def __init__(self, arr_t, order=2, axes=None):
tr_elems = norm_const(arr_t, order)
out_dtype = tr_elems.output.dtype
rd = Reduce(Type(out_dtype, arr_t.shape), predicate_sum(out_dtype), axes=axes)
res_t = rd.parameter.output
tr_sum = norm_const(res_t, 1. / order)
rd.parameter.input.connect(tr_elems, tr_elems.output, input_prime=tr_elems.input)
rd.parameter.output.connect(tr_sum, tr_sum.input, output_prime=tr_sum.output)
self._rd = rd
Computation.__init__(self, [
Parameter('output', Annotation(res_t, 'o')),
Parameter('input', Annotation(arr_t, 'i'))])
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 createNormalisationKernel(thread, shape):
footprint = thread.array(shape, dtype=numpy.complex)
fftshift = FFTShift(footprint)
norm = norm_const(footprint, 2)
fftshift.parameter.output.connect(norm,
norm.input,
output_prime=norm.output)
normalise = fftshift.compile(thread)
return normalise
def test_norm_const(some_thr, rc_dtype, order):
input_ = get_test_array((1000,), rc_dtype)
input_dev = some_thr.to_device(input_)
norm = tr.norm_const(input_dev, order)
output_dev = some_thr.empty_like(norm.output)
test = get_test_computation(output_dev)
test.parameter.input.connect(norm, norm.output, input_prime=norm.input)
testc = test.compile(some_thr)
testc(output_dev, input_dev)
assert diff_is_negligible(output_dev.get(), numpy.abs(input_) ** order)
def test_norm_const(some_thr, rc_dtype, order):
input_ = get_test_array((1000, ), rc_dtype)
input_dev = some_thr.to_device(input_)
norm = tr.norm_const(input_dev, order)
output_dev = some_thr.empty_like(norm.output)
test = get_test_computation(output_dev)
test.parameter.input.connect(norm, norm.output, input_prime=norm.input)
testc = test.compile(some_thr)
testc(output_dev, input_dev)
assert diff_is_negligible(output_dev.get(), numpy.abs(input_)**order)
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'))])
