def main(plots, lagtimes_ITS, nRuns, nStates, plot_ITS, plot_CK, save_plot, path):
if lagtimes_ITS is None:
lagtimes_ITS = [1, 50, 100, 200, 400, 600, 800, 1000]
MSM_object = estimate.MSM()
MSM_object.lagtimes = lagtimes_ITS
if plot_ITS and save_plot and not os.path.exists(os.path.join(path, "ck_plots")):
os.makedirs(os.path.join(path, "ck_plots"))
print("Analysing folder ", path)
for i in range(nRuns):
print("Plotting validity checks for run %d" % i)
MSM = utilities.readClusteringObject(os.path.join(path, "MSM_object_%d.pkl" % i))
assert len(MSM.dtrajs_full) == len(MSM.dtrajs_active)
MSM_object.MSM_object = MSM
MSM_object.dtrajs = MSM.dtrajs_full
if plot_ITS and (save_plot or plots):
MSM_object._calculateITS()
if save_plot:
plt.savefig(os.path.join(path, "its_%d.png" % i))
if plot_CK and (save_plot or plots):
# setting mlags to None chooses automatically the number of lagtimes
# according to the longest trajectory available
CK_test = MSM_object.MSM_object.cktest(nStates, mlags=None)
mplt.plot_cktest(CK_test)
if save_plot:
plt.savefig(os.path.join(path, "ck_plots/", "ck_%d.png" % i))
if plots:
plt.show()
plt.close('all')
def test_plot_kwargs_no_def_overriding(self):
plot_cktest(self.ck,
marker='o',
markerfacecolor='red',
linewidth=2,
label='testlabel',
color='blue',
linestyle='solid')
def plotChapmanKolmogorovTest(CKObject,
layout=None,
padding_between=0.1,
padding_top=0.075):
""" Plot the results of the Chapman-Kolgomorov tests"""
mplt.plot_cktest(CKObject,
layout=layout,
padding_between=padding_between,
padding_top=padding_top)
plt.savefig("CK.eps")
def test_plot(self):
P = np.array([[0.5, .25, .25, 0.],
[0., .25, .5, .25],
[.25, .25, .5, 0],
[.25, .25, .25, .25],
])
dtrajs = generate_trajs(P, 5, 1000)
msm_obj = pyemma.msm.MaximumLikelihoodMSM()
msm_obj.estimate(dtrajs)
ck = msm_obj.cktest(3)
plot_cktest(ck)
def cktest(self):
""" Conducts a Chapman-Kolmogorow test.
Returns
-------
"""
from copy import deepcopy
from pyemma.plots import plot_cktest
msm = deepcopy(self.msm)
ck = msm.cktest(self.macronum)
plot_cktest(ck)
def cktest(self):
""" Conducts a Chapman-Kolmogorow test.
Returns
-------
"""
from copy import deepcopy
from pyemma.plots import plot_cktest
msm = deepcopy(self.msm)
ck = msm.cktest(self.macronum)
plot_cktest(ck)
def test_plot_kwargs(self):
plot_cktest(self.ck,
marker='o',
markerfacecolor='red',
linewidth=2,
label='testlabel')
def test_plot(self):
plot_cktest(self.ck)
#print('%s macrostates chosen from timescale separation' %n_macrostates_timescales)
print('%s macrostates chosen because thats what we want' % n_macrostates)
plt.clf()
lags = [1, 2, 5, 10, 20, 50, 100, 200, 400]
its = msm.its(clkmeans.dtrajs, lags=lags)
mplt.plot_implied_timescales(its)
plt.savefig('implied_timescale_plot.png')
plt.clf()
print('fraction of states used = ', MSM.active_state_fraction)
print('fraction of counts used = ', MSM.active_count_fraction)
mplt.plot_cktest(MSM.cktest(3))
plt.savefig('cktest_msm.png')
plt.clf()
plt.figure(figsize=(8, 5))
mplt.plot_free_energy(np.hstack(Y1),
np.hstack(Y2),
weights=np.hstack(MSM.trajectory_weights()))
plt.xlabel('tic 1')
plt.ylabel('tic 2')
plt.savefig('reweighted_tic1_tic2.png')
print('running hmm')
HMM = msm.bayesian_hidden_markov_model(clkmeans.dtrajs, n_macrostates, lag=100)
lag_times=list(range(1, 100,2))
n_timescales=10
msm_timescales = implied_timescales(kmeanslabel, lag_times, n_timescales=n_timescales, msm=MarkovStateModel(verbose=False))
#for i in range(n_timescales):
# plt.plot(lag_times, msm_timescales[:, i], 'o-')
#plt.title('Discrete-time MSM Relaxation Timescales')
#plt.xlabel('lag time')
#plt.ylabel('Relaxation Time')
#plt.semilogy()
#plt.show()
cont_time_msm = ContinuousTimeMSM(lag_time=10, n_timescales=None)
time_model=cont_time_msm.fit(kmeanslabel)
print (time_model.ratemat_)
P=time_model.transmat_
M = msm.markov_model(P)
NetworkPlot.plot_network(P)
#mplt.plot_markov_model(M);
MLMSM_good = msm.estimate_markov_model(kmeanslabel, 4)
ck_good_msm=MLMSM_good.cktest(4)
fig, axes = mplt.plot_cktest(ck_good_msm)
fig.savefig('my_file.png', dpi=300)
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_pcca.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#####CK TEST, if specified
if args.ck:
ck = msm_from_data.cktest(n_sets, mlags=11)
mplt.plot_cktest(ck,
diag=False,
figsize=(7, 7),
layout=(n_sets, n_sets),
padding_top=0.1,
y01=False,
padding_between=0.3,
dt=0.1,
units='ns')
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_ck.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#####
#make hmm from msm and pcca clusters
hmm = msm_from_data.coarse_grain(n_sets)
print 'hmm'
ax = mplt.scatter_contour(cc_x[M.active_set],
cc_y[M.active_set],
eigenvectors_right[:, eig_ind],
fig=f)
plt.xlabel("IC 1")
plt.ylabel("IC %d" % (eig_ind + 1))
f.suptitle("%s eigenvector" % label.capitalize())
plt.savefig("%s_eig.png" % label)
# M = msm.bayesian_markov_model(dtrajs, msm_lag)
# M = msm.estimate_markov_model(dtrajs, msm_lag)
ck = M.cktest(n_sets, mlags=None, err_est=False, show_progress=False)
# mplt.plot_cktest(ck, diag=True, figsize=(7, 7), layout=(2, 2), padding_top=0.1, y01=False, padding_between=0.3, dt=0.1, units='ns')
mplt.plot_cktest(ck,
diag=False,
figsize=(7, 7),
y01=False,
padding_between=0.3)
plt.savefig("ck_test.png")
M.pcca(n_sets)
pcca_dist = M.metastable_distributions
membership = M.metastable_memberships # get PCCA memberships
# memberships over trajectory
# dist_all = [np.hstack([pcca_dist[i, :][dtraj] for dtraj in M.discrete_trajectories_full]) for i in range(n_sets)]
# mem_all = [np.hstack([membership[:, i][dtraj] for dtraj in M.discrete_trajectories_full]) for i in range(n_sets)]
# fig, axes = plt.subplots(1, n_sets, figsize=(16, 3))
# matplotlib.rcParams.update({'font.size': 12})
# axes = axes.flatten()
# np.seterr(invalid='warn')
print input_data.clustercenters[pcca_sets[number],0], input_data.clustercenters[pcca_sets[number],1]
for number in range(n_sets):
pp.scatter(input_data.clustercenters[pcca_sets[number],0], input_data.clustercenters[pcca_sets[number],1], color=color_list[number])
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_pcca.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#####CK TEST, if specified
if args.ck:
ck = msm_from_data.cktest(n_sets, mlags=11)
mplt.plot_cktest(ck, diag=False, figsize=(7,7), layout=(n_sets,n_sets), padding_top=0.1, y01=False, padding_between=0.3, dt=0.1, units='ns')
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_ck.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#####
#make hmm from msm and pcca clusters
hmm = msm_from_data.coarse_grain(n_sets)
print 'hmm'
print hmm.stationary_distribution
print hmm.transition_matrix
np.savetxt(os.path.join(args.save_destination, 'msm_populations.txt'), hmm.stationary_distribution)
np.savetxt(os.path.join(args.save_destination, 'msm_transmat.txt'), hmm.transition_matrix)