Skip to main content
`Ctrl`+`K`
scipy.integrate.

# romb

scipy.integrate.romb(y, dx=1.0, axis=-1, show=False)

  Romberg integration using samples of a function.

  Parameters:
    y array_like

      A vector of `2**k + 1` equally-spaced samples of a function.

    dx float, optional

      The sample spacing. Default is 1.

    axis int, optional

      The axis along which to integrate. Default is -1 (last axis).

    show bool, optional

      When y is a single 1-D array, then if this argument is True print the table showing Richardson extrapolation from the samples. Default is False.

  Returns:
    romb ndarray

      The integrated result for axis.

  See also

  `quad`

    adaptive quadrature using QUADPACK

  `fixed_quad`

    fixed-order Gaussian quadrature

  `dblquad`

    double integrals

  `tplquad`

    triple integrals

  `simpson`

    integrators for sampled data

  `cumulative_trapezoid`

    cumulative integration for sampled data

  Examples

      >>> from scipy import integrate
      >>> import numpy as np
      >>> x = np.arange(10, 14.25, 0.25)
      >>> y = np.arange(3, 12)

      >>> integrate.romb(y)
      56.0

      >>> y = np.sin(np.power(x, 2.5))
      >>> integrate.romb(y)
      -0.742561336672229

      >>> integrate.romb(y, show=True)
      Richardson Extrapolation Table for Romberg Integration
      ======================================================
      -0.81576
       4.63862  6.45674
      -1.10581 -3.02062 -3.65245
      -2.57379 -3.06311 -3.06595 -3.05664
      -1.34093 -0.92997 -0.78776 -0.75160 -0.74256
      ======================================================
      -0.742561336672229  # may vary