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

Author: chrisgorgo

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -66,7 +66,7 @@
 
 from nipype.algorithms.rapidart import ArtifactDetect
 from nipype.algorithms.misc import TSNR
-from nipype.algorithms.compcor import ACompCor
+from nipype.algorithms.confounds import ACompCor
 from nipype.interfaces.utility import Rename, Merge, IdentityInterface
 from nipype.utils.filemanip import filename_to_list
 from nipype.interfaces.io import DataSink, FreeSurferSource
@@ -75,7 +75,7 @@
 import numpy as np
 import scipy as sp
 import nibabel as nb
-from nipype.utils import NUMPY_MMAP
+from nipype.utils.config import NUMPY_MMAP
 
 imports = ['import os',
            'import nibabel as nb',

nipype/algorithms/rapidart.py

@@ -330,6 +330,8 @@ def _list_outputs(self):
         return outputs
 
     def _plot_outliers_with_wave(self, wave, outliers, name):
+        import matplotlib
+        matplotlib.use(config.get("execution", "matplotlib_backend"))
         import matplotlib.pyplot as plt
         plt.plot(wave)
         plt.ylim([wave.min(), wave.max()])

nipype/interfaces/freesurfer/preprocess.py

@@ -630,8 +630,26 @@ class ReconAllInputSpec(CommandLineInputSpec):
                            desc="Enable parallel execution")
     hires = traits.Bool(argstr="-hires", min_ver='6.0.0',
                         desc="Conform to minimum voxel size (for voxels < 1mm)")
+    mprage = traits.Bool(argstr='-mprage',
+                         desc=('Assume scan parameters are MGH MP-RAGE '
+                               'protocol, which produces darker gray matter'))
+    big_ventricles = traits.Bool(argstr='-bigventricles',
+                                 desc=('For use in subjects with enlarged '
+                                       'ventricles'))
+    brainstem = traits.Bool(argstr='-brainstem-structures',
+                            desc='Segment brainstem structures')
+    hippocampal_subfields_T1 = traits.Bool(
+        argstr='-hippocampal-subfields-T1', min_ver='6.0.0',
+        desc='segment hippocampal subfields using input T1 scan')
+    hippocampal_subfields_T2 = traits.Tuple(
+        File(exists=True), traits.Str(),
+        argstr='-hippocampal-subfields-T2 %s %s', min_ver='6.0.0',
+        desc=('segment hippocampal subfields using T2 scan, identified by '
+              'ID (may be combined with hippocampal_subfields_T1)'))
     expert = File(exists=True, argstr='-expert %s',
                   desc="Set parameters using expert file")
+    xopts = traits.Enum("use", "clean", "overwrite", argstr='-xopts-%s',
+                        desc="Use, delete or overwrite existing expert options file")
     subjects_dir = Directory(exists=True, argstr='-sd %s', hash_files=False,
                              desc='path to subjects directory', genfile=True)
     flags = traits.Str(argstr='%s', desc='additional parameters')
@@ -933,6 +951,13 @@ def _format_arg(self, name, trait_spec, value):
         if name == 'T1_files':
             if self._is_resuming():
                 return ''
+        if name == 'hippocampal_subfields_T1' and \
+                isdefined(self.inputs.hippocampal_subfields_T2):
+            return ''
+        if all((name == 'hippocampal_subfields_T2',
+                isdefined(self.inputs.hippocampal_subfields_T1) and
+                self.inputs.hippocampal_subfields_T1)):
+            trait_spec.argstr = trait_spec.argstr.replace('T2', 'T1T2')
         return super(ReconAll, self)._format_arg(name, trait_spec, value)
 
     @property
@@ -949,10 +974,22 @@ def cmdline(self):
         if not isdefined(subjects_dir):
             subjects_dir = self._gen_subjects_dir()
 
+        # Check only relevant steps
+        directive = self.inputs.directive
+        if not isdefined(directive):
+            steps = []
+        elif directive == 'autorecon1':
+            steps = self._autorecon1_steps
+        elif directive.startswith('autorecon2'):
+            steps = self._autorecon2_steps
+        elif directive == 'autorecon3':
+            steps = self._autorecon3_steps
+        else:
+            steps = self._steps
+
         no_run = True
         flags = []
-        for idx, step in enumerate(self._steps):
-            step, outfiles, infiles = step
+        for step, outfiles, infiles in steps:
             flag = '-{}'.format(step)
             noflag = '-no{}'.format(step)
             if noflag in cmd:
@@ -989,11 +1026,30 @@ def _prep_expert_file(self):
         if lines == []:
             return ''
 
+        contents = ''.join(lines)
+        if not isdefined(self.inputs.xopts) and \
+                self.get_expert_file() == contents:
+            return ' -xopts-use'
+
         expert_fname = os.path.abspath('expert.opts')
         with open(expert_fname, 'w') as fobj:
-            fobj.write(''.join(lines))
+            fobj.write(contents)
         return ' -expert {}'.format(expert_fname)
 
+    def _get_expert_file(self):
+        # Read pre-existing options file, if it exists
+        if isdefined(self.inputs.subjects_dir):
+            subjects_dir = self.inputs.subjects_dir
+        else:
+            subjects_dir = self._gen_subjects_dir()
+
+        xopts_file = os.path.join(subjects_dir, self.inputs.subject_id,
+                                  'scripts', 'expert-options')
+        if not os.path.exists(xopts_file):
+            return ''
+        with open(xopts_file, 'r') as fobj:
+            return fobj.read()
+
 
 class BBRegisterInputSpec(FSTraitedSpec):
     subject_id = traits.Str(argstr='--s %s',

nipype/interfaces/freesurfer/tests/test_auto_ReconAll.py

@@ -11,6 +11,10 @@ def test_ReconAll_inputs():
     ),
     args=dict(argstr='%s',
     ),
+    big_ventricles=dict(argstr='-bigventricles',
+    ),
+    brainstem=dict(argstr='-brainstem-structures',
+    ),
     directive=dict(argstr='-%s',
     position=0,
     usedefault=True,
@@ -24,12 +28,20 @@ def test_ReconAll_inputs():
     ),
     hemi=dict(argstr='-hemi %s',
     ),
+    hippocampal_subfields_T1=dict(argstr='-hippocampal-subfields-T1',
+    min_ver='6.0.0',
+    ),
+    hippocampal_subfields_T2=dict(argstr='-hippocampal-subfields-T2 %s %s',
+    min_ver='6.0.0',
+    ),
     hires=dict(argstr='-hires',
     min_ver='6.0.0',
     ),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
+    mprage=dict(argstr='-mprage',
+    ),
     mri_aparc2aseg=dict(xor=['expert'],
     ),
     mri_ca_label=dict(xor=['expert'],
@@ -98,6 +110,8 @@ def test_ReconAll_inputs():
     use_T2=dict(argstr='-T2pial',
     min_ver='5.3.0',
     ),
+    xopts=dict(argstr='-xopts-%s',
+    ),
     )
     inputs = ReconAll.input_spec()
 

nipype/interfaces/fsl/epi.py

@@ -26,7 +26,7 @@
 
 from ..base import (traits, TraitedSpec, InputMultiPath, File,
                     isdefined)
-from .base import FSLCommand, FSLCommandInputSpec
+from .base import FSLCommand, FSLCommandInputSpec, Info
 
 
 class PrepareFieldmapInputSpec(FSLCommandInputSpec):
@@ -140,6 +140,13 @@ class TOPUPInputSpec(FSLCommandInputSpec):
     out_field = File(argstr='--fout=%s', hash_files=False,
                      name_source=['in_file'], name_template='%s_field',
                      desc='name of image file with field (Hz)')
+    out_warp_prefix = traits.Str("warpfield", argstr='--dfout=%s', hash_files=False,
+                                 desc='prefix for the warpfield images (in mm)',
+                                 usedefault=True)
+    out_jac_prefix = traits.Str("jac", argstr='--jacout=%s',
+                                 hash_files=False,
+                                 desc='prefix for the warpfield images',
+                                 usedefault=True)
     out_corrected = File(argstr='--iout=%s', hash_files=False,
                          name_source=['in_file'], name_template='%s_corrected',
                          desc='name of 4D image file with unwarped images')
@@ -212,6 +219,8 @@ class TOPUPOutputSpec(TraitedSpec):
     out_movpar = File(exists=True, desc='movpar.txt output file')
     out_enc_file = File(desc='encoding directions file output for applytopup')
     out_field = File(desc='name of image file with field (Hz)')
+    out_warps = traits.List(File(exists=True), desc='warpfield images')
+    out_jacs = traits.List(File(exists=True), desc='Jacobian images')
     out_corrected = File(desc='name of 4D image file with unwarped images')
     out_logfile = File(desc='name of log-file')
 
@@ -237,7 +246,8 @@ class TOPUP(FSLCommand):
     >>> topup.cmdline # doctest: +ELLIPSIS +ALLOW_UNICODE
     'topup --config=b02b0.cnf --datain=topup_encoding.txt \
 --imain=b0_b0rev.nii --out=b0_b0rev_base --iout=b0_b0rev_corrected.nii.gz \
---fout=b0_b0rev_field.nii.gz --logout=b0_b0rev_topup.log'
+--fout=b0_b0rev_field.nii.gz --jacout=jac --logout=b0_b0rev_topup.log \
+--dfout=warpfield'
     >>> res = topup.run() # doctest: +SKIP
 
     """
@@ -270,6 +280,16 @@ def _list_outputs(self):
         outputs['out_movpar'] = self._gen_fname(base, suffix='_movpar',
                                                 ext='.txt', cwd=base_path)
 
+        n_vols = nb.load(self.inputs.in_file).shape[-1]
+        ext = Info.output_type_to_ext(self.inputs.output_type)
+        fmt = os.path.abspath('{prefix}_{i:02d}{ext}').format
+        outputs['out_warps'] = [
+            fmt(prefix=self.inputs.out_warp_prefix, i=i, ext=ext)
+            for i in range(1, n_vols + 1)]
+        outputs['out_jacs'] = [
+            fmt(prefix=self.inputs.out_jac_prefix, i=i, ext=ext)
+            for i in range(1, n_vols + 1)]
+
         if isdefined(self.inputs.encoding_direction):
             outputs['out_enc_file'] = self._get_encfilename()
         return outputs
@@ -423,6 +443,32 @@ class EddyInputSpec(FSLCommandInputSpec):
     flm = traits.Enum('linear', 'quadratic', 'cubic', argstr='--flm=%s',
                       desc='First level EC model')
 
+    slm = traits.Enum('none', 'linear', 'quadratic', argstr='--slm=%s',
+                      desc='Second level EC model')
+
+    fep = traits.Bool(False, argstr='--fep',
+                      desc='Fill empty planes in x- or y-directions')
+
+    interp = traits.Enum('spline', 'trilinear', argstr='--interp=%s',
+                         desc='Interpolation model for estimation step')
+
+    nvoxhp = traits.Int(1000, argstr='--nvoxhp=%s',
+                        desc=('# of voxels used to estimate the '
+                              'hyperparameters'))
+
+    fudge_factor = traits.Float(10.0, argstr='--ff=%s',
+                                desc=('Fudge factor for hyperparameter '
+                                      'error variance'))
+
+    dont_sep_offs_move = traits.Bool(False, argstr='--dont_sep_offs_move',
+                                     desc=('Do NOT attempt to separate '
+                                           'field offset from subject '
+                                           'movement'))
+
+    dont_peas = traits.Bool(False, argstr='--dont_peas',
+                           desc="Do NOT perform a post-eddy alignment of "
+                                "shells")
+
     fwhm = traits.Float(desc=('FWHM for conditioning filter when estimating '
                               'the parameters'), argstr='--fwhm=%s')
 

nipype/interfaces/fsl/tests/test_auto_Eddy.py

@@ -6,15 +6,23 @@
 def test_Eddy_inputs():
     input_map = dict(args=dict(argstr='%s',
     ),
+    dont_peas=dict(argstr='--dont_peas',
+    ),
+    dont_sep_offs_move=dict(argstr='--dont_sep_offs_move',
+    ),
     environ=dict(nohash=True,
     usedefault=True,
     ),
+    fep=dict(argstr='--fep',
+    ),
     field=dict(argstr='--field=%s',
     ),
     field_mat=dict(argstr='--field_mat=%s',
     ),
     flm=dict(argstr='--flm=%s',
     ),
+    fudge_factor=dict(argstr='--ff=%s',
+    ),
     fwhm=dict(argstr='--fwhm=%s',
     ),
     ignore_exception=dict(nohash=True,
@@ -43,6 +51,8 @@ def test_Eddy_inputs():
     ),
     in_topup_movpar=dict(requires=['in_topup_fieldcoef'],
     ),
+    interp=dict(argstr='--interp=%s',
+    ),
     is_shelled=dict(argstr='--data_is_shelled',
     ),
     method=dict(argstr='--resamp=%s',
@@ -52,6 +62,8 @@ def test_Eddy_inputs():
     num_threads=dict(nohash=True,
     usedefault=True,
     ),
+    nvoxhp=dict(argstr='--nvoxhp=%s',
+    ),
     out_base=dict(argstr='--out=%s',
     usedefault=True,
     ),
@@ -60,6 +72,8 @@ def test_Eddy_inputs():
     ),
     session=dict(argstr='--session=%s',
     ),
+    slm=dict(argstr='--slm=%s',
+    ),
     terminal_output=dict(nohash=True,
     ),
     use_cuda=dict(),
@@ -74,6 +88,9 @@ def test_Eddy_inputs():
 def test_Eddy_outputs():
     output_map = dict(out_corrected=dict(),
     out_movement_rms=dict(),
+    out_outlier_map=dict(),
+    out_outlier_n_sd_map=dict(),
+    out_outlier_n_sqr_sd_map=dict(),
     out_outlier_report=dict(),
     out_parameter=dict(),
     out_restricted_movement_rms=dict(),

nipype/interfaces/fsl/tests/test_auto_TOPUP.py

@@ -54,6 +54,14 @@ def test_TOPUP_inputs():
     name_source=['in_file'],
     name_template='%s_field',
     ),
+    out_warp_prefix=dict(argstr='--dfout=%s',
+    hash_files=False,
+    usedefault=True,
+    ),
+    out_jac_prefix=dict(argstr='--jacout=%s',
+    hash_files=False,
+    usedefault=True,
+    ),
     out_logfile=dict(argstr='--logout=%s',
     hash_files=False,
     keep_extension=True,
@@ -98,6 +106,8 @@ def test_TOPUP_outputs():
     out_fieldcoef=dict(),
     out_logfile=dict(),
     out_movpar=dict(),
+    out_warps=dict(),
+    out_jacs=dict(),
     )
     outputs = TOPUP.output_spec()