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working on better dispersion interpolation

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This commit is contained in:
Benoît Sierro
2021-06-24 10:13:12 +02:00
parent 97a19d4ffb
commit 5a7bf53e1c
13 changed files with 209 additions and 72 deletions
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1
src/scgenerator/__init__.py
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@@ -5,3 +5,4 @@ from .physics import fiber, materials, pulse, simulate, units
from .physics.simulate import RK4IP, new_simulation, resume_simulations
from .plotting import plot_avg, plot_results_1D, plot_results_2D, plot_spectrogram
from .spectra import Pulse
from . import utils

2
src/scgenerator/cli/cli.py
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@@ -104,7 +104,7 @@ def create_parser():
init_plot_parser.add_argument("config", help="path to the config file")
init_plot_parser.add_argument(
"--dispersion-limits",
"-s",
"-d",
default=None,
type=float,
nargs=2,

2
src/scgenerator/defaults.py
View File

@@ -23,7 +23,7 @@ default_parameters = dict(
parallel=True,
repeat=1,
tolerated_error=1e-11,
lower_wavelength_interp_limit=300e-9,
lower_wavelength_interp_limit=100e-9,
upper_wavelength_interp_limit=1900e-9,
interp_degree=8,
ideal_gas=False,

6
src/scgenerator/initialize.py
View File

@@ -85,8 +85,8 @@ class Params(BareParams):
logger = get_logger(__name__)
self.interp_range = (
max(self.lower_wavelength_interp_limit, units.m.inv(np.max(self.w[self.w > 0]))),
min(self.upper_wavelength_interp_limit, units.m.inv(np.min(self.w[self.w > 0]))),
max(self.lower_wavelength_interp_limit, self.l[self.l > 0].min()),
min(self.upper_wavelength_interp_limit, self.l[self.l > 0].max()),
)
temp_gamma = None
@@ -106,7 +106,7 @@ class Params(BareParams):
if self.gamma is None:
self.gamma = temp_gamma
logger.info(f"using computed \u0263 = {self.gamma:.2e} W/m^2")
logger.info(f"using computed \u0263 = {self.gamma:.2e} W/m\u00B2")
# Raman response
if "raman" in self.behaviors:

2
src/scgenerator/logger.py
View File

@@ -63,6 +63,6 @@ def configure_logger(logger: logging.Logger):
stream_handler.setLevel(print_lvl)
logger.addHandler(stream_handler)
logger.setLevel(min(print_lvl, file_lvl))
logger.setLevel(logging.DEBUG)
logger.already_configured = True
return logger

60
src/scgenerator/physics/fiber.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Dict, List, Literal, Tuple
from typing import Any, Dict, Iterable, List, Literal, Tuple, Union
import numpy as np
import toml
@@ -6,6 +6,8 @@ from numpy.fft import fft, ifft
from numpy.polynomial.chebyshev import Chebyshev, cheb2poly
from scipy.interpolate import interp1d
from ..logger import get_logger
from .. import io
from ..math import abs2, argclosest, power_fact, u_nm
from ..utils.parameter import BareParams, hc_model_specific_parameters
@@ -15,14 +17,14 @@ from . import units
from .units import c, pi
def lambda_for_dispersion():
def lambda_for_dispersion(left: float, right: float) -> np.ndarray:
"""Returns a wl vector for dispersion calculation
Returns
-------
array of wl values
"""
return np.linspace(190e-9, 3000e-9, 4000)
return np.arange(left - 2e-9, right + 3e-9, 1e-9)
def is_dynamic_dispersion(pressure=None):
@@ -679,7 +681,7 @@ def compute_dispersion(params: BareParams):
gamma = None
else:
interp_range = params.interp_range
lambda_ = lambda_for_dispersion()
lambda_ = lambda_for_dispersion(*interp_range)
beta2 = np.zeros_like(lambda_)
if params.model == "pcf":
@@ -773,7 +775,7 @@ def dispersion_coefficients(
beta2_coef : 1D array
Taylor coefficients in decreasing order
"""
logger = get_logger()
if interp_range is None:
r = slice(2, -2)
else:
@@ -783,15 +785,50 @@ def dispersion_coefficients(
r = (lambda_ > max(lambda_[2], interp_range[0])) & (
lambda_ < min(lambda_[-2], interp_range[1])
)
logger.debug(
f"interpolating dispersion between {lambda_[r].min()*1e9:.1f}nm and {lambda_[r].max()*1e9:.1f}nm"
)
# import matplotlib.pyplot as plt
# we get the beta2 Taylor coeffiecients by making a fit around w0
w_c = units.m(lambda_) - w0
# interp = interp1d(w_c[r], beta2[r])
# w_c = np.linspace(w_c)
# fig, ax = plt.subplots()
# ax.plot(w_c[r], beta2[r])
# fig.show()
fit = Chebyshev.fit(w_c[r], beta2[r], deg)
beta2_coef = cheb2poly(fit.convert().coef) * np.cumprod([1] + list(range(1, deg + 1)))
return beta2_coef
def dispersion_from_coefficients(
w_c: np.ndarray, beta: Union[list[float], np.ndarray]
) -> np.ndarray:
"""computes the dispersion profile (beta2) from the beta coefficients
Parameters
----------
w_c : np.ndarray, shape (n, )
centered angular frequency (0 <=> pump frequency)
beta : Iterable[float]
beta coefficients
Returns
-------
np.ndarray, shape (n, )
beta2 as function of w_c
"""
coef = np.array(beta) / np.cumprod([1] + list(range(1, len(beta))))
beta_arr = np.zeros_like(w_c)
for k, b in reversed(list(enumerate(coef))):
beta_arr = beta_arr + b * w_c ** k
return beta_arr
def delayed_raman_t(t, raman_type="stolen"):
"""
computes the unnormalized temporal Raman response function applied to the array t
@@ -1007,3 +1044,16 @@ def effective_core_radius(lambda_, core_radius, s=0.08, h=200e-9):
def effective_radius_HCARF(core_radius, t, f1, f2, lambda_):
"""eq. 3 in Hasan 2018"""
return f1 * core_radius * (1 - f2 * lambda_ ** 2 / (core_radius * t))
if __name__ == "__main__":
w = np.linspace(0, 1, 4096)
c = np.arange(8)
import time
t = time.time()
for _ in range(10000):
dispersion_from_coefficients(w, c)
print((time.time() - t) / 10, "ms")

61
src/scgenerator/physics/pulse.py
View File

@@ -12,13 +12,16 @@ n is the number of spectra at the same z position and nt is the size of the time
import itertools
import os
from pathlib import Path
from typing import Literal, Tuple
from typing import Literal, Tuple, TypeVar
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi
from numpy.fft import fft, fftshift, ifft
from scipy import optimize
from scipy.interpolate import UnivariateSpline
from scipy.optimize import minimize_scalar
from scipy.optimize.optimize import OptimizeResult
from .. import io
from ..defaults import default_plotting
@@ -30,7 +33,7 @@ from . import units
c = 299792458.0
hbar = 1.05457148e-34
T = TypeVar("T")
#
fwhm_to_T0_fac = dict(
@@ -535,14 +538,23 @@ def peak_ind(values, mam=None):
am = np.argmax(values)
else:
m, am = mam
left_ind = (
am
- np.where((values[am:0:-1] - values[am - 1 :: -1] < 0) & (values[am:0:-1] < m / 2))[0][0]
)
right_ind = (
am + np.where((values[am:-1] - values[am + 1 :] < 0) & (values[am:-1] < m / 2))[0][0]
)
return left_ind - 3, right_ind + 3
try:
left_ind = (
am
- np.where((values[am:0:-1] - values[am - 1 :: -1] <= 0) & (values[am:0:-1] < m / 2))[
0
][0]
) - 3
except IndexError:
left_ind = 0
try:
right_ind = (
am + np.where((values[am:-1] - values[am + 1 :] <= 0) & (values[am:-1] < m / 2))[0][0]
) + 3
except IndexError:
right_ind = len(values) - 1
return left_ind, right_ind
def setup_splines(x_axis, values, mam=None):
@@ -895,3 +907,32 @@ def measure_field(t: np.ndarray, field: np.ndarray) -> Tuple[float, float, float
peak_power = intensity.max()
energy = np.trapz(intensity, t)
return fwhm, peak_power, energy
def remove_2nd_order_dispersion(
spectrum: T, w_c: np.ndarray, beta2: float, max_z: float = -1.0
) -> tuple[T, OptimizeResult]:
"""attempts to remove 2nd order dispersion from a complex spectrum
Parameters
----------
spectrum : np.ndarray or Spectrum, shape (n, )
spectrum from which to remove 2nd order dispersion
w_c : np.ndarray, shape (n, )
corresponding centered angular frequencies (w-w0)
beta2 : float
2nd order dispersion coefficient
Returns
-------
np.ndarray, shape (n, )
spectrum with 2nd order dispersion removed
"""
# makeshift_t = np.linspace(0, 1, len(w_c))
propagate = lambda z: spectrum * np.exp(-0.5j * beta2 * w_c ** 2 * z)
def score(z):
return 1 / np.max(abs2(np.fft.ifft(propagate(z))))
opti = minimize_scalar(score, bracket=(max_z, 0))
return propagate(opti.x), opti

6
src/scgenerator/physics/units.py
View File

@@ -6,6 +6,9 @@ import re
from threading import settrace
from typing import Callable, TypeVar, Union
from dataclasses import dataclass
from matplotlib import pyplot as plt
from numpy.lib.arraysetops import isin
from ..utils.parameter import Parameter, type_checker
import numpy as np
from numpy import pi
@@ -255,7 +258,8 @@ def sort_axis(axis, plt_range: PlotRange):
# slice y according to the given ranges
y = y[ct][:, cw]
"""
if isinstance(plt_range, tuple):
plt_range = PlotRange(*plt_range)
r = np.array((plt_range.left, plt_range.right), dtype="float")
indices = np.arange(len(axis))[

5
src/scgenerator/plotting.py
View File

@@ -30,7 +30,7 @@ def plot_setup(
- an axis
"""
out_path = defaults["name"] if out_path is None else out_path
plot_name = out_path.stem
plot_name = out_path.name.replace(f".{file_type}", "")
out_dir = out_path.resolve().parent
file_name = plot_name + "." + file_type
@@ -286,9 +286,8 @@ def _finish_plot_2D(
if isinstance(ax, tuple) and len(ax) > 1:
ax, cbar_ax = ax[0], ax[1]
folder_name = ""
if is_new_plot:
out_path, fig, ax = plot_setup(out_path=Path(folder_name) / file_name, file_type=file_type)
out_path, fig, ax = plot_setup(out_path=Path(file_name), file_type=file_type)
else:
fig = ax.get_figure()

96
src/scgenerator/scripts/__init__.py
View File

@@ -1,7 +1,7 @@
from itertools import cycle
import itertools
from pathlib import Path
from typing import Iterable
from typing import Any, Iterable, Optional
from cycler import cycler
import matplotlib.pyplot as plt
@@ -12,10 +12,16 @@ from ..utils.parameter import BareParams
from ..initialize import ParamSequence
from ..physics import units, fiber
from ..spectra import Pulse
from ..utils import pretty_format_value
from ..utils import pretty_format_value, pretty_format_from_file_name, auto_crop
from .. import env, math
def fingerprint(params: BareParams):
h1 = hash(params.field_0.tobytes())
h2 = tuple(params.beta)
return h1, h2
def plot_all(sim_dir: Path, limits: list[str]):
for p in sim_dir.glob("*"):
if not p.is_dir():
@@ -26,7 +32,13 @@ def plot_all(sim_dir: Path, limits: list[str]):
left, right, unit = lim.split(",")
left = float(left)
right = float(right)
pulse.plot_2D(left, right, unit, file_name=p.parent / f"{p.name}_{left}_{right}_{unit}")
pulse.plot_2D(
left,
right,
unit,
file_name=p.parent
/ f"{pretty_format_from_file_name(p.name)} {left} {right} {unit}",
)
def plot_init_field_spec(
@@ -49,7 +61,7 @@ def plot_init_field_spec(
def plot_dispersion(config_path: Path, lim: tuple[float, float] = None):
fig, (left, right) = plt.subplots(1, 2, figsize=(12, 7), sharex=True)
fig, (left, right) = plt.subplots(1, 2, figsize=(12, 7))
left.grid()
right.grid()
all_labels = []
@@ -62,7 +74,7 @@ def plot_dispersion(config_path: Path, lim: tuple[float, float] = None):
lbl = plot_1_dispersion(lim, left, right, style, lbl, params)
all_labels.append(lbl)
finish_plot(fig, left, right, all_labels, params)
finish_plot(fig, right, all_labels, params)
def plot_init(
@@ -77,8 +89,8 @@ def plot_init(
all_labels = []
already_plotted = set()
for style, lbl, params in plot_helper(config_path):
if (bbb := hash(params.field_0.tobytes())) not in already_plotted:
already_plotted.add(bbb)
if (fp := fingerprint(params)) not in already_plotted:
already_plotted.add(fp)
else:
continue
lbl = plot_1_dispersion(lim_disp, tl, tr, style, lbl, params)
@@ -90,8 +102,8 @@ def plot_init(
def plot_1_init_spec_field(lim_t, lim_l, left, right, style, lbl, params):
field = math.abs2(params.field_0)
spec = math.abs2(params.spec_0)
t = units.fs.inv(params.t)
wl = units.nm.inv(params.w)
t = units.To.fs(params.t)
wl = units.To.nm(params.w)
lbl.append(f"max at {wl[spec.argmax()]:.1f} nm")
@@ -100,54 +112,68 @@ def plot_1_init_spec_field(lim_t, lim_l, left, right, style, lbl, params):
mt &= t >= lim_t[0]
mt &= t <= lim_t[1]
else:
mt = find_lim(t, field)
mt = auto_crop(t, field)
ml = np.ones_like(wl, dtype=bool)
if lim_l is not None:
ml &= t >= lim_l[0]
ml &= t <= lim_l[1]
ml &= wl >= lim_l[0]
ml &= wl <= lim_l[1]
else:
ml = find_lim(wl, spec)
ml = auto_crop(wl, spec)
left.plot(t[mt], field[mt])
right.plot(wl[ml], spec[ml], label=" ", **style)
return lbl
def plot_1_dispersion(lim, left, right, style, lbl, params):
coef = params.beta / np.cumprod([1] + list(range(1, len(params.beta))))
w_c = params.w_c
beta_arr = np.zeros_like(w_c)
for k, beta in reversed(list(enumerate(coef))):
beta_arr = beta_arr + beta * w_c ** k
def plot_1_dispersion(
lim: Optional[tuple[float, float]],
left: plt.Axes,
right: plt.Axes,
style: dict[str, Any],
lbl: list[str],
params: BareParams,
):
beta_arr = fiber.dispersion_from_coefficients(params.w_c, params.beta)
wl = units.m.inv(params.w)
D = fiber.beta2_to_D(beta_arr, wl) * 1e6
zdw = math.all_zeros(wl, beta_arr)
if len(zdw) > 0:
zdw = zdw[np.argmin(abs(zdw - params.wavelength))]
lbl.append(f"ZDW at {zdw*1e9:.1f}nm")
lbl.append(f"ZDW at {zdw:.1f}nm")
else:
lbl.append("")
m = np.ones_like(wl, dtype=bool)
if lim is None:
lim = params.interp_range
m &= wl >= lim[0]
m &= wl <= lim[1]
m &= wl >= (lim[0] if lim[0] < 1 else lim[0] * 1e-9)
m &= wl <= (lim[1] if lim[1] < 1 else lim[1] * 1e-9)
left.annotate(
rf"$\lambda_{{\mathrm{{min}}}}={np.min(params.l[params.l>0])*1e9:.1f}$ nm"
f"lower interpolation limit : {params.interp_range[0]*1e9:.1f} nm",
(0, 1),
xycoords="axes fraction",
va="top",
ha="left",
)
m = np.argwhere(m)[:, 0]
m = np.array(sorted(m, key=lambda el: wl[el]))
if len(m) == 0:
raise ValueError(f"nothing to plot in the range {lim!r}")
# plot D
D = fiber.beta2_to_D(beta_arr, wl) * 1e6
right.plot(1e9 * wl[m], D[m], label=" ", **style)
right.set_ylabel(units.D_ps_nm_km.label)
# plot beta
left.plot(1e9 * wl[m], units.beta2_fs_cm.inv(beta_arr[m]), label=" ", **style)
left.plot(units.To.Prad_s(params.w[m]), units.beta2_fs_cm.inv(beta_arr[m]), label=" ", **style)
left.set_ylabel(units.beta2_fs_cm.label)
left.set_xlabel("wavelength (nm)")
left.set_xlabel(units.Prad_s.label)
right.set_xlabel("wavelength (nm)")
return lbl
@@ -188,21 +214,3 @@ def plot_helper(config_path: Path) -> Iterable[tuple[dict, list[str], BareParams
for style, (variables, params) in zip(cc, pseq):
lbl = [pretty_format_value(name, value) for name, value in variables[1:-1]]
yield style, lbl, params
def find_lim(x: np.ndarray, y: np.ndarray, rel_thr: float = 0.01) -> int:
threshold = y.min() + rel_thr * (y.max() - y.min())
above_threshold = y > threshold
ind = np.argsort(x)
valid_ind = [
np.array(list(g)) for k, g in itertools.groupby(ind, key=lambda i: above_threshold[i]) if k
]
ind_above = sorted(valid_ind, key=lambda el: len(el), reverse=True)[0]
width = len(ind_above)
return np.concatenate(
(
np.arange(max(ind_above[0] - width, 0), ind_above[0]),
ind_above,
np.arange(ind_above[-1] + 1, min(len(y), ind_above[-1] + width)),
)
)

8
src/scgenerator/spectra.py
View File

@@ -47,7 +47,7 @@ class Spectrum(np.ndarray):
else:
return np.array([s.energy() for s in self])
def crop_wl(self, left: float, right: float) -> tuple[np.ndarray, np.ndarray]:
def crop_wl(self, left: float, right: float) -> np.ndarray:
cond = (self.params.l >= left) & (self.params.l <= right)
return cond
@@ -120,6 +120,9 @@ class Spectrum(np.ndarray):
-(((self.params.l - pos) / (pulse.fwhm_to_T0_fac["gaussian"] * width)) ** 2)
)
def measure(self) -> tuple[float, float, float]:
return pulse.measure_field(self.params.t, self.time_amp)
class Pulse(Sequence):
def __init__(self, path: os.PathLike, default_ind: Union[int, Iterable[int]] = None):
@@ -180,7 +183,7 @@ class Pulse(Sequence):
def __len__(self):
return self.nmax
def __getitem__(self, key):
def __getitem__(self, key) -> Spectrum:
return self.all_spectra(ind=range(self.nmax)[key]).squeeze()
def intensity(self, unit):
@@ -282,6 +285,7 @@ class Pulse(Sequence):
spectra = []
for i in ind:
spectra.append(self._load1(i))
spectra = Spectrum(spectra, self.params)
self.logger.debug(f"all spectra from {self.path} successfully loaded")
if len(ind) == 1:

30
src/scgenerator/utils/__init__.py
View File

@@ -4,6 +4,7 @@ scgenerator module but some function may be used in any python program
"""
from argparse import ArgumentTypeError
import itertools
import multiprocessing
import re
@@ -214,6 +215,17 @@ def pretty_format_value(name: str, value) -> str:
return getattr(BareParams, name).display(value)
def pretty_format_from_file_name(name: str) -> str:
s = name.split(PARAM_SEPARATOR)
out = []
for key, value in zip(s[::2], s[1::2]):
try:
out.append(getattr(BareParams, key).display(float(value)))
except (AttributeError, ValueError):
out.append(key + PARAM_SEPARATOR + value)
return PARAM_SEPARATOR.join(out)
def variable_iterator(config: BareConfig) -> Iterator[Tuple[List[Tuple[str, Any]], BareParams]]:
"""given a config with "variable" parameters, iterates through every possible combination,
yielding a a list of (parameter_name, value) tuples and a full config dictionary.
@@ -282,3 +294,21 @@ def override_config(new: Dict[str, Any], old: BareConfig = None) -> BareConfig:
for k in new:
variable.pop(k, None) # remove old ones
return replace(old, variable=variable, **{k: None for k in variable}, **new)
def auto_crop(x: np.ndarray, y: np.ndarray, rel_thr: float = 0.01) -> np.ndarray:
threshold = y.min() + rel_thr * (y.max() - y.min())
above_threshold = y > threshold
ind = np.argsort(x)
valid_ind = [
np.array(list(g)) for k, g in itertools.groupby(ind, key=lambda i: above_threshold[i]) if k
]
ind_above = sorted(valid_ind, key=lambda el: len(el), reverse=True)[0]
width = len(ind_above)
return np.concatenate(
(
np.arange(max(ind_above[0] - width, 0), ind_above[0]),
ind_above,
np.arange(ind_above[-1] + 1, min(len(y), ind_above[-1] + width)),
)
)

2
src/scgenerator/utils/parameter.py
View File

@@ -421,7 +421,7 @@ class BareParams:
dico : dict
dictionary
"""
forbiden_keys = ["w_c", "w_power_fact", "field_0", "spec_0", "w", "t", "z_targets"]
forbiden_keys = ["w_c", "w_power_fact", "field_0", "spec_0", "w", "t", "z_targets", "l"]
types = (np.ndarray, float, int, str, list, tuple, dict)
out = {}
for key, value in dico.items():