Hi there, for the purpose of explaining the process of a bi-dimensional wavelength/time…

Certainly! Here is an example Python script that creates a 3D plot of encoded pulses with wavelength, time, and intensity as the axes:

python Copy code import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D

Define the wavelength and time axes

wavelengths = np.linspace(1500, 1600, 101) times = np.linspace(0, 100, 101)

Create a 2D array to represent the encoded pulses

pulses = np.zeros((101, 101))

Encode the pulses using a unipolar encoding scheme

for i in range(10): for j in range(10): pulses[i10+j, i10+j] = 1

Create a 3D plot of the encoded pulses

fig = plt.figure() ax = fig.add_subplot(111, projection='3d') X, Y = np.meshgrid(wavelengths, times) ax.plot_surface(X, Y, pulses, cmap='viridis') ax.set_xlabel('Wavelength (nm)') ax.set_ylabel('Time (ns)') ax.set_zlabel('Intensity') plt.show() This script creates a 3D plot of encoded pulses with wavelength ranging from 1500 nm to 1600 nm and time ranging from 0 ns to 100 ns. The encoded pulses are spread within the wavelength/time space using a unipolar encoding scheme, where each pulse is represented by a spike in intensity at a particular wavelength and time. The resulting plot shows the pulses as a series of peaks within the wavelength/time space.

You can modify the script to change the range and resolution of the wavelength and time axes, as well as the encoding scheme used to create the pulses.