correlation3.inputs.in_mat = mat3 correlation3.inputs.typeF = 'Maps' correlation3.inputs.kind = 'correlation' Integ2 = Node(Function(input_names=['t1', 't2', 't3'], output_names=['Corre_files'], function=Integrate), name='Correlation_files') Graph = MapNode(Function( input_names=['Mat_D', 'Threshold', 'percentageConnections', 'complet'], output_names=['out_data', 'out_mat'], function=get_graph), name='Graph_Metricts', iterfield=['Mat_D']) Graph.iterables = ("Threshold", [0.6]) Graph.inputs.percentageConnections = False #Porcentaje de conexiones utilizadas ALFF_fALFF = MapNode(Function( input_names=['slow', 'ASamplePeriod', 'Time_s', 'plots'], output_names=['out_mat'], function=Calculate_ALFF_fALFF), name='ALFF_and_fALFF', iterfield=['Time_s']) ALFF_fALFF.iterables = ("slow", [2, 3, 4, 5]) ALFF_fALFF.inputs.ASamplePeriod = 1.6 #Time repetition ReHo = Node(Function(input_names=['func', 'nneigh', 'help_reho'], output_names=['out_ReHo'], function=Calculate_ReHo), name='Regional_homogeneity')
from nipype.interfaces import fsl # Node to grab data. grab = Node(DataGrabber(outfields=['t1', 'brain']), name='grabber') grab.inputs.base_directory = '/om/user/jakubk/meningioma/' grab.inputs.template = '*.nii.gz' # Change filenames later to specify T1. grab.inputs.field_template = { 't1': 'data/*.nii.gz', 'brain': 'ants_seg_output/brain/*.nii.gz' } grab.inputs.sort_filelist = True fast = MapNode(fsl.FAST(), iterfield=['in_files'], name='fast') fast.inputs.img_type = 1 fast.inputs.probability_maps = True fast.iterables = ('number_classes', [3, 4, 5]) sinker = Node(DataSink(), name='sinker') sinker.inputs.base_directory = op.abspath('fast_output') # How can we iterate over original NIFTI files and extracted brains together? # Run original NIFTI files. wf = Workflow(name='fast_brain', base_dir='/om/scratch/Wed/jakubk/') wf.connect(grab, 'brain', fast, 'in_files') wf.connect(fast, 'probability_maps', sinker, 'prob') wf.connect(fast, 'restored_image', sinker, 'restored') wf.connect(fast, 'tissue_class_files', sinker, 'tissue_files') wf.connect(fast, 'tissue_class_map', sinker, 'tissue_map') wf.run(plugin='SLURM', plugin_args={'sbatch_args': '--mem=50GB'})