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increased solution time for testing

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edoardo
2026-05-26 22:48:00 +02:00
parent df865759c3
commit 72517172c2
1 changed files with 9 additions and 8 deletions
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metapeg_v2.py
+9 -8
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@@ -13,14 +13,14 @@ import os
import xarray as xr import xarray as xr
from PIL import Image from PIL import Image
mp.verbosity(0) mp.verbosity(3)
if not os.path.exists("results"): if not os.path.exists("results"):
os.makedirs("results") os.makedirs("results")
###### PHYSICS SETUP ###### ###### PHYSICS SETUP ######
RESOLUTION = 20 #pixels per meep unit length (1um) RESOLUTION = 20 #pixels per meep unit length (1um)
MAX_RUN_TIME = 400 #meep units MAX_RUN_TIME = 700 #meep units
WAVELENGTH_MIN_UM = 1.50 WAVELENGTH_MIN_UM = 1.50
WAVELENGTH_MAX_UM = 1.60 WAVELENGTH_MAX_UM = 1.60
PML_UM = 1.0 #PML thickness PML_UM = 1.0 #PML thickness
@@ -118,7 +118,7 @@ def filter_and_project(
print("========================") print("========================")
print("weights shape", weights.shape) print("weights shape", weights.shape)
print("weights_filtered shape",weights_filtered.flatten().shape) print("weights_filtered shape",weights_filtered.shape)
print("type of weights", type(weights)) print("type of weights", type(weights))
print("========================") print("========================")
@@ -441,7 +441,7 @@ def normalization_sim() -> np.ndarray:
ref_fields = np.array( ref_fields = np.array(
[norm_sim.get_farfield(norm_near2far, point) for point in ff_points] [norm_sim.get_farfield(norm_near2far, point) for point in ff_points]
) )
#select only the Ex component for all frequencies, #select only the Ex component for all frequencies,
# which is the one we will optimize for # which is the one we will optimize for
@@ -588,12 +588,13 @@ def intensity_optimization(
measured_intensity = intensity_from_farfields(FarFields) measured_intensity = intensity_from_farfields(FarFields)
measured_intensity_mean = npa.mean(measured_intensity, axis=0) #measured_intensity_mean = npa.mean(measured_intensity, axis=0)
norm_measured_intensity = measured_intensity / ref_intensity * target_intensity_mean translated_measured_intensity = measured_intensity / npa.mean(ref_intensity,axis=0) * target_intensity_mean
norm_measured_intensity = npa.clip(norm_measured_intensity, a_min=0, a_max=1)
translated_measured_intensity = npa.clip(translated_measured_intensity, a_min=0, a_max=1)
distance = norm_measured_intensity - target_intensity_3d distance = translated_measured_intensity - target_intensity_3d
return npa.power(npa.sum(distance**2, axis=0),0.5) return npa.power(npa.sum(distance**2, axis=0),0.5)