pnpl.preprocessing.Pipeline

class pnpl.preprocessing.Pipeline(steps=<factory>, _context=<factory>)

MEG preprocessing pipeline.

A pipeline is a sequence of preprocessing steps that are applied in order to raw MEG data.

Parameters:

• steps (List) – List of step objects to apply

• _context (Dict[str, Any])

Example

>>> from pnpl.preprocessing import Pipeline, BadChannels, MaxwellFilter
>>> pipeline = Pipeline([
...     BadChannels(),
...     MaxwellFilter(),
... ])
>>>
>>> # Or from string:
>>> pipeline = Pipeline.from_string("bads+sss+notch+bp+ds")

__init__(steps=<factory>, _context=<factory>)

Parameters:

• steps (List)

• _context (Dict[str, Any])

Return type:

None

Methods

__init__([steps, _context])
from_string(spec[, config])	Parse a pipeline specification string.
get_output_filename(subject, session, task, run)	Generate output filename based on pipeline steps.
get_output_path(bids_root, subject, session, ...)	Generate full output path in derivatives directory.
run(raw, subject, session, task, run, bids_root)	Run the pipeline on raw data.
to_string()	Convert pipeline to specification string.

Attributes

steps