c80c66c2e5
Deep audit identified 106 findings; 102 fixed, 4 deferred. Covers 8 areas: - Settings & deploy: env-gated DEBUG/SECRET_KEY, HTTPS headers, gunicorn, celery worker - Auth (registered_user): password write_only, request.data fixes, transaction safety, proper HTTP status codes - Workout app: IDOR protection, get_object_or_404, prefetch_related N+1 fixes, transaction.atomic - Video/scripts: path traversal sanitization, HLS trigger guard, auth on cache wipe - Models (exercise/equipment/muscle/superset): null-safe __str__, stable IDs, prefetch support - Generator views: helper for registered_user lookup, logger.exception, bulk_update, transaction wrapping - Generator core (rules/selector/generator): push-pull ratio, type affinity normalization, modality checks, side-pair exact match, word-boundary regex, equipment cache clearing - Generator services (plan_builder/analyzer/normalizer): transaction.atomic, muscle cache, bulk_update, glutes classification fix Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import math
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import random
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from typing import Optional
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def clamp_duration_bias(duration_bias: float, duration_bias_range: Optional[tuple]) -> float:
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"""Clamp duration bias to [0,1] or workout-type specific range."""
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if not duration_bias_range:
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return max(0.0, min(1.0, duration_bias))
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low, high = duration_bias_range
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return max(float(low), min(float(high), duration_bias))
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def plan_superset_modalities(
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*,
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num_supersets: int,
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duration_bias: float,
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duration_bias_range: Optional[tuple],
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is_strength_workout: bool,
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rng=random,
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) -> list[bool]:
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"""Plan per-superset modality (True=duration, False=reps)."""
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if num_supersets <= 0:
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return []
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if is_strength_workout:
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return [False] * num_supersets
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if duration_bias_range:
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low, high = duration_bias_range
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target_bias = (float(low) + float(high)) / 2.0
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min_duration_sets = max(0, math.ceil(num_supersets * float(low)))
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max_duration_sets = min(num_supersets, math.floor(num_supersets * float(high)))
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else:
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target_bias = max(0.0, min(1.0, duration_bias))
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min_duration_sets = max(0, math.floor(num_supersets * max(0.0, target_bias - 0.15)))
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max_duration_sets = min(num_supersets, math.ceil(num_supersets * min(1.0, target_bias + 0.15)))
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duration_sets = int(round(num_supersets * target_bias))
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duration_sets = max(min_duration_sets, min(max_duration_sets, duration_sets))
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if num_supersets > 1 and duration_sets == num_supersets and max_duration_sets < num_supersets:
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duration_sets = max_duration_sets
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if num_supersets > 1 and duration_sets == 0 and min_duration_sets > 0:
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duration_sets = min_duration_sets
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modalities = [False] * num_supersets
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if duration_sets > 0:
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positions = list(range(num_supersets))
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rng.shuffle(positions)
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for idx in positions[:duration_sets]:
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modalities[idx] = True
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return modalities
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