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annotate python/indicators.py @ 692:9a258687af4c dev

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corrected some errors for ttc
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 29 Jun 2015 23:09:58 -0400
parents fa9aa5f08210
children 5ee22bf7e4d5
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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 # need for a class representing the indicators, their units, how to print them in graphs...
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12 class TemporalIndicator(object):
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13 '''Class for temporal indicators
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14 i.e. indicators that take a value at specific instants
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15
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16 values should be
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17 * a dict, for the values at specific time instants
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18 * or a list with a time interval object if continuous measurements
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19
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20 it should have more information like name, unit'''
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21
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22 def __init__(self, name, values, timeInterval=None, maxValue = None):
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23 self.name = name
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24 if timeInterval:
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25 assert len(values) == timeInterval.length()
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26 self.timeInterval = timeInterval
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27 self.values = {}
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28 for i in xrange(int(round(self.timeInterval.length()))):
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29 self.values[self.timeInterval[i]] = values[i]
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30 else:
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31 self.values = values
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32 instants = sorted(self.values.keys())
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33 if instants:
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34 self.timeInterval = moving.TimeInterval(instants[0], instants[-1])
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35 else:
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36 self.timeInterval = moving.TimeInterval()
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37 self.maxValue = maxValue
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38
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39 def __len__(self):
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40 return len(self.values)
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41
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42 def empty(self):
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43 return len(self.values) == 0
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44
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45 def __getitem__(self, t):
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46 'Returns the value at time t'
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47 if t in self.values.keys():
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48 return self.values[t]
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49 else:
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50 return None
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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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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 def valueSorted(self):
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91 ''' return the values after sort the keys in the indicator
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92 This should probably not be used: to delete'''
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93 print('Deprecated: values should not be accessed in this way')
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94 values=[]
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95 keys = self.values.keys()
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96 keys.sort()
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97 for key in keys:
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98 values.append(self.values[key])
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99 return values
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100
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101
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102 def l1Distance(x, y): # lambda x,y:abs(x-y)
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103 if x is None or y is None:
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104 return float('inf')
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105 else:
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106 return abs(x-y)
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107
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108 from utils import LCSS as utilsLCSS
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109
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110 class LCSS(utilsLCSS):
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111 '''Adapted LCSS class for indicators, same pattern'''
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112 def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min):
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113 utilsLCSS.__init__(self, similarityFunc, delta, aligned, lengthFunc)
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114 self.minLength = minLength
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115
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116 def checkIndicator(self, indicator):
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117 return indicator is not None and len(indicator) >= self.minLength
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118
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119 def compute(self, indicator1, indicator2, computeSubSequence = False):
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120 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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121 return self._compute(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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122 else:
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123 return 0
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124
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125 def computeNormalized(self, indicator1, indicator2, computeSubSequence = False):
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126 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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127 return self._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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128 else:
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129 return 0.
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130
376
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131 def computeDistance(self, indicator1, indicator2, computeSubSequence = False):
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132 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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133 return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
369
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134 else:
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135 return 1.
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136
244
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137 class SeverityIndicator(TemporalIndicator):
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138 '''Class for severity indicators
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139 field mostSevereIsMax is True
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140 if the most severe value taken by the indicator is the maximum'''
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141
286
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142 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None):
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143 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue)
244
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144 self.mostSevereIsMax = mostSevereIsMax
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145
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146 def getMostSevereValue(self, minNInstants=1): # TODO use np.percentile
244
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147 values = array(self.values.values())
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148 indices = range(len(values))
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149 if len(indices) >= minNInstants:
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150 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
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151 return mean(values[:minNInstants])
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152 else:
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153 return None
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154
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155 def getInstantOfMostSevereValue(self):
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156 '''Returns the instant at which the indicator reaches its most severe value'''
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157 if self.mostSevereIsMax:
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158 return max(self.values, key=self.values.get)
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159 else:
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160 return min(self.values, key=self.values.get)
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161
244
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162 # functions to aggregate discretized maps of indicators
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163 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap)
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164
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165 def indicatorMap(indicatorValues, trajectory, squareSize):
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166 '''Returns a dictionary
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167 with keys for the indices of the cells (squares)
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168 in which the trajectory positions are located
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169 at which the indicator values are attached
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170
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171 ex: speeds and trajectory'''
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172
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173 assert len(indicatorValues) == trajectory.length()
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174 indicatorMap = {}
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175 for k in xrange(trajectory.length()):
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176 p = trajectory[k]
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177 i = floor(p.x/squareSize)
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178 j = floor(p.y/squareSize)
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179 if indicatorMap.has_key((i,j)):
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180 indicatorMap[(i,j)].append(indicatorValues[k])
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181 else:
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182 indicatorMap[(i,j)] = [indicatorValues[k]]
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183 for k in indicatorMap.keys():
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184 indicatorMap[k] = mean(indicatorMap[k])
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185 return indicatorMap
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186
691
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187 # def indicatorMapFromPolygon(value, polygon, squareSize):
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188 # '''Fills an indicator map with the value within the polygon
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189 # (array of Nx2 coordinates of the polygon vertices)'''
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190 # points = []
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191 # for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize):
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192 # for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize):
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193 # points.append([x,y])
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194 # inside = nx.points_inside_poly(array(points), polygon)
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195 # indicatorMap = {}
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196 # for i in xrange(len(inside)):
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197 # if inside[i]:
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198 # indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0
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199 # return indicatorMap
244
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200
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201 def indicatorMapFromAxis(value, limits, squareSize):
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202 '''axis = [xmin, xmax, ymin, ymax] '''
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203 indicatorMap = {}
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204 for x in arange(limits[0], limits[1], squareSize):
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205 for y in arange(limits[2], limits[3], squareSize):
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206 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value
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207 return indicatorMap
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208
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209 def combineIndicatorMaps(maps, squareSize, combinationFunction):
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210 '''Puts many indicator maps together
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211 (averaging the values in each cell
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212 if more than one maps has a value)'''
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213 indicatorMap = {}
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214 for m in maps:
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215 for k,v in m.iteritems():
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216 if indicatorMap.has_key(k):
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217 indicatorMap[k].append(v)
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218 else:
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219 indicatorMap[k] = [v]
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220 for k in indicatorMap.keys():
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221 indicatorMap[k] = combinationFunction(indicatorMap[k])
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222 return indicatorMap
247
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223
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224 if __name__ == "__main__":
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225 import doctest
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226 import unittest
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227 suite = doctest.DocFileSuite('tests/indicators.txt')
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228 unittest.TextTestRunner().run(suite)
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229 # #doctest.testmod()
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230 # #doctest.testfile("example.txt")