original_docstring: 
      Map the input array to new coordinates by interpolation.
  
      The array of coordinates is used to find, for each point in the output,
      the corresponding coordinates in the input. The value of the input at
      those coordinates is determined by spline interpolation of the
      requested order.
  
      The shape of the output is derived from that of the coordinate
      array by dropping the first axis. The values of the array along
      the first axis are the coordinates in the input array at which the
      output value is found.
  
      Parameters
      ----------
      input : ndarray
          The input array.
      coordinates : array_like
          The coordinates at which `input` is evaluated.
      output : ndarray or dtype, optional
          The array in which to place the output, or the dtype of the returned
          array.
      order : int, optional
          The order of the spline interpolation, default is 3.
          The order has to be in the range 0-5.
      mode : str, optional
          Points outside the boundaries of the input are filled according
          to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
          Default is 'constant'.
      cval : scalar, optional
          Value used for points outside the boundaries of the input if
          ``mode='constant'``. Default is 0.0
      prefilter : bool, optional
          The parameter prefilter determines if the input is pre-filtered with
          `spline_filter` before interpolation (necessary for spline
          interpolation of order > 1).  If False, it is assumed that the input is
          already filtered. Default is True.
  
      Returns
      -------
      map_coordinates : ndarray
          The result of transforming the input. The shape of the output is
          derived from that of `coordinates` by dropping the first axis.
  
      See Also
      --------
      spline_filter, geometric_transform, scipy.interpolate
  
      Examples
      --------
      >>> from scipy import ndimage
      >>> a = np.arange(12.).reshape((4, 3))
      >>> a
      array([[  0.,   1.,   2.],
             [  3.,   4.,   5.],
             [  6.,   7.,   8.],
             [  9.,  10.,  11.]])
      >>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
      array([ 2.,  7.])
  
      Above, the interpolated value of a[0.5, 0.5] gives output[0], while
      a[2, 1] is output[1].
  
      >>> inds = np.array([[0.5, 2], [0.5, 4]])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
      array([  2. , -33.3])
      >>> ndimage.map_coordinates(a, inds, order=1, mode='nearest')
      array([ 2.,  8.])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
      array([ True, False], dtype=bool)
  
      
docstring: Map the input array to new coordinates by interpolation.
  The array of coordinates is used to find, for each point in the output,
  the corresponding coordinates in the input. The value of the input at
  those coordinates is determined by spline interpolation of the
  requested order.
  
  The shape of the output is derived from that of the coordinate
  array by dropping the first axis. The values of the array along
  the first axis are the coordinates in the input array at which the
  output value is found.
  
  Parameters
  
  input : ndarray
  The input array.
  coordinates : array_like
  The coordinates at which `input` is evaluated.
  output : ndarray or dtype, optional
  The array in which to place the output, or the dtype of the returned
  array.
  order : int, optional
  The order of the spline interpolation, default is 3.
  The order has to be in the range 0-5.
  
  Returns
  
  map_coordinates : ndarray
  The result of transforming the input. The shape of the output is
  derived from that of `coordinates` by dropping the first axis.
  
  See Also
  
  
  
  Examples
  
  
  
  Above, the interpolated value of a[0.5, 0.5] gives output[0], while
  a[2, 1] is output[1].
  
  
docstring_tokens: Map the input array to new coordinates by interpolation . The array of coordinates is used to find for each point in the output the corresponding coordinates in the input . The value of the input at those coordinates is determined by spline interpolation of the requested order . The shape of the output is derived from that of the coordinate array by dropping the first axis . The values of the array along the first axis are the coordinates in the input array at which the output value is found . Parameters input : ndarray The input array . coordinates : array_like The coordinates at which ` input ` is evaluated . output : ndarray or dtype optional The array in which to place the output or the dtype of the returned array . order : int optional The order of the spline interpolation default is 3 . The order has to be in the range 0 - 5 . Returns map_coordinates : ndarray The result of transforming the input . The shape of the output is derived from that of ` coordinates ` by dropping the first axis . See Also Examples Above the interpolated value of a [ 0 . 5 0 . 5 ] gives output [ 0 ] while a [ 2 1 ] is output [ 1 ] .
code: def map_coordinates(input, coordinates, output=None, order=3,
                      mode='constant', cval=0.0, prefilter=True):
      """
      Map the input array to new coordinates by interpolation.
  
      The array of coordinates is used to find, for each point in the output,
      the corresponding coordinates in the input. The value of the input at
      those coordinates is determined by spline interpolation of the
      requested order.
  
      The shape of the output is derived from that of the coordinate
      array by dropping the first axis. The values of the array along
      the first axis are the coordinates in the input array at which the
      output value is found.
  
      Parameters
      ----------
      input : ndarray
          The input array.
      coordinates : array_like
          The coordinates at which `input` is evaluated.
      output : ndarray or dtype, optional
          The array in which to place the output, or the dtype of the returned
          array.
      order : int, optional
          The order of the spline interpolation, default is 3.
          The order has to be in the range 0-5.
      mode : str, optional
          Points outside the boundaries of the input are filled according
          to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
          Default is 'constant'.
      cval : scalar, optional
          Value used for points outside the boundaries of the input if
          ``mode='constant'``. Default is 0.0
      prefilter : bool, optional
          The parameter prefilter determines if the input is pre-filtered with
          `spline_filter` before interpolation (necessary for spline
          interpolation of order > 1).  If False, it is assumed that the input is
          already filtered. Default is True.
  
      Returns
      -------
      map_coordinates : ndarray
          The result of transforming the input. The shape of the output is
          derived from that of `coordinates` by dropping the first axis.
  
      See Also
      --------
      spline_filter, geometric_transform, scipy.interpolate
  
      Examples
      --------
      >>> from scipy import ndimage
      >>> a = np.arange(12.).reshape((4, 3))
      >>> a
      array([[  0.,   1.,   2.],
             [  3.,   4.,   5.],
             [  6.,   7.,   8.],
             [  9.,  10.,  11.]])
      >>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
      array([ 2.,  7.])
  
      Above, the interpolated value of a[0.5, 0.5] gives output[0], while
      a[2, 1] is output[1].
  
      >>> inds = np.array([[0.5, 2], [0.5, 4]])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
      array([  2. , -33.3])
      >>> ndimage.map_coordinates(a, inds, order=1, mode='nearest')
      array([ 2.,  8.])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
      array([ True, False], dtype=bool)
  
      """
      if order < 0 or order > 5:
          raise RuntimeError('spline order not supported')
      input = numpy.asarray(input)
      if numpy.iscomplexobj(input):
          raise TypeError('Complex type not supported')
      coordinates = numpy.asarray(coordinates)
      if numpy.iscomplexobj(coordinates):
          raise TypeError('Complex type not supported')
      output_shape = coordinates.shape[1:]
      if input.ndim < 1 or len(output_shape) < 1:
          raise RuntimeError('input and output rank must be > 0')
      if coordinates.shape[0] != input.ndim:
          raise RuntimeError('invalid shape for coordinate array')
      mode = _extend_mode_to_code(mode)
      if prefilter and order > 1:
          filtered = spline_filter(input, order, output=numpy.float64)
      else:
          filtered = input
      output, return_value = _ni_support._get_output(output, input,
                                                     shape=output_shape)
      _geometric_transform(filtered, None, coordinates, None, None,
                           output, order, mode, cval, None, None)
      return return_value
code_tokens: def map_coordinates ( input , coordinates , output = None , order = 3 , mode = 'constant' , cval = 0.0 , prefilter = True ) : if order < 0 or order > 5 : raise RuntimeError ( 'spline order not supported' ) input = numpy . asarray ( input ) if numpy . iscomplexobj ( input ) : raise TypeError ( 'Complex type not supported' ) coordinates = numpy . asarray ( coordinates ) if numpy . iscomplexobj ( coordinates ) : raise TypeError ( 'Complex type not supported' ) output_shape = coordinates . shape [ 1 : ] if input . ndim < 1 or len ( output_shape ) < 1 : raise RuntimeError ( 'input and output rank must be > 0' ) if coordinates . shape [ 0 ] != input . ndim : raise RuntimeError ( 'invalid shape for coordinate array' ) mode = _extend_mode_to_code ( mode ) if prefilter and order > 1 : filtered = spline_filter ( input , order , output = numpy . float64 ) else : filtered = input output , return_value = _ni_support . _get_output ( output , input , shape = output_shape ) _geometric_transform ( filtered , None , coordinates , None , None , output , order , mode , cval , None , None ) return return_value
short_docstring: Map the input array to new coordinates by interpolation.
short_docstring_tokens: Map the input array to new coordinates by interpolation .
comment: """
      Map the input array to new coordinates by interpolation.
  
      The array of coordinates is used to find, for each point in the output,
      the corresponding coordinates in the input. The value of the input at
      those coordinates is determined by spline interpolation of the
      requested order.
  
      The shape of the output is derived from that of the coordinate
      array by dropping the first axis. The values of the array along
      the first axis are the coordinates in the input array at which the
      output value is found.
  
      Parameters
      ----------
      input : ndarray
          The input array.
      coordinates : array_like
          The coordinates at which `input` is evaluated.
      output : ndarray or dtype, optional
          The array in which to place the output, or the dtype of the returned
          array.
      order : int, optional
          The order of the spline interpolation, default is 3.
          The order has to be in the range 0-5.
      mode : str, optional
          Points outside the boundaries of the input are filled according
          to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
          Default is 'constant'.
      cval : scalar, optional
          Value used for points outside the boundaries of the input if
          ``mode='constant'``. Default is 0.0
      prefilter : bool, optional
          The parameter prefilter determines if the input is pre-filtered with
          `spline_filter` before interpolation (necessary for spline
          interpolation of order > 1).  If False, it is assumed that the input is
          already filtered. Default is True.
  
      Returns
      -------
      map_coordinates : ndarray
          The result of transforming the input. The shape of the output is
          derived from that of `coordinates` by dropping the first axis.
  
      See Also
      --------
      spline_filter, geometric_transform, scipy.interpolate
  
      Examples
      --------
      >>> from scipy import ndimage
      >>> a = np.arange(12.).reshape((4, 3))
      >>> a
      array([[  0.,   1.,   2.],
             [  3.,   4.,   5.],
             [  6.,   7.,   8.],
             [  9.,  10.,  11.]])
      >>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
      array([ 2.,  7.])
  
      Above, the interpolated value of a[0.5, 0.5] gives output[0], while
      a[2, 1] is output[1].
  
      >>> inds = np.array([[0.5, 2], [0.5, 4]])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
      array([  2. , -33.3])
      >>> ndimage.map_coordinates(a, inds, order=1, mode='nearest')
      array([ 2.,  8.])
      >>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
      array([ True, False], dtype=bool)
  
      """