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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
199032440 MDU6SXNzdWUxOTkwMzI0NDA= 1193 Reading netcdf containing empty N-d string arrays causes exception petercable 6645714 closed 0     4 2017-01-05T19:00:22Z 2019-08-30T23:18:04Z 2019-08-30T23:18:04Z CONTRIBUTOR      

For example:

``` import xarray as xr import numpy as np

a = np.array([[]], 'str')

ds = xr.Dataset() ds['x'] = (['dim0', 'dim1'], a, {}) ds.to_netcdf('test.nc')

ds = xr.open_dataset('test.nc') ds.load() ```

The exception occurs in the call to conventions.char_to_string:

def char_to_string(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') return arr.view(kind + str(arr.shape[-1]))[..., 0]

... return arr.view(kind + str(arr.shape[-1]))[..., 0] TypeError: data-type must not be 0-sized

I am able to avoid the exception locally by testing that the last dimension is > 0 prior to creating a view:

def char_to_string(arr): """Like netCDF4.chartostring, but faster and more flexible. """ # based on: http://stackoverflow.com/a/10984878/809705 arr = np.array(arr, copy=False, order='C') kind = arr.dtype.kind if kind not in ['U', 'S']: raise ValueError('argument must be a string') if arr.shape[-1] > 0: return arr.view(kind + str(arr.shape[-1]))[..., 0] return arr

 
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   completed xarray 13221727 issue
100601294 MDU6SXNzdWUxMDA2MDEyOTQ= 526 64-bit values are coerced to 32-bit when writing as NETCDF4_CLASSIC petercable 6645714 closed 0     5 2015-08-12T18:06:42Z 2015-09-01T21:02:08Z 2015-09-01T21:02:08Z CONTRIBUTOR      

``` import xray import numpy

ds = xray.Dataset() time_data = numpy.arange(10.0, 100.0, .5) raw_data = time_data.copy() times = xray.Variable('time', time_data) data = xray.DataArray(raw_data, dims=['time'], coords={'time': times}) ds['data'] = data ```

If you examine ds now, you’ll see:

<xray.Dataset> Dimensions: (time: 180) Coordinates: * time (time) float64 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ... Data variables: data (time) float64 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ...

Now, if we dump to netcdf and reload:

ds.to_netcdf('test1.nc', format='NETCDF4_CLASSIC') ds1 = xray.open_dataset('test1.nc')

The formerly 64-bit values are now 32-bit:

<xray.Dataset> Dimensions: (time: 180) Coordinates: * time (time) float32 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ... Data variables: data (time) float32 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 ...

It looks to me like coerce_nc3_dtype is being called for all formats that aren't straight NETCDF4.

 
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   completed xarray 13221727 issue

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