def getOnsets(task_name, line_bisection_log, delay):
if task_name == "line_bisection":
from parse_line_bisection_log import parse_line_bisection_log
_,_,correct_pictures, incorrect_pictures, noresponse_pictures = parse_line_bisection_log(line_bisection_log, delay)
return [correct_pictures["task"], incorrect_pictures["task"], noresponse_pictures["task"],
sorted(correct_pictures["rest"] + incorrect_pictures["rest"]), noresponse_pictures["rest"]]
else:
from variables import design_parameters
return design_parameters[task_name]['onsets']
Created on 29 Aug 2011
@author: filo
'''
import glob
from parse_line_bisection_log import parse_line_bisection_log
import numpy as np
logs = glob.glob("/media/data/2010reliability/data/*/*/logs/*-Line_Bisection.log")
print logs
task_accuracy = []
task_responses = []
rest_responses = []
for logfile in logs:
_,pictures ,correct_pictures, incorrect_pictures, noresponse_pictures = parse_line_bisection_log(logfile, 4*2.5)
cur_accuracy = float(len(correct_pictures['task']))/(len(correct_pictures['task'])+len(incorrect_pictures['task']))
task_accuracy.append(cur_accuracy)
cur_task_responses = float(len(pictures['task'])-len(noresponse_pictures['task']))/len(pictures['task'])
task_responses.append(cur_task_responses)
cur_rest_responses = float(len(pictures['rest'])-len(noresponse_pictures['rest']))/len(pictures['rest'])
rest_responses.append(cur_rest_responses)
print "task correct answers - mean: %f, std: %f"%(np.array(task_accuracy).mean(), np.array(task_accuracy).std())
print "task responses - mean: %f, std: %f"%(np.array(task_responses).mean(), np.array(task_responses).std())
print "rest responses - mean: %f, std: %f"%(np.array(rest_responses).mean(), np.array(rest_responses).std())
