def test_ntimescales_1():
# see issue #603
trajs = [np.random.randint(0, 30, size=500) for _ in range(5)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
assert len(np.unique(lumped_trajs)) == 11
def test_ntimescales_1():
# see issue #603
trajs = [np.random.randint(0, 30, size=500) for _ in range(5)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
assert len(np.unique(lumped_trajs)) == 11
def test_ntimescales_1():
# see issue #603
trajs = [random.randint(0, 100, size=500) for _ in range(15)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
observed_macros = len(np.unique(lumped_trajs))
assert observed_macros == 11, observed_macros
def test_ntimescales_1():
# see issue #603
trajs = [random.randint(0, 100, size=500) for _ in range(15)]
pccap = PCCAPlus(n_macrostates=11).fit(trajs)
lumped_trajs = pccap.transform(trajs)
observed_macros = len(np.unique(lumped_trajs))
assert observed_macros == 11, observed_macros
def test_ntimescales_3():
# see issue #603
trajs = [np.random.randint(0, 30, size=500) for _ in range(5)]
msm = MarkovStateModel(n_timescales=10).fit(trajs)
pccap = PCCAPlus.from_msm(msm, 11)
lumped_trajs = pccap.transform(trajs)
assert len(np.unique(lumped_trajs)) == 11
def test_ntimescales_3():
# see issue #603
trajs = [np.random.randint(0, 30, size=500) for _ in range(5)]
msm = MarkovStateModel(n_timescales=10).fit(trajs)
pccap = PCCAPlus.from_msm(msm, 11)
lumped_trajs = pccap.transform(trajs)
assert len(np.unique(lumped_trajs)) == 11
def test_ntimescales_2():
# see issue #603
trajs = [random.randint(0, 100, size=500) for _ in range(15)]
msm = MarkovStateModel().fit(trajs)
pccap = PCCAPlus.from_msm(msm, 11)
lumped_trajs = pccap.transform(trajs)
observed_macros = len(np.unique(lumped_trajs))
assert observed_macros == 11, observed_macros
def test_from_msm():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pcca = PCCA.from_msm(msm, 2)
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2)
def test_ntimescales_2():
# see issue #603
trajs = [random.randint(0, 100, size=500) for _ in range(15)]
msm = MarkovStateModel().fit(trajs)
pccap = PCCAPlus.from_msm(msm, 11)
lumped_trajs = pccap.transform(trajs)
observed_macros = len(np.unique(lumped_trajs))
assert observed_macros == 11, observed_macros
def test_from_msm():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pcca = PCCA.from_msm(msm, 2)
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2)
def test_pcca_plus_1():
assignments, ref_macrostate_assignments = _metastable_system()
pipeline = Pipeline([('msm', MarkovStateModel()), ('pcca+', PCCAPlus(2))])
macro_assignments = pipeline.fit_transform(assignments)[0]
# we need to consider any permutation of the state labels when we
# test for equality. Since it's only a 2-state that's simple using
# the logical_not to flip the assignments.
assert (np.all(macro_assignments == ref_macrostate_assignments) or np.all(
macro_assignments == np.logical_not(ref_macrostate_assignments)))
def generate_msm(self, clustered):
"""
Generates a MSM from the current cluster data
Returns: Msm
"""
# Generate microstate MSM
self.currtime = time.time()
msm = MarkovStateModel(lag_time=self.config.getint("model", "msm_lag"),
reversible_type="transpose",
ergodic_cutoff="off",
prior_counts=0.000001)
msm.fit(clustered)
print("TIME\tmicromsm:\t%f" % (time.time() - self.currtime))
utils.dump(msm, "msm_G%d.pkl" % self.generation)
# Lump into macrostates
self.currtime = time.time()
pcca = PCCAPlus.from_msm(msm,
n_macrostates=self.config.getint(
"model", "macrostates"))
mclustered = pcca.transform(clustered, mode="fill")
if any(any(np.isnan(x) for x in m) for m in mclustered): #pylint: disable=no-member
print(
"WARNING: Unassignable clusters in PCCA with %d macrostates!" %
self.config.getint("model", "macrostates"))
print("TIME\tpccaplus:\t%f" % (time.time() - self.currtime))
if self.save_extras:
utils.dump(pcca, "macrostater.pkl")
# Generate macrostate MSM
self.currtime = time.time()
mmsm = MarkovStateModel(lag_time=self.config.getint(
"model", "msm_lag"),
reversible_type="transpose",
ergodic_cutoff="off",
prior_counts=0.000001)
mmsm.fit(mclustered)
print("TIME\tmacromsm\t%f" % (time.time() - self.currtime))
utils.dump(mmsm, "mmsm_G%d.pkl" % self.generation)
return mmsm, mclustered
def test_from_msm_2():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2, 'crispness')
assert pccaplus.objective_function == 'crispness'
#msme.plot_free_energy(txx, obs=(0, 1), n_samples=10000,
# pi=msm.populations_[assignments],
# xlabel='tIC 1', ylabel='tIC 2')
#plt.figure()
#plt.scatter(clusterer.cluster_centers_[msm.state_labels_, 0],
# clusterer.cluster_centers_[msm.state_labels_, 1],
# s=1e4 * msm.populations_, # size by population
# c=msm.left_eigenvectors_[:, 1], # color by eigenvector
# cmap="coolwarm",
# zorder=3)
#plt.colorbar(label='First dynamical eigenvector')
#plt.tight_layout()
#plt.savefig('free_energy_plot.png')
# Macrostate model
pcca = PCCAPlus.from_msm(msm, n_macrostates=4)
macro_trajs = pcca.transform(clustered_trajs)
#msme.plot_free_energy(txx, obs=(0, 1), n_samples=10000,
# pi=msm.populations_[assignments],
# xlabel='tIC 1', ylabel='tIC 2')
#plt.figure()
#plt.scatter(clusterer.cluster_centers_[msm.state_labels_, 0],
# clusterer.cluster_centers_[msm.state_labels_, 1],
# s=50,
# c=pcca.microstate_mapping_,
# zorder=3
# )
#plt.tight_layout()
#plt.savefig('macrostate_clusters.png')
def test_from_msm_2():
assignments, _ = _metastable_system()
msm = MarkovStateModel()
msm.fit(assignments)
pccaplus = PCCAPlus.from_msm(msm, 2, 'crispness')
assert pccaplus.objective_function == 'crispness'
