def some_basic_tests():
import pyfftw
from mpi4py import MPI
comm = MPI.COMM_WORLD
N = 8
K0 = shenfun.fourier.bases.C2CBasis(N)
K1 = shenfun.fourier.bases.C2CBasis(N)
K2 = shenfun.fourier.bases.C2CBasis(N)
K3 = shenfun.fourier.bases.R2CBasis(N)
T = TensorProductSpace(comm, (K0, K1, K2, K3))
# Create data on rank 0 for testing
if comm.Get_rank() == 0:
f_g = np.random.random(T.shape())
f_g_hat = pyfftw.interfaces.numpy_fft.rfftn(f_g, axes=(0, 1, 2, 3))
else:
f_g = np.zeros(T.shape())
f_g_hat = np.zeros(T.spectral_shape(), dtype=np.complex)
# Distribute test data to all ranks
comm.Bcast(f_g, root=0)
comm.Bcast(f_g_hat, root=0)
# Create a function in real space to hold the test data
fj = shenfun.Array(T)
fj[:] = f_g[T.local_slice(False)]
# Perform forward transformation
f_hat = T.forward(fj)
assert np.allclose(f_g_hat[T.local_slice(True)], f_hat * N**4)
# Perform backward transformation
fj2 = shenfun.Array(T)
fj2 = T.backward(f_hat)
assert np.allclose(fj, fj2)
f_hat = T.scalar_product(fj)
# Padding
# Needs new instances of bases because arrays have new sizes
Kp0 = shenfun.fourier.bases.C2CBasis(N, padding_factor=1.5)
Kp1 = shenfun.fourier.bases.C2CBasis(N, padding_factor=1.5)
Kp2 = shenfun.fourier.bases.C2CBasis(N, padding_factor=1.5)
Kp3 = shenfun.fourier.bases.R2CBasis(N, padding_factor=1.5)
Tp = TensorProductSpace(comm, (Kp0, Kp1, Kp2, Kp3))
f_g_pad = Tp.backward(f_hat)
f_hat2 = Tp.forward(f_g_pad)
assert np.allclose(f_hat2, f_hat)
def test_to_ortho(basis, quad):
N = 10
if basis.family() == 'legendre':
B1 = lBasis[0](N, quad)
#B3 = lBasis[0](N, quad)
elif basis.family() == 'chebyshev':
if basis.short_name() == 'DU':
B1 = cBasisGC[0](N, quad)
else:
B1 = cBasis[0](N, quad)
#B3 = cBasis[0](N, quad)
elif basis.family() == 'laguerre':
B1 = laBasis[0](N, quad)
#B3 = laBasis[0](N, quad)
B0 = basis(N, quad=quad)
a = shenfun.Array(B0)
a_hat = shenfun.Function(B0)
b0_hat = shenfun.Function(B1)
b1_hat = shenfun.Function(B1)
a[:] = np.random.random(a.shape)
a_hat = a.forward(a_hat)
b0_hat = shenfun.project(a_hat, B1, output_array=b0_hat, fill=False, use_to_ortho=True)
b1_hat = shenfun.project(a_hat, B1, output_array=b1_hat, fill=False, use_to_ortho=False)
assert np.linalg.norm(b0_hat-b1_hat) < 1e-10
#B2 = basis(N, quad=quad)
TD = shenfun.TensorProductSpace(shenfun.comm, (B0, B0))
TC = shenfun.TensorProductSpace(shenfun.comm, (B1, B1))
a = shenfun.Array(TD)
a_hat = shenfun.Function(TD)
b0_hat = shenfun.Function(TC)
b1_hat = shenfun.Function(TC)
a[:] = np.random.random(a.shape)
a_hat = a.forward(a_hat)
b0_hat = shenfun.project(a_hat, TC, output_array=b0_hat, fill=False, use_to_ortho=True)
b1_hat = shenfun.project(a_hat, TC, output_array=b1_hat, fill=False, use_to_ortho=False)
assert np.linalg.norm(b0_hat-b1_hat) < 1e-10
F0 = shenfun.FunctionSpace(N, 'F')
TD = shenfun.TensorProductSpace(shenfun.comm, (B0, F0))
TC = shenfun.TensorProductSpace(shenfun.comm, (B1, F0))
a = shenfun.Array(TD)
a_hat = shenfun.Function(TD)
b0_hat = shenfun.Function(TC)
b1_hat = shenfun.Function(TC)
a[:] = np.random.random(a.shape)
a_hat = a.forward(a_hat)
b0_hat = shenfun.project(a_hat, TC, output_array=b0_hat, fill=False, use_to_ortho=True)
b1_hat = shenfun.project(a_hat, TC, output_array=b1_hat, fill=False, use_to_ortho=False)
assert np.linalg.norm(b0_hat-b1_hat) < 1e-10
def test_transforms(ST, quad, dim):
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST0 = ST(N, **kwargs)
fj = shenfun.Array(ST0)
fj[:] = np.random.random(fj.shape[0])
# Project function to space first
f_hat = shenfun.Function(ST0)
f_hat = ST0.forward(fj, f_hat)
fj = ST0.backward(f_hat, fj)
# Then check if transformations work as they should
u0 = shenfun.Function(ST0)
u1 = shenfun.Array(ST0)
u0 = ST0.forward(fj, u0)
u1 = ST0.backward(u0, u1)
assert np.allclose(fj, u1, rtol=1e-5, atol=1e-6)
u0 = ST0.forward(fj, u0)
u1 = ST0.backward(u0, u1)
assert np.allclose(fj, u1, rtol=1e-5, atol=1e-6)
u0 = ST0.forward(fj, u0, fast_transform=False)
u1 = ST0.backward(u0, u1, fast_transform=False)
assert np.allclose(fj, u1, rtol=1e-5, atol=1e-6)
# Multidimensional version
for axis in range(dim):
bc = [
np.newaxis,
] * dim
bc[axis] = slice(None)
fij = np.broadcast_to(fj[tuple(bc)], (N, ) * dim).copy()
ST1 = ST(N, **kwargs)
ST1.tensorproductspace = ABC(dim, ST0.coors)
ST1.plan((N, ) * dim, axis, fij.dtype, {})
u00 = shenfun.Function(ST1)
u11 = shenfun.Array(ST1)
u00 = ST1.forward(fij, u00)
u11 = ST1.backward(u00, u11)
cc = [
0,
] * dim
cc[axis] = slice(None)
cc = tuple(cc)
assert np.allclose(fij[cc], u11[cc], rtol=1e-5, atol=1e-6)
del ST1
def test_axis(ST, quad, axis):
kwargs = {'plan': True}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.points_and_weights(N)
f_hat = shenfun.Function(ST)
f_hat[:] = np.random.random(f_hat.shape[0])
B = inner_product((ST, 0), (ST, 0))
c = shenfun.Function(ST)
c = B.solve(f_hat, c)
# Multidimensional version
f0 = shenfun.Array(ST)
bc = [
np.newaxis,
] * 3
bc[axis] = slice(None)
ST.plan((N, ) * 3, axis, f0.dtype, {})
if hasattr(ST, 'bc'):
ST.bc.set_tensor_bcs(
ST
) # To set Dirichlet boundary conditions on multidimensional array
ck = shenfun.Function(ST)
fk = np.broadcast_to(f_hat[bc], ck.shape).copy()
ck = B.solve(fk, ck, axis=axis)
cc = [
0,
] * 3
cc[axis] = slice(None)
assert np.allclose(ck[cc], c)
def _setup_variational_problem(self):
self._setup_function_space()
u = sf.TrialFunction(self._V)
v = sf.TestFunction(self._V)
self._elastic_law.set_material_parameters(self._material_parameters)
self._dw_int = self._elastic_law.dw_int(u, v)
self._dw_ext = inner(
v, sf.Array(self._V.get_orthogonal(), buffer=(0, ) * self._dim))
if self._body_forces is not None:
V_body_forces = self._V.get_orthogonal()
body_forces_quad = sf.Array(V_body_forces,
buffer=self._body_forces)
self._dw_ext = inner(v, body_forces_quad)
def test_axis(ST, quad, axis):
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.points_and_weights(N)
f_hat = shenfun.Function(ST)
f_hat[:] = np.random.random(f_hat.shape[0])
B = inner_product((ST, 0), (ST, 0))
c = shenfun.Function(ST)
c = B.solve(f_hat, c)
# Multidimensional version
f0 = shenfun.Array(ST)
bc = [np.newaxis,]*3
bc[axis] = slice(None)
ST.tensorproductspace = ABC(3, ST.coors)
ST.plan((N,)*3, axis, f0.dtype, {})
if ST.has_nonhomogeneous_bcs:
ST.bc.set_tensor_bcs(ST, ST) # To set Dirichlet boundary conditions on multidimensional array
ck = shenfun.Function(ST)
fk = np.broadcast_to(f_hat[tuple(bc)], ck.shape).copy()
ck = B.solve(fk, ck, axis=axis)
cc = [1,]*3
cc[axis] = slice(None)
assert np.allclose(ck[tuple(cc)], c, rtol=1e-5, atol=1e-6)
def test_shear_test():
h = 0.1
length_ratio = 20
ell = h * length_ratio
N = (round(length_ratio / 5) * 30, 30)
domain = ((0., ell), (0., h))
print('Starting shear test ...')
for elastic_law in (LinearCauchyElasticity(), LinearGradientElasticity()):
for name_suffix in ('DisplacementControlled', 'TractionControlled'):
Shear = ShearTest(N, domain, elastic_law, name_suffix)
Shear.solve()
u_ana_dl = get_dimensionless_displacement(Shear.u_ana,
Shear._l_ref,
Shear._u_ref)
error_center = sf.Array(Shear.solution.function_space(),
buffer=u_ana_dl)[0, round(N[0] / 2), :] - \
Shear.solution.backward()[0, round(N[0] / 2), :]
error = np.linalg.norm(error_center)
assert error < 1e-5, 'Error tolerance not achieved'
Shear.postprocess()
print(
f'Error {elastic_law._name} ({name_suffix}):\t {error}\t N = {N}'
)
print('Finished tensile test (clamped)!')
def postprocess(self):
dirs = ['results', str(self.name), str(self.elastic_law.name)]
for d in dirs:
if not os.path.exists(d):
os.mkdir(d)
os.chdir(d)
output = []
# displacement
u = self.get_dimensional_solution()
V = u.function_space()
fl_disp_name = 'displacement'
fl_disp = sf.ShenfunFile(fl_disp_name, V, backend='hdf5', mode='w',
uniform=True)
output.append(fl_disp_name + '.h5')
for i in range(self.dim):
fl_disp.write(i, {'u': [u.backward(kind='uniform')]},
as_scalar=True)
# stress
stress, space = self.elastic_law.compute_cauchy_stresses(u)
fl_stress_name = 'cauchy_stress'
fl_stress = sf.ShenfunFile(fl_stress_name, space,
backend='hdf5', mode='w', uniform=True)
for i in range(self.dim):
for j in range(self.dim):
s = sf.Array(space, buffer=stress[i, j])
fl_stress.write(0, {f'S{i}{j}': [s]}, as_scalar=True)
output.append(fl_stress_name + '.h5')
# hyper stress
if self.elastic_law.name == 'LinearGradientElasticity':
stress, space = self.elastic_law.compute_hyper_stresses(u)
fl_stress_name = 'hyper_stress'
fl_stress = sf.ShenfunFile(fl_stress_name, space,
backend='hdf5', mode='w', uniform=True)
for i in range(self.dim):
for j in range(self.dim):
for k in range(self.dim):
s = sf.Array(space, buffer=stress[i, j])
fl_stress.write(0, {f'S{i}{j}{k}': [s]},
as_scalar=True)
output.append(fl_stress_name + '.h5')
self.write_xdmf_file(output)
os.chdir('../../..')
def test_tensor2():
B0 = shenfun.FunctionSpace(8, 'C')
T = shenfun.TensorProductSpace(comm, (B0, B0))
x, y = sp.symbols('x,y')
ue = x**2 + y**2
ua = shenfun.Array(T, buffer=ue)
uh = ua.forward()
M = shenfun.VectorSpace(T)
gradu = shenfun.project(grad(uh), M)
def test_transforms(ST, quad):
N = 10
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST0 = ST(N, **kwargs)
fj = shenfun.Array(ST0)
fj[:] = np.random.random(N)
fj = fj.forward().backward().copy()
assert np.allclose(fj, fj.forward().backward())
u0 = shenfun.Function(ST0)
u1 = shenfun.Array(ST0)
u0 = ST0.forward(fj, u0, fast_transform=False)
u1 = ST0.backward(u0, u1, fast_transform=False)
assert np.allclose(fj, u1, rtol=1e-5, atol=1e-6)
# Multidimensional version
ST0 = ST(N, **kwargs)
if ST0.short_name() in ('R2C', 'C2C'):
F0 = shenfun.FunctionSpace(N, 'F', dtype='D')
T0 = shenfun.TensorProductSpace(shenfun.comm, (F0, ST0))
else:
F0 = shenfun.FunctionSpace(N, 'F', dtype='d')
T0 = shenfun.TensorProductSpace(shenfun.comm, (F0, ST0))
fij = shenfun.Array(T0)
fij[:] = np.random.random(T0.shape(False))
fij = fij.forward().backward().copy()
assert np.allclose(fij, fij.forward().backward())
if ST0.short_name() in ('R2C', 'C2C'):
F0 = shenfun.FunctionSpace(N, 'F', dtype='D')
F1 = shenfun.FunctionSpace(N, 'F', dtype='D')
T = shenfun.TensorProductSpace(shenfun.comm, (F0, F1, ST0), dtype=ST0.dtype.char)
else:
F0 = shenfun.FunctionSpace(N, 'F', dtype='d')
F1 = shenfun.FunctionSpace(N, ST.family())
T = shenfun.TensorProductSpace(shenfun.comm, (F0, ST0, F1))
fij = shenfun.Array(T)
fij[:] = np.random.random(T.shape(False))
fij = fij.forward().backward().copy()
assert np.allclose(fij, fij.forward().backward())
def test_transforms(ST, quad, axis):
kwargs = {'plan': True}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.points_and_weights(N)
fj = shenfun.Array(ST)
fj[:] = np.random.random(fj.shape[0])
# Project function to space first
f_hat = shenfun.Function(ST)
f_hat = ST.forward(fj, f_hat)
fj = ST.backward(f_hat, fj)
# Then check if transformations work as they should
u0 = shenfun.Function(ST)
u1 = shenfun.Array(ST)
u0 = ST.forward(fj, u0)
u1 = ST.backward(u0, u1)
assert np.allclose(fj, u1)
u0 = ST.forward(fj, u0)
u1 = ST.backward(u0, u1)
assert np.allclose(fj, u1)
# Multidimensional version
bc = [
np.newaxis,
] * 3
bc[axis] = slice(None)
fj = np.broadcast_to(fj[bc], (N, ) * 3).copy()
ST.plan((N, ) * 3, axis, fj.dtype, {})
if hasattr(ST, 'bc'):
ST.bc.set_slices(ST) # To set Dirichlet boundary conditions
u00 = shenfun.Function(ST)
u11 = shenfun.Array(ST)
u00 = ST.forward(fj, u00)
u11 = ST.backward(u00, u11)
cc = [
0,
] * 3
cc[axis] = slice(None)
assert np.allclose(fj[cc], u11[cc])
def __call__(self, a_hat, b_hat, ab_hat=None):
"""Compute convolution of a_hat and b_hat without truncation
Parameters
----------
a_hat : Function
b_hat : Function
ab_hat : Function
"""
Tp = self.padding_space
T = self.newspace
if ab_hat is None:
ab_hat = shenfun.Function(T)
a = shenfun.Array(Tp)
b = shenfun.Array(Tp)
a = Tp.backward(a_hat, a)
b = Tp.backward(b_hat, b)
ab_hat = T.forward(a * b, ab_hat)
return ab_hat
def compute_numerical_error(u_ana, u_hat):
assert isinstance(u_hat, sf.Function)
V = u_hat.function_space()
# evaluate u_ana at quadrature points
error_array = sf.Array(V, buffer=u_ana)
# subtract numerical solution
error_array -= u_hat.backward()
# compute integral error
error = np.sqrt(sf.inner((1, 1), error_array**2))
return error
def test_scalarproduct(ST, quad):
"""Test fast scalar product against Vandermonde computed version"""
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
f = x*x+cos(pi*x)
fj = shenfun.Array(ST, buffer=f)
u0 = shenfun.Function(ST)
u1 = shenfun.Function(ST)
u0 = ST.scalar_product(fj, u0, fast_transform=True)
u1 = ST.scalar_product(fj, u1, fast_transform=False)
assert np.allclose(u1, u0)
assert not np.all(u1 == u0) # Check that fast is not the same as slow
def test_tensor2():
B0 = shenfun.FunctionSpace(8, 'C')
T = shenfun.TensorProductSpace(comm, (B0, B0))
x, y = sp.symbols('x,y')
ue = x**2 + y**2
ua = shenfun.Array(T, buffer=ue)
uh = ua.forward()
M = shenfun.VectorSpace(T)
gradu = shenfun.project(grad(uh), M)
V = shenfun.TensorSpace(T)
gradgradu = shenfun.project(grad(grad(uh)), V)
g = gradgradu.backward()
assert np.allclose(g.v[0], 2)
assert np.allclose(g.v[1], 0)
assert np.allclose(g.v[2], 0)
assert np.allclose(g.v[3], 2)
def test_project_1D(basis):
ue = sin(2*np.pi*x)*(1-x**2)
T = basis(12)
u = shenfun.TrialFunction(T)
v = shenfun.TestFunction(T)
u_tilde = shenfun.Function(T)
X = T.mesh()
ua = shenfun.Array(T, buffer=ue)
u_tilde = shenfun.inner(v, ua, output_array=u_tilde)
M = shenfun.inner(u, v)
u_p = shenfun.Function(T)
u_p = M.solve(u_tilde, u=u_p)
u_0 = shenfun.Function(T)
u_0 = shenfun.project(ua, T)
assert np.allclose(u_0, u_p)
u_1 = shenfun.project(ue, T)
assert np.allclose(u_1, u_p)
def test_scalarproduct(ST, quad):
"""Test fast scalar product against Vandermonde computed version"""
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.points_and_weights(N)
f = x * x + cos(pi * x)
fl = lambdify(x, f, 'numpy')
fj = shenfun.Array(ST)
fj[:] = fl(points)
u0 = shenfun.Function(ST)
u1 = shenfun.Function(ST)
u0 = ST.scalar_product(fj, u0, fast_transform=True)
u1 = ST.scalar_product(fj, u1, fast_transform=False)
assert np.allclose(u1, u0)
assert not np.all(u1 == u0) # Check that fast is not the same as slow
def test_eval(ST, quad):
"""Test eval against fast inverse"""
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.mpmath_points_and_weights(N)
fk = shenfun.Function(ST)
fj = shenfun.Array(ST)
fj[:] = np.random.random(fj.shape[0])
fk = ST.forward(fj, fk)
fj = ST.backward(fk, fj)
fk = ST.forward(fj, fk)
f = ST.eval(points, fk)
assert np.allclose(fj, f, rtol=1e-5, atol=1e-6), np.linalg.norm(fj-f)
fj = ST.backward(fk, fj, fast_transform=False)
fk = ST.forward(fj, fk, fast_transform=False)
f = ST.eval(points, fk)
assert np.allclose(fj, f, rtol=1e-5, atol=1e-6)
def check_with_pde(self, u_hat, material_parameters, body_forces):
assert isinstance(u_hat, sf.Function)
assert len(material_parameters) == self._n_material_parameters
for comp in body_forces:
assert isinstance(comp, (sp.Expr, float, int))
lambd, mu = material_parameters
V = u_hat.function_space().get_orthogonal()
# left hand side of pde
lhs = (lambd + mu) * grad(div(u_hat)) + mu * div(grad(u_hat))
error_array = sf.Array(V, buffer=body_forces)
error_array += sf.project(lhs, V).backward()
error = np.sqrt(inner((1, 1), error_array ** 2))
# scale by magnitude of solution
scale = np.sqrt(inner((1, 1), u_hat.backward() ** 2))
return error / scale
def test_eval(ST, quad):
"""Test eval agains fast inverse"""
kwargs = {}
if not ST.family() == 'fourier':
kwargs['quad'] = quad
ST = ST(N, **kwargs)
points, weights = ST.points_and_weights(N)
fk = shenfun.Function(ST)
fj = shenfun.Array(ST)
fj[:] = np.random.random(fj.shape[0])
fk = ST.forward(fj, fk)
fj = ST.backward(fk, fj)
fk = ST.forward(fj, fk)
f = ST.eval(points, fk)
#from IPython import embed; embed()
assert np.allclose(fj, f)
fj = ST.backward(fk, fj, fast_transform=False)
fk = ST.forward(fj, fk, fast_transform=False)
f = ST.eval(points, fk)
assert np.allclose(fj, f)
def test_CXXmat(test, trial):
test = test(N)
trial = trial(N)
CT = cBasis[0](N)
Cm = inner_product((test, 0), (trial, 1))
S2 = Cm.trialfunction[0]
S1 = Cm.testfunction[0]
fj = shenfun.Array(S2, buffer=np.random.randn(N))
# project to S2
f_hat = fj.forward()
fj = f_hat.backward(fj)
# Check S1.scalar_product(f) equals Cm*S2.forward(f)
f_hat = S2.forward(fj, f_hat)
cs = np.zeros_like(f_hat)
cs = Cm.matvec(f_hat, cs)
df = shenfun.project(shenfun.grad(f_hat), CT).backward()
cs2 = np.zeros(N)
cs2 = S1.scalar_product(df, cs2)
s = S1.slice()
assert np.allclose(cs[s], cs2[s], rtol=1e-5, atol=1e-6)
# 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 = np.zeros_like(f_hat)
cs = Cm.matvec(f_hat, cs)
cs2 = np.zeros((N, 4, 4), dtype=np.complex)
S1.tensorproductspace = ABC(3, S1.coors)
S1.plan((N, 4, 4), 0, np.complex, {})
cs2 = S1.scalar_product(df, cs2)
assert np.allclose(cs[s], cs2[s], rtol=1e-5, atol=1e-6)
def test_CXXmat(test, trial):
test = test(N)
trial = trial(N)
CT = cBasis[0](N)
Cm = inner_product((test, 0), (trial, 1))
S2 = Cm.trialfunction[0]
S1 = Cm.testfunction[0]
fj = shenfun.Array(S2, buffer=np.random.randn(N))
# project to S2
f_hat = fj.forward()
fj = f_hat.backward(fj)
# Check S1.scalar_product(f) equals Cm*S2.forward(f)
f_hat = S2.forward(fj, f_hat)
cs = np.zeros_like(f_hat)
cs = Cm.matvec(f_hat, cs)
df = shenfun.project(shenfun.grad(f_hat), CT).backward()
cs2 = np.zeros(N)
cs2 = S1.scalar_product(df, cs2)
s = S1.slice()
assert np.allclose(cs[s], cs2[s], rtol=1e-5, atol=1e-6)
import shenfun as sf
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
N = 10
L = sf.FunctionSpace(N, family='legendre', bc=(0, 0))
print(L.__class__)
# plot quadrature points
quad_points = L.mpmath_points_and_weights()[0]
plt.scatter(quad_points, np.zeros_like(quad_points))
# a projection
x = sp.symbols('x', real=True)
f = 1 - 1 / 2 * (3 * x**2 - 1) # the first Shen-Dirichlet basis function
P = sf.Function(L)
func = sf.project(f, L)
func_quad = sf.Array(L, buffer=f)
plt.scatter(quad_points, func_quad)
# should be orthogonal to all others except 0th and 2nd
scalprod = L.scalar_product(func_quad)
assert np.allclose(scalprod[np.r_[1, 3:N - 1]], 0)
assert np.logical_not(np.allclose(scalprod[np.r_[0, 2]], 0))
e = shenfun.Expr(basis)
e2 = -e
assert np.allclose(np.array(e.scales()).astype(np.int), (-np.array(e2.scales())).astype(np.int))
K0 = shenfun.FunctionSpace(N, 'F', dtype='D')
K1 = shenfun.FunctionSpace(N, 'F', dtype='D')
K2 = shenfun.FunctionSpace(N, 'F', dtype='d')
K3 = shenfun.FunctionSpace(N, 'C', dtype='d')
T = shenfun.TensorProductSpace(comm, (K0, K1, K2))
C = shenfun.TensorProductSpace(comm, (K1, K2, K3))
TT = shenfun.VectorTensorProductSpace(T)
CC = shenfun.VectorTensorProductSpace(C)
VT = shenfun.MixedTensorProductSpace([TT, T])
KK = shenfun.MixedTensorProductSpace([T, T, C])
vf = shenfun.Function(TT)
va = shenfun.Array(TT)
cf = shenfun.Function(CC)
ca = shenfun.Array(CC)
df = shenfun.Function(KK)
da = shenfun.Array(KK)
@pytest.mark.parametrize('u', (va, vf, cf, ca, df, da))
def test_index(u):
va0 = u[0]
va1 = u[1]
va2 = u[2]
assert (va0.index(), va1.index(), va2.index()) == (0, 1, 2)
assert va0.function_space() is u.function_space()[0]
assert va1.function_space() is u.function_space()[1]
assert va2.function_space() is u.function_space()[2]
