def test_chebvander3d(self) :
# also tests chebval3d for non-square coefficient array
x1, x2, x3 = self.x
c = np.random.random((2, 3, 4))
van = cheb.chebvander3d(x1, x2, x3, [1, 2, 3])
tgt = cheb.chebval3d(x1, x2, x3, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)
# check shape
van = cheb.chebvander3d([x1], [x2], [x3], [1, 2, 3])
assert_(van.shape == (1, 5, 24))
def test_chebvander3d(self):
# also tests chebval3d for non-square coefficient array
x1, x2, x3 = self.x
c = np.random.random((2, 3, 4))
van = cheb.chebvander3d(x1, x2, x3, [1, 2, 3])
tgt = cheb.chebval3d(x1, x2, x3, c)
res = np.dot(van, c.flat)
assert_almost_equal(res, tgt)
# check shape
van = cheb.chebvander3d([x1], [x2], [x3], [1, 2, 3])
assert_(van.shape == (1, 5, 24))
def chebForwardTransform(orders, locations, functionVals):
if len(locations.shape) == 1:
return np.array(cheb.chebfit(locations, functionVals, orders[0]))
else:
if locations.shape[1] == 2:
V = cheb.chebvander2d(locations[:, 0], locations[:, 1], orders)
elif locations.shape[1] == 3:
V = cheb.chebvander3d(locations[:, 0], locations[:, 1],
locations[:, 2], orders)
elif locations.shape[1] == 4:
V = chebVander4d(locations, orders)
elif locations.shape[1] == 5:
V = chebVander5d(locations, orders)
else:
raise NotImplementedError # there's a bad startup joke about this being good enough for the paper.
ret, _, _, _ = npl.lstsq(V, functionVals, rcond=None)
return np.reshape(ret, (np.array(orders) + 1).flatten())