def test_num_successful_go_nogo_trials():
assert num_successful_go_nogo_trials([
'StartSession', 'SuccessfulGoTrial', 'PokeOn1', 'RPressOn', 'PokeOn1'
]) == (1, 0)
assert num_successful_go_nogo_trials([
'StartSession', 'LPressOn', 'PokeOn1', 'PokeOn1', 'RPressOn', 'PokeOn1'
]) == (0, 0)
assert num_successful_go_nogo_trials([
'StartSession', 'SuccessfulNoGoTrial', 'SuccessfulNoGoTrial',
'LPressOn', 'RPressOn', 'SuccessfulGoTrial'
]) == (1, 2)
def Go_NoGo(loaded_file, i):
"""
:param loaded_file: file output from operant box
:param i: number of days analyzing
:return: data frame of all analysis extracted from file (one animal)
"""
(timecode, eventcode) = extract_info_from_file(loaded_file, 500)
(dippers, dippers_retrieved, retrieval_latency) = reward_retrieval(timecode, eventcode)
(go_trials, nogo_trials) = count_go_nogo_trials(eventcode)
(successful_go_trials, successful_nogo_trials) = num_successful_go_nogo_trials(eventcode)
go_latency = lever_press_lat_gng(timecode, eventcode, 'LLeverOn', 'SuccessfulGoTrial') + \
lever_press_lat_gng(timecode, eventcode, 'RLeverOn', 'SuccessfulGoTrial')
file_keys = list(loaded_file.keys())
for constant in ['File', 'Start Date', 'End Date', 'Subject', 'Experiment', 'Group', 'Box', 'Start Time', 'End Time', 'MSN', 'W']:
file_keys.remove(constant)
# All that's left in the list file_keys should be any group labels.
group_ids = []
for group in file_keys:
group_ids.append(loaded_file[group])
df2 = pd.DataFrame([[loaded_file['Subject'], int(i + 1), float(dippers),
float(successful_go_trials) / float(go_trials),
(float(nogo_trials) - float(successful_nogo_trials)) / float(nogo_trials),
float(successful_go_trials) / float(go_trials) - float(successful_nogo_trials) / float(nogo_trials),
float(go_latency), *group_ids]],
columns=column_list+file_keys)
return df2
def Go_NoGo(loaded_file, i):
"""
:param loaded_file: file output from operant box
:param i: number of days analyzing
:return: data frame of all analysis extracted from file (one animal)
"""
(timecode, eventcode) = extract_info_from_file(loaded_file, 500)
(dippers, dippers_retrieved,
retrieval_latency) = reward_retrieval(timecode, eventcode)
(go_trials, nogo_trials) = count_go_nogo_trials(eventcode)
(successful_go_trials,
successful_nogo_trials) = num_successful_go_nogo_trials(eventcode)
df2 = pd.DataFrame([[
loaded_file['Subject'], loaded_file['tts'],
int(i + 1),
float(dippers),
float(go_trials),
float(successful_go_trials)
]],
columns=column_list)
return df2