# Authors: Christoph Dinh # Martin Luessi # Matti Hamalainen # Alexandre Gramfort # Denis Engemann # # License: BSD (3-clause) import time import copy import numpy as np from .. import pick_channels, pick_types from ..utils import logger, verbose from ..baseline import rescale from ..epochs import _BaseEpochs from ..event import _find_events from ..filter import detrend from ..io.proj import setup_proj class RtEpochs(_BaseEpochs): """Realtime Epochs Can receive epochs in real time from an RtClient. For example, to get some epochs from a running mne_rt_server on 'localhost', you could use: client = mne.realtime.RtClient('localhost') event_id, tmin, tmax = 1, -0.2, 0.5 epochs = mne.realtime.RtEpochs(client, event_id, tmin, tmax) epochs.start() # start the measurement and start receiving epochs evoked_1 = epochs.average() # computed over all epochs evoked_2 = epochs[-5:].average() # computed over the last 5 epochs Parameters ---------- client : instance of mne.realtime.RtClient The realtime client. event_id : int | list of int The id of the event to consider. If int, only events with the ID specified by event_id are considered. Multiple event ID's can be specified using a list. tmin : float Start time before event. tmax : float End time after event. stim_channel : string or list of string Name of the stim channel or all the stim channels affected by the trigger. sleep_time : float Time in seconds to wait between checking for new epochs when epochs are requested and the receive queue is empty. name : string Comment that describes the Evoked data created. baseline : None (default) or tuple of length 2 The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. reject : dict Epoch rejection parameters based on peak to peak amplitude. Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'. If reject is None then no rejection is done. Values are float. Example: reject = dict(grad=4000e-13, # T / m (gradiometers) mag=4e-12, # T (magnetometers) eeg=40e-6, # uV (EEG channels) eog=250e-6 # uV (EOG channels) ) flat : dict Epoch rejection parameters based on flatness of signal Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg' If flat is None then no rejection is done. proj : bool, optional Apply SSP projection vectors decim : int Factor by which to downsample the data from the raw file upon import. Warning: This simply selects every nth sample, data is not filtered here. If data is not properly filtered, aliasing artifacts may occur. reject_tmin : scalar | None Start of the time window used to reject epochs (with the default None, the window will start with tmin). reject_tmax : scalar | None End of the time window used to reject epochs (with the default None, the window will end with tmax). detrend : int | None If 0 or 1, the data channels (MEG and EEG) will be detrended when loaded. 0 is a constant (DC) detrend, 1 is a linear detrend. None is no detrending. Note that detrending is performed before baseline correction. If no DC offset is preferred (zeroth order detrending), either turn off baseline correction, as this may introduce a DC shift, or set baseline correction to use the entire time interval (will yield equivalent results but be slower). add_eeg_ref : bool If True, an EEG average reference will be added (unless one already exists). isi_max : float The maximmum time in seconds between epochs. If no epoch arrives in the next isi_max seconds the RtEpochs stops. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose). Defaults to client.verbose. Attributes ---------- info : dict Measurement info. event_id : dict Names of of conditions corresponding to event_ids. ch_names : list of string List of channels' names. events : list of tuples The events associated with the epochs currently in the queue. verbose : bool, str, int, or None See above. """ @verbose def __init__(self, client, event_id, tmin, tmax, stim_channel='STI 014', sleep_time=0.1, baseline=(None, 0), picks=None, name='Unknown', reject=None, flat=None, proj=True, decim=1, reject_tmin=None, reject_tmax=None, detrend=None, add_eeg_ref=True, isi_max=2., verbose=None): info = client.get_measurement_info() # the measurement info of the data as we receive it self._client_info = copy.deepcopy(info) verbose = client.verbose if verbose is None else verbose # call _BaseEpochs constructor super(RtEpochs, self).__init__(info, event_id, tmin, tmax, baseline=baseline, picks=picks, name=name, reject=reject, flat=flat, decim=decim, reject_tmin=reject_tmin, reject_tmax=reject_tmax, detrend=detrend, add_eeg_ref=add_eeg_ref, verbose=verbose) self.proj = proj self._projector, self.info = setup_proj(self.info, add_eeg_ref, activate=self.proj) self._client = client if not isinstance(stim_channel, list): stim_channel = [stim_channel] stim_picks = pick_channels(self._client_info['ch_names'], include=stim_channel, exclude=[]) if len(stim_picks) == 0: raise ValueError('No stim channel found to extract event ' 'triggers.') self._stim_picks = stim_picks self._sleep_time = sleep_time # add calibration factors cals = np.zeros(self._client_info['nchan']) for k in range(self._client_info['nchan']): cals[k] = (self._client_info['chs'][k]['range'] * self._client_info['chs'][k]['cal']) self._cals = cals[:, None] # FIFO queues for received epochs and events self._epoch_queue = list() self.events = list() # variables needed for receiving raw buffers self._last_buffer = None self._first_samp = 0 self._event_backlog = list() # Number of good and bad epochs received self._n_good = 0 self._n_bad = 0 self._started = False self._last_time = time.time() self.isi_max = isi_max def start(self): """Start receiving epochs The measurement will be started if it has not already been started. """ if not self._started: # register the callback self._client.register_receive_callback(self._process_raw_buffer) # start the measurement and the receive thread nchan = self._client_info['nchan'] self._client.start_receive_thread(nchan) self._started = True self._last_time = np.inf # init delay counter. Will stop iterations def stop(self, stop_receive_thread=False, stop_measurement=False): """Stop receiving epochs Parameters ---------- stop_receive_thread : bool Stop the receive thread. Note: Other RtEpochs instances will also stop receiving epochs when the receive thread is stopped. The receive thread will always be stopped if stop_measurement is True. stop_measurement : bool Also stop the measurement. Note: Other clients attached to the server will also stop receiving data. """ if self._started: self._client.unregister_receive_callback(self._process_raw_buffer) self._started = False if stop_receive_thread or stop_measurement: self._client.stop_receive_thread(stop_measurement=stop_measurement) def next(self, return_event_id=False): """To make iteration over epochs easy. """ first = True while True: current_time = time.time() if current_time > (self._last_time + self.isi_max): logger.info('Time of %s seconds exceeded.' % self.isi_max) raise StopIteration if len(self._epoch_queue) > self._current: epoch = self._epoch_queue[self._current] event_id = self.events[self._current][-1] self._current += 1 self._last_time = current_time if return_event_id: return epoch, event_id else: return epoch if self._started: if first: logger.info('Waiting for epoch %d' % (self._current + 1)) first = False time.sleep(self._sleep_time) else: raise RuntimeError('Not enough epochs in queue and currently ' 'not receiving epochs, cannot get epochs!') def _get_data_from_disk(self): """Return the data for n_epochs epochs""" epochs = list() for epoch in self: epochs.append(epoch) data = np.array(epochs) return data def _process_raw_buffer(self, raw_buffer): """Process raw buffer (callback from RtClient) Note: Do not print log messages during regular use. It will be printed asynchronously which is annyoing when working in an interactive shell. Parameters ---------- raw_buffer : array of float, shape=(nchan, n_times) The raw buffer. """ verbose = 'ERROR' sfreq = self.info['sfreq'] n_samp = len(self._raw_times) # relative start and stop positions in samples tmin_samp = int(round(sfreq * self.tmin)) tmax_samp = tmin_samp + n_samp last_samp = self._first_samp + raw_buffer.shape[1] - 1 # apply calibration without inplace modification raw_buffer = self._cals * raw_buffer # detect events data = np.abs(raw_buffer[self._stim_picks]).astype(np.int) data = np.atleast_2d(data) buff_events = _find_events(data, self._first_samp, verbose=verbose) events = self._event_backlog for event_id in self.event_id.values(): idx = np.where(buff_events[:, -1] == event_id)[0] events.extend(zip(list(buff_events[idx, 0]), list(buff_events[idx, -1]))) events.sort() event_backlog = list() for event_samp, event_id in events: epoch = None if (event_samp + tmin_samp >= self._first_samp and event_samp + tmax_samp <= last_samp): # easy case: whole epoch is in this buffer start = event_samp + tmin_samp - self._first_samp stop = event_samp + tmax_samp - self._first_samp epoch = raw_buffer[:, start:stop] elif (event_samp + tmin_samp < self._first_samp and event_samp + tmax_samp <= last_samp): # have to use some samples from previous buffer if self._last_buffer is None: continue n_last = self._first_samp - (event_samp + tmin_samp) n_this = n_samp - n_last epoch = np.c_[self._last_buffer[:, -n_last:], raw_buffer[:, :n_this]] elif event_samp + tmax_samp > last_samp: # we need samples from next buffer if event_samp + tmin_samp < self._first_samp: raise RuntimeError('Epoch spans more than two raw ' 'buffers, increase buffer size!') # we will process this epoch with the next buffer event_backlog.append((event_samp, event_id)) else: raise RuntimeError('Unhandled case..') if epoch is not None: self._append_epoch_to_queue(epoch, event_samp, event_id) # set things up for processing of next buffer self._event_backlog = event_backlog self._first_samp = last_samp + 1 self._last_buffer = raw_buffer def _append_epoch_to_queue(self, epoch, event_samp, event_id): """Append a (raw) epoch to queue Note: Do not print log messages during regular use. It will be printed asynchronously which is annyoing when working in an interactive shell. Parameters ---------- epoch : array of float, shape=(nchan, n_times) The raw epoch (only calibration has been applied) over all channels. event_samp : int The time in samples when the epoch occurred. event_id : int The event ID of the epoch. """ # select the channels epoch = epoch[self.picks, :] # handle offset if self._offset is not None: epoch += self._offset # apply SSP if self.proj and self._projector is not None: epoch = np.dot(self._projector, epoch) # Detrend, baseline correct, decimate epoch = self._preprocess(epoch, verbose='ERROR') # Decide if this is a good epoch is_good, _ = self._is_good_epoch(epoch, verbose='ERROR') if is_good: self._epoch_queue.append(epoch) self.events.append((event_samp, 0, event_id)) self._n_good += 1 else: self._n_bad += 1 def __repr__(self): s = 'good / bad epochs received: %d / %d, epochs in queue: %d, '\ % (self._n_good, self._n_bad, len(self._epoch_queue)) s += ', tmin : %s (s)' % self.tmin s += ', tmax : %s (s)' % self.tmax s += ', baseline : %s' % str(self.baseline) return '' % s