def pairHmmRun2(QuerySeq,trSeq,tProb,eProb_M,eProb_X,qName):
###
InitLookupTable()
#print "QuerySeq ", QuerySeq
#print "trSeq ", trSeq
#print "tProb:\n", tProb
pen = 1.0
eProb_Y = np.array([pen, pen, pen, pen])
nq = len(QuerySeq) #QuerySeq represents the putative TR region in the Query from 3DP step
ntr = len(trSeq) #trSeq is the repeat Element
#coarse estimation of TRs in QuerySeq
#estTRinQ = int(nq/ntr)
estTRinQ = (nq/float(ntr))
#A factor reflecting unrestricted TR estimation
mltFactor = 1.5
#Converting QuerySeq to integers
q = np.arange(nq,dtype=np.int)
tempQ = map(ord,QuerySeq)
q = np.asarray(tempQ)
#Defining the repeatSeq for pairHmm
repeatSeq = trSeq*(int(mltFactor*estTRinQ)+1)
#print "estTRinQ: ",estTRinQ*mltFactor
#print "repeatSeq: ",repeatSeq
#Constructing the repeat array and converting to integers
nr = len(repeatSeq)
r = np.arange(nr,dtype=np.int)
tempR = map(ord,repeatSeq)
r = np.asarray(tempR)
#print "nr: ",nr," nq: ",nq, " ntr: ", ntr
#np.set_printoptions(suppress=True)
Lf_M = np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_X= np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_Y = np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_M,Lf_X,Lf_Y = pHf2.forwardProbLog(q,nq,r,nr,Lf_M,Lf_X,Lf_Y,eProb_M,eProb_X,eProb_Y,tProb)
print "##done_FWD_Calc##\n"
edf = np.zeros((nr+1))
num_TR,llr,index,ProbMax,edf,LendAt = pHf2.numTRlog(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
LLRcutOff = 0.01
LallQR,index = pHf2.getAllFwdProb(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
print "##AllFwdProb##\n"
#lowerBound, devList, diffLLR,LLR = getLowerBound3(LallQR,index,nq,ntr,LLRcutOff,qName)
diffLLR,LLR = getLowerBound_OldMethod(LallQR,index,nq,ntr,LLRcutOff,qName)
print "##LowerBound##\n"
#return num_TR, diffLLR, LLR,lowerBound,devList #UNCOMMENT THIS FOR THE NEW METHOD
return num_TR, diffLLR, LLR #THIS IS FOR THE OLD METHOD
def pairHmmRun2(QuerySeq, trSeq, tProb, eProb_M, eProb_X, qName):
###
InitLookupTable()
#print "QuerySeq ", QuerySeq
#print "trSeq ", trSeq
#print "tProb:\n", tProb
pen = 1.0
eProb_Y = np.array([pen, pen, pen, pen])
nq = len(
QuerySeq
) #QuerySeq represents the putative TR region in the Query from 3DP step
ntr = len(trSeq) #trSeq is the repeat Element
#coarse estimation of TRs in QuerySeq
#estTRinQ = int(nq/ntr)
estTRinQ = (nq / float(ntr))
#A factor reflecting unrestricted TR estimation
mltFactor = 1.5
#Converting QuerySeq to integers
q = np.arange(nq, dtype=np.int)
tempQ = map(ord, QuerySeq)
q = np.asarray(tempQ)
#Defining the repeatSeq for pairHmm
repeatSeq = trSeq * (int(mltFactor * estTRinQ) + 1)
#print "estTRinQ: ",estTRinQ*mltFactor
#print "repeatSeq: ",repeatSeq
#Constructing the repeat array and converting to integers
nr = len(repeatSeq)
r = np.arange(nr, dtype=np.int)
tempR = map(ord, repeatSeq)
r = np.asarray(tempR)
#print "nr: ",nr," nq: ",nq, " ntr: ", ntr
#np.set_printoptions(suppress=True)
Lf_M = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_X = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_Y = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_M, Lf_X, Lf_Y = pHf2.forwardProbLog(q, nq, r, nr, Lf_M, Lf_X, Lf_Y,
eProb_M, eProb_X, eProb_Y, tProb)
print "##done_FWD_Calc##\n"
edf = np.zeros((nr + 1))
num_TR, llr, index, ProbMax, edf, LendAt = pHf2.numTRlog(
Lf_M, Lf_X, Lf_Y, ntr, nr, nq)
LLRcutOff = 0.01
LallQR, index = pHf2.getAllFwdProb(Lf_M, Lf_X, Lf_Y, ntr, nr, nq)
print "##AllFwdProb##\n"
#lowerBound, devList, diffLLR,LLR = getLowerBound3(LallQR,index,nq,ntr,LLRcutOff,qName)
diffLLR, LLR = getLowerBound_OldMethod(LallQR, index, nq, ntr, LLRcutOff,
qName)
print "##LowerBound##\n"
#return num_TR, diffLLR, LLR,lowerBound,devList #UNCOMMENT THIS FOR THE NEW METHOD
return num_TR, diffLLR, LLR #THIS IS FOR THE OLD METHOD
def pairHmmRun(QuerySeq, trSeq, tProb, eProb_M, eProb_X):
###
InitLookupTable()
#print "QuerySeq ", QuerySeq
#print "trSeq ", trSeq
#print "tProb:\n", tProb
pen = 1.0
eProb_Y = np.array([pen, pen, pen, pen])
nq = len(
QuerySeq
) #QuerySeq represents the putative TR region in the Query from 3DP step
ntr = len(trSeq) #trSeq is the repeat Element
#coarse estimation of TRs in QuerySeq
#estTRinQ = int(nq/ntr)
estTRinQ = (nq / float(ntr))
#A factor reflecting unrestricted TR estimation
mltFactor = 1.5
#Converting QuerySeq to integers
q = np.arange(nq, dtype=np.int)
tempQ = map(ord, QuerySeq)
q = np.asarray(tempQ)
#Defining the repeatSeq for pairHmm
repeatSeq = trSeq * (int(mltFactor * estTRinQ) + 1)
#print "estTRinQ: ",estTRinQ*mltFactor
#print "repeatSeq: ",repeatSeq
#Constructing the repeat array and converting to integers
nr = len(repeatSeq)
r = np.arange(nr, dtype=np.int)
tempR = map(ord, repeatSeq)
r = np.asarray(tempR)
#print "nr: ",nr," nq: ",nq, " ntr: ", ntr
#np.set_printoptions(suppress=True)
Lf_M = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_X = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_Y = np.zeros((nq + 1, nr + 1)) - 1.0 * float("inf")
Lf_M, Lf_X, Lf_Y = pHf2.forwardProbLog(q, nq, r, nr, Lf_M, Lf_X, Lf_Y,
eProb_M, eProb_X, eProb_Y, tProb)
print "##done_FWD_Calc##\n"
edf = np.zeros((nr + 1))
num_TR, llr, index, ProbMax, edf, LendAt = pHf2.numTRlog(
Lf_M, Lf_X, Lf_Y, ntr, nr, nq)
#num_TR,llr,index,ProbMax,edf,LendAt = numTRlog(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
#print "LendAt: ", LendAt
#print "index: ", index
#print "edf: ", edf
samplesNumTR = generateSamples(edf, index, ntr, 50)
print "##sampling##\n"
LLRcutOff = 0.01
LallQR, index = pHf2.getAllFwdProb(Lf_M, Lf_X, Lf_Y, ntr, nr, nq)
#print "index where max prob exists: ", index
print "##AllFwdProb##\n"
#lowerBound, devList, maxLlrIdx = getLowerBound(LallQR,index,nq,ntr,LLRcutOff)
highDivLen, highDivRegion = getLowerBound2(LallQR, index, nq, ntr,
LLRcutOff)
print "##LowerBound##\n"
return num_TR, llr, ProbMax, samplesNumTR, highDivLen, highDivRegion
def pairHmmRun(QuerySeq,trSeq,tProb,eProb_M,eProb_X):
###
InitLookupTable()
#print "QuerySeq ", QuerySeq
#print "trSeq ", trSeq
#print "tProb:\n", tProb
pen = 1.0
eProb_Y = np.array([pen, pen, pen, pen])
nq = len(QuerySeq) #QuerySeq represents the putative TR region in the Query from 3DP step
ntr = len(trSeq) #trSeq is the repeat Element
#coarse estimation of TRs in QuerySeq
#estTRinQ = int(nq/ntr)
estTRinQ = (nq/float(ntr))
#A factor reflecting unrestricted TR estimation
mltFactor = 1.5
#Converting QuerySeq to integers
q = np.arange(nq,dtype=np.int)
tempQ = map(ord,QuerySeq)
q = np.asarray(tempQ)
#Defining the repeatSeq for pairHmm
repeatSeq = trSeq*(int(mltFactor*estTRinQ)+1)
#print "estTRinQ: ",estTRinQ*mltFactor
#print "repeatSeq: ",repeatSeq
#Constructing the repeat array and converting to integers
nr = len(repeatSeq)
r = np.arange(nr,dtype=np.int)
tempR = map(ord,repeatSeq)
r = np.asarray(tempR)
#print "nr: ",nr," nq: ",nq, " ntr: ", ntr
#np.set_printoptions(suppress=True)
Lf_M = np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_X= np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_Y = np.zeros((nq+1,nr+1))-1.0*float("inf")
Lf_M,Lf_X,Lf_Y = pHf2.forwardProbLog(q,nq,r,nr,Lf_M,Lf_X,Lf_Y,eProb_M,eProb_X,eProb_Y,tProb)
print "##done_FWD_Calc##\n"
edf = np.zeros((nr+1))
num_TR,llr,index,ProbMax,edf,LendAt = pHf2.numTRlog(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
#num_TR,llr,index,ProbMax,edf,LendAt = numTRlog(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
#print "LendAt: ", LendAt
#print "index: ", index
#print "edf: ", edf
samplesNumTR = generateSamples(edf,index,ntr,50)
print "##sampling##\n"
LLRcutOff = 0.01
LallQR,index = pHf2.getAllFwdProb(Lf_M,Lf_X,Lf_Y,ntr,nr,nq)
#print "index where max prob exists: ", index
print "##AllFwdProb##\n"
#lowerBound, devList, maxLlrIdx = getLowerBound(LallQR,index,nq,ntr,LLRcutOff)
highDivLen, highDivRegion = getLowerBound2(LallQR,index,nq,ntr,LLRcutOff)
print "##LowerBound##\n"
return num_TR, llr, ProbMax, samplesNumTR, highDivLen, highDivRegion
