text: """rbf - Radial basis functions for interpolation/smoothing scattered Nd data.
  
  Written by John Travers <jtravs@gmail.com>, February 2007
  Based closely on Matlab code by Alex Chirokov
  Additional, large, improvements by Robert Hetland
  Some additional alterations by Travis Oliphant
  
  Permission to use, modify, and distribute this software is given under the
  terms of the SciPy (BSD style) license.  See LICENSE.txt that came with
  this distribution for specifics.
  
  NO WARRANTY IS EXPRESSED OR IMPLIED.  USE AT YOUR OWN RISK.
  
  Copyright (c) 2006-2007, Robert Hetland <hetland@tamu.edu>
  Copyright (c) 2007, John Travers <jtravs@gmail.com>
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are
  met:
  
      * Redistributions of source code must retain the above copyright
         notice, this list of conditions and the following disclaimer.
  
      * Redistributions in binary form must reproduce the above
         copyright notice, this list of conditions and the following
         disclaimer in the documentation and/or other materials provided
         with the distribution.
  
      * Neither the name of Robert Hetland nor the names of any
         contributors may be used to endorse or promote products derived
         from this software without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  """
  from __future__ import division, print_function, absolute_import
  
  import sys
  
  from numpy import (sqrt, log, asarray, newaxis, all, dot, exp, eye,
                     float_)
  from scipy import linalg
  from scipy.lib.six import callable, get_method_function, \
       get_function_code
  
  __all__ = ['Rbf']
  
  
  class Rbf(object):
      """
      Rbf(*args)
  
      A class for radial basis function approximation/interpolation of
      n-dimensional scattered data.
  
      Parameters
      ----------
      *args : arrays
          x, y, z, ..., d, where x, y, z, ... are the coordinates of the nodes
          and d is the array of values at the nodes
      function : str or callable, optional
          The radial basis function, based on the radius, r, given by the norm
          (default is Euclidean distance); the default is 'multiquadric'::
  
              'multiquadric': sqrt((r/self.epsilon)**2 + 1)
              'inverse': 1.0/sqrt((r/self.epsilon)**2 + 1)
              'gaussian': exp(-(r/self.epsilon)**2)
              'linear': r
              'cubic': r**3
              'quintic': r**5
              'thin_plate': r**2 * log(r)
  
          If callable, then it must take 2 arguments (self, r).  The epsilon
          parameter will be available as self.epsilon.  Other keyword
          arguments passed in will be available as well.
  
      epsilon : float, optional
          Adjustable constant for gaussian or multiquadrics functions
          - defaults to approximate average distance between nodes (which is
          a good start).
      smooth : float, optional
          Values greater than zero increase the smoothness of the
          approximation.  0 is for interpolation (default), the function will
          always go through the nodal points in this case.
      norm : callable, optional
          A function that returns the 'distance' between two points, with
          inputs as arrays of positions (x, y, z, ...), and an output as an
          array of distance.  E.g, the default::
  
              def euclidean_norm(x1, x2):
                  return sqrt( ((x1 - x2)**2).sum(axis=0) )
  
          which is called with x1=x1[ndims,newaxis,:] and
          x2=x2[ndims,:,newaxis] such that the result is a matrix of the
          distances from each point in x1 to each point in x2.
  
      Examples
      --------
      >>> rbfi = Rbf(x, y, z, d)  # radial basis function interpolator instance
      >>> di = rbfi(xi, yi, zi)   # interpolated values
  
      """
  
      def _euclidean_norm(self, x1, x2):
          return sqrt(((x1 - x2)**2).sum(axis=0))
  
      def _h_multiquadric(self, r):
          return sqrt((1.0/self.epsilon*r)**2 + 1)
  
      def _h_inverse_multiquadric(self, r):
          return 1.0/sqrt((1.0/self.epsilon*r)**2 + 1)
  
      def _h_gaussian(self, r):
          return exp(-(1.0/self.epsilon*r)**2)
  
      def _h_linear(self, r):
          return r
  
      def _h_cubic(self, r):
          return r**3
  
      def _h_quintic(self, r):
          return r**5
  
      def _h_thin_plate(self, r):
          result = r**2 * log(r)
          result[r == 0] = 0  # the spline is zero at zero
          return result
  
      # Setup self._function and do smoke test on initial r
      def _init_function(self, r):
          if isinstance(self.function, str):
              self.function = self.function.lower()
              _mapped = {'inverse': 'inverse_multiquadric',
                         'inverse multiquadric': 'inverse_multiquadric',
                         'thin-plate': 'thin_plate'}
              if self.function in _mapped:
                  self.function = _mapped[self.function]
  
              func_name = "_h_" + self.function
              if hasattr(self, func_name):
                  self._function = getattr(self, func_name)
              else:
                  functionlist = [x[3:] for x in dir(self) if x.startswith('_h_')]
                  raise ValueError("function must be a callable or one of " +
                                       ", ".join(functionlist))
              self._function = getattr(self, "_h_"+self.function)
          elif callable(self.function):
              allow_one = False
              if hasattr(self.function, 'func_code') or \
                     hasattr(self.function, '__code__'):
                  val = self.function
                  allow_one = True
              elif hasattr(self.function, "im_func"):
                  val = get_method_function(self.function)
              elif hasattr(self.function, "__call__"):
                  val = get_method_function(self.function.__call__)
              else:
                  raise ValueError("Cannot determine number of arguments to function")
  
              argcount = get_function_code(val).co_argcount
              if allow_one and argcount == 1:
                  self._function = self.function
              elif argcount == 2:
                  if sys.version_info[0] >= 3:
                      self._function = self.function.__get__(self, Rbf)
                  else:
                      import new
                      self._function = new.instancemethod(self.function, self,
                                                          Rbf)
              else:
                  raise ValueError("Function argument must take 1 or 2 arguments.")
  
          a0 = self._function(r)
          if a0.shape != r.shape:
              raise ValueError("Callable must take array and return array of the same shape")
          return a0
  
      def __init__(self, *args, **kwargs):
          self.xi = asarray([asarray(a, dtype=float_).flatten()
                             for a in args[:-1]])
          self.N = self.xi.shape[-1]
          self.di = asarray(args[-1]).flatten()
  
          if not all([x.size == self.di.size for x in self.xi]):
              raise ValueError("All arrays must be equal length.")
  
          self.norm = kwargs.pop('norm', self._euclidean_norm)
          r = self._call_norm(self.xi, self.xi)
          self.epsilon = kwargs.pop('epsilon', None)
          if self.epsilon is None:
              self.epsilon = r.mean()
          self.smooth = kwargs.pop('smooth', 0.0)
  
          self.function = kwargs.pop('function', 'multiquadric')
  
          # attach anything left in kwargs to self
          #  for use by any user-callable function or
          #  to save on the object returned.
          for item, value in kwargs.items():
              setattr(self, item, value)
  
          self.A = self._init_function(r) - eye(self.N)*self.smooth
          self.nodes = linalg.solve(self.A, self.di)
  
      def _call_norm(self, x1, x2):
          if len(x1.shape) == 1:
              x1 = x1[newaxis, :]
          if len(x2.shape) == 1:
              x2 = x2[newaxis, :]
          x1 = x1[..., :, newaxis]
          x2 = x2[..., newaxis, :]
          return self.norm(x1, x2)
  
      def __call__(self, *args):
          args = [asarray(x) for x in args]
          if not all([x.shape == y.shape for x in args for y in args]):
              raise ValueError("Array lengths must be equal")
          shp = args[0].shape
          self.xa = asarray([a.flatten() for a in args], dtype=float_)
          r = self._call_norm(self.xa, self.xi)
          return dot(self._function(r), self.nodes).reshape(shp)
  