medkit.io._common ================= .. py:module:: medkit.io._common Attributes ---------- .. autoapisummary:: medkit.io._common.logger Classes ------- .. autoapisummary:: medkit.io._common.Entity medkit.io._common.Relation medkit.io._common.Segment medkit.io._common.TextAnnotation medkit.io._common.TextDocument Functions --------- .. autoapisummary:: medkit.io._common.get_anns_by_type Module Contents --------------- .. py:class:: Entity(label: str, text: str, spans: list[medkit.core.text.span.AnySpan], attrs: list[medkit.core.attribute.Attribute] | None = None, metadata: dict[str, Any] | None = None, uid: str | None = None, store: medkit.core.store.Store | None = None, attr_container_class: type[medkit.core.text.entity_attribute_container.EntityAttributeContainer] = EntityAttributeContainer) Bases: :py:obj:`Segment` Text entity referencing part of an :class:`~medkit.core.text.TextDocument`. :Attributes: **uid** : str The entity identifier. **label** : str The label for this entity (e.g., DISEASE) **text** : str Text of the entity. **spans** : list of AnySpan List of spans indicating which parts of the entity text correspond to which part of the document's full text. **attrs** : EntityAttributeContainer Attributes of the entity. Stored in a :class:{~medkit.core.EntityAttributeContainer} but can be passed as a list at init. **metadata** : dict of str to Any The metadata of the entity **keys** : set of str Pipeline output keys to which the entity belongs to. .. !! processed by numpydoc !! .. py:attribute:: attrs :type: medkit.core.text.entity_attribute_container.EntityAttributeContainer .. py:class:: Relation(label: str, source_id: str, target_id: str, attrs: list[medkit.core.attribute.Attribute] | None = None, metadata: dict[str, Any] | None = None, uid: str | None = None, store: medkit.core.store.Store | None = None, attr_container_class: type[medkit.core.attribute_container.AttributeContainer] = AttributeContainer) Bases: :py:obj:`TextAnnotation` Relation between two text entities. :Attributes: **uid** : str The identifier of the relation **label** : str The relation label **source_id** : str The identifier of the entity from which the relation is defined **target_id** : str The identifier of the entity to which the relation is defined **attrs** : AttributeContainer The attributes of the relation **metadata** : dict of str to Any The metadata of the relation **keys** : set of str Pipeline output keys to which the relation belongs to .. !! processed by numpydoc !! .. py:attribute:: source_id :type: str .. py:attribute:: target_id :type: str .. py:method:: to_dict() -> dict[str, Any] .. py:method:: from_dict(relation_dict: dict[str, Any]) -> typing_extensions.Self :classmethod: Create a Relation from a dict. :Parameters: **relation_dict** : dict of str to Any A dictionary from a serialized relation as generated by to_dict() .. !! processed by numpydoc !! .. py:class:: Segment(label: str, text: str, spans: list[medkit.core.text.span.AnySpan], attrs: list[medkit.core.attribute.Attribute] | None = None, metadata: dict[str, Any] | None = None, uid: str | None = None, store: medkit.core.store.Store | None = None, attr_container_class: type[medkit.core.attribute_container.AttributeContainer] = AttributeContainer) Bases: :py:obj:`TextAnnotation` Text segment referencing part of an :class:`~medkit.core.text.TextDocument`. :Attributes: **uid** : str The segment identifier. **label** : str The label for this segment (e.g., SENTENCE) **text** : str Text of the segment. **spans** : list of AnySpan List of spans indicating which parts of the segment text correspond to which part of the document's full text. **attrs** : AttributeContainer Attributes of the segment. Stored in a :class:{~medkit.core.AttributeContainer} but can be passed as a list at init. **metadata** : dict of str to Any The metadata of the segment **keys** : set of str Pipeline output keys to which the segment belongs to. .. !! processed by numpydoc !! .. py:attribute:: spans :type: list[medkit.core.text.span.AnySpan] .. py:attribute:: text :type: str .. py:method:: to_dict() -> dict[str, Any] .. py:method:: from_dict(segment_dict: dict[str, Any]) -> typing_extensions.Self :classmethod: Create a Segment from a dict. :Parameters: **segment_dict** : dict of str to Any A dictionary from a serialized segment as generated by to_dict() .. !! processed by numpydoc !! .. py:class:: TextAnnotation(label: str, attrs: list[medkit.core.attribute.Attribute] | None = None, metadata: dict[str, Any] | None = None, uid: str | None = None, attr_container_class: type[medkit.core.attribute_container.AttributeContainer] = AttributeContainer) Bases: :py:obj:`abc.ABC`, :py:obj:`medkit.core.dict_conv.SubclassMapping` Base abstract class for all text annotations. :Attributes: **uid** : str Unique identifier of the annotation. **label** : str The label for this annotation (e.g., SENTENCE) **attrs** : AttributeContainer Attributes of the annotation. Stored in a :class:{~medkit.core.AttributeContainer} but can be passed as a list at init. **metadata** : dict of str to Any The metadata of the annotation **keys** : set of str Pipeline output keys to which the annotation belongs to. .. !! processed by numpydoc !! .. py:attribute:: uid :type: str .. py:attribute:: label :type: str .. py:attribute:: attrs :type: medkit.core.attribute_container.AttributeContainer .. py:attribute:: metadata :type: dict[str, Any] .. py:attribute:: keys :type: set[str] .. py:method:: __init_subclass__() :classmethod: .. py:method:: from_dict(ann_dict: dict[str, Any]) -> typing_extensions.Self :classmethod: .. py:method:: to_dict() -> dict[str, Any] :abstractmethod: .. py:class:: TextDocument(text: str, anns: Sequence[medkit.core.text.annotation.TextAnnotation] | None = None, attrs: Sequence[medkit.core.Attribute] | None = None, metadata: dict[str, Any] | None = None, uid: str | None = None) Bases: :py:obj:`medkit.core.dict_conv.SubclassMapping` Document holding text annotations. Annotations must be subclasses of `TextAnnotation`. .. rubric:: Examples >>> doc = TextDocument(text="hello") >>> raw_text = doc.anns.get(label=TextDocument.RAW_LABEL)[0] :Attributes: **uid** : str Unique identifier of the document. **text** : str Full document text. **anns** : TextAnnotationContainer Annotations of the document. Stored in an :class:`~.text.TextAnnotationContainer` but can be passed as a list at init. **attrs** : AttributeContainer Attributes of the document. Stored in an :class:`~.core.AttributeContainer` but can be passed as a list at init **metadata** : dict of str to Any Document metadata. **raw_segment** : Segment Auto-generated segment containing the full unprocessed document text. To get the raw text as an annotation to pass to processing operations: .. !! processed by numpydoc !! .. py:attribute:: RAW_LABEL :type: ClassVar[str] :value: 'RAW_TEXT' .. py:attribute:: uid :type: str .. py:attribute:: anns :type: medkit.core.text.annotation_container.TextAnnotationContainer .. py:attribute:: attrs :type: medkit.core.AttributeContainer .. py:attribute:: metadata :type: dict[str, Any] .. py:attribute:: raw_segment :type: medkit.core.text.annotation.Segment .. py:method:: _generate_raw_segment(text: str, doc_id: str) -> medkit.core.text.annotation.Segment :classmethod: .. py:property:: text :type: str .. py:method:: __init_subclass__() :classmethod: .. py:method:: to_dict(with_anns: bool = True) -> dict[str, Any] .. py:method:: from_dict(doc_dict: dict[str, Any]) -> typing_extensions.Self :classmethod: Create a TextDocument from a dict. :Parameters: **doc_dict** : dict of str to Any A dictionary from a serialized TextDocument as generated by to_dict() .. !! processed by numpydoc !! .. py:method:: from_file(path: os.PathLike, encoding: str = 'utf-8') -> typing_extensions.Self :classmethod: Create a document from a text file. :Parameters: **path** : Path Path of the text file **encoding** : str, default="utf-8" Text encoding to use :Returns: TextDocument Text document with contents of `path` as text. The file path is included in the document metadata. .. !! processed by numpydoc !! .. py:method:: from_dir(path: os.PathLike, pattern: str = '*.txt', encoding: str = 'utf-8') -> list[typing_extensions.Self] :classmethod: Create documents from text files in a directory. :Parameters: **path** : Path Path of the directory containing text files **pattern** : str Glob pattern to match text files in `path` **encoding** : str Text encoding to use :Returns: list of TextDocument Text documents with contents of each file as text .. !! processed by numpydoc !! .. py:method:: get_snippet(segment: medkit.core.text.annotation.Segment, max_extend_length: int) -> str Return a portion of the original text containing the annotation. :Parameters: **segment** : Segment The annotation **max_extend_length** : int Maximum number of characters to use around the annotation :Returns: str A portion of the text around the annotation .. !! processed by numpydoc !! .. py:data:: logger .. py:function:: get_anns_by_type(medkit_doc: medkit.core.text.TextDocument, anns_labels: list[str] | None = None) -> dict[str, medkit.core.text.TextAnnotation] Filter annotations by labels and return a dictionary by type of annotation. :Parameters: **medkit_doc** : TextDocument Text document with annotations **anns_labels** : list of str, optional Labels to filter annotations. If not provided, all annotations will be in the dictionary :Returns: Dict[str, TextAnnotation] Annotations by type: 'entities', 'relations', and 'segments'. .. !! processed by numpydoc !!