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 smooghing 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()