issue_comments

closing as I believe this to be fixed by #1648

bisecting tells me that this seems to have been fixed by 8632cd7b3f468865db4dc9e5c09075f941dc70f2 (#1648): ```python import xarray as xr import numpy as np

da = xr.DataArray( np.linspace(0, 1, 2 * 3 * 4).reshape(2, 3, 4), coords={"x": ["a", "b"], "y": ["x", "y", "z"], "z": np.arange(4)}, dims=("x", "y", "z"), name="abc", ) da.loc["a", "x"].to_dataset().to_netcdf("test.nc") xr.open_dataset("test.nc").load() # to verify that the netcdf file is valid ``` only fails before that commit.

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It looks like we have some sort of special logic that calls len() when it shouldn't, thereby precluding scaling scalars string variables in netCDF files.

Yeah, I tend to agree, there should be some sort of auto-magic happening. But, I can think of at least two options:

1. Coerce to array-like, like you do manually in your first comment here. That makes sense if the dimension is important, i.e. it carries useful metadata or encodes something important.

2. Coerce to an attribute on the Dataset.

I use workflows where I concatenate things like multiple ensemble members into a single file, and I wind up with this pattern all the time. I usually just drop() the offending coordinate, and save it as part of the output filename. This is because tools like cdo really, really don't like non lat-lon-time dimensions, so that can interrupt my workflow sometimes. Saving as an attribute bypasses this issue, but then you lose the ability to retain any metadata that was associated with that coordinate.

Thanks, @darothen ! However, I believe I shouldn't have to, but xarray should do the right thing automatically.

Maybe I want to use the dataset after saving for something else, which would mean I'd have to do something like d_.drop(['category', 'species']).to_netcdf(...), which I don't like too much ...

@andreas-h you can drop the 0D dimensions:

python d_ = d_.drop(['category', 'species']) d_.to_netcdf(...)

Sure :smile:

I first open a DataArray:

grid_eu_coarse_noborder = xr.open_dataarray('tno-macc3_eu-no-nld-no-border_area.nc')
grid_eu_coarse_noborder

<xarray.DataArray 'TNO-MACC3_EU' (species: 12, category: 15, lon: 720, lat: 672)>
[87091200 values with dtype=float64]
Coordinates:
  * lat       (lat) float64 30.03 30.09 30.16 30.22 30.28 30.34 30.41 30.47 ...
  * category  (category) object 'pow' 'res' 'inc' 'pei' 'exf' 'sol' 'tra1' ...
  * species   (species) object 'CH4' 'CO' 'NH3' 'NMVOC' 'NOX' 'BC_1' ...
  * lon       (lon) float64 -29.94 -29.81 -29.69 -29.56 -29.44 -29.31 -29.19 ...

from which I select using .loc[] and convert to Dataset:

grid_eu_coarse_noborder.loc['NOX', 'pow'].to_dataset()

<xarray.Dataset>
Dimensions:       (lat: 672, lon: 720)
Coordinates:
  * lat           (lat) float64 30.03 30.09 30.16 30.22 30.28 30.34 30.41 ...
    category      <U3 'pow'
    species       <U3 'NOX'
  * lon           (lon) float64 -29.94 -29.81 -29.69 -29.56 -29.44 -29.31 ...
Data variables:
    TNO-MACC3_EU  (lon, lat) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...

which then cannot be saved:

grid_eu_coarse_noborder.loc['NOX', 'pow'].to_dataset().to_netcdf('test.nc')


---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/core/utils.py in __len__(self)
    395         try:
--> 396             return self.shape[0]
    397         except IndexError:

IndexError: tuple index out of range

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-8-fb260f3bec4b> in <module>()
----> 1 grid_eu_coarse_noborder.loc['NOX', 'pow'].to_dataset().to_netcdf('test.nc')

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/core/dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims)
    951         return to_netcdf(self, path, mode, format=format, group=group,
    952                          engine=engine, encoding=encoding,
--> 953                          unlimited_dims=unlimited_dims)
    954 
    955     def __unicode__(self):

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/api.py in to_netcdf(dataset, path, mode, format, group, engine, writer, encoding, unlimited_dims)
    567     try:
    568         dataset.dump_to_store(store, sync=sync, encoding=encoding,
--> 569                               unlimited_dims=unlimited_dims)
    570         if isinstance(path, BytesIO):
    571             return path.getvalue()

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/core/dataset.py in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims)
    891 
    892         store.store(variables, attrs, check_encoding,
--> 893                     unlimited_dims=unlimited_dims)
    894         if sync:
    895             store.sync()

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/common.py in store(self, variables, attributes, *args, **kwargs)
    233         cf_variables, cf_attrs = cf_encoder(variables, attributes)
    234         AbstractWritableDataStore.store(self, cf_variables, cf_attrs,
--> 235                                         *args, **kwargs)
    236 
    237

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, unlimited_dims)
    202         self.set_attributes(attributes)
    203         self.set_variables(variables, check_encoding_set,
--> 204                            unlimited_dims=unlimited_dims)
    205 
    206     def set_attributes(self, attributes):

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/common.py in set_variables(self, variables, check_encoding_set, unlimited_dims)
    214             check = vn in check_encoding_set
    215             target, source = self.prepare_variable(
--> 216                 name, v, check, unlimited_dims=unlimited_dims)
    217             self.writer.add(source, target)
    218

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/netCDF4_.py in prepare_variable(self, name, variable, check_encoding, unlimited_dims)
    272 
    273         if self.format == 'NETCDF4':
--> 274             variable, datatype = _nc4_values_and_dtype(variable)
    275         else:
    276             variable = encode_nc3_variable(variable)

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/backends/netCDF4_.py in _nc4_values_and_dtype(var)
     78     if var.dtype.kind == 'U':
     79         # this entire clause should not be necessary with netCDF4>=1.0.9
---> 80         if len(var) > 0:
     81             var = var.astype('O')
     82         dtype = str

/home/eb/software/LAMOSpy/0.2-intel-2016a-Python-3.5.1/lib/python3.5/site-packages/xarray-0.9.1-py3.5.egg/xarray/core/utils.py in __len__(self)
    396             return self.shape[0]
    397         except IndexError:
--> 398             raise TypeError('len() of unsized object')
    399 
    400

TypeError: len() of unsized object

Sorry I missed this earlier.

Do you know how you created this Dataset? Xarray should not let you make a dataset with a 0D dimension variable: that is a violation of its data model.