Dispersive wave generation in anomalous dispersion regimeΒΆ
Example of dispersive wave generation in anomalus dispersion regime at a central wavelength of 835 nm in a 15 centimeter long photonic crystal fiber using three different models to model Raman response.
import numpy as np
import matplotlib.pyplot as plt
import gnlse
import os
if __name__ == '__main__':
setup = gnlse.GNLSESetup()
# Numerical parameters
setup.resolution = 2**14
setup.time_window = 12.5 # ps
setup.z_saves = 200
# Physical parameters
setup.wavelength = 835 # nm
setup.fiber_length = 0.15 # m
setup.nonlinearity = 0.0 # 1/W/m
setup.raman_model = gnlse.raman_blowwood
setup.self_steepening = True
# The dispersion model is built from a Taylor expansion with coefficients
# given below.
loss = 0
betas = np.array([-0.024948815481502, 8.875391917212998e-05,
-9.247462376518329e-08, 1.508210856829677e-10])
# Input pulse parameters
power = 10000
# pulse duration [ps]
tfwhm = 0.05
# hyperbolic secant
setup.pulse_model = gnlse.SechEnvelope(power, tfwhm)
# Type of dyspersion operator: build from interpolation of given neffs
# read mat file for neffs
mat_path = os.path.join(os.path.dirname(__file__), '..',
'data', 'neff_pcf.mat')
mat = gnlse.read_mat(mat_path)
# neffs
neff = mat['neff'][:, 1]
# wavelengths in nm
lambdas = mat['neff'][:, 0] * 1e9
# Visualization
###########################################################################
# Set type of dispersion function
simulation_type = {
'Results for Taylor expansion': gnlse.DispersionFiberFromTaylor(
loss, betas),
'Results for interpolation': gnlse.DispersionFiberFromInterpolation(
loss, neff, lambdas, setup.wavelength)
}
count = len(simulation_type)
plt.figure(figsize=(15, 7), facecolor='w', edgecolor='k')
for (i, (name, dispersion_model)) in enumerate(simulation_type.items()):
setup.dispersion_model = dispersion_model
solver = gnlse.GNLSE(setup)
solution = solver.run()
plt.subplot(2, count, i + 1)
plt.title(name)
gnlse.plot_wavelength_vs_distance(solution, WL_range=[400, 1400])
plt.subplot(2, count, i + 1 + count)
gnlse.plot_delay_vs_distance(solution, time_range=[-.5, 5])
plt.tight_layout()
plt.show()
Output:
