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comparison python/indicators.py @ 286:fa95796a76b3

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simplified indicators (only non-measured values, whether measurable or not)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sun, 27 Jan 2013 00:22:55 -0500
parents 5957aa1d69e1
children 66691c06928c
comparison
equal deleted inserted replaced
285:5957aa1d69e1 286:fa95796a76b3
92 time = sorted(self.values.keys()) 92 time = sorted(self.values.keys())
93 plot([x/xfactor for x in time], [self.values[i] for i in time], options+marker, **kwargs) 93 plot([x/xfactor for x in time], [self.values[i] for i in time], options+marker, **kwargs)
94 if self.maxValue: 94 if self.maxValue:
95 ylim(ymax = self.maxValue) 95 ylim(ymax = self.maxValue)
96 96
97 @staticmethod 97 def computeDLCSS(indicator1, indicator2, threshold, delta = float('inf'), method= 'min'):
98 def computeDLCSS(indicator1, indicator2, threshold, delta = float('inf'), method= 'min'): 98 ''' compute the distance between two indicators using LCSS
99 ''' compute the distance between two indicators using LCSS 99 two common methods are used: min or mean of the indicators length'''
100 two common methods are used: min or mean of the indicators length''' 100 from utils import LCSS
101 # l1= TemporalIndicator1.valueSorted 101 lcss = LCSS(indicator1, indicator2, threshold, lambda x,y:abs(x-y), delta)
102 # l2= TemporalIndicator2.valueSorted 102 if method == 'min':
103 # if method = 'min': 103 denominator = min(len(indicator1), len(indicator2))
104 # DLCSS= 1- (LCSS(l1,l2, threshold, delta, distance))/min(len(l1),len(l2))) 104 elif method == 'mean':
105 # if method = 'mean': 105 denominator = float(len(indicator1) + len(indicator2))/2
106 # average= len(l1)+len(l2))/2 106 else:
107 # DLCSS= 1- ((LCSS(l1,l2, threshold, delta, distance))/average) 107 print('Unknown denominator method name')
108 # return DLCSS 108 denominator = 1.
109 return 0 109 return 1-float(lcss)/denominator
110 110
111 class SeverityIndicator(TemporalIndicator): 111 class SeverityIndicator(TemporalIndicator):
112 '''Class for severity indicators 112 '''Class for severity indicators
113 field mostSevereIsMax is True 113 field mostSevereIsMax is True
114 if the most severe value taken by the indicator is the maximum''' 114 if the most severe value taken by the indicator is the maximum'''
115 115
116 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, ignoredValue = None, maxValue = None): 116 def __init__(self, name, values, timeInterval=None, mostSevereIsMax=True, maxValue = None):
117 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue) 117 TemporalIndicator.__init__(self, name, values, timeInterval, maxValue)
118 self.mostSevereIsMax = mostSevereIsMax 118 self.mostSevereIsMax = mostSevereIsMax
119 self.ignoredValue = ignoredValue
120 119
121 def getMostSevereValue(self, minNInstants=1): # TODO use scoreatpercentile 120 def getMostSevereValue(self, minNInstants=1): # TODO use scoreatpercentile
122 from matplotlib.mlab import find 121 from matplotlib.mlab import find
123 from numpy.core.multiarray import array 122 from numpy.core.multiarray import array
124 from numpy.core.fromnumeric import mean 123 from numpy.core.fromnumeric import mean
125 values = array(self.values.values()) 124 values = array(self.values.values())
126 if self.ignoredValue: 125 indices = range(len(values))
127 indices = find(values != self.ignoredValue)
128 else:
129 indices = range(len(values))
130 if len(indices) >= minNInstants: 126 if len(indices) >= minNInstants:
131 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values 127 values = sorted(values[indices], reverse = self.mostSevereIsMax) # inverted if most severe is max -> take the first values
132 return mean(values[:minNInstants]) 128 return mean(values[:minNInstants])
133 else: 129 else:
134 return None 130 return None