Update app.py

app.py

@@ -57,10 +57,88 @@ def telemetry():
     json_data = request.json
     data=json_data
     print(data)
+
+    d = json_data
+    df_s = pd.DataFrame({
+        "deviceId": d["deviceId"],
+        "TELEMETRY_SEQ_GROUP_ID": d["TELEMETRY_SEQ_GROUP_ID"],
+        **{k: d[k] for k in d if isinstance(d[k], list)}
+    })
+    cols = [
+        'OAT', 'IAS', 'ALPHAI', 'BETAI', 'TETA', 'PHI', 'HDG', 'P', 'Q',
+        'R', 'NR', 'NP1', 'NG1', 'VZ', 'ZRS',
+        'E1A_TM_TOT', 'E1A_TM_T1', 'NGR1', 'DP1'
+    ]
+    X = df_s[cols]
+    assert list(X.columns) == cols
+
+    X = df_s[cols]
+    X = sm.add_constant(X)
+    y_pred = res_loaded.predict(X)
+    y_pred_100 = y_pred * 100
+    df_s['y_pred_100']=y_pred_100
+    #print(len(y_pred_100))
+    df_s["clogging_LH_Predicted"] = np.take(values, np.searchsorted(bins, df_s["y_pred_100"], side="right"))
+    mean_clogging = df_s["clogging_LH_Predicted"].mean()
+    #creo la cartella se non esiste
+    device_id = d["deviceId"]
+    csv_path = os.path.join(device_id, "clogging_mean_by_group.csv")
+    group_id = d["TELEMETRY_SEQ_GROUP_ID"]
+    if group_id == 0:
+        if os.path.exists(device_id):
+            shutil.rmtree(device_id)
+        os.makedirs(device_id)
+    #dataframe da scrivere
+    row = pd.DataFrame([{
+        "TELEMETRY_SEQ_GROUP_ID": group_id,
+        "mean_clogging_LH_Predicted": mean_clogging
+    }])
+    row.to_csv(
+        csv_path,
+        mode="a",
+        header=not os.path.exists(csv_path),
+        index=False
+    )
+    #regressione
+    dtf=pd.read_csv(csv_path)
+    #print(dtf)
+    l=len(dtf)
+    if l>5:
+        X=dtf[['TELEMETRY_SEQ_GROUP_ID']]
+        y=dtf['mean_clogging_LH_Predicted']
+        model = LinearRegression()
+        model.fit(X, y)
+
+        a = model.coef_[0]
+        b = model.intercept_
+
+        x_at_100 = ((100 - b) / a) if a != 0 else np.nan
+        if a != 0:
+          x_at_100 =x_at_100 - int(group_id)
+          slope= a
+          intercept= b
+          r2= model.score(X, y)
+          n_points= l
+        else:
+          x_at_100 =np.nan
+          slope=np.nan
+          intercept= np.nan
+          r2= np.nan
+          n_points= l
+
+    else:
+      x_at_100 =np.nan
+      slope=np.nan
+      intercept= np.nan
+      r2= np.nan
+      n_points= l
+    
     pic={}
-    pic['testo_prima_domanda']='hh'
-    pic['risultato']='hh'
-    pic['tabelle']='jjj'
+    pic['x_at_100']=x_at_100
+    pic['slope']=slope
+    pic['intercept']=intercept
+    pic['r2']=r2
+    pic['n_points']=l
     response = jsonify(pic)
     return response