def read_mat(file, target_chr = None):
"""Generator: for returning the contents of a mat file (e.g. hg18_genes.mat) on a given chromosome."""
wp = open(file)
for line in wp:
A = re.split("\s+", line.rstrip())
if (target_chr and A[1] != target_chr):
continue
partNo = int(A[0])
start = int(A[2])
finish = int(A[3])
family = A[5]
M = sparse2matrix(" ".join(A[8:]))
R = RptMatrix(None, None)
R.class_name = A[5]
R.rep_name = A[6]
R.M = reduce_64matrix(M)
yield partNo, start, finish, R
def FHK3(rpt_list, global_dist_file = None, tmp_file = None):
"""Calculate the fit by computing q and d values by aggrigrated family.
q: Estimated by averaging the q over the families.
d: Estimated for a family using the average over the instances of the family.
"""
# First: Create a list of fake repeats -- one for each family, where the C
# matrices are combined.
C_dic = {}
for r in rpt_list:
alpha = r.class_name
if alpha not in C_dic:
new_r = RptMatrix(None, None)
new_r.class_name = alpha
C_dic[alpha] = new_r
C_dic[alpha].M += r.M
family_rpt_list = C_dic.values()
# Now we call algorithm 2 on the new repeat list
return FHK1(family_rpt_list)
