plfit/plfit.py
def plotppf(self,x=None,xmin=None,alpha=None,dolog=True,**kwargs):
        """
        Plots the power-law-predicted value on the Y-axis against the real
        values along the X-axis.  Can be used as a diagnostic of the fit
        quality.
        """
        if not(xmin): xmin=self._xmin
        if not(alpha): alpha=self._alpha
        if not(x): x=np.sort(self.data[self.data>xmin])
        else: x=np.sort(x[x>xmin])

        # N = M^(-alpha+1)
        # M = N^(1/(-alpha+1))

        m0 = min(x)
        N = (1.0+np.arange(len(x)))[::-1]
        xmodel = m0 * N**(1/(1-alpha)) / max(N)**(1/(1-alpha))

        if dolog:
            pylab.loglog(x,xmodel,'.',**kwargs)
            pylab.gca().set_xlim(min(x),max(x))
            pylab.gca().set_ylim(min(x),max(x))
        else:
            pylab.plot(x,xmodel,'.',**kwargs)
        pylab.plot([min(x),max(x)],[min(x),max(x)],'k--')
        pylab.xlabel("Real Value")
        pylab.ylabel("Power-Law Model Value")
def plotppf(self,x=None,xmin=None,alpha=None,dolog=True,**kwargs):
        if not(xmin): xmin=self._xmin
        if not(alpha): alpha=self._alpha
        if not(x): x=np.sort(self.data[self.data>xmin])
        else: x=np.sort(x[x>xmin])

        # N = M^(-alpha+1)
        # M = N^(1/(-alpha+1))

        m0 = min(x)
        N = (1.0+np.arange(len(x)))[::-1]
        xmodel = m0 * N**(1/(1-alpha)) / max(N)**(1/(1-alpha))

        if dolog:
            pylab.loglog(x,xmodel,'.',**kwargs)
            pylab.gca().set_xlim(min(x),max(x))
            pylab.gca().set_ylim(min(x),max(x))
        else:
            pylab.plot(x,xmodel,'.',**kwargs)
        pylab.plot([min(x),max(x)],[min(x),max(x)],'k--')
        pylab.xlabel("Real Value")
        pylab.ylabel("Power-Law Model Value")
def plotppf ( self , x = None , xmin = None , alpha = None , dolog = True , * * kwargs ) : if not ( xmin ) : xmin = self . _xmin if not ( alpha ) : alpha = self . _alpha if not ( x ) : x = np . sort ( self . data [ self . data > xmin ] ) else : x = np . sort ( x [ x > xmin ] ) # N = M^(-alpha+1) # M = N^(1/(-alpha+1)) m0 = min ( x ) N = ( 1.0 + np . arange ( len ( x ) ) ) [ : : - 1 ] xmodel = m0 * N ** ( 1 / ( 1 - alpha ) ) / max ( N ) ** ( 1 / ( 1 - alpha ) ) if dolog : pylab . loglog ( x , xmodel , '.' , * * kwargs ) pylab . gca ( ) . set_xlim ( min ( x ) , max ( x ) ) pylab . gca ( ) . set_ylim ( min ( x ) , max ( x ) ) else : pylab . plot ( x , xmodel , '.' , * * kwargs ) pylab . plot ( [ min ( x ) , max ( x ) ] , [ min ( x ) , max ( x ) ] , 'k--' ) pylab . xlabel ( "Real Value" ) pylab . ylabel ( "Power-Law Model Value" )
Plots the power-law-predicted value on the Y-axis against the real
        values along the X-axis.  Can be used as a diagnostic of the fit
        quality.
Plots the power - law - predicted value on the Y - axis against the real values along the X - axis . Can be used as a diagnostic of the fit quality .