File size: 2,011 Bytes
a4e4cb2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | #!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")"
PYTHON_BIN="${PYTHON_BIN:-python3}"
"$PYTHON_BIN" - <<'__SKILL_EVOL_SOLVE_PY_0__'
from __future__ import annotations
import os
from pathlib import Path
from textwrap import dedent
PROJECT_ROOT = Path(os.environ.get("PROJECT_ROOT", "/root/task")).resolve()
FILES = {
"ordering_policy.py": dedent(
"""
from __future__ import annotations
import pandas as pd
def sort_logs(df: pd.DataFrame) -> pd.DataFrame:
return df.sort_values(["parsed_date", "time", "log_id"], kind="mergesort").reset_index(drop=True)
"""
),
"process_logs.py": dedent(
"""
from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
from date_parser import parse_display_dates
from ordering_policy import sort_logs
CSV_PATH = Path("server_logs.csv")
OUTPUT_PATH = Path("output.json")
def run(csv_path: Path = CSV_PATH, output_path: Path = OUTPUT_PATH) -> dict:
df = pd.read_csv(csv_path)
df["parsed_date"] = parse_display_dates(df["date"])
sorted_df = sort_logs(df)
export_df = sorted_df.drop(columns=["parsed_date"], errors="ignore")
payload = {
"row_count": int(len(export_df)),
"first_date": export_df.iloc[0]["date"],
"last_date": export_df.iloc[-1]["date"],
"records": export_df.to_dict(orient="records"),
}
output_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
return payload
if __name__ == "__main__":
run()
"""
),
}
for relative_path, content in FILES.items():
target = PROJECT_ROOT / relative_path
target.write_text(content, encoding="utf-8")
print(f"wrote {target}")
__SKILL_EVOL_SOLVE_PY_0__
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