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bug corrected generating last velocity twice and saving it (not saved, duplicated at loading time
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 30 Mar 2026 15:31:34 -0400
parents 76f5693b530c
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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1 >>> from trafficintelligence.utils import *
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2 >>> from trafficintelligence.moving import Point
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3 >>> from numpy import array, arange
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4
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5 >>> upperCaseFirstLetter('mmmm... donuts')
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6 'Mmmm... Donuts'
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7 >>> s = upperCaseFirstLetter('much ado about nothing')
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8 >>> s == 'Much Ado About Nothing'
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9 True
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10 >>> upperCaseFirstLetter(s) == s
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11 True
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12
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13 >>> computeChi2([],[])
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14 0
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15 >>> computeChi2(list(range(1,10)),list(range(1,10)))
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16 0.0
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17 >>> computeChi2(list(range(1,9)),list(range(1,10)))
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18 0.0
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19
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20 >>> ceilDecimals(1.23, 0)
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21 2.0
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22 >>> ceilDecimals(1.23, 1)
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23 1.3
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24
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25 >>> inBetween(1,2,1.5)
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26 True
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27 >>> inBetween(2.1,1,1.5)
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28 True
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29 >>> inBetween(1,2,0)
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30 False
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31
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32 >>> removeExtension('test-adfasdf.asdfa.txt')
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33 'test-adfasdf.asdfa'
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34 >>> removeExtension('test-adfasdf')
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35 'test-adfasdf'
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36
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37 >>> values = line2Ints('1 2 3 5 6')
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38 >>> values[0]
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39 1
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40 >>> values[-1]
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41 6
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42 >>> values = line2Floats('1.3 2.45 7.158e+01 5 6')
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43 >>> values[0]
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44 1.3
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45 >>> values[2] #doctest: +ELLIPSIS
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46 71.5...
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47 >>> values[-1]
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48 6.0
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49
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50 >>> filterCategoricalMovingWindow([3]*3 + [4]*4, 2)
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51 [3, 3, 3, 4, 4, 4, 4]
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52 >>> filterCategoricalMovingWindow([3]*6 + [4], 2)
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53 [3, 3, 3, 3, 3, 3, 3]
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54 >>> 'c' in filterCategoricalMovingWindow(['a']*3 + ['c'] + ['b']*3, 2)
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55 False
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56 >>> filterCategoricalMovingWindow([3], 2)
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57 [3]
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58
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59 >>> filterMovingWindow(arange(10), 3)
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60 array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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61 >>> filterMovingWindow(list(range(10)), 3)
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62 array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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63 >>> filterMovingWindow(arange(10.), 3)
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64 array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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65 >>> filterMovingWindow(arange(10.), 2)
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66 array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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67
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68 >>> stepPlot([3, 5, 7, 8], 1, 10, 0)
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69 ([1, 3, 3, 5, 5, 7, 7, 8, 8, 10], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
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70
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71 >>> mostCommon(['a','b','c','b'])
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72 'b'
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73 >>> mostCommon(['a','b','c','b', 'c'])
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74 'b'
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75 >>> mostCommon(list(range(10))+[1])
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76 1
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77 >>> mostCommon([(1,2), (2,3), (1,2)])
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78 (1, 2)
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79
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80 >>> res = sortByLength([list(range(3)), list(range(4)), list(range(1))])
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81 >>> [len(r) for r in res]
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82 [1, 3, 4]
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83 >>> res = sortByLength([list(range(3)), list(range(4)), list(range(1)), list(range(5))], reverse = True)
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84 >>> [len(r) for r in res]
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85 [5, 4, 3, 1]
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86
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87 >>> lcss = LCSS(similarityFunc = lambda x,y: abs(x-y) <= 0.1)
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88 >>> lcss.compute(list(range(5)), list(range(5)))
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89 np.int64(5)
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90 >>> lcss.compute(list(range(1,5)), list(range(5)))
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91 np.int64(4)
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92 >>> lcss.compute(list(range(5,10)), list(range(5)))
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93 np.int64(0)
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94 >>> lcss.compute(list(range(5)), list(range(10)))
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95 np.int64(5)
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96 >>> lcss.similarityFunc = lambda x,y: x == y
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97 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'])
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98 np.int64(3)
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99 >>> lcss.computeNormalized(['a','b','c'], ['a','b','c', 'd']) #doctest: +ELLIPSIS
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100 1.0
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101 >>> lcss.computeNormalized(['a','b','c','x'], ['a','b','c', 'd']) #doctest: +ELLIPSIS
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102 0.75
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103 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'])
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104 np.int64(3)
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105 >>> lcss.compute(['a','x','b','c'], ['a','b','c','d','x'])
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106 np.int64(3)
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107 >>> lcss.compute(['a','b','c','x','d'], ['a','b','c','d','x'])
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108 np.int64(4)
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109 >>> lcss.delta = 1
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110 >>> lcss.compute(['a','b','c'], ['a','b','x','x','c'])
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111 np.int64(2)
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112
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113 >>> lcss.delta = float('inf')
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114 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'], computeSubSequence = True)
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115 np.int64(3)
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116 >>> lcss.subSequenceIndices
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117 [(0, 0), (1, 1), (2, 2)]
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118 >>> lcss.compute(['a','b','c'], ['x','a','b','c'], computeSubSequence = True)
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119 np.int64(3)
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120 >>> lcss.subSequenceIndices
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121 [(0, 1), (1, 2), (2, 3)]
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122 >>> lcss.compute(['a','g','b','c'], ['a','b','c', 'd'], computeSubSequence = True)
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123 np.int64(3)
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124 >>> lcss.subSequenceIndices
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125 [(0, 0), (2, 1), (3, 2)]
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126
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127 >>> alignedLcss = LCSS(lambda x,y:(abs(x-y) <= 0.1), delta = 2, aligned = True)
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128 >>> alignedLcss.compute(list(range(5)), list(range(5)))
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129 np.int64(5)
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130 >>> alignedLcss.compute(list(range(1,5)), list(range(5)))
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131 np.int64(4)
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132
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133 >>> alignedLcss.compute(list(range(5,10)), list(range(10)))
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134 np.int64(5)
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135
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136 >>> lcss.delta = 2
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137 >>> lcss.compute(list(range(5,10)), list(range(10)))
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138 np.int64(0)
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139 >>> alignedLcss.delta = 6
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140 >>> alignedLcss.compute(list(range(5)), list(range(5)))
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141 np.int64(5)
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142 >>> alignedLcss.compute(list(range(5)), list(range(6)))
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143 np.int64(5)
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144 >>> lcss.delta = 10
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145 >>> alignedLcss.compute(list(range(1,7)), list(range(6)))
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146 np.int64(5)
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147 >>> lcss = LCSS(lambda x,y: x == y, delta = 2, aligned = True)
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148 >>> lcss.compute(list(range(20)), [2,4,6,7,8,9,11,13], True)
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149 np.int64(8)
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150 >>> lcss.subSequenceIndices
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151 [(2, 0), (4, 1), (6, 2), (7, 3), (8, 4), (9, 5), (11, 6), (13, 7)]
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152
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153 >>> lcss = LCSS(metric = 'cityblock', epsilon = 0.1)
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154 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(5)])
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155 np.int64(5)
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156 >>> lcss.compute([[i] for i in range(1,5)], [[i] for i in range(5)])
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157 np.int64(4)
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158 >>> lcss.compute([[i] for i in range(5,10)], [[i] for i in range(5)])
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159 np.int64(0)
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160 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(10)])
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161 np.int64(5)
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162
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163