def analyze_drive(drive_name, drive_data):
print("Processing " + drive_name + " With Columns: ")
print(list(drive_data))
n_sec = 1
drive_data = drive_data.iloc[::n_sec, :]
route = Route(latitudes=drive_data["Latitude"],
longitudes=drive_data["Longitude"])
min_dist = 0.0001
indices = route.not_too_close_points_indices(min_dist=min_dist)
drive_data = drive_data.iloc[indices, :]
route = Route(latitudes=drive_data["Latitude"],
longitudes=drive_data["Longitude"])
route_sector = route.get_sector()
way_set = WaySet.download_all_ways(route_sector,
tram_only=True,
timestamp="2012-09-14T15:00:00")
snapped_route = route.snap_points(way_set)
latitudes, longitudes = Route.get_lat_lon_from_points(snapped_route.points)
drive_data.insert(0, "Snapped_Lat", latitudes)
drive_data.insert(0, "Snapped_Lon", longitudes)
drive_data.insert(0, "Heading", snapped_route.headings) # Check length
drive_data.to_csv("Results/%s.csv" % drive_name)
wps = [{
"Time": row["Time"],
"Lat": row["Snapped_Lat"],
"Lon": row["Snapped_Lon"],
"Heading": row["Heading"],
"Value": row["1MinMean"]
} for index, row in drive_data.iterrows()]
with open("Results/%s.json" % drive_name, 'w') as outfile:
json.dump(wps, outfile, indent=4, sort_keys=True)
figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
route.plot(style='or')
snapped_route.plot()
way_set.plot()
#plt.show()
plt.savefig("Results/" + drive_name + ".svg")
plt.clf()
hm_image = generate_heatmap(drive_data["Longitude"],
drive_data["Latitude"],
drive_data["1MinMean"],
img_size=(200, 200))
hm_image = np.flip(hm_image.T, axis=0)
matplotlib.image.imsave("Results/" + drive_name + "_HeatMap.png",
hm_image,
cmap='hot')
figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
plt.plot(np.array(drive_data["1MinMean"]))
plt.savefig("Results/" + drive_name + "_DataProfile.png")
plt.clf()
