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)]
gtr = npcodon.get_gtr(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 = -3.61291826
log_gtr_exch = numpy.array([
0.76101439,
1.61870564,
0.2481876,
0.02708148,
1.39976982,
0])
log_omega = -2.26059034
d = 5.82284506
log_kb = -1.58612396
log_nt_weights = numpy.array([
-1.01321584,
-0.0838657,
0.32300651,
0])
D = get_sparse_D(
gtr, compo, log_counts, d, log_kb, log_nt_weights)
print >> out, D
print >> out, numpy.unique(D)
#
return out.getvalue()
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)]
gtr = npcodon.get_gtr(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 = -4.02576875,
log_gtr_exch = numpy.array(
[0.50335873, 1.31231415, 0.3491126, -0.17953527, 1.55231821, 0])
log_omega = -2.2685352
#
d = 2.86523675
log_kb = -0.63087496
log_nt_weights = numpy.array([-0.12604391, 0.46524165, 0.96822465, 0])
log_repop = -0.01399715
#
D = get_D(gtr, compo, log_counts, d, log_kb, log_nt_weights, log_repop)
print >> out, D
print >> out, numpy.unique(D)
mask = numpy.sum(gtr, axis=2)
D = D * mask
print >> out, 'unique in mask:', numpy.unique(mask)
print >> out, D
print >> out, numpy.unique(D)
#
return out.getvalue()
def submain_kacser_dominance_gtr(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)]
gtr = npcodon.get_gtr(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
log_kb = 0
theta = numpy.array([
d, log_kb,
0, 0, 0,
], dtype=float)
boxed_guess = [None]
fmin_args = (
mu_empirical, subs_counts, log_counts, v,
gtr, syn, nonsyn, compo, asym_compo,
boxed_guess,
)
f = eval_f_kacser_gtr
results = scipy.optimize.minimize(
f,
theta,
args=fmin_args,
method='Nelder-Mead',
)
print 'results:', results
xopt = results.x
print 'optimal solution vector:', xopt
print 'exp optimal solution vector:', numpy.exp(xopt)
print
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)]
gtr = npcodon.get_gtr(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 = -4.02576875,
log_gtr_exch = numpy.array([
0.50335873,
1.31231415,
0.3491126,
-0.17953527,
1.55231821,
0])
log_omega = -2.2685352
#
d = 2.86523675
log_kb = -0.63087496
log_nt_weights = numpy.array([
-0.12604391,
0.46524165,
0.96822465,
0])
log_repop = -0.01399715
#
D = get_D(
gtr, compo,
log_counts, d, log_kb, log_nt_weights, log_repop)
print >> out, D
print >> out, numpy.unique(D)
mask = numpy.sum(gtr, axis=2)
D = D * mask
print >> out, 'unique in mask:', numpy.unique(mask)
print >> out, D
print >> out, numpy.unique(D)
#
return out.getvalue()
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)]
gtr = npcodon.get_gtr(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
#
# predefine some plausible parameters but not the scaling parameter
log_mu = 0
log_g = numpy.zeros(6, dtype=float)
log_omega = -3
log_nt_weights = numpy.zeros(4, dtype=float)
#
# get the rate matrix associated with the initial guess
Q = get_Q(
gtr, syn, nonsyn, compo, asym_compo,
h,
log_counts,
log_mu, log_g, log_omega, 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,
0, 0, 0, 0, 0,
log_omega,
0, 0, 0,
])
#
# get the log likelihood associated with the initial guess
fmin_args = (
subs_counts, log_counts, v,
h,
gtr, 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
#
# search for the minimum negative log likelihood over multiple parameters
if args.fmin == 'simplex':
results = scipy.optimize.fmin(
eval_f,
theta,
args=fmin_args,
maxfun=10000,
maxiter=10000,
xtol=1e-8,
ftol=1e-8,
full_output=True,
)
elif args.fmin == 'bfgs':
results = scipy.optimize.fmin_bfgs(
eval_f,
theta,
args=fmin_args,
maxiter=10000,
full_output=True,
)
elif args.fmin == 'jeffopt':
results = jeffopt.fmin_jeff_unconstrained(
eval_f,
theta,
args=fmin_args,
)
elif args.fmin == 'ncg':
results = scipy.optimize.fmin_ncg(
eval_f,
theta,
fprime=eval_grad_f,
fhess=eval_hess_f,
args=fmin_args,
avextol=1e-6,
maxiter=10000,
full_output=True,
disp=True,
retall=True,
)
else:
raise Exception
print 'results:', results
xopt = results[0]
print 'optimal solution vector:', xopt
print 'exp optimal solution vector:', numpy.exp(xopt)
print
print 'inverse of hessian:'
print scipy.linalg.inv(eval_hess_f(xopt, *fmin_args))
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)]
gtr = npcodon.get_gtr(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
#
# predefine some plausible parameters but not the scaling parameter
log_mu = 0
log_g = numpy.zeros(6, dtype=float)
log_omega = -3
log_nt_weights = numpy.zeros(4, dtype=float)
#
# get the rate matrix associated with the initial guess
Q = get_Q(gtr, syn, nonsyn, compo, asym_compo, h, log_counts, log_mu,
log_g, log_omega, 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,
0,
0,
0,
0,
0,
log_omega,
0,
0,
0,
])
#
# get the log likelihood associated with the initial guess
fmin_args = (
subs_counts,
log_counts,
v,
h,
gtr,
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
#
# search for the minimum negative log likelihood over multiple parameters
if args.fmin == 'simplex':
results = scipy.optimize.fmin(
eval_f,
theta,
args=fmin_args,
maxfun=10000,
maxiter=10000,
xtol=1e-8,
ftol=1e-8,
full_output=True,
)
elif args.fmin == 'bfgs':
results = scipy.optimize.fmin_bfgs(
eval_f,
theta,
args=fmin_args,
maxiter=10000,
full_output=True,
)
elif args.fmin == 'jeffopt':
results = jeffopt.fmin_jeff_unconstrained(
eval_f,
theta,
args=fmin_args,
)
elif args.fmin == 'ncg':
results = scipy.optimize.fmin_ncg(
eval_f,
theta,
fprime=eval_grad_f,
fhess=eval_hess_f,
args=fmin_args,
avextol=1e-6,
maxiter=10000,
full_output=True,
disp=True,
retall=True,
)
else:
raise Exception
print 'results:', results
xopt = results[0]
print 'optimal solution vector:', xopt
print 'exp optimal solution vector:', numpy.exp(xopt)
print
print 'inverse of hessian:'
print scipy.linalg.inv(eval_hess_f(xopt, *fmin_args))
print