def nllk_drate_fix_drate_times_b(dRate,
multiAlign,
dRateTimesTreeTotalBranchLength,
tree,
qMat,
piProb,
cList,
qRates=[1.]):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
if dRate <= 0:
return 1.e10
seqNames = multiAlign.keys()
mLen = len(multiAlign.values()[0])
treeScaled = copy.deepcopy(tree)
treeScaled.scale_edges(dRateTimesTreeTotalBranchLength /
(dRate * tree.length()))
# print treeScaled.length()
# print dRate
# print treeScaled.length() * dRate
pc0 = pc0_from_dRate_and_tree(dRate, seqNames, treeScaled, qMat, piProb,
cList, qRates)
iRate = mle_irate_given_drate(dRate, treeScaled, pc0, mLen)
nllk = -logprob_msa(multiAlign, treeScaled, qMat, piProb, iRate, dRate,
cList, qRates)
# print nllk
return nllk
def nllk_rate(rate, multiAlign, tree, qMat, piProb, cList):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
iRate, dRate = rate
if iRate <= 0 or dRate <= 0:
return 1.e10
nllk = - logprob_msa(multiAlign, tree, qMat, piProb, iRate, dRate, cList)
return nllk
def nllk_rate(rate, multiAlign, tree, qMat, piProb, cList):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
iRate, dRate = rate
if iRate <= 0 or dRate <= 0:
return 1.e10
nllk = -logprob_msa(multiAlign, tree, qMat, piProb, iRate, dRate, cList)
return nllk
def nllk_drate(dRate, multiAlign, tree, qMat, piProb, cList, qRates=[1.]):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
if dRate <= 0:
return 1.e10
seqNames = multiAlign.keys()
mLen = len(multiAlign.values()[0])
pc0 = pc0_from_dRate_and_tree(dRate, seqNames, tree, qMat, piProb, cList, qRates)
iRate = mle_irate_given_drate(dRate, tree, pc0, mLen)
nllk = - logprob_msa(multiAlign, tree, qMat, piProb, iRate, dRate, cList, qRates)
return nllk
def nllk_drate(dRate, multiAlign, tree, qMat, piProb, cList, qRates=[1.]):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
if dRate <= 0:
return 1.e10
seqNames = multiAlign.keys()
mLen = len(multiAlign.values()[0])
pc0 = pc0_from_dRate_and_tree(dRate, seqNames, tree, qMat, piProb, cList,
qRates)
iRate = mle_irate_given_drate(dRate, tree, pc0, mLen)
nllk = -logprob_msa(multiAlign, tree, qMat, piProb, iRate, dRate, cList,
qRates)
return nllk
def nllk_drate_fix_drate_times_b(dRate, multiAlign, dRateTimesTreeTotalBranchLength, tree, qMat, piProb, cList, qRates=[1.]):
"""
negative log-likelihood for rate = (iRate, dRate) based on PIP
"""
if dRate <= 0:
return 1.e10
seqNames = multiAlign.keys()
mLen = len(multiAlign.values()[0])
treeScaled = copy.deepcopy(tree)
treeScaled.scale_edges(dRateTimesTreeTotalBranchLength / (dRate * tree.length()))
# print treeScaled.length()
# print dRate
# print treeScaled.length() * dRate
pc0 = pc0_from_dRate_and_tree(dRate, seqNames, treeScaled, qMat, piProb, cList, qRates)
iRate = mle_irate_given_drate(dRate, treeScaled, pc0, mLen)
nllk = - logprob_msa(multiAlign, treeScaled, qMat, piProb, iRate, dRate, cList, qRates)
# print nllk
return nllk
