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)
