For my own project I needed to create a graph based on a Delauney triangulation using NetworkX python library. And a special condition was that all the edges must be unique. Points for triangulation stored in a .shp-file.

I was lucky enough to find this tread on Delauney triangulation using NetworkX graphs. I made a nice function out of it for point NetworkX graphs processing. This function preserves nodes attributes (which are lost after triangulation) and calculates lengths of the edges. It can be further improved (and most likely will be) but even at the current state it is very handy.

Example of use:

Code for the function:

I was lucky enough to find this tread on Delauney triangulation using NetworkX graphs. I made a nice function out of it for point NetworkX graphs processing. This function preserves nodes attributes (which are lost after triangulation) and calculates lengths of the edges. It can be further improved (and most likely will be) but even at the current state it is very handy.

Example of use:

import networkx as nx import scipy.spatial import matplotlib.pyplot as plt path = '/directory/' f_path = path + 'filename.shp' G = nx.read_shp(f_path) GD = createTINgraph(G, show = True)

Code for the function:

import networkx as nx import scipy.spatial import matplotlib.pyplot as plt def createTINgraph(point_graph, show = False, calculate_distance = True): ''' Creates a graph based on Delaney triangulation @param point_graph: either a graph made by read_shp() from another NetworkX's point graph @param show: whether or not resulting graph should be shown, boolean @param calculate_distance: whether length of edges should be calculated @return - a graph made from a Delauney triangulation @Copyright notice: this code is an improved (by Yury V. Ryabov, 2014, riabovvv@gmail.com) version of Tom's code taken from this discussion https://groups.google.com/forum/#!topic/networkx-discuss/D7fMmuzVBAw ''' TIN = scipy.spatial.Delaunay(point_graph) edges = set() # for each Delaunay triangle for n in xrange(TIN.nsimplex): # for each edge of the triangle # sort the vertices # (sorting avoids duplicated edges being added to the set) # and add to the edges set edge = sorted([TIN.vertices[n,0], TIN.vertices[n,1]]) edges.add((edge[0], edge[1])) edge = sorted([TIN.vertices[n,0], TIN.vertices[n,2]]) edges.add((edge[0], edge[1])) edge = sorted([TIN.vertices[n,1], TIN.vertices[n,2]]) edges.add((edge[0], edge[1])) # make a graph based on the Delaunay triangulation edges graph = nx.Graph(list(edges)) #add nodes attributes to the TIN graph from the original points original_nodes = point_graph.nodes(data = True) for n in xrange(len(original_nodes)): XY = original_nodes[n][0] # X and Y tuple - coordinates of the original points graph.node[n]['XY'] = XY # add other attributes original_attributes = original_nodes[n][1] for i in original_attributes.iteritems(): # for tuple i = (key, value) graph.node[n][i[0]] = i[1] # calculate Euclidian length of edges and write it as edges attribute if calculate_distance: edges = graph.edges() for i in xrange(len(edges)): edge = edges[i] node_1 = edge[0] node_2 = edge[1] x1, y1 = graph.node[node_1]['XY'] x2, y2 = graph.node[node_2]['XY'] dist = sqrt( pow( (x2 - x1), 2 ) + pow( (y2 - y1), 2 ) ) dist = round(dist, 2) graph.edge[node_1][node_2]['distance'] = dist # plot graph if show: pointIDXY = dict(zip(range(len(point_graph)), point_graph)) nx.draw(graph, pointIDXY) plt.show() return graph

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