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)
medium=medium,
dopant=dopant,
steps=steps,
solver_order='alternating',
error=error,
propagate_pump=True,
save_all=True)
lt.link((pulse[0], fiber[0]), (pump[0], fiber[2]))
lt.run(pulse, pump)
x_datas = [
pulse.fields[0].nu, pump.fields[0].nu, pulse.fields[0].time,
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)
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)
dopant=dopant,
steps=steps,
solver_order='alternating',
error=error,
propagate_pump=True,
save_all=True)
lt.link((pulse[0], fiber[0]), (pump[0], fiber[2]))
lt.run(pulse, pump)
x_datas = [
pulse[0][0].nu, pump[0][0].nu, pulse[0][0].time, pump[0][0].time,
np.vstack((fiber[1][0].nu, fiber[1][1].nu)),
np.vstack((fiber[1][0].time, fiber[1][1].time))
]
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],
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)
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)
init_phi = [1.0, 0.0]
beta_2 = [-19.0, -17.0]
gamma = [4.3, 4.6]
soli = Soliton(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
bit_rate=bit_rate,
offset_nu=offset_nu,
order=order,
init_phi=init_phi,
beta_2=beta_2,
gamma=gamma,
save=True)
lt.run(soli)
x_datas = [soli[0][0].time, soli[0][0].nu]
y_datas = [
temporal_power(soli[0][0].channels),
spectral_power(soli[0][0].channels)
]
plot.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Soliton pulse"],
split=True)
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)
x_datas = [sech[0][0].time, sech[0][0].nu]
y_datas = [
temporal_power(sech[0][0].channels),
spectral_power(sech[0][0].channels)
]
plot.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Sech pulse"],
split=True)
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"
]
x_datas = [cw[0][0].time, cw[0][0].nu, cw[0][0].time]
y_datas = [
temporal_power(cw[0][0].channels),
spectral_power(cw[0][0].channels),
phase(cw[0][0].channels)
]
#plot.plot2d(x_datas, y_datas, x_labels=["t","nu","t"],
# y_labels=["P_t", "P_nu", "phi"], plot_titles=plot_titles,
# split=True, opacity=0.2)
samples = int(2**(10))
bit_width = 3.
lt = layout.Layout(Domain(samples_per_bit=samples, bit_width=bit_width))
channels = 1
center_lambda = [1552.0, 1549.0, 1596.0]
peak_power = [1e-3, 2e-3, 6e-3]
total_power = [1e-3]
center_lambda = [1552.0, 1549.0, 976.0]
position = [0.3, 0.5, 0.4]
width = [5.3, 6]
bit_rate = [0.03, 0.04]
offset_nu = [1.56, -1.6]
order = [2, 1]
init_phi = [1.0, 0.0]
beta_2 = [-19.0, -17.0]
gamma = [4.3, 4.6]
soli = Soliton(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
bit_rate=bit_rate,
offset_nu=offset_nu,
order=order,
init_phi=init_phi,
beta_2=beta_2,
gamma=gamma,
save=True)
lt.run(soli)
plot.plot([soli.fields[0].time, soli.fields[0].nu], [
temporal_power(soli.fields[0].channels),
spectral_power(soli.fields[0].channels)
], ["t", "nu"], ["P_t", "P_nu"],
plot_titles=["Soliton pulse"],
split=True)
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)