xcp_d.interfaces.censoring.RandomCensor

class xcp_d.interfaces.censoring.RandomCensor(from_file=None, resource_monitor=None, **inputs)[source]

Randomly flag volumes to censor.

Mandatory Inputs:

  • exact_scans (a list of items which are an integer) – Numbers of scans to retain. If None, no additional censoring will be performed.

  • temporal_mask (a pathlike object or string representing an existing file) – Temporal mask; all motion outlier volumes set to 1. This is a TSV file with one column: ‘framewise_displacement’.

Optional Inputs:

  • random_seed (an integer or None) – Random seed. (Nipype default value: None)

  • temporal_mask_metadata (a dictionary with keys which are any value and with values which are any value) – Metadata associated with the temporal_mask output.

Outputs:

  • temporal_mask (a pathlike object or string representing an existing file) – Temporal mask file.

  • temporal_mask_metadata (a dictionary with keys which are any value and with values which are any value) – Metadata associated with the temporal_mask output.

__init__(from_file=None, resource_monitor=None, **inputs)[source]

Subclasses must implement __init__

Methods

__init__([from_file, resource_monitor])

Subclasses must implement __init__

aggregate_outputs([runtime, needed_outputs])

Collate expected outputs and apply output traits validation.

help([returnhelp])

Prints class help

load_inputs_from_json(json_file[, overwrite])

A convenient way to load pre-set inputs from a JSON file.

run([cwd, ignore_exception])

Execute this interface.

save_inputs_to_json(json_file)

A convenient way to save current inputs to a JSON file.

Attributes

always_run

Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context.

can_resume

Defines if the interface can reuse partial results after interruption.

resource_monitor

version

interfaces should implement a version property