def _score(self, alignment):
if self.use_seq_weights:
seq_weights = alignment.get_seq_weights()
else:
seq_weights = [1.0] * len(alignment.msa)
if self.bg_distribution is None:
# Estimate bg distribution from this alignment
q = weighted_freq_count_pseudocount((aa for seq in alignment.msa for aa in seq), seq_weights, PSEUDOCOUNT)
else:
q = self.bg_distribution
scores = []
for i in xrange(len(alignment.msa[0])):
col = get_column(i, alignment.msa)
n_gaps = col.count("-")
assert n_gaps < len(col)
if self.gap_cutoff != 1 and n_gaps / len(col) > self.gap_cutoff:
score = self.SCORE_OVER_GAP_CUTOFF
else:
score = self._score_col(col, seq_weights, q)
if self.use_gap_penalty:
# vn_entropy has this commented out for some reason
score *= weighted_gap_penalty(col, seq_weights)
scores.append(score)
return scores
def _score(self, alignment):
if self.use_seq_weights:
seq_weights = alignment.get_seq_weights()
else:
seq_weights = [1.] * len(alignment.msa)
# Estimate bg distribution from this alignment
if hasattr(self, 'bg_distribution'):
if not self.bg_distribution:
q = dict((aa, 0) for aa in amino_acids)
for seq in self.msa:
for aa in seq:
q[aa] += 1
self.bg_distribution = q
scores = []
for i in xrange(len(alignment.msa[0])):
col = get_column(i, alignment.msa)
n_gaps = col.count('-')
assert n_gaps < len(col)
if self.gap_cutoff != 1 and n_gaps/len(col) > self.gap_cutoff:
score = self.SCORE_OVER_GAP_CUTOFF
else:
score = self._score_col(col, seq_weights)
if self.use_gap_penalty:
# vn_entropy has this commented out for some reason
score *= weighted_gap_penalty(col, seq_weights)
scores.append(score)
return scores
