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bundles / scipy 1.17.1 / scipy / signal / _savitzky_golay / savgol_coeffs

function

scipy.signal._savitzky_golay:savgol_coeffs

source: /scipy/signal/_savitzky_golay.py :11

Signature

def   savgol_coeffs ( window_length ,  polyorder ,  deriv = 0 ,  delta = 1.0 ,  pos = None ,  use = conv ,  * ,  xp = None ,  device = None )

Summary

Compute the coefficients for a 1-D Savitzky-Golay FIR filter.

Parameters

window_length : int

The length of the filter window (i.e., the number of coefficients).

polyorder : int

The order of the polynomial used to fit the samples. polyorder must be less than window_length.

deriv : int, optional

The order of the derivative to compute. This must be a nonnegative integer. The default is 0, which means to filter the data without differentiating.

delta : float, optional

The spacing of the samples to which the filter will be applied. This is only used if deriv > 0.

pos : int or None, optional

If pos is not None, it specifies evaluation position within the window. The default is the middle of the window.

use : str, optional

Either 'conv' or 'dot'. This argument chooses the order of the coefficients. The default is 'conv', which means that the coefficients are ordered to be used in a convolution. With use='dot', the order is reversed, so the filter is applied by dotting the coefficients with the data set.

Returns

coeffs : 1-D ndarray

The filter coefficients.

Notes

Array API Standard Support

savgol_coeffs 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                   ✅                     ✅                   
Dask                  ✅                     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

Examples

import numpy as np
from scipy.signal import savgol_coeffs
savgol_coeffs(5, 2)
✓
savgol_coeffs(5, 2, deriv=1)
✗
Note that use='dot' simply reverses the coefficients. 
savgol_coeffs(5, 2, pos=3)
savgol_coeffs(5, 2, pos=3, use='dot')
✓
savgol_coeffs(4, 2, pos=3, deriv=1, use='dot')
✗
`x` contains data from the parabola x = t**2, sampled at t = -1, 0, 1, 2, 3. `c` holds the coefficients that will compute the derivative at the last position. When dotted with `x` the result should be 6. 
x = np.array([1, 0, 1, 4, 9])
c = savgol_coeffs(5, 2, pos=4, deriv=1, use='dot')
✓
c.dot(x)
✗

See also

savgol_filter

Aliases

  • scipy.signal.savgol_coeffs

Referenced by

This package