582 lines
21 KiB
Python
582 lines
21 KiB
Python
"""
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Visualize module — BetaSpectrumAnalyzer class.
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Visualization and plotting tools for BetaSpectrum.
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All nuclides (single- and multi-branch) are treated uniformly as one or more
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branches — single-branch is just a special case with one branch.
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"""
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from __future__ import annotations
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import logging
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import pandas as pd
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from datetime import datetime, timezone
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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import numpy as np
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import matplotlib.pyplot as plt
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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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if TYPE_CHECKING:
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from matplotlib.figure import Figure
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from beta_spectrum.spectrum import BetaSpectrum, SpectrumConfig
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from beta_spectrum.components.detector_response import DetectorResponse
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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._branch_spectra_cache: Optional[List[np.ndarray]] = None
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self._logger = logger
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self._is_multi = spectrum._is_multi
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# Unified branch view: single-branch treated as one-branch multi-branch
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if not self._is_multi and self.spectrum.branch_spectra == []:
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self._unified_branches: List[_UnifiedBranch] = [
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_UnifiedBranch(
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endpoint_MeV=config.endpoint_MeV,
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transition_type=config.transition_type,
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intensity=1.0,
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)
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]
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else:
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self._unified_branches = [
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_UnifiedBranch(
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endpoint_MeV=b.endpoint_MeV,
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transition_type=b.transition_type,
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intensity=b.intensity,
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)
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for b in self.spectrum.branches
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]
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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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raw = self.spectrum.calculate_components(self.W)
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# Normalize keys: single-branch has bare names,
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# multi-branch has branch_0., branch_1., etc.
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# We keep the raw keys for export_to_csv compatibility.
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self._components_cache = raw
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assert self._components_cache is not None
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return self._components_cache
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@property
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def branch_spectra(self) -> List[np.ndarray]:
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"""Intensity-weighted branch spectra."""
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if self._branch_spectra_cache is None:
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self._branch_spectra_cache = self.spectrum.get_branch_normalized_spectra(
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self.W
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)
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assert self._branch_spectra_cache is not None
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return self._branch_spectra_cache
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def total_spectrum(self, normalize: bool = True) -> np.ndarray:
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"""Calculate the total spectrum."""
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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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# ---------------------------------------------------------------------------
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# Unified branch view
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# ---------------------------------------------------------------------------
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@property
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def _n_branches(self) -> int:
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"""Number of branches (unified view)."""
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return len(self._unified_branches)
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def _get_branch_spectrum(self, i: int) -> np.ndarray:
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"""Get the spectrum array for branch i."""
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if i < len(self.spectrum.branch_spectra):
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return self.spectrum.branch_spectra[i]
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# Single-branch: the spectrum itself is the only branch
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return self.spectrum(self.W)
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def _get_component_key(self, i: int, name: str) -> str:
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"""Get the canonical key for a per-branch component."""
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if self._is_multi:
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return f"branch_{i}.{name}"
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return name
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def _has_component(self, i: int, name: str) -> bool:
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"""Check if branch i has a given component."""
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key = self._get_component_key(i, name)
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return key in self.components
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def _get_component(self, i: int, name: str) -> np.ndarray:
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"""Get a per-branch component array."""
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key = self._get_component_key(i, name)
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return self.components[key]
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# ---------------------------------------------------------------------------
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# Plotting helpers
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# ---------------------------------------------------------------------------
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def _build_figure_header(self, fig: "Figure") -> None:
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"""Add a compact header with nuclide info, e_step, commit and timestamp outside axes."""
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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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e_step_keV = self.config.e_step_MeV * 1000
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header = (
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f"{parent}{self.config.A_number} -> {daughter}{self.config.A_number} "
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f"| E₀={self.config.endpoint_MeV * 1000:.1f} keV "
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f"| ΔE={e_step_keV:.1f} keV"
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)
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fig.text(
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0.01, 0.98, header,
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transform=fig.transFigure, fontsize=9,
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verticalalignment="top", horizontalalignment="left",
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fontfamily="monospace",
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)
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id_text = f"{get_git_short_hash(6)} | {self._timestamp}"
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fig.text(
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0.01, 0.01, id_text,
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transform=fig.transFigure, fontsize=8,
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verticalalignment="bottom", horizontalalignment="left",
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fontfamily="monospace", color="gray",
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)
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def _build_figure_title(self, fig: "Figure") -> str:
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"""Build a unified figure title."""
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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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n = self.config.A_number
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title = f"Beta-decay: {n}{parent} -> {n}{daughter}"
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if self._n_branches > 1:
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title += f" ({self._n_branches} branches)"
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return title
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def _format_branch_label(self, i: int) -> str:
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"""Abbreviated branch label for legend."""
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branch = self._unified_branches[i]
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e0 = branch.endpoint_MeV * 1000
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intensity_pct = branch.intensity * 100
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return f"Br. {i+1}: {e0:.1f} keV ({intensity_pct:.1f}%)"
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# ---------------------------------------------------------------------------
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# Plotting — unified multi-branch approach
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# ---------------------------------------------------------------------------
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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.
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If False, show only the spectrum plot.
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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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self._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(total, components, save_path)
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else:
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self._plot_spectra_view(total, components, save_path)
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def _plot_spectra_view(
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self,
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total: np.ndarray,
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components: Dict[str, np.ndarray],
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save_path: Optional[str],
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) -> None:
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"""Plot total spectrum with individual branch decomposition."""
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fig, ax = plt.subplots(figsize=(12, 7))
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branch_colors = [
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"#1f77b4", "#ff7f0e", "#2ca02c", "#d62728",
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"#9467bd", "#8c564b", "#e377c2", "#7f7f7f",
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"#bcbd22", "#17becf",
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]
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e_step_keV = self.config.e_step_MeV * 1000
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# Compute branch contributions (universal * intensity * branch_spectrum)
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branch_contribs = []
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for i in range(self._n_branches):
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universal_product = np.ones_like(self.W)
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for name in [
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"Fermi", "Screening", "FiniteSizeL0",
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"ChargeDistributionU", "Exchange",
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]:
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key = self._get_component_key(i, name)
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if key in components:
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universal_product *= components[key]
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bs = self._get_branch_spectrum(i)
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contrib = universal_product * self._unified_branches[i].intensity * bs
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branch_contribs.append(contrib)
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total_raw = sum(branch_contribs)
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ax.plot(
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self.energies_MeV, total_raw, "k-", lw=2.5,
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label="Total spectrum", zorder=10,
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)
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for i, contrib in enumerate(branch_contribs):
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color = branch_colors[i % len(branch_colors)]
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ax.plot(
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self.energies_MeV, contrib, color=color, lw=1.5,
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alpha=0.7, label=self._format_branch_label(i),
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)
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ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=12)
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ax.set_ylabel(
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f"Normalized Counts / {e_step_keV:.1f} keV", fontsize=12
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)
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fig.suptitle(
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self._build_figure_title(fig), fontsize=14, fontweight="bold"
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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(
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loc="best", fontsize=9,
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ncol=min(2, self._n_branches + 1),
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)
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self._build_figure_header(fig)
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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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def _plot_debug_view(
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self,
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total: np.ndarray,
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components: Dict[str, np.ndarray],
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save_path: Optional[str],
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) -> None:
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"""Debug view: vertical layout with 4 panels."""
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n_branches = self._n_branches
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e_step_keV = self.config.e_step_MeV * 1000
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fig, axes = plt.subplots(4, 1, figsize=(12, 16), sharex=True)
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fig.suptitle(
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self._build_figure_title(fig), fontsize=14, fontweight="bold"
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)
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branch_colors = [
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"#1f77b4", "#ff7f0e", "#2ca02c", "#d62728",
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"#9467bd", "#8c564b", "#e377c2", "#7f7f7f",
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"#bcbd22", "#17becf",
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]
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# Panel 1: Total spectrum + per-branch spectra
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branch_contribs = []
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for i in range(n_branches):
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universal_product = np.ones_like(self.W)
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for name in [
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"Fermi", "Screening", "FiniteSizeL0",
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"ChargeDistributionU", "Exchange",
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]:
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key = self._get_component_key(i, name)
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if key in components:
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universal_product *= components[key]
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bs = self._get_branch_spectrum(i)
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contrib = universal_product * self._unified_branches[i].intensity * bs
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branch_contribs.append(contrib)
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total_raw = sum(branch_contribs)
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ax = axes[0]
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ax.plot(
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self.energies_MeV, total_raw, "k-", lw=2,
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label="Total spectrum", zorder=10,
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)
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for i, contrib in enumerate(branch_contribs):
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color = branch_colors[i % len(branch_colors)]
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ax.plot(
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self.energies_MeV, contrib, color=color, lw=1.5,
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alpha=0.7, label=self._format_branch_label(i),
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)
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ax.set_yscale("log")
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ax.set_ylabel(
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f"Normalized Counts / {e_step_keV:.1f} keV", fontsize=10
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)
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ax.set_title("Spectrum", fontsize=11)
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=8, loc="best", ncol=min(2, n_branches + 1))
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# Panel 2: Fermi function + all phase spaces
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ax = axes[1]
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if "Fermi" in components:
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fermi_vals = components["Fermi"]
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fermi_scaled = fermi_vals / np.max(fermi_vals)
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ax.plot(
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self.energies_MeV, fermi_scaled, "r-", lw=1.5,
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label="Fermi", alpha=0.8,
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)
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for i in range(n_branches):
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color = branch_colors[i % len(branch_colors)]
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branch = self._unified_branches[i]
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key = self._get_component_key(i, "PhaseSpace")
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if key in components:
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ps_vals = components[key]
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W0_branch = T_to_W(branch.endpoint_MeV)
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mask = self.W <= W0_branch
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ps_masked = np.where(mask, ps_vals, 0.0)
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ps_max = np.max(ps_masked)
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if ps_max > 0:
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ps_scaled = ps_masked / ps_max
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else:
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ps_scaled = ps_masked
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ax.plot(
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self.energies_MeV, ps_scaled, color=color, lw=1.5,
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alpha=0.7, label=f"PS Br. {i+1}",
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)
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ax.set_ylabel("Normalized Factor", fontsize=10)
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ax.set_title("Fermi & Phase Space", fontsize=11)
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=8, loc="best")
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# Panel 3: Universal W0-independent corrections
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ax = axes[2]
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universal_components = [
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"Screening", "FiniteSizeL0",
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"ChargeDistributionU", "Exchange",
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]
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colors = ["green", "orange", "purple", "brown"]
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for name, color in zip(universal_components, colors):
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if name in components:
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values = components[name]
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values_scaled = values / np.max(values)
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ax.plot(
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self.energies_MeV, values_scaled, color=color, lw=1.5,
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label=name, alpha=0.8,
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)
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ax.set_ylabel("Normalized Factor", fontsize=10)
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ax.set_title("Universal Corrections", fontsize=11)
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=8, loc="best")
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# Panel 4: W0-dependent corrections (Radiative for each branch)
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ax = axes[3]
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for i in range(n_branches):
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color = branch_colors[i % len(branch_colors)]
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key = self._get_component_key(i, "Radiative")
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if key in components:
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rad_vals = components[key]
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ax.plot(
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self.energies_MeV, rad_vals, color=color, lw=1.5,
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alpha=0.7, label=f"Br. {i+1}",
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)
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ax.set_ylabel("Radiative Correction", fontsize=10)
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ax.set_xlabel(r"Electron kinetic energy $E$ [MeV]", fontsize=10)
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ax.set_title("Radiative Corrections", fontsize=11)
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ax.grid(True, alpha=0.3)
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ax.legend(fontsize=8, loc="best", ncol=min(2, n_branches + 1))
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self._build_figure_header(fig)
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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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|
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def _element_symbol(self, Z: int) -> str:
|
|
"""Return chemical symbol for atomic number Z."""
|
|
symbols = [
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"", "H", "He", "Li", "Be", "B", "C", "N", "O", "F",
|
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"Ne", "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K",
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"Ca", "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu",
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"Zn", "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y",
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"Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In",
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"Sn", "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr",
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"Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm",
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"Yb", "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au",
|
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"Hg", "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac",
|
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"Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es",
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]
|
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return symbols[Z] if Z < len(symbols) else f"Z{Z}"
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|
|
# ---------------------------------------------------------------------------
|
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# 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
|