def main(argv=None):
if(argv==None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
assert(not(options.refine_all and options.extend_all))
pool = Pool()
needy_nodes = pool.where("isa_partition and is_sampled").multilock()
# 1. Trying to detect fake convergence
for n in pool.where("state == 'converged'"):
means = kmeans(n.trajectory, k=2)
d = (means[0] - means[1]).norm2()
if(d > 2.0 and (options.refine_all or userinput("%s has converged but appears to have a bimodal distribution.\nDo you want to refine?"%n.name, "bool"))): #TODO decide upon threshold (per coordinate?)
refine(n, options)
# 2. Dealing with not-converged nodes
for n in pool.where("state == 'not-converged'"):
if(not(options.refine_all or options.extend_all)):
choice = userchoice("%s has not converged. What do you want to do?"%n.name, ['_refine', '_extend', '_ignore'])
if(options.refine_all or choice=="r"):
refine(n, options)
elif(options.extend_all or choice=="e"):
extend(n)
elif(choice=="i"):
continue
for n in needy_nodes:
n.save()
n.unlock()
zgf_setup_nodes.main()
zgf_grompp.main()
zgf_cleanup.main()
def mknodes_kmeans(parent, numnodes): frames_int = parent.trajectory fixed_clusters = [n.internals for n in parent.children] means = kmeans(frames_int, numnodes, fixed_clusters=fixed_clusters) # k-means finished - examine results chosen_idx = [ np.argmin( (frames_int-m).norm2() ) for m in means ] print "\nDiscretization overview:" frames_chosen = frames_int.getframes(chosen_idx) print "- Variance per int of presampling trajectory:" print frames_int.var().array print "- Variance per int of chosen nodes:" print frames_chosen.var().array print "- Relative variance per int of chosen nodes:" print (frames_chosen.var() / frames_int.var()).array return(chosen_idx)
def main(argv=None):
if (argv == None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
assert (not (options.refine_all and options.extend_all))
pool = Pool()
needy_nodes = pool.where("isa_partition and is_sampled").multilock()
# 1. Trying to detect fake convergence
for n in pool.where("state == 'converged'"):
means = kmeans(n.trajectory, k=2)
d = (means[0] - means[1]).norm2()
if (d > 2.0 and (options.refine_all or userinput(
"%s has converged but appears to have a bimodal distribution.\nDo you want to refine?"
% n.name,
"bool"))): #TODO decide upon threshold (per coordinate?)
refine(n, options)
# 2. Dealing with not-converged nodes
for n in pool.where("state == 'not-converged'"):
if (not (options.refine_all or options.extend_all)):
choice = userchoice(
"%s has not converged. What do you want to do?" % n.name,
['_refine', '_extend', '_ignore'])
if (options.refine_all or choice == "r"):
refine(n, options)
elif (options.extend_all or choice == "e"):
extend(n)
elif (choice == "i"):
continue
for n in needy_nodes:
n.save()
n.unlock()
zgf_setup_nodes.main()
zgf_grompp.main()
zgf_cleanup.main()
