[PyOpenSci] Documentation and Notebook Adjustments, manage SBCK

.github/workflows/upstream.yml

@@ -50,9 +50,10 @@ jobs:
         run: |
           conda --version
           echo "micromamba: $(micromamba --version)"
-      - name: Install upstream versions
+      - name: Install upstream versions and SBCK
         run: |
           python -m pip install -r requirements_upstream.txt
+          python -m pip install "sbck @ git+https://github.com/yrobink/SBCK-python.git@master"
       - name: Install xclim
         run: |
           python -m pip install --no-user --no-deps .

CHANGES.rst

@@ -46,6 +46,8 @@ Internal changes
 * `tox` now include `sbck` and `eofs` flags for easier testing of dependencies. CI builds now test against `sbck-python` @ master.  (:pull:`1328`).
 * `upstream` CI tests are now run on push to master, at midnight, and can also be triggered via `workflow_dispatch`. Failures from upstream build will open issues using `xarray-contrib/issue-from-pytest-log`. (:pull:`1327`).
 * Warnings from set ``_version_deprecated`` within Indicators now emit ``FutureWarning`` instead of ``DeprecationWarning`` for greater visibility. (:pull:`1319`).
+* The `Graphics` section of the `Usage` notebook has been expanded upon while grammar and spelling mistakes within the notebook-generated documentation have been reduced. (:issue:`1335`, :pull:`1338`, suggested from `PyOpenSci Software Review <https://github.com/pyOpenSci/software-review/issues/73>`_).
+* The Contributing guide now lists three separate subsections to help users understand the gains from optional dependencies. (:issue:`1335`, :pull:`1338`, suggested from `PyOpenSci Software Review <https://github.com/pyOpenSci/software-review/issues/73>`_).
 
 v0.41.0 (2023-02-28)
 --------------------

CONTRIBUTING.rst

@@ -304,10 +304,11 @@ When publishing on GitHub, maintainers will need to generate the release notes f
     # For ReStructuredText format (offered for convenience):
     $ xclim release_notes -r
 
-When publishing to GitHub, you will still need to replace subsection headers in the Markdown (`^^^^` -> `###`) and the history published should not extend past the changes for the current version. This behaviour may eventually change.
+.. note::
+    The changelog should not extend past those entries relevant for the current version.
 
 .. warning::
-    Be warned that a published package version on PyPI can never be overwritten. Be sure to verify that the package published at https://test.pypi.org/project/xclim/ matches expectations before publishing a version on GitHub.
+    A published version on PyPI can never be overwritten. Be sure to verify that the package published at https://test.pypi.org/project/xclim/ matches expectations before publishing a version on GitHub.
 
 The Manual Approach
 ~~~~~~~~~~~~~~~~~~~

README.rst

@@ -3,9 +3,9 @@ xclim: Climate services library |logo|
 ======================================
 
 +----------------------------+-----------------------------------------------------+
-| Versions                   | |pypi| |conda|                                      |
+| Versions                   | |pypi| |conda| |versions|                           |
 +----------------------------+-----------------------------------------------------+
-| Documentation and Support  | |docs| |gitter| |versions|                          |
+| Documentation and Support  | |docs| |gitter|                                     |
 +----------------------------+-----------------------------------------------------+
 | Open Source                | |license| |fair| |fossa| |zenodo|                   |
 +----------------------------+-----------------------------------------------------+
@@ -41,10 +41,29 @@ streamflow and sea ice concentration, numerous bias-adjustment algorithms, as we
 .. _xarray: https://docs.xarray.dev/
 .. _dask: https://docs.dask.org/
 
+Quick Install
+-------------
+`xclim` can be installed from PyPI:
+
+.. code-block:: shell
+
+    $ pip install xclim
+
+or from Anaconda (conda-forge):
+
+.. code-block:: shell
+
+    $ conda install -c conda-forge xclim
+
 Documentation
 -------------
 The official documentation is at https://xclim.readthedocs.io/
 
+How to make the most of xclim: `Basic Usage Examples`_ and `In-Depth Examples`_.
+
+.. _Basic Usage Examples: https://xclim.readthedocs.io/en/stable/notebooks/usage.html
+.. _In-Depth Examples: https://xclim.readthedocs.io/en/stable/notebooks/index.html
+
 Contributing to xclim
 ---------------------
 xclim is in active development and is being used in production by climate services specialists around the world.
@@ -66,7 +85,6 @@ License
 -------
 This is free software: you can redistribute it and/or modify it under the terms of the `Apache License 2.0`_. A copy of this license is provided in the code repository (`LICENSE`_).
 
-
 .. _Apache License 2.0: https://opensource.org/license/apache-2-0/
 .. _LICENSE: https://github.com/Ouranosinc/xclim/blob/master/LICENSE
 

docs/explanation.rst

@@ -14,8 +14,8 @@ Purpose
 
 The primary domains that `xclim` is built for are in calculating climate indicators, performing statistical correction / bias adjustment of climate model output variables or simulations, and in performing climate model simulation ensemble statistics.
 
-Other projects similar to xclim
-===============================
+Other Python projects similar to xclim
+======================================
 
 `xclim` has been developed within an ecosystem of several existing projects that deal with climate and statistical correction/downscaling and has both influenced and been influenced by their approaches:
 
@@ -31,11 +31,17 @@ Other projects similar to xclim
     - `MetPy` is built for reading, visualizing, and performing calculations specifically on standards-compliant, operational weather data. Like `xclim`, it makes use of `xarray`.
     - `xclim` adopted its standards and unit-handling approaches from `MetPy` and associated project `cf-xarray`.
 
+* `climpred` (`climpred Source Code <https://github.com/pangeo-data/climpred>`_; `climpred Documentation <https://climpred.readthedocs.io/en/stable/index.html>`_)
+    - `climpred` is designed to analyze and validate weather and climate forecast data against observations, reconstructions, and simulations. Similar to `xclim`, it leverages `xarray`, `dask`, and `cf_xarray` for object handling, distributed computation, and metadata validation, respectively.
+
 * `pyet` (`pyet Source Code <https://github.com/pyet-org/pyet>`_; `pyet Documentation <https://pyet.readthedocs.io/en/latest/>`_)
     - `pyet` is a tool for calculating/estimating evapotranspiration using many different accepted methodologies and employs a similar design approach as `xclim`, based on `xarray`-natives.
 
 * `xcdat` (`xcdat Source Code <https://github.com/xCDAT/xcdat>`_; `xcdat Documentation <https://xcdat.readthedocs.io/en/latest/>`_)
 
+* `GeoCAT` (`GeoCAT Documentation <https://geocat.ucar.edu/>`_)
+    - `GeoCAT` is an ensemble of tools developed specifically for scalable data analysis and visualization of structures and unstructured gridded earth science datasets. `GeoCAT` tools rely on many of the same tools that `xclim` uses in its stack (notably, `xarray`, `dask`, and `jupyter notebooks`).
+
 * `scikit-downscale` (`scikit-downscale Source Code <https://github.com/pangeo-data/scikit-downscale>`_, `scikit-downscale Documentation <https://scikit-downscale.readthedocs.io/en/latest/>`_)
     - `scikit-downscale` offers algorithms for statistical downscaling. `xclim` drew inspiration from its fit/predict architecture API approach. The suite of downscaling algorithms offered between both projects differs.
 

docs/installation.rst

@@ -6,13 +6,14 @@ Installation
 
 Stable release
 --------------
-To install xclim via pip, run this command in your terminal:
+
+To install `xclim` via `pip`, run this command in your terminal:
 
 .. code-block:: shell
 
     $ pip install xclim
 
-This is the preferred method to install xclim, as it will always install the most recent stable release.
+This is the preferred method to install `xclim`, as it will always install the most recent stable release.
 
 If you don't have `pip`_ installed, this `Python installation guide`_ can guide you through the process.
 
@@ -21,8 +22,9 @@ If you don't have `pip`_ installed, this `Python installation guide`_ can guide
 
 Anaconda release
 ----------------
+
 For ease of installation across operating systems, we also offer an Anaconda Python package hosted on conda-forge.
-This version tends to be updated at around the same frequency as the pip library, but can lag by a few days at times.
+This version tends to be updated at around the same frequency as the PyPI-hosted library, but can lag by a few days at times.
 
 `xclim` can be installed from conda-forge wth the following:
 
@@ -32,15 +34,41 @@ This version tends to be updated at around the same frequency as the pip library
 
 .. _extra-dependencies:
 
-Extra dependencies
+Extra Dependencies
 ------------------
+
+Speedups and Helper Libraries
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 To improve performance of `xclim`, we highly recommend you also install `flox`_ (see: :doc:`flox API <flox:api>`).
-This package integrates into xarray and significantly improves the performance of the grouping and resampling algorithms, especially when using `dask` on large datasets.
+This package seamlessly integrates into `xarray` and significantly improves the performance of the grouping and resampling algorithms, especially when using `dask` on large datasets.
+
+For grid subsetting, we also recommend using the tools found in `clisops`_ (see: :doc:`clisops.core.subset API <clisops:api>`) for spatial manipulation of geospatial data. `clisops` began as a component of `xclim` and is designed to alongside `xclim` and the `Pangeo`_ stack (`xarray`, `dask`, `jupyter`). In order to install `clisops`, the `GDAL`_ system libraries must be available.
+
+On Debian/Ubuntu, `GDAL` can be installed via `apt`:
+
+.. code-block:: shell
+
+    $ sudo apt-get install libgdal-dev
+
+If on Anaconda Python, `GDAL` will be installed if needed as a `clisops` dependency.
 
-We also recommend using the subsetting tools found in `clisops`_ (see: :doc:`clisops.core.subset API <clisops:api>`) for spatial manipulation of geospatial data.
+Both of these libraries are available on PyPI and conda-forge:
+
+.. code-block:: shell
+
+    $ pip install flox clisops
+    # Or, alternatively:
+    $ conda install -c conda-forge flox clisops
+
+.. _GDAL: https://gdal.org/download.html#binaries
+.. _Pangeo: https://pangeo.io/
+
+Upstream Dependencies
+^^^^^^^^^^^^^^^^^^^^^
 
 `xclim` is regularly tested against the main development branches of a handful of key base libraries (`cftime`, `flox`, `pint`, `xarray`).
-For convenience, these libraries can be installed alongside `xclim` using the following `pip`-installable recipe:
+For convenience, these libraries can be installed alongside `xclim` using the following `pip`-install command:
 
 .. code-block:: shell
 
@@ -55,29 +83,33 @@ Or, alternatively:
 .. _flox: https://github.com/xarray-contrib/flox
 .. _clisops: https://github.com/roocs/clisops
 
-Another optional library is `SBCK`_, which provides experimental adjustment methods to extend :doc:`xclim.sdba <sdba>`.
-`SBCK` is not installable directly from PyPI or conda-forge and has one complex dependency: `Eigen3`_.
+Experimental SDBA Algorithms
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-On Debian/Ubuntu, Eigen3 can be installed from via `apt`:
+`xclim` also offers support for a handful of experimental adjustment methods to extend :doc:`xclim.sdba <sdba>`, available only if some additional libraries are installed. These libraries are completely optional.
+
+One experimental library is `SBCK`_. `SBCK` is not available from PyPI nor conda-forge, and has one complex dependency: `Eigen3`_.
+As `SBCK` is compiled at installation time, a **C++** compiler (`GCC`, `Clang`, `MSVC`, etc.) must also be available.
+
+On Debian/Ubuntu, `Eigen3` can be installed via `apt`:
 
 .. code-block:: shell
 
     $ sudo apt-get install libeigen3-dev
 
-Eigen3  is also available on conda-forge, so one can do:
+Eigen3 is also available on conda-forge, so, if already using Anaconda, one can do:
 
 .. code-block:: shell
 
     $ conda install -c conda-forge eigen
 
-Afterwards, `SBCK can be installed from PyPI using `pip`:
+Afterwards, `SBCK` can be installed from PyPI using `pip`:
 
 .. code-block:: shell
 
-    $ pip install pybind11
     $ pip install "sbck @ git+https://github.com/yrobink/SBCK-python.git@master"
 
-Finally, the function :py:indicator:`xclim.sdba.property.first_eof` makes use of `eofs`_, another optional dependency, which is available on both pip and conda:
+Another experimental function :py:indicator:`xclim.sdba.property.first_eof` makes use of the `eofs`_ library, which is available on both PyPI and conda-forge:
 
 .. code-block:: shell
 
@@ -91,7 +123,8 @@ Finally, the function :py:indicator:`xclim.sdba.property.first_eof` makes use of
 
 From sources
 ------------
-.. Warning::
+
+.. warning::
     For Python3.11+ users: Many of the required scientific libraries do not currently have wheels that support the latest
     python. In order to ensure that installation of xclim doesn't fail, we suggest installing the `Cython` module
     before installing xclim in order to compile necessary libraries from source packages.
@@ -128,10 +161,11 @@ Alternatively, you can also install a local development copy via `flit`_:
 
 Creating a Conda environment
 ----------------------------
-To create a conda environment including all of `xclim`'s optional and development dependencies, run the following command from within your cloned repo:
+
+To create a conda environment including `xclim`'s dependencies and several optional libraries (notably: `clisops`, `eigen`, `eofs`, and `flox`) and development dependencies, run the following command from within your cloned repo:
 
 .. code-block:: console
 
-    $ conda create -n my_xclim_env python=3.8 --file=environment.yml
+    $ conda env create -n my_xclim_env python=3.8 --file=environment.yml
     $ conda activate my_xclim_env
     (my_xclim_env) $ pip install -e .