def test_set_materials(self):
def change_geom(sim):
t = sim.meep_time()
fn = t * 0.02
geom = [mp.Cylinder(radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn)),
mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn))]
sim.set_materials(geometry=geom)
c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5))
e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf))
sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3())
symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)]
sim = mp.Simulation(cell_size=mp.Vector3(10, 10),
geometry=[c, e],
boundary_layers=[mp.PML(1.0)],
sources=[sources],
symmetries=symmetries,
resolution=16)
eps = {'arr1': None, 'arr2': None}
def get_arr1(sim):
eps['arr1'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)))
def get_arr2(sim):
eps['arr2'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)))
sim.run(mp.at_time(50, get_arr1), mp.at_time(100, change_geom),
mp.at_end(get_arr2), until=200)
self.assertFalse(np.array_equal(eps['arr1'], eps['arr2']))
def test_set_materials(self):
def change_geom(sim):
t = sim.meep_time()
fn = t * 0.02
geom = [mp.Cylinder(radius=3, material=mp.Medium(index=3.5), center=mp.Vector3(fn, fn)),
mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf), center=mp.Vector3(fn, fn))]
sim.set_materials(geometry=geom)
c = mp.Cylinder(radius=3, material=mp.Medium(index=3.5))
e = mp.Ellipsoid(size=mp.Vector3(1, 2, mp.inf))
sources = mp.Source(src=mp.GaussianSource(1, fwidth=0.1), component=mp.Hz, center=mp.Vector3())
symmetries = [mp.Mirror(mp.X, -1), mp.Mirror(mp.Y, -1)]
sim = mp.Simulation(cell_size=mp.Vector3(10, 10),
geometry=[c, e],
boundary_layers=[mp.PML(1.0)],
sources=[sources],
symmetries=symmetries,
resolution=16)
eps = {'arr1': None, 'arr2': None}
def get_arr1(sim):
eps['arr1'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)))
def get_arr2(sim):
eps['arr2'] = sim.get_array(mp.Dielectric, mp.Volume(mp.Vector3(), mp.Vector3(10, 10)))
sim.run(mp.at_time(50, get_arr1), mp.at_time(100, change_geom),
mp.at_end(get_arr2), until=200)
self.assertFalse(np.array_equal(eps['arr1'], eps['arr2']))
def test_at_time(self):
sim = self.init_simple_simulation()
sim.use_output_directory(self.temp_dir)
sim.run(mp.at_time(100, mp.output_efield_z), until=200)
fname = os.path.join(self.temp_dir, 'simulation-ez-000100.00.h5')
self.assertTrue(os.path.exists(fname))
def test_at_time(self):
sim = self.init_simple_simulation()
sim.run(mp.at_time(100, mp.output_efield_z), until=200)
fname = 'simulation-ez-000100.00.h5'
self.assertTrue(os.path.exists(fname))
mp.all_wait()
if mp.am_master():
os.remove(fname)
def test_at_time(self):
sim = self.init_simple_simulation()
sim.run(mp.at_time(100, mp.output_efield_z), until=200)
fname = 'simulation-ez-000100.00.h5'
self.assertTrue(os.path.exists(fname))
mp.all_wait()
if mp.am_master():
os.remove(fname)
def main(from_um_factor, resolution_wlen, courant, submerged_index,
displacement, surface_index, wlen_center, wlen_width, nfreq,
air_wlen_factor, pml_wlen_factor, flux_wlen_factor, time_factor_cell,
second_time_factor, series, folder, parallel, n_processes,
split_chunks_evenly):
#%% CLASSIC INPUT PARAMETERS
"""
# Simulation size
from_um_factor = 100e-3 # Conversion of 1 μm to my length unit (=100nm/1μm)
resolution_wlen = 20 # >=8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)
courant = 0.5
# Nanoparticle specifications: Sphere in Vacuum :)
paper = "R"
reference = "Meep"
displacement = 0 # Displacement of the surface from the bottom of the sphere in nm
submerged_index = 1.33 # 1.33 for water
surface_index = 1.54 # 1.54 for glass
# Frequency and wavelength
wlen_center = 650 # Main wavelength range in nm
wlen_width = 200 # Wavelength band in nm
nfreq = 100
# Box dimensions
pml_wlen_factor = 0.38
air_wlen_factor = 0.15
flux_wlen_factor = 0.1
# Simulation time
time_factor_cell = 1.2
second_time_factor = 10
# Saving directories
series = "1stTest"
folder = "Test/TestDipole/DipoleGlass"
# Configuration
parallel = False
n_processes = 1
split_chunks_evenly = True
"""
#%% MORE INPUT PARAMETERS
# Simulation size
resolution = (from_um_factor * 1e3) * resolution_wlen / (wlen_center +
wlen_width / 2)
resolution = int(np.ceil(resolution / 2)) * 2
# Frequency and wavelength
cutoff = 3.2 # Gaussian planewave source's parameter of shape
nazimuthal = 16
npolar = 20
### TREATED INPUT PARAMETERS
# Nanoparticle specifications: Sphere in Vacuum :)
displacement = displacement / (from_um_factor * 1e3) # Now in Meep units
# Frequency and wavelength
wlen_center = wlen_center / (from_um_factor * 1e3) # Now in Meep units
wlen_width = wlen_width / (from_um_factor * 1e3) # Now in Meep units
freq_center = 1 / wlen_center # Hz center frequency in Meep units
freq_width = 1 / wlen_width # Hz range in Meep units from highest to lowest
# Space configuration
pml_width = pml_wlen_factor * (wlen_center + wlen_width / 2
) # 0.5 * max(wlen_range)
air_width = air_wlen_factor * (wlen_center + wlen_width / 2)
flux_box_size = flux_wlen_factor * (wlen_center + wlen_width / 2)
# Computation
enlapsed = []
if parallel:
np_process = mp.count_processors()
else:
np_process = 1
# Saving directories
if series is None:
series = "Test"
if folder is None:
folder = "Test"
params_list = [
"from_um_factor", "resolution_wlen", "resolution", "courant",
"submerged_index", "displacement", "surface_index", "wlen_center",
"wlen_width", "cutoff", "nfreq", "nazimuthal", "npolar",
"flux_box_size", "cell_width", "pml_width", "air_width",
"until_after_sources", "time_factor_cell", "second_time_factor",
"enlapsed", "parallel", "n_processes", "split_chunks_evenly", "script",
"sysname", "path"
]
#%% GENERAL GEOMETRY SETUP
air_width = air_width - air_width % (1 / resolution)
pml_width = pml_width - pml_width % (1 / resolution)
pml_layers = [mp.PML(thickness=pml_width)]
# symmetries = [mp.Mirror(mp.Y),
# mp.Mirror(mp.Z, phase=-1)]
# Two mirror planes reduce cell size to 1/4
# Issue related that lead me to comment this lines:
# https://github.com/NanoComp/meep/issues/1484
cell_width = 2 * (pml_width + air_width)
cell_width = cell_width - cell_width % (1 / resolution)
cell_size = mp.Vector3(cell_width, cell_width, cell_width)
# surface_center = r/4 - displacement/2 + cell_width/4
# surface_center = surface_center - surface_center%(1/resolution)
# displacement = r/2 + cell_width/2 - 2*surface_center
displacement = displacement - displacement % (1 / resolution)
flux_box_size = flux_box_size - flux_box_size % (1 / resolution)
sources = [
mp.Source(mp.GaussianSource(freq_center,
fwidth=freq_width,
is_integrated=True,
cutoff=cutoff),
center=mp.Vector3(),
component=mp.Ez)
]
# Ez-polarized point pulse
# (its size parameter is null and it is centered at zero)
# >> The planewave source extends into the PML
# ==> is_integrated=True must be specified
until_after_sources = time_factor_cell * cell_width * submerged_index
# Enough time for the pulse to pass through all the cell
# Originally: Aprox 3 periods of lowest frequency, using T=λ/c=λ in Meep units
# Now: Aprox 3 periods of highest frequency, using T=λ/c=λ in Meep units
# if surface_index != 1:
# geometry = [mp.Block(material=mp.Medium(index=surface_index),
# center=mp.Vector3(
# - displacement/2 + cell_width/4,
# 0, 0),
# size=mp.Vector3(
# cell_width/2 + displacement,
# cell_width, cell_width))]
# else:
# geometry = []
# A certain material surface underneath it
home = vs.get_home()
sysname = vs.get_sys_name()
path = os.path.join(home, folder, series)
if not os.path.isdir(path) and vm.parallel_assign(0, n_processes,
parallel):
os.makedirs(path)
file = lambda f: os.path.join(path, f)
#%% BASE SIMULATION: SETUP
measure_ram()
params = {}
for p in params_list:
params[p] = eval(p)
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
Courant=courant,
default_material=mp.Medium(index=submerged_index),
output_single_precision=True,
split_chunks_evenly=split_chunks_evenly)
measure_ram()
near2far_box = sim.add_near2far(
freq_center, freq_width, nfreq,
mp.Near2FarRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=+1))
measure_ram()
# used_ram.append(used_ram[-1])
#%% BASE SIMULATION: INITIALIZE
temp = time()
sim.init_sim()
enlapsed.append(time() - temp)
measure_ram()
#%% BASE SIMULATION: SIMULATION :D
step_ram_function = lambda sim: measure_ram()
temp = time()
sim.run(mp.at_beginning(step_ram_function),
mp.at_time(int(second_time_factor * until_after_sources / 2),
step_ram_function),
mp.at_end(step_ram_function),
until_after_sources=second_time_factor * until_after_sources)
# mp.stop_when_fields_decayed(
# np.mean(wlen_range), # dT = mean period of source
# mp.Ez, # Component of field to check
# mp.Vector3(0.5*cell_width - pml_width, 0, 0), # Where to check
# 1e-3)) # Factor to decay
enlapsed.append(time() - temp)
del temp
# Aprox 30 periods of lowest frequency, using T=λ/c=λ in Meep units
#%% BASE SIMULATION: ANGULAR PATTERN ANALYSIS
freqs = np.linspace(freq_center - freq_center / 2,
freq_center + freq_width / 2, nfreq)
wlens = 1 / freqs
# fraunhofer_distance = 8 * (r**2) / min(wlen_range)
# radial_distance = max( 10 * fraunhofer_distance , 1.5 * cell_width/2 )
# radius of far-field circle must be at least Fraunhofer distance
radial_distance = cell_width
azimuthal_angle = np.arange(0, 2 + 2 / nazimuthal,
2 / nazimuthal) # in multiples of pi
polar_angle = np.arange(0, 1 + 1 / npolar, 1 / npolar)
poynting_x = []
poynting_y = []
poynting_z = []
poynting_r = []
for phi in azimuthal_angle:
poynting_x.append([])
poynting_y.append([])
poynting_z.append([])
poynting_r.append([])
for theta in polar_angle:
farfield_dict = sim.get_farfields(
near2far_box,
1,
where=mp.Volume(center=mp.Vector3(
radial_distance * np.cos(np.pi * phi) *
np.sin(np.pi * theta),
radial_distance * np.sin(np.pi * phi) *
np.sin(np.pi *
theta), radial_distance * np.cos(np.pi * theta))))
Px = farfield_dict["Ey"] * np.conjugate(farfield_dict["Hz"])
Px -= farfield_dict["Ez"] * np.conjugate(farfield_dict["Hy"])
Py = farfield_dict["Ez"] * np.conjugate(farfield_dict["Hx"])
Py -= farfield_dict["Ex"] * np.conjugate(farfield_dict["Hz"])
Pz = farfield_dict["Ex"] * np.conjugate(farfield_dict["Hy"])
Pz -= farfield_dict["Ey"] * np.conjugate(farfield_dict["Hx"])
Px = np.real(Px)
Py = np.real(Py)
Pz = np.real(Pz)
poynting_x[-1].append(Px)
poynting_y[-1].append(Py)
poynting_z[-1].append(Pz)
poynting_r[-1].append(
np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz)))
del phi, theta, farfield_dict, Px, Py, Pz
poynting_x = np.array(poynting_x)
poynting_y = np.array(poynting_y)
poynting_z = np.array(poynting_z)
poynting_r = np.array(poynting_r)
#%% BASE SIMULATION: SAVE DATA
for p in params_list:
params[p] = eval(p)
os.chdir(path)
sim.save_near2far("BaseNear2Far", near2far_box)
if vm.parallel_assign(0, np_process, parallel):
f = h5.File("BaseNear2Far.h5", "r+")
for key, par in params.items():
f[list(f.keys())[0]].attrs[key] = par
f.close()
del f
os.chdir(syshome)
if vm.parallel_assign(1, np_process, parallel):
f = h5.File(file("BaseResults.h5"), "w")
f["Px"] = poynting_x
f["Py"] = poynting_y
f["Pz"] = poynting_z
f["Pr"] = poynting_r
for dset in f.values():
for key, par in params.items():
dset.attrs[key] = par
f.close()
del f
# if not split_chunks_evenly:
# vm.save_chunks(sim, params, path)
if parallel:
f = h5.File(file("BaseRAM.h5"),
"w",
driver='mpio',
comm=MPI.COMM_WORLD)
current_process = mp.my_rank()
f.create_dataset("RAM", (len(used_ram), np_process), dtype="float")
f["RAM"][:, current_process] = used_ram
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", (len(used_ram), np_process), dtype="int")
f["SWAP"][:, current_process] = swapped_ram
for a in params:
f["SWAP"].attrs[a] = params[a]
else:
f = h5.File(file("BaseRAM.h5"), "w")
f.create_dataset("RAM", data=used_ram)
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", data=swapped_ram)
for a in params:
f["SWAP"].attrs[a] = params[a]
f.close()
del f
sim.reset_meep()
#%% FURTHER SIMULATION
if surface_index != 1:
#% FURTHER SIMULATION: SETUP
measure_ram()
params = {}
for p in params_list:
params[p] = eval(p)
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
Courant=courant,
default_material=mp.Medium(index=surface_index),
output_single_precision=True,
split_chunks_evenly=split_chunks_evenly)
measure_ram()
near2far_box = sim.add_near2far(
freq_center, freq_width, nfreq,
mp.Near2FarRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=+1))
measure_ram()
# used_ram.append(used_ram[-1])
#% FURTHER SIMULATION: INITIALIZE
temp = time()
sim.init_sim()
enlapsed.append(time() - temp)
measure_ram()
#% FURTHER SIMULATION: SIMULATION :D
step_ram_function = lambda sim: measure_ram()
temp = time()
sim.run(mp.at_beginning(step_ram_function),
mp.at_time(int(second_time_factor * until_after_sources / 2),
step_ram_function),
mp.at_end(step_ram_function),
until_after_sources=second_time_factor * until_after_sources)
# mp.stop_when_fields_decayed(
# np.mean(wlen_range), # dT = mean period of source
# mp.Ez, # Component of field to check
# mp.Vector3(0.5*cell_width - pml_width, 0, 0), # Where to check
# 1e-3)) # Factor to decay
enlapsed.append(time() - temp)
del temp
# Aprox 30 periods of lowest frequency, using T=λ/c=λ in Meep units
#% FURTHER SIMULATION: ANGULAR PATTERN ANALYSIS
poynting_x2 = []
poynting_y2 = []
poynting_z2 = []
poynting_r2 = []
for phi in azimuthal_angle:
poynting_x2.append([])
poynting_y2.append([])
poynting_z2.append([])
poynting_r2.append([])
for theta in polar_angle:
farfield_dict = sim.get_farfields(
near2far_box,
1,
where=mp.Volume(center=mp.Vector3(
radial_distance * np.cos(np.pi * phi) *
np.sin(np.pi * theta),
radial_distance * np.sin(np.pi * phi) *
np.sin(np.pi *
theta), radial_distance *
np.cos(np.pi * theta))))
Px = farfield_dict["Ey"] * np.conjugate(farfield_dict["Hz"])
Px -= farfield_dict["Ez"] * np.conjugate(farfield_dict["Hy"])
Py = farfield_dict["Ez"] * np.conjugate(farfield_dict["Hx"])
Py -= farfield_dict["Ex"] * np.conjugate(farfield_dict["Hz"])
Pz = farfield_dict["Ex"] * np.conjugate(farfield_dict["Hy"])
Pz -= farfield_dict["Ey"] * np.conjugate(farfield_dict["Hx"])
Px = np.real(Px)
Py = np.real(Py)
Pz = np.real(Pz)
poynting_x2[-1].append(Px)
poynting_y2[-1].append(Py)
poynting_z2[-1].append(Pz)
poynting_r2[-1].append(
np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz)))
del phi, theta, farfield_dict, Px, Py, Pz
poynting_x2 = np.array(poynting_x2)
poynting_y2 = np.array(poynting_y2)
poynting_z2 = np.array(poynting_z2)
poynting_r2 = np.array(poynting_r2)
#% FURTHER SIMULATION: SAVE DATA
for p in params_list:
params[p] = eval(p)
os.chdir(path)
sim.save_near2far("FurtherNear2Far", near2far_box)
if vm.parallel_assign(0, np_process, parallel):
f = h5.File("FurtherNear2Far.h5", "r+")
for key, par in params.items():
f[list(f.keys())[0]].attrs[key] = par
f.close()
del f
os.chdir(syshome)
if vm.parallel_assign(1, np_process, parallel):
f = h5.File(file("FurtherResults.h5"), "w")
f["Px"] = poynting_x2
f["Py"] = poynting_y2
f["Pz"] = poynting_z2
f["Pr"] = poynting_r2
for dset in f.values():
for key, par in params.items():
dset.attrs[key] = par
f.close()
del f
# if not split_chunks_evenly:
# vm.save_chunks(sim, params, path)
if parallel:
f = h5.File(file("FurtherRAM.h5"),
"w",
driver='mpio',
comm=MPI.COMM_WORLD)
current_process = mp.my_rank()
f.create_dataset("RAM", (len(used_ram), np_process), dtype="float")
f["RAM"][:, current_process] = used_ram
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", (len(used_ram), np_process), dtype="int")
f["SWAP"][:, current_process] = swapped_ram
for a in params:
f["SWAP"].attrs[a] = params[a]
else:
f = h5.File(file("FurtherRAM.h5"), "w")
f.create_dataset("RAM", data=used_ram)
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", data=swapped_ram)
for a in params:
f["SWAP"].attrs[a] = params[a]
f.close()
del f
#%% NORMALIZE AND REARANGE DATA
if surface_index != 1:
max_poynting_r = np.max(
[np.max(np.abs(poynting_r)),
np.max(np.abs(poynting_r2))])
else:
max_poynting_r = np.max(np.abs(poynting_r))
poynting_x = np.array(poynting_x) / max_poynting_r
poynting_y = np.array(poynting_y) / max_poynting_r
poynting_z = np.array(poynting_z) / max_poynting_r
poynting_r = np.array(poynting_r) / max_poynting_r
if surface_index != 1:
poynting_x0 = np.array(poynting_x)
poynting_y0 = np.array(poynting_y)
poynting_z0 = np.array(poynting_z)
poynting_r0 = np.array(poynting_r)
poynting_x2 = np.array(poynting_x2) / max_poynting_r
poynting_y2 = np.array(poynting_y2) / max_poynting_r
poynting_z2 = np.array(poynting_z2) / max_poynting_r
poynting_r2 = np.array(poynting_r2) / max_poynting_r
#%%
"""
poynting_x = np.array(poynting_x0)
poynting_y = np.array(poynting_y0)
poynting_z = np.array(poynting_z0)
poynting_r = np.array(poynting_r0)
"""
if surface_index != 1:
polar_limit = np.arcsin(displacement / radial_distance) + .5
for i in range(len(polar_angle)):
if polar_angle[i] > polar_limit:
index_limit = i
break
poynting_x[:, index_limit:, :] = poynting_x2[:, index_limit:, :]
poynting_y[:, index_limit:, :] = poynting_y2[:, index_limit:, :]
poynting_z[:, index_limit:, :] = poynting_z2[:, index_limit:, :]
poynting_r[:, index_limit:, :] = poynting_r2[:, index_limit:, :]
poynting_x[:, index_limit - 1, :] = np.array([[
np.mean([p2, p0]) for p2, p0 in zip(poy2, poy0)
] for poy2, poy0 in zip(poynting_x2[:, index_limit -
1, :], poynting_x0[:, index_limit -
1, :])])
poynting_y[:, index_limit - 1, :] = np.array([[
np.mean([p2, p0]) for p2, p0 in zip(poy2, poy0)
] for poy2, poy0 in zip(poynting_y2[:, index_limit -
1, :], poynting_y0[:, index_limit -
1, :])])
poynting_z[:, index_limit - 1, :] = np.array([[
np.mean([p2, p0]) for p2, p0 in zip(poy2, poy0)
] for poy2, poy0 in zip(poynting_z2[:, index_limit -
1, :], poynting_z0[:, index_limit -
1, :])])
poynting_r[:, index_limit - 1, :] = np.sqrt(
np.squared(poynting_x[:, index_limit - 1, :]) +
np.squared(poynting_y[:, index_limit - 1, :]) +
np.squared(poynting_y[:, index_limit - 1, :]))
#%% SAVE FINAL DATA
if surface_index != 1 and vm.parallel_assign(0, np_process, parallel):
os.remove(file("BaseRAM.h5"))
os.rename(file("FurtherRAM.h5"), file("RAM.h5"))
elif vm.parallel_assign(0, np_process, parallel):
os.rename(file("BaseRAM.h5"), file("RAM.h5"))
#%% PLOT ANGULAR PATTERN IN 3D
if vm.parallel_assign(1, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
fig = plt.figure()
plt.suptitle(
f'Angular Pattern of point dipole molecule at {from_um_factor * 1e3 * wlen_center:.0f} nm'
)
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_surface(poynting_x[:, :, freq_index],
poynting_y[:, :, freq_index],
poynting_z[:, :, freq_index],
cmap=plt.get_cmap('jet'),
linewidth=1,
antialiased=False,
alpha=0.5)
ax.set_xlabel(r"$P_x$")
ax.set_ylabel(r"$P_y$")
ax.set_zlabel(r"$P_z$")
plt.savefig(file("AngularPattern.png"))
#%% PLOT ANGULAR PATTERN PROFILE FOR DIFFERENT POLAR ANGLES
if vm.parallel_assign(0, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(polar_angle).index(alpha) for alpha in [0, .25, .5, .75, 1]
]
plt.figure()
plt.suptitle(
f'Angular Pattern of point dipole molecule at {from_um_factor * 1e3 * wlen_center:.0f} nm'
)
ax_plain = plt.axes()
for i in index:
ax_plain.plot(poynting_x[:, i, freq_index],
poynting_y[:, i, freq_index],
".-",
label=rf"$\theta$ = {polar_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_x$")
ax_plain.set_ylabel(r"$P_y$")
ax_plain.set_aspect("equal")
plt.savefig(file("AngularPolar.png"))
#%% PLOT ANGULAR PATTERN PROFILE FOR DIFFERENT AZIMUTHAL ANGLES
if vm.parallel_assign(1, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(azimuthal_angle).index(alpha)
for alpha in [0, .25, .5, .75, 1, 1.25, 1.5, 1.75, 2]
]
plt.figure()
plt.suptitle(
f'Angular Pattern of point dipole molecule at {from_um_factor * 1e3 * wlen_center:.0f} nm'
)
ax_plain = plt.axes()
for i in index:
ax_plain.plot(poynting_x[i, :, freq_index],
poynting_z[i, :, freq_index],
".-",
label=rf"$\phi$ = {azimuthal_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_x$")
ax_plain.set_ylabel(r"$P_z$")
plt.savefig(file("AngularAzimuthal.png"))
if vm.parallel_assign(0, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(azimuthal_angle).index(alpha)
for alpha in [0, .25, .5, .75, 1, 1.25, 1.5, 1.75, 2]
]
plt.figure()
plt.suptitle(
f'Angular Pattern of point dipole molecule at {from_um_factor * 1e3 * wlen_center:.0f} nm'
)
ax_plain = plt.axes()
for i in index:
ax_plain.plot(np.sqrt(
np.square(poynting_x[i, :, freq_index]) +
np.square(poynting_y[i, :, freq_index])),
poynting_z[i, :, freq_index],
".-",
label=rf"$\phi$ = {azimuthal_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_\rho$")
ax_plain.set_ylabel(r"$P_z$")
plt.savefig(file("AngularAzimuthalAbs.png"))
def main(from_um_factor, resolution, courant, r, material, paper, reference,
submerged_index, displacement, surface_index, wlen_range, nfreq,
air_r_factor, pml_wlen_factor, flux_r_factor, time_factor_cell,
second_time_factor, series, folder, parallel, n_processes, n_cores,
n_nodes, split_chunks_evenly, load_flux, load_chunks, near2far):
#%% CLASSIC INPUT PARAMETERS
"""
# Simulation size
from_um_factor = 10e-3 # Conversion of 1 μm to my length unit (=10nm/1μm)
resolution = 2 # >=8 pixels per smallest wavelength, i.e. np.floor(8/wvl_min)
courant = 0.5
# Nanoparticle specifications: Sphere in Vacuum :)
r = 51.5 # Radius of sphere in nm
paper = "R"
reference = "Meep"
displacement = 0 # Displacement of the surface from the bottom of the sphere in nm
submerged_index = 1 # 1.33 for water
surface_index = 1 # 1.54 for glass
# Frequency and wavelength
wlen_range = np.array([350,500]) # Wavelength range in nm
nfreq = 100
# Box dimensions
pml_wlen_factor = 0.38
air_r_factor = 0.5
flux_r_factor = 0 #0.1
# Simulation time
time_factor_cell = 1.2
second_time_factor = 10
# Saving directories
series = "SilverRes4"
folder = "Test/TestSilver"
# Configuration
parallel = False
n_processes = 1
n_cores = 1
n_nodes = 1
split_chunks_evenly = True
load_flux = False
load_chunks = True
near2far = False
"""
#%% MORE INPUT PARAMETERS
# Frequency and wavelength
cutoff = 3.2 # Gaussian planewave source's parameter of shape
nazimuthal = 16
npolar = 20
### TREATED INPUT PARAMETERS
# Nanoparticle specifications: Sphere in Vacuum :)
r = r / (from_um_factor * 1e3) # Now in Meep units
if reference == "Meep":
medium = vmt.import_medium(material,
from_um_factor=from_um_factor,
paper=paper)
# Importing material constants dependant on frequency from Meep Library
elif reference == "RIinfo":
medium = vmt.MediumFromFile(material,
paper=paper,
reference=reference,
from_um_factor=from_um_factor)
# Importing material constants dependant on frequency from external file
else:
raise ValueError("Reference for medium not recognized. Sorry :/")
displacement = displacement / (from_um_factor * 1e3) # Now in Meep units
# Frequency and wavelength
wlen_range = np.array(wlen_range)
wlen_range = wlen_range / (from_um_factor * 1e3) # Now in Meep units
freq_range = 1 / wlen_range # Hz range in Meep units from highest to lowest
freq_center = np.mean(freq_range)
freq_width = max(freq_range) - min(freq_range)
# Space configuration
pml_width = pml_wlen_factor * max(wlen_range) # 0.5 * max(wlen_range)
air_width = air_r_factor * r # 0.5 * max(wlen_range)
flux_box_size = 2 * (1 + flux_r_factor) * r
# Saving directories
if series is None:
series = "Test"
if folder is None:
folder = "Test"
params_list = [
"from_um_factor", "resolution", "courant", "material", "r", "paper",
"reference", "submerged_index", "displacement", "surface_index",
"wlen_range", "nfreq", "nazimuthal", "npolar", "cutoff",
"flux_box_size", "cell_width", "pml_width", "air_width",
"source_center", "until_after_sources", "time_factor_cell",
"second_time_factor", "enlapsed", "parallel", "n_processes", "n_cores",
"n_nodes", "split_chunks_evenly", "near2far", "script", "sysname",
"path"
]
#%% GENERAL GEOMETRY SETUP
air_width = air_width - air_width % (1 / resolution)
pml_width = pml_width - pml_width % (1 / resolution)
pml_layers = [mp.PML(thickness=pml_width)]
# symmetries = [mp.Mirror(mp.Y),
# mp.Mirror(mp.Z, phase=-1)]
# Two mirror planes reduce cell size to 1/4
# Issue related that lead me to comment this lines:
# https://github.com/NanoComp/meep/issues/1484
cell_width = 2 * (pml_width + air_width + r)
cell_width = cell_width - cell_width % (1 / resolution)
cell_size = mp.Vector3(cell_width, cell_width, cell_width)
# surface_center = r/4 - displacement/2 + cell_width/4
# surface_center = surface_center - surface_center%(1/resolution)
# displacement = r/2 + cell_width/2 - 2*surface_center
displacement = displacement - displacement % (1 / resolution)
flux_box_size = flux_box_size - flux_box_size % (1 / resolution)
source_center = -0.5 * cell_width + pml_width
sources = [
mp.Source(mp.GaussianSource(freq_center,
fwidth=freq_width,
is_integrated=True,
cutoff=cutoff),
center=mp.Vector3(source_center),
size=mp.Vector3(0, cell_width, cell_width),
component=mp.Ez)
]
# Ez-polarized planewave pulse
# (its size parameter fills the entire cell in 2d)
# >> The planewave source extends into the PML
# ==> is_integrated=True must be specified
until_after_sources = time_factor_cell * cell_width * submerged_index
# Enough time for the pulse to pass through all the cell
# Originally: Aprox 3 periods of lowest frequency, using T=λ/c=λ in Meep units
# Now: Aprox 3 periods of highest frequency, using T=λ/c=λ in Meep units
geometry = [mp.Sphere(material=medium, center=mp.Vector3(), radius=r)]
# Au sphere with frequency-dependant characteristics imported from Meep.
if surface_index != 1:
geometry = [
mp.Block(material=mp.Medium(index=surface_index),
center=mp.Vector3(
r / 2 - displacement / 2 + cell_width / 4, 0, 0),
size=mp.Vector3(cell_width / 2 - r + displacement,
cell_width, cell_width)), *geometry
]
# A certain material surface underneath it
home = vs.get_home()
sysname = vs.get_sys_name()
path = os.path.join(home, folder, series)
if not os.path.isdir(path) and vm.parallel_assign(0, n_processes,
parallel):
os.makedirs(path)
file = lambda f: os.path.join(path, f)
# Computation
enlapsed = []
parallel_specs = np.array([n_processes, n_cores, n_nodes], dtype=int)
max_index = np.argmax(parallel_specs)
for index, item in enumerate(parallel_specs):
if item == 0: parallel_specs[index] = 1
parallel_specs[0:max_index] = np.full(parallel_specs[0:max_index].shape,
max(parallel_specs))
n_processes, n_cores, n_nodes = parallel_specs
parallel = max(parallel_specs) > 1
del parallel_specs, max_index, index, item
if parallel:
np_process = mp.count_processors()
else:
np_process = 1
#%% FIRST RUN
measure_ram()
params = {}
for p in params_list:
params[p] = eval(p)
stable, max_courant = vm.check_stability(params)
if stable:
print("As a whole, the simulation should be stable")
else:
print("As a whole, the simulation could not be stable")
print(f"Recommended maximum courant factor is {max_courant}")
if load_flux:
try:
flux_path = vm.check_midflux(params)[0]
flux_needed = False
except:
flux_needed = True
else:
flux_needed = True
if load_chunks and not split_chunks_evenly:
try:
chunks_path = vm.check_chunks(params)[0]
chunk_layout = os.path.join(chunks_path, "Layout.h5")
chunks_needed = False
except:
chunks_needed = True
flux_needed = True
else:
if not split_chunks_evenly:
chunks_needed = True
flux_needed = True
else:
chunk_layout = None
chunks_needed = False
if chunks_needed:
sim = mp.Simulation(
resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
Courant=courant,
default_material=mp.Medium(index=submerged_index),
output_single_precision=True,
split_chunks_evenly=split_chunks_evenly,
# symmetries=symmetries,
geometry=geometry)
sim.init_sim()
chunks_path = vm.save_chunks(sim, params, path)
chunk_layout = os.path.join(chunks_path, "Layout.h5")
del sim
if flux_needed:
#% FIRST RUN: SET UP
measure_ram()
sim = mp.Simulation(resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
Courant=courant,
default_material=mp.Medium(index=submerged_index),
split_chunks_evenly=split_chunks_evenly,
chunk_layout=chunk_layout,
output_single_precision=True) #,
# symmetries=symmetries)
# >> k_point zero specifies boundary conditions needed
# for the source to be infinitely extended
measure_ram()
# Scattered power --> Computed by surrounding it with closed DFT flux box
# (its size and orientation are irrelevant because of Poynting's theorem)
box_x1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size)))
box_x2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size)))
box_y1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size)))
box_y2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size)))
box_z1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0)))
box_z2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0)))
# Funny you can encase the sphere (r radius) so closely (2r-sided box)
measure_ram()
if near2far:
near2far_box = sim.add_near2far(
freq_center, freq_width, nfreq,
mp.Near2FarRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size,
flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size,
flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0,
flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0,
flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size,
0),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size,
0),
weight=+1))
measure_ram()
else:
near2far_box = None
# used_ram.append(used_ram[-1])
#% FIRST RUN: INITIALIZE
temp = time()
sim.init_sim()
enlapsed.append(time() - temp)
measure_ram()
step_ram_function = lambda sim: measure_ram()
#% FIRST RUN: SIMULATION NEEDED TO NORMALIZE
temp = time()
sim.run(mp.at_beginning(step_ram_function),
mp.at_time(int(until_after_sources / 2), step_ram_function),
mp.at_end(step_ram_function),
until_after_sources=until_after_sources)
# mp.stop_when_fields_decayed(
# np.mean(wlen_range), # dT = mean period of source
# mp.Ez, # Component of field to check
# mp.Vector3(0.5*cell_width - pml_width, 0, 0), # Where to check
# 1e-3)) # Factor to decay
enlapsed.append(time() - temp)
#% SAVE MID DATA
for p in params_list:
params[p] = eval(p)
flux_path = vm.save_midflux(sim, box_x1, box_x2, box_y1, box_y2,
box_z1, box_z2, near2far_box, params, path)
freqs = np.asarray(mp.get_flux_freqs(box_x1))
box_x1_flux0 = np.asarray(mp.get_fluxes(box_x1))
box_x2_flux0 = np.asarray(mp.get_fluxes(box_x2))
box_y1_flux0 = np.asarray(mp.get_fluxes(box_y1))
box_y2_flux0 = np.asarray(mp.get_fluxes(box_y2))
box_z1_flux0 = np.asarray(mp.get_fluxes(box_z1))
box_z2_flux0 = np.asarray(mp.get_fluxes(box_z2))
data_mid = np.array([
1e3 * from_um_factor / freqs, box_x1_flux0, box_x2_flux0,
box_y1_flux0, box_y2_flux0, box_z1_flux0, box_z2_flux0
]).T
header_mid = [
"Longitud de onda [nm]", "Flujo X10 [u.a.]", "Flujo X20 [u.a]",
"Flujo Y10 [u.a]", "Flujo Y20 [u.a]", "Flujo Z10 [u.a]",
"Flujo Z20 [u.a]"
]
if vm.parallel_assign(0, n_processes, parallel):
vs.savetxt(file("MidFlux.txt"),
data_mid,
header=header_mid,
footer=params)
if not split_chunks_evenly:
vm.save_chunks(sim, params, path)
if parallel:
f = h5.File(file("MidRAM.h5"),
"w",
driver='mpio',
comm=MPI.COMM_WORLD)
current_process = mp.my_rank()
f.create_dataset("RAM", (len(used_ram), np_process), dtype="float")
f["RAM"][:, current_process] = used_ram
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", (len(used_ram), np_process), dtype="int")
f["SWAP"][:, current_process] = swapped_ram
for a in params:
f["SWAP"].attrs[a] = params[a]
else:
f = h5.File(file("MidRAM.h5"), "w")
f.create_dataset("RAM", data=used_ram)
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", data=swapped_ram)
for a in params:
f["SWAP"].attrs[a] = params[a]
f.close()
del f
#% PLOT FLUX FOURIER MID DATA
if vm.parallel_assign(1, np_process, parallel):
ylims = (np.min(data_mid[:, 1:]), np.max(data_mid[:, 1:]))
ylims = (ylims[0] - .1 * (ylims[1] - ylims[0]),
ylims[1] + .1 * (ylims[1] - ylims[0]))
fig, ax = plt.subplots(3, 2, sharex=True)
fig.subplots_adjust(hspace=0, wspace=.05)
for a in ax[:, 1]:
a.yaxis.tick_right()
a.yaxis.set_label_position("right")
for a, h in zip(np.reshape(ax, 6), header_mid[1:]):
a.set_ylabel(h)
for d, a in zip(data_mid[:, 1:].T, np.reshape(ax, 6)):
a.plot(1e3 * from_um_factor / freqs, d)
a.set_ylim(*ylims)
ax[-1, 0].set_xlabel("Wavelength [nm]")
ax[-1, 1].set_xlabel("Wavelength [nm]")
plt.savefig(file("MidFlux.png"))
del fig, ax, ylims, a, h
sim.reset_meep()
#%% SECOND RUN: SETUP
measure_ram()
sim = mp.Simulation(
resolution=resolution,
cell_size=cell_size,
boundary_layers=pml_layers,
sources=sources,
k_point=mp.Vector3(),
Courant=courant,
default_material=mp.Medium(index=submerged_index),
output_single_precision=True,
split_chunks_evenly=split_chunks_evenly,
chunk_layout=chunk_layout,
# symmetries=symmetries,
geometry=geometry)
measure_ram()
box_x1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size)))
box_x2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size)))
box_y1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size)))
box_y2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size)))
box_z1 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0)))
box_z2 = sim.add_flux(
freq_center, freq_width, nfreq,
mp.FluxRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0)))
measure_ram()
if near2far:
near2far_box = sim.add_near2far(
freq_center, freq_width, nfreq,
mp.Near2FarRegion(center=mp.Vector3(x=-flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(x=+flux_box_size / 2),
size=mp.Vector3(0, flux_box_size, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(y=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(y=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, 0, flux_box_size),
weight=+1),
mp.Near2FarRegion(center=mp.Vector3(z=-flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=-1),
mp.Near2FarRegion(center=mp.Vector3(z=+flux_box_size / 2),
size=mp.Vector3(flux_box_size, flux_box_size, 0),
weight=+1))
measure_ram()
else:
near2far_box = None
# used_ram.append(used_ram[-1])
#%% SECOND RUN: INITIALIZE
temp = time()
sim.init_sim()
enlapsed.append(time() - temp)
measure_ram()
#%% LOAD FLUX FROM FILE
vm.load_midflux(sim, box_x1, box_x2, box_y1, box_y2, box_z1, box_z2,
near2far_box, flux_path)
measure_ram()
freqs = np.asarray(mp.get_flux_freqs(box_x1))
box_x1_flux0 = np.asarray(mp.get_fluxes(box_x1))
box_x1_data = sim.get_flux_data(box_x1)
box_x2_data = sim.get_flux_data(box_x2)
box_y1_data = sim.get_flux_data(box_y1)
box_y2_data = sim.get_flux_data(box_y2)
box_z1_data = sim.get_flux_data(box_z1)
box_z2_data = sim.get_flux_data(box_z2)
if near2far: near2far_data = sim.get_near2far_data(near2far_box)
temp = time()
sim.load_minus_flux_data(box_x1, box_x1_data)
sim.load_minus_flux_data(box_x2, box_x2_data)
sim.load_minus_flux_data(box_y1, box_y1_data)
sim.load_minus_flux_data(box_y2, box_y2_data)
sim.load_minus_flux_data(box_z1, box_z1_data)
sim.load_minus_flux_data(box_z2, box_z2_data)
if near2far: sim.load_minus_near2far_data(near2far_box, near2far_data)
enlapsed.append(time() - temp)
del box_x1_data, box_x2_data, box_y1_data, box_y2_data
del box_z1_data, box_z2_data
if near2far: del near2far_data
measure_ram()
#%% SECOND RUN: SIMULATION :D
step_ram_function = lambda sim: measure_ram()
temp = time()
sim.run(mp.at_beginning(step_ram_function),
mp.at_time(int(second_time_factor * until_after_sources / 2),
step_ram_function),
mp.at_end(step_ram_function),
until_after_sources=second_time_factor * until_after_sources)
# mp.stop_when_fields_decayed(
# np.mean(wlen_range), # dT = mean period of source
# mp.Ez, # Component of field to check
# mp.Vector3(0.5*cell_width - pml_width, 0, 0), # Where to check
# 1e-3)) # Factor to decay
enlapsed.append(time() - temp)
del temp
# Aprox 30 periods of lowest frequency, using T=λ/c=λ in Meep units
box_x1_flux = np.asarray(mp.get_fluxes(box_x1))
box_x2_flux = np.asarray(mp.get_fluxes(box_x2))
box_y1_flux = np.asarray(mp.get_fluxes(box_y1))
box_y2_flux = np.asarray(mp.get_fluxes(box_y2))
box_z1_flux = np.asarray(mp.get_fluxes(box_z1))
box_z2_flux = np.asarray(mp.get_fluxes(box_z2))
#%% SCATTERING ANALYSIS
scatt_flux = box_x1_flux - box_x2_flux
scatt_flux = scatt_flux + box_y1_flux - box_y2_flux
scatt_flux = scatt_flux + box_z1_flux - box_z2_flux
intensity = box_x1_flux0 / (flux_box_size)**2
# Flux of one of the six monitor planes / Área
# (the closest one, facing the planewave source)
# This is why the six sides of the flux box are separated
# (Otherwise, the box could've been one flux object with weights ±1 per side)
scatt_cross_section = np.divide(scatt_flux, intensity)
# Scattering cross section σ =
# = scattered power in all directions / incident intensity.
scatt_eff_meep = -1 * scatt_cross_section / (np.pi * r**2)
# Scattering efficiency =
# = scattering cross section / cross sectional area of the sphere
freqs = np.array(freqs)
scatt_eff_theory = [
ps.MieQ(np.sqrt(medium.epsilon(f)[0, 0] * medium.mu(f)[0, 0]),
1e3 * from_um_factor / f,
2 * r * 1e3 * from_um_factor,
nMedium=submerged_index,
asDict=True)['Qsca'] for f in freqs
]
# The simulation results are validated by comparing with
# analytic theory of PyMieScatt module
#%% ANGULAR PATTERN ANALYSIS
if near2far:
fraunhofer_distance = 8 * (r**2) / min(wlen_range)
radial_distance = max(10 * fraunhofer_distance, 1.5 * cell_width / 2)
# radius of far-field circle must be at least Fraunhofer distance
azimuthal_angle = np.arange(0, 2 + 2 / nazimuthal,
2 / nazimuthal) # in multiples of pi
polar_angle = np.arange(0, 1 + 1 / npolar, 1 / npolar)
poynting_x = []
poynting_y = []
poynting_z = []
poynting_r = []
for phi in azimuthal_angle:
poynting_x.append([])
poynting_y.append([])
poynting_z.append([])
poynting_r.append([])
for theta in polar_angle:
farfield_dict = sim.get_farfields(
near2far_box,
1,
where=mp.Volume(center=mp.Vector3(
radial_distance * np.cos(np.pi * phi) *
np.sin(np.pi * theta),
radial_distance * np.sin(np.pi * phi) *
np.sin(np.pi *
theta), radial_distance *
np.cos(np.pi * theta))))
Px = farfield_dict["Ey"] * np.conjugate(farfield_dict["Hz"])
Px -= farfield_dict["Ez"] * np.conjugate(farfield_dict["Hy"])
Py = farfield_dict["Ez"] * np.conjugate(farfield_dict["Hx"])
Py -= farfield_dict["Ex"] * np.conjugate(farfield_dict["Hz"])
Pz = farfield_dict["Ex"] * np.conjugate(farfield_dict["Hy"])
Pz -= farfield_dict["Ey"] * np.conjugate(farfield_dict["Hx"])
Px = np.real(Px)
Py = np.real(Py)
Pz = np.real(Pz)
poynting_x[-1].append(Px)
poynting_y[-1].append(Py)
poynting_z[-1].append(Pz)
poynting_r[-1].append(
np.sqrt(np.square(Px) + np.square(Py) + np.square(Pz)))
poynting_x = np.array(poynting_x)
poynting_y = np.array(poynting_y)
poynting_z = np.array(poynting_z)
poynting_r = np.array(poynting_r)
#%% SAVE FINAL DATA
for p in params_list:
params[p] = eval(p)
data = np.array(
[1e3 * from_um_factor / freqs, scatt_eff_meep, scatt_eff_theory]).T
header = [
"Longitud de onda [nm]", "Sección eficaz efectiva (Meep) [u.a.]",
"Sección eficaz efectiva (Theory) [u.a.]"
]
data_base = np.array([
1e3 * from_um_factor / freqs, box_x1_flux0, box_x1_flux, box_x2_flux,
box_y1_flux, box_y2_flux, box_z1_flux, box_z2_flux, intensity,
scatt_flux, scatt_cross_section
]).T
header_base = [
"Longitud de onda [nm]", "Flujo X10 [u.a.]", "Flujo X1 [u.a]",
"Flujo X2 [u.a]", "Flujo Y1 [u.a]", "Flujo Y2 [u.a]", "Flujo Z1 [u.a]",
"Flujo Z2 [u.a]", "Intensidad incidente [u.a.]",
"Flujo scattereado [u.a.]", "Sección eficaz de scattering [u.a.]"
]
if vm.parallel_assign(0, np_process, parallel):
vs.savetxt(file("Results.txt"), data, header=header, footer=params)
vs.savetxt(file("BaseResults.txt"),
data_base,
header=header_base,
footer=params)
if near2far:
header_near2far = [
"Poynting medio Px [u.a.]", "Poynting medio Py [u.a.]",
"Poynting medio Pz [u.a.]", "Poynting medio Pr [u.a.]"
]
data_near2far = [
poynting_x.reshape(poynting_x.size),
poynting_y.reshape(poynting_y.size),
poynting_z.reshape(poynting_z.size),
poynting_r.reshape(poynting_r.size)
]
if vm.parallel_assign(1, np_process, parallel):
vs.savetxt(file("Near2FarResults.txt"),
data_near2far,
header=header_near2far,
footer=params)
if not split_chunks_evenly:
vm.save_chunks(sim, params, path)
if parallel:
f = h5.File(file("RAM.h5"), "w", driver='mpio', comm=MPI.COMM_WORLD)
current_process = mp.my_rank()
f.create_dataset("RAM", (len(used_ram), np_process), dtype="float")
f["RAM"][:, current_process] = used_ram
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", (len(used_ram), np_process), dtype="int")
f["SWAP"][:, current_process] = swapped_ram
for a in params:
f["SWAP"].attrs[a] = params[a]
else:
f = h5.File(file("RAM.h5"), "w")
f.create_dataset("RAM", data=used_ram)
for a in params:
f["RAM"].attrs[a] = params[a]
f.create_dataset("SWAP", data=swapped_ram)
for a in params:
f["SWAP"].attrs[a] = params[a]
f.close()
del f
if flux_needed and vm.parallel_assign(0, np_process, parallel):
os.remove(file("MidRAM.h5"))
#%% PLOT ALL TOGETHER
if vm.parallel_assign(0, np_process, parallel) and surface_index == 1:
plt.figure()
plt.plot(1e3 * from_um_factor / freqs,
scatt_eff_meep,
'bo-',
label='Meep')
plt.plot(1e3 * from_um_factor / freqs,
scatt_eff_theory,
'ro-',
label='Theory')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Scattering efficiency [σ/πr$^{2}$]')
plt.legend()
plt.title('Scattering of Au Sphere With {:.1f} nm Radius'.format(
r * from_um_factor * 1e3))
plt.tight_layout()
plt.savefig(file("Comparison.png"))
#%% PLOT SEPARATE
if vm.parallel_assign(1, np_process, parallel):
plt.figure()
plt.plot(1e3 * from_um_factor / freqs,
scatt_eff_meep,
'bo-',
label='Meep')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Scattering efficiency [σ/πr$^{2}$]')
plt.legend()
plt.title('Scattering of Au Sphere With {:.1f} nm Radius'.format(
r * from_um_factor * 1e3))
plt.tight_layout()
plt.savefig(file("Meep.png"))
if vm.parallel_assign(0, np_process, parallel) and surface_index == 1:
plt.figure()
plt.plot(1e3 * from_um_factor / freqs,
scatt_eff_theory,
'ro-',
label='Theory')
plt.xlabel('Wavelength [nm]')
plt.ylabel('Scattering efficiency [σ/πr$^{2}$]')
plt.legend()
plt.title('Scattering of Au Sphere With {:.1f} nm Radius'.format(r *
10))
plt.tight_layout()
plt.savefig(file("Theory.png"))
#%% PLOT ONE ABOVE THE OTHER
if vm.parallel_assign(1, np_process, parallel) and surface_index == 1:
fig, axes = plt.subplots(nrows=2, sharex=True)
fig.subplots_adjust(hspace=0)
plt.suptitle('Scattering of Au Sphere With {:.1f} nm Radius'.format(
r * from_um_factor * 1e3))
axes[0].plot(1e3 * from_um_factor / freqs,
scatt_eff_meep,
'bo-',
label='Meep')
axes[0].yaxis.tick_right()
axes[0].set_ylabel('Scattering efficiency [σ/πr$^{2}$]')
axes[0].legend()
axes[1].plot(1e3 * from_um_factor / freqs,
scatt_eff_theory,
'ro-',
label='Theory')
axes[1].set_xlabel('Wavelength [nm]')
axes[1].set_ylabel('Scattering efficiency [σ/πr$^{2}$]')
axes[1].legend()
plt.savefig(file("SeparatedComparison.png"))
#%% PLOT FLUX FOURIER FINAL DATA
if vm.parallel_assign(0, np_process, parallel):
ylims = (np.min(data_base[:, 2:8]), np.max(data_base[:, 2:8]))
ylims = (ylims[0] - .1 * (ylims[1] - ylims[0]),
ylims[1] + .1 * (ylims[1] - ylims[0]))
fig, ax = plt.subplots(3, 2, sharex=True)
fig.subplots_adjust(hspace=0, wspace=.05)
plt.suptitle('Final flux of Au Sphere With {:.1f} nm Radius'.format(
r * from_um_factor * 1e3))
for a in ax[:, 1]:
a.yaxis.tick_right()
a.yaxis.set_label_position("right")
for a, h in zip(np.reshape(ax, 6), header_base[1:7]):
a.set_ylabel(h)
for d, a in zip(data_base[:, 3:9].T, np.reshape(ax, 6)):
a.plot(1e3 * from_um_factor / freqs, d)
a.set_ylim(*ylims)
ax[-1, 0].set_xlabel("Wavelength [nm]")
ax[-1, 1].set_xlabel("Wavelength [nm]")
plt.savefig(file("FinalFlux.png"))
#%% PLOT ANGULAR PATTERN IN 3D
if near2far and vm.parallel_assign(1, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
fig = plt.figure()
plt.suptitle(
'Angular Pattern of Au Sphere With {:.1f} nm Radius at {:.1f} nm'.
format(r * from_um_factor * 1e3,
from_um_factor * 1e3 / freqs[freq_index]))
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.plot_surface(poynting_x[:, :, freq_index],
poynting_y[:, :, freq_index],
poynting_z[:, :, freq_index],
cmap=plt.get_cmap('jet'),
linewidth=1,
antialiased=False,
alpha=0.5)
ax.set_xlabel(r"$P_x$")
ax.set_ylabel(r"$P_y$")
ax.set_zlabel(r"$P_z$")
plt.savefig(file("AngularPattern.png"))
#%% PLOT ANGULAR PATTERN PROFILE FOR DIFFERENT POLAR ANGLES
if near2far and vm.parallel_assign(0, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(polar_angle).index(alpha) for alpha in [0, .25, .5, .75, 1]
]
plt.figure()
plt.suptitle(
'Angular Pattern of Au Sphere With {:.1f} nm Radius at {:.1f} nm'.
format(r * from_um_factor * 1e3,
from_um_factor * 1e3 / freqs[freq_index]))
ax_plain = plt.axes()
for i in index:
ax_plain.plot(poynting_x[:, i, freq_index],
poynting_y[:, i, freq_index],
".-",
label=rf"$\theta$ = {polar_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_x$")
ax_plain.set_ylabel(r"$P_y$")
ax_plain.set_aspect("equal")
plt.savefig(file("AngularPolar.png"))
#%% PLOT ANGULAR PATTERN PROFILE FOR DIFFERENT AZIMUTHAL ANGLES
if near2far and vm.parallel_assign(1, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(azimuthal_angle).index(alpha)
for alpha in [0, .25, .5, .75, 1, 1.25, 1.5, 1.75, 2]
]
plt.figure()
plt.suptitle(
'Angular Pattern of Au Sphere With {:.1f} nm Radius at {:.1f} nm'.
format(r * from_um_factor * 1e3,
from_um_factor * 1e3 / freqs[freq_index]))
ax_plain = plt.axes()
for i in index:
ax_plain.plot(poynting_x[i, :, freq_index],
poynting_z[i, :, freq_index],
".-",
label=rf"$\phi$ = {azimuthal_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_x$")
ax_plain.set_ylabel(r"$P_z$")
plt.savefig(file("AngularAzimuthal.png"))
if near2far and vm.parallel_assign(0, np_process, parallel):
freq_index = np.argmin(np.abs(freqs - freq_center))
index = [
list(azimuthal_angle).index(alpha)
for alpha in [0, .25, .5, .75, 1, 1.25, 1.5, 1.75, 2]
]
plt.figure()
plt.suptitle(
'Angular Pattern of Au Sphere With {:.1f} nm Radius at {:.1f} nm'.
format(r * from_um_factor * 1e3,
from_um_factor * 1e3 / freqs[freq_index]))
ax_plain = plt.axes()
for i in index:
ax_plain.plot(np.sqrt(
np.square(poynting_x[i, :, freq_index]) +
np.square(poynting_y[i, :, freq_index])),
poynting_z[i, :, freq_index],
".-",
label=rf"$\phi$ = {azimuthal_angle[i]:.2f} $\pi$")
plt.legend()
ax_plain.set_xlabel(r"$P_\rho$")
ax_plain.set_ylabel(r"$P_z$")
plt.savefig(file("AngularAzimuthalAbs.png"))
sources=sources,
resolution=resolution)
# reflected flux
refl = sim.add_flux(fcen, df, nfreq, refl_fr)
tran_fr = mp.FluxRegion(center=mp.Vector3(0.5 * sx - dpml - 0.5, 4.0, 0),
size=mp.Vector3(0.0, 2.0 * width, 0))
tran = sim.add_flux(fcen, df, nfreq, tran_fr)
# for normal run, load negated fields to subtract incident from refl. fields
sim.load_minus_flux_data(refl, straight_refl_data)
pt = mp.Vector3(4.0, 0.5 * sx - dpml - 0.5)
sim.run(mp.at_time(200, mp.output_efield_z),
until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, pt, 1e-3))
bend_refl_flux = mp.get_fluxes(refl)
bend_tran_flux = mp.get_fluxes(tran)
flux_freqs = mp.get_flux_freqs(refl)
wl = []
Rs = []
Ts = []
for i in range(0, nfreq):
wl = np.append(wl, 1 / flux_freqs[i])
Rs = np.append(Rs, -bend_refl_flux[i] / straight_tran_flux[i])
Ts = np.append(Ts, bend_tran_flux[i] / straight_tran_flux[i])
def notch(w):
print("#----------------------------------------")
print("NOTCH WIDTH: %s nanometers" % (w * 1000))
print("#----------------------------------------")
# w is the width of the notch in the waveguide
#Waveguide Math
e = 0.4 # etch fraction [0 -> 1]
h2 = h * (1 - e) #height of the etched region
d = 1 #spacing of the waveguides
posd = d / 2 #pos half d
negd = -d / 2 #neg half d
ang = 80 #angle of the sidewall
angrad = ang * pi / 180
bottomoffset = h / tan(angrad)
c = bottomoffset / 2
r = sqrt(bottomoffset**2 + h**2)
fcen = 1 / wavelength
df = 0.05
n_e = 2.2012 # refractive index inside waveguide (at wavelength 0.6372)
n_c = 1.4569 # refractive index outside waveguide (at wavelength 0.6372)
#Waveguide Geometry
cell = mp.Vector3(a, H)
default_material = mp.Medium(epsilon=n_c**2)
core_material = mp.Medium(epsilon=n_e**2)
geometry = [
mp.Block(cell, center=mp.Vector3(0, 0), material=default_material),
mp.Block(mp.Vector3(a + 2 * h, h),
center=mp.Vector3(0, 0),
material=core_material),
# mp.Block(mp.Vector3(w, e * h),
mp.Block(mp.Vector3(w, e * h),
center=mp.Vector3(0, h2 / 2),
material=default_material)
]
# pml_layers = [mp.PML(0.2)]
pml_layers = [mp.Absorber(thickness=pmlthickness)]
# resolution = 50
r00 = None
r01 = None
r10 = None
r11 = None
t00 = None
t01 = None
t10 = None
t11 = None
su0 = None
sd0 = None
su1 = None
sd1 = None
only_fund = True
modes = [0, 1]
if only_fund:
modes = [0]
# for mode in [0, 1]:
for mode in modes:
if mode == 0:
eig_parity = mp.EVEN_Y # Fundamental
print("-----------")
print("MODE TYPE: FUNDAMENTAL")
else:
eig_parity = mp.ODD_Y # First Order
print("-----------")
print("MODE TYPE: FIRST ORDER")
sources = [
mp.EigenModeSource(mp.GaussianSource(frequency=fcen, fwidth=df),
size=mp.Vector3(0, H),
center=mp.Vector3(Ls, 0),
eig_parity=eig_parity)
]
sim = mp.Simulation(cell_size=cell,
boundary_layers=pml_layers,
geometry=geometry,
sources=sources,
resolution=resolution,
force_complex_fields=True)
'''
#--------------------------------------------------
#FOR DISPLAYING THE GEOMETRY
sim.run(until = 200)
eps_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Dielectric)
plt.figure(dpi=100)
plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')
#plt.axis('off')
plt.show()
quit()
#----------------------------------------------------
'''
'''
#------------------------------------------------------
#FOR GENERATING THE ELECTRIC FIELD GIF
#Note: After running this program, write the following commands in Terminal:
# $ source deactivate mp
# $ cd notch-out/
# $ python ../NotchIP.py
sim.use_output_directory()
sim.run(mp.at_beginning(mp.output_epsilon),
mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)),
until = 200)
#sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200)
#---------------------------------------------------------
'''
#---------------------------------------------------------
# FOR GENERATING THE TRANSMITTANCE SPECTRUM
nfreq = 20 # number of frequencies at which to compute flux
refl_fr1 = mp.FluxRegion(center=mp.Vector3(Lr1, 0),
size=mp.Vector3(
0, monitorheight)) # Reflected flux 1
refl_fr2 = mp.FluxRegion(center=mp.Vector3(Lr2, 0),
size=mp.Vector3(
0, monitorheight)) # Reflected flux 2
tran_fr = mp.FluxRegion(center=mp.Vector3(Lt, 0),
size=mp.Vector3(
0, monitorheight)) # Transmitted flux
su_fr = mp.FluxRegion(center=mp.Vector3(0, monitorheight / 2),
size=mp.Vector3(
a, 0)) # Flux loss above the waveguide
sd_fr = mp.FluxRegion(center=mp.Vector3(0, -monitorheight / 2),
size=mp.Vector3(
a, 0)) # Flux loss below the waveguide
refl1 = sim.add_flux(fcen, df, nfreq, refl_fr1)
refl2 = sim.add_flux(fcen, df, nfreq, refl_fr2)
tran = sim.add_flux(fcen, df, nfreq, tran_fr)
su = sim.add_flux(fcen, df, nfreq, su_fr)
sd = sim.add_flux(fcen, df, nfreq, sd_fr)
# ------------------------ CODE FOR SEPARATING FUND AND FIRST ORDER MODE STARTS HERE ------------------------
y_list = np.arange(-H / 2, H / 2, 1 / resolution)
refl_vals = []
tran_vals = []
def get_refl_slice(sim):
# print(sim.get_array(center=mp.Vector3(Lr1,0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True))
# refl_val = sim.get_array(center=mp.Vector3(Lr1,0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True)
refl_vals.append(
sim.get_array(center=mp.Vector3(Lr1, 0),
size=mp.Vector3(0, H),
component=mp.Ez,
cmplx=True))
def get_tran_slice(sim):
# print(sim.get_array(center=mp.Vector3(Lt, 0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True))
# tran_val = sim.get_array(center=mp.Vector3(Lt, 0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True)
tran_vals.append(
sim.get_array(center=mp.Vector3(Lt, 0),
size=mp.Vector3(0, H),
component=mp.Ez,
cmplx=True))
pt = mp.Vector3(9.75, 0) # Hardcoded?
gif = True
if gif: # and w == 0.1:
sim.use_output_directory()
sim.run(mp.at_beginning(mp.output_epsilon),
mp.at_end(get_refl_slice),
mp.at_end(get_tran_slice),
until=100)
# sim.run(mp.at_every(wavelength / 20, mp.output_efield_z), until=wavelength)
sim.run(mp.at_every(
wavelength / 20,
mp.output_png(
mp.Ez,
"-RZc bluered -A notch-out/notch-eps-000000.00.h5 -a gray:.2"
)),
until=19 * wavelength / 20)
sim.run(until_after_sources=mp.stop_when_fields_decayed(
50, mp.Ez, pt, 1e-3))
else:
sim.run(mp.at_beginning(mp.output_epsilon),
mp.at_time(100, get_refl_slice),
mp.at_time(100, get_tran_slice),
until_after_sources=mp.stop_when_fields_decayed(
50, mp.Ez, pt, 1e-3))
os.system("cp notch-out/notch-ez-000100.00.png " + str(int(w * 1000)) +
"-" + str(mode) + ".png")
os.system("convert notch-out/notch-ez-*.png " + str(int(w * 1000)) +
"-" + str(mode) + ".gif")
refl_val = refl_vals[0]
tran_val = tran_vals[0]
# n_eff must satisfy n_e <= n_eff <= n_c for the mode to be bound.
def fund_func(n_eff):
if n_eff >= n_c and n_eff <= n_e:
return sqrt(n_eff**2 - n_c**2) - sqrt(n_e**2 - n_eff**2) * tan(
pi * h / wavelength * sqrt(n_e**2 - n_eff**2))
def first_order_func(n_eff):
if n_eff >= n_c and n_eff <= n_e:
return sqrt(n_eff**2 - n_c**2) - sqrt(n_e**2 - n_eff**2) * tan(
pi * h / wavelength * sqrt(n_e**2 - n_eff**2) - pi / 2)
initial_guess = (n_c + n_e) / 2
n_eff_fund = fsolve(fund_func, initial_guess)
n_eff_first = fsolve(first_order_func, n_c)
print(n_eff_fund, n_eff_first)
assert (n_eff_fund > n_eff_first)
if len(n_eff_funds) == 0:
n_eff_funds.append(n_eff_fund[0])
if len(n_eff_firsts) == 0:
n_eff_firsts.append(n_eff_first[0])
ky0_fund = np.absolute(2 * pi / wavelength *
sqrt(n_e**2 - n_eff_fund**2))
ky0_first = np.absolute(2 * pi / wavelength *
sqrt(n_e**2 - n_eff_first**2))
ky1_fund = np.absolute(2 * pi / wavelength *
sqrt(n_eff_fund**2 - n_c**2))
ky1_first = np.absolute(2 * pi / wavelength *
sqrt(n_eff_first**2 - n_c**2))
E_fund = lambda y: cos(ky0_fund * y) if np.absolute(
y) < h / 2 else cos(ky0_fund * h / 2) * np.exp(-ky1_fund * (
np.absolute(y) - h / 2))
E_first_order = lambda y: sin(ky0_first * y) if np.absolute(
y) < h / 2 else sin(ky0_first * h / 2) * np.exp(-ky1_first * (
np.absolute(y) - h / 2)) * np.sign(y)
# y_list = np.arange(-H/2+.5/resolution, H/2-.5/resolution, 1/resolution)
#print("Y LIST: ", y_list)
#print("SIZE OF Y LIST: ", y_list.size)
E_fund_vec = np.zeros(y_list.size)
E_first_order_vec = np.zeros(y_list.size)
for index in range(y_list.size):
y = y_list[index]
E_fund_vec[index] = E_fund(y)
E_first_order_vec[index] = E_first_order(y)
# print("r VECTOR: ", refl_val)
# print("t VECTOR: ", tran_val)
# print("E0 VECTOR: ", E_fund_vec)
# print("E1 VECTOR: ", E_first_order_vec)
# plt.plot(y_list, refl_val*100,'bo-',label='reflectance')
# plt.plot(y_list, tran_val*1,'ro-',label='transmittance')
# plt.plot(y_list, E_fund_vec,'go-',label='E0')
# plt.plot(y_list, E_first_order_vec, 'co-', label='E1')
# # plt.axis([40.0, 300.0, 0.0, 100.0])
# plt.xlabel("y (um)")
# plt.ylabel("Field")
# plt.legend(loc="center right")
# plt.show()
fund_refl_amp = np.conj(
np.dot(refl_val, E_fund_vec) / np.dot(E_fund_vec, E_fund_vec)
) # Conjugate becasue MEEP uses physics exp(kz-wt) rather than engineering exp(wt-kz)
first_order_refl_amp = np.conj(
np.dot(refl_val, E_first_order_vec) /
np.dot(E_first_order_vec, E_first_order_vec))
fund_tran_amp = np.conj(
np.dot(tran_val, E_fund_vec) / np.dot(E_fund_vec, E_fund_vec))
first_order_tran_amp = np.conj(
np.dot(tran_val, E_first_order_vec) /
np.dot(E_first_order_vec, E_first_order_vec))
# fund_refl_power = np.abs(fund_tran_amp) ** 2
fund_refl_power = np.abs(fund_refl_amp)**2
first_order_refl_power = np.abs(first_order_refl_amp)**2
fund_tran_power = np.abs(fund_tran_amp)**2
first_order_tran_power = np.abs(first_order_tran_amp)**2
print(fund_refl_power, first_order_refl_power, fund_tran_power,
first_order_tran_power)
fund_refl_ratio = fund_refl_power / (fund_refl_power +
first_order_refl_power)
first_order_refl_ratio = first_order_refl_power / (
fund_refl_power + first_order_refl_power)
fund_tran_ratio = fund_tran_power / (fund_tran_power +
first_order_tran_power)
first_order_tran_ratio = first_order_tran_power / (
fund_tran_power + first_order_tran_power)
print("Percentage of reflected light in fundamental mode: ",
fund_refl_ratio * 100)
print("Percentage of reflected light in first order mode: ",
first_order_refl_ratio * 100)
print("Percentage of transmitted light in fundamental mode: ",
fund_tran_ratio * 100)
print("Percentage of transmitted light in first order mode: ",
first_order_tran_ratio * 100)
# ------------------------ CODE FOR SEPARATING FUND AND FIRST ORDER MODE ENDS HERE ------------------------
wl = [] #list of wavelengths
refl1_flux = mp.get_fluxes(refl1)
refl2_flux = mp.get_fluxes(refl2)
tran_flux = mp.get_fluxes(tran)
su_flux = mp.get_fluxes(su)
sd_flux = mp.get_fluxes(sd)
flux_freqs = mp.get_flux_freqs(refl1)
for i in range(nfreq):
wl = np.append(wl, 1 / flux_freqs[i])
print(1 / flux_freqs[i])
for ind, elt in enumerate(wl):
#print(round(elt, 4))
if round(elt, 3) == 0.637:
#print("ALERT: MATCH FOUND")
index = ind
R = -refl1_flux[index] / (refl2_flux[index] - refl1_flux[index])
T = tran_flux[index] / (refl2_flux[index] - refl1_flux[index])
S = (refl2_flux[index] - tran_flux[index]) / (refl2_flux[index] -
refl1_flux[index])
Su = su_flux[index] / (refl2_flux[index] - refl1_flux[index])
Sd = -sd_flux[index] / (refl2_flux[index] - refl1_flux[index])
S_correction = (1 - R - T) / (Su + Sd)
print(R, T, S, Su, Sd)
r = sqrt(R)
t = sqrt(T)
# The amplitude ... times the phase ... accounting for the distance to the detector (Reverse exp(-kz) of phase).
r_fund = (r * fund_refl_ratio) * (
fund_refl_amp / np.abs(fund_refl_amp)
) * (
np.exp(2j * pi * (-Lr1 - w / 2) * n_eff_fund / wavelength)
) # Lr1 is negative because it is the (negative) position of the monitor, not the distace from the monitor to the center.
r_first = (r * first_order_refl_ratio) * (
first_order_refl_amp / np.abs(first_order_refl_amp)) * (np.exp(
2j * pi * (-Lr1 - w / 2) * n_eff_first / wavelength))
t_fund = (t * fund_tran_ratio) * (
fund_tran_amp / np.abs(fund_tran_amp)) * (np.exp(
2j * pi * (Lt - w / 2) * n_eff_fund / wavelength))
t_first = (t * first_order_tran_ratio) * (
first_order_tran_amp / np.abs(first_order_tran_amp)) * (np.exp(
2j * pi * (Lt - w / 2) * n_eff_first / wavelength))
if mode == 0:
r00 = r_fund
r01 = r_first
t00 = t_fund
t01 = t_first
su0 = sqrt(np.abs(Su * S_correction))
sd0 = sqrt(np.abs(Sd * S_correction))
r00s.append(r00[0])
r01s.append(r01[0])
t00s.append(t00[0])
t01s.append(t01[0])
su0s.append(su0)
sd0s.append(sd0)
if only_fund:
r10s.append(0)
r11s.append(0)
t10s.append(0)
t11s.append(1)
su1s.append(0)
sd1s.append(0)
else:
r10 = r_fund
r11 = r_first
t10 = t_fund
t11 = t_first
su1 = sqrt(np.abs(Su * S_correction))
sd1 = sqrt(np.abs(Sd * S_correction))
r10s.append(r10[0])
r11s.append(r11[0])
t10s.append(t10[0])
t11s.append(t11[0])
su1s.append(su1)
sd1s.append(sd1)
norm_Su = S * Su / (Su + Sd)
NET = round((R + T + S) * 100, 0)
if NET > 100.0:
NET = 100.0
NET_LOSS = round((Su + Sd) / S * 100, 0)
if NET_LOSS > 100.0:
NET_LOSS = 100.0
'''
np.append(ws, [w * 1000])
np.append(Rs, [R * 100])
np.append(Ts, [T * 100])
np.append(Ss, [S * 100])
np.append(NET_LIST, [NET])
np.append(Sus, [Su * 100])
np.append(Sds, [Sd * 100])
np.append(NET_LOSS_LIST, [NET_LOSS])
np.append(norm_Sus, [norm_Su * 100])
'''
if mode == 0:
ws.append(w * 1000)
Rs.append(R * 100)
Ts.append(T * 100)
Ss.append(S * 100)
NET_LIST.append(NET)
Sus.append(Su * 100)
Sds.append(Sd * 100)
NET_LOSS_LIST.append(NET_LOSS)
norm_Sus.append(norm_Su * 100)
if mode == 0:
f1.write("--------------------------------------------------- \n")
f1.write("Notch Width: %s nanometers \n" % (w * 1000))
f1.write("Reflection Percentage: %s\n" % (R * 100))
f1.write("Transmission Percentage: %s\n" % (T * 100))
f1.write("Total Loss Percentage: %s\n" % (S * 100))
f1.write("Percentage of Light Accounted For: %s\n" % (NET))
f1.write("Upper Loss Percentage: %s\n" % (Su * 100))
f1.write("Lower Loss Percentage: %s\n" % (Sd * 100))
f1.write("Percentage of Total Loss Accounted For: %s\n" %
(NET_LOSS))
f1.write("Normalized Upper Loss Percentage: %s\n" %
(norm_Su * 100))
f1.write("\n \n")
f1.write("FUNDAMENTAL MODE \n")
f1.write("n_eff: %s\n" % (n_eff_fund[0]))
f1.write("Re(r00): %s\n" % (np.real(r00))[0])
f1.write("Im(r00): %s\n" % (np.imag(r00))[0])
f1.write("Re(r01): %s\n" % (np.real(r01))[0])
f1.write("Im(r01): %s\n" % (np.imag(r01))[0])
f1.write("Re(t00): %s\n" % (np.real(t00))[0])
f1.write("Im(t00): %s\n" % (np.imag(t00))[0])
f1.write("Re(t01): %s\n" % (np.real(t01))[0])
f1.write("Im(t01): %s\n" % (np.imag(t01))[0])
f1.write("Re(su0): %s\n" % (np.real(su0)))
f1.write("Im(su0): %s\n" % (np.imag(su0)))
f1.write("Re(sd0): %s\n" % (np.real(sd0)))
f1.write("Im(sd0): %s\n" % (np.imag(sd0)))
f1.write("\n")
else:
f1.write("FIRST ORDER MODE \n")
f1.write("n_eff: %s\n" % (n_eff_first[0]))
f1.write("Re(r10): %s\n" % (np.real(r10))[0])
f1.write("Im(r10): %s\n" % (np.imag(r10))[0])
f1.write("Re(r11): %s\n" % (np.real(r11))[0])
f1.write("Im(r11): %s\n" % (np.imag(r11))[0])
f1.write("Re(t10): %s\n" % (np.real(t10))[0])
f1.write("Im(t10): %s\n" % (np.imag(t10))[0])
f1.write("Re(t11): %s\n" % (np.real(t11))[0])
f1.write("Im(t11): %s\n" % (np.imag(t11))[0])
f1.write("Re(su1): %s\n" % (np.real(su1)))
f1.write("Im(su1): %s\n" % (np.imag(su1)))
f1.write("Re(sd1): %s\n" % (np.real(sd1)))
f1.write("Im(sd1): %s\n" % (np.imag(sd1)))
f1.write("--------------------------------------------------- \n")
sim.reset_meep()
def notch(w):
print("#----------------------------------------")
print("NOTCH WIDTH: %s nanometers" % (w * 1000))
print("#----------------------------------------")
# w is the width of the notch in the waveguide
angrad = ang * pi / 180
bottomoffset = e * h / tan(angrad)
vertices = [
mp.Vector3(w / 2 + bottomoffset, (.5 + e) * h),
mp.Vector3(-w / 2 - bottomoffset, (.5 + e) * h),
mp.Vector3(-w / 2 + bottomoffset, (.5 - e) * h),
mp.Vector3(w / 2 - bottomoffset, (.5 - e) * h)
]
if bottomoffset > w / 2:
ratio = (w / 2) / bottomoffset
vertices = [
mp.Vector3(w / 2, h / 2),
mp.Vector3(-w / 2, h / 2),
mp.Vector3(0, (.5 - e * ratio) * h)
]
print(vertices)
#Waveguide Geometry
cell = mp.Vector3(a, H)
geometry = [
mp.Block(cell, center=mp.Vector3(0, 0), material=default_material),
mp.Block(mp.Vector3(a, hu + h / 2),
center=mp.Vector3(0, (hu + h / 2) / 2),
material=upper_material),
mp.Block(mp.Vector3(a, hl + h / 2),
center=mp.Vector3(0, -(hl + h / 2) / 2),
material=lower_material),
mp.Block(mp.Vector3(a, h),
center=mp.Vector3(0, 0),
material=core_material)
]
if w > 0:
geometry.append(
mp.Prism(vertices, height=1, material=upper_material))
pml_layers = [mp.Absorber(thickness=dpml)]
r00 = None
r01 = None
r10 = None
r11 = None
t00 = None
t01 = None
t10 = None
t11 = None
su0 = None
sd0 = None
su1 = None
sd1 = None
modes = [0, 1]
if only_fund:
modes = [0]
# eig_parity_fund = mp.EVEN_Z+mp.EVEN_Y;
# eig_parity_first = mp.EVEN_Z+mp.ODD_Y;
eig_parity_fund = mp.EVEN_Y
eig_parity_first = mp.ODD_Y
# for mode in [0, 1]:
for mode in modes:
if mode == 0:
eig_parity = eig_parity_fund # Fundamental
print("-----------")
print("MODE TYPE: FUNDAMENTAL")
else:
eig_parity = eig_parity_first # First Order
print("-----------")
print("MODE TYPE: FIRST ORDER")
# sources = [ mp.EigenModeSource(
# mp.GaussianSource( frequency = fcen,
# fwidth = df),
# size = mp.Vector3(0,H),
# center = mp.Vector3(Ls, 0),
# eig_parity = eig_parity) ]
sources = [
mp.EigenModeSource(mp.ContinuousSource(frequency=fcen),
size=mp.Vector3(0, monitorheight),
center=mp.Vector3(Ls, 0),
eig_parity=eig_parity)
]
sim = mp.Simulation(cell_size=cell,
boundary_layers=pml_layers,
geometry=geometry,
sources=sources,
resolution=resolution,
force_complex_fields=True)
'''
#--------------------------------------------------
#FOR DISPLAYING THE GEOMETRY
sim.run(until = 200)
eps_data = sim.get_array(center=mp.Vector3(), size=cell, component=mp.Dielectric)
plt.figure(dpi=100)
plt.imshow(eps_data.transpose(), interpolation='spline36', cmap='binary')
#plt.axis('off')
plt.show()
quit()
#----------------------------------------------------
'''
'''
#------------------------------------------------------
#FOR GENERATING THE ELECTRIC FIELD GIF
#Note: After running this program, write the following commands in Terminal:
# $ source deactivate mp
# $ cd notchtest-out/
# $ python ../NotchIP.py
sim.use_output_directory()
sim.run(mp.at_beginning(mp.output_epsilon),
mp.to_appended("ez", mp.at_every(0.6, mp.output_efield_z)),
until = 200)
#sim.run(mp.at_every(0.6 , mp.output_png(mp.Ez, "-Zc dkbluered")), until=200)
#---------------------------------------------------------
'''
#---------------------------------------------------------
# FOR GENERATING THE TRANSMITTANCE SPECTRUM
# nfreq = 1 # number of frequencies at which to compute flux
#
# refl_fr1 = mp.FluxRegion(center=mp.Vector3(Lr1,0), size=mp.Vector3(0,monitorheight)) # Reflected flux 1
# refl_fr2 = mp.FluxRegion(center=mp.Vector3(Lr2,0), size=mp.Vector3(0,monitorheight)) # Reflected flux 2
# tran_fr = mp.FluxRegion(center=mp.Vector3(Lt,0), size=mp.Vector3(0,monitorheight)) # Transmitted flux
# su_fr = mp.FluxRegion(center=mp.Vector3(0, monitorheight/2), size=mp.Vector3(a,0)) # Flux loss above the waveguide
# sd_fr = mp.FluxRegion(center=mp.Vector3(0,-monitorheight/2), size=mp.Vector3(a,0)) # Flux loss below the waveguide
#
# refl1 = sim.add_flux(fcen, df, nfreq, refl_fr1)
# refl2 = sim.add_flux(fcen, df, nfreq, refl_fr2)
# tran = sim.add_flux(fcen, df, nfreq, tran_fr)
# su = sim.add_flux(fcen, df, nfreq, su_fr)
# sd = sim.add_flux(fcen, df, nfreq, sd_fr)
# ------------------------ CODE FOR SEPARATING FUND AND FIRST ORDER MODE STARTS HERE ------------------------
refl_vals = []
tran_vals = []
def get_refl_slice(sim):
# print(sim.get_array(center=mp.Vector3(Lr1,0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True))
# refl_val = sim.get_array(center=mp.Vector3(Lr1,0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True)
refl_vals.append(
sim.get_array(center=mp.Vector3(Lr1, 0),
size=mp.Vector3(
0, monitorheight - 2 / resolution),
component=mp.Ez,
cmplx=True))
def get_tran_slice(sim):
# print(sim.get_array(center=mp.Vector3(Lt, 0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True))
# tran_val = sim.get_array(center=mp.Vector3(Lt, 0), size=mp.Vector3(0,H), component=mp.Ez, cmplx=True)
tran_vals.append(
sim.get_array(center=mp.Vector3(Lt, 0),
size=mp.Vector3(
0, monitorheight - 2 / resolution),
component=mp.Ez,
cmplx=True))
gif = True
# T = 50*(a-4)/12;
T = 60
if resolution == 50:
epsform = "eps-000000.00"
else:
epsform = "eps-000000000"
if gif: # and w == 0.1:
sim.use_output_directory()
sim.run(mp.at_beginning(mp.output_epsilon),
mp.at_every(
1,
mp.output_png(
mp.Ez,
"-RZc bluered -A notchtest-out/notchtest-" +
epsform + ".h5 -a gray:.2")),
mp.at_end(get_refl_slice),
mp.at_end(get_tran_slice),
until=T)
os.system("convert notchtest-out/notchtest-ez-*.png " +
casedash + "-" + str(int(w * 1000)) + "-" +
str(mode) + "-full.gif")
os.system("rm notchtest-out/notchtest-ez-*.png")
nfreq = 1 # number of frequencies at which to compute flux
refl_fr1 = mp.FluxRegion(
center=mp.Vector3(Lr1, 0),
size=mp.Vector3(0, monitorheight)) # Reflected flux 1
refl_fr2 = mp.FluxRegion(
center=mp.Vector3(Lr2, 0),
size=mp.Vector3(0, monitorheight)) # Reflected flux 2
tran_fr = mp.FluxRegion(
center=mp.Vector3(Lt, 0),
size=mp.Vector3(0, monitorheight)) # Transmitted flux
su_fr = mp.FluxRegion(
center=mp.Vector3(0, monitorheight / 2),
size=mp.Vector3(a, 0)) # Flux loss above the waveguide
sd_fr = mp.FluxRegion(
center=mp.Vector3(0, -monitorheight / 2),
size=mp.Vector3(a, 0)) # Flux loss below the waveguide
refl1 = sim.add_flux(fcen, df, nfreq, refl_fr1)
refl2 = sim.add_flux(fcen, df, nfreq, refl_fr2)
tran = sim.add_flux(fcen, df, nfreq, tran_fr)
su = sim.add_flux(fcen, df, nfreq, su_fr)
sd = sim.add_flux(fcen, df, nfreq, sd_fr)
# sim.run(mp.at_every(wavelength / 20, mp.output_efield_z), until=wavelength)
# sim.run(mp.at_every(wavelength/20 , mp.output_png(mp.Ez, "-RZc bluered -A notchtest-out/notchtest-eps-000000000.h5 -a gray:.2")), until=19*wavelength/20)
sim.run(mp.at_every(
wavelength / 20,
mp.output_png(
mp.Ez, "-RZc bluered -A notchtest-out/notchtest-" +
epsform + ".h5 -a gray:.2")),
until=19 * wavelength / 20)
sim.run(until=20)
# sim.run(until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5))
else:
sim.run(mp.at_beginning(mp.output_epsilon),
mp.at_time(T, get_refl_slice),
mp.at_time(T, get_tran_slice)) #,
# until_after_sources=mp.stop_when_fields_decayed(50, mp.Ez, mp.Vector3(), 1e-5))
os.system("h5topng notchtest-out/notchtest-" + epsform +
".h5; mv notchtest-out/notchtest-" + epsform + ".png " +
casedash + "-" + str(int(w * 1000)) + "-" + str(mode) +
"-eps.png")
# os.system("cp notchtest-out/notchtest-ez-000100.00.png " + case + "-" + str(int(w*1000)) + "-" + str(mode) + ".png")
os.system("convert notchtest-out/notchtest-ez-*.png " +
casedash + "-" + str(int(w * 1000)) + "-" + str(mode) +
".gif")
os.system("rm notchtest-out/notchtest-ez-*.png")
# get_eigenmode(fcen, mp., refl_fr1, 1, kpoint)
# v = mp.volume(mp.vec(Lr1, -monitorheight/2), mp.vec(Lr1, monitorheight/2))
# mode = get_eigenmode(fcen, mp.X, v, v, 1, mp.vec(0, 0, 0), True, 0, 0, 1e-7, True)
# print(mode.amplitude)
# coef_refl_fund = mp.get_eigenmode_coefficients_and_kpoints(refl_fr1, 1, eig_parity=eig_parity_fund, eig_resolution=resolution, eig_tolerance=1e-7)
# coef_refl_first = mp.get_eigenmode_coefficients_and_kpoints(refl_fr1, 1, eig_parity=eig_parity_first, eig_resolution=resolution, eig_tolerance=1e-7)
#
# coef_tran_fund = mp.get_eigenmode_coefficients_and_kpoints(tran_fr, 1, eig_parity=eig_parity_fund, eig_resolution=resolution, eig_tolerance=1e-7)
# coef_tran_first = mp.get_eigenmode_coefficients_and_kpoints(tran_fr, 1, eig_parity=eig_parity_first, eig_resolution=resolution, eig_tolerance=1e-7)
ep = mp.ODD_Z
coef_refl_fund, vgrp, kpoints_fund = sim.get_eigenmode_coefficients(
refl1, [1],
eig_parity=ep,
eig_resolution=resolution,
eig_tolerance=1e-7)
coef_refl_first, vgrp, kpoints_first = sim.get_eigenmode_coefficients(
refl1, [2],
eig_parity=ep,
eig_resolution=resolution,
eig_tolerance=1e-7)
coef_tran_fund, vgrp, kpoints_fund = sim.get_eigenmode_coefficients(
tran, [1],
eig_parity=ep,
eig_resolution=resolution,
eig_tolerance=1e-7)
coef_tran_first, vgrp, kpoints_first = sim.get_eigenmode_coefficients(
tran, [2],
eig_parity=ep,
eig_resolution=resolution,
eig_tolerance=1e-7)
# print(kpoints_fund)
# print(kpoints_first)
# print(kpoints_fund[0])
# print(type(kpoints_fund[0]))
# print(dir(kpoints_fund[0]))
n_eff_fund = wavelength * kpoints_fund[0].x
n_eff_first = wavelength * kpoints_first[0].x
print(n_eff_fund)
print(n_eff_first)
# print(coef_refl_fund)
# print(type(coef_refl_fund))
# print(dir(coef_refl_fund))
# print(coef_refl_fund[0])
# print(coef_refl_fund[0][0,0,:])
#
# fund_refl_amp = coef_refl_fund[0][0,0,1];
# first_order_refl_amp = coef_refl_first[0][0,0,1];
# fund_tran_amp = coef_tran_fund[0][0,0,0];
# first_order_tran_amp = coef_tran_first[0][0,0,0];
print("get_eigenmode_coefficients:\n")
print(coef_refl_fund)
print(coef_refl_first)
print(coef_tran_fund)
print(coef_tran_first)
print("\n")
# print(coef_refl_fund[0,0,:])
fund_refl_amp = coef_refl_fund[0, 0, 1]
first_order_refl_amp = coef_refl_first[0, 0, 1]
fund_tran_amp = coef_tran_fund[0, 0, 0]
first_order_tran_amp = coef_tran_first[0, 0, 0]
refl_val = refl_vals[0]
tran_val = tran_vals[0]
# n_eff must satisfy n_e <= n_eff <= n_c for the mode to be bound.
def fund_func(n_eff):
if n_eff >= n_c and n_eff <= n_e:
return sqrt(
n_eff**2 - n_c**2) - sqrt(n_e**2 - n_eff**2) * tan(
pi * h / wavelength * sqrt(n_e**2 - n_eff**2))
def first_order_func(n_eff):
if n_eff >= n_c and n_eff <= n_e:
return sqrt(n_eff**2 -
n_c**2) - sqrt(n_e**2 - n_eff**2) * tan(
pi * h / wavelength *
sqrt(n_e**2 - n_eff**2) - pi / 2)
initial_guess = (n_c + n_e) / 2
# n_eff_fund = fsolve(fund_func, initial_guess)
# n_eff_first = fsolve(first_order_func, n_c)
print(n_eff_fund, n_eff_first)
assert (n_eff_fund > n_eff_first)
if len(n_eff_funds) == 0:
n_eff_funds.append(n_eff_fund)
if len(n_eff_firsts) == 0:
n_eff_firsts.append(n_eff_first)
ky0_fund = np.abs(2 * pi / wavelength *
sqrt(n_e**2 - n_eff_fund**2))
ky0_first = np.abs(2 * pi / wavelength *
sqrt(n_e**2 - n_eff_first**2))
ky1_fund = ky0_fund # np.abs(2 * pi / wavelength * sqrt(n_eff_fund **2 - n_c**2))
ky1_first = ky0_first # np.abs(2 * pi / wavelength * sqrt(n_eff_first**2 - n_c**2))
E_fund = lambda y: cos(ky0_fund * y) if np.abs(y) < h / 2 else cos(
ky0_fund * h / 2) * np.exp(-ky1_fund * (np.abs(y) - h / 2))
E_first_order = lambda y: sin(ky0_first * y) if np.abs(
y) < h / 2 else sin(ky0_first * h / 2) * np.exp(-ky1_first * (
np.abs(y) - h / 2)) * np.sign(y)
# y_list = np.arange(-H/2+.5/resolution, H/2-.5/resolution, 1/resolution)
#print("Y LIST: ", y_list)
#print("SIZE OF Y LIST: ", y_list.size)
E_fund_vec = np.zeros(y_list.size)
E_first_order_vec = np.zeros(y_list.size)
for index in range(y_list.size):
y = y_list[index]
E_fund_vec[index] = E_fund(y)
E_first_order_vec[index] = E_first_order(y)
# print(dir(sim))
# print(type(sim.get_eigenmode_coefficients))
# print(dir(sim.get_eigenmode_coefficients))
# print(type(sim.get_eigenmode))
# print(dir(sim.get_eigenmode))
# print(sim.get_eigenmode.__code__.co_varnames)
# print(sim.get_eigenmode.__defaults__)
# E1 = sim.get_eigenmode(fcen, mp.X, refl1.where, 1, None)
# E2 = sim.get_eigenmode(fcen, mp.X, refl1.where, 2, None)
# E3 = sim.get_eigenmode(fcen, mp.X, refl1.where, 3, None)
# E4 = sim.get_eigenmode(fcen, mp.X, refl1.where, 4, None)
# E1 = sim.get_eigenmode(fcen, mp.X, refl1.where, 1, mp.Vector3(0, 0, 0))
# E2 = sim.get_eigenmode(fcen, mp.X, refl1.where, 2, mp.Vector3(0, 0, 0))
# E3 = sim.get_eigenmode(fcen, mp.X, refl1.where, 3, mp.Vector3(0, 0, 0))
# E4 = sim.get_eigenmode(fcen, mp.X, refl1.where, 4, mp.Vector3(0, 0, 0))
# print(refl1.where)
# numEA = 0
# E1 = sim.fields.get_eigenmode(fcen, mp.X, refl1.where, refl1.where, 1, mp.vec(0,0,0), True, 0, numEA, 1e-7, True)
# print(type(E1))
# print(dir(E1))
#
# print(refl1.where)
# # print(E1, E2, E3, E4)
# print(E1.amplitude, E1.band_num, E1.group_velocity, E1.k)
# print(type(E1.amplitude))
# print(dir(E1.amplitude))
# print(doc(E1.amplitude))
# print(self(E1.amplitude))
# print(E1.amplitude(y_list))
# print(type(E1.amplitude(y_list)))
# print(dir(E1.amplitude(y_list)))
# print(E1.amplitude, E2.amplitude, E3.amplitude, E4.amplitude)
# E1 = sim.get_eigenmode(fcen, mp.X, refl1.where, refl1.where, 1, mp.vec(0, 0, 0))
# E2 = sim.get_eigenmode(fcen, mp.X, refl1.where, refl1.where, 2, mp.vec(0, 0, 0))
# E3 = sim.get_eigenmode(fcen, mp.X, refl1.where, refl1.where, 3, mp.vec(0, 0, 0))
# E4 = sim.get_eigenmode(fcen, mp.X, refl1.where, refl1.where, 4, mp.vec(0, 0, 0))
# print(y_list)
#
# E1a = np.zeros(y_list.size, dtype='Complex128');
# E2a = np.zeros(y_list.size, dtype='Complex128');
# E3a = np.zeros(y_list.size, dtype='Complex128');
# E4a = np.zeros(y_list.size, dtype='Complex128');
#
# # print(mp.eigenmode_amplitude.__code__.co_varnames)
# # print(mp.eigenmode_amplitude.__defaults__)
#
# for i in range(y_list.size):
# # print(E1)
# # print(E1.swigobj)
# # print(E1.amplitude)
# # print(mp.vec(Lr1, y_list[i], 0))
# # print(mp.Vector3(Lr1, y_list[i], 0))
# # print(mp.Ez)
# # E1a[i] = mp.eigenmode_amplitude(E1.swigobj, mp.vec(Lr1, y_list[i], 0), mp.Ez);
# eval_point = mp.vec(Lr1, y_list[i], 0)
# # print(eval_point)
# E1a[i] = mp.eigenmode_amplitude(E1.swigobj, eval_point, mp.Ex)
# E2a[i] = mp.eigenmode_amplitude(E2.swigobj, eval_point, mp.Ex)
# E3a[i] = mp.eigenmode_amplitude(E3.swigobj, eval_point, mp.Ex)
# E4a[i] = mp.eigenmode_amplitude(E4.swigobj, eval_point, mp.Ex)
# # E1a[i] = mp.eigenmode_amplitude(E1.amplitude, mp.Vector3(Lr1, y_list[i], 0), mp.Ez);
#
# plt.plot(y_list, np.abs(E1a)**2, 'bo-', label='E1')
# plt.plot(y_list, np.abs(E2a)**2, 'ro-', label='E2')
# plt.plot(y_list, np.abs(E3a)**2, 'go-', label='E3')
# plt.plot(y_list, np.abs(E4a)**2, 'co-', label='E4')
# # plt.axis([40.0, 300.0, 0.0, 100.0])
# plt.xlabel("y (um)")
# plt.ylabel("Field (a.u.)")
# plt.legend(loc="center right")
# plt.show()
# print("r VECTOR: ", refl_val)
# print("t VECTOR: ", tran_val)
# print("E0 VECTOR: ", E_fund_vec)
# print("E1 VECTOR: ", E_first_order_vec)
# fund_refl_amp_2 = np.conj(np.dot(refl_val, E_fund_vec) / np.dot(E_fund_vec, E_fund_vec)) # Conjugate becasue MEEP uses physics exp(kz-wt) rather than engineering exp(wt-kz)
# first_order_refl_amp_2 = np.conj(np.dot(refl_val, E_first_order_vec) / np.dot(E_first_order_vec, E_first_order_vec))
# fund_tran_amp_2 = np.conj(np.dot(tran_val, E_fund_vec) / np.dot(E_fund_vec, E_fund_vec))
# first_order_tran_amp_2 = np.conj(np.dot(tran_val, E_first_order_vec) / np.dot(E_first_order_vec, E_first_order_vec))
fund_refl_amp0 = fund_refl_amp
fund_tran_amp0 = fund_tran_amp
fund_refl_amp = np.conj(
np.dot(refl_val, E0) / np.dot(E0, E0)
) # Conjugate becasue MEEP uses physics exp(kz-wt) rather than engineering exp(wt-kz)
first_order_refl_amp = 0 # np.conj(np.dot(refl_val, E1[:,2]) / np.dot(E1[:,2], E1[:,2]))
fund_tran_amp = np.conj(np.dot(tran_val, E0) / np.dot(E0, E0))
first_order_tran_amp = 0 # np.conj(np.dot(tran_val, E1[:,2]) / np.dot(E1[:,2], E1[:,2]))
fund_refl = np.conj(fund_refl_amp) * E0
not_fund_refl = refl_val - fund_refl
fund_tran = np.conj(fund_tran_amp) * E0
not_fund_tran = tran_val - fund_tran
fund_refl_power = np.dot(np.conj(fund_refl), fund_refl)
not_fund_refl_power = np.dot(np.conj(not_fund_refl), not_fund_refl)
fund_tran_power = np.dot(np.conj(fund_tran), fund_tran)
not_fund_tran_power = np.dot(np.conj(not_fund_tran), not_fund_tran)
f2.write(
"%s, " %
[fund_refl_amp0, fund_tran_amp0, fund_refl_amp, fund_tran_amp])
# assert(fund_refl_power + not_fund_refl_power == 1)
# assert(fund_tran_power + not_fund_tran_power == 1)
# f2.write("\n");
fund_refl_ratio = np.abs(fund_refl_power /
(fund_refl_power + not_fund_refl_power))
first_order_refl_ratio = 0
fund_tran_ratio = np.abs(fund_tran_power /
(fund_tran_power + not_fund_tran_power))
first_order_tran_ratio = 0
f2.write("%s, " % [
fund_refl_ratio, fund_refl_power, not_fund_refl_power,
fund_refl_power + not_fund_refl_power, fund_tran_ratio,
fund_tran_power, not_fund_tran_power,
fund_tran_power + not_fund_tran_power
])
plt.plot(y_list, np.abs(refl_val), 'bo-', label='reflectance')
plt.plot(y_list, np.abs(tran_val), 'ro-', label='transmittance')
plt.plot(y_list, E0, 'go-', label='E0')
plt.plot(y_list, fund_refl, 'co-', label='over')
plt.plot(y_list, not_fund_refl, 'ko-', label='over')
# plt.axis([40.0, 300.0, 0.0, 100.0])
plt.xlabel("y (um)")
plt.ylabel("Field")
plt.legend(loc="center right")
plt.savefig(case + "fields.png")
plt.close()
#
# print("\n")
#
# print(tran_val.size, refl_val.size)
#
# print("\n")
#
# print(tran_val, refl_val, E0)
#
# print("\n")
# # print(np.conj(tran_val), tran_val, E1[:,2])
# #
# # print(np.conj(refl_val), refl_val, E1[:,2])
#
# refl_tot_power = np.abs(np.dot(np.conj(refl_val), refl_val))
# tran_tot_power = np.abs(np.dot(np.conj(tran_val), tran_val))
#
# print(fund_refl_amp, refl_tot_power, fund_tran_amp, tran_tot_power)
# print(fund_refl_amp , fund_refl_amp_2 )
# print(first_order_refl_amp , first_order_refl_amp_2)
# print(fund_tran_amp , fund_tran_amp_2 )
# print(first_order_tran_amp , first_order_tran_amp_2)
#
# print(np.angle(fund_refl_amp), np.angle(fund_refl_amp_2))
# print(np.angle(first_order_refl_amp), np.angle(first_order_refl_amp_2))
# print(np.angle(fund_tran_amp), np.angle(fund_tran_amp_2))
# print(np.angle(first_order_tran_amp), np.angle(first_order_tran_amp_2))
# fund_refl_power = np.abs(fund_tran_amp) ** 2
# fund_refl_power = np.abs(fund_refl_amp) ** 2
# first_order_refl_power = np.abs(first_order_refl_amp) ** 2
# fund_tran_power = np.abs(fund_tran_amp) ** 2
# first_order_tran_power = np.abs(first_order_tran_amp) ** 2
# print(fund_refl_power, first_order_refl_power, fund_tran_power, first_order_tran_power)
# fund_refl_ratio = fund_refl_power / (fund_refl_power + first_order_refl_power)
# first_order_refl_ratio = first_order_refl_power / (fund_refl_power + first_order_refl_power)
# fund_tran_ratio = fund_tran_power / (fund_tran_power + first_order_tran_power)
# first_order_tran_ratio = first_order_tran_power / (fund_tran_power + first_order_tran_power)
# fund_refl_power = np.abs(fund_refl_amp) ** 2
# first_order_refl_power = np.abs(first_order_refl_amp) ** 2
# fund_tran_power = np.abs(fund_tran_amp) ** 2
# first_order_tran_power = np.abs(first_order_tran_amp) ** 2
#
# fund_refl_ratio = fund_refl_power / refl_tot_power
# first_order_refl_ratio = first_order_refl_power / refl_tot_power
# fund_tran_ratio = fund_tran_power / tran_tot_power
# first_order_tran_ratio = first_order_tran_power / tran_tot_power
#
# fund_refl_ratio = 1
# first_order_refl_ratio = 0
# fund_tran_ratio = 1
# first_order_tran_ratio = 0
print("Percentage of reflected light in fundamental mode: ",
fund_refl_ratio * 100)
print("Percentage of reflected light in first order mode: ",
first_order_refl_ratio * 100)
print("Percentage of transmitted light in fundamental mode: ",
fund_tran_ratio * 100)
print("Percentage of transmitted light in first order mode: ",
first_order_tran_ratio * 100)
# ------------------------ CODE FOR SEPARATING FUND AND FIRST ORDER MODE ENDS HERE ------------------------
wl = [] #list of wavelengths
refl1_flux = mp.get_fluxes(refl1)
refl2_flux = mp.get_fluxes(refl2)
tran_flux = mp.get_fluxes(tran)
su_flux = mp.get_fluxes(su)
sd_flux = mp.get_fluxes(sd)
flux_freqs = mp.get_flux_freqs(refl1)
for i in range(nfreq):
wl = np.append(wl, 1 / flux_freqs[i])
print(1 / flux_freqs[i])
# for ind, elt in enumerate(wl):
# #print(round(elt, 4))
# if round(elt, 3) == 0.637:
# #print("ALERT: MATCH FOUND")
# index = ind
index = 0
rp = refl1_flux[index]
tp = tran_flux[index]
# print("rp/tp:\n")
# print(rp)
# print(tp)
# print("\n")
R = -refl1_flux[index] / (refl2_flux[index] - refl1_flux[index])
T = tran_flux[index] / (refl2_flux[index] - refl1_flux[index])
S = (refl2_flux[index] - tran_flux[index]) / (refl2_flux[index] -
refl1_flux[index])
Su = su_flux[index] / (refl2_flux[index] - refl1_flux[index])
Sd = -sd_flux[index] / (refl2_flux[index] - refl1_flux[index])
S_correction = (1 - R - T) / (Su + Sd)
# print(R, T, S, Su, Sd)
r = sqrt(R)
t = sqrt(T)
r_fund = (r * fund_refl_ratio) * np.exp(
1j * np.angle(fund_refl_amp) + 2j * pi *
(-Lr1 - w / 2) * n_eff_fund / wavelength
) # Lr1 is negative because it is the (negative) position of the monitor, not the distace from the monitor to the center.
r_first = (r * first_order_refl_ratio) * np.exp(
1j * np.angle(first_order_refl_amp) + 2j * pi *
(-Lr1 - w / 2) * n_eff_first / wavelength)
t_fund = (t * fund_tran_ratio
) * np.exp(1j * np.angle(fund_tran_amp) + 2j * pi *
(Lt - w / 2) * n_eff_fund / wavelength)
t_first = (t * first_order_tran_ratio) * np.exp(
1j * np.angle(first_order_tran_amp) + 2j * pi *
(Lt - w / 2) * n_eff_first / wavelength)
if mode == 0:
r00 = r_fund
r01 = r_first
t00 = t_fund
t01 = t_first
su0 = sqrt(np.abs(Su * S_correction))
sd0 = sqrt(np.abs(Sd * S_correction))
# su0 = sqrt(Su)
# sd0 = sqrt(Sd)
r00s.append(r00)
r01s.append(r01)
t00s.append(t00)
t01s.append(t01)
su0s.append(su0)
sd0s.append(sd0)
if only_fund:
r10s.append(0)
r11s.append(0)
t10s.append(0)
t11s.append(1)
su1s.append(0)
sd1s.append(0)
else:
r10 = r_fund
r11 = r_first
t10 = t_fund
t11 = t_first
su1 = sqrt(np.abs(Su * S_correction))
sd1 = sqrt(np.abs(Sd * S_correction))
# su1 = sqrt(Su)
# sd1 = sqrt(Sd)
r10s.append(r10)
r11s.append(r11)
t10s.append(t10)
t11s.append(t11)
su1s.append(su1)
sd1s.append(sd1)
norm_Su = S * Su / (Su + Sd)
NET = round((R + T + S) * 100, 0)
if NET > 100.0:
NET = 100.0
NET_LOSS = round((Su + Sd) / S * 100, 0)
if NET_LOSS > 100.0:
NET_LOSS = 100.0
'''
np.append(ws, [w * 1000])
np.append(Rs, [R * 100])
np.append(Ts, [T * 100])
np.append(Ss, [S * 100])
np.append(NET_LIST, [NET])
np.append(Sus, [Su * 100])
np.append(Sds, [Sd * 100])
np.append(NET_LOSS_LIST, [NET_LOSS])
np.append(norm_Sus, [norm_Su * 100])
'''
if mode == 0:
ws.append(w * 1000)
Rs.append(R * 100)
Ts.append(T * 100)
Ss.append(S * 100)
NET_LIST.append(NET)
Sus.append(Su * 100)
Sds.append(Sd * 100)
NET_LOSS_LIST.append(NET_LOSS)
norm_Sus.append(norm_Su * 100)
if mode == 0:
f1.write(
"--------------------------------------------------- \n")
f1.write("Notch Width: %s nanometers \n" % (w * 1000))
f1.write("Reflection Percentage: %s\n" % (R * 100))
f1.write("Transmission Percentage: %s\n" % (T * 100))
f1.write("Total Loss Percentage: %s\n" % (S * 100))
f1.write("Percentage of Light Accounted For: %s\n" % (NET))
f1.write("Upper Loss Percentage: %s\n" % (Su * 100))
f1.write("Lower Loss Percentage: %s\n" % (Sd * 100))
f1.write("Percentage of Total Loss Accounted For: %s\n" %
(NET_LOSS))
f1.write("Normalized Upper Loss Percentage: %s\n" %
(norm_Su * 100))
f1.write("\n \n")
f1.write("FUNDAMENTAL MODE \n")
f1.write("n_eff: %s\n" % (n_eff_fund))
f1.write("Re(r00): %s\n" % (np.real(r00)))
f1.write("Im(r00): %s\n" % (np.imag(r00)))
f1.write("Re(r01): %s\n" % (np.real(r01)))
f1.write("Im(r01): %s\n" % (np.imag(r01)))
f1.write("Re(t00): %s\n" % (np.real(t00)))
f1.write("Im(t00): %s\n" % (np.imag(t00)))
f1.write("Re(t01): %s\n" % (np.real(t01)))
f1.write("Im(t01): %s\n" % (np.imag(t01)))
f1.write("Re(su0): %s\n" % (np.real(su0)))
f1.write("Im(su0): %s\n" % (np.imag(su0)))
f1.write("Re(sd0): %s\n" % (np.real(sd0)))
f1.write("Im(sd0): %s\n" % (np.imag(sd0)))
f1.write("\n")
else:
f1.write("FIRST ORDER MODE \n")
f1.write("n_eff: %s\n" % (n_eff_first))
f1.write("Re(r10): %s\n" % (np.real(r10)))
f1.write("Im(r10): %s\n" % (np.imag(r10)))
f1.write("Re(r11): %s\n" % (np.real(r11)))
f1.write("Im(r11): %s\n" % (np.imag(r11)))
f1.write("Re(t10): %s\n" % (np.real(t10)))
f1.write("Im(t10): %s\n" % (np.imag(t10)))
f1.write("Re(t11): %s\n" % (np.real(t11)))
f1.write("Im(t11): %s\n" % (np.imag(t11)))
f1.write("Re(su1): %s\n" % (np.real(su1)))
f1.write("Im(su1): %s\n" % (np.imag(su1)))
f1.write("Re(sd1): %s\n" % (np.real(sd1)))
f1.write("Im(sd1): %s\n" % (np.imag(sd1)))
f1.write(
"--------------------------------------------------- \n")
sim.reset_meep()
