def startExperiment():
try:
expInfo, trialInfo, outputfile, monitorInfo = setupExperiment()
win = open_window(monitorInfo)
show_instructions(win, "Press spacebar to start experiment")
# We instanciate 4 staircases, we must decide the starting values for each of them
# The speed value is the speedValue in the for loop of staircases and is measured in [cm/s]
# The staircase will terminate when nTrials AND nReversals have been exceeded.
# If stepSizes was an array and has been exceeded before nTrials is
# exceeded then the staircase will continue to reverse
if (monitorInfo['RunSpeedTest']):
from common.draw_test_square import draw_test_square
draw_test_square(win)
nTrialCounter = {}
responses = {}
if expInfo['Block'] == 'Unilateral':
nTrialCounter['Unilateral-Left'] = 0
nTrialCounter['Unilateral-Right'] = 0
responses['Unilateral-Left'] = []
responses['Unilateral-Right'] = []
if expInfo['Block'] == 'Bilateral':
nTrialCounter['Bilateral-Left'] = 0
nTrialCounter['Bilateral-Right'] = 0
responses['Bilateral-Left'] = []
responses['Bilateral-Right'] = []
if expInfo['Block'] == 'Uni+Bi':
nTrialCounter['Unilateral-Left'] = 0
nTrialCounter['Unilateral-Right'] = 0
nTrialCounter['Bilateral-Left'] = 0
nTrialCounter['Bilateral-Right'] = 0
responses['Unilateral-Left'] = []
responses['Unilateral-Right'] = []
responses['Bilateral-Left'] = []
responses['Bilateral-Right'] = []
maxTrials = trialInfo['TotalTrialsPerCondition'] * 2
if (expInfo['Block'] == 'Uni+Bi'):
maxTrials *= 2
output = open(outputfile + "_fixed_tracking.txt", 'w')
output.write('Trial\tNTrial\tTrial Condition\tResponse\tStartTime\n')
# Generate a list of balanced random conditions
allConditions = []
for n in range(0, maxTrials):
condition = {}
if n % 2:
condition['Side'] = 'Left'
else:
condition['Side'] = 'Right'
if expInfo['Block'] == 'Unilateral':
condition['label'] = 'Unilateral' + '-' + condition['Side']
if expInfo['Block'] == 'Bilateral':
condition['label'] = 'Bilateral' + '-' + condition['Side']
if expInfo['Block'] == 'Uni+Bi':
if n % 4 == 0:
condition['label'] = 'Unilateral' + '-Right'
if n % 4 == 1:
condition['label'] = 'Unilateral' + '-Left'
if n % 4 == 2:
condition['label'] = 'Bilateral' + '-Right'
if n % 4 == 3:
condition['label'] = 'Bilateral' + '-Left'
allConditions.append(condition)
# Prepare some trials with catch trials
import copy
for i in range(0, len(allConditions)):
thisCondition = allConditions[i]
if np.random.rand() < 0.5 and (thisCondition['label'].split('-')[0]
== 'Bilateral'):
thisCondition = copy.deepcopy(allConditions[i])
thisCondition['isCatchTrial'] = True
allConditions.append(thisCondition)
if (trialInfo['Selection'] == 'random'):
from random import shuffle
shuffle(allConditions)
for thisCondition in allConditions:
print thisCondition
n = 0
expClock = core.Clock()
trigger_received = False # Indicates whether it received the '=' symbol from the fMRI (or from the keyboard)
while not trigger_received:
win.flip()
triggerKeys = event.getKeys(keyList=['equal'])
if len(triggerKeys) > 0: #'=' in triggerKeys: ##
trigger_received = True
expClock.reset()
if 'escape' in triggerKeys:
win.close()
core.quit()
event.clearEvents(eventType='keyboard')
for thisCondition in allConditions:
t0 = expClock.getTime()
if expInfo['Block'] == 'Unilateral':
if nTrialCounter[
'Unilateral-Left'] > maxTrials and nTrialCounter[
'Unilateral-Right'] > maxTrials:
break
if expInfo['Block'] == 'Bilateral':
if nTrialCounter[
'Bilateral-Left'] > maxTrials and nTrialCounter[
'Bilateral-Right'] > maxTrials:
break
if expInfo['Block'] == 'Uni+Bi':
if nTrialCounter[
'Bilateral-Left'] > maxTrials and nTrialCounter[
'Bilateral-Right'] > maxTrials:
break
# Extract the speed value from the input dialog for staircase for the
# current condition
speedValue = trialInfo[thisCondition['label'] + ' speed']
if 'isCatchTrial' in thisCondition:
# print "Catch trial, sarebbe ", thisCondition['Side'], "invece
# fa dall'altra"
if thisCondition['Side'] == 'Left':
thisCondition['Side'] = 'Right'
else:
thisCondition['Side'] = 'Left'
trackingTrial(win, expInfo, speedValue, thisCondition,
expInfo['SimulationMode'])
else:
# print thisCondition, speedValue
thisResp = trackingTrial(win, expInfo, speedValue,
thisCondition,
expInfo['SimulationMode'])
responses[thisCondition['label']].append(thisResp)
output.write(
str(n) + "\t" +
str(nTrialCounter[thisCondition['label']]) + "\t" +
thisCondition['label'] + "\t" + str(int(thisResp)) + "\t" +
str(t0) + "\n")
nTrialCounter[thisCondition['label']] += 1
n += 1
experiment_finished(win)
except:
raise
win.close()
else:
output.write('\n')
# Compute accuracy, we keep just the trials from 0 to maxTrials
accuracies = {}
for k in responses.keys():
accuracies[k] = float(sum(responses[k][0:maxTrials])) / len(
responses[k][0:maxTrials])
output.write('Accuracy ' + k + ' = ' + str(accuracies[k]) + "\n")
def startExperiment():
try:
expInfo, stairInfo, outputfile, monitorInfo = setupExperiment()
win = open_window(monitorInfo)
show_instructions(win, "Press spacebar to start experiment, doing " +
str(expInfo['TrainingTrials']) + " training trials")
# We instanciate 4 staircases, we must decide the starting values for each of them
# The speed value is the speedValue in the for loop of staircases and is measured in [cm/s]
# The staircase will terminate when nTrials AND nReversals have been exceeded.
# If stepSizes was an array and has been exceeded before nTrials is
# exceeded then the staircase will continue to reverse
# Convert the string of steps to a list of floats to feed to every
# staircase
stepSizes = [float(x)
for x in stairInfo['StepSizes'].replace('[', '').replace(']', '').split(',')]
# Convert the initial velocities of the staircases
SpeedBiRight = [float(x)
for x in stairInfo['SpeedBiRight'].replace('[', '').replace(']', '').split(',')]
SpeedBiLeft = [float(x)
for x in stairInfo['SpeedBiLeft'].replace('[', '').replace(']', '').split(',')]
SpeedUniRight = [float(x)
for x in stairInfo['SpeedUniRight'].replace('[', '').replace(']', '').split(',')]
SpeedUniLeft = [float(x)
for x in stairInfo['SpeedUniLeft'].replace('[', '').replace(']', '').split(',')]
conditionsBilateral = [
{'label': 'Bilateral-Right_0',
'Side': 'Right',
'startVal': SpeedBiRight[0],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpBiRight'],
'nDown':stairInfo['nDownBiRight'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Bilateral-Left_0',
'Side': 'Left',
'startVal': SpeedBiLeft[0],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpBiLeft'],
'nDown':stairInfo['nDownBiLeft'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Bilateral-Right_1',
'Side': 'Right',
'startVal': SpeedBiRight[1],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpBiRight'],
'nDown':stairInfo['nDownBiRight'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Bilateral-Left_1',
'Side': 'Left',
'startVal': SpeedBiLeft[1],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpBiLeft'],
'nDown':stairInfo['nDownBiLeft'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
}
]
########## CONDITIONS UNILATERAL ##########
conditionsUnilateral = [
{'label': 'Unilateral-Right_0',
'Side': 'Right',
'startVal': SpeedUniRight[0],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpUniRight'],
'nDown':stairInfo['nDownUniRight'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Unilateral-Left_0',
'Side': 'Left',
'startVal': SpeedUniLeft[0],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpUniLeft'],
'nDown':stairInfo['nDownUniLeft'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Unilateral-Right_1',
'Side': 'Right',
'startVal': SpeedUniRight[1],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpUniRight'],
'nDown':stairInfo['nDownUniRight'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
},
{'label': 'Unilateral-Left_1',
'Side': 'Left',
'startVal': SpeedUniLeft[1],
'method':'2AFC',
'stepType':stairInfo['StepType'],
'stepSizes':stepSizes,
'nUp':stairInfo['nUpUniLeft'],
'nDown':stairInfo['nDownUniLeft'],
'nTrials':stairInfo['MinTrials'],
'nReversals':stairInfo['MinReversals'],
'minVal':0
}
]
conditions = None
if expInfo['Block'] == 'Unilateral':
conditions = conditionsUnilateral
if expInfo['Block'] == 'Bilateral':
conditions = conditionsBilateral
if expInfo['Block'] == 'Uni+Bi':
conditions = conditionsUnilateral + conditionsBilateral
from psychopy import data
stairs = data.MultiStairHandler(
conditions=conditions, nTrials=2, method=stairInfo['Selection'])
if (monitorInfo['RunSpeedTest']):
from common.draw_test_square import draw_test_square
draw_test_square(win)
# Do some training trials with no variation in speed
for i in range(0, int(expInfo['TrainingTrials'])):
speedValue = 1.25 * (i + 1)
trackingTrial(win, expInfo, speedValue, conditions[randrange(0, 2)])
show_instructions(
win, "Finished training trials, press spacebar to begin")
velocityConditions = {}
velocityConditions['Left'] = []
velocityConditions['Right'] = []
# Start of the trial loop
# We save the last speed used for every side of stimulus presentation
nTrial = 0
import copy
# Has to initialize the first trial
speedValue, thisCondition = stairs.next()
nCatchTrials = 0
nValidTrials = 0
dfrows = [] # Collects all trials included catch trials
print thisCondition
while True: # Using while True is the correct way to insert catch trials
velocityConditions[thisCondition['Side']].append(speedValue)
# print thisCondition['Side'], speedValue
# Catch trial presentato al 25% di probabilita
if np.random.rand() < 0.25 and nTrial > 2:
nCatchTrials += 1
catchCondition = copy.deepcopy(thisCondition)
if thisCondition['Side'] == 'Left':
catchCondition['Side'] = 'Left'
catchSpeedValue = velocityConditions['Right'][-1]
else:
catchCondition['Side'] = 'Right'
catchSpeedValue = velocityConditions['Left'][-1]
catchResp = trackingTrial(win, expInfo, catchSpeedValue, catchCondition, simulation=expInfo['SimulationMode'], isCatchTrial=0) #doesn't print message
dfrows.append({'label':catchCondition['label'], 'Side':catchCondition['Side'], 'CatchCondition':1, 'Speed':speedValue, 'Response':int(not catchResp)})
else:
thisResp = trackingTrial(win, expInfo, speedValue, thisCondition, simulation=expInfo['SimulationMode'],isCatchTrial=0)
dfrows.append({'label':thisCondition['label'], 'Side':thisCondition['Side'], 'CatchCondition':0, 'Speed':speedValue, 'Response':int(not thisResp)})
if thisResp is not None:
stairs.addResponse(int(not thisResp))
nValidTrials += 1
try:
speedValue, thisCondition = stairs.next()
except StopIteration:
break
else:
raise
# Increase the trial counter and save the temporary results
nTrial = nTrial + 1
stairs.saveAsText(outputfile)
stairs.saveAsPickle(outputfile)
df = pd.DataFrame(dfrows) # this holds all trials in raw mode
stairs.saveAsExcel(outputfile)
experiment_finished(win)
df.to_excel(outputfile+'_trials_summary.xlsx')
except:
# If the experiments stops before a default response is inserted
stairs.addResponse(0)
stairs.saveAsExcel(outputfile)
df.to_excel(outputfile+'_trials_summary.xlsx')
win.close()
raise
def startExperiment():
try:
expInfo, stairInfo, outputfile, monitorInfo = setupExperiment()
win = open_window(monitorInfo)
show_instructions(
win, "Press spacebar to start experiment, doing " +
str(expInfo['TrainingTrials']) + " training trials")
# We instanciate 4 staircases, we must decide the starting values for each of them
# The speed value is the speedValue in the for loop of staircases and is measured in [cm/s]
# The staircase will terminate when nTrials AND nReversals have been exceeded.
# If stepSizes was an array and has been exceeded before nTrials is
# exceeded then the staircase will continue to reverse
# Convert the string of steps to a list of floats to feed to every
# staircase
stepSizes = [
float(x) for x in stairInfo['StepSizes'].replace('[', '').replace(
']', '').split(',')
]
# Convert the initial velocities of the staircases
SpeedBiRight = [
float(x)
for x in stairInfo['SpeedBiRight'].replace('[', '').replace(
']', '').split(',')
]
SpeedBiLeft = [
float(x)
for x in stairInfo['SpeedBiLeft'].replace('[', '').replace(
']', '').split(',')
]
SpeedUniRight = [
float(x)
for x in stairInfo['SpeedUniRight'].replace('[', '').replace(
']', '').split(',')
]
SpeedUniLeft = [
float(x)
for x in stairInfo['SpeedUniLeft'].replace('[', '').replace(
']', '').split(',')
]
conditionsBilateral = [{
'label': 'Bilateral-Right_0',
'Side': 'Right',
'startVal': SpeedBiRight[0],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpBiRight'],
'nDown': stairInfo['nDownBiRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Bilateral-Left_0',
'Side': 'Left',
'startVal': SpeedBiLeft[0],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpBiLeft'],
'nDown': stairInfo['nDownBiLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Bilateral-Right_1',
'Side': 'Right',
'startVal': SpeedBiRight[1],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpBiRight'],
'nDown': stairInfo['nDownBiRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Bilateral-Left_1',
'Side': 'Left',
'startVal': SpeedBiLeft[1],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpBiLeft'],
'nDown': stairInfo['nDownBiLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}]
########## CONDITIONS UNILATERAL ##########
conditionsUnilateral = [{
'label': 'Unilateral-Right_0',
'Side': 'Right',
'startVal': SpeedUniRight[0],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpUniRight'],
'nDown': stairInfo['nDownUniRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Unilateral-Left_0',
'Side': 'Left',
'startVal': SpeedUniLeft[0],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpUniLeft'],
'nDown': stairInfo['nDownUniLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Unilateral-Right_1',
'Side': 'Right',
'startVal': SpeedUniRight[1],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpUniRight'],
'nDown': stairInfo['nDownUniRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}, {
'label': 'Unilateral-Left_1',
'Side': 'Left',
'startVal': SpeedUniLeft[1],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpUniLeft'],
'nDown': stairInfo['nDownUniLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': 0
}]
conditions = None
if expInfo['Block'] == 'Unilateral':
conditions = conditionsUnilateral
if expInfo['Block'] == 'Bilateral':
conditions = conditionsBilateral
if expInfo['Block'] == 'Uni+Bi':
conditions = conditionsUnilateral + conditionsBilateral
from psychopy import data
stairs = data.MultiStairHandler(conditions=conditions,
nTrials=2,
method=stairInfo['Selection'])
if (monitorInfo['RunSpeedTest']):
from common.draw_test_square import draw_test_square
draw_test_square(win)
# Do some training trials with no variation in speed
for i in range(0, int(expInfo['TrainingTrials'])):
speedValue = 1.25 * (i + 1)
trackingTrial(win, expInfo, speedValue, conditions[randrange(0,
2)])
show_instructions(win,
"Finished training trials, press spacebar to begin")
velocityConditions = {}
velocityConditions['Left'] = []
velocityConditions['Right'] = []
# Start of the trial loop
# We save the last speed used for every side of stimulus presentation
nTrial = 0
import copy
for speedValue, thisCondition in stairs:
velocityConditions[thisCondition['Side']].append(speedValue)
# print thisCondition['Side'], speedValue
# Catch trial presentato al 25% di probabilita
if np.random.rand() < 0.25 and nTrial > 2:
catchCondition = copy.deepcopy(thisCondition)
if thisCondition['Side'] == 'Left':
catchCondition['Side'] = 'Left'
# print "Using speed value", velocityConditions['Right'][-1], " instead of ", speedValue
# get the last speed used element
speedValue = velocityConditions['Right'][-1]
else:
catchCondition['Side'] = 'Right'
# print "Using speed value", velocityConditions['Left'][-1], " instead of ", speedValue
# get the last speed used element
speedValue = velocityConditions['Left'][-1]
# print "0.25 catch trial, showing ", catchCondition['Side'], "
# instead of ", thisCondition['Side']
trackingTrial(win, expInfo, speedValue, catchCondition,
expInfo['SimulationMode'])
# Catch trial lanciato quando una delle due staircase finita
# e che randomizza il lato di presentazione
elif np.random.rand() < 0.5:
catchCondition = copy.deepcopy(thisCondition)
if thisCondition['Side'] == 'Left':
catchCondition['Side'] = 'Right'
else:
catchCondition['Side'] = 'Left'
# print "0.5 catch trial, showing ", catchCondition['Side'], "
# instead of ", thisCondition['Side']
trackingTrial(win, expInfo, speedValue, catchCondition,
expInfo['SimulationMode'])
else:
thisResp = trackingTrial(win, expInfo, speedValue,
thisCondition,
expInfo['SimulationMode'])
if thisResp is not None:
# print speedValue, thisCondition, nTrial
stairs.addData(not thisResp)
else:
raise
nTrial = nTrial + 1
stairs.saveAsText(outputfile)
# Finally save the results of the experiment
stairs.saveAsText(outputfile)
# stairs.saveAsExcel(outputfile)
stairs.saveAsPickle(outputfile)
experiment_finished(win)
except:
stairs.saveAsText(outputfile)
# stairs.saveAsExcel(outputfile)
stairs.saveAsPickle(outputfile)
win.close()
raise
def startExperiment():
try:
expInfo, stairInfo, outputfile, monitorInfo = setupExperiment()
win = open_window(monitorInfo, measureFPS=False)
win.measuredFPS = 59.95
show_instructions(
win, "Press spacebar to start experiment, doing " +
str(expInfo['TrainingTrials']) + " training trials")
# Convert the string of steps to a list of floats to feed to every
# staircase
stepSizes = [
float(x) for x in stairInfo['StepSizes'].replace('[', '').replace(
']', '').split(',')
]
expConditions = [{
'label': 'Right',
'startVal': stairInfo['ContrastRight'],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpRight'],
'nDown': stairInfo['nDownRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': min(stepSizes),
'maxVal': 1
}, {
'label': 'Left',
'startVal': stairInfo['ContrastLeft'],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpLeft'],
'nDown': stairInfo['nDownLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': min(stepSizes),
'maxVal': 1
}]
print "Minimum value for staircase = ", min(stepSizes)
from psychopy import data
stairs = data.MultiStairHandler(conditions=expConditions,
nTrials=1,
method=stairInfo['Selection'])
if (monitorInfo['RunSpeedTest']):
from common.draw_test_square import draw_test_square
draw_test_square(win)
# Do some training trials with no variation in speed
for i in np.linspace(0.0, 2.0, expInfo['TrainingTrials']):
contrastTrial(win,
expInfo,
contrastValue=i,
side='Left',
useSameStimuli=randrange(0, 2))
show_instructions(win,
"Finished training trials, press spacebar to begin")
for contrast, thisCondition in stairs:
sameStimuli = randrange(0, 100) < 25 # To present 4 equal stimuli
thisResp = contrastTrial(win,
expInfo,
contrastValue=contrast,
side=thisCondition['label'],
useSameStimuli=sameStimuli)
if thisResp is not None:
stairs.addData(not thisResp)
else:
print "skipped"
# save data as multiple formats for every trial
stairs.saveAsText(outputfile)
stairs.saveAsText(outputfile)
stairs.saveAsPickle(outputfile)
stairs.saveAsExcel(outputfile)
experiment_finished(win)
except:
win.close()
raise
def startExperiment():
"""
Begin the experiment
1. Set experimental parameters
2. Open the window and take measurements of the refresh rate
3. Start some training trials if needed
4. Start the interleaved staircase experiment
5. Save the result
"""
try:
expInfo, stairInfo, outputfile, monitorInfo = setupExperiment()
except:
raise
try:
win = open_window(monitorInfo)
show_instructions(
win, "Press spacebar to start experiment, doing " +
str(expInfo['TrainingTrials']) + " training trials")
# Convert the string of steps to a list of floats to feed to every
# staircase
stepSizes = [
float(x) for x in stairInfo['StepSizes'].replace('[', '').replace(
']', '').split(',')
]
expConditions = [{
'label': 'Right',
'startVal': stairInfo['FlickerFreqRight'],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpRight'],
'nDown': stairInfo['nDownRight'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': min(stepSizes)
}, {
'label': 'Left',
'startVal': stairInfo['FlickerFreqLeft'],
'method': '2AFC',
'stepType': stairInfo['StepType'],
'stepSizes': stepSizes,
'nUp': stairInfo['nUpLeft'],
'nDown': stairInfo['nDownLeft'],
'nTrials': stairInfo['MinTrials'],
'nReversals': stairInfo['MinReversals'],
'minVal': min(stepSizes)
}]
from psychopy import data
stairs = data.MultiStairHandler(conditions=expConditions,
nTrials=1,
method=stairInfo['Selection'])
if (monitorInfo['RunSpeedTest']):
from common.draw_test_square import draw_test_square
draw_test_square(win)
# Do some training trials with no variation in speed
for i in range(0, int(expInfo['TrainingTrials'])):
flickerTrial(win,
expInfo,
flickerFreq=1,
side='Left',
useOddBall=True)
show_instructions(win,
"Finished training trials, press spacebar to begin")
for flickerFreq, thisCondition in stairs:
thisResp = flickerTrial(win,
expInfo,
flickerFreq,
side=thisCondition['label'],
useOddBall=True)
if thisResp is not None:
stairs.addData(not thisResp)
stairs.saveAsText(outputfile)
stairs.saveAsText(outputfile)
stairs.saveAsPickle(outputfile)
stairs.saveAsExcel(outputfile)
experiment_finished(win)
except:
stairs.saveAsText(outputfile)
stairs.saveAsPickle(outputfile)
stairs.saveAsExcel(outputfile)
win.close()
raise