Merge remote-tracking branch 'origin/pr/847'

Conflicts:
	CHANGES
	nipype/algorithms/misc.py

Author: chrisgorgo

CHANGES

@@ -5,6 +5,7 @@ Next Release
        ``nipype`` namespace, and must be imported directly (e.g.
        ``from nipype.interfaces import fsl``).
 * ENH: New FSL interface: ProbTrackX2
+* ENH: New misc algorithm: NormalizeProbabilityMapSet
 * ENH: New ANTs interface: ApplyTransformsToPoints
 * ENH: New metrics group in algorithms. Now Distance, Overlap, and FuzzyOverlap
        are found in nipype.algorithms.metrics instead of misc

nipype/algorithms/misc.py

@@ -839,6 +839,110 @@ def calc_moments(timeseries_file, moment):
     zero = (m2 == 0)
     return np.where(zero, 0, m3 / m2**(moment/2.0))
 
+
+class NormalizeProbabilityMapSetInputSpec(TraitedSpec):
+    in_files = InputMultiPath(File(exists=True, mandatory=True,
+                    desc='The tpms to be normalized') )
+    in_mask = File(exists=True, mandatory=False,
+                    desc='Masked voxels must sum up 1.0, 0.0 otherwise.')
+
+class NormalizeProbabilityMapSetOutputSpec(TraitedSpec):
+    out_files = OutputMultiPath(File(exists=True),
+                                desc="normalized maps")
+
+
+class NormalizeProbabilityMapSet(BaseInterface):
+    """ Returns the input tissue probability maps (tpms, aka volume fractions)
+    normalized to sum up 1.0 at each voxel within the mask.
+
+    .. note:: Please recall this is not a spatial normalization algorithm
+
+    Example
+    -------
+
+    >>> import nipype.algorithms.misc as misc
+    >>> normalize = misc.NormalizeProbabilityMapSet()
+    >>> normalize.inputs.in_files = [ 'tpm_00.nii.gz', 'tpm_01.nii.gz', 'tpm_02.nii.gz' ]
+    >>> normalize.inputs.in_mask = 'tpms_msk.nii.gz'
+    >>> normalize.run() # doctest: +SKIP
+    """
+    input_spec = NormalizeProbabilityMapSetInputSpec
+    output_spec = NormalizeProbabilityMapSetOutputSpec
+
+    def _run_interface(self, runtime):
+        mask = None
+
+        if isdefined( self.inputs.in_mask ):
+            mask = self.inputs.in_mask
+
+        self._out_filenames = normalize_tpms( self.inputs.in_files, mask )
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs['out_files'] = self._out_filenames
+        return outputs
+
+
+def normalize_tpms( in_files, in_mask=None, out_files=[] ):
+    """
+    Returns the input tissue probability maps (tpms, aka volume fractions)
+    normalized to sum up 1.0 at each voxel within the mask.
+    """
+    import nibabel as nib
+    import numpy as np
+    import os.path as op
+
+    in_files = np.atleast_1d( in_files ).tolist()
+
+    if len(out_files)!=len(in_files):
+        for i,finname in enumerate( in_files ):
+            fname,fext = op.splitext( op.basename( finname ) )
+            if fext == '.gz':
+                fname,fext2 = op.splitext( fname )
+                fext = fext2 + fext
+
+            out_file = op.abspath(fname+'_norm'+('_%02d' % i)+fext)
+            out_files+= [out_file]
+
+    imgs = [nib.load(fim) for fim in in_files]
+
+    if len(in_files)==1:
+        img_data = imgs[0].get_data()
+        img_data[img_data>0.0] = 1.0
+        hdr = imgs[0].get_header().copy()
+        hdr['data_type']= 16
+        hdr.set_data_dtype( 'float32' )
+        nib.save( nib.Nifti1Image( img_data.astype(np.float32), imgs[0].get_affine(), hdr ), out_files[0] )
+        return out_files[0]
+
+    img_data = np.array( [ im.get_data() for im in imgs ] ).astype( 'f32' )
+    #img_data[img_data>1.0] = 1.0
+    img_data[img_data<0.0] = 0.0
+    weights = np.sum( img_data, axis=0 )
+
+    msk = np.ones_like( imgs[0].get_data() )
+    msk[ weights<= 0 ] = 0
+
+    if not in_mask is None:
+        msk = nib.load( in_mask ).get_data()
+        msk[ msk<=0 ] = 0
+        msk[ msk>0 ] = 1
+
+    msk = np.ma.masked_equal( msk, 0 )
+
+
+    for i,out_file in enumerate( out_files ):
+        data = np.ma.masked_equal( img_data[i], 0 )
+        probmap = data / weights
+        hdr = imgs[i].get_header().copy()
+        hdr['data_type']= 16
+        hdr.set_data_dtype( 'float32' )
+        nib.save( nib.Nifti1Image( probmap.astype(np.float32), imgs[i].get_affine(), hdr ), out_file )
+
+    return out_files
+
+
 # Deprecated interfaces ---------------------------------------------------------
 class Distance( nam.Distance ):
     """Calculates distance between two volumes.
@@ -877,3 +981,4 @@ def __init__(self, **inputs):
         warnings.warn(("This interface has been deprecated since 0.10.0,"
                       " please use nipype.algorithms.metrics.FuzzyOverlap"),
                       DeprecationWarning)
+

nipype/algorithms/tests/test_auto_NormalizeProbabilityMapSet.py

@@ -0,0 +1,24 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.algorithms.misc import NormalizeProbabilityMapSet
+
+def test_NormalizeProbabilityMapSet_inputs():
+    input_map = dict(in_files=dict(),
+    in_mask=dict(mandatory=False,
+    ),
+    )
+    inputs = NormalizeProbabilityMapSet.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_NormalizeProbabilityMapSet_outputs():
+    output_map = dict(out_files=dict(),
+    )
+    outputs = NormalizeProbabilityMapSet.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/algorithms/tests/test_normalize_tpms.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# @Author: oesteban - code@oscaresteban.es
+# @Date:   2014-05-28 17:57:20
+# @Last Modified by:   oesteban
+# @Last Modified time: 2014-05-29 13:43:09
+
+import os
+from shutil import rmtree
+from tempfile import mkdtemp
+
+from nipype.testing import (assert_equal,assert_raises,
+                            assert_almost_equal,example_data )
+
+import numpy as np
+import nibabel as nb
+import nipype.testing as nit
+
+from nipype.algorithms.misc import normalize_tpms
+
+def test_normalize_tpms():
+    tempdir = mkdtemp()
+
+    in_mask = example_data('tpms_msk.nii.gz' )
+    mskdata = nb.load( in_mask ).get_data()
+    mskdata[mskdata>0.0] = 1.0
+
+    mapdata = []
+    in_files = []
+    out_files = []
+
+    for i in range(3):
+        mapname = example_data('tpm_%02d.nii.gz' % i)
+        filename =  os.path.join(tempdir, 'modtpm_%02d.nii.gz' % i )
+        out_files.append(os.path.join(tempdir, 'normtpm_%02d.nii.gz' % i ))
+
+        im = nb.load(mapname)
+        data = im.get_data()
+        mapdata.append( data.copy() )
+
+        nb.Nifti1Image(2.0 * (data * mskdata), im.get_affine(),
+                    im.get_header() ).to_filename(filename)
+        in_files.append( filename )
+
+    normalize_tpms( in_files, in_mask, out_files=out_files )
+
+    sumdata = np.zeros_like(mskdata)
+
+    for i,tstfname in enumerate( out_files ):
+        normdata = nb.load( tstfname ).get_data()
+        sumdata+=normdata
+        yield assert_equal, np.all( normdata[mskdata==0]==0 ), True
+        yield assert_equal, np.allclose( normdata, mapdata[i] ), True
+
+    yield assert_equal, np.allclose(sumdata[sumdata>0.0], 1.0 ), True
+
+    rmtree(tempdir)