tooBig[:,j]=0
else:
tooBig[i,j]=1
print "modified",nChanged
print map(len,tables)
def rebuild_distance_matrix():
global dist
dist=[(float('inf'),None)]*(N*N)
for i in range(N):
for j in range(i+1,N):
d,res=table_distance(tables[i],tables[j])
dist[i*N+j]=d,res
dist[j*N+i]=d,res
def count_non_null_items_in_distance_matrix():
c=0
for i in geneRange:
for j in range(i+1,N):
if dist[i*N+j]!=(float('inf'),None):
c+=1
return c
def compute_sstate():
rebuild_tables()
trim_tables(5)
rebuild_distance_matrix()
sstate()
profilerun('compute_sstate()')
# compute_sstate()
genes=ft.keys()
N=len(genes)
genesIndex=dict(zip(genes,range(len(genes))))
# ft=bigFocalParamsValues
ssTable=map(lambda gene:map(lambda k:k[0]+((gene,k[1]),), ft[gene].items()), genes)
#we remove all ssStates where an item has two value mapped (the case for autoactivation)
ssNodes=[[row for row in table if not hasTwoValue(row)] for table in ssTable]
# ssNodes.sort(key=len)
reducedNodes=[]
for table in ssNodes:
rows=[]
for state in table:
s=["_"]*N
for var,val in state:
s[genesIndex[var]]=val
rows.append(tuple(s))
reducedNodes.append(rows)
tables=copy.copy(reducedNodes)
dist={}
for i in range(N):
for j in range(i+1,N):
d=table_distance(tables[i],tables[j])
dist[(i,j)]=d
dist[(j,i)]=d
# sstate()
# print dist
profilerun("sstate()")