numpy.trapz()

numpy.trapz(y, x=None, dx=1.0, axis=-1) [source]

Integrate along the given axis using the composite trapezoidal rule.

Integrate y (x) along given axis.

Parameters:

Parameters:	y : array_like Input array to integrate. x : array_like, optional The sample points corresponding to the `y` values. If `x` is None, the sample points are assumed to be evenly spaced `dx` apart. The default is None. dx : scalar, optional The spacing between sample points when `x` is None. The default is 1. axis : int, optional The axis along which to integrate.
Returns:	trapz : float Definite integral as approximated by trapezoidal rule.

y : array_like

Input array to integrate.

x : array_like, optional

The sample points corresponding to the y values. If x is None, the sample points are assumed to be evenly spaced dx apart. The default is None.

dx : scalar, optional

The spacing between sample points when x is None. The default is 1.

axis : int, optional

The axis along which to integrate.

Returns:

trapz : float

Definite integral as approximated by trapezoidal rule.

Notes

Image [R287] illustrates trapezoidal rule ? y-axis locations of points will be taken from y array, by default x-axis distances between points will be 1.0, alternatively they can be provided with x array or with dx scalar. Return value will be equal to combined area under the red lines.

References

[R286]

Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule

[R287]

(1, 2) Illustration image: http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png

Examples

>>> np.trapz([1,2,3])
4.0
>>> np.trapz([1,2,3], x=[4,6,8])
8.0
>>> np.trapz([1,2,3], dx=2)
8.0
>>> a = np.arange(6).reshape(2, 3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.trapz(a, axis=0)
array([ 1.5,  2.5,  3.5])
>>> np.trapz(a, axis=1)
array([ 2.,  8.])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.trapz.html

doc_NumPy

2017-01-10 18:19:02

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