# scipy.misc.central_diff_weights

scipy.misc.central_diff_weights(Np, ndiv=1)

  Return weights for an Np-point central derivative.

  Assumes equally-spaced function points.

  If weights are in the vector w, then derivative is w[0] * f(x-ho*dx) + … + w[-1] * f(x+h0*dx)

  Deprecated since version 1.10.0: `central_diff_weights` has been deprecated from `scipy.misc.central_diff_weights` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. You may consider using findiff: https://github.com/maroba/findiff or numdifftools: https://github.com/pbrod/numdifftools

  Parameters:
    Np int

      Number of points for the central derivative.

    ndiv int, optional

      Number of divisions. Default is 1.

  Returns:
    w ndarray

      Weights for an Np-point central derivative. Its size is Np.

  Notes

  Can be inaccurate for a large number of points.

  References

  [1]

  https://en.wikipedia.org/wiki/Finite_difference

  Examples

  We can calculate a derivative value of a function.

      >>> from scipy.misc import central_diff_weights
      >>> def f(x):
      ...     return 2 * x**2 + 3
      >>> x = 3.0 # derivative point
      >>> h = 0.1 # differential step
      >>> Np = 3 # point number for central derivative
      >>> weights = central_diff_weights(Np) # weights for first derivative
      >>> vals = [f(x + (i - Np/2) * h) for i in range(Np)]
      >>> sum(w * v for (w, v) in zip(weights, vals))/h
      11.79999999999998

  This value is close to the analytical solution: f’(x) = 4x, so f’(3) = 12