def test_unipartite_motif_roles(self):
for i in range(0, 2):
result = motif_roles(self.test_filename_u[i],
motifsize=i + 2,
allroles=True)
all_roles = result.nodes[result.nodes.keys()[0]].roles.keys()
roles = [[result.nodes[n].roles[r] for r in all_roles]
for n in result.nodes]
columns = [sum(x) for x in zip(*roles)]
check_total_nodes = sum(columns) == 3 * 13 or 2 * 2
check_columns = any(v == 0 for v in columns) == False
check_rows = [sum(x) for x in roles] == [1] * len(result.nodes)
self.assertTrue(check_columns and check_rows and check_total_nodes)
def test_bipartite_motif_roles(self):
for i in range(0, 5):
result = motif_roles(self.test_filename_b[i],
motifsize=i + 2,
networktype="bipartite",
allroles=False)
all_roles = result.nodes[result.nodes.keys()[0]].roles.keys()
roles = [[result.nodes[n].roles[r] for r in all_roles]
for n in result.nodes]
check_columns = any(v == 0
for v in [sum(x)
for x in zip(*roles)]) == False
check_rows = [sum(x) for x in roles] == [1] * len(result.nodes)
self.assertTrue(check_columns and check_rows)
def test_unipartite_link_roles(self):
for i in range(0, 2):
result = motif_roles(self.test_filename_u[i],
motifsize=i + 2,
links=True,
allroles=True)
all_roles = result.links[result.links.keys()[0]].roles.keys()
roles = [[result.links[n].roles[r] for r in all_roles]
for n in result.links]
columns = [sum(x) for x in zip(*roles)]
#Are there the number of links we think there are?
check_total_links = sum(columns) == sum(
links_per_motif[i + 2].values())
check_columns = any(
v == 0 for v in columns) == False # Every link has a role?
check_rows = [
sum(x) for x in roles
] == [1] * len(result.links) # Every link is in one position only
self.assertTrue(check_columns and check_rows and check_total_links)
def dalig(options, args, r_alignment=True):
#if options.degree==0 and options.normalization>0:
# sys.stderr.write("Not implemented yet the combination degree==0 and normalization>0.\n")
if r_alignment:
alignment, n1, n2 = read_alignment(args[2])
else:
alignment, n1, n2 = check_alignment(args[2])
try:
n1.remove("NULL")
except KeyError:
pass
try:
n2.remove("NULL")
except KeyError:
pass
##########THIS IS KIND OF SUPERSHIT.... BUT IT WORKS!
if options.normalization > 0:
if len(n1) <= len(n2):
reduce_n1 = False
#a_map={key:value for (value, key) in alignment}
a_map = {}
for (value, key) in alignment:
a_map[key] = value
else:
reduce_n1 = True
#a_map={key:value for (key, value) in alignment}
a_map = {}
for (key, value) in alignment:
a_map[key] = value
net1, net1type, n1 = read_network(args[0],
n1,
options.normalization,
red=reduce_n1,
a_map=a_map)
net2, net2type, n2 = read_network(args[1],
n2,
options.normalization,
red=reduce_n1 == False,
a_map=a_map)
alignment = rewrite_alignment(alignment, n1, n2, net1, net2)
else:
net1, net1type, n1 = read_network(args[0], n1)
net2, net2type, n2 = read_network(args[1], n2)
##########################################################
if alignment == []:
return 'optimal = [ (NULL,NULL:0,0:1)] \noverlap = (0,0)\nEnergy = nan\nNormalized energy = 1'
#Are the networks unipartite or bipartite?
if options.bipartite:
if net1type == False and net2type == False:
unipartite = net1type
else:
print "You are comparing a unipartite network with a bipartite one. Both will be considered as unipartite"
unipartite = True
else:
unipartite = True
if unipartite:
net1_roles = motif_roles(
copy.deepcopy(net1),
motifsize=2,
)
net1_roles2 = motif_roles(
copy.deepcopy(net1),
motifsize=3,
)
for i in net1_roles:
net1_roles[i].update(net1_roles2[i])
else:
net1_roles = motif_roles(
copy.deepcopy(net1),
motifsize=2,
networktype="bipartite",
)
for k in range(3, 5):
net1_roles2 = motif_roles(
copy.deepcopy(net1),
motifsize=k,
networktype="bipartite",
)
for i in net1_roles:
net1_roles[i].update(net1_roles2[i])
if unipartite:
net2_roles = motif_roles(
copy.deepcopy(net2),
motifsize=2,
)
net2_roles2 = motif_roles(
copy.deepcopy(net2),
motifsize=3,
)
for i in net2_roles:
net2_roles[i].update(net2_roles2[i])
else:
net2_roles = motif_roles(
copy.deepcopy(net2),
motifsize=2,
networktype="bipartite",
)
for k in range(3, 5):
net2_roles2 = motif_roles(
copy.deepcopy(net2),
motifsize=k,
networktype="bipartite",
)
for i in net2_roles:
net2_roles[i].update(net2_roles2[i])
dalig_in = dalig_input(net1, net2, net1_roles, net2_roles, alignment)
# get a random seed
rnd_seed = random.randint(0, sys.maxint)
# do we want to start from a random initial alignment?
if options.palignment:
aflag = "-a"
else:
aflag = ""
# do we want to start from a random initial alignment?
if options.neighbours:
sflag = "-s"
else:
sflag = ""
# call the dalig alignment code
command = "GSL_RNG_SEED=%s PATH=$PATH:/home/bbr36/.local/bin dalig.x -k %s -l %s -n %s %s %s" % (
rnd_seed, options.degree, options.cost_function, options.normalization,
aflag, sflag)
#dalig_out = tempfile.TemporaryFile()
process = subprocess.Popen(
command,
bufsize=0,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
)
# write the network and role data to dalig
process.stdin.write(dalig_in)
#process.stdin.close()
output = process.communicate()
# print out and store the dalig stdout line by line as it comes
#output=""
#for line in iter(poutput[0].readline, ''):
# output+=line
#process.wait()
# get rid of the GSL seed info and any empty lines from stderr
#dalig_stderr = [i for i in process.stderr.readlines() if "GSL_RNG_SEED" not in i and i != '']
#if dalig_stderr:
# sys.stderr.write(''.join(dalig_stderr))
return output[0]