Source: aplpy Version: 1.1.1-4 User: debian...@lists.debian.org Usertags: needs-update Control: affects -1 src:python-numpy
[X-Debbugs-CC: debian...@lists.debian.org, python-nu...@packages.debian.org ] Dear maintainers, With a recent upload of python-numpy the autopkgtest of aplpy fails in testing when that autopkgtest is run with the binary packages of python-numpy from unstable. It passes when run with only packages from testing. In tabular form: pass fail python-numpy from testing 1:1.16.0~rc1-2 aplpy from testing 1.1.1-4 all others from testing from testing I copied some of the output at the bottom of this report. I haven't checked them all, but it seems you need to accept or fix the deprecation warnings. Currently this regression is contributing to the delay of the migration of python-numpy to testing [1]. Of course, python-numpy shouldn't just break your autopkgtest (or even worse, your package), but it seems to me that the change in python-numpy was intended and your package needs to update to the new situation. If needed, please change the bug's severity. If this is a real problem in your package (and not only in your autopkgtest), the right binary package(s) from python-numpy should really add a versioned Breaks on the unfixed version of (one of your) package(s). Note: the Breaks is nice even if the issue is only in the autopkgtest as it helps the migration software to figure out the right versions to combine in the tests. More information about this bug and the reason for filing it can be found on https://wiki.debian.org/ContinuousIntegration/RegressionEmailInformation Paul [1] https://qa.debian.org/excuses.php?package=python-numpy https://ci.debian.net/data/autopkgtest/testing/amd64/a/aplpy/1592220/log.gz =================================== FAILURES =================================== _____________________________ test_beam_addremove ______________________________ def test_beam_addremove(): f = FITSFigure(HDU) > f.show_grayscale() /usr/lib/python3/dist-packages/aplpy/tests/test_beam.py:22: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ <string>:2: in show_grayscale ??? /usr/lib/python3/dist-packages/aplpy/decorators.py:25: in _auto_refresh return f(*args, **kwargs) /usr/lib/python3/dist-packages/aplpy/core.py:578: in show_grayscale interpolation=interpolation) <string>:2: in show_colorscale ??? /usr/lib/python3/dist-packages/aplpy/decorators.py:25: in _auto_refresh return f(*args, **kwargs) /usr/lib/python3/dist-packages/aplpy/core.py:670: in show_colorscale vmid=vmid, vmin=vmin, vmax=vmax) /usr/lib/python3/dist-packages/aplpy/normalize.py:51: in __init__ Normalize.__init__(self, vmin=vmin, vmax=vmax, clip=clip) /usr/lib/python3/dist-packages/matplotlib/colors.py:892: in __init__ self.vmin = _sanitize_extrema(vmin) /usr/lib/python3/dist-packages/matplotlib/colors.py:101: in _sanitize_extrema ret = np.asscalar(ex) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = 0.0 @array_function_dispatch(_asscalar_dispatcher) def asscalar(a): """ Convert an array of size 1 to its scalar equivalent. .. deprecated:: 1.16 Deprecated, use `numpy.ndarray.item()` instead. Parameters ---------- a : ndarray Input array of size 1. Returns ------- out : scalar Scalar representation of `a`. The output data type is the same type returned by the input's `item` method. Examples -------- >>> np.asscalar(np.array([24])) 24 """ # 2018-10-10, 1.16 warnings.warn('np.asscalar(a) is deprecated since NumPy v1.16, use ' > 'a.item() instead', DeprecationWarning, stacklevel=1) E DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead
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