jack
39 lines
977 B
Python
39 lines
977 B
Python
"""Per-frame descriptive statistics"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class Stats:
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min: float; min_index: int
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max: float; max_index: int
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diff: float; avg: float
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sigma: float; three_sigma: float
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def compute_stats(values: list[float]) -> Stats:
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clean = [(i, v) for i, v in enumerate(values) if not math.isnan(v)]
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if not clean:
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return Stats(0.0, -1, 0.0, -1, 0.0, 0.0, 0.0, 0.0)
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min_index, min_v = min(clean, key=lambda iv: iv[1])
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max_index, max_v = max(clean, key=lambda iv: iv[1])
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nums = [v for _, v in clean]
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avg = sum(nums) / len(nums)
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variance = sum((v - avg) ** 2 for v in nums) / len(nums)
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sigma = math.sqrt(variance)
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return Stats(
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min=min_v,
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min_index=min_index,
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max=max_v,
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max_index=max_index,
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diff=max_v - min_v,
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avg=avg,
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sigma=sigma,
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three_sigma=3 * sigma,
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)
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