meanap.pipeline.plotting_step2

Functions

plot_burst_detection_info(spike_times_dict, ...)

plot_firing_rate_distribution(ephys, out_path)

plot_heatmap(metric, chs, title, clabel, ...)

Electrode heatmap, port of electrodeHeatMaps.m / plotNodeHeatmap.m.

plot_neuronal_activity_checks(rec, params, ...)

plot_raster(spike_times_dict, duration_s, ...)

Two-panel raster, port of rasterPlot.m.

plot_step2_group_comparisons(recordings, ...)

Generate group comparison plots for step 2.
meanap.pipeline.plotting_step2.plot_burst_detection_info(spike_times_dict, ephys, duration_s, fs, out_path)[source]
Parameters:

  • spike_times_dict (dict)

  • ephys (dict)

  • duration_s (float)

  • fs (float)

  • out_path (Path)

meanap.pipeline.plotting_step2.plot_firing_rate_distribution(ephys, out_path)[source]
Parameters:

  • ephys (dict)

  • out_path (Path)

meanap.pipeline.plotting_step2.plot_heatmap(metric, chs, title, clabel, out_path, cmap='viridis', channel_layout='Axion64', batch_max=None)[source]

Electrode heatmap, port of electrodeHeatMaps.m / plotNodeHeatmap.m.

When batch_max is given, produce a two-panel figure (like MATLAB’s tiledlayout(1,2)): left scaled to this recording (color axis = 99th percentile of its own values), right scaled to the entire dataset (color axis = batch_max, the batch-wide max of this metric = MATLAB’s maxValStruct.(metric)), so levels are comparable across recordings. When batch_max is None, fall back to the original single panel.

Parameters:

  • metric (ndarray)

  • chs (ndarray)

  • title (str)

  • clabel (str)

  • out_path (Path)

  • channel_layout (str)

  • batch_max (float | None)

meanap.pipeline.plotting_step2.plot_neuronal_activity_checks(rec, params, spike_times_dict, n_channels, chs, fs, duration_s, ephys, output_root, spike_freq_max=None, batch_max=None)[source]
Parameters:

  • spike_times_dict (dict)

  • n_channels (int)

  • chs (ndarray)

  • fs (float)

  • duration_s (float)

  • ephys (dict)

  • output_root (Path)

  • spike_freq_max (float | None)

  • batch_max (dict | None)

meanap.pipeline.plotting_step2.plot_raster(spike_times_dict, duration_s, out_path, spike_freq_max=None, raster_upper_percentile=99.0)[source]

Two-panel raster, port of rasterPlot.m.

Top panel is scaled to this recording (color axis = the raster_upper_percentile of its own 1-second spike counts); bottom panel is scaled to the entire data batch (color axis = spike_freq_max, the batch-wide max firing rate). Sharing the bottom scale across every recording makes activity levels visually comparable between them — this is what MATLAB’s spikeFreqMax (maxValStruct.FR) does. When spike_freq_max is None (e.g. a single recording plotted in isolation) the batch panel falls back to this recording’s own percentile scale.

Parameters:

  • spike_times_dict (dict)

  • duration_s (float)

  • out_path (Path)

  • spike_freq_max (float | None)

  • raster_upper_percentile (float)

meanap.pipeline.plotting_step2.plot_step2_group_comparisons(recordings, all_ephys, out_dir, custom_grp_order=None)[source]

Generate group comparison plots for step 2.

Parameters:

  • recordings (list)

  • all_ephys (dict)

  • out_dir (Path)

  • custom_grp_order (list[str] | None)

Return type:

None