def PredictionAnalysis(align1, align2, outfile, cons_cut = 0.99, **kwargs):
if not os.path.exists(outfile):
mode = 'w'
last = None
elif check_headers(outfile):
mode = 'w'
last = None
else:
mode = 'a'
with open(outfile) as handle:
iterable = csv.DictReader(handle, delimiter = '\t')
last = get_last(iterable)
a1 = Alignment.alignment_from_file(align1)
a2 = Alignment.alignment_from_file(align2)
sprot = prot_from_path(align1)
tprot = prot_from_path(align2)
defaults = dict(zip(LINK_FIELDS, [None]*len(LINK_FIELDS)))
defaults['Source-Prot']=sprot
defaults['Target-Prot']=tprot
defaults.pop('Source-Start')
defaults.pop('Source-End')
defaults.pop('Target-Start')
defaults.pop('Target-End')
calculator = LinkCalculator()
rmheaders = dict((head, None) for head in calculator.get_fields())
headers = sorted(set(a1.seqs.keys()) & set(a2.seqs.keys()))
iterable = product(izip(count(), a1.iterate_columns(headers)),
izip(count(), a2.iterate_columns(headers)))
if last:
fiterable = dropwhile(iterable, lambda x:x[0][0]<last[2] and x[1][0]<last[3])
else:
fiterable = iterable
ohandle = open(outfile, mode)
writer = csv.DictWriter(ohandle, LINK_FIELDS, delimiter = '\t')
if mode == 'w':
writer.writerow(dict(zip(LINK_FIELDS, LINK_FIELDS)))
for (ind1, seq1), (ind2, seq2) in fiterable:
cseq1 = ''
cseq2 = ''
for s1, s2 in zip(seq1, seq2):
if s1 != '-' and s2 != '-':
cseq1 += s1
cseq2 += s2
if not cseq1 or not cseq2:
continue
c1 = make_counts(cseq1)
c2 = make_counts(cseq2)
row = dict()
row.update(defaults)
row['Source-Start'] = ind1
row['Source-End'] = ind1+1
row['Target-Start'] = ind2
row['Target-End'] = ind2+1
row['Source-Cons'] = max(x/len(cseq1) for x in c1.values())
row['Target-Cons'] = max(x/len(cseq2) for x in c2.values())
if row['Source-Cons'] == 1 or row['Target-Cons'] == 1:
writer.writerow(row)
continue
row['Source-Entropy'] = calculate_entropy(cseq1)
row['Target-Entropy'] = calculate_entropy(cseq2)
for field, val in calculator.calculate_all(cseq1, cseq2):
row[field] = val
mappings = prediction_mapping(cseq1, cseq2)
score = sum([z for _, _, z in mappings])/len(cseq1)
row['Total-Score'] = score
source, target, val = mappings[0]
row.update({'Source-Seq':source,
'Target-Seq':target,
'Correct-Num':val,
'Total-Num':c1[source],
'This-Score': val/c1[source]})
writer.writerow(row)
row.update(rmheaders)
for source, target, val in mappings[1:]:
row.update({'Source-Seq':source,
'Target-Seq':target,
'Correct-Num':val,
'Total-Num':c1[source],
'This-Score': val/c1[source]})
writer.writerow(row)
ohandle.flush()
os.fsync(ohandle.fileno())
