Mercurial Hosting > traffic-intelligence
annotate python/indicators.py @ 962:64259b9885bf
verbose option to print classification information (more to add)
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 06 Nov 2017 21:25:41 -0500 |
parents | c6d4ea05a2d0 |
children | 933670761a57 |
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 import moving |
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5 #import matplotlib.nxutils as nx |
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6 from matplotlib.pyplot import plot, ylim |
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7 from matplotlib.pylab import find |
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8 from numpy import array, arange, mean, floor, mean |
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9 |
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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 xrange(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): |
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78 return [self.__getitem__(t) for t in self.timeInterval] |
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79 |
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80 def plot(self, options = '', xfactor = 1., yfactor = 1., timeShift = 0, **kwargs): |
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81 if self.getTimeInterval().length() == 1: |
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82 marker = 'o' |
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83 else: |
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84 marker = '' |
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85 time = sorted(self.values.keys()) |
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86 plot([(x+timeShift)/xfactor for x in time], [self.values[i]/yfactor for i in time], options+marker, **kwargs) |
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87 if self.maxValue: |
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88 ylim(ymax = self.maxValue) |
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89 |
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90 @classmethod |
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91 def createMultivariate(cls, indicators): |
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92 '''Creates a new temporal indicator where the value at each instant is a list |
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93 of the indicator values at the instant, in the same order |
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94 the time interval will be the union of the time intervals of the indicators |
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95 name is concatenation of the indicator names''' |
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96 if len(indicators) < 2: |
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97 print('Error creating multivariate indicator with only {} indicator'.format(len(indicators))) |
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98 return None |
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99 |
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100 timeInterval = moving.TimeInterval.unionIntervals([indic.getTimeInterval() for indic in indicators]) |
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101 values = {} |
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102 for t in timeInterval: |
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103 tmpValues = [indic[t] for indic in indicators] |
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104 uniqueValues = set(tmpValues) |
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105 if len(uniqueValues) >= 2 or uniqueValues.pop() is not None: |
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106 values[t] = tmpValues |
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107 return cls(multivariateName([indic.name for indic in indicators]), values) |
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108 |
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109 # TODO static method avec class en parametre pour faire des indicateurs agrege, list par instant |
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110 |
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111 def l1Distance(x, y): # lambda x,y:abs(x-y) |
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112 if x is None or y is None: |
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113 return float('inf') |
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114 else: |
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115 return abs(x-y) |
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116 |
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117 def multiL1Matching(x, y, thresholds, proportionMatching=1.): |
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118 n = 0 |
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119 nDimensions = len(x) |
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120 for i in range(nDimensions): |
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121 if l1Distance(x[i], y[i]) <= thresholds[i]: |
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122 n += 1 |
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123 return n >= nDimensions*proportionMatching |
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124 |
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125 from utils import LCSS as utilsLCSS |
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126 |
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127 class LCSS(utilsLCSS): |
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128 '''Adapted LCSS class for indicators, same pattern''' |
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129 def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min): |
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130 utilsLCSS.__init__(self, similarityFunc = similarityFunc, delta = delta, aligned = aligned, lengthFunc = lengthFunc) |
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131 self.minLength = minLength |
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132 |
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133 def checkIndicator(self, indicator): |
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134 return indicator is not None and len(indicator) >= self.minLength |
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135 |
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136 def compute(self, indicator1, indicator2, computeSubSequence = False): |
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137 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): |
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138 return self._compute(indicator1.getValues(), indicator2.getValues(), computeSubSequence) |
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139 else: |
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140 return 0 |
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141 |
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142 def computeNormalized(self, indicator1, indicator2, computeSubSequence = False): |
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143 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): |
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144 return self._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence) |
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145 else: |
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146 return 0. |
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147 |
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148 def computeDistance(self, indicator1, indicator2, computeSubSequence = False): |
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149 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): |
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150 return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence) |
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151 else: |
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152 return 1. |
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153 |
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154 class SeverityIndicator(TemporalIndicator): |
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155 '''Class for severity indicators |
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156 field mostSevereIsMax is True |
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157 if the most severe value taken by the indicator is the maximum''' |
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158 |
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159 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None): |
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160 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue) |
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161 self.mostSevereIsMax = mostSevereIsMax |
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162 |
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163 def getMostSevereValue(self, minNInstants=1): # TODO use np.percentile |
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164 values = array(self.values.values()) |
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165 indices = range(len(values)) |
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166 if len(indices) >= minNInstants: |
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167 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values |
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168 return mean(values[:minNInstants]) |
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169 else: |
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170 return None |
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171 |
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172 def getInstantOfMostSevereValue(self): |
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173 '''Returns the instant at which the indicator reaches its most severe value''' |
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174 if self.mostSevereIsMax: |
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175 return max(self.values, key=self.values.get) |
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176 else: |
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177 return min(self.values, key=self.values.get) |
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178 |
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179 # functions to aggregate discretized maps of indicators |
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180 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap) |
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181 |
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182 def indicatorMap(indicatorValues, trajectory, squareSize): |
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183 '''Returns a dictionary |
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184 with keys for the indices of the cells (squares) |
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185 in which the trajectory positions are located |
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186 at which the indicator values are attached |
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187 |
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188 ex: speeds and trajectory''' |
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189 |
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190 assert len(indicatorValues) == trajectory.length() |
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191 indicatorMap = {} |
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192 for k in xrange(trajectory.length()): |
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193 p = trajectory[k] |
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194 i = floor(p.x/squareSize) |
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195 j = floor(p.y/squareSize) |
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196 if indicatorMap.has_key((i,j)): |
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197 indicatorMap[(i,j)].append(indicatorValues[k]) |
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198 else: |
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199 indicatorMap[(i,j)] = [indicatorValues[k]] |
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200 for k in indicatorMap.keys(): |
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201 indicatorMap[k] = mean(indicatorMap[k]) |
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202 return indicatorMap |
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203 |
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204 # def indicatorMapFromPolygon(value, polygon, squareSize): |
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205 # '''Fills an indicator map with the value within the polygon |
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206 # (array of Nx2 coordinates of the polygon vertices)''' |
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207 # points = [] |
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208 # for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize): |
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209 # for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize): |
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210 # points.append([x,y]) |
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211 # inside = nx.points_inside_poly(array(points), polygon) |
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212 # indicatorMap = {} |
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213 # for i in xrange(len(inside)): |
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214 # if inside[i]: |
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215 # indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0 |
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216 # return indicatorMap |
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217 |
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218 def indicatorMapFromAxis(value, limits, squareSize): |
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219 '''axis = [xmin, xmax, ymin, ymax] ''' |
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220 indicatorMap = {} |
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221 for x in arange(limits[0], limits[1], squareSize): |
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222 for y in arange(limits[2], limits[3], squareSize): |
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223 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value |
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224 return indicatorMap |
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225 |
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226 def combineIndicatorMaps(maps, squareSize, combinationFunction): |
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227 '''Puts many indicator maps together |
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228 (averaging the values in each cell |
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229 if more than one maps has a value)''' |
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230 indicatorMap = {} |
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231 for m in maps: |
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232 for k,v in m.iteritems(): |
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233 if indicatorMap.has_key(k): |
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234 indicatorMap[k].append(v) |
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235 else: |
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236 indicatorMap[k] = [v] |
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237 for k in indicatorMap.keys(): |
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238 indicatorMap[k] = combinationFunction(indicatorMap[k]) |
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239 return indicatorMap |
247
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240 |
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241 if __name__ == "__main__": |
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242 import doctest |
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243 import unittest |
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244 suite = doctest.DocFileSuite('tests/indicators.txt') |
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245 unittest.TextTestRunner().run(suite) |
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246 # #doctest.testmod() |
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247 # #doctest.testfile("example.txt") |