Merge branch 'pr/801'

Author: chrisgorgo

nipype/interfaces/afni/__init__.py

@@ -12,4 +12,5 @@
                          Fourier, Allineate, Maskave, SkullStrip, TCat, Fim,
                          BlurInMask, Autobox, TCorrMap, Bandpass, Retroicor,
                          TCorrelate, TCorr1D, BrickStat, ROIStats, AutoTcorrelate,
-                         AFNItoNIFTI)
+                         AFNItoNIFTI, Eval, Means)
+from .svm import (SVMTest, SVMTrain)

nipype/interfaces/afni/preprocess.py

@@ -1111,7 +1111,7 @@ def _format_arg(self, name, trait_spec, value):
     def _list_outputs(self):
         outputs = self.output_spec().get()
         if not isdefined(self.inputs.out_file):
-            outputs['out_file'] = self._gen_fname(self.inputs.in_file,
+            outputs['out_file'] = self._gen_filename(self.inputs.in_file,
                                                   suffix=self.inputs.suffix)
         else:
             outputs['out_file'] = os.path.abspath(self.inputs.out_file)
@@ -1209,7 +1209,7 @@ class TCatInputSpec(AFNICommandInputSpec):
         mandatory=True,
         copyfile=False)
     out_file = File(name_template="%s_tcat", desc='output image file name',
-                    argstr='-prefix %s', name_source="in_file")
+                    argstr='-prefix %s', name_source="in_files")
     rlt = traits.Str(desc='options', argstr='-rlt%s', position=1)
 
 
@@ -1935,5 +1935,107 @@ def _overload_extension(self, value):
 
     def _gen_filename(self, name):
         return os.path.abspath(super(AFNItoNIFTI, self)._gen_filename(name))
+        
+class EvalInputSpec(AFNICommandInputSpec):
+    in_file_a = File(desc='input file to 1deval',
+                     argstr='-a %s', position=0, mandatory=True, exists=True)
+    in_file_b = File(desc='operand file to 1deval',
+                     argstr=' -b %s', position=1, exists=True)
+    in_file_c = File(desc='operand file to 1deval',
+                     argstr=' -c %s', position=2, exists=True)
+    out_file = File(name_template="%s_calc", desc='output image file name',
+                    argstr='-prefix %s', name_source="in_file_a")
+    out1D = traits.Bool(desc="output in 1D",
+                    argstr='-1D')
+    expr = traits.Str(desc='expr', argstr='-expr "%s"', position=3,
+                      mandatory=True)
+    start_idx = traits.Int(desc='start index for in_file_a',
+                           requires=['stop_idx'])
+    stop_idx = traits.Int(desc='stop index for in_file_a',
+                          requires=['start_idx'])
+    single_idx = traits.Int(desc='volume index for in_file_a')
+    other = File(desc='other options', argstr='')
+    
+class Eval(AFNICommand):
+    """Evaluates an expression that may include columns of data from one or more text files
+
+    see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>
+    
+    Examples
+    ========
+    
+    >>> from nipype.interfaces import afni as afni
+    >>> eval = afni.Eval()
+    >>> eval.inputs.in_file_a = 'seed.1D'
+    >>> eval.inputs.in_file_b = 'resp.1D'
+    >>> eval.inputs.expr='a*b'
+    >>> eval.inputs.out1D = True
+    >>> eval.inputs.out_file =  'data_calc.1D'
+    >>> calc.cmdline #doctest: +SKIP
+    '3deval -a timeseries1.1D  -b timeseries2.1D -expr "a*b" -1D -prefix data_calc.1D'
+
+    """
+
+    _cmd = '1deval'
+    input_spec = EvalInputSpec
+    output_spec = AFNICommandOutputSpec
+
+    def _format_arg(self, name, trait_spec, value):
+        if name == 'in_file_a':
+            arg = trait_spec.argstr % value
+            if isdefined(self.inputs.start_idx):
+                arg += '[%d..%d]' % (self.inputs.start_idx,
+                                     self.inputs.stop_idx)
+            if isdefined(self.inputs.single_idx):
+                arg += '[%d]' % (self.inputs.single_idx)
+            return arg
+        return super(Eval, self)._format_arg(name, trait_spec, value)
+
+    def _parse_inputs(self, skip=None):
+        """Skip the arguments without argstr metadata
+        """
+        return super(Eval, self)._parse_inputs(
+            skip=('start_idx', 'stop_idx', 'out1D', 'other'))
+            
+class MeansInputSpec(AFNICommandInputSpec):
+    in_file_a = File(desc='input file to 3dMean',
+        argstr='%s',
+        position=0,
+        mandatory=True,
+        exists=True)
+    in_file_b = File(desc='another input file to 3dMean',
+        argstr='%s',
+        position=1,
+        exists=True)
+    out_file = File(name_template="%s_mean", desc='output image file name',
+                    argstr='-prefix %s', name_source="in_file_a")
+    scale = traits.Str(desc='scaling of output', argstr='-%sscale')
+    non_zero = traits.Bool(desc='use only non-zero values', argstr='-non_zero')
+    std_dev = traits.Bool(desc='calculate std dev', argstr='-stdev')
+    sqr = traits.Bool(desc='mean square instead of value', argstr='-sqr')
+    summ = traits.Bool(desc='take sum, (not average)', argstr='-sum')
+    count = traits.Bool(desc='compute count of non-zero voxels', argstr='-count')
+    mask_inter = traits.Bool(desc='create intersection mask', argstr='-mask_inter')
+    mask_union = traits.Bool(desc='create union mask', argstr='-mask_union')
 
+class Means(AFNICommand):
+    """Takes the voxel-by-voxel mean of all input datasets using 3dMean
 
+    see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dMean.html>
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni as afni
+    >>> means = afni.Means()
+    >>> means.inputs.in_file_a = 'im1.nii'
+    >>> means.inputs.in_file_b = 'im2.nii'
+    >>> means.inputs.out_file =  'output.nii'
+    >>> means.cmdline
+    '3dMean im1.nii im2.nii -prefix output.nii'
+
+    """
+
+    _cmd = '3dMean'
+    input_spec = MeansInputSpec
+    output_spec = AFNICommandOutputSpec

nipype/interfaces/afni/svm.py

@@ -0,0 +1,154 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft = python sts = 4 ts = 4 sw = 4 et:
+"""Afni svm interfaces
+
+    Change directory to provide relative paths for doctests
+    >>> import os
+    >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
+    >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data'))
+    >>> os.chdir(datadir)
+"""
+import warnings
+
+import os
+import re
+
+from ..base import (Directory, TraitedSpec,
+                    traits, isdefined, File, InputMultiPath, Undefined)
+from ...utils.filemanip import (load_json, save_json, split_filename)
+from nipype.utils.filemanip import fname_presuffix
+from .base import AFNICommand, AFNICommandInputSpec,\
+    AFNICommandOutputSpec
+from nipype.interfaces.base import CommandLineInputSpec, CommandLine,\
+    OutputMultiPath
+
+warn = warnings.warn
+warnings.filterwarnings('always', category=UserWarning)
+    
+class SVMTrainInputSpec(AFNICommandInputSpec):
+    #training options
+    ttype = traits.Str(desc='tname: classification or regression', 
+                argstr='-type %s',
+                mandatory=True)
+    in_file = File(desc='A 3D+t AFNI brik dataset to be used for training.',
+                argstr='-trainvol %s',
+                mandatory=True,
+                exists=True,
+                copyfile=False)
+    out_file = File(name_template="%s_vectors", 
+                desc='output sum of weighted linear support vectors file name',
+                argstr='-bucket %s',
+                suffix='_bucket', 
+                name_source="in_file")
+    model = File(name_template="%s_model",
+                desc='basename for the brik containing the SVM model',
+                argstr='-model %s',
+                suffix='_model', 
+                name_source="in_file")    
+    alphas = File(name_template="%s_alphas", 
+                desc='output alphas file name',
+                argstr='-alpha %s',
+                suffix='_alphas',
+                name_source="in_file")
+    mask = File(desc='byte-format brik file used to mask voxels in the analysis',
+                argstr='-mask %s',
+                position=-1,
+                exists=True,
+                copyfile=False)
+    nomodelmask = traits.Bool(desc='Flag to enable the omission of a mask file',
+                argstr='-nomodelmask')
+    trainlabels = File(desc='.1D labels corresponding to the stimulus paradigm for the training data.',
+                argstr='-trainlabels %s',
+                exists=True)
+    censor = File(desc='.1D censor file that allows the user to ignore certain samples in the training data.',
+                argstr='-censor %s',
+                exists=True)   
+    kernel = traits.Str(desc='string specifying type of kernel function:linear, polynomial, rbf, sigmoid',
+                argstr='-kernel %s')
+    max_iterations = traits.Int(desc='Specify the maximum number of iterations for the optimization.',
+                argstr='-max_iterations %d')
+    w_out = traits.Bool(desc='output sum of weighted linear support vectors',
+                argstr='-wout')
+    options = traits.Str(desc='additional options for SVM-light', argstr='%s')
+                
+class SVMTrainOutputSpec(TraitedSpec):
+    out_file = File(desc='sum of weighted linear support vectors file name')
+    model = File(desc='brik containing the SVM model file name')
+    alphas = File(desc='output alphas file name')
+                
+class SVMTrain(AFNICommand):
+    """Temporally predictive modeling with the support vector machine
+    SVM Train Only
+    For complete details, see the `3dsvm Documentation.
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni as afni
+    >>> svmTrain = afni.SVMTrain()
+    >>> svmTrain.inputs.in_file = 'run1+orig'
+    >>> svmTrain.inputs.trainlabels = 'run1_categories.1D'
+    >>> svmTrain.inputs.ttype = 'regression'
+    >>> svmTrain.inputs.mask = 'mask.nii'
+    >>> svmTrain.inputs.model = 'model_run1'
+    >>> svmTrain.inputs.alphas = 'alphas_run1'
+    >>> res = svmTrain.run() # doctest: +SKIP
+
+    """
+
+    _cmd = '3dsvm'
+    input_spec = SVMTrainInputSpec
+    output_spec = SVMTrainOutputSpec
+    _additional_metadata = ['suffix']
+
+    def _format_arg(self, name, trait_spec, value):
+        return super(SVMTrain, self)._format_arg(name, trait_spec, value)
+
+class SVMTestInputSpec(AFNICommandInputSpec):
+    #testing options
+    model = traits.Str(desc='modname is the basename for the brik containing the SVM model',
+                argstr='-model %s',
+                exists=True,
+                mandatory=True)
+    in_file = File(desc='A 3D or 3D+t AFNI brik dataset to be used for testing.',
+                argstr='-testvol %s',
+                exists=True,
+                mandatory=True)
+    out_file = File(name_template="%s_predictions",
+                desc='filename for .1D prediction file(s).',
+                argstr='-predictions %s')
+    testlabels = File(desc='*true* class category .1D labels for the test dataset. It is used to calculate the prediction accuracy performance',
+                exists=True,
+                argstr='-testlabels %s')
+    classout = traits.Bool(desc='Flag to specify that pname files should be integer-valued, corresponding to class category decisions.',
+                argstr='-classout')
+    nopredcensord = traits.Bool(desc='Flag to prevent writing predicted values for censored time-points',
+                argstr='-nopredcensord')
+    nodetrend = traits.Bool(desc='Flag to specify that pname files should not be linearly detrended',
+                argstr='-nodetrend')
+    multiclass = traits.Bool(desc='Specifies multiclass algorithm for classification',
+                argstr='-multiclass %s')
+    options = traits.Str(desc='additional options for SVM-light', argstr='%s')
+    
+class SVMTest(AFNICommand):
+    """Temporally predictive modeling with the support vector machine
+    SVM Test Only
+    For complete details, see the `3dsvm Documentation.
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni as afni
+    >>> svmTest = afni.SVMTest()
+    >>> svmTest.inputs.in_file= 'run2+orig'
+    >>> svmTest.inputs.model= 'run1+orig_model'
+    >>> svmTest.inputs.testlabels= 'run2_categories.1D'
+    >>> svmTest.inputs.out_file= 'pred2_model1'
+    >>> res = svmTest.run() # doctest: +SKIP
+
+    """
+    _cmd = '3dsvm'
+    input_spec = SVMTestInputSpec
+    output_spec = AFNICommandOutputSpec

nipype/interfaces/afni/tests/test_auto_Eval.py

@@ -0,0 +1,60 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.afni.preprocess import Eval
+
+def test_Eval_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    expr=dict(argstr='-expr "%s"',
+    mandatory=True,
+    position=3,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_file_a=dict(argstr='-a %s',
+    mandatory=True,
+    position=0,
+    ),
+    in_file_b=dict(argstr=' -b %s',
+    position=1,
+    ),
+    in_file_c=dict(argstr=' -c %s',
+    position=2,
+    ),
+    other=dict(argstr='',
+    ),
+    out1D=dict(argstr='-1D',
+    ),
+    out_file=dict(argstr='-prefix %s',
+    name_source='in_file_a',
+    name_template='%s_calc',
+    ),
+    outputtype=dict(),
+    single_idx=dict(),
+    start_idx=dict(requires=['stop_idx'],
+    ),
+    stop_idx=dict(requires=['start_idx'],
+    ),
+    terminal_output=dict(mandatory=True,
+    nohash=True,
+    ),
+    )
+    inputs = Eval.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_Eval_outputs():
+    output_map = dict(out_file=dict(),
+    )
+    outputs = Eval.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+