def calculate_relative_error(data_directory, method, step, A_true):
""" Calculate relative local error using A_true. """
method_step = "-".join([method, str(step)])
filename = ".".join([method_step, "npy"])
filename = os.path.join(data_directory, filename)
A_calc = np.load(filename)
delta_power = temporal_power(A_calc) - temporal_power(A_true)
mean_relative_error = np.mean(np.abs(delta_power))
mean_relative_error /= np.amax(temporal_power(A_true))
return mean_relative_error
def calculate_relative_error(data_directory, method, step, A_true):
""" Calculate relative local error using A_true. """
method_step = "-".join([method, str(step)])
filename = ".".join([method_step, "npy"])
filename = os.path.join(data_directory, filename)
A_calc = np.load(filename)
delta_power = temporal_power(A_calc) - temporal_power(A_true)
mean_relative_error = np.mean(np.abs(delta_power))
mean_relative_error /= np.amax(temporal_power(A_true))
return mean_relative_error
def calculate_relative_error(data_directory, method, target_error, A_true):
""" Calculate relative local error using reference field as A_true. """
method_error = "-".join([method, "%.0e" % (target_error)])
filename = ".".join([method_error, "npz"])
filename = os.path.join(data_directory, filename)
try:
npz_data = np.load(filename)
A_calc = npz_data["field"]
delta_power = temporal_power(A_calc) - temporal_power(A_true)
mean_relative_error = np.mean(np.abs(delta_power))
mean_relative_error /= np.amax(temporal_power(A_true))
#~mean_relative_error /= np.mean(temporal_power(A_true))
#~mean_relative_error /= np.sum(temporal_power(A_true))
number_of_ffts = npz_data["ffts"]
return (number_of_ffts, mean_relative_error)
except IOError:
print "Error opening file: %s" % filename
def calculate_relative_error(data_directory, method, target_error, A_true):
""" Calculate relative local error using A_true. """
method_error = "-".join([method, "%.0e" % (target_error)])
filename = ".".join([method_error, "npz"])
filename = os.path.join(data_directory, filename)
try:
npz_data = np.load(filename)
A_calc = npz_data["field"]
delta_power = temporal_power(A_calc) - temporal_power(A_true)
mean_relative_error = np.mean(np.abs(delta_power))
mean_relative_error /= np.amax(temporal_power(A_true))
#~mean_relative_error /= np.mean(temporal_power(A_true))
#~mean_relative_error /= np.sum(temporal_power(A_true))
number_of_ffts = npz_data["ffts"]
return (number_of_ffts, mean_relative_error)
except IOError:
print("Error opening file: %s" % filename)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, spectral_power, double_plot, labels
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(Fibre(length=5.0, beta=[0.0, 0.0, 0.0, 1.0],
gamma=1.0, total_steps=100))
system.run()
P_t = temporal_power(system.fields['fibre'])
P_nu_normalised = spectral_power(system.fields['fibre'], True)
double_plot(system.domain.t, P_t, system.domain.nu, P_nu_normalised,
labels['t'], labels['P_t'], labels['nu'], labels['P_nu'],
x_range=(95.0, 120.0), X_range=(192.6, 193.7), filename="4-15")
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, multi_plot, labels
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian("gaussian", peak_power=1.0, width=1.0))
system.run()
P_ts = [temporal_power(system.fields['gaussian'])]
fibres = [Fibre(length=5.0, beta=[0.0, 0.0, 0.0, 1.0], total_steps=100),
Fibre(length=5.0, beta=[0.0, 0.0, 1.0, 1.0], total_steps=100)]
for fibre in fibres:
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(fibre)
system.run()
P_ts.append(temporal_power(system.fields['fibre']))
z_labels = [r"$z = 0 \, km$",
r"$z = 5 \, km$, $\beta_2 = \, 0 \, ps / (nm \cdot km)$",
r"$z = 5 \, km$, $\beta_2 \neq \, 0 \, ps / (nm \cdot km)$"]
a high zoom level.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, multi_plot, labels
domain = Domain(bit_width=200.0, samples_per_bit=2048)
gaussian = Gaussian(peak_power=1.0, width=1.0)
P_ts = []
methods = ['ss_simple', 'ss_symmetric', 'ss_sym_rk4', 'rk4ip']
for m in methods:
sys = System(domain)
sys.add(gaussian)
sys.add(
Fibre(length=5.0,
method=m,
total_steps=50,
beta=[0.0, 0.0, 0.0, 1.0],
gamma=1.0))
sys.run()
P_ts.append(temporal_power(sys.field))
multi_plot(sys.domain.t,
P_ts,
methods,
labels["t"],
labels["P_t"],
methods,
x_range=(80.0, 140.0))
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, spectral_power, double_plot, labels
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(
Fibre(length=5.0, beta=[0.0, 0.0, 0.0, 1.0], gamma=1.0, total_steps=100))
system.run()
print system.domain
P_t = temporal_power(system.fields['fibre'])
P_nu_normalised = spectral_power(system.fields['fibre'], True)
P_lambda_normalised = P_nu_normalised
t = system.domain.t
nu = system.domain.nu
Lambda = system.domain.Lambda
double_plot(nu,
P_nu_normalised,
Lambda,
P_lambda_normalised,
labels['nu'],
labels['P_nu'],
labels['Lambda'],
labels['P_lambda'],
phase = self.initial_phase
phase -= 2.0 * pi * self.offset_nu * domain.t
sechh = 1./np.cosh(t_normalised)
sechh = np.where(sechh != 0, np.power(sechh, 1+1j*self.C), 0.)
magnitude = sqrt(self.peak_power)*sechh
if domain.channels > 1:
self.field[self.channel] += magnitude * exp(1j * phase)
else:
self.field += magnitude * exp(1j * phase)
return self.field
if __name__ == "__main__":
""" Plot a default Diss_soliton in temporal and spectral domain """
from pyofss import Domain, System, Diss_soliton
from pyofss import temporal_power, spectral_power, inst_freq
from pyofss import double_plot, labels
sys = System(Domain(bit_width=500.0))
sys.add(Diss_soliton())
sys.run()
double_plot(sys.domain.t, temporal_power(sys.field),
sys.domain.nu, spectral_power(sys.field, True),
labels["t"], labels["P_t"], labels["nu"], labels["P_nu"],
inst_freq = inst_freq(sys.field, sys.domain.dt), y2_label=labels["inst_nu"])
SYS.run()
stop = time.clock()
NO_OCL_DURATION = (stop - start) / 1000.0
NO_OCL_OUT = SYS.fields["fibre"]
sys = System(DOMAIN)
sys.add(Gaussian("gaussian", peak_power=1.0, width=1.0))
sys.add(OpenclFibre(TS, dorf="float", length=LENGTH, total_steps=STEPS))
start = time.clock()
sys.run()
stop = time.clock()
OCL_DURATION = (stop - start) / 1000.0
OCL_OUT = sys.fields["ocl_fibre"]
NO_OCL_POWER = temporal_power(NO_OCL_OUT)
OCL_POWER = temporal_power(OCL_OUT)
DELTA_POWER = NO_OCL_POWER - OCL_POWER
MEAN_RELATIVE_ERROR = np.mean(np.abs(DELTA_POWER))
MEAN_RELATIVE_ERROR /= np.amax(temporal_power(NO_OCL_OUT))
print("Run time without OpenCL: %e" % NO_OCL_DURATION)
print("Run time with OpenCL: %e" % OCL_DURATION)
print("Mean relative error: %e" % MEAN_RELATIVE_ERROR)
# Expect both plots to appear identical:
double_plot(SYS.domain.t, NO_OCL_POWER, SYS.domain.t, OCL_POWER,
x_label=labels["t"], y_label=labels["P_t"],
X_label=labels["t"], Y_label=labels["P_t"])
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, chirp, multi_plot, labels
P_ts = []
chirps = []
zs = [0.0, 2.0, 4.0]
for z in zs:
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(Fibre(length=z, beta=[0.0, 0.0, -1.0, 0.0]))
system.run()
field = system.fields['fibre']
P_ts.append(temporal_power(field))
temp = [f if abs(f) >= 5e-12 else 0.0 for f in field]
chirps.append(chirp(temp, system.domain.window_nu))
multi_plot(system.domain.t, P_ts, zs, labels["t"], labels["P_t"],
[r"$z = {0:.0f} \, km$"], (90.0, 110.0), filename="3-1")
multi_plot(system.domain.t, chirps, zs,
labels["t"], labels["chirp"], [r"$z = {0:.0f} \, km$"],
(90.0, 110.0), (-6.0, 6.0), filename="3-1_chirp")
return self.nonlinearity.exp_non(A, h, B)
if __name__ == "__main__":
"""
Plot the result of a Gaussian pulse propagating through optical fibre.
Simulates both (third-order) dispersion and nonlinearity.
Use five different methods: ss_simple, ss_symmetric, ss_sym_rk4,
ss_sym_rkf, and rk4ip. Expect all five methods to produce similar results;
plot traces should all overlap. Separate traces should only be seen at
a high zoom level.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, multi_plot, labels
domain = Domain(bit_width=200.0, samples_per_bit=2048)
gaussian = Gaussian(peak_power=1.0, width=1.0)
P_ts = []
methods = ['ss_simple', 'ss_symmetric', 'ss_sym_rk4', 'rk4ip']
for m in methods:
sys = System(domain)
sys.add(gaussian)
sys.add(Fibre(length=5.0, method=m, total_steps=50,
beta=[0.0, 0.0, 0.0, 1.0], gamma=1.0))
sys.run()
P_ts.append(temporal_power(sys.field))
multi_plot(sys.domain.t, P_ts, methods, labels["t"], labels["P_t"],
methods, x_range=(80.0, 140.0))
system = System(Domain(bit_width=30.0, samples_per_bit=8192, channels=2))
system.add(Gaussian(width=1.0, peak_power=100.0, channel=0))
system.add(Gaussian(width=1.0, peak_power=1.0, channel=1, offset_nu=offset_nu))
system.add(
Fibre('fibre',
length=0.4,
gamma=[1.0, 1.2],
beta=[[0.0, 0.0, 1.0, 0.0], [0.0, 10.0, 1.0, 0.0]],
centre_omega=(nu_to_omega(nu_0), nu_to_omega(nu_1)),
sim_type='wdm',
method='ARK4IP'))
system.run()
A_fs = system.fields['fibre']
P_t0 = temporal_power(A_fs[0])
P_t1 = temporal_power(A_fs[1])
double_plot(system.domain.t,
P_t0,
system.domain.t,
P_t1,
labels["t"],
labels["P_t"],
labels["t"],
labels["P_t"],
filename="7-6a")
P_nu0 = spectral_power(A_fs[0], True)
P_nu1 = spectral_power(A_fs[1], True)
from pyofss.domain import nu_to_omega
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, double_plot, labels
domain = Domain(bit_width=20.0, samples_per_bit=8192, channels=2)
nu_0 = 193.1
nu_1 = 1.2 * nu_0
offset_nu = 0.2 * 193.1
offset = 2.5 / domain.bit_width
system = System(domain)
system.add(Gaussian(width=1.0, peak_power=1000.0, channel=0))
system.add(Gaussian(width=1.0, peak_power=0.1, channel=1,
position=0.5 - offset, offset_nu=offset_nu))
system.add(Fibre('fibre', length=0.2, gamma=[0.1, 0.12],
beta=[[0.0, 0.0, 1.0, 0.0], [0.0, 10.0, 1.0, 0.0]],
centre_omega=(nu_to_omega(nu_0), nu_to_omega(nu_1)),
sim_type='wdm', method='ARK4IP'))
system.run()
A_fs = system.fields['fibre']
P_t0 = temporal_power(A_fs[0])
P_t1 = temporal_power(A_fs[1])
double_plot(system.domain.t, P_t0, system.domain.t, P_t1,
labels["t"], labels["P_t"], labels["t"], labels["P_t"],
x_range=(6.0, 14.0), X_range=(5.0, 10.0), filename="7-8")
def __str__(self):
"""
:return: Information string
:rtype: string
Output information on Gaussian.
"""
output_string = [
'position = {0:f}', 'width = {1:f} ps', 'fwhm = {2:f} ps',
'peak_power = {3:f} W', 'offset_nu = {4:f} THz', 'm = {5:d}',
'C = {6:f}', 'initial_phase = {7:f} rad', 'channel = {8:d}']
return "\n".join(output_string).format(
self.position, self.width, self.calculate_fwhm(), self.peak_power,
self.offset_nu, self.m, self.C, self.initial_phase, self.channel)
if __name__ == "__main__":
""" Plot a default Gaussian in temporal and spectral domain """
from pyofss import Domain, System, Gaussian
from pyofss import temporal_power, spectral_power
from pyofss import double_plot, labels
sys = System(Domain(bit_width=500.0))
sys.add(Gaussian())
sys.run()
double_plot(sys.domain.t, temporal_power(sys.field),
sys.domain.nu, spectral_power(sys.field, True),
labels["t"], labels["P_t"], labels["nu"], labels["P_nu"])
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, chirp, multi_plot, labels
P_ts = []
chirps = []
zs = [0.0, 2.0, 4.0]
for z in zs:
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(Fibre(length=z, beta=[0.0, 0.0, -1.0, 0.0]))
system.run()
field = system.fields['fibre']
P_ts.append(temporal_power(field))
temp = [f if abs(f) >= 5e-12 else 0.0 for f in field]
chirps.append(chirp(temp, system.domain.window_nu))
multi_plot(system.domain.t,
P_ts,
zs,
labels["t"],
labels["P_t"], [r"$z = {0:.0f} \, km$"], (-10.0, 10.0),
filename="3-1")
multi_plot(system.domain.t,
chirps,
zs,
labels["t"],
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from pyofss import Domain, System, Gaussian, Fibre
from pyofss import temporal_power, multi_plot, labels
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian("gaussian", peak_power=1.0, width=1.0))
system.run()
P_ts = [temporal_power(system.fields['gaussian'])]
fibres = [
Fibre(length=5.0, beta=[0.0, 0.0, 0.0, 1.0], total_steps=100),
Fibre(length=5.0, beta=[0.0, 0.0, 1.0, 1.0], total_steps=100)
]
for fibre in fibres:
system = System(Domain(bit_width=200.0, samples_per_bit=2048))
system.add(Gaussian(peak_power=1.0, width=1.0))
system.add(fibre)
system.run()
P_ts.append(temporal_power(system.fields['fibre']))
z_labels = [
r"$z = 0 \, km$", r"$z = 5 \, km$, $\beta_2 = \, 0 \, ps / (nm \cdot km)$",
self.width,
self.calculate_fwhm(),
self.peak_power,
self.offset_nu,
self.C,
self.initial_phase,
self.channel,
)
if __name__ == "__main__":
""" Plot a default Sech in temporal and spectral domain """
from pyofss import Domain, System, Sech
from pyofss import temporal_power, spectral_power
from pyofss import double_plot, labels
sys = System(Domain(bit_width=500.0))
sys.add(Sech())
sys.run()
double_plot(
sys.domain.t,
temporal_power(sys.field),
sys.domain.nu,
spectral_power(sys.field, True),
labels["t"],
labels["P_t"],
labels["nu"],
labels["P_nu"],
)
