issues

What happened:

Reading and writing zarr dataset multiple times into different paths changes bool dtype arrays to int8. I think this issue is related to #2937.

What you expected to happen:

My array's dtype in numpy/dask should not change, even if certain storage backends store dtypes a certain way.

Minimal Complete Verifiable Example:

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

ds = xr.Dataset({ "bool_field": xr.DataArray( np.random.randn(5) < 0.5, dims=('g'), coords={'g': np.arange(5)} ) }) ds.to_zarr('test.zarr', mode="w")

d2 = xr.open_zarr('test.zarr') print(d2.bool_field.dtype) print(d2.bool_field.encoding) d2.to_zarr("test2.zarr", mode="w")

d3 = xr.open_zarr('test2.zarr') print(d3.bool_field.dtype) `` The above snippet prints the following. In d3, the dtype ofbool_fieldisint8, presumably because d3 inherited d2'sencodingand it saysint8, despite the array having abool` dtype.

bool {'chunks': (5,), 'compressor': Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0), 'filters': None, 'dtype': dtype('int8')} int8 Anything else we need to know?:

Currently workaround is to explicitly set encodings. This fixes the problem:

python encoding = {k: {"dtype": d2[k].dtype} for k in d2} d2.to_zarr('test2.zarr', mode="w", encoding=encoding)

Environment:

• [x] Closes #3480
• [x] Tests added
• [x] Passes black . && mypy . && flake8
• [x] Fully documented, including whats-new.rst for all changes and api.rst for new API

AFAICT, the type checking in the _validate_datatypes_for_zarr_append is simply too strict, and relaxing it seems to work fine. But this is my first time digging into the xarray source code, so please let me know if this issue is more complex.

MCVE Code Sample

Zarr itself allows appending np.datetime and np.bool types.

```python

path = 'tmp/test.zarr' z1 = zarr.open(path, mode='w', shape=(10,), chunks=(10,), dtype='M8[D]') z1[:] = '1990-01-01' z2 = zarr.open(path, mode='a') a = np.array(['1992-01-01'] * 10, dtype='datetime64[D]') z2.append(a) (20,) z2 <zarr.core.Array (20,) datetime64[D]> ``` But it's equivalent in xarray throws an error:

```

ds = xr.Dataset( ... {'y': (('x',), np.array(['1991-01-01'] * 10, dtype='datetime64[D]'))} ... ) ds.to_zarr('tmp/test_xr.zarr', mode='w') <xarray.backends.zarr.ZarrStore object at 0x31f403170> ds2 = xr.Dataset( ... {'y': (('x',), np.array(['1992-01-01'] * 10, dtype='datetime64[D]'))} ... ) ds2.to_zarr('tmp/test_xr.zarr', mode='a', append_dim='x') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/personal/opt/anaconda3/lib/python3.7/site-packages/xarray/core/dataset.py", line 1616, in to_zarr append_dim=append_dim, File "/Users/personal/opt/anaconda3/lib/python3.7/site-packages/xarray/backends/api.py", line 1304, in to_zarr _validate_datatypes_for_zarr_append(dataset) File "/Users/personal/opt/anaconda3/lib/python3.7/site-packages/xarray/backends/api.py", line 1249, in _validate_datatypes_for_zarr_append check_dtype(k) File "/Users/personal/opt/anaconda3/lib/python3.7/site-packages/xarray/backends/api.py", line 1245, in check_dtype "unicode string or an object".format(var) ValueError: Invalid dtype for data variable: <xarray.DataArray 'y' (x: 10)> array(['1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000', '1992-01-01T00:00:00.000000000'], dtype='datetime64[ns]') Dimensions without coordinates: x dtype must be a subtype of number, a fixed sized string, a fixed size unicode string or an object ```

Expected Output

The append should succeed.

Problem Description

This function in xarray/api.py is too strict on types:

``` def _validate_datatypes_for_zarr_append(dataset): """DataArray.name and Dataset keys must be a string or None"""

def check_dtype(var):
    if (
        not np.issubdtype(var.dtype, np.number)
        and not coding.strings.is_unicode_dtype(var.dtype)
        and not var.dtype == object
    ):
        # and not re.match('^bytes[1-9]+$', var.dtype.name)):
        raise ValueError(
            "Invalid dtype for data variable: {} "
            "dtype must be a subtype of number, "
            "a fixed sized string, a fixed size "
            "unicode string or an object".format(var)
        )

for k in dataset.data_vars.values():
    check_dtype(k)

```

np.datetime64[.] and np.bool are not numbers: ```

np.issubdtype(np.dtype('datetime64[D]'), np.number) False np.issubdtype(np.dtype('bool'), np.number) False ```

Output of xr.show_versions()