scipy.integrate.simps¶
- scipy.integrate.simps(y, x=None, dx=1, axis=-1, even='avg')[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: y : array_like
Array to be integrated.
x : array_like, optional
If given, the points at which y is sampled.
dx : int, optional
Spacing of integration points along axis of y. Only used when x is None. Default is 1.
axis : int, optional
Axis along which to integrate. Default is the last axis.
even : str {‘avg’, ‘first’, ‘last’}, optional
- ‘avg’ : Average two results:1) use the first N-2 intervals with
a trapezoidal rule on the last interval and 2) use the last N-2 intervals with a trapezoidal rule on the first interval.
- ‘first’ : Use Simpson’s rule for the first N-2 intervals with
a trapezoidal rule on the last interval.
- ‘last’ : Use Simpson’s rule for the last N-2 intervals with a
trapezoidal rule on the first interval.
See also
- quad
- adaptive quadrature using QUADPACK
- romberg
- adaptive Romberg quadrature
- quadrature
- adaptive Gaussian quadrature
- fixed_quad
- fixed-order Gaussian quadrature
- dblquad
- double integrals
- tplquad
- triple integrals
- romb
- integrators for sampled data
- cumtrapz
- cumulative integration for sampled data
- ode
- ODE integrators
- odeint
- ODE integrators
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.