4.6. Marker

4.6.1. Description

The Marker is an object that is responsible for feature extraction. It primarily operates on data loaded into memory by Data Reader and stored in the data key of each data type in the Data object. In some cases, it can also operate on pre-processed data as obtained from the Preprocess step of the pipeline.

Important

This pre-process is not similar to pre-processing done by tools like FSL, SPM, AFNI, etc., . For example, one can perform confound removal on loaded data and then perform feature extraction.

Markers are meant to be used inside the DataGrabber context but you can operate on them outside the context as long as the actual data is in the memory and the Python runtime has not garbage-collected it.

If you are interested in using already provided Markers, please go to Built-in Pipeline Components. And, if you want to implement your own Marker, you need to provide concrete implementation of BaseMarker. Specifically, you need to override get_valid_inputs, get_output_type and compute methods.