main.py

import pandas as pd

def load_data(filepath):
    """CSV oder SQL-Daten einlesen."""
    # Beispiel: pd.read_csv(filepath)
    # Hier als Platzhalter:
    df = pd.read_csv(filepath)
    return df

def process_timeline(df):
    """Verarbeitet die Timeline-Daten, gruppiert nach 'ident' und 'timestamp'."""
    # Nach ident und timestamp sortieren
    df = df.sort_values(['ident', 'timestamp'])

    # Gruppiere nach ident, position_latitude, position_longitude
    grouped = df.groupby(['ident', 'position_latitude', 'position_longitude', 'ble_receiver_ident'])
    results = []

    for (ident, lat, lon, receiver), group in grouped:
        start_time = group['timestamp'].min()
        end_time = group['timestamp'].max()
        max_rssi = group['ble_beacon_rssi'].max()
        min_rssi = group['ble_beacon_rssi'].min()
        mean_rssi = group['ble_beacon_rssi'].mean()
        results.append({
            'ident': ident,
            'from': start_time,
            'to': end_time,
            'position_latitude': lat,
            'position_longitude': lon,
            'ble_receiver_ident': receiver,
            'max_rssi': max_rssi,
            'min_rssi': min_rssi,
            'mean_rssi': mean_rssi
        })
    return pd.DataFrame(results)

def show_timeline_on_map(df, output_html="timeline_map.html"):
    import folium
    import itertools

    timeline = process_timeline(df)
    center_lat = df['position_latitude'].mean()
    center_lon = df['position_longitude'].mean()
    m = folium.Map(location=[center_lat, center_lon], zoom_start=15)

    color_list = [
        "red", "blue", "green", "orange", "purple", "darkred", "lightred",
        "beige", "darkblue", "darkgreen", "cadetblue", "darkpurple", "white",
        "pink", "lightblue", "lightgreen", "gray", "black", "lightgray"
    ]
    color_cycle = itertools.cycle(color_list)
    ident_colors = {}

    for ident, group in df.groupby('ident'):
        color = next(color_cycle)
        ident_colors[ident] = color
        group = group.sort_values('timestamp')
        coords = list(zip(group['position_latitude'], group['position_longitude']))

        layer_name = f'<span style="color:{color};">&#9679;</span> {ident}'
        layer = folium.FeatureGroup(name=layer_name, show=False)

        if len(coords) > 1:
            folium.PolyLine(coords, color=color, weight=2.5, opacity=0.7, tooltip=ident).add_to(layer)

        # Marker für jede Koordinate mit zusammengefasstem Zeitraum, RSSI-Statistiken und receiver_ident
        coord_groups = timeline[timeline['ident'] == ident].groupby(['position_latitude', 'position_longitude', 'ble_receiver_ident'])
        for (lat, lon, receiver), coord_group in coord_groups:
            row = coord_group.iloc[0]
            popup_html = (
                f"<b>{ident}</b><br>"
                f"<b>Koordinate:</b> {lat}, {lon}<br>"
                f"<b>Receiver:</b> {receiver}<br><br>"
                f"<b>Zeitraum:</b> {row['from']} bis {row['to']}<br>"
                f"max_rssi: {row['max_rssi']}<br>"
                f"min_rssi: {row['min_rssi']}<br>"
                f"mean_rssi: {row['mean_rssi']:.2f}<br>"
            )
            folium.Marker(
                [lat, lon],
                icon=folium.Icon(color=color),
                popup=folium.Popup(popup_html, max_width=300)
            ).add_to(layer)

        layer.add_to(m)

    folium.LayerControl(collapsed=False).add_to(m)
    m.save(output_html)
    print("HTML Datei wurde generiert.")

def main():
    """Hauptfunktion zum Ausführen des Skripts."""
    # Datei einlesen
    df = load_data('data.csv')
    # Timeline-Berechnung und Ausgabe im Terminal entfernen
    # print(timeline)
    # Karte anzeigen
    show_timeline_on_map(df)

if __name__ == "__main__":
    main()