central_diff_weights(Np, ndiv=1)
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)
Can be inaccurate for a large number of points.
Number of points for the central derivative.
Number of divisions. Default is 1.
Weights for an Np-point central derivative. Its size is :None:None:`Np`.
Return weights for an Np-point central derivative.
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
See :The following pages refer to to this document either explicitly or contain code examples using this.
scipy.misc._common.central_diff_weights
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