bedpostx_parallel avoid computation of dyads uncertainty available

Author: oesteban

nipype/interfaces/afni/preprocess.py

@@ -1935,7 +1935,7 @@ def _overload_extension(self, value):
 
     def _gen_filename(self, name):
         return os.path.abspath(super(AFNItoNIFTI, self)._gen_filename(name))
-        
+
 class EvalInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 1deval',
                      argstr='-a %s', position=0, mandatory=True, exists=True)
@@ -1955,15 +1955,15 @@ class EvalInputSpec(AFNICommandInputSpec):
                           requires=['start_idx'])
     single_idx = traits.Int(desc='volume index for in_file_a')
     other = File(desc='other options', argstr='')
-    
+
 class Eval(AFNICommand):
     """Evaluates an expression that may include columns of data from one or more text files
 
     see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>
-    
+
     Examples
     ========
-    
+
     >>> from nipype.interfaces import afni as afni
     >>> eval = afni.Eval()
     >>> eval.inputs.in_file_a = 'seed.1D'
@@ -1996,7 +1996,7 @@ def _parse_inputs(self, skip=None):
         """
         return super(Eval, self)._parse_inputs(
             skip=('start_idx', 'stop_idx', 'out1D', 'other'))
-            
+
 class MeansInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 3dMean',
         argstr='%s',

nipype/interfaces/afni/svm.py

@@ -24,28 +24,28 @@
 
 warn = warnings.warn
 warnings.filterwarnings('always', category=UserWarning)
-    
+
 class SVMTrainInputSpec(AFNICommandInputSpec):
     #training options
-    ttype = traits.Str(desc='tname: classification or regression', 
+    ttype = traits.Str(desc='tname: classification or regression',
                 argstr='-type %s',
                 mandatory=True)
     in_file = File(desc='A 3D+t AFNI brik dataset to be used for training.',
                 argstr='-trainvol %s',
                 mandatory=True,
                 exists=True,
                 copyfile=False)
-    out_file = File(name_template="%s_vectors", 
+    out_file = File(name_template="%s_vectors",
                 desc='output sum of weighted linear support vectors file name',
                 argstr='-bucket %s',
-                suffix='_bucket', 
+                suffix='_bucket',
                 name_source="in_file")
     model = File(name_template="%s_model",
                 desc='basename for the brik containing the SVM model',
                 argstr='-model %s',
-                suffix='_model', 
-                name_source="in_file")    
-    alphas = File(name_template="%s_alphas", 
+                suffix='_model',
+                name_source="in_file")
+    alphas = File(name_template="%s_alphas",
                 desc='output alphas file name',
                 argstr='-alpha %s',
                 suffix='_alphas',
@@ -62,20 +62,20 @@ class SVMTrainInputSpec(AFNICommandInputSpec):
                 exists=True)
     censor = File(desc='.1D censor file that allows the user to ignore certain samples in the training data.',
                 argstr='-censor %s',
-                exists=True)   
+                exists=True)
     kernel = traits.Str(desc='string specifying type of kernel function:linear, polynomial, rbf, sigmoid',
                 argstr='-kernel %s')
     max_iterations = traits.Int(desc='Specify the maximum number of iterations for the optimization.',
                 argstr='-max_iterations %d')
     w_out = traits.Bool(desc='output sum of weighted linear support vectors',
                 argstr='-wout')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-                
+
 class SVMTrainOutputSpec(TraitedSpec):
     out_file = File(desc='sum of weighted linear support vectors file name')
     model = File(desc='brik containing the SVM model file name')
     alphas = File(desc='output alphas file name')
-                
+
 class SVMTrain(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Train Only
@@ -130,7 +130,7 @@ class SVMTestInputSpec(AFNICommandInputSpec):
     multiclass = traits.Bool(desc='Specifies multiclass algorithm for classification',
                 argstr='-multiclass %s')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-    
+
 class SVMTest(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Test Only

nipype/interfaces/camino/connectivity.py

@@ -37,7 +37,7 @@ class ConmatInputSpec(CommandLineInputSpec):
                              desc=("Tract statistic to use. See TractStats for other options."),
                              requires=['scalar_file'],xor=['tract_prop'])
 
-    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s', 
+    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s',
                              units='NA', xor=['tract_stat'],
                              desc=('Tract property average to compute in the connectivity matrix. '
                                    'See TractStats for details.'))

nipype/workflows/dmri/fsl/dti.py

@@ -124,7 +124,9 @@ def merge_and_mean(name='mm'):
     return wf
 
 
-def bedpostx_parallel(name='bedpostx_parallel', params={}):
+def bedpostx_parallel(name='bedpostx_parallel',
+                      compute_all_outputs=True,
+                      params={}):
     """
     Does the same as :func:`.create_bedpostx_pipeline` by splitting
     the input dMRI in small ROIs that are better suited for parallel
@@ -163,11 +165,16 @@ def bedpostx_parallel(name='bedpostx_parallel', params={}):
     xfibres = pe.MapNode(xfib_if, name='xfibres',
                          iterfield=['dwi', 'mask'])
 
-    make_dyads = pe.MapNode(fsl.MakeDyadicVectors(), name="make_dyads",
+    mrg_dyads = pe.MapNode(misc.MergeROIs(), name='Merge_dyads',
+                           iterfield=['in_files'])
+    make_dyads = pe.MapNode(fsl.MakeDyadicVectors(), name="Make_dyads",
                             iterfield=['theta_vol', 'phi_vol'])
-    out_fields = ['dyads', 'dyads_disp',
-                  'thsamples', 'phsamples', 'fsamples',
-                  'mean_thsamples', 'mean_phsamples', 'mean_fsamples']
+    out_fields = ['dyads']
+
+    if compute_all_outputs:
+        out_fields += ['dyads_disp', 'thsamples', 'phsamples',
+                       'fsamples', 'mean_thsamples', 'mean_phsamples',
+                       'mean_fsamples']
 
     outputnode = pe.Node(niu.IdentityInterface(fields=out_fields),
                          name='outputnode')
@@ -180,30 +187,35 @@ def bedpostx_parallel(name='bedpostx_parallel', params={}):
                                  ('out_masks', 'mask')]),
         (inputnode, xfibres,    [('bvecs', 'bvecs'),
                                  ('bvals', 'bvals')]),
-        (inputnode, make_dyads, [('mask', 'mask')])
+        (inputnode, make_dyads, [('mask', 'mask')]),
+        (inputnode, mrg_dyads,  [('mask', 'in_reference')]),
+        (xfibres,   mrg_dyads,  [(('dyads', transpose), 'in_files')]),
+        (slice_dwi, mrg_dyads,  [('out_index', 'in_index')])
+        (mrg_dyads, outputnode, [('merged_file', 'dyads')])
     ])
 
-    mms = {}
-    for k in ['thsamples', 'phsamples', 'fsamples']:
-        mms[k] = merge_and_mean_parallel(k)
+    if compute_all_outputs:
+        mms = {}
+        for k in ['thsamples', 'phsamples', 'fsamples']:
+            mms[k] = merge_and_mean_parallel(k)
+            wf.connect([
+                (slice_dwi, mms[k], [('out_index', 'inputnode.in_index')]),
+                (inputnode, mms[k], [('mask', 'inputnode.in_reference')]),
+                (xfibres, mms[k], [(k, 'inputnode.in_files')]),
+                (mms[k], outputnode, [('outputnode.merged', k),
+                                      ('outputnode.mean', 'mean_%s' % k)])
+
+            ])
+
+        # m_mdsamples = pe.Node(fsl.Merge(dimension="z"),
+        #                       name="merge_mean_dsamples")
         wf.connect([
-            (slice_dwi, mms[k], [('out_index', 'inputnode.in_index')]),
-            (inputnode, mms[k], [('mask', 'inputnode.in_reference')]),
-            (xfibres, mms[k], [(k, 'inputnode.in_files')]),
-            (mms[k], outputnode, [('outputnode.merged', k),
-                                  ('outputnode.mean', 'mean_%s' % k)])
-
+            (mms['thsamples'], make_dyads, [('outputnode.merged', 'theta_vol')]),
+            (mms['phsamples'], make_dyads, [('outputnode.merged', 'phi_vol')]),
+            #(xfibres, m_mdsamples,  [('mean_dsamples', 'in_files')]),
+            (make_dyads, outputnode, [('dispersion', 'dyads_disp')])
         ])
 
-    # m_mdsamples = pe.Node(fsl.Merge(dimension="z"),
-    #                       name="merge_mean_dsamples")
-    wf.connect([
-        (mms['thsamples'], make_dyads, [('outputnode.merged', 'theta_vol')]),
-        (mms['phsamples'], make_dyads, [('outputnode.merged', 'phi_vol')]),
-        #(xfibres, m_mdsamples,  [('mean_dsamples', 'in_files')]),
-        (make_dyads, outputnode, [('dyads', 'dyads'),
-                                  ('dispersion', 'dyads_disp')])
-    ])
     return wf