scipy.signal.
spline_filter#
- scipy.signal.spline_filter(Iin, lmbda=5.0)[source]#
- 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:
- Iinarray_like
- input data set 
- lmbdafloat, optional
- spline smoothing fall-off value, default is 5.0. 
 
- Returns:
- resndarray
- filtered input data 
 
 - Examples - We can filter an multi dimensional 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() 