mne_rt.viz.RawPlot#

class mne_rt.viz.RawPlot(ch_names: list[str], sfreq: float, time_window: float = 10.0, n_shown: int = 20, scale_uv: float = 100.0, info=None, verbose=None)[source]#

Bases: _MockQMainWindow

Scrolling raw M/EEG channel viewer.

Displays all channels stacked vertically in a dark-themed pyqtgraph.PlotWidget, colour-coded by channel index. Channels are scrollable via the mouse wheel on the plot or the scrollbar on the right edge of the signal area. Right-clicking any channel name on the Y-axis immediately opens an MNE sensor-position plot for that channel.

Parameters:
ch_nameslist of str

Channel names. One row is shown per channel.

sfreqfloat

Sampling frequency in Hz.

time_windowfloat, default 10.0

Visible time range in seconds at startup.

n_shownint, default 20

Number of channels visible simultaneously.

scale_uvfloat, default 100.0

Amplitude scale in µV. A signal of this peak amplitude occupies half the per-channel row height. For MEG (Tesla) pass scale_uv=1e-6 (i.e., 1 pT per half-row).

infomne.Info | None, default None

If provided: used to resolve channel types, apply SSP projectors, and show sensor positions on right-click.

verbosebool | str | None, default None

Verbosity level. See set_log_level().

See also

mne_rt.viz.NFPlot

Scrolling NF feature monitor.

mne_rt.RTStream.record_main

Drives the raw display from the NF loop.

Notes

The control panel (right sidebar) provides:

  • Playback — pause/resume, clear buffer, screenshot.

  • Amplitude — ÷2 / ×2 scale buttons with a live readout.

  • Display — time-window selector, grid toggle, DC-removal toggle.

  • Filter — online causal bandpass/highpass/lowpass/notch (scipy). Applied to new data from the moment “Apply filter” is clicked; data already in the buffer is not retroactively filtered.

  • Artifact Correction — LMS adaptive filter or ASR, applied from the moment “Apply from now” is clicked. LMS requires a reference channel; ASR calibrates on the current buffer content.

  • SSP — shown when info contains projectors; applied to new data from the moment the checkbox is ticked.

Use the mouse wheel on the signal area or the vertical scrollbar to the right of the traces to page through channels. Right-click any channel label on the Y-axis to open its sensor-position diagram.

Added in version 1.0.0.

__init__(ch_names: list[str], sfreq: float, time_window: float = 10.0, n_shown: int = 20, scale_uv: float = 100.0, info=None, verbose=None) → None[source]#

Methods

__init__(ch_names, sfreq[, time_window, ...])

closeEvent(event)

push(data)

Append a chunk of raw data and refresh the display.
push(data: ndarray) → None[source]#

Append a chunk of raw data and refresh the display.

All active online processing (filter, SSP, artifact correction) is applied to the incoming chunk before it enters the circular buffer. Data already in the buffer is never retroactively modified — the display transitions from unprocessed to processed as new data arrives.

Parameters:
datandarray, shape (n_channels, n_samples)

New raw data chunk.

Notes

The call is a no-op when the plot is paused.

property bad_channels: list[str]#

Channel names currently marked as bad.

property bad_segments: list[tuple[float, float]]#

Bad segments as list of (start_s, end_s) in absolute seconds.

to_annotations()[source]#

Return bad segments as mne.Annotations.

Returns:
annotationsmne.Annotations or None

None when no bad segments have been marked.

closeEvent(event) → None[source]#