def test_jacobi_ncc_solve(N, a0, b0, k_ncc, k_arg, dealias, dtype):
"""
This tests for aliasing errors when solving the equation
f(x)*u(x) = f(x)*g(x).
With 3/2 dealiasing, the RHS product will generally contain aliasing
errors in the last 2*max(k_ncc, k_arg) modes. We can eliminate these
by zeroing the corresponding number of modes of f(x) and/or g(x).
"""
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a=a0, b=b0, bounds=(0, 1), dealias=dealias)
b_ncc = b.clone_with(a=a0+k_ncc, b=b0+k_ncc)
b_arg = b.clone_with(a=a0+k_arg, b=b0+k_arg)
f = field.Field(dist=d, bases=(b_ncc,), dtype=dtype)
g = field.Field(dist=d, bases=(b_arg,), dtype=dtype)
u = field.Field(dist=d, bases=(b_arg,), dtype=dtype)
f['g'] = np.random.randn(*f['g'].shape)
g['g'] = np.random.randn(*g['g'].shape)
kmax = max(k_ncc, k_arg)
if kmax > 0 and dealias < 2:
f['c'][-kmax:] = 0
g['c'][-kmax:] = 0
problem = problems.LBVP([u])
problem.add_equation((f*u, f*g))
solver = solvers.LinearBoundaryValueSolver(problem)
solver.solve()
assert np.allclose(u['c'], g['c'])
def test_jacobi_differentiate(N, a, b, k, dtype):
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a0=a, b0=b, a=a+k, b=b+k, bounds=(0, 1))
x = b.local_grid(1)
f = field.Field(dist=d, bases=(b,), dtype=dtype)
f['g'] = x**5
fx = operators.Differentiate(f, c).evaluate()
assert np.allclose(fx['g'], 5*x**4)
def test_jacobi_convert_constant(N, a, b, k, dtype, layout):
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a0=a, b0=b, a=a+k, b=b+k, bounds=(0, 1))
fc = field.Field(dist=d, dtype=dtype)
fc['g'] = 1
fc[layout]
f = operators.convert(fc, (b,)).evaluate()
assert np.allclose(fc['g'], f['g'])
def test_jacobi_convert_explicit(N, a, b, k, dtype, layout):
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a0=a, b0=b, a=a+k, b=b+k, bounds=(0, 1))
x = b.local_grid(1)
f = field.Field(dist=d, bases=(b,), dtype=dtype)
f['g'] = x**5
fx = operators.Differentiate(f, c)
f[layout]
g = operators.convert(f, fx.domain.bases).evaluate()
assert np.allclose(g['g'], f['g'])
def test_jacobi_interpolate(N, a, b, k, dtype):
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a0=a, b0=b, a=a+k, b=b+k, bounds=(0, 1))
x = b.local_grid(1)
f = field.Field(dist=d, bases=(b,), dtype=dtype)
f['g'] = x**5
results = []
for p in [0, 1, np.random.rand()]:
fp = operators.Interpolate(f, c, p).evaluate()
results.append(np.allclose(fp['g'], p**5))
assert all(results)
def test_jacobi_ufunc(N, a, b, dtype, dealias, func):
c = coords.Coordinate('x')
d = distributor.Distributor((c, ))
b = basis.Jacobi(c, size=N, a=a, b=b, bounds=(0, 1), dealias=dealias)
x = b.local_grid(1)
f = field.Field(dist=d, bases=(b, ), dtype=dtype)
if func is np.arccosh:
f['g'] = 1 + x**2
else:
f['g'] = x**2
g0 = func(f['g'])
g = func(f).evaluate()
assert np.allclose(g['g'], g0)
def test_J_scalar_roundtrip(a, b, N, dealias, dtype):
if comm.size == 1:
c = coords.Coordinate('x')
d = distributor.Distributor([c])
xb = basis.Jacobi(c, a=a, b=b, size=N, bounds=(0, 1), dealias=dealias)
x = xb.local_grid(dealias)
# Scalar transforms
u = field.Field(dist=d, bases=(xb, ), dtype=dtype)
u.preset_scales(dealias)
u['g'] = ug = 2 * x**2 - 1
u['c']
assert np.allclose(u['g'], ug)
else:
pytest.skip("Can only test 1D transform in serial")
def test_jacobi_convert_implicit(N, a, b, k, dtype):
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a0=a, b0=b, a=a+k, b=b+k, bounds=(0, 1))
x = b.local_grid(1)
f = field.Field(dist=d, bases=(b,), dtype=dtype)
f['g'] = x**5
fx = operators.Differentiate(f, c).evaluate()
g = field.Field(dist=d, bases=(b,), dtype=dtype)
problem = problems.LBVP([g])
problem.add_equation((g, fx))
solver = solvers.LinearBoundaryValueSolver(problem)
solver.solve()
assert np.allclose(g['g'], fx['g'])
def build_CF_J(a, b, Nx, Ny, dealias_x, dealias_y):
c = coords.CartesianCoordinates('x', 'y')
d = distributor.Distributor((c, ))
xb = basis.ComplexFourier(c.coords[0],
size=Nx,
bounds=(0, np.pi),
dealias=dealias_x)
yb = basis.Jacobi(c.coords[1],
a=a,
b=b,
size=Ny,
bounds=(0, 1),
dealias=dealias_y)
x = xb.local_grid(dealias_x)
y = yb.local_grid(dealias_y)
return c, d, xb, yb, x, y
def test_jacobi_ncc_eval(N, a0, b0, k_ncc, k_arg, dealias, dtype):
"""
This tests for aliasing errors when evaluating the product
f(x) * g(x)
as both an NCC operator and with the pseudospectral method.
With 3/2 dealiasing, the product will generally contain aliasing
errors in the last 2*max(k_ncc, k_arg) modes. We can eliminate these
by zeroing the corresponding number of modes of f(x) and/or g(x).
"""
c = coords.Coordinate('x')
d = distributor.Distributor((c,))
b = basis.Jacobi(c, size=N, a=a0, b=b0, bounds=(0, 1), dealias=dealias)
b_ncc = b.clone_with(a=a0+k_ncc, b=b0+k_ncc)
b_arg = b.clone_with(a=a0+k_arg, b=b0+k_arg)
f = field.Field(dist=d, bases=(b_ncc,), dtype=dtype)
g = field.Field(dist=d, bases=(b_arg,), dtype=dtype)
f['g'] = np.random.randn(*f['g'].shape)
g['g'] = np.random.randn(*g['g'].shape)
x = b.local_grid(1)
f['g'] = np.cos(6*x)
g['g'] = np.cos(6*x)
kmax = max(k_ncc, k_arg)
if kmax > 0 and dealias < 2:
f['c'][-kmax:] = 0
g['c'][-kmax:] = 0
vars = [g]
w0 = f * g
w1 = w0.reinitialize(ncc=True, ncc_vars=vars)
problem = problems.LBVP(vars)
problem.add_equation((w1, 0))
solver = solvers.LinearBoundaryValueSolver(problem)
w1.store_ncc_matrices(vars, solver.subproblems)
w0 = w0.evaluate()
w2 = field.Field(dist=d, bases=(b,), dtype=dtype)
w2.preset_scales(w2.domain.dealias)
w2['g'] = w0['g']
w2['c']
w0['c']
print(np.max(np.abs(w2['g'] - w0['g'])))
w2 = operators.Convert(w2, w0.domain.bases[0]).evaluate()
w1 = w1.evaluate_as_ncc()
assert np.allclose(w0['c'], w2['c'])
