#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
if __name__ == '__main__':
points = Vlist(heatmap=True)
for line in taxi_it('test'):
for (lat, lon) in zip(line['latitude'], line['longitude']):
points.append(Point(lat, lon))
points.save('test positions')
#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
if __name__ == '__main__':
it = taxi_it('stands')
next(it)
points = Vlist()
for (i, line) in enumerate(it):
points.append(
Point(line['stands_latitude'], line['stands_longitude'],
'Stand (%d): %s' % (i + 1, line['stands_name'])))
points.save('stands')
#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
if __name__ == '__main__':
points = Vlist(heatmap=True)
for line in taxi_it('test'):
for (lat, lon) in zip(line['latitude'], line['longitude']):
points.append(Point(lat, lon))
points.save('test positions')
#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
if __name__ == '__main__':
it = taxi_it('stands')
next(it) # Ignore the "no stand" entry
points = Vlist()
for (i, line) in enumerate(it):
points.append(Point(line['stands_latitude'], line['stands_longitude'], 'Stand (%d): %s' % (i+1, line['stands_name'])))
points.save('stands')
#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
_sample_size = 5000
if __name__ == '__main__':
points = Vlist(cluster=True)
for line in taxi_it('train'):
if len(line['latitude']) > 0:
points.append(Point(line['latitude'][-1], line['longitude'][-1]))
if len(points) >= _sample_size:
break
points.save('destinations (cluster)')
points.cluster = False
points.heatmap = True
points.save('destinations (heatmap)')
#!/usr/bin/env python
from data.hdf5 import taxi_it
from visualizer import Vlist, Point
_sample_size = 5000
if __name__ == '__main__':
points = Vlist(cluster=True)
for line in taxi_it('train'):
if len(line['latitude'])>0:
points.append(Point(line['latitude'][-1], line['longitude'][-1]))
if len(points) >= _sample_size:
break
points.save('destinations (cluster)')
points.cluster = False
points.heatmap = True
points.save('destinations (heatmap)')
