*To*: submit@bugs.debian.org*Subject*: Bug#959134: astroml: (autopkgtest) needs update for new version of numpy: object of type <class 'float'> cannot be safely interpreted as an integer*From*: Paul Gevers <elbrus@debian.org>*Date*: Wed, 29 Apr 2020 21:30:10 +0200*Message-id*: <[🔎] 48f7c0c7-efb4-8da3-f3f3-ca5c49925a9d@debian.org>*Reply-to*: Paul Gevers <elbrus@debian.org>, 959134@bugs.debian.org

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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