plfit/plfit.py
def discrete_best_alpha(data, alpharangemults=(0.9,1.1), n_alpha=201, approximate=True, verbose=True):
    """
    Use the maximum L to determine the most likely value of alpha

    *alpharangemults* [ 2-tuple ]
        Pair of values indicating multiplicative factors above and below the
        approximate alpha from the MLE alpha to use when determining the
        "exact" alpha (by directly maximizing the likelihood function)
    """

    xmins = np.unique(data)
    if approximate:
        alpha_of_xmin = [ discrete_alpha_mle(data,xmin) for xmin in xmins ]
    else:
        alpha_approx = [ discrete_alpha_mle(data,xmin) for xmin in xmins ]
        alpharanges = [(0.9*a,1.1*a) for a in alpha_approx]
        alpha_of_xmin = [ most_likely_alpha(data,xmin,alpharange=ar,n_alpha=n_alpha) for xmin,ar in zip(xmins,alpharanges) ]
    ksvalues = [ discrete_ksD(data, xmin, alpha) for xmin,alpha in zip(xmins,alpha_of_xmin) ]

    best_index = argmin(ksvalues)
    best_alpha = alpha_of_xmin[best_index]
    best_xmin = xmins[best_index]
    best_ks = ksvalues[best_index]
    best_likelihood = discrete_likelihood(data, best_xmin, best_alpha)

    if verbose:
        print("alpha = %f   xmin = %f   ksD = %f   L = %f   (n<x) = %i  (n>=x) = %i" % (
                best_alpha, best_xmin, best_ks, best_likelihood,
                (data<best_xmin).sum(), (data>=best_xmin).sum()))

    return best_alpha,best_xmin,best_ks,best_likelihood
def discrete_best_alpha(data, alpharangemults=(0.9,1.1), n_alpha=201, approximate=True, verbose=True):
    xmins = np.unique(data)
    if approximate:
        alpha_of_xmin = [ discrete_alpha_mle(data,xmin) for xmin in xmins ]
    else:
        alpha_approx = [ discrete_alpha_mle(data,xmin) for xmin in xmins ]
        alpharanges = [(0.9*a,1.1*a) for a in alpha_approx]
        alpha_of_xmin = [ most_likely_alpha(data,xmin,alpharange=ar,n_alpha=n_alpha) for xmin,ar in zip(xmins,alpharanges) ]
    ksvalues = [ discrete_ksD(data, xmin, alpha) for xmin,alpha in zip(xmins,alpha_of_xmin) ]

    best_index = argmin(ksvalues)
    best_alpha = alpha_of_xmin[best_index]
    best_xmin = xmins[best_index]
    best_ks = ksvalues[best_index]
    best_likelihood = discrete_likelihood(data, best_xmin, best_alpha)

    if verbose:
        print("alpha = %f   xmin = %f   ksD = %f   L = %f   (n<x) = %i  (n>=x) = %i" % (
                best_alpha, best_xmin, best_ks, best_likelihood,
                (data<best_xmin).sum(), (data>=best_xmin).sum()))

    return best_alpha,best_xmin,best_ks,best_likelihood
def discrete_best_alpha ( data , alpharangemults = ( 0.9 , 1.1 ) , n_alpha = 201 , approximate = True , verbose = True ) : xmins = np . unique ( data ) if approximate : alpha_of_xmin = [ discrete_alpha_mle ( data , xmin ) for xmin in xmins ] else : alpha_approx = [ discrete_alpha_mle ( data , xmin ) for xmin in xmins ] alpharanges = [ ( 0.9 * a , 1.1 * a ) for a in alpha_approx ] alpha_of_xmin = [ most_likely_alpha ( data , xmin , alpharange = ar , n_alpha = n_alpha ) for xmin , ar in zip ( xmins , alpharanges ) ] ksvalues = [ discrete_ksD ( data , xmin , alpha ) for xmin , alpha in zip ( xmins , alpha_of_xmin ) ] best_index = argmin ( ksvalues ) best_alpha = alpha_of_xmin [ best_index ] best_xmin = xmins [ best_index ] best_ks = ksvalues [ best_index ] best_likelihood = discrete_likelihood ( data , best_xmin , best_alpha ) if verbose : print ( "alpha = %f   xmin = %f   ksD = %f   L = %f   (n<x) = %i  (n>=x) = %i" % ( best_alpha , best_xmin , best_ks , best_likelihood , ( data < best_xmin ) . sum ( ) , ( data >= best_xmin ) . sum ( ) ) ) return best_alpha , best_xmin , best_ks , best_likelihood
Use the maximum L to determine the most likely value of alpha

    *alpharangemults* [ 2-tuple ]
        Pair of values indicating multiplicative factors above and below the
        approximate alpha from the MLE alpha to use when determining the
        "exact" alpha (by directly maximizing the likelihood function)
Use the maximum L to determine the most likely value of alpha