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  • ghisvail · 4 ✖

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  • Test failures on Debian if built with bottleneck · 4 ✖

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  • CONTRIBUTOR · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
275392455 https://github.com/pydata/xarray/issues/1208#issuecomment-275392455 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3NTM5MjQ1NQ== ghisvail 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.testgroupby_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 ============== ``` 

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   Test failures on Debian if built with bottleneck 200908727
273561115 https://github.com/pydata/xarray/issues/1208#issuecomment-273561115 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3MzU2MTExNQ== ghisvail 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.

 
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   Test failures on Debian if built with bottleneck 200908727
273559627 https://github.com/pydata/xarray/issues/1208#issuecomment-273559627 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3MzU1OTYyNw== ghisvail 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.

 
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   Test failures on Debian if built with bottleneck 200908727
272832666 https://github.com/pydata/xarray/issues/1208#issuecomment-272832666 https://api.github.com/repos/pydata/xarray/issues/1208 MDEyOklzc3VlQ29tbWVudDI3MjgzMjY2Ng== ghisvail 1964655 2017-01-16T11:04:48Z 2017-01-16T11:04:48Z CONTRIBUTOR

Thanks, I'll iterate with the Debian maintainer of bottleneck.

 
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   Test failures on Debian if built with bottleneck 200908727

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