scipy.signal.

qspline1d#

scipy.signal.qspline1d(signal, lamb=0.0)[source]#

Compute quadratic spline coefficients for rank-1 array.

Parameters:

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

Returns:

cndarray: Quadratic spline coefficients.

See also

qspline1d_eval: Evaluate a quadratic spline at the new set of points.

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 .

Array API Standard Support

qspline1d has support for Python Array API Standard compatible backends in addition to NumPy. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	⛔
JAX	⚠️ no JIT	⛔
Dask	⚠️ computes graph	n/a

See Support for the array API standard for more information.

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()
>>> plt.show()

../../_images/scipy-signal-qspline1d-1.png