Bug#1000777: numpy breaks python-xarray autopkgtest on i386: numerical deltas

Paul Gevers elbrus at debian.org
Sun Nov 28 20:14:35 GMT 2021


Source: numpy, python-xarray
Control: found -1 numpy/1:1.21.4-2
Control: found -1 python-xarray/0.19.0-6
Severity: serious
Tags: sid bookworm
X-Debbugs-CC: debian-ci at lists.debian.org
User: debian-ci at lists.debian.org
Usertags: breaks needs-update

Dear maintainer(s),

With a recent upload of numpy the autopkgtest of python-xarray fails in 
testing when that autopkgtest is run with the binary packages of numpy 
from unstable on i386. It passes when run with only packages from 
testing. In tabular form:

                        pass            fail
numpy                  from testing    1:1.21.4-2
python-xarray          from testing    0.19.0-6
versioned deps [0]     from testing    from unstable
all others             from testing    from testing

I copied some of the output at the bottom of this report.

Currently this regression is blocking the migration of numpy to testing 
[1]. Due to the nature of this issue, I filed this bug report against 
both packages. Can you please investigate the situation and reassign the 
bug to the right package?

More information about this bug and the reason for filing it can be found on
https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation

Paul

[0] You can see what packages were added from the second line of the log 
file quoted below. The migration software adds source package from 
unstable to the list if they are needed to install packages from 
numpy/1:1.21.4-2. I.e. due to versioned dependencies or breaks/conflicts.
[1] https://qa.debian.org/excuses.php?package=numpy

https://ci.debian.net/data/autopkgtest/testing/i386/p/python-xarray/17095751/log.gz


=================================== FAILURES 
===================================
___________________ test_interpolate_chunk_advanced[linear] 
____________________

method = 'linear'

     @requires_scipy
     @requires_dask
     @pytest.mark.parametrize("method", ["linear", "nearest"])
     @pytest.mark.filterwarnings("ignore:Increasing number of chunks")
     def test_interpolate_chunk_advanced(method):
         """Interpolate nd array with an nd indexer sharing coordinates."""
         # Create original array
         x = np.linspace(-1, 1, 5)
         y = np.linspace(-1, 1, 7)
         z = np.linspace(-1, 1, 11)
         t = np.linspace(0, 1, 13)
         q = np.linspace(0, 1, 17)
         da = xr.DataArray(
             data=np.sin(x[:, np.newaxis, np.newaxis, np.newaxis, 
np.newaxis])
             * np.cos(y[:, np.newaxis, np.newaxis, np.newaxis])
             * np.exp(z[:, np.newaxis, np.newaxis])
             * t[:, np.newaxis]
             + q,
             dims=("x", "y", "z", "t", "q"),
             coords={"x": x, "y": y, "z": z, "t": t, "q": q, "label": 
"dummy_attr"},
         )
             # Create indexer into `da` with shared coordinate 
("full-twist" Möbius strip)
         theta = np.linspace(0, 2 * np.pi, 5)
         w = np.linspace(-0.25, 0.25, 7)
         r = xr.DataArray(
             data=1 + w[:, np.newaxis] * np.cos(theta),
             coords=[("w", w), ("theta", theta)],
         )
             x = r * np.cos(theta)
         y = r * np.sin(theta)
         z = xr.DataArray(
             data=w[:, np.newaxis] * np.sin(theta),
             coords=[("w", w), ("theta", theta)],
         )
             kwargs = {"fill_value": None}
         expected = da.interp(t=0.5, x=x, y=y, z=z, kwargs=kwargs, 
method=method)
             da = da.chunk(2)
         x = x.chunk(1)
         z = z.chunk(3)
         actual = da.interp(t=0.5, x=x, y=y, z=z, kwargs=kwargs, 
method=method)
>       assert_identical(actual, expected)
E       AssertionError: Left and right DataArray objects are not identical
E       E       Differing values:
E       L
E           array([[[ 3.302241e-01,  3.927241e-01, ...,  1.267724e+00, 
1.330224e+00],
E                   [ 1.239764e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-5.560517e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 3.302241e-01,  3.927241e-01, ...,  1.267724e+00, 
1.330224e+00]],
E           E                  [[ 3.603946e-01,  4.228946e-01, ..., 
1.297895e+00,  1.360395e+00],
E                   [ 1.346533e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-5.109700e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 3.603946e-01,  4.228946e-01, ...,  1.297895e+00, 
1.360395e+00]],
E           E                  ...,
E           E                  [[ 4.810764e-01,  5.435764e-01, ..., 
1.418576e+00,  1.481076e+00],
E                   [ 1.878775e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-3.662163e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 4.810764e-01,  5.435764e-01, ...,  1.418576e+00, 
1.481076e+00]],
E           E                  [[ 5.112469e-01,  5.737469e-01, ..., 
1.448747e+00,  1.511247e+00],
E                   [ 2.044535e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-3.371783e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 5.112469e-01,  5.737469e-01, ...,  1.448747e+00, 
1.511247e+00]]])
E       R
E           array([[[ 3.302241e-01,  3.927241e-01, ...,  1.267724e+00, 
1.330224e+00],
E                   [ 1.239764e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-5.560517e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 3.302241e-01,  3.927241e-01, ...,  1.267724e+00, 
1.330224e+00]],
E           E                  [[ 3.603946e-01,  4.228946e-01, ..., 
1.297895e+00,  1.360395e+00],
E                   [ 1.346533e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-5.109700e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 3.603946e-01,  4.228946e-01, ...,  1.297895e+00, 
1.360395e+00]],
E           E                  ...,
E           E                  [[ 4.810764e-01,  5.435764e-01, ..., 
1.418576e+00,  1.481076e+00],
E                   [ 1.878775e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-3.662163e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 4.810764e-01,  5.435764e-01, ...,  1.418576e+00, 
1.481076e+00]],
E           E                  [[ 5.112469e-01,  5.737469e-01, ..., 
1.448747e+00,  1.511247e+00],
E                   [ 2.044535e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   ...,
E                   [-3.371783e-17,  6.250000e-02, ...,  9.375000e-01, 
1.000000e+00],
E                   [ 5.112469e-01,  5.737469e-01, ...,  1.448747e+00, 
1.511247e+00]]])

/usr/lib/python3/dist-packages/xarray/tests/test_interp.py:874: 
AssertionError
_____________________ TestVariable.test_broadcasting_math 
______________________

self = <xarray.tests.test_variable.TestVariable object at 0xe234be20>

     def test_broadcasting_math(self):
         x = np.random.randn(2, 3)
         v = Variable(["a", "b"], x)
         # 1d to 2d broadcasting
         assert_identical(v * v, Variable(["a", "b"], 
np.einsum("ab,ab->ab", x, x)))
         assert_identical(v * v[0], Variable(["a", "b"], 
np.einsum("ab,b->ab", x, x[0])))
         assert_identical(v[0] * v, Variable(["b", "a"], 
np.einsum("b,ab->ba", x[0], x)))
         assert_identical(
             v[0] * v[:, 0], Variable(["b", "a"], np.einsum("b,a->ba", 
x[0], x[:, 0]))
         )
         # higher dim broadcasting
         y = np.random.randn(3, 4, 5)
         w = Variable(["b", "c", "d"], y)
         assert_identical(
             v * w, Variable(["a", "b", "c", "d"], 
np.einsum("ab,bcd->abcd", x, y))
         )
>       assert_identical(
             w * v, Variable(["b", "c", "d", "a"], 
np.einsum("bcd,ab->bcda", y, x))
         )
E       AssertionError: Left and right Variable objects are not identical
E       E       Differing values:
E       L
E           array([[[[ 2.599162e+00,  4.203630e-01],
E                    [-1.535482e-01, -2.483337e-02],
E                    [-7.007329e-01, -1.133297e-01],
E                    [-1.407709e+00, -2.276690e-01],
E                    [ 1.824711e-01,  2.951107e-02]],
E           E                   [[ 2.608622e+00,  4.218929e-01],
E                    [ 2.547940e+00,  4.120787e-01],
E                    [-7.653887e-01, -1.237864e-01],
E                    [-6.070710e-01, -9.818170e-02],
E                    [-2.362519e+00, -3.820905e-01]],
E           E                   [[ 1.020907e-01,  1.651115e-02],
E                    [ 2.661684e+00,  4.304746e-01],
E                    [ 1.208155e-02,  1.953951e-03],
E                    [-1.328791e+00, -2.149056e-01],
E                    [-1.154634e+00, -1.867391e-01]],
E           E                   [[ 1.191513e+00,  1.927036e-01],
E                    [-6.021055e-01, -9.737862e-02],
E                    [ 2.746716e+00,  4.442268e-01],
E                    [-2.387384e+00, -3.861119e-01],
E                    [-2.078187e+00, -3.361055e-01]]],
E           E           E                  [[[ 5.300545e-01, -2.128138e-02],
E                    [-2.946731e-01,  1.183095e-02],
E                    [ 3.155062e-01, -1.266739e-02],
E                    [ 3.608821e-01, -1.448921e-02],
E                    [ 8.299963e-01, -3.332387e-02]],
E           E                   [[ 4.979020e-02, -1.999048e-03],
E                    [-6.336254e-01,  2.543969e-02],
E                    [-3.603347e-01,  1.446723e-02],
E                    [-1.130619e+00,  4.539369e-02],
E                    [ 7.058588e-01, -2.833983e-02]],
E           E                   [[-9.765136e-01,  3.920646e-02],
E                    [-7.034547e-01,  2.824330e-02],
E                    [-8.960225e-01,  3.597479e-02],
E                    [ 2.066461e-01, -8.296722e-03],
E                    [-3.142470e-01,  1.261684e-02]],
E           E                   [[-5.447846e-01,  2.187279e-02],
E                    [-1.475410e+00,  5.923686e-02],
E                    [ 5.763690e-01, -2.314088e-02],
E                    [-1.794467e+00,  7.204682e-02],
E                    [-6.822498e-01,  2.739194e-02]]],
E           E           E                  [[[ 3.483035e-02,  5.116863e-02],
E                    [ 1.578827e-01,  2.319426e-01],
E                    [-2.378342e-01, -3.493979e-01],
E                    [ 3.567859e-01,  5.241477e-01],
E                    [ 1.027949e-01,  1.510142e-01]],
E           E                   [[ 8.731014e-02,  1.282657e-01],
E                    [ 1.240318e-01,  1.822129e-01],
E                    [-3.130521e-01, -4.598992e-01],
E                    [-2.779140e-01, -4.082784e-01],
E                    [ 3.416170e-01,  5.018633e-01]],
E           E                   [[ 1.688552e-01,  2.480621e-01],
E                    [ 1.079842e-01,  1.586376e-01],
E                    [ 3.249067e-01,  4.773145e-01],
E                    [ 7.240457e-01,  1.063682e+00],
E                    [-3.346429e-02, -4.916178e-02]],
E           E                   [[ 3.792953e-01,  5.572158e-01],
E                    [-5.941004e-02, -8.727821e-02],
E                    [ 2.575056e-01,  3.782968e-01],
E                    [-4.886430e-01, -7.178566e-01],
E                    [-3.724292e-01, -5.471290e-01]]]])
E       R
E           array([[[[ 2.599162e+00,  4.203630e-01],
E                    [-1.535482e-01, -2.483337e-02],
E                    [-7.007329e-01, -1.133297e-01],
E                    [-1.407709e+00, -2.276690e-01],
E                    [ 1.824711e-01,  2.951107e-02]],
E           E                   [[ 2.608622e+00,  4.218929e-01],
E                    [ 2.547940e+00,  4.120787e-01],
E                    [-7.653887e-01, -1.237864e-01],
E                    [-6.070710e-01, -9.818170e-02],
E                    [-2.362519e+00, -3.820905e-01]],
E           E                   [[ 1.020907e-01,  1.651115e-02],
E                    [ 2.661684e+00,  4.304746e-01],
E                    [ 1.208155e-02,  1.953951e-03],
E                    [-1.328791e+00, -2.149056e-01],
E                    [-1.154634e+00, -1.867391e-01]],
E           E                   [[ 1.191513e+00,  1.927036e-01],
E                    [-6.021055e-01, -9.737862e-02],
E                    [ 2.746716e+00,  4.442268e-01],
E                    [-2.387384e+00, -3.861119e-01],
E                    [-2.078187e+00, -3.361055e-01]]],
E           E           E                  [[[ 5.300545e-01, -2.128138e-02],
E                    [-2.946731e-01,  1.183095e-02],
E                    [ 3.155062e-01, -1.266739e-02],
E                    [ 3.608821e-01, -1.448921e-02],
E                    [ 8.299963e-01, -3.332387e-02]],
E           E                   [[ 4.979020e-02, -1.999048e-03],
E                    [-6.336254e-01,  2.543969e-02],
E                    [-3.603347e-01,  1.446723e-02],
E                    [-1.130619e+00,  4.539369e-02],
E                    [ 7.058588e-01, -2.833983e-02]],
E           E                   [[-9.765136e-01,  3.920646e-02],
E                    [-7.034547e-01,  2.824330e-02],
E                    [-8.960225e-01,  3.597479e-02],
E                    [ 2.066461e-01, -8.296722e-03],
E                    [-3.142470e-01,  1.261684e-02]],
E           E                   [[-5.447846e-01,  2.187279e-02],
E                    [-1.475410e+00,  5.923686e-02],
E                    [ 5.763690e-01, -2.314088e-02],
E                    [-1.794467e+00,  7.204682e-02],
E                    [-6.822498e-01,  2.739194e-02]]],
E           E           E                  [[[ 3.483035e-02,  5.116863e-02],
E                    [ 1.578827e-01,  2.319426e-01],
E                    [-2.378342e-01, -3.493979e-01],
E                    [ 3.567859e-01,  5.241477e-01],
E                    [ 1.027949e-01,  1.510142e-01]],
E           E                   [[ 8.731014e-02,  1.282657e-01],
E                    [ 1.240318e-01,  1.822129e-01],
E                    [-3.130521e-01, -4.598992e-01],
E                    [-2.779140e-01, -4.082784e-01],
E                    [ 3.416170e-01,  5.018633e-01]],
E           E                   [[ 1.688552e-01,  2.480621e-01],
E                    [ 1.079842e-01,  1.586376e-01],
E                    [ 3.249067e-01,  4.773145e-01],
E                    [ 7.240457e-01,  1.063682e+00],
E                    [-3.346429e-02, -4.916178e-02]],
E           E                   [[ 3.792953e-01,  5.572158e-01],
E                    [-5.941004e-02, -8.727821e-02],
E                    [ 2.575056e-01,  3.782968e-01],
E                    [-4.886430e-01, -7.178566e-01],
E                    [-3.724292e-01, -5.471290e-01]]]])

/usr/lib/python3/dist-packages/xarray/tests/test_variable.py:1630: 
AssertionError

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