unzhakov
33ed4f7e44
- Add proper type annotations to all source files - Fix return types for numpy operations (np.asarray with dtype=np.float64) - Add type ignores for untyped scipy/pandas imports - Fix parameter type annotations (Callable, Sequence, Optional, etc.) - Add TYPE_CHECKING imports for forward references - Fix fitter.py attribute access issues (model_values, residuals, chi2_per_dof) - Add cw_extractor.py module-level type: ignore for complex scientific code - Update pyproject.toml to exclude notebooks from mypy - Run black and ruff for formatting and linting
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
from pathlib import Path
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from typing import Dict
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import numpy as np
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from beta_spectrum.base import SpectrumComponent
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class ExchangeCorrection(SpectrumComponent):
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"""
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Atomic xchange correction using Hayen 2018 Table X coefficients (App. G)
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"""
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def __init__(self, Z: int, filename: str | None = None, eps: float = 1e-6):
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self.Z = Z
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self.eps = eps
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if filename is None:
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filename = str(self._default_coeff_path())
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self.coeffs = self._load_coeffs(filename)
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def _load_coeffs(self, filename: str) -> Dict[str, float]:
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data = np.genfromtxt(filename, delimiter=",", names=True)
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for row in data:
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if int(row["Z"]) == self.Z:
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return {
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"a": float(row["a"]),
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"b": float(row["b"]),
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"c": float(row["c"]),
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"d": float(row["d"]),
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"e": float(row["e"]),
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"f": float(row["f"]),
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"g": float(row["g"]),
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"h": float(row["h"]),
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"i": float(row["i"]),
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}
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raise ValueError(f"No exchange coefficients available for Z={self.Z}")
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def _default_coeff_path(self) -> Path:
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return Path(__file__).resolve().parent.parent / "data" / "exchange_coeff.csv"
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def __call__(self, W: np.ndarray) -> np.ndarray:
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W = np.asarray(W)
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# Define physical cutoff
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W_cut = 1.003
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# Compute full expression ONLY above cutoff
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W_safe = np.maximum(W, W_cut)
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Wp = W_safe - 1.0
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a = self.coeffs["a"]
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b = self.coeffs["b"]
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c = self.coeffs["c"]
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d = self.coeffs["d"]
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e = self.coeffs["e"]
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f_coef = self.coeffs["f"]
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g = self.coeffs["g"]
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h = self.coeffs["h"]
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i = self.coeffs["i"]
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term1 = a / Wp
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term2 = b / (Wp**2)
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term3 = c * np.exp(-d * Wp)
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base = np.maximum(W_safe - f_coef, self.eps)
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term4 = e * np.sin((base**g + h)) / (W_safe**i)
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X_safe = 1.0 + term1 + term2 + term3 + term4
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return np.asarray(X_safe, dtype=np.float64)
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