def load(self):
outdir = config.get_cache_filename(
config.CACHE_SIGNATURE_GROUPS_DIR,
self._context.dumpname)
inname = os.path.sep.join([outdir, self._name])
self._similarities = utils.int_array_cache(inname)
return
def _load(self):
## DO NOT SORT LIST. c'est des sequences. pas des sets.
myname = self.cacheFilenamePrefix+'.pinned'
sig = utils.int_array_cache(myname)
if sig is None:
log.info("Signature has to be calculated for %s. It's gonna take a while."%(self.name))
pointerSearcher = pointerfinder.PointerSearcher(self.mmap)
self.WORDSIZE = pointerSearcher.WORDSIZE
sig = []
# save first offset
last = self.mmap.start
for i in pointerSearcher: #returns the vaddr
sig.append(i-last) # save intervals between pointers
#print hex(i), 'value:', hex(self.mmap.readWord(i) )
last=i
# save it
sig = utils.int_array_save(myname, sig)
else:
log.debug("%d Signature intervals loaded from cache."%( len(sig) ))
self.sig = sig
#
self.addressCache[0] = self.mmap.start # previous pointer of interval 0 is start of mmap
self._loadAddressCache()
return
def load(self, _context):
inname = _context.get_filename_cache_signatures()
self._similarities = utils.int_array_cache(inname)
return
def load(self):
outdir = config.get_cache_filename(config.CACHE_SIGNATURE_GROUPS_DIR,
self._context.dumpname)
inname = os.path.sep.join([outdir, self._name])
self._similarities = utils.int_array_cache(inname)
return
def load(self, _context):
inname = _context.get_filename_cache_signatures()
self._similarities = utils.int_array_cache(inname)
return
def clean(digraph):
# clean solos
isolates = networkx.algorithms.isolate.isolates(digraph)
digraph.remove_nodes_from(isolates)
# clean solos clusters
graph = networkx.Graph(digraph) # undirected
subgraphs = networkx.algorithms.components.connected.connected_component_subgraphs(
graph)
isolates1 = set(utils.flatten(g.nodes() for g in subgraphs if len(g) == 1)) # self connected
isolates2 = set(utils.flatten(g.nodes() for g in subgraphs if len(g) == 2))
isolates3 = set(utils.flatten(g.nodes() for g in subgraphs if len(g) == 3))
digraph.remove_nodes_from(isolates1)
digraph.remove_nodes_from(isolates2)
digraph.remove_nodes_from(isolates3)
#
#graph = digraph.to_undirected()
#subgraphs = networkx.algorithms.components.connected.connected_component_subgraphs(graph)
subgraphs = [g for g in subgraphs if len(g) > 3]
isolatedGraphs = subgraphs[1:100]
# group by nodes number
isoDict = defaultdict(list)
[isoDict[len(g)].append(g) for g in isolatedGraphs]
# test isomorphism
isoGraphs = dict()
for numNodes, graphs in isoDict.items():
numgraphs = len(graphs)
if numgraphs == 1:
continue
isoGraph = networkx.Graph()
# quick find isomorphisms
todo = set(graphs)
for i, g1 in enumerate(graphs):
for g2 in graphs[i + 1:]:
if networkx.is_isomorphic(g1, g2):
print 'numNodes:%d graphs %d, %d are isomorphic' % (numNodes, i, i + 1)
isoGraph.add_edge(g1, g2, {'isomorphic': True})
if g2 in todo:
todo.remove(g2)
if g1 in todo:
todo.remove(g1)
# we can stop here, chain comparaison will work between g2
# and g3
break
if len(isoGraph) > 0:
isoGraphs[numNodes] = isoGraph
# draw the isomorphisms
for i, item in enumerate(isoGraphs.items()):
num, g = item
# networkx.draw(g)
for rg in g.nodes():
networkx.draw(rg)
fname = os.path.sep.join(
[config.imgCacheDir, 'isomorph_subgraphs_%d.png' % (num)])
plt.savefig(fname)
plt.clf()
# need to use gephi-like for rendering nicely on the same pic
bigGraph = networkx.DiGraph()
bigGraph.add_edges_from(digraph.edges(subgraphs[0].nodes()))
stack_addrs = utils.int_array_cache(
config.get_cache_filename(config.CACHE_STACK_VALUES, ctx.dumpname, ctx._heap_addr))
stack_addrs_txt = set(['%x' % (addr)
for addr in stack_addrs]) # new, no long
stacknodes = list(set(bigGraph.nodes()) & stack_addrs_txt)
print 'stacknodes left', len(stacknodes)
orig = list(set(graph.nodes()) & stack_addrs_txt)
print 'stacknodes orig', len(orig)
# identify strongly referenced allocators
degreesList = [(bigGraph.in_degree(node), node)
for node in bigGraph.nodes()]
degreesList.sort(reverse=True)
def clean(digraph):
# clean solos
isolates = networkx.algorithms.isolate.isolates(digraph)
digraph.remove_nodes_from(isolates)
# clean solos clusters
graph = networkx.Graph(digraph) # undirected
subgraphs = networkx.algorithms.components.connected.connected_component_subgraphs(
graph)
isolates1 = set(utils.flatten(g.nodes() for g in subgraphs
if len(g) == 1)) # self connected
isolates2 = set(utils.flatten(g.nodes() for g in subgraphs if len(g) == 2))
isolates3 = set(utils.flatten(g.nodes() for g in subgraphs if len(g) == 3))
digraph.remove_nodes_from(isolates1)
digraph.remove_nodes_from(isolates2)
digraph.remove_nodes_from(isolates3)
#
#graph = digraph.to_undirected()
#subgraphs = networkx.algorithms.components.connected.connected_component_subgraphs(graph)
subgraphs = [g for g in subgraphs if len(g) > 3]
isolatedGraphs = subgraphs[1:100]
# group by nodes number
isoDict = defaultdict(list)
[isoDict[len(g)].append(g) for g in isolatedGraphs]
# test isomorphism
isoGraphs = dict()
for numNodes, graphs in isoDict.items():
numgraphs = len(graphs)
if numgraphs == 1:
continue
isoGraph = networkx.Graph()
# quick find isomorphisms
todo = set(graphs)
for i, g1 in enumerate(graphs):
for g2 in graphs[i + 1:]:
if networkx.is_isomorphic(g1, g2):
print('numNodes:%d graphs %d, %d are isomorphic' %
(numNodes, i, i + 1))
isoGraph.add_edge(g1, g2, {'isomorphic': True})
if g2 in todo:
todo.remove(g2)
if g1 in todo:
todo.remove(g1)
# we can stop here, chain comparaison will work between g2
# and g3
break
if len(isoGraph) > 0:
isoGraphs[numNodes] = isoGraph
# draw the isomorphisms
for i, item in enumerate(isoGraphs.items()):
num, g = item
# networkx.draw(g)
for rg in g.nodes():
networkx.draw(rg)
fname = os.path.sep.join(
[config.imgCacheDir,
'isomorph_subgraphs_%d.png' % num])
plt.savefig(fname)
plt.clf()
# need to use gephi-like for rendering nicely on the same pic
bigGraph = networkx.DiGraph()
bigGraph.add_edges_from(digraph.edges(subgraphs[0].nodes()))
stack_addrs = utils.int_array_cache(
config.get_cache_filename(config.CACHE_STACK_VALUES, ctx.dumpname,
ctx._heap_addr))
stack_addrs_txt = set(['%x' % addr
for addr in stack_addrs]) # new, no long
stacknodes = list(set(bigGraph.nodes()) & stack_addrs_txt)
print('stacknodes left', len(stacknodes))
orig = list(set(graph.nodes()) & stack_addrs_txt)
print('stacknodes orig', len(orig))
# identify strongly referenced allocators
degreesList = [(bigGraph.in_degree(node), node)
for node in bigGraph.nodes()]
degreesList.sort(reverse=True)
