BaseHypnoPipe

Tip

See the 🚨 Abstract Class - This class is abstract and cannot be used directly. Subclass it and implement the required methods. for usage examples.

class sleepeegpy.base.BaseHypnoPipe(*, prec_pipe=None, path_to_eeg=NOTHING, output_dir=NOTHING, path_to_hypno=None, hypno_freq=NOTHING, hypno=NOTHING)

Bases: BasePipe, ABC

A base class for the sleep-stage-analysis pipeline segments.

Methods:

interpolate_bads	A wrapper for mne.io.Raw.interpolate_bads()
plot	A wrapper for mne.io.Raw.plot().
plot_sensors	A wrapper for mne.viz.plot_sensors() with a legend.
predict_hypno	Runs YASA's automatic sleep staging.
save_raw	A wrapper for mne.io.Raw.save().
set_eeg_reference	A wrapper for mne.io.Raw.set_eeg_reference().
sleep_stats	A wrapper for yasa.sleep_statistics().

Attributes:

bad_data_percent	Calculates percent of data segments annotated as BAD.
sf	A wrapper for raw.info["sfreq"].
path_to_hypno	Path to hypnogram.
hypno_freq	Sampling rate of the hypnogram in Hz.
hypno	Hypnogram with sampling frequency hypno_freq with int representing sleep stage.
hypno_up	Hypnogram upsampled to the sampling frequency of the raw data.
prec_pipe	Preceding pipe that hands over mne_raw object and output_dir.
path_to_eeg	Can be any eeg file type supported by mne.io.read_raw().
output_dir	Path to the directory where the output will be saved.
mne_raw	An instanse of mne.io.Raw.

path_to_hypno: Path

Path to hypnogram. Must be text file with every row being int representing sleep stage for the epoch.

hypno_freq: float

Sampling rate of the hypnogram in Hz.

E.g., 1/30 means 1 sample per 30 secs epoch, 250 means 1 sample per 1/250 sec epoch.

hypno: ndarray

Hypnogram with sampling frequency hypno_freq with int representing sleep stage.

hypno_up: array

Hypnogram upsampled to the sampling frequency of the raw data.

predict_hypno(eeg_name='E183', eog_name='E252', emg_name='E247', ref_name='E26', save=True)

Runs YASA’s automatic sleep staging.

Parameters:

• eeg_name (str) – Preferentially a central electrode. Defaults to “E183”.

• eog_name (str) – Preferentially, the left LOC channel. Defaults to “E252”.

• emg_name (str) – Preferentially a chin electrode. Defaults to “E247”.

• ref_name (str) – Reference channel, preferentially a mastoid. Defaults to “E26”.

• save (bool) – Whether to save the hypnogram to file. Defaults to True.

sleep_stats(save=False)

A wrapper for yasa.sleep_statistics().

Parameters:

save (bool) – Whether to save the stats to csv. Defaults to False.

property bad_data_percent

Calculates percent of data segments annotated as BAD.

Returns:

percent of bad data spans in raw data

Return type:

float

interpolate_bads(**interp_kwargs)

A wrapper for mne.io.Raw.interpolate_bads()

Parameters:

**interp_kwargs – Arguments passed to mne.io.Raw.interpolate_bads().

plot(save_annotations=False, save_bad_channels=False, overwrite=False, **kwargs)

A wrapper for mne.io.Raw.plot().

Parameters:

• save_annotations (bool) – Whether to save annotations as txt. Defaults to False.

• save_bad_channels (bool) – Whether to save bad channels as txt. Defaults to False.

• overwrite (bool) – Whether to overwrite annotations and bad_channels files if exist. Defaults to False.

• **kwargs – Arguments passed to mne.io.Raw.plot().

plot_sensors(legend=None, legend_args=None, **kwargs)

A wrapper for mne.viz.plot_sensors() with a legend.

Parameters:

• legend (Iterable[str]) – ch_groups names to connect to colors. Defaults to None.

• legend_args (dict) – Arguments passed to matplotlib.axes.Axes.legend(). Defaults to None.

• **kwargs – Arguments passed to mne.viz.plot_sensors().

save_raw(fname, **kwargs)

A wrapper for mne.io.Raw.save().

Parameters:

• fname (str) – Filename for the fif file being saved.

• **kwargs – Arguments passed to mne.io.Raw.save().

set_eeg_reference(ref_channels='average', projection=False, **kwargs)

A wrapper for mne.io.Raw.set_eeg_reference().

Parameters:

• ref_channels – ref_channels. Defaults to ‘average’.

• projection – projection. Defaults to False.

• **kwargs – Additional arguments passed to mne.io.Raw.set_eeg_reference().

property sf

A wrapper for raw.info["sfreq"].

Returns:

sampling frequency

Return type:

float

prec_pipe: Type[TypeVar(BasePipeType, bound= BasePipe)]

Preceding pipe that hands over mne_raw object and output_dir.

path_to_eeg: Path

Can be any eeg file type supported by mne.io.read_raw().

output_dir: Path

Path to the directory where the output will be saved.

mne_raw: Raw

An instanse of mne.io.Raw.