Task 1: Unit tests for common functions

tests/test_contact_common_functions.py

@@ -0,0 +1,181 @@
+__doc__ = """ Test common functions used in contact in Elastica.joint implementation"""
+
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose
+from elastica.joint import (
+    _dot_product,
+    _norm,
+    _clip,
+    _out_of_bounds,
+    _find_min_dist,
+    _aabbs_not_intersecting,
+)
+
+
+class TestDotProduct:
+    "class to test the dot product function"
+
+    @pytest.mark.parametrize("ndim", [3])
+    def test_dot_product_using_numpy(self, ndim):
+        """
+        This method was generated by "copilot" in VS code;
+        This method uses numpy dot product to compare with the output of our function,
+        numpy dot product uses an optimized implementation that takes advantage of
+        hardware-specific optimizations such as SIMD.
+        """
+        vector1 = np.random.randn(ndim)
+        vector2 = np.random.randn(ndim)
+        dot_product = _dot_product(vector1, vector2)
+        assert_allclose(dot_product, np.dot(vector1, vector2))
+
+    def test_dot_product_with_verified_values(self):
+        "Testing function with analytically verified values"
+
+        "test for parallel vectors"
+        vec1 = [1, 0, 0]
+        vec2 = [2, 0, 0]
+        dot_product = _dot_product(vec1, vec2)
+        assert_allclose(dot_product, 2)
+
+        "test for perpendicular vectors"
+        vec1 = [1, 0, 0]
+        vec2 = [0, 1, 0]
+        dot_product = _dot_product(vec1, vec2)
+        assert_allclose(dot_product, 0)
+
+        "test for opposite vectors"
+        vec1 = [1, 0, 0]
+        vec2 = [-1, 0, 0]
+        dot_product = _dot_product(vec1, vec2)
+        assert_allclose(dot_product, -1)
+
+        "test for complex vectors"
+        vec1 = [1, -2, 3]
+        vec2 = [-2, 1, 3]
+        dot_product = _dot_product(vec1, vec2)
+        assert_allclose(dot_product, 5)
+
+
+class TestNorm:
+    "class to test the _norm function"
+
+    def test_norm_with_verified_values(self):
+        "Testing function with analytically verified values"
+
+        "test for null vector"
+        vec1 = [0, 0, 0]
+        norm = _norm(vec1)
+        assert_allclose(norm, 0)
+
+        "test for unit vector"
+        vec1 = [1, 1, 1]
+        norm = _norm(vec1)
+        assert_allclose(norm, 1.7320508075688772)
+
+        "test for arbitrary natural number vector"
+        vec1 = [1, 2, 3]
+        norm = _norm(vec1)
+        assert_allclose(norm, 3.7416573867739413)
+
+        "test for decimal values vector"
+        vec1 = [0.001, 0.002, 0.003]
+        norm = _norm(vec1)
+        assert_allclose(norm, 0.0037416573867739412)
+
+
+class TestClip:
+    "class to test the _clip function"
+
+    @pytest.mark.parametrize(
+        "x, result",
+        [(0.5, 1), (1.5, 1.5), (2.5, 2)],
+    )
+    def test_norm_with_verified_values(self, x, result):
+        "Testing function with analytically verified values"
+
+        low = 1.0
+        high = 2.0
+        assert _clip(x, low, high) == result
+
+
+class TestOutOfBounds:
+    "class to test the _out_of_bounds function"
+
+    @pytest.mark.parametrize(
+        "x, result",
+        [(0.5, 1), (1.5, 0), (2.5, 1)],
+    )
+    def test_out_of_bounds_with_verified_values(self, x, result):
+        "testing function with analytically verified values"
+
+        low = 1.0
+        high = 2.0
+        assert _out_of_bounds(x, low, high) == result
+
+
+class TestFindMinDist:
+    "class to test the _find_min_dist function"
+
+    def test_find_min_dist(self):
+        "testing function with analytically verified values"
+
+        "intersecting lines"
+        x1 = np.array([0, 0, 0])
+        e1 = np.array([1, 1, 1])
+        x2 = np.array([0, 1, 0])
+        e2 = np.array([1, 0, 1])
+        min_dist_vec, closestpointofline1, closestpointofline2 = _find_min_dist(
+            x1, e1, x2, e2
+        )
+        assert_allclose(min_dist_vec, [0, 0, 0])
+        assert_allclose(closestpointofline1, [1, 1, 1])
+        assert_allclose(closestpointofline2, [-1, -1, -1])
+
+        "arbitrary close lines"
+        tol = 1e-5
+        x1 = np.array([0, 0, 0])
+        e1 = np.array([1, 1, 1])
+        x2 = np.array([0, 0, 1])
+        e2 = np.array([1.5, 2, 0])
+        min_dist_vec, closestpointofline1, closestpointofline2 = _find_min_dist(
+            x1, e1, x2, e2
+        )
+        assert_allclose(
+            min_dist_vec, [-0.153846, 0.115385, 0.038462], rtol=tol, atol=tol
+        )
+        assert_allclose(
+            closestpointofline1, [0.807692, 1.076923, 1], rtol=tol, atol=tol
+        )
+        assert_allclose(
+            closestpointofline2, [-0.961538, -0.961538, -0.961538], rtol=tol, atol=tol
+        )
+
+        "arbitrary away lines"
+        x1 = np.array([7, 0, 3.5])
+        e1 = np.array([1, 0, 0])
+        x2 = np.array([0, 70, 0])
+        e2 = np.array([0, 0, 11.5])
+        min_dist_vec, closestpointofline1, closestpointofline2 = _find_min_dist(
+            x1, e1, x2, e2
+        )
+        assert_allclose(min_dist_vec, [-7, 70, 0])
+        assert_allclose(closestpointofline1, [0, 70, 3.5])
+        assert_allclose(closestpointofline2, [7, 0, 3.5])
+
+
+class TestAABBSNotIntersecting:
+    "class to test the _aabb_intersecting function"
+
+    def test_aabbs_not_intersectin(self):
+        "testing function with analytically verified values"
+
+        " intersecting boxes"
+        aabb_one = np.array([[0, 0], [0, 0], [0, 0]])
+        aabb_two = np.array([[0, 0], [0, 0], [0, 0]])
+        assert _aabbs_not_intersecting(aabb_one, aabb_two) == 0
+
+        " non Intersecting boxes"
+        aabb_one = np.array([[0, 1], [0, 1], [0, 1]])
+        aabb_two = np.array([[2, 3], [2, 3], [2, 3]])
+        assert _aabbs_not_intersecting(aabb_one, aabb_two) == 1