def test_vector_distanceTo(self): v1 = Nodex((1, 0, 0)) v2 = Nodex((-2, 0, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 3.0, places=7) v1 = Nodex((0.707, 0.707, 0)) v2 = Nodex((-0.707, -0.707, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 2.0, places=2) v1 = Nodex((1200, 0, 0)) v2 = Nodex((-59.6, 0, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 1259.6, places=7) v1 = Nodex((-123.3, 51.5, 720)) v2 = Nodex((-123.3, 51.5, 550)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 720 - 550, places=7)
def test_vector_distanceTo(self): v1 = Nodex((1, 0, 0)) v2 = Nodex((-2, 0, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 3.0, places=7) v1 = Nodex((0.707, 0.707, 0)) v2 = Nodex((-0.707, -0.707, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 2.0, places=2) v1 = Nodex((1200, 0, 0)) v2 = Nodex((-59.6, 0, 0)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 1259.6, places=7) v1 = Nodex((-123.3, 51.5, 720)) v2 = Nodex((-123.3, 51.5, 550)) distance = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), 720-550, places=7)
def test_vector_length(self): v1 = Nodex((100, 0, 0)) length = v1.length() self.assertAlmostEqual(length.value(), 100.0, places=5) v1 = Nodex( (0.707, 0, 0.707)) # this is rough approximation of a unit vector length = v1.length() self.assertAlmostEqual(length.value(), 1.0, places=2) # Check distance in two ways and check if is equal v1 = Nodex((100, 0, 0)) v2 = Nodex((24, 25, 10)) diff = v1 - v2 distance = diff.length() distance_direct = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), distance_direct.value(), places=5) # Set some random vectors, normalize and check whether length == 1.0 v = Nodex((2154.0, 2315.98, -918)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) v = Nodex((500, 0.7, 50)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) v = Nodex((-4, 9, -7)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) # squared length v = Nodex((-4, 9, -7)) lengthSquared = v.length() ^ 2 lengthSquaredMethod = v.squareLength() self.assertAlmostEqual(lengthSquared.value(), lengthSquaredMethod.value(), places=5)
def test_vector_length(self): v1 = Nodex((100, 0, 0)) length = v1.length() self.assertAlmostEqual(length.value(), 100.0, places=5) v1 = Nodex((0.707, 0, 0.707)) # this is rough approximation of a unit vector length = v1.length() self.assertAlmostEqual(length.value(), 1.0, places=2) # Check distance in two ways and check if is equal v1 = Nodex((100, 0, 0)) v2 = Nodex((24, 25, 10)) diff = v1 - v2 distance = diff.length() distance_direct = v1.distanceTo(v2) self.assertAlmostEqual(distance.value(), distance_direct.value(), places=5) # Set some random vectors, normalize and check whether length == 1.0 v = Nodex((2154.0, 2315.98, -918)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) v = Nodex((500, 0.7, 50)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) v = Nodex((-4, 9, -7)) normal_length = v.normal().length() self.assertAlmostEqual(normal_length.value(), 1.0, places=5) # squared length v = Nodex((-4, 9, -7)) lengthSquared = v.length() ^ 2 lengthSquaredMethod = v.squareLength() self.assertAlmostEqual(lengthSquared.value(), lengthSquaredMethod.value(), places=5)