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annotate trafficintelligence/indicators.py @ 1107:a68e01bc3aa5

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Added tag v0.2.4 for changeset 799ef82caa1a
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 15 Mar 2019 16:09:56 -0400
parents b1ba6d44fcb9
children 956a66096e91
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1 #! /usr/bin/env python
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2 '''Class for indicators, temporal indicators, and safety indicators'''
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3
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4 from matplotlib.pyplot import plot, ylim
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5 from matplotlib.pylab import find
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6 from numpy import array, arange, mean, floor, mean
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7 from scipy import percentile
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8
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9 from trafficintelligence import moving
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10 from trafficintelligence.utils import LCSS as utilsLCSS
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11
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12 def multivariateName(indicatorNames):
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13 return '_'.join(indicatorNames)
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14
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15 # need for a class representing the indicators, their units, how to print them in graphs...
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16 class TemporalIndicator(object):
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17 '''Class for temporal indicators
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18 i.e. indicators that take a value at specific instants
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19
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20 values should be
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21 * a dict, for the values at specific time instants
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22 * or a list with a time interval object if continuous measurements
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24 it should have more information like name, unit'''
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25
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26 def __init__(self, name, values, timeInterval = None, maxValue = None):
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27 self.name = name
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28 if timeInterval is None:
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29 self.values = values
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30 instants = sorted(self.values.keys())
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31 if len(instants) > 0:
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32 self.timeInterval = moving.TimeInterval(instants[0], instants[-1])
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33 else:
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34 self.timeInterval = moving.TimeInterval()
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35 else:
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36 assert len(values) == timeInterval.length()
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37 self.timeInterval = timeInterval
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38 self.values = {}
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39 for i in range(int(round(self.timeInterval.length()))):
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40 self.values[self.timeInterval[i]] = values[i]
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41 self.maxValue = maxValue
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42
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43 def __len__(self):
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44 return len(self.values)
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45
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46 def empty(self):
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47 return len(self.values) == 0
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48
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49 def __getitem__(self, t):
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50 'Returns the value at time t'
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51 return self.values.get(t)
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52
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53 def getIthValue(self, i):
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54 sortedKeys = sorted(self.values.keys())
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55 if 0<=i<len(sortedKeys):
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56 return self.values[sortedKeys[i]]
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57 else:
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58 return None
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59
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60 def __iter__(self):
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61 self.iterInstantNum = 0 # index in the interval or keys of the dict
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62 return self
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63
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64 def __next__(self):
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65 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\
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66 # or (self.iterInstantNum >= self.values)
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67 raise StopIteration
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68 else:
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69 self.iterInstantNum += 1
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70 return self.getIthValue(self.iterInstantNum-1)
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71
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72 def getTimeInterval(self):
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73 return self.timeInterval
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74
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75 def getName(self):
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76 return self.name
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77
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78 def getValues(self):
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79 return [self.__getitem__(t) for t in self.timeInterval]
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80
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81 def plot(self, options = '', xfactor = 1., yfactor = 1., timeShift = 0, **kwargs):
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82 if self.getTimeInterval().length() == 1:
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83 marker = 'o'
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84 else:
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85 marker = ''
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86 time = sorted(self.values.keys())
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87 plot([(x+timeShift)/xfactor for x in time], [self.values[i]/yfactor for i in time], options+marker, **kwargs)
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88 if self.maxValue:
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89 ylim(ymax = self.maxValue)
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90
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91 @classmethod
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92 def createMultivariate(cls, indicators):
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93 '''Creates a new temporal indicator where the value at each instant is a list
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94 of the indicator values at the instant, in the same order
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95 the time interval will be the union of the time intervals of the indicators
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96 name is concatenation of the indicator names'''
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97 if len(indicators) < 2:
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98 print('Error creating multivariate indicator with only {} indicator'.format(len(indicators)))
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99 return None
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100
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101 timeInterval = moving.TimeInterval.unionIntervals([indic.getTimeInterval() for indic in indicators])
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102 values = {}
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103 for t in timeInterval:
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104 tmpValues = [indic[t] for indic in indicators]
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105 uniqueValues = set(tmpValues)
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106 if len(uniqueValues) >= 2 or uniqueValues.pop() is not None:
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107 values[t] = tmpValues
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108 return cls(multivariateName([indic.name for indic in indicators]), values)
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109
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110 # TODO static method avec class en parametre pour faire des indicateurs agrege, list par instant
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111
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112 def l1Distance(x, y): # lambda x,y:abs(x-y)
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113 if x is None or y is None:
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114 return float('inf')
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115 else:
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116 return abs(x-y)
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117
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118 def multiL1Matching(x, y, thresholds, proportionMatching=1.):
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119 n = 0
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120 nDimensions = len(x)
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121 for i in range(nDimensions):
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122 if l1Distance(x[i], y[i]) <= thresholds[i]:
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123 n += 1
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124 return n >= nDimensions*proportionMatching
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125
369
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126 class LCSS(utilsLCSS):
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127 '''Adapted LCSS class for indicators, same pattern'''
442
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128 def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min):
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129 utilsLCSS.__init__(self, similarityFunc = similarityFunc, delta = delta, aligned = aligned, lengthFunc = lengthFunc)
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130 self.minLength = minLength
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131
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132 def checkIndicator(self, indicator):
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133 return indicator is not None and len(indicator) >= self.minLength
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134
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135 def compute(self, indicator1, indicator2, computeSubSequence = False):
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136 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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137 return self._compute(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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138 else:
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139 return 0
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140
376
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141 def computeNormalized(self, indicator1, indicator2, computeSubSequence = False):
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142 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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143 return self._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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144 else:
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145 return 0.
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146
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147 def computeDistance(self, indicator1, indicator2, computeSubSequence = False):
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148 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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149 return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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150 else:
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151 return 1.
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152
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153 class SeverityIndicator(TemporalIndicator):
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154 '''Class for severity indicators
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155 field mostSevereIsMax is True
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156 if the most severe value taken by the indicator is the maximum'''
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157
286
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158 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None):
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159 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue)
244
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160 self.mostSevereIsMax = mostSevereIsMax
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161
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162 def getMostSevereValue(self, minNInstants=None, centile=None):
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163 '''if there are more than minNInstants observations,
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164 returns either the average of these maximum values
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165 or if centile is not None the n% centile from the most severe value
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166
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167 eg for TTC, centile = 15 returns the 15th centile (value such that 15% of observations are lower)'''
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168 values = list(self.values.values())
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169 if centile is not None:
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170 if self.mostSevereIsMax:
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171 c = 100-centile
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172 else:
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173 c = centile
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174 return percentile(values, c)
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175 elif minNInstants is not None and minNInstants <= self.__len__():
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176 values = sorted(values, reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
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177 return mean(values[:minNInstants])
244
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178 else:
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179 return None
244
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180
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181 def getInstantOfMostSevereValue(self):
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182 '''Returns the instant at which the indicator reaches its most severe value'''
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183 if self.mostSevereIsMax:
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184 return max(self.values, key=self.values.get)
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185 else:
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186 return min(self.values, key=self.values.get)
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187
244
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188 # functions to aggregate discretized maps of indicators
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189 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap)
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190
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191 def indicatorMap(indicatorValues, trajectory, squareSize):
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192 '''Returns a dictionary
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193 with keys for the indices of the cells (squares)
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194 in which the trajectory positions are located
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195 at which the indicator values are attached
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196
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197 ex: speeds and trajectory'''
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198
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199 assert len(indicatorValues) == trajectory.length()
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200 indicatorMap = {}
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201 for k in range(trajectory.length()):
244
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202 p = trajectory[k]
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203 i = floor(p.x/squareSize)
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204 j = floor(p.y/squareSize)
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205 if (i,j) in indicatorMap:
244
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206 indicatorMap[(i,j)].append(indicatorValues[k])
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207 else:
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208 indicatorMap[(i,j)] = [indicatorValues[k]]
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209 for k in indicatorMap:
244
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210 indicatorMap[k] = mean(indicatorMap[k])
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211 return indicatorMap
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212
691
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213 # def indicatorMapFromPolygon(value, polygon, squareSize):
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214 # '''Fills an indicator map with the value within the polygon
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215 # (array of Nx2 coordinates of the polygon vertices)'''
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216 # points = []
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217 # for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize):
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218 # for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize):
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219 # points.append([x,y])
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220 # inside = nx.points_inside_poly(array(points), polygon)
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221 # indicatorMap = {}
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222 # for i in range(len(inside)):
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223 # if inside[i]:
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224 # indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0
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225 # return indicatorMap
244
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226
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227 def indicatorMapFromAxis(value, limits, squareSize):
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228 '''axis = [xmin, xmax, ymin, ymax] '''
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229 indicatorMap = {}
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230 for x in arange(limits[0], limits[1], squareSize):
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231 for y in arange(limits[2], limits[3], squareSize):
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232 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value
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233 return indicatorMap
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234
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235 def combineIndicatorMaps(maps, squareSize, combinationFunction):
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236 '''Puts many indicator maps together
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237 (averaging the values in each cell
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238 if more than one maps has a value)'''
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239 indicatorMap = {}
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240 for m in maps:
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241 for k,v in m.items():
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242 if k in indicatorMap:
244
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243 indicatorMap[k].append(v)
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244 else:
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245 indicatorMap[k] = [v]
998
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246 for k in indicatorMap:
244
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247 indicatorMap[k] = combinationFunction(indicatorMap[k])
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248 return indicatorMap
247
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249
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250 if __name__ == "__main__":
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251 import doctest
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252 import unittest
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253 suite = doctest.DocFileSuite('tests/indicators.txt')
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254 unittest.TextTestRunner().run(suite)
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255 # #doctest.testmod()
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256 # #doctest.testfile("example.txt")