Add L grid to cfg

nidn/tests/trcwa_test.py

@@ -19,6 +19,8 @@ def test_single_layer():
     run_cfg.N_layers = 1
     run_cfg.target_frequencies = [0.5]
     run_cfg.N_freq = 1
+    run_cfg.TRCWA_L_grid = [[1.0, 0.0], [0.0, 1.0]]
+    run_cfg.TRCWA_NG = 11
 
     # Get eps shape
     shape = [
@@ -55,6 +57,8 @@ def test_uniform_layer():
     run_cfg.N_layers = 1
     run_cfg.target_frequencies = [0.05, 0.1]
     run_cfg.N_freq = 2
+    run_cfg.TRCWA_L_grid = [[1.0, 0.0], [0.0, 1.0]]
+    run_cfg.TRCWA_NG = 11
 
     # Get eps shape
     shape = [
@@ -96,6 +100,8 @@ def test_three_layer():
     run_cfg.N_layers = 3
     run_cfg.target_frequencies = [3.0]
     run_cfg.N_freq = 1
+    run_cfg.TRCWA_L_grid = [[1.0, 0.0], [0.0, 1.0]]
+    run_cfg.TRCWA_NG = 11
 
     # Get eps shape
     shape = [

nidn/training/utils/validate_config.py

@@ -34,6 +34,8 @@ def _validate_config(cfg: DotMap):
         "use_regularization_loss",
         "add_noise",
         "noise_scale",
+        "TRCWA_L_grid",
+        "TRCWA_NG",
     ]
 
     for key in required_keys:
@@ -50,6 +52,7 @@ def _validate_config(cfg: DotMap):
         "n_neurons",
         "eps_oversampling",
         "seed",
+        "TRCWA_NG",
     ]
     float_keys = [
         "L",
@@ -99,6 +102,7 @@ def _validate_config(cfg: DotMap):
         "iterations",
         "eps_oversampling",
         "noise_scale",
+        "TRCWA_NG",
     ]
     for key in positive_value_keys:
         if not (cfg[key] > 0):
@@ -111,3 +115,11 @@ def _validate_config(cfg: DotMap):
 
     if cfg.type != "classification" and cfg.type != "regression":
         raise ValueError(f"type must be either 'classification' or 'regression'")
+
+    if not cfg.TRCWA_L_grid[1][0] < cfg.TRCWA_L_grid[1][1]:
+        raise ValueError(f"TRCWA_L_grid dim1 must be ordered from low to high")
+    if not cfg.TRCWA_L_grid[0][0] > cfg.TRCWA_L_grid[0][1]:
+        raise ValueError(f"TRCWA_L_grid dim0 must be ordered from high to low")
+
+    if not all(cfg.TRCWA_L_grid) >= 0:
+        raise ValueError(f"TRCWA_L_grid must be positive")

nidn/trcwa/compute_spectrum.py

@@ -42,7 +42,7 @@ def compute_spectrum(eps_grid, run_cfg: DotMap):
     for idx, freq in enumerate(run_cfg.target_frequencies):
 
         # Create TRCWA Object for this frequency
-        trcwa = _init_trcwa(eps_grid[:, :, :, idx], freq)
+        trcwa = _init_trcwa(eps_grid[:, :, :, idx], freq, run_cfg)
 
         # Compute the spectrum
         reflectance, transmittance = trcwa.RT_Solve(normalize=1)

nidn/trcwa/constants.py

@@ -12,12 +12,6 @@
 # or doi.org/10.1364/OE.21.030812 for a more thorough explanation
 TRCWA_Q_ABS = 1e5
 
-# Truncation order (actual number might be smaller)
-TRCWA_NG = 11
-
-TRCWA_L1 = [0.1, 0]  # everything is set relative to this
-TRCWA_L2 = [0, 0.1]  # it is easiest to say 1 = 1 micron
-
 # Dielectric constant for top and bottom layer (vacuum layer)
 TRCWA_VACUUM_EPS = torch.tensor(1.0)
 

nidn/trcwa/init_trcwa.py

@@ -2,12 +2,13 @@
 from .trcwa import TRCWA
 
 
-def _init_trcwa(eps_grid, target_frequency):
+def _init_trcwa(eps_grid, target_frequency, run_cfg):
     """Creates a TRCWA object matching the given eps_grid and target_frequency.
 
     Args:
         eps_grid (torch.tensor): Grid of epsilon values. Should be [Nx, Ny, N_layers].
         target_frequency (float): Target frequency for this simulation run.
+        run_cfg (DotMap): Run configuration.
 
     Returns:
         TRCWA obj: The created object which is ready to compute the spectrum
@@ -25,7 +26,13 @@ def _init_trcwa(eps_grid, target_frequency):
 
     # Initialize TRCWA object
     trcwa = TRCWA(
-        TRCWA_NG, TRCWA_L1, TRCWA_L2, freqcmp, TRCWA_THETA, TRCWA_PHI, verbose=0,
+        run_cfg.TRCWA_NG,
+        run_cfg.TRCWA_L_grid[0],
+        run_cfg.TRCWA_L_grid[1],
+        freqcmp,
+        TRCWA_THETA,
+        TRCWA_PHI,
+        verbose=0,
     )
 
     # Add vacuum layer at the top

nidn/utils/resources/default_config.toml

@@ -33,3 +33,7 @@ N_layers = 1
 # Target frequencies
 physical_wavelength_range = [250e-9,10e-6]
 N_freq = 20
+
+# TRCWA parameters
+TRCWA_L_grid = [[0.1,0.0],[0.0,0.1]] # grid dimension for TRCWA
+TRCWA_NG = 11 # Truncation order (actual number might be smaller)