class_docstr: Base class for Bivariate spline s(x,y) interpolation on the rectangle
  [xb,xe] x [yb, ye] calculated from a given set of data points
  (x,y,z).
  
  See Also
  --------
  bisplrep, bisplev : an older wrapping of FITPACK
  BivariateSpline :
      implementation of bivariate spline interpolation on a plane grid
  SphereBivariateSpline :
      implementation of bivariate spline interpolation on a spherical grid
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docstr: Evaluate the spline or its derivatives at given positions.
  
  Parameters
  ----------
  x, y : array_like
      Input coordinates.
  
      If `grid` is False, evaluate the spline at points ``(x[i],
      y[i]), i=0, ..., len(x)-1``.  Standard Numpy broadcasting
      is obeyed.
  
      If `grid` is True: evaluate spline at the grid points
      defined by the coordinate arrays x, y. The arrays must be
      sorted to increasing order.
  
      Note that the axis ordering is inverted relative to
      the output of meshgrid.
  dx : int
      Order of x-derivative
  
      .. versionadded:: 0.14.0
  dy : int
      Order of y-derivative
  
      .. versionadded:: 0.14.0
  grid : bool
      Whether to evaluate the results on a grid spanned by the
      input arrays, or at points specified by the input arrays.
  
      .. versionadded:: 0.14.0
code: def __call__(self, x, y, dx=0, dy=0, grid=True):
      """
      Evaluate the spline or its derivatives at given positions.
  
      Parameters
      ----------
      x, y : array_like
          Input coordinates.
  
          If `grid` is False, evaluate the spline at points ``(x[i],
          y[i]), i=0, ..., len(x)-1``.  Standard Numpy broadcasting
          is obeyed.
  
          If `grid` is True: evaluate spline at the grid points
          defined by the coordinate arrays x, y. The arrays must be
          sorted to increasing order.
  
          Note that the axis ordering is inverted relative to
          the output of meshgrid.
      dx : int
          Order of x-derivative
  
          .. versionadded:: 0.14.0
      dy : int
          Order of y-derivative
  
          .. versionadded:: 0.14.0
      grid : bool
          Whether to evaluate the results on a grid spanned by the
          input arrays, or at points specified by the input arrays.
  
          .. versionadded:: 0.14.0
  
      """
      x = np.asarray(x)
      y = np.asarray(y)
  
      tx, ty, c = self.tck[:3]
      kx, ky = self.degrees
      if grid:
          if x.size == 0 or y.size == 0:
              return np.zeros((x.size, y.size), dtype=self.tck[2].dtype)
  
          if dx or dy:
              z, ier = dfitpack.parder(tx, ty, c, kx, ky, dx, dy, x, y)
              if not ier == 0:
                  raise ValueError(
                      "Error code returned by parder: %s" % ier
                  )
          else:
              z, ier = dfitpack.bispev(tx, ty, c, kx, ky, x, y)
              if not ier == 0:
                  raise ValueError(
                      "Error code returned by bispev: %s" % ier
                  )
      else:
          # standard Numpy broadcasting
          if x.shape != y.shape:
              x, y = np.broadcast_arrays(x, y)
  
          shape = x.shape
          x = x.ravel()
          y = y.ravel()
  
          if x.size == 0 or y.size == 0:
              return np.zeros(shape, dtype=self.tck[2].dtype)
  
          if dx or dy:
              z, ier = dfitpack.pardeu(tx, ty, c, kx, ky, dx, dy, x, y)
              if not ier == 0:
                  raise ValueError(
                      "Error code returned by pardeu: %s" % ier
                  )
          else:
              z, ier = dfitpack.bispeu(tx, ty, c, kx, ky, x, y)
              if not ier == 0:
                  raise ValueError(
                      "Error code returned by bispeu: %s" % ier
                  )
  
          z = z.reshape(shape)
      return z
  
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 4 for calling the _BivariateSplineBase obj's underlying member method code with example usage: obj.__call__(x, y, dx, dy, grid) and returns: z, np, np