def _model_for_L_mer(self, L_mer, gap_index, p_binding_site):
"""
Create a model initialised by this K-mer.
"""
# get the start position of the K-mer and a builder to make the model
mer_len = len(L_mer)
start, builder = self._make_builder(gap_index, mer_len)
# get the emission distribution
nucleo_dist = nucleo_dist_from_mer(
seq_to_numpy(L_mer),
self.options.pseudo_count_for_model_initialisation,
gap_index=gap_index
)
emissions = numpy.ones((self.options.K,4))/4.
emissions[start:start+mer_len+1] = nucleo_dist
# build the model
pssm, in_states, out_states = builder.create(
p_gap=.5,
emissions=emissions
)
model = hmm.as_model(
single_gap.add_to_simple_background_model(
model=pssm,
in_states=in_states,
out_states=out_states,
p_binding_site=p_binding_site
)
)
#print model.A
#from IPython.Debugger import Pdb; Pdb().set_trace();
return model
def model_for_initialisation_K_mer(self, K_mer, p_binding_site):
"""
Create a model initialised by this K-mer.
"""
emission_distributions = numpy.ones((self.K, 4)) * self.initialisation_pseudo_count
for k, base in enumerate(K_mer):
if 4 == base:
emission_distributions[k] += 0.25
else:
emission_distributions[k, base] += 1.0
gap_emissions = numpy.ones((4,)) / 4.0
pssm, in_states, out_states = self.builder.create(
p_gap=0.5, non_gap_emissions=emission_distributions, gap_emissions=gap_emissions
)
model = hmm.as_model(
single_gap.add_to_simple_background_model(
model=pssm, in_states=in_states, out_states=out_states, p_binding_site=p_binding_site
)
)
return model
numpy.array([hmm.dirichlet_draw(numpy.ones(builder.M) * strength) for k in xrange(builder.K)])
for strength in dirichlet_prior_strengths
]
gap_emissions = [hmm.dirichlet_draw(numpy.ones(builder.M) * strength) for strength in dirichlet_prior_strengths]
# create out single gapped pssms
pssms = [builder.create(p_gap, non_gap, gap) for non_gap, gap in zip(emissions, gap_emissions)]
# create our complete models (by adding a background model)
p_binding_site = exp_sites_per_sequence / L
models = [
hmm.as_model(
single_gap.add_to_simple_background_model(
model=pssm[0], in_states=pssm[1], out_states=pssm[2], p_binding_site=p_binding_site
)
)
for pssm in pssms
]
# write our logos
# convert to sequences and write fasta
def tag(sample_idx):
return "K%d-g%.2f-N%d-L%d-seed%d-%d" % (K, p_gap, N, L, seed, sample_idx)
print "Writing logos"
for i, model in enumerate(models):
emissions, gap_probs = builder.get_emissions_and_gap_probabilities(model, offset=1)
