Merge pull request #12 from nipy/master

Update new_interfaces with current Nipype Master

Author: pintohutch

.travis.yml

@@ -34,6 +34,7 @@ before_install:
   echo 'include_dirs = /usr/include:/usr/include/X11' >> $HOME/.numpy-site.cfg;
   fi
 install:
+- sudo apt-get install fusefat
 - conda update --yes conda
 - conda create -n testenv --yes pip python=$TRAVIS_PYTHON_VERSION
 - source activate testenv
@@ -56,7 +57,7 @@ install:
 - pip install -r requirements.txt  # finish remaining requirements
 - python setup.py install
 script:
-- python -W once:FSL:UserWarning:nipype `which nosetests` --with-doctest --with-cov --cover-package nipype --cov-config .coveragerc --logging-level=INFO
+- python -W once:FSL:UserWarning:nipype `which nosetests` --with-doctest --with-cov --cover-package nipype --cov-config .coveragerc --logging-level=DEBUG --verbosity=3
 after_success:
 - coveralls --config_file .coveragerc
 deploy:

CHANGES

@@ -1,11 +1,13 @@
 Next release
 ============
 
+* FIX: job execution on systems/approaches where locale is undefined (https://github.com/nipy/nipype/pull/1401)
+* FIX: Clean up byte/unicode issues using subprocess (https://github.com/nipy/nipype/pull/1394)
 * FIX: Prevent crash when tvtk is loaded - ETS_TOOLKIT=null (https://github.com/nipy/nipype/pull/973)
 * ENH: New interfaces in dipy: RESTORE, EstimateResponseSH, CSD and StreamlineTractography
        (https://github.com/nipy/nipype/pull/1090)
 * ENH: Added interfaces of AFNI (https://github.com/nipy/nipype/pull/1360,
-       https://github.com/nipy/nipype/pull/1361)
+       https://github.com/nipy/nipype/pull/1361, https://github.com/nipy/nipype/pull/1382)
 * ENH: Provides a Nipype wrapper for antsJointFusion (https://github.com/nipy/nipype/pull/1351)
 * ENH: Added support for PETPVC (https://github.com/nipy/nipype/pull/1335)
 * ENH: Merge S3DataSink into DataSink, added AWS documentation (https://github.com/nipy/nipype/pull/1316)
@@ -21,6 +23,8 @@ Next release
 * API: Default model level for the bedpostx workflow has been set to "2" following FSL 5.0.9 lead
 * ENH: New interfaces for interacting with AWS S3: S3DataSink and S3DataGrabber (https://github.com/nipy/nipype/pull/1201)
 * ENH: Interfaces for MINC tools (https://github.com/nipy/nipype/pull/1304)
+* FIX: Use realpath to determine hard link source (https://github.com/nipy/nipype/pull/1388)
+* FIX: Correct linking/copying fallback behavior (https://github.com/nipy/nipype/pull/1391)
 
 Release 0.11.0 (September 15, 2015)
 ============

README.rst

@@ -34,6 +34,10 @@ NIPYPE: Neuroimaging in Python: Pipelines and Interfaces
     :target: https://pypi.python.org/pypi/nipype/
     :alt: License
 
+.. image:: https://img.shields.io/badge/gitter-join%20chat%20%E2%86%92-brightgreen.svg?style=flat
+    :target: http://gitter.im/nipy/nipype
+    :alt: Chat
+
 Current neuroimaging software offer users an incredible opportunity to
 analyze data using a variety of different algorithms. However, this has
 resulted in a heterogeneous collection of specialized applications

circle.yml

@@ -12,7 +12,7 @@ dependencies:
     - bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh)
   override:
     # Install apt packages
-    - sudo apt-get install -y fsl-core fsl-atlases fsl-mni152-templates fsl-feeds afni swig python-vtk xvfb
+    - sudo apt-get install -y fsl-core fsl-atlases fsl-mni152-templates fsl-feeds afni swig python-vtk xvfb fusefat
     - echo 'source /etc/fsl/fsl.sh' >> $HOME/.profile
     - echo 'source /etc/afni/afni.sh' >> $HOME/.profile
     - mkdir -p ~/examples/ && ln -sf /usr/share/fsl-feeds/ ~/examples/feeds
@@ -29,7 +29,7 @@ dependencies:
     - pip install -e git+https://github.com/enthought/etsproxy.git#egg=etsproxy
     - pip install -e git+https://github.com/enthought/ets.git#egg=ets
     - gem install fakes3
-    - if [[ ! -d ~/examples/data ]]; then wget "http://tcpdiag.dl.sourceforge.net/project/nipy/nipype/nipype-0.2/nipype-tutorial.tar.bz2" && tar jxvf nipype-tutorial.tar.bz2 && mv nipype-tutorial/* ~/examples/; fi
+    - if [[ ! -d ~/examples/data ]]; then wget "https://dl.dropbox.com/s/jzgq2nupxyz36bp/nipype-tutorial.tar.bz2" && tar jxvf nipype-tutorial.tar.bz2 && mv nipype-tutorial/* ~/examples/; fi
     - if [[ ! -d ~/examples/fsl_course_data ]]; then wget -c "http://fsl.fmrib.ox.ac.uk/fslcourse/fdt1.tar.gz" && wget -c "http://fsl.fmrib.ox.ac.uk/fslcourse/fdt2.tar.gz" && wget -c "http://fsl.fmrib.ox.ac.uk/fslcourse/tbss.tar.gz" && mkdir ~/examples/fsl_course_data && tar zxvf fdt1.tar.gz -C ~/examples/fsl_course_data && tar zxvf fdt2.tar.gz -C ~/examples/fsl_course_data && tar zxvf tbss.tar.gz -C ~/examples/fsl_course_data; fi
     - bash ~/nipype/tools/install_spm_mcr.sh
     - mkdir -p ~/.nipype && echo '[logging]' > ~/.nipype/nipype.cfg && echo 'workflow_level = DEBUG' >> ~/.nipype/nipype.cfg && echo 'interface_level = DEBUG' >> ~/.nipype/nipype.cfg && echo 'filemanip_level = DEBUG' >> ~/.nipype/nipype.cfg

doc/documentation.rst

@@ -9,7 +9,7 @@ Documentation
    :Release: |version|
    :Date: |today|
 
-Previous versions:  `0.10.0 <http://nipy.org/nipype/0.10.0>`_ `0.9.2 <http://nipy.org/nipype/0.9.2>`_
+Previous versions:  `0.11.0 <http://nipy.org/nipype/0.11.0>`_ `0.10.0 <http://nipy.org/nipype/0.10.0>`_
 
 .. container:: doc2
 

doc/links_names.txt

@@ -46,7 +46,7 @@
 .. _pylint: http://www.logilab.org/project/pylint
 .. _pyflakes: http://divmod.org/trac/wiki/DivmodPyflakes
 .. _virtualenv: http://pypi.python.org/pypi/virtualenv
-.. _git: http://git.or.cz/
+.. _git: https://git-scm.com/
 .. _flymake: http://flymake.sourceforge.net/
 .. _rope: http://rope.sourceforge.net/
 .. _pymacs: http://pymacs.progiciels-bpi.ca/pymacs.html
@@ -60,21 +60,21 @@
 .. _`python coverage tester`: http://nedbatchelder.com/code/modules/coverage.html
 
 .. Other python projects
-.. _numpy: http://www.scipy.org/NumPy
+.. _numpy: http://www.numpy.org/
 .. _scipy: http://www.scipy.org
-.. _ipython: http://ipython.scipy.org
-.. _`ipython manual`: http://ipython.scipy.org/doc/manual/html
-.. _matplotlib: http://matplotlib.sourceforge.net
+.. _ipython: https://ipython.org
+.. _`ipython manual`: https://ipython.readthedocs.org/en/stable/
+.. _matplotlib: http://matplotlib.org/
 .. _ETS: http://code.enthought.com/projects/tool-suite.php
 .. _`Enthought Tool Suite`: http://code.enthought.com/projects/tool-suite.php
 .. _python: http://www.python.org
 .. _mayavi: http://mayavi.sourceforge.net/
-.. _sympy: http://code.google.com/p/sympy/
-.. _networkx: http://networkx.lanl.gov/
-.. _pythonxy: http://www.pythonxy.com/
+.. _sympy: http://www.sympy.org/
+.. _networkx: https://networkx.github.io/
+.. _pythonxy: https://python-xy.github.io/
 .. _EPD: http://www.enthought.com/products/epd.php
 .. _Traits: http://code.enthought.com/projects/traits/
-.. _Anaconda: https://store.continuum.io/cshop/anaconda/
+.. _Anaconda: https://www.continuum.io/downloads
 .. _Canopy: https://www.enthought.com/products/canopy/
 
 .. Python imaging projects

doc/users/pipeline_tutorial.rst

@@ -63,7 +63,7 @@ Step 0
 ~~~~~~
 
 Download and extract the `Pipeline tutorial data (429MB).
-<http://sourceforge.net/projects/nipy/files/nipype/nipype-0.2/nipype-tutorial.tar.bz2/download>`_
+<https://dl.dropbox.com/s/jzgq2nupxyz36bp/nipype-tutorial.tar.bz2>`_
 
 (checksum: 56ed4b7e0aac5627d1724e9c10cd26a7)
 

doc/users/plugins.rst

@@ -298,10 +298,11 @@ a custom template::
 In addition to overall workflow configuration, you can use node level
 configuration for OAR::
 
-    node.plugin_args = {'oarsub_args': '-l "nodes=1/cores=3"'}
+    node.plugin_args = {'overwrite': True, 'oarsub_args': '-l "nodes=1/cores=3"'}
 
 this would apply only to the node and is useful in situations, where a
 particular node might use more resources than other nodes in a workflow.
+You need to set the 'overwrite' flag to bypass the general settings-template you defined for the other nodes.
 
 
 ``qsub`` emulation

examples/README

@@ -1,4 +1,4 @@
 A dataset for use with these scripts can be downloaded from the nipype
 website. At the time of writing, it's at:
 
-http://nipy.sourceforge.net/nipype/users/pipeline_tutorial.html
+http://nipy.org/nipype/users/pipeline_tutorial.html

nipype/interfaces/afni/__init__.py

@@ -12,5 +12,6 @@
                          LFCD, Copy, Fourier, Allineate, Maskave, SkullStrip, TCat,
                          Fim, BlurInMask, Autobox, TCorrMap, Bandpass, Retroicor,
                          TCorrelate, TCorr1D, BrickStat, ROIStats, AutoTcorrelate,
-                         AFNItoNIFTI, Eval, Means, Hist, FWHMx)
+                         AFNItoNIFTI, Eval, Means, Hist, FWHMx, OutlierCount,
+                         QualityIndex)
 from .svm import (SVMTest, SVMTrain)

nipype/interfaces/afni/preprocess.py

@@ -16,8 +16,7 @@
 
 from .base import (AFNICommandBase, AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec,
                    Info, no_afni)
-from ..base import CommandLineInputSpec
-from ..base import (Directory, TraitedSpec,
+from ..base import (CommandLineInputSpec, CommandLine, Directory, TraitedSpec,
                     traits, isdefined, File, InputMultiPath, Undefined)
 from ...external.six import string_types
 from ...utils.filemanip import (load_json, save_json, split_filename)
@@ -2817,3 +2816,133 @@ def _list_outputs(self):
 
         outputs['fwhm'] = tuple(sout)
         return outputs
+
+
+class OutlierCountInputSpec(CommandLineInputSpec):
+    in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
+    mask = File(exists=True, argstr='-mask %s', xor=['autoclip', 'automask'],
+                desc='only count voxels within the given mask')
+    qthr = traits.Range(value=1e-3, low=0.0, high=1.0, argstr='-qthr %.5f',
+                        desc='indicate a value for q to compute alpha')
+
+    autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip', xor=['in_file'],
+                           desc='clip off small voxels')
+    automask = traits.Bool(False, usedefault=True, argstr='-automask', xor=['in_file'],
+                           desc='clip off small voxels')
+
+    fraction = traits.Bool(False, usedefault=True, argstr='-fraction',
+                           desc='write out the fraction of masked voxels'
+                                ' which are outliers at each timepoint')
+    interval = traits.Bool(False, usedefault=True, argstr='-range',
+                           desc='write out the median + 3.5 MAD of outlier'
+                                ' count with each timepoint')
+    save_outliers = traits.Bool(False, usedefault=True, desc='enables out_file option')
+    outliers_file = File(
+        name_template="%s_outliers", argstr='-save %s', name_source=["in_file"],
+        output_name='out_outliers', keep_extension=True, desc='output image file name')
+
+    polort = traits.Int(argstr='-polort %d',
+                        desc='detrend each voxel timeseries with polynomials')
+    legendre = traits.Bool(False, usedefault=True, argstr='-legendre',
+                           desc='use Legendre polynomials')
+    out_file = File(
+        name_template='%s_outliers', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class OutlierCountOutputSpec(TraitedSpec):
+    out_outliers = File(exists=True, desc='output image file name')
+    out_file = File(
+        name_template='%s_tqual', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class OutlierCount(CommandLine):
+    """Create a 3D dataset from 2D image files using AFNI to3d command
+
+    For complete details, see the `to3d Documentation
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> toutcount = afni.OutlierCount()
+    >>> toutcount.inputs.in_file = 'functional.nii'
+    >>> toutcount.cmdline #doctest: +ELLIPSIS
+    '3dToutcount functional.nii > functional_outliers'
+    >>> res = toutcount.run() #doctest: +SKIP
+
+   """
+
+    _cmd = '3dToutcount'
+    input_spec = OutlierCountInputSpec
+    output_spec = OutlierCountOutputSpec
+
+    def _parse_inputs(self, skip=None):
+        if skip is None:
+            skip = []
+
+        if not self.inputs.save_outliers:
+            skip += ['outliers_file']
+        return super(OutlierCount, self)._parse_inputs(skip)
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        if self.inputs.save_outliers:
+            outputs['out_outliers'] = op.abspath(self.inputs.outliers_file)
+        outputs['out_file'] = op.abspath(self.inputs.out_file)
+        return outputs
+
+
+class QualityIndexInputSpec(CommandLineInputSpec):
+    in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
+    mask = File(exists=True, argstr='-mask %s', xor=['autoclip', 'automask'],
+                desc='compute correlation only across masked voxels')
+    spearman = traits.Bool(False, usedefault=True, argstr='-spearman',
+                           desc='Quality index is 1 minus the Spearman (rank) '
+                                'correlation coefficient of each sub-brick '
+                                'with the median sub-brick. (default)')
+    quadrant = traits.Bool(False, usedefault=True, argstr='-quadrant',
+                           desc='Similar to -spearman, but using 1 minus the '
+                                'quadrant correlation coefficient as the '
+                                'quality index.')
+    autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip', xor=['mask'],
+                           desc='clip off small voxels')
+    automask = traits.Bool(False, usedefault=True, argstr='-automask', xor=['mask'],
+                           desc='clip off small voxels')
+    clip = traits.Float(argstr='-clip %f', desc='clip off values below')
+
+    interval = traits.Bool(False, usedefault=True, argstr='-range',
+                           desc='write out the median + 3.5 MAD of outlier'
+                                ' count with each timepoint')
+    out_file = File(
+        name_template='%s_tqual', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class QualityIndexOutputSpec(TraitedSpec):
+    out_file = File(desc='file containing the caputured standard output')
+
+
+class QualityIndex(CommandLine):
+    """Create a 3D dataset from 2D image files using AFNI to3d command
+
+    For complete details, see the `to3d Documentation
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> tqual = afni.QualityIndex()
+    >>> tqual.inputs.in_file = 'functional.nii'
+    >>> tqual.cmdline #doctest: +ELLIPSIS
+    '3dTqual functional.nii > functional_tqual'
+    >>> res = tqual.run() #doctest: +SKIP
+
+   """
+
+    _cmd = '3dTqual'
+    input_spec = QualityIndexInputSpec
+    output_spec = QualityIndexOutputSpec

nipype/interfaces/afni/tests/test_auto_OutlierCount.py

@@ -0,0 +1,80 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ....testing import assert_equal
+from ..preprocess import OutlierCount
+
+
+def test_OutlierCount_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    autoclip=dict(argstr='-autoclip',
+    usedefault=True,
+    xor=['in_file'],
+    ),
+    automask=dict(argstr='-automask',
+    usedefault=True,
+    xor=['in_file'],
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    fraction=dict(argstr='-fraction',
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_file=dict(argstr='%s',
+    mandatory=True,
+    position=-2,
+    ),
+    interval=dict(argstr='-range',
+    usedefault=True,
+    ),
+    legendre=dict(argstr='-legendre',
+    usedefault=True,
+    ),
+    mask=dict(argstr='-mask %s',
+    xor=['autoclip', 'automask'],
+    ),
+    out_file=dict(argstr='> %s',
+    keep_extension=False,
+    name_source=['in_file'],
+    name_template='%s_outliers',
+    position=-1,
+    ),
+    outliers_file=dict(argstr='-save %s',
+    keep_extension=True,
+    name_source=['in_file'],
+    name_template='%s_outliers',
+    output_name='out_outliers',
+    ),
+    polort=dict(argstr='-polort %d',
+    ),
+    qthr=dict(argstr='-qthr %.5f',
+    ),
+    save_outliers=dict(usedefault=True,
+    ),
+    terminal_output=dict(nohash=True,
+    ),
+    )
+    inputs = OutlierCount.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_OutlierCount_outputs():
+    output_map = dict(out_file=dict(argstr='> %s',
+    keep_extension=False,
+    name_source=['in_file'],
+    name_template='%s_tqual',
+    position=-1,
+    ),
+    out_outliers=dict(),
+    )
+    outputs = OutlierCount.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value