numkit.integration — Numerical integration of data

See also

scipy.integrate

numkit.integration.simps_error(dy, x=None, dx=1, axis=-1, even='avg')

Error on integral evaluated with Simpson’s rule from errors of points, dy.

Evaluate the integral with scipy.integrate.simps(). For a given vector dy of errors on the function values, the error on the integral is calculated via propagation of errors from the Newton-Cotes formula for the 3rd Lagrange interpolating polynomial. The results are exact for the cases of even spacing dx; for uneven spacing we currently average all spacings (exact solution is in the works...)

Arguments :

dy

errors for the tabulated values of the integrand f

x

values of abscissa at which f was tabulated (can be None and then dx should be provided)

dx

constant spacing of the abscissa

axis

axis in dy along which the data lies

even

see scipy.integrate.simps() (‘avg’, ‘first’, ‘last’)