Do not open nifti files with mmap if numpy < 1.12.0

.travis.yml

@@ -1,5 +1,6 @@
 cache:
-- apt
+  apt: true
+
 language: python
 python:
 - 2.7
@@ -10,11 +11,7 @@ env:
 - INSTALL_DEB_DEPENDECIES=false NIPYPE_EXTRAS="doc,tests,fmri,profiler"
 - INSTALL_DEB_DEPENDECIES=true NIPYPE_EXTRAS="doc,tests,fmri,profiler,duecredit"
 before_install:
-- function bef_inst {
-  wget http://repo.continuum.io/miniconda/Miniconda${TRAVIS_PYTHON_VERSION:0:1}-latest-Linux-x86_64.sh
-  -O /home/travis/.cache/miniconda.sh &&
-  bash /home/travis/.cache/miniconda.sh -b -p /home/travis/miniconda &&
-  export PATH=/home/travis/miniconda/bin:$PATH &&
+- function apt_inst {
   if $INSTALL_DEB_DEPENDECIES; then sudo rm -rf /dev/shm; fi &&
   if $INSTALL_DEB_DEPENDECIES; then sudo ln -s /run/shm /dev/shm; fi &&
   bash <(wget -q -O- http://neuro.debian.net/_files/neurodebian-travis.sh) &&
@@ -26,18 +23,24 @@ before_install:
     source /etc/fsl/fsl.sh;
     source /etc/afni/afni.sh;
     export FSLOUTPUTTYPE=NIFTI_GZ; fi }
-- travis_retry bef_inst
-install:
-#  Add install of vtk and mayavi to test mesh (disabled): conda install -y vtk mayavi &&
-- function inst {
+- function conda_inst {
+  export CONDA_HOME=$HOME/conda &&
+  wget https://repo.continuum.io/miniconda/Miniconda${TRAVIS_PYTHON_VERSION:0:1}-latest-Linux-x86_64.sh
+    -O /home/travis/.cache/conda.sh &&
+  bash /home/travis/.cache/conda.sh -b -p ${CONDA_HOME} &&
+  export PATH=${CONDA_HOME}/bin:$PATH &&
+  hash -r &&
+  conda config --set always_yes yes --set changeps1 no &&
+  conda update -q conda &&
+  conda install python=${TRAVIS_PYTHON_VERSION} &&
   conda config --add channels conda-forge &&
-  conda update --yes conda &&
-  conda update --all -y python=$TRAVIS_PYTHON_VERSION &&
   conda install -y nipype icu &&
-  rm -r /home/travis/miniconda/lib/python${TRAVIS_PYTHON_VERSION}/site-packages/nipype* &&
-  pip install -r requirements.txt &&
-  pip install -e .[$NIPYPE_EXTRAS]; }
-- travis_retry inst
+  rm -r ${CONDA_HOME}/lib/python${TRAVIS_PYTHON_VERSION}/site-packages/nipype*; }
+#  Add install of vtk and mayavi to test mesh (disabled): conda install -y vtk mayavi
+- travis_retry apt_inst
+- travis_retry conda_inst
+install:
+- travis_retry pip install -e .[$NIPYPE_EXTRAS]
 script:
 - py.test --doctest-modules nipype
 deploy:

examples/dmri_camino_dti.py

@@ -18,14 +18,14 @@
 Import necessary modules from nipype.
 """
 
+import os                                    # system functions
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.utility as util     # utility
 import nipype.pipeline.engine as pe          # pypeline engine
 import nipype.interfaces.camino as camino
 import nipype.interfaces.fsl as fsl
 import nipype.interfaces.camino2trackvis as cam2trk
 import nipype.algorithms.misc as misc
-import os                                    # system functions
 
 """
 We use the following functions to scrape the voxel and data dimensions of the input images. This allows the
@@ -36,27 +36,30 @@
 
 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)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     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)
+    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)
+    from nipype.utils import NUMPY_MMAP
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     return nii.affine
 
 subject_list = ['subj1']

examples/dmri_connectivity.py

@@ -47,6 +47,10 @@
 First, we import the necessary modules from nipype.
 """
 
+import inspect
+
+import os.path as op                      # system functions
+import cmp                                    # connectome mapper
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.utility as util     # utility
 import nipype.pipeline.engine as pe          # pypeline engine
@@ -56,10 +60,6 @@
 import nipype.interfaces.freesurfer as fs    # freesurfer
 import nipype.interfaces.cmtk as cmtk
 import nipype.algorithms.misc as misc
-import inspect
-
-import os.path as op                      # system functions
-import cmp                                    # connectome mapper
 
 """
 We define the following functions to scrape the voxel and data dimensions of the input images. This allows the
@@ -74,27 +74,30 @@
 
 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)
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     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)
+    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)
+    from nipype.utils import NUMPY_MMAP
+    nii = nb.load(volume, mmap=NUMPY_MMAP)
     return nii.affine
 
 

examples/fmri_ants_openfmri.py

@@ -36,6 +36,8 @@
 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
@@ -68,7 +70,7 @@ 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_fsl.py

@@ -106,10 +106,11 @@ 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).shape
+    _, _, _, timepoints = load(funcfile, mmap=NUMPY_MMAP).shape
     return int(timepoints / 2) - 1
 
 preproc.connect(inputnode, ('func', getmiddlevolume), extract_ref, 't_min')

examples/fmri_spm_auditory.py

@@ -29,6 +29,7 @@
 import nipype.algorithms.modelgen as model   # model specification
 import os                                    # system functions
 
+
 """
 
 Preliminaries
@@ -120,9 +121,10 @@
 
 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)
+    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

@@ -20,13 +20,13 @@
 from __future__ import division
 from builtins import range
 
+import os                                    # system functions
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.spm as spm          # spm
 import nipype.interfaces.matlab as mlab      # how to run matlab
 import nipype.interfaces.utility as util     # utility
 import nipype.pipeline.engine as pe          # pypeline engine
 import nipype.algorithms.modelgen as model   # model specification
-import os                                    # system functions
 
 """
 
@@ -114,9 +114,10 @@
 
 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)
+    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

@@ -76,6 +76,7 @@
 import scipy as sp
 import nibabel as nb
 
+
 imports = ['import os',
            'import nibabel as nb',
            'import numpy as np',
@@ -117,9 +118,10 @@ 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)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         data = np.median(img.get_data(), axis=3)
         if average is None:
             average = data
@@ -145,11 +147,12 @@ 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)
+        img = nb.load(filename, mmap=NUMPY_MMAP)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
         lowidx = int(timepoints / 2) + 1
@@ -260,11 +263,12 @@ def extract_noise_components(realigned_file, mask_file, num_components=5,
     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)
+    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]
@@ -329,10 +333,11 @@ 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)
+    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 +357,9 @@ 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()
+    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()
 
     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

@@ -75,6 +75,7 @@
 import numpy as np
 import scipy as sp
 import nibabel as nb
+from nipype.utils import NUMPY_MMAP
 
 imports = ['import os',
            'import nibabel as nb',
@@ -116,7 +117,7 @@ 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 +144,7 @@ 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 +269,9 @@ 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 +289,8 @@ 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]))