pdstools.adm.trees._multi
=========================

.. py:module:: pdstools.adm.trees._multi

.. autoapi-nested-parse::

   The :class:`MultiTrees` collection — multiple snapshots of one config.



Attributes
----------

.. autoapisummary::

   pdstools.adm.trees._multi.logger


Classes
-------

.. autoapisummary::

   pdstools.adm.trees._multi.MultiTrees


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

.. py:data:: logger

.. py:class:: MultiTrees

   A collection of :class:`ADMTreesModel` snapshots indexed by timestamp.

   Construct via :meth:`from_datamart`.


   .. py:attribute:: trees
      :type:  dict[str, pdstools.adm.trees._model.ADMTreesModel]


   .. py:attribute:: model_name
      :type:  str | None
      :value: None



   .. py:attribute:: context_keys
      :type:  list | None
      :value: None



   .. py:method:: __repr__() -> str


   .. py:method:: __getitem__(index: int | str) -> pdstools.adm.trees._model.ADMTreesModel

      Return the :class:`ADMTreesModel` at ``index``.

      Integer indices select by insertion order; string indices select
      by snapshot timestamp.  Use :meth:`items` if you need both keys
      and values together.



   .. py:method:: __len__() -> int


   .. py:method:: items()

      Iterate ``(timestamp, model)`` pairs in insertion order.



   .. py:method:: values()

      Iterate :class:`ADMTreesModel` instances in insertion order.



   .. py:method:: keys()

      Iterate snapshot timestamps in insertion order.



   .. py:method:: __iter__()


   .. py:method:: __add__(other: MultiTrees | pdstools.adm.trees._model.ADMTreesModel) -> MultiTrees


   .. py:property:: first
      :type: pdstools.adm.trees._model.ADMTreesModel



   .. py:property:: last
      :type: pdstools.adm.trees._model.ADMTreesModel



   .. py:method:: from_datamart(df: polars.DataFrame, n_threads: int = 1, configuration: str | None = None) -> MultiTrees
      :classmethod:


      Decode every Modeldata blob in ``df`` for a single configuration.

      Returns one :class:`MultiTrees` containing one
      :class:`ADMTreesModel` per snapshot.

      :param df: Datamart slice.  Must contain ``Modeldata``, ``SnapshotTime``
                 and ``Configuration`` columns and cover exactly one
                 Configuration.  Use :meth:`from_datamart_grouped` if ``df``
                 spans multiple configurations.
      :type df: pl.DataFrame
      :param n_threads: Worker count for parallel base64+zlib decoding.
      :type n_threads: int
      :param configuration: Optional explicit Configuration name; required if ``df``
                            doesn't already contain a single Configuration.
      :type configuration: str | None



   .. py:method:: from_datamart_grouped(df: polars.DataFrame, n_threads: int = 1) -> dict[str, MultiTrees]
      :classmethod:


      Decode every Modeldata blob in ``df``, grouped by Configuration.

      Returns a mapping of configuration name to :class:`MultiTrees`.
      Use :meth:`from_datamart` instead when the input has only one
      configuration.



   .. py:method:: _decode_datamart_frame(df: polars.DataFrame, n_threads: int = 1) -> list[tuple[str, str, pdstools.adm.trees._model.ADMTreesModel]]
      :staticmethod:


      Decode every blob in ``df`` and return ``(config, timestamp, model)`` rows.



   .. py:method:: compute_over_time(predictor_categorization: collections.abc.Callable | None = None) -> polars.DataFrame

      Return per-tree categorisation counts across snapshots, with a
      ``SnapshotTime`` column per row.