def test_mbar(self):
mbar = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=50000, maxerr=1.0E-13, estimator='mbar')
validate_thermodynamics(self, mbar)
check_serialization(mbar)
def test_wham(self):
wham = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=100000, maxerr=1.0E-13, estimator='wham')
validate_thermodynamics(self, wham)
check_serialization(wham)
def test_tram(self):
tram = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=10000, maxerr=1.0E-10, estimator='tram', lag=10)
validate_thermodynamics(self, tram)
validate_kinetics(self, tram)
def test_tram(self):
tram = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=10000, maxerr=1.0E-10, estimator='tram', lag=10, connectivity='reversible_pathways')
validate_thermodynamics(self, tram)
validate_kinetics(self, tram)
check_serialization(tram)
def test_dtram2(self):
''' check we have the temperatures attribute for every estimated lag time'''
dtram = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=50000, maxerr=1.0E-10, estimator='dtram', lag=[10, 11])
for t in dtram:
assert hasattr(t, 'temperatures')
def test_dtram(self):
dtram = estimate_multi_temperature(
self.energy_trajs, self.temp_trajs, self.dtrajs,
energy_unit='kT', temp_unit='kT',
maxiter=50000, maxerr=1.0E-10, estimator='dtram', lag=10)
validate_thermodynamics(self, dtram)
validate_kinetics(self, dtram)
check_serialization(dtram)
def multi_temperature_tram(feat, trajfiles, temperatures, dtrajs=None, stride=1, tica_lag=100,
keep_tica_dims=20, n_clusters=100, tram_lag=100, engfile="Etot.dat", usecols=(1,), kb=0.0083145):
"""
Parameters
----------
feat : obj, pyemma.coor.featurizer
Featurizer object that already has the appropriate features added.
trajfiles : list
Names of trajectories to include in estimation.
temperatures : list
Temperatures of corresponding trajectories.
stride : int
Number of frames to skip in tica and clustering.
tica_lag : int
Lagtime to use for constructing tica.
keep_tica_dims : int
Number of dimensions to keep from tica. Somewhat ambiguous.
n_clusters : int
Number of clusters for kmeans. Somewhat ambiguous.
"""
dirs = [ os.path.dirname(x) for x in trajfiles ]
beta = [ 1./(kb*x) for x in temperatures ]
if dtrajs is None:
inp = coor.source(trajfiles, feat)
tica_obj = coor.tica(inp, lag=tica_lag, dim=keep_tica_dims, stride=stride)
Y = tica_obj.get_output()
cl = coor.cluster_kmeans(data=Y, k=n_clusters, stride=stride)
dtrajs = cl.dtrajs
# dimensionless energy
if engfile.endswith("npy"):
energy_trajs = [ beta[i]*np.load("{}/{}".format(dirs[i], engfile)) for i in range(len(dirs)) ]
else:
energy_trajs = [ beta[i]*np.loadtxt("{}/{}".format(dirs[i], engfile), usecols=usecols) for i in range(len(dirs)) ]
temp_trajs = [ kb*temperatures[i]*np.ones(energy_trajs[i].shape[0], float) for i in range(len(dirs)) ]
# dTRAM approach
tram = thermo.estimate_multi_temperature(energy_trajs, temp_trajs,
dtrajs, energy_unit='kT', temp_unit='kT', estimator='tram',
lag=tram_lag, maxiter=2000000, maxerr=1e-10)
return dirs, dtrajs, tram
def test_multi_temperature_data():
req_keys = ('trajs', 'dtrajs', 'energy_trajs', 'temp_trajs', 'centers')
mt_data = get_multi_temperature_data()
for key in mt_data.keys():
assert key in req_keys
for key in req_keys:
assert key in mt_data
memm = estimate_multi_temperature(mt_data['energy_trajs'],
mt_data['temp_trajs'],
mt_data['dtrajs'],
energy_unit='kT',
temp_unit='kT',
reference_temperature=1.0,
estimator='dtram',
lag=[5],
maxiter=10000,
maxerr=1e-14)
assert memm.msm is not None
memm.msm.pcca(2)
pi = [memm.msm.pi[s].sum() for s in memm.msm.metastable_sets]
assert_allclose(pi, [0.3, 0.7], rtol=0.25, atol=0.1)