgrib | Yes | No | Yes |\n", + "| rasterio | Partial | Yes | No |\n", + "\n", + "\n", + "\n", + "**Can Read from Buffer**: Libraries that can read from buffers do not need to open a file using the operating system machinery and they allow the use of memory to open our files in whatever way we want as long as we have a seekable buffer (random access). \n", + "\n", + "**Handles Own I/O**: Some libraries have self contained code that can handle I/O, compression, codecs and data access. Some engines task their I/O to lower level libraries. This is the case with rasterio that uses GDAL to access raster files. If a Library is in control of its own I/O operations can be easily adapted to read from buffers.\n", + "\n", + "```{mermaid}\n", + "graph TD\n", + " A[\"netCDF-4 (.nc, .nc4) and most HDF5 files\"] -->|netcdf4| B[\"Remote Access: No\"]\n", + " A -->|h5netcdf| C[\"Remote Access: Yes\"]\n", + " \n", + " D[\"netCDF files (.nc, .cdf, .gz)\"] -->|scipy| E[\"Remote Access: Yes\"]\n", + " \n", + " F[\"zarr files (.zarr)\"] -->|zarr| G[\"Remote Access: Yes\"]\n", + "\n", + " H[\"OpenDAP\"] -->|pydap| I[\"Remote Access: Yes\"]\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "13", + "metadata": {}, + "source": [ + "## Remote Access and File Caching\n", + "\n", + "When we use fsspec to abstract a remote file we are in essence translating byte requests to HTTP range requests over the internet. An HTTP request is a costly I/O operation compared to accessing a local file. Because of this, it's common that libraries that handle over the network data transfers implement a cache to avoid requesting the same data over and over. In the case of fsspec there are different ways to ask the library to handle this **caching and this is one of the most relevant performance considerations** when we work with xarray and remote data.\n", + "\n", + "fsspec default cache is called `read-ahead` and as its name suggests it will read ahead of our request a fixed amount of bytes, this is good when we are working with text or tabular data but it's really an anti pattern when we work with scientific data formats. Benchmarks show that any of the caching schemas will perform better than using the default `read-ahead`.\n", + "\n", + "### fsspec caching implementations.\n", + "\n", + "#### simple cache + `open_local()`\n", + "\n", + "The simplest way to use fsspec is to cache remote files locally. Since we are using a local storage for our cache, backends like `netcdf4` will be reading from disk avoiding the issue of not being able to read directly from buffers. This pattern can be applied to different backends that don't support buffers with the disadvantage that we'll be caching whole files and using disk space.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], + "source": [ + "import fsspec\n", + "\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", + "\n", + "ds = xr.open_dataset(file, engine=\"netcdf4\")\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "15", + "metadata": {}, + "source": [ + "#### block cache + `open()`\n", + "\n", + "If our backend support reading from a buffer we can cache only the parts of the file that we are reading, this is useful but tricky. As we mentioned before fsspec default cache will request an overhead of 5MB ahead of the byte offset we request, and if we are reading small chunks from our file it will be really slow and incur in unnecessary transfers.\n", + "\n", + "Let's open the same file but using the `h5netcdf` engine and we'll use a block cache strategy that stores predefined block sizes from our remote file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], + "source": [ + "%%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", + "\n", + "fsspec_caching = {\n", + " \"cache_type\": \"blockcache\", # block cache stores blocks of fixed size and uses eviction using a LRU strategy.\n", + " \"block_size\": 8\n", + " * 1024\n", + " * 1024, # size in bytes per block, adjust depends on the file size but the recommended size is in the MB\n", + "}\n", + "\n", + "# Note that if we use a context, we'll close the file after the block so operations on xarray may fail if we don't load our data arrays.\n", + "with fs.open(uri, **fsspec_caching) as file:\n", + " ds = xr.open_dataset(file, engine=\"h5netcdf\")\n", + " mean = ds.sst.mean()\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "17", + "metadata": {}, + "source": [ + "### Reading data from cloud storage\n", + "\n", + "So far we have only used HTTP to access a remote file, however the commercial cloud has their own implementations with specific features. fsspec allows us to talk to different cloud storage implementations hiding these details from us and the libraries we use. Now we are going to access the same file using the S3 protocol. \n", + "\n", + "> Note: S3, Azure blob, etc all have their names and prefixes but under the hood they still work with the HTTP protocol.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "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", + "\n", + "fsspec_caching = {\n", + " \"cache_type\": \"blockcache\", # block cache stores blocks of fixed size and uses eviction using a LRU strategy.\n", + " \"block_size\": 8\n", + " * 1024\n", + " * 1024, # size in bytes per block, adjust depends on the file size but the recommended size is in the MB\n", + "}\n", + "\n", + "# we are not using a context, we can use ds until we manually close it.\n", + "ds = xr.open_dataset(fs.open(uri, **fsspec_caching), engine=\"h5netcdf\")\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "19", + "metadata": {}, + "source": [ + "## Key Takeaways\n", + "\n", + "1. **fsspec and remote access.**\n", + "\n", + ">fsspec is a Python library that provides a unified interface to various filesystems, enabling access to local, remote, and cloud storage systems.\n", + "It supports a wide range of protocols such as http, https, s3, gcs, ftp, and many more.\n", + "One of the key features of fsspec is its ability to cache remote files locally, improving performance by reducing latency and bandwidth usage.\n", + "\n", + "2. **xarray Backends.**\n", + "\n", + ">xarray backends offers flexible support for opening datasets stored in different formats and locations.\n", + "By leveraging various backends along with fsspec we can open, read, and analyze complex datasets efficiently, without worrying about the underlying file format or storage mechanism.\n", + "\n", + "3. **Combining fsspec with xarray**\n", + "\n", + "> xarray can work with fsspec filesystems to open and cache remote files and use caching strategies to optimize its data transfer.\n", + "\n", + "\n", + "\n", + "By leveraging these tools and techniques, you can efficiently manage and process large, remote datasets in a way that optimizes performance and accessibility." + ] + } + ], + "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/overview/get-started.md b/overview/get-started.md index fc4ca2be..ba33045d 100644 --- a/overview/get-started.md +++ b/overview/get-started.md @@ -4,18 +4,44 @@ # Get Started -Most of the tutorial content here is written as Jupyter Notebooks that mix +## Organization + +Tutorials are approximately divided into sections with increasing levels of complexity: `Fundamentals`, `Intermediate`, `Advanced`. You'll also find content specific to various `Workshops` hosted over the years, often with accompanying video recordings of instructors going over content and answering questions that come up. + +Most of the tutorial content is written as Jupyter Notebooks that mix code, text, visualization, and exercises. You can either browse rendered versions of these notebooks on this website, or _execute_ the code examples interactively. -You have two options for executing notebooks: +Many notebooks use special formatting ([Myst Markdown](https://mystmd.org/guide/quickstart-jupyter-lab-myst)) that renders best in a JupyterLab web interface. If you are new to JupyterLab, spend some time reviewing the [documentation and videos](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html). + +## Run code interactively + +### On the Cloud -**1. On the Cloud:** Clicking [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=overview/fundamental-path/index.ipynb) 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. +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) + +#### 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)! ``` -**1. On your computer:** Running tutorials on your computer requires some setup: +#### GitHub Codespaces + +This tutorial is available to run within [GitHub Codespaces](https://github.com/features/codespaces) - a preconfigured development environment running in Microsoft Azure. + +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial) + +☝️ Click the button above to go to options window to launch a GitHub codespace. + +You can choose from a selection of virtual machine types: 2 cores - 8 GB RAM should be sufficient for all code examples in this repository. +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. + +### 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 @@ -29,21 +55,3 @@ conda-lock install conda/conda-lock.yml --name xarray-tutorial conda activate xarray-tutorial jupyter lab ``` - -## Organization - -Tutorials are approximately divided into sections with increasing levels of complexity: `Fundamentals`, `Intermediate`, `Advanced`. You'll also find content specific to various `Workshops` hosted over the years, often with accompanying video recordings of instructors going over content and answering questions that come up. - -## Jupyter Lab - -JupyterLab is a next-generation web-based user interface for Project Jupyter. If you are new to this interface, spend some time reviewing the [documentation and videos](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html). - -## Jupyter Notebooks - -If you haven't used the Jupyter Notebooks before, the quick intro is - -1. There are two modes: command and edit -1. From command mode, press Enter to edit a cell (like this markdown cell) -1. From edit mode, press Esc to change to command mode -1. Press shift+enter to execute a cell and move to the next cell. -1. The toolbar has commands for executing, converting, and creating cells. diff --git a/workshops/online-tutorial-series/README.md b/workshops/online-tutorial-series/README.md index 01ebafaa..e777722c 100644 --- a/workshops/online-tutorial-series/README.md +++ b/workshops/online-tutorial-series/README.md @@ -1,4 +1,4 @@ -# Xarray Online Tutorial +# Xarray Online Tutorial 2020 Presented October 6 2020 by: diff --git a/workshops/scipy2023/README.md b/workshops/scipy2023/README.md index df5284f9..ea4009ee 100644 --- a/workshops/scipy2023/README.md +++ b/workshops/scipy2023/README.md @@ -14,6 +14,10 @@ Organized by: ## Instructions +:::{note} +You can access a recording of this tutorial [here](https://www.youtube.com/watch?v=L4FXcIOMlsY) +::: + ### Running Locally See instructions to set up the environment for running the tutorial material [here](get-started). diff --git a/workshops/scipy2024/README.md b/workshops/scipy2024/README.md deleted file mode 100644 index ee025838..00000000 --- a/workshops/scipy2024/README.md +++ /dev/null @@ -1,76 +0,0 @@ -# SciPy 2024 - -## Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis - -Organized by: - -- Scott Henderson (Univ. Washington) -- Jessica Scheick (Univ. New Hampshire) -- Negin Sobhani (National Center for Atmospheric Research) -- Tom Nicholas [C]worthy -- Max Jones (CarbonPlan) -- Wietze Suijker (Space Intelligence) - -## Learning Goals - -- Learn how to leverage Xarray’s powerful backend and extension capabilities to customize workflows and open a variety of scientific datasets -- Effectively use Xarray’s multidimensional indexing operations -- Understand how Xarray can wrap other array types in the scientific Python ecosystem -- Understand how to apply custom functions to Xarray data structures -- Orient yourself to Xarray resources to continue on your Xarray journey! - -## Outline - -These are links to rendered webpages if you'd just like to read. Further down this page is an 'Instructions' section that explains how to spin up an interactive computing environment. - -```{dropdown} Introduction -{doc}`../../overview/get-started` - -{doc}`../../fundamentals/02.1_indexing_Basic` - -TODO: Domain-agnositic data model, backends/engines -``` - -```{dropdown} Indexing and Computation -{doc}`../../intermediate/indexing/advanced-indexing` - -{doc}`../../intermediate/01-high-level-computation-patterns` - -{doc}`../../intermediate/xarray_and_dask` -``` - -```{dropdown} Extending & Customizing Xarray -{doc}`../../advanced/01_accessor_examples.ipynb` - -{doc}`../../advanced/1.Backend_without_Lazy_Loading.ipynb -``` - -```{dropdown} Synthesis -TODO: A Guided 'bring-your-own data exercise -``` - -## Instructions - -### Running Locally - -See instructions to set up the environment for running the tutorial material [here](get-started). - -### 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. - -[![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) - -☝️ 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. -You can choose from a selection of virtual machine types: 2 cores - 4 GB RAM - 32 GB storage, and 4 cores - 8 GB RAM - 32GB storage. -Additionally, you are able to chose from various Dev container configuration, for this specific workshop, please ensure that `Scipy2024` is selected. -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: - -- [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. diff --git a/workshops/scipy2024/index.ipynb b/workshops/scipy2024/index.ipynb index e626e17d..9cf8f355 100644 --- a/workshops/scipy2024/index.ipynb +++ b/workshops/scipy2024/index.ipynb @@ -5,18 +5,26 @@ "id": "0", "metadata": {}, "source": [ - "\n", + "# SciPy 2024\n", "\n", + "## Welcome to the Xarray SciPy 2024 Tutorial! \n", "\n", - "# Welcome to the Xarray SciPy 2024 Tutorial! \n", + "\n", "\n", "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n", "\n", - "July 8, 13:30–17:30 (US/Pacific), Room 317\n", + "July 8, 13:30–17:30 (US/Pacific), Tacoma Convention Center Ballroom B/C\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 won't cover it all today, but instead will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\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", - "## *Draft* Schedule \n", + ":::{admonition} Learning Goals\n", + "- Orient yourself to Xarray resources to continue on your Xarray journey!\n", + "- Effectively use Xarray’s multidimensional indexing and computational patterns\n", + "- Understand how Xarray integrates with other libraries in the scientific Python ecosystem\n", + "- Learn how to leverage Xarray’s powerful backend and extension capabilities to customize workflows and open a variety of scientific datasets\n", + ":::\n", + "\n", + "## Schedule \n", "\n", "*Times in US/Pacific Timezone (Tacoma, WA)\n", "\n", @@ -25,26 +33,57 @@ "| Topic | Time | Notebook Links | \n", "| :- | - | - | \n", "| Introduction and Setup | 1:30 (10 min) | --- | \n", - "| Xarray Data Model, Backends, Extensions | 1:40 (40 min) | [Quick Introduction to Indexing](../../fundamentals/02.1_indexing_Basic.ipynb)
[Boolean Indexing & Masking](../../intermediate/indexing/boolean-masking-indexing.ipynb) | \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", - "| Computational Patterns | 2:30 (50 min) | [Computation 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/01-high-level-computation-patterns.ipynb)
| \n", "| *10 minute Break* | \n", - "| Wrapping other arrays | 3:30 (50 min) | [Xarray and Dask](../../intermediate/xarray_and_dask.ipynb) | \n", + "| Xarray Integrations and Extensions | 3:30 (50 min) | [The Xarray Ecosystem](../../intermediate/xarray_ecosystem.ipynb) | \n", "| *10 minute Break* | \n", - "| Synthesis, Explore your data! | 4:30 (30 min)


5:00 (30 min) | Apply what you've learned, let's work together with your own data |\n", - "| | **End 5:30** | |\n", + "| Backends & Remote data| 4:30 (50 min) | [Remote Data](../../intermediate/remote_data/remote-data.ipynb) |\n", + "| | End 5:30 | |\n", + "\n", + "\n", + "### Tutorial Setup\n", + "\n", + "We recommend using a preconfigured GitHub Codespace for this tutorial. This section describes how to access and manage a GitHub Codespace.\n", + "\n", + ":::{note}\n", + "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", + "\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", + "☝️ Click the button above to go to options window to launch a Github Codespace.\n", + "\n", + "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).**\n", + "\n", + "Once your Codespace is launched, the following happens:\n", + "\n", + "- [Visual Studio Code](https://code.visualstudio.com/) Interface will open up within your browser.\n", + "- A built in terminal will open and it will execute `jupyter lab` automatically.\n", + "- Once you see a url to click within the terminal, simply `cmd + click` the given url.\n", + "- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface.\n", + "\n", + ":::{warning}\n", + "Consider Codespaces as ephemeral environments. You may lose your connection and any edits you make.\n", + ":::\n", "\n", "\n", "## Thanks for attending!\n", "\n", - "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)" + "Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)\n", + "\n", + "### SciPy 2024 Organized by:\n", + "\n", + "- Scott Henderson (Univ. Washington)\n", + "- Jessica Scheick (Univ. New Hampshire)\n", + "- Negin Sobhani (National Center for Atmospheric Research)\n", + "- Tom Nicholas [C]worthy\n", + "- Max Jones (CarbonPlan)\n", + "- Wietze Suijker (Space Intelligence)" ] - }, - { - "cell_type": "markdown", - "id": "1", - "metadata": {}, - "source": [] } ], "metadata": { diff --git a/workshops/thinking-like-xarray/README.md b/workshops/thinking-like-xarray/README.md index 7b26493e..3d9fdc70 100644 --- a/workshops/thinking-like-xarray/README.md +++ b/workshops/thinking-like-xarray/README.md @@ -1,4 +1,4 @@ -# Thinking like Xarray +# Thinking like Xarray 2022 Presented March 2022 for the [NCAR Python Seminar Series](https://ncar.github.io/esds/posts/2022/Thinking-with-Xarray/) by Deepak Cheerian