| """ |
| Generate synthetic Parquet fixtures for race_data unit tests. |
| Run once: python tests/fixtures/make_fixtures.py |
| """ |
|
|
| from pathlib import Path |
| import pandas as pd |
| import numpy as np |
|
|
| OUT_DIR = Path(__file__).parent |
| LAPS_COLUMNS = [ |
| "lap_number", "driver_code", "team", "lap_time_s", "compound", |
| "tyre_life", "position", "gap_to_leader_s", "pit_in", "pit_out", "sc_active", |
| ] |
|
|
| DRIVERS = ["VER", "HAM", "NOR", "PER", "ALO"] |
| TOTAL_LAPS = 52 |
|
|
|
|
| def _make_laps() -> pd.DataFrame: |
| rows = [] |
| for lap in range(1, TOTAL_LAPS + 1): |
| for i, drv in enumerate(DRIVERS): |
| rows.append({ |
| "lap_number": lap, |
| "driver_code": drv, |
| "team": "RBR" if drv in ("VER", "PER") else "MER", |
| "lap_time_s": 90.0 + i * 0.3 + np.random.uniform(-0.1, 0.1), |
| "compound": "MEDIUM" if lap <= 25 else "HARD", |
| "tyre_life": lap if lap <= 25 else lap - 25, |
| "position": i + 1, |
| "gap_to_leader_s": 0.0 if i == 0 else i * 2.5, |
| "pit_in": lap == 26 and i == 0, |
| "pit_out": lap == 27 and i == 0, |
| "sc_active": lap in (10, 11, 12), |
| }) |
| df = pd.DataFrame(rows, columns=LAPS_COLUMNS) |
| df["lap_number"] = df["lap_number"].astype("int32") |
| df["tyre_life"] = df["tyre_life"].astype("int32") |
| df["position"] = df["position"].astype("int32") |
| return df |
|
|
|
|
| if __name__ == "__main__": |
| laps = _make_laps() |
| path = OUT_DIR / "2023_British_GP_laps.parquet" |
| laps.to_parquet(path, index=False) |
| print(f"Written {len(laps)} rows to {path}") |
|
|
