def suggest_participant(event):
"""
Returns a suggested participant to be selected by the next wheel spin
:param event: Lambda event containing the API Gateway request path parameter wheel_id
{
"pathParameters":
{
"wheel_id": string ID of the wheel (DDB Hash Key)
},
}
:return: response dictionary containing a selected participant_id
{
"body":
{
"participant_id": string ID of the suggested participant (DDB Hash Key),
"rigged": True (if rigged, otherwise this key is not present)
}
}
"""
wheel_id = event['pathParameters']['wheel_id']
wheel = Wheel.get_existing_item(Key={'id': wheel_id})
if 'rigging' in wheel:
participant_id = wheel['rigging']['participant_id']
# Use rigging only if the rigged participant is still available
if 'Item' in WheelParticipant.get_item(Key={
'wheel_id': wheel_id,
'id': participant_id
}):
return_value = {'participant_id': participant_id}
# Only return rigged: True if we're not using hidden rigging
if not wheel['rigging'].get('hidden', False):
return_value['rigged'] = True
return return_value
return {'participant_id': choice_algorithm.suggest_participant(wheel)}
def test_selection_cycle(mock_dynamodb, setup_data, mock_participant_table):
def get_participant_with_id(participants, target_id):
for p in participants:
if p['id'] == target_id:
return p
return None
rngstate = random.getstate()
random.seed(0) # Make the (otherwise pseudorandom) test repeatable.
participants = WheelParticipant.scan({})['Items']
wheel = setup_data['wheel']
total_weight_of_chosens = 0
num_iterations = 200
distro = {}
for participant in participants:
distro[participant['name']] = 0
for _ in range(0, num_iterations):
chosen_id = choice_algorithm.suggest_participant(wheel)
chosen_was = get_participant_with_id(participants, chosen_id)
chosen_was_weight = chosen_was['weight']
distro[chosen_was['name']] = distro[chosen_was['name']] + 1
choice_algorithm.select_participant(wheel, chosen_was)
participants = WheelParticipant.scan({})['Items']
chosen_now = get_participant_with_id(participants, chosen_id)
chosen_now_weight = chosen_now['weight']
assert chosen_was_weight > 0.0
assert chosen_now_weight == 0
total_weight_of_chosens += chosen_was_weight
total_weight = sum(
[participant['weight'] for participant in participants])
assert abs(total_weight - len(participants)) < epsilon
# Must match human-inspected reasonable values for the RNG seed defined
# above for number of times each participant was chosen, and the total
# weight of participants selected. These are a rough equivalent to
# ensuring that the sequence of chosen participants matches the observed
# test run.
dv = list(distro.values())
list.sort(dv)
human_observed_selection_counts = [26, 27, 27, 29, 29, 31, 31]
human_observed_total_weight = 317.8786415239279
assert dv == human_observed_selection_counts
assert abs(float(total_weight_of_chosens) -
human_observed_total_weight) < epsilon
# Put things back the way they were.
random.setstate(rngstate)
def test_suggest_participant_no_participants(mock_dynamodb):
wheel = {'participant_count': 0}
with pytest.raises(BadRequestError):
choice_algorithm.suggest_participant(wheel)
def test_suggest_participant(mock_dynamodb, setup_data):
participant_ids = [
participant['id'] for participant in setup_data['participants']
]
assert choice_algorithm.suggest_participant(
setup_data['wheel']) in participant_ids
