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# numpy.polynomial.laguerre.laggrid2d

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Evaluate a 2-D Laguerre series on the Cartesian product of x and y.

This function returns the values:

.. math:: p(a,b) = \sum_{i,j} c_{i,j} * L_i(a) * L_j(b)

where the points (a, b) consist of all pairs formed by taking
a from x and b from y. The resulting points form a grid with
x in the first dimension and y in the second.
(more...)


        def laggrid2d(x, y, c):
"""
Evaluate a 2-D Laguerre series on the Cartesian product of x and y.

This function returns the values:

.. math:: p(a,b) = \sum_{i,j} c_{i,j} * L_i(a) * L_j(b)

where the points (a, b) consist of all pairs formed by taking
a from x and b from y. The resulting points form a grid with
x in the first dimension and y in the second.

The parameters x and y are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars. In either
case, either x and y or their elements must support multiplication
and addition both with themselves and with the elements of c.

If c has fewer than two dimensions, ones are implicitly appended to
its shape to make it 2-D. The shape of the result will be c.shape[2:] +
x.shape + y.shape.

Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points in the
Cartesian product of x and y.  If x or y is a list or
tuple, it is first converted to an ndarray, otherwise it is left
unchanged and, if it isn't an ndarray, it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term of
multi-degree i,j is contained in c[i,j]. If c has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.

Returns
-------
values : ndarray, compatible object
The values of the two dimensional Chebyshev series at points in the
Cartesian product of x and y.

--------
lagval, lagval2d, lagval3d, laggrid3d

Notes
-----

"""
c = lagval(x, c)
c = lagval(y, c)
return c


        #test values
tgt = np.einsum('i,j->ij', y1, y2)
res = lag.laggrid2d(x1, x2, self.c2d)
assert_almost_equal(res, tgt)

#test shape
z = np.ones((2,3))
res = lag.laggrid2d(z, z, self.c2d)


        #test values