def __init__(self, name="fibre", length=1.0, alpha=None,
beta=None, gamma=0.0, sim_type=None, traces=1,
local_error=1.0e-6, method="RK4IP", total_steps=100,
self_steepening=False, raman_scattering=False,
rs_factor=0.003, use_all=False, centre_omega=None,
tau_1=12.2e-3, tau_2=32.0e-3, f_R=0.18):
use_cache = not(method.upper().startswith('A'))
self.name = name
self.length = length
self.linearity = Linearity(alpha, beta, sim_type,
use_cache, centre_omega)
self.nonlinearity = Nonlinearity(gamma, sim_type, self_steepening,
raman_scattering, rs_factor,
use_all, tau_1, tau_2, f_R)
class Function():
""" Class to hold linear and nonlinear functions. """
def __init__(self, l, n, linear, nonlinear):
self.l = l
self.n = n
self.linear = linear
self.nonlinear = nonlinear
def __call__(self, A, z):
return self.l(A, z) + self.n(A, z)
self.function = Function(self.l, self.n, self.linear, self.nonlinear)
self.stepper = Stepper(traces, local_error, method, self.function,
self.length, total_steps)
def __init__(self,
name="fibre",
length=1.0,
alpha=None,
beta=None,
gamma=0.0,
sim_type=None,
traces=1,
local_error=1.0e-6,
method="RK4IP",
total_steps=100,
self_steepening=False,
raman_scattering=False,
rs_factor=0.003,
use_all=False,
centre_omega=None,
tau_1=12.2e-3,
tau_2=32.0e-3,
f_R=0.18):
use_cache = not (method.upper().startswith('A'))
self.name = name
self.length = length
self.linearity = Linearity(alpha, beta, sim_type, use_cache,
centre_omega)
self.nonlinearity = Nonlinearity(gamma, sim_type, self_steepening,
raman_scattering, rs_factor, use_all,
tau_1, tau_2, f_R)
class Function():
""" Class to hold linear and nonlinear functions. """
def __init__(self, l, n, linear, nonlinear):
self.l = l
self.n = n
self.linear = linear
self.nonlinear = nonlinear
def __call__(self, A, z):
return self.l(A, z) + self.n(A, z)
self.function = Function(self.l, self.n, self.linear, self.nonlinear)
self.stepper = Stepper(traces, local_error, method, self.function,
self.length, total_steps)
class Fibre(object):
"""
:param string name: Name of this module
:param double length: Length of fibre
:param object alpha: Attenuation of fibre
:param object beta: Dispersion of fibre
:param double gamma: Nonlinearity of fibre
:param string sim_type: Type of simulation
:param Uint traces: Number of field traces required
:param double local_error: Relative local error used in adaptive stepper
:param string method: Method to use in ODE solver
:param Uint total_steps: Number of steps to use for ODE integration
:param bool self_steepening: Toggles inclusion of self-steepening effects
:param bool raman_scattering: Toggles inclusion of raman-scattering effects
:param float rs_factor: Factor determining the amount of raman-scattering
:param bool use_all: Toggles use of general expression for nonlinearity
:param double centre_omega: Angular frequency used within dispersion class
:param double tau_1: Constant used in Raman scattering calculation
:param double tau_2: Constant used in Raman scattering calculation
:param double f_R: Constant setting the fraction of Raman scattering used
sim_type is either default or wdm.
traces: If greater than 1, will save the field at uniformly-spaced points
during fibre propagation. If zero, will output all saved points used.
This is useful if using an adaptive stepper which will likely save
points non-uniformly.
method: simulation method such as RK4IP, ARK4IP.
total_steps: If a non-adaptive stepper is used, this will be used
to set the step-size between successive points along the fibre.
local_error: Relative local error to aim for between propagtion points.
"""
def __init__(self,
name="fibre",
length=1.0,
alpha=None,
beta=None,
gamma=0.0,
sim_type=None,
traces=1,
local_error=1.0e-6,
method="RK4IP",
total_steps=100,
self_steepening=False,
raman_scattering=False,
rs_factor=0.003,
use_all=False,
centre_omega=None,
tau_1=12.2e-3,
tau_2=32.0e-3,
f_R=0.18):
use_cache = not (method.upper().startswith('A'))
self.name = name
self.length = length
self.linearity = Linearity(alpha, beta, sim_type, use_cache,
centre_omega)
self.nonlinearity = Nonlinearity(gamma, sim_type, self_steepening,
raman_scattering, rs_factor, use_all,
tau_1, tau_2, f_R)
class Function():
""" Class to hold linear and nonlinear functions. """
def __init__(self, l, n, linear, nonlinear):
self.l = l
self.n = n
self.linear = linear
self.nonlinear = nonlinear
def __call__(self, A, z):
return self.l(A, z) + self.n(A, z)
self.function = Function(self.l, self.n, self.linear, self.nonlinear)
self.stepper = Stepper(traces, local_error, method, self.function,
self.length, total_steps)
def __call__(self, domain, field):
self.linearity(domain)
self.nonlinearity(domain)
# Set temporal and spectral arrays for storage:
self.stepper.storage.t = domain.t
self.stepper.storage.nu = domain.nu
# Propagate field through fibre:
return self.stepper(field)
def l(self, A, z):
""" Linear term. """
return self.linearity.lin(A, z)
def linear(self, A, h):
""" Linear term in exponential factor. """
return self.linearity.exp_lin(A, h)
def n(self, A, z):
""" Nonlinear term. """
return self.nonlinearity.non(A, z)
def nonlinear(self, A, h, B):
""" Nonlinear term in exponential factor. """
return self.nonlinearity.exp_non(A, h, B)
class Fibre(object):
"""
:param string name: Name of this module
:param double length: Length of fibre
:param object alpha: Attenuation of fibre
:param object beta: Dispersion of fibre
:param double gamma: Nonlinearity of fibre
:param string sim_type: Type of simulation
:param Uint traces: Number of field traces required
:param double local_error: Relative local error used in adaptive stepper
:param string method: Method to use in ODE solver
:param Uint total_steps: Number of steps to use for ODE integration
:param bool self_steepening: Toggles inclusion of self-steepening effects
:param bool raman_scattering: Toggles inclusion of raman-scattering effects
:param float rs_factor: Factor determining the amount of raman-scattering
:param bool use_all: Toggles use of general expression for nonlinearity
:param double centre_omega: Angular frequency used within dispersion class
:param double tau_1: Constant used in Raman scattering calculation
:param double tau_2: Constant used in Raman scattering calculation
:param double f_R: Constant setting the fraction of Raman scattering used
sim_type is either default or wdm.
traces: If greater than 1, will save the field at uniformly-spaced points
during fibre propagation. If zero, will output all saved points used.
This is useful if using an adaptive stepper which will likely save
points non-uniformly.
method: simulation method such as RK4IP, ARK4IP.
total_steps: If a non-adaptive stepper is used, this will be used
to set the step-size between successive points along the fibre.
local_error: Relative local error to aim for between propagtion points.
"""
def __init__(self, name="fibre", length=1.0, alpha=None,
beta=None, gamma=0.0, sim_type=None, traces=1,
local_error=1.0e-6, method="RK4IP", total_steps=100,
self_steepening=False, raman_scattering=False,
rs_factor=0.003, use_all=False, centre_omega=None,
tau_1=12.2e-3, tau_2=32.0e-3, f_R=0.18):
use_cache = not(method.upper().startswith('A'))
self.name = name
self.length = length
self.linearity = Linearity(alpha, beta, sim_type,
use_cache, centre_omega)
self.nonlinearity = Nonlinearity(gamma, sim_type, self_steepening,
raman_scattering, rs_factor,
use_all, tau_1, tau_2, f_R)
class Function():
""" Class to hold linear and nonlinear functions. """
def __init__(self, l, n, linear, nonlinear):
self.l = l
self.n = n
self.linear = linear
self.nonlinear = nonlinear
def __call__(self, A, z):
return self.l(A, z) + self.n(A, z)
self.function = Function(self.l, self.n, self.linear, self.nonlinear)
self.stepper = Stepper(traces, local_error, method, self.function,
self.length, total_steps)
def __call__(self, domain, field):
self.linearity(domain)
self.nonlinearity(domain)
# Set temporal and spectral arrays for storage:
self.stepper.storage.t = domain.t
self.stepper.storage.nu = domain.nu
# Propagate field through fibre:
return self.stepper(field)
def l(self, A, z):
""" Linear term. """
return self.linearity.lin(A, z)
def linear(self, A, h):
""" Linear term in exponential factor. """
return self.linearity.exp_lin(A, h)
def n(self, A, z):
""" Nonlinear term. """
return self.nonlinearity.non(A, z)
def nonlinear(self, A, h, B):
""" Nonlinear term in exponential factor. """
return self.nonlinearity.exp_non(A, h, B)