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 2248614324,I_kwDOAMm_X86GByG0,8952,`isel(multi_index_level_name = MultiIndex.level)` corrupts the MultiIndex,2448579,open,0,,,1,2024-04-17T15:41:39Z,2024-04-18T13:14:46Z,,MEMBER,,,,"### What happened? From https://github.com/pydata/xarray/discussions/8951 if `d` is a MultiIndex-ed dataset with levels `(x, y, z)`, and `m` is a dataset with a single coord `x` `m.isel(x=d.x)` builds a dataset with a MultiIndex with levels `(y, z)`. This seems like it should work. cc @benbovy ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example ```Python import pandas as pd, xarray as xr, numpy as np xr.set_options(use_flox=True) test = pd.DataFrame() test[""x""] = np.arange(100) % 10 test[""y""] = np.arange(100) test[""z""] = np.arange(100) test[""v""] = np.arange(100) d = xr.Dataset.from_dataframe(test) d = d.set_index(index = [""x"", ""y"", ""z""]) print(d) m = d.groupby(""x"").mean() print(m) print(d.xindexes) print(m.isel(x=d.x).xindexes) xr.align(d, m.isel(x=d.x)) #res = d.groupby(""x"") - m #print(res) ``` ``` Dimensions: (index: 100) Coordinates: * index (index) object MultiIndex * x (index) int64 0 1 2 3 4 5 6 7 8 9 0 1 2 ... 8 9 0 1 2 3 4 5 6 7 8 9 * y (index) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 * z (index) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 Data variables: v (index) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 Dimensions: (x: 10) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 Data variables: v (x) float64 45.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0 Indexes: ┌ index PandasMultiIndex │ x │ y └ z Indexes: ┌ index PandasMultiIndex │ y └ z ValueError... ``` ### 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 _No response_ ### Anything else we need to know? _No response_ ### Environment
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8952/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 2213636579,I_kwDOAMm_X86D8Wnj,8887,resetting multiindex may be buggy,2448579,open,0,,,1,2024-03-28T16:23:38Z,2024-03-29T07:59:22Z,,MEMBER,,,,"### What happened? Resetting a MultiIndex dim coordinate preserves the MultiIndex levels as IndexVariables. We should either reset the indexes for the multiindex level variables, or warn asking the users to do so This seems to be the root cause exposed by https://github.com/pydata/xarray/pull/8809 cc @benbovy ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr # ND DataArray that gets stacked along a multiindex da = xr.DataArray(np.ones((3, 3)), coords={""dim1"": [1, 2, 3], ""dim2"": [4, 5, 6]}) da = da.stack(feature=[""dim1"", ""dim2""]) # Extract just the stacked coordinates for saving in a dataset ds = xr.Dataset(data_vars={""feature"": da.feature}) xr.testing.assertions._assert_internal_invariants(ds.reset_index([""feature"", ""dim1"", ""dim2""]), check_default_indexes=False) # succeeds xr.testing.assertions._assert_internal_invariants(ds.reset_index([""feature""]), check_default_indexes=False) # fails, but no warning either ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8887/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 2098659703,I_kwDOAMm_X859FwF3,8659,renaming index variables with `rename_vars` seems buggy,2448579,closed,0,,,1,2024-01-24T16:35:18Z,2024-03-15T19:21:51Z,2024-03-15T19:21:51Z,MEMBER,,,,"### What happened? (xref #8658) I'm not sure what the expected behaviour is here: ```python import xarray as xr import numpy as np from xarray.testing import _assert_internal_invariants ds = xr.Dataset() ds.coords[""1""] = (""1"", np.array([1], dtype=np.uint32)) ds[""1_""] = (""1"", np.array([1], dtype=np.uint32)) ds = ds.rename_vars({""1"": ""0""}) ds ``` It looks like this sequence of operations creates a default index But then ```python from xarray.testing import _assert_internal_invariants _assert_internal_invariants(ds, check_default_indexes=True) ``` fails with ``` ... File ~/repos/xarray/xarray/testing/assertions.py:301, in _assert_indexes_invariants_checks(indexes, possible_coord_variables, dims, check_default) 299 if check_default: 300 defaults = default_indexes(possible_coord_variables, dims) --> 301 assert indexes.keys() == defaults.keys(), (set(indexes), set(defaults)) 302 assert all(v.equals(defaults[k]) for k, v in indexes.items()), ( 303 indexes, 304 defaults, 305 ) AssertionError: ({'0'}, set()) ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8659/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2184871888,I_kwDOAMm_X86COn_Q,8830,"failing tests, all envs",2448579,closed,0,,,1,2024-03-13T20:56:34Z,2024-03-15T04:06:04Z,2024-03-15T04:06:04Z,MEMBER,,,,"### What happened? All tests are failing because of an error in `create_test_data` ``` from xarray.tests import create_test_data create_test_data() ``` ``` --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) Cell In[3], line 2 1 from xarray.tests import create_test_data ----> 2 create_test_data() File [~/repos/xarray/xarray/tests/__init__.py:329](http://localhost:8888/lab/workspaces/auto-P/tree/repos/devel/arraylake/~/repos/xarray/xarray/tests/__init__.py#line=328), in create_test_data(seed, add_attrs, dim_sizes) 327 obj.coords[""numbers""] = (""dim3"", numbers_values) 328 obj.encoding = {""foo"": ""bar""} --> 329 assert all(var.values.flags.writeable for var in obj.variables.values()) 330 return obj AssertionError: ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8830/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 2064480451,I_kwDOAMm_X857DXjD,8582,Adopt SPEC 0 instead of NEP-29,2448579,open,0,,,1,2024-01-03T18:36:24Z,2024-01-03T20:12:05Z,,MEMBER,,,,"### What is your issue? https://docs.xarray.dev/en/stable/getting-started-guide/installing.html#minimum-dependency-versions says that we follow NEP-29, and I think our min versions script also does that. I propose we follow https://scientific-python.org/specs/spec-0000/ In practice, I think this means we mostly drop Python versions earlier.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8582/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1943543755,I_kwDOAMm_X85z2B_L,8310,pydata/xarray as monorepo for Xarray and NamedArray,2448579,open,0,,,1,2023-10-14T20:34:51Z,2023-10-14T21:29:11Z,,MEMBER,,,,"### What is your issue? As we work through refactoring for NamedArray, it's pretty clear that Xarray will depend pretty closely on many files in `namedarray/`. For example various `utils.py`, `pycompat.py`, `*ops.py`, `formatting.py`, `formatting_html.py` at least. This promises to be quite painful if we did break NamedArray out in to its own repo (particularly around typing, e.g. https://github.com/pydata/xarray/pull/8309) I propose we use pydata/xarray as a monorepo that serves two packages: NamedArray and Xarray. - We can move as much as is needed to have NamedArray be independent of Xarray, but Xarray will depend quite closely on many utility functions in NamedArray. - We can release both at the same time similar to dask and distributed. - We can re-evaluate if and when NamedArray grows its own community.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8310/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1822982776,I_kwDOAMm_X85sqIJ4,8023,Possible autoray integration,2448579,open,0,,,1,2023-07-26T18:57:59Z,2023-07-26T19:26:05Z,,MEMBER,,,,"I'm opening this issue for discussion really. I stumbled on [autoray](https://autoray.readthedocs.io/en/latest/index.html) ([Github](https://github.com/jcmgray/autoray)) by @jcmgray which provides an abstract interface to a number of array types. What struck me was the very general [lazy compute](https://github.com/jcmgray/autoray#lazy-computation) system. This opens up the possibility of lazy-but-not-dask computation. Related: https://github.com/pydata/xarray/issues/2298 https://github.com/pydata/xarray/issues/1725 https://github.com/pydata/xarray/issues/5081 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8023/reactions"", ""total_count"": 2, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 2}",,,13221727,issue 1119647191,I_kwDOAMm_X85CvHXX,6220,[FEATURE]: Use fast path when grouping by unique monotonic decreasing variable,2448579,open,0,,,1,2022-01-31T16:24:29Z,2023-01-09T16:48:58Z,,MEMBER,,,,"### Is your feature request related to a problem? See https://github.com/pydata/xarray/pull/6213/files#r795716713 We check whether the `by` variable for groupby is unique and monotonically increasing. But the fast path would also apply to unique and monotonically decreasing variables. ### Describe the solution you'd like Update the condition to `is_monotonic_increasing or is_monotonic_decreasing` and add a test. ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6220/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1194945072,I_kwDOAMm_X85HOWow,6447,allow merging datasets where a variable might be a coordinate variable only in a subset of datasets,2448579,open,0,,,1,2022-04-06T17:53:51Z,2022-11-16T03:46:56Z,,MEMBER,,,,"### Is your feature request related to a problem? Here are two datasets, in one `a` is a data_var, in the other `a` is a coordinate variable. The following fails ``` python import xarray as xr ds1 = xr.Dataset({""a"": ('x', [1, 2, 3])}) ds2 = ds1.set_coords(""a"") ds2.update(ds1) ``` with ``` 649 ambiguous_coords = coord_names.intersection(noncoord_names) 650 if ambiguous_coords: --> 651 raise MergeError( 652 ""unable to determine if these variables should be "" 653 f""coordinates or not in the merged result: {ambiguous_coords}"" 654 ) 656 attrs = merge_attrs( 657 [var.attrs for var in coerced if isinstance(var, (Dataset, DataArray))], 658 combine_attrs, 659 ) 661 return _MergeResult(variables, coord_names, dims, out_indexes, attrs) MergeError: unable to determine if these variables should be coordinates or not in the merged result: {'a'} ``` ### Describe the solution you'd like I think we should replace this error with a warning and arbitrarily choose to either convert `a` to a coordinate variable or a data variable. ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6447/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1315480779,I_kwDOAMm_X85OaKTL,6817,wrong mean of complex values,2448579,closed,0,,,1,2022-07-22T23:09:47Z,2022-07-23T02:03:11Z,2022-07-23T02:03:11Z,MEMBER,,,,"### What happened? Seen in #4972 ``` python import xarray as xr import numpy as np array = np.array([0. +0.j, 0.+np.nan * 1j], dtype=np.complex64) var = xr.Variable(""x"", array) print(var.mean().data) print(array.mean()) ``` ``` 0j (nan+nanj) ``` ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example _No response_ ### 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 _No response_ ### Anything else we need to know? _No response_ ### Environment
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6817/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1207159549,I_kwDOAMm_X85H88r9,6497,restrict stale bot,2448579,closed,0,,,1,2022-04-18T15:25:56Z,2022-04-18T16:11:11Z,2022-04-18T16:11:11Z,MEMBER,,,,"### What is your issue? We have some stale issue but not that many. Can we restrict the bot to only issues that are untagged, or tagged as ""usage question"" or are not assigned to a ""project"" instead? This might reduce a lot of the noise.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6497/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1188406993,I_kwDOAMm_X85G1abR,6430,Bug in broadcasting with multi-indexes,2448579,closed,0,,,1,2022-03-31T17:25:57Z,2022-04-13T14:49:23Z,2022-04-13T14:49:23Z,MEMBER,,,,"### What happened? ``` python import numpy as np import xarray as xr ds = xr.Dataset( {""foo"": ((""x"", ""y"", ""z""), np.ones((3, 4, 2)))}, {""x"": [""a"", ""b"", ""c""], ""y"": [1, 2, 3, 4]}, ) expected = ds.sum(""z"") stacked = ds.stack(space=[""x"", ""y""]) broadcasted, _ = xr.broadcast(stacked, stacked.space) stacked.sum(""z"").unstack(""space"") # works broadcasted.sum(""z"").unstack(""space"") # error ``` ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [13], in 10 broadcasted, _ = xr.broadcast(stacked, stacked.space) 11 stacked.sum(""z"").unstack(""space"") ---> 12 broadcasted.sum(""z"").unstack(""space"") File ~/work/python/xarray/xarray/core/dataset.py:4332, in Dataset.unstack(self, dim, fill_value, sparse) 4330 non_multi_dims = set(dims) - set(stacked_indexes) 4331 if non_multi_dims: -> 4332 raise ValueError( 4333 ""cannot unstack dimensions that do not "" 4334 f""have exactly one multi-index: {tuple(non_multi_dims)}"" 4335 ) 4337 result = self.copy(deep=False) 4339 # we want to avoid allocating an object-dtype ndarray for a MultiIndex, 4340 # so we can't just access self.variables[v].data for every variable. 4341 # We only check the non-index variables. 4342 # https://github.com/pydata/xarray/issues/5902 ValueError: cannot unstack dimensions that do not have exactly one multi-index: ('space',) ``` ### What did you expect to happen? This should work. ### Minimal Complete Verifiable Example _No response_ ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment xarray main after the flexible indexes refactor","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6430/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1193704369,I_kwDOAMm_X85HJnux,6444,xr.where with scalar as second argument fails with keep_attrs=True,2448579,closed,0,,,1,2022-04-05T20:51:18Z,2022-04-12T02:12:39Z,2022-04-12T02:12:39Z,MEMBER,,,,"### What happened? ``` python import xarray as xr xr.where(xr.DataArray([1, 2, 3]) > 0, 1, 0) ``` fails with ``` 1809 if keep_attrs is True: 1810 # keep the attributes of x, the second parameter, by default to 1811 # be consistent with the `where` method of `DataArray` and `Dataset` -> 1812 keep_attrs = lambda attrs, context: attrs[1] 1814 # alignment for three arguments is complicated, so don't support it yet 1815 return apply_ufunc( 1816 duck_array_ops.where, 1817 cond, (...) 1823 keep_attrs=keep_attrs, 1824 ) IndexError: list index out of range ``` The workaround is to pass `keep_attrs=False` ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example _No response_ ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment xarray 2022.3.0","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6444/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 528168017,MDU6SXNzdWU1MjgxNjgwMTc=,3573,rasterio test failure,2448579,closed,0,,,1,2019-11-25T15:40:19Z,2022-04-09T01:17:32Z,2022-04-09T01:17:32Z,MEMBER,,,,"version ``` rasterio 1.1.1 py36h900e953_0 conda-forge ``` ``` =================================== FAILURES =================================== ________________________ TestRasterio.test_rasterio_vrt ________________________ self = def test_rasterio_vrt(self): import rasterio # tmp_file default crs is UTM: CRS({'init': 'epsg:32618'} with create_tmp_geotiff() as (tmp_file, expected): with rasterio.open(tmp_file) as src: with rasterio.vrt.WarpedVRT(src, crs=""epsg:4326"") as vrt: expected_shape = (vrt.width, vrt.height) expected_crs = vrt.crs expected_res = vrt.res # Value of single pixel in center of image lon, lat = vrt.xy(vrt.width // 2, vrt.height // 2) > expected_val = next(vrt.sample([(lon, lat)])) xarray/tests/test_backends.py:3966: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /usr/share/miniconda/envs/xarray-tests/lib/python3.6/site-packages/rasterio/sample.py:43: in sample_gen data = read(indexes, window=window, masked=masked, boundless=True) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ > ??? E ValueError: WarpedVRT does not permit boundless reads rasterio/_warp.pyx:978: ValueError ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3573/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1001197796,I_kwDOAMm_X847rRDk,5804,vectorized groupby binary ops,2448579,closed,0,,,1,2021-09-20T17:04:47Z,2022-03-29T07:11:28Z,2022-03-29T07:11:28Z,MEMBER,,,,"By switching to `numpy_groupies` we are vectorizing our groupby reductions. I think we can do the same for groupby's binary ops. Here's an example array ``` python import numpy as np import xarray as xr %load_ext memory_profiler N = 4 * 2000 da = xr.DataArray( np.random.random((N, N)), dims=(""x"", ""y""), coords={""labels"": (""x"", np.repeat([""a"", ""b"", ""c"", ""d"", ""e"", ""f"", ""g"", ""h""], repeats=N//8))}, ) ``` Consider this ""anomaly"" calculation, anomaly defined relative to the group mean ``` python def anom_current(da): grouped = da.groupby(""labels"") mean = grouped.mean() anom = grouped - mean return anom ``` With this approach, we loop over each group and apply the binary operation: https://github.com/pydata/xarray/blob/a1635d324753588e353e4e747f6058936fa8cf1e/xarray/core/computation.py#L502-L525 This saves some memory, but becomes slow for large number of groups. We could instead do ``` def anom_vectorized(da): mean = da.groupby(""labels"").mean() mean_expanded = mean.sel(labels=da.labels) anom = da - mean_expanded return anom ``` Now we are faster, but construct an extra array as big as the original array (I think this is an OK tradeoff). ``` %timeit anom_current(da) # 1.4 s ± 20.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit anom_vectorized(da) # 937 ms ± 5.26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` ------ (I haven't experimented with dask yet, so the following is just a theory). I think the real benefit comes with dask. Depending on where the groups are located relative to chunking, we could end up creating a lot of tiny chunks by splitting up existing chunks. With the vectorized approach we can do better. Ideally we would reindex the ""mean"" dask array with a numpy-array-of-repeated-ints such that the chunking of `mean_expanded` exactly matches the chunking of `da` along the grouped dimension. ~In practice, [dask.array.take](https://docs.dask.org/en/latest/_modules/dask/array/routines.html#take) doesn't allow specifying ""output chunks"" so we'd end up chunking ""mean_expanded"" based on dask's automatic heuristics, and then rechunking again for the binary operation.~ Thoughts? cc @rabernat","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5804/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1174177534,I_kwDOAMm_X85F_Ib-,6381,vectorized indexing with DataArray should not preserve IndexVariable,2448579,closed,0,,,1,2022-03-19T05:08:39Z,2022-03-21T04:47:47Z,2022-03-21T04:47:47Z,MEMBER,,,,"### What happened? After vectorized indexing a DataArray with dim `x`by a DataArray `z`, we get a DataArray with dim `z` and `x` as non-dim coordinate. But `x` is still an IndexVariable, not a normal variable. ### What did you expect to happen? `x` should be a normal variable. ### Minimal Complete Verifiable Example ```python import xarray as xr xr.set_options(display_style=""text"") da = xr.DataArray([1, 2, 3], dims=""x"", coords={""x"": [0, 1, 2]}) idxr = xr.DataArray([1], dims=""z"", name=""x"", coords={""z"": (""z"", [""a""])}) da.sel(x=idxr) ``` ``` array([2]) Coordinates: x (z) int64 1 * z (z) array([1]) ``` ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment xarray main but this bug was present prior to the explicit indexes refactor.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6381/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 514716299,MDU6SXNzdWU1MTQ3MTYyOTk=,3468,failure when roundtripping empty dataset to pandas,2448579,open,0,,,1,2019-10-30T14:28:31Z,2021-11-13T14:54:09Z,,MEMBER,,,,see https://github.com/pydata/xarray/pull/3285,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3468/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 520079199,MDU6SXNzdWU1MjAwNzkxOTk=,3497,how should xarray handle pandas attrs,2448579,open,0,,,1,2019-11-08T15:32:36Z,2021-07-04T03:31:02Z,,MEMBER,,,,"Continuing discussion form #3491. Pandas has added `attrs` to their objects. We should decide on what to do with them in the DataArray constructor. Many tests fail if we don't handle this case explicitly. @dcherian: > Not sure what we want to do about these attributes in the long term. One option would be to pop the name attribute, assign to DataArray.name and keep the rest as DataArray.attrs? But what if name clashes with the provided name? @max-sixty: > Agree! I think we could prioritize the supplied name above that in attrs. Another option would be raising an error if both were supplied.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3497/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 891240764,MDU6SXNzdWU4OTEyNDA3NjQ=,5299,failing RTD build,2448579,closed,0,,,1,2021-05-13T17:50:37Z,2021-05-14T01:04:22Z,2021-05-14T01:04:22Z,MEMBER,,,,"The RTD build is failing on all PRs with ``` Sphinx parallel build error: nbsphinx.NotebookError: UndefinedError in examples/ERA5-GRIB-example.ipynb: 'nbformat.notebooknode.NotebookNode object' has no attribute 'tags' ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5299/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 850473442,MDU6SXNzdWU4NTA0NzM0NDI=,5113,docs sidebar formatting has changed,2448579,closed,0,,,1,2021-04-05T16:06:43Z,2021-04-19T02:35:34Z,2021-04-19T02:35:34Z,MEMBER,,,," **What happened**: The formatting of section headings ""for users"", ""community"" etc. has changed: https://xarray.pydata.org/en/latest/ ![image](https://user-images.githubusercontent.com/2448579/113595647-86250a80-95f6-11eb-8dc3-804e338c8439.png) ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5113/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 819241806,MDU6SXNzdWU4MTkyNDE4MDY=,4980,fix bottleneck + Dask 1D rolling operations,2448579,closed,0,,,1,2021-03-01T20:38:34Z,2021-03-01T20:39:28Z,2021-03-01T20:39:27Z,MEMBER,,,,"Just as a reminder. Right now all rolling operations with dask arrays use `.construct().reduce()`. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4980/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 674414304,MDU6SXNzdWU2NzQ0MTQzMDQ=,4320,html repr doesn't work in some sphinx themes,2448579,closed,0,,,1,2020-08-06T15:45:54Z,2021-01-31T03:34:55Z,2021-01-31T03:34:54Z,MEMBER,,,,"Downstream issue: https://github.com/xarray-contrib/cf-xarray/issues/57 Example: no reprs displayed in https://cf-xarray.readthedocs.io/en/latest/examples/introduction.html @benbovy's diagnosis: > It looks like bootstrap 4 (used by sphinx-book-theme) forces all html elements with hidden attributes to be actually hidden (source), so the hack in pydata/xarray#4053 does not work here (the result is even worse). > I guess that a workaround would be to add some custom CSS such as .xr-wrap { display: block !important }, assuming that custom CSS is loaded after Bootstrap's CSS. Not ideal, though, it looks like a hack on top of another hack.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4320/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 722539100,MDU6SXNzdWU3MjI1MzkxMDA=,4515,show dimension coordinates at top of coordinates repr,2448579,closed,0,,,1,2020-10-15T17:44:28Z,2020-11-06T18:49:55Z,2020-11-06T18:49:55Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** I have datasets with lots of non-dim coord variables. Its annoying to search through and look at the dimension coordinates to get an idea of what subset of data I am looking at. ![image](https://user-images.githubusercontent.com/2448579/96166307-076ad280-0edb-11eb-8e87-470771b0a289.png) **Describe the solution you'd like** I think we should show dimension coordinate variables at the top of the coordinates repr. Example code ``` python ds = xr.Dataset() ds.coords[""as""] = 10 ds[""var""] = xr.DataArray(np.ones((10,)), dims=""x"", coords={""x"": np.arange(10)}) ds ``` ![image](https://user-images.githubusercontent.com/2448579/96166698-8c55ec00-0edb-11eb-825b-dabd460fe58b.png) Related #4409 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4515/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 663833847,MDU6SXNzdWU2NjM4MzM4NDc=,4249,RTD PR builds are timing out,2448579,closed,0,,,1,2020-07-22T15:04:22Z,2020-07-22T21:17:59Z,2020-07-22T21:17:59Z,MEMBER,,,,"See https://readthedocs.org/projects/xray/builds/ There's no useful information in the logs AFAICT: e.g. https://readthedocs.org/projects/xray/builds/11504571/","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4249/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 617579699,MDU6SXNzdWU2MTc1Nzk2OTk=,4056,flake8 failure,2448579,closed,0,,,1,2020-05-13T16:16:20Z,2020-05-13T17:35:46Z,2020-05-13T17:35:46Z,MEMBER,,,,"flake8 is failing on master (https://dev.azure.com/xarray/xarray/_build/results?buildId=2820&view=logs&jobId=a577607c-d99b-546f-eeb4-2341e9a21630&j=a577607c-d99b-546f-eeb4-2341e9a21630&t=7308a173-bf34-5af1-b6d9-30c4d79bebeb) with ``` ========================== Starting Command Output =========================== /bin/bash --noprofile --norc /home/vsts/work/_temp/e6322963-dd1c-4887-ba6a-2aa7ec888f4c.sh ./xarray/backends/memory.py:43:32: E741 ambiguous variable name 'l' ./xarray/backends/common.py:244:32: E741 ambiguous variable name 'l' ##[error]Bash exited with code '1'. Finishing: flake8 lint checks ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4056/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 582474355,MDU6SXNzdWU1ODI0NzQzNTU=,3861,CI not running?,2448579,closed,0,,,1,2020-03-16T17:23:13Z,2020-03-17T13:18:07Z,2020-03-17T13:18:07Z,MEMBER,,,,"Looks like the last run was on Thursday: https://dev.azure.com/xarray/xarray/_build?definitionId=1&_a=summary&view=runs No tests have been run for PRs #3826 #3836 #3858 and #3807 despite these having been updated recently. There is a workaround posted at this Azure issue: https://status.dev.azure.com/_event/179641421 but it looks like a fix is coming soon. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3861/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 433874617,MDU6SXNzdWU0MzM4NzQ2MTc=,2901,Link to dask documentation on chunks,2448579,closed,0,,,1,2019-04-16T16:29:13Z,2019-10-04T17:04:37Z,2019-10-04T17:04:37Z,MEMBER,,,,It would be good to link to https://docs.dask.org/en/latest/array-chunks.html in https://xarray.pydata.org/en/stable/dask.html#chunking-and-performance,"{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2901/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 389865283,MDU6SXNzdWUzODk4NjUyODM=,2600,Tests are failing on dask-dev,2448579,closed,0,,,1,2018-12-11T17:09:57Z,2018-12-12T03:13:30Z,2018-12-12T03:13:30Z,MEMBER,,,,"Sample error from https://travis-ci.org/pydata/xarray/jobs/466431752 ``` _______________________ test_dataarray_with_dask_coords ________________________ def test_dataarray_with_dask_coords(): import toolz x = xr.Variable('x', da.arange(8, chunks=(4,))) y = xr.Variable('y', da.arange(8, chunks=(4,)) * 2) data = da.random.random((8, 8), chunks=(4, 4)) + 1 array = xr.DataArray(data, dims=['x', 'y']) array.coords['xx'] = x array.coords['yy'] = y assert dict(array.__dask_graph__()) == toolz.merge(data.__dask_graph__(), x.__dask_graph__(), y.__dask_graph__()) > (array2,) = dask.compute(array) xarray/tests/test_dask.py:824: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ ../../../miniconda/envs/test_env/lib/python3.6/site-packages/dask/base.py:395: in compute dsk = collections_to_dsk(collections, optimize_graph, **kwargs) ../../../miniconda/envs/test_env/lib/python3.6/site-packages/dask/base.py:187: in collections_to_dsk for opt, val in groups.items()} ../../../miniconda/envs/test_env/lib/python3.6/site-packages/dask/base.py:187: in for opt, val in groups.items()} ../../../miniconda/envs/test_env/lib/python3.6/site-packages/dask/base.py:212: in _extract_graph_and_keys graph = merge(*graphs) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ dicts = , kwargs = {} factory = , rv = {} d = ('arange-36f53ab1e6153a63bbf7f4f8ff56693c', 0) def merge(*dicts, **kwargs): """""" Merge a collection of dictionaries >>> merge({1: 'one'}, {2: 'two'}) {1: 'one', 2: 'two'} Later dictionaries have precedence >>> merge({1: 2, 3: 4}, {3: 3, 4: 4}) {1: 2, 3: 3, 4: 4} See Also: merge_with """""" if len(dicts) == 1 and not isinstance(dicts[0], dict): dicts = dicts[0] factory = _get_factory(merge, kwargs) rv = factory() for d in dicts: > rv.update(d) E ValueError: dictionary update sequence element #0 has length 39; 2 is required ../../../miniconda/envs/test_env/lib/python3.6/site-packages/toolz/dicttoolz.py:39: ValueError ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2600/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue