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[docs] update urls throughout documentation (pydata#6262)
* update urls throughout documentation
* more url updates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update doc/internals/how-to-add-new-backend.rst
* Apply suggestions from code review
Co-authored-by: Anderson Banihirwe <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Anderson Banihirwe <[email protected]>
Copy file name to clipboardExpand all lines: design_notes/flexible_indexes_notes.md
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@@ -133,7 +133,7 @@ A possible, more explicit solution to reuse a `pandas.MultiIndex` in a DataArray
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New indexes may also be built from existing sets of coordinates or variables in a Dataset/DataArray using the `.set_index()` method.
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The [current signature](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.set_index.html#xarray.DataArray.set_index) of `.set_index()` is tailored to `pandas.MultiIndex` and tied to the concept of a dimension-index. It is therefore hardly reusable as-is in the context of flexible indexes proposed here.
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The [current signature](http://docs.xarray.dev/en/stable/generated/xarray.DataArray.set_index.html#xarray.DataArray.set_index) of `.set_index()` is tailored to `pandas.MultiIndex` and tied to the concept of a dimension-index. It is therefore hardly reusable as-is in the context of flexible indexes proposed here.
Copy file name to clipboardExpand all lines: doc/README.rst
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xarray
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------
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You can find information about building the docs at our `Contributing page <http://xarray.pydata.org/en/latest/contributing.html#contributing-to-the-documentation>`_.
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You can find information about building the docs at our `Contributing page <http:/docs.xarray.dev/en/latest/contributing.html#contributing-to-the-documentation>`_.
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PR checklist
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------------
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-**Properly comment and document your code.** See `"Documenting your code" <https://xarray.pydata.org/en/stable/contributing.html#documenting-your-code>`_.
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-**Test that the documentation builds correctly** by typing ``make html`` in the ``doc`` directory. This is not strictly necessary, but this may be easier than waiting for CI to catch a mistake. See `"Contributing to the documentation" <https://xarray.pydata.org/en/stable/contributing.html#contributing-to-the-documentation>`_.
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-**Properly comment and document your code.** See `"Documenting your code" <https://docs.xarray.dev/en/stable/contributing.html#documenting-your-code>`_.
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-**Test that the documentation builds correctly** by typing ``make html`` in the ``doc`` directory. This is not strictly necessary, but this may be easier than waiting for CI to catch a mistake. See `"Contributing to the documentation" <https://docs.xarray.dev/en/stable/contributing.html#contributing-to-the-documentation>`_.
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-**Test your code**.
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- Write new tests if needed. See `"Test-driven development/code writing" <https://xarray.pydata.org/en/stable/contributing.html#test-driven-development-code-writing>`_.
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- Write new tests if needed. See `"Test-driven development/code writing" <https://docs.xarray.dev/en/stable/contributing.html#test-driven-development-code-writing>`_.
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- Test the code using `Pytest <http://doc.pytest.org/en/latest/>`_. Running all tests (type ``pytest`` in the root directory) takes a while, so feel free to only run the tests you think are needed based on your PR (example: ``pytest xarray/tests/test_dataarray.py``). CI will catch any failing tests.
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- By default, the upstream dev CI is disabled on pull request and push events. You can override this behavior per commit by adding a <tt>[test-upstream]</tt> tag to the first line of the commit message. For documentation-only commits, you can skip the CI per commit by adding a "[skip-ci]" tag to the first line of the commit message.
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-**Properly format your code** and verify that it passes the formatting guidelines set by `Black <https://black.readthedocs.io/en/stable/>`_ and `Flake8<http://flake8.pycqa.org/en/latest/>`_. See `"Code formatting" <https://xarray.pydata.org/en/stablcontributing.html#code-formatting>`_. You can use `pre-commit <https://pre-commit.com/>`_ to run these automatically on each commit.
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-**Properly format your code** and verify that it passes the formatting guidelines set by `Black <https://black.readthedocs.io/en/stable/>`_ and `Flake8<http://flake8.pycqa.org/en/latest/>`_. See `"Code formatting" <https://docs.xarray.dev/en/stablcontributing.html#code-formatting>`_. You can use `pre-commit <https://pre-commit.com/>`_ to run these automatically on each commit.
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- Run ``pre-commit run --all-files`` in the root directory. This may modify some files. Confirm and commit any formatting changes.
Copy file name to clipboardExpand all lines: doc/ecosystem.rst
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- `infinite-diff <https://github.com/spencerahill/infinite-diff>`_: xarray-based finite-differencing, focused on gridded climate/meteorology data
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- `marc_analysis <https://github.com/darothen/marc_analysis>`_: Analysis package for CESM/MARC experiments and output.
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- `MetPy <https://unidata.github.io/MetPy/dev/index.html>`_: A collection of tools in Python for reading, visualizing, and performing calculations with weather data.
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- `MPAS-Analysis <http://mpas-analysis.readthedocs.io>`_: Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Accelerated Climate Model for Energy (ACME).
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- `OGGM <http://oggm.org/>`_: Open Global Glacier Model
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- `MPAS-Analysis <https://mpas-dev.github.io/MPAS-Analysis>`_: Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Accelerated Climate Model for Energy (ACME).
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- `OGGM <https://oggm.org/>`_: Open Global Glacier Model
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- `Oocgcm <https://oocgcm.readthedocs.io/>`_: Analysis of large gridded geophysical datasets
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- `Open Data Cube <https://www.opendatacube.org/>`_: Analysis toolkit of continental scale Earth Observation data from satellites.
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- `Pangaea: <https://pangaea.readthedocs.io/en/latest/>`_: xarray extension for gridded land surface & weather model output).
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- `Pangeo <https://pangeo-data.github.io>`_: A community effort for big data geoscience in the cloud.
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- `Pangeo <https://pangeo.io>`_: A community effort for big data geoscience in the cloud.
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- `PyGDX <https://pygdx.readthedocs.io/en/latest/>`_: Python 3 package for
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accessing data stored in GAMS Data eXchange (GDX) files. Also uses a custom
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subclass.
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- `wradlib <https://wradlib.org/>`_: An Open Source Library for Weather Radar Data Processing.
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- `wrf-python <https://wrf-python.readthedocs.io/>`_: A collection of diagnostic and interpolation routines for use with output of the Weather Research and Forecasting (WRF-ARW) Model.
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- `xarray-simlab <https://xarray-simlab.readthedocs.io>`_: xarray extension for computer model simulations.
- `xarray-topo <https://xarray-topo.readthedocs.io/>`_: xarray extension for topographic analysis and modelling.
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- `xbpch <https://github.com/darothen/xbpch>`_: xarray interface for bpch files.
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- `xclim <https://xclim.readthedocs.io/>`_: A library for calculating climate science indices with unit handling built from xarray and dask.
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- `xESMF <https://pangeo-xesmf.readthedocs.io/>`_: Universal regridder for geospatial data.
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- `xgcm <https://xgcm.readthedocs.io/>`_: Extends the xarray data model to understand finite volume grid cells (common in General Circulation Models) and provides interpolation and difference operations for such grids.
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- `xmitgcm <http://xgcm.readthedocs.io/>`_: a python package for reading `MITgcm <http://mitgcm.org/>`_ binary MDS files into xarray data structures.
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- `xmitgcm <http://xmitgcm.readthedocs.io/>`_: a python package for reading `MITgcm <https://mitgcm.org/>`_ binary MDS files into xarray data structures.
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- `xnemogcm <https://github.com/rcaneill/xnemogcm/>`_: a package to read `NEMO <https://nemo-ocean.eu/>`_ output files and add attributes to interface with xgcm.
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Machine Learning
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- `Elm <https://ensemble-learning-models.readthedocs.io>`_: Parallel machine learning on xarray data structures
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- `sklearn-xarray (1) <https://phausamann.github.io/sklearn-xarray>`_: Combines scikit-learn and xarray (1).
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- `sklearn-xarray (2) <https://sklearn-xarray.readthedocs.io/en/latest/>`_: Combines scikit-learn and xarray (2).
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- `xbatcher <https://xbatcher.readthedocs.io>`_: Batch Generation from Xarray Datasets.
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Other domains
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~~~~~~~~~~~~~
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Non-Python projects
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~~~~~~~~~~~~~~~~~~~
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- `xframe <https://github.com/QuantStack/xframe>`_: C++ data structures inspired by xarray.
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- `xframe <https://github.com/xtensor-stack/xframe>`_: C++ data structures inspired by xarray.
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- `AxisArrays <https://github.com/JuliaArrays/AxisArrays.jl>`_ and
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`NamedArrays <https://github.com/davidavdav/NamedArrays.jl>`_: similar data structures for Julia.
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