def test_lever_pressing():
assert lever_pressing([
'StartSession', 'PokeOn1', 'LPressOn', 'PokeOn1', 'RPressOn', 'PokeOn1'
], 'LPressOn', 'RPressOn') == (1, 1, 2)
assert lever_pressing([
'StartSession', 'LPressOn', 'PokeOn1', 'PokeOn1', 'RPressOn', 'PokeOn1'
], 'LPressOn') == (1, 0, 1)
assert lever_pressing([
'StartSession', 'PokeOn1', 'PokeOn1', 'LPressOn', 'RPressOn',
'RPressOn', 'PokeOn1'
], 'LPressOn', 'RPressOn') == (1, 2, 3)
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')
# Use this code for latencies and rates
#
# 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),
float(dippers),
float(dippers_retrieved),
float(retrieval_latency),
float(left_presses),
float(right_presses),
float(total_presses)
]],
columns=column_list)
return df2
def PIT_test_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)
(csplus_presses, csmin_presses,
total_presses) = lever_pressing(eventcode, 'ActivePress', 'InactivePress')
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(csplus_presses),
float(csmin_presses), *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')
df2 = pd.DataFrame([[
str(loaded_file['Subject'][1:]),
int(i + 1),
float(dippers),
float(dippers_retrieved),
float(retrieval_latency),
float(left_presses),
float(right_presses),
float(total_presses)
]],
columns=column_list)
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')
start_time = datetime.datetime.strptime(
f'{loaded_file["Start Date"]} {loaded_file["Start Time"]}',
'%m/%d/%y %H:%M:%S')
end_time = datetime.datetime.strptime(
f'{loaded_file["End Date"]} {loaded_file["End Time"]}',
'%m/%d/%y %H:%M:%S')
sess_length = end_time - start_time
sess_length = sess_length.seconds
# Use this code for latencies and rates
#
# 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')
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(left_presses),
float(right_presses),
float(total_presses),
float(sess_length), *group_ids
]],
columns=column_list + file_keys)
return df2
def habit_extinction_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)
(left_presses, right_presses, total_presses) = lever_pressing(eventcode, 'LPressOn', 'RPressOn')
pressing_across_test = bin_by_time(timecode, eventcode, (5 * 60), ['LPressOn', 'RPressOn'])
df2 = pd.DataFrame([[loaded_file['Subject'], loaded_file['Sex'], int(i + 1), loaded_file['Training'],
float(total_presses), pressing_across_test]], columns=column_list)
bins_df = df2['Bins'].apply(pd.Series)
bins_df = bins_df.rename(columns=lambda x: (x + 1) * 5)
df2 = pd.concat([df2[:], bins_df[:]], axis=1)
return df2