def test_partial_transform():
model = MarkovStateModel()
model.fit([['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
v = model.partial_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.partial_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.partial_transform(['a', 'b', 'c', 'd'], 'fill')
assert isinstance(v, np.ndarray)
assert len(v) == 4
assert v.dtype == np.float
np.testing.assert_array_equal(v, [0, 1, 2, np.nan])
v = model.partial_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_partial_transform():
model = MarkovStateModel()
model.fit([['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']])
assert model.mapping_ == {'a': 0, 'b': 1, 'c': 2}
v = model.partial_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.partial_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.partial_transform(['a', 'b', 'c', 'd'], 'fill')
assert isinstance(v, np.ndarray)
assert len(v) == 4
assert v.dtype == np.float
np.testing.assert_array_equal(v, [0, 1, 2, np.nan])
v = model.partial_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])
#_ = msme.plot_histogram(txx)
clusterer = MiniBatchKMeans(n_clusters=int(args.clusters),
random_state=42)
clustered_trajs = tica_trajs.fit_transform_with(clusterer,
'kmeans/',
fmt='dir-npy')
#plt.figure()
#plt.hexbin(txx[:,0], txx[:,1], bins='log', mincnt=1, cmap='viridis')
#plt.scatter(clusterer.cluster_centers_[:,0], clusterer.cluster_centers_[:,1], s=100, c='w')
#plt.savefig('microstate_clusters.png')
# We can construct an MSM from the labeled trajectories
msm = MarkovStateModel(lag_time=int(args.lag), n_timescales=20)
msm.fit(clustered_trajs)
assignments = clusterer.partial_transform(txx)
assignments = msm.partial_transform(assignments)
#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
"""Make a microstate MSM
msmbuilder autogenerated template version 2
created 2017-05-23T16:38:49.116944
please cite msmbuilder in any publications
"""
from msmbuilder.io import load_trajs, save_trajs, save_generic
from msmbuilder.msm import MarkovStateModel
## Load
meta, ktrajs = load_trajs('ktrajs')
## Fit
msm = MarkovStateModel(lag_time=2, n_timescales=10, verbose=False)
msm.fit(list(ktrajs.values()))
## Transform
microktrajs = {}
for k, v in ktrajs.items():
microktrajs[k] = msm.partial_transform(v)
## Save
print(msm.summarize())
save_generic(msm, 'msm.pickl')
save_trajs(microktrajs, 'microktrajs', meta)
"""Make a microstate MSM
{{header}}
"""
from msmbuilder.io import load_trajs, save_trajs, save_generic
from msmbuilder.msm import MarkovStateModel
## Load
meta, ktrajs = load_trajs('ktrajs')
## Fit
msm = MarkovStateModel(lag_time=2, n_timescales=10, verbose=False)
msm.fit(list(ktrajs.values()))
## Transform
microktrajs = {}
for k, v in ktrajs.items():
microktrajs[k] = msm.partial_transform(v)
## Save
print(msm.summarize())
save_generic(msm, 'msm.pickl')
save_trajs(microktrajs, 'microktrajs', meta)
