def test_graph1(self):
G = nx.OrderedGraph([
(3, 10),
(2, 13),
(1, 13),
(7, 11),
(0, 8),
(8, 13),
(0, 2),
(0, 7),
(0, 10),
(1, 7),
])
self.check_graph(G, is_planar=True)
def load_data(self):
savefile = os.path.join(self.datastore,"graphs", self.graph_name + ".adjlist.gz")
if os.path.isfile(savefile):
print(" loading from cache file" + savefile)
self.nx_graph = nx.read_adjlist(savefile)
else:
self.nx_graph = nx.OrderedGraph(
nx.readwrite.gpickle.read_gpickle(at.get(self.at_hash, datastore=self.datastore)))
print(" writing graph")
nx.write_adjlist(self.nx_graph, savefile)
def find_cycles(sub_network, weight='x_pu'):
"""
Find all cycles in the sub_network and record them in sub_network.C.
networkx collects the cycles with more than 2 edges; then the 2-edge cycles
from the MultiGraph must be collected separately (for cases where there
are multiple lines between the same pairs of buses).
Cycles with infinite impedance are skipped.
"""
branches_bus0 = sub_network.branches()["bus0"]
branches_i = branches_bus0.index
#reduce to a non-multi-graph for cycles with > 2 edges
mgraph = sub_network.graph(weight=weight, inf_weight=False)
graph = nx.OrderedGraph(mgraph)
cycles = nx.cycle_basis(graph)
#number of 2-edge cycles
num_multi = len(mgraph.edges()) - len(graph.edges())
sub_network.C = dok_matrix((len(branches_bus0), len(cycles) + num_multi))
for j, cycle in enumerate(cycles):
for i in range(len(cycle)):
branch = next(
iterkeys(mgraph[cycle[i]][cycle[(i + 1) % len(cycle)]]))
branch_i = branches_i.get_loc(branch)
sign = +1 if branches_bus0.iat[branch_i] == cycle[i] else -1
sub_network.C[branch_i, j] += sign
#counter for multis
c = len(cycles)
#add multi-graph 2-edge cycles for multiple branches between same pairs of buses
for u, v in graph.edges():
bs = list(mgraph[u][v].keys())
if len(bs) > 1:
first = bs[0]
first_i = branches_i.get_loc(first)
for b in bs[1:]:
b_i = branches_i.get_loc(b)
sign = -1 if branches_bus0.iat[b_i] == branches_bus0.iat[
first_i] else +1
sub_network.C[first_i, c] = 1
sub_network.C[b_i, c] = sign
c += 1
def load_data(self):
self.benchmark = self.datastore + "/graphs/HumanNet-XN.tsv"
edgelist = pd.read_csv(self.benchmark,
header=None,
sep="\t",
skiprows=1).values[:, :2].tolist()
self.nx_graph = nx.OrderedGraph(edgelist)
# Map nodes from ncbi to hugo names
self.nx_graph = nx.relabel.relabel_nodes(
self.nx_graph,
ncbi_to_hugo_map(self.nx_graph.nodes, datastore=self.datastore))
# Remove nodes which are not covered by the map
for node in list(self.nx_graph.nodes):
if isinstance(node, float):
self.nx_graph.remove_node(node)
def bfs_sample_neighbors(self, gene, num_neighbors, include_self=True):
neighbors = nx.OrderedGraph()
if include_self:
neighbors.add_node(gene)
bfs = nx.bfs_edges(self.nx_graph, gene)
for u, v in bfs:
if neighbors.number_of_nodes() == num_neighbors:
break
neighbors.add_node(v)
for node in neighbors.nodes():
for u, v, d in self.nx_graph.edges(node, data="weight"):
if neighbors.has_node(u) and neighbors.has_node(v):
neighbors.add_weighted_edges_from([(u, v, d)])
return neighbors
def test_precompute_obj(self):
G = nx.OrderedGraph()
elist = [[0, 1], [1, 2], [1, 3], [2, 3]]
G.add_edges_from(elist)
N = G.number_of_nodes()
w = nx.adjacency_matrix(G, nodelist=range(N))
obj = partial(maxcut_obj, w=w)
qc = QuantumCircuit(N, N)
qc.x([0])
backend = Aer.get_backend('statevector_simulator')
sv = execute(qc, backend=backend).result().get_statevector()
precomputed = precompute_obj(obj, N)
self.assertEqual(len(precomputed[np.where(sv)]), 1)
self.assertEqual(obj_from_statevector(sv, obj),
precomputed[np.where(sv)][0])
def load_data(self):
# previously human
# savefile = self.datastore + "/graphs/stringdb_graph_" + self.graph_type + "_edges.adjlist"
# currently mouse
if self.graph_type == 'all':
savefile = "../data/graphs/stringdb_all_mouse_graph_edges.adjlist"
else:
savefile = "../data/graphs/stringdb_coex_mouse_graph_" + self.graph_type + "_edges.adjlist"
if os.path.isfile(savefile):
print(" loading from cache file" + savefile)
self.nx_graph = nx.read_adjlist(savefile)
else:
print(
"Building StringDB Graph. It can take a while the first time..."
)
# previously human
# self.proteinlinks = self.datastore + "/graphs/9606.protein.links.detailed.v11.0.txt"
# currently mouse
self.proteinlinks = "../data/graphs/10090.protein.links.detailed.v11.0.txt.gz"
print(" ensp_to_ensg_map")
# fixed ensp_to_hugo_map to ensp_to_ensg for mouse
ensmap = ensp_to_hugo_map()
print(" reading self.proteinlinks")
# previously not gzipped
# currently unzipping
edges = pd.read_csv(self.proteinlinks, sep=' ', compression='gzip')
selected_edges = edges[self.name_to_edge[self.graph_type]] != 0
edgelist = edges[selected_edges][["protein1",
"protein2"]].values.tolist()
# previously human
# human ID has 4 numbers (9606), hence [5:] to remove that ID from protein ID
# edgelist = [[ensmap[edge[0][5:]], ensmap[edge[1][5:]]] for edge in edgelist
# if edge[0][5:] in ensmap.keys() and edge[1][5:] in ensmap.keys()]
# currently mouse (5 number ID 10090)
edgelist = [
[ensmap[edge[0][6:]], ensmap[edge[1][6:]]] for edge in edgelist
if edge[0][6:] in ensmap.keys()
and edge[1][6:] in ensmap.keys() and edge[0][6:] != edge[1][6:]
] # remove self edges
print(" creating OrderedGraph")
self.nx_graph = nx.OrderedGraph(edgelist)
print(" writing graph")
nx.write_adjlist(self.nx_graph, savefile)
print("Graph built !")
def load_data(self):
savefile = os.path.join(self.datastore,"graphs", 'hetio_{}'.format(self.graph_type) + ".adjlist.gz")
if os.path.isfile(savefile):
print(" loading from cache file" + savefile)
self.nx_graph = nx.read_adjlist(savefile)
else:
pkl_file = os.path.join(self.datastore,"graphs", 'hetio_{}_graph'.format(self.graph_type) + ".pkl")
if not os.path.isfile(pkl_file):
self._process_and_pickle(save_name=pkl_file)
self.nx_graph = nx.OrderedGraph(nx.read_gpickle(pkl_file))
print(" writing graph")
nx.write_adjlist(self.nx_graph, savefile)
def find_fragments(self,
coordinates: np.ndarray,
labels: list,
graph=False,
**kwargs) -> Union[list, nx.OrderedGraph]:
"""Reduces a set of labeled coordinates to a graph with edges drawn between species which are closer than a
distance cutoff (calculated from covalent radii) then extracts connected fragments from that graph.
Args:
coordinates: np.array, required, cartesian coordinate array
labels: list[str], required, list of particle labels
graph: bool, (optional),
**kwargs: additional keyword arguments
Returns:
list of Molecule objects or an nx.OrderedGraph object
"""
if len(coordinates) != len(
labels): # TODO: test the assertion in find_fragments
raise AssertionError(
"Coordinates and labels of different lengths! {} & {}".format(
coordinates.shape, len(labels)))
fragment_graph = nx.OrderedGraph()
for label, coordinate in zip(labels, coordinates):
graph_node = Particle(label, coordinate)
fragment_graph.add_node(graph_node)
all_r = get_all_magnitudes(coordinates)
for (particle1,
particle2), r in zip(it.combinations(fragment_graph.nodes, 2),
all_r):
if r <= self.get_cutoff_distance(particle1.label, particle2.label):
fragment_graph.add_edge(particle1,
particle2,
weight=r,
**kwargs)
if graph:
return fragment_graph
else:
return self.graph_to_fragments(fragment_graph)
def Cycles(n, branches_i=None):
"""
Light-weight function for finding all cycles a given network.
"""
branches = pd.concat({c: n.df(c)[['bus0', 'bus1']] for c in
sorted(n.branch_components)})\
.rename(columns={'bus0': 'source', 'bus1': 'target'})
if branches_i is None:
branches_i = branches.index.rename(['component', 'branch_i'])
else:
branches = branches.reindex(branches_i)
branches = branches.assign(index=branches_i.to_numpy())
branches_bus0 = branches['source']
mgraph = nx.from_pandas_edgelist(branches,
edge_attr=True,
create_using=nx.MultiGraph)
graph = nx.OrderedGraph(mgraph)
cycles = nx.cycle_basis(graph)
# number of 2-edge cycles
num_multi = len(mgraph.edges()) - len(graph.edges())
C = scipy.sparse.dok_matrix((len(branches_bus0), len(cycles) + num_multi))
for j, cycle in enumerate(cycles):
for i, start in enumerate(cycle):
end = cycle[(i + 1) % len(cycle)]
branch = branches_i.get_loc(graph[start][end]['index'])
sign = +1 if branches_bus0.iat[branch] == cycle[i] else -1
C[branch, j] += sign
# counter for multis
c = len(cycles)
# add multi-graph 2-edge cycles for multiple branches between same pairs
# of buses
for u, v in graph.edges():
bs = list(mgraph[u][v].values())
if len(bs) > 1:
first = branches_i.get_loc(bs[0]['index'])
for b in bs[1:]:
other = branches_i.get_loc(b['index'])
sign = - \
1 if branches_bus0.iat[other] == branches_bus0.iat[first] else +1
C[first, c] = 1
C[other, c] = sign
c += 1
return DataArray(C.todense(), {
'branch': branches_i,
'cycle': range(C.shape[1])
}, ('branch', 'cycle'))
def build_networkx_graph(nodes, edges):
# Build networkx datastructure
ascii_graph = networkx.OrderedGraph()
ascii_graph.add_nodes_from((node, {
"position": tuple(pos)
}) for pos, node in nodes.items())
ascii_graph.add_edges_from((edge['nodes'][0], edge['nodes'][1], {
"length": len(edge["points"]),
"points": [tuple(el) for el in edge["points"]]
}) for edge in edges)
networkx.set_edge_attributes(ascii_graph,
name="label",
values={
edge["nodes"]: edge["label"][1:-1]
for edge in edges if "label" in edge
})
return ascii_graph
def make_coupling_graph(block_interaction_data,
blau_coord_combinations,
on_attribute='coupling'):
"""
Make a networkx graph from block interaction
data.
Assumes pre computed blau coord combinations.
Parameters
-------------
block_interaction_data : pandas dataframe
Dataframe containing numbers of edges and interaction
strengths between different blocks within a larger graph.
blau:coord_combinations : list
List of tuples containing the different region name age range
paris
on_attribute : str (opt)
String specifying which variable to take as the edge weight.
"""
#coupling_graph = nx.Graph()
coupling_graph = nx.OrderedGraph()
for pair_1 in tqdm(blau_coord_combinations):
for pair_2 in blau_coord_combinations:
try:
coupling = list(block_interaction_data.loc[
(block_interaction_data['cat_1'] == pair_1)
& (block_interaction_data['cat_2'] == pair_2)]
[on_attribute])[0]
except:
pdb.set_trace()
coupling_graph.add_edge(pair_1, pair_2, weight=coupling)
return coupling_graph
def test_simulate_qiskit_amps():
elist = [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0], [0, 5], [1, 6], [2, 7],
[3, 8], [4, 9], [5, 7], [5, 8], [6, 8], [6, 9], [7, 9]]
G = nx.OrderedGraph()
G.add_edges_from(elist)
parameters = np.array([
5.192253984583296, 5.144373231492732, 5.9438949617723775,
5.807748946652058, 3.533458907810596, 6.006206583282401,
6.122313961527631, 6.218468942101044, 6.227704753217614,
0.3895570099244132, -0.1809282325810937, 0.8844522327007089,
0.7916086532373585, 0.21294534589417236, 0.4328896243354414,
0.8327451563500539, 0.7694639329585451, 0.4727893829336214
])
beta = parameters[:9]
gamma = -parameters[9:]
result = simulate_qiskit_amps(G, gamma, beta)
assert abs(abs(result) - 12) < 1e-2
def Grid(dimx = 9,dimy = 9, deltax=100, deltay=100, energy_list=[], **kwargs):
"Create a grid network with a 4-neighborhood"
if len(energy_list) == 0:
print("no energy list supplied for grid creation, aborting")
return None
x=0
y=0
G=nx.OrderedGraph()
for i in range(dimx):
for j in range(dimy):
#we want to increase x with j and i with j (fill rows first)
node = GraphNode(x+j*deltax,y+i*deltay, energy_list[j+i*dimx])
G.add_node(GraphNode(x+j*deltax,y+i*deltay, energy_list[j+i*dimx]), pos=node.pos, energy=node.energy)
for node1,node2 in combinations(G.nodes(),2):
dist = la.norm(node1.pos - node2.pos)
if dist <= 100:
G.add_edge(node1,node2)
return G
def load_data(self):
print("Building StringDB Graph. It can take a while the first time...")
savefile = "data/graphs/stringdb_graph_" + self.graph_type + "_edges.adjlist"
if os.path.isfile(savefile):
self.nx_graph = nx.read_adjlist(savefile)
else:
ensmap = ensp_to_hugo_map()
edges = pd.read_csv(self.proteinlinks, sep=' ')
selected_edges = edges[self.name_to_edge[self.graph_type]] != 0
edgelist = edges[selected_edges][["protein1",
"protein2"]].values.tolist()
edgelist = [[ensmap[edge[0][5:]], ensmap[edge[1][5:]]]
for edge in edgelist if edge[0][5:] in ensmap.keys()
and edge[1][5:] in ensmap.keys()]
self.nx_graph = nx.OrderedGraph(edgelist)
nx.write_adjlist(self.nx_graph, savefile)
print("Graph built !")
def test_graph3(self):
G = nx.OrderedGraph([
(0, 7),
(3, 11),
(3, 4),
(8, 9),
(4, 11),
(1, 7),
(1, 13),
(1, 11),
(3, 5),
(5, 7),
(1, 3),
(0, 4),
(5, 11),
(5, 13),
])
self.check_graph(G, is_planar=False)
def gfa_to_G(gfa, kmer_size):
# G = nx.DiGraph(k=kmer_size, name='gfa')
G = nx.OrderedGraph(k=kmer_size, name='gfa')
with open(gfa, 'r') as fin:
for line in fin:
record_type = line[0]
if record_type in ['#', 'H', 'C', 'P']:
continue
elif record_type == 'S':
name, attr = line_to_node(line)
G.add_node(name + '+',
seq=attr['seq'],
cov=attr['KC'] * 1.0 / len(attr['seq']),
len=len(attr['seq']),
A=attr['seq'].count('A') * 1.0 / len(attr['seq']),
C=attr['seq'].count('C') * 1.0 / len(attr['seq']),
G=attr['seq'].count('G') * 1.0 / len(attr['seq']),
T=attr['seq'].count('T') * 1.0 / len(attr['seq']))
G.add_node(name + '-',
seq=reverse_complement(attr['seq']),
cov=attr['KC'] * 1.0 / len(attr['seq']),
len=len(attr['seq']),
A=attr['seq'].count('T') * 1.0 / len(attr['seq']),
C=attr['seq'].count('G') * 1.0 / len(attr['seq']),
G=attr['seq'].count('C') * 1.0 / len(attr['seq']),
T=attr['seq'].count('A') * 1.0 / len(attr['seq']))
elif record_type == 'L':
cov = nx.get_node_attributes(G, 'cov')
u, v, attr = line_to_edge(line)
G.add_edge(u, v, **attr)
nx.set_edge_attributes(G, {
(u, v):
graphs.get_weight_attr(cov[u], cov[v], 0.05, 0.05)
})
u, v, attr = line_to_rc_edge(line)
G.add_edge(u, v, **attr)
nx.set_edge_attributes(G, {
(u, v):
graphs.get_weight_attr(cov[u], cov[v], 0.05, 0.05)
})
graphs.write_G_statistics(G)
return G
def countryStability(countryName):
countryCrops = [
x for x in list(
set(production.loc[(production['Area'] == countryName)
& (production['Y' + str(year)] > 0),
'Item'].tolist()))
if x in list(servingSizes.keys())
]
nutrientList = [x for x in list(foodNutrients)[4:-1] if x != 'Sodium']
stabilities = []
for thresh in threshes:
bnk = nx.OrderedGraph()
bnk.add_nodes_from(countryCrops, bipartite=0)
bnk.add_nodes_from(nutrientList, bipartite=0)
edges = []
weights = []
for crop in countryCrops:
newEdges = []
for nutrient in nutrientList:
weight = np.mean(
foodNutrients.loc[foodNutrients['FAO_name'] == crop,
nutrient])
weight = weight * 10**6 * 10**(
-2) * (1 / servingSizes[crop]
) / population[population['Country Name'] ==
countryName][year].iloc[0]
weight = weight * np.mean(
production.loc[(production['Area'] == countryName)
& (production['Item'] == crop)]['Y' + year])
if weight > 0.1:
newEdges.append((crop, nutrient, weight))
weights.append(weight)
edges.extend(newEdges)
bnk.add_edges_from(newEdges)
if bnk.degree[crop] == 0:
bnk.remove_node(crop)
countryCrops = [x for x in countryCrops if x != crop]
curve = multicurve_unweighted(bnk, len(countryCrops), 1000)
stabilities.append(
sum([curve[i] / len(curve) for i in range(len(curve))]))
return stabilities
def place_rooms(self, rooms):
rooms = [rooms]
nodes = list(self.grid)
self.rng.shuffle(nodes)
for start in nodes:
graph = nx.OrderedGraph()
room = rooms[0][0]
graph.add_node(start, id="r_{}".format(len(graph)), name=room, start=True)
for group in rooms:
self.nb_attempts = 0
graph = self._walk(graph, start, set(group) - {room})
if not graph:
break
if graph:
return graph
return None
def place_rooms(self, rooms):
nodes = list(self.grid)
self.rng.shuffle(nodes)
for start in nodes:
G = nx.OrderedGraph()
room = rooms[0][0]
G.add_node(start, id="r_{}".format(len(G)), name=room, start=True)
for group in rooms:
self.nb_attempts = 0
G = self._walk(G, start, set(group) - {room})
if not G:
break
if G:
return G
return None
def input(self, file):
tabfile = open(file, 'r')
nodes = () #set()
edges = () #set()
for line in tabfile:
elements = line.split("\t")
node1 = elements[0].strip()
node2 = elements[1].strip()
if (node1 not in nodes):
nodes += (node1, )
if (node2 not in nodes):
nodes += (node2, )
edge = (node1, node2)
if (edge not in edges):
edges += (edge, )
self.G = networkx.OrderedGraph()
self.G.add_nodes_from(nodes)
self.G.add_edges_from(edges)
def _make_graph(self):
import networkx as nx
# Keep graph nodes in order so we can reproduce the same layout.
from collections import OrderedDict
class OrderedGraph(nx.Graph):
node_dict_factory = OrderedDict
adjlist_dict_factory = OrderedDict
G = nx.OrderedGraph()
G.add_nodes_from(self.nodes)
edges = self.edges
if edges:
max_weight = float(max(e.weight for e in edges))
for e in edges:
G.add_edge(e.nodes[0],
e.nodes[1],
weight=e.weight / max_weight,
edge_object=e)
return G
def extract_network_scopes(
G: Union[nx.Graph, nx.OrderedGraph]
) -> Dict[str, Union[nx.Graph, nx.OrderedGraph]]:
scopes = {
"all": filter_graph(G, min_component_size=3),
}
# get world scopes
node_attr_world = lambda t: t[1].get("world")
sorted_nodes = sorted((tup for tup in G.nodes(data=True)),
key=node_attr_world)
for world, grp in groupby(sorted_nodes, key=node_attr_world):
sg = nx.OrderedGraph()
sg.add_nodes_from(grp)
sg.add_edges_from(
(src, dest) for src, dest in G.edges() if src in sg and dest in sg)
if sg.size():
scopes[world] = filter_graph(sg, min_component_size=3)
return scopes
def test_qasm_sv_obj_from_elist(self):
elist = [[3,1],[3,2],[0,1],[0,2],[1,2]]
G = nx.OrderedGraph()
G.add_edges_from(elist)
def obj_f_cut(x):
cut = 0
for i, j in G.edges():
if x[i] != x[j]:
# the edge is cut
cut -= 1
return cut
w = nx.adjacency_matrix(G, nodelist=range(4)).toarray()
obj = partial(maxcut_obj,w=w)
C, _ = get_maxcut_operator(w)
obj_sv = ObjectiveWrapper(obj,
varform_description={'name':'QAOA', 'p':10, 'num_qubits':4, 'cost_operator':C},
backend_description={'package':'qiskit', 'provider':'Aer', 'name':'statevector_simulator'},
execute_parameters={}).get_obj()
obj_qasm = ObjectiveWrapper(obj,
varform_description={'name':'QAOA', 'p':10, 'num_qubits':4, 'cost_operator':C},
backend_description={'package':'qiskit', 'provider':'Aer', 'name':'qasm_simulator'},
execute_parameters={'shots':10000}).get_obj()
obj_sv_custom = ObjectiveWrapper(obj_f_cut,
varform_description={'name':'QAOA', 'p':10, 'num_qubits':4, 'cost_operator':C},
backend_description={'package':'qiskit', 'provider':'Aer', 'name':'statevector_simulator'},
execute_parameters={}).get_obj()
obj_qasm_custom = ObjectiveWrapper(obj_f_cut,
varform_description={'name':'QAOA', 'p':10, 'num_qubits':4, 'cost_operator':C},
backend_description={'package':'qiskit', 'provider':'Aer', 'name':'qasm_simulator'},
execute_parameters={'shots':10000}).get_obj()
parameters = np.array([ 5.97337687, 2.58355601, 1.40698116, 1.41929411, -0.78430107,
-4.46418963, -0.61290647, -0.59975086, 0.48811492, 4.20269641,
-2.71558857, 2.82117292, 2.93922949, 2.06076731, 2.19543793,
2.42960372, -1.0079554 , 2.22741002, -1.06316475, 0.53106839])
sv_imported = obj_sv(parameters)
qasm_imported = obj_qasm(parameters)
sv_custom = obj_sv_custom(parameters)
qasm_custom = obj_qasm_custom(parameters)
self.assertTrue(np.isclose(sv_imported, sv_custom))
self.assertTrue(np.isclose(sv_imported, qasm_imported, rtol=0.01))
self.assertTrue(np.isclose(sv_custom, qasm_custom, rtol=0.01))
def random(num_nodes: int,
num_edges: int,
weight: int = 10000) -> nx.OrderedDiGraph:
"""
Generates a random undirected networkx DiGraph (as in always same edge both
ways)
Args:
num_nodes: number of nodes
num_edges: number of edges
weight: weight to be assigned to all edges
Returns:
A DiGraph generated as described
"""
graph = nx.OrderedGraph()
graph.add_nodes_from(range(num_nodes))
for _ in range(num_edges):
src = np.random.randint(0, num_nodes)
dst = np.random.randint(0, num_nodes)
graph.add_edge(src, dst, weight=weight)
return graph.to_directed()
def test_tuplelabels(self):
# https://github.com/networkx/networkx/pull/1048
# Writing tuple labels to GML failed.
G = nx.OrderedGraph()
G.add_edge((0, 1), (1, 0))
data = "\n".join(nx.generate_gml(G, stringizer=literal_stringizer))
answer = """graph [
node [
id 0
label "(0,1)"
]
node [
id 1
label "(1,0)"
]
edge [
source 0
target 1
]
]"""
assert data == answer
def test_graph2(self):
G = nx.OrderedGraph([
(1, 2),
(4, 13),
(0, 13),
(4, 5),
(7, 10),
(1, 7),
(0, 3),
(2, 6),
(5, 6),
(7, 13),
(4, 8),
(0, 8),
(0, 9),
(2, 13),
(6, 7),
(3, 6),
(2, 8),
])
self.check_graph(G, is_planar=False)
def generate_and_save_graph(results, path):
# Cleanup
remove_old_static_files()
# Generate network graph
G = nx.OrderedGraph()
G.add_nodes_from(results['nodes'])
G.add_edges_from(results['edges'])
degrees = dict(G.degree)
nx.draw(
G,
pos=nx.spring_layout(G),
edge_color='gray',
node_color='y',
node_size=[v * 50 for v in degrees.values()],
with_labels=True,
)
# Save graph file
plt.savefig(path)
plt.close()
def ManHattan(dimx = 9,dimy = 9, deltax=100, deltay=100, energy_list=[], posList = [], mobileNodeCount = 0, **kwargs):
G= nx.OrderedGraph()
if len(posList) == 0:
#print("no pos list supplied for grid creation, aborting")
#return None
if len(energy_list) == 0:
print("no energy list supplied for grid creation, aborting")
return None
x=50
y=50
#create fixed nodes:
for i in range(dimx):
for j in range(dimy):
#we want to increase x with j and i with j (fill rows first)
node = GraphNode(x+j*deltax,y+i*deltay, energy_list[j+i*dimx])
G.add_node(GraphNode(x+j*deltax,y+i*deltay, energy_list[j+i*dimx]), pos=node.pos, energy=node.energy)
x= 0
y = 0
intersections_x = []
intersections_y = []
for i in range(dimx):
intersections_x.append(x+i*deltax)
intersections_y.append(y+i*deltax)
np.random.seed(kwargs['seed'])
static_nodes = dimx*dimy
for i in range(mobileNodeCount):
node = GraphNode(np.random.choice(intersections_x), np.random.choice(intersections_y), energy_list[static_nodes+i])
G.add_node(GraphNode(np.random.choice(intersections_x), np.random.choice(intersections_y), energy_list[static_nodes+i]), pos=node.pos, energy = node.energy)
else:
for pos, energy in zip(posList,energy_list):
node = GraphNode(pos[0],pos[1],energy)
G.add_node(GraphNode(pos[0],pos[1],energy), pos =node.pos, energy = node.energy)
for node1,node2 in combinations(G.nodes(),2):
dist = la.norm(node1.pos - node2.pos)
if dist <= 100:
G.add_edge(node1,node2)
return G
def make_coupling_graph(self,
block_interaction_data,
on_attribute='coupling'):
"""
Assumes pre computed blau coord combinations.
"""
# coupling_graph = nx.Graph()
coupling_graph = nx.OrderedGraph()
for pair_1 in tqdm(self.blau_coord_combinations):
for pair_2 in self.blau_coord_combinations:
coupling = list(block_interaction_data.loc[
(block_interaction_data['cat_1'] == pair_1)
& (block_interaction_data['cat_2'] == pair_2)]
[on_attribute])[0]
coupling_graph.add_edge(pair_1, pair_2, weight=coupling)
return coupling_graph
