event.clearEvents()
# reset all triggers to zero
et.sendTriggers(port,0)
# experimental phases (unique things on screen)
fix_phase = fixDot[:] +[progress_bar,progress_bar_start,progress_bar_end]
stim_phase = fixDot[:] + [progress_bar,progress_bar_start,progress_bar_end,leftbar,rightbar,leftProb,rightProb,leftframe,rightframe]
select_phase = fixDot[:] +[progress_bar,progress_bar_start,progress_bar_end,selectbar,leftbar,rightbar,leftProb,rightProb,rightframe,leftframe]
feedback_phase = fixDot[:] +[progress_bar,progress_bar_start,progress_bar_end,progress_update,selectbar,leftbar,rightbar,leftProb,rightProb,rightframe,leftframe]
####################
### START EXP ###
####################
while 'c' not in event.getKeys():
et.drawFlip(win,[startExp])
######################
### START BLOCKS ###
######################
for block_no in range(param['n_blocks']):
trial_info['block_no']=block_no+1
block_correct = 0
# start block message
startBlock.text = stim_info["startBlock_text"].format(block_no+1, param['n_blocks'])
while True:
et.drawFlip(win,[startBlock])
cont=captureResponse(port,keys = [response_info['pause_resp'],None])
if cont == response_info['pause_resp']:
while captureResponse(port,keys=resp_keys+ [None]) in resp_keys:
win.flip()
### SET TIMING ###
####################
pause_frames = round(timing_info['pause_dur'] * win_info['framerate'])
feed_frames = round(timing_info['feed_dur'] * win_info['framerate'])
fix_frames = round(timing_info['fix_dur'] * win_info['framerate'])
noise_seq = np.random.uniform(
timing_info['noise_mean'] - timing_info['noise_range'],
timing_info['noise_mean'] + timing_info['noise_range'],
size=(n_blocks, n_trials + param['n_zero']))
####################
### START EXP ###
####################
# show block intro
while 'c' not in event.getKeys():
et.drawFlip(win, [startExp])
###########################
### START BLOCK LOOP # ###
###########################
for block_no in range(n_blocks):
# create trial sequence
trial_seq = np.tile(np.arange(n_cohs), int(n_trials / n_cohs))
# add zero-coherence trials
trial_seq = np.concatenate(
(trial_seq, np.ones(param['n_zero'], dtype=int) * n_cohs))
np.random.shuffle(trial_seq)
# make sure every coherence lvl is matched with both directions equal number of times
dir_list = [-1, 1] * int(n_trials / 2 / n_cohs)