def execute_task(task_type: dict, parameters: dict, save_folder: str) -> bool:
"""Excecute Task.
Executes the desired task by setting up the display window and
data acquisition, then passing on to the start_task funtion
which will initialize experiment.
Input:
parameters (dict): parameter dictionary
task_type (dict): type and mode of experiment
save_folder (str): path to save folder
"""
signal_model = None
language_model = None
filename = None
exp_type = ExperimentType(task_type['exp_type'])
fake = parameters['fake_data']
# Init EEG Model, if needed. Calibration Tasks Don't require probabilistic
# modules to be loaded.
if not fake and exp_type not in ExperimentType.calibration_tasks():
# Try loading in our signal_model and starting a langmodel(if enabled)
try:
signal_model, filename = load_signal_model()
except Exception as e:
logging.debug('Cannot load signal model. Exiting')
raise e
# if Language Model enabled init lm
if parameters['languagemodelenabled']:
language_model = init_language_model(parameters)
# Initialize DAQ
daq, server = init_eeg_acquisition(parameters, save_folder, server=fake)
# Initialize Display Window
# We have to wait until after the prompt to load the signal model before
# displaying the window, otherwise in fullscreen mode this throws an error
display = init_display_window(parameters)
print_message(display, "Initializing...")
# Start Task
try:
start_task(display,
daq,
exp_type,
parameters,
save_folder,
language_model=language_model,
signal_model=signal_model,
fake=fake,
auc_filename=filename)
# If exception, close all display and acquisition objects
except Exception as e:
_clean_up_session(display, daq, server)
raise e
return _clean_up_session(display, daq, server)
def test_init_display_window_returns_psychopy_window(self):
"""Test display window is created."""
display_window = init_display_window(self.parameters)
self.assertIsInstance(display_window, type(self.window))
self.assertEqual(display_window, self.window)
def test_img_path(self):
"""Test img_path method"""
parameters = self.parameters
parameters['window_height'] = 1
parameters['window_width'] = 1
parameters['is_txt_stim'] = False
img_path = 'bcipy/static/images/rsvp_images/'
parameters['path_to_presentation_images'] = img_path
fixation = 'bcipy/static/images/bci_main_images/PLUS.png'
# TODO: can this be mocked?
display = init_display_window(parameters)
daq = mock()
file_save = ""
signal_model = None
language_model = None
fake = True
auc_filename = ""
with patch('bcipy.tasks.rsvp.icon_to_icon.open', mock_open()):
task = RSVPIconToIconTask(display, daq, parameters, file_save,
signal_model, language_model, fake,
False, auc_filename)
self.assertTrue(len(task.alp) > 0)
self.assertTrue('PLUS' not in task.alp)
self.assertEqual('bcipy/static/images/rsvp_images/A.png',
task.img_path('A'))
self.assertEqual('A.png', task.img_path('A.png'))
self.assertEqual(fixation, task.img_path(fixation))
display.close()
def setUp(self):
"""Set up needed items for test."""
parameters_used = 'bcipy/parameters/parameters.json'
self.window = mock()
when(psychopy.visual).Window(size=any(),
screen=any(),
allowGUI=False,
useFBO=False,
fullscr=any(bool),
allowStencil=False,
monitor=any(str),
winType='pyglet',
units=any(),
waitBlanking=False,
color=any(str)).thenReturn(self.window)
self.parameters = load_json_parameters(parameters_used,
value_cast=True)
self.display_window = init_display_window(self.parameters)
set of variables.
"""
return self.width, self.height
def _reset_stimuli(self) -> None:
"""Reset Stimuli.
Used when redrawing stimuli. Sets the stimuli array and starting
x positions to their starting values. If using vertical bars
this should reset the y positions instead.
"""
# reset stimuli
self.stimuli = []
self.position_x = self.parameters['feedback_pos_x']
if __name__ == '__main__':
from bcipy.helpers.load import load_json_parameters
from bcipy.display.display_main import init_display_window
# Load a parameters file
parameters = load_json_parameters('bcipy/parameters/parameters.json', value_cast=True)
display = init_display_window(parameters)
clock = core.Clock()
feedback = LevelFeedback(display, parameters, clock)
for index, _ in enumerate(feedback.level_colors):
# the position argument is based on index starting at 1
index += 1
feedback.administer(position=index)
def execute_task(task_type: dict, parameters: dict, save_folder: str) -> bool:
"""Excecute Task.
Executes the desired task by setting up the display window and
data acquistion, then passing on to the start_task funtion
which will initialize experiment.
Input:
parameters (dict): parameter dictionary
task_type (dict): type and mode of experiment
save_folder (str): path to save folder
"""
fake = parameters['fake_data']
# Init EEG Model, if needed. Calibration Tasks Don't require probalistic
# modules to be loaded.
if task_type['exp_type'] > 1:
# Try loading in our classifier and starting a langmodel(if enabled)
try:
# EEG Model, Load in pre-trained classifier
if fake:
classifier = None
else:
classifier = load_classifier()
except Exception as e:
print("Cannot load EEG classifier. Exiting")
raise e
# if Language Model enabled and data not fake, init lm
if parameters['languagemodelenabled'] == 'true' \
and not fake:
try:
lmodel = init_language_model(parameters)
except:
print("Cannot init language model. Setting to None.")
lmodel = None
else:
lmodel = None
else:
classifier = None
lmodel = None
# Initialize DAQ
daq, server = init_eeg_acquisition(parameters, save_folder, server=fake)
# Initialize Display Window
display = init_display_window(parameters)
# Start Task
try:
start_task(display,
daq,
task_type,
parameters,
save_folder,
lmodel=lmodel,
classifier=classifier,
fake=fake)
# If exception, close all display and acquisition objects
except Exception as e:
_clean_up_session(display, daq, server)
raise e
return _clean_up_session(display, daq, server)