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refactor: rename 'analyzer' to 'visualize' — analysis belongs to fitter module

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unzhakov
2026-05-17 01:33:13 +03:00
parent 10ad3a867b
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beta_spectrum/visualize/__init__.py
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"""
Visualize module — BetaSpectrumAnalyzer class.
Visualization and plotting tools for BetaSpectrum.
All nuclides (single- and multi-branch) are treated uniformly as one or more
branches — single-branch is just a special case with one branch.
"""
from __future__ import annotations
import logging
import pandas as pd
from datetime import datetime, timezone
from dataclasses import dataclass
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
# Unified branch view: single-branch treated as one-branch multi-branch
if not self._is_multi and self.spectrum.branch_spectra == []:
self._unified_branches: List[_UnifiedBranch] = [
_UnifiedBranch(
endpoint_MeV=config.endpoint_MeV,
transition_type=config.transition_type,
intensity=1.0,
)
]
else:
self._unified_branches = [
_UnifiedBranch(
endpoint_MeV=b.endpoint_MeV,
transition_type=b.transition_type,
intensity=b.intensity,
)
for b in self.spectrum.branches
]
@property
def components(self) -> Dict[str, np.ndarray]:
if self._components_cache is None:
raw = self.spectrum.calculate_components(self.W)
# Normalize keys: single-branch has bare names,
# multi-branch has branch_0., branch_1., etc.
# We keep the raw keys for export_to_csv compatibility.
self._components_cache = raw
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
# ---------------------------------------------------------------------------
# Unified branch view
# ---------------------------------------------------------------------------
@property
def _n_branches(self) -> int:
"""Number of branches (unified view)."""
return len(self._unified_branches)
def _get_branch_spectrum(self, i: int) -> np.ndarray:
"""Get the spectrum array for branch i."""
if i < len(self.spectrum.branch_spectra):
return self.spectrum.branch_spectra[i]
# Single-branch: the spectrum itself is the only branch
return self.spectrum(self.W)
def _get_component_key(self, i: int, name: str) -> str:
"""Get the canonical key for a per-branch component."""
if self._is_multi:
return f"branch_{i}.{name}"
return name
def _has_component(self, i: int, name: str) -> bool:
"""Check if branch i has a given component."""
key = self._get_component_key(i, name)
return key in self.components
def _get_component(self, i: int, name: str) -> np.ndarray:
"""Get a per-branch component array."""
key = self._get_component_key(i, name)
return self.components[key]
# ---------------------------------------------------------------------------
# Plotting helpers
# ---------------------------------------------------------------------------
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"
)
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."""
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._n_branches > 1:
title += f" ({self._n_branches} branches)"
return title
def _format_branch_label(self, i: int) -> str:
"""Abbreviated branch label for legend."""
branch = self._unified_branches[i]
e0 = branch.endpoint_MeV * 1000
intensity_pct = branch.intensity * 100
return f"Br. {i+1}: {e0:.1f} keV ({intensity_pct:.1f}%)"
# ---------------------------------------------------------------------------
# Plotting — unified multi-branch approach
# ---------------------------------------------------------------------------
def plot_analysis(
self,
save_path: Optional[str] = None,
show_components: bool = True,
) -> None:
"""
Create visualization of the spectrum and all correction factors.
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.
"""
total = self.total_spectrum(normalize=True)
components = self.components
self._timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
if show_components:
self._plot_debug_view(total, components, save_path)
else:
self._plot_spectra_view(total, components, save_path)
def _plot_spectra_view(
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",
]
e_step_keV = self.config.e_step_MeV * 1000
# Compute branch contributions (universal * intensity * branch_spectrum)
branch_contribs = []
for i in range(self._n_branches):
universal_product = np.ones_like(self.W)
for name in [
"Fermi", "Screening", "FiniteSizeL0",
"ChargeDistributionU", "Exchange",
]:
key = self._get_component_key(i, name)
if key in components:
universal_product *= components[key]
bs = self._get_branch_spectrum(i)
contrib = universal_product * self._unified_branches[i].intensity * bs
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 / {e_step_keV:.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, self._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 _plot_debug_view(
self,
total: np.ndarray,
components: Dict[str, np.ndarray],
save_path: Optional[str],
) -> None:
"""Debug view: vertical layout with 4 panels."""
n_branches = self._n_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 in range(n_branches):
universal_product = np.ones_like(self.W)
for name in [
"Fermi", "Screening", "FiniteSizeL0",
"ChargeDistributionU", "Exchange",
]:
key = self._get_component_key(i, name)
if key in components:
universal_product *= components[key]
bs = self._get_branch_spectrum(i)
contrib = universal_product * self._unified_branches[i].intensity * bs
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 in range(n_branches):
color = branch_colors[i % len(branch_colors)]
branch = self._unified_branches[i]
key = self._get_component_key(i, "PhaseSpace")
if key in components:
ps_vals = components[key]
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 in range(n_branches):
color = branch_colors[i % len(branch_colors)]
key = self._get_component_key(i, "Radiative")
if key in components:
rad_vals = components[key]
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 _element_symbol(self, Z: int) -> str:
"""Return chemical symbol for atomic number Z."""
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",
]
return symbols[Z] if Z < len(symbols) else f"Z{Z}"
# ---------------------------------------------------------------------------
# Export
# ---------------------------------------------------------------------------
def export_to_csv(self, filename: str, source_type: str = "unknown") -> None:
"""Export spectrum data to CSV with metadata header."""
total = self.total_spectrum(normalize=True)
components = self.components
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")
parent_symbol = self._element_symbol(self.config.Z_parent)
daughter_symbol = self._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",
]
# Branch info — unified view
for i, branch in enumerate(self._unified_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}"
)
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()}")
data: Dict[str, np.ndarray] = {
"energy_MeV": self.energies_MeV,
"spectrum": total,
}
# Per-branch spectra and components
for i in range(self._n_branches):
data[f"branch_{i+1}_spectrum"] = self._get_branch_spectrum(i)
for comp_name in ["PhaseSpace", "Radiative"]:
key = self._get_component_key(i, comp_name)
if key in components:
data[f"branch_{i+1}_{comp_name}"] = components[key]
# Universal components (no prefix)
for name, values in components.items():
if not name.startswith("branch_"):
data[name] = values
df = pd.DataFrame(data)
df.to_csv(filename, index=False, float_format="%.4e")
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", 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,
}
@dataclass
class _UnifiedBranch:
"""Unified branch view for both single- and multi-branch modes."""
endpoint_MeV: float
transition_type: str
intensity: float