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PONetwork X , which depth searching option to select for this use?
 

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  1. PONetwork X , which depth searching option to select for this use?
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    <p>I am trying to ensure a global correct orientation of my normals. I.e make sure they point outwards everywhere.</p> <p>This is my method:</p> <ol> <li>I have a coordinate list <code>x y c n</code> . At each point in coordinate list I create edges from the point to its 5 nearest neighbours.</li> <li>I Weight each edge with (<code>1-n1.n2</code>).</li> <li>Compute a minimal spanning tree.</li> <li>Traverse this tree in depth first order.</li> <li>Start traversing at the point with the greatest <code>z</code> value, force the normal of this point to be orientated towards the positive <code>z</code> axis. </li> <li>Assign orientation to subsequent points that are consistent with their preceeding point, for example if current point <code>p1</code> has orientation <code>n1</code>, and next point visited is <code>p2</code>, if <code>n1.n2&lt;0</code> then <code>n2</code> is replaced by <code>-n2</code>.</li> </ol> <p>I am not sure how to do the depth first traversing and implement steps 5,6. My code is below.</p> <p>Many thanks!</p> <pre><code>import numpy as np from sklearn.neighbors import NearestNeighbors import networkx as nx #Get coordinates f_name = 'Bunnylarge' coord = np.genfromtxt(str(f_name)+'.txt',autostrip=True) #Fit nearest neighbours to coordinates neigh = NearestNeighbors(5) neigh.fit(coord[:,:3]) #Get the point with highest z value , this will be used as the starting point for my depth search z_max_point = np.where(coord[:,2]==np.max(coord[:,2])) z_max_point=int(z_max_point[0]) #Create a graph G = nx.Graph() #Add all points and there neighbours to graph, make the weight equal to the distance between points for i in range(0,len(coord)): d = neigh.kneighbors(coord[i,:3]) k = 5 for c in range(1,k): p1 = d[1][0][0] p2 = d[1][0][c] n1 = coord[d[1][0][0],3:6] n2 = coord[d[1][0][c],3:6] dot = np.dot(n1,n2) G.add_edge(p1,p2,weight =1-dot) #Get minimum spanning tree of graph #M = nx.minimum_spanning_edges(G,weight = 'weight',data=True) M = nx.minimum_spanning_edges(G) edgelist = list(M) #Convert minimum spanning tree into graph to be able to depth searched D = nx.from_edgelist(edgelist) if coord[z_max_point,5] &lt; 0 : #ie normal doesnt point out coord[z_max_point,3:6]=-coord[z_max_point,3:6] #Compute the DFS, start at the z_max_point T = nx.dfs_tree(D,source = z_max_point) #Go through the depth searched order and if subsequent points have for example a n1*n2&lt;0 then make n2=-n2 #for i in list(T.edges()): #for i in nx.dfs_successors(D,z_max_point): n1 = coord[z_max_point,3:6] #for i in nx.dfs_postorder_nodes(D,z_max_point): for i in nx.dfs_tree(D,z_max_point): print i n2 = coord[i,3:6] if np.dot(n1,n2)&lt;0: coord[i,3:6]=-coord[i,3:6] n2 = n1 print 'done' np.savetxt('testnormals2.xyz',coord) </code></pre>
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