decimate — SciPy v1.15.3 Manual (original) (raw)
scipy.signal.
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 lfilterwhen 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 up or down using the FFT method.
Resample using polyphase filtering and an FIR filter.
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(xnp.pifreq*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()
