def test_matrix_1d(self):
x = np.linspace(0, 6, 7)
d2_dx2 = FinDiff(0, x[1]-x[0], 2)
u = x**2
mat = d2_dx2.matrix(u.shape)
np.testing.assert_array_almost_equal(2*np.ones_like(x), mat.dot(u.reshape(-1)))
def test_matrix_3d_nonuni_performance(self):
x = y = z = np.linspace(0, 4, 30)
X, Y, Z = np.meshgrid(x, y, z, indexing='ij')
laplace = FinDiff(0, x, 2) + FinDiff(1, y, 2) + FinDiff(2, z, 2)
u = X**2 + Y**2 + Z**2
mat = laplace.matrix(u.shape)
np.testing.assert_array_almost_equal(6 * np.ones_like(X).reshape(-1),
mat.dot(u.reshape(-1)))
def test_matrix_2d_mixed_nonuni(self):
x, y = [np.linspace(0, 5, 6), np.linspace(0, 6, 7)]
X, Y = np.meshgrid(x, y, indexing='ij')
d2_dxdy = FinDiff((0, x), (1, y))
u = X**2 * Y**2
mat = d2_dxdy.matrix(u.shape)
expected = d2_dxdy(u).reshape(-1)
actual = mat.dot(u.reshape(-1))
np.testing.assert_array_almost_equal(expected, actual)
def test_matrix_2d(self):
thr = np.get_printoptions()["threshold"]
lw = np.get_printoptions()["linewidth"]
np.set_printoptions(threshold=np.inf)
np.set_printoptions(linewidth=500)
x, y = [np.linspace(0, 5, 6)] * 2
X, Y = np.meshgrid(x, y, indexing='ij')
laplace = FinDiff(0, x[1]-x[0], 2) + FinDiff(0, y[1]-y[0], 2)
u = X**2 + Y**2
mat = laplace.matrix(u.shape)
print(mat.toarray())
np.testing.assert_array_almost_equal(4 * np.ones_like(X).reshape(-1), mat.dot(u.reshape(-1)))
np.set_printoptions(threshold=thr)
np.set_printoptions(linewidth=lw)