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>
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
"""
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CI management command: check for drift between workout_research.md
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calibration values and WorkoutType DB records.
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Usage:
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python manage.py check_rules_drift
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python manage.py check_rules_drift --verbosity 2
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"""
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import sys
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from django.core.management.base import BaseCommand
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from generator.models import WorkoutType
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from generator.rules_engine import DB_CALIBRATION
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class Command(BaseCommand):
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help = (
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'Check for drift between research doc calibration values '
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'and WorkoutType DB records. Exits 1 if mismatches found.'
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)
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# Fields to compare between DB_CALIBRATION and WorkoutType model
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FIELDS_TO_CHECK = [
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'duration_bias',
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'typical_rest_between_sets',
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'typical_intensity',
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'rep_range_min',
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'rep_range_max',
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'round_range_min',
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'round_range_max',
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'superset_size_min',
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'superset_size_max',
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]
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def handle(self, *args, **options):
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verbosity = options.get('verbosity', 1)
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mismatches = []
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missing_in_db = []
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checked = 0
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for type_name, expected_values in DB_CALIBRATION.items():
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try:
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wt = WorkoutType.objects.get(name=type_name)
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except WorkoutType.DoesNotExist:
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missing_in_db.append(type_name)
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continue
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for field_name in self.FIELDS_TO_CHECK:
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if field_name not in expected_values:
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continue
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expected = expected_values[field_name]
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actual = getattr(wt, field_name, None)
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checked += 1
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if actual != expected:
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mismatches.append({
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'type': type_name,
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'field': field_name,
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'expected': expected,
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'actual': actual,
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})
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elif verbosity >= 2:
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self.stdout.write(
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f" OK {type_name}.{field_name} = {actual}"
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)
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# Report results
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self.stdout.write('')
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self.stdout.write(f'Checked {checked} field(s) across {len(DB_CALIBRATION)} workout types.')
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self.stdout.write('')
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if missing_in_db:
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self.stdout.write(self.style.WARNING(
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f'Missing from DB ({len(missing_in_db)}):'
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))
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for name in missing_in_db:
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self.stdout.write(f' - {name}')
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self.stdout.write('')
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if mismatches:
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self.stdout.write(self.style.ERROR(
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f'DRIFT DETECTED: {len(mismatches)} mismatch(es)'
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))
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self.stdout.write('')
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header = f'{"Workout Type":<35} {"Field":<30} {"Expected":<15} {"Actual":<15}'
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self.stdout.write(header)
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self.stdout.write('-' * len(header))
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for m in mismatches:
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self.stdout.write(
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f'{m["type"]:<35} {m["field"]:<30} '
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f'{str(m["expected"]):<15} {str(m["actual"]):<15}'
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)
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self.stdout.write('')
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self.stdout.write(self.style.ERROR(
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'To fix: update WorkoutType records in the DB or '
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'update DB_CALIBRATION in generator/rules_engine.py.'
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))
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sys.exit(1)
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else:
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self.stdout.write(self.style.SUCCESS(
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'No drift detected. DB values match research calibration.'
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))
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