def join_data():
from expyriment.misc import data_preprocessing
print "Joining data"
sys.stdout.write(" data subfolder [optional, default=data]? ")
folder = raw_input()
if len(folder) <= 0:
folder = "data"
start_with = raw_input(" data files start with [optional]? ")
d = data_preprocessing.Aggregator(folder, start_with)
return d
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Example analysis script for snarc_experiment.py
The current script produces two files for different analysis of the SNARC
effect (ANOVA vs. slopes analysis) using mean and median RTs
"""
from expyriment.misc import data_preprocessing, constants
agg = data_preprocessing.Aggregator(data_folder="./data/",
file_name="snarc_experiment")
agg.set_subject_variables(["mapping_order"])
agg.set_computed_variables(["parity = digit % 2", #0:odd, 1:even
"size = digit > 5", #0:small, 1:large
"space = btn == {0}".format(constants.K_RIGHT) #0:left, 1:right
])
# RTs: space x size
agg.set_exclusions(["RT > 1000", "RT < 200", "error == 1", "trial<0"])
agg.set_dependent_variables(["mean(RT)"])
agg.set_independent_variables(["size", "space"])
print agg
agg.aggregate(output_file="rt_size_space.csv")
# RTs: slopes analysis
agg.set_independent_variables(["digit"])
agg.aggregate(output_file="rt_digits.csv")
def import_one_subject(rootdir, file_name):
"""
Import the data of one subject, and do some basic preprocessing
"""
# Load data file
agg = data_preprocessing.Aggregator(data_folder=rootdir,
file_name=file_name)
# ignore lines that correspond to the register of >2 presses per trial
trial = agg.get_variable_data("TrialId").flatten()
n = len(trial)
ignore = []
for line in range(n):
if line > 0:
if trial[line] == trial[line - 1]:
ignore.append(1)
else:
ignore.append(0)
else:
ignore.append(0)
ignore = np.array(ignore)
trial = np.array(list(map(int, trial)))[ignore == 0]
multi_press = agg.get_variable_data("IsMultiKeyPress").flatten()
multi_press = np.array([
0 if e == 'False' else (1 if e == 'True' else -1) for e in multi_press
])
multi_press = multi_press[ignore == 0]
RT = agg.get_variable_data("RT").flatten()
RT = np.array([int(rt) if rt != 'None' else -1
for rt in RT]).astype('int64')
RT = RT[ignore == 0]
Correct = agg.get_variable_data("Correct").flatten()
Correct = np.array([1 if c == 'True' else 0 for c in Correct])
Correct = Correct[ignore == 0]
onset = agg.get_variable_data("StimulusOnset").flatten()
onset = onset[ignore == 0]
block = agg.get_variable_data("BlockId").flatten()
block = np.array(list(map(int, block)))[ignore == 0]
seq = agg.get_variable_data("Stimulus").flatten()
seq = np.array([1 if stim == 'True' else 0 for stim in seq])
seq = seq[ignore == 0]
serie = agg.get_variable_data("SerieId").flatten()
serie = np.array(list(map(int, serie)))[ignore == 0]
motor = agg.get_variable_data("Motricity").flatten()
motor = np.array(list(map(int, motor)))[ignore == 0]
ecc = agg.get_variable_data("Eccentricity").flatten().astype('int64')
ecc = np.array(list(map(int, ecc)))[ignore == 0]
delay = agg.get_variable_data("Delay").flatten()
delay = np.array(list(map(int, delay)))[ignore == 0]
rep = agg.get_variable_data("Repetition").flatten()
rep = np.array([1 if r == 'True' else 0 for r in rep])
rep = rep[ignore == 0]
return {
"trial": trial,
"multi_press": multi_press,
"RT": RT,
"Correct": Correct,
"onset": onset,
"block": block,
"seq": seq,
"serie": serie,
"motor": motor,
"ecc": ecc,
"delay": delay,
"rep": rep
}
def extractRecognitionAnswers(iFolder, iFile):
#
#
#
agg = data_preprocessing.Aggregator(data_folder=iFolder, file_name=iFile)
header = data_preprocessing.read_datafile(
iFolder + iFile, only_header_and_variable_names=True)
header = header[3].split('\n#e ')
pOrder = header[header.index('Presentation Order:') +
1:header.index('Presentation Order:') + 6]
pOrder = ''.join(pOrder)
non_decimal = re.compile(r'[^\d.]+')
pOrder = non_decimal.sub('', pOrder)
pOrder = pOrder.split('.')
pOrder = [int(x) for x in pOrder[0:-1]]
matrixA = ast.literal_eval(header[header.index('Learning:') +
1].split('\n')[0].split('\n')[0])
data = {}
for variable in agg.variables:
data[variable] = agg.get_variable_data(variable)
validCards = correctCards()
inValidCards = wrongCards()
aCorrect = np.logical_and(data['Matrix'] == 'MatrixA',
data['CorrectAnswer'] == 'True')
aNotCorrect = np.logical_and(data['Matrix'] == 'MatrixA',
data['CorrectAnswer'] == 'False')
rndCorrect = np.logical_and(data['Matrix'] == 'MatrixRandom',
data['CorrectAnswer'] == 'True')
rndNotCorrect = np.logical_and(data['Matrix'] == 'MatrixRandom',
data['CorrectAnswer'] == 'False')
for idx, val in enumerate(aCorrect):
if val[0]:
validCards.name.append(matrixA[pOrder[idx]])
validCards.position.append(pOrder[idx])
for idx, val in enumerate(aNotCorrect):
if val[0]:
inValidCards.name.append(matrixA[pOrder[idx]])
inValidCards.position.append(pOrder[idx])
validCards.animals = [word for word in validCards.name if word[0] == 'a']
validCards.clothes = [word for word in validCards.name if word[0] == 'c']
validCards.vehicules = [word for word in validCards.name if word[0] == 'v']
validCards.fruits = [word for word in validCards.name if word[0] == 'f']
inValidCards.animals = [
word for word in inValidCards.name if word[0] == 'a'
]
inValidCards.clothes = [
word for word in inValidCards.name if word[0] == 'c'
]
inValidCards.vehicules = [
word for word in inValidCards.name if word[0] == 'v'
]
inValidCards.fruits = [
word for word in inValidCards.name if word[0] == 'f'
]
return data, validCards, inValidCards, matrixA
def extractCorrectAnswers(iFolder, iFile):
#
#
#
agg = data_preprocessing.Aggregator(data_folder=iFolder, file_name=iFile)
header = data_preprocessing.read_datafile(
iFolder + iFile, only_header_and_variable_names=True)
header = header[3].split('\n#e ')
data = {}
for variable in agg.variables:
data[variable] = agg.get_variable_data(variable)
validCards = correctCards()
inValidCards = wrongCards()
indexBlocks = np.unique(data['NBlock'])
for block in indexBlocks:
correctAnswers = np.logical_and(data['Picture'] == data['Answers'],
data['NBlock'] == block)
wrongAnswers = np.logical_and(data['Picture'] != data['Answers'],
data['NBlock'] == block)
matrixPictures = ast.literal_eval(
header[header.index('Positions pictures:') +
1].split('\n')[0].split('\n')[0])
for idx, val in enumerate(correctAnswers):
if val:
validCards.name.append(data['Answers'][idx][0])
validCards.position.append(
matrixPictures.index(data['Answers'][idx]))
validCards.animals = [word for word in validCards.name if word[0] == 'a']
validCards.clothes = [word for word in validCards.name if word[0] == 'c']
validCards.vehicules = [word for word in validCards.name if word[0] == 'v']
validCards.fruits = [word for word in validCards.name if word[0] == 'f']
for idx, val in enumerate(wrongAnswers):
if val:
inValidCards.name.append(data['Answers'][idx][0])
inValidCards.picture.append(data['Picture'][idx][0])
if 'None' in data['Answers'][idx][0]:
inValidCards.position.append(100)
else:
inValidCards.position.append(
matrixPictures.index(data['Answers'][idx]))
inValidCards.animals = [
word for word in inValidCards.name if word[0] == 'a'
]
inValidCards.clothes = [
word for word in inValidCards.name if word[0] == 'c'
]
inValidCards.vehicules = [
word for word in inValidCards.name if word[0] == 'v'
]
inValidCards.fruits = [
word for word in inValidCards.name if word[0] == 'f'
]
return data, validCards, inValidCards, matrixPictures
import sys
from config import dataFolder
from expyriment.misc import data_preprocessing
import numpy as np
agg = data_preprocessing.Aggregator(data_folder=dataFolder,
file_name=sys.argv[1])
print 'Variable computed: '
data = {}
for variable in agg.variables:
data[variable] = agg.get_variable_data(variable)
indexBlocks = np.unique(data['NBlock'])
for block in indexBlocks:
print 'Block {}'.format(block)
correctAnswers = np.logical_and(data['Picture']==data['Answers'], data['NBlock']==block)
wrongAnswers = np.logical_and(data['Picture']!=data['Answers'], data['NBlock']==block)
correctRT = [int(i) for i in data['RT'][correctAnswers]]
print 'Correct answers: {}'.format(len(correctRT))
print 'Mean correct RT: {} ms'.format(np.mean(correctRT))