def test_with_save_traj(self):
path = pkg_resources.resource_filename(__name__, 'data') + os.path.sep
pdb_file = os.path.join(path, 'bpti_ca.pdb')
traj_files = [
os.path.join(path, 'bpti_001-033.xtc'),
os.path.join(path, 'bpti_034-066.xtc'),
os.path.join(path, 'bpti_067-100.xtc')
]
source_frag = coor.source([traj_files], top=pdb_file)
full_data = source_frag.get_output()[0]
last_frame_fragment_0 = [0,32]
first_frame_fragment_1 = [0,33]
first_frame_fragment_2 = [0,66]
reshape = lambda f: f.xyz.reshape((f.xyz.shape[0],f.xyz.shape[1] * f.xyz.shape[2])).squeeze()
# Frames in the first fragment:
frames = coor.save_traj(source_frag, [last_frame_fragment_0], None)
np.testing.assert_equal(reshape(frames), full_data[32])
# Frames the first and second fragments
frames = coor.save_traj(source_frag, [last_frame_fragment_0, first_frame_fragment_1], None)
np.testing.assert_equal(reshape(frames), full_data[np.array([32, 33])])
# Frames only in the second fragment
frames = coor.save_traj(source_frag, [first_frame_fragment_1], None)
np.testing.assert_equal(reshape(frames), full_data[33])
# Frames only in the second and third fragment
frames = coor.save_traj(source_frag, [first_frame_fragment_1, first_frame_fragment_2], None)
np.testing.assert_equal(reshape(frames), full_data[np.array([33, 66])])
def SaveEVFrames(dt, ev_traj, c, d, traj_inp=None, filename=None, topfile=None, nframes=None):
''' Save frames that correspond to eigenvector centers from md-trajectories
to separate trajectory.
Parameters:
--------------
traj_inp: List of underlying md-trajectories.
ev_traj: List of eigenfunction trajectories.
dt: Physical time step.
c: ndarray, shape(nc,M), centers.
d: ndarray, shape(nc,). admissible distances to the centers.
filename: str, name of the center-trajectories
topfile:str, topology-file
nframes: int, number of frames per center and per trajectory.
'''
# Get the number of trajectories:
ntraj = len(ev_traj)
# Get the number of centers and eigenfunctions:
nc, M = c.shape
# Create a reader of eigenfunction data:
ef = pco.source(ev_traj)
ef.chunksize = np.min(ef.trajectory_lengths())
# Get the output into memory, leaving out the first ef:
psidata = ef.get_output(dimensions=np.arange(1, M + 1, dtype=int))
cindices = []
# Write out frames to a trajectory file:
# Loop over the centers:
for i in range(nc):
# Create a list of possible frames:
indices = []
# Loop over the trajectory files:
for m in range(ntraj):
# Get the data for this traj:
mdata = psidata[m]
# Get the admissible frames for this trajectory:
mind = np.where(np.any(np.abs(mdata - c[i, :]) <= d[i], axis=1))[0]
# Make a random selection:
if not (nframes is None):
mind = dt * np.random.choice(mind, (nframes,))
else:
mind = dt * mind
# Put the information together:
mindices = np.zeros((mind.shape[0], 2), dtype=int)
mindices[:, 0] = m
mindices[:, 1] = mind
indices.append(mindices)
# Save to traj:
if not (traj_inp is None) and not (filename is None) and not (topfile is None):
pco.save_traj(traj_inp, indices, outfile=filename + "Center%d.xtc" % i, topfile=topfile)
cindices.append(indices)
return cindices
fig1, ax1 = mplt.plot_free_energy(
np.vstack(tica1)[:, 0],
np.vstack(tica1)[:, 2])
plt.scatter(cl.clustercenters[:, 0], cl.clustercenters[:, 2], c='k')
plt.xlabel('tIC 1', fontsize=18)
plt.ylabel('tIC 3', fontsize=18)
plt.savefig('cluster_centers1v3.pdf')
plt.clf()
a = np.arange(0.8, 15.5, 0.1)
b = list(range(num_clu))
ind = cl.sample_indexes_by_cluster(b, 1)
ind_traj = [i[0][0] for i in ind]
short_list = [a[i] for i in ind_traj]
topfile = '/scratch/shn13007/pgfolder/protein.pdb'
## create list of trajectories and Colvar files
traj_list = [indir + "/p60win{:2.1f}.xtc".format(i) for i in short_list]
f = coor.featurizer(topfile)
inp = coor.load(traj_list, f)
inp2 = [i[20000::10] for i in inp]
frame_list = [i[0][1] for i in ind]
#inp3 = inp2.tolist()
#frame_list2 = frame_list.tolist()
#coor.save_traj(inp2, , 'pg_cluster_centers.xtc', top=topfile)
for i, (j, k) in enumerate(zip(inp2, frame_list)):
coor.save_traj(j,
k,
'pg_cluster_centers' + str(i) + '_' + str(j) + '_' +
str(k) + '.pdb',
top=topfile)
s=100,
alpha=(state_1_colors[i]))
plt.scatter(clkmeans.clustercenters[i, 0],
clkmeans.clustercenters[i, 1],
color='cyan',
s=100,
alpha=(state_2_colors[i]))
plt.xlabel('tic 1')
plt.ylabel('tic 2')
plt.savefig('hmm_clusters_on_tic1_tic2_fade.png')
hmm_samples = HMM.sample_by_observation_probabilities(100)
for i in range(n_macrostates):
coor.save_traj(src, hmm_samples[i], './hmm%s_100samples.xtc' % i)
t = md.load('./hmm%s_100samples.xtc' % i, top=top)
t[0].save_pdb('./hmm%s_100samples.pdb' % i)
statdist = MSM.stationary_distribution
relative_counts = MSM.count_matrix_active.sum(0) / np.sum(
MSM.count_matrix_active)
plt.clf()
plt.figure()
plt.scatter(statdist, relative_counts)
plt.xlabel('MSM stationary distribution')
plt.ylabel('Relative counts')
plt.savefig('msm_sanity_check.png')