138 lines
3.9 KiB
Python
138 lines
3.9 KiB
Python
import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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import argparse
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plt.rcParams["figure.figsize"] = [40, 30]
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from pandas.plotting import register_matplotlib_converters
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register_matplotlib_converters()
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def readStats(name: str, min_block_number: int):
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df = pd.read_csv(name).convert_dtypes()
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df = df[df.block_number >= min_block_number]
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df.set_index("block_number", inplace=True)
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df.time /= 1000000000
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df.drop(columns=["gas"], inplace=True)
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df["bps"] = df.blocks / df.time
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df["tps"] = df.txs / df.time
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return df
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def prettySecs(s: float):
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sa = abs(int(s))
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ss = sa % 60
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m = sa // 60 % 60
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h = sa // (60 * 60)
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sign = "" if s >= 0 else "-"
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if h > 0:
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return f"{sign}{h}h{m}m{ss}s"
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elif m > 0:
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return f"{sign}{m}m{ss}s"
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else:
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return f"{sign}{ss}s"
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def formatBins(df: pd.DataFrame, bins: int):
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if bins > 0:
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bins = np.linspace(
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df.block_number.iloc[0], df.block_number.iloc[-1], bins, dtype=int
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)
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return df.groupby(pd.cut(df["block_number"], bins), observed=True)
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else:
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return df
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parser = argparse.ArgumentParser()
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parser.add_argument("baseline")
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parser.add_argument("contender")
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parser.add_argument("--plot", action="store_true")
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parser.add_argument(
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"--bins",
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default=10,
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type=int,
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help="Number of bins to group block ranges into in overview, 0=all rows",
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)
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parser.add_argument(
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"--min-block-number",
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default=500000,
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type=int,
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help="Skip block blocks below the given number",
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)
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args = parser.parse_args()
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baseline = readStats(args.baseline, args.min_block_number)
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contender = readStats(args.contender, args.min_block_number)
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# Pick out the rows to match - a more sophisticated version of this would
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# interpolate, perhaps - also, maybe should check for non-matching block/tx counts
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df = baseline.merge(contender, on=("block_number", "blocks", "txs"))
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df["bpsd"] = (df.bps_y - df.bps_x) / df.bps_x
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df["tpsd"] = (df.tps_y - df.tps_x) / df.tps_x
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df["timed"] = (df.time_y - df.time_x) / df.time_x
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df.reset_index(inplace=True)
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if args.plot:
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plt.rcParams["axes.grid"] = True
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fig = plt.figure()
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bps = fig.add_subplot(2, 2, 1, title="Blocks per second (more is better)")
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bpsd = fig.add_subplot(2, 2, 2, title="Difference (>0 is better)")
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tps = fig.add_subplot(2, 2, 3, title="Transactions per second (more is better)")
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tpsd = fig.add_subplot(2, 2, 4, title="Difference (>0 is better)")
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bps.plot(df.block_number, df.bps_x.rolling(3).mean(), label="baseline")
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bps.plot(df.block_number, df.bps_y.rolling(3).mean(), label="contender")
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bpsd.plot(df.block_number, df.bpsd.rolling(3).mean())
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tps.plot(df.block_number, df.tps_x.rolling(3).mean(), label="baseline")
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tps.plot(df.block_number, df.tps_y.rolling(3).mean(), label="contender")
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tpsd.plot(df.block_number, df.tpsd.rolling(3).mean())
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bps.legend()
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tps.legend()
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fig.subplots_adjust(bottom=0.05, right=0.95, top=0.95, left=0.05)
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plt.show()
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print(f"{os.path.basename(args.baseline)} vs {os.path.basename(args.contender)}")
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print(
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formatBins(df, args.bins)
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.agg(
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dict.fromkeys(
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["bps_x", "bps_y", "tps_x", "tps_y", "bpsd", "tpsd", "timed"], "mean"
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),
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)
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.to_string(
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formatters=dict(
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dict.fromkeys(["bpsd", "tpsd", "timed"], "{:,.2%}".format),
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**dict.fromkeys(["bps_x", "bps_y", "tps_x"], "{:,.2f}".format),
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)
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)
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)
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print(
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f"\nblocks: {df.blocks.sum()}, baseline: {prettySecs(df.time_x.sum())}, contender: {prettySecs(df.time_y.sum())}"
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)
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print(f"bpsd (mean): {df.bpsd.mean():.2%}")
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print(f"tpsd (mean): {df.tpsd.mean():.2%}")
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print(
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f"Time (sum): {prettySecs(df.time_y.sum()-df.time_x.sum())}, {df.timed.mean():.2%}"
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)
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print()
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print(
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"bpsd = blocks per sec diff (+), tpsd = txs per sec diff, timed = time to process diff (-)"
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)
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print("+ = more is better, - = less is better")
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