Hypothesis strategies in xarray.testing.strategies

doc/api.rst

@@ -1090,6 +1090,10 @@ See the :ref:`documentation page on testing <testing.hypothesis>` for a guide on
    testing.strategies.dimension_sizes
    testing.strategies.attrs
    testing.strategies.variables
+   testing.strategies.coordinate_variables
+   testing.strategies.dataarrays
+   testing.strategies.data_variables
+   testing.strategies.datasets
    testing.strategies.unique_subset_of
 
 Exceptions

doc/conf.py

@@ -317,6 +317,8 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "cftime": ("https://unidata.github.io/cftime", None),
+    "sparse": ("https://sparse.pydata.org/en/latest/", None),
+    "hypothesis": ("https://hypothesis.readthedocs.io/en/latest/", None),
     "cubed": ("https://cubed-dev.github.io/cubed/", None),
     "dask": ("https://docs.dask.org/en/latest", None),
     "datatree": ("https://xarray-datatree.readthedocs.io/en/latest/", None),

doc/user-guide/testing.rst

@@ -51,6 +51,10 @@ These strategies are accessible in the :py:mod:`xarray.testing.strategies` modul
    testing.strategies.dimension_sizes
    testing.strategies.attrs
    testing.strategies.variables
+   testing.strategies.coordinate_variables
+   testing.strategies.dataarrays
+   testing.strategies.data_variables
+   testing.strategies.datasets
    testing.strategies.unique_subset_of
 
 These build upon the numpy and array API strategies offered in :py:mod:`hypothesis.extra.numpy` and :py:mod:`hypothesis.extra.array_api`:
@@ -89,7 +93,6 @@ In your tests however you should not use ``.example()`` - instead you should par
     def test_function_that_acts_on_variables(var):
         assert func(var) == ...
 
-
 Chaining Strategies
 ~~~~~~~~~~~~~~~~~~~
 
@@ -145,6 +148,7 @@ objects your chained strategy will generate.
     fixed_x_variable_y_maybe_z = st.fixed_dictionaries(
         {"x": st.just(2), "y": st.integers(3, 4)}, optional={"z": st.just(2)}
     )
+
     fixed_x_variable_y_maybe_z.example()
 
     special_variables = xrst.variables(dims=fixed_x_variable_y_maybe_z)
@@ -156,6 +160,7 @@ Here we have used one of hypothesis' built-in strategies :py:func:`hypothesis.st
 strategy which generates mappings of dimension names to lengths (i.e. the ``size`` of the xarray object we want).
 This particular strategy will always generate an ``x`` dimension of length 2, and a ``y`` dimension of
 length either 3 or 4, and will sometimes also generate a ``z`` dimension of length 2.
+
 By feeding this strategy for dictionaries into the ``dims`` argument of xarray's :py:func:`~st.variables` strategy,
 we can generate arbitrary :py:class:`~xarray.Variable` objects whose dimensions will always match these specifications.
 

doc/whats-new.rst

@@ -446,6 +446,19 @@ Illviljan, Justus Magin, Katelyn FitzGerald, Kai Muehlbauer, Martin Durant, Maxi
 New Features
 ~~~~~~~~~~~~
 
+- Added a suite of hypothesis strategies for generating xarray objects containing arbitrary data, useful for testing.
+  Accessible under :py:func:`testing.strategies`, and documented in a new page on testing in the User Guide.
+  (:issue:`6911`, :pull:`6908`)
+  By `Tom Nicholas <https://github.com/TomNicholas>`_.
+- Enable taking the mean of dask-backed :py:class:`cftime.datetime` arrays
+  (:pull:`6556`, :pull:`6940`).  By `Deepak Cherian
+  <https://github.com/dcherian>`_ and `Spencer Clark
+  <https://github.com/spencerkclark>`_.
+- Allow creating Xarray objects where a multidimensional variable shares its name
+  with a dimension. Examples include output from finite volume models like FVCOM.
+  (:issue:`2233`, :pull:`7989`)
+  By `Deepak Cherian <https://github.com/dcherian>`_ and `Benoit Bovy <https://github.com/benbovy>`_.
+
 - Use `opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_ for :py:func:`xarray.dot` by default if installed.
   By `Deepak Cherian <https://github.com/dcherian>`_. (:issue:`7764`, :pull:`8373`).
 - Add ``DataArray.dt.total_seconds()`` method to match the Pandas API. (:pull:`8435`).