elev_plot display style update - gradient fill, heart_rate bmp Zones new display style
This commit is contained in:
229
fit_app.py
229
fit_app.py
@@ -423,8 +423,7 @@ def create_map_plot(df):
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######################
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# NEUE VERSION:
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# #####################
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def create_elevation_plot(df, smooth_points=500):
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# Originale Daten
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x = df['time']
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@@ -433,23 +432,17 @@ def create_elevation_plot(df, smooth_points=500):
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# Einfache Glättung: nur Y-Werte glätten, X-Werte beibehalten
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if len(y) >= 4: # Genug Punkte für cubic interpolation
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y_numeric = y.to_numpy()
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# Nur gültige Y-Punkte für Interpolation
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mask = ~np.isnan(y_numeric)
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if np.sum(mask) >= 4: # Genug gültige Punkte
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# Index-basierte Interpolation für Y-Werte
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valid_indices = np.where(mask)[0]
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valid_y = y_numeric[mask]
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# Interpolation über die Indizes
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f = interp1d(valid_indices, valid_y, kind='cubic',
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bounds_error=False, fill_value='extrapolate')
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f = interp1d(valid_indices, valid_y, kind='cubic', bounds_error=False, fill_value='extrapolate')
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# Neue Y-Werte für alle ursprünglichen X-Positionen
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all_indices = np.arange(len(y))
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y_smooth = f(all_indices)
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# Originale X-Werte beibehalten
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x_smooth = x
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else:
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@@ -461,40 +454,96 @@ def create_elevation_plot(df, smooth_points=500):
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fig = go.Figure()
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# Fläche unter der Kurve (mit geglätteten Daten)
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fig.add_trace(go.Scatter(
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x=x_smooth, y=y_smooth,
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mode='lines',
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line=dict(color='#1CAF50'), # Fill between color!
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fill='tozeroy',
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#fillcolor='rgba(226, 241, 248)',
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hoverinfo='skip',
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showlegend=False
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))
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# Separate Behandlung für positive und negative Bereiche
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y_array = np.array(y_smooth)
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x_array = np.array(x_smooth)
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# Hauptlinie (geglättet)
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# Positive Bereiche (Anstiege) - Gradient von transparent unten zu grün oben
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positive_mask = y_array >= 0
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if np.any(positive_mask):
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# Nulllinie für positive Bereiche
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fig.add_trace(go.Scatter(
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x=x_array,
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y=np.zeros_like(y_array),
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mode='lines',
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line=dict(width=0),
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hoverinfo='skip',
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showlegend=False
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))
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# Positive Bereiche mit Gradient nach oben
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fig.add_trace(go.Scatter(
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x=x_array,
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y=np.where(y_array >= 0, y_array, 0), # Nur positive Werte
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fill='tonexty', # Fill zur vorherigen Trace (Nulllinie)
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mode='lines',
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line=dict(width=0),
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fillgradient=dict(
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type="vertical",
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colorscale=[
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(0.0, "rgba(17, 17, 17, 0.0)"), # Transparent unten (bei y=0)
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(1.0, "rgba(43, 82, 144, 0.8)") # Blau oben (bei maximaler Höhe) Grün: 73, 189, 115
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]
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),
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hoverinfo='skip',
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showlegend=False
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))
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# Negative Bereiche (Abstiege) - Gradient von grün unten zu transparent oben
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negative_mask = y_array < 0
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if np.any(negative_mask):
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# Nulllinie für negative Bereiche
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fig.add_trace(go.Scatter(
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x=x_array,
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y=np.where(y_array < 0, y_array, 0), # Nur negative Werte
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mode='lines',
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line=dict(width=0),
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hoverinfo='skip',
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showlegend=False
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))
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# Negative Bereiche mit Gradient nach unten
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fig.add_trace(go.Scatter(
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x=x_array,
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y=np.zeros_like(y_array),
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fill='tonexty', # Fill zur vorherigen Trace (negative Werte)
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mode='lines',
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line=dict(width=0),
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fillgradient=dict(
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type="vertical",
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colorscale=[
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(0.0, "rgba(43, 82, 144, 0.8)"), # Blau unten (bei minimaler Tiefe)
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(1.0, "rgba(17, 17, 17, 0.0)") # Transparent oben (bei y=0)
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]
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),
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hoverinfo='skip',
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showlegend=False
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))
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# Hauptlinie (geglättet) - über allem
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fig.add_trace(go.Scatter(
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x=x_smooth, y=y_smooth,
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x=x_smooth,
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y=y_smooth,
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mode='lines',
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line=dict(color='#084C20', width=2), # Line color!
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line=dict(color='#2b5290', width=2), # Weiße Linie für bessere Sichtbarkeit
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name='Elevation',
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showlegend=False
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))
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# SUPDER IDEE, ABER GEHT NICHT WEGEN NEUEN smoothed POINTS! GEHT NUR BEI X
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#fig.update_traces(
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# hovertemplate=(
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# #"Time: %{customdata[0]}<br>" +
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# "Distance (km): %{customdata[0]:.2f}<br>" +
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# "Elevation: %{customdata[1]}<extra></extra>" +
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# "Elapsed Time: %{customdata[2]}<extra></extra>"
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# ),
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# customdata=df[['cum_dist_km','elev', 'time']]
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#
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# Add horizontal reference line at y=0
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fig.add_shape(
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type='line',
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x0=df['time_loc'].iloc[0],
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x1=df['time_loc'].iloc[-1],
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y0=0,
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y1=0,
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line=dict(color='gray', width=1, dash='dash'),
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name='Durchschnittstempo'
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)
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# Layout im Dark Theme
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fig.update_layout(
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title=dict(text='Höhenprofil relativ zum Startwert', font=dict(size=16, color='white')),
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title=dict(text='Höhenprofil (relativ zum Ausgangswert: 0m)', font=dict(size=16, color='white')),
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xaxis_title='Zeit',
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yaxis_title='Höhe relativ zum Start (m)',
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template='plotly_dark',
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@@ -507,6 +556,92 @@ def create_elevation_plot(df, smooth_points=500):
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return fig
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# Alte Version - normaler gradient fill between:
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# def create_elevation_plot(df, smooth_points=500):
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# # Originale Daten
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# x = df['time']
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# y = df['rel_elev']
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#
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# # Einfache Glättung: nur Y-Werte glätten, X-Werte beibehalten
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# if len(y) >= 4: # Genug Punkte für cubic interpolation
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# y_numeric = y.to_numpy()
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#
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# # Nur gültige Y-Punkte für Interpolation
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# mask = ~np.isnan(y_numeric)
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#
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# if np.sum(mask) >= 4: # Genug gültige Punkte
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# # Index-basierte Interpolation für Y-Werte
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# valid_indices = np.where(mask)[0]
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# valid_y = y_numeric[mask]
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#
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# # Interpolation über die Indizes
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# f = interp1d(valid_indices, valid_y, kind='cubic',
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# bounds_error=False, fill_value='extrapolate')
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#
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# # Neue Y-Werte für alle ursprünglichen X-Positionen
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# all_indices = np.arange(len(y))
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# y_smooth = f(all_indices)
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#
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# # Originale X-Werte beibehalten
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# x_smooth = x
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# else:
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# # Fallback: originale Daten
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# x_smooth, y_smooth = x, y
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# else:
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# # Zu wenige Punkte: originale Daten verwenden
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# x_smooth, y_smooth = x, y
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#
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# fig = go.Figure()
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#
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# # Fläche unter der Kurve (mit geglätteten Daten)
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# fig.add_trace(go.Scatter(
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# x=x_smooth, y=y_smooth,
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# mode='lines',
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# line=dict(color='#1CAF50'), # Fill between color!
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# fill='tozeroy',
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# #fillcolor='rgba(226, 241, 248)',
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# hoverinfo='skip',
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# showlegend=False
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# ))
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#
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# # Hauptlinie (geglättet)
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# fig.add_trace(go.Scatter(
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# x=x_smooth, y=y_smooth,
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# mode='lines',
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# line=dict(color='#084C20', width=2), # Line color!
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# name='Elevation',
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# showlegend=False
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# ))
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#
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# # SUPDER IDEE, ABER GEHT NICHT WEGE NEUEN smoothed POINTS! GEHT NUR BEI X
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# #fig.update_traces(
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# # hovertemplate=(
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# # #"Time: %{customdata[0]}<br>" +
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# # "Distance (km): %{customdata[0]:.2f}<br>" +
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# # "Elevation: %{customdata[1]}<extra></extra>" +
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# # "Elapsed Time: %{customdata[2]}<extra></extra>"
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# # ),
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# # customdata=df[['cum_dist_km','elev', 'time']]
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# #
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#
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# # Layout im Dark Theme
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# fig.update_layout(
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# title=dict(text='Höhenprofil relativ zum Startwert', font=dict(size=16, color='white')),
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# xaxis_title='Zeit',
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# yaxis_title='Höhe relativ zum Start (m)',
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# template='plotly_dark',
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# paper_bgcolor='#1e1e1e',
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# plot_bgcolor='#111111',
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# font=dict(color='white'),
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# margin=dict(l=40, r=40, t=50, b=40),
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# height=400
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# )
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#
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# return fig
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# #####################
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def create_deviation_plot(df): #Distanz-Zeit-Diagramm
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@@ -613,7 +748,7 @@ def create_heart_rate_plot(df):
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y=df['hr_smooth'][~mask],
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mode='lines',
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#name='Geglättete Herzfrequenz',
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line=dict(color='#E43D70', width=2),
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line=dict(color='#ff2c48', width=2),
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showlegend=False,
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hovertemplate=(
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"Zeit: %{x}<br>" +
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@@ -648,7 +783,7 @@ def create_heart_rate_plot(df):
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# Annotation für Durchschnittswert
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fig.add_annotation(
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x=df['time_loc'].iloc[int(len(df) * 0.8)], # Bei 80% der Zeit
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x=df['time_loc'].iloc[int(len(df) * 0.5)], # Bei 50% der Zeit
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y=mean_hr,
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text=f"Ø {mean_hr:.0f} bpm",
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showarrow=True,
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@@ -664,13 +799,20 @@ def create_heart_rate_plot(df):
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# Geschätzte maximale Herzfrequenz (Beispiel: 200 bpm)
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max_hr_estimated = 200 # oder z. B. 220 - alter
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# Definiere feste HR-Zonen in BPM
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## Definiere feste HR-Zonen in BPM
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#zones = [
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# {"name": "Zone 1", "lower": 0, "upper": 124, "color": "#F4A4A3"}, # Regeneration (Recovery)
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# {"name": "Zone 2", "lower": 124, "upper": 154, "color": "#EF7476"}, # Grundlagenausdauer (Endurance)
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# {"name": "Zone 3", "lower": 154, "upper": 169, "color": "#EA4748"}, # Tempo (Aerob)
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# {"name": "Zone 4", "lower": 169, "upper": 184, "color": "#E02628"}, # Schwelle (Threshold) (Anaerob)
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# {"name": "Zone 5", "lower": 184, "upper": max_hr_estimated, "color": "#B71316"}, # Neuromuskulär (Neuromuskulär)
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#]
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zones = [
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{"name": "Zone 1", "lower": 0, "upper": 124, "color": "#F4A4A3"}, # Regeneration (Recovery)
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{"name": "Zone 2", "lower": 124, "upper": 154, "color": "#EF7476"}, # Grundlagenausdauer (Endurance)
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{"name": "Zone 3", "lower": 154, "upper": 169, "color": "#EA4748"}, # Tempo
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{"name": "Zone 4", "lower": 169, "upper": 184, "color": "#E02628"}, # Schwelle (Threshold)
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{"name": "Zone 5", "lower": 184, "upper": max_hr_estimated, "color": "#B71316"}, # Anaerob
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{"name": "Zone 1", "lower": 0, "upper": 124, "color": "#4A4A4A"}, # Regeneration (Recovery) (#111111 Transparent)
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{"name": "Zone 2", "lower": 124, "upper": 154, "color": "#87CEFA"}, # Grundlagenausdauer (Endurance)
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{"name": "Zone 3", "lower": 154, "upper": 169, "color": "#90EE90"}, # Tempo (Aerob)
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{"name": "Zone 4", "lower": 169, "upper": 184, "color": "#FFDAB9"}, # Schwelle (Threshold) (Anaerob)
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{"name": "Zone 5", "lower": 184, "upper": max_hr_estimated, "color": "#FFB6C1"}, # Neuromuskulär (Neuromuskulär)
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]
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# Zeichne Zonen als Hintergrund (horizontale Rechtecke)
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@@ -698,7 +840,7 @@ def create_heart_rate_plot(df):
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),
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yaxis=dict(
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title='Herzfrequenz (bpm)',
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rangemode='tozero'
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range=[80, 200] # Statt rangemode='tozero'
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),
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template='plotly_dark',
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paper_bgcolor='#1e1e1e',
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@@ -762,13 +904,14 @@ def create_pace_bars_plot(df):
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# Step 4: Create Plotly bar chart
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=df['km_start'],
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x=df['km_start'], # Mittig unter jeder Bar
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y=df['pace_min_per_km'],
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width=df['segment_len'],
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text=[f"{v:.1f} min/km" if pd.notnull(v) else "" for v in df['pace_min_per_km']],
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#textposition='outside',
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textposition='inside',
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marker_color='#125595',
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opacity=0.9, # Transparenz
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name='Pace pro km',
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offset=0
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))
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