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Merge branch 'master' into fsl_model
* master: (57 commits) Fixed afni test Fixed AFNI tests. Escape the expr parameter in 3dcalc. fixing TSNR integer data bug fix: update getmask to dilate while binarizing sty: rest formatting ENH: Provide access to all relevant BET outputs when "surfaces" option used. Skipping dynamic traits tests for now. Fixed failing doctest. sty: fixed white space and removed traits fix Reverted the traits "fix" workaround. Removed redundant example, renamed the antsRegistration template. Put interpolation argument back in. Renamed the input/outputspec. Added changelog information. Revert "fix: added test for deepcopy bug" reverted traits change2 reverted traits change ENH: Future proof the API for new template building Fixed input/outputspec names. Refactoring. ...
2 parents 99f4a53 + 0a36f46 commit 66539dc

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‎CHANGES

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Next release
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============
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*ENH: New interfaces: ICC, Meshfix
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*ENH: added no_flatten option to Merge
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*FIX: fixed dynamic traits bug
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* ENH: New interfaces: ICC, Meshfix, ants.Register
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* ENH: New workflows: ants template building (both using 'ANTS' and the new 'antsRegistration')
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* ENH: New examples: how to use ANTS template building workflows (smri_ants_build_tmeplate),
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how to set SGE specific options (smri_ants_build_template_new)
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* ENH: added no_flatten option to Merge
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Release 0.6.0 (Jun 30, 2012)
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============================

‎doc/quickstart.rst

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Introductory slides. `Available here`__
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Beginner's tutorials (IPython Notebooks). `Availible here`__
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Michael Notter's guide. `Available here`__
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__ http://satra.github.com/intro2nipype
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__ https://github.com/mwaskom/nipype_concepts
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__ http://miykael.github.com/nipype-beginner-s-guide/index.html
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User guides

‎examples/smri_ants_build_template.py

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#!/usr/bin/env python
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
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===============================================
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sMRI: Using new ANTS for creating a T1 template
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===============================================
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In this tutorial we will use ANTS (old version aka "ANTS") based workflow to
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create a template out of multiple T1 volumes.
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1. Tell python where to find the appropriate functions.
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"""
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import os
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import nipype.interfaces.utility as util
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import nipype.interfaces.ants as ants
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import nipype.interfaces.io as io
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import nipype.pipeline.engine as pe # pypeline engine
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from nipype.workflows.smri.ants import ANTSTemplateBuildSingleIterationWF
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"""
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2. Download T1 volumes into home directory
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"""
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import urllib2
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homeDir=os.getenv("HOME")
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requestedPath=os.path.join(homeDir,'nipypeTestPath')
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mydatadir=os.path.realpath(requestedPath)
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if not os.path.exists(mydatadir):
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os.makedirs(mydatadir)
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print mydatadir
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MyFileURLs=[
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('http://slicer.kitware.com/midas3/download?bitstream=13121','01_T1_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13122','02_T1_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13124','03_T1_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13128','01_T1_inv_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13123','02_T1_inv_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13125','03_T1_inv_half.nii.gz'),
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]
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for tt in MyFileURLs:
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myURL=tt[0]
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localFilename=os.path.join(mydatadir,tt[1])
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if not os.path.exists(localFilename):
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remotefile = urllib2.urlopen(myURL)
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localFile = open(localFilename, 'wb')
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localFile.write(remotefile.read())
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localFile.close()
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print("Downloaded file: {0}".format(localFilename))
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else:
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print("File previously downloaded {0}".format(localFilename))
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input_images=[
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os.path.join(mydatadir,'01_T1_half.nii.gz'),
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os.path.join(mydatadir,'02_T1_half.nii.gz'),
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os.path.join(mydatadir,'03_T1_half.nii.gz')
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]
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input_passive_images=[
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{'INV_T1':os.path.join(mydatadir,'01_T1_inv_half.nii.gz')},
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{'INV_T1':os.path.join(mydatadir,'02_T1_inv_half.nii.gz')},
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{'INV_T1':os.path.join(mydatadir,'03_T1_inv_half.nii.gz')}
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]
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"""
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3. Define the workflow and its working directory
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"""
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tbuilder=pe.Workflow(name="ANTSTemplateBuilder")
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tbuilder.base_dir=requestedPath
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"""
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4. Define data sources. In real life these would be replace by DataGrabbers
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"""
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datasource = pe.Node(interface=util.IdentityInterface(fields=
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['imageList', 'passiveImagesDictionariesList']),
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run_without_submitting=True,
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name='InputImages' )
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datasource.inputs.imageList=input_images
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datasource.inputs.passiveImagesDictionariesList=input_passive_images
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"""
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5. Template is initialized by a simple average
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"""
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initAvg = pe.Node(interface=ants.AverageImages(), name ='initAvg')
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initAvg.inputs.dimension = 3
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initAvg.inputs.normalize = True
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tbuilder.connect(datasource, "imageList", initAvg, "images")
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"""
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6. Define the first iteration of template building
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"""
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buildTemplateIteration1=ANTSTemplateBuildSingleIterationWF('iteration01')
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tbuilder.connect(initAvg, 'output_average_image', buildTemplateIteration1, 'inputspec.fixed_image')
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tbuilder.connect(datasource, 'imageList', buildTemplateIteration1, 'inputspec.images')
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tbuilder.connect(datasource, 'passiveImagesDictionariesList', buildTemplateIteration1, 'inputspec.ListOfPassiveImagesDictionaries')
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"""
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7. Define the second iteration of template building
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"""
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buildTemplateIteration2 = ANTSTemplateBuildSingleIterationWF('iteration02')
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tbuilder.connect(buildTemplateIteration1, 'outputspec.template', buildTemplateIteration2, 'inputspec.fixed_image')
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tbuilder.connect(datasource, 'imageList', buildTemplateIteration2, 'inputspec.images')
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tbuilder.connect(datasource, 'passiveImagesDictionariesList', buildTemplateIteration2, 'inputspec.ListOfPassiveImagesDictionaries')
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"""
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8. Move selected files to a designated results folder
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"""
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datasink = pe.Node(io.DataSink(), name="datasink")
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datasink.inputs.base_directory = os.path.join(requestedPath, "results")
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tbuilder.connect(buildTemplateIteration2, 'outputspec.template',datasink,'PrimaryTemplate')
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tbuilder.connect(buildTemplateIteration2, 'outputspec.passive_deformed_templates',datasink,'PassiveTemplate')
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tbuilder.connect(initAvg, 'output_average_image', datasink,'PreRegisterAverage')
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"""
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8. Run the workflow
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"""
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tbuilder.run()

‎examples/smri_ants_registration.py

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#!/usr/bin/env python
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
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==================================
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sMRI: Using ANTS for registration
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==================================
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In this simple tutorial we will use the Registration interface from ANTS to
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coregister two T1 volumes.
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1. Tell python where to find the appropriate functions.
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"""
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import os
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import urllib2
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from nipype.interfaces.ants import Registration
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"""
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2. Download T1 volumes into home directory
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"""
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homeDir=os.getenv("HOME")
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requestedPath=os.path.join(homeDir,'nipypeTestPath')
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mydatadir=os.path.realpath(requestedPath)
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if not os.path.exists(mydatadir):
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os.makedirs(mydatadir)
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print mydatadir
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MyFileURLs=[
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('http://slicer.kitware.com/midas3/download?bitstream=13121','01_T1_half.nii.gz'),
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('http://slicer.kitware.com/midas3/download?bitstream=13122','02_T1_half.nii.gz'),
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]
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for tt in MyFileURLs:
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myURL=tt[0]
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localFilename=os.path.join(mydatadir,tt[1])
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if not os.path.exists(localFilename):
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remotefile = urllib2.urlopen(myURL)
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localFile = open(localFilename, 'wb')
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localFile.write(remotefile.read())
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localFile.close()
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print("Downloaded file: {0}".format(localFilename))
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else:
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print("File previously downloaded {0}".format(localFilename))
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input_images=[
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os.path.join(mydatadir,'01_T1_half.nii.gz'),
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os.path.join(mydatadir,'02_T1_half.nii.gz'),
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]
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"""
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3. Define the parameters of the registration
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"""
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reg = Registration()
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reg.inputs.fixed_image = [input_images[0], input_images[0] ]
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reg.inputs.moving_image = [input_images[1], input_images[1] ]
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reg.inputs.transforms = ['Affine', 'SyN']
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reg.inputs.transform_parameters = [(2.0,), (0.25, 3.0, 0.0)]
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reg.inputs.number_of_iterations = [[1500, 200], [100, 50, 30]]
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reg.inputs.dimension = 3
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reg.inputs.write_composite_transform = True
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reg.inputs.metric = ['Mattes']*2
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reg.inputs.metric_weight = [1]*2 # Default (value ignored currently by ANTs)
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reg.inputs.radius_or_number_of_bins = [32]*2
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reg.inputs.sampling_strategy = ['Random', None]
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reg.inputs.sampling_percentage = [0.05, None]
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reg.inputs.convergence_threshold = [1.e-8, 1.e-9]
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reg.inputs.convergence_window_size = [20]*2
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reg.inputs.smoothing_sigmas = [[1,0], [2,1,0]]
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reg.inputs.shrink_factors = [[2,1], [3,2,1]]
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reg.inputs.use_estimate_learning_rate_once = [True, True]
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reg.inputs.use_histogram_matching = [True, True] # This is the default
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reg.inputs.output_transform_prefix = 'thisTransform'
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reg.inputs.output_warped_image = 'INTERNAL_WARPED.nii.gz'
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reg.cmdline
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"""
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3. Run the registration
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"""
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reg.run()

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