import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import pytest
import numpy as np
import torch
import config


# ── FDM ───────────────────────────────────────────────────────────────────────

def test_fdm_full_run():
    """Il solver FDM produce un campo di temperatura fisicamente corretto."""
    from fdm.solver import solve
    T, x, t = solve()

    assert T.shape == (config.NX, config.NT)
    assert np.isfinite(T).all()
    assert T[:, -1].mean() > config.T0          # la sorgente ha scaldato il dominio
    assert T.max() > config.T0                   # picco sopra la IC
    assert T.min() >= config.T0 - 1e-6          # nessun raffreddamento sotto T0 (no sorgenti fredde)


def test_fdm_visualizer_creates_html(tmp_path, monkeypatch):
    """Il visualizer FDM scrive almeno un file HTML senza errori."""
    import fdm.visualizer as fdm_vis
    monkeypatch.setattr(fdm_vis, 'BASE_DIR', str(tmp_path))

    from fdm.solver import solve
    T, x, t = solve()
    fdm_vis.visualize_fdm(T, x, t)

    html_files = list(tmp_path.rglob('*.html'))
    assert len(html_files) >= 1, "Nessun file HTML generato dal visualizer FDM"


def test_pinn_visualizer_creates_html(tmp_path, monkeypatch):
    """Il visualizer PINN scrive i tre file HTML senza errori."""
    import visualizer as pinn_vis
    monkeypatch.setattr(pinn_vis, 'BASE_DIR', str(tmp_path))

    from fdm.solver import solve as fdm_solve
    T_fdm, _, _ = fdm_solve()

    nx, nt = 20, 20
    x_vals = np.linspace(0, config.L, nx)
    t_vals = np.linspace(0, config.T_END, nt)
    T_pred = np.full((nx, nt), config.T_AMB)    # predizione costante (dummy)

    pinn_vis.visualize_heat_field(T_pred, x_vals, t_vals, T_fdm)

    html_files = list(tmp_path.rglob('*.html'))
    assert len(html_files) == 3, f"Attesi 3 HTML, trovati {len(html_files)}"


# ── PINN training (lento) ─────────────────────────────────────────────────────

@pytest.mark.slow
def test_pinn_training_saves_checkpoint(tmp_path, monkeypatch):
    """Training per 30 epoche: il checkpoint viene salvato."""
    import engine
    save_path = str(tmp_path / 'model.pth')
    monkeypatch.setattr(engine, 'MODEL_SAVE_PATH', save_path)
    monkeypatch.setattr(engine, 'MODELS_DIR', str(tmp_path))

    from engine import prepare_data, train_model
    data = prepare_data(N_f=300, N_ic=100, N_bc=100)
    train_model(data, epochs=30, patience=30)

    assert os.path.exists(save_path)
    ckpt = torch.load(save_path, map_location='cpu', weights_only=True)
    assert 'state_dict' in ckpt


@pytest.mark.slow
def test_pinn_evaluate_after_training(tmp_path, monkeypatch):
    """evaluate_model gira senza errori dopo un training minimo."""
    import engine
    save_path = str(tmp_path / 'model.pth')
    monkeypatch.setattr(engine, 'MODEL_SAVE_PATH', save_path)
    monkeypatch.setattr(engine, 'MODELS_DIR', str(tmp_path))

    from engine import prepare_data, train_model, evaluate_model
    data = prepare_data(N_f=300, N_ic=100, N_bc=100)
    train_model(data, epochs=30, patience=30)
    evaluate_model(data)