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Merge pull request #104 from dmarx/tensor_support
Tensor support
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*.egg-info | ||
Pipfile | ||
sandbox.ipynb | ||
Pipfile.lock | ||
Pipfile.lock | ||
.DS_Store |
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import numpy as np | ||
from keyframed import Curve | ||
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def test_vector_interpolation_linear(): | ||
# Test linear interpolation with vectors | ||
start_vec = np.array([0, 0, 0]) | ||
end_vec = np.array([10, 10, 10]) | ||
curve = Curve({0: start_vec, 10: end_vec}, default_interpolation='linear') | ||
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assert np.allclose(curve[5], np.array([5, 5, 5])) | ||
assert np.allclose(curve[2.5], np.array([2.5, 2.5, 2.5])) | ||
assert np.allclose(curve[7.5], np.array([7.5, 7.5, 7.5])) | ||
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# def test_vector_interpolation_custom(): | ||
# # Test custom interpolation function for vectors | ||
# def custom_interp(t, t0, value0, t1, value1): | ||
# # Example: simple linear interpolation | ||
# return value0 + (value1 - value0) * (t - t0) / (t1 - t0) | ||
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# start_vec = np.array([1, 2, 3]) | ||
# end_vec = np.array([4, 5, 6]) | ||
# curve = Curve({0: start_vec, 10: end_vec}) | ||
# curve.set_interpolation(custom_interp) | ||
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# assert np.allclose(curve[5], np.array([2.5, 3.5, 4.5])) | ||
# assert np.allclose(curve[2], np.array([1.3, 2.3, 3.3])) | ||
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def test_vector_keyframe_insertion(): | ||
# Test inserting vector keyframes | ||
curve = Curve() | ||
curve[0] = np.array([1, 2, 3]) | ||
curve[5] = np.array([4, 5, 6]) | ||
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assert np.allclose(curve[0], np.array([1, 2, 3])) | ||
assert np.allclose(curve[5], np.array([4, 5, 6])) | ||
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# def test_vector_interpolation_bounds(): | ||
# # Test vector interpolation at curve bounds | ||
# curve = Curve({0: np.array([0, 0]), 10: np.array([10, 10])}, default_interpolation='linear') | ||
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# assert np.allclose(curve[-5], np.array([0, 0])) # Test extrapolation if your curve supports it | ||
# assert np.allclose(curve[15], np.array([10, 10])) | ||
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# def test_vector_mixed_interpolation(): | ||
# # Test curves with mixed scalar and vector interpolation | ||
# curve = Curve({0: 0, 5: np.array([5, 10, 15]), 10: 10}, default_interpolation='linear') | ||
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# assert curve[2.5] == 2.5 # Scalar interpolation | ||
# assert np.allclose(curve[7.5], np.array([7.5, 12.5, 17.5])) # Vector interpolation | ||
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# Add more tests for edge cases, different vector lengths, and other interpolation methods as needed |
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import torch | ||
from keyframed import Curve | ||
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def test_matrix_interpolation(): | ||
start_matrix = torch.tensor([[0, 0], [0, 0]], dtype=torch.float32) | ||
end_matrix = torch.tensor([[1, 1], [1, 1]], dtype=torch.float32) | ||
curve = Curve({0: start_matrix, 10: end_matrix}, default_interpolation='linear') | ||
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expected_mid = torch.tensor([[0.5, 0.5], [0.5, 0.5]], dtype=torch.float32) | ||
assert torch.allclose(curve[5], expected_mid) |
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import pytest | ||
import numpy as np | ||
import torch | ||
from keyframed import Curve, Keyframe | ||
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# Test linear interpolation with PyTorch tensors | ||
def test_linear_interpolation_tensor(): | ||
start_tensor = torch.tensor([0, 0, 0], dtype=torch.float32) | ||
end_tensor = torch.tensor([10, 10, 10], dtype=torch.float32) | ||
curve = Curve({0: start_tensor, 10: end_tensor}, default_interpolation='linear') | ||
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assert torch.allclose(curve[5], torch.tensor([5, 5, 5], dtype=torch.float32)) | ||
assert torch.allclose(curve[2.5], torch.tensor([2.5, 2.5, 2.5], dtype=torch.float32)) | ||
assert torch.allclose(curve[7.5], torch.tensor([7.5, 7.5, 7.5], dtype=torch.float32)) | ||
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# Test mixed NumPy array and PyTorch tensor interpolation | ||
# def test_mixed_interpolation(): | ||
# start_array = np.array([1, 2, 3]) | ||
# end_tensor = torch.tensor([4, 5, 6]) | ||
# curve = Curve({0: start_array, 10: end_tensor}, default_interpolation='linear') | ||
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# expected_midpoint = torch.tensor([2.5, 3.5, 4.5]) | ||
# assert torch.allclose(curve[5], expected_midpoint) | ||
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# Test PyTorch tensor interpolation with custom interpolation method | ||
# def test_custom_tensor_interpolation(): | ||
# def custom_interp(t, t0, value0, t1, value1): | ||
# value0, value1 = torch.tensor(value0), torch.tensor(value1) | ||
# return value0 + (value1 - value0) * (t - t0) / (t1 - t0) | ||
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# start_tensor = torch.tensor([1, 1, 1]) | ||
# end_tensor = torch.tensor([2, 2, 2]) | ||
# curve = Curve({0: start_tensor, 1: end_tensor}) | ||
# curve.set_interpolation(custom_interp) | ||
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# assert torch.allclose(curve[0.5], torch.tensor([1.5, 1.5, 1.5])) | ||
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# Test tensor keyframe insertion | ||
def test_tensor_keyframe_insertion(): | ||
curve = Curve() | ||
curve[0] = torch.tensor([1, 2, 3]) | ||
curve[5] = torch.tensor([4, 5, 6]) | ||
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assert torch.allclose(curve[0], torch.tensor([1, 2, 3])) | ||
assert torch.allclose(curve[5], torch.tensor([4, 5, 6])) | ||
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# Test tensor interpolation at curve bounds | ||
# def test_tensor_interpolation_bounds(): | ||
# curve = Curve({0: torch.tensor([0, 0]), 10: torch.tensor([10, 10])}, default_interpolation='linear') | ||
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# assert torch.allclose(curve[-5], torch.tensor([0, 0])) # Test extrapolation if your curve supports it | ||
# assert torch.allclose(curve[15], torch.tensor([10, 10])) | ||
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# Add more tests for edge cases and different tensor shapes if necessary |