class_docstr: Bivariate spline approximation over a rectangular mesh on a sphere.
  
  Can be used for smoothing data.
  
  .. versionadded:: 0.11.0
  
  Parameters
  ----------
  u : array_like
      1-D array of latitude coordinates in strictly ascending order.
      Coordinates must be given in radians and lie within the interval
      (0, pi).
  v : array_like
      1-D array of longitude coordinates in strictly ascending order.
      Coordinates must be given in radians. First element (v[0]) must lie
      within the interval [-pi, pi). Last element (v[-1]) must satisfy
      v[-1] <= v[0] + 2*pi.
  r : array_like
      2-D array of data with shape ``(u.size, v.size)``.
  s : float, optional
      Positive smoothing factor defined for estimation condition
      (``s=0`` is for interpolation).
  pole_continuity : bool or (bool, bool), optional
      Order of continuity at the poles ``u=0`` (``pole_continuity[0]``) and
      ``u=pi`` (``pole_continuity[1]``).  The order of continuity at the pole
      will be 1 or 0 when this is True or False, respectively.
      Defaults to False.
  pole_values : float or (float, float), optional
      Data values at the poles ``u=0`` and ``u=pi``.  Either the whole
      parameter or each individual element can be None.  Defaults to None.
  pole_exact : bool or (bool, bool), optional
      Data value exactness at the poles ``u=0`` and ``u=pi``.  If True, the
      value is considered to be the right function value, and it will be
      fitted exactly. If False, the value will be considered to be a data
      value just like the other data values.  Defaults to False.
  pole_flat : bool or (bool, bool), optional
      For the poles at ``u=0`` and ``u=pi``, specify whether or not the
      approximation has vanishing derivatives.  Defaults to False.
  
  See Also
  --------
  RectBivariateSpline : bivariate spline approximation over a rectangular
      mesh
  
  Notes
  -----
  Currently, only the smoothing spline approximation (``iopt[0] = 0`` and
  ``iopt[0] = 1`` in the FITPACK routine) is supported.  The exact
  least-squares spline approximation is not implemented yet.
  
  When actually performing the interpolation, the requested `v` values must
  lie within the same length 2pi interval that the original `v` values were
  chosen from.
  
  For more information, see the FITPACK_ site about this function.
  
  .. _FITPACK: http://www.netlib.org/dierckx/spgrid.f
  
  Examples
  --------
  Suppose we have global data on a coarse grid
  
  >>> lats = np.linspace(10, 170, 9) * np.pi / 180.
  >>> lons = np.linspace(0, 350, 18) * np.pi / 180.
  >>> data = np.dot(np.atleast_2d(90. - np.linspace(-80., 80., 18)).T,
  ...               np.atleast_2d(180. - np.abs(np.linspace(0., 350., 9)))).T
  
  We want to interpolate it to a global one-degree grid
  
  >>> new_lats = np.linspace(1, 180, 180) * np.pi / 180
  >>> new_lons = np.linspace(1, 360, 360) * np.pi / 180
  >>> new_lats, new_lons = np.meshgrid(new_lats, new_lons)
  
  We need to set up the interpolator object
  
  >>> from scipy.interpolate import RectSphereBivariateSpline
  >>> lut = RectSphereBivariateSpline(lats, lons, data)
  
  Finally we interpolate the data.  The `RectSphereBivariateSpline` object
  only takes 1-D arrays as input, therefore we need to do some reshaping.
  
  >>> data_interp = lut.ev(new_lats.ravel(),
  ...                      new_lons.ravel()).reshape((360, 180)).T
  
  Looking at the original and the interpolated data, one can see that the
  interpolant reproduces the original data very well:
  
  >>> import matplotlib.pyplot as plt
  >>> fig = plt.figure()
  >>> ax1 = fig.add_subplot(211)
  >>> ax1.imshow(data, interpolation='nearest')
  >>> ax2 = fig.add_subplot(212)
  >>> ax2.imshow(data_interp, interpolation='nearest')
  >>> plt.show()
  
  Choosing the optimal value of ``s`` can be a delicate task. Recommended
  values for ``s`` depend on the accuracy of the data values.  If the user
  has an idea of the statistical errors on the data, she can also find a
  proper estimate for ``s``. By assuming that, if she specifies the
  right ``s``, the interpolator will use a spline ``f(u,v)`` which exactly
  reproduces the function underlying the data, she can evaluate
  ``sum((r(i,j)-s(u(i),v(j)))**2)`` to find a good estimate for this ``s``.
  For example, if she knows that the statistical errors on her
  ``r(i,j)``-values are not greater than 0.1, she may expect that a good
  ``s`` should have a value not larger than ``u.size * v.size * (0.1)**2``.
  
  If nothing is known about the statistical error in ``r(i,j)``, ``s`` must
  be determined by trial and error.  The best is then to start with a very
  large value of ``s`` (to determine the least-squares polynomial and the
  corresponding upper bound ``fp0`` for ``s``) and then to progressively
  decrease the value of ``s`` (say by a factor 10 in the beginning, i.e.
  ``s = fp0 / 10, fp0 / 100, ...``  and more carefully as the approximation
  shows more detail) to obtain closer fits.
  
  The interpolation results for different values of ``s`` give some insight
  into this process:
  
  >>> fig2 = plt.figure()
  >>> s = [3e9, 2e9, 1e9, 1e8]
  >>> for ii in range(len(s)):
  ...     lut = RectSphereBivariateSpline(lats, lons, data, s=s[ii])
  ...     data_interp = lut.ev(new_lats.ravel(),
  ...                          new_lons.ravel()).reshape((360, 180)).T
  ...     ax = fig2.add_subplot(2, 2, ii+1)
  ...     ax.imshow(data_interp, interpolation='nearest')
  ...     ax.set_title("s = %g" % s[ii])
  >>> plt.show()
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code: def __init__(
      self,
      u,
      v,
      r,
      s=0.0,
      pole_continuity=False,
      pole_values=None,
      pole_exact=False,
      pole_flat=False,
  ):
      iopt = np.array([0, 0, 0], dtype=int)
      ider = np.array([-1, 0, -1, 0], dtype=int)
      if pole_values is None:
          pole_values = (None, None)
      elif isinstance(pole_values, (float, np.float32, np.float64)):
          pole_values = (pole_values, pole_values)
      if isinstance(pole_continuity, bool):
          pole_continuity = (pole_continuity, pole_continuity)
      if isinstance(pole_exact, bool):
          pole_exact = (pole_exact, pole_exact)
      if isinstance(pole_flat, bool):
          pole_flat = (pole_flat, pole_flat)
  
      r0, r1 = pole_values
      iopt[1:] = pole_continuity
      if r0 is None:
          ider[0] = -1
      else:
          ider[0] = pole_exact[0]
  
      if r1 is None:
          ider[2] = -1
      else:
          ider[2] = pole_exact[1]
  
      ider[1], ider[3] = pole_flat
  
      u, v = np.ravel(u), np.ravel(v)
      if not np.all(np.diff(u) > 0.0):
          raise ValueError("u must be strictly increasing")
      if not np.all(np.diff(v) > 0.0):
          raise ValueError("v must be strictly increasing")
  
      if not u.size == r.shape[0]:
          raise ValueError(
              "u dimension of r must have same number of "
              "elements as u"
          )
      if not v.size == r.shape[1]:
          raise ValueError(
              "v dimension of r must have same number of "
              "elements as v"
          )
  
      if pole_continuity[1] is False and pole_flat[1] is True:
          raise ValueError(
              "if pole_continuity is False, so must be " "pole_flat"
          )
      if pole_continuity[0] is False and pole_flat[0] is True:
          raise ValueError(
              "if pole_continuity is False, so must be " "pole_flat"
          )
  
      r = np.ravel(r)
      nu, tu, nv, tv, c, fp, ier = dfitpack.regrid_smth_spher(
          iopt, ider, u.copy(), v.copy(), r.copy(), r0, r1, s
      )
  
      if ier not in [0, -1, -2]:
          msg = _spfit_messages.get(ier, "ier=%s" % (ier))
          raise ValueError(msg)
  
      self.fp = fp
      self.tck = tu[:nu], tv[:nv], c[: (nu - 4) * (nv - 4)]
      self.degrees = (3, 3)
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  from numpy import zeros, concatenate, alltrue, ravel, all, diff, array, ones
  import numpy
  from .None import fitpack
  from .None import dfitpack
label_desc: Use image node_id 1 to create a new RectSphereBivariateSpline object from inherited base classes: SphereBivariateSpline with example: obj = RectSphereBivariateSpline(u, v, r, s, pole_continuity, pole_values, pole_exact, pole_flat)