68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
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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 torch
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import config
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from engine import set_seed, _get_device, prepare_data
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def test_set_seed_reproducibility():
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set_seed(42)
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r1 = torch.rand(10)
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set_seed(42)
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r2 = torch.rand(10)
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torch.testing.assert_close(r1, r2)
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def test_get_device_valid():
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device = _get_device()
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assert isinstance(device, torch.device)
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assert device.type in ('cpu', 'cuda', 'mps')
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def test_prepare_data_keys():
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data = prepare_data(N_f=100, N_ic=50, N_bc=50)
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assert set(data.keys()) == {'device', 'x_f', 't_f', 'x_ic', 't_bc'}
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def test_prepare_data_shapes():
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N_f, N_ic, N_bc = 100, 50, 50
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data = prepare_data(N_f=N_f, N_ic=N_ic, N_bc=N_bc)
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# engine.py aggiunge 2 * (N_f // 4) punti di clustering
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expected_f = N_f + 2 * (N_f // 4)
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assert data['x_f'].shape == (expected_f,)
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assert data['t_f'].shape == (expected_f,)
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assert data['x_ic'].shape == (N_ic,)
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assert data['t_bc'].shape == (N_bc,)
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def test_prepare_data_x_bounds():
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data = prepare_data(N_f=500, N_ic=100, N_bc=100)
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assert data['x_f'].min().item() >= 0.0
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assert data['x_f'].max().item() <= config.L
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assert data['x_ic'].min().item() >= 0.0
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assert data['x_ic'].max().item() <= config.L
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def test_prepare_data_t_bounds():
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data = prepare_data(N_f=500, N_ic=100, N_bc=100)
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assert data['t_f'].min().item() >= 0.0
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assert data['t_f'].max().item() <= config.T_END
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def test_prepare_data_device_consistency():
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data = prepare_data(N_f=100, N_ic=50, N_bc=50)
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expected = data['device'].type
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for key in ('x_f', 't_f', 'x_ic', 't_bc'):
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assert data[key].device.type == expected, f"{key} sul device sbagliato"
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def test_prepare_data_deterministic():
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"""Due chiamate con lo stesso seed (fissato in prepare_data) producono dati identici."""
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d1 = prepare_data(N_f=100, N_ic=50, N_bc=50)
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d2 = prepare_data(N_f=100, N_ic=50, N_bc=50)
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torch.testing.assert_close(d1['x_f'], d2['x_f'])
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torch.testing.assert_close(d1['t_f'], d2['t_f'])
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torch.testing.assert_close(d1['x_ic'], d2['x_ic'])
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