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big cleanup

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This commit is contained in:
Benoît Sierro
2023-10-09 10:25:02 +02:00
parent 31298df023
commit edb9dfade3
13 changed files with 78 additions and 398 deletions
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31
src/scgenerator/defaults.py
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@@ -1,31 +0,0 @@
from pathlib import Path
import matplotlib.pyplot as plt
default_plotting = dict(
figsize=(10, 7),
interpolation_2D="bicubic",
vmin=-40,
vmax=0,
vmax_with_headroom=2,
out_path=Path("plot"),
avg_main_to_coherence_ratio=4,
avg_line_labels=["individual values", "mean"],
muted_style=dict(linewidth=0.5, c=(0.8, 0.8, 0.8, 0.4)),
highlighted_style=dict(c="red"),
color_cycle=plt.rcParams["axes.prop_cycle"].by_key()["color"],
light_color=(1, 1, 1, 0.7),
markers=["*", "+", ".", "D", "x", "d", "v", "s", "1", "^"],
cmap="viridis",
label_quality_factor=r"$F_\mathrm{Q}$",
label_mean_g12=r"$\langle | g_{12} |\rangle$",
label_g12=r"|$g_{12}$|",
label_z="propagation distance z (m)",
label_fwhm=r"$T_\mathrm{FWHM}$ (fs)",
label_wb_distance=r"$L_\mathrm{WB}$",
label_t_jitter="timing jitter (fs)",
label_fwhm_noise="FWHM noise (%)",
label_int_noise="RIN (%)",
text_topright_style=dict(verticalalignment="top", horizontalalignment="right"),
text_topleft_style=dict(verticalalignment="top", horizontalalignment="left"),
)

28
src/scgenerator/env.py
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@@ -36,4 +36,32 @@ class Config(BaseSettings):
log_file: Path | None = None
default_plotting = dict(
figsize=(10, 7),
interpolation_2D="bicubic",
vmin=-40,
vmax=0,
vmax_with_headroom=2,
out_path=Path("plot"),
avg_main_to_coherence_ratio=4,
avg_line_labels=["individual values", "mean"],
muted_style=dict(linewidth=0.5, c=(0.8, 0.8, 0.8, 0.4)),
highlighted_style=dict(c="red"),
color_cycle=plt.rcParams["axes.prop_cycle"].by_key()["color"],
light_color=(1, 1, 1, 0.7),
markers=["*", "+", ".", "D", "x", "d", "v", "s", "1", "^"],
cmap="viridis",
label_quality_factor=r"$F_\mathrm{Q}$",
label_mean_g12=r"$\langle | g_{12} |\rangle$",
label_g12=r"|$g_{12}$|",
label_z="propagation distance z (m)",
label_fwhm=r"$T_\mathrm{FWHM}$ (fs)",
label_wb_distance=r"$L_\mathrm{WB}$",
label_t_jitter="timing jitter (fs)",
label_fwhm_noise="FWHM noise (%)",
label_int_noise="RIN (%)",
text_topright_style=dict(verticalalignment="top", horizontalalignment="right"),
text_topleft_style=dict(verticalalignment="top", horizontalalignment="left"),
)
CONFIG = Config()

6
src/scgenerator/evaluator.py
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@@ -9,10 +9,10 @@ from typing import Any, Callable, MutableMapping, NamedTuple, Optional, Union
import numpy as np
from scgenerator import io, math, operators, utils
from scgenerator import io, math, operators
from scgenerator.const import INF, MANDATORY_PARAMETERS
from scgenerator.logger import get_logger
from scgenerator.physics import fiber, materials, plasma, pulse, units
from scgenerator.utils import get_logger
class ErrorRecord(NamedTuple):
@@ -464,7 +464,7 @@ default_rules: list[Rule] = [
Rule("w0_ind", math.argclosest, ["w", "w0"]),
Rule("w_num", len, ["w"]),
Rule("dw", lambda w: w[1] - w[0]),
Rule(["fft", "ifft"], utils.fft_functions, priorities=1),
Rule(["fft", "ifft"], math.fft_functions, priorities=1),
Rule("wavelength_window", lambda dt, wavelength: (math.min_wl_from_dt(dt, wavelength), 8e-6)),
Rule("wavelength_window", fiber.valid_wavelength_window),
Rule("dispersion_ind", fiber.dispersion_indices),

20
src/scgenerator/io.py
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@@ -6,9 +6,10 @@ import json
import os
import re
from dataclasses import dataclass
from functools import cache
from io import BytesIO
from pathlib import Path
from typing import BinaryIO, Protocol, Sequence
from typing import Any, BinaryIO, Protocol, Sequence
from zipfile import ZipFile
import numpy as np
@@ -67,6 +68,23 @@ def custom_decode_hook(obj):
return obj
@cache
def load_material_dico(name: str) -> dict[str, Any]:
"""
loads a material dictionary
Parameters
----------
name : str
name of the material
Returns
-------
material_dico : dict
"""
return json.loads(data_file("materials.json").read_text())[name]
class PropagationIOHandler(Protocol):
def __len__(self) -> int:
...

11
src/scgenerator/math.py
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@@ -6,7 +6,7 @@ import math
import warnings
from dataclasses import dataclass
from functools import cache
from typing import Sequence
from typing import Callable, Sequence
import numba
import numpy as np
@@ -17,6 +17,15 @@ pi = np.pi
c = 299792458.0
def fft_functions(
full_field: bool,
) -> tuple[Callable[[np.ndarray], np.ndarray], Callable[[np.ndarray], np.ndarray]]:
if full_field:
return np.fft.rfft, np.fft.irfft
else:
return np.fft.fft, np.fft.ifft
def expm1_int(y: np.ndarray, dx: float) -> np.ndarray:
"""evaluates 1 - exp( -∫func(y(x))dx ) from x=-inf to x"""
return -np.expm1(-cumulative_simpson(y) * dx)

2
src/scgenerator/physics/fiber.py
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@@ -377,7 +377,7 @@ def V_parameter_koshiba(l: np.ndarray, pcf_pitch: float, pcf_pitch_ratio: float)
n_co = 1.45
r_eff = pcf_pitch / np.sqrt(3)
pi2a = pipi * r_eff
A, B = saitoh_paramters(pcf_pitch_ratio)
A, _ = saitoh_paramters(pcf_pitch_ratio)
V = A[0] + A[1] / (1 + A[2] * np.exp(A[3] * ratio_l))

6
src/scgenerator/physics/pulse.py
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@@ -24,7 +24,7 @@ from scipy.optimize import minimize_scalar
from scipy.optimize._optimize import OptimizeResult
from scgenerator import math
from scgenerator.defaults import default_plotting
from scgenerator.env import default_plotting
from scgenerator.io import DataFile
from scgenerator.physics import units
@@ -186,8 +186,6 @@ def modify_field_ratio(
pre_field_0: np.ndarray,
peak_power: float = None,
energy: float = None,
intensity_noise: float = None,
noise_correlation: float = 0,
) -> float:
"""
multiply a field by this number to get the desired specifications
@@ -735,7 +733,7 @@ def avg_g12(values: np.ndarray):
avg_values = np.mean(math.abs2(values), axis=0)
coherence = g12(values)
return np.sum(coherence * avg_values) / np.sum(avg_values)
return np.sum(coherence * avg_values, axis=-1) / np.sum(avg_values, axis=-1)
def fwhm_ind(values: np.ndarray, mam=None):

5
src/scgenerator/physics/units.py
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@@ -349,11 +349,6 @@ def get_unit(unit: Union[str, Callable]) -> Callable[[float], float]:
return unit
def is_unit(name, value):
if not hasattr(get_unit(value), "inv"):
raise TypeError("invalid unit specified")
def beta2_coef(beta2_coefficients):
fac = 1e27
out = np.zeros_like(beta2_coefficients)

4
src/scgenerator/plotting.py
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@@ -14,12 +14,12 @@ from scipy.interpolate import UnivariateSpline, interp1d
from scgenerator import math
from scgenerator.const import PARAM_SEPARATOR
from scgenerator.defaults import default_plotting as defaults
from scgenerator.env import default_plotting as defaults
from scgenerator.math import abs2, linear_interp_2d, span
from scgenerator.parameter import Parameters
from scgenerator.physics import pulse, units
from scgenerator.physics.units import PlotRange, sort_axis
from scgenerator.spectra import Propagation, Spectrum
from scgenerator.spectra import Spectrum
RangeType = tuple[float, float, Union[str, Callable]]
NO_LIM = object()

7
src/scgenerator/spectra.py
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@@ -150,6 +150,13 @@ class Spectrum(np.ndarray):
def measure(self) -> tuple[float, float, float]:
return pulse.measure_field(self.t, self.time_amp)
def coherence(self, axis: int = 0) -> np.ndarray:
"""
returns the coherence of the spectrum, computed by collapsing axis `axis` and aligned
on the wavelength grid
"""
return pulse.g12(self, axis)[..., self.wl_order]
freq_int = afreq_int
freq_amp = afreq_amp

11
src/scgenerator/threading.py
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@@ -1,11 +1,14 @@
from multiprocessing import Process, Queue
from multiprocessing import Queue
from threading import Thread
from tqdm import tqdm
class Multibar(Thread):
queue: Queue
bars:list[tqdm]
def __init__(self, bars:list[tqdm], queue:Queue):
bars: list[tqdm]
def __init__(self, bars: list[tqdm], queue: Queue):
self.queue = queue
self.bars = bars
@@ -14,5 +17,3 @@ class Multibar(Thread):
bar_id, amount = self.queue.get(True, None)
self.bars[bar_id].update(amount)
self.bars[0].update(amount)

48
src/scgenerator/transform.py
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@@ -1,48 +0,0 @@
import numpy as np
import scgenerator.math as math
import scgenerator.physics.units as units
def normalize_range(
axis: np.ndarray, _range: tuple | units.PlotRange | None, num: int
) -> tuple[units.PlotRange, np.ndarray]:
if _range is None:
_range = units.PlotRange(axis.min(), axis.max(), units.no_unit)
elif not isinstance(_range, units.PlotRange):
_range = units.PlotRange(*_range)
new_axis = np.linspace(_range[0], _range[1], num)
return _range, new_axis
def prop_2d(
values: np.ndarray,
h_axis: np.ndarray,
v_axis: np.ndarray,
h_range: tuple | units.PlotRange | None = None,
v_range: tuple | units.PlotRange | None = None,
h_num: int = 1024,
v_num: int = 1024,
z_lim: tuple[float, float] | None = None,
):
if values.ndim != 2:
raise TypeError("prop_2d can only transform 2d data")
if np.iscomplexobj(values):
values = math.abs2(values)
horizontal_range, horizontal = normalize_range(h_axis, h_range, h_num)
vertical_range, vertical = normalize_range(v_axis, v_range, v_num)
values = math.interp_2d(
h_axis, v_axis, values, horizontal_range.unit(horizontal), vertical_range.unit(vertical)
)
if horizontal_range.must_correct_wl:
values = np.apply_along_axis(
lambda x: units.to_WL(x, horizontal_range.unit.to.m(horizontal)), 1, values
)
elif vertical_range.must_correct_wl:
values = np.apply_along_axis(
lambda x: units.to_WL(x, vertical_range.unit.to.m(vertical)), 0, values
)
return horizontal, vertical, values

297
src/scgenerator/utils.py
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@@ -1,297 +0,0 @@
"""
This files includes utility functions designed more or less to be used specifically with the
scgenerator module but some function may be used in any python program
"""
from __future__ import annotations
import itertools
import json
import os
import re
import tomllib
from functools import cache, lru_cache
from pathlib import Path
from string import printable as str_printable
from typing import Any, Callable, MutableMapping, Sequence, TypeVar, Union
import numpy as np
from scgenerator import io
from scgenerator.const import PARAM_FN, PARAM_SEPARATOR, SPEC1_FN
from scgenerator.logger import get_logger
T_ = TypeVar("T_")
def conform_variable_entry(d) -> list[dict[str, list]]:
if isinstance(d, MutableMapping):
d = [{k: v} for k, v in d.items()]
return d
def load_previous_spectrum(prev_data_dir: str) -> np.ndarray:
prev_data_dir = Path(prev_data_dir)
num = find_last_spectrum_num(prev_data_dir)
return load_spectrum(prev_data_dir / SPEC1_FN.format(num))
@lru_cache(20000)
def load_spectrum(file: os.PathLike) -> np.ndarray:
return np.load(file)
def conform_toml_path(path: os.PathLike) -> Path:
path: str = str(path)
if not path.lower().endswith(".toml"):
path = path + ".toml"
return Path(path)
def open_single_config(path: os.PathLike) -> dict[str, Any]:
d = _open_config(path)
f = d.pop("Fiber", [{}])[0]
return d | f
def _open_config(path: os.PathLike):
"""
returns a dictionary parsed from the specified toml file
This also handle having a 'INCLUDE' argument that will fill
otherwise unspecified keys with what's in the INCLUDE file(s)
"""
path = conform_toml_path(path)
dico = resolve_loadfile_arg(load_toml(path))
if "Fiber" not in dico:
dico = dict(name=path.name, Fiber=[dico])
resolve_relative_paths(dico, path.parent)
return dico
def resolve_relative_paths(d: dict[str, Any], root: os.PathLike | None = None):
root = Path(root) if root is not None else Path.cwd()
for k, v in d.items():
if isinstance(v, MutableMapping):
resolve_relative_paths(v, root)
elif not isinstance(v, str) and isinstance(v, Sequence):
for el in v:
if isinstance(el, MutableMapping):
resolve_relative_paths(el, root)
elif "file" in k:
d[k] = str(root / v)
def resolve_loadfile_arg(dico: dict[str, Any]) -> dict[str, Any]:
if (f_list := dico.pop("INCLUDE", None)) is not None:
if isinstance(f_list, str):
f_list = [f_list]
for to_load in f_list:
loaded = load_toml(to_load)
for k, v in loaded.items():
if k not in dico and k not in dico.get("variable", {}):
dico[k] = v
for k, v in dico.items():
if isinstance(v, MutableMapping):
dico[k] = resolve_loadfile_arg(v)
elif isinstance(v, Sequence):
for i, vv in enumerate(v):
if isinstance(vv, MutableMapping):
dico[k][i] = resolve_loadfile_arg(vv)
return dico
def load_toml(descr: os.PathLike) -> dict[str, Any]:
descr = str(descr)
if ":" in descr:
path, entry = descr.split(":", 1)
with open(path, "rb") as file:
return tomllib.load(file)[entry]
else:
with open(descr, "rb") as file:
return tomllib.load(file)
def load_flat(descr: os.PathLike) -> dict[str, Any]:
with open(descr, "rb") as file:
d = tomllib.load(file)
if "Fiber" in d:
for fib in d["Fiber"]:
for k, v in fib.items():
d[k] = v
break
return d
@cache
def load_material_dico(name: str) -> dict[str, Any]:
"""
loads a material dictionary
Parameters
----------
name : str
name of the material
Returns
-------
material_dico : dict
"""
return json.loads(io.data_file("materials.json").read_text())[name]
def save_data(data: Union[np.ndarray, MutableMapping], data_dir: Path, file_name: str):
"""
saves numpy array to disk
Parameters
----------
data : Union[np.ndarray, MutableMapping]
data to save
file_name : str
file name
task_id : int
id that uniquely identifies the process
identifier : str, optional
identifier in the main data folder of the task, by default ""
"""
path = data_dir / file_name
if isinstance(data, np.ndarray):
np.save(path, data)
elif isinstance(data, MutableMapping):
np.savez(path, **data)
get_logger(__name__).debug(f"saved data in {path}")
return
def ensure_folder(path: Path, prevent_overwrite: bool = True, mkdir=True) -> Path:
"""
ensure a folder exists and doesn't overwrite anything if required
Parameters
----------
path : Path
desired path
prevent_overwrite : bool, optional
whether to create a new directory when one already exists, by default True
Returns
-------
Path
final path
"""
path = path.resolve()
# is path root ?
if len(path.parts) < 2:
return path
# is a part of path an existing *file* ?
parts = path.parts
path = Path(path.root)
for part in parts:
if path.is_file():
path = ensure_folder(path, mkdir=mkdir, prevent_overwrite=False)
path /= part
folder_name = path.name
for i in itertools.count():
if not path.is_file() and (not prevent_overwrite or not path.is_dir()):
if mkdir:
path.mkdir(exist_ok=True)
return path
path = path.parent / (folder_name + f"_{i}")
def branch_id(branch: Path) -> tuple[int, int]:
sim_match = branch.resolve().parent.name.split()[0]
if sim_match.isdigit():
s_int = int(sim_match)
else:
s_int = 0
branch_match = re.search(r"(?<=b_)[0-9]+", branch.name)
if branch_match is None:
b_int = 0
else:
b_int = int(branch_match[0])
return s_int, b_int
def find_last_spectrum_num(data_dir: Path):
for num in itertools.count(1):
p_to_test = data_dir / SPEC1_FN.format(num)
if not p_to_test.is_file() or os.path.getsize(p_to_test) == 0:
return num - 1
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)),
)
)
def to_62(i: int) -> str:
arr = []
if i == 0:
return "0"
i = abs(i)
while i:
i, value = divmod(i, 62)
arr.append(str_printable[value])
return "".join(reversed(arr))
def fft_functions(
full_field: bool,
) -> tuple[Callable[[np.ndarray], np.ndarray], Callable[[np.ndarray], np.ndarray]]:
if full_field:
return np.fft.rfft, np.fft.irfft
else:
return np.fft.fft, np.fft.ifft
def update_path_name(p: str) -> str:
return re.sub(r"( ?num [0-9]+)|(u_[0-9]+ )", "", p)
def fiber_folder(i: int, sim_name: str, fiber_name: str) -> str:
return PARAM_SEPARATOR.join([format(i), sim_name, fiber_name])
def simulations_list(path: os.PathLike) -> list[Path]:
"""
finds simulations folders contained in a parent directory
Parameters
----------
path : os.PathLike
parent path
Returns
-------
list[Path]
Absolute Path to the simulation folder
"""
paths: list[Path] = []
for pwd, _, files in os.walk(path):
if PARAM_FN in files and SPEC1_FN.format(0) in files:
paths.append(Path(pwd))
paths.sort(key=branch_id)
return [p for p in paths if p.parent.name == paths[-1].parent.name]