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comparison python/indicators.py @ 244:5027c174ab90

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moved indicators to new file, added ExtrapolatedTrajectory class to extrapolation file
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 17 Jul 2012 00:15:42 -0400
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children 8f0ed138d373
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243:e0988a8ace0c 244:5027c174ab90
1 #! /usr/bin/env python
2 '''Class for indicators, temporal indicators, and safety indicators'''
3
4 __metaclass__ = type
5
6 # need for a class representing the indicators, their units, how to print them in graphs...
7 class TemporalIndicator:
8 '''Class for temporal indicators
9 i.e. indicators that take a value at specific instants
10
11 values should be
12 * a dict, for the values at specific time instants
13 * or a list with a time interval object if continuous measurements
14
15 it should have more information like name, unit'''
16
17 def __init__(self, name, values, timeInterval=None):
18 self.name = name
19 self.isCosine = name.find('Cosine')
20 self.values = values
21 self.timeInterval = timeInterval
22 if timeInterval:
23 assert len(values) == timeInterval.length()
24
25 def empty(self):
26 return len(self.values) == 0
27
28 def __getitem__(self, i):
29 if self.timeInterval:
30 if self.timeInterval.contains(i):
31 return self.values[i-self.timeInterval.first]
32 else:
33 if i in self.values.keys():
34 return self.values[i]
35 return None # default
36
37 def __iter__(self):
38 self.iterInstantNum = 0 # index in the interval or keys of the dict
39 return self
40
41 def next(self):
42 if self.iterInstantNum >= len(self.values):#(self.timeInterval and self.iterInstantNum>=self.timeInterval.length())\
43 # or (self.iterInstantNum >= self.values)
44 raise StopIteration
45 else:
46 self.iterInstantNum += 1
47 if self.timeInterval:
48 return self.values[self.iterInstantNum-1]
49 else:
50 return self.values.values()[self.iterInstantNum-1]
51
52 def getTimeInterval(self):
53 if not self.timeInterval and type(self.values)==dict:
54 instants = self.values.keys()
55 if instants:
56 self.timeInterval = TimeInterval(instants[0], instants[-1])
57 else:
58 self.timeInterval = TimeInterval()
59 return self.timeInterval
60
61 def getValues(self):
62 if self.timeInterval:
63 return self.values
64 else:
65 return self.values.values()
66
67 def getAngleValues(self):
68 '''if the indicator is a function of an angle,
69 transform it to an angle (eg cos)
70 (no transformation otherwise)'''
71 from numpy import arccos
72 values = self.getValues()
73 if self.isCosine >= 0:
74 return [arccos(c) for c in values]
75 else:
76 return values
77
78 class SeverityIndicator(TemporalIndicator):
79 '''Class for severity indicators
80 field mostSevereIsMax is True
81 if the most severe value taken by the indicator is the maximum'''
82
83 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, ignoredValue = None):
84 TemporalIndicator.__init__(self, name, values, timeInterval)
85 self.mostSevereIsMax = mostSevereIsMax
86 self.ignoredValue = ignoredValue
87
88 def getMostSevereValue(self, minNInstants=1):
89 from matplotlib.mlab import find
90 from numpy.core.multiarray import array
91 from numpy.core.fromnumeric import mean
92 values = array(self.values.values())
93 if self.ignoredValue:
94 indices = find(values != self.ignoredValue)
95 else:
96 indices = range(len(values))
97 if len(indices) >= minNInstants:
98 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
99 return mean(values[:minNInstants])
100 else:
101 return None
102
103 # functions to aggregate discretized maps of indicators
104 # TODO add values in the cells between the positions (similar to discretizing vector graphics to bitmap)
105
106 def indicatorMap(indicatorValues, trajectory, squareSize):
107 '''Returns a dictionary
108 with keys for the indices of the cells (squares)
109 in which the trajectory positions are located
110 at which the indicator values are attached
111
112 ex: speeds and trajectory'''
113
114 from numpy import floor, mean
115 assert len(indicatorValues) == trajectory.length()
116 indicatorMap = {}
117 for k in xrange(trajectory.length()):
118 p = trajectory[k]
119 i = floor(p.x/squareSize)
120 j = floor(p.y/squareSize)
121 if indicatorMap.has_key((i,j)):
122 indicatorMap[(i,j)].append(indicatorValues[k])
123 else:
124 indicatorMap[(i,j)] = [indicatorValues[k]]
125 for k in indicatorMap.keys():
126 indicatorMap[k] = mean(indicatorMap[k])
127 return indicatorMap
128
129 def indicatorMapFromPolygon(value, polygon, squareSize):
130 '''Fills an indicator map with the value within the polygon
131 (array of Nx2 coordinates of the polygon vertices)'''
132 import matplotlib.nxutils as nx
133 from numpy.core.multiarray import array, arange
134 from numpy import floor
135
136 points = []
137 for x in arange(min(polygon[:,0])+squareSize/2, max(polygon[:,0]), squareSize):
138 for y in arange(min(polygon[:,1])+squareSize/2, max(polygon[:,1]), squareSize):
139 points.append([x,y])
140 inside = nx.points_inside_poly(array(points), polygon)
141 indicatorMap = {}
142 for i in xrange(len(inside)):
143 if inside[i]:
144 indicatorMap[(floor(points[i][0]/squareSize), floor(points[i][1]/squareSize))] = 0
145 return indicatorMap
146
147 def indicatorMapFromAxis(value, limits, squareSize):
148 '''axis = [xmin, xmax, ymin, ymax] '''
149 from numpy.core.multiarray import arange
150 from numpy import floor
151 indicatorMap = {}
152 for x in arange(limits[0], limits[1], squareSize):
153 for y in arange(limits[2], limits[3], squareSize):
154 indicatorMap[(floor(x/squareSize), floor(y/squareSize))] = value
155 return indicatorMap
156
157 def combineIndicatorMaps(maps, squareSize, combinationFunction):
158 '''Puts many indicator maps together
159 (averaging the values in each cell
160 if more than one maps has a value)'''
161 #from numpy import mean
162 indicatorMap = {}
163 for m in maps:
164 for k,v in m.iteritems():
165 if indicatorMap.has_key(k):
166 indicatorMap[k].append(v)
167 else:
168 indicatorMap[k] = [v]
169 for k in indicatorMap.keys():
170 indicatorMap[k] = combinationFunction(indicatorMap[k])
171 return indicatorMap