davide
b8301a4329
- pytest.ini: configura testpaths, marker slow, output verboso - tests/conftest.py: fixture condivise (device, small_data, pinn_model) - tests/test_config.py: sanità parametri fisici e numerici, CFL, _pde_scale - tests/test_model.py: HeatPINN.forward e heat_pinn_loss (shape, finiti, zero-weight analytici per IC e BC, scaling dei pesi) - tests/test_engine_data.py: set_seed, _get_device, prepare_data (shape, bounds, device consistency, determinismo) - tests/test_integration_pinn.py: pipeline dati→modello→loss→backward - tests/test_e2e.py: FDM completo, visualizer FDM/PINN con tmp_path, training breve (2 test @slow) - requirements.txt: aggiunge pytest>=7.0.0 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
88 lines
3.3 KiB
Python
88 lines
3.3 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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import pytest
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import numpy as np
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import torch
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import config
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# ── FDM ───────────────────────────────────────────────────────────────────────
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def test_fdm_full_run():
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"""Il solver FDM produce un campo di temperatura fisicamente corretto."""
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from fdm.solver import solve
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T, x, t = solve()
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assert T.shape == (config.NX, config.NT)
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assert np.isfinite(T).all()
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assert T[:, -1].mean() > config.T0 # la sorgente ha scaldato il dominio
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assert T.max() > config.T0 # picco sopra la IC
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assert T.min() >= config.T0 - 1e-6 # nessun raffreddamento sotto T0 (no sorgenti fredde)
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def test_fdm_visualizer_creates_html(tmp_path, monkeypatch):
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"""Il visualizer FDM scrive almeno un file HTML senza errori."""
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import fdm.visualizer as fdm_vis
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monkeypatch.setattr(fdm_vis, 'BASE_DIR', str(tmp_path))
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from fdm.solver import solve
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T, x, t = solve()
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fdm_vis.visualize_fdm(T, x, t)
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html_files = list(tmp_path.rglob('*.html'))
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assert len(html_files) >= 1, "Nessun file HTML generato dal visualizer FDM"
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def test_pinn_visualizer_creates_html(tmp_path, monkeypatch):
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"""Il visualizer PINN scrive i tre file HTML senza errori."""
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import visualizer as pinn_vis
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monkeypatch.setattr(pinn_vis, 'BASE_DIR', str(tmp_path))
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from fdm.solver import solve as fdm_solve
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T_fdm, _, _ = fdm_solve()
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nx, nt = 20, 20
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x_vals = np.linspace(0, config.L, nx)
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t_vals = np.linspace(0, config.T_END, nt)
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T_pred = np.full((nx, nt), config.T_AMB) # predizione costante (dummy)
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pinn_vis.visualize_heat_field(T_pred, x_vals, t_vals, T_fdm)
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html_files = list(tmp_path.rglob('*.html'))
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assert len(html_files) == 3, f"Attesi 3 HTML, trovati {len(html_files)}"
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# ── PINN training (lento) ─────────────────────────────────────────────────────
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@pytest.mark.slow
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def test_pinn_training_saves_checkpoint(tmp_path, monkeypatch):
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"""Training per 30 epoche: il checkpoint viene salvato."""
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import engine
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save_path = str(tmp_path / 'model.pth')
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monkeypatch.setattr(engine, 'MODEL_SAVE_PATH', save_path)
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monkeypatch.setattr(engine, 'MODELS_DIR', str(tmp_path))
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from engine import prepare_data, train_model
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data = prepare_data(N_f=300, N_ic=100, N_bc=100)
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train_model(data, epochs=30, patience=30)
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assert os.path.exists(save_path)
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ckpt = torch.load(save_path, map_location='cpu', weights_only=True)
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assert 'state_dict' in ckpt
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@pytest.mark.slow
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def test_pinn_evaluate_after_training(tmp_path, monkeypatch):
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"""evaluate_model gira senza errori dopo un training minimo."""
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import engine
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save_path = str(tmp_path / 'model.pth')
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monkeypatch.setattr(engine, 'MODEL_SAVE_PATH', save_path)
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monkeypatch.setattr(engine, 'MODELS_DIR', str(tmp_path))
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from engine import prepare_data, train_model, evaluate_model
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data = prepare_data(N_f=300, N_ic=100, N_bc=100)
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train_model(data, epochs=30, patience=30)
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evaluate_model(data)
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