# jax.numpy.trapezoid#

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

Integrate along the given axis using the composite trapezoidal rule.

LAX-backend implementation of numpy.trapezoid().

Original docstring below.

If x is provided, the integration happens in sequence along its elements - they are not sorted.

Integrate y (x) along each 1d slice on the given axis, compute $$\int y(x) dx$$. When x is specified, this integrates along the parametric curve, computing $$\int_t y(t) dt = \int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt$$.

Parameters:
• y (array_like) â€“ Input array to integrate.

• x (array_like, optional) â€“ 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) â€“ The spacing between sample points when x is None. The default is 1.

• axis (int, optional) â€“ The axis along which to integrate.

Returns:

trapezoid â€“ Definite integral of y = n-dimensional array as approximated along a single axis by the trapezoidal rule. If y is a 1-dimensional array, then the result is a float. If n is greater than 1, then the result is an n-1 dimensional array.

Return type:

float or ndarray

References