def checked_resonances2(elt, neigs=0, tol=1e-6):
"""
Compute resonances with two densities in order to check convergence.
If the same answer occurs to within tol, accept the pole as converged.
Inputs:
elt - coarse mesh
neigs - number of poles desired? (default: 0 -> compute all)
tol - absolute estimated error tolerance (default: 1e-6)
Use tol = 0 to return everything
"""
(l, V) = compute_resonances(elt, neigs)
if neigs == 0:
neigs = len(l)
N = problem_size(elt)
dl = np.zeros((neigs,1))
V = V[0:N,:]
for k in range(0,neigs):
dl[k] = errest_resonance(elt, l[k], V[:,k])
if tol > 0:
# Filter eigenvalues
is_good = np.abs(dl) < tol
l = l[is_good]
dl = dl[is_good]
V = V[:,is_good]
def checked_resonances(elt, neigs=0, tol=1e-6):
"""
Compute resonances with two densities in order to check convergence.
If the same answer occurs to within tol, accept the pole as converged.
Inputs:
elt -- coarse mesh
neigs -- number of poles desired? (default: 0 -> compute all)
tol -- match tolerance (default: 1e-6)
Output:
l --
"""
elt2 = copy.deepcopy(elt)
for i in range(len(elt)):
elt2[i]['order'] = int(np.ceil(elt[i]['order'] * 1.5))
l1 = compute_resonances(elt=elt, neigs=neigs)
l2 = compute_resonances(elt=elt2, neigs=neigs)
l = compare_eigs(l2, l1, tol)
return l
