sorry, that was stupid. there is no inversion (apart from 1/m), just the integration.
still, improving the integration would allow larger steps. andrew andrew cooke wrote: > > why are you dong this point by point? surely you can express the physics > as a set of equations and invert the matrix? wouldn't that be a lot > faster? you'd replace the iteration over all combinations of points with > a faster matrix inversion. > > see for example > http://www.medwelljournals.com/fulltext/ajit/2006/324-338.pdf page 331 > onwards. > > there's a very nice into to the verlet integration mentioned here - > http://teknikus.dk/tj/gdc2001.htm > > andrew > > > jeffg wrote: >> If anyone wants to take this on... I would really really like to have >> the spring_layout modified to support multi-threading if at all >> possible. >> My test data is 20,000, which makes this process 20,000 x 20,000 or >> 400,000,000 (400 million) calculations. This is taking somewhere >> between 2-3 hours an iteration. >> I plan to plot out over 1,000,000 data points or more, which would put >> this at 1,000,000,000,000 (1 trillion) calculations. Any help in >> making this more efficient would be much appreciated. >> >> def spring_layout(G, iterations=50, dim=2, node_pos=None, >> verbose=False): >> """Spring force model layout""" >> if node_pos==None : # set the initial positions randomly in 1x1 >> box >> vpos=random_layout(G, dim=dim) >> else: >> vpos=node_pos >> if iterations==0: >> return vpos >> if G.order()==0: >> k=1.0 >> else: >> k=N.sqrt(1.0/G.order()) # optimal distance between nodes >> disp={} # displacements >> >> # initial "temperature" (about .1 of domain area) >> # this is the largest step allowed in the dynamics >> # linearly step down by dt on each iteration so >> # on last iteration it is size dt. >> t=0.1 >> dt=0.1/float(iterations+1) >> for i in range(0,iterations): >> for v in G: >> if verbose==True: >> print("Interation: " + str(i + 1) + ", Calculating: " >> + str(v.encode('iso-8859-15', "replace"))) >> disp[v]=N.zeros(dim) >> for u in G: >> delta=vpos[v]-vpos[u] >> dn=max(sqrt(N.dot(delta,delta)),0.01) >> # repulsive force between all >> deltaf=delta*k**2/dn**2 >> disp[v]=disp[v]+deltaf >> # attractive force between neighbors >> if G.has_edge(v,u): >> deltaf=-delta*dn**2/(k*dn) >> disp[v]=disp[v]+deltaf >> >> # update positions >> for v in G: >> l=max(sqrt(N.dot(disp[v],disp[v])),0.01) >> vpos[v]=vpos[v]+ disp[v]*t/l >> t-=dt >> return vpos >> -- >> http://mail.python.org/mailman/listinfo/python-list >> >> > > > -- > http://mail.python.org/mailman/listinfo/python-list > > -- http://mail.python.org/mailman/listinfo/python-list
