remove cdms2 (#8441)

* remove `cdms2` from all environments

* remove any code related to `cdms2`

* remove the `mypy` override for `cdms2`

* remove the special environment files for python 3.11

* whats-new

* more info on the replacement

* remove the `cdms2` methods from the api docs

* remove all mentions of UV-CDAT / cdms2 from the faq

* also change the binder environment

Author: keewis

.binder/environment.yml

@@ -6,7 +6,6 @@ dependencies:
   - boto3
   - bottleneck
   - cartopy
-  - cdms2
   - cfgrib
   - cftime
   - coveralls
@@ -38,5 +37,4 @@ dependencies:
   - toolz
   - xarray
   - zarr
-  - pip:
-    - numbagg
+  - numbagg

.github/workflows/ci.yaml

@@ -67,13 +67,7 @@ jobs:
         run: |
           echo "TODAY=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
 
-          if [[ "${{matrix.python-version}}" == "3.11" ]]; then
-            if [[ ${{matrix.os}} == windows* ]]; then
-              echo "CONDA_ENV_FILE=ci/requirements/environment-windows-py311.yml" >> $GITHUB_ENV
-            else
-              echo "CONDA_ENV_FILE=ci/requirements/environment-py311.yml" >> $GITHUB_ENV
-            fi
-          elif [[ ${{ matrix.os }} == windows* ]] ;
+          if [[ ${{ matrix.os }} == windows* ]] ;
           then
             echo "CONDA_ENV_FILE=ci/requirements/environment-windows.yml" >> $GITHUB_ENV
           elif [[ "${{ matrix.env }}" != "" ]] ;

ci/requirements/all-but-dask.yml

@@ -3,13 +3,11 @@ channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.10
   - black
   - aiobotocore
   - boto3
   - bottleneck
   - cartopy
-  - cdms2
   - cftime
   - coveralls
   - flox

ci/requirements/environment-py311.yml

@@ -5,7 +5,6 @@ dependencies:
   - boto3
   - bottleneck
   - cartopy
-  # - cdms2  # Not available on Windows
   - cftime
   - dask-core
   - distributed

ci/requirements/environment-windows-py311.yml

@@ -7,7 +7,6 @@ dependencies:
   - boto3
   - bottleneck
   - cartopy
-  - cdms2
   - cftime
   - dask-core
   - distributed

ci/requirements/environment-windows.yml

@@ -11,7 +11,6 @@ dependencies:
   - boto3=1.24
   - bottleneck=1.3
   - cartopy=0.20
-  - cdms2=3.1
   - cftime=1.6
   - coveralls
   - dask-core=2022.7

ci/requirements/environment.yml

@@ -628,11 +628,9 @@ DataArray methods
    load_dataarray
    open_dataarray
    DataArray.as_numpy
-   DataArray.from_cdms2
    DataArray.from_dict
    DataArray.from_iris
    DataArray.from_series
-   DataArray.to_cdms2
    DataArray.to_dask_dataframe
    DataArray.to_dataframe
    DataArray.to_dataset

ci/requirements/min-all-deps.yml

@@ -168,18 +168,11 @@ integration with Cartopy_.
 .. _Iris: https://scitools-iris.readthedocs.io/en/stable/
 .. _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`__.
-
-__ 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
-and CDAT have some great domain specific functionality, and xarray includes
-methods for converting back and forth between xarray and these libraries. See
-:py:meth:`~xarray.DataArray.to_iris` and :py:meth:`~xarray.DataArray.to_cdms2`
-for more details.
+has some great domain specific functionality, and xarray includes
+methods for converting back and forth between xarray and Iris. See
+:py:meth:`~xarray.DataArray.to_iris` for more details.
 
 What other projects leverage xarray?
 ------------------------------------

doc/api.rst

@@ -29,6 +29,9 @@ New Features
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
+- drop support for `cdms2 <https://github.com/CDAT/cdms>`_. Please use
+  `xcdat <https://github.com/xCDAT/xcdat>`_ instead (:pull:`8441`).
+  By `Justus Magin <https://github.com/keewis`_.
 
 - Bump minimum tested pint version to ``>=0.22``. By `Deepak Cherian <https://github.com/dcherian>`_.
 

doc/getting-started-guide/faq.rst

@@ -88,7 +88,6 @@ module = [
   "affine.*",
   "bottleneck.*",
   "cartopy.*",
-  "cdms2.*",
   "cf_units.*",
   "cfgrib.*",
   "cftime.*",

doc/whats-new.rst

@@ -3,7 +3,6 @@
 from collections import Counter
 
 import numpy as np
-import pandas as pd
 
 from xarray.coding.times import CFDatetimeCoder, CFTimedeltaCoder
 from xarray.conventions import decode_cf
@@ -12,7 +11,6 @@
 from xarray.core.dtypes import get_fill_value
 from xarray.core.pycompat import array_type
 
-cdms2_ignored_attrs = {"name", "tileIndex"}
 iris_forbidden_keys = {
     "standard_name",
     "long_name",
@@ -60,92 +58,6 @@ def _filter_attrs(attrs, ignored_attrs):
     return {k: v for k, v in attrs.items() if k not in ignored_attrs}
 
 
-def from_cdms2(variable):
-    """Convert a cdms2 variable into an DataArray"""
-    values = np.asarray(variable)
-    name = variable.id
-    dims = variable.getAxisIds()
-    coords = {}
-    for axis in variable.getAxisList():
-        coords[axis.id] = DataArray(
-            np.asarray(axis),
-            dims=[axis.id],
-            attrs=_filter_attrs(axis.attributes, cdms2_ignored_attrs),
-        )
-    grid = variable.getGrid()
-    if grid is not None:
-        ids = [a.id for a in grid.getAxisList()]
-        for axis in grid.getLongitude(), grid.getLatitude():
-            if axis.id not in variable.getAxisIds():
-                coords[axis.id] = DataArray(
-                    np.asarray(axis[:]),
-                    dims=ids,
-                    attrs=_filter_attrs(axis.attributes, cdms2_ignored_attrs),
-                )
-    attrs = _filter_attrs(variable.attributes, cdms2_ignored_attrs)
-    dataarray = DataArray(values, dims=dims, coords=coords, name=name, attrs=attrs)
-    return decode_cf(dataarray.to_dataset())[dataarray.name]
-
-
-def to_cdms2(dataarray, copy=True):
-    """Convert a DataArray into a cdms2 variable"""
-    # we don't want cdms2 to be a hard dependency
-    import cdms2
-
-    def set_cdms2_attrs(var, attrs):
-        for k, v in attrs.items():
-            setattr(var, k, v)
-
-    # 1D axes
-    axes = []
-    for dim in dataarray.dims:
-        coord = encode(dataarray.coords[dim])
-        axis = cdms2.createAxis(coord.values, id=dim)
-        set_cdms2_attrs(axis, coord.attrs)
-        axes.append(axis)
-
-    # Data
-    var = encode(dataarray)
-    cdms2_var = cdms2.createVariable(
-        var.values, axes=axes, id=dataarray.name, mask=pd.isnull(var.values), copy=copy
-    )
-
-    # Attributes
-    set_cdms2_attrs(cdms2_var, var.attrs)
-
-    # Curvilinear and unstructured grids
-    if dataarray.name not in dataarray.coords:
-        cdms2_axes = {}
-        for coord_name in set(dataarray.coords.keys()) - set(dataarray.dims):
-            coord_array = dataarray.coords[coord_name].to_cdms2()
-
-            cdms2_axis_cls = (
-                cdms2.coord.TransientAxis2D
-                if coord_array.ndim
-                else cdms2.auxcoord.TransientAuxAxis1D
-            )
-            cdms2_axis = cdms2_axis_cls(coord_array)
-            if cdms2_axis.isLongitude():
-                cdms2_axes["lon"] = cdms2_axis
-            elif cdms2_axis.isLatitude():
-                cdms2_axes["lat"] = cdms2_axis
-
-        if "lon" in cdms2_axes and "lat" in cdms2_axes:
-            if len(cdms2_axes["lon"].shape) == 2:
-                cdms2_grid = cdms2.hgrid.TransientCurveGrid(
-                    cdms2_axes["lat"], cdms2_axes["lon"]
-                )
-            else:
-                cdms2_grid = cdms2.gengrid.AbstractGenericGrid(
-                    cdms2_axes["lat"], cdms2_axes["lon"]
-                )
-            for axis in cdms2_grid.getAxisList():
-                cdms2_var.setAxis(cdms2_var.getAxisIds().index(axis.id), axis)
-            cdms2_var.setGrid(cdms2_grid)
-
-    return cdms2_var
-
-
 def _pick_attrs(attrs, keys):
     """Return attrs with keys in keys list"""
     return {k: v for k, v in attrs.items() if k in keys}

pyproject.toml

@@ -56,7 +56,6 @@
     ReprObject,
     _default,
     either_dict_or_kwargs,
-    emit_user_level_warning,
 )
 from xarray.core.variable import (
     IndexVariable,
@@ -81,10 +80,6 @@
         from dask.delayed import Delayed
     except ImportError:
         Delayed = None  # type: ignore
-    try:
-        from cdms2 import Variable as cdms2_Variable
-    except ImportError:
-        cdms2_Variable = None
     try:
         from iris.cube import Cube as iris_Cube
     except ImportError:
@@ -4402,47 +4397,6 @@ def from_series(cls, series: pd.Series, sparse: bool = False) -> DataArray:
         result.name = series.name
         return result
 
-    def to_cdms2(self) -> cdms2_Variable:
-        """Convert this array into a cdms2.Variable
-
-        .. deprecated:: 2023.06.0
-            The `cdms2`_ library has been deprecated. Please consider using the
-            `xcdat`_ library instead.
-
-        .. _cdms2: https://github.com/CDAT/cdms
-        .. _xcdat: https://github.com/xCDAT/xcdat
-        """
-        from xarray.convert import to_cdms2
-
-        emit_user_level_warning(
-            "The cdms2 library has been deprecated."
-            " Please consider using the xcdat library instead.",
-            DeprecationWarning,
-        )
-
-        return to_cdms2(self)
-
-    @classmethod
-    def from_cdms2(cls, variable: cdms2_Variable) -> Self:
-        """Convert a cdms2.Variable into an xarray.DataArray
-
-        .. deprecated:: 2023.06.0
-            The `cdms2`_ library has been deprecated. Please consider using the
-            `xcdat`_ library instead.
-
-        .. _cdms2: https://github.com/CDAT/cdms
-        .. _xcdat: https://github.com/xCDAT/xcdat
-        """
-        from xarray.convert import from_cdms2
-
-        emit_user_level_warning(
-            "The cdms2 library has been deprecated."
-            " Please consider using the xcdat library instead.",
-            DeprecationWarning,
-        )
-
-        return from_cdms2(variable)
-
     def to_iris(self) -> iris_Cube:
         """Convert this array into a iris.cube.Cube"""
         from xarray.convert import to_iris