res = math.sqrt(res)
else: # numpy.ndarray
res = np.ones(Lambda.shape)
for i in range(len(self._Bs)):
res += (self._Bs[i]*Lambda**2) / (Lambda**2 - self._Cs[i])
res = np.sqrt(res)
return res
if __name__ == "__main__":
import optcom.utils.plot as plot
from optcom.domain import Domain
sellmeier = Sellmeier("sio2")
center_omega = Domain.lambda_to_omega(1050.0)
print(sellmeier.n(center_omega))
Lambda = np.linspace(120, 2120, 2000)
omega = Domain.lambda_to_omega(Lambda)
n = sellmeier.n(omega)
x_labels = ['Lambda']
y_labels = ['Refractive index']
plot_titles = ["Refractive index of Silica from Sellmeier equations"]
plot.plot2d(Lambda, n, x_labels=x_labels, y_labels=y_labels,
plot_titles=plot_titles, opacity=0.0)
print(
NLCoefficient.calc_nl_coefficient(omega,
nl_index=nl_index,
eff_area=eff_area))
# With numpy ndarray
lambdas = np.linspace(900, 1550, 10)
omegas = Domain.lambda_to_omega(lambdas)
n_core = np.linspace(1.42, 1.43, 10)
n_clad = np.linspace(1.415, 1.425, 10)
eff_area = EffectiveArea.calc_effective_area(omegas,
core_radius=core_radius,
n_core=n_core,
n_clad=n_clad)
nl_index = NLIndex.calc_nl_index(omega, medium=medium)
res = NLCoefficient.calc_nl_coefficient(omegas,
nl_index=nl_index,
eff_area=eff_area)
x_labels = ['Lambda']
y_labels = ['gamma']
plot_titles = ["Non linear coefficient of Silica"]
plot.plot2d(lambdas,
res,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
opacity=0.0)
beta_2 = Dispersion.calc_beta_deriv(omega, 2, "SiO2")
x_data = [Lambda]
y_data = [beta_2]
x_labels = ['Lambda']
y_labels = ['beta2']
plot_titles = ["Group velocity dispersion coefficients in Silica"]
disp = Dispersion.calc_dispersion(Lambda, beta_2)
x_data.append(Lambda)
y_data.append(disp)
x_labels.append('Lambda')
y_labels.append('dispersion')
plot_titles.append('Dispersion of Silica')
beta_3 = Dispersion.calc_beta_deriv(omega, 3, "Sio2")
slope = Dispersion.calc_dispersion_slope(Lambda, beta_2, beta_3)
x_data.append(Lambda)
y_data.append(slope)
x_labels.append('Lambda')
y_labels.append('dispersion_slope')
plot_titles.append('Dispersion slope of Silica')
plot.plot2d(x_data,
y_data,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
split=True,
opacity=0.0)
temp_phase = [phase_shift[0](0), phase_shift[1](0)]
plot_titles += [
"Pulses coming out of the {} with phase "
"shift {} and {}".format(default_name, str(round(temp_phase[0],
2)),
str(round(temp_phase[1], 2)))
]
v_pi = [1.0]
v_mod = [
lambda t: math.sin(math.pi * t), lambda t: math.sin(math.pi / 2.0 * t)
]
v_bias = [1.2, 2.1]
mz = IdealMZ(v_pi=v_pi, v_mod=v_mod, v_bias=v_bias)
lt.link((pulse[0], mz[0]))
lt.run(pulse)
# Plot parameters and get waves
y_datas.append(temporal_power(mz[1][0].channels))
plot_titles += ["Pulses coming out of the {}".format(default_name)]
y_datas = [temporal_power(pulse[0][0].channels)] + y_datas
x_datas = [pulse[0][0].time, mz[1][0].time]
plot.plot2d(x_datas,
y_datas,
split=True,
plot_titles=plot_titles,
x_labels=['t'],
y_labels=['P_t'],
opacity=0.3)
]
y_datas = [
spectral_power(pulse[0][0].channels),
spectral_power(pump[0][0].channels),
temporal_power(pulse[0][0].channels),
temporal_power(pump[0][0].channels),
spectral_power(np.vstack(
(fiber[1][0].channels, fiber[1][1].channels))),
temporal_power(np.vstack((fiber[1][0].channels, fiber[1][1].channels)))
]
x_labels = ['nu', 't', 'nu', 't']
y_labels = ['P_nu', 'P_t', 'P_nu', 'P_t']
plot.plot2d(x_datas,
y_datas,
x_labels=x_labels,
y_labels=y_labels,
plot_groups=[0, 0, 1, 1, 2, 3],
opacity=0.3)
x_data_raw = np.linspace(0.0, length, steps)
x_data = np.zeros((0, len(x_data_raw)))
for i in range(nbr_ch_s + 2 + nbr_ch_p):
x_data = np.vstack((x_data, x_data_raw))
x_data = [x_data, x_data_raw.reshape((1, -1))]
power_signal = fiber.power_signal_forward + fiber.power_signal_backward
power_ase_forward = np.sum(fiber.power_ase_forward, axis=0)
power_ase_backward = np.sum(fiber.power_ase_backward, axis=0)
power_pump = fiber.power_pump_forward + fiber.power_pump_backward
powers = np.vstack(
a = 5.0
d = 15.0
n_0 = 1.02
norm_d = d / a
cpl = Coupling(V=V, a=a, d=d, n_0=n_0)
Lambda = 1550.0
omega = Domain.lambda_to_omega(Lambda)
print(cpl.get_kappa(omega))
Lambda = np.linspace(900, 1600, 3)
omega = Domain.lambda_to_omega(Lambda)
print(cpl.get_kappa(omega))
Lambda = np.linspace(900, 1600, 1000)
omega = Domain.lambda_to_omega(Lambda)
kappas = cpl.get_kappa(omega)
plot_titles = [
'Coupling coefficient as a function of the wavelength '
'for norm. spacing = {}'.format(norm_d)
]
plot.plot2d(Lambda,
kappas,
x_labels=['Lambda'],
y_labels=['Kappa km^{-1}'],
plot_titles=plot_titles,
split=True,
opacity=0.0)
import numpy as np
import optcom.utils.plot as plot
from optcom.domain import Domain
# With float
omega = Domain.lambda_to_omega(1552.0)
nl_index = NLIndex(medium="SiO2")
print(nl_index(omega))
print(nl_index.calc_nl_index(omega, medium="SiO2"))
# With numpy ndarray
lambdas = np.linspace(500, 1600, 1000)
omegas = Domain.lambda_to_omega(lambdas)
nl_index = NLIndex(medium="SiO2")
res = nl_index(omegas)
x_labels = ['Lambda']
y_labels = ['n_2']
plot_titles = ["Non linear index of Silica from fitting formula"]
plot.plot2d(lambdas,
res,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
opacity=0.0,
y_ranges=[(2.5 * 1e-20, 3.2 * 1e-20)])
lt.run(pulse_1, pulse_2, pulse_3)
plot_titles.extend([
"Original pulses",
"Pulses coming out of the {} with 'combine=True'".format(default_name)
])
plot_groups.extend([2, 2, 2, 3])
plot_labels.extend(['port 0', 'port 1', 'port 2', None])
y_datas.extend([
temporal_power(pulse_1[0][0].channels),
temporal_power(pulse_2[0][0].channels),
temporal_power(pulse_3[0][0].channels),
temporal_power(pm[1][0].channels)
])
x_datas.extend([
pulse_1[0][0].time, pulse_2[0][0].time, pulse_3[0][0].time,
pm[1][0].time
])
plot.plot2d(x_datas,
y_datas,
plot_labels=plot_labels,
plot_groups=plot_groups,
plot_titles=plot_titles,
x_labels=['t'],
y_labels=['P_t'],
opacity=0.3)
sigmas = stimu.get_cross_section(omegas, center_omega, N_0, N_1, T)
x_data.append(lambdas)
y_data.append(sigmas)
dopant_name = dopant[0].upper() + dopant[1:]
plot_titles.append("Cross sections {} from formula and McCumber "
"relations".format(dopant_name))
for i, file in enumerate(files):
nbr_samples = 1000
lambdas = np.linspace(file_range[i][0], file_range[i][1], nbr_samples)
omegas = Domain.lambda_to_omega(lambdas)
file_name = folder + file + '.txt'
predict = CSVFit(file_name=file_name, conv_factor=[1e9, 1e18]) # in nm
stimu = StimulatedEmission(predict=predict)
sigmas = stimu.get_cross_section(omegas)
x_data.append(lambdas)
y_data.append(sigmas)
dopant_name = file[0].upper() + file[1:]
plot_titles.append(
"Emission ross sections {} from data file".format(dopant_name))
plot.plot2d(x_data,
y_data,
x_labels=['Lambda'],
y_labels=['sigma_e'],
split=True,
plot_colors='red',
plot_titles=plot_titles,
plot_linestyles='-.',
opacity=0.0)
order = [1, 2, 1]
chirp = [0.0, 0.5, 0.1]
init_phi = [0.0, 1.0, 0.0]
gssn = Gaussian(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
peak_power=peak_power,
bit_rate=bit_rate,
offset_nu=offset_nu,
order=order,
chirp=chirp,
init_phi=init_phi,
save=True)
lt.run(gssn)
x_datas = [gssn[0][0].time, gssn[0][0].nu]
y_datas = [
temporal_power(gssn[0][0].channels),
spectral_power(gssn[0][0].channels)
]
plot.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Gaussian pulse"],
split=True)
f_c: float = cst.F_C
x_data.append(time)
h_R = Raman.calc_h_R(time, f_a=f_a, f_b=f_b, f_c=f_c)
y_data.append(h_R)
plot_labels.append('Isotropic and anisotropic part')
f_a = 1.0
f_b = 0.0
f_c = 1.0
x_data.append(time)
h_R = Raman.calc_h_R(time, f_a=f_a, f_b=f_b, f_c=f_c)
y_data.append(h_R)
plot_labels.append('W/o anisotropic part')
f_a = 0.0
f_b = 1.0
f_c = 0.0
x_data.append(time)
h_R = Raman.calc_h_R(time, f_a=f_a, f_b=f_b, f_c=f_c)
y_data.append(h_R)
plot_labels.append('W/o isotropic part')
plot_titles = ['Raman response function', 'Raman gain']
plot.plot2d(x_data,
y_data,
x_labels=['t'],
y_labels=['h_R'],
plot_groups=[0, 0, 0],
plot_titles=plot_titles,
opacity=0.0,
plot_labels=plot_labels)
conv_func=[domain.Domain.lambda_to_omega],
root_dir=root_dir)
Lambda = 976.0
omega = domain.Domain.lambda_to_omega(Lambda)
print(csv(omega, 1))
Lambda = np.arange(300) + 850.
omega = domain.Domain.lambda_to_omega(Lambda)
res_1 = csv(omega, 0)
root_dir = "./data/fiber_amp/cross_section/emission/"
file_name = ('yb.txt')
csv = CSVFit(file_name, ',', conv_factor=[1e9, 1e18],
conv_func=[domain.Domain.lambda_to_omega],
root_dir=root_dir)
res_2 = csv(omega, 0)
print('1010 : ', csv(domain.Domain.lambda_to_omega(1010.), 0))
print('1015 : ', csv(domain.Domain.lambda_to_omega(1015.), 0))
print('1020 : ', csv(domain.Domain.lambda_to_omega(1020.), 0))
print('1025 : ', csv(domain.Domain.lambda_to_omega(1025.), 0))
print('1030 : ', csv(domain.Domain.lambda_to_omega(1030.), 0))
Lambda_temp = np.arange(150) + 1000.
omega_temp = domain.Domain.lambda_to_omega(Lambda_temp)
res_temp = csv(omega_temp, 0)
print('max at ',
domain.Domain.omega_to_lambda(omega_temp[np.argmax(res_temp)]),
' : ', np.amax(res_temp))
plot.plot2d([Lambda], [res_1, res_2], x_labels=['Lambda'],
y_labels=['sigma_a'], line_labels=['absorption', 'emission'])
channels = 1
center_lambda = [1552.0, 1549.0, 1596.0]
peak_power = [1e-3, 2e-3, 6e-3]
total_power = [1e-3]
offset_nu = [0.0, 1.56, -1.6]
init_phi = [1.0, 1.0, 0.0]
cw = CW(channels=channels,
center_lambda=center_lambda,
peak_power=peak_power,
offset_nu=offset_nu,
init_phi=init_phi,
save=True)
lt.run(cw)
plot_titles = ["CW pulse spectral power"]
x_datas = [cw[0][0].nu]
y_datas = [spectral_power(cw[0][0].channels)]
for i in range((int(samples / 2) - 4), (int(samples / 2) + 4)):
print(y_datas[0][0][i])
plot.plot2d(x_datas,
y_datas,
x_labels=["nu"],
y_labels=["P_nu"],
plot_titles=plot_titles,
split=True,
opacity=0.2)
RS=False,
XPM=False,
ASYM=True,
COUP=True,
approx_type=1,
nlse_method='ssfm_super_sym',
steps=steps,
ode_method=method,
save=True,
wait=False,
NOISE=True)
lt.add_link(pulse[0], coupler[0])
lt.run(pulse)
lt.reset()
# Plot parameters and get waves
x_datas.append(coupler[2][0].time)
y_datas.append(Field.temporal_power(coupler[2][0].channels))
plot_groups.append(0)
line_labels.extend(ode_methods)
plot_titles.extend(["ODE solvers test with n={}".format(str(steps))])
# -------------------- Plotting results ------------------------
plot.plot2d(x_datas,
y_datas,
plot_groups=plot_groups,
plot_titles=plot_titles,
x_labels=['t'],
y_labels=['P_t'],
line_labels=line_labels,
line_opacities=[0.1])
CSVFit
lt = layout.Layout(domain.Domain(samples_per_bit=512,bit_width=20.0))
pulse = gaussian.Gaussian(channels=2, peak_power=[10.0, 10e-1],
width=[1.0, 5.0], center_lambda=[1050.0, 1048.0])
gamma_data = CSVFit('./data/gamma_test.txt')
fiber = Fiber(length=.10, method="ssfm_symmetric", alpha=[0.046],
alpha_order=4, beta_order=4, gamma=1.5,
nl_approx=False, ATT=True, DISP=True,
SPM=True, XPM=False, SS=False, RS=False, approx_type=1,
steps=1000, medium='sio2')
lt.link((pulse[0], fiber[0]))
lt.run(pulse)
x_datas = [pulse[0][0].nu, fiber[1][0].nu,
pulse[0][0].time, fiber[1][0].time]
y_datas = [spectral_power(pulse[0][0].channels),
spectral_power(fiber[1][0].channels),
temporal_power(pulse[0][0].channels),
temporal_power(fiber[1][0].channels)]
x_labels = ['nu', 'nu', 't', 't']
y_labels = ['P_nu', 'P_nu', 'P_t', 'P_t']
plot_titles = ["Original Pulse", "Pulse at the end of the fiber"]
plot_titles.extend(plot_titles)
plot.plot2d(x_datas, y_datas, x_labels=x_labels, y_labels=y_labels,
plot_titles=plot_titles, plot_groups=[0,1,2,3], opacity=0.3)
