sccala.asynphot.integrate
=========================

.. py:module:: sccala.asynphot.integrate


Functions
---------

.. autoapisummary::

   sccala.asynphot.integrate._basic_mod_simpson
   sccala.asynphot.integrate.mod_simpson


Module Contents
---------------

.. py:function:: _basic_mod_simpson(y, start, stop, x, dx, axis)

.. py:function:: mod_simpson(y, x=None, dx=1.0, axis=-1, even='avg')

   Integrate y(x) using samples along the given axis and a modified composite
   Simpson's rule. Equal to Gaussian error propagation of regular 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.