Merge remote-tracking branch 'upstream/master' into doc/sphinxext-interfaces

Author: effigies

.mailmap

@@ -67,6 +67,7 @@ Gio Piantoni <[email protected]>
 Guillaume Flandin <[email protected]>
 Hans Johnson <[email protected]> <[email protected]>
 Horea Christian <[email protected]> <[email protected]>
+Hrvoje Stojic <[email protected]>
 Isaac Schwabacher <[email protected]>
 Jakub Kaczmarzyk <[email protected]>
 James Kent <[email protected]>

.zenodo.json

@@ -514,6 +514,11 @@
     {
       "name": "Blair, Ross"
     },
+    {
+      "affiliation": "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London",
+      "name": "Stojic, Hrvoje",
+      "orcid": "0000-0002-9699-9052"
+    },
     {
       "affiliation": "The University of Texas at Austin",
       "name": "Floren, Andrew",

examples/dmri_camino_dti.py

@@ -35,30 +35,27 @@
 
 def get_vox_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 
 
 def get_data_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     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
-    from nipype.utils import NUMPY_MMAP
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     return nii.affine
 
 

examples/dmri_connectivity.py

@@ -72,30 +72,27 @@
 
 def get_vox_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
 
 
 def get_data_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     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
-    from nipype.utils import NUMPY_MMAP
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     return nii.affine
 
 

examples/fmri_ants_openfmri.py

@@ -41,7 +41,6 @@
 from nipype.workflows.fmri.fsl import (create_featreg_preproc,
                                        create_modelfit_workflow,
                                        create_fixed_effects_flow)
-from nipype.utils import NUMPY_MMAP
 
 config.enable_provenance()
 version = 0

examples/fmri_fsl.py

@@ -101,11 +101,10 @@ def pickfirst(files):
 
 def getmiddlevolume(func):
     from nibabel import load
-    from nipype.utils import NUMPY_MMAP
     funcfile = func
     if isinstance(func, list):
         funcfile = func[0]
-    _, _, _, timepoints = load(funcfile, mmap=NUMPY_MMAP).shape
+    _, _, _, timepoints = load(funcfile).shape
     return int(timepoints / 2) - 1
 
 

examples/fmri_spm_auditory.py

@@ -108,10 +108,9 @@
 
 def get_vox_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

examples/fmri_spm_face.py

@@ -103,10 +103,9 @@
 
 def get_vox_dims(volume):
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     if isinstance(volume, list):
         volume = volume[0]
-    nii = nb.load(volume, mmap=NUMPY_MMAP)
+    nii = nb.load(volume)
     hdr = nii.header
     voxdims = hdr.get_zooms()
     return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

examples/rsfmri_vol_surface_preprocessing.py

@@ -117,10 +117,9 @@ def median(in_files):
     """
     import numpy as np
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
-        img = nb.load(filename, mmap=NUMPY_MMAP)
+        img = nb.load(filename)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data
@@ -146,12 +145,11 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
     from nipype.utils.filemanip import split_filename, list_to_filename
     import numpy as np
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     out_files = []
     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, mmap=NUMPY_MMAP)
+        img = nb.load(filename)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
         lowidx = int(timepoints / 2) + 1
@@ -264,12 +262,11 @@ def extract_noise_components(realigned_file,
     from scipy.linalg.decomp_svd import svd
     import numpy as np
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     import os
-    imgseries = nb.load(realigned_file, mmap=NUMPY_MMAP)
+    imgseries = nb.load(realigned_file)
     components = None
     for filename in filename_to_list(mask_file):
-        mask = nb.load(filename, mmap=NUMPY_MMAP).get_data()
+        mask = nb.load(filename).get_data()
         if len(np.nonzero(mask > 0)[0]) == 0:
             continue
         voxel_timecourses = imgseries.get_data()[mask > 0]
@@ -334,11 +331,10 @@ def extract_subrois(timeseries_file, label_file, indices):
     """
     from nipype.utils.filemanip import split_filename
     import nibabel as nb
-    from nipype.utils import NUMPY_MMAP
     import os
-    img = nb.load(timeseries_file, mmap=NUMPY_MMAP)
+    img = nb.load(timeseries_file)
     data = img.get_data()
-    roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
+    roiimg = nb.load(label_file)
     rois = roiimg.get_data()
     prefix = split_filename(timeseries_file)[1]
     out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
@@ -359,9 +355,8 @@ def combine_hemi(left, right):
     """
     import os
     import numpy as np
-    from nipype.utils import NUMPY_MMAP
-    lh_data = nb.load(left, mmap=NUMPY_MMAP).get_data()
-    rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
+    lh_data = nb.load(left).get_data()
+    rh_data = nb.load(right).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

@@ -76,7 +76,6 @@
 import numpy as np
 import scipy as sp
 import nibabel as nb
-from nipype.utils.config import NUMPY_MMAP
 
 """
 A list of modules and functions to import inside of nodes
@@ -129,7 +128,7 @@ def median(in_files):
     """
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
-        img = nb.load(filename, mmap=NUMPY_MMAP)
+        img = nb.load(filename)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data
@@ -156,7 +155,7 @@ 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, mmap=NUMPY_MMAP)
+        img = nb.load(filename)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
         lowidx = int(timepoints / 2) + 1
@@ -282,9 +281,9 @@ 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, mmap=NUMPY_MMAP)
+    img = nb.load(timeseries_file)
     data = img.get_data()
-    roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
+    roiimg = nb.load(label_file)
     rois = roiimg.get_data()
     prefix = split_filename(timeseries_file)[1]
     out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
@@ -303,8 +302,8 @@ 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, mmap=NUMPY_MMAP).get_data()
-    rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
+    lh_data = nb.load(left).get_data()
+    rh_data = nb.load(right).get_data()
 
     indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
                          2000000 + np.arange(0, rh_data.shape[0])[:, None]))