workout generator audit: rules engine, structure rules, split patterns, injury UX, metadata cleanup
- 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>
This commit is contained in:
Trey t
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generator/tests/test_movement_enforcement.py
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505
generator/tests/test_movement_enforcement.py
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"""
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Tests for _build_working_supersets() — Items #4, #6, #7:
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- Movement pattern enforcement (WorkoutStructureRule merging)
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- Modality consistency check (duration_bias warning)
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- Straight-set strength (first superset = single main lift)
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"""
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from django.contrib.auth import get_user_model
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from django.test import TestCase
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from unittest.mock import patch, MagicMock, PropertyMock
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from generator.models import (
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MuscleGroupSplit,
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MovementPatternOrder,
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UserPreference,
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WorkoutStructureRule,
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WorkoutType,
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)
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from generator.services.workout_generator import (
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WorkoutGenerator,
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STRENGTH_WORKOUT_TYPES,
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WORKOUT_TYPE_DEFAULTS,
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)
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from registered_user.models import RegisteredUser
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User = get_user_model()
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class MovementEnforcementTestBase(TestCase):
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"""Shared setup for movement enforcement tests."""
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@classmethod
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def setUpTestData(cls):
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cls.auth_user = User.objects.create_user(
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username='testmove', password='testpass123',
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)
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cls.registered_user = RegisteredUser.objects.create(
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first_name='Test', last_name='Move', user=cls.auth_user,
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)
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# Create workout types
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cls.strength_type = WorkoutType.objects.create(
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name='traditional strength',
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typical_rest_between_sets=120,
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typical_intensity='high',
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rep_range_min=3,
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rep_range_max=6,
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duration_bias=0.0,
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superset_size_min=1,
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superset_size_max=3,
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)
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cls.hiit_type = WorkoutType.objects.create(
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name='hiit',
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typical_rest_between_sets=30,
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typical_intensity='high',
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rep_range_min=10,
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rep_range_max=20,
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duration_bias=0.7,
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superset_size_min=3,
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superset_size_max=6,
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)
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# Create MovementPatternOrder records
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MovementPatternOrder.objects.create(
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position='early', movement_pattern='lower push - squat',
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frequency=20, section_type='working',
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)
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MovementPatternOrder.objects.create(
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position='early', movement_pattern='upper push - horizontal',
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frequency=15, section_type='working',
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)
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MovementPatternOrder.objects.create(
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position='middle', movement_pattern='upper pull',
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frequency=18, section_type='working',
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)
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MovementPatternOrder.objects.create(
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position='late', movement_pattern='isolation',
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frequency=12, section_type='working',
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)
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# Create WorkoutStructureRule for strength
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cls.strength_rule = WorkoutStructureRule.objects.create(
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workout_type=cls.strength_type,
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section_type='working',
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movement_patterns=['lower push - squat', 'hip hinge', 'upper push - horizontal'],
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typical_rounds=5,
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typical_exercises_per_superset=2,
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goal_type='general_fitness',
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)
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def _make_preference(self, **kwargs):
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"""Create a UserPreference for testing."""
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defaults = {
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'registered_user': self.registered_user,
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'days_per_week': 3,
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'fitness_level': 2,
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'primary_goal': 'general_fitness',
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}
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defaults.update(kwargs)
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return UserPreference.objects.create(**defaults)
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def _make_generator(self, pref):
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"""Create a WorkoutGenerator with mocked dependencies."""
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with patch('generator.services.workout_generator.ExerciseSelector') as MockSelector, \
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patch('generator.services.workout_generator.PlanBuilder'):
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gen = WorkoutGenerator(pref)
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# Make the exercise selector return mock exercises
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self.mock_selector = gen.exercise_selector
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return gen
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def _create_mock_exercise(self, name='Mock Exercise', is_duration=False,
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is_weight=True, is_reps=True, is_compound=True,
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exercise_tier='primary', movement_patterns='lower push - squat',
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hr_elevation_rating=5):
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"""Create a mock Exercise object."""
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ex = MagicMock()
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ex.pk = id(ex) # unique pk
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ex.name = name
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ex.is_duration = is_duration
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ex.is_weight = is_weight
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ex.is_reps = is_reps
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ex.is_compound = is_compound
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ex.exercise_tier = exercise_tier
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ex.movement_patterns = movement_patterns
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ex.hr_elevation_rating = hr_elevation_rating
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ex.side = None
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ex.stretch_position = 'mid'
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return ex
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class TestMovementPatternEnforcement(MovementEnforcementTestBase):
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"""Item #4: WorkoutStructureRule patterns merged with position patterns."""
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def test_movement_patterns_passed_to_selector(self):
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"""select_exercises should receive combined movement pattern preferences
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when both position patterns and structure rule patterns exist."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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# Setup mock exercises
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mock_exercises = [
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self._create_mock_exercise(f'Exercise {i}')
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for i in range(3)
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]
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gen.exercise_selector.select_exercises.return_value = mock_exercises
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gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises
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muscle_split = {
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'muscles': ['chest', 'back'],
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'split_type': 'full_body',
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'label': 'Full Body',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
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supersets = gen._build_working_supersets(
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muscle_split, self.strength_type, wt_params,
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)
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# Verify select_exercises was called
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self.assertTrue(gen.exercise_selector.select_exercises.called)
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# Check the movement_pattern_preference argument in the first call
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first_call_kwargs = gen.exercise_selector.select_exercises.call_args_list[0]
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# The call could be positional or keyword - check kwargs
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if first_call_kwargs.kwargs.get('movement_pattern_preference') is not None:
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patterns = first_call_kwargs.kwargs['movement_pattern_preference']
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# Should be combined patterns (intersection of position + rule, or rule[:3])
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self.assertIsInstance(patterns, list)
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self.assertTrue(len(patterns) > 0)
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pref.delete()
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class TestStrengthStraightSets(MovementEnforcementTestBase):
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"""Item #7: First working superset in strength = single main lift."""
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def test_strength_first_superset_single_exercise(self):
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"""For traditional strength, the first working superset should request
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exactly 1 exercise (straight set of a main lift)."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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mock_ex = self._create_mock_exercise('Barbell Squat')
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gen.exercise_selector.select_exercises.return_value = [mock_ex]
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gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]
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muscle_split = {
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'muscles': ['quads', 'hamstrings'],
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'split_type': 'lower',
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'label': 'Lower',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
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supersets = gen._build_working_supersets(
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muscle_split, self.strength_type, wt_params,
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)
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self.assertGreaterEqual(len(supersets), 1)
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# First superset should have been requested with count=1
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first_call = gen.exercise_selector.select_exercises.call_args_list[0]
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self.assertEqual(first_call.kwargs.get('count', first_call.args[1] if len(first_call.args) > 1 else None), 1)
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pref.delete()
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def test_strength_first_superset_more_rounds(self):
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"""First superset of a strength workout should have 4-6 rounds."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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mock_ex = self._create_mock_exercise('Deadlift')
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gen.exercise_selector.select_exercises.return_value = [mock_ex]
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gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]
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muscle_split = {
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'muscles': ['hamstrings', 'glutes'],
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'split_type': 'lower',
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'label': 'Lower',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
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# Run multiple times to check round ranges
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round_counts = set()
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for _ in range(50):
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gen.exercise_selector.select_exercises.return_value = [mock_ex]
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supersets = gen._build_working_supersets(
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muscle_split, self.strength_type, wt_params,
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)
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if supersets:
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round_counts.add(supersets[0]['rounds'])
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# All first-superset round counts should be in [4, 6]
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for r in round_counts:
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self.assertGreaterEqual(r, 4, f"Rounds {r} below minimum 4")
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self.assertLessEqual(r, 6, f"Rounds {r} above maximum 6")
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pref.delete()
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def test_strength_first_superset_rest_period(self):
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"""First superset of a strength workout should use the workout type's
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typical_rest_between_sets for rest."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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mock_ex = self._create_mock_exercise('Bench Press')
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gen.exercise_selector.select_exercises.return_value = [mock_ex]
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gen.exercise_selector.balance_stretch_positions.return_value = [mock_ex]
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muscle_split = {
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'muscles': ['chest', 'triceps'],
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'split_type': 'push',
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'label': 'Push',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
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supersets = gen._build_working_supersets(
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muscle_split, self.strength_type, wt_params,
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)
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if supersets:
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# typical_rest_between_sets for our strength_type is 120
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self.assertEqual(supersets[0]['rest_between_rounds'], 120)
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pref.delete()
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def test_strength_accessories_still_superset(self):
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"""2nd+ supersets in strength workouts should still have 2+ exercises
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(the min_ex_per_ss rule still applies to non-first supersets)."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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mock_exercises = [
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self._create_mock_exercise(f'Accessory {i}', exercise_tier='accessory')
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for i in range(3)
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]
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gen.exercise_selector.select_exercises.return_value = mock_exercises
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gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises
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muscle_split = {
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'muscles': ['chest', 'back', 'shoulders'],
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'split_type': 'upper',
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'label': 'Upper',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
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supersets = gen._build_working_supersets(
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muscle_split, self.strength_type, wt_params,
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)
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# Should have multiple supersets
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if len(supersets) >= 2:
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# Check that the second superset's select_exercises call
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# requested count >= 2 (min_ex_per_ss)
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second_call = gen.exercise_selector.select_exercises.call_args_list[1]
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count_arg = second_call.kwargs.get('count')
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if count_arg is None and len(second_call.args) > 1:
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count_arg = second_call.args[1]
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self.assertGreaterEqual(count_arg, 2)
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pref.delete()
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def test_non_strength_no_single_exercise_override(self):
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"""Non-strength workouts should NOT have the single-exercise first superset."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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mock_exercises = [
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self._create_mock_exercise(f'HIIT Move {i}', is_duration=True, is_weight=False)
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for i in range(5)
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]
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gen.exercise_selector.select_exercises.return_value = mock_exercises
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gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises
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muscle_split = {
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'muscles': ['chest', 'back', 'quads'],
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'split_type': 'full_body',
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'label': 'Full Body',
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}
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])
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supersets = gen._build_working_supersets(
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muscle_split, self.hiit_type, wt_params,
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)
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# First call to select_exercises should NOT have count=1
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first_call = gen.exercise_selector.select_exercises.call_args_list[0]
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count_arg = first_call.kwargs.get('count')
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if count_arg is None and len(first_call.args) > 1:
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count_arg = first_call.args[1]
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self.assertGreater(count_arg, 1, "Non-strength first superset should have > 1 exercise")
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pref.delete()
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class TestModalityConsistency(MovementEnforcementTestBase):
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"""Item #6: Modality consistency warning for duration-dominant workouts."""
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def test_duration_dominant_warns_on_low_ratio(self):
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"""When duration_bias >= 0.6 and most exercises are rep-based,
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a warning should be appended to self.warnings."""
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pref = self._make_preference()
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gen = self._make_generator(pref)
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# Create mostly rep-based (non-duration) exercises
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mock_exercises = [
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self._create_mock_exercise(f'Rep Exercise {i}', is_duration=False)
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for i in range(4)
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]
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gen.exercise_selector.select_exercises.return_value = mock_exercises
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gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises
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muscle_split = {
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'muscles': ['chest', 'back'],
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'split_type': 'full_body',
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'label': 'Full Body',
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}
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# Use HIIT params (duration_bias = 0.7 >= 0.6)
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wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])
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# But force rep-based by setting duration_bias low in actual randomization
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# We need to make superset_is_duration = False for all supersets
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# Override duration_bias to be very low so random.random() > it
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# But wt_params['duration_bias'] stays at 0.7 for the post-check
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# Actually, the modality check uses wt_params['duration_bias'] which is 0.7
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# The rep-based exercises come from select_exercises mock returning
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# exercises with is_duration=False
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supersets = gen._build_working_supersets(
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muscle_split, self.hiit_type, wt_params,
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)
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# The exercises are not is_duration so the check should fire
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# Look for the modality mismatch warning
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modality_warnings = [
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w for w in gen.warnings if 'Modality mismatch' in w
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]
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# Note: This depends on whether the supersets ended up rep-based
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# Since duration_bias is 0.7, most supersets will be duration-based
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# and our mock exercises don't have is_duration=True, so they'd be
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# skipped in the duration superset builder (the continue clause).
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# The modality check counts exercises that ARE is_duration.
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# With is_duration=False mocks in duration supersets, they'd be skipped.
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# So total_exercises could be 0 (if all were skipped).
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# Let's verify differently: the test should check the logic directly.
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# Create a scenario where the duration check definitely triggers:
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# Set duration_bias high but exercises are rep-based
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gen.warnings = [] # Reset warnings
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# Create supersets manually to test the post-check
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# Simulate: wt_params has high duration_bias, but exercises are rep-based
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wt_params_high_dur = dict(wt_params)
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wt_params_high_dur['duration_bias'] = 0.8
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# Return exercises that won't be skipped (rep-based supersets with non-duration exercises)
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rep_exercises = [
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self._create_mock_exercise(f'Rep Ex {i}', is_duration=False)
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for i in range(3)
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]
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gen.exercise_selector.select_exercises.return_value = rep_exercises
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gen.exercise_selector.balance_stretch_positions.return_value = rep_exercises
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# Force non-strength workout type with low actual random for duration
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# Use a non-strength type so is_strength_workout is False
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# Create a real WorkoutType to avoid MagicMock pk issues with Django ORM
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non_strength_type = WorkoutType.objects.create(
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name='circuit training',
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typical_rest_between_sets=30,
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duration_bias=0.7,
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)
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# Patch random to make all supersets rep-based despite high duration_bias
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import random
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original_random = random.random
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random.random = lambda: 0.99 # Always > duration_bias, so rep-based
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try:
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supersets = gen._build_working_supersets(
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muscle_split, non_strength_type, wt_params_high_dur,
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)
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finally:
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random.random = original_random
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# Now check warnings
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modality_warnings = [
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w for w in gen.warnings if 'Modality mismatch' in w
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]
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if supersets and any(ss.get('exercises') for ss in supersets):
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self.assertTrue(
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len(modality_warnings) > 0,
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f"Expected modality mismatch warning but got: {gen.warnings}",
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)
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pref.delete()
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|
||||
def test_no_warning_when_duration_bias_low(self):
|
||||
"""When duration_bias < 0.6, no modality consistency warning
|
||||
should be emitted even if exercises are all rep-based."""
|
||||
pref = self._make_preference()
|
||||
gen = self._make_generator(pref)
|
||||
|
||||
mock_exercises = [
|
||||
self._create_mock_exercise(f'Rep Exercise {i}', is_duration=False)
|
||||
for i in range(3)
|
||||
]
|
||||
gen.exercise_selector.select_exercises.return_value = mock_exercises
|
||||
gen.exercise_selector.balance_stretch_positions.return_value = mock_exercises
|
||||
|
||||
muscle_split = {
|
||||
'muscles': ['chest', 'back'],
|
||||
'split_type': 'full_body',
|
||||
'label': 'Full Body',
|
||||
}
|
||||
|
||||
# Use strength params (duration_bias = 0.0 < 0.6)
|
||||
wt_params = dict(WORKOUT_TYPE_DEFAULTS['traditional strength'])
|
||||
|
||||
supersets = gen._build_working_supersets(
|
||||
muscle_split, self.strength_type, wt_params,
|
||||
)
|
||||
|
||||
modality_warnings = [
|
||||
w for w in gen.warnings if 'Modality mismatch' in w
|
||||
]
|
||||
self.assertEqual(
|
||||
len(modality_warnings), 0,
|
||||
f"Should not have modality warning for low duration_bias but got: {modality_warnings}",
|
||||
)
|
||||
|
||||
pref.delete()
|
||||
|
||||
def test_no_warning_when_duration_ratio_sufficient(self):
|
||||
"""When duration_bias >= 0.6 and duration exercises >= 50%,
|
||||
no warning should be emitted."""
|
||||
pref = self._make_preference()
|
||||
gen = self._make_generator(pref)
|
||||
|
||||
# Create mostly duration exercises
|
||||
duration_exercises = [
|
||||
self._create_mock_exercise(f'Duration Ex {i}', is_duration=True, is_weight=False)
|
||||
for i in range(4)
|
||||
]
|
||||
gen.exercise_selector.select_exercises.return_value = duration_exercises
|
||||
gen.exercise_selector.balance_stretch_positions.return_value = duration_exercises
|
||||
|
||||
muscle_split = {
|
||||
'muscles': ['chest', 'back'],
|
||||
'split_type': 'full_body',
|
||||
'label': 'Full Body',
|
||||
}
|
||||
wt_params = dict(WORKOUT_TYPE_DEFAULTS['hiit'])
|
||||
|
||||
supersets = gen._build_working_supersets(
|
||||
muscle_split, self.hiit_type, wt_params,
|
||||
)
|
||||
|
||||
modality_warnings = [
|
||||
w for w in gen.warnings if 'Modality mismatch' in w
|
||||
]
|
||||
self.assertEqual(
|
||||
len(modality_warnings), 0,
|
||||
f"Should not have modality warning when duration ratio is sufficient but got: {modality_warnings}",
|
||||
)
|
||||
|
||||
pref.delete()
|
||||
Reference in New Issue
Block a user