Source: astroml
Version: 0.4.post1-5
Severity: serious
X-Debbugs-CC: debian-ci@lists.debian.org, numpy@packages.debian.org
Tags: sid bullseye
User: debian-ci@lists.debian.org
Usertags: needs-update
Control: affects -1 src:numpy
Dear maintainer(s),
With a recent upload of numpy the autopkgtest of astroml fails in
testing when that autopkgtest is run with the binary packages of numpy
from unstable. It passes when run with only packages from testing. In
tabular form:
pass fail
numpy from testing 1:1.18.3-1
astroml from testing 0.4.post1-5
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]. Of course, numpy shouldn't just break your autopkgtest (or even
worse, your package), but it seems to me that the change in numpy was
intended and your package needs to update to the new situation.
If this is a real problem in your package (and not only in your
autopkgtest), the right binary package(s) from 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=numpy
https://ci.debian.net/data/autopkgtest/testing/amd64/a/astroml/5208422/log.gz
=================================== FAILURES
===================================
_______________________________ test_gaussian1d
________________________________
start = -6, stop = 10, num = 1000.0, endpoint = True, retstep = False
dtype = None, axis = 0
@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False,
dtype=None,
axis=0):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
Parameters
----------
start : array_like
The starting value of the sequence.
stop : array_like
The end value of the sequence, unless `endpoint` is set to
False.
In that case, the sequence consists of all but the last of
``num + 1``
evenly spaced samples, so that `stop` is excluded. Note
that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be
non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not
included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given,
infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
axis : int, optional
The axis in the result to store the samples. Relevant only
if start
or stop are array-like. By default (0), the samples will be
along a
new axis inserted at the beginning. Use -1 to get an axis at
the end.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
geomspace : Similar to `linspace`, but with numbers spaced
evenly on a log
scale (a geometric progression).
logspace : Similar to `geomspace`, but with the end points
specified as
logarithms.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
try:
> num = operator.index(num)
E TypeError: 'float' object cannot be interpreted as an integer
/usr/lib/python3/dist-packages/numpy/core/function_base.py:117: TypeError
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