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