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rename search_direction to direction

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jan 2017-12-17 21:32:59 -08:00
parent 000cfabd78
commit 4a9596921f
1 changed files with 6 additions and 6 deletions
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12
fdfd_tools/bloch.py
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@ -68,7 +68,7 @@ This module contains functions for generating and solving the
k, f = find_k(frequency=1/1550, k, f = find_k(frequency=1/1550,
tolerance=(1/1550 - 1/1551), tolerance=(1/1550 - 1/1551),
search_direction=[1, 0, 0], direction=[1, 0, 0],
G_matrix=recip_lattice, G_matrix=recip_lattice,
epsilon=epsilon, epsilon=epsilon,
band=0) band=0)
@ -369,7 +369,7 @@ def eigsolve(num_modes: int,
def find_k(frequency: float, def find_k(frequency: float,
tolerance: float, tolerance: float,
search_direction: numpy.ndarray, direction: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: field_t,
mu: field_t = None, mu: field_t = None,
@ -380,7 +380,7 @@ def find_k(frequency: float,
:param frequency: Target frequency. :param frequency: Target frequency.
:param tolerance: Target frequency tolerance. :param tolerance: Target frequency tolerance.
:param search_direction: k-vector direction to search along. :param direction: k-vector direction to search along.
:param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns. :param G_matrix: 3x3 matrix, with reciprocal lattice vectors as columns.
:param epsilon: Dielectric constant distribution for the simulation. :param epsilon: Dielectric constant distribution for the simulation.
All fields are sampled at cell centers (i.e., NOT Yee-gridded) All fields are sampled at cell centers (i.e., NOT Yee-gridded)
@ -390,10 +390,10 @@ def find_k(frequency: float,
return: (k, actual_frequency) The found k-vector and its frequency return: (k, actual_frequency) The found k-vector and its frequency
""" """
search_direction = numpy.array(search_direction) / norm(search_direction) direction = numpy.array(direction) / norm(direction)
def get_f(k0_mag: float, band: int = 0): def get_f(k0_mag: float, band: int = 0):
k0 = search_direction * k0_mag k0 = direction * k0_mag
n, _v = eigsolve(band + 1, k0, G_matrix=G_matrix, epsilon=epsilon) n, _v = eigsolve(band + 1, k0, G_matrix=G_matrix, epsilon=epsilon)
f = numpy.sqrt(numpy.abs(numpy.real(n[band]))) f = numpy.sqrt(numpy.abs(numpy.real(n[band])))
return f return f
@ -401,6 +401,6 @@ def find_k(frequency: float,
res = scipy.optimize.minimize_scalar(lambda x: abs(get_f(x, band) - frequency), 0.25, res = scipy.optimize.minimize_scalar(lambda x: abs(get_f(x, band) - frequency), 0.25,
method='Bounded', bounds=(0, 0.5), method='Bounded', bounds=(0, 0.5),
options={'xatol': abs(tolerance)}) options={'xatol': abs(tolerance)})
return res.x * search_direction, res.fun + frequency return res.x * direction, res.fun + frequency