def constructOverlap(bamfile, ah, edge, tolerance):
qid = ah.query_name
qs = ah.query_alignment_start
qe = ah.query_alignment_end
ql = ah.query_length
tid = bamfile.getrname(ah.reference_id)
ts = ah.reference_start
te = ah.reference_end
tl = ah.reference_length
strand = 0
score = ah.mapping_quality
if ah.is_reverse:
strand = 1
ov = overlap(qid, tid, score, -1, strand, qs, qe, ql, 0, ts, te, tl)
return ov
# proc.communicate()
#Make sure you load the Overlap.py from the mrsa_analysis directory
from Overlap import overlap
from Overlap import read_overlaps
c=0
blasr_file = "/sc/orga/scratch/webste01/mrsa_analysis/unitig_54_blasr_out"
with open(blasr_file,"r") as bf:
for l in bf:
name1, name2, score, pctiden, strand1, start1, end1, len1, strand2, start2, end2, len2 = l.split()[0:12]
#check to see that the read is in an overhanging read
if name1 in overhanging_reads_list:
print "in list"
o = overlap(name1, name2, score, pctiden, strand1, start1, end1, len1, strand2, start2, end2, len2)
if o.hasFullOverlap():
print name1, "overlapping"
#else:
# print name1, "not full overlap"
#else:
# print "non overhanging"
#Check whether the overhanging reads are also overlapping reads
#Check whether the branching reads align to neighbors of the unitig in the graph
doors = []
has_door_been_init = True
door_occup = np.zeros(len(doors), dtype=np.int16)
print(doors, "\n", door_occup)
#HOG sliding window is 64x128
ped = detectors.ped_det(image) #Ped np array
if zero_f_exists == False: #First frame
ovlp_o = [False] * len(
doors) #create empty array to fill in/vector of 1xn
ct = 0
for object in doors:
ovlp_o[ct] = overlap(object, ped, 0.3)
ct += 1
ped_len_o = len(ped)
zero_f_exists = True
print("Init conditions scanned")
else:
ct_d = 0
ped_len_n = len(ped)
ovlp_n = [False] * len(doors)
for object in doors: #Object is door num
#Please check for errors again
ovlp_n_v = overlap(object, ped, 0.3)
ovlp_n[ct_d] = ovlp_n_v
