3607965c88
- Complete routing engine: ingest, optimizer (CG+B&B), maintenance monitor, metrics, pipeline, quality checks - Streamlit dashboard with Input/Output tab structure, editable data editors, interactive Folium map with satellite layer and maintenance base highlights, FH stacked bar chart with TTM availability - CSV data files: AERONAVES, CHECKS, AIRPORTS, ESCALA DE VOO - README, CONTEXTO and CHANGELOG added - Remove legacy pre_process scripts and raw binary files (PDFs/xlsx) - Update .gitignore to exclude outputs/, data/, raw/*.pdf, raw/*.xlsx Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""
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Script 02 – Build fleet reference table.
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Reads AERONAVES.csv + CHECKS.csv, computes TTM and the full check-cycle
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sequence for each aircraft, and saves the reference to data/reference/.
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"""
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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import pandas as pd
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from datetime import datetime
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from src.routing_engine.config import DEFAULT_CONFIG
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from src.routing_engine.inspect_files import read_aircraft, read_checks
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from src.routing_engine.ingest import build_fleet, _check_cycles
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cfg = DEFAULT_CONFIG
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aircraft_df = read_aircraft(cfg.raw_dir / cfg.aircraft_file)
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checks_df = read_checks(cfg.raw_dir / cfg.checks_file)
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print("Aircraft loaded:")
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print(aircraft_df.to_string(index=False))
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print()
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print("Checks loaded:")
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print(checks_df.to_string(index=False))
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print()
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# Build fleet
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planning_start = datetime(cfg.planning_year, 3, 18) # first OFRAG departure in the sample data
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fleet = build_fleet(aircraft_df, checks_df, planning_start)
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print("Fleet reference:")
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for _, row in fleet.iterrows():
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print(f"\n {row['tail_number']} ({row['model']}) FH total = {row['fh_total']:.0f}")
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print(f" TTM before first check: {row['ttm_hours']:.1f} FH")
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for i, c in enumerate(row["checks"]):
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print(f" Cycle {i}: threshold={c['fh_threshold']:.0f} FH, "
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f"TTM={c['ttm']:.0f} FH, duration={c['duration_hours']:.0f} h")
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# Save to reference
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cfg.reference_dir.mkdir(parents=True, exist_ok=True)
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out_path = cfg.reference_dir / "fleet_reference.csv"
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flat_rows = []
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for _, row in fleet.iterrows():
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flat_rows.append(
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{
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"tail_number": row["tail_number"],
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"model": row["model"],
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"fh_total": row["fh_total"],
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"ttm_hours_cycle0": row["ttm_hours"],
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"n_check_cycles": len(row["checks"]),
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"cycles_json": str(row["checks"]),
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}
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)
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pd.DataFrame(flat_rows).to_csv(out_path, index=False)
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print(f"\nFleet reference saved -> {out_path}")
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