# scipy.integrate.newton_cotes¶

scipy.integrate.newton_cotes(rn, equal=0)[source]

Return weights and error coefficient for Newton-Cotes integration.

Suppose we have (N+1) samples of f at the positions x_0, x_1, ..., x_N. Then an N-point Newton-Cotes formula for the integral between x_0 and x_N is:

$$\int_{x_0}^{x_N} f(x)dx = \Delta x \sum_{i=0}^{N} a_i f(x_i) + B_N (\Delta x)^{N+2} f^{N+1} (\xi)$$

where $$\xi \in [x_0,x_N]$$ and $$\Delta x = \frac{x_N-x_0}{N}$$ is the average samples spacing.

If the samples are equally-spaced and N is even, then the error term is $$B_N (\Delta x)^{N+3} f^{N+2}(\xi)$$.

Parameters: rn : int The integer order for equally-spaced data or the relative positions of the samples with the first sample at 0 and the last at N, where N+1 is the length of rn. N is the order of the Newton-Cotes integration. equal : int, optional Set to 1 to enforce equally spaced data. an : ndarray 1-D array of weights to apply to the function at the provided sample positions. B : float Error coefficient.

Notes

Normally, the Newton-Cotes rules are used on smaller integration regions and a composite rule is used to return the total integral.