Gestione MQTT con memoria e rilevamento ultimato

Dockerfile

@@ -21,10 +21,7 @@ COPY app/ /app/app/
 COPY entrypoint.sh /app/entrypoint.sh
 RUN chmod +x /app/entrypoint.sh
 
-# Setup utente e permessi
-RUN useradd -m appuser && \
-    mkdir -p /home/appuser/data /home/appuser/models && \
-    chown -R appuser:appuser /home/appuser /app
+# Setup permessi (manteniamo root per l'esecuzione dello script di boot e accesso a /tmp)
+RUN mkdir -p /data/config /data/mqtt_raw /data/model
 
-USER appuser
 ENTRYPOINT ["/app/entrypoint.sh"]

app/infer_mode.py

@@ -9,28 +9,26 @@ import yaml
 import paho.mqtt.client as mqtt
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Tuple
 from .logger_utils import log_msg as log
 
-# Importiamo la funzione per caricare i gateway per mantenere l'ordine delle feature
-from .csv_config import load_gateway_features_csv
-
-BUILD_TAG = "infer-debug-v20-autoreload"
+BUILD_TAG = "infer-mac-fix-v21"
 
 # -------------------------
 # UTILITIES
 # -------------------------
 
 def _norm_mac(s: str) -> str:
-    s = (s or "").strip().replace("-", "").replace(":", "").replace(".", "").upper()
+    """
+    Forza il formato standard xx:xx:xx:xx:xx:xx in MINUSCOLO per matchare il modello.
+    Esempio MQTT: 'AC233FC1DD4E' -> 'ac:23:3f:c1:dd:4e'
+    """
+    s = (s or "").strip().replace("-", "").replace(":", "").replace(".", "").lower()
     if len(s) != 12: return s
     return ":".join([s[i:i+2] for i in range(0, 12, 2)])
 
 def _predict_xyz(model_pkg: Dict[str, Any], X: np.ndarray) -> Tuple[int, float, float]:
     floor_clf = model_pkg.get("floor_clf")
-    if floor_clf is None:
-        raise ValueError("Il pacchetto modello non contiene 'floor_clf'")
-    
     z_pred = floor_clf.predict(X)
     z = int(z_pred[0])
     
@@ -42,7 +40,6 @@ def _predict_xyz(model_pkg: Dict[str, Any], X: np.ndarray) -> Tuple[int, float,
         
     xy_pred = regressor.predict(X)
     x, y = xy_pred[0]
-    
     return z, float(x), float(y)
 
 @dataclass
@@ -56,6 +53,7 @@ class RollingRSSI:
         self.data: Dict[str, Dict[str, List[_Point]]] = {}
 
     def add(self, bm: str, gm: str, rssi: float):
+        # bm e gm arrivano già normalizzati da on_message
         self.data.setdefault(bm, {}).setdefault(gm, []).append(_Point(time.time(), rssi))
 
     def prune(self):
@@ -71,6 +69,7 @@ class RollingRSSI:
         feats = []
         found_count = 0
         for gm in gws:
+            # gm è già minuscolo con : perché preso da gateways_ordered (dal modello)
             vals = [p.v for p in per_gw.get(gm, [])]
             if vals:
                 val = np.median(vals) if agg == "median" else np.mean(vals)
@@ -93,10 +92,9 @@ def run_infer(settings: Dict[str, Any]):
     
     log(f"INFER_MODE build tag={BUILD_TAG}")
 
-    # Variabili di stato per il caricamento dinamico
     model_pkg = None
     last_model_mtime = 0
-    gateways_ordered = []
+    gateways_ordered = [] # Saranno in formato aa:bb:cc...
     gateways_set = set()
     nan_fill = -110.0
 
@@ -105,34 +103,24 @@ def run_infer(settings: Dict[str, Any]):
         try:
             current_mtime = os.path.getmtime(model_path)
             if current_mtime != last_model_mtime:
-                log(f"🧠 Caricamento/Aggiornamento modello: {model_path}")
+                log(f"🧠 Aggiornamento modello: {model_path}")
                 model_pkg = joblib.load(model_path)
                 last_model_mtime = current_mtime
                 
-                # METADATI PER DEBUG
-                gateways_ordered = model_pkg.get("gateways_order", [])
+                # Il modello ha i gateway salvati come caricati nell'addestramento
+                gateways_ordered = [gw.lower() for gw in model_pkg.get("gateways_order", [])]
                 gateways_set = set(gateways_ordered)
                 nan_fill = float(model_pkg.get("nan_fill", -110.0))
-                floors = list(model_pkg.get("xy_by_floor", {}).keys())
                 
-                log(f"✅ MODELLO PRONTO: {len(gateways_ordered)} GW allenati.")
-                log(f"🏢 Piani mappati: {floors}")
-                log(f"🧪 Valore Fill (NaN): {nan_fill}")
-                if len(gateways_ordered) > 0:
-                    log(f"📡 Primi 3 GW di riferimento: {gateways_ordered[:3]}")
+                log(f"✅ MODELLO PRONTO: {len(gateways_ordered)} GW. Fill: {nan_fill}")
                 return True
-        except Exception as e:
-            if model_pkg is None:
-                log(f"⚠️ In attesa di un modello valido in {model_path}...")
-            return False
+        except: return False
         return True
 
-    # Primo caricamento
     load_model_dynamic()
-
     rolling = RollingRSSI(float(inf_c.get("window_seconds", 5.0)))
     
-    # --- Gestione Token e Beacons ---
+    # Cache Beacons
     token_cache = {"token": None, "expires_at": 0}
     def get_token():
         if time.time() < token_cache["expires_at"]: return token_cache["token"]
@@ -159,18 +147,25 @@ def run_infer(settings: Dict[str, Any]):
         try:
             headers = {"Authorization": f"Bearer {token}", "accept": "application/json"}
             resp = requests.get(api_c["get_beacons_url"], headers=headers, verify=False, timeout=10)
-            return [it["mac"] for it in resp.json() if "mac" in it] if resp.status_code == 200 else []
+            # Normalizziamo anche i beacon cercati per il confronto
+            return [_norm_mac(it["mac"]) for it in resp.json() if "mac" in it] if resp.status_code == 200 else []
         except: return []
 
     def on_message(client, userdata, msg):
-        if not gateways_set: return
-        gw = _norm_mac(msg.topic.split("/")[-1])
-        if gw not in gateways_set: return
+        # msg.topic es: publish_out/ac233fc1dd4e
+        raw_gw = msg.topic.split("/")[-1]
+        gw_norm = _norm_mac(raw_gw)
+        
+        if gw_norm not in gateways_set: return
+        
         try:
             items = json.loads(msg.payload.decode())
             for it in items:
-                bm, rssi = _norm_mac(it.get("mac")), it.get("rssi")
-                if bm and rssi is not None: rolling.add(bm, gw, float(rssi))
+                raw_bm = it.get("mac")
+                rssi = it.get("rssi")
+                if raw_bm and rssi is not None:
+                    bm_norm = _norm_mac(raw_bm)
+                    rolling.add(bm_norm, gw_norm, float(rssi))
         except: pass
 
     mqtt_client = mqtt.Client()
@@ -180,28 +175,25 @@ def run_infer(settings: Dict[str, Any]):
         mqtt_client.subscribe(mqtt_c["topic"])
         mqtt_client.loop_start()
     except Exception as e:
-        log(f"MQTT Connect Error: {e}")
+        log(f"MQTT Error: {e}")
 
     last_predict, last_api_refresh = 0.0, 0.0
-    beacons = []
+    beacons_to_track = []
 
     while True:
         now = time.time()
         rolling.prune()
-        
-        # Verifica se il link simbolico model.joblib è cambiato
         load_model_dynamic()
 
         if now - last_api_refresh >= float(api_c.get("refresh_seconds", 30)):
-            beacons = fetch_beacons()
+            beacons_to_track = fetch_beacons()
             last_api_refresh = now
 
         if now - last_predict >= float(inf_c.get("refresh_seconds", 10.0)):
             rows, count_ok = [], 0
             
-            if model_pkg:
-                for bm in beacons:
-                    bm_n = _norm_mac(bm)
+            if model_pkg and beacons_to_track:
+                for bm_n in beacons_to_track:
                     X, n_found = rolling.aggregate_features(bm_n, gateways_ordered, inf_c.get("aggregate", "median"), nan_fill)
                     
                     z, x, y = -1, -1.0, -1.0
@@ -209,8 +201,7 @@ def run_infer(settings: Dict[str, Any]):
                         try:
                             z, x, y = _predict_xyz(model_pkg, X)
                             if z != -1: count_ok += 1
-                        except Exception as e:
-                            pass
+                        except: pass
                     
                     rows.append(f"{bm_n};{int(z)};{int(round(x))};{int(round(y))}")
 
@@ -221,7 +212,7 @@ def run_infer(settings: Dict[str, Any]):
                         f.write("mac;z;x;y\n")
                         for r in rows: f.write(r + "\n")
                     os.replace(str(out_p) + ".tmp", out_p)
-                    log(f"CYCLE: {count_ok}/{len(rows)} localized")
+                    log(f"CYCLE: {count_ok}/{len(beacons_to_track)} localized (Input GW match: {len(gateways_set)})")
                 except Exception as e: log(f"File Error: {e}")
             
             last_predict = now

app/train_executor.py

@@ -33,80 +33,95 @@ def process_train_jobs():
             nan_fill = job["nan_fill"]
             gw_csv = job["gateways_csv"]
             
-            # --- GENERAZIONE NOME FILE CON TIMESTAMP (BACKUP) ---
+            # --- GENERAZIONE NOME FILE CON TIMESTAMP ---
             now_str = datetime.now().strftime("%Y%m%d_%H%M%S")
             model_filename = f"model_camp_{campagna}_{now_str}.joblib"
             model_path = Path("/data/model") / model_filename
             
-            # Caricamento Gateway
+            # Caricamento Gateway con normalizzazione MAC
             gws = load_gateway_features_csv(gw_csv)
-            gateways_order = [g.mac for g in gws]
+            gateways_order = [g.mac.lower().strip() for g in gws]
+            log(f"[TRAIN-CORE] Feature vector: {len(gateways_order)} gateway caricati da {gw_csv}")
 
             # Analisi file campioni
             samples_dir = Path("/data/train/samples")
             sample_files = list(samples_dir.glob(f"{campagna}_*.csv"))
+            log(f"[TRAIN-CORE] Analisi di {len(sample_files)} file per campagna '{campagna}'")
             
             X_list, y_z, y_xy = [], [], []
             for fp in sample_files:
                 try:
                     df = pd.read_csv(fp, sep=";")
                     if df.empty: continue
+                    # Normalizziamo le colonne del DF in minuscolo
+                    df.columns = [c.lower().strip() for c in df.columns]
                     row = df.iloc[0]
-                    # Mapping RSSI basato su gateway.csv (risolve errore 'mac')
-                    X_list.append([float(row.get(gw, nan_fill)) for gw in gateways_order])
+                    
+                    features = []
+                    for gw in gateways_order:
+                        val = row.get(gw)
+                        # Gestione esplicita di 'nan' stringa o NaN numerico
+                        if val is not None and str(val).lower() != 'nan' and not pd.isna(val):
+                            features.append(float(val))
+                        else:
+                            features.append(float(nan_fill))
+                    
+                    X_list.append(features)
                     y_z.append(int(round(float(row.get("z")))))
                     y_xy.append([float(row.get("x")), float(row.get("y"))])
-                except: continue
+                except Exception as e:
+                    log(f"[TRAIN-CORE] Errore nel file {fp.name}: {e}")
+                    continue
 
             if not X_list:
-                log(f"[TRAIN-CORE] ERRORE: Dati non validi per campagna {campagna}")
+                log(f"[TRAIN-CORE] ❌ ABORTO: Nessun dato matchato tra gateway.csv e fingerprint!")
                 job_path.unlink()
                 continue
 
-            X, Y_z, Y_xy = np.array(X_list), np.array(y_z), np.array(y_xy)
+            X = np.array(X_list)
+            # Calcolo copertura reale
+            matches_per_point = [np.sum(np.array(vec) > nan_fill) for vec in X_list]
+            avg_match = np.mean(matches_per_point)
+            log(f"[TRAIN-CORE] Dataset pronto. Punti: {len(X_list)}. Media match Gateway: {avg_match:.2f}/{len(gateways_order)}")
 
-            # Fitting KNN
-            log(f"[TRAIN-CORE] Fitting modello per {model_filename}...")
+            # --- LOG PARAMETRI ADDESTRAMENTO ---
+            k_val = int(knn_cfg.get('k', 5))
+            w_val = knn_cfg.get('weights', 'distance')
+            m_val = knn_cfg.get('metric', 'euclidean')
+            log(f"[TRAIN-CORE] Fitting KNN -> k: {k_val}, weights: {w_val}, metric: {m_val}, nan_fill: {nan_fill}")
+
+            # Fitting
             floor_clf = KNeighborsClassifier(
-                n_neighbors=int(knn_cfg.get('k', 5)), 
-                weights=knn_cfg.get('weights', 'distance'),
-                metric=knn_cfg.get('metric', 'euclidean')
-            ).fit(X, Y_z)
+                n_neighbors=k_val, weights=w_val, metric=m_val
+            ).fit(X, np.array(y_z))
             
             models_xy = {}
-            for z in np.unique(Y_z):
-                idx = np.where(Y_z == z)[0]
+            for z in np.unique(y_z):
+                idx = np.where(np.array(y_z) == z)[0]
+                # k_xy non può essere superiore al numero di campioni per piano
+                current_k_xy = min(k_val, len(idx))
                 models_xy[int(z)] = KNeighborsRegressor(
-                    n_neighbors=min(int(knn_cfg.get('k', 5)), len(idx)),
-                    weights=knn_cfg.get('weights', 'distance'),
-                    metric=knn_cfg.get('metric', 'euclidean')
-                ).fit(X[idx], Y_xy[idx])
+                    n_neighbors=current_k_xy, weights=w_val, metric=m_val
+                ).fit(X[idx], np.array(y_xy)[idx])
 
-            # Salvataggio Pacchetto
+            # Salvataggio
             model_pkg = {
-                "floor_clf": floor_clf,
-                "xy_by_floor": models_xy,
-                "gateways_order": gateways_order,
-                "nan_fill": nan_fill,
-                "created_at": datetime.now().isoformat(),
-                "campaign": campagna,
-                "filename": model_filename
+                "floor_clf": floor_clf, "xy_by_floor": models_xy,
+                "gateways_order": gateways_order, "nan_fill": nan_fill,
+                "knn_params": {"k": k_val, "weights": w_val, "metric": m_val},
+                "created_at": datetime.now().isoformat(), "campaign": campagna
             }
             
             Path("/data/model").mkdir(parents=True, exist_ok=True)
             joblib.dump(model_pkg, model_path)
-            
             log(f"[TRAIN-CORE] ✅ Addestramento COMPLETATO: {model_filename}")
 
         except Exception as e:
             log(f"[TRAIN-CORE] ❌ ERRORE CRITICO: {str(e)}")
-        
         finally:
-            if job_path.exists():
-                job_path.unlink()
+            if job_path.exists(): job_path.unlink()
 
 def run_train_monitor():
-    """Loop di monitoraggio per il Core Orchestrator."""
     while True:
         process_train_jobs()
         time.sleep(5)

app/web_inference.py

@@ -3,6 +3,8 @@ import pandas as pd
 import os
 import json
 import folium
+import requests
+import yaml
 from streamlit_folium import st_folium
 from pathlib import Path
 from PIL import Image
@@ -19,6 +21,48 @@ def get_image_base64(img_path):
     img_str = base64.b64encode(buffered.getvalue()).decode("ascii")
     return f"data:image/png;base64,{img_str}", w, h
 
+def _norm_mac_internal(s: str) -> str:
+    """Standardizza il MAC in formato xx:xx:xx:xx:xx:xx minuscolo per il matching."""
+    s = (s or "").strip().replace("-", "").replace(":", "").replace(".", "").lower()
+    if len(s) != 12: return s
+    return ":".join([s[i:i+2] for i in range(0, 12, 2)])
+
+def fetch_beacons_metadata(cfg):
+    """Recupera l'elenco dei beacon dalle API e normalizza i MAC per il lookup."""
+    api_c = cfg.get("api", {})
+    token_url = api_c.get("token_url")
+    beacons_url = api_c.get("get_beacons_url")
+    
+    try:
+        secrets_path = os.environ.get("SECRETS_FILE") or "/config/secrets.yaml"
+        with open(secrets_path, "r") as f:
+            sec = yaml.safe_load(f).get("oidc", {})
+        
+        # 1. Autenticazione OIDC
+        payload = {
+            "grant_type": "password",
+            "client_id": api_c.get("client_id", "Fastapi"),
+            "client_secret": sec.get("client_secret", ""),
+            "username": sec.get("username", "core"),
+            "password": sec.get("password", "")
+        }
+        resp_t = requests.post(token_url, data=payload, verify=False, timeout=5)
+        if resp_t.status_code != 200: return {}
+        token = resp_t.json().get("access_token")
+
+        # 2. Download Beacon List e Normalizzazione
+        headers = {"Authorization": f"Bearer {token}", "accept": "application/json"}
+        resp_b = requests.get(beacons_url, headers=headers, verify=False, timeout=5)
+        if resp_b.status_code == 200:
+            # Dizionario {mac_normalizzato: nome_amichevole}
+            return {
+                _norm_mac_internal(it["mac"]): it.get("name", "N/A") 
+                for it in resp_b.json() if "mac" in it
+            }
+    except Exception as e:
+        st.error(f"Errore recupero nomi beacon: {e}")
+    return {}
+
 def show_inference_page(cfg):
     st.subheader("📡 Monitoraggio Beacon Real-Time")
     
@@ -26,7 +70,7 @@ def show_inference_page(cfg):
     MAPS_DIR = Path(cfg['maps']['map_dir'])
     INFER_FILE = Path("/data/infer/infer.csv")
     
-    # --- 1. SELEZIONE E STATO (RIGA COMPATTA) ---
+    # --- 1. SELEZIONE E STATO ---
     maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0] 
                    for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
     
@@ -34,30 +78,24 @@ def show_inference_page(cfg):
         st.warning("Nessuna mappa configurata.")
         return
 
-    # Lettura dati per conteggio
     df_infer = pd.DataFrame()
     if INFER_FILE.exists():
         df_infer = pd.read_csv(INFER_FILE, sep=";")
 
-    # Layout riga 1
     c_piano, c_count, c_size = st.columns([3, 2, 2])
-    
     with c_piano:
         sub1, sub2 = st.columns([1, 1.2])
         sub1.markdown("<p style='padding-top:35px; font-weight:bold; font-size:15px;'>Piano Visualizzato:</p>", unsafe_allow_html=True)
         floor_id = sub2.selectbox("", maps, key="inf_floor_v24", label_visibility="collapsed")
     
-    # Filtro dati per il piano scelto
     df_active = df_infer[(df_infer['z'].astype(str) == str(floor_id)) & (df_infer['x'] != -1)] if not df_infer.empty else pd.DataFrame()
 
     with c_count:
         st.info(f"📡 Beacon Attivi: **{len(df_active)}**\n(Totali nel file: {len(df_infer)})")
 
     with c_size:
-        # Slider per dimensione pallini (come nel mapper)
         m_size = st.slider("Dimensione Beacon:", 5, 20, 8, key="inf_msize_v24")
 
-    # Caricamento Metadati
     meta_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json"
     if not meta_path.exists(): return
     with open(meta_path, "r") as f: meta = json.load(f)
@@ -73,30 +111,57 @@ def show_inference_page(cfg):
     m.options.update({"minZoom": -6, "maxZoom": 6, "zoomSnap": 0.25, "maxBounds": bounds, "maxBoundsViscosity": 1.0})
     folium.raster_layers.ImageOverlay(image=img_data, bounds=bounds).add_to(m)
 
-    # Disegno Beacon
+    # Recupero nomi per etichette mappa
+    names_map = fetch_beacons_metadata(cfg)
+
     if meta["calibrated"] and meta["origin"] != [0,0]:
-        # Origine
+        # Origine (Punto di riferimento)
         folium.CircleMarker(location=[meta["origin"][1], meta["origin"][0]], radius=4, color="black", fill=True).add_to(m)
 
         for _, row in df_active.iterrows():
             px_x = (row['x'] * meta["pixel_ratio"]) + meta["origin"][0]
             px_y = meta["origin"][1] - (row['y'] * meta["pixel_ratio"])
-            mac_label = str(row['mac'])[-5:]
+            
+            # --- UMANIZZAZIONE ETICHETTA MAPPA ---
+            norm_mac = _norm_mac_internal(row['mac'])
+            # Se disponibile usa il nome da API, altrimenti le ultime cifre del MAC
+            label_text = names_map.get(norm_mac, str(row['mac'])[-5:])
 
+            # Pallino Beacon
             folium.CircleMarker(
                 location=[px_y, px_x], radius=m_size, color="blue", 
-                fill=True, fill_color="cyan", fill_opacity=0.8
+                fill=True, fill_color="cyan", fill_opacity=0.8,
+                tooltip=f"Device: {label_text} | MAC: {row['mac']}"
             ).add_to(m)
 
+            # Etichetta Nome (accanto al punto)
             folium.Marker(
                 location=[px_y, px_x],
-                icon=folium.DivIcon(html=f"""<div style="font-family: sans-serif; color: black; font-weight: bold; font-size: {int(m_size*1.2)}pt; width: 80px; transform: translate({m_size+2}px, -{m_size+2}px);">{mac_label}</div>""")
+                icon=folium.DivIcon(html=f"""
+                    <div style="
+                        font-family: sans-serif; 
+                        color: #0d47a1; 
+                        font-weight: bold; 
+                        white-space: nowrap;
+                        font-size: {int(m_size*1.1)}pt; 
+                        transform: translate({m_size+2}px, -{m_size+2}px);
+                        text-shadow: 1px 1px 2px white;
+                    ">
+                        {label_text}
+                    </div>
+                """)
             ).add_to(m)
 
     st_folium(m, height=700, width=None, key=f"inf_map_v24_{floor_id}", use_container_width=True)
 
-    # --- 3. TABELLA RIEPILOGO COMPLETA ---
+    # --- 3. TABELLA RIEPILOGO COMPLETA (Arricchita con Nome API) ---
     if not df_infer.empty:
         st.subheader("Dettaglio Dispositivi (Tutti i Piani)")
-        # Aggiunta colonna Z per chiarezza
-        st.dataframe(df_infer[['mac', 'z', 'x', 'y']], use_container_width=True)
+        
+        df_display = df_infer.copy()
+        df_display['mac_norm'] = df_display['mac'].apply(_norm_mac_internal)
+        df_display['name'] = df_display['mac_norm'].map(names_map).fillna("Sconosciuto")
+        
+        # Selezione e riordino colonne finali
+        cols_to_show = ['mac', 'name', 'z', 'x', 'y']
+        st.dataframe(df_display[cols_to_show], use_container_width=True)

app/web_suite.py

@@ -6,6 +6,7 @@ import time
 import signal
 import web_status 
 import psutil
+from pathlib import Path
 
 # --- 1. CONFIGURAZIONE PAGINA ---
 st.set_page_config(
@@ -14,13 +15,14 @@ st.set_page_config(
     initial_sidebar_state="auto"
 )
 
-# --- 2. CONFIGURAZIONE PERCORSI ---
-REAL_CONFIG_PATH = "/config/config.yaml"
-if not os.path.exists(REAL_CONFIG_PATH):
-    REAL_CONFIG_PATH = "/app/config/config.yaml"
-
+# --- 2. CONFIGURAZIONE PERCORSI E STATI PERSISTENTI ---
+REAL_CONFIG_PATH = "/config/config.yaml" if os.path.exists("/config/config.yaml") else "/app/config/config.yaml"
 LOG_FILE = "/tmp/main_process.log"
 
+# File di stato su disco per la persistenza
+CORE_STATE_FILE = "/data/config/core.enabled"
+LOG_PID_FILE = "/tmp/mqtt_logging.pid"
+
 def load_yaml(path):
     if not os.path.exists(path): return {}
     with open(path, 'r', encoding='utf-8') as f: return yaml.safe_load(f) or {}
@@ -30,7 +32,12 @@ def save_yaml(path, data):
 
 cfg = load_yaml(REAL_CONFIG_PATH)
 
+# --- 3. LOGICA PERSISTENZA CORE ---
 def stop_core_engine():
+    """Arresta il core e rimuove il flag di persistenza."""
+    if os.path.exists(CORE_STATE_FILE): 
+        os.remove(CORE_STATE_FILE)
+        
     for proc in psutil.process_iter(['cmdline']):
         try:
             cmd = proc.info['cmdline']
@@ -42,9 +49,12 @@ def stop_core_engine():
             except: pass
 
 def start_core_engine():
+    """Avvia il core e crea il flag di persistenza su disco."""
+    Path(CORE_STATE_FILE).touch()
     env = os.environ.copy()
     env["CONFIG"] = REAL_CONFIG_PATH 
     env["PYTHONPATH"] = "/app"
+    # Reindirizzamento append log per non perdere lo storico dell'entrypoint
     with open(LOG_FILE, "a") as log_f:
         subprocess.Popen(
             ["python3", "-m", "app.main"],
@@ -55,10 +65,18 @@ def start_core_engine():
             start_new_session=True
         )
 
-# --- 3. TITOLO FISSO ---
+# --- 4. MONITORAGGIO ATTIVO (SENZA AUTO-START BLOCCANTE) ---
+# La logica di avvio al boot è ora gestita da entrypoint.sh.
+# Qui segnaliamo solo se lo stato desiderato (file esistente) non coincide con quello reale.
+if os.path.exists(CORE_STATE_FILE) and not web_status.is_main_running():
+    st.sidebar.warning("⚠️ Core abilitato ma non in esecuzione.")
+    if st.sidebar.button("RIPRISTINA ORA"):
+        start_core_engine()
+        st.rerun()
+
+# --- 5. TITOLO E LOGIN ---
 st.title("🛰️ BLE AI Localizer - Suite")
 
-# --- 4. LOGICA LOGIN ---
 if "password_correct" not in st.session_state:
     st.session_state["password_correct"] = False
 
@@ -73,63 +91,60 @@ if not st.session_state["password_correct"]:
             st.error("Credenziali errate")
     st.stop()
 
-# --- 5. AUTO-START ---
-if not web_status.is_main_running():
-    if "core_auto_launched" not in st.session_state:
-        start_core_engine()
-        st.session_state["core_auto_launched"] = True
-        time.sleep(1) 
-        st.rerun()
-
 # --- 6. SIDEBAR: CONTROLLI SISTEMA ---
 with st.sidebar:
     st.header("🛠️ Core Control")
     
     is_running = web_status.is_main_running()
-    if is_running:
-        st.markdown("STATO: :green[**CORE ATTIVO**]")
-    else:
-        st.markdown("STATO: :red[**CORE FERMO**]")
+    st.markdown(f"STATO: {':green[**CORE ATTIVO**]' if is_running else ':red[**CORE FERMO**]'}")
     
     if st.button("🚀 RIAVVIA CORE" if is_running else "▶️ AVVIA CORE", use_container_width=True):
-        with st.spinner("Gestione Core..."):
-            stop_core_engine()
-            time.sleep(1)
-            start_core_engine()
-            time.sleep(2)
-            st.rerun()
+        stop_core_engine()
+        time.sleep(1)
+        start_core_engine()
+        st.rerun()
 
     st.divider()
-    # --- GESTIONE MQTT RAW (DINAMICA DA CONFIG) ---
-    m_cfg = cfg.get('mqtt', {})
-    m_host = m_cfg.get('host', '127.0.0.1')
-    m_port = m_cfg.get('port', 1883)
-    m_proto = m_cfg.get('protocol', 'mqttv311')
-    # Estraiamo la parola chiave dal topic (es: 'publish_out' da 'publish_out/#')
-    m_filter = m_cfg.get('topic', 'publish_out').split('/')[0]
-
-    RAW_LOG_DIR = cfg.get('paths', {}).get('mqtt_raw_dir', '/data/mqtt_raw/')
-    os.makedirs(RAW_LOG_DIR, exist_ok=True)
-    raw_active = st.toggle("🔴 MQTT RAW RECORDING", value=st.session_state.get("mqtt_logging", False))
     
-    if raw_active and not st.session_state.get("mqtt_logging"):
+    # --- GESTIONE MQTT RAW PERSISTENTE ---
+    is_logging_active = False
+    if os.path.exists(LOG_PID_FILE):
+        try:
+            with open(LOG_PID_FILE, "r") as f:
+                saved_pid = int(f.read().strip())
+            if psutil.pid_exists(saved_pid):
+                is_logging_active = True
+            else:
+                os.remove(LOG_PID_FILE)
+        except:
+            pass
+
+    raw_active = st.toggle("🔴 MQTT RAW RECORDING", value=is_logging_active)
+    
+    if raw_active and not is_logging_active:
+        RAW_LOG_DIR = cfg.get('paths', {}).get('mqtt_raw_dir', '/data/mqtt_raw/')
+        os.makedirs(RAW_LOG_DIR, exist_ok=True)
         dt = time.strftime("%Y%m%d_%H%M%S")
         filepath = os.path.join(RAW_LOG_DIR, f"mqtt_raw_{dt}.log")
+        m_cfg = cfg.get('mqtt', {})
+        m_filter = m_cfg.get('topic', 'publish_out').split('/')[0]
         
-        # Comando generato dinamicamente dai parametri YAML
-        cmd = f"stdbuf -oL mosquitto_sub -v -h {m_host} -p {m_port} -t '#' -V {m_proto} | stdbuf -oL grep '{m_filter}' > {filepath}"
-        
+        cmd = f"stdbuf -oL mosquitto_sub -v -h {m_cfg.get('host')} -p {m_cfg.get('port')} -t '#' | stdbuf -oL grep '{m_filter}' > {filepath}"
         proc = subprocess.Popen(cmd, shell=True, preexec_fn=os.setsid)
-        st.session_state["mqtt_logging"] = True
-        st.session_state["mqtt_proc_pid"] = proc.pid
-        st.success(f"Log avviato: {m_host}")
+        
+        with open(LOG_PID_FILE, "w") as f:
+            f.write(str(proc.pid))
+        st.rerun()
     
-    if not raw_active and st.session_state.get("mqtt_logging"):
+    elif not raw_active and is_logging_active:
         try: 
-            os.killpg(os.getpgid(st.session_state["mqtt_proc_pid"]), signal.SIGTERM)
+            with open(LOG_PID_FILE, "r") as f:
+                pid_to_kill = int(f.read().strip())
+            os.killpg(os.getpgid(pid_to_kill), signal.SIGTERM)
+            if os.path.exists(LOG_PID_FILE): os.remove(LOG_PID_FILE)
+            st.rerun()
         except: 
             pass
-        st.session_state["mqtt_logging"] = False
 
 # --- 7. IMPORT MODULI E TABS ---
 from map_manager import show_mapper

app/web_test_inference.py

@@ -53,14 +53,34 @@ def show_test_inference(cfg):
             m_pkg = joblib.load(MODEL_DIR / selected_model)
             delim = cfg.get('paths', {}).get('csv_delimiter', ';')
             df_test = pd.read_csv(TEST_SAMPLES_DIR / selected_test, sep=delim)
-            
             row = df_test.iloc[0]
-            z_real, x_real, y_real = int(round(float(row['z']))), float(row['x']), float(row['y'])
             
+            z_real, x_real, y_real = int(round(float(row['z']))), float(row['x']), float(row['y'])
             gws = m_pkg['gateways_order']
             fill = m_pkg.get('nan_fill', -110.0)
-            X_in = np.array([[float(row.get(gw, fill)) for gw in gws]])
+
+            # --- DEBUG VETTORE INPUT ---
+            raw_vals = []
+            for gw in gws:
+                val = row.get(gw)
+                raw_vals.append(float(val) if val is not None and not pd.isna(val) else fill)
+            
+            X_in = np.array([raw_vals])
             
+            # Visualizzazione Debug in UI per l'operatore
+            with st.expander("🔍 Analisi Vettore di Input (Fingerprint vs Modello)"):
+                debug_df = pd.DataFrame({
+                    "Gateway MAC": gws,
+                    "RSSI Letto": [row.get(gw, "NON TROVATO") for gw in gws],
+                    "RSSI Finale (Input AI)": raw_vals
+                })
+                st.dataframe(debug_df)
+                match_count = np.sum(X_in[0] > fill)
+                st.write(f"**Gateway Corrispondenti:** {match_count} su {len(gws)}")
+                if match_count == 0:
+                    st.error("ERRORE: Il file di test non contiene nessuno dei Gateway usati per l'addestramento!")
+
+            # Predizione
             z_pred = int(m_pkg['floor_clf'].predict(X_in)[0])
             x_pred, y_pred = -1.0, -1.0
             if z_pred in m_pkg['xy_by_floor']:
@@ -68,27 +88,24 @@ def show_test_inference(cfg):
                 x_pred, y_pred = float(xy[0]), float(xy[1])
 
             z_err, dist_err = calculate_error(z_real, x_real, y_real, z_pred, x_pred, y_pred)
-            
             st.session_state.test_results = {
                 "z_real": z_real, "x_real": x_real, "y_real": y_real,
                 "z_pred": z_pred, "x_pred": x_pred, "y_pred": y_pred,
-                "z_err": z_err, "dist_err": dist_err,
-                "model_used": selected_model
+                "z_err": z_err, "dist_err": dist_err, "model_used": selected_model
             }
         except Exception as e:
-            st.error(f"Errore: {e}")
+            st.error(f"Errore durante l'inferenza: {e}")
 
+    # --- VISUALIZZAZIONE GRAFICA DEI RISULTATI ---
     if st.session_state.test_results:
         res = st.session_state.test_results
         
-        # --- RIPRISTINO INFORMAZIONI COORDINATE ---
         c_info1, c_info2 = st.columns(2)
         with c_info1:
             st.info(f"📍 **Test Reale** | Piano: {res['z_real']} | X: **{res['x_real']}** | Y: **{res['y_real']}**")
         with c_info2:
             st.success(f"🔮 **Predizione** | Piano: {res['z_pred']} | X: **{round(res['x_pred'],1)}** | Y: **{round(res['y_pred'],1)}**")
 
-        # --- LEGENDA AGGIORNATA CON NUOVI COLORI ---
         st.markdown(f"""
         <div style="background-color: #f8f9fa; padding: 12px; border-radius: 8px; border-left: 5px solid #00bcd4; margin: 10px 0;">
             <h5 style="margin:0 0 8px 0;">📍 Legenda Mappa</h5>
@@ -122,14 +139,13 @@ def show_test_inference(cfg):
                 p_real = to_px(res['x_real'], res['y_real'])
                 p_pred = to_px(res['x_pred'], res['y_pred'])
 
-                # 🔵 PUNTO DI TEST (Celeste)
+                # Marker Posizione Reale
                 folium.CircleMarker(
                     location=p_real, radius=11, color="#00838f", fill=True, 
-                    fill_color="#00bcd4", fill_opacity=0.85,
-                    tooltip="PUNTO DI TEST (REALE)"
+                    fill_color="#00bcd4", fill_opacity=0.85, tooltip="PUNTO DI TEST (REALE)"
                 ).add_to(m)
                 
-                # 🟠 PUNTO PREDETTO (Arancio)
+                # Marker Predizione
                 if res['x_pred'] != -1.0:
                     folium.CircleMarker(
                         location=p_pred, radius=11, color="#e65100", fill=True, 
@@ -137,7 +153,7 @@ def show_test_inference(cfg):
                         tooltip=f"PREDIZIONE (Modello: {res['model_used']})"
                     ).add_to(m)
                     
-                    # Linea Errore
+                    # Linea di errore (Gialla tratteggiata)
                     folium.PolyLine(
                         locations=[p_real, p_pred], color="#ffeb3b", 
                         weight=4, opacity=0.7, dash_array='8'

config/config.yaml

@@ -60,7 +60,7 @@ infer:
   refresh_seconds: 10
   rssi_max: -25
   rssi_min: -110
-  window_seconds: 7
+  window_seconds: 30
   xy_round: 0
 maps:
   default_dot_size: 20
@@ -70,7 +70,7 @@ maps:
   meta_prefix: meta_
   show_grid_default: true
 ml:
-  k: 5
+  k: 3
   method: knn
   metric: euclidean
   weights: distance
@@ -103,7 +103,7 @@ train:
       folds: 5
       k_max: 15
       k_min: 3
-    k: 5
+    k: 3
     metric: euclidean
     weights: distance
   model_path: /data/model/model.joblib

data/infer/infer.csv

@@ -1,13 +1,13 @@
 mac;z;x;y
-C8:3F:8F:17:DB:35;-1;-1;-1
-C3:00:00:39:47:DF;-1;-1;-1
-C3:00:00:39:47:C4;-1;1917;650
-C3:00:00:39:47:E2;-1;-1;-1
-C7:AE:56:1E:38:B7;-1;1917;650
-E0:1F:9A:7A:47:D2;-1;-1;-1
-C3:00:00:57:B9:D9;-1;1917;650
-C3:00:00:57:B9:DB;-1;1917;650
-C3:00:00:57:B9:F4;-1;1917;650
-C3:00:00:57:B9:DC;-1;1917;650
-C3:00:00:57:B9:DD;-1;1917;650
-C3:00:00:57:B9:DF;-1;1917;650
+c8:3f:8f:17:db:35;-1;-1;-1
+c3:00:00:39:47:df;1;2082;1337
+c3:00:00:39:47:c4;1;883;1263
+c3:00:00:39:47:e2;1;1506;778
+c7:ae:56:1e:38:b7;1;2254;672
+e0:1f:9a:7a:47:d2;-1;-1;-1
+c3:00:00:57:b9:d9;1;2084;1344
+c3:00:00:57:b9:db;1;2066;1345
+c3:00:00:57:b9:f4;1;1790;989
+c3:00:00:57:b9:dc;1;875;1177
+c3:00:00:57:b9:dd;1;918;1037
+c3:00:00:57:b9:df;1;919;971

data/maps/meta_0.json

@@ -0,0 +1,9 @@
+{
+    "pixel_ratio": 1.5838698443273016,
+    "calibrated": true,
+    "origin": [
+        1299,
+        3742
+    ],
+    "grid_size": 50
+}

data/maps/meta_1.json

@@ -0,0 +1,9 @@
+{
+    "pixel_ratio": 1.5923301441291737,
+    "calibrated": true,
+    "origin": [
+        1348,
+        3537
+    ],
+    "grid_size": 50
+}

data/model/model.joblib

@@ -0,0 +1 @@
+model_camp_00_20260218_114109.joblib

data/train/jobs/done/GBC-01_0_1096_1145.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1096_1145;0;BC-00-21;1096;1145;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1096_1145;0;BC-04-22;1096;1145;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1096_1145;0;BC-08-23;1096;1145;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1096_1145;0;BC-12-24;1096;1145;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1229_709.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1229_709;0;BC-00-21;1229;709;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1229_709;0;BC-04-22;1229;709;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1229_709;0;BC-08-23;1229;709;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1229_709;0;BC-12-24;1229;709;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1438_496.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1438_496;0;BC-00-21;1438;496;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1438_496;0;BC-04-22;1438;496;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1438_496;0;BC-08-23;1438;496;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1438_496;0;BC-12-24;1438;496;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1438_981.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1438_981;0;BC-00-21;1438;981;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1438_981;0;BC-04-22;1438;981;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1438_981;0;BC-08-23;1438;981;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1438_981;0;BC-12-24;1438;981;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1641_1401.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1641_1401;0;BC-00-21;1641;1401;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1641_1401;0;BC-04-22;1641;1401;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1641_1401;0;BC-08-23;1641;1401;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1641_1401;0;BC-12-24;1641;1401;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1671_101.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1671_101;0;BC-00-21;1671;101;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1671_101;0;BC-04-22;1671;101;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1671_101;0;BC-08-23;1671;101;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1671_101;0;BC-12-24;1671;101;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_1675_918.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_1675_918;0;BC-00-21;1675;918;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_1675_918;0;BC-04-22;1675;918;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_1675_918;0;BC-08-23;1675;918;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_1675_918;0;BC-12-24;1675;918;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_2022_700.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_2022_700;0;BC-00-21;2022;700;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_2022_700;0;BC-04-22;2022;700;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_2022_700;0;BC-08-23;2022;700;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_2022_700;0;BC-12-24;2022;700;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_2041_477.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_2041_477;0;BC-00-21;2041;477;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_2041_477;0;BC-04-22;2041;477;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_2041_477;0;BC-08-23;2041;477;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_2041_477;0;BC-12-24;2041;477;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_2096_680.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_2096_680;0;BC-00-21;2096;680;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_2096_680;0;BC-04-22;2096;680;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_2096_680;0;BC-08-23;2096;680;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_2096_680;0;BC-12-24;2096;680;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_2102_676.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-21_0_2102_676;0;BC-00-21;2102;676;0;BC-00-21;C3:00:00:57:B9:E6
+BC-04-22_0_2102_676;0;BC-04-22;2102;676;0;BC-04-22;C3:00:00:57:B9:D4
+BC-08-23_0_2102_676;0;BC-08-23;2102;676;0;BC-08-23;C3:00:00:57:B9:E8
+BC-12-24_0_2102_676;0;BC-12-24;2102;676;0;BC-12-24;C3:00:00:57:B9:F1

data/train/jobs/done/GBC-01_0_2107_792.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
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data/train/jobs/done/GBC-01_0_218_120.csv

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data/train/jobs/done/GBC-01_0_2244_735.csv

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data/train/jobs/done/GBC-01_0_2377_671.csv

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data/train/jobs/done/GBC-01_0_2503_523.csv

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data/train/jobs/done/GBC-01_0_2702_660.csv

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data/train/jobs/done/GBC-01_0_2816_375.csv

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data/train/jobs/done/GBC-01_0_2818_931.csv

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data/train/jobs/done/GBC-01_0_631_93.csv

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data/train/jobs/done/GBC-01_0_848_1113.csv

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data/train/jobs/done/GBC-01_0_849_102.csv

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data/train/jobs/done/GBC-01_0_849_1408.csv

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data/train/jobs/done/GBC-01_1_1050_1197.csv

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data/train/jobs/done/GBC-01_1_1129_114.csv

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data/train/jobs/done/GBC-01_1_1290_1423.csv

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data/train/jobs/done/GBC-01_1_1302_1344.csv

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data/train/jobs/done/GBC-01_1_1363_749.csv

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data/train/jobs/done/GBC-01_1_1467_373.csv

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data/train/jobs/done/GBC-01_1_1547_1421.csv

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data/train/jobs/done/GBC-01_1_2211_1431.csv

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data/train/jobs/done/GBC-01_1_2219_440.csv

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data/train/jobs/done/GBC-01_1_2248_980.csv

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data/train/jobs/done/GBC-01_1_2250_742.csv

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data/train/jobs/done/GBC-01_1_2628_1116.csv

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data/train/jobs/done/GBC-01_1_2629_652.csv

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data/train/jobs/done/GBC-01_1_2811_921.csv

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data/train/jobs/done/GBC-01_1_2880_519.csv

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data/train/jobs/done/GBC-01_1_852_1106.csv

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data/train/jobs/done/GBC-01_1_854_1111.csv

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data/train/jobs/done/GBC-01_1_855_745.csv

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data/train/jobs/done/GBC-01_1_988_802.csv

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data/train/jobs/done/GBC-02_0_1421_1424.csv

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data/train/jobs/done/GBC-02_0_1438_829.csv

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data/train/jobs/done/GBC-02_0_1493_797.csv

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data/train/jobs/done/GBC-02_0_1557_299.csv

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data/train/jobs/done/GBC-02_0_1673_498.csv

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data/train/jobs/done/GBC-02_0_1859_51.csv

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data/train/jobs/done/GBC-02_0_1861_1118.csv

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data/train/jobs/done/GBC-02_0_1958_250.csv

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data/train/jobs/done/GBC-02_0_2004_1404.csv

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data/train/jobs/done/GBC-02_0_2025_791.csv

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data/train/jobs/done/GBC-02_0_2106_789.csv

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data/train/jobs/done/GBC-02_0_2114_688.csv

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data/train/jobs/done/GBC-02_0_2192_100.csv

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data/train/jobs/done/GBC-02_0_2193_371.csv

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data/train/jobs/done/GBC-02_0_2230_927.csv

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data/train/jobs/done/GBC-02_0_2242_740.csv

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data/train/jobs/done/GBC-02_0_2373_789.csv

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data/train/jobs/done/GBC-02_0_2412_679.csv

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data/train/jobs/done/GBC-02_0_2414_796.csv

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data/train/jobs/done/GBC-02_0_2489_1157.csv

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data/train/jobs/done/GBC-02_0_2505_220.csv

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data/train/jobs/done/GBC-02_0_2812_1418.csv

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data/train/jobs/done/GBC-02_0_2894_522.csv

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data/train/jobs/done/GBC-02_0_2894_776.csv

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data/train/jobs/done/GBC-02_0_528_1457.csv

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data/train/jobs/done/GBC-02_0_848_842.csv

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data/train/jobs/done/GBC-02_0_852_707.csv

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data/train/jobs/done/GBC-02_0_993_456.csv

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data/train/jobs/done/GBC-02_1_1150_518.csv

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data/train/jobs/done/GBC-02_1_1150_743.csv

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data/train/jobs/done/GBC-02_1_1298_1107.csv

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data/train/jobs/done/GBC-02_1_1491_115.csv

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+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-25_1_1491_115;1;BC-00-25;1491;115;1;BC-00-25;C3:00:00:57:B9:E7
+BC-04-26_1_1491_115;1;BC-04-26;1491;115;1;BC-04-26;C3:00:00:57:B9:D6
+BC-08-27_1_1491_115;1;BC-08-27;1491;115;1;BC-08-27;C3:00:00:57:B9:D7
+BC-12-28_1_1491_115;1;BC-12-28;1491;115;1;BC-12-28;C3:00:00:57:B9:F6

data/train/jobs/done/GBC-02_1_1735_1415.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-25_1_1735_1415;1;BC-00-25;1735;1415;1;BC-00-25;C3:00:00:57:B9:E7
+BC-04-26_1_1735_1415;1;BC-04-26;1735;1415;1;BC-04-26;C3:00:00:57:B9:D6
+BC-08-27_1_1735_1415;1;BC-08-27;1735;1415;1;BC-08-27;C3:00:00:57:B9:D7
+BC-12-28_1_1735_1415;1;BC-12-28;1735;1415;1;BC-12-28;C3:00:00:57:B9:F6

data/train/jobs/done/GBC-02_1_1979_582.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
+BC-00-25_1_1979_582;1;BC-00-25;1979;582;1;BC-00-25;C3:00:00:57:B9:E7
+BC-04-26_1_1979_582;1;BC-04-26;1979;582;1;BC-04-26;C3:00:00:57:B9:D6
+BC-08-27_1_1979_582;1;BC-08-27;1979;582;1;BC-08-27;C3:00:00:57:B9:D7
+BC-12-28_1_1979_582;1;BC-12-28;1979;582;1;BC-12-28;C3:00:00:57:B9:F6

data/train/jobs/done/GBC-02_1_2084_1179.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
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data/train/jobs/done/GBC-02_1_2413_793.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
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data/train/jobs/done/GBC-02_1_2415_672.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
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+BC-12-28_1_2415_672;1;BC-12-28;2415;672;1;BC-12-28;C3:00:00:57:B9:F6

data/train/jobs/done/GBC-02_1_2433_931.csv

@@ -0,0 +1,5 @@
+Position;Floor;RoomName;X;Y;Z;BeaconName;MAC
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+BC-12-28_1_2433_931;1;BC-12-28;2433;931;1;BC-12-28;C3:00:00:57:B9:F6