def __init__(self, **params) :
self.params = params
if not os.path.exists(config.MENG_GRAPH_PATH) :
log.debug("Meng graph file not found. Building one at '%s'" % config.MENG_GRAPH_PATH)
mb = MengModelBuilder()
self.graph = mb.build()
del mb
log.debug("Meng graph built. %d nodes and %d edges."
% (self.graph.number_of_nodes(), self.graph.number_of_edges()))
utils.ensure_folder(os.path.dirname(config.MENG_GRAPH_PATH))
nx.write_gexf(self.graph, config.MENG_GRAPH_PATH)
log.debug("Meng graph saved.")
else:
log.debug("Reading Meng graph file at '%s'" % config.MENG_GRAPH_PATH)
self.graph = nx.read_gexf(config.MENG_GRAPH_PATH, node_type=int)
python类read_gexf()的实例源码
def lpu_parser(filename):
"""
GEXF LPU specification parser.
Extract LPU specification data from a GEXF file and store it in
Python data structures.
TODO: Update
Parameters
----------
filename : str
GEXF filename.
Returns
-------
TODO: Update
"""
graph = nx.read_gexf(filename)
return LPU.graph_to_dicts(graph)
def test_edges_to_graph_gexf(self):
graph = nx.read_gexf(fixtures_path('graph.gexf'))
with open(fixtures_path('graph.json'), 'r') as json_graph:
edges = json.load(json_graph)
out = nx.read_gexf(io.StringIO(utils.edges_to_graph(edges,
fmt='gexf')))
assert nodeset(out) == nodeset(graph)
assert edgeset(out) == edgeset(graph)
def test_command_graph_gexf(self):
matches = [
((0, 0), (3, 3)),
((1, 11), (4, 14)),
((8, 12), (11, 15)),
]
regions = utils.serialize_json(matches)
result = self.runner.invoke(
cli.graph,
[regions, '-f', 'gexf', self.image_grid]
)
out = nx.read_gexf(io.StringIO(result.output.strip()))
graph = nx.read_gexf(fixtures_path('graph.gexf'))
assert nodeset(out) == nodeset(graph)
assert edgeset(out) == edgeset(graph)
def test_command_graph_gexf_tolerance(self):
matches = [
((0, 0), (3, 3)),
((1, 11), (4, 14)),
((8, 12), (11, 15)),
]
regions = utils.serialize_json(matches)
result = self.runner.invoke(
cli.graph,
[regions, '-f', 'gexf', '-st', 0, self.image_grid]
)
out = nx.read_gexf(io.StringIO(result.output.strip()))
graph = nx.read_gexf(fixtures_path('graph.gexf'))
assert nodeset(out) == nodeset(graph)
assert edgeset(out) == edgeset(graph)
def rank_nodes_baselines(graph, method="katz", limit=20) :
# If 'graph' is a string then a path was provided, so we load the graph from it
if (isinstance(graph, basestring)) :
graph = nx.read_gexf(graph, node_type=int)
if method=="katz" :
r = katz_centrality(graph, alpha=0.01, beta=1.0)
elif method=="hits_hub" :
hubs, auth = nx.hits(graph, max_iter=500)
r = hubs
elif method=="hits_auth" :
hubs, auth = nx.hits(graph, max_iter=500)
r = auth
else :
raise ValueError("Invalid method parameter: '%s'" % method)
rank = sorted(r.items(), key=lambda (k,v):v, reverse=True)
results = []
for node_id, score in rank :
if graph.node[node_id]["type"]=="paper" :
results.append((node_id, graph.node[node_id]["paper_id"], score))
if len(results) == limit :
break
return results
def rank_nodes_baselines(graph, method="katz", limit=20) :
# If 'graph' is a string then a path was provided, so we load the graph from it
if (isinstance(graph, basestring)) :
graph = nx.read_gexf(graph, node_type=int)
if method=="katz" :
r = katz_centrality(graph, alpha=0.01, beta=1.0)
elif method=="hits_hub" :
hubs, auth = nx.hits(graph, max_iter=500)
r = hubs
elif method=="hits_auth" :
hubs, auth = nx.hits(graph, max_iter=500)
r = auth
else :
raise ValueError("Invalid method parameter: '%s'" % method)
rank = sorted(r.items(), key=lambda (k,v):v, reverse=True)
results = []
for node_id, score in rank :
if graph.node[node_id]["type"]=="paper" :
results.append((node_id, graph.node[node_id]["paper_id"], score))
if len(results) == limit :
break
return results
def build_graph(conf_name, year, H, min_topic_lift, min_ngram_lift, exclude=[], force=False, save=True, load=False):
"""
Utility method to build and return the graph model. First we check if a graph file
exists. If not, we check if the builder class is already instantiated. If not, we do
it and proceed to build the graph.
"""
global builder
model_folder = config.IN_MODELS_FOLDER % (config.DATASET, H)
# Creates model folder if non existing
if not os.path.exists(model_folder):
os.makedirs(model_folder)
graph_file = utils.get_graph_file_name(conf_name, model_folder)
if force or (not os.path.exists(graph_file)):
if not builder:
builder = kddcup_model.ModelBuilder()
# Builds the graph file
graph = builder.build(conf_name, year, H, min_topic_lift, min_ngram_lift, exclude)
# Stores gexf copy for caching purposes
if save:
nx.write_gexf(graph, graph_file)
return graph
else:
# A gexf copy already exists in disk. Just load it and return
# print graph_file
try:
graph = nx.read_gexf(graph_file, node_type=int)
except:
print "Problem opening '%s'." % graph_file
sys.exit(1)
return graph
def build_graph(query, K, H, min_topic_lift, min_ngram_lift, exclude=[], force=False, save=True, load=False):
"""
Utility method to build and return the graph model. First we check if a graph file
exists. If not, we check if the builder class is already instantiated. If not, we do
it and proceed to build the graph.
"""
global builder
model_folder = config.IN_MODELS_FOLDER % (config.DATASET, K, H)
# Creates model folder if non existing
if not os.path.exists(model_folder):
os.makedirs(model_folder)
graph_file = utils.get_graph_file_name(query, model_folder)
if force or (not os.path.exists(graph_file)):
if not builder:
builder = model.ModelBuilder()
# Builds the graph file
graph = builder.build(query, K, H, min_topic_lift, min_ngram_lift, exclude)
# Stores gexf copy for caching purposes
if save:
nx.write_gexf(graph, graph_file)
return graph
else:
# A gexf copy already exists in disk. Just load it and return
# print graph_file
try:
graph = nx.read_gexf(graph_file, node_type=int)
except:
print "Problem opening '%s'." % graph_file
sys.exit(1)
return graph
def lpu_parser_legacy(filename):
"""
TODO: Update
"""
graph = nx.read_gexf(filename)
return LPU.graph_to_dicts(LPU.conv_legacy_graph(graph))
def run(out_name):
V = vis.visualizer()
# Assumes that generic_lpu_0_input.h5 and generic_lpu_1_input.h5
# contain the same data:
V.add_LPU('./data/generic_lpu_0_input.h5', LPU='Sensory', is_input=True)
V.add_plot({'type': 'waveform', 'uids': [['sensory_0']], 'variable':'I'},
'input_Sensory')
for i in [0, 1]:
G = nx.read_gexf('./data/generic_lpu_%s.gexf.gz' % i)
neu_proj = sorted([k for k, n in G.node.items() if \
n['name'][:4] == 'proj' and \
n['class'] == 'LeakyIAF'])
N = len(neu_proj)
V.add_LPU('generic_lpu_%s_%s_output.h5' % (i, out_name),
'Generic LPU %s' % i,
gexf_file='./data/generic_lpu_%s.gexf.gz' % i)
V.add_plot({'type': 'raster', 'uids': [neu_proj],
'variable': 'spike_state',
'yticks': range(1, 1+N),
'yticklabels': neu_proj, 'title': 'Output'},
'Generic LPU %s' % i)
V.rows = 3
V.cols = 1
V.fontsize = 8
V.out_filename = '%s.mp4' % out_name
V.codec = 'mpeg4'
V.xlim = [0, 1.0]
V.run()
#V.run('%s.png' % out_name)
# Run the visualizations in parallel:
