numpy.fft.hfft(a, n=None, axis=1, norm=None)
[source]
Compute the FFT of a signal that has Hermitian symmetry, i.e., a real spectrum.
Parameters: 


Returns: 

Raises: 

hfft
/ihfft
are a pair analogous to rfft
/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 hfft
for which you must supply the length of the result if it is to be odd.
ihfft(hfft(a, 2*len(a)  2) == a
, within roundoff error,ihfft(hfft(a, 2*len(a)  1) == a
, within roundoff error.>>> 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]) # may vary >>> 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.]])
© 2005–2019 NumPy Developers
Licensed under the 3clause BSD License.
https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.fft.hfft.html