Mercurial Hosting > traffic-intelligence
annotate python/indicators.py @ 251:4978b5baf8f1
Win32 version for traffic intelligence. Use the README-Win32.txt to know how to
fetch the dependencies. A batch file is included to automate most of the process
author | Jeep-Tour@Jeep-Tour-PC |
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date | Mon, 23 Jul 2012 12:27:50 -0400 |
parents | 8f0ed138d373 |
children | 7a3bf04cf016 |
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 __metaclass__ = type |
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5 |
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6 # need for a class representing the indicators, their units, how to print them in graphs... |
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7 class TemporalIndicator: |
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8 '''Class for temporal indicators |
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9 i.e. indicators that take a value at specific instants |
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10 |
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11 values should be |
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12 * a dict, for the values at specific time instants |
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13 * or a list with a time interval object if continuous measurements |
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14 |
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15 it should have more information like name, unit''' |
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16 |
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17 def __init__(self, name, values, timeInterval=None): |
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18 self.name = name |
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19 self.isCosine = name.find('Cosine') |
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20 self.values = values |
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21 self.timeInterval = timeInterval |
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22 if timeInterval: |
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23 assert len(values) == timeInterval.length() |
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24 |
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25 def empty(self): |
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26 return len(self.values) == 0 |
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27 |
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28 def __getitem__(self, i): |
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29 if self.timeInterval: |
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30 if self.timeInterval.contains(i): |
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31 return self.values[i-self.timeInterval.first] |
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32 else: |
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33 if i in self.values.keys(): |
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34 return self.values[i] |
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35 return None # default |
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36 |
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37 def __iter__(self): |
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38 self.iterInstantNum = 0 # index in the interval or keys of the dict |
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39 return self |
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40 |
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41 def next(self): |
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42 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\ |
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43 # or (self.iterInstantNum >= self.values) |
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44 raise StopIteration |
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45 else: |
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46 self.iterInstantNum += 1 |
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47 if self.timeInterval: |
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48 return self.values[self.iterInstantNum-1] |
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49 else: |
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50 return self.values.values()[self.iterInstantNum-1] |
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51 |
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52 def getTimeInterval(self): |
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53 if not self.timeInterval and type(self.values)==dict: |
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54 instants = self.values.keys() |
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55 if instants: |
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56 self.timeInterval = TimeInterval(instants[0], instants[-1]) |
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57 else: |
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58 self.timeInterval = TimeInterval() |
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59 return self.timeInterval |
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60 |
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61 def getValues(self): |
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62 if self.timeInterval: |
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63 return self.values |
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64 else: |
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65 return self.values.values() |
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66 |
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67 def getAngleValues(self): |
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68 '''if the indicator is a function of an angle, |
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69 transform it to an angle (eg cos) |
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70 (no transformation otherwise)''' |
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71 from numpy import arccos |
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72 values = self.getValues() |
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73 if self.isCosine >= 0: |
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74 return [arccos(c) for c in values] |
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75 else: |
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76 return values |
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77 |
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78 class SeverityIndicator(TemporalIndicator): |
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79 '''Class for severity indicators |
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80 field mostSevereIsMax is True |
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81 if the most severe value taken by the indicator is the maximum''' |
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82 |
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83 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, ignoredValue = None): |
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84 TemporalIndicator.__init__(self, name, values, timeInterval) |
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85 self.mostSevereIsMax = mostSevereIsMax |
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86 self.ignoredValue = ignoredValue |
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87 |
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88 def getMostSevereValue(self, minNInstants=1): |
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89 from matplotlib.mlab import find |
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90 from numpy.core.multiarray import array |
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91 from numpy.core.fromnumeric import mean |
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92 values = array(self.values.values()) |
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93 if self.ignoredValue: |
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94 indices = find(values != self.ignoredValue) |
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95 else: |
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96 indices = range(len(values)) |
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97 if len(indices) >= minNInstants: |
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98 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values |
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99 return mean(values[:minNInstants]) |
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100 else: |
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101 return None |
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102 |
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103 # functions to aggregate discretized maps of indicators |
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104 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap) |
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105 |
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106 def indicatorMap(indicatorValues, trajectory, squareSize): |
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107 '''Returns a dictionary |
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108 with keys for the indices of the cells (squares) |
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109 in which the trajectory positions are located |
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110 at which the indicator values are attached |
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111 |
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112 ex: speeds and trajectory''' |
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113 |
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114 from numpy import floor, mean |
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115 assert len(indicatorValues) == trajectory.length() |
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116 indicatorMap = {} |
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117 for k in xrange(trajectory.length()): |
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118 p = trajectory[k] |
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119 i = floor(p.x/squareSize) |
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120 j = floor(p.y/squareSize) |
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121 if indicatorMap.has_key((i,j)): |
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122 indicatorMap[(i,j)].append(indicatorValues[k]) |
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123 else: |
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124 indicatorMap[(i,j)] = [indicatorValues[k]] |
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125 for k in indicatorMap.keys(): |
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126 indicatorMap[k] = mean(indicatorMap[k]) |
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127 return indicatorMap |
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128 |
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129 def indicatorMapFromPolygon(value, polygon, squareSize): |
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130 '''Fills an indicator map with the value within the polygon |
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131 (array of Nx2 coordinates of the polygon vertices)''' |
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132 import matplotlib.nxutils as nx |
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133 from numpy.core.multiarray import array, arange |
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134 from numpy import floor |
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135 |
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136 points = [] |
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137 for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize): |
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138 for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize): |
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139 points.append([x,y]) |
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140 inside = nx.points_inside_poly(array(points), polygon) |
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141 indicatorMap = {} |
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142 for i in xrange(len(inside)): |
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143 if inside[i]: |
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144 indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0 |
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145 return indicatorMap |
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146 |
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147 def indicatorMapFromAxis(value, limits, squareSize): |
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148 '''axis = [xmin, xmax, ymin, ymax] ''' |
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149 from numpy.core.multiarray import arange |
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150 from numpy import floor |
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151 indicatorMap = {} |
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152 for x in arange(limits[0], limits[1], squareSize): |
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153 for y in arange(limits[2], limits[3], squareSize): |
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154 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value |
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155 return indicatorMap |
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156 |
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157 def combineIndicatorMaps(maps, squareSize, combinationFunction): |
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158 '''Puts many indicator maps together |
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159 (averaging the values in each cell |
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160 if more than one maps has a value)''' |
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161 #from numpy import mean |
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162 indicatorMap = {} |
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163 for m in maps: |
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164 for k,v in m.iteritems(): |
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165 if indicatorMap.has_key(k): |
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166 indicatorMap[k].append(v) |
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167 else: |
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168 indicatorMap[k] = [v] |
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169 for k in indicatorMap.keys(): |
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170 indicatorMap[k] = combinationFunction(indicatorMap[k]) |
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171 return indicatorMap |
247
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172 |
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173 if __name__ == "__main__": |
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174 import doctest |
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175 import unittest |
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176 suite = doctest.DocFileSuite('tests/indicators.txt') |
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177 unittest.TextTestRunner().run(suite) |
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178 # #doctest.testmod() |
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179 # #doctest.testfile("example.txt") |