def main():
args = parse_args()
try:
os.makedirs(args.directory)
except OSError:
pass
target = Volume.fromfile(args.target)
structure = Structure.fromfile(args.template)
center = structure.coor.mean(axis=1)
radius = np.linalg.norm((structure.coor - center.reshape(-1, 1)), axis=0).max() + 0.5 * args.resolution
template = zeros_like(target)
rottemplate = zeros_like(target)
mask = zeros_like(target)
rotmask = zeros_like(target)
structure_to_shape(structure.coor, args.resolution, out=template, shape='vol', weights=structure.atomnumber)
structure_to_shape(structure.coor, args.resolution, out=mask, shape='mask')
if args.laplace:
target.array = laplace(target.array, mode='constant')
template.array = laplace(template.array, mode='constant')
if args.core_weighted:
mask.array = determine_core_indices(mask.array)
# Normalize the template density
ind = mask.array != 0
N = ind.sum()
template.array *= mask.array
template.array[ind] -= template.array[ind].mean()
template.array[ind] /= template.array[ind].std()
rotmat = quat_to_rotmat(proportional_orientations(args.angle)[0])
lcc_list = []
center -= target.origin
center /= template.voxelspacing
radius /= template.voxelspacing
time0 = time()
for n, rot in enumerate(rotmat):
rotate_grid(template.array, rot, center, radius, rottemplate.array)
rotate_grid(mask.array, rot, center, radius, rotmask.array, nearest=True)
lcc = calc_lcc(target.array, rottemplate.array, rotmask.array, N)
lcc_list.append(lcc)
print '{:d} \r'.format(n),
print 'Searching took: {:.0f}m {:.0f}s'.format(*divmod(time() - time0, 60))
ind = np.argsort(lcc_list)[::-1]
with open(os.path.join(args.directory, args.outfile), 'w') as f:
line = ' '.join(['{:.4f}'] + ['{:7.4f}'] * 9) + '\n'
for n in xrange(min(args.nsolutions, len(lcc_list))):
f.write(line.format(lcc_list[ind[n]], *rotmat[ind[n]].ravel()))
def main():
structure = Structure.fromfile(sys.argv[1])
template = structure_to_shape(
structure.coor, resolution=float(sys.argv[2]),
weights=structure.atomnumber, shape='vol'
)
template.tofile(sys.argv[3])