class_docstr: Smooth bivariate spline approximation in spherical coordinates.
  
  .. 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]``.
  w : array_like, optional
      Positive 1-D sequence of weights.
  s : float, optional
      Positive smoothing factor defined for estimation condition:
      ``sum((w(i)*(r(i) - s(theta(i), phi(i))))**2, axis=0) <= s``
      Default ``s=len(w)`` which should be a good value if ``1/w[i]`` is an
      estimate of the standard deviation of ``r[i]``.
  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
  LSQSphereBivariateSpline :
      a bivariate spline in spherical coordinates using weighted
      least-squares fitting
  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):
  
  >>> theta = np.linspace(0., np.pi, 7)
  >>> phi = np.linspace(0., 2*np.pi, 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
  
  >>> lats, lons = np.meshgrid(theta, phi)
  >>> from scipy.interpolate import SmoothSphereBivariateSpline
  >>> lut = SmoothSphereBivariateSpline(lats.ravel(), lons.ravel(),
  ...                                   data.T.ravel(), s=3.5)
  
  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_smth = lut(fine_lats, fine_lons)
  
  >>> import matplotlib.pyplot as plt
  >>> 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_smth, interpolation='nearest')
  >>> plt.show()
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code: def __call__(self, theta, phi, dtheta=0, dphi=0, grid=True):
      theta = np.asarray(theta)
      phi = np.asarray(phi)
  
      if phi.size > 0 and (phi.min() < 0.0 or phi.max() > 2.0 * np.pi):
          raise ValueError("requested phi out of bounds.")
  
      return SphereBivariateSpline.__call__(
          self, theta, phi, dtheta=dtheta, dphi=dphi, grid=grid
      )
  
imports: import warnings
  from numpy import zeros, concatenate, ravel, diff, array, ones
  import numpy
  from .None import fitpack
  from .None import dfitpack
label_desc: Use image node_id 2 for calling the SmoothSphereBivariateSpline obj's underlying member method code with example usage: obj.__call__(theta, phi, dtheta, dphi, grid) and returns: SphereBivariateSpline