def test_profile_mut_bin_noerr(self):
""" Test the ability of mpathic.profile_mut to compute mutation rates
"""
print '\nIn test_profile_mut_bin_noerr_...'
library_files = glob.glob(self.input_dir+'library_*.txt')
library_files += glob.glob(self.input_dir+'dataset_*.txt')
good_bin_num = 2
bad_bin_num = 5
for file_name in library_files:
print '\t%s ='%file_name,
description = file_name.split('_')[-1].split('.')[0]
executable = lambda:\
profile_mut.main(io.load_dataset(file_name),bin=good_bin_num,err=False)
print '(bin=%d)'%good_bin_num,
# If bad or library, then profile_mut.main should raise SortSeqError
if ('_bad' in file_name) or ('library' in file_name):
try:
self.assertRaises(SortSeqError,executable)
print 'badtype,',
except:
print 'good (ERROR).'
raise
# If good, then profile_mut.main should produce a valid df
elif '_good' in file_name:
try:
df = executable()
qc.validate_profile_mut(df)
out_file = self.output_dir+\
'profile_mut_bin_noerr_%s.txt'%description
io.write(df,out_file) # Test writing
io.load_profile_mut(out_file) # Test loading
print 'good,',
except:
print 'bad (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')
# Should always raise an error if bin num is too large
executable = lambda:\
profile_mut.main(io.load_dataset(file_name),bin=bad_bin_num)
print '(bin=%d)'%bad_bin_num,
try:
self.assertRaises(SortSeqError,executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
def test_profile_mut_bin_noerr(self):
""" Test the ability of mpathic.profile_mut to compute mutation rates
"""
print '\nIn test_profile_mut_bin_noerr_...'
library_files = glob.glob(self.input_dir + 'library_*.txt')
library_files += glob.glob(self.input_dir + 'dataset_*.txt')
good_bin_num = 2
bad_bin_num = 5
for file_name in library_files:
print '\t%s =' % file_name,
description = file_name.split('_')[-1].split('.')[0]
executable = lambda:\
profile_mut.main(io.load_dataset(file_name),bin=good_bin_num,err=False)
print '(bin=%d)' % good_bin_num,
# If bad or library, then profile_mut.main should raise SortSeqError
if ('_bad' in file_name) or ('library' in file_name):
try:
self.assertRaises(SortSeqError, executable)
print 'badtype,',
except:
print 'good (ERROR).'
raise
# If good, then profile_mut.main should produce a valid df
elif '_good' in file_name:
try:
df = executable()
qc.validate_profile_mut(df)
out_file = self.output_dir+\
'profile_mut_bin_noerr_%s.txt'%description
io.write(df, out_file) # Test writing
io.load_profile_mut(out_file) # Test loading
print 'good,',
except:
print 'bad (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')
# Should always raise an error if bin num is too large
executable = lambda:\
profile_mut.main(io.load_dataset(file_name),bin=bad_bin_num)
print '(bin=%d)' % bad_bin_num,
try:
self.assertRaises(SortSeqError, executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
def test_profile_mut_total_err(self):
""" Test the ability of mpathic.profile_mut to compute mutation rates based on total count values
"""
print '\nIn test_profile_mut_total_err...'
library_files = glob.glob(self.input_dir + 'library_*.txt')
library_files += glob.glob(self.input_dir + 'dataset_*.txt')
for file_name in library_files:
print '\t%s =' % file_name,
description = file_name.split('_')[-1].split('.')[0]
executable = lambda: profile_mut.main(io.load_dataset(file_name),
err=True)
# If good, then profile_mut.main should produce a valid df
if '_good' in file_name:
try:
df = executable()
qc.validate_profile_mut(df)
out_file = self.output_dir+\
'profile_mut_total_err_%s.txt'%description
io.write(df, out_file)
io.load_profile_mut(out_file)
print 'good.'
except:
print 'bad (ERROR).'
raise
# If bad, then profile_mut.main should raise SortSeqError
elif '_bad' in file_name:
try:
self.assertRaises(SortSeqError, executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')
def test_profile_mut_total_err(self):
""" Test the ability of mpathic.profile_mut to compute mutation rates based on total count values
"""
print '\nIn test_profile_mut_total_err...'
library_files = glob.glob(self.input_dir+'library_*.txt')
library_files += glob.glob(self.input_dir+'dataset_*.txt')
for file_name in library_files:
print '\t%s ='%file_name,
description = file_name.split('_')[-1].split('.')[0]
executable = lambda: profile_mut.main(io.load_dataset(file_name),err=True)
# If good, then profile_mut.main should produce a valid df
if '_good' in file_name:
try:
df = executable()
qc.validate_profile_mut(df)
out_file = self.output_dir+\
'profile_mut_total_err_%s.txt'%description
io.write(df,out_file)
io.load_profile_mut(out_file)
print 'good.'
except:
print 'bad (ERROR).'
raise
# If bad, then profile_mut.main should raise SortSeqError
elif '_bad' in file_name:
try:
self.assertRaises(SortSeqError,executable)
print 'badtype.'
except:
print 'good (ERROR).'
raise
# There are no other options
else:
raise SortSeqError('Unrecognized class of file_name.')