Disabled external gits
This commit is contained in:
281
cs457-gc/assignment_3_1/test/test.py
Normal file
281
cs457-gc/assignment_3_1/test/test.py
Normal file
@@ -0,0 +1,281 @@
|
||||
import time
|
||||
import pytest
|
||||
import json
|
||||
import sys
|
||||
import igl
|
||||
import numpy as np
|
||||
sys.path.append('../')
|
||||
sys.path.append('../src')
|
||||
from smooth_surfaces import *
|
||||
|
||||
eps = 1E-6
|
||||
|
||||
with open('test_data.json', 'r') as infile:
|
||||
homework_datas = json.load(infile)
|
||||
|
||||
# Derivatives of a paraboloid
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[0])
|
||||
def test_paraboloid_points(data):
|
||||
P, a, b, c, d, e, x = data
|
||||
P = np.array(P, dtype=float)
|
||||
x = np.array(x, dtype=float)
|
||||
|
||||
x_student = compute_paraboloid_points(P, a, b, c, d, e)
|
||||
assert np.linalg.norm(x_student - x) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[1])
|
||||
def test_paraboloid_first_derivatives(data):
|
||||
P, a, b, c, d, e, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
|
||||
x_u_student, x_v_student = compute_paraboloid_first_derivatives(P, a, b, c, d, e)
|
||||
|
||||
assert np.linalg.norm(x_u_student - x_u) < eps
|
||||
assert np.linalg.norm(x_v_student - x_v) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[2])
|
||||
def test_paraboloid_second_derivatives(data):
|
||||
P, a, b, c, d, e, x_uu, x_uv, x_vv = data
|
||||
P = np.array(P, dtype=float)
|
||||
x_uu = np.array(x_uu, dtype=float)
|
||||
x_uv = np.array(x_uv, dtype=float)
|
||||
x_vv = np.array(x_vv, dtype=float)
|
||||
|
||||
student_x_uu, student_x_uv, student_x_vv = compute_paraboloid_second_derivatives(P, a, b, c, d, e)
|
||||
|
||||
assert np.linalg.norm(student_x_uu - x_uu) < eps
|
||||
assert np.linalg.norm(student_x_uv - x_uv) < eps
|
||||
assert np.linalg.norm(student_x_vv - x_vv) < eps
|
||||
|
||||
##############################
|
||||
|
||||
# Derivatives of a torus
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[3])
|
||||
def test_torus_points(data):
|
||||
P, R, r, x = data
|
||||
P = np.array(P, dtype=float)
|
||||
x = np.array(x, dtype=float)
|
||||
|
||||
x_student = compute_torus_points(P, R, r)
|
||||
assert np.linalg.norm(x_student - x) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[4])
|
||||
def test_torus_first_derivatives(data):
|
||||
P, R, r, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
|
||||
x_u_student, x_v_student = compute_torus_first_derivatives(P, R, r)
|
||||
|
||||
assert np.linalg.norm(x_u_student - x_u) < eps
|
||||
assert np.linalg.norm(x_v_student - x_v) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[5])
|
||||
def test_torus_second_derivatives(data):
|
||||
P, R, r, x_uu, x_uv, x_vv = data
|
||||
P = np.array(P, dtype=float)
|
||||
x_uu = np.array(x_uu, dtype=float)
|
||||
x_uv = np.array(x_uv, dtype=float)
|
||||
x_vv = np.array(x_vv, dtype=float)
|
||||
|
||||
student_x_uu, student_x_uv, student_x_vv = compute_torus_second_derivatives(P, R, r)
|
||||
|
||||
assert np.linalg.norm(student_x_uu - x_uu) < eps
|
||||
assert np.linalg.norm(student_x_uv - x_uv) < eps
|
||||
assert np.linalg.norm(student_x_vv - x_vv) < eps
|
||||
|
||||
##############################
|
||||
|
||||
# Shape Operator
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[6])
|
||||
def test_paraboloid_first_fundamental_form(data):
|
||||
P, a, b, c, d, e, I, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
I = np.array(I, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
I_student = compute_first_fundamental_form(x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(I_student - I) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[7])
|
||||
def test_torus_first_fundamental_form(data):
|
||||
P, R, r, I, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
I = np.array(I, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
I_student = compute_first_fundamental_form(x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(I_student - I) < eps
|
||||
|
||||
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[8])
|
||||
def test_paraboloid_surface_normal(data):
|
||||
P, a, b, c, d, e, n, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
n = np.array(n, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
n_student = compute_surface_normal(x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(n_student - n) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[9])
|
||||
def test_torus_surface_normal(data):
|
||||
P, R, r, n, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
n = np.array(n, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
n_student = compute_surface_normal( x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(n_student - n) < eps
|
||||
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[10])
|
||||
def test_paraboloid_second_fundamental_form(data):
|
||||
P, a, b, c, d, e, II, x_uu, x_uv, x_vv, n = data
|
||||
P = np.array(P, dtype=float)
|
||||
II = np.array(II, dtype=float)
|
||||
x_uu = np.array(x_uu, dtype=float)
|
||||
x_uv = np.array(x_uv, dtype=float)
|
||||
x_vv = np.array(x_vv, dtype=float)
|
||||
n = np.array(n, dtype=float)
|
||||
|
||||
II_student = compute_second_fundamental_form(x_uu, x_uv, x_vv, n )
|
||||
assert np.linalg.norm(II_student - II) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[11])
|
||||
def test_torus_second_fundamental_form(data):
|
||||
P, R, r, II, x_uu, x_uv, x_vv, n = data
|
||||
P = np.array(P, dtype=float)
|
||||
II = np.array(II, dtype=float)
|
||||
x_uu = np.array(x_uu, dtype=float)
|
||||
x_uv = np.array(x_uv, dtype=float)
|
||||
x_vv = np.array(x_vv, dtype=float)
|
||||
n = np.array(n, dtype=float)
|
||||
|
||||
II_student = compute_second_fundamental_form(x_uu, x_uv, x_vv, n)
|
||||
assert np.linalg.norm(II_student - II) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[12])
|
||||
def test_paraboloid_shape_operator(data):
|
||||
P, a, b, c, d, e, S, I, II = data
|
||||
P = np.array(P, dtype=float)
|
||||
S = np.array(S, dtype=float)
|
||||
I = np.array(I, dtype=float)
|
||||
II = np.array(II, dtype=float)
|
||||
|
||||
S_student = compute_shape_operator(I, II)
|
||||
assert np.linalg.norm(S_student - S) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[13])
|
||||
def test_torus_shape_operator(data):
|
||||
P, R, r, S, I, II = data
|
||||
P = np.array(P, dtype=float)
|
||||
S = np.array(S, dtype=float)
|
||||
I = np.array(I, dtype=float)
|
||||
II = np.array(II, dtype=float)
|
||||
|
||||
S_student = compute_shape_operator(I, II)
|
||||
assert np.linalg.norm(S_student - S) < eps
|
||||
|
||||
##############################
|
||||
|
||||
# Principal Curvatures
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[14])
|
||||
def test_paraboloid_principal_curvatures(data):
|
||||
P, a, b, c, d, e, k1, k2, e1, e2, S, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
k1 = np.array(k1, dtype=float)
|
||||
k2 = np.array(k2, dtype=float)
|
||||
e1 = np.array(e1, dtype=float)
|
||||
e2 = np.array(e2, dtype=float)
|
||||
S = np.array(S, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
k_1_student, k_2_student, e_1_student, e_2_student = compute_principal_curvatures(S, x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(k_1_student - k1) < eps
|
||||
assert np.linalg.norm(k_2_student - k2) < eps
|
||||
assert np.linalg.norm(e_1_student - e1) < eps
|
||||
assert np.linalg.norm(e_2_student - e2) < eps
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[15])
|
||||
def test_torus_principal_curvatures(data):
|
||||
P, R, r, k1, k2, e1, e2, S, x_u, x_v = data
|
||||
P = np.array(P, dtype=float)
|
||||
k1 = np.array(k1, dtype=float)
|
||||
k2 = np.array(k2, dtype=float)
|
||||
e1 = np.array(e1, dtype=float)
|
||||
e2 = np.array(e2, dtype=float)
|
||||
S = np.array(S, dtype=float)
|
||||
x_u = np.array(x_u, dtype=float)
|
||||
x_v = np.array(x_v, dtype=float)
|
||||
k_1_student, k_2_student, e_1_student, e_2_student = compute_principal_curvatures(S, x_u, x_v)
|
||||
|
||||
assert np.linalg.norm(k_1_student - k1) < eps
|
||||
assert np.linalg.norm(k_2_student - k2) < eps
|
||||
assert np.linalg.norm(e_1_student - e1) < eps
|
||||
assert np.linalg.norm(e_2_student - e2) < eps
|
||||
|
||||
##############################
|
||||
|
||||
# Asymptotic Directions
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[16])
|
||||
def test_paraboloid_asymptotic_direction(data):
|
||||
P, a, b, c, d, e, a1, a2, k1, k2, e1, e2 = data
|
||||
P = np.array(P, dtype=float)
|
||||
a1 = np.array(a1, dtype=float)
|
||||
a2 = np.array(a2, dtype=float)
|
||||
k1 = np.array(k1, dtype=float)
|
||||
k2 = np.array(k2, dtype=float)
|
||||
e1 = np.array(e1, dtype=float)
|
||||
e2 = np.array(e2, dtype=float)
|
||||
a_1_student, a_2_student = compute_asymptotic_directions(k1, k2, e1, e2)
|
||||
|
||||
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)
|
||||
|
||||
@pytest.mark.timeout(0.5)
|
||||
@pytest.mark.parametrize("data", homework_datas[17])
|
||||
def test_torus_asymptotic_direction(data):
|
||||
P, R, r, a1, a2, k1, k2, e1, e2 = data
|
||||
P = np.array(P, dtype=float)
|
||||
a1 = np.array(a1, dtype=float)
|
||||
a2 = np.array(a2, dtype=float)
|
||||
k1 = np.array(k1, dtype=float)
|
||||
k2 = np.array(k2, dtype=float)
|
||||
e1 = np.array(e1, dtype=float)
|
||||
e2 = np.array(e2, dtype=float)
|
||||
|
||||
a_1_student, a_2_student = compute_asymptotic_directions(k1, k2, e1, e2)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
##############################
|
||||
Reference in New Issue
Block a user