def p2x(self, p):
        """ Map parameters ``p`` to vector in x-space.

        x-space is a vector space of dimension ``p.size``. Its axes are
        in the directions specified by the eigenvectors of ``p``'s covariance
        matrix, and distance along an axis is in units of the standard
        deviation in that direction.
        """
        if hasattr(p, 'keys'):
            dp = BufferDict(p, keys=self.g.keys())._buf[:self.meanflat.size] - self.meanflat
        else:
            dp = numpy.asarray(p).reshape(-1) - self.meanflat
        return self.vec_isig.dot(dp)
def p2x ( self , p ) : if hasattr ( p , 'keys' ) : dp = BufferDict ( p , keys = self . g . keys ( ) ) . _buf [ : self . meanflat . size ] - self . meanflat else : dp = numpy . asarray ( p ) . reshape ( - 1 ) - self . meanflat return self . vec_isig . dot ( dp )
Map parameters ``p`` to vector in x-space.

        x-space is a vector space of dimension ``p.size``. Its axes are
        in the directions specified by the eigenvectors of ``p``'s covariance
        matrix, and distance along an axis is in units of the standard
        deviation in that direction.