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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 |
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494210818 | MDExOlB1bGxSZXF1ZXN0MzE4MDA5MjU3 | 3312 | convert DataArray to DataSet before combine | friedrichknuth 10554254 | open | 0 | 10 | 2019-09-16T18:37:35Z | 2022-06-09T14:50:17Z | FIRST_TIME_CONTRIBUTOR | 0 | pydata/xarray/pulls/3312 | Enables combine_by_coords on DataArrays. Will convert DataArray to DataSet before proceeding. As mentioned in #3248, this will still fail if the DataArray is unnamed, but at least the error message tells the user why. Previously, combining both named ``` da1 = xr.DataArray(name='foo', data=np.random.rand(3,3), coords=[('x', [1, 2, 3]), ('y', [1, 2, 3])]) da2 = xr.DataArray(name='foo2', data=np.random.rand(3,3), coords=[('x', [5, 6, 7]), ('y', [5, 6, 7])]) xr.combine_by_coords([da1, da2]) ``` and unnamed DataArrays ``` da1 = xr.DataArray(data=np.random.rand(3,3), coords=[('x', [1, 2, 3]), ('y', [1, 2, 3])]) da2 = xr.DataArray(data=np.random.rand(3,3), coords=[('x', [5, 6, 7]), ('y', [5, 6, 7])]) xr.combine_by_coords([da1, da2])
With this PR, combining the named DataArrays results in a combined DataSet, while the latter example will result in
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494906646 | MDU6SXNzdWU0OTQ5MDY2NDY= | 3315 | xr.combine_nested() fails when passed nested DataSets | friedrichknuth 10554254 | open | 0 | 8 | 2019-09-17T23:47:44Z | 2021-07-08T17:42:53Z | NONE |
xr.combine_nested() works when passed a nested list of DataArray objects.
```KeyError Traceback (most recent call last) <ipython-input-8-c0035883fc68> in <module> 3 ds3 = da3.to_dataset() 4 ds4 = da4.to_dataset() ----> 5 xr.combine_nested([[ds1, ds2], [ds3, ds4]], concat_dim=["x", "y"]) ~/repos/contribute/xarray/xarray/core/combine.py in combine_nested(datasets, concat_dim, compat, data_vars, coords, fill_value, join) 462 ids=False, 463 fill_value=fill_value, --> 464 join=join, 465 ) 466 ~/repos/contribute/xarray/xarray/core/combine.py in _nested_combine(datasets, concat_dims, compat, data_vars, coords, ids, fill_value, join) 305 coords=coords, 306 fill_value=fill_value, --> 307 join=join, 308 ) 309 return combined ~/repos/contribute/xarray/xarray/core/combine.py in _combine_nd(combined_ids, concat_dims, data_vars, coords, compat, fill_value, join) 196 compat=compat, 197 fill_value=fill_value, --> 198 join=join, 199 ) 200 (combined_ds,) = combined_ids.values() ~/repos/contribute/xarray/xarray/core/combine.py in _combine_all_along_first_dim(combined_ids, dim, data_vars, coords, compat, fill_value, join) 218 datasets = combined_ids.values() 219 new_combined_ids[new_id] = _combine_1d( --> 220 datasets, dim, compat, data_vars, coords, fill_value, join 221 ) 222 return new_combined_ids ~/repos/contribute/xarray/xarray/core/combine.py in _combine_1d(datasets, concat_dim, compat, data_vars, coords, fill_value, join) 246 compat=compat, 247 fill_value=fill_value, --> 248 join=join, 249 ) 250 except ValueError as err: ~/repos/contribute/xarray/xarray/core/concat.py in concat(objs, dim, data_vars, coords, compat, positions, fill_value, join) 131 "objects, got %s" % type(first_obj) 132 ) --> 133 return f(objs, dim, data_vars, coords, compat, positions, fill_value, join) 134 135 ~/repos/contribute/xarray/xarray/core/concat.py in _dataset_concat(datasets, dim, data_vars, coords, compat, positions, fill_value, join) 363 for k in datasets[0].variables: 364 if k in concat_over: --> 365 vars = ensure_common_dims([ds.variables[k] for ds in datasets]) 366 combined = concat_vars(vars, dim, positions) 367 assert isinstance(combined, Variable) ~/repos/contribute/xarray/xarray/core/concat.py in <listcomp>(.0) 363 for k in datasets[0].variables: 364 if k in concat_over: --> 365 vars = ensure_common_dims([ds.variables[k] for ds in datasets]) 366 combined = concat_vars(vars, dim, positions) 367 assert isinstance(combined, Variable) ~/repos/contribute/xarray/xarray/core/utils.py in getitem(self, key) 383 384 def getitem(self, key: K) -> V: --> 385 return self.mapping[key] 386 387 def iter(self) -> Iterator[K]: KeyError: 'a' ``` |
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xarray 13221727 | issue | ||||||||
212561278 | MDU6SXNzdWUyMTI1NjEyNzg= | 1301 | open_mfdataset() significantly slower on 0.9.1 vs. 0.8.2 | friedrichknuth 10554254 | closed | 0 | 17 | 2017-03-07T21:16:53Z | 2017-11-16T15:02:48Z | 2017-11-16T15:02:00Z | NONE | I noticed a big speed discrepancy between xarray versions 0.8.2 and 0.9.1 when using open_mfdataset() on a dataset ~ 1.2 GB in size, consisting of 3 files and using netcdf4 as the engine. 0.8.2 was run first, so this is probably not a disk caching issue. Test ``` import xarray as xr import time start_time = time.time() ds0 = xr.open_mfdataset('./*.nc') print("--- %s seconds ---" % (time.time() - start_time)) ``` Result xarray==0.8.2, dask==0.11.1, netcdf4==1.2.4
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