flight_data_analyzer/range.py at master · ntlhui/flight_data_analyzer · GitHub

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#!/usr/bin/env python3

import glob
import os
from arducopter_extract import arducopter_extract
from dji_extract import dji_extract
import numpy as np
import matplotlib.pyplot as plt; plt.ion()
from multiprocessing import Pool, Queue
from itertools import product
import utm

import argparse

def process_ac(logfile):
	'''Processes the provided ArduCopter logfile and outputs the maximum distance
	from home.

	param:	logfile		Path to the ArduCopter binary log file
	return:	max_rng		Maximum range from start point, or None if bad file
	'''
	if os.path.getsize(logfile) == 0:
		return
	log = arducopter_extract.ArduLog(logfile)
	try:
		positions_unfiltered = log.extract_6dof3()[:,(5, 6, 4)]
	except KeyError:
		badlogs = open('badlogs.txt', 'a')
		badlogs.write('%s\n' % (logfile))
		badlogs.close()
		return
	positions = []
	for i in range(len(positions_unfiltered)):
		if positions_unfiltered[i,0] != 0:
			utm_coord = utm.from_latlon(positions_unfiltered[i,0], positions_unfiltered[i,1])
			positions.append([utm_coord[0], utm_coord[1], positions_unfiltered[i, 2]])
	if len(positions) == 0:
		return
	positions = np.array(positions)
	distances = np.zeros(len(positions))
	for i in range(len(distances)):
		distances[i] = np.linalg.norm(positions[i,:] - positions[0,:])
	max_rng = np.max(distances)


	return max_rng

def process_dji(logfile):
	'''Processes the provided DJI logfile as a CVS and outputs the maximum distance
	from home.

	param:	logfile		Path to the DJI csv log file
	return:	max_rng		Maximum range from start point, or None if bad file
	'''
	if os.path.getsize(logfile) == 0:
		return

	log = dji_extract.DJILog(logfile)
	try:
		positions_unfiltered = log.extract_6dof()
	except KeyError:
		badlogs = open('badlogs.txt', 'a')
		badlogs.write('%s\n' % (logfile))
		badlogs.close()
		return
	positions = []
	for i in range(len(positions_unfiltered)):
		if positions_unfiltered[i,0] != 0:
			utm_coord = utm.from_latlon(positions_unfiltered[i,2], positions_unfiltered[i,1])
			positions.append([utm_coord[0], utm_coord[1], positions_unfiltered[i, 2]])

	if len(positions) == 0:
		return
	positions = np.array(positions)
	distances = np.zeros(len(positions))
	for i in range(len(distances)):
		distances[i] = np.linalg.norm(positions[i,:] - positions[0,:])
	distance = np.max(distances)


	return distance


if __name__ == '__main__':

	parser = argparse.ArgumentParser(description='Calculates the distribution of max range flown in the specified logs')
	parser.add_argument('--input_dir', '-i', metavar='input_dir', help='Directory containing logs', default='/root/gdrive')
	parser.add_argument('--output_file', '-o', metavar='output_file', help='Path to histogram filename', default='.outputs/range.png')

	args = parser.parse_args()

	data_dir = args.input_dir
	arducopterlogs = glob.glob(os.path.join(data_dir, "**", "*.BIN"), recursive=True) + glob.glob(os.path.join(data_dir, "**", "*.bin"), recursive=True)
	djilogs = glob.glob(os.path.join(data_dir, "**", "*.csv"), recursive=True)
	output_dir = '/tmp/'

	p = Pool(7)
	max_range_ac = p.map(process_ac, arducopterlogs)
	max_range_dji = p.map(process_dji, djilogs)

	distances = max_range_ac + max_range_dji
	distances = np.array([rng for rng in distances if rng is not None])
	np.save('data/ranges.npy', (distances))


	q_distances = np.round((distances) / 100) * 100
	fig = plt.figure()
	plt.hist(q_distances)
	plt.xlabel('Distance (m)')
	plt.ylabel('Number of Flights')
	plt.title('Distribution of Flight Distances')
	plt.savefig(args.output_file)
	plt.close(fig)

	mean = np.mean(distances)
	median = np.median(distances)
	max_d = np.max(distances)
	min_d = np.min(distances[np.nonzero(distances)])

	print('Mean distance: %.0f m' % (mean))
	print('Median distance: %.0f m' % (median))
	print('Max distance: %.0f m' % (max_d))
	print('Min distance: %.0f m' % (min_d))