# Authors: Jaakko Leppakangas # # License: BSD (3-clause) from datetime import datetime, timedelta import time import os.path as op import re from copy import deepcopy from itertools import takewhile import collections import numpy as np from .utils import (_pl, check_fname, _validate_type, verbose, warn, logger, _check_pandas_installed, _mask_to_onsets_offsets) from .utils import _DefaultEventParser from .io.write import (start_block, end_block, write_float, write_name_list, write_double, start_file) from .io.constants import FIFF from .io.open import fiff_open from .io.tree import dir_tree_find from .io.tag import read_tag def _check_o_d_s(onset, duration, description): onset = np.atleast_1d(np.array(onset, dtype=float)) if onset.ndim != 1: raise ValueError('Onset must be a one dimensional array, got %s ' '(shape %s).' % (onset.ndim, onset.shape)) duration = np.array(duration, dtype=float) if duration.ndim == 0 or duration.shape == (1,): duration = np.repeat(duration, len(onset)) if duration.ndim != 1: raise ValueError('Duration must be a one dimensional array, ' 'got %d.' % (duration.ndim,)) description = np.array(description, dtype=str) if description.ndim == 0 or description.shape == (1,): description = np.repeat(description, len(onset)) if description.ndim != 1: raise ValueError('Description must be a one dimensional array, ' 'got %d.' % (description.ndim,)) if any([';' in desc for desc in description]): raise ValueError('Semicolons in descriptions not supported.') if not (len(onset) == len(duration) == len(description)): raise ValueError('Onset, duration and description must be ' 'equal in sizes, got %s, %s, and %s.' % (len(onset), len(duration), len(description))) return onset, duration, description class Annotations(object): """Annotation object for annotating segments of raw data. Parameters ---------- onset : array of float, shape (n_annotations,) The starting time of annotations in seconds after ``orig_time``. duration : array of float, shape (n_annotations,) Durations of the annotations in seconds. description : array of str, shape (n_annotations,) | str Array of strings containing description for each annotation. If a string, all the annotations are given the same description. To reject epochs, use description starting with keyword 'bad'. See example above. orig_time : float | int | instance of datetime.datetime | array of int | None | str A POSIX Timestamp, datetime or an array containing the timestamp as the first element and microseconds as the second element. Determines the starting time of annotation acquisition. If None (default), starting time is determined from beginning of raw data acquisition. In general, ``raw.info['meas_date']`` (or None) can be used for syncing the annotations with raw data if their acquisiton is started at the same time. If it is a string, it should conform to the ISO8601 format. More precisely to this '%Y-%m-%d %H:%M:%S.%f' particular case of the ISO8601 format where the delimiter between date and time is ' '. Notes ----- Annotations are added to instance of :class:`mne.io.Raw` as the attribute :attr:`raw.annotations `. To reject bad epochs using annotations, use annotation description starting with 'bad' keyword. The epochs with overlapping bad segments are then rejected automatically by default. To remove epochs with blinks you can do: >>> eog_events = mne.preprocessing.find_eog_events(raw) # doctest: +SKIP >>> n_blinks = len(eog_events) # doctest: +SKIP >>> onset = eog_events[:, 0] / raw.info['sfreq'] - 0.25 # doctest: +SKIP >>> duration = np.repeat(0.5, n_blinks) # doctest: +SKIP >>> description = ['bad blink'] * n_blinks # doctest: +SKIP >>> annotations = mne.Annotations(onset, duration, description) # doctest: +SKIP >>> raw.set_annotations(annotations) # doctest: +SKIP >>> epochs = mne.Epochs(raw, events, event_id, tmin, tmax) # doctest: +SKIP **orig_time** If ``orig_time`` is None, the annotations are synced to the start of the data (0 seconds). Otherwise the annotations are synced to sample 0 and ``raw.first_samp`` is taken into account the same way as with events. When setting annotations, the following alignments between ``raw.info['meas_date']`` and ``annotation.orig_time`` take place: :: ----------- meas_date=XX, orig_time=YY ----------------------------- | +------------------+ |______________| RAW | | | | | +------------------+ meas_date first_samp . . | +------+ . |_________| ANOT | . | | | . | +------+ . orig_time onset[0] . | +------+ |___________________| | | | | | +------+ orig_time onset[0]' ----------- meas_date=XX, orig_time=None --------------------------- | +------------------+ |______________| RAW | | | | | +------------------+ . N +------+ . o_________| ANOT | . n | | . e +------+ . | +------+ |________________________| | | | | | +------+ orig_time onset[0]' ----------- meas_date=None, orig_time=YY --------------------------- N +------------------+ o______________| RAW | n | | e +------------------+ | +------+ |_________| ANOT | | | | | +------+ [[[ CRASH ]]] ----------- meas_date=None, orig_time=None ------------------------- N +------------------+ o______________| RAW | n | | e +------------------+ . N +------+ . o_________| ANOT | . n | | . e +------+ . N +------+ o________________________| | n | | e +------+ orig_time onset[0]' """ # noqa: E501 def __init__(self, onset, duration, description, orig_time=None): # noqa: D102 if orig_time is not None: orig_time = _handle_meas_date(orig_time) self.orig_time = orig_time self.onset, self.duration, self.description = _check_o_d_s( onset, duration, description) self._sort() # ensure we're sorted def __repr__(self): """Show the representation.""" counter = collections.Counter(self.description) kinds = ['%s (%s)' % k for k in counter.items()] kinds = ', '.join(kinds[:3]) + ('' if len(kinds) <= 3 else '...') kinds = (': ' if len(kinds) > 0 else '') + kinds if self.orig_time is None: orig = 'orig_time : None' else: orig = 'orig_time : %s' % datetime.utcfromtimestamp(self.orig_time) return ('' % (len(self.onset), _pl(len(self.onset)), kinds, orig)) def __len__(self): """Return the number of annotations.""" return len(self.duration) def __add__(self, other): """Add (concatencate) two Annotation objects.""" out = self.copy() out += other return out def __iadd__(self, other): """Add (concatencate) two Annotation objects in-place. Both annotations must have the same orig_time """ if len(self) == 0: self.orig_time = other.orig_time if self.orig_time != other.orig_time: raise ValueError("orig_time should be the same to " "add/concatenate 2 annotations " "(got %s != %s)" % (self.orig_time, other.orig_time)) return self.append(other.onset, other.duration, other.description) def __iter__(self): """Iterate over the annotations.""" for idx in range(len(self.onset)): yield self.__getitem__(idx) def __getitem__(self, key): """Propagate indexing and slicing to the underlying numpy structure.""" if isinstance(key, int): out_keys = ('onset', 'duration', 'description', 'orig_time') out_vals = (self.onset[key], self.duration[key], self.description[key], self.orig_time) return collections.OrderedDict(zip(out_keys, out_vals)) else: key = list(key) if isinstance(key, tuple) else key return Annotations(onset=self.onset[key], duration=self.duration[key], description=self.description[key], orig_time=self.orig_time) def append(self, onset, duration, description): """Add an annotated segment. Operates inplace. Parameters ---------- onset : float | array-like Annotation time onset from the beginning of the recording in seconds. duration : float | array-like Duration of the annotation in seconds. description : str | array-like Description for the annotation. To reject epochs, use description starting with keyword 'bad' Returns ------- self : mne.Annotations The modified Annotations object. Notes ----- The array-like support for arguments allows this to be used similarly to not only ``list.append``, but also `list.extend `__. """ # noqa: E501 onset, duration, description = _check_o_d_s( onset, duration, description) self.onset = np.append(self.onset, onset) self.duration = np.append(self.duration, duration) self.description = np.append(self.description, description) self._sort() return self def copy(self): """Return a deep copy of self.""" return deepcopy(self) def delete(self, idx): """Remove an annotation. Operates inplace. Parameters ---------- idx : int | array-like of int Index of the annotation to remove. Can be array-like to remove multiple indices. """ self.onset = np.delete(self.onset, idx) self.duration = np.delete(self.duration, idx) self.description = np.delete(self.description, idx) def save(self, fname): """Save annotations to FIF, CSV or TXT. Typically annotations get saved in the FIF file for raw data (e.g., as ``raw.annotations``), but this offers the possibility to also save them to disk separately in different file formats which are easier to share between packages. Parameters ---------- fname : str The filename to use. """ check_fname(fname, 'annotations', ('-annot.fif', '-annot.fif.gz', '_annot.fif', '_annot.fif.gz', '.txt', '.csv')) if fname.endswith(".txt"): _write_annotations_txt(fname, self) elif fname.endswith(".csv"): _write_annotations_csv(fname, self) else: with start_file(fname) as fid: _write_annotations(fid, self) def _sort(self): """Sort in place.""" # instead of argsort here we use sorted so that it gives us # the onset-then-duration hierarchy vals = sorted(zip(self.onset, self.duration, range(len(self)))) order = list(list(zip(*vals))[-1]) if len(vals) else [] self.onset = self.onset[order] self.duration = self.duration[order] self.description = self.description[order] def crop(self, tmin=None, tmax=None, emit_warning=False): """Remove all annotation that are outside of [tmin, tmax]. The method operates inplace. Parameters ---------- tmin : float | None Start time of selection in seconds. tmax : float | None End time of selection in seconds. emit_warning : bool Whether to emit warnings when limiting or omitting annotations. Defaults to False. Returns ------- self : instance of Annotations The cropped Annotations object. """ if len(self) == 0: return # no annotations, nothing to do offset = 0 if self.orig_time is None else self.orig_time absolute_onset = self.onset + offset absolute_offset = absolute_onset + self.duration tmin = tmin if tmin is not None else absolute_onset.min() tmax = tmax if tmax is not None else absolute_offset.max() if tmin > tmax: raise ValueError('tmax should be greater than tmin.') out_of_bounds = (absolute_onset > tmax) | (absolute_offset < tmin) # clip the left side clip_left_elem = (absolute_onset < tmin) & ~out_of_bounds self.onset[clip_left_elem] = tmin - offset diff = tmin - absolute_onset[clip_left_elem] self.duration[clip_left_elem] = self.duration[clip_left_elem] - diff # clip the right side clip_right_elem = (absolute_offset > tmax) & ~out_of_bounds diff = absolute_offset[clip_right_elem] - tmax self.duration[clip_right_elem] = self.duration[clip_right_elem] - diff # remove out of bounds self.onset = self.onset.compress(~out_of_bounds) self.duration = self.duration.compress(~out_of_bounds) self.description = self.description.compress(~out_of_bounds) if emit_warning: omitted = out_of_bounds.sum() if omitted > 0: warn('Omitted %s annotation(s) that were outside data' ' range.' % omitted) limited = clip_left_elem.sum() + clip_right_elem.sum() if limited > 0: warn('Limited %s annotation(s) that were expanding outside the' ' data range.' % limited) return self def _combine_annotations(one, two, one_n_samples, one_first_samp, two_first_samp, sfreq, meas_date): """Combine a tuple of annotations.""" if one is None and two is None: return None elif two is None: return one elif one is None: one = Annotations([], [], [], None) # Compute the shift necessary for alignment: # 1. The shift (in time) due to concatenation shift = one_n_samples / sfreq meas_date = _handle_meas_date(meas_date) # 2. Shift by the difference in meas_date and one.orig_time if one.orig_time is not None: shift += one_first_samp / sfreq shift += meas_date - one.orig_time # 3. Shift by the difference in meas_date and two.orig_time if two.orig_time is not None: shift -= two_first_samp / sfreq shift -= meas_date - two.orig_time onset = np.concatenate([one.onset, two.onset + shift]) duration = np.concatenate([one.duration, two.duration]) description = np.concatenate([one.description, two.description]) return Annotations(onset, duration, description, one.orig_time) def _handle_meas_date(meas_date): """Convert meas_date to seconds. If `meas_date` is a string, it should conform to the ISO8601 format. More precisely to this '%Y-%m-%d %H:%M:%S.%f' particular case of the ISO8601 format where the delimiter between date and time is ' '. Otherwise, this function returns 0. Note that ISO8601 allows for ' ' or 'T' as delimiters between date and time. """ if meas_date is None: meas_date = 0 elif isinstance(meas_date, str): ACCEPTED_ISO8601 = '%Y-%m-%d %H:%M:%S.%f' try: meas_date = datetime.strptime(meas_date, ACCEPTED_ISO8601) except ValueError: meas_date = 0 else: unix_ref_time = datetime.utcfromtimestamp(0) meas_date = (meas_date - unix_ref_time).total_seconds() meas_date = round(meas_date, 6) # round that 6th decimal elif isinstance(meas_date, datetime): meas_date = float(time.mktime(meas_date.timetuple())) elif not np.isscalar(meas_date): if len(meas_date) > 1: meas_date = meas_date[0] + meas_date[1] / 1000000. else: meas_date = meas_date[0] return float(meas_date) def _sync_onset(raw, onset, inverse=False): """Adjust onsets in relation to raw data.""" meas_date = _handle_meas_date(raw.info['meas_date']) if raw.annotations.orig_time is None: annot_start = onset else: offset = -raw._first_time if inverse else raw._first_time annot_start = (raw.annotations.orig_time - meas_date) - offset + onset return annot_start def _annotations_starts_stops(raw, kinds, name='unknown', invert=False): """Get starts and stops from given kinds. onsets and ends are inclusive. """ _validate_type(kinds, (str, list, tuple), str(type(kinds)), "str, list or tuple") if isinstance(kinds, str): kinds = [kinds] else: for kind in kinds: _validate_type(kind, 'str', "All entries") if len(raw.annotations) == 0: onsets, ends = np.array([], int), np.array([], int) else: idxs = [idx for idx, desc in enumerate(raw.annotations.description) if any(desc.upper().startswith(kind.upper()) for kind in kinds)] # onsets are already sorted onsets = raw.annotations.onset[idxs] onsets = _sync_onset(raw, onsets) ends = onsets + raw.annotations.duration[idxs] onsets = raw.time_as_index(onsets, use_rounding=True) ends = raw.time_as_index(ends, use_rounding=True) assert (onsets <= ends).all() # all durations >= 0 if invert: # We need to eliminate overlaps here, otherwise wacky things happen, # so we carefully invert the relationship mask = np.zeros(len(raw.times), bool) for onset, end in zip(onsets, ends): mask[onset:end] = True mask = ~mask extras = (onsets == ends) extra_onsets, extra_ends = onsets[extras], ends[extras] onsets, ends = _mask_to_onsets_offsets(mask) # Keep ones where things were exactly equal del extras # we could do this with a np.insert+np.searchsorted, but our # ordered-ness should get us it for free onsets = np.sort(np.concatenate([onsets, extra_onsets])) ends = np.sort(np.concatenate([ends, extra_ends])) assert (onsets <= ends).all() return onsets, ends def _write_annotations(fid, annotations): """Write annotations.""" start_block(fid, FIFF.FIFFB_MNE_ANNOTATIONS) write_float(fid, FIFF.FIFF_MNE_BASELINE_MIN, annotations.onset) write_float(fid, FIFF.FIFF_MNE_BASELINE_MAX, annotations.duration + annotations.onset) # To allow : in description, they need to be replaced for serialization write_name_list(fid, FIFF.FIFF_COMMENT, [d.replace(':', ';') for d in annotations.description]) if annotations.orig_time is not None: write_double(fid, FIFF.FIFF_MEAS_DATE, annotations.orig_time) end_block(fid, FIFF.FIFFB_MNE_ANNOTATIONS) def _write_annotations_csv(fname, annot): pd = _check_pandas_installed(strict=True) meas_date = _handle_meas_date(annot.orig_time) dt = datetime.utcfromtimestamp(meas_date) onsets_dt = [dt + timedelta(seconds=o) for o in annot.onset] df = pd.DataFrame(dict(onset=onsets_dt, duration=annot.duration, description=annot.description)) df.to_csv(fname, index=False) def _write_annotations_txt(fname, annot): content = "# MNE-Annotations\n" if annot.orig_time is not None: meas_date = _handle_meas_date(annot.orig_time) orig_dt = datetime.utcfromtimestamp(meas_date) content += "# orig_time : %s \n" % orig_dt content += "# onset, duration, description\n" data = np.array([annot.onset, annot.duration, annot.description], dtype=str).T with open(fname, 'wb') as fid: fid.write(content.encode()) np.savetxt(fid, data, delimiter=',', fmt="%s") def read_annotations(fname, sfreq='auto', uint16_codec=None): r"""Read annotations from a file. This function reads a .fif, .fif.gz, .vrmk, .edf, .txt, .csv .cnt, .cef, or .set file and makes an :class:`mne.Annotations` object. Parameters ---------- fname : str The filename. sfreq : float | 'auto' The sampling frequency in the file. This parameter is necessary for \*.vmrk and \*.cef files as Annotations are expressed in seconds and \*.vmrk/\*.cef files are in samples. For any other file format, ``sfreq`` is omitted. If set to 'auto' then the ``sfreq`` is taken from the respective info file of the same name with according file extension (\*.vhdr for brainvision; \*.dap for Curry 7; \*.cdt.dpa for Curry 8). So data.vrmk looks for sfreq in data.vhdr, data.cef looks in data.dap and data.cdt.cef looks in data.cdt.dpa. uint16_codec : str | None This parameter is only used in EEGLAB (\*.set) and omitted otherwise. If your \*.set file contains non-ascii characters, sometimes reading it may fail and give rise to error message stating that "buffer is too small". ``uint16_codec`` allows to specify what codec (for example: 'latin1' or 'utf-8') should be used when reading character arrays and can therefore help you solve this problem. Returns ------- annot : instance of Annotations | None The annotations. Notes ----- The annotations stored in a .csv require the onset columns to be timestamps. If you have onsets as floats (in seconds), you should use the .txt extension. """ from .io.brainvision.brainvision import _read_annotations_brainvision from .io.eeglab.eeglab import _read_annotations_eeglab from .io.edf.edf import _read_annotations_edf from .io.cnt.cnt import _read_annotations_cnt from .io.curry.curry import _read_annotations_curry from .io.ctf.markers import _read_annotations_ctf name = op.basename(fname) if name.endswith(('fif', 'fif.gz')): # Read FiF files ff, tree, _ = fiff_open(fname, preload=False) with ff as fid: annotations = _read_annotations_fif(fid, tree) elif name.endswith('txt'): orig_time = _read_annotations_txt_parse_header(fname) onset, duration, description = _read_annotations_txt(fname) annotations = Annotations(onset=onset, duration=duration, description=description, orig_time=orig_time) elif name.endswith('vmrk'): annotations = _read_annotations_brainvision(fname, sfreq=sfreq) elif name.endswith('csv'): annotations = _read_annotations_csv(fname) elif name.endswith('cnt'): annotations = _read_annotations_cnt(fname) elif name.endswith('ds'): annotations = _read_annotations_ctf(fname) elif name.endswith('cef'): annotations = _read_annotations_curry(fname, sfreq=sfreq) elif name.endswith('set'): annotations = _read_annotations_eeglab(fname, uint16_codec=uint16_codec) elif name.endswith(('edf', 'bdf', 'gdf')): onset, duration, description = _read_annotations_edf(fname) onset = np.array(onset, dtype=float) duration = np.array(duration, dtype=float) annotations = Annotations(onset=onset, duration=duration, description=description, orig_time=None) elif name.startswith('events_') and fname.endswith('mat'): annotations = _read_brainstorm_annotations(fname) else: raise IOError('Unknown annotation file format "%s"' % fname) if annotations is None: raise IOError('No annotation data found in file "%s"' % fname) return annotations def _read_annotations_csv(fname): """Read annotations from csv. Parameters ---------- fname : str The filename. Returns ------- annot : instance of Annotations The annotations. """ pd = _check_pandas_installed(strict=True) df = pd.read_csv(fname) orig_time = df['onset'].values[0] try: float(orig_time) warn('It looks like you have provided annotation onsets as floats. ' 'These will be interpreted as MILLISECONDS. If that is not what ' 'you want, save your CSV as a TXT file; the TXT reader accepts ' 'onsets in seconds.') except ValueError: pass orig_time = _handle_meas_date(orig_time) onset_dt = pd.to_datetime(df['onset']) onset = (onset_dt - onset_dt[0]).dt.total_seconds() duration = df['duration'].values.astype(float) description = df['description'].values if orig_time == 0: orig_time = None return Annotations(onset, duration, description, orig_time) def _read_brainstorm_annotations(fname, orig_time=None): """Read annotations from a Brainstorm events_ file. Parameters ---------- fname : str The filename orig_time : float | int | instance of datetime | array of int | None A POSIX Timestamp, datetime or an array containing the timestamp as the first element and microseconds as the second element. Determines the starting time of annotation acquisition. If None (default), starting time is determined from beginning of raw data acquisition. In general, ``raw.info['meas_date']`` (or None) can be used for syncing the annotations with raw data if their acquisiton is started at the same time. Returns ------- annot : instance of Annotations | None The annotations. """ from scipy import io def get_duration_from_times(t): return t[1] - t[0] if t.shape[0] == 2 else np.zeros(len(t[0])) annot_data = io.loadmat(fname) onsets, durations, descriptions = (list(), list(), list()) for label, _, _, _, times, _, _ in annot_data['events'][0]: onsets.append(times[0]) durations.append(get_duration_from_times(times)) n_annot = len(times[0]) descriptions += [str(label[0])] * n_annot return Annotations(onset=np.concatenate(onsets), duration=np.concatenate(durations), description=descriptions, orig_time=orig_time) def _is_iso8601(candidate_str): ISO8601 = r'^\d{4}-\d{2}-\d{2}[ T]\d{2}:\d{2}:\d{2}\.\d{6}$' return re.compile(ISO8601).match(candidate_str) is not None def _read_annotations_txt_parse_header(fname): def is_orig_time(x): return x.startswith('# orig_time :') with open(fname) as fid: header = list(takewhile(lambda x: x.startswith('#'), fid)) orig_values = [h[13:].strip() for h in header if is_orig_time(h)] orig_values = [_handle_meas_date(orig) for orig in orig_values if _is_iso8601(orig)] return None if not orig_values else orig_values[0] def _read_annotations_txt(fname): onset, duration, desc = np.loadtxt(fname, delimiter=',', dtype=np.bytes_, unpack=True) onset = [float(o.decode()) for o in onset] duration = [float(d.decode()) for d in duration] desc = [str(d.decode()).strip() for d in desc] return onset, duration, desc def _read_annotations_fif(fid, tree): """Read annotations.""" annot_data = dir_tree_find(tree, FIFF.FIFFB_MNE_ANNOTATIONS) if len(annot_data) == 0: annotations = None else: annot_data = annot_data[0] orig_time = None onset, duration, description = list(), list(), list() for ent in annot_data['directory']: kind = ent.kind pos = ent.pos tag = read_tag(fid, pos) if kind == FIFF.FIFF_MNE_BASELINE_MIN: onset = tag.data onset = list() if onset is None else onset elif kind == FIFF.FIFF_MNE_BASELINE_MAX: duration = tag.data duration = list() if duration is None else duration - onset elif kind == FIFF.FIFF_COMMENT: description = tag.data.split(':') description = [d.replace(';', ':') for d in description] elif kind == FIFF.FIFF_MEAS_DATE: orig_time = float(tag.data) assert len(onset) == len(duration) == len(description) annotations = Annotations(onset, duration, description, orig_time) return annotations def _ensure_annotation_object(obj): """Check that the object is an Annotations instance. Raise error otherwise. """ if not isinstance(obj, Annotations): raise ValueError('Annotations must be an instance of ' 'mne.Annotations. Got %s.' % obj) def _select_annotations_based_on_description(descriptions, event_id, regexp): """Get a collection of descriptions and returns index of selected.""" regexp_comp = re.compile('.*' if regexp is None else regexp) event_id_ = dict() dropped = [] # Iterate over the sorted descriptions so that the Counter mapping # is slightly less arbitrary for desc in sorted(descriptions): if desc in event_id_: continue if regexp_comp.match(desc) is None: continue if isinstance(event_id, dict): if desc in event_id: event_id_[desc] = event_id[desc] else: continue else: trigger = event_id(desc) if trigger is not None: event_id_[desc] = trigger else: dropped.append(desc) event_sel = [ii for ii, kk in enumerate(descriptions) if kk in event_id_] if len(event_sel) == 0 and regexp is not None: raise ValueError('Could not find any of the events you specified.') return event_sel, event_id_ def _check_event_id(event_id, raw): from .io.brainvision.brainvision import _BVEventParser from .io.brainvision.brainvision import _check_bv_annot from .io.brainvision.brainvision import RawBrainVision from .io import RawFIF, RawArray if event_id is None: return _DefaultEventParser() elif event_id == 'auto': if isinstance(raw, RawBrainVision): return _BVEventParser() elif (isinstance(raw, (RawFIF, RawArray)) and _check_bv_annot(raw.annotations.description)): logger.info('Non-RawBrainVision raw using branvision markers') return _BVEventParser() else: return _DefaultEventParser() elif callable(event_id) or isinstance(event_id, dict): return event_id else: raise ValueError('Invalid input event_id') @verbose def events_from_annotations(raw, event_id="auto", regexp=r'^(?![Bb][Aa][Dd]|[Ee][Dd][Gg][Ee]).*$', use_rounding=True, chunk_duration=None, verbose=None): """Get events and event_id from an Annotations object. Parameters ---------- raw : instance of Raw The raw data for which Annotations are defined. event_id : dict | callable | None | 'auto' Can be: - **dict**: map descriptions (keys) to integer event codes (values). Only the descriptions present will be mapped, others will be ignored. - **callable**: must take a string input and returns an integer event code or None to ignore it. - **None**: Map descriptions to unique integer values based on their ``sorted`` order. - **'auto' (default)**: prefer a raw-format-specific parser: - Brainvision: map stimulus events to their integer part; response events to integer part + 1000; optic events to integer part + 2000; 'SyncStatus/Sync On' to 99998; 'New Segment/' to 99999; all others like ``None`` with an offset of 10000. - Other raw formats: Behaves like None. .. versionadded:: 0.18 regexp : str | None Regular expression used to filter the annotations whose descriptions is a match. The default ignores descriptions beginning ``'bad'`` or ``'edge'`` (case-insensitive). .. versionchanged:: 0.18 Default ignores bad and edge descriptions. use_rounding : boolean If True, use rounding (instead of truncation) when converting times to indices. This can help avoid non-unique indices. chunk_duration: float | None Chunk duration in seconds. If ``chunk_duration`` is set to None (default), generated events correspond to the annotation onsets. If not, :func:`mne.events_from_annotations` returns as many events as they fit within the annotation duration spaced according to ``chunk_duration``. As a consequence annotations with duration shorter than ``chunk_duration`` will not contribute events. %(verbose)s Returns ------- events : ndarray, shape (n_events, 3) The events. event_id : dict The event_id variable that can be passed to Epochs. """ if len(raw.annotations) == 0: event_id = dict() if not isinstance(event_id, dict) else event_id return np.empty((0, 3), dtype=int), event_id annotations = raw.annotations event_id = _check_event_id(event_id, raw) event_sel, event_id_ = _select_annotations_based_on_description( annotations.description, event_id=event_id, regexp=regexp) if chunk_duration is None: inds = raw.time_as_index(annotations.onset, use_rounding=use_rounding, origin=annotations.orig_time) + raw.first_samp values = [event_id_[kk] for kk in annotations.description[event_sel]] inds = inds[event_sel] else: inds = values = np.array([]).astype(int) for annot in annotations[event_sel]: annot_offset = annot['onset'] + annot['duration'] _onsets = np.arange(start=annot['onset'], stop=annot_offset, step=chunk_duration) good_events = annot_offset - _onsets >= chunk_duration if good_events.any(): _onsets = _onsets[good_events] _inds = raw.time_as_index(_onsets, use_rounding=use_rounding, origin=annotations.orig_time) _inds += raw.first_samp inds = np.append(inds, _inds) _values = np.full(shape=len(_inds), fill_value=event_id_[annot['description']], dtype=int) values = np.append(values, _values) events = np.c_[inds, np.zeros(len(inds)), values].astype(int) logger.info('Used Annotations descriptions: %s' % (list(event_id_.keys()),)) return events, event_id_