def test_estimate_transition_matrix_1():
np.random.seed(42)
count_matrix = np.array([[6, 3, 7], [4, 6, 9], [2, 6, 7]])
t = MSMLib.estimate_transition_matrix(count_matrix)
eq(t, np.array([[0.375, 0.1875, 0.4375],
[0.21052632, 0.31578947, 0.47368421],
[0.13333333, 0.4, 0.46666667]]))
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):
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):
num_macro = 5
TC = get("PCCA_ref/tProb.mtx")
A = get("PCCA_ref/Assignments.Fixed.h5")['arr_0']
print A
macro_map, macro_assign = PCCA.run_pcca(num_macro, A, TC)
r_macro_map = get("PCCA_ref/MacroMapping.dat")
macro_map = macro_map.astype(np.int)
r_macro_map = r_macro_map.astype(np.int)
# The order of macrostates might be different between the reference and
# new lumping. We therefore find a permutation to match them.
permutation_mapping = np.zeros(macro_assign.max() + 1, 'int')
for i in range(num_macro):
j = np.where(macro_map == i)[0][0]
permutation_mapping[i] = r_macro_map[j]
macro_map_permuted = permutation_mapping[macro_map]
MSMLib.apply_mapping_to_assignments(macro_assign, permutation_mapping)
r_macro_assign = get("PCCA_ref/MacroAssignments.h5")['arr_0']
eq(macro_map_permuted, r_macro_map)
eq(macro_assign, r_macro_assign)
def test(self):
num_macro = 5
TC = get("PCCA_ref/tProb.mtx")
A = get("PCCA_ref/Assignments.Fixed.h5")['arr_0']
macro_map, macro_assign = PCCA.run_pcca(num_macro, A, TC)
r_macro_map = get("PCCA_ref/MacroMapping.dat")
macro_map = macro_map.astype(np.int)
r_macro_map = r_macro_map.astype(np.int)
# The order of macrostates might be different between the reference and
# new lumping. We therefore find a permutation to match them.
permutation_mapping = np.zeros(macro_assign.max() + 1, 'int')
for i in range(num_macro):
j = np.where(macro_map == i)[0][0]
permutation_mapping[i] = r_macro_map[j]
macro_map_permuted = permutation_mapping[macro_map]
MSMLib.apply_mapping_to_assignments(macro_assign, permutation_mapping)
r_macro_assign = get("PCCA_ref/MacroAssignments.h5")['arr_0']
eq(macro_map_permuted, r_macro_map)
eq(macro_assign, r_macro_assign)
def test_get_count_matrix_from_assignments_1():
assignments = np.zeros((10, 10), 'int')
val = MSMLib.get_count_matrix_from_assignments(assignments).todense()
correct = np.matrix([[90.0]])
eq(val, correct)
def test_estimate_rate_matrix_1():
np.random.seed(42)
assignments = np.random.randint(2, size=(10, 10))
counts = MSMLib.get_count_matrix_from_assignments(assignments)
K = MSMLib.estimate_rate_matrix(counts, assignments).todense()
correct = np.matrix([[-40.40909091, 0.5], [0.33928571, -50.55357143]])
eq(K, correct)
def test_apply_mapping_to_assignments_1():
l = 100
assignments = np.random.randint(l, size=(10, 10))
mapping = np.ones(l)
MSMLib.apply_mapping_to_assignments(assignments, mapping)
eq(assignments, np.ones((10, 10)))
def test_get_count_matrix_from_assignments_1():
assignments = np.zeros((10, 10), "int")
val = MSMLib.get_count_matrix_from_assignments(assignments).todense()
correct = np.matrix([[90.0]])
eq(val, correct)
def test_estimate_rate_matrix_1():
np.random.seed(42)
assignments = np.random.randint(2, size=(10, 10))
counts = MSMLib.get_count_matrix_from_assignments(assignments)
K = MSMLib.estimate_rate_matrix(counts, assignments).todense()
correct = np.matrix([[-40.40909091, 0.5], [0.33928571, -50.55357143]])
eq(K, correct)
def test_apply_mapping_to_assignments_1():
l = 100
assignments = np.random.randint(l, size=(10, 10))
mapping = np.ones(l)
MSMLib.apply_mapping_to_assignments(assignments, mapping)
eq(assignments, np.ones((10, 10)))
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_get_count_matrix_from_assignments_2():
np.random.seed(42)
assignments = np.random.randint(3, size=(10, 10))
val = MSMLib.get_count_matrix_from_assignments(assignments).todense()
correct = np.matrix([[11., 9., 10.], [9., 17., 7.], [10., 7., 10.]])
eq(val, correct)
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_get_count_matrix_from_assignments_3():
np.random.seed(42)
assignments = np.random.randint(3, size=(10, 10))
val = MSMLib.get_count_matrix_from_assignments(assignments, lag_time=2, sliding_window=False).todense()
eq(val, np.matrix([[5.0, 3.0, 4.0], [2.0, 12.0, 3.0], [4.0, 3.0, 4.0]]))
val = MSMLib.get_count_matrix_from_assignments(assignments, lag_time=2, sliding_window=True).todense()
eq(val, np.matrix([[8.0, 9.0, 11.0], [5.0, 18.0, 6.0], [11.0, 5.0, 7.0]]))
def test_estimate_transition_matrix_1():
np.random.seed(42)
count_matrix = np.array([[6, 3, 7], [4, 6, 9], [2, 6, 7]])
t = MSMLib.estimate_transition_matrix(count_matrix)
eq(
t,
np.array([[0.375, 0.1875, 0.4375],
[0.21052632, 0.31578947, 0.47368421],
[0.13333333, 0.4, 0.46666667]]))
def test_get_count_matrix_from_assignments_2():
np.random.seed(42)
assignments = np.random.randint(3, size=(10, 10))
val = MSMLib.get_count_matrix_from_assignments(assignments).todense()
correct = np.matrix([[11.0, 9.0, 10.0], [9.0, 17.0, 7.0], [10.0, 7.0, 10.0]])
eq(val, correct)
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):
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_CalculateTPT():
T = get("transition_path_theory_reference/tProb.mtx")
sources = [0] # chosen arb. for ref. by TJL
sinks = [70] # chosen arb. for ref. by TJL
script_out = CalculateTPT.run(T, sources, sinks)
committors_ref = get(pjoin("transition_path_theory_reference",
"committors.h5"))['Data']
net_flux_ref = get(pjoin("transition_path_theory_reference",
"net_flux.h5"))['Data']
eq(script_out[0], committors_ref)
eq(script_out[1].toarray(), net_flux_ref)
def test_CalculateTPT():
T = get("transition_path_theory_reference/tProb.mtx")
sources = [0] # chosen arb. for ref. by TJL
sinks = [70] # chosen arb. for ref. by TJL
script_out = CalculateTPT.run(T, sources, sinks)
committors_ref = get(pjoin("transition_path_theory_reference",
"committors.h5"))['Data']
net_flux_ref = get(pjoin("transition_path_theory_reference",
"net_flux.h5"))['Data']
eq(script_out[0], committors_ref)
eq(script_out[1].toarray(), net_flux_ref)
def test_get_count_matrix_from_assignments_3():
np.random.seed(42)
assignments = np.random.randint(3, size=(10, 10))
val = MSMLib.get_count_matrix_from_assignments(
assignments, lag_time=2, sliding_window=False).todense()
eq(val, np.matrix([[5., 3., 4.], [2., 12., 3.], [4., 3., 4.]]))
val = MSMLib.get_count_matrix_from_assignments(
assignments, lag_time=2, sliding_window=True).todense()
eq(val, np.matrix([[8., 9., 11.], [5., 18., 6.], [11., 5., 7.]]))
def test_trim_states():
# run the (just tested) ergodic trim
counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
trimmed, mapping = MSMLib.ergodic_trim(counts)
# now try the segmented method
states_to_trim = MSMLib.ergodic_trim_indices(counts)
trimmed_counts = MSMLib.trim_states(
states_to_trim, counts, assignments=None)
eq(trimmed.todense(), trimmed_counts.todense())
def test_FindPaths():
tprob = get("transition_path_theory_reference/tProb.mtx")
sources = [0]
sinks = [70]
paths, bottlenecks, fluxes = FindPaths.run(tprob, sources, sinks, 10)
# paths are hard to test due to type issues, adding later --TJL
bottlenecks_ref = get(pjoin("transition_path_theory_reference",
"dijkstra_bottlenecks.h5"))['Data']
fluxes_ref = get(pjoin("transition_path_theory_reference",
"dijkstra_fluxes.h5"))['Data']
eq(bottlenecks, bottlenecks_ref)
eq(fluxes, fluxes_ref)
def test_FindPaths():
tprob = get("transition_path_theory_reference/tProb.mtx")
sources = [0]
sinks = [70]
paths, bottlenecks, fluxes = FindPaths.run(tprob, sources, sinks, 10)
# paths are hard to test due to type issues, adding later --TJL
bottlenecks_ref = get(pjoin("transition_path_theory_reference",
"dijkstra_bottlenecks.h5"))['Data']
fluxes_ref = get(pjoin("transition_path_theory_reference",
"dijkstra_fluxes.h5"))['Data']
eq(bottlenecks, bottlenecks_ref)
eq(fluxes, fluxes_ref)
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 compare_kyle_to_lutz(self, raw_counts):
"""Kyle wrote the most recent MLE code. We compare to the previous
code that was written by Lutz.
"""
counts = MSMLib.ergodic_trim(raw_counts)[0]
x_kyle = MSMLib.mle_reversible_count_matrix(counts)
x_kyle /= x_kyle.sum()
x_lutz = MSMLib.__mle_reversible_count_matrix_lutz__(counts)
x_lutz /= x_lutz.sum()
eq(x_kyle.toarray(), x_lutz.toarray())
def compare_kyle_to_lutz(self, raw_counts):
"""Kyle wrote the most recent MLE code. We compare to the previous
code that was written by Lutz.
"""
counts = MSMLib.ergodic_trim(raw_counts)[0]
x_kyle = MSMLib.mle_reversible_count_matrix(counts)
x_kyle /= x_kyle.sum()
x_lutz = MSMLib.__mle_reversible_count_matrix_lutz__(counts)
x_lutz /= x_lutz.sum()
eq(x_kyle.toarray(), x_lutz.toarray())
def test_apply_mapping_to_assignments_2():
"preseve the -1s"
l = 100
assignments = np.random.randint(l, size=(10, 10))
assignments[0, 0] = -1
mapping = np.ones(l)
correct = np.ones((10, 10))
correct[0, 0] = -1
MSMLib.apply_mapping_to_assignments(assignments, mapping)
eq(assignments, correct)
def test_renumber_states_1():
a = np.random.randint(3, size=(2, 10))
a[np.where(a == 0)] = 1
a[0, 0] = -1
# since its inplace
new_a = a.copy()
mapping = MSMLib.renumber_states(new_a)
eq(int(new_a[0, 0]), -1)
eq(np.where(a == 2)[0], np.where(new_a == 1)[0])
eq(np.where(a == 2)[1], np.where(new_a == 1)[1])
eq(mapping, np.array([1, 2]))
eq(mapping[new_a][np.where(a != -1)], a[np.where(a != -1)])
def test_renumber_states_1():
a = np.random.randint(3, size=(2, 10))
a[np.where(a == 0)] = 1
a[0, 0] = -1
# since its inplace
new_a = a.copy()
mapping = MSMLib.renumber_states(new_a)
eq(int(new_a[0, 0]), -1)
eq(np.where(a == 2)[0], np.where(new_a == 1)[0])
eq(np.where(a == 2)[1], np.where(new_a == 1)[1])
eq(mapping, np.array([1, 2]))
eq(mapping[new_a][np.where(a != -1)], a[np.where(a != -1)])
def test_apply_mapping_to_assignments_2():
"preseve the -1s"
l = 100
assignments = np.random.randint(l, size=(10, 10))
assignments[0, 0] = -1
mapping = np.ones(l)
correct = np.ones((10, 10))
correct[0, 0] = -1
MSMLib.apply_mapping_to_assignments(assignments, mapping)
eq(assignments, correct)
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_estimate_rate_matrix_2():
np.random.seed(42)
counts_dense = np.random.randint(100, size=(4, 4))
counts_sparse = scipy.sparse.csr_matrix(counts_dense)
t_mat_dense = MSMLib.estimate_transition_matrix(counts_dense)
t_mat_sparse = MSMLib.estimate_transition_matrix(counts_sparse)
correct = np.array([[0.22368421, 0.40350877, 0.06140351, 0.31140351],
[0.24193548, 0.08064516, 0.33064516, 0.34677419],
[0.22155689, 0.22155689, 0.26047904, 0.29640719],
[0.23469388, 0.02040816, 0.21428571, 0.53061224]])
eq(t_mat_dense, correct)
eq(t_mat_dense, np.array(t_mat_sparse.todense()))
def test_estimate_rate_matrix_2():
np.random.seed(42)
counts_dense = np.random.randint(100, size=(4, 4))
counts_sparse = scipy.sparse.csr_matrix(counts_dense)
t_mat_dense = MSMLib.estimate_transition_matrix(counts_dense)
t_mat_sparse = MSMLib.estimate_transition_matrix(counts_sparse)
correct = np.array([[0.22368421, 0.40350877, 0.06140351, 0.31140351],
[0.24193548, 0.08064516, 0.33064516, 0.34677419],
[0.22155689, 0.22155689, 0.26047904, 0.29640719],
[0.23469388, 0.02040816, 0.21428571, 0.53061224]])
eq(t_mat_dense, correct)
eq(t_mat_dense, np.array(t_mat_sparse.todense()))
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):
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_1(self):
C = MSMLib.get_count_matrix_from_assignments(self.assignments, 2)
rc, t, p, m = MSMLib.build_msm(C, symmetrize="MLE", ergodic_trimming=True)
eq(rc.todense(), np.matrix([[6.46159184, 4.61535527], [4.61535527, 2.30769762]]), decimal=4)
eq(t.todense(), np.matrix([[0.58333689, 0.41666311], [0.66666474, 0.33333526]]), decimal=4)
eq(p, np.array([0.61538595, 0.38461405]), decimal=5)
eq(m, np.array([0, 1]))
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'))
def test_trim_states():
# run the (just tested) ergodic trim
counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
trimmed, mapping = MSMLib.ergodic_trim(counts)
# now try the segmented method
states_to_trim = MSMLib.ergodic_trim_indices(counts)
trimmed_counts = MSMLib.trim_states(
states_to_trim, counts, assignments=None)
eq(trimmed.todense(), trimmed_counts.todense())
assignments = np.array([np.arange(counts.shape[0])])
states_to_trim = MSMLib.ergodic_trim_indices(counts)
trimmed_counts, trimmed_assignments = MSMLib.trim_states(states_to_trim, counts, assignments=assignments) # Test that code works with assignments given
trimmed_assignments_ref = np.array([[0, 1, -1]]) # State 2 is strong-disconnected so set to -1
eq(trimmed_assignments, trimmed_assignments_ref)
def test(self):
from msmbuilder.scripts.SaveStructures import save
project = get('ProjectInfo.yaml')
assignments = get('Assignments.h5')['arr_0']
which_states = [0, 1, 2]
list_of_trajs = project.get_random_confs_from_states(assignments,
which_states, num_confs=2, replacement=True,
random=np.random.RandomState(42))
assert isinstance(list_of_trajs, list)
assert isinstance(list_of_trajs[0], Trajectory)
eq(len(list_of_trajs), len(which_states))
for t in list_of_trajs:
eq(len(t), 2)
print list_of_trajs[0].keys()
# sep, tps, one
save(list_of_trajs, which_states, style='sep', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='tps', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='one', format='lh5', outdir=self.td)
names = ['State0-0.lh5', 'State0-1.lh5', 'State0.lh5', 'State1-0.lh5',
'State1-1.lh5', 'State1.lh5', 'State2-0.lh5', 'State2-1.lh5',
'State2.lh5']
for name in names:
t = Trajectory.load_trajectory_file(pjoin(self.td, name))
eq(t, get('save_structures/' + name))
def test(self):
from msmbuilder.scripts.SaveStructures import save
os.chdir(self.td)
project = get('ProjectInfo.yaml')
assignments = get('Assignments.h5')['arr_0']
which_states = [0, 1, 2]
list_of_trajs = project.get_random_confs_from_states(assignments,
which_states, num_confs=2, replacement=True,
random=np.random.RandomState(42))
assert isinstance(list_of_trajs, list)
assert isinstance(list_of_trajs[0], md.Trajectory)
eq(len(list_of_trajs), len(which_states))
for t in list_of_trajs:
eq(len(t), 2)
# sep, tps, one
save(list_of_trajs, which_states, style='sep', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='tps', format='lh5', outdir=self.td)
save(list_of_trajs, which_states, style='one', format='lh5', outdir=self.td)
names = ['State0-0.lh5', 'State0-1.lh5', 'State0.lh5', 'State1-0.lh5',
'State1-1.lh5', 'State1.lh5', 'State2-0.lh5', 'State2-1.lh5',
'State2.lh5']
for name in names:
t = md.load(pjoin(self.td, name))
eq(t.xyz, get('save_structures/' + name).xyz) # Just checking coordinates because atom names / bonds in reference data are incompatible with MDTraj.
def test_1(self):
C = MSMLib.get_count_matrix_from_assignments(self.assignments, 2)
rc, t, p, m = MSMLib.build_msm(C,
symmetrize='MLE',
ergodic_trimming=True)
eq(rc.todense(),
np.matrix([[6.46159184, 4.61535527], [4.61535527, 2.30769762]]),
decimal=4)
eq(t.todense(),
np.matrix([[0.58333689, 0.41666311], [0.66666474, 0.33333526]]),
decimal=4)
eq(p, np.array([0.61538595, 0.38461405]), decimal=5)
eq(m, np.array([0, 1]))
def test_trim_states():
# run the (just tested) ergodic trim
counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
trimmed, mapping = MSMLib.ergodic_trim(counts)
# now try the segmented method
states_to_trim = MSMLib.ergodic_trim_indices(counts)
trimmed_counts = MSMLib.trim_states(states_to_trim,
counts,
assignments=None)
eq(trimmed.todense(), trimmed_counts.todense())
assignments = np.array([np.arange(counts.shape[0])])
states_to_trim = MSMLib.ergodic_trim_indices(counts)
trimmed_counts, trimmed_assignments = MSMLib.trim_states(
states_to_trim, counts,
assignments=assignments) # Test that code works with assignments given
trimmed_assignments_ref = np.array(
[[0, 1, -1]]) # State 2 is strong-disconnected so set to -1
eq(trimmed_assignments, trimmed_assignments_ref)
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_CalculateMFPTs(mfpt_state=70):
mfpt = CalculateMFPTs.run(get('transition_path_theory_reference/tProb.mtx'), mfpt_state)
mfpt0 = get(pjoin("transition_path_theory_reference", "mfpt.h5"))['Data']
eq(mfpt, mfpt0)
def test_CalculateImpliedTimescales():
impTimes = CalculateImpliedTimescales.run(MinLagtime=3, MaxLagtime=5,
Interval=1, NumEigen=10, AssignmentsFn=get('Assignments.h5', just_filename=True),
trimming=True, symmetrize='Transpose', nProc=1)
eq(impTimes, get('ImpliedTimescales.dat'))
def test(self):
project = get('ProjectInfo.yaml')
asgn = AssignHierarchical.main(k=100, d=None,
zmatrix_fn=get('ZMatrix.h5', just_filename=True), stride=10, project=project)
eq(asgn, get('WardAssignments.h5')['arr_0'])
def test_CreateAtomIndices():
indices = CreateAtomIndices.run(get('native.pdb', just_filename=True),
'minimal')
eq(indices, get('AtomIndices.dat'))
def compare_kyle_to_reference(self, raw_counts, reference):
"""Compare MLE estimated reversible counts to a reference matrix"""
counts = MSMLib.ergodic_trim(raw_counts)[0]
x_kyle = MSMLib.mle_reversible_count_matrix(counts)
eq(x_kyle.toarray(), reference.toarray())
def test_ergodic_trim():
counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
trimmed, mapping = MSMLib.ergodic_trim(counts)
eq(trimmed.todense(), np.matrix('2 1; 1 2'))
eq(mapping, np.array([0, 1, -1]))
def test_CreateAtomIndices():
indices = CreateAtomIndices.run(get('native.pdb', just_filename=True),
'minimal')
eq(indices, get('AtomIndices.dat'))
def test_CalculateClusterRadii():
cr = CalculateClusterRadii.main(get("Assignments.h5")['arr_0'],
get("Assignments.h5.distances")['arr_0'])
cr_r = get("ClusterRadii.dat")
eq(cr, cr_r)
