""" ================================ Workshop: Dartmouth College 2010 ================================ First lets go to the directory with the data we'll be working on and start the interactive python interpreter (with some nipype specific configuration). Note that nipype does not need to be run through ipython - it is just much nicer to do interactive work in it. .. sourcecode:: bash cd $TDPATH ipython -p nipype For every neuroimaging procedure supported by nipype there exists a wrapper - a small piece of code managing the underlying software (FSL, SPM, AFNI etc.). We call those interfaces. They are standarised so we can hook them up together. Lets have a look at some of them. .. sourcecode:: ipython In [1]: import nipype.interfaces.fsl as fsl In [2]: fsl.BET.help() Inputs ------ Mandatory: in_file: input file to skull strip Optional: args: Additional parameters to the command center: center of gravity in voxels environ: Environment variables (default={}) frac: fractional intensity threshold functional: apply to 4D fMRI data mutually exclusive: functional, reduce_bias mask: create binary mask image mesh: generate a vtk mesh brain surface no_output: Don't generate segmented output out_file: name of output skull stripped image outline: create surface outline image output_type: FSL output type radius: head radius reduce_bias: bias field and neck cleanup mutually exclusive: functional, reduce_bias skull: create skull image threshold: apply thresholding to segmented brain image and mask vertical_gradient: vertical gradient in fractional intensity threshold (-1, 1) Outputs ------- mask_file: path/name of binary brain mask (if generated) meshfile: path/name of vtk mesh file (if generated) out_file: path/name of skullstripped file outline_file: path/name of outline file (if generated) In [3]: import nipype.interfaces.freesurfer as fs In [4]: fs.Smooth.help() Inputs ------ Mandatory: in_file: source volume num_iters: number of iterations instead of fwhm mutually exclusive: surface_fwhm reg_file: registers volume to surface anatomical surface_fwhm: surface FWHM in mm mutually exclusive: num_iters requires: reg_file Optional: args: Additional parameters to the command environ: Environment variables (default={}) proj_frac: project frac of thickness a long surface normal mutually exclusive: proj_frac_avg proj_frac_avg: average a long normal min max delta mutually exclusive: proj_frac smoothed_file: output volume subjects_dir: subjects directory vol_fwhm: volumesmoothing outside of surface Outputs ------- args: Additional parameters to the command environ: Environment variables smoothed_file: smoothed input volume subjects_dir: subjects directory You can read about all of the interfaces implemented in nipype at our online documentation at http://nipy.sourceforge.net/nipype/documentation.html#documentation . Check it out now. Using interfaces ---------------- Having interfaces allows us to use third party software (like FSL BET) as function. Look how simple it is. """ import nipype.interfaces.fsl as fsl result = fsl.BET(in_file='data/s1/struct.nii').run() print result """ Running a single program is not much of a breakthrough. Lets run motion correction followed by smoothing (isotropic - in other words not using SUSAN). Notice that in the first line we are setting the output data type for all FSL interfaces. """ fsl.FSLCommand.set_default_output_type('NIFTI_GZ') result1 = fsl.MCFLIRT(in_file='data/s1/f3.nii').run() result2 = fsl.Smooth(in_file='f3_mcf.nii.gz', fwhm=6).run() """ Simple workflow --------------- In the previous example we knew that fsl.MCFLIRT will produce a file called f3_mcf.nii.gz and we have hard coded this as an input to fsl.Smooth. This is quite limited, but luckily nipype supports joining interfaces in pipelines. This way output of one interface will be used as an input of another without having to hard code anything. Before connecting Interfaces we need to put them into (separate) Nodes and give them unique names. This way every interface will process data in a separate folder. """ import nipype.pipeline.engine as pe import os motion_correct = pe.Node(interface=fsl.MCFLIRT(in_file=os.path.abspath('data/s1/f3.nii')), name="motion_correct") smooth = pe.Node(interface=fsl.Smooth(fwhm=6), name="smooth") motion_correct_and_smooth = pe.Workflow(name="motion_correct_and_smooth") motion_correct_and_smooth.base_dir = os.path.abspath('.') # define where will be the root folder for the workflow motion_correct_and_smooth.connect([ (motion_correct, smooth, [('out_file', 'in_file')]) ]) # we are connecting 'out_file' output of motion_correct to 'in_file' input of smooth motion_correct_and_smooth.run() """ Another workflow ---------------- Another example of a simple workflow (calculate the mean of fMRI signal and subtract it). This time we'll be assigning inputs after defining the workflow. """ calc_mean = pe.Node(interface=fsl.ImageMaths(), name="calc_mean") calc_mean.inputs.op_string = "-Tmean" subtract = pe.Node(interface=fsl.ImageMaths(), name="subtract") subtract.inputs.op_string = "-sub" demean = pe.Workflow(name="demean") demean.base_dir = os.path.abspath('.') demean.connect([ (calc_mean, subtract, [('out_file', 'in_file2')]) ]) demean.inputs.calc_mean.in_file = os.path.abspath('data/s1/f3.nii') demean.inputs.subtract.in_file = os.path.abspath('data/s1/f3.nii') demean.run() """ Reusing workflows ----------------- The beauty of the workflows is that they are reusable. We can just import a workflow made by someone else and feed it with our data. """ from fmri_fsl import preproc preproc.base_dir = os.path.abspath('.') preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii') preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii') preproc.run() """ ... and we can run it again and it won't actually rerun anything because none of the parameters have changed. """ 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'), ('maskfunc3.out_file', 'funcruns')]) ]) preprocess.run() """ Datagrabber ----------- Datagrabber is (surprise, surprise) an interface for collecting files from hard drive. It is very flexible and supports almost any file organisation of your data you can imagine. """ datasource1 = nio.DataGrabber() datasource1.inputs.template = 'data/s1/f3.nii' results = datasource1.run() print results.outputs datasource2 = nio.DataGrabber() datasource2.inputs.template = 'data/s*/f*.nii' results = datasource2.run() print results.outputs datasource3 = nio.DataGrabber(infields=['run']) datasource3.inputs.template = 'data/s1/f%d.nii' datasource3.inputs.run = [3, 7] results = datasource3.run() print results.outputs datasource4 = nio.DataGrabber(infields=['subject_id', 'run']) datasource4.inputs.template = 'data/%s/f%d.nii' datasource4.inputs.run = [3, 7] datasource4.inputs.subject_id = ['s1', 's3'] results = datasource4.run() print results.outputs """ Iterables --------- Iterables is a special field of the Node class that enables to iterate all workfloes/nodes connected to it over some parameters. Here we'll use it to iterate over two subjects. """ import nipype.interfaces.utility as util infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']), name="infosource") infosource.iterables = ('subject_id', ['s1', 's3']) datasource = pe.Node(nio.DataGrabber(infields=['subject_id'], outfields=['func', 'struct']), name="datasource") datasource.inputs.template = '%s/%s.nii' datasource.inputs.base_directory = os.path.abspath('data') datasource.inputs.template_args = dict(func=[['subject_id','f3']], struct=[['subject_id','struct']]) my_workflow = pe.Workflow(name="my_workflow") my_workflow.base_dir = os.path.abspath('.') my_workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')]), (datasource, preproc, [('func', 'inputspec.func'), ('struct', 'inputspec.struct')])]) my_workflow.run() """ and we can change a node attribute and run it again """ smoothnode = my_workflow.get_node('preproc.smooth') assert(str(smoothnode)=='preproc.smooth') smoothnode.iterables = ('fwhm', [5.,10.]) my_workflow.run() """ Visualizing workflows 2 ----------------------- In the case of nested workflows, we might want to look at expanded forms of the workflow. """