What happened: When using the hue keyword in a scatter plot to color the points based on a string variable, the color assignment in the plot is wrong (whereas the legend is correct).

What you expected to happen: In the example, data of category "A" ranges between 0 and 2 in u-direction and 0 and 0.5 in v-direction. Points in that square should be orange (the color for "A") but currently are blue.

Minimal Complete Verifiable Example:

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

u = np.random.rand(50, 2) * np.array([1, 2]) v = np.random.rand(50, 2) * np.array([1, 0.5])

ds = xr.Dataset( { "u": (("x", "category"), u), "v": (("x", "category"), v), }, coords={"category": ["B", "A"],} )

g = ds.plot.scatter( y="u", x="v", hue="category", ); ```

Anything else we need to know?: I think that this might be related to sorting at some point. If the variable by which I color is sorted alphabetically (["A", "B"] instead of ["B", "A"]), the color assignment is correct.

Not sure if this issue is related to https://github.com/pydata/xarray/issues/4126, bit it looks different to me (the problem is not the legend, but the colors in the plot itself).

Environment:

Code Sample

```python import numpy as np import xarray as xr import dask.array as da import dask from dask.distributed import Client

@dask.delayed def run_sim(n_time): result = np.array([np.random.randn(n_time)]) return result

client = Client()

Parameters

n_sims = 5 n_time = 100 output_file = 'out.nc'

if I use this as output, computing the data after reopening the file

produces an error

out = da.stack([da.from_delayed(run_sim(n_time), (1,n_time,),np.float64) for i in range(n_sims)])

If I use this as output, reopening the netcdf file is no problem

out = np.random.randn(n_sims,2,n_time)

ds = xr.Dataset({'var1': (['realization', 'time'], out[:,0,:])}, coords={'realization': np.arange(n_sims), 'time': np.arange(n_time)*.1})

Save to a netcdf file -> at this point, computations will be carried out

ds.to_netcdf(output_file, engine='netcdf4')

Reopen the file

with xr.open_dataset(output_file, chunks={'realization': 2}, engine='netcdf4')as ds: # Now acces the data ds.compute()

```

Problem description

When I generate a Dataset using a dask delayed object and save the Dataset to a netcdf file, it seems that the file is not properly closed. When trying to reopen it, I get an error (see below). Also, ncdump -h fails on the output file after it has been written. However, after the first unsuccessful attempt to open the file, the file seems to be closed. I can run ncdump -h on it and a second attempt to open it works.

Note that the problem only arises if I

• store output form a dask delayed object in the Dataset (not if I store a simple numpy array of random numbers)
• start a dask.distributed.Client()

This issue is related to my question on stackoverflow.

Traceback of the python code:

```python-traceback

OSError Traceback (most recent call last) <ipython-input-2-83478559c186> in <module>() 36 with xr.open_dataset(output_file, chunks={'realization': 2}, engine='netcdf4')as ds: 37 # Now acces the data ---> 38 ds.compute()

~/miniconda3/lib/python3.6/site-packages/xarray/core/dataset.py in compute(self, kwargs) 592 """ 593 new = self.copy(deep=False) --> 594 return new.load(kwargs) 595 596 def _persist_inplace(self, **kwargs):

~/miniconda3/lib/python3.6/site-packages/xarray/core/dataset.py in load(self, kwargs) 489 490 # evaluate all the dask arrays simultaneously --> 491 evaluated_data = da.compute(*lazy_data.values(), kwargs) 492 493 for k, data in zip(lazy_data, evaluated_data):

~/miniconda3/lib/python3.6/site-packages/dask/base.py in compute(args, kwargs) 400 keys = [x.dask_keys() for x in collections] 401 postcomputes = [x.dask_postcompute() for x in collections] --> 402 results = schedule(dsk, keys, kwargs) 403 return repack([f(r, a) for r, (f, a) in zip(results, postcomputes)]) 404

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, **kwargs) 2191 try: 2192 results = self.gather(packed, asynchronous=asynchronous, -> 2193 direct=direct) 2194 finally: 2195 for f in futures.values():

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in gather(self, futures, errors, maxsize, direct, asynchronous) 1566 return self.sync(self._gather, futures, errors=errors, 1567 direct=direct, local_worker=local_worker, -> 1568 asynchronous=asynchronous) 1569 1570 @gen.coroutine

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in sync(self, func, args, kwargs) 651 return future 652 else: --> 653 return sync(self.loop, func, args, **kwargs) 654 655 def repr(self):

~/miniconda3/lib/python3.6/site-packages/distributed/utils.py in sync(loop, func, args, kwargs) 275 e.wait(10) 276 if error[0]: --> 277 six.reraise(error[0]) 278 else: 279 return result[0]

~/miniconda3/lib/python3.6/site-packages/six.py in reraise(tp, value, tb) 691 if value.traceback is not tb: 692 raise value.with_traceback(tb) --> 693 raise value 694 finally: 695 value = None

~/miniconda3/lib/python3.6/site-packages/distributed/utils.py in f() 260 if timeout is not None: 261 future = gen.with_timeout(timedelta(seconds=timeout), future) --> 262 result[0] = yield future 263 except Exception as exc: 264 error[0] = sys.exc_info()

~/miniconda3/lib/python3.6/site-packages/tornado/gen.py in run(self) 1131 1132 try: -> 1133 value = future.result() 1134 except Exception: 1135 self.had_exception = True

~/miniconda3/lib/python3.6/site-packages/tornado/gen.py in run(self) 1139 if exc_info is not None: 1140 try: -> 1141 yielded = self.gen.throw(*exc_info) 1142 finally: 1143 # Break up a reference to itself

~/miniconda3/lib/python3.6/site-packages/distributed/client.py in _gather(self, futures, errors, direct, local_worker) 1445 six.reraise(type(exception), 1446 exception, -> 1447 traceback) 1448 if errors == 'skip': 1449 bad_keys.add(key)

~/miniconda3/lib/python3.6/site-packages/six.py in reraise(tp, value, tb) 690 value = tp() 691 if value.traceback is not tb: --> 692 raise value.with_traceback(tb) 693 raise value 694 finally:

~/miniconda3/lib/python3.6/site-packages/dask/array/core.py in getter() 87 c = a[b] 88 if asarray: ---> 89 c = np.asarray(c) 90 finally: 91 if lock:

~/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray() 490 491 """ --> 492 return array(a, dtype, copy=False, order=order) 493 494

~/miniconda3/lib/python3.6/site-packages/xarray/core/indexing.py in array() 600 601 def array(self, dtype=None): --> 602 return np.asarray(self.array, dtype=dtype) 603 604 def getitem(self, key):

~/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py in asarray() 490 491 """ --> 492 return array(a, dtype, copy=False, order=order) 493 494

~/miniconda3/lib/python3.6/site-packages/xarray/core/indexing.py in array() 506 def array(self, dtype=None): 507 array = as_indexable(self.array) --> 508 return np.asarray(array[self.key], dtype=None) 509 510 def transpose(self, order):

~/miniconda3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py in getitem() 62 getitem = operator.getitem 63 ---> 64 with self.datastore.ensure_open(autoclose=True): 65 try: 66 array = getitem(self.get_array(), key.tuple)

~/miniconda3/lib/python3.6/contextlib.py in enter() 79 def enter(self): 80 try: ---> 81 return next(self.gen) 82 except StopIteration: 83 raise RuntimeError("generator didn't yield") from None

~/miniconda3/lib/python3.6/site-packages/xarray/backends/common.py in ensure_open() 502 if not self._isopen: 503 try: --> 504 self._ds = self._opener() 505 self._isopen = True 506 yield

~/miniconda3/lib/python3.6/site-packages/xarray/backends/netCDF4_.py in _open_netcdf4_group() 229 import netCDF4 as nc4 230 --> 231 ds = nc4.Dataset(filename, mode=mode, **kwargs) 232 233 with close_on_error(ds):

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.init()

netCDF4/_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()

OSError: [Errno -101] NetCDF: HDF error: b'/home/user/code/test/out.nc'

`` Output ofncdump -h` after writing the file (before reopening):

``` HDF5-DIAG: Error detected in HDF5 (1.10.2) thread 139952254916352: #000: H5F.c line 511 in H5Fopen(): unable to open file major: File accessibilty minor: Unable to open file #001: H5Fint.c line 1519 in H5F_open(): unable to lock the file major: File accessibilty minor: Unable to open file #002: H5FD.c line 1650 in H5FD_lock(): driver lock request failed major: Virtual File Layer minor: Can't update object #003: H5FDsec2.c line 941 in H5FD_sec2_lock(): unable to lock file, errno = 11, error message = 'Resource temporarily unavailable' major: File accessibilty minor: Bad file ID accessed ncdump: out.nc: NetCDF: HDF error

```

Expected Output

The netcdf-file is closed after writing it with to_netcdf().

Output of xr.show_versions()