mne.HEDAnnotations#

class mne.HEDAnnotations(onset, duration, description, hed_string, hed_version='8.3.0', orig_time=None, ch_names=None, *, extras=None)[source]#

Annotations object for annotating segments of raw data with HED tags.

Parameters:
onsetarray of float, shape (n_annotations,)

The starting time of annotations in seconds after orig_time.

durationarray of float, shape (n_annotations,) | float

Durations of the annotations in seconds. If a float, all the annotations are given the same duration.

descriptionarray 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.

hed_stringarray of str, shape (n_annotations,) | str

Sequence of strings containing a HED tag (or comma-separated list of HED tags) for each annotation. If a single string is provided, all annotations are assigned the same HED string.

hed_versionstr

The HED schema version against which to validate the HED strings.

orig_timefloat | str | datetime | tuple of int | None

A POSIX Timestamp, datetime or a tuple 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 acquisition 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 ‘ ‘.

ch_nameslist | None

List of lists of channel names associated with the annotations. Empty entries are assumed to be associated with no specific channel, i.e., with all channels or with the time slice itself. None (default) is the same as passing all empty lists. For example, this creates three annotations, associating the first with the time interval itself, the second with two channels, and the third with a single channel:

Annotations(onset=[0, 3, 10], duration=[1, 0.25, 0.5],
            description=['Start', 'BAD_flux', 'BAD_noise'],
            ch_names=[[], ['MEG0111', 'MEG2563'], ['MEG1443']])
extraslist[dict[str, int | float | str | None] | None] | None

Optional list of dicts containing extra fields for each annotation. The number of items must match the number of annotations.

Attributes:
extras

The extras of the Annotations.

orig_time

The time base of the Annotations.

Methods

__add__(other)

Add (concatencate) two Annotation objects.

__getitem__(key, *[, with_ch_names, with_extras])

Propagate indexing and slicing to the underlying structure.

__iter__()

Iterate over the annotations.

__len__()

Return the number of annotations.

append(*, onset, duration, description, ...)

Add an annotated segment.

copy()

Return a copy of the Annotations.

count()

Count annotations.

crop([tmin, tmax, emit_warning, ...])

Remove all annotations that are outside of [tmin, tmax].

delete(idx)

Remove an annotation.

rename(mapping[, verbose])

Rename annotation description(s).

save(fname, *[, overwrite, verbose])

Save annotations to FIF, CSV or TXT.

set_durations(mapping[, verbose])

Set annotation duration(s).

to_data_frame([time_format])

Export annotations in tabular structure as a pandas DataFrame.

See also

mne.Annotations

Notes

New in v1.12.

__add__(other)[source]#

Add (concatencate) two Annotation objects.

__getitem__(key, *, with_ch_names=None, with_extras=True)[source]#

Propagate indexing and slicing to the underlying structure.

__iter__()[source]#

Iterate over the annotations.

__len__()[source]#

Return the number of annotations.

Returns:
n_annotint

The number of annotations.

append(*, onset, duration, description, hed_string, ch_names=None, extras=None)[source]#

Add an annotated segment. Operates inplace.

Parameters:
onsetfloat | array_like

Annotation time onset from the beginning of the recording in seconds.

durationfloat | array_like

Duration of the annotation in seconds.

descriptionstr | array_like

Description for the annotation. To reject epochs, use description starting with keyword ‘bad’.

hed_stringarray of str, shape (n_annotations,) | str

Sequence of strings containing a HED tag (or comma-separated list of HED tags) for each annotation. If a single string is provided, all annotations are assigned the same HED string.

ch_nameslist | None

List of lists of channel names associated with the annotations. Empty entries are assumed to be associated with no specific channel, i.e., with all channels or with the time slice itself. None (default) is the same as passing all empty lists. For example, this creates three annotations, associating the first with the time interval itself, the second with two channels, and the third with a single channel:

Annotations(onset=[0, 3, 10], duration=[1, 0.25, 0.5],
            description=['Start', 'BAD_flux', 'BAD_noise'],
            ch_names=[[], ['MEG0111', 'MEG2563'], ['MEG1443']])
extraslist[dict[str, int | float | str | None] | None] | None

Optional list of dicts containing extras fields for each annotation.

Returns:
selfmne.HEDAnnotations

The modified HEDAnnotations object.

copy()[source]#

Return a copy of the Annotations.

Returns:
instinstance of Annotations

A copy of the object.

count()[source]#

Count annotations.

Returns:
countsdict

A dictionary containing unique annotation descriptions as keys with their counts as values.

crop(tmin=None, tmax=None, emit_warning=False, use_orig_time=True, verbose=None)[source]#

Remove all annotations that are outside of [tmin, tmax].

The method operates inplace.

Parameters:
tminfloat | datetime | None

Start time of selection in seconds.

tmaxfloat | datetime | None

End time of selection in seconds.

emit_warningbool

Whether to emit warnings when limiting or omitting annotations. Defaults to False.

use_orig_timebool

Whether to use orig_time as an offset. Defaults to True.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
selfinstance of HEDAnnotations

The cropped HEDAnnotations object.

delete(idx)[source]#

Remove an annotation. Operates inplace.

Parameters:
idxint | array_like of int

Index of the annotation to remove. Can be array-like to remove multiple indices.

property extras#

The extras of the Annotations.

The extras attribute is a list of dictionaries. It can easily be converted to a pandas DataFrame using: pd.DataFrame(extras).

property orig_time#

The time base of the Annotations.

rename(mapping, verbose=None)[source]#

Rename annotation description(s). Operates inplace.

Parameters:
mappingdict

A dictionary mapping the old description to a new description, e.g. {‘1.0’ : ‘Control’, ‘2.0’ : ‘Stimulus’}.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
selfmne.Annotations

The modified Annotations object.

Notes

New in v0.24.0.

save(fname, *, overwrite=False, verbose=None)[source]#

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:
fnamepath-like

The filename to use.

overwritebool

If True (default False), overwrite the destination file if it exists.

New in v0.23.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Notes

The format of the information stored in the saved annotation objects depends on the chosen file format. .csv files store the onset as timestamps (e.g., 2002-12-03 19:01:56.676071), whereas .txt files store onset as seconds since start of the recording (e.g., 45.95597082905339).

set_durations(mapping, verbose=None)[source]#

Set annotation duration(s). Operates inplace.

Parameters:
mappingdict | float

A dictionary mapping the annotation description to a duration in seconds e.g. {'ShortStimulus' : 3, 'LongStimulus' : 12}. Alternatively, if a number is provided, then all annotations durations are set to the single provided value.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
selfmne.Annotations

The modified Annotations object.

Notes

New in v0.24.0.

to_data_frame(time_format='datetime')[source]#

Export annotations in tabular structure as a pandas DataFrame.

Parameters:
time_formatstr | None

Desired time format. If None, no conversion is applied, and time values remain as float values in seconds. If 'ms', time values will be rounded to the nearest millisecond and converted to integers. If 'timedelta', time values will be converted to pandas.Timedelta values. If 'datetime', time values will be converted to pandas.Timestamp values, relative to raw.info['meas_date'] and offset by raw.first_samp. Default is None unless specified otherwise.

Returns:
resultpandas.DataFrame

Returns a pandas DataFrame with onset, duration, description, and hed_string columns. A column named ch_names is added if any annotations are channel-specific.