Updated the elevation related part and the elevation line plot. Also correction at the pace_bars_plot right tickz entries positioning bug.

2026-05-07 15:19:37 +02:00
parent 9b329232c6
commit bbf99a59f2
2 changed files with 72 additions and 105 deletions
--- a/DashboardApp_WebVersion.png
+++ b/DashboardApp_WebVersion.png
--- a/jogging_dashboard_browser_app.py
+++ b/jogging_dashboard_browser_app.py
@@ -239,12 +239,15 @@ def process_fit(file_path):
        df['time_diff_sec'] = df['time_diff'].dt.total_seconds()
        df['duration_hms'] = df['time_diff'].apply(lambda td: str(td).split('.')[0])

-        # Cumulative distance (km)
-        distances = [0]
-        for i in range(1, len(df)):
-            d = haversine(df.loc[i-1, 'lon'], df.loc[i-1, 'lat'], df.loc[i, 'lon'], df.loc[i, 'lat'])
-            distances.append(distances[-1] + d)
-        df['cum_dist_km'] = distances
+        # Cumulative distance (km) (vektorisiert)
+        lat_r = np.radians(df['lat'].values)
+        lon_r = np.radians(df['lon'].values)
+        dlat  = np.diff(lat_r, prepend=lat_r[0])
+        dlon  = np.diff(lon_r, prepend=lon_r[0])
+        a     = np.sin(dlat/2)**2 + np.cos(lat_r) * np.cos(np.roll(lat_r,1)) * np.sin(dlon/2)**2
+        a[0]  = 0
+        step_dist = 2 * 6371 * np.arcsin(np.sqrt(np.clip(a, 0, 1)))
+        df['cum_dist_km'] = np.cumsum(step_dist)

        # Elevation handling
        if 'elev' in df.columns:
@@ -275,37 +278,27 @@ def process_fit(file_path):
        df['speed_kmh_smooth'] = df['speed_kmh'].rolling(window=10, win_type="gaussian", center=True).mean(std=2)


-        # Heart rate handling (NEU!)
-        # ##############
-        # UPDATE: Da NaN-Problem mit heart_rate, manuell nochmal neu einlesen und überschreiben:
-        # save heart rate data into variable
+        # Heart rate handling
+        # Zweiter Durchlauf nötig: nach dropna() hat df weniger Zeilen als records,
+        # safe_add_column_to_dataframe behandelt den Längenunterschied korrekt.
        heart_rate = []
        for record in fit_file.get_messages("record"):
-            # Records can contain multiple pieces of data (ex: timestamp, latitude, longitude, etc)
            for data in record:
-                # Print the name and value of the data (and the units if it has any)
                if data.name == 'heart_rate':
                    heart_rate.append(data.value)
-        # Hier variable neu überschrieben:
        df = safe_add_column_to_dataframe(df, 'heart_rate', heart_rate)
-        # ##############

-        # ######################################
-        # IF issues with heart_rate values, usw these DEBUG prints:
-        #print(heart_rate)
        if 'heart_rate' in df.columns:
            df['heart_rate'] = pd.to_numeric(df['heart_rate'], errors='coerce')
-            df['hr_smooth'] = df['heart_rate'].rolling(window=5, center=True).mean()
-        #    print(f"Heart rate range: {df['heart_rate'].min():.0f} - {df['heart_rate'].max():.0f} bpm")     # Uncomment if needed - DEBUG purpose!
+            #df['hr_smooth']  = df['heart_rate'].rolling(window=5, center=True).mean()
+            df['hr_smooth'] = df['heart_rate'].rolling(window=2, center=True, min_periods=1).mean()
        else:
-            print("Keine Heart Rate Daten gefunden!")
            df['heart_rate'] = np.nan
-            df['hr_smooth'] = np.nan
+            df['hr_smooth']  = np.nan

            print(f"Verarbeitete FIT-Datei: {len(df)} Datenpunkte")
            print(f"Distanz: {df['cum_dist_km'].iloc[-1]:.2f} km")
            print(f"Dauer: {df['duration_hms'].iloc[-1]}")
-        # ######################################

        return df

@@ -366,12 +359,15 @@ def process_gpx(file_path):
            ((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]
-        for i in range(1, len(df)):
-            d = haversine(df.loc[i-1, 'lon'], df.loc[i-1, 'lat'], df.loc[i, 'lon'], df.loc[i, 'lat'])
-            distances.append(distances[-1] + d)
-        df['cum_dist_km'] = distances
+        # Cumulative distance (km) (vektorisiert)
+        lat_r = np.radians(df['lat'].values)
+        lon_r = np.radians(df['lon'].values)
+        dlat  = np.diff(lat_r, prepend=lat_r[0])
+        dlon  = np.diff(lon_r, prepend=lon_r[0])
+        a     = np.sin(dlat/2)**2 + np.cos(lat_r) * np.cos(np.roll(lat_r,1)) * np.sin(dlon/2)**2
+        a[0]  = 0
+        step_dist = 2 * 6371 * np.arcsin(np.sqrt(np.clip(a, 0, 1)))
+        df['cum_dist_km'] = np.cumsum(step_dist)

        # Elevation (gleiche Logik wie in deiner FIT-Funktion)
        df['elev'] = df['elev'].bfill()
@@ -508,15 +504,12 @@ def calculate_calories_burned(df):

    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
+    dt   = np.diff(ts, prepend=ts[0])
+    mask = (dt > 0) & (dt <= 10)
+    frac = np.clip((hr - hr_rest) / (hr_max - hr_rest), 0, None)
+    met  = np.clip((frac * vo2max) / 3.5, 1.0, 18.0)
+    kcal_per_s = met * _WEIGHT_KG * 3.5 / 12000.0
+    cumulative = float(np.sum(kcal_per_s[mask] * dt[mask]))

    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))
@@ -1070,8 +1063,7 @@ def create_pixel_heatmap(dataframes,
        if len(lats_r) < 2:
            continue
        xs, ys = to_px(lats_r, lons_r)
-        for x, y in zip(xs, ys):
-            count_grid[y, x] += 1
+        np.add.at(count_grid, (ys, xs), 1)

    max_count = max(count_grid.max(), 1)
    log_max   = np.log1p(max_count)
@@ -1107,21 +1099,30 @@ def create_pixel_heatmap(dataframes,
    # - Einzellauf-Modus: nur highlight_df, aber count_grid kam von allen
    draw_frames = dataframes if (mode == 'city' or highlight_df is None) else [highlight_df]

-    for df in draw_frames:        # ← draw_frames statt dataframes
+    from matplotlib.collections import LineCollection
+
+    # (LineCollection - alle Segmente in einem Aufruf):
+    for df in draw_frames:
        lats_r = df['lat'].dropna().values
        lons_r = df['lon'].dropna().values
        if len(lats_r) < 2:
            continue
-
        xs, ys = to_px(lats_r, lons_r)

-        for i in range(len(xs) - 1):
-            mid_x = np.clip((xs[i] + xs[i+1]) // 2, 0, img_width  - 1)
-            mid_y = np.clip((ys[i] + ys[i+1]) // 2, 0, img_height - 1)
-            norm_val = np.log1p(count_grid[mid_y, mid_x]) / log_max if log_max > 0 else 0
-            ax.plot([xs[i], xs[i+1]], [ys[i], ys[i+1]],
-                    color=cmap(norm_val), linewidth=line_width,
-                    solid_capstyle='round', solid_joinstyle='round')
+        # Segmente als Array: shape (N-1, 2, 2)
+        points   = np.array([xs, ys]).T.reshape(-1, 1, 2)
+        segments = np.concatenate([points[:-1], points[1:]], axis=1)
+
+        # Farbe pro Segment aus count_grid
+        mid_xs = np.clip((xs[:-1] + xs[1:]) // 2, 0, img_width  - 1)
+        mid_ys = np.clip((ys[:-1] + ys[1:]) // 2, 0, img_height - 1)
+        counts = count_grid[mid_ys, mid_xs]
+        norm_vals = np.log1p(counts) / log_max if log_max > 0 else np.zeros_like(counts)
+        colors = cmap(norm_vals)
+
+        lc = LineCollection(segments, colors=colors, linewidths=line_width,
+                            capstyle='round', joinstyle='round')
+        ax.add_collection(lc)

    # Colorbar als Inset-Axes (verändert NICHT die Figure-Größe)
    #cbar_ax = fig_mpl.add_axes([0.88, 0.10, 0.025, 0.70])  #  # verticale Position: rechts
@@ -1633,32 +1634,17 @@ def create_pixel_hr_map(df, img_width=900, img_height=900,
 # ELEVATION-PLOT:
 # -----------------------------------------------------------------------------
 def create_elevation_plot(df, smooth_points=500):
-    # Originale Daten
    x = df['time']
    y = df['rel_elev']

-    # Einfache Glättung: nur Y-Werte glätten, X-Werte beibehalten
-    if len(y) >= 4:  # Genug Punkte für cubic interpolation
-        y_numeric = y.to_numpy()
-        # Nur gültige Y-Punkte für Interpolation
-        mask = ~np.isnan(y_numeric)
-        if np.sum(mask) >= 4:  # Genug gültige Punkte
-            # Index-basierte Interpolation für Y-Werte
-            valid_indices = np.where(mask)[0]
-            valid_y = y_numeric[mask]
-            # Interpolation über die Indizes
-            f = interp1d(valid_indices, valid_y, kind='cubic', bounds_error=False, fill_value='extrapolate')
-            # Neue Y-Werte für alle ursprünglichen X-Positionen
-            all_indices = np.arange(len(y))
-            y_smooth = f(all_indices)
-            # Originale X-Werte beibehalten
-            x_smooth = x
-        else:
-            # Fallback: originale Daten
-            x_smooth, y_smooth = x, y
-    else:
-        # Zu wenige Punkte: originale Daten verwenden
-        x_smooth, y_smooth = x, y
+    y_smooth = (
+        pd.Series(y.values)
+        .interpolate(method='linear', limit=10, limit_direction='both')  # kurze Lücken schließen
+        .rolling(window=15, center=True, min_periods=1)
+        .mean()
+        .values
+    )
+    x_smooth = x

    fig = go.Figure()

@@ -1957,12 +1943,12 @@ def create_heart_rate_plot(df):
    fig = go.Figure()

    # Heart Rate Linie (geglättet)
+    mask = df['heart_rate'].isna()
    fig.add_trace(go.Scatter(
        x=df['time'][~mask],
-        y=df['hr_smooth'][~mask],
+        y=df['heart_rate'][~mask],
        mode='lines',
-        #name='Geglättete Herzfrequenz',
-        line=dict(color='#ff2c48', width=2),
+        line=dict(color='#ff2c48', width=1.5),   # etwas dünner für gezackte Linie
        showlegend=False,
        hovertemplate=(
            "Zeit: %{x}<br>" +
@@ -2232,7 +2218,7 @@ def create_pace_bars_plot(df, formatted_pace=None):
    # -------------------------------------------------------------------------
    fig.add_trace(go.Bar(
        x=pace_vals,
-        y=y_labels,
+        y=list(range(len(y_labels))),   # ← 0,1,2,3... statt Strings  y=y_labels,
        orientation='h',
        marker=dict(
            color=bar_colors,
@@ -2296,7 +2282,7 @@ def create_pace_bars_plot(df, formatted_pace=None):
        if right_text:
            fig.add_annotation(
                x=x_max,
-                y=km_lbl,
+                y=i,            # ← numerischer Index statt km_lbl String
                text=right_text,
                showarrow=False,
                xanchor='right',
@@ -2329,13 +2315,16 @@ def create_pace_bars_plot(df, formatted_pace=None):
        yaxis=dict(
            title='',
            autorange='reversed',     # km 1 oben, letzter km unten (wie Strava)
+            tickmode='array',
+            tickvals=list(range(len(y_labels))),   # ← 0,1,2...
+            ticktext=y_labels,                      # ← "1","2",...,"0.4"
            tickfont=dict(size=11, color='white'),
            showgrid=False,
            zeroline=False,
        ),
        template='plotly_dark',
        height=plot_height,
-        margin=dict(l=50, r=80, t=45, b=45),
+        margin=dict(l=40, r=40, t=45, b=45),
        plot_bgcolor='#111111',
        paper_bgcolor='#1e1e1e',
        font=dict(color='white'),
@@ -2567,7 +2556,7 @@ def update_all_plots(json_data):
    Output('heatmap-city-info', 'children'),
    Input('file-dropdown', 'value'),
    Input('heatmap-mode-toggle', 'value'),
-    prevent_initial_call=True
+    #prevent_initial_call=True                 # Sonst beim Start der App kein Renderprozess
 )
 def update_pixel_heatmap(selected_file, toggle_value):
    """
@@ -2578,11 +2567,11 @@ def update_pixel_heatmap(selected_file, toggle_value):
    """
    import os

-    if not selected_file or selected_file in ['NO_FILES', 'NO_FOLDER', 'ERROR']:
-        fig = go.Figure()
-        fig.update_layout(paper_bgcolor='#1e1e1e', font=dict(color='white'),
-                          title=dict(text='Keine Datei ausgewählt'))
-        return fig, ''
+    if not selected_file or selected_file in ['NO_FILES', 'NO_FOLDER', 'ERROR', None]:
+        empty = go.Figure()
+        empty.update_layout(paper_bgcolor='#1e1e1e', font=dict(color='white'),
+                            title=dict(text='Datei wählen...', font=dict(color='white')))
+        return empty, ''

    city_code = extract_city_code(selected_file)
    city_info_text = f'Erkannte Region: {city_code}' if city_code else 'Kein Stadtcode erkannt'
@@ -2631,28 +2620,6 @@ def update_pixel_heatmap(selected_file, toggle_value):

    return fig, city_info_text

-    # Lade alle Läufe dieser Stadt
-    all_options = list_files()
-    city_dfs, city_paths = load_runs_for_city(city_code, all_options)
-
-    if not city_dfs:
-        # Fallback: nur aktuelle Datei
-        try:
-            df_single = process_selected_file(selected_file)
-            city_dfs = [df_single]
-        except Exception:
-            city_dfs = []
-
-    fig = create_pixel_heatmap(
-        dataframes=city_dfs,
-        mode='city',
-        city_code=city_code,
-        n_city_runs=len(city_dfs),
-    )
-
-    city_info_text = f'Region {city_code} · {len(city_dfs)} Läufe geladen'
-    return fig, city_info_text
-


 # Callback 3: Export SVG