unzhakov
d6328d4b24
- Add commit hash (6 chars) and UTC timestamp to all plots via text boxes - Add nuclear data header (nuclide, endpoint, transition, corrections) to plots - Two display modes: - DEBUG (-vv): 4-panel debug view with all correction factors - Normal (default): single spectrum plot with nuclear data header - CLI: --plot now passes show_components=(args.verbose >= 2)
981 lines
29 KiB
Python
981 lines
29 KiB
Python
# spectrum.py
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from __future__ import annotations
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import logging
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from datetime import datetime, timezone
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import numpy as np
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import matplotlib.pyplot as plt
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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from dataclasses import dataclass
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from beta_spectrum.base import SpectrumComponent
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from beta_spectrum.components.phase_space import PhaseSpace
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from beta_spectrum.components.fermi import FermiFunction
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from beta_spectrum.components.finite_size import FiniteSizeL0, ChargeDistributionU
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from beta_spectrum.components.screening import ScreeningCorrection
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from beta_spectrum.components.exchange import ExchangeCorrection
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from beta_spectrum.components.radiative import RadiativeCorrection
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from beta_spectrum.logging_utils import get_git_short_hash
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from beta_spectrum.utils import T_to_W
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from beta_spectrum.constants import ME_MEV
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if TYPE_CHECKING:
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from beta_spectrum.components.detector_response import DetectorResponse
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@dataclass
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class SpectrumConfig:
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"""
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Configuration for beta spectrum calculation.
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Supports declarative detector response specification:
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set detector_model, detector_sigma_a_keV, and related parameters
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to enable automatic detector smearing via convolve_with_detector().
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"""
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Z_parent: int
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Z_daughter: int
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A_number: int
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endpoint_MeV: float
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transition_type: str = "A" # should be [A, F1, F1U, F2, F2U, F3, F3U, F4]
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e_step_MeV: float = 0.001
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# Toggle components and corrections
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use_phase_space: bool = True
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use_fermi: bool = True
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use_screening: bool = True
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use_finite_size: bool = True
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use_charge_dist: bool = True
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use_radiative: bool = True
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use_exchange: bool = True
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# Detector response convolution (analytical model)
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use_detector_response: bool = False
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detector_model: str = "gaussian"
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detector_sigma_a_keV: float = 1.0
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detector_sigma_b: float = 0.0
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detector_tail_fraction: float = 0.0
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detector_tau_keV: float = 5.0
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detector_fano_factor: float = 0.12
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detector_n_channels: int = 4096
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detector_channel_energy_range: tuple[float, float] = (
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0.0,
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0.35,
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) # in m_e units (total energy)
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class BetaSpectrum:
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"""
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Precise beta spectrum calculator.
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Combines basic spectrum shape (fermi * phase_space) with mutplicative corrections.
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Parameters
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----------
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components : List[SpectrumComponent]
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List of enabled spectral components.
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logger : logging.Logger, optional
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Logger for debug/info output.
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"""
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def __init__(
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self,
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components: List[SpectrumComponent],
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logger: Optional[logging.Logger] = None,
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):
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self.components = components
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self._component_names = [self._get_component_name(c) for c in components]
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self._logger = logger
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def __call__(self, W: np.ndarray) -> np.ndarray:
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"""
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Calculate total spectrum as a product of enabled components
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"""
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result: np.ndarray = np.ones_like(W, dtype=float)
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for comp in self.components:
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if self._logger:
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self._logger.debug(
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"Evaluating %s at %d energy points", comp.__class__.__name__, len(W)
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)
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result = result * comp(W)
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return result.astype(np.float64)
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def _get_component_name(self, comp: SpectrumComponent) -> str:
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"""
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Get human-readable component names
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"""
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class_name = comp.__class__.__name__
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return class_name.replace("Correction", "").replace("Function", "")
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def calculate_components(self, W: np.ndarray) -> Dict[str, np.ndarray]:
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"""
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Calculate individual components for debugging and analysis
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"""
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components_dict = {}
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for name, comp in zip(self._component_names, self.components):
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components_dict[name] = comp(W)
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return components_dict
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@classmethod
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def from_config(
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cls, config: SpectrumConfig, logger: Optional[logging.Logger] = None
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) -> BetaSpectrum:
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"""
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Create a BetaSpectrum from configuration.
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Parameters
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----------
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config : SpectrumConfig
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Configuration with decay parameters and correction toggles.
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logger : logging.Logger, optional
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Logger for progress output.
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"""
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components: List[SpectrumComponent] = []
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W0 = float(T_to_W(config.endpoint_MeV))
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if config.use_phase_space:
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components.append(
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PhaseSpace(W0=W0, transition_type=config.transition_type, logger=logger)
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)
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if config.use_fermi:
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components.append(
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FermiFunction(Z=config.Z_daughter, A=config.A_number, logger=logger)
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)
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if config.use_finite_size:
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components.append(
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FiniteSizeL0(Z=config.Z_daughter, A=config.A_number, logger=logger)
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)
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if config.use_charge_dist:
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components.append(
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ChargeDistributionU(
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Z=config.Z_daughter, A=config.A_number, logger=logger
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)
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)
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if config.use_screening:
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components.append(
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ScreeningCorrection(
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FermiFunction(Z=config.Z_parent, A=config.A_number, logger=logger),
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logger=logger,
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)
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)
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if config.use_exchange:
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components.append(ExchangeCorrection(Z=config.Z_parent, logger=logger))
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if config.use_radiative:
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components.append(
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RadiativeCorrection(
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W0=W0,
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Z=config.Z_daughter,
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A=config.A_number,
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use_endpoint_resummation=True,
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logger=logger,
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)
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)
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return cls(components, logger=logger)
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def get_energy_grid(self, config: SpectrumConfig) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Generate array of energy values for spectrum calculation.
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"""
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kinetic_MeV = np.arange(
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config.e_step_MeV, config.endpoint_MeV, config.e_step_MeV
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)
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W = T_to_W(kinetic_MeV)
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return W, kinetic_MeV.astype(np.float64)
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@staticmethod
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def create_detector_from_config(
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config: SpectrumConfig,
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) -> "DetectorResponse":
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"""
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Create a DetectorResponse from SpectrumConfig detector parameters.
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Converts keV-based resolution parameters to m_e units internally.
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Parameters
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----------
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config : SpectrumConfig
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Configuration with detector response parameters set.
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Returns
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-------
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DetectorResponse
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Detector response object ready for convolution.
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"""
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from beta_spectrum.components.detector_response import DetectorResponse
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from beta_spectrum.utils import T_to_W
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sigma_a_me = config.detector_sigma_a_keV / ME_MEV
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tau_me = config.detector_tau_keV / ME_MEV
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W0 = T_to_W(config.endpoint_MeV)
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channel_range = config.detector_channel_energy_range
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# Extend range to cover endpoint
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if channel_range[1] < W0:
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channel_range = (channel_range[0], float(W0 + 0.05))
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detector = DetectorResponse.from_gaussian_params(
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channel_energy_range=channel_range,
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n_channels=config.detector_n_channels,
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sigma_a=sigma_a_me,
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sigma_b=config.detector_sigma_b,
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tail_fraction=config.detector_tail_fraction,
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tau=tau_me,
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model=config.detector_model,
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fano_factor=config.detector_fano_factor,
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)
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return detector
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def convolve_with_detector(
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self,
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detector_response: "DetectorResponse",
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W: Optional[np.ndarray] = None,
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config: Optional[SpectrumConfig] = None,
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) -> np.ndarray:
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"""
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Convolve theoretical spectrum with detector response.
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Returns the predicted measured spectrum after detector smearing.
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Parameters
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----------
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detector_response : DetectorResponse
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Detector response object (analytical or tabulated).
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W : np.ndarray, optional
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Energy grid in m_e units. If None, generated from config.
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config : SpectrumConfig, optional
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Used to generate energy grid if W is None.
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Returns
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-------
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np.ndarray
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Convolved spectrum (predicted measured counts per channel).
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"""
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if W is None:
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if config is None:
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raise ValueError("Either W or config must be provided")
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W, _ = self.get_energy_grid(config)
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theoretical_spectrum = self(W)
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if self._logger:
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self._logger.debug(
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"Convolution: %d channels, %d spectrum points",
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len(detector_response.channel_energies),
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len(W),
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)
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convolved = detector_response.convolve(W, theoretical_spectrum, normalize=True)
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return convolved
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def convolve_detector(
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self,
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config: SpectrumConfig,
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W: Optional[np.ndarray] = None,
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detector_response: Optional["DetectorResponse"] = None,
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) -> np.ndarray:
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"""
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Convolve theoretical spectrum with detector response from config.
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Convenience method: creates DetectorResponse from config parameters
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and convolves the spectrum in a single call.
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Parameters
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----------
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config : SpectrumConfig
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Configuration with detector response parameters set.
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W : np.ndarray, optional
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Energy grid in m_e units. If None, generated from config.
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detector_response : DetectorResponse, optional
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Pre-built detector response. If None, created from config.
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Returns
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-------
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np.ndarray
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Convolved spectrum (predicted measured counts per channel).
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Examples
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--------
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>>> config = SpectrumConfig(
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... Z_parent=43, Z_daughter=44, A_number=99, endpoint_MeV=0.294,
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... use_detector_response=True,
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... detector_sigma_a_keV=1.0,
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... )
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>>> spectrum = BetaSpectrum.from_config(config)
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>>> W, _ = spectrum.get_energy_grid(config)
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>>> convolved = spectrum.convolve_detector(config, W=W)
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"""
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if detector_response is None:
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detector_response = self.create_detector_from_config(config)
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return self.convolve_with_detector(detector_response, W=W, config=config)
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class BetaSpectrumAnalyzer:
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"""
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Introspection, analysis and debugging tools for BetaSpectrum.
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Parameters
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----------
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spectrum : BetaSpectrum
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The spectrum to analyze.
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config : SpectrumConfig
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Configuration used to create the spectrum.
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logger : logging.Logger, optional
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Logger for progress output.
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"""
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def __init__(
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self,
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spectrum: BetaSpectrum,
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config: SpectrumConfig,
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logger: Optional[logging.Logger] = None,
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):
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"""
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Initialize analyzer with a spectrum and configuration.
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"""
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self.spectrum = spectrum
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self.config = config
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self.W, self.energies_MeV = spectrum.get_energy_grid(config)
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self._components_cache: Optional[Dict[str, np.ndarray]] = None
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self._logger = logger
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@property
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def components(self) -> Dict[str, np.ndarray]:
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if self._components_cache is None:
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self._components_cache = self.spectrum.calculate_components(self.W)
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assert self._components_cache is not None
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return self._components_cache
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def total_spectrum(self, normalize: bool = True) -> np.ndarray:
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"""
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Calculate the total_spectrum
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"""
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total = self.spectrum(self.W)
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if normalize:
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integral = np.trapezoid(total, self.energies_MeV)
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total = total / integral
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return total
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def convolved_spectrum(
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self,
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detector_response: Optional["DetectorResponse"] = None,
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normalize: bool = True,
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) -> np.ndarray:
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"""
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Calculate spectrum convolved with detector response.
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If detector_response is None and use_detector_response is True in config,
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creates a detector response from config parameters.
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Parameters
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----------
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detector_response : DetectorResponse, optional
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Detector response to convolve with. Created from config if None.
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normalize : bool
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If True, normalize to unit area.
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Returns
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-------
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np.ndarray
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Convolved spectrum.
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"""
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if detector_response is None and self.config.use_detector_response:
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detector_response = BetaSpectrum.create_detector_from_config(self.config)
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if detector_response is None:
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raise ValueError(
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"detector_response must be provided, or set "
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"use_detector_response=True in config"
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)
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convolved = self.spectrum.convolve_with_detector(
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detector_response, W=self.W, config=self.config
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)
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if normalize:
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integral = np.trapezoid(convolved, detector_response.channel_energies)
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if integral > 0:
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convolved = convolved / integral
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return convolved
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def plot_analysis(
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self,
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save_path: Optional[str] = None,
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show_components: bool = True,
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) -> None:
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"""
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Create visualization of the spectrum and all correction factors.
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Parameters
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----------
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save_path : str, optional
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Path to save the figure.
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show_components : bool
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If True, show full debug view (4-panel with all corrections).
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If False, show only the spectrum plot with nuclear data header.
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"""
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total = self.total_spectrum(normalize=True)
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components = self.components
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commit = get_git_short_hash(6)
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timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
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if show_components:
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self._plot_debug_view(
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total, components, save_path, commit, timestamp
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)
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else:
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self._plot_spectrum_only(
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total, save_path, commit, timestamp
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)
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def _add_id_textbox(
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self, ax: plt.Axes, commit: str, timestamp: str
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) -> None:
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"""Add ID text box with commit hash and timestamp to plot."""
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id_text = f"commit: {commit} | {timestamp}"
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ax.text(
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0.01,
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0.01,
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id_text,
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transform=ax.transAxes,
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fontsize=8,
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color="gray",
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verticalalignment="bottom",
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horizontalalignment="left",
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fontfamily="monospace",
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)
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def _add_nuclear_data_header(self, ax: plt.Axes) -> None:
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"""Add nuclear data information header to plot."""
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parent = self._element_symbol(self.config.Z_parent)
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daughter = self._element_symbol(self.config.Z_daughter)
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enabled_corrections = []
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if self.config.use_phase_space:
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enabled_corrections.append("phase_space")
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if self.config.use_fermi:
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enabled_corrections.append("fermi")
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if self.config.use_screening:
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enabled_corrections.append("screening")
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if self.config.use_finite_size:
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enabled_corrections.append("finite_size")
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if self.config.use_charge_dist:
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enabled_corrections.append("charge_dist")
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if self.config.use_radiative:
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enabled_corrections.append("radiative")
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if self.config.use_exchange:
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enabled_corrections.append("exchange")
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header_lines = [
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f"Nuclide: {parent}{self.config.A_number} -> {daughter}{self.config.A_number}",
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f"Endpoint: {self.config.endpoint_MeV * 1000:.1f} keV",
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f"Transition: {self.config.transition_type}",
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f"Corrections: {', '.join(enabled_corrections)}",
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]
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if self.config.use_detector_response:
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header_lines.append(
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f"Detector: {self.config.detector_model} "
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f"(σ={self.config.detector_sigma_a_keV} keV)"
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)
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header_text = "\n".join(header_lines)
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ax.text(
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0.99,
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0.99,
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header_text,
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transform=ax.transAxes,
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fontsize=8,
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verticalalignment="top",
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horizontalalignment="right",
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bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.8, edgecolor="none"),
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)
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def _plot_spectrum_only(
|
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self,
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total: np.ndarray,
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save_path: Optional[str],
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commit: str,
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timestamp: str,
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) -> None:
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"""Plot only the total spectrum with nuclear data header."""
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.plot(
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self.energies_MeV, total, "b-", lw=2, label="Normalized spectrum"
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)
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ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=11)
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ax.set_ylabel("Normalized Counts", fontsize=11)
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ax.set_title(
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f"Beta-decay: {self.config.Z_parent} -> {self.config.Z_daughter}, A={self.config.A_number}",
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fontsize=13,
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)
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ax.set_yscale("log")
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ax.grid(True, alpha=0.3)
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ax.legend(loc="best", fontsize=10)
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self._add_nuclear_data_header(ax)
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self._add_id_textbox(ax, commit, timestamp)
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plt.tight_layout()
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if save_path:
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fig.savefig(save_path, dpi=150, bbox_inches="tight")
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print(f"Figure saved to {save_path}")
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plt.show()
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def _plot_debug_view(
|
||
self,
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total: np.ndarray,
|
||
components: Dict[str, np.ndarray],
|
||
save_path: Optional[str],
|
||
commit: str,
|
||
timestamp: str,
|
||
) -> None:
|
||
"""Full debug view with 4-panel spectrum analysis."""
|
||
fig = plt.figure(figsize=(14, 10))
|
||
|
||
# 1. Main spectrum plot (top-left)
|
||
ax1 = plt.subplot(2, 2, 1)
|
||
ax1.plot(
|
||
self.energies_MeV, total, "b-", lw=2, label="Total spectrum (log scale)"
|
||
)
|
||
ax1.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=10)
|
||
ax1.set_ylabel(
|
||
f"Normalized Counts per {self.config.e_step_MeV:.3e} MeV", fontsize=10
|
||
)
|
||
ax1.set_title(
|
||
f"Beta-decay: Z={self.config.Z_parent} -> {self.config.Z_daughter}, A={self.config.A_number}",
|
||
fontsize=12,
|
||
)
|
||
ax1.set_yscale("log")
|
||
ax1.grid(True, alpha=0.3)
|
||
ax1.legend()
|
||
|
||
# 2. Correction plots (top-right)
|
||
ax2 = plt.subplot(2, 2, 2)
|
||
|
||
# Set color order for correction plots
|
||
component_order = [
|
||
"PhaseSpace",
|
||
"Fermi",
|
||
"Screening",
|
||
"Exchange",
|
||
"FiniteSizeL0",
|
||
"ChargeDistributionU",
|
||
"Radiative",
|
||
]
|
||
colors = ["gray", "red", "green", "blue", "orange", "purple", "brown"]
|
||
|
||
for name, color in zip(component_order, colors):
|
||
if name in components:
|
||
values = components[name]
|
||
|
||
# For Fermi and PhaseSpace, scale for visibility
|
||
if name in ["Fermi", "PhaseSpace"]:
|
||
values = values / np.max(values)
|
||
label = f"{name} (norm)"
|
||
else:
|
||
label = name
|
||
|
||
ax2.plot(
|
||
self.energies_MeV,
|
||
values,
|
||
color=color,
|
||
lw=1.5,
|
||
label=label,
|
||
alpha=0.8,
|
||
)
|
||
|
||
ax2.set_xlabel("Electron kinetic energy E [MeV]", fontsize=10)
|
||
ax2.set_ylabel("Correction factor", fontsize=10)
|
||
ax2.set_title("Spectrum components", fontsize=12)
|
||
ax2.grid(True, alpha=0.3)
|
||
ax2.legend(loc="best", fontsize=8)
|
||
|
||
# 3. Cumulative effect (botom-left)
|
||
ax3 = plt.subplot(2, 2, 3)
|
||
|
||
# Cumulative product
|
||
cumulative = np.ones_like(self.W)
|
||
baseline_norm = np.ones_like(cumulative)
|
||
|
||
# Plot baseline
|
||
ax3.plot(
|
||
self.energies_MeV, baseline_norm, "k--", lw=1.5, label="Baseline", alpha=0.7
|
||
)
|
||
|
||
# Add components one by one
|
||
for name, color in zip(component_order, colors):
|
||
if name in components:
|
||
cumulative *= components[name]
|
||
norm_cumulative = cumulative / np.max(cumulative)
|
||
ax3.plot(
|
||
self.energies_MeV,
|
||
norm_cumulative,
|
||
color=color,
|
||
lw=1.5,
|
||
label=f"+ {name}",
|
||
alpha=0.7,
|
||
)
|
||
|
||
ax3.set_xlabel("Electron kinetic energy E [MeV]", fontsize=10)
|
||
ax3.set_ylabel("Normalized spectrum", fontsize=10)
|
||
ax3.set_title("Cumulative effect", fontsize=12)
|
||
ax3.grid(True, alpha=0.3)
|
||
ax3.legend(loc="best", fontsize=8)
|
||
|
||
# 4. Deviation from unity (bottom-right)
|
||
ax4 = plt.subplot(2, 2, 4)
|
||
|
||
for name, color in zip(component_order, colors):
|
||
if name in components and name not in ["Fermi", "PhaseSpace"]:
|
||
deviation = components[name] - 1.0
|
||
ax4.plot(
|
||
self.energies_MeV,
|
||
deviation,
|
||
color=color,
|
||
lw=1.5,
|
||
label=f"{name}",
|
||
alpha=0.7,
|
||
)
|
||
|
||
ax4.set_xlabel("Electron kinetic energy E [MeV]", fontsize=10)
|
||
ax4.set_ylabel("Deviation from unity", fontsize=10)
|
||
ax4.set_title("Correction deviations (C - 1)", fontsize=12)
|
||
ax4.grid(True, alpha=0.3)
|
||
ax4.legend(loc="best", fontsize=8)
|
||
ax4.axhline(y=0, color="k", linestyle="-", lw=0.5)
|
||
|
||
plt.suptitle(
|
||
f"Beta-decay spectrum {self.config.A_number}{self._element_symbol(self.config.Z_parent)} -> {self.config.A_number}{self._element_symbol(self.config.Z_daughter)}",
|
||
fontsize=14,
|
||
fontweight="bold",
|
||
)
|
||
plt.tight_layout()
|
||
|
||
if save_path:
|
||
fig.savefig(save_path, dpi=150, bbox_inches="tight")
|
||
print(f"Figure saved to {save_path}")
|
||
|
||
plt.show()
|
||
|
||
def _element_symbol(self, Z: int) -> str:
|
||
symbols = [
|
||
"",
|
||
"H",
|
||
"He",
|
||
"Li",
|
||
"Be",
|
||
"B",
|
||
"C",
|
||
"N",
|
||
"O",
|
||
"F",
|
||
"Ne",
|
||
"Na",
|
||
"Mg",
|
||
"Al",
|
||
"Si",
|
||
"P",
|
||
"S",
|
||
"Cl",
|
||
"Ar",
|
||
"K",
|
||
"Ca",
|
||
"Sc",
|
||
"Ti",
|
||
"V",
|
||
"Cr",
|
||
"Mn",
|
||
"Fe",
|
||
"Co",
|
||
"Ni",
|
||
"Cu",
|
||
"Zn",
|
||
"Ga",
|
||
"Ge",
|
||
"As",
|
||
"Se",
|
||
"Br",
|
||
"Kr",
|
||
"Rb",
|
||
"Sr",
|
||
"Y",
|
||
"Zr",
|
||
"Nb",
|
||
"Mo",
|
||
"Tc",
|
||
"Ru",
|
||
"Rh",
|
||
"Pd",
|
||
"Ag",
|
||
"Cd",
|
||
"In",
|
||
"Sn",
|
||
"Sb",
|
||
"Te",
|
||
"I",
|
||
"Xe",
|
||
"Cs",
|
||
"Ba",
|
||
"La",
|
||
"Ce",
|
||
"Pr",
|
||
"Nd",
|
||
"Pm",
|
||
"Sm",
|
||
"Eu",
|
||
"Gd",
|
||
"Tb",
|
||
"Dy",
|
||
"Ho",
|
||
"Er",
|
||
"Tm",
|
||
"Yb",
|
||
"Lu",
|
||
"Hf",
|
||
"Ta",
|
||
"W",
|
||
"Re",
|
||
"Os",
|
||
"Ir",
|
||
"Pt",
|
||
"Au",
|
||
"Hg",
|
||
"Tl",
|
||
"Pb",
|
||
"Bi",
|
||
"Po",
|
||
"At",
|
||
"Rn",
|
||
"Fr",
|
||
"Ra",
|
||
"Ac",
|
||
"Th",
|
||
"Pa",
|
||
"U",
|
||
"Np",
|
||
"Pu",
|
||
"Am",
|
||
"Cm",
|
||
"Bk",
|
||
"Cf",
|
||
"Es",
|
||
"Fm",
|
||
]
|
||
return symbols[Z] if Z < len(symbols) else f"Z{Z}"
|
||
|
||
def export_to_csv(self, filename: str, source_type: str = "unknown") -> None:
|
||
"""
|
||
Export spectrum data to CSV with metadata header.
|
||
|
||
Parameters
|
||
----------
|
||
filename : str
|
||
Output CSV file path.
|
||
source_type : str
|
||
Data source type (paceENSDF, json, cli) for the header.
|
||
"""
|
||
total = self.total_spectrum(normalize=True)
|
||
components = self.components
|
||
|
||
# Build enabled corrections list
|
||
enabled = []
|
||
if self.config.use_phase_space:
|
||
enabled.append("phase_space")
|
||
if self.config.use_fermi:
|
||
enabled.append("fermi")
|
||
if self.config.use_screening:
|
||
enabled.append("screening")
|
||
if self.config.use_finite_size:
|
||
enabled.append("finite_size")
|
||
if self.config.use_charge_dist:
|
||
enabled.append("charge_dist")
|
||
if self.config.use_radiative:
|
||
enabled.append("radiative")
|
||
if self.config.use_exchange:
|
||
enabled.append("exchange")
|
||
|
||
# Build metadata header
|
||
from datetime import datetime, timezone
|
||
|
||
def _element_symbol(Z: int) -> str:
|
||
symbols = [
|
||
"",
|
||
"H",
|
||
"He",
|
||
"Li",
|
||
"Be",
|
||
"B",
|
||
"C",
|
||
"N",
|
||
"O",
|
||
"F",
|
||
"Ne",
|
||
"Na",
|
||
"Mg",
|
||
"Al",
|
||
"Si",
|
||
"P",
|
||
"S",
|
||
"Cl",
|
||
"Ar",
|
||
"K",
|
||
"Ca",
|
||
"Sc",
|
||
"Ti",
|
||
"V",
|
||
"Cr",
|
||
"Mn",
|
||
"Fe",
|
||
"Co",
|
||
"Ni",
|
||
"Cu",
|
||
"Zn",
|
||
"Ga",
|
||
"Ge",
|
||
"As",
|
||
"Se",
|
||
"Br",
|
||
"Kr",
|
||
"Rb",
|
||
"Sr",
|
||
"Y",
|
||
"Zr",
|
||
"Nb",
|
||
"Mo",
|
||
"Tc",
|
||
"Ru",
|
||
"Rh",
|
||
"Pd",
|
||
"Ag",
|
||
"Cd",
|
||
"In",
|
||
"Sn",
|
||
"Sb",
|
||
"Te",
|
||
"I",
|
||
"Xe",
|
||
"Cs",
|
||
"Ba",
|
||
"La",
|
||
"Ce",
|
||
"Pr",
|
||
"Nd",
|
||
"Pm",
|
||
"Sm",
|
||
"Eu",
|
||
"Gd",
|
||
"Tb",
|
||
"Dy",
|
||
"Ho",
|
||
"Er",
|
||
"Tm",
|
||
"Yb",
|
||
"Lu",
|
||
"Hf",
|
||
"Ta",
|
||
"W",
|
||
"Re",
|
||
"Os",
|
||
"Ir",
|
||
"Pt",
|
||
"Au",
|
||
"Hg",
|
||
"Tl",
|
||
"Pb",
|
||
"Bi",
|
||
"Po",
|
||
"At",
|
||
"Rn",
|
||
"Fr",
|
||
"Ra",
|
||
"Ac",
|
||
"Th",
|
||
"Pa",
|
||
"U",
|
||
"Np",
|
||
"Pu",
|
||
"Am",
|
||
"Cm",
|
||
"Bk",
|
||
"Cf",
|
||
"Es",
|
||
"Fm",
|
||
]
|
||
return symbols[Z] if Z < len(symbols) else f"Z{Z}"
|
||
|
||
parent_symbol = _element_symbol(self.config.Z_parent)
|
||
daughter_symbol = _element_symbol(self.config.Z_daughter)
|
||
|
||
header_lines = [
|
||
f"# beta-spectrum v{__import__('beta_spectrum').__version__}",
|
||
f"# timestamp: {datetime.now(timezone.utc).isoformat()}",
|
||
f"# source: {source_type}",
|
||
f"# nuclide: {parent_symbol}{self.config.A_number} -> {daughter_symbol}{self.config.A_number} (Z={self.config.Z_parent}->{self.config.Z_daughter})",
|
||
f"# endpoint: {self.config.endpoint_MeV * 1000:.1f} keV",
|
||
f"# transition: {self.config.transition_type}",
|
||
f"# corrections: {', '.join(enabled)}",
|
||
f"# e_step: {self.config.e_step_MeV:.4f} MeV",
|
||
]
|
||
|
||
if self.config.use_detector_response:
|
||
header_lines.append(
|
||
f"# detector: {self.config.detector_model} "
|
||
f"(sigma={self.config.detector_sigma_a_keV} keV, "
|
||
f"tail={self.config.detector_tail_fraction})"
|
||
)
|
||
else:
|
||
header_lines.append("# detector: disabled")
|
||
|
||
header_lines.append(f"# git_commit: {get_git_short_hash()}")
|
||
|
||
# Write CSV with header
|
||
import pandas as pd
|
||
|
||
data: Dict[str, np.ndarray] = {
|
||
"energy_MeV": self.energies_MeV,
|
||
"spectrum": total,
|
||
}
|
||
for name, values in components.items():
|
||
data[name] = values
|
||
|
||
df = pd.DataFrame(data)
|
||
df.to_csv(filename, index=False, float_format="%.4e")
|
||
|
||
# Prepend header
|
||
with open(filename, "r") as f:
|
||
content = f.read()
|
||
with open(filename, "w") as f:
|
||
f.write("\n".join(header_lines) + "\n" + content)
|
||
|
||
if self._logger:
|
||
self._logger.info("CSV exported to %s with metadata header", filename)
|
||
|
||
def get_data(self) -> Dict[str, Any]:
|
||
"""Get all numerical data for custom analysis."""
|
||
return {
|
||
"energies_MeV": self.energies_MeV,
|
||
"energies_W": self.W,
|
||
"spectrum": self.total_spectrum(normalize=True),
|
||
"components": self.components,
|
||
"config": self.config,
|
||
}
|