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’.


The signal to be downsampled, as an N-dimensional array.


The downsampling factor. 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.


The down-sampled signal.

See also


Resample up or down using the FFT method.


Resample using polyphase filtering and an FIR filter.


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.


>>> 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')