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()))
import sys import numpy as np from numpy.fft import fftn from powerfit import Volume from powerfit._powerfit import fsc_curve vol1 = Volume.fromfile(sys.argv[1]) vol2 = Volume.fromfile(sys.argv[2]) ft_vol1 = fftn(vol1.array) ft_vol2 = fftn(vol2.array) fsc = fsc_curve(ft_vol1, ft_vol2) res = [n / vol1.dimensions[0] for n in xrange(fsc.size)] inv_res = [1 / r for r in res[1:]] print fsc print print res print print inv_res
def main():
time0 = time()
args = parse_args()
mkdir_p(args.directory)
# Configure logging file
logging.basicConfig(filename=join(args.directory, 'powerfit.log'),
level=logging.INFO,
format='%(asctime)s %(message)s')
logging.info(' '.join(argv))
# Get GPU queue if requested
queues = None
if args.gpu:
import pyopencl as cl
p = cl.get_platforms()[0]
devs = p.get_devices()
context = cl.Context(devices=devs)
# For clFFT each queue should have its own Context
queues = [cl.CommandQueue(context, device=dev) for dev in devs]
write('Target file read from: {:s}'.format(abspath(args.target.name)))
target = Volume.fromfile(args.target)
write('Target resolution: {:.2f}'.format(args.resolution))
resolution = args.resolution
write(('Initial shape of density:' + ' {:d}' * 3).format(*target.shape))
# Resample target density if requested
if not args.no_resampling:
factor = 2 * args.resampling_rate * target.voxelspacing / resolution
if factor < .9:
target = resample(target, factor)
write(('Shape after resampling:' +
' {:d}' * 3).format(*target.shape))
# Trim target density if requested
if not args.no_trimming:
if args.trimming_cutoff is None:
args.trimming_cutoff = target.array.max() / 10
target = trim(target, args.trimming_cutoff)
write(('Shape after trimming:' + ' {:d}' * 3).format(*target.shape))
# Extend the density to a multiple of 2, 3, 5, and 7 for clFFT
extended_shape = [nearest_multiple2357(n) for n in target.shape]
target = extend(target, extended_shape)
write(('Shape after extending:' + ' {:d}' * 3).format(*target.shape))
# Read in structure or high-resolution map
write('Template file read from: {:s}'.format(abspath(args.template.name)))
structure = Structure.fromfile(args.template)
if args.chain is not None:
write('Selecting chains: ' + args.chain)
structure = structure.select('chain', args.chain.split(','))
if structure.data.size == 0:
raise ValueError("No atoms were selected.")
# Move structure to origin of density
structure.translate(target.origin - structure.coor.mean(axis=1))
template = structure_to_shape_like(target,
structure.coor,
resolution=resolution,
weights=structure.atomnumber,
shape='vol')
mask = structure_to_shape_like(target,
structure.coor,
resolution=resolution,
shape='mask')
# Read in the rotations to sample
write('Reading in rotations.')
q, w, degree = proportional_orientations(args.angle)
rotmat = quat_to_rotmat(q)
write('Requested rotational sampling density: {:.2f}'.format(args.angle))
write('Real rotational sampling density: {:}'.format(degree))
# Apply core-weighted mask if requested
if args.core_weighted:
write('Calculating core-weighted mask.')
mask.array = determine_core_indices(mask.array)
pf = PowerFitter(target, laplace=args.laplace)
pf._rotations = rotmat
pf._template = template
pf._mask = mask
pf._nproc = args.nproc
pf.directory = args.directory
pf._queues = queues
if args.gpu:
write('Using GPU-accelerated search.')
else:
write('Requested number of processors: {:d}'.format(args.nproc))
write('Starting search')
time1 = time()
pf.scan()
write(
'Time for search: {:.0f}m {:.0f}s'.format(*divmod(time() - time1, 60)))
write('Analyzing results')
# calculate the molecular volume of the structure
mv = structure_to_shape_like(
target,
structure.coor,
resolution=resolution,
radii=structure.rvdw,
shape='mask').array.sum() * target.voxelspacing**3
z_sigma = fisher_sigma(mv, resolution)
analyzer = Analyzer(pf._lcc,
rotmat,
pf._rot,
voxelspacing=target.voxelspacing,
origin=target.origin,
z_sigma=z_sigma)
write('Writing solutions to file.')
Volume(pf._lcc, target.voxelspacing,
target.origin).tofile(join(args.directory, 'lcc.mrc'))
analyzer.tofile(join(args.directory, 'solutions.out'))
write('Writing PDBs to file.')
n = min(args.num, len(analyzer.solutions))
write_fits_to_pdb(structure,
analyzer.solutions[:n],
basename=join(args.directory, 'fit'))
write('Total time: {:.0f}m {:.0f}s'.format(*divmod(time() - time0, 60)))
def main():
time0 = clock()
args = parse_args()
mkdir_p(args.directory)
# Configure logging file
logging.basicConfig(filename=join(args.directory, 'powerfit.log'),
level=logging.INFO, format='%(asctime)s %(message)s')
logging.info(' '.join(argv))
# Get GPU queue if requested
queues = None
if args.gpu:
import pyopencl as cl
p = cl.get_platforms()[0]
devs = p.get_devices()
ctx = cl.Context(devices=devs)
# For clFFT each queue should have its own Context
contexts = [cl.Context(devices=[dev]) for dev in devs]
queues = [cl.CommandQueue(ctx, device=dev) for ctx, dev in zip(contexts, devs)]
write('Target file read from: {:s}'.format(abspath(args.target.name)))
target = Volume.fromfile(args.target)
write('Target resolution: {:.2f}'.format(args.resolution))
resolution = args.resolution
write(('Initial shape of density:' + ' {:d}'*3).format(*target.shape))
# Resample target density if requested
if not args.no_resampling:
factor = 2 * args.resampling_rate * target.voxelspacing / resolution
if factor < .9:
target = resample(target, factor)
write(('Shape after resampling:' + ' {:d}'*3).format(*target.shape))
# Trim target density if requested
if not args.no_trimming:
if args.trimming_cutoff is None:
args.trimming_cutoff = target.array.max() / 10
target = trim(target, args.trimming_cutoff)
write(('Shape after trimming:' + ' {:d}'*3).format(*target.shape))
# Extend the density to a multiple of 2, 3, 5, and 7 for clFFT
extended_shape = [nearest_multiple2357(n) for n in target.shape]
target = extend(target, extended_shape)
write(('Shape after extending:' + ' {:d}'*3).format(*target.shape))
# Read in structure or high-resolution map
if args.ft_template == 'pdb':
write('Template file read from: {:s}'.format(abspath(args.template.name)))
structure = Structure.fromfile(args.template)
if args.chain is not None:
write('Selecting chains: ' + args.chain)
structure = structure.select('chain', args.chain.split(','))
if structure.data.size == 0:
raise ValueError("No atoms were selected.")
template = structure_to_shape_like(
target, structure.coor, resolution=resolution,
weights=structure.atomnumber, shape='vol'
)
mask = structure_to_shape_like(
target, structure.coor, resolution=resolution, shape='mask'
)
elif args.ft_template == 'map':
template_high_res = Volume.fromfile(args.template)
#TODO handle correctly
# Read in the rotations to sample
write('Reading in rotations.')
q, w, degree = proportional_orientations(args.angle)
rotmat = quat_to_rotmat(q)
write('Requested rotational sampling density: {:.2f}'.format(args.angle))
write('Real rotational sampling density: {:}'.format(degree))
# Apply core-weighted mask if requested
if args.core_weighted:
write('Calculating core-weighted mask.')
mask.array = determine_core_indices(mask.array)
pf = PowerFitter(target, laplace=args.laplace)
pf._rotations = rotmat
pf._template = template
pf._mask = mask
pf._nproc = args.nproc
pf.directory = args.directory
pf._queues = queues
if args.gpu:
write('Using GPU-accelerated search.')
else:
write('Requested number of processors: {:d}'.format(args.nproc))
write('Starting search')
time1 = clock()
pf.scan()
write('Time for search: {:.0f}m {:.0f}s'.format(*divmod(clock() - time1, 60)))
write('Analyzing results')
# calculate the molecular volume of the structure
mv = structure_to_shape_like(
target, structure.coor, resolution=resolution, radii=structure.rvdw, shape='mask'
).array.sum() * target.voxelspacing ** 3
z_sigma = fisher_sigma(mv, resolution)
analyzer = Analyzer(
pf._lcc, rotmat, pf._rot, voxelspacing=target.voxelspacing,
origin=target.origin, z_sigma=z_sigma
)
write('Writing solutions to file.')
Volume(pf._lcc, target.voxelspacing, target.origin).tofile(join(args.directory, 'lcc.mrc'))
analyzer.tofile(join(args.directory, 'solutions.out'))
if args.ft_template == 'pdb':
write('Writing PDBs to file.')
n = min(args.num, len(analyzer.solutions))
write_fits_to_pdb(structure, analyzer.solutions[:n],
basename=join(args.directory, 'fit'))
write('Total time: {:.0f}m {:.0f}s'.format(*divmod(clock() - time0, 60)))