spikegen 0.1.0

Generate spike trains (Poisson, gamma renewal, regular, inhomogeneous) in pure Python with zero dependencies.

spikegen

Generate spike trains in pure Python with zero dependencies. Poisson, gamma renewal, regular, and inhomogeneous processes, returned as plain sorted lists of spike times, with explicit seeds for reproducibility.

Install

pip install spikegen

30-second example

from spikegen import homogeneous_poisson, regular, gamma_renewal, with_refractory

homogeneous_poisson(rate=50.0, duration=2.0, seed=0)   # Poisson spikes in [0, 2)
regular(rate=10.0, duration=1.0)                        # [0.0, 0.1, 0.2, ...]
gamma_renewal(rate=20.0, shape=2.0, duration=1.0, seed=0)  # more regular than Poisson

spikes = homogeneous_poisson(rate=80.0, duration=1.0, seed=0)
with_refractory(spikes, refractory=0.002)              # enforce a 2 ms refractory period

Times are in the same units as 1 / rate (seconds if rate is in Hz). Seeded processes are reproducible: the same seed gives the same train.

Why this exists

Generating synthetic spike trains is a daily need, but the generators live inside heavy frameworks: elephant requires neo and quantities, pyspike is NumPy-based, and other options are old or partial. spikegen is a small, dependency-free generator that returns plain lists of floats, so reproducible spike trains are one import away. It pairs with spikedist: generate trains, then measure the distance between them.

Processes

• regular(rate, duration): evenly spaced spikes. Deterministic.
• homogeneous_poisson(rate, duration, seed): constant-rate Poisson process.
• inhomogeneous_poisson(rate_fn, max_rate, duration, seed): time-varying rate by thinning.
• gamma_renewal(rate, shape, duration, seed): gamma inter-spike intervals; shape 1 is Poisson, larger shape is more regular.
• with_refractory(times, refractory): drop spikes within a minimum interval.

All parameters are keyword-only and explicit.

Testing

pip install -e ".[dev]"
pytest

Tests cover exact values for the deterministic generators, seeded reproducibility, the rate-bound and ordering invariants, and the validation paths, with property tests via Hypothesis.

Contributing

Issues and pull requests are welcome. See CONTRIBUTING.md.

License

MIT. See LICENSE.