Problem description

Given the original time dimension: python ds = xr.open_mfdataset("C:\\Users\\janis\\.cate\\data_stores\\local\\local.SST_should_fail\\*.nc", decode_cf=False) <xarray.DataArray 'time' (time: 32)> array([788961600, 789048000, 789134400, 789220800, 789307200, 789393600, 789480000, 789566400, 789652800, 789739200, 789825600, 789912000, 789998400, 790084800, 790171200, 790257600, 790344000, 790430400, 790516800, 790603200, 790689600, 790776000, 790862400, 790948800, 791035200, 791121600, 791208000, 791294400, 791380800, 791467200, 791553600, 791640000], dtype=int64) Coordinates: * time (time) int64 788961600 789048000 789134400 789220800 789307200 ... Attributes: standard_name: time axis: T comment: bounds: time_bnds long_name: reference time of sst file _ChunkSizes: 1 units: seconds since 1981-01-01 calendar: gregorian Produces this decoded time dimension with xarray >= 0.10.1: python ds = xr.open_mfdataset("C:\\Users\\janis\\.cate\\data_stores\\local\\local.SST_should_fail\\*.nc", decode_cf=True) <xarray.DataArray 'time' (time: 32)> array(['1981-01-01T00:00:00.627867648', '1980-12-31T23:59:58.770774016', '1981-01-01T00:00:01.208647680', '1980-12-31T23:59:59.351554048', '1981-01-01T00:00:01.789427712', '1980-12-31T23:59:59.932334080', '1980-12-31T23:59:58.075240448', '1981-01-01T00:00:00.513114112', '1980-12-31T23:59:58.656020480', '1981-01-01T00:00:01.093894144', '1980-12-31T23:59:59.236800512', '1981-01-01T00:00:01.674674176', '1980-12-31T23:59:59.817580544', '1980-12-31T23:59:57.960486912', '1981-01-01T00:00:00.398360576', '1980-12-31T23:59:58.541266944', '1981-01-01T00:00:00.979140608', '1980-12-31T23:59:59.122046976', '1981-01-01T00:00:01.559920640', '1980-12-31T23:59:59.702827008', '1981-01-01T00:00:02.140700672', '1981-01-01T00:00:00.283607040', '1980-12-31T23:59:58.426513408', '1981-01-01T00:00:00.864387072', '1980-12-31T23:59:59.007293440', '1981-01-01T00:00:01.445167104', '1980-12-31T23:59:59.588073472', '1981-01-01T00:00:02.025947136', '1981-01-01T00:00:00.168853504', '1980-12-31T23:59:58.311759872', '1981-01-01T00:00:00.749633536', '1980-12-31T23:59:58.892539904'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1981-01-01T00:00:00.627867648 ... Attributes: standard_name: time axis: T comment: bounds: time_bnds long_name: reference time of sst file _ChunkSizes: 1

Expected Output

With xarray == 0.10.0 the output is as expected: python ds = xr.open_mfdataset("C:\\Users\\janis\\.cate\\data_stores\\local\\local.SST_should_fail\\*.nc", decode_cf=True) <xarray.DataArray 'time' (time: 32)> array(['2006-01-01T12:00:00.000000000', '2006-01-02T12:00:00.000000000', '2006-01-03T12:00:00.000000000', '2006-01-04T12:00:00.000000000', '2006-01-05T12:00:00.000000000', '2006-01-06T12:00:00.000000000', '2006-01-07T12:00:00.000000000', '2006-01-08T12:00:00.000000000', '2006-01-09T12:00:00.000000000', '2006-01-10T12:00:00.000000000', '2006-01-11T12:00:00.000000000', '2006-01-12T12:00:00.000000000', '2006-01-13T12:00:00.000000000', '2006-01-14T12:00:00.000000000', '2006-01-15T12:00:00.000000000', '2006-01-16T12:00:00.000000000', '2006-01-17T12:00:00.000000000', '2006-01-18T12:00:00.000000000', '2006-01-19T12:00:00.000000000', '2006-01-20T12:00:00.000000000', '2006-01-21T12:00:00.000000000', '2006-01-22T12:00:00.000000000', '2006-01-23T12:00:00.000000000', '2006-01-24T12:00:00.000000000', '2006-01-25T12:00:00.000000000', '2006-01-26T12:00:00.000000000', '2006-01-27T12:00:00.000000000', '2006-01-28T12:00:00.000000000', '2006-01-29T12:00:00.000000000', '2006-01-30T12:00:00.000000000', '2006-01-31T12:00:00.000000000', '2006-02-01T12:00:00.000000000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2006-01-01T12:00:00 2006-01-02T12:00:00 ... Attributes: standard_name: time axis: T comment: bounds: time_bnds long_name: reference time of sst file _ChunkSizes: 1

Output of xr.show_versions()

We're close to a minor/bug-fix release (0.10.1). What do we need to get done before that can happen?

• [x] #1800 Performance improvements to Zarr (@jhamman)
• [ ] #1793 Fix for to_netcdf writes with dask-distributed (@jhamman, could use help)
• [x] #1819 Normalisation for RGB imshow

Help wanted / bugs that no-one is working on: - [ ] #1792 Comparison to masked numpy arrays - [ ] #1764 groupby_bins fails for empty bins

What else?

This is what is getting rendered on RTD http://xarray.pydata.org/en/latest/io.html#zarr

``` In [26]: ds = xr.Dataset({'foo': (('x', 'y'), np.random.rand(4, 5))}, ....: coords={'x': [10, 20, 30, 40], ....: 'y': pd.date_range('2000-01-01', periods=5), ....: 'z': ('x', list('abcd'))}) ....:

In [27]: ds.to_zarr('path/to/directory.zarr')

AttributeError Traceback (most recent call last) <ipython-input-27-8c5f1b00edbc> in <module>() ----> 1 ds.to_zarr('path/to/directory.zarr')

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/core/dataset.py in to_zarr(self, store, mode, synchronizer, group, encoding) 1165 from ..backends.api import to_zarr 1166 return to_zarr(self, store=store, mode=mode, synchronizer=synchronizer, -> 1167 group=group, encoding=encoding) 1168 1169 def unicode(self):

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/backends/api.py in to_zarr(dataset, store, mode, synchronizer, group, encoding) 752 # I think zarr stores should always be sync'd immediately 753 # TODO: figure out how to properly handle unlimited_dims --> 754 dataset.dump_to_store(store, sync=True, encoding=encoding) 755 return store

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/core/dataset.py in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims) 1068 1069 store.store(variables, attrs, check_encoding, -> 1070 unlimited_dims=unlimited_dims) 1071 if sync: 1072 store.sync()

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/backends/zarr.py in store(self, variables, attributes, args, kwargs) 378 def store(self, variables, attributes, args, kwargs): 379 AbstractWritableDataStore.store(self, variables, attributes, --> 380 *args, kwargs) 381 382

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, unlimited_dims) 275 variables, attributes = self.encode(variables, attributes) 276 --> 277 self.set_attributes(attributes) 278 self.set_dimensions(variables, unlimited_dims=unlimited_dims) 279 self.set_variables(variables, check_encoding_set,

/home/docs/checkouts/readthedocs.org/user_builds/xray/conda/latest/lib/python3.5/site-packages/xarray-0.10.0+dev55.g1d32399-py3.5.egg/xarray/backends/zarr.py in set_attributes(self, attributes) 341 342 def set_attributes(self, attributes): --> 343 self.ds.attrs.put(attributes) 344 345 def encode_variable(self, variable):

AttributeError: 'Attributes' object has no attribute 'put' ```

Code Sample, a copy-pastable example if possible

```python import xarray as xr import netCDF4 as nc product = '/Users/stefano/src/s5p/products/NO2/'\ 'S5P_OFFL_L2__NO2____20171107T195219_20171107T213349_00361_01_001107_20171108T122727.nc'

opening the product with netCDF4 works fine

no2 = nc.Dataset(product) no2.groups

correctly shows groups' content

OrderedDict([('PRODUCT', <class 'netCDF4._netCDF4.Group'> group /PRODUCT: dimensions(sizes): scanline(5640), ground_pixel(450), corner(4), time(1), polynomial_exponents(6), layer(34), vertices(2) variables(dimensions): int32 scanline(scanline), int32 ground_pixel(ground_pixel), int32 time(time), int32 corner(corner), int32 polynomial_exponents(polynomial_exponents), int32 layer(layer), int32 vertices(vertices), float32 latitude(time,scanline,ground_pixel), float32 longitude(time,scanline,ground_pixel), int32 delta_time(time,scanline), <class 'str'> time_utc(time,scanline), uint8 qa_value(time,scanline,ground_pixel), float32 nitrogendioxide_tropospheric_column(time,scanline,ground_pixel), float32 nitrogendioxide_tropospheric_column_precision(time,scanline,ground_pixel), float32 averaging_kernel(time,scanline,ground_pixel,layer), float32 air_mass_factor_troposphere(time,scanline,ground_pixel), float32 air_mass_factor_total(time,scanline,ground_pixel), int32 tm5_tropopause_layer_index(time,scanline,ground_pixel), float32 tm5_constant_a(layer,vertices), float32 tm5_constant_b(layer,vertices) groups: SUPPORT_DATA), ('METADATA', <class 'netCDF4._netCDF4.Group'> group /METADATA: dimensions(sizes): variables(dimensions): groups: QA_STATISTICS, ALGORITHM_SETTINGS, GRANULE_DESCRIPTION, ISO_METADATA, EOP_METADATA, ESA_METADATA)])

opening the product with xarray raises an UnboundLocalError exception

no2 = xr.open_dataset(product, group='/PRODUCT') ```

```

UnboundLocalError Traceback (most recent call last) <ipython-input-26-2052b95f2d95> in <module>() ----> 1 no2 = xr.open_dataset(product, group='/PRODUCT')

/Users/stefano/anaconda/lib/python3.6/site-packages/xarray/backends/api.py in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables) 303 lock = _default_lock(filename_or_obj, engine) 304 with close_on_error(store): --> 305 return maybe_decode_store(store, lock) 306 else: 307 if engine is not None and engine != 'scipy':

/Users/stefano/anaconda/lib/python3.6/site-packages/xarray/backends/api.py in maybe_decode_store(store, lock) 223 store, mask_and_scale=mask_and_scale, decode_times=decode_times, 224 concat_characters=concat_characters, decode_coords=decode_coords, --> 225 drop_variables=drop_variables) 226 227 _protect_dataset_variables_inplace(ds, cache)

/Users/stefano/anaconda/lib/python3.6/site-packages/xarray/conventions.py in decode_cf(obj, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables) 1153 vars, attrs, coord_names = decode_cf_variables( 1154 vars, attrs, concat_characters, mask_and_scale, decode_times, -> 1155 decode_coords, drop_variables=drop_variables) 1156 ds = Dataset(vars, attrs=attrs) 1157 ds = ds.set_coords(coord_names.union(extra_coords).intersection(vars))

/Users/stefano/anaconda/lib/python3.6/site-packages/xarray/conventions.py in decode_cf_variables(variables, attributes, concat_characters, mask_and_scale, decode_times, decode_coords, drop_variables) 1086 k, v, concat_characters=concat_characters, 1087 mask_and_scale=mask_and_scale, decode_times=decode_times, -> 1088 stack_char_dim=stack_char_dim) 1089 if decode_coords: 1090 var_attrs = new_vars[k].attrs

/Users/stefano/anaconda/lib/python3.6/site-packages/xarray/conventions.py in decode_cf_variable(name, var, concat_characters, mask_and_scale, decode_times, decode_endianness, stack_char_dim) 998 if (has_fill or scale_factor is not None or add_offset is not None): 999 if has_fill and np.array(fill_value).dtype.kind in ['U', 'S', 'O']: -> 1000 if string_encoding is not None: 1001 raise NotImplementedError( 1002 'variable %r has a _FillValue specified, but '

UnboundLocalError: local variable 'string_encoding' referenced before assignment python

Opening another group with xarray works fine

no2 = xr.open_dataset(product, group='/METADATA/QA_STATISTICS') no2 <xarray.Dataset> Dimensions: (nitrogendioxide_stratospheric_column_histogram_axis: 100, nitrogendioxide_stratospheric_column_pdf_axis: 400, nitrogendioxide_total_column_histogram_axis: 100, nitrogendioxide_total_column_pdf_axis: 400, nitrogendioxide_tropospheric_column_histogram_axis: 100, nitrogendioxide_tropospheric_column_pdf_axis: 400, vertices: 2) [...] ```

Problem description

An UnboundLocalError exception is raised while trying to open a netCDF file on a specific group ('/PRODUCT'). Opening the file within another group works fine. The same file can be correctly opened with the netCDF4 library.

Expected Output

File correctly loaded.

Output of xr.show_versions()

Code Sample, a copy-pastable example if possible

See my code here

Problem description

I received AttributeError: 'PydapArrayWrapper' object has no attribute 'shape' when I tried to open PydapDataStore. However, everything works when I use pydap instead of xarray.

Expected Output

xarray.Dataset object

Output of xr.show_versions()