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annotate python/indicators.py @ 631:2d1d33ae1c69

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major work on pPET and pet, issues remain for pPET computed with predicted trajectories
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 17 Feb 2015 03:55:55 +0100
parents eb8baa080470
children 3058e00887bc
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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 if timeInterval:
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22 assert len(values) == timeInterval.length()
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23 self.timeInterval = timeInterval
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24 self.values = {}
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25 for i in xrange(int(round(self.timeInterval.length()))):
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26 self.values[self.timeInterval[i]] = values[i]
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27 else:
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28 self.values = values
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29 instants = sorted(self.values.keys())
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30 if instants:
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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 self.maxValue = maxValue
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35
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36 def __len__(self):
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37 return len(self.values)
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38
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39 def empty(self):
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40 return len(self.values) == 0
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41
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42 def __getitem__(self, i):
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43 'Returns ith value in time interval'
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44 if i in self.values.keys():
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45 return self.values[i]
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46 else:
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47 return None
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48
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49 def getIthValue(self, i):
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50 sortedKeys = sorted(self.values.keys())
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51 if 0<=i<len(sortedKeys):
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52 return self.values[sortedKeys[i]]
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53 else:
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54 return None
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55
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56 def __iter__(self):
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57 self.iterInstantNum = 0 # index in the interval or keys of the dict
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58 return self
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59
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60 def next(self):
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61 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\
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62 # or (self.iterInstantNum >= self.values)
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63 raise StopIteration
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64 else:
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65 self.iterInstantNum += 1
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66 return self.getIthValue(self.iterInstantNum-1)
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67
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68 def getTimeInterval(self):
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69 return self.timeInterval
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70
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71 def getName(self):
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72 return self.name
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73
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74 def getValues(self):
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75 return [self.__getitem__(t) for t in self.timeInterval]
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76
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77 def plot(self, options = '', xfactor = 1., yfactor = 1., timeShift = 0, **kwargs):
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78 from matplotlib.pylab import plot,ylim
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79 if self.getTimeInterval().length() == 1:
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80 marker = 'o'
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81 else:
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82 marker = ''
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83 time = sorted(self.values.keys())
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84 plot([(x+timeShift)/xfactor for x in time], [self.values[i]/yfactor for i in time], options+marker, **kwargs)
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85 if self.maxValue:
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86 ylim(ymax = self.maxValue)
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87
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88 def valueSorted(self):
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89 ''' return the values after sort the keys in the indicator
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90 This should probably not be used: to delete'''
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91 print('Deprecated: values should not be accessed in this way')
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92 values=[]
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93 keys = self.values.keys()
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94 keys.sort()
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95 for key in keys:
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96 values.append(self.values[key])
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97 return values
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98
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99
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100 def l1Distance(x, y): # lambda x,y:abs(x-y)
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101 if x == None or y == None:
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102 return float('inf')
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103 else:
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104 return abs(x-y)
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105
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106 from utils import LCSS as utilsLCSS
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107
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108 class LCSS(utilsLCSS):
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109 '''Adapted LCSS class for indicators, same pattern'''
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110 def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min):
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111 utilsLCSS.__init__(self, similarityFunc, delta, aligned, lengthFunc)
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112 self.minLength = minLength
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113
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114 def checkIndicator(self, indicator):
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115 return indicator != None and len(indicator) >= self.minLength
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116
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117 def compute(self, indicator1, indicator2, computeSubSequence = False):
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118 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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119 return self._compute(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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120 else:
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121 return 0
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122
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123 def computeNormalized(self, indicator1, indicator2, computeSubSequence = False):
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124 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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125 return self._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
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126 else:
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127 return 0.
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128
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129 def computeDistance(self, indicator1, indicator2, computeSubSequence = False):
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130 if self.checkIndicator(indicator1) and self.checkIndicator(indicator2):
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diff changeset
131 return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence)
369
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132 else:
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133 return 1.
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134
244
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135 class SeverityIndicator(TemporalIndicator):
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136 '''Class for severity indicators
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137 field mostSevereIsMax is True
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138 if the most severe value taken by the indicator is the maximum'''
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139
286
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140 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None):
269
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141 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue)
244
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142 self.mostSevereIsMax = mostSevereIsMax
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143
631
2d1d33ae1c69 major work on pPET and pet, issues remain for pPET computed with predicted trajectories
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diff changeset
144 def getMostSevereValue(self, minNInstants=1): # TODO use np.percentile
244
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145 from matplotlib.mlab import find
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parents:
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146 from numpy.core.multiarray import array
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147 from numpy.core.fromnumeric import mean
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148 values = array(self.values.values())
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diff changeset
149 indices = range(len(values))
244
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150 if len(indices) >= minNInstants:
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151 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
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152 return mean(values[:minNInstants])
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153 else:
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154 return None
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155
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156 # functions to aggregate discretized maps of indicators
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157 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap)
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158
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159 def indicatorMap(indicatorValues, trajectory, squareSize):
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160 '''Returns a dictionary
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161 with keys for the indices of the cells (squares)
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162 in which the trajectory positions are located
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163 at which the indicator values are attached
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parents:
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164
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parents:
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165 ex: speeds and trajectory'''
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parents:
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166
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parents:
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167 from numpy import floor, mean
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parents:
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168 assert len(indicatorValues) == trajectory.length()
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parents:
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169 indicatorMap = {}
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parents:
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170 for k in xrange(trajectory.length()):
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parents:
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171 p = trajectory[k]
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parents:
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172 i = floor(p.x/squareSize)
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parents:
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173 j = floor(p.y/squareSize)
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parents:
diff changeset
174 if indicatorMap.has_key((i,j)):
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parents:
diff changeset
175 indicatorMap[(i,j)].append(indicatorValues[k])
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parents:
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176 else:
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parents:
diff changeset
177 indicatorMap[(i,j)] = [indicatorValues[k]]
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parents:
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178 for k in indicatorMap.keys():
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parents:
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179 indicatorMap[k] = mean(indicatorMap[k])
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180 return indicatorMap
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181
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parents:
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182 def indicatorMapFromPolygon(value, polygon, squareSize):
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parents:
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183 '''Fills an indicator map with the value within the polygon
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parents:
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184 (array of Nx2 coordinates of the polygon vertices)'''
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parents:
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185 import matplotlib.nxutils as nx
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parents:
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186 from numpy.core.multiarray import array, arange
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parents:
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187 from numpy import floor
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parents:
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188
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parents:
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189 points = []
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parents:
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190 for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize):
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parents:
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191 for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize):
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parents:
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192 points.append([x,y])
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parents:
diff changeset
193 inside = nx.points_inside_poly(array(points), polygon)
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parents:
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194 indicatorMap = {}
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parents:
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195 for i in xrange(len(inside)):
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parents:
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196 if inside[i]:
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parents:
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197 indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0
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198 return indicatorMap
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199
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200 def indicatorMapFromAxis(value, limits, squareSize):
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201 '''axis = [xmin, xmax, ymin, ymax] '''
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parents:
diff changeset
202 from numpy.core.multiarray import arange
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parents:
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203 from numpy import floor
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parents:
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204 indicatorMap = {}
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parents:
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205 for x in arange(limits[0], limits[1], squareSize):
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parents:
diff changeset
206 for y in arange(limits[2], limits[3], squareSize):
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parents:
diff changeset
207 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value
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parents:
diff changeset
208 return indicatorMap
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parents:
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209
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parents:
diff changeset
210 def combineIndicatorMaps(maps, squareSize, combinationFunction):
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parents:
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211 '''Puts many indicator maps together
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parents:
diff changeset
212 (averaging the values in each cell
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parents:
diff changeset
213 if more than one maps has a value)'''
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parents:
diff changeset
214 #from numpy import mean
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parents:
diff changeset
215 indicatorMap = {}
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
diff changeset
216 for m in maps:
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parents:
diff changeset
217 for k,v in m.iteritems():
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parents:
diff changeset
218 if indicatorMap.has_key(k):
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parents:
diff changeset
219 indicatorMap[k].append(v)
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parents:
diff changeset
220 else:
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parents:
diff changeset
221 indicatorMap[k] = [v]
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parents:
diff changeset
222 for k in indicatorMap.keys():
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parents:
diff changeset
223 indicatorMap[k] = combinationFunction(indicatorMap[k])
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parents:
diff changeset
224 return indicatorMap
247
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diff changeset
225
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 244
diff changeset
226 if __name__ == "__main__":
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diff changeset
227 import doctest
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parents: 244
diff changeset
228 import unittest
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parents: 244
diff changeset
229 suite = doctest.DocFileSuite('tests/indicators.txt')
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parents: 244
diff changeset
230 unittest.TextTestRunner().run(suite)
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parents: 244
diff changeset
231 # #doctest.testmod()
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parents: 244
diff changeset
232 # #doctest.testfile("example.txt")