bundles / scipy 1.17.1 / scipy / signal / _spline_filters / cspline1d_eval
function
scipy.signal._spline_filters:cspline1d_eval
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: ndarraycublic spline coefficients
newx: ndarrayNew set of points.
dx: float, optionalOld sample-spacing, the default value is 1.0.
x0: int, optionalOld origin, the default value is 0.
Returns
res: ndarrayEvaluated 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-arrayapifor 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()
✓
See also
- cspline1d
Compute cubic spline coefficients for rank-1 array.
Aliases
-
scipy.signal.cspline1d_eval