ProjectInfo = Serializer.LoadFromHDF(
'/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/ProjectInfo.h5'
)
Counts = -1 * np.ones(
(ProjectInfo['NumTrajs'], max(ProjectInfo['TrajLengths'])))
print Counts.shape
Savepath = '/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/result/NvOfTrajectory'
plt.figure()
plt.xlabel('Steps')
plt.ylabel('Nv')
plt.hold(False)
for i in range(0, 93):
T = Trajectory.LoadFromHDF(
'/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/Trajectories/trj%d_hc.h5'
% i)
Hcount = hct.count_Helix(T)
plt.title('Nv-steps of Traj%d' % i)
plt.plot(range(len(Hcount)), Hcount, '.')
print 'Save figure to %s/Nvoftraj%d.png' % (Savepath, i)
plt.savefig('%s/Nvoftraj%d.png' % (Savepath, i))
Counts[i, :len(Hcount)] = Hcount[:]
Counts_ma = np.ma.array(Counts, mask=[Counts == -1])
H_mean = Counts_ma.mean(0)
H_std = Counts_ma.std(0)
print H_mean
plt.figure()
plt.plot(range(len(H_mean)), H_mean, 'b')
def Nvprediction():
try:
mean_numhelix_states = loadtxt('mean_numhelix_states.dat')
except IOError:
StatesAsi = hct.get_StatesAssignments(Assignments)
NumHelix_states = hct.compute_numhelix_states(StatesAsi)
#print "NumHelix_states",NumHelix_states
#savetxt('NumHelix_states',NumHelix_states)
states = [int(i) for i in NumHelix_states.keys()]
states.sort()
mean_numhelix_states = []
std_numhelix_states = []
for state in states:
mean_numhelix_states.append(np.mean(NumHelix_states['%d' % state]))
std_numhelix_states.append(np.std(NumHelix_states['%d' % state]))
savetxt('mean_numhelix_states.dat', mean_numhelix_states)
savetxt('std_numhelix_states.dat', std_numhelix_states)
#plt.figure()
#plt.errorbar(states,mean_numhelix_states,std_numhelix_states)
#plt.xlabel("State ID")
#plt.ylabel("Number of Helix")
#plt.savefig("Numhelix_states")
#plt.show()
P0 = np.zeros(len(Population))
for data in Assignments['Data']:
P0[data[0]] += 1
P0 = P0 / P0.sum()
populationslist = []
for k in range(140): # tau = 50, so 140*50 = 7000
populationslist.append(P0)
P0 *= Tmatrix
numhelix = np.dot(np.array(populationslist),
np.array(mean_numhelix_states).reshape(-1, 1))
print numhelix
numhelix = numhelix.reshape(1, -1)[0]
plt.figure()
plt.plot(
np.arange(0, 7000, 50),
numhelix,
'ro',
) # tau = 50, so 140*50 = 7000
plt.hold(True)
Counts = -1 * np.ones(
(ProjectInfo['NumTrajs'], max(ProjectInfo['TrajLengths'])))
print Counts.shape
for i in range(0, 93):
T = Trajectory.LoadFromHDF(
'/Users/tud51931/projects/MSM/msm/ff03-hybridkcenter/sourcedata/Trajectories/trj%d_hc.h5'
% i)
Hcount = hct.count_Helix(T)
Counts[i, :len(Hcount)] = Hcount[:]
Counts_ma = np.ma.array(Counts, mask=[Counts == -1])
H_mean = Counts_ma.mean(0)
H_std = Counts_ma.std(0)
print H_mean
plt.plot(range(len(H_mean)), H_mean, 'b')
plt.title('Nv-steps')
plt.xlabel('Steps')
plt.ylabel('Nv')
plt.legend(('Nv_msm', 'Nv_rawdata'), loc='upper left')
figname = 'Nv_prediction_%sCluster%0.1f_tau%d.png' % (metrics, cutoff, tau)
plt.savefig(figname)
print "Save to %s" % figname
