Mercurial > hg > nsaunier > traffic-intelligence

comparison trafficintelligence/tests/prediction.txt @ 1028:cc5cb04b04b0

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major update using the trafficintelligence package name and install through pip
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 15 Jun 2018 11:19:10 -0400
parents python/tests/prediction.txt@933670761a57
children aafbc0bab925
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1027:6129296848d3 1028:cc5cb04b04b0
1 >>> from prediction import *
2 >>> import moving, storage, utils
3 >>> from numpy import absolute, array
4
5 >>> et = PredictedTrajectoryConstant(moving.Point(0,0), moving.Point(1,0))
6 >>> et.predictPosition(4) # doctest:+ELLIPSIS
7 (4.0...,0.0...)
8 >>> et.predictPosition(1) # doctest:+ELLIPSIS
9 (1.0...,0.0...)
10
11 >>> et = PredictedTrajectoryConstant(moving.Point(0,0), moving.Point(1,0), moving.NormAngle(0.1,0), maxSpeed = 2)
12 >>> et.predictPosition(10) # doctest:+ELLIPSIS
13 (15.5...,0.0...)
14 >>> et.predictPosition(11) # doctest:+ELLIPSIS
15 (17.5...,0.0...)
16 >>> et.predictPosition(12) # doctest:+ELLIPSIS
17 (19.5...,0.0...)
18
19 >>> import random
20 >>> acceleration = lambda: random.uniform(-0.5,0.5)
21 >>> steering = lambda: random.uniform(-0.1,0.1)
22 >>> et = PredictedTrajectoryRandomControl(moving.Point(0,0),moving.Point(1,1), acceleration, steering, maxSpeed = 2)
23 >>> p = et.predictPosition(500)
24 >>> from numpy import max
25 >>> max(et.getPredictedSpeeds()) <= 2.
26 True
27
28 >>> p = moving.Point(3,4)
29 >>> sp = SafetyPoint(p, 0.1, 0)
30 >>> print(sp)
31 3 4 0.1 0
32
33 >>> et1 = PredictedTrajectoryConstant(moving.Point(-5.,0.), moving.Point(1.,0.))
34 >>> et2 = PredictedTrajectoryConstant(moving.Point(0.,-5.), moving.Point(0.,1.))
35 >>> collision, t, cp1, cp2 = computeCollisionTime(et1, et2, 0.1, 10)
36 >>> collision
37 True
38 >>> t
39 5
40 >>> collision, t, cp1, cp2 = computeCollisionTime(et1, et2, 0.1, 5)
41 >>> collision
42 True
43 >>> t
44 5
45 >>> collision, t, cp1, cp2 = computeCollisionTime(et1, et2, 0.1, 4)
46 >>> collision
47 False
48
49 >>> proto = storage.loadTrajectoriesFromSqlite('../samples/laurier.sqlite', 'feature', [1204])[0]
50 >>> proto.getPositions().computeCumulativeDistances()
51 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.5, 0.5), proto.getVelocityAt(10)*0.9, proto, True)
52 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.9) < 1e-5
53 True
54 >>> for t in range(int(proto.length())): x=et.predictPosition(t)
55 >>> traj = et.getPredictedTrajectory()
56 >>> traj.computeCumulativeDistances()
57 >>> absolute(array(traj.distances).mean() - et.initialSpeed < 1e-3)
58 True
59
60 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(10)+moving.Point(0.6, 0.6), proto.getVelocityAt(10)*0.7, proto, False)
61 >>> absolute(et.initialSpeed - proto.getVelocityAt(10).norm2()*0.7) < 1e-5
62 True
63 >>> proto = moving.MovingObject.generate(1, moving.Point(-5.,0.), moving.Point(1.,0.), moving.TimeInterval(0,10))
64 >>> et = PredictedTrajectoryPrototype(proto.getPositionAt(0)+moving.Point(0., 1.), proto.getVelocityAt(0)*0.5, proto, False)
65 >>> for t in range(int(proto.length()/0.5)): x=et.predictPosition(t)
66 >>> et.predictPosition(10) # doctest:+ELLIPSIS
67 (0.0...,1.0...)
68 >>> et.predictPosition(12) # doctest:+ELLIPSIS
69 (1.0...,1.0...)
70
71