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1167883842 | I_kwDOAMm_X85FnH5C | 6352 | to_netcdf from subsetted Dataset with strings loaded from char array netCDF can sometimes fail | DocOtak 868027 | open | 0 | 0 | 2022-03-14T04:52:38Z | 2022-04-09T16:59:52Z | CONTRIBUTOR | What happened?Not quite sure what to actually title this, so feel free to edit it. I have some netcdf files modeled after the Argo _prof file format (CF Discrete sampling geometry incomplete multidimensional array representation). While working on splitting these into individual profiles, I would occasionally get exceptions thrown complaining about broadcasting. I eventually narrowed this down to some string variables we maintain for historic purposes. Depending on the row split apart, the string data in each cell could be shorter which would result in a stringN having some different N (e.g. string4 = 3 in the CDL). If while serializing, a different string variable is being encoded that actually has length 4, it would reuse the now incorrect string4 dim name. The above situation seems to only occur when a netCDF file is read back into xarray and the What did you expect to happen?Successful serialization to netCDF. Minimal Complete Verifiable Example```Python setupimport numpy as np import xarray as xr one_two = xr.DataArray(np.array(["a", "aa"], dtype="object"), dims=["dim0"]) two_two = xr.DataArray(np.array(["aa", "aa"], dtype="object"), dims=["dim0"]) ds = xr.Dataset({"var0": one_two, "var1": two_two}) ds.var0.encoding["dtype"] = "S1" ds.var1.encoding["dtype"] = "S1" need to write out and read back inds.to_netcdf("test.nc") only selecting the shorter string will failds1 = xr.load_dataset("test.nc") ds1[{"dim0": 1}].to_netcdf("ok.nc") ds1[{"dim0": 0}].to_netcdf("error.nc") will work if the char dim name is removed from encoding of the now shorter arrds1 = xr.load_dataset("test.nc") del ds1.var0.encoding["char_dim_name"] ds1[{"dim0": 0}].to_netcdf("will_work.nc") ``` Relevant log output```PythonIndexError Traceback (most recent call last) /var/folders/y1/63dlf4614h5d2cgr5g1t_5lh0000gn/T/ipykernel_64155/447008818.py in <module> 2 ds1 = xr.load_dataset("test.nc") 3 ds1[{"dim0": 1}].to_netcdf("ok.nc") ----> 4 ds1[{"dim0": 0}].to_netcdf("error.nc") ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/core/dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf) 1899 from ..backends.api import to_netcdf 1900 -> 1901 return to_netcdf( 1902 self, 1903 path, ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf) 1070 # TODO: allow this work (setting up the file for writing array data) 1071 # to be parallelized with dask -> 1072 dump_to_store( 1073 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims 1074 ) ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims) 1117 variables, attrs = encoder(variables, attrs) 1118 -> 1119 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims) 1120 1121 ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, writer, unlimited_dims) 263 self.set_attributes(attributes) 264 self.set_dimensions(variables, unlimited_dims=unlimited_dims) --> 265 self.set_variables( 266 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims 267 ) ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in set_variables(self, variables, check_encoding_set, writer, unlimited_dims) 305 ) 306 --> 307 writer.add(source, target) 308 309 def set_dimensions(self, variables, unlimited_dims=None): ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in add(self, source, target, region) 154 target[region] = source 155 else: --> 156 target[...] = source 157 158 def sync(self, compute=True): ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/netCDF4_.py in setitem(self, key, value) 70 with self.datastore.lock: 71 data = self.get_array(needs_lock=False) ---> 72 data[key] = value 73 if self.datastore.autoclose: 74 self.datastore.close(needs_lock=False) src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.setitem() src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable._put() IndexError: size of data array does not conform to slice ``` Anything else we need to know?I've been unable to recreate the specific error I'm getting in a minimal example. However, removing the When digging in the xarray issues, these looked maybe relevant: #2219 #2895 Actual traceback I get with my data```python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /var/folders/y1/63dlf4614h5d2cgr5g1t_5lh0000gn/T/ipykernel_64155/3328648456.py in <module> ----> 1 ds[{"N_PROF": 0}].to_netcdf("test.nc") ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/core/dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf) 1899 from ..backends.api import to_netcdf 1900 -> 1901 return to_netcdf( 1902 self, 1903 path, ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf) 1070 # TODO: allow this work (setting up the file for writing array data) 1071 # to be parallelized with dask -> 1072 dump_to_store( 1073 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims 1074 ) ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims) 1117 variables, attrs = encoder(variables, attrs) 1118 -> 1119 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims) 1120 1121 ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, writer, unlimited_dims) 263 self.set_attributes(attributes) 264 self.set_dimensions(variables, unlimited_dims=unlimited_dims) --> 265 self.set_variables( 266 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims 267 ) ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in set_variables(self, variables, check_encoding_set, writer, unlimited_dims) 305 ) 306 --> 307 writer.add(source, target) 308 309 def set_dimensions(self, variables, unlimited_dims=None): ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in add(self, source, target, region) 154 target[region] = source 155 else: --> 156 target[...] = source 157 158 def sync(self, compute=True): ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/netCDF4_.py in __setitem__(self, key, value) 70 with self.datastore.lock: 71 data = self.get_array(needs_lock=False) ---> 72 data[key] = value 73 if self.datastore.autoclose: 74 self.datastore.close(needs_lock=False) src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__setitem__() ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/netCDF4/utils.py in _StartCountStride(elem, shape, dimensions, grp, datashape, put, use_get_vars) 354 fullslice = False 355 if fullslice and datashape and put and not hasunlim: --> 356 datashape = broadcasted_shape(shape, datashape) 357 358 # pad datashape with zeros for dimensions not being sliced (issue #906) ~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/netCDF4/utils.py in broadcasted_shape(shp1, shp2) 962 a = as_strided(x, shape=shp1, strides=[0] * len(shp1)) 963 b = as_strided(x, shape=shp2, strides=[0] * len(shp2)) --> 964 return np.broadcast(a, b).shape ValueError: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (5,) and arg 1 with shape (6,). ```EnvironmentINSTALLED VERSIONScommit: None python: 3.9.9 (main, Jan 5 2022, 11:21:18) [Clang 13.0.0 (clang-1300.0.29.30)] python-bits: 64 OS: Darwin OS-release: 21.3.0 machine: arm64 processor: arm byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.13.0 libnetcdf: 4.8.1 xarray: 2022.3.0 pandas: 1.3.5 numpy: 1.22.0 scipy: None netCDF4: 1.5.8 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.5.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None fsspec: None cupy: None pint: 0.18 sparse: None setuptools: 58.1.0 pip: 21.2.4 conda: None pytest: 6.2.5 IPython: 7.31.0 sphinx: 4.4.0 |
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477427854 | MDExOlB1bGxSZXF1ZXN0MzA0NzU0OTc2 | 3187 | reduce the size of example dataset in dask docs | DocOtak 868027 | closed | 0 | 4 | 2019-08-06T14:50:27Z | 2019-08-06T20:41:39Z | 2019-08-06T20:41:38Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/3187 | Another attempt at getting the docs to build again on RTD (#3182). The current failure is due to high memory usage in the dask examples. I've converted the two most memory expensive code blocks into |
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477084478 | MDExOlB1bGxSZXF1ZXN0MzA0NDgxOTY3 | 3186 | bump rasterio to 1.0.24 in doc building environment | DocOtak 868027 | closed | 0 | 2 | 2019-08-05T22:24:59Z | 2019-08-06T01:20:16Z | 2019-08-06T01:20:15Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/3186 | This is hopefully a fix for #3182 but I wasn't sure how to really test this on read the docs (RTD) itself. There may be a few things going on:
* Local testing showed removing the "auto_gallery" dir would cause the failing gallery examples to actually be recognized as failing (i.e. bust the cache)
* https://github.com/conda-forge/rasterio-feedstock/issues/118 and some examining of the |
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476323960 | MDExOlB1bGxSZXF1ZXN0MzAzOTAzNjg1 | 3180 | enable sphinx.ext.napoleon | DocOtak 868027 | closed | 0 | 3 | 2019-08-02T19:26:46Z | 2019-08-02T21:17:43Z | 2019-08-02T21:17:43Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/3180 | Enables the napoleon extension in sphinx. This will interpret the numpydoc style parameters and types and convert them to sphinx Eventually the numpydoc dependency might be able to be removed but currently removing it makes the wrapped
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476317653 | MDExOlB1bGxSZXF1ZXN0MzAzODk4NDE4 | 3179 | remove type annotations from autodoc method signatures | DocOtak 868027 | closed | 0 | 1 | 2019-08-02T19:07:44Z | 2019-08-02T20:17:59Z | 2019-08-02T20:17:58Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/3179 | This PR removes all the type hints from method signatures generated by sphinx.ext.autodoc. See http://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#confval-autodoc_typehints The sphinx documentation doesn't say which version this was added in, but I imagine it is quite recent.
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354539078 | MDExOlB1bGxSZXF1ZXN0MjExMjc3OTMz | 2386 | fix typo in uri in the docs | DocOtak 868027 | closed | 0 | 1 | 2018-08-28T01:46:56Z | 2018-08-28T01:49:02Z | 2018-08-28T01:48:59Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/2386 | Seems I left off a trailing |
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345322908 | MDExOlB1bGxSZXF1ZXN0MjA0NTA2MTg2 | 2322 | BUG: modify behavior of Dataset.filter_by_attrs to match netCDF4.Data… | DocOtak 868027 | closed | 0 | 5 | 2018-07-27T18:25:42Z | 2018-08-28T01:48:00Z | 2018-08-28T01:21:20Z | CONTRIBUTOR | 0 | pydata/xarray/pulls/2322 | Here is my fix for #2315 which matches the behavior of Dataset.filter_by_attrs to be that of its netCDF4 inspiration. I followed the pattern seen in the netCDF4 library which set a boolean flag while looping over attributes and short circuits the loop if any test returns false. Only if all the
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344631360 | MDU6SXNzdWUzNDQ2MzEzNjA= | 2315 | Behavior of filter_by_attrs() does not match netCDF4.Dataset.get_variables_by_attributes | DocOtak 868027 | closed | 0 | 8 | 2018-07-25T22:35:14Z | 2018-08-28T01:21:20Z | 2018-08-28T01:21:20Z | CONTRIBUTOR | When using the Minimal Example
Example Output
Expected Output
Alternatively, chaining calls to filter_by_attrs will result in the expected behavior: ```python import xarray as xr example_dataset = xr.Dataset({ "var1": xr.DataArray([], attrs={"standard_name": "example1", "priority": 0}), "var2": xr.DataArray([], attrs={"standard_name": "example2"}) }) example_dataset.filter_by_attrs(standard_name="example2").filter_by_attrs(priority=0) <xarray.Dataset> Dimensions: () Data variables: empty ``` Output of
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