gssn_1: oc.Gaussian = oc.Gaussian(channels=1, width=[5.0],
field_name='field 1 to be saved in file')
field_saver_1: oc.SaveField = oc.SaveField()
gssn_2: oc.Gaussian = oc.Gaussian(channels=1, width=[10.0],
field_name='field 2 to be saved in file')
field_saver_2: oc.SaveField = oc.SaveField()
lt.add_links((gssn_1[0], field_saver_1[0]), (gssn_2[0], field_saver_2[0]))
lt.run(gssn_1, gssn_2)
fields: List[oc.Field] = field_saver_1.fields + field_saver_2.fields
lt_: oc.Layout = oc.Layout()
load_field: oc.LoadField = oc.LoadField(fields=fields)
lt.run(load_field)
fields = load_field[0].fields
x_datas: List[np.ndarray] = [fields[0].time, fields[1].time,
fields[0].nu, fields[1].nu]
y_datas: List[np.ndarray] = [oc.temporal_power(fields[0].channels),
oc.temporal_power(fields[1].channels),
oc.spectral_power(fields[0].channels),
oc.spectral_power(fields[1].channels)]
oc.plot2d(x_datas, y_datas, x_labels=["t", "t", "nu","nu"],
y_labels=["P_t", "P_t", "P_nu", "P_nu"],
plot_titles=["Gaussian pulse which has been saved and loaded"],
plot_groups=[0,0,1,1])
noise=100 * np.ones(domain.noise_samples))
filter: oc.GaussianFilter = oc.GaussianFilter(nu_bw=nu_bw,
nu_offset=0.,
order=1,
center_nu=center_nu)
lt.add_link(pulse[0], filter[0])
lt.run(pulse)
plot_titles: List[str] = [
"Original pulse", r"After Gaussian filter with "
"frequency bandwidth {} THz.".format(round(nu_bw, 2))
]
plot_titles += plot_titles
y_datas: List[np.ndarray] = [
oc.temporal_power(pulse[0][0].channels),
oc.temporal_power(filter[1][0].channels),
oc.spectral_power(pulse[0][0].channels),
oc.spectral_power(filter[1][0].channels), pulse[0][0].noise,
filter[1][0].noise
]
x_datas: List[np.ndarray] = [
pulse[0][0].time, filter[1][0].time, pulse[0][0].nu, filter[1][0].nu,
pulse[0][0].domain.noise_nu, filter[1][0].domain.noise_nu
]
x_labels: List[str] = ['t', 't', 'nu', 'nu', 'nu', 'nu']
y_labels: List[str] = ['P_t', 'P_t', 'P_nu', 'P_nu', 'P (W)', 'P (W)']
nu_range = (center_nu - .1, center_nu + .1)
time_range = (bit_width / 2. + 75., bit_width / 2. - 75.)
noise_range = (x_datas[-1][0], x_datas[-1][-1])
x_ranges = [time_range, time_range, nu_range, nu_range, noise_range]
oc.plot2d(x_datas,
rep_freq: List[float] = [0.03, 0.04]
offset_nu: List[float] = [1.56, -1.6]
chirp: List[float] = [0.5, 0.1]
init_phi: List[float] = [1.0, 0.0]
sech = oc.Sech(channels=channels,
center_lambda=center_lambda,
position=position,
fwhm=width,
peak_power=peak_power,
rep_freq=rep_freq,
offset_nu=offset_nu,
chirp=chirp,
init_phi=init_phi,
save=True)
lt.run(sech)
x_datas: List[np.ndarray] = [sech[0][0].time, sech[0][0].nu]
y_datas: List[np.ndarray] = [
oc.temporal_power(sech[0][0].channels),
oc.spectral_power(sech[0][0].channels)
]
oc.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Sech pulse"],
split=True)
cw: oc.CW = oc.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: List[str] = [
"CW pulse temporal power", "CW pulse spectral power",
"CW pulse temporal phase", "CW pulse spectral phase"
]
x_datas: List[np.ndarray] = [
cw[0][0].time, cw[0][0].nu, cw[0][0].time, cw[0][0].nu
]
y_datas: List[np.ndarray] = [
oc.temporal_power(cw[0][0].channels),
oc.spectral_power(cw[0][0].channels),
oc.temporal_phase(cw[0][0].channels),
oc.spectral_phase(cw[0][0].channels)
]
oc.plot2d(x_datas,
y_datas,
x_labels=["t", "nu", "t", "nu"],
y_labels=["P_t", "P_nu", "phi_t", "phi_nu"],
plot_titles=plot_titles,
split=True,
line_opacities=[0.2])
init_phi: List[float] = [1.0, 0.0]
beta_2: List[float] = [-19.0, -17.0]
gamma: List[float] = [4.3, 4.6]
soli = oc.Soliton(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
rep_freq=rep_freq,
offset_nu=offset_nu,
order=order,
init_phi=init_phi,
beta_2=beta_2,
gamma=gamma,
save=True)
lt.run(soli)
x_datas: List[np.ndarray] = [soli[0][0].time, soli[0][0].nu]
y_datas: List[np.ndarray] = [
oc.temporal_power(soli[0][0].channels),
oc.spectral_power(soli[0][0].channels)
]
oc.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Soliton pulse"],
split=True)
order: List[int] = [1, 2, 1]
chirp: List[float] = [0.0, 0.5, 0.1]
init_phi: List[float] = [0.0, 1.0, 0.0]
gssn = oc.Gaussian(channels=channels,
center_lambda=center_lambda,
position=position,
width=width,
peak_power=peak_power,
rep_freq=rep_freq,
offset_nu=offset_nu,
order=order,
chirp=chirp,
init_phi=init_phi,
save=True)
lt.run(gssn)
x_datas: List[np.ndarray] = [gssn[0][0].time, gssn[0][0].nu]
y_datas: List[np.ndarray] = [
oc.temporal_power(gssn[0][0].channels),
oc.spectral_power(gssn[0][0].channels)
]
oc.plot2d(x_datas,
y_datas,
x_labels=["t", "nu"],
y_labels=["P_t", "P_nu"],
plot_titles=["Gaussian pulse"],
split=True)
STEP_UPDATE=False,
save_all=True,
INTRA_COMP_DELAY=True,
INTRA_PORT_DELAY=False,
INTER_PORT_DELAY=False,
noise_ode_method='rk1',
NOISE=True)
lt.add_link(pulse[0], fiber[0])
lt.run(pulse)
x_datas: List[np.ndarray] = [
pulse[0][0].nu, fiber[1][0].nu, pulse[0][0].time, fiber[1][0].time
]
y_datas: List[np.ndarray] = [
oc.spectral_power(pulse[0][0].channels),
oc.spectral_power(fiber[1][0].channels),
oc.temporal_power(pulse[0][0].channels),
oc.temporal_power(fiber[1][0].channels)
]
x_labels: List[str] = ['nu', 'nu', 't', 't']
y_labels: List[str] = ['P_nu', 'P_nu', 'P_t', 'P_t']
plot_titles: List[str] = [
"Original Pulses", "Pulses at the end of the fiber"
]
plot_titles.extend(plot_titles)
oc.plot2d(x_datas,
y_datas,
x_labels=x_labels,