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Fonction simpson - module scipy.integrate

Signature de la fonction simpson

def simpson(y, x=None, dx=1.0, axis=-1, even='avg') 

Description

simpson.__doc__

    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 : float, optional
        Spacing of integration points along axis of `x`. 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
    cumulative_trapezoid: 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.

    Examples
    --------
    >>> from scipy import integrate
    >>> x = np.arange(0, 10)
    >>> y = np.arange(0, 10)

    >>> integrate.simpson(y, x)
    40.5

    >>> y = np.power(x, 3)
    >>> integrate.simpson(y, x)
    1642.5
    >>> integrate.quad(lambda x: x**3, 0, 9)[0]
    1640.25

    >>> integrate.simpson(y, x, even='first')
    1644.5