width=[10., 6.],
center_lambda=[center_lambda],
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]
import optcom as oc
# Create 2 Gaussian channels
pulse = oc.Gaussian(channels=2,
center_lambda=[1030., 1550.],
peak_power=[0.5, 1.0])
# Create fiber with a user-defined attenuation coefficient
fiber = oc.Fiber(length=1.0,
alpha=[0.4],
ATT=True,
DISP=True,
SPM=True,
save_all=True)
# Create an optical layout and link the first port of 'pulse' to the first port of 'fiber'
layout = oc.Layout()
layout.add_link(pulse.get_port(0), fiber.get_port(0))
layout.run_all()
# Extract outputs and plot
time = fiber.storage.time
power = oc.temporal_power(fiber.storage.channels)
space = fiber.storage.space
oc.animation2d(time,
power,
space,
x_label='t',
y_label='P_t',
plot_title='My first Optcom example',
line_labels=['1030. nm channel', '1550. nm channel'])
#filename='example_anim_readme.mp4')
combiner: oc.IdealCombiner = oc.IdealCombiner(arms=3, combine=False)
lt.add_links((pulse_1[0], combiner[0]), (pulse_2[0], combiner[1]),
(pulse_3[0], combiner[2]), (combiner[3], pm[0]))
lt.run(pulse_1, pulse_2, pulse_3)
plot_titles: List[str] = ([
"Original pulses", "Pulses coming out of the "
"ideal coupler \n without combination"
])
plot_groups: List[int] = [0, 0, 0, 1]
line_labels: List[Optional[str]] = ['port 0', 'port 1', 'port 2', None]
out_channels: np.ndarray = oc.temporal_power(pm[1][0].channels)
for i in range(len(pm[1])):
out_channels = np.vstack(
(out_channels, oc.temporal_power(pm[1][i].channels)))
y_datas: List[np.ndarray] = [
oc.temporal_power(pulse_1[0][0].channels),
oc.temporal_power(pulse_2[0][0].channels),
oc.temporal_power(pulse_3[0][0].channels), out_channels
]
x_datas: List[np.ndarray] = [
pulse_1[0][0].time, pulse_2[0][0].time, pulse_3[0][0].time,
pm[1][0].time
]
lt.reset()
pm = oc.IdealPhaseMod()
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])
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
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"],
plot_groups=[0, 0, 1, 1])
random_phase: float = random.random() * math.pi
random_phase_bis: float = random.random() * math.pi
phase_shifts: List[List[float]] = [[random_phase, random_phase],
[math.pi / 2, 0.0],
[random_phase, random_phase_bis]]
y_datas: List[np.ndarray] = []
plot_titles: List[str] = ["Original pulse"]
mz: oc.IdealMZM
for i, phase_shift in enumerate(phase_shifts):
# Propagation
mz = oc.IdealMZM(phase_shift=phase_shift, loss=loss)
lt.add_link(pulse[0], mz[0])
lt.run(pulse)
lt.reset()
# Plot parameters and get waves
y_datas.append(oc.temporal_power(mz[1][0].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 Ideal MZM with phase "
"shift {} and {}".format(str(round(temp_phase[0], 2)),
str(round(temp_phase[1], 2)))
]
v_pi: List[float] = [1.0]
v_mod: List[Callable] = [
lambda t: math.sin(math.pi * t), lambda t: math.sin(math.pi / 2.0 * t)
]
v_bias: List[float] = [1.2, 2.1]
mz = oc.IdealMZM(v_pi=v_pi, v_mod=v_mod, v_bias=v_bias)
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)
import numpy as np
import optcom as oc
lt: oc.Layout = oc.Layout()
pulse: oc.Gaussian = oc.Gaussian(channels=1, peak_power=[10.0])
isolator_1: oc.IdealIsolator = oc.IdealIsolator(blocked_port=1, save=True)
lt.add_link(pulse[0], isolator_1[0])
lt.run(pulse)
plot_titles: List[str] = ([
'Initial Pulse', 'Output of first isolator (pass)'
])
y_datas: List[np.ndarray] = [
oc.temporal_power(pulse[0][0].channels),
oc.temporal_power(isolator_1[1][0].channels)
]
x_datas: List[np.ndarray] = [pulse[0][0].time, isolator_1[1][0].time]
oc.plot2d(x_datas,
y_datas,
split=True,
plot_titles=plot_titles,
x_labels=['t'],
y_labels=['P_t'],
line_opacities=[0.3])
lt: oc.Layout = oc.Layout(domain)
lambda_bw = 0.05 # nm
nu_bw = oc.lambda_bw_to_nu_bw(lambda_bw, center_lambda)
pulse: oc.Gaussian = oc.Gaussian(channels=2, peak_power=[10.0, 19.0],
width=[10., 6.],
center_lambda=[center_lambda],
noise=100*np.ones(domain.noise_samples))
filter: oc.FlatTopFilter = oc.FlatTopFilter(nu_bw=nu_bw, nu_offset=0.,
center_nu=center_nu)
lt.add_link(pulse[0], filter[0])
lt.run(pulse)
plot_titles: List[str] = ["Original pulse", r"After flat top 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]
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)
import numpy as np
import optcom as oc
# with division
pulse: oc.Gaussian = oc.Gaussian(channels=1, peak_power=[10.0])
lt: oc.Layout = oc.Layout()
arms: int = 3
ratios: List[float] = [round(random.uniform(0, 1), 2) for i in range(arms)]
divider: oc.IdealDivider = oc.IdealDivider(arms=arms,
divide=True,
ratios=ratios,
save=True)
lt.add_link(pulse[0], divider[0])
lt.run(pulse)
y_datas: List[np.ndarray] = [oc.temporal_power(pulse[0][0].channels)]
x_datas: List[np.ndarray] = [pulse[0][0].time]
plot_titles: List[str] = ([
"Original pulse", "Pulses coming out of the "
"ideal divider (3 ports) \n with ratios {}".format(str(ratios))
])
plot_groups: List[int] = [0] + [1 for i in range(arms)]
line_labels: List[Optional[str]] = [None]
line_labels.extend(["port {}".format(str(i)) for i in range(arms)])
for i in range(1, arms + 1):
y_datas.append(oc.temporal_power(divider[i][0].channels))
x_datas.append(divider[i][0].time)
# Without division
arms = 3
save=True,
wait=True,
UNI_OMEGA=True,
STEP_UPDATE=False,
INTRA_COMP_DELAY=True,
INTRA_PORT_DELAY=False,
INTER_PORT_DELAY=False,
noise_ode_method='rk4',
NOISE=True)
lt.add_link(pulse_1[0], coupler[0])
lt.add_link(pulse_2[0], coupler[1])
lt.run(pulse_1, pulse_2)
y_datas: List[np.ndarray] = [
oc.temporal_power(pulse_1[0][0].channels),
oc.temporal_power(pulse_2[0][0].channels),
oc.temporal_power(coupler[2][0].channels),
oc.temporal_power(coupler[3][0].channels), pulse_1[0][0].noise,
coupler[2][0].noise, pulse_2[0][0].noise, coupler[3][0].noise
]
x_datas: List[np.ndarray] = [
pulse_1[0][0].time, pulse_2[0][0].time, coupler[2][0].time,
coupler[3][0].time, pulse_1[0][0].domain.noise_omega,
coupler[2][0].domain.noise_omega, pulse_2[0][0].domain.noise_omega,
coupler[3][0].domain.noise_omega
]
plot_groups: List[int] = [0, 1, 2, 3, 4, 4, 5, 5]
plot_titles: List[str] = [
"Original pulse", "Original pulse", "Pulse coming out of the "
"coupler with Lk = {}".format(str(round(length * kappa_, 2)))
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)
lt: oc.Layout = oc.Layout(oc.Domain(bit_width=100.0, samples_per_bit=4096))
pulse: oc.Gaussian = oc.Gaussian(channels=2, peak_power=[0.5, 1.0], width=[0.5, 0.8])
steps: int = int(5e3)
fiber: oc.Fiber
SS: bool = True
for j, nlse_method in enumerate(nlse_methods):
if (j == len(nlse_methods)-2): # To compute rk4ip and rk4ip_gnlse
oc.set_rk4ip_opti_gnlse(False) # Can make slighty diff. output
else:
oc.set_rk4ip_opti_gnlse(True)
# Propagation
fiber = oc.Fiber(length=0.2, nlse_method=nlse_method, alpha=[0.5],
beta_order=3, gamma=4.0, nl_approx=False, SPM=True,
XPM=True, SS=True, RS=True, steps=steps, save=True)
lt.add_link(pulse[0], fiber[0])
lt.run(pulse)
lt.reset()
# Plot parameters and get waves
x_datas.append(fiber[1][0].time)
y_datas.append(oc.temporal_power(fiber[1][0].channels))
plot_groups.append(0)
line_labels.extend(nlse_methods[:-1] + ["rk4ip_gnlse"])
plot_titles.extend(["NLSE solvers test with n={}".format(str(steps))])
# -------------------- Plotting results ------------------------
oc.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=[0.3])
