fix: re-exporting in rscraping __init__.py

findrace.py

@@ -30,6 +30,8 @@ def _parse_arguments():
 def main(race_id: str, datasource: str, is_female: bool, with_lineups: bool, save: bool, day: int | None):
     if not Datasource.has_value(datasource):
         raise ValueError(f"invalid datasource={datasource}")
+    if datasource == Datasource.TABULAR.value:
+        raise ValueError(f"datasource={datasource} is not supported in this script")
 
     race = find_race(
         race_id=race_id,

rscraping/__init__.py

@@ -1,118 +1,4 @@
-import os
-from typing import Any
-from collections.abc import Generator
-
-from pyutils.strings import normalize_synonyms, remove_conjunctions, remove_symbols, unaccent
-from rscraping.data.constants import SYNONYMS
-from rscraping.data.models import Datasource, Lineup, Race
-from simplemma.simplemma import text_lemmatizer
-from rscraping.clients import Client
-
-from rscraping.ocr import ImageProcessor
-from rscraping.parsers.df import DataFrameParser
-
-
-def find_race(
-    race_id: str,
-    datasource: Datasource,
-    is_female: bool,
-    category: str | None = None,
-    day: int | None = None,
-    with_lineup: bool = False,
-) -> Race | None:
-    """
-    Find a race based on the provided parameters.
-
-    Parameters:
-    - race_id (str): The ID of the race to find.
-    - datasource (Datasource): The data source to use for retrieving race information.
-    - is_female (bool): Whether the race is for females (True) or not (False).
-    - category (Optional[str]): The category of the race (optional).
-    - day (Optional[int]): The day of the race (optional).
-    - with_lineup (bool): Whether to include lineup information for participants (default: False).
-
-    Returns:
-    - Optional[Race]: The found Race object if the race is found, otherwise None.
-
-    This function retrieves race information using the provided datasource and parameters.
-    If with_lineup is True, it attempts to find lineup information for each participant in the race and attaches it to
-    the participant object.
-
-    Note that lineup retrieval may raise NotImplementedError, in which case it will be caught and
-    the function will proceed without adding lineup information.
-    """
-
-    client = Client(source=datasource, is_female=is_female, category=category)
-    race = client.get_race_by_id(race_id, day=day)
-
-    if race is not None and with_lineup:
-        try:
-            lineups = client.get_lineup_by_race_id(race_id)
-            for participant in race.participants:
-                lineup = [lin for lin in lineups if lin.club == participant.participant]
-                participant.lineup = lineup[0] if len(lineup) == 1 else None
-        except NotImplementedError:
-            pass
-
-    return race
-
-
-def parse_race_image(
-    path: str,
-    datasource: Datasource,
-    header_size: int = 3,
-    allow_plot: bool = False,
-) -> Generator[Race, Any, Any]:
-    """
-    Parse race information from an image file using provided data source and parameters.
-
-    Parameters:
-    - path (str): The path to the image file to be processed.
-    - datasource (Datasource): The data source to use for retrieving additional data.
-    - header_size (int): The ammount of the image that represents the header (default: 3) -> 1/3.
-    - allow_plot (bool): Whether to allow plotting for debugging during processing (default: False).
-
-    Yields:
-    - Generator[Race, Any, Any]: A generator that yields Race objects as they are parsed.
-
-    This function processes race information from an image file. It uses an ImageProcessor and a
-    DataFrameParser for retrieving header data and tabular data from the image, respectively.
-    The header data is retrieved based on the specified header size, and the tabular data is
-    extracted using the DataFrameParser.
-
-    The function yields Race objects as they are parsed from the data. The file name, header data,
-    and tabular data are used as input for the parsing process.
-    """
-
-    processor = ImageProcessor(source=datasource, allow_plot=allow_plot)  # pyright: ignore
-    parser = DataFrameParser(source=datasource, allow_plot=allow_plot)  # pyright: ignore
-
-    header_data = processor.retrieve_header_data(path=path, header_size=header_size)
-    df = processor.retrieve_tabular_dataframe(path=path, header_size=header_size)
-
-    return parser.parse_races_from(
-        data=df,
-        file_name=os.path.splitext(os.path.basename(path))[0],
-        header=header_data,
-    )
-
-
-def find_lineup(race_id: str, datasource: Datasource, is_female: bool) -> Generator[Lineup, Any, Any]:
-    client = Client(source=datasource, is_female=is_female)
-    return client.get_lineup_by_race_id(race_id)
-
-
-def lemmatize(phrase: str, lang: str = "es") -> list[str]:
-    """
-    Lemmatize a phrase using the simplemma library. The phrase is preprocessed before lemmatization.
-    Synonyms are normalized, conjunctions are removed, symbols are removed, and accents are removed.
-
-    Parameters:
-    - phrase (str): The phrase to lemmatize.
-    - lang (str): The language of the phrase (default: "es").
-
-    Returns: list[str]: A list of lemmatized words from the phrase.
-    """
-    phrase = normalize_synonyms(phrase, SYNONYMS)
-    phrase = unaccent(remove_symbols(remove_conjunctions(phrase)))
-    return list(set(text_lemmatizer(phrase, lang=lang)))
+from ._functions import find_race as find_race
+from ._functions import parse_race_image as parse_race_image
+from ._functions import lemmatize as lemmatize
+from ._functions import find_lineup as find_lineup

rscraping/_functions.py

@@ -0,0 +1,118 @@
+import os
+from collections.abc import Generator
+from typing import Any
+
+from simplemma.simplemma import text_lemmatizer
+
+from pyutils.strings import normalize_synonyms, remove_conjunctions, remove_symbols, unaccent
+from rscraping.clients import Client
+from rscraping.data.constants import SYNONYMS
+from rscraping.data.models import Datasource, Lineup, Race
+from rscraping.ocr import ImageProcessor
+from rscraping.parsers.df import DataFrameParser
+
+
+def find_race(
+    race_id: str,
+    datasource: Datasource,
+    is_female: bool,
+    category: str | None = None,
+    day: int | None = None,
+    with_lineup: bool = False,
+) -> Race | None:
+    """
+    Find a race based on the provided parameters.
+
+    Parameters:
+    - race_id (str): The ID of the race to find.
+    - datasource (Datasource): The data source to use for retrieving race information.
+    - is_female (bool): Whether the race is for females (True) or not (False).
+    - category (Optional[str]): The category of the race (optional).
+    - day (Optional[int]): The day of the race (optional).
+    - with_lineup (bool): Whether to include lineup information for participants (default: False).
+
+    Returns:
+    - Optional[Race]: The found Race object if the race is found, otherwise None.
+
+    This function retrieves race information using the provided datasource and parameters.
+    If with_lineup is True, it attempts to find lineup information for each participant in the race and attaches it to
+    the participant object.
+
+    Note that lineup retrieval may raise NotImplementedError, in which case it will be caught and
+    the function will proceed without adding lineup information.
+    """
+
+    client = Client(source=datasource, is_female=is_female, category=category)
+    race = client.get_race_by_id(race_id, day=day)
+
+    if race is not None and with_lineup:
+        try:
+            lineups = client.get_lineup_by_race_id(race_id)
+            for participant in race.participants:
+                lineup = [lin for lin in lineups if lin.club == participant.participant]
+                participant.lineup = lineup[0] if len(lineup) == 1 else None
+        except NotImplementedError:
+            pass
+
+    return race
+
+
+def parse_race_image(
+    path: str,
+    datasource: Datasource,
+    header_size: int = 3,
+    allow_plot: bool = False,
+) -> Generator[Race, Any, Any]:
+    """
+    Parse race information from an image file using provided data source and parameters.
+
+    Parameters:
+    - path (str): The path to the image file to be processed.
+    - datasource (Datasource): The data source to use for retrieving additional data.
+    - header_size (int): The ammount of the image that represents the header (default: 3) -> 1/3.
+    - allow_plot (bool): Whether to allow plotting for debugging during processing (default: False).
+
+    Yields:
+    - Generator[Race, Any, Any]: A generator that yields Race objects as they are parsed.
+
+    This function processes race information from an image file. It uses an ImageProcessor and a
+    DataFrameParser for retrieving header data and tabular data from the image, respectively.
+    The header data is retrieved based on the specified header size, and the tabular data is
+    extracted using the DataFrameParser.
+
+    The function yields Race objects as they are parsed from the data. The file name, header data,
+    and tabular data are used as input for the parsing process.
+    """
+
+    processor = ImageProcessor(source=datasource, allow_plot=allow_plot)  # pyright: ignore
+    parser = DataFrameParser(source=datasource, allow_plot=allow_plot)  # pyright: ignore
+
+    header_data = processor.retrieve_header_data(path=path, header_size=header_size)
+    df = processor.retrieve_tabular_dataframe(path=path, header_size=header_size)
+
+    return parser.parse_races_from(
+        data=df,
+        file_name=os.path.splitext(os.path.basename(path))[0],
+        header=header_data,
+    )
+
+
+def find_lineup(race_id: str, datasource: Datasource, is_female: bool) -> Generator[Lineup, Any, Any]:
+    client = Client(source=datasource, is_female=is_female)
+    return client.get_lineup_by_race_id(race_id)
+
+
+def lemmatize(phrase: str, lang: str = "es") -> list[str]:
+    """
+    Lemmatize a phrase using the simplemma library. The phrase is preprocessed before lemmatization.
+    Synonyms are normalized, conjunctions are removed, symbols are removed, and accents are removed.
+
+    Parameters:
+    - phrase (str): The phrase to lemmatize.
+    - lang (str): The language of the phrase (default: "es").
+
+    Returns: list[str]: A list of lemmatized words from the phrase.
+    """
+    phrase = normalize_synonyms(phrase, SYNONYMS)
+    phrase = unaccent(remove_symbols(remove_conjunctions(phrase)))
+    return list(set(text_lemmatizer(phrase, lang=lang)))

rscraping/clients/tabular.py

@@ -151,9 +151,9 @@ def _load_dataframe(self, config: TabularClientConfig) -> pd.DataFrame:
         # remove unused dataframe parts
         # 1. remove rows with NaN index
         # 2. keep only the first 15 columns
-        # 3. remove columns "Temp." and "Puesto"
+        # 3. remove column "Puesto"
         # 4. remove rows with empty "Nome" column
-        df = df[df.index.notnull()].iloc[:, :15].drop(columns=["Temp.", "Puesto"]).loc[df[COLUMN_NAME] != ""]
+        df = df[df.index.notnull()].iloc[:, :15].drop(columns=["Puesto"]).loc[df[COLUMN_NAME] != ""]
 
         return df