def test_2():
model = MarkovStateModel(verbose=False)
C = np.array([[4380, 153, 15, 2, 0, 0], [211, 4788, 1, 0, 0, 0],
[169, 1, 4604, 226, 0, 0], [3, 13, 158, 4823, 3, 0],
[0, 0, 0, 4, 4978, 18], [7, 5, 0, 0, 62, 4926]],
dtype=float)
C = C + 1.0 / 6.0
model.n_states_ = C.shape[0]
model.countsmat_ = C
model.transmat_, model.populations_ = model._fit_mle(C)
n_trials = 5000
random = np.random.RandomState(0)
all_timescales = np.zeros((n_trials, model.n_states_ - 1))
all_eigenvalues = np.zeros((n_trials, model.n_states_))
for i in range(n_trials):
T = np.vstack([random.dirichlet(C[i]) for i in range(C.shape[0])])
u = _solve_msm_eigensystem(T, k=6)[0]
all_eigenvalues[i] = u
all_timescales[i] = -1 / np.log(u[1:])
pp.figure(figsize=(12, 8))
for i in range(3):
pp.subplot(2, 3, i + 1)
pp.title('Timescale %d' % i)
kde = scipy.stats.gaussian_kde(all_timescales[:, i])
xx = np.linspace(all_timescales[:, i].min(), all_timescales[:,
i].max())
r = scipy.stats.norm(loc=model.timescales_[i],
scale=model.uncertainty_timescales()[i])
pp.plot(xx, kde.evaluate(xx), c='r', label='Samples')
pp.plot(xx, r.pdf(xx), c='b', label='Analytic')
for i in range(1, 4):
pp.subplot(2, 3, 3 + i)
pp.title('Eigenvalue %d' % i)
kde = scipy.stats.gaussian_kde(all_eigenvalues[:, i])
xx = np.linspace(all_eigenvalues[:, i].min(), all_eigenvalues[:,
i].max())
r = scipy.stats.norm(loc=model.eigenvalues_[i],
scale=model.uncertainty_eigenvalues()[i])
pp.plot(xx, kde.evaluate(xx), c='r', label='Samples')
pp.plot(xx, r.pdf(xx), c='b', label='Analytic')
pp.tight_layout()
pp.legend(loc=4)
pp.savefig('test_msm_uncertainty_plots.png')
def test_2():
model = MarkovStateModel(verbose=False)
C = np.array([
[4380, 153, 15, 2, 0, 0],
[211, 4788, 1, 0, 0, 0],
[169, 1, 4604, 226, 0, 0],
[3, 13, 158, 4823, 3, 0],
[0, 0, 0, 4, 4978, 18],
[7, 5, 0, 0, 62, 4926]], dtype=float)
C = C + 1.0 / 6.0
model.n_states_ = C.shape[0]
model.countsmat_ = C
model.transmat_, model.populations_ = model._fit_mle(C)
n_trials = 5000
random = np.random.RandomState(0)
all_timescales = np.zeros((n_trials, model.n_states_ - 1))
all_eigenvalues = np.zeros((n_trials, model.n_states_))
for i in range(n_trials):
T = np.vstack([random.dirichlet(C[i]) for i in range(C.shape[0])])
u = _solve_msm_eigensystem(T, k=6)[0]
all_eigenvalues[i] = u
all_timescales[i] = -1 / np.log(u[1:])
pp.figure(figsize=(12, 8))
for i in range(3):
pp.subplot(2,3,i+1)
pp.title('Timescale %d' % i)
kde = scipy.stats.gaussian_kde(all_timescales[:, i])
xx = np.linspace(all_timescales[:,i].min(), all_timescales[:,i].max())
r = scipy.stats.norm(loc=model.timescales_[i], scale=model.uncertainty_timescales()[i])
pp.plot(xx, kde.evaluate(xx), c='r', label='Samples')
pp.plot(xx, r.pdf(xx), c='b', label='Analytic')
for i in range(1, 4):
pp.subplot(2,3,3+i)
pp.title('Eigenvalue %d' % i)
kde = scipy.stats.gaussian_kde(all_eigenvalues[:, i])
xx = np.linspace(all_eigenvalues[:,i].min(), all_eigenvalues[:,i].max())
r = scipy.stats.norm(loc=model.eigenvalues_[i], scale=model.uncertainty_eigenvalues()[i])
pp.plot(xx, kde.evaluate(xx), c='r', label='Samples')
pp.plot(xx, r.pdf(xx), c='b', label='Analytic')
pp.tight_layout()
pp.legend(loc=4)
pp.savefig('test_msm_uncertainty_plots.png')
f = open('macrostate_info-%s.txt' % (it + 1), 'w')
f.write('Cluster centers \n\n')
for val in clusterer.cluster_centers_:
f.write(str(val) + '\n')
f.write('\nPopulations\n\n')
for val in msm.populations_:
f.write(str(val) + '\n')
f.write('\nState labels\n\n')
for val in msm.state_labels_:
f.write(str(val) + '\n')
f.write('\nEigen uncertainty\n\n')
for val in msm.uncertainty_eigenvalues():
f.write(str(val) + '\n')
f.write('\nEigen values\n\n')
for val in msm.eigenvalues_:
f.write(str(val) + '\n')
f.write('\nLeft eigenvectors\n\n')
for val in msm.left_eigenvectors_:
f.write(str(val) + '\n')
f.write('\nRight eigenvectors\n\n')
for val in msm.right_eigenvectors_:
f.write(str(val) + '\n')
f.close()