add: plot of pulse collisions with dimensions

scripts/debug.py

@@ -1,3 +1,6 @@
+"""
+Just a debug script for functionalities
+"""
 import numpy as np
 import matplotlib.pyplot as plt
 

scripts/modules/plot_collisions_fig1.py

@@ -0,0 +1,85 @@
+import numpy as np
+from pynlin.nlin import m_th_time_integral_general
+import matplotlib.pyplot as plt
+from matplotlib.ticker import ScalarFormatter
+import os
+
+formatter = ScalarFormatter()
+formatter.set_scientific(True)
+formatter.set_powerlimits([0, 0])
+
+
+def plot_illustrative(fiber, pulse, wdm, recompute=False):
+    """
+    Plot of Marco
+    """
+    print("Plotting Fig.1 (pulse collisions)...")
+    m = [-10, -90]
+    dgds = [1e-12, 1e-12]
+    m1 = -10
+    m2 = -90
+    dgd_hi = 100e-15
+    dgd_lo = 1e-16
+    beta2a = -35e-27  # TODO, check con Marco
+    beta2b = -75e-27  
+    z = np.linspace(0, fiber.length, 1000)
+    # 1. the two pulses 
+    cases = [(dgd_hi, beta2a, beta2b, m1), 
+             (dgd_hi, beta2a, beta2b, m2),]
+    I_list = []
+    for dgd, beta2a, beta2b, m in cases:
+        I = np.real(m_th_time_integral_general(pulse, fiber, wdm, (0, 0), (0, 0), 0.0, m, z, dgd, None, beta2a, beta2b))
+        I_list.append(I)
+
+    # 2. the set of pulse peaks:
+    zw = 1/(pulse.baud_rate * dgd_hi)
+    m_max = fiber.length / zw
+    print(f"  > m_max: {m_max}")
+    m_axis = -np.array(range(int(round(m_max))))
+    peaks   = np.zeros(2)[np.newaxis, :].repeat(len(m_axis), axis=0)
+    z_peaks = np.zeros(2)[np.newaxis, :].repeat(len(m_axis), axis=0)
+    #
+    if not os.path.exists("results/fig1_peaks.npy") or recompute:
+      print("  Computing peaks...")
+      for im, m in enumerate(m_axis):
+        print(f"  m: {m:>10}")
+        for ic, (dgd, beta2a, beta2b, _) in enumerate(cases):
+          I = np.real(m_th_time_integral_general(pulse, fiber, wdm, (0, 0), (0, 0), 0.0, m, z, dgd_hi, None, beta2a, beta2b))
+          peaks[im, ic]   = np.max(I)
+          z_peaks[im, ic] = z[np.argmax(I)]
+      np.save("results/fig1_peaks.npy", (peaks, z_peaks))
+      print("  Done computing and saving peaks.")
+    else:
+      print("  Loading peaks...")
+      peaks, z_peaks = np.load("results/fig1_peaks.npy")
+      print("  Done loading peaks.")
+
+
+    # 3. Plotting
+    cases = [(dgd_hi, beta2a, beta2a, m1), 
+             (dgd_lo, beta2b, beta2b, m2),]
+    colors = ["blue", "grey"]
+    plt.figure(figsize=(4, 3))
+    # plotting the pulses
+    for I in I_list:
+      plt.plot(z*1e-3, I*1e-12, label=f'try', color="red")
+    # plotting the peaks
+    for ip, (peak, z_peak) in enumerate(zip(peaks, z_peaks)):
+      # print(peak, z_peak)
+      for ic in range(len(cases)):
+        if ip == 0:
+          plt.plot(z_peak[ic]*1e-3, peak[ic]*1e-12, 
+                  marker="x",
+                  markersize=0.1,
+                  label='case'+str(ic),
+                  color=colors[ic])
+        plt.plot(z_peak[ic]*1e-3, peak[ic]*1e-12, 
+                marker="x",
+                color=colors[ic])
+    plt.legend()
+    plt.xlabel(r'$z \; [\mathrm{km}]$')
+    plt.ylabel(r'$I(z) \; [\mathrm{ps^{-1}}]$')
+    plt.gca().yaxis.set_major_formatter(formatter)
+    plt.tight_layout()
+    plt.savefig("media/1-quovadis.pdf")
+    print("Done plotting Fig.1.")

scripts/noise.py

@@ -1,55 +1,28 @@
 """
-script for finding the overall noise on a single channel 
-given a fiber link configuration consisting 
+Finds the overall noise on a single channel 
+given a fiber link configuration.
+
+Generates figs:
+
+- Statistics of channels over DGD
+- NLIN thresholding and approximation
+
+TODO: 
+
+- Final computation of the noise including the fB
+
 """
 import pynlin.fiber
 from scripts.modules.space_integrals_general import *
 from scripts.modules.time_integrals import do_time_integrals
 from scripts.modules.load_fiber_values import *
-import matplotlib.pyplot as plt
-import scipy
 import pynlin.fiber
-from pynlin.nlin import m_th_time_integral_general
 import pynlin.wdm
 import pynlin.pulses
 from scripts.modules.load_fiber_values import load_group_delay
 from modules import cfg
-from matplotlib.ticker import ScalarFormatter
-formatter = ScalarFormatter()
-formatter.set_scientific(True)
-formatter.set_powerlimits([0, 0])
-
-def plot_illustrative(fiber, pulse, wdm):
-    """
-    Plot an illustrative case of the time integrals.
-    """
-    m = [-10, -90]
-    dgds = [1e-12, 1e-12]
-    m1 = -10
-    m2 = -90
-    dgd_hi = 100e-15
-    dgd_lo = 1e-16
-    beta2a = -75e-27  # TODO , CHECK
-    beta2b = -75e-27  
-    z = np.linspace(0, fiber.length, 1000)
-    # cases=[(dgd, beta2a, beta2b, m), 
-    cases = [(dgd_hi, beta2a, beta2b, m1), 
-             (dgd_hi, beta2a, beta2b, m2),]
-    I_list = []
-    for dgd, beta2a, beta2b, m in cases:
-        I = np.real(m_th_time_integral_general(pulse, fiber, wdm, (0, 0), (0, 0), 0.0, m, z, dgd, None, beta2a, beta2b))
-        I_list.append(I)
-
-    plt.figure(figsize=(4, 3))
-    for I in I_list:
-      plt.plot(z*1e-3, I*1e-12, label=f'try')
-    plt.xlabel(r'$z \; [\mathrm{km}]$')
-    plt.ylabel(r'$I(z) \; [\mathrm{ps^{-1}}]$')
-    plt.gca().yaxis.set_major_formatter(formatter)
-    plt.tight_layout()
-    plt.savefig("media/quo_vadis.pdf")
-
-
+from modules.plot_collisions_fig1 import plot_illustrative
+
 cf = cfg.load_toml_to_struct("./input/config_collision.toml")
 oi_fit = np.load('oi_fit.npy')
 oi_avg = np.load('oi_avg.npy')
@@ -72,8 +45,6 @@ def plot_illustrative(fiber, pulse, wdm):
       n_modes = 4
   )
 
-# print(f"beta_2 = {beta2:.9e}")
-
 # make the time integral take as an input (pulse, fiber, wdm)
 pulse = pynlin.pulses.GaussianPulse(
     baud_rate=cf.baud_rate,
@@ -93,25 +64,9 @@ def plot_illustrative(fiber, pulse, wdm):
 # print(l_freq * 1e-12)
 delta = (s_freq - l_freq) * 1e-12
 avg = ((s_freq + l_freq) * 1e-12 / 2)
-# beta_file = './results/fitBeta.mat'
-# mat = scipy.io.loadmat(beta_file)['fitParams'] * 1.0
-
-# beta_file = './results/fitBeta.mat'
-# omega = 2 * np.pi * freqs
-# omega_norm = scipy.io.loadmat(beta_file)['omega_std']
-# omega_n = (omega - scipy.io.loadmat(beta_file)['omega_mean']) / omega_norm
-# beta1 = np.zeros((4, len(freqs)))
-
-# write the results file in ../results/general_results.h5 with the correct time integrals
-# the file contains, for each interferent channel, the values (z, m, I) of the z
-# channel of interest is set to channel 0, and interferent channel index start from 0 for simplicity
-# a_chan = (0, 0)
-# print("@@@@@@@@ Time integrals  @@@@@@@@")
-# # do_time_integrals(a_chan, fiber, wdm, pulse, overwrite=True)
-# print("@@@@@@@@ Space integrals @@@@@@@@")
-# b_chan = (1, 99)
-# print("dgd: ", pynlin.nlin.get_dgd(a_chan, b_chan, fiber, wdm))
 
 ### Manually set the DGD and beta2 values for both the channels
-plot_illustrative(fiber, pulse, wdm)
-# compare_interferent(fiber, wdm, pulse, dgd=dgd, beta2a=beta2a, beta2b=beta2b)
+plot_illustrative(fiber, 
+                  pulse, 
+                  wdm, 
+                  recompute=True)

scripts/optimize.py

@@ -1,3 +1,11 @@
+"""
+Optimize the pumps and calculate signal and ASE power evolution
+
+Generates figs: 
+
+- Power profiles
+
+"""
 import os
 import tqdm
 from multiprocessing import Pool

scripts/watch_convergence.py

@@ -1,3 +1,7 @@
+""" 
+When run in background, continously
+update a plot of the final channel on off gain. 
+"""
 import numpy as np
 import matplotlib.pyplot as plt
 from watchdog.observers import Observer