Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/indicators.py @ 1156:f7fbe624fff7
added helper functions for categorical variables
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 25 Sep 2020 11:56:59 -0400 |
parents | 6baa915dd8bd |
children | bb14f919d1cb |
rev | line source |
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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 numpy import array, arange, mean, floor, mean |
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6 from scipy import percentile |
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7 |
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8 from trafficintelligence import moving |
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9 from trafficintelligence.utils import LCSS as utilsLCSS |
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10 |
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11 def multivariateName(indicatorNames): |
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12 return '_'.join(indicatorNames) |
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13 |
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14 # need for a class representing the indicators, their units, how to print them in graphs... |
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15 class TemporalIndicator(object): |
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16 '''Class for temporal indicators |
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17 i.e. indicators that take a value at specific instants |
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18 |
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19 values should be |
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20 * a dict, for the values at specific time instants |
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21 * or a list with a time interval object if continuous measurements |
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22 |
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23 it should have more information like name, unit''' |
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24 |
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25 def __init__(self, name, values, timeInterval = None, maxValue = None): |
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26 self.name = name |
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27 if timeInterval is None: |
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28 self.values = values |
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29 instants = sorted(self.values.keys()) |
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30 if len(instants) > 0: |
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31 self.timeInterval = moving.TimeInterval(instants[0], instants[-1]) |
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32 else: |
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33 self.timeInterval = moving.TimeInterval() |
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34 else: |
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35 assert len(values) == timeInterval.length() |
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36 self.timeInterval = timeInterval |
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37 self.values = {} |
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38 for i in range(int(round(self.timeInterval.length()))): |
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39 self.values[self.timeInterval[i]] = values[i] |
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40 self.maxValue = maxValue |
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41 |
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42 def __len__(self): |
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43 return len(self.values) |
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44 |
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45 def empty(self): |
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46 return len(self.values) == 0 |
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47 |
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48 def __getitem__(self, t): |
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49 'Returns the value at time t' |
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50 return self.values.get(t) |
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51 |
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52 def getIthValue(self, i): |
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53 sortedKeys = sorted(self.values.keys()) |
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54 if 0<=i<len(sortedKeys): |
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55 return self.values[sortedKeys[i]] |
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56 else: |
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57 return None |
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58 |
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59 def __iter__(self): |
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60 self.iterInstantNum = 0 # index in the interval or keys of the dict |
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61 return self |
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62 |
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63 def __next__(self): |
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64 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\ |
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65 # or (self.iterInstantNum >= self.values) |
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66 raise StopIteration |
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67 else: |
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68 self.iterInstantNum += 1 |
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69 return self.getIthValue(self.iterInstantNum-1) |
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70 |
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71 def getTimeInterval(self): |
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72 return self.timeInterval |
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73 |
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74 def getName(self): |
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75 return self.name |
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76 |
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77 def getValues(self, withNone = True): |
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78 result = [self.__getitem__(t) for t in self.timeInterval] |
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79 if withNone: |
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80 return result |
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81 else: |
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82 return [x for x in result if x is not None] |
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83 |
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84 def getInstants(self): |
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85 return list(self.values.keys()) |
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86 |
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87 def plot(self, options = '', xfactor = 1., yfactor = 1., timeShift = 0, **kwargs): |
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88 if self.getTimeInterval().length() == 1: |
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89 marker = 'o' |
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90 else: |
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91 marker = '' |
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92 time = sorted(self.values.keys()) |
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93 plot([(x+timeShift)/xfactor for x in time], [self.values[i]/yfactor for i in time], options+marker, **kwargs) |
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94 if self.maxValue: |
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95 ylim(ymax = self.maxValue) |
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96 |
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97 @classmethod |
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98 def createMultivariate(cls, indicators): |
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99 '''Creates a new temporal indicator where the value at each instant is a list |
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100 of the indicator values at the instant, in the same order |
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101 the time interval will be the union of the time intervals of the indicators |
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102 name is concatenation of the indicator names''' |
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103 if len(indicators) < 2: |
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104 print('Error creating multivariate indicator with only {} indicator'.format(len(indicators))) |
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105 return None |
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106 |
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107 timeInterval = moving.TimeInterval.unionIntervals([indic.getTimeInterval() for indic in indicators]) |
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108 values = {} |
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109 for t in timeInterval: |
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110 tmpValues = [indic[t] for indic in indicators] |
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111 uniqueValues = set(tmpValues) |
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112 if len(uniqueValues) >= 2 or uniqueValues.pop() is not None: |
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113 values[t] = tmpValues |
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114 return cls(multivariateName([indic.name for indic in indicators]), values) |
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115 |
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116 # TODO static method avec class en parametre pour faire des indicateurs agrege, list par instant |
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117 |
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118 def l1Distance(x, y): # lambda x,y:abs(x-y) |
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119 if x is None or y is None: |
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120 return float('inf') |
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121 else: |
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122 return abs(x-y) |
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123 |
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124 def multiL1Matching(x, y, thresholds, proportionMatching=1.): |
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125 n = 0 |
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126 nDimensions = len(x) |
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127 for i in range(nDimensions): |
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128 if l1Distance(x[i], y[i]) <= thresholds[i]: |
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129 n += 1 |
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130 return n >= nDimensions*proportionMatching |
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131 |
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132 class LCSS(utilsLCSS): |
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133 '''Adapted LCSS class for indicators, same pattern''' |
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134 def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min): |
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135 utilsLCSS.__init__(self, similarityFunc = similarityFunc, delta = delta, aligned = aligned, lengthFunc = lengthFunc) |
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136 self.minLength = minLength |
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137 |
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138 def checkIndicator(self, indicator): |
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139 return indicator is not None and len(indicator) >= self.minLength |
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140 |
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141 def compute(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._compute(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 computeNormalized(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._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence) |
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150 else: |
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151 return 0. |
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152 |
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153 def computeDistance(self, indicator1, indicator2, computeSubSequence = False): |
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154 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): |
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155 return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence) |
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156 else: |
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157 return 1. |
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158 |
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159 class SeverityIndicator(TemporalIndicator): |
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160 '''Class for severity indicators |
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161 field mostSevereIsMax is True |
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162 if the most severe value taken by the indicator is the maximum''' |
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163 |
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164 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None): |
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165 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue) |
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166 self.mostSevereIsMax = mostSevereIsMax |
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167 |
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168 def getMostSevereValue(self, minNInstants=None, centile=None): |
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169 '''if there are more than minNInstants observations, |
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170 returns either the average of these maximum values |
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171 or if centile is not None the n% centile from the most severe value |
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172 |
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173 eg for TTC, centile = 15 returns the 15th centile (value such that 15% of observations are lower)''' |
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174 values = list(self.values.values()) |
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175 if centile is not None: |
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176 if self.mostSevereIsMax: |
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177 c = 100-centile |
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178 else: |
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179 c = centile |
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180 return percentile(values, c) |
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181 elif minNInstants is not None and minNInstants <= self.__len__(): |
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182 values = sorted(values, reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values |
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183 return mean(values[:minNInstants]) |
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184 else: |
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185 return None |
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186 |
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187 def getInstantOfMostSevereValue(self): |
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188 '''Returns the instant at which the indicator reaches its most severe value''' |
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189 if self.mostSevereIsMax: |
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190 return max(self.values, key=self.values.get) |
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191 else: |
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192 return min(self.values, key=self.values.get) |
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193 |
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194 # functions to aggregate discretized maps of indicators |
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195 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap) |
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196 |
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197 def indicatorMap(indicatorValues, trajectory, squareSize): |
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198 '''Returns a dictionary |
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199 with keys for the indices of the cells (squares) |
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200 in which the trajectory positions are located |
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201 at which the indicator values are attached |
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202 |
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203 ex: speeds and trajectory''' |
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204 |
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205 assert len(indicatorValues) == trajectory.length() |
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206 indicatorMap = {} |
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207 for k in range(trajectory.length()): |
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208 p = trajectory[k] |
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209 i = floor(p.x/squareSize) |
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210 j = floor(p.y/squareSize) |
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211 if (i,j) in indicatorMap: |
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212 indicatorMap[(i,j)].append(indicatorValues[k]) |
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213 else: |
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214 indicatorMap[(i,j)] = [indicatorValues[k]] |
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215 for k in indicatorMap: |
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216 indicatorMap[k] = mean(indicatorMap[k]) |
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217 return indicatorMap |
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218 |
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219 # def indicatorMapFromPolygon(value, polygon, squareSize): |
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220 # '''Fills an indicator map with the value within the polygon |
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221 # (array of Nx2 coordinates of the polygon vertices)''' |
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222 # points = [] |
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223 # for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize): |
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224 # for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize): |
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225 # points.append([x,y]) |
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226 # inside = nx.points_inside_poly(array(points), polygon) |
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227 # indicatorMap = {} |
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228 # for i in range(len(inside)): |
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229 # if inside[i]: |
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230 # indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0 |
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231 # return indicatorMap |
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232 |
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233 def indicatorMapFromAxis(value, limits, squareSize): |
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234 '''axis = [xmin, xmax, ymin, ymax] ''' |
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235 indicatorMap = {} |
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236 for x in arange(limits[0], limits[1], squareSize): |
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237 for y in arange(limits[2], limits[3], squareSize): |
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238 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value |
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239 return indicatorMap |
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240 |
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241 def combineIndicatorMaps(maps, squareSize, combinationFunction): |
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242 '''Puts many indicator maps together |
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243 (averaging the values in each cell |
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244 if more than one maps has a value)''' |
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245 indicatorMap = {} |
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246 for m in maps: |
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247 for k,v in m.items(): |
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248 if k in indicatorMap: |
244
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249 indicatorMap[k].append(v) |
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250 else: |
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251 indicatorMap[k] = [v] |
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252 for k in indicatorMap: |
244
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253 indicatorMap[k] = combinationFunction(indicatorMap[k]) |
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254 return indicatorMap |
247
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255 |
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256 if __name__ == "__main__": |
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257 import doctest |
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258 import unittest |
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259 suite = doctest.DocFileSuite('tests/indicators.txt') |
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260 unittest.TextTestRunner().run(suite) |
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261 # #doctest.testmod() |
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262 # #doctest.testfile("example.txt") |