def _select_exercise(exercise_filter, previous_exercise=None, retry_mode=False, logger=None):
logger = logger or mock.MagicMock()
if previous_exercise is not None:
for related_muscle_group_set in MuscleGroup.get_rings():
if previous_exercise.muscle_group_id in related_muscle_group_set:
logger.log_muscle_restriction(previous_exercise, related_muscle_group_set)
exercise_filter.restrict_to_muscle_group_ids(related_muscle_group_set)
break
elif not retry_mode:
logger.log_retry_mode()
exercise_filter = exercise_filter.compound_only()
elif retry_mode and previous_exercise is None:
logger.log_rollback_mode()
rollback_filter = exercise_filter.copy()
exercise_filter = exercise_filter.compound_only()
if exercise_filter.count() == 0:
exercise_filter = rollback_filter
if exercise_filter.count() == 0:
raise DeadEndException("Need a bigger exercise pool to continue")
exercise_list = [exercise for exercise in exercise_filter.query]
logger.log_available_exercises(exercise_list)
exercise_list = evenly_distribute_exercises_by_muscle_group(exercise_list)
try:
exercise = random.choice(exercise_list)
except IndexError:
raise NoExercisesAvailableException("No exercises left")
logger.log_exercise_selection(exercise)
return exercise
def test_evenly_distribute_exercises_by_muscle_group(self):
exercise_list = Exercise().query
distributed_exercise_list = evenly_distribute_exercises_by_muscle_group(exercise_list)
muscle_group_counts = defaultdict(int)
for exercise in distributed_exercise_list:
muscle_group_counts[exercise.muscle_group_id] += 1
for count in muscle_group_counts.values():
self.assertEqual(count, 100)
def _select_superset_exercise(self, first_exercise):
self.superset_filter.discard_exercise_id(first_exercise.id)
self.superset_filter.discard_mutually_exclusive(first_exercise.id)
possible_muscle_ids = [first_exercise.muscle_group_id]
possible_muscle_ids += MuscleGroup.ALLOWABLE_RELATED_FOR_SUPERSETS.get(first_exercise.muscle_group_id, [])
exercise_filter = self.superset_filter.copy().restrict_to_muscle_group_ids(possible_muscle_ids)
exercise_list = [exercise for exercise in exercise_filter.query]
self.logger.log_available_superset_exercise(exercise_list)
exercise_list = evenly_distribute_exercises_by_muscle_group(exercise_list)
try:
exercise = random.choice(exercise_list)
except IndexError:
exercise = None
self.logger.log_superset_exercise(exercise)
return exercise
