meeg_utils.steps.FixedLengthEpochs#
- class meeg_utils.steps.FixedLengthEpochs(duration=2.0, *, overlap=0.0, reject_by_annotation=True)[source]#
Cut a continuous recording into consecutive epochs of equal length (e.g. resting state).
Wraps
mne.make_fixed_length_epochs(). Epochs overlappingBADannotations are dropped.- Parameters:
Notes
Figures (
plot()):"drop_log".Methods
__init__([duration, overlap, ...])fit(inst[, y, system])Learn the step's state from the data.
fit_transform(inst[, y, system, copy])Fit the step on the data and transform the same data.
get_metadata_routing()Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
plot([kind, inst])Draw quality-control figures.
set_fit_request(*[, inst, system])Configure whether metadata should be requested to be passed to the
fitmethod.set_params(**params)Set the parameters of this estimator.
set_transform_request(*[, copy, inst])Configure whether metadata should be requested to be passed to the
transformmethod.transform(inst, *[, copy])Apply the fitted step to the data.
Attributes
Data types the step accepts.
adds_annotationsWhether the step may add
BAD_annotations (existing ones are kept).changes_channelsWhether the step may add, drop or reorder channels.
Whether the step may change the time axis (e.g. resampling, cropping).
drops_epochsWhether the step may drop epochs (e.g. artifact rejection).
kind -> whether it needs data (
inst).systemsAcquisition systems the step applies to;
Nonemeans all systems.- accepts: ClassVar[tuple[type, ...]] = (<class 'mne.io.base.BaseRaw'>,)#
Data types the step accepts.
- returns#
alias of
BaseEpochs