Simulate VC portfolios, calculate percent above benchmarks

bigdraws.py

import numpy as np

def vcdraws(alpha,n):
    # prob density distribution = 1/3 0x, 1/3 1x, 1/3 power law with alpha=alpha
    # returns list of n draws from the distribution
    pn=np.random.randint(0,3, size=n)
    td = powerlaw.Power_Law(xmin=1.0, parameters=[alpha])
    return [td.generate_random(1)[0] if pn[i]==2 else pn[i] for i in range(n)]

def vcportdraws(alpha,portsize,runs):
    # generate runs portfolios of size portsize
    # returns % of portfolios whose average is above 1..15 in 15-item list
    numd=portsize*runs
    ports = vcdraws(alpha,numd)
    avgs = [average(ports[i:i+portsize]) for i in range(0,numd,portsize)]
    count=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
    inc = 1./runs
    for i in avgs:
        for j in range(15):
            if i>(j+1): count[j]+=inc
    return [int(i*1000)/10. for i in count]