def generateTrials(myDict, nTestTrials):
'''
generates a list of trials to write to csv file
supply 3 columns to be factorially combined
myDict = {'contrast': [1.0, 0.75, 0.5, 0.25], 'orientation':[225, 315],
'probeMatch':[1, 0], 'correctKey':[1]}
'''
columnHeaders = myDict.keys()
trialList = data.createFactorialTrialList(myDict)
for item in trialList:
if item['probeMatch'] == 1:
item['correctKey'] = expInfo['sameKey']
else:
item['correctKey'] =expInfo['differentKey']
# write trial list for experiment loop:
if expInfo['experimentType']=='practise':
trialList = rand.sample(trialList, nTestTrials)
with open('mainTrials.csv', 'wb') as mainTrialsFile:
writer = csv.writer(mainTrialsFile, dialect='excel')
writer.writerow(columnHeaders)
for row in trialList:
writer.writerow(row.values())
if expInfo['experimentType']=='practise':
# generate trial list for practise loop:
practiseTrialList = rand.sample(trialList, nTestTrials)
# write trial list for practise loop:
with open('practiseTrials.csv', 'wb') as practiseTrialsFile:
writer = csv.writer(practiseTrialsFile, dialect='excel')
writer.writerow(columnHeaders)
for row in practiseTrialList:
writer.writerow(row.values())
def test_default(self):
exp = data.ExperimentHandler(name='testExp',
version='0.1',
extraInfo={
'participant': 'jwp',
'ori': 45
},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName=self.tmpDir + 'default')
# First loop: Training.
conds = data.createFactorialTrialList({
'faceExpression': ['happy', 'sad'],
'presTime': [0.2, 0.3]
})
training = data.TrialHandler(
trialList=conds,
nReps=3,
name='train',
method='random',
seed=100 # Global seed - so fixed for whole experiment.
)
exp.addLoop(training)
for trial in training:
training.addData('training.rt', random.random() * 0.5 + 0.5)
if random.random() > 0.5:
training.addData('training.key', 'left')
else:
training.addData('training.key', 'right')
exp.nextEntry()
# Then run 3 repeats of a staircase.
outerLoop = data.TrialHandler(trialList=[],
nReps=3,
name='stairBlock',
method='random')
exp.addLoop(outerLoop)
for thisRep in outerLoop: # The outer loop doesn't save any data.
staircase = data.StairHandler(startVal=10,
name='staircase',
nTrials=5)
exp.addLoop(staircase)
for thisTrial in staircase:
id = random.random()
if random.random() > 0.5:
staircase.addData(1)
else:
staircase.addData(0)
exp.addData('id', id)
exp.nextEntry()
def generateTrialsDict(myDict):
columnHeaders = myDict.keys()
trialList = data.createFactorialTrialList(myDict)
for item in trialList:
if item['orientation'] > 0:
item['correctKey'] = expInfo['rightKey']
elif item['orientation'] < 0:
item['correctKey'] =expInfo['leftKey']
return trialList
def test_ExperimentHandler():
exp = data.ExperimentHandler(name='testExp',
version='0.1',
extraInfo={
'participant': 'jwp',
'ori': 45
},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName=tmpFile + 'x')
#a first loop (like training?)
conds = data.createFactorialTrialList({
'faceExpression': ['happy', 'sad'],
'presTime': [0.2, 0.3]
})
training = data.TrialHandler(
trialList=conds, nReps=3, name='train', method='random',
seed=100) #this will set the global seed - so fixed for whole exp
exp.addLoop(training)
#run those trials
for trial in training:
training.addData('training.rt', random.random() * 0.5 + 0.5)
if random.random() > 0.5:
training.addData('training.key', 'left')
else:
training.addData('training.key', 'right')
exp.nextEntry()
#then run 3 repeats of a staircase
outerLoop = data.TrialHandler(trialList=[],
nReps=3,
name='stairBlock',
method='random')
exp.addLoop(outerLoop)
for thisRep in outerLoop: #the outer loop doesn't save any data
staircase = data.StairHandler(startVal=10, name='staircase', nTrials=5)
exp.addLoop(staircase)
for thisTrial in staircase:
id = random.random()
if random.random() > 0.5:
staircase.addData(1)
else:
staircase.addData(0)
exp.addData('id', id)
exp.nextEntry()
#exp should then automatically save the pickle and csv data files
for e in exp.entries:
print e
print 'done'
def test_default(self):
exp = data.ExperimentHandler(
name='testExp',
version='0.1',
extraInfo={'participant': 'jwp', 'ori': 45},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName=self.tmpDir + 'default'
)
# First loop: Training.
conds = data.createFactorialTrialList(
{'faceExpression': ['happy', 'sad'], 'presTime': [0.2, 0.3]}
)
training = data.TrialHandler(
trialList=conds, nReps=3, name='train',
method='random',
seed=self.random_seed)
exp.addLoop(training)
rng = np.random.RandomState(seed=self.random_seed)
for trial in training:
training.addData('training.rt', rng.rand() * 0.5 + 0.5)
if rng.rand() > 0.5:
training.addData('training.key', 'left')
else:
training.addData('training.key', 'right')
exp.nextEntry()
# Then run 3 repeats of a staircase.
outerLoop = data.TrialHandler(
trialList=[], nReps=3,name='stairBlock', method='random'
)
exp.addLoop(outerLoop)
for thisRep in outerLoop: # The outer loop doesn't save any data.
staircase = data.StairHandler(
startVal=10, name='staircase', nTrials=5
)
exp.addLoop(staircase)
for thisTrial in staircase:
id = rng.rand()
if rng.rand() > 0.5:
staircase.addData(1)
else:
staircase.addData(0)
exp.addData('id', id)
exp.nextEntry()
def sart_block(win, fb, omitNum, reps, bNum, fixed):
mouse = event.Mouse(visible=0)
xStim = visual.TextStim(win, text="X", height=3.35, color="white",
pos=(0, 0))
circleStim = visual.Circle(win, radius=1.50, lineWidth=8,
lineColor="white", pos=(0, -.2))
numStim = visual.TextStim(win, font="Arial", color="white", pos=(0, 0))
correctStim = visual.TextStim(win, text="CORRECT", color="green",
font="Arial", pos=(0, 0))
incorrectStim = visual.TextStim(win, text="INCORRECT", color="red",
font="Arial", pos=(0, 0))
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]
if fb == True:
fontSizes=[1.20, 3.00]
else:
fontSizes=[1.20, 1.80, 2.35, 2.50, 3.00]
list= data.createFactorialTrialList({"number" : numbers,
"fontSize" : fontSizes})
seqList = []
for i in range(len(fontSizes)):
for number in numbers:
random.shuffle(list)
for trial in list:
if trial["number"] == number and trial not in seqList:
seqList.append(trial)
break
if fixed == True:
trials = data.TrialHandler(seqList, nReps=reps, method='sequential')
else:
trials = data.TrialHandler(list, nReps=reps, method='random')
clock = core.Clock()
tNum = 0
resultList =[]
startTime = time.clock()
for trial in trials:
tNum += 1
resultList.append(sart_trial(win, fb, omitNum, xStim, circleStim,
numStim, correctStim, incorrectStim, clock,
trials.thisTrial['fontSize'],
trials.thisTrial['number'], tNum, bNum, mouse))
endTime = time.clock()
totalTime = endTime - startTime
for row in resultList:
row.append(totalTime/tNum)
print ("\n\n#### Block " + str(bNum) + " ####\nDes. Time Per P Trial: " +
str(2.05*1000) + " ms\nDes. Time Per Non-P Trial: " +
str(1.15*1000) + " ms\nActual Time Per Trial: " +
str((totalTime/tNum)*1000) + " ms\n\n")
return resultList
def generateTrials(myDict):
'''
generates a list of trials to write to csv file
supply 3 columns to be factorially combined
myDict = {'contrast': [1.0, 0.75, 0.5, 0.25], 'orientation':[225, 315],
'probeMatch':[1, 0]}
'''
columnHeaders = myDict.keys()
# header = ",".join(columnHeaders)
trialList = data.createFactorialTrialList(myDict)
with open('mainTrials.csv', 'wb') as mainTrialsFile:
writer = csv.writer(mainTrialsFile, dialect='excel')
writer.writerow(columnHeaders)
for row in trialList:
writer.writerow(row.values())
def generateTrialsCSV(myDict):
columnHeaders = myDict.keys()
trialList = data.createFactorialTrialList(myDict)
for item in trialList:
if item['orientation'] > 0:
item['correctKey'] = 'j'
elif item['orientation'] < 0:
item['correctKey'] = 'f'
with open('ssv_loop.csv', 'wb') as conditionsFile:
writer = csv.writer(conditionsFile, dialect='excel')
writer.writerow(columnHeaders)
for row in trialList:
writer.writerow(row.values())
def generateTrials(myDict):
'''
generates a list of trials to write to csv file
'''
columnHeaders = myDict.keys()
trialList = data.createFactorialTrialList(myDict)
# write trial list for experiment loop:
# if expInfo['experimentType']=='practise':
# trialList = rand.sample(trialList, nTestTrials)
with open('conditions.csv', 'wb') as conditionsFile:
writer = csv.writer(conditionsFile, dialect='excel')
writer.writerow(columnHeaders)
for row in trialList:
writer.writerow(row.values())
def test_ExperimentHandler():
exp = data.ExperimentHandler(name='testExp',
version='0.1',
extraInfo={'participant':'jwp','ori':45},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName=tmpFile+'x')
#a first loop (like training?)
conds = data.createFactorialTrialList({'faceExpression':['happy','sad'],'presTime':[0.2,0.3]})
training=data.TrialHandler(trialList=conds, nReps=3,name='train',
method='random',
seed=100)#this will set the global seed - so fixed for whole exp
exp.addLoop(training)
#run those trials
for trial in training:
training.addData('training.rt',random.random()*0.5+0.5)
if random.random()>0.5:
training.addData('training.key','left')
else:
training.addData('training.key','right')
exp.nextEntry()
#then run 3 repeats of a staircase
outerLoop=data.TrialHandler(trialList=[], nReps=3,name='stairBlock',
method='random')
exp.addLoop(outerLoop)
for thisRep in outerLoop:#the outer loop doesn't save any data
staircase=data.StairHandler(startVal=10, name='staircase', nTrials=5)
exp.addLoop(staircase)
for thisTrial in staircase:
id=random.random()
if random.random()>0.5:
staircase.addData(1)
else:
staircase.addData(0)
exp.addData('id',id)
exp.nextEntry()
#exp should then automatically save the pickle and csv data files
for e in exp.entries:
print e
print 'done'
def rand_trials_sample(self):
"""
generates a randomized list of trial/stimulus parameters, of length numTrials
"""
factors = {
"ISI_s": self.ISI,
"flash_dur_ms": self.flash_dur,
"luminance": self.luminance,
"wave_freq_Hz": self.wave_freq, #waveFreq = freq of the tone
"pulse_dur_ms":
self.pulse_dur, #pulseDur = length of time the tone is played
"wave_amp_dB": self.wave_amp, #waveAmp = sound levels
"stimulus": self.stimulus,
"delay_ms": self.delay # delay between presenting A and V
}
stimList = data.createFactorialTrialList(factors)
randSample = random.sample(stimList, self.numTrials)
return randSample
from ppc import csvWriter
opacities = [0.2, 1.0]
images = ['cat.jpg', 'lion.jpg', 'fish.gif']
reps = 2
"""
1. PSYCHOPY VERSION
% No ability to save on trial-to-trial basis! (reason enough to discard)
+ Short and simple
i Suitable for simple factorial designs but not much more
% Messy output: adds extra columns ("ran" and "order") and floats integers (1 becomes 1.0)
"""
from psychopy import data
conditions = data.createFactorialTrialList({
'opacity': opacities,
'image': images
}) # tuple list of unique trials
trials = data.TrialHandler(
conditions, reps,
method='fullRandom') # as a TrialHandler object with repetitions of trials
for trial in trials:
trials.addData('answer', random.choice(['left', 'right']))
trials.addData('score', random.randint(0, 1))
trials.saveAsWideText('result_trialHandler (' +
time.strftime('%Y-%m-%d %H-%M-%S', time.localtime()) +
').csv',
appendFile=False)
"""
2. GENERIC PYTHON VERSION
exp = data.ExperimentHandler(name='testExp',
version='0.1',
extraInfo={
'participant': 'jwp',
'ori': 45
},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName='testExp')
# a first loop (like training?)
conds = data.createFactorialTrialList({
'faceExpression': ['happy', 'sad'],
'presTime': [0.2, 0.3]
})
training = data.TrialHandler(
trialList=conds, nReps=3, name='train', method='random',
seed=100) # this will set the global seed - for the whole exp
exp.addLoop(training)
# run those trials
for trial in training:
training.addData('training.rt', random.random() * 0.5 + 0.5)
if random.random() > 0.5:
training.addData('training.key', 'left')
else:
training.addData('training.key', 'right')
exp.nextEntry()
# then run 3 repeats of a staircase
from psychopy import data, log
from numpy import random
log.console.setLevel(log.DEBUG)
exp = data.ExperimentHandler(name='testExp',
version='0.1',
extraInfo={'participant':'jwp','ori':45},
runtimeInfo=None,
originPath=None,
savePickle=True,
saveWideText=True,
dataFileName='testExp')
#a first loop (like training?)
conds = data.createFactorialTrialList({'faceExpression':['happy','sad'],'presTime':[0.2,0.3]})
training=data.TrialHandler(trialList=conds, nReps=3,name='train',
method='random',
seed=100)#this will set the global seed - so fixed for whole exp
exp.addLoop(training)
#run those trials
for trial in training:
training.addData('training.rt',random.random()*0.5+0.5)
if random.random()>0.5:
training.addData('training.key','left')
else:
training.addData('training.key','right')
exp.nextEntry()
#then run 3 repeats of a staircase
outerLoop=data.TrialHandler(trialList=[], nReps=3,name='stairBlock',
method='random')
opacities = [0.2, 1.0]
images = ['cat.jpg', 'lion.jpg', 'fish.gif']
reps = 2
"""
1. PSYCHOPY VERSION
% No ability to save on trial-to-trial basis! (reason enough to discard)
+ Short and simple
i Suitable for simple factorial designs but not much more
% Messy output: adds extra columns ("ran" and "order") and floats integers (1 becomes 1.0)
"""
from psychopy import data
conditions = data.createFactorialTrialList({'opacity': opacities, 'image': images}) # tuple list of unique trials
trials = data.TrialHandler(conditions, reps, method='fullRandom') # as a TrialHandler object with repetitions of trials
for trial in trials:
trials.addData('answer', random.choice(['left', 'right']))
trials.addData('score', random.randint(0,1))
trials.saveAsWideText('result_trialHandler (' + time.strftime('%Y-%m-%d %H-%M-%S', time.localtime()) +').csv', appendFile=False)
"""
2. GENERIC PYTHON VERSION
+ good control
+ pure core python (occam's razor)
% Sensitive to identical keys in all trial-dicts
"""
"""
3) line orientation judgment trials
using the rough SVV estimate as centre for line orientations
"""
# GO signal for line orientation discrimination trials
print("Waiting for 2AFC task to start\n")
draw_fixation(color='green')
sequence = list(np.linspace(start=svv-RANGE, stop=svv+RANGE, num=N_ORI))
conditions = {'orientation': sequence}
trial_list = data.createFactorialTrialList(conditions)
trials = data.TrialHandler(trialList=trial_list, nReps=N_REPS, method='random')
experiment.addLoop(trials)
"""
start trial loop
"""
# initial interval
frame_n = 0
while frame_n < ITI_DURATION:
frame_n += 1
win.flip()
[i.setTex(noise_texture) for i in noise]
[i.draw() for i in noise]
win.flip()
"""
3) line orientation judgment trials
using the rough SVV estimate as centre for line orientations
"""
# GO signal for line orientation discrimination trials
print("Waiting for 2AFC task to start\n")
draw_fixation(color='green')
sequence = list(np.linspace(start=svv - RANGE, stop=svv + RANGE, num=N_ORI))
conditions = {'orientation': sequence}
trial_list = data.createFactorialTrialList(conditions)
trials = data.TrialHandler(trialList=trial_list, nReps=N_REPS, method='random')
experiment.addLoop(trials)
"""
start trial loop
"""
# initial interval
frame_n = 0
while frame_n < ITI_DURATION:
frame_n += 1
win.flip()
# start trial
draw_lines(lines, trials)
luminance=0)
flash.autoDraw = True
#factorially combine the options into a dict. required by psychopy
factors = {
"ISI": ISI,
"flash_dur": flash_dur,
"luminance": luminance,
"frequency": frequency, #waveFreq
"duration": duration, #pulseDur
"sound_levels": sound_levels, #waveAmp
"auditory_on": auditory_on
}
#make a randomized stimulus list
stimList = data.createFactorialTrialList(factors)
randNs = random.sample(
range(0,
len(stimList) - 1),
numTrials) # generate random numbers equal to the # of trials
trials_subset = [
None
] * numTrials # to get a subset of values to use for the inputs from the overall massive stimlist
for i in range(0, numTrials):
randNum = randNs[i]
trials_subset[i] = stimList[randNum]
#making experimentHandler and trials
exp = data.ExperimentHandler(name='testExp',
if infoBox.OK: # this will be True (user hit OK) or False (cancelled)
metaData.update(metaData2) # add new metaData to existing metaData
else:
sys.exit("User cancelled gui.")
# set up counterbalancing
if metaData['override automated counter-balancing'] == True: # user has chosen to override automated counter-balancing
cb = {'sequenceOrder': ['X', 'Y'], 'testOrder': ['A', 'B']} # set up info for infoBox gui
infoBox = gui.DlgFromDict(dictionary=cb,
title='Choose counter-balancing parameters') # display gui to get info from user
sequenceOrder = cb['sequenceOrder']
testOrder = cb['testOrder']
if not infoBox.OK: # if user hit cancel
quitExp() # quit
else: # otherwise use automated counter-balancing
trialList = data.createFactorialTrialList(
{'sequenceOrder': ['X', 'Y'], 'testOrder': ['A', 'B']}) # get counter balancing conditions
# assign to counter-balanced conditions [the 4 unique arrangements are assigned cyclically]
sequenceOrder = trialList[int(metaData['participant']) % 4]['sequenceOrder']
testOrder = trialList[int(metaData['participant']) % 4]['testOrder']
metaData.update({'expName': expName, 'date': date, 'session': session, 'sequenceOrder': sequenceOrder,
'testOrder': testOrder}) # record the counterbalancing info in the metaData
# Customise sequences for this participant
if sequenceOrder == 'X':
oldSequence = '41324'
newSequence = '23142'
elif sequenceOrder == 'Y':
oldSequence = '23142'
newSequence = '41324'
originPath=None,
savePickle=False,
saveWideText=True,
autoLog=False,
dataFileName=filename)
temp_file = codecs.open(filename + '_temp.csv', 'w+', 'utf-8')
# number of repetitions for all conditions
nReps = 3
condsList = data.createFactorialTrialList({
'ctpd_prime': [-80, -60, -40, -30, -20, -10, 0, 10, 20, 30, 40, 60, 80],
'dsd_prime': [10],
'dsd_test': [5],
'dtype_prime': ['norm'],
'dtype_test': ['norm'],
'streak_length_prime': [2, 3, 5, 6],
'prime_set_size': [36],
'test_set_size': [36]
})
trialsList = []
totBlockN = 0
curTrialN = 0
coordinates_by_trial = {}
oris_by_trial = {}
colors_by_trial = {}
set_sizes = {}
totalTrials = 0
###############################################################################
imList_changed[change_pos].setImage("%s_%s/%03d.png" % tuple(parMatrix[change_pos][0:3]))
# print imList[change_pos]._imName
screenshot_changed = visual.BufferImageStim(win, stim=imList)
return (screenshot, screenshot_changed, parMatrix, imList, imList_changed)
probe = visual.Circle(win, 0.2, 96, units="deg", fillColor="#a8a8a8", lineWidth=0)
maskTime = 5
showTime = 15
fpTime = 1 # in second
change_types = ["shape"]
trainingList = data.createFactorialTrialList(factors={"change_type": change_types, "change_pos": range(nImages)})
shuffle(trainingList)
fullPosList = range(nImages)
shuffle(fullPosList)
# mainTrials=[]
ecc_list = [x - 0.5 for x in range(1, 8)]
angle_list = range(0, 360, 30)
# for i in change_types:
# print i
# print sample(range(nImages),5)
factorsList = data.createFactorialTrialList(
factors={
iFBT = 0.500 # immediate feedback interval
bFBT = 15. # break interval between blocks
# parameters of the visual stimuli
size = 50 # size of the stimuli (in pixels)
XY = [[-size/2,0],[0,size/2],[0,size/4],[size/2,size/4],[size/2,-size/4],[0,-size/4],[0,-size/2]] # basic coordinates of a leftwards-pointing arrow
CLRS = ['green', 'red'] # colors of the go and stop arrows, respectively
# define response keys (in this case: the arrow keys and esc key)
keys = ('left', 'right', 'escape')
### DO NOT CHANGE THE DESIGN (UNLESS YOU REALLY KNOW WHAT YOU ARE DOING)
# Specify the design (2 by 4 in this instance)
go = [0,1] # left (0) vs. right (1) arrows
signal = [0,0,0,1] # 1/4 of the trials are signal (1) trials
design = data.createFactorialTrialList({"goStim": go, "signal": signal})
#####################################################################
### Initialise experiment
#####################################################################
# By default, Psychopy changes the working directory based on the
# location of the .py file. The output folder will be located here.
# The output folder will be created if it does not exist yet.
wd = os.getcwd()
data_path = wd + "/output"
if not os.path.isdir('output'):
os.mkdir('output')
# Get some user info and use this to determine the file name
info = {"Participant": "", "Session": "", "X extra: Gender": "", "X extra: Age": ""}
already_exists = True
imList_changed[change_pos].setImage('%s_%s/%03d.png' % tuple(parMatrix[change_pos][0:3]))
#print imList[change_pos]._imName
screenshot_changed = visual.BufferImageStim(win, stim=imList)
return (screenshot, screenshot_changed,parMatrix, imList, imList_changed)
probe = visual.Circle(win, 0.2, 96, units='deg', fillColor='#a8a8a8', lineWidth=0)
maskTime=5
showTime=15
fpTime=1 #in second
change_types=['color','rot','shape']
trainingList = data.createFactorialTrialList(factors={'change_type': change_types,'change_pos':range(nImages)})
shuffle(trainingList)
fullPosList=range(nImages)
shuffle(fullPosList)
mainTrials=[]
ecc_list=[x - .5 for x in range(1, 8)]
angle_list=range(0,360,30)
#for i in change_types:
# print i
# print sample(range(nImages),5)
factorsList = data.createFactorialTrialList(factors={'change_type': change_types, 'trial_probe_ecc':ecc_list, 'trial_probe_angle':angle_list})
#print 'factorsList: %g'%len(factorsList)
def randomize(c_b, block):
# Variables and their Parameters
bins = ['Bin_1', 'Bin_2', 'Bin_3', 'Bin_4', 'Bin_5']
scaleType = ['Prob', 'Det']
congruency = ['L_Con', 'R_Con', 'L_Incon', 'R_Incon']
# Inserting the parameters into the block design
if block != 0:
if block % 2 == c_b:
Design = data.createFactorialTrialList({
"Bins": bins,
"Congruency": congruency
})
dataFrame = pandas.DataFrame.from_dict(Design)
dataFrame["ScaleType"] = "Prob"
else:
Design = data.createFactorialTrialList({
"Bins": bins,
"Congruency": congruency
})
dataFrame = pandas.DataFrame.from_dict(Design)
dataFrame["ScaleType"] = "Det"
else:
Design = data.createFactorialTrialList({
"ScaleType": scaleType,
"Bins": bins,
"Congruency": congruency
})
dataFrame = pandas.DataFrame.from_dict(Design)
# Adding in index tags and correct response column via DataFrame()
##adding in index tags
dataFrame["Tag"] = range(len(dataFrame))
##adding in correct responses
dataFrame['CorAns'] = dataFrame["Congruency"]
dataFrame['CorAns'].replace(['L_Con', 'R_Con', 'L_Incon', 'R_Incon'],
[left_key, right_key, left_key, right_key],
inplace=True)
##adding in reward magnitude
dataFrame['BinSize'] = dataFrame['Bins']
dataFrame['BinSize'].replace(['Bin_1', 'Bin_2', 'Bin_3', 'Bin_4', 'Bin_5'],
[0, 20, 50, 80, 100],
inplace=True)
#Convert dataframe back to list of dictionaries
trial_list = pandas.DataFrame.to_dict(dataFrame, orient="records")
# TESTRUN
# If the experimenter wishes to test-run option, reduced amount of trials are given
if int(info["testrun"]) == 1:
## including a practice block
if block == 0:
trials = data.TrialHandler(trialList=random.sample(
trial_list, n_practice_trials),
nReps=1,
method="random")
else:
trials = data.TrialHandler(trialList=random.sample(
trial_list, n_test_trials),
nReps=1,
method='random')
thisExp.addLoop(trials)
else:
if block == 0:
trials = data.TrialHandler(trialList=random.sample(
trial_list, n_practice_trials),
nReps=1,
method='random')
else:
trials = data.TrialHandler(trialList=trial_list,
nReps=n_trial_rep,
method='random')
thisExp.addLoop(trials)
return trials
opacities = [0.2, 1.0]
images = ["cat.jpg", "lion.jpg", "fish.gif"]
reps = 2
"""
1. PSYCHOPY VERSION
% No ability to save on trial-to-trial basis! (reason enough to discard)
+ Short and simple
i Suitable for simple factorial designs but not much more
% Messy output: adds extra columns ("ran" and "order") and floats integers (1 becomes 1.0)
"""
from psychopy import data
conditions = data.createFactorialTrialList({"opacity": opacities, "image": images}) # tuple list of unique trials
trials = data.TrialHandler(conditions, reps, method="fullRandom") # as a TrialHandler object with repetitions of trials
for trial in trials:
trials.addData("answer", random.choice(["left", "right"]))
trials.addData("score", random.randint(0, 1))
trials.saveAsWideText(
"result_trialHandler (" + time.strftime("%Y-%m-%d %H-%M-%S", time.localtime()) + ").csv", appendFile=False
)
"""
2. GENERIC PYTHON VERSION
+ good control
+ pure core python (occam's razor)
