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name: plotting description: "Plot raster, spike-count, trace, and animation views for time-first snnTorch spike tensors." disable-model-invocation: true metadata: disco-role: operating license: MIT

Plotting

Use this sub-skill when the task is to visualize spike tensors with snntorch.spikeplot helpers rather than to train, encode, load datasets, or exchange NIR graphs.

Route here for

• Raster plots from time-first binary spike tensors with spikeplot.raster.
• Output-neuron spike-count panels and spike-count animations with spikeplot.spike_count.
• Membrane/current traces, optionally with spike overlays, using spikeplot.traces.
• Image-like spike-frame animations with spikeplot.animator in notebooks or scripts.
• Headless matplotlib setup for CI, SSH, and batch jobs.

Do not handle here

• Training loops, losses, surrogate gradients, or utils.reset: route to encoding-training.
• Spike encoding from raw data with spikegen: route to encoding-training.
• NIR export/import: route to nir-interoperability.
• Dataset download/loading and legacy neuromorphic dataset wrappers: route to spikevision.
• Core neuron construction or state lifecycle beyond using recorded spk_rec/mem_rec: route to core-neurons.

Operating workflow

1. Identify the recorded tensor: most spikeplot workflows use one sample with time as dimension 0, such as [num_steps, num_outputs] for spike counts/traces or [num_steps, height, width] for frame animations.
2. Normalize shapes before plotting. For classic rasters, prefer [num_steps, num_neurons]; flatten trailing spatial dimensions if needed. For minibatch recordings, index one sample first, for example spk_rec[:, sample_idx].detach().cpu().
3. Choose the helper and exact arguments from API reference; use workflows for notebook and headless-script patterns.
4. For spike_count, pass a Python list/tuple/array of labels whose length matches the output dimension; do not pass a torch tensor as labels.
5. For servers or CI, set an Agg backend before importing matplotlib.pyplot, create explicit fig, ax objects, and close figures after rendering.
6. If plotting fails or animations do not save/display, use troubleshooting before changing model or tensor semantics.

Bundled script

• scripts/spikeplot_smoke.py: headless Agg smoke that builds synthetic spike/trace tensors, exercises raster, spike-count, traces, and a lightweight animation object, and prints a pass summary.

Start with workflows for practical recipes and API reference for live snnTorch 1.0.0 signatures.