matplotlib.axes.Axes.psd#

Axes.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)[source]#

Plot the power spectral density.

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.

Parameters:

x1-D array or sequence: Array or sequence containing the data
Fsfloat, default: 2: The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit.
windowcallable or ndarray, default: window_hanning: 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.
sides{'default', 'onesided', 'twosided'}, optional: 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.
pad_toint, optional: 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
NFFTint, default: 256: 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.
detrend{'none', 'mean', 'linear'} or callable, default: 'none': 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 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.
scale_by_freqbool, default: True: Whether the resulting density values should be scaled by the scaling frequency, which gives density in units of 1/Hz. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.
noverlapint, default: 0 (no overlap): The number of points of overlap between segments.
Fcint, default: 0: 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.
return_linebool, default: False: Whether to include the line object plotted in the returned values.

Returns:

Pxx1-D array: The values for the power spectrum \(P_{xx}\) before scaling (real valued).
freqs1-D array: The frequencies corresponding to the elements in Pxx.
lineLine2D: The line created by this function. Only returned if return_line is True.

Other Parameters:

dataindexable object, optional

If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception):

**kwargs

Keyword arguments control the Line2D properties:

Property	Description
`agg_filter`	a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
`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'}
`gapcolor`	color or None
`gid`	str
`in_layout`	bool
`label`	object
`linestyle` or ls	{'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
`linewidth` or lw	float
`marker`	marker style string, `Path` or `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]
`mouseover`	bool
`path_effects`	`AbstractPathEffect`
`picker`	float or callable[[Artist, Event], tuple[bool, dict]]
`pickradius`	unknown
`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

Examples using `matplotlib.axes.Axes.psd`#

Psd Demo

matplotlib.axes.Axes.psd#

Examples using matplotlib.axes.Axes.psd#

Examples using `matplotlib.axes.Axes.psd`#