plfit/plfit_py.py
def plexp(x,xm=1,a=2.5):
    """
    CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin
    This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
    """

    C = 1/(-xm/(1 - a) - xm/a + math.exp(a)*xm/a)
    Ppl = lambda X: 1+C*(xm/(1-a)*(X/xm)**(1-a))
    Pexp = lambda X: C*xm/a*math.exp(a)-C*(xm/a)*math.exp(-a*(X/xm-1))
    d=Ppl(x)
    d[x<xm]=Pexp(x)
    return d
def plexp(x,xm=1,a=2.5):
    """
    CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin
    This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
    """

    C = 1/(-xm/(1 - a) - xm/a + math.exp(a)*xm/a)
    Ppl = lambda X: 1+C*(xm/(1-a)*(X/xm)**(1-a))
    Pexp = lambda X: C*xm/a*math.exp(a)-C*(xm/a)*math.exp(-a*(X/xm-1))
    d=Ppl(x)
    d[x<xm]=Pexp(x)
    return d
def plexp ( x , xm = 1 , a = 2.5 ) : C = 1 / ( - xm / ( 1 - a ) - xm / a + math . exp ( a ) * xm / a ) Ppl = lambda X : 1 + C * ( xm / ( 1 - a ) * ( X / xm ) ** ( 1 - a ) ) Pexp = lambda X : C * xm / a * math . exp ( a ) - C * ( xm / a ) * math . exp ( - a * ( X / xm - 1 ) ) d = Ppl ( x ) d [ x < xm ] = Pexp ( x ) return d
CDF(x) for the piecewise distribution exponential x<xmin, powerlaw x>=xmin
    This is the CDF version of the distributions drawn in fig 3.4a of Clauset et al.
CDF ( x ) for the piecewise distribution exponential x<xmin powerlaw x > = xmin This is the CDF version of the distributions drawn in fig 3 . 4a of Clauset et al .