"""Auto-generated example: 0 Float, 0 Categorical (over time)."""

import random
import bencher as bn
from datetime import datetime, timedelta


class BaselineCheck(bn.ParametrizedSweep):
    """Measures a fixed baseline metric with no swept parameters."""

    baseline = bn.ResultFloat(units="ms", doc="Baseline latency")

    _time_offset = 0.0

    def benchmark(self):
        self.baseline = 42.0
        self.baseline += random.gauss(0, 0.1 * 5)
        self.baseline += self._time_offset * 10


def example_sweep_0_float_0_cat_over_time(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
    """0 Float, 0 Categorical (over time)."""
    if run_cfg is None:
        run_cfg = bn.BenchRunCfg()
    benchable = BaselineCheck()
    bench = benchable.to_bench(run_cfg)
    _base_time = datetime(2000, 1, 1)
    for i, offset in enumerate([0.0, 0.5, 1.0]):
        benchable._time_offset = offset
        run_cfg.clear_cache = True
        run_cfg.clear_history = i == 0
        res = bench.plot_sweep(
            "over_time",
            input_vars=[],
            result_vars=["baseline"],
            description="A 0 float + 0 categorical parameter sweep tracked over time. Setting over_time=True records multiple time snapshots that can be scrubbed via a slider. Each call to plot_sweep with a new time_src appends a snapshot to the history. This is designed for nightly benchmarks or CI pipelines where you want to track how metrics evolve across commits, releases, or environmental changes. Use clear_history=True on the first snapshot to reset, and clear_cache=True to force re-evaluation. With no input variables, this is a 0D sweep that measures a single baseline metric.",
            post_description="The time slider lets you scrub through snapshots. The 'All Time Points (aggregated)' tab pools all snapshots into one view, smoothing out per-snapshot noise to reveal long-term trends.",
            run_cfg=run_cfg,
            time_src=_base_time + timedelta(seconds=i),
        )

    return bench


if __name__ == "__main__":
    bn.run(example_sweep_0_float_0_cat_over_time, level=4, over_time=True)
