75d271c696
- Remplace le continuum exponentiel par un modèle réaliste CsI(Tl) dans l'entraînement (bosse asymétrique ~110 keV + queue Compton) - Ajoute l'injection de background mesuré (70% mesuré / 30% synthétique) via --measured_background et MEASURED_BACKGROUND_PATH - Ajoute l'endpoint /api/background/continuum et le toggle "Continuum CsI" sur le dashboard background - Exclut le canal 1023 (overflow bin) de l'affichage web (NUM_CHANNELS=1023) - Corrige le lissage Gaussien du background (normalisation locale aux bords) - Met à jour README.md, CLAUDE.md, TUTORIEL.md, TOTO.md, vega_ml/README.md Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
584 lines
21 KiB
Python
584 lines
21 KiB
Python
"""
|
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Synthetic Spectra Generation Script v3
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|
|
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Optimized for 2D model training with:
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- Fixed 60-second duration (60 time intervals)
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- Better isotope combinations including decay chain scenarios
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- Enhanced background-only samples
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- More diverse mixing scenarios
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Usage:
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python -m synthetic_spectra.generate_spectra_v3 --num_samples 200000 --workers 8
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"""
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import argparse
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import sys
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from pathlib import Path
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import json
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from datetime import datetime
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import numpy as np
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from multiprocessing import Pool, cpu_count
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from functools import partial
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import time
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from typing import List, Tuple, Dict, Optional
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import os
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# Add parent directory to path for imports
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from synthetic_spectra.generator import (
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SpectrumGenerator,
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SpectrumConfig,
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IsotopeSource,
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GeneratedSpectrum,
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save_spectrum,
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)
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from synthetic_spectra.config import RADIACODE_CONFIGS
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from synthetic_spectra.ground_truth import get_isotope
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# =============================================================================
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# ISOTOPE POOLS - Organized for realistic scenarios
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# =============================================================================
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# Calibration/check sources (individual isotopes)
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CALIBRATION_ISOTOPES = [
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"Cs-137", "Co-60", "Am-241", "Ba-133", "Eu-152", "Na-22", "Co-57", "Mn-54"
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]
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# Medical isotopes (often found individually)
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MEDICAL_ISOTOPES = [
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"Tc-99m", "I-131", "I-123", "F-18", "Ga-67", "Ga-68", "In-111", "Lu-177", "Tl-201"
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]
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# Industrial sources
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INDUSTRIAL_ISOTOPES = [
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"Ir-192", "Se-75", "Zn-65", "Co-58", "Cd-109"
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]
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# Natural decay chains - these ALWAYS appear together in nature
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URANIUM_238_CHAIN = ["U-238", "Ra-226", "Pb-214", "Bi-214"] # Secular equilibrium
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THORIUM_232_CHAIN = ["Th-232", "Ac-228", "Pb-212", "Bi-212", "Tl-208"]
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URANIUM_235_CHAIN = ["U-235"] # Daughters have low gamma yield
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# Fallout/contamination (often appear in specific combinations)
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CHERNOBYL_FUKUSHIMA = ["Cs-137", "Cs-134"] # Classic reactor fallout signature
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FRESH_FALLOUT = ["I-131", "Cs-137", "Cs-134", "Zr-95", "Nb-95"]
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OLDER_FALLOUT = ["Cs-137", "Sr-90"] # Long-lived only
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# Natural background (what you'd see with no source)
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NATURAL_BACKGROUND = ["K-40"] # Potassium in environment
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# NORM - Naturally Occurring Radioactive Material
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NORM_MATERIALS = ["K-40", "Ra-226", "Th-232", "U-238"]
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def get_valid_isotopes(isotope_list: List[str]) -> List[str]:
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"""Filter to isotopes with gamma lines."""
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valid = []
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for name in isotope_list:
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iso = get_isotope(name)
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if iso and len(iso.gamma_lines) > 0:
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valid.append(name)
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return valid
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# Pre-validate all pools
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VALID_CALIBRATION = get_valid_isotopes(CALIBRATION_ISOTOPES)
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VALID_MEDICAL = get_valid_isotopes(MEDICAL_ISOTOPES)
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VALID_INDUSTRIAL = get_valid_isotopes(INDUSTRIAL_ISOTOPES)
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VALID_U238_CHAIN = get_valid_isotopes(URANIUM_238_CHAIN)
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VALID_TH232_CHAIN = get_valid_isotopes(THORIUM_232_CHAIN)
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VALID_FALLOUT = get_valid_isotopes(CHERNOBYL_FUKUSHIMA + FRESH_FALLOUT)
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VALID_NORM = get_valid_isotopes(NORM_MATERIALS)
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# All valid isotopes for random selection
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ALL_VALID_ISOTOPES = list(set(
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VALID_CALIBRATION + VALID_MEDICAL + VALID_INDUSTRIAL +
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VALID_U238_CHAIN + VALID_TH232_CHAIN + VALID_FALLOUT + VALID_NORM
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))
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# =============================================================================
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# SAMPLE SCENARIOS
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# =============================================================================
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class SampleScenario:
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"""Defines a type of sample to generate."""
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def __init__(self, name: str, fraction: float):
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self.name = name
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self.fraction = fraction
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def generate_sources(self, rng: np.random.Generator, activity_range: Tuple[float, float]) -> List[IsotopeSource]:
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"""Generate isotope sources for this scenario."""
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raise NotImplementedError
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class BackgroundOnlyScenario(SampleScenario):
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"""Pure background - no identifiable sources."""
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def __init__(self, fraction: float = 0.15):
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super().__init__("background_only", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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return [] # No sources - just background
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class SingleCalibrationScenario(SampleScenario):
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"""Single calibration source."""
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def __init__(self, fraction: float = 0.20):
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super().__init__("single_calibration", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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isotope = rng.choice(VALID_CALIBRATION)
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activity = rng.uniform(*activity_range)
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return [IsotopeSource(isotope, activity, include_daughters=True)]
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class SingleMedicalScenario(SampleScenario):
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"""Single medical isotope."""
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def __init__(self, fraction: float = 0.10):
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super().__init__("single_medical", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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if not VALID_MEDICAL:
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return []
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isotope = rng.choice(VALID_MEDICAL)
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activity = rng.uniform(*activity_range)
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return [IsotopeSource(isotope, activity, include_daughters=True)]
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class SingleIndustrialScenario(SampleScenario):
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"""Single industrial source."""
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def __init__(self, fraction: float = 0.05):
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super().__init__("single_industrial", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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if not VALID_INDUSTRIAL:
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return []
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isotope = rng.choice(VALID_INDUSTRIAL)
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activity = rng.uniform(*activity_range)
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return [IsotopeSource(isotope, activity, include_daughters=True)]
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class UraniumChainScenario(SampleScenario):
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"""Natural uranium with decay chain in equilibrium."""
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def __init__(self, fraction: float = 0.08):
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super().__init__("uranium_chain", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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# All daughters at ~same activity (secular equilibrium)
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base_activity = rng.uniform(*activity_range)
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sources = []
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for iso in VALID_U238_CHAIN:
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# Slight variation to simulate real-world
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activity = base_activity * rng.uniform(0.8, 1.2)
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sources.append(IsotopeSource(iso, activity, include_daughters=False))
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return sources
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class ThoriumChainScenario(SampleScenario):
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"""Natural thorium with decay chain."""
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def __init__(self, fraction: float = 0.08):
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super().__init__("thorium_chain", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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base_activity = rng.uniform(*activity_range)
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sources = []
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for iso in VALID_TH232_CHAIN:
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activity = base_activity * rng.uniform(0.8, 1.2)
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sources.append(IsotopeSource(iso, activity, include_daughters=False))
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return sources
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class NORMScenario(SampleScenario):
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"""NORM - naturally occurring radioactive material (multiple natural isotopes)."""
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def __init__(self, fraction: float = 0.08):
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super().__init__("norm", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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# Pick 2-4 NORM isotopes
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num_isotopes = rng.integers(2, 5)
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selected = rng.choice(VALID_NORM, size=min(num_isotopes, len(VALID_NORM)), replace=False)
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sources = []
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for iso in selected:
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activity = rng.uniform(*activity_range)
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sources.append(IsotopeSource(iso, activity, include_daughters=True))
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return sources
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class FalloutScenario(SampleScenario):
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"""Reactor fallout signature (Cs-137 + Cs-134 fingerprint)."""
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def __init__(self, fraction: float = 0.06):
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super().__init__("fallout", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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sources = []
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# Cs-137/Cs-134 ratio varies with age of fallout
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cs137_activity = rng.uniform(*activity_range)
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# Fresh fallout: ~1:1 ratio, aged: Cs-134 decays faster
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age_factor = rng.uniform(0.1, 1.0) # How "fresh" the fallout is
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cs134_activity = cs137_activity * age_factor
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if "Cs-137" in VALID_FALLOUT:
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sources.append(IsotopeSource("Cs-137", cs137_activity, include_daughters=True))
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if "Cs-134" in VALID_FALLOUT and cs134_activity > 0.5:
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sources.append(IsotopeSource("Cs-134", cs134_activity, include_daughters=True))
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# Sometimes include I-131 (very fresh fallout only)
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if rng.random() < 0.3 and "I-131" in VALID_FALLOUT:
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sources.append(IsotopeSource("I-131", rng.uniform(1, 50), include_daughters=True))
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return sources
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class MixedSourcesScenario(SampleScenario):
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"""Random mix of 2-3 different source types."""
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def __init__(self, fraction: float = 0.10):
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super().__init__("mixed", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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num_isotopes = rng.integers(2, 4)
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selected = rng.choice(ALL_VALID_ISOTOPES, size=num_isotopes, replace=False)
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sources = []
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for iso in selected:
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activity = rng.uniform(*activity_range)
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sources.append(IsotopeSource(iso, activity, include_daughters=True))
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return sources
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class ComplexMixScenario(SampleScenario):
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"""Complex scenario: 4-6 isotopes from various categories."""
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def __init__(self, fraction: float = 0.05):
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super().__init__("complex_mix", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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num_isotopes = rng.integers(4, 7)
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selected = set()
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# Try to get variety from different pools
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pools = [VALID_CALIBRATION, VALID_MEDICAL, VALID_INDUSTRIAL, VALID_U238_CHAIN, VALID_TH232_CHAIN]
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for pool in pools:
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if len(selected) >= num_isotopes:
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break
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if pool:
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iso = rng.choice(pool)
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selected.add(iso)
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# Fill remaining with random
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while len(selected) < num_isotopes:
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iso = rng.choice(ALL_VALID_ISOTOPES)
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selected.add(iso)
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sources = []
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for iso in selected:
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activity = rng.uniform(*activity_range)
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sources.append(IsotopeSource(iso, activity, include_daughters=True))
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return sources
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class WeakSourceScenario(SampleScenario):
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"""Very weak sources - near detection limit."""
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def __init__(self, fraction: float = 0.05):
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super().__init__("weak_source", fraction)
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def generate_sources(self, rng, activity_range) -> List[IsotopeSource]:
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# Very low activity - near background
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weak_activity_range = (0.1, 5.0) # Much weaker than normal
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isotope = rng.choice(ALL_VALID_ISOTOPES)
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activity = rng.uniform(*weak_activity_range)
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return [IsotopeSource(isotope, activity, include_daughters=True)]
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|
|
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# All scenarios with their fractions (should sum to 1.0)
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DEFAULT_SCENARIOS = [
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BackgroundOnlyScenario(0.15), # 15% - important for "no detection" cases
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SingleCalibrationScenario(0.20), # 20% - common check sources
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SingleMedicalScenario(0.08), # 8% - medical isotopes
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SingleIndustrialScenario(0.05), # 5% - industrial sources
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UraniumChainScenario(0.10), # 10% - natural uranium + daughters
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ThoriumChainScenario(0.10), # 10% - natural thorium + daughters
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NORMScenario(0.07), # 7% - NORM materials
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FalloutScenario(0.05), # 5% - reactor fallout signature
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MixedSourcesScenario(0.10), # 10% - random 2-3 isotope mixes
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ComplexMixScenario(0.05), # 5% - complex 4-6 isotope scenarios
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WeakSourceScenario(0.05), # 5% - near-detection-limit sources
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]
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|
|
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# =============================================================================
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# BACKGROUND VARIATION
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# =============================================================================
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|
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class BackgroundConfig:
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"""Configuration for varied background generation."""
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def __init__(
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self,
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intensity_min: float = 0.3,
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intensity_max: float = 3.0,
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k40_prob: float = 0.95,
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radon_prob: float = 0.8,
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thorium_prob: float = 0.6,
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):
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self.intensity_min = intensity_min
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self.intensity_max = intensity_max
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self.k40_prob = k40_prob
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self.radon_prob = radon_prob
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self.thorium_prob = thorium_prob
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def sample(self, rng: np.random.Generator) -> dict:
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"""Sample a random background configuration."""
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return {
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'background_cps': rng.uniform(self.intensity_min, self.intensity_max) * 5.0,
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'include_k40': rng.random() < self.k40_prob,
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'include_radon': rng.random() < self.radon_prob,
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'include_thorium': rng.random() < self.thorium_prob,
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}
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|
|
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# =============================================================================
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# SAMPLE GENERATION
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# =============================================================================
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def generate_single_sample(args: Tuple[int, dict]) -> Optional[str]:
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"""
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Generate a single sample for parallel processing.
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Args:
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args: Tuple of (sample_index, config_dict)
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Returns:
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Sample ID if successful, None if failed
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"""
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sample_idx, config = args
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try:
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# Create RNG with unique seed per sample
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rng = np.random.default_rng(config['base_seed'] + sample_idx)
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# Initialize generator
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detector_config = RADIACODE_CONFIGS.get(config['detector_name'])
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generator = SpectrumGenerator(detector_config=detector_config)
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# Select scenario based on cumulative probabilities
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scenarios = config['scenarios']
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scenario_probs = [s.fraction for s in scenarios]
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scenario = rng.choice(scenarios, p=scenario_probs)
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# Generate sources for this scenario
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sources = scenario.generate_sources(rng, config['activity_range'])
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# Background configuration
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bg_config = BackgroundConfig(
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intensity_min=config.get('bg_intensity_min', 0.3),
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intensity_max=config.get('bg_intensity_max', 3.0),
|
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)
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bg_params = bg_config.sample(rng)
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|
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# FIXED 60-second duration for 2D model
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duration = 60.0
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|
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# Create spectrum config
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spec_config = SpectrumConfig(
|
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duration_seconds=duration,
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time_interval_seconds=1.0, # 1 second per interval = 60 intervals
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sources=sources,
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include_background=True,
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background_cps=bg_params['background_cps'],
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include_k40=bg_params['include_k40'],
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include_radon=bg_params['include_radon'],
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include_thorium=bg_params['include_thorium'],
|
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detector_name=config['detector_name'],
|
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measured_background_path=config.get('measured_background_path'),
|
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)
|
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|
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# Generate spectrum
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spectrum = generator.generate_spectrum(spec_config)
|
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|
|
# Save spectrum
|
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output_dir = Path(config['output_dir']) / "spectra"
|
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save_spectrum(
|
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spectrum,
|
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output_dir,
|
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save_image=True, # Save NPY file
|
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image_format='npy' # Skip PNG for speed
|
|
)
|
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|
|
return spectrum.sample_id
|
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|
|
except Exception as e:
|
|
print(f"Error generating sample {sample_idx}: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
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return None
|
|
|
|
|
|
def generate_training_data_v3(
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num_samples: int,
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output_dir: Path,
|
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detector_name: str = "radiacode_103",
|
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activity_range: Tuple[float, float] = (1.0, 100.0),
|
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bg_intensity_range: Tuple[float, float] = (0.3, 3.0),
|
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scenarios: Optional[List[SampleScenario]] = None,
|
|
num_workers: int = None,
|
|
random_seed: int = None,
|
|
measured_background_path: Optional[str] = None,
|
|
) -> int:
|
|
"""
|
|
Generate training samples in parallel.
|
|
|
|
Args:
|
|
num_samples: Total number of samples to generate
|
|
output_dir: Output directory
|
|
detector_name: Detector to simulate
|
|
activity_range: (min, max) activity in Bq
|
|
bg_intensity_range: Background intensity multiplier range
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|
scenarios: List of SampleScenario objects (default: DEFAULT_SCENARIOS)
|
|
num_workers: Number of parallel workers
|
|
random_seed: Base random seed
|
|
|
|
Returns:
|
|
Number of successfully generated samples
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|
"""
|
|
if num_workers is None:
|
|
num_workers = max(1, cpu_count() - 1)
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|
|
if random_seed is None:
|
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random_seed = int(time.time())
|
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|
|
if scenarios is None:
|
|
scenarios = DEFAULT_SCENARIOS
|
|
|
|
# Normalize scenario fractions
|
|
total_fraction = sum(s.fraction for s in scenarios)
|
|
for s in scenarios:
|
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s.fraction /= total_fraction
|
|
|
|
# Create output directory
|
|
output_dir = Path(output_dir)
|
|
spectra_dir = output_dir / "spectra"
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|
spectra_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
print(f"=" * 70)
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|
print(f"SYNTHETIC SPECTRA GENERATION v3 - Optimized for 2D Model")
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|
print(f"=" * 70)
|
|
print(f"\nConfiguration:")
|
|
print(f" Samples: {num_samples:,}")
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|
print(f" Output: {output_dir}")
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|
print(f" Detector: {detector_name}")
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|
print(f" Duration: 60 seconds (fixed)")
|
|
print(f" Activity range: {activity_range[0]:.1f} - {activity_range[1]:.1f} Bq")
|
|
print(f" Workers: {num_workers}")
|
|
print(f"\nScenario distribution:")
|
|
for s in scenarios:
|
|
count = int(num_samples * s.fraction)
|
|
print(f" {s.name}: {s.fraction*100:.1f}% (~{count:,} samples)")
|
|
print()
|
|
|
|
# Shared config for all workers
|
|
shared_config = {
|
|
'detector_name': detector_name,
|
|
'output_dir': str(output_dir),
|
|
'activity_range': activity_range,
|
|
'bg_intensity_min': bg_intensity_range[0],
|
|
'bg_intensity_max': bg_intensity_range[1],
|
|
'base_seed': random_seed,
|
|
'scenarios': scenarios,
|
|
'measured_background_path': measured_background_path,
|
|
}
|
|
|
|
# Create work items
|
|
work_items = [(i, shared_config) for i in range(num_samples)]
|
|
|
|
# Progress tracking
|
|
start_time = time.time()
|
|
completed = 0
|
|
failed = 0
|
|
last_report = 0
|
|
|
|
print(f"Starting generation...")
|
|
|
|
# Generate in parallel
|
|
with Pool(num_workers) as pool:
|
|
for result in pool.imap_unordered(generate_single_sample, work_items, chunksize=100):
|
|
if result is not None:
|
|
completed += 1
|
|
else:
|
|
failed += 1
|
|
|
|
total = completed + failed
|
|
|
|
# Progress report every 1%
|
|
if total - last_report >= num_samples // 100 or total == num_samples:
|
|
elapsed = time.time() - start_time
|
|
rate = completed / elapsed if elapsed > 0 else 0
|
|
eta = (num_samples - total) / rate if rate > 0 else 0
|
|
|
|
print(f"\r Progress: {total:,}/{num_samples:,} ({100*total/num_samples:.1f}%) | "
|
|
f"Rate: {rate:.1f}/s | "
|
|
f"ETA: {eta/60:.1f}m | "
|
|
f"Failed: {failed}", end="", flush=True)
|
|
last_report = total
|
|
|
|
total_time = time.time() - start_time
|
|
|
|
print(f"\n\nGeneration complete!")
|
|
print(f" Total time: {total_time/60:.1f} minutes")
|
|
print(f" Successful: {completed:,}")
|
|
print(f" Failed: {failed}")
|
|
print(f" Rate: {completed/total_time:.1f} samples/second")
|
|
|
|
return completed
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description='Generate synthetic gamma spectra v3')
|
|
parser.add_argument('--num_samples', '-n', type=int, default=200000,
|
|
help='Number of samples to generate')
|
|
parser.add_argument('--output_dir', '-o', type=str, default='data/synthetic',
|
|
help='Output directory')
|
|
parser.add_argument('--detector', '-d', type=str, default='radiacode_103',
|
|
help='Detector type')
|
|
parser.add_argument('--workers', '-w', type=int, default=None,
|
|
help='Number of parallel workers')
|
|
parser.add_argument('--seed', '-s', type=int, default=None,
|
|
help='Random seed')
|
|
parser.add_argument('--activity_min', type=float, default=1.0,
|
|
help='Minimum activity in Bq')
|
|
parser.add_argument('--activity_max', type=float, default=100.0,
|
|
help='Maximum activity in Bq')
|
|
parser.add_argument('--measured_background', type=str, default=None,
|
|
help='Path to measured background .npy file for hybrid training')
|
|
|
|
args = parser.parse_args()
|
|
|
|
generate_training_data_v3(
|
|
num_samples=args.num_samples,
|
|
output_dir=Path(args.output_dir),
|
|
detector_name=args.detector,
|
|
activity_range=(args.activity_min, args.activity_max),
|
|
num_workers=args.workers,
|
|
random_seed=args.seed,
|
|
measured_background_path=args.measured_background,
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|