I have a time series
x(t) that is a NumPy array. My assignment tells me that I need to find the integral of this data with time.
How am I supposed to do this? It's not a function that I need to integrate, it's a list of data.
It depends on the statement of the problem. A rude approach would be something like this
import numpy as np import scipy as sp t = np.linspace(-1, 1, 100) x = t*t delta = t - t I = sum(delta*x)
You can use Simpson's Rule. A routine that does that for you is
>>> help(scipy.integrate.simps) Help on function simps in module scipy.integrate.quadrature: simps(y, x=None, dx=1, axis=-1, even='avg') Integrate y(x) using samples along the given axis and the composite Simpson's rule. If x is None, spacing of dx is assumed. If there are an even number of samples, N, then there are an odd number of intervals (N-1), but Simpson's rule requires an even number of intervals. The parameter 'even' controls how this is handled.