Merge pull request #697 from satra/fix/reporting

WIP: Multiple fixes

Author: satra

CHANGES

@@ -38,6 +38,16 @@ Next release
 * ENH: Added simplified outputname generation for command line interfaces.
 * FIX: Cleaned up input and output spec metadata
 
+* ENH: Allow ants use a single mask image
+* ENH: Create configuration option for parameterizing directories with hashes
+* ENH: arrange nodes by topological sort with disconnected subgraphs
+* ENH: uses the nidm iri namespace for uuids
+* ENH: remove old reporting webpage (this is a WIP i hope to finish in a day or two)
+
+* FIX: example openfmri script now makes the contrast spec a hashed input
+* FIX: FILMGLS compatibility with FSL 5.0.5
+* FIX: Freesurfer recon-all resume now avoids setting inputs
+
 Release 0.8.0 (May 8, 2013)
 ===========================
 

doc/users/config_file.rst

@@ -114,6 +114,12 @@ Execution
     version information is available. Please notify developers or submit a
     patch.
 
+*parameterize_dirs*
+    If this is set to True, the node's output directory will contain full
+    parameterization of any iterable, otherwise parameterizations over 32
+    characters will be replaced by their hash. (possible values: ``true`` and
+	``false``; default value: ``true``)
+
 Example
 ~~~~~~~
 

doc/users/joinnode_and_itersource.rst

@@ -70,7 +70,7 @@ the *unique* flag, e.g.:
 
 ::
 
-d = pe.JoinNode(interface=D(), joinsource="b", unique=True, name="d")
+  d = pe.JoinNode(interface=D(), joinsource="b", unique=True, name="d")
 
 synchronize
 ===========

examples/fmri_openfmri.py

@@ -129,8 +129,9 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
                                                        'task_id']),
                          name='infosource')
     if subject is None:
-        infosource.iterables = [('subject_id', subjects),
-            ('model_id', [model_id])]
+        infosource.iterables = [('subject_id', subjects[:2]),
+                                ('model_id', [model_id]),
+                                ('task_id', [task_id])]
     else:
         infosource.iterables = [('subject_id',
                                  [subjects[subjects.index(subject)]]),
@@ -150,18 +151,22 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
 
     datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
                                                    'task_id', 'model_id'],
-                                         outfields=['anat', 'bold', 'behav']),
+                                         outfields=['anat', 'bold', 'behav',
+                                                    'contrasts']),
                          name='datasource')
     datasource.inputs.base_directory = data_dir
     datasource.inputs.template = '*'
     datasource.inputs.field_template = {'anat': '%s/anatomy/highres001.nii.gz',
                                 'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
                                 'behav': ('%s/model/model%03d/onsets/task%03d_'
-                                          'run%03d/cond*.txt')}
+                                          'run%03d/cond*.txt'),
+                                'contrasts': ('models/model%03d/'
+                                              'task_contrasts.txt')}
     datasource.inputs.template_args = {'anat': [['subject_id']],
                                        'bold': [['subject_id', 'task_id']],
                                        'behav': [['subject_id', 'model_id',
-                                                  'task_id', 'run_id']]}
+                                                  'task_id', 'run_id']],
+                                       'contrasts': [['model_id']]}
     datasource.inputs.sort_filelist = True
 
     """
@@ -192,11 +197,8 @@ def get_highpass(TR, hpcutoff):
     Setup a basic set of contrasts, a t-test per condition
     """
 
-    def get_contrasts(base_dir, model_id, task_id, conds):
+    def get_contrasts(contrast_file, task_id, conds):
         import numpy as np
-        import os
-        contrast_file = os.path.join(base_dir, 'models', 'model%03d' % model_id,
-                                     'task_contrasts.txt')
         contrast_def = np.genfromtxt(contrast_file, dtype=object)
         contrasts = []
         for row in contrast_def:
@@ -207,12 +209,11 @@ def get_contrasts(base_dir, model_id, task_id, conds):
             contrasts.append(con)
         return contrasts
 
-    contrastgen = pe.Node(niu.Function(input_names=['base_dir', 'model_id',
+    contrastgen = pe.Node(niu.Function(input_names=['contrast_file',
                                                     'task_id', 'conds'],
                                        output_names=['contrasts'],
                                        function=get_contrasts),
                           name='contrastgen')
-    contrastgen.inputs.base_dir = data_dir
 
     art = pe.MapNode(interface=ra.ArtifactDetect(use_differences=[True, False],
                                                  use_norm=True,
@@ -232,7 +233,7 @@ def get_contrasts(base_dir, model_id, task_id, conds):
     wf.connect(datasource, 'behav', modelspec, 'event_files')
     wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
     wf.connect(subjinfo, 'conds', contrastgen, 'conds')
-    wf.connect(infosource, 'model_id', contrastgen, 'model_id')
+    wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
     wf.connect(infosource, 'task_id', contrastgen, 'task_id')
     wf.connect(contrastgen, 'contrasts', modelfit, 'inputspec.contrasts')
 

nipype/external/d3.v3.min.js

@@ -98,14 +98,14 @@ class AFNICommandInputSpec(CommandLineInputSpec):
     out_file = File(name_template="%s_afni", desc='output image file name',
                     argstr='-prefix %s',
                     name_source=["in_file"])
-    
+
 class AFNICommandOutputSpec(TraitedSpec):
     out_file = File(desc='output file',
                     exists=True)
 
 
 class AFNICommand(CommandLine):
-    
+
     input_spec = AFNICommandInputSpec
     _outputtype = None
 
@@ -143,7 +143,7 @@ def set_default_output_type(cls, outputtype):
             cls._outputtype = outputtype
         else:
             raise AttributeError('Invalid AFNI outputtype: %s' % outputtype)
-        
+
     def _overload_extension(self, value):
         path, base, _ = split_filename(value)
         return os.path.join(path, base + Info.outputtype_to_ext(self.inputs.outputtype))
@@ -157,5 +157,5 @@ def _list_outputs(self):
                 if outputs[name]:
                     _,_,ext = split_filename(outputs[name])
                     if ext == "":
-                        outputs[name] = outputs[name] + "+orig.BRIK" 
+                        outputs[name] = outputs[name] + "+orig.BRIK"
         return outputs

nipype/interfaces/afni/base.py

@@ -194,11 +194,11 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
                             usedefault=True, desc='image dimension (2 or 3)')
     fixed_image = InputMultiPath(File(exists=True), mandatory=True,
                                  desc='image to apply transformation to (generally a coregistered functional)')
-    fixed_image_mask = File(requires=['moving_image_mask'],
-                            exists=True, desc='')
+    fixed_image_mask = File(argstr='%s', exists=True,
+                            desc='mask used to limit registration region')
     moving_image = InputMultiPath(File(exists=True), mandatory=True,
                                   desc='image to apply transformation to (generally a coregistered functional)')
-    moving_image_mask = File(argstr='%s', requires=['fixed_image_mask'],
+    moving_image_mask = File(requires=['fixed_image_mask'],
                              exists=True, desc='')
     initial_moving_transform = File(argstr='%s', exists=True, desc='',
                                     xor=['initial_moving_transform_com'])
@@ -558,8 +558,12 @@ def _formatCollapseLinearTransformsToFixedImageHeader(self):
             return '--collapse-linear-transforms-to-fixed-image-header 0'
 
     def _format_arg(self, opt, spec, val):
-        if opt == 'moving_image_mask':
-            return '--masks [ %s, %s ]' % (self.inputs.fixed_image_mask, self.inputs.moving_image_mask)
+        if opt == 'fixed_image_mask':
+            if isdefined(self.inputs.moving_image_mask):
+                return '--masks [ %s, %s ]' % (self.inputs.fixed_image_mask,
+                                               self.inputs.moving_image_mask)
+            else:
+                return '--masks %s' % self.inputs.fixed_image_mask
         elif opt == 'transforms':
             return self._formatRegistration()
         elif opt == 'initial_moving_transform':

nipype/interfaces/ants/registration.py

@@ -591,17 +591,22 @@ def _gen_filename(self, name):
 
 
 class ReconAllInputSpec(CommandLineInputSpec):
-    subject_id = traits.Str("recon_all", argstr='-subjid %s', desc='subject name',
-                            usedefault=True)
+    subject_id = traits.Str("recon_all", argstr='-subjid %s',
+                            desc='subject name', usedefault=True)
     directive = traits.Enum('all', 'autorecon1', 'autorecon2', 'autorecon2-cp',
-                            'autorecon2-wm', 'autorecon2-inflate1', 'autorecon2-perhemi',
-                            'autorecon3', 'localGI', 'qcache', argstr='-%s',
-                            desc='process directive', usedefault=True,
-                            position=0)
-    hemi = traits.Enum('lh', 'rh', desc='hemisphere to process', argstr="-hemi %s")
+                            'autorecon2-wm', 'autorecon2-inflate1',
+                            'autorecon2-perhemi', 'autorecon3', 'localGI',
+                            'qcache', argstr='-%s', desc='process directive',
+                            usedefault=True, position=0)
+    hemi = traits.Enum('lh', 'rh', desc='hemisphere to process',
+                       argstr="-hemi %s")
     T1_files = InputMultiPath(File(exists=True), argstr='-i %s...',
                               desc='name of T1 file to process')
-    subjects_dir = Directory(exists=True, argstr='-sd %s',
+    T2_file = File(exists=True, argstr="-T2 %s", min_ver='5.3.0',
+                   desc='Use a T2 image to refine the cortical surface')
+    openmp = traits.Int(argstr="-openmp %d",
+                        desc="Number of processors to use in parallel")
+    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')
 
@@ -742,42 +747,54 @@ def _list_outputs(self):
         outputs = self._outputs().get()
 
         outputs.update(FreeSurferSource(subject_id=self.inputs.subject_id,
-                         subjects_dir=subjects_dir, hemi=hemi)._list_outputs())
+                                        subjects_dir=subjects_dir,
+                                        hemi=hemi)._list_outputs())
         outputs['subject_id'] = self.inputs.subject_id
         outputs['subjects_dir'] = subjects_dir
         return outputs
 
-    @property
-    def cmdline(self):
+    def _is_resuming(self):
         subjects_dir = self.inputs.subjects_dir
         if not isdefined(subjects_dir):
             subjects_dir = self._gen_subjects_dir()
-        if not os.path.isdir(
-            os.path.join(subjects_dir,self.inputs.subject_id,'mri')):
-            return super(ReconAll, self).cmdline
-        self._check_mandatory_inputs()
-        skip = ['T1_files']
+        if os.path.isdir(os.path.join(subjects_dir, self.inputs.subject_id,
+                                      'mri')):
+            return True
+        return False
+
+    def _format_arg(self, name, trait_spec, value):
+        if name == 'T1_files':
+            if self._is_resuming():
+                return ''
+        return super(ReconAll, self)._format_arg(name, trait_spec, value)
+
+    @property
+    def cmdline(self):
+        cmd = super(ReconAll, self).cmdline
+        if not self._is_resuming():
+            return cmd
         subjects_dir = self.inputs.subjects_dir
         if not isdefined(subjects_dir):
             subjects_dir = self._gen_subjects_dir()
+        #cmd = cmd.replace(' -all ', ' -make all ')
+        iflogger.info('Overriding recon-all directive')
         flags = []
         directive = 'all'
         for idx, step in enumerate(self._steps):
             step, outfiles = step
-            if all([os.path.exists(os.path.join(subjects_dir,self.inputs.subject_id,f)) for f  in outfiles]):
+            if all([os.path.exists(os.path.join(subjects_dir,
+                                                self.inputs.subject_id,f)) for
+                    f in outfiles]):
                 flags.append('-no%s'%step)
                 if idx > 4:
                     directive = 'autorecon2'
                 elif idx > 23:
                     directive = 'autorecon3'
             else:
                 flags.append('-%s'%step)
-        self.inputs.args = ' '.join([self.inputs.args] + flags)
-        self.inputs.directive = directive
-        allargs = self._parse_inputs(skip=skip)
-        allargs.insert(0, self.cmd)
-        cmd = ' '.join(allargs)
-        iflogger.info('resume recon-all : %s'%cmd)
+        cmd = cmd.replace(' -%s ' % self.inputs.directive, ' -%s ' % directive)
+        cmd += ' ' + ' '.join(flags)
+        iflogger.info('resume recon-all : %s' % cmd)
         return cmd
 
 

nipype/interfaces/freesurfer/preprocess.py

@@ -29,9 +29,8 @@
 import os
 import warnings
 
-from nipype.utils.filemanip import fname_presuffix
-from nipype.interfaces.base import (CommandLine, traits, CommandLineInputSpec,
-                                    isdefined)
+from ...utils.filemanip import fname_presuffix
+from ..base import (CommandLine, traits, CommandLineInputSpec, isdefined)
 
 warn = warnings.warn
 warnings.filterwarnings('always', category=UserWarning)

nipype/interfaces/fsl/base.py

@@ -18,15 +18,16 @@
 
 import numpy as np
 
-from nipype.interfaces.fsl.base import (FSLCommand, FSLCommandInputSpec, Info)
-from nipype.interfaces.base import (load_template, File, traits, isdefined,
+from nibabel import load
+
+from ... import LooseVersion
+from .base import (FSLCommand, FSLCommandInputSpec, Info)
+from ..base import (load_template, File, traits, isdefined,
                                     TraitedSpec, BaseInterface, Directory,
                                     InputMultiPath, OutputMultiPath,
                                     BaseInterfaceInputSpec)
-from nipype.utils.filemanip import (
-    list_to_filename, filename_to_list, fname_presuffix)
-from nibabel import load
-from nipype.utils.misc import human_order_sorted
+from ...utils.filemanip import (list_to_filename, filename_to_list)
+from ...utils.misc import human_order_sorted
 
 warn = warnings.warn
 warnings.filterwarnings('always', category=UserWarning)
@@ -473,9 +474,6 @@ def _list_outputs(self):
         return outputs
 
 
-# interface to fsl command line model fit routines
-# ohinds: 2009-12-28
-
 class FILMGLSInputSpec(FSLCommandInputSpec):
     in_file = File(exists=True, mandatory=True, position=-3,
                    argstr='%s',
@@ -513,6 +511,43 @@ class FILMGLSInputSpec(FSLCommandInputSpec):
     results_dir = Directory('results', argstr='-rn %s', usedefault=True,
                             desc='directory to store results in')
 
+class FILMGLSInputSpec505(FSLCommandInputSpec):
+    in_file = File(exists=True, mandatory=True, position=-3,
+                   argstr='--in=%s', desc='input data file')
+    design_file = File(exists=True, position=-2,
+                       argstr='--pd=%s', desc='design matrix file')
+    threshold = traits.Range(default=1000., low=0.0, argstr='--thr=%f',
+                             position=-1, usedefault=True, desc='threshold')
+    smooth_autocorr = traits.Bool(argstr='--sa',
+                                  desc='Smooth auto corr estimates')
+    mask_size = traits.Int(argstr='--ms=%d', desc="susan mask size")
+    brightness_threshold = traits.Range(low=0, argstr='--epith=%d',
+                                      desc=('susan brightness threshold, '
+                                            'otherwise it is estimated'))
+    full_data = traits.Bool(argstr='-v', desc='output full data')
+    _estimate_xor = ['autocorr_estimate_only', 'fit_armodel', 'tukey_window',
+                     'multitaper_product', 'use_pava', 'autocorr_noestimate']
+    autocorr_estimate_only = traits.Bool(argstr='--ac', xor=_estimate_xor,
+                                         desc=('perform autocorrelation '
+                                               'estimation only'))
+    fit_armodel = traits.Bool(argstr='--ar', xor=_estimate_xor,
+                              desc=('fits autoregressive model - default is to '
+                                    'use tukey with M=sqrt(numvols)'))
+    tukey_window = traits.Int(argstr='--tukey=%d', xor=_estimate_xor,
+                              desc='tukey window size to estimate autocorr')
+    multitaper_product = traits.Int(argstr='--mt=%d', xor=_estimate_xor,
+                                    desc=('multitapering with slepian tapers '
+                                          'and num is the time-bandwidth '
+                                          'product'))
+    use_pava = traits.Bool(argstr='--pava', desc='estimates autocorr using PAVA')
+    autocorr_noestimate = traits.Bool(argstr='--noest', xor=_estimate_xor,
+                                      desc='do not estimate autocorrs')
+    output_pwdata = traits.Bool(argstr='--outputPWdata',
+                                desc=('output prewhitened data and average '
+                                      'design matrix'))
+    results_dir = Directory('results', argstr='--rn=%s', usedefault=True,
+                            desc='directory to store results in')
+
 
 class FILMGLSOutputSpec(TraitedSpec):
     param_estimates = OutputMultiPath(File(exists=True),
@@ -561,7 +596,11 @@ class FILMGLS(FSLCommand):
     """
 
     _cmd = 'film_gls'
-    input_spec = FILMGLSInputSpec
+
+    if LooseVersion(Info.version()) > LooseVersion('5.0.4'):
+        input_spec = FILMGLSInputSpec505
+    else:
+        input_spec = FILMGLSInputSpec
     output_spec = FILMGLSOutputSpec
 
     def _get_pe_files(self, cwd):