user1425750 - 2 months ago 45
Python Question

# Using scipy to perform discrete integration of the sample

I am trying to port from labview to python. In labview there is a function "Integral x(t) VI" that given a set of samples will perform a discrete integration of a list of samples and return a output list of values of the areas under the curve according to Simpsons rule.

I tried to find an equivalent function in scipy, e.g., scipy.integrate.simps, but those functions return the summed integral across the set of samples, as a float.

Can anyone suggest how to achieve this? Am I just looking at the problem the wrong way around?

I think you may be using scipy.integrate.simps slightly incorrectly. The area returned by `scipy.integrate.simps` is the total area under `y` (the first parameter passed). The second parameter is optional, and are sample values for the x-axis (the actual x values for each of the y values). ie:

``````>>> import numpy as np
>>> import scipy
>>> a=np.array([1,1,1,1,1])
>>> scipy.integrate.simps(a)
4.0
>>> scipy.integrate.simps(a,np.array([0,10,20,30,40]))
40.0
``````

I think you want to return the areas under the same curve between different limits? To do that you pass the part of the curve you want, like this:

``````>>> a=np.array([0,1,1,1,1,10,10,10,10,0])
>>> scipy.integrate.simps(a)
44.916666666666671
>>> scipy.integrate.simps(a[:5])
3.6666666666666665
>>> scipy.integrate.simps(a[5:])
36.666666666666664
``````