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RF: Drop NUMPY_MMAP constant
1 parent 4fa5f53 commit 714868d

8 files changed

+23
-39
lines changed

‎package/niflow/nipype1/examples/dmri_camino_dti.py

+3-6
Original file line numberDiff line numberDiff line change
@@ -35,30 +35,27 @@
3535

3636
def get_vox_dims(volume):
3737
import nibabel as nb
38-
from nipype.utils import NUMPY_MMAP
3938
if isinstance(volume, list):
4039
volume = volume[0]
41-
nii = nb.load(volume, mmap=NUMPY_MMAP)
40+
nii = nb.load(volume)
4241
hdr = nii.header
4342
voxdims = hdr.get_zooms()
4443
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
4544

4645

4746
def get_data_dims(volume):
4847
import nibabel as nb
49-
from nipype.utils import NUMPY_MMAP
5048
if isinstance(volume, list):
5149
volume = volume[0]
52-
nii = nb.load(volume, mmap=NUMPY_MMAP)
50+
nii = nb.load(volume)
5351
hdr = nii.header
5452
datadims = hdr.get_data_shape()
5553
return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
5654

5755

5856
def get_affine(volume):
5957
import nibabel as nb
60-
from nipype.utils import NUMPY_MMAP
61-
nii = nb.load(volume, mmap=NUMPY_MMAP)
58+
nii = nb.load(volume)
6259
return nii.affine
6360

6461

‎package/niflow/nipype1/examples/dmri_connectivity.py

+3-6
Original file line numberDiff line numberDiff line change
@@ -73,30 +73,27 @@
7373

7474
def get_vox_dims(volume):
7575
import nibabel as nb
76-
from nipype.utils import NUMPY_MMAP
7776
if isinstance(volume, list):
7877
volume = volume[0]
79-
nii = nb.load(volume, mmap=NUMPY_MMAP)
78+
nii = nb.load(volume)
8079
hdr = nii.header
8180
voxdims = hdr.get_zooms()
8281
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
8382

8483

8584
def get_data_dims(volume):
8685
import nibabel as nb
87-
from nipype.utils import NUMPY_MMAP
8886
if isinstance(volume, list):
8987
volume = volume[0]
90-
nii = nb.load(volume, mmap=NUMPY_MMAP)
88+
nii = nb.load(volume)
9189
hdr = nii.header
9290
datadims = hdr.get_data_shape()
9391
return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
9492

9593

9694
def get_affine(volume):
9795
import nibabel as nb
98-
from nipype.utils import NUMPY_MMAP
99-
nii = nb.load(volume, mmap=NUMPY_MMAP)
96+
nii = nb.load(volume)
10097
return nii.affine
10198

10299

‎package/niflow/nipype1/examples/fmri_ants_openfmri.py

-1
Original file line numberDiff line numberDiff line change
@@ -41,7 +41,6 @@
4141
from nipype.workflows.fmri.fsl import (create_featreg_preproc,
4242
create_modelfit_workflow,
4343
create_fixed_effects_flow)
44-
from nipype.utils import NUMPY_MMAP
4544

4645
config.enable_provenance()
4746
version = 0

‎package/niflow/nipype1/examples/fmri_fsl.py

+1-2
Original file line numberDiff line numberDiff line change
@@ -101,11 +101,10 @@ def pickfirst(files):
101101

102102
def getmiddlevolume(func):
103103
from nibabel import load
104-
from nipype.utils import NUMPY_MMAP
105104
funcfile = func
106105
if isinstance(func, list):
107106
funcfile = func[0]
108-
_, _, _, timepoints = load(funcfile, mmap=NUMPY_MMAP).shape
107+
_, _, _, timepoints = load(funcfile).shape
109108
return int(timepoints / 2) - 1
110109

111110

‎package/niflow/nipype1/examples/fmri_spm_auditory.py

+1-2
Original file line numberDiff line numberDiff line change
@@ -107,10 +107,9 @@
107107

108108
def get_vox_dims(volume):
109109
import nibabel as nb
110-
from nipype.utils import NUMPY_MMAP
111110
if isinstance(volume, list):
112111
volume = volume[0]
113-
nii = nb.load(volume, mmap=NUMPY_MMAP)
112+
nii = nb.load(volume)
114113
hdr = nii.header
115114
voxdims = hdr.get_zooms()
116115
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

‎package/niflow/nipype1/examples/fmri_spm_face.py

+1-2
Original file line numberDiff line numberDiff line change
@@ -101,10 +101,9 @@
101101

102102
def get_vox_dims(volume):
103103
import nibabel as nb
104-
from nipype.utils import NUMPY_MMAP
105104
if isinstance(volume, list):
106105
volume = volume[0]
107-
nii = nb.load(volume, mmap=NUMPY_MMAP)
106+
nii = nb.load(volume)
108107
hdr = nii.header
109108
voxdims = hdr.get_zooms()
110109
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

‎package/niflow/nipype1/examples/rsfmri_vol_surface_preprocessing.py

+8-13
Original file line numberDiff line numberDiff line change
@@ -117,10 +117,9 @@ def median(in_files):
117117
"""
118118
import numpy as np
119119
import nibabel as nb
120-
from nipype.utils import NUMPY_MMAP
121120
average = None
122121
for idx, filename in enumerate(filename_to_list(in_files)):
123-
img = nb.load(filename, mmap=NUMPY_MMAP)
122+
img = nb.load(filename)
124123
data = np.median(img.get_data(), axis=3)
125124
if average is None:
126125
average = data
@@ -146,12 +145,11 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
146145
from nipype.utils.filemanip import split_filename, list_to_filename
147146
import numpy as np
148147
import nibabel as nb
149-
from nipype.utils import NUMPY_MMAP
150148
out_files = []
151149
for filename in filename_to_list(files):
152150
path, name, ext = split_filename(filename)
153151
out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
154-
img = nb.load(filename, mmap=NUMPY_MMAP)
152+
img = nb.load(filename)
155153
timepoints = img.shape[-1]
156154
F = np.zeros((timepoints))
157155
lowidx = int(timepoints / 2) + 1
@@ -264,12 +262,11 @@ def extract_noise_components(realigned_file,
264262
from scipy.linalg.decomp_svd import svd
265263
import numpy as np
266264
import nibabel as nb
267-
from nipype.utils import NUMPY_MMAP
268265
import os
269-
imgseries = nb.load(realigned_file, mmap=NUMPY_MMAP)
266+
imgseries = nb.load(realigned_file)
270267
components = None
271268
for filename in filename_to_list(mask_file):
272-
mask = nb.load(filename, mmap=NUMPY_MMAP).get_data()
269+
mask = nb.load(filename).get_data()
273270
if len(np.nonzero(mask > 0)[0]) == 0:
274271
continue
275272
voxel_timecourses = imgseries.get_data()[mask > 0]
@@ -334,11 +331,10 @@ def extract_subrois(timeseries_file, label_file, indices):
334331
"""
335332
from nipype.utils.filemanip import split_filename
336333
import nibabel as nb
337-
from nipype.utils import NUMPY_MMAP
338334
import os
339-
img = nb.load(timeseries_file, mmap=NUMPY_MMAP)
335+
img = nb.load(timeseries_file)
340336
data = img.get_data()
341-
roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
337+
roiimg = nb.load(label_file)
342338
rois = roiimg.get_data()
343339
prefix = split_filename(timeseries_file)[1]
344340
out_ts_file = os.path.join(os.getcwd(), '%s_subcortical_ts.txt' % prefix)
@@ -359,9 +355,8 @@ def combine_hemi(left, right):
359355
"""
360356
import os
361357
import numpy as np
362-
from nipype.utils import NUMPY_MMAP
363-
lh_data = nb.load(left, mmap=NUMPY_MMAP).get_data()
364-
rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
358+
lh_data = nb.load(left).get_data()
359+
rh_data = nb.load(right).get_data()
365360

366361
indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
367362
2000000 + np.arange(0, rh_data.shape[0])[:, None]))

‎package/niflow/nipype1/examples/rsfmri_vol_surface_preprocessing_nipy.py

+6-7
Original file line numberDiff line numberDiff line change
@@ -76,7 +76,6 @@
7676
import numpy as np
7777
import scipy as sp
7878
import nibabel as nb
79-
from nipype.utils.config import NUMPY_MMAP
8079

8180
"""
8281
A list of modules and functions to import inside of nodes
@@ -129,7 +128,7 @@ def median(in_files):
129128
"""
130129
average = None
131130
for idx, filename in enumerate(filename_to_list(in_files)):
132-
img = nb.load(filename, mmap=NUMPY_MMAP)
131+
img = nb.load(filename)
133132
data = np.median(img.get_data(), axis=3)
134133
if average is None:
135134
average = data
@@ -156,7 +155,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
156155
for filename in filename_to_list(files):
157156
path, name, ext = split_filename(filename)
158157
out_file = os.path.join(os.getcwd(), name + '_bp' + ext)
159-
img = nb.load(filename, mmap=NUMPY_MMAP)
158+
img = nb.load(filename)
160159
timepoints = img.shape[-1]
161160
F = np.zeros((timepoints))
162161
lowidx = int(timepoints / 2) + 1
@@ -282,9 +281,9 @@ def extract_subrois(timeseries_file, label_file, indices):
282281
The first four columns are: freesurfer index, i, j, k positions in the
283282
label file
284283
"""
285-
img = nb.load(timeseries_file, mmap=NUMPY_MMAP)
284+
img = nb.load(timeseries_file)
286285
data = img.get_data()
287-
roiimg = nb.load(label_file, mmap=NUMPY_MMAP)
286+
roiimg = nb.load(label_file)
288287
rois = roiimg.get_data()
289288
prefix = split_filename(timeseries_file)[1]
290289
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):
303302
def combine_hemi(left, right):
304303
"""Combine left and right hemisphere time series into a single text file
305304
"""
306-
lh_data = nb.load(left, mmap=NUMPY_MMAP).get_data()
307-
rh_data = nb.load(right, mmap=NUMPY_MMAP).get_data()
305+
lh_data = nb.load(left).get_data()
306+
rh_data = nb.load(right).get_data()
308307

309308
indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
310309
2000000 + np.arange(0, rh_data.shape[0])[:, None]))

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