def run_test_c2r(self, shape, axes):
ishape = list(shape)
ishape[axes[-1]] = shape[axes[-1]] // 2 + 1
ishape[-1] *= 2 # For complex
known_data = np.random.uniform(size=ishape).astype(np.float32).view(
np.complex64)
idata = bf.ndarray(known_data, space='cuda')
odata = bf.ndarray(shape=shape, dtype='f32', space='cuda')
fft = Fft()
fft.init(idata, odata, axes=axes)
fft.execute(idata, odata)
# Note: Numpy applies normalization while CUFFT does not
norm = reduce(lambda a, b: a * b, [shape[d] for d in axes])
known_result = gold_irfftn(known_data, axes=axes) * norm
np.testing.assert_allclose(odata.copy('system'), known_result, RTOL,
ATOL)
def run_test_c2r_impl(self, shape, axes, fftshift=False):
ishape = list(shape)
oshape = list(shape)
ishape[axes[-1]] = shape[axes[-1]] // 2 + 1
oshape[axes[-1]] = (ishape[axes[-1]] - 1) * 2
ishape[-1] *= 2 # For complex
known_data = np.random.normal(size=ishape).astype(np.float32).view(np.complex64)
idata = bf.ndarray(known_data, space='cuda')
odata = bf.ndarray(shape=oshape, dtype='f32', space='cuda')
fft = Fft()
fft.init(idata, odata, axes=axes, apply_fftshift=fftshift)
fft.execute(idata, odata)
# Note: Numpy applies normalization while CUFFT does not
norm = reduce(lambda a, b: a * b, [shape[d] for d in axes])
if fftshift:
known_data = np.fft.ifftshift(known_data, axes=axes)
known_result = gold_irfftn(known_data, axes=axes) * norm
compare(odata.copy('system'), known_result)