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annotate python/indicators.py @ 313:43e62b9cb652

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solved issues to make the files importable (caution with getDLCSS)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 11 Apr 2013 22:50:04 -0400
parents 6c068047edbf
children a5e40bd04cf4
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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 __metaclass__ = type
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5
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6 import moving
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7
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8 # need for a class representing the indicators, their units, how to print them in graphs...
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9 class TemporalIndicator:
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10 '''Class for temporal indicators
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11 i.e. indicators that take a value at specific instants
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12
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13 values should be
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14 * a dict, for the values at specific time instants
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15 * or a list with a time interval object if continuous measurements
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16
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17 it should have more information like name, unit'''
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18
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19 def __init__(self, name, values, timeInterval=None, maxValue = None):
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20 self.name = name
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21 self.isCosine = (name.find('Cosine') >= 0)
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22 if timeInterval:
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23 assert len(values) == timeInterval.length()
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24 self.timeInterval = timeInterval
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25 self.values = {}
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26 for i in xrange(int(round(self.timeInterval.length()))):
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27 self.values[self.timeInterval[i]] = values[i]
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28 else:
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29 self.values = values
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30 instants = sorted(self.values.keys())
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31 if instants:
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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 self.maxValue = maxValue
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36
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37 def __len__(self):
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38 return len(self.values)
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39
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40 def empty(self):
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41 return len(self.values) == 0
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42
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43 def __getitem__(self, i):
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44 'Returns ith value in time interval'
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45 if i in self.values.keys():
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46 return self.values[i]
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47 else:
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48 return None
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49
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50 def getIthValue(self, i):
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51 sortedKeys = sorted(self.values.keys())
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52 if 0<=i<len(sortedKeys):
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53 return self.values[sortedKeys[i]]
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54 else:
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55 return None
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56
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57 def __iter__(self):
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58 self.iterInstantNum = 0 # index in the interval or keys of the dict
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59 return self
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60
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61 def next(self):
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62 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\
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63 # or (self.iterInstantNum >= self.values)
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64 raise StopIteration
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65 else:
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66 self.iterInstantNum += 1
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67 return self.getIthValue(self.iterInstantNum-1)
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68
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69 def getTimeInterval(self):
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70 return self.timeInterval
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71
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72 def getValues(self):
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73 return [self.__getitem__(t) for t in self.timeInterval]
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74
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75 def getAngleValues(self):
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76 '''if the indicator is a function of an angle,
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77 transform it to an angle (eg cos)
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78 (no transformation otherwise)'''
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79 from numpy import arccos
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80 values = self.getValues()
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81 if self.isCosine:
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82 return [arccos(c) for c in values]
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83 else:
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84 return values
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85
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86 def plot(self, options = '', xfactor = 1., **kwargs):
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87 from matplotlib.pylab import plot,ylim
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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/xfactor for x in time], [self.values[i] 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 def valueSorted(self):
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98 ''' return the values after sort the keys in the indicator
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99 This should probably not be used: to delete'''
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100 values=[]
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101 keys = self.values.keys()
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102 keys.sort()
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103 for key in keys:
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104 values.append(self.values[key])
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105 return values
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106
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107 @staticmethod
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108 def getDLCSS(indic1, indic2, threshold, delta = float('inf') , method ='min' ):
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109 ''' compute the distance between two indicators using LCSS
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110 two common methods are used: min or mean of the indicators length'''
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111 l1 = indic1.valueSorted
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112 l2 = indic2.valueSorted
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113 DLCSS = None
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114 if method == 'min':
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115 DLCSS = 1- (LCSS(l1,l2, threshold, delta, distance))/min(len(l1),len(l2))
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116 if method == 'mean':
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117 average = len(l1)+len(l2)/2
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118 DLCSS = 1- ((LCSS(l1,l2, threshold, delta, distance))/average)
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119 return DLCSS
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120
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121
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122 def computeDLCSS(indicator1, indicator2, threshold, delta = float('inf'), method= 'min'):
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123 ''' compute the distance between two indicators using LCSS
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124 two common methods are used: min or mean of the indicators length'''
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125 from utils import LCSS
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126
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127 def distance(x, y): # lambda x,y:abs(x-y)
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128 if x == None or y == None:
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129 return float('inf')
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130 else:
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131 return abs(x-y)
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132
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133 lcss = LCSS(indicator1.getValues(), indicator2.getValues(), threshold, distance, delta)
286
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134 if method == 'min':
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135 denominator = min(len(indicator1), len(indicator2))
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136 elif method == 'mean':
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137 denominator = float(len(indicator1) + len(indicator2))/2
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138 else:
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139 print('Unknown denominator method name')
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140 denominator = 1.
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141 return 1-float(lcss)/denominator
285
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142
244
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143 class SeverityIndicator(TemporalIndicator):
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144 '''Class for severity indicators
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145 field mostSevereIsMax is True
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146 if the most severe value taken by the indicator is the maximum'''
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147
286
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148 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None):
269
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149 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue)
244
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150 self.mostSevereIsMax = mostSevereIsMax
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151
285
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152 def getMostSevereValue(self, minNInstants=1): # TODO use scoreatpercentile
244
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153 from matplotlib.mlab import find
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154 from numpy.core.multiarray import array
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155 from numpy.core.fromnumeric import mean
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156 values = array(self.values.values())
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157 indices = range(len(values))
244
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158 if len(indices) >= minNInstants:
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159 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
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160 return mean(values[:minNInstants])
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161 else:
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162 return None
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163
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164 # functions to aggregate discretized maps of indicators
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165 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap)
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166
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167 def indicatorMap(indicatorValues, trajectory, squareSize):
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168 '''Returns a dictionary
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169 with keys for the indices of the cells (squares)
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170 in which the trajectory positions are located
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171 at which the indicator values are attached
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172
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173 ex: speeds and trajectory'''
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174
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175 from numpy import floor, mean
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176 assert len(indicatorValues) == trajectory.length()
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177 indicatorMap = {}
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178 for k in xrange(trajectory.length()):
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179 p = trajectory[k]
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180 i = floor(p.x/squareSize)
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181 j = floor(p.y/squareSize)
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182 if indicatorMap.has_key((i,j)):
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183 indicatorMap[(i,j)].append(indicatorValues[k])
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184 else:
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185 indicatorMap[(i,j)] = [indicatorValues[k]]
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diff changeset
186 for k in indicatorMap.keys():
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diff changeset
187 indicatorMap[k] = mean(indicatorMap[k])
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188 return indicatorMap
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189
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190 def indicatorMapFromPolygon(value, polygon, squareSize):
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191 '''Fills an indicator map with the value within the polygon
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diff changeset
192 (array of Nx2 coordinates of the polygon vertices)'''
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193 import matplotlib.nxutils as nx
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194 from numpy.core.multiarray import array, arange
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195 from numpy import floor
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196
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197 points = []
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198 for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize):
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199 for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize):
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diff changeset
200 points.append([x,y])
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diff changeset
201 inside = nx.points_inside_poly(array(points), polygon)
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202 indicatorMap = {}
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diff changeset
203 for i in xrange(len(inside)):
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parents:
diff changeset
204 if inside[i]:
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parents:
diff changeset
205 indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0
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206 return indicatorMap
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207
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diff changeset
208 def indicatorMapFromAxis(value, limits, squareSize):
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diff changeset
209 '''axis = [xmin, xmax, ymin, ymax] '''
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parents:
diff changeset
210 from numpy.core.multiarray import arange
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parents:
diff changeset
211 from numpy import floor
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parents:
diff changeset
212 indicatorMap = {}
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parents:
diff changeset
213 for x in arange(limits[0], limits[1], squareSize):
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parents:
diff changeset
214 for y in arange(limits[2], limits[3], squareSize):
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parents:
diff changeset
215 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value
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parents:
diff changeset
216 return indicatorMap
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parents:
diff changeset
217
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diff changeset
218 def combineIndicatorMaps(maps, squareSize, combinationFunction):
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diff changeset
219 '''Puts many indicator maps together
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parents:
diff changeset
220 (averaging the values in each cell
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parents:
diff changeset
221 if more than one maps has a value)'''
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parents:
diff changeset
222 #from numpy import mean
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diff changeset
223 indicatorMap = {}
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parents:
diff changeset
224 for m in maps:
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parents:
diff changeset
225 for k,v in m.iteritems():
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parents:
diff changeset
226 if indicatorMap.has_key(k):
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parents:
diff changeset
227 indicatorMap[k].append(v)
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parents:
diff changeset
228 else:
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parents:
diff changeset
229 indicatorMap[k] = [v]
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parents:
diff changeset
230 for k in indicatorMap.keys():
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diff changeset
231 indicatorMap[k] = combinationFunction(indicatorMap[k])
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parents:
diff changeset
232 return indicatorMap
247
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diff changeset
233
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parents: 244
diff changeset
234 if __name__ == "__main__":
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diff changeset
235 import doctest
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parents: 244
diff changeset
236 import unittest
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diff changeset
237 suite = doctest.DocFileSuite('tests/indicators.txt')
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parents: 244
diff changeset
238 unittest.TextTestRunner().run(suite)
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diff changeset
239 # #doctest.testmod()
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diff changeset
240 # #doctest.testfile("example.txt")