def main(argv=None):
if (argv == None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
pool = Pool()
found_parents = [n for n in pool if n.name == options.parent_node]
assert (len(found_parents) == 1)
parent = found_parents[0]
chosen_idx = np.linspace(start=0,
stop=parent.trajectory.n_frames - 1,
num=options.numnodes).astype(int)
print "choosen_idx: ", chosen_idx
for i in chosen_idx:
n = Node()
n.parent_frame_num = i
n.parent = parent
n.state = "created"
n.extensions_counter = 0
n.extensions_max = 0
n.extensions_length = 0
n.sampling_length = parent.sampling_length * 3
n.internals = parent.trajectory.getframe(i)
pool.append(n)
n.save()
def main(argv=None):
if argv == None:
argv = sys.argv
options = options_desc.parse_args(argv)[0]
pool = Pool()
found_parents = [n for n in pool if n.name == options.parent_node]
assert len(found_parents) == 1
parent = found_parents[0]
chosen_idx = np.linspace(start=0, stop=parent.trajectory.n_frames - 1, num=options.numnodes).astype(int)
print "choosen_idx: ", chosen_idx
for i in chosen_idx:
n = Node()
n.parent_frame_num = i
n.parent = parent
n.state = "created"
n.extensions_counter = 0
n.extensions_max = 0
n.extensions_length = 0
n.sampling_length = parent.sampling_length * 3
n.internals = parent.trajectory.getframe(i)
pool.append(n)
n.save()
def main(argv=None):
if(argv==None):
argv = sys.argv
options = options_desc.parse_args(argv)[0]
print("Options:\n%s\n"%pformat(eval(str(options))))
if(options.random_seed):
# using numpy-random because python-random differs beetween 32 and 64 bit
np.random.seed(hash(options.random_seed))
pool = Pool()
old_pool_size = len(pool)
print "pool", pool
if(options.parent_node == "root"):
parent = pool.root
else:
found = [n for n in pool if n.name == options.parent_node]
assert(len(found) == 1)
parent = found[0]
print "### Generate nodes: %s ###" % options.methodnodes
if(options.methodnodes == "kmeans"):
chosen_idx = mknodes_kmeans(parent, options.numnodes)
elif(options.methodnodes == "equidist"):
chosen_idx = mknodes_equidist(parent, options.numnodes)
elif(options.methodnodes == "maxdist"):
chosen_idx = mknodes_maxdist(parent, options.numnodes)
elif(options.methodnodes == "all"):
chosen_idx = mknodes_all(parent)
else:
raise(Exception("Method unknown: "+options.methodnodes))
chosen_idx.sort() # makes preview-trajectory easier to understand
if(options.write_preview):
write_node_preview(pool, parent, chosen_idx)
for i in chosen_idx:
n = Node()
n.parent_frame_num = i
n.parent = parent
n.state = "creating-a-partition" # will be set to "created" at end of script
n.extensions_counter = 0
n.extensions_max = options.ext_max
n.extensions_length = options.ext_length
n.sampling_length = options.sampling_length
n.internals = parent.trajectory.getframe(i)
pool.append(n)
print "\n### Obtain alpha: %s ###" % options.methodalphas
old_alpha = pool.alpha
if(options.methodalphas == "theta"):
pool.alpha = calc_alpha_theta(pool)
elif(options.methodalphas == "user"):
pool.alpha = userinput("Please enter a value for alpha", "float")
else:
raise(Exception("Method unknown: "+options.methodalphas))
pool.history.append({'refined_node': (parent.name, parent.state), 'size':old_pool_size, 'alpha':old_alpha, 'timestamp':datetime.now()})
pool.save() # alpha might have changed
print "\n### Obtain phi fit: %s ###" % options.methodphifit
if(options.methodphifit == "harmonic"):
do_phifit_harmonic(pool)
elif(options.methodphifit == "switch"):
do_phifit_switch(pool)
elif(options.methodphifit == "leastsq"):
do_phifit_leastsq(pool)
else:
raise(Exception("Method unkown: "+options.methodphifit))
for n in pool.where("state == 'creating-a-partition'"):
n.state = "created"
n.save()
print "saving " +str(n)
zgf_cleanup.main()
def main():
options = options_desc.parse_args(sys.argv)[0]
pool = Pool()
active_nodes = pool.where("isa_partition")
if options.transition_level == "clusters":
npz_file = np.load(pool.chi_mat_fn)
chi_matrix = npz_file['matrix']
n_clusters = npz_file['n_clusters']
default_cluster_threshold = options.coreset_power
# determine cluster
#TODO this part is too cryptic
# amount_phi[j] = amount of basis functions per cluster j
amount_phi=np.ones(n_clusters,dtype=np.uint64)
amount_phi=amount_phi*len(chi_matrix)
amount_phi_total=len(chi_matrix)
# sort columns of chi and return new sorted args
arg_sort_cluster=np.argsort(chi_matrix,axis=0)
# sort columns of chi and return new sorted chi
# notice that the last row has to be [1 ... 1]
sort_cluster=np.sort(chi_matrix,axis=0)
# show_cluster contains arrays of the type [a b] where a is the row
# and b the column of the entry from chi matrix where
# chi_sorted(a,b) > default_cluster_threshold
show_cluster=np.argwhere(sort_cluster > 0.5 )
# from the above it could be clear
# that the amount of phi function
# of cluster i is given by x where x the number so that
# [x i] is in show_cluster and for all
# [y i] in show_cluster we have x>y
# we define amount_phi[i]=x
for element in show_cluster:
index=element[0]
cluster=element[1]
if amount_phi[cluster]>index:
amount_phi[cluster]=index
# create cluster list which contains arrays
# each array consinst of a set of numbers corresponding to
# the phi function of node_number
cluster=[]
for i in range(0,n_clusters):
cluster_set=[]
for j in range(amount_phi[i],amount_phi_total):
#if (j < amount_phi[i] + 3):
cluster_set.append(arg_sort_cluster[j][i])
cluster.append(cluster_set)
for i in range(len(cluster)):
counter = 0
for node_index in cluster[i]:
counter += 1
# and ignore nodes which have a higher chi value then default_cluster_threshold
if( chi_matrix[node_index][i] > default_cluster_threshold and counter>options.min_nodes):
continue
node = active_nodes[node_index]
trajectory= node.trajectory
print "-----"
print "Generating transition nodes for node %s..."%node.name
neighbour_frames = get_indices_equidist(node, options.num_tnodes)
# create transition node for node_index
for frame_number in neighbour_frames:
print "Using frame %d as starting configuration."%frame_number
n = Node()
n.parent_frame_num = frame_number
n.parent = node
n.state = "created"
n.extensions_counter = 0
n.extensions_max = options.num_runs-1
n.extensions_length = options.sampling_length
n.sampling_length = options.sampling_length
n.internals = trajectory.getframe(frame_number)
n.save_mode = options.save_mode
pool.append(n)
n.save()
print "%d transition nodes generated."%options.num_tnodes
print "-----"
zgf_setup_nodes.main()
zgf_grompp.main()
cluster_dict = {}
for (ic,c) in enumerate(cluster):
cluster_dict['cluster_%d'%ic] = c
# save cluster
np.savez(pool.analysis_dir+"core_set_cluster.npz", **cluster_dict)
elif options.transition_level == "nodes":
for node in active_nodes:
trajectory= node.trajectory
# TODO duplicate code... use the one above
print "-----"
print "Generating transition nodes for node %s..."%node.name
neighbour_frames = get_indices_equidist(node, options.num_tnodes)
# create transition point for node_index
for frame_number in neighbour_frames:
print "Using frame %d as starting configuration."%frame_number
n = Node()
n.parent_frame_num = frame_number
n.parent = node
n.state = "created"
n.extensions_counter = 0
n.extensions_max = options.num_runs-1
n.extensions_length = options.sampling_length
n.sampling_length = options.sampling_length
n.internals = trajectory.getframe(frame_number)
n.save_mode = options.save_mode
pool.append(n)
n.save()
print "%d transition nodes generated."%options.num_tnodes
print "-----"
zgf_setup_nodes.main()
zgf_grompp.main()
instructionFile = pool.analysis_dir+"instruction.txt"
f = open(instructionFile, "w")
f.write("{'power': %f, 'tnodes': %d, 'level': '%s', 'min_nodes': %d}"%(options.coreset_power, options.num_tnodes, options.transition_level, options.min_nodes))
f.close()
