Use import torchio as tio in some docstrings

fepegar · Nov 23, 2020 · 20e40a2 · 20e40a2
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diff --git a/torchio/data/dataset.py b/torchio/data/dataset.py
@@ -22,35 +22,34 @@ class SubjectsDataset(Dataset):
     :class:`torchio.data.subject.Subject`.
 
     Args:
-        subjects: Sequence of instances of
+        subjects: List of instances of
             :class:`~torchio.data.subject.Subject`.
         transform: An instance of :class:`torchio.transforms.Transform`
             that will be applied to each subject.
 
     Example:
-        >>> from torchio import SubjectsDataset, ScalarImage, LabelMap, Subject
-        >>> from torchio.transforms import RescaleIntensity, RandomAffine, Compose
-        >>> subject_a = Subject(
-        ...     t1=ScalarImage('t1.nrrd',),
-        ...     t2=ScalarImage('t2.mha',),
-        ...     label=LabelMap('t1_seg.nii.gz'),
+        >>> import torchio as tio
+        >>> subject_a = tio.Subject(
+        ...     t1=tio.ScalarImage('t1.nrrd',),
+        ...     t2=tio.ScalarImage('t2.mha',),
+        ...     label=tio.LabelMap('t1_seg.nii.gz'),
         ...     age=31,
         ...     name='Fernando Perez',
         ... )
-        >>> subject_b = Subject(
-        ...     t1=ScalarImage('colin27_t1_tal_lin.minc',),
-        ...     t2=ScalarImage('colin27_t2_tal_lin_dicom',),
-        ...     label=LabelMap('colin27_seg1.nii.gz'),
+        >>> subject_b = tio.Subject(
+        ...     t1=tio.ScalarImage('colin27_t1_tal_lin.minc',),
+        ...     t2=tio.ScalarImage('colin27_t2_tal_lin_dicom',),
+        ...     label=tio.LabelMap('colin27_seg1.nii.gz'),
         ...     age=56,
         ...     name='Colin Holmes',
         ... )
         >>> subjects_list = [subject_a, subject_b]
         >>> transforms = [
-        ...     RescaleIntensity((0, 1)),
-        ...     RandomAffine(),
+        ...     tio.RescaleIntensity((0, 1)),
+        ...     tio.RandomAffine(),
         ... ]
-        >>> transform = Compose(transforms)
-        >>> subjects_dataset = SubjectsDataset(subjects_list, transform=transform)
+        >>> transform = tio.Compose(transforms)
+        >>> subjects_dataset = tio.SubjectsDataset(subjects_list, transform=transform)
         >>> subject = subjects_dataset[0]
 
     .. _NiBabel: https://nipy.org/nibabel/#nibabel

diff --git a/torchio/data/subject.py b/torchio/data/subject.py
@@ -15,24 +15,24 @@ class Subject(dict):
 
     Example:
 
-        >>> from torchio import ScalarImage, LabelMap, Subject
+        >>> import torchio as tio
         >>> # One way:
-        >>> subject = Subject(
-        ...     one_image=ScalarImage('path_to_image.nii.gz'),
-        ...     a_segmentation=LabelMap('path_to_seg.nii.gz'),
+        >>> subject = tio.Subject(
+        ...     one_image=tio.ScalarImage('path_to_image.nii.gz'),
+        ...     a_segmentation=tio.LabelMap('path_to_seg.nii.gz'),
         ...     age=45,
         ...     name='John Doe',
         ...     hospital='Hospital Juan Negrín',
         ... )
         >>> # If you want to create the mapping before, or have spaces in the keys:
         >>> subject_dict = {
-        ...     'one image': ScalarImage('path_to_image.nii.gz'),
-        ...     'a segmentation': LabelMap('path_to_seg.nii.gz'),
+        ...     'one image': tio.ScalarImage('path_to_image.nii.gz'),
+        ...     'a segmentation': tio.LabelMap('path_to_seg.nii.gz'),
         ...     'age': 45,
         ...     'name': 'John Doe',
         ...     'hospital': 'Hospital Juan Negrín',
         ... }
-        >>> Subject(subject_dict)
+        >>> subject = tio.Subject(subject_dict)
 
     """