def __init__(self,
name: str = default_name,
channels: int = 1,
center_lambda: List[float] = [cst.DEF_LAMBDA],
peak_power: List[float] = [1e-3],
total_power: Optional[List[float]] = None,
offset_nu: List[float] = [0.0],
init_phi: List[float] = [0.0],
save: bool = False) -> None:
r"""
Parameters
----------
name :
The name of the component.
channels :
The number of channels in the field.
center_lambda :
The center wavelength of the channels. :math:`[nm]`
peak_power :
Peak power of the pulses. :math:`[W]`
total_power :
Total power of the pulses. :math:`[W]` (peak_power will be
ignored if total_power provided)
offset_nu :
The offset frequency. :math:`[THz]`
init_phi :
The initial phase of the pulses.
save :
If True, the last wave to enter/exit a port will be saved.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.OPTI_OUT]
super().__init__(name, default_name, ports_type, save)
# Attr types check ---------------------------------------------
util.check_attr_type(channels, 'channels', int)
util.check_attr_type(center_lambda, 'center_lambda', float, list)
util.check_attr_type(peak_power, 'peak_power', float, list)
util.check_attr_type(total_power, 'total_power', None, float, list)
util.check_attr_type(offset_nu, 'offset_nu', float, list)
util.check_attr_type(init_phi, 'init_phi', float, list)
# Attr ---------------------------------------------------------
self.channels: int = channels
self.center_lambda: List[float] = util.make_list(
center_lambda, channels)
self.peak_power: List[float] = util.make_list(peak_power, channels)
self.total_power: List[float] = []
if (total_power is not None):
self.total_power = util.make_list(total_power, channels)
self.offset_nu: List[float] = util.make_list(offset_nu, channels)
self.init_phi: List[float] = util.make_list(init_phi, channels)
def __init__(self, kappa: Optional[List[float]] = None,
V: float = cst.V, a: float = cst.CORE_RADIUS,
d: float = cst.C2C_SPACING,
lambda_0: float = cst.DEF_LAMBDA,
n_0: float = cst.REF_INDEX,
omega: Optional[Array[float]] = None) -> None:
r"""
Parameters
----------
kappa :
The coupling coefficients. :math:`[km^{-1}]`
V :
The fiber parameter.
a :
The core radius. :math:`[\mu m]`
d :
The center to center spacing between the two cores.
:math:`[\mu m]`
lambda_0 :
The wavelength in the vacuum for the considered wave.
:math:`[nm]`
n_0 :
The refractive index outside of the two fiber cores.
omega :
The angular frequency. :math:`[ps^{-1}]`
"""
super().__init__(omega)
self._kappa: List[float]
if (kappa is None):
self._kappa = [Coupling.calc_kappa(V, a, d, lambda_0, n_0)]
else:
self._kappa = util.make_list(kappa)
def __init__(self, kappa: Optional[List[float]] = None,
V: float = cst.V, a: float = cst.CORE_RADIUS,
d: float = cst.C2C_SPACING,
n_0: float = cst.REF_INDEX) -> None:
r"""
Parameters
----------
kappa :
The coupling coefficients. :math:`[km^{-1}]`
V :
The fiber parameter.
a :
The core radius. :math:`[\mu m]`
d :
The center to center spacing between the two cores.
:math:`[\mu m]`
n_0 :
The refractive index outside of the two fiber cores.
"""
super().__init__()
self._kappa: List[float]
self._predict: Optional[Callable] = None
self._V: float = V
self._a: float = a
self._d: float = d
self._n_0: float = n_0
if (kappa is not None):
kappa = util.make_list(kappa) # make sure is list
self._kappa = np.asarray(kappa).reshape((1,-1))
else:
self._predict = self.get_kappa
def phase_shift(self, phase_shift: Union[List[float],
List[Callable]]) -> None:
phase_shift_ = util.make_list(phase_shift, 2, 0.0)
self._phasemod_1 = IdealPhaseMod(name='nocount',
phase_shift=phase_shift_[0])
self._phasemod_2 = IdealPhaseMod(name='nocount',
phase_shift=phase_shift_[1])
def __init__(self, name: str = default_name,
fields: Union[Field, List[Field]] = [], save: bool = False,
pre_call_code: str = '', post_call_code: str = '') -> None:
r"""
Parameters
----------
name :
The name of the component.
fields :
A field or a list of Field to launch into the simulation.
pre_call_code :
A string containing code which will be executed prior to
the call to the function :func:`__call__`. The two
parameters `input_ports` and `input_fields` are available.
post_call_code :
A string containing code which will be executed posterior to
the call to the function :func:`__call__`. The two
parameters `output_ports` and `output_fields` are available.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.ANY_OUT]
super().__init__(name, default_name, ports_type, save,
pre_call_code=pre_call_code,
post_call_code=post_call_code)
# Attr types check ---------------------------------------------
util.check_attr_type(fields, 'fields', Field, list)
# Attr ---------------------------------------------------------
self.fields: List[Field] = util.make_list(fields)
def __init__(self, kappa: Union[List[float], Callable],
order_taylor: int = 1, start_taylor: int = 0,
skip_taylor: List[int] = []) -> None:
r"""
Parameters
----------
kappa :
The coupling coefficients. :math:`[km^{-1}]` If a callable
is provided, variable must be angular frequency.
:math:`[ps^{-1}]`
order_taylor :
The order of kappa coefficients Taylor series expansion to
take into account. (will be set to the length of the kappa
array if one is provided)
start_taylor :
The order of the derivative from which to start the Taylor
series expansion.
skip_taylor :
The order_taylors of the derivative to not consider.
"""
super().__init__()
self._order_taylor: int = order_taylor
self._start_taylor: int = start_taylor
self._skip_taylor: List[int] = skip_taylor
# The coupling coefficient -------------------------------------
self._op: np.ndarray = np.array([])
self._kappa_op: np.ndarray = np.array([])
self._kappa: Callable
if (callable(kappa)):
self._kappa = kappa
else:
kappa_ = np.asarray(util.make_list(kappa))
self._kappa = lambda omega: util.hstack_like(kappa_, omega)
self._order_taylor = len(kappa_) - 1
def transfer_function(time: np.ndarray, v_pi: List[float],
v_bias: List[float],
v_mod: List[Union[float, Callable]]) -> np.ndarray:
v_pi = util.make_list(v_pi, 2)
v_bias = util.make_list(v_bias, 2)
v_mod = util.make_list(v_mod, 2)
v_mod_: List[Callable] = []
for v in v_mod:
if (callable(v)):
v_mod_.append(v)
else:
v_mod_.append(lambda t: v)
print(v_pi, v_bias, v_mod_)
phase_shift = [
lambda t: cst.PI * (v_bias[0] + v_mod_[0](t)) / v_pi[0],
lambda t: cst.PI * (v_bias[1] + v_mod_[1](t)) / v_pi[1]
]
tf = np.cos((phase_shift[0](time) - phase_shift[1](time)) / 2.)
return Field.temporal_power(tf)
def add_single_plot(plt_to_add, x_data, y_data, x_label, y_label, x_range,
y_range, plot_title, plot_label, plot_linestyle,
plot_color, opacity):
if (y_data.ndim == 1): # not multidimentsional
y_data = y_data.reshape((1, -1))
if (cst.AUTO_PAD_PLOT):
x_data = np.asarray(x_data)
x_data, y_data = util.auto_pad(x_data, y_data)
multi_channel = len(y_data) > 1
labels_on_plot = plot_label is not None
colors_on_plot = plot_color is not None
if (multi_channel):
plot_label = util.make_list(plot_label, len(y_data))
for i in range(len(y_data)):
if (multi_channel):
if (labels_on_plot):
plot_label_temp = plot_label[i] + " (ch.{})".format(i)
else:
plot_label_temp = "channel {}".format(i)
else:
plot_label_temp = plot_label
if (not colors_on_plot):
if (labels_on_plot or multi_channel):
plt_to_add.plot(x_data,
y_data[i],
ls=plot_linestyle,
label=plot_label_temp)
else:
plt_to_add.plot(x_data, y_data[i], ls=plot_linestyle)
plt_to_add.fill_between(x_data, y_data[i], alpha=opacity)
else:
if (labels_on_plot or multi_channel):
plt_to_add.plot(x_data,
y_data[i],
ls=plot_linestyle,
c=plot_color,
label=plot_label_temp)
else:
plt_to_add.plot(x_data,
y_data[i],
ls=plot_linestyle,
c=plot_color)
plt_to_add.fill_between(x_data,
y_data[i],
alpha=opacity,
facecolor=plot_color)
add_subplot_para(plt_to_add, x_label, y_label, x_range, y_range,
plot_title)
if (labels_on_plot or multi_channel):
plt_to_add.legend(loc="best")
def __init__(
self,
name: str = default_name,
ratios_ports: List[List[float]] = [[0.5, 0.5]],
max_nbr_pass: Optional[List[int]] = None,
save: bool = False,
) -> None:
"""
Parameters
----------
name :
The name of the component.
ratios_ports :
Each element of the list contain a list with the two
dividing percentages for the two output ports.
max_nbr_pass :
The maximum number of times a field can enter at the
corresponding index-number port.
save :
If True, the last wave to enter/exit a port will be saved.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.ANY_ALL for i in range(4)]
super().__init__(name,
default_name,
ports_type,
save,
max_nbr_pass=max_nbr_pass)
# Attr types check ---------------------------------------------
util.check_attr_type(ratios_ports, 'ratios_ports', list)
# Attr ---------------------------------------------------------
ratios_ports = util.make_list(ratios_ports, 4)
# N.B. name='nocount' to avoid inc. default name counter
self._divider_0 = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=ratios_ports[0])
self._divider_1 = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=ratios_ports[1])
self._divider_2 = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=ratios_ports[2])
self._divider_3 = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=ratios_ports[3])
def __init__(self,
coeff: Union[List[float], Callable],
order_taylor: int = 1,
start_taylor: int = 0,
skip_taylor: List[int] = [],
UNI_OMEGA: bool = False) -> None:
r"""
Parameters
----------
coeff :
The derivatives of the coefficients.
order_taylor :
The order of coeff coefficients Taylor series expansion to
take into account. (will be set to the length of the coeff
array if one is provided)
start_taylor :
The order of the derivative from which to start the Taylor
series expansion.
skip_taylor :
The order_taylors of the derivative to not consider.
UNI_OMEGA :
If True, consider only the center omega for computation.
Otherwise, considered omega discretization.
"""
super().__init__()
self._UNI_OMEGA = UNI_OMEGA
self._order_taylor: int = order_taylor
self._start_taylor: int = start_taylor
self._skip_taylor: List[int] = skip_taylor
# The attenuation constant -------------------------------------
self._op: np.ndarray = np.array([])
self._coeff_op: np.ndarray = np.array([])
self._coeff: Union[np.ndarray, Callable]
if (callable(coeff)):
self._coeff = coeff
else:
self._coeff_values = np.asarray(util.make_list(coeff))
#fct2pickle = lambda omega, order: util.hstack_like(coeff_, omega)
#self._coeff = CallableContainer(fct2pickle)
self._coeff = self._hstack_like
max_order_taylor: int = len(self._coeff_values) - 1
if (self._order_taylor > max_order_taylor):
self._order_taylor = max_order_taylor
warning_message = (
"The requested order is higher than the "
"provided coefficients, max order of {} will be set.".
format(max_order_taylor))
warnings.warn(warning_message, TaylorOrderWarning)
def add_2D_subplot(plt_to_add, x_data, y_data, x_label, y_label, x_range,
y_range, plot_title, line_label, line_style, line_width,
line_color, line_opacity):
"""Plot a 2D graph."""
x_data_ = np.array([x_data]) if (x_data.ndim < 2) else x_data
y_data_ = np.array([y_data]) if (y_data.ndim < 2) else y_data
x_data_ = util.modify_length_ndarray(x_data_, len(y_data_))
multi_channel = len(y_data_) > 1
labels_on_plot = line_label is not None
colors_on_plot = line_color is not None
if (multi_channel):
line_label = util.make_list(line_label, len(y_data_))
for i in range(len(y_data_)):
if (multi_channel):
if (labels_on_plot):
line_label_ = line_label[i] + " (ch.{})".format(i)
else:
line_label_ = "channel {}".format(i)
else:
line_label_ = line_label
if (not colors_on_plot):
line_color = linecolors[add_2D_subplot.counter % len(linecolors)]
add_2D_subplot.counter += 1
if (labels_on_plot or multi_channel):
plt_to_add.plot(x_data_[i],
y_data_[i],
ls=line_style,
lw=line_width,
c=line_color,
label=line_label_)
else:
plt_to_add.plot(x_data_[i],
y_data_[i],
ls=line_style,
lw=line_width,
c=line_color)
plt_to_add.fill_between(x_data_[i],
y_data_[i],
alpha=line_opacity,
facecolor=line_color)
add_subplot_para(plt_to_add,
x_label=x_label,
y_label=y_label,
x_range=x_range,
y_range=y_range,
plot_title=plot_title)
if (labels_on_plot or multi_channel):
plt_to_add.legend(loc="best")
def __init__(self,
beta: Optional[Union[List[float], Callable]] = None,
order: int = 2,
medium: str = cst.DEF_FIBER_MEDIUM,
start_taylor: int = 0,
skip_taylor: List[int] = []) -> None:
r"""
Parameters
----------
beta :
The derivatives of the propagation constant.
:math:`[km^{-1}, ps\cdot km^{-1}, ps^2\cdot km^{-1},
ps^3\cdot km^{-1}, \ldots]` If a callable is provided,
variable must be angular frequency. :math:`[ps^{-1}]`
order :
The order of beta coefficients to take into account. (will
be ignored if beta values are provided - no file)
medium :
The medium in which the dispersion is considered.
start_taylor :
The order of the derivative from which to start the Taylor
Series expansion.
"""
super().__init__()
self._order: int = order
self._medium: str = medium
self._start_taylor: int = start_taylor
self._skip_taylor: List[int] = skip_taylor
self._predict: Optional[Callable] = None
self._beta: Array[float]
self._class_n: Optional[Callable] = None
if (beta is not None):
if (callable(beta)):
self._predict = beta
else:
beta = util.make_list(beta) # make sure is list
self._beta = np.asarray(beta).reshape((1, -1))
self._order = self._beta.shape[1] - 1
else:
self._predict = self.calc_beta_coeffs
self._class_n = Sellmeier(medium)
def __init__(self,
alpha: Optional[Union[List[float], Callable]] = None,
order: int = 1,
medium: str = cst.DEF_FIBER_MEDIUM,
start_taylor: int = 0,
skip_taylor: List[int] = []) -> None:
r"""
Parameters
----------
alpha :
The derivatives of the attenuation coefficients.
:math:`[km^{-1}, ps\cdot km^{-1}, ps^2\cdot km^{-1},
ps^3\cdot km^{-1}, \ldots]` If a callable is provided,
variable must be angular frequency. :math:`[ps^{-1}]`
order :
The order of alpha coefficients to take into account. (will
be ignored if alpha values are provided - no file)
medium :
The medium in which the attenuation is considered.
start_taylor :
The order of the derivative from which to start the Taylor
Series expansion.
"""
super().__init__()
self._order: int = order
self._medium: str = medium
self._start_taylor: int = start_taylor
self._skip_taylor: List[int] = skip_taylor
self._predict: Optional[Callable] = None
self._alpha: Array[float]
self._future_eq_to_calc_alpha: bool = False
if (alpha is not None):
if (callable(alpha)):
self._predict = alpha
else: # alpha is a array of float
alpha = util.make_list(alpha) # make sure is list
self._alpha = np.asarray(alpha).reshape((1, -1))
self._order = self._alpha.shape[1] - 1
else:
self._future_eq_to_calc_alpha = True
def __init__(self,
re: REFiber,
alpha: Optional[List[float]] = None) -> None:
r"""
Parameters
----------
re : REFiber
The rate equations object.
alpha :
The attenuation coefficient. :math:`[km^{-1}]`
"""
super().__init__()
self._re: REFiber = re
self._alpha: Array[float]
if (alpha is None):
self._alpha = np.zeros(0.0)
else:
alpha = util.make_list(alpha)
self._alpha = np.asarray(alpha)
self._factor: Array[float]
def add_2D_subplot(plt_to_add, x_data, y_data, x_label, y_label, x_range,
y_range, plot_title, plot_label, plot_linestyle,
plot_color, opacity):
x_data_temp = np.asarray(x_data)
x_data = np.array([])
y_data_temp = np.asarray(y_data)
y_data = np.array([])
x_data, y_data = util.auto_pad(x_data_temp, y_data_temp)
multi_channel = len(y_data) > 1
labels_on_plot = plot_label is not None
colors_on_plot = plot_color is not None
if (multi_channel):
plot_label = util.make_list(plot_label, len(y_data))
print('in plot', x_data.shape, y_data.shape)
print(x_data)
for i in range(len(y_data)):
if (multi_channel):
if (labels_on_plot):
plot_label_temp = plot_label[i] + " (ch.{})".format(i)
else:
plot_label_temp = "channel {}".format(i)
else:
plot_label_temp = plot_label
if (not colors_on_plot):
plot_color = linecolors[add_2D_subplot.counter]
add_2D_subplot.counter += 1
if (labels_on_plot or multi_channel):
plt_to_add.plot(x_data , y_data[i], ls=plot_linestyle,
c=plot_color, label=plot_label_temp)
else:
plt_to_add.plot(x_data , y_data[i], ls=plot_linestyle,
c=plot_color)
plt_to_add.fill_between(x_data , y_data[i], alpha=opacity,
facecolor=plot_color)
add_subplot_para(plt_to_add, x_label=x_label, y_label=y_label,
x_range=x_range, y_range=y_range,
plot_title=plot_title)
if (labels_on_plot or multi_channel):
plt_to_add.legend(loc = "best")
def __init__(self,
name: str = default_name,
channels: int = 1,
center_lambda: List[float] = [cst.DEF_LAMBDA],
position: List[float] = [0.5],
width: List[float] = [10.0],
fwhm: Optional[List[float]] = None,
peak_power: List[float] = [1e-3],
rep_freq: List[float] = [0.0],
offset_nu: List[float] = [0.0],
chirp: List[float] = [0.0],
init_phi: List[float] = [0.0],
noise: Optional[np.ndarray] = None,
field_name: str = '',
save: bool = False,
pre_call_code: str = '',
post_call_code: str = '') -> None:
r"""
Parameters
----------
name :
The name of the component.
channels :
The number of channels in the field.
center_lambda :
The center wavelength of the channels. :math:`[nm]`
position :
Relative position of the pulses in the time window.
:math:`\in [0,1]`
width :
Half width of the pulse. :math:`[ps]`
fwhm :
Full band width at half maximum. :math:`[ps]` If fwhm is
provided, the width will be ignored. If fwhm is not provided
or set to None, will use the width.
peak_power :
Peak power of the pulses. :math:`[W]`
rep_freq :
The repetition frequency of the pulse in the time window.
:math:`[THz]`
offset_nu :
The offset frequency. :math:`[THz]`
chirp :
The chirp parameter for chirped pulses.
init_phi :
The initial phase of the pulses.
noise :
The initial noise along the pulses.
field_name :
The name of the field.
save :
If True, the last wave to enter/exit a port will be saved.
pre_call_code :
A string containing code which will be executed prior to
the call to the function :func:`__call__`. The two
parameters `input_ports` and `input_fields` are available.
post_call_code :
A string containing code which will be executed posterior to
the call to the function :func:`__call__`. The two
parameters `output_ports` and `output_fields` are available.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.OPTI_OUT]
super().__init__(name,
default_name,
ports_type,
save,
pre_call_code=pre_call_code,
post_call_code=post_call_code)
# Attr types check ---------------------------------------------
util.check_attr_type(channels, 'channels', int)
util.check_attr_type(center_lambda, 'center_lambda', float, list)
util.check_attr_type(position, 'position', float, list)
util.check_attr_type(width, 'width', float, list)
util.check_attr_type(fwhm, 'fwhm', float, list, None)
util.check_attr_type(peak_power, 'peak_power', float, list)
util.check_attr_type(rep_freq, 'rep_freq', float, list)
util.check_attr_type(offset_nu, 'offset_nu', float, list)
util.check_attr_type(chirp, 'chirp', float, list)
util.check_attr_type(init_phi, 'init_phi', float, list)
util.check_attr_type(noise, 'noise', None, np.ndarray)
util.check_attr_type(field_name, 'field_name', str)
# Attr ---------------------------------------------------------
self.channels: int = channels
self.center_lambda: List[float] = util.make_list(
center_lambda, channels)
self.position: List[float] = util.make_list(position, channels)
self.width: List[float] = util.make_list(width, channels)
self._fwhm: Optional[List[float]]
self.fwhm = fwhm
self.peak_power: List[float] = util.make_list(peak_power, channels)
self.rep_freq: List[float] = util.make_list(rep_freq, channels)
self.offset_nu: List[float] = util.make_list(offset_nu, channels)
self.chirp: List[float] = util.make_list(chirp, channels)
self.init_phi: List[float] = util.make_list(init_phi, channels)
self.noise: Optional[np.ndarray] = noise
self.field_name: str = field_name
def __init__(self,
name: str = default_name,
phase_shift: Union[List[float], List[Callable]] = [0.0, 0.0],
loss: float = 0.0,
extinction: Optional[float] = None,
v_pi: Optional[List[float]] = None,
v_bias: Optional[List[float]] = None,
v_mod: Optional[List[Callable]] = None,
save: bool = False,
max_nbr_pass: Optional[List[int]] = None,
pre_call_code: str = '',
post_call_code: str = '') -> None:
r"""
Parameters
----------
name :
The name of the component.
phase_shift :
The phase difference induced between the two arms of the MZ.
Can be a list of callable with time variable. :math:`[ps]`
(will be ignored if (v_pi and v_bias) or (v_pi and v_mod)
are provided)
loss :
The loss induced by the MZ. :math:`[dB]`
extinction :
The extinction ratio. :math:`[dB]`
v_pi :
The half-wave voltage. :math:`[V]`
v_bias :
The bias voltage. :math:`[V]`
v_mod :
The modulation voltage :math:`[V]`. Must be a callable with
time variable. :math:`[ps]`
save :
If True, the last wave to enter/exit a port will be saved.
max_nbr_pass :
No fields will be propagated if the number of
fields which passed through a specific port exceed the
specified maximum number of pass for this port.
pre_call_code :
A string containing code which will be executed prior to
the call to the function :func:`__call__`. The two
parameters `input_ports` and `input_fields` are available.
post_call_code :
A string containing code which will be executed posterior to
the call to the function :func:`__call__`. The two
parameters `output_ports` and `output_fields` are available.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.ANY_ALL, cst.ANY_ALL]
super().__init__(name,
default_name,
ports_type,
save,
max_nbr_pass=max_nbr_pass,
pre_call_code=pre_call_code,
post_call_code=post_call_code)
# Attr types check ---------------------------------------------
util.check_attr_type(phase_shift, 'phase_shift', float, Callable, list)
util.check_attr_type(loss, 'loss', float)
util.check_attr_type(extinction, 'extinction', None, float)
util.check_attr_type(v_pi, 'v_pi', None, float, list)
util.check_attr_type(v_bias, 'v_bias', None, float, list)
util.check_attr_type(v_mod, 'v_mod', None, Callable, list)
# Attr ---------------------------------------------------------
self._v_pi: Optional[List[float]]
self._v_pi = v_pi if v_pi is None else util.make_list(v_pi, 2)
self._v_bias: Optional[List[float]]
self._v_bias = v_bias if v_bias is None else util.make_list(v_bias, 2)
self._v_mod: Optional[List[Callable]]
self._v_mod = v_mod if v_mod is None else util.make_list(v_mod, 2)
if (v_pi is not None and (v_bias is not None or v_mod is not None)):
self._update_phase_shift()
else:
self.phase_shift = phase_shift
self.loss = loss
self._extinction: Optional[float]
self.extinction = extinction
self._divider = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=[0.5, 0.5])
# Policy -------------------------------------------------------
self.add_port_policy(([0], [1], True))
def fwhm(self, fwhm: Optional[List[float]]) -> None:
if (fwhm is None):
self._fwhm = None
else:
self._fwhm = util.make_list(fwhm, self.channels)
def __init__(self,
solvers: List[AbstractSolver],
noise_solvers: List[Optional[AbstractSolver]] = [None],
length: float = 1.0,
steps: List[int] = [100],
step_method: List[str] = [FIXED],
solver_order: str = FOLLOWING,
stepper_method: List[str] = [FORWARD],
solver_sequence: Optional[List[int]] = None,
boundary_cond: Optional[AbstractBoundaryConditions] = None,
conv_checker: Optional[AbstractConvergenceChecker] = None,
save_all: bool = False) -> None:
r"""
Parameters
----------
solvers :
The solvers used to solve the equations.
nlse_solvers :
The solvers used to solve the noise equations.
length :
The length over which the equation is considered.
:math:`[km]`
steps :
The number of steps used for the computation.
step_method :
The method used to update the step size.
solver_order :
The order type in which the methods will be computed. (will
be ignored if solver_sequence is provided)
stepper_method :
The method used to converge to solution.
solver_sequence :
The sequence in which the methods will be computed.
boundary_cond : AbstractBoundaryConditions
The boundaries conditions.
conv_checker : AbstractConvergenceChecker
The convergence checker.
save_all :
If True, will save all channels of all fields at each space
step during the equation resolution. The number of step
recorded depends on the :attr:`memory_storage` attribute
of :class:`layout.Layout`.
"""
# Attr types check ---------------------------------------------
util.check_attr_type(length, 'length', int, float)
util.check_attr_type(steps, 'steps', int, list)
util.check_attr_type(step_method, 'step_method', str, list)
util.check_attr_type(solver_order, 'solver_order', str)
util.check_attr_type(stepper_method, 'stepper_method', str, list)
util.check_attr_type(boundary_cond, 'boundary_cond',
AbstractBoundaryConditions, None)
util.check_attr_type(conv_checker, 'conv_checker',
AbstractConvergenceChecker, None)
util.check_attr_type(save_all, 'save_all', bool)
# Attr ---------------------------------------------------------
self._solvers: List[AbstractSolver] = util.make_list(solvers)
self._nbr_solvers: int = len(self._solvers)
self._noise_solvers: List[Optional[AbstractSolver]]
self._noise_solvers = util.make_list(noise_solvers, self._nbr_solvers,
None)
self._eqs: List[SOLVER_CALLABLE_TYPE]
self._eqs = [self._solvers[i].f for i in range(self._nbr_solvers)]
self._steps: List[int] = util.make_list(steps, self._nbr_solvers)
self._length: float = length
# Stepper method
stepper_method = util.make_list(stepper_method, self._nbr_solvers)
self._stepper_method_str: List[str] = []
self._stepper_method: List[STEPPER_METHOD_TYPE] = []
for i in range(len(stepper_method)):
self._stepper_method_str.append(
util.check_attr_value(stepper_method[i].lower(),
STEPPER_METHODS, FORWARD))
self._stepper_method.append(
getattr(
self,
"_{}_method".format(self._stepper_method_str[i].lower())))
# Step method
step_method = util.make_list(step_method, self._nbr_solvers)
self._step_method_str: List[str] = []
self._step_method: List[STEP_METHOD_TYPE] = []
for i in range(len(step_method)):
self._step_method_str.append(
util.check_attr_value(step_method[i].lower(), STEP_METHODS,
FIXED))
self._step_method.append(
getattr(self,
"_{}_step".format(self._step_method_str[i].lower())))
# Solver order
solver_order = util.check_attr_value(solver_order.lower(),
SOLVER_ORDERS, FOLLOWING)
if (solver_sequence is None):
if (solver_order.lower() == ALTERNATING):
solver_sequence = [0 for i in range(self._nbr_solvers)]
if (solver_order.lower() == FOLLOWING):
solver_sequence = [i for i in range(self._nbr_solvers)]
else:
solver_sequence = solver_sequence
self._solver_seq_ids: List[List[int]] =\
self._get_seq_ids(solver_sequence)
self.save_all: bool = save_all
self._avail_memory: float = 0.
self._save_step: int = 0
self._enough_space: bool = False
self._storage: Storage = Storage()
self._channels: np.ndarray = np.array([])
self._noises: np.ndarray = np.array([])
self._space: np.ndarray = np.array([])
self._conv_checker: Optional[AbstractConvergenceChecker] = conv_checker
self._boundary_cond: Optional[
AbstractBoundaryConditions] = boundary_cond
def __init__(self,
eqs: List[AbstractEquation],
method: List[str] = [cst.DEFAULT_SOLVER],
length: float = 1.0,
steps: List[int] = [100],
step_method: List[str] = [FIXED],
solver_order: str = FOLLOWING,
stepper_method: List[str] = [FORWARD],
solver_sequence: Optional[List[int]] = None,
error: float = 0.01,
save_all: bool = False) -> None:
r"""
Parameters
----------
eqs : list of AbstractEquation
The equation to solve.
method :
The method used to solve the equation.
length :
The length over which the equation is considered.
:math:`[km]`
steps :
The number of steps used for the computation.
step_method :
The method used to update the step size.
solver_order :
The order type in which the methods will be computed. (will
be ignored if solver_sequence is provided)
stepper_method :
The method used to converge to solution.
solver_sequence :
The sequence in which the methods will be computed.
error :
The error for convergence criterion of stepper resolution.
save_all :
If True, will save all channels of all fields at each space
step during the equation resolution. The number of step
recorded depends on the :attr:`memory_storage` attribute
of :class:`layout.Layout`.
"""
self._eqs: List[AbstractEquation] = util.make_list(eqs)
self._nbr_solvers: int = len(self._eqs)
self._methods: List[str] = util.make_list(method, self._nbr_solvers)
self._steps: List[int] = util.make_list(steps, self._nbr_solvers)
self._length: float = length
self._solvers: List[Solver] = [
Solver(self._eqs[i], self._methods[i])
for i in range(self._nbr_solvers)
]
stepper_method = util.make_list(stepper_method, self._nbr_solvers)
self._stepper_method_str = stepper_method
self._stepper_method = [
getattr(self, "_{}_method".format(stepper_method[i].lower()))
for i in range(self._nbr_solvers)
]
step_method = util.make_list(step_method, self._nbr_solvers)
self._step_method_str = step_method
self._step_method = [
getattr(self, "_{}_step".format(step_method[i].lower()))
for i in range(self._nbr_solvers)
]
self._start_shooting_forward: bool = True
if (solver_sequence is None):
if (solver_order.lower() == ALTERNATING):
solver_sequence = [0 for i in range(self._nbr_solvers)]
if (solver_order.lower() == FOLLOWING):
solver_sequence = [i for i in range(self._nbr_solvers)]
else:
solver_sequence = solver_sequence
self._solver_seq_ids: List[List[int]] =\
self._get_seq_ids(solver_sequence)
self._error: float = error
self.save_all: bool = save_all
self._storage: Storage = Storage()
def open(self, domain: Domain, *fields: List[Field]) -> None:
super().open(domain, *fields)
# Initialize angular frequency ---------------------------------
self._samples = len(self._omega)
self._omega_s = self._in_eq_waves(self._omega_all, 0)
self._omega_p = self._in_eq_waves(self._omega_all, 1)
# Iniate array for center omegas data --------------------------
self._center_omega_s = self._in_eq_waves(self._center_omega, 0)
self._center_omega_p = self._in_eq_waves(self._center_omega, 1)
# Signal channel width ----------------------------------------
signal_width = np.array(
util.make_list(self._signal_width, len(self._center_omega_s)))
self._width_omega_s = (1.0 / signal_width) * 2.0 * cst.PI
# Initiate the variables array - power and pop. density --------
self._shape_step_s = (len(self._center_omega_s), self._samples)
self._power_s_f = np.zeros((1, ) + self._shape_step_s)
self._power_s_b = np.zeros((1, ) + self._shape_step_s)
self._power_s_ref = np.zeros(self._shape_step_s)
self._power_ase_f = np.zeros((1, ) + self._shape_step_s)
self._power_ase_b = np.zeros((1, ) + self._shape_step_s)
self._shape_step_p = (len(self._center_omega_p), self._samples)
self._power_p_f = np.zeros((1, ) + self._shape_step_p)
self._power_p_b = np.zeros((1, ) + self._shape_step_p)
self._power_p_ref = np.zeros(self._shape_step_p)
self._N_1 = np.zeros(1)
# Initiate refractive index ------------------------------------
if (self._calc_n_core):
self._n_0_s = self._calc_n_core.n(self._omega_s)
self._n_0_p = self._calc_n_core.n(self._omega_p)
else:
self._n_0_s = np.ones(self._shape_step_s) * self._n_0_s_value
self._n_0_p = np.ones(self._shape_step_p) * self._n_0_p_value
if (self._calc_n_clad):
NA_s = np.ones(self._shape_step_s) * self._NA_value_s
NA_p = np.ones(self._shape_step_p) * self._NA_value_p
self._n_clad_s = self._calc_n_clad(NA_s, self._n_0_s)
self._n_clad_p = self._calc_n_clad(NA_p, self._n_0_p)
else:
self._n_clad_s = np.ones(self._shape_step_s) * self._n_clad_s_value
self._n_clad_p = np.ones(self._shape_step_p) * self._n_clad_p_value
self._n_tot_s = np.zeros(self._shape_step_s)
self._n_tot_p = np.zeros(self._shape_step_p)
# Initiate cross sections for each frequency -------------------
self._A_eff_s = np.zeros(self._shape_step_s)
self._Gamma_s = np.zeros(self._shape_step_s)
for i in range(len(self._center_omega_s)):
NA = NumericalAperture.calc_NA(self._n_0_s[i], self._n_clad_s[i])
self._A_eff_s[i] = self._eff_area_s(self._omega_s[i], NA)
self._Gamma_s[i] = self._overlap_s(self._A_eff_s[i])
self._A_eff_p = np.zeros(self._shape_step_p)
self._Gamma_p = np.zeros(self._shape_step_p)
for i in range(len(self._center_omega_p)):
NA = NumericalAperture.calc_NA(self._n_0_p[i], self._n_clad_p[i])
self._A_eff_p[i] = self._eff_area_s(self._omega_p[i], NA)
self._Gamma_p[i] = self._overlap_p(self._A_eff_p[i])
# Initiate cross sections for each frequency -------------------
if (self._absorp is not None):
self._sigma_a_s = np.zeros(self._shape_step_s)
self._sigma_a_p = np.zeros(self._shape_step_p)
for i in range(len(self._center_omega_s)):
self._sigma_a_s[i] =\
self._absorp.get_cross_section(self._omega_s[i])
for i in range(len(self._center_omega_p)):
self._sigma_a_p[i] =\
self._absorp.get_cross_section(self._omega_p[i])
else:
self._sigma_a_s = np.ones(
self._shape_step_s) * self._sigma_a_s_value
self._sigma_a_p = np.ones(
self._shape_step_p) * self._sigma_a_p_value
if (self._stimu is not None):
self._sigma_e_s = np.zeros(self._shape_step_s)
self._sigma_e_p = np.zeros(self._shape_step_p)
for i in range(len(self._center_omega_s)):
self._sigma_e_s[i] =\
self._stimu.get_cross_section(self._omega_s[i])
for i in range(len(self._center_omega_p)):
self._sigma_e_p[i] =\
self._stimu.get_cross_section(self._omega_p[i])
else:
if (self._sigma_e_mccumber):
self._sigma_e_s = np.zeros(self._shape_step_s)
self._sigma_e_p = np.zeros(self._shape_step_p)
# must initiate here
else:
self._sigma_e_s = (np.ones(self._shape_step_s) *
self._sigma_e_s_value)
self._sigma_e_p = (np.ones(self._shape_step_p) *
self._sigma_e_p_value)
# Initiate counter ---------------------------------------------
self._iter = -1 # -1 bcs increment in the first init. cond.
self._call_counter = 0
self._step = 0
self._forward = True
def __init__(self,
name: str = default_name,
phase_shift: Union[List[float], List[Callable]] = [0.0, 0.0],
loss: float = 0.0,
ext_ratio: float = 0.0,
v_pi: Optional[List[float]] = None,
v_bias: Optional[List[float]] = None,
v_mod: Optional[List[Callable]] = None,
save: bool = False,
max_nbr_pass: Optional[List[int]] = None) -> None:
r"""
Parameters
----------
name :
The name of the component.
phase_shift :
The phase difference induced between the two arms of the MZ.
Can be a list of callable with time variable. :math:`[ps]`
(will be ignored if (v_pi and v_bias) or (v_pi and v_mod)
are provided)
loss :
The loss induced by the MZ. :math:`[dB]`
ext_ratio :
The extinction ratio.
v_pi :
The half-wave voltage. :math:`[V]`
v_bias :
The bias voltage. :math:`[V]`
v_mod :
The modulation voltage :math:`[V]`. Must be a callable with
time variable. :math:`[ps]`
save :
If True, the last wave to enter/exit a port will be saved.
max_nbr_pass :
No fields will be propagated if the number of
fields which passed through a specific port exceed the
specified maximum number of pass for this port.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.ANY_ALL, cst.ANY_ALL]
super().__init__(name,
default_name,
ports_type,
save,
max_nbr_pass=max_nbr_pass)
# Attr types check ---------------------------------------------
util.check_attr_type(phase_shift, 'phase_shift', float, Callable, list)
util.check_attr_type(loss, 'loss', float)
util.check_attr_type(ext_ratio, 'ext_ratio', float)
util.check_attr_type(v_pi, 'v_pi', None, float, list)
util.check_attr_type(v_bias, 'v_bias', None, float, list)
util.check_attr_type(v_mod, 'v_mod', None, Callable, list)
# Attr ---------------------------------------------------------
if (v_pi is not None and (v_bias is not None or v_mod is not None)):
pi_ = util.make_list(v_pi, 2)
bias_ = util.make_list(v_bias, 2) if v_bias is not None\
else [0.0, 0.0]
mod_ = util.make_list(v_mod, 2) if v_mod is not None\
else [lambda t: 0.0, lambda t: 0.0]
phase_shift_ = [
lambda t: cst.PI * (bias_[0] + mod_[0](t)) / pi_[0],
lambda t: cst.PI * (bias_[1] + mod_[1](t)) / pi_[1]
]
else:
phase_shift_ = util.make_list(phase_shift, 2, 0.0)
# N.B. name='nocount' to avoid inc. default name counter
self._divider = IdealDivider(name='nocount',
arms=2,
divide=True,
ratios=[0.5, 0.5])
self._combiner = IdealCombiner(name='nocount',
arms=2,
combine=True,
ratios=[0.5, 0.5])
self._phasemod_1 = IdealPhaseMod(name='nocount',
phase_shift=phase_shift_[0])
self._phasemod_2 = IdealPhaseMod(name='nocount',
phase_shift=phase_shift_[1])
self._amp = IdealAmplifier(name='nocount', gain=-loss)
# Policy -------------------------------------------------------
self.add_port_policy(([0], [1], True))
def __init__(self,
name: str = default_name,
channels: int = 1,
center_lambda: List[float] = [cst.DEF_LAMBDA],
position: List[float] = [0.5],
width: List[float] = [10.0],
peak_power: List[float] = [1e-3],
bit_rate: List[float] = [0.0],
offset_nu: List[float] = [0.0],
chirp: List[float] = [0.0],
init_phi: List[float] = [0.0],
save: bool = False) -> None:
r"""
Parameters
----------
name :
The name of the component.
channels :
The number of channels in the field.
center_lambda :
The center wavelength of the channels. :math:`[nm]`
position :
Relative position of the pulses in the time window.
:math:`\in [0,1]`
width :
Half width of the pulse. :math:`[ps]`
peak_power :
Peak power of the pulses. :math:`[W]`
bit_rate :
Bit rate (repetition rate) of the pulse in the time window.
:math:`[THz]`
offset_nu :
The offset frequency. :math:`[THz]`
chirp :
The chirp parameter for chirped pulses.
init_phi :
The initial phase of the pulses.
save :
If True, the last wave to enter/exit a port will be saved.
"""
# Parent constructor -------------------------------------------
ports_type = [cst.OPTI_OUT]
super().__init__(name, default_name, ports_type, save)
# Attr types check ---------------------------------------------
util.check_attr_type(channels, 'channels', int)
util.check_attr_type(center_lambda, 'center_lambda', float, list)
util.check_attr_type(position, 'position', float, list)
util.check_attr_type(width, 'width', float, list)
util.check_attr_type(peak_power, 'peak_power', float, list)
util.check_attr_type(bit_rate, 'bit_rate', float, list)
util.check_attr_type(offset_nu, 'offset_nu', float, list)
util.check_attr_type(chirp, 'chirp', float, list)
util.check_attr_type(init_phi, 'init_phi', float, list)
# Attr ---------------------------------------------------------
self.channels: int = channels
self.center_lambda: List[float] = util.make_list(
center_lambda, channels)
self.position: List[float] = util.make_list(position, channels)
self.width: List[float] = util.make_list(width, channels)
self.peak_power: List[float] = util.make_list(peak_power, channels)
self.bit_rate: List[float] = util.make_list(bit_rate, channels)
self.offset_nu: List[float] = util.make_list(offset_nu, channels)
self.chirp: List[float] = util.make_list(chirp, channels)
self.init_phi: List[float] = util.make_list(init_phi, channels)
def __init__(self, nbr_fibers: int, beta: Optional[Union[List[List[float]],
Callable]],
kappa: Optional[List[List[List[float]]]],
sigma_cross: List[List[float]],
c2c_spacing: List[List[float]], core_radius: List[float],
V: List[float], n_0: List[float], ASYM: bool, COUP: bool,
XPM: bool, medium: str) -> None:
r"""
Parameters
----------
nbr_fibers :
The number of fibers in the coupler.
beta :
The derivatives of the propagation constant.
:math:`[km^{-1}, ps\cdot km^{-1}, ps^2\cdot km^{-1},
ps^3\cdot km^{-1}, \ldots]`
kappa :
The coupling coefficients. :math:`[km^{-1}]`
sigma_cross :
Positive term multiplying the XPM term of the NLSE inbetween
the fibers.
c2c_spacing :
The center to center distance between two cores.
:math:`[\mu m]`
core_radius :
The core radius. :math:`[\mu m]`
V :
The fiber parameter.
n_0 :
The refractive index outside of the waveguides.
ASYM :
If True, trigger the asymmetry effects between cores.
COUP :
If True, trigger the coupling effects between cores.
XPM :
If True, trigger the cross-phase modulation.
medium :
The main medium of the fiber.
"""
super().__init__(nbr_fibers)
if (beta is not None):
beta_: Union[List[List[float]], List[List[Callable]]] =\
util.make_matrix(beta, nbr_fibers, nbr_fibers)
sigma_cross = util.make_matrix(sigma_cross,
nbr_fibers,
nbr_fibers,
sym=True)
if (kappa is not None):
kappa = util.make_tensor(kappa, nbr_fibers, nbr_fibers, 0)
V = util.make_list(V, nbr_fibers)
n_0 = util.make_list(n_0, nbr_fibers)
c2c_spacing = util.make_matrix(c2c_spacing,
nbr_fibers,
nbr_fibers,
sym=True)
core_radius = util.make_list(core_radius, nbr_fibers)
for i in range(nbr_fibers):
for j in range(nbr_fibers):
if (i != j):
if (ASYM):
if (beta is not None):
self._effects_lin[i][j].append(
Asymmetry(beta_01=beta_[i][0],
beta_02=beta_[j][0]))
else:
self._effects_lin[i][j].append(
Asymmetry(medium=medium))
if (COUP):
if (kappa is not None):
self._effects_all[i][j].append(
Coupling(kappa[i][j]))
else:
same_V = sum(V) / len(V) == V[0]
same_a = (sum(core_radius) /
len(core_radius) == core_radius[0])
same_n_0 = sum(n_0) / len(n_0) == n_0[0]
if (not (same_V and same_a and same_n_0)):
util.warning_terminal(
"Automatic calculation "
"of coupling coefficient assumes same "
"V, core_radius and n_0 for now, "
"different ones provided, might lead to "
"unrealistic results.")
self._effects_all[i][j].append(
Coupling(V=V[i],
a=core_radius[i],
d=c2c_spacing[i][j],
n_0=n_0[i]))
if (XPM):
self._effects_non_lin[i][j].append(
Kerr(SPM=False,
XPM=True,
FWM=False,
sigma=sigma_cross[i][j]))
def __init__(self, re: REFiber, alpha: Optional[Union[List[float],
Callable]],
alpha_order: int, beta: Optional[Union[List[float],
Callable]],
beta_order: int, gamma: Optional[Union[float, Callable]],
gain_order: int, R_0: float, R_L: float,
nl_index: Optional[Union[float, Callable]], ATT: bool,
DISP: bool, GS: bool, medium: str, dopant: str) -> None:
r"""
Parameters
----------
re :
A fiber rate equation object.
alpha :
The derivatives of the attenuation coefficients.
:math:`[km^{-1}, ps\cdot km^{-1}, ps^2\cdot km^{-1},
ps^3\cdot km^{-1}, \ldots]` If a callable is provided,
variable must be angular frequency. :math:`[ps^{-1}]`
alpha_order :
The order of alpha coefficients to take into account. (will
be ignored if alpha values are provided - no file)
beta :
The derivatives of the propagation constant.
:math:`[km^{-1}, ps\cdot km^{-1}, ps^2\cdot km^{-1},
ps^3\cdot km^{-1}, \ldots]` If a callable is provided,
variable must be angular frequency. :math:`[ps^{-1}]`
beta_order :
The order of beta coefficients to take into account. (will
be ignored if beta values are provided - no file)
gamma :
The non linear coefficient.
:math:`[rad\cdot W^{-1}\cdot km^{-1}]` If a callable is
provided, variable must be angular frequency.
:math:`[ps^{-1}]`
gain_order :
The order of the gain coefficients to take into account.
(from the Rate Equations resolution)
R_0 :
The reflectivity at the fiber start.
R_L :
The reflectivity at the fiber end.
nl_index :
The non linear index. Used to calculate the non linear
parameter. :math:`[m^2\cdot W^{-1}]`
ATT :
If True, trigger the attenuation.
DISP :
If True, trigger the dispersion.
GS :
If True, trigger the gain saturation.
medium :
The main medium of the fiber amplifier.
dopant :
The doped medium of the fiber amplifier.
"""
# keep all methods from NLSE but bypass constructor
AbstractEquation.__init__(self) # grand parent constructor
self._medium: str = medium
self._dopant: str = dopant
self._re: REFiber = re
self._R_L: float = R_L
self._R_0: float = R_0
self._delay_time: Array[float]
self._coprop: bool
# Effects ------------------------------------------------------
self._att_ind: int = -1
self._disp_ind: int = -1
self._gs_ind: int = -1
self._gain_ind: int = -1
self._gain_order: int = gain_order
self._beta_order: int = beta_order
if (ATT):
self._effects_lin.append(Attenuation(alpha, alpha_order))
self._att_ind = len(self._effects_lin) - 1
if (DISP):
self._effects_lin.append(Dispersion(beta, beta_order))
self._disp_ind = len(self._effects_lin) - 1
if (GS):
start_taylor_gain = 1
self._effects_lin.append(GainSaturation(re, [0.0]))
self._gs_ind = len(self._effects_lin) - 1
else:
start_taylor_gain = 0
alpha_temp = [0.0 for i in range(self._gain_order + 1)]
self._effects_lin.append(
Attenuation(alpha_temp, start_taylor=start_taylor_gain))
self._gain_ind = len(self._effects_lin) - 1
# Gamma --------------------------------------------------------
self._nl_index: Union[float, Callable] = NLIndex(medium=medium) if\
(nl_index is None) else nl_index
self._gamma: Array[float]
self._predict_gamma: Optional[Callable] = None
self._custom_gamma: bool = False
if (gamma is not None):
if (callable(gamma)):
self._custom_gamma = True
self._predict_gamma = gamma
else:
self._gamma = np.asarray(util.make_list(gamma))
else:
self._predict_gamma = NLCoefficient.calc_nl_coefficient
def v_pi(self, v_pi: Optional[List[float]]) -> None:
self._v_pi = util.make_list(v_pi, 2)
self._update_phase_shift()
def __init__(self,
sigma_a: Optional[Union[List[float], Callable]] = None,
sigma_e: Optional[Union[List[float], Callable]] = None,
n_core: Optional[Union[float, List[float]]] = None,
n_clad: Optional[Union[float, List[float]]] = None,
NA: Optional[Union[float, List[float]]] = None,
temperature: float = 293.15,
tau_meta: float = cst.TAU_META,
N_T: float = cst.N_T,
core_radius: float = cst.CORE_RADIUS,
clad_radius: float = cst.CLAD_RADIUS,
area_doped: Optional[float] = None,
eta_s: float = cst.ETA_SIGNAL,
eta_p: float = cst.ETA_PUMP,
R_0: float = cst.R_0,
R_L: float = cst.R_L,
signal_width: List[float] = [1.0],
medium: str = cst.DEF_FIBER_MEDIUM,
dopant: str = cst.DEF_FIBER_DOPANT,
step_update: bool = False) -> None:
r"""
Parameters
----------
sigma_a :
The absorption cross sections of the signal and the pump
(1<=len(sigma_a)<=2). :math:`[nm^2]` If a callable is
provided, varibale must be wavelength. :math:`[nm]`
sigma_e :
The emission cross sections of the signal and the pump
(1<=len(sigma_a)<=2). :math:`[nm^2]` If a callable is
provided, varibale must be wavelength. :math:`[nm]`
n_core :
The refractive index of the core. If the medium is not
recognised by Optcom, at least two elements should be
provided out of those three: n_core, n_clad, NA.
n_clad :
The refractive index of the cladding. If the medium is not
recognised by Optcom, at least two elements should be
provided out of those three: n_core, n_clad, NA.
NA :
The numerical aperture. If the medium is not
recognised by Optcom, at least two elements should be
provided out of those three: n_core, n_clad, NA.
temperature :
The temperature of the medium. :math:`[K]`
tau_meta :
The metastable level lifetime. :math:`[\mu s]`
N_T :
The total doping concentration. :math:`[nm^{-3}]`
core_radius :
The radius of the core. :math:`[\mu m]`
clad_radius :
The radius of the cladding. :math:`[\mu m]`
area_doped :
The doped area. :math:`[\mu m^2]` If None, will be
approximated to the core area.
eta_s :
The background signal loss. :math:`[km^{-1}]`
eta_p :
The background pump loss. :math:`[km^{-1}]`
R_0 :
The reflectivity at the fiber start.
R_L :
The reflectivity at the fiber end.
signal_width :
The width of each channel of the signal. :math:`[ps]`
medium :
The main medium of the fiber amplifier.
dopant :
The doped medium of the fiber amplifier.
step_update :
If True, update the signal and pump power from the wave
arrays at each space step.
"""
super().__init__(2) # 2 equations
# Variable declaration -----------------------------------------
self._power_s_f: Array[float] = np.array([])
self._power_s_b: Array[float] = np.array([])
self._power_s_ref: Array[float] = np.array([])
self._power_p_f: Array[float] = np.array([])
self._power_p_b: Array[float] = np.array([])
self._power_p_ref: Array[float] = np.array([])
self._power_ase_f: Array[float] = np.array([])
self._power_ase_b: Array[float] = np.array([])
self._N_1: Array[float] = np.array([])
self._sigma_a_s: Array[float] = np.array([])
self._sigma_a_p: Array[float] = np.array([])
self._sigma_e_s: Array[float] = np.array([])
self._sigma_e_p: Array[float] = np.array([])
self._n_tot_s: Array[float] = np.array([])
self._n_tot_p: Array[float] = np.array([])
self._n_0_s: Array[float] = np.array([])
self._n_0_p: Array[float] = np.array([])
self._n_clad_s: Array[float] = np.array([])
self._n_clad_p: Array[float] = np.array([])
self._Gamma_s: Array[float] = np.array([])
self._Gamma_p: Array[float] = np.array([])
self._A_eff_s: Array[float] = np.array([])
self._A_eff_p: Array[float] = np.array([])
# Variable initialization --------------------------------------
self._coprop: bool = True
self._step_update: bool = step_update
self._signal_width = signal_width
self._medium: str = medium
self._dopant: str = dopant
self._T: float = temperature
self._N_T: float = N_T
self._tau: float = tau_meta * 1e6 # us -> ps
self._decay: float = 1 / self._tau
if (area_doped is None):
area_doped = (cst.PI * core_radius**2)
# TO DO: make option cladding doped and thus A_d_p = A_cladding
A_d_p = (cst.PI * core_radius**2)
A_d_s = area_doped
self._overlap_s = OverlapFactor(A_d_s)
self._overlap_p = OverlapFactor(A_d_p)
self._A_d_s = A_d_s * 1e6 # um^2 -> nm^2
# Doping region area, can be cladding or core
self._A_d_p = area_doped * 1e6 # um^2 -> nm^2
self._core_radius = core_radius
self._res_index: ResonantIndex = ResonantIndex(medium=self._dopant)
# Set n_core and n_clad depending on NA (forget NA afterwards)
# Assume that only core medium can be calculated with Sellmeier
# equations. Could be changed later. Would be easier to have
# also cladding medium set by Sellmeier but cladding medium
# not always available. -> To Discuss
self._calc_n_core: Optional[Sellmeier] = None
self._calc_n_clad: Optional[Callable] = None
if (NA is None and n_clad is None):
util.warning_terminal("Must specify at least NA or n_clad, "
"n_clad will be set to 0.")
self._n_clad_s_value = 0.0
self._n_clad_p_value = 0.0
if (n_clad is not None): # default is NA is None
n_clad_temp = util.make_list(n_clad, 2)
self._n_clad_s_value = n_clad_temp[0]
self._n_clad_p_value = n_clad_temp[1]
if (n_core is None):
if (NA is not None and n_clad is not None):
n_clad_temp = util.make_list(n_clad, 2)
self._n_0_s_value =\
NumericalAperture.calc_n_core(NA, n_clad_temp[0])
self._n_0_p_value =\
NumericalAperture.calc_n_core(NA, n_clad_temp[1])
else:
self._calc_n_core = Sellmeier(medium=self._medium)
if (NA is not None and n_clad is None):
self._calc_n_clad = NumericalAperture.calc_n_clad
NA_temp = util.make_list(NA, 2)
self._NA_value_s = NA_temp[0]
self._NA_value_p = NA_temp[1]
else:
n_core_: List[float] = util.make_list(n_core, 2)
self._n_0_s_value = n_core_[0]
self._n_0_p_value = n_core_[1]
if (NA is not None and n_clad is None):
self._n_clad_s_value =\
NumericalAperture.calc_n_clad(NA, n_core_[0])
self._n_clad_p_value =\
NumericalAperture.calc_n_clad(NA, n_core_[1])
self._eff_area_s = EffectiveArea(core_radius)
self._eff_area_p = EffectiveArea(core_radius)
self._eta_s = eta_s * 1e-12 # km^{-1} -> nm^{-1}
self._eta_p = eta_p * 1e-12 # km^{-1} -> nm^{-1}
self._factor_s = 1 / (cst.HBAR * self._A_d_s)
self._factor_p = 1 / (cst.HBAR * self._A_d_p)
self._absorp: Optional[Absorption] = None
if (sigma_a is None):
self._absorp = Absorption(dopant=dopant)
else:
if (callable(sigma_a)):
self._absorp = Absorption(predict=sigma_a)
else:
sigma_a_: List[float] = util.make_list(sigma_a, 2)
self._sigma_a_s_value = sigma_a_[0]
self._sigma_a_p_value = sigma_a_[1]
self._sigma_e_mccumber = False
self._stimu: Optional[StimulatedEmission] = None
if (sigma_e is None):
self._sigma_e_mccumber = True
else:
if (callable(sigma_e)):
self._stimu = StimulatedEmission(predict=sigma_e)
else:
sigma_e_: List[float] = util.make_list(sigma_e, 2)
self._sigma_e_s_value = sigma_e_[0]
self._sigma_e_p_value = sigma_e_[1]
self._R_0 = R_0
self._R_L = R_L
def v_bias(self, v_bias: Optional[List[float]]) -> None:
self._v_bias = util.make_list(v_bias, 2)
self._update_phase_shift()
def __init__(self,
name: str,
default_name: str,
ports_type: List[int],
save: bool,
wait: bool = False,
max_nbr_pass: Optional[List[int]] = None) -> None:
"""
Parameters
----------
name :
The name of the component.
default_name :
The default name of the component.
ports_type :
Type of each port of the component, give also the number of
ports in the component. For types, see
:mod:`optcom/utils/constant_values/port_types`.
save :
If True, will save each field going through each port. The
recorded fields can be accessed with the attribute
:attr:`fields`.
wait :
If True, will wait for specified waiting port policy added
with the function :func:`AbstractComponent.add_wait_policy`.
max_nbr_pass :
If not None, no fields will be propagated if the number of
fields which passed through a specific port exceed the
specified maximum number of pass for this port.
"""
# Attr type check ----------------------------------------------
util.check_attr_type(name, 'name', str)
util.check_attr_type(default_name, 'default_name', str)
util.check_attr_type(ports_type, 'ports_type', list)
util.check_attr_type(save, 'save', bool)
util.check_attr_type(wait, 'wait', bool)
util.check_attr_type(max_nbr_pass, 'max_nbr_pass', None, list)
# Nbr of instances and default name management -----------------
self.inc_nbr_instances()
self.name: str = name
if (name == default_name):
if (self._nbr_instances_with_default_name):
self.name += ' ' + str(self._nbr_instances_with_default_name)
self.inc_nbr_instances_with_default_name()
# Others var ---------------------------------------------------
self._nbr_ports: int = len(ports_type)
self.save: bool = save
self._storages: Storage = []
self._ports: Port = []
for i in range(self._nbr_ports):
self._ports.append(Port(self, i, ports_type[i]))
self._port_policy: Dict[Tuple[int, ...], Tuple[int, ...]] = {}
self._wait_policy: List[List[int]] = []
self._wait: bool = wait
self._counter_pass: List[int] = [0 for i in range(self._nbr_ports)]
self._max_nbr_pass: List[int]
if (max_nbr_pass is None):
# 999 bcs max default recursion depth with python
self._max_nbr_pass = [999 for i in range(self._nbr_ports)]
else:
self._max_nbr_pass = util.make_list(max_nbr_pass, self._nbr_ports)
def v_mod(self, v_mod: Optional[List[Callable]]) -> None:
self._v_mod = util.make_list(v_mod, 2)
self._update_phase_shift()
