From dc33b198d3aef774725757ce4642c321cf0641f6 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Wed, 14 Aug 2024 09:50:15 +0200 Subject: [PATCH] fix: update dwi workflow to new metadata crawling --- mriqc/workflows/diffusion/base.py | 94 ++++++++++++------------------- 1 file changed, 35 insertions(+), 59 deletions(-) diff --git a/mriqc/workflows/diffusion/base.py b/mriqc/workflows/diffusion/base.py index 11573409a..a8e4e1009 100644 --- a/mriqc/workflows/diffusion/base.py +++ b/mriqc/workflows/diffusion/base.py @@ -43,9 +43,6 @@ This workflow is orchestrated by :py:func:`dmri_qc_workflow`. """ -from pathlib import Path - -import numpy as np from nipype.interfaces import utility as niu from nipype.pipeline import engine as pe @@ -87,45 +84,29 @@ def dmri_qc_workflow(name='dwiMRIQC'): from mriqc.messages import BUILDING_WORKFLOW from mriqc.workflows.shared import synthstrip_wf as dmri_bmsk_workflow - workflow = pe.Workflow(name=name) - - dataset = config.workflow.inputs.get('dwi', []) - - full_data = [] - - for dwi_path in dataset: - bval = config.execution.layout.get_bval(dwi_path) - if bval and Path(bval).exists() and len(np.loadtxt(bval)) > config.workflow.min_len_dwi: - full_data.append(dwi_path) - else: - config.loggers.workflow.warn( - f'Dismissing {dwi_path} for processing. b-values are missing or ' - 'insufficient in number to execute the workflow.' - ) - - if set(dataset) - set(full_data): - config.workflow.inputs['dwi'] = full_data - config.to_filename() - + # Enable if necessary + # mem_gb = config.workflow.biggest_file_gb['dwi'] + dataset = config.workflow.inputs['dwi'] + metadata = config.workflow.inputs_metadata['dwi'] + entities = config.workflow.inputs_entities['dwi'] message = BUILDING_WORKFLOW.format( modality='diffusion', - detail=( - f'for {len(full_data)} NIfTI files.' - if len(full_data) > 2 - else f"({' and '.join('<%s>' % v for v in full_data)})." - ), + detail=f'for {len(dataset)} NIfTI files.', ) config.loggers.workflow.info(message) - if config.execution.datalad_get: - from mriqc.utils.misc import _datalad_get - - _datalad_get(full_data) - # Define workflow, inputs and outputs # 0. Get data, put it in RAS orientation - inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']), name='inputnode') - inputnode.iterables = [('in_file', full_data)] + workflow = pe.Workflow(name=name) + inputnode = pe.Node(niu.IdentityInterface( + fields=['in_file', 'metadata', 'entities'], + ), name='inputnode') + inputnode.synchronize = True # Do not test combinations of iterables + inputnode.iterables = [ + ('in_file', dataset), + ('metadata', metadata), + ('entities', entities), + ] sanitize = pe.Node( SanitizeImage( @@ -245,7 +226,11 @@ def dmri_qc_workflow(name='dwiMRIQC'): (inputnode, dwi_report_wf, [ ('in_file', 'inputnode.name_source'), ]), - (inputnode, iqms_wf, [('in_file', 'inputnode.in_file')]), + (inputnode, iqms_wf, [ + ('in_file', 'inputnode.in_file'), + ('metadata', 'inputnode.metadata'), + ('entities', 'inputnode.entities'), + ]), (inputnode, sanitize, [('in_file', 'in_file')]), (sanitize, dwi_ref, [('out_file', 'in_file')]), (sanitize, sp_mask, [('out_file', 'in_file')]), @@ -335,7 +320,6 @@ def compute_iqms(name='ComputeIQMs'): wf = compute_iqms() """ - from niworkflows.interfaces.bids import ReadSidecarJSON from mriqc.interfaces import IQMFileSink from mriqc.interfaces.diffusion import DiffusionQC @@ -348,6 +332,8 @@ def compute_iqms(name='ComputeIQMs'): niu.IdentityInterface( fields=[ 'in_file', + 'metadata', + 'entities', 'in_shells', 'n_shells', 'b_values_file', @@ -377,7 +363,6 @@ def compute_iqms(name='ComputeIQMs'): niu.IdentityInterface( fields=[ 'out_file', - 'meta_sidecar', 'noise_floor', ] ), @@ -389,8 +374,6 @@ def compute_iqms(name='ComputeIQMs'): name='estimate_sigma', ) - meta = pe.Node(ReadSidecarJSON(index_db=config.execution.bids_database_dir), name='metadata') - measures = pe.Node(DiffusionQC(), name='measures') addprov = pe.Node( @@ -413,10 +396,11 @@ def compute_iqms(name='ComputeIQMs'): # fmt: off workflow.connect([ (inputnode, datasink, [('in_file', 'in_file'), + ('entities', 'entities'), + (('metadata', _filter_metadata), 'metadata'), ('n_shells', 'NumberOfShells'), ('b_values_shells', 'bValuesEstimation'), (('b_values_file', _bvals_report), 'bValues')]), - (inputnode, meta, [('in_file', 'in_file')]), (inputnode, measures, [('in_file', 'in_file'), ('b_values_file', 'in_bval_file'), ('b_values_shells', 'in_shells_bval'), @@ -439,15 +423,7 @@ def compute_iqms(name='ComputeIQMs'): ('piesno_sigma', 'piesno_sigma')]), (inputnode, addprov, [('in_file', 'in_file')]), (addprov, datasink, [('out_prov', 'provenance')]), - (meta, datasink, [('subject', 'subject_id'), - ('session', 'session_id'), - ('task', 'task_id'), - ('acquisition', 'acq_id'), - ('reconstruction', 'rec_id'), - ('run', 'run_id'), - (('out_dict', _filter_metadata), 'metadata')]), (datasink, outputnode, [('out_file', 'out_file')]), - (meta, outputnode, [('out_dict', 'meta_sidecar')]), (measures, datasink, [('out_qc', 'root')]), (inputnode, estimate_sigma, [('in_noise', 'in_file'), ('brain_mask', 'mask')]), @@ -676,23 +652,23 @@ def epi_mni_align(name='SpatialNormalization'): def _mean(inlist): - import numpy as np + from numpy import mean - return np.mean(inlist) + return mean(inlist) def _parse_tqual(in_file): - import numpy as np + from numpy import mean with open(in_file) as fin: lines = fin.readlines() - return np.mean([float(line.strip()) for line in lines if not line.startswith('++')]) + return mean([float(line.strip()) for line in lines if not line.startswith('++')]) def _parse_tout(in_file): - import numpy as np + from numpy import loadtxt - data = np.loadtxt(in_file) # pylint: disable=no-member + data = loadtxt(in_file) # pylint: disable=no-member return data.mean() @@ -701,9 +677,9 @@ def _tolist(value): def _get_bvals(bmatrix): - import numpy as np + from numpy import squeeze - return np.squeeze(bmatrix[:, -1]).tolist() + return squeeze(bmatrix[:, -1]).tolist() def _first(inlist): @@ -722,11 +698,11 @@ def _all_but_first(inlist): def _estimate_sigma(in_file, mask): import nibabel as nb - import numpy as np + from numpy import median msk = nb.load(mask).get_fdata() > 0.5 return round( - float(np.median(nb.load(in_file).get_fdata()[msk])), + float(median(nb.load(in_file).get_fdata()[msk])), 6, )