:

>>> np.set_printoptions(suppress=True)

Create a signal `x` of length `m` with `k` ones:

>>> m = 8
>>> k = 3
>>> x = np.zeros(m)
>>> x[:k] = 1

Use the FFT to compute the Fourier transform of `x`, and
inspect the magnitudes of the coefficients:

>>> np.abs(np.fft.fft(x))
array([ 3.        ,  2.41421356,  1.        ,  0.41421356,  1.        ,
        0.41421356,  1.        ,  2.41421356])

Now find the same values (up to sign) using `diric`. We multiply
by `k` to account for the different scaling conventions of
`numpy.fft.fft` and `diric`:

>>> theta = np.linspace(0, 2*np.pi, m, endpoint=False)
>>> k * special.diric(theta, k)
array([ 3.        ,  2.41421356,  1.        , -0.41421356, -1.        ,
       -0.41421356,  1.        ,  2.41421356])
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