Merge pull request #1780 from wmpauli/master

satra · web-flow · commit 8119d78d02ac · 2017-03-17T23:32:16.000-04:00
FSL FIX wrapper
diff --git a/nipype/interfaces/fsl/fix.py b/nipype/interfaces/fsl/fix.py
@@ -0,0 +1,298 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+"""The fix module provides classes for interfacing with the `FSL FIX
+<http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX/index.html>` command line tools.  
+
+This was written to work with FSL version v5.0
+
+The following example assumes that melodic has already been run, so
+the datagrabber is configured to start from there (a list of melodic
+output directories). If no hand_labels_noise.txt exists already, this
+will fail and comment on that.
+
+EXAMPLE:
+subject_list = ['1','2','3']
+
+fix_pipeline = pe.Workflow(name='fix_pipeline')
+fix_pipeline.base_dir = os.path.abspath('./')
+
+info = dict(mel_ica=[['subject_id']])
+
+datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'], outfields=['mel_ica']), name='datasource')
+datasource.inputs.base_directory = os.path.abspath('<path_to_base_directory>')
+datasource.inputs.template = '%s/<path_to_melodic_ica>'
+datasource.inputs.template_args = info
+datasource.inputs.subject_id =  subject_list
+datasource.inputs.sort_filelist = True
+datasource.iterables = ('subject_id', subject_list)
+
+# create training set by looking into which mel_icas have hand_labels_noise.txt files in them
+create_training_set = pe.JoinNode(interface=fix.TrainingSetCreator(), joinfield=['mel_icas_in'], joinsource='datasource', name='trainingset_creator')
+
+# train the classifier
+train_node = pe.Node(interface=fix.Training(trained_wts_filestem='foo'), name='train_node')
+
+# test accuracy. Probably not necessary, and also failing on my setup because of fix itself (no error msg)
+accuracy_tester = pe.Node(interface=fix.AccuracyTester(output_directory='more_foo'), name='accuracy_tester')
+
+# classify components
+classify_node = pe.Node(interface=fix.Classifier(), name='classify')
+
+# apply cleanup
+cleaner_node = pe.Node(interface=fix.Cleaner(), name='cleaner')
+
+fix_pipeline.connect(datasource, 'mel_ica', create_training_set, 'mel_icas_in')
+fix_pipeline.connect(create_training_set, 'mel_icas_out', train_node, 'mel_icas')
+fix_pipeline.connect(train_node, 'trained_wts_file', accuracy_tester, 'trained_wts_file')
+fix_pipeline.connect(datasource, 'mel_ica', accuracy_tester, 'mel_icas')
+fix_pipeline.connect(train_node, 'trained_wts_file', classify_node, 'trained_wts_file')
+fix_pipeline.connect(datasource, 'mel_ica', classify_node, 'mel_ica')
+fix_pipeline.connect(classify_node, 'artifacts_list_file', cleaner_node, 'artifacts_list_file')
+
+fix_pipeline.write_graph()
+outgraph = fix_pipeline.run()
+
+"""
+
+from nipype.interfaces.base import (
+    TraitedSpec,
+    CommandLineInputSpec,
+    CommandLine,
+    InputMultiPath,
+    OutputMultiPath,
+    BaseInterface,
+    BaseInterfaceInputSpec,
+    traits
+)
+from nipype.interfaces.traits_extension import (
+    Directory,
+    File,
+    isdefined
+)
+import os
+
+class TrainingSetCreatorInputSpec(BaseInterfaceInputSpec):
+    mel_icas_in = InputMultiPath(Directory(exists=True), copyfile=False,
+                              desc='Melodic output directories',
+                              argstr='%s', position=-1)
+
+class TrainingSetCreatorOutputSpec(TraitedSpec):
+    mel_icas_out = OutputMultiPath(Directory(exists=True), copyfile=False,
+                              desc='Hand labels for noise vs signal',
+                              argstr='%s', position=-1)
+    
+
+class TrainingSetCreator(BaseInterface):
+    '''Goes through set of provided melodic output directories, to find all
+    the ones that have a hand_labels_noise.txt file in them.
+
+    This is outsourced as a separate class, so that the pipeline is
+    rerun everytime a handlabeled file has been changed, or a new one
+    created.
+
+    '''
+    input_spec = TrainingSetCreatorInputSpec
+    output_spec = TrainingSetCreatorOutputSpec
+    _always_run = True
+
+    def _run_interface(self, runtime):
+        mel_icas = []
+        for item in self.inputs.mel_icas_in:
+            if os.path.exists(os.path.join(item,'hand_labels_noise.txt')):
+                mel_icas.append(item)
+
+        if len(mel_icas) == 0:
+            raise Exception('%s did not find any hand_labels_noise.txt files in the following directories: %s' % (self.__class__.__name__, mel_icas))
+
+        return runtime
+
+    def _list_outputs(self):
+        mel_icas = []
+        for item in self.inputs.mel_icas_in:
+            if os.path.exists(os.path.join(item,'hand_labels_noise.txt')):
+                mel_icas.append(item)
+        outputs = self._outputs().get()
+        outputs['mel_icas_out'] = mel_icas
+        return outputs
+
+
+
+
+class FeatureExtractorInputSpec(CommandLineInputSpec):    
+    mel_ica = Directory(exists=True, copyfile=False, desc='Melodic output directory or directories',
+                        argstr='%s', position=-1)
+
+
+class FeatureExtractorOutputSpec(TraitedSpec):
+    mel_ica = Directory(exists=True, copyfile=False, desc='Melodic output directory or directories',
+                        argstr='%s', position=-1)
+
+
+class FeatureExtractor(CommandLine):
+    '''
+    Extract features (for later training and/or classifying)
+    '''
+    input_spec = FeatureExtractorInputSpec
+    output_spec = FeatureExtractorOutputSpec
+    cmd = 'fix -f'
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs['mel_ica'] = self.inputs.mel_ica
+        return outputs
+
+
+class TrainingInputSpec(CommandLineInputSpec):
+    mel_icas = InputMultiPath(Directory(exists=True), copyfile=False,
+                              desc='Melodic output directories',
+                              argstr='%s', position=-1)
+
+    trained_wts_filestem = traits.Str(desc='trained-weights filestem, used for trained_wts_file and output directories', argstr='%s', position=1)  
+
+    loo = traits.Bool(argstr='-l', desc='full leave-one-out test with classifier training', position=2)
+
+
+class TrainingOutputSpec(TraitedSpec):
+    trained_wts_file = File(exists=True, desc='Trained-weights file')
+
+
+class Training(CommandLine):
+    '''
+    Train the classifier based on your own FEAT/MELODIC output directory.
+    '''
+    input_spec = TrainingInputSpec
+    output_spec = TrainingOutputSpec
+    cmd = 'fix -t'
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        if isdefined(self.inputs.trained_wts_filestem):
+            outputs['trained_wts_file'] = os.path.abspath(self.inputs.trained_wts_filestem + '.RData')
+        else:
+            outputs['trained_wts_file'] = os.path.abspath('trained_wts_file.RData')
+        return outputs
+
+
+
+
+class AccuracyTesterInputSpec(CommandLineInputSpec):
+    mel_icas = InputMultiPath(Directory(exists=True), copyfile=False,
+                              desc='Melodic output directories',
+                              argstr='%s', position=3, mandatory=True)
+
+    trained_wts_file = File(desc='trained-weights file', argstr='%s', position=1, mandatory=True)  
+
+    output_directory = Directory(desc='Path to folder in which to store the results of the accuracy test.', argstr='%s', position=2, mandatory=True)  
+
+
+class AccuracyTesterOutputSpec(TraitedSpec):
+    output_directory = Directory(desc='Path to folder in which to store the results of the accuracy test.', argstr='%s', position=1)  
+
+
+class AccuracyTester(CommandLine):
+    '''
+    Test the accuracy of an existing training dataset on a set of hand-labelled subjects.
+    Note: This may or may not be working. Couldn't presently not confirm because fix fails on this (even outside of nipype) without leaving an error msg. 
+    '''
+    input_spec = AccuracyTesterInputSpec
+    output_spec = AccuracyTesterOutputSpec
+    cmd = 'fix -C'
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        if isdefined(self.inputs.output_directory):
+            outputs['output_directory'] = Directory(exists=False, value=self.inputs.output_directory)
+        else:
+            outputs['output_directory'] = Directory(exists=False, value='accuracy_test')
+        return outputs
+
+
+
+class ClassifierInputSpec(CommandLineInputSpec):
+    mel_ica = Directory(exists=True, copyfile=False, desc='Melodic output directory or directories',
+                        argstr='%s', position=1)
+
+    trained_wts_file = File(exists=True, desc='trained-weights file', argstr='%s', position=2, mandatory=True, copyfile=False)  
+
+    thresh = traits.Int(argstr='%d', desc='Threshold for cleanup.', position=-1, mandatory=True)
+
+    artifacts_list_file = File(desc='Text file listing which ICs are artifacts; can be the output from classification or can be created manually')
+
+class ClassifierOutputSpec(TraitedSpec):
+    artifacts_list_file = File(desc='Text file listing which ICs are artifacts; can be the output from classification or can be created manually')
+
+
+class Classifier(CommandLine):
+    '''
+    Classify ICA components using a specific training dataset (<thresh> is in the range 0-100, typically 5-20).
+    '''
+    input_spec = ClassifierInputSpec
+    output_spec = ClassifierOutputSpec
+    cmd = 'fix -c'
+
+    def _gen_artifacts_list_file(self, mel_ica, thresh):
+        
+        _, trained_wts_file = os.path.split(self.inputs.trained_wts_file)
+        trained_wts_filestem = trained_wts_file.split('.')[0]
+        filestem = 'fix4melview_' + trained_wts_filestem + '_thr'
+
+        fname = os.path.join(mel_ica, filestem + str(thresh) + '.txt')
+        return fname
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs['artifacts_list_file'] = self._gen_artifacts_list_file(self.inputs.mel_ica, self.inputs.thresh)
+
+        return outputs
+
+
+
+
+class CleanerInputSpec(CommandLineInputSpec):
+    artifacts_list_file = File(exists=True, argstr='%s', position=1, mandatory=True, desc='Text file listing which ICs are artifacts; can be the output from classification or can be created manually')  
+
+    cleanup_motion = traits.Bool(argstr='-m', 
+                                 desc='cleanup motion confounds, looks for design.fsf for highpass filter cut-off', 
+                                 position=2)
+
+    highpass = traits.Float(argstr='-m -h %f',
+                            desc='cleanup motion confounds', value=100, position=2)
+
+    aggressive = traits.Bool(argstr='-A',
+                             desc='Apply aggressive (full variance) cleanup, instead of the default less-aggressive (unique variance) cleanup.', position=3)
+
+    confound_file = traits.File(argstr='-x %s',
+                                desc='Include additional confound file.', position=4)
+
+    confound_file_1 = traits.File(argstr='-x %s',
+                                  desc='Include additional confound file.', position=5)
+
+    confound_file_2 = traits.File(argstr='-x %s',
+                                  desc='Include additional confound file.', position=6)
+
+
+class CleanerOutputSpec(TraitedSpec):
+    cleaned_functional_file = File(exists=True, desc='Cleaned session data')
+
+
+class Cleaner(CommandLine):
+    '''
+    Extract features (for later training and/or classifying)
+    '''
+    input_spec = CleanerInputSpec
+    output_spec = CleanerOutputSpec
+    cmd = 'fix -a'
+
+    def _get_cleaned_functional_filename(self, artifacts_list_filename):
+        ''' extract the proper filename from the first line of the artifacts file '''
+        artifacts_list_file = open(artifacts_list_filename, 'r')
+        functional_filename, extension = artifacts_list_file.readline().split('.')
+        artifacts_list_file_path, artifacts_list_filename = os.path.split(artifacts_list_filename)
+        
+        return(os.path.join(artifacts_list_file_path, functional_filename + '_clean.nii.gz'))
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        outputs['cleaned_functional_file'] = self._get_cleaned_functional_filename(self.inputs.artifacts_list_file)
+        return outputs
+
