FIX: Removes unnecessary methods from smri freesurfer workflow utils.

Author: David Ellis

nipype/workflows/smri/freesurfer/recode_tables/fs2abc.csv

@@ -79,8 +79,7 @@ def link_masks(subjects_dir, subject_id):
     wf.connect(autorecon_resume, "subject_id", outputnode, "subject_id")
     return wf
 
-def create_reconall_workflow(name="ReconAll", plugin_args=None,
-                             recoding_file=None):
+def create_reconall_workflow(name="ReconAll", plugin_args=None):
     """Creates the ReconAll workflow in nipype.
 
     Example
@@ -510,15 +509,6 @@ def completemethod(datasinked_files, subject_id):
                       (inputspec, completion, [('subject_id', 'subject_id')]),
                       (completion, postds_outputspec, [('subject_id', 'subject_id')])])
 
-
-    #### Workflow additions go here
-    if recoding_file:
-        from utils import create_recoding_wf
-        recode = create_recoding_wf(recoding_file)
-        reconall.connect([(ar3_wf, recode, [('outputspec.aseg', 'inputspec.labelmap')]),
-                          (recode, outputspec, [('outputspec.recodedlabelmap', 'recoded_labelmap')])])
-
-
     return reconall
 
 

nipype/workflows/smri/freesurfer/recon.py

@@ -493,79 +493,6 @@ def checkenv(exitonfail=False):
     else:
         print("Warning: " + msg)
 
-def center_volume(in_file):
-    import SimpleITK as sitk
-    import os
-    img = sitk.ReadImage(in_file)
-    size = img.GetSize()
-    origin = img.GetOrigin()
-    new_origin = [0,0,0]
-    for i, xx in enumerate(origin):
-        new_origin[i] = float(size[i])/2
-        if xx < 0:
-            new_origin[i] = -new_origin[i]
-    img.SetOrigin(new_origin)
-    out_file = os.path.abspath(os.path.basename(in_file))
-    sitk.WriteImage(img, out_file)
-    return out_file
-
-
-def recodeLabelMap(in_file, out_file, recode_file):
-    """This function has been adapted from BRAINSTools and serves
-    as a means to recode a label map based upon an input csv
-    file."""
-    import SimpleITK as sitk
-    import os
-    import csv
-    import sys
-
-    # Convert csv to RECODE_TABLE
-    CSVFile = open(recode_file, 'rb')
-    reader = csv.reader(CSVFile)
-    header = reader.next()
-    n_cols = len(header)
-    if n_cols == 4:
-        # ignore label names
-        label_keys = (0, 2)
-    elif n_cols == 2:
-        # no label names present
-        label_keys = (0, 1)
-    else:
-        # csv does not match format requirements
-        print("ERROR: input csv file for label recoding does meet requirements")
-        sys.exit()
-
-    # read csv file
-    RECODE_TABLE = list()
-    for line in reader:
-        RECODE_TABLE.append((int(line[label_keys[0]]), int(line[label_keys[1]])))
-
-    def minimizeSizeOfImage(outlabels):
-        """This function will find the largest integer value in the labelmap, and
-        cast the image to the smallest possible integer size so that no loss of data
-        results."""
-        measureFilt  = sitk.StatisticsImageFilter()
-        measureFilt.Execute(outlabels)
-        imgMin=measureFilt.GetMinimum()
-        imgMax=measureFilt.GetMaximum()
-        if imgMax < (2**8)-1:
-            outlabels = sitk.Cast( outlabels, sitk.sitkUInt8 )
-        elif imgMax < (2**16)-1:
-            outlabels = sitk.Cast( outlabels, sitk.sitkUInt16 )
-        elif imgMax < (2**32)-1:
-            outlabels = sitk.Cast( outlabels, sitk.sitkUInt32 )
-        elif imgMax < (2**64)-1:
-            outlabels = sitk.Cast( outlabels, sitk.sitkUInt64 )
-        return outlabels
-
-    LabelImage=sitk.Cast(sitk.ReadImage(in_file), sitk.sitkUInt32)
-    for (old, new) in RECODE_TABLE:
-        LabelImage = sitk.Cast((LabelImage == old), sitk.sitkUInt32)*(new - old)+LabelImage
-    LabelImage = minimizeSizeOfImage(LabelImage)
-    out_file = os.path.abspath(out_file)
-    sitk.WriteImage(LabelImage, out_file)
-    return out_file
-
 
 def create_recoding_wf(in_file, out_file=None):
     wf = nipype.Workflow(name="RecodeLabels")
@@ -729,139 +656,3 @@ def createsrcsubj(source_directory):
         rh_entorhinal_exvivo_thresh='label/rh.entorhinal_exvivo.thresh.label',
         rh_perirhinal_exvivo_thresh='label/rh.perirhinal_exvivo.thresh.label')
     return datasource, outfields
-
-def source_long_files_workflow(name="Source_Longitudinal_Files"):
-    """Creates a workflow to source the longitudinal files from a freesurfer directory.
-    This should only be used when the files are not in a prexisting workflow"""
-
-    wf = Workflow(name=name)
-
-    inputspec = Node(IdentityInterface(fields=['subject_id',
-                                               'subjects_dir',
-                                               'timepoints']),
-                     name="inputspec")
-
-    # TODO: Create outputspec
-
-    # grab files from the initial single session run
-    grab_inittp_files = pe.Node(nio.DataGrabber(), name="Grab_Initial_Files",
-                                infields=['subject_id'],
-                                outfileds=['inputvols', 'iscales', 'ltas'])
-    grab_inittp_files.inputs.template = '*'
-    grab_inittp_files.inputs.base_directory = config['subjects_dir']
-    grab_inittp_files.inputs.field_template = dict(inputvols='%s/mri/orig/0*.mgz',
-                                                   iscales='%s/mri/orig/0*-iscale.txt',
-                                                   ltas='%s/mri/orig/0*.lta')
-
-    grab_inittp_files.inputs.template_args = dict(inputvols=[['subject_id']],
-                                                  iscales=[['subject_id']],
-                                                  ltas=[['subject_id']])
-
-    wf.connect([(grab_inittp_files, outputspec, [('inputvols', 'inputspec.in_T1s'),
-                                                   ('iscales', 'inputspec.iscales'),
-                                                   ('ltas', 'inputspec.ltas')])])
-
-    merge_norms = pe.Node(Merge(len(config['timepoints'])), name="Merge_Norms")
-    merge_segs = pe.Node(Merge(len(config['timepoints'])), name="Merge_Segmentations")
-    merge_segs_noCC = pe.Node(Merge(len(config['timepoints'])), name="Merge_Segmentations_noCC")
-    merge_template_ltas = pe.Node(Merge(len(config['timepoints'])), name="Merge_Template_ltas")
-
-    for i, tp in enumerate(config['timepoints']):
-        # datasource timepoint files
-        tp_data_source = pe.Node(FreeSurferSource(), name="{0}_DataSource".format(tp))
-        tp_data_source.inputs.subject_id = tp
-        tp_data_source.inputs.subjects_dir = config['subjects_dir']
-
-        tp_data_grabber = pe.Node(nio.DataGrabber(), name="{0}_DataGrabber".format(tp),
-                                  infields=['tp', 'long_tempate'],
-                                  outfileds=['subj_to_template_lta', 'seg_noCC', 'seg_presurf'])
-        tp_data_grabber.inputs.template = '*'
-        tp_data_grabber.inputs.base_directory = config['subjects_dir']
-        tp_data_grabber.inputs.field_template = dict(
-            subj_to_template_lta='%s/mri/transforms/%s_to_%s.lta',
-            seg_noCC='%s/mri/aseg.auto_noCCseg.mgz',
-            seg_presurf='%s/mri/aseg.presurf.mgz',)
-
-        tp_data_grabber.inputs.template_args = dict(
-            subj_to_template_lta=[['long_template', 'tp', 'long_template']],
-            seg_noCC=[['tp']],
-            seg_presurf=[['tp']])
-
-        wf.connect([(tp_data_source, merge_norms, [('norm',
-                                                          'in{0}'.format(i))]),
-                          (tp_data_grabber, merge_segs, [('seg_presurf',
-                                                          'in{0}'.format(i))]),
-                          (tp_data_grabber, merge_segs_noCC, [('seg_noCC',
-                                                               'in{0}'.format(i))]),
-                          (tp_data_grabber, merge_template_ltas, [('subj_to_template_lta',
-                                                                   'in{0}'.format(i))])])
-
-        if tp == config['subject_id']:
-            wf.connect([(tp_data_source, outputspec, [('wm', 'inputspec.init_wm')]),
-                              (tp_data_grabber, outputspec, [('subj_to_template_lta',
-                                                          'inputspec.subj_to_template_lta')]),
-                              (tp_data_grabber, outputspec, [('subj_to_template_lta',
-                                                          'inputspec.subj_to_template_lta')])])
-
-    wf.connect([(merge_norms, outputspec, [('out', 'inputspec.alltps_norms')]),
-                      (merge_segs, outputspec, [('out', 'inputspec.alltps_segs')]),
-                      (merge_template_ltas, outputspec, [('out', 'inputspec.alltps_to_template_ltas')]),
-                      (merge_segs_noCC, outputspec, [('out', 'inputspec.alltps_segs_noCC')])])
-
-
-
-    # datasource files from the template run
-    ds_template_files = pe.Node(FreeSurferSource(), name="Datasource_Template_Files")
-    ds_template_files.inputs.subject_id = config['subject_id']
-    ds_template_files.inputs.subjects_dir = config['subjects_dir']
-
-    wf.connect([(ds_template_files, ar1_wf, [('brainmask', 'inputspec.template_brainmask')]),
-                      (ds_template_files, outputspec, [('aseg', 'inputspec.template_aseg')])])
-
-    # grab files from template run
-    grab_template_files = pe.Node(nio.DataGrabber(), name="Grab_Template_Files",
-                                  infields=['subject_id', 'long_template'],
-                                  outfields=['template_talairach_xfm',
-                                             'template_talairach_lta',
-                                             'template_talairach_m3z',
-                                             'template_label_intensities',
-                                             'template_lh_white',
-                                             'template_rh_white',
-                                             'template_lh_pial',
-                                             'template_rh_pial'])
-    grab_template_files.inputs.template = '*'
-    grab_template_files.inputs.base_directory = config['subjects_dir']
-    grab_template_files.inputs.subject_id = config['subject_id']
-    grab_template_files.inputs.long_template = config['long_template']
-    grab_template_files.inputs.field_template = dict(
-        template_talairach_xfm='%s/mri/transfroms/talairach.xfm',
-        template_talairach_lta='%s/mri/transfroms/talairach.lta',
-        template_talairach_m3z='%s/mri/transfroms/talairach.m3z',
-        template_label_intensities='%s/mri/aseg.auto_noCCseg.label_intensities.txt',
-        template_lh_white='%s/surf/lh.white',
-        template_rh_white='%s/surf/rh.white',
-        template_lh_pial='%s/surf/lh.pial',
-        template_rh_pial='%s/surf/rh.pial')
-
-    grab_template_files.inputs.template_args = dict(
-        template_talairach_xfm=[['long_template']],
-        template_talairach_lta=[['long_template']],
-        template_talairach_m3z=[['long_template']],
-        template_lh_white=[['long_template']],
-        template_rh_white=[['long_template']],
-        template_lh_pial=[['long_template']],
-        template_rh_pial=[['long_template']])
-    wf.connect([(grab_template_files, outputspec, [('template_talairach_xfm',
-                                                    'inputspec.template_talairach_xfm'),
-                                                   ('template_talairach_lta',
-                                                    'inputspec.template_talairach_lta'),
-                                                   ('template_talairach_m3z',
-                                                    'inputspec.template_talairach_m3z'),
-                                                   ('template_label_intensities',
-                                                    'inputspec.template_label_intensities'),
-                                                   ('template_lh_white', 'inputspec.template_lh_white'),
-                                                   ('template_rh_white', 'inputspec.template_rh_white'),
-                                                   ('template_lh_pial', 'inputspec.template_lh_pial'),
-                                                   ('template_rh_pial', 'inputspec.template_rh_pial')])
-            ])
-    return wf