Bug#898526: h5py: FTBFS with HDF5 1.10.2

[Gilles Filippini]

Control: severity -1 serious
Control: retitle -1 h5py: FTBFS - FAIL: test_out_of_order_offsets

On Sun, 13 May 2018 02:49:12 +0200 Gilles Filippini <pini at debian.org> wrote:
> Source: h5py
> Version: 2.7.1-2
> Severity: normal
> 
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA512
> 
> Hi,
> 
> h5py FTBFS with HDF5 1.10.2 currently in experimental. The failure occurs
> during the tests where two of them fail:
> 
> ======================================================================
> FAIL: test_out_of_order_offsets (h5py.tests.hl.test_datatype.TestOffsets)
> - ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "h5py/tests/hl/test_datatype.py", line 198, in test_out_of_order_offsets
>     self.assertArrayEqual(fd['data'], data)
>   File "h5py/tests/common.py", line 124, in assertArrayEqual
>     "Dtype mismatch (%s vs %s)%s" % (dset.dtype, arr.dtype, message)
> AssertionError: Dtype mismatch ({'names':['f1','f3','f2'], 'formats':['<f4','<f8','<i4'], 'offsets':[0,8,16], 'itemsize':20} vs {'names':['f1','f2','f3'], 'formats':['<f4','<i4','<f8'], 'offsets':[0,16,8], 'itemsize':20})
> 
> ======================================================================
> FAIL: test_out_of_order_offsets (h5py.tests.old.test_h5t.TestCompound)
> - ----------------------------------------------------------------------
> Traceback (most recent call last):
>   File "h5py/tests/old/test_h5t.py", line 61, in test_out_of_order_offsets
>     self.assertEqual(tid.dtype, expected_dtype)
> AssertionError: dtype({'names':['f1','f3','f2'], 'formats':['<f4','<f8','<i4'], 'offsets':[0,8,16], 'itemsize':20}) != dtype({'names':['f1','f2','f3'], 'formats':['<f4','<i4','<f8'], 'offsets':[0,16,8], 'itemsize':20})
> 
> - ----------------------------------------------------------------------
> Ran 447 tests in 1.206s
> 
> FAILED (failures=2, skipped=18, expected failures=6)

Actually it FTBFS with the very same failure on unstable as well. Then
raising severity to serious.

This seems tied to the recent python-numpy upgrade to 1.14.3. It builds
fine against python-numpy 1.13.3.

Thanks,

_g.

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