def map(self, traj):
# TODO: can we merge phi_inds and psi_inds to only call
# compute_dihedrals once?
y1 = compute_dihedrals(traj, self._phi_inds).astype(np.float32)
y2 = compute_dihedrals(traj, self._psi_inds).astype(np.float32)
rad = np.hstack((y1, y2))
if self.deg:
return np.rad2deg(rad)
else:
return rad
def map(self, traj):
# TODO: can we merge phi_inds and psi_inds to only call
# compute_dihedrals once?
y1 = compute_dihedrals(traj, self._phi_inds).astype(np.float32)
y2 = compute_dihedrals(traj, self._psi_inds).astype(np.float32)
rad = np.hstack((y1, y2))
if self.deg:
return np.rad2deg(rad)
else:
return rad
def read_and_featurize_custom(traj_file, condition=None, location=None, dihedral_residues = None, distance_residues = None):
top = md.load_frame(traj_file,index = 0).topology
#atom_indices = [a.index for a in top.atoms if a.residue.resSeq != 130]
atom_indices = [a.index for a in top.atoms]
traj = md.load(traj_file, atom_indices=atom_indices)
print traj_file
#print traj
#print("loaded trajectory")
'''
a = time.time()
featurizer = DihedralFeaturizer(types = ['phi', 'psi', 'chi2'])
features = featurizer.transform(traj)
b = time.time()
#print(b-a)
print("original features has dim")
print(np.shape(features))
'''
a = time.time()
phi_tuples = phi_indices(traj.topology, dihedral_residues)
psi_tuples = psi_indices(traj.topology, dihedral_residues)
chi2_tuples = chi2_indices(traj.topology, dihedral_residues)
#if distance_residues is not None:
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
phi_angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=phi_tuples))
psi_angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=psi_tuples))
chi2_angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=chi2_tuples))
manual_features = np.concatenate([np.sin(phi_angles), np.cos(phi_angles), np.sin(psi_angles), np.cos(psi_angles), np.sin(chi2_angles), np.cos(chi2_angles)])
b = time.time()
#print(b-a)
print("new features has shape: ")
print(np.shape(manual_features))
if condition is None:
condition = get_condition(traj_file)
if location is None:
location = "/scratch/users/enf/b2ar_analysis/features_allprot"
verbosedump(manual_features, "%s/%s.h5" %(location, condition))
def __init__(self, directory="/home/harrigan/projects/msmbuilder/Tutorial/"):
# Load from disk
print "Loading trajectories"
traj_list_in = get_data.get_from_fs(os.path.join(directory, 'XTC'), os.path.join(directory, 'native.pdb'))
# Compute dihedrals
traj_list_angles = list()
traj_list = list()
print "Computing Dihedrals"
for traj in traj_list_in:
# Compute actual angles
diheds = dihedral.compute_dihedrals(traj, Dipeptide.INDICES, opt=False)
# Compute sin and cos
num_diheds = diheds.shape[1]
per_diheds = np.zeros((diheds.shape[0], num_diheds * 2))
per_diheds[:, 0:num_diheds] = np.sin(diheds)
per_diheds[:, num_diheds:num_diheds * 2] = np.cos(diheds)
distances = distance.compute_distances(traj, Dipeptide.PAIRS, periodic=True, opt=False)
distances = np.log(distances)
# Save
traj_list.append(distances)
traj_list_angles.append(diheds)
# traj_list.append(per_diheds)
self.traj_list = traj_list
self.traj_list_angles = traj_list_angles
def get_redundant_internal_coordinates(trajectory, **kwargs):
"""Compute internal coordinates from the cartesian coordinates
This extracts all of the bond lengths, bond angles and dihedral angles
from every frame in a trajectory.
Parameters
----------
trajectory : mdtraj.Trajectory
Trajectory object containing the internal coordinates
Other Parameters
----------------
ibonds : np.ndarray, optional, shape[n_bonds, 2], dtype=int
Each row gives the indices of two atoms involved in a bond
iangles : np.ndarray, optional shape[n_angles, 3], dtype=int
Each row gives the indices of three atoms which together make an angle
idihedrals : np.ndarray, optional, shape[n_dihedrals, 4], dtype=int
Each row gives the indices of the four atoms which together make a
dihedral
Notes
-----
ibonds, iangles, and idihedrals will be computed usig the first
frame in the trajectory, if not supplied
Returns
-------
internal_coords : np.ndarray, shape=[n_frames, n_bonds+n_angles+n_dihedrals]
All of the internal coordinates collected into a big array, such that
internal_coords[i,j] gives the jth coordinate for the ith frame.
"""
if 'ibonds' in kwargs and 'iangles' in kwargs and 'idihedrals' in kwargs:
ibonds = kwargs['ibonds']
iangles = kwargs['iangles']
idihedrals = kwargs['idihedrals']
else:
ibonds, iangles, idihedrals = get_connectivity(trajectory)
# convert everything to the right shape and C ordering, since
# all of these methods are in C and are going to need things to be
# the right type. The methods will all do a copy for things that
# aren't the right type, but hopefully we can only do the copy once
# instead of three times if xyzlist really does need to be reordered
# in memory
xyzlist = np.array(trajectory.xyz, dtype=np.float32, order='c')
ibonds = np.array(ibonds, dtype=np.int32, order='c')
iangles = np.array(iangles, dtype=np.int32, order='c')
idihedrals = np.array(idihedrals, dtype=np.int32, order='c')
b = compute_distances(xyzlist, ibonds)
a = compute_angles(xyzlist, iangles)
d = compute_dihedrals(xyzlist, idihedrals, degrees=False)
return np.hstack((b, a, d))
def get_redundant_internal_coordinates(trajectory, **kwargs):
"""Compute internal coordinates from the cartesian coordinates
This extracts all of the bond lengths, bond angles and dihedral angles
from every frame in a trajectory.
Parameters
----------
trajectory : mdtraj.Trajectory
Trajectory object containing the internal coordinates
Other Parameters
----------------
ibonds : np.ndarray, optional, shape[n_bonds, 2], dtype=int
Each row gives the indices of two atoms involved in a bond
iangles : np.ndarray, optional shape[n_angles, 3], dtype=int
Each row gives the indices of three atoms which together make an angle
idihedrals : np.ndarray, optional, shape[n_dihedrals, 4], dtype=int
Each row gives the indices of the four atoms which together make a
dihedral
Notes
-----
ibonds, iangles, and idihedrals will be computed usig the first
frame in the trajectory, if not supplied
Returns
-------
internal_coords : np.ndarray, shape=[n_frames, n_bonds+n_angles+n_dihedrals]
All of the internal coordinates collected into a big array, such that
internal_coords[i,j] gives the jth coordinate for the ith frame.
"""
if 'ibonds' in kwargs and 'iangles' in kwargs and 'idihedrals' in kwargs:
ibonds = kwargs['ibonds']
iangles = kwargs['iangles']
idihedrals = kwargs['idihedrals']
else:
ibonds, iangles, idihedrals = get_connectivity(trajectory)
# convert everything to the right shape and C ordering, since
# all of these methods are in C and are going to need things to be
# the right type. The methods will all do a copy for things that
# aren't the right type, but hopefully we can only do the copy once
# instead of three times if xyzlist really does need to be reordered
# in memory
xyzlist = np.array(trajectory.xyz, dtype=np.float32, order='c')
ibonds = np.array(ibonds, dtype=np.int32, order='c')
iangles = np.array(iangles, dtype=np.int32, order='c')
idihedrals = np.array(idihedrals, dtype=np.int32, order='c')
b = compute_distances(xyzlist, ibonds)
a = compute_angles(xyzlist, iangles)
d = compute_dihedrals(xyzlist, idihedrals, degrees=False)
return np.hstack((b, a, d))
def prepare_trajectory(self, trajectory):
"""Prepare the dihedral angle representation of a trajectory, suitable
for distance calculations.
Parameters
----------
trajectory : mdtraj.Trajectory
An MDTraj trajectory to prepare
Returns
-------
projected_angles : ndarray
A 2D array of dimension len(trajectory) x (2*number of dihedral
angles per frame), such that in each row, the first half of the entries
contain the cosine of the dihedral angles and the later dihedral angles
contain the sine of the dihedral angles. This transform is necessary so
that distance calculations preserve the periodic symmetry.
"""
traj_length = trajectory.n_frames
if self.angles == 'user':
indices = self.read_dihedral_indices(self.userfilename)
dihedrals = _dihedralcalc.compute_dihedrals(trajectory, indices)
else:
if self.indices is None:
dihedrals = np.hstack(getattr(_dihedralcalc, 'compute_%s' % e)(trajectory)[1] for e in self.angles.split('/'))
else:
dihedrals = _dihedralcalc.compute_dihedrals(trajectory, self.indices)
# these dihedrals go between -pi and pi but obviously because of the
# periodicity, when we take distances we want the distance between -179
# and +179 to be very close, so we need to do a little transform
num_dihedrals = dihedrals.shape[1]
transformed = np.empty((traj_length, 2 * num_dihedrals))
transformed[:, 0:num_dihedrals] = np.cos(dihedrals)
transformed[:, num_dihedrals:2 * num_dihedrals] = np.sin(dihedrals)
return np.double(transformed)
def read_and_featurize(traj_file, features_dir = None, condition=None, dihedral_types = ["phi", "psi", "chi1", "chi2"], dihedral_residues = None, resSeq_pairs = None, iterative = True):
a = time.time()
dihedral_indices = []
residue_order = []
if len(dihedral_residues) > 0:
for dihedral_type in dihedral_types:
if dihedral_type == "phi": dihedral_indices.append(phi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "psi": dihedral_indices.append(psi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi1": dihedral_indices.append(chi1_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi2": dihedral_indices.append(chi2_indices(fix_topology(top), dihedral_residues))
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
dihedral_angles = []
for dihedral_type in dihedral_indices:
angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=dihedral_type))
dihedral_angles.append(np.sin(angles))
dihedral_angles.append(np.cos(angles))
manual_features = np.transpose(np.concatenate(dihedral_angles))
if len(resSeq_pairs) > 0:
top = md.load_frame(traj_file, index=0).topology
resIndex_pairs = convert_resSeq_to_resIndex(top, resSeq_pairs)
contact_features = []
if iterative:
try:
for chunk in md.iterload(traj_file, chunk = 1000):
# chunk = fix_traj(chunk)
#chunk = md.load(traj_file,stride=1000)
#print(resIndex_pairs[0:10])
chunk_features = md.compute_contacts(chunk, contacts = resIndex_pairs, scheme = 'closest-heavy', ignore_nonprotein=False)[0]
print(np.shape(chunk_features))
contact_features.append(chunk_features)
contact_features = np.concatenate(contact_features)
except Exception,e:
print str(e)
print("Failed")
return
#traj = md.load(traj_file)
#contact_features = md.compute_contacts(chunk, contacts = contact_residue_pairs, scheme = 'closest-heavy', ignore_nonprotein=False)[0]
else:
try:
traj = md.load(traj_file)
contact_features = md.compute_contacts(traj, contacts = resIndex_pairs, scheme = 'closest-heavy', ignore_nonprotein=False)[0]
except Exception,e:
print str(e)
print("Failed for traj")
return
def __init__(self,
directory="/home/harrigan/projects/msmbuilder/Tutorial/"):
# Load from disk
print "Loading trajectories"
traj_list_in = get_data.get_from_fs(
os.path.join(directory, 'XTC'),
os.path.join(directory, 'native.pdb'))
# Compute dihedrals
traj_list_angles = list()
traj_list = list()
print "Computing Dihedrals"
for traj in traj_list_in:
# Compute actual angles
diheds = dihedral.compute_dihedrals(traj,
Dipeptide.INDICES,
opt=False)
# Compute sin and cos
num_diheds = diheds.shape[1]
per_diheds = np.zeros((diheds.shape[0], num_diheds * 2))
per_diheds[:, 0:num_diheds] = np.sin(diheds)
per_diheds[:, num_diheds:num_diheds * 2] = np.cos(diheds)
distances = distance.compute_distances(traj,
Dipeptide.PAIRS,
periodic=True,
opt=False)
distances = np.log(distances)
# Save
traj_list.append(distances)
traj_list_angles.append(diheds)
# traj_list.append(per_diheds)
self.traj_list = traj_list
self.traj_list_angles = traj_list_angles
def read_and_featurize_custom(traj_file,
condition=None,
location=None,
dihedral_residues=None,
distance_residues=None):
top = md.load_frame(traj_file, index=0).topology
#atom_indices = [a.index for a in top.atoms if a.residue.resSeq != 130]
atom_indices = [a.index for a in top.atoms]
traj = md.load(traj_file, atom_indices=atom_indices)
print(traj_file)
#print traj
#print("loaded trajectory")
'''
a = time.time()
featurizer = DihedralFeaturizer(types = ['phi', 'psi', 'chi2'])
features = featurizer.transform(traj)
b = time.time()
#print(b-a)
print("original features has dim")
print(np.shape(features))
'''
a = time.time()
phi_tuples = phi_indices(traj.topology, dihedral_residues)
psi_tuples = psi_indices(traj.topology, dihedral_residues)
chi2_tuples = chi2_indices(traj.topology, dihedral_residues)
#if distance_residues is not None:
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
phi_angles = np.transpose(
ManualDihedral.compute_dihedrals(traj=traj, indices=phi_tuples))
psi_angles = np.transpose(
ManualDihedral.compute_dihedrals(traj=traj, indices=psi_tuples))
chi2_angles = np.transpose(
ManualDihedral.compute_dihedrals(traj=traj, indices=chi2_tuples))
manual_features = np.concatenate([
np.sin(phi_angles),
np.cos(phi_angles),
np.sin(psi_angles),
np.cos(psi_angles),
np.sin(chi2_angles),
np.cos(chi2_angles)
])
b = time.time()
#print(b-a)
print("new features has shape: ")
print((np.shape(manual_features)))
if condition is None:
condition = get_condition(traj_file)
if location is None:
location = "/scratch/users/enf/b2ar_analysis/features_allprot"
verbosedump(manual_features, "%s/%s.h5" % (location, condition))
def read_and_featurize_iter(traj_file, features_dir = None, condition=None, dihedral_types = ["phi", "psi", "chi1", "chi2"], dihedral_residues = None, contact_residues = None):
a = time.time()
dihedral_indices = []
residue_order = []
if len(dihedral_residues) > 0:
for dihedral_type in dihedral_types:
if dihedral_type == "phi": dihedral_indices.append(phi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "psi": dihedral_indices.append(psi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi1": dihedral_indices.append(chi1_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi2": dihedral_indices.append(chi2_indices(fix_topology(top), dihedral_residues))
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
dihedral_angles = []
for dihedral_type in dihedral_indices:
angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=dihedral_type))
dihedral_angles.append(np.sin(angles))
dihedral_angles.append(np.cos(angles))
manual_features = np.transpose(np.concatenate(dihedral_angles))
if len(contact_residues) > 0:
contact_features = []
for chunk in md.iterload(traj_file, chunk = 10000):
fixed_traj = fix_traj(chunk)
fixed_top = fixed_traj.topology
distance_residues = []
res_objects = [r for r in fixed_top.residues]
for r in contact_residues:
for res in res_objects:
if res.resSeq == r and len(res._atoms) > 5:
#print res._atoms
distance_residues.append(res.index)
if len(contact_residues) != len(distance_residues):
print "Residues are missing"
print len(contact_residues)
print len(distance_residues)
#sys.exit()
#return None
combinations = itertools.combinations(distance_residues, 2)
pairs = [c for c in combinations]
#print pairs
contact_features.append(md.compute_contacts(fixed_traj, contacts = pairs, scheme = 'closest-heavy', ignore_nonprotein=False)[0])
contact_features = np.concatenate(contact_features)
if len(dihedral_residues) > 0:
manual_features = np.column_stack((manual_features, contact_features))
else:
manual_features = contact_features
b = time.time()
print("new features %s has shape: " %traj_file)
print(np.shape(manual_features))
if condition is None:
condition = get_condition(traj_file)
verbosedump(manual_features, "%s/%s.h5" %(features_dir, condition))
def read_and_featurize_custom(traj_file, features_dir = None, condition=None, dihedral_types = ["phi", "psi", "chi1", "chi2"], dihedral_residues = None, contact_residues = None):
#if "23" not in traj_file and "24" not in traj_file: return
top = md.load_frame(traj_file,index = 0).topology
#atom_indices = [a.index for a in top.atoms if a.residue.resSeq != 130]
atom_indices = [a.index for a in top.atoms]
traj = md.load(traj_file, atom_indices=atom_indices)
print traj_file
#print traj
#print("loaded trajectory")
'''
a = time.time()
featurizer = DihedralFeaturizer(types = ['phi', 'psi', 'chi2'])
features = featurizer.transform(traj)
b = time.time()
#print(b-a)
print("original features has dim")
print(np.shape(features))
'''
a = time.time()
dihedral_indices = []
residue_order = []
if len(dihedral_residues) > 0:
for dihedral_type in dihedral_types:
if dihedral_type == "phi": dihedral_indices.append(phi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "psi": dihedral_indices.append(psi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi1": dihedral_indices.append(chi1_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi2": dihedral_indices.append(chi2_indices(fix_topology(top), dihedral_residues))
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
dihedral_angles = []
for dihedral_type in dihedral_indices:
angles = np.transpose(ManualDihedral.compute_dihedrals(traj=traj,indices=dihedral_type))
dihedral_angles.append(np.sin(angles))
dihedral_angles.append(np.cos(angles))
manual_features = np.transpose(np.concatenate(dihedral_angles))
if len(contact_residues) > 0:
fixed_traj = fix_traj(traj)
fixed_top = fixed_traj.topology
distance_residues = []
res_objects = [r for r in fixed_top.residues]
for r in contact_residues:
for res in res_objects:
if res.resSeq == r and len(res._atoms) > 5:
#print res._atoms
distance_residues.append(res.index)
if len(contact_residues) != len(distance_residues):
print "Residues are missing"
print len(contact_residues)
print len(distance_residues)
#sys.exit()
#return None
combinations = itertools.combinations(distance_residues, 2)
pairs = [c for c in combinations]
#print pairs
contact_features = md.compute_contacts(traj, contacts = pairs, scheme = 'closest-heavy', ignore_nonprotein=False)[0]
#print contact_features
#print(np.shape(contact_features))
if len(dihedral_residues) > 0:
manual_features = np.column_stack((manual_features, contact_features))
else:
manual_features = contact_features
b = time.time()
print("new features %s has shape: " %traj_file)
print(np.shape(manual_features))
if condition is None:
condition = get_condition(traj_file)
verbosedump(manual_features, "%s/%s.h5" %(features_dir, condition))
def read_and_featurize_iter(traj_file,
features_dir=None,
condition=None,
dihedral_types=["phi", "psi", "chi1", "chi2"],
dihedral_residues=None,
contact_residues=None):
a = time.time()
dihedral_indices = []
residue_order = []
if len(dihedral_residues) > 0:
for dihedral_type in dihedral_types:
if dihedral_type == "phi":
dihedral_indices.append(
phi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "psi":
dihedral_indices.append(
psi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi1":
dihedral_indices.append(
chi1_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi2":
dihedral_indices.append(
chi2_indices(fix_topology(top), dihedral_residues))
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
dihedral_angles = []
for dihedral_type in dihedral_indices:
angles = np.transpose(
ManualDihedral.compute_dihedrals(traj=traj,
indices=dihedral_type))
dihedral_angles.append(np.sin(angles))
dihedral_angles.append(np.cos(angles))
manual_features = np.transpose(np.concatenate(dihedral_angles))
if len(contact_residues) > 0:
contact_features = []
for chunk in md.iterload(traj_file, chunk=10000):
fixed_traj = fix_traj(chunk)
fixed_top = fixed_traj.topology
distance_residues = []
res_objects = [r for r in fixed_top.residues]
for r in contact_residues:
for res in res_objects:
if res.resSeq == r and len(res._atoms) > 5:
#print res._atoms
distance_residues.append(res.index)
if len(contact_residues) != len(distance_residues):
print("Residues are missing")
print(len(contact_residues))
print(len(distance_residues))
#sys.exit()
#return None
combinations = itertools.combinations(distance_residues, 2)
pairs = [c for c in combinations]
#print pairs
contact_features.append(
md.compute_contacts(fixed_traj,
contacts=pairs,
scheme='closest-heavy',
ignore_nonprotein=False)[0])
contact_features = np.concatenate(contact_features)
if len(dihedral_residues) > 0:
manual_features = np.column_stack(
(manual_features, contact_features))
else:
manual_features = contact_features
b = time.time()
print(("new features %s has shape: " % traj_file))
print((np.shape(manual_features)))
if condition is None:
condition = get_condition(traj_file)
verbosedump(manual_features, "%s/%s.h5" % (features_dir, condition))
def read_and_featurize_custom(traj_file,
features_dir=None,
condition=None,
dihedral_types=["phi", "psi", "chi1", "chi2"],
dihedral_residues=None,
contact_residues=None):
#if "23" not in traj_file and "24" not in traj_file: return
top = md.load_frame(traj_file, index=0).topology
#atom_indices = [a.index for a in top.atoms if a.residue.resSeq != 130]
atom_indices = [a.index for a in top.atoms]
traj = md.load(traj_file, atom_indices=atom_indices)
print(traj_file)
#print traj
#print("loaded trajectory")
'''
a = time.time()
featurizer = DihedralFeaturizer(types = ['phi', 'psi', 'chi2'])
features = featurizer.transform(traj)
b = time.time()
#print(b-a)
print("original features has dim")
print(np.shape(features))
'''
a = time.time()
dihedral_indices = []
residue_order = []
if len(dihedral_residues) > 0:
for dihedral_type in dihedral_types:
if dihedral_type == "phi":
dihedral_indices.append(
phi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "psi":
dihedral_indices.append(
psi_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi1":
dihedral_indices.append(
chi1_indices(fix_topology(top), dihedral_residues))
if dihedral_type == "chi2":
dihedral_indices.append(
chi2_indices(fix_topology(top), dihedral_residues))
#print("new features has dim %d" %(2*len(phi_tuples) + 2*len(psi_tuples) + 2*len(chi2_tuples)))
#print("feauturizing manually:")
dihedral_angles = []
for dihedral_type in dihedral_indices:
angles = np.transpose(
ManualDihedral.compute_dihedrals(traj=traj,
indices=dihedral_type))
dihedral_angles.append(np.sin(angles))
dihedral_angles.append(np.cos(angles))
manual_features = np.transpose(np.concatenate(dihedral_angles))
if len(contact_residues) > 0:
fixed_traj = fix_traj(traj)
fixed_top = fixed_traj.topology
distance_residues = []
res_objects = [r for r in fixed_top.residues]
for r in contact_residues:
for res in res_objects:
if res.resSeq == r and len(res._atoms) > 5:
#print res._atoms
distance_residues.append(res.index)
if len(contact_residues) != len(distance_residues):
print("Residues are missing")
print(len(contact_residues))
print(len(distance_residues))
#sys.exit()
#return None
combinations = itertools.combinations(distance_residues, 2)
pairs = [c for c in combinations]
#print pairs
contact_features = md.compute_contacts(traj,
contacts=pairs,
scheme='closest-heavy',
ignore_nonprotein=False)[0]
#print contact_features
#print(np.shape(contact_features))
if len(dihedral_residues) > 0:
manual_features = np.column_stack(
(manual_features, contact_features))
else:
manual_features = contact_features
b = time.time()
print(("new features %s has shape: " % traj_file))
print((np.shape(manual_features)))
if condition is None:
condition = get_condition(traj_file)
verbosedump(manual_features, "%s/%s.h5" % (features_dir, condition))
