gpd_plot

mport datetime as dt
import json
import os
import contextily as cx
import folium
import geopandas as gp
import mapclassify
import matplotlib.pyplot as plt
import pandas as pd
import shapely.geometry as sg
from matplotlib import cm
from matplotlib.lines import Line2D

json_folder = "Historique des positions/"


def extract_activity(record):
    activity_type = record['activitySegment']['activityType']
    confidence = record['activitySegment']['confidence']
    start_time = dt.datetime.fromisoformat(record['activitySegment']['duration']['startTimestamp'].rstrip('Z'))
    end_time = dt.datetime.fromisoformat(record['activitySegment']['duration']['endTimestamp'].rstrip('Z'))
    distance = record['activitySegment']['distance']
    start_lat = record['activitySegment']['startLocation']['latitudeE7'] / 10 ** 7
    start_lon = record['activitySegment']['startLocation']['longitudeE7'] / 10 ** 7
    end_lat = record['activitySegment']['endLocation']['latitudeE7'] / 10 ** 7
    end_lon = record['activitySegment']['endLocation']['longitudeE7'] / 10 ** 7
    activities = record['activitySegment']['activities']
    return activity_type, confidence, start_time, end_time, distance, start_lat, start_lon, end_lat, end_lon


def extract_placevisite(record):
    location_name = record['placeVisit']['location']['name'] if 'name' in record['placeVisit']['location'] else ''
    start_time = dt.datetime.fromisoformat(record['placeVisit']['duration']['startTimestamp'].rstrip('Z'))
    end_time = dt.datetime.fromisoformat(record['placeVisit']['duration']['endTimestamp'].rstrip('Z'))
    start_lat = record['placeVisit']['location']['latitudeE7'] / 10 ** 7 if 'latitudeE7' in record['placeVisit'][
        'location'] else 0
    start_lon = record['placeVisit']['location']['longitudeE7'] / 10 ** 7 if 'longitudeE7' in record['placeVisit'][
        'location'] else 0
    address = record['placeVisit']['location']['address'] if 'address' in record['placeVisit']['location'] else ''
    return location_name, address, start_time, end_time, start_lat, start_lon


def parse_json_activities(json_file):
    with open(json_file, 'r') as f:
        data = json.load(f)
    records = data['timelineObjects']
    activities = []
    for record in records:
        if 'activitySegment' in record:
            activity = extract_activity(record)
            activities.append(activity)

    df_activity = pd.DataFrame(activities, columns=['activity_type', 'confidence', 'start_time', 'end_time', 'distance',
                                                    'start_lat', 'start_lon', 'end_lat', 'end_lon'])
    return df_activity


def parse_json_visites(folder):
    place_visites = []
    points = []
    for root, dirs, files in os.walk(folder):
        for file in files:
            if file.endswith('.json') and file[0] == '2' and file[4] == '_':
                with open(os.path.join(root, file), 'r') as f:
                    data = json.load(f)
                records = data['timelineObjects']
                for record in records:
                    if 'placeVisit' in record:
                        visites = extract_placevisite(record)
                        place_visites.append(visites)

    dff = pd.DataFrame(place_visites,
                       columns=['location_name', 'address', 'start_time', 'end_time', 'start_lat', 'start_lon'])
    points = [sg.Point(lon, lat) for lat, lon in zip(dff['start_lat'], dff['start_lon'])]
    df = gp.GeoDataFrame(place_visites,
                         columns=['location_name', 'address', 'start_time', 'end_time', 'start_lat', 'start_lon'],
                         geometry=points, crs=4326)

    return df


gdf = parse_json_visites(json_folder)
world = gp.read_file('./TM_WORLD_BORDERS-0.3.shp', allow_override=True).to_crs('EPSG:4326')
gdf.set_crs(epsg=4326, allow_override=True)


def compute_legend(df, ax, cmap, steps):
    vals = [x / (steps - 1) for x in range(steps)]
    custom_lines = [Line2D([0], [0], color=cmap(step), lw=4) for step in vals]
    labels = [df.start_time.quantile(step).strftime("%Y-%m-%d") for step in vals]
    ax.legend(custom_lines, labels, loc="lower right")


def seconds(timestamp):
    return timestamp.to_pydatetime().timestamp()


def calculate_pal(df2, cmap):
    min_ts = seconds(df2.start_time.min())
    max_ts = seconds(df2.start_time.max())
    return df2.start_time.apply(lambda ts: cmap((seconds(ts) - min_ts) / (max_ts - min_ts))).tolist()


def draw_map(df, box):
    box_only = df[df.geometry.within(box)]
    minx, miny, maxx, maxy = box.bounds
    base = world.plot(color='white', edgecolor='silver', figsize=(16, 12))
    base.set_xlim(minx, maxx)
    base.set_ylim(miny, maxy)
    cmap = cm.get_cmap('viridis')
    pal = calculate_pal(box_only, cmap)
    ax = box_only.plot(ax=None, marker='o', color=pal, markersize=8)
    cx.add_basemap(ax, source=cx.providers.OpenTopoMap, zoom=8)
    compute_legend(box_only, ax, cmap, 5)
    plt.show()


draw_map(gdf, sg.box(1.2, 43.75, 1.3, 43.80))