import sys
import os.path
import os
import numpy as np
import csv
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from ngs_scripts.Aggregate import returnSampleDict
alldict = returnSampleDict()
varnumberntdict = {}
for key in alldict:
if alldict[key].strain.upper() == 'FLUB':
varcounter = 0
# print alldict[key].smartid
varlist = alldict[key].varlist
for var in varlist:
if var.segment == 'HA':
if var.totalcount >= 200 and var.binocheck.upper() == 'TRUE':
# print var.ntpos,var.major,var.majorfreq,var.minor,var.minorfreq
varcounter+=1
varnumberntdict[alldict[key].smartid] = (key,str(varcounter))
thefile = open('flub_varcount.txt','w')
print>>thefile,'smartid,sampleid,varcount'
for key in varnumberntdict:
print>>thefile, key+','+varnumberntdict[key][0]+','+varnumberntdict[key][1]
else:
seq.append(line)
if name:
yield (name, "".join(seq))
condict = {}
with open(consensuspath) as fp:
for name, seq in read_fasta(fp):
# print(name, seq)
condict[name[1:].split(" ")[0]] = seq
# for key in condict:
# print key, condict[key]
somedict = returnSampleDict()
ultvarlist = []
for key in somedict:
if somedict[key].strain == "H3N2":
# print key,somedict[key].smartid
for eachvar in somedict[key].varlist:
if eachvar.segment == "HA":
if eachvar.binocheck == "TRUE":
if eachvar.totalcount >= 100:
ultvarlist.append(eachvar.ntpos)
# printrow1 = [somedict[key].smartid,eachvar.segment,eachvar.ntpos,eachvar.major,eachvar.majorfreq,'major']
# printrow2 = [somedict[key].smartid,eachvar.segment,eachvar.ntpos,eachvar.minor,eachvar.minorfreq,'minor']
# print ','.join(map(str, printrow1))
# print ','.join(map(str, printrow2))
ultvarlist = list(set(ultvarlist))
def l1_norm(somestrain, CUTOFF = 0.03, COVERCUTOFF = 200):
#Obtain reference files
if somestrain.upper() == 'FLUB':
refdict = snplists.Reference.open_fasta('flub_reference.fa')
elif somestrain.upper() == 'H3N2':
refdict = snplists.Reference.open_fasta('flua_reference.fa')
#Retrieves variant information for samples
alldict = returnSampleDict()
#Find samples that only have the strain we specified
sampdict = {}
for key in alldict:
if alldict[key].strain.upper() == somestrain.upper():
sampdict[key] = alldict[key]
keylist = list(sampdict.keys())
keylist.sort()
officialnamelist = []
for key in keylist:
officialnamelist.append(sampdict[key].smartid)
#Peform L1_norm for each segment
for SEGMENT in refdict:
# if SEGMENT == 'HA':
dismatrix = np.zeros((len(keylist),len(keylist)))
for aidx,asamp in enumerate(keylist): #column
for bidx,bsamp in enumerate(keylist): #row
if aidx == bidx:
dismatrix[aidx,bidx] = 0
elif aidx > bidx: #Because matrix is symmetrical we cut work in half
dismatrix[aidx,bidx] = dismatrix[bidx,aidx]
else:
# print aidx,bidx
avarlist = sampdict[asamp].varlist
bvarlist = sampdict[bsamp].varlist
asegntlist = []
bsegntlist = []
asegntdict = {}
bsegntdict = {}
#Filter for good quality variants
for var in avarlist:
if var.totalcount > COVERCUTOFF and var.binocheck.upper() == 'TRUE' and var.minorfreq > CUTOFF and var.segment == SEGMENT:
asegntlist.append(var.segment+'_'+str(var.ntpos))
asegntdict[var.segment+'_'+str(var.ntpos)] = var
for var in bvarlist:
if var.totalcount > COVERCUTOFF and var.binocheck.upper() == 'TRUE' and var.minorfreq > CUTOFF and var.segment == SEGMENT:
bsegntlist.append(var.segment+'_'+str(var.ntpos))
bsegntdict[var.segment+'_'+str(var.ntpos)] = var
#Combine list of variants
unionsegntlist = list(set(asegntlist).union(set(bsegntlist)))
unionsegntlist.sort()
sampindexlist=[]
#Three cases to generating L1_NORM.
#Case 1 -> The variant is found in both samples
#Case 2 -> The variant is found only in sample A, in that case we take B info from reference.
#Case 3 -> The variant is found only in Sample B
for segnt in unionsegntlist:
#Case 1
if segnt in asegntlist and segnt in bsegntlist:
a_afreq = asegntdict[segnt].afreq
a_cfreq = asegntdict[segnt].cfreq
a_gfreq = asegntdict[segnt].gfreq
a_tfreq = asegntdict[segnt].tfreq
b_afreq = bsegntdict[segnt].afreq
b_cfreq = bsegntdict[segnt].cfreq
b_gfreq = bsegntdict[segnt].gfreq
b_tfreq = bsegntdict[segnt].tfreq
#Case 2
elif segnt in asegntlist:
a_afreq = asegntdict[segnt].afreq
a_cfreq = asegntdict[segnt].cfreq
a_gfreq = asegntdict[segnt].gfreq
a_tfreq = asegntdict[segnt].tfreq
refseg,refpos = segnt.split('_')
ref_nt = refdict[refseg][int(refpos)-1]
ref_nt = ref_nt.upper()
b_afreq = 0.0
b_cfreq = 0.0
b_gfreq = 0.0
b_tfreq = 0.0
if ref_nt == 'A':
b_afreq = 1.0
elif ref_nt == 'C':
b_cfreq = 1.0
elif ref_nt == 'G':
b_gfreq = 1.0
elif ref_nt == 'T':
b_tfreq = 1.0
else:
print 'ERROR_ERROR'
#Case 3
elif segnt in bsegntlist:
b_afreq = bsegntdict[segnt].afreq
b_cfreq = bsegntdict[segnt].cfreq
b_gfreq = bsegntdict[segnt].gfreq
b_tfreq = bsegntdict[segnt].tfreq
refseg,refpos = segnt.split('_')
ref_nt = refdict[refseg][int(refpos)-1]
ref_nt = ref_nt.upper()
a_afreq = 0.0
a_cfreq = 0.0
a_gfreq = 0.0
a_tfreq = 0.0
if ref_nt == 'A':
a_afreq = 1.0
elif ref_nt == 'C':
a_cfreq = 1.0
elif ref_nt == 'G':
a_gfreq = 1.0
elif ref_nt == 'T':
a_tfreq = 1.0
else:
print 'ERROR_ERROR'
indexvalue = abs(a_afreq-b_afreq)+abs(a_cfreq-b_cfreq)+abs(a_gfreq-b_gfreq)+abs(a_tfreq-b_tfreq)
sampindexlist.append(indexvalue)
#Sum over sampindexlist (all variants in that segment)
inputvalue = sum(sampindexlist)#/float(len(refdict[SEGMENT])) #normalize
dismatrix[aidx,bidx] = inputvalue
relpath = os.getcwd()
filepath = relpath+'/../FILES/output/'
# thefile = open(filepath+somestrain+'.namelist.csv','w')
# for row in dismatrix:
# print row
# df = pd.DataFrame(dismatrix)
# print df
df = pd.DataFrame(dismatrix, index=officialnamelist, columns=officialnamelist)
df.to_csv(filepath+somestrain+'.'+SEGMENT+'.dissim_all.csv', index=True, header=True, sep=',')