forked from jan/fdfd_tools
Cleanup
This commit is contained in:
parent
0e47fdd5fb
commit
e8f836c908
@ -73,7 +73,7 @@ This module contains functions for generating and solving the
|
||||
|
||||
'''
|
||||
|
||||
from typing import List, Tuple, Callable, Dict
|
||||
from typing import Tuple, Callable
|
||||
import logging
|
||||
import numpy
|
||||
from numpy import pi, real, trace
|
||||
@ -83,7 +83,6 @@ import scipy.optimize
|
||||
from scipy.linalg import norm
|
||||
import scipy.sparse.linalg as spalg
|
||||
|
||||
from .eigensolvers import rayleigh_quotient_iteration
|
||||
from . import field_t
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -256,7 +255,7 @@ def hmn_2_hxyz(k0: numpy.ndarray,
|
||||
:return: Function for converting h_mn into H_xyz
|
||||
"""
|
||||
shape = epsilon[0].shape + (1,)
|
||||
k_mag, m, n = generate_kmn(k0, G_matrix, shape)
|
||||
_k_mag, m, n = generate_kmn(k0, G_matrix, shape)
|
||||
|
||||
def operator(h: numpy.ndarray):
|
||||
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
|
||||
@ -379,7 +378,6 @@ def find_k(frequency: float,
|
||||
return res.x * direction, res.fun + frequency
|
||||
|
||||
|
||||
|
||||
def eigsolve(num_modes: int,
|
||||
k0: numpy.ndarray,
|
||||
G_matrix: numpy.ndarray,
|
||||
@ -432,10 +430,8 @@ def eigsolve(num_modes: int,
|
||||
Z = y0
|
||||
|
||||
while True:
|
||||
Z *= num_modes / norm(Z)
|
||||
ZtZ = Z.conj().T @ Z
|
||||
Z_norm = numpy.sqrt(real(trace(ZtZ))) / num_modes
|
||||
Z /= Z_norm
|
||||
ZtZ /= Z_norm * Z_norm
|
||||
try:
|
||||
U = numpy.linalg.inv(ZtZ)
|
||||
except numpy.linalg.LinAlgError:
|
||||
@ -449,7 +445,7 @@ def eigsolve(num_modes: int,
|
||||
continue
|
||||
break
|
||||
|
||||
for iter in range(max_iters):
|
||||
for i in range(max_iters):
|
||||
ZtZ = Z.conj().T @ Z
|
||||
U = numpy.linalg.inv(ZtZ)
|
||||
AZ = scipy_op @ Z
|
||||
@ -460,22 +456,22 @@ def eigsolve(num_modes: int,
|
||||
E = numpy.abs(E_signed)
|
||||
G = (AZU - Z @ U @ ZtAZU) * sgn
|
||||
|
||||
if iter > 0 and abs(E - prev_E) < tolerance * 0.5 * (E + prev_E + 1e-7):
|
||||
if i > 0 and abs(E - prev_E) < tolerance * 0.5 * (E + prev_E + 1e-7):
|
||||
logging.info('Optimization succeded: {} - 5e-8 < {} * {} / 2'.format(abs(E - prev_E), tolerance, E + prev_E))
|
||||
break
|
||||
|
||||
KG = scipy_iop @ G
|
||||
traceGtKG = _rtrace_AtB(G, KG)
|
||||
|
||||
if prev_traceGtKG == 0 or iter % reset_iters == 0:
|
||||
if prev_traceGtKG == 0 or i % reset_iters == 0:
|
||||
logger.info('CG reset')
|
||||
gamma = 0
|
||||
else:
|
||||
gamma = traceGtKG / prev_traceGtKG
|
||||
|
||||
D = gamma * d_scale * D + KG
|
||||
d_scale = numpy.sqrt(_rtrace_AtB(D, D)) / num_modes
|
||||
D /= d_scale
|
||||
D = gamma / d_scale * D + KG
|
||||
d_scale = num_modes / norm(D)
|
||||
D *= d_scale
|
||||
|
||||
ZtAZ = Z.conj().T @ AZ
|
||||
|
||||
@ -486,22 +482,6 @@ def eigsolve(num_modes: int,
|
||||
symZtD = _symmetrize(Z.conj().T @ D)
|
||||
symZtAD = _symmetrize(Z.conj().T @ AD)
|
||||
|
||||
'''
|
||||
U_sZtD = U @ symZtD
|
||||
|
||||
dE = 2.0 * (_rtrace_AtB(U, symZtAD) -
|
||||
_rtrace_AtB(ZtAZU, U_sZtD))
|
||||
|
||||
d2E = 2 * (_rtrace_AtB(U, DtAD) -
|
||||
_rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD)) -
|
||||
4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
|
||||
|
||||
# Newton-Raphson to find a root of the first derivative:
|
||||
theta = -dE/d2E
|
||||
|
||||
if d2E < 0 or abs(theta) >= pi:
|
||||
theta = -abs(prev_theta) * numpy.sign(dE)
|
||||
'''
|
||||
|
||||
def Qi_func(theta, memo=[None, None]):
|
||||
if memo[0] == theta:
|
||||
@ -549,12 +529,25 @@ def eigsolve(num_modes: int,
|
||||
|
||||
trace_deriv *= 2
|
||||
return trace_deriv * sgn
|
||||
'''
|
||||
|
||||
U_sZtD = U @ symZtD
|
||||
|
||||
dE = 2.0 * (_rtrace_AtB(U, symZtAD) -
|
||||
_rtrace_AtB(ZtAZU, U_sZtD))
|
||||
|
||||
d2E = 2 * (_rtrace_AtB(U, DtAD) -
|
||||
_rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD)) -
|
||||
4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
|
||||
|
||||
# Newton-Raphson to find a root of the first derivative:
|
||||
theta = -dE/d2E
|
||||
|
||||
if d2E < 0 or abs(theta) >= pi:
|
||||
theta = -abs(prev_theta) * numpy.sign(dE)
|
||||
|
||||
# theta, new_E, new_dE = linmin(theta, E, dE, 0.1, min(tolerance, 1e-6), 1e-14, 0, -numpy.sign(dE) * K_PI, trace_func)
|
||||
theta, n, _, new_E, _, _new_dE = scipy.optimize.line_search(trace_func, trace_deriv, xk=theta, pk=numpy.ones((1,1)), gfk=dE, old_fval=E, c1=min(tolerance, 1e-6), c2=0.1, amax=pi)
|
||||
'''
|
||||
theta, new_E, new_dE = linmin(theta, E, dE, 0.1, min(tolerance, 1e-6), 1e-14, 0, -numpy.sign(dE) * K_PI, trace_func)
|
||||
'''
|
||||
#theta, n, _, new_E, _, _new_dE = scipy.optimize.line_search(trace_func, trace_deriv, xk=theta, pk=numpy.ones((1,1)), gfk=dE, old_fval=E, c1=min(tolerance, 1e-6), c2=0.1, amax=pi)
|
||||
result = scipy.optimize.minimize_scalar(trace_func, bounds=(0, pi), tol=tolerance)
|
||||
new_E = result.fun
|
||||
theta = result.x
|
||||
@ -591,32 +584,33 @@ def eigsolve(num_modes: int,
|
||||
order = numpy.argsort(numpy.abs(eigvals))
|
||||
return eigvals[order], eigvecs.T[order]
|
||||
|
||||
#def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_tol=1e-14, x_min=0, linmin_func):
|
||||
# if df0 > 0:
|
||||
# x0, f0, df0 = linmin(-x_guess, f0, -df0, -x_max, f_tol, df_tol, x_tol, -x_min, lambda q, dq: -linmin_func(q, dq))
|
||||
# return -x0, f0, -df0
|
||||
# elif df0 == 0:
|
||||
# return 0, f0, df0
|
||||
# else:
|
||||
# x = x_guess
|
||||
# fx = f0
|
||||
# dfx = df0
|
||||
'''
|
||||
def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_tol=1e-14, x_min=0, linmin_func):
|
||||
if df0 > 0:
|
||||
x0, f0, df0 = linmin(-x_guess, f0, -df0, -x_max, f_tol, df_tol, x_tol, -x_min, lambda q, dq: -linmin_func(q, dq))
|
||||
return -x0, f0, -df0
|
||||
elif df0 == 0:
|
||||
return 0, f0, df0
|
||||
else:
|
||||
x = x_guess
|
||||
fx = f0
|
||||
dfx = df0
|
||||
|
||||
# isave = numpy.zeros((2,), numpy.intc)
|
||||
# dsave = numpy.zeros((13,), float)
|
||||
isave = numpy.zeros((2,), numpy.intc)
|
||||
dsave = numpy.zeros((13,), float)
|
||||
|
||||
# x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,
|
||||
# x_min, x_max, isave, dsave)
|
||||
# for i in range(int(1e6)):
|
||||
# if task != 'F':
|
||||
# logging.info('search converged in {} iterations'.format(i))
|
||||
# break
|
||||
# fx = f(x, dfx)
|
||||
# x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,
|
||||
# x_min, x_max, isave, dsave)
|
||||
|
||||
# return x, fx, dfx
|
||||
x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,
|
||||
x_min, x_max, isave, dsave)
|
||||
for i in range(int(1e6)):
|
||||
if task != 'F':
|
||||
logging.info('search converged in {} iterations'.format(i))
|
||||
break
|
||||
fx = f(x, dfx)
|
||||
x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,
|
||||
x_min, x_max, isave, dsave)
|
||||
|
||||
return x, fx, dfx
|
||||
'''
|
||||
|
||||
def _rtrace_AtB(A, B):
|
||||
return real(numpy.sum(A.conj() * B))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user