tlbox.apps.bazel_parser.refinement
==================================

.. py:module:: tlbox.apps.bazel_parser.refinement


Attributes
----------

.. autoapisummary::

   tlbox.apps.bazel_parser.refinement.logger


Classes
-------

.. autoapisummary::

   tlbox.apps.bazel_parser.refinement.Verbosity
   tlbox.apps.bazel_parser.refinement.RefinementConfig


Functions
---------

.. autoapisummary::

   tlbox.apps.bazel_parser.refinement.refine_dataframe
   tlbox.apps.bazel_parser.refinement.remove_node_from_repo
   tlbox.apps.bazel_parser.refinement.full_refinement


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

.. py:data:: logger

.. py:class:: Verbosity(*args, **kwds)

   Bases: :py:obj:`enum.Enum`


   Create a collection of name/value pairs.

   Example enumeration:

   >>> class Color(Enum):
   ...     RED = 1
   ...     BLUE = 2
   ...     GREEN = 3

   Access them by:

   - attribute access::

   >>> Color.RED
   <Color.RED: 1>

   - value lookup:

   >>> Color(1)
   <Color.RED: 1>

   - name lookup:

   >>> Color['RED']
   <Color.RED: 1>

   Enumerations can be iterated over, and know how many members they have:

   >>> len(Color)
   3

   >>> list(Color)
   [<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

   Methods can be added to enumerations, and members can have their own
   attributes -- see the documentation for details.


   .. py:attribute:: SILENT
      :value: 'SILENT'



   .. py:attribute:: COUNT
      :value: 'COUNT'



   .. py:attribute:: LIST
      :value: 'LIST'



.. py:class:: RefinementConfig

   This specifies how to refine a dataframe of nodes to a smaller set.

   We expect node_name to be the index of the dataframe.

   All of these fields are exclusionary.


   .. py:attribute:: name_patterns
      :type:  list[str]


   .. py:attribute:: class_patterns
      :type:  list[str]


   .. py:attribute:: class_pattern_to_name_patterns
      :type:  dict[str, list[str]]


.. py:function:: refine_dataframe(df: pandas.DataFrame, refinement: RefinementConfig, verbosity: Verbosity) -> pandas.DataFrame

.. py:function:: remove_node_from_repo(node: str, repo: tlbox.apps.bazel_parser.repo_graph_data.RepoGraphData) -> None

   Modify graph by removing node, but preserving edges.

   Removed nodes are expected to be noise/artifact targets (probability=1.0,
   duration=0), so dropping their attributes is correct. Callers should call
   repo.refresh() afterward to recompute all metrics on the reduced graph.


.. py:function:: full_refinement(repo: tlbox.apps.bazel_parser.repo_graph_data.RepoGraphData, refinement: RefinementConfig, verbosity: Verbosity) -> pandas.DataFrame