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 2163608564,I_kwDOAMm_X86A9gv0,8802,Error when using `apply_ufunc` with `datetime64` as output dtype,44147817,open,0,,,4,2024-03-01T15:09:57Z,2024-05-03T12:19:14Z,,CONTRIBUTOR,,,,"### What happened? When using `apply_ufunc` with `datetime64[ns]` as output dtype, code throws error about converting from specific units to generic datetime units. ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example ```Python import xarray as xr import numpy as np def _fn(arr: np.ndarray, time: np.ndarray) -> np.ndarray: return time[:10] def fn(da: xr.DataArray) -> xr.DataArray: dim_out = ""time_cp"" return xr.apply_ufunc( _fn, da, da.time, input_core_dims=[[""time""], [""time""]], output_core_dims=[[dim_out]], vectorize=True, dask=""parallelized"", output_dtypes=[""datetime64[ns]""], dask_gufunc_kwargs={""allow_rechunk"": True, ""output_sizes"": {dim_out: 10},}, exclude_dims=set((""time"",)), ) da_fake = xr.DataArray(np.random.rand(5,5,5), coords=dict(x=range(5), y=range(5), time=np.array(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'], dtype='datetime64[ns]') )).chunk(dict(x=2,y=2)) fn(da_fake.compute()).compute() # ValueError: Cannot convert from specific units to generic units in NumPy datetimes or timedeltas fn(da_fake).compute() # same errors as above ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. - [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output ```Python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[211], line 1 ----> 1 fn(da_fake).compute() File /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataarray.py:1163, in DataArray.compute(self, **kwargs) 1144 """"""Manually trigger loading of this array's data from disk or a 1145 remote source into memory and return a new array. The original is 1146 left unaltered. (...) 1160 dask.compute 1161 """""" 1162 new = self.copy(deep=False) -> 1163 return new.load(**kwargs) File /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataarray.py:1137, in DataArray.load(self, **kwargs) 1119 def load(self, **kwargs) -> Self: 1120 """"""Manually trigger loading of this array's data from disk or a 1121 remote source into memory and return this array. 1122 (...) 1135 dask.compute 1136 """""" -> 1137 ds = self._to_temp_dataset().load(**kwargs) 1138 new = self._from_temp_dataset(ds) 1139 self._variable = new._variable File /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/dataset.py:853, in Dataset.load(self, **kwargs) 850 chunkmanager = get_chunked_array_type(*lazy_data.values()) 852 # evaluate all the chunked arrays simultaneously --> 853 evaluated_data = chunkmanager.compute(*lazy_data.values(), **kwargs) 855 for k, data in zip(lazy_data, evaluated_data): 856 self.variables[k].data = data File /srv/conda/envs/notebook/lib/python3.10/site-packages/xarray/core/daskmanager.py:70, in DaskManager.compute(self, *data, **kwargs) 67 def compute(self, *data: DaskArray, **kwargs) -> tuple[np.ndarray, ...]: 68 from dask.array import compute ---> 70 return compute(*data, **kwargs) File /srv/conda/envs/notebook/lib/python3.10/site-packages/dask/base.py:628, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs) 625 postcomputes.append(x.__dask_postcompute__()) 627 with shorten_traceback(): --> 628 results = schedule(dsk, keys, **kwargs) 630 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) File /srv/conda/envs/notebook/lib/python3.10/site-packages/numpy/lib/function_base.py:2372, in vectorize.__call__(self, *args, **kwargs) 2369 self._init_stage_2(*args, **kwargs) 2370 return self -> 2372 return self._call_as_normal(*args, **kwargs) File /srv/conda/envs/notebook/lib/python3.10/site-packages/numpy/lib/function_base.py:2365, in vectorize._call_as_normal(self, *args, **kwargs) 2362 vargs = [args[_i] for _i in inds] 2363 vargs.extend([kwargs[_n] for _n in names]) -> 2365 return self._vectorize_call(func=func, args=vargs) File /srv/conda/envs/notebook/lib/python3.10/site-packages/numpy/lib/function_base.py:2446, in vectorize._vectorize_call(self, func, args) 2444 """"""Vectorized call to `func` over positional `args`."""""" 2445 if self.signature is not None: -> 2446 res = self._vectorize_call_with_signature(func, args) 2447 elif not args: 2448 res = func() File /srv/conda/envs/notebook/lib/python3.10/site-packages/numpy/lib/function_base.py:2506, in vectorize._vectorize_call_with_signature(self, func, args) 2502 outputs = _create_arrays(broadcast_shape, dim_sizes, 2503 output_core_dims, otypes, results) 2505 for output, result in zip(outputs, results): -> 2506 output[index] = result 2508 if outputs is None: 2509 # did not call the function even once 2510 if otypes is None: ValueError: Cannot convert from specific units to generic units in NumPy datetimes or timedeltas ``` ### Anything else we need to know? _No response_ ### Environment
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8802/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 963006707,MDExOlB1bGxSZXF1ZXN0NzA1NzEzMTc1,5680,ENH: Add default fill values for decode_cf,44147817,open,0,,,8,2021-08-06T19:54:05Z,2022-06-09T14:50:16Z,,CONTRIBUTOR,,0,pydata/xarray/pulls/5680,"- [x] Closes #2374 - [x] Tests added - [x] Passes `pre-commit run --all-files` - [ ] User visible changes (including notable bug fixes) are documented in `whats-new.rst` This is a work in progress, mostly so that I can ask some clarifying questions. I see that `netCDF4` is an optional dependency for `xarray`, so probably `import netCDF4` can't be used. Should `xarray` simply hard-code default fill values ? From the issue's conversation, it wasn't clear to me whether an argument should control the use of the default fill value. Since some tests fail now I guess the answer is yes. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5680/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull 1178365524,I_kwDOAMm_X85GPG5U,6405,Docstring of `open_zarr` fails to mention that `decode_coords` could be a string too,44147817,open,0,,,0,2022-03-23T16:30:11Z,2022-03-23T16:49:14Z,,CONTRIBUTOR,,,,"### What is your issue? The docstring of `open_zarr` fails to mention that `decode_coords` could be a string too (and what the accepted string values mean) https://github.com/pydata/xarray/blob/fed852073eee883c0ed1e13e28e508ff0cf9d5c1/xarray/backends/zarr.py#L687-L689","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6405/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue