dpnp.fft.hfft

dpnp.fft.hfft(a, n=None, axis=-1, norm=None, out=None)[source]

Compute the FFT of a signal that has Hermitian symmetry, i.e., a real spectrum.

For full documentation refer to numpy.fft.hfft.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- Input array.
n ({None, int}, optional) -- Length of the transformed axis of the output. For n output points, n//2+1 input points are necessary. If the input is longer than this, it is cropped. If it is shorter than this, it is padded with zeros. If n is not given, it is taken to be 2*(m-1) where m is the length of the input along the axis specified by axis. Default: None.
axis (int, optional) -- Axis over which to compute the FFT. If not given, the last axis is used. Default: -1.
norm ({None, "backward", "ortho", "forward"}, optional) -- Normalization mode (see dpnp.fft). Indicates which direction of the forward/backward pair of transforms is scaled and with what normalization factor. None is an alias of the default option "backward". Default: "backward".
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. Default: None.

Returns:

out -- The truncated or zero-padded input, transformed along the axis indicated by axis, or the last one if axis is not specified. The length of the transformed axis is n, or, if n is not given, 2*(m-1) where m is the length of the transformed axis of the input. To get an odd number of output points, n must be specified, for instance as 2*m - 1 in the typical case.

Return type:

dpnp.ndarray

See also

dpnp.fft: For definition of the DFT and conventions used.
dpnp.fft.rfft: The one-dimensional FFT of real input.
dpnp.fft.ihfft: The inverse of dpnp.fft.hfft.

Notes

dpnp.fft.hfft/dpnp.fft.ihfft are a pair analogous to dpnp.fft.rfft/dpnp.fft.irfft, but for the opposite case: here the signal has Hermitian symmetry in the time domain and is real in the frequency domain. So here it's dpnp.fft.hfft for which you must supply the length of the result if it is to be odd.

even: ihfft(hfft(a, 2*len(a) - 2)) == a, within round-off error,
odd: ihfft(hfft(a, 2*len(a) - 1)) == a, within round-off error.

The correct interpretation of the Hermitian input depends on the length of the original data, as given by n. This is because each input shape could correspond to either an odd or even length signal. By default, dpnp.fft.hfft assumes an even output length which puts the last entry at the Nyquist frequency; aliasing with its symmetric counterpart. By Hermitian symmetry, the value is thus treated as purely real. To avoid losing information, the correct length of the real input must be given.

Examples

>>> import dpnp as np
>>> signal = np.array([1, 2, 3, 4, 3, 2])
>>> np.fft.fft(signal)
array([15.+0.j, -4.+0.j,  0.+0.j, -1.-0.j,  0.+0.j, -4.+0.j])
>>> np.fft.hfft(signal[:4]) # Input first half of signal
array([15., -4.,  0., -1.,  0., -4.])
>>> np.fft.hfft(signal, 6)  # Input entire signal and truncate
array([15., -4.,  0., -1.,  0., -4.])

>>> signal = np.array([[1, 1.j], [-1.j, 2]])
>>> np.conj(signal.T) - signal   # check Hermitian symmetry
array([[ 0.-0.j, -0.+0.j], # may vary
       [ 0.+0.j,  0.-0.j]])
>>> freq_spectrum = np.fft.hfft(signal)
>>> freq_spectrum
array([[ 1.,  1.],
       [ 2., -2.]])