def backtrace(self, first, second, f):
m, n = f.shape
alignments = list()
alignment = self.emptyAlignment(first, second)
if self.minScore is None:
minScore = self.bestScore(f)
else:
minScore = self.minScore
for i in range(m):
for j in range(n):
if f[i, j] >= minScore:
self.backtraceFrom(first, second, f, i, j, alignments,
alignment)
return alignments
def __unicode__(self):
first = [text_type(e) for e in self.first.elements]
second = [text_type(e) for e in self.second.elements]
for i in range(len(first)):
n = max(len(first[i]), len(second[i]))
format = u'%-' + text_type(n) + u's'
first[i] = format % first[i]
second[i] = format % second[i]
return u'%s\n%s' % (u' '.join(first), u' '.join(second))
def computeAlignmentMatrix(self, first, second):
m = len(first) + 1
n = len(second) + 1
f = numpy.zeros((m, n), int)
for i in range(1, m):
for j in range(1, n):
# Match elements.
ab = f[i - 1, j - 1] \
+ self.scoring(first[i - 1], second[j - 1])
# Gap on sequenceA.
ga = f[i, j - 1] + self.gapScore
# Gap on sequenceB.
gb = f[i - 1, j] + self.gapScore
f[i, j] = max(0, max(ab, max(ga, gb)))
return f
def __str__(self):
first = [str(e) for e in self.first.elements]
second = [str(e) for e in self.second.elements]
for i in range(len(first)):
n = max(len(first[i]), len(second[i]))
format = '%-' + str(n) + 's'
first[i] = format % first[i]
second[i] = format % second[i]
return '%s\n%s' % (' '.join(first), ' '.join(second))