def test_read_file(self):
text = 'Hello World!'
# read regular file
fp = join(self.tmpdir, 'test.txt')
with open(fp, 'w') as f:
f.write(text)
with read_file(fp) as f:
obs = f.read()
self.assertEqual(obs, text)
# read compressed file
fp = join(self.tmpdir, 'test.txt.gz')
with gzip.open(fp, 'wb') as f:
f.write(text.encode())
with read_file(fp) as f:
obs = f.read()
self.assertEqual(obs, text)
remove(fp)
def read_search_results(file, maxhits=None):
"""Read homology search results of one sample.
Parameters
----------
file : str
input filepath
maxhits : int
maximum number of hits per protein to preserve
Returns
-------
list of dict
search results
"""
p = re.compile(r'# (\S+): (.*)')
data = []
with read_file(file) as f:
for line in f:
line = line.rstrip('\r\n')
m = p.match(line)
if m:
if m.group(1) == 'ID':
data.append({'id': m.group(2), 'hits': []})
elif m.group(1) == 'Length':
data[-1]['length'] = int(m.group(2))
elif m.group(1) == 'Product':
data[-1]['product'] = m.group(2)
elif m.group(1) == 'Score':
data[-1]['score'] = float(m.group(2))
else:
data[-1]['hits'].append(line)
if len(data[-1]['hits']) == (maxhits or 0):
break
# convert hit table to DataFrame
for i in range(len(data)):
data[i]['hits'] = pd.read_csv(StringIO('\n'.join(data[i]['hits'])),
sep='\t',
na_values='*',
names=[
'id', 'identity', 'evalue',
'score', 'coverage', 'taxid'
],
dtype={
'id': str,
'identity': np.float32,
'evalue': np.float64,
'score': np.float32,
'coverage': np.float32,
'taxid': str
}).set_index('id')
return data
def read_search_results(file,
maxhits=None,
evalue=None,
identity=None,
coverage=None):
"""Read homology search results of one sample.
Parameters
----------
file : str
input filepath
maxhits : int
maximum number of hits per protein to preserve
evalue : float
maximum E-value cutoff
identity : int
minimum percent identity cutoff
coverage : int
minimum percent query coverage cutoff
Returns
-------
list of dict
search results
"""
# read research result file
p = re.compile(r'# (\S+): (.*)')
data = []
with read_file(file) as f:
for line in f:
line = line.rstrip('\r\n')
m = p.match(line)
if m:
key = m.group(1)
if key == 'ID':
data.append({'id': m.group(2), 'hits': []})
elif key == 'Length':
data[-1]['length'] = int(m.group(2))
elif key == 'Product':
data[-1]['product'] = m.group(2)
elif key == 'Score':
data[-1]['score'] = float(m.group(2))
else:
data[-1]['hits'].append(line)
for i in range(len(data)):
# convert hit table to DataFrame
hits = pd.read_csv(StringIO('\n'.join(data[i]['hits'])),
sep='\t',
na_values='*',
names=[
'id', 'identity', 'evalue', 'score',
'coverage', 'taxid'
],
dtype={
'id': str,
'identity': np.float32,
'evalue': np.float64,
'score': np.float32,
'coverage': np.float32,
'taxid': str
}).set_index('id')
# filter hits by thresholds
hits = hits.query(
'evalue <= {} & identity >= {} & coverage >= {}'.format(
(evalue or 100), (identity or 0), (coverage or 0)))
# limit number of hits
data[i]['hits'] = hits.head(maxhits) if maxhits else hits
return data