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# Authors: Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# 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 numpy as np
from .utils import _pl, check_fname, _validate_type, verbose, warn, logger
from .utils import _check_pandas_installed
from .utils import _Counter as Counter
from .externals.six import string_types
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
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 | 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 <mne.io.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
onset = 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 isinstance(description, string_types):
description = np.repeat(description, len(onset))
if duration.ndim != 1:
raise ValueError('Duration must be a one dimensional array.')
if not (len(onset) == len(duration) == len(description)):
raise ValueError('Onset, duration and description must be '
'equal in sizes.')
if any([';' in desc for desc in description]):
raise ValueError('Semicolons in descriptions not supported.')
self.onset = onset
self.duration = duration
self.description = np.array(description, dtype=str)
def __repr__(self):
"""Show the representation."""
kinds = sorted(set('%s' % d.split(' ')[0].lower()
for d in self.description))
kinds = ['%s (%s)' % (kind, sum(d.lower().startswith(kind)
for d in self.description))
for kind in kinds]
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 ('<Annotations | %s segment%s %s, %s>'
% (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 append(self, onset, duration, description):
"""Add an annotated segment. Operates inplace.
Parameters
----------
onset : float
Annotation time onset from the beginning of the recording in
seconds.
duration : float
Duration of the annotation in seconds.
description : str
Description for the annotation. To reject epochs, use description
starting with keyword 'bad'
Returns
-------
self : mne.Annotations
The modified Annotations object.
"""
self.onset = np.append(self.onset, onset)
self.duration = np.append(self.duration, duration)
self.description = np.append(self.description, description)
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 | list of int
Index of the annotation to remove.
"""
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 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.
"""
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.')
if tmin < 0:
raise ValueError('tmin should be positive.')
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, string_types):
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, (string_types, list, tuple), str(type(kinds)),
"str, list or tuple")
if isinstance(kinds, string_types):
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 = raw.annotations.onset[idxs]
onsets = _sync_onset(raw, onsets)
ends = onsets + raw.annotations.duration[idxs]
order = np.argsort(onsets)
onsets = raw.time_as_index(onsets[order], use_rounding=True)
ends = raw.time_as_index(ends[order], use_rounding=True)
if invert:
# We invert the relationship (i.e., get segments that do not satisfy)
if len(onsets) == 0 or onsets[0] != 0:
onsets = np.concatenate([[0], onsets])
ends = np.concatenate([[0], ends])
if len(ends) == 1 or ends[-1] != len(raw.times):
onsets = np.concatenate([onsets, [len(raw.times)]])
ends = np.concatenate([ends, [len(raw.times)]])
onsets, ends = ends[:-1], onsets[1:]
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, 'w') as fid:
fid.write(content)
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 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 files as Annotations are expressed in seconds and \*.vmrk files
are in samples. For any other file format, ``sfreq`` is omitted.
If set to 'auto' then the ``sfreq`` is taken from the \*.vhdr
file that has the same name (without file extension). So data.vrmk
looks for sfreq in data.vhdr.
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.
"""
from .io.brainvision.brainvision import _read_annotations_brainvision
from .io.eeglab.eeglab import _read_annotations_eeglab
from .io.edf.edf import _read_annotations_edf
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('set'):
annotations = _read_annotations_eeglab(fname,
uint16_codec=uint16_codec)
elif name.endswith('edf'):
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]
orig_time = _handle_meas_date(orig_time)
onset_dt = pd.to_datetime(df['onset'])
onset = (onset_dt - onset_dt[0]).dt.seconds.astype(float)
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):
import re
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=str, unpack=True)
onset = [float(o) for o in onset]
duration = [float(d) for d in duration]
desc = [str(d).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)
@verbose
def events_from_annotations(raw, event_id=None, regexp=None, use_rounding=True,
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
Dictionary of string keys and integer values as used in mne.Epochs
to map annotation descriptions to integer event codes. Only the
keys present will be mapped and the annotations with other descriptions
will be ignored. Otherwise, a callable that provides an integer given
a string or that returns None for an event to ignore.
If None, all descriptions of annotations are mapped
and assigned arbitrary unique integer values.
regexp : str | None
Regular expression used to filter the annotations whose
descriptions is a match.
use_rounding : boolean
If True, use rounding (instead of truncation) when converting
times to indices. This can help avoid non-unique indices.
verbose : bool, str, int, or None
If not None, override default verbose level (see
:func:`mne.verbose` and :ref:`Logging documentation <tut_logging>`
for more). Defaults to self.verbose.
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:
return np.empty((0, 3), dtype=int), event_id
annotations = raw.annotations
inds = raw.time_as_index(annotations.onset, use_rounding=use_rounding,
origin=annotations.orig_time) + raw.first_samp
# Filter out the annotations that do not match regexp
regexp_comp = re.compile('.*' if regexp is None else regexp)
if event_id is None:
event_id = Counter()
event_id_ = dict()
dropped = []
for desc in annotations.description:
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(annotations.description)
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.')
values = [event_id_[kk] for kk in
annotations.description[event_sel]]
previous_value = np.zeros(len(event_sel))
inds = inds[event_sel]
events = np.c_[inds, previous_value, values].astype(int)
logger.info('Used Annotations descriptions: %s' %
(list(event_id_.keys()),))
return events, event_id_
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