Added example notebooks for FDTD, one how to use on its own, and one …

nidn/fdtd_integration/calculate_transmission_reflection_coefficients.py

@@ -121,11 +121,6 @@ def _check_for_all_zero_signal(signals):
         raise ValueError(
             "The free-space signal is all zero. Increase the number of FDTD_niter to ensure that the signal reaches the detector."
         )
-    if _mean_square(signals[1]) <= 1e-15:
-        logger.warning(
-            "WARNING:The signal through the material layer(s) never reaches the detector. Increase FDTD_niter to ensure that the signal reaches the detector. The signal usually travels slower in a material than in free space."
-        )
-
 
 # From : https://stackoverflow.com/questions/54498775/pytorch-argrelmax-function-or-c
 def _torch_find_peaks(signal):

nidn/fdtd_integration/init_fdtd.py

@@ -28,6 +28,8 @@ def init_fdtd(cfg: DotMap, include_object, wavelength, permittivity):
         fdtd:Grid: Grid with all the added object, ready to be run
     """
     set_backend("torch")
+    if (len(cfg.PER_LAYER_THICKNESS) == 1) and (cfg.N_layers > 1):
+        cfg.PER_LAYER_THICKNESS = [cfg.PER_LAYER_THICKNESS] * cfg.N_layers
     # The scaling is the number of grid points per unit magnitude. This is the maximum of the reation between the unit magnitude and 1/10th of the smallest wavelength,
     # and a constant which is defaulted to 10. If this scaling becomes too low, i.e. below 2, there might be some errors in creating the grid,
     # as there is too feew grid points for ceartian elements to be placed correctly.

nidn/training/utils/validate_config.py

@@ -224,11 +224,6 @@ def _validate_config(cfg: DotMap):
     ):
         raise ValueError(f"PER_LAYER_THICKNESS must have length 1 or N_layers")
 
-    if cfg.solver == "FDTD" and (not len(cfg.PER_LAYER_THICKNESS) == cfg.N_layers):
-        raise ValueError(
-            f"PER_LAYER_THICKNESS must have length N_layers when using FDTD"
-        )
-
     if not (cfg.freq_distribution == "linear" or cfg.freq_distribution == "log"):
         raise ValueError(f"freq_distribution must be either 'linear' or 'log'")