def assert_full_output(method):
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.4)
K = dipolarkernel(t, r)
L = regoperator(r, 2)
V = K @ P
alpha, alphas_evaled, functional, residuals, penalties = selregparam(
V,
K,
cvxnnls,
method='aic',
algorithm=method,
full_output=True,
regop=L)
errors = []
if np.size(alpha) != 1:
errors.append("alphaopt is not a scalar")
if len(functional) != len(alphas_evaled):
errors.append(
"The number of elements of functional values and evaluated alphas are different."
)
if len(residuals) != len(penalties):
errors.append(
"The number of elements of evluated residuals and penalties are different"
)
if not alpha in alphas_evaled:
errors.append("The optimal alpha is not part of the evaluated alphas")
assert not errors, f"Errors occured:\n{chr(10).join(errors)}"
def test_algorithms():
#=======================================================================
"Check that the value returned by the the grid and Brent algorithms coincide"
t = np.linspace(0, 5, 80)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3, 0.2)
K = dipolarkernel(t, r)
L = regoperator(r, 2)
V = K @ P + whitegaussnoise(t, 0.02, seed=1)
alpha_grid = selregparam(V,
K,
cvxnnls,
method='aic',
algorithm='grid',
regop=L)
alpha_brent = selregparam(V,
K,
cvxnnls,
method='aic',
algorithm='brent',
regop=L)
assert abs(1 - alpha_grid / alpha_brent) < 0.15
def get_alpha_from_method(method):
t = np.linspace(0, 5, 500)
r = np.linspace(2, 5, 80)
P = dd_gauss(r, 3.0, 0.16986436005760383)
K = dipolarkernel(t, r)
L = regoperator(r, 2, includeedges=True)
V = K @ P
alpha = selregparam(V, K, cvxnnls, method=method, noiselvl=0, regop=L)
return np.log10(alpha)
def test_confinter_tikh():
#============================================================
"Check that the confidence intervals are correctly computed using Tikhonov regularization"
t = np.linspace(-2, 4, 300)
r = np.linspace(2, 6, 100)
P = dd_gauss(r, 3, 0.2)
K = dipolarkernel(t, r)
V = K @ P
fit = snlls(V, K, lbl=np.zeros_like(r))
assert_confidence_intervals(fit.paramUncert, fit.param)
