sty: mass ruffication

nipreps/synthstrip/__main__.py

@@ -21,12 +21,14 @@
 #     https://www.nipreps.org/community/licensing/
 #
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     import sys
+
     from nipreps.synthstrip.cli import main
+
     from . import __name__ as module
 
     # `python -m <module>` typically displays the command as __main__.py
-    if "__main__.py" in sys.argv[0]:
-        sys.argv[0] = "%s -m %s" % (sys.executable, module)
+    if '__main__.py' in sys.argv[0]:
+        sys.argv[0] = f'{sys.executable} -m {module}'
     main()

nipreps/synthstrip/cli.py

@@ -49,63 +49,60 @@ def main():
     """Entry point to SynthStrip."""
     import os
     from argparse import ArgumentParser
+
+    import nibabel as nb
     import numpy as np
     import scipy
-    import nibabel as nb
     import torch
+
     from .model import StripModel
 
     # parse command line
     parser = ArgumentParser(description=__doc__)
     parser.add_argument(
-        "-i",
-        "--image",
-        metavar="file",
+        '-i',
+        '--image',
+        metavar='file',
         required=True,
-        help="Input image to skullstrip.",
+        help='Input image to skullstrip.',
     )
+    parser.add_argument('-o', '--out', metavar='file', help='Save stripped image to path.')
+    parser.add_argument('-m', '--mask', metavar='file', help='Save binary brain mask to path.')
+    parser.add_argument('-g', '--gpu', action='store_true', help='Use the GPU.')
+    parser.add_argument('-n', '--num-threads', action='store', type=int, help='number of threads')
     parser.add_argument(
-        "-o", "--out", metavar="file", help="Save stripped image to path."
-    )
-    parser.add_argument(
-        "-m", "--mask", metavar="file", help="Save binary brain mask to path."
-    )
-    parser.add_argument("-g", "--gpu", action="store_true", help="Use the GPU.")
-    parser.add_argument(
-        "-n", "--num-threads", action="store", type=int, help="number of threads")
-    parser.add_argument(
-        "-b",
-        "--border",
+        '-b',
+        '--border',
         default=1,
         type=int,
-        help="Mask border threshold in mm. Default is 1.",
+        help='Mask border threshold in mm. Default is 1.',
     )
-    parser.add_argument("--model", metavar="file", help="Alternative model weights.")
+    parser.add_argument('--model', metavar='file', help='Alternative model weights.')
     args = parser.parse_args()
 
     # sanity check on the inputs
     if not args.out and not args.mask:
-        parser.fatal("Must provide at least --out or --mask output flags.")
+        parser.fatal('Must provide at least --out or --mask output flags.')
 
     # necessary for speed gains (I think)
     torch.backends.cudnn.benchmark = True
     torch.backends.cudnn.deterministic = True
 
     # configure GPU device
     if args.gpu:
-        os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-        device = torch.device("cuda")
-        device_name = "GPU"
+        os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+        device = torch.device('cuda')
+        device_name = 'GPU'
     else:
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-        device = torch.device("cpu")
-        device_name = "CPU"
+        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+        device = torch.device('cpu')
+        device_name = 'CPU'
 
         if args.num_threads and args.num_threads > 0:
             torch.set_num_threads(args.num_threads)
 
     # configure model
-    print(f"Configuring model on the {device_name}")
+    print(f'Configuring model on the {device_name}')
 
     with torch.no_grad():
         model = StripModel()
@@ -115,20 +112,20 @@ def main():
     # load model weights
     if args.model is not None:
         modelfile = args.model
-        print("Using custom model weights")
+        print('Using custom model weights')
     else:
-        raise RuntimeError("A model must be provided.")
+        raise RuntimeError('A model must be provided.')
 
     checkpoint = torch.load(modelfile, map_location=device)
-    model.load_state_dict(checkpoint["model_state_dict"])
+    model.load_state_dict(checkpoint['model_state_dict'])
 
     # load input volume
-    print(f"Input image read from: {args.image}")
+    print(f'Input image read from: {args.image}')
 
     # normalize intensities
     image = nb.load(args.image)
     conformed = conform(image)
-    in_data = conformed.get_fdata(dtype="float32")
+    in_data = conformed.get_fdata(dtype='float32')
     in_data -= in_data.min()
     in_data = np.clip(in_data / np.percentile(in_data, 99), 0, 1)
     in_data = in_data[np.newaxis, np.newaxis]
@@ -142,10 +139,10 @@ def main():
     sdt_target = resample_like(
         nb.Nifti1Image(sdt, conformed.affine, None),
         image,
-        output_dtype="int16",
+        output_dtype='int16',
         cval=100,
     )
-    sdt_data = np.asanyarray(sdt_target.dataobj).astype("int16")
+    sdt_data = np.asanyarray(sdt_target.dataobj).astype('int16')
 
     # find largest CC (just do this to be safe for now)
     components = scipy.ndimage.label(sdt_data.squeeze() < args.border)[0]
@@ -161,25 +158,25 @@ def main():
         nb.Nifti1Image(img_data, image.affine, image.header).to_filename(
             args.out,
         )
-        print(f"Masked image saved to: {args.out}")
+        print(f'Masked image saved to: {args.out}')
 
     # write the brain mask
     if args.mask:
         hdr = image.header.copy()
-        hdr.set_data_dtype("uint8")
+        hdr.set_data_dtype('uint8')
         nb.Nifti1Image(mask, image.affine, hdr).to_filename(args.mask)
-        print(f"Binary brain mask saved to: {args.mask}")
+        print(f'Binary brain mask saved to: {args.mask}')
 
-    print("If you use SynthStrip in your analysis, please cite:")
-    print("----------------------------------------------------")
-    print("SynthStrip: Skull-Stripping for Any Brain Image.")
-    print("A Hoopes, JS Mora, AV Dalca, B Fischl, M Hoffmann.")
+    print('If you use SynthStrip in your analysis, please cite:')
+    print('----------------------------------------------------')
+    print('SynthStrip: Skull-Stripping for Any Brain Image.')
+    print('A Hoopes, JS Mora, AV Dalca, B Fischl, M Hoffmann.')
 
 
 def conform(input_nii):
     """Resample image as SynthStrip likes it."""
-    import numpy as np
     import nibabel as nb
+    import numpy as np
     from nitransforms.linear import Affine
 
     shape = np.array(input_nii.shape[:3])
@@ -199,10 +196,7 @@ def conform(input_nii):
     )
 
     # Get corner voxel centers in mm
-    corners_xyz = (
-        affine
-        @ np.hstack((corner_centers_ijk, np.ones((len(corner_centers_ijk), 1)))).T
-    )
+    corners_xyz = affine @ np.hstack((corner_centers_ijk, np.ones((len(corner_centers_ijk), 1)))).T
 
     # Target affine is 1mm voxels in LIA orientation
     target_affine = np.diag([-1.0, 1.0, -1.0, 1.0])[:, (0, 2, 1, 3)]
@@ -212,9 +206,7 @@ def conform(input_nii):
     target_shape = ((extent[1] - extent[0]) / 1.0 + 0.999).astype(int)
 
     # SynthStrip likes dimensions be multiple of 64 (192, 256, or 320)
-    target_shape = np.clip(
-        np.ceil(np.array(target_shape) / 64).astype(int) * 64, 192, 320
-    )
+    target_shape = np.clip(np.ceil(np.array(target_shape) / 64).astype(int) * 64, 192, 320)
 
     # Ensure shape ordering is LIA too
     target_shape[2], target_shape[1] = target_shape[1:3]
@@ -239,5 +231,5 @@ def resample_like(image, target, output_dtype=None, cval=0):
     return Affine(reference=target).apply(image, output_dtype=output_dtype, cval=cval)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

nipreps/synthstrip/model.py

@@ -44,9 +44,9 @@
 
 """
 
+import numpy as np
 import torch
 import torch.nn as nn
-import numpy as np
 
 
 class StripModel(nn.Module):
@@ -60,7 +60,6 @@ def __init__(
         max_pool=2,
         return_mask=False,
     ):
-
         super().__init__()
 
         # dimensionality
@@ -69,19 +68,15 @@ def __init__(
         # build feature list automatically
         if isinstance(nb_features, int):
             if nb_levels is None:
-                raise ValueError(
-                    "must provide unet nb_levels if nb_features is an integer"
-                )
-            feats = np.round(nb_features * feat_mult ** np.arange(nb_levels)).astype(
-                int
-            )
+                raise ValueError('must provide unet nb_levels if nb_features is an integer')
+            feats = np.round(nb_features * feat_mult ** np.arange(nb_levels)).astype(int)
             feats = np.clip(feats, 1, max_features)
             nb_features = [
                 np.repeat(feats[:-1], nb_conv_per_level),
                 np.repeat(np.flip(feats), nb_conv_per_level),
             ]
         elif nb_levels is not None:
-            raise ValueError("cannot use nb_levels if nb_features is not an integer")
+            raise ValueError('cannot use nb_levels if nb_features is not an integer')
 
         # extract any surplus (full resolution) decoder convolutions
         enc_nf, dec_nf = nb_features
@@ -94,11 +89,9 @@ def __init__(
             max_pool = [max_pool] * self.nb_levels
 
         # cache downsampling / upsampling operations
-        MaxPooling = getattr(nn, "MaxPool%dd" % ndims)
+        MaxPooling = getattr(nn, 'MaxPool%dd' % ndims)
         self.pooling = [MaxPooling(s) for s in max_pool]
-        self.upsampling = [
-            nn.Upsample(scale_factor=s, mode="nearest") for s in max_pool
-        ]
+        self.upsampling = [nn.Upsample(scale_factor=s, mode='nearest') for s in max_pool]
 
         # configure encoder (down-sampling path)
         prev_nf = 1
@@ -128,7 +121,7 @@ def __init__(
 
         # now we take care of any remaining convolutions
         self.remaining = nn.ModuleList()
-        for num, nf in enumerate(final_convs):
+        for nf in final_convs:
             self.remaining.append(ConvBlock(ndims, prev_nf, nf))
             prev_nf = nf
 
@@ -140,7 +133,6 @@ def __init__(
             self.remaining.append(ConvBlock(ndims, prev_nf, 1, activation=None))
 
     def forward(self, x):
-
         # encoder forward pass
         x_history = [x]
         for level, convs in enumerate(self.encoder):
@@ -169,17 +161,17 @@ class ConvBlock(nn.Module):
     Specific convolutional block followed by leakyrelu for unet.
     """
 
-    def __init__(self, ndims, in_channels, out_channels, stride=1, activation="leaky"):
+    def __init__(self, ndims, in_channels, out_channels, stride=1, activation='leaky'):
         super().__init__()
 
-        Conv = getattr(nn, "Conv%dd" % ndims)
+        Conv = getattr(nn, 'Conv%dd' % ndims)
         self.conv = Conv(in_channels, out_channels, 3, stride, 1)
-        if activation == "leaky":
+        if activation == 'leaky':
             self.activation = nn.LeakyReLU(0.2)
         elif activation is None:
             self.activation = None
         else:
-            raise ValueError(f"Unknown activation: {activation}")
+            raise ValueError(f'Unknown activation: {activation}')
 
     def forward(self, x):
         out = self.conv(x)

nipreps/synthstrip/wrappers/nipype.py

@@ -21,19 +21,21 @@
 #     https://www.nipreps.org/community/licensing/
 #
 """SynthStrip interface."""
+
 import os
 from pathlib import Path
+
 from nipype.interfaces.base import (
     CommandLine,
     CommandLineInputSpec,
     File,
     TraitedSpec,
-    traits,
     Undefined,
+    traits,
 )
 
-_fs_home = os.getenv("FREESURFER_HOME", None)
-_default_model_path = Path(_fs_home) / "models" / "synthstrip.1.pt" if _fs_home else Undefined
+_fs_home = os.getenv('FREESURFER_HOME', None)
+_default_model_path = Path(_fs_home) / 'models' / 'synthstrip.1.pt' if _fs_home else Undefined
 
 if _fs_home and not _default_model_path.exists():
     _default_model_path = Undefined
@@ -43,43 +45,39 @@ class _SynthStripInputSpec(CommandLineInputSpec):
     in_file = File(
         exists=True,
         mandatory=True,
-        argstr="-i %s",
-        desc="Input image to be brain extracted",
-    )
-    use_gpu = traits.Bool(
-        False, usedefault=True, argstr="-g", desc="Use GPU", nohash=True
+        argstr='-i %s',
+        desc='Input image to be brain extracted',
     )
+    use_gpu = traits.Bool(False, usedefault=True, argstr='-g', desc='Use GPU', nohash=True)
     model = File(
         str(_default_model_path),
         usedefault=True,
         exists=True,
-        argstr="--model %s",
+        argstr='--model %s',
         desc="file containing model's weights",
     )
-    border_mm = traits.Int(
-        1, usedefault=True, argstr="-b %d", desc="Mask border threshold in mm"
-    )
+    border_mm = traits.Int(1, usedefault=True, argstr='-b %d', desc='Mask border threshold in mm')
     out_file = File(
-        name_source=["in_file"],
-        name_template="%s_desc-brain.nii.gz",
-        argstr="-o %s",
-        desc="store brain-extracted input to file",
+        name_source=['in_file'],
+        name_template='%s_desc-brain.nii.gz',
+        argstr='-o %s',
+        desc='store brain-extracted input to file',
     )
     out_mask = File(
-        name_source=["in_file"],
-        name_template="%s_desc-brain_mask.nii.gz",
-        argstr="-m %s",
-        desc="store brainmask to file",
+        name_source=['in_file'],
+        name_template='%s_desc-brain_mask.nii.gz',
+        argstr='-m %s',
+        desc='store brainmask to file',
     )
-    num_threads = traits.Int(desc="Number of threads", argstr="-n %d", nohash=True)
+    num_threads = traits.Int(desc='Number of threads', argstr='-n %d', nohash=True)
 
 
 class _SynthStripOutputSpec(TraitedSpec):
-    out_file = File(desc="brain-extracted image")
-    out_mask = File(desc="brain mask")
+    out_file = File(desc='brain-extracted image')
+    out_mask = File(desc='brain mask')
 
 
 class SynthStrip(CommandLine):
-    _cmd = "nipreps-synthstrip"
+    _cmd = 'nipreps-synthstrip'
     input_spec = _SynthStripInputSpec
     output_spec = _SynthStripOutputSpec