def test_total_head_pokes():
assert total_head_pokes([
'StartSession', 'PokeOn1', 'LPressOn', 'PokeOn1', 'RPressOn', 'PokeOn1'
]) == 3
assert total_head_pokes([
'StartSession', 'LPressOn', 'PokeOn1', 'PokeOn1', 'RPressOn', 'PokeOn1'
]) == 3
assert total_head_pokes([
'StartSession', 'PokeOn1', 'PokeOn1', 'LPressOn', 'RPressOn',
'RPressOn', 'PokeOn1'
]) == 3
def trough_train_function(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)
(ind_dur, tot_dur, ind_dur_iti, tot_dur_iti) = cue_responding_duration(timecode, eventcode, 'StartSession', 'EndSession', "PokeOn1", "PokeOff1")
# ITI is meaningless here because we are using the whole session
total_pokes = total_head_pokes(eventcode)
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(dippers_retrieved),
float(retrieval_latency), float(ind_dur), float(tot_dur), float(total_pokes), *group_ids]],
columns=column_list+file_keys)
return df2
def PCER_function(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)
cue_iti_responding(timecode, eventcode, 'StartTrial', 'EndTrial', 'PokeOn1')
(ind_dur, tot_dur, ind_dur_iti, tot_dur_iti) = cue_responding_duration(timecode, eventcode, 'StartTrial',
'EndTrial', 'PokeOn1', 'PokeOff1')
total_pokes = total_head_pokes(eventcode)
(all_cue_length_poke_rates, all_iti_length_poke_rates, subtracted_poke_rates) = \
response_rate_across_cue_iti(timecode, eventcode, 'StartTrial', 'EndTrial', 'PokeOn1')
new_cols = ['Subject'] + ['Cue_' + str(i + 1) for i in range(len(all_cue_length_poke_rates))] + \
['ITI_' + str(i + 1) for i in range(len(all_cue_length_poke_rates))] + \
['ES_' + str(i + 1) for i in range(len(all_cue_length_poke_rates))]
across_cue_df = pd.DataFrame([([loaded_file['Subject']]+all_cue_length_poke_rates+all_iti_length_poke_rates+subtracted_poke_rates)],
columns=new_cols)
df2 = pd.DataFrame([[loaded_file['Subject'], int(i + 1), float(dippers), float(dippers_retrieved),
float(retrieval_latency), float(ind_dur), float(tot_dur), float(total_pokes)]],
columns=column_list)
df2 = pd.merge(df2, across_cue_df, how='left', on=['Subject'])
return df2