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ea1896a544
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b2dc2b341b
| Author | SHA1 | Date | |
|---|---|---|---|
| b2dc2b341b | |||
| d3dcc6a30b |
@@ -97,7 +97,7 @@ df_results = load_results()
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def run_solver_subprocess(time_limit_sec, output_placeholder):
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"""Spawns the optimization worker and streams logs to the UI."""
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process = subprocess.Popen(
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["uv", "run", "python", "-u", "solver_worker.py", str(time_limit_sec)],
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["uv", "run", "python", "-u", "src/fleet_assignment/optimizer.py", str(time_limit_sec)],
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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@@ -17,11 +17,10 @@ import sqlite3
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# Ensure src module is visible if run standalone
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from src.fleet_assignment.config import DB_PATH, RESULTS_FILE, COST_FILE
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from src.fleet_assignment.config import DB_PATH, RESULTS_FILE, COST_FILE, JSON_AEROPORTOS, JSON_EMISSORES
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def run_solver(time_limit_sec=300):
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print(f"--- Starting Global Solver Worker (Limit: {time_limit_sec}s) ---")
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JSON_AEROPORTOS = "airports.json"
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conn = sqlite3.connect(DB_PATH)
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df_frota_db = pd.read_sql_query("SELECT * FROM frota", conn)
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@@ -34,7 +33,7 @@ def run_solver(time_limit_sec=300):
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pax_demanda_completa = demands_db.groupby(['Data Apenas', 'Localidade Decolagem', 'Localidade Pouso'])['PAX'].sum().reset_index()
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demands = pax_demanda_completa[pax_demanda_completa['PAX'] > 0].copy()
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with open('emissores.json', 'r') as f:
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with open(JSON_EMISSORES, 'r') as f:
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emissores_data = json.load(f)[0]
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icaos_emissores = list(emissores_data.values())
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