"""Auto-generated example: 2 Float, 0 Categorical (no repeats)."""

import math

import bencher as bn


class CompressionBench(bn.ParametrizedSweep):
    """Measures compression ratio across block size and input entropy."""

    block_size = bn.FloatSweep(default=4096, bounds=[512, 65536], doc="Block size in bytes")
    entropy = bn.FloatSweep(default=0.5, bounds=[0.0, 1.0], doc="Input data entropy")

    ratio = bn.ResultFloat(units="x", doc="Compression ratio")

    def benchmark(self):
        self.ratio = (1.0 - 0.7 * self.entropy) * (1.0 + 0.3 * math.log2(self.block_size / 512))


def example_sweep_2_float_0_cat_no_repeats(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
    """2 Float, 0 Categorical (no repeats)."""
    bench = CompressionBench().to_bench(run_cfg)
    bench.plot_sweep(
        input_vars=["block_size", "entropy"],
        result_vars=["ratio"],
        description="A 2 float + 0 categorical parameter sweep with a single sample per combination. Bencher calculates the Cartesian product of all input variables and evaluates the benchmark function at each point. With no repeats, each combination appears exactly once -- useful for deterministic functions or quick exploration before committing to longer runs. A 2D float sweep produces a heatmap. Additional categorical variables create faceted heatmaps, one per category combination.",
        post_description="Each tab shows a different view of the same data: interactive plots, tabular summaries, and raw data. Use the tabs to explore the sweep results from different angles.",
    )

    return bench


if __name__ == "__main__":
    bn.run(example_sweep_2_float_0_cat_no_repeats, level=4)
