papyri
{ } Raw JSON

bundles / scipy latest / scipy / signal / _spline_filters / cspline1d_eval

function

scipy.signal._spline_filters:cspline1d_eval

source: /scipy/signal/_spline_filters.py :526

Signature

def   cspline1d_eval ( cj ,  newx ,  dx = 1.0 ,  x0 = 0 )

Summary

Evaluate a cubic spline at the new set of points.

Extended Summary

dx is the old sample-spacing while x0 was the old origin. In other-words the old-sample points (knot-points) for which the cj represent spline coefficients were at equally-spaced points of:

oldx = x0 + j*dx j=0...N-1, with N=len(cj)

Edges are handled using mirror-symmetric boundary conditions.

Parameters

cj : ndarray

cublic spline coefficients

newx : ndarray

New set of points.

dx : float, optional

Old sample-spacing, the default value is 1.0.

x0 : int, optional

Old origin, the default value is 0.

Returns

res : ndarray

Evaluated a cubic spline points.

Notes

Array API Standard Support

cspline1d_eval has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. 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 dev-arrayapi for more information.

Examples

We can filter a signal to reduce and smooth out high-frequency noise with a cubic spline: 
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import cspline1d, cspline1d_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 = cspline1d_eval(cspline1d(sig), time)
✓
plt.plot(sig, label="signal")
plt.plot(time, filtered, label="filtered")
plt.legend()
✗
plt.show()
✓
fig-cf928e1cd66daf84.png

See also

cspline1d

Compute cubic spline coefficients for rank-1 array.

Aliases

  • scipy.signal.cspline1d_eval

Referenced by

This package