smotif = ud.getSmotif(s1_profile, s2_profile)
rdc_file, ss_seq, cathdb = ud.initData()
cath_entries = ud.extractCath(smotif, cathdb)
print sys.argv[1], sys.argv[2], s1_profile, s2_profile
print smotif
print len(cath_entries)
#*********************************
# Search for needles in the haystack
#*********************************
B0 = 18.8
TinK = 298.0
Sorder = 1.0
scal = rf.rdcScal(Sorder, B0, TinK)
homologs=[] # specify homologs to exclude from calculation
sort_nchi={}
for domain in cath_entries:
# ['5gstB02', 'GLU', '90', 'MET', '112', 'ASP', '118', 'LEU', '141']
if domain[0] in homologs:
continue
rdcd = ParaData(['rdc', ss_seq, rdc_file])
rdc_data = rdcd.processParaData()
temp_nchi, temp_tensor = [], [] # to store tensor and control datasets
for i in range(0, len(rdc_data)): # iterate through each RDC data set
vector_data, data_error = rf.matchRdcToSmotif(s1_profile, s2_profile, domain, rdc_data[i])
if not data_error:
# [[-0.6490001678466797, -0.3989999294281006, 0.6469993591308594], -7252794860688272.0, -6.593]]
# X Y Z gm1*gm2 rdc
