nipype: added RandomiseGroupLevel in new file 'stats.py'

code/nipype/__init__.py

@@ -8,4 +8,4 @@
 
 from .base import (BROCCOLICommand, Info)
 from .preprocess import (RegisterTwoVolumes,Smoothing,MotionCorrection)
-
+from .stats import (RandomiseGroupLevel)

code/nipype/stats.py

@@ -0,0 +1,141 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+"""The BROCCOLI module provides classes for interfacing with the `BROCCOLI
+<http://github.com/wanderine/BROCCOLI>`_ command line tools.
+
+    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 os
+import os.path as op
+import warnings
+
+import numpy as np
+
+from nipype.interfaces.broccoli.base import BROCCOLICommand, BROCCOLICommandInputSpec, BROCCOLICommandOutputSpec
+from nipype.interfaces.base import (TraitedSpec, File, InputMultiPath,
+                                    OutputMultiPath, Undefined, traits,
+                                    isdefined, OutputMultiPath)
+from nipype.utils.filemanip import split_filename
+
+from nibabel import load
+
+
+warn = warnings.warn
+warnings.filterwarnings('always', category=UserWarning)
+
+
+class RandomiseGroupLevelInputSpec(BROCCOLICommandInputSpec):
+    in_file = File(desc='input volume to use for randomisation testing',
+                   argstr='%s',
+                   position=0,
+                   mandatory=True,
+                   exists=True,
+                   copyfile=False)
+
+    design = traits.Str(argstr='-design %s', desc='The design matrix to apply in each permutation')
+
+    contrasts = traits.Str(argstr='-contrasts %s', desc='The contrast vector(s) to apply to the estimated beta values')
+
+    groupmean = traits.Bool(argstr='-groupmean', desc='Test for group mean, using sign flipping (design and contrast not needed)')
+
+    mask = traits.Str(argstr='-mask %s', desc='A mask that defines which voxels to permute (default none)')
+
+    permutations = traits.Int(argstr='-permutations %d', desc='Number of permutations to use (default 5,000)')
+
+    teststatistics = traits.Int(argstr='-teststatistics %d', desc='Test statistics to use, 0 = GLM t-test, 1 = GLM F-test  (default 0)')
+
+    inferencemode = traits.Int(argstr='-inferencemode %d', desc='Inference mode to use, 0 = voxel, 1 = cluster extent, 2 = cluster mass, 3 = TFCE (default 1)')
+
+    cdt = traits.Float(argstr='-cdt %s', desc='Cluster defining threshold for cluster inference (default 2.5)')
+
+    significance = traits.Float(argstr='-significance %s', desc='The significance level to calculate the threshold for (default 0.05)')
+
+    output = traits.Str(argstr='-output %s', desc='Set output filename (default volumes_perm_tvalues.nii and volumes_perm_pvalues.nii)')
+
+    writepermutationvalues = traits.Str(argstr='-writepermutationvalues %s', desc='Write all the permutation values to a text file')
+
+    writepermutations = traits.Str(argstr='-writepermutations %s', desc='Write all the random permutations (or sign flips) to a text file')
+
+    permutationfile = traits.Str(argstr='-permutationfile', desc='Use a specific permutation file or sign flipping file (e.g. from FSL)')
+
+    quiet = traits.Bool(argstr='-quiet', desc="Use a specific permutation file or sign flipping file (e.g. from FSL)")
+
+    verbose = traits.Bool(argstr='-verbose', desc='Print extra stuff (default false)')
+
+
+class RandomiseGroupLevelOutputSpec(TraitedSpec):
+    pvalues_file = File(exists=True,
+        desc="path/name of volume(s) showing p values of contrast(s)")
+    stats_file = File(exists=True,
+        desc="path/name of volume(s) showing test statistics (t or F) of contrast(s)")
+    permutationvalues_file = File(
+        desc="path/name of text file with permutation values")
+    permutations_file = File(
+        desc="path/name of text file with random permutations (or sign flips)")
+
+
+class RandomiseGroupLevel(BROCCOLICommand):
+    """The function performs permutation testing for group analyses.
+
+    Examples
+    ========
+
+    General usage:
+
+    >>> from nipype.interfaces import broccoli
+    >>> rgl = broccoli.RandomiseGroupLevel()
+    >>> rgl.inputs.in_file = 'volumes.nii'
+    >>> rgl.inputs.design = 'design.mat'
+    >>> rgl.inputs.contrasts = 'design.con'
+    >>> rgl.inputs.platform = 1
+    >>> rgl.inputs.device = 2
+    >>> rgl.cmdline
+    'RandomiseGroupLevel volumes.nii -design design.mat -contrasts design.con -device 2 -platform 1'
+
+    Testing a group mean:
+
+    >>> from nipype.interfaces import broccoli
+    >>> rgl = broccoli.RandomiseGroupLevel()
+    >>> rgl.inputs.in_file = 'volumes.nii'
+    >>> rgl.inputs.groupmean = True
+    >>> rgl.inputs.platform = 1
+    >>> rgl.inputs.device = 2
+    >>> rgl.cmdline
+    'RandomiseGroupLevel volumes.nii -design design.mat -contrasts design.con -device 2 -platform 1' 
+
+    """
+
+    _cmd = 'RandomiseGroupLevel'
+    input_spec = RandomiseGroupLevelInputSpec
+    output_spec = RandomiseGroupLevelOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        if (not isdefined(self.inputs.output)):
+            pvalues_file = self._gen_fname(self.inputs.in_file,
+                                           suffix='_pvalues')
+
+            stats_file = self._gen_fname(self.inputs.in_file,
+                                         suffix='_tvalues')
+
+        else:
+            pvalues_file = self._gen_fname(self.inputs.output,
+                                           suffix='_pvalues')
+
+            stats_file = self._gen_fname(self.inputs.output,
+                                         suffix='_tvalues')
+
+        if (isdefined(self.inputs.writepermutationvalues)):
+            outputs['permutationvalues_file'] = self._gen_fname(self.inputs.writepermutationvalues,
+                                                                ext='.txt')
+
+        if (isdefined(self.inputs.writepermutations)):
+            outputs['permutations_file'] = self._gen_fname(self.inputs.writepermutations,
+                                                           ext='.txt')
+
+        return outputs