Merge branch 'master' of https://github.com/nipy/nipype into enh/workbench

mgxd · mgxd · commit a221f1b7c949 · 2018-05-24T18:06:30.000-04:00
diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -54,7 +54,7 @@ _build_main_image_py36: &build_main_image_py36
         --tag nipype/nipype:py36 \
         --build-arg BUILD_DATE="$(date -u +"%Y-%m-%dT%H:%M:%SZ")" \
         --build-arg VCS_REF="$(git rev-parse --short HEAD)" \
-        --build-arg VERSION="${CIRCLE_TAG}" /home/circleci/nipype \
+        --build-arg VERSION="${CIRCLE_TAG}" /home/circleci/nipype
 
 _build_main_image_py27: &build_main_image_py27
   name: Build main image (py27)
@@ -68,7 +68,7 @@ _build_main_image_py27: &build_main_image_py27
         --build-arg PYTHON_VERSION_MINOR=7 \
         --build-arg BUILD_DATE="$(date -u +"%Y-%m-%dT%H:%M:%SZ")" \
         --build-arg VCS_REF="$(git rev-parse --short HEAD)" \
-        --build-arg VERSION="${CIRCLE_TAG}-py27" /home/circleci/nipype \
+        --build-arg VERSION="${CIRCLE_TAG}-py27" /home/circleci/nipype
 
 _download_test_data: &_download_test_data
   name: Download test data
@@ -299,7 +299,7 @@ jobs:
       - run: *_run_codecov_smoke
       - store_artifacts: *store_artifacts_kwds
 
-  deploy:
+  deploy_dockerhub:
     docker:
       - image: docker:17.10.0-ce-git
     steps:
@@ -330,6 +330,18 @@ jobs:
             - /tmp/docker/cache/Dockerfile.base-pruned
           key: dockerfile-cache-v1-{{ .Branch }}-{{ checksum "/tmp/docker/cache/Dockerfile.base-pruned" }}
 
+  deploy_pypi:
+    machine: *machine_kwds
+    working_directory: /home/circleci/nipype
+    steps:
+      - checkout:
+          path: /home/circleci/nipype
+      - run:
+          name: Deploy to PyPI
+          command: |
+            pip install twine future wheel
+            python setup.py sdist bdist_wheel
+            twine upload dist/*
 
 workflows:
   version: 2
@@ -348,7 +360,7 @@ workflows:
       - test_fmri_spm_nested_fsl_feeds:
           requires:
             - compare_base_dockerfiles
-      - deploy:
+      - deploy_dockerhub:
           filters:
             branches:
               only: master
@@ -357,3 +369,14 @@ workflows:
             - test_fmri_spm_nested_fsl_feeds
             - test_py3_fmri_fsl_spm
             - test_py3_fmri_spm_dartel_multiproc
+      - deploy_pypi:
+          filters:
+            branches:
+              ignore: /.*/
+            tags:
+              only: /.*/
+          requires:
+            - test_pytest
+            - test_fmri_spm_nested_fsl_feeds
+            - test_py3_fmri_fsl_spm
+            - test_py3_fmri_spm_dartel_multiproc
diff --git a/.travis.yml b/.travis.yml
@@ -60,16 +60,5 @@ install:
 script:
 - py.test -v --cov nipype --cov-config .coveragerc --cov-report xml:cov.xml -c nipype/pytest.ini --doctest-modules nipype
 
-deploy:
-  provider: pypi
-  user: satra
-  password:
-    secure: OCO0FXb4f+pH4Uw7zWCIRp3qOJ1t7rhky4K8MjNU8tyVCJgd6O/Bv8GJgceS0LktPodlAAjB8SxAhTORPAQZ1D/44PJYy3NQIisvej1zjLpaA9TEGfl6W7MqhDpRyMHW+cnSi/n84SAmdr+Z4vOxScDHdwr13EPmGyOIlHMAGnE=
-  on:
-    tags: true
-    repo: nipy/nipype
-    branch: master
-  distributions: "sdist bdist_wheel"
-
 after_script:
 - codecov --file cov.xml --flags unittests -e TRAVIS_JOB_NUMBER
diff --git a/nipype/interfaces/afni/__init__.py b/nipype/interfaces/afni/__init__.py
@@ -18,7 +18,7 @@
 from .utils import (
     ABoverlap, AFNItoNIFTI, Autobox, Axialize, BrickStat, Bucket, Calc, Cat,
     CatMatvec, CenterMass, ConvertDset, Copy, Dot, Edge3, Eval, FWHMx,
-    MaskTool, Merge, Notes, NwarpApply, NwarpAdjust, NwarpCat, OneDToolPy,
-    Refit, Resample, TCat, TCatSubBrick, TStat, To3D, Unifize, Undump, ZCutUp,
-    GCOR, Zcat, Zeropad)
+    LocalBistat, MaskTool, Merge, Notes, NwarpApply, NwarpAdjust, NwarpCat,
+    OneDToolPy, Refit, Resample, TCat, TCatSubBrick, TStat, To3D, Unifize,
+    Undump, ZCutUp, GCOR, Zcat, Zeropad)
 from .model import (Deconvolve, Remlfit, Synthesize)
diff --git a/nipype/interfaces/afni/tests/test_auto_LocalBistat.py b/nipype/interfaces/afni/tests/test_auto_LocalBistat.py
@@ -0,0 +1,73 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from __future__ import unicode_literals
+from ..utils import LocalBistat
+
+
+def test_LocalBistat_inputs():
+    input_map = dict(
+        args=dict(argstr='%s', ),
+        automask=dict(
+            argstr='-automask',
+            xor=['weight_file'],
+        ),
+        environ=dict(
+            nohash=True,
+            usedefault=True,
+        ),
+        ignore_exception=dict(
+            deprecated='1.0.0',
+            nohash=True,
+            usedefault=True,
+        ),
+        in_file1=dict(
+            argstr='%s',
+            mandatory=True,
+            position=-2,
+        ),
+        in_file2=dict(
+            argstr='%s',
+            mandatory=True,
+            position=-1,
+        ),
+        mask_file=dict(argstr='-mask %s', ),
+        neighborhood=dict(
+            argstr="-nbhd '%s(%s)'",
+            mandatory=True,
+        ),
+        num_threads=dict(
+            nohash=True,
+            usedefault=True,
+        ),
+        out_file=dict(
+            argstr='-prefix %s',
+            keep_extension=True,
+            name_source='in_file1',
+            name_template='%s_bistat',
+            position=0,
+        ),
+        outputtype=dict(),
+        stat=dict(
+            argstr='-stat %s...',
+            mandatory=True,
+        ),
+        terminal_output=dict(
+            deprecated='1.0.0',
+            nohash=True,
+        ),
+        weight_file=dict(
+            argstr='-weight %s',
+            xor=['automask'],
+        ),
+    )
+    inputs = LocalBistat.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(inputs.traits()[key], metakey) == value
+def test_LocalBistat_outputs():
+    output_map = dict(out_file=dict(), )
+    outputs = LocalBistat.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(outputs.traits()[key], metakey) == value
diff --git a/nipype/interfaces/afni/utils.py b/nipype/interfaces/afni/utils.py
@@ -1338,6 +1338,121 @@ def _list_outputs(self):
         return outputs
 
 
+class LocalBistatInputSpec(AFNICommandInputSpec):
+    in_file1 = File(
+        exists=True,
+        mandatory=True,
+        argstr='%s',
+        position=-2,
+        desc='Filename of the first image')
+    in_file2 = File(
+        exists=True,
+        mandatory=True,
+        argstr='%s',
+        position=-1,
+        desc='Filename of the second image')
+    neighborhood = traits.Either(
+        traits.Tuple(traits.Enum('SPHERE', 'RHDD', 'TOHD'), traits.Float()),
+        traits.Tuple(traits.Enum('RECT'), traits.Tuple(traits.Float(),
+                                                       traits.Float(),
+                                                       traits.Float())),
+        mandatory=True,
+        desc='The region around each voxel that will be extracted for '
+             'the statistics calculation. Possible regions are: '
+             '\'SPHERE\', \'RHDD\' (rhombic dodecahedron), \'TOHD\' '
+             '(truncated octahedron) with a given radius in mm or '
+             '\'RECT\' (rectangular block) with dimensions to specify in mm.',
+        argstr="-nbhd '%s(%s)'")
+    _stat_names = ['pearson', 'spearman', 'quadrant', 'mutinfo', 'normuti',
+                   'jointent', 'hellinger', 'crU', 'crM', 'crA', 'L2slope',
+                   'L1slope', 'num', 'ALL']
+    stat = InputMultiPath(
+        traits.Enum(_stat_names),
+        mandatory=True,
+        desc='statistics to compute. Possible names are :'
+             '  * pearson  = Pearson correlation coefficient'
+             '  * spearman = Spearman correlation coefficient'
+             '  * quadrant = Quadrant correlation coefficient'
+             '  * mutinfo  = Mutual Information'
+             '  * normuti  = Normalized Mutual Information'
+             '  * jointent = Joint entropy'
+             '  * hellinger= Hellinger metric'
+             '  * crU      = Correlation ratio (Unsymmetric)'
+             '  * crM      = Correlation ratio (symmetrized by Multiplication)'
+             '  * crA      = Correlation ratio (symmetrized by Addition)'
+             '  * L2slope  = slope of least-squares (L2) linear regression of '
+             '               the data from dataset1 vs. the dataset2 '
+             '               (i.e., d2 = a + b*d1 ==> this is \'b\')'
+             '  * L1slope  = slope of least-absolute-sum (L1) linear '
+             '               regression of the data from dataset1 vs. '
+             '               the dataset2'
+             '  * num      = number of the values in the region: '
+             '               with the use of -mask or -automask, '
+             '               the size of the region around any given '
+             '               voxel will vary; this option lets you '
+             '               map that size.'
+             '  * ALL      = all of the above, in that order'
+             'More than one option can be used.',
+        argstr='-stat %s...')
+    mask_file = traits.File(
+        exists=True,
+        desc='mask image file name. Voxels NOT in the mask will not be used '
+             'in the neighborhood of any voxel. Also, a voxel NOT in the mask '
+             'will have its statistic(s) computed as zero (0).',
+        argstr='-mask %s')
+    automask = traits.Bool(
+        desc='Compute the mask as in program 3dAutomask.',
+        argstr='-automask',
+        xor=['weight_file'])
+    weight_file = traits.File(
+        exists=True,
+        desc='File name of an image to use as a weight.  Only applies to '
+             '\'pearson\' statistics.',
+        argstr='-weight %s',
+        xor=['automask'])
+    out_file = traits.File(
+        desc='Output dataset.',
+        argstr='-prefix %s',
+        name_source='in_file1',
+        name_template='%s_bistat',
+        keep_extension=True,
+        position=0)
+
+
+class LocalBistat(AFNICommand):
+    """3dLocalBistat - computes statistics between 2 datasets, at each voxel,
+    based on a local neighborhood of that voxel.
+
+    For complete details, see the `3dLocalBistat Documentation.
+    <https://afni.nimh.nih.gov/pub../pub/dist/doc/program_help/3dLocalBistat.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> bistat = afni.LocalBistat()
+    >>> bistat.inputs.in_file1 = 'functional.nii'
+    >>> bistat.inputs.in_file2 = 'structural.nii'
+    >>> bistat.inputs.neighborhood = ('SPHERE', 1.2)
+    >>> bistat.inputs.stat = 'pearson'
+    >>> bistat.inputs.outputtype = 'NIFTI'
+    >>> bistat.cmdline
+    "3dLocalBistat -prefix functional_bistat.nii -nbhd 'SPHERE(1.2)' -stat pearson functional.nii structural.nii"
+    >>> res = automask.run()  # doctest: +SKIP
+
+    """
+
+    _cmd = '3dLocalBistat'
+    input_spec = LocalBistatInputSpec
+    output_spec = AFNICommandOutputSpec
+
+    def _format_arg(self, name, spec, value):
+        if name == 'neighborhood' and value[0] == 'RECT':
+            value = ('RECT', '%s,%s,%s' % value[1])
+
+        return super(LocalBistat, self)._format_arg(name, spec, value)
+
+
 class MaskToolInputSpec(AFNICommandInputSpec):
     in_file = File(
         desc='input file or files to 3dmask_tool',
diff --git a/nipype/interfaces/dcm2nii.py b/nipype/interfaces/dcm2nii.py
@@ -264,7 +264,7 @@ class Dcm2niixInputSpec(CommandLineInputSpec):
         position=-1,
         copyfile=False,
         mandatory=True,
-        desc=('A set of filenames to be converted. Note that the current ' 
+        desc=('A set of filenames to be converted. Note that the current '
               'version (1.0.20180328) of dcm2niix converts any files in the '
               'directory. To only convert specific files they should be in an '
               'isolated directory'),
diff --git a/nipype/tests/test_nipype.py b/nipype/tests/test_nipype.py
@@ -12,7 +12,7 @@ def test_nipype_info():
     assert exception_not_raised
 
 
-@pytest.mark.skipif(not get_nipype_gitversion(), 
+@pytest.mark.skipif(not get_nipype_gitversion(),
                     reason="not able to get version from get_nipype_gitversion")
 def test_git_hash():
     # removing the first "g" from gitversion
diff --git a/nipype/workflows/dmri/dtitk/tensor_registration.py b/nipype/workflows/dmri/dtitk/tensor_registration.py
@@ -52,10 +52,11 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
                                   params={'array_size': (128, 128, 64)}):
     """
     Workflow that performs a diffeomorphic registration
-    (Rigid and Affine follwed by Diffeomorphic)
+    (Rigid and Affine followed by Diffeomorphic)
     Note: the requirements for a diffeomorphic registration specify that
     the dimension 0 is a power of 2 so images are resliced prior to
-    registration
+    registration. Remember to move origin and reslice prior to applying xfm to
+    another file!
 
     Example
     -------
@@ -75,7 +76,9 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
                          fields=['out_file', 'out_file_xfm',
                                  'fixed_resliced', 'moving_resliced']),
                          name='outputnode')
-
+    origin_node_fixed = pe.Node(dtitk.TVAdjustVoxSp(origin=(0, 0, 0)),
+                                name='origin_node_fixed')
+    origin_node_moving = origin_node_fixed.clone(name='origin_node_moving')
     reslice_node_pow2 = pe.Node(dtitk.TVResample(
                                 origin=(0, 0, 0),
                                 array_size=params['array_size']),
@@ -92,14 +95,23 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
     compose_xfm_node = pe.Node(dtitk.ComposeXfm(), name='compose_xfm_node')
     apply_xfm_node = pe.Node(dtitk.DiffeoSymTensor3DVol(),
                              name='apply_xfm_node')
+    adjust_vs_node_to_input = pe.Node(dtitk.TVAdjustVoxSp(),
+                                      name='adjust_vs_node_to_input')
+    reslice_node_to_input = pe.Node(dtitk.TVResample(),
+                                    name='reslice_node_to_input')
+    input_fa = pe.Node(dtitk.TVtool(in_flag='fa'), name='input_fa')
 
     wf = pe.Workflow(name=name)
 
+    # calculate input FA image for origin reference
+    wf.connect(inputnode, 'fixed_file', input_fa, 'in_file')
     # Reslice input images
-    wf.connect(inputnode, 'fixed_file', reslice_node_pow2, 'in_file')
+    wf.connect(inputnode, 'fixed_file', origin_node_fixed, 'in_file')
+    wf.connect(origin_node_fixed, 'out_file', reslice_node_pow2, 'in_file')
     wf.connect(reslice_node_pow2, 'out_file',
                reslice_node_moving, 'target_file')
-    wf.connect(inputnode, 'moving_file', reslice_node_moving, 'in_file')
+    wf.connect(inputnode, 'moving_file', origin_node_moving, 'in_file')
+    wf.connect(origin_node_moving, 'out_file', reslice_node_moving, 'in_file')
     # Rigid registration
     wf.connect(reslice_node_pow2, 'out_file', rigid_node, 'fixed_file')
     wf.connect(reslice_node_moving, 'out_file', rigid_node, 'moving_file')
@@ -118,8 +130,13 @@ def diffeomorphic_tensor_pipeline(name='DiffeoTen',
     # Apply transform
     wf.connect(reslice_node_moving, 'out_file', apply_xfm_node, 'in_file')
     wf.connect(compose_xfm_node, 'out_file', apply_xfm_node, 'transform')
+    # Move origin and reslice to match original fixed input image
+    wf.connect(apply_xfm_node, 'out_file', adjust_vs_node_to_input, 'in_file')
+    wf.connect(input_fa, 'out_file', adjust_vs_node_to_input, 'target_file')
+    wf.connect(adjust_vs_node_to_input, 'out_file', reslice_node_to_input, 'in_file')
+    wf.connect(input_fa, 'out_file', reslice_node_to_input, 'target_file')
     # Send to output
-    wf.connect(apply_xfm_node, 'out_file', outputnode, 'out_file')
+    wf.connect(reslice_node_to_input, 'out_file', outputnode, 'out_file')
     wf.connect(compose_xfm_node, 'out_file', outputnode, 'out_file_xfm')
     wf.connect(reslice_node_pow2, 'out_file', outputnode, 'fixed_resliced')
     wf.connect(reslice_node_moving, 'out_file', outputnode, 'moving_resliced')
diff --git a/tools/checkspecs.py b/tools/checkspecs.py
@@ -287,7 +287,7 @@ def test_specs(self, uri):
                         and "requires" not in trait.__dict__\
                         and "xor" not in trait.__dict__:
                     if trait.trait_type.__class__.__name__ is "Range"\
-                            and trait.default == trait.trait_type._low: 
+                            and trait.default == trait.trait_type._low:
                         continue
                     bad_specs.append(
                         [uri, c, 'Inputs', traitname, 'default value is set, no value for usedefault'])
