def spearman_similarity(A, B):
m = maximum_distance(tree, script_tree.getRoot(tree))
#print m,A,B
#print spearman_distance(m[0],m[1]),spearman_distance(A,B)
#print (spearman_distance(m[0],m[1]) - spearman_distance(A,B))/(spearman_distance(m[0],m[1]))
return (spearman_distance(m[0], m[1]) -
spearman_distance(A, B)) / (spearman_distance(m[0], m[1]))
def order_from_tree(tree):
order = []
nodes = script_tree.getNodes(tree)
root = script_tree.getRoot(tree)
for n in nodes:
if not script_tree.isLeaf(tree, n):
order.append(n)
# print order
order.sort(lambda x, y: cmp(script_tree.distanceFrom(tree, x, root), script_tree.distanceFrom(tree, y, root)))
for i in range(len(order)):
order[i] = script_tree.getBootstrap(tree, order[i])
return order
def order_from_tree(tree):
order = []
nodes = script_tree.getNodes(tree)
root = script_tree.getRoot(tree)
for n in nodes:
if not script_tree.isLeaf(tree,n):
order.append(n)
#print order
order.sort(lambda x,y: cmp(script_tree.distanceFrom(tree,x,root),script_tree.distanceFrom(tree,y,root)))
for i in range(len(order)):
order[i] = script_tree.getBootstrap(tree,order[i])
return order
def donnor_search(t, n):
current_node = n
result = -1
while result == -1 and not script_tree.isRoot(t, current_node):
current_node = script_tree.getParent(t, current_node)
annot = script_tree.getBootstrap(t, current_node)
event = annot[1:].split(".")[0]
if event[:2] != "T@" and event[:2] != "D@":
result = event
if annot.find("@T") >= 0:
current_node = script_tree.getRoot(t)
result = -1
return result
def donnor_search(t,n):
current_node = n
result = -1
while result == -1 and not script_tree.isRoot(t,current_node):
current_node = script_tree.getParent(t,current_node)
annot = script_tree.getBootstrap(t,current_node)
event = annot[1:].split(".")[0]
if event[:2] != "T@" and event[:2] != "D@":
result = event
if annot.find("@T") >= 0:
current_node = script_tree.getRoot(t)
result = -1
return result
def kendall_similarity(A, B):
m = maximum_distance(tree, script_tree.getRoot(tree))
# print m
# print kendall_distance(m[0],m[1]) , kendall_distance(A,B)
return (kendall_distance(m[0], m[1]) - kendall_distance(A, B)) / (kendall_distance(m[0], m[1]))
elif words[0] == "ls":
TIME_FOR_SEARCH = float(words[1])
elif words[0] == "d":
MIN_TRANSFER_DIST = int(words[1])
elif words[0] == "rd":
RANDOM = int(words[1])
elif words[0] == "ts":
THRESHOLD_CONSTRAINTS = float(words[1])
else:
print
"unused parameter (bad format):", p
name_constraint_file = parameters[1]
tree = script_tree.readTree(open(parameters[0], "r").readline())
constraints = open(name_constraint_file, "r").readlines()
root = script_tree.getRoot(tree)
print
name_constraint_file
def maximum_spearman(n):
if n % 2 == 0:
m = n / 2
return 2 * m * m
else:
m = (n - 1) / 2
return 2 * m * m + 2 * m
# def maximum_distance(tree,root):
elif words[0] == "r":
RANDOM_TYPE = int(words[1])
elif words[0] == "ls":
TIME_FOR_SEARCH = float(words[1])
elif words[0] == "d":
MIN_TRANSFER_DIST = int(words[1])
elif words[0] == "rd":
RANDOM = int(words[1])
elif words[0] == "ts":
THRESHOLD_CONSTRAINTS = float(words[1])
else:
print "unused parameter (bad format):",p
tree = script_tree.readTree(open(parameters[0],"r").readline())
root = script_tree.getRoot(tree)
tree1 = script_tree.readTree(open(parameters[0],"r").readline())
tree2 = script_tree.readTree(open(parameters[1],"r").readline())
root1 = script_tree.getRoot(tree1)
root2 = script_tree.getRoot(tree2)
def maximum_spearman(n):
if n % 2 == 0:
m = n /2
return 2 * m * m
else:
m = (n-1)/2
output_trf.write("#family,older,younger,support\n")
for gt in gene_trees:
file_rec = open(gt,"r").readlines()
print gt,
constraints_rec = {}
constraints_trf = {}
i = 0
while i < len(file_rec):
words = file_rec[i].split()
#print words,len(words)
if len(words) > 1 and words[1] == "reconciled":
number = int(words[0])
for j in range(i+2,i+2+number):
#print file_rec[j]
tree = script_tree.readTree(file_rec[j]) # one reconciliation
root = script_tree.getRoot(tree)
#annot = script_tree.getBootstrap(tree,root)
#event = annot.split(".")[-1]
#if event[:2] == "T@" or event[:2] == "D@":
#origination = event[2:].split("->")[0]
#else:
#origination = event # detect origination species
#nodes = script_tree.getNodes(species_tree)
##for n in nodes:
#if script_tree.isLeaf(species_tree,n):
#name = script_tree.getName(species_tree,n)
#if name == origination:
#species_under_origin = [name]
#else:
#name = script_tree.getBootstrap(species_tree,n)
#if name == origination:
def kendall_similarity(A,B): m = maximum_distance(tree,script_tree.getRoot(tree)) #print m #print kendall_distance(m[0],m[1]) , kendall_distance(A,B) return (kendall_distance(m[0],m[1]) - kendall_distance(A,B))/(kendall_distance(m[0],m[1]))
def spearman_similarity(A,B): m = maximum_distance(tree,script_tree.getRoot(tree)) #print m,A,B #print spearman_distance(m[0],m[1]),spearman_distance(A,B) #print (spearman_distance(m[0],m[1]) - spearman_distance(A,B))/(spearman_distance(m[0],m[1])) return (spearman_distance(m[0],m[1]) - spearman_distance(A,B))/(spearman_distance(m[0],m[1]))
