mne.viz.plot_tfr_topomap(tfr, tmin=None, tmax=None, fmin=None, fmax=None, ch_type=None, baseline=None, mode='mean', layout=None, vmin=None, vmax=None, cmap=None, sensors=True, colorbar=True, unit=None, res=64, size=2, cbar_fmt='%1.1e', show_names=False, title=None, axes=None, show=True, outlines='head', head_pos=None)

Plot topographic maps of specific time-frequency intervals of TFR data


tfr : AvereageTFR

The AvereageTFR object.

tmin : None | float

The first time instant to display. If None the first time point available is used.

tmax : None | float

The last time instant to display. If None the last time point available is used.

fmin : None | float

The first frequency to display. If None the first frequency available is used.

fmax : None | float

The last frequency to display. If None the last frequency available is used.

ch_type : ‘mag’ | ‘grad’ | ‘planar1’ | ‘planar2’ | ‘eeg’ | None

The channel type to plot. For ‘grad’, the gradiometers are collected in pairs and the RMS for each pair is plotted. If None, then channels are chosen in the order given above.

baseline : tuple or list of length 2

The time interval to apply rescaling / baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used.

mode : ‘logratio’ | ‘ratio’ | ‘zscore’ | ‘mean’ | ‘percent’

Do baseline correction with ratio (power is divided by mean power during baseline) or z-score (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)) If None, baseline no correction will be performed.

layout : None | Layout

Layout instance specifying sensor positions (does not need to be specified for Neuromag data). If possible, the correct layout file is inferred from the data; if no appropriate layout file was found, the layout is automatically generated from the sensor locations.

vmin : float | callable | None

The value specifying the lower bound of the color range. If None, and vmax is None, -vmax is used. Else np.min(data) or in case data contains only positive values 0. If callable, the output equals vmin(data). Defaults to None.

vmax : float | callable | None

The value specifying the upper bound of the color range. If None, the maximum value is used. If callable, the output equals vmax(data). Defaults to None.

cmap : matplotlib colormap | None

Colormap. If None and the plotted data is all positive, defaults to ‘Reds’. If None and data contains also negative values, defaults to ‘RdBu_r’. Defaults to None.

sensors : bool | str

Add markers for sensor locations to the plot. Accepts matplotlib plot format string (e.g., ‘r+’ for red plusses). If True, a circle will be used (via .add_artist). Defaults to True.

colorbar : bool

Plot a colorbar.

unit : str | None

The unit of the channel type used for colorbar labels.

res : int

The resolution of the topomap image (n pixels along each side).

size : float

Side length per topomap in inches.

cbar_fmt : str

String format for colorbar values.

show_names : bool | callable

If True, show channel names on top of the map. If a callable is passed, channel names will be formatted using the callable; e.g., to delete the prefix ‘MEG ‘ from all channel names, pass the function lambda x: x.replace(‘MEG ‘, ‘’). If mask is not None, only significant sensors will be shown.

title : str | None

Title. If None (default), no title is displayed.

axes : instance of Axis | None

The axes to plot to. If None the axes is defined automatically.

show : bool

Show figure if True.

outlines : ‘head’ | ‘skirt’ | dict | None

The outlines to be drawn. If ‘head’, the default head scheme will be drawn. If ‘skirt’ the head scheme will be drawn, but sensors are allowed to be plotted outside of the head circle. If dict, each key refers to a tuple of x and y positions, the values in ‘mask_pos’ will serve as image mask, and the ‘autoshrink’ (bool) field will trigger automated shrinking of the positions due to points outside the outline. Alternatively, a matplotlib patch object can be passed for advanced masking options, either directly or as a function that returns patches (required for multi-axis plots). If None, nothing will be drawn. Defaults to ‘head’.

head_pos : dict | None

If None (default), the sensors are positioned such that they span the head circle. If dict, can have entries ‘center’ (tuple) and ‘scale’ (tuple) for what the center and scale of the head should be relative to the electrode locations.


fig : matplotlib.figure.Figure

The figure containing the topography.