pdstools.explanations.ExplanationsUtils
=======================================

.. py:module:: pdstools.explanations.ExplanationsUtils


Attributes
----------

.. autoapisummary::

   pdstools.explanations.ExplanationsUtils.defaults


Classes
-------

.. autoapisummary::

   pdstools.explanations.ExplanationsUtils.ContextInfo
   pdstools.explanations.ExplanationsUtils.ContextOperations


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

.. py:data:: defaults

.. py:class:: ContextInfo

   Bases: :py:obj:`TypedDict`


   dict() -> new empty dictionary
   dict(mapping) -> new dictionary initialized from a mapping object's
       (key, value) pairs
   dict(iterable) -> new dictionary initialized as if via:
       d = {}
       for k, v in iterable:
           d[k] = v
   dict(**kwargs) -> new dictionary initialized with the name=value pairs
       in the keyword argument list.  For example:  dict(one=1, two=2)


   .. py:attribute:: context_key
      :type:  str


   .. py:attribute:: context_value
      :type:  str


.. py:class:: ContextOperations(aggregate: pdstools.explanations.Aggregate.Aggregate)

   Bases: :py:obj:`pdstools.utils.namespaces.LazyNamespace`


   Context related operations such as to filter unique contexts.

   :param aggregate (Aggregate):
   :type aggregate (Aggregate): The aggregate object to operate on.

   .. attribute:: aggregate (Aggregate)

      

      :type: The aggregate object.

   .. attribute:: _df (Optional[pl.DataFrame])

      

      :type: DataFrame containing context information.

   .. attribute:: _context_keys (Optional[list[str]])

      

      :type: list of context keys.

   .. attribute:: initialized (bool)

      

      :type: Flag indicating if the context operations have been initialized.

   .. method:: get_context_keys():

      Returns the list of context keys from loaded data.
      Eg. ['pyChannel', 'pyDirection', ...]
      

   .. method:: get_df(context_infos=None, with_partition_col=False):

      Returns a DataFrame containing unique contexts
      If `with_partition_col` is True, includes the partition column.
      If `context_infos` is None, returns the full unique contexts,
      else filtered by the context
      Eg. with partition column:
      | pyChannel | pyDirection | ... | partition |
      |-----------|-------------|-----|-----------|
      | channel1  | direction1  | ... | {"partition": {"pyChannel": "channel1", "pyDirection": "direction1"}} |
      | channel1  | direction2  | ... | {"partition": {"pyChannel": "channel1", "pyDirection": "direction2"}} |
      

   .. method:: get_list(context_infos=None, with_partition_col=False):

      Returns a list[ContextInfo] containing unique contexts
      If `with_partition_col` is True, includes the partition column.
      If `context_infos` is None, returns the full unique contexts,
      else filtered by the context
      Eg. without partition column:
      [
          {"pyChannel": "channel1", "pyDirection": "direction1", ...},
          {"pyChannel": "channel1", "pyDirection": "direction2", ...},
      ]
      

   .. method:: get_context_info_str(context_info, sep="-"):

      Returns a string representation of a single context information.
      Eg. channel1-direction1-...


   .. py:attribute:: dependencies
      :value: ['polars']



   .. py:attribute:: dependency_group
      :value: 'explanations'



   .. py:attribute:: aggregate


   .. py:attribute:: initialized
      :value: False



   .. py:method:: get_context_keys()


   .. py:method:: get_df(context_infos: list[ContextInfo] | None = None, with_partition_col: bool = False) -> polars.DataFrame

      Get the DataFrame filtered by the provided context information.



   .. py:method:: get_list(context_infos: list[ContextInfo] | None = None, with_partition_col: bool = False) -> list[ContextInfo]

      Get the list of context information filtered by the provided context information.



   .. py:method:: get_context_info_str(context_info: ContextInfo, sep: str = '-') -> str
      :staticmethod: