How to use the networkit.powerlaw.Distribution function in networkit

def fit(self, data=None):
        if data==None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
        self.n = len(data)
        from numpy import log, sum
        if not self.discrete and not self.xmax:
            self.alpha = 1 + ( self.n / sum( log( data / self.xmin ) ))
            if not self.in_range():
                Distribution.fit(self, data, suppress_output=True)
            self.KS(data)
        elif self.discrete and self.estimate_discrete and not self.xmax:
            self.alpha = 1 + ( self.n / sum( log( data / ( self.xmin - .5 ) ) ))
            if not self.in_range():
                Distribution.fit(self, data, suppress_output=True)
            self.KS(data)
        else:
            Distribution.fit(self, data, suppress_output=True)

        if not self.in_range():
            self.noise_flag=True
        else:
            self.noise_flag=False

#Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            from numpy import inf
            loglikelihoods[loglikelihoods==-inf] = log(10**float_info.min_10_exp)
        else:
            loglikelihoods = Distribution.loglikelihoods(self, data)
        return loglikelihoods

    def _generate_random_continuous(self, r):
        from numpy import log
#        return ( (self.xmin**self.beta) -
#            (1/self.Lambda) * log(1-r) )**(1/self.beta)
        return (1/self.Lambda)* ( (self.Lambda*self.xmin)**self.beta -
            log(1-r) )**(1/self.beta)

class Truncated_Power_Law(Distribution):

    def parameters(self, params):
        self.alpha = params[0]
        self.parameter1 = self.alpha
        self.parameter1_name = 'alpha'
        self.Lambda = params[1]
        self.parameter2 = self.Lambda
        self.parameter2_name = 'lambda'

    @property
    def name(self):
        return "truncated_power_law"

    def _initial_parameters(self, data):
        from numpy import log, sum, mean
        alpha = 1 + len(data)/sum( log( data / (self.xmin) ))

from numpy import log
#        likelihoods = exp(-Lambda*data)*\
#                Lambda*exp(Lambda*xmin)
            loglikelihoods = log(self.Lambda) + (self.Lambda*(self.xmin-data))
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            loglikelihoods[loglikelihoods==0] = log(10**float_info.min_10_exp)
        else:
            loglikelihoods = Distribution.loglikelihoods(self, data)
        return loglikelihoods

    def _generate_random_continuous(self, r):
        from numpy import log
        return self.xmin - (1/self.Lambda) * log(1-r)

class Streched_Exponential(Distribution):

    def parameters(self, params):
        self.Lambda = params[0]
        self.parameter1 = self.Lambda
        self.parameter1_name = 'lambda'
        self.beta = params[1]
        self.parameter2 = self.beta
        self.parameter2_name = 'beta'

    @property
    def name(self):
        return "stretched_exponential"

    def _initial_parameters(self, data):
        from numpy import mean
        return (1/mean(data), 1)

def pdf(self, data=None):
        if data==None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import exp
#        likelihoods = exp(-Lambda*data)*\
#                Lambda*exp(Lambda*xmin)
            likelihoods = self.Lambda*exp(self.Lambda*(self.xmin-data))
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            likelihoods[likelihoods==0] = 10**float_info.min_10_exp
        else:
            likelihoods = Distribution.pdf(self, data)
        return likelihoods

[self._double_search_discrete(R) for R in r]
                    ).astype('float')
        return x

    def _double_search_discrete(self, r):
        #Find a range from x1 to x2 that our random probability fits between
        x2 = int(self.xmin)
        while self.ccdf(data=[x2]) >= (1 - r):
            x1 = x2
            x2 = 2*x1
        #Use binary search within that range to find the exact answer, up to
        #the limit of being between two integers.
        x = bisect_map(x1, x2, self.ccdf, 1-r)
        return x

class Power_Law(Distribution):

    def __init__(self, estimate_discrete=True, **kwargs):
        self.estimate_discrete = estimate_discrete
        Distribution.__init__(self, **kwargs)

    def parameters(self, params):
        self.alpha = params[0]
        self.parameter1 = self.alpha
        self.parameter1_name = 'alpha'

    @property
    def name(self):
        return "power_law"

    @property
    def sigma(self):

def __init__(self, estimate_discrete=True, **kwargs):
        self.estimate_discrete = estimate_discrete
        Distribution.__init__(self, **kwargs)

if data==None and hasattr(self, 'parent_Fit'):
            data = self.parent_Fit.data
        if not self.discrete and self.in_range() and not self.xmax:
            data = trim_to_range(data, xmin=self.xmin, xmax=self.xmax)
            from numpy import log
            loglikelihoods = ( 
                    log((data*self.Lambda)**(self.beta-1) *
                        self.beta * self. Lambda) + 
                    (self.Lambda*self.xmin)**self.beta - 
                        (self.Lambda*data)**self.beta)
            #Simplified so as not to throw a nan from infs being divided by each other
            from sys import float_info
            from numpy import inf
            loglikelihoods[loglikelihoods==-inf] = log(10**float_info.min_10_exp)
        else:
            loglikelihoods = Distribution.loglikelihoods(self, data)
        return loglikelihoods

return likelihoods

    def _generate_random_continuous(self, r):
        def helper(r):
            from numpy import log
            from numpy.random import rand
            while 1:
                x = self.xmin - (1/self.Lambda) * log(1-r)
                p = ( x/self.xmin )**-self.alpha
                if rand()

    @property
    def _pdf_discrete_normalizer(self):
        C = 1.0 - self._cdf_xmin
        if self.xmax:
            C -= 1 - self._cdf_base_function(self.xmax+1)
        C = 1.0/C
        return C

    def _generate_random_continuous(self, r):
            return self.xmin * (1 - r) ** (-1/(self.alpha - 1))
    def _generate_random_discrete_estimate(self, r):
            x = (self.xmin - 0.5) * (1 - r) ** (-1/(self.alpha - 1)) + 0.5
            from numpy import around
            return around(x)

class Exponential(Distribution):

    def parameters(self, params):
        self.Lambda = params[0]
        self.parameter1 = self.Lambda
        self.parameter1_name = 'lambda'

    @property
    def name(self):
        return "exponential"

    def _initial_parameters(self, data):
        from numpy import mean
        return 1/mean(data)

    def _in_standard_parameter_range(self):
        return self.Lambda>0