mne_denoise.viz.plot_power_ratio_map#

mne_denoise.viz.plot_power_ratio_map(inst_before, inst_after, info, vlim=(None, None), cmap='viridis', colorbar_label='Power Ratio (After / Before)', title='Power Ratio Map', show=True, ax=None, fname=None)[source]#

Plot a topomap of preserved power ratio after denoising.

Parameters:

  • inst_before (MNE object | ndarray) – Inputs used to estimate per-channel variance. Accepted forms: 1D channel variances, 2D channel-by-time arrays, 3D epoch arrays, or MNE Raw/Epochs/Evoked objects.

  • inst_after (MNE object | ndarray) – Inputs used to estimate per-channel variance. Accepted forms: 1D channel variances, 2D channel-by-time arrays, 3D epoch arrays, or MNE Raw/Epochs/Evoked objects.

  • info (mne.Info) – Sensor metadata used by mne.viz.plot_topomap.

  • vlim (tuple[float | None, float | None]) – Lower and upper limits passed to mne.viz.plot_topomap.

  • cmap (str | matplotlib.colors.Colormap) – Colormap passed to mne.viz.plot_topomap.

  • colorbar_label (str) – Colorbar label.

  • title (str) – Panel title.

  • show (bool) – If True, display the figure.

  • ax (matplotlib.axes.Axes | None) – Target axes. If None, create a new figure and axes.

  • fname (path-like | None) – Optional output path used to save the figure.

Returns:

fig – Figure handle.

Return type:

matplotlib.figure.Figure

Raises:

ValueError – If info is missing or if channel counts do not match info.

Notes

Ratio values are computed as var_after / var_before channel-wise.

Examples

>>> from mne_denoise.viz import plot_power_ratio_map
>>> fig = plot_power_ratio_map(
...     before_array,
...     after_array,
...     info=info,
...     show=False,
... )