def test_transform():
model = MarkovStateModel()
model.fit([['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
v = model.transform([['a', 'b', 'c']])
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 1, 2])
v = model.transform([['a', 'b', 'c', 'd']], 'clip')
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 1, 2])
v = model.transform([['a', 'b', 'c', 'd']], 'fill')
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.float
np.testing.assert_array_equal(v[0], [0, 1, 2, np.nan])
v = model.transform([['a', 'a', 'SPLIT', 'b', 'b', 'b']], 'clip')
assert isinstance(v, list)
assert len(v) == 2
assert v[0].dtype == np.int
assert v[1].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 0])
np.testing.assert_array_equal(v[1], [1, 1, 1])
def test_transform():
model = MarkovStateModel()
model.fit([['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
v = model.transform([['a', 'b', 'c']])
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 1, 2])
v = model.transform([['a', 'b', 'c', 'd']], 'clip')
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 1, 2])
v = model.transform([['a', 'b', 'c', 'd']], 'fill')
assert isinstance(v, list)
assert len(v) == 1
assert v[0].dtype == np.float
np.testing.assert_array_equal(v[0], [0, 1, 2, np.nan])
v = model.transform([['a', 'a', 'SPLIT', 'b', 'b', 'b']], 'clip')
assert isinstance(v, list)
assert len(v) == 2
assert v[0].dtype == np.int
assert v[1].dtype == np.int
np.testing.assert_array_equal(v[0], [0, 0])
np.testing.assert_array_equal(v[1], [1, 1, 1])
def fit_msms(yaml_file):
mdl_params = yaml_file["mdl_params"]
current_mdl_params={}
for i in mdl_params.keys():
if i.startswith("msm__"):
current_mdl_params[i.split("msm__")[1]] = mdl_params[i]
for protein in yaml_file["protein_list"]:
with enter_protein_mdl_dir(yaml_file, protein):
print(protein)
assignments = verboseload("assignments.pkl")
msm_mdl = MarkovStateModel(**current_mdl_params).fit(
[assignments[i] for i in assignments.keys()])
verbosedump(msm_mdl, "msm_mdl.pkl")
fixed_assignments = {}
for i in assignments.keys():
fixed_assignments[i] = msm_mdl.transform(
assignments[i], mode='fill')[0]
verbosedump(fixed_assignments, 'fixed_assignments.pkl')
return
def fit_msms(yaml_file):
mdl_params = yaml_file["mdl_params"]
current_mdl_params={}
for i in mdl_params.keys():
if i.startswith("msm__"):
current_mdl_params[i.split("msm__")[1]] = mdl_params[i]
for protein in yaml_file["protein_list"]:
with enter_protein_mdl_dir(yaml_file, protein):
print(protein)
assignments = verboseload("assignments.pkl")
msm_mdl = MarkovStateModel(**current_mdl_params).fit(
[assignments[i] for i in assignments.keys()])
verbosedump(msm_mdl, "msm_mdl.pkl")
fixed_assignments = {}
for i in assignments.keys():
fixed_assignments[i] = msm_mdl.transform(
assignments[i], mode='fill')[0]
verbosedump(fixed_assignments, 'fixed_assignments.pkl')
return
