From b055e50f6c408e01afdccfce0e963eb22e22f5c4 Mon Sep 17 00:00:00 2001 From: mikeqfu Date: Mon, 26 Sep 2022 11:59:17 +0100 Subject: [PATCH] Modify `find_closest_points()` and `find_shortest_path()` --- pyhelpers/geom.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyhelpers/geom.py b/pyhelpers/geom.py index 3372753..861bc4c 100644 --- a/pyhelpers/geom.py +++ b/pyhelpers/geom.py @@ -686,7 +686,7 @@ def find_closest_points(pts, ref_pts, k=1, unique_pts=False, as_geom=False, ret_ >>> cities = [[-2.9916800, 53.4071991], # Liverpool ... [-4.2488787, 55.8609825], # Glasgow ... [-1.6131572, 54.9738474]] # Newcastle - >>> ref_cities = example_dataframe().to_numpy() + >>> ref_cities = example_df.to_numpy() >>> closest_to_each = find_closest_points(pts=cities, ref_pts=ref_cities, k=1) >>> closest_to_each # Liverpool: Manchester; Glasgow: Manchester; Newcastle: Leeds @@ -719,7 +719,7 @@ def find_closest_points(pts, ref_pts, k=1, unique_pts=False, as_geom=False, ret_ 'MULTIPOINT (-2.2451148 53.4794892, -2.2451148 53.4794892, -1.5437941 53.7974185)' """ - ckdtree = _check_dependency(name='scipy.spatial.ckdtree') + ckdtree = _check_dependency(name='scipy.spatial') if isinstance(ref_pts, np.ndarray): ref_pts_ = copy.copy(ref_pts) @@ -866,7 +866,7 @@ def find_shortest_path(points_sequence, ret_dist=False, as_geom=False, **kwargs) nn_clf = sklearn_neighbors.NearestNeighbors(n_neighbors=2, **kwargs).fit(points_sequence) kn_g = nn_clf.kneighbors_graph() - nx_g = nx.from_scipy_sparse_matrix(kn_g) + nx_g = nx.from_scipy_sparse_array(kn_g) possible_paths = [list(nx.dfs_preorder_nodes(nx_g, i)) for i in range(len(points_sequence))] @@ -1036,7 +1036,7 @@ def get_geometric_midpoint_calc(pt1, pt2, as_geom=False): >>> geometric_midpoint = get_geometric_midpoint_calc(pt_1, pt_2, as_geom=True) >>> geometric_midpoint.wkt - 'POINT (1.516897742074818 52.6309028455831)' + 'POINT (1.5168977420748175 52.630902845583094)' .. seealso:: @@ -1082,7 +1082,7 @@ def get_rectangle_centroid(rectangle, as_geom=False): >>> rect_cen = get_rectangle_centroid(rectangle=rectangle_obj) >>> rect_cen - array([0.5, 0.5]) + array([[0.5, 0.5]]) """ if isinstance(rectangle, np.ndarray):