""" Analyzer module — BetaSpectrumAnalyzer class. Introspection, analysis, and debugging tools for BetaSpectrum. """ from __future__ import annotations import logging import pandas as pd from datetime import datetime, timezone from typing import TYPE_CHECKING, Any, Dict, List, Optional import numpy as np import matplotlib.pyplot as plt from beta_spectrum.logging_utils import get_git_short_hash from beta_spectrum.utils import T_to_W if TYPE_CHECKING: from matplotlib.figure import Figure from beta_spectrum.spectrum import BetaSpectrum, SpectrumConfig from beta_spectrum.components.detector_response import DetectorResponse 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._branch_spectra_cache: Optional[List[np.ndarray]] = None self._logger = logger self._is_multi = spectrum._is_multi @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 @property def branch_spectra(self) -> List[np.ndarray]: """Intensity-weighted branch spectra.""" if self._branch_spectra_cache is None: self._branch_spectra_cache = self.spectrum.get_branch_normalized_spectra( self.W ) assert self._branch_spectra_cache is not None return self._branch_spectra_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 _build_figure_header(self, fig: "Figure") -> None: """Add a compact header with nuclide info, e_step, commit and timestamp outside axes.""" parent = self._element_symbol(self.config.Z_parent) daughter = self._element_symbol(self.config.Z_daughter) e_step_keV = self.config.e_step_MeV * 1000 header = ( f"{parent}{self.config.A_number} -> {daughter}{self.config.A_number} " f"| E₀={self.config.endpoint_MeV * 1000:.1f} keV " f"| ΔE={e_step_keV:.1f} keV " f"| {self.config.transition_type}" ) fig.text( 0.01, 0.98, header, transform=fig.transFigure, fontsize=9, verticalalignment="top", horizontalalignment="left", fontfamily="monospace", ) id_text = f"{get_git_short_hash(6)} | {self._timestamp}" fig.text( 0.01, 0.01, id_text, transform=fig.transFigure, fontsize=8, verticalalignment="bottom", horizontalalignment="left", fontfamily="monospace", color="gray", ) def _build_figure_title(self, fig: "Figure") -> str: """Build a unified figure title and return the figure for suptitle.""" parent = self._element_symbol(self.config.Z_parent) daughter = self._element_symbol(self.config.Z_daughter) n = self.config.A_number title = f"Beta-decay: {n}{parent} -> {n}{daughter}" if self._is_multi and self.spectrum.branches: title += f" ({len(self.spectrum.branches)} branches)" return title def _format_branch_label(self, i: int) -> str: """Abbreviated branch label for legend.""" if i >= len(self.spectrum.branches): return f"Br. {i+1}" branch = self.spectrum.branches[i] e0 = branch.endpoint_MeV * 1000 intensity_pct = branch.intensity * 100 return f"Br. {i+1}: {e0:.1f} keV ({intensity_pct:.1f}%)" def plot_analysis( self, save_path: Optional[str] = None, show_components: bool = True, ) -> None: """ Create visualization of the spectrum and all correction factors. In single-branch mode: standard 4-panel debug view or single spectrum plot. In multi-branch mode: spectrum plot with individual branches shown, and debug view with vertical layout for branch comparison. Parameters ---------- save_path : str, optional Path to save the figure. show_components : bool If True, show full debug view. If False, show only the spectrum plot with nuclear data header. If True, use logarithmic y-scale. Set to False for linear scale, which is better when branch intensities are similar. """ total = self.total_spectrum(normalize=True) components = self.components self._timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC") if self._is_multi: if show_components: self._plot_multi_branch_debug( total, components, save_path ) else: self._plot_multi_branch_spectra( total, components, save_path ) else: if show_components: self._plot_debug_view(total, components, save_path) else: self._plot_spectrum_only(total, save_path) def _plot_spectrum_only( self, total: np.ndarray, save_path: Optional[str], ) -> None: """Plot only the total spectrum.""" fig, ax = plt.subplots(figsize=(10, 6)) ax.plot(self.energies_MeV, total, "b-", lw=2, label="Normalized spectrum") ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=12) ax.set_ylabel( f"Normalized Counts / {self.config.e_step_MeV * 1000:.1f} keV", fontsize=12 ) fig.suptitle( self._build_figure_title(fig), fontsize=14, fontweight="bold" ) ax.set_yscale("log") ax.grid(True, alpha=0.3) ax.legend(loc="best", fontsize=10) self._build_figure_header(fig) 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 _plot_debug_view( self, total: np.ndarray, components: Dict[str, np.ndarray], save_path: Optional[str], ) -> None: """Full debug view with 4-panel spectrum analysis.""" fig = plt.figure(figsize=(14, 10)) fig.suptitle( self._build_figure_title(fig), fontsize=14, fontweight="bold" ) e_step_keV = self.config.e_step_MeV * 1000 # 1. Main spectrum plot (top-left) ax1 = plt.subplot(2, 2, 1) ax1.plot(self.energies_MeV, total, "b-", lw=2, label="Total spectrum") ax1.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=10) ax1.set_ylabel( f"Normalized Counts / {e_step_keV:.1f} keV", fontsize=10 ) ax1.set_yscale("log") ax1.grid(True, alpha=0.3) ax1.legend(fontsize=8) # 2. Correction plots (top-right) ax2 = plt.subplot(2, 2, 2) 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] if name in ["Fermi", "PhaseSpace"]: values = values / np.max(values) label = name 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=11) ax2.grid(True, alpha=0.3) ax2.legend(loc="best", fontsize=8) # 3. Cumulative effect (bottom-left) ax3 = plt.subplot(2, 2, 3) cumulative = np.ones_like(self.W) ax3.plot( self.energies_MeV, np.ones_like(cumulative), "k--", lw=1.5, label="Baseline", alpha=0.7, ) 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=11) 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=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=11) ax4.grid(True, alpha=0.3) ax4.legend(loc="best", fontsize=8) ax4.axhline(y=0, color="k", linestyle="-", lw=0.5) self._build_figure_header(fig) 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}" # --------------------------------------------------------------------------- # Multi-branch plotting # --------------------------------------------------------------------------- def _plot_multi_branch_spectra( self, total: np.ndarray, components: Dict[str, np.ndarray], save_path: Optional[str], ) -> None: """Plot total spectrum with individual branch decomposition.""" fig, ax = plt.subplots(figsize=(12, 7)) branch_colors = [ "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", ] # Compute branch contributions (universal * intensity * branch_spectrum) branch_contribs = [] for i, branch_spec in enumerate(self.spectrum.branch_spectra): universal_product = np.ones_like(self.W) for name in [ "Fermi", "Screening", "FiniteSizeL0", "ChargeDistributionU", "Exchange", ]: if name in components: universal_product *= components[name] bs = self.spectrum.get_branch_spectra(self.W) contrib = universal_product * self.spectrum.branches[i].intensity * bs[i] branch_contribs.append(contrib) total_raw = sum(branch_contribs) ax.plot( self.energies_MeV, total_raw, "k-", lw=2.5, label="Total spectrum", zorder=10, ) for i, contrib in enumerate(branch_contribs): color = branch_colors[i % len(branch_colors)] ax.plot( self.energies_MeV, contrib, color=color, lw=1.5, alpha=0.7, label=self._format_branch_label(i), ) ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=12) ax.set_ylabel( f"Normalized Counts / {self.config.e_step_MeV * 1000:.1f} keV", fontsize=12 ) fig.suptitle( self._build_figure_title(fig), fontsize=14, fontweight="bold" ) ax.set_yscale("log") ax.grid(True, alpha=0.3) ax.legend( loc="best", fontsize=9, ncol=min(2, len(self.spectrum.branches) + 1), ) self._build_figure_header(fig) plt.tight_layout() if save_path: fig.savefig(save_path, dpi=150, bbox_inches="tight") print(f"Figure saved to {save_path}") def _plot_multi_branch_debug( self, total: np.ndarray, components: Dict[str, np.ndarray], save_path: Optional[str], ) -> None: """Debug view for multi-branch: vertical layout with 4 panels.""" n_branches = len(self.spectrum.branches) e_step_keV = self.config.e_step_MeV * 1000 fig, axes = plt.subplots(4, 1, figsize=(12, 16), sharex=True) fig.suptitle( self._build_figure_title(fig), fontsize=14, fontweight="bold" ) branch_colors = [ "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", ] # Panel 1: Total spectrum + per-branch spectra branch_contribs = [] for i, branch_spec in enumerate(self.spectrum.branch_spectra): universal_product = np.ones_like(self.W) for name in [ "Fermi", "Screening", "FiniteSizeL0", "ChargeDistributionU", "Exchange", ]: if name in components: universal_product *= components[name] bs = self.spectrum.get_branch_spectra(self.W) contrib = universal_product * self.spectrum.branches[i].intensity * bs[i] branch_contribs.append(contrib) total_raw = sum(branch_contribs) ax = axes[0] ax.plot( self.energies_MeV, total_raw, "k-", lw=2, label="Total spectrum", zorder=10, ) for i, contrib in enumerate(branch_contribs): color = branch_colors[i % len(branch_colors)] ax.plot( self.energies_MeV, contrib, color=color, lw=1.5, alpha=0.7, label=self._format_branch_label(i), ) ax.set_yscale("log") ax.set_ylabel( f"Normalized Counts / {e_step_keV:.1f} keV", fontsize=10 ) ax.set_title("Spectrum", fontsize=11) ax.grid(True, alpha=0.3) ax.legend(fontsize=8, loc="best", ncol=min(2, n_branches + 1)) # Panel 2: Fermi function + all phase spaces ax = axes[1] if "Fermi" in components: fermi_vals = components["Fermi"] fermi_scaled = fermi_vals / np.max(fermi_vals) ax.plot( self.energies_MeV, fermi_scaled, "r-", lw=1.5, label="Fermi", alpha=0.8, ) for i, branch in enumerate(self.spectrum.branches): color = branch_colors[i % len(branch_colors)] if f"branch_{i}.PhaseSpace" in components: ps_vals = components[f"branch_{i}.PhaseSpace"] W0_branch = T_to_W(branch.endpoint_MeV) mask = self.W <= W0_branch ps_masked = np.where(mask, ps_vals, 0.0) ps_max = np.max(ps_masked) if ps_max > 0: ps_scaled = ps_masked / ps_max else: ps_scaled = ps_masked ax.plot( self.energies_MeV, ps_scaled, color=color, lw=1.5, alpha=0.7, label=f"PS Br. {i+1}", ) ax.set_ylabel("Normalized Factor", fontsize=10) ax.set_title("Fermi & Phase Space", fontsize=11) ax.grid(True, alpha=0.3) ax.legend(fontsize=8, loc="best") # Panel 3: Universal W0-independent corrections ax = axes[2] universal_components = [ "Screening", "FiniteSizeL0", "ChargeDistributionU", "Exchange", ] colors = ["green", "orange", "purple", "brown"] for name, color in zip(universal_components, colors): if name in components: values = components[name] values_scaled = values / np.max(values) ax.plot( self.energies_MeV, values_scaled, color=color, lw=1.5, label=name, alpha=0.8, ) ax.set_ylabel("Normalized Factor", fontsize=10) ax.set_title("Universal Corrections", fontsize=11) ax.grid(True, alpha=0.3) ax.legend(fontsize=8, loc="best") # Panel 4: W0-dependent corrections (Radiative for each branch) ax = axes[3] for i, branch in enumerate(self.spectrum.branches): color = branch_colors[i % len(branch_colors)] if f"branch_{i}.Radiative" in components: rad_vals = components[f"branch_{i}.Radiative"] ax.plot( self.energies_MeV, rad_vals, color=color, lw=1.5, alpha=0.7, label=f"Br. {i+1}", ) ax.set_ylabel("Radiative Correction", fontsize=10) ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=10) ax.set_title("Radiative Corrections", fontsize=11) ax.grid(True, alpha=0.3) ax.legend(fontsize=8, loc="best", ncol=min(2, n_branches + 1)) self._build_figure_header(fig) plt.tight_layout() if save_path: fig.savefig(save_path, dpi=150, bbox_inches="tight") print(f"Figure saved to {save_path}") 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 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._is_multi and self.spectrum.branches: header_lines.append(f"# branches: {len(self.spectrum.branches)}") for i, branch in enumerate(self.spectrum.branches): header_lines.append( f"# branch_{i+1}: E₀={branch.endpoint_MeV*1000:.1f} keV, " f"transition={branch.transition_type}, " f"intensity={branch.intensity:.4f}" ) else: header_lines.append("# branches: 1 (single)") 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 data: Dict[str, np.ndarray] = { "energy_MeV": self.energies_MeV, "spectrum": total, } if self._is_multi and self.spectrum.branches: # Multi-branch mode: total spectrum + per-branch spectra + components # (branch info — intensity, endpoint, transition — is in the header, not repeated columns) for i, branch_spec in enumerate(self.spectrum.branch_spectra): data[f"branch_{i+1}_spectrum"] = branch_spec(self.W) # Per-branch components (PhaseSpace, Radiative, etc.) for i, branch_spec in enumerate(self.spectrum.branch_spectra): branch_comps = branch_spec.calculate_components(self.W) for comp_name, values in branch_comps.items(): data[f"branch_{i+1}_{comp_name}"] = values # Universal components (same for all branches — no prefix needed) for name, values in components.items(): if not name.startswith("branch_"): data[name] = values else: # Single-branch mode: standard export 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: mode = "multi-branch" if self._is_multi else "single-branch" self._logger.info("CSV exported to %s (%s mode)", filename, mode) 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, }