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annotate python/indicators.py @ 962:64259b9885bf

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verbose option to print classification information (more to add)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 06 Nov 2017 21:25:41 -0500
parents c6d4ea05a2d0
children 933670761a57
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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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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
369
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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):
375
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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
376
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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
244
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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
286
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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)
244
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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
244
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164 values = array(self.values.values())
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165 indices = range(len(values))
244
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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
244
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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
691
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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
244
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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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diff changeset
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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diff changeset
246 # #doctest.testmod()
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247 # #doctest.testfile("example.txt")