def findDiffSeries(series1,diffInterval=1):
series1 = smoothArray(series1)
diffSeries =[]
lenSeries=len(series1)
if (diffInterval>lenSeries):
print "diffInterval can not be greater than the length of series"
return
i=diffInterval
while(i<lenSeries):
diffSeries.append((i-diffInterval,i,((series1[i]-series1[i-diffInterval])/series1[i-diffInterval])*100))
i=i+1
(N,P) = MADThreshold(diffSeries)
print "N & P ::::::::"+ str(N)+":::"+ str(P)
return diffSeries
def slopeBasedDetection(series1,smoothed1,series2,smoothed2,next_val_to_consider = 7, default_threshold = True, threshold = 0, what_to_consider = 1):
if(smoothed1 == False):
series1 = smoothArray(series1)
if(smoothed2 == False):
series2 = smoothArray(series2)
n = len(series1)
positive_slopes = []
anomalous_pts = []
negative_slopes = []
i = 0
while(i<(n-next_val_to_consider+1)):
if((series2[i+next_val_to_consider-1] - series2[i]) == 0):
i= i+ next_val_to_consider
continue
diff = ((series1[i+next_val_to_consider-1] - series1[i]) * series2[i] )/ ((series2[i+next_val_to_consider-1] - series2[i]) * series1[i])
if(diff < 0):
negative_slopes.append((i,i+next_val_to_consider-1,diff))
else:
positive_slopes.append((i,i+next_val_to_consider-1,diff))
i= i+ next_val_to_consider
if(default_threshold == True):
temp = []
for x in positive_slopes:
temp.append(x[2])
if(len(temp)>0):
(_,positive_threshold) = MADThreshold(temp)
# print "Positive Threshold Value:" + str(positive_threshold)
if(default_threshold == True):
temp = []
for x in negative_slopes:
temp.append(x[2])
if(len(temp)>0):
(negative_threshold,_) = MADThreshold(temp)
# print "Negative Threshold Value:" + str(negative_threshold)
if(what_to_consider == 1):
positive_anomalous_pts = []
if(default_threshold):
print "Possitive Threshold of Slope Based : " + str(positive_threshold)
for i in range(0,len(positive_slopes)):
if(positive_slopes[i][2] > positive_threshold):
positive_anomalous_pts.append(positive_slopes[i])
else:
print "Possitive Threshold of Slope Based User Given: " + str(threshold)
for i in range(0,len(positive_slopes)):
if(positive_slopes[i][2] > threshold):
positive_anomalous_pts.append(positive_slopes[i])
return positive_anomalous_pts
elif(what_to_consider == -1):
negative_anomalous_pts = []
if(default_threshold):
print "Negative Threshold of Slope Based : " + str(negative_threshold)
for i in range(0,len(negative_slopes)):
if(negative_slopes[i][2] < negative_threshold):
negative_anomalous_pts.append(negative_slopes[i])
else:
print "Negative Threshold of Slope Based User Given: " + str(threshold)
for i in range(0,len(negative_slopes)):
if(negative_slopes[i][2] < threshold):
negative_anomalous_pts.append(negative_slopes[i])
return negative_anomalous_pts
elif(what_to_consider == 0):
if(default_threshold):
print "Possitive Threshold of Slope Based : " + str(positive_threshold)
print "Negative Threshold of Slope Based : " + str(negative_threshold)
positive_anomalous_pts = []
for i in range(0,len(positive_slopes)):
if(positive_slopes[i][2] > positive_threshold):
positive_anomalous_pts.append(positive_slopes[i])
negative_anomalous_pts = []
for i in range(0,len(negative_slopes)):
if(negative_slopes[i][2] < negative_threshold):
negative_anomalous_pts.append(negative_slopes[i])
anomalous_pts = positive_anomalous_pts + negative_anomalous_pts
# Sort array according to start of window
sorted(anomalous_pts, key=lambda x: x[0])
return anomalous_pts
else:
print "Threshold of Slope Based User Given: " + str(threshold)
positive_anomalous_pts = []
for i in range(0,len(positive_slopes)):
if(positive_slopes[i][2] > threshold):
positive_anomalous_pts.append(positive_slopes[i])
negative_anomalous_pts = []
for i in range(0,len(negative_slopes)):
if(abs(negative_slopes[i][2]) > threshold):
negative_anomalous_pts.append(negative_slopes[i])
anomalous_pts = positive_anomalous_pts + negative_anomalous_pts
# Sort array according to start of window
sorted(anomalous_pts, key=lambda x: x[0])
return anomalous_pts
pass
