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5e0b58b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | """Pure feature calculations used by the signal interpreter."""
from __future__ import annotations
from collections import Counter
from collections.abc import Iterable
from .models import SpikeSample
def population_rate_hz(
spikes: Iterable[SpikeSample], *, neuron_count: int, duration_ms: float
) -> float:
"""Return population-averaged firing rate in hertz."""
if neuron_count <= 0 or duration_ms <= 0.0:
return 0.0
spike_count = sum(1 for _ in spikes)
seconds = duration_ms / 1000.0
return spike_count / (neuron_count * seconds)
def burst_index(spikes: Iterable[SpikeSample]) -> float:
"""Return a bounded heuristic burst score based on same-tick spike concentration."""
counts = Counter(sample.tick for sample in spikes)
total = sum(counts.values())
if total == 0:
return 0.0
peak = max(counts.values(), default=0)
return min(1.0, peak / total)
def synchrony(spikes: Iterable[SpikeSample], *, neuron_count: int) -> float:
"""Return a bounded same-tick synchrony score for the observed population."""
if neuron_count <= 0:
return 0.0
per_tick: dict[int, set[int]] = {}
for sample in spikes:
per_tick.setdefault(sample.tick, set()).add(sample.neuron_id)
if not per_tick:
return 0.0
peak = max(len(ids) for ids in per_tick.values())
return min(1.0, peak / neuron_count)
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