smriprep.workflows.outputs module
Writing outputs.
- smriprep.workflows.outputs.init_anat_reports_wf(*, spaces, freesurfer, output_dir, sloppy=False, name='anat_reports_wf')[source]
Set up a battery of datasinks to store reports in the right location.
- Parameters:
- Inputs:
source_file – Input T1w image
std_t1w – T1w image resampled to standard space
std_mask – Mask of skull-stripped template
subject_dir – FreeSurfer SUBJECTS_DIR
subject_id – FreeSurfer subject ID
t1w_conform_report – Conformation report
t1w_preproc – The T1w reference map, which is calculated as the average of bias-corrected and preprocessed T1w images, defining the anatomical space.
t1w_dseg – Segmentation in T1w space
t1w_mask – Brain (binary) mask estimated by brain extraction.
template – Template space and specifications
- smriprep.workflows.outputs.init_ds_anat_volumes_wf(*, bids_root: str, output_dir: str, name='ds_anat_volumes_wf', tpm_labels=('GM', 'WM', 'CSF')) Workflow[source]
- smriprep.workflows.outputs.init_ds_dseg_wf(*, output_dir: str, extra_entities: dict | None = None, name: str = 'ds_dseg_wf')[source]
Save discrete segmentations
- Parameters:
- Inputs:
source_files – List of input anatomical images
anat_dseg – Segmentation in anatomical space
- Outputs:
anat_dseg – The location in the output directory of the discrete segmentation
- smriprep.workflows.outputs.init_ds_fs_registration_wf(*, output_dir: str, image_type: Literal['T1w', 'T2w'], name: str = 'ds_fs_registration_wf')[source]
Save rigid registration between subject anatomical template and either FreeSurfer T1.mgz or T2.mgz
- Parameters:
- Inputs:
source_files – List of input anatomical images
fsnative2anat_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1/T2
- Outputs:
anat2fsnative_xfm – LTA-style affine matrix translating from T1/T2 to FreeSurfer-conformed subject space
fsnative2anat_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
- smriprep.workflows.outputs.init_ds_fs_segs_wf(*, bids_root: str, output_dir: str, extra_entities: dict | None = None, name='ds_fs_segs_wf')[source]
Set up a battery of datasinks to store derivatives in the right location.
- Parameters:
- Inputs:
anat_fs_aparc – FreeSurfer’s aparc+aseg segmentation, in native anatomical space
anat_fs_aseg – FreeSurfer’s aseg segmentation, in native anatomical space
source_files – List of input anatomical images
- smriprep.workflows.outputs.init_ds_grayord_metrics_wf(*, bids_root: str, output_dir: str, metrics: list[str], cifti_output: Literal['91k', '170k'], name='ds_grayord_metrics_wf') LiterateWorkflow[source]
Save CIFTI-2 surface metrics
- Parameters:
- Inputs:
source_files – List of input T1w images
``<metric>`` – CIFTI-2 scalar file for each metric passed to
metrics``<metric>_metadata`` – JSON file containing metadata for each metric passed to
metrics
- Outputs:
``<metric>`` – CIFTI-2 scalar file in
output_dirfor each metric passed tometrics
- smriprep.workflows.outputs.init_ds_mask_wf(*, bids_root: str, output_dir: str, mask_type: Literal['brain', 'roi', 'ribbon'], extra_entities: dict | None = None, name='ds_mask_wf')[source]
Save the subject brain mask
- Parameters:
- Inputs:
source_files – List of input anat images
mask_file – Mask to save
- Outputs:
mask_file – The location in the output directory of the mask
- smriprep.workflows.outputs.init_ds_surface_metrics_wf(*, bids_root: str, output_dir: str, metrics: list[str], name='ds_surface_metrics_wf') LiterateWorkflow[source]
Save GIFTI surface metrics
- Parameters:
- Inputs:
source_files – List of input T1w images
``<metric>`` – Left and right GIFTIs for each metric passed to
metrics
- Outputs:
``<metric>`` – Left and right GIFTIs in
output_dirfor each metric passed tometrics
- smriprep.workflows.outputs.init_ds_surfaces_wf(*, output_dir: str, surfaces: list[str], name='ds_surfaces_wf') LiterateWorkflow[source]
Save GIFTI surfaces
- Parameters:
- Inputs:
source_files – List of input anatomical images
``<surface>`` – Left and right GIFTIs for each surface passed to
surfaces
- Outputs:
``<surface>`` – Left and right GIFTIs in
output_dirfor each surface passed tosurfaces
- smriprep.workflows.outputs.init_ds_template_registration_wf(*, output_dir: str, image_type: Literal['T1w', 'T2w'], name: str = 'ds_template_registration_wf')[source]
Save template registration transforms
- Parameters:
- Inputs:
template – Template space and specifications
source_files – List of input anatomical images
anat2std_xfm – Nonlinear spatial transform to resample imaging data given in anatomical space into standard space.
std2anat_xfm – Inverse transform of
anat2std_xfm
- smriprep.workflows.outputs.init_ds_template_wf(*, num_anat: int, output_dir: str, image_type: Literal['T1w', 'T2w'], name: str = 'ds_template_wf')[source]
Save the subject-specific template
- Parameters:
- Inputs:
source_files – List of input anatomical images
anat_ref_xfms – List of affine transforms to realign input anatomical images
anat_preproc – The anatomical reference map, which is calculated as the average of bias-corrected and preprocessed anatomical images, defining the anatomical space.
- Outputs:
anat_preproc – The location in the output directory of the preprocessed anatomical image
- smriprep.workflows.outputs.init_ds_tpms_wf(*, output_dir: str, extra_entities: dict | None = None, name: str = 'ds_tpms_wf', tpm_labels: tuple = ('GM', 'WM', 'CSF'))[source]
Save tissue probability maps
- Parameters:
- Inputs:
source_files – List of input anatomical images
anat_tpms – Tissue probability maps in anatomical space
- Outputs:
anat_tpms – The location in the output directory of the tissue probability maps
- smriprep.workflows.outputs.init_template_iterator_wf(*, spaces: SpatialReferences, sloppy: bool = False, name='template_iterator_wf')[source]
Prepare the necessary components to resample an image to a template space
This produces a workflow with an unjoined iterable named “spacesource”.
It takes as input a collated list of template specifiers and transforms to that space.
The fields in outputnode can be used as if they come from a single template.