Module « scipy.interpolate »
Classe « LSQSphereBivariateSpline »
Informations générales
Héritage
builtins.object
_BivariateSplineBase
SphereBivariateSpline
LSQSphereBivariateSpline
Définition
class LSQSphereBivariateSpline(SphereBivariateSpline):
Description [extrait de LSQSphereBivariateSpline.__doc__]
Weighted least-squares bivariate spline approximation in spherical
coordinates.
Determines a smoothing bicubic spline according to a given
set of knots in the `theta` and `phi` directions.
.. versionadded:: 0.11.0
Parameters
----------
theta, phi, r : array_like
1-D sequences of data points (order is not important). Coordinates
must be given in radians. Theta must lie within the interval
``[0, pi]``, and phi must lie within the interval ``[0, 2pi]``.
tt, tp : array_like
Strictly ordered 1-D sequences of knots coordinates.
Coordinates must satisfy ``0 < tt[i] < pi``, ``0 < tp[i] < 2*pi``.
w : array_like, optional
Positive 1-D sequence of weights, of the same length as `theta`, `phi`
and `r`.
eps : float, optional
A threshold for determining the effective rank of an over-determined
linear system of equations. `eps` should have a value within the
open interval ``(0, 1)``, the default is 1e-16.
See Also
--------
BivariateSpline :
a base class for bivariate splines.
UnivariateSpline :
a smooth univariate spline to fit a given set of data points.
SmoothBivariateSpline :
a smoothing bivariate spline through the given points
LSQBivariateSpline :
a bivariate spline using weighted least-squares fitting
RectSphereBivariateSpline :
a bivariate spline over a rectangular mesh on a sphere
SmoothSphereBivariateSpline :
a smoothing bivariate spline in spherical coordinates
RectBivariateSpline :
a bivariate spline over a rectangular mesh.
bisplrep :
a function to find a bivariate B-spline representation of a surface
bisplev :
a function to evaluate a bivariate B-spline and its derivatives
Notes
-----
For more information, see the FITPACK_ site about this function.
.. _FITPACK: http://www.netlib.org/dierckx/sphere.f
Examples
--------
Suppose we have global data on a coarse grid (the input data does not
have to be on a grid):
>>> from scipy.interpolate import LSQSphereBivariateSpline
>>> import matplotlib.pyplot as plt
>>> theta = np.linspace(0, np.pi, num=7)
>>> phi = np.linspace(0, 2*np.pi, num=9)
>>> data = np.empty((theta.shape[0], phi.shape[0]))
>>> data[:,0], data[0,:], data[-1,:] = 0., 0., 0.
>>> data[1:-1,1], data[1:-1,-1] = 1., 1.
>>> data[1,1:-1], data[-2,1:-1] = 1., 1.
>>> data[2:-2,2], data[2:-2,-2] = 2., 2.
>>> data[2,2:-2], data[-3,2:-2] = 2., 2.
>>> data[3,3:-2] = 3.
>>> data = np.roll(data, 4, 1)
We need to set up the interpolator object. Here, we must also specify the
coordinates of the knots to use.
>>> lats, lons = np.meshgrid(theta, phi)
>>> knotst, knotsp = theta.copy(), phi.copy()
>>> knotst[0] += .0001
>>> knotst[-1] -= .0001
>>> knotsp[0] += .0001
>>> knotsp[-1] -= .0001
>>> lut = LSQSphereBivariateSpline(lats.ravel(), lons.ravel(),
... data.T.ravel(), knotst, knotsp)
As a first test, we'll see what the algorithm returns when run on the
input coordinates
>>> data_orig = lut(theta, phi)
Finally we interpolate the data to a finer grid
>>> fine_lats = np.linspace(0., np.pi, 70)
>>> fine_lons = np.linspace(0., 2*np.pi, 90)
>>> data_lsq = lut(fine_lats, fine_lons)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(131)
>>> ax1.imshow(data, interpolation='nearest')
>>> ax2 = fig.add_subplot(132)
>>> ax2.imshow(data_orig, interpolation='nearest')
>>> ax3 = fig.add_subplot(133)
>>> ax3.imshow(data_lsq, interpolation='nearest')
>>> plt.show()
Constructeur(s)
Liste des opérateurs
Opérateurs hérités de la classe object
__eq__,
__ge__,
__gt__,
__le__,
__lt__,
__ne__
Liste des méthodes
Toutes les méthodes
Méthodes d'instance
Méthodes statiques
Méthodes dépréciées
Méthodes héritées de la classe SphereBivariateSpline
__init_subclass__, __subclasshook__, ev
Méthodes héritées de la classe _BivariateSplineBase
get_coeffs, get_knots, get_residual
Méthodes héritées de la classe object
__delattr__,
__dir__,
__format__,
__getattribute__,
__hash__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__sizeof__,
__str__
Améliorations / Corrections
Vous avez des améliorations (ou des corrections) à proposer pour ce document : je vous remerçie par avance de m'en faire part, cela m'aide à améliorer le site.
Emplacement :
Description des améliorations :