xcp_d.interfaces.plotting.QCPlotsES

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

Plot fd, dvars, and carpet plots of the bold data before and after regression/filtering.

This is essentially equivalent to the QCPlots (which are paired pre- and post-processing FMRIPlots), but adapted for the executive summary.

It takes in the data that’s regressed, the data that’s filtered and regressed, as well as the segmentation files, TR, FD, bold_mask and unprocessed data.

It outputs the .SVG files before after processing has taken place.

Mandatory Inputs:

  • filtered_motion (a pathlike object or string representing an existing file) – TSV file with filtered motion parameters.

  • interpolated_filtered_bold (a pathlike object or string representing an existing file) – Data after filtering, interpolation, etc. This is not plotted.

  • preprocessed_bold (a pathlike object or string representing an existing file) – Preprocessed BOLD file, after mean-centering and detrending using only the low-motion volumes.

  • standardize (a boolean) – Whether to standardize the data or not. If False, then the preferred DCAN version of the plot will be generated, where the BOLD data are not rescaled, and the carpet plot has color limits of -600 and 600. If True, then the BOLD data will be z-scored and the color limits will be -2 and 2.

  • uncensored_denoised_bold (a pathlike object or string representing an existing file) – Data after regression and interpolation, but not filtering.The preprocessed BOLD data are censored, mean-centered, detrended, and denoised to get the betas, and then the full, uncensored preprocessed BOLD data are denoised using those betas.

Optional Inputs:

  • TR (a float) – Repetition time.

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

  • run_index (a list of items which are an integer or a _Undefined or None) – An index indicating splits between runs, for concatenated data. If not Undefined, this should be a list of integers, indicating the volumes.

  • seg_data (a pathlike object or string representing an existing file) – Segmentation file.

Outputs:

  • after_process (a pathlike object or string representing an existing file) – .SVG file after processing.

  • before_process (a pathlike object or string representing an existing file) – .SVG file before processing.

__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