def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
def makeHMM(Trajectories, topology):
top = md.load_prmtop(topology)
alpha_carbons = [a.index for a in top.atoms if a.name == 'CA']
filenames = sorted(glob(Trajectories))
first_frame = md.load_frame(filenames[0], 0, top=top)
f = SuperposeFeaturizer(alpha_carbons, first_frame)
dataset = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=top):
dataset.append(f.partial_transform(chunk))
hmm = GaussianHMM(n_states=8)
hmm.fit(dataset)
print(hmm.timescales_)
return hmm
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [
atom.index for atom in topology.atoms
if atom.element.symbol in ['C', 'O', 'N']
]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk=100, top=topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [n for n in range(2, 11, 2)]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
#pdb.set_trace()
strided_data = [
s[i::lag_time] for s in sequences for i in range(lag_time)
]
hmm = GaussianHMM(n_states=n, n_init=1).fit(strided_data)
from glob import glob
filenames = sorted(glob("05_Prod_*.nc"))
topology = md.load_prmtop(glob("*nowat.prmtop")[0])
first_frame = md.load_frame(filenames[0], 0, top=topology)
indices = [atom.index for atom in topology.atoms if atom.element.symbol in ['C', 'O', 'N']]
featurizer = SuperposeFeaturizer(indices, first_frame)
sequences = []
for fragment in filenames:
for chunk in md.iterload(fragment, chunk = 100, top = topology):
sequences.append(featurizer.partial_transform(chunk))
lag_times = [1, 20, 50, 100, 200, 400]
hmm_ts0 = {}
hmm_ts1 = {}
n_states = [n for n in range(2,11,2)]
for n in n_states:
hmm_ts0[n] = []
hmm_ts1[n] = []
for lag_time in lag_times:
#pdb.set_trace()
strided_data = [s[i::lag_time] for s in sequences for i in range(lag_time)]
hmm = GaussianHMM(n_states=n, n_init=1).fit(strided_data)
timescales = hmm.timescales_ * lag_time
