docs: include docstrings with rst format to autogenerate in RDT

docs/source/conf.py

@@ -12,6 +12,7 @@
     "sphinx.ext.mathjax",
     "sphinx.ext.viewcode",
     "sphinx.ext.intersphinx",
+    "sphinx.ext.napoleon",
     "nbsphinx",
 ]
 

pysus/api/_impl/databases.py

@@ -38,9 +38,45 @@ def _fetch_data(
     show_progress: bool = True,
     **kwargs,
 ) -> pd.DataFrame:
-    """Query, download, and concatenate Parquet files for a given dataset."""
+    """Query, download, and concatenate Parquet files for a given dataset.
+
+    Internally creates an async event loop, queries the PySUS API for matching
+    files, downloads them, and reads them into a single DataFrame.
+
+    Parameters
+    ----------
+    dataset : str
+        Name of the dataset (e.g. ``"sinan"``, ``"sinasc"``).
+    group : str, optional
+        Group or disease code to filter by.
+    state : str, optional
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int], optional
+        Year or list of years to fetch.
+    month : int | list[int], optional
+        Month or list of months to fetch.
+    show_progress : bool, optional
+        Whether to display a tqdm progress bar during download.  Default is
+        ``True``.
+    **kwargs
+        Additional arguments forwarded to :meth:`PySUS.read_parquet`.
+
+    Returns
+    -------
+    pd.DataFrame
+        Concatenated data from all matching Parquet files.  Returns an empty
+        DataFrame when no files are found.
+
+    Raises
+    ------
+    RuntimeError
+        If an event loop is already running but ``nest_asyncio`` is not
+        installed.
+    """
 
     async def _fetch():
+        """Coroutine that performs the actual API query, download, and read."""
+
         async with PySUS() as pysus:
             years = [year] if isinstance(year, int) else (year or [None])
             months = [month] if isinstance(month, int) else (month or [None])
@@ -157,7 +193,25 @@ def sinan(
     year: int | list[int],
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch SINAN records for a given disease and year(s)."""
+    """Fetch SINAN records for a given disease and year(s).
+
+    SINAN (Sistema de Informação de Agravos de Notificação) is the Brazilian
+    notifiable-disease information system.
+
+    Parameters
+    ----------
+    disease : Literal
+        Disease code (e.g. ``"DENG"`` for dengue, ``"ZIKA"`` for zika).
+    year : int | list[int]
+        Year or list of years to fetch.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        SINAN records for the specified disease and year(s).
+    """
     return _fetch_data(
         dataset="sinan",
         group=disease.upper(),
@@ -171,7 +225,27 @@ def sinasc(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch SINASC birth certificates for a given state, year(s), and group."""
+    """Fetch SINASC birth certificates for a given state, year(s), and group.
+
+    SINASC (Sistema de Informação sobre Nascidos Vivos) is the Brazilian live
+    birth information system.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        SINASC birth records for the specified state, year(s), and group.
+    """
     return _fetch_data(
         dataset="sinasc",
         state=state.upper(),
@@ -186,7 +260,27 @@ def sim(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch SIM mortality records for a given state, year(s), and group."""
+    """Fetch SIM mortality records for a given state, year(s), and group.
+
+    SIM (Sistema de Informação sobre Mortalidade) is the Brazilian mortality
+    information system.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        SIM mortality records for the specified state, year(s), and group.
+    """
     return _fetch_data(
         dataset="sim",
         state=state.upper(),
@@ -202,7 +296,29 @@ def sih(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch SIH hospital admissions for a state, year, month, and group."""
+    """Fetch SIH hospital admissions for a state, year, month, and group.
+
+    SIH (Sistema de Informação Hospitalar) is the Brazilian hospital
+    admission information system.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    month : int | list[int]
+        Month or list of months to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        SIH hospital admission records.
+    """
     return _fetch_data(
         dataset="sih",
         state=state.upper(),
@@ -219,7 +335,29 @@ def sia(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch SIA ambulatory care for a state, year, month, and group."""
+    """Fetch SIA ambulatory care for a state, year, month, and group.
+
+    SIA (Sistema de Informação Ambulatorial) is the Brazilian ambulatory care
+    information system.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    month : int | list[int]
+        Month or list of months to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        SIA ambulatory care records.
+    """
     return _fetch_data(
         dataset="sia",
         state=state.upper(),
@@ -235,7 +373,27 @@ def pni(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch PNI immunisation records for a given state, year(s), and group."""
+    """Fetch PNI immunisation records for a given state, year(s), and group.
+
+    PNI (Programa Nacional de Imunizações) is the Brazilian national
+    immunisation programme.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        PNI immunisation records.
+    """
     return _fetch_data(
         dataset="pni",
         state=state.upper(),
@@ -249,7 +407,25 @@ def ibge(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch IBGE census data for given year(s) and optional group."""
+    """Fetch IBGE census data for given year(s) and optional group.
+
+    IBGE (Instituto Brasileiro de Geografia e Estatística) provides census
+    and demographic data.
+
+    Parameters
+    ----------
+    year : int | list[int]
+        Year or list of years to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        IBGE census data for the specified year(s) and group.
+    """
     return _fetch_data(dataset="ibge", group=group, year=year)
 
 
@@ -260,7 +436,29 @@ def cnes(
     group: str | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch CNES health facilities for a state, year, month, and group."""
+    """Fetch CNES health facilities for a state, year, month, and group.
+
+    CNES (Cadastro Nacional de Estabelecimentos de Saúde) is the Brazilian
+    registry of health-care facilities.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    month : int | list[int]
+        Month or list of months to fetch.
+    group : str, optional
+        Additional grouping code.
+    **kwargs
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        CNES health-facility records.
+    """
     return _fetch_data(
         dataset="cnes",
         state=state.upper(),
@@ -277,7 +475,29 @@ def ciha(
     group: str | None = "CIHA",
     **kwargs,
 ) -> pd.DataFrame:
-    """Fetch CIHA hospitalisation records for state, year, month, and group."""
+    """Fetch CIHA hospitalisation records for state, year, month, and group.
+
+    CIHA (Comunicação de Internação Hospitalar) provides hospitalisation
+    records.
+
+    Parameters
+    ----------
+    state : State
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int]
+        Year or list of years to fetch.
+    month : int | list[int]
+        Month or list of months to fetch.
+    group : str, optional
+        Additional grouping code.  Default is ``"CIHA"``.
+    ``**kwargs``
+        Additional arguments forwarded to :func:`_fetch_data`.
+
+    Returns
+    -------
+    pd.DataFrame
+        CIHA hospitalisation records.
+    """
     return _fetch_data(
         dataset="ciha",
         state=state.upper(),
@@ -306,9 +526,39 @@ def list_files(
     month: int | list[int] | None = None,
     **kwargs,
 ) -> pd.DataFrame:
-    """List catalog files filtered by client, group, state, year, and month."""
+    """List catalog files filtered by client, group, state, year, and month.
+
+    Queries the PySUS API metadata and returns a DataFrame with file name,
+    path, dataset, group, year, month, state, and last-modified timestamp for
+    every matching file without downloading the actual data.
+
+    Parameters
+    ----------
+    dataset : Literal
+        Dataset name (e.g. ``"SINAN"``, ``"SINASC"``, etc.).
+    client : Literal["FTP", "DadosGov"], optional
+        Data source client to query.
+    group : str, optional
+        Group or disease code to filter by.
+    state : str, optional
+        Two-letter state abbreviation (e.g. ``"RJ"``).
+    year : int | list[int], optional
+        Year or list of years to filter by.
+    month : int | list[int], optional
+        Month or list of months to filter by.
+    **kwargs
+        Additional arguments forwarded to :meth:`PySUS.query`.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with columns ``name``, ``path``, ``dataset``, ``group``,
+        ``year``, ``month``, ``state``, and ``modify``.
+    """
 
     async def _list():
+        """Coroutine that queries the PySUS API and builds the file list."""
+
         async with PySUS() as pysus:
             years = [year] if isinstance(year, int) else (year or [None])
             months = [month] if isinstance(month, int) else (month or [None])