def record(state, amp_name, amp_serial, record_dir, eeg_only):
# set data file name
filename = time.strftime(record_dir + "/%Y%m%d-%H%M%S-raw.pcl",
time.localtime())
qc.print_c('>> Output file: %s' % (filename), 'W')
# test writability
try:
qc.make_dirs(record_dir)
open(
filename,
'w').write('The data will written when the recording is finished.')
except:
raise RuntimeError('Problem writing to %s. Check permission.' %
filename)
# start a server for sending out data filename when software trigger is used
outlet = start_server('StreamRecorderInfo', channel_format='string',\
source_id=filename, stype='Markers')
# connect to EEG stream server
sr = StreamReceiver(amp_name=amp_name,
amp_serial=amp_serial,
eeg_only=eeg_only)
# start recording
qc.print_c('\n>> Recording started (PID %d).' % os.getpid(), 'W')
qc.print_c('\n>> Press Enter to stop recording', 'G')
tm = qc.Timer(autoreset=True)
next_sec = 1
while state.value == 1:
sr.acquire()
if sr.get_buflen() > next_sec:
duration = str(datetime.timedelta(seconds=int(sr.get_buflen())))
print('RECORDING %s' % duration)
next_sec += 1
tm.sleep_atleast(0.01)
# record stop
qc.print_c('>> Stop requested. Copying buffer', 'G')
buffers, times = sr.get_buffer()
signals = buffers
events = None
# channels = total channels from amp, including trigger channel
data = {
'signals': signals,
'timestamps': times,
'events': events,
'sample_rate': sr.get_sample_rate(),
'channels': sr.get_num_channels(),
'ch_names': sr.get_channel_names()
}
qc.print_c('Saving raw data ...', 'W')
qc.save_obj(filename, data)
print('Saved to %s\n' % filename)
qc.print_c('Converting raw file into a fif format.', 'W')
pcl2fif(filename)
def record(recordState, amp_name, amp_serial, record_dir, eeg_only, recordLogger=logger, queue=None):
redirect_stdout_to_queue(recordLogger, queue, 'INFO')
# set data file name
timestamp = time.strftime('%Y%m%d-%H%M%S', time.localtime())
pcl_file = "%s/%s-raw.pcl" % (record_dir, timestamp)
eve_file = '%s/%s-eve.txt' % (record_dir, timestamp)
recordLogger.info('>> Output file: %s' % (pcl_file))
# test writability
try:
qc.make_dirs(record_dir)
open(pcl_file, 'w').write('The data will written when the recording is finished.')
except:
raise RuntimeError('Problem writing to %s. Check permission.' % pcl_file)
# start a server for sending out data pcl_file when software trigger is used
outlet = start_server('StreamRecorderInfo', channel_format='string',\
source_id=eve_file, stype='Markers')
# connect to EEG stream server
sr = StreamReceiver(buffer_size=0, amp_name=amp_name, amp_serial=amp_serial, eeg_only=eeg_only)
# start recording
recordLogger.info('\n>> Recording started (PID %d).' % os.getpid())
qc.print_c('\n>> Press Enter to stop recording', 'G')
tm = qc.Timer(autoreset=True)
next_sec = 1
while recordState.value == 1:
sr.acquire()
if sr.get_buflen() > next_sec:
duration = str(datetime.timedelta(seconds=int(sr.get_buflen())))
recordLogger.info('RECORDING %s' % duration)
next_sec += 1
tm.sleep_atleast(0.001)
# record stop
recordLogger.info('>> Stop requested. Copying buffer')
buffers, times = sr.get_buffer()
signals = buffers
events = None
# channels = total channels from amp, including trigger channel
data = {'signals':signals, 'timestamps':times, 'events':events,
'sample_rate':sr.get_sample_rate(), 'channels':sr.get_num_channels(),
'ch_names':sr.get_channel_names(), 'lsl_time_offset':sr.lsl_time_offset}
recordLogger.info('Saving raw data ...')
qc.save_obj(pcl_file, data)
recordLogger.info('Saved to %s\n' % pcl_file)
# automatically convert to fif and use event file if it exists (software trigger)
if os.path.exists(eve_file):
recordLogger.info('Found matching event file, adding events.')
else:
eve_file = None
recordLogger.info('Converting raw file into fif.')
pcl2fif(pcl_file, external_event=eve_file)
if __name__ == '__main__':
sr = StreamReceiver(window_size=w_seconds,
amp_name=amp_name,
amp_serial=amp_serial)
sfreq = sr.sample_rate
psde = PSDEstimator(sfreq=sfreq,
fmin=fmin,
fmax=fmax,
bandwidth=None,
adaptive=False,
low_bias=True,
n_jobs=1,
normalization='length',
verbose=None)
ch_names = sr.get_channel_names()
fq_res = 1 / w_seconds
hz_list = []
f = fmin
while f < fmax:
hz_list.append(f)
f += fq_res
picks = [ch_names.index(ch) for ch in channel_picks]
psd = get_psd(sr, psde, picks).T # freq x ch
assert len(hz_list) == psd.shape[0], (len(hz_list), psd.shape[0])
cv2.namedWindow("img", cv2.WINDOW_AUTOSIZE)
cv2.moveWindow("img", screen_offset_x, screen_offset_y)
#cv2.setWindowProperty("img", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN);
mul_x = 50
mul_y = 20
fq_width = 55
class MainView(QMainWindow, SubjectInfo, TaskManager, SequenceManager, ExpProtocol, EventNumber, FilePathManager,\
ChannelScaleManager, ChannelSelector, ChannelFilter, BadEpochMonitor, MRCPExtractor, MainSwitch,\
ScopeSwitch, RecordSwitch, TaskSwitch, EventPlot, SSVEPExpProtocol, EyeTracker, RunTimer, GUITimer):
"""
MainView class controls the GUI frontend interaction
"""
def __init__(self,
amp_name,
amp_serial,
state=mp.Value('i', 1),
queue=None):
"""
Initialize experimenter window GUI and subject view window GUI
:amp_name: amplifier name passed from LSL
:amp_serial: amplifier serial passed from LSL
"""
super(MainView, self).__init__()
self.router = Router()
self.ui = main_layout.Ui_MainWindow()
self.ui.setupUi(self)
self.window = QMainWindow()
self.SV_window = subject_layout.Ui_SV()
self.SV_window.setupUi(self.window)
self.eye_tracker_dialog = QDialog()
self.eye_tracker_window = eye_tracker_layout.Ui_Dialog()
self.eye_tracker_window.setupUi(self.eye_tracker_dialog)
# redirect_stdout_to_queue(logger, queue, 'INFO')
logger.info('Viewer launched')
self.amp_name = amp_name
self.amp_serial = amp_serial
self.state = state
self.init_all()
def init_all(self):
"""
Initialize specialized functions inside GUI
"""
self.init_config_file()
self.init_loop()
self.init_panel_GUI()
self.init_event_functions()
self.init_SV_GUI()
self.init_scope_GUI()
self.init_timer() # timer for scope refreshing
self.init_Runtimer() # timer for record, train and test
self.init_eye_tracker()
def init_config_file(self):
"""
Initialize config file
"""
self.scope_settings = RawConfigParser(allow_no_value=True,
inline_comment_prefixes=('#',
';'))
if (len(sys.argv) == 1):
self.show_channel_names = 0
self.device_name = ""
else:
if (sys.argv[1].find("gtec") > -1):
self.device_name = "gtec"
self.show_channel_names = 1
elif (sys.argv[1].find("biosemi") > -1):
self.device_name = "biosemi"
self.show_channel_names = 1
elif (sys.argv[1].find("hiamp") > -1):
self.device_name = "hiamp"
self.show_channel_names = 1
else:
self.device_name = ""
self.show_channel_names = 0
# self.scope_settings.read(os.getenv("HOME") + "/.scope_settings.ini")
self.scope_settings.read('.scope_settings.ini')
def init_loop(self):
"""
Initialize loop related variables like StreamReceiver and self.eeg
"""
self.updating = False
logger.info("init_loop runs")
self.sr = StreamReceiver(window_size=1,
buffer_size=10,
amp_serial=Variables.get_amp_serial(),
amp_name=Variables.get_amp_name())
srate = int(self.sr.sample_rate)
# n_channels= self.sr.channels
# 12 unsigned ints (4 bytes)
########## TODO: assumkng 32 samples chunk => make it read from LSL header
data = [
'EEG', srate, ['L', 'R'], 32,
len(self.sr.get_eeg_channels()), 0,
self.sr.get_trigger_channel(), None, None, None, None, None
]
logger.info('Trigger channel is %d' % self.sr.get_trigger_channel())
self.config = {
'id': data[0],
'sf': data[1],
'labels': data[2],
'samples': data[3],
'eeg_channels': data[4],
'exg_channels': data[5],
'tri_channels': data[6],
'eeg_type': data[8],
'exg_type': data[9],
'tri_type': data[10],
'lbl_type': data[11],
'tim_size': 1,
'idx_size': 1
}
self.tri = np.zeros(self.config['samples'])
self.last_tri = 0
self.eeg = np.zeros(
(self.config['samples'], self.config['eeg_channels']),
dtype=np.float)
self.exg = np.zeros(
(self.config['samples'], self.config['exg_channels']),
dtype=np.float)
self.ts_list = []
self.ts_list_tri = []
def init_event_functions(self):
"""
Initialize event listeners for widgets in GUI
"""
# Control buttons
self.ui.pushButton_Main_switch.clicked.connect(
self.onClicked_button_Main_switch)
self.ui.pushButton_start_SV.clicked.connect(
self.onClicked_button_start_SV)
self.ui.pushButton_scope_switch.clicked.connect(
self.onClicked_button_scope_switch)
self.ui.pushButton_rec.clicked.connect(self.onClicked_button_rec)
# self.ui.pushButton_start_train.clicked.connect(self.onClicked_button_train)
# self.ui.pushButton_start_test.clicked.connect(self.onClicked_button_test)
# Subject information
self.ui.pushButton_save.clicked.connect(
self.onClicked_button_save_subject_information)
# Experimental protocol
self.ui.pushButton_define_task_done.clicked.connect(
self.onClicked_button_define_task_done)
self.ui.pushButton_define_task_add.clicked.connect(
self.onClicked_button_define_task_add)
self.ui.pushButton_create_sequence.clicked.connect(
self.onClicked_button_create_sequence)
self.ui.pushButton_randomize.clicked.connect(
self.onClicked_button_randomize)
self.ui.toolButton_choose_image_task.clicked.connect(
self.onClicked_toolButton_choose_image_task)
self.ui.toolButton_choose_sound_task.clicked.connect(
self.onClicked_toolButton_choose_sound_task)
self.ui.pushButton_experimental_protocol_finish.clicked.connect(
self.onClicked_experimental_protocol_finish)
self.ui.pushButton_save_protocol.clicked.connect(
self.onClicked_button_save_protocol)
self.ui.toolButton_load_protocol.clicked.connect(
self.onClicked_toolButton_load_protocol)
# Event management tab
self.ui.pushButton_save_event_number.clicked.connect(
self.onClicked_button_save_event_number)
# Oscilloscope
self.ui.comboBox_scale.activated.connect(
self.onActivated_combobox_scale)
self.ui.spinBox_time.valueChanged.connect(
self.onValueChanged_spinbox_time)
self.ui.checkBox_car.stateChanged.connect(
self.onActivated_checkbox_car)
self.ui.checkBox_bandpass.stateChanged.connect(
self.onActivated_checkbox_bandpass)
self.ui.checkBox_notch.stateChanged.connect(
self.onActivated_checkbox_notch)
self.ui.pushButton_bp.clicked.connect(self.onClicked_button_bp)
self.ui.pushButton_apply_notch.clicked.connect(
self.onClicked_button_notch)
self.ui.table_channels.itemSelectionChanged.connect(
self.onSelectionChanged_table)
self.ui.table_channels.doubleClicked.connect(
self.onDoubleClicked_channel_table)
self.ui.pushButton_update_channel_name.clicked.connect(
self.onClicked_button_update_channel_name)
self.ui.table_channels.viewport().installEventFilter(self)
# SSVEP
self.ui.pushButton_ssvep_task.clicked.connect(
self.onClicked_pushButton_ssvep_task)
# eye tracker
self.ui.pushButton_open_eye_tracker_ui.clicked.connect(
self.onClicked_pushButton_open_eye_tracker_ui)
# MRCP tab
self.ui.pushButton_temp_clear.clicked.connect(
self.onClicked_button_temp_clear)
self.ui.pushButton_temp_mean.clicked.connect(
self.onClicked_button_temp_mean)
self.ui.pushButton_temp_view.clicked.connect(
self.onClicked_button_temp_view)
self.ui.pushButton_temp_remove.clicked.connect(
self.onClicked_button_temp_remove)
def init_panel_GUI(self):
"""
Initialize experimenter GUI
"""
# Tabs
self.ui.tab_experimental_protocol.setEnabled(False)
self.ui.tab_subjec_information.setEnabled(False)
self.ui.tab_event_and_file_management.setEnabled(False)
# self.ui.tab_Oscilloscope.setEnabled(False)
self.ui.tab_experiment_type.setEnabled(False)
# Experimental protocol
self.task_list = []
self.new_task_list = []
self.task_descriptor_list = []
self.task_image_path = ""
self.task_image_path_list = []
self.task_sound_path = ""
self.task_sound_path_list = []
self.task_table = np.ndarray([])
self.new_task_table = np.ndarray([])
self.task_counter = 0
self.protocol_path = ""
# Button
self.init_task_name_table()
self.ui.groupBox_sequence_manager.setEnabled(False)
# Event management tab
self.event_timestamp_list = []
self.init_task_event_number_table()
self.event_list = []
# Button
self.ui.pushButton_save_event_number.clicked.connect(
self.onClicked_button_save_event_number)
self.event_file_path = ""
self.mrcp_template_file_path = ""
self.raw_eeg_file_path = ""
self.raw_mrcp_file_path = ""
self.subject_file_path = ""
# Oscilloscope
self.ui.comboBox_scale.setCurrentIndex(4)
self.ui.checkBox_notch.setChecked(True)
# self.ui.checkBox_car.setChecked(
# int(self.scope_settings.get("filtering", "apply_car_filter")))
# self.ui.checkBox_bandpass.setChecked(
# int(self.scope_settings.get("filtering", "apply_bandpass_filter")))
self.ui.pushButton_apply_notch.setEnabled(True)
self.ui.doubleSpinBox_lc_notch.setEnabled(True)
self.ui.doubleSpinBox_hc_notch.setEnabled(True)
# initialize channel selection panel in main view GUI
self.channels_to_show_idx = []
idx = 0
for y in range(0, 4):
for x in range(0, NUM_X_CHANNELS):
if idx < self.config['eeg_channels']:
# self.table_channels.item(x,y).setTextAlignment(QtCore.Qt.AlignCenter)
self.ui.table_channels.item(x, y).setSelected(True) # Qt5
# self.table_channels.setItemSelected(self.table_channels.item(x, y), True) # Qt4 only
self.channels_to_show_idx.append(idx)
else:
self.ui.table_channels.setItem(x, y,
QTableWidgetItem("N/A"))
self.ui.table_channels.item(x, y).setFlags(
QtCore.Qt.NoItemFlags)
self.ui.table_channels.item(x, y).setTextAlignment(
QtCore.Qt.AlignCenter)
idx += 1
self.ui.table_channels.verticalHeader().setStretchLastSection(True)
self.ui.table_channels.horizontalHeader().setStretchLastSection(True)
self.channel_to_scale_row_index = -1
self.channel_to_scale_column_index = -1
self.selected_channel_row_index = 0
self.selected_channel_column_index = 0
self.single_channel_scale = 1
# MRCP tab
self.init_class_epoch_counter_table()
self.init_class_bad_epoch_table()
self.show_TID_events = False
self.show_LPT_events = False
self.show_Key_events = False
self.raw_trial_MRCP = np.ndarray([])
self.processed_trial_MRCP = np.ndarray([])
self.total_trials_MRCP = []
self.total_trials_raw_MRCP = []
self.total_MRCP_inds = []
self.temp_counter = 0
self.temp_counter_list = []
self.input_temp_list = []
self.display_temp_list = []
self.selected_temp = ""
self.list_selected_temp = []
self.template_buffer = np.zeros(
(6 * int(self.sr.sample_rate), self.config['eeg_channels']),
dtype=float)
self.b_lp, self.a_lp = Utils.butter_lowpass(3,
int(self.sr.sample_rate),
2)
self.b_hp, self.a_hp = Utils.butter_highpass(0.05,
int(self.sr.sample_rate),
2)
self.initial_condition_list_lp = Utils.construct_initial_condition_list(
self.b_lp, self.a_lp, self.config['eeg_channels'])
self.initial_condition_list_hp = Utils.construct_initial_condition_list(
self.b_hp, self.a_hp, self.config['eeg_channels'])
self.ui.pushButton_bad_epoch.clicked.connect(
self.onClicked_button_bad_epoch)
self.screen_width = 522
self.screen_height = 160
# self.setGeometry(100,100, self.screen_width, self.screen_height)
# self.setFixedSize(self.screen_width, self.screen_height)
self.setWindowTitle('EEG Scope Panel')
self.setFocusPolicy(QtCore.Qt.ClickFocus)
self.setFocus()
logger.info('GUI show')
self.show()
def init_panel_GUI_stop_recording(self):
"""
Initialize experimenter GUI when stop recording button pressed. This is used to
prepare for next run.
"""
# Tabs
self.ui.tab_experimental_protocol.setEnabled(False)
self.ui.tab_subjec_information.setEnabled(False)
self.ui.tab_event_and_file_management.setEnabled(False)
# self.ui.tab_Oscilloscope.setEnabled(False)
self.ui.tab_experiment_type.setEnabled(False)
# Experimental protocol
self.task_list = []
self.new_task_list = []
self.task_descriptor_list = []
self.task_image_path = ""
self.task_image_path_list = []
self.task_sound_path = ""
self.task_sound_path_list = []
self.task_table = np.ndarray([])
self.new_task_table = np.ndarray([])
self.task_counter = 0
self.protocol_path = ""
# Button
self.init_task_name_table()
self.ui.groupBox_sequence_manager.setEnabled(False)
# Event management tab
self.event_timestamp_list = []
self.init_task_event_number_table()
self.event_list = []
# Button
self.ui.pushButton_save_event_number.clicked.connect(
self.onClicked_button_save_event_number)
self.event_file_path = ""
self.mrcp_template_file_path = ""
self.raw_eeg_file_path = ""
self.raw_mrcp_file_path = ""
self.subject_file_path = ""
# Oscilloscope
self.ui.comboBox_scale.setCurrentIndex(4)
self.ui.checkBox_notch.setChecked(True)
# self.ui.checkBox_car.setChecked(
# int(self.scope_settings.get("filtering", "apply_car_filter")))
# self.ui.checkBox_bandpass.setChecked(
# int(self.scope_settings.get("filtering", "apply_bandpass_filter")))
# self.ui.pushButton_apply_notch.setEnabled(False)
self.ui.doubleSpinBox_lc_notch.setEnabled(False)
self.ui.doubleSpinBox_hc_notch.setEnabled(False)
# # initialize channel selection panel in main view GUI
# self.channels_to_show_idx = []
# idx = 0
# for y in range(0, 4):
# for x in range(0, NUM_X_CHANNELS):
# if idx < self.config['eeg_channels']:
# # self.table_channels.item(x,y).setTextAlignment(QtCore.Qt.AlignCenter)
# self.ui.table_channels.item(x, y).setSelected(True) # Qt5
# # self.table_channels.setItemSelected(self.table_channels.item(x, y), True) # Qt4 only
# self.channels_to_show_idx.append(idx)
# else:
# self.ui.table_channels.setItem(x, y,
# QTableWidgetItem("N/A"))
# self.ui.table_channels.item(x, y).setFlags(
# QtCore.Qt.NoItemFlags)
# self.ui.table_channels.item(x, y).setTextAlignment(
# QtCore.Qt.AlignCenter)
# idx += 1
self.ui.table_channels.verticalHeader().setStretchLastSection(True)
self.ui.table_channels.horizontalHeader().setStretchLastSection(True)
self.channel_to_scale_row_index = -1
self.channel_to_scale_column_index = -1
self.selected_channel_row_index = 0
self.selected_channel_column_index = 0
self.single_channel_scale = 1
# MRCP tab
self.init_class_epoch_counter_table()
self.init_class_bad_epoch_table()
self.show_TID_events = False
self.show_LPT_events = False
self.show_Key_events = False
self.raw_trial_MRCP = np.ndarray([])
self.processed_trial_MRCP = np.ndarray([])
self.total_trials_MRCP = []
self.total_trials_raw_MRCP = []
self.total_MRCP_inds = []
self.temp_counter = 0
self.temp_counter_list = []
self.input_temp_list = []
self.display_temp_list = []
self.selected_temp = ""
self.list_selected_temp = []
self.template_buffer = np.zeros(
(6 * int(self.sr.sample_rate), self.config['eeg_channels']),
dtype=float)
self.b_lp, self.a_lp = Utils.butter_lowpass(3,
int(self.sr.sample_rate),
2)
self.b_hp, self.a_hp = Utils.butter_highpass(0.05,
int(self.sr.sample_rate),
2)
self.initial_condition_list_lp = Utils.construct_initial_condition_list(
self.b_lp, self.a_lp, self.config['eeg_channels'])
self.initial_condition_list_hp = Utils.construct_initial_condition_list(
self.b_hp, self.a_hp, self.config['eeg_channels'])
self.ui.pushButton_bad_epoch.clicked.connect(
self.onClicked_button_bad_epoch)
self.screen_width = 522
self.screen_height = 160
# self.setGeometry(100,100, self.screen_width, self.screen_height)
# self.setFixedSize(self.screen_width, self.screen_height)
self.setWindowTitle('EEG Scope Panel')
self.setFocusPolicy(QtCore.Qt.ClickFocus)
self.setFocus()
self.show()
def init_SV_GUI(self):
"""
Initialize subject view GUI
"""
self.SVStatus = 0
self.starttime = 0
self.SV_time = 0
self.idle_time = int(self.ui.idleTimeLineEdit.text())
self.focus_time = self.idle_time + int(
self.ui.focusTimeLineEdit.text())
self.prepare_time = self.focus_time + int(
self.ui.prepareTimeLineEdit.text())
self.two_time = self.prepare_time + int(self.ui.twoTimeLineEdit.text())
self.one_time = self.two_time + int(self.ui.oneTimeLineEdit.text())
self.task_time = self.one_time + int(self.ui.taskTimeLineEdit.text())
self.relax_time = self.task_time + 2
self.cycle_time = self.relax_time
self.is_experiment_on = False
def init_scope_GUI(self):
"""
Initialize oscilloscope GUI
"""
self.bool_parser = {True: '1', False: '0'}
# PyQTGraph plot initialization
self.win = pg.GraphicsWindow()
self.win.setWindowTitle('EEG Scope')
self.win.setWindowFlags(QtCore.Qt.WindowMinimizeButtonHint)
self.win.keyPressEvent = self.keyPressEvent
# self.win.show()
self.main_plot_handler = self.win.addPlot()
self.win.resize(1280, 800)
# Scales available in the GUI. If you change the options in the GUI
# you should change them here as well
self.scales_range = [1, 10, 25, 50, 100, 250, 500, 1000, 2500, 100000]
self.single_scales_range = [
0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.5, 1.7, 1.8, 2
]
# Scale in uV
self.scale = 100
# Time window to show in seconds
self.seconds_to_show = 10
# Y Tick labels. Use values from the config file.
self.channel_labels = []
values = []
''' For non-LSL systems having no channel names
for x in range(0, self.config['eeg_channels']):
if (self.show_channel_names):
self.channel_labels.append("(" + str(x + 1) + ") " +
self.scope_settings.get("internal",
"channel_names_" + self.device_name + str(
self.config['eeg_channels'])).split(', ')[x])
else:
self.channel_labels.append('CH ' + str(x + 1))
'''
ch_names = np.array(self.sr.get_channel_names())
self.channel_labels = ch_names[self.sr.get_eeg_channels()]
for x in range(0, len(self.channels_to_show_idx)):
values.append((-x * self.scale,
self.channel_labels[self.channels_to_show_idx[x]]))
values_axis = []
values_axis.append(values)
values_axis.append([])
# Update table labels with current names
idx = 0
for y in range(0, 4):
for x in range(0, NUM_X_CHANNELS):
if (idx < self.config['eeg_channels']):
self.ui.table_channels.item(x, y).setText(
self.channel_labels[idx])
idx += 1
# Plot initialization
# Plotting colors. If channels > 16, colors will roll back to the beginning
self.colors = np.array([[255, 0, 0], [0, 255, 0], [0, 0, 255],
[255, 255, 0], [0, 255, 255], [255, 0, 255],
[128, 100, 100], [0, 128, 0], [0, 128, 128],
[128, 128, 0], [255, 128, 128], [128, 0, 128],
[128, 255, 0], [255, 128, 0], [0, 255, 128],
[128, 0, 255]])
# pen = pg.mkColor(self.colors)
# self.main_plot_handler.getAxis('left').setTextPen('b')
self.main_plot_handler.getAxis('left').setTicks(values_axis)
self.main_plot_handler.setRange(
xRange=[0, self.seconds_to_show],
yRange=[
+1.5 * self.scale,
-0.5 * self.scale - self.scale * self.config['eeg_channels']
])
self.main_plot_handler.disableAutoRange()
self.main_plot_handler.showGrid(y=True)
self.main_plot_handler.setLabel(axis='left',
text='Scale (uV): ' + str(self.scale))
self.main_plot_handler.setLabel(axis='bottom', text='Time (s)')
# X axis
self.x_ticks = np.zeros(self.config['sf'] * self.seconds_to_show)
for x in range(0, self.config['sf'] * self.seconds_to_show):
self.x_ticks[x] = (x * 1) / float(self.config['sf'])
# We want a lightweight scope, so we downsample the plotting to 64 Hz
self.subsampling_value = self.config['sf'] / 64
# EEG data for plotting
self.data_plot = np.zeros((self.config['sf'] * self.seconds_to_show,
self.config['eeg_channels']))
print('self.data plot shape: ', self.data_plot.shape)
self.curve_eeg = []
for x in range(0, len(self.channels_to_show_idx)):
self.curve_eeg.append(
self.main_plot_handler.plot(
x=self.x_ticks,
y=self.data_plot[:, self.channels_to_show_idx[x]],
pen=pg.mkColor(self.colors[self.channels_to_show_idx[x] %
16, :])))
# self.curve_eeg[-1].setDownsampling(ds=self.subsampling_value, auto=False, method="mean")
# Events data
self.events_detected = []
self.events_curves = []
self.events_text = []
# CAR initialization
self.apply_car = False
self.matrix_car = np.zeros(
(self.config['eeg_channels'], self.config['eeg_channels']),
dtype=float)
self.matrix_car[:, :] = -1 / float(self.config['eeg_channels'])
np.fill_diagonal(self.matrix_car,
1 - (1 / float(self.config['eeg_channels'])))
# Laplacian initalization. TO BE DONE
self.matrix_lap = np.zeros(
(self.config['eeg_channels'], self.config['eeg_channels']),
dtype=float)
np.fill_diagonal(self.matrix_lap, 1)
self.matrix_lap[2, 0] = -1
self.matrix_lap[0, 2] = -0.25
self.matrix_lap[0, 2] = -0.25
# BP initialization
self.apply_bandpass = 1
if (self.apply_bandpass):
self.ui.doubleSpinBox_lp.setValue(40.0)
self.ui.doubleSpinBox_hp.setValue(1.0)
self.ui.doubleSpinBox_lp.setMinimum(0)
self.ui.doubleSpinBox_lp.setMaximum(self.sr.sample_rate / 2 - 0.1)
self.ui.doubleSpinBox_lp.setSingleStep(1)
self.ui.doubleSpinBox_hp.setMinimum(0)
self.ui.doubleSpinBox_hp.setMaximum(self.sr.sample_rate / 2 - 0.1)
self.ui.doubleSpinBox_hp.setSingleStep(1)
self.ui.pushButton_bp.click()
# notch initialization
self.apply_notch = 1
if (self.apply_notch):
self.ui.doubleSpinBox_lc_notch.setValue(58.0)
self.ui.doubleSpinBox_hc_notch.setValue(62.0)
self.ui.doubleSpinBox_lc_notch.setMinimum(0.1)
self.ui.doubleSpinBox_lc_notch.setMaximum(self.sr.sample_rate / 2 -
0.1)
self.ui.doubleSpinBox_lc_notch.setSingleStep(1)
self.ui.doubleSpinBox_hc_notch.setMinimum(0.1)
self.ui.doubleSpinBox_hc_notch.setMaximum(self.sr.sample_rate / 2 -
0.1)
self.ui.doubleSpinBox_hc_notch.setSingleStep(1)
self.ui.pushButton_apply_notch.click()
self.ui.checkBox_bandpass.setChecked(self.apply_bandpass)
self.b_bandpass_scope_refilter = self.b_bandpass_scope
self.a_bandpass_scope_refilter = self.a_bandpass_scope
self.zi_bandpass_scope_refilter = self.zi_bandpass_scope
self.b_notch_scope_refilter = self.b_notch_scope
self.a_notch_scope_refilter = self.a_notch_scope
self.zi_notch_scope_refilter = self.zi_notch_scope
self.update_title_scope()
# Help variables
self.show_help = 0
self.help = pg.TextItem(
"CNBI EEG Scope v0.3 \n" +
"----------------------------------------------------------------------------------\n"
+ "C: De/activate CAR Filter\n" +
"B: De/activate Bandpass Filter (with current settings)\n" +
"T: Show/hide TiD events\n" + "L: Show/hide LPT events\n" +
"K: Show/hide Key events. If not shown, they are NOT recorded!\n" +
"0-9: Add a user-specific Key event. Do not forget to write down why you marked it.\n"
+
"Up, down arrow keys: Increase/decrease the scale, steps of 10 uV\n"
+
"Left, right arrow keys: Increase/decrease the time to show, steps of 1 s\n"
+
"Spacebar: Stop the scope plotting, whereas data acquisition keeps running (EXPERIMENTAL)\n"
+ "Esc: Exits the scope",
anchor=(0, 0),
border=(70, 70, 70),
fill=pg.mkColor(20, 20, 20, 200),
color=(255, 255, 255))
# Stop plot functionality
self.stop_plot = 0
# Force repaint even when we shouldn't repaint.
self.force_repaint = 1
def init_timer(self):
"""
Initialize main timer used for refreshing oscilloscope window. This refreshes every 20ms.
"""
self.os_time_list1 = []
QtCore.QCoreApplication.processEvents()
QtCore.QCoreApplication.flush()
self.timer = QtCore.QTimer(self)
self.timer.setTimerType(QtCore.Qt.PreciseTimer)
self.timer.timeout.connect(self.update_loop)
self.timer.start(20)
def init_Runtimer(self):
"""
Initialize task related timer which controls the timing for visual cues
"""
self.time_show = 0
self.os_time_list = []
self.Runtimer = task.LoopingCall(self.Time)
def init_eye_tracker(self):
self.eye_tracker_window.tableWidget.setRowCount(9)
self.eye_tracker_window.pushButton_1.clicked.connect(self.update_cal1)
self.eye_tracker_window.pushButton_2.clicked.connect(self.update_cal2)
self.eye_tracker_window.pushButton_3.clicked.connect(self.update_cal3)
self.eye_tracker_window.pushButton_4.clicked.connect(self.update_cal4)
self.eye_tracker_window.pushButton_5.clicked.connect(self.update_cal5)
self.eye_tracker_window.pushButton_6.clicked.connect(self.update_cal6)
self.eye_tracker_window.pushButton_7.clicked.connect(self.update_cal7)
self.eye_tracker_window.pushButton_8.clicked.connect(self.update_cal8)
self.eye_tracker_window.pushButton_9.clicked.connect(self.update_cal9)
self.eye_tracker_window.pushButton_12.clicked.connect(
self.update_current_gaze_loc)
self.eye_tracker_window.pushButton_13.clicked.connect(
self.recording_data)
self.eye_tracker_window.pushButton_14.clicked.connect(
self.recording_stop)
self.rec_time = int(self.eye_tracker_window.LineEdit_rec.text())
# self.LineEdit_rec.clicked.conntect(self.update_rec_time(int(self.LineEdit_rec.text())))
self.gaze_x = 0
self.gaze_y = 0
self.table_row = 0
self.table_col = 0
# print(self.gaze_x, self.gaze_y)
self.UTC_time = 0
# List of values in 9 points
self.points = np.zeros((9, 2))
self.gaze_loc = 0
def trigger_help(self):
"""Shows / hide help in the scope window"""
if self.show_help:
self.help.setPos(0, self.scale)
self.main_plot_handler.addItem(self.help)
self.help.setZValue(1)
else:
self.main_plot_handler.removeItem(self.help)
def eventFilter(self, source, event):
"""
Select single channel to scale by right clicking
:param source: channel table content
:param event: right mouse button press
:return: ID of the selected channel
"""
if (event.type() == QtCore.QEvent.MouseButtonPress
and event.buttons() == QtCore.Qt.RightButton
and source is self.ui.table_channels.viewport()):
item = self.ui.table_channels.itemAt(event.pos())
# print('Global Pos:', event.globalPos())
if item is not None:
self.channel_to_scale_row_index = item.row()
self.channel_to_scale_column_index = item.column()
print("RRRRRRRRR", self.channel_to_scale_row_index,
self.channel_to_scale_column_index)
# print('Table Item:', item.row(), item.column())
# self.menu = QMenu(self)
# self.menu.addAction(item.text()) #(QAction('test'))
# menu.exec_(event.globalPos())
return super(MainView, self).eventFilter(source, event)
class RecordingFromHardWare():
def __init__(self):
self.start_recording = False
self.new_data = []
self.sr = StreamReceiver(buffer_size=0)
self.record_dir = '%s/records' % os.getcwd()
self.current_window = np.ndarray([])
self.current_time_stamps = np.ndarray([])
self.MRCP_window_size = 6
print("I RUMNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN")
# =============================================================================
# def runInfinitecording(self):
# while self.start_recording:
# self.sr.acquire()
# if self.sr.get_buflen() > 20:
# duration = str(datetime.timedelta(seconds=int(self.sr.get_buflen())))
# #recordLogger.info('RECORDING %s' % duration)
# #next_sec += 1
# buffers, times = self.sr.get_buffer()
# signals = buffers
# events = None
#
# # channels = total channels from amp, including trigger channel
# data = {'signals':signals, 'timestamps':times, 'events':events,
# 'sample_rate':self.sr.get_sample_rate(), 'channels':self.sr.get_num_channels(),
# 'ch_names':self.sr.get_channel_names(), 'lsl_time_offset':self.sr.lsl_time_offset}
# print(data)
# self.new_data.append(data)
# =============================================================================
def startRecording(self):
# do some stuff
self.start_recording = True
#download_thread = threading.Thread(target=self.runInfinitecording)
recordLogger = logger
thread = threading.Thread(target=self.record)
thread.start()
def stopRecording(self):
self.start_recording = False
#Write into file
#self.new_data = []
def isRecordingIsRunning(self):
return self.start_recording
def set_MRCP_window_size(self, MRCP_window_size):
self.MRCP_window_size = MRCP_window_size
def record(self):
recordLogger = logger
#redirect_stdout_to_queue(recordLogger, 'INFO')
# set data file name
timestamp = time.strftime('%Y%m%d-%H%M%S', time.localtime())
pcl_file = "%s/%s-raw.pcl" % (self.record_dir, timestamp)
eve_file = '%s/%s-eve.txt' % (self.record_dir, timestamp)
recordLogger.info('>> Output file: %s' % (pcl_file))
# test writability
try:
qc.make_dirs(self.record_dir)
open(pcl_file, 'w').write(
'The data will written when the recording is finished.')
except:
raise RuntimeError('Problem writing to %s. Check permission.' %
pcl_file)
# start a server for sending out data pcl_file when software trigger is used
outlet = start_server('StreamRecorderInfo', channel_format='string',\
source_id=eve_file, stype='Markers')
# connect to EEG stream server
#sr = StreamReceiver(buffer_size=0, eeg_only=eeg_only)
# start recording
recordLogger.info('\n>> Recording started (PID %d).' % os.getpid())
qc.print_c('\n>> Press Enter to stop recording', 'G')
tm = qc.Timer(autoreset=True)
next_sec = 1
while self.start_recording:
self.sr.acquire()
if self.sr.get_buflen() > next_sec:
duration = str(
datetime.timedelta(seconds=int(self.sr.get_buflen())))
#recordLogger.info('RECORDING %s' % duration)
next_sec += 1
#current_buffer, current_times = self.sr.get_buffer()
self.sr.set_window_size(self.MRCP_window_size)
self.current_window, self.current_time_stamps = self.sr.get_window(
)
#print("window shape: {} time stamps shape: {}".format(self.current_window.shape, self.current_time_stamps.shape))
tm.sleep_atleast(0.001)
# record stop
recordLogger.info('>> Stop requested. Copying buffer')
buffers, times = self.sr.get_buffer()
signals = buffers
events = None
# channels = total channels from amp, including trigger channel
data = {
'signals': signals,
'timestamps': times,
'events': events,
'sample_rate': self.sr.get_sample_rate(),
'channels': self.sr.get_num_channels(),
'ch_names': self.sr.get_channel_names(),
'lsl_time_offset': self.sr.lsl_time_offset
}
print("data length : {}".format(data['signals'].shape))
self.new_data = data['signals']
recordLogger.info('Saving raw data ...')
qc.save_obj(pcl_file, data)
recordLogger.info('Saved to %s\n' % pcl_file)
# automatically convert to fif and use event file if it exists (software trigger)
if os.path.exists(eve_file):
recordLogger.info('Found matching event file, adding events.')
else:
eve_file = None
recordLogger.info('Converting raw file into fif.')
pcl2fif(pcl_file, external_event=eve_file)
class BCIDecoder(object):
"""
Decoder class
The label order of self.labels and self.label_names match likelihood orders computed by get_prob()
"""
def __init__(self, classifier=None, buffer_size=1.0, fake=False, amp_serial=None, amp_name=None):
"""
Params
------
classifier: classifier file
spatial: spatial filter to use
buffer_size: length of the signal buffer in seconds
"""
self.classifier = classifier
self.buffer_sec = buffer_size
self.fake = fake
self.amp_serial = amp_serial
self.amp_name = amp_name
if self.fake == False:
model = qc.load_obj(self.classifier)
if model == None:
self.print('Error loading %s' % model)
sys.exit(-1)
self.cls = model['cls']
self.psde = model['psde']
self.labels = list(self.cls.classes_)
self.label_names = [model['classes'][k] for k in self.labels]
self.spatial = model['spatial']
self.spectral = model['spectral']
self.notch = model['notch']
self.w_seconds = model['w_seconds']
self.w_frames = model['w_frames']
self.wstep = model['wstep']
self.sfreq = model['sfreq']
if not int(self.sfreq * self.w_seconds) == self.w_frames:
raise RuntimeError('sfreq * w_sec %d != w_frames %d' % (int(self.sfreq * self.w_seconds), self.w_frames))
if 'multiplier' in model:
self.multiplier = model['multiplier']
else:
self.multiplier = 1
# Stream Receiver
self.sr = StreamReceiver(window_size=self.w_seconds, amp_name=self.amp_name, amp_serial=self.amp_serial)
if self.sfreq != self.sr.sample_rate:
raise RuntimeError('Amplifier sampling rate (%.1f) != model sampling rate (%.1f). Stop.' % (
self.sr.sample_rate, self.sfreq))
# Map channel indices based on channel names of the streaming server
self.spatial_ch = model['spatial_ch']
self.spectral_ch = model['spectral_ch']
self.notch_ch = model['notch_ch']
self.ref_new = model['ref_new']
self.ref_old = model['ref_old']
self.ch_names = self.sr.get_channel_names()
mc = model['ch_names']
self.picks = [self.ch_names.index(mc[p]) for p in model['picks']]
if self.spatial_ch is not None:
self.spatial_ch = [self.ch_names.index(mc[p]) for p in model['spatial_ch']]
if self.spectral_ch is not None:
self.spectral_ch = [self.ch_names.index(mc[p]) for p in model['spectral_ch']]
if self.notch_ch is not None:
self.notch_ch = [self.ch_names.index(mc[p]) for p in model['notch_ch']]
if self.ref_new is not None:
self.ref_new = self.ch_names.index(mc[model['ref_new']])
if self.ref_old is not None:
self.ref_old = self.ch_names.index(mc[model['ref_old']])
# PSD buffer
psd_temp = self.psde.transform(np.zeros((1, len(self.picks), self.w_frames)))
self.psd_shape = psd_temp.shape
self.psd_size = psd_temp.size
self.psd_buffer = np.zeros((0, self.psd_shape[1], self.psd_shape[2]))
self.ts_buffer = []
else:
# Fake left-right decoder
model = None
self.psd_shape = None
self.psd_size = None
# TODO: parameterize directions using fake_dirs
self.labels = [11, 9]
self.label_names = ['LEFT_GO', 'RIGHT_GO']
def print(self, *args):
if len(args) > 0: print('[BCIDecoder] ', end='')
print(*args)
def get_labels(self):
"""
Returns
-------
Class labels numbers in the same order as the likelihoods returned by get_prob()
"""
return self.labels
def get_label_names(self):
"""
Returns
-------
Class label names in the same order as get_labels()
"""
return self.label_names
def start(self):
pass
def stop(self):
pass
def get_prob(self):
"""
Read the latest window
Returns
-------
The likelihood P(X|C), where X=window, C=model
"""
if self.fake:
# fake deocder: biased likelihood for the first class
probs = [random.uniform(0.0, 1.0)]
# others class likelihoods are just set to equal
p_others = (1 - probs[0]) / (len(self.labels) - 1)
for x in range(1, len(self.labels)):
probs.append(p_others)
time.sleep(0.0625) # simulated delay for PSD + RF
else:
self.sr.acquire()
w, ts = self.sr.get_window() # w = times x channels
w = w.T # -> channels x times
# apply filters. Important: maintain the original channel order at this point.
pu.preprocess(w, sfreq=self.sfreq, spatial=self.spatial, spatial_ch=self.spatial_ch,
spectral=self.spectral, spectral_ch=self.spectral_ch, notch=self.notch,
notch_ch=self.notch_ch, multiplier=self.multiplier)
# select the same channels used for training
w = w[self.picks]
# debug: show max - min
# c=1; print( '### %d: %.1f - %.1f = %.1f'% ( self.picks[c], max(w[c]), min(w[c]), max(w[c])-min(w[c]) ) )
# psd = channels x freqs
psd = self.psde.transform(w.reshape((1, w.shape[0], w.shape[1])))
# update psd buffer ( < 1 msec overhead )
self.psd_buffer = np.concatenate((self.psd_buffer, psd), axis=0)
self.ts_buffer.append(ts[0])
if ts[0] - self.ts_buffer[0] > self.buffer_sec:
# search speed comparison for ordered arrays:
# http://stackoverflow.com/questions/16243955/numpy-first-occurence-of-value-greater-than-existing-value
t_index = np.searchsorted(self.ts_buffer, ts[0] - 1.0)
self.ts_buffer = self.ts_buffer[t_index:]
self.psd_buffer = self.psd_buffer[t_index:, :, :] # numpy delete is slower
# assert ts[0] - self.ts_buffer[0] <= self.buffer_sec
# make a feautre vector and classify
feats = np.concatenate(psd[0]).reshape(1, -1)
# compute likelihoods
probs = self.cls.predict_proba(feats)[0]
return probs
def get_prob_unread(self):
return self.get_prob()
def get_psd(self):
"""
Returns
-------
The latest computed PSD
"""
return self.psd_buffer[-1].reshape((1, -1))
def is_ready(self):
"""
Ready to decode? Returns True if buffer is not empty.
"""
return self.sr.is_ready()
class BCIDecoder(object):
"""
Decoder class
The label order of self.labels and self.label_names match likelihood orders computed by get_prob()
"""
def __init__(self, classifier=None, buffer_size=1.0, fake=False, amp_serial=None, amp_name=None):
"""
Params
------
classifier: classifier file
spatial: spatial filter to use
buffer_size: length of the signal buffer in seconds
"""
self.classifier = classifier
self.buffer_sec = buffer_size
self.fake = fake
self.amp_serial = amp_serial
self.amp_name = amp_name
if self.fake == False:
model = qc.load_obj(self.classifier)
if model is None:
logger.error('Classifier model is None.')
raise ValueError
self.cls = model['cls']
self.psde = model['psde']
self.labels = list(self.cls.classes_)
self.label_names = [model['classes'][k] for k in self.labels]
self.spatial = model['spatial']
self.spectral = model['spectral']
self.notch = model['notch']
self.w_seconds = model['w_seconds']
self.w_frames = model['w_frames']
self.wstep = model['wstep']
self.sfreq = model['sfreq']
if 'decim' not in model:
model['decim'] = 1
self.decim = model['decim']
if not int(round(self.sfreq * self.w_seconds)) == self.w_frames:
logger.error('sfreq * w_sec %d != w_frames %d' % (int(round(self.sfreq * self.w_seconds)), self.w_frames))
raise RuntimeError
if 'multiplier' in model:
self.multiplier = model['multiplier']
else:
self.multiplier = 1
# Stream Receiver
self.sr = StreamReceiver(window_size=self.w_seconds, amp_name=self.amp_name, amp_serial=self.amp_serial)
if self.sfreq != self.sr.sample_rate:
logger.error('Amplifier sampling rate (%.3f) != model sampling rate (%.3f). Stop.' % (self.sr.sample_rate, self.sfreq))
raise RuntimeError
# Map channel indices based on channel names of the streaming server
self.spatial_ch = model['spatial_ch']
self.spectral_ch = model['spectral_ch']
self.notch_ch = model['notch_ch']
#self.ref_ch = model['ref_ch'] # not supported yet
self.ch_names = self.sr.get_channel_names()
mc = model['ch_names']
self.picks = [self.ch_names.index(mc[p]) for p in model['picks']]
if self.spatial_ch is not None:
self.spatial_ch = [self.ch_names.index(mc[p]) for p in model['spatial_ch']]
if self.spectral_ch is not None:
self.spectral_ch = [self.ch_names.index(mc[p]) for p in model['spectral_ch']]
if self.notch_ch is not None:
self.notch_ch = [self.ch_names.index(mc[p]) for p in model['notch_ch']]
# PSD buffer
#psd_temp = self.psde.transform(np.zeros((1, len(self.picks), self.w_frames // self.decim)))
#self.psd_shape = psd_temp.shape
#self.psd_size = psd_temp.size
#self.psd_buffer = np.zeros((0, self.psd_shape[1], self.psd_shape[2]))
#self.psd_buffer = None
self.ts_buffer = []
logger.info_green('Loaded classifier %s (sfreq=%.3f, decim=%d)' % (' vs '.join(self.label_names), self.sfreq, self.decim))
else:
# Fake left-right decoder
model = None
self.psd_shape = None
self.psd_size = None
# TODO: parameterize directions using fake_dirs
self.labels = [11, 9]
self.label_names = ['LEFT_GO', 'RIGHT_GO']
def get_labels(self):
"""
Returns
-------
Class labels numbers in the same order as the likelihoods returned by get_prob()
"""
return self.labels
def get_label_names(self):
"""
Returns
-------
Class label names in the same order as get_labels()
"""
return self.label_names
def start(self):
pass
def stop(self):
pass
def get_prob(self, timestamp=False):
"""
Read the latest window
Input
-----
timestamp: If True, returns LSL timestamp of the leading edge of the window used for decoding.
Returns
-------
The likelihood P(X|C), where X=window, C=model
"""
if self.fake:
# fake deocder: biased likelihood for the first class
probs = [random.uniform(0.0, 1.0)]
# others class likelihoods are just set to equal
p_others = (1 - probs[0]) / (len(self.labels) - 1)
for x in range(1, len(self.labels)):
probs.append(p_others)
time.sleep(0.0625) # simulated delay
t_prob = pylsl.local_clock()
else:
self.sr.acquire(blocking=True)
w, ts = self.sr.get_window() # w = times x channels
t_prob = ts[-1]
w = w.T # -> channels x times
# re-reference channels
# TODO: add re-referencing function to preprocess()
# apply filters. Important: maintain the original channel order at this point.
w = pu.preprocess(w, sfreq=self.sfreq, spatial=self.spatial, spatial_ch=self.spatial_ch,
spectral=self.spectral, spectral_ch=self.spectral_ch, notch=self.notch,
notch_ch=self.notch_ch, multiplier=self.multiplier, decim=self.decim)
# select the same channels used for training
w = w[self.picks]
# debug: show max - min
# c=1; print( '### %d: %.1f - %.1f = %.1f'% ( self.picks[c], max(w[c]), min(w[c]), max(w[c])-min(w[c]) ) )
# psd = channels x freqs
psd = self.psde.transform(w.reshape((1, w.shape[0], w.shape[1])))
# make a feautre vector and classify
feats = np.concatenate(psd[0]).reshape(1, -1)
# compute likelihoods
probs = self.cls.predict_proba(feats)[0]
# update psd buffer ( < 1 msec overhead )
'''
if self.psd_buffer is None:
self.psd_buffer = psd
else:
self.psd_buffer = np.concatenate((self.psd_buffer, psd), axis=0)
# TODO: CHECK THIS BLOCK
self.ts_buffer.append(ts[0])
if ts[0] - self.ts_buffer[0] > self.buffer_sec:
# search speed comparison for ordered arrays:
# http://stackoverflow.com/questions/16243955/numpy-first-occurence-of-value-greater-than-existing-value
#t_index = np.searchsorted(self.ts_buffer, ts[0] - 1.0)
t_index = np.searchsorted(self.ts_buffer, ts[0] - self.buffer_sec)
self.ts_buffer = self.ts_buffer[t_index:]
self.psd_buffer = self.psd_buffer[t_index:, :, :] # numpy delete is slower
# assert ts[0] - self.ts_buffer[0] <= self.buffer_sec
'''
if timestamp:
return probs, t_prob
else:
return probs
def get_prob_unread(self, timestamp=False):
return self.get_prob(timestamp)
def get_psd(self):
"""
Returns
-------
The latest computed PSD
"""
raise NotImplementedError('Sorry! PSD buffer is under testing.')
return self.psd_buffer[-1].reshape((1, -1))
def is_ready(self):
"""
Ready to decode? Returns True if buffer is not empty.
"""
return self.sr.is_ready()