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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 smooth_surfaces import *
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eps = 1E-6
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with open('test_data.json', 'r') as infile:
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homework_datas = json.load(infile)
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# Derivatives of a paraboloid
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[0])
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def test_paraboloid_points(data):
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P, a, b, c, d, e, x = data
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P = np.array(P, dtype=float)
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x = np.array(x, dtype=float)
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x_student = compute_paraboloid_points(P, a, b, c, d, e)
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assert np.linalg.norm(x_student - x) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[1])
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def test_paraboloid_first_derivatives(data):
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P, a, b, c, d, e, x_u, x_v = data
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P = np.array(P, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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x_u_student, x_v_student = compute_paraboloid_first_derivatives(P, a, b, c, d, e)
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assert np.linalg.norm(x_u_student - x_u) < eps
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assert np.linalg.norm(x_v_student - x_v) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[2])
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def test_paraboloid_second_derivatives(data):
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P, a, b, c, d, e, x_uu, x_uv, x_vv = data
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P = np.array(P, dtype=float)
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x_uu = np.array(x_uu, dtype=float)
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x_uv = np.array(x_uv, dtype=float)
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x_vv = np.array(x_vv, dtype=float)
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student_x_uu, student_x_uv, student_x_vv = compute_paraboloid_second_derivatives(P, a, b, c, d, e)
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assert np.linalg.norm(student_x_uu - x_uu) < eps
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assert np.linalg.norm(student_x_uv - x_uv) < eps
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assert np.linalg.norm(student_x_vv - x_vv) < eps
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##############################
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# Derivatives of a torus
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[3])
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def test_torus_points(data):
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P, R, r, x = data
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P = np.array(P, dtype=float)
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x = np.array(x, dtype=float)
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x_student = compute_torus_points(P, R, r)
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assert np.linalg.norm(x_student - x) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[4])
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def test_torus_first_derivatives(data):
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P, R, r, x_u, x_v = data
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P = np.array(P, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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x_u_student, x_v_student = compute_torus_first_derivatives(P, R, r)
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assert np.linalg.norm(x_u_student - x_u) < eps
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assert np.linalg.norm(x_v_student - x_v) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[5])
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def test_torus_second_derivatives(data):
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P, R, r, x_uu, x_uv, x_vv = data
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P = np.array(P, dtype=float)
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x_uu = np.array(x_uu, dtype=float)
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x_uv = np.array(x_uv, dtype=float)
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x_vv = np.array(x_vv, dtype=float)
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student_x_uu, student_x_uv, student_x_vv = compute_torus_second_derivatives(P, R, r)
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assert np.linalg.norm(student_x_uu - x_uu) < eps
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assert np.linalg.norm(student_x_uv - x_uv) < eps
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assert np.linalg.norm(student_x_vv - x_vv) < eps
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##############################
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# Shape Operator
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[6])
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def test_paraboloid_first_fundamental_form(data):
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P, a, b, c, d, e, I, x_u, x_v = data
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P = np.array(P, dtype=float)
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I = np.array(I, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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I_student = compute_first_fundamental_form(x_u, x_v)
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assert np.linalg.norm(I_student - I) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[7])
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def test_torus_first_fundamental_form(data):
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P, R, r, I, x_u, x_v = data
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P = np.array(P, dtype=float)
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I = np.array(I, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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I_student = compute_first_fundamental_form(x_u, x_v)
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assert np.linalg.norm(I_student - I) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[8])
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def test_paraboloid_surface_normal(data):
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P, a, b, c, d, e, n, x_u, x_v = data
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P = np.array(P, dtype=float)
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n = np.array(n, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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n_student = compute_surface_normal(x_u, x_v)
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assert np.linalg.norm(n_student - n) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[9])
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def test_torus_surface_normal(data):
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P, R, r, n, x_u, x_v = data
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P = np.array(P, dtype=float)
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n = np.array(n, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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n_student = compute_surface_normal( x_u, x_v)
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assert np.linalg.norm(n_student - n) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[10])
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def test_paraboloid_second_fundamental_form(data):
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P, a, b, c, d, e, II, x_uu, x_uv, x_vv, n = data
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P = np.array(P, dtype=float)
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II = np.array(II, dtype=float)
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x_uu = np.array(x_uu, dtype=float)
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x_uv = np.array(x_uv, dtype=float)
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x_vv = np.array(x_vv, dtype=float)
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n = np.array(n, dtype=float)
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II_student = compute_second_fundamental_form(x_uu, x_uv, x_vv, n )
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assert np.linalg.norm(II_student - II) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[11])
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def test_torus_second_fundamental_form(data):
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P, R, r, II, x_uu, x_uv, x_vv, n = data
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P = np.array(P, dtype=float)
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II = np.array(II, dtype=float)
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x_uu = np.array(x_uu, dtype=float)
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x_uv = np.array(x_uv, dtype=float)
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x_vv = np.array(x_vv, dtype=float)
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n = np.array(n, dtype=float)
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II_student = compute_second_fundamental_form(x_uu, x_uv, x_vv, n)
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assert np.linalg.norm(II_student - II) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[12])
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def test_paraboloid_shape_operator(data):
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P, a, b, c, d, e, S, I, II = data
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P = np.array(P, dtype=float)
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S = np.array(S, dtype=float)
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I = np.array(I, dtype=float)
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II = np.array(II, dtype=float)
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S_student = compute_shape_operator(I, II)
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assert np.linalg.norm(S_student - S) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[13])
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def test_torus_shape_operator(data):
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P, R, r, S, I, II = data
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P = np.array(P, dtype=float)
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S = np.array(S, dtype=float)
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I = np.array(I, dtype=float)
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II = np.array(II, dtype=float)
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S_student = compute_shape_operator(I, II)
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assert np.linalg.norm(S_student - S) < eps
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##############################
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# Principal Curvatures
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[14])
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def test_paraboloid_principal_curvatures(data):
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P, a, b, c, d, e, k1, k2, e1, e2, S, x_u, x_v = data
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P = np.array(P, dtype=float)
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k1 = np.array(k1, dtype=float)
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k2 = np.array(k2, dtype=float)
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e1 = np.array(e1, dtype=float)
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e2 = np.array(e2, dtype=float)
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S = np.array(S, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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k_1_student, k_2_student, e_1_student, e_2_student = compute_principal_curvatures(S, x_u, x_v)
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assert np.linalg.norm(k_1_student - k1) < eps
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assert np.linalg.norm(k_2_student - k2) < eps
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assert np.linalg.norm(e_1_student - e1) < eps
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assert np.linalg.norm(e_2_student - e2) < eps
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[15])
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def test_torus_principal_curvatures(data):
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P, R, r, k1, k2, e1, e2, S, x_u, x_v = data
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P = np.array(P, dtype=float)
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k1 = np.array(k1, dtype=float)
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k2 = np.array(k2, dtype=float)
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e1 = np.array(e1, dtype=float)
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e2 = np.array(e2, dtype=float)
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S = np.array(S, dtype=float)
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x_u = np.array(x_u, dtype=float)
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x_v = np.array(x_v, dtype=float)
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k_1_student, k_2_student, e_1_student, e_2_student = compute_principal_curvatures(S, x_u, x_v)
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assert np.linalg.norm(k_1_student - k1) < eps
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assert np.linalg.norm(k_2_student - k2) < eps
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assert np.linalg.norm(e_1_student - e1) < eps
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assert np.linalg.norm(e_2_student - e2) < eps
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##############################
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# Asymptotic Directions
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[16])
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def test_paraboloid_asymptotic_direction(data):
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P, a, b, c, d, e, a1, a2, k1, k2, e1, e2 = data
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P = np.array(P, dtype=float)
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a1 = np.array(a1, dtype=float)
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a2 = np.array(a2, dtype=float)
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k1 = np.array(k1, dtype=float)
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k2 = np.array(k2, dtype=float)
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e1 = np.array(e1, dtype=float)
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e2 = np.array(e2, dtype=float)
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a_1_student, a_2_student = compute_asymptotic_directions(k1, k2, e1, e2)
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assert (np.linalg.norm(a_1_student - a1) < eps and np.linalg.norm(a_2_student - a2) < eps) or (np.linalg.norm(a_1_student - a2) < eps and np.linalg.norm(a_2_student - a1) < eps)
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@pytest.mark.timeout(0.5)
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@pytest.mark.parametrize("data", homework_datas[17])
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def test_torus_asymptotic_direction(data):
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P, R, r, a1, a2, k1, k2, e1, e2 = data
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P = np.array(P, dtype=float)
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a1 = np.array(a1, dtype=float)
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a2 = np.array(a2, dtype=float)
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k1 = np.array(k1, dtype=float)
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k2 = np.array(k2, dtype=float)
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e1 = np.array(e1, dtype=float)
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e2 = np.array(e2, dtype=float)
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a_1_student, a_2_student = compute_asymptotic_directions(k1, k2, e1, e2)
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assert (np.linalg.norm(a_1_student - a1) < eps and np.linalg.norm(a_2_student - a2) < eps) or (np.linalg.norm(a_1_student - a2) < eps and np.linalg.norm(a_2_student - a1) < eps)
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##############################
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@@ -0,0 +1 @@
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[[[[[-1.0, -1.0], [-0.9, -1.0], [-0.8, -1.0], [-0.7, -1.0], [-0.6, -1.0], [-0.5, -1.0], [-0.3999999999999999, -1.0], [-0.29999999999999993, -1.0], [-0.19999999999999996, -1.0], [-0.09999999999999998, -1.0], [0.0, -1.0], [0.10000000000000009, -1.0], [0.20000000000000018, -1.0], [0.30000000000000004, -1.0], [0.40000000000000013, -1.0], [0.5, -1.0], [0.6000000000000001, -1.0], [0.7000000000000002, -1.0], [0.8, -1.0], [0.9000000000000001, -1.0], [1.0, -1.0], [-1.0, -0.9], [-0.9, -0.9], [-0.8, -0.9], [-0.7, -0.9], [-0.6, -0.9], [-0.5, -0.9], [-0.3999999999999999, -0.9], [-0.29999999999999993, -0.9], [-0.19999999999999996, -0.9], [-0.09999999999999998, -0.9], [0.0, -0.9], [0.10000000000000009, -0.9], [0.20000000000000018, -0.9], [0.30000000000000004, -0.9], [0.40000000000000013, -0.9], [0.5, -0.9], [0.6000000000000001, -0.9], [0.7000000000000002, -0.9], [0.8, -0.9], [0.9000000000000001, -0.9], [1.0, -0.9], [-1.0, -0.8], [-0.9, -0.8], [-0.8, -0.8], [-0.7, -0.8], [-0.6, -0.8], [-0.5, -0.8], [-0.3999999999999999, -0.8], [-0.29999999999999993, -0.8], [-0.19999999999999996, -0.8], [-0.09999999999999998, -0.8], [0.0, -0.8], [0.10000000000000009, -0.8], [0.20000000000000018, -0.8], [0.30000000000000004, -0.8], [0.40000000000000013, -0.8], [0.5, -0.8], [0.6000000000000001, -0.8], [0.7000000000000002, -0.8], [0.8, -0.8], [0.9000000000000001, -0.8], [1.0, -0.8], [-1.0, -0.7], [-0.9, -0.7], [-0.8, -0.7], [-0.7, -0.7], [-0.6, -0.7], [-0.5, -0.7], [-0.3999999999999999, -0.7], [-0.29999999999999993, -0.7], [-0.19999999999999996, -0.7], [-0.09999999999999998, -0.7], [0.0, -0.7], [0.10000000000000009, -0.7], [0.20000000000000018, -0.7], [0.30000000000000004, -0.7], [0.40000000000000013, -0.7], [0.5, -0.7], [0.6000000000000001, -0.7], [0.7000000000000002, -0.7], [0.8, -0.7], [0.9000000000000001, -0.7], [1.0, -0.7], [-1.0, -0.6], [-0.9, -0.6], [-0.8, -0.6], [-0.7, -0.6], [-0.6, -0.6], [-0.5, -0.6], [-0.3999999999999999, -0.6], [-0.29999999999999993, -0.6], [-0.19999999999999996, -0.6], [-0.09999999999999998, -0.6], [0.0, -0.6], [0.10000000000000009, -0.6], [0.20000000000000018, -0.6], [0.30000000000000004, -0.6], [0.40000000000000013, -0.6], [0.5, -0.6], [0.6000000000000001, -0.6], [0.7000000000000002, -0.6], [0.8, -0.6], [0.9000000000000001, -0.6], [1.0, -0.6], [-1.0, -0.5], [-0.9, -0.5], [-0.8, -0.5], [-0.7, -0.5], [-0.6, -0.5], [-0.5, -0.5], [-0.3999999999999999, -0.5], [-0.29999999999999993, -0.5], [-0.19999999999999996, -0.5], [-0.09999999999999998, -0.5], [0.0, -0.5], [0.10000000000000009, -0.5], [0.20000000000000018, -0.5], [0.30000000000000004, -0.5], [0.40000000000000013, -0.5], [0.5, -0.5], [0.6000000000000001, -0.5], [0.7000000000000002, -0.5], [0.8, -0.5], [0.9000000000000001, -0.5], [1.0, -0.5], [-1.0, -0.3999999999999999], [-0.9, -0.3999999999999999], [-0.8, -0.3999999999999999], [-0.7, -0.3999999999999999], [-0.6, -0.3999999999999999], [-0.5, -0.3999999999999999], [-0.3999999999999999, -0.3999999999999999], [-0.29999999999999993, -0.3999999999999999], [-0.19999999999999996, -0.3999999999999999], [-0.09999999999999998, -0.3999999999999999], [0.0, -0.3999999999999999], [0.10000000000000009, -0.3999999999999999], [0.20000000000000018, -0.3999999999999999], [0.30000000000000004, -0.3999999999999999], [0.40000000000000013, -0.3999999999999999], [0.5, -0.3999999999999999], [0.6000000000000001, -0.3999999999999999], [0.7000000000000002, -0.3999999999999999], [0.8, -0.3999999999999999], [0.9000000000000001, -0.3999999999999999], [1.0, -0.3999999999999999], [-1.0, -0.29999999999999993], [-0.9, -0.29999999999999993], [-0.8, -0.29999999999999993], [-0.7, -0.29999999999999993], [-0.6, -0.29999999999999993], [-0.5, -0.29999999999999993], [-0.3999999999999999, -0.29999999999999993], [-0.29999999999999993, -0.29999999999999993], [-0.19999999999999996, -0.29999999999999993], [-0.09999999999999998, -0.29999999999999993], [0.0, -0.29999999999999993], [0.10000000000000009, -0.29999999999999993], [0.20000000000000018, -0.29999999999999993], [0.30000000000000004, -0.29999999999999993], [0.40000000000000013, -0.29999999999999993], [0.5, -0.29999999999999993], [0.6000000000000001, -0.29999999999999993], [0.7000000000000002, -0.29999999999999993], [0.8, -0.29999999999999993], [0.9000000000000001, -0.29999999999999993], [1.0, -0.29999999999999993], [-1.0, -0.19999999999999996], [-0.9, -0.19999999999999996], [-0.8, -0.19999999999999996], [-0.7, -0.19999999999999996], [-0.6, -0.19999999999999996], [-0.5, -0.19999999999999996], [-0.3999999999999999, -0.19999999999999996], [-0.29999999999999993, -0.19999999999999996], [-0.19999999999999996, -0.19999999999999996], [-0.09999999999999998, -0.19999999999999996], [0.0, -0.19999999999999996], [0.10000000000000009, -0.19999999999999996], [0.20000000000000018, -0.19999999999999996], [0.30000000000000004, -0.19999999999999996], [0.40000000000000013, -0.19999999999999996], [0.5, -0.19999999999999996], [0.6000000000000001, -0.19999999999999996], [0.7000000000000002, -0.19999999999999996], [0.8, -0.199999Line truncated
|
||||
Reference in new issue
Block a user