Merge branch 'master' into fix_afni_allineate

Author: rmarkello

nipype/algorithms/confounds.py

@@ -345,6 +345,9 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
              'unspecified')
     save_pre_filter = traits.Either(
         traits.Bool, File, desc='Save pre-filter basis as text file')
+    ignore_initial_volumes = traits.Range(
+        low=0, usedefault=True,
+        desc='Number of volumes at start of series to ignore')
 
 
 class CompCorOutputSpec(TraitedSpec):
@@ -357,6 +360,26 @@ class CompCor(BaseInterface):
     """
     Interface with core CompCor computation, used in aCompCor and tCompCor
 
+    CompCor provides three pre-filter options, all of which include per-voxel
+    mean removal:
+      - polynomial: Legendre polynomial basis
+      - cosine: Discrete cosine basis
+      - False: mean-removal only
+
+    In the case of ``polynomial`` and ``cosine`` filters, a pre-filter file may
+    be saved with a row for each volume/timepoint, and a column for each
+    non-constant regressor.
+    If no non-constant (mean-removal) columns are used, this file may be empty.
+
+    If ``ignore_initial_volumes`` is set, then the specified number of initial
+    volumes are excluded both from pre-filtering and CompCor component
+    extraction.
+    Each column in the components and pre-filter files are prefixe with zeros
+    for each excluded volume so that the number of rows continues to match the
+    number of volumes in the input file.
+    In addition, for each excluded volume, a column is added to the pre-filter
+    file with a 1 in the corresponding row.
+
     Example
     -------
 
@@ -417,6 +440,12 @@ def _run_interface(self, runtime):
                                  header=imgseries.header)
             mask_images = [img]
 
+        skip_vols = self.inputs.ignore_initial_volumes
+        if skip_vols:
+            imgseries = imgseries.__class__(
+                imgseries.get_data()[..., skip_vols:], imgseries.affine,
+                imgseries.header)
+
         mask_images = self._process_masks(mask_images, imgseries.get_data())
 
         TR = 0
@@ -441,6 +470,13 @@ def _run_interface(self, runtime):
             imgseries.get_data(), mask_images, self.inputs.num_components,
             self.inputs.pre_filter, degree, self.inputs.high_pass_cutoff, TR)
 
+        if skip_vols:
+            old_comp = components
+            nrows = skip_vols + components.shape[0]
+            components = np.zeros((nrows, components.shape[1]),
+                                  dtype=components.dtype)
+            components[skip_vols:] = old_comp
+
         components_file = os.path.join(os.getcwd(), self.inputs.components_file)
         np.savetxt(components_file, components, fmt=b"%.10f", delimiter='\t',
                    header=self._make_headers(components.shape[1]), comments='')
@@ -451,6 +487,15 @@ def _run_interface(self, runtime):
                      'cosine': 'cos'}[self.inputs.pre_filter]
             ncols = filter_basis.shape[1] if filter_basis.size > 0 else 0
             header = ['{}{:02d}'.format(ftype, i) for i in range(ncols)]
+            if skip_vols:
+                old_basis = filter_basis
+                nrows = filter_basis.shape[0] if filter_basis.size > 0 else 0
+                filter_basis = np.zeros((nrows + skip_vols, ncols + skip_vols),
+                                        dtype=filter_basis.dtype)
+                filter_basis[skip_vols:, :ncols] = old_basis
+                filter_basis[:skip_vols, -skip_vols:] = np.eye(skip_vols)
+                header.extend(['SteadyState{:02d}'.format(i)
+                               for i in range(skip_vols)])
             np.savetxt(pre_filter_file, filter_basis, fmt=b'%.10f',
                        delimiter='\t', header='\t'.join(header), comments='')
 

nipype/algorithms/tests/test_auto_ACompCor.py

@@ -12,6 +12,8 @@ def test_ACompCor_inputs():
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
+    ignore_initial_volumes=dict(usedefault=True,
+    ),
     mask_files=dict(),
     mask_index=dict(requires=['mask_files'],
     xor=['merge_method'],

nipype/algorithms/tests/test_auto_TCompCor.py

@@ -12,6 +12,8 @@ def test_TCompCor_inputs():
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
+    ignore_initial_volumes=dict(usedefault=True,
+    ),
     mask_files=dict(),
     mask_index=dict(requires=['mask_files'],
     xor=['merge_method'],

nipype/interfaces/freesurfer/longitudinal.py

@@ -13,55 +13,66 @@
 from __future__ import print_function, division, unicode_literals, absolute_import
 
 import os
-#import itertools
 
 from ... import logging
 from ..base import (TraitedSpec, File, traits,
                     InputMultiPath, OutputMultiPath, isdefined)
-from .base import FSCommand, FSTraitedSpec
+from .base import (FSCommand, FSTraitedSpec, FSCommandOpenMP,
+                   FSTraitedSpecOpenMP)
 
 __docformat__ = 'restructuredtext'
 iflogger = logging.getLogger('interface')
 
 
-class RobustTemplateInputSpec(FSTraitedSpec):
+class RobustTemplateInputSpec(FSTraitedSpecOpenMP):
     # required
-    in_files = InputMultiPath(File(exists=True), mandatory=True, argstr='--mov %s',
-                             desc='input movable volumes to be aligned to common mean/median template')
+    in_files = InputMultiPath(
+        File(exists=True), mandatory=True, argstr='--mov %s',
+        desc='input movable volumes to be aligned to common mean/median '
+             'template')
     out_file = File('mri_robust_template_out.mgz', mandatory=True,
                     usedefault=True, argstr='--template %s',
                     desc='output template volume (final mean/median image)')
-    auto_detect_sensitivity = traits.Bool(argstr='--satit', xor=['outlier_sensitivity'], mandatory=True,
-                                          desc='auto-detect good sensitivity (recommended for head or full brain scans)')
-    outlier_sensitivity = traits.Float(argstr='--sat %.4f', xor=['auto_detect_sensitivity'], mandatory=True,
-                                       desc='set outlier sensitivity manually (e.g. "--sat 4.685" ). Higher values mean ' +
-                                       'less sensitivity.')
+    auto_detect_sensitivity = traits.Bool(
+        argstr='--satit', xor=['outlier_sensitivity'], mandatory=True,
+        desc='auto-detect good sensitivity (recommended for head or full '
+             'brain scans)')
+    outlier_sensitivity = traits.Float(
+        argstr='--sat %.4f', xor=['auto_detect_sensitivity'], mandatory=True,
+        desc='set outlier sensitivity manually (e.g. "--sat 4.685" ). Higher '
+             'values mean less sensitivity.')
     # optional
-    transform_outputs = InputMultiPath(File(exists=False),
-                                       argstr='--lta %s',
-                                       desc='output xforms to template (for each input)')
-    intensity_scaling = traits.Bool(default_value=False,
-                                    argstr='--iscale',
-                                    desc='allow also intensity scaling (default off)')
-    scaled_intensity_outputs = InputMultiPath(File(exists=False),
-                                              argstr='--iscaleout %s',
-                                              desc='final intensity scales (will activate --iscale)')
-    subsample_threshold = traits.Int(argstr='--subsample %d',
-                                     desc='subsample if dim > # on all axes (default no subs.)')
-    average_metric = traits.Enum('median', 'mean', argstr='--average %d',
-                                 desc='construct template from: 0 Mean, 1 Median (default)')
-    initial_timepoint = traits.Int(argstr='--inittp %d',
-                                   desc='use TP# for spacial init (default random), 0: no init')
-    fixed_timepoint = traits.Bool(default_value=False, argstr='--fixtp',
-                                  desc='map everthing to init TP# (init TP is not resampled)')
-    no_iteration = traits.Bool(default_value=False, argstr='--noit',
-                               desc='do not iterate, just create first template')
-    initial_transforms = InputMultiPath(File(exists=True),
-                                        argstr='--ixforms %s',
-                                        desc='use initial transforms (lta) on source')
-    in_intensity_scales = InputMultiPath(File(exists=True),
-                                         argstr='--iscalein %s',
-                                         desc='use initial intensity scales')
+    transform_outputs = InputMultiPath(
+        File(exists=False), argstr='--lta %s',
+        desc='output xforms to template (for each input)')
+    intensity_scaling = traits.Bool(
+        default_value=False, argstr='--iscale',
+        desc='allow also intensity scaling (default off)')
+    scaled_intensity_outputs = InputMultiPath(
+        File(exists=False), argstr='--iscaleout %s',
+        desc='final intensity scales (will activate --iscale)')
+    subsample_threshold = traits.Int(
+        argstr='--subsample %d',
+        desc='subsample if dim > # on all axes (default no subs.)')
+    average_metric = traits.Enum(
+        'median', 'mean', argstr='--average %d',
+        desc='construct template from: 0 Mean, 1 Median (default)')
+    initial_timepoint = traits.Int(
+        argstr='--inittp %d',
+        desc='use TP# for spacial init (default random), 0: no init')
+    fixed_timepoint = traits.Bool(
+        default_value=False, argstr='--fixtp',
+        desc='map everthing to init TP# (init TP is not resampled)')
+    no_iteration = traits.Bool(
+        default_value=False, argstr='--noit',
+        desc='do not iterate, just create first template')
+    initial_transforms = InputMultiPath(
+        File(exists=True), argstr='--ixforms %s',
+        desc='use initial transforms (lta) on source')
+    in_intensity_scales = InputMultiPath(
+        File(exists=True), argstr='--iscalein %s',
+        desc='use initial intensity scales')
+
 
 class RobustTemplateOutputSpec(TraitedSpec):
     out_file = File(
@@ -72,7 +83,7 @@ class RobustTemplateOutputSpec(TraitedSpec):
         File(exists=True), desc="output final intensity scales")
 
 
-class RobustTemplate(FSCommand):
+class RobustTemplate(FSCommandOpenMP):
     """ construct an unbiased robust template for longitudinal volumes
 
     Examples
@@ -92,8 +103,10 @@ class RobustTemplate(FSCommand):
     >>> template.cmdline  #doctest: +NORMALIZE_WHITESPACE +ALLOW_UNICODE
     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --subsample 200'
 
-    >>> template.inputs.transform_outputs = ['structural.lta', 'functional.lta']
-    >>> template.inputs.scaled_intensity_outputs = ['structural-iscale.txt', 'functional-iscale.txt']
+    >>> template.inputs.transform_outputs = ['structural.lta',
+    ...                                      'functional.lta']
+    >>> template.inputs.scaled_intensity_outputs = ['structural-iscale.txt',
+    ...                                             'functional-iscale.txt']
     >>> template.cmdline    #doctest: +NORMALIZE_WHITESPACE +ALLOW_UNICODE
     'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout structural-iscale.txt functional-iscale.txt --subsample 200 --lta structural.lta functional.lta'
 
@@ -151,9 +164,11 @@ class FuseSegmentationsInputSpec(FSTraitedSpec):
         must include the corresponding norm file for all given timepoints \
         as well as for the current subject")
 
+
 class FuseSegmentationsOutputSpec(TraitedSpec):
     out_file = File(exists=False, desc="output fused segmentation file")
 
+
 class FuseSegmentations(FSCommand):
 
     """ fuse segmentations together from multiple timepoints

nipype/interfaces/freesurfer/tests/test_auto_RobustTemplate.py

@@ -33,6 +33,7 @@ def test_RobustTemplate_inputs():
     ),
     no_iteration=dict(argstr='--noit',
     ),
+    num_threads=dict(),
     out_file=dict(argstr='--template %s',
     mandatory=True,
     usedefault=True,