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Fixes several typos #8712

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Fixes several typos #8712

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36631e8
Added letter
benediktjohannes Jan 20, 2026
63b8ea9
Another typo fix
benediktjohannes Jan 20, 2026
c901ad9
Fixes several typos
benediktjohannes Jan 20, 2026
899c9bd
Consistent with the other self.data_obj
benediktjohannes Jan 20, 2026
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12 changes: 6 additions & 6 deletions monai/data/image_writer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -405,7 +405,7 @@ def set_data_array(
``None`` indicates data without any channel dimension.
squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
currently support ``spatial_ndim`` and ``contiguous``, defauting to ``3`` and ``False`` respectively.
currently support ``spatial_ndim`` and ``contiguous``, defaulting to ``3`` and ``False`` respectively.
"""
n_chns = data_array.shape[channel_dim] if channel_dim is not None else 0
self.data_obj = self.convert_to_channel_last(
Expand All @@ -424,7 +424,7 @@ def set_data_array(

def set_metadata(self, meta_dict: Mapping | None = None, resample: bool = True, **options):
"""
Resample ``self.dataobj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
Resample ``self.data_obj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.

Args:
meta_dict: a metadata dictionary for affine, original affine and spatial shape information.
Expand Down Expand Up @@ -575,7 +575,7 @@ def set_data_array(
``None`` indicates data without any channel dimension.
squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
currently support ``spatial_ndim``, defauting to ``3``.
currently support ``spatial_ndim``, defaulting to ``3``.
"""
self.data_obj = self.convert_to_channel_last(
data=data_array,
Expand All @@ -586,7 +586,7 @@ def set_data_array(

def set_metadata(self, meta_dict: Mapping | None, resample: bool = True, **options):
"""
Resample ``self.dataobj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
Resample ``self.data_obj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.

Args:
meta_dict: a metadata dictionary for affine, original affine and spatial shape information.
Expand Down Expand Up @@ -726,7 +726,7 @@ def set_data_array(
squeeze_end_dims: if ``True``, any trailing singleton dimensions will be removed.
contiguous: if ``True``, the data array will be converted to a contiguous array. Default is ``False``.
kwargs: keyword arguments passed to ``self.convert_to_channel_last``,
currently support ``spatial_ndim``, defauting to ``2``.
currently support ``spatial_ndim``, defaulting to ``2``.
"""
self.data_obj = self.convert_to_channel_last(
data=data_array,
Expand All @@ -738,7 +738,7 @@ def set_data_array(

def set_metadata(self, meta_dict: Mapping | None = None, resample: bool = True, **options):
"""
Resample ``self.dataobj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.
Resample ``self.data_obj`` if needed. This method assumes ``self.data_obj`` is a 'channel-last' ndarray.

Args:
meta_dict: a metadata dictionary for affine, original affine and spatial shape information.
Expand Down
2 changes: 1 addition & 1 deletion tests/data/test_cachedataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def test_shape(self, transform, expected_shape):
self.assertEqual(len(data3), 1)

if transform is None:
# Check without providing transfrom
# Check without providing transform
dataset2 = CacheDataset(data=test_data, cache_rate=0.5, as_contiguous=True)
for k in ["image", "label", "extra"]:
self.assertEqual(dataset[0][k], dataset2[0][k])
Expand Down
2 changes: 1 addition & 1 deletion tests/data/test_smartcachedataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def test_shape(self, replace_rate, num_replace_workers, transform):
num_replace_workers=num_replace_workers,
)
if transform is None:
# Check without providing transfrom
# Check without providing transform
dataset2 = SmartCacheDataset(
data=test_data,
replace_rate=replace_rate,
Expand Down