922 lines
30 KiB
Python
922 lines
30 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jul 30th 2025
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@author: Marcel Weschke
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@email: marcel.weschke@directbox.de
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"""
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# %% Load libraries
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import os
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import base64
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import io
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import datetime
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from math import radians, sin, cos, sqrt, asin
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import dash
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from dash import dcc, html, Input, Output, Dash
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import dash_bootstrap_components as dbc
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import pandas as pd
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import numpy as np
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import plotly.express as px
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import plotly.graph_objects as go
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from scipy.interpolate import interp1d
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import gpxpy
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from fitparse import FitFile
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# === Helper Functions ===
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def list_fit_files():
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"""
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Listet alle .fit Files im Verzeichnis auf und sortiert sie nach Datum
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"""
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folder = './fit_files'
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# Prüfe ob Ordner existiert
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if not os.path.exists(folder):
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print(f"Ordner {folder} existiert nicht!")
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return [{'label': 'Ordner nicht gefunden', 'value': 'NO_FOLDER'}]
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# Hole alle .fit Files
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try:
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all_files = os.listdir(folder)
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files = [f for f in all_files if f.lower().endswith('.fit')]
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except Exception as e:
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print(f"Fehler beim Lesen des Ordners: {e}")
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return [{'label': 'Fehler beim Lesen', 'value': 'ERROR'}]
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def extract_date(filename):
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"""Extrahiert Datum aus Filename für Sortierung"""
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try:
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# Versuche verschiedene Datumsformate
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return datetime.datetime.strptime(filename[:10], '%d.%m.%Y')
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except ValueError:
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try:
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return datetime.datetime.strptime(filename[:10], '%Y-%m-%d')
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except ValueError:
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try:
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# Versuche auch andere Formate
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return datetime.datetime.strptime(filename[:8], '%Y%m%d')
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except ValueError:
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# Wenn kein Datum erkennbar, nutze Datei-Änderungsdatum
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try:
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file_path = os.path.join(folder, filename)
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return datetime.datetime.fromtimestamp(os.path.getmtime(file_path))
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except:
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return datetime.datetime.min
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# Sortiere Files nach Datum (neueste zuerst)
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files.sort(key=extract_date, reverse=True)
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# Erstelle Dropdown-Optionen
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if files:
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options = []
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for f in files:
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file_path = os.path.join(folder, f)
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# Zeige auch Dateigröße und Änderungsdatum an
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try:
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size_mb = os.path.getsize(file_path) / (1024 * 1024)
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mod_time = datetime.datetime.fromtimestamp(os.path.getmtime(file_path))
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label = f"{f}"
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#label = f"{f} ({size_mb:.1f}MB - {mod_time.strftime('%d.%m.%Y %H:%M')}\n)" # For debugging purpose
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except:
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label = f
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options.append({
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'label': label,
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'value': file_path
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})
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return options
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else:
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return [{'label': 'Keine .fit Dateien gefunden', 'value': 'NO_FILE'}]
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def haversine(lon1, lat1, lon2, lat2):
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"""
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Berechnet die Entfernung zwischen zwei GPS-Koordinaten in km
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"""
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R = 6371 # Erdradius in km
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dlon = radians(lon2 - lon1)
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dlat = radians(lat2 - lat1)
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a = sin(dlat/2)**2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon/2)**2
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return 2 * R * asin(sqrt(a))
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def process_fit(file_path):
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"""
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Verarbeitet eine FIT-Datei und erstellt einen DataFrame
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"""
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if file_path in ['NO_FILE', 'NO_FOLDER', 'ERROR']:
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print(f"Ungültiger Dateipfad: {file_path}")
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return pd.DataFrame()
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if not os.path.exists(file_path):
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print(f"Datei nicht gefunden: {file_path}")
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return pd.DataFrame()
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try:
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fit_file = FitFile(file_path)
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print(f"Verarbeite FIT-Datei: {file_path}")
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# Sammle alle record-Daten
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records = []
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for record in fit_file.get_messages("record"):
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record_data = {}
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for data in record:
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# Sammle alle verfügbaren Datenfelder
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record_data[data.name] = data.value
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records.append(record_data)
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if not records:
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print("Keine Aufzeichnungsdaten in der FIT-Datei gefunden")
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return pd.DataFrame()
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# Erstelle DataFrame
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df = pd.DataFrame(records)
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print(f"DataFrame erstellt mit {len(df)} Zeilen und Spalten: {list(df.columns)}")
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# Debugging: Schaue welche Spalten verfügbar sind
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print(f"Verfügbare Spalten: {df.columns.tolist()}")
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# Suche nach Heart Rate in verschiedenen Formaten
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possible_hr_cols = [col for col in df.columns if 'heart' in col.lower() or 'hr' in col.lower()]
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print(f"Mögliche Heart Rate Spalten: {possible_hr_cols}")
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# Standard-Spaltennamen für verschiedene FIT-Formate
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lat_cols = ['position_lat', 'lat', 'latitude']
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lon_cols = ['position_long', 'lon', 'longitude']
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elev_cols = ['altitude', 'elev', 'elevation', 'enhanced_altitude']
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time_cols = ['timestamp', 'time']
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hr_cols = ['heart_rate', 'hr'] + possible_hr_cols
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speed_cols = ['speed', 'enhanced_speed']
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dist_cols = ['distance', 'total_distance']
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# Finde die richtigen Spaltennamen
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lat_col = next((col for col in lat_cols if col in df.columns), None)
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lon_col = next((col for col in lon_cols if col in df.columns), None)
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elev_col = next((col for col in elev_cols if col in df.columns), None)
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time_col = next((col for col in time_cols if col in df.columns), None)
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hr_col = next((col for col in hr_cols if col in df.columns), None)
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speed_col = next((col for col in speed_cols if col in df.columns), None)
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# Prüfe ob wichtige Daten vorhanden sind
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if not lat_col or not lon_col or not time_col:
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raise ValueError(f"Wichtige Daten fehlen! Lat: {lat_col}, Lon: {lon_col}, Time: {time_col}")
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# Benenne Spalten einheitlich um
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df = df.rename(columns={
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lat_col: 'lat',
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lon_col: 'lon',
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elev_col: 'elev' if elev_col else None,
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time_col: 'time',
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hr_col: 'heart_rate' if hr_col else None,
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speed_col: 'speed_ms' if speed_col else None
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})
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# FIT lat/lon sind oft in semicircles - konvertiere zu Grad
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if df['lat'].max() > 180: # Semicircles detection
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df['lat'] = df['lat'] * (180 / 2**31)
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df['lon'] = df['lon'] * (180 / 2**31)
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# Entferne Zeilen ohne GPS-Daten
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df = df.dropna(subset=['lat', 'lon', 'time']).reset_index(drop=True)
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# Basic cleanup
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df['time'] = pd.to_datetime(df['time'])
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df['time_loc'] = df['time'].dt.tz_localize(None)
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df['time_diff'] = df['time'] - df['time'].iloc[0]
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df['time_diff_sec'] = df['time_diff'].dt.total_seconds()
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df['duration_hms'] = df['time_diff'].apply(lambda td: str(td).split('.')[0])
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# Cumulative distance (km)
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distances = [0]
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for i in range(1, len(df)):
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d = haversine(df.loc[i-1, 'lon'], df.loc[i-1, 'lat'], df.loc[i, 'lon'], df.loc[i, 'lat'])
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distances.append(distances[-1] + d)
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df['cum_dist_km'] = distances
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# Elevation handling
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if 'elev' in df.columns:
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df['elev'] = df['elev'].bfill()
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df['delta_elev'] = df['elev'].diff().fillna(0)
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df['rel_elev'] = df['elev'] - df['elev'].iloc[0]
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else:
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# Fallback wenn keine Elevation vorhanden
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df['elev'] = 0
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df['delta_elev'] = 0
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df['rel_elev'] = 0
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# Speed calculation
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if 'speed_ms' in df.columns:
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# Konvertiere m/s zu km/h
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df['speed_kmh'] = df['speed_ms'] * 3.6
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else:
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# Fallback: Berechne Speed aus GPS-Daten
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df['delta_t'] = df['time'].diff().dt.total_seconds()
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df['delta_d'] = df['cum_dist_km'].diff()
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df['speed_kmh'] = (df['delta_d'] / df['delta_t']) * 3600
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df['speed_kmh'] = df['speed_kmh'].replace([np.inf, -np.inf], np.nan)
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# Velocity (used in pace calculations)
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df['vel_kmps'] = np.gradient(df['cum_dist_km'], df['time_diff_sec'])
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# Smoothed speed (Gaussian rolling)
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df['speed_kmh_smooth'] = df['speed_kmh'].rolling(window=10, win_type="gaussian", center=True).mean(std=2)
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# Heart rate handling (NEU!)
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# ##############
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# UPDATE: Da NaN-Problem mit heart_rate, manuell nochmal neu einlesen und überschreiben:
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# save heart rate data into variable
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heart_rate = []
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for record in fit_file.get_messages("record"):
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# Records can contain multiple pieces of data (ex: timestamp, latitude, longitude, etc)
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for data in record:
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# Print the name and value of the data (and the units if it has any)
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if data.name == 'heart_rate':
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heart_rate.append(data.value)
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# Hier variable neu überschrieben:
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df = safe_add_column_to_dataframe(df, 'heart_rate', heart_rate)
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# ##############
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# MY DEBUG:
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#print(heart_rate)
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if 'heart_rate' in df.columns:
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df['heart_rate'] = pd.to_numeric(df['heart_rate'], errors='coerce')
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df['hr_smooth'] = df['heart_rate'].rolling(window=5, center=True).mean()
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print(f"Heart rate range: {df['heart_rate'].min():.0f} - {df['heart_rate'].max():.0f} bpm")
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else:
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print("Keine Heart Rate Daten gefunden!")
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df['heart_rate'] = np.nan
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df['hr_smooth'] = np.nan
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print(f"Verarbeitete FIT-Datei: {len(df)} Datenpunkte")
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print(f"Distanz: {df['cum_dist_km'].iloc[-1]:.2f} km")
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print(f"Dauer: {df['duration_hms'].iloc[-1]}")
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return df
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except Exception as e:
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print(f"Fehler beim Verarbeiten der FIT-Datei {file_path}: {str(e)}")
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return pd.DataFrame()
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def safe_add_column_to_dataframe(df, column_name, values):
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"""
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Fügt eine Spalte sicher zu einem DataFrame hinzu, auch wenn die Längen nicht übereinstimmen
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"""
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if df.empty:
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return df
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df_len = len(df)
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values_len = len(values) if hasattr(values, '__len__') else 0
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if values_len == df_len:
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# Perfekt - gleiche Länge
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df[column_name] = values
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elif values_len > df_len:
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# Zu viele Werte - kürze sie
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print(f"WARNUNG: {column_name} hat {values_len} Werte, DataFrame hat {df_len} Zeilen. Kürze Werte.")
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df[column_name] = values[:df_len]
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elif values_len < df_len:
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# Zu wenige Werte - fülle mit NaN auf
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print(f"WARNUNG: {column_name} hat {values_len} Werte, DataFrame hat {df_len} Zeilen. Fülle mit NaN auf.")
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extended_values = list(values) + [None] * (df_len - values_len)
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df[column_name] = extended_values
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else:
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# Keine Werte - fülle mit NaN
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print(f"WARNUNG: Keine Werte für {column_name}. Fülle mit NaN.")
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df[column_name] = [None] * df_len
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return df
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# =============================================================================
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# INFO BANNER
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# =============================================================================
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def create_info_banner(df):
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# Total distance in km
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total_distance_km = df['cum_dist_km'].iloc[-1]
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# Total time as timedelta
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total_seconds = df['time_diff_sec'].iloc[-1]
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hours, remainder = divmod(int(total_seconds), 3600)
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minutes, seconds = divmod(remainder, 60)
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formatted_total_time = f"{hours:02d}:{minutes:02d}:{seconds:02d}"
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# Average pace (min/km)
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if total_distance_km > 0:
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pace_sec_per_km = total_seconds / total_distance_km
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pace_min = int(pace_sec_per_km // 60)
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pace_sec = int(pace_sec_per_km % 60)
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formatted_pace = f"{pace_min}:{pace_sec:02d} min/km"
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else:
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formatted_pace = "N/A"
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# Build the info banner layout
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info_banner = html.Div([
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html.Div([
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html.H4("Total Distance", style={'margin-bottom': '5px'}),
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html.H2(f"{total_distance_km:.2f} km")
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], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
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html.Div([
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html.H4("Total Time", style={'margin-bottom': '5px'}),
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html.H2(formatted_total_time)
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], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
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html.Div([
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html.H4("Average Pace", style={'margin-bottom': '5px'}),
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html.H2(formatted_pace)
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], style={'width': '30%', 'display': 'inline-block', 'textAlign': 'center'}),
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], style={
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'display': 'flex',
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'justifyContent': 'space-around',
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'backgroundColor': '#1e1e1e',
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'color': 'white',
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'padding': '20px',
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'marginBottom': '5px',
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'borderRadius': '10px',
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'width': '100%',
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#'maxWidth': '1200px',
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'margin': 'auto'
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})
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return info_banner
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# =============================================================================
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# START OF THE PLOTS
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# =============================================================================
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def create_map_plot(df):
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# fig = px.line_map(
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# df,
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# lat='lat',
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# lon='lon',
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# hover_name='time',
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# hover_data={
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# 'cum_dist_km': ':.2f',
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# 'duration_hms': True,
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# 'lat': False,
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# 'lon': False,
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# 'time': False
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# },
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# labels={
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# 'cum_dist_km': 'Distance (km) ',
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# 'duration_hms': 'Elapsed Time '
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# },
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# zoom=13,
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# height=800
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# )
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fig = px.line_map(
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df,
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lat='lat',
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lon='lon',
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zoom=13,
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height=800
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)
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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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"Speed (Km/h): %{customdata[1]:.2f}<br>" +
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"Heart Rate (bpm): %{customdata[2]}<br>" +
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"Elapsed Time: %{customdata[3]}<extra></extra>"
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),
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#customdata=df[['time', 'cum_dist_km', 'duration_hms']]
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customdata=df[['cum_dist_km', 'speed_kmh', 'heart_rate', 'duration_hms']]
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)
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# Define map style and the line ontop
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fig.update_layout(map_style="open-street-map")
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# The built-in plotly.js styles are: carto-darkmatter, carto-positron, open-street-map, stamen-terrain, stamen-toner, stamen-watercolor, white-bg
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# The built-in Mapbox styles are: basic, streets, outdoors, light, dark, satellite, satellite-streets
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fig.update_traces(line=dict(color="#f54269", width=3))
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# Start / Stop marker
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start = df.iloc[0]
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end = df.iloc[-1]
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fig.add_trace(go.Scattermap(
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lat=[start['lat']], lon=[start['lon']], mode='markers+text',
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marker=dict(size=12, color='#fca062'), text=['Start'], name='Start', textposition='bottom left'
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))
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fig.add_trace(go.Scattermap(
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lat=[end['lat']], lon=[end['lon']], mode='markers+text',
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marker=dict(size=12, color='#b9fc62'), text=['Stop'], name='Stop', textposition='bottom left'
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))
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fig.update_layout(paper_bgcolor='#1e1e1e', font=dict(color='white'))
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fig.update_layout(
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legend=dict(
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orientation='h', # horizontal layout
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||
yanchor='top',
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y=-0.01, # move legend below the map
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xanchor='center',
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x=0.5,
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font=dict(color='white')
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)
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)
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return fig
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######################
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||
# NEUE VERSION:
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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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# 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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# 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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# 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
|
||
x_smooth, y_smooth = x, y
|
||
|
||
fig = go.Figure()
|
||
|
||
# Fläche unter der Kurve (mit geglätteten Daten)
|
||
fig.add_trace(go.Scatter(
|
||
x=x_smooth, y=y_smooth,
|
||
mode='lines',
|
||
line=dict(color='#1CAF50'), # Fill between color!
|
||
fill='tozeroy',
|
||
#fillcolor='rgba(226, 241, 248)',
|
||
hoverinfo='skip',
|
||
showlegend=False
|
||
))
|
||
|
||
# Hauptlinie (geglättet)
|
||
fig.add_trace(go.Scatter(
|
||
x=x_smooth, y=y_smooth,
|
||
mode='lines',
|
||
line=dict(color='#084C20', width=2), # Line color!
|
||
name='Elevation',
|
||
showlegend=False
|
||
))
|
||
|
||
# SUPDER IDEE, ABER GEHT NICHT WEGE NEUEN smoothed POINTS! GEHT NUR BEI X
|
||
#fig.update_traces(
|
||
# hovertemplate=(
|
||
# #"Time: %{customdata[0]}<br>" +
|
||
# "Distance (km): %{customdata[0]:.2f}<br>" +
|
||
# "Elevation: %{customdata[1]}<extra></extra>" +
|
||
# "Elapsed Time: %{customdata[2]}<extra></extra>"
|
||
# ),
|
||
# customdata=df[['cum_dist_km','elev', 'time']]
|
||
#
|
||
|
||
# Layout im Dark Theme
|
||
fig.update_layout(
|
||
title=dict(text='Höhenprofil relativ zum Startwert', font=dict(size=16, color='white')),
|
||
xaxis_title='Zeit',
|
||
yaxis_title='Höhe relativ zum Start (m)',
|
||
template='plotly_dark',
|
||
paper_bgcolor='#1e1e1e',
|
||
plot_bgcolor='#111111',
|
||
font=dict(color='white'),
|
||
margin=dict(l=40, r=40, t=50, b=40),
|
||
height=400
|
||
)
|
||
|
||
return fig
|
||
|
||
|
||
|
||
def create_deviation_plot(df): #Distanz-Zeit-Diagramm
|
||
# Compute mean velocity in km/s
|
||
vel_kmps_mean = df['cum_dist_km'].iloc[-1] / df['time_diff_sec'].iloc[-1]
|
||
# Expected cumulative distance assuming constant mean velocity
|
||
df['cum_dist_km_qmean'] = df['time_diff_sec'] * vel_kmps_mean
|
||
# Deviation from mean velocity distance
|
||
df['del_dist_km_qmean'] = df['cum_dist_km'] - df['cum_dist_km_qmean']
|
||
# Plot the deviation
|
||
fig = px.line(
|
||
df,
|
||
x='time_loc',
|
||
y='del_dist_km_qmean',
|
||
labels={
|
||
'time_loc': 'Zeit',
|
||
'del_dist_km_qmean': 'Δ Strecke (km)'
|
||
},
|
||
template='plotly_dark',
|
||
)
|
||
fig.update_layout(
|
||
title=dict(text='Abweichung von integriertem Durchschnittstempo', font=dict(size=16)),
|
||
yaxis_title='Abweichung (km)',
|
||
xaxis_title='Zeit',
|
||
height=400,
|
||
paper_bgcolor='#1e1e1e',
|
||
plot_bgcolor='#111111',
|
||
font=dict(color='white', size=14),
|
||
margin=dict(l=40, r=40, t=50, b=40)
|
||
)
|
||
# Add horizontal reference line at y=0
|
||
fig.add_shape(
|
||
type='line',
|
||
x0=df['time_loc'].iloc[0],
|
||
x1=df['time_loc'].iloc[-1],
|
||
y0=0,
|
||
y1=0,
|
||
line=dict(color='gray', width=1, dash='dash'),
|
||
name='Durchschnittstempo'
|
||
)
|
||
return fig
|
||
|
||
|
||
def create_speed_plot(df):
|
||
mask = df['speed_kmh_smooth'].isna()
|
||
mean_speed_kmh = df['speed_kmh'].mean()
|
||
fig = go.Figure()
|
||
fig.add_trace(go.Scatter(
|
||
x=df['time'][~mask],
|
||
y=df['speed_kmh_smooth'][~mask],
|
||
mode='lines',
|
||
name='Geglättete Geschwindigkeit',
|
||
line=dict(color='royalblue')
|
||
))
|
||
fig.update_layout(
|
||
title=dict(text=f'Tempo über die Zeit (geglättet) - Durchschnittstempo: {mean_speed_kmh:.2f} km/h', font=dict(size=16)),
|
||
xaxis=dict(title='Zeit', tickformat='%H:%M', type='date'),
|
||
yaxis=dict(title='Geschwindigkeit (km/h)', rangemode='tozero'),
|
||
template='plotly_dark',
|
||
paper_bgcolor='#1e1e1e',
|
||
plot_bgcolor='#111111',
|
||
font=dict(color='white'),
|
||
margin=dict(l=40, r=40, t=40, b=40)
|
||
)
|
||
# Add horizontal reference line at y=mean_speed_kmh
|
||
fig.add_shape(
|
||
type='line',
|
||
x0=df['time_loc'].iloc[0],
|
||
x1=df['time_loc'].iloc[-1],
|
||
y0=mean_speed_kmh,
|
||
y1=mean_speed_kmh,
|
||
line=dict(color='gray', width=1, dash='dash'),
|
||
name='Durchschnittstempo'
|
||
)
|
||
return fig
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
# heart_rate Plot NEW !!!
|
||
def create_heart_rate_plot(df):
|
||
# Maske für gültige Heart Rate Daten
|
||
mask = df['hr_smooth'].isna()
|
||
|
||
# Durchschnittliche Heart Rate berechnen (nur gültige Werte)
|
||
valid_hr = df['heart_rate'].dropna()
|
||
if len(valid_hr) > 0:
|
||
mean_hr = valid_hr.mean()
|
||
min_hr = valid_hr.min()
|
||
max_hr = valid_hr.max()
|
||
else:
|
||
mean_hr = 0
|
||
min_hr = 0
|
||
max_hr = 0
|
||
|
||
fig = go.Figure()
|
||
|
||
# Heart Rate Linie (geglättet)
|
||
fig.add_trace(go.Scatter(
|
||
x=df['time'][~mask],
|
||
y=df['hr_smooth'][~mask],
|
||
mode='lines',
|
||
#name='Geglättete Herzfrequenz',
|
||
line=dict(color='#E43D70', width=2),
|
||
showlegend=False,
|
||
hovertemplate=(
|
||
"Zeit: %{x}<br>" +
|
||
"Herzfrequenz: %{y:.0f} bpm<br>" +
|
||
"<extra></extra>"
|
||
)
|
||
))
|
||
|
||
# # Optional: Raw Heart Rate als dünnere, transparente Linie
|
||
# if not df['heart_rate'].isna().all():
|
||
# fig.add_trace(go.Scatter(
|
||
# x=df['time'],
|
||
# y=df['heart_rate'],
|
||
# mode='lines',
|
||
# name='Raw Herzfrequenz',
|
||
# line=dict(color='#E43D70', width=1, dash='dot'),
|
||
# opacity=0.3,
|
||
# showlegend=False,
|
||
# hoverinfo='skip'
|
||
# ))
|
||
|
||
# Durchschnittslinie
|
||
if mean_hr > 0:
|
||
fig.add_shape(
|
||
type='line',
|
||
x0=df['time_loc'].iloc[0],
|
||
x1=df['time_loc'].iloc[-1],
|
||
y0=mean_hr,
|
||
y1=mean_hr,
|
||
line=dict(color='gray', width=1, dash='dash'),
|
||
)
|
||
|
||
# Annotation für Durchschnittswert
|
||
fig.add_annotation(
|
||
x=df['time_loc'].iloc[int(len(df) * 0.8)], # Bei 80% der Zeit
|
||
y=mean_hr,
|
||
text=f"Ø {mean_hr:.0f} bpm",
|
||
showarrow=True,
|
||
arrowhead=2,
|
||
arrowcolor="gray",
|
||
bgcolor="rgba(128,128,128,0.1)",
|
||
bordercolor="gray",
|
||
font=dict(color="white", size=10)
|
||
)
|
||
|
||
# Heart Rate Zonen (optional)
|
||
if mean_hr > 0:
|
||
# Geschätzte maximale Herzfrequenz (Beispiel: 200 bpm)
|
||
# Heart Rate Zonen (optional)
|
||
# Geschätzte maximale Herzfrequenz
|
||
max_hr_estimated = 200 # oder z. B. 220 - alter
|
||
|
||
# Definiere feste HR-Zonen in BPM
|
||
zones = [
|
||
{"name": "Zone 1", "lower": 0, "upper": 124, "color": "#F4A4A3"},
|
||
{"name": "Zone 2", "lower": 124, "upper": 154, "color": "#EF7476"},
|
||
{"name": "Zone 3", "lower": 154, "upper": 169, "color": "#EA4748"},
|
||
{"name": "Zone 4", "lower": 169, "upper": 184, "color": "#E02628"},
|
||
{"name": "Zone 5", "lower": 184, "upper": max_hr_estimated, "color": "#B71316"},
|
||
]
|
||
|
||
# Zeichne Zonen als Hintergrund (horizontale Rechtecke)
|
||
for zone in zones:
|
||
fig.add_hrect(
|
||
y0=zone["lower"], y1=zone["upper"],
|
||
fillcolor=zone["color"],
|
||
opacity=0.1,
|
||
line_width=0,
|
||
annotation_text=zone["name"], # optional: Name der Zone einblenden
|
||
annotation_position="top left"
|
||
)
|
||
|
||
# Layout
|
||
title_text = f'Herzfrequenz über die Zeit (geglättete)'
|
||
if mean_hr > 0:
|
||
title_text += f' - Ø {mean_hr:.0f} bpm (Range: {min_hr:.0f}-{max_hr:.0f})'
|
||
|
||
fig.update_layout(
|
||
title=dict(text=title_text, font=dict(size=16, color='white')),
|
||
xaxis=dict(
|
||
title='Zeit',
|
||
tickformat='%H:%M',
|
||
type='date'
|
||
),
|
||
yaxis=dict(
|
||
title='Herzfrequenz (bpm)',
|
||
rangemode='tozero'
|
||
),
|
||
template='plotly_dark',
|
||
paper_bgcolor='#1e1e1e',
|
||
plot_bgcolor='#111111',
|
||
font=dict(color='white'),
|
||
margin=dict(l=40, r=40, t=50, b=40),
|
||
height=400
|
||
)
|
||
|
||
return fig
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
def create_pace_bars_plot(df):
|
||
# Ensure time column is datetime
|
||
if not pd.api.types.is_datetime64_any_dtype(df['time']):
|
||
df['time'] = pd.to_datetime(df['time'], errors='coerce')
|
||
|
||
# Assign km segments
|
||
df['km'] = df['cum_dist_km'].astype(int)
|
||
|
||
# Time in seconds from start
|
||
df['time_sec'] = (df['time'] - df['time'].iloc[0]).dt.total_seconds()
|
||
|
||
# Step 3: Compute pace manually per km group
|
||
df['km_start'] = np.nan
|
||
df['segment_len'] = np.nan
|
||
df['pace_min_per_km'] = np.nan
|
||
|
||
for km_val, group in df.groupby('km'):
|
||
dist_start = group['cum_dist_km'].iloc[0]
|
||
dist_end = group['cum_dist_km'].iloc[-1]
|
||
segment_len = dist_end - dist_start
|
||
|
||
time_start = group['time_sec'].iloc[0]
|
||
time_end = group['time_sec'].iloc[-1]
|
||
elapsed_time_sec = time_end - time_start
|
||
|
||
if segment_len > 0:
|
||
pace_min_per_km = (elapsed_time_sec / 60) / segment_len
|
||
else:
|
||
pace_min_per_km = np.nan
|
||
|
||
df.loc[group.index, 'km_start'] = km_val
|
||
df.loc[group.index, 'segment_len'] = segment_len
|
||
df.loc[group.index, 'pace_min_per_km'] = pace_min_per_km
|
||
|
||
# Clean types
|
||
df['km_start'] = df['km_start'].astype(int)
|
||
df['segment_len'] = df['segment_len'].astype(float)
|
||
df['pace_min_per_km'] = pd.to_numeric(df['pace_min_per_km'], errors='coerce')
|
||
|
||
# Step 4: Create Plotly bar chart
|
||
fig = go.Figure()
|
||
fig.add_trace(go.Bar(
|
||
x=df['km_start'],
|
||
y=df['pace_min_per_km'],
|
||
width=df['segment_len'],
|
||
text=[f"{v:.1f} min/km" if pd.notnull(v) else "" for v in df['pace_min_per_km']],
|
||
textposition='outside',
|
||
marker_color='dodgerblue',
|
||
name='Pace pro km',
|
||
offset=0
|
||
))
|
||
|
||
|
||
|
||
|
||
# #########
|
||
# Add horizontal reference line - X-Werte für gesamte Breite
|
||
# Calculate average pace
|
||
total_distance_km = df['cum_dist_km'].iloc[-1]
|
||
total_seconds = df['time_diff_sec'].iloc[-1]
|
||
|
||
# Average pace (min/km) - KORRIGIERT
|
||
if total_distance_km > 0:
|
||
pace_sec_per_km = total_seconds / total_distance_km
|
||
pace_min_per_km = pace_sec_per_km / 60 # Konvertiere zu Minuten pro km
|
||
else:
|
||
pace_min_per_km = 0
|
||
|
||
fig.add_shape(
|
||
type='line',
|
||
x0=0, # Start bei 0
|
||
x1=total_distance_km, # Ende bei maximaler Distanz
|
||
y0=pace_min_per_km,
|
||
y1=pace_min_per_km,
|
||
line=dict(color='gray', width=1, dash='dash'),
|
||
)
|
||
|
||
## Optional: Text-Annotation für die durchschnittliche Pace
|
||
#fig.add_annotation(
|
||
# x=total_distance_km * 0.8, # Position bei 80% der Distanz
|
||
# y=pace_min_per_km,
|
||
# text=f"Ø {pace_min_per_km:.1f} min/km",
|
||
# showarrow=True,
|
||
# arrowhead=2,
|
||
# arrowcolor="gray",
|
||
# bgcolor="rgba(255,0,0,0.1)",
|
||
# bordercolor="gray",
|
||
# font=dict(color="white")
|
||
#)
|
||
|
||
|
||
|
||
|
||
fig.update_layout(
|
||
title=dict(text='Tempo (min/km) je Kilometer', font=dict(size=16)),
|
||
xaxis_title='Distanz (km)',
|
||
yaxis_title='Minuten pro km',
|
||
barmode='overlay',
|
||
bargap=0,
|
||
bargroupgap=0,
|
||
xaxis=dict(
|
||
type='linear',
|
||
range=[0, df['cum_dist_km'].iloc[-1]],
|
||
tickmode='linear',
|
||
dtick=1,
|
||
showgrid=True
|
||
),
|
||
template='plotly_dark',
|
||
height=400,
|
||
margin=dict(l=40, r=40, t=30, b=40),
|
||
plot_bgcolor='#111111',
|
||
paper_bgcolor='#1e1e1e',
|
||
font=dict(color='white')
|
||
)
|
||
return fig
|
||
|
||
|
||
|
||
|
||
|
||
# === App Setup ===
|
||
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.SLATE])
|
||
app.title = "FIT Dashboard"
|
||
|
||
app.layout = html.Div([
|
||
html.H1("Running Dashboard", style={'textAlign': 'center'}),
|
||
dcc.Store(id='stored-df'),
|
||
|
||
html.Div([
|
||
html.Label("FIT-Datei wählen:", style={'color': 'white'}),
|
||
dcc.Dropdown(
|
||
id='fit-file-dropdown',
|
||
options=list_fit_files(),
|
||
value=list_fit_files()[0]['value'], # immer gültig
|
||
clearable=False,
|
||
style={'width': '300px', 'color': 'black'}
|
||
)
|
||
], style={'padding': '20px', 'backgroundColor': '#1e1e1e'}),
|
||
|
||
html.Div(id='info-banner'),
|
||
dcc.Graph(id='fig-map'),
|
||
dcc.Graph(id='fig-elevation'),
|
||
dcc.Graph(id='fig_deviation'),
|
||
dcc.Graph(id='fig_speed'),
|
||
dcc.Graph(id='fig_hr'),
|
||
dcc.Graph(id='fig_pace_bars')
|
||
])
|
||
|
||
|
||
# === Callbacks ===
|
||
# Callback 1: Load GPX File and Store as JSON
|
||
@app.callback(
|
||
Output('stored-df', 'data'),
|
||
Input('fit-file-dropdown', 'value')
|
||
)
|
||
def load_fit_data(path):
|
||
df = process_fit(path)
|
||
return df.to_json(date_format='iso', orient='split')
|
||
|
||
# Callback 2: Update All Plots
|
||
@app.callback(
|
||
Output('info-banner', 'children'),
|
||
Output('fig-map', 'figure'),
|
||
Output('fig-elevation', 'figure'),
|
||
Output('fig_deviation', 'figure'),
|
||
Output('fig_speed', 'figure'),
|
||
Output('fig_hr', 'figure'),
|
||
Output('fig_pace_bars', 'figure'),
|
||
Input('stored-df', 'data')
|
||
)
|
||
def update_all_plots(json_data):
|
||
df = pd.read_json(io.StringIO(json_data), orient='split')
|
||
|
||
info = create_info_banner(df)
|
||
fig_map = create_map_plot(df)
|
||
fig_elev = create_elevation_plot(df)
|
||
fig_dev = create_deviation_plot(df)
|
||
fig_speed = create_speed_plot(df)
|
||
fig_hr = create_heart_rate_plot(df)
|
||
fig_pace = create_pace_bars_plot(df)
|
||
|
||
return info, fig_map, fig_elev, fig_dev, fig_speed, fig_hr, fig_pace
|
||
|
||
|
||
# === Run Server ===
|
||
if __name__ == '__main__':
|
||
app.run(debug=True, port=8051)
|
||
|
||
|
||
# NOTE:
|
||
# Zusammenhang zwischen Pace und Geschwindigkeit
|
||
# - Pace = Minuten pro Kilometer (z. B. 5:40/km)
|
||
# - Geschwindigkeit = Kilometer pro Stunde (z. B. 10.71 km/h)
|
||
#
|