Bump min deps

ci/requirements/min-all-deps.yml

@@ -10,46 +10,45 @@ dependencies:
   - python=3.8
   - boto3=1.13
   - bottleneck=1.3
-  # cartopy 0.18 conflicts with pynio
-  - cartopy=0.17
+  - cartopy=0.19
   - cdms2=3.1
   - cfgrib=0.9
-  - cftime=1.2
+  - cftime=1.4
   - coveralls
-  - dask-core=2.30
-  - distributed=2.30
-  - h5netcdf=0.8
-  - h5py=2.10
-  # hdf5 1.12 conflicts with h5py=2.10
+  - dask-core=2021.04
+  - distributed=2021.04
+  - h5netcdf=0.11
+  - h5py=3.1
+  # hdf5 1.12 conflicts with h5py=3.1
   - hdf5=1.10
   - hypothesis
   - iris=2.4
   - lxml=4.6  # Optional dep of pydap
-  - matplotlib-base=3.3
+  - matplotlib-base=3.4
   - nc-time-axis=1.2
   # netcdf follows a 1.major.minor[.patch] convention
   # (see https://github.com/Unidata/netcdf4-python/issues/1090)
   # bumping the netCDF4 version is currently blocked by #4491
   - netcdf4=1.5.3
-  - numba=0.51
-  - numpy=1.18
+  - numba=0.53
+  - numpy=1.19
   - packaging=20.0
-  - pandas=1.1
-  - pint=0.16
+  - pandas=1.2
+  - pint=0.17
   - pip
   - pseudonetcdf=3.1
   - pydap=3.2
-  - pynio=1.5
+  # - pynio=1.5.5
   - pytest
   - pytest-cov
   - pytest-env
   - pytest-xdist
-  - rasterio=1.1
-  - scipy=1.5
+  - rasterio=1.2
+  - scipy=1.6
   - seaborn=0.11
-  - sparse=0.11
+  - sparse=0.12
   - toolz=0.11
   - typing_extensions=3.7
-  - zarr=2.5
+  - zarr=2.8
   - pip:
     - numbagg==0.1

doc/getting-started-guide/installing.rst

@@ -7,9 +7,9 @@ Required dependencies
 ---------------------
 
 - Python (3.8 or later)
-- `numpy <https://www.numpy.org/>`__ (1.18 or later)
+- `numpy <https://www.numpy.org/>`__ (1.19 or later)
 - `packaging <https://packaging.pypa.io/en/latest/#>`__ (20.0 or later)
-- `pandas <https://pandas.pydata.org/>`__ (1.1 or later)
+- `pandas <https://pandas.pydata.org/>`__ (1.2 or later)
 
 .. _optional-dependencies:
 

doc/whats-new.rst

@@ -45,6 +45,26 @@ New Features
 Breaking changes
 ~~~~~~~~~~~~~~~~
 
+- PyNIO support is now untested. The minimum versions of some dependencies were changed:
+
+  =============== ===== ====
+  Package         Old   New
+  =============== ===== ====
+  cftime          1.2   1.4
+  dask            2.30  2021.4
+  distributed     2.30  2021.4
+  h5netcdf        0.8   0.11
+  matplotlib-base 3.3   3.4
+  numba           0.51  0.53
+  numpy           1.18  1.19
+  pandas          1.1   1.2
+  pint            0.16  0.17
+  rasterio        1.1   1.2
+  scipy           1.5   1.6
+  sparse          0.11  0.12
+  zarr            2.5   2.8
+  =============== ===== ====
+
 - The Dataset and DataArray ``rename*`` methods do not implicitly add or drop
   indexes. (:pull:`5692`).
   By `Benoît Bovy <https://github.com/benbovy>`_.

setup.cfg

@@ -75,8 +75,8 @@ zip_safe = False  # https://mypy.readthedocs.io/en/latest/installed_packages.htm
 include_package_data = True
 python_requires = >=3.8
 install_requires =
-    numpy >= 1.18
-    pandas >= 1.1
+    numpy >= 1.19
+    pandas >= 1.2
     packaging >= 20.0
 
 [options.extras_require]

xarray/core/dask_array_compat.py

@@ -1,9 +1,6 @@
 import warnings
 
 import numpy as np
-from packaging.version import Version
-
-from .pycompat import dask_version
 
 try:
     import dask.array as da
@@ -57,127 +54,7 @@ def pad(array, pad_width, mode="constant", **kwargs):
     return padded
 
 
-if dask_version > Version("2.30.0"):
-    ensure_minimum_chunksize = da.overlap.ensure_minimum_chunksize
-else:
-
-    # copied from dask
-    def ensure_minimum_chunksize(size, chunks):
-        """Determine new chunks to ensure that every chunk >= size
-
-        Parameters
-        ----------
-        size : int
-            The maximum size of any chunk.
-        chunks : tuple
-            Chunks along one axis, e.g. ``(3, 3, 2)``
-
-        Examples
-        --------
-        >>> ensure_minimum_chunksize(10, (20, 20, 1))
-        (20, 11, 10)
-        >>> ensure_minimum_chunksize(3, (1, 1, 3))
-        (5,)
-
-        See Also
-        --------
-        overlap
-        """
-        if size <= min(chunks):
-            return chunks
-
-        # add too-small chunks to chunks before them
-        output = []
-        new = 0
-        for c in chunks:
-            if c < size:
-                if new > size + (size - c):
-                    output.append(new - (size - c))
-                    new = size
-                else:
-                    new += c
-            if new >= size:
-                output.append(new)
-                new = 0
-            if c >= size:
-                new += c
-        if new >= size:
-            output.append(new)
-        elif len(output) >= 1:
-            output[-1] += new
-        else:
-            raise ValueError(
-                f"The overlapping depth {size} is larger than your "
-                f"array {sum(chunks)}."
-            )
-
-        return tuple(output)
-
-
-if dask_version > Version("2021.03.0"):
+if da is not None:
     sliding_window_view = da.lib.stride_tricks.sliding_window_view
 else:
-
-    def sliding_window_view(x, window_shape, axis=None):
-        from dask.array.overlap import map_overlap
-        from numpy.core.numeric import normalize_axis_tuple
-
-        from .npcompat import sliding_window_view as _np_sliding_window_view
-
-        window_shape = (
-            tuple(window_shape) if np.iterable(window_shape) else (window_shape,)
-        )
-
-        window_shape_array = np.array(window_shape)
-        if np.any(window_shape_array <= 0):
-            raise ValueError("`window_shape` must contain positive values")
-
-        if axis is None:
-            axis = tuple(range(x.ndim))
-            if len(window_shape) != len(axis):
-                raise ValueError(
-                    f"Since axis is `None`, must provide "
-                    f"window_shape for all dimensions of `x`; "
-                    f"got {len(window_shape)} window_shape elements "
-                    f"and `x.ndim` is {x.ndim}."
-                )
-        else:
-            axis = normalize_axis_tuple(axis, x.ndim, allow_duplicate=True)
-            if len(window_shape) != len(axis):
-                raise ValueError(
-                    f"Must provide matching length window_shape and "
-                    f"axis; got {len(window_shape)} window_shape "
-                    f"elements and {len(axis)} axes elements."
-                )
-
-        depths = [0] * x.ndim
-        for ax, window in zip(axis, window_shape):
-            depths[ax] += window - 1
-
-        # Ensure that each chunk is big enough to leave at least a size-1 chunk
-        # after windowing (this is only really necessary for the last chunk).
-        safe_chunks = tuple(
-            ensure_minimum_chunksize(d + 1, c) for d, c in zip(depths, x.chunks)
-        )
-        x = x.rechunk(safe_chunks)
-
-        # result.shape = x_shape_trimmed + window_shape,
-        # where x_shape_trimmed is x.shape with every entry
-        # reduced by one less than the corresponding window size.
-        # trim chunks to match x_shape_trimmed
-        newchunks = tuple(
-            c[:-1] + (c[-1] - d,) for d, c in zip(depths, x.chunks)
-        ) + tuple((window,) for window in window_shape)
-
-        kwargs = dict(
-            depth=tuple((0, d) for d in depths),  # Overlap on +ve side only
-            boundary="none",
-            meta=x._meta,
-            new_axis=range(x.ndim, x.ndim + len(axis)),
-            chunks=newchunks,
-            trim=False,
-            window_shape=window_shape,
-            axis=axis,
-        )
-
-        return map_overlap(_np_sliding_window_view, x, align_arrays=False, **kwargs)
+    sliding_window_view = None