This is documentation for an old release of SciPy (version 0.19.1). Read this page in the documentation of the latest stable release (version 1.14.1).
scipy.special.ellip_harm¶
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scipy.special.
ellip_harm
(h2, k2, n, p, s, signm=1, signn=1)[source]¶ 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:
(s2−h2)(s2−k2)E″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 thanh**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
Notes
The geometric intepretation of the ellipsoidal functions is explained in [R399], [R400], [R401]. 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
[R398] Digital Libary of Mathematical Functions 29.12 http://dlmf.nist.gov/29.12 [R399] (1, 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. [R400] (1, 2) David J.and Dechambre P, “Computation of Ellipsoidal Gravity Field Harmonics for small solar system bodies” pp. 30-36, 2000 [R401] (1, 2) 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:
>>> 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)