plfit/plfit.py
def plot_lognormal_cdf(self,**kwargs):
        """
        Plot the fitted lognormal distribution
        """
        if not hasattr(self,'lognormal_dist'):
            return

        x=np.sort(self.data)
        n=len(x)
        xcdf = np.arange(n,0,-1,dtype='float')/float(n)
        lcdf = self.lognormal_dist.sf(x)

        D_location = argmax(xcdf-lcdf)
        pylab.vlines(x[D_location],xcdf[D_location],lcdf[D_location],color='m',linewidth=2)

        pylab.plot(x, lcdf,',',**kwargs)
def plot_lognormal_cdf(self,**kwargs):
        """
        Plot the fitted lognormal distribution
        """
        if not hasattr(self,'lognormal_dist'):
            return

        x=np.sort(self.data)
        n=len(x)
        xcdf = np.arange(n,0,-1,dtype='float')/float(n)
        lcdf = self.lognormal_dist.sf(x)

        D_location = argmax(xcdf-lcdf)
        pylab.vlines(x[D_location],xcdf[D_location],lcdf[D_location],color='m',linewidth=2)

        pylab.plot(x, lcdf,',',**kwargs)
def plot_lognormal_cdf ( self , * * kwargs ) : if not hasattr ( self , 'lognormal_dist' ) : return x = np . sort ( self . data ) n = len ( x ) xcdf = np . arange ( n , 0 , - 1 , dtype = 'float' ) / float ( n ) lcdf = self . lognormal_dist . sf ( x ) D_location = argmax ( xcdf - lcdf ) pylab . vlines ( x [ D_location ] , xcdf [ D_location ] , lcdf [ D_location ] , color = 'm' , linewidth = 2 ) pylab . plot ( x , lcdf , ',' , * * kwargs )
Plot the fitted lognormal distribution
Plot the fitted lognormal distribution