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beta_shape_pnpi/beta_spectrum/analyzer.py
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"""
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.axes import Axes
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 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
commit = get_git_short_hash(6)
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, commit, timestamp
)
else:
self._plot_multi_branch_spectra(
total, components, save_path, commit, timestamp
)
else:
if show_components:
self._plot_debug_view(total, components, save_path, commit, timestamp)
else:
self._plot_spectrum_only(total, save_path, commit, timestamp)
def _add_id_textbox(self, ax: "Axes", commit: str, timestamp: str) -> None:
"""Add ID text box with commit hash and timestamp to plot."""
id_text = f"commit: {commit} | {timestamp}"
ax.text(
0.01,
0.01,
id_text,
transform=ax.transAxes,
fontsize=8,
color="gray",
verticalalignment="bottom",
horizontalalignment="left",
fontfamily="monospace",
)
def _add_nuclear_data_header(self, ax: "Axes") -> None:
"""Add nuclear data information header to plot."""
parent = self._element_symbol(self.config.Z_parent)
daughter = self._element_symbol(self.config.Z_daughter)
enabled_corrections = []
if self.config.use_phase_space:
enabled_corrections.append("phase_space")
if self.config.use_fermi:
enabled_corrections.append("fermi")
if self.config.use_screening:
enabled_corrections.append("screening")
if self.config.use_finite_size:
enabled_corrections.append("finite_size")
if self.config.use_charge_dist:
enabled_corrections.append("charge_dist")
if self.config.use_radiative:
enabled_corrections.append("radiative")
if self.config.use_exchange:
enabled_corrections.append("exchange")
header_lines = [
f"Nuclide: {parent}{self.config.A_number} -> {daughter}{self.config.A_number}",
f"Endpoint: {self.config.endpoint_MeV * 1000:.1f} keV",
f"Transition: {self.config.transition_type}",
f"Corrections: {', '.join(enabled_corrections)}",
]
if self.config.use_detector_response:
header_lines.append(
f"Detector: {self.config.detector_model} "
f"(σ={self.config.detector_sigma_a_keV} keV)"
)
header_text = "\n".join(header_lines)
ax.text(
0.99,
0.99,
header_text,
transform=ax.transAxes,
fontsize=8,
verticalalignment="top",
horizontalalignment="right",
bbox=dict(
boxstyle="round,pad=0.3", facecolor="white", alpha=0.8, edgecolor="none"
),
)
def _plot_spectrum_only(
self,
total: np.ndarray,
save_path: Optional[str],
commit: str,
timestamp: str,
) -> None:
"""Plot only the total spectrum with nuclear data header."""
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=11)
ax.set_ylabel("Normalized Counts", fontsize=11)
ax.set_title(
f"Beta-decay: {self.config.Z_parent} -> {self.config.Z_daughter}, A={self.config.A_number}",
fontsize=13,
)
ax.set_yscale("log")
ax.grid(True, alpha=0.3)
ax.legend(loc="best", fontsize=10)
self._add_nuclear_data_header(ax)
self._add_id_textbox(ax, commit, timestamp)
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],
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}"
# ---------------------------------------------------------------------------
# Multi-branch plotting
# ---------------------------------------------------------------------------
def _get_branch_label(self, i: int) -> str:
"""Generate branch label for legend."""
if i >= len(self.spectrum.branches):
return f"Branch {i+1}"
branch = self.spectrum.branches[i]
return f"Branch {i+1}: {branch.transition_type}, E₀={branch.endpoint_MeV*1000:.1f} keV"
def _plot_multi_branch_spectra(
self,
total: np.ndarray,
components: Dict[str, np.ndarray],
save_path: Optional[str],
commit: str,
timestamp: str,
) -> None:
"""Plot total spectrum with individual branch decomposition.
All curves use raw (un-normalized) values so that the total is
the arithmetic sum of branch contributions, and branch amplitudes
are proportional to their intensities.
"""
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 = arithmetic sum of all branches (raw, un-normalized)
total_raw = sum(branch_contribs)
ax.plot(
self.energies_MeV,
total_raw,
"k-",
lw=2.5,
label="Total spectrum",
zorder=10,
)
# Plot each branch contribution (raw, un-normalized)
for i, (contrib, branch) in enumerate(
zip(branch_contribs, self.spectrum.branches)
):
color = branch_colors[i % len(branch_colors)]
ax.plot(
self.energies_MeV,
contrib,
color=color,
lw=1.5,
alpha=0.7,
label=self._get_branch_label(i),
)
ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=12)
ax.set_ylabel("Normalized Counts", fontsize=12)
parent = self._element_symbol(self.config.Z_parent)
daughter = self._element_symbol(self.config.Z_daughter)
ax.set_title(
f"Beta-decay: {parent}{self.config.A_number} -> {daughter}{self.config.A_number} — {len(self.spectrum.branches)} branches",
fontsize=14,
)
ax.set_yscale("log")
ax.grid(True, alpha=0.3)
ax.legend(loc="best", fontsize=10, ncol=min(2, len(self.spectrum.branches) + 1))
self._add_nuclear_data_header(ax)
self._add_id_textbox(ax, commit, timestamp)
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],
commit: str,
timestamp: str,
) -> None:
"""Debug view for multi-branch: vertical layout with 4 panels.
Panel 1: Total spectrum + intensity-scaled branch spectra (log scale)
Panel 2: Fermi function + all phase spaces (shape comparison, no intensity)
Panel 3: Universal W0-independent corrections (Fermi, Screening, etc.)
Panel 4: W0-dependent corrections (Radiative for each branch)
"""
n_branches = len(self.spectrum.branches)
fig, axes = plt.subplots(4, 1, figsize=(12, 16), sharex=True)
branch_colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
]
parent = self._element_symbol(self.config.Z_parent)
daughter = self._element_symbol(self.config.Z_daughter)
# Panel 1: Total spectrum + per-branch spectra
# All curves use raw (un-normalized) values so total = sum of branches
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, branch) in enumerate(
zip(branch_contribs, self.spectrum.branches)
):
color = branch_colors[i % len(branch_colors)]
ax.plot(
self.energies_MeV,
contrib,
color=color,
lw=1.5,
alpha=0.7,
label=f"Branch {i+1}: {branch.transition_type}, "
f"E₀={branch.endpoint_MeV*1000:.1f} keV",
)
ax.set_yscale("log")
ax.set_ylabel("Normalized Counts", fontsize=10)
ax.set_title(
f"Multi-branch: {parent}{self.config.A_number} -> {daughter}{self.config.A_number} ({n_branches} branches)",
fontsize=12,
fontweight="bold",
)
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 (shape comparison)
# Phase space is masked beyond endpoint before normalizing to avoid
# negative values from the sqrt term
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 (normalized)",
alpha=0.8,
)
for i, (branch_spec, branch) in enumerate(
zip(self.spectrum.branch_spectra, self.spectrum.branches)
):
color = branch_colors[i % len(branch_colors)]
if f"branch_{i}.PhaseSpace" in components:
ps_vals = components[f"branch_{i}.PhaseSpace"]
# Mask beyond endpoint to avoid negative values
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 Branch {i+1} (normalized)",
)
ax.set_ylabel("Normalized Factor", fontsize=10)
ax.set_title(
"Fermi Function & Phase Space Shapes", fontsize=11, fontweight="bold"
)
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=f"{name} (normalized)",
alpha=0.8,
)
ax.set_ylabel("Normalized Factor", fontsize=10)
ax.set_title(
"Universal W0-Independent Corrections", fontsize=11, fontweight="bold"
)
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_spec, branch) in enumerate(
zip(self.spectrum.branch_spectra, 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"Branch {i+1} Radiative (E₀={branch.endpoint_MeV*1000:.1f} keV)",
)
ax.set_ylabel("Radiative Correction", fontsize=10)
ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=10)
ax.set_title(
"W0-Dependent Corrections (Radiative)", fontsize=11, fontweight="bold"
)
ax.grid(True, alpha=0.3)
ax.legend(fontsize=8, loc="best", ncol=min(2, n_branches + 1))
self._add_id_textbox(axes[3], commit, timestamp)
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,
}