xcp_d.interfaces.connectivity.TSVConnect

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

Extract timeseries and compute connectivity matrices.

Write out time series using Nilearn’s NiftiLabelMasker Then write out functional correlation matrix of timeseries using numpy.

Optional Inputs:

  • temporal_mask (a pathlike object or string representing an existing file) – Temporal mask, after dummy scan removal.

  • timeseries (a pathlike object or string representing an existing file) – Parcellated time series TSV file.

Outputs:

  • correlations (a pathlike object or string representing an existing file) – Correlation matrix file.

  • correlations_exact (a list of items which are a pathlike object or string representing an existing file or None) – Correlation matrix files limited to an exact number of volumes.

__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