def build_futamura_intersection_table(self, solvent_radius):
outside_barrier = 4.0
grid_spacing = 4 * solvent_radius
grid = FutamuraHash(self, outside_barrier, grid_spacing)
T = grid.T
block_assignments = grid.atom_block_assignments
print 'locating intersections'
# now locate the intersections
x_table = {}
radii = {
'C': solvent_radius + 1.75,
'O': solvent_radius + 1.4,
'N': solvent_radius + 1.55,
'S': solvent_radius + 1.8,
'P': solvent_radius + 2.0,
'H': solvent_radius + 1.17,
'Z': solvent_radius + 3.0
}
default_distance = solvent_radius + 1.8
for atom in self.atoms:
x_table['%s' % (atom.atom_number)] = []
r1 = radii.get(atom.atom_type[0], default_distance)
block = block_assignments['%s' % (atom.atom_number)]
key_tokens = string.split(block)
keys = [
string.atoi(key_tokens[0]),
string.atoi(key_tokens[1]),
string.atoi(key_tokens[2])
]
# put 'this' block first, so that intersection table accesses search here first
for second_atom in T[block]:
if atom != second_atom:
r2 = radii.get(second_atom.atom_type[0], default_distance)
if atom.dist(second_atom) <= r1 + r2:
x_table['%s' % (atom.atom_number)].append([
second_atom.x, second_atom.y, second_atom.z, r2,
second_atom.res_number, second_atom.chain_name,
second_atom.atom_type
])
start_array = [0, 0, 0]
end_array = [0, 0, 0]
# figure out starts and ends
counts = [
grid.volume_count_x, grid.volume_count_y, grid.volume_count_z
]
for ind in [0, 1, 2]:
if keys[ind] == 0:
start_array[ind] = 0
end_array[ind] = 2
elif keys[ind] == counts[ind] - 1:
start_array[ind] = keys[ind] - 1
end_array[ind] = keys[ind] + 1
else:
start_array[ind] = keys[ind] - 1
end_array[ind] = keys[ind] + 2
for i in range(start_array[0], end_array[0]):
for j in range(start_array[1], end_array[1]):
for k in range(start_array[2], end_array[2]):
key2 = '%s %s %s' % (i, j, k)
if key2 == block:
continue # did this one earlier
if key2 in T.keys():
for second_atom in T[key2]:
if atom != second_atom:
r2 = radii.get(second_atom.atom_type[0],
default_distance)
if atom.dist(second_atom) <= r1 + r2:
x_table['%s' %
(atom.atom_number)].append([
second_atom.x,
second_atom.y,
second_atom.z, r2,
second_atom.res_number,
second_atom.chain_name,
second_atom.atom_type
])
self.x_table = x_table
def assign_core_atom_distances(self, core_cutoff, neighbor_thresh=3, mindist=0.0, maxdist=12.0):
f_hash = FutamuraHash(self, 0.0, maxdist, 2, core_cutoff, neighbor_thresh)
atom_list = self.get_core_alpha_carbons(core_cutoff, neighbor_thresh)
for cp in atom_list:
cp.data['nearby_cps'] = f_hash.get_atoms_between(cp.atom_number, mindist, maxdist)
def assign_core_atom_distances(self, core_cutoff, neighbor_thresh=3, mindist=0.0, maxdist=12.0):
f_hash = FutamuraHash(self, 0.0, maxdist, 2, core_cutoff, neighbor_thresh)
atom_list = self.get_core_alpha_carbons(core_cutoff, neighbor_thresh)
for cp in atom_list:
cp.data['nearby_cps'] = f_hash.get_atoms_between(cp.atom_number, mindist, maxdist)
def assign_atom_distances(self, mindist=0.0, maxdist=12.0):
f_hash = FutamuraHash(self, 0.0, maxdist, 1)
atom_list = self.get_central_atom_list()
for cp in atom_list:
cp.data['nearby_cps'] = f_hash.get_atoms_between(cp.atom_number, mindist, maxdist)
def assign_atom_distances(self, mindist=0.0, maxdist=12.0):
f_hash = FutamuraHash(self, 0.0, maxdist, 1)
atom_list = self.get_central_atom_list()
for cp in atom_list:
cp.data['nearby_cps'] = f_hash.get_atoms_between(
cp.atom_number, mindist, maxdist)
