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.trapz().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) – 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 – 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