Merge pull request #2190 from oliver-contier/mc_ref_mod

create_featreg_preproc: reference run modification

Author: satra

nipype/workflows/fmri/fsl/preprocess.py

@@ -18,6 +18,28 @@ def getthreshop(thresh):
     return ['-thr %.10f -Tmin -bin' % (0.1 * val[1]) for val in thresh]
 
 
+def pickrun(files, whichrun):
+    """pick file from list of files"""
+
+    filemap = {'first': 0, 'last': -1, 'middle': len(files) // 2}
+
+    if isinstance(files, list):
+
+        # whichrun is given as integer
+        if isinstance(whichrun, int):
+            return files[whichrun]
+        # whichrun is given as string
+        elif isinstance(whichrun, str):
+            if whichrun not in filemap.keys():
+                raise(KeyError, 'Sorry, whichrun must be either integer index'
+                                'or string in form of "first", "last" or "middle')
+            else:
+                return files[filemap[whichrun]]
+    else:
+        # in case single file name is given
+        return files
+
+
 def pickfirst(files):
     if isinstance(files, list):
         return files[0]
@@ -401,7 +423,7 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
     return featpreproc
 
 
-def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle'):
+def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle', whichrun=0):
     """Create a FEAT preprocessing workflow with registration to one volume of the first run
 
     Parameters
@@ -412,6 +434,7 @@ def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle')
         name : name of workflow (default: featpreproc)
         highpass : boolean (default: True)
         whichvol : which volume of the first run to register to ('first', 'middle', 'last', 'mean')
+        whichrun : which run to draw reference volume from (integer index or 'first', 'middle', 'last')
 
     Inputs::
 
@@ -511,8 +534,8 @@ def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle')
     if whichvol != 'mean':
         extract_ref = pe.Node(interface=fsl.ExtractROI(t_size=1),
                               iterfield=['in_file'],
-                              name='extractref')
-        featpreproc.connect(img2float, ('out_file', pickfirst), extract_ref, 'in_file')
+                              name = 'extractref')
+        featpreproc.connect(img2float, ('out_file', pickrun, whichrun), extract_ref, 'in_file')
         featpreproc.connect(img2float, ('out_file', pickvol, 0, whichvol), extract_ref, 't_min')
         featpreproc.connect(extract_ref, 'roi_file', outputnode, 'reference')
 
@@ -530,7 +553,7 @@ def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle')
         featpreproc.connect(extract_ref, 'roi_file', motion_correct, 'ref_file')
     else:
         motion_correct.inputs.mean_vol = True
-        featpreproc.connect(motion_correct, ('mean_img', pickfirst), outputnode, 'reference')
+        featpreproc.connect(motion_correct, ('mean_img', pickrun, whichrun), outputnode, 'reference')
 
     featpreproc.connect(motion_correct, 'par_file', outputnode, 'motion_parameters')
     featpreproc.connect(motion_correct, 'out_file', outputnode, 'realigned_files')
@@ -550,10 +573,9 @@ def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle')
     Extract the mean volume of the first functional run
     """
 
-    meanfunc = pe.Node(interface=fsl.ImageMaths(op_string='-Tmean',
-                                                suffix='_mean'),
+    meanfunc = pe.Node(interface=fsl.ImageMaths(op_string = '-Tmean', suffix='_mean'),
                        name='meanfunc')
-    featpreproc.connect(motion_correct, ('out_file', pickfirst), meanfunc, 'in_file')
+    featpreproc.connect(motion_correct, ('out_file', pickrun, whichrun), meanfunc, 'in_file')
 
     """
     Strip the skull from the mean functional to generate a mask
@@ -699,7 +721,7 @@ def create_featreg_preproc(name='featpreproc', highpass=True, whichvol='middle')
                         iterfield=['in_file'],
                         name='meanfunc3')
 
-    featpreproc.connect(meanscale, ('out_file', pickfirst), meanfunc3, 'in_file')
+    featpreproc.connect(meanscale, ('out_file', pickrun, whichrun), meanfunc3, 'in_file')
     featpreproc.connect(meanfunc3, 'out_file', outputnode, 'mean')
 
     """

nipype/workflows/fmri/fsl/tests/test_preprocess.py

@@ -0,0 +1,25 @@
+__author__ = 'oliver'
+
+from ..preprocess import create_featreg_preproc, pickrun
+
+
+def test_pickrun():
+    files = ['1', '2', '3', '4']
+    assert pickrun(files, 0) == '1'
+    assert pickrun(files, 'first') == '1'
+    assert pickrun(files, -1) == '4'
+    assert pickrun(files, 'last') == '4'
+    assert pickrun(files, 'middle') == '3'
+
+
+def test_create_featreg_preproc():
+    """smoke test"""
+    wf = create_featreg_preproc(whichrun=0)
+
+    # test type
+    import nipype
+    assert type(wf) == nipype.pipeline.engine.Workflow
+
+    # test methods
+    assert wf.get_node('extractref')
+    assert wf._get_dot()