personal_heatmap.py

import os
import glob

import folium
import gpxpy

import numpy as np
import pandas as pd
from geopy.geocoders import Nominatim

geolocator = Nominatim()
location = geolocator.geocode(
    "Montreal Quebec"
)  # Change this to change location centering
lat_check = float(location.raw["lat"])
lon_check = float(location.raw["lon"])

data = glob.glob("*.gpx")
fitdata = glob.glob("*.fit")

if not len(fitdata) == 0:
    print("Converting Garmin FIT files")
    os.system("python fit_to_csv.py")
    os.system("mkdir fit_files")
    os.system("mv *.fit ./fit_files")

csvdata = glob.glob("*.csv")

lat = []
lon = []

all_lat = []
all_long = []

print("Loading data")

for activity in data:
    gpx_filename = activity
    gpx_file = open(gpx_filename, "r")
    gpx = gpxpy.parse(gpx_file)

    for track in gpx.tracks:
        for segment in track.segments:
            for point in segment.points:
                lat.append(point.latitude)
                lon.append(point.longitude)

    check1 = np.any(
        np.isclose(lat, lat_check, atol=0.5)
    )  # Change the tolerance 'atol' to include a larger or smaller area around the centering point
    check2 = np.any(
        np.isclose(lon, lon_check, atol=0.5)
    )  # Change the tolerance 'atol' to include a larger or smaller area around the centering point

    if check1 and check2:
        all_lat.append(lat)
        all_long.append(lon)

    lon = []
    lat = []

for activity in csvdata:
    csv_filename = activity
    csv_file = pd.read_csv(csv_filename)

    for i in range(len(csv_file)):
        lat.append(csv_file["position_lat"][i])
        lon.append(csv_file["position_long"][i])

    check1 = np.any(
        np.isclose(lat, lat_check, atol=0.5)
    )  # Change the tolerance 'atol' to include a larger or smaller area around the centering point
    check2 = np.any(
        np.isclose(lon, lon_check, atol=0.5)
    )  # Change the tolerance 'atol' to include a larger or smaller area around the centering point

    if check1 and check2:
        all_lat.append(lat)
        all_long.append(lon)

    lon = []
    lat = []

all_lat = all_lat[0]
all_long = all_long[0]

central_long = sum(all_long) / float(len(all_long))
central_lat = sum(all_lat) / float(len(all_lat))

print("Initializing map")
m = folium.Map(
    location=[central_lat, central_long], tiles="Stamen Toner", zoom_start=14.2
)  # Recommended map styles are "Stamen Terrain", "Stamen Toner"

print("Plotting gpx data")

for activity in data:
    gpx_filename = activity
    gpx_file = open(gpx_filename, "r")
    gpx = gpxpy.parse(gpx_file)

    for track in gpx.tracks:
        for segment in track.segments:
            for point in segment.points:
                lat.append(point.latitude)
                lon.append(point.longitude)

    points = zip(lat, lon)
    points = [item for item in zip(lat, lon)]

    folium.PolyLine(points, color="red", weight=2.5, opacity=0.5).add_to(m)
    lat = []
    lon = []

print("Plotting csv data")
color = "red"
hr = []
for activity in csvdata:
    csv_filename = activity
    csv_file = pd.read_csv(csv_filename)
    for i in range(len(csv_file)):
        lat.append(csv_file["position_lat"][i])
        lon.append(csv_file["position_long"][i])
        hr.append(csv_file["heart_rate"][i])
    points = zip(lat, lon)
    points = [item for item in zip(lat, lon)]

    # color = []
    # print('heart_rate',csv_file['heart_rate'])
    # hr = hr / max(hr)
    # for value in hr:
    #    if value < 0.2:
    #        color.append("darkred")
    #    elif value >= 0.2 and value < 0.4:
    #        color.append("red")
    #    elif value >= 0.4 and value < 0.6:
    #        color.append("lightred")
    #    elif value >= 0.6 and value < 0.8:
    #        color.append("lightyellow")
    #    elif value >= 0.6:
    #        color.append("yellow")

    folium.PolyLine(points, color=color, weight=2.5, opacity=0.5).add_to(m)
    lat = []
    lon = []
    hr = []

m.save("heatmap.html")