Varriation method for fractal dimension in Python Numpy

import numpy as np

PRECISION = 100

def osc(tau, f, x, precision=10000):
    ens = np.linspace(x-tau, x+tau, precision)
    # Computes (precision)-values of f between x-tau and x+tau
    voisinage = f(ens)
    # returns the maximum amplitude
    return abs(np.amax(voisinage) - np.amin(voisinage))
vect_osc = np.vectorize(osc)

def var(tau, f, a, b):
    global PRECISION
    x = np.linspace(a, b, PRECISION)
    # Computes the oscillation of f for different values of x
    y = vect_osc(tau, f, x)
    # returns the average oscillation for each x around tau
    return np.average(y)
vect_var = np.vectorize(var)

def variation_dimension(f, a, b):
    global PRECISION

    # from 0.00...001 to 0.99...99 since log(1) = 0 and log(0) is undefined
    taus = np.linspace(1e-9, 1-1e-9, PRECISION)
    # Computes the variation for different values of tau
    variations = vect_var(taus, f, a, b)
    # Fits linearly the  x=log(taus) and y=log(variations)
    coeffs = np.polyfit(np.log(taus), np.log(variations), 1)
    # Computes the estimated fractal dimension
    dim = 2 - coeffs[0]
    return dim, taus, variations