fix display in early blocks and time avg

This commit is contained in:
Jacek Sieka 2024-06-07 12:29:34 +02:00
parent b3a5c67532
commit ce80ed79a5
No known key found for this signature in database
GPG Key ID: A1B09461ABB656B8
1 changed files with 10 additions and 5 deletions

View File

@ -14,8 +14,9 @@ register_matplotlib_converters()
def readStats(name: str, min_block_number: int): def readStats(name: str, min_block_number: int):
df = pd.read_csv(name).convert_dtypes() df = pd.read_csv(name).convert_dtypes()
if len(df.index) > 2 * min_block_number: if df.block_number.iloc[-1] > min_block_number:
df = df[df.block_number >= min_block_number] cutoff = min(df.block_number.iloc[-1] - min_block_number, min_block_number)
df = df[df.block_number >= cutoff]
df.set_index("block_number", inplace=True) df.set_index("block_number", inplace=True)
df.time /= 1000000000 df.time /= 1000000000
df.drop(columns=["gas"], inplace=True) df.drop(columns=["gas"], inplace=True)
@ -74,8 +75,8 @@ contender = readStats(args.contender, args.min_block_number)
# interpolate, perhaps - also, maybe should check for non-matching block/tx counts # interpolate, perhaps - also, maybe should check for non-matching block/tx counts
df = baseline.merge(contender, on=("block_number", "blocks", "txs")) df = baseline.merge(contender, on=("block_number", "blocks", "txs"))
df["bpsd"] = (df.bps_y - df.bps_x) / df.bps_x df["bpsd"] = ((df.bps_y - df.bps_x) / df.bps_x).fillna(0)
df["tpsd"] = (df.tps_y - df.tps_x) / df.tps_x df["tpsd"] = ((df.tps_y - df.tps_x) / df.tps_x).fillna(0)
df["timed"] = (df.time_y - df.time_x) / df.time_x df["timed"] = (df.time_y - df.time_x) / df.time_x
df.reset_index(inplace=True) df.reset_index(inplace=True)
@ -127,8 +128,12 @@ print(
) )
print(f"bpsd (mean): {df.bpsd.mean():.2%}") print(f"bpsd (mean): {df.bpsd.mean():.2%}")
print(f"tpsd (mean): {df.tpsd.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( print(
f"Time (sum): {prettySecs(df.time_y.sum()-df.time_x.sum())}, {df.timed.mean():.2%}" f"Time (total): {prettySecs(timet)}, {(timet/time_xt):.2%}"
) )
print() print()