proteometer.utils
=================

.. py:module:: proteometer.utils


Functions
---------

.. autoapisummary::

   proteometer.utils.flatten
   proteometer.utils.generate_index
   proteometer.utils.check_missingness
   proteometer.utils.filter_missingness
   proteometer.utils.expsum


Module Contents
---------------

.. py:function:: flatten(s: List[Any]) -> List[Any]

   Flattens a nested list into a single list.
   :param s: A list that may contain nested lists.
   :type s: List[Any]

   :returns: A flattened list containing all elements from the input list.
   :rtype: List[Any]


.. py:function:: generate_index(df: pandas.DataFrame, prot_col: str, level_col: str | None = None, id_separator: str = '@', id_col: str = 'id') -> pandas.DataFrame

   Generate a unique index for a DataFrame based on protein column identifier and optional level column identifier.

   :param df: Input DataFrame.
   :type df: pd.DataFrame
   :param prot_col: Column name for protein identifiers.
   :type prot_col: str
   :param level_col: Column name for level identifiers. Defaults to None.
   :type level_col: str | None, optional
   :param id_separator: Separator for combining protein and level identifiers. Defaults to "@".
   :type id_separator: str, optional
   :param id_col: Name of the new column for the generated index. Defaults to "id".
   :type id_col: str, optional

   :returns: DataFrame with the generated index.
   :rtype: pd.DataFrame


.. py:function:: check_missingness(df: pandas.DataFrame, groups: collections.abc.Sequence[str], group_cols: collections.abc.Sequence[collections.abc.Sequence[str]]) -> pandas.DataFrame

   Calculate missingness for specified groups in a DataFrame.

   :param df: Input DataFrame.
   :type df: pd.DataFrame
   :param groups: Names of the groups.
   :type groups: Sequence[str]
   :param group_cols: Columns corresponding to each group.
   :type group_cols: Sequence[Sequence[str]]

   :returns: DataFrame with missingness information added.
   :rtype: pd.DataFrame


.. py:function:: filter_missingness(df: pandas.DataFrame, groups: collections.abc.Sequence[str], group_cols: collections.abc.Sequence[collections.abc.Sequence[str]], min_replicates_qc: int = 2, method: Literal['all', 'any'] = 'any') -> pandas.DataFrame

   Filter rows in a DataFrame based on missingness thresholds for specified groups.

   :param df: Input DataFrame.
   :type df: pd.DataFrame
   :param groups: Names of the groups.
   :type groups: Sequence[str]
   :param group_cols: Columns corresponding to each group.
   :type group_cols: Sequence[Sequence[str]]
   :param min_replicates_qc: Threshold for minimal number of
                             replicates that are not NA. Defaults to 2.
   :type min_replicates_qc: float, optional
   :param method: Method for filtering. Can be "all" or "any".
                  Defaults to "all". If "all", all groups must meet the threshold.
                  If "any", at least one group must meet the threshold.
   :type method: str, optional

   :returns: Filtered DataFrame.
   :rtype: pd.DataFrame


.. py:function:: expsum(x: pandas.Series[float]) -> float