cleanup of todos

pynlin/raman/pytorch/gain_optimizer.py

@@ -107,7 +107,7 @@ def optimize(
                     self.pump_wavelengths, *self.wavelength_scaling
                 )
                 signal_spectrum = dBm(self.forward( # the physics works in Watt
-                    pump_wavelengths, watt(self.pump_powers))) # FIXME : where are the signal
+                    pump_wavelengths, watt(self.pump_powers))) 
                 loss = loss_function(signal_spectrum, _target_spectrum) # the loss function is in dBm
                 loss.backward()
                 torch_optimizer.step()

pynlin/raman/solvers.py

@@ -791,8 +791,6 @@ def optim_fun(x0):
 
             # Compute the phonon occupancy factor
             Hinv = np.exp(h_planck * np.abs(frequency_shifts) / (kB * temperature)) - 1
-            # FIXME evidentemente stiamo considerando onde che hanno la stessa frequenza. Dobbiamo 
-            # sommare l'effetto solo sullo stesso modo, e lasciare che il coupling misceli tutto.
             eta = 1 + 1 / Hinv
             np.fill_diagonal(eta, 0)
             # print(eta)

scripts/modules/nlin_fB.py

@@ -260,4 +260,6 @@ def antonio_rescale_min(dgd):
 plt.xscale('log')
 plt.ylabel(r'channel-pair NLIN [km$^2$/ps$^2$]')
 plt.tight_layout()
-plt.savefig(f"media/dispersion/partial_NLIN_fB.png", dpi=dpi)
+plt.savefig(f"media/dispersion/partial_NLIN_fB.png", dpi=dpi)
+
+# TODO start again from here!!

scripts/optimize.py

@@ -227,8 +227,7 @@ def repropagate_numpy(fiber,
         cf.launch_power = signal_power
         cfg.save_struct_to_toml("./input/config.toml", cf)
         output_file = f"results/ct_solution{signal_power}_gain_{cf.raman_gain}.npy"
-
-
+
         signal_wavelengths = wdm.wavelength_grid()
 
         if not os.path.exists(output_file) or recompute: