Pipeline complet Radiacode 103 - identification automatique d'isotopes
- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Jacquin Antoine
477
train/vega_ml/synthetic_spectra/spectrum_viewer.py
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477
train/vega_ml/synthetic_spectra/spectrum_viewer.py
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@ -0,0 +1,477 @@
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"""
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Spectrum Viewer Application
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A simple GUI application to browse and visualize generated synthetic spectra.
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Randomly samples from the available spectra to avoid loading all files at once.
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Usage:
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python -m synthetic_spectra.spectrum_viewer
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Or with options:
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python -m synthetic_spectra.spectrum_viewer --num_samples 200 --data_dir ./data/synthetic/spectra
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"""
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import tkinter as tk
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from tkinter import ttk
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import numpy as np
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import json
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from pathlib import Path
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import random
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from typing import Optional, List, Dict, Any
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from .config import RADIACODE_CONFIGS, get_default_config
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# Try to import matplotlib for plotting
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try:
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import matplotlib
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matplotlib.use('TkAgg')
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import matplotlib.pyplot as plt
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
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from matplotlib.figure import Figure
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HAS_MATPLOTLIB = True
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except ImportError:
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HAS_MATPLOTLIB = False
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print("Warning: matplotlib not found. Install with: pip install matplotlib")
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class SpectrumViewer:
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"""
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GUI application for viewing synthetic gamma spectra.
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"""
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def __init__(
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self,
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data_dir: str = "./data/synthetic/spectra",
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num_samples: int = 100,
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random_seed: Optional[int] = None
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):
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"""
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Initialize the spectrum viewer.
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Args:
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data_dir: Directory containing spectrum .npy and .json files
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num_samples: Number of random samples to load (for performance)
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random_seed: Random seed for reproducible sample selection
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"""
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self.data_dir = Path(data_dir)
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self.num_samples = num_samples
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if random_seed is not None:
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random.seed(random_seed)
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# Find and sample spectrum files
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self.spectrum_files = self._discover_and_sample_files()
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if not self.spectrum_files:
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raise ValueError(f"No spectrum files found in {self.data_dir}")
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print(f"Loaded {len(self.spectrum_files)} spectrum samples")
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# Current state
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self.current_index = 0
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self.current_spectrum: Optional[np.ndarray] = None
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self.current_metadata: Optional[Dict[str, Any]] = None
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# Setup GUI
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self._setup_gui()
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# Load first spectrum
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self._load_current_spectrum()
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def _discover_and_sample_files(self) -> List[Path]:
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"""Find all spectrum files and randomly sample them."""
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# Find all .npy files
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all_npy_files = list(self.data_dir.glob("spectrum_*.npy"))
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if not all_npy_files:
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# Try without prefix
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all_npy_files = list(self.data_dir.glob("*.npy"))
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print(f"Found {len(all_npy_files)} total spectrum files")
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# Randomly sample if we have more than requested
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if len(all_npy_files) > self.num_samples:
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sampled = random.sample(all_npy_files, self.num_samples)
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else:
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sampled = all_npy_files
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# Sort by name for consistent ordering in dropdown
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return sorted(sampled, key=lambda p: p.stem)
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def _setup_gui(self):
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"""Setup the tkinter GUI."""
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self.root = tk.Tk()
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self.root.title("Spectrum Viewer - Synthetic Gamma Spectra")
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self.root.geometry("1200x800")
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# Main container
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main_frame = ttk.Frame(self.root, padding="10")
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main_frame.grid(row=0, column=0, sticky="nsew")
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# Configure grid weights for resizing
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self.root.columnconfigure(0, weight=1)
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self.root.rowconfigure(0, weight=1)
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main_frame.columnconfigure(0, weight=1)
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main_frame.rowconfigure(1, weight=1)
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# === Top controls ===
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controls_frame = ttk.Frame(main_frame)
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controls_frame.grid(row=0, column=0, sticky="ew", pady=(0, 10))
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controls_frame.columnconfigure(1, weight=1)
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# Dropdown for spectrum selection
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ttk.Label(controls_frame, text="Select Spectrum:").grid(row=0, column=0, padx=(0, 10))
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self.spectrum_var = tk.StringVar()
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self.spectrum_dropdown = ttk.Combobox(
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controls_frame,
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textvariable=self.spectrum_var,
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values=[f.stem for f in self.spectrum_files],
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state="readonly",
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width=50
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)
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self.spectrum_dropdown.grid(row=0, column=1, sticky="ew", padx=(0, 10))
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self.spectrum_dropdown.bind("<<ComboboxSelected>>", self._on_spectrum_selected)
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self.spectrum_dropdown.current(0)
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# Navigation buttons
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nav_frame = ttk.Frame(controls_frame)
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nav_frame.grid(row=0, column=2)
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ttk.Button(nav_frame, text="◀ Prev", command=self._prev_spectrum).pack(side="left", padx=2)
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ttk.Button(nav_frame, text="Next ▶", command=self._next_spectrum).pack(side="left", padx=2)
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ttk.Button(nav_frame, text="🎲 Random", command=self._random_spectrum).pack(side="left", padx=2)
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# Sample count label
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self.count_label = ttk.Label(
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controls_frame,
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text=f"Showing {len(self.spectrum_files)} of available spectra"
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)
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self.count_label.grid(row=0, column=3, padx=(10, 0))
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# === Plotting area ===
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plot_frame = ttk.Frame(main_frame)
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plot_frame.grid(row=1, column=0, sticky="nsew")
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plot_frame.columnconfigure(0, weight=1)
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plot_frame.rowconfigure(0, weight=1)
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if HAS_MATPLOTLIB:
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# Create matplotlib figure with 2 subplots
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self.fig = Figure(figsize=(12, 6), dpi=100)
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# 2D spectrogram (heatmap)
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self.ax_2d = self.fig.add_subplot(121)
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self.ax_2d.set_title("2D Spectrogram (Time vs Energy)")
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self.ax_2d.set_xlabel("Energy Channel")
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self.ax_2d.set_ylabel("Time Interval (s)")
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# 1D summed spectrum
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self.ax_1d = self.fig.add_subplot(122)
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self.ax_1d.set_title("Summed Spectrum")
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self.ax_1d.set_xlabel("Energy (keV)")
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self.ax_1d.set_ylabel("Counts (normalized)")
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self.fig.tight_layout()
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# Embed in tkinter
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self.canvas = FigureCanvasTkAgg(self.fig, master=plot_frame)
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self.canvas.draw()
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self.canvas.get_tk_widget().grid(row=0, column=0, sticky="nsew")
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# Toolbar
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toolbar_frame = ttk.Frame(plot_frame)
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toolbar_frame.grid(row=1, column=0, sticky="ew")
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self.toolbar = NavigationToolbar2Tk(self.canvas, toolbar_frame)
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self.toolbar.update()
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else:
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ttk.Label(
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plot_frame,
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text="matplotlib not installed. Install with: pip install matplotlib",
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font=("Arial", 14)
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).grid(row=0, column=0, pady=50)
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# === Metadata panel ===
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metadata_frame = ttk.LabelFrame(main_frame, text="Spectrum Metadata", padding="10")
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metadata_frame.grid(row=2, column=0, sticky="ew", pady=(10, 0))
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self.metadata_text = tk.Text(
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metadata_frame,
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height=10,
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wrap="word",
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font=("Consolas", 10)
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)
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self.metadata_text.pack(fill="both", expand=True)
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# Scrollbar for metadata
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scrollbar = ttk.Scrollbar(metadata_frame, orient="vertical", command=self.metadata_text.yview)
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scrollbar.pack(side="right", fill="y")
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self.metadata_text.configure(yscrollcommand=scrollbar.set)
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def _load_current_spectrum(self):
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"""Load the currently selected spectrum and its metadata."""
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if not self.spectrum_files:
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return
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spectrum_path = self.spectrum_files[self.current_index]
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json_path = spectrum_path.with_suffix(".json")
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# Load numpy array
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try:
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self.current_spectrum = np.load(spectrum_path)
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print(f"Loaded spectrum: {spectrum_path.name}, shape: {self.current_spectrum.shape}")
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except Exception as e:
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print(f"Error loading spectrum: {e}")
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self.current_spectrum = None
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# Load metadata JSON
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if json_path.exists():
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try:
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with open(json_path, 'r') as f:
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self.current_metadata = json.load(f)
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except Exception as e:
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print(f"Error loading metadata: {e}")
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self.current_metadata = None
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else:
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self.current_metadata = None
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# Update display
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self._update_plot()
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self._update_metadata()
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def _update_plot(self):
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"""Update the matplotlib plots."""
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if not HAS_MATPLOTLIB or self.current_spectrum is None:
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return
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# Clear previous plots
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self.ax_2d.clear()
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self.ax_1d.clear()
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spectrum = self.current_spectrum
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num_channels = spectrum.shape[1] if len(spectrum.shape) > 1 else len(spectrum)
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# Energy axis: use the same mapping as generation whenever possible.
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detector_name = None
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if isinstance(self.current_metadata, dict):
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detector_name = (
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self.current_metadata.get('detector')
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or self.current_metadata.get('detector_name')
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or (self.current_metadata.get('config') or {}).get('detector_name')
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)
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detector_config = RADIACODE_CONFIGS.get(detector_name, get_default_config())
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energy_bins = detector_config.get_energy_bins()
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if len(energy_bins) != num_channels:
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# Fallback: linear mapping for the available channel count.
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energy_bins = np.linspace(
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detector_config.energy_min_kev,
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detector_config.energy_max_kev,
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num_channels,
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dtype=np.float64
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)
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energy_min = float(energy_bins[0])
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energy_max = float(energy_bins[-1])
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if len(spectrum.shape) == 2:
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# 2D spectrogram
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num_intervals = spectrum.shape[0]
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# Plot 2D heatmap
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im = self.ax_2d.imshow(
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spectrum,
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aspect='auto',
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origin='lower',
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extent=[energy_min, energy_max, 0, num_intervals],
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cmap='viridis'
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)
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self.ax_2d.set_title(f"2D Spectrogram ({num_intervals} time intervals)")
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self.ax_2d.set_xlabel("Energy (keV)")
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self.ax_2d.set_ylabel("Time Interval (s)")
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# Add colorbar - use a dedicated axes to avoid removal issues
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if not hasattr(self, '_cbar_ax') or self._cbar_ax is None:
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# Create a dedicated colorbar axes on first use
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self._cbar_ax = self.fig.add_axes([0.46, 0.55, 0.01, 0.35])
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else:
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self._cbar_ax.clear()
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self._colorbar = self.fig.colorbar(im, cax=self._cbar_ax, label='Counts')
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# Sum across time for 1D spectrum
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summed_spectrum = spectrum.sum(axis=0)
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else:
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# 1D spectrum
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self.ax_2d.text(
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0.5, 0.5, "1D Spectrum\n(No time dimension)",
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ha='center', va='center', transform=self.ax_2d.transAxes
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)
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summed_spectrum = spectrum
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# Plot 1D summed spectrum
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self.ax_1d.plot(energy_bins, summed_spectrum, 'b-', linewidth=0.8)
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self.ax_1d.fill_between(energy_bins, 0, summed_spectrum, alpha=0.3)
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self.ax_1d.set_title("Summed Spectrum")
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self.ax_1d.set_xlabel("Energy (keV)")
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self.ax_1d.set_ylabel("Counts (normalized)")
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self.ax_1d.set_xlim(energy_min, energy_max)
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self.ax_1d.set_ylim(0, None)
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self.ax_1d.grid(True, alpha=0.3)
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# Add vertical lines for common peaks if metadata available
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if self.current_metadata:
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isotopes = self.current_metadata.get('isotopes', [])
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if isotopes:
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# Add some common reference lines
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peak_energies = self._get_peak_energies_from_metadata()
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for energy, label in peak_energies[:5]: # Show top 5 peaks
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if energy_min < energy < energy_max:
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self.ax_1d.axvline(x=energy, color='red', linestyle='--', alpha=0.5, linewidth=0.8)
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self.ax_1d.annotate(
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label,
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xy=(energy, self.ax_1d.get_ylim()[1] * 0.95),
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fontsize=8,
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rotation=90,
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ha='right',
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va='top'
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)
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# Use subplots_adjust instead of tight_layout to avoid colorbar axes conflict
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self.fig.subplots_adjust(left=0.08, right=0.95, top=0.92, bottom=0.12, wspace=0.3)
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self.canvas.draw()
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def _get_peak_energies_from_metadata(self) -> List[tuple]:
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"""Extract key peak energies from metadata for annotation."""
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peaks = []
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if not self.current_metadata:
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return peaks
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isotopes = self.current_metadata.get('isotopes', [])
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# Common isotope peak energies
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isotope_peaks = {
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'Cs-137': [(661.66, 'Cs-137')],
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'Co-60': [(1173.23, 'Co-60'), (1332.49, 'Co-60')],
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'Am-241': [(59.54, 'Am-241')],
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'Ba-133': [(356.0, 'Ba-133'), (81.0, 'Ba-133')],
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'Na-22': [(511.0, 'Na-22'), (1274.54, 'Na-22')],
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'K-40': [(1460.83, 'K-40')],
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'Eu-152': [(344.28, 'Eu-152'), (1408.0, 'Eu-152')],
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'I-131': [(364.49, 'I-131')],
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'Tc-99m': [(140.51, 'Tc-99m')],
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'Co-57': [(122.06, 'Co-57')],
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}
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for iso_info in isotopes:
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iso_name = iso_info.get('name', '') if isinstance(iso_info, dict) else str(iso_info)
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if iso_name in isotope_peaks:
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peaks.extend(isotope_peaks[iso_name])
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return peaks
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def _update_metadata(self):
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"""Update the metadata text display."""
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self.metadata_text.delete(1.0, tk.END)
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if self.current_spectrum is not None:
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# Add spectrum shape info
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info = f"Spectrum Shape: {self.current_spectrum.shape}\n"
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info += f"Data type: {self.current_spectrum.dtype}\n"
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info += f"Value range: [{self.current_spectrum.min():.4f}, {self.current_spectrum.max():.4f}]\n"
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info += f"Mean value: {self.current_spectrum.mean():.4f}\n"
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info += "\n" + "="*50 + "\n\n"
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self.metadata_text.insert(tk.END, info)
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if self.current_metadata:
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# Pretty print JSON metadata
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formatted = json.dumps(self.current_metadata, indent=2)
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self.metadata_text.insert(tk.END, formatted)
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else:
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self.metadata_text.insert(tk.END, "No metadata JSON file found for this spectrum.")
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def _on_spectrum_selected(self, event=None):
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"""Handle spectrum selection from dropdown."""
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selection = self.spectrum_var.get()
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for i, f in enumerate(self.spectrum_files):
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if f.stem == selection:
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self.current_index = i
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break
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self._load_current_spectrum()
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def _prev_spectrum(self):
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"""Go to previous spectrum."""
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self.current_index = (self.current_index - 1) % len(self.spectrum_files)
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self.spectrum_dropdown.current(self.current_index)
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self._load_current_spectrum()
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def _next_spectrum(self):
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"""Go to next spectrum."""
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self.current_index = (self.current_index + 1) % len(self.spectrum_files)
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self.spectrum_dropdown.current(self.current_index)
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self._load_current_spectrum()
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def _random_spectrum(self):
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"""Jump to a random spectrum."""
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self.current_index = random.randint(0, len(self.spectrum_files) - 1)
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self.spectrum_dropdown.current(self.current_index)
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self._load_current_spectrum()
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def run(self):
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"""Start the GUI main loop."""
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self.root.mainloop()
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def main():
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"""Main entry point."""
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import argparse
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parser = argparse.ArgumentParser(
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description="Visualize synthetic gamma spectra"
|
||||
)
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parser.add_argument(
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"--data_dir",
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||||
type=str,
|
||||
default="./data/synthetic/spectra",
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||||
help="Directory containing spectrum files (default: ./data/synthetic/spectra)"
|
||||
)
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||||
parser.add_argument(
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||||
"--num_samples",
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||||
type=int,
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||||
default=100,
|
||||
help="Number of random samples to load (default: 100)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Random seed for reproducible sample selection"
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not HAS_MATPLOTLIB:
|
||||
print("ERROR: matplotlib is required for visualization.")
|
||||
print("Install with: pip install matplotlib")
|
||||
return
|
||||
|
||||
print(f"Starting Spectrum Viewer...")
|
||||
print(f"Data directory: {args.data_dir}")
|
||||
print(f"Loading up to {args.num_samples} random samples...")
|
||||
|
||||
try:
|
||||
viewer = SpectrumViewer(
|
||||
data_dir=args.data_dir,
|
||||
num_samples=args.num_samples,
|
||||
random_seed=args.seed
|
||||
)
|
||||
viewer.run()
|
||||
except ValueError as e:
|
||||
print(f"Error: {e}")
|
||||
except Exception as e:
|
||||
print(f"Unexpected error: {e}")
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user