7bf7d34d0f
* Fix adaptive refinement (#571) --------- Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com> * Remove collector * Fixes * Fixes * rm unnecessary comment * fix advection (#581) * Fix tutorial .html link (#580) * fix problem data collection for v0.1 (#584) * Message Passing Module (#516) * add deep tensor network block * add interaction network block * add radial field network block * add schnet block * add equivariant network block * fix + tests + doc files * fix egnn + equivariance/invariance tests Co-authored-by: Dario Coscia <dariocos99@gmail.com> --------- Co-authored-by: giovanni <giovanni.canali98@yahoo.it> Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it> * add type checker (#527) --------- Co-authored-by: Filippo Olivo <filippo@filippoolivo.com> Co-authored-by: Giovanni Canali <115086358+GiovanniCanali@users.noreply.github.com> Co-authored-by: giovanni <giovanni.canali98@yahoo.it> Co-authored-by: AleDinve <giuseppealessio.d@student.unisi.it>
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import torch
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import pytest
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from pina import LabelTensor
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from pina.utils import merge_tensors, check_consistency, check_positive_integer
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from pina.domain import EllipsoidDomain, CartesianDomain, DomainInterface
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def test_merge_tensors():
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tensor1 = LabelTensor(torch.rand((20, 3)), ["a", "b", "c"])
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tensor2 = LabelTensor(torch.zeros((20, 3)), ["d", "e", "f"])
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tensor3 = LabelTensor(torch.ones((30, 3)), ["g", "h", "i"])
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merged_tensor = merge_tensors((tensor1, tensor2, tensor3))
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assert tuple(merged_tensor.labels) == (
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"a",
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"b",
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"c",
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"d",
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"e",
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"f",
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"g",
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"h",
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"i",
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)
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assert merged_tensor.shape == (20 * 20 * 30, 9)
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assert torch.all(merged_tensor.extract(("d", "e", "f")) == 0)
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assert torch.all(merged_tensor.extract(("g", "h", "i")) == 1)
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def test_check_consistency_correct():
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ellipsoid1 = EllipsoidDomain({"x": [1, 2], "y": [-2, 1]})
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example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ["x", "y", "z"])
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check_consistency(example_input_pts, torch.Tensor)
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check_consistency(CartesianDomain, DomainInterface, subclass=True)
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check_consistency(ellipsoid1, DomainInterface)
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def test_check_consistency_incorrect():
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ellipsoid1 = EllipsoidDomain({"x": [1, 2], "y": [-2, 1]})
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example_input_pts = LabelTensor(torch.tensor([[0, 0, 0]]), ["x", "y", "z"])
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with pytest.raises(ValueError):
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check_consistency(example_input_pts, DomainInterface)
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with pytest.raises(ValueError):
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check_consistency(torch.Tensor, DomainInterface, subclass=True)
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with pytest.raises(ValueError):
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check_consistency(ellipsoid1, torch.Tensor)
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@pytest.mark.parametrize("value", [0, 1, 2, 3, 10])
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@pytest.mark.parametrize("strict", [True, False])
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def test_check_positive_integer(value, strict):
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if value != 0:
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check_positive_integer(value, strict=strict)
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else:
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check_positive_integer(value, strict=False)
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# Should fail if value is negative
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with pytest.raises(AssertionError):
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check_positive_integer(-1, strict=strict)
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# Should fail if value is not an integer
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with pytest.raises(AssertionError):
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check_positive_integer(1.5, strict=strict)
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# Should fail if value is not a number
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with pytest.raises(AssertionError):
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check_positive_integer("string", strict=strict)
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