def test_order2string(self):
self.assertEquals("1st", util.order2string(1))
self.assertEquals("2nd", util.order2string(2))
self.assertEquals("3rd", util.order2string(3))
self.assertEquals("4th", util.order2string(4))
self.assertEquals("11th", util.order2string(11))
self.assertEquals("12th", util.order2string(12))
self.assertEquals("21st", util.order2string(21))
self.assertEquals("22nd", util.order2string(22))
self.assertEquals("23rd", util.order2string(23))
def test_order2string(self):
self.assertEquals("1st", util.order2string(1))
self.assertEquals("2nd", util.order2string(2))
self.assertEquals("3rd", util.order2string(3))
self.assertEquals("4th", util.order2string(4))
self.assertEquals("11th", util.order2string(11))
self.assertEquals("12th", util.order2string(12))
self.assertEquals("21st", util.order2string(21))
self.assertEquals("22nd", util.order2string(22))
self.assertEquals("23rd", util.order2string(23))
def make_background_file(bgseqs, use_revcomp, bgorder):
"""create a meme background file and returns its name and the model itself as
a tuple"""
def make_seqs(seqs):
"""prepare the input sequences for feeding into meme.
This means only taking the unique sequences and their reverse
complement if desired"""
meme_input_seqs = []
for locseq in seqs.values():
seq = locseq[1]
if seq not in meme_input_seqs:
meme_input_seqs.append(seq)
if use_revcomp:
revseq = st.revcomp(seq)
if revseq not in meme_input_seqs:
meme_input_seqs.append(revseq)
return meme_input_seqs
filename = None
bgmodel = st.markov_background(make_seqs(bgseqs), bgorder)
with tempfile.NamedTemporaryFile(mode='w+', prefix='memebg',
delete=False) as outfile:
filename = outfile.name
#logging.info("make background file '%s'", filename)
outfile.write("# %s order Markov background model\n" %
util.order2string(len(bgmodel) - 1))
for order_row in bgmodel:
for seq, frequency in order_row.items():
outfile.write('%s %10s\n' % (seq, str(round(frequency, 8))))
return (filename, bgmodel)
def make_background_file(bgseqs, use_revcomp, bgorder):
"""create a meme background file and returns its name and the model itself as
a tuple"""
def make_seqs(seqs):
"""prepare the input sequences for feeding into meme.
This means only taking the unique sequences and their reverse
complement if desired"""
meme_input_seqs = []
for locseq in seqs.values():
seq = locseq[1]
if seq not in meme_input_seqs:
meme_input_seqs.append(seq)
if use_revcomp:
revseq = st.revcomp(seq)
if revseq not in meme_input_seqs:
meme_input_seqs.append(revseq)
return meme_input_seqs
filename = None
bgmodel = st.markov_background(make_seqs(bgseqs), bgorder)
with tempfile.NamedTemporaryFile(mode='w+', prefix='memebg',
delete=False) as outfile:
filename = outfile.name
#logging.info("make background file '%s'", filename)
outfile.write("# %s order Markov background model\n" %
util.order2string(len(bgmodel) - 1))
for order_row in bgmodel:
for seq, frequency in order_row.items():
outfile.write('%s %10s\n' %
(seq, str(round(frequency, 8))))
return (filename, bgmodel)
