`scipy.signal._spline_filters:qspline1d`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_spline_filters.py:360

Signature

def qspline1d(signal, lamb = 0.0)

Summary

Compute quadratic spline coefficients for rank-1 array.

Parameters

signal : ndarray: A rank-1 array representing samples of a signal.
lamb : float, optional: Smoothing coefficient (must be zero for now).

Returns

c : ndarray: Quadratic spline coefficients.

Notes

Find the quadratic spline coefficients for a 1-D signal assuming mirror-symmetric boundary conditions. To obtain the signal back from the spline representation mirror-symmetric-convolve these coefficients with a length 3 FIR window [1.0, 6.0, 1.0]/ 8.0 .

Examples

We can filter a signal to reduce and smooth out high-frequency noise with a quadratic spline:

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import qspline1d, qspline1d_eval
rng = np.random.default_rng()
sig = np.repeat([0., 1., 0.], 100)
sig += rng.standard_normal(len(sig))*0.05  # add noise
time = np.linspace(0, len(sig))
filtered = qspline1d_eval(qspline1d(sig), time)

✓

plt.plot(sig, label="signal")
plt.plot(time, filtered, label="filtered")
plt.legend()

✗

<matplotlib.legend.Legend object at 0x7f3f687d9a90>

plt.show()

✓

Aliases

scipy.signal.qspline1d