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Fonction spline_filter1d - module scipy.ndimage

Signature de la fonction spline_filter1d

def spline_filter1d(input, order=3, axis=-1, output=<class 'numpy.float64'>, mode='mirror') 

Description

spline_filter1d.__doc__

    Calculate a 1-D 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.

    Parameters
    ----------
    input : array_like
        The input array.
    order : int, optional
        The order of the spline, default is 3.
    axis : int, optional
        The axis along which the spline filter is applied. Default is the last
        axis.
    output : ndarray or dtype, optional
        The array in which to place the output, or the dtype of the returned
        array. Default is ``numpy.float64``.
    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 'mirror'. 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.

    Returns
    -------
    spline_filter1d : ndarray
        The filtered input.

    Notes
    -----
    All functions in `ndimage.interpolation` do spline interpolation of
    the input image. If using B-splines of `order > 1`, the input image
    values have to be converted to B-spline coefficients first, which is
    done by applying this 1-D filter sequentially along all
    axes of the input. All functions that require B-spline coefficients
    will automatically filter their inputs, a behavior controllable with
    the `prefilter` keyword argument. For functions that accept a `mode`
    parameter, the result will only be correct if it matches the `mode`
    used when filtering.

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

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

    See Also
    --------
    spline_filter : Multidimensional spline filter.

    Examples
    --------
    We can filter an image using 1-D spline along the given axis:

    >>> from scipy.ndimage import spline_filter1d
    >>> import matplotlib.pyplot as plt
    >>> orig_img = np.eye(20)  # create an image
    >>> orig_img[10, :] = 1.0
    >>> sp_filter_axis_0 = spline_filter1d(orig_img, axis=0)
    >>> sp_filter_axis_1 = spline_filter1d(orig_img, axis=1)
    >>> f, ax = plt.subplots(1, 3, sharex=True)
    >>> for ind, data in enumerate([[orig_img, "original image"],
    ...             [sp_filter_axis_0, "spline filter (axis=0)"],
    ...             [sp_filter_axis_1, "spline filter (axis=1)"]]):
    ...     ax[ind].imshow(data[0], cmap='gray_r')
    ...     ax[ind].set_title(data[1])
    >>> plt.tight_layout()
    >>> plt.show()