Merge pull request #3121 from steve19922/stop-using-numpy_mmap-constant

[REF] removed all uses of numpy_mmap

Author: effigies

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

@@ -73,30 +73,27 @@
 
 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

@@ -107,10 +107,9 @@
 
 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

@@ -101,10 +101,9 @@
 
 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]))

nipype/algorithms/confounds.py

@@ -26,7 +26,6 @@
     OutputMultiPath,
     SimpleInterface,
 )
-from ..utils import NUMPY_MMAP
 from ..utils.misc import normalize_mc_params
 
 IFLOGGER = logging.getLogger("nipype.interface")
@@ -599,7 +598,7 @@ def _run_interface(self, runtime):
             else 0
         )
 
-        imgseries = nb.load(self.inputs.realigned_file, mmap=NUMPY_MMAP)
+        imgseries = nb.load(self.inputs.realigned_file)
 
         if len(imgseries.shape) != 4:
             raise ValueError(
@@ -917,7 +916,7 @@ class TSNR(BaseInterface):
     output_spec = TSNROutputSpec
 
     def _run_interface(self, runtime):
-        img = nb.load(self.inputs.in_file[0], mmap=NUMPY_MMAP)
+        img = nb.load(self.inputs.in_file[0])
         header = img.header.copy()
         vollist = [nb.load(filename) for filename in self.inputs.in_file]
         data = np.concatenate(
@@ -1263,7 +1262,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
                     )
                 )
         if mask_index < len(mask_files):
-            mask = nb.load(mask_files[mask_index], mmap=NUMPY_MMAP)
+            mask = nb.load(mask_files[mask_index])
             return [mask]
         raise ValueError(
             ("mask_index {0} must be less than number of mask " "files {1}").format(
@@ -1273,7 +1272,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
     masks = []
     if mask_method == "none":
         for filename in mask_files:
-            masks.append(nb.load(filename, mmap=NUMPY_MMAP))
+            masks.append(nb.load(filename))
         return masks
 
     if mask_method == "union":

nipype/algorithms/icc.py

@@ -11,7 +11,6 @@
     traits,
     File,
 )
-from ..utils import NUMPY_MMAP
 
 
 class ICCInputSpec(BaseInterfaceInputSpec):
@@ -46,7 +45,7 @@ def _run_interface(self, runtime):
 
         session_datas = [
             [
-                nb.load(fname, mmap=NUMPY_MMAP).get_fdata()[maskdata].reshape(-1, 1)
+                nb.load(fname).get_fdata()[maskdata].reshape(-1, 1)
                 for fname in sessions
             ]
             for sessions in self.inputs.subjects_sessions

nipype/algorithms/misc.py

@@ -28,7 +28,6 @@
     Undefined,
 )
 from ..utils.filemanip import fname_presuffix, split_filename, ensure_list
-from ..utils import NUMPY_MMAP
 
 from . import confounds
 
@@ -210,7 +209,7 @@ def _gen_output_filename(self, name):
 
     def _run_interface(self, runtime):
         for fname in self.inputs.volumes:
-            img = nb.load(fname, mmap=NUMPY_MMAP)
+            img = nb.load(fname)
 
             affine = img.affine
             affine = np.dot(self.inputs.transformation_matrix, affine)
@@ -1320,7 +1319,7 @@ def split_rois(in_file, mask=None, roishape=None):
     if roishape is None:
         roishape = (10, 10, 1)
 
-    im = nb.load(in_file, mmap=NUMPY_MMAP)
+    im = nb.load(in_file)
     imshape = im.shape
     dshape = imshape[:3]
     nvols = imshape[-1]
@@ -1411,7 +1410,7 @@ def merge_rois(in_files, in_idxs, in_ref, dtype=None, out_file=None):
         except:
             pass
 
-    ref = nb.load(in_ref, mmap=NUMPY_MMAP)
+    ref = nb.load(in_ref)
     aff = ref.affine
     hdr = ref.header.copy()
     rsh = ref.shape
@@ -1469,7 +1468,7 @@ def merge_rois(in_files, in_idxs, in_ref, dtype=None, out_file=None):
                 data[idata] = cdata[0:nels]
                 nb.Nifti1Image(data.reshape(rsh[:3]), aff, hdr).to_filename(fname)
 
-        imgs = [nb.load(im, mmap=NUMPY_MMAP) for im in nii]
+        imgs = [nb.load(im) for im in nii]
         allim = nb.concat_images(imgs)
         allim.to_filename(out_file)
 

nipype/algorithms/modelgen.py

@@ -17,7 +17,6 @@
 from nibabel import load
 import numpy as np
 
-from ..utils import NUMPY_MMAP
 from ..interfaces.base import (
     BaseInterface,
     TraitedSpec,
@@ -474,7 +473,7 @@ def _generate_standard_design(
             for i, out in enumerate(outliers):
                 numscans = 0
                 for f in ensure_list(sessinfo[i]["scans"]):
-                    shape = load(f, mmap=NUMPY_MMAP).shape
+                    shape = load(f).shape
                     if len(shape) == 3 or shape[3] == 1:
                         iflogger.warning(
                             "You are using 3D instead of 4D "
@@ -604,7 +603,7 @@ def _concatenate_info(self, infolist):
             if isinstance(f, list):
                 numscans = len(f)
             elif isinstance(f, (str, bytes)):
-                img = load(f, mmap=NUMPY_MMAP)
+                img = load(f)
                 numscans = img.shape[3]
             else:
                 raise Exception("Functional input not specified correctly")
@@ -984,7 +983,7 @@ def _generate_clustered_design(self, infolist):
             infoout[i].onsets = None
             infoout[i].durations = None
             if info.conditions:
-                img = load(self.inputs.functional_runs[i], mmap=NUMPY_MMAP)
+                img = load(self.inputs.functional_runs[i])
                 nscans = img.shape[3]
                 reg, regnames = self._cond_to_regress(info, nscans)
                 if hasattr(infoout[i], "regressors") and infoout[i].regressors:

nipype/algorithms/rapidart.py

@@ -18,7 +18,6 @@
 from nibabel import load, funcs, Nifti1Image
 import numpy as np
 
-from ..utils import NUMPY_MMAP
 from ..interfaces.base import (
     BaseInterface,
     traits,
@@ -485,12 +484,12 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
 
         # read in functional image
         if isinstance(imgfile, (str, bytes)):
-            nim = load(imgfile, mmap=NUMPY_MMAP)
+            nim = load(imgfile)
         elif isinstance(imgfile, list):
             if len(imgfile) == 1:
-                nim = load(imgfile[0], mmap=NUMPY_MMAP)
+                nim = load(imgfile[0])
             else:
-                images = [load(f, mmap=NUMPY_MMAP) for f in imgfile]
+                images = [load(f) for f in imgfile]
                 nim = funcs.concat_images(images)
 
         # compute global intensity signal