width=[10., 6.],
center_lambda=[center_lambda],
noise=100*np.ones(domain.noise_samples))
filter: oc.GenericFilter = oc.GenericFilter(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_omega,
filter[1][0].domain.noise_omega]
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, y_datas, plot_titles=plot_titles, x_labels=x_labels,
y_labels=y_labels, split=True, line_opacities=[0.3],
x_ranges=x_ranges)
y_datas.append(oc.temporal_power(divider[i][0].channels))
x_datas.append(divider[i][0].time)
# Without division
arms = 3
pulse = oc.Gaussian(channels=2, peak_power=[10.0, 7.0])
divider = oc.IdealDivider(arms=arms, divide=False, save=True)
lt.reset()
lt.add_link(pulse[0], divider[0])
lt.run(pulse)
plot_titles.extend([
"Original pulse", "Pulses coming out of the ideal "
"divider (3 ports) \n with no division."
])
plot_groups.extend([2] + [3 for i in range(arms)])
line_labels.extend([None])
line_labels.extend(["port {}".format(str(i)) for i in range(arms)])
y_datas.extend([oc.temporal_power(pulse[0][0].channels)])
x_datas.extend([pulse[0][0].time])
for i in range(1, arms + 1):
y_datas.append(oc.temporal_power(divider[i][0].channels))
x_datas.append(divider[i][0].time)
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])
"""
from typing import List
import numpy as np
import optcom as oc
# With float
omega: float = oc.lambda_to_omega(1552.0)
nl_index: oc.NLIndex = oc.NLIndex(medium="SiO2")
print(nl_index(omega))
# With numpy ndarray
lambdas: np.ndarray = np.linspace(500, 1600, 1000)
omegas: np.ndarray = oc.lambda_to_omega(lambdas)
res: np.ndarray = nl_index(omegas)
x_labels: List[str] = ['Lambda']
y_labels: List[str] = ['n_2']
plot_titles: List[str] = [
"Non linear index as a function of the "
"wavelength for Silica core."
]
oc.plot2d([lambdas], [res],
x_labels=x_labels,
y_labels=y_labels,
plot_titles=plot_titles,
line_opacities=[0.0],
y_ranges=[(2.5 * 1e-20, 3.2 * 1e-20)])
import optcom as oc
# With float
n_clad: float = 1.44
omega: float = oc.lambda_to_omega(1050.0)
sellmeier: oc.Sellmeier = oc.Sellmeier("sio2")
NA_inst: oc.NumericalAperture = oc.NumericalAperture(sellmeier, n_clad)
print(NA_inst(omega))
n_core: float = sellmeier(omega)
NA_inst = oc.NumericalAperture(n_core, n_clad)
NA: float = NA_inst(omega)
print(NA, oc.NumericalAperture.calc_NA(n_core, n_clad))
print(n_core, oc.NumericalAperture.calc_n_core(NA, n_clad))
print(n_clad, oc.NumericalAperture.calc_n_clad(NA, n_core))
# With numpy ndarray
lambdas: np.ndarray = np.linspace(900, 1550, 100)
omegas: np.ndarray = oc.lambda_to_omega(lambdas)
NA_inst = oc.NumericalAperture(sellmeier, n_clad)
res: np.ndarray = NA_inst(omegas)
x_labels: List[str] = ['Lambda']
y_labels: List[str] = ['Numerical aperture']
plot_titles: List[str] = ["Numercial aperture as a function of the "
"wavelength \n for Silica core with constant "
"cladding refractive index."]
oc.plot2d([lambdas], [res], x_labels=x_labels, y_labels=y_labels,
plot_titles=plot_titles, line_opacities=[0.0],
y_ranges=[(0.1, 0.2)])
width=[10.0],
field_name='field 2 to be saved in file')
field_saver_2: oc.SaveFieldToFile = oc.SaveFieldToFile(file_name=file_name,
add_fields=True,
root_dir=root_dir)
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] = []
with open(file_name, 'rb') as file_to_load:
fields.append(pickle.load(file_to_load)[0])
fields.append(pickle.load(file_to_load)[0])
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])
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)