Numpy or SciPy Derivative function for non-uniform spacing?

Question

I was wondering if numpy or scipy had a method in their libraries to find the numerical derivative of a list of values with non-uniform spacing. The idea is to feed in the timestamps that correspond to the values and then for it to use the timestamps to find the numerical derivative.

Answer 1

You can create your own functions using numpy. For the derivatives using forward differences (edit thanks to @EOL, but note that NumPy's diff() is not a differentiate function):

def diff_fwd(x, y): 
    return np.diff(y)/np.diff(x)

"central" differences (it is not necessarily central, depending on your data spacing):

def diff_central(x, y):
    x0 = x[:-2]
    x1 = x[1:-1]
    x2 = x[2:]
    y0 = y[:-2]
    y1 = y[1:-1]
    y2 = y[2:]
    f = (x2 - x1)/(x2 - x0)
    return (1-f)*(y2 - y1)/(x2 - x1) + f*(y1 - y0)/(x1 - x0)

where y contains the function evaluations and x the corresponding "times", such that you can use an arbitrary interval.