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>
116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
"""
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Django management command to analyze existing workouts and extract ML patterns.
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Usage:
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python manage.py analyze_workouts
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python manage.py analyze_workouts --dry-run
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python manage.py analyze_workouts --verbosity 2
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"""
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import time
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from django.core.management.base import BaseCommand
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from generator.services.workout_analyzer import WorkoutAnalyzer
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from generator.models import (
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MuscleGroupSplit,
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MovementPatternOrder,
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WeeklySplitPattern,
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WorkoutStructureRule,
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WorkoutType,
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)
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class Command(BaseCommand):
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help = (
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'Analyze existing workouts in the database and extract ML patterns '
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'into WorkoutType, MuscleGroupSplit, WeeklySplitPattern, '
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'WorkoutStructureRule, and MovementPatternOrder models.'
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)
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def add_arguments(self, parser):
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parser.add_argument(
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'--dry-run',
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action='store_true',
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default=False,
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help='Print what would be done without writing to the database.',
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)
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def handle(self, *args, **options):
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dry_run = options.get('dry_run', False)
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if dry_run:
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self.stdout.write(self.style.WARNING(
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'DRY RUN mode - no changes will be written to the database.\n'
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'Remove --dry-run to actually run the analysis.\n'
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))
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self._print_current_state()
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return
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start_time = time.time()
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analyzer = WorkoutAnalyzer()
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analyzer.analyze()
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elapsed = time.time() - start_time
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self.stdout.write('')
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self._print_current_state()
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self.stdout.write(self.style.SUCCESS(
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f'\nAnalysis complete in {elapsed:.2f}s!'
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))
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def _print_current_state(self):
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"""Print a summary of the current state of all ML pattern models."""
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self.stdout.write(self.style.MIGRATE_HEADING('\nCurrent ML Pattern Model State:'))
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self.stdout.write(f' WorkoutType: {WorkoutType.objects.count()} records')
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self.stdout.write(f' MuscleGroupSplit: {MuscleGroupSplit.objects.count()} records')
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self.stdout.write(f' WeeklySplitPattern: {WeeklySplitPattern.objects.count()} records')
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self.stdout.write(f' WorkoutStructureRule: {WorkoutStructureRule.objects.count()} records')
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self.stdout.write(f' MovementPatternOrder: {MovementPatternOrder.objects.count()} records')
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# List WorkoutTypes
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wts = WorkoutType.objects.all().order_by('name')
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if wts.exists():
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self.stdout.write(self.style.MIGRATE_HEADING('\n WorkoutTypes:'))
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for wt in wts:
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self.stdout.write(
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f' - {wt.name}: reps {wt.rep_range_min}-{wt.rep_range_max}, '
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f'rounds {wt.round_range_min}-{wt.round_range_max}, '
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f'intensity={wt.typical_intensity}'
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)
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# List MuscleGroupSplits
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splits = MuscleGroupSplit.objects.all().order_by('-frequency')
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if splits.exists():
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self.stdout.write(self.style.MIGRATE_HEADING('\n Top MuscleGroupSplits:'))
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for s in splits[:10]:
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muscles_str = ', '.join(s.muscle_names[:5])
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if len(s.muscle_names) > 5:
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muscles_str += f' (+{len(s.muscle_names) - 5} more)'
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self.stdout.write(
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f' - [{s.split_type}] {s.label} | '
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f'freq={s.frequency}, ex_count={s.typical_exercise_count} | '
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f'{muscles_str}'
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)
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# List WeeklySplitPatterns
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patterns = WeeklySplitPattern.objects.all().order_by('-frequency')
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if patterns.exists():
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self.stdout.write(self.style.MIGRATE_HEADING('\n Top WeeklySplitPatterns:'))
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for p in patterns[:10]:
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self.stdout.write(
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f' - {p.days_per_week}-day: {p.pattern_labels} '
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f'(freq={p.frequency}, rest_days={p.rest_day_positions})'
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)
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# List WorkoutStructureRule goal distribution
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rules = WorkoutStructureRule.objects.all()
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if rules.exists():
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from collections import Counter
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goal_counts = Counter(rules.values_list('goal_type', flat=True))
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self.stdout.write(self.style.MIGRATE_HEADING('\n WorkoutStructureRule by goal:'))
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for goal, count in sorted(goal_counts.items()):
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self.stdout.write(f' - {goal}: {count} rules')
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