Source: aplpy
Version: 1.1.1-4
User: debian-ci@lists.debian.org
Usertags: needs-update
Control: affects -1 src:python-numpy
[X-Debbugs-CC: debian-ci@lists.debian.org,
python-numpy@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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