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
168
train/vega_ml/analyzer/analyze_last_inference.py
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train/vega_ml/analyzer/analyze_last_inference.py
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"""Analyze a captured middleware inference log (request+response).
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Reads a JSON file like analyzer/out/last_inference_detail_*.json produced by
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analyzer/fetch_last_inference.ps1 and prints diagnostics focused on:
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- input spectrum shape/range
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- quantization / clamping artifacts
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- energy-window evidence for uranium chain peaks
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- server output probabilities for U-234/U-235/U-238
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Usage:
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python analyzer/analyze_last_inference.py --path analyzer/out/last_inference_detail_*.json
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Exit code is always 0; this is a reporting tool.
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"""
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from __future__ import annotations
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import argparse
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import json
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Iterable
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import numpy as np
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@dataclass(frozen=True)
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class ModelGrid:
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emin_kev: float = 20.0
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emax_kev: float = 3000.0
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num_channels: int = 1023
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@property
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def step_kev(self) -> float:
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return (self.emax_kev - self.emin_kev) / self.num_channels
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def energy_to_channel(self, energy_kev: float) -> int:
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# Mirror how the repo’s helper scripts commonly approximate channel index.
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ch = int(round((energy_kev - self.emin_kev) / self.step_kev))
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return int(np.clip(ch, 0, self.num_channels - 1))
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def channel_to_energy(self, channel: int) -> float:
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return self.emin_kev + channel * self.step_kev
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def _head(values: Iterable[float], n: int = 12) -> str:
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vals = list(values)
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return ", ".join(f"{v:.6g}" for v in vals[:n])
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--path", required=True, help="Path to last_inference_detail_*.json")
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ap.add_argument("--window", type=int, default=2, help="Half-window (channels) for peak window sum")
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args = ap.parse_args()
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path = Path(args.path)
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# PowerShell may write UTF-8 with BOM; handle both.
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obj = json.loads(path.read_text(encoding="utf-8-sig"))
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req = obj.get("request", {}).get("json", {})
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resp = obj.get("response", {}).get("json", {})
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spectrum = req.get("spectrum")
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if spectrum is None:
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raise SystemExit("No request.json.spectrum found in the log detail JSON")
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arr = np.asarray(spectrum, dtype=np.float64)
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grid = ModelGrid()
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print(f"file: {path}")
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print(f"request spectrum shape: {arr.shape}")
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print(f"request spectrum range: min={arr.min():.6g} max={arr.max():.6g} mean={arr.mean():.6g}")
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# Quantization / clamping check
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flat = arr.ravel()
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# Sample to keep this quick on huge logs
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sample = flat[:: max(1, flat.size // 200_000)]
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uniq = np.unique(np.round(sample, 12))
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print(f"unique(sampled,rounded) count={len(uniq)}")
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print(f"unique head: {_head(uniq)}")
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# “Looks like quantized steps” heuristic
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if len(uniq) <= 64:
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steps = np.diff(uniq)
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steps = steps[steps > 0]
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if steps.size:
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step_med = float(np.median(steps))
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print(f"quantization hint: median_step≈{step_med:.6g}")
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# Channel energy distribution
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channel_sums = arr.sum(axis=0)
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nonzero_channels = int(np.count_nonzero(channel_sums))
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print(f"channels with any signal: {nonzero_channels}/{grid.num_channels} ({nonzero_channels/grid.num_channels:.1%})")
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# Top channels (where energy actually is)
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top_k = 12
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top_idx = np.argsort(channel_sums)[::-1][:top_k]
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print("top channels by sum (time-collapsed):")
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for ch in top_idx:
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s = float(channel_sums[ch])
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e = grid.channel_to_energy(int(ch))
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print(f" ch={int(ch):4d} E≈{e:7.1f} keV sum={s:.6g}")
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# Window-sum helper
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w = int(args.window)
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def window_sum(center_ch: int) -> float:
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lo = max(0, center_ch - w)
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hi = min(grid.num_channels - 1, center_ch + w)
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return float(arr[:, lo : hi + 1].sum())
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# Evidence around key uranium chain energies.
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energies = {
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"U-238 49.6": 49.6,
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"U-238 113.5": 113.5,
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"Ra-226 186.2": 186.2,
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"Pb-214 295.2": 295.2,
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"Pb-214 351.9": 351.9,
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"Bi-214 609.3": 609.3,
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"Bi-214 1120": 1120.3,
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"Bi-214 1764": 1764.5,
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"Tl-208 2614": 2614.5,
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}
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print(f"energy-window sums (±{w} channels):")
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for name, e in energies.items():
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ch = grid.energy_to_channel(e)
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s = window_sum(ch)
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print(f" {name:12s} ch={ch:4d} window_sum={s:.6g}")
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# Server response: uranium-related probabilities
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names = resp.get("isotope_names") or []
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probs = resp.get("probabilities") or []
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thr = resp.get("threshold_used")
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if names and probs and len(names) == len(probs):
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name_to_idx = {n: i for i, n in enumerate(names)}
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print("server output (selected):")
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if thr is not None:
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print(f" threshold_used={thr}")
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for iso in ("U-234", "U-235", "U-238", "Pb-214", "Bi-214", "Ra-226", "Th-232", "Th-234"):
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i = name_to_idx.get(iso)
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if i is None:
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print(f" {iso}: not in isotope_names")
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else:
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p = float(probs[i])
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flag = "PRESENT" if (thr is not None and p >= float(thr)) else "-"
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print(f" {iso:6s} idx={i:2d} prob={p:.6g} {flag}")
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# Top-10
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pairs = sorted(((n, float(probs[i])) for i, n in enumerate(names)), key=lambda x: x[1], reverse=True)[:10]
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print("top-10 probabilities:")
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for n, p in pairs:
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print(f" {n:8s} {p:.6g}")
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else:
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print("No response.json.isotope_names/probabilities found (or lengths mismatch).")
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print("\nInterpretation hints:")
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print("- If the uranium/daughter energy-window sums are ~0, the client is likely rebinning/calibrating incorrectly, zeroing high-energy channels, or over-normalizing/quantizing.")
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print("- If the spectrum is already [0,1] with very few unique values, the client is likely clamping/quantizing (lossy) before sending to the server.")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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