% Sensitive to identical keys in all trial-dicts
"""
# Generate trial list
trials = [{
'opacity': opacity,
'image': image,
'answer': '',
'score': ''
} for opacity in opacities for image in images for rep in range(reps)]
trials = random.sample(trials, len(trials))
for i, trial in enumerate(trials):
trial['no'] = i + 1 # randomize and add trial numbers
# Update trials and save
writer = csvWriter('results_manual', '', trials[0])
for trial in trials:
trial['answer'] = random.choice(['left', 'right'])
trial['score'] = random.randint(0, 1)
writer.write(trial)
"""
3. PANDAS VERSION
+ Great for analysis of cross-trial data
% Too complex. Rather do the manual version and convert trials to DataFrame when you need to process data
"""
# Generate trial list
trials = [{
'opacity': opacity,
'image': image,
'answer': '',
def rate_media():
full_txt = "How many hours per week do you spend following the news?"
msg = visual.TextStim(win, text=full_txt, pos=(0, 0.1))
media_rating.reset()
while media_rating.noResponse:
media_rating.draw()
msg.draw()
win.flip()
return (media_rating.getRating())
writer = ppc.csvWriter(ID,
saveFolder="data",
headerTrial=person_stories_trials[1])
####unite dataframes to df
for i in intro:
show_info(i)
if k == 0:
person_stories_function()
topic_function()
else:
topic_function()
person_stories_function()
news_rating = {
'ID': ID,
REPETITIONS = 4 # 3 reps* 4 different setsizes * 2 (presence) * 4 (conjunction) = 128 trials
condition = 'visual_search' #Just a variable. If the script can run several exp. then this can be called in GUI. Not relevant here.
""" OUTPUT """
#KEYS
KEYS_present = ['t'] #T is response key for target present.
KEYS_absent = [
'y', 'r'
] #Y and R (present next to T) are response key for target absent. One can be used for righthanders, the other for lefthanders
#response keys for quitting and starting
KEYS_QUIT = ['escape'] # Keys that quits the experiment
KEYS_start = ['space'] # press space to start
# Prepare a csv log-file using the ppc script
writer = ppc.csvWriter(
str(V['ID']), saveFolder=SAVE_FOLDER
) # writer.write(trial) will write individual trials with low latency
""" FUNCTIONS FOR EXPERIMENTAL LOOP"""
#Generate a trial list with different combinations of the experimental factors
def make_trial_list(condition):
# Factorial design
trial_list = []
for rowsize in rowsizes: # number of letters in stim rows
for conjunct in conjunction:
for present in presence:
for rep in range(REPETITIONS):
# Add a dictionary for every trial
trial_list += [{
'ID': V['ID'],
# Create psychopy window
my_monitor = monitors.Monitor(
'testMonitor'
) # Create monitor object from the variables above. This is needed to control size of stimuli in degrees.
win = visual.Window(
monitor=my_monitor,
size=MON_SIZE,
color=[-1, -1, -1],
units='deg',
fullscr=True,
allowGUI=False
) # Initiate psychopy Window as the object "win", using the myMon object from last line. Use degree as units!
# Stuff
clock = core.Clock()
writer = ppc.csvWriter(str(V['subject']), saveFolder=SAVE_FOLDER)
# create base stimli
square1 = visual.Rect(win, width=2, height=2, pos=([-5, 0]))
square2 = visual.Rect(win, width=2, height=2, pos=([0, 0]))
square3 = visual.Rect(win, width=2, height=2, pos=([5, 0]))
fixation = visual.TextStim(win, text='+', pos=([0, .05]),
height=1) # check sizes, should be 1x1 degrees
# Responses and keys
KEYS_QUIT = ['q'] # Keys that quits the experiment
RESPONSES = {'left': 'lctrl', 'right': 'rctrl'}
RESP_POS = {'lctrl': 'X', 'rctrl': 'O'}
# Create stimuli
"""
2. GENERIC PYTHON VERSION
+ good control
+ pure core python (occam's razor)
% Sensitive to identical keys in all trial-dicts
"""
# Generate trial list
trials = [{'opacity': opacity, 'image': image, 'answer':'', 'score':''}
for opacity in opacities for image in images for rep in range(reps)]
trials = random.sample(trials, len(trials))
for i, trial in enumerate(trials):
trial['no'] = i + 1 # randomize and add trial numbers
# Update trials and save
writer = csvWriter('results_manual', '', trials[0])
for trial in trials:
trial['answer'] = random.choice(['left', 'right'])
trial['score'] = random.randint(0,1)
writer.write(trial)
"""
3. PANDAS VERSION
+ Great for analysis of cross-trial data
% Too complex. Rather do the manual version and convert trials to DataFrame when you need to process data
"""
# Generate trial list
trials = [{'opacity': opacity, 'image': image, 'answer':'', 'score':''}
for opacity in opacities for image in images for rep in range(reps)]
import ppc
print 'the physical diameter of the gabor patch should be', ppc.deg2cm(GABOR_SIZE, MON_DISTANCE), 'cm'
print 'the physical size of the fixation cross should be', ppc.deg2cm(FIX_HEIGHT, MON_DISTANCE), 'cm'
from psychopy import core, visual, gui, monitors, sound, event
import random
# Intro-dialogue. Get subject-id and other variables.
# Save input variables in "V" dictionary (V for "variables")
V = {'subject':'', 'condition': ['trueFix', 'falseFix'], 'age':'', 'gender':['male', 'female']}
if not gui.DlgFromDict(V, order=['subject', 'age', 'gender']).OK:
core.quit()
# Stuff
clock = core.Clock() # A clock wich will be used throughout the experiment to time events on a trial-per-trial basis (stimuli and reaction times).
writer = ppc.csvWriter(str(V['subject']), saveFolder=SAVE_FOLDER) # writer.write(trial) will write individual trials with low latency
# Create psychopy window
my_monitor = monitors.Monitor('testMonitor', width=MON_WIDTH, distance=MON_DISTANCE) # Create monitor object from the variables above. This is needed to control size of stimuli in degrees.
my_monitor.setSizePix(MON_SIZE)
win = visual.Window(monitor=my_monitor, units='deg', fullscr=True, allowGUI=False, color='black') # Initiate psychopy Window as the object "win", using the myMon object from last line. Use degree as units!
# Stimuli.
stim_gabor = visual.GratingStim(win, mask='gauss', sf=GABOR_SF, size=GABOR_SIZE) # A gabor patch. Again, units are inherited.
stim_fix = visual.TextStim(win, '+', height=FIX_HEIGHT) # Fixation cross is just the character "+". Units are inherited from Window when not explicitly specified.
stim_text = visual.TextStim(win, pos=MESSAGE_POS, height=MESSAGE_HEIGHT, wrapWidth=999) # Message / question stimulus. Will be used to display instructions and questions.
sound_success = sound.Sound('C', secs=0.1, octave=6) # Obs, ppc.Sound() is much more accurate, but only works on windows.
sound_fail = sound.Sound('C', secs=0.4, octave=4)
"""
% Sensitive to identical keys in all trial-dicts
"""
# Generate trial list
trials = [
{"opacity": opacity, "image": image, "answer": "", "score": ""}
for opacity in opacities
for image in images
for rep in range(reps)
]
trials = random.sample(trials, len(trials))
for i, trial in enumerate(trials):
trial["no"] = i + 1 # randomize and add trial numbers
# Update trials and save
writer = csvWriter("results_manual", "", trials[0])
for trial in trials:
trial["answer"] = random.choice(["left", "right"])
trial["score"] = random.randint(0, 1)
writer.write(trial)
"""
3. PANDAS VERSION
+ Great for analysis of cross-trial data
% Too complex. Rather do the manual version and convert trials to DataFrame when you need to process data
"""
# Generate trial list
trials = [
{"opacity": opacity, "image": image, "answer": "", "score": ""}
