Example #1
0
def getScreenMeasurements(dpath, et_model, et_model_display_configs):
    """

    :param dpath:
    :param et_model:
    :param et_model_display_configs:
    :return:
    """
#    et_model, _ = getInfoFromPath(dpath)
    display_param = et_model_display_configs.get(et_model)
    if display_param is None:
        et_config_path = glob.glob('./configs/*%s.yaml' % (et_model))
        if et_config_path:
            et_config_path = nabs(et_config_path[0])
            display_config = load(file(et_config_path, 'r'), Loader=Loader)
            dev_list = display_config.get('monitor_devices')
            for d in dev_list:
                if d.keys()[0] == 'Display':
                    d = d['Display']
                    width = d.get('physical_dimensions', {}).get('width')
                    height = d.get('physical_dimensions', {}).get('height')
                    eye_dist = d.get('default_eye_distance', {}).get(
                        'surface_center')
                    et_model_display_configs[et_model] = OrderedDict()
                    et_model_display_configs[et_model][
                        screen_measure_fields[0]] = width
                    et_model_display_configs[et_model][
                        screen_measure_fields[1]] = height
                    et_model_display_configs[et_model][
                        screen_measure_fields[2]] = eye_dist
                    return et_model_display_configs[et_model]
    return display_param
def getGeometry(data):
    return np.mean((1/(np.degrees(2*np.arctan(data['screen_width']/(2*data['eye_distance'])))/data['display_width_pix']),
                    1/(np.degrees(2*np.arctan(data['screen_height']/(2*data['eye_distance'])))/data['display_height_pix'])))

if not os.path.exists(OUTPUT_DIR):
    os.mkdir(OUTPUT_DIR)
                
OUTPUT_DIR = getFullOutputFolderPath(OUTPUT_DIR)
if not os.path.exists(OUTPUT_DIR):
    os.mkdir(OUTPUT_DIR)

print 'OUTPUT_FOLDER:', OUTPUT_DIR

                
DATA_FILES = [nabs(fpath) for fpath in glob.glob(GLOB_PATH_PATTERN) if analyseit(fpath, INCLUDE_SUB, INCLUDE_TRACKERS)]

stim_all = []

if __name__ == '__main__':
    
    for file_path in DATA_FILES:
        file_log = open(OUTPUT_DIR + '/win_select.log', 'a')
        try:        
#        if 1:
            t1 = time.time()
            DATA = np.load(file_path)
            et_model, sub = getInfoFromPath(file_path)

            #Removes every second sample for LC Tech EyeFollower
            #TODO: come up with a better alternative
Example #3
0
        'min_cal_points': 8,
        'win_select_func': 'roll',
        'win_size': 0.175,  
        'win_type': 'sample',     
        'window_skip': 0.2,
        'wsa': 'fiona' ,
        'units': 'gaze',
    }]

if not os.path.exists(OUTPUT_FOLDER):
    os.mkdir(OUTPUT_FOLDER)
    
OUTPUT_FOLDER = getFullOutputFolderPath(OUTPUT_FOLDER)
print 'OUTPUT_FOLDER:', OUTPUT_FOLDER

DATA_FILES = [nabs(fpath) for fpath in glob.glob(GLOB_PATH_PATTERN) if
              analyseit(fpath)]

#Check for dublicates
sub_dict = dict()
for et_model in INCLUDE_TRACKERS:
    sub_dict[et_model] = []
    for file_path in DATA_FILES:
        et, sub = getInfoFromPath(file_path)
        if et==et_model:
            sub_dict[et_model].append(sub)
    if len(sub_dict[et_model]) != len(np.unique(sub_dict[et_model])):
        unique_ids, unique_counts = np.unique(sub_dict[et_model], return_counts=True)
        
        print 'Dublicate subject ids in %s:'%et_model, \
        unique_ids[np.argwhere(unique_counts > 1).flatten()]