def test_cartesian_distance(self):
v1 = [1,0]
v2 = [0,1]
dist_1 = vector_utils.cartesian_distance_2d(v1, v2)
dist_2 = math.sqrt((v1[0]-v2[0])**2+(v1[1]-v2[1])**2)
self.assertEqual(dist_1, dist_2)
v1 = [1,0]
v2 = [0,0]
dist_1 = vector_utils.cartesian_distance_2d(v1, v2)
self.assertEqual(dist_1, 1)
v1 = [1,0]
v2 = [-1,0]
dist_1 = vector_utils.cartesian_distance_2d(v1, v2)
self.assertEqual(dist_1, 2)
def test_neighbour_distance(self):
"""
TODO: This test contains no asserts and has just been used as a way of eye-balling distances when debug is
uncommented.
Create a world with a fixed number of cells, where the expected approximate distance between neighbouring
nodes is know, so aberrant configurations can be detected
"""
# Calculate min_dist
# Create world
total_cells=50
w = world.World(total_cells_desired=total_cells)
starting_node = random.choice(w.all_boundary_nodes)
for node in w.all_boundary_nodes:
# Confirm node has neighbours
self.assertGreater(len(starting_node.neighbours), 0)
for neighbour in node.neighbours:
#print("{0} to {1}".format(node.loc, neighbour.loc))
angular_dist = math.degrees(geographic_utils.radians_between_locations(node.loc, neighbour.loc))
node_cartesian_pos = geographic_utils.geographic_to_cartesian([np.array(node.loc)])[0]
neighbour_cartesian_pos = geographic_utils.geographic_to_cartesian([np.array(neighbour.loc)])[0]
direct_dist = vector_utils.cartesian_distance_2d(node_cartesian_pos, neighbour_cartesian_pos)
def geographic_direct_distance(a, b):
"""
Calculates direct distance between two coordinate locations, with a path that passes through the sphere
"""
cartesian_points = geographic_to_cartesian([a, b])
return cartesian_distance_2d(cartesian_points[0], cartesian_points[1])