def get_response_content(fs):
numpy.set_printoptions(
linewidth=1000000,
threshold=1000000,
)
out = StringIO()
#
args = construct_args()
#
#
# Precompute some ndarrays
# according to properties of DNA and the genetic code.
if args.mtdna:
code = npcodon.g_code_mito
stop = npcodon.g_stop_mito
else:
code = npcodon.g_code
stop = npcodon.g_stop
#
all_codons = npcodon.enum_codons(stop)
codons = all_codons[:-len(stop)]
ts, tv = npcodon.get_ts_tv(codons)
syn, nonsyn = npcodon.get_syn_nonsyn(code, codons)
compo = npcodon.get_compo(codons)
asym_compo = npcodon.get_asym_compo(codons)
ham = npcodon.get_hamming(codons)
#
subs_counts = yangdata.get_subs_counts_from_data_files(args)
codon_counts = (
numpy.sum(subs_counts, axis=0) + numpy.sum(subs_counts, axis=1))
codon_counts = codon_counts[:len(codons)]
subs_counts = subs_counts[:len(codons), :len(codons)]
v = codon_counts / float(numpy.sum(codon_counts))
log_counts = numpy.log(codon_counts)
#
log_mu, log_kappa, log_omega, d, log_kb, nta, ntc, ntg = g_opt_x.tolist()
log_nt_weights = numpy.array([nta, ntc, ntg, 0])
D = get_sparse_D(
ts, tv, compo, log_counts, d, log_kb, log_nt_weights)
print >> out, D
print >> out, numpy.unique(D)
#
return out.getvalue()
def submain_unconstrained_dominance(args):
#
# Precompute some ndarrays
# according to properties of DNA and the genetic code.
if args.mtdna:
code = npcodon.g_code_mito
stop = npcodon.g_stop_mito
else:
code = npcodon.g_code
stop = npcodon.g_stop
#
all_codons = npcodon.enum_codons(stop)
codons = all_codons[:-len(stop)]
ts, tv = npcodon.get_ts_tv(codons)
syn, nonsyn = npcodon.get_syn_nonsyn(code, codons)
compo = npcodon.get_compo(codons)
asym_compo = npcodon.get_asym_compo(codons)
ham = npcodon.get_hamming(codons)
#
subs_counts = yangdata.get_subs_counts_from_data_files(args)
codon_counts = (
numpy.sum(subs_counts, axis=0) + numpy.sum(subs_counts, axis=1))
for a, b in zip(codons, codon_counts):
print a, ':', b
print 'raw codon total:', numpy.sum(codon_counts)
print 'raw codon counts:', codon_counts
codon_counts = codon_counts[:len(codons)]
print 'non-stop codon total:', numpy.sum(codon_counts)
subs_counts = subs_counts[:len(codons), :len(codons)]
v = codon_counts / float(numpy.sum(codon_counts))
log_counts = numpy.log(codon_counts)
#
# get the minimum expected number of substitutions between codons
mu_empirical = npcodon.get_lb_expected_subs(ham, subs_counts)
print 'lower bound on expected mutations per codon site:', mu_empirical
print
print 'entropy lower bound on negative log likelihood:',
print npcodon.get_lb_neg_ll(subs_counts)
print
#
# initialize parameter value guesses
d = 0.5
theta = numpy.array([
d,
0, 0, 0,
], dtype=float)
boxed_guess = [None]
fmin_args = (
mu_empirical, subs_counts, log_counts, v,
ts, tv, syn, nonsyn, compo, asym_compo,
boxed_guess,
)
f = eval_f_unconstrained
results = scipy.optimize.fmin(
f,
theta,
args=fmin_args,
maxfun=10000,
maxiter=10000,
xtol=1e-8,
ftol=1e-8,
full_output=True,
)
print 'results:', results
xopt = results[0]
print 'optimal solution vector:', xopt
print 'exp optimal solution vector:', numpy.exp(xopt)
print
def submain_constrained_dominance(args):
#
# Precompute some ndarrays
# according to properties of DNA and the genetic code.
if args.mtdna or args.force_mtcode:
code = npcodon.g_code_mito
stop = npcodon.g_stop_mito
else:
code = npcodon.g_code
stop = npcodon.g_stop
#
all_codons = npcodon.enum_codons(stop)
codons = all_codons[:-len(stop)]
ts, tv = npcodon.get_ts_tv(codons)
syn, nonsyn = npcodon.get_syn_nonsyn(code, codons)
compo = npcodon.get_compo(codons)
asym_compo = npcodon.get_asym_compo(codons)
ham = npcodon.get_hamming(codons)
#
subs_counts = yangdata.get_subs_counts_from_data_files(args)
codon_counts = numpy.sum(subs_counts, axis=0) + numpy.sum(
subs_counts, axis=1)
for a, b in zip(codons, codon_counts):
print a, ':', b
print 'raw codon total:', numpy.sum(codon_counts)
print 'raw codon counts:', codon_counts
codon_counts = codon_counts[:len(codons)]
print 'non-stop codon total:', numpy.sum(codon_counts)
subs_counts = subs_counts[:len(codons), :len(codons)]
v = codon_counts / float(numpy.sum(codon_counts))
log_counts = numpy.log(codon_counts)
#
if args.disease == 'genic':
h = get_fixation_genic
elif args.disease == 'recessive':
h = get_fixation_recessive_disease
elif args.disease == 'dominant':
h = get_fixation_dominant_disease
else:
raise Exception
#
# initialize parameter values
mu_r = 1.0
kappa = 2.0
omega = 0.1
#pA = 0.25
#pC = 0.25
#pG = 0.25
#pT = 0.25
theta = numpy.array([mu_r, kappa, omega, 0, 0, 0, 0])
#
# adjust the expected rate parameter
Q = get_Q_slsqp(
ts, tv, syn, nonsyn, compo, asym_compo,
h,
log_counts, v,
theta)
expected_rate = -algopy.dot(algopy.diag(Q), v)
mu_n = 1. / expected_rate
mu_r = npcodon.get_lb_expected_subs(ham, subs_counts)
theta = numpy.array([mu_r, kappa, omega, 0, 0, 0, 0])
#
# get the log likelihood associated with the initial guess
fmin_args = (
subs_counts, log_counts, v,
h,
ts, tv, syn, nonsyn, compo, asym_compo,
)
initial_cost = eval_f(theta, *fmin_args)
print 'negative log likelihood of initial guess:',
print initial_cost
print
print 'entropy bound on negative log likelihood:',
print npcodon.get_lb_neg_ll(subs_counts)
print
do_opt(args, eval_f, theta, fmin_args)
def submain_unconstrained_dominance(args):
#
# Precompute some ndarrays
# according to properties of DNA and the genetic code.
if args.mtdna or args.force_mtcode:
code = npcodon.g_code_mito
stop = npcodon.g_stop_mito
else:
code = npcodon.g_code
stop = npcodon.g_stop
#
all_codons = npcodon.enum_codons(stop)
codons = all_codons[:-len(stop)]
ts, tv = npcodon.get_ts_tv(codons)
syn, nonsyn = npcodon.get_syn_nonsyn(code, codons)
compo = npcodon.get_compo(codons)
asym_compo = npcodon.get_asym_compo(codons)
ham = npcodon.get_hamming(codons)
#
subs_counts = yangdata.get_subs_counts_from_data_files(args)
codon_counts = (
numpy.sum(subs_counts, axis=0) + numpy.sum(subs_counts, axis=1))
for a, b in zip(codons, codon_counts):
print a, ':', b
print 'raw codon total:', numpy.sum(codon_counts)
print 'raw codon counts:', codon_counts
codon_counts = codon_counts[:len(codons)]
print 'non-stop codon total:', numpy.sum(codon_counts)
subs_counts = subs_counts[:len(codons), :len(codons)]
v = codon_counts / float(numpy.sum(codon_counts))
log_counts = numpy.log(codon_counts)
#
"""
if args.disease == 'unconstrained':
if args.integrate == 'quadrature':
h = get_fixation_unconstrained_quad
elif args.integrate == 'special':
h = get_fixation_unconstrained
else:
raise Exception
else:
raise Exception
"""
#FIXME: the h parameter is becoming obsolete
h = None
# predefine some plausible parameters but not the scaling parameter
log_mu = 0
log_kappa = 1
log_omega = -3
d = 1.6
#d = 0.5
#d = -1.2
log_nt_weights = numpy.zeros(4)
#
# get the rate matrix associated with the initial guess
Q = get_Q_unconstrained(
ts, tv, syn, nonsyn, compo, asym_compo,
h,
log_counts,
log_mu, log_kappa, log_omega, d, log_nt_weights)
#
# get the minimum expected number of substitutions between codons
mu_empirical = npcodon.get_lb_expected_subs(ham, subs_counts)
mu_implied = -numpy.sum(numpy.diag(Q) * v)
log_mu = math.log(mu_empirical) - math.log(mu_implied)
print 'lower bound on expected mutations per codon site:', mu_empirical
print
# construct the initial guess
theta = numpy.array([
log_mu,
log_kappa,
log_omega,
d,
0,
0,
0,
])
#
# get the log likelihood associated with the initial guess
fmin_args = (
subs_counts, log_counts, v,
h,
ts, tv, syn, nonsyn, compo, asym_compo,
)
initial_cost = eval_f_unconstrained(theta, *fmin_args)
print 'negative log likelihood of initial guess:',
print initial_cost
print
print 'entropy bound on negative log likelihood:',
print npcodon.get_lb_neg_ll(subs_counts)
print
do_opt(args, eval_f_unconstrained, theta, fmin_args)
