__docformat__ = 'restructuredtext'

import numpy as N

class VarianceFunction:
    """
    Variance function that relates the variance of a random variable
    to its mean. Defaults to 1.
    """

    def __call__(self, mu):
        """
        Default variance function

        INPUTS:
           mu  -- mean parameters

        OUTPUTS: v
           v   -- ones(mu.shape)
        """   
         
        return N.ones(mu.shape, N.float64)

constant = VarianceFunction()

class Power:
    """
    Power variance function:

    V(mu) = fabs(mu)**power
    
    INPUTS:
       power -- exponent used in power variance function

    """

    def __init__(self, power=1.):
        self.power = power

    def __call__(self, mu):
        
        """
        Power variance function

        INPUTS:
           mu  -- mean parameters

        OUTPUTS: v
           v   -- fabs(mu)**self.power
        """   
        return N.power(N.fabs(mu), self.power)

class Binomial:
    """
    Binomial variance function

    p = mu / n; V(mu) = p * (1 - p) * n

    INPUTS:
       n -- number of trials in Binomial 
    """

    tol = 1.0e-10

    def __init__(self, n=1):
        self.n = n

    def clean(self, p):
        return N.clip(p, Binomial.tol, 1 - Binomial.tol)

    def __call__(self, mu):
        """
        Binomial variance function

        INPUTS:
           mu  -- mean parameters

        OUTPUTS: v
           v   -- mu / self.n * (1 - mu / self.n) * self.n
        """   
        p = self.clean(mu / self.n)
        return p * (1 - p) * self.n

mu = Power()
mu_squared = Power(power=2)
mu_cubed = Power(power=3)
binary = Binomial()