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 2083501344,I_kwDOAMm_X858L7Ug,8612,more frequency string updates?,10194086,closed,0,,,5,2024-01-16T09:56:48Z,2024-02-15T16:53:37Z,2024-02-15T16:53:37Z,MEMBER,,,,"### What is your issue? I looked a bit into the frequency string update & found 3 issues we could improve upon. 1. Apart from `""M""`, pandas also deprecated `""Y""`, and `""Q""`, in favor of `""YE""` and `""QE""`. (And they are discussing renaming `""MS""` to `""MB""`). Should we do the same? 2. Should we translate the new freq strings to the old ones if pandas < 2.2 is installed? Otherwise we get the following situation: ```python import xarray as xr xr.date_range(""1600-02-01"", periods=3, freq=""M"") # deprecation warning xr.date_range(""1600-02-01"", periods=3, freq=""ME"") # ValueError: Invalid frequency: ME ``` 3. `date_range_like` can emit deprecation warnings without a way to mitigate them if pandas < 2.2 is installed. (When a `DatetimeIndex`) is passed. Could be nice to translate the old freq string to the new one without a warning. I have played around with 2. and 3. and can open a PR if you are on board. @spencerkclark @aulemahal - pandas-dev/pandas#55792 - pandas-dev/pandas#55553 - pandas-dev/pandas#56840 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8612/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1918089795,I_kwDOAMm_X85yU7pD,8252,cannot use negative step to sel from zarr (without dask),10194086,closed,0,,,0,2023-09-28T18:52:07Z,2024-02-10T02:57:33Z,2024-02-10T02:57:33Z,MEMBER,,,,"### What happened? As per: https://github.com/pydata/xarray/pull/8246#discussion_r1340357405 Passing a negative step in a `slice` to select a non-chunked zarr-backed datasets raises an error. ### What did you expect to happen? zarr should allow negative step (probably?) ### Minimal Complete Verifiable Example ```Python import xarray as xr # create a zarr dataset air = xr.tutorial.open_dataset(""air_temperature"") air.to_zarr(""test.zarr"") ds = xr.open_dataset(""test.zarr"", engine=""zarr"") ds.air[::-1, ].load() # note that this works if the dataset is backed by dask ds_dask = xr.open_dataset(""test.zarr"", engine=""zarr"", chunks=""auto"") ds_dask.air[::-1, ].load() ``` ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] 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. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python File ~/code/xarray/xarray/core/parallelcompat.py:93, in guess_chunkmanager(manager) 91 if isinstance(manager, str): 92 if manager not in chunkmanagers: ---> 93 raise ValueError( 94 f""unrecognized chunk manager {manager} - must be one of: {list(chunkmanagers)}"" 95 ) 97 return chunkmanagers[manager] 98 elif isinstance(manager, ChunkManagerEntrypoint): 99 # already a valid ChunkManager so just pass through ValueError: unrecognized chunk manager dask - must be one of: [] ``` ### Anything else we need to know? The error comes from https://github.com/zarr-developers/zarr-python/blob/6ec746ef1242dd9fec26b128cc0b3455d28ad6f0/zarr/indexing.py#L174 so it would need an upstream fix first. cc @dcherian is this what you had in mind? ### Environment
INSTALLED VERSIONS ------------------ commit: f6d69a1f6d952dcd67609c97f3fb3069abdda586 python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] python-bits: 64 OS: Linux OS-release: 6.2.0-33-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.14.2 libnetcdf: 4.9.2 xarray: 2023.9.1.dev8+gf6d69a1f pandas: 2.1.1 numpy: 1.24.4 scipy: 1.11.3 netCDF4: 1.6.4 pydap: installed h5netcdf: 1.2.0 h5py: 3.9.0 Nio: None zarr: 2.16.1 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.7.0 bottleneck: 1.3.7 dask: 2023.9.2 distributed: None matplotlib: 3.8.0 cartopy: 0.22.0 seaborn: 0.12.2 numbagg: 0.2.2 fsspec: 2023.9.2 cupy: None pint: 0.20.1 sparse: 0.14.0 flox: 0.7.2 numpy_groupies: 0.10.1 setuptools: 68.2.2 pip: 23.2.1 conda: None pytest: 7.4.2 mypy: None IPython: 8.15.0 sphinx: None
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8252/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1455395909,I_kwDOAMm_X85Wv5RF,7298,html repr fails for empty cftime arrays,10194086,closed,0,,,1,2022-11-18T16:09:00Z,2024-01-15T21:49:36Z,2024-01-15T21:49:35Z,MEMBER,,,,"### What happened? The html repr of a cftime array wants to display the ""calendar"", which it cannot if it is empty. ### What did you expect to happen? No error. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr data_obs = np.random.randn(3) time_obs = xr.date_range(""2000-01-01"", periods=3, freq=""YS"", calendar=""noleap"") obs = xr.DataArray(data_obs, coords={""time"": time_obs}) o = obs[:0] xr.core.formatting_html.array_repr(o) ``` ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] 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. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python ValueError Traceback (most recent call last) Input In [1], in () 8 obs = xr.DataArray(data_obs, coords={""time"": time_obs}) 10 o = obs[:0] ---> 12 xr.core.formatting_html.array_repr(o) File ~/code/xarray/xarray/core/formatting_html.py:318, in array_repr(arr) 316 if hasattr(arr, ""xindexes""): 317 indexes = _get_indexes_dict(arr.xindexes) --> 318 sections.append(index_section(indexes)) 320 sections.append(attr_section(arr.attrs)) 322 return _obj_repr(arr, header_components, sections) File ~/code/xarray/xarray/core/formatting_html.py:195, in _mapping_section(mapping, name, details_func, max_items_collapse, expand_option_name, enabled) 188 expanded = _get_boolean_with_default( 189 expand_option_name, n_items < max_items_collapse 190 ) 191 collapsed = not expanded 193 return collapsible_section( 194 name, --> 195 details=details_func(mapping), 196 n_items=n_items, 197 enabled=enabled, 198 collapsed=collapsed, 199 ) File ~/code/xarray/xarray/core/formatting_html.py:155, in summarize_indexes(indexes) 154 def summarize_indexes(indexes): --> 155 indexes_li = """".join( 156 f""
  • {summarize_index(v, i)}
    • "" 157 for v, i in indexes.items() 158 ) 159 return f"""" File ~/code/xarray/xarray/core/formatting_html.py:156, in (.0) 154 def summarize_indexes(indexes): 155 indexes_li = """".join( --> 156 f""
  • {summarize_index(v, i)}
    • "" 157 for v, i in indexes.items() 158 ) 159 return f"""" File ~/code/xarray/xarray/core/formatting_html.py:140, in summarize_index(coord_names, index) 138 index_id = f""index-{uuid.uuid4()}"" 139 preview = escape(inline_index_repr(index)) --> 140 details = short_index_repr_html(index) 142 data_icon = _icon(""icon-database"") 144 return ( 145 f""
    {name}
    "" 146 f""
    {preview}
    "" (...) 150 f""
    {details}
    "" 151 ) File ~/code/xarray/xarray/core/formatting_html.py:132, in short_index_repr_html(index) 129 if hasattr(index, ""_repr_html_""): 130 return index._repr_html_() --> 132 return f""
    {escape(repr(index))}
    "" File ~/code/xarray/xarray/core/indexes.py:547, in PandasIndex.__repr__(self) 546 def __repr__(self): --> 547 return f""PandasIndex({repr(self.index)})"" File ~/code/xarray/xarray/coding/cftimeindex.py:353, in CFTimeIndex.__repr__(self) 345 end_str = format_times( 346 self.values[-REPR_ELLIPSIS_SHOW_ITEMS_FRONT_END:], 347 display_width, 348 offset=offset, 349 first_row_offset=offset, 350 ) 351 datastr = ""\n"".join([front_str, f""{' '*offset}..."", end_str]) --> 353 attrs_str = format_attrs(self) 354 # oneliner only if smaller than display_width 355 full_repr_str = f""{klass_name}([{datastr}], {attrs_str})"" File ~/code/xarray/xarray/coding/cftimeindex.py:272, in format_attrs(index, separator) 267 def format_attrs(index, separator="", ""): 268 """"""Format attributes of CFTimeIndex for __repr__."""""" 269 attrs = { 270 ""dtype"": f""'{index.dtype}'"", 271 ""length"": f""{len(index)}"", --> 272 ""calendar"": f""'{index.calendar}'"", 273 ""freq"": f""'{index.freq}'"" if len(index) >= 3 else None, 274 } 276 attrs_str = [f""{k}={v}"" for k, v in attrs.items()] 277 attrs_str = f""{separator}"".join(attrs_str) File ~/code/xarray/xarray/coding/cftimeindex.py:698, in CFTimeIndex.calendar(self) 695 """"""The calendar used by the datetimes in the index."""""" 696 from .times import infer_calendar_name --> 698 return infer_calendar_name(self) File ~/code/xarray/xarray/coding/times.py:374, in infer_calendar_name(dates) 371 return sample.calendar 373 # Error raise if dtype is neither datetime or ""O"", if cftime is not importable, and if element of 'O' dtype is not cftime. --> 374 raise ValueError(""Array does not contain datetime objects."") ValueError: Array does not contain datetime objects. ``` ### Anything else we need to know? Bisected to 7379923de756a2bcc59044d548f8ab7a68b91d4e use `_repr_inline_` for indexes that define it. ### Environment
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7298/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1959175248,I_kwDOAMm_X850xqRQ,8367,`da.xindexes` or `da.indexes` raises an error if there are none (in the repr),10194086,closed,0,,,1,2023-10-24T12:45:12Z,2023-12-06T17:06:16Z,2023-12-06T17:06:16Z,MEMBER,,,,"### What happened? `da.xindexes` or `da.indexes` raises an error when trying to generate the repr if there are no coords (indexes) ### What did you expect to happen? Displaying an empty Mappable? ### Minimal Complete Verifiable Example ```Python xr.DataArray([3, 5]).indexes xr.DataArray([3, 5]).xindexes ``` ### 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 Out[9]: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File ~/.conda/envs/xarray_dev/lib/python3.10/site-packages/IPython/core/formatters.py:708, in PlainTextFormatter.__call__(self, obj) 701 stream = StringIO() 702 printer = pretty.RepresentationPrinter(stream, self.verbose, 703 self.max_width, self.newline, 704 max_seq_length=self.max_seq_length, 705 singleton_pprinters=self.singleton_printers, 706 type_pprinters=self.type_printers, 707 deferred_pprinters=self.deferred_printers) --> 708 printer.pretty(obj) 709 printer.flush() 710 return stream.getvalue() File ~/.conda/envs/xarray_dev/lib/python3.10/site-packages/IPython/lib/pretty.py:410, in RepresentationPrinter.pretty(self, obj) 407 return meth(obj, self, cycle) 408 if cls is not object \ 409 and callable(cls.__dict__.get('__repr__')): --> 410 return _repr_pprint(obj, self, cycle) 412 return _default_pprint(obj, self, cycle) 413 finally: File ~/.conda/envs/xarray_dev/lib/python3.10/site-packages/IPython/lib/pretty.py:778, in _repr_pprint(obj, p, cycle) 776 """"""A pprint that just redirects to the normal repr function."""""" 777 # Find newlines and replace them with p.break_() --> 778 output = repr(obj) 779 lines = output.splitlines() 780 with p.group(): File ~/code/xarray/xarray/core/indexes.py:1659, in Indexes.__repr__(self) 1657 def __repr__(self): 1658 indexes = formatting._get_indexes_dict(self) -> 1659 return formatting.indexes_repr(indexes) File ~/code/xarray/xarray/core/formatting.py:474, in indexes_repr(indexes, max_rows) 473 def indexes_repr(indexes, max_rows: int | None = None) -> str: --> 474 col_width = _calculate_col_width(chain.from_iterable(indexes)) 476 return _mapping_repr( 477 indexes, 478 ""Indexes"", (...) 482 max_rows=max_rows, 483 ) File ~/code/xarray/xarray/core/formatting.py:341, in _calculate_col_width(col_items) 340 def _calculate_col_width(col_items): --> 341 max_name_length = max(len(str(s)) for s in col_items) if col_items else 0 342 col_width = max(max_name_length, 7) + 6 343 return col_width ValueError: max() arg is an empty sequence ``` ### Anything else we need to know? _No response_ ### Environment
    INSTALLED VERSIONS ------------------ commit: ccc8f9987b553809fb6a40c52fa1a8a8095c8c5f python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] python-bits: 64 OS: Linux OS-release: 6.2.0-35-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.14.2 libnetcdf: 4.9.2 xarray: 2023.9.1.dev8+gf6d69a1f pandas: 2.1.1 numpy: 1.24.4 scipy: 1.11.3 netCDF4: 1.6.4 pydap: installed h5netcdf: 1.2.0 h5py: 3.9.0 Nio: None zarr: 2.16.1 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.7.0 bottleneck: 1.3.7 dask: 2023.9.2 distributed: None matplotlib: 3.8.0 cartopy: 0.22.0 seaborn: 0.12.2 numbagg: 0.2.2 fsspec: 2023.9.2 cupy: None pint: 0.20.1 sparse: 0.14.0 flox: 0.7.2 numpy_groupies: 0.10.1 setuptools: 68.2.2 pip: 23.2.1 conda: None pytest: 7.4.2 mypy: 1.5.1 IPython: 8.15.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8367/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 722168932,MDU6SXNzdWU3MjIxNjg5MzI=,4513,"where should keep_attrs be set in groupby, resample, weighted etc.?",10194086,closed,0,,,2,2020-10-15T09:36:43Z,2023-11-10T16:58:35Z,2023-11-10T16:58:35Z,MEMBER,,,,"I really should not open this can of worms but per https://github.com/pydata/xarray/issues/4450#issuecomment-697507489: > I'm always confused about whether `ds.groupby(..., keep_attrs=True).mean()` or `ds.groupby(...).mean(keep_attrs=True)` is correct. (similarly for rolling, coarsen etc.) Also as I try to fix the `keep_attr` behavior in #4510 it would be good to know where they should go. So I tried to figure out how this is currently handled and found the following: **`ds.xxx(keep_attrs=True).yyy()`** - all fixed **`ds.xxx().yyy(keep_attrs=True)`** - `coarsen` (fixed in #5227) - `groupby` - `groupby_bin` - `resample` - `rolling` (adjusted in #4510) - `rolling_exp` (fixed in #4592) - `weighted` So the working consensus seems to be to to `ds.xxx().yyy(keep_attrs=True)` - any comments on that? (Edit: looking at this it is only half as bad, ""only"" `coarsen`, `rolling` (#4510), and `rolling_exp` would need to be fixed.) ### Detailed analysis
    ```python import xarray as xr ds = xr.tutorial.open_dataset(""air_temperature"") da = ds.air ``` ### coarsen ```python ds.coarsen(time=2, keep_attrs=True).mean() # keeps global attributes ds.coarsen(time=2).mean(keep_attrs=True) # keeps DataArray attributes ds.coarsen(time=2, keep_attrs=True).mean(keep_attrs=True) # keeps both da.coarsen(time=2).mean(keep_attrs=True) # error da.coarsen(time=2, keep_attrs=True).mean() # keeps DataArray attributes ``` ### groupby ```python ds.groupby(""time.month"").mean(keep_attrs=True) # keeps both da.groupby(""time.month"").mean(keep_attrs=True) # keeps DataArray attributes ds.groupby(""time.month"", keep_attrs=True).mean() # error da.groupby(""time.month"", keep_attrs=True).mean() # error ``` ### groupby_bins ```python ds.groupby_bins(ds.lat, np.arange(0, 90, 10)).mean(keep_attrs=True) # keeps both da.groupby_bins(ds.lat, np.arange(0, 90, 10)).mean(keep_attrs=True) # keeps DataArray attrs ds.groupby_bins(ds.lat, np.arange(0, 90, 10), keep_attrs=True) # errors da.groupby_bins(ds.lat, np.arange(0, 90, 10), keep_attrs=True) # errors ``` ### resample ```python ds.resample(time=""A"").mean(keep_attrs=True) # keeps both da.resample(time=""A"").mean(keep_attrs=True) # keeps DataArray attributes ds.resample(time=""A"", keep_attrs=False).mean() # ignored da.resample(time=""A"", keep_attrs=False).mean() # ignored ``` ### rolling ```python ds.rolling(time=2).mean(keep_attrs=True) # keeps both da.rolling(time=2).mean(keep_attrs=True) # keeps DataArray attributes ds.rolling(time=2, keep_attrs=True).mean() # DeprecationWarning; keeps both da.rolling(time=2, keep_attrs=True).mean() # DeprecationWarning; keeps DataArray attributes ``` see #4510 ### rolling_exp ```python ds.rolling_exp(time=5, keep_attrs=True).mean() # ignored da.rolling_exp(time=5, keep_attrs=True).mean() # ignored ds.rolling_exp(time=5).mean(keep_attrs=True) # keeps both da.rolling_exp(time=5).mean(keep_attrs=True) # keeps DataArray attributes ``` ### weighted ```python ds.weighted(ds.lat).mean(keep_attrs=True) # keeps both da.weighted(ds.lat).mean(keep_attrs=True) # keeps DataArray attrs ```
    edit: moved `rolling` after #4510, moved `rolling_exp` after #4592 and `coarsen` after #5227","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4513/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1986324822,I_kwDOAMm_X852ZOlW,8436,align fails when more than one xindex is set,10194086,closed,0,,,2,2023-11-09T20:07:52Z,2023-11-10T12:53:49Z,2023-11-10T12:53:49Z,MEMBER,,,,"### What happened? I tried a DataArray with more than one dimension coordinate. Unfortunately `xr.align` fails, which disallows any arithmetic operation - even when the coords are exactly the same. ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr data = np.arange(12).reshape(3, 4) y = [10, 20, 30] s = [""a"", ""b"", ""c""] x = [1, 2, 3, 4] da = xr.DataArray(data, dims=(""y"", ""x""), coords={""x"": x, ""y"": y, ""s"": (""y"", s)}) da = da.set_xindex(""s"") xr.align(da, da.y) # errors da + da # errors da + da.x # errors ``` ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] 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. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. - [ ] Recent environment — the issue occurs with the latest version of xarray and its dependencies. ### Relevant log output ```Python --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/mathause/code/mesmer/devel/prepare_for_surfer.ipynb Cell 28 line 1 12 da = xr.DataArray(data, dims=(""y"", ""x""), coords={""x"": x, ""y"": y, ""s"": (""y"", s)}) 13 da = da.set_xindex(""s"") ---> 15 xr.align(da, da.y) # errors 17 da + da.x # errors File ~/.conda/envs/mesmer_dev/lib/python3.9/site-packages/xarray/core/alignment.py:888, in align(join, copy, indexes, exclude, fill_value, *objects) 692 """""" 693 Given any number of Dataset and/or DataArray objects, returns new 694 objects with aligned indexes and dimension sizes. ref='~/.conda/envs/mesmer_dev/lib/python3.9/site-packages/xarray/core/alignment.py:0'>0;32m (...) 878 879 """""" 880 aligner = Aligner( 881 objects, 882 join=join, ref='~/.conda/envs/mesmer_dev/lib/python3.9/site-packages/xarray/core/alignment.py:0'>0;32m (...) 886 fill_value=fill_value, 887 ) --> 888 aligner.align() 889 return aligner.results File ~/.conda/envs/mesmer_dev/lib/python3.9/site-packages/xarray/core/alignment.py:573, in Aligner.align(self) 571 self.find_matching_indexes() 572 self.find_matching_unindexed_dims() --> 573 self.assert_no_index_conflict() 574 self.align_indexes() 575 self.assert_unindexed_dim_sizes_equal() File ~/.conda/envs/mesmer_dev/lib/python3.9/site-packages/xarray/core/alignment.py:318, in Aligner.assert_no_index_conflict(self) 314 if dup: 315 items_msg = "", "".join( 316 f""{k!r} ({v} conflicting indexes)"" for k, v in dup.items() 317 ) --> 318 raise ValueError( 319 ""cannot re-index or align objects with conflicting indexes found for "" 320 f""the following {msg}: {items_msg}\n"" 321 ""Conflicting indexes may occur when\n"" 322 ""- they relate to different sets of coordinate and/or dimension names\n"" 323 ""- they don't have the same type\n"" 324 ""- they may be used to reindex data along common dimensions"" 325 ) ValueError: cannot re-index or align objects with conflicting indexes found for the following dimensions: 'y' (2 conflicting indexes) Conflicting indexes may occur when - they relate to different sets of coordinate and/or dimension names - they don't have the same type - they may be used to reindex data along common dimensions ``` ### Anything else we need to know? _No response_ ### Environment
    INSTALLED VERSIONS ------------------ commit: feba6984aa914327408fee3c286dae15969d2a2f python: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:40:32) [GCC 12.3.0] python-bits: 64 OS: Linux OS-release: 6.2.0-36-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.14.2 libnetcdf: 4.9.2 xarray: 2023.9.1.dev8+gf6d69a1f pandas: 2.1.1 numpy: 1.24.4 scipy: 1.11.3 netCDF4: 1.6.4 pydap: installed h5netcdf: 1.2.0 h5py: 3.9.0 Nio: None zarr: 2.16.1 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.7.0 bottleneck: 1.3.7 dask: 2023.9.2 distributed: None matplotlib: 3.8.0 cartopy: 0.22.0 seaborn: 0.12.2 numbagg: 0.2.2 fsspec: 2023.9.2 cupy: None pint: 0.20.1 sparse: 0.14.0 flox: 0.7.2 numpy_groupies: 0.10.1 setuptools: 68.2.2 pip: 23.2.1 conda: None pytest: 7.4.2 mypy: 1.5.1 IPython: 8.15.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8436/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1657036222,I_kwDOAMm_X85ixF2-,7730,flox performance regression for cftime resampling,10194086,closed,0,,,8,2023-04-06T09:38:03Z,2023-10-15T03:48:44Z,2023-10-15T03:48:44Z,MEMBER,,,,"### What happened? Running an in-memory `groupby` operation took much longer than expected. Turning off flox fixed this - but I don't think that's the idea ;-) ### What did you expect to happen? flox to be at least on par with our naive implementation ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr arr = np.random.randn(10, 10, 365*30) time = xr.date_range(""2000"", periods=30*365, calendar=""noleap"") da = xr.DataArray(arr, dims=(""y"", ""x"", ""time""), coords={""time"": time}) # using max print(""max:"") xr.set_options(use_flox=True) %timeit da.groupby(""time.year"").max(""time"") %timeit da.groupby(""time.year"").max(""time"", engine=""flox"") xr.set_options(use_flox=False) %timeit da.groupby(""time.year"").max(""time"") # as reference %timeit [da.sel(time=str(year)).max(""time"") for year in range(2000, 2030)] # using mean print(""mean:"") xr.set_options(use_flox=True) %timeit da.groupby(""time.year"").mean(""time"") %timeit da.groupby(""time.year"").mean(""time"", engine=""flox"") xr.set_options(use_flox=False) %timeit da.groupby(""time.year"").mean(""time"") # as reference %timeit [da.sel(time=str(year)).mean(""time"") for year in range(2000, 2030)] ``` ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] 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. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python max: 158 ms ± 4.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 28.1 ms ± 318 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 11.5 ms ± 52.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) mean: 95.6 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 34.8 ms ± 2.88 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 15.2 ms ± 232 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` ### Anything else we need to know? _No response_ ### Environment
    INSTALLED VERSIONS ------------------ commit: f8127fc9ad24fe8b41cce9f891ab2c98eb2c679a python: 3.10.10 | packaged by conda-forge | (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 5.15.0-69-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: 4.9.1 xarray: main pandas: 1.5.3 numpy: 1.23.5 scipy: 1.10.1 netCDF4: 1.6.3 pydap: installed h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: 2.14.2 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.4.1 bottleneck: 1.3.7 dask: 2023.3.2 distributed: 2023.3.2.1 matplotlib: 3.7.1 cartopy: 0.21.1 seaborn: 0.12.2 numbagg: 0.2.2 fsspec: 2023.3.0 cupy: None pint: 0.20.1 sparse: 0.14.0 flox: 0.6.10 numpy_groupies: 0.9.20 setuptools: 67.6.1 pip: 23.0.1 conda: None pytest: 7.2.2 mypy: None IPython: 8.12.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7730/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 235224055,MDU6SXNzdWUyMzUyMjQwNTU=,1449,time.units truncated when saving to_netcdf,10194086,closed,0,,,6,2017-06-12T12:58:37Z,2023-09-13T13:25:25Z,2023-09-13T13:25:24Z,MEMBER,,,,"When I manually specify the `units` attribute for time, and then save the Dataset `to_netcdf` the string is truncated. See exaple import pandas as pd import xarray as xr time = pd.date_range('2000-01-01', '2000-01-31', freq='6h') ds = xr.Dataset(coords=dict(time=time)) units = 'days since 1975-01-01 00:00:00' calendar = 'gregorian' encoding=dict(time=dict(units=units, calendar=calendar)) ds.to_netcdf('test.nc', format='NETCDF4_CLASSIC', encoding=encoding) ! ncdump -h test.nc # time:units = ""days since 1975-01-01"" ; Some programs seem to require the hours to be present to interpret the time properly (e.g. panoply). When specifying the hour, a 'T' is added. units = 'days since 1975-01-01 01:00:00' ! ncdump -h test.nc # time:units = ""days since 1975-01-01T01:00:00"" ; When xarray defines the `time.units` it works fine. ds = xr.Dataset(coords=dict(time=time)) ds.to_netcdf('test.nc', format='NETCDF4_CLASSIC',) ! ncdump -h test.nc # time:units = ""hours since 2000-01-01 00:00:00"" ; xarray version 0.9.6","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1449/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 715730538,MDU6SXNzdWU3MTU3MzA1Mzg=,4491,deprecate pynio backend,10194086,closed,0,,,21,2020-10-06T14:27:20Z,2022-11-26T15:40:37Z,2022-11-26T15:40:37Z,MEMBER,,,," We are currently not testing with the newest version of netCDF4 because it is incompatible with pynio (the newest version is [1.5.4](https://github.com/Unidata/netcdf4-python/blob/master/Changelog), we are at [1.5.3](https://dev.azure.com/xarray/xarray/_build/results?buildId=3974&view=logs&j=ba13898e-1dfb-5ace-9966-8b7af3677790&t=0a8d5551-2e87-575f-4689-3e85d9688898&l=220)). This is unlikely to be fixed, see conda-forge/pynio-feedstock#90. Therefore we need to think how to setup the tests so we use the newest version of netCDF4. Maybe just remove it from `py38.yml`? And long term what to do with the pynio backend? Deprecate? Move to an external repo? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4491/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1372729718,I_kwDOAMm_X85R0jF2,7036,index refactor: more `_coord_names` than `_variables` on Dataset,10194086,closed,0,,,3,2022-09-14T10:19:00Z,2022-09-27T10:35:40Z,2022-09-27T10:35:40Z,MEMBER,,,,"### What happened? `xr.core.dataset.DataVariables` assumes that everything that is in `ds._dataset._variables` and not in `self._dataset._coord_names` is a ""data variable"". However, since the index refactor we can end up with more `_coord_names` than `_variables` which breaks a number of stuff (e.g. the repr). ### What did you expect to happen? Well it seems this assumption is now wrong. ### Minimal Complete Verifiable Example ```Python ds = xr.Dataset(coords={""a"": (""x"", [1, 2, 3]), ""b"": (""x"", ['a', 'b', 'c'])}) ds.set_index(z=['a', 'b']).reset_index(""z"", drop=True) ``` ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] 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. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python ValueError: __len__() should return >= 0 ``` ### Anything else we need to know? The error comes from here https://github.com/pydata/xarray/blob/63ba862d03c8d0cd8b44d2071bc360e9fed4519d/xarray/core/dataset.py#L368 Bisected to #5692 - which probably does not help too much. ### Environment
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7036/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 144630996,MDU6SXNzdWUxNDQ2MzA5OTY=,810,correct DJF mean,10194086,closed,0,,,4,2016-03-30T15:36:42Z,2022-04-06T16:19:47Z,2016-05-04T12:56:30Z,MEMBER,,,,"This started as a question and I add it as reference. Maybe you have a comment. There are several ways to calculate time series of seasonal data (starting from monthly or daily data): ``` # load libraries import pandas as pd import matplotlib.pyplot import numpy as np import xarray as xr # Create Example Dataset time = pd.date_range('2000.01.01', '2010.12.31', freq='M') data = np.random.rand(*time.shape) ds = xr.DataArray(data, coords=dict(time=time)) # (1) using resample ds_res = ds.resample('Q-FEB', 'time') ds_res = ds_res.sel(time=ds_res['time.month'] == 2) ds_res = ds_res.groupby('time.year').mean('time') # (2) this is wrong ds_season = ds.where(ds['time.season'] == 'DJF').groupby('time.year').mean('time') # (3) using where and rolling # mask other months with nan ds_DJF = ds.where(ds['time.season'] == 'DJF') # rolling mean -> only Jan is not nan # however, we loose Jan/ Feb in the first year and Dec in the last ds_DJF = ds_DJF.rolling(min_periods=3, center=True, time=3).mean() # make annual mean ds_DJF = ds_DJF.groupby('time.year').mean('time') ds_res.plot(marker='*') ds_season.plot() ds_DJF.plot() plt.show() ``` (1) The first is to use resample with 'Q-FEB' as argument. This works fine. It does include Jan/ Feb in the first year, and Dec in the last year + 1. If this makes sense can be debated. One case where this does not work is when you have, say, two regions in your data set, for one you want to calculate DJF and for the other you want NovDecJan. (2) Using 'time.season' is wrong as it combines Jan, Feb and Dec from the same year. (3) The third uses `where` and `rolling` and you lose 'incomplete' seasons. If you replace `ds.where(ds['time.season'] == 'DJF')` with `ds.groupby('time.month').where(summer_months)`, where `summer_months` is a boolean array it works also for non-standard 'summers' (or seasons) across the globe. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/810/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1111644832,I_kwDOAMm_X85CQlqg,6186,upstream dev CI: enable bottleneck again,10194086,closed,0,,,2,2022-01-22T18:11:25Z,2022-02-07T09:05:24Z,2022-02-07T09:05:24Z,MEMBER,,,,bottleneck cannot be built with python 3.10. See https://github.com/pydata/xarray/actions/runs/1731371015,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6186/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 975385095,MDU6SXNzdWU5NzUzODUwOTU=,5721,pandas deprecates Index.get_loc with method,10194086,closed,0,,,7,2021-08-20T08:24:16Z,2022-01-27T21:06:40Z,2022-01-27T21:06:40Z,MEMBER,,,,"pandas deprecates the `method` keyword in `Index.get_loc`, see pandas-dev/pandas#42269. Therefore we end up with about 5000 warnings in our upstream tests: `FutureWarning: Passing method to Index.get_loc is deprecated and will raise in a future version. Use index.get_indexer([item], method=...) instead` We should fix this before pandas releases because the warning will not be silent (`FutureWarning`) or ask pandas to give us more time and use a `DeprecationWarning` at the moment.
    We use this here: https://github.com/pydata/xarray/blob/4bb9d9c6df77137f05e85c7cc6508fe7a93dc0e4/xarray/core/indexes.py#L233-L235 Is this only ever called with one item? Then we might be able to use ```python indexer = self.index.get_indexer( [label_value], method=method, tolerance=tolerance ).item() if indexer == -1: raise KeyError(label_value) ``` --- https://github.com/pydata/xarray/blob/3956b73a7792f41e4410349f2c40b9a9a80decd2/xarray/core/missing.py#L571-L572 This one could be easy to fix (replace with `imin = index.get_indexer([minval], method=""nearest"").item()`) --- It is also defined in `CFTimeIndex`, which complicates things: https://github.com/pydata/xarray/blob/eea76733770be03e78a0834803291659136bca31/xarray/coding/cftimeindex.py#L461-L466 because `get_indexer` expects an iterable and thus the `if isinstance(key, str)` test no longer works.
    @benbovy @spencerkclark ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5721/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 432074821,MDU6SXNzdWU0MzIwNzQ4MjE=,2889,nansum vs nanmean for all-nan vectors,10194086,closed,0,,,3,2019-04-11T15:04:39Z,2022-01-05T21:59:48Z,2019-04-11T16:08:02Z,MEMBER,,,," ```python import xarray as xr import numpy as np ds = xr.DataArray([np.NaN, np.NaN]) ds.mean() ds.sum() ``` #### Problem description `ds.mean()` returns `NaN`, `ds.sum()` returns `0`. This comes from numpy (cp `np.nanmean` vs. `np.nansum`), so it might have to be discussed upstream, but I wanted to ask the xarray community first on their opinion. This is also relevant for #422 (what happens if the all weights are NaN or sum up to 0). #### Expected Output I would expect both to return `np.nan`. #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Mar 27 2019, 23:01:00) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.4.176-96-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.2 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: None h5netcdf: 0.7.1 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: 1.0.22 cfgrib: None iris: None bottleneck: 1.2.1 dask: 1.1.5 distributed: 1.26.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.0 pip: 19.0.3 conda: None pytest: 4.4.0 IPython: 7.4.0 sphinx: 2.0.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2889/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1086797050,I_kwDOAMm_X85AxzT6,6101,enable pytest-xdist again (after dask release),10194086,closed,0,,,0,2021-12-22T12:57:03Z,2022-01-03T08:29:48Z,2022-01-03T08:29:48Z,MEMBER,,,,I disabled pytest-xdist because a dask issue renders our CI unusable. As soon as dask releases a new version we should revert #6077 again.,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6101/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 834641104,MDU6SXNzdWU4MzQ2NDExMDQ=,5053,ImportError: module 'xarray.backends.*' has no attribute '*_backend',10194086,closed,0,,,3,2021-03-18T10:44:33Z,2021-04-25T16:23:20Z,2021-04-25T16:23:19Z,MEMBER,,,,"**What happened**: I could not open the test dataset on master. It's a bit strange that this is not picked up by the tests, so probably something to do with the environment I have (I just updated all packages). @alexamici @aurghs does that tell you anything? I can also try to figure it out. **Minimal Complete Verifiable Example**: calling `open_dataset` with `""""` is enough to trigger the error: ```python import xarray as xr air = xr.open_dataset("""") ``` **Anything else we need to know?**: And the traceback:
    ```python-traceback --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) ~/conda/envs/xarray_dev/lib/python3.8/site-packages/pkg_resources/__init__.py in resolve(self) 2479 try: -> 2480 return functools.reduce(getattr, self.attrs, module) 2481 except AttributeError as exc: AttributeError: module 'xarray.backends.cfgrib_' has no attribute 'cfgrib_backend' The above exception was the direct cause of the following exception: ImportError Traceback (most recent call last) in ----> 1 air = xr.tutorial.open_dataset(""air_temperature"") ~/code/xarray/xarray/tutorial.py in open_dataset(name, cache, cache_dir, github_url, branch, **kws) 93 raise OSError(msg) 94 ---> 95 ds = _open_dataset(localfile, **kws) 96 97 if not cache: ~/code/xarray/xarray/backends/api.py in open_dataset(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, backend_kwargs, *args, **kwargs) 491 492 if engine is None: --> 493 engine = plugins.guess_engine(filename_or_obj) 494 495 backend = plugins.get_backend(engine) ~/code/xarray/xarray/backends/plugins.py in guess_engine(store_spec) 99 100 def guess_engine(store_spec): --> 101 engines = list_engines() 102 103 for engine, backend in engines.items(): ~/code/xarray/xarray/backends/plugins.py in list_engines() 95 def list_engines(): 96 pkg_entrypoints = pkg_resources.iter_entry_points(""xarray.backends"") ---> 97 return build_engines(pkg_entrypoints) 98 99 ~/code/xarray/xarray/backends/plugins.py in build_engines(pkg_entrypoints) 82 backend_entrypoints = BACKEND_ENTRYPOINTS.copy() 83 pkg_entrypoints = remove_duplicates(pkg_entrypoints) ---> 84 external_backend_entrypoints = backends_dict_from_pkg(pkg_entrypoints) 85 backend_entrypoints.update(external_backend_entrypoints) 86 backend_entrypoints = sort_backends(backend_entrypoints) ~/code/xarray/xarray/backends/plugins.py in backends_dict_from_pkg(pkg_entrypoints) 56 for pkg_ep in pkg_entrypoints: 57 name = pkg_ep.name ---> 58 backend = pkg_ep.load() 59 backend_entrypoints[name] = backend 60 return backend_entrypoints ~/conda/envs/xarray_dev/lib/python3.8/site-packages/pkg_resources/__init__.py in load(self, require, *args, **kwargs) 2470 if require: 2471 self.require(*args, **kwargs) -> 2472 return self.resolve() 2473 2474 def resolve(self): ~/conda/envs/xarray_dev/lib/python3.8/site-packages/pkg_resources/__init__.py in resolve(self) 2480 return functools.reduce(getattr, self.attrs, module) 2481 except AttributeError as exc: -> 2482 raise ImportError(str(exc)) from exc 2483 2484 def require(self, env=None, installer=None): ImportError: module 'xarray.backends.cfgrib_' has no attribute 'cfgrib_backend' ```
    **Environment**:
    Output of xr.show_versions() ``` INSTALLED VERSIONS ------------------ commit: a6f51c680f4e4c3ed5101b9c1111f0b94d28a781 python: 3.8.6 | packaged by conda-forge | (default, Jan 25 2021, 23:21:18) [GCC 9.3.0] python-bits: 64 OS: Linux OS-release: 5.4.0-67-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.2.dev111+g0d93c4f9.d20201219 pandas: 1.2.3 numpy: 1.20.1 scipy: 1.6.1 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.10.0 h5py: 3.1.0 Nio: None zarr: 2.6.1 cftime: 1.4.1 nc_time_axis: 1.2.0 PseudoNetCDF: installed rasterio: 1.2.1 cfgrib: 0.9.8.5 iris: 2.4.0 bottleneck: 1.3.2 dask: 2021.03.0 distributed: 2021.03.0 matplotlib: 3.3.4 cartopy: 0.18.0 seaborn: 0.11.1 numbagg: installed pint: 0.16.1 setuptools: 49.6.0.post20210108 pip: 21.0.1 conda: None pytest: 6.2.2 IPython: 7.21.0 sphinx: None ```
    and my conda list:
    ``` # packages in environment at /home/mathause/conda/envs/xarray_dev: # # Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 1_gnu conda-forge affine 2.3.0 py_0 conda-forge antlr-python-runtime 4.7.2 py38h578d9bd_1002 conda-forge apipkg 1.5 py_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge asciitree 0.3.3 py_2 conda-forge attrs 20.3.0 pyhd3deb0d_0 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 py_2 conda-forge backports.functools_lru_cache 1.6.1 py_0 conda-forge beautifulsoup4 4.9.3 pyhb0f4dca_0 conda-forge black 20.8b1 py_1 conda-forge bokeh 2.3.0 py38h578d9bd_0 conda-forge boost-cpp 1.72.0 h9d3c048_4 conda-forge boto3 1.17.30 pyhd8ed1ab_0 conda-forge botocore 1.20.30 pyhd8ed1ab_0 conda-forge bottleneck 1.3.2 py38h5c078b8_3 conda-forge brotlipy 0.7.0 py38h497a2fe_1001 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.17.1 h7f98852_1 conda-forge ca-certificates 2020.12.5 ha878542_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cairo 1.16.0 h7979940_1007 conda-forge cartopy 0.18.0 py38hab71064_13 conda-forge cdat_info 8.2.1 pyh9f0ad1d_1 conda-forge cdms2 3.1.5 pypi_0 pypi cdtime 3.1.4 py38h49bcaf2_2 conda-forge certifi 2020.12.5 py38h578d9bd_1 conda-forge cf-units 2.1.4 py38hab2c0dc_2 conda-forge cffi 1.14.5 py38ha65f79e_0 conda-forge cfgrib 0.9.8.5 pyhd8ed1ab_0 conda-forge cfgv 3.2.0 py_0 conda-forge cfitsio 3.470 hb418390_7 conda-forge cftime 1.4.1 py38h5c078b8_0 conda-forge chardet 4.0.0 py38h578d9bd_1 conda-forge click 7.1.2 pyh9f0ad1d_0 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.7.1 pyhd8ed1ab_0 conda-forge cloudpickle 1.6.0 py_0 conda-forge coverage 5.5 py38h497a2fe_0 conda-forge coveralls 3.0.1 pyhd8ed1ab_0 conda-forge cryptography 3.4.6 py38ha5dfef3_0 conda-forge curl 7.75.0 h979ede3_0 conda-forge cycler 0.10.0 py_2 conda-forge cytoolz 0.11.0 py38h497a2fe_3 conda-forge dask 2021.3.0 pyhd8ed1ab_0 conda-forge dask-core 2021.3.0 pyhd8ed1ab_0 conda-forge dataclasses 0.8 pyhc8e2a94_1 conda-forge dbus 1.13.6 hfdff14a_1 conda-forge decorator 4.4.2 py_0 conda-forge distarray 2.12.2 py_1 conda-forge distlib 0.3.1 pyh9f0ad1d_0 conda-forge distributed 2021.3.0 py38h578d9bd_0 conda-forge docopt 0.6.2 py_1 conda-forge eccodes 2.20.0 ha0e6eb6_0 conda-forge editdistance 0.5.3 py38h709712a_3 conda-forge esmf 8.0.1 mpi_mpich_h3cbecb6_102 conda-forge esmpy 8.0.1 mpi_mpich_py38h6f0bf2d_102 conda-forge execnet 1.8.0 pyh44b312d_0 conda-forge expat 2.2.10 h9c3ff4c_0 conda-forge fasteners 0.14.1 py_3 conda-forge filelock 3.0.12 pyh9f0ad1d_0 conda-forge flake8 3.9.0 pyhd8ed1ab_0 conda-forge fontconfig 2.13.1 hba837de_1004 conda-forge freetype 2.10.4 h0708190_1 conda-forge freexl 1.0.6 h7f98852_0 conda-forge fsspec 0.8.7 pyhd8ed1ab_0 conda-forge future 0.18.2 py38h578d9bd_3 conda-forge g2clib 1.6.0 hf3f1b0b_9 conda-forge geos 3.9.1 h9c3ff4c_2 conda-forge geotiff 1.6.0 h11d48b3_4 conda-forge gettext 0.19.8.1 h0b5b191_1005 conda-forge giflib 5.2.1 h516909a_2 conda-forge glib 2.66.7 h9c3ff4c_1 conda-forge glib-tools 2.66.7 h9c3ff4c_1 conda-forge gprof2dot 2019.11.30 py_0 conda-forge gst-plugins-base 1.18.4 h29181c9_0 conda-forge gstreamer 1.18.4 h76c114f_0 conda-forge h5netcdf 0.10.0 pyhd8ed1ab_0 conda-forge h5py 3.1.0 nompi_py38hafa665b_100 conda-forge hdf4 4.2.13 h10796ff_1004 conda-forge hdf5 1.10.6 mpi_mpich_h996c276_1014 conda-forge heapdict 1.0.1 py_0 conda-forge hypothesis 6.8.1 pyhd8ed1ab_0 conda-forge icu 68.1 h58526e2_0 conda-forge identify 2.1.3 pyhd8ed1ab_0 conda-forge idna 2.10 pyh9f0ad1d_0 conda-forge importlib-metadata 3.7.3 py38h578d9bd_0 conda-forge importlib_metadata 3.7.3 hd8ed1ab_0 conda-forge importlib_resources 5.1.2 py38h578d9bd_0 conda-forge iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge ipython 7.21.0 py38h81c977d_0 conda-forge ipython_genutils 0.2.0 py_1 conda-forge iris 2.4.0 py38h578d9bd_1 conda-forge isort 5.7.0 pyhd8ed1ab_0 conda-forge jasper 1.900.1 h07fcdf6_1006 conda-forge jedi 0.18.0 py38h578d9bd_2 conda-forge jinja2 2.11.3 pyh44b312d_0 conda-forge jmespath 0.10.0 pyh9f0ad1d_0 conda-forge jpeg 9d h516909a_0 conda-forge json-c 0.15 h98cffda_0 conda-forge jsonschema 3.2.0 py38h32f6830_1 conda-forge jupyter_core 4.7.1 py38h578d9bd_0 conda-forge kealib 1.4.14 hcc255d8_2 conda-forge kiwisolver 1.3.1 py38h1fd1430_1 conda-forge krb5 1.17.2 h926e7f8_0 conda-forge lazy-object-proxy 1.5.2 py38h497a2fe_1 conda-forge lcms2 2.12 hddcbb42_0 conda-forge ld_impl_linux-64 2.35.1 hea4e1c9_2 conda-forge libaec 1.0.4 he1b5a44_1 conda-forge libblas 3.8.0 17_openblas conda-forge libcblas 3.8.0 17_openblas conda-forge libcdms 3.1.2 h981a4fd_113 conda-forge libcf 1.0.3 py38h88b7cc0_109 conda-forge libclang 11.1.0 default_ha53f305_0 conda-forge libcst 0.3.17 py38h578d9bd_0 conda-forge libcurl 7.75.0 hc4aaa36_0 conda-forge libdap4 3.20.6 hd7c4107_1 conda-forge libdrs 3.1.2 h7918d09_113 conda-forge libdrs_f 3.1.2 h5026c31_111 conda-forge libedit 3.1.20191231 he28a2e2_2 conda-forge libev 4.33 h516909a_1 conda-forge libevent 2.1.10 hcdb4288_3 conda-forge libffi 3.3 h58526e2_2 conda-forge libgcc-ng 9.3.0 h2828fa1_18 conda-forge libgdal 3.2.1 h38ff51b_7 conda-forge libgfortran-ng 9.3.0 hff62375_18 conda-forge libgfortran5 9.3.0 hff62375_18 conda-forge libglib 2.66.7 h3e27bee_1 conda-forge libgomp 9.3.0 h2828fa1_18 conda-forge libiconv 1.16 h516909a_0 conda-forge libkml 1.3.0 hd79254b_1012 conda-forge liblapack 3.8.0 17_openblas conda-forge libllvm10 10.0.1 he513fc3_3 conda-forge libllvm11 11.1.0 hf817b99_0 conda-forge libnetcdf 4.7.4 mpi_mpich_hdef422e_7 conda-forge libnghttp2 1.43.0 h812cca2_0 conda-forge libopenblas 0.3.10 pthreads_h4812303_5 conda-forge libpng 1.6.37 hed695b0_2 conda-forge libpq 13.1 hfd2b0eb_2 conda-forge librttopo 1.1.0 h1185371_6 conda-forge libspatialite 5.0.1 he52d314_3 conda-forge libssh2 1.9.0 ha56f1ee_6 conda-forge libstdcxx-ng 9.3.0 h6de172a_18 conda-forge libtiff 4.2.0 hdc55705_0 conda-forge libuuid 2.32.1 h14c3975_1000 conda-forge libwebp-base 1.2.0 h7f98852_2 conda-forge libxcb 1.13 h7f98852_1003 conda-forge libxkbcommon 1.0.3 he3ba5ed_0 conda-forge libxml2 2.9.10 h72842e0_3 conda-forge libxslt 1.1.33 h15afd5d_2 conda-forge line_profiler 3.1.0 py38h82cb98a_1 conda-forge llvmlite 0.36.0 py38h4630a5e_0 conda-forge locket 0.2.0 py_2 conda-forge lxml 4.6.2 py38hf1fe3a4_1 conda-forge lz4-c 1.9.3 h9c3ff4c_0 conda-forge markupsafe 1.1.1 py38h497a2fe_3 conda-forge matplotlib 3.3.4 py38h578d9bd_0 conda-forge matplotlib-base 3.3.4 py38h0efea84_0 conda-forge mccabe 0.6.1 py_1 conda-forge mechanicalsoup 1.0.0 pyhd8ed1ab_0 conda-forge monkeytype 20.5.0 pyh516909a_0 conda-forge monotonic 1.5 py_0 conda-forge more-itertools 8.7.0 pyhd8ed1ab_0 conda-forge mpi 1.0 mpich conda-forge mpi4py 3.0.3 py38he865349_5 conda-forge mpich 3.4.1 h846660c_104 conda-forge msgpack-python 1.0.2 py38h1fd1430_1 conda-forge mypy 0.812 pyhd8ed1ab_0 conda-forge mypy_extensions 0.4.3 py38h578d9bd_3 conda-forge mysql-common 8.0.23 ha770c72_1 conda-forge mysql-libs 8.0.23 h935591d_1 conda-forge nbformat 5.1.2 pyhd8ed1ab_1 conda-forge nc-time-axis 1.2.0 py_1 conda-forge ncurses 6.2 h58526e2_4 conda-forge netcdf-fortran 4.5.3 mpi_mpich_h7ad8bfe_1 conda-forge netcdf4 1.5.6 nompi_py38h1cdf482_100 conda-forge nodeenv 1.5.0 pyh9f0ad1d_0 conda-forge nspr 4.30 h9c3ff4c_0 conda-forge nss 3.62 hb5efdd6_0 conda-forge numba 0.53.0 py38h5e62926_1 conda-forge numbagg 0.1 pypi_0 pypi numcodecs 0.7.3 py38h709712a_0 conda-forge numpy 1.20.1 py38h18fd61f_0 conda-forge olefile 0.46 pyh9f0ad1d_1 conda-forge openblas 0.3.10 pthreads_h04b7a96_5 conda-forge openjpeg 2.4.0 hf7af979_0 conda-forge openssl 1.1.1j h7f98852_0 conda-forge packaging 20.9 pyh44b312d_0 conda-forge pandas 1.2.3 py38h51da96c_0 conda-forge parso 0.8.1 pyhd8ed1ab_0 conda-forge partd 1.1.0 py_0 conda-forge pathspec 0.8.1 pyhd3deb0d_0 conda-forge patsy 0.5.1 py_0 conda-forge pcre 8.44 he1b5a44_0 conda-forge pexpect 4.8.0 py38h32f6830_1 conda-forge pickleshare 0.7.5 py38h32f6830_1002 conda-forge pillow 8.1.2 py38ha0e1e83_0 conda-forge pint 0.16.1 py_0 conda-forge pip 21.0.1 pyhd8ed1ab_0 conda-forge pixman 0.40.0 h36c2ea0_0 conda-forge pluggy 0.13.1 py38h578d9bd_4 conda-forge poppler 0.89.0 h2de54a5_5 conda-forge poppler-data 0.4.10 0 conda-forge postgresql 13.1 h6303168_2 conda-forge pre-commit 2.11.1 py38h578d9bd_0 conda-forge proj 7.2.0 h277dcde_2 conda-forge prompt-toolkit 3.0.17 pyha770c72_0 conda-forge pseudonetcdf 3.1.0 py_1 conda-forge psutil 5.8.0 py38h497a2fe_1 conda-forge pthread-stubs 0.4 h36c2ea0_1001 conda-forge ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge py 1.10.0 pyhd3deb0d_0 conda-forge pycodestyle 2.7.0 pyhd8ed1ab_0 conda-forge pycparser 2.20 pyh9f0ad1d_2 conda-forge pydap 3.2.2 py38_1000 conda-forge pyflakes 2.3.0 pyhd8ed1ab_0 conda-forge pygments 2.8.1 pyhd8ed1ab_0 conda-forge pyke 1.1.1 py38h578d9bd_1003 conda-forge pyopenssl 20.0.1 pyhd8ed1ab_0 conda-forge pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge pyqt 5.12.3 py38h578d9bd_7 conda-forge pyqt-impl 5.12.3 py38h7400c14_7 conda-forge pyqt5-sip 4.19.18 py38h709712a_7 conda-forge pyqtchart 5.12 py38h7400c14_7 conda-forge pyqtwebengine 5.12.1 py38h7400c14_7 conda-forge pyrsistent 0.17.3 py38h497a2fe_2 conda-forge pyshp 2.1.3 pyh44b312d_0 conda-forge pysocks 1.7.1 py38h578d9bd_3 conda-forge pytest 6.2.2 py38h578d9bd_0 conda-forge pytest-cov 2.11.1 pyh44b312d_0 conda-forge pytest-env 0.6.2 py_0 conda-forge pytest-forked 1.3.0 pyhd3deb0d_0 conda-forge pytest-profiling 1.7.0 py_1 conda-forge pytest-xdist 2.2.1 pyhd8ed1ab_0 conda-forge python 3.8.6 hffdb5ce_5_cpython conda-forge python-dateutil 2.8.1 py_0 conda-forge python-xxhash 2.0.0 py38h497a2fe_1 conda-forge python_abi 3.8 1_cp38 conda-forge pytz 2021.1 pyhd8ed1ab_0 conda-forge pyyaml 5.4.1 py38h497a2fe_0 conda-forge qt 5.12.9 hda022c4_4 conda-forge rasterio 1.2.1 py38h57accd2_2 conda-forge readline 8.0 he28a2e2_2 conda-forge regex 2020.11.13 py38h497a2fe_1 conda-forge regrid2 3.1.5 pypi_0 pypi requests 2.25.1 pyhd3deb0d_0 conda-forge s3transfer 0.3.4 pyhd8ed1ab_0 conda-forge scipy 1.6.1 py38hb2138dd_0 conda-forge seaborn 0.11.1 ha770c72_0 conda-forge seaborn-base 0.11.1 pyhd8ed1ab_1 conda-forge setuptools 49.6.0 py38h578d9bd_3 conda-forge shapely 1.7.1 py38h4fc1155_4 conda-forge six 1.15.0 pyh9f0ad1d_0 conda-forge snuggs 1.4.7 py_0 conda-forge sortedcontainers 2.3.0 pyhd8ed1ab_0 conda-forge soupsieve 2.0.1 py38h32f6830_0 conda-forge sparse 0.11.2 py_0 conda-forge sqlite 3.34.0 h74cdb3f_0 conda-forge statsmodels 0.12.2 py38h5c078b8_0 conda-forge tblib 1.6.0 py_0 conda-forge tiledb 2.2.5 h91fcb0e_0 conda-forge tk 8.6.10 hed695b0_1 conda-forge toml 0.10.2 pyhd8ed1ab_0 conda-forge toolz 0.11.1 py_0 conda-forge tornado 6.1 py38h497a2fe_1 conda-forge traitlets 5.0.5 py_0 conda-forge typed-ast 1.4.2 py38h497a2fe_0 conda-forge typing_extensions 3.7.4.3 py_0 conda-forge typing_inspect 0.6.0 pyh9f0ad1d_0 conda-forge tzcode 2021a h7f98852_1 conda-forge tzdata 2021a he74cb21_0 conda-forge udunits2 2.2.27.27 h360fe7b_0 conda-forge urllib3 1.26.4 pyhd8ed1ab_0 conda-forge virtualenv 20.4.3 py38h578d9bd_0 conda-forge wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge webob 1.8.6 py_0 conda-forge wheel 0.36.2 pyhd3deb0d_0 conda-forge xarray 0.16.2.dev111+g0d93c4f9.d20201219 dev_0 xerces-c 3.2.3 h9d8b166_2 conda-forge xorg-kbproto 1.0.7 h14c3975_1002 conda-forge xorg-libice 1.0.10 h516909a_0 conda-forge xorg-libsm 1.2.3 hd9c2040_1000 conda-forge xorg-libx11 1.7.0 h36c2ea0_0 conda-forge xorg-libxau 1.0.9 h14c3975_0 conda-forge xorg-libxdmcp 1.1.3 h516909a_0 conda-forge xorg-libxext 1.3.4 h7f98852_1 conda-forge xorg-libxrender 0.9.10 h7f98852_1003 conda-forge xorg-renderproto 0.11.1 h14c3975_1002 conda-forge xorg-xextproto 7.3.0 h14c3975_1002 conda-forge xorg-xproto 7.0.31 h14c3975_1007 conda-forge xxhash 0.8.0 h7f98852_3 conda-forge xz 5.2.5 h516909a_1 conda-forge yaml 0.2.5 h516909a_0 conda-forge zarr 2.6.1 pyhd8ed1ab_0 conda-forge zict 2.0.0 py_0 conda-forge zipp 3.4.1 pyhd8ed1ab_0 conda-forge zlib 1.2.11 h516909a_1010 conda-forge zstd 1.4.9 ha95c52a_0 conda-forge ```
    --- edit: added the traceback","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5053/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 817951965,MDU6SXNzdWU4MTc5NTE5NjU=,4970,minimum version and non-semantic versioning (netCDF4),10194086,closed,0,,,1,2021-02-27T15:33:48Z,2021-03-08T00:20:38Z,2021-03-08T00:20:38Z,MEMBER,,,,"We currently pin netCDF4 to [version 1.5](https://github.com/pydata/xarray/blob/48378c4b11c5c2672ff91396d4284743165b4fbe/ci/requirements/py37-min-all-deps.yml#L28). However, I think netCDF4 does not really follow semantic versioning, e.g. python 2 support was dropped in version 1.5.6. So they may actually be doing something like `1.major.minor[.patch]` - I asked about their versioning scheme in Unidata/netcdf4-python#1090. So I wonder if we would need to pin netCDF to version to version 1.5.4.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4970/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 814806676,MDU6SXNzdWU4MTQ4MDY2NzY=,4945,Upstream CI failing silently,10194086,closed,0,,,1,2021-02-23T20:30:29Z,2021-02-24T08:14:00Z,2021-02-24T08:14:00Z,MEMBER,,,,"The last 5 days our Upstream CI failed silently with a timeout after 6h: https://github.com/pydata/xarray/actions/workflows/upstream-dev-ci.yaml?query=branch%3Amaster+event%3Aschedule This was probably caused by #4934. As mentioned in dask/dask#4934 this is probably dask/dask#6738 which was merged 5 days ago.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4945/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 803402841,MDU6SXNzdWU4MDM0MDI4NDE=,4881,check mypy at the end of some CI runs?,10194086,closed,0,,,2,2021-02-08T10:04:44Z,2021-02-22T16:33:50Z,2021-02-22T16:33:50Z,MEMBER,,,,"We currently run mypy in the `pre-commit hooks` CI. However, this is done in an environment where no dependencies are installed. Therefore we missed the errors that pop up when running mypy with numpy 1.20 installed. (Please correct my if I misunderstood this). Should we add a new step to our CI and run mypy? I think we should at least add this to `ubuntu-latest py3.8`. For more complete checks we could also go for ` ubuntu-latest py37-min-all-deps` and `upstream-dev`.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4881/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 739008382,MDU6SXNzdWU3MzkwMDgzODI=,4570,fix compatibility with h5py version 3 and unpin tests,10194086,closed,0,,,6,2020-11-09T13:00:01Z,2021-02-17T08:41:20Z,2021-02-17T08:41:20Z,MEMBER,,,,"h5py version 3.1 broke our tests. I pinned it to version 2.10 in #4569. We should therefore * fix the issues * unpin h5py again The failures could be related to a change how strings are read: https://docs.h5py.org/en/latest/strings.html I am not sure if this has to be fixed in xarray or in h5necdf. I'd be happy if someone else took this one. Failed tests:
    ``` FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_zero_dimensional_variable FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_write_store - As... FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_roundtrip_test_data FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_load - Assertion... FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_dataset_compute FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_roundtrip_object_dtype FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_roundtrip_string_data FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_orthogonal_indexing FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_vectorized_indexing FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_isel_dataarray FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_array_type_after_indexing FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_append_write - A... FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_append_overwrite_values FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_write_groups - A... FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_encoding_kwarg_vlen_string FAILED xarray/tests/test_backends.py::TestH5NetCDFData::test_compression_encoding FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_zero_dimensional_variable FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_write_store FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_roundtrip_test_data FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_load - Ass... FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_dataset_compute FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_roundtrip_object_dtype FAILED xarray/tests/test_backends.py::TestH5NetCDFViaDaskData::test_encoding_kwarg_vlen_string FAILED xarray/tests/test_backends.py::TestH5NetCDFViaDaskData::test_compression_encoding FAILED xarray/tests/test_distributed.py::test_dask_distributed_netcdf_roundtrip[h5netcdf-NETCDF4] FAILED xarray/tests/test_distributed.py::test_dask_distributed_read_netcdf_integration_test[h5netcdf-NETCDF4] ```
    **Example failure:** ```python traceback > assert_allclose(original, computed) E AssertionError: Left and right Dataset objects are not close E E Differing coordinates: E L * dim3 (dim3) **What happened**: Our doctests fail since numpy 1.20 came out: https://github.com/pydata/xarray/pull/4760/checks?check_run_id=1818512841#step:8:69 **What you expected to happen**: They don't ;-) **Minimal Complete Verifiable Example**: The following fails with numpy 1.20 while it converted `np.NaN` to an integer before ([xarray.DataArray.pad](http://xarray.pydata.org/en/v0.16.2/generated/xarray.DataArray.pad.html#xarray.DataArray.pad) at the bottom) ```python import numpy as np x = np.arange(10) x = np.pad(x, 1, ""constant"", constant_values=np.nan) ``` requires numpy 1.20 **Anything else we need to know?**: - that's probably related to https://numpy.org/doc/stable/release/1.20.0-notes.html#numpy-scalars-are-cast-when-assigned-to-arrays - I asked if this behavior will stay: https://github.com/numpy/numpy/issues/16499#issuecomment-772342087 - One possibility is to add a check `np.can_cast(constant_values.dtype, array.dtype)` (or similar) for a better error message. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4858/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 785825401,MDU6SXNzdWU3ODU4MjU0MDE=,4808,"Upstream dev CI does not show ""version info""",10194086,closed,0,,,3,2021-01-14T09:24:04Z,2021-01-15T01:01:04Z,2021-01-14T23:45:07Z,MEMBER,,,,"Would be nice if it did :-) e.g. https://github.com/pydata/xarray/runs/1698988952","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4808/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 722437965,MDU6SXNzdWU3MjI0Mzc5NjU=,4514,"encode_cf_datetime: reference_date can not be ""2000-02-30""",10194086,closed,0,,,3,2020-10-15T15:25:10Z,2021-01-05T15:42:37Z,2021-01-05T15:42:37Z,MEMBER,,,,"**What happened**: I try to save the result of `ds.resample(""time"": ""Q-FEB"").mean()` with a `360_day` calendar as netcdf. Thus, my first date is `cftime.Datetime360Day(2000-02-30)` (note the day). xarray then tries to use `units='days since 2000-02-30'` which fails with `ValueError: day is out of range for month`. **What you expected to happen**: The dataset can be saved. **Minimal Complete Verifiable Example**: ```python import cftime import xarray as xr time = xr.cftime_range(""2000-02-30"", ""2001-01-01"", freq=""3M"", calendar=""360_day"") dates = np.asarray(time) reference_date = xr.coding.times.infer_datetime_units(dates) # 'days since 2000-02-30 00:00:00.000000' xr.coding.times.encode_cf_datetime(time) # ValueError ``` Traceback:
    ```python-traceback --------------------------------------------------------------------------- OutOfBoundsDatetime Traceback (most recent call last) ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/coding/times.py in encode_cf_datetime(dates, units, calendar) 367 # parse with cftime instead --> 368 raise OutOfBoundsDatetime 369 assert dates.dtype == ""datetime64[ns]"" OutOfBoundsDatetime: During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) in 4 time = xr.cftime_range(""2000-02-30"", ""2001-01-01"", freq=""3M"", calendar=""360_day"") 5 ----> 6 xr.coding.times.encode_cf_datetime(time) ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/coding/times.py in encode_cf_datetime(dates, units, calendar) 385 386 except (OutOfBoundsDatetime, OverflowError): --> 387 num = _encode_datetime_with_cftime(dates, units, calendar) 388 389 num = cast_to_int_if_safe(num) ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/coding/times.py in _encode_datetime_with_cftime(dates, units, calendar) 332 return np.nan if d is None else cftime.date2num(d, units, calendar) 333 --> 334 return np.vectorize(encode_datetime)(dates) 335 336 ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/numpy/lib/function_base.py in __call__(self, *args, **kwargs) 2089 vargs.extend([kwargs[_n] for _n in names]) 2090 -> 2091 return self._vectorize_call(func=func, args=vargs) 2092 2093 def _get_ufunc_and_otypes(self, func, args): ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/numpy/lib/function_base.py in _vectorize_call(self, func, args) 2159 res = func() 2160 else: -> 2161 ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args) 2162 2163 # Convert args to object arrays first ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/numpy/lib/function_base.py in _get_ufunc_and_otypes(self, func, args) 2119 2120 inputs = [arg.flat[0] for arg in args] -> 2121 outputs = func(*inputs) 2122 2123 # Performance note: profiling indicates that -- for simple ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/coding/times.py in encode_datetime(d) 330 331 def encode_datetime(d): --> 332 return np.nan if d is None else cftime.date2num(d, units, calendar) 333 334 return np.vectorize(encode_datetime)(dates) cftime/_cftime.pyx in cftime._cftime.date2num() cftime/_cftime.pyx in cftime._cftime._dateparse() ValueError: day is out of range for month ```
    **Anything else we need to know?**: This goes wrong here: https://github.com/pydata/xarray/blob/15537497136345ed67e9e8b089bcd4573df0b2ea/xarray/coding/times.py#L249-L250 A possible fix is to add the following lines: ```python try: cftime._cftime._dateparse(reference_date) except ValueError: reference_date = type(dates[0])(dates[0].year, dates[0].month, 1) reference_date = xr.coding.times.format_cftime_datetime(reference_date) ``` To workaround set the encoding manually: ```python encoding = {} encoding['time'] = {'units': 'days since 1850-01-01'} ds.to_netcdf(filename, encoding=encoding) ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4514/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 761266480,MDU6SXNzdWU3NjEyNjY0ODA=,4671,windows test timeout,10194086,closed,0,,,3,2020-12-10T14:00:54Z,2020-12-15T16:40:38Z,2020-12-15T16:40:38Z,MEMBER,,,,"The windows tests seem to regularly time out after 60 min, which is annoying. All other tests seem to run about twice as fast... There is some stuff we can try (which may or may not help): - [ ] switch to the newest windows image - [ ] use mamba to resolve dependencies - [ ] use matplotlib-base - [ ] find the slowest tests using `py.test xarray/tests/ --durations=100` - [ ] de-parametrize tests with a slow setup (if possible) - [ ] reduce the number of xfails - [ ] other ideas? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4671/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 766450436,MDU6SXNzdWU3NjY0NTA0MzY=,4690,Linux py36-bare-minimum will likely fail,10194086,closed,0,,,1,2020-12-14T13:41:56Z,2020-12-14T19:47:59Z,2020-12-14T19:47:59Z,MEMBER,,,,"The Linux py36-bare-minimum will likely fail with a `TypeError: 'NoneType' object is not callable` due to python/importlib_metadata#270 This could be fixed by adding `typing_extensions` to the CI or maybe they fix that upstream.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4690/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 755566566,MDU6SXNzdWU3NTU1NjY1NjY=,4643,mamba does not use strict channel priority (in our CI),10194086,closed,0,,,0,2020-12-02T19:54:19Z,2020-12-05T00:30:11Z,2020-12-05T00:30:11Z,MEMBER,,,,"We use mamba in the new CI: https://github.com/pydata/xarray/blob/7152b41fa80a56db0ce88b241fbe4092473cfcf0/.github/workflows/upstream-dev-ci.yaml#L39 I just tested it and it is awesomely fast! However, it does not enforce strict channel priority, so packages from main get mixed in (see e.g. https://github.com/pydata/xarray/runs/1487637678#step:5:112 & search for `main`) & we don't want that. So I'd suggest to do: ```bash mamba env update --override-channels -c conda-forge -f ci/requirements/py38.yml ``` (note there is a `--strict-channel-priority` option in mamba but it still installed packages from main when I tested it) cc @andersy005: mybe you could add this to #4604? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4643/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 714993891,MDU6SXNzdWU3MTQ5OTM4OTE=,4487,overloaded functions have several signatures in the docs,10194086,closed,0,,,5,2020-10-05T16:37:02Z,2020-11-24T14:52:09Z,2020-11-24T14:52:09Z,MEMBER,,,,"See ![image](https://user-images.githubusercontent.com/10194086/95106783-3f368500-0739-11eb-80dc-a9572ef98e1e.png) (http://xarray.pydata.org/en/latest/generated/xarray.concat.html) Can this be fixed? Or do we actually want to show both?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4487/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 741115905,MDU6SXNzdWU3NDExMTU5MDU=,4574,nightly upstream test,10194086,closed,0,,,6,2020-11-11T22:31:53Z,2020-11-22T02:37:14Z,2020-11-22T02:37:14Z,MEMBER,,,,"As discussed in the call today it would be nice to remove the upstream test from our ci and setup a ""nightly"" job to test against the development versions of pandas etc. This should lead to less failures unrelated to specific PRs. Question: how are we going to see that the upstream build failed? Having the failure in random PRs is not clean but very visible... This could be done via github actions. See xpublish for an example setup: https://github.com/xarray-contrib/xpublish/tree/master/.github/workflows xref #4313","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4574/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 723181209,MDU6SXNzdWU3MjMxODEyMDk=,4516,py38-upstream-dev failure: 'CFTimeIndex' object has no attribute '_id',10194086,closed,0,,,6,2020-10-16T12:39:01Z,2020-10-23T01:08:50Z,2020-10-23T01:08:50Z,MEMBER,,,,"Now that #4502 was fixed upstream we get a new issue in `py38-upstream-dev` (which I am pretty sure is unrelated as dask is not involved). `xarray/tests/test_cftimeindex_resample.py::test_resample` fails with: ```python-traceback AttributeError: 'CFTimeIndex' object has no attribute '_id' ``` See: https://dev.azure.com/xarray/xarray/_build/results?buildId=4038&view=logs&jobId=603f3fdc-8af6-5e0a-f594-fa71bc949352&j=603f3fdc-8af6-5e0a-f594-fa71bc949352&t=51624cf6-228d-5319-1d6f-8cd30bcca2e7 The failure did not happen on the 14. Oct. (https://github.com/pydata/xarray/commit/db4f03e467d13229512f8f7924dc142db1b9486b the failures here are #4502) but it appeared on the 15. Oct. (https://github.com/pydata/xarray/commit/15537497136345ed67e9e8b089bcd4573df0b2ea) Maybe a change in pandas? I have not looked at it closely - maybe @spencerkclark sees what's going on? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4516/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 724020766,MDU6SXNzdWU3MjQwMjA3NjY=,4521,cfgrib does not work with newest eccodes version,10194086,closed,0,,,0,2020-10-18T14:35:37Z,2020-10-19T22:21:13Z,2020-10-19T22:21:13Z,MEMBER,,,,"See ecmwf/cfgrib#167. This causes our doc build to fail (#4520). The tests do not fail because `import cfgrib` issues a `ModuleNotFoundError` therfore `@requires_cfgrib` skips the test... todo - [ ] unpin docs again - [ ] it's probably quite a corner case but - should the tests fail? How would this be achieved...?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4521/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 721833440,MDU6SXNzdWU3MjE4MzM0NDA=,4509,test_dataarray.py not run in py38-all-but-dask,10194086,closed,0,,,2,2020-10-14T22:25:00Z,2020-10-18T15:30:59Z,2020-10-18T15:30:59Z,MEMBER,,,,"The `DataArray` tests are skipped in `py38-all-but-dask` - I have no idea why... See https://dev.azure.com/xarray/xarray/_build/results?buildId=4028&view=logs&j=15c5a299-0936-5372-ef76-f6800a28dcef&t=b8653be4-abae-5992-d733-6af037900ab6&l=108 `test_dataarray.py` should be just before `test_dataset.py`","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4509/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 718869334,MDU6SXNzdWU3MTg4NjkzMzQ=,4502,dask dev upstream test failiures,10194086,closed,0,,,1,2020-10-11T16:12:13Z,2020-10-16T12:17:58Z,2020-10-16T12:17:57Z,MEMBER,,,,"Our upstream tests fail due to a change in dask. The likely culprit is dask/dask#6680 - `dask.array.zeros_like` does now take the `meta` keyword and thus it tries to coerce `str` to a `bool` which fails (and it didn't do before). The following should trigger the error: ```python import xarray as xr import dask da = xr.DataArray(dask.array.array("""")) xr.zeros_like(da, dtype=bool) ``` Note that `zeros_like` is called in `isnull` (which triggered the test failures): https://github.com/pydata/xarray/blob/080caf4246fe2f4d6aa0c5dcb65a99b376fa669b/xarray/core/duck_array_ops.py#L100-L102 **What happened**: ```python /home/vsts/work/1/s/xarray/tests/test_duck_array_ops.py:499: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /home/vsts/work/1/s/xarray/testing.py:139: in compat_variable return a.dims == b.dims and (a._data is b._data or equiv(a.data, b.data)) /home/vsts/work/1/s/xarray/testing.py:36: in _data_allclose_or_equiv return duck_array_ops.array_equiv(arr1, arr2) /home/vsts/work/1/s/xarray/core/duck_array_ops.py:246: in array_equiv flag_array = (arr1 == arr2) | (isnull(arr1) & isnull(arr2)) /home/vsts/work/1/s/xarray/core/duck_array_ops.py:104: in isnull return zeros_like(data, dtype=bool) /home/vsts/work/1/s/xarray/core/duck_array_ops.py:56: in f return wrapped(*args, **kwargs) /usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/creation.py:174: in zeros_like return zeros( /usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/wrap.py:78: in wrap_func_shape_as_first_arg return Array(dsk, name, chunks, dtype=dtype, meta=kwargs.get(""meta"", None)) /usr/share/miniconda/envs/xarray-tests/lib/python3.8/site-packages/dask/array/core.py:1083: in __new__ meta = meta_from_array(meta, dtype=dtype) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ x = array('', dtype=' meta = meta.astype(dtype) E ValueError: invalid literal for int() with base 10: '' ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4502/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 532878771,MDU6SXNzdWU1MzI4Nzg3NzE=,3595,xr.Dataset.map drops attrs of DataArray,10194086,closed,0,,,6,2019-12-04T19:11:35Z,2020-10-14T16:29:52Z,2020-10-14T16:29:52Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr import numpy as np ds = xr.DataArray([1, 2], attrs=dict(tst=""DataArray"")).to_dataset(name=""data"") ds.attrs[""tst""] = ""Dataset"" ds.map(np.mean, keep_attrs=True).data ``` returns ```python array(1.5) ``` #### Expected Output ```python array(1.5) Attributes: tst: DataArray ``` #### Problem Description Applying `xr.Dataset.map(..., keep_attrs=True)` does not retain the attributes of the DataArrays. Should it? In constrast `ds.mean(keep_attrs=True)` retains DataArray-level attrs. EDIT: corrected example #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: bd4f048bdb5a5a356a5603904d96a676037d1b6e python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.32-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.14.0+117.gbd4f048b.dirty pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.1.2 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: 1.5.5 zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.7.2 iris: 2.2.0 bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: installed setuptools: 41.6.0.post20191029 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3595/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 679445858,MDU6SXNzdWU2Nzk0NDU4NTg=,4342, unpin matplotlib in ci/requirements/doc.yml again,10194086,closed,0,,,0,2020-08-14T23:15:23Z,2020-10-06T14:43:50Z,2020-10-06T14:43:50Z,MEMBER,,,,"Matplotlib 3.3.1 broke the doc build so I pinned it to 3.3.0 - see #4340 The likely culprit is https://github.com/matplotlib/matplotlib/issues/18254 - thanks @jthielen for finding the matplotlib issue","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4342/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 681627420,MDU6SXNzdWU2ODE2Mjc0MjA=,4352,da.sum(min_count=1) errors for integer data,10194086,closed,0,,,1,2020-08-19T07:52:35Z,2020-10-02T09:28:27Z,2020-10-02T09:28:27Z,MEMBER,,,," **What happened**: `da.sum(min_count=1)` returns a `TypeError` if `da` has an integer dtype. Of course min_count is not necessary for integer data as it cannot contain `NaN`. **What you expected to happen**: `min_count` should be ignored **Minimal Complete Verifiable Example**: ```python import xarray as xr da = xr.DataArray([[1, 2, 3], [4, 5, 6]]) da.sum(min_count=1) ``` **Anything else we need to know?**: Full traceback
    ```python In [37]: da.sum(min_count=1) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in ----> 1 da.sum(min_count=1) ~/code/xarray/xarray/core/common.py in wrapped_func(self, dim, axis, skipna, **kwargs) 44 45 def wrapped_func(self, dim=None, axis=None, skipna=None, **kwargs): ---> 46 return self.reduce(func, dim, axis, skipna=skipna, **kwargs) 47 48 else: ~/code/xarray/xarray/core/dataarray.py in reduce(self, func, dim, axis, keep_attrs, keepdims, **kwargs) 2336 """""" 2337 -> 2338 var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs) 2339 return self._replace_maybe_drop_dims(var) 2340 ~/code/xarray/xarray/core/variable.py in reduce(self, func, dim, axis, keep_attrs, keepdims, allow_lazy, **kwargs) 1591 data = func(input_data, axis=axis, **kwargs) 1592 else: -> 1593 data = func(input_data, **kwargs) 1594 1595 if getattr(data, ""shape"", ()) == self.shape: ~/code/xarray/xarray/core/duck_array_ops.py in f(values, axis, skipna, **kwargs) 310 311 try: --> 312 return func(values, axis=axis, **kwargs) 313 except AttributeError: 314 if not isinstance(values, dask_array_type): ~/code/xarray/xarray/core/duck_array_ops.py in f(*args, **kwargs) 46 else: 47 wrapped = getattr(eager_module, name) ---> 48 return wrapped(*args, **kwargs) 49 50 else: <__array_function__ internals> in sum(*args, **kwargs) TypeError: _sum_dispatcher() got an unexpected keyword argument 'min_count' ```
    **Environment**:
    Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: a7fb5a9fa1a2b829181ea9e4986b959f315350dd python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 5.4.0-42-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.15.2.dev64+g2542a63f pandas: 0.25.3 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.4 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: None zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.5.4 iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.3.1 cartopy: 0.18.0 seaborn: 0.9.0 numbagg: None pint: None setuptools: 49.6.0.post20200814 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.17.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4352/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 688115687,MDU6SXNzdWU2ODgxMTU2ODc=,4385,warnings from internal use of apply_ufunc,10194086,closed,0,,,4,2020-08-28T14:28:56Z,2020-08-30T16:37:52Z,2020-08-30T16:37:52Z,MEMBER,,,,"Another follow up from #4060: `quantile` now emits a `FutureWarning`: **Minimal Complete Verifiable Example**: ```python xr.DataArray([1, 2, 3]).quantile(q=0.5) ``` ``` ~/.conda/envs/ipcc_ar6/lib/python3.7/site-packages/xarray/core/variable.py:1866: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version. kwargs={""q"": q, ""axis"": axis, ""interpolation"": interpolation}, ``` We should probably check the warnings in the test suite - there may be others. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4385/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 587735698,MDU6SXNzdWU1ODc3MzU2OTg=,3890,error for apply_ufunc with exclude_dims and vectorize,10194086,closed,0,,,2,2020-03-25T14:26:36Z,2020-08-24T13:37:49Z,2020-08-24T13:37:49Z,MEMBER,,,,"I tried to use `apply_ufunc` for a function that takes input of unequal length and requires `vectorize=True` which resulted in a `ValueError`. I think the problem stems from the way `np.vectorize` is called. #### MCVE Code Sample ```python import xarray as xr import scipy as sp import scipy.stats import numpy as np # create dataarrays of unequal length ds = xr.tutorial.open_dataset(""air_temperature"") da1 = ds.air da2 = ds.air.isel(time=slice(None, 50)) # function that takes arguments of unequal length and requires vectorizing def mannwhitneyu(x, y): _, p = sp.stats.mannwhitneyu(x, y) return p # test that the function takes arguments of unequal length mannwhitneyu(da1.isel(lat=0, lon=0), da2.isel(lat=0, lon=0)) xr.apply_ufunc( mannwhitneyu, da1, da2, input_core_dims=[[""time""], [""time""]], exclude_dims=set([""time""]), vectorize=True, ) ``` Returns ```python ValueError: inconsistent size for core dimension 'n': 50 vs 2920 ``` Note: the error stems from numpy. #### Expected Output A DataArray. #### Problem Description I can reproduce the problem in pure numpy: ``` python vec_wrong = np.vectorize(mannwhitneyu, signature=""(n),(n)->()"", otypes=[np.float]) vec_wrong(da1.values.T, da2.values.T) ``` The correct result is returned when the `signature` is changed: ``` python vec_correct = np.vectorize(mannwhitneyu, signature=""(m),(n)->()"", otypes=[np.float]) vec_correct(da1.values.T, da2.values.T) ``` So I think the signature needs to be changed when `exclude_dims` are present. #### Versions
    Output of `xr.show_versions()` This is my development environment, so i think xarray should be 'master'. **PNC:/home/mathause/conda/envs/xarray_devel/lib/python3.7/site-packages/PseudoNetCDF/pncwarn.py:24:UserWarning: pyproj could not be found, so IO/API coordinates cannot be converted to lat/lon; to fix, install pyproj or basemap (e.g., `pip install pyproj)` INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.15.0-91-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.1 xarray: 0.11.1+335.gb0c336f6 pandas: 0.25.3 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.3 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: None zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.5.4 iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.2 cartopy: None seaborn: 0.9.0 numbagg: None setuptools: 41.6.0.post20191101 pip: 19.3.1 conda: installed pytest: 5.2.2 IPython: 7.9.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3890/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 681904962,MDU6SXNzdWU2ODE5MDQ5NjI=,4354,sum: min_count is not available for reduction with more than one dimensions,10194086,closed,0,,,0,2020-08-19T14:52:41Z,2020-08-20T16:22:55Z,2020-08-20T16:22:55Z,MEMBER,,,,"**Is your feature request related to a problem? Please describe.** `sum` with `min_count` errors when passing more than one dim: ```python import xarray as xr da = xr.DataArray([[1., 2, 3], [4, 5, 6]]) da.sum([""dim_0"", ""dim_1""], min_count=1) ``` **Describe the solution you'd like** The logic to calculate the number of valid elements is here: https://github.com/pydata/xarray/blob/1be777fe725a85b8cc0f65a2bc41f4bc2ba18043/xarray/core/nanops.py#L35 I *think* this can be fixed by replacing `mask.shape[axis]` with `np.take(a.shape, axis).prod()` **Additional context** Potentially relevant for #4351 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4354/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 592629388,MDU6SXNzdWU1OTI2MjkzODg=,3927,use level of a MultiIndex for plotting?,10194086,closed,0,,,1,2020-04-02T13:24:05Z,2020-05-25T16:32:15Z,2020-05-25T16:32:15Z,MEMBER,,,,"It would be nice to be able to use a level of a MultiIndex for plotting. #### MCVE Code Sample ```python import numpy as np import xarray as xr da = xr.DataArray( np.random.randn(10), dims=""x"", coords=dict( a=(""x"", np.arange(10, 20)), b=(""x"", np.arange(1, 11)) ) ) da = da.set_index(x=[""a"", ""b""]) da ``` This creates the following DataArray ```python array([-1.34516338, 0.97644817, -0.24032189, -0.70112418, -0.8686898 , -0.55607078, 0.56618151, 1.62847463, 0.84947296, -0.5775504 ]) Coordinates: * x (x) MultiIndex - a (x) int64 10 11 12 13 14 15 16 17 18 19 - b (x) int64 1 2 3 4 5 6 7 8 9 10 ``` Is there a way to plot a line using one of the levels of the MultiIindex? ```python da.plot(x=""a"") ``` returns ```python ValueError: x must be either None or one of ('x') ``` ```python da.plot() ``` returns ```python TypeError: Plotting requires coordinates to be numeric or dates of type np.datetime64, datetime.datetime, cftime.datetime or pd.Interval. ``` (which makes sense). If `da` is a 2D Variable the error is ```python ValueError: x and y must be coordinate variables ``` #### Expected Output A line plot #### Versions
    Output of `xr.show_versions()` INSTALLED VERSIONS ------------------ commit: b3bafeefbd6e6d70bce505ae1f0d9d5a2b015089 python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.15.0-91-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.1 xarray: 0.11.1+335.gb0c336f6 pandas: 0.25.3 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.3 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: None zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: None PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.5.4 iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.2 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None pint: None setuptools: 41.6.0.post20191101 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3927/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 620351521,MDU6SXNzdWU2MjAzNTE1MjE=,4074,[bug] when passing boolean weights to weighted mean,10194086,closed,0,,,0,2020-05-18T16:38:18Z,2020-05-23T21:06:19Z,2020-05-23T21:06:19Z,MEMBER,,,," #### MCVE Code Sample ```python import numpy as np import xarray as xr dta = xr.DataArray([1., 1., 1.]) wgt = xr.DataArray(np.array([1, 1, 0], dtype=np.bool)) dta.weighted(wgt).mean() ``` Returns ``` array(2.) ``` #### Expected Output ``` array(1.) ``` #### Problem Description Passing a boolean array as weights to the weighted mean returns the wrong result because the `weights` are not properly normalized (in this case). Internally the `sum_of_weights` is calculated as ```python xr.dot(dta.notnull(), wgt) ``` i.e. the dot product of two boolean arrays. This yields: ``` array(True) ``` We'll need to convert it to int or float: ```python xr.dot(dta.notnull(), wgt * 1) ``` which is correct ``` array(2) ``` #### Versions
    Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 23:03:20) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 5.3.0-51-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.15.1 pandas: 1.0.3 numpy: 1.18.1 scipy: 1.4.1 netCDF4: 1.5.3 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.1.1.2 nc_time_axis: None PseudoNetCDF: None rasterio: 1.1.3 cfgrib: None iris: None bottleneck: None dask: 2.16.0 distributed: 2.16.0 matplotlib: 3.2.1 cartopy: 0.17.0 seaborn: None numbagg: None setuptools: 46.1.3.post20200325 pip: 20.1 conda: None pytest: 5.4.1 IPython: 7.13.0 sphinx: 3.0.3
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4074/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 559864146,MDU6SXNzdWU1NTk4NjQxNDY=,3750,isort pre-commit hook does not skip text files,10194086,closed,0,,,4,2020-02-04T17:18:31Z,2020-05-06T01:50:29Z,2020-03-28T20:58:15Z,MEMBER,,,,"#### MCVE Code Sample Add arbitrary change to the file `doc/pandas.rst` ``` bash git add doc/pandas.rst git commit -m ""test"" ``` The pre-commit hook will fail. #### Expected Output the pre-commit hook to pass #### Problem Description running `isort -rc doc/*` will change the following files: ``` bash modified: contributing.rst modified: howdoi.rst modified: internals.rst modified: io.rst modified: pandas.rst modified: quick-overview.rst ``` unfortunately it does not behave properly and deletes/ changes arbitrary lines. Can the pre-commit hook be told to only run on *.py files? On the command line this would be `isort -rc *.py` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3750/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 522457033,MDU6SXNzdWU1MjI0NTcwMzM=,3524,xr.plot infers sequential colormap on diverging levels,10194086,closed,0,,,2,2019-11-13T20:32:46Z,2020-04-05T13:41:25Z,2020-04-05T13:41:25Z,MEMBER,,,,"#### MCVE Code Sample ```python import numpy as np import xarray as xr data = np.random.randn(10, 10) data = np.abs(data) da = xr.DataArray(data) # returns a diverging colormap da.plot(vmax=2, center=0, extend=""both"") # returns a sequential colormap da.plot(levels=[-2, -1, 0, 1, 2], extend=""both"") ``` ![tst](https://user-images.githubusercontent.com/10194086/68801234-f32fad00-065b-11ea-81c8-b7d0efbf57c4.png) #### Expected Output A diverging colormap, maybe? #### Problem Description I was surprised by getting the viridis colormap until I realised that my data must all be positive and the colormap is infered from the data and not from levels. However, when specifying the range via `vmax=2, center=0` it is not inferred from the data. #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.25-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.14.0+44.g4dce93f1 pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.0.1 pydap: None h5netcdf: 0.7.4 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.0.22 cfgrib: None iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.4.0 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: 2.2.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3524/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 308030789,MDU6SXNzdWUzMDgwMzA3ODk=,2010,rolling.mean vs rolling.construct.mean,10194086,closed,0,,,8,2018-03-23T13:34:05Z,2020-03-26T02:02:59Z,2020-03-26T02:02:59Z,MEMBER,,,,"#### Code Sample, a copy-pastable example if possible ```python import xarray as xr import numpy as np x = np.arange(4.) ds = xr.DataArray(x, dims=('dim', )) ds.rolling(dim=3, center=True).mean() # RESULT: array([nan, 1., 2., nan]) ds.rolling(dim=3, center=True).construct('window').mean('window') # RESULT: array([0.5, 1. , 2. , 2.5]) ``` #### Problem description `ds.rolling(...).mean()` and `ds.rolling(...).construct().mean()` yields different results. Because `mean` does `skipna=True` per default. #### Expected Output I would expect both ways to yield the same result. #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Linux OS-release: 4.4.114-42-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.2+dev6.g9261601 pandas: 0.22.0 numpy: 1.14.2 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: None h5py: None Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: 1.0.0 dask: 0.17.1 distributed: 1.21.3 matplotlib: 2.2.2 cartopy: None seaborn: None setuptools: 39.0.1 pip: 9.0.2 conda: None pytest: 3.4.2 IPython: 6.2.1 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2010/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 180641734,MDU6SXNzdWUxODA2NDE3MzQ=,1029,`flatten` as convinience method for stack all?,10194086,closed,0,,,6,2016-10-03T13:35:28Z,2020-03-25T08:31:18Z,2020-03-25T08:31:18Z,MEMBER,,,,"There is a bunch of operations that need to be conducted on a 1D array. It could be handy to have a convinience method that mimicks numpys `flatten`, i.e. works along the lines: ``` def flatten(dataarray, name='stacked'): return dataarray.stack(**{name: dataarray.dims}) ``` What do you think? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1029/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 84127296,MDU6SXNzdWU4NDEyNzI5Ng==,422,add average function,10194086,closed,0,,,23,2015-06-02T17:53:53Z,2020-03-19T14:29:42Z,2020-03-19T14:29:42Z,MEMBER,,,,"It would be nice to be able to do `ds.average()` to compute weighted averages (e.g. for geo data). Of course this would require the axes to be in a predictable order. Or to give a weight per dimension... ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/422/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 559794979,MDU6SXNzdWU1NTk3OTQ5Nzk=,3747,The seaborn.apionly entry-point has been removed.,10194086,closed,0,,,2,2020-02-04T15:28:57Z,2020-02-05T16:03:52Z,2020-02-05T16:03:52Z,MEMBER,,,,"Seaborn [recently dropped](https://github.com/mwaskom/seaborn/releases) the `apionly` entry point (in version 0.10.0). There is a dedicated `import_seaborn` function https://github.com/pydata/xarray/blob/47a6fc0c9b796997996d79d32358585d08021b9c/xarray/plot/utils.py#L24 but it is not used in one place https://github.com/pydata/xarray/blob/47a6fc0c9b796997996d79d32358585d08021b9c/xarray/plot/utils.py#L146 which now causes test failures https://dev.azure.com/xarray/xarray/_build/results?buildId=2111&view=ms.vss-test-web.build-test-results-tab&runId=29632&paneView=debug&resultId=108176 Todo: double check if the supported versions of seaborn still require this workaround. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3747/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 549679475,MDU6SXNzdWU1NDk2Nzk0NzU=,3694,"xr.dot requires equal indexes (join=""exact"")",10194086,closed,0,,,5,2020-01-14T16:28:15Z,2020-01-20T12:09:27Z,2020-01-20T12:09:27Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr import numpy as np d1 = xr.DataArray(np.arange(4), dims=[""a""], coords=dict(a=[0, 1, 2, 3])) d2 = xr.DataArray(np.arange(4), dims=[""a""], coords=dict(a=[0, 1, 2, 3])) # note: different coords d3 = xr.DataArray(np.arange(4), dims=[""a""], coords=dict(a=[1, 2, 3, 4])) (d1 * d2).sum() # -> array(14) xr.dot(d1, d2) # -> array(14) (d2 * d3).sum() # -> array(8) xr.dot(d2, d3) # -> ValueError ``` #### Expected Output ```python array(8) ``` #### Problem Description The last statement results in an ```python ValueError: indexes along dimension 'a' are not equal ``` because `xr.apply_ufunc` defaults to `join='exact'`. However, I think this should work - but maybe there is a good reason for this to fail? This is a problem for #2922 (weighted operations) - I think it is fine for the weights and data to not align. Fixing this may be as easy as specifying `join='inner'` in https://github.com/pydata/xarray/blob/e0fd48052dbda34ee35d2491e4fe856495c9621b/xarray/core/computation.py#L1181-L1187 @fujiisoup #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: 5afc6f32b18f5dbb9a89e30f156b626b0a83597d python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.36-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.6.2 xarray: 0.14.0+164.g5afc6f32.dirty pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.1.2 pydap: installed h5netcdf: 0.7.4 h5py: 2.10.0 Nio: 1.5.5 zarr: 2.3.2 cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: installed rasterio: 1.1.0 cfgrib: 0.9.7.2 iris: 2.2.0 bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: installed setuptools: 41.6.0.post20191029 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3694/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 545764524,MDU6SXNzdWU1NDU3NjQ1MjQ=,3665,Cannot roundtrip time in NETCDF4_CLASSIC,10194086,closed,0,,,4,2020-01-06T14:47:48Z,2020-01-16T18:27:15Z,2020-01-16T18:27:14Z,MEMBER,,,,"#### MCVE Code Sample ``` python import numpy as np import xarray as xr time = xr.cftime_range(""2006-01-01"", periods=2, calendar=""360_day"") da = xr.DataArray(time, dims=[""time""]) da.encoding[""dtype""] = np.float da.to_netcdf(""tst.nc"", format=""NETCDF4_CLASSIC"") ds = xr.open_dataset(""tst.nc"") ds.to_netcdf(""tst2.nc"", format=""NETCDF4_CLASSIC"") ``` yields: ```python ValueError: could not safely cast array from dtype int64 to int32 ``` Or an example without `to_netcdf`: ```python import numpy as np import xarray as xr time = xr.cftime_range(""2006-01-01"", periods=2, calendar=""360_day"") da = xr.DataArray(time, dims=[""time""]) da.encoding[""_FillValue""] = np.array([np.nan]) xr.backends.netcdf3.encode_nc3_variable(xr.conventions.encode_cf_variable(da)) ``` #### Expected Output Xarray can save the dataset/ an `xr.Variable`. #### Problem Description If there is a time variable that can be encoded using integers only, but that has a `_FillValue` set to `NaN`, saving `to_netcdf(name, format=""NETCDF4_CLASSIC"")` fails. The problem is that xarray adds a (unnecessary) `_FillValue` when saving a file. Note: if the time cannot be encoded using integers only, it works: ``` python da = xr.DataArray(time, dims=[""time""]) da.encoding[""_FillValue""] = np.array([np.nan]) da.encoding[""units""] = ""days since 2006-01-01T12:00:00"" xr.backends.netcdf3.encode_nc3_variable(xr.conventions.encode_cf_variable(da)) ``` Another note: when saving with NETCDF4 ``` python da = xr.DataArray(time, dims=[""time""]) da.encoding[""_FillValue""] = np.array([np.nan]) xr.backends.netCDF4_._encode_nc4_variable(xr.conventions.encode_cf_variable(da)) ``` The following is returned: ``` array([0, 1]) Attributes: units: days since 2006-01-01 00:00:00.000000 calendar: proleptic_gregorian _FillValue: [-9223372036854775808] ``` #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.36-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.1 xarray: 0.14.1 pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.3 pydap: None h5netcdf: 0.7.4 h5py: 2.10.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.1.1 cfgrib: None iris: None bottleneck: 1.3.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.2 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.4.0 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: 2.2.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3665/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 539010474,MDU6SXNzdWU1MzkwMTA0NzQ=,3634,"""ValueError: Percentiles must be in the range [0, 100]""",10194086,closed,0,,,1,2019-12-17T11:34:35Z,2019-12-17T13:50:06Z,2019-12-17T13:50:06Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr da = xr.DataArray([0, 1, 2]) da.quantile(q=50) >>> ValueError: Percentiles must be in the range [0, 100] ``` #### Expected Output ```python ValueError: Quantiles must be in the range [0, 1] ``` #### Problem Description By wrapping `np.nanpercentile` (xref: #3559) we also get the numpy error. However, the error message is wrong as xarray needs it to be in 0..1. BTW: thanks for #3559, makes my life easier! #### Output of ``xr.show_versions()`` --- Edit: uses `nanpercentile` internally.
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.36-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.5 libnetcdf: 4.7.1 xarray: 0.14.1+28.gf2b2f9f6 (current master) pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.3 pydap: None h5netcdf: 0.7.4 h5py: 2.10.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.1.1 cfgrib: None iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.2 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.4.0 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: 2.2.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3634/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 523037716,MDU6SXNzdWU1MjMwMzc3MTY=,3535,subtracting CFTimeIndex can cause pd.TimedeltaIndex to overflow,10194086,closed,0,,,2,2019-11-14T18:45:03Z,2019-12-07T20:38:00Z,2019-12-07T20:38:00Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr i1 = xr.cftime_range(""4991-01-01"", periods=1) i2 = xr.cftime_range(""7190-12-31"", periods=1) i2 - i1 ``` #### Expected Output a timedelta #### Problem Description returns `OverflowError: Python int too large to convert to C long`. Originally I stumbled upon this when trying to `open_mfdataset` files from a long simulation (piControl). I did not figure out yet where this subtraction happens in `open_mfdataset`. (Opening the single files and using `xr.concat` works). The offending lines are here https://github.com/pydata/xarray/blob/40588dc38ddc2d573e3dc8c63b2e6533eb978656/xarray/coding/cftimeindex.py#L433 Ultimately this is probably a pandas problem as it tries to convert `datetime.timedelta(days=803532)` to `' INSTALLED VERSIONS ------------------ commit: None python: 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 21:52:21) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.25-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.14.0+44.g4dce93f1 pandas: 0.25.2 numpy: 1.17.3 scipy: 1.3.1 netCDF4: 1.5.0.1 pydap: None h5netcdf: 0.7.4 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.4.2 nc_time_axis: 1.2.0 PseudoNetCDF: None rasterio: 1.0.22 cfgrib: None iris: None bottleneck: 1.2.1 dask: 2.6.0 distributed: 2.6.0 matplotlib: 3.1.1 cartopy: 0.17.0 seaborn: 0.9.0 numbagg: None setuptools: 41.4.0 pip: 19.3.1 conda: None pytest: 5.2.2 IPython: 7.9.0 sphinx: 2.2.1 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3535/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 509851476,MDU6SXNzdWU1MDk4NTE0NzY=,3423,add `ALL_DIMS` in `xr.dot`,10194086,closed,0,,,0,2019-10-21T09:45:24Z,2019-10-29T19:12:51Z,2019-10-29T19:12:51Z,MEMBER,,,,"#### MCVE Code Sample ```python import numpy as np import xarray as xr da_a = xr.DataArray(np.arange(3 * 2).reshape(3, 2), dims=['a', 'b']) da_b = xr.DataArray(np.arange(3 * 2 * 2).reshape(3, 2, 2), dims=['a', 'b', 'c']) xr.dot(da_a, da_b, dims=None) ``` This sums over the common dimensions: ```python array([110, 125]) Dimensions without coordinates: c ``` To sum over all dimensions: ```python xr.dot(da_a, da_b, dims=['a', 'b', 'c']) ``` ```python array([110, 125]) Dimensions without coordinates: c ``` #### Problem Description `xr.dot` with `dims=None` currently sums over `all the common dimensions`. However, there are cases when a sum over all dimensions is desired. E.g. `xr.dot(da_a, da_b, dims=['a', 'b', 'c']) ` is a a memory efficient way to compute `(da_a * da_b).sum()` (if `a` and `b` don't share the same dimensions). This is currently used in #2922 ([example](https://github.com/pydata/xarray/blob/dc7f6057c1a2533569ff6b5995d9a3c3e4c1fd85/xarray/core/weighted.py#L159)). Therefore I suggest to allow `xr.dot(da_a, da_b, dims=xr.ALL_DIMS)` as shortcut to sum over all dimensions. I assume there is no intent to change the behavior of `xr.dot(..., dims=None)`? (As it is a bit in contrast to other functions that are mostly applied over all dimensions.) @fujiisoup @max-sixty #### Output of ``xr.show_versions()``
    # Paste the output here xr.show_versions() here
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3423/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 490229690,MDU6SXNzdWU0OTAyMjk2OTA=,3284,specifying list of colors does not work for me,10194086,closed,0,,,1,2019-09-06T09:36:08Z,2019-09-09T18:31:16Z,2019-09-09T18:31:16Z,MEMBER,,,,"#### MCVE Code Sample ```python import xarray as xr import numpy as np airtemps = xr.tutorial.load_dataset('air_temperature') air = airtemps.air.isel(time=0) levels = np.arange(225, 301, 25) colors = ['#ffffb2', '#fecc5c', '#fd8d3c', '#e31a1c'] # this does not work for me air.plot.pcolormesh(levels=levels, colors=colors) ``` #### Expected Output Should create a plot with the specified colors. According to the the docstring this should work. Or maybe I am doing something wrong here? #### Problem Description Instead I get the following error: ```python /usr/local/Miniconda3-envs/envs/2019/envs/iacpy3_2019/lib/python3.7/site-packages/xarray/plot/utils.py in _process_cmap_cbar_kwargs(func, kwargs, data) 683 # colors is only valid when levels is supplied or the plot is of type 684 # contour or contourf --> 685 if colors and (('contour' not in func.__name__) and (not levels)): 686 raise ValueError(""Can only specify colors with contour or levels"") 687 ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ``` Instead I use the following, which works: ```python air.plot.pcolormesh(levels=levels, cmap=colors) ``` #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.1 | packaged by conda-forge | (default, Feb 18 2019, 01:42:00) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.12.14-lp151.28.13-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.2 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: installed h5netcdf: 0.7.1 h5py: 2.9.0 Nio: None zarr: 2.3.1 cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: 1.0.22 cfgrib: 0.9.7 iris: 2.2.0 bottleneck: 1.2.1 dask: 1.1.5 distributed: 1.26.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.0 pip: 19.0.3 conda: None pytest: 4.4.0 IPython: 7.4.0 sphinx: 2.0.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3284/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 441341354,MDU6SXNzdWU0NDEzNDEzNTQ=,2948,xr.merge fails when passing dict,10194086,closed,0,,,5,2019-05-07T17:06:07Z,2019-06-12T15:33:26Z,2019-06-12T15:33:26Z,MEMBER,,,,"#### Code Sample ```python import xarray as xr a = xr.DataArray([1, 2], dims=('foo', ), name='a') b = xr.DataArray([3], dims=('bar', ), name='b') objects = dict(a=a, b=b) xr.merge(objects) ``` #### Problem description This returns an `AttributeError`, while the documentation states that a dict is a valid input. ``` python def merge(objects, compat='no_conflicts', join='outer'): """"""Merge any number of xarray objects into a single Dataset as variables. Parameters ---------- objects : Iterable[Union[xarray.Dataset, xarray.DataArray, dict]] Merge together all variables from these objects. If any of them are DataArray objects, they must have a name. ... ``` #### Expected Output Either the docs or the `dict_like_objects` loop should be changed. #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.7.1 | packaged by conda-forge | (default, Feb 18 2019, 01:42:00) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.4.138-59-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.12.1 pandas: 0.24.2 numpy: 1.16.2 scipy: 1.2.1 netCDF4: 1.5.0.1 pydap: None h5netcdf: 0.7.1 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 nc_time_axis: 1.2.0 PseudonetCDF: None rasterio: 1.0.22 cfgrib: 0.9.6.2 iris: 2.2.0 bottleneck: 1.2.1 dask: 1.1.5 distributed: 1.26.1 matplotlib: 3.0.3 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 41.0.0 pip: 19.0.3 conda: None pytest: 4.4.0 IPython: 7.4.0 sphinx: 2.0.1
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2948/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 407864596,MDU6SXNzdWU0MDc4NjQ1OTY=,2754,silence warning for decode_cf_datetime?,10194086,closed,0,,,7,2019-02-07T19:38:38Z,2019-02-19T20:47:26Z,2019-02-19T20:47:26Z,MEMBER,,,,"#### Code Sample, a copy-pastable example if possible ```python import xarray as xr import numpy as np x = np.arange(100) * 365 x = xr.coding.times.decode_cf_datetime(x, 'days since 2400-01-01', calendar='proleptic_gregorian') ``` #### Problem description xarray still throws an error when decoding out-of-bounds dates for proleptic_gregorian - should this be silenced? #### Output of ``xr.show_versions()``
    # Paste the output here xr.show_versions() here NSTALLED VERSIONS ------------------ commit: None python: 3.7.1 | packaged by conda-forge | (default, Nov 13 2018, 18:33:04) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.4.172-86-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.11.3 pandas: 0.24.1 numpy: 1.16.1 scipy: 1.2.0 netCDF4: 1.4.2 pydap: None h5netcdf: 0.6.2 h5py: 2.9.0 Nio: None zarr: None cftime: 1.0.3.4 PseudonetCDF: None rasterio: 1.0.17 cfgrib: None iris: None bottleneck: 1.2.1 cyordereddict: None dask: 1.1.1 distributed: 1.25.3 matplotlib: 3.0.2 cartopy: 0.17.0 seaborn: 0.9.0 setuptools: 40.7.3 pip: 19.0.1 conda: None pytest: 4.2.0 IPython: 7.2.0 sphinx: 1.8.4
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2754/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 106595746,MDU6SXNzdWUxMDY1OTU3NDY=,577,wrap lon coordinates to 360,10194086,closed,0,,,4,2015-09-15T16:36:37Z,2019-01-17T09:34:56Z,2019-01-15T20:15:01Z,MEMBER,,,,"Assume I have two datasets with the same lat/ lon grid. However, one has `lon = 0...359`and the other `lon = -180...179`. How can I wrap around one of them? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/577/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 84068169,MDU6SXNzdWU4NDA2ODE2OQ==,418,select range/ slice with method='nearest',10194086,closed,0,,,5,2015-06-02T15:17:27Z,2019-01-15T20:10:20Z,2019-01-15T20:10:19Z,MEMBER,,,,"It would be nice to support nearest neighbour selection with a slice like so: `ds.sel(lat=slice(30, 50), method='nearest')`. Maybe the `step` has to be disallowed. Or is there another possibility how to achieve this functionality? use case: Often I look at a region with different data sets that don't have the same resolutions. It would be nice to be able to select the closest-matching region with an easy syntax. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/418/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 310819233,MDU6SXNzdWUzMTA4MTkyMzM=,2036,better error message for to_netcdf -> unlimited_dims,10194086,closed,0,,,4,2018-04-03T12:39:21Z,2018-05-18T14:48:32Z,2018-05-18T14:48:32Z,MEMBER,,,,"#### Code Sample, a copy-pastable example if possible ```python # Your code here import numpy as np import xarray as xr x = np.arange(10) da = xr.Dataset(data_vars=dict(data=('dim1', x)), coords=dict(dim1=('dim1', x), dim2=('dim2', x))) da.to_netcdf('tst.nc', format='NETCDF4_CLASSIC', unlimited_dims='dim1') ``` #### Problem description This creates the error `RuntimeError: NetCDF: NC_UNLIMITED size already in use`. With `format='NETCDF4'` silently creates the dimensions `d`, `i`, `m`, and `\1`. The correct syntax is `unlimited_dims=['dim1']`. With `format='NETCDF4_CLASSIC'` and `unlimited_dims=['dim1', 'dim2']`, still raises the not-so-helpful `NC_UNLIMITED` error. I only tested with netCDF4 as backend. #### Expected Output * better error message * work with `unlimited_dims='dim1'` #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.6.5.final.0 python-bits: 64 OS: Linux OS-release: 4.4.120-45-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.2 pandas: 0.22.0 numpy: 1.14.2 scipy: 1.0.1 netCDF4: 1.3.1 h5netcdf: 0.5.0 h5py: 2.7.1 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: 1.0.0 dask: 0.17.2 distributed: 1.21.5 matplotlib: 2.2.2 cartopy: 0.16.0 seaborn: 0.8.1 setuptools: 39.0.1 pip: 9.0.3 conda: None pytest: 3.5.0 IPython: 6.3.0 sphinx: 1.7.2
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2036/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 317954266,MDU6SXNzdWUzMTc5NTQyNjY=,2085,units = 'days' leads to timedelta64 for data variable,10194086,closed,0,,,5,2018-04-26T09:47:11Z,2018-04-26T16:44:25Z,2018-04-26T15:55:09Z,MEMBER,,,,"#### Code Sample ```python import numpy as np import xarray as xr def test_units(units): x = np.arange(10) data = np.random.randn(10) ds = xr.Dataset(data_vars=dict(data=('x', data)), coords=dict(x=('x', x))) ds.data.attrs['units'] = units ds.to_netcdf('tst.nc') decoded = xr.open_dataset('tst.nc') print(units.ljust(8), decoded.data.dtype) ds.close() decoded.close() test_units('seconds') test_units('second') test_units('minutes') test_units('minute') test_units('days') test_units('day') test_units('months') test_units('years') ``` #### Problem description Returns: ``` seconds timedelta64[ns] second float64 minutes timedelta64[ns] minute float64 days timedelta64[ns] day float64 months float64 years float64 ``` #### Expected Output I would expect type float for all of them. Or is this expected behaviour? I have a dataset that reports 'consecutive dry days' and the dataset creator correctly set the units of the data to 'days', but I don't want this to be decoded (but the time axis should).... #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.6.4.final.0 python-bits: 64 OS: Linux OS-release: 4.4.126-48-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_GB.UTF-8 LOCALE: en_GB.UTF-8 xarray: 0.10.2+dev6.g9261601 pandas: 0.22.0 numpy: 1.14.2 scipy: 1.0.1 netCDF4: 1.3.1 h5netcdf: 0.5.0 h5py: 2.7.1 Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: 1.0.0 dask: 0.17.2 distributed: 1.21.3 matplotlib: 2.2.2 cartopy: None seaborn: None setuptools: 39.0.1 pip: 9.0.2 conda: None pytest: 3.4.2 IPython: 6.2.1 sphinx: None
    ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2085/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 295498327,MDU6SXNzdWUyOTU0OTgzMjc=,1896,_color_palette not using full range of colors if seaborn is installed,10194086,closed,0,,,2,2018-02-08T12:43:45Z,2018-02-16T21:08:32Z,2018-02-16T21:08:32Z,MEMBER,,,,"```python from xarray.plot.utils import import_seaborn _color_palette('Greys', 3) ``` Returns 0.85, ... 0.58, ... 0.31, ... if seaborn is installed, and 1.00, ... 0.58, ... 0.00, ... otherwise #### Problem description the return value of `_color_palette('Greys', 3)` is different when seaborn is installed or not. The relevant code is here: https://github.com/pydata/xarray/blob/6aa225f5dae9cc997e232c11a63072923c8c0238/xarray/plot/utils.py#L115 https://github.com/pydata/xarray/blob/6aa225f5dae9cc997e232c11a63072923c8c0238/xarray/plot/utils.py#L143 The same logic in seaborn https://github.com/mwaskom/seaborn/blob/0beede57152ce80ce1d4ef5d0c0f1cb61d118375/seaborn/palettes.py#L411 Intuitively I prefer the xarray solution because this uses the full range of colors which I find beneficial, however there may be a reason for this I'm not aware of. Maybe @mwaskom will answer: mwaskom/seaborn#1372 #### Expected Output #### Output of ``xr.show_versions()``
    INSTALLED VERSIONS ------------------ commit: None python: 3.5.4.final.0 python-bits: 64 OS: Linux OS-release: 3.13.0-141-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 xarray: 0.10.0 pandas: 0.21.0 numpy: 1.13.3 scipy: 1.0.0 netCDF4: 1.3.1 h5netcdf: 0.5.0 Nio: None bottleneck: 1.2.1 cyordereddict: None dask: 0.16.0 matplotlib: 2.1.1 cartopy: 0.15.1 seaborn: 0.8.1 setuptools: 38.2.4 pip: 9.0.1 conda: None pytest: None IPython: 6.2.1 sphinx: None
    ~~ edit: sorry I pressed the button to early ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1896/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 106581329,MDU6SXNzdWUxMDY1ODEzMjk=,576,define fill value for where,10194086,closed,0,,,4,2015-09-15T15:27:32Z,2017-08-08T17:00:30Z,2017-08-08T17:00:30Z,MEMBER,,,,"It would be nice if `where` accepts an `other` argument: ``` def where(self, cond, other=np.NaN): pass ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/576/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 105442957,MDU6SXNzdWUxMDU0NDI5NTc=,561,calculate percentiles,10194086,closed,0,,,3,2015-09-08T18:28:12Z,2017-01-23T18:22:14Z,2017-01-23T18:22:14Z,MEMBER,,,,"How would I calculate percentiles over a certain dimension? ``` ds.groupby('time.month').apply(np.percentile, q=5) ``` Does not work. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/561/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 144575316,MDU6SXNzdWUxNDQ1NzUzMTY=,808,bottleneck version,10194086,closed,0,,,3,2016-03-30T12:36:54Z,2017-01-07T07:43:34Z,2017-01-07T07:43:34Z,MEMBER,,,,"Not sure if this is possible to specify in the `setup.py` as bottleneck is not required. However, if bottleneck is used it should probably be in version 1.0. For version 0.8.0 I get the following error: `>>> move_mean() got an unexpected keyword argument 'min_count'` Feel free to close. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/808/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 171956399,MDU6SXNzdWUxNzE5NTYzOTk=,975,invalid timestamps in the future,10194086,closed,0,,,6,2016-08-18T17:29:02Z,2016-08-25T22:39:26Z,2016-08-25T22:39:26Z,MEMBER,,,,"If I have a netCDF file that has invalid timesteps from the 'future', it is wrongly converted to datetime64[ns]. ``` import netCDF4 as nc import numpy as np import xarray as xr # create netCDF file ncf = nc.Dataset('test_future.nc', 'w') ncf.createDimension('time') ncf.createVariable('time', np.int, dimensions=('time')) ncf.variables['time'].units = 'days since 1850-01-01 00:00:00' ncf.variables['time'].calendar = 'standard' ncf.variables['time'][:] = np.arange(850) * 365 ncf.close() # open with xr ds = xr.open_dataset('test_future.nc') # this works ds # ds.time is a datetime64[ns] object # this fails ds.time ``` If I choose chalendar='noleap' the dates wrap around! ``` ncf = nc.Dataset('test_future_noleap.nc', 'w') ncf.createDimension('time') ncf.createVariable('time', np.int, dimensions=('time')) ncf.variables['time'].units = 'days since 1850-01-01 00:00:00' ncf.variables['time'].calendar = 'noleap' ncf.variables['time'][:] = np.arange(850) * 365 ncf.close() # open with xr ds = xr.open_dataset('test_future_noleap.nc') # after 2262 they go back to 1678! ds.time ``` If my 'invalid' time is from the 'past' it works as expected: ``` ncf = nc.Dataset('test_past.nc', 'w') ncf.createDimension('time') ncf.createVariable('time', np.int, dimensions=('time')) ncf.variables['time'].units = 'days since 1000-01-01 00:00:00' ncf.variables['time'].calendar = 'standard' ncf.variables['time'][:] = np.arange(850) * 365 ncf.close() # open with xr ds = xr.open_dataset('test_past.nc') # this works ds # ds.time is a object ds.time ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/975/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 138443211,MDU6SXNzdWUxMzg0NDMyMTE=,784,almost-equal grids,10194086,closed,0,,,6,2016-03-04T10:50:34Z,2016-03-29T08:46:07Z,2016-03-29T08:46:07Z,MEMBER,,,,"Sometimes it happens that you have two grids that are equal up to precision or ""for practical purposes"". xarray does not align them. Is there a possibility to still get them aligned (and not having to copy the coordinates from one to the other)? Examples: - CESM output from the land model and the atmosphere model. - I have a file with a region definition on a fine grid. If I regrid that to the model resolution with cdo the grids are equal up to `~1*10**-4`. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/784/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 138519359,MDU6SXNzdWUxMzg1MTkzNTk=,786,reindex and netCDF indexing,10194086,closed,0,,,2,2016-03-04T16:37:32Z,2016-03-04T18:49:54Z,2016-03-04T18:49:54Z,MEMBER,,,,"reindex can cause problems as long as the data is not loaded ``` import xarray as xr data = xr.DataArray([1, 2, 3], dims='x').to_dataset('name') data.to_netcdf('tst.nc') # works fine a = xr.open_dataset('tst.nc') a.load() a.reindex(x=[2, 0, 1]) # problem b = xr.open_dataset('tst.nc') b.reindex(x=[2, 0, 1]) ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/786/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 134321867,MDU6SXNzdWUxMzQzMjE4Njc=,766,colormap with vmin vmax,10194086,closed,0,,,3,2016-02-17T16:02:30Z,2016-02-18T08:38:50Z,2016-02-18T08:06:05Z,MEMBER,,,,"When specifying a negative vmin and a positive vmax the `viridis` colormap is used. Is this intentional? ``` import xarray as xr import matplotlib.pyplot as plt airtemps = xr.tutorial.load_dataset('air_temperature') air2d = airtemps.air.isel(time=500) - 273.15 # diverging colormap air2d.plot.pcolormesh() plt.show() # viridis air2d.plot.pcolormesh(vmin=-24, vmax=24) plt.show() # ------------------------------------------------------------ # the solution: # diverging colormap air2d.plot.pcolormesh(vmin=-24, center=0) plt.show() ``` EDIT: solution and working example ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/766/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 67356079,MDU6SXNzdWU2NzM1NjA3OQ==,388,DataArray and virtual variables,10194086,closed,0,,,2,2015-04-09T12:59:08Z,2015-09-15T15:26:29Z,2015-09-15T15:26:28Z,MEMBER,,,,"Now that we are at it... Why don't DataArrays have virtual variables? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/388/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 105414928,MDU6SXNzdWUxMDU0MTQ5Mjg=,560,replace time coordinates for whole year,10194086,closed,0,,,3,2015-09-08T16:09:52Z,2015-09-08T16:40:52Z,2015-09-08T16:40:52Z,MEMBER,,,,"It is quite convenient to select a whole year `ds.sel(time='1991')`. However, sel should not be used to assign data. Thus you can not do `ds.sel(time='1991').coords['time'] = time` and `ds[dict(time='1991')]` does not work. 1) Should `ds[dict(time='1991')]` work? 2) A possible workaround is: ``` idx = xray.core.indexing.remap_label_indexers(ds, {'time' : '1999'}) ds[idx] = time ``` Should this be added to the documentation or is this case too uncommon? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/560/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 83530814,MDU6SXNzdWU4MzUzMDgxNA==,415,Nearest neighbor lookups,10194086,closed,0,,,3,2015-06-01T14:34:03Z,2015-06-02T07:29:24Z,2015-06-01T18:27:21Z,MEMBER,,,,"Nearest neighbor lookups does not seem to work: ``` import xray xray.__version__ ``` 0.4.1 ``` data = xray.DataArray([1, 2, 3], dims='x') data.sel(x=[1.1, 1.9], method='nearest') ``` ValueError: not all values found in index 'x' Example from: http://xray.readthedocs.org/en/latest/indexing.html#nearest-neighbor-lookups (pandas version 0.16.1) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/415/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 67332234,MDU6SXNzdWU2NzMzMjIzNA==,386,"""loosing"" virtual variables",10194086,closed,0,,,4,2015-04-09T10:35:31Z,2015-04-20T03:55:44Z,2015-04-20T03:55:44Z,MEMBER,,,,"Once I take a mean over virtual variables, they are not available any more. ``` import pandas as pd import numpy as np import xray t = pd.date_range('2000-01-01', '2000-12-31', freq='6H') x = np.random.rand(*t.shape) time = xray.DataArray(t, name='t', dims='time') ts = xray.Dataset({'x' : ('time', x), 'time' : time}) ts_mean = ts.groupby('time.date').mean() ts_mean.virtual_variables ``` Is this intended behaviour? And could I get them back somehow? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/386/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 67343193,MDU6SXNzdWU2NzM0MzE5Mw==,387,Accessing virtual_variables,10194086,closed,0,,,1,2015-04-09T11:41:08Z,2015-04-09T13:45:29Z,2015-04-09T13:45:29Z,MEMBER,,,,"Virtual variables such as time.date can not be accessed by dot notation. ``` In [81]: ts.time.date --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in () ----> 1 ts.time.date /home/mathause/.local/lib/python2.7/site-packages/xray/core/common.pyc in __getattr__(self, name) 115 pass 116 raise AttributeError(""%r object has no attribute %r"" % --> 117 (type(self).__name__, name)) 118 119 def __dir__(self): AttributeError: 'DataArray' object has no attribute 'date' ``` Compare ``` ts.__getattr__('time.date') ``` So maybe time.date is not a useful name. time_date? Or don't show these attributes to the user? ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/387/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue