replace all nibabel imports and the nibabel.load to have the NUMPY_MMAP

Author: oesteban

examples/dmri_camino_dti.py

@@ -38,7 +38,7 @@ def get_vox_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
@@ -48,15 +48,15 @@ def get_data_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     datadims = hdr.get_data_shape()
     return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
 
 
 def get_affine(volume):
     import nibabel as nb
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     return nii.affine
 
 subject_list = ['subj1']

examples/dmri_connectivity.py

@@ -76,7 +76,7 @@ def get_vox_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
@@ -86,15 +86,15 @@ def get_data_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     datadims = hdr.get_data_shape()
     return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
 
 
 def get_affine(volume):
     import nibabel as nb
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     return nii.affine
 
 

examples/fmri_ants_openfmri.py

@@ -68,7 +68,7 @@ def median(in_files):
     """
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
-        img = nb.load(filename)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data

examples/fmri_spm_auditory.py

@@ -122,7 +122,7 @@ def get_vox_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

examples/fmri_spm_face.py

@@ -116,7 +116,7 @@ def get_vox_dims(volume):
     import nibabel as nb
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

examples/rsfmri_vol_surface_preprocessing.py

@@ -119,7 +119,7 @@ def median(in_files):
     import nibabel as nb
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
-        img = nb.load(filename)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data
@@ -149,7 +149,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
     for filename in filename_to_list(files):
         path, name, ext = split_filename(filename)
         out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
-        img = nb.load(filename)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
         lowidx = int(timepoints / 2) + 1
@@ -261,10 +261,10 @@ def extract_noise_components(realigned_file, mask_file, num_components=5,
     import numpy as np
     import nibabel as nb
     import os
-    imgseries = nb.load(realigned_file)
+    imgseries = nb.load(realigned_file, mmap=NUMPY_MMAP)
     components = None
     for filename in filename_to_list(mask_file):
-        mask = nb.load(filename).get_data()
+        mask = nb.load(filename, mmap=NUMPY_MMAP).get_data()
         if len(np.nonzero(mask > 0)[0]) == 0:
             continue
         voxel_timecourses = imgseries.get_data()[mask > 0]
@@ -330,9 +330,9 @@ def extract_subrois(timeseries_file, label_file, indices):
     from nipype.utils.filemanip import split_filename
     import nibabel as nb
     import os
-    img = nb.load(timeseries_file)
+    img = nb.load(timeseries_file, mmap=NUMPY_MMAP)
     data = img.get_data()
-    roiimg = nb.load(label_file)
+    roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
     rois = roiimg.get_data()
     prefix = split_filename(timeseries_file)[1]
     out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
@@ -352,8 +352,8 @@ def combine_hemi(left, right):
     """
     import os
     import numpy as np
-    lh_data = nb.load(left).get_data()
-    rh_data = nb.load(right).get_data()
+    lh_data = nb.load(left, mmap=NUMPY_MMAP).get_data()
+    rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
 
     indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
                          2000000 + np.arange(0, rh_data.shape[0])[:, None]))

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -116,7 +116,7 @@ def median(in_files):
     """
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
-        img = nb.load(filename)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data
@@ -143,7 +143,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
     for filename in filename_to_list(files):
         path, name, ext = split_filename(filename)
         out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
-        img = nb.load(filename)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
         lowidx = int(timepoints / 2) + 1
@@ -268,9 +268,9 @@ def extract_subrois(timeseries_file, label_file, indices):
         The first four columns are: freesurfer index, i, j, k positions in the
         label file
     """
-    img = nb.load(timeseries_file)
+    img = nb.load(timeseries_file, mmap=NUMPY_MMAP)
     data = img.get_data()
-    roiimg = nb.load(label_file)
+    roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
     rois = roiimg.get_data()
     prefix = split_filename(timeseries_file)[1]
     out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
@@ -288,8 +288,8 @@ def extract_subrois(timeseries_file, label_file, indices):
 def combine_hemi(left, right):
     """Combine left and right hemisphere time series into a single text file
     """
-    lh_data = nb.load(left).get_data()
-    rh_data = nb.load(right).get_data()
+    lh_data = nb.load(left, mmap=NUMPY_MMAP).get_data()
+    rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
 
     indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
                          2000000 + np.arange(0, rh_data.shape[0])[:, None]))

nipype/algorithms/icc.py

@@ -37,7 +37,7 @@ def _run_interface(self, runtime):
         maskdata = nb.load(self.inputs.mask).get_data()
         maskdata = np.logical_not(np.logical_or(maskdata == 0, np.isnan(maskdata)))
 
-        session_datas = [[nb.load(fname).get_data()[maskdata].reshape(-1, 1) for fname in sessions] for sessions in self.inputs.subjects_sessions]
+        session_datas = [[nb.load(fname, mmap=NUMPY_MMAP).get_data()[maskdata].reshape(-1, 1) for fname in sessions] for sessions in self.inputs.subjects_sessions]
         list_of_sessions = [np.dstack(session_data) for session_data in session_datas]
         all_data = np.hstack(list_of_sessions)
         icc = np.zeros(session_datas[0][0].shape)

nipype/algorithms/metrics.py

@@ -410,8 +410,8 @@ def _run_interface(self, runtime):
         assert(ncomp == len(self.inputs.in_tst))
         weights = np.ones(shape=ncomp)
 
-        img_ref = np.array([nb.load(fname).get_data() for fname in self.inputs.in_ref])
-        img_tst = np.array([nb.load(fname).get_data() for fname in self.inputs.in_tst])
+        img_ref = np.array([nb.load(fname, mmap=NUMPY_MMAP).get_data() for fname in self.inputs.in_ref])
+        img_tst = np.array([nb.load(fname, mmap=NUMPY_MMAP).get_data() for fname in self.inputs.in_tst])
 
         msk = np.sum(img_ref, axis=0)
         msk[msk > 0] = 1.0

nipype/algorithms/misc.py

@@ -140,7 +140,7 @@ class SimpleThreshold(BaseInterface):
 
     def _run_interface(self, runtime):
         for fname in self.inputs.volumes:
-            img = nb.load(fname)
+            img = nb.load(fname, mmap=NUMPY_MMAP)
             data = np.array(img.get_data())
 
             active_map = data > self.inputs.threshold
@@ -196,7 +196,7 @@ def _gen_output_filename(self, name):
 
     def _run_interface(self, runtime):
         for fname in self.inputs.volumes:
-            img = nb.load(fname)
+            img = nb.load(fname, mmap=NUMPY_MMAP)
 
             affine = img.affine
             affine = np.dot(self.inputs.transformation_matrix, affine)
@@ -1158,7 +1158,7 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
     Returns the input tissue probability maps (tpms, aka volume fractions)
     normalized to sum up 1.0 at each voxel within the mask.
     """
-    import nibabel as nib
+    import nibabel as nb
     import numpy as np
     import os.path as op
 
@@ -1174,15 +1174,15 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
             out_file = op.abspath('%s_norm_%02d%s' % (fname, i, fext))
             out_files += [out_file]
 
-    imgs = [nib.load(fim) for fim in in_files]
+    imgs = [nb.load(fim, mmap=NUMPY_MMAP) for fim in in_files]
 
     if len(in_files) == 1:
         img_data = imgs[0].get_data()
         img_data[img_data > 0.0] = 1.0
         hdr = imgs[0].header.copy()
         hdr['data_type'] = 16
         hdr.set_data_dtype(np.float32)
-        nib.save(nib.Nifti1Image(img_data.astype(np.float32), imgs[0].affine,
+        nb.save(nb.Nifti1Image(img_data.astype(np.float32), imgs[0].affine,
                                  hdr), out_files[0])
         return out_files[0]
 
@@ -1195,7 +1195,7 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
     msk[weights <= 0] = 0
 
     if in_mask is not None:
-        msk = nib.load(in_mask).get_data()
+        msk = nb.load(in_mask, mmap=NUMPY_MMAP).get_data()
         msk[msk <= 0] = 0
         msk[msk > 0] = 1
 
@@ -1207,7 +1207,7 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
         hdr = imgs[i].header.copy()
         hdr['data_type'] = 16
         hdr.set_data_dtype('float32')
-        nib.save(nib.Nifti1Image(probmap.astype(np.float32), imgs[i].affine,
+        nb.save(nb.Nifti1Image(probmap.astype(np.float32), imgs[i].affine,
                                  hdr), out_file)
 
     return out_files
@@ -1225,15 +1225,15 @@ def split_rois(in_file, mask=None, roishape=None):
     if roishape is None:
         roishape = (10, 10, 1)
 
-    im = nb.load(in_file)
+    im = nb.load(in_file, mmap=NUMPY_MMAP)
     imshape = im.shape
     dshape = imshape[:3]
     nvols = imshape[-1]
     roisize = roishape[0] * roishape[1] * roishape[2]
     droishape = (roishape[0], roishape[1], roishape[2], nvols)
 
     if mask is not None:
-        mask = nb.load(mask).get_data()
+        mask = nb.load(mask, mmap=NUMPY_MMAP).get_data()
         mask[mask > 0] = 1
         mask[mask < 1] = 0
     else:
@@ -1314,7 +1314,7 @@ def merge_rois(in_files, in_idxs, in_ref,
         except:
             pass
 
-    ref = nb.load(in_ref)
+    ref = nb.load(in_ref, mmap=NUMPY_MMAP)
     aff = ref.affine
     hdr = ref.header.copy()
     rsh = ref.shape
@@ -1335,7 +1335,7 @@ def merge_rois(in_files, in_idxs, in_ref,
         for cname, iname in zip(in_files, in_idxs):
             f = np.load(iname)
             idxs = np.squeeze(f['arr_0'])
-            cdata = nb.load(cname).get_data().reshape(-1, ndirs)
+            cdata = nb.load(cname, mmap=NUMPY_MMAP).get_data().reshape(-1, ndirs)
             nels = len(idxs)
             idata = (idxs, )
             try:
@@ -1363,15 +1363,15 @@ def merge_rois(in_files, in_idxs, in_ref,
             idxs = np.squeeze(f['arr_0'])
 
             for d, fname in enumerate(nii):
-                data = nb.load(fname).get_data().reshape(-1)
-                cdata = nb.load(cname).get_data().reshape(-1, ndirs)[:, d]
+                data = nb.load(fname, mmap=NUMPY_MMAP).get_data().reshape(-1)
+                cdata = nb.load(cname, mmap=NUMPY_MMAP).get_data().reshape(-1, ndirs)[:, d]
                 nels = len(idxs)
                 idata = (idxs, )
                 data[idata] = cdata[0:nels]
                 nb.Nifti1Image(data.reshape(rsh[:3]),
                                aff, hdr).to_filename(fname)
 
-        imgs = [nb.load(im) for im in nii]
+        imgs = [nb.load(im, mmap=NUMPY_MMAP) for im in nii]
         allim = nb.concat_images(imgs)
         allim.to_filename(out_file)