Merge branch 'master' of git://github.com/nipy/nipype into enh/selectdirs

Author: mgxd

.zenodo.json

@@ -518,6 +518,11 @@
       "affiliation": "MIT, HMS",
       "name": "Ghosh, Satrajit",
       "orcid": "0000-0002-5312-6729"
+    },
+    {
+      "affiliation": "University of Amsterdam",
+      "name": "Lukas Snoek",
+      "orcid": "0000-0001-8972-204X"
     }
   ],
   "keywords": [

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
@@ -441,16 +470,33 @@ 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='')
 
         if self.inputs.pre_filter and self.inputs.save_pre_filter:
             pre_filter_file = self._list_outputs()['pre_filter_file']
-            ftype = {'polynomial': 'poly',
-                     'cosine': 'cos'}[self.inputs.pre_filter]
+            ftype = {'polynomial': 'Legendre',
+                     'cosine': 'Cosine'}[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 defined above
+                filter_basis = np.zeros((nrows, ncols + skip_vols),
+                                        dtype=filter_basis.dtype)
+                if old_basis.size > 0:
+                    filter_basis[skip_vols:, :ncols] = old_basis
+                filter_basis[:skip_vols, -skip_vols:] = np.eye(skip_vols)
+                header.extend(['NonSteadyStateOutlier{: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/info.py

@@ -105,7 +105,7 @@ def get_nipype_gitversion():
 PYTEST_MIN_VERSION = '3.0'
 FUTURE_MIN_VERSION = '0.16.0'
 SIMPLEJSON_MIN_VERSION = '3.8.0'
-PROV_MIN_VERSION = '1.5.0'
+PROV_VERSION = '1.5.0'
 CLICK_MIN_VERSION = '6.6.0'
 
 NAME = 'nipype'
@@ -136,7 +136,7 @@ def get_nipype_gitversion():
     'traits>=%s' % TRAITS_MIN_VERSION,
     'future>=%s' % FUTURE_MIN_VERSION,
     'simplejson>=%s' % SIMPLEJSON_MIN_VERSION,
-    'prov>=%s' % PROV_MIN_VERSION,
+    'prov==%s' % PROV_VERSION,
     'click>=%s' % CLICK_MIN_VERSION,
     'funcsigs',
     'pytest>=%s' % PYTEST_MIN_VERSION,

nipype/interfaces/afni/__init__.py

@@ -8,17 +8,21 @@
 """
 
 from .base import Info
-from .preprocess import (Allineate, Automask, AutoTcorrelate,
+from .preprocess import (AlignEpiAnatPy, Allineate, Automask,
+                         AutoTcorrelate, AutoTLRC,
                          Bandpass, BlurInMask, BlurToFWHM,
                          ClipLevel, DegreeCentrality, Despike,
                          Detrend, ECM, Fim, Fourier, Hist, LFCD,
                          Maskave, Means, OutlierCount,
                          QualityIndex, ROIStats, Retroicor,
                          Seg, SkullStrip, TCorr1D, TCorrMap, TCorrelate,
+                         TNorm,
                          TShift, Volreg, Warp, QwarpPlusMinus, Qwarp)
 from .svm import (SVMTest, SVMTrain)
-from .utils import (AFNItoNIFTI, Autobox, Axialize, BrickStat, Calc, Cat, Copy,
+from .utils import (ABoverlap, AFNItoNIFTI, Autobox, Axialize, BrickStat,
+                    Bucket, Calc, Cat, CatMatvec, Copy, Dot,
                     Edge3, Eval, FWHMx, MaskTool, Merge, Notes, NwarpApply,
-                    Refit, Resample, TCat, TStat, To3D, Unifize, ZCutUp, GCOR,
+                    OneDToolPy,
+                    Refit, Resample, TCat, TCatSubBrick, TStat, To3D, Unifize, ZCutUp, GCOR,
                     Zcat, Zeropad)
 from .model import (Deconvolve, Remlfit)

nipype/interfaces/afni/base.py

@@ -8,6 +8,7 @@
 
 import os
 from sys import platform
+from distutils import spawn
 
 from ... import logging
 from ...utils.filemanip import split_filename, fname_presuffix
@@ -144,7 +145,6 @@ class AFNICommandOutputSpec(TraitedSpec):
     out_file = File(desc='output file',
                     exists=True)
 
-
 class AFNICommand(AFNICommandBase):
     """Shared options for several AFNI commands """
     input_spec = AFNICommandInputSpec
@@ -283,3 +283,23 @@ def no_afni():
     if Info.version() is None:
         return True
     return False
+
+
+class AFNIPythonCommandInputSpec(CommandLineInputSpec):
+    outputtype = traits.Enum('AFNI', list(Info.ftypes.keys()),
+                             desc='AFNI output filetype')
+    py27_path = traits.Either('python2', File(exists=True),
+        usedefault=True,
+        default='python2')
+
+class AFNIPythonCommand(AFNICommand):
+    @property
+    def cmd(self):
+        if spawn.find_executable(super(AFNIPythonCommand, self).cmd) is not None:
+            return spawn.find_executable(super(AFNIPythonCommand, self).cmd)
+        else:
+            return super(AFNIPythonCommand, self).cmd
+
+    @property
+    def cmdline(self):
+        return "{} {}".format(self.inputs.py27_path, super(AFNIPythonCommand, self).cmdline)