import random


# make a random sequence of data of length n that has regular peaks
def make_random_sequence(n, period = 10):
    L = []
    for i in range(n):
        if (i % period == 0):
            L.append(10*(.5+random.random()))
        else:
            L.append(random.random())
    return L


# make a collection of m random data sequences, each of length n
def make_sequence_collection(m, n, period = 10):
    sequences = []
    for i in range(m):
        sequences.append(make_random_sequence(n, period))
    return sequences

def get_outliers(S):
    # compute the average
    mean_S = sum(S)/len(S)

    print "the mean is %f" % mean_S
    for i in range(len(S)):
        if (S[i] >= 2*mean_S):
            print "outlier detected at position %d" % i

def printList(S):
    for item in S:
        print "%0.2f" % item,
    print
    
    
n = 15

S = make_random_sequence(n,5)
printList(S)

get_outliers(S)


m = 3
L = make_sequence_collection(m, n, 5)
print L


for list in L:
    printList(list)
print




# compute the average at each of the n positions in m datasets
profile = []
for j in range(n):
    sum_j = 0.0
    for i in range(m):
        sum_j += L[i][j] 
    profile.append(sum_j/m)

for item in profile:
    print "%0.2f" % item,    
print

get_outliers(profile)