def test_align_multi(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
tsk.run()
with open(
os.path.join(ddir, 'output_data',
'reference_alignment_target.csv')) as f:
result = f.readlines()
with open('tests/data/3sak/3sak_self.csv') as f:
template = f.readlines()
self.assertEqual(result, template)
def test_analysis(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
tsk.run()
with open(os.path.join(ddir, 'output_pdbs', 'replica.pdb')) as f:
result = f.readlines()
with open('tests/data/1hpw/replica.pdb') as f:
template = f.readlines()
inds = numpy.random.choice(range(len(result)), 50)
result = list(numpy.array(result)[inds])
template = list(numpy.array(template)[inds])
self.assertEqual(result, template)
def test_rmsf(self, ddir, prsr):
"""Plik tests/data/2gb1.rmsf zawiera znormalizowane do max rmsf z wersji serwerowej."""
if cla.fast:
raise SkipTest
tsk = FlexTask(**vars(prsr))
tsk.run()
with open(os.path.join(ddir, 'plots/RMSF.csv')) as f:
rmsf = [float(l.split()[1]) for l in f.readlines()]
rmsf = [i / max(rmsf) for i in rmsf]
with open('tests/data/2gb1.rmsf') as f:
ref = [float(l.split()[1]) for l in f.readlines()]
diffs = [i - j for i, j in zip(rmsf, ref)]
av = sum(map(abs, diffs)) / len(diffs)
self.assertLess(av, .15) # average lower than .15
mavs = [sum(diffs[i:i + 6]) / 6. for i in range(len(diffs) - 6)]
self.assertGreater(len([i for i in map(abs, mavs) if i < .2]),
.8 * len(diffs))
def test_run_default(self, ddir, prsr):
if cla.fast:
raise SkipTest
tsk = FlexTask(**vars(prsr))
tsk.run()
def clc_len(fn):
with open(fn) as f:
return len(f.raedlines())
csvs = {
i: clc_len(ddir + '/plots/' + i)
for i in os.listdir(ddir + '/plots') if 'csv' in i
}
for i, j in csvs.items():
self.assertEqual(j, 129 if i == 'RMSF.csv' else 1000)
rmtree(ddir)
def test_default(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
self.assertEqual(tsk.mc_steps, 50)
self.assertEqual(tsk.replicas, 1)
self.assertEqual(tsk.mc_annealing, 20)
self.assertEqual(tsk.mc_cycles, 50)
self.assertEqual(tsk.input_protein, '1hpw')
self.assertEqual(tsk.protein_restraints, ('ss2', 3, 3.8, 8.0))
self.assertEqual(tsk.work_dir, ddir)
self.assertEqual(tsk.verbose, 0)
def test_output_completeness(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
tsk.run()
lst = os.listdir(ddir)
outSet = {
'output_pdbs': [
'%s_%i.pdb' % (tp, i) for tp in ('model', 'cluster')
for i in range(10)
] + ['replica.pdb'],
'output_data': [
'all_rmsds_A.txt', 'lowest_rmsds_A.txt', 'medoids_rmsds_A.txt',
'reference_alignment_target.csv'
],
'plots': [
'%s.%s' % (n, e) for e in ('csv', 'svg')
for n in ('E_RMSD_A_total', 'RMSD_frame_A_replica_0')
]
}
self.assertEqual(set(lst).intersection(outSet), set(outSet.keys()))
for i, j in outSet.items():
lst = os.listdir(ddir + '/' + i)
self.assertEqual(set(lst).intersection(j), set(j))
def test_FTinit(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
def test_raises_VE_wrong_reference(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
ftraf = tsk.file_TRAF
fseq = tsk.file_SEQ
tsk.setup_job()
tsk.parse_reference(tsk.reference_pdb)
tsk.load_output(ftraf, fseq)
tsk.score_results(n_filtered=tsk.filtering_count,
number_of_medoids=tsk.clustering_medoids,
number_of_iterations=tsk.clustering_iterations)
with self.assertRaises(ValueError):
tsk.calculate_rmsd()
def test_save_CABS_files(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
tsk.run()
self.assertTrue(any('.cbs' in i for i in os.listdir(ddir)))
def test_run(self, ddir, prsr):
tsk = FlexTask(**vars(prsr))
tsk.run()