import matplotlib is failing when it imports PIL

```python

E ImportError: dlopen(/usr/local/miniconda/envs/xarray-tests/lib/python3.8/site-packages/PIL/_imaging.cpython-38-darwin.so, 2): Library not loaded: @rpath/libwebp.7.dylib E Referenced from: /usr/local/miniconda/envs/xarray-tests/lib/libtiff.5.dylib E Reason: Incompatible library version: libtiff.5.dylib requires version 9.0.0 or later, but libwebp.7.dylib provides version 8.0.0

/usr/local/miniconda/envs/xarray-tests/lib/python3.8/site-packages/PIL/Image.py:69: ImportError ```

I have a data 3D DataArray with Time, Latitude and Longitude coordinates.

I want to plot maps of this dataset, faceted by Time. The following code

import cartopy.crs as ccrs proj = ccrs.PlateCarree() data.plot(transform=proj, col='Time', col_wrap=3, robust=True)

fails with

ValueError: Axes should be an instance of GeoAxes, got <class 'matplotlib.axes._subplots.AxesSubplot'>

this is because to plot with a transform, the axes must be a GeoAxes, which is done with something like plt.subplot(111, projection=proj). The implicit subplotting done when faceting does not do that. To make the faceting works, I had to do

import cartopy.crs as ccrs proj = ccrs.PlateCarree() data.plot(transform=proj, col='Time', col_wrap=3, robust=True, subplot_kws={'projection':proj})

I propose that, when plot faceting is requested with a transform kw, the content of that keyword should be passed to the subplot function as a projection argument automatically by default. If a projection is provided explicitely like in the call above, use that one.

MCVE Code Sample

```python

Your code here

Original_DataArray = <xarray.DataArray 'Conexões Domésticas e Piscinas' (projetos_resi: 9, res_segmentacao: 21, time: 133, bands: 10)>

Shift_Map = <xarray.DataArray 'Anos por Desconto' (res_segmentacao: 21)> array([ 0, 0, 0, 24, 0, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 36, 60, 36, 0])

"Multi" Shift Function

def _fn(dataArray,over_shift_index,multi_shift_index,shift_map,initialValues=None): """
-dataArray: dataarray to shift -over_shift_index: index over apply shit of dataarray -multi_shift_index: index with the elements to apply a different value shift -shift_map: datarray indexed by multi_shift_index with the values to shift for each element """

_da = dataArray.copy()
_shift_map = shift_map.astype(int)

for name, sl in _da.groupby(multi_shift_index.name):
    _shift = subscript(_shift_map, multi_shift_index, name ).values.tolist()
    _sl = sl.squeeze(multi_shift_index.name).shift(time = _shift)

    _dict = {multi_shift_index.name : name}
    _da.loc[_dict] = _sl


return _da.fillna(0.)

```

Expected Output

Same estructure DataArray shifted along the "time" coordinate by a different value for each element of the "res_segmentacao" coordinate.

python exp_output= multidynamic( Original_DataArray , time, res_segmentacao, Shift_Map )

Problem Description

I´ve reached my objective but I wanted to consult if anyone had donde this in a more efficient way. Thanks!

Code Sample, a copy-pastable example if possible

```python import numpy as np import pandas as pd import xarray as xr

time = np.arange('2005-02-01', '2007-03-01', dtype='datetime64') arr = xr.DataArray( np.arange(time.size), coords=[time,], dims=('time',), name='data' ) arr.resample(time='M').interpolate('linear')

ValueError Traceback (most recent call last) <ipython-input-7-6a92b6afe08e> in <module>() 7 np.arange(time.size), coords=[time,], dims=('time',), name='data' 8 ) ----> 9 arr.resample(time='M').interpolate('linear')

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/xarray/core/resample.py in interpolate(self, kind) 108 109 """ --> 110 return self._interpolate(kind=kind) 111 112 def _interpolate(self, kind='linear'):

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/xarray/core/resample.py in _interpolate(self, kind) 218 elif self._dim not in v.dims: 219 coords[k] = v --> 220 return DataArray(f(new_x), coords, dims, name=dummy.name, 221 attrs=dummy.attrs) 222

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/polyint.py in call(self, x) 77 """ 78 x, x_shape = self._prepare_x(x) ---> 79 y = self._evaluate(x) 80 return self._finish_y(y, x_shape) 81

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/interpolate.py in _evaluate(self, x_new) 632 y_new = self._call(self, x_new) 633 if not self._extrapolate: --> 634 below_bounds, above_bounds = self._check_bounds(x_new) 635 if len(y_new) > 0: 636 # Note fill_value must be broadcast up to the proper size

~/devel/c3s-cns/venv_op/lib/python3.6/site-packages/scipy/interpolate/interpolate.py in _check_bounds(self, x_new) 664 "range.") 665 if self.bounds_error and above_bounds.any(): --> 666 raise ValueError("A value in x_new is above the interpolation " 667 "range.") 668

ValueError: A value in x_new is above the interpolation range. ```

Problem description

The internal format of arr.time is datetime64[D]

```python arr.time

<xarray.DataArray 'time' (time: 758)> array(['2005-02-01', '2005-02-02', '2005-02-03', ..., '2007-02-26', '2007-02-27', '2007-02-28'], dtype='datetime64[D]') Coordinates: * time (time) datetime64[D] 2005-02-01 2005-02-02 2005-02-03 ... ``` Internally there is a cast to float, for both the old time indices x and the new time indices new_x, but the new time indices are in datetime64[ns], so they don't match.

DataArrayResample._interpolate

```python x = self._obj[self._dim].astype('float') y = self._obj.data

   axis = self._obj.get_axis_num(self._dim)

   f = interp1d(x, y, kind=kind, axis=axis, bounds_error=True,
                assume_sorted=True)
   new_x = self._full_index.values.astype('float')

``` With a cast to datetime64[ns] it works:

```python import numpy as np import pandas as pd import xarray as xr

time = np.arange('2005-02-01', '2007-03-01', dtype='datetime64').astype('datetime64[ns]') arr = xr.DataArray( np.arange(time.size), coords=[time,], dims=('time',), name='data' ) arr.resample(time='M').interpolate('linear') <xarray.DataArray 'data' (time: 25)> array([ 27., 58., 88., 119., 149., 180., 211., 241., 272., 302., 333., 364., 392., 423., 453., 484., 514., 545., 576., 606., 637., 667., 698., 729., 757.]) Coordinates: * time (time) datetime64[ns] 2005-02-28 2005-03-31 2005-04-30 ... ```

Expected Output

python <xarray.DataArray 'data' (time: 25)> array([ 27., 58., 88., 119., 149., 180., 211., 241., 272., 302., 333., 364., 392., 423., 453., 484., 514., 545., 576., 606., 637., 667., 698., 729., 757.]) Coordinates: * time (time) datetime64[ns] 2005-02-28 2005-03-31 2005-04-30 ...

Output of xr.show_versions()

To me this name is a bit confusing as it actually encodes an in memory object to look like a decoded netcdf file.

I have a class which inherits (I know about dataset_accessor, still seems easier to simply inherit...) xarray.Dataset to which I've added a method _make_cf:

```python import xarray as xr class XResult(xr.Dataset): def init(self, data=None, coords=None, attrs=None, kwargs): if isinstance(data, str): kwargs = dict(READ_KWARGS, kwargs) with xr.open_dataset(data, **kwargs) as data: attrs = data.attrs

    super().__init__(data, coords, attrs)
    self._make_cf()

def _make_cf(self):
    self = xr.decode_cf(self)

``` I expect the XResult object to be decoded but it is not.

if I do xresult = xarray.decode_cf(XResult(...)) than the object is indeed decoded but is no longer an XResult object and loses the attached functionality.

It would be quite convenient to have a decode_cf or encode_cf method part of the Dataset class that will operate inplace.

Code Sample, a copy-pastable example if possible

Minimal example here: https://github.com/lvankampenhout/bug-reports/blob/master/Facetgrid_cmap_extend.ipynb

Problem description

The extreme colors of neither the pcolormesh or colorbar (using extend='both') are not saturated as they should when faceting.

Output of xr.show_versions()

MCVE Code Sample

```python xr.DataArray(coords=dict(a=' '.join(['hello world' for _ in range(100)])))

<xarray.DataArray ()> array(nan) Coordinates: a <U5999 'hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world hello world' ```

Expected Output

<xarray.DataArray ()> array(nan) Coordinates: a <U5999 'hello world ... hello world'

Problem Description

I think mostly the same as https://github.com/pydata/xarray/issues/1319 but for coords

Output of xr.show_versions()

In DataArray.copy, the _indexes attributes is not deep copied. After pull request #3840, this causes deleting a coordinate of a copy will also delete that coordinate from the original, even for deep copies.

MCVE Code Sample

```python a0 = xr.DataArray( np.array([[1, 2, 3], [4, 5, 6]]), dims=["y", "x"], coords={"x": ["a", "b", "c"], "y": [-1, 1]}, )

a1 = a0.copy() del a1.coords["y"]

xr.tests.assert_identical(a0, a0) ```

The result is:

``` xarray/testing.py:272: in _assert_internal_invariants _assert_dataarray_invariants(xarray_obj) xarray/testing.py:222: in _assert_dataarray_invariants _assert_indexes_invariants_checks(da._indexes, da._coords, da.dims)

indexes = {'x': Index(['a', 'b', 'c'], dtype='object', name='x')}, possible_coord_variables = {'x': <xarray.IndexVariable 'x' (x: 3)> array(['a', 'b', 'c'], dtype='<U1'), 'y': <xarray.IndexVariable 'y' (y: 2)> array([-1, 1])} dims = ('y', 'x')

def _assert_indexes_invariants_checks(indexes, possible_coord_variables, dims):
    assert isinstance(indexes, dict), indexes
    assert all(isinstance(v, pd.Index) for v in indexes.values()), {
        k: type(v) for k, v in indexes.items()
    }

    index_vars = {
        k for k, v in possible_coord_variables.items() if isinstance(v, IndexVariable)
    }
    assert indexes.keys() <= index_vars, (set(indexes), index_vars)

    # Note: when we support non-default indexes, these checks should be opt-in
    # only!
    defaults = default_indexes(possible_coord_variables, dims)

  assert indexes.keys() == defaults.keys(), (set(indexes), set(defaults))

E AssertionError: ({'x'}, {'y', 'x'})

xarray/testing.py:185: AssertionError ```

Expected Output

The test should pass.

Problem Description

Doing a deep copy should make a copy of everything. Changing a deep copy should not alter the original in any way.

Should match the pandas function: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.idxmax.html