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Lots of progress, switching to tomli

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This commit is contained in:
Benoît Sierro
2021-10-25 09:25:37 +02:00
parent 7b490d110e
commit 25a201543f
15 changed files with 484 additions and 136 deletions
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4
developement_help.md
View File

@@ -5,3 +5,7 @@
- optional : add a default value
- optional : add to valid_variable
- optional : add to mandatory_parameters
## complicated Rule conditions
- add the desired parameters to the init of the operator
- raise OperatorError if the conditions are not met

3
requirements.txt
View File

@@ -1,5 +1,6 @@
numpy
matplotlib
scipy
toml
tomli
tomli-w
tqdm

2
setup.cfg
View File

@@ -23,7 +23,7 @@ install_requires =
numba
matplotlib
scipy
toml
tomli
tqdm

4
src/scgenerator/errors.py
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@@ -42,3 +42,7 @@ class EvaluatorError(Exception):
class NoDefaultError(EvaluatorError):
pass
class OperatorError(EvaluatorError):
pass

13
src/scgenerator/evaluator.py
View File

@@ -24,7 +24,7 @@ class Rule:
targets = list(target) if isinstance(target, (list, tuple)) else [target]
self.func = func
if priorities is None:
priorities = [1] * len(targets)
priorities = [0] * len(targets)
elif isinstance(priorities, (int, float, np.integer, np.floating)):
priorities = [priorities]
self.targets = dict(zip(targets, priorities))
@@ -293,7 +293,7 @@ class Evaluator:
default_rules: list[Rule] = [
# Grid
*Rule.deduce(
["z_targets", "t", "time_window", "t_num", "dt", "w_c", "w0", "w", "l"],
["z_targets", "t", "time_window", "t_num", "dt", "w_c", "w0", "w", "w_order", "l"],
math.build_sim_grid,
["time_window", "t_num", "dt"],
2,
@@ -403,11 +403,16 @@ default_rules: list[Rule] = [
Rule("raman_op", operators.Raman),
Rule("raman_op", operators.NoRaman, priorities=-1),
Rule("nonlinear_operator", operators.EnvelopeNonLinearOperator),
Rule("loss_op", operators.CustomConstantLoss, priorities=3),
Rule("loss_op", operators.CustomLoss, priorities=3),
Rule("loss_op", operators.CapillaryLoss, priorities=2, conditions=dict(loss="capillary")),
Rule("loss_op", operators.ConstantLoss, priorities=1),
Rule("loss_op", operators.NoLoss, priorities=-1),
Rule("n_op", operators.ConstantRefractiveIndex),
Rule("n_op", operators.MarcatiliRefractiveIndex),
Rule("n_op", operators.MarcatiliAdjustedRefractiveIndex),
Rule("n_op", operators.HasanRefractiveIndex),
Rule("dispersion_op", operators.ConstantPolyDispersion),
Rule("dispersion_op", operators.DirectDispersion),
Rule("linear_operator", operators.LinearOperator),
Rule("conserved_quantity", operators.ConservedQuantity),
Rule("conserved_quantity", operators.conserved_quantity),
]

11
src/scgenerator/legacy.py
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@@ -5,7 +5,8 @@ from pathlib import Path
from typing import Any, Set
import numpy as np
import toml
import tomli
import tomli_w
from .const import SPEC1_FN, SPEC1_FN_N, SPECN_FN1
from .parameter import Configuration, Parameters
@@ -15,8 +16,8 @@ from .variationer import VariationDescriptor
def load_config(path: os.PathLike) -> dict[str, Any]:
with open(path) as file:
d = toml.load(file)
with open(path, "rb") as file:
d = tomli.load(file)
d.setdefault("variable", {})
return d
@@ -40,8 +41,8 @@ def convert_sim_folder(path: os.PathLike):
os.makedirs(new_root, exist_ok=True)
_, configs = load_config_sequence(path)
master_config = dict(name=path.name, Fiber=configs)
with open(new_root / "initial_config.toml", "w") as f:
toml.dump(master_config, f, encoder=toml.TomlNumpyEncoder())
with open(new_root / "initial_config.toml", "wb") as f:
tomli_w.dump(Parameters.strip_params_dict(master_config), f)
configuration = Configuration(path, final_output_path=new_root)
pbar = PBars(configuration.total_num_steps, "Converting")

57
src/scgenerator/math.py
View File

@@ -2,6 +2,7 @@ from typing import Union
import numpy as np
from scipy.special import jn_zeros
from .cache import np_cache
pi = np.pi
@@ -244,7 +245,6 @@ def build_sim_grid(
length: float,
z_num: int,
wavelength: float,
interpolation_degree: int,
time_window: float = None,
t_num: int = None,
dt: float = None,
@@ -286,6 +286,8 @@ def build_sim_grid(
pump angular frequency
w : np.ndarray, shape (t_num, )
actual angualr frequency grid in rad/s
w_order : np.ndarray, shape (t_num, )
result of w.argsort()
l : np.ndarray, shape (t_num)
wavelengths in m
"""
@@ -295,31 +297,34 @@ def build_sim_grid(
dt = t[1] - t[0]
t_num = len(t)
z_targets = np.linspace(0, length, z_num)
w_c, w0, w = update_frequency_domain(t, wavelength, interpolation_degree)
l = 2 * pi * c / w
return z_targets, t, time_window, t_num, dt, w_c, w0, w, l
def update_frequency_domain(
t: np.ndarray, wavelength: float, deg: int
) -> tuple[np.ndarray, float, np.ndarray, np.ndarray]:
"""updates the frequency grid
Parameters
----------
t : np.ndarray
time array
wavelength : float
wavelength
deg : int
interpolation degree of the dispersion
Returns
-------
Tuple[np.ndarray, float, np.ndarray, np.ndarray]
w_c, w0, w
"""
w_c = wspace(t)
w0 = 2 * pi * c / wavelength
w = w_c + w0
return w_c, w0, w
w_order = np.argsort(w)
l = 2 * pi * c / w
return z_targets, t, time_window, t_num, dt, w_c, w0, w, w_order, l
def linear_extrapolation(y: np.ndarray) -> np.ndarray:
"""extrapolates IN PLACE linearily on both side of the support
Parameters
----------
y : np.ndarray
array
left_ind : int
first value we want to keep (extrapolate to the left of that)
right_ind : int
last value we want to keep (extrapolate to the right of that)
"""
out = y.copy()
order = np.argsort(y)
left_ind, *_, right_ind = np.nonzero(out[order])[0]
_linear_extrapolation_in_place(out[order], left_ind, right_ind)
return out
def _linear_extrapolation_in_place(y: np.ndarray, left_ind: int, right_ind: int):
y[:left_ind] = -(1 + np.arange(left_ind))[::-1] * (y[left_ind + 1] - y[left_ind]) + y[left_ind]
y[right_ind:] = np.arange(len(y) - right_ind) * (y[right_ind] - y[right_ind - 1]) + y[right_ind]
return y

384
src/scgenerator/operators.py
View File

@@ -4,15 +4,19 @@ Nothing except the solver should depend on this file
"""
from __future__ import annotations
from abc import ABC, abstractmethod
import dataclasses
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any
import numpy as np
from scipy.interpolate import interp1d
from . import math
from .errors import OperatorError
from .logger import get_logger
from .physics import fiber, pulse
from .physics import fiber, materials, pulse, units
from .utils import load_material_dico
class SpectrumDescriptor:
@@ -57,10 +61,12 @@ class CurrentState:
class Operator(ABC):
def __repr__(self) -> str:
value_pair_list = list(self.__dict__.items())
if len(value_pair_list) > 1:
value_pair_str_list = [k + "=" + self.__value_repr(k, v) for k, v in value_pair_list]
else:
if len(value_pair_list) == 0:
value_pair_str_list = ""
elif len(value_pair_list) == 1:
value_pair_str_list = [self.__value_repr(value_pair_list[0][0], value_pair_list[0][1])]
else:
value_pair_str_list = [k + "=" + self.__value_repr(k, v) for k, v in value_pair_list]
return self.__class__.__name__ + "(" + ", ".join(value_pair_str_list) + ")"
@@ -79,6 +85,238 @@ class NoOp:
self.arr_const = np.zeros(t_num)
##################################################
###################### GAS #######################
##################################################
class AbstractGas(ABC):
@abstractmethod
def pressure(self, state: CurrentState) -> float:
"""returns the pressure at the current
Parameters
----------
state : CurrentState
Returns
-------
float
pressure un bar
"""
@abstractmethod
def number_density(self, state: CurrentState) -> float:
"""returns the number density in 1/m^3 of at the current state
Parameters
----------
state : CurrentState
Returns
-------
float
number density in 1/m^3
"""
@abstractmethod
def square_index(self, state: CurrentState) -> np.ndarray:
"""returns the square of the material refractive index at the current state
Parameters
----------
state : CurrentState
Returns
-------
np.ndarray
n^2
"""
class ConstantGas(AbstractGas):
pressure_const: float
number_density_const: float
n_gas_2_const: np.ndarray
def __init__(
self,
gas_name: str,
pressure: float,
temperature: float,
ideal_gas: bool,
wl_for_disp: np.ndarray,
):
gas_dico = load_material_dico(gas_name)
self.pressure_const = pressure
if ideal_gas:
self.number_density_const = materials.number_density_van_der_waals(
pressure=pressure, temperature=temperature, material_dico=gas_dico
)
else:
self.number_density_const = self.pressure_const / (units.kB * temperature)
self.n_gas_2_const = materials.n_gas_2(
wl_for_disp, gas_name, self.pressure_const, temperature, ideal_gas
)
def pressure(self, state: CurrentState = None) -> float:
return self.pressure_const
def number_density(self, state: CurrentState = None) -> float:
return self.number_density_const
def square_index(self, state: CurrentState = None) -> float:
return self.n_gas_2_const
class PressureGradientGas(AbstractGas):
name: str
p_in: float
p_out: float
temperature: float
gas_dico: dict[str, Any]
def __init__(
self,
gas_name: str,
pressure_in: float,
pressure_out: float,
temperature: float,
ideal_gas: bool,
wl_for_disp: np.ndarray,
):
self.name = gas_name
self.p_in = pressure_in
self.p_out = pressure_out
self.gas_dico = load_material_dico(gas_name)
self.temperature = temperature
self.ideal_gas = ideal_gas
self.wl_for_disp = wl_for_disp
def pressure(self, state: CurrentState) -> float:
return materials.pressure_from_gradient(state.z_ratio, self.p_in, self.p_out)
def number_density(self, state: CurrentState) -> float:
if self.ideal:
return self.pressure(state) / (units.kB * self.temperature)
else:
return materials.number_density_van_der_waals(
pressure=self.pressure(state),
temperature=self.temperature,
material_dico=self.gas_dico,
)
def square_index(self, state: CurrentState) -> np.ndarray:
return materials.fast_n_gas_2(
self.wl_for_disp, self.pressure(state), self.temperature, self.ideal_gas, self.gas_dico
)
##################################################
################### DISPERSION ###################
##################################################
class AbstractRefractiveIndex(Operator):
@abstractmethod
def __call__(self, state: CurrentState) -> np.ndarray:
"""returns the total/effective refractive index at this state
Parameters
----------
state : CurrentState
Returns
-------
np.ndarray
refractive index
"""
class ConstantRefractiveIndex(AbstractRefractiveIndex):
n_eff_arr: np.ndarray
def __init__(self, n_eff: np.ndarray):
self.n_eff_arr = n_eff
def __call__(self, state: CurrentState = None) -> np.ndarray:
return self.n_eff_arr
class PCFRefractiveIndex(AbstractRefractiveIndex):
def __int__(self, wl_for_disp: np.ndarray, pitch: float, pitch_ratio: float):
self.n_eff_const = fiber.n_eff_pcf(wl_for_disp, pitch, pitch_ratio)
class MarcatiliRefractiveIndex(AbstractRefractiveIndex):
gas_op: AbstractGas
core_radius: float
wl_for_disp: np.ndarray
def __init__(self, gas_op: ConstantGas, core_radius: float, wl_for_disp: np.ndarray):
self.gas_op = gas_op
self.core_radius = core_radius
self.wl_for_disp = wl_for_disp
def __call__(self, state: CurrentState) -> np.ndarray:
return fiber.n_eff_marcatili(
self.wl_for_disp, self.gas_op.square_index(state), self.core_radius
)
class MarcatiliAdjustedRefractiveIndex(MarcatiliRefractiveIndex):
def __call__(self, state: CurrentState) -> np.ndarray:
return fiber.n_eff_marcatili_adjusted(
self.wl_for_disp, self.gas_op.square_index(state), self.core_radius
)
@dataclass(repr=False, eq=False)
class HasanRefractiveIndex(AbstractRefractiveIndex):
gas_op: ConstantGas
core_radius: float
capillary_num: int
capillary_nested: int
capillary_thickness: float
capillary_radius: float
capillary_resonance_strengths: list[float]
wl_for_disp: np.ndarray
# def __init__(
# self,
# gas_op: ConstantGas,
# core_radius: float,
# capillary_num: int,
# capillary_nested: int,
# capillary_thickness: float,
# capillary_radius: float,
# capillary_resonance_strengths: list[float],
# wl_for_disp: np.ndarray,
# ):
# self.gas_op = gas_op
# self.core_radius = core_radius
# self.capillary_num = capillary_num
# self.capillary_nested = capillary_nested
# self.capillary_thickness = capillary_thickness
# self.capillary_radius = capillary_radius
# self.capillary_resonance_strengths = capillary_resonance_strengths
# self.wl_for_disp = wl_for_disp
def __call__(self, state: CurrentState) -> np.ndarray:
return fiber.n_eff_hasan(
self.wl_for_disp,
self.gas_op.square_index(state),
self.core_radius,
self.capillary_num,
self.capillary_nested,
self.capillary_thickness,
fiber.capillary_spacing_hasan(
self.capillary_num, self.capillary_radius, self.core_radius
),
self.capillary_resonance_strengths,
)
##################################################
################### DISPERSION ###################
##################################################
@@ -92,7 +330,6 @@ class AbstractDispersion(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -118,6 +355,10 @@ class ConstantPolyDispersion(AbstractDispersion):
w_c: np.ndarray,
interpolation_degree: int,
):
if interpolation_degree < 1:
raise OperatorError(
f"interpolation degree of degree {interpolation_degree} incompatible"
)
self.coefs = fiber.dispersion_coefficients(w_for_disp, beta2_arr, w0, interpolation_degree)
self.w_c = w_c
self.w_power_fact = np.array(
@@ -133,24 +374,66 @@ class ConstantDirectDispersion(AbstractDispersion):
Direct dispersion for when the refractive index is known
"""
disp_arr_const: np.ndarray
disp_arr: np.ndarray
def __init__(
self,
w_for_disp: np.ndarray,
w0: np.ndarray,
t_num: int,
n_eff: np.ndarray,
n_op: ConstantRefractiveIndex,
dispersion_ind: np.ndarray,
w_order: np.ndarray,
):
self.disp_arr_const = np.zeros(t_num)
self.disp_arr = np.zeros(t_num, dtype=complex)
w0_ind = math.argclosest(w_for_disp, w0)
self.disp_arr_const[dispersion_ind] = fiber.fast_direct_dispersion(
w_for_disp, w0, n_eff, w0_ind
self.disp_arr[dispersion_ind] = fiber.fast_direct_dispersion(
w_for_disp, w0, n_op(), w0_ind
)[2:-2]
left_ind, *_, right_ind = np.nonzero(self.disp_arr[w_order])[0]
self.disp_arr[w_order] = math._linear_extrapolation_in_place(
self.disp_arr[w_order], left_ind, right_ind
)
def __call__(self, state: CurrentState = None):
return self.disp_arr_const
def __call__(self, state: CurrentState = None) -> np.ndarray:
return self.disp_arr
class DirectDispersion(AbstractDispersion):
def __new__(
cls,
w_for_disp: np.ndarray,
w0: np.ndarray,
t_num: int,
n_op: ConstantRefractiveIndex,
dispersion_ind: np.ndarray,
w_order: np.ndarray,
):
if isinstance(n_op, ConstantRefractiveIndex):
return ConstantDirectDispersion(w_for_disp, w0, t_num, n_op, dispersion_ind, w_order)
return object.__new__(cls)
def __init__(
self,
w_for_disp: np.ndarray,
w0: np.ndarray,
t_num: int,
n_op: ConstantRefractiveIndex,
dispersion_ind: np.ndarray,
w_order: np.ndarray,
):
self.w_for_disp = w_for_disp
self.disp_ind = dispersion_ind
self.n_op = n_op
self.disp_arr = np.zeros(t_num)
self.w0 = w0
self.w0_ind = math.argclosest(w_for_disp, w0)
def __call__(self, state: CurrentState) -> np.ndarray:
self.disp_arr[self.disp_ind] = fiber.fast_direct_dispersion(
self.w_for_disp, self.w0, self.n_op(state), self.w0_ind
)[2:-2]
return self.disp_arr
##################################################
@@ -166,7 +449,6 @@ class AbstractLoss(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -203,18 +485,18 @@ class CapillaryLoss(ConstantLoss):
self.arr = np.zeros(t_num)
self.arr[dispersion_ind] = alpha[2:-2]
def __call__(self, state: CurrentState) -> np.ndarray:
def __call__(self, state: CurrentState = None) -> np.ndarray:
return self.arr
class CustomConstantLoss(ConstantLoss):
class CustomLoss(ConstantLoss):
def __init__(self, l: np.ndarray, loss_file: str):
loss_data = np.load(loss_file)
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) -> np.ndarray:
def __call__(self, state: CurrentState = None) -> np.ndarray:
return self.arr
@@ -234,7 +516,6 @@ class LinearOperator(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -259,7 +540,6 @@ class AbstractRaman(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -297,7 +577,6 @@ class AbstractSPM(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -332,7 +611,6 @@ class AbstractSelfSteepening(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -367,7 +645,6 @@ class AbstractGamma(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -414,7 +691,6 @@ class NonLinearOperator(Operator):
Parameters
----------
state : CurrentState
current state of the simulation
Returns
-------
@@ -450,39 +726,19 @@ class EnvelopeNonLinearOperator(NonLinearOperator):
##################################################
class ConservedQuantity(Operator):
@classmethod
def create(
cls, raman_op: AbstractGamma, gamma_op: AbstractGamma, loss_op: AbstractLoss, w: np.ndarray
) -> ConservedQuantity:
logger = get_logger(__name__)
raman = not isinstance(raman_op, NoRaman)
loss = not isinstance(raman_op, NoLoss)
if raman and loss:
logger.debug("Conserved quantity : photon number with loss")
return PhotonNumberLoss(w, gamma_op, loss_op)
elif raman:
logger.debug("Conserved quantity : photon number without loss")
return PhotonNumberNoLoss(w, gamma_op)
elif loss:
logger.debug("Conserved quantity : energy with loss")
return EnergyLoss(w, loss_op)
else:
logger.debug("Conserved quantity : energy without loss")
return EnergyNoLoss(w)
class AbstractConservedQuantity(Operator):
@abstractmethod
def __call__(self, state: CurrentState) -> float:
pass
class NoConservedQuantity(ConservedQuantity):
class NoConservedQuantity(AbstractConservedQuantity):
def __call__(self, state: CurrentState) -> float:
return 0.0
class PhotonNumberLoss(ConservedQuantity):
def __init__(self, w: np.ndarray, gamma_op: AbstractGamma, loss_op=AbstractLoss):
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
@@ -494,7 +750,7 @@ class PhotonNumberLoss(ConservedQuantity):
)
class PhotonNumberNoLoss(ConservedQuantity):
class PhotonNumberNoLoss(AbstractConservedQuantity):
def __init__(self, w: np.ndarray, gamma_op: AbstractGamma):
self.w = w
self.dw = w[1] - w[0]
@@ -504,7 +760,7 @@ class PhotonNumberNoLoss(ConservedQuantity):
return pulse.photon_number(state.spec2, self.w, self.dw, self.gamma_op(state))
class EnergyLoss(ConservedQuantity):
class EnergyLoss(AbstractConservedQuantity):
def __init__(self, w: np.ndarray, loss_op: AbstractLoss):
self.dw = w[1] - w[0]
self.loss_op = loss_op
@@ -515,9 +771,35 @@ class EnergyLoss(ConservedQuantity):
)
class EnergyNoLoss(ConservedQuantity):
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.C_to_A_factor * state.spectrum), self.dw)
def conserved_quantity(
adapt_step_size: bool,
raman_op: AbstractGamma,
gamma_op: AbstractGamma,
loss_op: AbstractLoss,
w: np.ndarray,
) -> AbstractConservedQuantity:
if not adapt_step_size:
return NoConservedQuantity()
logger = get_logger(__name__)
raman = not isinstance(raman_op, NoRaman)
loss = not isinstance(raman_op, NoLoss)
if raman and loss:
logger.debug("Conserved quantity : photon number with loss")
return PhotonNumberLoss(w, gamma_op, loss_op)
elif raman:
logger.debug("Conserved quantity : photon number without loss")
return PhotonNumberNoLoss(w, gamma_op)
elif loss:
logger.debug("Conserved quantity : energy with loss")
return EnergyLoss(w, loss_op)
else:
logger.debug("Conserved quantity : energy without loss")
return EnergyNoLoss(w)

23
src/scgenerator/parameter.py
View File

@@ -1,5 +1,6 @@
from __future__ import annotations
import datetime as datetime_module
import enum
import os
@@ -16,7 +17,7 @@ from . import env, legacy, utils
from .const import MANDATORY_PARAMETERS, PARAM_FN, VALID_VARIABLE, __version__
from .evaluator import Evaluator, pdict
from .logger import get_logger
from .operators import LinearOperator, NonLinearOperator
from .operators import AbstractConservedQuantity, LinearOperator, NonLinearOperator
from .utils import fiber_folder, update_path_name
from .variationer import VariationDescriptor, Variationer
@@ -261,7 +262,6 @@ class Parameters:
name: str = Parameter(string, default="no name")
prev_data_dir: str = Parameter(string)
recovery_data_dir: str = Parameter(string)
previous_config_file: str = Parameter(string)
output_path: Path = Parameter(type_checker(Path), default=Path("sc_data"), converter=Path)
# # fiber
@@ -334,7 +334,7 @@ class Parameters:
tolerated_error: float = Parameter(in_range_excl(1e-15, 1e-3), default=1e-11)
step_size: float = Parameter(non_negative(float, int), default=0)
interpolation_range: tuple[float, float] = Parameter(float_pair)
interpolation_degree: int = Parameter(positive(int), default=8)
interpolation_degree: int = Parameter(non_negative(int))
prev_sim_dir: str = Parameter(string)
recovery_last_stored: int = Parameter(non_negative(int), default=0)
parallel: bool = Parameter(boolean, default=True)
@@ -343,6 +343,9 @@ class Parameters:
# computed
linear_operator: LinearOperator = Parameter(type_checker(LinearOperator))
nonlinear_operator: NonLinearOperator = Parameter(type_checker(NonLinearOperator))
conserved_quantity: AbstractConservedQuantity = Parameter(
type_checker(AbstractConservedQuantity)
)
field_0: np.ndarray = Parameter(type_checker(np.ndarray))
spec_0: np.ndarray = Parameter(type_checker(np.ndarray))
beta2: float = Parameter(type_checker(int, float))
@@ -372,7 +375,13 @@ class Parameters:
self._evaluator.set(self._param_dico)
def __repr__(self) -> str:
return "Parameter(" + ", ".join(f"{k}={v}" for k, v in self.dump_dict().items()) + ")"
return "Parameter(" + ", ".join(self.__repr_list__()) + ")"
def __pformat__(self) -> str:
return "\n".join(["Parameter(", *list(self.__repr_list__()), ")"])
def __repr_list__(self) -> Iterator[str]:
yield from (f"{k}={v}" for k, v in self.dump_dict().items())
def dump_dict(self) -> dict[str, Any]:
param = Parameters.strip_params_dict(self._param_dico)
@@ -427,7 +436,7 @@ class Parameters:
"nonlinear_op",
"linear_op",
}
types = (np.ndarray, float, int, str, list, tuple, dict)
types = (np.ndarray, float, int, str, list, tuple, dict, Path)
out = {}
for key, value in dico.items():
if key in forbiden_keys:
@@ -436,8 +445,12 @@ class Parameters:
continue
if isinstance(value, dict):
out[key] = Parameters.strip_params_dict(value)
elif isinstance(value, Path):
out[key] = str(value)
elif isinstance(value, np.ndarray) and value.dtype == complex:
continue
elif isinstance(value, np.ndarray):
out[key] = value.tolist()
else:
out[key] = value

2
src/scgenerator/physics/fiber.py
View File

@@ -1087,7 +1087,7 @@ def capillary_loss(wl: np.ndarray, he_mode: tuple[int, int], core_radius: float)
np.ndarray
loss in 1/m
"""
chi_silica = mat.sellmeier(wl, utils.load_material_dico("silica"))
chi_silica = abs(mat.sellmeier(wl, utils.load_material_dico("silica")))
nu_n = 0.5 * (chi_silica + 2) / np.sqrt(chi_silica)
return nu_n * (u_nm(*he_mode) * wl / pipi) ** 2 * core_radius ** -3

13
src/scgenerator/physics/materials.py
View File

@@ -1,4 +1,4 @@
from typing import Callable
from typing import Any, Callable
import numpy as np
import scipy.special
@@ -18,6 +18,17 @@ def n_gas_2(
"""Returns the sqare of the index of refraction of the specified gas"""
material_dico = utils.load_material_dico(gas_name)
n_gas_2 = fast_n_gas_2(wl_for_disp, pressure, temperature, ideal_gas, material_dico)
return n_gas_2
def fast_n_gas_2(
wl_for_disp: np.ndarray,
pressure: float,
temperature: float,
ideal_gas: bool,
material_dico: dict[str, Any],
):
if ideal_gas:
n_gas_2 = sellmeier(wl_for_disp, material_dico, pressure, temperature) + 1
else:

40
src/scgenerator/physics/simulate.py
View File

@@ -2,16 +2,21 @@ import multiprocessing
import multiprocessing.connection
import os
from datetime import datetime
from pathlib import Path
from typing import Any, Generator, Type, Union, Optional
from logging import Logger
from pathlib import Path
from typing import Any, Generator, Optional, Type, Union
import numpy as np
from .. import utils
from ..errors import EvaluatorError
from ..logger import get_logger
from ..operators import (
AbstractConservedQuantity,
CurrentState,
)
from ..parameter import Configuration, Parameters
from ..pbar import PBars, ProgressBarActor, progress_worker
from ..operators import CurrentState, ConservedQuantity, NoConservedQuantity
try:
import ray
@@ -35,7 +40,7 @@ class RK4IP:
cons_qty: list[float]
state: CurrentState
conserved_quantity_func: ConservedQuantity
conserved_quantity: AbstractConservedQuantity
stored_spectra: list[np.ndarray]
def __init__(
@@ -82,22 +87,8 @@ class RK4IP:
params.tolerated_error if self.params.adapt_step_size else self.params.step_size
)
self.set_cons_qty()
self._setup_sim_parameters()
def set_cons_qty(self):
# Set up which quantity is conserved for adaptive step size
if self.params.adapt_step_size:
self.conserved_quantity_func = ConservedQuantity.create(
self.params.nonlinear_operator.raman_op,
self.params.nonlinear_operator.gamma_op,
self.params.linear_operator.loss_op,
self.params.w,
)
else:
self.logger.debug(f"Using constant step size of {1e6*self.error_ok:.3f}μm")
self.conserved_quantity_func = NoConservedQuantity()
def _setup_sim_parameters(self):
# making sure to keep only the z that we want
self.z_stored = list(self.z_targets.copy()[0 : self.params.recovery_last_stored + 1])
@@ -106,7 +97,10 @@ class RK4IP:
self.store_num = len(self.z_targets)
# Setup initial values for every physical quantity that we want to track
C_to_A_factor = (self.params.A_eff_arr / self.params.A_eff_arr[0]) ** (1 / 4)
try:
C_to_A_factor = (self.params.A_eff_arr / self.params.A_eff_arr[0]) ** (1 / 4)
except EvaluatorError:
C_to_A_factor = 1.0
z = self.z_targets.pop(0)
# Initial step size
if self.params.adapt_step_size:
@@ -124,7 +118,7 @@ class RK4IP:
self.state.spectrum.copy()
]
self.cons_qty = [
self.conserved_quantity_func(self.state),
self.params.conserved_quantity(self.state),
0,
]
self.size_fac = 2 ** (1 / 5)
@@ -267,7 +261,7 @@ class RK4IP:
new_state = self.state.replace(expD * (A_I + k1 / 6 + k2 / 3 + k3 / 3) + k4 / 6)
if self.params.adapt_step_size:
self.cons_qty[step] = self.conserved_quantity_func(new_state)
self.cons_qty[step] = self.params.conserved_quantity(new_state)
curr_p_change = np.abs(self.cons_qty[step - 1] - self.cons_qty[step])
cons_qty_change_ok = self.error_ok * self.cons_qty[step - 1]
@@ -531,7 +525,7 @@ class MultiProcSimulations(Simulations, priority=1):
super().run()
def new_sim(self, params: Parameters):
self.queue.put((params,), block=True, timeout=None)
self.queue.put(params.dump_dict(), block=True, timeout=None)
def finish(self):
"""0 means finished"""
@@ -556,7 +550,7 @@ class MultiProcSimulations(Simulations, priority=1):
if raw_data == 0:
queue.task_done()
return
(params,) = raw_data
params = Parameters(**raw_data)
MutliProcRK4IP(
params,
p_queue,

36
src/scgenerator/plotting.py
View File

@@ -174,7 +174,6 @@ def create_zoom_axis(
# copy the plot content
if plot:
lines = axis.get_lines()
ymin, ymax = 0, 0
for line in lines:
xdata = line.get_xdata()
xdata, ind, _ = sort_axis(xdata, (*xlim, units.s))
@@ -1071,9 +1070,36 @@ def partial_plot(root: os.PathLike):
spec_list = sorted(
path.glob(SPEC1_FN.format("*")), key=lambda el: int(re.search("[0-9]+", el.name)[0])
)
params = Parameters.load(path / "params.toml")
params.z_targets = params.z_targets[: len(spec_list)]
raw_values = np.array([load_spectrum(s) for s in spec_list])
specs = units.to_log2D(math.abs2(np.fft.fftshift(raw_values, axes=-1)))
fields = math.abs2(np.fft.ifft(raw_values))
left.imshow(specs, origin="lower", aspect="auto", vmin=-60, interpolation="nearest")
right.imshow(fields, origin="lower", aspect="auto", interpolation="nearest")
wl, z, values = transform_2D_propagation(
raw_values,
PlotRange(
0.5 * params.interpolation_range[0] * 1e9,
1.1 * params.interpolation_range[1] * 1e9,
"nm",
),
params,
log="2D",
)
left.imshow(
values,
origin="lower",
aspect="auto",
vmin=-60,
interpolation="nearest",
extent=get_extent(wl, z),
)
t, z, values = transform_2D_propagation(
np.fft.ifft(raw_values),
PlotRange(-10, 10, "ps"),
params,
log=False,
)
right.imshow(
values, origin="lower", aspect="auto", interpolation="nearest", extent=get_extent(t, z)
)
return left, right

3
src/scgenerator/scripts/__init__.py
View File

@@ -13,7 +13,8 @@ from ..parameter import Configuration, Parameters
from ..physics import fiber, units
from ..plotting import plot_setup
from ..spectra import SimulationSeries
from ..utils import _open_config, auto_crop, save_toml, simulations_list, translate_parameters
from ..utils import _open_config, auto_crop, save_toml, simulations_list
from ..legacy import translate_parameters
def fingerprint(params: Parameters):

25
src/scgenerator/utils.py
View File

@@ -18,7 +18,8 @@ from typing import Any, Callable, MutableMapping, Sequence, TypeVar, Set
import numpy as np
import pkg_resources as pkg
import toml
import tomli
import tomli_w
from .const import PARAM_FN, PARAM_SEPARATOR, SPEC1_FN, Z_FN, ROOT_PARAMETERS
from .logger import get_logger
@@ -47,8 +48,8 @@ class Paths:
def get(cls, key):
if key not in cls.paths:
if os.path.exists("paths.toml"):
with open("paths.toml") as file:
paths_dico = toml.load(file)
with open("paths.toml", "rb") as file:
paths_dico = tomli.load(file)
for k, v in paths_dico.items():
cls.paths[k] = v
if key not in cls.paths:
@@ -182,18 +183,18 @@ def load_toml(descr: os.PathLike) -> dict[str, Any]:
descr = str(descr)
if ":" in descr:
path, entry = descr.split(":", 1)
with open(path) as file:
return toml.load(file)[entry]
with open(path, "rb") as file:
return tomli.load(file)[entry]
else:
with open(descr) as file:
return toml.load(file)
with open(descr, "rb") as file:
return tomli.load(file)
def save_toml(path: os.PathLike, dico):
"""saves a dictionary into a toml file"""
path = conform_toml_path(path)
with open(path, mode="w") as file:
toml.dump(dico, file)
with open(path, mode="wb") as file:
tomli_w.dump(dico, file)
return dico
@@ -247,7 +248,7 @@ def load_material_dico(name: str) -> dict[str, Any]:
----------
material_dico : dict
"""
return toml.loads(Paths.gets("materials"))[name]
return tomli.loads(Paths.gets("materials"))[name]
def save_data(data: np.ndarray, data_dir: Path, file_name: str):
@@ -478,8 +479,8 @@ def save_parameters(
os.makedirs(file_path.parent, exist_ok=True)
# save toml of the simulation
with open(file_path, "w") as file:
toml.dump(params, file, encoder=toml.TomlNumpyEncoder())
with open(file_path, "wb") as file:
tomli_w.dump(params, file)
return file_path