bencher.results.manim_cartesian.cartesian_product_cfg
=====================================================

.. py:module:: bencher.results.manim_cartesian.cartesian_product_cfg

.. autoapi-nested-parse::

   Data model for Cartesian product animation configuration.

   No manim dependency — pure Python dataclasses that bridge BenchCfg
   to the animation scene.



Classes
-------

.. autoapisummary::

   bencher.results.manim_cartesian.cartesian_product_cfg.SweepVar
   bencher.results.manim_cartesian.cartesian_product_cfg.CartesianProductCfg


Functions
---------

.. autoapisummary::

   bencher.results.manim_cartesian.cartesian_product_cfg.from_bench_cfg


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

.. py:class:: SweepVar

   A single dimension of the Cartesian product.

   .. attribute:: name

      Human-readable variable name (e.g. "theta", "repeat").

   .. attribute:: values

      The sampled values for this dimension.


   .. py:attribute:: name
      :type:  str


   .. py:attribute:: values
      :type:  list[Any]
      :value: []



.. py:class:: CartesianProductCfg

   Configuration describing an N-dimensional Cartesian product sweep.

   ``all_vars`` includes every dimension — input variables *and* meta
   variables (repeat, over_time).  They are all first-class dimensions
   of the product.

   .. attribute:: all_vars

      Every dimension of the Cartesian product.

   .. attribute:: result_names

      Names of the result variables being collected.


   .. py:attribute:: all_vars
      :type:  list[SweepVar]
      :value: []



   .. py:attribute:: result_names
      :type:  list[str]
      :value: []



   .. py:attribute:: strobe_color
      :type:  tuple[int, int, int]
      :value: (80, 80, 80)



   .. py:attribute:: strobe_pad
      :type:  int
      :value: 12



   .. py:attribute:: strobe_mark_size
      :type:  int
      :value: 2



   .. py:attribute:: strobe_mark_gap
      :type:  int
      :value: 4



   .. py:attribute:: strobe_mark_row_h
      :type:  int
      :value: 16



   .. py:attribute:: strobe_border_radius
      :type:  int
      :value: 4



   .. py:attribute:: strobe_base_border_w
      :type:  int
      :value: 2



   .. py:property:: ndim
      :type: int


      Number of dimensions.


   .. py:property:: shape
      :type: tuple[int, Ellipsis]


      Shape of the result tensor.


   .. py:property:: total_cells
      :type: int


      Total number of cells in the Cartesian product.


.. py:function:: from_bench_cfg(bench_cfg) -> CartesianProductCfg

   Build a :class:`CartesianProductCfg` from a ``BenchCfg`` instance.

   Uses ``bench_cfg.all_vars`` (input_vars + meta_vars) so that repeat
   and over_time are included as full dimensions — they are just more
   dimensions of the Cartesian product.

   This mirrors the extraction pattern in ``DimsCfg.__init__``
   (``bench_cfg.py``).