def mfptGraph(self):
""" Not yet implemented
"""
import pyemma.plots as mplt
self._intergrityCheck()
#pos = np.array([[3,3],[4.25,0],[0,1],[1.75,0],[6,1.0]])
#state_colors = ['green', 'blue', 'yellow', 'cyan', 'purple']
# Color states by committor! on gradient from blue to red
msm = self.model.msm
state_sizes = msm.stationary_distribution ** 0.25 # Scale eq prob down to make states visible
fig, pos = mplt.plot_markov_model(msm, state_sizes=state_sizes)
def mfptGraph(self):
""" Not yet implemented
"""
import pyemma.plots as mplt
self._intergrityCheck()
#pos = np.array([[3,3],[4.25,0],[0,1],[1.75,0],[6,1.0]])
#state_colors = ['green', 'blue', 'yellow', 'cyan', 'purple']
# Color states by committor! on gradient from blue to red
msm = self.model.msm
state_sizes = msm.stationary_distribution ** 0.25 # Scale eq prob down to make states visible
fig, pos = mplt.plot_markov_model(msm, state_sizes=state_sizes)
from pyemma import msm, plots
from pyemma import plots as mplt
import numpy as np
import pdb
import matplotlib.pyplot as plt
P = np.array([[0.8, 0.15, 0.05, 0.0, 0.0], [0.1, 0.75, 0.05, 0.05, 0.05],
[0.05, 0.1, 0.8, 0.0, 0.05], [0.0, 0.2, 0.0, 0.8, 0.0],
[0.0, 0.02, 0.02, 0.0, 0.96]])
M = msm.markov_model(P)
pos = np.array([[2.0, -1.5], [1, 0], [2.0, 1.5], [0.0, -1.5], [0.0, 1.5]])
pl = mplt.plot_markov_model(M, pos=pos)
#pl[0].show()
A = [0]
B = [4]
tpt = msm.tpt(M, A, B)
# get tpt gross flux
F = tpt.gross_flux
print('**Flux matrix**:')
print(F)
print('**forward committor**:')
print(tpt.committor)
print('**backward committor**:')
print(tpt.backward_committor)
# we position states along the y-axis according to the commitor
tptpos = np.array([tpt.committor, [0, 0, 0.5, -0.5, 0]]).transpose()
print('\n**Gross flux illustration**: ')
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)
#plot msm using pyemma function
mplt.plot_markov_model(hmm, minflux=4e-4, arrow_label_format='%.3f')
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_hmm_markovmodel.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#plot hmm timescales
print hmm.metastable_assignments
pp.plot(msm_from_data.timescales()[:-1] / msm_from_data.timescales()[1:],
linewidth=0,
marker='o')
pp.xlabel('index')
pp.ylabel('timescale separation')
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)
#plot msm using pyemma function
mplt.plot_markov_model(hmm, minflux=4e-4, arrow_label_format='%.3f')
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_hmm_markovmodel.png'))
if args.display:
pp.show()
pp.clf()
pp.close()
#plot hmm timescales
print hmm.metastable_assignments
pp.plot(msm_from_data.timescales()[:-1]/msm_from_data.timescales()[1:], linewidth=0,marker='o')
pp.xlabel('index'); pp.ylabel('timescale separation');
if args.save:
pp.savefig(os.path.join(args.save_destination, 'msm_hmm_timescales.png'))
if args.display: