def test_dilate_points(self):
shape = (9, 8, 7)
vol = volume.zeros(shape, voxelspacing=1, origin=(1,0,0))
dilate_points(np.asarray([[1, 0, 0]]), np.asarray([1]), vol)
answer = volume.zeros(shape, voxelspacing=1, origin=(1,0,0))
answer.array[0, 0, 0] = 1
answer.array[0, 0, 1] = 1
answer.array[0, 0, -1] = 1
answer.array[0, 1, 0] = 1
answer.array[0, -1, 0] = 1
answer.array[1, 0, 0] = 1
answer.array[-1, 0, 0] = 1
self.assertTrue(np.allclose(answer.array, vol.array))
def rsurface(points, radius, shape, voxelspacing):
dimensions = [x*voxelspacing for x in shape]
origin = volume_origin(points, dimensions)
rsurf = volume.zeros(shape, voxelspacing, origin)
rsurf = dilate_points(points, radius, rsurf)
return rsurf
print(pdbs[2 * n + 1], pdbs[2 * n])
try:
p1 = PDB.fromfile(pdbs[2 * n + 1])
p2 = PDB.fromfile(pdbs[2 * n])
shape = grid_shape(p1.coor, p2.coor, VOXELSPACING)
vdw_radii = p1.vdw_radius
radii = vdw_radii + 3.0
rsurf = rsurface(p1.coor, radii, shape, VOXELSPACING)
rcore = rsurface(p1.coor, vdw_radii, shape, VOXELSPACING)
lsurf = rsurf.duplicate()
lsurf._array.fill(0)
lsurf = dilate_points(p2.coor, p2.vdw_radius, lsurf)
#rsurf.tofile('rsurf.mrc')
#rcore.tofile('rcore.mrc')
#lsurf.tofile('lsurf.mrc')
overlap = (rsurf.array * lsurf.array).sum()
clash = (rcore.array * lsurf.array).sum()
overlaps.append(overlap)
cores.append(clash)
print(pdbs[2 * n][:4])
print('overlap: ', overlap)
print('clash: ', clash)
print()
except:
print(pdbs[2 * n + 1], pdbs[2 * n])
try:
p1 = PDB.fromfile(pdbs[2 * n + 1])
p2 = PDB.fromfile(pdbs[2 * n])
shape = grid_shape(p1.coor, p2.coor, VOXELSPACING)
vdw_radii = p1.vdw_radius
radii = vdw_radii + 3.0
rsurf = rsurface(p1.coor, radii, shape, VOXELSPACING)
rcore = rsurface(p1.coor, vdw_radii, shape, VOXELSPACING)
lsurf = rsurf.duplicate()
lsurf._array.fill(0)
lsurf = dilate_points(p2.coor, p2.vdw_radius, lsurf)
# rsurf.tofile('rsurf.mrc')
# rcore.tofile('rcore.mrc')
# lsurf.tofile('lsurf.mrc')
overlap = (rsurf.array * lsurf.array).sum()
clash = (rcore.array * lsurf.array).sum()
overlaps.append(overlap)
cores.append(clash)
print(pdbs[2 * n][:4])
print("overlap: ", overlap)
print("clash: ", clash)
print()
except:
def _initialize(self):
# check if requirements are set
if any(x is None for x in (self.receptor, self.ligand)):
raise ValueError("Not all requirements are met for a search")
if self.weights is None:
self.weights = np.ones(self.rotations.shape[0], dtype=np.float64)
if len(self.weights) != len(self.rotations):
raise ValueError("")
d = self.data
# determine size for grid
shape = grid_shape(self.receptor.coor, self.ligand.coor, self.voxelspacing)
# calculate the interaction surface and core of the receptor
vdw_radii = self.receptor.vdw_radius
radii = vdw_radii + self.interaction_radius
d['rsurf'] = rsurface(self.receptor.coor, radii,
shape, self.voxelspacing)
d['rcore'] = rsurface(self.receptor.coor, vdw_radii,
shape, self.voxelspacing)
# keep track of some data for later calculations
d['origin'] = np.asarray(d['rcore'].origin, dtype=np.float64)
d['shape'] = d['rcore'].shape
d['start'] = d['rcore'].start
d['nrot'] = self.rotations.shape[0]
# set ligand center to the origin of the receptor map
# and make a grid of the ligand
radii = self.ligand.vdw_radius
d['lsurf'] = dilate_points((self.ligand.coor - self.ligand.center \
+ self.receptor.center), radii, volume.zeros_like(d['rcore']))
d['im_center'] = np.asarray((self.receptor.center - d['rcore'].origin)/self.voxelspacing, dtype=np.float64)
d['max_clash'] = self.max_clash/self.voxelspacing**3
d['min_interaction'] = self.min_interaction/self.voxelspacing**3
# SAXS data
d['q'] = self._q
d['targetIq'] = self._Iq
d['sq'] = self._sq
if self.coarse_grain:
e1, xyz1 = coarse_grain(self.receptor, bpr=self.beads_per_residue)
e2, xyz2 = coarse_grain(self.ligand, bpr=self.beads_per_residue)
else:
e1, xyz1 = self.receptor.elements, self.receptor.coor
e2, xyz2 = self.ligand.elements, self.ligand.coor
d['base_Iq'] = scattering_curve(self._q, e1, xyz1, bpr=self.beads_per_residue)
d['base_Iq'] += scattering_curve(self._q, e2, xyz2, bpr=self.beads_per_residue)
d['fifj'], d['rind'], d['lind'] = create_fifj_lookup_table(d['q'], e1, e2, bpr=self.beads_per_residue)
d['rxyz'] = xyz1
d['lxyz'] = xyz2 - self.ligand.center
d['chi2'] = np.zeros(d['rcore'].shape, dtype=np.float64)
d['best_chi2'] = np.zeros_like(d['chi2'])