Merge pull request #4 from nipy/master

Update fork.

Author: sgiavasis

.travis.yml

@@ -1,5 +1,6 @@
 language: python
 python:
+  - 2.6
   - 2.7
 # Setup anaconda
 before_install:
@@ -23,7 +24,9 @@ before_install:
 
 # Install packages
 install:
-  - conda install --yes pip python=$TRAVIS_PYTHON_VERSION numpy scipy nose traits networkx dateutil
+  - conda create -n testenv --yes pip python=$TRAVIS_PYTHON_VERSION
+  - source activate testenv
+  - conda install --yes numpy scipy nose traits networkx dateutil
   - pip install nibabel --use-mirrors
   - pip install python-coveralls --use-mirrors
   - pip install nose-cov --use-mirrors

doc/quickstart.rst

@@ -15,15 +15,18 @@ Downloading and installing
 Beginner's guide
 ================
 
-Introductory slides. `Available here`__
-
 Beginner's tutorials (IPython Notebooks). `Availible here`__
 
-Michael Notter's guide. `Available here`__
+Michael Notter's Nipype guide. `Available here`__
+
+Dissecting Nipype Workflows. `Available here`__
+
+Introductory slides [older]. `Available here`__
 
-__ http://satra.github.com/intro2nipype
 __ https://github.com/mwaskom/nipype_concepts
 __ http://miykael.github.com/nipype-beginner-s-guide/index.html
+__ http://slideviewer.herokuapp.com/url/raw.github.com/nipy/nipype/master/examples/nipype_tutorial.ipynb?theme=sky
+__ http://satra.github.com/intro2nipype
 
 User guides
 ===========

nipype/external/provcopy.py

@@ -21,7 +21,7 @@
     from rdflib.term import URIRef, BNode
     from rdflib.term import Literal as RDFLiteral
     from rdflib.graph import ConjunctiveGraph, Graph
-    from rdflib.namespace import RDF
+    from rdflib.namespace import RDF, RDFS
 except ImportError:
     pass
 
@@ -760,7 +760,12 @@ def rdf(self, graph=None, subj=None):
                 graph.add((subj, pred, obj))
         if self._extra_attributes:
             for (attr, value) in self._extra_attributes:
-                pred = attr.rdf_representation() if attr != PROV['type'] else RDF.type
+                if attr == PROV['type']:
+                    pred = RDF.type
+                elif attr == PROV['label']:
+                    pred = RDFS.label
+                else:
+                    pred = attr.rdf_representation()
                 try:
                     # try if there is a RDF representation defined
                     obj = value.rdf_representation()
@@ -825,7 +830,12 @@ def rdf(self, graph=None):
             for (attr, value) in self._extra_attributes:
                 if not value:
                     continue
-                pred = attr.rdf_representation() if attr != PROV['type'] else RDF.type
+                if attr == PROV['type']:
+                    pred = RDF.type
+                elif attr == PROV['label']:
+                    pred = RDFS.label
+                else:
+                    pred = attr.rdf_representation()
                 try:
                     # try if there is a RDF representation defined
                     otherobj = value.rdf_representation()

nipype/interfaces/ants/registration.py

@@ -233,7 +233,7 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         requires=['metric_weight'],
         desc='the number of bins in each stage for the MI and Mattes metric, '
         'the radius for other metrics')
-    sampling_strategy_item_trait = traits.Enum("Dense", "Regular", "Random", None)
+    sampling_strategy_item_trait = traits.Enum("None", "Regular", "Random", None)
     sampling_strategy_stage_trait = traits.Either(
         sampling_strategy_item_trait, traits.List(sampling_strategy_item_trait))
     sampling_strategy = traits.List(
@@ -390,9 +390,9 @@ class Registration(ANTSCommand):
     >>> 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-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, 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 CC[ fixed1.nii, moving1.nii, 0.5, 32, Dense, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, Dense, 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-linear-transforms-to-fixed-image-header 0 --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric CC[ fixed1.nii, moving1.nii, 1, 4, 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 CC[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, 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'
     """
-    DEF_SAMPLING_STRATEGY = 'Dense'
+    DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling stratey argument."""
 
     _cmd = 'antsRegistration'

nipype/interfaces/camino/__init__.py

@@ -9,6 +9,6 @@
 from .dti import (DTIFit, ModelFit, DTLUTGen, PicoPDFs, Track, TrackPICo,
                   TrackBayesDirac, TrackDT, TrackBallStick, TrackBootstrap,
                   ComputeFractionalAnisotropy, ComputeMeanDiffusivity,
-                  ComputeTensorTrace,  ComputeEigensystem)
+                  ComputeTensorTrace,  ComputeEigensystem, DTMetric)
 from .calib import (SFPICOCalibData, SFLUTGen)
 from .odf import (QBallMX, LinRecon, SFPeaks)

nipype/interfaces/camino/convert.py

@@ -563,7 +563,7 @@ class AnalyzeHeader(StdOutCommandLine):
 
     >>> import nipype.interfaces.camino as cmon
     >>> hdr = cmon.AnalyzeHeader()
-    >>> hdr.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> hdr.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> hdr.inputs.scheme_file = 'A.scheme'
     >>> hdr.inputs.data_dims = [256,256,256]
     >>> hdr.inputs.voxel_dims = [1,1,1]

nipype/interfaces/camino/dti.py

@@ -8,7 +8,7 @@
 """
 from nipype.interfaces.base import (CommandLineInputSpec, CommandLine, traits,
                                     TraitedSpec, File, StdOutCommandLine,
-                                    StdOutCommandLineInputSpec)
+                                    StdOutCommandLineInputSpec, isdefined)
 from nipype.utils.filemanip import split_filename
 import os
 
@@ -49,7 +49,7 @@ class DTIFit(StdOutCommandLine):
     >>> import nipype.interfaces.camino as cmon
     >>> fit = cmon.DTIFit()
     >>> fit.inputs.scheme_file = 'A.scheme'
-    >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> fit.run()                  # doctest: +SKIP
     """
     _cmd = 'dtfit'
@@ -65,6 +65,107 @@ def _gen_outfilename(self):
         _, name , _ = split_filename(self.inputs.in_file)
         return name + '_DT.Bdouble'
 
+class DTMetricInputSpec(CommandLineInputSpec):
+    eigen_data = File(exists=True, argstr='-inputfile %s', mandatory=True,
+                      desc='voxel-order data filename')
+
+    metric = traits.Enum('fa','md','rd','l1', 'l2', 'l3', 'tr', 'ra', '2dfa','cl','cp','cs',
+                         argstr='-stat %s', mandatory=True,
+                         desc=('Specifies the metric to compute. Possible choices are: '
+                               '"fa", "md", "rd", "l1", "l2", "l3", "tr", "ra", "2dfa", "cl", "cp" or "cs".'))
+
+    inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+                                argstr='-inputdatatype %s', usedefault=True,
+                                desc=('Specifies the data type of the input data. '
+                                      'The data type can be any of the following strings: '
+                                      '"char", "short", "int", "long", "float" or "double".'
+                                      'Default is double data type'))
+
+    outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+                                 argstr='-outputdatatype %s', usedefault=True,
+                                 desc=('Specifies the data type of the output data. '
+                                       'The data type can be any of the following strings: '
+                                       '"char", "short", "int", "long", "float" or "double".'
+                                       'Default is double data type'))
+
+    data_header = File(argstr='-header %s', exists=True,
+                       desc=('A Nifti .nii or .nii.gz file containing the header information. '
+                             'Usually this will be the header of the raw data file from which '
+                             'the diffusion tensors were reconstructed.'))
+
+    outputfile = File(argstr='-outputfile %s', genfile=True,
+                      desc=('Output name. Output will be a .nii.gz file if data_header is provided and'
+                            'in voxel order with outputdatatype datatype (default: double) otherwise.'))
+
+class DTMetricOutputSpec(TraitedSpec):
+    metric_stats = File(exists=True, desc='Diffusion Tensor statistics of the chosen metric')
+
+class DTMetric(CommandLine):
+    """
+    Computes tensor metric statistics based on the eigenvalues l1 >= l2 >= l3
+    typically obtained from ComputeEigensystem.
+
+    The full list of statistics is:
+
+     <cl> = (l1 - l2) / l1 , a measure of linearity
+     <cp> = (l2 - l3) / l1 , a measure of planarity
+     <cs> = l3 / l1 , a measure of isotropy
+      with: cl + cp + cs = 1
+
+     <l1> = first eigenvalue
+     <l2> = second eigenvalue
+     <l3> = third eigenvalue
+
+     <tr> = l1 + l2 + l3
+     <md> = tr / 3
+     <rd> = (l2 + l3) / 2
+     <fa> = fractional anisotropy. (Basser et al, J Magn Reson B 1996)
+     <ra> = relative anisotropy (Basser et al, J Magn Reson B 1996)
+
+     <2dfa> = 2D FA of the two minor eigenvalues l2 and l3
+      i.e. sqrt( 2 * [(l2 - <l>)^2 + (l3 - <l>)^2] / (l2^2 + l3^2) )
+           with: <l> = (l2 + l3) / 2
+
+    Example
+    -------
+    Compute the CP planar metric as float data type.
+
+    >>> import nipype.interfaces.camino as cam
+    >>> dtmetric = cam.DTMetric()
+    >>> dtmetric.inputs.eigen_data = 'dteig.Bdouble'
+    >>> dtmetric.inputs.metric = 'cp'
+    >>> dtmetric.inputs.outputdatatype = 'float'
+    >>> dtmetric.run()                  # doctest: +SKIP
+    """
+    _cmd = 'dtshape'
+    input_spec=DTMetricInputSpec
+    output_spec=DTMetricOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs['metric_stats'] = os.path.abspath(self._gen_outfilename())
+        return outputs
+
+    def _gen_outfilename(self):
+        return self._gen_outputfile()
+
+    def _gen_outputfile(self):
+        outputfile = self.inputs.outputfile
+        if not isdefined(outputfile):
+            outputfile = self._gen_filename('outputfile')
+        return outputfile
+
+    def _gen_filename(self, name):
+        if name == 'outputfile':
+            _, name , _ = split_filename(self.inputs.eigen_data)
+            metric = self.inputs.metric
+            datatype= self.inputs.outputdatatype
+            if isdefined(self.inputs.data_header):
+                filename = name + '_' + metric + '.nii.gz'
+            else:
+                filename = name + '_' + metric + '.B' + datatype
+        return filename
+
 class ModelFitInputSpec(StdOutCommandLineInputSpec):
     def _gen_model_options(): #@NoSelf
         """
@@ -134,7 +235,7 @@ class ModelFit(StdOutCommandLine):
     >>> fit = cmon.ModelFit()
     >>> fit.model = 'dt'
     >>> fit.inputs.scheme_file = 'A.scheme'
-    >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> fit.run()                  # doctest: +SKIP
     """
     _cmd = 'modelfit'
@@ -381,7 +482,7 @@ class TrackDT(Track):
 
     >>> import nipype.interfaces.camino as cmon
     >>> track = cmon.TrackDT()
-    >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> track.inputs.seed_file = 'seed_mask.nii'
     >>> track.run()                 # doctest: +SKIP
     """
@@ -447,7 +548,7 @@ class TrackBayesDirac(Track):
 
     >>> import nipype.interfaces.camino as cmon
     >>> track = cmon.TrackBayesDirac()
-    >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> track.inputs.seed_file = 'seed_mask.nii'
     >>> track.inputs.scheme_file = 'bvecs.scheme'
     >>> track.run()                  # doctest: +SKIP
@@ -547,7 +648,7 @@ class ComputeMeanDiffusivity(StdOutCommandLine):
 
     >>> import nipype.interfaces.camino as cmon
     >>> md = cmon.ComputeMeanDiffusivity()
-    >>> md.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> md.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> md.inputs.scheme_file = 'A.scheme'
     >>> md.run()                  # doctest: +SKIP
     """
@@ -606,7 +707,7 @@ class ComputeFractionalAnisotropy(StdOutCommandLine):
 
     >>> import nipype.interfaces.camino as cmon
     >>> fa = cmon.ComputeFractionalAnisotropy()
-    >>> fa.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> fa.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> fa.inputs.scheme_file = 'A.scheme'
     >>> fa.run()                  # doctest: +SKIP
     """
@@ -667,7 +768,7 @@ class ComputeTensorTrace(StdOutCommandLine):
 
     >>> import nipype.interfaces.camino as cmon
     >>> trace = cmon.ComputeTensorTrace()
-    >>> trace.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> trace.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> trace.inputs.scheme_file = 'A.scheme'
     >>> trace.run()                 # doctest: +SKIP
     """
@@ -690,11 +791,21 @@ class ComputeEigensystemInputSpec(StdOutCommandLineInputSpec):
 
     inputmodel = traits.Enum('dt', 'multitensor', argstr='-inputmodel %s', desc='Specifies the model that the input data contains parameters for. Possible model types are: "dt" (diffusion-tensor data) and "multitensor"')
 
-    maxcomponents = traits.Int(argstr='-maxcomponents %s', desc='The maximum number of tensor components in a voxel of the input data.')
+    maxcomponents = traits.Int(argstr='-maxcomponents %d', desc='The maximum number of tensor components in a voxel of the input data.')
 
-    inputdatatype = traits.Enum("double", "char", "short", "int", "long", "float", argstr='-inputdatatype %s', desc='Specifies the data type of the input file. The data type can be any of the following strings: "char", "short", "int", "long", "float" or "double".')
+    inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+                                 argstr='-inputdatatype %s', usedefault=True,
+                                 desc=('Specifies the data type of the input data. '
+                                       'The data type can be any of the following strings: '
+                                       '"char", "short", "int", "long", "float" or "double".'
+                                       'Default is double data type'))
 
-    outputdatatype = traits.Enum("double", "char", "short", "int", "long", "float", argstr='-outputdatatype %s', desc='Specifies the data type of the output data. The data type can be any of the following strings: "char", "short", "int", "long", "float" or "double".')
+    outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+                                 argstr='-outputdatatype %s', usedefault=True,
+                                 desc=('Specifies the data type of the output data. '
+                                       'The data type can be any of the following strings: '
+                                       '"char", "short", "int", "long", "float" or "double".'
+                                       'Default is double data type'))
 
 class ComputeEigensystemOutputSpec(TraitedSpec):
     eigen = File(exists=True, desc='Trace of the diffusion tensor')
@@ -716,7 +827,7 @@ class ComputeEigensystem(StdOutCommandLine):
 
     >>> import nipype.interfaces.camino as cmon
     >>> dteig = cmon.ComputeEigensystem()
-    >>> dteig.inputs.in_file = 'tensor_fitted_data.Bfloat'
+    >>> dteig.inputs.in_file = 'tensor_fitted_data.Bdouble'
     >>> dteig.run()                  # doctest: +SKIP
     """
     _cmd = 'dteig'
@@ -730,4 +841,5 @@ def _list_outputs(self):
 
     def _gen_outfilename(self):
         _, name , _ = split_filename(self.inputs.in_file)
-        return name + "_Eigen.img"     #Need to change to self.inputs.outputdatatype
+        datatype= self.inputs.outputdatatype
+        return name + '_eig.B' + datatype

nipype/interfaces/camino/tests/test_auto_ComputeEigensystem.py

@@ -11,13 +11,15 @@ def test_ComputeEigensystem_inputs():
     ),
     inputmodel=dict(argstr='-inputmodel %s',
     ),
-    outputdatatype=dict(argstr='-outputdatatype %s',
+    outputdatatype=dict(usedefault=True,
+    argstr='-outputdatatype %s',
     ),
     args=dict(argstr='%s',
     ),
-    inputdatatype=dict(argstr='-inputdatatype %s',
+    inputdatatype=dict(usedefault=True,
+    argstr='-inputdatatype %s',
     ),
-    maxcomponents=dict(argstr='-maxcomponents %s',
+    maxcomponents=dict(argstr='-maxcomponents %d',
     ),
     environ=dict(nohash=True,
     usedefault=True,