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

import math
import config


def test_x_src_within_domain():
    assert 0.0 <= config.X_SRC <= config.L


def test_t_step_before_t_end():
    assert 0.0 < config.T_STEP < config.T_END


def test_gauss_sigma_positive():
    assert config.GAUSS_SIGMA > 0.0


def test_physics_positive():
    assert config.ALPHA > 0.0
    assert config.K > 0.0
    assert config.H_CONV > 0.0
    assert config.L > 0.0
    assert config.T_END > 0.0


def test_training_hyperparameters_positive():
    assert config.PATIENCE > 0
    assert config.EPOCHS > 0
    assert config.LR_ADAM > 0.0
    assert config.SCHED_MIN_LR > 0.0
    assert config.SCHED_FACTOR > 0.0
    assert config.SCHED_PATIENCE > 0


def test_lr_ordering():
    """Min LR deve essere inferiore all'LR iniziale."""
    assert config.SCHED_MIN_LR < config.LR_ADAM


def test_sched_patience_lt_patience():
    """Lo scheduler deve poter agire prima che scatti l'early stopping."""
    assert config.SCHED_PATIENCE < config.PATIENCE


def test_cfl_stability():
    """La griglia FDM deve soddisfare la condizione CFL (r ≤ 0.5)."""
    dx = config.L / (config.NX - 1)
    dt = config.T_END / (config.NT - 1)
    r = config.ALPHA * dt / dx ** 2
    assert r <= 0.5, f"CFL violata: r={r:.4f} > 0.5"


def test_grid_dimensions():
    assert config.NX >= 2
    assert config.NT >= 2
    assert config.N_F >= 1
    assert config.N_IC >= 1
    assert config.N_BC >= 1


def test_pde_scale_covers_source_peak():
    """_pde_scale in model.py deve coprire il picco gaussiano della sorgente."""
    from model import _pde_scale
    src_peak = config.ALPHA * config.Q_VAL / (
        config.K * config.GAUSS_SIGMA * math.sqrt(2 * math.pi)
    )
    assert _pde_scale >= src_peak ** 2 - 1e-6, (
        f"_pde_scale={_pde_scale:.1f} < src_peak²={src_peak**2:.1f}: "
        "la loss PDE non è normalizzata correttamente"
    )