unzhakov
36174b5d25
- DEFAULT_JSON_SCHEMA: drop the 8 detector_* keys left over from the removed detector_response module; CLI dry-run drops the dead 'Detector: not configured (Phase 4)' line - CSV export: single-mode no longer writes branch_1_PhaseSpace / branch_1_Radiative duplicates next to the bare PhaseSpace/Radiative columns; per-branch core factors only in multi-branch mode - Visualizer: debug panels fall back to the unprefixed core-factor columns for single-mode CSVs instead of skipping them - Spectrum ylabel now states the actual units: normalized spectrum [MeV^-1], not 'Normalized Counts / keV' - tests: defaults assert detector keys gone; column layout pinned for single vs multi; panel and ylabel assertions
469 lines
15 KiB
Python
469 lines
15 KiB
Python
"""
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Visualization module — plotting built ON TOP OF the CSV export.
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All graphical representations are produced from the exported CSV file
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(data columns + metadata header) and never recomputed from the analyzer in
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parallel. The CSV file is the single source of truth: a plot is produced
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exactly from the bytes a researcher would download, which turns every plot
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into an additional quality check of the export routine.
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"""
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from __future__ import annotations
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import re
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import shutil
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import tempfile
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.figure import Figure
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from .csv_export import CSVEmitter
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if TYPE_CHECKING:
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from beta_spectrum.output.core import BetaSpectrumAnalyzer
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_BRANCH_LINE_RE = re.compile(
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r"^\s*branch_(\d+):\s*E₀=([0-9.]+)\s*keV,\s*transition=(\w+),\s*"
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r"intensity=([0-9.]+)(.*)$"
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)
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_EXCLUDED_SUFFIX_RE = re.compile(r"-\s*excluded:\s*(.*)$")
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_BRANCH_COLORS = [
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"#1f77b4",
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"#ff7f0e",
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"#2ca02c",
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"#d62728",
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"#9467bd",
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"#8c564b",
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"#e377c2",
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"#7f7f7f",
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"#bcbd22",
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"#17becf",
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]
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def parse_spectrum_csv(
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csv_path: str,
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) -> Tuple[pd.DataFrame, Dict[str, Any]]:
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"""Read an exported spectrum CSV and parse its metadata header.
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Parameters
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----------
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csv_path : str
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Path to a CSV file produced by CSVEmitter.export_to_csv.
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Returns
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-------
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tuple[pandas.DataFrame, dict]
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The data frame (comment lines skipped, separator auto-detected)
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and a metadata dict with keys: version, build, timestamp, source,
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nuclide, endpoint_keV, neutrino_mass_MeV, corrections, e_step_MeV,
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n_points, normalization, branches.
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Raises
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------
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ValueError
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If the file cannot be read or parsed.
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"""
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try:
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df = pd.read_csv(csv_path, sep=None, engine="python", comment="#")
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raw_lines = []
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with open(csv_path, "r") as f:
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for line in f:
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if line.startswith("#"):
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raw_lines.append(line.rstrip("\n"))
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else:
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break # header block ended
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except OSError as exc:
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raise ValueError(f"Cannot read spectrum CSV {csv_path}: {exc}") from exc
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meta: Dict[str, Any] = {
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"version": None,
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"build": None,
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"timestamp": None,
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"source": None,
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"nuclide": None,
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"endpoint_keV": None,
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"neutrino_mass_MeV": None,
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"corrections": [],
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"e_step_MeV": None,
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"n_points": None,
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"normalization": None,
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"branches": [],
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}
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for line in raw_lines:
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body = line.lstrip("#").strip()
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m = re.match(r"beta-spectrum v([\d.]+)\(build\.(\w+)\)", body)
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if m:
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meta["version"] = m.group(1)
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meta["build"] = m.group(2)
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continue
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m = re.match(r"timestamp:\s*(.+)", body)
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if m:
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meta["timestamp"] = m.group(1).strip()
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continue
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m = re.match(r"source:\s*(\S+)", body)
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if m:
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meta["source"] = m.group(1)
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continue
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m = re.match(r"nuclide:\s*(.+?)\s+\(Z=", body)
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if m:
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meta["nuclide"] = m.group(1).strip()
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continue
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m = re.match(r"basic_endpoint:\s*([0-9.]+)\s*keV", body)
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if m:
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meta["endpoint_keV"] = float(m.group(1))
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continue
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m = re.match(r"neutrino_mass_MeV:\s*([0-9.]+)", body)
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if m:
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meta["neutrino_mass_MeV"] = float(m.group(1))
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continue
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m = re.match(r"corrections:\s*(.*)", body)
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if m:
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meta["corrections"] = [
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c.strip() for c in m.group(1).split(",") if c.strip()
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]
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continue
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m = re.match(r"e_step:\s*([0-9.]+)\s*MeV", body)
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if m:
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meta["e_step_MeV"] = float(m.group(1))
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continue
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m = re.match(r"n_points:\s*(\d+)", body)
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if m:
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meta["n_points"] = int(m.group(1))
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continue
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m = re.match(r"branch normalization:\s*(\S+)", body)
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if m:
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meta["normalization"] = m.group(1)
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continue
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m = _BRANCH_LINE_RE.match(body)
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if m:
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excluded = _EXCLUDED_SUFFIX_RE.search(m.group(5))
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meta["branches"].append(
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{
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"index": int(m.group(1)),
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"endpoint_keV": float(m.group(2)),
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"transition": m.group(3),
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"intensity": float(m.group(4)),
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"excluded": None if excluded is None else excluded.group(1).strip(),
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}
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)
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continue
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if meta["n_points"] is None:
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meta["n_points"] = len(df)
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return df, meta
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def _branch_column(df: pd.DataFrame, index: int) -> Optional[str]:
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"""Find the branch spectrum column name for a 1-based branch index."""
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for candidate in (f"branch_{index}_spectrum", f"branch_{index}"):
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if candidate in df.columns:
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return candidate
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return None
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def _active_branches(meta: Dict[str, Any]) -> List[Dict[str, Any]]:
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return [b for b in meta["branches"] if b["excluded"] is None]
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def _spectrum_title(meta: Dict[str, Any]) -> str:
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n_branches = len(meta["branches"])
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active = len(_active_branches(meta))
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title = f"Beta-decay: {meta['nuclide']}"
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if n_branches > 1:
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if active < n_branches:
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title += f" ({active} of {n_branches} branches)"
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else:
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title += f" ({n_branches} branches)"
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return title
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def _apply_figure_header(fig: Figure, meta: Dict[str, Any]) -> None:
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"""Write the figure header/footer lines from the CSV metadata."""
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e_step_keV = (meta["e_step_MeV"] or 0.0) * 1000.0
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endpoint = meta["endpoint_keV"]
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header = f"{meta['nuclide']} | E₀={endpoint:.1f} keV | ΔE={e_step_keV:.1f} keV"
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fig.text(
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0.01,
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0.98,
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header,
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transform=fig.transFigure,
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fontsize=9,
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verticalalignment="top",
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horizontalalignment="left",
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fontfamily="monospace",
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)
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if meta["neutrino_mass_MeV"]:
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fig.text(
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0.01,
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0.96,
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f"| m_ν={meta['neutrino_mass_MeV']:.6f} MeV",
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transform=fig.transFigure,
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fontsize=8,
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verticalalignment="top",
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horizontalalignment="left",
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fontfamily="monospace",
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color="red",
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)
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if meta["build"]:
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fig.text(
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0.01,
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0.01,
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f"{meta['build']} | {meta['timestamp']}",
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transform=fig.transFigure,
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fontsize=8,
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verticalalignment="bottom",
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horizontalalignment="left",
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fontfamily="monospace",
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color="gray",
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)
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def _plot_spectra_view_from_df(
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df: pd.DataFrame,
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meta: Dict[str, Any],
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save_path: Optional[str],
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) -> Figure:
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"""Plot total spectrum and per-branch spectra from CSV columns."""
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energies = df["energy_MeV"].to_numpy()
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fig, ax = plt.subplots(figsize=(12, 7))
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if "total_spectrum" in df.columns:
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ax.plot(
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energies,
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df["total_spectrum"].to_numpy(),
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"k-",
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lw=2.5,
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label="Total spectrum",
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zorder=10,
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)
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for color_i, b in enumerate(_active_branches(meta)):
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col = _branch_column(df, int(b["index"]))
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if col is None:
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continue
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ax.plot(
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energies,
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df[col].to_numpy(),
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color=_BRANCH_COLORS[color_i % len(_BRANCH_COLORS)],
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lw=1.5,
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alpha=0.7,
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label=(
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f"Br. {b['index']}: {b['endpoint_keV']:.1f} keV "
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f"({b['intensity'] * 100:.1f}%)"
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),
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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(r"Normalized spectrum [MeV$^{-1}$]", fontsize=12)
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fig.suptitle(_spectrum_title(meta), fontsize=14, fontweight="bold")
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ax.set_yscale("log")
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ax.grid(True, alpha=0.3)
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ax.legend(loc="best", fontsize=9, ncol=2)
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_apply_figure_header(fig, meta)
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fig.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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return fig
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def _plot_debug_view_from_df(
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df: pd.DataFrame,
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meta: Dict[str, Any],
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save_path: Optional[str],
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) -> Figure:
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"""Debug view (4 panels) built entirely from CSV columns."""
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energies = df["energy_MeV"].to_numpy()
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active = _active_branches(meta)
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fig, axes = plt.subplots(4, 1, figsize=(12, 16), sharex=True)
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fig.suptitle(_spectrum_title(meta), fontsize=14, fontweight="bold")
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# Panel 1: total spectrum + per-branch spectra
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ax = axes[0]
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if "total_spectrum" in df.columns:
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ax.plot(
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energies,
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df["total_spectrum"].to_numpy(),
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"k-",
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lw=2,
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label="Total spectrum",
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zorder=10,
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)
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for color_i, b in enumerate(active):
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col = _branch_column(df, int(b["index"]))
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if col is None:
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continue
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ax.plot(
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energies,
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df[col].to_numpy(),
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color=_BRANCH_COLORS[color_i % len(_BRANCH_COLORS)],
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lw=1.5,
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alpha=0.7,
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label=f"Br. {b['index']} ({b['intensity'] * 100:.1f}%)",
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)
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ax.set_yscale("log")
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ax.set_ylabel(r"Normalized spectrum [MeV$^{-1}$]", fontsize=10)
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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=2)
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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 df.columns:
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fermi = df["Fermi"].to_numpy()
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fermi_scaled = fermi / np.max(fermi)
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ax.plot(energies, fermi_scaled, "r-", lw=1.5, label="Fermi", alpha=0.8)
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for color_i, b in enumerate(active):
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col = f"branch_{b['index']}_PhaseSpace"
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if col not in df.columns:
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# Single-branch CSVs carry the core factors unprefixed.
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col = "PhaseSpace"
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if col not in df.columns:
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continue
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ps_vals = df[col].to_numpy()
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ps_max = np.max(ps_vals) if np.any(ps_vals > 0) else 1.0
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ax.plot(
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energies,
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ps_vals / ps_max if ps_max > 0 else ps_vals,
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color=_BRANCH_COLORS[color_i % len(_BRANCH_COLORS)],
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lw=1.5,
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alpha=0.7,
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label=f"PS Br. {b['index']}",
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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 higher-order corrections
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ax = axes[2]
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correction_colors = ["green", "orange", "purple", "brown"]
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for corr_i, name in enumerate(
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["Screening", "FiniteSizeL0", "ChargeDistributionU", "Exchange"]
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):
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if name in df.columns:
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values = df[name].to_numpy()
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values_scaled = values / np.max(values)
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ax.plot(
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energies,
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values_scaled,
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color=correction_colors[corr_i],
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lw=1.5,
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label=name,
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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 per branch)
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ax = axes[3]
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for color_i, b in enumerate(active):
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col = f"branch_{b['index']}_Radiative"
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if col not in df.columns:
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# Single-branch CSVs carry the core factors unprefixed.
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col = "Radiative"
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if col not in df.columns:
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continue
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ax.plot(
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energies,
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df[col].to_numpy(),
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color=_BRANCH_COLORS[color_i % len(_BRANCH_COLORS)],
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lw=1.5,
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alpha=0.7,
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label=f"Br. {b['index']}",
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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=2)
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_apply_figure_header(fig, meta)
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fig.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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return fig
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class Visualizer:
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"""Visualization built from the exported CSV data (single source of truth)."""
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@staticmethod
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def plot_from_csv(
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csv_path: str,
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save_path: Optional[str] = None,
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show_components: bool = True,
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) -> Figure:
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"""Create a visualization from an exported CSV spectrum file.
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Parameters
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----------
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csv_path : str
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Path to a CSV exported by CSVEmitter.export_to_csv. The plots
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are built exclusively from the file contents (data columns and
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metadata header) — nothing is recomputed from the model.
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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, produce the full 4-panel debug view; requires the
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'full' CSV verbosity. If the file lacks component columns, a
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warning is printed and the simple spectrum view is produced.
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"""
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df, meta = parse_spectrum_csv(csv_path)
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if show_components and "Fermi" in df.columns:
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return _plot_debug_view_from_df(df, meta, save_path)
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if show_components:
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print(
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"Warning: requested component panels but CSV lacks component "
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"columns (export with --csv-verbosity full); falling back to "
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"simple spectrum view."
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)
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return _plot_spectra_view_from_df(df, meta, save_path)
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@staticmethod
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def plot_analysis(
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analyzer: BetaSpectrumAnalyzer,
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save_path: Optional[str] = None,
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show_components: bool = True,
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) -> Figure:
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"""Create a visualization by exporting CSV and plotting from it.
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The export goes to a temporary file using the 'full' verbosity so
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that the plots exercise the same data pipeline (header + columns)
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a researcher receives.
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"""
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tmp_dir = tempfile.mkdtemp(prefix="bs_plot_")
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try:
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csv_path = f"{tmp_dir}/spectrum.csv"
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CSVEmitter.export_to_csv(
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analyzer,
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csv_path,
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source_type="plot",
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csv_verbosity="full",
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)
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return Visualizer.plot_from_csv(
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csv_path,
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save_path=save_path,
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show_components=show_components,
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)
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finally:
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shutil.rmtree(tmp_dir, ignore_errors=True)
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