scipy.integrate.

# simpson#

scipy.integrate.simpson(y, *, x=None, dx=1.0, axis=-1)[source]#

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.

Parameters:
yarray_like

Array to be integrated.

xarray_like, optional

If given, the points at which y is sampled.

dxfloat, optional

Spacing of integration points along axis of x. Only used when x is None. Default is 1.

axisint, optional

Axis along which to integrate. Default is the last axis.

Returns:
float

The estimated integral computed with the composite Simpson’s rule.

`quad`

`fixed_quad`

`dblquad`

double integrals

`tplquad`

triple integrals

`romb`

integrators for sampled data

`cumulative_trapezoid`

cumulative integration for sampled data

`cumulative_simpson`

cumulative integration using Simpson’s 1/3 rule

Notes

For an odd number of samples that are equally spaced the result is exact if the function is a polynomial of order 3 or less. If the samples are not equally spaced, then the result is exact only if the function is a polynomial of order 2 or less.

References

[1]

Cartwright, Kenneth V. Simpson’s Rule Cumulative Integration with MS Excel and Irregularly-spaced Data. Journal of Mathematical Sciences and Mathematics Education. 12 (2): 1-9

Examples

```>>> from scipy import integrate
>>> import numpy as np
>>> x = np.arange(0, 10)
>>> y = np.arange(0, 10)
```
```>>> integrate.simpson(y, x=x)
40.5
```
```>>> y = np.power(x, 3)
>>> integrate.simpson(y, x=x)
1640.5
>>> integrate.quad(lambda x: x**3, 0, 9)[0]
1640.25
```