def prepare_trajectory(self, trajectory):
"""Prepare a trajectory for distance calculations based on the contact map.
Each frame in the trajectory will be represented by a vector where
each entries represents the distance between two residues in the structure.
Depending on what contacts you pick to use, this can be a 'native biased'
picture or not.
Paramters
---------
trajectory : msmbuilder.Trajectory
The trajectory to prepare
Returns
-------
pairwise_distances : ndarray
1D array of various residue-residue distances
"""
xyzlist = trajectory['XYZList']
traj_length = len(xyzlist)
num_residues = trajectory.GetNumberOfResidues()
num_atoms = trajectory.GetNumberOfAtoms()
if self.contacts == 'all':
contacts = np.empty(
((num_residues - 2) * (num_residues - 3) / 2, 2),
dtype=np.int32)
p = 0
for (a, b) in itertools.combinations(range(num_residues), 2):
if max(a, b) > min(a, b) + 2:
contacts[p, :] = [a, b]
p += 1
assert p == len(contacts), 'Something went wrong generating "all"'
else:
num, width = self.contacts.shape
contacts = self.contacts
if not width == 2:
raise ValueError('contacts must be width 2')
if not (0 < len(np.unique(contacts[:, 0])) < num_residues):
raise ValueError(
'contacts should refer to zero-based indexing of the residues'
)
if not np.all(
np.logical_and(0 <= np.unique(contacts),
np.unique(contacts) < num_residues)):
raise ValueError(
'contacts should refer to zero-based indexing of the residues'
)
if self.scheme == 'ca':
# not all residues have a CA
#alpha_indices = np.where(trajectory['AtomNames'] == 'CA')[0]
atom_contacts = np.zeros_like(contacts)
residue_to_alpha = np.zeros(num_residues) # zero based indexing
for i in range(num_atoms):
if trajectory['AtomNames'][i] == 'CA':
residue = trajectory['ResidueID'][i] - 1
residue_to_alpha[residue] = i
#print 'contacts (residues)', contacts
#print 'residue_to_alpja', residue_to_alpha.shape
#print 'residue_to_alpja', residue_to_alpha
atom_contacts = residue_to_alpha[contacts]
#print 'atom_contacts', atom_contacts
output = _contactcalc.atom_distances(xyzlist, atom_contacts)
elif self.scheme in ['closest', 'closest-heavy']:
if self.scheme == 'closest':
residue_membership = [None for i in range(num_residues)]
for i in range(num_residues):
residue_membership[i] = np.where(
trajectory['ResidueID'] == i + 1)[0]
elif self.scheme == 'closest-heavy':
residue_membership = [[] for i in range(num_residues)]
for i in range(num_atoms):
residue = trajectory['ResidueID'][i] - 1
if not trajectory['AtomNames'][i].lstrip(
'0123456789').startswith('H'):
residue_membership[residue].append(i)
#print 'Residue Membership'
#print residue_membership
#for row in residue_membership:
# for col in row:
# print "%s-%s" % (trajectory['AtomNames'][col], trajectory['ResidueID'][col]),
# print
output = _contactcalc.residue_distances(xyzlist,
residue_membership,
contacts)
else:
raise ValueError('This is not supposed to happen!')
return np.double(output)
