# scipy.special.sinc

scipy.special.sinc(x)

  Return the sinc function.

  The sinc function is \(\sin(\pi x)/(\pi x)\).

  Parameters
    x ndarray

      Array (possibly multi-dimensional) of values for which to to calculate `sinc(x)`.

  Returns
    out ndarray

      `sinc(x)`, which has the same shape as the input.

  Notes

  `sinc(0)` is the limit value 1.

  The name sinc is short for “sine cardinal” or “sinus cardinalis”.

  The sinc function is used in various signal processing applications, including in anti-aliasing, in the construction of a Lanczos resampling filter, and in interpolation.

  For bandlimited interpolation of discrete-time signals, the ideal interpolation kernel is proportional to the sinc function.

  References

  1

    Weisstein, Eric W. “Sinc Function.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/SincFunction.html

  2

    Wikipedia, “Sinc function”, https://en.wikipedia.org/wiki/Sinc_function

  Examples

      >>> import matplotlib.pyplot as plt
      >>> x = np.linspace(-4, 4, 41)
      >>> np.sinc(x)
       array([-3.89804309e-17,  -4.92362781e-02,  -8.40918587e-02, # may vary
              -8.90384387e-02,  -5.84680802e-02,   3.89804309e-17,
              6.68206631e-02,   1.16434881e-01,   1.26137788e-01,
              8.50444803e-02,  -3.89804309e-17,  -1.03943254e-01,
              -1.89206682e-01,  -2.16236208e-01,  -1.55914881e-01,
              3.89804309e-17,   2.33872321e-01,   5.04551152e-01,
              7.56826729e-01,   9.35489284e-01,   1.00000000e+00,
              9.35489284e-01,   7.56826729e-01,   5.04551152e-01,
              2.33872321e-01,   3.89804309e-17,  -1.55914881e-01,
             -2.16236208e-01,  -1.89206682e-01,  -1.03943254e-01,
             -3.89804309e-17,   8.50444803e-02,   1.26137788e-01,
              1.16434881e-01,   6.68206631e-02,   3.89804309e-17,
              -5.84680802e-02,  -8.90384387e-02,  -8.40918587e-02,
              -4.92362781e-02,  -3.89804309e-17])

      >>> plt.plot(x, np.sinc(x))
      [<matplotlib.lines.Line2D object at 0x...>]
      >>> plt.title("Sinc Function")
      Text(0.5, 1.0, 'Sinc Function')
      >>> plt.ylabel("Amplitude")
      Text(0, 0.5, 'Amplitude')
      >>> plt.xlabel("X")
      Text(0.5, 0, 'X')
      >>> plt.show()

  It works in 2-D as well:

      >>> x = np.linspace(-4, 4, 401)
      >>> xx = np.outer(x, x)
      >>> plt.imshow(np.sinc(xx))
      <matplotlib.image.AxesImage object at 0x...>

scipy.special.exprel Statistical functions (`scipy.stats`)