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fdfd_tools/meanas/fdtd/base.py

88 lines
2.2 KiB
Python

"""
Basic FDTD field updates
"""
from typing import List, Callable, Tuple, Dict
import numpy
from .. import dx_lists_t, field_t, field_updater
__author__ = 'Jan Petykiewicz'
def curl_h(dxes: dx_lists_t = None) -> field_updater:
"""
Curl operator for use with the H field.
:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
:return: Function for taking the discretized curl of the H-field, F(H) -> curlH
"""
if dxes:
dxyz_b = numpy.meshgrid(*dxes[1], indexing='ij')
def dh(f, ax):
return (f - numpy.roll(f, 1, axis=ax)) / dxyz_b[ax]
else:
def dh(f, ax):
return f - numpy.roll(f, 1, axis=ax)
def ch_fun(h: field_t) -> field_t:
output = numpy.empty_like(h)
output[0] = dh(h[2], 1)
output[1] = dh(h[0], 2)
output[2] = dh(h[1], 0)
output[0] -= dh(h[1], 2)
output[1] -= dh(h[2], 0)
output[2] -= dh(h[0], 1)
return output
return ch_fun
def curl_e(dxes: dx_lists_t = None) -> field_updater:
"""
Curl operator for use with the E field.
:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types
:return: Function for taking the discretized curl of the E-field, F(E) -> curlE
"""
if dxes is not None:
dxyz_a = numpy.meshgrid(*dxes[0], indexing='ij')
def de(f, ax):
return (numpy.roll(f, -1, axis=ax) - f) / dxyz_a[ax]
else:
def de(f, ax):
return numpy.roll(f, -1, axis=ax) - f
def ce_fun(e: field_t) -> field_t:
output = numpy.empty_like(e)
output[0] = de(e[2], 1)
output[1] = de(e[0], 2)
output[2] = de(e[1], 0)
output[0] -= de(e[1], 2)
output[1] -= de(e[2], 0)
output[2] -= de(e[0], 1)
return output
return ce_fun
def maxwell_e(dt: float, dxes: dx_lists_t = None) -> field_updater:
curl_h_fun = curl_h(dxes)
def me_fun(e: field_t, h: field_t, epsilon: field_t):
e += dt * curl_h_fun(h) / epsilon
return e
return me_fun
def maxwell_h(dt: float, dxes: dx_lists_t = None) -> field_updater:
curl_e_fun = curl_e(dxes)
def mh_fun(e: field_t, h: field_t):
h -= dt * curl_e_fun(e)
return h
return mh_fun