psd(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, *, data=None, **kwargs)
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 $P_{xx}$ , with a scaling to correct for power loss due to windowing.
If len(x) < NFFT, it will be zero padded to NFFT.
For plotting, the power is plotted as $10\log_{10}(P_{xx})$ for decibels, though Pxx itself is returned.
If given, the following parameters also accept a string s
, which is interpreted as data[s]
(unless this raises an exception):
x
Keyword arguments control the .Line2D
properties:
Properties: agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array alpha: scalar or None animated: bool antialiased or aa: bool clip_box: .Bbox
clip_on: bool clip_path: Patch or (Path, Transform) or None color or c: color dash_capstyle: .CapStyle
or {'butt', 'projecting', 'round'} dash_joinstyle: .JoinStyle
or {'miter', 'round', 'bevel'} dashes: sequence of floats (on/off ink in points) or (None, None) data: (2, N) array or two 1D arrays drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default' figure: .Figure
fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'} gid: str in_layout: bool label: object linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...} linewidth or lw: float marker: marker style string, ~.path.Path
or ~.markers.MarkerStyle
markeredgecolor or mec: color markeredgewidth or mew: float markerfacecolor or mfc: color markerfacecoloralt or mfcalt: color markersize or ms: float markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool] path_effects: .AbstractPathEffect
picker: float or callable[[Artist, Event], tuple[bool, dict]] pickradius: float rasterized: bool sketch_params: (scale: float, length: float, randomness: float) snap: bool or None solid_capstyle: .CapStyle
or {'butt', 'projecting', 'round'} solid_joinstyle: .JoinStyle
or {'miter', 'round', 'bevel'} transform: unknown url: str visible: bool xdata: 1D array ydata: 1D array zorder: float
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 center frequency of x, which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.
Whether to include the line object plotted in the returned values.
The values for the power spectrum $P_{xx}$ before scaling (real valued).
The frequencies corresponding to the elements in Pxx.
The line created by this function. Only returned if return_line is True.
Plot the power spectral density.
csd
Plots the spectral density between two signals.
magnitude_spectrum
Plots the magnitude spectrum.
specgram
Differs in the default overlap; in not returning the mean of the segment periodograms; in returning the times of the segments; and in plotting a colormap instead of a line.
The following pages refer to to this document either explicitly or contain code examples using this.
matplotlib.pyplot.specgram
matplotlib.pyplot.magnitude_spectrum
matplotlib.pyplot.csd
matplotlib.pyplot.plotting
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them