Python MarkovStateModel.draw_samples方法代码示例

# 需要导入模块: from msmbuilder.msm import MarkovStateModel [as 别名]
# 或者: from msmbuilder.msm.MarkovStateModel import draw_samples [as 别名]
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)

# 需要导入模块: from msmbuilder.msm import MarkovStateModel [as 别名]
# 或者: from msmbuilder.msm.MarkovStateModel import draw_samples [as 别名]
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')

# 需要导入模块: from msmbuilder.msm import MarkovStateModel [as 别名]
# 或者: from msmbuilder.msm.MarkovStateModel import draw_samples [as 别名]
# 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"))