def smoothen(ride): # TODO: also try without smoothing
for i in range(2, len(ride)):
if util.euclidian_distance(ride[i-2], ride[i]) < max( \
util.euclidian_distance(ride[i-2], ride[i-1]),
util.euclidian_distance(ride[i-1], ride[i])):
ride[i-1] = [(ride[i-2][0] + ride[i][0]) / 2, (ride[i-2][1] + ride[i][1]) / 2]
return ride
def smoothen(ride): # TODO: also try without smoothing
for i in range(2, len(ride)):
if util.euclidian_distance(ride[i-2], ride[i]) < max( \
util.euclidian_distance(ride[i-2], ride[i-1]),
util.euclidian_distance(ride[i-1], ride[i])):
ride[i - 1] = [(ride[i - 2][0] + ride[i][0]) / 2,
(ride[i - 2][1] + ride[i][1]) / 2]
return ride
def segment_driver(driver_id):
''' this generated the segments in settings.SEGMENTS_FOLDER[1] '''
da = DataAccess()
for ride_id_minus_1, ride in enumerate(da.get_rides(driver_id)):
ride_id = ride_id_minus_1 + 1
if da.skip_segment(driver_id, ride_id):
continue
# apply the Ramer-Douglas-Peucker algorithm
ride = [p + [i] for i, p in enumerate(smoothen(ride))] # enrich with timestamp
ride = rdp(ride, epsilon=10)
lengths = [util.euclidian_distance(ride[i-1], ride[i]) for i in xrange(1, len(ride))]
times = [ride[i][2] - ride[i-1][2] for i in xrange(1, len(ride))]
angles = [util.get_angle(ride[i-2], ride[i-1], ride[i]) for i in xrange(2, len(ride))]
# bucket the values
lengths = util.bucket(np.log(lengths), 25, [2.2,8]) # [int(l) for l in lengths]
times = util.bucket(np.log(times), 20, [1,5.5]) # [int(t) for t in times]
angles = util.bucket(angles, 30, [0,180]) # [int(a) for a in angles]
# write results
da.write_ride_segments(driver_id, ride_id, lengths, times, angles)
logging.info('finished segmenting driver %s' % driver_id)
def segment_driver(driver_id):
''' this generated the segments in settings.SEGMENTS_FOLDER[1] '''
da = DataAccess()
for ride_id_minus_1, ride in enumerate(da.get_rides(driver_id)):
ride_id = ride_id_minus_1 + 1
if da.skip_segment(driver_id, ride_id):
continue
# apply the Ramer-Douglas-Peucker algorithm
ride = [p + [i]
for i, p in enumerate(smoothen(ride))] # enrich with timestamp
ride = rdp(ride, epsilon=10)
lengths = [
util.euclidian_distance(ride[i - 1], ride[i])
for i in xrange(1, len(ride))
]
times = [ride[i][2] - ride[i - 1][2] for i in xrange(1, len(ride))]
angles = [
util.get_angle(ride[i - 2], ride[i - 1], ride[i])
for i in xrange(2, len(ride))
]
# bucket the values
lengths = util.bucket(np.log(lengths), 25,
[2.2, 8]) # [int(l) for l in lengths]
times = util.bucket(np.log(times), 20,
[1, 5.5]) # [int(t) for t in times]
angles = util.bucket(angles, 30, [0, 180]) # [int(a) for a in angles]
# write results
da.write_ride_segments(driver_id, ride_id, lengths, times, angles)
logging.info('finished segmenting driver %s' % driver_id)
def segment_driver_v2(driver_id):
''' this generated the segments in settings.SEGMENTS_FOLDER[2] '''
da = DataAccess()
for ride_id_minus_1, ride in enumerate(da.get_rides(driver_id)):
ride_id = ride_id_minus_1 + 1
if da.skip_segment(driver_id, ride_id, version=2):
continue
# apply the Ramer-Douglas-Peucker algorithm
ride = [p + [i] for i, p in enumerate(ride)] # enrich with timestamp
ride = rdp(ride, epsilon=4)
lengths = [
util.euclidian_distance(ride[i - 1], ride[i])
for i in range(1, len(ride))
]
times = [ride[i][2] - ride[i - 1][2] for i in range(1, len(ride))]
angles = [
util.get_angle(ride[i - 2], ride[i - 1], ride[i])
for i in range(2, len(ride))
]
lengths = np.histogram(lengths,
bins=list(range(0, 700, 20)) + [1000000000])[0]
times = np.histogram(times,
bins=list(range(0, 60, 4)) + [1000000000])[0]
angles = np.histogram(angles, bins=list(range(0, 181, 20)))[0]
# write results
da.write_ride_segments(driver_id,
ride_id,
lengths,
times,
angles,
version=2)
logging.info('finished segmenting driver %s' % driver_id)
def segment_driver_v2(driver_id):
''' this generated the segments in settings.SEGMENTS_FOLDER[2] '''
da = DataAccess()
for ride_id_minus_1, ride in enumerate(da.get_rides(driver_id)):
ride_id = ride_id_minus_1 + 1
if da.skip_segment(driver_id, ride_id, version=2):
continue
# apply the Ramer-Douglas-Peucker algorithm
ride = [p + [i] for i, p in enumerate(ride)] # enrich with timestamp
ride = rdp(ride, epsilon=4)
lengths = [util.euclidian_distance(ride[i-1], ride[i]) for i in xrange(1, len(ride))]
times = [ride[i][2] - ride[i-1][2] for i in xrange(1, len(ride))]
angles = [util.get_angle(ride[i-2], ride[i-1], ride[i]) for i in xrange(2, len(ride))]
lengths = np.histogram(lengths, bins=range(0, 700, 20) + [1000000000])[0]
times = np.histogram(times, bins=range(0, 60, 4) + [1000000000])[0]
angles = np.histogram(angles, bins=range(0, 181, 20))[0]
# write results
da.write_ride_segments(driver_id, ride_id, lengths, times, angles, version=2)
logging.info('finished segmenting driver %s' % driver_id)
def get_ride_heading(ride, variations=False, moving_average_window=3, stops=False, version=1):
'''
I don't know exactly what this does. I was drunk when I wrote it.
'''
ride = np.array(ride)
if moving_average_window:
ride = util.movingaverage(ride, moving_average_window)
ROLL_STEP = 3
ride2 = np.array(ride)
ride1 = np.roll(ride2, ROLL_STEP, axis=0)
l = len(ride)
ride0 = np.hstack((np.ones((l,1)), np.zeros((l,1))))
ride0 = ride0[ROLL_STEP:]
ride1 = ride1[ROLL_STEP:]
ride2 = ride2[ROLL_STEP:]
a1 = ride0
a2 = ride2 - ride1
distances1 = np.linalg.norm(a1, axis=1)
distances = np.linalg.norm(a2, axis=1)
x = (a1 * a2).sum(1) / np.maximum(distances1 * distances, 0.1)
y = np.sign(a2[:,1])
np.seterr(all='ignore')
angles = np.arccos(x) * 180 / np.pi
np.seterr(all='print')
angles[y<0] = 360 - angles[y<0]
angles[distances < 2] = np.nan
is_stopped = []
angle = []
angle_window = []
WINDOW_SIZE = 2
MIN_SPEED = 2
direction = []
for i, dist in enumerate(distances):
if dist > MIN_SPEED:
angle_window.append(a2[i])
angle_window = angle_window[-WINDOW_SIZE:]
if len(angle_window) < WINDOW_SIZE:
direction.append(np.nan)
else:
d = get_angle(angle_window[-WINDOW_SIZE/2:]) - get_angle(angle_window[:WINDOW_SIZE/2])
if d > 180:
d -= 360
if d < -180:
d += 360
direction.append(d)
else:
direction.append(np.nan)
if dist < MIN_SPEED or len(angle_window) < WINDOW_SIZE:
is_stopped.append(True)
else:
is_stopped.append(False)
angle.append(get_angle(angle_window))
windows = []
current_window = []
current_window_type = 0
for i in range(len(direction)):
if np.isnan(direction[i]):
current_window = []
windows.append(0)
continue
current_window.append(direction[i])
current_window = current_window[-4:]
t = np.mean(current_window)
if current_window_type == 0:
if np.abs(t) > 3:
current_window_type = np.sign(t)
else:
if np.sign(current_window[-1]) != current_window_type:
current_window_type = 0
windows.append(current_window_type)
windows = windows[2:] + [0, 0]
sw = True
while sw:
sw = False
for i in range(1, len(windows) - 1):
if windows[i] != windows[i-1] and windows[i] != windows[i+1]:
windows[i] = windows[i+1]
sw = True
for i in range(3, len(windows)):
if windows[i-3] != windows[i-2] and \
windows[i-2] == windows[i-1] and \
windows[i-1] != windows[i]:
windows[i-2] = windows[i-3]
windows[i-1] = windows[i]
sw = True
description = []
current_window_type = 'stop' if stops else 'straight'
new_type = current_window_type
current_window_length = 0
for i in range(1, len(windows)):
if stops:
if is_stopped[i]:
if current_window_type != 'stop':
new_type = 'stop'
if windows[i] == 0 and not is_stopped[i]:
if current_window_type != 'straight':
new_type = 'straight'
else:
if windows[i] == 0 or is_stopped[i]:
if current_window_type != 'straight':
new_type = 'straight'
if windows[i] == 1:
if current_window_type != 'left':
new_type = 'left'
if windows[i] == -1:
if current_window_type != 'right':
new_type = 'right'
if new_type == current_window_type:
current_window_length += 1
else:
if current_window_length:
description.append([
current_window_type,
current_window_length,
util.euclidian_distance(ride2[i-current_window_length], ride2[i]),
-np.sum(direction[i-current_window_length : i-1])
])
current_window_type = new_type
current_window_length = 0
i = 0
while i < len(description) - 1:
if description[i][0] in ['right', 'left'] and np.abs(description[i][3]) < 15:
description[i+1][2] += description[i][2]
description.pop(i)
else:
i += 1
i = 0
while i < len(description) - 1:
if description[i][0] == description[i+1][0]:
description[i+1][1] += description[i][1]
description[i+1][2] += description[i][2]
description[i+1][3] += description[i][3]
description.pop(i)
else:
i += 1
# convert to words
DIST_TH = [0, 10, 50, 100, 250, 500, 1500]
if version == 1:
TIME_TH = [0, 2, 8, 50]
ANGLE_TH = [0, 35, 70, 110, 145]
else:
TIME_TH = [0, 8, 50]
ANGLE_TH = [0, 10, 30, 55, 80, 110, 145]
mirrored = {'straight': 'straight', 'left': 'right', 'right': 'left', 'stop': 'stop'}
words_original = []
words_mirror = []
for row in description:
if row[0] == 'stop':
v = np.digitize([row[1]], TIME_TH)[0]
elif row[0] == 'straight':
v = np.digitize([row[2]], DIST_TH)[0]
else:
v = np.digitize([np.abs(row[3])], ANGLE_TH)[0]
word = '%s_%s' % (row[0], v)
words_original.append(word)
word = '%s_%s' % (mirrored[row[0]], v)
words_mirror.append(word)
if variations:
words_inverted = list(reversed(words_mirror))
words_mirror_inverted = list(reversed(words_original))
return [words_original, words_mirror, words_inverted, words_mirror_inverted]
return words_original
def test_euclidian(self):
a = Point(2, 3)
b = Point(-2, 4)
self.assertAlmostEqual(4.12311, util.euclidian_distance(a, b), 5)
def beaconDistance(self, uuid, point):
beacon = self.findBeacon(uuid)
if not beacon:
raise Exception("Beacon not found!")
return util.euclidian_distance(beacon.point, point)