def test_CXXmat(SXSX):
S1, S2 = SXSX
if S1.__class__.__name__ == "ShenDirichletBasis" and S2.__class__.__name__ == "ShenDirichletBasis":
Cm = CDDmat(np.arange(N).astype(np.float))
elif S1.__class__.__name__ == "ShenDirichletBasis":
Cm = CDNmat(np.arange(N).astype(np.float))
elif S1.__class__.__name__ == "ShenNeumannBasis":
Cm = CNDmat(np.arange(N).astype(np.float))
fj = np.random.randn(N)
# project to S2
f_hat = np.zeros(N)
f_hat = S2.fst(fj, f_hat)
fj = S2.ifst(f_hat, fj)
# Check S1.fss(f) equals Cm*S2.fst(f)
f_hat = S2.fst(fj, f_hat)
cs = Cm.matvec(f_hat)
df = np.zeros(N)
df = S2.fastChebDerivative(fj, df)
cs2 = np.zeros(N)
cs2 = S1.fastShenScalar(df, cs2)
# from IPython import embed; embed()
assert np.allclose(cs, cs2)
# Multidimensional version
f_hat = f_hat.repeat(4 * 4).reshape((N, 4, 4)) + 1j * f_hat.repeat(4 * 4).reshape((N, 4, 4))
df = df.repeat(4 * 4).reshape((N, 4, 4)) + 1j * df.repeat(4 * 4).reshape((N, 4, 4))
cs = Cm.matvec(f_hat)
cs2 = np.zeros((N, 4, 4), dtype=np.complex)
cs2 = S1.fastShenScalar(df, cs2)
assert np.allclose(cs, cs2)
def test_CDDmat(SD):
M = 256
u = (1 - x ** 2) * sin(np.pi * 6 * x)
dudx = u.diff(x, 1)
points, weights = SD.points_and_weights(M)
dudx_j = np.array([dudx.subs(x, h) for h in points], dtype=np.float)
uj = np.array([u.subs(x, h) for h in points], dtype=np.float)
dudx_j = np.zeros(M)
u_hat = np.zeros(M)
u_hat = SD.fst(uj, u_hat)
uj = SD.ifst(u_hat, uj)
u_hat = SD.fst(uj, u_hat)
uc_hat = np.zeros(M)
uc_hat = SD.fct(uj, uc_hat)
du_hat = np.zeros(M)
dudx_j = SD.fastChebDerivative(uj, dudx_j)
Cm = CDDmat(np.arange(M).astype(np.float))
TDMASolver = TDMA(SD.quad, False)
cs = Cm.matvec(u_hat)
# Should equal (but not exact so use extra resolution)
cs2 = np.zeros(M)
cs2 = SD.fastShenScalar(dudx_j, cs2)
assert np.allclose(cs, cs2)
cs = TDMASolver(cs)
du = np.zeros(M)
du = SD.ifst(cs, du)
assert np.linalg.norm(du - dudx_j) / M < 1e-10
# Multidimensional version
u3_hat = u_hat.repeat(4 * 4).reshape((M, 4, 4)) + 1j * u_hat.repeat(4 * 4).reshape((M, 4, 4))
cs = Cm.matvec(u3_hat)
cs2 = np.zeros((M, 4, 4), dtype=np.complex)
du3 = dudx_j.repeat(4 * 4).reshape((M, 4, 4)) + 1j * dudx_j.repeat(4 * 4).reshape((M, 4, 4))
cs2 = SD.fastShenScalar(du3, cs2)
assert np.allclose(cs, cs2, 1e-10)
cs = TDMASolver(cs)
d3 = np.zeros((M, 4, 4), dtype=np.complex)
d3 = SD.ifst(cs, d3)
# from IPython import embed; embed()
assert np.linalg.norm(du3 - d3) / (M * 16) < 1e-10
