def read(dir, keys=[]):
assert os.path.exists(dir)
if len(keys)==0:
keys = inspect(dir)
datadict = {}
for key in keys:
fname = os.path.join(dir,key+'.npy')
if os.path.exists(fname):
datadict[key] = np.load(fname)
fname = os.path.join(dir,'ma_segment_graph.pickle')
if os.path.exists(fname):
datadict['ma_segment_graph'] = igraph.read(fname)
fname = os.path.join(dir,'ma_segment_lidx.pickle')
if os.path.exists(fname):
datadict['ma_segment_lidx'] = pickle.load(open(fname, 'rb'))
fname = os.path.join(dir, 'ma_clusters')
if os.path.exists(fname):
datadict['ma_clusters'] = []
for f in glob(os.path.join(fname, '*.pickle')):
datadict['ma_clusters'].append(igraph.read(f))
return datadict
def main():
g1 = igraph.read("/Users/irene/Desktop/pfe/arguments_politique start.gml", format= "gml")
g2 = igraph.read("/Users/irene/Desktop/pfe/arguments_politiques_complets.gml", format = "gml")
g3 = igraph.read("/Users/irene/Desktop/pfe/arguments_politique1.gml", format = "gml")
gcomp1 = get_giant_component(g1)
gcomp2 = igraph.Graph.induced_subgraph(g2,gcomp1.vs["name"])
print graph_summary(g1)
print graph_summary(g2)
def construir_red_autocorrelacion(self, conjuntos, nombre, contar_coinc,
etiquetas_nodos):
'''Construye la red de autocorrelación de un conjunto de conjuntos
Parámetros
Conjuntos es una lista de listas'''
print(len(conjuntos))
cantidad_nodos = len(conjuntos)
red = open(nombre + ".net", 'w')
red.write("*Vertices " + str(cantidad_nodos) + "\n")
if etiquetas_nodos != None:
for i, etiqueta in enumerate(etiquetas_nodos):
red.write(str(i + 1) + ' "' + etiqueta + '"\n')
red.write("*Edges\n")
for i in range(cantidad_nodos - 1):
for j in range(i + 1, cantidad_nodos):
#print(i,j)
coinc = contar_coinc(conjuntos[i], conjuntos[j])
if (coinc > 0):
print(i, j, " ", coinc)
red.write(
str(i + 1) + " " + str(j + 1) + " " + str(coinc) +
"\n")
red.close()
return igraph.read(nombre + ".net", format="pajek"), red.name
def main():
parser = argparse.ArgumentParser(description="""Run CONGO from the command line. Mostly meant as a demo -- only prints one cover.""")
parser.add_argument('-d', '--demo', action='store_true', help="""Run a demo with the famous Zachary's Karate Club data set. Overrides all other options.""")
parser.add_argument('-l', '--label', default='CONGA_index', nargs='?', const='label', help="""Choose which attribute of the graph to print.
When this option is present with no parameters, defaults to 'label'. When the option is not
present, defaults to the index.""")
parser.add_argument('-n', '--num_clusters', type=int, help="""Specify the number of clusters to use.""")
parser.add_argument('-w', '--height', default=2, type=int, help="""The lengh of the longest shortest paths that CONGO considers.""")
parser.add_argument('file', nargs='?', help="""The path to the file in igraph readable format.""")
args = parser.parse_args()
if args.demo:
run_demo()
return
if not args.file:
print("CONGO.py: error: no file specified.\n")
print(parser.parse_args('-h'.split()))
return
# only works for undirected
G = ig.read(args.file).as_undirected()
result = congo(G, args.height)
if args.num_clusters:
result.pretty_print_cover(args.num_clusters, label=args.label)
else:
result.pretty_print_cover(result.optimal_count, label=args.label)
def main():
with rasengan.tictoc("Loading Graph"):
graph = igraph.read(args.graph_fn)
graph.to_undirected(mode="collapse",
combine_edges=dict(weights="first"))
with rasengan.tictoc("Creating Adjacent Node List"):
adjacent_edge_list = graph.get_inclist()
adjacent_node_list = graph.get_adjlist()
# 1234 is just a random number.
assert (adjacent_node_list[1234][0]
in graph.es[adjacent_edge_list[1234][0]].tuple)
total_vertices = float(len(adjacent_edge_list))
with rasengan.tictoc("Creating Local Node Prob List"):
edge_prob_list = []
for idx, edges in enumerate(adjacent_edge_list):
if idx % 1000 == 0:
print idx / total_vertices * 100
weights = np.array(graph.es[edges]["weight"])
edge_prob_list.append(weights / weights.sum())
queries = read_queries(args.query_fn)
for qid, query in queries.iteritems():
for start in query:
weighted_random_walk(graph,
start,
adjacent_node_list,
edge_prob_list,
path_maxlength=args.path_maxlength,
n_runs=args.n_runs)
def svg_from_graph(topo_file="topology.graphml",
gfx_file="graph.svg",
layout="lgl",
width=800,
height=800,
node_size=14,
node_color="lightblue",
edge_color="grey",
label_key="id"):
g = igraph.read(topo_file)
# g.to_undirected() # Use this if you want the graph to be undirected
# Example of alternative layout: g.layout_reingold_tilford(root=0)
width = int(width)
height = int(height)
node_size = int(node_size)
node_colors = [node_color] * len(g.vs)
edge_colors = [edge_color] * len(g.es)
node_labels = [v[label_key][:] for v in g.vs]
g.write_svg(gfx_file,
layout=layout,
width=width,
height=height,
vertex_size=node_size,
font_size=node_size,
labels=node_labels,
colors=node_colors,
edge_colors=edge_colors)
def igraph_draw_traj(filname,pold,polar=True,layout=None):
import igraph as ig
g = ig.read(filname,format="graphml")
pols=[]
for i in g.vs:
pols.append(pold[i['id']])
# print pols
if polar:
rgbs = [(1-(i+1.)/2,(i+1.)/2,0) for i in pols]
else:
rgbs = [(1-i,i,0) for i in pols]
# print filname
GGG=nx.read_graphml(filname)
g.vs["label"] = GGG.nodes()
visual_style = {}
visual_style["vertex_size"] = 15
visual_style['vertex_color']=rgbs#'pink'
visual_style['vertex_label_size']='10'
visual_style["vertex_label"] = g.vs["label"]
if layout==None:
layout=g.layout("kk")
# else:
visual_style["layout"] = layout
visual_style["bbox"] = (700, 700)
visual_style["margin"] = 100
return g,visual_style,layout
def compute_wl_embeddings_discrete(data_directory, h):
graph_filenames = retrieve_graph_filenames(data_directory)
graphs = [ig.read(filename) for filename in graph_filenames]
wl = WeisfeilerLehman()
label_dicts = wl.fit_transform(graphs, h)
# Each entry in the list represents the label sequence of a single
# graph. The label sequence contains the vertices in its rows, and
# the individual iterations in its columns.
#
# Hence, (i, j) will contain the label of vertex i at iteration j.
label_sequences = [
np.full((len(graph.vs), h + 1), np.nan) for graph in graphs
]
for iteration in sorted(label_dicts.keys()):
for graph_index, graph in enumerate(graphs):
labels_raw, labels_compressed = label_dicts[iteration][graph_index]
# Store label sequence of the current iteration, i.e. *all*
# of the compressed labels.
label_sequences[graph_index][:, iteration] = labels_compressed
return label_sequences
def construir_red_autocorrelacion(self, conjuntos, nombre, contar_coinc, etiquetas_nodos):
'''Construye la red de autocorrelación de un conjunto de conjuntos en formato pajek y la escribe en un archivo nombre.net
Parámetros
conjuntos : Lista de listas de identificadores de papers
nombre : Nombre del archivo .net
contar_coinc: Función para contar los elementos de la intersección entre dos conjuntos cualesquiera
etiquetas_nodos : etiqueta de cada nodo de la red que corresponde a cada lista de conjuntos.
Retorna
Tupla con un objeto igraph que sirve para calcular medidas de la red y el nombre de la red'''
print(len(conjuntos))
cantidad_nodos = len(conjuntos)
red = open(nombre+".net", 'w')
red.write("*Vertices "+str(cantidad_nodos)+"\n")
if etiquetas_nodos!=None:
for i,etiqueta in enumerate(etiquetas_nodos):
red.write(str(i+1)+' "'+etiqueta.encode('utf-8', "ignore")+'"\n')
red.write("*Edges\n")
for i in range(cantidad_nodos-1):
for j in range(i+1, cantidad_nodos):
#print(i,j)
coinc = contar_coinc(conjuntos[i],conjuntos[j])
if(coinc > 0):
print(i,j," ",coinc)
red.write(str(i+1)+" "+str(j+1)+" "+str(coinc)+"\n")
red.close()
return igraph.read(nombre+".net",format="pajek"), red.name
def clustering(filename, counter):
G = igraph.read(filename,directed=False,format='ncol')
D = G.community_multilevel()
MIN_CLUSTER_SIZE = 1
style = {}
style['vertex_size'] = 10
kept_clusters = 0
clusters = []
for i in D.subgraphs():
if len(i.vs) > MIN_CLUSTER_SIZE:
kept_clusters += 1
clusters.append(i)
style["layout"] = i.layout("fr", maxiter=250) # "fr" or "lgl" work nicely
igraph.plot(i, 'cluster_visualization/cluster_'+str(counter)+'_'+str(kept_clusters)+'.pdf', **style)
print('total clusters -->',len(D.subgraphs()))
print('kept clusters -->',kept_clusters)
return clusters
def main(gpath):
G = ig.read(gpath)
components = G.components()
nxG = nx.read_gpickle(gpath.replace('.gml', ''))
nodes = sorted(nxG.nodes())
node_ix = dict(zip(nodes, range(len(nodes))))
node_id_to_comp = np.zeros((G.vcount(), 2), dtype=int)
Ps = []
for i, component in enumerate(
sorted(components, key=lambda c: len(c), reverse=True)):
sG = G.subgraph(component)
if sG.vcount() > 1:
print("Component %d" % sG.vcount())
P = sG.shortest_paths_dijkstra()
for n in range(sG.vcount()):
label = sG.vs[n]['label']
node_n = node_ix[label]
assert np.sum(node_id_to_comp[node_n, :]) == 0
node_id_to_comp[node_n, :] = (i, n)
Ps.append(P)
output_path = "../generated-data/pairwise_features/%s_shortest_path_len_sparse" % (
os.path.basename(gpath).replace('.gml', ''))
np.savez(output_path, Ps=Ps, node_id_to_comp=node_id_to_comp)
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('input', metavar='graph.pickle',
help='Input graph file')
parser.add_argument(
'--connection',
default='postgresql://*****:*****@localhost/osrm',
help='Postgres connection string. Default %(default)s'
)
parser.add_argument('--verbose', action='store_true',
help='Increase logging level')
args = parser.parse_args()
logging.basicConfig(
level=logging.INFO if args.verbose else logging.WARNING)
log.info("Creating DB engine")
engine = create_engine(args.connection, echo=False)
log.info("Creating DB session")
Session = sessionmaker(bind=engine)
session = Session()
log.info("Creating OSRM tables")
models.Base.metadata.create_all(engine)
session.query(models.OSRMRouteNode).delete()
g = igraph.read(args.input)
export_nodes(g, session)
def construir_red_autocorrelacion(self, conjuntos, nombre, contar_coinc, etiquetas_nodos):
'''Construye la red de autocorrelación de un conjunto de conjuntos
Parámetros
Conjuntos es una lista de listas'''
print(len(conjuntos))
cantidad_nodos = len(conjuntos)
red = open(nombre+".net", 'w')
red.write("*Vertices "+str(cantidad_nodos)+"\n")
if etiquetas_nodos!=None:
for i,etiqueta in enumerate(etiquetas_nodos):
red.write(str(i+1)+' "'+str(etiqueta)+'"\n')
red.write("*Edges\n")
for i in range(cantidad_nodos-1):
#print i
for j in range(i+1, cantidad_nodos):
#print(i,j)
coinc = contar_coinc(conjuntos[i],conjuntos[j])
if(coinc > 0):
#print(i,j," ",coinc)
red.write(str(i+1)+" "+str(j+1)+" "+str(coinc)+"\n")
red.close()
print 'acabó la red'
return igraph.read(nombre+".net",format="pajek"), red.name
def main():
parser = argparse.ArgumentParser(description="""Run CONGA from the command line. Mostly meant as a demo -- only prints one cover.""")
parser.add_argument('-m', '--modularity_measure', choices=['lazar'],
help="""Calculate the modularities using the specified
modularity measure. Currently only supports lazar.""")
parser.add_argument('-n', '--num_clusters', type=int, help="""Specify the number of clusters to use.""")
parser.add_argument('-d', '--demo', action='store_true', help="""Run a demo with the famous Zachary's Karate Club data set. Overrides all other options.""")
parser.add_argument('-l', '--label', default='CONGA_index', nargs='?', const='label', help="""Choose which attribute of the graph to print.
When this option is present with no parameters, defaults to 'label'. When the option is not
present, defaults to the index.""")
parser.add_argument('file', nargs='?', help="""The path to the file in igraph readable format.""")
args = parser.parse_args()
if args.demo:
run_demo()
return
if not args.file:
print("CONGA.py: error: no file specified.\n")
print(parser.parse_args('-h'.split()))
return
# only works for undirected
G = ig.read(args.file).as_undirected()
result = CONGA(G, calculate_modularities=args.modularity_measure, optimal_count=args.num_clusters)
result.pretty_print_cover(result.optimal_count, label=args.label)
def convert_graph(gfn, informat, save_dir, *outformats):
"""
Convert between igraph supported formats. No conversion to MAT or NPY available.
Positional arguments:
====================
gfn - the graph file name
informat - the input format of the graph
save_dir - the directory where we save result to
outformat - a list of output formats
"""
try:
if informat in [
"graphml", "ncol", "edgelist", "lgl", "pajek", "graphdb"
]:
g = igraph.read(gfn, None)
elif informat == "mat":
g = csc_to_igraph(loadAnyMat(gfn))
elif informat == "npy":
g = csc_to_igraph(np.load(gfn).item())
elif informat == "attredge":
g = attredge_to_igraph(gfn)
else:
err_msg = "[ERROR]: Unknown format '%s'. Please check format and retry!" % informat
print err_msg
return (None, err_msg)
except Exception, err_msg:
print err_msg
return (None, "[ERROR]: " + str(err_msg))
def igraph_draw_traj(filname, pold, polar=True, layout=None):
import igraph as ig
g = ig.read(filname, format="graphml")
pols = []
for i in g.vs:
pols.append(pold[i['id']])
# print pols
if polar:
rgbs = [(1 - (i + 1.) / 2, (i + 1.) / 2, 0) for i in pols]
else:
rgbs = [(1 - i, i, 0) for i in pols]
# print filname
GGG = nx.read_graphml(filname)
g.vs["label"] = GGG.nodes()
visual_style = {}
visual_style["vertex_size"] = 15
visual_style['vertex_color'] = rgbs #'pink'
visual_style['vertex_label_size'] = '10'
visual_style["vertex_label"] = g.vs["label"]
if layout == None:
layout = g.layout("kk")
# else:
visual_style["layout"] = layout
visual_style["bbox"] = (700, 700)
visual_style["margin"] = 100
return g, visual_style, layout
def main(gpath, steps):
G = ig.read(gpath)
output = G.community_walktrap(steps=steps)
if hasattr(output, 'as_clustering'):
output = output.as_clustering()
comms = {
G.vs['label'][i]: "comm %d" % output.membership[i]
for i in range(G.vcount())
}
print("Num communities: %d" % (len(np.unique(list(comms.values())))))
comms_to_nodes = defaultdict(set)
for g, c in comms.items():
comms_to_nodes[c].add(g)
comm_sizes = np.array([len(comms_to_nodes[c]) for c in comms_to_nodes])
print("Median communitiy size: %0.2f" % np.median(comm_sizes))
print("# communities with 1 node: %d" % np.sum(comm_sizes == 1))
output_path = '../generated-data/communities/%s_%dsteps.json' % (
os.path.basename(gpath).replace('.gml', ''), steps)
with open(output_path, 'w') as f:
json.dump(comms, f)
def svg_from_graph(topo_file="topology.graphml",
gfx_file="graph.svg",
layout="lgl",
width=800,
height=800,
node_size=14,
node_color="lightblue",
edge_color="grey",
label_key="id"):
g = igraph.read(topo_file)
# g.to_undirected() # Use this if you want the graph to be undirected
# Example of alternative layout: g.layout_reingold_tilford(root=0)
width = int(width)
height = int (height)
node_size = int(node_size)
node_colors = [node_color]*len(g.vs)
edge_colors = [edge_color]*len(g.es)
node_labels = [v[label_key][:] for v in g.vs]
g.write_svg(gfx_file, layout=layout, width=width, height=height,
vertex_size=node_size, font_size=node_size,
labels=node_labels, colors=node_colors,
edge_colors=edge_colors)
def write_degree_sequences(gml_dir, deg_dir):
gml_df = buildGMLcatalog(gml_dir)
fpV = gml_df['fp_gml']
analysis_df = pd.DataFrame(columns=['num_edges', 'Weighted', 'Directed',
'Bipartite', 'Multigraph', 'Multiplex',
'fp_gml', 'n', 'alpha', 'xmin','ntail',
'Lpl', 'ppl', 'dexp', 'dln', 'dstrexp',
'dplwc', 'meandeg'])
for fp in fpV:
g = igraph.read(fp)
#### find what kind of graph this is (follow hierarchical ordering of types)
# check first for multiplex
row = gml_df[gml_df.fp_gml==fp]
if row['Multiplex'].item() == 1:
processmultiplex(g,fp, deg_dir, analysis_df)
elif row['Bipartite'].item() == 1:
processbipartite(g, fp, deg_dir,analysis_df)
elif row['Multigraph'].item() == 1:
processmultigraph(g, fp, deg_dir,analysis_df)
elif row['Weighted'].item() == 1:
processweighted(g, fp, deg_dir,analysis_df)
elif row['Directed'].item() == 1:
processdirected(g, fp, deg_dir,analysis_df)
else:
readdeg(g, fp,deg_dir,analysis_df)
return analysis_df
def CompareWithKSplit():
refGraph = igraph.read(os.path.join(sourcesDir,sourceFunctionName))
refGraph['name'] = sourceFunctionName
if (os.name != "nt"):
#p = multiprocessing.Pool(initializer=workerInit,initargs=[len(refGraph.vs)])
p = multiprocessing.Pool()
mapper = p.imap_unordered
#mapper = itertools.imap
else:
mapper = itertools.imap
#workerInit(len(refGraph.vs))
reportFile = CounterXlsReport(csvName + "-K=" + str(myK) + "-" + sourceFunctionName)
print "Prepping db - (func files) - target=" +os.path.join(sourcesDir,sourceFunctionName)
sourcesList3k = list(mapper(identity,split2k.ksplitGraphObject(myK,refGraph,True)))
print "For k=" + str(myK) + " we have - " + str(len(sourcesList3k))
print "end db prep"
from simple_db import DBSimpleClient
db = DBSimpleClient()
params = [doOneFunctionFileRW,itertools.izip(db.get_all(),itertools.cycle([sourcesList3k]))]
#if (mapper != itertools.imap):
# params.append(50)
for allFields in mapper(*params):
for fields in allFields:
reportFile.writeLine(fields)
def start_create_gml(workfolder):
workfolder_gml = gt.createFolder(workfolder + "GML\\")
workfolder_att = gt.createFolder(workfolder + "ATT\\")
i = 0
for fp in os.listdir(workfolder):
fname = os.path.splitext(fp)
i += 1
if fname[-1] == '.repost': # or fname[-1]=='.comment':
wbfilep = workfolder + fp
print i, wbfilep
gmlfp = workfolder_gml + fname[0] + '.gml'
g = ig.Graph()
if not os.path.exists(gmlfp):
g = weibo2g.start(wbfilep, gmlfp,
addcommentlist=True) #ig.read(gmlfp)#
else:
g = ig.read(gmlfp)
"add time slice function"
for percent in percentlist:
lengthNow = int(round(len(timelist) * percent))
lengthNow = lengthNow if lengthNow > 1 else 1
timelistPercentNow = timelist[:lengthNow]
timelistPeriodNow = selecTime(timelistPercentNow, periodcnt)
for timep in timelistPeriodNow:
g = g.subgraph_edges(es.select(createdtimetos_le=timep))
#grt.analyzeNetNodes(g,workfolder_att,str(fname[0]))
grt.analyzeNetNodes_New(g, workfolder_att, str(fname[0]))
print grt.analysisNet(g)
async def query(request):
req_file = request.files.get("file")
file_name = req_file.name
body = req_file.body.decode("unicode_escape")
with open("files/" + file_name, "w+") as file:
file.write(body)
file_path = "files/" + file_name
graph = ig.read(file_path, format="graphml")
colors = {
"#3217A7":10008,
"#F0F007": 205005,
"White":163001,
"#9725A4":135007,
"#000000": 164010,
"#156911":189010,
'#A71717':157000
}
for pos, x in enumerate(graph.vs['color']):
graph.vs[pos]['color'] = colors[x]
for pos, x in enumerate(graph.es['color']):
graph.es[pos]['color'] = colors[x]
return redirect(
graphistry.bind(
source='src',
destination='dst',
point_color='color',
edge_color='color'
).plot(graph)
)
def main():
graph_file_name = f'{DATA_DIR}/graph_{sys.argv[1]}.gml'
if not os.path.isfile(graph_file_name):
print('First argument must be name of existing file with model')
return
start_date = datetime.datetime.now()
print('Reading graph...')
g = igraph.read(graph_file_name)
alg = sys.argv[2]
print('Looking for community')
if alg == 'lp':
lp = find_communities_label_propagation(g)
save(g, lp)
elif alg == 'ml':
ml = find_communities_multilevel(g)
save(g, ml)
elif alg == 'im':
im = find_communities_infomap(g)
save(g, im)
else:
print('Incorrect second argument. Must be one of [lp, ml, im]')
return
end_date = datetime.datetime.now()
print(f'{sys.argv[1]},{alg},{(end_date - start_date).total_seconds()}')
def superFunctionsGenerator():
for exeName in filter(lambda x: inWhiteList(x, 'exe'),
os.listdir(targetsDir)):
print "loading - " + exeName + " ... "
currentExeDir = os.path.join(
targetsDir, os.path.join(exeName,
functionsGraphsDirectoryName))
for funcFileName in filter(
lambda x: inWhiteList(
os.path.splitext(x)[0], 'functionName'),
filter(lambda x: x.endswith(myExt),
os.listdir(currentExeDir))):
print "FUNC begin - " + funcFileName + " ... "
tarGraph = igraph.read(
os.path.join(currentExeDir, funcFileName))
tarGraph['name'] = funcFileName
#funcFileName and exe are only for the timeout print
yield {
'tarGraph': tarGraph,
'refGraph': refGraph,
'sourcesList3k': sourcesList3k,
'funcFileName': funcFileName,
'exeName': exeName
}
print "FUNC finished " + funcFileName
print "finished loading " + exeName
def convert_graph(gfn, informat, save_dir, *outformats):
"""
Convert between igraph supported formats. No conversion to MAT or NPY available.
Positional arguments:
====================
gfn - the graph file name
informat - the input format of the graph
save_dir - the directory where we save result to
outformat - a list of output formats
"""
try:
if informat in ["graphml", "ncol", "edgelist", "lgl", "pajek", "graphdb"]:
g = igraph.read(gfn, None)
elif informat == "mat":
g = csc_to_igraph(loadAnyMat(gfn))
elif informat == "npy":
g = csc_to_igraph(np.load(gfn).item())
else:
err_msg = "[ERROR]: Unknown format '%s'. Please check format and re-try!" % informat
print err_msg
return err_msg
except Exception, err_msg:
print err_msg
return "[ERROR]: "+str(err_msg)
def main():
parser = argparse.ArgumentParser(description="Convert an igraph to a csc object")
parser.add_argument("graph_fn", action="store", help="The name of the igraph to read from disk. *Must be gml format!")
parser.add_argument("-g", "--gen_graph", action="store_true", help="Generate a new ER graph")
parser.add_argument("-n", "--num_nodes", action="store", type=int, help="The number of nodes in the ER graph")
parser.add_argument("-p", "--probability", action="store", type=float, help="The probability of connectivity of each node to another in the graph")
parser.add_argument("-s", "--save", action="store_true", help="Save conversion to disk")
parser.add_argument("-f", "--save_fn", action="store", default="csc_matlab", help="Save file name")
parser.add_argument("-t", "--test", action="store_true", help="Run test only!")
result = parser.parse_args()
if result.test:
test()
exit(1)
if os.path.exists(result.graph_fn):
g = igraph.read(result.graph_fn, format="gml")
elif result.gen_graph or result.num_nodes or result.probability:
assert (result.gen_graph and result.num_nodes and result.probability), "You must set all ER parameters i.e. n, p"
g = igraph.Graph.Erdos_Renyi(n=result.num_nodes, p=result.probability)
igraph.write(g, result.save_fn+".gml", format="gml")
else:
sys.stderr.writelines("Invalid path %s ... and all (i.e. n, p, g) ER parameters not set so no action taken. \n EXITING NOW! \n")
exit(-1)
igraph_to_csc(g, result.save, result.save_fn)
def main(timestamp):
nng = ig.read(f'/tmp/{timestamp}_leiden_graph.gml',
format='graphml')
partition = la.find_partition(nng, la.ModularityVertexPartition)
clusters = partition.membership
clusters = np.array(clusters).astype(str)
np.savetxt(f'/tmp/{timestamp}_leiden_clusters.csv',
clusters, delimiter=',', newline='\n', fmt='%s')
def nx_to_igraph(G):
temp_dir = tempfile.mkdtemp()
tmpfile = os.path.join(temp_dir, 'graph.graphml')
nx.write_graphml(G, tmpfile)
ig = igraph.read(tmpfile, format="graphml")
os.remove(tmpfile)
os.rmdir(temp_dir)
return (ig)
def main():
G = ig.read("enron.gml")
test_degree_centrality_hypothesis(G)
test_closeness_centrality_hypothesis(G)
test_betweenness_centrality_hypothesis(G)
test_eigenvector_centrality_hypothesis(G)
test_pagerank_hypothesis(G)
test_hits_authority_hypothesis(G)
def max_bcentrality_igraph(filename): G = ig.read(filename) bcs = ig.betweenness(G) index, value = max(enumerate(bcs), key=operator.itemgetter(1)) vs = ig.VertexSeq(G) print "node with max betweenness centrality =", vs[index] print "indegree of this node =", vs[index].indegree() print "outdegree of this node =", vs[index].outdegree()
def max_bcentrality_igraph(filename): G = ig.read(filename) bcs = ig.betweenness(G) index, value = max(enumerate(bcs), key=operator.itemgetter(1)) vs = ig.VertexSeq(G) print "node with max betweenness centrality =", vs[index] print "indegree of this node =", vs[index].indegree() print "outdegree of this node =", vs[index].outdegree()
def main(graph_path, selected_features=None):
G = nx.read_gpickle(graph_path)
nodes = list(sorted(G.nodes()))
nodes_to_features = collections.defaultdict(dict)
extract_features_networkx(G, nx_features, nodes_to_features,
selected_features)
iG = ig.read(graph_path + '.gml')
extract_features_ig(iG, ig_features, nodes_to_features, selected_features)
# features are ordered according to nodes
F = []
feature_labels = []
selected_features = nx_features + ig_features if selected_features is None else selected_features
for feature in selected_features:
Ff = []
for n in nodes:
v = nodes_to_features[n]
Ff.append(v[feature])
Ff = np.array(Ff)
if len(Ff.shape) == 1:
Ff = Ff[:, None]
if Ff.shape[1] == 1:
feature_labels.append(feature)
else:
for i in range(Ff.shape[1]):
feature_labels.append("%s_%d" % (feature, i))
F.append(Ff)
F = np.hstack(F)
mu = np.mean(F, axis=0)
std = np.std(F, axis=0)
print(mu)
print(std)
# normalize
F = stats.zscore(F, axis=0)
print(F.shape)
print(np.min(F, axis=0))
print(np.max(F, axis=0))
print(np.mean(F, axis=0))
print(np.std(F, axis=0))
print(feature_labels)
output_path = '../generated-data/features/%s_topology' % (
os.path.basename(graph_path))
np.savez(output_path, F=F, feature_labels=feature_labels, mu=mu, std=std)
def __init__(self, network=None, time_serie=None, output='network.NET'):
self.time_serie = time_serie
if network is None:
self.network = None
self.N = len(self.time_serie)
else:
self.network = igraph.read(network, format="pajek")
self.N = self.network.vcount()
self.output = output
def load(filename):
print("Loading "+filename)
g = ig.read(
filename, format="ncol",
directed=True, names=True
)
g.delete_vertices([0,1])
print("Done")
return(g)
def main():
parser = argparse.ArgumentParser(
description=
"""Run CONGA from the command line. Mostly meant as a demo -- only prints one cover."""
)
parser.add_argument(
"-m",
"--modularity_measure",
choices=["lazar"],
help="""Calculate the modularities using the specified
modularity measure. Currently only supports lazar.""",
)
parser.add_argument(
"-n",
"--num_clusters",
type=int,
help="""Specify the number of clusters to use.""",
)
parser.add_argument(
"-d",
"--demo",
action="store_true",
help=
"""Run a demo with the famous Zachary's Karate Club data set. Overrides all other options.""",
)
parser.add_argument(
"-l",
"--label",
default="CONGA_index",
nargs="?",
const="label",
help="""Choose which attribute of the graph to print.
When this option is present with no parameters, defaults to 'label'. When the option is not
present, defaults to the index.""",
)
parser.add_argument(
"file",
nargs="?",
help="""The path to the file in igraph readable format.""")
args = parser.parse_args()
if args.demo:
run_demo()
return
if not args.file:
print("conga.py: error: no file specified.\n")
print(parser.parse_args("-h".split()))
return
# only works for undirected
G = ig.read(args.file).as_undirected()
result = conga(
G,
calculate_modularities=args.modularity_measure,
optimal_count=args.num_clusters,
)
result.pretty_print_cover(result.optimal_count, label=args.label)
def main():
g = igraph.read('tag_wordnet.lgl', 'lgl')
# draw graph
for v in g.vs:
v['label'] = v['name']
p = igraph.drawing.Plot('tag_wordnet.pdf', igraph.drawing.BoundingBox(6000, 6000))
p.add(g)
p.save()
def q7():
G = read_gdf("../../data/network_analysis/MiddleEastern.gdf")
nx.write_gml(G, "/tmp/MiddleEastern.gml")
G = ig.read("/tmp/MiddleEastern.gml")
bcs = G.betweenness()
labels = [v["label"] for v in G.vs()]
label_bcs = sorted(zip(labels, bcs), key=operator.itemgetter(1), reverse=True)
ingredient_set = set(["coriander", "garlic", "walnut", "dill"])
print [x for x in label_bcs if x[0] in ingredient_set]
def runSimulation(self, plot=True):
full = self.time
if plot == True:
self.movie = gm.GraphMovie()
# attrs = {'fillcolor':fields ,'width':0.5,'height':0.5,'fontsize':0.0,'nodedegreesize':False, 'plotlabel':'agent0_'+str(full-ttime) }
# A = nx.to_agraph(net);
# 1 A.layout(prog = 'twopi');
while full > 0:
if plot == True and self.time == full:
self.net = igraph.read(self.string + ".gml")
for i, v in enumerate(self.net.vs):
v['label'] = str(i)
v['color'] = self.fields[0, i]
self.movie.addGraph(self.net)
elif plot == True and self.time != full:
self.net = igraph.read(self.string + ".gml")
for i, v in enumerate(self.net.vs):
v['label'] = str(i)
if i == self.edges[sampvar[0]][0] or i == self.edges[
sampvar[0]][1]:
v['color'] = 0.0
else:
v['color'] = self.fields[0, i]
# e = self.net.es[int(sampvar[0])];
# print e;
# print "edges"
# print sampvar[0]
# print self.edges[sampvar[0]][0]; print self.edges[sampvar[0]][1];
# e['color'] = 0.0; # set edge color
self.movie.addGraph(self.net)
sampvar = np.random.randint(0, len(self.edges), (1))
# the edge(interaction) sampling process
interact = float(self.fields[0, self.edges[sampvar[0]][0]] +
self.fields[0, self.edges[sampvar[0]][1]]) / 2
self.fields[0, self.edges[sampvar[0]][0]] = float(
self.fields[0, self.edges[sampvar[0]][0]] + interact) / 2
self.fields[0, self.edges[sampvar[0]][1]] = float(
self.fields[0, self.edges[sampvar[0]][1]] + interact) / 2
# attrs['fillcolor'] = fields;
full = full - 1
# attrs['plotlabel'] = 'agent0_'+str(full-ttime);
self.movie.doMovieLayout()
self.movie.renderMovie(name=self.string)
def setGraph(self, g: Union[str, Graph]):
if isinstance(g, str):
g = igraph.read(g)
self.g = wrapGraph(g)
for mode in self.modes:
if mode.onSetGraph():
break
self.resetViewRect()
self.update()
def simple_read_net():
'''读取pajek格式文件'''
g = ig.read("testdata/GR3_60.NET", format="pajek")
# 设置边和顶点颜色
g.vs["color"] = "#3d679d"
g.es["color"] = "red"
graphStyle = {"vertex_size": 12, 'margin': 6}
graphStyle["layout"] = g.layout("fr") #设置布局
g.write_svg('GR3_60_graph.svg', width=600, height=600, **graphStyle)
def main():
g = igraph.read('tag_wordnet.lgl', 'lgl')
for v in g.vs:
v['label'] = v['name']
d = g.community_fastgreedy()
p = igraph.drawing.Plot('graphtree.pdf', igraph.drawing.BoundingBox(5000, 13000))
# p.add(g)
p.add(d)
p.save()
def igraph_from_tuples(v_names):
tmp = tempfile.mktemp(suffix='.ncol', prefix='graph_', dir='tmp/')
fp = open(tmp, "w")
for (name_a, name_b) in v_names:
fp.write("{} {}\n".format(name_a, name_b))
fp.close()
# will store node names under the 'name' vertex attribute.
g = igraph.read(tmp, format='ncol', directed=False, names=True)
os.remove(tmp)
return g
def to_igraph(graph):
# Initialise the optimiser, using default settings
import networkx as nx
import igraph as ig
g = ig.Graph()
if anonymize:
nx.write_graphml(anonymize(graph), "/tmp/test_graph.graphml")
else:
nx.write_graphml(graph, "/tmp/test_graph.graphml")
return ig.read("/tmp/test_graph.graphml", format='graphml')
def nx_to_igraph(network):
try:
import igraph
except ImportError:
raise ImportError('requires igraph ')
file_descriptor, file_path = tempfile.mkstemp(suffix='.gml')
nx.write_gml(network, file_path)
igraph_network = igraph.read(file_path)
return igraph_network
def nx_to_igraph(network):
try:
import igraph
except ImportError:
raise ImportError('requires igraph ', )
file_descriptor, file_path = tempfile.mkstemp(suffix='.gml')
nx.write_gml(network, file_path)
igraph_network = igraph.read(file_path)
return igraph_network
def CompareWithKSplit():
refGraph = igraph.read(os.path.join(sourcesDir,sourceFunctionName))
refGraph['name'] = sourceFunctionName
if (os.name != "nt"):
#p = multiprocessing.Pool(initializer=workerInit,initargs=[len(refGraph.vs)])
p = multiprocessing.Pool()
mapper = p.imap_unordered
#mapper = itertools.imap
else:
mapper = itertools.imap
#workerInit(len(refGraph.vs))
reportFile = CounterXlsReport(csvName + "-K=" + str(myK) + "-" + sourceFunctionName)
print "Prepping db - (func files) - target=" +os.path.join(sourcesDir,sourceFunctionName)
sourcesList3k = list(mapper(identity,split2k.ksplitGraphObject(myK,refGraph,True)))
print "For k=" + str(myK) + " we have - " + str(len(sourcesList3k))
print "end db prep"
def inWhiteList(value,atrbName):
for whiteListEntry in filter(lambda x: x['source']==sourceName,whiteList):
#print "CHECK " + atrbName
if whiteListEntry[atrbName] == value:
return True
return False
def superFunctionsGenerator():
for exeName in filter(lambda x:inWhiteList(x,'exe'),os.listdir(targetsDir)):
print "loading - " + exeName + " ... "
currentExeDir = os.path.join(targetsDir,os.path.join(exeName,functionsGraphsDirectoryName))
for funcFileName in filter(lambda x:inWhiteList(os.path.splitext(x)[0],'functionName'),filter(lambda x:x.endswith(myExt),os.listdir(currentExeDir))):
print "FUNC begin - " + funcFileName + " ... "
tarGraph = igraph.read(os.path.join(currentExeDir,funcFileName))
tarGraph['name'] = funcFileName
#funcFileName and exe are only for the timeout print
yield {'tarGraph':tarGraph,'refGraph':refGraph,'sourcesList3k':sourcesList3k,'funcFileName':funcFileName,'exeName':exeName}
print "FUNC finished " + funcFileName
print "finished loading " + exeName
params = [doOneFunctionFileRW,superFunctionsGenerator()]
#if (mapper != itertools.imap):
# params.append(50)
for allFields in mapper(*params):
for fields in allFields:
reportFile.writeLine(fields)
def make_projection(graph, atts):
""" makes bipartite projections, returns seller projection"""
# PREPARE EDGE ATTRIBUTES
graph.es['val'] = list(atts['vals'])
graph.es['hs'] = list(atts['hs'])
graph.es['dest'] = list(atts['dest'])
graph.es['hss'] = list(atts['hss'])
graph.es['dest_source'] = list(atts['dest_source'])
graph.es['imp_name'] = list(atts['imp_name'])
# PREPARE VERTEX ATTRIBUTES
# The strength member function sums all of the edge values
graph.vs['val'] = graph.strength(graph.vs, weights='val')
# Get list of exporters who sell to the US
us_list = what_sellers(graph.es, 'USA')
graph.vs['US'] = 0
graph.vs[us_list]['US'] = 1
# Get list of exporters who sell to a seleted foreign coutnry
us_list = what_sellers(graph.es, 'VEN')
graph.vs['VEN'] = 0
graph.vs[us_list]['VEN'] = 1
# Get most frequent hs by exporter
hs_tup = source_hs(graph.es,'hss')
graph.vs['hs_source'] = 0
graph.vs[hs_tup[0]]['hs_source'] = hs_tup[1]
# Get most frequent destimation
dest_tup = source_hs(graph.es,'dest_source')
graph.vs['dest_source'] = 0
graph.vs[dest_tup[0]]['dest_source'] = dest_tup[1]
# SIZES FROM graph.csv
size = 10046
edge_size = 58031
big_size = 40789
sub = size
# MAKE THE TWO TYPES (SELLER AND BUYER)
graph.vs['type'] = [1] * big_size
graph.vs[sub:]['type'] = [0] * (big_size - sub)
# PROEJECT AND ADD ATTRIBUTES
proj2, proj1 = graph.bipartite_projection()
proj1.vs['val'] = graph.vs[0:sub]['val']
proj1.vs['val'] = graph.vs[0:sub]['val']
# Get most valuable importer
max_imp = pd.read_pickle('max_imp.pickle')
proj1.vs['imp_name'] = max_imp
# WRITE AND READ
proj1.write_pickle('proj1.pickle')
proj1 = ig.read('proj1.pickle')
print(ig.summary(proj1))
return proj1, proj2
def load_dat():
graph = ig.read('igraph_small.csv',format='edge')
vals = pd.read_csv('vals_small.csv')['val']
hs = pd.read_csv('vals_small.csv')['hs10']
hss = pd.read_csv('vals_small.csv')['hs_source']
dest = pd.read_csv('vals_small.csv')['dest_alf']
dest_source = pd.read_csv('vals_small.csv')['dest']
imp_name = pd.read_csv('vals_small.csv')['imp_name']
atts = {'vals': vals,'hs': hs,'dest': dest,
'hss': hss,'dest_source': dest_source,
'imp_name': imp_name}
return graph, atts
def main(filepath,feature):
pwd = os.getcwd() # This should be your home directory (e.g. /Users/blahblah).
filename = os.path.split(filepath)[1]
f = open(pwd + "/%s/"%feature + filename + ".%s.txt"%feature,"w")
writer = csv.writer(f)
G = igraph.read(filepath)
G.simplify()
G.to_undirected()
largest_c = G.clusters(mode="STRONG").giant()
exec("f_value = %s(largest_c)"%feature)
writer.writerow((filepath,f_value))
f.close()
def _read_celltype_graph(self,
connmatrix_file,
format='gml',
cellcount_file=None):
celltype_graph = None
cellcount_dict = {}
if cellcount_file is not None:
with open(cellcount_file, 'r') as popfile:
reader = csv.reader(popfile)
for line in reader:
if len(line) > 0:
cellcount_dict[line[0]] = int(line[1])
if format == 'csv':
celltype_graph = igraph.Graph(len(cellcount_dict), directed=True)
index = 0
for celltype, cellcount in cellcount_dict.items():
celltype_graph.vs[index]['label'] = celltype
celltype_graph.vs[index]['count'] = cellcount
index += 1
reader = csv.reader(file(connmatrix_file))
header = reader.next()
row = 0
for line in reader:
if len(line) <= 0:
continue
pre = header[row]
source = celltype_graph.vs.select(label_eq=pre)
if len(source) > 0:
source = source[0]
row += 1
col = 0
for entry in line:
post = header[col]
target = celltype_graph.vs.select(label_eq=post)
if len(target) > 0:
target = target[0]
value = int(entry)
celltype_graph.add_edges((source.index, target.index))
eid = celltype_graph.get_eid(source.index, target.index)
celltype_graph.es[eid]['weight'] = value
col += 1
elif format == 'gml':
celltype_graph = igraph.read(f=connmatrix_file, format=format)
celltype_graph['doc'] = 'Celltype-based connectivity data. \
count of node *n* is the number of cells of type *n* \
that are present in the model. weight of edge (a, b) \
is the number of cells of type *a* that connect to \
each cell of type *b*.'
return celltype_graph
def HACK_convert_nx_igraph(nx_graph):
"""
There exist no current method to convert a nx graph to an igraph.
So this is a hack which does sp.
Args:
nx_graph: input nx_graph to be converted to igraph
Returns:
ig_graph: converted igraph
"""
nx.write_pajek(nx_graph, "/tmp/rohan.net")
ig_graph = igraph.Graph()
ig_graph = igraph.read("/tmp/rohan.net", format="pajek")
return ig_graph
def superFunctionsGenerator():
for exeName in os.listdir(targetsDir):
print "loading - " + exeName + " ... " ,
currentExeDir = os.path.join(targetsDir,os.path.join(exeName,functionsGraphsDirectoryName))
for funcFileName in filter(lambda x:x.endswith(myExt),os.listdir(currentExeDir)):
tarGraph = igraph.read(os.path.join(currentExeDir,funcFileName))
tarGraph['name'] = funcFileName
#funcFileName and exe are only for the timeout print
yield {'tarGraph':tarGraph,'refGraph':refGraph,'sourcesList3k':sourcesList3k,'funcFileName':funcFileName,'exeName':exeName}
print "finished loading " + exeName
def main(infile):
g = igraph.read(infile, 'lgl')
d = g.community_fastgreedy()
mem = d.cut(CUT)
categories = [[] for i in xrange(CUT)]
r = re.compile(r'(?P<name>.+)\((?P<count>\d+)\)')
for i, cat_no in enumerate(mem):
m = r.match(g.vs[i]['name'])
categories[cat_no].append((int(m.group('count')), m.group('name')))
for i, cat in enumerate(categories):
print '** category %d **' % i
cat.sort(lambda x, y: -cmp(x, y))
for c in cat:
print "%s : %d" % (c[1], c[0])
def load_gml(path, attr_name="value"):
g = igraph.read(path)
elist = g.get_edgelist()
min_id = min(chain.from_iterable(elist))
if min_id == 1:
offset = True
else:
offset = False
if offset:
elist = [(i-1, j-1) for i, j in elist]
try:
labels = [int(v) for v in g.vs.get_attribute_values(attr_name)]
except:
labels = [v for v in g.vs.get_attribute_values(attr_name)]
return elist, labels
def plotDegDistr(gml_file):
g = read(gml_file)
print g.summary()
xs, ys = zip(*[(left, count) for left, _, count
in g.degree_distribution().bins()])
_sum = sum(ys)
_novy = [float(ys[i]) / _sum for i in range(0, len(ys))]
plt.stem(xs, _novy)
plt.yscale("log")
plt.xscale("log")
plt.ylabel('p(k)')
plt.xlabel('k')
plt.show()
def main(infile, outfile):
g = igraph.read(infile, 'lgl')
# write XGMML
root = etree.XML('<?xml version="1.0" encoding="UTF-8" standalone="yes"?><graph label="Network" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:cy="http://www.cytoscape.org" xmlns="http://www.cs.rpi.edu/XGMML" directed="0" />')
for v in g.vs:
etree.SubElement(root, 'node', label = v['name'].decode('utf-8'),
id = str(v.index))
for e in g.es:
etree.SubElement(root, 'edge', source = str(e.source),
target = str(e.target),
label = '')
file = open(outfile, 'w')
file.write(etree.tostring(root, encoding = 'utf-8',
pretty_print = True))
file.close()
def read_graph_paper(self, graph_str):
try:
graph_file = open(self.path+graph_str+'.txt')
except IOError:
print 'The file '+self.path+graph_str+'.txt'+' does not exist!'
return None
g = ig.read(self.path+graph_str+'.net')
for r in graph_file:
lines = r.split('\t')
id = int(lines[0])
cluster = str(lines[1])
label = str(lines[2]).rstrip('\n')
g.vs[id]['class'] = cluster
g.vs[id]['label'] = label
graph_file.close()
return g
