mne.viz.plot_raw_psd#

mne.viz.plot_raw_psd(raw, fmin=0, fmax=inf, tmin=None, tmax=None, proj=False, n_fft=None, n_overlap=0, reject_by_annotation=True, picks=None, ax=None, color='black', xscale='linear', area_mode='std', area_alpha=0.33, dB=True, estimate='auto', show=True, n_jobs=None, average=False, line_alpha=None, spatial_colors=True, sphere=None, window='hamming', exclude='bads', verbose=None)[source]#

Warning

LEGACY: New code should use Raw.compute_psd().plot().

Plot power or amplitude spectra.

Separate plots are drawn for each channel type. When the data have been processed with a bandpass, lowpass or highpass filter, dashed lines (╎) indicate the boundaries of the filter. The line noise frequency is also indicated with a dashed line (⋮). If average=False, the plot will be interactive, and click-dragging on the spectrum will generate a scalp topography plot for the chosen frequency range in a new figure.

Parameters:

rawinstance of Raw: The raw object.
fmin, fmaxfloat: The lower- and upper-bound on frequencies of interest. Default is fmin=0, fmax=np.inf (spans all frequencies present in the data).
tmin, tmaxfloat | None: First and last times to include, in seconds. None uses the first or last time present in the data. Default is tmin=None, tmax=None (all times).
projbool: Whether to apply SSP projection vectors before spectral estimation. Default is False.
n_fftint | None: Number of points to use in Welch FFT calculations. Default is None, which uses the minimum of 2048 and the number of time points.
n_overlapint: The number of points of overlap between blocks. The default value is 0 (no overlap).
reject_by_annotationbool: Whether to omit bad spans of data before spectral estimation. If True, spans with annotations whose description begins with bad will be omitted.
picksstr | array_like | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
axinstance of Axes | list of Axes | None: The axes to plot to. If None, a new Figure will be created with the correct number of axes. If Axes are provided (either as a single instance or a list of axes), the number of axes provided must match the number of channel types present in the object..Default is None.
colorstr | tuple: A matplotlib-compatible color to use. Has no effect when spatial_colors=True.
xscale‘linear’ | ‘log’: Scale of the frequency axis. Default is 'linear'.
area_modestr | None: Mode for plotting area. If ‘std’, the mean +/- 1 STD (across channels) will be plotted. If ‘range’, the min and max (across channels) will be plotted. Bad channels will be excluded from these calculations. If None, no area will be plotted. If average=False, no area is plotted.
area_alphafloat: Alpha for the area.
dBbool: Plot Power Spectral Density (PSD), in units (amplitude**2/Hz (dB)) if dB=True, and estimate='power' or estimate='auto'. Plot PSD in units (amplitude**2/Hz) if dB=False and, estimate='power'. Plot Amplitude Spectral Density (ASD), in units (amplitude/sqrt(Hz)), if dB=False and estimate='amplitude' or estimate='auto'. Plot ASD, in units (amplitude/sqrt(Hz) (dB)), if dB=True and estimate='amplitude'.
estimatestr, {‘auto’, ‘power’, ‘amplitude’}: Can be “power” for power spectral density (PSD), “amplitude” for amplitude spectrum density (ASD), or “auto” (default), which uses “power” when dB is True and “amplitude” otherwise.
showbool: Show the figure if True.
n_jobsint | None: The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.
averagebool: If False, the PSDs of all channels is displayed. No averaging is done and parameters area_mode and area_alpha are ignored. When False, it is possible to paint an area (hold left mouse button and drag) to plot a topomap.
line_alphafloat | None: Alpha for the PSD line. Can be None (default) to use 1.0 when average=True and 0.1 when average=False.
spatial_colorsbool: Whether to color spectrum lines by channel location. Ignored if average=True.
spherefloat | array_like | instance of ConductorModel | None | ‘auto’ | ‘eeglab’: The sphere parameters to use for the head outline. Can be array-like of shape (4,) to give the X/Y/Z origin and radius in meters, or a single float to give just the radius (origin assumed 0, 0, 0). Can also be an instance of a spherical ConductorModel to use the origin and radius from that object. If 'auto' the sphere is fit to digitization points. If 'eeglab' the head circle is defined by EEG electrodes 'Fpz', 'Oz', 'T7', and 'T8' (if 'Fpz' is not present, it will be approximated from the coordinates of 'Oz'). None (the default) is equivalent to 'auto' when enough extra digitization points are available, and (0, 0, 0, 0.095) otherwise.

New in version 0.20.

Changed in version 1.1: Added 'eeglab' option.
windowstr | float | tuple: Windowing function to use. See scipy.signal.get_window().

New in version 0.22.0.
excludelist of str | ‘bads’: Channels names to exclude from being shown. If ‘bads’, the bad channels are excluded. Pass an empty list to plot all channels (including channels marked “bad”, if any).

New in version 0.24.0.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

figinstance of Figure: Figure with frequency spectra of the data channels.

Notes

This function exists to support legacy code; for new code the preferred idiom is inst.compute_psd().plot() (where inst is an instance of Raw, Epochs, or Evoked).