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"""Handling of objective functions and objective quantities."""
from abc import ABC, abstractmethod
import numpy as np
import meep as mp
from .filter_source import FilteredSource
from .optimization_problem import Grid
from meep.simulation import py_v3_to_vec
class ObjectiveQuantitiy(ABC):
@abstractmethod
def __init__(self):
return
@abstractmethod
def register_monitors(self):
return
@abstractmethod
def place_adjoint_source(self):
return
@abstractmethod
def __call__(self):
return
@abstractmethod
def get_evaluation(self):
return
class EigenmodeCoefficient(ObjectiveQuantitiy):
def __init__(self,sim,volume,mode,forward=True,kpoint_func=None,**kwargs):
'''
'''
self.sim = sim
self.volume=volume
self.mode=mode
self.forward = 0 if forward else 1
self.normal_direction = None
self.kpoint_func = kpoint_func
self.eval = None
self.EigenMode_kwargs = kwargs
return
def register_monitors(self,frequencies):
self.frequencies = np.asarray(frequencies)
self.monitor = self.sim.add_mode_monitor(frequencies,mp.ModeRegion(center=self.volume.center,size=self.volume.size),yee_grid=True)
self.normal_direction = self.monitor.normal_direction
return self.monitor
def place_adjoint_source(self,dJ):
'''Places an equivalent eigenmode monitor facing the opposite direction. Calculates the
correct scaling/time profile.
dJ ........ the user needs to pass the dJ/dMonitor evaluation
'''
dJ = np.atleast_1d(dJ)
dt = self.sim.fields.dt # the timestep size from sim.fields.dt of the forward sim
# determine starting kpoint for reverse mode eigenmode source
direction_scalar = 1 if self.forward else -1
if self.kpoint_func is None:
if self.normal_direction == 0:
k0 = direction_scalar * mp.Vector3(x=1)
elif self.normal_direction == 1:
k0 = direction_scalar * mp.Vector3(y=1)
elif self.normal_direction == 2:
k0 == direction_scalar * mp.Vector3(z=1)
else:
k0 = direction_scalar * self.kpoint_func(self.time_src.frequency,1)
if dJ.ndim == 2:
dJ = np.sum(dJ,axis=1)
da_dE = 0.5 * self.cscale # scalar popping out of derivative
scale = adj_src_scale(self, dt)
if self.frequencies.size == 1:
# Single frequency simulations. We need to drive it with a time profile.
amp = da_dE * dJ * scale # final scale factor
src = self.time_src
else:
# multi frequency simulations
scale = da_dE * dJ * scale
src = FilteredSource(self.time_src.frequency,self.frequencies,scale,dt) # generate source from broadband response
amp = 1
# generate source object
self.source = [mp.EigenModeSource(src,
eig_band=self.mode,
direction=mp.NO_DIRECTION,
eig_kpoint=k0,
amplitude=amp,
eig_match_freq=True,
size=self.volume.size,
center=self.volume.center,
**self.EigenMode_kwargs)]
return self.source
def __call__(self):
# We just need a workable time profile, so just grab the first available time profile and use that.
self.time_src = self.sim.sources[0].src
# Eigenmode data
direction = mp.NO_DIRECTION if self.kpoint_func else mp.AUTOMATIC
ob = self.sim.get_eigenmode_coefficients(self.monitor,[self.mode],direction=direction,kpoint_func=self.kpoint_func,**self.EigenMode_kwargs)
self.eval = np.squeeze(ob.alpha[:,:,self.forward]) # record eigenmode coefficients for scaling
self.cscale = ob.cscale # pull scaling factor
return self.eval
def get_evaluation(self):
'''Returns the requested eigenmode coefficient.
'''
try:
return self.eval
except AttributeError:
raise RuntimeError("You must first run a forward simulation before resquesting an eigenmode coefficient.")
class FourierFields(ObjectiveQuantitiy):
def __init__(self,sim,volume, component):
self.sim = sim
self.volume=volume
self.eval = None
self.component = component
return
def register_monitors(self,frequencies):
self.frequencies = np.asarray(frequencies)
self.num_freq = len(self.frequencies)
self.monitor = self.sim.add_dft_fields([self.component], self.frequencies, where=self.volume, yee_grid=False)
return self.monitor
def place_adjoint_source(self,dJ):
dt = self.sim.fields.dt # the timestep size from sim.fields.dt of the forward sim
self.sources = []
scale = adj_src_scale(self, dt)
x_dim, y_dim, z_dim = len(self.dg.x), len(self.dg.y), len(self.dg.z)
if self.num_freq == 1:
amp = -dJ[0].copy().reshape(x_dim, y_dim, z_dim) * scale
src = self.time_src
if self.component in [mp.Hx, mp.Hy, mp.Hz]:
amp = -amp
for zi in range(z_dim):
for yi in range(y_dim):
for xi in range(x_dim):
if amp[xi, yi, zi] != 0:
self.sources += [mp.Source(src, component=self.component, amplitude=amp[xi, yi, zi],
center=mp.Vector3(self.dg.x[xi], self.dg.y[yi], self.dg.z[zi]))]
else:
dJ_4d = np.array([dJ[f].copy().reshape(x_dim, y_dim, z_dim) for f in range(self.num_freq)])
if self.component in [mp.Hx, mp.Hy, mp.Hz]:
dJ_4d = -dJ_4d
for zi in range(z_dim):
for yi in range(y_dim):
for xi in range(x_dim):
final_scale = -dJ_4d[:,xi,yi,zi] * scale
src = FilteredSource(self.time_src.frequency,self.frequencies,final_scale,dt)
self.sources += [mp.Source(src, component=self.component, amplitude=1,
center=mp.Vector3(self.dg.x[xi], self.dg.y[yi], self.dg.z[zi]))]
return self.sources
def __call__(self):
self.time_src = self.sim.sources[0].src
self.dg = Grid(*self.sim.get_array_metadata(dft_cell=self.monitor))
self.eval = np.array([self.sim.get_dft_array(self.monitor, self.component, i) for i in range(self.num_freq)])
return self.eval
def get_evaluation(self):
try:
return self.eval
except AttributeError:
raise RuntimeError("You must first run a forward simulation.")
class Near2FarFields(ObjectiveQuantitiy):
def __init__(self,sim,Near2FarRegions, far_pts):
self.sim = sim
self.Near2FarRegions=Near2FarRegions
self.eval = None
self.far_pts = far_pts #list of far pts
self.nfar_pts = len(far_pts)
return
def register_monitors(self,frequencies):
self.frequencies = np.asarray(frequencies)
self.num_freq = len(self.frequencies)
self.monitor = self.sim.add_near2far(self.frequencies, *self.Near2FarRegions, yee_grid=True)
return self.monitor
def place_adjoint_source(self,dJ):
dt = self.sim.fields.dt # the timestep size from sim.fields.dt of the forward sim
self.sources = []
if dJ.ndim == 4:
dJ = np.sum(dJ,axis=0)
dJ = dJ.flatten()
farpt_list = np.array([list(pi) for pi in self.far_pts]).flatten()
far_pt0 = self.far_pts[0]
far_pt_vec = py_v3_to_vec(self.sim.dimensions, far_pt0, self.sim.is_cylindrical)
self.all_nearsrcdata = self.monitor.swigobj.near_sourcedata(far_pt_vec, farpt_list, self.nfar_pts, dJ)
for near_data in self.all_nearsrcdata:
cur_comp = near_data.near_fd_comp
amp_arr = np.array(near_data.amp_arr).reshape(-1, self.num_freq)
scale = amp_arr * adj_src_scale(self, dt, include_resolution=False)
if self.num_freq == 1:
self.sources += [mp.IndexedSource(self.time_src, near_data, scale[:,0])]
else:
src = FilteredSource(self.time_src.frequency,self.frequencies,scale,dt)
(num_basis, num_pts) = src.nodes.shape
for basis_i in range(num_basis):
self.sources += [mp.IndexedSource(src.time_src_bf[basis_i], near_data, src.nodes[basis_i])]
return self.sources
def __call__(self):
self.time_src = self.sim.sources[0].src
self.eval = np.array([self.sim.get_farfield(self.monitor, far_pt) for far_pt in self.far_pts]).reshape((self.nfar_pts, self.num_freq, 6))
return self.eval
def get_evaluation(self):
try:
return self.eval
except AttributeError:
raise RuntimeError("You must first run a forward simulation.")
def adj_src_scale(obj_quantity, dt, include_resolution=True):
# -------------------------------------- #
# Get scaling factor
# -------------------------------------- #
# leverage linearity and combine source for multiple frequencies
T = obj_quantity.sim.meep_time()
if not include_resolution:
dV = 1
elif obj_quantity.sim.cell_size.y == 0:
dV = 1/obj_quantity.sim.resolution
elif obj_quantity.sim.cell_size.z == 0:
dV = 1/obj_quantity.sim.resolution * 1/obj_quantity.sim.resolution
else:
dV = 1/obj_quantity.sim.resolution * 1/obj_quantity.sim.resolution * 1/obj_quantity.sim.resolution
iomega = (1.0 - np.exp(-1j * (2 * np.pi * obj_quantity.frequencies) * dt)) * (1.0 / dt) # scaled frequency factor with discrete time derivative fix
src = obj_quantity.time_src
# an ugly way to calcuate the scaled dtft of the forward source
y = np.array([src.swigobj.current(t,dt) for t in np.arange(0,T,dt)]) # time domain signal
fwd_dtft = np.matmul(np.exp(1j*2*np.pi*obj_quantity.frequencies[:,np.newaxis]*np.arange(y.size)*dt), y)*dt/np.sqrt(2*np.pi) # dtft
# we need to compensate for the phase added by the time envelope at our freq of interest
src_center_dtft = np.matmul(np.exp(1j*2*np.pi*np.array([src.frequency])[:,np.newaxis]*np.arange(y.size)*dt), y)*dt/np.sqrt(2*np.pi)
adj_src_phase = np.exp(1j*np.angle(src_center_dtft))
if obj_quantity.frequencies.size == 1:
# Single frequency simulations. We need to drive it with a time profile.
scale = dV * iomega / fwd_dtft / adj_src_phase # final scale factor
else:
# multi frequency simulations
scale = dV * iomega / adj_src_phase
return scale
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