replaced all operators with functions
This commit is contained in:
Benoît Sierro
@@ -4,13 +4,13 @@ Nothing except the solver should depend on this file
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from abc import abstractmethod
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from copy import deepcopy
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from dataclasses import dataclass
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from typing import Any, Callable
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from typing import Any, Callable, Protocol
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import numpy as np
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from scipy.interpolate import interp1d
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from matplotlib.cbook import operator
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from scgenerator import math
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from scgenerator.logger import get_logger
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@@ -102,21 +102,33 @@ class CurrentState:
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)
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class NoOpTime(Operator):
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def __init__(self, t_num: int):
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self.arr_const = np.zeros(t_num)
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def __call__(self, state: CurrentState) -> np.ndarray:
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"""returns 0"""
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return self.arr_const
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Operator = Callable[[CurrentState], np.ndarray]
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Qualifier = Callable[[CurrentState], float]
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class NoOpFreq(Operator):
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def __init__(self, w_num: int):
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self.arr_const = np.zeros(w_num)
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def no_op_time(t_num) -> Operator:
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arr_const = np.zeros(t_num)
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def __call__(self, state: CurrentState) -> np.ndarray:
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return self.arr_const
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def operate(state: CurrentState) -> np.ndarray:
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return arr_const
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return operate
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def no_op_freq(w_num) -> Operator:
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arr_const = np.zeros(w_num)
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def operate(state: CurrentState) -> np.ndarray:
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return arr_const
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return operate
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def const_op(array: np.ndarray) -> Operator:
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def operate(state: CurrentState) -> np.ndarray:
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return array
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return operate
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##################################################
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@@ -124,13 +136,7 @@ class NoOpFreq(Operator):
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##################################################
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class AbstractGas(Operator):
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gas: materials.Gas
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def __call__(self, state: CurrentState) -> np.ndarray:
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return self.square_index(state)
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@abstractmethod
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class GasOp(Protocol):
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def pressure(self, state: CurrentState) -> float:
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"""returns the pressure at the current
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@@ -144,7 +150,6 @@ class AbstractGas(Operator):
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pressure un bar
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"""
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@abstractmethod
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def number_density(self, state: CurrentState) -> float:
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"""returns the number density in 1/m^3 of at the current state
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@@ -158,7 +163,6 @@ class AbstractGas(Operator):
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number density in 1/m^3
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"""
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@abstractmethod
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def square_index(self, state: CurrentState) -> np.ndarray:
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"""returns the square of the material refractive index at the current state
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@@ -173,7 +177,8 @@ class AbstractGas(Operator):
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"""
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class ConstantGas(AbstractGas):
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class ConstantGas:
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gas: materials.Gas
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pressure_const: float
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number_density_const: float
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n_gas_2_const: np.ndarray
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@@ -201,7 +206,8 @@ class ConstantGas(AbstractGas):
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return self.n_gas_2_const
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class PressureGradientGas(AbstractGas):
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class PressureGradientGas:
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gas: materials.Gas
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p_in: float
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p_out: float
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temperature: float
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@@ -237,79 +243,55 @@ class PressureGradientGas(AbstractGas):
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##################################################
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class AbstractRefractiveIndex(Operator):
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@abstractmethod
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def __call__(self, state: CurrentState) -> np.ndarray:
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"""returns the total/effective refractive index at this state
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def constant_refractive_index(n_eff: np.ndarray) -> Operator:
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def operate(state: CurrentState) -> np.ndarray:
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return n_eff
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Parameters
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----------
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state : CurrentState
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Returns
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-------
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np.ndarray
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refractive index
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"""
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return operate
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class ConstantRefractiveIndex(AbstractRefractiveIndex):
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n_eff_arr: np.ndarray
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def marcatili_refractive_index(
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gas_op: GasOp, core_radius: float, wl_for_disp: np.ndarray
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) -> Operator:
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def operate(state: CurrentState) -> np.ndarray:
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return fiber.n_eff_marcatili(wl_for_disp, gas_op.square_index(state), core_radius)
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def __init__(self, n_eff: np.ndarray):
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self.n_eff_arr = n_eff
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def __call__(self, state: CurrentState = None) -> np.ndarray:
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return self.n_eff_arr
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return operate
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class MarcatiliRefractiveIndex(AbstractRefractiveIndex):
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gas_op: AbstractGas
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core_radius: float
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wl_for_disp: np.ndarray
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def marcatili_adjusted_refractive_index(
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gas_op: GasOp, core_radius: float, wl_for_disp: np.ndarray
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) -> Operator:
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def operate(state: CurrentState) -> np.ndarray:
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return fiber.n_eff_marcatili_adjusted(wl_for_disp, gas_op.square_index(state), core_radius)
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def __init__(self, gas_op: ConstantGas, core_radius: float, wl_for_disp: np.ndarray):
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self.gas_op = gas_op
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self.core_radius = core_radius
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self.wl_for_disp = wl_for_disp
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return operate
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def __call__(self, state: CurrentState) -> np.ndarray:
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return fiber.n_eff_marcatili(
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self.wl_for_disp, self.gas_op.square_index(state), self.core_radius
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def vincetti_refracrive_index(
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gas_op: GasOp,
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core_radius: float,
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wl_for_disp: np.ndarray,
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wavelength: float,
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wall_thickness: float,
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tube_radius: float,
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gap: float,
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n_tubes: int,
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n_terms: int,
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) -> Operator:
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def operate(state: CurrentState) -> np.ndarray:
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return fiber.n_eff_vincetti(
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wl_for_disp,
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wavelength,
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gas_op.square_index(state),
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wall_thickness,
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tube_radius,
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gap,
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n_tubes,
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n_terms,
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)
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class MarcatiliAdjustedRefractiveIndex(MarcatiliRefractiveIndex):
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def __call__(self, state: CurrentState) -> np.ndarray:
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return fiber.n_eff_marcatili_adjusted(
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self.wl_for_disp, self.gas_op.square_index(state), self.core_radius
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)
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@dataclass(repr=False, eq=False)
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class HasanRefractiveIndex(AbstractRefractiveIndex):
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gas_op: ConstantGas
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core_radius: float
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capillary_num: int
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capillary_nested: int
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capillary_thickness: float
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capillary_radius: float
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capillary_resonance_strengths: list[float]
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wl_for_disp: np.ndarray
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def __call__(self, state: CurrentState) -> np.ndarray:
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return fiber.n_eff_hasan(
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self.wl_for_disp,
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self.gas_op.square_index(state),
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self.core_radius,
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self.capillary_num,
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self.capillary_nested,
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self.capillary_thickness,
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fiber.capillary_spacing_hasan(
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self.capillary_num, self.capillary_radius, self.core_radius
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),
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self.capillary_resonance_strengths,
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)
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return operate
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##################################################
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@@ -317,109 +299,67 @@ class HasanRefractiveIndex(AbstractRefractiveIndex):
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##################################################
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class AbstractDispersion(Operator):
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@abstractmethod
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def __call__(self, state: CurrentState) -> np.ndarray:
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"""returns the dispersion in the frequency domain
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Parameters
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----------
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state : CurrentState
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Returns
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-------
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np.ndarray
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dispersive component
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"""
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class ConstantPolyDispersion(AbstractDispersion):
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def constant_polynomial_dispersion(
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beta2_coefficients: np.ndarray,
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w_c: np.ndarray,
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) -> Operator:
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"""
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dispersion approximated by fitting a polynome on the dispersion and
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evaluating on the envelope
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"""
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w_power_fact = np.array(
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[math.power_fact(w_c, k) for k in range(2, len(beta2_coefficients) + 2)]
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)
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disp_arr = fiber.fast_poly_dispersion_op(w_c, beta2_coefficients, w_power_fact)
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disp_arr: np.ndarray
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def operate(state: CurrentState) -> np.ndarray:
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return disp_arr
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def __init__(
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self,
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beta2_coefficients: np.ndarray,
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w_c: np.ndarray,
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):
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w_power_fact = np.array(
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[math.power_fact(w_c, k) for k in range(2, len(beta2_coefficients) + 2)]
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)
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self.disp_arr = fiber.fast_poly_dispersion_op(w_c, beta2_coefficients, w_power_fact)
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def __call__(self, state: CurrentState = None) -> np.ndarray:
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return self.disp_arr
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return operate
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class ConstantDirectDispersion(AbstractDispersion):
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def constant_direct_diersion(
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w_for_disp: np.ndarray,
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w0: np.ndarray,
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t_num: int,
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n_eff: np.ndarray,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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) -> Operator:
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"""
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Direct dispersion for when the refractive index is known
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"""
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disp_arr = np.zeros(t_num, dtype=complex)
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w0_ind = math.argclosest(w_for_disp, w0)
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disp_arr[dispersion_ind] = fiber.fast_direct_dispersion(w_for_disp, w0, n_eff, w0_ind)[2:-2]
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left_ind, *_, right_ind = np.nonzero(disp_arr[w_order])[0]
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disp_arr[w_order] = math._polynom_extrapolation_in_place(
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disp_arr[w_order], left_ind, right_ind, 1
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)
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disp_arr: np.ndarray
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def operate(state: CurrentState) -> np.ndarray:
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return disp_arr
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def __init__(
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self,
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w_for_disp: np.ndarray,
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w0: np.ndarray,
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t_num: int,
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n_op: ConstantRefractiveIndex,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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):
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self.disp_arr = np.zeros(t_num, dtype=complex)
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w0_ind = math.argclosest(w_for_disp, w0)
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self.disp_arr[dispersion_ind] = fiber.fast_direct_dispersion(
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w_for_disp, w0, n_op(), w0_ind
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return operate
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def direct_dispersion(
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w_for_disp: np.ndarray,
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w0: np.ndarray,
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t_num: int,
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n_eff_op: Operator,
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dispersion_ind: np.ndarray,
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) -> Operator:
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disp_arr = np.zeros(t_num, dtype=complex)
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w0_ind = math.argclosest(w_for_disp, w0)
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def operate(state: CurrentState) -> np.ndarray:
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disp_arr[dispersion_ind] = fiber.fast_direct_dispersion(
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w_for_disp, w0, n_eff_op(state), w0_ind
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)[2:-2]
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left_ind, *_, right_ind = np.nonzero(self.disp_arr[w_order])[0]
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self.disp_arr[w_order] = math._polynom_extrapolation_in_place(
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self.disp_arr[w_order], left_ind, right_ind, 1
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)
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return disp_arr
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def __call__(self, state: CurrentState = None) -> np.ndarray:
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return self.disp_arr
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class DirectDispersion(AbstractDispersion):
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def __new__(
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cls,
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w_for_disp: np.ndarray,
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w0: np.ndarray,
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t_num: int,
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n_op: ConstantRefractiveIndex,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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):
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if isinstance(n_op, ConstantRefractiveIndex):
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return ConstantDirectDispersion(w_for_disp, w0, t_num, n_op, dispersion_ind, w_order)
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return object.__new__(cls)
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def __init__(
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self,
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w_for_disp: np.ndarray,
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w0: np.ndarray,
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t_num: int,
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n_op: ConstantRefractiveIndex,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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):
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self.w_for_disp = w_for_disp
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self.disp_ind = dispersion_ind
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self.n_op = n_op
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self.disp_arr = np.zeros(t_num, dtype=complex)
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self.w0 = w0
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self.w0_ind = math.argclosest(w_for_disp, w0)
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def __call__(self, state: CurrentState) -> np.ndarray:
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self.disp_arr[self.disp_ind] = fiber.fast_direct_dispersion(
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self.w_for_disp, self.w0, self.n_op(state), self.w0_ind
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)[2:-2]
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return self.disp_arr
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return operate
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##################################################
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@@ -427,106 +367,24 @@ class DirectDispersion(AbstractDispersion):
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##################################################
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class AbstractWaveVector(Operator):
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@abstractmethod
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def __call__(self, state: CurrentState) -> np.ndarray:
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"""returns the wave vector beta in the frequency domain
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def constant_wave_vector(
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n_eff: np.ndarray,
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w_for_disp: np.ndarray,
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w_num: int,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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):
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beta_arr = np.zeros(w_num, dtype=float)
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beta_arr[dispersion_ind] = fiber.beta(w_for_disp, n_eff)[2:-2]
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left_ind, *_, right_ind = np.nonzero(beta_arr[w_order])[0]
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beta_arr[w_order] = math._polynom_extrapolation_in_place(
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beta_arr[w_order], left_ind, right_ind, 1.0
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)
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Parameters
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----------
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state : CurrentState
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def operate(state: CurrentState) -> np.ndarray:
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return beta_arr
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Returns
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-------
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np.ndarray
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wave vector
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"""
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class ConstantWaveVector(AbstractWaveVector):
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beta_arr: np.ndarray
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def __init__(
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self,
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n_op: ConstantRefractiveIndex,
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w_for_disp: np.ndarray,
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w_num: int,
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dispersion_ind: np.ndarray,
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w_order: np.ndarray,
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):
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self.beta_arr = np.zeros(w_num, dtype=float)
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self.beta_arr[dispersion_ind] = fiber.beta(w_for_disp, n_op())[2:-2]
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left_ind, *_, right_ind = np.nonzero(self.beta_arr[w_order])[0]
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self.beta_arr[w_order] = math._polynom_extrapolation_in_place(
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self.beta_arr[w_order], left_ind, right_ind, 1.0
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)
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def __call__(self, state: CurrentState) -> np.ndarray:
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return self.beta_arr
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##################################################
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###################### LOSS ######################
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##################################################
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class AbstractLoss(Operator):
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@abstractmethod
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def __call__(self, state: CurrentState) -> np.ndarray:
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"""returns the loss in the frequency domain
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Parameters
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----------
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state : CurrentState
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Returns
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-------
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np.ndarray
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loss in 1/m
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"""
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class ConstantLoss(AbstractLoss):
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arr_const: np.ndarray
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def __init__(self, alpha: float, w_num: int):
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self.arr_const = alpha * np.ones(w_num)
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def __call__(self, state: CurrentState = None) -> np.ndarray:
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return self.arr_const
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class NoLoss(ConstantLoss):
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def __init__(self, w_num: int):
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super().__init__(0, w_num)
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class CapillaryLoss(ConstantLoss):
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def __init__(
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self,
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wl_for_disp: np.ndarray,
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dispersion_ind: np.ndarray,
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w_num: int,
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core_radius: float,
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he_mode: tuple[int, int],
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):
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alpha = fiber.capillary_loss(wl_for_disp, he_mode, core_radius)
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self.arr = np.zeros(w_num)
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self.arr[dispersion_ind] = alpha[2:-2]
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def __call__(self, state: CurrentState = None) -> np.ndarray:
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return self.arr
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class CustomLoss(ConstantLoss):
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def __init__(self, l: np.ndarray, loss_file: str):
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loss_data = np.load(loss_file)
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||||
wl = loss_data["wavelength"]
|
||||
loss = loss_data["loss"]
|
||||
self.arr = interp1d(wl, loss, fill_value=0, bounds_error=False)(l)
|
||||
|
||||
def __call__(self, state: CurrentState = None) -> np.ndarray:
|
||||
return self.arr
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
@@ -534,134 +392,53 @@ class CustomLoss(ConstantLoss):
|
||||
##################################################
|
||||
|
||||
|
||||
class AbstractRaman(Operator):
|
||||
fraction: float = 0.0
|
||||
def envelope_raman(raman_type: str, raman_fraction: float, t: np.ndarray) -> Operator:
|
||||
hr_w = fiber.delayed_raman_w(t, raman_type)
|
||||
|
||||
@abstractmethod
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
"""returns the raman component
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return raman_fraction * np.fft.ifft(hr_w * np.fft.fft(state.field2))
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : CurrentState
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
raman component
|
||||
"""
|
||||
return operate
|
||||
|
||||
|
||||
class NoEnvelopeRaman(NoOpTime, AbstractRaman):
|
||||
pass
|
||||
def full_field_raman(
|
||||
raman_type: str, raman_fraction: float, t: np.ndarray, w: np.ndarray, chi3: float
|
||||
) -> Operator:
|
||||
hr_w = fiber.delayed_raman_w(t, raman_type)
|
||||
factor_in = units.epsilon0 * chi3 * raman_fraction
|
||||
factor_out = 1j * w**2 / (2.0 * units.c**2 * units.epsilon0)
|
||||
|
||||
|
||||
class NoFullFieldRaman(NoOpFreq, AbstractRaman):
|
||||
pass
|
||||
|
||||
|
||||
class EnvelopeRaman(AbstractRaman):
|
||||
def __init__(self, raman_type: str, t: np.ndarray):
|
||||
self.hr_w = fiber.delayed_raman_w(t, raman_type)
|
||||
self.fraction = 0.245 if raman_type == "agrawal" else 0.18
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.fraction * np.fft.ifft(self.hr_w * np.fft.fft(state.field2))
|
||||
|
||||
|
||||
class FullFieldRaman(AbstractRaman):
|
||||
def __init__(self, raman_type: str, t: np.ndarray, w: np.ndarray, chi3: float):
|
||||
self.hr_w = fiber.delayed_raman_w(t, raman_type)
|
||||
self.fraction = 0.245 if raman_type == "agrawal" else 0.18
|
||||
self.factor_in = units.epsilon0 * chi3 * self.fraction
|
||||
self.factor_out = 1j * w**2 / (2.0 * units.c**2 * units.epsilon0)
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.factor_out * np.fft.rfft(
|
||||
self.factor_in * state.field * np.fft.irfft(self.hr_w * np.fft.rfft(state.field2))
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return factor_out * np.fft.rfft(
|
||||
factor_in * state.field * np.fft.irfft(hr_w * np.fft.rfft(state.field2))
|
||||
)
|
||||
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
####################### SPM ######################
|
||||
##################################################
|
||||
|
||||
|
||||
class AbstractSPM(Operator):
|
||||
fraction: float = 1.0
|
||||
def envelope_spm(raman_fraction: float) -> Operator:
|
||||
fraction = 1 - raman_fraction
|
||||
|
||||
@abstractmethod
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
"""returns the SPM component
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return fraction * state.field2
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : CurrentState
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
SPM component
|
||||
"""
|
||||
return operate
|
||||
|
||||
|
||||
class NoEnvelopeSPM(NoOpFreq, AbstractSPM):
|
||||
pass
|
||||
def full_field_spm(raman_fraction: float, w: np.ndarray, chi3: float) -> Operator:
|
||||
fraction = 1 - raman_fraction
|
||||
factor_out = 1j * w**2 / (2.0 * units.c**2 * units.epsilon0)
|
||||
factor_in = fraction * chi3 * units.epsilon0
|
||||
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return factor_out * np.fft.rfft(factor_in * state.field2 * state.field)
|
||||
|
||||
class NoFullFieldSPM(NoOpTime, AbstractSPM):
|
||||
pass
|
||||
|
||||
|
||||
class EnvelopeSPM(AbstractSPM):
|
||||
def __init__(self, raman_op: AbstractRaman):
|
||||
self.fraction = 1 - raman_op.fraction
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.fraction * state.field2
|
||||
|
||||
|
||||
class FullFieldSPM(AbstractSPM):
|
||||
def __init__(self, raman_op: AbstractRaman, w: np.ndarray, chi3: float):
|
||||
self.fraction = 1 - raman_op.fraction
|
||||
self.factor_out = 1j * w**2 / (2.0 * units.c**2 * units.epsilon0)
|
||||
self.factor_in = self.fraction * chi3 * units.epsilon0
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.factor_out * np.fft.rfft(self.factor_in * state.field2 * state.field)
|
||||
|
||||
|
||||
##################################################
|
||||
################# SELF-STEEPENING ################
|
||||
##################################################
|
||||
|
||||
|
||||
class AbstractSelfSteepening(Operator):
|
||||
@abstractmethod
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
"""returns the self-steepening component
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : CurrentState
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
self-steepening component
|
||||
"""
|
||||
|
||||
|
||||
class NoSelfSteepening(NoOpFreq, AbstractSelfSteepening):
|
||||
pass
|
||||
|
||||
|
||||
class SelfSteepening(AbstractSelfSteepening):
|
||||
def __init__(self, w_c: np.ndarray, w0: float):
|
||||
self.arr = w_c / w0
|
||||
|
||||
def __call__(self, state: CurrentState = None) -> np.ndarray:
|
||||
return self.arr
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
@@ -669,57 +446,14 @@ class SelfSteepening(AbstractSelfSteepening):
|
||||
##################################################
|
||||
|
||||
|
||||
class AbstractGamma(Operator):
|
||||
@abstractmethod
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
"""returns the gamma component
|
||||
def variable_gamma(gas_op: GasOp, w0: float, A_eff: float, t_num: int) -> Operator:
|
||||
arr = np.ones(t_num)
|
||||
|
||||
Parameters
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
n2 = gas_op.square_index(state)
|
||||
return arr * fiber.gamma_parameter(n2, w0, A_eff)
|
||||
|
||||
----------
|
||||
state : CurrentState
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
gamma component
|
||||
"""
|
||||
|
||||
|
||||
class ConstantScalarGamma(AbstractGamma):
|
||||
def __init__(self, gamma: float, t_num: int):
|
||||
self.arr_const = gamma * np.ones(t_num)
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.arr_const
|
||||
|
||||
|
||||
class NoGamma(ConstantScalarGamma):
|
||||
def __init__(self, w: np.ndarray) -> None:
|
||||
super().__init__(0, w)
|
||||
|
||||
|
||||
class ConstantGamma(AbstractGamma):
|
||||
def __init__(self, gamma_arr: np.ndarray):
|
||||
self.arr = gamma_arr
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.arr
|
||||
|
||||
|
||||
class VariableScalarGamma(AbstractGamma):
|
||||
def __init__(
|
||||
self, gas_op: AbstractGas, temperature: float, w0: float, A_eff: float, t_num: int
|
||||
):
|
||||
self.gas_op = gas_op
|
||||
self.temperature = temperature
|
||||
self.w0 = w0
|
||||
self.A_eff = A_eff
|
||||
self.arr = np.ones(t_num)
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
n2 = self.gas_op.gas.n2(self.temperature, self.gas_op.pressure(state))
|
||||
return self.arr * fiber.gamma_parameter(n2, self.w0, self.A_eff)
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
@@ -727,24 +461,16 @@ class VariableScalarGamma(AbstractGamma):
|
||||
##################################################
|
||||
|
||||
|
||||
class Plasma(Operator):
|
||||
mat_plasma: plasma.Plasma
|
||||
gas_op: AbstractGas
|
||||
def ionization(w: np.ndarray, gas_op: GasOp, plasma_op: plasma.Plasma) -> Operator:
|
||||
factor_out = -w / (2.0 * units.c**2 * units.epsilon0)
|
||||
|
||||
def __init__(self, dt: float, w: np.ndarray, gas_op: AbstractGas):
|
||||
self.gas_op = gas_op
|
||||
self.mat_plasma = plasma.Plasma(dt, self.gas_op.gas["ionization_energy"])
|
||||
self.factor_out = -w / (2.0 * units.c**2 * units.epsilon0)
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
N0 = self.gas_op.number_density(state)
|
||||
plasma_info = self.mat_plasma(state.field, N0)
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
N0 = gas_op.number_density(state)
|
||||
plasma_info = plasma_op(state.field, N0)
|
||||
state.stats["ionization_fraction"] = plasma_info.electron_density[-1] / N0
|
||||
return self.factor_out * np.fft.rfft(plasma_info.polarization)
|
||||
return factor_out * np.fft.rfft(plasma_info.polarization)
|
||||
|
||||
|
||||
class NoPlasma(NoOpFreq, Plasma):
|
||||
pass
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
@@ -752,91 +478,78 @@ class NoPlasma(NoOpFreq, Plasma):
|
||||
##################################################
|
||||
|
||||
|
||||
class AbstractConservedQuantity(Operator):
|
||||
@abstractmethod
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
pass
|
||||
def photon_number_with_loss(w: np.ndarray, gamma_op: Operator, loss_op: Operator) -> Qualifier:
|
||||
w = w
|
||||
dw = w[1] - w[0]
|
||||
|
||||
|
||||
class NoConservedQuantity(AbstractConservedQuantity):
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
return 0.0
|
||||
|
||||
|
||||
class PhotonNumberLoss(AbstractConservedQuantity):
|
||||
def __init__(self, w: np.ndarray, gamma_op: AbstractGamma, loss_op: AbstractLoss):
|
||||
self.w = w
|
||||
self.dw = w[1] - w[0]
|
||||
self.gamma_op = gamma_op
|
||||
self.loss_op = loss_op
|
||||
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
def qualify(state: CurrentState) -> float:
|
||||
return pulse.photon_number_with_loss(
|
||||
state.spectrum2,
|
||||
self.w,
|
||||
self.dw,
|
||||
self.gamma_op(state),
|
||||
self.loss_op(state),
|
||||
w,
|
||||
dw,
|
||||
gamma_op(state),
|
||||
loss_op(state),
|
||||
state.current_step_size,
|
||||
)
|
||||
|
||||
|
||||
class PhotonNumberNoLoss(AbstractConservedQuantity):
|
||||
def __init__(self, w: np.ndarray, gamma_op: AbstractGamma):
|
||||
self.w = w
|
||||
self.dw = w[1] - w[0]
|
||||
self.gamma_op = gamma_op
|
||||
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
return pulse.photon_number(state.spectrum2, self.w, self.dw, self.gamma_op(state))
|
||||
return qualify
|
||||
|
||||
|
||||
class EnergyLoss(AbstractConservedQuantity):
|
||||
def __init__(self, w: np.ndarray, loss_op: AbstractLoss):
|
||||
self.dw = w[1] - w[0]
|
||||
self.loss_op = loss_op
|
||||
def photon_number_without_loss(w: np.ndarray, gamma_op: Operator) -> Qualifier:
|
||||
dw = w[1] - w[0]
|
||||
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
def qualify(state: CurrentState) -> float:
|
||||
return pulse.photon_number(state.spectrum2, w, dw, gamma_op(state))
|
||||
|
||||
return qualify
|
||||
|
||||
|
||||
def energy_with_loss(w: np.ndarray, loss_op: Operator) -> Qualifier:
|
||||
dw = w[1] - w[0]
|
||||
|
||||
def qualify(state: CurrentState) -> float:
|
||||
return pulse.pulse_energy_with_loss(
|
||||
math.abs2(state.conversion_factor * state.spectrum),
|
||||
self.dw,
|
||||
self.loss_op(state),
|
||||
dw,
|
||||
loss_op(state),
|
||||
state.current_step_size,
|
||||
)
|
||||
|
||||
return qualify
|
||||
|
||||
class EnergyNoLoss(AbstractConservedQuantity):
|
||||
def __init__(self, w: np.ndarray):
|
||||
self.dw = w[1] - w[0]
|
||||
|
||||
def __call__(self, state: CurrentState) -> float:
|
||||
return pulse.pulse_energy(math.abs2(state.conversion_factor * state.spectrum), self.dw)
|
||||
def energy_without_loss(w: np.ndarray) -> Qualifier:
|
||||
dw = w[1] - w[0]
|
||||
|
||||
def qualify(state: CurrentState) -> float:
|
||||
return pulse.pulse_energy(math.abs2(state.conversion_factor * state.spectrum), dw)
|
||||
|
||||
return qualify
|
||||
|
||||
|
||||
def conserved_quantity(
|
||||
adapt_step_size: bool,
|
||||
raman_op: AbstractGamma,
|
||||
gamma_op: AbstractGamma,
|
||||
loss_op: AbstractLoss,
|
||||
raman: bool,
|
||||
loss: bool,
|
||||
gamma_op: Operator,
|
||||
loss_op: Operator,
|
||||
w: np.ndarray,
|
||||
) -> AbstractConservedQuantity:
|
||||
) -> Qualifier:
|
||||
if not adapt_step_size:
|
||||
return NoConservedQuantity()
|
||||
return lambda state: 0.0
|
||||
logger = get_logger(__name__)
|
||||
raman = not isinstance(raman_op, NoEnvelopeRaman)
|
||||
loss = not isinstance(loss_op, NoLoss)
|
||||
if raman and loss:
|
||||
logger.debug("Conserved quantity : photon number with loss")
|
||||
return PhotonNumberLoss(w, gamma_op, loss_op)
|
||||
return photon_number_with_loss(w, gamma_op, loss_op)
|
||||
elif raman:
|
||||
logger.debug("Conserved quantity : photon number without loss")
|
||||
return PhotonNumberNoLoss(w, gamma_op)
|
||||
return photon_number_without_loss(w, gamma_op)
|
||||
elif loss:
|
||||
logger.debug("Conserved quantity : energy with loss")
|
||||
return EnergyLoss(w, loss_op)
|
||||
return energy_with_loss(w, loss_op)
|
||||
else:
|
||||
logger.debug("Conserved quantity : energy without loss")
|
||||
return EnergyNoLoss(w)
|
||||
return energy_without_loss(w)
|
||||
|
||||
|
||||
##################################################
|
||||
@@ -844,44 +557,26 @@ def conserved_quantity(
|
||||
##################################################
|
||||
|
||||
|
||||
class LinearOperator(Operator):
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
"""returns the linear operator to be multiplied by the spectrum in the frequency domain
|
||||
def envelope_linear_operator(dispersion_op: Operator, loss_op: Operator) -> Operator:
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return dispersion_op(state) - loss_op(state) / 2
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : CurrentState
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
linear component
|
||||
"""
|
||||
return operate
|
||||
|
||||
|
||||
class EnvelopeLinearOperator(LinearOperator):
|
||||
def __init__(self, dispersion_op: AbstractDispersion, loss_op: AbstractLoss):
|
||||
self.dispersion_op = dispersion_op
|
||||
self.loss_op = loss_op
|
||||
def full_field_linear_operator(
|
||||
self,
|
||||
beta_op: Operator,
|
||||
loss_op: Operator,
|
||||
frame_velocity: float,
|
||||
w: np.ndarray,
|
||||
) -> Operatore:
|
||||
delay = w / frame_velocity
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return self.dispersion_op(state) - self.loss_op(state) / 2
|
||||
def operate(state: CurrentState) -> np.ndarray:
|
||||
return 1j * (wave_vector(state) - delay) - loss_op(state) / 2
|
||||
|
||||
|
||||
class FullFieldLinearOperator(LinearOperator):
|
||||
def __init__(
|
||||
self,
|
||||
beta_op: AbstractWaveVector,
|
||||
loss_op: AbstractLoss,
|
||||
frame_velocity: float,
|
||||
w: np.ndarray,
|
||||
):
|
||||
self.delay = w / frame_velocity
|
||||
self.wave_vector = beta_op
|
||||
self.loss_op = loss_op
|
||||
|
||||
def __call__(self, state: CurrentState) -> np.ndarray:
|
||||
return 1j * (self.wave_vector(state) - self.delay) - self.loss_op(state) / 2
|
||||
return operate
|
||||
|
||||
|
||||
##################################################
|
||||
|
||||
@@ -885,6 +885,11 @@ def effective_radius_HCARF(core_radius, t, f1, f2, wl_for_disp):
|
||||
return f1 * core_radius * (1 - f2 * wl_for_disp**2 / (core_radius * t))
|
||||
|
||||
|
||||
def scalar_loss(alpha: float, w_num: int) -> np.ndarray:
|
||||
"""simple, wavelength independent scalar loss"""
|
||||
return alpha * np.ones(w_num)
|
||||
|
||||
|
||||
def capillary_loss(wl: np.ndarray, he_mode: tuple[int, int], core_radius: float) -> np.ndarray:
|
||||
"""computes the wavelenth dependent capillary loss according to Marcatili
|
||||
|
||||
@@ -908,6 +913,19 @@ def capillary_loss(wl: np.ndarray, he_mode: tuple[int, int], core_radius: float)
|
||||
return nu_n * (u_nm(*he_mode) * wl / pipi) ** 2 * core_radius**-3
|
||||
|
||||
|
||||
def safe_constant_loss(
|
||||
wl_for_disp: np.ndarray,
|
||||
dispersion_ind: np.ndarray,
|
||||
w_num: int,
|
||||
core_radius: float,
|
||||
he_mode: tuple[int, int],
|
||||
)->np.ndarray:
|
||||
alpha = capillary_loss(wl_for_disp, he_mode, core_radius)
|
||||
loss_arr = np.zeros(w_num)
|
||||
loss_arr[dispersion_ind] = alpha[2:-2]
|
||||
return loss_arr
|
||||
|
||||
|
||||
def extinction_distance(loss: T, ratio=1 / e) -> T:
|
||||
return np.log(ratio) / -loss
|
||||
|
||||
|
||||
Reference in New Issue
Block a user