def test_get_trial_timing_exclusivity_assertions(go, catch, auto_rewarded, hit,
false_alarm, aborted,
errortext):
with pytest.raises(AssertionError) as e:
trials_processing.get_trial_timing(None, None, None, go, catch,
auto_rewarded, hit, false_alarm,
aborted)
assert errortext in str(e.value)
def test_get_trial_timing():
event_dict = {
('trial_start', ''): {
'rebased_time': 306.4785879253758,
'frame': 18075
},
('initial_blank', 'enter'): {
'rebased_time': 306.47868008512637,
'frame': 18075
},
('initial_blank', 'exit'): {
'rebased_time': 306.4787637603285,
'frame': 18075
},
('pre_change', 'enter'): {
'rebased_time': 306.47883573270514,
'frame': 18075
},
('pre_change', 'exit'): {
'rebased_time': 306.4789062422286,
'frame': 18075
},
('stimulus_window', 'enter'): {
'rebased_time': 306.478977629464,
'frame': 18075
},
('stimulus_changed', ''): {
'rebased_time': 310.9827406729944,
'frame': 18345
},
('auto_reward', ''): {
'rebased_time': 310.98279450599154,
'frame': 18345
},
('response_window', 'enter'): {
'rebased_time': 311.13223900212347,
'frame': 18354
},
('response_window', 'exit'): {
'rebased_time': 311.73284526699706,
'frame': 18390
},
('miss', ''): {
'rebased_time': 311.7330193465259,
'frame': 18390
},
('stimulus_window', 'exit'): {
'rebased_time': 315.2356723770604,
'frame': 18600
},
('no_lick', 'exit'): {
'rebased_time': 315.23582480636213,
'frame': 18600
},
('trial_end', ''): {
'rebased_time': 315.23590438557534,
'frame': 18600
}
}
licks = [
312.24876,
312.58027,
312.73126,
312.86627,
313.02635,
313.16292,
313.54016,
314.04408,
314.47449,
314.61011,
314.75495,
]
nan = np.nan
stimulus_presentations_df = pd.DataFrame({
'duration': {
2: 0.2499599999999873,
3: nan,
4: 0.24966999999998052,
5: 0.24990999999999985,
6: 0.2499800000000505,
7: 0.2499300000000062,
8: 0.24996999999996206,
9: 0.24968999999998687,
10: 0.25983999999999696,
11: nan,
12: 0.24995000000001255,
13: 0.2500800000000254,
14: 0.2500800000000254
},
'end_frame': {
2: 18091.0,
3: nan,
4: 18181.0,
5: 18226.0,
6: 18271.0,
7: 18316.0,
8: 18361.0,
9: 18406.0,
10: 18451.0,
11: nan,
12: 18541.0,
13: 18586.0,
14: 18631.0
},
'image_index': {
2: 0,
3: 8,
4: 0,
5: 0,
6: 0,
7: 0,
8: 5,
9: 5,
10: 5,
11: 8,
12: 5,
13: 5,
14: 5
},
'image_name': {
2: 'im065',
3: 'omitted',
4: 'im065',
5: 'im065',
6: 'im065',
7: 'im065',
8: 'im062',
9: 'im062',
10: 'im062',
11: 'omitted',
12: 'im062',
13: 'im062',
14: 'im062'
},
'image_set': {
2: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
3: 'omitted',
4: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
5: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
6: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
7: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
8: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
9: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
10: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
11: 'omitted',
12: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
13: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2',
14: 'Natural_Images_Lum_Matched_set_training_2017.07.14_2'
},
'index': {
2: 2,
3: 0,
4: 3,
5: 4,
6: 5,
7: 6,
8: 7,
9: 8,
10: 9,
11: 1,
12: 10,
13: 11,
14: 12
},
'omitted': {
2: False,
3: True,
4: False,
5: False,
6: False,
7: False,
8: False,
9: False,
10: False,
11: True,
12: False,
13: False,
14: False
},
'orientation': {
2: nan,
3: nan,
4: nan,
5: nan,
6: nan,
7: nan,
8: nan,
9: nan,
10: nan,
11: nan,
12: nan,
13: nan,
14: nan
},
'start_frame': {
2: 18076,
3: 18120,
4: 18166,
5: 18211,
6: 18256,
7: 18301,
8: 18346,
9: 18391,
10: 18436,
11: 18480,
12: 18526,
13: 18571,
14: 18616
},
'start_time': {
2: 307.2712,
3: 308.00111,
4: 308.77121,
5: 309.52094,
6: 310.27088,
7: 311.02089,
8: 311.77086,
9: 312.52112,
10: 313.27098,
11: 314.01115,
12: 314.78083,
13: 315.53094,
14: 316.2808
},
'stop_time': {
2: 307.52116,
3: nan,
4: 309.02088,
5: 309.77085,
6: 310.52086,
7: 311.27082,
8: 312.02083,
9: 312.77081,
10: 313.53082,
11: nan,
12: 315.03078,
13: 315.78102,
14: 316.53088
}
})
result = trials_processing.get_trial_timing(event_dict,
stimulus_presentations_df,
licks,
go=False,
catch=False,
auto_rewarded=True,
hit=False,
false_alarm=False,
aborted=False)
expected_result = {
'start_time': 306.4785879253758,
'stop_time': 315.23590438557534,
'trial_length': 8.757316460199547,
'response_time': 312.24876,
'change_frame': 18345,
'change_time': 311.77086,
'response_latency': 0.4778999999999769
}
# use assert_frame_equal to take advantage of the nice way it deals with NaNs
pd.testing.assert_frame_equal(pd.DataFrame(result, index=[0]),
pd.DataFrame(expected_result, index=[0]),
check_names=False)
def test_get_trial_timing():
event_dict = {
('trial_start', ''): {
'timestamp': 306.4785879253758,
'frame': 18075
},
('initial_blank', 'enter'): {
'timestamp': 306.47868008512637,
'frame': 18075
},
('initial_blank', 'exit'): {
'timestamp': 306.4787637603285,
'frame': 18075
},
('pre_change', 'enter'): {
'timestamp': 306.47883573270514,
'frame': 18075
},
('pre_change', 'exit'): {
'timestamp': 306.4789062422286,
'frame': 18075
},
('stimulus_window', 'enter'): {
'timestamp': 306.478977629464,
'frame': 18075
},
('stimulus_changed', ''): {
'timestamp': 310.9827406729944,
'frame': 18345
},
('auto_reward', ''): {
'timestamp': 310.98279450599154,
'frame': 18345
},
('response_window', 'enter'): {
'timestamp': 311.13223900212347,
'frame': 18354
},
('response_window', 'exit'): {
'timestamp': 311.73284526699706,
'frame': 18390
},
('miss', ''): {
'timestamp': 311.7330193465259,
'frame': 18390
},
('stimulus_window', 'exit'): {
'timestamp': 315.2356723770604,
'frame': 18600
},
('no_lick', 'exit'): {
'timestamp': 315.23582480636213,
'frame': 18600
},
('trial_end', ''): {
'timestamp': 315.23590438557534,
'frame': 18600
}
}
licks = [
312.24876,
312.58027,
312.73126,
312.86627,
313.02635,
313.16292,
313.54016,
314.04408,
314.47449,
314.61011,
314.75495,
]
# Only need to worry about the timestamp
# value at change_frame
# because get_trial_timing will only use
# timestamps to lookup the timestamp of
# change_frame
timestamps = np.zeros(20000, dtype=float)
timestamps[18345] = 311.77086
result = trials_processing.get_trial_timing(event_dict,
licks,
go=False,
catch=False,
auto_rewarded=True,
hit=False,
false_alarm=False,
aborted=False,
timestamps=timestamps,
monitor_delay=0.0)
expected_result = {
'start_time': 306.4785879253758,
'stop_time': 315.23590438557534,
'trial_length': 8.757316460199547,
'response_time': 312.24876,
'change_frame': 18345,
'change_time': 311.77086,
'response_latency': 0.4778999999999769
}
# use assert_frame_equal to take advantage of the
# nice way it deals with NaNs
pd.testing.assert_frame_equal(pd.DataFrame(result, index=[0]),
pd.DataFrame(expected_result, index=[0]),
check_names=False)
