def test_create_tics_array():
n_traj = 100
max_traj_length = 100
n_states = 10
n_tics = 5
test_kmeans_mdl = fake_kmeans(n_states, n_tics)
tica_data = {}
test_assignments = {}
for i in range(n_traj):
test_assignments[str(i)] = np.random.randint(n_states,
size=max_traj_length)
tica_data[str(i)] = np.random.normal(size=(max_traj_length,
n_tics))
key_mapping, tics_array = create_tics_array(test_assignments,
test_kmeans_mdl,
tica_data)
assert tics_array.shape == (n_traj, max_traj_length, n_tics)
for i in range(10):
temp_num = np.random.randint(n_traj)
assert (tics_array[temp_num] == tica_data[key_mapping[temp_num]]).all()
return
def test_create_tics_array():
n_traj = 100
max_traj_length = 100
n_states = 10
n_tics = 5
test_kmeans_mdl = fake_kmeans(n_states, n_tics)
tica_data = {}
test_assignments = {}
for i in range(n_traj):
test_assignments[str(i)] = np.random.randint(n_states,
size=max_traj_length)
tica_data[str(i)] = np.random.normal(size=(max_traj_length, n_tics))
key_mapping, tics_array = create_tics_array(test_assignments,
test_kmeans_mdl, tica_data)
assert tics_array.shape == (n_traj, max_traj_length, n_tics)
for i in range(10):
temp_num = np.random.randint(n_traj)
assert (tics_array[temp_num] == tica_data[key_mapping[temp_num]]).all()
return
def _load_protein_matrices(yaml_file, protein_name):
"""
Helper routine to load matrices for a protein
:param yaml_file: yaml file to work with
:param protein_name: name of the protein
:return:
prj :The protein Series
prt : The protein project
key_mapping: mapping of the assigment matrix 0-axis to traj names
assignment_matrix: Massive matrix of
tics_mapping: mapping of the tics_array matrix 0-axis to traj names
tics_array: Multi dimensional array where the 0th axis is equal to the
number of trajectors, the 1st axis is equal to largest traj and the
3rd dimension is equal to the number of tics in the mdl.
"""
prj = ProteinSeries(yaml_file)
prt = Protein(prj, protein_name)
key_mapping, assignment_matrix = create_assignment_matrix(prt.fixed_assignments)
tics_mapping , tics_array = create_tics_array(prt.fixed_assignments, prt.kmeans_mdl,
prt.tica_data)
return prj, prt, key_mapping, assignment_matrix, tics_mapping, tics_array
def _load_protein_matrices(yaml_file, protein_name):
"""
Helper routine to load matrices for a protein
:param yaml_file: yaml file to work with
:param protein_name: name of the protein
:return:
prj :The protein Series
prt : The protein project
key_mapping: mapping of the assigment matrix 0-axis to traj names
assignment_matrix: Massive matrix of
tics_mapping: mapping of the tics_array matrix 0-axis to traj names
tics_array: Multi dimensional array where the 0th axis is equal to the
number of trajectors, the 1st axis is equal to largest traj and the
3rd dimension is equal to the number of tics in the mdl.
"""
prj = ProteinSeries(yaml_file)
prt = Protein(prj, protein_name)
key_mapping, assignment_matrix = create_assignment_matrix(
prt.fixed_assignments)
tics_mapping, tics_array = create_tics_array(prt.fixed_assignments,
prt.kmeans_mdl, prt.tica_data)
return prj, prt, key_mapping, assignment_matrix, tics_mapping, tics_array