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[Autoloop: perf-comparison] Iteration 375: Add 3 resample benchmark pairs (ohlc, first_last, std_var_size)
Run: https://github.com/githubnext/tsb/actions/runs/28260214481 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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‎benchmarks/pandas/bench_resample_first_last.py‎

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"""Benchmark: resample_first_last — pd.Series.resample("H").first() / .last()."""
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import time
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import pandas as pd
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import numpy as np
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SIZE = 50_000
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WARMUP = 3
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ITERATIONS = 30
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base = pd.Timestamp("2020-01-01T00:00:00Z")
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idx = pd.date_range(start=base, periods=SIZE, freq="min")
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data = [(i % 100) * 2.5 + np.cos(i * 0.01) * 10 for i in range(SIZE)]
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s = pd.Series(data, index=idx)
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for _ in range(WARMUP):
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s.resample("H").first()
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s.resample("H").last()
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times = []
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for _ in range(ITERATIONS):
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t0 = time.perf_counter()
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s.resample("H").first()
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s.resample("H").last()
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times.append(time.perf_counter() - t0)
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total = sum(times)
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mean_ms = (total / ITERATIONS) * 1000
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total_ms = total * 1000
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print(f'{{"function": "resample_first_last", "mean_ms": {mean_ms:.6f}, "iterations": {ITERATIONS}, "total_ms": {total_ms:.6f}}}')

‎benchmarks/pandas/bench_resample_ohlc.py‎

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"""Benchmark: resample_ohlc — pd.Series.resample("H").ohlc() — OHLC aggregation."""
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import time
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import pandas as pd
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import numpy as np
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SIZE = 50_000
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WARMUP = 3
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ITERATIONS = 30
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base = pd.Timestamp("2020-01-01T00:00:00Z")
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idx = pd.date_range(start=base, periods=SIZE, freq="min")
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data = [np.sin(i * 0.03) * 100 + 200 for i in range(SIZE)]
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s = pd.Series(data, index=idx)
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for _ in range(WARMUP):
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s.resample("H").ohlc()
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times = []
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for _ in range(ITERATIONS):
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t0 = time.perf_counter()
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s.resample("H").ohlc()
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times.append(time.perf_counter() - t0)
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total = sum(times)
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mean_ms = (total / ITERATIONS) * 1000
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total_ms = total * 1000
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print(f'{{"function": "resample_ohlc", "mean_ms": {mean_ms:.6f}, "iterations": {ITERATIONS}, "total_ms": {total_ms:.6f}}}')

‎benchmarks/pandas/bench_resample_std_var_size.py‎

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"""Benchmark: resample_std_var_size — pd.Series.resample("H").std() / .var() / .size()."""
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import time
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import pandas as pd
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import numpy as np
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SIZE = 50_000
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WARMUP = 3
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ITERATIONS = 30
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base = pd.Timestamp("2020-01-01T00:00:00Z")
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idx = pd.date_range(start=base, periods=SIZE, freq="min")
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data = [np.sin(i * 0.05) * 50 + (i % 60) * 0.5 for i in range(SIZE)]
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s = pd.Series(data, index=idx)
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for _ in range(WARMUP):
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s.resample("H").std()
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s.resample("H").var()
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s.resample("H").size()
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times = []
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for _ in range(ITERATIONS):
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t0 = time.perf_counter()
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s.resample("H").std()
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s.resample("H").var()
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s.resample("H").size()
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times.append(time.perf_counter() - t0)
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total = sum(times)
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mean_ms = (total / ITERATIONS) * 1000
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total_ms = total * 1000
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print(f'{{"function": "resample_std_var_size", "mean_ms": {mean_ms:.6f}, "iterations": {ITERATIONS}, "total_ms": {total_ms:.6f}}}')

‎benchmarks/tsb/bench_resample_first_last.ts‎

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/**
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* Benchmark: resample_first_last — SeriesResampler.first() and .last() on hourly resampling.
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*
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* Mirrors pandas: pd.Series.resample("H").first() / .last()
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* first() returns the first non-null value per bin; last() returns the last.
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*
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* Outputs JSON: {"function": "resample_first_last", "mean_ms": ..., "iterations": ..., "total_ms": ...}
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*/
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import { Series, resampleSeries } from "../../src/index.ts";
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const SIZE = 50_000;
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const WARMUP = 3;
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const ITERATIONS = 30;
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const base = new Date("2020-01-01T00:00:00Z").getTime();
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const idx = Array.from({ length: SIZE }, (_, i) => new Date(base + i * 60_000));
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const data = Array.from({ length: SIZE }, (_, i) => (i % 100) * 2.5 + Math.cos(i * 0.01) * 10);
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const s = new Series({ data, index: idx });
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for (let i = 0; i < WARMUP; i++) {
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resampleSeries(s, "H").first();
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resampleSeries(s, "H").last();
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}
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const times: number[] = [];
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for (let i = 0; i < ITERATIONS; i++) {
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const t0 = performance.now();
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resampleSeries(s, "H").first();
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resampleSeries(s, "H").last();
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times.push(performance.now() - t0);
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}
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const total = times.reduce((a, b) => a + b, 0);
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console.log(
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JSON.stringify({
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function: "resample_first_last",
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mean_ms: total / ITERATIONS,
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iterations: ITERATIONS,
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total_ms: total,
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}),
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);

‎benchmarks/tsb/bench_resample_ohlc.ts‎

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/**
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* Benchmark: resample_ohlc — SeriesResampler.ohlc() — OHLC aggregation on daily resampling.
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*
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* Mirrors pandas: pd.Series.resample("D").ohlc()
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* ohlc() returns a DataFrame with open/high/low/close columns, one row per time bin.
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*
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* Outputs JSON: {"function": "resample_ohlc", "mean_ms": ..., "iterations": ..., "total_ms": ...}
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*/
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import { Series, resampleSeries } from "../../src/index.ts";
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const SIZE = 50_000;
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const WARMUP = 3;
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const ITERATIONS = 30;
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const base = new Date("2020-01-01T00:00:00Z").getTime();
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const idx = Array.from({ length: SIZE }, (_, i) => new Date(base + i * 60_000));
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const data = Array.from({ length: SIZE }, (_, i) => Math.sin(i * 0.03) * 100 + 200);
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const s = new Series({ data, index: idx });
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for (let i = 0; i < WARMUP; i++) {
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resampleSeries(s, "H").ohlc();
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}
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const times: number[] = [];
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for (let i = 0; i < ITERATIONS; i++) {
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const t0 = performance.now();
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resampleSeries(s, "H").ohlc();
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times.push(performance.now() - t0);
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}
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const total = times.reduce((a, b) => a + b, 0);
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console.log(
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JSON.stringify({
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function: "resample_ohlc",
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mean_ms: total / ITERATIONS,
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iterations: ITERATIONS,
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total_ms: total,
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}),
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);

‎benchmarks/tsb/bench_resample_std_var_size.ts‎

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/**
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* Benchmark: resample_std_var_size — SeriesResampler.std(), .var(), .size() on hourly bins.
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*
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* Mirrors pandas: pd.Series.resample("H").std() / .var() / .size()
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* std() computes standard deviation per bin, var() computes variance,
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* size() returns the count per bin.
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*
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* Outputs JSON: {"function": "resample_std_var_size", "mean_ms": ..., "iterations": ..., "total_ms": ...}
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*/
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import { Series, resampleSeries } from "../../src/index.ts";
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const SIZE = 50_000;
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const WARMUP = 3;
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const ITERATIONS = 30;
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const base = new Date("2020-01-01T00:00:00Z").getTime();
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const idx = Array.from({ length: SIZE }, (_, i) => new Date(base + i * 60_000));
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const data = Array.from({ length: SIZE }, (_, i) => Math.sin(i * 0.05) * 50 + (i % 60) * 0.5);
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const s = new Series({ data, index: idx });
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for (let i = 0; i < WARMUP; i++) {
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resampleSeries(s, "H").std();
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resampleSeries(s, "H").var();
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resampleSeries(s, "H").size();
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}
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const times: number[] = [];
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for (let i = 0; i < ITERATIONS; i++) {
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const t0 = performance.now();
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resampleSeries(s, "H").std();
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resampleSeries(s, "H").var();
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resampleSeries(s, "H").size();
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times.push(performance.now() - t0);
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}
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const total = times.reduce((a, b) => a + b, 0);
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console.log(
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JSON.stringify({
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function: "resample_std_var_size",
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mean_ms: total / ITERATIONS,
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iterations: ITERATIONS,
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total_ms: total,
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}),
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);

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