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Visualisation Module

The btorch.visualisation module provides plotting functions for neural simulation analysis.

Modules

timeseries.py

Time-series visualization for spike and continuous data.

Function Description
plot_raster Spike raster with grouping, styling, events, regions, and tracks
plot_traces Continuous traces (voltage, currents)
plot_spectrum Frequency spectrum (Welch method)
plot_grouped_spectrum Spectral analysis by neuron groups
plot_log_hist Log-log histogram
plot_neuron_traces Multi-panel neuron state plots (voltage, ASC, PSC)

Dataclasses: - NeuronSpec: Per-neuron styling (color, marker, linestyle) - SimulationStates: Container for simulation data - TracePlotFormat: Figure formatting options

dynamics.py

Multiscale dynamics analysis visualization.

Function Description
plot_multiscale_fano Fano factor across time windows
plot_dfa_analysis Detrended Fluctuation Analysis
plot_isi_cv ISI Coefficient of Variation
plot_avalanche_analysis Avalanche size/duration distributions
plot_eigenvalue_spectrum Weight matrix eigenvalue spectrum
plot_lyapunov_spectrum Lyapunov exponents spectrum
plot_firing_rate_distribution Firing rate histogram

Dataclasses: - DynamicsData: Container for spike data - DynamicsPlotFormat: Visualization mode (individual/grouped/distribution) - FanoFactorConfig: Fano analysis parameters - DFAConfig: DFA parameters

hexmap.py

Hexagonal heatmap visualization using Plotly.

Function Description
hex_heatmap Interactive hex-grid heatmap with slider for time series

aggregation.py

Grouped distribution and neuropil time-series visualization.

Function Description
plot_group_distribution Generic grouped plot API with violin, box, or ecdf
plot_group_violin Grouped violin plot convenience wrapper
plot_group_box Grouped box plot convenience wrapper
plot_group_ecdf Grouped ECDF plot convenience wrapper
plot_neuropil_timeseries_overview Aggregated neuropil overview in wave/heatmap style
plot_neuropil_timeseries_panels Region-wise subplot grid for detailed comparison

Usage Examples

from btorch.visualisation.timeseries import plot_raster, plot_neuron_traces, NeuronSpec

# Basic raster
plot_raster(spikes, dt=0.1, marker="|", markersize=5)

# Grouped raster with colors
plot_raster(
    spikes,
    neurons_df=df,
    group_by="cell_type",
    color={"excitatory": "red", "inhibitory": "blue"},
    show_separators=True,
    events=[100, 200],  # Event markers
    regions=[(50, 80)],  # Shaded regions
    show_tracks=True,
)

# Neuron traces with per-neuron styling
specs = [NeuronSpec(color="red"), NeuronSpec(color="blue")]
plot_neuron_traces(voltage=V, dt=0.1, neuron_specs=specs)