"""Auto-generated example: 3 Float, 0 Categorical (over time)."""
import random
import math
import bencher as bn
from datetime import datetime, timedelta
class HashBenchmark(bn.ParametrizedSweep):
"""Hash throughput across key size, payload size, and iterations."""
key_size = bn.FloatSweep(default=32, bounds=[8, 256], doc="Key size in bytes")
payload_size = bn.FloatSweep(default=1024, bounds=[64, 65536], doc="Payload size in bytes")
iterations = bn.FloatSweep(default=100, bounds=[10, 1000], doc="Hash iterations")
throughput = bn.ResultFloat(units="MB/s", doc="Hash throughput")
_time_offset = 0.0
def benchmark(self):
self.throughput = 500.0 / (1.0 + 0.5 * math.log2(self.key_size / 8)) / (1.0 + 0.3 * math.log2(self.payload_size / 64)) * (self.iterations / 100)
self.throughput += random.gauss(0, 0.1 * 30)
self.throughput += self._time_offset * 10
def example_sweep_3_float_0_cat_over_time(run_cfg: bn.BenchRunCfg | None = None) -> bn.Bench:
"""3 Float, 0 Categorical (over time)."""
if run_cfg is None:
run_cfg = bn.BenchRunCfg()
benchable = HashBenchmark()
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=['key_size', 'payload_size', 'iterations'],
result_vars=['throughput'],
description='A 3 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. A 3D float sweep produces a volumetric representation. This is useful for visualising scalar fields in 3D parameter spaces.',
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_3_float_0_cat_over_time, subsampling_divisions=4, over_time=True)