Merge branch 'master' into enh/MRTrix3

Author: oesteban

CHANGES

@@ -55,6 +55,12 @@ Next release
 * FIX: typo in nipype.interfaces.freesurfer.utils.py Tkregister2 (https://github.com/nipy/nipype/pull/1083)
 * FIX: SSHDataGrabber outputs now return full path to the grabbed/downloaded files. (https://github.com/nipy/nipype/pull/1086)
 * FIX: Add QA output for TSNR to resting workflow (https://github.com/nipy/nipype/pull/1088)
+* FIX: Change N4BiasFieldCorrection to use short tag for dimensionality (backward compatible) (https://github.com/nipy/nipype/pull/1096)
+* ENH: Added -newgrid input to Warp in AFNI (3dWarp wrapper) (https://github.com/nipy/nipype/pull/1128)
+* FIX: Fixed AFNI Copy interface to use positional inputs as required (https://github.com/nipy/nipype/pull/1131)
+* ENH: Added a check in Dcm2nii to check if nipype created the config.ini file and remove if true (https://github.com/nipy/nipype/pull/1132)
+* ENH: Use a while loop to wait for Xvfb (up to a max wait time "xvfb_max_wait" in config file, default 10)
+       (https://github.com/nipy/nipype/pull/1142)
 
 Release 0.10.0 (October 10, 2014)
 ============

doc/users/config_file.rst

@@ -125,6 +125,9 @@ Execution
     all pending jobs and checking for job completion. To be nice to cluster
     schedulers the default is set to 60 seconds.
 
+*xvfb_max_wait*
+    Maximum time (in seconds) to wait for Xvfb to start, if the _redirect_x parameter of an Interface is True.
+
 Example
 ~~~~~~~
 

examples/fmri_ants_openfmri.py

@@ -124,6 +124,7 @@ def create_reg_workflow(name='registration'):
                                                                  'transformed_files',
                                                                  'transformed_mean',
                                                                  'anat2target',
+                                                                 'mean2anat_mask'
                                                                  ]),
                          name='outputspec')
 
@@ -171,6 +172,13 @@ def create_reg_workflow(name='registration'):
     register.connect(mean2anat, 'out_matrix_file',
                      mean2anatbbr, 'in_matrix_file')
 
+    """
+    Create a mask of the median image coregistered to the anatomical image
+    """
+    
+    mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
+    register.connect(mean2anatbbr, 'out_file', mean2anat_mask, 'in_file')
+
     """
     Convert the BBRegister transformation to ANTS ITK format
     """
@@ -276,6 +284,8 @@ def create_reg_workflow(name='registration'):
     register.connect(warpall, 'output_image', outputnode, 'transformed_files')
     register.connect(mean2anatbbr, 'out_matrix_file',
                      outputnode, 'func2anat_transform')
+    register.connect(mean2anat_mask, 'mask_file',
+                     outputnode, 'mean2anat_mask')
     register.connect(reg, 'composite_transform',
                      outputnode, 'anat2target_transform')
 
@@ -333,7 +343,8 @@ def create_fs_reg_workflow(name='registration'):
                                                           'transformed_files',
                                                           'min_cost_file',
                                                           'anat2target',
-                                                          'aparc'
+                                                          'aparc',
+                                                          'mean2anat_mask'
                                                           ]),
                       name='outputspec')
 
@@ -349,7 +360,7 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(fssource, 'T1', convert, 'in_file')
 
     # Coregister the median to the surface
-    bbregister = Node(freesurfer.BBRegister(),
+    bbregister = Node(freesurfer.BBRegister(registered_file=True),
                     name='bbregister')
     bbregister.inputs.init = 'fsl'
     bbregister.inputs.contrast_type = 't2'
@@ -359,6 +370,10 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(inputnode, 'mean_image', bbregister, 'source_file')
     register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
 
+    # Create a mask of the median coregistered to the anatomical image
+    mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
+    register.connect(bbregister, 'registered_file', mean2anat_mask, 'in_file')
+
     """
     use aparc+aseg's brain mask
     """
@@ -500,6 +515,8 @@ def create_fs_reg_workflow(name='registration'):
                      outputnode, 'out_reg_file')
     register.connect(bbregister, 'min_cost_file',
                      outputnode, 'min_cost_file')
+    register.connect(mean2anat_mask, 'mask_file',
+                     outputnode, 'mean2anat_mask')
     register.connect(reg, 'composite_transform',
                      outputnode, 'anat2target_transform')
     register.connect(merge, 'out', outputnode, 'transforms')
@@ -964,6 +981,8 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
         subs.append(('/model%03d/task%03d_' % (model_id, task_id), '/'))
         subs.append(('_bold_dtype_mcf_bet_thresh_dil', '_mask'))
         subs.append(('_output_warped_image', '_anat2target'))
+        subs.append(('median_flirt_brain_mask', 'median_brain_mask'))
+        subs.append(('median_bbreg_brain_mask', 'median_brain_mask'))
         return subs
 
     subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds', 'run_id',
@@ -998,6 +1017,7 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
                                      ('outputspec.motion_plots',
                                       'qa.motion.plots'),
                                      ('outputspec.mask', 'qa.mask')])])
+    wf.connect(registration, 'outputspec.mean2anat_mask', datasink, 'qa.mask.mean2anat')
     wf.connect(art, 'norm_files', datasink, 'qa.art.@norm')
     wf.connect(art, 'intensity_files', datasink, 'qa.art.@intensity')
     wf.connect(art, 'outlier_files', datasink, 'qa.art.@outlier_files')

nipype/interfaces/afni/preprocess.py

@@ -233,6 +233,9 @@ class WarpInputSpec(AFNICommandInputSpec):
                    argstr="-gridset %s",
                    exists=True)
 
+    newgrid = traits.Float(desc="specify grid of this size (mm)",
+                           argstr="-newgrid %f")
+
     zpad = traits.Int(desc="pad input dataset with N planes" +
                       " of zero on all sides.",
                       argstr="-zpad %d")
@@ -254,7 +257,13 @@ class Warp(AFNICommand):
     >>> warp.inputs.out_file = "trans.nii.gz"
     >>> warp.cmdline
     '3dWarp -deoblique -prefix trans.nii.gz structural.nii'
-    >>> res = warp.run() # doctest: +SKIP
+
+    >>> warp_2 = afni.Warp()
+    >>> warp_2.inputs.in_file = 'structural.nii'
+    >>> warp_2.inputs.newgrid = 1.0
+    >>> warp_2.inputs.out_file = "trans.nii.gz"
+    >>> warp_2.cmdline
+    '3dWarp -newgrid 1.000000 -prefix trans.nii.gz structural.nii'
 
     """
 
@@ -667,7 +676,7 @@ class CopyInputSpec(AFNICommandInputSpec):
                    exists=True,
                    copyfile=False)
     out_file = File(name_template="%s_copy", desc='output image file name',
-                    argstr='-prefix %s', name_source="in_file")
+                    argstr='%s', position=-1, name_source="in_file")
 
 
 class Copy(AFNICommand):
@@ -681,11 +690,26 @@ class Copy(AFNICommand):
     ========
 
     >>> from nipype.interfaces import afni as afni
-    >>> copy = afni.Copy()
-    >>> copy.inputs.in_file = 'functional.nii'
-    >>> copy.inputs.out_file = 'new_func.nii'
-    >>> res = copy.run() # doctest: +SKIP
-
+    >>> copy3d = afni.Copy()
+    >>> copy3d.inputs.in_file = 'functional.nii'
+    >>> copy3d.cmdline
+    '3dcopy functional.nii functional_copy'
+
+    >>> from copy import deepcopy
+    >>> copy3d_2 = deepcopy(copy3d)
+    >>> copy3d_2.inputs.outputtype = 'NIFTI'
+    >>> copy3d_2.cmdline
+    '3dcopy functional.nii functional_copy.nii'
+
+    >>> copy3d_3 = deepcopy(copy3d)
+    >>> copy3d_3.inputs.outputtype = 'NIFTI_GZ'
+    >>> copy3d_3.cmdline
+    '3dcopy functional.nii functional_copy.nii.gz'
+
+    >>> copy3d_4 = deepcopy(copy3d)
+    >>> copy3d_4.inputs.out_file = 'new_func.nii'
+    >>> copy3d_4.cmdline
+    '3dcopy functional.nii new_func.nii'
     """
 
     _cmd = '3dcopy'

nipype/interfaces/afni/tests/test_auto_Copy.py

@@ -16,9 +16,10 @@ def test_Copy_inputs():
     mandatory=True,
     position=-2,
     ),
-    out_file=dict(argstr='-prefix %s',
+    out_file=dict(argstr='%s',
     name_source='in_file',
     name_template='%s_copy',
+    position=-1,
     ),
     outputtype=dict(),
     terminal_output=dict(nohash=True,

nipype/interfaces/afni/tests/test_auto_Warp.py

@@ -26,6 +26,8 @@ def test_Warp_inputs():
     ),
     mni2tta=dict(argstr='-mni2tta',
     ),
+    newgrid=dict(argstr='-newgrid %f',
+    ),
     out_file=dict(argstr='-prefix %s',
     name_source='in_file',
     name_template='%s_warp',

nipype/interfaces/ants/segmentation.py

@@ -223,7 +223,7 @@ def _list_outputs(self):
 
 
 class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
-    dimension = traits.Enum(3, 2, argstr='--image-dimension %d',
+    dimension = traits.Enum(3, 2, argstr='-d %d',
                             usedefault=True,
                             desc='image dimension (2 or 3)')
     input_image = File(argstr='--input-image %s', mandatory=True,
@@ -237,8 +237,7 @@ class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
     bspline_fitting_distance = traits.Float(argstr="--bspline-fitting %s")
     bspline_order = traits.Int(requires=['bspline_fitting_distance'])
     shrink_factor = traits.Int(argstr="--shrink-factor %d")
-    n_iterations = traits.List(traits.Int(), argstr="--convergence %s",
-                               requires=['convergence_threshold'])
+    n_iterations = traits.List(traits.Int(), argstr="--convergence %s")
     convergence_threshold = traits.Float(requires=['n_iterations'])
     save_bias = traits.Bool(False, mandatory=True, usedefault=True,
                             desc=('True if the estimated bias should be saved'
@@ -277,26 +276,33 @@ class N4BiasFieldCorrection(ANTSCommand):
     >>> n4.inputs.bspline_fitting_distance = 300
     >>> n4.inputs.shrink_factor = 3
     >>> n4.inputs.n_iterations = [50,50,30,20]
-    >>> n4.inputs.convergence_threshold = 1e-6
     >>> n4.cmdline
     'N4BiasFieldCorrection --bspline-fitting [ 300 ] \
---image-dimension 3 --input-image structural.nii \
---convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii \
+-d 3 --input-image structural.nii \
+--convergence [ 50x50x30x20 ] --output structural_corrected.nii \
 --shrink-factor 3'
 
 	>>> n4_2 = copy.deepcopy(n4)
-    >>> n4_2.inputs.bspline_order = 5
+    >>> n4_2.inputs.convergence_threshold = 1e-6
     >>> n4_2.cmdline
-    'N4BiasFieldCorrection --bspline-fitting [ 300, 5 ] \
---image-dimension 3 --input-image structural.nii \
+    'N4BiasFieldCorrection --bspline-fitting [ 300 ] \
+-d 3 --input-image structural.nii \
 --convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii \
 --shrink-factor 3'
 
-    >>> n4_3 = N4BiasFieldCorrection()
-    >>> n4_3.inputs.input_image = 'structural.nii'
-    >>> n4_3.inputs.save_bias = True
+    >>> n4_3 = copy.deepcopy(n4_2)
+    >>> n4_3.inputs.bspline_order = 5
     >>> n4_3.cmdline
-    'N4BiasFieldCorrection --image-dimension 3 --input-image structural.nii \
+    'N4BiasFieldCorrection --bspline-fitting [ 300, 5 ] \
+-d 3 --input-image structural.nii \
+--convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii \
+--shrink-factor 3'
+
+    >>> n4_4 = N4BiasFieldCorrection()
+    >>> n4_4.inputs.input_image = 'structural.nii'
+    >>> n4_4.inputs.save_bias = True
+    >>> n4_4.cmdline
+    'N4BiasFieldCorrection -d 3 --input-image structural.nii \
 --output [ structural_corrected.nii, structural_bias.nii ]'
     """
 
@@ -335,10 +341,12 @@ def _format_arg(self, name, trait_spec, value):
                 newval = '[ %g ]' % value
             return trait_spec.argstr % newval
 
-        if ((name == 'n_iterations') and
-           (isdefined(self.inputs.convergence_threshold))):
-            newval = '[ %s, %g ]' % ('x'.join([str(elt) for elt in value]),
-                                     self.inputs.convergence_threshold)
+        if name == 'n_iterations':
+            if isdefined(self.inputs.convergence_threshold):
+                newval = '[ %s, %g ]' % ('x'.join([str(elt) for elt in value]),
+                                         self.inputs.convergence_threshold)
+            else:
+                newval = '[ %s ]' % 'x'.join([str(elt) for elt in value])
             return trait_spec.argstr % newval
 
         return super(N4BiasFieldCorrection,

nipype/interfaces/ants/tests/test_auto_N4BiasFieldCorrection.py

@@ -13,7 +13,7 @@ def test_N4BiasFieldCorrection_inputs():
     ),
     convergence_threshold=dict(requires=['n_iterations'],
     ),
-    dimension=dict(argstr='--image-dimension %d',
+    dimension=dict(argstr='-d %d',
     usedefault=True,
     ),
     environ=dict(nohash=True,
@@ -28,7 +28,6 @@ def test_N4BiasFieldCorrection_inputs():
     mask_image=dict(argstr='--mask-image %s',
     ),
     n_iterations=dict(argstr='--convergence %s',
-    requires=['convergence_threshold'],
     ),
     num_threads=dict(nohash=True,
     usedefault=True,

nipype/interfaces/base.py

@@ -994,9 +994,13 @@ def run(self, **inputs):
                     xvfb_proc = subprocess.Popen(xvfb_cmd,
                                                  stdout=open(os.devnull),
                                                  stderr=open(os.devnull))
-                    time.sleep(0.2)  # give Xvfb time to start
-                    if xvfb_proc.poll() is not None:
-                        raise Exception('Error: Xvfb did not start')
+                    wait_step = 0.2
+                    wait_time = 0
+                    while xvfb_proc.poll() is not None:
+                        if wait_time > config.get('execution', 'xvfb_max_wait'):
+                            raise Exception('Error: Xvfb did not start')
+                        time.sleep(wait_step)  # give Xvfb time to start
+                        wait_time += wait_step
 
                     runtime.environ['DISPLAY'] = ':%s' % vdisplay_num