Merge remote-tracking branch 'upstream/master' into new_fsl

Author: akeshavan

dmriprep/__init__.py

@@ -9,6 +9,12 @@
 import errno
 import logging
 import os
+import warnings
+
+# Filter warnings that are visible whenever you import another package that
+# was compiled against an older numpy than is installed.
+warnings.filterwarnings("ignore", message="numpy.dtype size changed")
+warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
 
 from . import data
 from . import io

dmriprep/data.py

@@ -251,6 +251,16 @@ def list_all_subjects(self):
         """
         raise NotImplementedError
 
+    def filter_keys(self, subject):
+        """Study-specific S3 key filtering
+
+        Parameters
+        ----------
+        subject : dmriprep.data.Subject
+            subject instance
+        """
+        raise NotImplementedError
+
     def postprocess(self, subject):
         """Study-specific postprocessing steps
 
@@ -362,6 +372,34 @@ def get_subs_from_tsv_key(s3_key):
 
         return all_subjects
 
+    def filter_keys(self, subject):
+        """Filter S3 keys based on HBN specific vagaries
+
+        HBN Site-CBIC has multiple anatomy folders due to
+        motion correction software at the scanner level.
+        If subject.site == "Site-CBIC" then choose only the
+        anatomy files in the T1W_VNavNorm files
+
+        Parameters
+        ----------
+        subject : dmriprep.data.Subject
+            subject instance
+        """
+        if subject.site == "Site-CBIC":
+            t1w_keys = subject.s3_keys['t1w']
+            freesurfer_keys = subject.s3_keys['freesurfer']
+            correct_dir = "T1w_VNavNorm"
+
+            subject._s3_keys['t1w'] = list(filter(
+                lambda x: correct_dir in x,
+                t1w_keys
+            ))
+
+            subject._s3_keys['freesurfer'] = list(filter(
+                lambda x: correct_dir in x,
+                freesurfer_keys
+            ))
+
     def postprocess(self, subject):
         """Move the T1 file back into the freesurfer directory.
 
@@ -416,6 +454,7 @@ def __init__(self, subject_id, study, site=None):
         self._valid = False
         self._organize_s3_keys()
         if self.valid:
+            self.study.filter_keys(self)
             self._s3_keys = self._determine_directions(self._s3_keys)
             self._files = None
 

dmriprep/qc.py

@@ -10,7 +10,6 @@
 import matplotlib.pyplot as plt
 
 
-
 def reorient_array(data, aff):
     # rearrange the matrix to RAS orientation
     orientation = nib.orientations.io_orientation(aff)

dmriprep/run.py

@@ -1,11 +1,5 @@
 import os.path as op
 from shutil import copyfile
-import warnings
-
-# Filter warnings that are visible whenever you import another package that
-# was compiled against an older numpy than is installed.
-warnings.filterwarnings("ignore", message="numpy.dtype size changed")
-warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
 
 
 def run_dmriprep(dwi_file, bvec_file, bval_file,
@@ -407,7 +401,8 @@ def num_outliers(scan, outliers):
             return len([d for d in outliers if d['scan'] == scan])
 
         if 0 < threshold < 1:
-            threshold *= nib.load(dwi_file).shape[2]
+            img = nib.load(dwi_file)
+            threshold *= img.shape[img.header.get_n_slices()]
 
         drop_scans = np.array([
             s for s in scans
@@ -488,8 +483,19 @@ def drop_outliers_fn(in_file, in_bval, in_bvec, drop_scans):
 
         img = nib.load(op.abspath(in_file))
         img_data = img.get_fdata()
-        img_data_thinned = np.delete(img_data, drop_scans, axis=3)
-        img_thinned = nib.Nifti1Image(img_data_thinned.astype(np.float64), img.affine, header=img.header)
+        img_data_thinned = np.delete(img_data,
+                                     drop_scans,
+                                     axis=3)
+        if isinstance(img, nib.nifti1.Nifti1Image):
+            img_thinned = nib.Nifti1Image(img_data_thinned.astype(np.float64),
+                                          img.affine,
+                                          header=img.header)
+        elif isinstance(img, nib.nifti2.Nifti2Image):
+            img_thinned = nib.Nifti2Image(img_data_thinned.astype(np.float64),
+                                          img.affine,
+                                          header=img.header)
+        else:
+            raise TypeError("in_file does not contain Nifti image datatype.")
 
         out_file = fname_presuffix(in_file, suffix="_thinned", newpath=op.abspath('.'))
         nib.save(img_thinned, op.abspath(out_file))