xcp_d.interfaces.plotting.CensoringPlot

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

Generate a censoring figure.

This is a line plot showing both the raw and filtered framewise displacement time series, with vertical lines/bands indicating volumes removed by the post-processing workflow.

Mandatory Inputs:

  • TR (a float) – Repetition Time.

  • dummy_scans (an integer) – Dummy time to drop.

  • fd_thresh (a float) – Framewise displacement threshold.

  • filtered_motion (a pathlike object or string representing an existing file) – Filtered motion file.

  • fmriprep_confounds_file (a pathlike object or string representing an existing file) – FMRIPrep confounds file.

  • head_radius (a float) – Head radius for FD calculation.

  • motion_filter_type (a string or None)

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

Outputs:

out_file (a pathlike object or string representing an existing file) – Censoring plot.

__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