# spectrum.py from __future__ import annotations import logging import numpy as np import matplotlib.pyplot as plt from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple from dataclasses import dataclass from beta_spectrum.base import SpectrumComponent from beta_spectrum.components.phase_space import PhaseSpace from beta_spectrum.components.fermi import FermiFunction from beta_spectrum.components.finite_size import FiniteSizeL0, ChargeDistributionU from beta_spectrum.components.screening import ScreeningCorrection from beta_spectrum.components.exchange import ExchangeCorrection from beta_spectrum.components.radiative import RadiativeCorrection from beta_spectrum.logging_utils import get_git_short_hash from beta_spectrum.utils import T_to_W from beta_spectrum.constants import ME_MEV if TYPE_CHECKING: from beta_spectrum.components.detector_response import DetectorResponse @dataclass class SpectrumConfig: """ Configuration for beta spectrum calculation. Supports declarative detector response specification: set detector_model, detector_sigma_a_keV, and related parameters to enable automatic detector smearing via convolve_with_detector(). """ Z_parent: int Z_daughter: int A_number: int endpoint_MeV: float transition_type: str = "A" # should be [A, F1, F1U, F2, F2U, F3, F3U, F4] e_step_MeV: float = 0.001 # Toggle components and corrections use_phase_space: bool = True use_fermi: bool = True use_screening: bool = True use_finite_size: bool = True use_charge_dist: bool = True use_radiative: bool = True use_exchange: bool = True # Detector response convolution (analytical model) use_detector_response: bool = False detector_model: str = "gaussian" detector_sigma_a_keV: float = 1.0 detector_sigma_b: float = 0.0 detector_tail_fraction: float = 0.0 detector_tau_keV: float = 5.0 detector_fano_factor: float = 0.12 detector_n_channels: int = 4096 detector_channel_energy_range: tuple[float, float] = ( 0.0, 0.35, ) # in m_e units (total energy) class BetaSpectrum: """ Precise beta spectrum calculator. Combines basic spectrum shape (fermi * phase_space) with mutplicative corrections. Parameters ---------- components : List[SpectrumComponent] List of enabled spectral components. logger : logging.Logger, optional Logger for debug/info output. """ def __init__( self, components: List[SpectrumComponent], logger: Optional[logging.Logger] = None, ): self.components = components self._component_names = [self._get_component_name(c) for c in components] self._logger = logger def __call__(self, W: np.ndarray) -> np.ndarray: """ Calculate total spectrum as a product of enabled components """ result: np.ndarray = np.ones_like(W, dtype=float) for comp in self.components: if self._logger: self._logger.debug( "Evaluating %s at %d energy points", comp.__class__.__name__, len(W) ) result = result * comp(W) return result.astype(np.float64) def _get_component_name(self, comp: SpectrumComponent) -> str: """ Get human-readable component names """ class_name = comp.__class__.__name__ return class_name.replace("Correction", "").replace("Function", "") def calculate_components(self, W: np.ndarray) -> Dict[str, np.ndarray]: """ Calculate individual components for debugging and analysis """ components_dict = {} for name, comp in zip(self._component_names, self.components): components_dict[name] = comp(W) return components_dict @classmethod def from_config( cls, config: SpectrumConfig, logger: Optional[logging.Logger] = None ) -> BetaSpectrum: """ Create a BetaSpectrum from configuration. Parameters ---------- config : SpectrumConfig Configuration with decay parameters and correction toggles. logger : logging.Logger, optional Logger for progress output. """ components: List[SpectrumComponent] = [] W0 = float(T_to_W(config.endpoint_MeV)) if config.use_phase_space: components.append( PhaseSpace(W0=W0, transition_type=config.transition_type, logger=logger) ) if config.use_fermi: components.append( FermiFunction(Z=config.Z_daughter, A=config.A_number, logger=logger) ) if config.use_finite_size: components.append( FiniteSizeL0(Z=config.Z_daughter, A=config.A_number, logger=logger) ) if config.use_charge_dist: components.append( ChargeDistributionU( Z=config.Z_daughter, A=config.A_number, logger=logger ) ) if config.use_screening: components.append( ScreeningCorrection( FermiFunction(Z=config.Z_parent, A=config.A_number, logger=logger), logger=logger, ) ) if config.use_exchange: components.append(ExchangeCorrection(Z=config.Z_parent, logger=logger)) if config.use_radiative: components.append( RadiativeCorrection( W0=W0, Z=config.Z_daughter, A=config.A_number, use_endpoint_resummation=True, logger=logger, ) ) return cls(components, logger=logger) def get_energy_grid(self, config: SpectrumConfig) -> Tuple[np.ndarray, np.ndarray]: """ Generate array of energy values for spectrum calculation. """ kinetic_MeV = np.arange( config.e_step_MeV, config.endpoint_MeV, config.e_step_MeV ) W = T_to_W(kinetic_MeV) return W, kinetic_MeV.astype(np.float64) @staticmethod def create_detector_from_config( config: SpectrumConfig, ) -> "DetectorResponse": """ Create a DetectorResponse from SpectrumConfig detector parameters. Converts keV-based resolution parameters to m_e units internally. Parameters ---------- config : SpectrumConfig Configuration with detector response parameters set. Returns ------- DetectorResponse Detector response object ready for convolution. """ from beta_spectrum.components.detector_response import DetectorResponse from beta_spectrum.utils import T_to_W sigma_a_me = config.detector_sigma_a_keV / ME_MEV tau_me = config.detector_tau_keV / ME_MEV W0 = T_to_W(config.endpoint_MeV) channel_range = config.detector_channel_energy_range # Extend range to cover endpoint if channel_range[1] < W0: channel_range = (channel_range[0], float(W0 + 0.05)) detector = DetectorResponse.from_gaussian_params( channel_energy_range=channel_range, n_channels=config.detector_n_channels, sigma_a=sigma_a_me, sigma_b=config.detector_sigma_b, tail_fraction=config.detector_tail_fraction, tau=tau_me, model=config.detector_model, fano_factor=config.detector_fano_factor, ) return detector def convolve_with_detector( self, detector_response: "DetectorResponse", W: Optional[np.ndarray] = None, config: Optional[SpectrumConfig] = None, ) -> np.ndarray: """ Convolve theoretical spectrum with detector response. Returns the predicted measured spectrum after detector smearing. Parameters ---------- detector_response : DetectorResponse Detector response object (analytical or tabulated). W : np.ndarray, optional Energy grid in m_e units. If None, generated from config. config : SpectrumConfig, optional Used to generate energy grid if W is None. Returns ------- np.ndarray Convolved spectrum (predicted measured counts per channel). """ if W is None: if config is None: raise ValueError("Either W or config must be provided") W, _ = self.get_energy_grid(config) theoretical_spectrum = self(W) if self._logger: self._logger.debug( "Convolution: %d channels, %d spectrum points", len(detector_response.channel_energies), len(W), ) convolved = detector_response.convolve(W, theoretical_spectrum, normalize=True) return convolved def convolve_detector( self, config: SpectrumConfig, W: Optional[np.ndarray] = None, detector_response: Optional["DetectorResponse"] = None, ) -> np.ndarray: """ Convolve theoretical spectrum with detector response from config. Convenience method: creates DetectorResponse from config parameters and convolves the spectrum in a single call. Parameters ---------- config : SpectrumConfig Configuration with detector response parameters set. W : np.ndarray, optional Energy grid in m_e units. If None, generated from config. detector_response : DetectorResponse, optional Pre-built detector response. If None, created from config. Returns ------- np.ndarray Convolved spectrum (predicted measured counts per channel). Examples -------- >>> config = SpectrumConfig( ... Z_parent=43, Z_daughter=44, A_number=99, endpoint_MeV=0.294, ... use_detector_response=True, ... detector_sigma_a_keV=1.0, ... ) >>> spectrum = BetaSpectrum.from_config(config) >>> W, _ = spectrum.get_energy_grid(config) >>> convolved = spectrum.convolve_detector(config, W=W) """ if detector_response is None: detector_response = self.create_detector_from_config(config) return self.convolve_with_detector(detector_response, W=W, config=config) class BetaSpectrumAnalyzer: """ Introspection, analysis and debugging tools for BetaSpectrum. Parameters ---------- spectrum : BetaSpectrum The spectrum to analyze. config : SpectrumConfig Configuration used to create the spectrum. logger : logging.Logger, optional Logger for progress output. """ def __init__( self, spectrum: BetaSpectrum, config: SpectrumConfig, logger: Optional[logging.Logger] = None, ): """ Initialize analyzer with a spectrum and configuration. """ self.spectrum = spectrum self.config = config self.W, self.energies_MeV = spectrum.get_energy_grid(config) self._components_cache: Optional[Dict[str, np.ndarray]] = None self._logger = logger @property def components(self) -> Dict[str, np.ndarray]: if self._components_cache is None: self._components_cache = self.spectrum.calculate_components(self.W) assert self._components_cache is not None return self._components_cache def total_spectrum(self, normalize: bool = True) -> np.ndarray: """ Calculate the total_spectrum """ total = self.spectrum(self.W) if normalize: integral = np.trapezoid(total, self.energies_MeV) total = total / integral return total def convolved_spectrum( self, detector_response: Optional["DetectorResponse"] = None, normalize: bool = True, ) -> np.ndarray: """ Calculate spectrum convolved with detector response. If detector_response is None and use_detector_response is True in config, creates a detector response from config parameters. Parameters ---------- detector_response : DetectorResponse, optional Detector response to convolve with. Created from config if None. normalize : bool If True, normalize to unit area. Returns ------- np.ndarray Convolved spectrum. """ if detector_response is None and self.config.use_detector_response: detector_response = BetaSpectrum.create_detector_from_config(self.config) if detector_response is None: raise ValueError( "detector_response must be provided, or set " "use_detector_response=True in config" ) convolved = self.spectrum.convolve_with_detector( detector_response, W=self.W, config=self.config ) if normalize: integral = np.trapezoid(convolved, detector_response.channel_energies) if integral > 0: convolved = convolved / integral return convolved def plot_analysis(self, save_path: Optional[str] = None) -> None: """ Create visualization of the spectrum and all correction. """ total = self.total_spectrum(normalize=True) components = self.components 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, }