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gistfile1.txt
$ python
Python 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:35:11)
[Clang 11.1.0 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
>>> scipy.__file__
'/Users/ondrej/repos/scipy/scipy/__init__.py'
>>> scipy.test()
============================= test session starts ==============================
platform darwin -- Python 3.9.6, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
rootdir: /Users/ondrej/repos/scipy, configfile: pytest.ini
plugins: cov-2.12.1, xdist-2.3.0, forked-1.3.0
collected 46069 items / 11135 deselected / 34934 selected
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=================================== FAILURES ===================================
___________________________ test_banded_ode_solvers ____________________________
scipy/integrate/tests/test_banded_ode_solvers.py:218: in test_banded_ode_solvers
check_complex(idx, "zvode", meth, use_jac, with_jac, banded)
a = array([[-0.6+0.3j , 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0. +0.j , -0.5+0.25j, 0. +0.j , 0. +... 0. +0.j , 0. +0.j , -0.9+0.45j, 0. +0.j ],
[ 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j , -0.7+0.35j]])
a_complex = array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. +... 0.3-0.15j, -0.1+0.05j, -0.9+0.45j, -0.3+0.15j],
[ 0. +0.j , 0. +0.j , 0.1-0.05j, 0.1-0.05j, -0.7+0.35j]])
a_complex_diag = array([[-0.6+0.3j , 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0. +0.j , -0.5+0.25j, 0. +0.j , 0. +... 0. +0.j , 0. +0.j , -0.9+0.45j, 0. +0.j ],
[ 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j , -0.7+0.35j]])
a_real = array([[-0.6, 0.1, 0. , 0. , 0. ],
[ 0.2, -0.5, 0.9, 0. , 0. ],
[ 0.1, 0.1, -0.4, 0.1, 0. ],
[ 0. , 0.3, -0.1, -0.9, -0.3],
[ 0. , 0. , 0.1, 0.1, -0.7]])
a_real_diag = array([[-0.6, 0. , 0. , 0. , 0. ],
[ 0. , -0.5, 0. , 0. , 0. ],
[ 0. , 0. , -0.4, 0. , 0. ],
[ 0. , 0. , 0. , -0.9, 0. ],
[ 0. , 0. , 0. , 0. , -0.7]])
a_real_lower = array([[-0.6, 0. , 0. , 0. , 0. ],
[ 0.2, -0.5, 0. , 0. , 0. ],
[ 0.1, 0.1, -0.4, 0. , 0. ],
[ 0. , 0.3, -0.1, -0.9, 0. ],
[ 0. , 0. , 0.1, 0.1, -0.7]])
a_real_upper = array([[-0.6, 0.1, 0. , 0. , 0. ],
[ 0. , -0.5, 0.9, 0. , 0. ],
[ 0. , 0. , -0.4, 0.1, 0. ],
[ 0. , 0. , 0. , -0.9, -0.3],
[ 0. , 0. , 0. , 0. , -0.7]])
banded = False
check_complex = <function test_banded_ode_solvers.<locals>.check_complex at 0x1683b6a60>
check_real = <function test_banded_ode_solvers.<locals>.check_real at 0x168405f70>
complex_matrices = [array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. ... 0. +0.j , 0. +0.j , -0.9+0.45j, 0. +0.j ],
[ 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j , -0.7+0.35j]])]
complex_solutions = [(array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j]), array([0. , 0.25, 0.5 , 0.75, 1. ]), array([[1. +1.j ...1j, 1.02529209+0.88779126j,
1.83770279+1.05647438j, 1.2875341 +1.07346963j,
2.16211541+1.31786874j]]))]
idx = 0
meth = 'bdf'
p = [['bdf', 'adams'], [False, True], [False, True], [False, True]]
real_matrices = [array([[-0.6, 0.1, 0. , 0. , 0. ],
[ 0.2, -0.5, 0.9, 0. , 0. ],
[ 0.1, 0.1, -0.4, 0.1, 0. ],
...
[ 0. , 0. , -0.4, 0. , 0. ],
[ 0. , 0. , 0. , -0.9, 0. ],
[ 0. , 0. , 0. , 0. , -0.7]])]
real_solutions = [(array([1, 2, 3, 4, 5]), array([0. , 0.25, 0.5 , 0.75, 1. ]), array([[1. +5.66269967e-17j, 2. +1.0916....37457856, 2.22245466, 2.03662568, 2.95777682],
[0.54881164, 1.21306132, 2.01096014, 1.62627864, 2.48292652]]))]
solver = 'lsoda'
t_exact = array([0. , 0.25, 0.5 , 0.75, 1. ])
use_jac = False
with_jac = False
y0 = array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j])
y_exact = array([[1. +1.j , 2. +1.j ,
3. +1.j , 4. +1.j ,
...471j, 1.02529209+0.88779126j,
1.83770279+1.05647438j, 1.2875341 +1.07346963j,
2.16211541+1.31786874j]])
scipy/integrate/tests/test_banded_ode_solvers.py:201: in check_complex
t, y = _solve_linear_sys(a, y0,
a = array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. +... 0.3-0.15j, -0.1+0.05j, -0.9+0.45j, -0.3+0.15j],
[ 0. +0.j , 0. +0.j , 0.1-0.05j, 0.1-0.05j, -0.7+0.35j]])
banded = False
complex_matrices = [array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. ... 0. +0.j , 0. +0.j , -0.9+0.45j, 0. +0.j ],
[ 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j , -0.7+0.35j]])]
complex_solutions = [(array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j]), array([0. , 0.25, 0.5 , 0.75, 1. ]), array([[1. +1.j ...1j, 1.02529209+0.88779126j,
1.83770279+1.05647438j, 1.2875341 +1.07346963j,
2.16211541+1.31786874j]]))]
idx = 0
meth = 'bdf'
solver = 'zvode'
t_exact = array([0. , 0.25, 0.5 , 0.75, 1. ])
use_jac = False
with_jac = False
y0 = array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j])
y_exact = array([[1. +1.j , 2. +1.j ,
3. +1.j , 4. +1.j ,
...753j, 3.49020872+1.06056879j,
2.42592262+1.12330981j, 0.93474257+0.9849586j ,
2.57287977+1.38244112j]])
scipy/integrate/tests/test_banded_ode_solvers.py:103: in _solve_linear_sys
r.integrate(r.t + dt)
a = array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. +... 0.3-0.15j, -0.1+0.05j, -0.9+0.45j, -0.3+0.15j],
[ 0. +0.j , 0. +0.j , 0.1-0.05j, 0.1-0.05j, -0.7+0.35j]])
banded = False
dt = 0.25
lband = None
method = 'bdf'
r = <scipy.integrate._ode.ode object at 0x1683c5820>
solver = 'zvode'
t = [0]
t0 = 0
tend = 1.0
uband = None
use_jac = False
with_jacobian = False
y = [array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j])]
y0 = array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j])
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x1683c5b50>>
relax = False
self = <scipy.integrate._ode.ode object at 0x1683c5820>
step = False
t = 0.25
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function _linear_func at 0x11d4e4e50>, <function ode.integrate.<locals>.<lambda> at 0x16840f040>, array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j]), 0, 0.25, 1e-09, ...)
f = <function _linear_func at 0x11d4e4e50>
f_params = (array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. ...0.3-0.15j, -0.1+0.05j, -0.9+0.45j, -0.3+0.15j],
[ 0. +0.j , 0. +0.j , 0.1-0.05j, 0.1-0.05j, -0.7+0.35j]]),)
istate = -1
jac = <function ode.integrate.<locals>.<lambda> at 0x16840f040>
jac_params = (array([[-0.6+0.3j , 0.1-0.05j, 0. +0.j , 0. +0.j , 0. +0.j ],
[ 0.2-0.1j , -0.5+0.25j, 0.9-0.45j, 0. ...0.3-0.15j, -0.1+0.05j, -0.9+0.45j, -0.3+0.15j],
[ 0. +0.j , 0. +0.j , 0.1-0.05j, 0.1-0.05j, -0.7+0.35j]]),)
self = <scipy.integrate._ode.zvode object at 0x1683c5b50>
t = 0.0014324127418710484
t0 = 0
t1 = 0.25
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1.+1.j, 2.+1.j, 3.+1.j, 4.+1.j, 5.+1.j])
y1 = array([0.99906963+0.99957098j, 2.00314966+0.99949701j,
2.99921202+1.00021471j, 3.99241394+1.0020111j ,
4.99563249+1.00129052j])
______________________________ TestOde.test_zvode ______________________________
scipy/integrate/tests/test_integrate.py:107: in test_zvode
self._do_problem(problem, 'zvode', 'adams')
problem = <scipy.integrate.tests.test_integrate.SimpleOscillator object at 0x16842cfa0>
problem_cls = <class 'scipy.integrate.tests.test_integrate.SimpleOscillator'>
self = <scipy.integrate.tests.test_integrate.TestOde object at 0x168410580>
scipy/integrate/tests/test_integrate.py:80: in _do_problem
z = ig.integrate(problem.stop_t)
f = <function TestODEClass._do_problem.<locals>.<lambda> at 0x168405e50>
ig = <scipy.integrate._ode.ode object at 0x16842cd00>
integrator = 'zvode'
integrator_params = {}
jac = None
method = 'adams'
problem = <scipy.integrate.tests.test_integrate.SimpleOscillator object at 0x16842cfa0>
self = <scipy.integrate.tests.test_integrate.TestOde object at 0x168410580>
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x16842c370>>
relax = False
self = <scipy.integrate._ode.ode object at 0x16842cd00>
step = False
t = 1.09
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function TestODEClass._do_problem.<locals>.<lambda> at 0x168405e50>, <function ode.integrate.<locals>.<lambda> at 0x1684598b0>, array([1. +0.j, 0.1+0.j]), 0.0, 1.09, 1.0000000000000002e-06, ...)
f = <function TestODEClass._do_problem.<locals>.<lambda> at 0x168405e50>
f_params = ()
istate = -1
jac = <function ode.integrate.<locals>.<lambda> at 0x1684598b0>
jac_params = ()
self = <scipy.integrate._ode.zvode object at 0x16842c370>
t = 0.03249684834867783
t0 = 0.0
t1 = 1.09
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1. +0.j, 0.1+0.j])
y1 = array([ 1.0011345 -0.00211519j, -0.03010698-0.00011959j])
__________________________ TestOde.test_concurrent_ok __________________________
scipy/integrate/tests/test_integrate.py:172: in test_concurrent_ok
assert_allclose(r.y, 0.1)
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 0.02059729
E Max relative difference: 0.20597291
E x: array([0.1-0.020597j])
E y: array(0.1)
f = <function TestOde.test_concurrent_ok.<locals>.<lambda> at 0x168459ee0>
k = 0
r = <scipy.integrate._ode.ode object at 0x168471dc0>
r2 = <scipy.integrate._ode.ode object at 0x168471fd0>
self = <scipy.integrate.tests.test_integrate.TestOde object at 0x168471730>
sol = 'zvode'
__________________ TestZVODECheckParameterUse.test_no_params ___________________
scipy/integrate/tests/test_integrate.py:602: in test_no_params
self._check_solver(solver)
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x16846f310>
solver = <scipy.integrate._ode.ode object at 0x16846fdc0>
scipy/integrate/tests/test_integrate.py:597: in _check_solver
solver.integrate(pi)
ic = [1.0, 0.0]
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x16846f310>
solver = <scipy.integrate._ode.ode object at 0x16846fdc0>
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x16846fd90>>
relax = False
self = <scipy.integrate._ode.ode object at 0x16846fdc0>
step = False
t = 3.141592653589793
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function f at 0x11d640ee0>, <function jac at 0x11d625280>, array([1.+0.j, 0.+0.j]), 0.0, 3.141592653589793, 1e-07, ...)
f = <function f at 0x11d640ee0>
f_params = ()
istate = -1
jac = <function jac at 0x11d625280>
jac_params = ()
self = <scipy.integrate._ode.zvode object at 0x16846fd90>
t = 0.012148892003676552
t0 = 0.0
t1 = 3.141592653589793
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1.+0.j, 0.+0.j])
y1 = array([ 0.9999262 -7.37968809e-05j, -0.01214859+2.98852130e-07j])
_______________ TestZVODECheckParameterUse.test_one_scalar_param _______________
scipy/integrate/tests/test_integrate.py:610: in test_one_scalar_param
self._check_solver(solver)
omega = 1.0
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684c2f10>
solver = <scipy.integrate._ode.ode object at 0x1684c2c10>
scipy/integrate/tests/test_integrate.py:597: in _check_solver
solver.integrate(pi)
ic = [1.0, 0.0]
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684c2f10>
solver = <scipy.integrate._ode.ode object at 0x1684c2c10>
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x1684c2b50>>
relax = False
self = <scipy.integrate._ode.ode object at 0x1684c2c10>
step = False
t = 3.141592653589793
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function f1 at 0x11d625310>, <function jac1 at 0x11d6253a0>, array([1.+0.j, 0.+0.j]), 0.0, 3.141592653589793, 1e-07, ...)
f = <function f1 at 0x11d625310>
f_params = (1.0,)
istate = -1
jac = <function jac1 at 0x11d6253a0>
jac_params = (1.0,)
self = <scipy.integrate._ode.zvode object at 0x1684c2b50>
t = 0.012148892003676552
t0 = 0.0
t1 = 3.141592653589793
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1.+0.j, 0.+0.j])
y1 = array([ 0.9999262 -7.37968809e-05j, -0.01214859+2.98852130e-07j])
______________ TestZVODECheckParameterUse.test_two_scalar_params _______________
scipy/integrate/tests/test_integrate.py:619: in test_two_scalar_params
self._check_solver(solver)
omega1 = 1.0
omega2 = 1.0
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684fa0a0>
solver = <scipy.integrate._ode.ode object at 0x1684faf10>
scipy/integrate/tests/test_integrate.py:597: in _check_solver
solver.integrate(pi)
ic = [1.0, 0.0]
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684fa0a0>
solver = <scipy.integrate._ode.ode object at 0x1684faf10>
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x1684fae50>>
relax = False
self = <scipy.integrate._ode.ode object at 0x1684faf10>
step = False
t = 3.141592653589793
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function f2 at 0x11d625430>, <function jac2 at 0x11d6254c0>, array([1.+0.j, 0.+0.j]), 0.0, 3.141592653589793, 1e-07, ...)
f = <function f2 at 0x11d625430>
f_params = (1.0, 1.0)
istate = -1
jac = <function jac2 at 0x11d6254c0>
jac_params = (1.0, 1.0)
self = <scipy.integrate._ode.zvode object at 0x1684fae50>
t = 0.012148892003676552
t0 = 0.0
t1 = 3.141592653589793
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1.+0.j, 0.+0.j])
y1 = array([ 0.9999262 -7.37968809e-05j, -0.01214859+2.98852130e-07j])
_________________ TestZVODECheckParameterUse.test_vector_param _________________
scipy/integrate/tests/test_integrate.py:627: in test_vector_param
self._check_solver(solver)
omega = [1.0, 1.0]
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684c1df0>
solver = <scipy.integrate._ode.ode object at 0x1684c1b80>
scipy/integrate/tests/test_integrate.py:597: in _check_solver
solver.integrate(pi)
ic = [1.0, 0.0]
self = <scipy.integrate.tests.test_integrate.TestZVODECheckParameterUse object at 0x1684c1df0>
solver = <scipy.integrate._ode.ode object at 0x1684c1b80>
scipy/integrate/_ode.py:433: in integrate
self._y, self.t = mth(self.f, self.jac or (lambda: None),
mth = <bound method vode.run of <scipy.integrate._ode.zvode object at 0x1684c1ee0>>
relax = False
self = <scipy.integrate._ode.ode object at 0x1684c1b80>
step = False
t = 3.141592653589793
scipy/integrate/_ode.py:1013: in run
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
E UserWarning: zvode: Excess work done on this call. (Perhaps wrong MF.)
args = (<function fv at 0x11d625550>, <function jacv at 0x11d6255e0>, array([1.+0.j, 0.+0.j]), 0.0, 3.141592653589793, 1e-07, ...)
f = <function fv at 0x11d625550>
f_params = ([1.0, 1.0],)
istate = -1
jac = <function jacv at 0x11d6255e0>
jac_params = ([1.0, 1.0],)
self = <scipy.integrate._ode.zvode object at 0x1684c1ee0>
t = 0.012148892003676552
t0 = 0.0
t1 = 3.141592653589793
unexpected_istate_msg = 'Unexpected istate=-1'
y0 = array([1.+0.j, 0.+0.j])
y1 = array([ 0.9999262 -7.37968809e-05j, -0.01214859+2.98852130e-07j])
_________________________ TestBarycentric.test_append __________________________
scipy/interpolate/tests/test_polyint.py:318: in test_append
P.add_xi(self.xs[3:], self.ys[3:])
P = <scipy.interpolate.polyint.BarycentricInterpolator object at 0x14acc24f0>
self = <scipy.interpolate.tests.test_polyint.TestBarycentric object at 0x14acbca60>
scipy/interpolate/polyint.py:617: in add_xi
self.wi **= -1
E RuntimeWarning: divide by zero encountered in reciprocal
old_n = 3
self = <scipy.interpolate.polyint.BarycentricInterpolator object at 0x14acc24f0>
xi = array([0.5, 1. ])
yi = array([[-1.],
[ 3.]])
____________________ TestSolve.test_simple_her_actuallysym _____________________
scipy/linalg/tests/test_basic.py:579: in test_simple_her_actuallysym
assert_array_almost_equal(dot(a, x), b)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 1 / 2 (50%)
E Max absolute difference: 1.03161697
E Max relative difference: 0.
E x: array([0. +1.j , 0.748905+0.709489j])
E y: array([0.+1.j, 0.+0.j])
a = [[2, 3], [3, -5]]
b = [1j, 0]
lower = 1
self = <scipy.linalg.tests.test_basic.TestSolve object at 0x14ae94100>
x = array([ 0.11824818+0.37518248j, -0.07883212+0.08321168j])
____________________ TestLstsq.test_simple_overdet_complex _____________________
scipy/linalg/tests/test_basic.py:1059: in test_simple_overdet_complex
assert_allclose(abs((dot(a, x) - b)**2).sum(axis=0),
E AssertionError:
E Not equal to tolerance rtol=2.98023e-06, atol=2.98023e-06
E driver: gelsd
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 6.891783
E Max relative difference: 0.96745825
E x: array(14.015381, dtype=float32)
E y: array(7.123598, dtype=float32)
a = array([[1.+2.j, 2.+0.j],
[4.+0.j, 5.+0.j],
[3.+0.j, 4.+0.j]], dtype=complex64)
a1 = array([[1.+2.j, 2.+0.j],
[4.+0.j, 5.+0.j],
[3.+0.j, 4.+0.j]], dtype=complex64)
b = array([1.+0.j, 2.+4.j, 3.+0.j], dtype=complex64)
b1 = array([1.+0.j, 2.+4.j, 3.+0.j], dtype=complex64)
dtype = <class 'numpy.complex64'>
lapack_driver = 'gelsd'
out = (array([-0.48314652+0.02559727j, 0.94566095+0.08548044j], dtype=complex64), 7.1235976, 2, array([8.517418 , 1.5663979], dtype=float32))
overwrite = True
r = 2
residuals = 7.1235976
self = <scipy.linalg.tests.test_basic.TestLstsq object at 0x14ae821f0>
x = array([-0.48314652+0.02559727j, 0.94566095+0.08548044j], dtype=complex64)
_____________________ TestLstsq.test_random_complex_exact ______________________
scipy/linalg/tests/test_basic.py:1149: in test_random_complex_exact
assert_allclose(
E AssertionError:
E Not equal to tolerance rtol=4.76837e-05, atol=4.76837e-05
E driver: gelsd
E Mismatched elements: 60 / 60 (100%)
E Max absolute difference: 8.837243
E Max relative difference: 338.7324
E x: array([[ 1.023868-0.516378j, 0.93382 -0.630808j, 1.028059-0.395364j],
E [ 1.487115+0.568498j, 1.259903+0.727326j, 1.122598+0.464395j],
E [-2.461137+8.200006j, -2.912361+8.148932j, -2.881072+8.141109j],...
E y: array([[0.616139+0.j, 0.219291+0.j, 0.872524+0.j],
E [0.647865+0.j, 0.473003+0.j, 0.23419 +0.j],
E [0.833822+0.j, 0.098516+0.j, 0.205027+0.j],...
a = array([[5.8303890e+00+1.42998781e+01j, 6.2210876e-01+7.24091470e-01j,
4.3772775e-01+1.86764393e-02j, 7.8535861...505e-01+3.70635867e-01j,
2.2599553e-01+7.59584546e-01j, 8.0830746e+00+6.12367821e+00j]],
dtype=complex64)
a1 = array([[5.8303890e+00+1.42998781e+01j, 6.2210876e-01+7.24091470e-01j,
4.3772775e-01+1.86764393e-02j, 7.8535861...505e-01+3.70635867e-01j,
2.2599553e-01+7.59584546e-01j, 8.0830746e+00+6.12367821e+00j]],
dtype=complex64)
b = array([[0.61613923+0.j, 0.21929118+0.j, 0.87252367+0.j],
[0.64786494+0.j, 0.47300294+0.j, 0.23419032+0.j],
...52694 +0.j, 0.1732783 +0.j, 0.57995284+0.j],
[0.7273775 +0.j, 0.73040235+0.j, 0.4039958 +0.j]], dtype=complex64)
b1 = array([[0.61613923+0.j, 0.21929118+0.j, 0.87252367+0.j],
[0.64786494+0.j, 0.47300294+0.j, 0.23419032+0.j],
...52694 +0.j, 0.1732783 +0.j, 0.57995284+0.j],
[0.7273775 +0.j, 0.73040235+0.j, 0.4039958 +0.j]], dtype=complex64)
dtype = <class 'numpy.complex64'>
i = 0
lapack_driver = 'gelsd'
n = 20
out = (array([[ 0.01294096+0.01694867j, -0.00192093+0.01694867j,
0.01272957+0.01694867j],
[ 0.08152949-0.093...519 , 10.6915245, 8.947641 ,
8.566785 , 8.184578 , 7.360665 , 4.6518607, 3.4158041],
dtype=float32))
overwrite = True
r = 20
self = <scipy.linalg.tests.test_basic.TestLstsq object at 0x14ae82a30>
x = array([[ 0.01294096+0.01694867j, -0.00192093+0.01694867j,
0.01272957+0.01694867j],
[ 0.08152949-0.0939...181478j],
[ 0.02685548-0.3564865j , 0.01576587-0.37385136j,
0.00933838-0.35475665j]], dtype=complex64)
____________________ TestLstsq.test_random_complex_overdet _____________________
scipy/linalg/tests/test_basic.py:1211: in test_random_complex_overdet
assert_allclose(
E AssertionError:
E Not equal to tolerance rtol=2.98023e-06, atol=2.98023e-06
E driver: gelsd
E Mismatched elements: 45 / 45 (100%)
E Max absolute difference: 0.7172899
E Max relative difference: 149.43454
E x: array([[ 0.062104+6.638021e-01j, 0.057934+6.638021e-01j,
E 0.002216+6.638021e-01j],
E [-0.058405-3.299081e-02j, -0.058758-1.167682e-02j,...
E y: array([[ 0.062104-0.053488j, 0.057934-0.045925j, 0.002216+0.00382j ],
E [ 0.033628-0.025135j, -0.009612+0.005401j, 0.016862-0.020744j],
E [-0.004304+0.002065j, 0.0038 -0.002958j, 0.004777-0.00959j ],...
a = array([[5.8303890e+00+5.71755362e+00j, 6.2210876e-01+9.86747205e-01j,
4.3772775e-01+4.31800842e-01j, 7.8535861...8.2215977e-01+5.79493463e-01j, 6.2796509e-01+6.17560923e-01j,
1.1792306e-01+1.77117482e-01j]], dtype=complex64)
a1 = array([[5.8303890e+00+5.71755362e+00j, 6.2210876e-01+9.86747205e-01j,
4.3772775e-01+4.31800842e-01j, 7.8535861...8.2215977e-01+5.79493463e-01j, 6.2796509e-01+6.17560923e-01j,
1.1792306e-01+1.77117482e-01j]], dtype=complex64)
b = array([[0.8649818 +0.j, 0.7102707 +0.j, 0.17063169+0.j],
[0.85508263+0.j, 0.01985662+0.j, 0.64168376+0.j],
...500082+0.j, 0.02463633+0.j, 0.6935125 +0.j],
[0.42121193+0.j, 0.85834944+0.j, 0.57507354+0.j]], dtype=complex64)
b1 = array([[0.8649818 +0.j, 0.7102707 +0.j, 0.17063169+0.j],
[0.85508263+0.j, 0.01985662+0.j, 0.64168376+0.j],
...500082+0.j, 0.02463633+0.j, 0.6935125 +0.j],
[0.42121193+0.j, 0.85834944+0.j, 0.57507354+0.j]], dtype=complex64)
dtype = <class 'numpy.complex64'>
i = 0
lapack_driver = 'gelsd'
m = 15
n = 20
out = (array([[ 0.06210371+6.6380215e-01j, 0.05793425+6.6380215e-01j,
0.00221577+6.6380215e-01j],
[-0.05840...38003 , 15.058733, 13.859146, 11.219447, 9.0597 , 8.560572,
8.029268, 7.581321, 6.87882 ], dtype=float32))
overwrite = True
r = 15
self = <scipy.linalg.tests.test_basic.TestLstsq object at 0x149481f10>
x = array([[ 0.06210371+6.6380215e-01j, 0.05793425+6.6380215e-01j,
0.00221577+6.6380215e-01j],
[-0.058405...
[ 0.02510021-1.1387596e-01j, 0.0231653 -1.1449404e-01j,
0.02585518-9.7305194e-02j]], dtype=complex64)
________________________ TestFBLAS2Simple.test_syr_her _________________________
scipy/linalg/tests/test_blas.py:317: in test_syr_her
assert_allclose(f(1.0, z, lower=True), resz.T, rtol=rtol)
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0
E
E Mismatched elements: 10 / 16 (62.5%)
E Max absolute difference: 53.15072906
E Max relative difference: 2.23606798
E x: array([[ -1. +3.j, 0. +0.j, 0. +0.j, 0. +0.j],
E [ -9. +13.j, -3. +21.j, 0. +0.j, 0. +0.j],
E [-25. +31.j, -15. +43.j, -5. +55.j, 0. +0.j],
E [-49. +57.j, -35. +73.j, -21. +89.j, -7.+105.j]], dtype=complex64)
E y: array([[ -3. +4.j, 0. +0.j, 0. +0.j, 0. +0.j],
E [ -5. +10.j, -7. +24.j, 0. +0.j, 0. +0.j],
E [ -7. +16.j, -9. +38.j, -11. +60.j, 0. +0.j],
E [ -9. +22.j, -11. +52.j, -13. +82.j, -15.+112.j]])
a = array([[ 1., 2., 3., 4.],
[ 0., 4., 6., 8.],
[ 0., 0., 9., 12.],
[ 0., 0., 0., 16.]])
b = array([[ 3., 6., 9., 12.],
[ 0., 12., 18., 24.],
[ 0., 0., 27., 36.],
[ 0., 0., 0., 48.]])
f = <fortran object>
p = 'c'
rehz = array([[ 5.+0.j, 11.+2.j, 17.+4.j, 23.+6.j],
[ 0.+0.j, 25.+0.j, 39.+2.j, 53.+4.j],
[ 0.+0.j, 0.+0.j, 61.+0.j, 83.+2.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, 113.+0.j]])
rehz_reverse = array([[113.+0.j, 83.-2.j, 53.-4.j, 23.-6.j],
[ 0.+0.j, 61.+0.j, 39.-2.j, 17.-4.j],
[ 0.+0.j, 0.+0.j, 25.+0.j, 11.-2.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, 5.+0.j]])
resx = array([[ 1., 2., 3., 4.],
[ 0., 4., 6., 8.],
[ 0., 0., 9., 12.],
[ 0., 0., 0., 16.]])
resx_reverse = array([[16., 12., 8., 4.],
[ 0., 9., 6., 3.],
[ 0., 0., 4., 2.],
[ 0., 0., 0., 1.]])
resz = array([[ -3. +4.j, -5. +10.j, -7. +16.j, -9. +22.j],
[ 0. +0.j, -7. +24.j, -9. +38.j, -11. +52.j],
[ 0. +0.j, 0. +0.j, -11. +60.j, -13. +82.j],
[ 0. +0.j, 0. +0.j, 0. +0.j, -15.+112.j]])
resz_reverse = array([[-15.+112.j, -13. +82.j, -11. +52.j, -9. +22.j],
[ 0. +0.j, -11. +60.j, -9. +38.j, -7. +16.j],
[ 0. +0.j, 0. +0.j, -7. +24.j, -5. +10.j],
[ 0. +0.j, 0. +0.j, 0. +0.j, -3. +4.j]])
rtol = 1e-07
self = <scipy.linalg.tests.test_blas.TestFBLAS2Simple object at 0x14949abb0>
w = array([0.+0.j, 1.+2.j, 0.+0.j, 0.+0.j, 3.+4.j, 0.+0.j, 0.+0.j, 5.+6.j,
0.+0.j, 0.+0.j, 7.+8.j, 0.+0.j])
x = array([1., 2., 3., 4.])
y = array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,
6.5, 7. , 7.5, 8. ])
z = array([1.+2.j, 3.+4.j, 5.+6.j, 7.+8.j])
__________________________ TestEig.test_make_eigvals ___________________________
scipy/linalg/tests/test_decomp.py:351: in test_make_eigvals
self._check_gen_eig(A, B)
A = array([[0.36488598+0.56809865j, 0.61539618+0.86912739j,
0.07538124+0.43617342j],
[0.36882401+0.80214764... 0.65137814+0.70426097j],
[0.39720258+0.70458131j, 0.78873014+0.21879211j,
0.31683612+0.92486763j]])
B = array([[0.44214076+0.56143308j, 0.90931596+0.32966845j,
0.05980922+0.50296683j],
[0.18428708+0.11189432... 0.67488094+0.56594464j],
[0.59462478+0.00676406j, 0.53331016+0.61744171j,
0.04332406+0.91212289j]])
self = <scipy.linalg.tests.test_decomp.TestEig object at 0x149468bb0>
scipy/linalg/tests/test_decomp.py:235: in _check_gen_eig
assert_allclose(val1[:, i], val2[:, i],
E AssertionError:
E Not equal to tolerance rtol=1e-13, atol=1e-13
E
E array([[0.36488598+0.56809865j, 0.61539618+0.86912739j,
E 0.07538124+0.43617342j],
E [0.36882401+0.80214764j, 0.9331401 +0.14376682j,
E 0.65137814+0.70426097j],
E [0.39720258+0.70458131j, 0.78873014+0.21879211j,
E 0.31683612+0.92486763j]])
E array([[0.44214076+0.56143308j, 0.90931596+0.32966845j,
E 0.05980922+0.50296683j],
E [0.18428708+0.11189432j, 0.04735528+0.60719371j,
E 0.67488094+0.56594464j],
E [0.59462478+0.00676406j, 0.53331016+0.61744171j,
E 0.04332406+0.91212289j]])
E Mismatched elements: 3 / 3 (100%)
E Max absolute difference: 0.90239291
E Max relative difference: 0.30065
E x: array([ 0.666376-2.307768j, -0.150503-2.942555j, 0.3752 -2.735541j])
E y: array([0.087702-3.000192j, 0.331766-2.40094j , 0.403462-2.903364j])
A = array([[0.36488598+0.56809865j, 0.61539618+0.86912739j,
0.07538124+0.43617342j],
[0.36882401+0.80214764... 0.65137814+0.70426097j],
[0.39720258+0.70458131j, 0.78873014+0.21879211j,
0.31683612+0.92486763j]])
B = array([[0.44214076+0.56143308j, 0.90931596+0.32966845j,
0.05980922+0.50296683j],
[0.18428708+0.11189432... 0.67488094+0.56594464j],
[0.59462478+0.00676406j, 0.53331016+0.61744171j,
0.04332406+0.91212289j]])
B0 = array([[0.44214076+0.56143308j, 0.90931596+0.32966845j,
0.05980922+0.50296683j],
[0.18428708+0.11189432... 0.67488094+0.56594464j],
[0.59462478+0.00676406j, 0.53331016+0.61744171j,
0.04332406+0.91212289j]])
i = 0
msg = '\narray([[0.36488598+0.56809865j, 0.61539618+0.86912739j,\n 0.07538124+0.43617342j],\n [0.36882401+0.802... 0.67488094+0.56594464j],\n [0.59462478+0.00676406j, 0.53331016+0.61744171j,\n 0.04332406+0.91212289j]])'
self = <scipy.linalg.tests.test_decomp.TestEig object at 0x149468bb0>
val1 = array([[ 0.66637578-2.30776823j, -0.01908767+0.00947095j,
0.09765692-0.15772331j],
[-0.15050348-2.9425... -0.55883967-0.15461035j],
[ 0.37520026-2.735541j , 0.14129369-0.05101752j,
-0.1945099 -0.13107334j]])
val2 = array([[ 8.77023558e-02-3.00019153e+00j, 4.65650672e-03-2.64720113e-03j,
3.87012365e-04-2.61067454e-04j],
...],
[ 4.03462148e-01-2.90336431e+00j, -1.57577078e-02+5.65717628e-03j,
-5.46545800e-04+2.63856650e-03j]])
vr = array([[-0.43040566-0.43040566j, -0.59839128-0.59839128j,
0.54544764+0.54544764j],
[-0.36063072-0.3606... -0.32737811-0.32737811j],
[-0.42976326-0.42976326j, 0.20434679+0.20434679j,
-0.3087239 -0.3087239j ]])
w = array([[ 2.67726000e+00+0.00000000e+00j, 2.83445479e-02-1.01134379e-15j,
-2.49703427e-03+2.88834243e-03j],
[ 1.99500344e+00+0.00000000e+00j, 5.88559241e-01+0.00000000e+00j,
1.16801448e+00+0.00000000e+00j]])
wt = array([[ 2.67726000e+00+0.00000000e+00j, 2.83445479e-02-1.01134379e-15j,
-2.49703427e-03+2.88834243e-03j],
[ 1.99500344e+00+0.00000000e+00j, 5.88559241e-01+0.00000000e+00j,
1.16801448e+00+0.00000000e+00j]])
__________________________ TestEigBanded.test_zhbevd ___________________________
scipy/linalg/tests/test_decomp.py:502: in test_zhbevd
assert_array_almost_equal(abs(evec_), abs(self.evec_herm_lin))
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 1.58968705
E Max relative difference: 6.82730566
E x: array([[0.359877, 0.031817, 0.638579, 0.030717, 0.772035, 0.227172,
E 0.696111, 0.244334, 0.462393, 1.014976],
E [0.159978, 0.440427, 0.222026, 0.754656, 0.076127, 0.741219,...
E y: array([[0.254471, 0.022498, 0.451543, 0.02172 , 0.545911, 0.160635,
E 0.492225, 0.17277 , 0.326962, 0.165873],
E [0.113121, 0.311429, 0.156995, 0.533622, 0.05383 , 0.524121,...
evec = array([[-0.25447137-0.2544714j , -0.0224981 -0.0224981j ,
-0.45154314-0.45154325j, 0.0217204 +0.0217204j ,
...,
-0.4922247 +0.4922247j , 0.17277077-0.17276965j,
-0.32696153+0.32696153j, -0.14312147+0.16587337j]])
evec_ = array([[-0.25447137-0.2544714j , -0.0224981 -0.0224981j ,
-0.45154314-0.45154325j, 0.0217204 +0.0217204j ,
...,
-0.4922247 +0.4922247j , 0.17277077-0.17276965j,
-0.32696153+0.32696153j, -0.14312147+0.16587337j]])
info = 0
self = <scipy.linalg.tests.test_decomp.TestEigBanded object at 0x14ae13af0>
w = array([-4.6749823 , -4.49981592, -3.43214201, -2.84350424, -1.66090423,
-0.86217766, 0.43023792, 0.52994106, 2.64944971, 4.36389767])
__________________________ TestEigBanded.test_zhbevx ___________________________
scipy/linalg/tests/test_decomp.py:513: in test_zhbevx
assert_array_almost_equal(abs(evec_), abs(self.evec_herm_lin))
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 0.84910218
E Max relative difference: 5.11897831
E x: array([[3.598769e-01, 3.181712e-02, 6.385785e-01, 3.071729e-02,
E 7.720354e-01, 2.271717e-01, 6.961108e-01, 2.443340e-01,
E 4.623934e-01, 1.014976e+00],...
E y: array([[0.254471, 0.022498, 0.451543, 0.02172 , 0.545911, 0.160635,
E 0.492225, 0.17277 , 0.326962, 0.165873],
E [0.113121, 0.311429, 0.156995, 0.533622, 0.05383 , 0.524121,...
N = 10
evec = array([[-0.25447137-2.54471398e-01j, -0.0224981 -2.24981016e-02j,
-0.45154314-4.51543251e-01j, 0.0217204 +2.1... +2.56791415e-30j, 0. +1.18038862e-11j,
0. +0.00000000e+00j, 0. +9.99983105e-01j]])
evec_ = array([[-0.25447137-2.54471398e-01j, -0.0224981 -2.24981016e-02j,
-0.45154314-4.51543251e-01j, 0.0217204 +2.1... +2.56791415e-30j, 0. +1.18038862e-11j,
0. +0.00000000e+00j, 0. +9.99983105e-01j]])
ifail = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)
info = 0
num = 10
self = <scipy.linalg.tests.test_decomp.TestEigBanded object at 0x14ae13d90>
w = array([-4.6749823 , -4.49981592, -3.43214201, -2.84350424, -1.66090423,
-0.86217766, 0.43023792, 0.52994106, 2.64944971, 4.36389767])
________________________ TestEigBanded.test_eig_banded _________________________
scipy/linalg/tests/test_decomp.py:568: in test_eig_banded
assert_array_almost_equal(abs(evec_herm_), abs(self.evec_herm_lin))
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 1.58968705
E Max relative difference: 6.82730566
E x: array([[0.359877, 0.031817, 0.638579, 0.030717, 0.772035, 0.227172,
E 0.696111, 0.244334, 0.462393, 1.014976],
E [0.159978, 0.440427, 0.222026, 0.754656, 0.076127, 0.741219,...
E y: array([[0.254471, 0.022498, 0.451543, 0.02172 , 0.545911, 0.160635,
E 0.492225, 0.17277 , 0.326962, 0.165873],
E [0.113121, 0.311429, 0.156995, 0.533622, 0.05383 , 0.524121,...
evec_herm = array([[-0.25447137-0.2544714j , -0.0224981 -0.0224981j ,
-0.45154314-0.45154325j, 0.0217204 +0.0217204j ,
...,
-0.4922247 +0.4922247j , 0.17277077-0.17276965j,
-0.32696153+0.32696153j, -0.14312147+0.16587337j]])
evec_herm_ = array([[-0.25447137-0.2544714j , -0.0224981 -0.0224981j ,
-0.45154314-0.45154325j, 0.0217204 +0.0217204j ,
...,
-0.4922247 +0.4922247j , 0.17277077-0.17276965j,
-0.32696153+0.32696153j, -0.14312147+0.16587337j]])
evec_sym = array([[ 0.16587337, 0.32696153, 0.17276965, -0.4922247 , 0.16063466,
0.54591149, 0.0217204 , 0.45154314...6153, -0.17276965, -0.4922247 , 0.16063466,
-0.54591149, -0.0217204 , 0.45154314, -0.0224981 , -0.25447137]])
evec_sym_ = array([[ 0.16587337, 0.32696153, 0.17276965, -0.4922247 , 0.16063466,
0.54591149, 0.0217204 , 0.45154314...6153, -0.17276965, -0.4922247 , 0.16063466,
-0.54591149, -0.0217204 , 0.45154314, -0.0224981 , -0.25447137]])
self = <scipy.linalg.tests.test_decomp.TestEigBanded object at 0x149453dc0>
w_herm = array([-4.6749823 , -4.49981592, -3.43214201, -2.84350424, -1.66090423,
-0.86217766, 0.43023792, 0.52994106, 2.64944971, 4.36389767])
w_sym = array([-4.36389767, -2.64944971, -0.52994106, -0.43023792, 0.86217766,
1.66090423, 2.84350424, 3.43214201, 4.49981592, 4.6749823 ])
_____________ TestEigh.test_various_drivers_standard[ev-complex64] _____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.000119209
E
E Mismatched elements: 400 / 400 (100%)
E Max absolute difference: 9.878377
E Max relative difference: inf
E x: array([[-1.024111e-01-1.129053e-01j, -9.103125e-02-3.533325e-01j,
E 1.826477e-01-3.730512e-02j, 2.729376e-01+2.975390e-01j,
E -1.050508e-01+2.498122e-01j, 3.369594e-01-3.583230e-02j,...
E y: array(0.)
a = array([[0.6445826 +0.00000000e+00j, 0.32437274+9.31576341e-02j,
0.61983734+6.31575584e-02j, 0.32045603+1.76963...j, 0.41159117-4.80479747e-02j,
0.68176913+2.78819323e-01j, 0.0289948 +0.00000000e+00j]],
dtype=complex64)
driver = 'ev'
dtype_ = <class 'numpy.complex64'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14942eee0>
v = array([[-0.0739059 -7.39059001e-02j, -0.30122298-3.01222980e-01j,
0.14191413+1.41914129e-01j, 0.17931043+1.7...-0.05785928-5.78592792e-02j,
0.01692364+9.84401166e-01j, 0.21668212+2.16682121e-01j]],
dtype=complex64)
w = array([-2.3175747 , -1.6421384 , -1.5190917 , -1.3529652 , -1.0369561 ,
-0.91279966, -0.8082594 , -0.59898156, ... 0.9107243 , 1.1070666 ,
1.2276475 , 1.6616762 , 2.0099874 , 2.3414361 , 10.686417 ],
dtype=float32)
____________ TestEigh.test_various_drivers_standard[ev-complex128] _____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-13
E
E Mismatched elements: 400 / 400 (100%)
E Max absolute difference: 4.96917367
E Max relative difference: inf
E x: array([[-2.983289e-02-2.611873e-02j, -2.292372e-01-1.617507e-01j,
E 2.889755e-01+2.519307e-01j, 6.053284e-02+5.959726e-01j,
E 4.928402e-02-3.507084e-02j, -8.426184e-02-3.250729e-01j,...
E y: array(0.)
a = array([[0.37492896+0.j , 0.45935991-0.18855491j,
0.82462976-0.20893167j, 0.20393551+0.34284233j,
...255j,
0.56518081-0.23093414j, 0.29804915+0.15078988j,
0.40743044-0.35303982j, 0.15557592+0.j ]])
driver = 'ev'
dtype_ = <class 'numpy.complex128'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14ae38df0>
v = array([[ 9.12043145e-02+9.12043145e-02j, -2.28230214e-01-2.28230214e-01j,
3.26174267e-01+3.26174267e-01j, 2....0736e-01j, -2.91865337e-02-2.91865732e-02j,
-6.23002395e-02+6.33701440e-01j, 1.82498824e-01+1.82498824e-01j]])
w = array([-2.32053699, -1.92537705, -1.69232193, -1.33074409, -1.0528957 ,
-0.76211562, -0.54819113, -0.24444331, ...028108, 0.68326401, 0.79165356, 0.93821424,
1.32300894, 1.51791058, 1.76330908, 2.3285108 , 10.21982614])
____________ TestEigh.test_various_drivers_standard[evd-complex64] _____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.000119209
E
E Mismatched elements: 20 / 400 (5%)
E Max absolute difference: 0.17769675
E Max relative difference: inf
E x: array([[-1.341105e-07+1.483355e-02j, 3.576279e-07+2.980232e-08j,
E 2.980232e-07+2.980232e-07j, 2.980232e-08+8.940697e-08j,
E -7.450581e-08-3.576279e-07j, -8.195639e-08+2.980232e-08j,...
E y: array(0.)
a = array([[0.93991697+0.00000000e+00j, 0.39091605+1.65591612e-01j,
0.26158276-4.73397635e-02j, 0.37927064-1.44070...j, 0.16194409+1.27978563e-01j,
0.4030868 -2.19227582e-01j, 0.38564578+0.00000000e+00j]],
dtype=complex64)
driver = 'evd'
dtype_ = <class 'numpy.complex64'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x149436fd0>
v = array([[-1.07141308e-01-0.10309255j, -1.86949298e-01-0.1869493j ,
-3.60869735e-01-0.36086974j, -1.99490339e-01... 6.41181841e-02-0.40363362j,
-3.97245467e-01+0.00978629j, 1.36577025e-01+0.17734522j]],
dtype=complex64)
w = array([-2.2366235e+00, -2.1211147e+00, -1.6599302e+00, -1.3560791e+00,
-1.0950742e+00, -8.0291504e-01, -5.99710...21013e+00, 1.2699474e+00,
1.6432656e+00, 1.9971005e+00, 2.2034056e+00, 9.8387175e+00],
dtype=float32)
____________ TestEigh.test_various_drivers_standard[evd-complex128] ____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-13
E
E Mismatched elements: 20 / 400 (5%)
E Max absolute difference: 3.91507493
E Max relative difference: inf
E x: array([[ 5.551115e-16+1.072247e-01j, -2.775558e-16-5.551115e-16j,
E 3.053113e-16+4.996004e-16j, -1.110223e-16-2.775558e-16j,
E -2.775558e-16+5.551115e-17j, -1.804112e-16-2.914335e-16j,...
E y: array(0.)
a = array([[0.80326946+0.j , 0.59936528+0.05300101j,
0.95832685+0.0462222j , 0.55680251+0.17499864j,
...294j,
0.60840317-0.22914422j, 0.59827221-0.00135214j,
0.26349336-0.23383477j, 0.60505255+0.j ]])
driver = 'evd'
dtype_ = <class 'numpy.complex128'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x149436160>
v = array([[-7.20684005e-02+9.15398123e-01j, 1.51473516e-01+1.51473516e-01j,
-8.83182172e-02-8.83182172e-02j, 2....5449e-01j, -3.57786128e-01-2.07193101e-01j,
1.11792605e-01+1.76430821e-01j, 2.37966225e-01+2.04786508e-01j]])
w = array([-2.1933784 , -1.94651785, -1.5997267 , -1.57196017, -0.97569209,
-0.88542367, -0.71470547, -0.3696185 , ...027221, 0.58746605, 0.83714941, 1.03364908,
1.26355445, 1.46203797, 1.7543098 , 2.06181122, 10.41503829])
____________ TestEigh.test_various_drivers_standard[evx-complex64] _____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.000119209
E
E Mismatched elements: 400 / 400 (100%)
E Max absolute difference: 13.744765
E Max relative difference: inf
E x: array([[-3.651766e-01+1.175622e+01j, -6.825393e-02-5.268404e-01j,
E 3.930492e-01+1.110123e-01j, -4.181564e-04-3.892285e-02j,
E 1.544308e-01+3.418234e-02j, -2.727565e-01-1.666747e-01j,...
E y: array(0.)
a = array([[0.9203447 +0.j , 0.2483745 -0.03459179j,
0.6773846 +0.12101765j, 0.5054712 -0.07335676j,
...704623 +0.23423882j, 0.63310504+0.28264895j,
0.38397732+0.3441984j , 0.07380515+0.j ]], dtype=complex64)
driver = 'evx'
dtype_ = <class 'numpy.complex64'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14945c7f0>
v = array([[-0.0429727 +1.00964117e+00j, -0.14481622-1.44816220e-01j,
0.26343495+2.63434947e-01j, -0.00761492-7.6... 0.04240745+4.24074531e-02j,
-0.17201637-1.72016367e-01j, 0.2040696 +2.04069585e-01j]],
dtype=complex64)
w = array([-2.2410452 , -1.9704098 , -1.5324193 , -1.3974583 , -1.078885 ,
-0.72428995, -0.64166534, -0.3583088 , ... 0.9276952 , 1.150788 ,
1.3305326 , 1.5351841 , 1.7345213 , 2.1826546 , 10.071262 ],
dtype=float32)
____________ TestEigh.test_various_drivers_standard[evx-complex128] ____________
scipy/linalg/tests/test_decomp.py:851: in test_various_drivers_standard
assert_allclose(a @ v - (v * w), 0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-13
E
E Mismatched elements: 400 / 400 (100%)
E Max absolute difference: 7.80396382
E Max relative difference: inf
E x: array([[ 3.311647e-01+4.732797e-01j, -4.936677e-01-4.542667e-01j,
E -3.088349e-01-9.947181e-02j, -2.556849e-02+1.133738e-02j,
E 2.515436e-03-5.583135e-02j, -5.922909e-02-4.024031e-01j,...
E y: array(0.)
a = array([[0.26914684+0.00000000e+00j, 0.29146127-4.10195053e-01j,
0.95705696-3.48075063e-01j, 0.87025858+1.15636...33202859-3.21654094e-01j, 0.59800323+1.94424008e-01j,
0.55331016+4.14019946e-01j, 0.25478618+0.00000000e+00j]])
driver = 'evx'
dtype_ = <class 'numpy.complex128'>
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14942fdf0>
v = array([[ 3.30891400e-01+3.30891400e-01j, -4.17037287e-01-4.17037287e-01j,
-1.07798794e-01-1.07798794e-01j, -1....3780e-02j, -1.89615754e-01-1.89615754e-01j,
-4.41563754e-02+8.01356524e-01j, 1.85068789e-01+1.85068789e-01j]])
w = array([-2.27173441e+00, -2.03989104e+00, -1.62632289e+00, -1.41799595e+00,
-1.07636252e+00, -8.08232864e-01, -6...9124e-01, 1.21517278e+00, 1.39670038e+00,
1.80985410e+00, 1.96426791e+00, 2.36322476e+00, 1.00990438e+01])
_________________ TestEigh.test_eigh[6-F-True-True-True-None] __________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.46809623
E Max relative difference: 1.0000427
E x: array([-0.485601, -0.312531, -0.074911, 0.219079, 0.5228 , 0.936172],
E dtype=float32)
E y: array([-0.2428 +0.j, -0.156266+0.j, -0.037455+0.j, 0.109539+0.j,
E 0.2614 +0.j, 0.468076+0.j], dtype=complex64)
a = array([[0.94189006+0.j, 0.3177541 +0.j, 0.48921168+0.j, 0.29285413+0.j,
0.62844056+0.j, 0.11751232+0.j],
...1751232+0.j, 0.47093302+0.j, 0.41464418+0.j, 0.5263543 +0.j,
0.6622156 +0.j, 0.56324327+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.8814993 +0.j, 0.7532733 +0.j, 0.5667193 +0.j, 0.91612804+0.j,
0.18487613+0.j, 0.43611383+0.j],
...3611383+0.j, 0.39468226+0.j, 0.4902285 +0.j, 0.3416944 +0.j,
0.246093 +0.j, 4.1263437 +0.j]], dtype=complex64)
diag1_ = array([-0.48560095, -0.31253147, -0.0749108 , 0.21907865, 0.5228004 ,
0.9361725 ], dtype=float32)
diag_ = array([-0.60781467, -0.1966563 , 0.19238439, 0.44767025, 0.61062247,
2.7847323 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = True
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14942d3a0>
turbo = True
w = array([-0.24280049+0.j, -0.15626569+0.j, -0.03745542+0.j, 0.10953932+0.j,
0.2614001 +0.j, 0.46807626+0.j], dtype=complex64)
z = array([[ 0.02692915+0.02692915j, -0.021659 -0.021659j ,
0.19803704+0.19803704j, 0.1290862 +0.1290862j ,
....33830327j, -0.27845508-0.27845508j,
-0.15060899-0.15060899j, -0.1726871 -0.1711923j ]],
dtype=complex64)
_________________ TestEigh.test_eigh[6-F-True-True-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.32953268
E Max relative difference: 1.0000012
E x: array([-0.541171, -0.24827 , -0.16693 , 0.01691 , 0.242608, 0.149311],
E dtype=float32)
E y: array([-0.270585+0.j, -0.124142+0.j, -0.083465+0.j, 0.008455+0.j,
E 0.121304+0.j, 0.478843+0.j], dtype=complex64)
a = array([[0.24046203+0.j, 0.7203457 +0.j, 0.51699066+0.j, 0.72209704+0.j,
0.46539277+0.j, 0.29535893+0.j],
...9535893+0.j, 0.8227661 +0.j, 0.7007535 +0.j, 0.4971041 +0.j,
0.5259398 +0.j, 0.06233544+0.j]], dtype=complex64)
atol = 0.0001
b = array([[4.3692007 +0.j, 0.5208768 +0.j, 0.4924582 +0.j, 0.6876096 +0.j,
0.7258201 +0.j, 0.33202532+0.j],
...3202532+0.j, 0.48081577+0.j, 0.39746702+0.j, 0.4460055 +0.j,
0.4292883 +0.j, 4.4085026 +0.j]], dtype=complex64)
diag1_ = array([-0.54117084, -0.2482701 , -0.16692963, 0.01691022, 0.24260822,
0.14931066], dtype=float32)
diag_ = array([-0.44902846, -0.16516139, 0.14273405, 0.51106656, 0.8109742 ,
3.2629323 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = True
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14942df40>
turbo = False
w = array([-0.27058533+0.j, -0.12414176+0.j, -0.08346476+0.j, 0.00845512+0.j,
0.12130409+0.j, 0.47884333+0.j], dtype=complex64)
z = array([[ 0.23658736+0.23658854j, -0.05110337-0.04964942j,
0.2828052 +0.28268144j, -0.27289012-0.2729052j ,
....13521922j, 0.20471877+0.2047004j ,
0.02705213+0.02705213j, 0.15438505-0.02807979j]],
dtype=complex64)
_________________ TestEigh.test_eigh[6-F-True-False-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.54828775
E Max relative difference: 1.1236883
E x: array([-0.478913, -0.163269, -0.036441, 0.180954, 0.371176, 1.036224],
E dtype=float32)
E y: array([-0.239456+0.j, -0.081634+0.j, -0.018221+0.j, 0.090477+0.j,
E 0.185588+0.j, 0.487936+0.j], dtype=complex64)
a = array([[0.14704211+0.j, 0.89636266+0.j, 0.6964647 +0.j, 0.45620486+0.j,
0.08488958+0.j, 0.5465375 +0.j],
...465375 +0.j, 0.4186488 +0.j, 0.64523673+0.j, 0.3573673 +0.j,
0.44854876+0.j, 0.13988845+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.7057762 +0.j, 0.5976716 +0.j, 0.7405352 +0.j, 0.38507336+0.j,
0.18701029+0.j, 0.75921696+0.j],
...5921696+0.j, 0.07421535+0.j, 0.699367 +0.j, 0.57520294+0.j,
0.1353806 +0.j, 4.269169 +0.j]], dtype=complex64)
diag1_ = array([-0.4789126 , -0.16326874, -0.03644104, 0.1809543 , 0.3711757 ,
1.0362235 ], dtype=float32)
diag_ = array([-0.69455516, -0.35041702, -0.06705053, 0.13802083, 0.39961326,
2.6240501 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = False
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14940e460>
turbo = True
w = array([-0.23945613+0.j, -0.08163433+0.j, -0.01822052+0.j, 0.09047712+0.j,
0.18558776+0.j, 0.48793578+0.j], dtype=complex64)
z = array([[ 0.452138 +0.452138j , 0.13338469+0.13338473j,
0.15458602+0.15457486j, -0.01659884-0.01659893j,
....09108649j, -0.15876791-0.15876791j,
0.08394218+0.08394222j, 0.11395784+0.49494687j]],
dtype=complex64)
________________ TestEigh.test_eigh[6-F-True-False-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.27752507
E Max relative difference: 1.0000007
E x: array([-0.55505 , -0.213289, 0.045369, 0.183871, 0.273931, 0.396452],
E dtype=float32)
E y: array([-0.277525+0.j, -0.106645+0.j, 0.022685+0.j, 0.091935+0.j,
E 0.136965+0.j, 0.453675+0.j], dtype=complex64)
a = array([[0.4506064 +0.j, 0.38382778+0.j, 0.81977063+0.j, 0.26001203+0.j,
0.53073037+0.j, 0.19195576+0.j],
...9195576+0.j, 0.59642404+0.j, 0.4405579 +0.j, 0.5363573 +0.j,
0.48572755+0.j, 0.21342213+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.5921583 +0.j, 0.14073387+0.j, 0.620896 +0.j, 0.46062562+0.j,
0.2517855 +0.j, 0.49150977+0.j],
...9150977+0.j, 0.5499142 +0.j, 0.54769075+0.j, 0.5205428 +0.j,
0.3506582 +0.j, 4.38437 +0.j]], dtype=complex64)
diag1_ = array([-0.55505013, -0.21328928, 0.04536902, 0.1838706 , 0.2739307 ,
0.396452 ], dtype=float32)
diag_ = array([-0.856958 , -0.4250862 , -0.15582177, -0.01355576, 0.23185635,
2.9385388 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = False
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14940ea90>
turbo = False
w = array([-0.27752507+0.j, -0.1066446 +0.j, 0.02268453+0.j, 0.09193532+0.j,
0.13696538+0.j, 0.45367455+0.j], dtype=complex64)
z = array([[-0.24175027-0.24175027j, 0.13737267+0.13737267j,
-0.19659264-0.19659264j, 0.35927033+0.35927033j,
....01595859j, -0.26116204-0.26116204j,
-0.08484881-0.08484881j, -0.10266025+0.35609558j]],
dtype=complex64)
_________________ TestEigh.test_eigh[6-F-False-True-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.49825108
E Max relative difference: 1.0000006
E x: array([-0.44047 , -0.188464, 0.038233, 0.278568, 0.352137, 0.996502],
E dtype=float32)
E y: array([-0.220235+0.j, -0.094232+0.j, 0.019117+0.j, 0.139284+0.j,
E 0.176069+0.j, 0.498251+0.j], dtype=complex64)
a = array([[0.882543 +0.j, 0.60269994+0.j, 0.3709318 +0.j, 0.5176232 +0.j,
0.5265752 +0.j, 0.3746794 +0.j],
...746794 +0.j, 0.68633467+0.j, 0.63627464+0.j, 0.4105417 +0.j,
0.69689256+0.j, 0.76453674+0.j]], dtype=complex64)
atol = 0.0001
b = array([[4.4315524 +0.j, 0.41940913+0.j, 0.14003824+0.j, 0.5052338 +0.j,
0.65201926+0.j, 0.96257436+0.j],
...6257436+0.j, 0.9009159 +0.j, 0.21293898+0.j, 0.35946187+0.j,
0.5895454 +0.j, 3.6991673 +0.j]], dtype=complex64)
diag1_ = array([-0.44046974, -0.18846393, 0.03823304, 0.2785678 , 0.35213748,
0.9965023 ], dtype=float32)
diag_ = array([-0.49769592, -0.42596987, -0.07926966, 0.35028592, 0.5796132 ,
2.511098 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = True
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14ac61dc0>
turbo = True
w = array([-0.22023499+0.j, -0.09423202+0.j, 0.01911653+0.j, 0.13928385+0.j,
0.17606875+0.j, 0.4982512 +0.j], dtype=complex64)
z = array([[ 0.02304962+0.02304962j, 0.19031185+0.19031185j,
0.00609896+0.00609896j, 0.26555067+0.26555067j,
....38324526j, -0.13731356-0.13731356j,
-0.23705967-0.23705967j, -0.20846404-0.2084637j ]],
dtype=complex64)
________________ TestEigh.test_eigh[6-F-False-True-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.22122192
E Max relative difference: 1.0000068
E x: array([-0.442444, -0.201545, 0.017607, 0.16953 , 0.357098, 0.721418],
E dtype=float32)
E y: array([-0.221222+0.j, -0.100773+0.j, 0.008804+0.j, 0.084765+0.j,
E 0.178549+0.j, 0.525691+0.j], dtype=complex64)
a = array([[0.22101411+0.j, 0.33586907+0.j, 0.63221896+0.j, 0.53261286+0.j,
0.24382536+0.j, 0.35554168+0.j],
...5554168+0.j, 0.50479794+0.j, 0.7114129 +0.j, 0.64866424+0.j,
0.73286283+0.j, 0.47943765+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.853972 +0.j, 0.6447499 +0.j, 0.48021883+0.j, 0.4430055 +0.j,
0.3101057 +0.j, 0.365514 +0.j],
...65514 +0.j, 0.46312022+0.j, 0.6176184 +0.j, 0.4594969 +0.j,
0.60431015+0.j, 4.433647 +0.j]], dtype=complex64)
diag1_ = array([-0.44244403, -0.20154493, 0.01760732, 0.16953002, 0.3570978 ,
0.7214176 ], dtype=float32)
diag_ = array([-0.7402807 , -0.37563217, 0.06704548, 0.12726681, 0.40285635,
3.2228992 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = True
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x1494072b0>
turbo = False
w = array([-0.2212221 +0.j, -0.10077266+0.j, 0.00880363+0.j, 0.08476513+0.j,
0.17854875+0.j, 0.5256907 +0.j], dtype=complex64)
z = array([[-0.17789046-0.17789167j, -0.21407811-0.21442269j,
-0.3454858 -0.3451161j , -0.25057662-0.25060064j,
....32072693j, -0.1055024 -0.10548996j,
-0.04871838-0.04871838j, 0.14641598-0.0470806j ]],
dtype=complex64)
________________ TestEigh.test_eigh[6-F-False-False-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.47770208
E Max relative difference: 1.0000014
E x: array([-0.111534, -0.045158, 0.016268, 0.269463, 0.379338, 0.955574],
E dtype=float32)
E y: array([-0.055767+0.j, -0.022579+0.j, 0.008134+0.j, 0.134731+0.j,
E 0.189669+0.j, 0.477872+0.j], dtype=complex64)
a = array([[0.6452997 +0.j, 0.5964542 +0.j, 0.5424254 +0.j, 0.5148393 +0.j,
0.57869583+0.j, 0.58155155+0.j],
...8155155+0.j, 0.38993406+0.j, 0.45339584+0.j, 0.44374526+0.j,
0.26262194+0.j, 0.35480455+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.587556 +0.j, 0.35207134+0.j, 0.3048609 +0.j, 0.28332126+0.j,
0.22228493+0.j, 0.38722858+0.j],
...8722858+0.j, 0.7595627 +0.j, 0.65089786+0.j, 0.46203086+0.j,
0.33025602+0.j, 3.6631072 +0.j]], dtype=complex64)
diag1_ = array([-0.11153392, -0.04515787, 0.01626768, 0.26946256, 0.37933812,
0.9555737 ], dtype=float32)
diag_ = array([-0.54924893, -0.46740282, -0.08804972, 0.21839237, 0.5904868 ,
3.1221344 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = False
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x149407610>
turbo = True
w = array([-0.05576696+0.j, -0.02257896+0.j, 0.00813383+0.j, 0.13473132+0.j,
0.18966901+0.j, 0.4778716 +0.j], dtype=complex64)
z = array([[-0.28307393-0.28307393j, -0.24174953-0.24174953j,
-0.26248735-0.26248735j, 0.01137766+0.01137766j,
....33739296j, 0.11381758+0.11381758j,
-0.05777605-0.05777605j, -0.12800555-0.12734008j]],
dtype=complex64)
________________ TestEigh.test_eigh[6-F-False-False-False-None] ________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0001
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.5541795
E Max relative difference: 1.0005932
E x: array([-0.419803, -0.325263, -0.18648 , -0.031227, 0.369174, 1.10803 ],
E dtype=float32)
E y: array([-0.209902+0.j, -0.162631+0.j, -0.09324 +0.j, -0.015613+0.j,
E 0.184587+0.j, 0.553851+0.j], dtype=complex64)
a = array([[0.8223416 +0.j, 0.7209296 +0.j, 0.17268606+0.j, 0.4822605 +0.j,
0.5141309 +0.j, 0.91035676+0.j],
...1035676+0.j, 0.42631674+0.j, 0.44917846+0.j, 0.57719296+0.j,
0.73331213+0.j, 0.13215497+0.j]], dtype=complex64)
atol = 0.0001
b = array([[3.9971466 +0.j, 0.4122611 +0.j, 0.6195147 +0.j, 0.27250645+0.j,
0.37227485+0.j, 0.27615562+0.j],
...7615562+0.j, 0.77285135+0.j, 0.6765339 +0.j, 0.56973404+0.j,
0.21664503+0.j, 3.7920017 +0.j]], dtype=complex64)
diag1_ = array([-0.41980323, -0.325263 , -0.18648006, -0.03122663, 0.36917424,
1.1080304 ], dtype=float32)
diag_ = array([-0.72434413, -0.25737715, 0.12102426, 0.35222927, 0.89393467,
2.8990715 ], dtype=float32)
dim = 6
dtype_ = 'F'
eigvals = None
lower = False
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14ac88610>
turbo = False
w = array([-0.20990156+0.j, -0.16263144+0.j, -0.09324001+0.j, -0.01561319+0.j,
0.18458709+0.j, 0.55385095+0.j], dtype=complex64)
z = array([[ 0.20411697+0.20411697j, -0.15473579-0.15473579j,
-0.00154966-0.00154966j, 0.05437683+0.05437683j,
....15470162j, 0.157751 +0.157751j ,
-0.11361784-0.11361784j, -0.17066842-0.16511473j]],
dtype=complex64)
_________________ TestEigh.test_eigh[6-D-True-True-True-None] __________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.46721899
E Max relative difference: 1.
E x: array([-0.208976, 0.012785, 0.053826, 0.312004, 0.48156 , 0.934438])
E y: array([-0.104488+0.j, 0.006393+0.j, 0.026913+0.j, 0.156002+0.j,
E 0.24078 +0.j, 0.467219+0.j])
a = array([[0.71151329+0.j, 0.3093607 +0.j, 0.52717286+0.j, 0.46442694+0.j,
0.40183626+0.j, 0.58061673+0.j],
...0.j],
[0.58061673+0.j, 0.12754241+0.j, 0.24559284+0.j, 0.3463392 +0.j,
0.85971392+0.j, 0.78403115+0.j]])
atol = 1e-11
b = array([[3.53282904+0.j, 0.56861146+0.j, 0.47723147+0.j, 0.58576209+0.j,
0.66667433+0.j, 0.56435813+0.j],
...0.j],
[0.56435813+0.j, 0.5475766 +0.j, 0.3108049 +0.j, 0.73431137+0.j,
0.61806327+0.j, 4.14883347+0.j]])
diag1_ = array([-0.20897559, 0.01278527, 0.05382603, 0.31200375, 0.48156019,
0.93443799])
diag_ = array([-0.40828611, -0.26025893, 0.14991251, 0.31064419, 0.67324649,
2.68692907])
dim = 6
dtype_ = 'D'
eigvals = None
lower = True
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x149439ee0>
turbo = True
w = array([-0.10448779+0.j, 0.00639263+0.j, 0.02691302+0.j, 0.15600187+0.j,
0.2407801 +0.j, 0.467219 +0.j])
z = array([[ 0.10179151+0.10179151j, -0.19182457-0.19182457j,
-0.28036969-0.28036969j, 0.38349856+0.38349856j,
...,
-0.00571041-0.00571041j, -0.04042499-0.04042499j,
0.3445809 +0.3445809j , -0.14721246-0.14723448j]])
_________________ TestEigh.test_eigh[6-D-True-True-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.48914139
E Max relative difference: 1.
E x: array([-0.440268, -0.108758, -0.045163, 0.209923, 0.304468, 0.978286])
E y: array([-0.220134+0.j, -0.054379+0.j, -0.022581+0.j, 0.104961+0.j,
E 0.152234+0.j, 0.489144+0.j])
a = array([[0.83184019+0.j, 0.44850066+0.j, 0.48224936+0.j, 0.93117523+0.j,
0.7562725 +0.j, 0.61425982+0.j],
...0.j],
[0.61425982+0.j, 0.10912077+0.j, 0.19157452+0.j, 0.4631397 +0.j,
0.41174307+0.j, 0.36281503+0.j]])
atol = 1e-11
b = array([[4.44894656+0.j, 0.38731322+0.j, 0.53842472+0.j, 0.40216099+0.j,
0.2844065 +0.j, 0.48004812+0.j],
...0.j],
[0.48004812+0.j, 0.9264048 +0.j, 0.40715136+0.j, 0.50295856+0.j,
0.697232 +0.j, 4.43435686+0.j]])
diag1_ = array([-0.44026798, -0.10875835, -0.04516259, 0.2099229 , 0.30446826,
0.97828587])
diag_ = array([-0.82087746, -0.36496181, -0.16815352, 0.37702657, 0.79248229,
2.88464801])
dim = 6
dtype_ = 'D'
eigvals = None
lower = True
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x149439040>
turbo = False
w = array([-0.22013399+0.j, -0.05437918+0.j, -0.0225813 +0.j, 0.10496145+0.j,
0.15223413+0.j, 0.48914448+0.j])
z = array([[ 0.18728137+0.18728137j, -0.01264387-0.01264387j,
-0.30275018-0.30275018j, 0.02387516+0.02387516j,
...,
0.36218632+0.36218632j, -0.04173572-0.04173572j,
-0.30788209-0.30788209j, -0.04906664-0.0492157j ]])
_________________ TestEigh.test_eigh[6-D-True-False-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.45220174
E Max relative difference: 1.
E x: array([-0.430458, -0.343752, -0.126716, 0.142294, 0.225441, 0.904403])
E y: array([-0.215229+0.j, -0.171876+0.j, -0.063358+0.j, 0.071147+0.j,
E 0.11272 +0.j, 0.452202+0.j])
a = array([[0.20165145+0.j, 0.57751939+0.j, 0.46180506+0.j, 0.54345016+0.j,
0.50644849+0.j, 0.3592125 +0.j],
...0.j],
[0.3592125 +0.j, 0.43640971+0.j, 0.45532387+0.j, 0.95986398+0.j,
0.27387325+0.j, 0.45295321+0.j]])
atol = 1e-11
b = array([[4.29417849+0.j, 0.86871958+0.j, 0.47298458+0.j, 0.76512999+0.j,
0.75171527+0.j, 0.3393883 +0.j],
...0.j],
[0.3393883 +0.j, 0.76192083+0.j, 0.1011976 +0.j, 0.80656604+0.j,
0.92261296+0.j, 3.48071444+0.j]])
diag1_ = array([-0.4304576 , -0.3437524 , -0.12671572, 0.14229393, 0.22544075,
0.9044035 ])
diag_ = array([-0.71297136, -0.50447194, -0.25125495, -0.08989238, 0.30587581,
2.84549194])
dim = 6
dtype_ = 'D'
eigvals = None
lower = False
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14941b550>
turbo = True
w = array([-0.2152288 +0.j, -0.1718762 +0.j, -0.06335786+0.j, 0.07114697+0.j,
0.11272037+0.j, 0.45220175+0.j])
z = array([[-0.01940712-0.01940712j, -0.28205045-0.28205045j,
-0.34314316-0.34314316j, -0.20374353-0.20374353j,
...,
0.12946989+0.12946989j, 0.3215643 +0.3215643j ,
-0.17511595-0.17511595j, -0.20654279-0.20652544j]])
________________ TestEigh.test_eigh[6-D-True-False-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.21458406
E Max relative difference: 1.
E x: array([-0.429168, -0.226661, -0.057259, 0.03991 , 0.368466, 0.453574])
E y: array([-0.214584+0.j, -0.11333 +0.j, -0.028629+0.j, 0.019955+0.j,
E 0.184233+0.j, 0.534686+0.j])
a = array([[0.84857284+0.j, 0.54979222+0.j, 0.61389136+0.j, 0.71715856+0.j,
0.71611654+0.j, 0.14062217+0.j],
...0.j],
[0.14062217+0.j, 0.28443538+0.j, 0.38469069+0.j, 0.09659502+0.j,
0.75176294+0.j, 0.47611267+0.j]])
atol = 1e-11
b = array([[3.56819358+0.j, 0.09731193+0.j, 0.76233493+0.j, 0.61009798+0.j,
0.50667643+0.j, 0.22415343+0.j],
...0.j],
[0.22415343+0.j, 0.5987129 +0.j, 0.79354542+0.j, 0.35780076+0.j,
0.52505517+0.j, 4.03412453+0.j]])
diag1_ = array([-0.42916812, -0.2266605 , -0.05725854, 0.03990991, 0.36846629,
0.45357373])
diag_ = array([-0.45961248, -0.27479489, 0.03087143, 0.18251432, 0.37059721,
3.67821052])
dim = 6
dtype_ = 'D'
eigvals = None
lower = False
overwrite = True
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14937ef10>
turbo = False
w = array([-0.21458406+0.j, -0.11333025+0.j, -0.02862927+0.j, 0.01995495+0.j,
0.18423314+0.j, 0.534686 +0.j])
z = array([[-0.07062765-0.07062764j, -0.15635187-0.15635196j,
0.15145832+0.15145832j, 0.37445072+0.37445072j,
...,
0.31546351+0.31546351j, -0.01766316-0.01766316j,
0.35818957+0.35818957j, 0.04611843+0.51517196j]])
_________________ TestEigh.test_eigh[6-D-False-True-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.26617536
E Max relative difference: 1.
E x: array([-0.532351, -0.32691 , -0.032601, 0.087917, 0.275316, 0.862828])
E y: array([-0.266175+0.j, -0.163455+0.j, -0.0163 +0.j, 0.043958+0.j,
E 0.137658+0.j, 0.608115+0.j])
a = array([[0.19726992+0.j, 0.74086873+0.j, 0.247372 +0.j, 0.36777871+0.j,
0.74380484+0.j, 0.6093195 +0.j],
...0.j],
[0.6093195 +0.j, 0.4378835 +0.j, 0.7100433 +0.j, 0.34691623+0.j,
0.83602676+0.j, 0.58308823+0.j]])
atol = 1e-11
b = array([[3.9406136 +0.j, 0.54713909+0.j, 0.13773987+0.j, 0.23951298+0.j,
0.48738011+0.j, 0.69066462+0.j],
...0.j],
[0.69066462+0.j, 0.5388175 +0.j, 0.64299084+0.j, 0.29212669+0.j,
0.45186728+0.j, 4.04272396+0.j]])
diag1_ = array([-0.53235071, -0.32691028, -0.03260072, 0.08791695, 0.27531566,
0.86282839])
diag_ = array([-0.67218586, -0.07246483, -0.0063822 , 0.43931345, 0.87416187,
3.23329803])
dim = 6
dtype_ = 'D'
eigvals = None
lower = True
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14937e790>
turbo = True
w = array([-0.26617536+0.j, -0.16345514+0.j, -0.01630036+0.j, 0.04395847+0.j,
0.13765783+0.j, 0.60811538+0.j])
z = array([[-1.77013896e-01-1.77013896e-01j, 3.42370878e-01+3.42370878e-01j,
-1.95549287e-01-1.95549287e-01j, 2....5464e-01j, -1.64962022e-01-1.64962022e-01j,
2.10487466e-01+2.10487466e-01j, 1.36618340e-01+3.10923431e-01j]])
________________ TestEigh.test_eigh[6-D-False-True-False-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.53240441
E Max relative difference: 1.57960175
E x: array([-0.869454, -0.277809, 0.139136, 0.220895, 0.452656, 0.861342])
E y: array([-0.33705 +0.j, -0.138904+0.j, 0.069568+0.j, 0.110448+0.j,
E 0.226328+0.j, 0.430671+0.j])
a = array([[0.27261062+0.j, 0.04205495+0.j, 0.51513428+0.j, 0.13874065+0.j,
0.44880645+0.j, 0.81168894+0.j],
...0.j],
[0.81168894+0.j, 0.87849289+0.j, 0.06339601+0.j, 0.24770736+0.j,
0.58057842+0.j, 0.22153806+0.j]])
atol = 1e-11
b = array([[3.85261617+0.j, 0.35970308+0.j, 0.58669934+0.j, 0.34498049+0.j,
0.32272017+0.j, 0.6520558 +0.j],
...0.j],
[0.6520558 +0.j, 0.49537484+0.j, 0.6639669 +0.j, 0.61580777+0.j,
0.7446846 +0.j, 3.50357128+0.j]])
diag1_ = array([-0.86945419, -0.27780894, 0.13913612, 0.22089507, 0.45265554,
0.8613416 ])
diag_ = array([-0.80106543, -0.59442604, -0.00617281, 0.15573334, 0.4056252 ,
3.01405565])
dim = 6
dtype_ = 'D'
eigvals = None
lower = True
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14ac6e3d0>
turbo = False
w = array([-0.33704977+0.j, -0.13890447+0.j, 0.06956806+0.j, 0.11044753+0.j,
0.22632777+0.j, 0.4306708 +0.j])
z = array([[ 0.27253022+0.6251033j , 0.14597259+0.14597259j,
-0.2026133 -0.2026133j , -0.32725165-0.32725165j,
...,
0.07314678+0.07314679j, -0.0589045 -0.05890449j,
0.34644687+0.34644687j, -0.14780682-0.14780682j]])
________________ TestEigh.test_eigh[6-D-False-False-True-None] _________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.46400199
E Max relative difference: 1.
E x: array([-0.47534 , -0.277137, -0.075012, 0.075452, 0.183818, 0.020822])
E y: array([-0.23767 +0.j, -0.138569+0.j, -0.037506+0.j, 0.037726+0.j,
E 0.091909+0.j, 0.484824+0.j])
a = array([[0.16360209+0.j, 0.74914928+0.j, 0.30434003+0.j, 0.57516177+0.j,
0.52795367+0.j, 0.72242201+0.j],
...0.j],
[0.72242201+0.j, 0.46992984+0.j, 0.60366644+0.j, 0.66528242+0.j,
0.30804763+0.j, 0.37233133+0.j]])
atol = 1e-11
b = array([[3.4803405 +0.j, 0.65967944+0.j, 0.65800516+0.j, 0.34342059+0.j,
0.52096372+0.j, 0.40808915+0.j],
...0.j],
[0.40808915+0.j, 0.12017123+0.j, 0.55640349+0.j, 0.50448368+0.j,
0.26358134+0.j, 3.94508881+0.j]])
diag1_ = array([-0.47534025, -0.27713705, -0.07501199, 0.07545225, 0.18381825,
0.0208216 ])
diag_ = array([-0.47864621, 0.09167204, 0.24625594, 0.42640824, 0.5104482 ,
3.23696316])
dim = 6
dtype_ = 'D'
eigvals = None
lower = False
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14ac6e8b0>
turbo = True
w = array([-0.23767012+0.j, -0.13856853+0.j, -0.03750599+0.j, 0.03772612+0.j,
0.09190912+0.j, 0.48482359+0.j])
z = array([[ 0.43073616+0.43073616j, -0.14501967-0.14501967j,
-0.05811003-0.05811003j, 0.16165742+0.16165743j,
...,
-0.39225138-0.39225139j, -0.10643115-0.10643115j,
0.1075131 +0.1075131j , 0.19312526+0.51353835j]])
________________ TestEigh.test_eigh[6-D-False-False-False-None] ________________
scipy/linalg/tests/test_decomp.py:892: in test_eigh
assert_allclose(diag1_, w, rtol=0., atol=atol)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1e-11
E
E Mismatched elements: 6 / 6 (100%)
E Max absolute difference: 0.5927337
E Max relative difference: 1.
E x: array([-0.3406 , -0.109432, -0.048392, 0.114588, 0.43081 , 1.185471])
E y: array([-0.1703 +0.j, -0.054716+0.j, -0.024196+0.j, 0.057294+0.j,
E 0.215405+0.j, 0.592737+0.j])
a = array([[0.66935268+0.j, 0.77780905+0.j, 0.55409121+0.j, 0.61948759+0.j,
0.61420033+0.j, 0.55203713+0.j],
...0.j],
[0.55203713+0.j, 0.64195696+0.j, 0.64273712+0.j, 0.83521969+0.j,
0.34542958+0.j, 0.41701111+0.j]])
atol = 1e-11
b = array([[4.1987233 +0.j, 0.30511017+0.j, 0.44010466+0.j, 0.7037236 +0.j,
0.31646723+0.j, 0.57026433+0.j],
...0.j],
[0.57026433+0.j, 0.40125308+0.j, 0.73838623+0.j, 0.30892016+0.j,
0.06773588+0.j, 4.12817453+0.j]])
diag1_ = array([-0.34059977, -0.10943169, -0.04839208, 0.11458819, 0.4308101 ,
1.18547053])
diag_ = array([-1.04998803, -0.49886791, -0.01795759, 0.26587977, 0.49271342,
3.33323287])
dim = 6
dtype_ = 'D'
eigvals = None
lower = False
overwrite = False
self = <scipy.linalg.tests.test_decomp.TestEigh object at 0x14935b580>
turbo = False
w = array([-0.17029989+0.j, -0.05471584+0.j, -0.02419604+0.j, 0.05729409+0.j,
0.21540505+0.j, 0.59273683+0.j])
z = array([[-0.045203 -0.045203j , -0.26777574-0.26777574j,
0.2606524 +0.2606524j , -0.24979522-0.24979522j,
...,
0.16915973+0.16915973j, 0.28749646+0.28749646j,
-0.01672724-0.01672724j, 0.15803141+0.15789633j]])
______________________ TestSVD_GESVD.test_simple_complex _______________________
scipy/linalg/tests/test_decomp.py:1111: in test_simple_complex
assert_array_almost_equal(u.conj().T @ u, eye(u.shape[1]))
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 9 / 9 (100%)
E Max absolute difference: 0.97054768
E Max relative difference: 0.97054768
E x: array([[ 1.863167-1.286402e-17j, 0.252408-5.630535e-02j,
E 0.024053+4.787066e-04j],
E [ 0.252408+5.630535e-02j, 0.915003-1.847309e-17j,...
E y: array([[1., 0., 0.],
E [0., 1., 0.],
E [0., 0., 1.]])
a = [[1, 2, 3], [1, 2j, 3], [2, 5, 6]]
full_matrices = True
s = array([9.34187884e+00, 2.39359557e+00, 6.05073700e-16])
self = <scipy.linalg.tests.test_decomp.TestSVD_GESVD object at 0x14935bee0>
u = array([[-0.39828431-0.37357977j, -0.09163318-0.09133861j,
0.90369977+0.90369977j],
[-0.29651332-0.3668... -0.11583439-0.11583439j],
[-0.8576312 -0.77905014j, 0.15293019+0.15386718j,
-0.39393269-0.39393269j]])
vh = array([[-0.25798451+0.00000000e+00j, -0.57043932-9.50732193e-02j,
-0.77395353-4.44968492e-18j],
[-0.182...9496108e-16j],
[-0.9486833 -0.00000000e+00j, -0. +6.24500451e-17j,
0.31622777-4.49574451e-17j]])
______________________ TestSVD_GESVD.test_random_complex _______________________
scipy/linalg/tests/test_decomp.py:1127: in test_random_complex
assert_array_almost_equal(u.conj().T @ u,
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 375 / 400 (93.8%)
E Max absolute difference: 19.52939442
E Max relative difference: 19.52939442
E x: array([[ 8.586701e-01+1.046603e-18j, 6.314249e-03+2.182309e-08j,
E 2.876587e-03+2.722746e-05j, -4.915836e-02-2.165741e-15j,
E 5.267591e-03+2.317472e-16j, -2.283455e-02-1.240581e-15j,...
E y: array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
E 0., 0., 0., 0.],
E [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,...
a = array([[0.19151945+0.86498179j, 0.62210877+0.71027069j,
0.43772774+0.17063169j, 0.78535858+0.85508266j,
...856j, 0.60667522+0.8112992j ,
0.82215979+0.8727098j , 0.62796507+0.66598823j,
0.11792306+0.58878655j]])
full_matrices = True
i = 0
m = 15
n = 20
s = array([12.86143638, 2.94843302, 2.55481777, 2.3477689 , 2.11704004,
2.09284945, 1.8806212 , 1.70856509, 1.42069197, 1.32153852,
1.04277929, 0.93685169, 0.78054485, 0.71154826, 0.38778732])
self = <scipy.linalg.tests.test_decomp.TestSVD_GESVD object at 0x14ac68820>
u = array([[-1.65378616e-01-1.65378616e-01j, 1.16847751e-01+1.16848109e-01j,
2.29235212e-02+2.29707931e-02j, -3....0115e-01j, -8.46257457e-02+1.85156223e-01j,
1.85181013e-02-1.78106928e-02j, 4.70380837e-01+4.10219065e-02j]])
vh = array([[-0.25763384+0.j , -0.24905824+0.02672578j,
-0.26160805-0.00707567j, -0.2333506 +0.02518936j,
..., -0.18131298+0.0098072j ,
-0.06945942-0.10975804j, 0.12818036+0.03224759j,
0.21116326+0.40193129j]])
____________________________ TestQZ.test_qz_complex ____________________________
scipy/linalg/tests/test_decomp.py:2102: in test_qz_complex
assert_array_almost_equal(Q @ AA @ Z.conj().T, A)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 25 / 25 (100%)
E Max absolute difference: 1.24268613
E Max relative difference: 1.04389533
E x: array([[-0.004922+0.003162j, 0.03643 -0.042272j, -0.004731+0.007782j,
E 0.007437-0.010201j, -0.017222+0.015339j],
E [-0.003225-0.000939j, 0.017724-0.004525j, -0.002029+0.001507j,...
E y: array([[0.929616+0.729689j, 0.316376+0.994015j, 0.183919+0.676874j,
E 0.20456 +0.790823j, 0.567725+0.170914j],
E [0.595545+0.026849j, 0.964515+0.80037j , 0.653177+0.903723j,...
A = array([[0.92961609+0.72968908j, 0.31637555+0.99401459j,
0.18391881+0.67687371j, 0.20456028+0.79082252j,
...906j, 0.71745362+0.46810575j,
0.46759901+0.4593732j , 0.32558468+0.70950978j,
0.43964461+0.17805301j]])
AA = array([[-1.23486358e-01-7.15399961e-15j, -1.81158310e-01+7.82707232e-15j,
3.58634296e-04-5.29225448e-04j, -8....j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
4.87840651e-02-6.69110860e-02j]])
B = array([[0.53144988+0.22563761j, 0.16774223+0.24497444j,
0.76881392+0.7928007j , 0.92817055+0.49517241j,
...536j, 0.49541476+0.33605516j,
0.35161653+0.82638043j, 0.71423037+0.89810064j,
0.50392912+0.0427153j ]])
BB = array([[ 0.00161829+0.00000000e+00j, -0.06737054+9.71445147e-17j,
0.00277447-4.09419490e-03j, 0.03008124+1.8...00000000e+00j,
0. +0.00000000e+00j, 0. +0.00000000e+00j,
0.78879555+0.00000000e+00j]])
Q = array([[-0.0124049 -0.0124049j , -0.08098177-0.08098177j,
-0.00857385-0.00857385j, 0.00053118+0.00053118j,
..., -0.13537673-0.13537673j,
-0.01433514-0.01433514j, 0.00358038+0.00358038j,
-0.09321653-0.09321653j]])
Z = array([[ 7.69175751e-05+9.66024136e-17j, 1.28001480e-02+2.56045185e-15j,
-2.95299146e-03+4.35763742e-03j, 1....j,
-7.15035354e-03+1.05515538e-02j, 2.66907399e-01+4.51503378e-08j,
3.83963303e-01-5.26635112e-01j]])
n = 5
self = <scipy.linalg.tests.test_decomp.TestQZ object at 0x14ac639a0>
___________________________ TestQZ.test_qz_complex64 ___________________________
scipy/linalg/tests/test_decomp.py:2114: in test_qz_complex64
assert_array_almost_equal(Q @ AA @ Z.conj().T, A, decimal=5)
E AssertionError:
E Arrays are not almost equal to 5 decimals
E
E Mismatched elements: 25 / 25 (100%)
E Max absolute difference: 1.3936292
E Max relative difference: 2.9828136
E x: array([[ 0.07049+0.40556j, 0.06169+0.15344j, 0.00871+0.00982j,
E 0.00099+0.09088j, -0.03962+0.14536j],
E [ 0.36104-0.14102j, -0.03418-0.17164j, -0.05379-0.05545j,...
E y: array([[0.92962+0.72969j, 0.31638+0.99401j, 0.18392+0.67687j,
E 0.20456+0.79082j, 0.56773+0.17091j],
E [0.59554+0.02685j, 0.96451+0.80037j, 0.65318+0.90372j,...
A = array([[0.9296161 +0.72968906j, 0.31637555+0.99401456j,
0.18391882+0.6768737j , 0.20456028+0.7908225j ,
...0.46810576j,
0.467599 +0.4593732j , 0.32558468+0.7095098j ,
0.4396446 +0.178053j ]], dtype=complex64)
AA = array([[-3.17302674e-01-3.1730002e-01j, -1.10790467e+00+5.9604645e-08j,
-9.15605366e-01-5.3644180e-07j, 2.068...00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
-1.09895885e-01-6.9619828e-01j]], dtype=complex64)
B = array([[0.5314499 +0.2256376j , 0.16774222+0.24497443j,
0.7688139 +0.7928007j , 0.92817056+0.4951724j ,
...0.33605516j,
0.35161653+0.82638043j, 0.71423036+0.8981006j ,
0.50392914+0.0427153j ]], dtype=complex64)
BB = array([[ 0. +0.0000000e+00j, -0.37612104+0.0000000e+00j,
-0.15505296-8.1956387e-08j, 0.53143716-3.2782...
0. +0.0000000e+00j, 0. +0.0000000e+00j,
0.3922476 +0.0000000e+00j]], dtype=complex64)
Q = array([[-0.02583549-0.13805246j, -0.0003476 -0.00034761j,
0.00028997+0.00028999j, 0.01062595+0.01062595j,
...0810727j,
0.00789832+0.00789865j, 0.15082175+0.15082175j,
0.23858337+0.23858342j]], dtype=complex64)
Z = array([[ 0.17593992+1.7593990e-01j, 0.73558056-2.9802322e-08j,
0.37611586+2.0861626e-07j, -0.8283269 +5.3644...
0.06691903+1.8626451e-08j, -0.6479421 +4.1723251e-07j,
-0.13334364-8.4474075e-01j]], dtype=complex64)
n = 5
self = <scipy.linalg.tests.test_decomp.TestQZ object at 0x1497dcdc0>
________________________ TestQZ.test_qz_double_complex _________________________
scipy/linalg/tests/test_decomp.py:2125: in test_qz_double_complex
AA, BB, Q, Z = qz(A, B, output='complex')
A = array([[0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503],
[0.5955447 , 0.96451452, 0.6531771 , 0.748... 0.80981255, 0.87217591, 0.9646476 , 0.72368535],
[0.64247533, 0.71745362, 0.46759901, 0.32558468, 0.43964461]])
B = array([[0.72968908, 0.99401459, 0.67687371, 0.79082252, 0.17091426],
[0.02684928, 0.80037024, 0.90372254, 0.024... 0.50022275, 0.81018941, 0.09596853, 0.21895004],
[0.25871906, 0.46810575, 0.4593732 , 0.70950978, 0.17805301]])
n = 5
self = <scipy.linalg.tests.test_decomp.TestQZ object at 0x1497dc790>
scipy/linalg/_decomp_qz.py:258: in qz
result, _ = _qz(A, B, output=output, lwork=lwork, sort=sort,
A = array([[0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503],
[0.5955447 , 0.96451452, 0.6531771 , 0.748... 0.80981255, 0.87217591, 0.9646476 , 0.72368535],
[0.64247533, 0.71745362, 0.46759901, 0.32558468, 0.43964461]])
B = array([[0.72968908, 0.99401459, 0.67687371, 0.79082252, 0.17091426],
[0.02684928, 0.80037024, 0.90372254, 0.024... 0.50022275, 0.81018941, 0.09596853, 0.21895004],
[0.25871906, 0.46810575, 0.4593732 , 0.70950978, 0.17805301]])
check_finite = True
lwork = None
output = 'complex'
overwrite_a = False
overwrite_b = False
sort = None
scipy/linalg/_decomp_qz.py:127: in _qz
warnings.warn("The QZ iteration failed. (a,b) are not in Schur "
E scipy.linalg.misc.LinAlgWarning: The QZ iteration failed. (a,b) are not in Schur form, but ALPHAR(j), ALPHAI(j), and BETA(j) should be correct for J=4,...,N
A = array([[0.92961609, 0.31637555, 0.18391881, 0.20456028, 0.56772503],
[0.5955447 , 0.96451452, 0.6531771 , 0.748... 0.80981255, 0.87217591, 0.9646476 , 0.72368535],
[0.64247533, 0.71745362, 0.46759901, 0.32558468, 0.43964461]])
B = array([[0.72968908, 0.99401459, 0.67687371, 0.79082252, 0.17091426],
[0.02684928, 0.80037024, 0.90372254, 0.024... 0.50022275, 0.81018941, 0.09596853, 0.21895004],
[0.25871906, 0.46810575, 0.4593732 , 0.70950978, 0.17805301]])
a1 = array([[0.92961609+0.j, 0.31637555+0.j, 0.18391881+0.j, 0.20456028+0.j,
0.56772503+0.j],
[0.5955447 +0.... 0.72368535+0.j],
[0.64247533+0.j, 0.71745362+0.j, 0.46759901+0.j, 0.32558468+0.j,
0.43964461+0.j]])
a_m = 5
a_n = 5
b1 = array([[0.72968908+0.j, 0.99401459+0.j, 0.67687371+0.j, 0.79082252+0.j,
0.17091426+0.j],
[0.02684928+0.... 0.21895004+0.j],
[0.25871906+0.j, 0.46810575+0.j, 0.4593732 +0.j, 0.70950978+0.j,
0.17805301+0.j]])
b_m = 5
b_n = 5
check_finite = True
gges = <fortran object>
info = 5
lwork = 165
output = 'complex'
overwrite_a = True
overwrite_b = True
result = (array([[-2.34524812e-01-2.34524833e-01j, 5.96844304e-01+5.97497505e-01j,
3.16520812e-01+3.16520812e-01j, -1...6647458-0.0664754j ,
-0.00333645-0.00333645j, 0.01643819+0.0165189j ,
-0.21114442-0.21115542j]]), ...)
sfunction = <function _qz.<locals>.<lambda> at 0x149360af0>
sort = None
typa = 'D'
typb = 'D'
______________________________ TestOrdQZ.test_lhp ______________________________
scipy/linalg/tests/test_decomp.py:2366: in test_lhp
self.check_all('lhp')
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497edd00>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[ -31.1141381 -6.46012214e-13j, -10.8963297 -4.14218588e-09j,
-157.68589005+1.84081236e-08j, 10.33...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[ -31.1141381 -6.46012214e-13j, -10.8963297 -4.14218588e-09j,
-157.68589005+1.84081236e-08j, 10.335...8402-1.38261738e-15j, -0.07905359+3.10199171e-12j,
0.26549587-3.32887046e-11j, 0.75392979-7.45269858e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497edd00>
sort = 'lhp'
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-2.726199e-19j, 0.009174-4.493415e-18j,
E 0.010538-1.450496e-18j, 0.005477-1.979775e-17j],
E [ 0.009174+4.636163e-18j, 0.012032+1.067203e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[ -31.1141381 -6.46012214e-13j, -10.8963297 -4.14218588e-09j,
-157.68589005+1.84081236e-08j, 10.3350...0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 22.19705256-2.19420886e+01j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 0.83578309+0.00000000e+00j, -4.65649438-2.87034546e-10j,
-4.33106291+4.03125777e-10j, -1.77606997+1.7... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 5.05710627+0.00000000e+00j]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[ 7.46840448e-02+7.46840448e-02j, -5.77557803e-03-5.77557803e-03j,
4.80710267e-03+4.80710267e-03j, -4....2355e-05j, -1.50548800e-03-1.50548800e-03j,
3.29492283e-04+3.29492283e-04j, -6.49405396e-01-6.49405396e-01j]])
Z = array([[ 0.05099366-3.58927444e-16j, -0.55491431-2.82923687e-11j,
-0.26510151+1.83312152e-11j, 0.00117845-1.1...38402-1.38261738e-15j, -0.07905359+3.10199171e-12j,
0.26549587-3.32887046e-11j, 0.75392979-7.45269858e-01j]])
alpha = array([-31.1141381 -6.46012214e-13j, -7.22439206+5.68310534e-10j,
11.80413294+3.74449419e-15j, 22.19705256-2.19420886e+01j])
beta = array([0.83578309+0.j, 3.14087286+0.j, 0.09375939+0.j, 5.05710627+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497edd00>
sort = 'lhp'
______________________________ TestOrdQZ.test_rhp ______________________________
scipy/linalg/tests/test_decomp.py:2369: in test_rhp
self.check_all('rhp')
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497ed5b0>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[134.39477527+4.26325641e-14j, -8.94635726+1.86775391e+01j,
69.27716232-6.76779276e+00j, 53.836549...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[134.39477527+4.26325641e-14j, -8.94635726+1.86775391e+01j,
69.27716232-6.76779276e+00j, 53.8365497...5001+1.38777878e-16j, 0.06082859-1.73544744e-01j,
-0.59212176+3.79578056e-01j, 0.57441682-5.35857893e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497ed5b0>
sort = 'rhp'
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-3.547364e-19j, 0.009174-4.922254e-18j,
E 0.010538-2.343044e-18j, 0.005477-1.931867e-17j],
E [ 0.009174+5.530040e-18j, 0.012032+1.565478e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[134.39477527+4.26325641e-14j, -8.94635726+1.86775391e+01j,
69.27716232-6.76779276e+00j, 53.83654971... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, -7.2906963 +5.73525227e-10j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 1.06748818+0.j , -1.51349933+0.6342589j ,
2.50124443+0.68327563j, 5.93919865-2.79324658j],
...,
[ 0. +0.j , 0. +0.j ,
0. +0.j , 3.16969926+0.j ]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[-7.43612701e-02-7.43612701e-02j, -3.70941403e-04-4.54338691e-03j,
-5.87340373e-03+6.80560633e-03j, -1....6865e-18j, -7.14045418e-01+2.51211141e-01j,
-3.82062145e-01-7.77407639e-03j, -2.69248399e-01-2.27958306e-01j]])
Z = array([[ 0.06688086+2.77555756e-17j, 0.05007278-1.10312020e-01j,
-0.41418762+9.10066489e-03j, -0.41835419+1.2...05001+1.38777878e-16j, 0.06082859-1.73544744e-01j,
-0.59212176+3.79578056e-01j, 0.57441682-5.35857893e-01j]])
alpha = array([134.39477527+4.26325641e-14j, 4.98648107-4.92920441e+00j,
-12.05432554-2.48689958e-13j, -7.2906963 +5.73525227e-10j])
beta = array([1.06748818+0.j, 1.13605915+0.j, 0.3238014 +0.j, 3.16969926+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497ed5b0>
sort = 'rhp'
______________________________ TestOrdQZ.test_iuc ______________________________
scipy/linalg/tests/test_decomp.py:2372: in test_iuc
self.check_all('iuc')
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497dee80>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.570688...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.5706889...5001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497dee80>
sort = 'iuc'
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-3.505470e-19j, 0.009174-5.035516e-18j,
E 0.010538-2.263648e-18j, 0.005477-1.979775e-17j],
E [ 0.009174+5.557735e-18j, 0.012032+1.067203e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.57068897... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 22.19705256-2.19420886e+01j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 1.06748818+0.00000000e+00j, -1.18208167-2.42028619e-14j,
6.47769615-5.09570209e-10j, 2.14942102-2.1... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 5.05710627+0.00000000e+00j]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[-7.43612701e-02-7.43612701e-02j, 7.01704469e-03+7.01704469e-03j,
-7.43872879e-03-7.43872879e-03j, -4....6865e-18j, -9.01568861e-04-9.01568861e-04j,
-1.25368789e-03-1.25368789e-03j, -6.49405396e-01-6.49405396e-01j]])
Z = array([[ 0.06688086+2.77555756e-17j, 0.03340062+7.07767178e-16j,
-0.61255237+4.81867324e-11j, 0.00117845-1.1...05001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]])
alpha = array([134.39477527+4.26325641e-14j, -3.50777004-7.28306304e-14j,
-5.62833156+4.42755610e-10j, 22.19705256-2.19420886e+01j])
beta = array([1.06748818+0.j, 0.09422517+0.j, 2.44697045+0.j, 5.05710627+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497dee80>
sort = 'iuc'
______________________________ TestOrdQZ.test_ouc ______________________________
scipy/linalg/tests/test_decomp.py:2375: in test_ouc
self.check_all('ouc')
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497efc70>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.570688...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.5706889...5001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497efc70>
sort = 'ouc'
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-3.505470e-19j, 0.009174-5.035516e-18j,
E 0.010538-2.263648e-18j, 0.005477-1.979775e-17j],
E [ 0.009174+5.557735e-18j, 0.012032+1.067203e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.57068897... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 22.19705256-2.19420886e+01j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 1.06748818+0.00000000e+00j, -1.18208167-2.42028619e-14j,
6.47769615-5.09570209e-10j, 2.14942102-2.1... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 5.05710627+0.00000000e+00j]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[-7.43612701e-02-7.43612701e-02j, 7.01704469e-03+7.01704469e-03j,
-7.43872879e-03-7.43872879e-03j, -4....6865e-18j, -9.01568861e-04-9.01568861e-04j,
-1.25368789e-03-1.25368789e-03j, -6.49405396e-01-6.49405396e-01j]])
Z = array([[ 0.06688086+2.77555756e-17j, 0.03340062+7.07767178e-16j,
-0.61255237+4.81867324e-11j, 0.00117845-1.1...05001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]])
alpha = array([134.39477527+4.26325641e-14j, -3.50777004-7.28306304e-14j,
-5.62833156+4.42755610e-10j, 22.19705256-2.19420886e+01j])
beta = array([1.06748818+0.j, 0.09422517+0.j, 2.44697045+0.j, 5.05710627+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1497efc70>
sort = 'ouc'
______________________________ TestOrdQZ.test_ref ______________________________
scipy/linalg/tests/test_decomp.py:2386: in test_ref
self.check_all(sort)
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0b20>
sort = <function TestOrdQZ.test_ref.<locals>.sort at 0x149353940>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.570688...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.5706889...5001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0b20>
sort = <function TestOrdQZ.test_ref.<locals>.sort at 0x149353940>
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-3.505470e-19j, 0.009174-5.035516e-18j,
E 0.010538-2.263648e-18j, 0.005477-1.979775e-17j],
E [ 0.009174+5.557735e-18j, 0.012032+1.067203e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.57068897... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 22.19705256-2.19420886e+01j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 1.06748818+0.00000000e+00j, -1.18208167-2.42028619e-14j,
6.47769615-5.09570209e-10j, 2.14942102-2.1... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 5.05710627+0.00000000e+00j]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[-7.43612701e-02-7.43612701e-02j, 7.01704469e-03+7.01704469e-03j,
-7.43872879e-03-7.43872879e-03j, -4....6865e-18j, -9.01568861e-04-9.01568861e-04j,
-1.25368789e-03-1.25368789e-03j, -6.49405396e-01-6.49405396e-01j]])
Z = array([[ 0.06688086+2.77555756e-17j, 0.03340062+7.07767178e-16j,
-0.61255237+4.81867324e-11j, 0.00117845-1.1...05001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]])
alpha = array([134.39477527+4.26325641e-14j, -3.50777004-7.28306304e-14j,
-5.62833156+4.42755610e-10j, 22.19705256-2.19420886e+01j])
beta = array([1.06748818+0.j, 0.09422517+0.j, 2.44697045+0.j, 5.05710627+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0b20>
sort = <function TestOrdQZ.test_ref.<locals>.sort at 0x149353940>
______________________________ TestOrdQZ.test_cef ______________________________
scipy/linalg/tests/test_decomp.py:2397: in test_cef
self.check_all(sort)
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0cd0>
sort = <function TestOrdQZ.test_cef.<locals>.sort at 0x149353f70>
scipy/linalg/tests/test_decomp.py:2363: in check_all
self.check(Ai, Bi, sort, *reti)
Ai = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
Bi = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
ret = ((array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.570688...]]), array([1.+0.j, 0.+0.j]), array([1., 0.]), array([[1., 0.],
[0., 1.]]), array([[1., 0.],
[0., 1.]])))
reti = (array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.5706889...5001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]]))
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0cd0>
sort = <function TestOrdQZ.test_cef.<locals>.sort at 0x149353f70>
scipy/linalg/tests/test_decomp.py:2316: in check
assert_array_almost_equal(Q @ Q.T.conj(), Id)
E AssertionError:
E Arrays are not almost equal to 6 decimals
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98869617
E Max relative difference: 0.98869617
E x: array([[ 0.011304-3.505470e-19j, 0.009174-5.035516e-18j,
E 0.010538-2.263648e-18j, 0.005477-1.979775e-17j],
E [ 0.009174+5.557735e-18j, 0.012032+1.067203e-19j,...
E y: array([[1., 0., 0., 0.],
E [0., 1., 0., 0.],
E [0., 0., 1., 0.],
E [0., 0., 0., 1.]])
A = array([[-21.1 -22.5j , 53.5 -50.5j , -34.5 +127.5j , 7.5 +0.5j ],
[ -0.46 -7.78j, -3.5 -37.5j , -15.5....7 -17.1j , -68.5 +12.5j , -7.5 -3.5j ],
[ 5.5 +4.4j , 14.4 +43.3j , -32.5 -46.j , -19. -32.5j ]])
AA = array([[134.39477527+4.26325641e-14j, -7.48252835-1.22568622e-13j,
89.4978431 -7.04037702e-09j, -10.57068897... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 22.19705256-2.19420886e+01j]])
B = array([[ 1. -5.j , 1.6+1.2j, -3. +0.j , 0. -1.j ],
[ 0.8-0.6j, 0. -5.j , -4. +3.j , -2.4-3.2j],
[ 1. +0.j , 2.4+1.8j, -4. -5.j , 0. -3.j ],
[ 0. +1.j , -1.8+2.4j, 0. -4.j , 4. -5.j ]])
BB = array([[ 1.06748818+0.00000000e+00j, -1.18208167-2.42028619e-14j,
6.47769615-5.09570209e-10j, 2.14942102-2.1... +0.00000000e+00j, 0. +0.00000000e+00j,
0. +0.00000000e+00j, 5.05710627+0.00000000e+00j]])
Id = array([[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.]])
Q = array([[-7.43612701e-02-7.43612701e-02j, 7.01704469e-03+7.01704469e-03j,
-7.43872879e-03-7.43872879e-03j, -4....6865e-18j, -9.01568861e-04-9.01568861e-04j,
-1.25368789e-03-1.25368789e-03j, -6.49405396e-01-6.49405396e-01j]])
Z = array([[ 0.06688086+2.77555756e-17j, 0.03340062+7.07767178e-16j,
-0.61255237+4.81867324e-11j, 0.00117845-1.1...05001+1.38777878e-16j, 0.13130749+2.80331314e-15j,
0.06726816-5.29180866e-12j, 0.75392979-7.45269858e-01j]])
alpha = array([134.39477527+4.26325641e-14j, -3.50777004-7.28306304e-14j,
-5.62833156+4.42755610e-10j, 22.19705256-2.19420886e+01j])
beta = array([1.06748818+0.j, 0.09422517+0.j, 2.44697045+0.j, 5.05710627+0.j])
self = <scipy.linalg.tests.test_decomp.TestOrdQZ object at 0x1495d0cd0>
sort = <function TestOrdQZ.test_cef.<locals>.sort at 0x149353f70>
____________________ TestOrdQZWorkspaceSize.test_decompose _____________________
scipy/linalg/tests/test_decomp.py:2474: in test_decompose
_ = ordqz(A, B, sort=lambda alpha, beta: alpha < beta,
A = array([[0.3400652 +0.j, 0.40679654+0.j, 0.03785227+0.j, ...,
0.46569912+0.j, 0.34000689+0.j, 0.18197288+0.j],
...
[0.35160158+0.j, 0.41201777+0.j, 0.68358587+0.j, ...,
0.50537399+0.j, 0.08104583+0.j, 0.39046819+0.j]])
B = array([[0.01923255+0.j, 0.58262855+0.j, 0.18256915+0.j, ...,
0.98463685+0.j, 0.60828429+0.j, 0.56107299+0.j],
...
[0.3774488 +0.j, 0.22516928+0.j, 0.08706877+0.j, ...,
0.9015293 +0.j, 0.18932018+0.j, 0.78947954+0.j]])
N = 202
_ = (array([[-4.07674921e+00, -3.65758548e-01, 6.84469640e-03, ...,
1.01177726e+00, -5.76916826e+00, 2.12013153...28, -0.10160696],
[-0.02071906, -0.03215941, -0.04681432, ..., 0.01070236,
0.07517671, 0.03073703]]))
ddtype = <class 'numpy.complex128'>
self = <scipy.linalg.tests.test_decomp.TestOrdQZWorkspaceSize object at 0x1497ddd30>
scipy/linalg/_decomp_qz.py:353: in ordqz
(AA, BB, _, *ab, Q, Z, _, _), typ = _qz(A, B, output=output, sort=None,
A = array([[0.3400652 +0.j, 0.40679654+0.j, 0.03785227+0.j, ...,
0.46569912+0.j, 0.34000689+0.j, 0.18197288+0.j],
...
[0.35160158+0.j, 0.41201777+0.j, 0.68358587+0.j, ...,
0.50537399+0.j, 0.08104583+0.j, 0.39046819+0.j]])
B = array([[0.01923255+0.j, 0.58262855+0.j, 0.18256915+0.j, ...,
0.98463685+0.j, 0.60828429+0.j, 0.56107299+0.j],
...
[0.3774488 +0.j, 0.22516928+0.j, 0.08706877+0.j, ...,
0.9015293 +0.j, 0.18932018+0.j, 0.78947954+0.j]])
check_finite = True
output = 'complex'
overwrite_a = False
overwrite_b = False
sort = <function TestOrdQZWorkspaceSize.test_decompose.<locals>.<lambda> at 0x149353670>
scipy/linalg/_decomp_qz.py:127: in _qz
warnings.warn("The QZ iteration failed. (a,b) are not in Schur "
E scipy.linalg.misc.LinAlgWarning: The QZ iteration failed. (a,b) are not in Schur form, but ALPHAR(j), ALPHAI(j), and BETA(j) should be correct for J=98,...,N
A = array([[0.3400652 +0.j, 0.40679654+0.j, 0.03785227+0.j, ...,
0.46569912+0.j, 0.34000689+0.j, 0.18197288+0.j],
...
[0.35160158+0.j, 0.41201777+0.j, 0.68358587+0.j, ...,
0.50537399+0.j, 0.08104583+0.j, 0.39046819+0.j]])
B = array([[0.01923255+0.j, 0.58262855+0.j, 0.18256915+0.j, ...,
0.98463685+0.j, 0.60828429+0.j, 0.56107299+0.j],
...
[0.3774488 +0.j, 0.22516928+0.j, 0.08706877+0.j, ...,
0.9015293 +0.j, 0.18932018+0.j, 0.78947954+0.j]])
a1 = array([[0.3400652 +0.j, 0.40679654+0.j, 0.03785227+0.j, ...,
0.46569912+0.j, 0.34000689+0.j, 0.18197288+0.j],
...
[0.35160158+0.j, 0.41201777+0.j, 0.68358587+0.j, ...,
0.50537399+0.j, 0.08104583+0.j, 0.39046819+0.j]])
a_m = 202
a_n = 202
b1 = array([[0.01923255+0.j, 0.58262855+0.j, 0.18256915+0.j, ...,
0.98463685+0.j, 0.60828429+0.j, 0.56107299+0.j],
...
[0.3774488 +0.j, 0.22516928+0.j, 0.08706877+0.j, ...,
0.9015293 +0.j, 0.18932018+0.j, 0.78947954+0.j]])
b_m = 202
b_n = 202
check_finite = True
gges = <fortran object>
info = 99
lwork = 6666
output = 'complex'
overwrite_a = False
overwrite_b = False
result = (array([[ 2.52544948e+00+2.52544986e+00j, -3.15241315e-01-3.15241315e-01j,
3.33983793e-01+3.33983793e-01j, ..... -1.82661396e-10-1.82661396e-10j, -3.33609739e-08-3.33609739e-08j,
-4.46818968e-02-4.46818968e-02j]]), ...)
sfunction = <function _qz.<locals>.<lambda> at 0x1493535e0>
sort = None
typa = 'D'
typb = 'D'
______________________ test_cossin[True-4-2-2-complex64] _______________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.000476837
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.984457
E Max relative difference: 43.62083
E x: array([[ 0.247539+0.212893j, -0.097059+0.043832j, 0.495105+0.6285j ,
E 0.305295+0.385632j],
E [-0.378363-0.106404j, -0.07387 +0.060195j, 0.551738-0.08671j ,...
E y: array([[ 0.247539+0.250358j, -0.078446-0.101413j, -0.40529 +1.026551j,
E 0.196049+0.111241j],
E [-0.378363-0.376737j, -0.056753-0.073382j, -0.062781+0.191695j,...
cs = array([[ 0.5111532 , 0. , 0.8594896 , 0. ],
[ 0. , 0.14221044, 0. , 0.9898364... , 0.5111532 , 0. ],
[-0. , -0.98983645, 0. , 0.14221044]],
dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 4
p = 2
q = 2
swap_sign = True
u = array([[ 0.4789903 +0.48450702j, 0.68741924+0.68741924j,
0. +0.j , 0. +0.j ],
....j , 0. +0.j ,
-0.55586 -0.5809152j , -0.81093484-0.81093484j]],
dtype=complex64)
vh = array([[ 0.99958915+0.j , 0.02842978+0.00364315j,
0. +0.j , 0. +0.j ],
....j , 0. +0.j ,
0.3809979 +0.8601899j , -0.31575269+0.12334777j]],
dtype=complex64)
x = array([[ 0.24753878+0.21289341j, -0.09705902+0.04383202j,
0.49510536+0.6285002j , 0.30529493+0.3856325j ],
....0961457j , -0.76420313-0.24125303j,
-0.08124338-0.188753j , -0.19499378+0.13314982j]],
dtype=complex64)
______________________ test_cossin[True-4-2-2-complex128] ______________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=8.88178e-13
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98445686
E Max relative difference: 43.6209201
E x: array([[ 0.247539+0.212893j, -0.097059+0.043832j, 0.495105+0.6285j ,
E 0.305295+0.385632j],
E [-0.378363-0.106405j, -0.07387 +0.060195j, 0.551738-0.08671j ,...
E y: array([[ 0.247539+0.250358j, -0.078446-0.101413j, -0.405289+1.026551j,
E 0.196049+0.111241j],
E [-0.378363-0.376737j, -0.056753-0.073382j, -0.062781+0.191695j,...
cs = array([[ 0.51115322, 0. , 0.85948961, 0. ],
[ 0. , 0.14221044, 0. , 0.9898364... [-0.85948961, -0. , 0.51115322, 0. ],
[-0. , -0.98983645, 0. , 0.14221044]])
dtype_ = <class 'numpy.complex128'>
m = 4
p = 2
q = 2
swap_sign = True
u = array([[ 0.47899024+0.48450695j, 0.68741938+0.68741938j,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
-0.55586001-0.58091521j, -0.81093484-0.81093484j]])
vh = array([[ 0.99958915+0.j , 0.02842975+0.00364314j,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
0.38099787+0.86018965j, -0.31575257+0.12334789j]])
x = array([[ 0.24753878+0.21289341j, -0.09705902+0.04383202j,
0.49510535+0.62850021j, 0.30529494+0.3856325j ],
...,
[ 0.50056658-0.0961457j , -0.76420315-0.24125303j,
-0.08124338-0.188753j , -0.19499378+0.13314982j]])
_____________________ test_cossin[True-40-12-20-complex64] _____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.00476837
E
E Mismatched elements: 1599 / 1600 (99.9%)
E Max absolute difference: 0.86879224
E Max relative difference: 102.203026
E x: array([[ 0.067269-0.105123j, -0.132009+0.072522j, 0.191574-0.130776j,
E ..., -0.045967-0.092064j, 0.032634-0.191569j,
E 0.083053-0.026689j],...
E y: array([[ 1.148676e-02+0.123114j, 3.815025e-03-0.267871j,
E 1.696894e-02+0.366338j, ..., 2.460654e-02-0.116443j,
E -9.248574e-03+0.074464j, 2.038984e-03+0.164055j],...
cs = array([[0.95636225, 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0.93... ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ]], dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 40
p = 12
q = 20
swap_sign = True
u = array([[ 0.64554113+0.64554113j, 0.10857041+0.10856328j,
-0.1511025 -0.1511025j , ..., 0. +0.j ... , ..., 0.12548961+0.1452842j ,
0.27403247-0.03251076j, -0.08519427+0.02226634j]],
dtype=complex64)
vh = array([[ 0.02462583+0.02462583j, -0.02892695-0.02892695j,
0.12876168+0.12876168j, ..., 0. +0.j ... , ..., 0.04119656+0.04119656j,
0.03000575+0.03000575j, 0.05606353+0.05606353j]],
dtype=complex64)
x = array([[ 0.06726895-0.10512291j, -0.13200909+0.07252232j,
0.19157375-0.13077633j, ..., -0.04596726-0.09206396...7043j, ..., 0.00936194+0.3188879j ,
-0.11522646+0.04287184j, 0.09563287+0.13534644j]],
dtype=complex64)
____________________ test_cossin[True-40-12-20-complex128] _____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=8.88178e-12
E
E Mismatched elements: 1600 / 1600 (100%)
E Max absolute difference: 0.42390936
E Max relative difference: 36.40584215
E x: array([[ 0.067269-0.105123j, -0.132009+0.072522j, 0.191574-0.130776j,
E ..., -0.045967-0.092064j, 0.032634-0.191569j,
E 0.083053-0.026689j],...
E y: array([[ 0.172392-0.037854j, -0.204531-0.059487j, 0.32235 +0.060797j,
E ..., 0.046097-0.138031j, 0.224203-0.158936j,
E 0.109742+0.056365j],...
cs = array([[0.95636219, 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0.93... ,
0. ],
[0. , 0. , 0. , ..., 0. , 0. ,
0. ]])
dtype_ = <class 'numpy.complex128'>
m = 40
p = 12
q = 20
swap_sign = True
u = array([[-0.64554115-0.64554115j, -0.10857071-0.10857071j,
0.15110294+0.15110294j, ..., 0. +0.j ... 0. +0.j , ..., 0.12548952+0.14528416j,
0.27403258-0.03251085j, -0.08519428+0.02226641j]])
vh = array([[-0.02462583+0.1486336j , 0.02892693-0.16463658j,
-0.12876166+0.26929783j, ..., 0. +0.j ... 0. +0.j , ..., -0.04119643+0.09759015j,
-0.0300057 +0.1937469j , -0.05606345+0.1462833j ]])
x = array([[ 0.06726894-0.10512291j, -0.13200909+0.07252232j,
0.19157375-0.13077633j, ..., -0.04596726-0.09206396... -0.10764146+0.14217044j, ..., 0.00936194+0.31888788j,
-0.11522645+0.04287184j, 0.09563286+0.13534645j]])
____________________ test_cossin[True-100-50-50-complex64] _____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0119209
E
E Mismatched elements: 10000 / 10000 (100%)
E Max absolute difference: 1.0675421
E Max relative difference: 2.151536
E x: array([[ 0.034611+0.054412j, -0.079631+0.022368j, 0.10308 +0.037791j,
E ..., 0.01909 +0.024557j, 0.043826-0.034464j,
E -0.013095-0.097166j],...
E y: array([[-0.78022 +0.043341j, -0.758549+0.155613j, -0.79798 +0.113554j,
E ..., -0.214067+0.062071j, -0.207819+0.011519j,
E -0.214761+0.176216j],...
cs = array([[ 0.999994 , 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. ... ],
[-0. , -0. , -0. , ..., 0. ,
0. , 0.00767171]], dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 100
p = 50
q = 50
swap_sign = True
u = array([[-0.04304973-0.04304973j, 0.04634938+0.04634938j,
0.09702699+0.09702699j, ..., 0. +0.j ... , ..., -0.08185943-0.08185943j,
0.0702801 +0.0702801j , 0.18293446+0.18293446j]],
dtype=complex64)
vh = array([[-0.10104161-0.10104161j, -0.2913747 -0.2913747j ,
-0.07868081-0.07868081j, ..., 0. +0.j ... , ..., 0.09845314+0.09845314j,
0.0831342 +0.0831342j , 0.0927339 +0.0927339j ]],
dtype=complex64)
x = array([[ 0.03461061+0.05441161j, -0.07963095+0.02236791j,
0.10307966+0.03779104j, ..., 0.01909042+0.02455692...9684j, ..., 0.03251724-0.0567811j ,
-0.06798539-0.04788655j, 0.05670264-0.05009903j]],
dtype=complex64)
____________________ test_cossin[True-100-50-50-complex128] ____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-11
E
E Mismatched elements: 10000 / 10000 (100%)
E Max absolute difference: 0.33195028
E Max relative difference: 345.98066458
E x: array([[ 0.034611+0.054412j, -0.079631+0.022368j, 0.10308 +0.037791j,
E ..., 0.01909 +0.024557j, 0.043826-0.034464j,
E -0.013095-0.097166j],...
E y: array([[ 0.034611+0.034611j, -0.013353+0.041097j, 0.023184+0.171712j,
E ..., -0.006756+0.055343j, 0.021558+0.020605j,
E 0.095482+0.112137j],...
cs = array([[ 0.99999396, 0. , 0. , ..., 0. ,
0. , 0. ],
[ 0. ...058, 0. ],
[-0. , -0. , -0. , ..., 0. ,
0. , 0.00767166]])
dtype_ = <class 'numpy.complex128'>
m = 100
p = 50
q = 50
swap_sign = True
u = array([[-0.04304969-0.04304969j, -0.04634962-0.04634962j,
0.09702676+0.09702675j, ..., 0. +0.j ... 0. +0.j , ..., 0.08185913+0.08185913j,
-0.07028021-0.07028021j, 0.18293438+0.18293438j]])
vh = array([[-0.10104178-0.j , -0.29137451+0.07533856j,
-0.07868081+0.04881494j, ..., 0. +0.j ... 0. +0.j , ..., 0.09845379+0.05974957j,
0.0831347 -0.13753482j, 0.09273391-0.12071081j]])
x = array([[ 0.03461061+0.05441161j, -0.07963095+0.02236791j,
0.10307966+0.03779104j, ..., 0.01909042+0.02455692... -0.05204128-0.07969684j, ..., 0.03251724-0.0567811j ,
-0.0679854 -0.04788655j, 0.05670264-0.05009903j]])
______________________ test_cossin[False-4-2-2-complex64] ______________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.000476837
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.9844577
E Max relative difference: 41.969162
E x: array([[ 0.247539+0.212893j, -0.097059+0.043832j, 0.495105+0.6285j ,
E 0.305295+0.385632j],
E [-0.378363-0.106404j, -0.07387 +0.060195j, 0.551738-0.08671j ,...
E y: array([[ 0.247539+0.250358j, -0.078446-0.101413j, -0.405291+1.026549j,
E 0.19605 +0.11124j ],
E [-0.378363-0.376737j, -0.056752-0.073382j, -0.062781+0.191693j,...
cs = array([[ 0.5111532 , 0. , -0.8594896 , -0. ],
[ 0. , 0.14221044, -0. , -0.9898364... , 0.5111532 , 0. ],
[ 0. , 0.98983645, 0. , 0.14221044]],
dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 4
p = 2
q = 2
swap_sign = False
u = array([[ 0.47899032+0.48450702j, 0.68741846+0.68741846j,
0. +0.j , 0. +0.j ],
....j , 0. +0.j ,
0.55586004+0.5830938j , 0.8109352 +0.8109352j ]],
dtype=complex64)
vh = array([[ 0.99958915+0.j , 0.0284297 +0.00364321j,
0. +0.j , 0. +0.j ],
....j , 0. +0.j ,
-0.38099572-0.86019063j, 0.3157529 -0.1233472j ]],
dtype=complex64)
x = array([[ 0.24753878+0.21289341j, -0.09705902+0.04383202j,
0.49510536+0.6285002j , 0.30529493+0.3856325j ],
....0961457j , -0.76420313-0.24125303j,
-0.08124338-0.188753j , -0.19499378+0.13314982j]],
dtype=complex64)
_____________________ test_cossin[False-4-2-2-complex128] ______________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=8.88178e-13
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 0.98445686
E Max relative difference: 41.96895252
E x: array([[ 0.247539+0.212893j, -0.097059+0.043832j, 0.495105+0.6285j ,
E 0.305295+0.385632j],
E [-0.378363-0.106405j, -0.07387 +0.060195j, 0.551738-0.08671j ,...
E y: array([[ 0.247539+0.250358j, -0.078446-0.101413j, -0.405289+1.026551j,
E 0.196049+0.111241j],
E [-0.378363-0.376737j, -0.056753-0.073382j, -0.062781+0.191695j,...
cs = array([[ 0.51115322, 0. , -0.85948961, -0. ],
[ 0. , 0.14221044, -0. , -0.9898364... [ 0.85948961, 0. , 0.51115322, 0. ],
[ 0. , 0.98983645, 0. , 0.14221044]])
dtype_ = <class 'numpy.complex128'>
m = 4
p = 2
q = 2
swap_sign = False
u = array([[ 0.47899024+0.48450695j, -0.68741938-0.68741938j,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
0.55586001+0.58309381j, -0.81093484-0.81093484j]])
vh = array([[ 0.99958915+0.j , 0.02842975+0.00364314j,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
0.38099787+0.86018965j, -0.31575257+0.12334789j]])
x = array([[ 0.24753878+0.21289341j, -0.09705902+0.04383202j,
0.49510535+0.62850021j, 0.30529494+0.3856325j ],
...,
[ 0.50056658-0.0961457j , -0.76420315-0.24125303j,
-0.08124338-0.188753j , -0.19499378+0.13314982j]])
____________________ test_cossin[False-40-12-20-complex64] _____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.00476837
E
E Mismatched elements: 1598 / 1600 (99.9%)
E Max absolute difference: 0.86875063
E Max relative difference: 176.46999
E x: array([[ 0.067269-0.105123j, -0.132009+0.072522j, 0.191574-0.130776j,
E ..., -0.045967-0.092064j, 0.032634-0.191569j,
E 0.083053-0.026689j],...
E y: array([[ 0.011464+0.123139j, 0.00379 -0.267844j, 0.016914+0.366396j,
E ..., -0.001062-0.090938j, -0.034848+0.099892j,
E -0.023596+0.18952j ],...
cs = array([[ 0.95636225, 0. , 0. , ..., -0. ,
-0. , -0. ],
[ 0. ... ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ]], dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 40
p = 12
q = 20
swap_sign = False
u = array([[ 0.6455412 +0.6455412j , 0.10856988+0.1085629j ,
-0.15110286-0.15110286j, ..., 0. +0.j ... , ..., -0.12548956-0.14528416j,
-0.27403256+0.03251077j, 0.08519419-0.02226649j]],
dtype=complex64)
vh = array([[ 0.02462588+0.02462588j, -0.02892709-0.02892709j,
0.12876171+0.12876171j, ..., 0. +0.j ... , ..., 0.04119648+0.04119648j,
0.03000554+0.03000554j, 0.05606308+0.05606308j]],
dtype=complex64)
x = array([[ 0.06726895-0.10512291j, -0.13200909+0.07252232j,
0.19157375-0.13077633j, ..., -0.04596726-0.09206396...7043j, ..., 0.00936194+0.3188879j ,
-0.11522646+0.04287184j, 0.09563287+0.13534644j]],
dtype=complex64)
____________________ test_cossin[False-40-12-20-complex128] ____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=8.88178e-12
E
E Mismatched elements: 1600 / 1600 (100%)
E Max absolute difference: 0.42390936
E Max relative difference: 36.40584215
E x: array([[ 0.067269-0.105123j, -0.132009+0.072522j, 0.191574-0.130776j,
E ..., -0.045967-0.092064j, 0.032634-0.191569j,
E 0.083053-0.026689j],...
E y: array([[ 0.172392-0.037854j, -0.204531-0.059487j, 0.32235 +0.060797j,
E ..., 0.046097-0.138031j, 0.224203-0.158936j,
E 0.109742+0.056365j],...
cs = array([[ 0.95636219, 0. , 0. , ..., -0. ,
-0. , -0. ],
[ 0. ... , 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0. ]])
dtype_ = <class 'numpy.complex128'>
m = 40
p = 12
q = 20
swap_sign = False
u = array([[-0.64554115-0.64554115j, 0.10857071+0.10857071j,
0.15110294+0.15110294j, ..., 0. +0.j ... 0. +0.j , ..., -0.12548952-0.14528416j,
-0.27403258+0.03251085j, 0.08519428-0.02226641j]])
vh = array([[-0.02462583+0.1486336j , 0.02892693-0.16463658j,
-0.12876166+0.26929783j, ..., 0. +0.j ... 0. +0.j , ..., 0.04119643-0.09759015j,
0.0300057 -0.1937469j , 0.05606345-0.1462833j ]])
x = array([[ 0.06726894-0.10512291j, -0.13200909+0.07252232j,
0.19157375-0.13077633j, ..., -0.04596726-0.09206396... -0.10764146+0.14217044j, ..., 0.00936194+0.31888788j,
-0.11522645+0.04287184j, 0.09563286+0.13534645j]])
____________________ test_cossin[False-100-50-50-complex64] ____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0119209
E
E Mismatched elements: 9953 / 10000 (99.5%)
E Max absolute difference: 1.2559024
E Max relative difference: 8.623419
E x: array([[ 0.034611+0.054412j, -0.079631+0.022368j, 0.10308 +0.037791j,
E ..., 0.01909 +0.024557j, 0.043826-0.034464j,
E -0.013095-0.097166j],...
E y: array([[-0.028618+0.061885j, -0.022327+0.015702j, -0.031088+0.195908j,
E ..., 0.350568+0.040654j, 0.372103-0.020275j,
E 0.356554+0.182658j],...
cs = array([[ 0.999994 , 0. , 0. , ..., -0. ,
-0. , -0. ],
[ 0. ... ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0.00767171]], dtype=float32)
dtype_ = <class 'numpy.complex64'>
m = 100
p = 50
q = 50
swap_sign = False
u = array([[ 0.04304946+0.04304946j, -0.04634916-0.04634916j,
-0.09702732-0.09702732j, ..., 0. +0.j ... , ..., 0.08185969+1.0072651j ,
0.07027877+0.07027877j, -0.18293476-0.18293476j]],
dtype=complex64)
vh = array([[ 0.1010414 +0.1010414j , 0.29137468+0.29137468j,
0.07868065+0.07868065j, ..., 0. +0.j ... , ..., -0.09845369-0.09845369j,
-0.08313435-0.08313435j, -0.09273385-0.09273385j]],
dtype=complex64)
x = array([[ 0.03461061+0.05441161j, -0.07963095+0.02236791j,
0.10307966+0.03779104j, ..., 0.01909042+0.02455692...9684j, ..., 0.03251724-0.0567811j ,
-0.06798539-0.04788655j, 0.05670264-0.05009903j]],
dtype=complex64)
___________________ test_cossin[False-100-50-50-complex128] ____________________
scipy/linalg/tests/test_decomp_cossin.py:39: in test_cossin
assert_allclose(x, u @ cs @ vh, rtol=0., atol=m*1e3*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-11
E
E Mismatched elements: 10000 / 10000 (100%)
E Max absolute difference: 0.33195028
E Max relative difference: 345.98019149
E x: array([[ 0.034611+0.054412j, -0.079631+0.022368j, 0.10308 +0.037791j,
E ..., 0.01909 +0.024557j, 0.043826-0.034464j,
E -0.013095-0.097166j],...
E y: array([[ 0.034611+0.034611j, -0.013353+0.041097j, 0.023184+0.171712j,
E ..., -0.006756+0.055343j, 0.021558+0.020605j,
E 0.095482+0.112137j],...
cs = array([[ 0.99999396, 0. , 0. , ..., -0. ,
-0. , -0. ],
[ 0. ...058, 0. ],
[ 0. , 0. , 0. , ..., 0. ,
0. , 0.00767166]])
dtype_ = <class 'numpy.complex128'>
m = 100
p = 50
q = 50
swap_sign = False
u = array([[-0.04304969-0.04304969j, 0.04634962+0.04634962j,
0.09702676+0.09702676j, ..., 0. +0.j ... 0. +0.j , ..., -0.08185913-0.08185913j,
0.07028021+0.07028021j, 0.18293438+0.18293438j]])
vh = array([[-0.10104178-0.j , -0.29137451+0.07533856j,
-0.07868081+0.04881494j, ..., 0. +0.j ... 0. +0.j , ..., 0.09845379+0.05974957j,
0.0831347 -0.13753482j, 0.09273391-0.12071081j]])
x = array([[ 0.03461061+0.05441161j, -0.07963095+0.02236791j,
0.10307966+0.03779104j, ..., 0.01909042+0.02455692... -0.05204128-0.07969684j, ..., 0.03251724-0.0567811j ,
-0.0679854 -0.04788655j, 0.05670264-0.05009903j]])
___________________________ test_cossin_mixed_types ____________________________
scipy/linalg/tests/test_decomp_cossin.py:82: in test_cossin_mixed_types
assert_allclose(x, u @ cs @ vh, rtol=0.,
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-12
E
E Mismatched elements: 16 / 16 (100%)
E Max absolute difference: 1.09238379
E Max relative difference: 0.9420253
E x: array([[ 0.242126, 0.668159, -0.2244 , 0.666771],
E [-0.611676, 0.174841, 0.715847, 0.28783 ],
E [ 0.735828, -0.224535, 0.617059, 0.165469],
E [-0.160576, -0.687443, -0.237586, 0.667226]])
E y: array([[ 0.242126+0.198519j, 0.668159+0.603551j, -0.2244 -0.17953j ,
E 0.666771+0.605688j],
E [-0.611676-0.476692j, 0.174841+0.374838j, 0.715847+0.576951j,...
cs = array([[ 0.71694778, 0. , -0.69712688, -0. ],
[ 0. , 0.62909834, -0. , -0.7773257... [ 0.69712688, 0. , 0.71694778, 0. ],
[ 0. , 0.77732572, 0. , 0.62909834]])
u = array([[ 0.96139997+0.85267945j, -0.27515469-0.27515469j,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
-0.94622056+0.62145418j, 0.32352226+0.32352226j]])
vh = array([[ 0.55943437+0.j, 0.82887465+0.j, 0. +0.j,
0. +0.j],
[ 0.82887465+0.j, -0.55943...74+0.j,
-0.80593007+0.j],
[ 0. +0.j, 0. +0.j, 0.80593007+0.j,
0.59201074+0.j]])
x = array([[ 0.24212565, 0.66815912, -0.2243999 , 0.66677076],
[-0.61167641, 0.17484098, 0.71584656, 0.2878303... [ 0.73582782, -0.22453492, 0.61705864, 0.16546941],
[-0.16057573, -0.68744315, -0.23758581, 0.66722585]])
_________________________________ test_simple __________________________________
scipy/linalg/tests/test_decomp_ldl.py:61: in test_simple
assert_allclose(l.dot(d).dot(l.T), a, atol=spacing(1000.), rtol=0)
E AssertionError:
E Not equal to tolerance rtol=0, atol=1.13687e-13
E
E Mismatched elements: 1 / 16 (6.25%)
E Max absolute difference: 2.12985581
E Max relative difference: 3.71888956
E x: array([[-0.39 -0.71j , 5.14 -0.64j , -7.86 -2.96j ,
E 3.8 +0.92j ],
E [ 5.14 -0.64j , 8.86 +1.81j , -3.52 +0.58j ,...
E y: array([[-0.39-0.71j, 5.14-0.64j, -7.86-2.96j, 3.8 +0.92j],
E [ 5.14-0.64j, 8.86+1.81j, -3.52+0.58j, 5.32-1.59j],
E [-7.86-2.96j, -3.52+0.58j, -2.83-0.03j, -1.54-2.86j],
E [ 3.8 +0.92j, 5.32-1.59j, -1.54-2.86j, -0.56+0.12j]])
a = array([[-0.39-0.71j, 5.14-0.64j, -7.86-2.96j, 3.8 +0.92j],
[ 5.14-0.64j, 8.86+1.81j, -3.52+0.58j, 5.32-1.59... [-7.86-2.96j, -3.52+0.58j, -2.83-0.03j, -1.54-2.86j],
[ 3.8 +0.92j, 5.32-1.59j, -1.54-2.86j, -0.56+0.12j]])
b = array([[ 5., 10., 1., 18.],
[10., 2., 11., 1.],
[ 1., 11., 19., 9.],
[18., 1., 9., 0.]])
c = array([[ 52., 97., 112., 107., 50.],
[ 97., 114., 89., 98., 13.],
[112., 89., 64., 33., 6.],
[107., 98., 33., 60., 73.],
[ 50., 13., 6., 73., 77.]])
d = array([[-0.39 -0.71j , -7.86 -2.96j ,
0. +0.j , 0. +0.j ],
...,
[ 0. +0.j , 0. +0.j ,
0. +0.j , -0.28986036-1.07142312j]])
e = array([[-1.36+0.j , 0. +0.j , 0. +0.j , 0. +0.j ],
[ 1.58-0.9j , -8.87+0.j , 0. +0.j , 0. +0.j ... [ 2.21+0.21j, -1.84+0.03j, -4.63+0.j , 0. +0.j ],
[ 3.91-1.5j , -1.78-1.18j, 0.11-0.11j, -1.84+0.j ]])
l = array([[ 1. +0.j , 0. +0.j ,
0. +0.j , 0. +0.j ],
...,
[ 0.44255824+0.19364837j, -0.4822823 +0.01498936j,
-0.10708219-0.31567809j, 1. +0.j ]])
p = array([0, 2, 1, 3])
u = array([[ 1. , -0.2777036 , -0.19672131, 0.14754098, 0.4 ],
[ 0. , 1. , -0.4262295... , 0. , 1. , -0.6 ],
[ 0. , 0. , 0. , 0. , 1. ]])
x = array([[ 2., 2., -4., 0., 4.],
[ 2., -2., -2., 10., -8.],
[-4., -2., 6., -8., -4.],
[ 0., 10., -8., 6., -6.],
[ 4., -8., -4., -6., 10.]])
_______________________ test_ldl_type_size_combinations ________________________
scipy/linalg/tests/test_decomp_ldl.py:134: in test_ldl_type_size_combinations
assert_allclose(l.dot(d1).dot(l.T), x, rtol=rtol, err_msg=msg2)
E AssertionError:
E Not equal to tolerance rtol=0.0001, atol=0
E Sym failed for size: 30, dtype: <class 'numpy.complex64'>
E x and y nan location mismatch:
E x: array([[nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj,
E nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj,
E nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj, nan+nanj,...
E y: array([[4.286979e+04+8.179955e-01j, 6.061730e-01+1.105522e+00j,
E 1.237040e+00+1.046835e+00j, 7.228262e-01+9.566016e-01j,
E 4.196392e-01+5.735544e-01j, 1.478078e+00+9.985576e-01j,...
complex_dtypes = [<class 'numpy.complex64'>, <class 'numpy.complex128'>]
d1 = array([[ 4.2869793e+04+8.1799555e-01j, 0.0000000e+00+0.0000000e+00j,
0.0000000e+00+0.0000000e+00j, 0.000000...0000e+00+0.0000000e+00j,
0.0000000e+00+0.0000000e+00j, nan +nanj]],
dtype=complex64)
d2 = array([[42869.793+8.1678516e-01j, 0. +0.0000000e+00j,
0. +0.0000000e+00j, 0. +0.0000000e+00j...00e+00j, 0. +0.0000000e+00j,
0. +0.0000000e+00j, 42870.035+1.4343451e+00j]],
dtype=complex64)
dtype = <class 'numpy.complex64'>
l = array([[ 1.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0....nan +nanj,
nan +nanj, 1.00000000e+00+0.00000000e+00j]],
dtype=complex64)
msg = "Failed for size: 750, dtype: <class 'numpy.float64'>"
msg1 = "Her failed for size: 30, dtype: <class 'numpy.complex64'>"
msg2 = "Sym failed for size: 30, dtype: <class 'numpy.complex64'>"
n = 30
p = array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])
real_dtypes = [<class 'numpy.float32'>, <class 'numpy.float64'>]
rtol = 0.0001
sizes = [30, 750]
u = array([[1.00000000e+00+0.00000000e+00j, 1.41362871e-05+2.57564643e-05j,
2.88519022e-05+2.43907343e-05j, 1.6859...0e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 1.00000000e+00+0.00000000e+00j]],
dtype=complex64)
x = array([[4.2869793e+04+8.1799555e-01j, 6.0617304e-01+1.1055222e+00j,
1.2370398e+00+1.0468354e+00j, 7.2282618e-0...100193e-01+7.3975587e-01j,
1.1208339e+00+1.4640008e+00j, 4.2870035e+04+1.4343451e+00j]],
dtype=complex64)
______________________ TestLeastSquaresSolvers.test_gelsd ______________________
scipy/linalg/tests/test_lapack.py:299: in test_gelsd
assert_allclose(x[:-1],
E AssertionError:
E Not equal to tolerance rtol=2.98023e-06, atol=0
E
E Mismatched elements: 2 / 2 (100%)
E Max absolute difference: 1.9126699
E Max relative difference: 0.85848874
E x: array([1.161754+0.011594j, 1.767737+0.026065j], dtype=complex64)
E y: array([1.161754-1.901076j, 1.735882+1.521241j], dtype=complex64)
a1 = array([[1.+4.j , 2.+0.j ],
[4.+0.5j, 5.-3.j ],
[7.-2.j , 8.+0.7j]], dtype=complex64)
b1 = array([16.+0.j, 17.+2.j, 20.-4.j], dtype=complex64)
dtype = <class 'numpy.complex64'>
gelsd = <fortran object>
gelsd_lwork = <fortran object>
info = 0
iwork = 22
iwork_size = 22
lwork = 132
m = 3
n = 2
nrhs = 1
rank = 2
rwork = 871.0
rwork_size = 871
s = array([13.035514, 4.337667], dtype=float32)
self = <scipy.linalg.tests.test_lapack.TestLeastSquaresSolvers object at 0x14933b070>
work = (132+0j)
x = array([1.1617541+0.0115942j , 1.767737 +0.02606473j,
9.860377 +2.9825528j ], dtype=complex64)
_______________________ TestBlockedQR.test_tpqrt_tpmqrt ________________________
scipy/linalg/tests/test_lapack.py:1564: in test_tpqrt_tpmqrt
assert_allclose(Q.T.conj() @ Q, np.eye(2 * n, dtype=dtype),
E AssertionError:
E Not equal to tolerance rtol=0, atol=1.19209e-05
E
E Mismatched elements: 1599 / 1600 (99.9%)
E Max absolute difference: 12.374693
E Max relative difference: 12.374693
E x: array([[ 1. +3.690115e-09j, -0.090805+5.450196e-01j,
E -0.451658+4.996298e-01j, ..., 0.002614-1.333150e-01j,
E -0.051659-2.247769e-02j, -0.060915+1.937353e-02j],...
E y: array([[1.+0.j, 0.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j],
E [0.+0.j, 1.+0.j, 0.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j],
E [0.+0.j, 0.+0.j, 1.+0.j, ..., 0.+0.j, 0.+0.j, 0.+0.j],...
A = array([[3.74928951e-01+0.09681033j, 1.32822515e-02+0.05674176j,
9.14296210e-01+0.55676717j, 3.05153906e-01+0.9...j, 2.86833107e-01+0.3042861j ,
8.13285913e-03+0.12858835j, 1.55575916e-01+0.03962879j]],
dtype=complex64)
B = array([[1.40036270e-01+7.83941671e-02j, 6.77633703e-01+5.40406883e-01j,
4.26636606e-01+4.66444403e-01j, 3.6103...7e-01+1.65434197e-01j,
3.41157168e-01+2.07217753e-01j, 8.07256639e-01+2.73718208e-01j]],
dtype=complex64)
B_pent = array([[1.40036270e-01+7.83941671e-02j, 6.77633703e-01+5.40406883e-01j,
4.26636606e-01+4.66444403e-01j, 3.6103...7e-01+1.65434197e-01j,
3.41157168e-01+2.07217753e-01j, 8.07256639e-01+2.73718208e-01j]],
dtype=complex64)
C = array([[0.93991697+0.14093672j, 0.38034382+0.45281383j,
0.51122963+0.7398031j , 0.30554125+0.66841525j,
...3952322+0.16903687j, 0.08666288+0.59353966j,
0.04896488+0.5521449j , 0.38564578+0.91247076j]], dtype=complex64)
CD = array([[0.93991697+0.14093672j, 0.38034382+0.45281383j,
0.51122963+0.7398031j , 0.30554125+0.66841525j,
...040912 +0.41593793j, 0.21937418+0.5795122j ,
0.20674385+0.41599753j, 0.60505253+0.63885045j]], dtype=complex64)
D = array([[0.80326945+0.7476997j , 0.2536164 +0.92167217j,
0.96988416+0.71148026j, 0.4311564 +0.44658327j,
...040912 +0.41593793j, 0.21937418+0.5795122j ,
0.20674385+0.41599753j, 0.60505253+0.63885045j]], dtype=complex64)
Q = array([[-1.6816545e-01-0.04342198j, 3.0066213e-01-0.5138871j ,
1.4023140e-01-0.5244207j , ..., -1.5535165e-0...., -8.5352756e-02+0.10020322j,
-5.7839133e-02-0.08398443j, 6.5650374e-01-0.00515014j]],
dtype=complex64)
R = array([[-2.229524 +0.j , -2.2234719 -0.05083947j,
-2.5715108 -0.53294176j, -1.8654728 -0.61433774j,
....j , 0. +0.j ,
0. +0.j , 0. +0.j ]],
dtype=complex64)
a = array([[-2.229524 +0.j , -2.2234719 -0.05083947j,
-2.5715108 -0.53294176j, -1.8654728 -0.61433774j,
....26411065j, 0.2868331 +0.3042861j ,
0.00813286+0.12858835j, -2.0977418 +0.j ]],
dtype=complex64)
b = array([[ 5.48111387e-02+2.80626640e-02j, 2.35243499e-01+1.16861470e-01j,
4.12007757e-02+1.09041989e-01j, 6....-01-6.75631687e-04j,
-7.00440584e-03-3.00287344e-02j, -1.51779339e-01-1.54117987e-01j]],
dtype=complex64)
b_pent = array([[ 5.48111387e-02+2.80626640e-02j, 2.35243499e-01+1.16861470e-01j,
4.12007757e-02+1.09041989e-01j, 6....-01-6.75631687e-04j,
-7.00440584e-03-3.00287344e-02j, -1.51779339e-01-1.54117987e-01j]],
dtype=complex64)
c = array([[-1.28262043e-01-1.18303549e+00j, -1.11611533e+00-6.45811200e-01j,
-5.87609410e-01+2.04570353e-01j, -1....-01+8.70146096e-01j,
-9.32434648e-02-4.57058311e-01j, -8.68649840e-01-5.05026519e-01j]],
dtype=complex64)
c_default = array([[-7.0786059e-01-0.22903423j, -1.0925199e+00-0.62839687j,
-9.9235940e-01-0.05771828j, -1.1866330e+00+0.0... , -2.0603260e-01-0.69598025j,
5.0595883e-02-0.77386236j, 3.3600682e-01-1.109668j ]],
dtype=complex64)
cd = array([[-1.28262043e-01-1.18303549e+00j, -1.11611533e+00-6.45811200e-01j,
-5.87609410e-01+2.04570353e-01j, -1....-01-9.33840275e-02j,
-1.62131771e-01+3.30779135e-01j, 1.46363646e-01+7.92887866e-01j]],
dtype=complex64)
d = array([[-0.16231656+1.0507054j , -0.0201031 +0.45571172j,
0.22240877-0.24867892j, -0.7251109 -0.05300257j,
....41283867j, -0.1239416 -0.09338403j,
-0.16213177+0.33077914j, 0.14636365+0.79288787j]],
dtype=complex64)
d_default = array([[ 1.034925 +9.94576216e-02j, 0.6551479 -4.68598783e-01j,
0.56472033-3.70133042e-01j, 0.7713597 -5.6... 0.05344653-1.47254825e-01j,
0.2880966 -4.96875942e-02j, 0.14850661+3.24122995e-01j]],
dtype=complex64)
dtype = <class 'numpy.complex64'>
ind = 2
info = 0
l = 10
n = 20
q = array([[-0.11637557+0.03004932j, 0. -0.j ,
0. -0.j , ..., -0.24739806+0.18624334...4468j, ..., 0.01684452-0.00963912j,
-0.04350069-0.04030803j, 0.6330348 +0.07676551j]],
dtype=complex64)
qCD = array([[-1.28261700e-01-1.18303573e+00j, -1.11611521e+00-6.45811379e-01j,
-5.87609708e-01+2.04570472e-01j, -1....-01-9.33841690e-02j,
-1.62132040e-01+3.30779016e-01j, 1.46363825e-01+7.92887926e-01j]],
dtype=complex64)
self = <scipy.linalg.tests.test_lapack.TestBlockedQR object at 0x149756760>
side = 'R'
t = array([[ 1.16816545e+00+4.34219763e-02j, -3.00662130e-01+5.13887107e-01j,
-1.40231401e-01+5.24420679e-01j, -7....+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 1.07416356e+00+1.88911669e-02j]],
dtype=complex64)
tol = 1.1920928955078125e-05
tpmqrt = <fortran object>
tpqrt = <fortran object>
trans = 'C'
transpose = 'C'
v = array([[ 1.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0....-01-6.75631687e-04j,
-7.00440584e-03-3.00287344e-02j, -1.51779339e-01-1.54117987e-01j]],
dtype=complex64)
_______________________ test_pteqr[1-complex64-float32] ________________________
scipy/linalg/tests/test_lapack.py:2341: in test_pteqr
assert_allclose(z_pteqr @ np.conj(z_pteqr).T, np.identity(n),
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0.000119209
E
E Mismatched elements: 96 / 100 (96%)
E Max absolute difference: 1.0001061
E Max relative difference: 1.0001061
E x: array([[ 2.000106e+00-3.021374e-08j, 5.503352e-05+1.112606e-04j,
E -1.181042e-04-4.784209e-05j, 4.950785e-05-4.735091e-05j,
E -1.976794e-04-3.417278e-04j, -1.611617e-05+2.036547e-04j,...
E y: array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],...
A = array([[ 4.47456567+0.00000000e+00j, 0.04507503-1.52557934e-01j,
-0.0922049 +1.38601588e-01j, 0.25380873-2.2...52491-2.67349083e-02j, -0.25085083-2.54874506e-01j,
0.29112434+2.72393159e-01j, 4.22380331+1.91403149e-17j]])
atol = 0.00011920928955078125
compute_z = 1
d = array([4.6420317, 4.08414 , 4.1616287, 4.8985543, 4.606429 , 4.009197 ,
4.1014714, 4.6635017, 4.0050616, 4.160808 ], dtype=float32)
d_pteqr = array([5.485662 , 5.331787 , 5.1133327, 4.910609 , 4.4465175, 4.1877375,
4.025056 , 3.4652476, 3.1968386, 3.1700296], dtype=float32)
dtype = <class 'numpy.complex64'>
e = array([0.5487338 , 0.6918952 , 0.65196127, 0.22426932, 0.71217924,
0.23724909, 0.3253997 , 0.74649143, 0.6496329 ], dtype=float32)
e_pteqr = array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)
info = 0
n = 10
pteqr = <fortran object>
realtype = <class 'numpy.float32'>
z = array([[ 0.36796927-0.00000000e+00j, 0.33682111+0.00000000e+00j,
-0.32197994-0.00000000e+00j, -0.32232592-0.0...81006-1.17313586e-01j, -0.08329753-8.59727474e-02j,
0.19303824+1.90972428e-01j, 0.25702479+5.66484777e-02j]])
z_pteqr = array([[-8.26988444e-02-0.08333663j, 1.68895304e-01+0.16889837j,
-2.89405584e-02-0.02894056j, -7.16031134e-01... 4.09283876e-01+0.40928388j,
-3.06050181e-01-0.30605018j, -2.95010842e-02-0.02950108j]],
dtype=complex64)
_______________________ test_pteqr[1-complex128-float64] _______________________
scipy/linalg/tests/test_lapack.py:2341: in test_pteqr
assert_allclose(z_pteqr @ np.conj(z_pteqr).T, np.identity(n),
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=2.22045e-13
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 1.00035563
E Max relative difference: 1.00035563
E x: array([[ 2.000356e+00+1.301325e-17j, 9.395033e-06-1.059863e-04j,
E 4.856275e-05-3.100908e-04j, 6.815647e-05-6.781831e-05j,
E -5.866721e-05+1.415251e-04j, 4.634710e-04-1.124743e-04j,...
E y: array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],...
A = array([[ 3.96268517e+00+0.00000000e+00j, -1.32443318e-01-7.68858349e-03j,
9.85335335e-03-4.18432171e-01j, -3....0368e-02j, -3.83619169e-01+1.48004077e-01j,
3.12337359e-01-2.72761538e-01j, 4.37401453e+00+9.80935927e-17j]])
atol = 2.220446049250313e-13
compute_z = 1
d = array([4.64203165, 4.08413996, 4.16162871, 4.89855419, 4.60642906,
4.00919705, 4.10147154, 4.66350177, 4.00506158, 4.16080805])
d_pteqr = array([5.48566661, 5.3317876 , 5.11332967, 4.91061042, 4.44651701,
4.18773978, 4.02505584, 3.4652485 , 3.19683758, 3.17003057])
dtype = <class 'numpy.complex128'>
e = array([0.54873379, 0.6918952 , 0.65196126, 0.22426931, 0.71217922,
0.23724909, 0.3253997 , 0.74649141, 0.6496329 ])
e_pteqr = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
info = 0
n = 10
pteqr = <fortran object>
realtype = <class 'numpy.float64'>
z = array([[ 0.36796928-0.00000000e+00j, 0.3368211 +0.00000000e+00j,
-0.32197992-0.00000000e+00j, 0.3223259 -0.0...81008+1.17313583e-01j, -0.08329752-8.59727498e-02j,
0.19303822+1.90972439e-01j, -0.25702479-5.66484767e-02j]])
z_pteqr = array([[ 2.14866926e-01+2.15688800e-01j, -2.94097613e-01-2.94100623e-01j,
1.98376706e-01+1.98376706e-01j, -2....5782e-02j, 9.14865110e-02+9.14865110e-02j,
-3.43791939e-01-3.43791939e-01j, -3.19445060e-01-3.19445060e-01j]])
_______________________ test_pteqr[2-complex64-float32] ________________________
scipy/linalg/tests/test_lapack.py:2341: in test_pteqr
assert_allclose(z_pteqr @ np.conj(z_pteqr).T, np.identity(n),
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0.000119209
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 3.4252677
E Max relative difference: 3.4252677
E x: array([[3.421343+5.173621e-10j, 1.804402+6.662359e-01j,
E 1.604396+6.672151e-02j, 1.804605+3.335824e-01j,
E 1.875262+1.218040e+00j, 1.970888+3.718435e-01j,...
E y: array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],...
A = array([[4.3745403 , 0.02058449, 0. , 0. , 0. ,
0. , 0. , 0. , 0. ... , 0. , 0. ,
0. , 0. , 0. , 0.43194503, 4.7080727 ]],
dtype=float32)
atol = 0.00011920928955078125
compute_z = 2
d = array([4.3745403, 4.950714 , 4.7319937, 4.5986586, 4.1560187, 4.1559944,
4.0580835, 4.866176 , 4.601115 , 4.7080727], dtype=float32)
d_pteqr = array([6.061098 , 5.42447 , 4.816393 , 4.791948 , 4.3754225, 4.3666797,
4.1330028, 3.986744 , 3.789294 , 3.456317 ], dtype=float32)
dtype = <class 'numpy.complex64'>
e = array([0.02058449, 0.96990985, 0.83244264, 0.21233912, 0.18182497,
0.1834045 , 0.30424225, 0.52475643, 0.43194503], dtype=float32)
e_pteqr = array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)
info = 0
n = 10
pteqr = <fortran object>
realtype = <class 'numpy.float32'>
z = array([[4.3745403 , 0.02058449, 0. , 0. , 0. ,
0. , 0. , 0. , 0. ... , 0. , 0. ,
0. , 0. , 0. , 0.43194503, 4.7080727 ]],
dtype=float32)
z_pteqr = array([[ 7.35389395e-03+7.35389395e-03j, -1.33029052e-05-1.33029052e-05j,
3.66518996e-03+3.66625469e-03j, 3....-01-9.99040484e-01j,
1.39560521e-01+1.39559269e-01j, 8.66624061e-04+2.04886124e-01j]],
dtype=complex64)
_______________________ test_pteqr[2-complex128-float64] _______________________
scipy/linalg/tests/test_lapack.py:2341: in test_pteqr
assert_allclose(z_pteqr @ np.conj(z_pteqr).T, np.identity(n),
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=2.22045e-13
E
E Mismatched elements: 100 / 100 (100%)
E Max absolute difference: 1.00003519
E Max relative difference: 1.00003519
E x: array([[ 1.999984e+00+9.856252e-18j, 3.112064e-06+4.626447e-06j,
E -2.487773e-06-3.363064e-06j, -1.119989e-06-2.650340e-06j,
E 8.210364e-06+1.111426e-05j, 1.163509e-05+1.618926e-05j,...
E y: array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
E [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],...
A = array([[4.37454012, 0.02058449, 0. , 0. , 0. ,
0. , 0. , 0. , 0. ..., 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.43194502, 4.70807258]])
atol = 2.220446049250313e-13
compute_z = 2
d = array([4.37454012, 4.95071431, 4.73199394, 4.59865848, 4.15601864,
4.15599452, 4.05808361, 4.86617615, 4.60111501, 4.70807258])
d_pteqr = array([6.06109887, 5.42447071, 4.8163913 , 4.79194854, 4.37542418,
4.36667628, 4.13300124, 3.98674299, 3.78929723, 3.45631601])
dtype = <class 'numpy.complex128'>
e = array([0.02058449, 0.96990985, 0.83244264, 0.21233911, 0.18182497,
0.18340451, 0.30424224, 0.52475643, 0.43194502])
e_pteqr = array([0., 0., 0., 0., 0., 0., 0., 0., 0.])
info = 0
n = 10
pteqr = <fortran object>
realtype = <class 'numpy.float64'>
z = array([[4.37454012, 0.02058449, 0. , 0. , 0. ,
0. , 0. , 0. , 0. ..., 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.43194502, 4.70807258]])
z_pteqr = array([[ 7.35392347e-03+7.35392347e-03j, -1.33025976e-05-1.33025976e-05j,
3.66546676e-03+3.66550241e-03j, 3....2596e-01j, 2.81914294e-01+2.81914426e-01j,
1.39560737e-01+1.39560732e-01j, 8.66560940e-04+8.64192818e-04j]])
_________________________ test_orcsd_uncsd[complex64] __________________________
scipy/linalg/tests/test_lapack.py:2580: in test_orcsd_uncsd
assert_allclose(X, Xc, rtol=0., atol=1e4*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.00119209
E
E Mismatched elements: 62500 / 62500 (100%)
E Max absolute difference: 2.06105279
E Max relative difference: 1.13639798
E x: array([[-0.003369-0.089671j, 0.023887-0.037137j, -0.008538-0.037053j,
E ..., 0.015854-0.000855j, -0.015245+0.030977j,
E -0.070348+0.010963j],...
E y: array([[-1.353576+0.029958j, -1.340595+0.095377j, -1.334501-0.041374j,
E ..., 1.807032-0.13475j , 1.740764+0.040992j,
E 1.720917+0.034306j],...
S = array([[0.9999632 +0.j, 0. +0.j, 0. +0.j, ...,
0. +0.j, 0. +0.j, 0. +0.j],
... +0.j, 0. +0.j, 0. +0.j, ...,
0. +0.j, 0. +0.j, 0. +0.j]], dtype=complex64)
U = array([[ 0.07988413+0.07988413j, 0.05782943+0.05782943j,
0.02824698+0.02824698j, ..., 0. +0.j ... , ..., -0.01874552-0.01212016j,
-0.02953853-0.04205358j, 0.51570034+0.03584292j]],
dtype=complex64)
VH = array([[-0.15891863-0.15891863j, 0.03217187+0.03217187j,
0.0428614 +0.0428614j , ..., 0. +0.j ... , ..., 0.10454153+0.10454153j,
0.10110135+0.10110135j, -0.03779661-0.03779661j]],
dtype=complex64)
X = array([[-0.00336949-0.08967093j, 0.02388683-0.03713678j,
-0.00853815-0.0370533j , ..., 0.01585419-0.00085457... -0.02441464-0.02295167j, ..., -0.06886998-0.14571939j,
0.00023097+0.00542734j, -0.02792829-0.01951423j]])
Xc = array([[-1.3535756+0.02995761j, -1.3405954+0.09537749j,
-1.3345006-0.04137411j, ..., 1.8070318-0.13475046j,
...86-0.16701436j, ..., -1.3649768-0.05799366j,
-1.3314868-0.08228168j, -1.3028332-0.20417815j]], dtype=complex64)
cs11 = array([[-0.03950119+2.1980256e-03j, -0.00881335+8.9165792e-03j,
0.02004194+4.0721864e-02j, ..., -0.06352181+1...., 0.20033415-2.3184717e-04j,
-0.06482288-2.0680267e-02j, 0.03652084+1.2903346e-01j]],
dtype=complex64)
cs12 = array([[ 1.00000000e+00+0.j , 9.27328039e-03+0.01988285j,
-4.37983796e-02-0.03541439j, ..., 4.9463190...-6.79804385e-02+0.9010644j ,
-2.54274398e-01-0.08880071j, 1.00000000e+00+0.j ]],
dtype=complex64)
cs21 = array([[ 1.0000000e+00-0.00000000e+00j, 1.2352501e-02-1.24206748e-02j,
-2.7828384e-02-5.69836907e-02j, ...,
...0225971e-08-1.53348623e-07j, -7.2488199e-08+8.10794454e-09j,
1.0000000e+00-0.00000000e+00j]], dtype=complex64)
cs22 = array([[ 1. +0.j , 1. -0.j ,
-0.08899979-0.06636783j, ..., 0.10197296+0.11223032...8245j, ..., -0.1200927 +0.03457491j,
-0.00936232+0.05628107j, 0.07879356+0.03218741j]],
dtype=complex64)
dlw = <fortran object>
drv = <fortran object>
dtype_ = <class 'numpy.complex64'>
i = 79
info = 0
lwval = (5941, 1356)
lwvals = {'lrwork': 1356, 'lwork': 5941}
m = 250
n11 = 0
n12 = 0
n21 = 90
n22 = 0
one = (1+0j)
p = 80
pfx = 'un'
q = 170
r = 80
theta = array([0.0085781 , 0.02248442, 0.03334695, 0.05084195, 0.06963684,
0.07842147, 0.08943271, 0.0985026 , 0.124120...9602 , 1.0484569 , 1.0646789 ,
1.0994385 , 1.1222991 , 1.1310798 , 1.1416228 , 1.1646581 ],
dtype=float32)
u1 = array([[ 0.07988413+0.07988413j, 0.05782943+0.05782943j,
0.02824698+0.02824698j, ..., -0.02080848-0.02080848...9114j, ..., 0.03051069+0.03051069j,
-0.02597722-0.02597722j, 0.0460772 +0.0460772j ]],
dtype=complex64)
u2 = array([[ 3.87129523e-02+0.03871295j, -3.27097438e-02-0.03270974j,
-3.01225558e-02-0.03012256j, ..., 4.2209238...-1.87455248e-02-0.01212016j,
-2.95385290e-02-0.04205358j, 5.15700340e-01+0.03584292j]],
dtype=complex64)
v1t = array([[-0.15891863-0.15891863j, 0.03217187+0.03217187j,
0.0428614 +0.0428614j , ..., 0.00702289+0.00702289...5151j, ..., 0.01484766-0.03556336j,
-0.00866541-0.01905349j, -0.03602626+0.05676633j]],
dtype=complex64)
v2t = array([[-0.10825238-0.10825238j, 0.04659836+0.04659836j,
0.08225404+0.08225404j, ..., -0.05718694-0.05718694...8335j, ..., 0.10454153+0.10454153j,
0.10110135+0.10110135j, -0.03779661-0.03779661j]],
dtype=complex64)
_________________________ test_orcsd_uncsd[complex128] _________________________
scipy/linalg/tests/test_lapack.py:2580: in test_orcsd_uncsd
assert_allclose(X, Xc, rtol=0., atol=1e4*np.finfo(dtype_).eps)
E AssertionError:
E Not equal to tolerance rtol=0, atol=2.22045e-12
E
E Mismatched elements: 62500 / 62500 (100%)
E Max absolute difference: 0.20821762
E Max relative difference: 158.76993265
E x: array([[-3.258482e-02+0.043419j, 2.797335e-02-0.009264j,
E 9.460888e-02+0.032588j, ..., 4.591140e-02-0.029226j,
E 5.545141e-02+0.040103j, 3.014565e-02+0.076146j],...
E y: array([[-0.072847+0.001457j, 0.025793-0.034843j, 0.046778+0.101149j,
E ..., 0.03061 +0.025313j, 0.026404+0.046587j,
E -0.038257+0.089164j],...
S = array([[0.99992634+0.j, 0. +0.j, 0. +0.j, ...,
0. +0.j, 0. +0.j, 0. +0.j],
...
[0. +0.j, 0. +0.j, 0. +0.j, ...,
0. +0.j, 0. +0.j, 0. +0.j]])
U = array([[-0.00320732-0.00320732j, -0.03410694-0.03410694j,
-0.01686753-0.01686753j, ..., 0. +0.j ... 0. +0.j , ..., 0.00787045-0.02639398j,
0.08239674-0.02021737j, 0.49223654+0.02381977j]])
VH = array([[-0.09914769+0.09679266j, -0.0969658 +0.02545378j,
-0.01460127-0.02505031j, ..., 0. +0.j ... 0. +0.j , ..., 0.0085658 +0.03856545j,
0.12410058-0.05568307j, -0.04600079+0.10195166j]])
X = array([[-3.25848173e-02+0.04341854j, 2.79733519e-02-0.00926409j,
9.46088802e-02+0.03258825j, ..., 4.5911396...e-02+0.03316959j, ..., 1.03335243e-02-0.03330612j,
-1.54300250e-02-0.04607005j, -3.22918940e-02+0.00488006j]])
Xc = array([[-0.07284679+0.00145678j, 0.02579336-0.03484265j,
0.04677831+0.10114934j, ..., 0.03060963+0.02531281... 0.04367192+0.01500596j, ..., -0.03757676+0.03047612j,
0.01175489+0.01601474j, 0.0225538 +0.02901953j]])
cs11 = array([[ 1.59198433e-02-0.01989984j, -1.49491565e-03+0.04985768j,
-2.64355656e-02+0.00995325j, ..., -7.0482001...e-02+0.02233653j, ..., -1.73723126e-02-0.16793607j,
1.17141234e-01+0.03730273j, -2.67614322e-02+0.03158167j]])
cs12 = array([[ 1.00000000e+00+0.j , -4.03850163e-02+0.00120639j,
2.19921095e-03-0.02013995j, ..., -1.8940490...e-01-0.08396555j, ..., -6.67443195e-02+0.34585953j,
-3.14664310e-01-0.64279191j, 1.00000000e+00+0.j ]])
cs21 = array([[ 1.00000000e+00-0.00000000e+00j, 1.88253621e-05-7.44887898e-02j,
3.90185914e-02-1.60303749e-02j, .......,
1.80072456e-16-6.33272056e-16j, 4.10524726e-16-3.74664719e-16j,
1.00000000e+00-0.00000000e+00j]])
cs22 = array([[ 1. +0.j , 1. -0.j ,
0.00468288-0.04395682j, ..., -0.04137687-0.01881958... -0.00185675+0.0551248j , ..., -0.14763155+0.08193283j,
0.09765526+0.13218974j, -0.09116572+0.04384868j]])
dlw = <fortran object>
drv = <fortran object>
dtype_ = <class 'numpy.complex128'>
i = 79
info = 0
lwval = (5941, 1356)
lwvals = {'lrwork': 1356, 'lwork': 5941}
m = 250
n11 = 0
n12 = 0
n21 = 90
n22 = 0
one = (1+0j)
p = 80
pfx = 'un'
q = 170
r = 80
theta = array([0.01213793, 0.02157956, 0.03200007, 0.05619727, 0.06795927,
0.08145713, 0.0859745 , 0.09337222, 0.113391...02, 0.99565929, 1.01890903, 1.04909933, 1.05345039,
1.06397967, 1.07704039, 1.09967668, 1.13471527, 1.17598369])
u1 = array([[-0.00320732-0.00320732j, -0.03410694-0.03410694j,
-0.01686753-0.01686753j, ..., -0.02266968-0.02266972... -0.00131217-0.00131217j, ..., 0.1281776 +0.12817757j,
0.04525891+0.04525891j, -0.06198617-0.06198617j]])
u2 = array([[-0.00256843-0.00256843j, -0.10828445-0.10828445j,
0.01789632+0.01789632j, ..., -0.05910242-0.01773732... 0.05523842+0.05523842j, ..., 0.00787045-0.02639398j,
0.08239674-0.02021737j, 0.49223654+0.02381977j]])
v1t = array([[-0.09914769+0.09679266j, -0.0969658 +0.02545378j,
-0.01460127-0.02505031j, ..., -0.00351876+0.0495459j... 0.09523747+0.00771924j, ..., -0.05704606-0.03436656j,
0.03065735-0.00179158j, -0.0687944 -0.02585313j]])
v2t = array([[ 0.09007952+0.j , 0.10201691+0.03147212j,
-0.02799111-0.03053774j, ..., 0.02778914-0.02584502... 0.00973552+0.13050744j, ..., 0.0085658 +0.03856545j,
0.12410058-0.05568307j, -0.04600079+0.10195166j]])
__________________________ test_gges_tgexc[complex64] __________________________
scipy/linalg/tests/test_lapack.py:3003: in test_gges_tgexc
d1 = s[0, 0] / t[0, 0]
E RuntimeWarning: divide by zero encountered in cfloat_scalars
a = array([[0.19151945+0.7671166j , 0.62210876+0.70811534j,
0.43772775+0.7968672j , 0.7853586 +0.55776083j,
...3687817+0.604334j , 0.81920207+0.10310444j,
0.05711564+0.8023742j , 0.66942173+0.94555324j]], dtype=complex64)
atol = 1.1920928955078125e-05
b = array([[0.97903883+0.28587767j, 0.88123226+0.9867472j ,
0.6276819 +0.43180084j, 0.93048656+0.5742344j ,
...066752 +0.47332987j, 0.82215977+0.90255505j,
0.6279651 +0.22599553j, 0.11792306+0.30415374j]], dtype=complex64)
dtype = <class 'numpy.complex64'>
gges = <fortran object>
n = 10
q = array([[-9.96816605e-02-9.70854983e-02j, -7.83794820e-02-7.83779323e-02j,
-7.52691645e-03-7.53007969e-03j, -2....-02+4.18403521e-02j,
3.19793187e-02+3.19792219e-02j, -2.90764198e-02-7.63684809e-02j]],
dtype=complex64)
result = (array([[ 0.7632798 +7.6328647e-01j, 0.92274415+9.2274243e-01j,
1.1924442 -1.0255642e+00j, 1.4903862 +1.227...18403521e-02j,
3.19793187e-02+3.19792219e-02j, -2.90764198e-02-7.63684809e-02j]],
dtype=complex64), ...)
s = array([[ 0.7632798 +7.6328647e-01j, 0.92274415+9.2274243e-01j,
1.1924442 -1.0255642e+00j, 1.4903862 +1.2278...j, 0. +0.0000000e+00j,
0. +0.0000000e+00j, 0.63525987-5.2902943e-01j]],
dtype=complex64)
t = array([[ 0.00000000e+00+0.0000000e+00j, 1.21357460e-02+1.2137553e-02j,
-1.24537861e+00+1.0710891e+00j, 1.168...00e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j]],
dtype=complex64)
tgexc = <fortran object>
z = array([[ 0.2900852 +2.9008514e-01j, -0.00980273-9.8028919e-03j,
0.11081608-9.5307469e-02j, -0.51829416-4.2915...j, 0.51072454+2.3841858e-07j,
0.35566515+3.5566533e-01j, -0.04741958-2.0157309e-01j]],
dtype=complex64)
_________________________ test_gges_tgexc[complex128] __________________________
scipy/linalg/tests/test_lapack.py:2996: in test_gges_tgexc
assert_equal(result[-1], 0)
E AssertionError:
E Items are not equal:
E ACTUAL: 6
E DESIRED: 0
a = array([[0.19151945+0.76711663j, 0.62210877+0.70811536j,
0.43772774+0.79686718j, 0.78535858+0.55776083j,
...987j,
0.53687819+0.604334j , 0.81920207+0.10310444j,
0.05711564+0.80237418j, 0.66942174+0.94555324j]])
atol = 2.220446049250313e-14
b = array([[0.97903882+0.28587767j, 0.88123225+0.98674722j,
0.62768192+0.43180085j, 0.93048653+0.57423441j,
...469j,
0.60667522+0.47332988j, 0.82215979+0.90255503j,
0.62796507+0.22599553j, 0.11792306+0.30415374j]])
dtype = <class 'numpy.complex128'>
gges = <fortran object>
n = 10
result = (array([[ 1.53552504e+00+1.53552504e+00j, -2.17808642e-02-2.17808642e-02j,
4.95265518e-02+4.95265518e-02j, 3...06j, 5.50566438e-04+5.50566438e-04j,
-8.22721396e-03-8.22721396e-03j, -9.86414377e-02-9.86414377e-02j]]), ...)
tgexc = <fortran object>
__________________________ test_solve_continuous_are ___________________________
scipy/linalg/tests/test_solvers.py:307: in test_solve_continuous_are
_test_factory(case, min_decimal[ind])
_test_factory = <function test_solve_continuous_are.<locals>._test_factory at 0x149b33310>
case = (array([[ 4. , 3. ],
[-4.5, -3.5]]), array([[ 1],
[-1]]), array([[9., 6.],
[6., 4.]]), 1, None)
cases = [(array([[0., 1.],
[0., 0.]]), array([[0],
[1]]), array([[1., 0.],
[0., 2.]]), 1, None), (array([...e+00, 0.00000000e+00, 0.00000000e+00]]), array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]), None), ...]
ind = 1
mat15 = {'A': array([[-1., -0., 0., ..., 0., 0., 0.],
[ 1., 0., -1., ..., 0., 0., 0.],
[ 0., 0., -1., .... 0., 0., 1., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 1.]])}
mat18 = {'A': array([[-3.71945896e+02, 2.63663585e+02, -7.06484447e+01, ...,
3.26531309e-53, -8.70750158e-54, 2.176... 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]), 'R': array([[1]], dtype=uint8)}
mat19 = {'A': array([[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 0., 0., 0.],
[ 0., 0., 0., ...., 0, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 0, 0, 1]], dtype=uint8), 'R': array([[1, 0],
[0, 1]], dtype=uint8)}
mat20 = {'A': array([[-65.10321489, -0.37813875, 0. , ..., 0. ,
0. , 0. ],
... ...,
[0, 0, 0, ..., 1, 0, 0],
[0, 0, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 0, 0, 1]], dtype=uint8)}
mat6 = {'A': array([[-4.3280e+00, 1.7140e-01, 5.3760e+00, 4.0160e+02, -7.2460e+02,
-1.9330e+00, 1.0200e+00, -9.82...0000000e+00, 0.00000000e+00, 0.00000000e+00]]), 'R': array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])}
min_decimal = (14, 12, 13, 14, 11, 6, ...)
scipy/linalg/tests/test_solvers.py:300: in _test_factory
x = solve_continuous_are(a, b, q, r)
a = array([[ 4. , 3. ],
[-4.5, -3.5]])
b = array([[ 1],
[-1]])
case = (array([[ 4. , 3. ],
[-4.5, -3.5]]), array([[ 1],
[-1]]), array([[9., 6.],
[6., 4.]]), 1, None)
dec = 12
knownfailure = None
q = array([[9., 6.],
[6., 4.]])
r = 1
scipy/linalg/_solvers.py:478: in solve_continuous_are
elwisescale = sca[:, None] * np.reciprocal(sca)
E RuntimeWarning: divide by zero encountered in reciprocal
H = array([[ 4. , 3. , 0. , 0. , 1. ],
[-4.5, -3.5, 0. , 0. , -1. ],
[-9. , -6. , -4. , 4.5, 0. ],
[-6. , -4. , -3. , 3.5, 0. ],
[ 0. , 0. , 1. , -1. , 1. ]])
J = array([[1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.]])
M = array([[0. , 3. , 0. , 0. , 1. ],
[4.5, 0. , 0. , 0. , 1. ],
[9. , 6. , 0. , 4.5, 0. ],
[6. , 4. , 3. , 0. , 0. ],
[0. , 0. , 1. , 1. , 0. ]])
_ = array([0, 1, 2, 3, 4])
a = array([[ 4. , 3. ],
[-4.5, -3.5]])
b = array([[ 1],
[-1]])
balanced = True
e = None
gen_are = False
m = 2
n = 1
q = array([[9., 6.],
[6., 4.]])
r = array([[1]])
r_or_c = <class 'float'>
s = array([0., 0.])
sca = array([1., 1., 1., 1., 1.])
___________________________ test_solve_discrete_are ____________________________
scipy/linalg/tests/test_solvers.py:528: in test_solve_discrete_are
_test_factory(case, min_decimal[ind])
_test_factory = <function test_solve_discrete_are.<locals>._test_factory at 0x149b33820>
case = (array([[ 2.+0.j, 1.-2.j],
[ 0.+0.j, -0.-3.j]]), array([[0],
[1]]), array([[1, 0],
[0, 2]]), array([[1]]), None)
cases = [(array([[ 2.+0.j, 1.-2.j],
[ 0.+0.j, -0.-3.j]]), array([[0],
[1]]), array([[1, 0],
[0, 2]]), ar...0.005, 0. ],
[0. , 0.02 ]]), array([[0.33333333, 0. ],
[0. , 3. ]]), None), ...]
ind = 0
min_decimal = (12, 14, 13, 14, 13, 16, ...)
scipy/linalg/tests/test_solvers.py:520: in _test_factory
x = solve_discrete_are(a, b, q, r)
a = array([[ 2.+0.j, 1.-2.j],
[ 0.+0.j, -0.-3.j]])
b = array([[0],
[1]])
case = (array([[ 2.+0.j, 1.-2.j],
[ 0.+0.j, -0.-3.j]]), array([[0],
[1]]), array([[1, 0],
[0, 2]]), array([[1]]), None)
dec = 12
knownfailure = None
q = array([[1, 0],
[0, 2]])
r = array([[1]])
scipy/linalg/_solvers.py:683: in solve_discrete_are
elwisescale = sca[:, None] * np.reciprocal(sca)
E RuntimeWarning: divide by zero encountered in reciprocal
H = array([[ 2.+0.j, 1.-2.j, 0.+0.j, 0.+0.j, 0.+0.j],
[ 0.+0.j, -0.-3.j, 0.+0.j, 0.+0.j, 1.+0.j],
[-1... 0.+0.j],
[ 0.+0.j, -2.+0.j, 0.+0.j, 1.+0.j, 0.+0.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j]])
J = array([[ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[ 0... 0.+0.j],
[ 0.+0.j, 0.+0.j, 1.+2.j, -0.+3.j, 0.+0.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, -1.+0.j, 0.+0.j]])
M = array([[0. , 2.23606798, 0. , 0. , 0. ],
[0. , 0. , 0. , 0. ... 2. , 2.23606798, 0. , 0. ],
[0. , 0. , 0. , 1. , 0. ]])
_ = array([0, 1, 2, 3, 4])
a = array([[ 2.+0.j, 1.-2.j],
[ 0.+0.j, -0.-3.j]])
b = array([[0],
[1]])
balanced = True
e = None
gen_are = False
m = 2
n = 1
q = array([[1, 0],
[0, 2]])
r = array([[1]])
r_or_c = <class 'complex'>
s = array([0., 1.])
sca = array([1. , 2. , 1. , 0.5, 1. ])
_________________________ TestPlacePoles.test_complex __________________________
scipy/signal/tests/test_ltisys.py:82: in test_complex
self._check(A, B, P)
A = array([[0. , 7. , 0. , 0. ],
[0. , 0. , 0. , 2.33333333],
[0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. ]])
B = array([[0, 0],
[0, 0],
[1, 0],
[0, 1]])
P = array([-3.+0.j, -1.+0.j, -2.-1.j, -2.+1.j])
self = <scipy.signal.tests.test_ltisys.TestPlacePoles object at 0x176ff9c40>
scipy/signal/tests/test_ltisys.py:45: in _check
fsf = place_poles(A, B, P, **kwargs)
A = array([[0. , 7. , 0. , 0. ],
[0. , 0. , 0. , 2.33333333],
[0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. ]])
B = array([[0, 0],
[0, 0],
[1, 0],
[0, 1]])
P = array([-3.+0.j, -1.+0.j, -2.-1.j, -2.+1.j])
kwargs = {}
self = <scipy.signal.tests.test_ltisys.TestPlacePoles object at 0x176ff9c40>
scipy/signal/ltisys.py:3348: in place_poles
stop, cur_rtol, nb_iter = update_loop(ker_pole, transfer_matrix,
A = array([[0. , 7. , 0. , 0. ],
[0. , 0. , 0. , 2.33333333],
[0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. ]])
B = array([[0, 0],
[0, 0],
[1, 0],
[0, 1]])
Q = array([[-0.27216553-1.36082763e-01j, 0.16000261+2.13336816e-01j,
0. +0.00000000e+00j, 0.79653151+4.4... +0.00000000e+00j, -0.96001567+2.77555756e-17j,
0. +0.00000000e+00j, 0.25632374-1.12552418e-01j]])
_ = array([[7.34846923+0.j , 1.90515869+0.95257934j],
[0. +0.j , 2.43051587+0.j ],
[0. +0.j , 0. +0.j ],
[0. +0.j , 0. +0.j ]])
cur_rtol = 0
j = 3
ker_pole = [array([[ 0. +0.j, 0.81997603+0.j],
[ 0. +0.j, -0.3514183 +0.j],
[ 1. +0.j, 0. ... [ 1. +0.j , 0. +0.j ],
[ 0. +0.j , 0.25632374-0.11255242j]])]
ker_pole_j = array([[ 0. +0.j , 0.79653151+0.44925663j],
[ 0. +0.j , -0.29175995-0.01456882j],
[ 1. +0.j , 0. +0.j ],
[ 0. +0.j , 0.25632374-0.11255242j]])
maxiter = 30
method = 'YT'
nb_iter = 0
pole_space_j = array([[-2. -1.j, 0. +0.j],
[-7. +0.j, -2. -1.j],
[ 0. +0.j, 0. +0.j],
[ 0. +0.j, -2.33333333+0.j]])
poles = array([-3.+0.j, -1.+0.j, -2.-1.j, -2.+1.j])
rankB = 2
rtol = 0.001
skip_conjugate = False
transfer_matrix = array([[ 0.57981061+0.j, 0.69871782+0.j, 0.56323283+0.j,
0.31767241+0.j],
[-0.24849026+0.j, -0.09981...78+0.j,
0. +0.j],
[ 0.31948748+0.j, 0.04277864+0.j, 0.18124825+0.j,
-0.07958658+0.j]])
transfer_matrix_j = array([[ 0.56323283, 0.31767241],
[-0.20630544, -0.01030171],
[ 0.70710678, 0. ],
[ 0.18124825, -0.07958658]])
u = array([[ 0., 0., -1., 0.],
[-0., 0., 0., -1.],
[-1., -0., 0., 0.],
[-0., -1., 0., 0.]])
u0 = array([[ 0., 0.],
[-0., 0.],
[-1., -0.],
[-0., -1.]])
u1 = array([[-1., 0.],
[ 0., -1.],
[ 0., 0.],
[ 0., 0.]])
update_loop = <function _YT_loop at 0x12a5a8670>
z = array([[-1., 0.],
[ 0., -1.]])
scipy/signal/ltisys.py:2989: in _YT_loop
det_transfer_matrixb = np.abs(np.linalg.det(transfer_matrix))
B = array([[0, 0],
[0, 0],
[1, 0],
[0, 1]])
hnb = 1
i = 1
ker_pole = [array([[ 0. +0.j, 0.81997603+0.j],
[ 0. +0.j, -0.3514183 +0.j],
[ 1. +0.j, 0. ... [ 1. +0.j , 0. +0.j ],
[ 0. +0.j , 0.25632374-0.11255242j]])]
maxiter = 30
nb_real = 2
nb_try = 0
poles = array([-3.+0.j, -1.+0.j, -2.-1.j, -2.+1.j])
r_comp = array([3])
r_j = array([], dtype=int64)
r_p = array([1])
rtol = 0.001
stop = False
transfer_matrix = array([[ 0.57981061+0.j, 0.69871782+0.j, 0.56323283+0.j,
0.31767241+0.j],
[-0.24849026+0.j, -0.09981...78+0.j,
0. +0.j],
[ 0.31948748+0.j, 0.04277864+0.j, 0.18124825+0.j,
-0.07958658+0.j]])
update_order = array([[1, 0],
[2, 3],
[0, 1],
[2, 3],
[2, 3],
[2, 3],
[0, 1],
[2, 3]])
<__array_function__ internals>:5: in det
???
args = (array([[ 0.57981061+0.j, 0.69871782+0.j, 0.56323283+0.j,
0.31767241+0.j],
[-0.24849026+0.j, -0.0998...+0.j,
0. +0.j],
[ 0.31948748+0.j, 0.04277864+0.j, 0.18124825+0.j,
-0.07958658+0.j]]),)
kwargs = {}
relevant_args = (array([[ 0.57981061+0.j, 0.69871782+0.j, 0.56323283+0.j,
0.31767241+0.j],
[-0.24849026+0.j, -0.0998...+0.j,
0. +0.j],
[ 0.31948748+0.j, 0.04277864+0.j, 0.18124825+0.j,
-0.07958658+0.j]]),)
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/linalg/linalg.py:2158: in det
r = _umath_linalg.det(a, signature=signature)
E RuntimeWarning: divide by zero encountered in det
a = array([[ 0.57981061+0.j, 0.69871782+0.j, 0.56323283+0.j,
0.31767241+0.j],
[-0.24849026+0.j, -0.09981...78+0.j,
0. +0.j],
[ 0.31948748+0.j, 0.04277864+0.j, 0.18124825+0.j,
-0.07958658+0.j]])
result_t = <class 'numpy.complex128'>
signature = 'D->D'
t = <class 'numpy.complex128'>
_____________________________ test_hermitian_modes _____________________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:418: in test_hermitian_modes
eval_evec(symmetric, D, typ, k, which,
D = <std-hermitian>
k = 2
mattype = <class 'scipy.sparse.csr.csr_matrix'>
params = <scipy.sparse.linalg.eigen.arpack.tests.test_arpack.SymmetricParams object at 0x177d34310>
sigma = None
symmetric = True
typ = 'F'
which = 'LM'
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:244: in eval_evec
eigenvalues, evec = eigs_func(ac, k, **kwargs)
OPpart = None
a = array([[4.1003466-5.5486800e-18j, 2.7116542-6.1170468e-03j,
3.3070924-9.8987085e-01j, 2.8662996+5.3747588e-01j...91e-01j, 2.850687 +1.3113559e+00j,
4.365537 +5.4241574e-01j, 5.113538 -4.6394601e-19j]],
dtype=complex64)
ac = <6x6 sparse matrix of type '<class 'numpy.complex64'>'
with 36 stored elements in Compressed Sparse Row format>
atol = 0.00035762786865234375
d = <std-hermitian>
eigs_func = <function eigsh at 0x11cf53e50>
err = 'error for eigsh:standard, typ=F, which=LM, sigma=None, mattype=csr_matrix, OPpart=None, mode=normal'
exact_eval = array([ 2.4328265+0.j, 20.968052 +0.j], dtype=complex64)
general = False
ind = array([4, 5])
k = 2
kwargs = {'mode': 'normal', 'sigma': None, 'tol': 0, 'v0': None, ...}
mattype = <class 'scipy.sparse.csr.csr_matrix'>
mode = 'normal'
ntries = 0
rtol = 0.0017881393432617188
sigma = None
symmetric = True
typ = 'F'
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:1567: in eigsh
ret = eigs(A, k, M=M, sigma=sigma, which=which, v0=v0,
A = <6x6 sparse matrix of type '<class 'numpy.complex64'>'
with 36 stored elements in Compressed Sparse Row format>
M = None
Minv = None
OPinv = None
k = 2
maxiter = None
mode = 'normal'
ncv = None
return_eigenvectors = True
sigma = None
tol = 0
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:1346: in eigs
params.iterate()
A = <6x6 sparse matrix of type '<class 'numpy.complex64'>'
with 36 stored elements in Compressed Sparse Row format>
M = None
M_matvec = None
Minv = None
Minv_matvec = None
OPinv = None
OPpart = None
k = 2
matvec = <bound method LinearOperator.matvec of <6x6 MatrixLinearOperator with dtype=complex64>>
maxiter = None
mode = 1
n = 6
ncv = None
params = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177aa2910>
return_eigenvectors = True
sigma = None
tol = 0
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:758: in iterate
raise ArpackError(self.info, infodict=self.iterate_infodict)
E scipy.sparse.linalg.eigen.arpack.arpack.ArpackError: ARPACK error -8: Error return from LAPACK eigenvalue calculation;
self = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177aa2910>
xslice = slice(12, 18, None)
yslice = slice(6, 12, None)
_________________________ test_real_nonsymmetric_modes _________________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:448: in test_real_nonsymmetric_modes
params = NonSymmetricParams()
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:387: in __init__
GNC['eval'] = eig(GNC['mat'], GNC['bmat'], left=False, right=False)
Ac = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
Ar = array([[0.0336116 , 0.73179173, 0.58046758, 0.62399656, 0.66970158,
0.07444273],
[0.94646305, 0.4887148...5475069,
0.61204159],
[0.27085361, 0.54668945, 0.83312142, 0.8873266 , 0.47043726,
0.86179483]])
GNC = <gen-cmplx-nonsym>
GNR = <gen-real-nonsym>
M = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
N = 6
SNC = <std-cmplx-nonsym>
SNR = <std-real-nonsym>
self = <scipy.sparse.linalg.eigen.arpack.tests.test_arpack.NonSymmetricParams object at 0x177a8b910>
v0 = array([0.79362396, 0.61849376, 0.09206729, 0.60362149, 0.95504774,
0.41524815])
scipy/linalg/decomp.py:222: in eig
return _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
a = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
b = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
check_finite = True
homogeneous_eigvals = False
left = False
overwrite_a = False
overwrite_b = False
right = False
scipy/linalg/decomp.py:89: in _geneig
_check_info(info, 'generalized eig algorithm (ggev)')
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
alpha = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
beta = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
cvl = False
cvr = False
ggev = <fortran object>
homogeneous_eigvals = False
info = 6
left = False
lwork = 198
overwrite_a = False
overwrite_b = False
res = (array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), arr...+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]]), array([198.+0.j]), 0)
right = False
vl = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
vr = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
w = array([nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
work = array([ 1.98000000e+02+0.j , 1.52474242e+00+0.j ,
1.86040856e+00+0.j , 1.75889142e+00+0...0000000e+00+0.j , 0.00000000e+00+0.j ,
0.00000000e+00+0.j , 0.00000000e+00+0.j ])
scipy/linalg/decomp.py:1354: in _check_info
raise LinAlgError(("%s " + positive) % (driver, info,))
E numpy.linalg.LinAlgError: generalized eig algorithm (ggev) did not converge (LAPACK info=6)
driver = 'generalized eig algorithm (ggev)'
info = 6
positive = 'did not converge (LAPACK info=%d)'
_______________________ test_complex_nonsymmetric_modes ________________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:462: in test_complex_nonsymmetric_modes
params = NonSymmetricParams()
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:387: in __init__
GNC['eval'] = eig(GNC['mat'], GNC['bmat'], left=False, right=False)
Ac = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
Ar = array([[0.0336116 , 0.73179173, 0.58046758, 0.62399656, 0.66970158,
0.07444273],
[0.94646305, 0.4887148...5475069,
0.61204159],
[0.27085361, 0.54668945, 0.83312142, 0.8873266 , 0.47043726,
0.86179483]])
GNC = <gen-cmplx-nonsym>
GNR = <gen-real-nonsym>
M = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
N = 6
SNC = <std-cmplx-nonsym>
SNR = <std-real-nonsym>
self = <scipy.sparse.linalg.eigen.arpack.tests.test_arpack.NonSymmetricParams object at 0x177aa2a90>
v0 = array([0.79362396, 0.61849376, 0.09206729, 0.60362149, 0.95504774,
0.41524815])
scipy/linalg/decomp.py:222: in eig
return _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
a = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
b = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
check_finite = True
homogeneous_eigvals = False
left = False
overwrite_a = False
overwrite_b = False
right = False
scipy/linalg/decomp.py:89: in _geneig
_check_info(info, 'generalized eig algorithm (ggev)')
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
alpha = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
beta = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
cvl = False
cvr = False
ggev = <fortran object>
homogeneous_eigvals = False
info = 6
left = False
lwork = 198
overwrite_a = False
overwrite_b = False
res = (array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), arr...+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]]), array([198.+0.j]), 0)
right = False
vl = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
vr = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
w = array([nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
work = array([ 1.98000000e+02+0.j , 1.52474242e+00+0.j ,
1.86040856e+00+0.j , 1.75889142e+00+0...0000000e+00+0.j , 0.00000000e+00+0.j ,
0.00000000e+00+0.j , 0.00000000e+00+0.j ])
scipy/linalg/decomp.py:1354: in _check_info
raise LinAlgError(("%s " + positive) % (driver, info,))
E numpy.linalg.LinAlgError: generalized eig algorithm (ggev) did not converge (LAPACK info=6)
driver = 'generalized eig algorithm (ggev)'
info = 6
positive = 'did not converge (LAPACK info=%d)'
__________________ test_standard_nonsymmetric_starting_vector __________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:475: in test_standard_nonsymmetric_starting_vector
params = NonSymmetricParams()
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:387: in __init__
GNC['eval'] = eig(GNC['mat'], GNC['bmat'], left=False, right=False)
Ac = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
Ar = array([[0.0336116 , 0.73179173, 0.58046758, 0.62399656, 0.66970158,
0.07444273],
[0.94646305, 0.4887148...5475069,
0.61204159],
[0.27085361, 0.54668945, 0.83312142, 0.8873266 , 0.47043726,
0.86179483]])
GNC = <gen-cmplx-nonsym>
GNR = <gen-real-nonsym>
M = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
N = 6
SNC = <std-cmplx-nonsym>
SNR = <std-real-nonsym>
self = <scipy.sparse.linalg.eigen.arpack.tests.test_arpack.NonSymmetricParams object at 0x177972d90>
v0 = array([0.79362396, 0.61849376, 0.09206729, 0.60362149, 0.95504774,
0.41524815])
scipy/linalg/decomp.py:222: in eig
return _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
a = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
b = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
check_finite = True
homogeneous_eigvals = False
left = False
overwrite_a = False
overwrite_b = False
right = False
scipy/linalg/decomp.py:89: in _geneig
_check_info(info, 'generalized eig algorithm (ggev)')
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
alpha = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
beta = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
cvl = False
cvr = False
ggev = <fortran object>
homogeneous_eigvals = False
info = 6
left = False
lwork = 198
overwrite_a = False
overwrite_b = False
res = (array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), arr...+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]]), array([198.+0.j]), 0)
right = False
vl = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
vr = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
w = array([nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
work = array([ 1.98000000e+02+0.j , 1.52474242e+00+0.j ,
1.86040856e+00+0.j , 1.75889142e+00+0...0000000e+00+0.j , 0.00000000e+00+0.j ,
0.00000000e+00+0.j , 0.00000000e+00+0.j ])
scipy/linalg/decomp.py:1354: in _check_info
raise LinAlgError(("%s " + positive) % (driver, info,))
E numpy.linalg.LinAlgError: generalized eig algorithm (ggev) did not converge (LAPACK info=6)
driver = 'generalized eig algorithm (ggev)'
info = 6
positive = 'did not converge (LAPACK info=%d)'
__________________ test_general_nonsymmetric_starting_vector ___________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:488: in test_general_nonsymmetric_starting_vector
params = NonSymmetricParams()
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:387: in __init__
GNC['eval'] = eig(GNC['mat'], GNC['bmat'], left=False, right=False)
Ac = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
Ar = array([[0.0336116 , 0.73179173, 0.58046758, 0.62399656, 0.66970158,
0.07444273],
[0.94646305, 0.4887148...5475069,
0.61204159],
[0.27085361, 0.54668945, 0.83312142, 0.8873266 , 0.47043726,
0.86179483]])
GNC = <gen-cmplx-nonsym>
GNR = <gen-real-nonsym>
M = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
N = 6
SNC = <std-cmplx-nonsym>
SNR = <std-real-nonsym>
self = <scipy.sparse.linalg.eigen.arpack.tests.test_arpack.NonSymmetricParams object at 0x177956250>
v0 = array([0.79362396, 0.61849376, 0.09206729, 0.60362149, 0.95504774,
0.41524815])
scipy/linalg/decomp.py:222: in eig
return _geneig(a1, b1, left, right, overwrite_a, overwrite_b,
a = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
b = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
check_finite = True
homogeneous_eigvals = False
left = False
overwrite_a = False
overwrite_b = False
right = False
scipy/linalg/decomp.py:89: in _geneig
_check_info(info, 'generalized eig algorithm (ggev)')
a1 = array([[0.48870811+0.17661709j, 0.67635185+0.87407565j,
0.59099644+0.82099736j, 0.25166088+0.97449869j,
...967j,
0.80909383+0.01877711j, 0.98349106+0.498656j ,
0.25772843+0.91582274j, 0.73846334+0.55299526j]])
alpha = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
b1 = array([[2.52668095, 1.76407337, 1.6089164 , 1.02004337, 2.55297852,
1.0468024 ],
[1.76407337, 1.9263074...5434713,
1.49642396],
[1.0468024 , 1.40759039, 1.88324583, 0.89514232, 1.49642396,
2.00774384]])
beta = array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j])
cvl = False
cvr = False
ggev = <fortran object>
homogeneous_eigvals = False
info = 6
left = False
lwork = 198
overwrite_a = False
overwrite_b = False
res = (array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), array([0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]), arr...+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]]), array([198.+0.j]), 0)
right = False
vl = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
vr = array([[0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j]])
w = array([nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j, nan+0.j])
work = array([ 1.98000000e+02+0.j , 1.52474242e+00+0.j ,
1.86040856e+00+0.j , 1.75889142e+00+0...0000000e+00+0.j , 0.00000000e+00+0.j ,
0.00000000e+00+0.j , 0.00000000e+00+0.j ])
scipy/linalg/decomp.py:1354: in _check_info
raise LinAlgError(("%s " + positive) % (driver, info,))
E numpy.linalg.LinAlgError: generalized eig algorithm (ggev) did not converge (LAPACK info=6)
driver = 'generalized eig algorithm (ggev)'
info = 6
positive = 'did not converge (LAPACK info=%d)'
__________________ test_standard_nonsymmetric_no_convergence ___________________
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:505: in test_standard_nonsymmetric_no_convergence
w, v = eigs(m, 4, which='LM', v0=m[:, 0], maxiter=5, tol=tol)
atol = 4.440892098500626e-13
k = 0
m = array([[0.19151945+8.93352260e-01j, 0.62210877+4.48584019e-01j,
0.43772774+2.44383579e-01j, 0.78535858+8.14172...8112992 +8.68899271e-01j, 0.8727098 +3.75823074e-01j,
0.66598823+2.81110432e-01j, 0.58878655+1.01350637e-01j]])
rtol = 4.440892098500626e-13
tol = 0
scipy/sparse/linalg/eigen/arpack/arpack.py:1346: in eigs
params.iterate()
A = array([[0.19151945+8.93352260e-01j, 0.62210877+4.48584019e-01j,
0.43772774+2.44383579e-01j, 0.78535858+8.14172...8112992 +8.68899271e-01j, 0.8727098 +3.75823074e-01j,
0.66598823+2.81110432e-01j, 0.58878655+1.01350637e-01j]])
M = None
M_matvec = None
Minv = None
Minv_matvec = None
OPinv = None
OPpart = None
k = 4
matvec = <bound method LinearOperator.matvec of <30x30 MatrixLinearOperator with dtype=complex128>>
maxiter = 5
mode = 1
n = 30
ncv = None
params = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177959a30>
return_eigenvectors = True
sigma = None
tol = 0
v0 = array([0.19151945+0.89335226j, 0.86912739+0.15381227j,
0.28525096+0.13413814j, 0.15257277+0.3618351j ,
0...571851j,
0.98436901+0.65821638j, 0.59697377+0.94320689j,
0.37845461+0.79574615j, 0.02798429+0.06622806j])
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:756: in iterate
self._raise_no_convergence()
self = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177959a30>
xslice = slice(60, 90, None)
yslice = slice(30, 60, None)
scipy/sparse/linalg/eigen/arpack/arpack.py:376: in _raise_no_convergence
raise ArpackNoConvergence(msg % (num_iter, k_ok, self.k), ev, vec)
E scipy.sparse.linalg.eigen.arpack.arpack.ArpackNoConvergence: ARPACK error -1: No convergence (6 iterations, 0/4 eigenvectors converged) [ARPACK error -14: ZNAUPD did not find any eigenvalues to sufficient accuracy.]
ev = array([], dtype=float64)
k_ok = 0
msg = 'No convergence (%d iterations, %d/%d eigenvectors converged) [ARPACK error -14: ZNAUPD did not find any eigenvalues to sufficient accuracy.]'
num_iter = 6
self = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177959a30>
vec = array([], shape=(30, 0), dtype=float64)
The above exception was the direct cause of the following exception:
scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py:510: in test_standard_nonsymmetric_no_convergence
raise AssertionError("Spurious no-eigenvalues-found case") from err
E AssertionError: Spurious no-eigenvalues-found case
atol = 4.440892098500626e-13
k = 0
m = array([[0.19151945+8.93352260e-01j, 0.62210877+4.48584019e-01j,
0.43772774+2.44383579e-01j, 0.78535858+8.14172...8112992 +8.68899271e-01j, 0.8727098 +3.75823074e-01j,
0.66598823+2.81110432e-01j, 0.58878655+1.01350637e-01j]])
rtol = 4.440892098500626e-13
tol = 0
________________________________ test_hermitian ________________________________
scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py:234: in test_hermitian
assert_allclose(np.linalg.norm(H.dot(vx) - B.dot(vx) * wx)
E AssertionError:
E Not equal to tolerance rtol=0, atol=0.0005
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 0.08907774
E Max relative difference: inf
E x: array(0.089078)
E y: array(0)
B = array([[12.24688894+4.53961057e-17j, 1.74679671-7.75084491e-01j,
2.25524057-5.05988710e-01j],
[ 1.746...2473602e-01j],
[ 2.25524057+5.05988710e-01j, 2.32889858-4.12473602e-01j,
13.2674624 -2.08640148e-17j]])
H = array([[10.57050192+0.j , 0.82059188+0.26689052j,
0.9297422 -0.31266156j],
[ 0.82059188-0.2668... 1.03587843-0.18753497j],
[ 0.9297422 +0.31266156j, 1.03587843+0.18753497j,
10.2478854 +0.j ]])
X = array([[0.00620852, 0.30064171, 0.43689317],
[0.612149 , 0.91819808, 0.62573667],
[0.70599757, 0.14983372, 0.74606341]])
_ = array([[-0.12166085-0.11056284j, 0.19910733+0.1846047j ,
-0.17498795-0.20407711j],
[ 0.02021919-0.1590... -0.11754575+0.31589616j],
[-0.1574893 -0.20881762j, -0.1085624 -0.26646251j,
0.05196623-0.05925546j]])
gen = True
gens = [True, False]
j = 2
k = 3
ks = [1, 3, 10, 50]
size = 3
sizes = [3, 10, 50]
v = array([[-0.11061823-0.13594845j, 0.20013978+0.19097171j,
-0.18456201-0.17213689j],
[-0.0739353 -0.0633... 0.10067272+0.10088279j],
[-0.18315346-0.18315346j, -0.18978531-0.18391033j,
-0.00364458-0.00364471j]])
vx = array([-0.11061823-0.13594845j, -0.0739353 -0.06337963j,
-0.18315346-0.18315346j])
w = array([0.70568316, 0.89526432, 0.99224451])
w0 = array([0.70568316, 0.89526432, 0.99224451])
wx = 0.705683157602587
________________________________ test_svd_linop ________________________________
scipy/sparse/linalg/eigen/tests/test_svds.py:275: in test_svd_linop
U1, s1, VH1 = reorder(svds(A, k, which="LM",
A = array([[-0.14301033-0.17070043j, 0.02583255-1.1687512j ,
1.7158403 +1.3977058j , 0.7938058 +0.1196542j ,
...840015j ,
0.13054843+0.83423215j, 0.01331851+1.0741159j ,
0.15345834+0.3275901j ]], dtype=complex64)
L = <6x7 CheckingLinearOperator with dtype=complex64>
U1 = array([[ 0.47100006+0.j , -0.10600049+0.j ,
0.50958393+0.j ],
[-0.405697 -0.4397... 0.13793735+0.11385535j],
[-0.03005731+0.01011889j, 0.53675494-0.38499414j,
0.20508357+0.25645228j]])
U2 = array([[ 0.47100006+0.j , -0.10600049+0.j ,
0.50958393+0.j ],
[-0.405697 -0.4397... 0.13793735+0.11385535j],
[-0.03005731+0.01011889j, 0.53675494-0.38499414j,
0.20508357+0.25645228j]])
VH1 = array([[-0.27339419-0.09549013j, 0.12644833-0.09046217j,
0.28334144+0.29343357j, -0.02417932-0.49182758j,
..., 0.12342614+0.34945522j,
-0.03441411+0.35626839j, 0.50795104+0.25338317j,
-0.10325167-0.02591817j]])
VH2 = array([[-0.27339419-0.09549013j, 0.12644833-0.09046217j,
0.28334144+0.29343357j, -0.02417932-0.49182758j,
..., 0.12342614+0.34945522j,
-0.03441411+0.35626839j, 0.50795104+0.25338317j,
-0.10325167-0.02591817j]])
dt = <class 'numpy.complex64'>
eps = 0.001
k = 3
m = 7
n = 6
nmks = [(6, 7, 3), (9, 5, 4), (10, 8, 5)]
reorder = <function test_svd_linop.<locals>.reorder at 0x17797a790>
rng = RandomState(MT19937) at 0x1771BF440
s1 = array([3.68408079, 4.62436463, 5.64904755])
s2 = array([3.68408079, 4.62436463, 5.64904755])
solver = None
v0 = array([1., 1., 1., 1., 1., 1.])
scipy/sparse/linalg/eigen/_svds.py:225: in svds
eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter,
A = array([[-0.14301033-0.17070043j, 0.02583255-1.1687512j ,
1.7158403 +1.3977058j , 0.7938058 +0.1196542j ,
...840015j ,
0.13054843+0.83423215j, 0.01331851+1.0741159j ,
0.15345834+0.3275901j ]], dtype=complex64)
XH_X = <6x6 _CustomLinearOperator with dtype=complex64>
XH_dot = <built-in method dot of numpy.ndarray object at 0x177b8a270>
XH_mat = <built-in method dot of numpy.ndarray object at 0x177b8a270>
X_dot = <built-in method dot of numpy.ndarray object at 0x177b8af90>
X_matmat = <built-in method dot of numpy.ndarray object at 0x177b8af90>
k = 3
largest = True
m = 7
matmat_XH_X = <function svds.<locals>.matmat_XH_X at 0x17797aaf0>
matvec_XH_X = <function svds.<locals>.matvec_XH_X at 0x17797ae50>
maxiter = None
n = 6
ncv = None
options = None
return_singular_vectors = True
solver = None
tol = 0
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:1567: in eigsh
ret = eigs(A, k, M=M, sigma=sigma, which=which, v0=v0,
A = <6x6 _CustomLinearOperator with dtype=complex64>
M = None
Minv = None
OPinv = None
k = 3
maxiter = None
mode = 'normal'
ncv = None
return_eigenvectors = True
sigma = None
tol = 0
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:1346: in eigs
params.iterate()
A = <6x6 _CustomLinearOperator with dtype=complex64>
M = None
M_matvec = None
Minv = None
Minv_matvec = None
OPinv = None
OPpart = None
k = 3
matvec = <bound method LinearOperator.matvec of <6x6 _CustomLinearOperator with dtype=complex64>>
maxiter = None
mode = 1
n = 6
ncv = None
params = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177959b80>
return_eigenvectors = True
sigma = None
tol = 0
v0 = None
which = 'LM'
scipy/sparse/linalg/eigen/arpack/arpack.py:758: in iterate
raise ArpackError(self.info, infodict=self.iterate_infodict)
E scipy.sparse.linalg.eigen.arpack.arpack.ArpackError: ARPACK error -8: Error return from LAPACK eigenvalue calculation;
self = <scipy.sparse.linalg.eigen.arpack.arpack._UnsymmetricArpackParams object at 0x177959b80>
xslice = slice(12, 18, None)
yslice = slice(6, 12, None)
_____________ TestHyp2f1.test_a_b_negative_int[hyp2f1_test_case2] ______________
scipy/special/tests/test_hyp2f1.py:389: in test_a_b_negative_int
assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
E AssertionError:
E Not equal to tolerance rtol=5e-11, atol=0
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 2.69263521e-14
E Max relative difference: 8.43561629e-11
E x: array(-0.000305-9.261359e-05j)
E y: array(-0.000305-9.261359e-05j)
a = -8
b = 8.095813935368371
c = 4.0013768449590685
expected = (-0.0003054674127221263-9.261359291755414e-05j)
hyp2f1_test_case = Hyp2f1TestCase(a=-8, b=8.095813935368371, c=4.0013768449590685, z=(0.9473684210526314-0.10526315789473695j), expected=(-0.0003054674127221263-9.261359291755414e-05j), rtol=5e-11)
rtol = 5e-11
self = <scipy.special.tests.test_hyp2f1.TestHyp2f1 object at 0x299cc6940>
z = (0.9473684210526314-0.10526315789473695j)
_____________ TestHyp2f1.test_a_b_negative_int[hyp2f1_test_case3] ______________
scipy/special/tests/test_hyp2f1.py:389: in test_a_b_negative_int
assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
E AssertionError:
E Not equal to tolerance rtol=1e-13, atol=0
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 5.49472805e-16
E Max relative difference: 1.17502468e-13
E x: array(-0.002081-0.004188j)
E y: array(-0.002081-0.004188j)
a = -4
b = -3.956227226099288
c = -3.9316537064827854
expected = (-0.0020809502580892937-0.0041877333232365095j)
hyp2f1_test_case = Hyp2f1TestCase(a=-4, b=-3.956227226099288, c=-3.9316537064827854, z=(1.1578947368421053-0.3157894736842106j), expected=(-0.0020809502580892937-0.0041877333232365095j), rtol=1e-13)
rtol = 1e-13
self = <scipy.special.tests.test_hyp2f1.TestHyp2f1 object at 0x299cef880>
z = (1.1578947368421053-0.3157894736842106j)
_ TestHyp2f1.test_a_b_neg_int_after_euler_hypergeometric_transformation[hyp2f1_test_case1] _
scipy/special/tests/test_hyp2f1.py:454: in test_a_b_neg_int_after_euler_hypergeometric_transformation
assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
E AssertionError:
E Not equal to tolerance rtol=5e-12, atol=0
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 6.31453328e-11
E Max relative difference: 5.78868492e-12
E x: array(4.079317-10.116942j)
E y: array(4.079317-10.116942j)
a = 8.5
b = -3.9316537064827854
c = 1.5
expected = (4.0793167523167675-10.11694246310966j)
hyp2f1_test_case = Hyp2f1TestCase(a=8.5, b=-3.9316537064827854, c=1.5, z=(0.9473684210526314-0.10526315789473695j), expected=(4.0793167523167675-10.11694246310966j), rtol=5e-12)
rtol = 5e-12
self = <scipy.special.tests.test_hyp2f1.TestHyp2f1 object at 0x299c09be0>
z = (0.9473684210526314-0.10526315789473695j)
__________________ TestHyp2f1.test_region1[hyp2f1_test_case3] __________________
scipy/special/tests/test_hyp2f1.py:556: in test_region1
assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
E AssertionError:
E Not equal to tolerance rtol=1e-12, atol=0
E
E Mismatched elements: 1 / 1 (100%)
E Max absolute difference: 6.19291275e-13
E Max relative difference: 1.49879074e-12
E x: array(-0.407528-0.068193j)
E y: array(-0.407528-0.068193j)
a = 2.02764642551431
b = 16.088264119063613
c = 8.031683612216888
expected = (-0.4075277891264672-0.06819344579666956j)
hyp2f1_test_case = Hyp2f1TestCase(a=2.02764642551431, b=16.088264119063613, c=8.031683612216888, z=(0.3157894736842106-0.736842105263158j), expected=(-0.4075277891264672-0.06819344579666956j), rtol=1e-12)
rtol = 1e-12
self = <scipy.special.tests.test_hyp2f1.TestHyp2f1 object at 0x299c095e0>
z = (0.3157894736842106-0.736842105263158j)
______________________ test_cont_basic[500-200-beta-arg4] ______________________
scipy/stats/tests/test_continuous_basic.py:152: in test_cont_basic
check_cdf_ppf(distfn, arg, distname)
arg = (2.3098496451481823, 0.6268795430096368)
distfn = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
distname = 'beta'
m = array(0.78653818)
n_fit_samples = 200
rng = RandomState(MT19937) at 0x11AEDFB40
rvs = array([0.82658367, 0.86232975, 0.96288766, 0.76777878, 0.98445302,
0.90940266, 0.9101755 , 0.97958165, 0.932096...42, 0.99978436, 0.28187789, 0.74720748, 0.35576611,
0.98107928, 0.42713941, 0.88318304, 0.60112207, 0.99380819])
sm = 0.7853597191930264
sn = 500
sv = 0.04724445035750433
v = array(0.04264857)
scipy/stats/tests/test_continuous_basic.py:500: in check_cdf_ppf
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
arg = (2.3098496451481823, 0.6268795430096368)
distfn = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
msg = 'beta'
values = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array(2.30984965), array(0.62687954))
cond = array([ True, True, True])
cond0 = True
cond1 = array([ True, True, True])
cond2 = array([False, False, False])
cond3 = array([False, False, False])
goodargs = [array([0.001, 0.5 , 0.999]), array([2.30984965]), array([0.62687954])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array([nan, nan, nan])
q = array([0.001, 0.5 , 0.999])
scale = array([1])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([2.30984965])
b = array([0.62687954])
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_____________________ test_cont_basic[500-200-fisk-arg23] ______________________
scipy/stats/tests/test_continuous_basic.py:152: in test_cont_basic
check_cdf_ppf(distfn, arg, distname)
arg = (3.085754862225318,)
distfn = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
m = array(1.19619763)
n_fit_samples = 200
rng = RandomState(MT19937) at 0x171F94840
rvs = array([0.39660239, 0.64089145, 0.58309115, 2.40778694, 0.69897509,
1.19728895, 1.58168894, 1.25456152, 1.639071...33, 0.68337915, 2.48249301, 0.96536794, 0.89252507,
0.68112488, 1.27363911, 2.06405464, 0.7910704 , 1.82491887])
sm = 1.2116968934029964
sn = 500
sv = 0.6702848337002123
v = array(0.84763509)
scipy/stats/tests/test_continuous_basic.py:500: in check_cdf_ppf
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
arg = (3.085754862225318,)
distfn = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
msg = 'fisk'
values = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = array([ True, True, True])
cond0 = True
cond1 = array([ True, True, True])
cond2 = array([False, False, False])
cond3 = array([False, False, False])
goodargs = [array([0.001, 0.5 , 0.999]), array([3.08575486])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array([nan, nan, nan])
q = array([0.001, 0.5 , 0.999])
scale = array([1])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
upper_bound = inf
scipy/stats/_continuous_distns.py:1127: in _ppf
return burr._ppf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.001, 0.5 , 0.999])
scipy/stats/_continuous_distns.py:962: in _ppf
return (q**(-1.0/d) - 1)**(-1.0/c)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
____________________ test_cont_basic[500-200-kappa3-arg56] _____________________
scipy/stats/tests/test_continuous_basic.py:183: in test_cont_basic
check_random_state_property(distfn, arg)
alpha = 0.01
arg = (1.0,)
distfn = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
distname = 'kappa3'
locscale_defaults = (0, 1)
m = array(nan)
meths = [<bound method rv_continuous.pdf of <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>>, <bound method ...0x11e8db6a0>>, <bound method rv_continuous.logsf of <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>>]
n_fit_samples = 200
rng = RandomState(MT19937) at 0x171F94340
rvs = array([5.76265988e-02, 2.53386956e-01, 1.89286706e-01, 1.50514995e+01,
3.31166900e-01, 1.74302199e+00, 4.115653...8.96945299e-01, 7.04087667e-01, 3.05758560e-01,
2.10934395e+00, 9.35734064e+00, 4.85195608e-01, 6.39931647e+00])
sm = 7.115095284584419
sn = 500
spec_x = {'levy_l': -0.5, 'pareto': 1.5, 'rv_histogram_instance': 5.0, 'tukeylambda': 0.3, ...}
sv = 2325.778875348396
v = array(nan)
x = 0.5
scipy/stats/tests/common_tests.py:205: in check_random_state_property
distfn.rvs(*args, size=1, random_state=rng)
args = (1.0,)
distfn = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
r0 = array([0.23688814, 1.64626412, 0.77849784, 3.6589331 , 3.54495477,
0.37474544, 0.38210172, 4.04724671])
r1 = array([0.23688814, 1.64626412, 0.77849784, 3.6589331 , 3.54495477,
0.37474544, 0.38210172, 4.04724671])
r2 = array([0.23688814, 1.64626412, 0.77849784, 3.6589331 , 3.54495477,
0.37474544, 0.38210172, 4.04724671])
rndm = RandomState(MT19937) at 0x1153B6E40
rng = Generator(PCG64) at 0x17327C200
scipy/stats/_distn_infrastructure.py:1092: in rvs
vals = self._rvs(*args, size=size, random_state=random_state)
args = [array(1.)]
cond = True
discrete = None
kwds = {'size': 1}
loc = array(0)
random_state = Generator(PCG64) at 0x17327C200
random_state_saved = RandomState(MT19937) at 0x1733B8440
rndm = Generator(PCG64) at 0x17327C200
scale = array(1)
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = (1,)
scipy/stats/_distn_infrastructure.py:1010: in _rvs
Y = self._ppf(U, *args)
U = array([0.97669977])
args = (array(1.),)
random_state = Generator(PCG64) at 0x17327C200
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = (1,)
scipy/stats/_continuous_distns.py:5948: in _ppf
return (a/(q**-a - 1.0))**(1.0/a)
E RuntimeWarning: divide by zero encountered in reciprocal
a = array(1.)
q = array([0.97669977])
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
_____________________ test_cont_basic[500-200-rdist-arg85] _____________________
scipy/stats/tests/test_continuous_basic.py:152: in test_cont_basic
check_cdf_ppf(distfn, arg, distname)
arg = (1.6,)
distfn = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
distname = 'rdist'
m = array(0.)
n_fit_samples = 200
rng = RandomState(MT19937) at 0x1722BE340
rvs = array([-0.67476749, -0.52707987, 0.72277652, -0.64387104, -0.23803441,
0.73426926, 0.15423458, -0.05223934, ...955993, 0.38732151, -0.85042831, -0.65962329,
-0.13036107, 0.96821079, 0.82602293, 0.65081821, 0.04353538])
sm = 0.004314273648814117
sn = 500
sv = 0.3653987108705205
v = array(0.38461538)
scipy/stats/tests/test_continuous_basic.py:500: in check_cdf_ppf
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
arg = (1.6,)
distfn = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
msg = 'rdist'
values = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = (array(1.6),)
cond = array([ True, True, True])
cond0 = True
cond1 = array([ True, True, True])
cond2 = array([False, False, False])
cond3 = array([False, False, False])
goodargs = [array([0.001, 0.5 , 0.999]), array([1.6])]
kwds = {}
loc = array([0])
lower_bound = -1.0
output = array([nan, nan, nan])
q = array([0.001, 0.5 , 0.999])
scale = array([1])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = array([1.6])
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([0.8])
b = array([0.8])
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_________________ test_cont_basic[500-200-semicircular-arg89] __________________
scipy/stats/tests/test_continuous_basic.py:152: in test_cont_basic
check_cdf_ppf(distfn, arg, distname)
arg = ()
distfn = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
distname = 'semicircular'
m = array(0.)
n_fit_samples = 200
rng = RandomState(MT19937) at 0x173243340
rvs = array([ 2.32794779e-01, -4.49362996e-01, 1.28233946e-01, -9.41968328e-01,
-1.76262410e-01, 4.09552262e-01, -8...1665e-01, 6.31238201e-01, 1.15932012e-01,
-3.56685522e-01, -8.32108883e-01, 6.16700117e-02, -6.05982480e-01])
sm = -0.022750590093737345
sn = 500
sv = 0.257186961507024
v = array(0.25)
scipy/stats/tests/test_continuous_basic.py:500: in check_cdf_ppf
npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
arg = ()
distfn = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
msg = 'semicircular'
values = [0.001, 0.5, 0.999]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = ()
cond = array([1, 1, 1])
cond0 = 1
cond1 = array([ True, True, True])
cond2 = array([0, 0, 0])
cond3 = array([0, 0, 0])
goodargs = [array([0.001, 0.5 , 0.999])]
kwds = {}
loc = array([0])
lower_bound = -1.0
output = array([nan, nan, nan])
q = array([0.001, 0.5 , 0.999])
scale = array([1])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:7328: in _ppf
return rdist._ppf(q, 3)
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = 3
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = 1.5
b = 1.5
q = array([0.001, 0.5 , 0.999])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_________________________ test_rvs_scalar[fisk-arg23] __________________________
scipy/stats/tests/test_continuous_basic.py:233: in test_rvs_scalar
assert np.isscalar(distfn.rvs(*arg))
arg = (3.085754862225318,)
distfn = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
scipy/stats/_distn_infrastructure.py:1092: in rvs
vals = self._rvs(*args, size=size, random_state=random_state)
args = [array(3.08575486)]
cond = True
discrete = None
kwds = {}
loc = array(0)
random_state = RandomState(MT19937) at 0x1153B6E40
rndm = None
scale = array(1)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
size = ()
scipy/stats/_distn_infrastructure.py:1010: in _rvs
Y = self._ppf(U, *args)
U = array(0.10831149)
args = (array(3.08575486),)
random_state = RandomState(MT19937) at 0x1153B6E40
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
size = ()
scipy/stats/_continuous_distns.py:1127: in _ppf
return burr._ppf(x, c, 1.0)
c = array(3.08575486)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array(0.10831149)
scipy/stats/_continuous_distns.py:962: in _ppf
return (q**(-1.0/d) - 1)**(-1.0/c)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array(3.08575486)
d = 1.0
q = array(0.10831149)
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
________________________ test_rvs_scalar[kappa3-arg56] _________________________
scipy/stats/tests/test_continuous_basic.py:233: in test_rvs_scalar
assert np.isscalar(distfn.rvs(*arg))
arg = (1.0,)
distfn = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
distname = 'kappa3'
scipy/stats/_distn_infrastructure.py:1092: in rvs
vals = self._rvs(*args, size=size, random_state=random_state)
args = [array(1.)]
cond = True
discrete = None
kwds = {}
loc = array(0)
random_state = RandomState(MT19937) at 0x1733B8440
rndm = None
scale = array(1)
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = ()
scipy/stats/_distn_infrastructure.py:1010: in _rvs
Y = self._ppf(U, *args)
U = array(0.95813935)
args = (array(1.),)
random_state = RandomState(MT19937) at 0x1733B8440
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = ()
scipy/stats/_continuous_distns.py:5948: in _ppf
return (a/(q**-a - 1.0))**(1.0/a)
E RuntimeWarning: divide by zero encountered in reciprocal
a = array(1.)
q = array(0.95813935)
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
____________________ test_rvs_broadcast[fisk-shape_args23] _____________________
scipy/stats/tests/test_continuous_basic.py:338: in test_rvs_broadcast
check_rvs_broadcast(distfunc, dist, allargs, bshape, shape_only, 'd')
allargs = [array([[[3.08575486]],
[[3.08575486]],
[[3.08575486]],
[[3.08575486]]]), array([0., 0.]), array([[1.],
[1.],
[1.]])]
bshape = [4, 3, 2]
dist = 'fisk'
distfunc = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
k = 0
loc = array([0., 0.])
nargs = 1
scale = array([[1.],
[1.],
[1.]])
shape_args = (3.085754862225318,)
shape_only = False
shp = (4, 1, 1)
scipy/stats/tests/common_tests.py:336: in check_rvs_broadcast
expected = rvs(*allargs)
allargs = [array([[[3.08575486]],
[[3.08575486]],
[[3.08575486]],
[[3.08575486]]]), array([0., 0.]), array([[1.],
[1.],
[1.]])]
distfunc = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
otype = 'd'
rvs = <numpy.vectorize object at 0x172bfd5b0>
sample = array([[[1.30885548, 0.74358848],
[0.67208762, 1.06903171],
[1.35692128, 0.90440986]],
[[3.575... 0.60562832]],
[[1.04180398, 1.04217857],
[1.19555334, 1.75188064],
[1.36788899, 1.1576074 ]]])
shape = [4, 3, 2]
shape_only = False
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2163: in __call__
return self._vectorize_call(func=func, args=vargs)
args = (array([[[3.08575486]],
[[3.08575486]],
[[3.08575486]],
[[3.08575486]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
excluded = set()
func = <function check_rvs_broadcast.<locals>.<lambda> at 0x172be6670>
kwargs = {}
self = <numpy.vectorize object at 0x172bfd5b0>
vargs = (array([[[3.08575486]],
[[3.08575486]],
[[3.08575486]],
[[3.08575486]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2246: in _vectorize_call
outputs = ufunc(*inputs)
args = (array([[[3.08575486]],
[[3.08575486]],
[[3.08575486]],
[[3.08575486]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
func = <function check_rvs_broadcast.<locals>.<lambda> at 0x172be6670>
inputs = [array([[[3.085754862225318]],
[[3.085754862225318]],
[[3.085754862225318]],
[[3.085754862225318]]], dtype=object), array([0.0, 0.0], dtype=object), array([[1.0],
[1.0],
[1.0]], dtype=object)]
otypes = 'd'
self = <numpy.vectorize object at 0x172bfd5b0>
ufunc = <ufunc '<lambda> (vectorized)'>
scipy/stats/tests/common_tests.py:334: in <lambda>
rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
allargs = (3.085754862225318, 0.0, 1.0)
distfunc = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
scipy/stats/_distn_infrastructure.py:1092: in rvs
vals = self._rvs(*args, size=size, random_state=random_state)
args = [array(3.08575486)]
cond = True
discrete = None
kwds = {}
loc = array(0.)
random_state = RandomState(MT19937) at 0x1153B6E40
rndm = None
scale = array(1.)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
size = ()
scipy/stats/_distn_infrastructure.py:1010: in _rvs
Y = self._ppf(U, *args)
U = array(0.69646919)
args = (array(3.08575486),)
random_state = RandomState(MT19937) at 0x1153B6E40
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
size = ()
scipy/stats/_continuous_distns.py:1127: in _ppf
return burr._ppf(x, c, 1.0)
c = array(3.08575486)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array(0.69646919)
scipy/stats/_continuous_distns.py:962: in _ppf
return (q**(-1.0/d) - 1)**(-1.0/c)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array(3.08575486)
d = 1.0
q = array(0.69646919)
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
___________________ test_rvs_broadcast[kappa3-shape_args56] ____________________
scipy/stats/tests/test_continuous_basic.py:338: in test_rvs_broadcast
check_rvs_broadcast(distfunc, dist, allargs, bshape, shape_only, 'd')
allargs = [array([[[1.]],
[[1.]],
[[1.]],
[[1.]]]), array([0., 0.]), array([[1.],
[1.],
[1.]])]
bshape = [4, 3, 2]
dist = 'kappa3'
distfunc = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
k = 0
loc = array([0., 0.])
nargs = 1
scale = array([[1.],
[1.],
[1.]])
shape_args = (1.0,)
shape_only = False
shp = (4, 1, 1)
scipy/stats/tests/common_tests.py:336: in check_rvs_broadcast
expected = rvs(*allargs)
allargs = [array([[[1.]],
[[1.]],
[[1.]],
[[1.]]]), array([0., 0.]), array([[1.],
[1.],
[1.]])]
distfunc = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
distname = 'kappa3'
otype = 'd'
rvs = <numpy.vectorize object at 0x172506580>
sample = array([[[ 7.06013731, 0.55718918],
[ 1.00398844, 2.15918769],
[ 2.48070677, 0.58793362]],
[...93211]],
[[ 3.73328289, 0.46377763],
[ 1.31534355, 6.64101821],
[ 0.77359501, 4.05427386]]])
shape = [4, 3, 2]
shape_only = False
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2163: in __call__
return self._vectorize_call(func=func, args=vargs)
args = (array([[[1.]],
[[1.]],
[[1.]],
[[1.]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
excluded = set()
func = <function check_rvs_broadcast.<locals>.<lambda> at 0x172be6a60>
kwargs = {}
self = <numpy.vectorize object at 0x172506580>
vargs = (array([[[1.]],
[[1.]],
[[1.]],
[[1.]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2246: in _vectorize_call
outputs = ufunc(*inputs)
args = (array([[[1.]],
[[1.]],
[[1.]],
[[1.]]]), array([0., 0.]), array([[1.],
[1.],
[1.]]))
func = <function check_rvs_broadcast.<locals>.<lambda> at 0x172be6a60>
inputs = [array([[[1.0]],
[[1.0]],
[[1.0]],
[[1.0]]], dtype=object), array([0.0, 0.0], dtype=object), array([[1.0],
[1.0],
[1.0]], dtype=object)]
otypes = 'd'
self = <numpy.vectorize object at 0x172506580>
ufunc = <ufunc '<lambda> (vectorized)'>
scipy/stats/tests/common_tests.py:334: in <lambda>
rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
allargs = (1.0, 0.0, 1.0)
distfunc = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
scipy/stats/_distn_infrastructure.py:1092: in rvs
vals = self._rvs(*args, size=size, random_state=random_state)
args = [array(1.)]
cond = True
discrete = None
kwds = {}
loc = array(0.)
random_state = RandomState(MT19937) at 0x1733B8440
rndm = None
scale = array(1.)
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = ()
scipy/stats/_distn_infrastructure.py:1010: in _rvs
Y = self._ppf(U, *args)
U = array(0.14376682)
args = (array(1.),)
random_state = RandomState(MT19937) at 0x1733B8440
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
size = ()
scipy/stats/_continuous_distns.py:5948: in _ppf
return (a/(q**-a - 1.0))**(1.0/a)
E RuntimeWarning: divide by zero encountered in reciprocal
a = array(1.)
q = array(0.14376682)
self = <scipy.stats._continuous_distns.kappa3_gen object at 0x11e8db6a0>
___________________ test_methods_with_lists[beta-args4-ppf] ____________________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = (2.3098496451481823, 0.6268795430096368)
dist = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
distname = 'beta'
f = <bound method rv_continuous.ppf of <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>>
loc = [0, 0.1]
method = 'ppf'
scale = [1, 1.01]
shape2 = [[2.3098496451481823, 2.3098496451481823], [0.6268795430096368, 0.6268795430096368]]
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array([2.30984965, 2.30984965]), array([0.62687954, 0.62687954]))
cond = array([ True, True])
cond0 = array([ True, True])
cond1 = array([ True, True])
cond2 = array([False, False])
cond3 = array([False, False])
goodargs = [array([0.1, 0.2]), array([2.30984965, 2.30984965]), array([0.62687954, 0.62687954])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([0. , 0.1])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
upper_bound = array([1. , 1.11])
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([2.30984965, 2.30984965])
b = array([0.62687954, 0.62687954])
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
___________________ test_methods_with_lists[beta-args4-isf] ____________________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = (2.3098496451481823, 0.6268795430096368)
dist = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
distname = 'beta'
f = <bound method rv_continuous.isf of <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>>
loc = [0, 0.1]
method = 'isf'
scale = [1, 1.01]
shape2 = [[2.3098496451481823, 2.3098496451481823], [0.6268795430096368, 0.6268795430096368]]
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2181: in isf
place(output, cond, self._isf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array([2.30984965, 2.30984965]), array([0.62687954, 0.62687954]))
cond = array([ True, True])
cond0 = array([ True, True])
cond1 = array([ True, True])
cond2 = array([False, False])
cond3 = array([False, False])
goodargs = [array([0.1, 0.2]), array([2.30984965, 2.30984965]), array([0.62687954, 0.62687954])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([0. , 0.1])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
upper_bound = array([1. , 1.11])
scipy/stats/_continuous_distns.py:623: in _isf
return _boost._beta_isf(x, a, b)
E RuntimeWarning: overflow encountered in _beta_isf
a = array([2.30984965, 2.30984965])
b = array([0.62687954, 0.62687954])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
x = array([0.1, 0.2])
___________________ test_methods_with_lists[fisk-args23-cdf] ___________________
scipy/stats/tests/test_continuous_basic.py:712: in test_methods_with_lists
[f(*v) for v in zip(x, *shape2, loc, scale)],
args = (3.085754862225318,)
dist = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
f = <bound method rv_continuous.cdf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
loc = [0, 0.1]
method = 'cdf'
result = array([0.00082014, 0.00079536])
scale = [1, 1.01]
shape2 = [[3.085754862225318, 3.085754862225318]]
x = [0.1, 0.2]
scipy/stats/tests/test_continuous_basic.py:712: in <listcomp>
[f(*v) for v in zip(x, *shape2, loc, scale)],
.0 = <zip object at 0x17253ec80>
f = <bound method rv_continuous.cdf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
v = (0.1, 3.085754862225318, 0, 1)
scipy/stats/_distn_infrastructure.py:1963: in cdf
place(output, cond, self._cdf(*goodargs))
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = True
cond0 = True
cond1 = True
cond2 = False
dtyp = dtype('float64')
goodargs = [array([0.1]), array([3.08575486])]
kwds = {}
loc = array(0)
output = array(0.)
scale = array(1)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array(0.1)
scipy/stats/_continuous_distns.py:1111: in _cdf
return burr._cdf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.1])
scipy/stats/_continuous_distns.py:950: in _cdf
return (1 + x**(-c))**(-d)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
x = array([0.1])
___________________ test_methods_with_lists[fisk-args23-sf] ____________________
scipy/stats/tests/test_continuous_basic.py:712: in test_methods_with_lists
[f(*v) for v in zip(x, *shape2, loc, scale)],
args = (3.085754862225318,)
dist = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
f = <bound method rv_continuous.sf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
loc = [0, 0.1]
method = 'sf'
result = array([0.99917986, 0.99920464])
scale = [1, 1.01]
shape2 = [[3.085754862225318, 3.085754862225318]]
x = [0.1, 0.2]
scipy/stats/tests/test_continuous_basic.py:712: in <listcomp>
[f(*v) for v in zip(x, *shape2, loc, scale)],
.0 = <zip object at 0x1727bbb40>
f = <bound method rv_continuous.sf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
v = (0.1, 3.085754862225318, 0, 1)
scipy/stats/_distn_infrastructure.py:2046: in sf
place(output, cond, self._sf(*goodargs))
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = True
cond0 = True
cond1 = True
cond2 = False
dtyp = dtype('float64')
goodargs = [array([0.1]), array([3.08575486])]
kwds = {}
loc = array(0)
output = array(0.)
scale = array(1)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array(0.1)
scipy/stats/_continuous_distns.py:1114: in _sf
return burr._sf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.1])
scipy/stats/_continuous_distns.py:956: in _sf
return np.exp(self._logsf(x, c, d))
c = array([3.08575486])
d = 1.0
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
x = array([0.1])
scipy/stats/_continuous_distns.py:959: in _logsf
return np.log1p(- (1 + x**(-c))**(-d))
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
x = array([0.1])
__________________ test_methods_with_lists[fisk-args23-logsf] __________________
scipy/stats/tests/test_continuous_basic.py:712: in test_methods_with_lists
[f(*v) for v in zip(x, *shape2, loc, scale)],
args = (3.085754862225318,)
dist = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
f = <bound method rv_continuous.logsf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
loc = [0, 0.1]
method = 'logsf'
result = array([-0.00082048, -0.00079568])
scale = [1, 1.01]
shape2 = [[3.085754862225318, 3.085754862225318]]
x = [0.1, 0.2]
scipy/stats/tests/test_continuous_basic.py:712: in <listcomp>
[f(*v) for v in zip(x, *shape2, loc, scale)],
.0 = <zip object at 0x1735f6240>
f = <bound method rv_continuous.logsf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
v = (0.1, 3.085754862225318, 0, 1)
scipy/stats/_distn_infrastructure.py:2091: in logsf
place(output, cond, self._logsf(*goodargs))
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = True
cond0 = True
cond1 = True
cond2 = False
dtyp = dtype('float64')
goodargs = [array([0.1]), array([3.08575486])]
kwds = {}
loc = array(0)
output = array(-inf)
scale = array(1)
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array(0.1)
scipy/stats/_continuous_distns.py:1124: in _logsf
return burr._logsf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.1])
scipy/stats/_continuous_distns.py:959: in _logsf
return np.log1p(- (1 + x**(-c))**(-d))
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
x = array([0.1])
___________________ test_methods_with_lists[fisk-args23-ppf] ___________________
scipy/stats/tests/test_continuous_basic.py:712: in test_methods_with_lists
[f(*v) for v in zip(x, *shape2, loc, scale)],
args = (3.085754862225318,)
dist = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
f = <bound method rv_continuous.ppf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
loc = [0, 0.1]
method = 'ppf'
result = array([0.49063532, 0.74448365])
scale = [1, 1.01]
shape2 = [[3.085754862225318, 3.085754862225318]]
x = [0.1, 0.2]
scipy/stats/tests/test_continuous_basic.py:712: in <listcomp>
[f(*v) for v in zip(x, *shape2, loc, scale)],
.0 = <zip object at 0x17306f600>
f = <bound method rv_continuous.ppf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
v = (0.1, 3.085754862225318, 0, 1)
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = True
cond0 = True
cond1 = True
cond2 = False
cond3 = False
goodargs = [array([0.1]), array([3.08575486])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array(nan)
q = array(0.1)
scale = array([1])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
upper_bound = inf
scipy/stats/_continuous_distns.py:1127: in _ppf
return burr._ppf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.1])
scipy/stats/_continuous_distns.py:962: in _ppf
return (q**(-1.0/d) - 1)**(-1.0/c)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
q = array([0.1])
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
___________________ test_methods_with_lists[fisk-args23-isf] ___________________
scipy/stats/tests/test_continuous_basic.py:712: in test_methods_with_lists
[f(*v) for v in zip(x, *shape2, loc, scale)],
args = (3.085754862225318,)
dist = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
distname = 'fisk'
f = <bound method rv_continuous.isf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
loc = [0, 0.1]
method = 'isf'
result = array([2.03817369, 1.6828175 ])
scale = [1, 1.01]
shape2 = [[3.085754862225318, 3.085754862225318]]
x = [0.1, 0.2]
scipy/stats/tests/test_continuous_basic.py:712: in <listcomp>
[f(*v) for v in zip(x, *shape2, loc, scale)],
.0 = <zip object at 0x173080500>
f = <bound method rv_continuous.isf of <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>>
v = (0.1, 3.085754862225318, 0, 1)
scipy/stats/_distn_infrastructure.py:2181: in isf
place(output, cond, self._isf(*goodargs) * scale + loc)
_a = 0.0
_b = inf
args = (array(3.08575486),)
cond = True
cond0 = True
cond1 = True
cond2 = False
cond3 = False
goodargs = [array([0.1]), array([3.08575486])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array(nan)
q = array(0.1)
scale = array([1])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
upper_bound = inf
scipy/stats/_distn_infrastructure.py:1028: in _isf
return self._ppf(1.0-q, *args) # use correct _ppf for subclasses
args = (array([3.08575486]),)
q = array([0.1])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
scipy/stats/_continuous_distns.py:1127: in _ppf
return burr._ppf(x, c, 1.0)
c = array([3.08575486])
self = <scipy.stats._continuous_distns.fisk_gen object at 0x11dafcf10>
x = array([0.9])
scipy/stats/_continuous_distns.py:962: in _ppf
return (q**(-1.0/d) - 1)**(-1.0/c)
E RuntimeWarning: divide by zero encountered in reciprocal
c = array([3.08575486])
d = 1.0
q = array([0.9])
self = <scipy.stats._continuous_distns.burr_gen object at 0x11dafc880>
__________________ test_methods_with_lists[rdist-args86-ppf] ___________________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = (1.6,)
dist = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
distname = 'rdist'
f = <bound method rv_continuous.ppf of <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>>
loc = [0, 0.1]
method = 'ppf'
scale = [1, 1.01]
shape2 = [[1.6, 1.6]]
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = (array([1.6, 1.6]),)
cond = array([ True, True])
cond0 = array([ True, True])
cond1 = array([ True, True])
cond2 = array([False, False])
cond3 = array([False, False])
goodargs = [array([0.1, 0.2]), array([1.6, 1.6])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([-1. , -0.91])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
upper_bound = array([1. , 1.11])
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = array([1.6, 1.6])
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([0.8, 0.8])
b = array([0.8, 0.8])
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
__________________ test_methods_with_lists[rdist-args86-isf] ___________________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = (1.6,)
dist = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
distname = 'rdist'
f = <bound method rv_continuous.isf of <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>>
loc = [0, 0.1]
method = 'isf'
scale = [1, 1.01]
shape2 = [[1.6, 1.6]]
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2181: in isf
place(output, cond, self._isf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = (array([1.6, 1.6]),)
cond = array([ True, True])
cond0 = array([ True, True])
cond1 = array([ True, True])
cond2 = array([False, False])
cond3 = array([False, False])
goodargs = [array([0.1, 0.2]), array([1.6, 1.6])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([-1. , -0.91])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
upper_bound = array([1. , 1.11])
scipy/stats/_distn_infrastructure.py:1028: in _isf
return self._ppf(1.0-q, *args) # use correct _ppf for subclasses
args = (array([1.6, 1.6]),)
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = array([1.6, 1.6])
q = array([0.9, 0.8])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([0.8, 0.8])
b = array([0.8, 0.8])
q = array([0.9, 0.8])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_______________ test_methods_with_lists[semicircular-args90-ppf] _______________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = ()
dist = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
distname = 'semicircular'
f = <bound method rv_continuous.ppf of <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>>
loc = [0, 0.1]
method = 'ppf'
scale = [1, 1.01]
shape2 = []
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = ()
cond = array([1, 1])
cond0 = array([1, 1])
cond1 = array([ True, True])
cond2 = array([0, 0])
cond3 = array([0, 0])
goodargs = [array([0.1, 0.2])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([-1. , -0.91])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
upper_bound = array([1. , 1.11])
scipy/stats/_continuous_distns.py:7328: in _ppf
return rdist._ppf(q, 3)
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = 3
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = 1.5
b = 1.5
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_______________ test_methods_with_lists[semicircular-args90-isf] _______________
scipy/stats/tests/test_continuous_basic.py:710: in test_methods_with_lists
result = f(x, *shape2, loc=loc, scale=scale)
args = ()
dist = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
distname = 'semicircular'
f = <bound method rv_continuous.isf of <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>>
loc = [0, 0.1]
method = 'isf'
scale = [1, 1.01]
shape2 = []
x = [0.1, 0.2]
scipy/stats/_distn_infrastructure.py:2181: in isf
place(output, cond, self._isf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = ()
cond = array([1, 1])
cond0 = array([1, 1])
cond1 = array([ True, True])
cond2 = array([0, 0])
cond3 = array([0, 0])
goodargs = [array([0.1, 0.2])]
kwds = {'loc': [0, 0.1], 'scale': [1, 1.01]}
loc = array([0. , 0.1])
lower_bound = array([-1. , -0.91])
output = array([nan, nan])
q = array([0.1, 0.2])
scale = array([1. , 1.01])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
upper_bound = array([1. , 1.11])
scipy/stats/_distn_infrastructure.py:1028: in _isf
return self._ppf(1.0-q, *args) # use correct _ppf for subclasses
args = ()
q = array([0.1, 0.2])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
scipy/stats/_continuous_distns.py:7328: in _ppf
return rdist._ppf(q, 3)
q = array([0.9, 0.8])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x11e856520>
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = 3
q = array([0.9, 0.8])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = 1.5
b = 1.5
q = array([0.9, 0.8])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
___________________ test_discrete_basic[nbinom-arg13-False] ____________________
scipy/stats/tests/test_discrete_basic.py:42: in test_discrete_basic
check_cdf_ppf(distfn, arg, supp, distname + ' cdf_ppf')
arg = (5, 0.5)
distfn = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
distname = 'nbinom'
first_case = False
m = array(5.)
rvs = array([0, 2, 6, ..., 3, 3, 3])
supp = array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 21])
v = array(10.)
scipy/stats/tests/test_discrete_basic.py:178: in check_cdf_ppf
npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, *arg), *arg),
arg = (5, 0.5)
distfn = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
msg = 'nbinom cdf_ppf'
supp = array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 21])
scipy/stats/_distn_infrastructure.py:3385: in ppf
place(output, cond, self._ppf(*goodargs) + loc)
_ = 1
_a = 0
_b = inf
args = (array(5), array(0.5))
cond = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True])
cond0 = True
cond1 = array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True])
cond2 = array([False, False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False, False,
False, False, False])
goodargs = [array([0.03125 , 0.109375 , 0.2265625 , 0.36328125, 0.5 ,
0.62304688, 0.72558594, 0.80615234, 0.86657...9459,
0.99409103, 0.99640131, 0.99782825, 0.99870026, 0.99922806,
0.99973324]), array([5]), array([0.5])]
kwds = {}
loc = array([0])
output = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan, nan, nan])
q = array([0.03125 , 0.109375 , 0.2265625 , 0.36328125, 0.5 ,
0.62304688, 0.72558594, 0.80615234, 0.866577...7547913, 0.98455811, 0.99039459,
0.99409103, 0.99640131, 0.99782825, 0.99870026, 0.99922806,
0.99973324])
self = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
scipy/stats/_discrete_distns.py:339: in _ppf
return _boost._nbinom_ppf(q, n, p)
E RuntimeWarning: overflow encountered in _nbinom_ppf
n = array([5])
p = array([0.5])
q = array([0.03125 , 0.109375 , 0.2265625 , 0.36328125, 0.5 ,
0.62304688, 0.72558594, 0.80615234, 0.866577...7547913, 0.98455811, 0.99039459,
0.99409103, 0.99640131, 0.99782825, 0.99870026, 0.99922806,
0.99973324])
self = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
__________________________ test_moments[nbinom-arg13] __________________________
scipy/stats/tests/test_discrete_basic.py:82: in test_moments
check_normalization(distfn, arg, distname)
arg = (5, 0.5)
distfn = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
distname = 'nbinom'
k = array(1.3)
m = array(5.)
s = array(0.9486833)
v = array(10.)
scipy/stats/tests/common_tests.py:33: in check_normalization
normalization_expect = distfn.expect(lambda x: 1, args=args)
args = (5, 0.5)
atol = 1e-07
distfn = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
distname = 'nbinom'
norm_moment = 1.0
rtol = 1e-07
scipy/stats/_distn_infrastructure.py:3532: in expect
x0 = self.ppf(0.5, *args)
_a = 0
_b = inf
args = (5, 0.5)
chunksize = 32
conditional = False
fun = <function rv_discrete.expect.<locals>.fun at 0x172b92f70>
func = <function check_normalization.<locals>.<lambda> at 0x172b92940>
invfac = 1.0
lb = 0
loc = 0
maxcount = 1000
self = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
tolerance = 1e-10
ub = inf
scipy/stats/_distn_infrastructure.py:3385: in ppf
place(output, cond, self._ppf(*goodargs) + loc)
_ = 1
_a = 0
_b = inf
args = (array(5), array(0.5))
cond = True
cond0 = True
cond1 = True
cond2 = False
goodargs = [array([0.5]), array([5]), array([0.5])]
kwds = {}
loc = array([0])
output = array(nan)
q = array(0.5)
self = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
scipy/stats/_discrete_distns.py:339: in _ppf
return _boost._nbinom_ppf(q, n, p)
E RuntimeWarning: overflow encountered in _nbinom_ppf
n = array([5])
p = array([0.5])
q = array([0.5])
self = <scipy.stats._discrete_distns.nbinom_gen object at 0x11dabaa00>
___________________ TestNCH.test_nchypergeom_wallenius_naive ___________________
scipy/stats/tests/test_discrete_distns.py:416: in test_nchypergeom_wallenius_naive
mean(N, m1, n, w), rtol=2e-2)
N = array([[[ 80, 50, 132],
[ 91, 114, 172],
[145, 55, 106],
[146, 180, 108]],
[[153, 129, 117],
[109, 142, 26],
[129, 104, 148],
[ 55, 83, 99]]])
m1 = array([[[41, 16, 73],
[23, 44, 83],
[76, 8, 35],
[50, 96, 76]],
[[86, 48, 64],
[32, 91, 21],
[38, 40, 68],
[ 5, 43, 52]]])
m2 = array([[[39, 34, 59],
[68, 70, 89],
[69, 47, 71],
[96, 84, 32]],
[[67, 81, 53],
[77, 51, 5],
[91, 64, 80],
[50, 40, 47]]])
max_m = 100
mean = <numpy.vectorize object at 0x172db2970>
n = array([[[ 8, 45, 15],
[ 8, 17, 150],
[117, 9, 57],
[105, 110, 90]],
[[ 83, 96, 43],
[ 32, 26, 18],
[ 76, 10, 40],
[ 34, 60, 9]]])
self = <scipy.stats.tests.test_discrete_distns.TestNCH object at 0x172ac80a0>
shape = (2, 4, 3)
support = <function TestNCH.test_nchypergeom_wallenius_naive.<locals>.support at 0x172761700>
w = array([[[1.42243449, 1.03486153, 1.7689432 ],
[1.10726768, 1.14728499, 0.78786659],
[1.8530935 , 0.011... 1.82895114, 1.65273601],
[0.82349288, 1.26168533, 0.6838232 ],
[0.76338588, 1.92491914, 1.29402846]]])
x = array([[[ 6, 14, 8],
[ 2, 1, 65],
[56, 1, 6],
[46, 44, 69]],
[[68, 25, 8],
[26, 3, 16],
[32, 0, 21],
[ 3, 36, 8]]])
xl = array([[[ 0, 11, 0],
[ 0, 0, 61],
[48, 0, 0],
[ 9, 26, 58]],
[[16, 15, 0],
[ 0, 0, 13],
[ 0, 0, 0],
[ 0, 20, 0]]])
xu = array([[[ 8, 16, 15],
[ 8, 17, 83],
[76, 8, 35],
[50, 96, 76]],
[[83, 48, 43],
[32, 26, 18],
[38, 10, 40],
[ 5, 43, 9]]])
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2163: in __call__
return self._vectorize_call(func=func, args=vargs)
args = (array([[[ 80, 50, 132],
[ 91, 114, 172],
[145, 55, 106],
[146, 180, 108]],
[[153, 1...1.82895114, 1.65273601],
[0.82349288, 1.26168533, 0.6838232 ],
[0.76338588, 1.92491914, 1.29402846]]]))
excluded = set()
func = <function TestNCH.test_nchypergeom_wallenius_naive.<locals>.mean at 0x172761670>
kwargs = {}
self = <numpy.vectorize object at 0x172db2970>
vargs = (array([[[ 80, 50, 132],
[ 91, 114, 172],
[145, 55, 106],
[146, 180, 108]],
[[153, 1...1.82895114, 1.65273601],
[0.82349288, 1.26168533, 0.6838232 ],
[0.76338588, 1.92491914, 1.29402846]]]))
../../mambaforge/envs/scipy-dev/lib/python3.9/site-packages/numpy/lib/function_base.py:2246: in _vectorize_call
outputs = ufunc(*inputs)
E RuntimeWarning: invalid value encountered in mean (vectorized)
args = (array([[[ 80, 50, 132],
[ 91, 114, 172],
[145, 55, 106],
[146, 180, 108]],
[[153, 1...1.82895114, 1.65273601],
[0.82349288, 1.26168533, 0.6838232 ],
[0.76338588, 1.92491914, 1.29402846]]]))
func = <function TestNCH.test_nchypergeom_wallenius_naive.<locals>.mean at 0x172761670>
inputs = [array([[[80, 50, 132],
[91, 114, 172],
[145, 55, 106],
[146, 180, 108]],
[[153, 129, ...29178, 0.6838231997605593],
[0.7633858816811221, 1.9249191439419642, 1.294028457081314]]],
dtype=object)]
otypes = 'd'
self = <numpy.vectorize object at 0x172db2970>
ufunc = <ufunc 'mean (vectorized)'>
___________________________ TestFrozen.test_pickling ___________________________
scipy/stats/tests/test_distributions.py:3727: in test_pickling
medians = [distfn.ppf(0.5), unpickled.ppf(0.5)]
beta = <scipy.stats._distn_infrastructure.rv_frozen object at 0x172a17910>
distfn = <scipy.stats._distn_infrastructure.rv_frozen object at 0x172a17910>
poiss = <scipy.stats._distn_infrastructure.rv_frozen object at 0x172a04190>
r0 = array([0.83575711, 0.99631806, 0.79494185, 0.32267884, 0.97873207,
0.66175512, 0.99026476, 0.95294174])
r1 = array([0.83575711, 0.99631806, 0.79494185, 0.32267884, 0.97873207,
0.66175512, 0.99026476, 0.95294174])
s = b'\x80\x04\x95\x98\x1f\x00\x00\x00\x00\x00\x00\x8c!scipy.stats._distn_infrastructure\x94\x8c\trv_frozen\x94\x93\x94)\x...n=False)\n >>> plt.show()\n \n\n \x94ubh8G\x00\x00\x00\x00\x00\x00\x00\x00h9G?\xf0\x00\x00\x00\x00\x00\x00ub.'
sample = <scipy.stats._distn_infrastructure.rv_sample object at 0x172a0bdf0>
self = <scipy.stats.tests.test_distributions.TestFrozen object at 0x172a17400>
unpickled = <scipy.stats._distn_infrastructure.rv_frozen object at 0x172a04640>
scipy/stats/_distn_infrastructure.py:465: in ppf
return self.dist.ppf(q, *self.args, **self.kwds)
q = 0.5
self = <scipy.stats._distn_infrastructure.rv_frozen object at 0x172a17910>
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array(2.30984965), array(0.62687954))
cond = True
cond0 = True
cond1 = True
cond2 = False
cond3 = False
goodargs = [array([0.5]), array([2.30984965]), array([0.62687954])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array(nan)
q = array(0.5)
scale = array([1])
self = <scipy.stats._continuous_distns.beta_gen object at 0x172a17e80>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([2.30984965])
b = array([0.62687954])
q = array([0.5])
self = <scipy.stats._continuous_distns.beta_gen object at 0x172a17e80>
_____________________________ TestExpect.test_beta _____________________________
scipy/stats/tests/test_distributions.py:3780: in test_beta
ub = stats.beta.ppf(0.95, 10, 10, loc=5, scale=2)
m = 6.333333333333336
self = <scipy.stats.tests.test_distributions.TestExpect object at 0x172a34a30>
v = 0.055555555555555594
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array(10), array(10))
cond = True
cond0 = True
cond1 = True
cond2 = False
cond3 = False
goodargs = [array([0.95]), array([10]), array([10])]
kwds = {'loc': 5, 'scale': 2}
loc = array([5])
lower_bound = 5.0
output = array(nan)
q = array(0.95)
scale = array([2])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
upper_bound = 7.0
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([10])
b = array([10])
q = array([0.95])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
____________ TestNumericalInverseHermite.test_basic[rdist-shapes86] ____________
scipy/stats/tests/test_stats.py:6978: in test_basic
p_tol = np.max(np.abs(dist.ppf(x)-fni.ppf(x))/np.abs(dist.ppf(x)))
dist = <scipy.stats._distn_infrastructure.rv_frozen object at 0x1682099a0>
distname = 'rdist'
fail_dists = {'beta', 'gausshyper', 'genhyperbolic', 'geninvgauss', 'ncf', 'nct', ...}
fni = <scipy.stats._rvs_sampling.NumericalInverseHermite object at 0x16821b8b0>
self = <scipy.stats.tests.test_stats.TestNumericalInverseHermite object at 0x168217bb0>
shapes = (1.6,)
slow_dists = {'ksone', 'kstwo', 'levy_stable', 'skewnorm'}
sup = <numpy.testing._private.utils.suppress_warnings object at 0x16821baf0>
x = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
scipy/stats/_distn_infrastructure.py:465: in ppf
return self.dist.ppf(q, *self.args, **self.kwds)
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._distn_infrastructure.rv_frozen object at 0x1682099a0>
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = (array(1.6),)
cond = array([ True, True, True, True, True, True, True, True, True,
True])
cond0 = True
cond1 = array([ True, True, True, True, True, True, True, True, True,
True])
cond2 = array([False, False, False, False, False, False, False, False, False,
False])
cond3 = array([False, False, False, False, False, False, False, False, False,
False])
goodargs = [array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152]), array([1.6])]
kwds = {}
loc = array([0])
lower_bound = -1.0
output = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
scale = array([1])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x1682092e0>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = array([1.6])
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x1682092e0>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = array([0.8])
b = array([0.8])
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
________ TestNumericalInverseHermite.test_basic[semicircular-shapes90] _________
scipy/stats/tests/test_stats.py:6978: in test_basic
p_tol = np.max(np.abs(dist.ppf(x)-fni.ppf(x))/np.abs(dist.ppf(x)))
dist = <scipy.stats._distn_infrastructure.rv_frozen object at 0x158e55d60>
distname = 'semicircular'
fail_dists = {'beta', 'gausshyper', 'genhyperbolic', 'geninvgauss', 'ncf', 'nct', ...}
fni = <scipy.stats._rvs_sampling.NumericalInverseHermite object at 0x158e2f550>
self = <scipy.stats.tests.test_stats.TestNumericalInverseHermite object at 0x158e55eb0>
shapes = ()
slow_dists = {'ksone', 'kstwo', 'levy_stable', 'skewnorm'}
sup = <numpy.testing._private.utils.suppress_warnings object at 0x158e2f490>
x = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
scipy/stats/_distn_infrastructure.py:465: in ppf
return self.dist.ppf(q, *self.args, **self.kwds)
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._distn_infrastructure.rv_frozen object at 0x158e55d60>
scipy/stats/_distn_infrastructure.py:2136: in ppf
place(output, cond, self._ppf(*goodargs) * scale + loc)
_a = -1.0
_b = 1.0
args = ()
cond = array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
cond0 = 1
cond1 = array([ True, True, True, True, True, True, True, True, True,
True])
cond2 = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
cond3 = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
goodargs = [array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])]
kwds = {}
loc = array([0])
lower_bound = -1.0
output = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
scale = array([1])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x158e553d0>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:7328: in _ppf
return rdist._ppf(q, 3)
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._continuous_distns.semicircular_gen object at 0x158e553d0>
scipy/stats/_continuous_distns.py:6955: in _ppf
return 2*beta._ppf(q, c/2, c/2) - 1
c = 3
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._continuous_distns.rdist_gen object at 0x11daf2e80>
scipy/stats/_continuous_distns.py:626: in _ppf
return _boost._beta_ppf(q, a, b)
E RuntimeWarning: overflow encountered in _beta_ppf
a = 1.5
b = 1.5
q = array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152])
self = <scipy.stats._continuous_distns.beta_gen object at 0x11dbd5910>
_______________ TestNumericalInverseHermite.test_inaccurate_CDF ________________
scipy/stats/tests/test_stats.py:7109: in test_inaccurate_CDF
stats.NumericalInverseHermite(stats.beta(*shapes))
match = 'The interpolating spline could not be created.'
self = <scipy.stats.tests.test_stats.TestNumericalInverseHermite object at 0x2b54df2b0>
shapes = (2.3098496451481823, 0.6268795430096368)
scipy/stats/_rvs_sampling.py:339: in __init__
res = _fast_numerical_inverse(dist, tol, max_intervals)
dist = <scipy.stats._distn_infrastructure.rv_frozen object at 0x29a8fd3a0>
max_intervals = 100000
self = <scipy.stats._rvs_sampling.NumericalInverseHermite object at 0x172ef7a30>
tol = 1e-12
scipy/stats/_rvs_sampling.py:563: in _fast_numerical_inverse
p = np.array([dist.ppf(tol/10), dist.isf(tol/10)]) # initial interval
dist = <scipy.stats._distn_infrastructure.rv_frozen object at 0x29a8fd3a0>
max_intervals = 100000
tol = 1e-12
scipy/stats/_distn_infrastructure.py:468: in isf
return self.dist.isf(q, *self.args, **self.kwds)
q = 1e-13
self = <scipy.stats._distn_infrastructure.rv_frozen object at 0x29a8fd3a0>
scipy/stats/_distn_infrastructure.py:2181: in isf
place(output, cond, self._isf(*goodargs) * scale + loc)
_a = 0.0
_b = 1.0
args = (array(2.30984965), array(0.62687954))
cond = True
cond0 = True
cond1 = True
cond2 = False
cond3 = False
goodargs = [array([1.e-13]), array([2.30984965]), array([0.62687954])]
kwds = {}
loc = array([0])
lower_bound = 0.0
output = array(nan)
q = array(1.e-13)
scale = array([1])
self = <scipy.stats._continuous_distns.beta_gen object at 0x2b63f1d30>
upper_bound = 1.0
scipy/stats/_continuous_distns.py:623: in _isf
return _boost._beta_isf(x, a, b)
E RuntimeWarning: overflow encountered in _beta_isf
a = array([2.30984965])
b = array([0.62687954])
self = <scipy.stats._continuous_distns.beta_gen object at 0x2b63f1d30>
x = array([1.e-13])
=========================== short test summary info ============================
FAILED scipy/integrate/tests/test_banded_ode_solvers.py::test_banded_ode_solvers
FAILED scipy/integrate/tests/test_integrate.py::TestOde::test_zvode - UserWar...
FAILED scipy/integrate/tests/test_integrate.py::TestOde::test_concurrent_ok
FAILED scipy/integrate/tests/test_integrate.py::TestZVODECheckParameterUse::test_no_params
FAILED scipy/integrate/tests/test_integrate.py::TestZVODECheckParameterUse::test_one_scalar_param
FAILED scipy/integrate/tests/test_integrate.py::TestZVODECheckParameterUse::test_two_scalar_params
FAILED scipy/integrate/tests/test_integrate.py::TestZVODECheckParameterUse::test_vector_param
FAILED scipy/interpolate/tests/test_polyint.py::TestBarycentric::test_append
FAILED scipy/linalg/tests/test_basic.py::TestSolve::test_simple_her_actuallysym
FAILED scipy/linalg/tests/test_basic.py::TestLstsq::test_simple_overdet_complex
FAILED scipy/linalg/tests/test_basic.py::TestLstsq::test_random_complex_exact
FAILED scipy/linalg/tests/test_basic.py::TestLstsq::test_random_complex_overdet
FAILED scipy/linalg/tests/test_blas.py::TestFBLAS2Simple::test_syr_her - Asse...
FAILED scipy/linalg/tests/test_decomp.py::TestEig::test_make_eigvals - Assert...
FAILED scipy/linalg/tests/test_decomp.py::TestEigBanded::test_zhbevd - Assert...
FAILED scipy/linalg/tests/test_decomp.py::TestEigBanded::test_zhbevx - Assert...
FAILED scipy/linalg/tests/test_decomp.py::TestEigBanded::test_eig_banded - As...
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[ev-complex64]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[ev-complex128]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[evd-complex64]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[evd-complex128]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[evx-complex64]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_various_drivers_standard[evx-complex128]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-True-True-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-True-True-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-True-False-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-True-False-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-False-True-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-False-True-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-False-False-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-F-False-False-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-True-True-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-True-True-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-True-False-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-True-False-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-False-True-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-False-True-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-False-False-True-None]
FAILED scipy/linalg/tests/test_decomp.py::TestEigh::test_eigh[6-D-False-False-False-None]
FAILED scipy/linalg/tests/test_decomp.py::TestSVD_GESVD::test_simple_complex
FAILED scipy/linalg/tests/test_decomp.py::TestSVD_GESVD::test_random_complex
FAILED scipy/linalg/tests/test_decomp.py::TestQZ::test_qz_complex - Assertion...
FAILED scipy/linalg/tests/test_decomp.py::TestQZ::test_qz_complex64 - Asserti...
FAILED scipy/linalg/tests/test_decomp.py::TestQZ::test_qz_double_complex - sc...
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_lhp - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_rhp - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_iuc - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_ouc - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_ref - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZ::test_cef - AssertionError:
FAILED scipy/linalg/tests/test_decomp.py::TestOrdQZWorkspaceSize::test_decompose
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-4-2-2-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-4-2-2-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-40-12-20-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-40-12-20-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-100-50-50-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[True-100-50-50-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-4-2-2-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-4-2-2-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-40-12-20-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-40-12-20-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-100-50-50-complex64]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin[False-100-50-50-complex128]
FAILED scipy/linalg/tests/test_decomp_cossin.py::test_cossin_mixed_types - As...
FAILED scipy/linalg/tests/test_decomp_ldl.py::test_simple - AssertionError:
FAILED scipy/linalg/tests/test_decomp_ldl.py::test_ldl_type_size_combinations
FAILED scipy/linalg/tests/test_lapack.py::TestLeastSquaresSolvers::test_gelsd
FAILED scipy/linalg/tests/test_lapack.py::TestBlockedQR::test_tpqrt_tpmqrt - ...
FAILED scipy/linalg/tests/test_lapack.py::test_pteqr[1-complex64-float32] - A...
FAILED scipy/linalg/tests/test_lapack.py::test_pteqr[1-complex128-float64] - ...
FAILED scipy/linalg/tests/test_lapack.py::test_pteqr[2-complex64-float32] - A...
FAILED scipy/linalg/tests/test_lapack.py::test_pteqr[2-complex128-float64] - ...
FAILED scipy/linalg/tests/test_lapack.py::test_orcsd_uncsd[complex64] - Asser...
FAILED scipy/linalg/tests/test_lapack.py::test_orcsd_uncsd[complex128] - Asse...
FAILED scipy/linalg/tests/test_lapack.py::test_gges_tgexc[complex64] - Runtim...
FAILED scipy/linalg/tests/test_lapack.py::test_gges_tgexc[complex128] - Asser...
FAILED scipy/linalg/tests/test_solvers.py::test_solve_continuous_are - Runtim...
FAILED scipy/linalg/tests/test_solvers.py::test_solve_discrete_are - RuntimeW...
FAILED scipy/signal/tests/test_ltisys.py::TestPlacePoles::test_complex - Runt...
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_hermitian_modes
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_real_nonsymmetric_modes
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_complex_nonsymmetric_modes
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_standard_nonsymmetric_starting_vector
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_general_nonsymmetric_starting_vector
FAILED scipy/sparse/linalg/eigen/arpack/tests/test_arpack.py::test_standard_nonsymmetric_no_convergence
FAILED scipy/sparse/linalg/eigen/lobpcg/tests/test_lobpcg.py::test_hermitian
FAILED scipy/sparse/linalg/eigen/tests/test_svds.py::test_svd_linop - scipy.s...
FAILED scipy/special/tests/test_hyp2f1.py::TestHyp2f1::test_a_b_negative_int[hyp2f1_test_case2]
FAILED scipy/special/tests/test_hyp2f1.py::TestHyp2f1::test_a_b_negative_int[hyp2f1_test_case3]
FAILED scipy/special/tests/test_hyp2f1.py::TestHyp2f1::test_a_b_neg_int_after_euler_hypergeometric_transformation[hyp2f1_test_case1]
FAILED scipy/special/tests/test_hyp2f1.py::TestHyp2f1::test_region1[hyp2f1_test_case3]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-beta-arg4]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-fisk-arg23]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-kappa3-arg56]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-rdist-arg85]
FAILED scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-semicircular-arg89]
FAILED scipy/stats/tests/test_continuous_basic.py::test_rvs_scalar[fisk-arg23]
FAILED scipy/stats/tests/test_continuous_basic.py::test_rvs_scalar[kappa3-arg56]
FAILED scipy/stats/tests/test_continuous_basic.py::test_rvs_broadcast[fisk-shape_args23]
FAILED scipy/stats/tests/test_continuous_basic.py::test_rvs_broadcast[kappa3-shape_args56]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[beta-args4-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[beta-args4-isf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[fisk-args23-cdf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[fisk-args23-sf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[fisk-args23-logsf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[fisk-args23-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[fisk-args23-isf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[rdist-args86-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[rdist-args86-isf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[semicircular-args90-ppf]
FAILED scipy/stats/tests/test_continuous_basic.py::test_methods_with_lists[semicircular-args90-isf]
FAILED scipy/stats/tests/test_discrete_basic.py::test_discrete_basic[nbinom-arg13-False]
FAILED scipy/stats/tests/test_discrete_basic.py::test_moments[nbinom-arg13]
FAILED scipy/stats/tests/test_discrete_distns.py::TestNCH::test_nchypergeom_wallenius_naive
FAILED scipy/stats/tests/test_distributions.py::TestFrozen::test_pickling - R...
FAILED scipy/stats/tests/test_distributions.py::TestExpect::test_beta - Runti...
FAILED scipy/stats/tests/test_stats.py::TestNumericalInverseHermite::test_basic[rdist-shapes86]
FAILED scipy/stats/tests/test_stats.py::TestNumericalInverseHermite::test_basic[semicircular-shapes90]
FAILED scipy/stats/tests/test_stats.py::TestNumericalInverseHermite::test_inaccurate_CDF
= 119 failed, 32138 passed, 2550 skipped, 11135 deselected, 118 xfailed, 9 xpassed in 603.65s (0:10:03) =
False
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