xcp_d.interfaces.utils.ConvertTo32

class xcp_d.interfaces.utils.ConvertTo32(from_file=None, resource_monitor=None, **inputs)[source]

Downcast files from >32-bit to 32-bit if necessary.

Optional Inputs:

  • anat_dseg (a pathlike object or string representing an existing file or None) – T1-space segmentation file. (Nipype default value: None)

  • bold_file (a pathlike object or string representing an existing file or None) – BOLD file. (Nipype default value: None)

  • bold_mask (a pathlike object or string representing an existing file or None) – BOLD mask file. (Nipype default value: None)

  • boldref (a pathlike object or string representing an existing file or None) – BOLD reference file. (Nipype default value: None)

  • t1w (a pathlike object or string representing an existing file or None) – T1-weighted anatomical file. (Nipype default value: None)

  • t2w (a pathlike object or string representing an existing file or None) – T2-weighted anatomical file. (Nipype default value: None)

Outputs:

  • anat_dseg (a pathlike object or string representing an existing file or None) – T1-space segmentation file.

  • bold_file (a pathlike object or string representing an existing file or None) – BOLD file.

  • bold_mask (a pathlike object or string representing an existing file or None) – BOLD mask file.

  • boldref (a pathlike object or string representing an existing file or None) – BOLD reference file.

  • t1w (a pathlike object or string representing an existing file or None) – T1-weighted anatomical file.

  • t2w (a pathlike object or string representing an existing file or None) – T2-weighted anatomical file.

__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