danielbrianbennett / sql_abet_analyzer Goto Github PK

Can save time, and produces more performance variables (about 70 organised behavioural outcomes measures) than default ABET method.

• The program can save time as databases doesn't have to be analysed separately, e.g., run a single analysis on as many ABETdb files you want.
• The program recognises the Animal IDs even though the animal was run on different sets and stored in different databases.
• The program calculates collapsed mean, for all the sessions for each animal that the user specified to analyse, for all outcomes variables, in seperate excel sheet.

To run, place all databases (.ABETdb files) that should be analysed in same folder as the program and run abet_cal.py.

• 1 - Animal IDs: Extract and Analyse data defined by animal IDs
• 2 - Calender: Extract and Analyse data defined by experiment dates
• 3 - Animal IDs / Calender: Extract and Analyse data defined by experiment dates AND animal IDs
• 4 - Schedule: Extract and Analyse data defined by schedule/stage name(s)
• 5 - Computer: Extract and Analyse data from specific computer(s)
• 6 - All data: Extract and Analyse entire database(s)

• Hits, misses, false alarms, and correct rejections
• Hit rate, False alarm rate
• Response criterion (c)
• Discrimination sensivitity (d')
• Hit latency, false alarm latency, reward retrieval latency
• ISI touches
• Front beam breakes, Back beam breaks
• The data in 5min bins for: Hit rate, False alarm rate, Discrimination sensivitity (d'), Response criterion (c)

• Latency Between Hits (MEAN)
• Latency Between Hits (STDEV)
• Latency Between Hits (MAX)
• Latency Between Stimuli Responses (MEAN)
• Latency Between Stimuli Responses (STDEV)
• Latency Between Stimuli Responses (MAX)
• Trials Between Stimuli Responses (MEAN)
• Trials Between Stimuli Responses (STDEV)
• Trials Between Stimuli Responses (MAX)
• False Alarm Bout Length (MEAN)
• False Alarm Bout Length (STDEV)
• False Alarm Bout Length (MAX)
• Hit Bout Length (MEAN)
• Hit Bout Length (STDEV)
• Hit Bout Length (MAX)
• Latency Reward Retrieval --> Front Beam Break (MEAN)
• Latency Reward Retrieval --> Front Beam Break (STDEV)
• Latency Retrieval --> Front Beam Break (MAX)

• Time Between Hits (MIN)
• Time Between Hits (RANGE)
• Time Between Hits (SKEW)
• Time Between Hits (KURTOSIS)
• Time Between Hits (VARIANCE)
• Inactivity time (MIN)
• Inactivity time (RANGE)
• Inactivity time (SKEW)
• Inactivity time (KURTOSIS)
• Inactivity time (VARIANCE)
• Inactivity trials (MIN)
• Inactivity trials (RANGE)
• Inactivity trials (SKEW)
• Inactivity trials (KURTOSIS)
• Inactivity trials (VARIANCE)
• False Alarm Bout (MIN)
• False Alarm Bout (RANGE)
• False Alarm Bout (SKEW)
• False Alarm Bout (KURTOSIS)
• False Alarm Bout (VARIANCE)
• Hit Bout (MIN)
• Hit Bout (RANGE)
• Hit Bout (SKEW)
• Hit Bout (KURTOSIS)
• Hit Bout (VARIANCE)
• Latency Retrieval --> Front Beam Break (MIN)
• Latency Retrieval --> Front Beam Break (RANGE)
• Latency Retrieval --> Front Beam Break (SKEW)
• Latency Retrieval --> Front Beam Break (KURTOSIS)
• Latency Retrieval --> Front Beam Break (VARIANCE)

• FAR stimulus 1/2
• FAR stimulus 3
• FAR stimulus 4
• FAR stimulus 5

• The microsoftdB --> SQLite conversion requires mdb-export-all
• statistics
• pandas
• sqlite3
• scipy
• tqdm
• csv
• shutil
• tabulate
• glob
• xlwt