1c61b80731
- Add rules_engine.py with quantitative rules for all 8 workout types - Add quality gate retry loop in generate_single_workout() - Expand calibrate_structure_rules to all 120 combinations (8 types × 5 goals × 3 sections) - Wire WeeklySplitPattern DB records into _pick_weekly_split() - Enforce movement patterns from WorkoutStructureRule in exercise selection - Add straight-set strength support (single main lift, 4-6 rounds) - Add modality consistency check for duration-dominant workout types - Add InjuryStep component to onboarding and preferences - Add sibling exercise exclusion in regenerate and preview_day endpoints - Display generator warnings on dashboard - Expand fix_rep_durations, fix_exercise_flags, fix_movement_pattern_typo - Add audit_exercise_data and check_rules_drift management commands - Add Next.js frontend with dashboard, onboarding, preferences, history pages - Add generator app with ML-powered workout generation pipeline - 96 new tests across 7 test modules Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
353 lines
10 KiB
Python
353 lines
10 KiB
Python
"""
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Muscle name normalization and split classification.
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The DB contains ~38 muscle entries with casing duplicates (e.g. "Quads" vs "quads",
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"Abs" vs "abs", "Core" vs "core"). This module provides a single source of truth
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for mapping raw muscle names to canonical lowercase names, organizing them into
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split categories, and classifying a set of muscles into a split type.
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"""
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from __future__ import annotations
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from typing import Set, List, Optional
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# ---------------------------------------------------------------------------
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# Raw name -> canonical name
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# Keys are lowercased for lookup; values are the canonical form we store.
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# ---------------------------------------------------------------------------
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MUSCLE_NORMALIZATION_MAP: dict[str, str] = {
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# --- quads ---
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'quads': 'quads',
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'quadriceps': 'quads',
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'quad': 'quads',
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# --- hamstrings ---
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'hamstrings': 'hamstrings',
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'hamstring': 'hamstrings',
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'hams': 'hamstrings',
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# --- glutes ---
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'glutes': 'glutes',
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'glute': 'glutes',
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'gluteus': 'glutes',
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'gluteus maximus': 'glutes',
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# --- calves ---
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'calves': 'calves',
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'calf': 'calves',
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'gastrocnemius': 'calves',
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'soleus': 'calves',
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# --- chest ---
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'chest': 'chest',
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'pecs': 'chest',
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'pectorals': 'chest',
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# --- deltoids / shoulders ---
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'deltoids': 'deltoids',
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'deltoid': 'deltoids',
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'shoulders': 'deltoids',
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'shoulder': 'deltoids',
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'front deltoids': 'front deltoids',
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'front deltoid': 'front deltoids',
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'front delts': 'front deltoids',
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'rear deltoids': 'rear deltoids',
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'rear deltoid': 'rear deltoids',
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'rear delts': 'rear deltoids',
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'side deltoids': 'side deltoids',
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'side deltoid': 'side deltoids',
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'side delts': 'side deltoids',
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'lateral deltoids': 'side deltoids',
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'medial deltoids': 'side deltoids',
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# --- triceps ---
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'triceps': 'triceps',
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'tricep': 'triceps',
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# --- biceps ---
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'biceps': 'biceps',
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'bicep': 'biceps',
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# --- upper back ---
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'upper back': 'upper back',
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'rhomboids': 'upper back',
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# --- lats ---
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'lats': 'lats',
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'latissimus dorsi': 'lats',
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'lat': 'lats',
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# --- middle back ---
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'middle back': 'middle back',
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'mid back': 'middle back',
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# --- lower back ---
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'lower back': 'lower back',
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'erector spinae': 'lower back',
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'spinal erectors': 'lower back',
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# --- traps ---
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'traps': 'traps',
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'trapezius': 'traps',
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# --- abs ---
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'abs': 'abs',
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'abdominals': 'abs',
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'rectus abdominis': 'abs',
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# --- obliques ---
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'obliques': 'obliques',
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'oblique': 'obliques',
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'external obliques': 'obliques',
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'internal obliques': 'obliques',
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# --- core (general) ---
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'core': 'core',
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# --- intercostals ---
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'intercostals': 'intercostals',
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# --- hip flexor ---
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'hip flexor': 'hip flexors',
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'hip flexors': 'hip flexors',
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'iliopsoas': 'hip flexors',
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'psoas': 'hip flexors',
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# --- hip abductors ---
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'hip abductors': 'hip abductors',
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'hip abductor': 'hip abductors',
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# --- hip adductors ---
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'hip adductors': 'hip adductors',
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'hip adductor': 'hip adductors',
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'adductors': 'hip adductors',
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'groin': 'hip adductors',
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# --- rotator cuff ---
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'rotator cuff': 'rotator cuff',
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# --- forearms ---
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'forearms': 'forearms',
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'forearm': 'forearms',
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'wrist flexors': 'forearms',
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'wrist extensors': 'forearms',
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# --- arms (general) ---
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'arms': 'arms',
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# --- feet ---
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'feet': 'feet',
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'foot': 'feet',
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# --- it band ---
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'it band': 'it band',
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'iliotibial band': 'it band',
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}
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# ---------------------------------------------------------------------------
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# Muscles grouped by functional split category.
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# Used to classify a workout's primary split type.
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# ---------------------------------------------------------------------------
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MUSCLE_GROUP_CATEGORIES: dict[str, list[str]] = {
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'upper_push': [
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'chest', 'front deltoids', 'deltoids', 'triceps', 'side deltoids',
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],
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'upper_pull': [
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'upper back', 'lats', 'biceps', 'rear deltoids', 'middle back',
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'traps', 'forearms', 'rotator cuff',
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],
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'lower_push': [
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'quads', 'calves', 'glutes', 'hip abductors', 'hip adductors',
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],
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'lower_pull': [
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'hamstrings', 'glutes', 'lower back', 'hip flexors',
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],
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'core': [
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'abs', 'obliques', 'core', 'intercostals', 'hip flexors',
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],
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}
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# Reverse lookup: canonical muscle -> list of categories it belongs to
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_MUSCLE_TO_CATEGORIES: dict[str, list[str]] = {}
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for _cat, _muscles in MUSCLE_GROUP_CATEGORIES.items():
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for _m in _muscles:
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_MUSCLE_TO_CATEGORIES.setdefault(_m, []).append(_cat)
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# Broader split groupings for classifying entire workouts
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SPLIT_CATEGORY_MAP: dict[str, str] = {
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'upper_push': 'upper',
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'upper_pull': 'upper',
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'lower_push': 'lower',
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'lower_pull': 'lower',
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'core': 'core',
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}
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def normalize_muscle_name(name: Optional[str]) -> Optional[str]:
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"""
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Map a raw muscle name string to its canonical lowercase form.
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Returns None if the name is empty, None, or unrecognized.
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"""
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if not name:
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return None
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key = name.strip().lower()
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if not key:
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return None
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canonical = MUSCLE_NORMALIZATION_MAP.get(key)
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if canonical:
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return canonical
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# Fallback: return the lowered/stripped version so we don't silently
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# drop unknown muscles -- the analyzer can decide what to do.
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return key
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def get_muscles_for_exercise(exercise) -> Set[str]:
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"""
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Return the set of normalized muscle names for a given Exercise instance.
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Uses the ExerciseMuscle join table (exercise.exercise_muscle_exercise).
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Falls back to the comma-separated Exercise.muscle_groups field if no
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ExerciseMuscle rows exist.
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"""
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from muscle.models import ExerciseMuscle
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muscles: Set[str] = set()
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# Primary source: ExerciseMuscle join table
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em_qs = ExerciseMuscle.objects.filter(exercise=exercise).select_related('muscle')
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for em in em_qs:
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if em.muscle and em.muscle.name:
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normalized = normalize_muscle_name(em.muscle.name)
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if normalized:
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muscles.add(normalized)
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# Fallback: comma-separated muscle_groups CharField on Exercise
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if not muscles and exercise.muscle_groups:
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for raw in exercise.muscle_groups.split(','):
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normalized = normalize_muscle_name(raw)
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if normalized:
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muscles.add(normalized)
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return muscles
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def get_movement_patterns_for_exercise(exercise) -> List[str]:
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"""
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Parse the comma-separated movement_patterns CharField on Exercise and
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return a list of normalized (lowered, stripped) pattern strings.
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"""
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if not exercise.movement_patterns:
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return []
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patterns = []
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for raw in exercise.movement_patterns.split(','):
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cleaned = raw.strip().lower()
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if cleaned:
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patterns.append(cleaned)
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return patterns
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def classify_split_type(muscle_names: set[str] | list[str]) -> str:
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"""
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Given a set/list of canonical muscle names from a workout, return the
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best-fit split_type string.
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Returns one of: 'push', 'pull', 'legs', 'upper', 'lower', 'full_body',
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'core'.
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Note: This function intentionally does not return 'cardio' because split
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classification is muscle-based and cardio is not a muscle group. Cardio
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workout detection happens via ``WorkoutAnalyzer._infer_workout_type()``
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which examines movement patterns (cardio/locomotion) rather than muscles.
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"""
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if not muscle_names:
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return 'full_body'
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muscle_set = set(muscle_names) if not isinstance(muscle_names, set) else muscle_names
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# Count how many muscles fall into each category
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category_scores: dict[str, int] = {
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'upper_push': 0,
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'upper_pull': 0,
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'lower_push': 0,
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'lower_pull': 0,
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'core': 0,
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}
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for m in muscle_set:
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cats = _MUSCLE_TO_CATEGORIES.get(m, [])
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for cat in cats:
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category_scores[cat] += 1
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total = sum(category_scores.values())
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if total == 0:
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return 'full_body'
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upper_push = category_scores['upper_push']
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upper_pull = category_scores['upper_pull']
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lower_push = category_scores['lower_push']
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lower_pull = category_scores['lower_pull']
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core_score = category_scores['core']
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upper_total = upper_push + upper_pull
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lower_total = lower_push + lower_pull
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# -- Core dominant --
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if core_score > 0 and core_score >= total * 0.6:
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return 'core'
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# -- Full body: both upper and lower have meaningful representation --
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if upper_total > 0 and lower_total > 0:
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upper_ratio = upper_total / total
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lower_ratio = lower_total / total
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# If neither upper nor lower dominates heavily, it's full body
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if 0.2 <= upper_ratio <= 0.8 and 0.2 <= lower_ratio <= 0.8:
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return 'full_body'
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# -- Upper dominant --
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if upper_total > lower_total and upper_total >= total * 0.5:
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if upper_push > 0 and upper_pull == 0:
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return 'push'
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if upper_pull > 0 and upper_push == 0:
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return 'pull'
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if upper_push > upper_pull * 2:
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return 'push'
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if upper_pull > upper_push * 2:
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return 'pull'
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return 'upper'
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# -- Lower dominant --
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if lower_total > upper_total and lower_total >= total * 0.5:
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if lower_push > 0 and lower_pull == 0:
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return 'legs'
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if lower_pull > 0 and lower_push == 0:
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return 'legs'
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return 'lower'
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# -- Push dominant (upper push + lower push) --
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push_total = upper_push + lower_push
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pull_total = upper_pull + lower_pull
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if push_total > pull_total * 2:
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return 'push'
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if pull_total > push_total * 2:
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return 'pull'
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return 'full_body'
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def get_broad_split_category(split_type: str) -> str:
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"""
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Simplify a split type for weekly-pattern analysis.
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Returns one of: 'upper', 'lower', 'push', 'pull', 'core', 'full_body', 'cardio'.
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"""
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mapping = {
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'push': 'push',
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'pull': 'pull',
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'legs': 'lower',
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'upper': 'upper',
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'lower': 'lower',
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'full_body': 'full_body',
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'core': 'core',
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'cardio': 'cardio',
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}
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return mapping.get(split_type, 'full_body')
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