Merge pull request #2823 from jguillon/fix/mrtrix3-tckgen-option

ENH: Support tckgen -select in MRtrix3 v3+

Author: effigies

.mailmap

@@ -93,7 +93,7 @@ Josh Warner <[email protected]> JDWarner <[email protected]
 Josh Warner <[email protected]> Josh Warner (Mac) <[email protected]>
 Kai Schlamp <[email protected]> medihack <[email protected]>Jessica Forbes <[email protected]> jessicaforbes <[email protected]>
 Katie Bottenhorn <[email protected]> 62442katieb <[email protected]>
-Kesshi Jordan <[email protected]> Kesshi Jordan <[email protected]> 
+Kesshi Jordan <[email protected]> Kesshi Jordan <[email protected]>
 Kesshi Jordan <[email protected]> Kesshi Jordan <[email protected]>
 Kesshi Jordan <[email protected]> Kesshi Jordan <[email protected]>
 Kesshi Jordan <[email protected]> Kesshi Jordan <[email protected]>

README.rst

@@ -91,7 +91,7 @@ To participate in the Nipype development related discussions please use the foll
 
 Please add *[nipype]* to the subject line when posting on the mailing list.
 
-You can even hangout with the Nipype developers in their 
+You can even hangout with the Nipype developers in their
 `Gitter <https://gitter.im/nipy/nipype>`_ channel or in the BrainHack `Slack <https://brainhack.slack.com/messages/C1FR76RAL>`_ channel. (Click `here <https://brainhack-slack-invite.herokuapp.com>`_ to join the Slack workspace.)
 
 

doc/devel/interface_specs.rst

@@ -12,8 +12,8 @@ In case of trouble, we encourage you to post on `NeuroStars <https://neurostars.
 NeuroStars.org is a platform similar to StackOverflow but dedicated to neuroinformatics.
 You can also post on the nipype developers mailing list: http://mail.python.org/mailman/listinfo/neuroimaging.
 As we are sharing a mailing list with the nipy community, please add ``[nipype]`` to the message title.
-Alternatively, you're welcome to chat with us in the Nipype 
-`Gitter <https://gitter.im/nipy/nipype>`_ channel or in the 
+Alternatively, you're welcome to chat with us in the Nipype
+`Gitter <https://gitter.im/nipy/nipype>`_ channel or in the
 BrainHack `Slack <https://brainhack.slack.com/messages/C1FR76RAL>`_ channel.
 (Click `here <https://brainhack-slack-invite.herokuapp.com>`_ to join the Slack workspace.)
 

nipype/interfaces/dcm2nii.py

@@ -374,7 +374,7 @@ class Dcm2niix(CommandLine):
     converts any files in the directory containing the files in the list. We
     also do not support nested filenames with this option. **Thus all files
     must have a common root directory.**
-    
+
     >>> converter = Dcm2niix()
     >>> converter.inputs.source_names = ['functional_1.dcm', 'functional_2.dcm']
     >>> converter.inputs.compression = 5

nipype/interfaces/mrtrix3/tests/test_auto_Tractography.py

@@ -40,7 +40,10 @@ def test_Tractography_inputs():
             argstr='-samples %d',
             usedefault=True,
         ),
-        n_tracks=dict(argstr='-number %d', ),
+        n_tracks=dict(
+            argstr='-number %d',
+            max_ver='0.4',
+        ),
         n_trials=dict(argstr='-trials %d', ),
         noprecompt=dict(argstr='-noprecomputed', ),
         nthreads=dict(
@@ -77,6 +80,10 @@ def test_Tractography_inputs():
             xor=['seed_image', 'seed_grid_voxel'],
         ),
         seed_sphere=dict(argstr='-seed_sphere %f,%f,%f,%f', ),
+        select=dict(
+            argstr='-select %d',
+            min_ver='3',
+        ),
         sph_trait=dict(argstr='%f,%f,%f,%f', ),
         step_size=dict(argstr='-step %f', ),
         stop=dict(argstr='-stop', ),

nipype/interfaces/mrtrix3/tracking.py

@@ -76,6 +76,13 @@ class TractographyInputSpec(MRTrix3BaseInputSpec):
               'is 90deg x stepsize / voxelsize)'))
     n_tracks = traits.Int(
         argstr='-number %d',
+        max_ver='0.4',
+        desc=('set the desired number of tracks. The program will continue'
+              ' to generate tracks until this number of tracks have been '
+              'selected and written to the output file'))
+    select = traits.Int(
+        argstr='-select %d',
+        min_ver='3',
         desc=('set the desired number of tracks. The program will continue'
               ' to generate tracks until this number of tracks have been '
               'selected and written to the output file'))

nipype/interfaces/spm/__init__.py

@@ -5,9 +5,9 @@
 
 from .base import (Info, SPMCommand, logger, no_spm, scans_for_fname,
                    scans_for_fnames)
-from .preprocess import (FieldMap, SliceTiming, Realign, RealignUnwarp, 
+from .preprocess import (FieldMap, SliceTiming, Realign, RealignUnwarp,
                          Coregister, Normalize, Normalize12, Segment,
-                         Smooth, NewSegment, DARTEL, DARTELNorm2MNI, 
+                         Smooth, NewSegment, DARTEL, DARTELNorm2MNI,
                          CreateWarped, VBMSegment)
 from .model import (Level1Design, EstimateModel, EstimateContrast, Threshold,
                     OneSampleTTestDesign, TwoSampleTTestDesign,

nipype/interfaces/spm/preprocess.py

@@ -443,7 +443,7 @@ class RealignUnwarpInputSpec(SPMCommandInputSpec):
         traits.Either(ImageFileSPM(exists=True),
                       traits.List(ImageFileSPM(exists=True))),
         field='data.scans',
-        mandatory=True, 
+        mandatory=True,
         copyfile=True,
         desc='list of filenames to realign and unwarp')
     phase_map = File(
@@ -452,52 +452,52 @@ class RealignUnwarpInputSpec(SPMCommandInputSpec):
              'behaviour, the same map will be used for all sessions',
         copyfile=False)
     quality = traits.Range(
-        low=0.0, 
-        high=1.0, 
+        low=0.0,
+        high=1.0,
         field='eoptions.quality',
         desc='0.1 = fast, 1.0 = precise')
     fwhm = traits.Range(
-        low=0.0, 
+        low=0.0,
         field='eoptions.fwhm',
         desc='gaussian smoothing kernel width')
     separation = traits.Range(
-        low=0.0, 
+        low=0.0,
         field='eoptions.sep',
         desc='sampling separation in mm')
     register_to_mean = traits.Bool(
         field='eoptions.rtm',
         desc='Indicate whether realignment is done to the mean image')
     weight_img = File(
-        exists=True, 
+        exists=True,
         field='eoptions.weight',
         desc='filename of weighting image')
     interp = traits.Range(
-        low=0, 
-        high=7, 
+        low=0,
+        high=7,
         field='eoptions.einterp',
         desc='degree of b-spline used for interpolation')
     wrap = traits.List(
-        traits.Int(), 
-        minlen=3, 
+        traits.Int(),
+        minlen=3,
         maxlen=3,
         field='eoptions.ewrap',
         desc='Check if interpolation should wrap in [x,y,z]')
     est_basis_func = traits.List(
-        traits.Int(), 
-        minlen=2, 
+        traits.Int(),
+        minlen=2,
         maxlen=2,
         field='uweoptions.basfcn',
         desc='Number of basis functions to use for each dimension')
     est_reg_order = traits.Range(
-        low=0, 
-        high=3, 
+        low=0,
+        high=3,
         field='uweoptions.regorder',
         desc=('This parameter determines how to balance the compromise between likelihood '
               'maximization and smoothness maximization of the estimated field.'))
     est_reg_factor = traits.ListInt(
-        [100000], 
+        [100000],
         field='uweoptions.lambda',
-        minlen=1, 
+        minlen=1,
         maxlen=1,
         usedefault=True,
         desc='Regularisation factor. Default: 100000 (medium).')
@@ -506,60 +506,60 @@ class RealignUnwarpInputSpec(SPMCommandInputSpec):
         desc=('Jacobian deformations. In theory a good idea to include them, '
              ' in practice a bad idea. Default: No.'))
     est_first_order_effects = traits.List(
-        traits.Int(), 
-        minlen=1, 
+        traits.Int(),
+        minlen=1,
         maxlen=6,
         field='uweoptions.fot',
         desc='First order effects should only depend on pitch and roll, i.e. [4 5]')
     est_second_order_effects = traits.List(
-        traits.Int(), 
-        minlen=1, 
+        traits.Int(),
+        minlen=1,
         maxlen=6,
         field='uweoptions.sot',
         desc='List of second order terms to model second derivatives of.')
     est_unwarp_fwhm = traits.Range(
-        low=0.0, 
+        low=0.0,
         field='uweoptions.uwfwhm',
         desc='gaussian smoothing kernel width for unwarp')
     est_re_est_mov_par = traits.Bool(
         field='uweoptions.rem',
         desc='Re-estimate movement parameters at each unwarping iteration.')
     est_num_of_iterations = traits.ListInt(
-        [5], 
+        [5],
         field='uweoptions.noi',
-        minlen=1, 
-        maxlen=1, 
+        minlen=1,
+        maxlen=1,
         usedefault=True,
         desc='Number of iterations.')
     est_taylor_expansion_point = traits.String(
-        'Average', 
+        'Average',
         field='uweoptions.expround',
         usedefault=True,
         desc='Point in position space to perform Taylor-expansion around.')
     reslice_which = traits.ListInt(
-        [2, 1], 
+        [2, 1],
         field='uwroptions.uwwhich',
-        minlen=2, 
-        maxlen=2, 
+        minlen=2,
+        maxlen=2,
         usedefault=True,
         desc='determines which images to reslice')
     reslice_interp = traits.Range(
-        low=0, 
-        high=7, 
+        low=0,
+        high=7,
         field='uwroptions.rinterp',
         desc='degree of b-spline used for interpolation')
     reslice_wrap = traits.List(
-        traits.Int(), 
-        minlen=3, 
+        traits.Int(),
+        minlen=3,
         maxlen=3,
         field='uwroptions.wrap',
         desc='Check if interpolation should wrap in [x,y,z]')
     reslice_mask = traits.Bool(
         field='uwroptions.mask',
         desc='True/False mask output image')
     out_prefix = traits.String(
-        'u', 
-        field='uwroptions.prefix', 
+        'u',
+        field='uwroptions.prefix',
         usedefault=True,
         desc='realigned and unwarped output prefix')
 
@@ -585,7 +585,7 @@ class RealignUnwarpOutputSpec(TraitedSpec):
 
 class RealignUnwarp(SPMCommand):
     """Use spm_uw_estimate for estimating within subject registration and unwarping
-    of time series. Function accepts only one single field map. If in_files is a 
+    of time series. Function accepts only one single field map. If in_files is a
     list of files they will be treated as separate sessions but associated to the
     same fieldmap.
 
@@ -617,7 +617,7 @@ def _format_arg(self, opt, spec, val):
                                     keep4d=False,
                                     separate_sessions=True)
         return super(RealignUnwarp, self)._format_arg(opt, spec, val)
-   
+
 
     def _parse_inputs(self, skip=()):
 
@@ -630,7 +630,7 @@ def _parse_inputs(self, skip=()):
 
         if isdefined(self.inputs.in_files):
             if isinstance(self.inputs.in_files, list):
-                data = [dict(scans = sess, pmscan = pmscan) 
+                data = [dict(scans = sess, pmscan = pmscan)
                                           for sess in spmdict['data']['scans']]
             else:
                 data = [dict(scans = spmdict['data']['scans'], pmscan = pmscan)]

nipype/pipeline/engine/tests/test_engine.py

@@ -441,7 +441,7 @@ def test_write_graph_runs(tmpdir):
 
             assert os.path.exists('graph.dot') or os.path.exists(
                 'graph_detailed.dot')
-       
+
             try:
                 os.remove('graph.dot')
             except OSError:

nipype/pipeline/plugins/legacymultiproc.py

@@ -81,7 +81,7 @@ class NonDaemonMixin(object):
     @property
     def daemon(self):
         return False
-    
+
     @daemon.setter
     def daemon(self, val):
         pass