def read_and_featurize(filename, dihedrals=['chi2'], stride=10):
#print("reading and featurizing %s" %(filename))
top = md.load_frame(filename, 0).topology
#print("got top")
atom_indices = [a.index for a in top.atoms if a.residue.resSeq == 93 and a.residue != "POPC" and str(a.residue)[0] == "H"]
print(len(atom_indices))
#atom_indices = [a.index for a in top.atoms if a.residue.chain.index == 0 and a.residue.resSeq != 93 and a.residue != "POPC" and a.residue.resSeq != 130 and a.residue.resSeq != 172 and a.residue.resSeq != 79 and a.residue.resSeq != 341]
#print("got indices")
traj = md.load(filename, stride=1000, atom_indices=atom_indices)
#print("got traj")
featurizer = DihedralFeaturizer(types = dihedrals)
features = featurizer.transform(traj_list = traj)
#print(np.shape(features))
#print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory)-2]
dcd_file = directory[len(directory)-1]
new_file = "%s_features_stride%d.h5" %(dcd_file.rsplit( ".", 1 )[ 0 ] , stride)
new_root_dir = "/scratch/users/enf/b2ar_analysis/subsampled_features"
new_condition_dir = "%s/%s" %(new_root_dir, condition)
new_file_full = "%s/%s/%s" %(new_root_dir, condition, new_file)
#print("saving features as %s" %new_file_full)
verbosedump(features, new_file_full)
return features
def read_and_featurize(filename, dihedrals=['phi', 'psi', 'chi2'], stride=10):
print("reading and featurizing %s" %(filename))
traj = md.load(filename)
#test_traj_init = md.load_frame(filename,5)
#test_traj_init.save_pdb("/scratch/users/enf/b2ar_analysis/test_init.pdb")
#traj.topology = fix_topology(traj.topology)
#traj[-1].save_pdb("/scratch/users/enf/b2ar_analysis/test_fixed.pdb")
#traj.save_dcd("/scratch/users/enf/b2ar_analysis/test_fixed.dcd")
#print("got traj")
featurizer = DihedralFeaturizer(types = dihedrals)
features = featurizer.transform(traj_list = traj)
#print("finished featurizing")
directory = filename.split("/")
traj_file = directory[len(directory)-1]
condition = traj_file.split("_")[0].split(".")[0]
print("Condition %s has features of shape %s" %(condition, np.shape(features)))
new_file = "/scratch/users/enf/b2ar_analysis/combined_features/%s_features.h5" %condition
verbosedump(features, new_file)
def read_and_featurize(filename, dihedrals=['phi', 'psi', 'chi2'], stride=10):
print(("reading and featurizing %s" % (filename)))
traj = md.load(filename)
#test_traj_init = md.load_frame(filename,5)
#test_traj_init.save_pdb("/scratch/users/enf/b2ar_analysis/test_init.pdb")
#traj.topology = fix_topology(traj.topology)
#traj[-1].save_pdb("/scratch/users/enf/b2ar_analysis/test_fixed.pdb")
#traj.save_dcd("/scratch/users/enf/b2ar_analysis/test_fixed.dcd")
#print("got traj")
featurizer = DihedralFeaturizer(types=dihedrals)
features = featurizer.transform(traj_list=traj)
#print("finished featurizing")
directory = filename.split("/")
traj_file = directory[len(directory) - 1]
condition = traj_file.split("_")[0].split(".")[0]
print(("Condition %s has features of shape %s" %
(condition, np.shape(features))))
new_file = "/scratch/users/enf/b2ar_analysis/combined_features/%s_features.h5" % condition
verbosedump(features, new_file)
def read_and_featurize_divided(filename, dihedrals=['phi', 'psi', 'chi2'], stride=10):
#print("reading and featurizing %s" %(filename))
traj_top = md.load_frame(filename,0).topology
atom_indices = [a.index for a in traj_top.atoms if a.residue.name[0:2] != "HI"]
traj = md.load(filename,atom_indices=atom_indices)
#print("got traj")
featurizer = DihedralFeaturizer(types = dihedrals)
features = featurizer.transform(traj_list = traj)
#print(np.shape(features))
#print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory)-2]
dcd_file = directory[len(directory)-1]
new_file = "%s_features_stride%d.h5" %(dcd_file.rsplit( ".", 1 )[ 0 ] , stride)
new_root_dir = "/scratch/users/enf/b2ar_analysis/subsampled_features"
new_condition_dir = "%s/%s" %(new_root_dir, condition)
new_file_full = "%s/%s/%s" %(new_root_dir, condition, new_file)
#print("saving features as %s" %new_file_full)
verbosedump(features, new_file_full)
return features
def read_and_featurize_divided(filename,
dihedrals=['phi', 'psi', 'chi2'],
stride=10):
#print("reading and featurizing %s" %(filename))
traj_top = md.load_frame(filename, 0).topology
atom_indices = [
a.index for a in traj_top.atoms if a.residue.name[0:2] != "HI"
]
traj = md.load(filename, atom_indices=atom_indices)
#print("got traj")
featurizer = DihedralFeaturizer(types=dihedrals)
features = featurizer.transform(traj_list=traj)
#print(np.shape(features))
#print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory) - 2]
dcd_file = directory[len(directory) - 1]
new_file = "%s_features_stride%d.h5" % (dcd_file.rsplit(".", 1)[0], stride)
new_root_dir = "/scratch/users/enf/b2ar_analysis/subsampled_features"
new_condition_dir = "%s/%s" % (new_root_dir, condition)
new_file_full = "%s/%s/%s" % (new_root_dir, condition, new_file)
#print("saving features as %s" %new_file_full)
verbosedump(features, new_file_full)
return features
def read_and_featurize(filename, dihedrals=['chi2'], stride=10):
#print("reading and featurizing %s" %(filename))
top = md.load_frame(filename, 0).topology
#print("got top")
atom_indices = [a.index for a in top.atoms if a.residue.resSeq == 93 and a.residue != "POPC" and str(a.residue)[0] == "H"]
print((len(atom_indices)))
#atom_indices = [a.index for a in top.atoms if a.residue.chain.index == 0 and a.residue.resSeq != 93 and a.residue != "POPC" and a.residue.resSeq != 130 and a.residue.resSeq != 172 and a.residue.resSeq != 79 and a.residue.resSeq != 341]
#print("got indices")
traj = md.load(filename, stride=1000, atom_indices=atom_indices)
#print("got traj")
featurizer = DihedralFeaturizer(types = dihedrals)
features = featurizer.transform(traj_list = traj)
#print(np.shape(features))
#print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory)-2]
dcd_file = directory[len(directory)-1]
new_file = "%s_features_stride%d.h5" %(dcd_file.rsplit( ".", 1 )[ 0 ] , stride)
new_root_dir = "/scratch/users/enf/b2ar_analysis/subsampled_features"
new_condition_dir = "%s/%s" %(new_root_dir, condition)
new_file_full = "%s/%s/%s" %(new_root_dir, condition, new_file)
#print("saving features as %s" %new_file_full)
verbosedump(features, new_file_full)
return features
def test_function_featurizer():
trajectories = AlanineDipeptide().get_cached().trajectories
trj0 = trajectories[0]
# use the dihedral to compute phi for ala
atom_ind = [[4, 6, 8, 14]]
func = compute_dihedrals
# test with args
f = FunctionFeaturizer(func, func_args={"indices": atom_ind})
res1 = f.transform([trj0])
# test with function in a function without any args
def funcception(trj):
return compute_phi(trj)[1]
f = FunctionFeaturizer(funcception)
res2 = f.transform([trj0])
# know results
f3 = DihedralFeaturizer(['phi'], sincos=False)
res3 = f3.transform([trj0])
# compare all
for r in [res2, res3]:
np.testing.assert_array_almost_equal(res1, r)
def test_function_featurizer():
trajectories = AlanineDipeptide().get_cached().trajectories
trj0 = trajectories[0]
# use the dihedral to compute phi for ala
atom_ind = [[4, 6, 8, 14]]
func = compute_dihedrals
# test with args
f = FunctionFeaturizer(func, func_args={"indices": atom_ind})
res1 = f.transform([trj0])
# test with function in a function without any args
def funcception(trj):
return compute_phi(trj)[1]
f = FunctionFeaturizer(funcception)
res2 = f.transform([trj0])
# know results
f3 = DihedralFeaturizer(['phi'], sincos=False)
res3 = f3.transform([trj0])
# compare all
for r in [res2, res3]:
np.testing.assert_array_almost_equal(res1, r)
def test_get_common_features():
yaml_file = load_yaml_file(os.path.join(base_dir,"mdl_dir","project.yaml"))
aligned_dict={}
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
aligned_dict[protein] = t.top.to_fasta(chain=0)
f= DihedralFeaturizer()
common_feature_dic,_ = _get_common_features(yaml_file,f, aligned_dict, False)
for protein in yaml_file["protein_list"]:
t = load_random_traj(yaml_file, protein)
assert(len(common_feature_dic[protein])==f.transform(t)[0].shape[1])
return
def test_code_works():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = DihedralFeaturizer(['phi', 'psi'], trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = VonMisesHMM(n_states=4, n_init=1)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
def test_code_works():
# creates a 4-state HMM on the ALA2 data. Nothing fancy, just makes
# sure the code runs without erroring out
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = DihedralFeaturizer(['phi', 'psi'], trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = VonMisesHMM(n_states=4, n_init=1)
hmm.fit(sequences)
assert len(hmm.timescales_ == 3)
assert np.any(hmm.timescales_ > 50)
def test_DihedralFeaturizer_describe_features_nosincos():
feat = DihedralFeaturizer(sincos=False)
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def test_DihedralFeaturizer_describe_features_nosincos():
feat = DihedralFeaturizer(sincos=False)
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
feature_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
if feat.sincos:
func = getattr(np, '%s' % df.iloc[f_index].otherinfo)
feature_value = func(feature_value)
assert (features[0][:, f_index] == feature_value.flatten()).all()
def fit_and_transform(directory, stride=5):
projected_data_filename = "/scratch/users/enf/b2ar_analysis/phi_psi_chi_stride%d_projected.h5" % stride
fit_model_filename = "/scratch/users/enf/b2ar_analysis/phi_psi_chi2_stride%s_tica_coords.h5" % stride
#active_pdb_file = "/scratch/users/enf/b2ar_analysis/3P0G_pymol_prepped.pdb"
active_pdb_file = "/scratch/users/enf/b2ar_analysis/system_B.pdb"
tica_model = tICA(n_components=4)
if not os.path.exists(projected_data_filename):
print("loading feature files")
feature_files = get_trajectory_files(directory)
pool = mp.Pool(mp.cpu_count())
features = pool.map(load_features, feature_files)
pool.terminate()
if not os.path.exists(fit_model_filename):
print("fitting data to tICA model")
fit_model = tica_model.fit(features)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(features)
verbosedump(transformed_data, projected_data_filename)
else:
print("loading tICA model")
fit_model = verboseload(fit_model_filename)
transformed_data = fit_model.transform(features)
verbosedump(transformed_data, projected_data_filename)
else:
fit_model = verboseload(fit_model_filename)
transformed_data = verboseload(projected_data_filename)
active_pdb = md.load(active_pdb_file)
top = active_pdb.topology
atom_indices = [
a.index for a in top.atoms
if a.residue.is_protein and a.residue.resSeq != 341
and a.residue.name[0:2] != "HI" and a.residue.resSeq != 79
and a.residue.resSeq != 296 and a.residue.resSeq != 269 and a.residue.
resSeq != 178 and a.residue.resSeq != 93 and a.residue.name != "NMA"
and a.residue.name != "NME" and a.residue.name != "ACE"
]
active_pdb = md.load(active_pdb_file, atom_indices=atom_indices)
featurizer = DihedralFeaturizer(types=['phi', 'psi', 'chi2'])
active_pdb_features = featurizer.transform(active_pdb)
active_pdb_projected = fit_model.transform(active_pdb_features)
print((active_pdb_projected[0:4]))
def test_pickle():
"""Test pickling an HMM"""
trajectories = AlanineDipeptide().get_cached().trajectories
topology = trajectories[0].topology
indices = topology.select('symbol C or symbol O or symbol N')
featurizer = DihedralFeaturizer(['phi', 'psi'], trajectories[0][0])
sequences = featurizer.transform(trajectories)
hmm = VonMisesHMM(n_states=4, n_init=1)
hmm.fit(sequences)
logprob, hidden = hmm.predict(sequences)
with tempfile.TemporaryFile() as savefile:
pickle.dump(hmm, savefile)
savefile.seek(0, 0)
hmm2 = pickle.load(savefile)
logprob2, hidden2 = hmm2.predict(sequences)
assert (logprob == logprob2)
def read_and_featurize(filename, dihedrals=['phi','psi','chi2'], stride=10):
print("reading and featurizing %s" %(filename))
traj = md.load(filename).select('chain A and protein')
featurizer = DihedralFeaturizer(types = dihedrals)
features = featurizer.transform(traj_list = traj)
print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory)-2]
dcd_file = directory[len(directory)-1]
new_file = "%s_features_stride%d.h5" %(dcd_file.rsplit( ".", 1 )[ 0 ] , stride)
new_root_dir = "/home/enf/b2ar_analysis/subsampled_features/"
new_condition_dir = "%s/%s" %(new_root_dir, condition)
if not os.path.exists(new_condition_dir):
os.makedirs(new_condition_dir)
new_file_full = "%s/%s/%s" %(new_root_dir, condition, new_file)
print("saving features as %s" %new_file_full)
verbosedump(features, new_file_full)
return features
def read_and_featurize(filename, dihedrals=['phi', 'psi', 'chi2'], stride=10):
print(("reading and featurizing %s" % (filename)))
traj = md.load(filename).select('chain A and protein')
featurizer = DihedralFeaturizer(types=dihedrals)
features = featurizer.transform(traj_list=traj)
print("finished featurizing")
directory = filename.split("/")
condition = directory[len(directory) - 2]
dcd_file = directory[len(directory) - 1]
new_file = "%s_features_stride%d.h5" % (dcd_file.rsplit(".", 1)[0], stride)
new_root_dir = "/home/enf/b2ar_analysis/subsampled_features/"
new_condition_dir = "%s/%s" % (new_root_dir, condition)
if not os.path.exists(new_condition_dir):
os.makedirs(new_condition_dir)
new_file_full = "%s/%s/%s" % (new_root_dir, condition, new_file)
print(("saving features as %s" % new_file_full))
verbosedump(features, new_file_full)
return features
def fit_and_transform(directory, stride=5):
projected_data_filename = "/scratch/users/enf/b2ar_analysis/phi_psi_chi_stride%d_projected.h5" %stride
fit_model_filename = "/scratch/users/enf/b2ar_analysis/phi_psi_chi2_stride%s_tica_coords.h5" %stride
#active_pdb_file = "/scratch/users/enf/b2ar_analysis/3P0G_pymol_prepped.pdb"
active_pdb_file = "/scratch/users/enf/b2ar_analysis/system_B.pdb"
tica_model = tICA(n_components=4)
if not os.path.exists(projected_data_filename):
print("loading feature files")
feature_files = get_trajectory_files(directory)
pool = mp.Pool(mp.cpu_count())
features = pool.map(load_features, feature_files)
pool.terminate()
if not os.path.exists(fit_model_filename):
print("fitting data to tICA model")
fit_model = tica_model.fit(features)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(features)
verbosedump(transformed_data, projected_data_filename)
else:
print("loading tICA model")
fit_model = verboseload(fit_model_filename)
transformed_data = fit_model.transform(features)
verbosedump(transformed_data, projected_data_filename)
else:
fit_model = verboseload(fit_model_filename)
transformed_data = verboseload(projected_data_filename)
active_pdb = md.load(active_pdb_file)
top = active_pdb.topology
atom_indices = [a.index for a in top.atoms if a.residue.is_protein and a.residue.resSeq != 341 and a.residue.name[0:2] != "HI" and a.residue.resSeq != 79 and a.residue.resSeq != 296 and a.residue.resSeq != 269 and a.residue.resSeq != 178 and a.residue.resSeq != 93 and a.residue.name != "NMA" and a.residue.name != "NME" and a.residue.name != "ACE"]
active_pdb = md.load(active_pdb_file, atom_indices=atom_indices)
featurizer = DihedralFeaturizer(types=['phi', 'psi', 'chi2'])
active_pdb_features = featurizer.transform(active_pdb)
active_pdb_projected = fit_model.transform(active_pdb_features)
print(active_pdb_projected[0:4])
def test_FeatureSelector_describe_features():
rnd_traj = np.random.randint(len(trajectories))
f_ca = ContactFeaturizer(scheme='CA', ignore_nonprotein=True)
f1 = f_ca.transform([trajectories[rnd_traj]])
df1 = pd.DataFrame(f_ca.describe_features(trajectories[rnd_traj]))
f_dih = DihedralFeaturizer()
f2 = f_dih.transform([trajectories[rnd_traj]])
df2 = pd.DataFrame(f_dih.describe_features(trajectories[rnd_traj]))
df_dict = {}
df_dict["ca"] = df1
df_dict["dih"] = df2
f_comb = FeatureSelector([('ca', f_ca), ('dih', f_dih)])
f3 = f_comb.transform([trajectories[rnd_traj]])
df3 = pd.DataFrame(f_comb.describe_features(trajectories[rnd_traj]))
assert len(df3) == len(df1) + len(df2)
df4 = pd.concat([df_dict[i] for i in f_comb.feat_list])
# lets randomly compare 40 features
for i in np.random.choice(range(len(df3)), 40):
for j in df3.columns:
assert eq(df3.iloc[i][j], df4.iloc[i][j])
def test_FeatureSelector_describe_features():
rnd_traj = np.random.randint(len(trajectories))
f_ca = ContactFeaturizer(scheme='CA', ignore_nonprotein=True)
f1 = f_ca.transform([trajectories[rnd_traj]])
df1 = pd.DataFrame(f_ca.describe_features(trajectories[rnd_traj]))
f_dih = DihedralFeaturizer()
f2 = f_dih.transform([trajectories[rnd_traj]])
df2 = pd.DataFrame(f_dih.describe_features(trajectories[rnd_traj]))
df_dict = {}
df_dict["ca"] = df1
df_dict["dih"] = df2
f_comb = FeatureSelector([('ca', f_ca), ('dih', f_dih)])
f3 = f_comb.transform([trajectories[rnd_traj]])
df3 = pd.DataFrame(f_comb.describe_features(trajectories[rnd_traj]))
assert len(df3) == len(df1) + len(df2)
df4 = pd.concat([df_dict[i] for i in f_comb.feat_list])
# lets randomly compare 40 features
for i in np.random.choice(range(len(df3)), 40):
for j in df3.columns:
assert eq(df3.iloc[i][j], df4.iloc[i][j])
### Featurization based on dihedral angles for the protein folding trajectories ### Required packages: mdtraj, msmbuilder, glob ### @Chuankai Zhao, [email protected] import mdtraj as md import glob from msmbuilder.featurizer import DihedralFeaturizer from msmbuilder.utils import verbosedump, verboseload # Set the path of MD trajectories and the name of topology files. trajaddress = "/home/amoffet2/msm_network_project/folding/lindorff-larsen_2011_trajs/protein_g-350K/DESRES-Trajectory_NuG2-*-protein/NuG2-*-protein/*.dcd" top = "/home/amoffet2/msm_network_project/folding/lindorff-larsen_2011_trajs/protein_g-350K/protein_g.pdb" # Load the trajectories using mdtraj files = glob.glob(trajaddress) traj_list = [] for f in files: t = md.load(f, top=top, stride=10) traj_list.append(t) # Featurize the trajectories based on phi, psi, chi1 dihedral angles model = DihedralFeaturizer(types=['phi', 'psi', 'chi1']) features = model.transform(traj_list) # Set the path of output file and save the output. pkl = "/home/czhao37/2-SAXS-Adaptive_Samping/6-protein-G/features/featurized_1mio.pkl" verbosedump(features, pkl)
if traj.endswith(".dcd"):
traj_files.append("%s/%s" %(traj_dir,traj))
traj_files.sort()
traj = md.load(traj_files, top = "/home/harrigan/compute/wetmsm/gpcr/des/system_mae_to_pdb/des_trajs/DESRES-Trajectory_pnas2011b-H-05-all/system.pdb", stride=10)
traj = traj[0].join(traj[1:])
traj.save("/home/enf/b2ar_analysis/H-05/%s" %("combined_traj_stride10.h5"))
else:
'''
#print("loading h5 traj")
#traj = md.load("combined_traj_stride10.h5")
'''
'''
if not (os.path.isfile("phi_psi_chi2_features_vd_stride10.h5")):
print("featurizing")
phi_psi_chi2 = DihedralFeaturizer(types=['phi','psi','chi2'])
features = phi_psi_chi2.transform(traj_list = traj)
print("finished featurizing")
verbosedump(features, "phi_psi_chi2_features_vd_stride10.h5")
else:
print("loading existing features")
features = verboseload("phi_psi_chi2_features_vd_stride10.h5")
features = [np.concatenate(features)]
if not (os.path.isfile("reduced_phi_psi_chi_stride10.h5")):
print("Fitting tICA model")
tica_model = tICA(n_components=4)
fitted_model = tica_model.fit(features)
reduced_data = fitted_model.transform(features)
verbosedump(reduced_data, "reduced_phi_psi_chi_stride10.h5")
print(tica_model.summarize())
else:
import mdtraj as md
import pandas as pd
from msmbuilder.msm import MarkovStateModel
from msmbuilder.cluster import KMeans
flist = glob.glob("../trajectory.xtc")
top = md.load("../top.pdb")
trj_list = [md.load(i, top=top) for i in flist]
print("Found %d trajs" % len(trj_list))
f = DihedralFeaturizer(sincos=False)
dump(f, "raw_featurizer.pkl")
feat = f.transform(trj_list)
dump(feat, "raw_features.pkl")
f = load("./featurizer.pkl")
dump(f, "featurizer.pkl")
df1 = pd.DataFrame(f.describe_features(trj_list[0]))
dump(df1, "feature_descriptor.pkl")
feat = f.transform(trj_list)
dump(feat, "features.pkl")
t = tICA(lag_time=100, n_components=2, kinetic_mapping=False)
tica_feat = t.fit_transform(feat)
if traj.endswith(".dcd"):
traj_files.append("%s/%s" %(traj_dir,traj))
traj_files.sort()
traj = md.load(traj_files, top = "/home/harrigan/compute/wetmsm/gpcr/des/system_mae_to_pdb/des_trajs/DESRES-Trajectory_pnas2011b-H-05-all/system.pdb", stride=10)
traj = traj[0].join(traj[1:])
traj.save("/home/enf/b2ar_analysis/H-05/%s" %("combined_traj_stride10.h5"))
else:
'''
#print("loading h5 traj")
#traj = md.load("combined_traj_stride10.h5")
'''
'''
if not (os.path.isfile("phi_psi_chi2_features_vd_stride10.h5")):
print("featurizing")
phi_psi_chi2 = DihedralFeaturizer(types=['phi', 'psi', 'chi2'])
features = phi_psi_chi2.transform(traj_list=traj)
print("finished featurizing")
verbosedump(features, "phi_psi_chi2_features_vd_stride10.h5")
else:
print("loading existing features")
features = verboseload("phi_psi_chi2_features_vd_stride10.h5")
features = [np.concatenate(features)]
if not (os.path.isfile("reduced_phi_psi_chi_stride10.h5")):
print("Fitting tICA model")
tica_model = tICA(n_components=4)
fitted_model = tica_model.fit(features)
reduced_data = fitted_model.transform(features)
verbosedump(reduced_data, "reduced_phi_psi_chi_stride10.h5")
print((tica_model.summarize()))
else:
