SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Function summary

• affine_transform(input, matrix, offset = 0.0, output_shape = None, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)
• geometric_transform(input, mapping, output_shape = None, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True, extra_arguments = (), extra_keywords = { })
• map_coordinates(input, coordinates, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)
• rotate(input, angle, axes = (1, 0), reshape = True, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)
• shift(input, shift, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)
• spline_filter(input, order = 3, output = numpy.float64, output_type = None)
• spline_filter1d(input, order = 3, axis = -Const(1), output = numpy.float64, output_type = None)
• zoom(input, zoom, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

Functions

• affine_transform(input, matrix, offset = 0.0, output_shape = None, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

  Apply an affine transformation.

  The given matrix and offset are used to find for each point in the output the corresponding coordinates in the input by an affine transformation. The value of the input at those coordinates is determined by spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode. The output shape can optionally be given. If not given it is equal to the input shape. The parameter prefilter determines if the input is pre-filtered before interpolation, if False it is assumed that the input is already filtered.

  The matrix must be two-dimensional or can also be given as a one-dimensional sequence or array. In the latter case, it is assumed that the matrix is diagonal. A more efficient algorithms is then applied that exploits the separability of the problem.

• geometric_transform(input, mapping, output_shape = None, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True, extra_arguments = (), extra_keywords = { })

  Apply an arbritrary geometric transform.

  The given mapping function 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.

  mapping must be a callable object that accepts a tuple of length equal to the output array rank and returns the corresponding input coordinates as a tuple of length equal to the input array rank. Points outside the boundaries of the input are filled according to the given mode ('constant', 'nearest', 'reflect' or 'wrap'). The output shape can optionally be given. If not given, it is equal to the input shape. The parameter prefilter determines if the input is pre-filtered before interpolation (necessary for spline interpolation of order > 1). If False it is assumed that the input is already filtered. The extra_arguments and extra_keywords arguments can be used to provide extra arguments and keywords that are passed to the mapping function at each call.

  Example usage:

  >>> a = arange(12.).reshape((4,3))
  >>> def shift_func(output_coordinates):
  ...     return (output_coordinates[0]-0.5, output_coordinates[1]-0.5)
  ...
  >>> print geometric_transform(a,shift_func)
  array([[ 0.    ,  0.    ,  0.    ],
         [ 0.    ,  1.3625,  2.7375],
         [ 0.    ,  4.8125,  6.1875],
         [ 0.    ,  8.2625,  9.6375]])
  

• map_coordinates(input, coordinates, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

  Apply an arbritrary coordinate transformation.

  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 that 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. For example, if the input has dimensions (100,200,3), then the shape of coordinates will be (3,100,200,3), where coordinates[:,1,2,3] specify the input coordinate at which output[1,2,3] is found.

  Points outside the boundaries of the input are filled according to the given mode ('constant', 'nearest', 'reflect' or 'wrap'). The parameter prefilter determines if the input is pre-filtered before interpolation (necessary for spline interpolation of order > 1). If False it is assumed that the input is already filtered.

  Example usage:

  >>> a = arange(12.).reshape((4,3))
  >>> print a
  [[  0.   1.   2.]
   [  3.   4.   5.]
   [  6.   7.   8.]
   [  9.  10.  11.]]
  >>> output = map_coordinates(a,[[0.5, 2], [0.5, 1]],order=1)
  >>> print output
  [ 2. 7.]
  

  Here, the interpolated value of a[0.5,0.5] gives output[0], while a[2,1] is output[1].

• rotate(input, angle, axes = (1, 0), reshape = True, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

  Rotate an array.

  The array is rotated in the plane defined by the two axes given by the axes parameter using spline interpolation of the requested order. The angle is given in degrees. Points outside the boundaries of the input are filled according to the given mode. If reshape is true, the output shape is adapted so that the input array is contained completely in the output. The parameter prefilter determines if the input is pre- filtered before interpolation, if False it is assumed that the input is already filtered.

• shift(input, shift, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

  Shift an array.

  The array is shifted using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode. The parameter prefilter determines if the input is pre-filtered before interpolation, if False it is assumed that the input is already filtered.

• spline_filter(input, order = 3, output = numpy.float64, output_type = None)

  Multi-dimensional spline filter.

  Note: The multi-dimensional filter is implemented as a sequence of one-dimensional spline filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

• spline_filter1d(input, order = 3, axis = -Const(1), output = numpy.float64, output_type = None)

  Calculates a one-dimensional spline filter along the given axis.

  The lines of the array along the given axis are filtered by a spline filter. The order of the spline must be >= 2 and <= 5.

• zoom(input, zoom, output_type = None, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True)

  Zoom an array.

  The array is zoomed using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode. The parameter prefilter determines if the input is pre- filtered before interpolation, if False it is assumed that the input is already filtered.

Imported Names