def test_quadtree_similar_point(): # Introduce a point into a quad tree where a similar point already exists. # Test will hang if it doesn't complete. Xs = [] # check the case where points are actually different Xs.append(np.array([[1, 2], [3, 4]], dtype=np.float32)) # check the case where points are the same on X axis Xs.append(np.array([[1.0, 2.0], [1.0, 3.0]], dtype=np.float32)) # check the case where points are arbitrarily close on X axis Xs.append(np.array([[1.00001, 2.0], [1.00002, 3.0]], dtype=np.float32)) # check the case where points are the same on Y axis Xs.append(np.array([[1.0, 2.0], [3.0, 2.0]], dtype=np.float32)) # check the case where points are arbitrarily close on Y axis Xs.append(np.array([[1.0, 2.00001], [3.0, 2.00002]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes Xs.append(np.array([[1.00001, 2.00001], [1.00002, 2.00002]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes # close to machine epsilon - x axis Xs.append(np.array([[1, 0.0003817754041], [2, 0.0003817753750]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes # close to machine epsilon - y axis Xs.append(np.array([[0.0003817754041, 1.0], [0.0003817753750, 2.0]], dtype=np.float32)) for X in Xs: tree = _QuadTree(n_dimensions=2, verbose=0) tree.build_tree(X) tree._check_coherence()
def test_quadtree_similar_point(): # Introduce a point into a quad tree where a similar point already exists. # Test will hang if it doesn't complete. Xs = [] # check the case where points are actually different Xs.append(np.array([[1, 2], [3, 4]], dtype=np.float32)) # check the case where points are the same on X axis Xs.append(np.array([[1.0, 2.0], [1.0, 3.0]], dtype=np.float32)) # check the case where points are arbitrarily close on X axis Xs.append(np.array([[1.00001, 2.0], [1.00002, 3.0]], dtype=np.float32)) # check the case where points are the same on Y axis Xs.append(np.array([[1.0, 2.0], [3.0, 2.0]], dtype=np.float32)) # check the case where points are arbitrarily close on Y axis Xs.append(np.array([[1.0, 2.00001], [3.0, 2.00002]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes Xs.append(np.array([[1.00001, 2.00001], [1.00002, 2.00002]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes # close to machine epsilon - x axis Xs.append(np.array([[1, 0.0003817754041], [2, 0.0003817753750]], dtype=np.float32)) # check the case where points are arbitrarily close on both axes # close to machine epsilon - y axis Xs.append(np.array([[0.0003817754041, 1.0], [0.0003817753750, 2.0]], dtype=np.float32)) for X in Xs: tree = _QuadTree(n_dimensions=2, verbose=0) tree.build_tree(X) tree._check_coherence()
def test_quad_tree_pickle(n_dimensions, protocol): rng = check_random_state(0) X = rng.random_sample((10, n_dimensions)) tree = _QuadTree(n_dimensions=n_dimensions, verbose=0) tree.build_tree(X) s = pickle.dumps(tree, protocol=protocol) bt2 = pickle.loads(s) for x in X: cell_x_tree = tree.get_cell(x) cell_x_bt2 = bt2.get_cell(x) assert cell_x_tree == cell_x_bt2
def check_insert_duplicate(n_dimensions=2): X = rng.random_sample((10, n_dimensions)) Xd = np.r_[X, X[:5]] tree = _QuadTree(n_dimensions=n_dimensions, verbose=0) tree.build_tree(Xd) cumulative_size = tree.cumulative_size leafs = tree.leafs # Assert that the first 5 are indeed duplicated and that the next # ones are single point leaf for i, x in enumerate(X): cell_id = tree.get_cell(x) assert leafs[cell_id] assert cumulative_size[cell_id] == 1 + (i < 5)
def check_insert_duplicate(n_dimensions=2): X = rng.random_sample((10, n_dimensions)) Xd = np.r_[X, X[:5]] tree = _QuadTree(n_dimensions=n_dimensions, verbose=0) tree.build_tree(Xd) cumulative_size = tree.cumulative_size leafs = tree.leafs # Assert that the first 5 are indeed duplicated and that the next # ones are single point leaf for i, x in enumerate(X): cell_id = tree.get_cell(x) assert leafs[cell_id] assert cumulative_size[cell_id] == 1 + (i < 5)
def test_quadtree_boundary_computation(): # Introduce a point into a quad tree with boundaries not easy to compute. Xs = [] # check a random case Xs.append(np.array([[-1, 1], [-4, -1]], dtype=np.float32)) # check the case where only 0 are inserted Xs.append(np.array([[0, 0], [0, 0]], dtype=np.float32)) # check the case where only negative are inserted Xs.append(np.array([[-1, -2], [-4, 0]], dtype=np.float32)) # check the case where only small numbers are inserted Xs.append(np.array([[-1e-6, 1e-6], [-4e-6, -1e-6]], dtype=np.float32)) for X in Xs: tree = _QuadTree(n_dimensions=2, verbose=0) tree.build_tree(X) tree._check_coherence()
def test_quadtree_boundary_computation(): # Introduce a point into a quad tree with boundaries not easy to compute. Xs = [] # check a random case Xs.append(np.array([[-1, 1], [-4, -1]], dtype=np.float32)) # check the case where only 0 are inserted Xs.append(np.array([[0, 0], [0, 0]], dtype=np.float32)) # check the case where only negative are inserted Xs.append(np.array([[-1, -2], [-4, 0]], dtype=np.float32)) # check the case where only small numbers are inserted Xs.append(np.array([[-1e-6, 1e-6], [-4e-6, -1e-6]], dtype=np.float32)) for X in Xs: tree = _QuadTree(n_dimensions=2, verbose=0) tree.build_tree(X) tree._check_coherence()
def test_quad_tree_pickle(): rng = check_random_state(0) for n_dimensions in (2, 3): X = rng.random_sample((10, n_dimensions)) tree = _QuadTree(n_dimensions=n_dimensions, verbose=0) tree.build_tree(X) def check_pickle_protocol(protocol): s = pickle.dumps(tree, protocol=protocol) bt2 = pickle.loads(s) for x in X: cell_x_tree = tree.get_cell(x) cell_x_bt2 = bt2.get_cell(x) assert cell_x_tree == cell_x_bt2 for protocol in (0, 1, 2): yield check_pickle_protocol, protocol
def test_quad_tree_pickle(): rng = check_random_state(0) for n_dimensions in (2, 3): X = rng.random_sample((10, n_dimensions)) tree = _QuadTree(n_dimensions=n_dimensions, verbose=0) tree.build_tree(X) def check_pickle_protocol(protocol): s = pickle.dumps(tree, protocol=protocol) bt2 = pickle.loads(s) for x in X: cell_x_tree = tree.get_cell(x) cell_x_bt2 = bt2.get_cell(x) assert cell_x_tree == cell_x_bt2 for protocol in (0, 1, 2): yield check_pickle_protocol, protocol