def generate_graphs():
# creates all graphs up to 7 nodes (up to isomorphism)
# max valid number is 1252
graph_num = 0
for i in range(2, 1253):
G = nx.graph_atlas(i)
if not nx.is_connected(G):
continue
graph_num += 1
nx.write_gpickle(G, '../hpc/atlas/' + str(graph_num) + '.gpickle')
def generate_graphs():
# creates all graphs up to 7 nodes (up to isomorphism)
# max is 1252
graph_num = 0
for i in range(2, 1253):
G = nx.graph_atlas(i)
if not nx.is_connected(G):
continue
graph_num += 1
nx.write_edgelist(G, 'graphs/atlas/' + str(graph_num) + '.edgelist')
def iso_graphs(version=2):
# Return the list of all graphs with up to seven nodes named in the Graph Atlas.
if version == 1:
graphs = []
n = len(nx.graph_atlas_g())
for i in range(n):
graphs.append([nx.graph_atlas(i)])
return graphs
elif version == 2:
graphs = nx.graph_atlas_g()
graphs = [[g] for g in graphs]
return graphs[1:]
def simple_network():
g = nx.graph_atlas(150)
h = nx.OrderedDiGraph()
for u, v in g.edges():
h.add_edge(u, v)
net = pg.create_empty_network()
for n in h.nodes:
pg.create_bus(net, level="BP", name="BUS{}".format(n))
for n in [0, 3, "F"]:
pg.create_bus(net, level="MP", name="BUSMP{}".format(n))
for u, v in h.edges:
pg.create_pipe(net,
"BUS{}".format(u),
"BUS{}".format(v),
length_m=1e4,
diameter_m=0.05,
name="PIPE{}-{}".format(u, v))
for i in [2, 4, 5]:
pg.create_load(net,
"BUS{}".format(i),
p_kW=10.0,
name="LOAD{}".format(i))
for i in [0, 3]:
bus_mp = "BUSMP{}".format(i)
bus_bp = "BUS{}".format(i)
pg.create_station(net,
bus_mp,
bus_bp,
p_lim_kW=50,
p_Pa=1.022e5,
name="STATION{}".format(i))
pg.create_pipe(net,
"BUSMP0",
"BUSMP3",
length_m=300,
diameter_m=0.05,
name="PIPEMP0-3")
pg.create_pipe(net,
"BUSMPF",
"BUSMP0",
length_m=300,
diameter_m=0.05,
name="PIPEMPF-0")
pg.create_feeder(net, "BUSMPF", p_lim_kW=50, p_Pa=0.9e5, name="FEEDER")
return net
def graphlet_list(k):
assert k > 0
foo = 1
loc_graphlet_list = {n: [] for n in range(1, k + 1)}
while True:
G = nx.graph_atlas(foo)
n = G.number_of_nodes()
if n > k:
break
if nx.is_connected(G):
loc_graphlet_list[n].append(G)
foo += 1
return loc_graphlet_list
def test_graph(self):
G = graph_atlas(6)
assert_nodes_equal(G.nodes(), range(3))
assert_edges_equal(G.edges(), [(0, 1), (0, 2)])
def test_index_too_large(self):
with pytest.raises(ValueError):
graph_atlas(NUM_GRAPHS)
def test_index_too_small(self):
with pytest.raises(ValueError):
graph_atlas(-1)
def generate(args):
return nx.graph_atlas(i=args.index)
def test_graph(self):
G = graph_atlas(6)
assert_nodes_equal(G.nodes(), range(3))
assert_edges_equal(G.edges(), [(0, 1), (0, 2)])
def test_index_too_large(self):
graph_atlas(NUM_GRAPHS)
def test_index_too_small(self):
graph_atlas(-1)
def _random_motif():
g = nx.graph_atlas(random.randint(7, 30))
while len([c for c in nx.connected_components(g)]) != 1:
g = nx.graph_atlas(random.randint(7, 30))
return nx.relabel_nodes(g, lambda x: str(x + 1))
from classical import brute_force
# Defining classical optimizer necessary for QAOA
optimizer = optimizers.SPSA()
# Backend
backend = Aer.get_backend('qasm_simulator') #simulator
# Producing the data
p_min, p_max = 1, 3
# Putting the whole atlas in one data frame
output = pd.DataFrame()
for i in range(3, len(nx.graph_atlas_g())):
G = nx.graph_atlas(i)
n = len(G.nodes)
m = len(G.edges)
d = m / (n * (n - 1) / 2
) # density of graph - number of edges / number of possible edges
optimum = brute_force(G)
print("\nGraph " + str(i) + ": n = " + str(n), ", m = " + str(m),
", d = " + str(d), "the optimum cut is " + str(optimum) + "\n")
if m > 0: # Method does not work if there are no edges
for p in range(p_min, p_max + 1):
# specifications
specs = {
'graph_atlas index': i,
'p': p,
from networkx import graph_atlas, write_gml graph = graph_atlas(15) write_gml(graph, "/Users/erans/PycharmProjects/metabolic_pathways_/g.gml")
def test_index_too_small(self):
graph_atlas(-1)
def test_index_too_large(self):
graph_atlas(NUM_GRAPHS)
for i in range(p):
state = common.phase_separator(state, C, gamma)
state = common.mixer(state, M, beta)
return state
def opt(args, *a):
state = qaoa(a[0], a[1], a[2], a[3], a[4], args[0], args[1])
return -common.expectation(a[0], state)
def dicke_ps_ring(G, k, p, num_steps):
C = common.create_C(G)
M = common.create_ring_M(len(G.nodes))
kwargs = {
'method': 'L-BFGS-B',
'args': (G, C, M, k, p),
'bounds': [[0, pi / 2], [0, pi]]
}
optimal = basinhopping(opt,
np.array([pi / 4, pi / 2]),
minimizer_kwargs=kwargs,
niter=num_steps)
return -optimal.fun, optimal.x
if __name__ == '__main__':
G = nx.graph_atlas(6)
exp, angles = dicke_ps_ring(G, int(len(G.nodes) / 2), 1, 20)
print('exp: ' + str(exp))
def test_graph(self):
G = graph_atlas(6)
assert_equal(set(G), set(range(3)))
assert_equal(list(G.edges()), [(0, 1), (0, 2)])
