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add support for preliz distributions and update dependencies (#70)
* add support for preliz distributions and update dependencies * update rtd config * support scipy RVs and add type hints * update version and rerun notebooks
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‎.github/workflows/publish.yml

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- v*
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jobs:
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publish:
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build-package:
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runs-on: ubuntu-latest
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permissions:
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# write attestations and id-token are necessary for attest-build-provenance-github
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attestations: write
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id-token: write
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install build dependencies
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run: python -m pip install build
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- name: Build package
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run: python -m build
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fetch-depth: 0
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persist-credentials: false
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- uses: hynek/build-and-inspect-python-package@v2
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with:
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# Prove that the packages were built in the context of this workflow.
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attest-build-provenance-github: true
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publish:
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runs-on: ubuntu-latest
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
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# Use the `release` GitHub environment to protect the Trusted Publishing (OIDC)
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# workflow by requiring signoff from a maintainer.
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environment:
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name: publish
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url: https://pypi.org/p/xarray-einstats
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needs: build-package
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permissions:
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# write id-token is necessary for trusted publishing (OIDC)
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id-token: write
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steps:
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- name: Download Distribution Artifacts
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uses: actions/download-artifact@v4
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with:
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# The build-and-inspect-python-package action invokes upload-artifact.
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# These are the correct arguments from that action.
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name: Packages
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path: dist
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')

‎.github/workflows/test.yml

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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.11", "3.12", "3.13"]
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fail-fast: false
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steps:
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- uses: actions/checkout@v4

‎.readthedocs.yaml

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version: 2
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build:
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os: ubuntu-22.04
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os: ubuntu-24.04
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tools:
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python: "3.10"
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python: "3.12"
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sphinx:
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fail_on_warning: true
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configuration: docs/source/conf.py
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install:

‎docs/source/api/linalg.md

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solve
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inv
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pinv
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```
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## Convenience functions

‎docs/source/changelog.md

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# Change Log
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## v0.9.0 (2025 May 22)
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### New features
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* Support PreliZ distributions as input to RV wrappers {pull}`70`
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* Support new SciPy random variables as input to RV wrappers {pull}`70`
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* Allow specifying the shape of `.rvs` output through `coords` argument {pull}`70`
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### Maintenance and fixes
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* Update publish workflow to follow best practices {pull}`70`
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* Use `dim` in `xarray.dot` instead of deprecated `dims` {pull}`70`
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* Add type hints from docstrings through `docstub` {pull}`70`
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* Remove coordinate values of sorted dimension in `sort` {pull}`70`
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## v0.8.0 (2024 Sep 19)
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### New features
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* Add `numpy.linalg.pinv` wrapper {pull}`63`

‎docs/source/conf.py

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numpydoc_show_class_members = False
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numpydoc_xref_param_type = True
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numpydoc_xref_ignore = {"of", "or", "optional", "scalar"}
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numpydoc_xref_ignore = {"of", "or", "optional"}
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singulars = ("int", "list", "dict", "float")
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numpydoc_xref_aliases = {
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"DataArray": ":class:`xarray.DataArray`",
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"Dataset": ":class:`xarray.Dataset`",
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"pattern_list": "list of str, list or dict",
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"DimHandler": ":class:`~xarray_einstats.einops.DimHandler`",
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**{f"{singular}s": f":any:`{singular}s <{singular}>`" for singular in singulars},
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"scalar": ":term:`numpy:scalar`",
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"iterable": ":term:`python:iterable`",
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"sequence": ":term:`python:sequence`",
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"mapping": ":term:`python:mapping`",
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"hashable": ":term:`python:hashable`",
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}
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# Include the example source for plots in API docs

‎docs/source/tutorials/einops-basics-port.ipynb

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" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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"\n",
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"html[theme=dark],\n",
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"body[data-theme=dark],\n",
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"html[theme=\"dark\"],\n",
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"html[data-theme=\"dark\"],\n",
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"body[data-theme=\"dark\"],\n",
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"body.vscode-dark {\n",
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" --xr-font-color0: rgba(255, 255, 255, 1);\n",
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" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
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" --xr-border-color: #1f1f1f;\n",
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".xr-sections {\n",
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" grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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".xr-section-item {\n",
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".xr-section-item input {\n",
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".xr-section-item input:focus + label {\n",
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" border: 2px solid var(--xr-font-color0);\n",
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"}\n",
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"\n",
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".xr-section-item input:enabled + label:hover {\n",
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".xr-section-summary-in + label:before {\n",
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" stroke: currentColor;\n",
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"}\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1MB\n",
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"Dimensions: (batch: 6, height: 96, width: 96, channel: 3)\n",
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"Dimensions without coordinates: batch, height, width, channel\n",
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"Data variables:\n",
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" ims (batch, height, width, channel) float64 1.0 0.902 ... 1.0 0.8039</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3eec4040-5856-441f-a836-5e03835a0b88' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3eec4040-5856-441f-a836-5e03835a0b88' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>batch</span>: 6</li><li><span>height</span>: 96</li><li><span>width</span>: 96</li><li><span>channel</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c5bf0d5-07dc-410d-bccd-6bfff020901b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c5bf0d5-07dc-410d-bccd-6bfff020901b' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-acd7bcef-aff7-4348-9b27-01a4199bfc36' class='xr-section-summary-in' type='checkbox' checked><label for='section-acd7bcef-aff7-4348-9b27-01a4199bfc36' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ims</span></div><div class='xr-var-dims'>(batch, height, width, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.902 0.902 ... 1.0 1.0 0.8039</div><input id='attrs-f0841d19-41a9-4c43-8827-96261613a4f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0841d19-41a9-4c43-8827-96261613a4f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e88cda-9cfd-4a98-9234-0151154149e7' class='xr-var-data-in' type='checkbox'><label for='data-f3e88cda-9cfd-4a98-9234-0151154149e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1. , 0.90196078, 0.90196078],\n",
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" ims (batch, height, width, channel) float64 1MB 1.0 0.902 ... 0.8039</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-183bfbe5-29c2-4615-8069-a8d0dcdd35e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-183bfbe5-29c2-4615-8069-a8d0dcdd35e2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>batch</span>: 6</li><li><span>height</span>: 96</li><li><span>width</span>: 96</li><li><span>channel</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b3551b06-e4e0-40cb-a65a-03044daab9b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3551b06-e4e0-40cb-a65a-03044daab9b1' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-9f26d6c0-72b4-469a-8b15-df753c57cca2' class='xr-section-summary-in' type='checkbox' checked><label for='section-9f26d6c0-72b4-469a-8b15-df753c57cca2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ims</span></div><div class='xr-var-dims'>(batch, height, width, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.902 0.902 ... 1.0 1.0 0.8039</div><input id='attrs-63bae9eb-1de0-4adf-b6ef-293a1d0d95f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63bae9eb-1de0-4adf-b6ef-293a1d0d95f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ead5f62-d0fc-43fd-8b90-7358da393ba9' class='xr-var-data-in' type='checkbox'><label for='data-7ead5f62-d0fc-43fd-8b90-7358da393ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[1. , 0.90196078, 0.90196078],\n",
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" [1. , 0.90196078, 0.90196078],\n",
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" ...,\n",
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" ...,\n",
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