def CI_training_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)
(A_responding, A_iti) = cue_iti_responding(timecode, eventcode,
'ExcitorATrialStart',
'ExcitorATrialEnd', 'PokeOn1')
(B_responding, B_iti) = cue_iti_responding(timecode, eventcode,
'ExcitorBTrialStart',
'ExcitorBTrialEnd', 'PokeOn1')
df2 = pd.DataFrame([[
loaded_file['Subject'], loaded_file['MSN'],
int(i + 1),
float(A_responding),
float(A_iti),
float(B_responding),
float(B_iti),
float(dippers),
float(dippers_retrieved),
float(retrieval_latency)
]],
columns=column_list)
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
def test_cue_iti_responding():
assert cue_iti_responding([0, 1, 2, 3, 4, 5], [
'StartSession', 'PokeOn1', 'LPressOn', 'PokeOn1', 'RPressOn', 'PokeOn1'
], 'LPressOn', 'RPressOn', 'PokeOn1') == (30.0, 30.0)
assert cue_iti_responding([0, 1, 2, 3, 4, 5], [
'StartSession', 'LPressOn', 'PokeOn1', 'PokeOn1', 'RPressOn', 'PokeOn1'
], 'LPressOn', 'RPressOn', 'PokeOn1') == (40.0, 0.0)
assert cue_iti_responding([0, 1, 2, 3, 4, 5], [
'StartSession', 'PokeOn1', 'PokeOn1', 'LPressOn', 'RPressOn', 'PokeOn1'
], 'LPressOn', 'RPressOn', 'PokeOn1') == (0.0, 60.0)
def CI_summation_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)
(B_responding, B_iti) = cue_iti_responding(timecode, eventcode, 'ExcitorBTrialStart', 'ExcitorBTrialEnd', 'PokeOn1')
(inhibitor_responding, inhibitor_iti) = cue_iti_responding(timecode, eventcode, 'InhibitorTrialStart', 'InhibitorTrialEnd', 'PokeOn1')
(B_responding_5, B_iti_5) = binned_responding(timecode, eventcode, 'ExcitorBTrialStart', 'ExcitorBTrialEnd', 'PokeOn1', 5)
(inhibitor_responding_5, inhibitor_iti_5) = binned_responding(timecode, eventcode, 'InhibitorTrialStart', 'InhibitorTrialEnd', 'PokeOn1', 5)
df2 = pd.DataFrame([[loaded_file['Subject'], loaded_file['MSN'], int(i + 1), float(B_responding),
float(B_iti), float(inhibitor_responding), float(inhibitor_iti), float(B_responding_5),
float(B_iti_5), float(inhibitor_responding_5), float(inhibitor_iti_5)]], columns=column_list)
return df2
def PIT_training_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)
(A_responding, A_iti) = cue_iti_responding(timecode, eventcode,
'ExcitorATrialStart',
'ExcitorATrialEnd', 'PokeOn1')
(B_responding, B_iti) = cue_iti_responding(timecode, eventcode,
'ExcitorBTrialStart',
'ExcitorBTrialEnd', 'PokeOn1')
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'], loaded_file['MSN'],
int(i + 1),
float(A_responding),
float(A_iti),
float(B_responding),
float(B_iti),
float(A_responding - A_iti),
float(B_responding - B_iti), *group_ids
]],
columns=column_list + file_keys)
return df2
def crf_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)
(left_presses, right_presses,
total_presses) = lever_pressing(eventcode, 'LPressOn', 'RPressOn')
if 'LLeverOn' in eventcode:
press_latency = lever_press_latency(timecode, eventcode, 'LLeverOn',
'LPressOn')
(lever_press_rate,
iti_rate) = cue_iti_responding(timecode, eventcode, 'StartSession',
'EndSession', 'LPressOn')
elif 'RLeverOn' in eventcode:
press_latency = lever_press_latency(timecode, eventcode, 'RLeverOn',
'RPressOn')
(lever_press_rate,
iti_rate) = cue_iti_responding(timecode, eventcode, 'StartSession',
'EndSession', 'RPressOn')
df2 = pd.DataFrame([[
loaded_file['Subject'],
int(i + 1), loaded_file['tts'],
float(dippers),
float(dippers_retrieved),
float(retrieval_latency),
float(total_presses),
float(press_latency),
float(lever_press_rate)
]],
columns=column_list)
return df2
def signtracking(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)
(inactive_poke,
inactive_iti_poke) = cue_iti_responding(timecode, eventcode,
'NoRewardTrialStart',
'NoRewardTrialEnd', 'PokeOn1')
(inactive_press, inactive_iti_press) = cue_iti_responding(
timecode, eventcode, 'NoRewardTrialStart', 'NoRewardTrialEnd',
'InactivePress')
(active_poke,
active_iti_poke) = cue_iti_responding(timecode, eventcode,
'RewardTrialStart',
'RewardTrialEnd', 'PokeOn1')
(active_press,
active_iti_press) = cue_iti_responding(timecode, eventcode,
'RewardTrialStart',
'RewardTrialEnd', 'ActivePress')
df2 = pd.DataFrame([[
loaded_file['Subject'],
int(i + 1),
float(dippers),
float(inactive_poke),
float(inactive_press),
float(active_poke),
float(active_press)
]],
columns=column_list)
return df2
def CI_retardation_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)
(X_responding, X_iti) = cue_iti_responding(timecode, eventcode,
'InhibitorTrialStart',
'InhibitorTrialEnd', 'PokeOn1')
df2 = pd.DataFrame([[
loaded_file['Subject'], loaded_file['tts'], loaded_file['CI'],
int(i + 1),
float(X_responding),
float(X_iti)
]],
columns=column_list)
return df2
