def _xyinit(self,y=None):
if y is None: y = rand_ortho1(self.g.order())
x = rand_ortho1(self.g.order())
# translate and normalize:
x = x-x[0]
y = y-y[0]
sfactor = 1.0/max(y.max(),x.max())
return matrix(zip(x*sfactor,y*sfactor))
def optimal_arrangement(self):
b = self.balance()
y = rand_ortho1(self.g.order())
return self._conjugate_gradient_L(y,b)
