Evaluate bivariate polynomials
Hi all, how do I evaluate a bivariate polynomial p(x,y)=c_0 + c_1 x + c_2 y +c_3 x**2 + c_4 x*y+ c_5 y**2 + c_6 x**3 + c_7 x**2*y + c_8 x*y**2+c_9*y**3 + \dots in numpy ? In case of univariate polynomials I can use np.polyval. Any pointer would be appreciated. Nils
On Wed, Oct 19, 2011 at 5:58 AM, Nils Wagner <nwagner@iam.unistuttgart.de>wrote:
Hi all,
how do I evaluate a bivariate polynomial
p(x,y)=c_0 + c_1 x + c_2 y +c_3 x**2 + c_4 x*y+ c_5 y**2 + c_6 x**3 + c_7 x**2*y + c_8 x*y**2+c_9*y**3 + \dots
in numpy ?
In case of univariate polynomials I can use np.polyval.
Any pointer would be appreciated.
Here's a version for Bernstein polynomials that you could adapt. It's possible to fool with the 2d version to have it evaluate on the grid defined by x,y. As is, it evaluates on the x,y pairs. The coefficient c is a rectangular array. def bval(x, c): x = np.asarray(x) c = np.asarray(c) if len(c) == 1: c = c*np.ones(x.shape) else: t = 1  x for i in range(len(c)  1): c = c[:1]*t + c[1:]*x return c[0] def bval2d(x, y, c): f = bval(y, bval(x, c[...,None])) return f I use Bernstein polynomials for nonlinear least squares for numerical reasons and because they tend to work better with numerical differentiation. Chuck
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Charles R Harris

Nils Wagner