def test(self):
BuildMSM.run(lagtime=1, assignments=get('Assignments.h5')['arr_0'], symmetrize='MLE',
out_dir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'), decimal=5)
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'), decimal=3)
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
def test(self):
BuildMSM.run(lagtime=1, assignments=get('Assignments.h5')['arr_0'], symmetrize='MLE',
out_dir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'), decimal=5)
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'), decimal=3)
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
def test(self):
BuildMSM.run(LagTime=1, assignments=get('Assignments.h5')['arr_0'], Symmetrize='MLE',
OutDir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'))
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'))
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
def test(self):
args, metric = Assign.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-g', get('Gens.lh5', just_filename=True),
'-o', self.td,
'rmsd', '-a', get('AtomIndices.dat', just_filename=True)],
print_banner=False)
Assign.main(args, metric)
eq(load(pjoin(self.td, 'Assignments.h5')),
get('assign/Assignments.h5'))
eq(load(pjoin(self.td, 'Assignments.h5.distances')),
get('assign/Assignments.h5.distances'))
def test(self):
args, metric = Cluster.parser.parse_args([
'-p', get('points_on_cube/ProjectInfo.yaml', just_filename=True),
'-o', self.td,
'rmsd', '-a', get('points_on_cube/AtomIndices.dat', just_filename=True),
'kcenters', '-k', '4'], print_banner=False)
Cluster.main(args, metric)
assignments = load(pjoin(self.td, 'Assignments.h5'))["arr_0"]
assignment_counts = np.bincount(assignments.flatten())
eq(assignment_counts, np.array([2, 2, 2, 2]))
distances = load(pjoin(self.td, 'Assignments.h5.distances'))["arr_0"]
eq(distances, np.zeros((1,8)))
def test(self):
# extract xtcs to a temp dir
xtc_fn = get('XTC.tgz', just_filename=True)
fh = tarfile.open(xtc_fn, mode='r:gz')
fh.extractall(self.td)
fh.close()
outfn = pjoin(self.td, 'ProjectInfo.yaml')
# move to that directory
os.chdir(self.td)
atom_indices = np.arange(4)
ConvertDataToHDF.run(projectfn=outfn,
conf_filename=get('native.pdb', just_filename=True),
input_dir=pjoin(self.td, 'XTC'),
source='file',
min_length=0,
stride=1,
rmsd_cutoff=np.inf,
atom_indices=atom_indices, iext=".xtc")
project = load(outfn)
traj = project.load_conf()
eq(traj.n_atoms, 4)
def test(self):
args, metric = Assign.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-g', get('Gens.lh5', just_filename=True),
'-o', self.td,
'rmsd', '-a', get('OldAtomIndices.dat', just_filename=True)],
print_banner=False)
if os.getenv('TRAVIS', None) == 'true':
raise nose.SkipTest('Skipping test_Assign on TRAVIS')
Assign.main(args, metric)
eq(load(pjoin(self.td, 'Assignments.h5')),
get('assign/Assignments.h5'))
eq(load(pjoin(self.td, 'Assignments.h5.distances')),
get('assign/Assignments.h5.distances'), decimal=5)
def test(self):
prep_metric = metrics.Dihedral(angles='phi/psi')
project = get('ProjectInfo.yaml')
os.chdir(self.td)
tICA_train.run(prep_metric, project, delta_time=10, atom_indices=None,
output='tICAtest.h5', min_length=0, stride=1)
ref_tICA = get('tICA_ref_mle.h5')
ref_vals = ref_tICA['vals']
ref_vecs = ref_tICA['vecs']
ref_inds = np.argsort(ref_vals)
ref_vals = ref_vals[ref_inds]
ref_vecs = ref_vecs[:, ref_inds]
test_tICA = load('tICAtest.h5')
test_vals = test_tICA['vals']
test_vecs = test_tICA['vecs']
test_inds = np.argsort(test_vals)
test_vals = test_vals[test_inds]
test_vecs = test_vecs[:, test_inds]
eq(test_vals, ref_vals)
eq(test_vecs, test_vecs)
def test(self):
args, metric = Cluster.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-a', pjoin(self.td, 'Assignments.h5'),
'-d', pjoin(self.td, 'Assignments.h5.distances'),
'-g', pjoin(self.td, 'Gens.lh5'),
'rmsd', '-a', get('AtomIndices.dat', just_filename=True),
'kcenters', '-k', '100'], print_banner=False)
Cluster.main(args, metric)
eq(load(pjoin(self.td, 'Assignments.h5')),
get('Assignments.h5'))
eq(load(pjoin(self.td, 'Assignments.h5.distances')),
get('Assignments.h5.distances'))
eq(load(pjoin(self.td, 'Gens.lh5')),
get('Gens.lh5'))
def test(self):
args, metric = Cluster.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-s', '10',
'-o', self.td,
'rmsd', '-a', get('AtomIndices.dat', just_filename=True),
'hierarchical'],
print_banner=False)
Cluster.main(args, metric)
eq(load(pjoin(self.td, 'ZMatrix.h5')), get('ZMatrix.h5'))
def test(self):
try:
import fastcluster
except ImportError:
raise nose.SkipTest("Cannot find fastcluster, so skipping hierarchical clustering test.")
args, metric = Cluster.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-s', '10',
'-o', self.td,
'rmsd', '-a', get('AtomIndices.dat', just_filename=True),
'hierarchical'],
print_banner=False)
Cluster.main(args, metric)
eq(load(pjoin(self.td, 'ZMatrix.h5')), get('ZMatrix.h5'))
def test(self):
# extract xtcs to a temp dir
xtc_fn = get('XTC.tgz', just_filename=True)
fh = tarfile.open(xtc_fn, mode='r:gz')
fh.extractall(self.td)
fh.close()
outfn = pjoin(self.td, 'ProjectInfo.yaml')
# mode to that directory
os.chdir(self.td)
ConvertDataToHDF.run(projectfn=outfn,
PDBfn=get('native.pdb', just_filename=True),
InputDir=pjoin(self.td, 'XTC'),
source='file',
min_length=0,
stride=1,
rmsd_cutoff=np.inf)
eq(load(outfn), get('ProjectInfo.yaml'))
def test(self):
if os.getenv('TRAVIS', None) == 'true':
raise nose.SkipTest('Skipping test_Assign on TRAVIS')
try:
import fastcluster
except ImportError:
raise nose.SkipTest("Cannot find fastcluster, so skipping hierarchical clustering test.")
args, metric = Cluster.parser.parse_args([
'-p', get('ProjectInfo.yaml', just_filename=True),
'-s', '10',
'-o', self.td,
'rmsd', '-a', get('AtomIndices.dat', just_filename=True),
'hierarchical'],
print_banner=False)
Cluster.main(args, metric)
eq(load(pjoin(self.td, 'ZMatrix.h5')), get('ZMatrix.h5'))
