def test_prime_power_hash_examples():
# Tests that two C-shaped prime power graphs are equivalent
prime, power = 2, 2
e1 = [((0, 1), (1, 1), 1), ((1, 0), (0, 0), 1), ((0, 0), (0, 1), 1)]
e2 = [((0, 1), (1, 1), 1), ((1, 0), (0, 0), 1), ((1, 0), (1, 1), 1)]
g1 = create_prime_power_graph(e1, prime, power)
g2 = create_prime_power_graph(e2, prime, power)
assert hash_graph(g1) == hash_graph(g2)
# Tests upper ququart bar and lower ququart bar inequivalent
e1 = [((1, 0), (0, 0), 1)]
e2 = [((0, 1), (1, 1), 1)]
g1 = create_prime_power_graph(e1, prime, power)
g2 = create_prime_power_graph(e2, prime, power)
assert hash_graph(g1) == hash_graph(g2)
# Tests both ququart zigzags are equivalent
e1 = [((0, 1), (1, 1), 1), ((1, 0), (0, 0), 1), ((0, 0), (1, 1), 1)]
e2 = [((0, 1), (1, 1), 1), ((1, 0), (0, 0), 1), ((0, 1), (1, 0), 1)]
g1 = create_prime_power_graph(e1, prime, power)
g2 = create_prime_power_graph(e2, prime, power)
assert hash_graph(g1) == hash_graph(g2)
# Checks two-bar and rotated two-bar ququarts are inequivalent
e1 = [((0, 1), (1, 1), 1), ((0, 0), (1, 0), 1)]
e2 = [((0, 0), (0, 1), 1), ((1, 0), (1, 1), 1)]
g1 = create_prime_power_graph(e1, prime, power)
g2 = create_prime_power_graph(e2, prime, power)
assert hash_graph(g1) == hash_graph(g2)
def queued_orbit_search(init_graph, local_ops, save_edges, verbose):
# Initialises class graph with init_graph
init_edges = list(init_graph.edges())
init_hash = hash_graph(init_graph)
class_graph = nx.Graph()
class_graph.add_node(0,
nx_graph=init_graph,
edges=init_edges,
hash=init_hash)
class_graph.member_hash_table = {init_hash: 0}
# Loops over queue members until empty
queue = [0]
visited = 0
while queue:
# Prints live count of explored/known
if verbose:
out = \
str(visited) + '/' + str(len(queue) + visited) + ' visited (' \
+ str(int(100 * float(visited)/(len(queue) + visited))) + '%)'
sys.stdout.write('%s\r' % out)
sys.stdout.flush()
visited += 1
# Gets next graph on queue and finds representative nodes
graph_label = queue.pop()
graph = class_graph.node[graph_label]['nx_graph']
node_equivs = find_rep_nodes(graph)
# Applies set of local ops to each representative node
for rep_node, equiv_nodes in node_equivs.iteritems():
for op_label, local_op in local_ops:
new_graph = local_op(graph, rep_node)
new_edges = list(new_graph.edges())
new_hash = hash_graph(new_graph)
# Checks new graph is difference to original
if sorted(new_graph.edges(data='weight')) == \
sorted(graph.edges(data='weight')):
continue
# If different, tries to find new graph in class
try:
old_label = class_graph.member_hash_table[new_hash]
if save_edges:
# If new edge adds new edge between members
if not class_graph.has_edge(graph_label, old_label):
class_graph.add_edge(graph_label,
old_label,
equivs=[equiv_nodes],
ops=[op_label])
# Else adds any new local ops to edge label
elif op_label not in \
class_graph[graph_label][old_label]['ops']:
class_graph[graph_label][old_label]['ops']\
.append(op_label)
class_graph[graph_label][old_label]['equivs']\
.append(equiv_nodes)
continue
# If not in class, creates new class graph node
except KeyError:
new_label = max(class_graph.nodes()) + 1
class_graph.add_node(new_label,
nx_graph=new_graph,
edges=new_edges,
hash=new_hash)
if save_edges:
class_graph.add_edge(graph_label,
new_label,
equivs=[equiv_nodes],
ops=[op_label])
class_graph.member_hash_table.update({new_hash: new_label})
queue.append(new_label)
return class_graph
def test_hash_graph():
for _ in range(100):
g = gen_random_connected_graph(10)
relab_g = random_relabel(g)
assert hash_graph(g) == hash_graph(relab_g)
def find_all_classes(directory, power, prime):
""" Finds all members of all classes """
# Gets edge indices and state params and generates edge map
filename = directory + '/edge_index.csv'
with open(filename, 'r') as file:
reader = csv.reader(file)
edge_index = [tuple(map(int, edge)) for edge in reader]
filename = directory + '/state_params.csv'
with open(filename, 'r') as file:
reader = csv.reader(file)
p, m, n = map(int, next(reader))
c_map = gen_psuedo_graph_edge_map(p, m)
# Creates function to produce config isomorphs
isomorph_configs = make_isomorph_func(edge_index, n)
pprint(c_map)
pprint(edge_index)
# Initialises progress bar
rg_file = directory + '/remaining_graphs.csv'
rem_graphs_size = os.path.getsize(rg_file)
pbar = tqdm(total=rem_graphs_size)
while True:
rem_update = rem_graphs_size - os.path.getsize(rg_file)
if rem_update >= 0:
pbar.update(rem_update)
rem_graphs_size = os.path.getsize(rg_file)
# Waits until a graph is available to process
edge_config = get_next_graph(directory)
if not edge_config:
break
tqdm.write("Psuedo edge config: %s" % (edge_config, ))
# Create initial graph
c_edges = [(u, v, w) for (u, v), w in zip(edge_index, edge_config)]
init_graph = create_psuedo_graph(c_edges, p, m, c_map)
# Checks if graph is connected
if not nx.is_connected(init_graph.to_undirected()):
tqdm.write("Disconnected. Removing isomorphs... ")
# Removes any isomorphic graphs from remaining
remove_disconnected_configs(directory, edge_config,
isomorph_configs)
tqdm.write("Done")
continue
init_graph = psuedo_to_real(init_graph)
# Check if graph has already been found for hash test
graph_hash = hash_graph(init_graph)
if found_hash(directory, graph_hash):
tqdm.write("Already found %d" % graph_hash)
iso_configs = isomorph_configs(edge_config)
remove_found_graphs(directory, iso_configs)
continue
# Explore class graph
tqdm.write("Exploring class...")
class_graph = explore_lc_orbit(init_graph, False, False)
class_register = [[
node, attrs['edges'], attrs['hash'], attrs['nx_graph']
] for node, attrs in class_graph.node.iteritems()]
nodes, edges, hashes, graphs = zip(*class_register)
# Formats edge list based on state parameters
if power == 1:
edges = [[(u, v, c) for (u, i), (v, j), c in edge_set]
for edge_set in edges]
if prime == 2:
edges = [[(u, v) for u, v, c in edge_set] for edge_set in edges]
# Exports class_register to file
tqdm.write("Writing register to file...")
class_register = zip(nodes, edges, hashes)
config_label = '_'.join(map(str, edge_config))
filename = directory + '/classes/' + config_label + '.csv'
with open(filename, 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(class_register)
# Finds and removes any isomorphs
tqdm.write("Removing isomorphs...")
psu_edges = [
real_graph_to_psu_edges(graph, c_map, edge_index)
for graph in graphs
]
edge_configs = [[c for u, v, c in w_edges] for w_edges in psu_edges]
iso_configs = [
iso_config for config in edge_configs
for iso_config in isomorph_configs(config)
]
remove_found_graphs(directory, iso_configs)
# Adds hashes to found hash directory
write_hashes(directory, hashes)
tqdm.write("Done")
pbar.close()