Using scipy to perform discrete integration of the sample

Question

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?

Accepted Answer

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
```

Using scipy to perform discrete integration of the sample

Solution

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