def test_complete_compile_experiment_data_3():
df = get_csv_as_df('experiment', 3)
data = compile_data.compile_experiment_data(df)
# effort and discomfort ratings
assert data['prop_effort_ups'] == 0.166666667 # 3/6
assert data['prop_effort_downs'] == 0.333333333 # 2/6
assert data['prop_effort_sames'] == 0.500000000 # 1/6
assert data['prop_discomfort_ups'] == 0.333333333 # 2/6
assert data['prop_discomfort_downs'] == 0.333333333 # 2/6
assert data['prop_discomfort_sames'] == 0.333333333 # 2/6
def test_complete_compile_experiment_data_1():
df = get_csv_as_df('experiment', 1)
data = compile_data.compile_experiment_data(df)
assert data['condition'] == 5
assert data['block_order'] == ('medium,medium,hard,medium,easy,'
'medium,medium,medium,medium')
assert data['num_blocks'] == 9
assert data['block_hard'] == 3
assert data['block_easy'] == 5
assert data['anticipated_enjoyment'] == 3
assert data['anticipated_performance'] == 4
assert data['anticipated_effort'] == 4
assert data['anticipated_discomfort'] == 4
assert data['anticipated_fatigue'] == 4
# real-time data for each block
assert data['accuracy_1'] == 0.571428571
assert data['accuracy_2'] == 0.428571429
assert data['accuracy_3'] == 0.357142857
assert data['accuracy_4'] == 0.5
assert data['accuracy_5'] == 0.571428571
assert data['accuracy_6'] == 0.285714286
assert data['accuracy_7'] == 0.357142857
assert data['accuracy_8'] == 0.428571429
assert data['accuracy_9'] == 0.357142857
assert 'accuracy_10' not in data.keys()
assert data['effort_1'] == 5
assert data['effort_2'] == 4
assert data['effort_3'] == 4
assert data['effort_4'] == 4
assert data['effort_5'] == 4
assert data['effort_6'] == 7
assert data['effort_7'] == 1
assert data['effort_8'] == 2
assert data['effort_9'] == 7
assert 'effort_10' not in data.keys()
assert data['discomfort_1'] == 5
assert data['discomfort_2'] == 4
assert data['discomfort_3'] == 4
assert data['discomfort_4'] == 4
assert data['discomfort_5'] == 4
assert data['discomfort_6'] == 7
assert data['discomfort_7'] == 1
assert data['discomfort_8'] == 2
assert data['discomfort_9'] == 7
assert 'discomfort_10' not in data.keys()
# minimum/maximum discomfort and effort ratings
assert data['min_effort'] == 1
assert data['max_effort'] == 7
assert data['min_discomfort'] == 1
assert data['max_discomfort'] == 7
# slope and intercept values for all blocks
assert data['accuracy_slope'] == -0.012189013
assert data['accuracy_intercept'] == 0.525361222
assert data['effort_slope'] == -0.011834331
assert data['effort_intercept'] == 4.316195575
assert data['discomfort_slope'] == -0.011834331
assert data['discomfort_intercept'] == 4.316195575
# real-time data by block type
assert data['medium_accuracy'] == 0.418367347
assert data['medium_effort'] == 4.285714286
assert data['medium_discomfort'] == 4.285714286
assert data['medium_accuracy_slope'] == -0.014672245
assert data['medium_accuracy_intercept'] == 0.541020328
assert data['medium_effort_slope'] == -0.019802768
assert data['medium_effort_intercept'] == 4.451256
assert data['medium_discomfort_slope'] == -0.019802768
assert data['medium_discomfort_intercept'] == 4.451256
assert data['hard_accuracy'] == 0.357142857
assert data['hard_effort'] == 4
assert data['hard_discomfort'] == 4
assert data['easy_accuracy'] == 0.571428571
assert data['easy_effort'] == 4
assert data['easy_discomfort'] == 4
assert data['start_discomfort'] == 5
assert data['peak_discomfort'] == 7
assert data['end_discomfort'] == 7
assert data['avg_discomfort'] == 4.222222222
assert data['start_effort'] == 5
assert data['peak_effort'] == 7
assert data['end_effort'] == 7
assert data['avg_effort'] == 4.222222222
assert data['avg_accuracy'] == 0.428571429
assert data['max_accuracy'] == 0.571428571
assert data['min_accuracy'] == 0.285714286
assert data['start_accuracy'] == 0.571428571
assert data['end_accuracy'] == 0.357142857
assert data['auc_accuracy'] == 3.392857143
assert data['auc_effort'] == 32.0
assert data['auc_discomfort'] == 32.0
assert data['time_experiment_ms'] == 831136
def test_complete_compile_experiment_data():
pid = "104"
df = get_csv_as_df('experiment', pid)
ed = compile_data.compile_experiment_data(df)
assert ed['num_trials'] == 646
assert ed['trials_per_block'] == 82
assert ed['num_blocks'] == 8
assert ed['forecasted_enjoyment'] == 5
assert ed['forecasted_performance'] == 4
assert ed['forecasted_effort'] == 6
assert ed['forecasted_discomfort'] == 5
assert ed['forecasted_fatigue'] == 2
assert ed['forecasted_motivation'] == 4
# check keys for each block's real-time data
blk_summary_keys = [
'anticipated', 'nogo_next4_avg', 'nogo_prev4_avg', 'go_errors',
'effort', 'num_trials', 'discomfort', 'rt_avg', 'nogo_errors',
'accuracy'
]
for i in range(1, 9):
blk_key_prefix = "blk{}".format(i)
blk_keys = [k for k in ed.keys() if k.startswith(blk_key_prefix)]
assert len(blk_keys) == 13
for k in blk_summary_keys:
expected_blk_key = "{}_{}".format(blk_key_prefix, k)
assert expected_blk_key in blk_keys
# effort and discomfort ratings
assert ed['prop_effort_ups'] == 0.428571429 # 3/7
assert ed['prop_effort_downs'] == 0.571428571 # 4/7
assert ed['prop_effort_sames'] == 0.0 # 0/7
assert ed['prop_discomfort_ups'] == 0.285714286 # 2/7
assert ed['prop_discomfort_downs'] == 0.142857143 # 1/7
assert ed['prop_discomfort_sames'] == 0.571428571 # 4/7
# no-go error variable weighted averages
assert ed['nogo_num_errors'] == 43
assert ed['nogo_error_prev_rt_avg'] == 346.66257668704293
assert ed['nogo_error_next_rt_avg'] == 336.88535031840127
# regression variables for blocks
assert ed['accuracy_slope'] == 0.002454737
assert ed['accuracy_intercept'] == 0.829650186
assert ed['effort_slope'] == 0.022726243
assert ed['effort_intercept'] == 3.92036912
assert ed['discomfort_slope'] == 0.202292011
assert ed['discomfort_intercept'] == 3.178529021
# peak-end calculations
assert ed['start_effort'] == 2
assert ed['peak_effort'] == 7
assert ed['min_effort'] == 2
assert ed['end_effort'] == 3
assert ed['avg_effort'] == 4.125
assert ed['start_discomfort'] == 4
assert ed['peak_discomfort'] == 7
assert ed['min_discomfort'] == 4
assert ed['end_discomfort'] == 6
assert ed['avg_discomfort'] == 5
assert ed['avg_accuracy'] == 0.851393189
assert ed['max_accuracy'] == 0.951219512
assert ed['min_accuracy'] == 0.62195122
assert ed['start_accuracy'] == 0.865853659
assert ed['end_accuracy'] == 0.875
assert ed['auc_accuracy'] == 5.943597561
assert ed['auc_effort'] == 30.5
assert ed['auc_discomfort'] == 35.0
# post-experiment evaluation of valence and arousal
assert ed['arousal_post_mind_body'] == '2'
assert ed['arousal_post_feeling'] == '5'
assert ed['time_experiment_ms'] == 940644