# scipy.special.ellip_harm

scipy.special.ellip_harm(h2, k2, n, p, s, signm=1, signn=1)

  Ellipsoidal harmonic functions E^p_n(l)

  These are also known as Lame functions of the first kind, and are solutions to the Lame equation:

  \[(s^2 - h^2)(s^2 - k^2)E''(s) + s(2s^2 - h^2 - k^2)E'(s) + (a - q s^2)E(s) = 0\]

  where \(q = (n+1)n\) and \(a\) is the eigenvalue (not returned) corresponding to the solutions.

  Parameters:
    h2 float

      `h**2`

    k2 float

      `k**2`; should be larger than `h**2`

    n int

      Degree

    s float

      Coordinate

    p int

      Order, can range between [1,2n+1]

    signm{1, -1}, optional

      Sign of prefactor of functions. Can be +/-1. See Notes.

    signn{1, -1}, optional

      Sign of prefactor of functions. Can be +/-1. See Notes.

  Returns:
    E float

      the harmonic \(E^p_n(s)\)

  See also

  `ellip_harm_2`, `ellip_normal`

  Notes

  The geometric interpretation of the ellipsoidal functions is explained in [2], [3], [4]. The signm and signn arguments control the sign of prefactors for functions according to their type:

      K : +1
      L : signm
      M : signn
      N : signm*signn

  New in version 0.15.0.

  References

  [1]

  Digital Library of Mathematical Functions 29.12 https://dlmf.nist.gov/29.12

  [2]

  Bardhan and Knepley, “Computational science and re-discovery: open-source implementations of ellipsoidal harmonics for problems in potential theory”, Comput. Sci. Disc. 5, 014006 (2012) DOI:10.1088/1749-4699/5/1/014006.

  [3]

  David J.and Dechambre P, “Computation of Ellipsoidal Gravity Field Harmonics for small solar system bodies” pp. 30-36, 2000

  [4]

  George Dassios, “Ellipsoidal Harmonics: Theory and Applications” pp. 418, 2012

  Examples

      >>> from scipy.special import ellip_harm
      >>> w = ellip_harm(5,8,1,1,2.5)
      >>> w
      2.5

  Check that the functions indeed are solutions to the Lame equation:

      >>> import numpy as np
      >>> from scipy.interpolate import UnivariateSpline
      >>> def eigenvalue(f, df, ddf):
      ...     r = ((s**2 - h**2)*(s**2 - k**2)*ddf + s*(2*s**2 - h**2 - k**2)*df - n*(n+1)*s**2*f)/f
      ...     return -r.mean(), r.std()
      >>> s = np.linspace(0.1, 10, 200)
      >>> k, h, n, p = 8.0, 2.2, 3, 2
      >>> E = ellip_harm(h**2, k**2, n, p, s)
      >>> E_spl = UnivariateSpline(s, E)
      >>> a, a_err = eigenvalue(E_spl(s), E_spl(s,1), E_spl(s,2))
      >>> a, a_err
      (583.44366156701483, 6.4580890640310646e-11)