stat_tools.py

import math

from random import gauss
from random import random

def MakeCallable(callble, param):
    def subroutine(x):
        return callble(x, param)
    return subroutine

def GetMeanValue(seq):
    return sum(seq) / len(seq)

def SigmaSubroutine(xi, mean):
    return (xi - mean) ** 2

def GetSigma(seq):
    m = GetMeanValue(seq)
    sub_sum = MakeCallable(SigmaSubroutine, m)
    mp = map(sub_sum, seq)
    summary, n = 0, 0
    for i in mp:
        n += 1
        summary += i
    return math.sqrt(summary / n)

def GeneratePositiveGauss(a=0, sigma=1, max_n=10, precision=2):
    val = -1
    while not 0 < val <= max_n:
        val = round(gauss(a, sigma), precision)
    return val

def LoadedToss(probability):
    return random() < probability


def GetMaxIncome(home_fame, away_fame, importance):
    def _attendance(price):
        return -0.005 * (price ** 2) + 2 * home_fame + 1.5 * away_fame + importance

    def _price_provider():
        for i in range(101):
            yield i * 10



    prices = [(_attendance(p) * p, p, _attendance(p)) for p in _price_provider()]
    return max(prices)