unify spelling of metdata in docstrings

monai/apps/deepedit/transforms.py

@@ -614,12 +614,12 @@ class AddGuidanceFromPointsDeepEditd(Transform):
     Args:
         ref_image: key to reference image to fetch current and original image details.
         guidance: output key to store guidance.
-        meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.
+        meta_keys: explicitly indicate the key of the metadata dictionary of `ref_image`.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             if None, will try to construct meta_keys by `{ref_image}_{meta_key_postfix}`.
-        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
 

monai/apps/deepgrow/transforms.py

@@ -372,13 +372,13 @@ class SpatialCropForegroundd(MapTransform):
         allow_smaller: when computing box size with `margin`, whether allow the image size to be smaller
             than box size, default to `True`. if the margined size is bigger than image size, will pad with
             specified `mode`.
-        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.
+        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             it can be a sequence of string, map to the `keys`.
             if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
-        meta_key_postfix: if meta_keys is None, use `{key}_{meta_key_postfix}` to fetch/store the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_keys is None, use `{key}_{meta_key_postfix}` to fetch/store the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
         start_coord_key: key to record the start coordinate of spatial bounding box for foreground.
@@ -475,12 +475,12 @@ class AddGuidanceFromPointsd(Transform):
         depth_first: if depth (slices) is positioned at first dimension.
         spatial_dims: dimensions based on model used for deepgrow (2D vs 3D).
         slice_key: key that represents applicable slice to add guidance.
-        meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.
+        meta_keys: explicitly indicate the key of the metadata dictionary of `ref_image`.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             if None, will try to construct meta_keys by `{ref_image}_{meta_key_postfix}`.
-        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to fetch the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to fetch the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
 
@@ -589,13 +589,13 @@ class SpatialCropGuidanced(MapTransform):
         guidance: key to the guidance. It is used to generate the bounding box of foreground
         spatial_size: minimal spatial size of the image patch e.g. [128, 128, 128] to fit in.
         margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
-        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.
+        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             it can be a sequence of string, map to the `keys`.
             if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
-        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to fetch the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_keys is None, use `key_{postfix}` to fetch the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
         start_coord_key: key to record the start coordinate of spatial bounding box for foreground.
@@ -712,12 +712,12 @@ class ResizeGuidanced(Transform):
     Args:
         guidance: key to guidance
         ref_image: key to reference image to fetch current and original image details
-        meta_keys: explicitly indicate the key of the meta data dictionary of `ref_image`.
+        meta_keys: explicitly indicate the key of the metadata dictionary of `ref_image`.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             if None, will try to construct meta_keys by `{ref_image}_{meta_key_postfix}`.
-        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_key is None, use `{ref_image}_{meta_key_postfix}` to to fetch the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
         cropped_shape_key: key that records cropped shape for foreground.
@@ -787,13 +787,13 @@ class RestoreLabeld(MapTransform):
         align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
             See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
             It also can be a sequence of bool, each element corresponds to a key in ``keys``.
-        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.
+        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             it can be a sequence of string, map to the `keys`.
             if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
-        meta_key_postfix: if meta_key is None, use `key_{meta_key_postfix} to fetch the meta data according
-            to the key data, default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: if meta_key is None, use `key_{meta_key_postfix} to fetch the metadata according
+            to the key data, default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
         start_coord_key: key that records the start coordinate of spatial bounding box for foreground.
@@ -897,13 +897,13 @@ class Fetch2DSliced(MapTransform):
         keys: keys of the corresponding items to be transformed.
         guidance: key that represents guidance.
         axis: axis that represents slice in 3D volume.
-        meta_keys: explicitly indicate the key of the corresponding meta data dictionary.
+        meta_keys: explicitly indicate the key of the corresponding metadata dictionary.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-            the meta data is a dictionary object which contains: filename, original_shape, etc.
+            the metadata is a dictionary object which contains: filename, original_shape, etc.
             it can be a sequence of string, map to the `keys`.
             if None, will try to construct meta_keys by `key_{meta_key_postfix}`.
-        meta_key_postfix: use `key_{meta_key_postfix}` to fetch the meta data according to the key data,
-            default is `meta_dict`, the meta data is a dictionary object.
+        meta_key_postfix: use `key_{meta_key_postfix}` to fetch the metadata according to the key data,
+            default is `meta_dict`, the metadata is a dictionary object.
             For example, to handle key `image`,  read/write affine matrices from the
             metadata `image_meta_dict` dictionary's `affine` field.
         allow_missing_keys: don't raise exception if key is missing.

monai/bundle/scripts.py

@@ -591,7 +591,7 @@ def ckpt_export(
     else:
         copy_model_state(dst=net, src=ckpt_file_ if key_in_ckpt_ == "" else ckpt_file_[key_in_ckpt_])
 
-    # convert to TorchScript model and save with meta data, config content
+    # convert to TorchScript model and save with metadata, config content
     net = convert_to_torchscript(model=net)
 
     extra_files: Dict = {}

monai/data/csv_saver.py

@@ -27,7 +27,7 @@ class CSVSaver:
     Save the data in a dictionary format cache, and write to a CSV file finally.
     Typically, the data can be classification predictions, call `save` for single data
     or call `save_batch` to save a batch of data together, and call `finalize` to write
-    the cached data into CSV file. If no meta data provided, use index from 0 to save data.
+    the cached data into CSV file. If no metadata provided, use index from 0 to save data.
     Note that this saver can't support multi-processing because it reads / writes single
     CSV file and can't guarantee the data order in multi-processing situation.
 
@@ -88,7 +88,7 @@ def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict]
 
         Args:
             data: target data content that save into cache.
-            meta_data: the meta data information corresponding to the data.
+            meta_data: the metadata information corresponding to the data.
 
         """
         save_key = meta_data[Key.FILENAME_OR_OBJ] if meta_data else str(self._data_index)

monai/data/dataset_summary.py

@@ -54,12 +54,12 @@ def __init__(
             dataset: dataset from which to load the data.
             image_key: key name of images (default: ``image``).
             label_key: key name of labels (default: ``label``).
-            meta_key: explicitly indicate the key of the corresponding meta data dictionary.
+            meta_key: explicitly indicate the key of the corresponding metadata dictionary.
                 for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
-                the meta data is a dictionary object which contains: filename, affine, original_shape, etc.
+                the metadata is a dictionary object which contains: filename, affine, original_shape, etc.
                 if None, will try to construct meta_keys by `{image_key}_{meta_key_postfix}`.
-            meta_key_postfix: use `{image_key}_{meta_key_postfix}` to fetch the meta data from dict,
-                the meta data is a dictionary object (default: ``meta_dict``).
+            meta_key_postfix: use `{image_key}_{meta_key_postfix}` to fetch the metadata from dict,
+                the metadata is a dictionary object (default: ``meta_dict``).
             num_workers: how many subprocesses to use for data loading.
                 ``0`` means that the data will be loaded in the main process (default: ``0``).
             kwargs: other parameters (except `batch_size` and `num_workers`) for DataLoader,
@@ -76,12 +76,12 @@ def __init__(
 
     def collect_meta_data(self):
         """
-        This function is used to collect the meta data for all images of the dataset.
+        This function is used to collect the metadata for all images of the dataset.
         """
 
         for data in self.data_loader:
             if self.meta_key not in data:
-                raise ValueError(f"To collect meta data for the dataset, key `{self.meta_key}` must exist in `data`.")
+                raise ValueError(f"To collect metadata for the dataset, key `{self.meta_key}` must exist in `data`.")
             self.all_meta_data.append(data[self.meta_key])
 
     def get_target_spacing(self, spacing_key: str = "pixdim", anisotropic_threshold: int = 3, percentile: float = 10.0):
@@ -93,7 +93,7 @@ def get_target_spacing(self, spacing_key: str = "pixdim", anisotropic_threshold:
         After loading with `monai.DataLoader`, "pixdim" is in the form of `torch.Tensor` with size `(batch_size, 8)`.
 
         Args:
-            spacing_key: key of spacing in meta data (default: ``pixdim``).
+            spacing_key: key of spacing in metadata (default: ``pixdim``).
             anisotropic_threshold: threshold to decide if the target spacing is anisotropic (default: ``3``).
             percentile: for anisotropic target spacing, use the percentile of all spacings of the anisotropic axis to
                 replace that axis.

monai/data/image_dataset.py

@@ -59,7 +59,7 @@ def __init__(
             image_only: if True return only the image volume, otherwise, return image volume and the metadata.
             transform_with_metadata: if True, the metadata will be passed to the transforms whenever possible.
             dtype: if not None convert the loaded image to this data type.
-            reader: register reader to load image file and meta data, if None, will use the default readers.
+            reader: register reader to load image file and metadata, if None, will use the default readers.
                 If a string of reader name provided, will construct a reader object with the `*args` and `**kwargs`
                 parameters, supported reader name: "NibabelReader", "PILReader", "ITKReader", "NumpyReader"
             args: additional parameters for reader if providing a reader name.