def test_sample_dimension():
np.random.seed(42)
X = np.random.randn(500, 5)
data = [X, X, X]
tica = tICA(n_components=2, lag_time=1).fit(data)
tica_trajs = {k: tica.partial_transform(v) for k, v in enumerate(data)}
res = sample_dimension(tica_trajs, 0, 10, scheme="linear")
res2 = sample_dimension(tica_trajs, 1, 10, scheme="linear")
assert len(res) == len(res2) == 10
def respawn_from_tICs(self, n_spawns, dims=(0, 1)):
"""
Find candidate frames in the trajectories to spawn new simulations from.
Look for frames in the trajectories that are nearby the edge regions of the tIC converted space
Parameters
----------
dims: tuple of ints, tICs to sample from
n_spawns: int, number of spawns to generate
Returns
-------
chosen_frames: list of tuples, each tuple is (traj_id, frame_id)
"""
if self.ttrajs is None:
self.ttrajs = self.get_tica_trajs()
frames_per_tIC = max(1, int(n_spawns / len(dims)))
chosen_frames = []
for d in dims:
sampled_pairs = sample_dimension(self.ttrajs,
dimension=d,
n_frames=frames_per_tIC,
scheme='edge')
for pair in sampled_pairs:
chosen_frames.append(pair)
return chosen_frames
def sampling_along_tIC(resultdir, opath, tica_trajs, xtc_traj_folder,
traj_list_array, pdb_name, tIC_a):
transformed = np.concatenate(tica_trajs)
draw_tica_histogram_core(transformed[:, 0], transformed[:, 1], '1', '2')
tica_trajs = {i: tica_trajs[i]
for i in range(len(tica_trajs))
} #tica_trajs is now a dictionary
inds = sample_dimension(tica_trajs,
dimension=tIC_a - 1,
n_frames=200,
scheme='random') #sample 200 conformations
#make trajectory
traj = md.join(
md.load_frame(xtc_traj_folder + traj_list_array[i],
index=frame_i,
top=xtc_traj_folder + pdb_name) for i, frame_i in inds)
#save the trajectory
traj.save("%s/tica-dimension-tIC%s.xtc" % (resultdir, tIC_a - 1))
#show the samples on tICA projections
samples_coord = []
for i, frame_i in inds:
samples_coord.append(
[tica_trajs[i][frame_i][0], tica_trajs[i][frame_i][1]])
samples_coord = np.array(samples_coord)
print(samples_coord.shape)
plt.plot(samples_coord[:, 0], samples_coord[:, 1], 'o-')
plt.legend('sample')
plt.savefig(resultdir + '/' + opath)
def sample_tica_dim(dim=0, n_frames=200, meta=None, ttrajs=None):
## Load
if (not meta is None) & (not ttrajs is None):
## Sample
# These are apparently ordered according tica value
inds = sample_dimension(ttrajs,
dimension=dim,
n_frames=n_frames,
scheme='random')
save_generic(inds, "tica-dimension-{}-inds.pickl".format(dim + 1))
## Get tica components
tica_values = np.array(
[ttrajs[traj_i][frame_i][dim] for traj_i, frame_i in inds])
tica_values = (tica_values - tica_values.min()) / (tica_values.max() -
tica_values.min())
tica_values *= 10
## Make trajectory
top = preload_top(meta)
# Use loc because sample_dimension is nice
traj = md.join(
md.load_frame(meta.loc[traj_i]['traj_fn'], index=frame_i, top=top)
for traj_i, frame_i in inds)
## Supperpose
## Save
traj_fn = "tica-dimension-{}.dcd".format(dim + 1)
backup(traj_fn)
traj.save(traj_fn)
else:
raise ValueError('Specify meta data and trajectory objects')
depends:
- ../top.pdb
- ../trajs
"""
import mdtraj as md
from msmbuilder.io.sampling import sample_dimension
from msmbuilder.io import load_trajs, save_generic, preload_top, backup
## Load
meta, ttrajs = load_trajs('ttrajs')
## Sample
inds = sample_dimension(ttrajs,
dimension=0,
n_frames=200, scheme='random')
save_generic(inds, "tica-dimension-0-inds.pickl")
## Make trajectory
top = preload_top(meta)
# Use loc because sample_dimension is nice
traj = md.join(
md.load_frame(meta.loc[traj_i]['traj_fn'], index=frame_i, top=top)
for traj_i, frame_i in inds
)
## Save
traj_fn = "tica-dimension-0.xtc"
