psd(x, NFFT=None, Fs=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None)
The power spectral density $P_{xx}$ by Welch's average periodogram method. The vector x is divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The $|\mathrm{fft}(i)|^2$ of each segment $i$ are averaged to compute $P_{xx}$ .
If len(x) < NFFT, it will be zero padded to NFFT.
Array or sequence containing the data
The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit.
A function or a vector of length NFFT. To create window vectors see .window_hanning
, .window_none
, numpy.blackman
, numpy.hamming
, numpy.bartlett
, scipy.signal
, scipy.signal.get_window
, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.
Which sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided.
The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT
The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead.
The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib it is a function. The ~matplotlib.mlab
module defines .detrend_none
, .detrend_mean
, and .detrend_linear
, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls .detrend_none
. 'mean' calls .detrend_mean
. 'linear' calls .detrend_linear
.
Whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.
The number of points of overlap between segments.
The values for the power spectrum $P_{xx}$ (real valued)
The frequencies corresponding to the elements in Pxx
Compute the power spectral density.
csd
returns the spectral density between two signals.
magnitude_spectrum
returns the magnitude spectrum.
specgram
:None:None:`specgram` differs in the default overlap; in not returning the mean of the segment periodograms; and in returning the times of the segments.
The following pages refer to to this document either explicitly or contain code examples using this.
matplotlib.mlab.csd
matplotlib.mlab.cohere
matplotlib.mlab.specgram
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