def test_split_combine_complex(some_thr):
i1 = get_test_array((1000, ), numpy.float32)
i2 = get_test_array((1000, ), numpy.float32)
i1_dev = some_thr.to_device(i1)
i2_dev = some_thr.to_device(i2)
o1_dev = some_thr.empty_like(i1)
o2_dev = some_thr.empty_like(i2)
base_t = Type(numpy.complex64, shape=1000)
test = get_test_computation(base_t)
combine = tr.combine_complex(base_t)
split = tr.split_complex(base_t)
test.parameter.input.connect(combine,
combine.output,
i_real=combine.real,
i_imag=combine.imag)
test.parameter.output.connect(split,
split.input,
o_real=split.real,
o_imag=split.imag)
testc = test.compile(some_thr)
testc(o1_dev, o2_dev, i1_dev, i2_dev)
assert diff_is_negligible(o1_dev.get(), i1)
assert diff_is_negligible(o2_dev.get(), i2)
def __init__(self,
x,
NFFT=256,
noverlap=128,
pad_to=None,
window=hanning_window):
# print("x Data type = %s" % x.dtype)
# print("Is Real = %s" % dtypes.is_real(x.dtype))
# print("dim = %s" % x.ndim)
assert dtypes.is_real(x.dtype)
assert x.ndim == 1
rolling_frame_trf = rolling_frame(x, NFFT, noverlap, pad_to)
complex_dtype = dtypes.complex_for(x.dtype)
fft_arr = Type(complex_dtype, rolling_frame_trf.output.shape)
real_fft_arr = Type(x.dtype, rolling_frame_trf.output.shape)
window_trf = window(real_fft_arr, NFFT)
broadcast_zero_trf = transformations.broadcast_const(real_fft_arr, 0)
to_complex_trf = transformations.combine_complex(fft_arr)
amplitude_trf = transformations.norm_const(fft_arr, 1)
crop_trf = crop_frequencies(amplitude_trf.output)
fft = FFT(fft_arr, axes=(1, ))
fft.parameter.input.connect(to_complex_trf,
to_complex_trf.output,
input_real=to_complex_trf.real,
input_imag=to_complex_trf.imag)
fft.parameter.input_imag.connect(broadcast_zero_trf,
broadcast_zero_trf.output)
fft.parameter.input_real.connect(window_trf,
window_trf.output,
unwindowed_input=window_trf.input)
fft.parameter.unwindowed_input.connect(
rolling_frame_trf,
rolling_frame_trf.output,
flat_input=rolling_frame_trf.input)
fft.parameter.output.connect(amplitude_trf,
amplitude_trf.input,
amplitude=amplitude_trf.output)
fft.parameter.amplitude.connect(crop_trf,
crop_trf.input,
cropped_amplitude=crop_trf.output)
self._fft = fft
self._transpose = Transpose(fft.parameter.cropped_amplitude)
Computation.__init__(self, [
Parameter('output',
Annotation(self._transpose.parameter.output, 'o')),
Parameter('input', Annotation(fft.parameter.flat_input, 'i'))
])
def rfft(self, a, nthreads=ncpu):
a = self.check_array(a, RTYPES, RTYPE)
if SCIK and self.is_gpu_memory_enough(a):
shape = [s for s in a.shape]
shape[-1] = shape[-1]//2 + 1
dtype = G_RTYPES[a.dtype.type]
func = fft.fft
af = self._fft_scik(a, func, shape, dtype)
elif REIK and self.is_gpu_memory_enough(a):
thr = self.api.Thread(self.dev)
plan = FFT(Type(complex_for(a.dtype), a.shape))
# combines two real-valued inputs into a complex-valued input of the same shape
cc = combine_complex(plan.parameter.input)
# supplies a constant output
bc = broadcast_const(cc.imag, 0)
plan.parameter.input.connect(cc, cc.output, real_input=cc.real, imag_input=cc.imag)
plan.parameter.imag_input.connect(bc, bc.output)
fftc = plan.compile(thr, fast_math=True)
a_dev = thr.to_device(a)
a_out_dev = thr.empty_like(plan.parameter.output)
fftc(a_out_dev, a_dev)
af = a_out_dev.get()
af = N.fft.fftshift(af)
elif FFTW:
func = pyfftw.builders.rfftn
af = self._fftw(a, func, nthreads)
else:
af = N.fft.rfftn(a)
return af
def rfft(self, a, nthreads=ncpu):
a = self.check_array(a, RTYPES, RTYPE)
if SCIK and self.is_gpu_memory_enough(a):
shape = [s for s in a.shape]
shape[-1] = shape[-1]//2 + 1
dtype = G_RTYPES[a.dtype.type]
func = fft.fft
af = self._fft_scik(a, func, shape, dtype)
elif REIK and self.is_gpu_memory_enough(a):
thr = self.api.Thread(self.dev)
plan = FFT(Type(complex_for(a.dtype), a.shape))
# combines two real-valued inputs into a complex-valued input of the same shape
cc = combine_complex(plan.parameter.input)
# supplies a constant output
bc = broadcast_const(cc.imag, 0)
plan.parameter.input.connect(cc, cc.output, real_input=cc.real, imag_input=cc.imag)
plan.parameter.imag_input.connect(bc, bc.output)
fftc = plan.compile(thr, fast_math=True)
a_dev = thr.to_device(a)
a_out_dev = thr.empty_like(plan.parameter.output)
fftc(a_out_dev, a_dev)
af = a_out_dev.get()
af = N.fft.fftshift(af)
elif FFTW:
func = pyfftw.builders.rfftn
af = self._fftw(a, func, nthreads)
else:
af = N.fft.rfftn(a)
return af
def test_split_combine_complex(some_thr):
i1 = get_test_array((1000,), numpy.float32)
i2 = get_test_array((1000,), numpy.float32)
i1_dev = some_thr.to_device(i1)
i2_dev = some_thr.to_device(i2)
o1_dev = some_thr.empty_like(i1)
o2_dev = some_thr.empty_like(i2)
base_t = Type(numpy.complex64, shape=1000)
test = get_test_computation(base_t)
combine = tr.combine_complex(base_t)
split = tr.split_complex(base_t)
test.parameter.input.connect(combine, combine.output, i_real=combine.real, i_imag=combine.imag)
test.parameter.output.connect(split, split.input, o_real=split.real, o_imag=split.imag)
testc = test.compile(some_thr)
testc(o1_dev, o2_dev, i1_dev, i2_dev)
assert diff_is_negligible(o1_dev.get(), i1)
assert diff_is_negligible(o2_dev.get(), i2)
def __init__(self, x, NFFT=256, noverlap=128, pad_to=None, window=hanning_window):
assert dtypes.is_real(x.dtype)
assert x.ndim == 1
rolling_frame_trf = rolling_frame(x, NFFT, noverlap, pad_to)
complex_dtype = dtypes.complex_for(x.dtype)
fft_arr = Type(complex_dtype, rolling_frame_trf.output.shape)
real_fft_arr = Type(x.dtype, rolling_frame_trf.output.shape)
window_trf = window(real_fft_arr, NFFT)
broadcast_zero_trf = transformations.broadcast_const(real_fft_arr, 0)
to_complex_trf = transformations.combine_complex(fft_arr)
amplitude_trf = transformations.norm_const(fft_arr, 1)
crop_trf = crop_frequencies(amplitude_trf.output)
fft = FFT(fft_arr, axes=(1,))
fft.parameter.input.connect(
to_complex_trf, to_complex_trf.output,
input_real=to_complex_trf.real, input_imag=to_complex_trf.imag)
fft.parameter.input_imag.connect(
broadcast_zero_trf, broadcast_zero_trf.output)
fft.parameter.input_real.connect(
window_trf, window_trf.output, unwindowed_input=window_trf.input)
fft.parameter.unwindowed_input.connect(
rolling_frame_trf, rolling_frame_trf.output, flat_input=rolling_frame_trf.input)
fft.parameter.output.connect(
amplitude_trf, amplitude_trf.input, amplitude=amplitude_trf.output)
fft.parameter.amplitude.connect(
crop_trf, crop_trf.input, cropped_amplitude=crop_trf.output)
self._fft = fft
self._transpose = Transpose(fft.parameter.cropped_amplitude)
Computation.__init__(self,
[Parameter('output', Annotation(self._transpose.parameter.output, 'o')),
Parameter('input', Annotation(fft.parameter.flat_input, 'i'))])
