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Fonction geometric_transform - module scipy.ndimage

Signature de la fonction geometric_transform

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

Description

geometric_transform.__doc__

    Apply an arbitrary 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.

    Parameters
    ----------
    input : array_like
        The input array.
    mapping : {callable, scipy.LowLevelCallable}
        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.
    output_shape : tuple of ints, optional
        Shape tuple.
    output : array or dtype, optional
        The array in which to place the output, or the dtype of the
        returned array. By default an array of the same dtype as input
        will be created.
    order : int, optional
        The order of the spline interpolation, default is 3.
        The order has to be in the range 0-5.
    mode : {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest',            'mirror', 'grid-wrap', 'wrap'}, optional
        The `mode` parameter determines how the input array is extended
        beyond its boundaries. Default is 'constant'. Behavior for each valid
        value is as follows (see additional plots and details on
        :ref:`boundary modes <ndimage-interpolation-modes>`):
    
        'reflect' (`d c b a | a b c d | d c b a`)
            The input is extended by reflecting about the edge of the last
            pixel. This mode is also sometimes referred to as half-sample
            symmetric.
    
        'grid-mirror'
            This is a synonym for 'reflect'.
    
        'constant' (`k k k k | a b c d | k k k k`)
            The input is extended by filling all values beyond the edge with
            the same constant value, defined by the `cval` parameter. No
            interpolation is performed beyond the edges of the input.
    
        'grid-constant' (`k k k k | a b c d | k k k k`)
            The input is extended by filling all values beyond the edge with
            the same constant value, defined by the `cval` parameter. Interpolation
            occurs for samples outside the input's extent  as well.
    
        'nearest' (`a a a a | a b c d | d d d d`)
            The input is extended by replicating the last pixel.
    
        'mirror' (`d c b | a b c d | c b a`)
            The input is extended by reflecting about the center of the last
            pixel. This mode is also sometimes referred to as whole-sample
            symmetric.
    
        'grid-wrap' (`a b c d | a b c d | a b c d`)
            The input is extended by wrapping around to the opposite edge.
    
        'wrap' (`d b c d | a b c d | b c a b`)
            The input is extended by wrapping around to the opposite edge, but in a
            way such that the last point and initial point exactly overlap. In this
            case it is not well defined which sample will be chosen at the point of
            overlap.
    cval : scalar, optional
        Value to fill past edges of input if `mode` is 'constant'. Default
        is 0.0.
    prefilter : bool, optional
        Determines if the input array is prefiltered with `spline_filter`
        before interpolation. The default is True, which will create a
        temporary `float64` array of filtered values if `order > 1`. If
        setting this to False, the output will be slightly blurred if
        `order > 1`, unless the input is prefiltered, i.e. it is the result
        of calling `spline_filter` on the original input.
    extra_arguments : tuple, optional
        Extra arguments passed to `mapping`.
    extra_keywords : dict, optional
        Extra keywords passed to `mapping`.

    Returns
    -------
    output : ndarray
        The filtered input.

    See Also
    --------
    map_coordinates, affine_transform, spline_filter1d


    Notes
    -----
    This function also accepts low-level callback functions with one
    the following signatures and wrapped in `scipy.LowLevelCallable`:

    .. code:: c

       int mapping(npy_intp *output_coordinates, double *input_coordinates,
                   int output_rank, int input_rank, void *user_data)
       int mapping(intptr_t *output_coordinates, double *input_coordinates,
                   int output_rank, int input_rank, void *user_data)

    The calling function iterates over the elements of the output array,
    calling the callback function at each element. The coordinates of the
    current output element are passed through ``output_coordinates``. The
    callback function must return the coordinates at which the input must
    be interpolated in ``input_coordinates``. The rank of the input and
    output arrays are given by ``input_rank`` and ``output_rank``
    respectively. ``user_data`` is the data pointer provided
    to `scipy.LowLevelCallable` as-is.

    The callback function must return an integer error status that is zero
    if something went wrong and one otherwise. If an error occurs, you should
    normally set the Python error status with an informative message
    before returning, otherwise a default error message is set by the
    calling function.

    In addition, some other low-level function pointer specifications
    are accepted, but these are for backward compatibility only and should
    not be used in new code.

    For complex-valued `input`, this function transforms the real and imaginary
    components independently.

    .. versionadded:: 1.6.0
        Complex-valued support added.

    Examples
    --------
    >>> import numpy as np
    >>> from scipy.ndimage import geometric_transform
    >>> a = np.arange(12.).reshape((4, 3))
    >>> def shift_func(output_coords):
    ...     return (output_coords[0] - 0.5, output_coords[1] - 0.5)
    ...
    >>> geometric_transform(a, shift_func)
    array([[ 0.   ,  0.   ,  0.   ],
           [ 0.   ,  1.362,  2.738],
           [ 0.   ,  4.812,  6.187],
           [ 0.   ,  8.263,  9.637]])

    >>> b = [1, 2, 3, 4, 5]
    >>> def shift_func(output_coords):
    ...     return (output_coords[0] - 3,)
    ...
    >>> geometric_transform(b, shift_func, mode='constant')
    array([0, 0, 0, 1, 2])
    >>> geometric_transform(b, shift_func, mode='nearest')
    array([1, 1, 1, 1, 2])
    >>> geometric_transform(b, shift_func, mode='reflect')
    array([3, 2, 1, 1, 2])
    >>> geometric_transform(b, shift_func, mode='wrap')
    array([2, 3, 4, 1, 2])