def test_target_number_less_than_alp(self):
"""Test when requested number of targets is less than the length of
the alphabet."""
alp = list(range(10))
targets = generate_targets(alp, 5)
self.assertEqual(len(targets), 5)
self.assertEqual(len(targets), len(set(targets)))
def test_remainder(self):
"""Test behavior when number of targets is greater than the length of
the alphabet by a value other than a multiple of the alphabet length.
"""
alp = list(range(5))
targets = generate_targets(alp, 12)
counts = Counter(targets)
for item in alp:
self.assertGreaterEqual(counts[item], 2)
self.assertLessEqual(counts[item], 3)
def test_target_greater_than_alp(self):
"""Test behavior when number of targets is greater than the length
of the alphabet"""
alp = list(range(5))
targets = generate_targets(alp, 10)
self.assertEqual(len(targets), 10)
counts = Counter(targets)
for item in alp:
self.assertEqual(counts[item], 2)
def execute(self):
"""Execute the task"""
self.logger.debug('Starting Icon to Icon Task!')
icons = generate_targets(self.alp, self.stim_number)
self.logger.debug('Icon sequence: %s', icons)
selections = []
copy_phrase_task = self.init_copy_phrase_task(task_list=[(icons, [])])
epoch = EpochManager(icons, copy_phrase_task, self.timing[0])
correct_trials = 0
data = self.init_session_data(save=True)
run = self.await_start()
epoch.next()
while run and self.user_wants_to_continue():
sequence_timing = self.present_sequence(
epoch.current_sequence,
epoch.current_durations,
show_target=epoch.first_sequence)
# Write triggers to file
write_triggers_from_sequence_icon_to_icon(
sequence_timing,
self.trigger_file,
epoch.target,
target_displayed=epoch.first_sequence)
core.wait(self.buffer_val)
# Delete calibration
if epoch.first_stimulus:
del sequence_timing[0]
# Delete the target presentation
if epoch.first_sequence:
del sequence_timing[0]
# Reshape the data and triggers as needed for analysis.
raw_data, triggers, target_info = process_data_for_decision(
sequence_timing, self.daq, self.window, self.parameters,
self.rsvp.first_stim_time)
# Data Record for session.json
entry = {
'stimuli': epoch.stimuli,
'eeg_len': len(raw_data),
'timing_sti': epoch.timing_sti,
'triggers': triggers,
'target_info': target_info,
'target_letter': epoch.target
}
if self.fake:
new_epoch = True
correct_trials += 1
selections.append(epoch.target)
self.display_feedback(selection=epoch.target, correct=True)
else:
# Evaluate the data and make a decision.
decision_made, new_sti = copy_phrase_task.evaluate_sequence(
raw_data, triggers, target_info,
self.collection_window_len)
new_epoch = decision_made
# Add the evidence to the data record.
ev_hist = copy_phrase_task.conjugator.evidence_history
likelihood = copy_phrase_task.conjugator.likelihood
entry['lm_evidence'] = ev_hist['LM'][0].tolist()
entry['eeg_evidence'] = ev_hist['ERP'][-1].tolist()
entry['likelihood'] = likelihood.tolist()
if decision_made:
selection = copy_phrase_task.decision_maker.last_selection
selections.append(selection)
correct = selection == epoch.target
if correct:
correct_trials += 1
self.display_feedback(selection, correct)
epoch.add_stim_data(entry)
self.update_session_data(data, epoch, save=True)
if new_epoch:
epoch.next()
else:
epoch.next_stimulus(new_sti)
run = self.stoppage_criteria_ok(epoch.seq_counter,
data['total_time_spent'])
# end while loop
self.write_data(correct_trials, len(selections))
self.exit_display()
return self.file_save