html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1208#issuecomment-275392455,https://api.github.com/repos/pydata/xarray/issues/1208,275392455,MDEyOklzc3VlQ29tbWVudDI3NTM5MjQ1NQ==,1964655,2017-01-26T13:51:15Z,2017-01-26T13:51:15Z,CONTRIBUTOR,"Re-opening. Debian now has a version of Numpy with the fix which broke `bottleneck`. However, the tests still do not pass with the following log:
```
=================================== FAILURES ===================================
___________________ TestDataArray.test_groupby_apply_center ____________________

self = <xarray.tests.test_dataarray.TestDataArray testMethod=test_groupby_apply_center>

    def test_groupby_apply_center(self):
        def center(x):
            return x - np.mean(x)
    
        array = self.make_groupby_example_array()
        grouped = array.groupby('abc')
    
        expected_ds = array.to_dataset()
        exp_data = np.hstack([center(self.x[:, :9]),
                              center(self.x[:, 9:10]),
                              center(self.x[:, 10:])])
        expected_ds['foo'] = (['x', 'y'], exp_data)
        expected_centered = expected_ds['foo']
>       self.assertDataArrayAllClose(expected_centered, grouped.apply(center))

xarray/tests/test_dataarray.py:1495: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/__init__.py:169: in assertDataArrayAllClose
    assert_allclose(ar1, ar2, rtol=rtol, atol=atol)
xarray/testing.py:125: in assert_allclose
    assert_allclose(a.variable, b.variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = <xarray.Variable (x: 10, y: 20)>
array([[  1.086411e-01,  -4.940766e-01,  -3.4...e-01,
          3.025169e-01,  -4.970776e-03,  -2.526321e-02,   4.464467e-01]])
b = <xarray.Variable (x: 10, y: 20)>
array([[  1.086411e-01,  -4.940766e-01,  -3.4...e-01,
          3.025169e-01,  -4.970776e-03,  -2.526321e-02,   4.464467e-01]])
rtol = 1e-05, atol = 1e-08, decode_bytes = True

    def assert_allclose(a, b, rtol=1e-05, atol=1e-08, decode_bytes=True):
        """"""Like :py:func:`numpy.testing.assert_allclose`, but for xarray objects.
    
        Raises an AssertionError if two objects are not equal up to desired
        tolerance.
    
        Parameters
        ----------
        a : xarray.Dataset, xarray.DataArray or xarray.Variable
            The first object to compare.
        b : xarray.Dataset, xarray.DataArray or xarray.Variable
            The second object to compare.
        rtol : float, optional
            Relative tolerance.
        atol : float, optional
            Absolute tolerance.
        decode_bytes : bool, optional
            Whether byte dtypes should be decoded to strings as UTF-8 or not.
            This is useful for testing serialization methods on Python 3 that
            return saved strings as bytes.
    
        See also
        --------
        assert_identical, assert_equal, numpy.testing.assert_allclose
        """"""
        import xarray as xr
        ___tracebackhide__ = True  # noqa: F841
        assert type(a) == type(b)
        if isinstance(a, xr.Variable):
            assert a.dims == b.dims
            allclose = _data_allclose_or_equiv(a.values, b.values,
                                               rtol=rtol, atol=atol,
                                               decode_bytes=decode_bytes)
>           assert allclose, '{}\n{}'.format(a.values, b.values)
E           AssertionError: [[  1.08641053e-01  -4.94076627e-01  -3.45099073e-01   2.39968246e-01
E               1.70028797e-04  -3.54496330e-01   2.22851030e-01   3.72358514e-01
E              -1.43769756e-01  -2.74687658e-01  -2.45407871e-01   2.15556579e-01
E              -4.03911607e-01   3.87184795e-01  -4.18200302e-01  -4.06039565e-01
E              -3.83684191e-01  -2.96458046e-01   4.74874038e-02   1.26236739e-01]
E            [  3.61580852e-01  -4.79206943e-02  -2.66600721e-01   1.55428994e-01
E               2.61949104e-01  -2.14716556e-01  -3.06136650e-01   1.22278069e-01
E              -6.66015156e-02  -1.69577684e-01   3.87460083e-01   1.76306072e-01
E              -4.44331607e-01  -5.25418189e-01   1.02862502e-01  -2.85689326e-02
E              -2.02277044e-02   9.84154078e-02   2.13794060e-01   3.01328151e-01]
E            [  2.95736742e-01   4.25678417e-02   1.09718836e-01  -4.88189636e-01
E               4.03312049e-02  -2.29350832e-01   4.14023579e-01   2.97767319e-02
E              -2.67128794e-01   3.21459151e-01   3.17043658e-01  -2.23716814e-01
E              -2.66104828e-01  -3.28529266e-01   8.57802322e-02  -3.77281481e-01
E               3.19405419e-01  -2.14206353e-01   1.44674807e-01  -3.10715376e-01]
E            [  2.81673580e-01   2.73754200e-03  -3.38780740e-01  -3.34309419e-01
E              -3.53325764e-01   3.60925598e-01  -1.82147600e-01   4.78926180e-01
E              -5.15467328e-01  -3.67560456e-02  -4.19994747e-01  -1.07446917e-01
E               2.66933947e-01  -1.08693646e-01   4.91030682e-03   2.77558294e-01
E              -1.86235207e-01   2.48140256e-01   4.43512756e-02   1.26844065e-01]
E            [  3.01363974e-01  -1.09877347e-01   1.22309126e-01   4.31480566e-01
E               4.76386916e-01   2.85348322e-01   2.76157706e-02   4.35295440e-01
E               1.70634220e-02  -1.45109715e-01   3.74386086e-01  -3.66322521e-01
E              -3.46253595e-02  -2.91631220e-01  -2.43084987e-01   2.23311739e-01
E              -4.16467323e-01   2.14347435e-02  -3.60138536e-01  -9.76142770e-02]
E            [ -2.41033567e-01  -3.46475587e-01  -1.15366138e-01   4.41278309e-01
E              -1.18403292e-01   2.82655028e-01   4.28244101e-01  -1.79257638e-01
E              -1.03689353e-01  -2.18645467e-02   4.46969306e-02   5.81598169e-02
E               2.65938587e-01   1.92085853e-01  -4.32495899e-01  -2.24346584e-01
E               5.34276468e-02   5.48206231e-02  -5.89989991e-03  -3.15437391e-01]
E            [ -1.62051319e-01  -9.45765818e-02  -5.03109630e-01   4.17684367e-01
E              -3.06186685e-01  -1.46334025e-01  -9.56430059e-02  -4.25805199e-01
E               1.03351228e-01   4.13053193e-01  -1.06769941e-01   3.80051241e-01
E              -2.82681942e-01  -5.40183672e-02  -1.20729983e-01   2.51349129e-01
E               3.98594071e-01  -1.25509013e-02  -2.20384829e-01   3.53787835e-01]
E            [  8.63994358e-02   3.30289251e-01   3.34951245e-01   4.74269877e-01
E              -3.76742368e-01  -1.06150098e-01   2.75894947e-01   2.58433673e-01
E               1.72780099e-01  -1.23719179e-01   1.87148326e-01   2.30802594e-02
E              -1.91651687e-02  -1.24840109e-01  -3.47666075e-02   1.94775931e-01
E               1.57854118e-01   2.36980122e-01   4.19620283e-01   3.92725774e-01]
E            [ -1.20034155e-01   2.87247357e-01   3.46581678e-02  -2.91391233e-01
E              -1.41828417e-01  -1.02849154e-01  -2.22434220e-01   5.88958162e-02
E               5.10373849e-02   8.00973720e-02  -4.20339680e-01   3.77197946e-02
E              -1.19250042e-01   1.89674423e-01  -3.08228692e-02   2.09467152e-01
E              -2.95852048e-01   3.88376081e-02  -7.35402315e-02   2.08679192e-01]
E            [ -5.81460294e-02  -4.49176762e-01   1.06408912e-01   4.43941698e-01
E              -4.43932980e-01   8.99339636e-02  -1.99292909e-01  -4.60542036e-01
E               1.91585641e-01  -4.28948878e-02  -4.56597716e-01   1.85430017e-01
E               3.29358074e-01   1.29811129e-01   2.80559136e-01   3.70777734e-01
E               3.02516908e-01  -4.97077643e-03  -2.52632109e-02   4.46446732e-01]]
E           [[  1.08641053e-01  -4.94076627e-01  -3.45099073e-01   2.39968246e-01
E               1.70028797e-04  -3.54496330e-01   2.22851030e-01   3.72358514e-01
E              -1.43769756e-01   1.03282389e-01  -2.45407871e-01   2.15556579e-01
E              -4.03911607e-01   3.87184795e-01  -4.18200302e-01  -4.06039565e-01
E              -3.83684191e-01  -2.96458046e-01   4.74874038e-02   1.26236739e-01]
E            [  3.61580852e-01  -4.79206943e-02  -2.66600721e-01   1.55428994e-01
E               2.61949104e-01  -2.14716556e-01  -3.06136650e-01   1.22278069e-01
E              -6.66015156e-02   2.08392364e-01   3.87460083e-01   1.76306072e-01
E              -4.44331607e-01  -5.25418189e-01   1.02862502e-01  -2.85689326e-02
E              -2.02277044e-02   9.84154078e-02   2.13794060e-01   3.01328151e-01]
E            [  2.95736742e-01   4.25678417e-02   1.09718836e-01  -4.88189636e-01
E               4.03312049e-02  -2.29350832e-01   4.14023579e-01   2.97767319e-02
E              -2.67128794e-01   6.99429199e-01   3.17043658e-01  -2.23716814e-01
E              -2.66104828e-01  -3.28529266e-01   8.57802322e-02  -3.77281481e-01
E               3.19405419e-01  -2.14206353e-01   1.44674807e-01  -3.10715376e-01]
E            [  2.81673580e-01   2.73754200e-03  -3.38780740e-01  -3.34309419e-01
E              -3.53325764e-01   3.60925598e-01  -1.82147600e-01   4.78926180e-01
E              -5.15467328e-01   3.41214002e-01  -4.19994747e-01  -1.07446917e-01
E               2.66933947e-01  -1.08693646e-01   4.91030682e-03   2.77558294e-01
E              -1.86235207e-01   2.48140256e-01   4.43512756e-02   1.26844065e-01]
E            [  3.01363974e-01  -1.09877347e-01   1.22309126e-01   4.31480566e-01
E               4.76386916e-01   2.85348322e-01   2.76157706e-02   4.35295440e-01
E               1.70634220e-02   2.32860332e-01   3.74386086e-01  -3.66322521e-01
E              -3.46253595e-02  -2.91631220e-01  -2.43084987e-01   2.23311739e-01
E              -4.16467323e-01   2.14347435e-02  -3.60138536e-01  -9.76142770e-02]
E            [ -2.41033567e-01  -3.46475587e-01  -1.15366138e-01   4.41278309e-01
E              -1.18403292e-01   2.82655028e-01   4.28244101e-01  -1.79257638e-01
E              -1.03689353e-01   3.56105501e-01   4.46969306e-02   5.81598169e-02
E               2.65938587e-01   1.92085853e-01  -4.32495899e-01  -2.24346584e-01
E               5.34276468e-02   5.48206231e-02  -5.89989991e-03  -3.15437391e-01]
E            [ -1.62051319e-01  -9.45765818e-02  -5.03109630e-01   4.17684367e-01
E              -3.06186685e-01  -1.46334025e-01  -9.56430059e-02  -4.25805199e-01
E               1.03351228e-01   7.91023240e-01  -1.06769941e-01   3.80051241e-01
E              -2.82681942e-01  -5.40183672e-02  -1.20729983e-01   2.51349129e-01
E               3.98594071e-01  -1.25509013e-02  -2.20384829e-01   3.53787835e-01]
E            [  8.63994358e-02   3.30289251e-01   3.34951245e-01   4.74269877e-01
E              -3.76742368e-01  -1.06150098e-01   2.75894947e-01   2.58433673e-01
E               1.72780099e-01   2.54250868e-01   1.87148326e-01   2.30802594e-02
E              -1.91651687e-02  -1.24840109e-01  -3.47666075e-02   1.94775931e-01
E               1.57854118e-01   2.36980122e-01   4.19620283e-01   3.92725774e-01]
E            [ -1.20034155e-01   2.87247357e-01   3.46581678e-02  -2.91391233e-01
E              -1.41828417e-01  -1.02849154e-01  -2.22434220e-01   5.88958162e-02
E               5.10373849e-02   4.58067419e-01  -4.20339680e-01   3.77197946e-02
E              -1.19250042e-01   1.89674423e-01  -3.08228692e-02   2.09467152e-01
E              -2.95852048e-01   3.88376081e-02  -7.35402315e-02   2.08679192e-01]
E            [ -5.81460294e-02  -4.49176762e-01   1.06408912e-01   4.43941698e-01
E              -4.43932980e-01   8.99339636e-02  -1.99292909e-01  -4.60542036e-01
E               1.91585641e-01   3.35075160e-01  -4.56597716e-01   1.85430017e-01
E               3.29358074e-01   1.29811129e-01   2.80559136e-01   3.70777734e-01
E               3.02516908e-01  -4.97077643e-03  -2.52632109e-02   4.46446732e-01]]

xarray/testing.py:123: AssertionError
_______________________ TestDataArray.test_groupby_math ________________________

self = <xarray.tests.test_dataarray.TestDataArray testMethod=test_groupby_math>

    def test_groupby_math(self):
        array = self.make_groupby_example_array()
        for squeeze in [True, False]:
            grouped = array.groupby('x', squeeze=squeeze)
    
            expected = array + array.coords['x']
            actual = grouped + array.coords['x']
            self.assertDataArrayIdentical(expected, actual)
    
            actual = array.coords['x'] + grouped
            self.assertDataArrayIdentical(expected, actual)
    
            ds = array.coords['x'].to_dataset('X')
            expected = array + ds
            actual = grouped + ds
            self.assertDatasetIdentical(expected, actual)
    
            actual = ds + grouped
            self.assertDatasetIdentical(expected, actual)
    
        grouped = array.groupby('abc')
        expected_agg = (grouped.mean() - np.arange(3)).rename(None)
        actual = grouped - DataArray(range(3), [('abc', ['a', 'b', 'c'])])
        actual_agg = actual.groupby('abc').mean()
>       self.assertDataArrayAllClose(expected_agg, actual_agg)

xarray/tests/test_dataarray.py:1541: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/__init__.py:169: in assertDataArrayAllClose
    assert_allclose(ar1, ar2, rtol=rtol, atol=atol)
xarray/testing.py:125: in assert_allclose
    assert_allclose(a.variable, b.variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = <xarray.Variable (abc: 3)>
array([ 0.504303, -0.53899 , -1.97593 ])
b = <xarray.Variable (abc: 3)>
array([ 0.504303, -0.53899 , -0.175924])
rtol = 1e-05, atol = 1e-08, decode_bytes = True

    def assert_allclose(a, b, rtol=1e-05, atol=1e-08, decode_bytes=True):
        """"""Like :py:func:`numpy.testing.assert_allclose`, but for xarray objects.
    
        Raises an AssertionError if two objects are not equal up to desired
        tolerance.
    
        Parameters
        ----------
        a : xarray.Dataset, xarray.DataArray or xarray.Variable
            The first object to compare.
        b : xarray.Dataset, xarray.DataArray or xarray.Variable
            The second object to compare.
        rtol : float, optional
            Relative tolerance.
        atol : float, optional
            Absolute tolerance.
        decode_bytes : bool, optional
            Whether byte dtypes should be decoded to strings as UTF-8 or not.
            This is useful for testing serialization methods on Python 3 that
            return saved strings as bytes.
    
        See also
        --------
        assert_identical, assert_equal, numpy.testing.assert_allclose
        """"""
        import xarray as xr
        ___tracebackhide__ = True  # noqa: F841
        assert type(a) == type(b)
        if isinstance(a, xr.Variable):
            assert a.dims == b.dims
            allclose = _data_allclose_or_equiv(a.values, b.values,
                                               rtol=rtol, atol=atol,
                                               decode_bytes=decode_bytes)
>           assert allclose, '{}\n{}'.format(a.values, b.values)
E           AssertionError: [ 0.5043027  -0.53899037 -1.97592983]
E           [ 0.5043027  -0.53899037 -0.17592373]

xarray/testing.py:123: AssertionError
----------------------------- Captured stderr call -----------------------------
/<<PKGBUILDDIR>>/.pybuild/pythonX.Y_2.7/build/xarray/tests/test_dataarray.py:1529: FutureWarning: the order of the arguments on DataArray.to_dataset has changed; you now need to supply ``name`` as a keyword argument
  ds = array.coords['x'].to_dataset('X')
________________________ TestDataArray.test_groupby_sum ________________________

self = <xarray.tests.test_dataarray.TestDataArray testMethod=test_groupby_sum>

    def test_groupby_sum(self):
        array = self.make_groupby_example_array()
        grouped = array.groupby('abc')
    
        expected_sum_all = Dataset(
            {'foo': Variable(['abc'], np.array([self.x[:, :9].sum(),
                                                self.x[:, 10:].sum(),
                                                self.x[:, 9:10].sum()]).T),
             'abc': Variable(['abc'], np.array(['a', 'b', 'c']))})['foo']
        self.assertDataArrayAllClose(expected_sum_all, grouped.reduce(np.sum))
>       self.assertDataArrayAllClose(expected_sum_all, grouped.sum())

xarray/tests/test_dataarray.py:1440: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/__init__.py:169: in assertDataArrayAllClose
    assert_allclose(ar1, ar2, rtol=rtol, atol=atol)
xarray/testing.py:125: in assert_allclose
    assert_allclose(a.variable, b.variable)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = <xarray.Variable (abc: 3)>
array([ 45.861725,  46.894773,   4.057272])
b = <xarray.Variable (abc: 3)>
array([ 45.861725,  46.894773,   0.877923])
rtol = 1e-05, atol = 1e-08, decode_bytes = True

    def assert_allclose(a, b, rtol=1e-05, atol=1e-08, decode_bytes=True):
        """"""Like :py:func:`numpy.testing.assert_allclose`, but for xarray objects.
    
        Raises an AssertionError if two objects are not equal up to desired
        tolerance.
    
        Parameters
        ----------
        a : xarray.Dataset, xarray.DataArray or xarray.Variable
            The first object to compare.
        b : xarray.Dataset, xarray.DataArray or xarray.Variable
            The second object to compare.
        rtol : float, optional
            Relative tolerance.
        atol : float, optional
            Absolute tolerance.
        decode_bytes : bool, optional
            Whether byte dtypes should be decoded to strings as UTF-8 or not.
            This is useful for testing serialization methods on Python 3 that
            return saved strings as bytes.
    
        See also
        --------
        assert_identical, assert_equal, numpy.testing.assert_allclose
        """"""
        import xarray as xr
        ___tracebackhide__ = True  # noqa: F841
        assert type(a) == type(b)
        if isinstance(a, xr.Variable):
            assert a.dims == b.dims
            allclose = _data_allclose_or_equiv(a.values, b.values,
                                               rtol=rtol, atol=atol,
                                               decode_bytes=decode_bytes)
>           assert allclose, '{}\n{}'.format(a.values, b.values)
E           AssertionError: [ 45.86172541  46.89477337   4.05727211]
E           [ 45.86172541  46.89477337   0.87792268]

xarray/testing.py:123: AssertionError
============= 3 failed, 1161 passed, 341 skipped in 40.75 seconds ==============
``` ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,200908727
https://github.com/pydata/xarray/issues/1208#issuecomment-273561115,https://api.github.com/repos/pydata/xarray/issues/1208,273561115,MDEyOklzc3VlQ29tbWVudDI3MzU2MTExNQ==,1964655,2017-01-18T18:36:38Z,2017-01-18T18:36:38Z,CONTRIBUTOR,We'd need to wait for numpy-1.12.1 to be absolutely sure. I don't have time to deploy a dev version of numpy to test.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,200908727
https://github.com/pydata/xarray/issues/1208#issuecomment-273559627,https://api.github.com/repos/pydata/xarray/issues/1208,273559627,MDEyOklzc3VlQ29tbWVudDI3MzU1OTYyNw==,1964655,2017-01-18T18:31:01Z,2017-01-18T18:31:01Z,CONTRIBUTOR,"It turned out to be a bug in numpy 1.12.0, fixed in 1.12.1, which made `bottleneck` fail. Closing.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,200908727
https://github.com/pydata/xarray/issues/1208#issuecomment-272832666,https://api.github.com/repos/pydata/xarray/issues/1208,272832666,MDEyOklzc3VlQ29tbWVudDI3MjgzMjY2Ng==,1964655,2017-01-16T11:04:48Z,2017-01-16T11:04:48Z,CONTRIBUTOR,"Thanks, I'll iterate with the Debian maintainer of bottleneck.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,200908727