""" ... and we can change a parameter and run it again. Only the dependent nodes are rerun and that too only if the input state has changed. """ preproc.inputs.meanfuncmask.frac = 0.5 preproc.run() """ Visualizing workflows 1 ----------------------- So what did we run in this precanned workflow """ preproc.write_graph() """ Datasink -------- Datasink is a special interface for copying and arranging results. """ import nipype.interfaces.io as nio preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii') preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii') datasink = pe.Node(interface=nio.DataSink(), name='sinker') preprocess = pe.Workflow(name='preprocout') preprocess.base_dir = os.path.abspath('.')
preproc.run() """ ... and we can change a parameter and run it again. Only the dependent nodes are rerun and that too only if the input state has changed. """ preproc.inputs.meanfuncmask.frac = 0.5 preproc.run() """ Visualizing workflows 1 ----------------------- So what did we run in this precanned workflow """ preproc.write_graph() """ Datasink -------- Datasink is a special interface for copying and arranging results. """ import nipype.interfaces.io as nio preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii') preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii') datasink = pe.Node(interface=nio.DataSink(), name='sinker') preprocess = pe.Workflow(name='preprocout') preprocess.base_dir = os.path.abspath('.') preprocess.connect([(preproc, datasink, [('meanfunc2.out_file', 'meanfunc'),