bencher.results.manim_cartesian.cartesian_product_cfg

Data model for Cartesian product animation configuration.

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

Classes

`SweepVar`	A single dimension of the Cartesian product.
`CartesianProductCfg`	Configuration describing an N-dimensional Cartesian product sweep.

Functions

from_bench_cfg(→ CartesianProductCfg)

Build a CartesianProductCfg from a BenchCfg instance.

Module Contents

class bencher.results.manim_cartesian.cartesian_product_cfg.SweepVar

A single dimension of the Cartesian product.

name: Human-readable variable name (e.g. “theta”, “repeat”).

values: The sampled values for this dimension.

name: str

values: list[Any] = []

class bencher.results.manim_cartesian.cartesian_product_cfg.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.

all_vars: Every dimension of the Cartesian product.

result_names: Names of the result variables being collected.

all_vars: list[SweepVar] = []

result_names: list[str] = []

strobe_color: tuple[int, int, int] = (80, 80, 80)

strobe_pad: int = 12

strobe_mark_size: int = 2

strobe_mark_gap: int = 4

strobe_mark_row_h: int = 16

strobe_border_radius: int = 4

strobe_base_border_w: int = 2

property ndim: int: Number of dimensions.

property shape: tuple[int, Ellipsis]: Shape of the result tensor.

property total_cells: int: Total number of cells in the Cartesian product.

bencher.results.manim_cartesian.cartesian_product_cfg.from_bench_cfg(bench_cfg) → CartesianProductCfg

Build a 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).