medkit.core.text.utils ====================== .. py:module:: medkit.core.text.utils Functions --------- .. autoapisummary:: medkit.core.text.utils.clean_newline_character medkit.core.text.utils.clean_parentheses_eds medkit.core.text.utils.clean_multiple_whitespaces_in_sentence medkit.core.text.utils.replace_point_after_keywords medkit.core.text.utils.replace_multiple_newline_after_sentence medkit.core.text.utils.replace_newline_inside_sentence medkit.core.text.utils.replace_point_in_uppercase medkit.core.text.utils.replace_point_in_numbers medkit.core.text.utils.replace_point_before_keywords medkit.core.text.utils.lstrip medkit.core.text.utils.rstrip medkit.core.text.utils.strip Module Contents --------------- .. py:function:: clean_newline_character(text: str, spans: list[medkit.core.text.span.AnySpan], keep_endlines: bool = False) -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace the newline character depending on its position in the text. The endlines characters that are not suppressed can be either kept as endlines, or replaced by spaces. This method combines :func:`replace_multiple_newline_after_sentence` and :func:`replace_newline_inside_sentence`. :Parameters: **text** : str The text to be modified **spans** : list of AnySpan Spans associated to the `text` **keep_endlines** : bool, default=False Whether to keep the endlines as '.\\\\n' or replace them with '. ' :Returns: **text** : str The cleaned text **spans** : list of AnySpan The list of modified spans .. rubric:: Examples >>> text = "This is\\n\\n\\ta sentence\\nAnother\\nsentence\\n\\nhere" >>> spans = [Span(0, len(text))] >>> text, spans = clean_newline_character(text, spans, keep_endlines=False) >>> print(text) This is a sentence. Another sentence here >>> text, spans = clean_newline_character(text, spans, keep_endlines=True) >>> print(text) This is a sentence. Another sentence here .. !! processed by numpydoc !! .. py:function:: clean_parentheses_eds(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Modify the text near the parentheses depending on its content. The rules are adapted for French documents. .. rubric:: Examples >>> text = \"\"\" ... Le test PCR est (-), pas de nouvelles. ... L'examen d'aujourd'hui est (+). ... Les bilans réalisés (biologique, métabolique en particulier à la recherche ... de GAMT et X fragile) sont revenus négatifs. ... Le patient a un traitement(debuté le 3/02). ... \"\"\" >>> spans = [Span(0, len(text))] >>> text, spans = clean_parentheses_eds(text, spans) >>> print(text) Le test PCR est negatif , pas de nouvelles. L'examen d'aujourd'hui est positif . Les bilans réalisés sont revenus négatifs ; biologique, métabolique en particulier à la recherche de GAMT et X fragile. Le patient a un traitement,debuté le 3/02,. .. !! processed by numpydoc !! .. py:function:: clean_multiple_whitespaces_in_sentence(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Normalize consecutive whitespaces in a sentence. Replace multiple white-spaces between alphanumeric characters and lowercase characters with a single whitespace. .. rubric:: Examples >>> text = "A phrase with multiple spaces " >>> spans = [Span(0, len(text))] >>> text, spans = clean_multiple_whitespaces_in_sentence(text, spans) >>> print(text) A phrase with multiple spaces .. !! processed by numpydoc !! .. py:function:: replace_point_after_keywords(text: str, spans: list[medkit.core.text.span.AnySpan], keywords: list[str], strict: bool = False, replace_by: str = ' ') -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace the dot character after a keyword and update its span. Could be used to replace dots that indicate the title of a person (i.e. M. or Mrs.) or some dots that appear by mistake after `keywords`. :Parameters: **text** : str The text to be modified **spans** : list of AnySpan Spans associated to the `text` **keywords** : list of str Word or pattern to match before a point **strict** : bool, default=False If True, the keyword must be followed by a point. If False, the keyword could have zero or many whitespaces before a point **replace_by** : str, default=" " Replacement string :Returns: **text** : str The text with the replaced matches **spans** : list of AnySpan The list of modified spans .. rubric:: Examples >>> text = "Le Dr. a un rdv. Mme. Bernand est venue à 14h" >>> spans = [Span(0, len(text))] >>> keywords = ["Dr", "Mme"] >>> text, spans = replace_point_after_keywords(text, spans, keywords, replace_by="") >>> print(text) Le Dr a un rdv. Mme Bernand est venue à 14h .. !! processed by numpydoc !! .. py:function:: replace_multiple_newline_after_sentence(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Normalize consecutive newlines between sentences. Replace multiple space characters between a newline character \\\\n and a capital letter or a number with a single newline character. :Parameters: **text** : str The text to be modified **spans** : list of AnySpan Spans associated to the `text` :Returns: **text** : str The cleaned text **spans** : list of AnySpan The list of modified spans .. !! processed by numpydoc !! .. py:function:: replace_newline_inside_sentence(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace newline in a sentence. Replace the newline character \\\\n between lowercase letters or punctuation marks with a space. :Parameters: **text** : str The text to be modified **spans** : list of AnySpan Spans associated to the `text` :Returns: **text** : str The cleaned text **spans** : list of AnySpan The list of modified spans .. !! processed by numpydoc !! .. py:function:: replace_point_in_uppercase(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace the dot character between uppercase characters with a space and update its span. .. rubric:: Examples >>> text = "Abréviation ING.DRT or RTT.J" >>> spans = [Span(0, len(text))] >>> text, spans = replace_point_in_uppercase(text, spans) >>> print(text) Abréviation ING DRT or RTT J .. !! processed by numpydoc !! .. py:function:: replace_point_in_numbers(text: str, spans: list[medkit.core.text.span.AnySpan]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace the dot character between numbers with a comma and update its span. .. rubric:: Examples >>> text = "La valeur est de 3.456." >>> spans = [Span(0, len(text))] >>> text, spans = replace_point_in_numbers(text, spans) >>> print(text) La valeur est de 3,456. .. !! processed by numpydoc !! .. py:function:: replace_point_before_keywords(text: str, spans: list[medkit.core.text.span.AnySpan], keywords: list[str]) -> tuple[str, list[medkit.core.text.span.AnySpan]] Replace the dot character before a keyword with a space and update its span. .. !! processed by numpydoc !! .. py:function:: lstrip(text: str, start: int = 0, chars: str | None = None) -> tuple[str, int] Return a copy of the string with leading characters removed and its corresponding new start index. :Parameters: **text** : str The text to strip. **start** : int, default=0 The start index from the original text if any. **chars** : str, optional The list of characters to strip. Default behaviour is like `str.lstrip([chars])`. :Returns: **new_text** : str New text **new_start** : int New start index .. !! processed by numpydoc !! .. py:function:: rstrip(text: str, end: int | None = None, chars: str | None = None) -> tuple[str, int] Return a copy of the string with trailing characters removed and its corresponding new end index. :Parameters: **text** : str The text to strip. **end** : int, optional The end index from the original text if any. **chars** : str, optional The list of characters to strip. Default behaviour is like `str.rstrip([chars])`. :Returns: **new_text** : str New text **new_end** : int New end index .. !! processed by numpydoc !! .. py:function:: strip(text: str, start: int = 0, chars: str | None = None) -> tuple[str, int, int] Return a copy of the string with leading characters removed and its corresponding new start and end indexes. :Parameters: **text** : str The text to strip. **start** : int, default=0 The start index from the original text if any. **chars** : str, optional The list of characters to strip. Default behaviour is like `str.lstrip([chars])`. :Returns: **new_text** : str New text **new_start** : int New start index **new_end** : int New end index .. !! processed by numpydoc !!