issues

If a variable does not define a _FillValue value the 'default fill value' is normally used where data is masked. The default netCDF4 library does this by default and can be controlled with the set_auto_mask() function.

For example loading a NetCDF with no explicit fill value set:

```python In [92]: from netCDF4 import Dataset

In [93]: osd = Dataset('os150nb.nc', 'r')

In [94]: osd['u'] Out[94]: <class 'netCDF4._netCDF4.Variable'> float32 u(time, depth_cell) missing_value: 1e+38 long_name: Zonal velocity component units: meter second-1 C_format: %7.2f data_min: -0.6097069 data_max: 0.6496426 unlimited dimensions: current shape = (6830, 60) filling on, default _FillValue of 9.969209968386869e+36 used

In [95]: u[1000] Out[95]: masked_array(data=[0.09373848885297775, 0.08173848688602448, 0.0697384923696518, 0.12273849546909332, 0.11573849618434906, 0.1387384980916977, 0.17173849046230316, 0.17673850059509277, 0.17673850059509277, 0.16373848915100098, 0.1857384890317917, 0.17673850059509277, 0.20173849165439606, 0.20973849296569824, 0.2037384957075119, 0.2297385036945343, 0.23273849487304688, 0.22873848676681519, 0.24073849618434906, 0.22873848676681519, 0.23073849081993103, 0.23273849487304688, 0.24973849952220917, 0.2467384934425354, 0.2207385003566742, 0.22773849964141846, 0.2387385070323944, 0.21473848819732666, 0.23973849415779114, 0.23673850297927856, 0.2517384886741638, 0.25273850560188293, 0.21973849833011627, 0.2387385070323944, 0.2207385003566742, 0.22373849153518677, 0.23473849892616272, 0.21073849499225616, 0.2247384935617447, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --, --], mask=[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True], fill_value=9.96921e+36, dtype=float32) ```

The resulting array is a masked array where missing values are masked. You can see the default fill value has been given in the variable output.

When loading the same NetCDF with xarray that fill value gets used where the values would be masked by NetCDF4.

``` python In [107]: os150 = xr.open_dataset('os150nb.nc', decode_cf=True, mask_and_scale=True, decode_coords=True)

In [108]: os150.u[1000] Out[108]: <xarray.DataArray 'u' (depth_cell: 60)> array([9.373849e-02, 8.173849e-02, 6.973849e-02, 1.227385e-01, 1.157385e-01, 1.387385e-01, 1.717385e-01, 1.767385e-01, 1.767385e-01, 1.637385e-01, 1.857385e-01, 1.767385e-01, 2.017385e-01, 2.097385e-01, 2.037385e-01, 2.297385e-01, 2.327385e-01, 2.287385e-01, 2.407385e-01, 2.287385e-01, 2.307385e-01, 2.327385e-01, 2.497385e-01, 2.467385e-01, 2.207385e-01, 2.277385e-01, 2.387385e-01, 2.147385e-01, 2.397385e-01, 2.367385e-01, 2.517385e-01, 2.527385e-01, 2.197385e-01, 2.387385e-01, 2.207385e-01, 2.237385e-01, 2.347385e-01, 2.107385e-01, 2.247385e-01, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36, 9.969210e+36]) Coordinates: time datetime64[ns] 2018-11-26T10:24:53.971200 Dimensions without coordinates: depth_cell Attributes: long_name: Zonal velocity component units: meter second-1 C_format: %7.2f data_min: -0.6097069 data_max: 0.6496426 ```

While this behaviour is correct in the sense that xarray has followed the NetCDF specification it's now no longer clear that those values were missing in the original NetCDF.

The attributes don't mention the fill value so even though this is outside the specified data range one could be forgiven for thinking that's the actual value in the DataArray. It's especially confusing when you've asked to have CF decoded and these values are still present.

Further more if you look at the encoding for this DataArray you can see that it incorrectly states that the _FillVaule is the missing_value:

python In [136]: os150['u'].encoding Out[136]: {'source': 'C:\\Data\\adcp_processing\\in2018_v06\\postproc\\os150nb\\contour\\os150nb.nc', 'original_shape': (6830, 60), '_FillValue': 1e+38, 'dtype': dtype('float32')}

Unless I'm missing something I think this behaviour should be changed to either: * Explicitly mention that the default fill value is being used in the DataArray attributes or have some other way of identifying it or * Mask this value with nan/missing_vlaue in the resulting DataArray

Note that the NetCDF file I've used here isn't publicly available yet but I can add a link to it soon once it is.

```python from collections import OrderedDict import xarray as xr import numpy as np

sensor_string_np = np.zeros([12, 100], dtype='|S1') data_vars = {} data_vars['sensorName'] = xr.DataArray(data=sensor_string_np.copy(), attrs=OrderedDict([('_FillValue', ' '),]), name="sensorName", dims=("sensor", "string"))

scanfile = xr.Dataset(data_vars=data_vars) scanfile.sensorName[0, :len("test")] = np.frombuffer("test".encode(), dtype='|S1') scanfile.to_netcdf('test.nc') ```

(py37) C:\Data\in2019_v02>ncdump -h test.nc netcdf test { dimensions: sensor = 12 ; string = 100 ; string1 = 1 ; variables: char sensorName(sensor, string, string1) ; sensorName:_FillValue = " " ; }

Problem description

I'm not entirely sure if this is a bug or user error. The above code is a minimal example of an issue we've been having in the latest version of xarray (or since about version 11).

We are trying to preserve the old fixed width char array style of strings for backwards compatibility purposes. However the above code adds in the extra 'string1' dimension when saving to NetCDF.

From what I can understand this is a feature of encoding described at http://xarray.pydata.org/en/stable/io.html#string-encoding. I think xarray is treating each byte of the S1 array as a 'string' which it is then encoding again by splitting each character into byte arrays of one byte each.

What I would expect is that since I'm explicitly working with a char arrays rather than strings is for the array to be written to the disk as is.

I can work around this by setting the encoding for the variable to be 'str' and removing the _FillValue:

python data_vars['sensorName'] = xr.DataArray(data=sensor_string_np.copy(), name="sensorName", dims=("sensor", "string")) ... scanfile.to_netcdf(r'test.nc', encoding={'sensorName': {'dtype': 'str'}})

(py37) C:\Data\in2019_v02>ncdump -h test.nc netcdf test { dimensions: sensor = 12 ; string = 100 ; variables: string sensorName(sensor, string) ; }

However this seems like a painful work around.

Is there another way I should be doing this?

Output of xr.show_versions()