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bundles / scipy 1.17.1 / scipy / signal / _signaltools / decimate

function

scipy.signal._signaltools:decimate

source: /scipy/signal/_signaltools.py :5193

Signature

def   decimate ( x ,  q ,  n = None ,  ftype = iir ,  axis = -1 ,  zero_phase = True )

Summary

Downsample the signal after applying an anti-aliasing filter.

Extended Summary

By default, an order 8 Chebyshev type I filter is used. A 30 point FIR filter with Hamming window is used if ftype is 'fir'.

Parameters

x : array_like

The input signal made up of equidistant samples. If x is a multidimensional array, the parameter axis specifies the time axis.

q : int

The downsampling factor, which is a postive integer. When using IIR downsampling, it is recommended to call decimate multiple times for downsampling factors higher than 13.

n : int, optional

The order of the filter (1 less than the length for 'fir'). Defaults to 8 for 'iir' and 20 times the downsampling factor for 'fir'.

ftype : str {'iir', 'fir'} or ``dlti`` instance, optional

If 'iir' or 'fir', specifies the type of lowpass filter. If an instance of an dlti object, uses that object to filter before downsampling.

axis : int, optional

The axis along which to decimate.

zero_phase : bool, optional

Prevent phase shift by filtering with filtfilt instead of lfilter when using an IIR filter, and shifting the outputs back by the filter's group delay when using an FIR filter. The default value of True is recommended, since a phase shift is generally not desired.

Returns

y : ndarray

The down-sampled signal.

Notes

For non-integer downsampling factors, resample can be used. Consult the scipy.interpolate module for methods of resampling signals with non-constant sampling intervals.

The zero_phase keyword was added in 0.18.0. The possibility to use instances of dlti as ftype was added in 0.18.0.

Array API Standard Support

decimate 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 import signal
import matplotlib.pyplot as plt
✓
Define wave parameters. 
wave_duration = 3
sample_rate = 100
freq = 2
q = 5
✓
Calculate number of samples. 
samples = wave_duration*sample_rate
samples_decimated = int(samples/q)
✓
Create cosine wave. 
x = np.linspace(0, wave_duration, samples, endpoint=False)
y = np.cos(x*np.pi*freq*2)
✓
Decimate cosine wave. 
ydem = signal.decimate(y, q)
xnew = np.linspace(0, wave_duration, samples_decimated, endpoint=False)
✓
Plot original and decimated waves. 
plt.plot(x, y, '.-', xnew, ydem, 'o-')
plt.xlabel('Time, Seconds')
plt.legend(['data', 'decimated'], loc='best')
✗
plt.show()
✓
fig-1d3b2c347f9ab9a4.png

See also

resample

Resample up or down using the FFT method.

resample_poly

Resample using polyphase filtering and an FIR filter.

Aliases

  • scipy.signal.decimate

Referenced by

This package