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'))])