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Merge remote-tracking branch 'upstream/master' into doc/sphinxext-interfaces
2 parents 9a02161 + 2125c0b commit 4c4c00c

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lines changed

‎.mailmap

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Original file line numberDiff line numberDiff line change
@@ -67,6 +67,7 @@ Gio Piantoni <[email protected]>
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Guillaume Flandin <[email protected]>
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Hans Johnson <[email protected]> <[email protected]>
6969
Horea Christian <[email protected]> <[email protected]>
70+
Hrvoje Stojic <[email protected]>
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Isaac Schwabacher <[email protected]>
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Jakub Kaczmarzyk <[email protected]>
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James Kent <[email protected]>

‎.zenodo.json

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@@ -514,6 +514,11 @@
514514
{
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"name": "Blair, Ross"
516516
},
517+
{
518+
"affiliation": "Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London",
519+
"name": "Stojic, Hrvoje",
520+
"orcid": "0000-0002-9699-9052"
521+
},
517522
{
518523
"affiliation": "The University of Texas at Austin",
519524
"name": "Floren, Andrew",

‎examples/dmri_camino_dti.py

Lines changed: 3 additions & 6 deletions
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

‎examples/dmri_connectivity.py

Lines changed: 3 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -72,30 +72,27 @@
7272

7373
def get_vox_dims(volume):
7474
import nibabel as nb
75-
from nipype.utils import NUMPY_MMAP
7675
if isinstance(volume, list):
7776
volume = volume[0]
78-
nii = nb.load(volume, mmap=NUMPY_MMAP)
77+
nii = nb.load(volume)
7978
hdr = nii.header
8079
voxdims = hdr.get_zooms()
8180
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
8281

8382

8483
def get_data_dims(volume):
8584
import nibabel as nb
86-
from nipype.utils import NUMPY_MMAP
8785
if isinstance(volume, list):
8886
volume = volume[0]
89-
nii = nb.load(volume, mmap=NUMPY_MMAP)
87+
nii = nb.load(volume)
9088
hdr = nii.header
9189
datadims = hdr.get_data_shape()
9290
return [int(datadims[0]), int(datadims[1]), int(datadims[2])]
9391

9492

9593
def get_affine(volume):
9694
import nibabel as nb
97-
from nipype.utils import NUMPY_MMAP
98-
nii = nb.load(volume, mmap=NUMPY_MMAP)
95+
nii = nb.load(volume)
9996
return nii.affine
10097

10198

‎examples/fmri_ants_openfmri.py

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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

‎examples/fmri_fsl.py

Lines changed: 1 addition & 2 deletions
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

‎examples/fmri_spm_auditory.py

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -108,10 +108,9 @@
108108

109109
def get_vox_dims(volume):
110110
import nibabel as nb
111-
from nipype.utils import NUMPY_MMAP
112111
if isinstance(volume, list):
113112
volume = volume[0]
114-
nii = nb.load(volume, mmap=NUMPY_MMAP)
113+
nii = nb.load(volume)
115114
hdr = nii.header
116115
voxdims = hdr.get_zooms()
117116
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

‎examples/fmri_spm_face.py

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -103,10 +103,9 @@
103103

104104
def get_vox_dims(volume):
105105
import nibabel as nb
106-
from nipype.utils import NUMPY_MMAP
107106
if isinstance(volume, list):
108107
volume = volume[0]
109-
nii = nb.load(volume, mmap=NUMPY_MMAP)
108+
nii = nb.load(volume)
110109
hdr = nii.header
111110
voxdims = hdr.get_zooms()
112111
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]

‎examples/rsfmri_vol_surface_preprocessing.py

Lines changed: 8 additions & 13 deletions
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]))

‎examples/rsfmri_vol_surface_preprocessing_nipy.py

Lines changed: 6 additions & 7 deletions
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]))

‎nipype/algorithms/confounds.py

Lines changed: 4 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,6 @@
2626
OutputMultiPath,
2727
SimpleInterface,
2828
)
29-
from ..utils import NUMPY_MMAP
3029
from ..utils.misc import normalize_mc_params
3130

3231
IFLOGGER = logging.getLogger("nipype.interface")
@@ -599,7 +598,7 @@ def _run_interface(self, runtime):
599598
else 0
600599
)
601600

602-
imgseries = nb.load(self.inputs.realigned_file, mmap=NUMPY_MMAP)
601+
imgseries = nb.load(self.inputs.realigned_file)
603602

604603
if len(imgseries.shape) != 4:
605604
raise ValueError(
@@ -915,7 +914,7 @@ class TSNR(BaseInterface):
915914
output_spec = TSNROutputSpec
916915

917916
def _run_interface(self, runtime):
918-
img = nb.load(self.inputs.in_file[0], mmap=NUMPY_MMAP)
917+
img = nb.load(self.inputs.in_file[0])
919918
header = img.header.copy()
920919
vollist = [nb.load(filename) for filename in self.inputs.in_file]
921920
data = np.concatenate(
@@ -1266,7 +1265,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
12661265
)
12671266
)
12681267
if mask_index < len(mask_files):
1269-
mask = nb.load(mask_files[mask_index], mmap=NUMPY_MMAP)
1268+
mask = nb.load(mask_files[mask_index])
12701269
return [mask]
12711270
raise ValueError(
12721271
("mask_index {0} must be less than number of mask " "files {1}").format(
@@ -1276,7 +1275,7 @@ def combine_mask_files(mask_files, mask_method=None, mask_index=None):
12761275
masks = []
12771276
if mask_method == "none":
12781277
for filename in mask_files:
1279-
masks.append(nb.load(filename, mmap=NUMPY_MMAP))
1278+
masks.append(nb.load(filename))
12801279
return masks
12811280

12821281
if mask_method == "union":

‎nipype/algorithms/icc.py

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,6 @@
1111
traits,
1212
File,
1313
)
14-
from ..utils import NUMPY_MMAP
1514

1615

1716
class ICCInputSpec(BaseInterfaceInputSpec):
@@ -46,7 +45,7 @@ def _run_interface(self, runtime):
4645

4746
session_datas = [
4847
[
49-
nb.load(fname, mmap=NUMPY_MMAP).get_fdata()[maskdata].reshape(-1, 1)
48+
nb.load(fname).get_fdata()[maskdata].reshape(-1, 1)
5049
for fname in sessions
5150
]
5251
for sessions in self.inputs.subjects_sessions

‎nipype/algorithms/misc.py

Lines changed: 4 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,6 @@
2626
Undefined,
2727
)
2828
from ..utils.filemanip import fname_presuffix, split_filename, ensure_list
29-
from ..utils import NUMPY_MMAP
3029

3130
from . import confounds
3231

@@ -208,7 +207,7 @@ def _gen_output_filename(self, name):
208207

209208
def _run_interface(self, runtime):
210209
for fname in self.inputs.volumes:
211-
img = nb.load(fname, mmap=NUMPY_MMAP)
210+
img = nb.load(fname)
212211

213212
affine = img.affine
214213
affine = np.dot(self.inputs.transformation_matrix, affine)
@@ -1324,7 +1323,7 @@ def split_rois(in_file, mask=None, roishape=None):
13241323
if roishape is None:
13251324
roishape = (10, 10, 1)
13261325

1327-
im = nb.load(in_file, mmap=NUMPY_MMAP)
1326+
im = nb.load(in_file)
13281327
imshape = im.shape
13291328
dshape = imshape[:3]
13301329
nvols = imshape[-1]
@@ -1415,7 +1414,7 @@ def merge_rois(in_files, in_idxs, in_ref, dtype=None, out_file=None):
14151414
except:
14161415
pass
14171416

1418-
ref = nb.load(in_ref, mmap=NUMPY_MMAP)
1417+
ref = nb.load(in_ref)
14191418
aff = ref.affine
14201419
hdr = ref.header.copy()
14211420
rsh = ref.shape
@@ -1473,7 +1472,7 @@ def merge_rois(in_files, in_idxs, in_ref, dtype=None, out_file=None):
14731472
data[idata] = cdata[0:nels]
14741473
nb.Nifti1Image(data.reshape(rsh[:3]), aff, hdr).to_filename(fname)
14751474

1476-
imgs = [nb.load(im, mmap=NUMPY_MMAP) for im in nii]
1475+
imgs = [nb.load(im) for im in nii]
14771476
allim = nb.concat_images(imgs)
14781477
allim.to_filename(out_file)
14791478

‎nipype/algorithms/modelgen.py

Lines changed: 3 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -13,7 +13,6 @@
1313
from nibabel import load
1414
import numpy as np
1515

16-
from ..utils import NUMPY_MMAP
1716
from ..interfaces.base import (
1817
BaseInterface,
1918
TraitedSpec,
@@ -468,7 +467,7 @@ def _generate_standard_design(
468467
for i, out in enumerate(outliers):
469468
numscans = 0
470469
for f in ensure_list(sessinfo[i]["scans"]):
471-
shape = load(f, mmap=NUMPY_MMAP).shape
470+
shape = load(f).shape
472471
if len(shape) == 3 or shape[3] == 1:
473472
iflogger.warning(
474473
"You are using 3D instead of 4D "
@@ -598,7 +597,7 @@ def _concatenate_info(self, infolist):
598597
if isinstance(f, list):
599598
numscans = len(f)
600599
elif isinstance(f, (str, bytes)):
601-
img = load(f, mmap=NUMPY_MMAP)
600+
img = load(f)
602601
numscans = img.shape[3]
603602
else:
604603
raise Exception("Functional input not specified correctly")
@@ -976,7 +975,7 @@ def _generate_clustered_design(self, infolist):
976975
infoout[i].onsets = None
977976
infoout[i].durations = None
978977
if info.conditions:
979-
img = load(self.inputs.functional_runs[i], mmap=NUMPY_MMAP)
978+
img = load(self.inputs.functional_runs[i])
980979
nscans = img.shape[3]
981980
reg, regnames = self._cond_to_regress(info, nscans)
982981
if hasattr(infoout[i], "regressors") and infoout[i].regressors:

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