def test_idft_init(impl):
# Just check if the code runs at all; this uses the init function of
# DiscreteFourierTransform, so we don't need exhaustive tests here
shape = (4, 5)
ran = _dft_space(shape, dtype='complex128')
ran_hc = _dft_space(shape, dtype='float64')
dom = _dft_space(shape, dtype='complex128')
dom_hc = _dft_space((3, 5), dtype='complex128')
# Implicit range
DiscreteFourierTransformInverse(dom, impl=impl)
# Explicit range
DiscreteFourierTransformInverse(ran, domain=dom, impl=impl)
DiscreteFourierTransformInverse(ran_hc, domain=dom_hc, axes=(0,),
impl=impl, halfcomplex=True)
def test_dft_call(impl):
# 2d, complex, all ones and random back & forth
shape = (4, 5)
dft_dom = odl.discr_sequence_space(shape, dtype='complex64')
dft = DiscreteFourierTransform(domain=dft_dom, impl=impl)
idft = DiscreteFourierTransformInverse(range=dft_dom, impl=impl)
assert dft.domain == idft.range
assert dft.range == idft.domain
one = dft.domain.one()
one_dft1 = dft(one, flags=('FFTW_ESTIMATE', ))
one_dft2 = dft.inverse.inverse(one, flags=('FFTW_ESTIMATE', ))
one_dft3 = dft.adjoint.adjoint(one, flags=('FFTW_ESTIMATE', ))
true_dft = [
[20, 0, 0, 0, 0], # along all axes by default
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]
]
assert np.allclose(one_dft1, true_dft)
assert np.allclose(one_dft2, true_dft)
assert np.allclose(one_dft3, true_dft)
one_idft1 = idft(one_dft1, flags=('FFTW_ESTIMATE', ))
one_idft2 = dft.inverse(one_dft1, flags=('FFTW_ESTIMATE', ))
one_idft3 = dft.adjoint(one_dft1, flags=('FFTW_ESTIMATE', ))
assert np.allclose(one_idft1, one)
assert np.allclose(one_idft2, one)
assert np.allclose(one_idft3, one)
rand_arr = noise_element(dft_dom)
rand_arr_dft = dft(rand_arr, flags=('FFTW_ESTIMATE', ))
rand_arr_idft = idft(rand_arr_dft, flags=('FFTW_ESTIMATE', ))
assert (rand_arr_idft - rand_arr).norm() < 1e-6
# 2d, halfcomplex, first axis
shape = (4, 5)
axes = 0
dft_dom = odl.discr_sequence_space(shape, dtype='float32')
dft = DiscreteFourierTransform(domain=dft_dom,
impl=impl,
halfcomplex=True,
axes=axes)
idft = DiscreteFourierTransformInverse(range=dft_dom,
impl=impl,
halfcomplex=True,
axes=axes)
assert dft.domain == idft.range
assert dft.range == idft.domain
one = dft.domain.one()
one_dft = dft(one, flags=('FFTW_ESTIMATE', ))
true_dft = [
[4, 4, 4, 4, 4], # transform axis shortened
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]
]
assert np.allclose(one_dft, true_dft)
one_idft1 = idft(one_dft, flags=('FFTW_ESTIMATE', ))
one_idft2 = dft.inverse(one_dft, flags=('FFTW_ESTIMATE', ))
assert np.allclose(one_idft1, one)
assert np.allclose(one_idft2, one)
rand_arr = noise_element(dft_dom)
rand_arr_dft = dft(rand_arr, flags=('FFTW_ESTIMATE', ))
rand_arr_idft = idft(rand_arr_dft, flags=('FFTW_ESTIMATE', ))
assert (rand_arr_idft - rand_arr).norm() < 1e-6