Test workflow for the new MINC interface in Nipype.

mincworkflow.py

# Artificial example (averaging and then dumping) using the new
# MINC interface in Nipype.
#
# Carlo Hamalainen <carlo@carlo-hamalainen.net>

import os
import nipype.interfaces.minc as minc
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
import nipype.pipeline.engine as pe
import nipype.interfaces.base as base

# Our top level working directory.
experiment_dir = '/home/carlo/work/vmwork/experiment'

# Averaging node. Note that this node accepts
# a list of files (input_files) and produces
# a single output file (output_file, seen in avg.outputs).
avg = pe.Node(interface=minc.AverageTask(), name='mincaverage')

# Dump node.
dump = pe.Node(interface=minc.DumpTask(), name='mincdump')

# Top level workflow.
main_workflow = pe.Workflow(name='main_workflow')
main_workflow.base_dir = experiment_dir

# Source data: glob for the test 2D files.
datagrabber = nio.DataGrabber(sort_filelist=True,
                              outfields=['testfiles'],
                              base_directory = experiment_dir,
                              template = 'data/minc_test_2D_??.mnc')

datasource = pe.Node(interface=datagrabber, name='testinputfiles')

# Output directory:
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = os.path.abspath(os.path.join(experiment_dir, 'output'))

main_workflow.add_nodes([avg, dump])

main_workflow.connect(datasource, 'testfiles', avg, 'input_files')
main_workflow.connect(avg, 'output_file', dump, 'input_file')
main_workflow.connect(dump, 'output_file', datasink, 'average_file')

main_workflow.run()