| Total Complexity | 5 |
| Total Lines | 43 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | """ |
||
| 2 | Calculations. |
||
| 3 | """ |
||
| 4 | import math |
||
| 5 | from collections import defaultdict |
||
| 6 | from typing import Collection, Dict, Sequence, Set, Tuple |
||
| 7 | |||
| 8 | from typeddfs import TypedDfs |
||
|
|
|||
| 9 | |||
| 10 | from mandos.model.hits import AbstractHit, HitFrame, Pair |
||
| 11 | |||
| 12 | SimilarityDf = TypedDfs.typed("DistanceDf").symmetric().build() |
||
| 13 | |||
| 14 | |||
| 15 | class AnalysisUtils: |
||
| 16 | @classmethod |
||
| 17 | def elle(cls, x: float) -> float: |
||
| 18 | return math.log10(1 + x) |
||
| 19 | |||
| 20 | @classmethod |
||
| 21 | def hit_multidict(cls, hits: Sequence[AbstractHit], key: str): |
||
| 22 | x_to_hits = defaultdict(list) |
||
| 23 | for hit in hits: |
||
| 24 | x_to_hits[getattr(hit, key)].append(hit) |
||
| 25 | return x_to_hits |
||
| 26 | |||
| 27 | @classmethod |
||
| 28 | def weights_of_pairs( |
||
| 29 | cls, hits1: Collection[AbstractHit], hits2: Collection[AbstractHit] |
||
| 30 | ) -> Dict[Pair, Tuple[float, float]]: |
||
| 31 | """ |
||
| 32 | Calculates the sum of |
||
| 33 | """ |
||
| 34 | union = {h.to_pair for h in hits1}.union({h.to_pair for h in hits2}) |
||
| 35 | return {p: (cls._score(hits1, p), cls._score(hits2, p)) for p in union} |
||
| 36 | |||
| 37 | @classmethod |
||
| 38 | def _score(cls, hits: Collection[AbstractHit], pair: Pair) -> int: |
||
| 39 | return sum([h.value for h in hits if h.to_pair == pair]) |
||
| 40 | |||
| 41 | |||
| 42 | __all__ = ["AnalysisUtils", "SimilarityDf"] |
||
| 43 |