`scipy.signal._spline_filters:spline_filter`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_spline_filters.py:26

Signature

def spline_filter(Iin, lmbda = 5.0)

Summary

Smoothing spline (cubic) filtering of a rank-2 array.

Extended Summary

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 smoothing fall-off value, default is 5.0.

Returns

res : ndarray: 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])

✗

<matplotlib.image.AxesImage object at 0x7f3f68130b90>
Text(0.5, 1.0, 'original image')
<matplotlib.image.AxesImage object at 0x7f3f68561bd0>
Text(0.5, 1.0, 'spline filter')

plt.tight_layout()
plt.show()

✓

Aliases

scipy.signal.spline_filter