class DataTransformation(object):
"""
Args:
source ([dict]): list of records
config (Configuration): configuration to apply to each record
"""
def __init__(self, source, config, single_process=False, processes=cpu_count()):
if single_process:
self.results = imap(transform, izip(source, repeat(config)))
else:
self.pool = Pool(processes)
self.results = self.pool.imap(transform, izip(source, repeat(config)))
def __iter__(self):
return self
def next(self):
return self.results.next()
def __del__(self):
try:
self.pool.close()
except:
pass
def main(args):
dens = EMData(args.map)
star = parse_star(args.input, keep_index=False)
star[["ImageNumber", "ImageName"]] = star['rlnImageName'].str.split("@", expand=True)
grouped = star.groupby("ImageName")
pool = None
if args.nproc > 1:
pool = Pool(processes=args.nproc)
results = pool.imap(lambda x: project_stack(x, dens, args.dest), (group for name, group in grouped))
else:
results = (project_stack(group, dens, args.dest) for name, group in grouped)
i = 0
t = 0
for r in results:
i += 1
t += r
sys.stdout.write("\rProjected %d particles in %d stacks" % (t, i))
sys.stdout.flush()
if pool is not None:
pool.close()
pool.join()
sys.stdout.write('\n')
sys.stdout.flush()
return 0
class DataFusion(object):
"""
Args:
source ([tuple]): list of tuples, each containing the records to merge
config (Configuration): configuration to apply to each tuple
"""
def __init__(self, source, config, processes=cpu_count()):
self._pool = Pool(processes)
self._results = self._pool.imap(_merge_records, izip(source, repeat(config)))
def __iter__(self):
return self
def next(self):
return self._results.next()
def __del__(self):
self._pool.close()
def main(options):
"""
Projection subtraction program entry point.
:param options: Command-line arguments parsed by ArgumentParser.parse_args()
:return: Exit status
"""
rchop = lambda x, y: x if not x.endswith(y) or len(y) == 0 else x[:-len(y)]
options.output = rchop(options.output, ".star")
options.suffix = rchop(options.suffix, ".mrc")
options.suffix = rchop(options.suffix, ".mrcs")
star = StarFile(options.input)
npart = len(star['rlnImageName'])
sub_dens = EMData(options.submap)
if options.wholemap is not None:
dens = EMData(options.wholemap)
else:
print "Reference map is required."
return 1
# Write star header for output.star.
top_header = "\ndata_\n\nloop_\n"
headings = star.keys()
output_star = open("{0}.star".format(options.output), 'w')
output_star.write(top_header)
for i, heading in enumerate(headings):
output_star.write("_{0} #{1}\n".format(heading, i + 1))
if options.recenter: # Compute difference vector between new and old mass centers.
if options.wholemap is None:
print "Reference map required for recentering."
return 1
new_dens = dens - sub_dens
# Note the sign of the shift in coordinate frame is opposite the shift in the CoM.
recenter = Vec3f(*dens.phase_cog()[:3]) - Vec3f(
*new_dens.phase_cog()[:3])
else:
recenter = None
pool = None
if options.nproc > 1: # Compute subtraction in parallel.
pool = Pool(processes=options.nproc)
results = pool.imap(
lambda x: subtract(x,
dens,
sub_dens,
recenter=recenter,
no_frc=options.no_frc,
low_cutoff=options.low_cutoff,
high_cutoff=options.high_cutoff),
particles(star),
chunksize=min(npart / options.nproc, options.maxchunk))
else: # Use serial generator.
results = (subtract(x,
dens,
sub_dens,
recenter=recenter,
no_frc=options.no_frc,
low_cutoff=options.low_cutoff,
high_cutoff=options.high_cutoff)
for x in particles(star))
# Write subtraction results to .mrcs and .star files.
i = 0
nfile = 1
starpath = None
mrcs = None
mrcs_orig = None
for r in results:
if i % options.maxpart == 0:
mrcsuffix = options.suffix + "_%d" % nfile
nfile += 1
starpath = "{0}.mrcs".format(
os.path.sep.join(
os.path.relpath(mrcsuffix,
options.output).split(os.path.sep)[1:]))
mrcs = "{0}.mrcs".format(mrcsuffix)
mrcs_orig = "{0}_original.mrcs".format(mrcsuffix)
if os.path.exists(mrcs):
os.remove(mrcs)
if os.path.exists(mrcs_orig):
os.remove(mrcs_orig)
r.ptcl_norm_sub.append_image(mrcs)
if options.original:
r.ptcl.append_image(mrcs_orig)
if logger.getEffectiveLevel(
) == logging.DEBUG: # Write additional debug output.
ptcl_sub_img = r.ptcl.process("math.sub.optimal", {
"ref": r.ctfproj,
"actual": r.ctfproj_sub,
"return_subim": True
})
ptcl_lowpass = r.ptcl.process("filter.lowpass.gauss", {
"apix": 1.22,
"cutoff_freq": 0.05
})
ptcl_sub_lowpass = r.ptcl_norm_sub.process("filter.lowpass.gauss",
{
"apix": 1.22,
"cutoff_freq": 0.05
})
ptcl_sub_img.write_image("poreclass_subimg.mrcs", -1)
ptcl_lowpass.write_image("poreclass_lowpass.mrcs", -1)
ptcl_sub_lowpass.write_image("poreclass_sublowpass.mrcs", -1)
r.ctfproj.write_image("poreclass_ctfproj.mrcs", -1)
r.ctfproj_sub.write_image("poreclass_ctfprojsub.mrcs", -1)
assert r.meta.i == i # Assert particle order is preserved.
star['rlnImageName'][i] = "{0:06d}@{1}".format(
i % options.maxpart + 1, starpath) # Set new image name.
r.meta.update(star) # Update StarFile with altered fields.
line = ' '.join(str(star[key][i]) for key in headings)
output_star.write("{0}\n".format(line))
i += 1
output_star.close()
if pool is not None:
pool.close()
pool.join()
return 0
