1-Phn &ddlmZmZmZmZddZdS))MCP MCP_Geometric MCP_Connect MCP_FlexibleTct|t|}}|rt}nt}|||}||g|g\}}||||fS)a Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters ---------- array : ndarray Array of costs. start : iterable n-d index into `array` defining the starting point end : iterable n-d index into `array` defining the end point fully_connected : bool (optional) If True, diagonal moves are permitted, if False, only axial moves. geometric : bool (optional) If True, the MCP_Geometric class is used to calculate costs, if False, the MCP base class is used. See the class documentation for an explanation of the differences between MCP and MCP_Geometric. Returns ------- path : list List of n-d index tuples defining the path from `start` to `end`. cost : float Cost of the path. If `geometric` is False, the cost of the path is the sum of the values of `array` along the path. If `geometric` is True, a finer computation is made (see the documentation of the MCP_Geometric class). See Also -------- MCP, MCP_Geometric Examples -------- >>> import numpy as np >>> from skimage.graph import route_through_array >>> >>> image = np.array([[1, 3], [10, 12]]) >>> image array([[ 1, 3], [10, 12]]) >>> # Forbid diagonal steps >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False) ([(0, 0), (0, 1), (1, 1)], 9.5) >>> # Now allow diagonal steps: the path goes directly from start to end >>> route_through_array(image, [0, 0], [1, 1]) ([(0, 0), (1, 1)], 9.19238815542512) >>> # Cost is the sum of array values along the path (16 = 1 + 3 + 12) >>> route_through_array(image, [0, 0], [1, 1], fully_connected=False, ... geometric=False) ([(0, 0), (0, 1), (1, 1)], 16.0) >>> # Larger array where we display the path that is selected >>> image = np.arange((36)).reshape((6, 6)) >>> image array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35]]) >>> # Find the path with lowest cost >>> indices, weight = route_through_array(image, (0, 0), (5, 5)) >>> indices = np.stack(indices, axis=-1) >>> path = np.zeros_like(image) >>> path[indices[0], indices[1]] = 1 >>> path array([[1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]]) )fully_connected)tuplerr find_costs traceback) arraystartendr geometric mcp_classmcoststraceback_arrays Q/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/graph/mcp.pyroute_through_arrayrsyZuuSzz3E!   %999A\\5'C599E? ;;s  U3Z ''N)TT)_mcprrrrrrrrsM????????????T(T(T(T(T(T(r