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Merge branch 'master' into enh/DipyMultiTensorSimulate
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‎.gitignore

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.pydevproject
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.idea/
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/documentation.zip
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.DS_Store

‎.travis.yml

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@@ -26,9 +26,9 @@ install:
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- source activate testenv
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- if [ ${TRAVIS_PYTHON_VERSION:0:1} == "2" ]; then pip install ordereddict; fi
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- conda install --yes numpy scipy nose traits networkx dateutil
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- pip install nibabel --use-mirrors
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- pip install python-coveralls --use-mirrors
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- pip install nose-cov --use-mirrors
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- pip install nibabel
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- pip install python-coveralls
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- pip install nose-cov
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- pip install https://github.com/RDFLib/rdflib/archive/master.zip
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- pip install https://github.com/trungdong/prov/archive/rdf.zip
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- python setup.py install

‎bin/nipype2boutiques

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#!/usr/bin/env python
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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import sys
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from nipype.utils.nipype2boutiques import main
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if __name__ == '__main__':
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main(sys.argv)

‎doc/quickstart.rst

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devel/gitwash/index
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.. include:: links_names.txt
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Useful links for beginners
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===========================
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Getting started with Python - Tutorials. `Available here`__
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Python for Beginners `Available here`__
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__ http://www.codecademy.com/en/tracks/python
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__ https://www.python.org/about/gettingstarted/

‎doc/users/tutorial_101.rst

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@@ -99,15 +99,15 @@ This results in a workflow containing two isolated nodes:
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**5. Connecting nodes to each other**
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We want to connect the output produced by realignment to the input of
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smoothing. This is done as follows.
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We want to connect the output produced by the node realignment to the input of
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the node smoothing. This is done as follows.
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.. testcode::
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workflow.connect(realigner, 'realigned_files', smoother, 'in_files')
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or alternatively, a more flexible notation can be used. Although not shown here,
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the following notation can be used to connect multiple outputs from one node to
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Although not shown here, the following notation can be used to connect multiple outputs from one node to
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multiple inputs (see step 7 below).
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.. testcode::
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of the nodes). The outputs of these routines are in these folders.
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.. include:: ../links_names.txt
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.. glossary::
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pipeline
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Connected series of processes (processes can be run parallel and or sequential)
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workflow
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(kind of synonymous to pipeline) = hosting the nodes
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node
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= switching-point within a pipeline, you can give it a name (in the above example e.g. realigner),
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a node usually requires an or several inputs and will produce an or several outputs
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interface
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= specific software (e.g. FSL, SPM ...) are wrapped in interfaces, within a node instances of an
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interface can be run
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modules
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for each interface the according modules have to be imported in the usual pythonic manner

‎nipype/interfaces/dipy/__init__.py

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from .tracks import TrackDensityMap
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from .tensors import TensorMode
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from .tensors import TensorMode, DTI
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from .preprocess import Resample, Denoise
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from .simulate import SimulateMultiTensor

‎nipype/interfaces/dipy/tensors.py

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have_dipy = False
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else:
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import dipy.reconst.dti as dti
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from dipy.core.gradients import GradientTable
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from dipy.core.gradients import gradient_table
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from dipy.io.utils import nifti1_symmat
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32+
def tensor_fitting(data, bvals, bvecs, mask_file=None):
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"""
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Use dipy to fit DTI
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Parameters
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----------
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in_file : str
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Full path to a DWI data file.
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bvals : str
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Full path to a file containing gradient magnitude information (b-values).
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bvecs : str
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Full path to a file containing gradient direction information (b-vectors).
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mask_file : str, optional
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Full path to a file containing a binary mask. Defaults to use the entire volume.
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Returns
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-------
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TensorFit object, affine
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"""
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img = nb.load(in_file).get_data()
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data = img.get_data()
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affine = img.get_affine()
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if mask_file is not None:
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mask = nb.load(self.inputs.mask_file).get_data()
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else:
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mask=None
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# Load information about the gradients:
60+
gtab = grad.gradient_table(self.inputs.bvals, self.inputs.bvecs)
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# Fit it
63+
tenmodel = dti.TensorModel(gtab)
64+
return tenmodel.fit(data, mask), affine
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class DTIInputSpec(TraitedSpec):
68+
in_file = File(exists=True, mandatory=True,
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desc='The input 4D diffusion-weighted image file')
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bvecs = File(exists=True, mandatory=True,
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desc='The input b-vector text file')
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bvals = File(exists=True, mandatory=True,
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desc='The input b-value text file')
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mask_file = File(exists=True, mandatory=False,
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desc='An optional white matter mask')
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out_filename = File(
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genfile=True, desc='The output filename for the DTI parameters image')
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class DTIOutputSpec(TraitedSpec):
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out_file = File(exists=True)
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83+
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class DTI(BaseInterface):
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"""
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Calculates the diffusion tensor model parameters
87+
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Example
89+
-------
90+
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>>> import nipype.interfaces.dipy as dipy
92+
>>> dti = dipy.DTI()
93+
>>> dti.inputs.in_file = 'diffusion.nii'
94+
>>> dti.inputs.bvecs = 'bvecs'
95+
>>> dti.inputs.bvals = 'bvals'
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>>> dti.run() # doctest: +SKIP
97+
"""
98+
input_spec = DTIInputSpec
99+
output_spec = DTIOutputSpec
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101+
def _run_interface(self, runtime):
102+
ten_fit, affine = tensor_fitting(self.inputs.in_file,
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self.inputs.bvals,
104+
self.inputs.bvecs,
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self.inputs.mask_file)
106+
lower_triangular = tenfit.lower_triangular()
107+
img = nifti1_symmat(lower_triangular, affine)
108+
out_file = op.abspath(self._gen_outfilename())
109+
nb.save(img, out_file)
110+
iflogger.info('DTI parameters image saved as {i}'.format(i=out_file))
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return runtime
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def _list_outputs(self):
114+
outputs = self._outputs().get()
115+
outputs['out_file'] = op.abspath(self._gen_outfilename())
116+
return outputs
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def _gen_filename(self, name):
119+
if name is 'out_filename':
120+
return self._gen_outfilename()
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else:
122+
return None
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def _gen_outfilename(self):
125+
_, name, _ = split_filename(self.inputs.in_file)
126+
return name + '_dti.nii'
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31129
class TensorModeInputSpec(TraitedSpec):
32130
in_file = File(exists=True, mandatory=True,
33131
desc='The input 4D diffusion-weighted image file')
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bvecs = File(exists=True, mandatory=True,
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desc='The input b-vector text file')
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bvals = File(exists=True, mandatory=True,
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desc='The input b-value text file')
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mask_file = File(exists=True, mandatory=False,
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desc='An optional white matter mask')
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out_filename = File(
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genfile=True, desc='The output filename for the Tensor mode image')
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output_spec = TensorModeOutputSpec
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def _run_interface(self, runtime):
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## Load the 4D image files
73-
img = nb.load(self.inputs.in_file)
74-
data = img.get_data()
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affine = img.get_affine()
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## Load the gradient strengths and directions
78-
bvals = np.loadtxt(self.inputs.bvals)
79-
gradients = np.loadtxt(self.inputs.bvecs).T
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## Place in Dipy's preferred format
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gtab = GradientTable(gradients)
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gtab.bvals = bvals
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85-
## Mask the data so that tensors are not fit for
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## unnecessary voxels
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mask = data[..., 0] > 50
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## Fit the tensors to the data
90-
tenmodel = dti.TensorModel(gtab)
91-
tenfit = tenmodel.fit(data, mask)
92-
93-
## Calculate the mode of each voxel's tensor
94-
mode_data = tenfit.mode
172+
ten_fit = tensor_fitting(self.inputs.in_file, self.inputs.bvals, self.inputs.bvecs,
173+
self.inputs.mask_file)
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96175
## Write as a 3D Nifti image with the original affine
97-
img = nb.Nifti1Image(mode_data, affine)
176+
img = nb.Nifti1Image(tenfit.mode, affine)
98177
out_file = op.abspath(self._gen_outfilename())
99178
nb.save(img, out_file)
100179
iflogger.info('Tensor mode image saved as {i}'.format(i=out_file))

‎nipype/interfaces/dipy/tests/test_auto_DTI.py

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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from nipype.testing import assert_equal
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from nipype.interfaces.dipy.tensors import DTI
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def test_DTI_inputs():
6+
input_map = dict(bvals=dict(mandatory=True,
7+
),
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bvecs=dict(mandatory=True,
9+
),
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in_file=dict(mandatory=True,
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),
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mask_file=dict(mandatory=False,
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),
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out_filename=dict(genfile=True,
15+
),
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)
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inputs = DTI.input_spec()
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for key, metadata in input_map.items():
20+
for metakey, value in metadata.items():
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yield assert_equal, getattr(inputs.traits()[key], metakey), value
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def test_DTI_outputs():
24+
output_map = dict(out_file=dict(),
25+
)
26+
outputs = DTI.output_spec()
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28+
for key, metadata in output_map.items():
29+
for metakey, value in metadata.items():
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yield assert_equal, getattr(outputs.traits()[key], metakey), value
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‎nipype/interfaces/dipy/tests/test_auto_TensorMode.py

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),
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in_file=dict(mandatory=True,
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),
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mask_file=dict(mandatory=False,
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),
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out_filename=dict(genfile=True,
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),
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)

‎nipype/interfaces/fsl/__init__.py

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ImageStats, FilterRegressor, Overlay, Slicer,
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PlotTimeSeries, PlotMotionParams, ConvertXFM,
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SwapDimensions, PowerSpectrum, Reorient2Std,
19-
Complex, InvWarp, WarpUtils, ConvertWarp, WarpPoints, WarpPointsToStd)
19+
Complex, InvWarp, WarpUtils, ConvertWarp, WarpPoints,
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WarpPointsToStd, RobustFOV)
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2122
from .epi import (PrepareFieldmap, TOPUP, ApplyTOPUP, Eddy, EPIDeWarp,
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SigLoss, EddyCorrect, EpiReg)

‎nipype/interfaces/fsl/preprocess.py

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return super(FUGUE, self)._parse_inputs(skip=skip)
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1391-
def _list_outputs(self):
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outputs = self._outputs().get()
1393-
if self.inputs.forward_warping:
1394-
out_field = 'warped_file'
1395-
else:
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out_field = 'unwarped_file'
1397-
out_file = getattr(self.inputs, out_field)
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if not isdefined(out_file):
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if isdefined(self.inputs.in_file):
1400-
out_file = self._gen_fname(self.inputs.in_file,
1401-
suffix='_'+out_field[:-5])
1402-
if isdefined(out_file):
1403-
outputs[out_field] = os.path.abspath(out_file)
1404-
if isdefined(self.inputs.fmap_out_file):
1405-
outputs['fmap_out_file'] = os.path.abspath(
1406-
self.inputs.fmap_out_file)
1407-
if isdefined(self.inputs.shift_out_file):
1408-
outputs['shift_out_file'] = os.path.abspath(
1409-
self.inputs.shift_out_file)
1410-
return outputs
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14121392
class PRELUDEInputSpec(FSLCommandInputSpec):
14131393
complex_phase_file = File(exists=True, argstr='--complex=%s',

‎nipype/interfaces/fsl/tests/test_auto_RobustFOV.py

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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from nipype.testing import assert_equal
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from nipype.interfaces.fsl.utils import RobustFOV
4+
5+
def test_RobustFOV_inputs():
6+
input_map = dict(args=dict(argstr='%s',
7+
),
8+
environ=dict(nohash=True,
9+
usedefault=True,
10+
),
11+
ignore_exception=dict(nohash=True,
12+
usedefault=True,
13+
),
14+
in_file=dict(argstr='-i %s',
15+
mandatory=True,
16+
position=0,
17+
),
18+
out_roi=dict(argstr='-r %s',
19+
hash_files=False,
20+
name_source=['in_file'],
21+
name_template='%s_ROI',
22+
),
23+
output_type=dict(),
24+
terminal_output=dict(nohash=True,
25+
),
26+
)
27+
inputs = RobustFOV.input_spec()
28+
29+
for key, metadata in input_map.items():
30+
for metakey, value in metadata.items():
31+
yield assert_equal, getattr(inputs.traits()[key], metakey), value
32+
33+
def test_RobustFOV_outputs():
34+
output_map = dict(out_roi=dict(),
35+
)
36+
outputs = RobustFOV.output_spec()
37+
38+
for key, metadata in output_map.items():
39+
for metakey, value in metadata.items():
40+
yield assert_equal, getattr(outputs.traits()[key], metakey), value
41+

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