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  • xarray · 4 ✖
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
304314787 MDU6SXNzdWUzMDQzMTQ3ODc= 1982 NetCDF coordinates in parent group is not used when reading sub group jacklovell 4849151 open 0     10 2018-03-12T10:26:54Z 2021-12-27T18:19:22Z   NONE      

Code Sample, a copy-pastable example if possible

```python ncfile_cf = "x07z00017_cf.nc" with xr.open_dataset(ncfile_cf, group="x07") as ds: ds_data_cf = ds.copy(deep=True) print(ds_data_cf)

<xarray.Dataset> Dimensions: (time1: 100000, time2: 2) Dimensions without coordinates: time1, time2 Data variables: aps (time1) float64 ... iact (time1) float64 ... vact (time1) float64 ... dps (time1) float64 ... tss (time2) float64 ...

```

Problem description

When reading a sub group from a netCDF file with dimensions defined in the root group, the dimensions are not read from the root group. This contradicts the netCDF documentation, which states that dimensions are scoped such that they can be seen by all sub groups.

The attached netCDF file demonstrates this issue. x07z00017_cf.nc.zip

Expected Output

The dimensions from the root group should be used when reading the sub-group.

```python with xr.open_dataset(ncfile_cf) as ds: for coord in ds.coords: ds_data_cf.coords[coord] = ds[coord] print(ds_data_cf)

<xarray.Dataset> Dimensions: (time1: 100000, time2: 2) Coordinates: * time1 (time1) float64 0.0 1e-06 2e-06 3e-06 4e-06 5e-06 6e-06 7e-06 ... * time2 (time2) float64 0.0 0.1 Data variables: aps (time1) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 ... iact (time1) float64 -0.00125 -0.000625 -0.00125 -0.0009375 ... vact (time1) float64 -0.009375 -0.009375 -0.01875 -0.01875 -0.009375 ... dps (time1) float64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... tss (time2) float64 0.0 0.0 ```

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Linux OS-release: 4.13.0-36-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.0 pandas: 0.22.0 numpy: 1.13.3 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: None Nio: None bottleneck: 1.2.1 cyordereddict: None dask: 0.15.3 matplotlib: 2.1.0 cartopy: None seaborn: 0.8.0 setuptools: 38.5.1 pip: 9.0.1 conda: 4.4.11 pytest: 3.2.1 IPython: 6.2.1 sphinx: 1.6.3 
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     xarray 13221727 issue
322849322 MDU6SXNzdWUzMjI4NDkzMjI= 2129 Using `DataArray.where()` with a DataArray as the condition drops the name jacklovell 4849151 closed 0     4 2018-05-14T14:46:18Z 2020-04-14T08:54:33Z 2020-04-14T08:54:33Z NONE      

Code Sample, a copy-pastable example if possible

Create a boolean DataArray to use as a mask for another DataArray, with the same coordinates:

python da_1 = xr.DataArray(np.array([0.861185, 0.301491, 0.642744, 0.773298, 0.786516, 0.011693, 0.659326, 0.07877 , 0.108488, 0.747863]), coords=[('coord1', np.arange(10))], name='da1') da_2 = xr.DataArray(np.array([0.116959, 0.955742, 0.121562, 0.635573, 0.304895, 0.064529, 0.042461, 0.41751 , 0.607457, 0.110104]), coords=[('coord1', np.arange(10))], name='da2') da_3 = np.rint(da_2).astype('bool') print(da_1) <xarray.DataArray 'da1' (coord1: 10)> array([0.861185, 0.301491, 0.642744, 0.773298, 0.786516, 0.011693, 0.659326, 0.07877 , 0.108488, 0.747863]) Coordinates: * coord1 (coord1) int64 0 1 2 3 4 5 6 7 8 9 print(da_1.where(da_3)) <xarray.DataArray (coord1: 10)> array([ nan, 0.301491, nan, 0.773298, nan, nan, nan, nan, 0.108488, nan]) Coordinates: * coord1 (coord1) int64 0 1 2 3 4 5 6 7 8 9

Problem description

When using a DataArray (da_3 in the example) as the condition in the where() method of another DataArray (da_1), the name of the DataArray being masked (da_1) is dropped.

Expected Output

The name should be retained. python print(da_1.where(da_3)) <xarray.DataArray 'da_1` (coord1: 10)> array([ nan, 0.301491, nan, 0.773298, nan, nan, nan, nan, 0.108488, nan]) Coordinates: * coord1 (coord1) int64 0 1 2 3 4 5 6 7 8 9

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.5.final.0 python-bits: 64 OS: Linux OS-release: 4.13.0-38-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.3 pandas: 0.22.0 numpy: 1.14.3 scipy: 1.1.0 netCDF4: 1.3.2 h5netcdf: None h5py: 2.7.1 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: 0.17.4 distributed: 1.21.8 matplotlib: 2.2.2 cartopy: None seaborn: 0.8.1 setuptools: 39.1.0 pip: 10.0.1 conda: 4.5.2 pytest: 3.5.1 IPython: 6.4.0 sphinx: 1.7.4 
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   completed xarray 13221727 issue
303809308 MDU6SXNzdWUzMDM4MDkzMDg= 1977 Netcdf char array not being decoded to string in compound dtype jacklovell 4849151 open 0     3 2018-03-09T11:23:04Z 2020-02-14T13:26:11Z   NONE      

Code Sample, a copy-pastable example if possible

python script_nc_file = "bolo_geom_fromscript.nc" with xr.open_dataset(script_nc_file, group='/bolo/sxd', concat_characters=True) as ds: ds = ds.copy(deep=True) print(ds.slits) <xarray.DataArray 'slits' (slit_no: 4)> array([ ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'1', b'', b'', b'', b'', b''], 1, (-0.06458486, 0.21803484, -0.97380162), ( 0.95881973, 0.28401534, 0.), (-0.52069675, 1.77104629, -1.564 ), 0.005, 0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'1', b'', b'', b'', b'', b''], 0), ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'2', b'', b'', b'', b'', b''], 1, (-0.16038567, 0.54145294, -0.82529095), ( 0.95881973, 0.28401534, 0.), (-0.5278879 , 1.76891617, -1.564 ), 0.005, 0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'O', b'u', b't', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'2', b'', b'', b'', b'', b''], 1), ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'3', b'', b'', b'', b'', b''], 1, (-0.26470454, 0.89362754, -0.36243804), ( 0.95881973, 0.28401534, 0.), (-0.31231469, 1.06756025, -1.57072314), 0.005, 0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'3', b'', b'', b'', b'', b''], 2), ([b'B', b'o', b'l', b'o', b'm', b'e', b't', b'e', b'r', b'S', b'l', b'i', b't', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b'', b''], [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'4', b'', b'', b'', b'', b''], 1, (-0.19640032, 0.66303636, 0.72236396), ( 0.95881973, 0.28401534, 0.), (-0.31950584, 1.06543013, -1.57072314), 0.005, 0.005, [b'M', b'A', b'S', b'T', b'-', b'U', b' ', b'S', b'X', b'D', b' ', b'-', b' ', b'U', b'p', b'p', b'e', b'r', b' ', b'S', b'l', b'i', b't', b' ', b'4', b'', b'', b'', b'', b''], 3)], dtype={'names':['Object_type','ID','Version','basis_1','basis_2','centre_point','width','height','slit_id','slit_no'], 'formats':[('S1', (30,)),('S1', (30,)),'<i4',[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],'<f8','<f8',('S1', (30,)),'<i4'], 'offsets':[0,30,60,64,88,112,136,144,152,184], 'itemsize':192, 'aligned':True}) Coordinates: * slit_no (slit_no) int64 0 1 2 3 Attributes: units: m

Problem description

When opening the attached dataset ,the char arrays in the compound dtype are not being converted into strings, despite concat_characters=True. bolo_geom_fromscript.nc.zip

Expected Output

The char arrays should be converted into strings (or at the very least, bytes if an encoding is not present): ```python import netCDF4 as nc longest_str = np.max([ds.slits.values[field].shape[-1] for field in ds.slits.values.dtype.fields if ds.slits.values[field].dtype.kind in ('S', 'U')]) str_dtype = '<U{}'.format(longest_str) cartesian_coord = np.dtype([('x', np.float64), ('y', np.float64), ('z', np.float64)]) aperture_dtype_str = np.dtype([('Object_type', str_dtype), ('ID', str_dtype), ('Version', np.int32), ('basis_1', cartesian_coord), ('basis_2', cartesian_coord), ('centre_point', cartesian_coord), ('width', np.float64), ('height', np.float64), ('slit_id', str_dtype), ('slit_no', np.int32)]) ds['slits_str'] = xr.DataArray(np.empty(ds.slits.size, aperture_dtype_str), coords=[('slit_no', ds.coords['slit_no'])]) for key in ds.slits.values.dtype.fields: if key in ('Object_type', 'ID', 'slit_id'): string_key = nc.chartostring(ds.slits.values[key]) ds.slits_str.values[key] = string_key else: ds.slits_str.values[key] = ds.slits.values[key] print(ds.slits_str)

<xarray.DataArray 'slits_str' (slit_no: 4)> array([ ('BolometerSlit', 'MAST-U SXD - Outer Slit 1', 1, (-0.06458486, 0.21803484, -0.97380162), ( 0.95881973, 0.28401534, 0.), (-0.52069675, 1.77104629, -1.564 ), 0.005, 0.005, 'MAST-U SXD - Outer Slit 1', 0), ('BolometerSlit', 'MAST-U SXD - Outer Slit 2', 1, (-0.16038567, 0.54145294, -0.82529095), ( 0.95881973, 0.28401534, 0.), (-0.5278879 , 1.76891617, -1.564 ), 0.005, 0.005, 'MAST-U SXD - Outer Slit 2', 1), ('BolometerSlit', 'MAST-U SXD - Upper Slit 3', 1, (-0.26470454, 0.89362754, -0.36243804), ( 0.95881973, 0.28401534, 0.), (-0.31231469, 1.06756025, -1.57072314), 0.005, 0.005, 'MAST-U SXD - Upper Slit 3', 2), ('BolometerSlit', 'MAST-U SXD - Upper Slit 4', 1, (-0.19640032, 0.66303636, 0.72236396), ( 0.95881973, 0.28401534, 0.), (-0.31950584, 1.06543013, -1.57072314), 0.005, 0.005, 'MAST-U SXD - Upper Slit 4', 3)], dtype=[('Object_type', '<U30'), ('ID', '<U30'), ('Version', '<i4'), ('basis_1', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('basis_2', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('centre_point', [('x', '<f8'), ('y', '<f8'), ('z', '<f8')]), ('width', '<f8'), ('height', '<f8'), ('slit_id', '<U30'), ('slit_no', '<i4')]) Coordinates: * slit_no (slit_no) int64 0 1 2 3 ```

Output of xr.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Linux OS-release: 4.13.0-32-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.0 pandas: 0.22.0 numpy: 1.13.3 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: None Nio: None bottleneck: 1.2.1 cyordereddict: None dask: 0.15.3 matplotlib: 2.1.0 cartopy: None seaborn: 0.8.0 setuptools: 38.5.1 pip: 9.0.1 conda: 4.4.11 pytest: 3.2.1 IPython: 6.2.1 sphinx: 1.6.3 
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     xarray 13221727 issue
201617371 MDU6SXNzdWUyMDE2MTczNzE= 1217 Using where() in datasets with dataarrays with different dimensions results in huge RAM consumption jacklovell 4849151 closed 0     6 2017-01-18T16:09:50Z 2019-02-23T07:47:01Z 2019-02-23T07:47:01Z NONE      

I have a dataset containing groups of data with different dimensions. e.g.:

Python ds = xr.Dataset() ds['data1'] = xr.DataArray(data1, coords={'t1': t1}) ds['data2'] = xr.DataArray(data2, coords={'t2': t2})

If I do something like ds.where(ds.data1 < 0.1), Python ends up allocating huge amounts of memory (>30GB for a dataset of <1MB) and seems to loop indefinitely, until the call is interrupted with CTRL-C.

To use where() successfully, I have to use a subset of the dataset with only one dimension.

 
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   completed xarray 13221727 issue

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