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spline_filter�bspline�gauss_spline�cubic� quadratic� cspline1d� qspline1d�cspline1d_eval�qspline1d_eval� @c �� � | j j }t g d�d� � dz }|dv r}| � d� � } t | j |� � }t | j |� � }t |||� � }t |||� � }|d|z z � |� � }nJ|dv r7t | |� � }t |||� � }|� |� � }nt d� � �|S ) a3 Smoothing spline (cubic) filtering of a rank-2 array.
Filter an input data set, `Iin`, using a (cubic) smoothing spline of
fall-off `lmbda`.
Parameters
----------
Iin : array_like
input data set
lmbda : float, optional
spline smooghing fall-off value, default is `5.0`.
Returns
-------
res : ndarray
filterd input data
Examples
--------
We can filter an multi dimentional signal (ex: 2D image) using cubic
B-spline filter:
>>> import numpy as np
>>> from scipy.signal import spline_filter
>>> import matplotlib.pyplot as plt
>>> orig_img = np.eye(20) # create an image
>>> orig_img[10, :] = 1.0
>>> sp_filter = spline_filter(orig_img, lmbda=0.1)
>>> f, ax = plt.subplots(1, 2, sharex=True)
>>> for ind, data in enumerate([[orig_img, "original image"],
... [sp_filter, "spline filter"]]):
... ax[ind].imshow(data[0], cmap='gray_r')
... ax[ind].set_title(data[1])
>>> plt.tight_layout()
>>> plt.show()
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