=================================== FAILURES ===================================
____________________________ test_odeint_banded_jac ____________________________
[gw1] linux -- Python 3.9.6 /opt/hostedtoolcache/Python/3.9.6/x64/bin/python
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:75: in test_odeint_banded_jac
check_odeint(JACTYPE_BANDED)
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:52: in check_odeint
sol, info = odeint(rhs, y0, t,
atol = 1e-13
dt = 0.125
jacobian = <function bjac at 0x7f2a5a09a670>
jactype = 4
ml = 2
mu = 1
nsteps = 64
rtol = 1e-11
t = array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875, 1. ,
1.125, 1.25 , 1.375, 1.5 , 1.625, 1.75 ,...6.25 , 6.375, 6.5 , 6.625,
6.75 , 6.875, 7. , 7.125, 7.25 , 7.375, 7.5 , 7.625, 7.75 ,
7.875, 8. ])
y0 = array([1., 2., 3., 4., 5.])
lib/python3.9/site-packages/scipy/integrate/odepack.py:241: in odeint
output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,
Dfun = <function bjac at 0x7f2a5a09a670>
args = ()
atol = 1e-13
col_deriv = 0
dt = array([0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125,...0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125])
full_output = True
func = <function rhs at 0x7f2a5a0b2040>
h0 = 0.0
hmax = 0.0
hmin = 0.0
ixpr = 0
ml = 2
mu = 1
mxhnil = 0
mxordn = 12
mxords = 5
mxstep = 0
printmessg = 0
rtol = 1e-11
t = array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875, 1. ,
1.125, 1.25 , 1.375, 1.5 , 1.625, 1.75 ,...6.25 , 6.375, 6.5 , 6.625,
6.75 , 6.875, 7. , 7.125, 7.25 , 7.375, 7.5 , 7.625, 7.75 ,
7.875, 8. ])
tcrit = None
tfirst = False
y0 = array([1., 2., 3., 4., 5.])
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:10: in rhs
banded5x5.banded5x5(t, y, dydt)
E AttributeError: module 'scipy.integrate._test_odeint_banded' has no attribute 'banded5x5'
dydt = array([0., 0., 0., 0., 0.])
t = 0.0
y = array([1., 2., 3., 4., 5.])
_____________________________ test_odeint_full_jac _____________________________
[gw0] linux -- Python 3.9.6 /opt/hostedtoolcache/Python/3.9.6/x64/bin/python
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:71: in test_odeint_full_jac
check_odeint(JACTYPE_FULL)
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:52: in check_odeint
sol, info = odeint(rhs, y0, t,
atol = 1e-13
dt = 0.125
jacobian = <function jac at 0x7f957ded74c0>
jactype = 1
ml = None
mu = None
nsteps = 64
rtol = 1e-11
t = array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875, 1. ,
1.125, 1.25 , 1.375, 1.5 , 1.625, 1.75 ,...6.25 , 6.375, 6.5 , 6.625,
6.75 , 6.875, 7. , 7.125, 7.25 , 7.375, 7.5 , 7.625, 7.75 ,
7.875, 8. ])
y0 = array([1., 2., 3., 4., 5.])
lib/python3.9/site-packages/scipy/integrate/odepack.py:241: in odeint
output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,
Dfun = <function jac at 0x7f957ded74c0>
args = ()
atol = 1e-13
col_deriv = 0
dt = array([0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125,...0.125, 0.125, 0.125, 0.125, 0.125,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,
0.125])
full_output = True
func = <function rhs at 0x7f957ded78b0>
h0 = 0.0
hmax = 0.0
hmin = 0.0
ixpr = 0
ml = -1
mu = -1
mxhnil = 0
mxordn = 12
mxords = 5
mxstep = 0
printmessg = 0
rtol = 1e-11
t = array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875, 1. ,
1.125, 1.25 , 1.375, 1.5 , 1.625, 1.75 ,...6.25 , 6.375, 6.5 , 6.625,
6.75 , 6.875, 7. , 7.125, 7.25 , 7.375, 7.5 , 7.625, 7.75 ,
7.875, 8. ])
tcrit = None
tfirst = False
y0 = array([1., 2., 3., 4., 5.])
lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py:10: in rhs
banded5x5.banded5x5(t, y, dydt)
E AttributeError: module 'scipy.integrate._test_odeint_banded' has no attribute 'banded5x5'
dydt = array([0., 0., 0., 0., 0.])
t = 0.0
y = array([1., 2., 3., 4., 5.])
_________________________________ test_gh12922 _________________________________
[gw0] linux -- Python 3.9.6 /opt/hostedtoolcache/Python/3.9.6/x64/bin/python
lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/tests/test_report.py:27: in test_gh12922
result = minimize(objective, x0=x0, method='trust-constr',
cons = {'fun': <function test_gh12922.<locals>.<lambda> at 0x7f9570680670>, 'type': 'ineq'}
n = 25
objective = <function test_gh12922.<locals>.objective at 0x7f9570680430>
opts = {'maxiter': 1000, 'verbose': 2}
x0 = array([-5. , -4.58333333, -4.16666667, -3.75 , -3.33333333,
-2.91666667, -2.5 , -2.08333333, ...666667, 2.08333333, 2.5 , 2.91666667,
3.33333333, 3.75 , 4.16666667, 4.58333333, 5. ])
lib/python3.9/site-packages/scipy/optimize/_minimize.py:634: in minimize
return _minimize_trustregion_constr(fun, x0, args, jac, hess, hessp,
args = ()
bounds = None
callback = None
constraints = [<scipy.optimize._constraints.NonlinearConstraint object at 0x7f95706902e0>]
fun = <function test_gh12922.<locals>.objective at 0x7f9570680430>
hess = None
hessp = None
jac = '2-point'
meth = 'trust-constr'
method = 'trust-constr'
options = {'maxiter': 1000, 'verbose': 2}
tol = None
x0 = array([-5. , -4.58333333, -4.16666667, -3.75 , -3.33333333,
-2.91666667, -2.5 , -2.08333333, ...666667, 2.08333333, 2.5 , 2.91666667,
3.33333333, 3.75 , 4.16666667, 4.58333333, 5. ])
lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:509: in _minimize_trustregion_constr
_, result = tr_interior_point(
J_eq0 = array([], shape=(0, 25), dtype=float64)
J_ineq0 = array([[-10.0000001, 0. , 0. , 0. , 0. ,
0. , 0. , 0. ... , -0. , -0. , -0. ,
-0. , -0. , -0. , -0. , -0. ]])
args = ()
barrier_tol = 1e-08
bounds = None
c_eq0 = array([], dtype=float64)
c_ineq0 = array([25.])
callback = None
canonical = <scipy.optimize._trustregion_constr.canonical_constraint.CanonicalConstraint object at 0x7f9570690880>
canonical_all = [<scipy.optimize._trustregion_constr.canonical_constraint.CanonicalConstraint object at 0x7f9570690880>]
constraints = [<scipy.optimize._constraints.NonlinearConstraint object at 0x7f95706902e0>]
disp = False
factorization_method = None
finite_diff_bounds = (-inf, inf)
finite_diff_rel_step = None
fun = <function test_gh12922.<locals>.objective at 0x7f9570680430>
grad = '2-point'
gtol = 1e-08
hess = <scipy.optimize._hessian_update_strategy.BFGS object at 0x7f9570690d00>
hessp = None
initial_barrier_parameter = 0.1
initial_barrier_tolerance = 0.1
initial_constr_penalty = 1.0
initial_tr_radius = 1.0
lagrangian_hess = <scipy.optimize._trustregion_constr.minimize_trustregion_constr.LagrangianHessian object at 0x7f95706907f0>
maxiter = 1000
method = 'tr_interior_point'
n_sparse = 0
n_vars = 25
objective = <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>
prepared_constraints = [<scipy.optimize._constraints.PreparedConstraint object at 0x7f95706903a0>]
sparse_jacobian = False
start_time = 1626880845.5189075
state = barrier_parameter: 2.048000000000001e-09
barrier_tolerance: 2.048000000000001e-09
cg_niter: 99
cg_st...1, -9.99996971e-01,
-1.00000212e+00, -9.99998856e-01, -9.99998509e-01, -1.00000035e+00,
-9.99999303e-01])
stop_criteria = <function _minimize_trustregion_constr.<locals>.stop_criteria at 0x7f957069f040>
verbose = 2
x0 = array([-5. , -4.58333333, -4.16666667, -3.75 , -3.33333333,
-2.91666667, -2.5 , -2.08333333, ...666667, 2.08333333, 2.5 , 2.91666667,
3.33333333, 3.75 , 4.16666667, 4.58333333, 5. ])
xtol = 1e-08
lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py:321: in tr_interior_point
z, state = equality_constrained_sqp(
BARRIER_DECAY_RATIO = 0.2
BOUNDARY_PARAMETER = 0.995
TRUST_ENLARGEMENT = 5
barrier_parameter = 2.048000000000001e-09
constr = <function CanonicalConstraint._greater_to_canonical.<locals>.fun at 0x7f957069f280>
constr0_subprob = array([5.97175363e-16])
constr_eq0 = array([], dtype=float64)
constr_ineq0 = array([25.])
enforce_feasibility = array([False])
factorization_method = None
fun = <bound method ScalarFunction.fun of <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>>
fun0 = 250.69444444444446
fun0_subprob = 1.0000000555070645
grad = <bound method ScalarFunction.grad of <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>>
grad0 = array([-8.00000038, -7.16666735, -6.3333334 , -5.50000051, -4.66666716,
-3.83333393, -3. , -2.16666686, ...333357, 6.16666806, 7. , 7.83333438,
8.66666677, 9.5 , 10.33333369, 11.16666708, 12. ])
grad0_subprob = array([ 1.99999997e+00, 7.45058060e-09, 2.98023224e-08, 4.47034836e-08,
4.47034836e-08, 8.94069672e-08, 1...8,
-0.00000000e+00, -7.45058037e-08, -8.94069632e-08, -6.70552234e-08,
-8.19563841e-08, -2.04800000e-09])
initial_barrier_parameter = 0.1
initial_penalty = 1.0
initial_tolerance = 0.1
initial_trust_radius = 1.0
jac = <function CanonicalConstraint._greater_to_canonical.<locals>.jac at 0x7f957069f4c0>
jac0_subprob = array([[-3.14342542e-08, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
... 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 5.28839571e-16]])
jac_eq0 = array([], shape=(0, 25), dtype=float64)
jac_ineq0 = array([[-10.0000001, 0. , 0. , 0. , 0. ,
0. , 0. , 0. ... , -0. , -0. , -0. ,
-0. , -0. , -0. , -0. , -0. ]])
lagr_hess = <scipy.optimize._trustregion_constr.minimize_trustregion_constr.LagrangianHessian object at 0x7f95706907f0>
n_eq = 0
n_ineq = 1
n_vars = 25
s0 = array([1.])
state = barrier_parameter: 2.048000000000001e-09
barrier_tolerance: 2.048000000000001e-09
cg_niter: 99
cg_st...1, -9.99996971e-01,
-1.00000212e+00, -9.99998856e-01, -9.99998509e-01, -1.00000035e+00,
-9.99999303e-01])
stop_criteria = <function _minimize_trustregion_constr.<locals>.stop_criteria at 0x7f957069f040>
subprob = <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>
tolerance = 2.048000000000001e-09
trust_lb = array([ -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -0.995])
trust_radius = 1.0
trust_ub = array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,
inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])
x0 = array([-5. , -4.58333333, -4.16666667, -3.75 , -3.33333333,
-2.91666667, -2.5 , -2.08333333, ...666667, 2.08333333, 2.5 , 2.91666667,
3.33333333, 3.75 , 4.16666667, 4.58333333, 5. ])
xtol = 1e-08
z = array([-8.26654652e-09, -9.99999988e-01, -9.99999978e-01, -9.99999970e-01,
-9.99999971e-01, -9.99999949e-01, -9...1,
-9.99999994e-01, -1.00000003e+00, -1.00000004e+00, -1.00000003e+00,
-1.00000004e+00, 5.28839571e-16])
lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py:165: in equality_constrained_sqp
f_soc, b_soc = fun_and_constr(x_soc)
A = array([[-2.69181670e-08, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
... 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 2.64419786e-18]])
BOX_FACTOR = 0.5
H = <26x26 _CustomLinearOperator with dtype=float64>
INTERMEDIARY_REDUCTION_RATIO = 0.3
LARGE_REDUCTION_RATIO = 0.9
LS = <1x26 _CustomLinearOperator with dtype=float64>
MAX_TRUST_REDUCTION = 0.5
MIN_TRUST_REDUCTION = 0.1
PENALTY_FACTOR = 0.3
S = <26x26 _CustomLinearOperator with dtype=float64>
SOC_THRESHOLD = 0.1
SUFFICIENT_REDUCTION_RATIO = 1e-08
TRUST_ENLARGEMENT_FACTOR_L = 7.0
TRUST_ENLARGEMENT_FACTOR_S = 2.0
TR_FACTOR = 0.8
Y = <26x1 _CustomLinearOperator with dtype=float64>
Z = <26x26 _CustomLinearOperator with dtype=float64>
_ = 0
actual_reduction = -1.8834886983398746e-09
b = array([3.87463048e-17])
b_next = array([2.35207988e-17])
b_t = array([0.])
c = array([ 1.99999997e+00, 3.09199095e-06, 9.22381878e-06, -7.22706059e-07,
-8.53087843e-06, -7.41330030e-06, 1...6,
-4.26172308e-06, 2.27242708e-06, 2.96533108e-06, -7.15255482e-07,
1.37835741e-06, -2.04800000e-09])
c_t = array([ 2.28549660e+00, 1.77356434e-06, 5.29700086e-06, -3.99091525e-07,
-4.87347004e-06, -4.21476252e-06, 5...6,
-2.44275525e-06, 1.27143740e-06, 1.66209417e-06, -4.36310798e-07,
7.54939587e-07, -2.04800000e-09])
cg_info = {'hits_boundary': False, 'niter': 1, 'stop_cond': 4}
constr0 = array([5.97175363e-16])
constr_violation = 3.874630476438635e-17
d = array([ 1.43941097e-09, -5.31177310e-07, -1.58643620e-06, 1.19526740e-07,
1.45958996e-06, 1.26230899e-06, -1...6,
7.31598023e-07, -3.80791767e-07, -4.97792321e-07, 1.30673803e-07,
-2.26102189e-07, 5.46130927e-10])
dn = array([ 1.43941097e-09, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,
-0.00000000e+00, -0.00000000e+00, -0...0,
-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,
-0.00000000e+00, -1.41394747e-19])
dt = array([ 5.36469749e-20, -5.31177310e-07, -1.58643620e-06, 1.19526740e-07,
1.45958996e-06, 1.26230899e-06, -1...6,
7.31598023e-07, -3.80791767e-07, -4.97792321e-07, 1.30673803e-07,
-2.26102189e-07, 5.46130927e-10])
f = 1.0000000710636558
f_next = 1.0000000738347012
factorization_method = None
fun0 = 1.0000000555070645
fun_and_constr = <bound method BarrierSubproblem.function_and_constraints of <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>>
grad0 = array([ 1.99999997e+00, 7.45058060e-09, 2.98023224e-08, 4.47034836e-08,
4.47034836e-08, 8.94069672e-08, 1...8,
-0.00000000e+00, -7.45058037e-08, -8.94069632e-08, -6.70552234e-08,
-8.19563841e-08, -2.04800000e-09])
grad_and_jac = <bound method BarrierSubproblem.gradient_and_jacobian of <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>>
initial_penalty = 1.0
initial_trust_radius = 1.0
intersect = True
jac0 = array([[-3.14342542e-08, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
... 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 5.28839571e-16]])
lagr_hess = <bound method BarrierSubproblem.lagrangian_hessian of <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>>
last_iteration_failed = False
lb_t = array([ -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -0.995])
linearized_constr = array([0.])
merit_function = 1.0000000733223358
merit_function_next = 1.0000000752058245
n = 26
new_penalty = 43528981.208981454
optimality = 1.0184943675994873e-05
penalty = 58294072.700174615
predicted_reduction = 2.7823785853887594e-09
previous_penalty = 58294072.700174615
quadratic_model = 3.0470286846287016e-09
reduction_ratio = -0.6769347306763828
scaling = <bound method BarrierSubproblem.scaling of <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>>
state = barrier_parameter: 2.048000000000001e-09
barrier_tolerance: 2.048000000000001e-09
cg_niter: 99
cg_st...1, -9.99996971e-01,
-1.00000212e+00, -9.99998856e-01, -9.99998509e-01, -1.00000035e+00,
-9.99999303e-01])
stop_criteria = <bound method BarrierSubproblem.stop_criteria of <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>>
t = 1
trust_lb = array([ -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf,
-inf, -0.995])
trust_radius = 6.96500000066631
trust_radius_t = 6.96500000066631
trust_ub = array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,
inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])
ub_t = array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,
inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf])
v = array([74299263.13440967])
vpred = 1e-16
x = array([-6.00850288e-09, -9.99998445e-01, -9.99995380e-01, -1.00000035e+00,
-1.00000426e+00, -1.00000370e+00, -9...1,
-1.00000212e+00, -9.99998856e-01, -9.99998509e-01, -1.00000035e+00,
-9.99999303e-01, 2.64419786e-18])
x0 = array([-8.26654652e-09, -9.99999988e-01, -9.99999978e-01, -9.99999970e-01,
-9.99999971e-01, -9.99999949e-01, -9...1,
-9.99999994e-01, -1.00000003e+00, -1.00000004e+00, -1.00000003e+00,
-1.00000004e+00, 5.28839571e-16])
x_next = array([-4.56909192e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01, 2.64419786e-18])
x_soc = array([-3.69530289e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01, 2.64419786e-18])
y = array([ 8.73789022e-10, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,
-0.00000000e+00, -0.00000000e+00, -0...0,
-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00,
-0.00000000e+00, -8.58331499e-20])
lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py:82: in function_and_constraints
f = self.fun(x)
s = array([2.64419786e-18])
self = <scipy.optimize._trustregion_constr.tr_interior_point.BarrierSubproblem object at 0x7f957af19e50>
x = array([-3.69530289e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1, -9.99998013e-01,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01])
z = array([-3.69530289e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01, 2.64419786e-18])
lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:258: in fun
self._update_x_impl(x)
self = <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>
x = array([-3.69530289e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1, -9.99998013e-01,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01])
lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:230: in update_x
self._update_hess()
self = <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>
x = array([-3.69530289e-09, -9.99998977e-01, -9.99996967e-01, -1.00000023e+00,
-1.00000280e+00, -1.00000244e+00, -9...1, -9.99998013e-01,
-1.00000139e+00, -9.99999237e-01, -9.99999007e-01, -1.00000022e+00,
-9.99999529e-01])
lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:253: in _update_hess
self._update_hess_impl()
self = <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>
lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py:215: in update_hess
self.H.update(self.x - self.x_prev, self.g - self.g_prev)
self = <scipy.optimize._differentiable_functions.ScalarFunction object at 0x7f9570690b50>
lib/python3.9/site-packages/scipy/optimize/_hessian_update_strategy.py:182: in update
warn('delta_grad == 0.0. Check if the approximated '
E UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.
delta_grad = array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.])
delta_x = array([8.73789022e-10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.000000...000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00])
self = <scipy.optimize._hessian_update_strategy.BFGS object at 0x7f9570690d00>
----------------------------- Captured stdout call -----------------------------
| niter |f evals|CG iter| obj func |tr radius | opt | c viol |
|-------|-------|-------|-------------|----------|----------|----------|
| 1 | 26 | 0 | +2.5069e+02 | 1.00e+00 | 1.20e+01 | 2.50e+01 |
| 2 | 52 | 1 | +2.2693e+02 | 7.00e+00 | 1.15e+01 | 1.77e+01 |
| 3 | 78 | 2 | +6.6230e+01 | 4.90e+01 | 6.32e+00 | 4.07e+00 |
| 4 | 104 | 3 | +1.2726e+01 | 4.90e+01 | 2.79e+00 | 7.77e-01 |
| 5 | 130 | 4 | +4.9841e-01 | 4.90e+01 | 1.85e-01 | 8.74e-02 |
| 6 | 156 | 6 | +7.3244e-01 | 4.90e+01 | 3.96e-02 | 2.12e-02 |
| 7 | 156 | 6 | +7.3244e-01 | 2.45e+02 | 3.96e-02 | 2.12e-02 |
| 8 | 182 | 7 | +8.6782e-01 | 2.45e+02 | 9.68e-04 | 4.68e-03 |
| 9 | 182 | 7 | +8.6782e-01 | 1.23e+03 | 9.68e-04 | 4.68e-03 |
| 10 | 208 | 9 | +9.3326e-01 | 1.23e+03 | 1.63e-02 | 1.17e-03 |
| 11 | 234 | 10 | +9.6631e-01 | 1.23e+03 | 2.46e-04 | 2.89e-04 |
| 12 | 234 | 10 | +9.6631e-01 | 6.13e+03 | 2.46e-04 | 2.89e-04 |
| 13 | 234 | 10 | +9.6631e-01 | 3.06e+04 | 2.46e-04 | 2.89e-04 |
| 14 | 260 | 13 | +9.8308e-01 | 3.06e+04 | 1.71e-04 | 7.22e-05 |
| 15 | 312 | 16 | +9.8308e-01 | 3.06e+03 | 1.71e-04 | 7.22e-05 |
| 16 | 364 | 19 | +9.8308e-01 | 3.06e+02 | 1.71e-04 | 7.22e-05 |
| 17 | 416 | 22 | +9.8308e-01 | 3.87e+01 | 1.71e-04 | 7.22e-05 |
| 18 | 468 | 25 | +9.8308e-01 | 1.94e+01 | 1.71e-04 | 7.22e-05 |
| 19 | 494 | 28 | +9.9160e-01 | 1.94e+01 | 8.68e-05 | 1.77e-05 |
| 20 | 494 | 28 | +9.9160e-01 | 9.68e+01 | 8.68e-05 | 1.77e-05 |
| 21 | 520 | 31 | +9.9580e-01 | 9.68e+01 | 1.07e-04 | 4.43e-06 |
| 22 | 546 | 32 | +9.9790e-01 | 9.68e+01 | 1.06e-05 | 1.11e-06 |
| 23 | 546 | 32 | +9.9790e-01 | 4.84e+02 | 1.06e-05 | 1.11e-06 |
| 24 | 572 | 33 | +9.9895e-01 | 4.84e+02 | 1.99e-05 | 2.75e-07 |
| 25 | 598 | 34 | +9.9948e-01 | 4.84e+02 | 1.10e-05 | 6.70e-08 |
| 26 | 624 | 37 | +9.9974e-01 | 4.84e+02 | 2.28e-06 | 1.67e-08 |
| 27 | 624 | 37 | +9.9974e-01 | 2.42e+03 | 2.28e-06 | 1.67e-08 |
| 28 | 650 | 38 | +9.9987e-01 | 2.42e+03 | 1.23e-06 | 4.18e-09 |
| 29 | 650 | 38 | +9.9987e-01 | 1.21e+04 | 1.23e-06 | 4.18e-09 |
| 30 | 676 | 39 | +9.9994e-01 | 1.21e+04 | 1.23e-06 | 1.03e-09 |
| 31 | 702 | 43 | +9.9997e-01 | 1.21e+04 | 3.60e-05 | 2.59e-10 |
| 32 | 728 | 44 | +9.9998e-01 | 1.21e+04 | 3.05e-06 | 6.46e-11 |
| 33 | 754 | 45 | +9.9999e-01 | 1.21e+04 | 3.65e-07 | 1.61e-11 |
| 34 | 780 | 46 | +1.0000e+00 | 1.21e+04 | 6.11e-07 | 4.00e-12 |
| 35 | 806 | 47 | +1.0000e+00 | 1.21e+04 | 3.95e-07 | 9.54e-13 |
| 36 | 832 | 48 | +1.0000e+00 | 1.21e+04 | 1.42e-07 | 1.92e-13 |
| 37 | 832 | 48 | +1.0000e+00 | 6.05e+04 | 1.42e-07 | 1.92e-13 |
| 38 | 858 | 53 | +1.0000e+00 | 6.05e+04 | 4.44e-06 | 4.94e-14 |
| 39 | 884 | 54 | +1.0000e+00 | 6.05e+04 | 2.82e-06 | 1.29e-14 |
| 40 | 910 | 55 | +1.0000e+00 | 6.05e+04 | 8.20e-07 | 3.38e-15 |
| 41 | 936 | 56 | +1.0000e+00 | 6.05e+04 | 1.34e-07 | 8.02e-16 |
| 42 | 988 | 57 | +1.0000e+00 | 6.05e+03 | 1.34e-07 | 8.02e-16 |
| 43 | 1014 | 58 | +1.0000e+00 | 6.05e+02 | 1.34e-07 | 8.02e-16 |
| 44 | 1066 | 59 | +1.0000e+00 | 6.05e+01 | 1.34e-07 | 8.02e-16 |
| 45 | 1092 | 60 | +1.0000e+00 | 6.05e+00 | 1.34e-07 | 8.02e-16 |
| 46 | 1144 | 61 | +1.0000e+00 | 6.05e-01 | 1.34e-07 | 8.02e-16 |
| 47 | 1170 | 62 | +1.0000e+00 | 6.05e-02 | 1.34e-07 | 8.02e-16 |
| 48 | 1222 | 63 | +1.0000e+00 | 6.05e-03 | 1.34e-07 | 8.02e-16 |
| 49 | 1248 | 64 | +1.0000e+00 | 6.05e-04 | 1.34e-07 | 8.02e-16 |
| 50 | 1300 | 65 | +1.0000e+00 | 6.05e-05 | 1.34e-07 | 8.02e-16 |
| 51 | 1326 | 66 | +1.0000e+00 | 6.05e-06 | 1.34e-07 | 8.02e-16 |
| 52 | 1378 | 67 | +1.0000e+00 | 6.05e-07 | 1.34e-07 | 8.02e-16 |
| 53 | 1404 | 68 | +1.0000e+00 | 1.04e-07 | 1.34e-07 | 8.02e-16 |
| 54 | 1430 | 69 | +1.0000e+00 | 5.20e-08 | 1.34e-07 | 8.02e-16 |
| 55 | 1456 | 70 | +1.0000e+00 | 2.60e-08 | 1.34e-07 | 8.02e-16 |
| 56 | 1482 | 71 | +1.0000e+00 | 1.30e-08 | 1.34e-07 | 8.02e-16 |
| 57 | 1508 | 72 | +1.0000e+00 | 1.30e-08 | 1.42e-07 | 3.21e-16 |
| 58 | 1534 | 73 | +1.0000e+00 | 6.50e-09 | 1.42e-07 | 3.21e-16 |
| 59 | 1560 | 73 | +1.0000e+00 | 1.00e+00 | 1.42e-07 | 3.21e-16 |
| 60 | 1586 | 75 | +1.0000e+00 | 1.00e-01 | 1.42e-07 | 3.21e-16 |
| 61 | 1612 | 77 | +1.0000e+00 | 1.00e-02 | 1.42e-07 | 3.21e-16 |
| 62 | 1638 | 78 | +1.0000e+00 | 1.00e-03 | 1.42e-07 | 3.21e-16 |
| 63 | 1664 | 79 | +1.0000e+00 | 1.00e-04 | 1.42e-07 | 3.21e-16 |
| 64 | 1716 | 81 | +1.0000e+00 | 1.00e-05 | 1.42e-07 | 3.21e-16 |
| 65 | 1742 | 83 | +1.0000e+00 | 1.00e-06 | 1.42e-07 | 3.21e-16 |
| 66 | 1794 | 84 | +1.0000e+00 | 1.93e-07 | 1.42e-07 | 3.21e-16 |
| 67 | 1820 | 86 | +1.0000e+00 | 9.66e-08 | 1.42e-07 | 3.21e-16 |
| 68 | 1846 | 87 | +1.0000e+00 | 4.83e-08 | 1.42e-07 | 3.21e-16 |
| 69 | 1872 | 88 | +1.0000e+00 | 2.41e-08 | 1.42e-07 | 3.21e-16 |
| 70 | 1898 | 89 | +1.0000e+00 | 1.21e-08 | 1.42e-07 | 3.21e-16 |
| 71 | 1924 | 90 | +1.0000e+00 | 1.21e-08 | 1.34e-07 | 6.83e-17 |
| 72 | 1950 | 91 | +1.0000e+00 | 5.87e-09 | 1.34e-07 | 6.83e-17 |
| 73 | 1976 | 91 | +1.0000e+00 | 1.00e+00 | 1.34e-07 | 6.83e-17 |
| 74 | 2002 | 99 | +1.0000e+00 | 6.97e+00 | 1.02e-05 | 3.61e-17 |
______________________ test_cont_basic[500-200-ncf-arg74] ______________________
[gw1] linux -- Python 3.9.6 /opt/hostedtoolcache/Python/3.9.6/x64/bin/python
lib/python3.9/site-packages/scipy/stats/tests/test_continuous_basic.py:189: in test_cont_basic
check_entropy(distfn, arg, distname)
alpha = 0.01
arg = (27, 27, 0.41578441799226107)
distfn = <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>
distname = 'ncf'
locscale_defaults = (0, 1)
m = array(1.09663138)
meths = [<bound method rv_continuous.pdf of <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>>, <bound method ...f2a596f3370>>, <bound method rv_continuous.logsf of <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>>]
n_fit_samples = 200
rng = RandomState(MT19937) at 0x7F2A3C3E7940
rvs = array([0.90888841, 0.87523216, 0.73228753, 1.33584017, 0.93548576,
1.09504212, 1.15670442, 0.36786914, 0.791225...03, 1.73540023, 0.87035866, 0.73702581, 0.81477748,
0.87362182, 1.58673099, 1.88455316, 0.61564337, 1.09912842])
sm = 1.0740685247899555
sn = 500
spec_x = {'levy_l': -0.5, 'pareto': 1.5, 'rv_histogram_instance': 5.0, 'tukeylambda': 0.3, ...}
sv = 0.1911827409537913
v = array(0.2013794)
x = 0.5
lib/python3.9/site-packages/scipy/stats/tests/common_tests.py:93: in check_entropy
ent = distfn.entropy(*arg)
arg = (27, 27, 0.41578441799226107)
distfn = <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>
msg = 'ncf'
lib/python3.9/site-packages/scipy/stats/_distn_infrastructure.py:1254: in entropy
place(output, cond0, self.vecentropy(*goodargs) + log(goodscale))
args = (array(27), array(27), array(0.41578442))
cond0 = True
goodargs = [array([27]), array([27]), array([0.41578442])]
goodscale = array([1])
kwds = {}
loc = array(0)
output = array(0.)
scale = array(1)
self = <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>
/opt/hostedtoolcache/Python/3.9.6/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2163: in __call__
return self._vectorize_call(func=func, args=vargs)
args = (array([27]), array([27]), array([0.41578442]))
excluded = set()
func = <bound method rv_continuous._entropy of <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>>
kwargs = {}
self = <numpy.vectorize object at 0x7f2a59c6a850>
vargs = (array([27]), array([27]), array([0.41578442]))
/opt/hostedtoolcache/Python/3.9.6/x64/lib/python3.9/site-packages/numpy/lib/function_base.py:2246: in _vectorize_call
outputs = ufunc(*inputs)
args = (array([27]), array([27]), array([0.41578442]))
func = <bound method rv_continuous._entropy of <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>>
inputs = [array([27], dtype=object), array([27], dtype=object), array([0.41578441799226107], dtype=object)]
otypes = 'd'
self = <numpy.vectorize object at 0x7f2a59c6a850>
ufunc = <ufunc '_entropy (vectorized)'>
lib/python3.9/site-packages/scipy/stats/_distn_infrastructure.py:2621: in _entropy
h = integrate.quad(integ, _a, _b)[0]
_a = 0.0
_b = inf
args = (27, 27, 0.41578441799226107)
integ = <function rv_continuous._entropy.<locals>.integ at 0x7f2a30fe8700>
self = <scipy.stats._continuous_distns.ncf_gen object at 0x7f2a596f3370>
lib/python3.9/site-packages/scipy/integrate/quadpack.py:400: in quad
warnings.warn(msg, IntegrationWarning, stacklevel=2)
E scipy.integrate.quadpack.IntegrationWarning: The occurrence of roundoff error is detected, which prevents
E the requested tolerance from being achieved. The error may be
E underestimated.
a = 0.0
args = ()
b = inf
epsabs = 1.49e-08
epsrel = 1.49e-08
flip = False
full_output = 0
func = <function rv_continuous._entropy.<locals>.integ at 0x7f2a30fe8700>
ier = 2
limit = 50
limlst = 50
maxp1 = 50
msg = 'The occurrence of roundoff error is detected, which prevents \n the requested tolerance from being achieved. The error may be \n underestimated.'
msgs = {80: 'A Python error occurred possibly while calling the function.', 1: 'The maximum number of subdivisions (50) has b...\n underestimated.', 3: 'Extremely bad integrand behavior occurs at some points of the\n integration interval.', ...}
points = None
retval = (nan, nan, 2)
weight = None
wopts = None
wvar = None
=========================== short test summary info ============================
FAILED lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py::test_odeint_banded_jac
FAILED lib/python3.9/site-packages/scipy/integrate/tests/test_odeint_jac.py::test_odeint_full_jac
FAILED lib/python3.9/site-packages/scipy/optimize/_trustregion_constr/tests/test_report.py::test_gh12922
FAILED lib/python3.9/site-packages/scipy/stats/tests/test_continuous_basic.py::test_cont_basic[500-200-ncf-arg74]
= 4 failed, 32588 passed, 2086 skipped, 107 xfailed, 8 xpassed in 350.96s (0:05:50) =
~/work/scipy/scipy
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