pythonscipynumerical-integrationintegral

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 takes a set of samples as input, performs a discrete integration of the samples and returns a list of values (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.

How do I get the list of integrated values as opposed to the sum of the integrated values?

Am I just looking at the problem the wrong way around?


Solution

  • 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