Merge remote-tracking branch 'upstream/master' into fix/frompy3

* upstream/master:
  fix: deploy only nipy/nipype master branch
  fix for identifying if one or multiple durations are employed to describe the condition
  corrected line of modelgen and added test for variable concatenated events
  fix
  test
  fix: bad copy paste.
  tst: added more duration checking tests
  tst: added duration checking
  fix: use float for computation input
  fix: update scripts to use single file ants composite_transform output
  doc: changed description
  fix: address multimodal and unimodal inputs
  Update test_auto_MRIConvert.py
  FIX: add TR, TE and TI flag to FreeSurfer's MRIConvert

Author: satra

.travis.yml

@@ -45,4 +45,5 @@ deploy:
     secure: ZaN6Y4hfybPMi1rW3Qe03irCe/3GU4J4BJsjG+tClVN5Fjc3FCJaYU0k4wNd9q2kMjuRDv1efA9BN/5QasJS32oqs6wHvxzIWA18ucel4BBnAAkuviC1vqi/8Hk/DGB6e1gnmZ1Nv58zVLfn0MwlOKmsGDtvGQWc+HbaV2fS5NA=
   on:
     tags: true
+    repo: nipy/nipype
     branch: master

examples/fmri_ants_openfmri.py

@@ -236,7 +236,7 @@ def create_reg_workflow(name='registration'):
 
     merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target
@@ -429,7 +429,7 @@ def create_fs_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'qsub_args': '-pe orte 4',
@@ -445,7 +445,7 @@ def create_fs_reg_workflow(name='registration'):
 
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target

examples/rsfmri_vol_surface_preprocessing.py

@@ -517,7 +517,7 @@ def create_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
@@ -528,11 +528,9 @@ def create_reg_workflow(name='registration'):
     Concatenate the affine and ants transforms into a list
     """
 
-    pickfirst = lambda x: x[0]
-
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
     """
     Transform the mean image. First to anatomical and then to target

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -498,7 +498,7 @@ def create_reg_workflow(name='registration'):
     reg.inputs.use_histogram_matching = [False] * 2 + [True]
     reg.inputs.winsorize_lower_quantile = 0.005
     reg.inputs.winsorize_upper_quantile = 0.995
-    reg.inputs.args = '--float'
+    reg.inputs.float = True
     reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
     reg.inputs.num_threads = 4
     reg.plugin_args = {'sbatch_args': '-c%d' % 4}
@@ -510,11 +510,9 @@ def create_reg_workflow(name='registration'):
     Concatenate the affine and ants transforms into a list
     """
 
-    pickfirst = lambda x: x[0]
-
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
-    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+    register.connect(reg, 'composite_transform', merge, 'in1')
 
 
     """

nipype/algorithms/modelgen.py

@@ -462,6 +462,10 @@ def _concatenate_info(self, infolist):
         # names, onsets, durations, amplitudes, pmod, tmod, regressor_names,
         # regressors
         infoout = infolist[0]
+        for j, val in enumerate(infolist[0].durations):
+            if len(infolist[0].onsets[j]) > 1 and len(val) == 1:
+                infoout.durations[j] = (infolist[0].durations[j] *
+                                        len(infolist[0].onsets[j]))
         for i, info in enumerate(infolist[1:]):
             #info.[conditions, tmod] remain the same
             if info.onsets:
@@ -476,8 +480,15 @@ def _concatenate_info(self, infolist):
                                  sum(nscans[0:(i + 1)])
                         infoout.onsets[j].extend(onsets.tolist())
                 for j, val in enumerate(info.durations):
-                    if len(val) > 1:
+                    if len(info.onsets[j]) > 1 and len(val) == 1:
+                        infoout.durations[j].extend(info.durations[j] *
+                                                    len(info.onsets[j]))
+                    elif len(info.onsets[j]) == len(val):
                         infoout.durations[j].extend(info.durations[j])
+                    else:
+                        raise ValueError('Mismatch in number of onsets and \
+                                         durations for run {0}, condition \
+                                         {1}'.format(i+2, j+1))
                 if hasattr(info, 'amplitudes') and info.amplitudes:
                     for j, val in enumerate(info.amplitudes):
                         infoout.amplitudes[j].extend(info.amplitudes[j])

nipype/algorithms/tests/test_modelgen.py

@@ -38,6 +38,20 @@ def test_modelgen1():
     yield assert_equal, len(res.outputs.session_info[0]['regress']), 0
     yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
     yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([12, 300, 600, 1080])
+    info = [Bunch(conditions=['cond1'], onsets=[[2]], durations=[[1]]),
+            Bunch(conditions=['cond1'], onsets=[[3]], durations=[[1]])]
+    s.inputs.subject_info = deepcopy(info)
+    res = s.run()
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6.])
+    yield assert_almost_equal, np.array(res.outputs.session_info[1]['cond'][0]['duration']), np.array([6.])
+    info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
+            Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]])]
+    s.inputs.subject_info = deepcopy(info)
+    s.inputs.input_units = 'scans'
+    res = s.run()
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([6., 6.])
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([6.,])
+    yield assert_almost_equal, np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([6., 6.])
     rmtree(tempdir)
 
 
@@ -47,6 +61,7 @@ def test_modelgen_spm_concat():
     filename2 = os.path.join(tempdir, 'test2.nii')
     Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename1)
     Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename2)
+    # Test case when only one duration is passed, as being the same for all onsets.
     s = SpecifySPMModel()
     s.inputs.input_units = 'secs'
     s.inputs.concatenate_runs = True
@@ -64,16 +79,41 @@ def test_modelgen_spm_concat():
     yield assert_equal, np.sum(res.outputs.session_info[0]['regress'][0]['val']), 30
     yield assert_equal, len(res.outputs.session_info[0]['cond']), 1
     yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1., 1., 1.])
+    # Test case of scans as output units instead of seconds
     setattr(s.inputs, 'output_units', 'scans')
     yield assert_equal, s.inputs.output_units, 'scans'
     s.inputs.subject_info = deepcopy(info)
     res = s.run()
     yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0, 210.0, 220.0, 280.0, 330.0])/6
+    # Test case for no concatenation with seconds as output units
     s.inputs.concatenate_runs = False
     s.inputs.subject_info = deepcopy(info)
     s.inputs.output_units = 'secs'
     res = s.run()
     yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['onset']), np.array([2.0, 50.0, 100.0, 170.0])
+    # Test case for variable number of events in separate runs, sometimes unique.
+    filename3 = os.path.join(tempdir, 'test3.nii')
+    Nifti1Image(np.random.rand(10, 10, 10, 30), np.eye(4)).to_filename(filename3)
+    s.inputs.functional_runs = [filename1, filename2, filename3]
+    info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
+            Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]]),
+            Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
+    s.inputs.subject_info = deepcopy(info)
+    res = s.run()
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1.])
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1.,])
+    yield assert_almost_equal, np.array(res.outputs.session_info[1]['cond'][1]['duration']), np.array([1., 1.])
+    yield assert_almost_equal, np.array(res.outputs.session_info[2]['cond'][1]['duration']), np.array([1.,])
+    # Test case for variable number of events in concatenated runs, sometimes unique.
+    s.inputs.concatenate_runs = True
+    info = [Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]]),
+            Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2, 4]], durations=[[1, 1], [1, 1]]),
+            Bunch(conditions=['cond1', 'cond2'], onsets=[[2, 3], [2]], durations=[[1, 1], [1]])]
+    s.inputs.subject_info = deepcopy(info)
+    res = s.run()
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][0]['duration']), np.array([1., 1., 1., 1., 1., 1.])
+    yield assert_almost_equal, np.array(res.outputs.session_info[0]['cond'][1]['duration']), np.array([1., 1., 1., 1.])
     rmtree(tempdir)
 
 

nipype/interfaces/ants/registration.py

@@ -7,11 +7,10 @@
    >>> os.chdir(datadir)
 """
 
-from ..base import (TraitedSpec, File, traits)
+from ..base import (TraitedSpec, File, traits, InputMultiPath)
 from .base import ANTSCommand, ANTSCommandInputSpec
 import os
-from nipype.interfaces.base import InputMultiPath
-from nipype.interfaces.traits_extension import isdefined
+from ..traits_extension import isdefined
 import numpy as np
 
 
@@ -458,15 +457,22 @@ class Registration(ANTSCommand):
 
     >>> # Test multiple metrics per stage
     >>> reg5 = copy.deepcopy(reg)
-    >>> reg5.inputs.fixed_image = [ 'fixed1.nii', 'fixed2.nii' ]
-    >>> reg5.inputs.moving_image = [ 'moving1.nii', 'moving2.nii' ]
+    >>> reg5.inputs.fixed_image = 'fixed1.nii'
+    >>> reg5.inputs.moving_image = 'moving1.nii'
     >>> reg5.inputs.metric = ['Mattes', ['Mattes', 'CC']]
     >>> reg5.inputs.metric_weight = [1, [.5,.5]]
-    >>> reg5.inputs.radius_or_number_of_bins = [32, [32,4] ]
+    >>> reg5.inputs.radius_or_number_of_bins = [32, [32, 4] ]
     >>> reg5.inputs.sampling_strategy = ['Random', None] # use default strategy in second stage
     >>> reg5.inputs.sampling_percentage = [0.05, [0.05, 0.10]]
     >>> reg5.cmdline
-    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+
+    >>> # Test multiple inputs
+    >>> reg6 = copy.deepcopy(reg5)
+    >>> reg6.inputs.fixed_image = ['fixed1.nii', 'fixed2.nii']
+    >>> reg6.inputs.moving_image = ['moving1.nii', 'moving2.nii']
+    >>> reg6.cmdline
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1   --write-composite-transform 1'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -520,12 +526,12 @@ def _formatMetric(self, index):
             specs = list()
             for i in indexes:
                 temp = dict([(k, v[i]) for k, v in items])
-                if i > len( self.inputs.fixed_image ):
+                if len(self.inputs.fixed_image) == 1:
                     temp["fixed_image"] = self.inputs.fixed_image[0]
                 else:
                     temp["fixed_image"] = self.inputs.fixed_image[i]
 
-                if i > len( self.inputs.moving_image ):
+                if len(self.inputs.moving_image) == 1:
                     temp["moving_image"] = self.inputs.moving_image[0]
                 else:
                     temp["moving_image"] = self.inputs.moving_image[i]

nipype/interfaces/freesurfer/preprocess.py

@@ -212,6 +212,12 @@ class MRIConvertInputSpec(FSTraitedSpec):
                           desc='dont rescale values for COR')
     no_change = traits.Bool(argstr='--nochange',
                            desc="don't change type of input to that of template")
+    tr = traits.Int(argstr='-tr %d',
+                    desc='TR in msec')
+    te = traits.Int(argstr='-te %d',
+                    desc='TE in msec')
+    ti = traits.Int(argstr='-ti %d',
+                    desc='TI in msec (note upper case flag)')
     autoalign_matrix = File(exists=True, argstr='--autoalign %s',
                         desc='text file with autoalign matrix')
     unwarp_gradient = traits.Bool(argstr='--unwarp_gradient_nonlinearity',

nipype/interfaces/freesurfer/tests/test_auto_MRIConvert.py

@@ -162,11 +162,17 @@ def test_MRIConvert_inputs():
     subject_name=dict(argstr='--subject_name %s',
     ),
     subjects_dir=dict(),
+    te=dict(argstr='-te %d',
+    ),
     template_info=dict(),
     template_type=dict(argstr='--template_type %s',
     ),
     terminal_output=dict(nohash=True,
     ),
+    ti=dict(argstr='-ti %d',
+    ),
+    tr=dict(argstr='-tr %d',
+    ),
     unwarp_gradient=dict(argstr='--unwarp_gradient_nonlinearity',
     ),
     vox_size=dict(argstr='-voxsize %f %f %f',
@@ -190,4 +196,3 @@ def test_MRIConvert_outputs():
     for key, metadata in output_map.items():
         for metakey, value in metadata.items():
             yield assert_equal, getattr(outputs.traits()[key], metakey), value
-