def test_fspace_simple_attributes():
intv = odl.IntervalProd(0, 1)
fspace = FunctionSpace(intv)
fspace_r = FunctionSpace(intv, field=odl.RealNumbers())
fspace_c = FunctionSpace(intv, field=odl.ComplexNumbers())
assert fspace.domain == intv
assert fspace.range == odl.RealNumbers()
assert fspace_r.range == odl.RealNumbers()
assert fspace_c.range == odl.ComplexNumbers()
def test_fourier_trafo_range(exponent, odl_floating_dtype):
# Check if the range is initialized correctly. Encompasses the init test
dtype = odl_floating_dtype
# Testing R2C for real dtype, else C2C
# 1D
shape = 10
space_discr = odl.uniform_discr(0,
1,
shape,
exponent=exponent,
impl='numpy',
dtype=dtype)
dft = FourierTransform(space_discr, halfcomplex=True, shift=True)
assert dft.range.field == odl.ComplexNumbers()
halfcomplex = True if is_real_dtype(dtype) else False
assert dft.range.grid == reciprocal_grid(dft.domain.grid,
halfcomplex=halfcomplex,
shift=True)
assert dft.range.exponent == conj_exponent(exponent)
# 3D
shape = (3, 4, 5)
space_discr = odl.uniform_discr([0] * 3, [1] * 3,
shape,
exponent=exponent,
impl='numpy',
dtype=dtype)
dft = FourierTransform(space_discr, halfcomplex=True, shift=True)
assert dft.range.field == odl.ComplexNumbers()
halfcomplex = True if is_real_dtype(dtype) else False
assert dft.range.grid == reciprocal_grid(dft.domain.grid,
halfcomplex=halfcomplex,
shift=True)
assert dft.range.exponent == conj_exponent(exponent)
# shift must be True in the last axis
if halfcomplex:
with pytest.raises(ValueError):
FourierTransform(space_discr, shift=(True, True, False))
if exponent != 2.0:
with pytest.raises(NotImplementedError):
dft.adjoint
with pytest.raises(TypeError):
FourierTransform(dft.domain.partition)
def test_init(exponent):
# Validate that the different init patterns work and do not crash.
space = odl.FunctionSpace(odl.IntervalProd(0, 1))
part = odl.uniform_partition_fromintv(space.domain, 10)
rn = odl.rn(10, exponent=exponent)
odl.DiscreteLp(space, part, rn, exponent=exponent)
odl.DiscreteLp(space, part, rn, exponent=exponent, interp='linear')
# Normal discretization of unit interval with complex
complex_space = odl.FunctionSpace(odl.IntervalProd(0, 1),
field=odl.ComplexNumbers())
cn = odl.cn(10, exponent=exponent)
odl.DiscreteLp(complex_space, part, cn, exponent=exponent)
space = odl.FunctionSpace(odl.IntervalProd([0, 0], [1, 1]))
part = odl.uniform_partition_fromintv(space.domain, (10, 10))
rn = odl.rn(100, exponent=exponent)
odl.DiscreteLp(space, part, rn, exponent=exponent,
interp=['nearest', 'linear'])
# Real space should not work with complex
with pytest.raises(ValueError):
odl.DiscreteLp(space, part, cn)
# Complex space should not work with reals
with pytest.raises(ValueError):
odl.DiscreteLp(complex_space, part, rn)
# Wrong size of underlying space
rn_wrong_size = odl.rn(20)
with pytest.raises(ValueError):
odl.DiscreteLp(space, part, rn_wrong_size)
def test_dspace_type_numpy():
# Plain function set -> Ntuples-like
fset = odl.FunctionSet(odl.Interval(0, 1), odl.Strings(2))
assert dspace_type(fset, 'numpy') == odl.Ntuples, None
assert dspace_type(fset, 'numpy', np.int) == odl.Ntuples
# Real space
rspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.RealNumbers())
assert dspace_type(rspc, 'numpy') == odl.Fn
assert dspace_type(rspc, 'numpy', np.float32) == odl.Fn
assert dspace_type(rspc, 'numpy', np.int) == odl.Fn
with pytest.raises(TypeError):
dspace_type(rspc, 'numpy', np.complex)
with pytest.raises(TypeError):
dspace_type(rspc, 'numpy', np.dtype('<U2'))
# Complex space
cspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.ComplexNumbers())
assert dspace_type(cspc, 'numpy') == odl.Fn
assert dspace_type(cspc, 'numpy', np.complex64) == odl.Fn
with pytest.raises(TypeError):
dspace_type(cspc, 'numpy', np.float)
with pytest.raises(TypeError):
assert dspace_type(cspc, 'numpy', np.int)
with pytest.raises(TypeError):
dspace_type(cspc, 'numpy', np.dtype('<U2'))
def test_nearest_interpolation_1d_complex(fn_impl):
intv = odl.IntervalProd(0, 1)
part = odl.uniform_partition_fromintv(intv, 5, nodes_on_bdry=False)
# Coordinate vectors are:
# [0.1, 0.3, 0.5, 0.7, 0.9]
space = odl.FunctionSpace(intv, field=odl.ComplexNumbers())
dspace = odl.cn(part.size)
interp_op = NearestInterpolation(space, part, dspace)
function = interp_op([0 + 1j, 1 + 2j, 2 + 3j, 3 + 4j, 4 + 5j])
# Evaluate at single point
val = function(0.35) # closest to index 1 -> 1 + 2j
assert val == 1.0 + 2.0j
# Input array, with and without output array
pts = np.array([0.4, 0.0, 0.65, 0.95])
true_arr = [1 + 2j, 0 + 1j, 3 + 4j, 4 + 5j]
assert all_equal(function(pts), true_arr)
# Should also work with a (1, N) array
pts = pts[None, :]
assert all_equal(function(pts), true_arr)
out = np.empty(4, dtype='complex128')
function(pts, out=out)
assert all_equal(out, true_arr)
# Input meshgrid, with and without output array
# Same as array for 1d
mg = sparse_meshgrid([0.4, 0.0, 0.65, 0.95])
true_mg = [1 + 2j, 0 + 1j, 3 + 4j, 4 + 5j]
assert all_equal(function(mg), true_mg)
function(mg, out=out)
assert all_equal(out, true_mg)
def test_dspace_type_cuda():
# Plain function set -> Ntuples-like
fset = odl.FunctionSet(odl.Interval(0, 1), odl.Strings(2))
assert dspace_type(fset, 'cuda') == odl.CudaNtuples
assert dspace_type(fset, 'cuda', np.int) == odl.CudaNtuples
# Real space
rspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.RealNumbers())
assert dspace_type(rspc, 'cuda') == odl.CudaFn
assert dspace_type(rspc, 'cuda', np.float64) == odl.CudaFn
assert dspace_type(rspc, 'cuda', np.int) == odl.CudaFn
with pytest.raises(TypeError):
dspace_type(rspc, 'cuda', np.complex)
with pytest.raises(TypeError):
dspace_type(rspc, 'cuda', np.dtype('<U2'))
# Complex space (not implemented)
cspc = odl.FunctionSpace(odl.Interval(0, 1), field=odl.ComplexNumbers())
with pytest.raises(NotImplementedError):
dspace_type(cspc, 'cuda')
with pytest.raises(NotImplementedError):
dspace_type(cspc, 'cuda', np.complex64)
with pytest.raises(TypeError):
dspace_type(cspc, 'cuda', np.float)
with pytest.raises(TypeError):
assert dspace_type(cspc, 'cuda', np.int)
def test_fspace_vector_real_imag():
rect, _, mg = _standard_setup_2d()
cspace = FunctionSpace(rect, field=odl.ComplexNumbers())
f = cspace.element(cfunc_2d_vec_oop)
# real / imag on complex functions
assert all_equal(f.real(mg), cfunc_2d_vec_oop(mg).real)
assert all_equal(f.imag(mg), cfunc_2d_vec_oop(mg).imag)
out_mg = np.empty((2, 3))
f.real(mg, out=out_mg)
assert all_equal(out_mg, cfunc_2d_vec_oop(mg).real)
f.imag(mg, out=out_mg)
assert all_equal(out_mg, cfunc_2d_vec_oop(mg).imag)
# real / imag on real functions, should be the function itself / zero
rspace = FunctionSpace(rect)
f = rspace.element(func_2d_vec_oop)
assert all_equal(f.real(mg), f(mg))
assert all_equal(f.imag(mg), rspace.zero()(mg))
# Complex conjugate
f = cspace.element(cfunc_2d_vec_oop)
fbar = f.conj()
assert all_equal(fbar(mg), cfunc_2d_vec_oop(mg).conj())
out_mg = np.empty((2, 3), dtype='complex128')
fbar(mg, out=out_mg)
assert all_equal(out_mg, cfunc_2d_vec_oop(mg).conj())
def test_fspace_vector_init():
# 1d, real
intv = odl.IntervalProd(0, 1)
fspace = FunctionSpace(intv)
fspace.element(func_1d_oop)
fspace.element(func_1d_oop, vectorized=False)
fspace.element(func_1d_oop, vectorized=True)
fspace.element(func_1d_ip, vectorized=True)
fspace.element(func_1d_dual, vectorized=True)
# 2d, real
rect = odl.IntervalProd([0, 0], [1, 2])
fspace = FunctionSpace(rect)
fspace.element(func_2d_novec, vectorized=False)
fspace.element(func_2d_vec_oop)
fspace.element(func_2d_vec_oop, vectorized=True)
fspace.element(func_2d_vec_ip, vectorized=True)
fspace.element(func_2d_vec_dual, vectorized=True)
# 2d, complex
fspace = FunctionSpace(rect, field=odl.ComplexNumbers())
fspace.element(cfunc_2d_novec, vectorized=False)
fspace.element(cfunc_2d_vec_oop)
fspace.element(cfunc_2d_vec_oop, vectorized=True)
fspace.element(cfunc_2d_vec_ip, vectorized=True)
fspace.element(cfunc_2d_vec_dual, vectorized=True)
def test_uniform_discr_init_complex(tspace_impl):
"""Test initialization and basic properties with uniform_discr, complex."""
if tspace_impl != 'numpy':
pytest.xfail(reason='complex dtypes not supported')
discr = odl.uniform_discr(0, 1, 10, dtype='complex', impl=tspace_impl)
assert discr.is_complex
assert discr.dtype == discr.tspace.default_dtype(odl.ComplexNumbers())
def test_fspace_vector_eval_complex():
rect, points, mg = _standard_setup_2d()
fspace = FunctionSpace(rect, field=odl.ComplexNumbers())
f_novec = fspace.element(cfunc_2d_novec, vectorized=False)
f_vec_oop = fspace.element(cfunc_2d_vec_oop, vectorized=True)
f_vec_ip = fspace.element(cfunc_2d_vec_ip, vectorized=True)
f_vec_dual = fspace.element(cfunc_2d_vec_dual, vectorized=True)
true_arr = cfunc_2d_vec_oop(points)
true_mg = cfunc_2d_vec_oop(mg)
# Out-of-place
assert f_novec([0.5, 1.5]) == cfunc_2d_novec([0.5, 1.5])
assert f_vec_oop([0.5, 1.5]) == cfunc_2d_novec([0.5, 1.5])
assert all_equal(f_vec_oop(points), true_arr)
assert all_equal(f_vec_oop(mg), true_mg)
# In-place standard implementation
out_arr = np.empty((5, ), dtype='complex128')
out_mg = np.empty((2, 3), dtype='complex128')
f_vec_oop(points, out=out_arr)
f_vec_oop(mg, out=out_mg)
assert all_equal(out_arr, true_arr)
assert all_equal(out_mg, true_mg)
with pytest.raises(TypeError): # ValueError: invalid vectorized input
f_vec_oop(points[0])
# In-place-only
out_arr = np.empty((5, ), dtype='complex128')
out_mg = np.empty((2, 3), dtype='complex128')
f_vec_ip(points, out=out_arr)
f_vec_ip(mg, out=out_mg)
assert all_equal(out_arr, true_arr)
assert all_equal(out_mg, true_mg)
# Standard out-of-place evaluation
assert f_vec_ip([0.5, 1.5]) == cfunc_2d_novec([0.5, 1.5])
assert all_equal(f_vec_ip(points), true_arr)
assert all_equal(f_vec_ip(mg), true_mg)
# Dual use
assert f_vec_dual([0.5, 1.5]) == cfunc_2d_novec([0.5, 1.5])
assert all_equal(f_vec_dual(points), true_arr)
assert all_equal(f_vec_dual(mg), true_mg)
out_arr = np.empty((5, ), dtype='complex128')
out_mg = np.empty((2, 3), dtype='complex128')
f_vec_dual(points, out=out_arr)
f_vec_dual(mg, out=out_mg)
assert all_equal(out_arr, true_arr)
assert all_equal(out_mg, true_mg)
def test_fspace_init():
intv = odl.IntervalProd(0, 1)
FunctionSpace(intv)
FunctionSpace(intv, field=odl.RealNumbers())
FunctionSpace(intv, field=odl.ComplexNumbers())
rect = odl.IntervalProd([0, 0], [1, 2])
FunctionSpace(rect)
FunctionSpace(rect, field=odl.RealNumbers())
FunctionSpace(rect, field=odl.ComplexNumbers())
cube = odl.IntervalProd([0, 0, 0], [1, 2, 3])
FunctionSpace(cube)
FunctionSpace(cube, field=odl.RealNumbers())
FunctionSpace(cube, field=odl.ComplexNumbers())
ndbox = odl.IntervalProd([0] * 10, np.arange(1, 11))
FunctionSpace(ndbox)
FunctionSpace(ndbox, field=odl.RealNumbers())
FunctionSpace(ndbox, field=odl.ComplexNumbers())
def test_fspace_equality():
intv = odl.Interval(0, 1)
intv2 = odl.Interval(-1, 1)
fspace = FunctionSpace(intv)
fspace_r = FunctionSpace(intv, field=odl.RealNumbers())
fspace_c = FunctionSpace(intv, field=odl.ComplexNumbers())
fspace_intv2 = FunctionSpace(intv2)
assert fspace == fspace_r
assert fspace != fspace_c
assert fspace != fspace_intv2
def test_equals():
"""Test equality check and hash."""
intv = odl.IntervalProd(0, 1)
intv2 = odl.IntervalProd(-1, 1)
fspace = FunctionSpace(intv)
fspace_r = FunctionSpace(intv, field=odl.RealNumbers())
fspace_c = FunctionSpace(intv, field=odl.ComplexNumbers())
fspace_intv2 = FunctionSpace(intv2)
_test_eq(fspace, fspace)
_test_eq(fspace, fspace_r)
_test_eq(fspace_c, fspace_c)
_test_neq(fspace, fspace_c)
_test_neq(fspace, fspace_intv2)
def test_factory(exponent):
discr = odl.uniform_discr(0, 1, 10, impl='numpy', exponent=exponent)
assert isinstance(discr.dspace, odl.Fn)
assert discr.is_rn
assert discr.dspace.exponent == exponent
assert discr.dtype == odl.Fn.default_dtype(odl.RealNumbers())
# Complex
discr = odl.uniform_discr(0, 1, 10, dtype='complex',
impl='numpy', exponent=exponent)
assert isinstance(discr.dspace, odl.Fn)
assert discr.is_cn
assert discr.dspace.exponent == exponent
assert discr.dtype == odl.Fn.default_dtype(odl.ComplexNumbers())
def test_fspace_attributes():
"""Check attribute access and correct values."""
intv = odl.IntervalProd(0, 1)
# Scalar-valued function spaces
fspace = FunctionSpace(intv)
fspace_r = FunctionSpace(intv, out_dtype=float)
fspace_c = FunctionSpace(intv, out_dtype=complex)
fspace_s = FunctionSpace(intv, out_dtype='U1')
scalar_spaces = (fspace, fspace_r, fspace_c, fspace_s)
assert fspace.domain == intv
assert fspace.field == odl.RealNumbers()
assert fspace_r.field == odl.RealNumbers()
assert fspace_c.field == odl.ComplexNumbers()
assert fspace_s.field is None
assert fspace.out_dtype == float
assert fspace_r.out_dtype == float
assert fspace_r.real_out_dtype == float
assert fspace_r.complex_out_dtype == complex
assert fspace_c.out_dtype == complex
assert fspace_c.real_out_dtype == float
assert fspace_c.complex_out_dtype == complex
assert fspace_s.out_dtype == np.dtype('U1')
assert fspace.is_real
assert not fspace.is_complex
assert fspace_r.is_real
assert not fspace_r.is_complex
assert fspace_c.is_complex
assert not fspace_c.is_real
with pytest.raises(AttributeError):
fspace_s.real_out_dtype
with pytest.raises(AttributeError):
fspace_s.complex_out_dtype
assert all(spc.scalar_out_dtype == spc.out_dtype for spc in scalar_spaces)
assert all(spc.out_shape == () for spc in scalar_spaces)
assert all(not spc.tensor_valued for spc in scalar_spaces)
# Vector-valued function space
fspace_vec = FunctionSpace(intv, out_dtype=(float, (2, )))
assert fspace_vec.field == odl.RealNumbers()
assert fspace_vec.out_dtype == np.dtype((float, (2, )))
assert fspace_vec.scalar_out_dtype == float
assert fspace_vec.out_shape == (2, )
assert fspace_vec.tensor_valued
def test_fspace_one():
rect, points, mg = _standard_setup_2d()
# real
fspace = FunctionSpace(rect)
one_vec = fspace.one()
assert one_vec([0.5, 1.5]) == 1.0
assert all_equal(one_vec(points), np.ones(5, dtype=float))
assert all_equal(one_vec(mg), np.ones((2, 3), dtype=float))
# complex
fspace = FunctionSpace(rect, field=odl.ComplexNumbers())
one_vec = fspace.one()
assert one_vec([0.5, 1.5]) == 1.0 + 1j * 0.0
assert all_equal(one_vec(points), np.ones(5, dtype=complex))
assert all_equal(one_vec(mg), np.ones((2, 3), dtype=complex))
def test_fspace_astype():
rspace = FunctionSpace(odl.IntervalProd(0, 1))
cspace = FunctionSpace(odl.IntervalProd(0, 1), field=odl.ComplexNumbers())
rspace_s = FunctionSpace(odl.IntervalProd(0, 1), out_dtype='float32')
cspace_s = FunctionSpace(odl.IntervalProd(0, 1), out_dtype='complex64')
assert rspace.astype('complex64') == cspace_s
assert rspace.astype('complex128') == cspace
assert rspace.astype('complex128') is rspace.complex_space
assert rspace.astype('float32') == rspace_s
assert rspace.astype('float64') is rspace.real_space
assert cspace.astype('float32') == rspace_s
assert cspace.astype('float64') == rspace
assert cspace.astype('float64') is cspace.real_space
assert cspace.astype('complex64') == cspace_s
assert cspace.astype('complex128') is cspace.complex_space
def test_fourier_trafo_scaling():
# Test if the FT scales correctly
# Characteristic function of [0, 1], its Fourier transform is
# given by exp(-1j * y / 2) * sinc(y/2)
def char_interval(x):
return (x >= 0) & (x <= 1)
def char_interval_ft(x):
return np.exp(-1j * x / 2) * sinc(x / 2) / np.sqrt(2 * np.pi)
fspace = odl.FunctionSpace(odl.IntervalProd(-2, 2),
field=odl.ComplexNumbers())
discr = odl.uniform_discr_fromspace(fspace, 40, impl='numpy')
dft = FourierTransform(discr)
for factor in (2, 1j, -2.5j, 1 - 4j):
func_true_ft = factor * dft.range.element(char_interval_ft)
func_dft = dft(factor * fspace.element(char_interval))
assert (func_dft - func_true_ft).norm() < 1e-6
