Source: python-numpy, astropy
Control: found -1 python-numpy/1:1.16.0~rc1-2
Control: found -1 astropy/3.0.5-1
X-Debbugs-CC: debian...@lists.debian.org
User: debian...@lists.debian.org
Usertags: breaks needs-update
Dear maintainers,
With a recent upload of python-numpy the autopkgtest of astropy 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:
passfail
python-numpy from testing1:1.16.0~rc1-2
astropyfrom testing3.0.5-1
all others from testingfrom testing
I copied some of the output at the bottom of this report.
Currently this regression is contributing to the delay of the migration
of python-numpy to testing [1]. Due to the nature of this issue, I filed
this bug report against both packages. Can you please investigate the
situation and reassign the bug to the right package? If needed, please
change the bug's severity.
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/astropy/1592290/log.gz
ERRORS
ERROR at setup of TestJointFitter.test_joint_parameter
self =
def setup_class(self):
"""
Create 2 gaussian models and some data with noise.
Create a fitter for the two models keeping the amplitude
parameter
common for the two models.
"""
self.g1 = models.Gaussian1D(10, mean=14.9, stddev=.3)
self.g2 = models.Gaussian1D(10, mean=13, stddev=.4)
self.jf = JointFitter([self.g1, self.g2],
{self.g1: ['amplitude'],
self.g2: ['amplitude']}, [9.8])
self.x = np.arange(10, 20, .1)
y1 = self.g1(self.x)
y2 = self.g2(self.x)
with NumpyRNGContext(_RANDOM_SEED):
n = np.random.randn(100)
self.ny1 = y1 + 2 * n
self.ny2 = y2 + 2 * n
> self.jf(self.x, self.ny1, self.x, self.ny2)
/usr/lib/python3/dist-packages/astropy/modeling/tests/test_fitters.py:156:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
/usr/lib/python3/dist-packages/astropy/modeling/fitting.py:1215: in __call__
self.fitparams, args=args)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _
func = >
x0 = array([9.8, array([14.9]), array([0.3]), array([13.]), array([0.4])],
dtype=object)
args = (array([10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8,
10.9, 11. ,
11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 1...595e+00, -1.30030932e+00,
-2.36494839e+00,
-2.25212374e+00, 3.2674e+00, 1.37397670e+00, 4.73034211e-02]))
Dfun = None, full_output = 0, col_deriv = 0, ftol = 1.49012e-08
xtol = 1.49012e-08, gtol = 0.0, maxfev = 1200, epsfcn =
2.220446049250313e-16
factor = 100, diag = None
def leastsq(func, x0, args=(), Dfun=None, full_output=0,
col_deriv=0, ftol=1.49012e-8, xtol=1.49012e-8,
gtol=0.0, maxfev=0, epsfcn=None, factor=100, diag=None):
"""
Minimize the sum of squares of a set of equations.
::
x = arg min(sum(func(y)**2,axis=0))
y
Parameters
--
func : callable
should take at least one (possibly length N vector) argument and
returns M floating point numbers. It must not return NaNs or
fitting might fail.
x0 : ndarray
The starting estimate for the minimization.
args : tuple, optional
Any extra arguments to func are placed in this tuple.
Dfun : callable, optional
A function or method to compute the Jacobian of func with
derivatives
across the rows. If this is None, the Jacobian will be
estimated.
full_output : bool, optional
non-zero to return all optional outputs.
col_deriv : bool, optional
non-zero to specify that the Jacobian function computes
derivatives
down the columns (faster, because there is no transpose
operation).
ftol : float, optional
Relative error desired in the sum of squares.
xtol : float, optional
Relative error desired in the approximate solution.
gtol : float, optional
Orthogonality desired between the function vector and the
columns of
the Jacobian.
maxfev : int, optional
The maximum number of calls to the function. If `Dfun` is
provided
then the default `maxfev` is 100*(N+1) where N is the number
of elements
in x0,
