On 12/17/2012 11:11 AM, Guillaume Gay wrote: > Hi list. > > I am using graph-tool to model an /apical junctions/ network, based on the > model by Farhadifar et al. > <http://www.sciencedirect.com/science/article/pii/S0960982207023342>. In > short, it corresponds to the outer surface of the cells in a particular > region of the embryo, where each cell is represented by a polygon. Topology > of the network changes due to cell division and cell death. > > The physical model involves the local minimization of an energy depending on > the local geometry. > > I got a working model, but the energy optimization is time consuming. I think > that this is due to the way I compute the components of the vector formed by > each edge of the graph, by iterating over the edges., eg: > > |for edge in graph.edges(): > v0, v1 = edge.source(), edge.target() > deltax[edge] = x[v1] - x[v0] > deltay[edge] = y[v1] - y[v0]| > > This is further complicated (to a little extent) by periodic conditions to > the coordinate systems, and some other details, but I think this is the core > issue. > > I have the feeling I can do better, perhaps by using the adjacency matrix? > This would be particularly interesting as the graph topology doesn't change > for a given optimization. >
Hi Guillaume, Take a look at the edge_difference() function, since I think it does what you want, and should be significantly faster. Cheers, Tiago -- Tiago de Paula Peixoto <[email protected]>
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