87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# import pytest
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import time
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import pytest
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import json
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import sys
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import igl
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import numpy as np
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sys.path.append('../')
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sys.path.append('../src')
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from src.energies import *
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eps = 1E-6
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with open('test_data2.json', 'r') as infile:
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homework_datas = json.load(infile)
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# @pytest.mark.timeout(1)
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@pytest.mark.parametrize("data", homework_datas[0])
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def test_shape_energy_correctness(data):
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V = np.array(data[0], dtype=float)
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E = np.array(data[1], dtype=int)
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l0 = np.array(data[2], dtype=float)
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shape_energy_ground_truth = np.array(data[3])
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shape_energy_student = compute_shape_energy(V, E, l0)
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assert np.linalg.norm(shape_energy_ground_truth - shape_energy_student) < eps
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# @pytest.mark.timeout(1)
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@pytest.mark.parametrize("data", homework_datas[1])
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def test_equilibrium_engery_correctness(data):
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V = np.array(data[0], dtype=float)
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F = np.array(data[1], dtype=int)
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x_csl = data[2]
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equilibrium_engery_ground_truth = np.array(data[3])
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equilibrium_engery_student = compute_equilibrium_energy(V, F, x_csl)
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assert np.linalg.norm(equilibrium_engery_ground_truth - equilibrium_engery_student) < eps
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@pytest.mark.timeout(1)
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@pytest.mark.parametrize("data", homework_datas[2])
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def test_faces_area_gradient_correctness(data):
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V = np.array(data[0], dtype=float)
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F = np.array(data[1], dtype=int)
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faces_area_gradient_ground_truth_x = np.array(data[2])
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faces_area_gradient_ground_truth_y = np.array(data[3])
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[faces_area_gradient_student_x, faces_area_gradient_student_y] = compute_faces_area_gradient(V, F)
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assert np.linalg.norm(faces_area_gradient_ground_truth_x - faces_area_gradient_student_x) < eps \
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and np.linalg.norm(faces_area_gradient_ground_truth_y - faces_area_gradient_student_y) < eps
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@pytest.mark.timeout(1)
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@pytest.mark.parametrize("data", homework_datas[3])
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def test_equilibrium_energy_gradient_correctness(data):
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V = np.array(data[0], dtype=float)
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F = np.array(data[1], dtype=int)
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x_csl = data[2]
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equilibrium_energy_gradient = np.array(data[3])
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equilibrium_energy_student = compute_equilibrium_energy_gradient(V, F, x_csl)
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assert np.linalg.norm(equilibrium_energy_gradient - equilibrium_energy_student) < eps
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@pytest.mark.timeout(1)
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@pytest.mark.parametrize("data", homework_datas[4])
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def test_shape_energy_gradient_correctness(data):
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V = np.array(data[0], dtype=float)
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E = np.array(data[1], dtype=int)
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l0 = np.array(data[2], dtype=float)
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shape_energy_gradient_ground_truth = np.array(data[3])
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shape_energy_gradient_student = compute_shape_energy_gradient(V, E, l0)
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assert np.linalg.norm(shape_energy_gradient_ground_truth - shape_energy_gradient_student) < eps
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n = 100000
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F_big = np.arange(3 * n).reshape((n, 3))
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V_big = np.random.random((3 * n, 3))
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l0_big = np.zeros(3 * n)
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E_big = igl.edges(F_big)
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@pytest.mark.timeout(1)
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def test_shape_energy_timing():
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compute_shape_energy(V_big, E_big, l0_big)
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@pytest.mark.timeout(1)
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def test_faces_area_gradient_timing():
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compute_faces_area_gradient(V_big, F_big)
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@pytest.mark.timeout(1)
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def test_equilibrium_energy_gradient_timing():
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compute_equilibrium_energy_gradient(V_big, F_big, 0)
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@pytest.mark.timeout(1)
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def test_shape_energy_gradient_timing():
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compute_shape_energy_gradient(V_big, E_big, l0_big) |