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 373653203,MDU6SXNzdWUzNzM2NTMyMDM=,2508,groupby fails on generic ndarray functions,11671536,open,0,,,4,2018-10-24T20:00:22Z,2020-10-04T16:05:58Z,,NONE,,,,"This seems related to #326. #### Code Sample, a copy-pastable example if possible ```python import numpy as np import xarray as xr da = xr.DataArray(np.random.randint(0, 1, (10, 10, 3)), dims=['row', 'col', 'time']) da.groupby('time').apply(np.linalg.norm) ``` #### Problem description I would expect xarary to know how to apply generic numpy functions along specified axis. However, it currently raises the following exception: ```python AttributeError Traceback (most recent call last) in () ----> 1 da.groupby('time').apply(np.linalg.norm) ~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in apply(self, func, shortcut, **kwargs) 514 applied = (maybe_wrap_array(arr, func(arr, **kwargs)) 515 for arr in grouped) --> 516 return self._combine(applied, shortcut=shortcut) 517 518 def _combine(self, applied, shortcut=False): ~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _combine(self, applied, shortcut) 519 """"""Recombine the applied objects like the original."""""" 520 applied_example, applied = peek_at(applied) --> 521 coord, dim, positions = self._infer_concat_args(applied_example) 522 if shortcut: 523 combined = self._concat_shortcut(applied, dim, positions) ~/anaconda3/lib/python3.7/site-packages/xarray/core/groupby.py in _infer_concat_args(self, applied_example) 289 290 def _infer_concat_args(self, applied_example): --> 291 if self._group_dim in applied_example.dims: 292 coord = self._group 293 positions = self._group_indices AttributeError: 'numpy.float64' object has no attribute 'dims' ``` #### Expected Output a data array whit a time coordinate of size 3 (ie same shape as ``da.groupby('time').mean()``) #### Output of ``xr.show_versions()``
INSTALLED VERSIONS ------------------ commit: None python: 3.7.0.final.0 python-bits: 64 OS: Linux OS-release: 4.4.0-138-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 xarray: 0.10.9 pandas: 0.23.4 numpy: 1.15.1 scipy: 1.1.0 netCDF4: 1.4.1 h5netcdf: 0.6.2 h5py: 2.8.0 Nio: None zarr: None cftime: 1.0.1 PseudonetCDF: None rasterio: None iris: None bottleneck: 1.2.1 cyordereddict: None dask: 0.19.1 distributed: 1.23.1 matplotlib: 2.2.3 cartopy: None seaborn: 0.9.0 setuptools: 40.2.0 pip: 18.1 conda: 4.5.11 pytest: 3.8.0 IPython: 6.5.0 sphinx: 1.7.9
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2508/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 231715344,MDU6SXNzdWUyMzE3MTUzNDQ=,1428,changes made to coords using groupby and apply do not persist,11671536,open,0,,,1,2017-05-26T19:28:06Z,2020-03-29T15:28:53Z,,NONE,,,,"I am running Ubuntu 16 with Xarray 0.9.1 on python 3.6.0. I have found that any changes made to coordinates in a function that is called by a groupby object's apply method do not persist. The following code illustrates the problem: ```python import numpy as np import xarray as xr def change_new_coord(dar): """""" change the new_coord coord from 1 to 0 """""" dar.coords['new_coord'] = 0 return dar # setup data array data = np.ones((10, 10, 1000)) time = np.linspace(0, 10, 1000) coords = {'time': time, 'd2': range(10), 'd3': range(10)} dims = ['d2', 'd3', 'time'] dar = xr.DataArray(data, coords=coords, dims=dims) # attach coordinate based on d2 and d3 dar.coords['new_coord'] = (('d2', 'd3'), np.ones((10, 10))) # stack stacked = dar.stack(z=('d2', 'd3')) # groupby gr = stacked.groupby('z') # apply out = gr.apply(change_new_coord).unstack('z') # raises; all values in new_coord should be 0, but they are still 1 assert np.all(out.coords['new_coord'] == 0) ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1428/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 231838537,MDU6SXNzdWUyMzE4Mzg1Mzc=,1431,inconsistent behavior in stack/unstack along one dimension,11671536,closed,0,,,4,2017-05-28T01:07:09Z,2019-05-22T21:49:13Z,2019-05-22T21:49:13Z,NONE,,,,"I am using Ubuntu 16, python 3.6, and xarray 0.9.1 A DataArray can be stacked along one dimension, but when unstack is called a ValueError is raised. It seems that either unstack should work, or calling stack should also raise a ValueError. ```python import xarray as xr dims = ['a', 'b'] coords = {'a': range(2), 'b':range(2)} values = [[0, 0], [0, 0]] dar = xr.DataArray(values, coords, dims) stacked = dar.stack(z=('a',)) # this works unstack = stacked.unstack('z') # this raises ValueError ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1431/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 231835326,MDU6SXNzdWUyMzE4MzUzMjY=,1430,setting values with getattr performs wrong opperation with multi-dimensional coordinate,11671536,closed,0,,,3,2017-05-27T23:44:14Z,2019-04-30T03:17:20Z,2019-04-30T03:17:20Z,NONE,,,,"I am using Ubuntu 16, python 3.6, and xarray 0.9.1 Consider the following code: ```python import xarray as xr import numpy as np dims = ['a', 'b'] coords = {'a': range(2), 'b':range(2), 'group': (('a', 'b'), [[0, 0], [0, 1]])} values = [[0, 0], [0, 0]] dar = xr.DataArray(values, coords, dims) dar[dict(group=0)] = 1 # group is only 0 for three of the four elements expected_values = np.array([[1, 1], [1, 0]]) # yet this raises because all four values are set to 1 assert np.all(np.isclose(dar.values, expected_values)) ``` I suspect trying to assign values in this way using a multi-dimensional coordinate should raise a ValueError as this does: ```python dar[dict(group=0)] ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1430/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 232726778,MDU6SXNzdWUyMzI3MjY3Nzg=,1436,Wrong dimension referenced in boolean indexing with .loc,11671536,closed,0,,,3,2017-05-31T23:35:08Z,2017-10-19T23:54:03Z,2017-10-19T23:54:03Z,NONE,,,,"I am using Ubuntu 16, python 3.6, and xarary 0.9.5. ```python import numpy as np import xarray as xr # setup for a simple grid DX = 50 X = np.arange(0, 2010, DX) Y = np.arange(0, 2010, DX) Z = np.arange(0, 2010, DX) grid_shape = (len(X), len(Y), len(Z)) # Create data array dims = 'X Y Z'.split() coords = {'X': X, 'Y': Y, 'Z': Z} dar = xr.DataArray(np.ones(grid_shape), dims=dims, coords=coords) # slice the data array so that all Z values are greater than 1000 dar2 = dar.loc[dar.Z > 1000] assert np.all(dar2.Z > 1000) # fails becase dar is sliced along X, not Z ``` Since the object returned from dar.Z > 1000 is a data array with ""Z"" as the only dim I would expect this to slice the ""Z"" dim rather than X. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1436/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 228872889,MDU6SXNzdWUyMjg4NzI4ODk=,1410,to_netcdf without path argument returns None rather than bytes,11671536,closed,0,,,2,2017-05-15T23:18:54Z,2017-09-05T13:50:55Z,2017-09-05T13:50:55Z,NONE,,,,"I am using: Ubuntu 16.04 python 3.6.0 xarray 0.9.1 scipy 0.18.1 The [documentation states](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.to_netcdf.html) that if no path argument is passed to the to_netcdf method of the DataArray class it returns a bytes object. However, it returns None. ```python import xarray as xr dar = xr.DataArray([1, 2, 3]) output = dar.to_netcdf() assert isinstance(output, bytes) # fails because output is None ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1410/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 208676242,MDU6SXNzdWUyMDg2NzYyNDI=,1276,DataArray doesnt use bottleneck implementation on rolling variance,11671536,closed,0,,,2,2017-02-18T22:22:16Z,2017-03-06T01:32:14Z,2017-03-06T01:32:14Z,NONE,,,,"It appears that the DataArray is not using the bottleneck move_var when calling var on a rolling object but it does for std. I have attached some simple benchmarks to demonstrate. python version 3.6 pandas version 0.19.2 xarray version 0.9.1 bottleneck version 1.2.0 ```python import numpy as np import xarray as xr import bottleneck import pandas as pd data = np.random.rand(10000) # xarray setup ar = xr.DataArray(data, dims=['time']) xrol = ar.rolling(time=30) # pandas setup df = ar.to_pandas() prol = df.rolling(30) ``` ```python # xray std profile %time _ = xrol.std() ``` CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 209 µs ```python # xray var profile %time _ = xrol.var() ``` CPU times: user 8.69 s, sys: 132 ms, total: 8.82 s Wall time: 8.52 s ```python # pandas std profile %time _ = prol.std() ``` CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 1.13 ms ```python # pandas var profile %time _ = prol.var() ``` CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 617 µs ```python # bottleneck std profile %time _ = bottleneck.move_std(data, 30) ``` CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 138 µs ```python # bottleneck var profile %time _ = bottleneck.move_var(data, 30) ``` CPU times: user 0 ns, sys: 0 ns, total: 0 ns Wall time: 143 µs ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1276/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue