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

Returns:

See also

Notes

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.

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()