timeseries

plot.timeseries

Time-series, EEG, and power-spectrum plotting for SimulationResult.

Functions

Name	Description
plot_eeg	EEG-like stacked-channel plot.
plot_power_spectrum	FFT power spectrum with frequency-band annotations.
plot_timeseries	Default timeseries line plot with per-unit subplots.

plot_eeg

plot.timeseries.plot_eeg(
    result,
    VOI=None,
    mode=0,
    spacing=None,
    normalize=False,
    channel_labels=True,
    ax=None,
    linewidth=0.5,
    **kwargs,
)

EEG-like stacked-channel plot.

Parameters

result : SimulationResult Must have .data (xr.DataArray with node dim). VOI : str, optional Variable of interest. Defaults to first variable. mode : int Mode index. spacing : float, optional Vertical spacing; auto-computed from data if None. normalize : bool Z-score each channel before plotting. channel_labels : bool Show region labels on y-axis. ax : matplotlib.axes.Axes, optional linewidth : float

plot_power_spectrum

plot.timeseries.plot_power_spectrum(
    result,
    VOI=None,
    ROI='mean',
    mode=0,
    bands=None,
    ax=None,
    label='simulation',
    **kwargs,
)

FFT power spectrum with frequency-band annotations.

Parameters

result : SimulationResult VOI : str, optional Variable of interest. ROI : str or int ‘mean’ or region index. mode : int bands : dict, optional ax : matplotlib.axes.Axes, optional

plot_timeseries

plot.timeseries.plot_timeseries(result, ax=None, **kwargs)

Default timeseries line plot with per-unit subplots.

Parameters

result : SimulationResult Must have .data (xr.DataArray) and ._units dict. ax : matplotlib.axes.Axes, optional Target axes (single-panel only; ignored for multi-unit). **kwargs Forwarded to ax.plot().

Returns

matplotlib.figure.Figure or None