def test_sample_1():
# Test that the code actually runs and gives something non-crazy
# Make an ergodic dataset with two gaussian centers offset by 25 units.
chunk = np.random.normal(size=(20000, 3))
data = [np.vstack((chunk, chunk + 25)), np.vstack((chunk + 25, chunk))]
clusterer = cluster.KMeans(n_clusters=2)
msm = MarkovStateModel()
pipeline = sklearn.pipeline.Pipeline([("clusterer", clusterer),
("msm", msm)])
pipeline.fit(data)
trimmed_assignments = pipeline.transform(data)
# Now let's make make the output assignments start with
# zero at the first position.
i0 = trimmed_assignments[0][0]
if i0 == 1:
for m in trimmed_assignments:
m *= -1
m += 1
pairs = msm.draw_samples(trimmed_assignments, 2000)
samples = map_drawn_samples(pairs, data)
mu = np.mean(samples, axis=1)
eq(mu, np.array([[0., 0., 0.0], [25., 25., 25.]]), decimal=1)
# We should make sure we can sample from Trajectory objects too...
# Create a fake topology with 1 atom to match our input dataset
top = md.Topology.from_dataframe(pd.DataFrame({
"serial": [0],
"name": ["HN"],
"element": ["H"],
"resSeq": [1],
"resName": "RES",
"chainID": [0]
}),
bonds=np.zeros(shape=(0, 2), dtype='int'))
# np.newaxis reshapes the data to have a 40000 frames, 1 atom, 3 xyz
trajectories = [md.Trajectory(x[:, np.newaxis], top) for x in data]
trj_samples = map_drawn_samples(pairs, trajectories)
mu = np.array([t.xyz.mean(0)[0] for t in trj_samples])
eq(mu, np.array([[0., 0., 0.0], [25., 25., 25.]]), decimal=1)
def test_sample_1():
# Test that the code actually runs and gives something non-crazy
# Make an ergodic dataset with two gaussian centers offset by 25 units.
chunk = np.random.normal(size=(20000, 3))
data = [np.vstack((chunk, chunk + 25)), np.vstack((chunk + 25, chunk))]
clusterer = cluster.KMeans(n_clusters=2)
msm = MarkovStateModel()
pipeline = sklearn.pipeline.Pipeline(
[("clusterer", clusterer), ("msm", msm)]
)
pipeline.fit(data)
trimmed_assignments = pipeline.transform(data)
# Now let's make make the output assignments start with
# zero at the first position.
i0 = trimmed_assignments[0][0]
if i0 == 1:
for m in trimmed_assignments:
m *= -1
m += 1
pairs = msm.draw_samples(trimmed_assignments, 2000)
samples = map_drawn_samples(pairs, data)
mu = np.mean(samples, axis=1)
eq(mu, np.array([[0., 0., 0.0], [25., 25., 25.]]), decimal=1)
# We should make sure we can sample from Trajectory objects too...
# Create a fake topology with 1 atom to match our input dataset
top = md.Topology.from_dataframe(
pd.DataFrame({
"serial": [0], "name": ["HN"], "element": ["H"], "resSeq": [1],
"resName": "RES", "chainID": [0]
}), bonds=np.zeros(shape=(0, 2), dtype='int')
)
# np.newaxis reshapes the data to have a 40000 frames, 1 atom, 3 xyz
trajectories = [md.Trajectory(x[:, np.newaxis], top)
for x in data]
trj_samples = map_drawn_samples(pairs, trajectories)
mu = np.array([t.xyz.mean(0)[0] for t in trj_samples])
eq(mu, np.array([[0., 0., 0.0], [25., 25., 25.]]), decimal=1)
clL = cluster.labels_
msm = MarkovStateModel(lag_time=10,n_timescales=10)
msm.fit_transform(clL)
trjs = clL
N = n_samples
inits = ad.findStarting([trjs], N, method=method)
T = []
for trj in sorted(glob.glob(Trjs)):
T.append(trj)
count = 0
for init in inits:
structure = msm.draw_samples(clL, 1)[init]
print structure
top = findTop(T[structure[0][0]])
rawTrj = findRawtrj(T[structure[0][0]])
print top
#f = md.load(rawTrj, top=top, frame=structure[0][1])
#f.save_pdb(name_sys+str(count)+'_'+name_round+'.pdb')
#shutil.copy(top, name_sys+str(count)+'_'+name_round+'.prmtop')
#f.save_mdcrd(name_sys+str(count)+'_'+name_round+'.mdcrd')
frame = structure[0][1]
newTop = name_sys+str(count)+'_'+name_round+'.prmtop'
newrst = name_sys+str(count)+'_'+name_round+'-00.rst'
f = open('cppASample_'+str(count)+'.in', 'w')
f.write('parm ' + top + '\n')
f.write('trajin ' + rawTrj + '\n')
# findStarting(trjs, N, method='random')
import adaptivsamplingMSM as ad
from msmbuilder.msm import MarkovStateModel
cluster=pickle.load(open('clustering.pkl','rb'))
trjs = cluster.labels_
N = n_samples
T = []
for trj in sorted(glob.glob('rawTrj/MD1-rwTrj/*.mdcrd')):
T.append(trj)
inits = ad.findStarting([trjs], N, method='leastPop')
msm=MarkovStateModel(lag_time=1, n_timescales=10)
msm.fit_transform(cluster.labels_)
OPF = []
structure = msm.draw_samples(trjs, 1)
for i in range(n_samples):
try:
init = structure[msm.mapping_[inits[i]]]
except KeyError:
print KeyError
traj = T[init[0][0]]
frame = init[0][1]
OPF.append({'traj':traj, 'frame':frame})
json.dump(OPF, open("ClsInf.txt",'w'))
### Step 5: making the CPPtraj inputs
import json
topFile='mytopfile.top'
inf = json.load(open("ClsInf.txt"))
