def test_regression_2359(self):
# Check regression --- for certain point sets, gradient
# estimation could end up in an infinite loop
points = np.load(data_file('estimate_gradients_hang.npy'))
values = np.random.rand(points.shape[0])
tri = qhull.Delaunay(points)
# This should not hang
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=interpnd.GradientEstimationWarning)
interpnd.estimate_gradients_2d_global(tri, values, maxiter=1)
def test_regression_2359(self):
# Check regression --- for certain point sets, gradient
# estimation could end up in an infinite loop
points = np.load(data_file('estimate_gradients_hang.npy'))
values = np.random.rand(points.shape[0])
tri = qhull.Delaunay(points)
# This should not hang
with suppress_warnings() as sup:
sup.filter(interpnd.GradientEstimationWarning,
"Gradient estimation did not converge")
interpnd.estimate_gradients_2d_global(tri, values, maxiter=1)
def test_regression_2359(self):
# Check regression --- for certain point sets, gradient
# estimation could end up in an infinite loop
points = np.load(data_file('estimate_gradients_hang.npy'))
values = np.random.rand(points.shape[0])
tri = qhull.Delaunay(points)
# This should not hang
with suppress_warnings() as sup:
sup.filter(interpnd.GradientEstimationWarning,
"Gradient estimation did not converge")
interpnd.estimate_gradients_2d_global(tri, values, maxiter=1)
def test_smoketest(self):
x = np.array([(0, 0), (0, 2), (1, 0), (1, 2), (0.25, 0.75), (0.6, 0.8)], dtype=float)
tri = qhull.Delaunay(x)
# Should be exact for linear functions, independent of triangulation
funcs = [
(lambda x, y: 0 * x + 1, (0, 0)),
(lambda x, y: 0 + x, (1, 0)),
(lambda x, y: -2 + y, (0, 1)),
(lambda x, y: 3 + 3 * x + 14.15 * y, (3, 14.15)),
]
for j, (func, grad) in enumerate(funcs):
z = func(x[:, 0], x[:, 1])
dz = interpnd.estimate_gradients_2d_global(tri, z, tol=1e-6)
assert_equal(dz.shape, (6, 2))
assert_allclose(dz, np.array(grad)[None, :] + 0 * dz, rtol=1e-5, atol=1e-5, err_msg="item %d" % j)
def test_smoketest(self):
x = np.array([(0, 0), (0, 2),
(1, 0), (1, 2), (0.25, 0.75), (0.6, 0.8)], dtype=float)
tri = qhull.Delaunay(x)
# Should be exact for linear functions, independent of triangulation
funcs = [
(lambda x, y: 0*x + 1, (0, 0)),
(lambda x, y: 0 + x, (1, 0)),
(lambda x, y: -2 + y, (0, 1)),
(lambda x, y: 3 + 3*x + 14.15*y, (3, 14.15))
]
for j, (func, grad) in enumerate(funcs):
z = func(x[:,0], x[:,1])
dz = interpnd.estimate_gradients_2d_global(tri, z, tol=1e-6)
assert_equal(dz.shape, (6, 2))
assert_allclose(dz, np.array(grad)[None,:] + 0*dz,
rtol=1e-5, atol=1e-5, err_msg="item %d" % j)
