sigmas = absorp.get_cross_section(omegas)
x_data.append(lambdas)
y_data.append(sigmas)
dopant_name = dopant[0].upper() + dopant[1:]
plot_titles.append(
'Cross sections {} from formula'.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.omega_to_lambda(lambdas)
file_name = folder + file + '.txt'
predict = CSVFit(file_name=file_name, conv_factor=[1e9, 1e18]) # in nm
absorp = Absorption(predict=predict)
sigmas = absorp.get_cross_section(omegas)
x_data.append(lambdas)
y_data.append(sigmas)
dopant_name = file[0].upper() + file[1:]
plot_titles.append(
'Absorption cross sections {} from data file'.format(dopant_name))
plot.plot(x_data,
y_data,
x_labels=['Lambda'],
y_labels=['sigma_a'],
split=True,
plot_colors='red',
plot_titles=plot_titles,
plot_linestyles='-.',
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.plot(x_data,
y_data,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
split=True,
opacity=0.0)
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.fields[0].nu, fiber.fields[1].nu,
pulse.fields[0].time, fiber.fields[1].time]
y_datas = [spectral_power(pulse.fields[0].channels),
spectral_power(fiber.fields[1].channels),
temporal_power(pulse.fields[0].channels),
temporal_power(fiber.fields[1].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.plot(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)
n_clad=n_clad))
effective_area = EffectiveArea(core_radius=core_radius, NA=NA)
print(effective_area(omega))
print(
EffectiveArea.calc_effective_area(omega,
core_radius=core_radius,
NA=NA))
# With numpy ndarray
lambdas = np.linspace(900, 1550, 100)
omegas = Domain.lambda_to_omega(lambdas)
n_core = np.linspace(1.42, 1.43, 100)
n_clad = np.linspace(1.415, 1.425, 100)
NA = NumericalAperture.calc_NA(n_core, n_clad)
effective_area = EffectiveArea(core_radius=core_radius, NA=NA)
res = effective_area(omegas)
x_labels = ['Lambda']
y_labels = ['Effective Area']
plot_titles = ["Effective Area as a function of the wavelength"]
plot.plot(lambdas,
res,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
opacity=0.0,
y_ranges=[2.5 * 1e-20, 3.5 * 1e-20])
arms = 3
ratios = [round(random.uniform(0, 1), 2) for i in range(arms)]
divider = IdealDivider(arms=arms, ratios=ratios, save=True)
lt.link((pulse[0], divider[0]))
lt.run(pulse)
plot_titles = ([
"Original pulse", "Pulses coming out of the {} with "
"ratios {}".format(default_name, str(ratios))
])
plot_groups: List[int] = [0] + [1 for i in range(arms)]
plot_labels: List[Optional[str]] = [None]
plot_labels.extend(["port {}".format(str(i)) for i in range(arms)])
fields = [temporal_power(pulse.fields[0].channels)]
times = [pulse.fields[0].time]
for i in range(1, arms + 1):
fields.append(temporal_power(divider.fields[i].channels))
times.append(divider.fields[i].time)
plot.plot(times,
fields,
plot_groups=plot_groups,
plot_titles=plot_titles,
x_labels=['t'],
y_labels=['P_t'],
plot_labels=plot_labels,
opacity=0.3)
pump.fields[0].time, fiber.fields[1].nu, fiber.fields[1].time
]
y_datas = [
spectral_power(pulse.fields[0].channels),
spectral_power(pump.fields[0].channels),
temporal_power(pulse.fields[0].channels),
temporal_power(pump.fields[0].channels),
spectral_power(fiber.fields[1].channels),
temporal_power(fiber.fields[1].channels)
]
x_labels = ['nu', 'nu', 't', 't', 'nu', 't']
y_labels = ['P_nu', 'P_nu', 'P_t', 'P_t', 'P_nu', 'P_t']
plot.plot(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(
channels = 3
center_lambda = [1552.0, 1549.0, 1596.0]
position = [0.3, 0.5]
width = [5.3, 6]
peak_power = [1e-3, 2e-3, 6e-3]
bit_rate = [0.03, 0.04]
offset_nu = [1.56, -1.6]
chirp = [0.5, 0.1]
init_phi = [1.0, 0.0]
sech = Sech(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
peak_power=peak_power,
bit_rate=bit_rate,
offset_nu=offset_nu,
chirp=chirp,
init_phi=init_phi,
save=True)
lt.run(sech)
plot.plot([sech.fields[0].time, sech.fields[0].nu], [
temporal_power(sech.fields[0].channels),
spectral_power(sech.fields[0].channels)
], ["t", "nu"], ["P_t", "P_nu"],
plot_titles=["Sech pulse"],
split=True)
phase
lt = layout.Layout(Domain(samples_per_bit=4096))
channels = 3
center_lambda = [1552.0, 1549.0, 1596.0]
peak_power = [1e-3, 2e-3, 6e-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 temporal power", "CW pulse spectral power", "CW pulse phase"
]
plot.plot([cw.fields[0].time, cw.fields[0].nu, cw.fields[0].time], [
temporal_power(cw.fields[0].channels),
spectral_power(cw.fields[0].channels),
phase(cw.fields[0].channels)
], ["t", "nu", "t"], ["P_t", "P_nu", "phi"],
plot_titles=plot_titles,
split=True)
if __name__ == "__main__":
import optcom.utils.plot as plot
from optcom.domain import Domain
V = 2.0
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.plot(Lambda, kappas, x_labels=['Lambda'], y_labels=['Kappa km^{-1}'],
plot_titles=plot_titles, split=True, opacity=0.0)
print(resonant.n(omega, n_0, N_1))
Lambda = np.linspace(500, 1500, 1000)
omega = Domain.lambda_to_omega(Lambda)
delta_n = resonant.n(omega, n_0, N_1)
delta_n_data = [delta_n]
x_labels = ['Lambda']
y_labels = ['Index change']
plot_titles = [
'Resonant index change with constant n_0 = {} (pumped)'.format(n_0)
]
sellmeier = Sellmeier("siO2")
n_0 = sellmeier.n(omega)
delta_n = resonant.n(omega, n_0, N_1)
delta_n_data.append(delta_n)
x_labels.append('Lambda')
y_labels.append('Index change')
plot_titles.append("Resonant index change with n_0 from Silica Sellmeier "
"equations. (pumped)")
plot.plot([Lambda, Lambda],
delta_n_data,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
split=True,
opacity=0.0)
center_lambda = [1552.0, 1549.0, 1596.0]
position = [0.5, 0.3, 0.5]
width = [2.0, 5.3, 6]
peak_power = [1e-3, 2e-3, 6e-3]
bit_rate = [0.0, 0.03, 0.04]
offset_nu = [0.0, 1.56, -1.6]
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)
plot.plot([gssn.fields[0].time, gssn.fields[0].nu], [
temporal_power(gssn.fields[0].channels),
spectral_power(gssn.fields[0].channels)
], ["t", "nu"], ["P_t", "P_nu"],
plot_titles=["Gaussian pulse"],
split=True)
mz = IdealMZ(phase_shift=phase_shift, loss=loss)
lt.link((pulse[0], mz[0]))
lt.run(pulse)
lt.reset()
# Plot parameters and get waves
fields.append(temporal_power(mz.fields[1].channels))
if (isinstance(phase_shift[0], float)):
temp_phase = phase_shift
else:
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
fields.append(temporal_power(mz.fields[1].channels))
plot_titles += ["Pulses coming out of the {}".format(default_name)]
fields = [temporal_power(pulse.fields[0].channels)] + fields
time = [pulse.fields[0].time, mz.fields[1].time]
plot.plot(time, fields, split=True, plot_titles=plot_titles,
x_labels=['t'], y_labels=['P_t'], opacity=0.3)
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.plot(Lambda,
n,
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
opacity=0.0)
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.plot(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)