def getSSAroundSS(self, solarSystemID, jumps):
ss = self.getSSInfo(solarSystemID)
color = 0
if ss[2] > 0.5:
color = "green"
else:
color = "red"
ssRegion = colored(ss[0], color)
ssName = colored(ss[1], color)
ssSecruity = colored("%.1f" % ss[2], color)
if self.ssgraph:
gr = self.ssgraph
else:
gr = graph()
nodes = self.getAllSS()
gr.add_nodes(nodes)
for edge in self.getAllSSEdges():
gr.add_edge(edge)
print "Searching for Solar Systems around %s: %s(%s) in %d jumps." % (ssRegion, ssName, ssSecruity, jumps)
ssinrad = breadth_first_search(gr, solarSystemID, radius(jumps))
ssinrad = ssinrad[1]
text = "Found %d systems" % len(ssinrad)
text = colored(text, "cyan")
print "Done. %s, including current one." % text
return ssinrad
def sample_gene_interactions(c, args, idx_to_sample):
#fetch variant gene dict for all samples
get_variant_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
k = []
variants = []
#calculate nodes from the graph
hprd_genes = gr.nodes()
if args.gene == None or args.gene not in hprd_genes:
sys.stderr.write("gene name not given else not represented in the p-p interaction file\n")
elif args.gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=args.gene,filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
dot = write(gst)
out.write(dot)
st, sd = shortest_path(gst, args.gene)
if args.var_mode:
for sample in sam.iterkeys():
var = sam[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for x in range(0, (args.radius+1)):
for each in var:
for key, value in sd.iteritems():
if value == x and key == each[0]:
print "\t".join([str(sample),str(args.gene), \
str(x), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3])])
elif (not args.var_mode):
for sample in sam.iterkeys():
for each in sam[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius+1)):
for key, value in sd.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
def testbfs_in_empty_graph(self):
gr = graph()
st, lo = breadth_first_search(gr, filter=radius(2))
assert st == {}
assert lo == []
def test_dfs_in_digraph(self):
gr = testlib.new_graph()
st, pre, post = depth_first_search(gr, root=0, filter=radius(3))
for each in st:
assert (st[each] == None or st[each] == 0
or st[st[each]] == 0 or st[st[st[each]]] == 0)
def test_dfs_in_empty_graph(self):
gr = graph()
st, pre, post = depth_first_search(gr, filter=radius(2))
assert st == {}
assert pre == []
assert post == []
def test_bfs_in_digraph(self):
gr = testlib.new_digraph()
st, lo = breadth_first_search(gr, root=0, filter=radius(3))
for each in st:
assert (st[each] == None or st[each] == 0
or st[st[each]] == 0 or st[st[st[each]]] == 0)
def sample_lof_interactions(c, args, idx_to_sample, samples):
lof = get_lof_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
#calculate nodes from the graph
hprd_genes = gr.nodes()
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene,\
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
# for each level return interacting genes
# if they are variants in the sample.
for rad in range(1, (args.radius+1)):
for key, value in sd.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:", \
"none"])
#initialize k
k = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene, \
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
for rad in range(1, (args.radius+1)):
for each in var:
for key, value in sd.iteritems():
if value == rad and key == each[0]:
print "\t".join([str(sample), \
str(gene), \
str(rad), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10])])
def sample_gene_interactions(c, args, idx_to_sample):
out = open("file.dot", 'w')
#fetch variant gene dict for all samples
samples = get_variant_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
k = []
variants = []
#calculate nodes from the graph
hprd_genes = gr.nodes()
if args.gene == None or args.gene not in hprd_genes:
sys.stderr.write("Gene name not found or")
sys.stderr.write(" gene not in p-p interaction file\n")
elif args.gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=args.gene,filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
dot = write(gst)
out.write(dot)
st, sd = shortest_path(gst, args.gene)
if args.var_mode:
for sample in samples.iterkeys():
var = samples[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for x in range(0, (args.radius + 1)):
for each in var:
for key, value in sd.iteritems():
if value == x and key == each[0]:
print "\t".join([str(sample),str(args.gene), \
str(x), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
elif (not args.var_mode):
for sample in samples.iterkeys():
for each in samples[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius + 1)):
for key, value in sd.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
#initialize variants list for next iteration
variants = []
def sample_lof_interactions(c, args, idx_to_sample, samples):
lof = get_lof_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
#calculate nodes from the graph
hprd_genes = gr.nodes()
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene,\
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
# for each level return interacting genes
# if they are variants in the sample.
for rad in range(1, (args.radius + 1)):
for key, value in sd.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:", \
"none"])
#initialize k
k = []
#initialize variants list for next iteration
variants = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene, \
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
for rad in range(1, (args.radius + 1)):
for each in var:
for key, value in sd.iteritems():
if value == rad and key == each[0]:
print "\t".join([str(sample), \
str(gene), \
str(rad), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
def test_bfs_in_graph(self):
gr = testlib.new_graph()
st, lo = breadth_first_search(gr, root=0, filter=radius(3))
for each in st:
assert (st[each] == None or st[each] == 0 or st[st[each]] == 0
or st[st[st[each]]] == 0)
def testbfs_in_empty_graph(self):
gr = graph()
st, lo = breadth_first_search(gr, filter=radius(2))
assert st == {}
assert lo == []
def test_dfs_in_digraph(self):
gr = testlib.new_graph()
st, pre, post = depth_first_search(gr, root=0, filter=radius(3))
for each in st:
assert (st[each] == None or st[each] == 0 or st[st[each]] == 0
or st[st[st[each]]] == 0)
def test_dfs_in_empty_graph(self):
gr = graph()
st, pre, post = depth_first_search(gr, filter=radius(2))
assert st == {}
assert pre == []
assert post == []
