[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]>

CONTRIBUTING.md

@@ -1 +1 @@
-Xarray's contributor guidelines [can be found in our online documentation](http://xarray.pydata.org/en/stable/contributing.html)
+Xarray's contributor guidelines [can be found in our online documentation](http://docs.xarray.dev/en/stable/contributing.html)

README.rst

@@ -6,7 +6,7 @@ xarray: N-D labeled arrays and datasets
 .. image:: https://codecov.io/gh/pydata/xarray/branch/main/graph/badge.svg
    :target: https://codecov.io/gh/pydata/xarray
 .. image:: https://readthedocs.org/projects/xray/badge/?version=latest
-   :target: https://xarray.pydata.org/
+   :target: https://docs.xarray.dev/
 .. image:: https://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat
   :target: https://pandas.pydata.org/speed/xarray/
 .. image:: https://img.shields.io/pypi/v/xarray.svg
@@ -69,12 +69,12 @@ powerful and concise interface. For example:
 Documentation
 -------------
 
-Learn more about xarray in its official documentation at https://xarray.pydata.org/
+Learn more about xarray in its official documentation at https://docs.xarray.dev/
 
 Contributing
 ------------
 
-You can find information about contributing to xarray at our `Contributing page <https://xarray.pydata.org/en/latest/contributing.html#>`_.
+You can find information about contributing to xarray at our `Contributing page <https://docs.xarray.dev/en/latest/contributing.html#>`_.
 
 Get in touch
 ------------

asv_bench/asv.conf.json

@@ -7,7 +7,7 @@
     "project": "xarray",
 
     // The project's homepage
-    "project_url": "http://xarray.pydata.org/",
+    "project_url": "http://docs.xarray.dev/",
 
     // The URL or local path of the source code repository for the
     // project being benchmarked

ci/install-upstream-wheels.sh

@@ -40,7 +40,7 @@ python -m pip install \
     git+https://github.com/dask/distributed \
     git+https://github.com/zarr-developers/zarr \
     git+https://github.com/Unidata/cftime \
-    git+https://github.com/mapbox/rasterio \
+    git+https://github.com/rasterio/rasterio \
     git+https://github.com/pypa/packaging \
     git+https://github.com/hgrecco/pint \
     git+https://github.com/pydata/bottleneck \

design_notes/flexible_indexes_notes.md

@@ -133,7 +133,7 @@ A possible, more explicit solution to reuse a `pandas.MultiIndex` in a DataArray
 
 New indexes may also be built from existing sets of coordinates or variables in a Dataset/DataArray using the `.set_index()` method.
 
-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.
+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.
 
 The new signature may look like one of these:
 

doc/README.rst

@@ -3,4 +3,4 @@
 xarray
 ------
 
-You can find information about building the docs at our `Contributing page <http://xarray.pydata.org/en/latest/contributing.html#contributing-to-the-documentation>`_.
+You can find information about building the docs at our `Contributing page <http:/docs.xarray.dev/en/latest/contributing.html#contributing-to-the-documentation>`_.

doc/conf.py

@@ -260,12 +260,12 @@
 
 
 # configuration for sphinxext.opengraph
-ogp_site_url = "https://xarray.pydata.org/en/latest/"
-ogp_image = "https://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png"
+ogp_site_url = "https://docs.xarray.dev/en/latest/"
+ogp_image = "https://docs.xarray.dev/en/stable/_static/dataset-diagram-logo.png"
 ogp_custom_meta_tags = [
     '<meta name="twitter:card" content="summary_large_image" />',
     '<meta property="twitter:site" content="@xarray_dev" />',
-    '<meta name="image" property="og:image" content="https://xarray.pydata.org/en/stable/_static/dataset-diagram-logo.png" />',
+    '<meta name="image" property="og:image" content="https://docs.xarray.dev/en/stable/_static/dataset-diagram-logo.png" />',
 ]
 
 # Redirects for pages that were moved to new locations

doc/contributing.rst

@@ -95,14 +95,14 @@ version control to allow many people to work together on the project.
 
 Some great resources for learning Git:
 
-* the `GitHub help pages <http://help.github.com/>`_.
-* the `NumPy's documentation <http://docs.scipy.org/doc/numpy/dev/index.html>`_.
-* Matthew Brett's `Pydagogue <http://matthew-brett.github.io/pydagogue/>`_.
+* the `GitHub help pages <https://help.github.com/>`_.
+* the `NumPy's documentation <https://numpy.org/doc/stable/dev/index.html>`_.
+* Matthew Brett's `Pydagogue <https://matthew-brett.github.io/pydagogue/>`_.
 
 Getting started with Git
 ------------------------
 
-`GitHub has instructions <http://help.github.com/set-up-git-redirect>`__ for installing git,
+`GitHub has instructions <https://help.github.com/set-up-git-redirect>`__ for installing git,
 setting up your SSH key, and configuring git.  All these steps need to be completed before
 you can work seamlessly between your local repository and GitHub.
 
@@ -455,7 +455,7 @@ it is worth getting in the habit of writing tests ahead of time so that this is
 Like many packages, *xarray* uses `pytest
 <http://doc.pytest.org/en/latest/>`_ and the convenient
 extensions in `numpy.testing
-<http://docs.scipy.org/doc/numpy/reference/routines.testing.html>`_.
+<https://numpy.org/doc/stable/reference/routines.testing.html>`_.
 
 Writing tests
 ~~~~~~~~~~~~~
@@ -855,15 +855,15 @@ GitHub. To delete it there do::
 PR checklist
 ------------
 
-- **Properly comment and document your code.** See `"Documenting your code" <https://xarray.pydata.org/en/stable/contributing.html#documenting-your-code>`_.
-- **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>`_.
+- **Properly comment and document your code.** See `"Documenting your code" <https://docs.xarray.dev/en/stable/contributing.html#documenting-your-code>`_.
+- **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>`_.
 - **Test your code**.
 
-    - Write new tests if needed. See `"Test-driven development/code writing" <https://xarray.pydata.org/en/stable/contributing.html#test-driven-development-code-writing>`_.
+    - Write new tests if needed. See `"Test-driven development/code writing" <https://docs.xarray.dev/en/stable/contributing.html#test-driven-development-code-writing>`_.
     - 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.
     - 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.
 
-- **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.
+- **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.
 
     - Run ``pre-commit run --all-files`` in the root directory. This may modify some files. Confirm and commit any formatting changes.
 

doc/ecosystem.rst

@@ -20,12 +20,12 @@ Geosciences
 - `infinite-diff <https://github.com/spencerahill/infinite-diff>`_: xarray-based finite-differencing, focused on gridded climate/meteorology data
 - `marc_analysis <https://github.com/darothen/marc_analysis>`_: Analysis package for CESM/MARC experiments and output.
 - `MetPy <https://unidata.github.io/MetPy/dev/index.html>`_: A collection of tools in Python for reading, visualizing, and performing calculations with weather data.
-- `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).
-- `OGGM <http://oggm.org/>`_: Open Global Glacier Model
+- `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).
+- `OGGM <https://oggm.org/>`_: Open Global Glacier Model
 - `Oocgcm <https://oocgcm.readthedocs.io/>`_: Analysis of large gridded geophysical datasets
 - `Open Data Cube <https://www.opendatacube.org/>`_: Analysis toolkit of continental scale Earth Observation data from satellites.
 - `Pangaea: <https://pangaea.readthedocs.io/en/latest/>`_: xarray extension for gridded land surface & weather model output).
-- `Pangeo <https://pangeo-data.github.io>`_: A community effort for big data geoscience in the cloud.
+- `Pangeo <https://pangeo.io>`_: A community effort for big data geoscience in the cloud.
 - `PyGDX <https://pygdx.readthedocs.io/en/latest/>`_: Python 3 package for
   accessing data stored in GAMS Data eXchange (GDX) files. Also uses a custom
   subclass.
@@ -41,13 +41,13 @@ Geosciences
 - `wradlib <https://wradlib.org/>`_: An Open Source Library for Weather Radar Data Processing.
 - `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.
 - `xarray-simlab <https://xarray-simlab.readthedocs.io>`_: xarray extension for computer model simulations.
-- `xarray-spatial <https://makepath.github.io/xarray-spatial>`_: Numba-accelerated raster-based spatial processing tools (NDVI, curvature, zonal-statistics, proximity, hillshading, viewshed, etc.)
-- `xarray-topo <https://gitext.gfz-potsdam.de/sec55-public/xarray-topo>`_: xarray extension for topographic analysis and modelling.
+- `xarray-spatial <https://xarray-spatial.org/>`_: Numba-accelerated raster-based spatial processing tools (NDVI, curvature, zonal-statistics, proximity, hillshading, viewshed, etc.)
+- `xarray-topo <https://xarray-topo.readthedocs.io/>`_: xarray extension for topographic analysis and modelling.
 - `xbpch <https://github.com/darothen/xbpch>`_: xarray interface for bpch files.
 - `xclim <https://xclim.readthedocs.io/>`_: A library for calculating climate science indices with unit handling built from xarray and dask.
 - `xESMF <https://pangeo-xesmf.readthedocs.io/>`_: Universal regridder for geospatial data.
 - `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.
-- `xmitgcm <http://xgcm.readthedocs.io/>`_: a python package for reading `MITgcm <http://mitgcm.org/>`_ binary MDS files into xarray data structures.
+- `xmitgcm <http://xmitgcm.readthedocs.io/>`_: a python package for reading `MITgcm <https://mitgcm.org/>`_ binary MDS files into xarray data structures.
 - `xnemogcm <https://github.com/rcaneill/xnemogcm/>`_: a package to read `NEMO <https://nemo-ocean.eu/>`_ output files and add attributes to interface with xgcm.
 
 Machine Learning
@@ -57,6 +57,7 @@ Machine Learning
 - `Elm <https://ensemble-learning-models.readthedocs.io>`_: Parallel machine learning on xarray data structures
 - `sklearn-xarray (1) <https://phausamann.github.io/sklearn-xarray>`_: Combines scikit-learn and xarray (1).
 - `sklearn-xarray (2) <https://sklearn-xarray.readthedocs.io/en/latest/>`_: Combines scikit-learn and xarray (2).
+- `xbatcher <https://xbatcher.readthedocs.io>`_: Batch Generation from Xarray Datasets.
 
 Other domains
 ~~~~~~~~~~~~~
@@ -90,7 +91,7 @@ Visualization
 
 Non-Python projects
 ~~~~~~~~~~~~~~~~~~~
-- `xframe <https://github.com/QuantStack/xframe>`_: C++ data structures inspired by xarray.
+- `xframe <https://github.com/xtensor-stack/xframe>`_: C++ data structures inspired by xarray.
 - `AxisArrays <https://github.com/JuliaArrays/AxisArrays.jl>`_ and
   `NamedArrays <https://github.com/davidavdav/NamedArrays.jl>`_: similar data structures for Julia.
 

doc/gallery.rst

@@ -116,7 +116,7 @@ External Examples
     ---
     :img-top: https://avatars.githubusercontent.com/u/60833341?s=200&v=4
     ++++
-    .. link-button:: http://gallery.pangeo.io/
+    .. link-button:: https://gallery.pangeo.io/
         :type: url
         :text: Xarray and dask on the cloud with Pangeo
         :classes: btn-outline-dark btn-block stretched-link

doc/gallery/plot_rasterio.py

@@ -23,7 +23,7 @@
 import xarray as xr
 
 # Read the data
-url = "https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif"
+url = "https://github.com/rasterio/rasterio/raw/master/tests/data/RGB.byte.tif"
 da = xr.open_rasterio(url)
 
 # Compute the lon/lat coordinates with pyproj

doc/gallery/plot_rasterio_rgb.py

@@ -18,7 +18,7 @@
 import xarray as xr
 
 # Read the data
-url = "https://github.com/mapbox/rasterio/raw/master/tests/data/RGB.byte.tif"
+url = "https://github.com/rasterio/rasterio/raw/master/tests/data/RGB.byte.tif"
 da = xr.open_rasterio(url)
 
 # The data is in UTM projection. We have to set it manually until

doc/getting-started-guide/faq.rst

@@ -136,7 +136,7 @@ With xarray, we draw a firm line between labels that the library understands
 example, we do not automatically interpret and enforce units or `CF
 conventions`_. (An exception is serialization to and from netCDF files.)
 
-.. _CF conventions: http://cfconventions.org/latest.html
+.. _CF conventions: https://cfconventions.org/latest.html
 
 An implication of this choice is that we do not propagate ``attrs`` through
 most operations unless explicitly flagged (some methods have a ``keep_attrs``
@@ -155,7 +155,7 @@ xarray, and have contributed a number of improvements and fixes upstream. Xarray
 does not yet support all of netCDF4-python's features, such as modifying files
 on-disk.
 
-__ https://github.com/Unidata/netcdf4-python
+__ https://unidata.github.io/netcdf4-python/
 
 Iris_ (supported by the UK Met office) provides similar tools for in-
 memory manipulation of labeled arrays, aimed specifically at weather and
@@ -166,13 +166,13 @@ different approaches to handling metadata: Iris strictly interprets
 integration with Cartopy_.
 
 .. _Iris: https://scitools-iris.readthedocs.io/en/stable/
-.. _Cartopy: http://scitools.org.uk/cartopy/docs/latest/
+.. _Cartopy: https://scitools.org.uk/cartopy/docs/latest/
 
 `UV-CDAT`__ is another Python library that implements in-memory netCDF-like
 variables and `tools for working with climate data`__.
 
-__ http://uvcdat.llnl.gov/
-__ http://drclimate.wordpress.com/2014/01/02/a-beginners-guide-to-scripting-with-uv-cdat/
+__ https://uvcdat.llnl.gov/
+__ https://drclimate.wordpress.com/2014/01/02/a-beginners-guide-to-scripting-with-uv-cdat/
 
 We think the design decisions we have made for xarray (namely, basing it on
 pandas) make it a faster and more flexible data analysis tool. That said, Iris
@@ -197,7 +197,7 @@ would certainly appreciate it. We recommend two citations.
 
      - Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and
        Datasets in Python. Journal of Open Research Software. 5(1), p.10.
-       DOI: http://doi.org/10.5334/jors.148
+       DOI: https://doi.org/10.5334/jors.148
 
        Here’s an example of a BibTeX entry::
 
@@ -210,7 +210,7 @@ would certainly appreciate it. We recommend two citations.
              year      = {2017},
              publisher = {Ubiquity Press},
              doi       = {10.5334/jors.148},
-             url       = {http://doi.org/10.5334/jors.148}
+             url       = {https://doi.org/10.5334/jors.148}
            }
 
   2. You may also want to cite a specific version of the xarray package. We