118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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def print_header():
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print("=" * 42)
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print(" Inverse Heat PINN")
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print(" Identifica: α, k, h_conv da misure")
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print("=" * 42)
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_ALL_PARAMS = ('alpha', 'k', 'h_conv')
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def _ask_identify():
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print(f"\nParametri disponibili: {', '.join(_ALL_PARAMS)}")
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raw = input("Quali identificare? (invio = tutti, oppure es: alpha k): ").strip()
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chosen = [p for p in (raw.split() if raw else _ALL_PARAMS) if p in _ALL_PARAMS]
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invalid = [p for p in raw.split() if p not in _ALL_PARAMS] if raw else []
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if invalid:
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print(f" Ignorati (non validi): {invalid}")
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if not chosen:
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print(" Nessun parametro valido — uso tutti.")
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chosen = list(_ALL_PARAMS)
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print(f" Identifico: {chosen}")
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import config as _cfg
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_true = {'alpha': _cfg.ALPHA, 'k': _cfg.K, 'h_conv': _cfg.H_CONV}
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init_vals = {}
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for p in chosen:
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true_val = _true[p]
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raw_v = input(f" Valore iniziale per {p} (vero: {true_val}): ").strip()
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try:
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init_vals[p] = float(raw_v) if raw_v else true_val
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except ValueError:
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init_vals[p] = true_val
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print(f" → {p} inizia da {init_vals[p]}")
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from inverse.config_inverse import W_PDE, W_IC, W_BC, W_DATA
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print(f"\nPesi loss (default — PDE:{W_PDE} IC:{W_IC} BC:{W_BC} Data:{W_DATA})")
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weights = {}
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for wname, wdefault in [('w_pde', W_PDE), ('w_ic', W_IC), ('w_bc', W_BC), ('w_data', W_DATA)]:
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raw_w = input(f" {wname} (default {wdefault}): ").strip()
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try:
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weights[wname] = float(raw_w) if raw_w else wdefault
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except ValueError:
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weights[wname] = wdefault
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raw_ep = input("\nNumero di epoch (default 10000): ").strip()
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try:
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epochs = int(raw_ep) if raw_ep else 10000
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except ValueError:
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epochs = 10000
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return chosen, init_vals, weights, epochs
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def main():
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print_header()
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from inverse.engine import prepare_data_inverse, _get_device
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print("\nGenerazione punti di collocazione...")
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data = prepare_data_inverse()
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print(f"Pronti — device: {data['device']}\n")
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x_s = None
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t_s = None
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T_meas = None
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while True:
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print("\n" + "-" * 34)
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print(" MAIN MENU")
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print("-" * 34)
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print("1. Carica misure")
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print("2. Addestra (identifica α, k, h)")
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print("3. Valuta risultati")
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print("4. Visualizza")
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print("0. Esci")
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print("-" * 34)
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choice = input("Scelta (0-4): ").strip()
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if choice == "1":
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from inverse.data import load_measurements
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x_s, t_s, T_meas = load_measurements(data['device'])
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elif choice == "2":
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if x_s is None:
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print("Caricare prima le misure (opzione 1).")
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continue
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identify, init_vals, weights, epochs = _ask_identify()
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from inverse.engine import train_inverse
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try:
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train_inverse(data, x_s, t_s, T_meas, identify=identify, init_vals=init_vals, epochs=epochs, **weights)
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except KeyboardInterrupt:
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print("\nTraining interrotto. Il miglior modello trovato è stato salvato.")
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elif choice == "3":
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from inverse.engine import evaluate_inverse
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evaluate_inverse()
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elif choice == "4":
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from inverse.engine import generate_visualization_inverse
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generate_visualization_inverse()
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elif choice == "0":
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print("Uscita.")
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sys.exit(0)
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else:
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print("Scelta non valida.")
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if __name__ == "__main__":
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main()
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