scipy.signal.

decimate#

scipy.signal.decimate(x, q, n=None, ftype='iir', axis=-1, zero_phase=True)[source]#

Downsample the signal after applying an anti-aliasing filter.

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:

xarray_like: The input signal made up of equidistant samples. If x is a multidimensional array, the parameter axis specifies the time axis.
qint: 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.
nint, 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’.
ftypestr {‘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.
axisint, optional: The axis along which to decimate.
zero_phasebool, 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.

Added in version 0.18.0.

Returns:

yndarray: The down-sampled signal.

See also

resample: Resample up or down using the FFT method.
resample_poly: Resample using polyphase filtering and an FIR filter.

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 Support for the array API standard 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()

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