issues

• [x] Closes https://github.com/pydata/xarray/issues/7971
• [x] Closes https://github.com/pydata/xarray/issues/8437.

We were previously pinned to <0.21 Removing the pin didn't change the env but with >=0.21 we get 0.22 which works.

Is your feature request related to a problem?

We can do plain reductions, and groupby reductions with numbagg. Rolling is the last one left!

I don't think coarsen will benefit since it's basically a reshape and reduce on that view, so it should already be accelerated. There may be small gains in handling the boundary conditions but that's probably it.

• [ ] Tests added - check bottleneck tests
• [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

• [x] Closes #7841
• [x] Closes #7892

• [x] Closes #7797
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst

cc @slevang

Pandas is expecting to release v2 in two weeks (pandas-dev/pandas#46776 (comment)). But we are still incompatible with their main branch: - #7441 - #7420

This PR pins pandas to <2

There was a bug in estimating the last index of the slice. Index a range object instead.

• [x] Closes #6560
• [x] Tests added

Oops. I used "Github Codespaces" to edit whats-new, but it turns if you commit in there, it just commits to main!

This fixes the pre-commit error.

Discussed in https://github.com/pydata/xarray/discussions/6901

• [x] Closes #3141, Closes #3417
• [x] Tests added
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [x] New functions/methods are listed in api.rst

Discussed in https://github.com/pydata/xarray/discussions/6762

What happened?

Really hard to describe in words =)

concat = xr.concat([da.isel(time=0), da.isel(time=[1])], dim="time") xr.align(concat, da, dim="time")

fails when concat and da should be identical. This is causing failures in cf-xarray:https://github.com/xarray-contrib/cf-xarray/issues/319

cc @benbovy

What did you expect to happen?

No response

Minimal Complete Verifiable Example

```Python import numpy as np import xarray as xr

time = xr.DataArray( np.array( ["2013-01-01T00:00:00.000000000", "2013-01-01T06:00:00.000000000"], dtype="datetime64[ns]", ), dims="time", name="time", )

da = time concat = xr.concat([da.isel(time=0), da.isel(time=[1])], dim="time") xr.align(da, concat, join="exact") # works

da = xr.DataArray(np.ones(time.shape), dims="time", coords={"time": time}) concat = xr.concat([da.isel(time=0), da.isel(time=[1])], dim="time") xr.align(da, concat, join="exact") ```

Relevant log output

```

ValueError Traceback (most recent call last) Input In [27], in <module> 17 da = xr.DataArray(np.ones(time.shape), dims="time", coords={"time": time}) 18 concat = xr.concat([da.isel(time=0), da.isel(time=[1])], dim="time") ---> 19 xr.align(da, concat, join="exact")

File ~/work/python/xarray/xarray/core/alignment.py:761, in align(join, copy, indexes, exclude, fill_value, *objects) 566 """ 567 Given any number of Dataset and/or DataArray objects, returns new 568 objects with aligned indexes and dimension sizes. (...) 751 752 """ 753 aligner = Aligner( 754 objects, 755 join=join, (...) 759 fill_value=fill_value, 760 ) --> 761 aligner.align() 762 return aligner.results

File ~/work/python/xarray/xarray/core/alignment.py:549, in Aligner.align(self) 547 self.find_matching_unindexed_dims() 548 self.assert_no_index_conflict() --> 549 self.align_indexes() 550 self.assert_unindexed_dim_sizes_equal() 552 if self.join == "override":

File ~/work/python/xarray/xarray/core/alignment.py:395, in Aligner.align_indexes(self) 393 if need_reindex: 394 if self.join == "exact": --> 395 raise ValueError( 396 "cannot align objects with join='exact' where " 397 "index/labels/sizes are not equal along " 398 "these coordinates (dimensions): " 399 + ", ".join(f"{name!r} {dims!r}" for name, dims in key[0]) 400 ) 401 joiner = self._get_index_joiner(index_cls) 402 joined_index = joiner(matching_indexes)

ValueError: cannot align objects with join='exact' where index/labels/sizes are not equal along these coordinates (dimensions): 'time' ('time',) ```

Anything else we need to know?

No response

Environment

xarray main

What is your issue?

The zarr backend has a list of "valid" encodings that needs to be updated any time zarr adds something new (e.g. https://github.com/pydata/xarray/pull/6348)

https://github.com/pydata/xarray/blob/53172cb1e03a98759faf77ef48efaa64676ad24a/xarray/backends/zarr.py#L215-L234

Can we get rid of this? I don't know the backends code well, but won't all our encoding parameters have been removed by this stage? The raise_on_invalid kwarg suggests so.

@tomwhite points out that zarr will raise a warning: ``` python

zarr.create((1,), blah=1) /Users/tom/miniconda/envs/sgkit-3.8/lib/python3.8/site-packages/zarr/creation.py:221: UserWarning: ignoring keyword argument 'blah' warn('ignoring keyword argument %r' % k) <zarr.core.Array (1,) float64> ```

• xref #5409
• [x] Tests added
• [x] Passes pre-commit run --all-files

Some questions: 1. flake8 fails with some false positives. What do we do about that? 2. I am importing the da and ds fitures from test_dataarray and test_dataset. Is that the pattern we want to follow?

xarray/tests/test_coarsen.py:9:1: F401 '.test_dataarray.da' imported but unused xarray/tests/test_coarsen.py:10:1: F401 '.test_dataset.ds' imported but unused xarray/tests/test_coarsen.py:13:36: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:20:26: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:35:25: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:51:5: F811 redefinition of unused 'da' from line 9 xarray/tests/test_coarsen.py:62:5: F811 redefinition of unused 'da' from line 9 xarray/tests/test_coarsen.py:84:5: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:156:5: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:186:25: F811 redefinition of unused 'ds' from line 10 xarray/tests/test_coarsen.py:217:5: F811 redefinition of unused 'da' from line 9 xarray/tests/test_coarsen.py:269:5: F811 redefinition of unused 'da' from line 9

Any thoughts on adding

1. .chunksize or .nbytes_chunk
2. .ntasks : this would be len(obj.__dask_graph__())

• [x] Closes #4373
• [x] Tests added
• [x] Passes isort . && black . && mypy . && flake8
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [x] New functions/methods are listed in api.rst

I could use some help with adding tests and parameter checking if someone wants to help :)

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

ds = xr.Dataset() ds.coords["x"] = ("x", np.arange(10)) ds.coords["y"] = ("y", np.arange(20)) ds.coords["t"] = ("t", np.arange(4)) ds["u"] = np.sin((ds.x - 5) / 5) * np.sin((ds.y - 10) / 10) ds["v"] = np.sin((ds.x - 5) / 5) * np.cos((ds.y - 10) / 10)

ds = ds * 2*np.cos((ds.t) * 2 * 3.14 /0.75)

ds["u"].attrs["units"] = "m/s" ds["mag"] = np.hypot(ds.u, ds.v)

ds.mag.plot(col="t", x="x")

fg = ds.plot.quiver(x="x", y="y", u="u", v="v", col="t", hue="mag") ```

What happened:

The HTML repr is using the first element in a chunks tuple;

What you expected to happen:

it should be using whatever dask does in this case

Minimal Complete Verifiable Example:

```python

import xarray as xr import dask

test = xr.DataArray( dask.array.zeros( (12, 901, 1001), chunks=( (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), (1, 899, 1), (1, 199, 1, 199, 1, 199, 1, 199, 1, 199, 1), ), ) ) test.to_dataset(name="a") ```

EDIT: The text repr has the same issue <xarray.Dataset> Dimensions: (dim_0: 12, dim_1: 901, dim_2: 1001) Dimensions without coordinates: dim_0, dim_1, dim_2 Data variables: a (dim_0, dim_1, dim_2) float64 dask.array<chunksize=(1, 1, 1), meta=np.ndarray>

• [x] Closes #4019
• [x] Tests added
• [x] Passes isort -rc . && black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

~Doesn't have a proper test. Need some help here~. cc @bnaul

• [x] Closes #3569
• [x] Tests added
• [x] Passes isort -rc . && black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

Not sure how to test this.

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 ```

• [x] Closes #3681
• [x] Tests added
• [x] Passes isort -rc . && black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

I added an example on using apply_ufunc to vectorize a 1D example over a DataArray. Comments and feedback welcome.

I added an example of using numba's guvectorize too. Thoughts on keeping that bit?

• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

cc @rabernat @TomNicholas

MCVE Code Sample

Example 1

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

da = xr.DataArray(np.random.randn(10, 20), name="a") ds = da.to_dataset() xr.testing.assert_equal(da, da.chunk({"dim_0": 2})) # works xr.testing.assert_equal(da.chunk(), da.chunk({"dim_0": 2})) # works

xr.testing.assert_equal(ds, ds.chunk({"dim_0": 2})) # works xr.testing.assert_equal(ds.chunk(), ds.chunk({"dim_0": 2})) # does not work

``` I get

```

ValueError Traceback (most recent call last) <ipython-input-1-bc8216a67408> in <module> 8 9 xr.testing.assert_equal(ds, ds.chunk({"dim_0": 2})) # works ---> 10 xr.testing.assert_equal(ds.chunk(), ds.chunk({"dim_0": 2})) # does not work

~/work/python/xarray/xarray/testing.py in assert_equal(a, b) 56 assert a.equals(b), formatting.diff_array_repr(a, b, "equals") 57 elif isinstance(a, Dataset): ---> 58 assert a.equals(b), formatting.diff_dataset_repr(a, b, "equals") 59 else: 60 raise TypeError("{} not supported by assertion comparison".format(type(a)))

~/work/python/xarray/xarray/core/dataset.py in equals(self, other) 1322 """ 1323 try: -> 1324 return self._all_compat(other, "equals") 1325 except (TypeError, AttributeError): 1326 return False

~/work/python/xarray/xarray/core/dataset.py in _all_compat(self, other, compat_str) 1285 1286 return self._coord_names == other._coord_names and utils.dict_equiv( -> 1287 self._variables, other._variables, compat=compat 1288 ) 1289

~/work/python/xarray/xarray/core/utils.py in dict_equiv(first, second, compat) 335 """ 336 for k in first: --> 337 if k not in second or not compat(first[k], second[k]): 338 return False 339 for k in second:

~/work/python/xarray/xarray/core/dataset.py in compat(x, y) 1282 # require matching order for equality 1283 def compat(x: Variable, y: Variable) -> bool: -> 1284 return getattr(x, compat_str)(y) 1285 1286 return self._coord_names == other._coord_names and utils.dict_equiv(

~/work/python/xarray/xarray/core/variable.py in equals(self, other, equiv) 1558 try: 1559 return self.dims == other.dims and ( -> 1560 self._data is other._data or equiv(self.data, other.data) 1561 ) 1562 except (TypeError, AttributeError):

~/work/python/xarray/xarray/core/duck_array_ops.py in array_equiv(arr1, arr2) 201 warnings.filterwarnings("ignore", "In the future, 'NAT == x'") 202 flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) --> 203 return bool(flag_array.all()) 204 205

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/array/core.py in bool(self) 1380 ) 1381 else: -> 1382 return bool(self.compute()) 1383 1384 nonzero = bool # python 2

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/base.py in compute(self, kwargs) 173 dask.base.compute 174 """ --> 175 (result,) = compute(self, traverse=False, kwargs) 176 return result 177

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/base.py in compute(args, kwargs) 444 keys = [x.dask_keys() for x in collections] 445 postcomputes = [x.dask_postcompute() for x in collections] --> 446 results = schedule(dsk, keys, kwargs) 447 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)]) 448

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/threaded.py in get(dsk, result, cache, num_workers, pool, kwargs) 80 get_id=_thread_get_id, 81 pack_exception=pack_exception, ---> 82 kwargs 83 ) 84

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs) 489 _execute_task(task, data) # Re-execute locally 490 else: --> 491 raise_exception(exc, tb) 492 res, worker_id = loads(res_info) 493 state["cache"][key] = res

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/compatibility.py in reraise(exc, tb) 128 if exc.traceback is not tb: 129 raise exc.with_traceback(tb) --> 130 raise exc 131 132 import pickle as cPickle

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/local.py in execute_task(key, task_info, dumps, loads, get_id, pack_exception) 231 try: 232 task, data = loads(task_info) --> 233 result = _execute_task(task, data) 234 id = get_id() 235 result = dumps((result, id))

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk) 117 func, args = arg[0], arg[1:] 118 args2 = [_execute_task(a, cache) for a in args] --> 119 return func(*args2) 120 elif not ishashable(arg): 121 return arg

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/optimization.py in call(self, *args) 1057 if not len(args) == len(self.inkeys): 1058 raise ValueError("Expected %d args, got %d" % (len(self.inkeys), len(args))) -> 1059 return core.get(self.dsk, self.outkey, dict(zip(self.inkeys, args))) 1060 1061 def reduce(self):

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/core.py in get(dsk, out, cache) 147 for key in toposort(dsk): 148 task = dsk[key] --> 149 result = _execute_task(task, cache) 150 cache[key] = result 151 result = _execute_task(out, cache)

~/miniconda3/envs/dcpy/lib/python3.7/site-packages/dask/core.py in _execute_task(arg, cache, dsk) 117 func, args = arg[0], arg[1:] 118 args2 = [_execute_task(a, cache) for a in args] --> 119 return func(*args2) 120 elif not ishashable(arg): 121 return arg

ValueError: operands could not be broadcast together with shapes (0,20) (2,20) ```

Example 2

The relevant xarray line in the previous traceback is flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)), so I tried

python (ds.isnull() & ds.chunk({"dim_0": 1}).isnull()).compute() # works (ds.chunk().isnull() & ds.chunk({"dim_0": 1}).isnull()).compute() # does not work?!

```

ValueError Traceback (most recent call last) <ipython-input-4-abdfbeda355a> in <module> 1 (ds.isnull() & ds.chunk({"dim_0": 1}).isnull()).compute() # works ----> 2 (ds.chunk().isnull() & ds.chunk({"dim_0": 1}).isnull()).compute() # does not work?!

~/work/python/xarray/xarray/core/dataset.py in compute(self, kwargs) 791 """ 792 new = self.copy(deep=False) --> 793 return new.load(kwargs) 794 795 def _persist_inplace(self, **kwargs) -> "Dataset":

~/work/python/xarray/xarray/core/dataset.py in load(self, **kwargs) 645 646 for k, data in zip(lazy_data, evaluated_data): --> 647 self.variables[k].data = data 648 649 # load everything else sequentially

~/work/python/xarray/xarray/core/variable.py in data(self, data) 331 data = as_compatible_data(data) 332 if data.shape != self.shape: --> 333 raise ValueError("replacement data must match the Variable's shape") 334 self._data = data 335

ValueError: replacement data must match the Variable's shape ```

Problem Description

I don't know what's going on here. I expect assert_equal should return True for all these examples.

Our test for isnull with dask always calls load before comparing:

def test_isnull_with_dask(): da = construct_dataarray(2, np.float32, contains_nan=True, dask=True) assert isinstance(da.isnull().data, dask_array_type) assert_equal(da.isnull().load(), da.load().isnull())

Output of xr.show_versions()

xarray master & dask 2.3.0

Dask has updated it's percentile, quantile implementation: https://github.com/dask/dask/pull/4677

Can we now update our median method to work with dask arrays?

Dask arrays with the same graph have the same name. We can use this to quickly compare dask-backed variables without computing. (see https://github.com/pydata/xarray/issues/3068#issuecomment-508853564)

I will work on adding extra tests but could use feedback on the approach.

• [x] Closes #3068, closes #3311, closes #3454
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

@djhoese, thanks for the great example code!

• [x] Closes #508
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

Downstream issue: https://github.com/marbl-ecosys/cesm2-marbl/issues/1

Basically, we are currently raising an error when attempting to merge variables that are present in some datasets but not others that are provided to concat. This seems unnecessarily strict and it turns out we had an issue for it! (#508)

This fixes the following behaviour. (downstream issue https://github.com/xgcm/xgcm/issues/154)

def test_concat(self, data): split_data = [ data.isel(dim1=slice(3)), data.isel(dim1=3), # this wouldn't work on master data.isel(dim1=slice(4, None)), ] assert_identical(data, concat(split_data, "dim1"))

• partially addresses #2909 , closes #2901, closes #2908

Unfortunately my fix in #2935 broke some major functionality. A proper fix would involve some facetgrid refactoring I think; so that'll take some time. This reverts that commit and adds a test.

xr.DataArray(np.random.randn(10, 20)).plot(levels=10, cmap=mpl.cm.RdBu)

• [x] Closes #1354
• [x] Tests added
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

I won't work on it for the next few days if someone else wants to take over...

• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

Adds a labels boolean kwarg for contour that adds contour labels. Also adds clabel_kwargs that is passed on to Axes.clabel()

air.isel(time=0).plot.contour(labels=True, colors='k', clabel_kwargs={'fmt': '%.1f'})

• [x] Closes #2583
• [x] Tests added (for all bug fixes or enhancements)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

```python =================================== FAILURES =================================== ____ TestVariable.test_index_0d_not_a_time _______ self = <xarray.tests.test_variable.TestVariable object at 0x7f0dd7b6bda0> def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d])

  self._assertIndexedLikeNDArray(x, d)

xarray/tests/test_variable.py:197:

self = <xarray.tests.test_variable.TestVariable object at 0x7f0dd7b6bda0> variable = <xarray.Variable (x: 1)> array(['NaT'], dtype='datetime64[ns]') expected_value0 = numpy.datetime64('NaT'), expected_dtype = None def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ assert variable[0].shape == () assert variable[0].ndim == 0 assert variable[0].size == 1 # test identity assert variable.equals(variable.copy()) assert variable.identical(variable.copy()) # check value is equal for both ndarray and Variable with warnings.catch_warnings(): warnings.filterwarnings('ignore', "In the future, 'NAT == x'")

      assert variable.values[0] == expected_value0

E AssertionError: assert numpy.datetime64('NaT') == numpy.datetime64('NaT') E -numpy.datetime64('NaT') E +numpy.datetime64('NaT') xarray/tests/test_variable.py:143: AssertionError

____ TestVariableWithDask.test_index_0d_not_a_time _____ self = <xarray.tests.test_variable.TestVariableWithDask object at 0x7f0e00bc5978> def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d])

  self._assertIndexedLikeNDArray(x, d)

xarray/tests/test_variable.py:197:

self = <xarray.tests.test_variable.TestVariableWithDask object at 0x7f0e00bc5978> variable = <xarray.Variable (x: 1)> dask.array<shape=(1,), dtype=datetime64[ns], chunksize=(1,)> expected_value0 = numpy.datetime64('NaT'), expected_dtype = None def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ assert variable[0].shape == () assert variable[0].ndim == 0 assert variable[0].size == 1 # test identity assert variable.equals(variable.copy()) assert variable.identical(variable.copy()) # check value is equal for both ndarray and Variable with warnings.catch_warnings(): warnings.filterwarnings('ignore', "In the future, 'NAT == x'")

      assert variable.values[0] == expected_value0

E AssertionError: assert numpy.datetime64('NaT') == numpy.datetime64('NaT') E -numpy.datetime64('NaT') E +numpy.datetime64('NaT') xarray/tests/test_variable.py:143: AssertionError

___ TestIndexVariable.testindex_0d_not_a_time ____ self = <xarray.tests.test_variable.TestIndexVariable object at 0x7f0e01063390> def test_index_0d_not_a_time(self): d = np.datetime64('NaT', 'ns') x = self.cls(['x'], [d])

  self._assertIndexedLikeNDArray(x, d)

xarray/tests/test_variable.py:197:

self = <xarray.tests.test_variable.TestIndexVariable object at 0x7f0e01063390> variable = <xarray.IndexVariable 'x' (x: 1)> array(['NaT'], dtype='datetime64[ns]') expected_value0 = numpy.datetime64('NaT'), expected_dtype = None def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None): """Given a 1-dimensional variable, verify that the variable is indexed like a numpy.ndarray. """ assert variable[0].shape == () assert variable[0].ndim == 0 assert variable[0].size == 1 # test identity assert variable.equals(variable.copy()) assert variable.identical(variable.copy()) # check value is equal for both ndarray and Variable with warnings.catch_warnings(): warnings.filterwarnings('ignore', "In the future, 'NAT == x'")

      assert variable.values[0] == expected_value0

E AssertionError: assert numpy.datetime64('NaT') == numpy.datetime64('NaT') E -numpy.datetime64('NaT') E +numpy.datetime64('NaT') xarray/tests/test_variable.py:143: AssertionError ```

• [x] Closes #2381
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

• [x] Tests passed (for all non-documentation changes)

Remove some warnings.

• [x] Closes #2224 (remove if there is no corresponding issue, which should only be the case for minor changes)
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

This PR adds support for xscale, yscale, xticks, yticks, xlim, ylim kwargs following discussion in #2224 and the Pandas API (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot).

Haven't added FacetGrid support yet. I'll get to that soon.

It would be useful to add the boolean kwargs logx, logy, loglog to DataArray.plot() just as in pandas.DataFrame.plot()

https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html#pandas.DataFrame.plot

• [x] Closes #2284
• [x] Tests added (for all bug fixes or enhancements)
• [x] Tests passed (for all non-documentation changes)
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API (remove if this change should not be visible to users, e.g., if it is an internal clean-up, or if this is part of a larger project that will be documented later)

```python da = xr.DataArray([1, 5], dims=['time'], coords={'time': [np.datetime64('2014-05-06'), np.datetime64('2014-05-10')]})

da.interp(time='2014-05-07') <xarray.DataArray ()> array(2.) Coordinates: time datetime64[ns] 2014-05-07 ```

• [ ] Closes #xxxx
• [x] Tests added / passed
• [x] Passes git diff upstream/master **/*py | flake8 --diff
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

This PR teaches plot.contourf() to contour variables with both a 1D co-ordinate (e.g. time) and a 2D co-ordinate (e.g. time-varying depth) da: <xarray.DataArray 'S' (z: 5, time: 85646)> array([[ nan, nan, nan, ..., nan, nan, nan], [ 35.02816 , 34.729567, 34.779223, ..., 34.57513 , 34.671975, 34.334675], [ 35.206943, 35.163239, 34.938674, ..., 34.780728, 34.836331, 34.70386 ], [ nan, 35.184057, 35.10592 , ..., 34.656925, 34.776915, 34.429442], [ 34.85562 , 34.81994 , 35.00963 , ..., 35.014522, 34.9747 , 35.033848]]) Coordinates: depth (z, time) float16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 53.5 ... * time (time) datetime64[ns] 2013-12-19T06:00:01 2013-12-19T06:10:01 ... Dimensions without coordinates: z

Now we can do da.plot(x='time', y='depth')

Couple of questions: 1. I've added a test, but am not sure how to work in an assert statement. 2. How do I test that I haven't messed up the syntax in whats-new.rst