Dashboard web FastAPI + Chart.js

- 4 vues : spectre temps reel, historique detections, background, timeline CPS
- API REST : /api/status, /api/spectrum/current, /api/spectrum/difference,
  /api/background, /api/background/spectrum, /api/history, /api/cps/timeline
- Frontend vanilla JS + Chart.js (pas de Node.js, leger pour Pi 4)
- Moniteur modifie pour exporter son etat dans /data/monitor_state.json
  et le CPS dans /data/cps_log.jsonl chaque cycle
- Nouveau conteneur Docker 'web' sur port 8080
- Theme sombre, calibration energie (E = 0.33 + 2.97 * canal)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

detect/radiacode_monitor.py

@@ -25,6 +25,10 @@ THRESHOLD = float(os.environ.get("THRESHOLD", "0.5"))
 SAMPLE_INTERVAL = int(os.environ.get("SAMPLE_INTERVAL", "60"))
 REPORT_HOUR = int(os.environ.get("REPORT_HOUR", "0"))
 MIN_LIVE_TIME = int(os.environ.get("MIN_LIVE_TIME", "3600"))
+STATE_PATH = os.environ.get("STATE_PATH", "/data/monitor_state.json")
+CPS_LOG_PATH = os.environ.get("CPS_LOG_PATH", "/data/cps_log.jsonl")
+ENERGY_OFFSET = float(os.environ.get("ENERGY_CALIBRATION_OFFSET", "0.33"))
+ENERGY_SLOPE = float(os.environ.get("ENERGY_CALIBRATION_SLOPE", "2.97"))
 
 # Logging
 logging.basicConfig(
@@ -86,6 +90,7 @@ class RadiacodeMonitor:
         self.cumulated_counts = np.zeros(1024, dtype=np.float64)
         self.cumulated_live_time = 0.0
         self.last_report_date = None
+        self.connected = False
 
     def try_connect(self):
         """Tente de se connecter au Radiacode. Retourne le device ou None."""
@@ -94,9 +99,11 @@ class RadiacodeMonitor:
 
             device = RadiaCode()
             log.info("Radiacode connecté")
+            self.connected = True
             return device
         except Exception as e:
             log.debug(f"Détecteur non disponible : {e}")
+            self.connected = False
             return None
 
     def sample_once(self):
@@ -131,6 +138,55 @@ class RadiacodeMonitor:
                 except Exception:
                     pass
 
+    def save_state(self):
+        """Ecrit l'etat actuel du moniteur dans un fichier JSON atomique."""
+        energy_kev = [round(ENERGY_OFFSET + ENERGY_SLOPE * i, 2) for i in range(1024)]
+        cps = float(self.cumulated_counts.sum() / self.cumulated_live_time) if self.cumulated_live_time > 0 else 0.0
+
+        isotopes = []
+        if self.cumulated_live_time > 0:
+            rate = self.cumulated_counts / self.cumulated_live_time
+            if self.bg_counts is not None and self.bg_live_time is not None:
+                bg_rate = self.bg_counts / self.bg_live_time
+                net_rate = np.clip(rate - bg_rate, 0, None)
+            else:
+                net_rate = rate
+            isotopes = self.run_inference(net_rate)
+
+        state = {
+            "timestamp": datetime.now().isoformat(),
+            "connected": self.connected,
+            "cumulated_live_time_s": round(self.cumulated_live_time, 1),
+            "cumulated_live_time_h": round(self.cumulated_live_time / 3600, 2),
+            "total_counts": int(self.cumulated_counts.sum()),
+            "cps": round(cps, 2),
+            "background_subtracted": self.bg_counts is not None,
+            "isotopes_detected": isotopes,
+            "energy_kev": energy_kev,
+            "counts": [round(float(c), 1) for c in self.cumulated_counts],
+        }
+
+        state_path = Path(STATE_PATH)
+        state_path.parent.mkdir(parents=True, exist_ok=True)
+        tmp_path = state_path.with_suffix(".tmp")
+        with open(tmp_path, "w") as f:
+            json.dump(state, f)
+        os.replace(tmp_path, state_path)
+
+    def log_cps(self, counts, live_time):
+        """Ajoute un point CPS au journal horodaté."""
+        cps = float(counts.sum() / live_time) if live_time > 0 else 0.0
+        entry = {
+            "ts": datetime.now().isoformat(),
+            "cps": round(cps, 2),
+            "live_time_s": round(live_time, 1),
+            "total_counts": int(counts.sum()),
+        }
+        log_path = Path(CPS_LOG_PATH)
+        log_path.parent.mkdir(parents=True, exist_ok=True)
+        with open(log_path, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+
     def run_inference(self, spectrum_rate):
         """Exécute l'inférence PyTorch sur le spectre cumulé."""
         if spectrum_rate.max() > 0:
@@ -239,7 +295,12 @@ class RadiacodeMonitor:
                 self.generate_report()
                 self.last_report_date = now.date()
 
-            self.sample_once()
+            success = self.sample_once()
+            if success:
+                self.save_state()
+                self.log_cps(self.cumulated_counts, self.cumulated_live_time)
+            else:
+                self.save_state()
             time.sleep(SAMPLE_INTERVAL)