Updated the banner, added: Elevation and Calories

jogging_dashboard_browser_app.py

@@ -352,7 +352,11 @@ def process_gpx(file_path):
         df['time_loc'] = df['time'].dt.tz_localize(None)
         df['time_diff'] = df['time'] - df['time'].iloc[0]
         df['time_diff_sec'] = df['time_diff'].dt.total_seconds()
-        df['duration_hms'] = df['time_diff'].apply(lambda td: str(td).split('.')[0])
+        #df['duration_hms'] = df['time_diff'].apply(lambda td: str(td).split('.')[0])
+        secs = df['time_diff'].dt.total_seconds().astype(int)
+        df['duration_hms'] = (secs // 3600).astype(int).astype(str).str.zfill(2) + ':' + \
+            ((secs % 3600) // 60).astype(int).astype(str).str.zfill(2) + ':' + \
+            (secs % 60).astype(int).astype(str).str.zfill(2)
 
         # Kumulative Distanz (gleiche Logik wie in deiner FIT-Funktion)
         distances = [0]
@@ -445,6 +449,70 @@ def safe_add_column_to_dataframe(df, column_name, values):
 
 
 
+# =============================================================================
+# NEU: Elevation Gain & Calories – Hilfsfunktionen
+# =============================================================================
+
+# ── Biometrics (kalibriert gegen Strava) ─────────────────────────────────────
+_WEIGHT_KG  = 75.0
+_HEIGHT_CM  = 178.0
+_AGE_YEARS  = 35
+_IS_MALE    = True
+_HR_REST    = 64.5   # Ruhepuls in bpm – kalibriert gegen Strava 550 kcal
+# ─────────────────────────────────────────────────────────────────────────────
+
+def calculate_elevation_gain(df):
+    if 'elev' not in df.columns or df['elev'].isna().all():
+        return 0
+
+    elev_smooth = df['elev'].rolling(
+        window=30, win_type='gaussian', center=True, min_periods=1
+    ).mean(std=5)
+
+    delta = elev_smooth.diff().fillna(0)
+    # Schwellwert relativ zur Gesamtamplitude statt absolut fest
+    amplitude = df['elev'].max() - df['elev'].min()
+    threshold = amplitude * 0.00095  # ~0.4% der Gesamtamplitude
+    gain = delta[delta > threshold].sum()
+
+    print(f"DEBUG elev: min={df['elev'].min():.1f}, max={df['elev'].max():.1f}, delta>0.03={delta[delta>0.03].sum():.1f}")
+    return int(round(gain))
+
+
+def calculate_calories_burned(df):
+    # Geschlechts- und altersbasierte HR-Zonen nach Karvonen
+    # Kein fixer HR_REST — nutze den tatsächlichen Minimum-HR aus dem Lauf
+    # als Annäherung an den Ruhepuls
+    hr_max = 220 - _AGE_YEARS
+
+    use_hr = ('heart_rate' in df.columns and
+              df['heart_rate'].notna().sum() > len(df) * 0.5)
+
+    if not use_hr:
+        return 0
+
+    hr = df['heart_rate'].ffill().fillna(df['heart_rate'].bfill()).values
+    ts = df['time_diff_sec'].values
+
+    # Ruhepuls aus dem Lauf selbst schätzen: 5. Perzentile der HR-Werte
+    hr_rest = float(df['heart_rate'].quantile(0.05))
+    hr_rest = max(45.0, min(hr_rest, 80.0))   # Plausibilitätsgrenze
+
+    vo2max = 15.0 * (hr_max / hr_rest)
+
+    cumulative = 0.0
+    for i in range(1, len(ts)):
+        dt = ts[i] - ts[i - 1]
+        if dt <= 0 or dt > 10:
+            continue
+        frac       = max(0.0, (hr[i] - hr_rest) / (hr_max - hr_rest))
+        met        = max(1.0, min((frac * vo2max) / 3.5, 18.0))
+        kcal_per_s = met * _WEIGHT_KG * 3.5 / 12000.0
+        cumulative += kcal_per_s * dt
+
+    print(f"DEBUG calories: rows={len(df)}, hr_valid={df['heart_rate'].notna().sum()}, duration={df['time_diff_sec'].iloc[-1]:.0f}s, hr_mean={df['heart_rate'].mean():.1f}")
+    return int(round(cumulative))
+
 
 # =============================================================================
 # INFO BANNER
@@ -468,22 +536,38 @@ def create_info_banner(df):
     else:
         formatted_pace = "N/A"
 
+    # Elevation Gain
+    elevation_gain_m = calculate_elevation_gain(df)
+
+    # Calories
+    total_calories = calculate_calories_burned(df)
+
     # Build the info banner layout
     info_banner = html.Div([
         html.Div([
             html.H4("Total Distance", style={'margin-bottom': '5px'}),
             html.H2(f"{total_distance_km:.2f} km")
-        ], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
+        ], style={'width': '18%', 'display': 'inline-block', 'textAlign': 'center'}),
 
         html.Div([
             html.H4("Total Time", style={'margin-bottom': '5px'}),
             html.H2(formatted_total_time)
-        ], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
+        ], style={'width': '18%', 'display': 'inline-block', 'textAlign': 'center'}),
 
         html.Div([
             html.H4("Average Pace", style={'margin-bottom': '5px'}),
             html.H2(formatted_pace)
-        ], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
+        ], style={'width': '18%', 'display': 'inline-block', 'textAlign': 'center'}),
+
+        html.Div([
+            html.H4("Elevation", style={'margin-bottom': '5px'}),
+            html.H2(f"{elevation_gain_m} m")
+        ], style={'width': '18%', 'display': 'inline-block', 'textAlign': 'center'}),
+
+        html.Div([
+            html.H4("Calories", style={'margin-bottom': '5px'}),
+            html.H2(f"{total_calories} kcal")
+        ], style={'width': '18%', 'display': 'inline-block', 'textAlign': 'center'}),
     ], style={
         'display': 'flex',
         'justifyContent': 'space-around',
@@ -1395,7 +1479,9 @@ app.layout = html.Div([
     Input('file-dropdown', 'value')
 )
 def load_data(selected_file):                   # Dateipfad der ausgewählten Datei
+    print(f"DEBUG load_data: {selected_file}")
     df = process_selected_file(selected_file)   # Verarbeitet diese Datei
+    print(f"DEBUG load_data: rows={len(df)}")
     return df.to_json(date_format='iso', orient='split')
 
 # Callback 2: Update All (static) Plots