Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/tests/moving.txt @ 1029:c6cf75a2ed08
reorganization of imports
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 18 Jun 2018 22:50:59 -0400 |
parents | cc5cb04b04b0 |
children | aafbc0bab925 |
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1 >>> from moving import * |
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some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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2 >>> import storage |
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3 >>> import numpy as np |
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4 |
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5 >>> Interval().empty() |
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6 True |
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7 >>> Interval(0,1).empty() |
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8 False |
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9 >>> Interval(0,1) |
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10 [0, 1] |
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11 >>> Interval(0,1).length() |
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12 1.0 |
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13 >>> Interval(23.2,24.9).length() |
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14 1.6999999999999993 |
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15 >>> Interval(10,8).length() |
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16 0.0 |
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17 |
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18 >>> TimeInterval(0,1).length() |
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19 2.0 |
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20 >>> TimeInterval(10,8).length() |
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21 0.0 |
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22 >>> TimeInterval(10,8) == TimeInterval(10,8) |
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23 True |
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24 >>> TimeInterval(10,8) == TimeInterval(8,10) |
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25 True |
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26 >>> TimeInterval(11,8) == TimeInterval(10,8) |
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27 False |
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28 |
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29 >>> [i for i in TimeInterval(9,13)] |
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30 [9, 10, 11, 12, 13] |
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31 |
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32 >>> TimeInterval(2,5).equal(TimeInterval(2,5)) |
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33 True |
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34 >>> TimeInterval(2,5).equal(TimeInterval(2,4)) |
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35 False |
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36 >>> TimeInterval(2,5).equal(TimeInterval(5,2)) |
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37 False |
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38 |
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39 >>> TimeInterval(3,6).distance(TimeInterval(4,6)) |
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40 0 |
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41 >>> TimeInterval(3,6).distance(TimeInterval(6,10)) |
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42 0 |
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43 >>> TimeInterval(3,6).distance(TimeInterval(8,10)) |
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44 2 |
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45 >>> TimeInterval(20,30).distance(TimeInterval(3,15)) |
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46 5 |
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modification for moving tests (unionIntrvals)
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47 >>> TimeInterval.unionIntervals([TimeInterval(3,6), TimeInterval(8,10),TimeInterval(11,15)]) |
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48 [3, 15] |
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49 |
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50 >>> Point(0,3) == Point(0,3) |
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51 True |
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52 >>> Point(0,3) == Point(0,3.2) |
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53 False |
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54 >>> Point(3,4)-Point(1,7) |
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55 (2.000000,-3.000000) |
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Point/CurvilinearTrajectory/Interaction utiles
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56 >>> -Point(1,2) |
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57 (-1.000000,-2.000000) |
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verified motion prediction with prototypes at constant speed (test needed)
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58 >>> Point(1,2)*0.5 |
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59 (0.500000,1.000000) |
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60 |
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61 >>> Point(3,2).norm2Squared() |
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62 13 |
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63 |
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64 >>> Point.distanceNorm2(Point(3,4),Point(1,7)) |
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65 3.605551275463989 |
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66 |
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major work on pPET and pet, issues remain for pPET computed with predicted trajectories
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67 >>> Point(3,2).inPolygon(np.array([[0,0],[1,0],[1,1],[0,1]])) |
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68 False |
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69 >>> Point(3,2).inPolygon(np.array([[0,0],[4,0],[4,3],[0,3]])) |
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70 True |
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71 |
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72 >>> predictPositionNoLimit(10, Point(0,0), Point(1,1)) # doctest:+ELLIPSIS |
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73 ((1.0...,1.0...), (10.0...,10.0...)) |
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74 |
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75 >>> segmentIntersection(Point(0,0), Point(0,1), Point(1,1), Point(2,3)) |
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76 >>> segmentIntersection(Point(0,1), Point(0,3), Point(1,0), Point(3,1)) |
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77 >>> segmentIntersection(Point(0.,0.), Point(2.,2.), Point(0.,2.), Point(2.,0.)) |
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78 (1.000000,1.000000) |
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79 >>> segmentIntersection(Point(0,0), Point(4,4), Point(0,4), Point(4,0)) |
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80 (2.000000,2.000000) |
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81 >>> segmentIntersection(Point(0,1), Point(1,2), Point(2,0), Point(3,2)) |
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82 |
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83 >>> t1 = Trajectory.fromPointList([(92.2, 102.9), (56.7, 69.6)]) |
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84 >>> t2 = Trajectory.fromPointList([(92.2, 102.9), (56.7, 69.6)]) |
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85 >>> t1 == t2 |
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86 True |
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87 >>> t3 = Trajectory.fromPointList([(92.24, 102.9), (56.7, 69.6)]) |
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88 >>> t1 == t3 |
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89 False |
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90 >>> t3 = Trajectory.fromPointList([(92.2, 102.9), (56.7, 69.6), (56.7, 69.6)]) |
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91 >>> t1 == t3 |
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92 False |
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93 |
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94 >>> left = Trajectory.fromPointList([(92.291666666666686, 102.99239033124439), (56.774193548387103, 69.688898836168306)]) |
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95 >>> middle = Trajectory.fromPointList([(87.211021505376351, 93.390778871978512), (59.032258064516128, 67.540286481647257)]) |
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96 >>> right = Trajectory.fromPointList([(118.82392473118281, 115.68263205013426), (63.172043010752688, 66.600268576544309)]) |
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97 >>> alignments = [left, middle, right] |
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98 >>> for a in alignments: a.computeCumulativeDistances() |
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99 >>> getSYfromXY(Point(73, 82), alignments) |
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100 [1, 0, (73.819977,81.106170), 18.172277808821125, 18.172277808821125, 1.2129694042343868] |
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101 >>> getSYfromXY(Point(78, 83), alignments, 0.5) |
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102 [1, 0, (77.033188,84.053889), 13.811799123113715, 13.811799123113715, -1.4301775140225983] |
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re-organized code to avoid cyclic python module dependencies
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103 |
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104 >>> Trajectory().length() |
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105 0 |
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106 >>> t1 = Trajectory([[0.5,1.5,2.5],[0.5,3.5,6.5]]) |
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107 >>> t1.length() == 3. |
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108 True |
69
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109 >>> t1[1] |
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110 (1.500000,3.500000) |
284
f2cf16ad798f
added LCSS for trajectories
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diff
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|
111 |
542
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added differentiate function for curvilinear trajectories and modified the addPosition functions
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112 >>> t1.differentiate() |
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113 (1.000000,3.000000) (1.000000,3.000000) |
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114 >>> t1.differentiate(True) |
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115 (1.000000,3.000000) (1.000000,3.000000) (1.000000,3.000000) |
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116 >>> t1 = Trajectory([[0.5,1.5,3.5],[0.5,2.5,7.5]]) |
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117 >>> t1.differentiate() |
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118 (1.000000,2.000000) (2.000000,5.000000) |
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119 |
576
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120 >>> t1.computeCumulativeDistances() |
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121 >>> t1.getDistance(0) |
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122 2.23606797749979 |
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123 >>> t1.getDistance(1) |
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124 5.385164807134504 |
577
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125 >>> t1.getDistance(2) |
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126 Index 2 beyond trajectory length 3-1 |
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127 >>> t1.getCumulativeDistance(0) |
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128 0.0 |
576
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129 >>> t1.getCumulativeDistance(1) |
577
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130 2.23606797749979 |
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131 >>> t1.getCumulativeDistance(2) |
576
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132 7.6212327846342935 |
577
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133 >>> t1.getCumulativeDistance(3) |
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134 Index 3 beyond trajectory length 3 |
576
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135 |
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136 |
369
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lcss subclass for indicators
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137 >>> from utils import LCSS |
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138 >>> lcss = LCSS(lambda x,y: Point.distanceNorm2(x,y) <= 0.1) |
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139 >>> Trajectory.lcss(t1, t1, lcss) |
284
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140 3 |
369
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141 >>> lcss = LCSS(lambda p1, p2: (p1-p2).normMax() <= 0.1) |
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142 >>> Trajectory.lcss(t1, t1, lcss) |
284
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143 3 |
345
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added simple classification based on speed
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144 |
504
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optimized direct time to collision computation and added tests
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145 >>> p1=Point(0,0) |
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146 >>> p2=Point(1,0) |
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147 >>> v1 = Point(0.1,0.1) |
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148 >>> v2 = Point(-0.1, 0.1) |
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149 >>> abs(Point.timeToCollision(p1, p2, v1, v2, 0.)-5.0) < 0.00001 |
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150 True |
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151 >>> abs(Point.timeToCollision(p1, p2, v1, v2, 0.1)-4.5) < 0.00001 |
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152 True |
531
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153 >>> p1=Point(0,1) |
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154 >>> p2=Point(1,0) |
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155 >>> v1 = Point(0,0.1) |
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156 >>> v2 = Point(0.1, 0) |
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157 >>> Point.timeToCollision(p1, p2, v1, v2, 0.) == None |
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|
158 True |
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159 >>> Point.timeToCollision(p2, p1, v2, v1, 0.) == None |
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|
160 True |
583
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161 >>> Point.midPoint(p1, p2) |
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162 (0.500000,0.500000) |
987
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163 >>> p1=Point(0.,0.) |
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164 >>> p2=Point(5.,0.) |
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165 >>> v1 = Point(2.,0.) |
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166 >>> v2 = Point(1.,0.) |
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167 >>> Point.timeToCollision(p1, p2, v1, v2, 0.) |
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|
168 5.0 |
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169 >>> Point.timeToCollision(p1, p2, v1, v2, 1.) |
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|
170 4.0 |
504
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optimized direct time to collision computation and added tests
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|
171 |
661
dc70d9e711f5
some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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172 >>> objects = storage.loadTrajectoriesFromSqlite('../samples/laurier.sqlite', 'object') |
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173 >>> len(objects) |
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174 5 |
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175 >>> objects[0].hasFeatures() |
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176 False |
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177 >>> features = storage.loadTrajectoriesFromSqlite('../samples/laurier.sqlite', 'feature') |
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178 >>> for o in objects: o.setFeatures(features) |
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179 >>> objects[0].hasFeatures() |
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180 True |
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181 |
918
3a06007a4bb7
modularized save trajectories, added slice to Trajectory, etc
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|
182 >>> o1 = MovingObject.generate(1, Point(-5.,0.), Point(1.,0.), TimeInterval(0,10)) |
3a06007a4bb7
modularized save trajectories, added slice to Trajectory, etc
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|
183 >>> o2 = MovingObject.generate(2, Point(0.,-5.), Point(0.,1.), TimeInterval(0,10)) |
631
2d1d33ae1c69
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|
184 >>> MovingObject.computePET(o1, o2, 0.1) |
887
e2452abba0e7
added option to compute PET in safety analysis script, and save in database
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|
185 (0.0, 5, 5) |
918
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|
186 >>> o2 = MovingObject.generate(2, Point(0.,-5.), Point(0.,1.), TimeInterval(5,15)) |
631
2d1d33ae1c69
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|
187 >>> MovingObject.computePET(o1, o2, 0.1) |
887
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added option to compute PET in safety analysis script, and save in database
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188 (5.0, 5, 10) |
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modularized save trajectories, added slice to Trajectory, etc
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|
189 >>> o2 = MovingObject.generate(2, Point(0.,-5.), Point(0.,1.), TimeInterval(15,30)) |
887
e2452abba0e7
added option to compute PET in safety analysis script, and save in database
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|
190 >>> MovingObject.computePET(o1, o2, 0.1) |
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|
191 (15.0, 5, 20) |
631
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major work on pPET and pet, issues remain for pPET computed with predicted trajectories
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|
192 |
542
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193 >>> t = CurvilinearTrajectory(S = [1., 2., 3., 5.], Y = [0.5, 0.5, 0.6, 0.7], lanes = ['1']*4) |
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194 >>> t.differentiate() # doctest:+ELLIPSIS |
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195 [1.0, 0.0, '1'] [1.0, 0.099..., '1'] [2.0, 0.099..., '1'] |
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196 >>> t.differentiate(True) # doctest:+ELLIPSIS |
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197 [1.0, 0.0, '1'] [1.0, 0.099..., '1'] [2.0, 0.099..., '1'] [2.0, 0.099..., '1'] |
582
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corrected bug for curvilinear trajectory with only one position and differentiation
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|
198 >>> t = CurvilinearTrajectory(S = [1.], Y = [0.5], lanes = ['1']) |
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|
199 >>> t.differentiate().empty() |
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corrected bug for curvilinear trajectory with only one position and differentiation
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|
200 True |
542
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added differentiate function for curvilinear trajectories and modified the addPosition functions
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531
diff
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|
201 |
1018
d7afc59f6966
work in progress
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998
diff
changeset
|
202 >>> o1 = MovingObject.generate(1, Point(1., 2.), Point(1., 1.), TimeInterval(0,10)) |
1019
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work on object concatenation
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|
203 >>> o1.features = [o1] |
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work in progress
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diff
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|
204 >>> o2 = MovingObject.generate(2, Point(14., 14.), Point(1., 0.), TimeInterval(14,20)) |
1019
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work on object concatenation
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|
205 >>> o2.features = [o2] |
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|
206 >>> o3 = MovingObject.generate(3, Point(2., 2.), Point(1., 1.), TimeInterval(2,12)) |
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|
207 >>> o3.features = [o3] |
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208 >>> o13 = MovingObject.concatenate(o1, o3, 4) |
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|
209 >>> o13.getNum() |
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|
210 4 |
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211 >>> o13.getTimeInterval() == TimeInterval(0,12) |
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212 True |
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213 >>> t=5 |
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214 >>> o13.getPositionAtInstant(t) == (o1.getPositionAtInstant(t)+o3.getPositionAtInstant(t)).divide(2) |
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215 True |
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216 >>> len(o13.getFeatures()) |
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217 2 |
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218 >>> o12 = MovingObject.concatenate(o1, o2, 5) |
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219 >>> o12.getTimeInterval() == TimeInterval(o1.getFirstInstant(), o2.getLastInstant()) |
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220 True |
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221 >>> v = o12.getVelocityAtInstant(12) |
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222 >>> v == Point(3./4, 2./4) |
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223 True |
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224 >>> o12.getPositionAtInstant(11) == o1.getPositionAtInstant(10)+v |
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225 True |
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226 >>> len(o12.getFeatures()) |
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227 3 |
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228 |
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229 >>> o1 = MovingObject.generate(1, Point(0., 2.), Point(0., 1.), TimeInterval(0,2)) |
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230 >>> o1.classifyUserTypeSpeedMotorized(0.5, np.median) |
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231 >>> userTypeNames[o1.getUserType()] |
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232 'car' |
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233 >>> o1.classifyUserTypeSpeedMotorized(1.5, np.median) |
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234 >>> userTypeNames[o1.getUserType()] |
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235 'pedestrian' |
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236 |
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237 >>> o1 = MovingObject.generate(1, Point(0.,0.), Point(1.,0.), TimeInterval(0,10)) |
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238 >>> gt1 = BBMovingObject(1, TimeInterval(0,10), MovingObject.generate(1, Point(0.2,0.6), Point(1.,0.), TimeInterval(0,10)), MovingObject.generate(2, Point(-0.2,-0.4), Point(1.,0.), TimeInterval(0,10))) |
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239 >>> gt1.computeCentroidTrajectory() |
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240 >>> computeClearMOT([gt1], [], 0.2, 0, 10) |
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241 (None, 0.0, 11, 0, 0, 11, None, None) |
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242 >>> computeClearMOT([], [o1], 0.2, 0, 10) |
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243 (None, None, 0, 0, 11, 0, None, None) |
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244 >>> computeClearMOT([gt1], [o1], 0.2, 0, 10) # doctest:+ELLIPSIS |
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245 (0.0999..., 1.0, 0, 0, 0, 11, None, None) |
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246 >>> computeClearMOT([gt1], [o1], 0.05, 0, 10) |
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247 (None, -1.0, 11, 0, 11, 11, None, None) |
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248 |
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249 >>> o1 = MovingObject(1, TimeInterval(0,3), positions = Trajectory([list(range(4)), [0.1, 0.1, 1.1, 1.1]])) |
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250 >>> o2 = MovingObject(2, TimeInterval(0,3), positions = Trajectory([list(range(4)), [0.9, 0.9, -0.1, -0.1]])) |
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251 >>> gt1 = BBMovingObject(1, TimeInterval(0,3), MovingObject(positions = Trajectory([list(range(4)), [0.]*4])), MovingObject(positions = Trajectory([list(range(4)), [0.]*4]))) |
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252 >>> gt1.computeCentroidTrajectory() |
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253 >>> gt2 = BBMovingObject(2, TimeInterval(0,3), MovingObject(positions = Trajectory([list(range(4)), [1.]*4])), MovingObject(positions = Trajectory([list(range(4)), [1.]*4]))) |
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254 >>> gt2.computeCentroidTrajectory() |
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255 >>> computeClearMOT([gt1, gt2], [o1, o2], 0.2, 0, 3) # doctest:+ELLIPSIS |
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256 (0.1..., 0.75, 0, 2, 0, 8, None, None) |
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257 >>> computeClearMOT([gt2, gt1], [o2, o1], 0.2, 0, 3) # doctest:+ELLIPSIS |
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258 (0.1..., 0.75, 0, 2, 0, 8, None, None) |
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259 >>> computeClearMOT([gt1], [o1, o2], 0.2, 0, 3) |
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260 (0.1, -0.25, 0, 1, 4, 4, None, None) |
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261 >>> computeClearMOT([gt1], [o2, o1], 0.2, 0, 3) # symmetry |
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262 (0.1, -0.25, 0, 1, 4, 4, None, None) |
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263 >>> computeClearMOT([gt1, gt2], [o1], 0.2, 0, 3) # doctest:+ELLIPSIS |
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264 (0.100..., 0.375, 4, 1, 0, 8, None, None) |
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265 >>> computeClearMOT([gt2, gt1], [o1], 0.2, 0, 3) # doctest:+ELLIPSIS |
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266 (0.100..., 0.375, 4, 1, 0, 8, None, None) |
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267 >>> computeClearMOT([gt1, gt2], [o1, o2], 0.08, 0, 3) |
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268 (None, -1.0, 8, 0, 8, 8, None, None) |