: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
(days, df) = loop_over_days(column_list, habit_extinction_function)
binned_columns = [col for col in df.columns if type(col) == int]
dfnew = pd.melt(df,
id_vars=['Subject', 'Sex', 'Day', 'Training', 'Lever Presses'],
value_vars=binned_columns,
var_name='Bin',
value_name='Lever_Pressing_During_Bin')
"""
(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
(days, df) = loop_over_days(column_list, CI_training_function)
print(df.to_string())
df.to_excel("output.xlsx")
from operantanalysis import loop_over_days, extract_info_from_file, cue_responding_duration
import pandas as pd
column_list = ['Subject', 'Condition', 'Day', 'Light Poke Duration', 'LightITI']
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_dur_individual, X_dur_total, XITI_dur_individual, XITI_dur_total) = cue_responding_duration(timecode, eventcode, 'InhibitorTrialStart', 'InhibitorTrialEnd', 'PokeOn1', 'PokeOff1')
df2 = pd.DataFrame([[loaded_file['Subject'], loaded_file['MSN'], int(i + 1),
float(X_dur_total), float(XITI_dur_total)]], columns=column_list)
return df2
(days, df) = loop_over_days(column_list, CI_retardation_function)
print(df.to_string())
df.to_excel("output.xlsx")
(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
(days, df) = loop_over_days(column_list, Go_NoGo)
print(df.to_string())
df.to_excel("output.xlsx")
group_means = df.groupby(['Day', 'tts'])['Dippers',
'Successful Go Trials'].mean()
group_sems = df.groupby(['Day', 'tts'])['Dippers',
'Successful Go Trials'].sem()
print(
df.groupby(['Day',
'tts'])['Dippers',
'Successful Go Trials'].mean().unstack().to_string())
print(
df.groupby(['Day',
'tts'])['Dippers',
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)
(large_rewards, small_rewards) = num_switch_trials(eventcode)
(forced_latency) = lever_press_latency_Switch(timecode, eventcode)
df2 = pd.DataFrame([[
loaded_file['Subject'], loaded_file['MSN'],
int(i + 1),
float(dippers),
float(large_rewards),
float(small_rewards),
float(forced_latency)
]],
columns=column_list)
return df2
(days, df) = loop_over_days(column_list, crf_function)
print(df.to_string())
df.to_excel("output.xlsx")
float(total_presses)
]],
columns=column_list)
return df2
# If user provided an argument at execution, use this to find data.
try:
data_directory = argv[1]
# Otherwise, store it as an empty string so loop_over_days knows to use GUI.
except IndexError:
data_directory = ''
(days, df) = loop_over_days(column_list,
crf_function,
master_data_folder=data_directory)
print(df.to_string())
# If user provided multiple arguments at execution, use the second one as the save path for the output folder.
try:
save_path = os.path.join(argv[2], 'output.xlsx')
df.to_excel(save_path)
# Otherwise, save the DataFrame to the current working directory.
except IndexError:
df.to_excel("output.xlsx")
graph_toggle = input(
'Would you like to see graphs of dipper retrieval, latency, and lever presses (Y/n)? '
)
['Paired_ES_' + str(i + 1) for i in range(len(paired_length_poke_rates))] + \
['Unpaired_' + str(i + 1) for i in range(len(paired_length_poke_rates))] + \
['Unpaired_ITI_' + str(i + 1) for i in range(len(paired_length_poke_rates))] + \
['Unpaired_ES_' + str(i + 1) for i in range(len(paired_length_poke_rates))]
across_cue_df = pd.DataFrame(
[([loaded_file['Subject']] + paired_length_poke_rates +
paired_iti_length_poke_rates + paired_subtracted_poke_rates +
unpaired_length_poke_rates + unpaired_iti_length_poke_rates +
unpaired_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
(days, df) = loop_over_days(column_list, PCER_P2_function)
print(df.to_string())
df.to_excel("output.xlsx")
float(B_responding - B_iti), *group_ids
]],
columns=column_list + file_keys)
return df2
# If user provided an argument at execution, use this to find data.
try:
data_directory = argv[1]
# Otherwise, store it as an empty string so loop_over_days knows to use GUI.
except IndexError:
data_directory = ''
(days, df) = loop_over_days(column_list,
PIT_training_function,
master_data_folder=data_directory)
print(df.to_string())
# If user provided multiple arguments at execution, use the second one as the save path for the output folder.
try:
save_path = os.path.join(argv[2], 'output.xlsx')
df.to_excel(save_path)
# Otherwise, save the DataFrame to the current working directory.
except IndexError:
df.to_excel("output.xlsx")
graph_toggle = input(
'Would you like to see graphs of CS+ Responding (Y/n)? ')
if graph_toggle == 'Y':
'BInhibitorTrialEnd',
'PokeOn1', 'PokeOff1')
df2 = pd.DataFrame([[
loaded_file['Subject'], loaded_file['tts'], loaded_file['CI'],
int(i + 1),
float(B_dur_total),
float(BITI_dur_total),
float(BX_dur_total),
float(BXITI_dur_total)
]],
columns=column_list)
return df2
(days, df) = loop_over_days(column_list, CI_summation_function)
print(df.to_string())
df.to_excel("output.xlsx")
group_means = df.groupby(['Day',
'Condition'])['Noise Poke Duration', 'Noise ITI',
'Inhibitor Poke Duration',
'Inhibitor ITI'].mean()
group_sems = df.groupby(['Day',
'Condition'])['Noise Poke Duration', 'Noise ITI',
'Inhibitor Poke Duration',
'Inhibitor ITI'].sem()
print(
df.groupby(['Day', 'tts',
'Condition'])['Noise Poke Duration', 'Noise ITI',
if loaded_file['MSN'] == 'PavCA_LeftUnpaired_2020' or loaded_file['MSN'] == 'PavCA_RightUnpaired_2020':
(inactive_poke, inactive_iti_poke, trials_w_poke) = cue_iti_responding_PavCA(timecode, eventcode, 'NoRewardTrialStart', 'NoRewardTrialEnd', 'PokeOn1')
(inactive_press, inactive_iti_press, trials_w_press) = cue_iti_responding_PavCA(timecode, eventcode, 'NoRewardTrialStart', 'NoRewardTrialEnd', 'InactivePress')
poke_lat = lever_press_latency_PavCA(timecode, eventcode, 'NoRewardTrialStart', 'PokeOn1', 10)
press_lat = lever_press_latency_PavCA(timecode, eventcode, 'NoRewardTrialStart', 'InactivePress', 10)
poke = inactive_poke
press = inactive_press
prob_poke = trials_w_poke / 35
prob_press = trials_w_press / 35
iti_poke = inactive_iti_poke
elif loaded_file['MSN'] == 'PavCA_LeftPaired_2020' or loaded_file['MSN'] == 'PavCA_RightPaired_2020':
(active_poke, active_iti_poke, trials_w_poke) = cue_iti_responding_PavCA(timecode, eventcode, 'RewardTrialStart', 'RewardTrialEnd', 'PokeOn1')
(active_press, active_iti_press, trials_w_press) = cue_iti_responding_PavCA(timecode, eventcode, 'RewardTrialStart', 'RewardTrialEnd', 'ActivePress')
poke_lat = lever_press_latency_PavCA(timecode, eventcode, 'RewardTrialStart', 'PokeOn1', 10)
press_lat = lever_press_latency_PavCA(timecode, eventcode, 'RewardTrialStart', 'ActivePress', 10)
poke = active_poke
press = active_press
prob_poke = trials_w_poke / 35
prob_press = trials_w_press / 35
iti_poke = active_iti_poke
df2 = pd.DataFrame([[loaded_file['Subject'], int(i + 1), float(dippers), float(iti_poke), float(poke),
float(press), float(prob_poke), float(prob_press), float(poke_lat), float(press_lat)]],
columns=column_list)
return df2
(days, df) = loop_over_days(column_list, pavCA)
print(df.to_string())
df.to_excel("output.xlsx")
'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
(days, df) = loop_over_days(column_list, signtracking)
print(df.to_string())
df.to_excel("output.xlsx")
(dippers, dippers_retrieved,
retrieval_latency) = reward_retrieval(timecode, eventcode)
df2 = pd.DataFrame([[
loaded_file['Subject'],
int(i + 1),
float(dippers),
float(dippers_retrieved),
float(retrieval_latency)
]],
columns=column_list)
return df2
(days, df) = loop_over_days(column_list, trough_train_function)
print(df)
group_means = df.groupby(['Day'])['Dippers Retrieved',
'Retrieval Latency'].mean()
group_sems = df.groupby(['Day'])['Dippers Retrieved',
'Retrieval Latency'].sem()
plt.subplot(221)
group_means['Dippers Retrieved'].plot(legend=True,
yerr=group_sems['Dippers Retrieved'],
ylim=[0, 60],
xlim=[0, days + 1],
xticks=(range(1, days + 1, 1)),
marker='o',
(small_go_trials, large_go_trials, small_go_success,
large_go_success) = (eventcode.count('GoTrialBegSmallReward'),
eventcode.count('GoTrialBegLargeReward'),
eventcode.count('GoTrialSuccessSmallReward'),
eventcode.count('GoTrialSuccessLargeReward'))
small_go_latency = lever_press_latency(timecode, eventcode,
'GoTrialBegSmallReward',
'GoTrialSuccessSmallReward')
large_go_latency = lever_press_latency(timecode, eventcode,
'GoTrialBegLargeReward',
'GoTrialSuccessLargeReward')
df2 = pd.DataFrame([[
loaded_file['Subject'],
int(i + 1),
float(small_go_trials),
float(large_go_trials),
float(small_go_success),
float(large_go_success),
float(small_go_latency),
float(large_go_latency)
]],
columns=column_list)
return df2
(days, df) = loop_over_days(column_list, RVI_Go_NoGo_P1)
print(df.to_string())
df.to_excel("output.xlsx")
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt # noqa
column_list = ['Subject', 'Sex', 'Day', 'Training', 'Lever Presses', 'Bins']
def habit_reacquisition_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
(days, df) = loop_over_days(column_list, habit_reacquisition_function)
binned_columns = [col for col in df.columns if type(col) == int]
dfnew = pd.melt(df, id_vars=['Subject', 'Sex', 'Day', 'Training', 'Lever Presses'], value_vars=binned_columns,
var_name='Bin', value_name='Lever_Pressing_During_Bin')
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
# If user provided an argument at execution, use this to find data.
try:
data_directory = argv[1]
# Otherwise, store it as an empty string so loop_over_days knows to use GUI.
except IndexError:
data_directory = ''
(days, df) = loop_over_days(column_list, trough_train_function, master_data_folder=data_directory)
print(df.to_string())
# If user provided multiple arguments at execution, use the second one as the save path for the output folder.
try:
save_path = os.path.join(argv[2], 'output.xlsx')
df.to_excel(save_path)
# Otherwise, save the DataFrame to the current working directory.
except IndexError:
df.to_excel("output.xlsx")
graph_toggle = input('Would you like to see graphs of dipper retrieval and latency (Y/n)? ')
float(retrieval_latency), *group_ids
]],
columns=column_list + file_keys)
return df2
# If user provided an argument at execution, use this to find data.
try:
data_directory = argv[1]
# Otherwise, store it as an empty string so loop_over_days knows to use GUI.
except IndexError:
data_directory = ''
(days, df) = loop_over_days(column_list,
Go_NoGo,
master_data_folder=data_directory)
print(df.to_string())
# If user provided multiple arguments at execution, use the second one as the save path for the output folder.
try:
save_path = os.path.join(argv[2], 'output.xlsx')
df.to_excel(save_path)
# Otherwise, save the DataFrame to the current working directory.
except IndexError:
df.to_excel("output.xlsx")
df.to_excel("output.xlsx")
graph_toggle = input(
'Would you like to see graphs of Succesful Go Trials and Retrieval Latency (Y/n)? '
