def filterlocal(ui, vi, m, falloff, myid, main_node, number_of_proc):
if myid == main_node:
nx = vi.get_xsize()
ny = vi.get_ysize()
nz = vi.get_zsize()
# Round all resolution numbers to two digits
for x in range(nx):
for y in range(ny):
for z in range(nz):
ui.set_value_at_fast(x, y, z, round(ui.get_value_at(x, y, z), 2))
dis = [nx, ny, nz]
else:
falloff = 0.0
radius = 0
dis = [0, 0, 0]
falloff = sp_utilities.bcast_number_to_all(falloff, main_node)
dis = sp_utilities.bcast_list_to_all(dis, myid, source_node=main_node)
if myid != main_node:
nx = int(dis[0])
ny = int(dis[1])
nz = int(dis[2])
vi = sp_utilities.model_blank(nx, ny, nz)
ui = sp_utilities.model_blank(nx, ny, nz)
sp_utilities.bcast_EMData_to_all(vi, myid, main_node)
sp_utilities.bcast_EMData_to_all(ui, myid, main_node)
sp_fundamentals.fftip(vi) # volume to be filtered
st = EMAN2_cppwrap.Util.infomask(ui, m, True)
filteredvol = sp_utilities.model_blank(nx, ny, nz)
cutoff = max(st[2] - 0.01, 0.0)
while cutoff < st[3]:
cutoff = round(cutoff + 0.01, 2)
# if(myid == main_node): print cutoff,st
pt = EMAN2_cppwrap.Util.infomask(
sp_morphology.threshold_outside(ui, cutoff - 0.00501, cutoff + 0.005),
m,
True,
) # Ideally, one would want to check only slices in question...
if pt[0] != 0.0:
# print cutoff,pt[0]
vovo = sp_fundamentals.fft(filt_tanl(vi, cutoff, falloff))
for z in range(myid, nz, number_of_proc):
for x in range(nx):
for y in range(ny):
if m.get_value_at(x, y, z) > 0.5:
if round(ui.get_value_at(x, y, z), 2) == cutoff:
filteredvol.set_value_at_fast(
x, y, z, vovo.get_value_at(x, y, z)
)
mpi.mpi_barrier(mpi.MPI_COMM_WORLD)
sp_utilities.reduce_EMData_to_root(filteredvol, myid, main_node, mpi.MPI_COMM_WORLD)
return filteredvol
def helicalshiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1):
nproc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("helical-shiftali_MPI")
max_iter = int(maxit)
if (myid == main_node):
infils = EMUtil.get_all_attributes(stack, "filament")
ptlcoords = EMUtil.get_all_attributes(stack, 'ptcl_source_coord')
filaments = ordersegments(infils, ptlcoords)
total_nfils = len(filaments)
inidl = [0] * total_nfils
for i in range(total_nfils):
inidl[i] = len(filaments[i])
linidl = sum(inidl)
nima = linidl
tfilaments = []
for i in range(total_nfils):
tfilaments += filaments[i]
del filaments
else:
total_nfils = 0
linidl = 0
total_nfils = bcast_number_to_all(total_nfils, source_node=main_node)
if myid != main_node:
inidl = [-1] * total_nfils
inidl = bcast_list_to_all(inidl, myid, source_node=main_node)
linidl = bcast_number_to_all(linidl, source_node=main_node)
if myid != main_node:
tfilaments = [-1] * linidl
tfilaments = bcast_list_to_all(tfilaments, myid, source_node=main_node)
filaments = []
iendi = 0
for i in range(total_nfils):
isti = iendi
iendi = isti + inidl[i]
filaments.append(tfilaments[isti:iendi])
del tfilaments, inidl
if myid == main_node:
print_msg("total number of filaments: %d" % total_nfils)
if total_nfils < nproc:
ERROR(
'number of CPUs (%i) is larger than the number of filaments (%i), please reduce the number of CPUs used'
% (nproc, total_nfils),
myid=myid)
# balanced load
temp = chunks_distribution([[len(filaments[i]), i]
for i in range(len(filaments))],
nproc)[myid:myid + 1][0]
filaments = [filaments[temp[i][1]] for i in range(len(temp))]
nfils = len(filaments)
#filaments = [[0,1]]
#print "filaments",filaments
list_of_particles = []
indcs = []
k = 0
for i in range(nfils):
list_of_particles += filaments[i]
k1 = k + len(filaments[i])
indcs.append([k, k1])
k = k1
data = EMData.read_images(stack, list_of_particles)
ldata = len(data)
sxprint("ldata=", ldata)
nx = data[0].get_xsize()
ny = data[0].get_ysize()
if maskfile == None:
mrad = min(nx, ny) // 2 - 2
mask = pad(model_blank(2 * mrad + 1, ny, 1, 1.0), nx, ny, 1, 0.0)
else:
mask = get_im(maskfile)
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(ldata):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], p['tx'], p['ty'],
p['mirror'], p['scale'])
if CTF:
from sp_filter import filt_ctf
from sp_morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
ctf_abs_sum = None
from sp_utilities import info
for im in range(ldata):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
qctf = data[im].get_attr("ctf_applied")
if qctf == 0:
data[im] = filt_ctf(fft(data[im]), ctf_params)
data[im].set_attr('ctf_applied', 1)
elif qctf != 1:
ERROR('Incorrectly set qctf flag', myid=myid)
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
else:
data[im] = fft(data[im])
del list_of_particles
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
tsnr = 1. / snr
for i in range(0, nx + 2, 2):
for j in range(ny):
temp.set_value_at(i, j, tsnr)
temp.set_value_at(i + 1, j, 0.0)
#info(ctf_2_sum)
Util.add_img(ctf_2_sum, temp)
#info(ctf_2_sum)
del temp
total_iter = 0
shift_x = [0.0] * ldata
for Iter in range(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n" % (total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in range(ldata):
Util.add_img(avg, fshift(data[im], shift_x[im]))
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF: tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0 / float(nima))
else:
tavg = model_blank(nx, ny)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask(tavg, mask, False)
tavg -= stat[0]
Util.mul_img(tavg, mask)
tavg.write_image("tavg.hdf", Iter)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
tavg = fft(tavg)
sx_sum = 0.0
nxc = nx // 2
for ifil in range(nfils):
"""
# Calculate filament average
avg = EMData(nx, ny, 1, False)
filnima = 0
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
Util.add_img(avg, data[im])
filnima += 1
tavg = Util.mult_scalar(avg, 1.0/float(filnima))
"""
# Calculate 1D ccf between each segment and filament average
nsegms = indcs[ifil][1] - indcs[ifil][0]
ctx = [None] * nsegms
pcoords = [None] * nsegms
for im in range(indcs[ifil][0], indcs[ifil][1]):
ctx[im - indcs[ifil][0]] = Util.window(ccf(tavg, data[im]), nx,
1)
pcoords[im - indcs[ifil][0]] = data[im].get_attr(
'ptcl_source_coord')
#ctx[im-indcs[ifil][0]].write_image("ctx.hdf",im-indcs[ifil][0])
#print " CTX ",myid,im,Util.infomask(ctx[im-indcs[ifil][0]], None, True)
# search for best x-shift
cents = nsegms // 2
dst = sqrt(
max((pcoords[cents][0] - pcoords[0][0])**2 +
(pcoords[cents][1] - pcoords[0][1])**2,
(pcoords[cents][0] - pcoords[-1][0])**2 +
(pcoords[cents][1] - pcoords[-1][1])**2))
maxincline = atan2(ny // 2 - 2 - float(search_rng), dst)
kang = int(dst * tan(maxincline) + 0.5)
#print " settings ",nsegms,cents,dst,search_rng,maxincline,kang
# ## C code for alignment. @ming
results = [0.0] * 3
results = Util.helixshiftali(ctx, pcoords, nsegms, maxincline,
kang, search_rng, nxc)
sib = int(results[0])
bang = results[1]
qm = results[2]
#print qm, sib, bang
# qm = -1.e23
#
# for six in xrange(-search_rng, search_rng+1,1):
# q0 = ctx[cents].get_value_at(six+nxc)
# for incline in xrange(kang+1):
# qt = q0
# qu = q0
# if(kang>0): tang = tan(maxincline/kang*incline)
# else: tang = 0.0
# for kim in xrange(cents+1,nsegms):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# #print " A ", ifil,six,incline,kim,xl,ixl,dxl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# for kim in xrange(cents):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# if( qt > qm ):
# qm = qt
# sib = six
# bang = tang
# if( qu > qm ):
# qm = qu
# sib = six
# bang = -tang
#if incline == 0: print "incline = 0 ",six,tang,qt,qu
#print qm,six,sib,bang
#print " got results ",indcs[ifil][0], indcs[ifil][1], ifil,myid,qm,sib,tang,bang,len(ctx),Util.infomask(ctx[0], None, True)
for im in range(indcs[ifil][0], indcs[ifil][1]):
kim = im - indcs[ifil][0]
dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 +
(pcoords[cents][1] - pcoords[kim][1])**2)
if (kim < cents): xl = -dst * bang + sib
else: xl = dst * bang + sib
shift_x[im] = xl
# Average shift
sx_sum += shift_x[indcs[ifil][0] + cents]
# #print myid,sx_sum,total_nfils
sx_sum = mpi.mpi_reduce(sx_sum, 1, mpi.MPI_FLOAT, mpi.MPI_SUM,
main_node, mpi.MPI_COMM_WORLD)
if myid == main_node:
sx_sum = float(sx_sum[0]) / total_nfils
print_msg("Average shift %6.2f\n" % (sx_sum))
else:
sx_sum = 0.0
sx_sum = 0.0
sx_sum = bcast_number_to_all(sx_sum, source_node=main_node)
for im in range(ldata):
shift_x[im] -= sx_sum
#print " %3d %6.3f"%(im,shift_x[im])
#exit()
# combine shifts found with the original parameters
for im in range(ldata):
t1 = Transform()
##import random
##shix=random.randint(-10, 10)
##t1.set_params({"type":"2D","tx":shix})
t1.set_params({"type": "2D", "tx": shift_x[im]})
# combine t0 and t1
tt = t1 * init_params[im]
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi.mpi_barrier(mpi.MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from sp_utilities import file_type
if (file_type(stack) == "bdb"):
from sp_utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata,
nproc)
else:
from sp_utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, 0, ldata, nproc)
else:
send_attr_dict(main_node, data, par_str, 0, ldata)
if myid == main_node: print_end_msg("helical-shiftali_MPI")
def main():
arglist = []
for arg in sys.argv:
arglist.append(arg)
progname = optparse.os.path.basename(arglist[0])
usage = progname + """ inputvolume locresvolume maskfile outputfile --radius --falloff --MPI
Locally filer a volume based on local resolution volume (sxlocres.py) within area outlined by the maskfile
"""
parser = optparse.OptionParser(usage, version=sp_global_def.SPARXVERSION)
parser.add_option(
"--radius",
type="int",
default=-1,
help=
"if there is no maskfile, sphere with r=radius will be used, by default the radius is nx/2-1"
)
parser.add_option("--falloff",
type="float",
default=0.1,
help="falloff of tanl filter (default 0.1)")
parser.add_option("--MPI",
action="store_true",
default=False,
help="use MPI version")
(options, args) = parser.parse_args(arglist[1:])
if len(args) < 3 or len(args) > 4:
sp_global_def.sxprint("See usage " + usage)
sp_global_def.ERROR(
"Wrong number of parameters. Please see usage information above.")
return
if sp_global_def.CACHE_DISABLE:
pass #IMPORTIMPORTIMPORT from sp_utilities import disable_bdb_cache
sp_utilities.disable_bdb_cache()
if options.MPI:
number_of_proc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
if (myid == main_node):
#print sys.argv
vi = sp_utilities.get_im(sys.argv[1])
ui = sp_utilities.get_im(sys.argv[2])
#print Util.infomask(ui, None, True)
radius = options.radius
nx = vi.get_xsize()
ny = vi.get_ysize()
nz = vi.get_zsize()
dis = [nx, ny, nz]
else:
falloff = 0.0
radius = 0
dis = [0, 0, 0]
vi = None
ui = None
dis = sp_utilities.bcast_list_to_all(dis, myid, source_node=main_node)
if (myid != main_node):
nx = int(dis[0])
ny = int(dis[1])
nz = int(dis[2])
radius = sp_utilities.bcast_number_to_all(radius, main_node)
if len(args) == 3:
if (radius == -1):
radius = min(nx, ny, nz) // 2 - 1
m = sp_utilities.model_circle(radius, nx, ny, nz)
outvol = args[2]
elif len(args) == 4:
if (myid == main_node):
m = sp_morphology.binarize(sp_utilities.get_im(args[2]), 0.5)
else:
m = sp_utilities.model_blank(nx, ny, nz)
outvol = args[3]
sp_utilities.bcast_EMData_to_all(m, myid, main_node)
pass #IMPORTIMPORTIMPORT from sp_filter import filterlocal
filteredvol = sp_filter.filterlocal(ui, vi, m, options.falloff, myid,
main_node, number_of_proc)
if (myid == 0):
filteredvol.write_image(outvol)
else:
vi = sp_utilities.get_im(args[0])
ui = sp_utilities.get_im(
args[1]
) # resolution volume, values are assumed to be from 0 to 0.5
nn = vi.get_xsize()
falloff = options.falloff
if len(args) == 3:
radius = options.radius
if (radius == -1):
radius = nn // 2 - 1
m = sp_utilities.model_circle(radius, nn, nn, nn)
outvol = args[2]
elif len(args) == 4:
m = sp_morphology.binarize(sp_utilities.get_im(args[2]), 0.5)
outvol = args[3]
sp_fundamentals.fftip(vi) # this is the volume to be filtered
# Round all resolution numbers to two digits
for x in range(nn):
for y in range(nn):
for z in range(nn):
ui.set_value_at_fast(x, y, z,
round(ui.get_value_at(x, y, z), 2))
st = EMAN2_cppwrap.Util.infomask(ui, m, True)
filteredvol = sp_utilities.model_blank(nn, nn, nn)
cutoff = max(st[2] - 0.01, 0.0)
while (cutoff < st[3]):
cutoff = round(cutoff + 0.01, 2)
pt = EMAN2_cppwrap.Util.infomask(
sp_morphology.threshold_outside(ui, cutoff - 0.00501,
cutoff + 0.005), m, True)
if (pt[0] != 0.0):
vovo = sp_fundamentals.fft(
sp_filter.filt_tanl(vi, cutoff, falloff))
for x in range(nn):
for y in range(nn):
for z in range(nn):
if (m.get_value_at(x, y, z) > 0.5):
if (round(ui.get_value_at(x, y, z),
2) == cutoff):
filteredvol.set_value_at_fast(
x, y, z, vovo.get_value_at(x, y, z))
sp_global_def.write_command(optparse.os.path.dirname(outvol))
filteredvol.write_image(outvol)
def filterlocal(ui, vi, m, falloff, myid, main_node, number_of_proc): from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv, mpi_send, mpi_recv from mpi import MPI_SUM, MPI_FLOAT, MPI_INT from sp_utilities import bcast_number_to_all, bcast_list_to_all, model_blank, bcast_EMData_to_all, reduce_EMData_to_root from sp_morphology import threshold_outside from sp_filter import filt_tanl from sp_fundamentals import fft, fftip if(myid == main_node): nx = vi.get_xsize() ny = vi.get_ysize() nz = vi.get_zsize() # Round all resolution numbers to two digits for x in range(nx): for y in range(ny): for z in range(nz): ui.set_value_at_fast( x,y,z, round(ui.get_value_at(x,y,z), 2) ) dis = [nx,ny,nz] else: falloff = 0.0 radius = 0 dis = [0,0,0] falloff = bcast_number_to_all(falloff, main_node) dis = bcast_list_to_all(dis, myid, source_node = main_node) if(myid != main_node): nx = int(dis[0]) ny = int(dis[1]) nz = int(dis[2]) vi = model_blank(nx,ny,nz) ui = model_blank(nx,ny,nz) bcast_EMData_to_all(vi, myid, main_node) bcast_EMData_to_all(ui, myid, main_node) fftip(vi) # volume to be filtered st = Util.infomask(ui, m, True) filteredvol = model_blank(nx,ny,nz) cutoff = max(st[2] - 0.01,0.0) while(cutoff < st[3] ): cutoff = round(cutoff + 0.01, 2) #if(myid == main_node): print cutoff,st pt = Util.infomask( threshold_outside(ui, cutoff - 0.00501, cutoff + 0.005), m, True) # Ideally, one would want to check only slices in question... if(pt[0] != 0.0): #print cutoff,pt[0] vovo = fft( filt_tanl(vi, cutoff, falloff) ) for z in range(myid, nz, number_of_proc): for x in range(nx): for y in range(ny): if(m.get_value_at(x,y,z) > 0.5): if(round(ui.get_value_at(x,y,z),2) == cutoff): filteredvol.set_value_at_fast(x,y,z,vovo.get_value_at(x,y,z)) mpi_barrier(MPI_COMM_WORLD) reduce_EMData_to_root(filteredvol, myid, main_node, MPI_COMM_WORLD) return filteredvol
def resample( prjfile, outdir, bufprefix, nbufvol, nvol, seedbase,\
delta, d, snr, CTF, npad,\
MPI, myid, ncpu, verbose = 0 ):
from sp_utilities import even_angles
from random import seed, jumpahead, shuffle
import os
from sys import exit
nprj = EMUtil.get_image_count( prjfile )
if MPI:
if myid == 0:
if os.path.exists(outdir): nx = 1
else: nx = 0
else: nx = 0
ny = bcast_number_to_all(nx, source_node = 0)
if ny == 1: ERROR('Output directory exists, please change the name and restart the program', "resample", 1,myid)
mpi.mpi_barrier( mpi.MPI_COMM_WORLD )
if myid == 0:
os.makedirs(outdir)
sp_global_def.write_command(outdir)
mpi.mpi_barrier( mpi.MPI_COMM_WORLD )
else:
if os.path.exists(outdir):
ERROR('Output directory exists, please change the name and restart the program', "resample", 1,0)
os.makedirs(outdir)
sp_global_def.write_command(outdir)
if(verbose == 1): finfo=open( os.path.join(outdir, "progress%04d.txt" % myid), "w" )
else: finfo = None
#print " before evenangles",myid
from sp_utilities import getvec
from numpy import array, reshape
refa = even_angles(delta)
nrefa = len(refa)
refnormal = zeros((nrefa,3),'float32')
tetref = [0.0]*nrefa
for i in range(nrefa):
tr = getvec( refa[i][0], refa[i][1] )
for j in range(3): refnormal[i][j] = tr[j]
tetref[i] = refa[i][1]
del refa
vct = array([0.0]*(3*nprj),'float32')
if myid == 0:
sxprint(" will read ",myid)
tr = EMUtil.get_all_attributes(prjfile,'xform.projection')
tetprj = [0.0]*nprj
for i in range(nprj):
temp = tr[i].get_params("spider")
tetprj[i] = temp["theta"]
if(tetprj[i] > 90.0): tetprj[i] = 180.0 - tetprj[i]
vct[3*i+0] = tr[i].at(2,0)
vct[3*i+1] = tr[i].at(2,1)
vct[3*i+2] = tr[i].at(2,2)
del tr
else:
tetprj = [0.0]*nprj
#print " READ ",myid
if MPI:
#print " will bcast",myid
vct = mpi.mpi_bcast( vct, len(vct), mpi.MPI_FLOAT, 0, mpi.MPI_COMM_WORLD )
from sp_utilities import bcast_list_to_all
tetprj = bcast_list_to_all(tetprj, myid, 0)
#print " reshape ",myid
vct = reshape(vct,(nprj,3))
assignments = [[] for i in range(nrefa)]
dspn = 1.25*delta
for k in range(nprj):
best_s = -1.0
best_i = -1
for i in range( nrefa ):
if(abs(tetprj[k] - tetref[i]) <= dspn):
s = abs(refnormal[i][0]*vct[k][0] + refnormal[i][1]*vct[k][1] + refnormal[i][2]*vct[k][2])
if s > best_s:
best_s = s
best_i = i
assignments[best_i].append(k)
am = len(assignments[0])
mufur = 1.0/am
for i in range(1,len(assignments)):
ti = len(assignments[i])
am = min(am, ti)
if(ti>0): mufur += 1.0/ti
del tetprj,tetref
dp = 1.0 - d # keep that many in each direction
keep = int(am*dp +0.5)
mufur = keep*nrefa/(1.0 - mufur*keep/float(nrefa))
if myid == 0:
sxprint(" Number of projections ",nprj,". Number of reference directions ",nrefa,", multiplicative factor for the variance ",mufur)
sxprint(" Minimum number of assignments ",am," Number of projections used per stratum ", keep," Number of projections in resampled structure ",int(am*dp +0.5)*nrefa)
if am <2 or am == keep:
sxprint("incorrect settings")
exit() # FIX
if(seedbase < 1):
seed()
jumpahead(17*myid+123)
else:
seed(seedbase)
jumpahead(17*myid+123)
volfile = os.path.join(outdir, "bsvol%04d.hdf" % myid)
from random import randint
niter = nvol/ncpu/nbufvol
for kiter in range(niter):
if(verbose == 1):
finfo.write( "Iteration %d: \n" % kiter )
finfo.flush()
iter_start = time()
# the following has to be converted to resample mults=1 means take given projection., mults=0 means omit
mults = [ [0]*nprj for i in range(nbufvol) ]
for i in range(nbufvol):
for l in range(nrefa):
mass = assignments[l][:]
shuffle(mass)
mass = mass[:keep]
mass.sort()
#print l, " * ",mass
for k in range(keep):
mults[i][mass[k]] = 1
'''
lout = []
for l in xrange(len(mults[i])):
if mults[i][l] == 1: lout.append(l)
write_text_file(lout, os.path.join(outdir, "list%04d_%03d.txt" %(i, myid)))
del lout
'''
del mass
rectors, fftvols, wgtvols = resample_prepare( prjfile, nbufvol, snr, CTF, npad )
resample_insert( bufprefix, fftvols, wgtvols, mults, CTF, npad, finfo )
del mults
resample_finish( rectors, fftvols, wgtvols, volfile, kiter, nprj, finfo )
rectors = None
fftvols = None
wgtvols = None
if(verbose == 1):
finfo.write( "time for iteration: %10.3f\n" % (time() - iter_start) )
finfo.flush()
def main():
arglist = []
for arg in sys.argv:
arglist.append(arg)
progname = os.path.basename(arglist[0])
usage = progname + """ firstvolume secondvolume maskfile directory --prefix --wn --step --cutoff --radius --fsc --res_overall --out_ang_res --apix --MPI
Compute local resolution in real space within area outlined by the maskfile and within regions wn x wn x wn
"""
parser = optparse.OptionParser(usage, version=sp_global_def.SPARXVERSION)
parser.add_option("--prefix",
type="str",
default='localres',
help="Prefix for the output files. (default localres)")
parser.add_option(
"--wn",
type="int",
default=7,
help=
"Size of window within which local real-space FSC is computed. (default 7)"
)
parser.add_option(
"--step",
type="float",
default=1.0,
help="Shell step in Fourier size in pixels. (default 1.0)")
parser.add_option("--cutoff",
type="float",
default=0.143,
help="Resolution cut-off for FSC. (default 0.143)")
parser.add_option(
"--radius",
type="int",
default=-1,
help=
"If there is no maskfile, sphere with r=radius will be used. By default, the radius is nx/2-wn (default -1)"
)
parser.add_option(
"--fsc",
type="string",
default=None,
help=
"Save overall FSC curve (might be truncated). By default, the program does not save the FSC curve. (default none)"
)
parser.add_option(
"--res_overall",
type="float",
default=-1.0,
help=
"Overall resolution at the cutoff level estimated by the user [abs units]. (default None)"
)
parser.add_option(
"--out_ang_res",
action="store_true",
default=False,
help=
"Additionally creates a local resolution file in Angstroms. (default False)"
)
parser.add_option(
"--apix",
type="float",
default=1.0,
help=
"Pixel size in Angstrom. Effective only with --out_ang_res options. (default 1.0)"
)
parser.add_option("--MPI",
action="store_true",
default=False,
help="Use MPI version.")
(options, args) = parser.parse_args(arglist[1:])
if len(args) < 3 or len(args) > 4:
sxprint("Usage: " + usage)
ERROR(
"Invalid number of parameters used. Please see usage information above."
)
return
if sp_global_def.CACHE_DISABLE:
sp_utilities.disable_bdb_cache()
res_overall = options.res_overall
if options.MPI:
number_of_proc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
sp_global_def.MPI = True
cutoff = options.cutoff
nk = int(options.wn)
if (myid == main_node):
#print sys.argv
vi = sp_utilities.get_im(sys.argv[1])
ui = sp_utilities.get_im(sys.argv[2])
nx = vi.get_xsize()
ny = vi.get_ysize()
nz = vi.get_zsize()
dis = [nx, ny, nz]
else:
dis = [0, 0, 0, 0]
sp_global_def.BATCH = True
dis = sp_utilities.bcast_list_to_all(dis, myid, source_node=main_node)
if (myid != main_node):
nx = int(dis[0])
ny = int(dis[1])
nz = int(dis[2])
vi = sp_utilities.model_blank(nx, ny, nz)
ui = sp_utilities.model_blank(nx, ny, nz)
if len(args) == 3:
m = sp_utilities.model_circle((min(nx, ny, nz) - nk) // 2, nx, ny,
nz)
outdir = args[2]
elif len(args) == 4:
if (myid == main_node):
m = sp_morphology.binarize(sp_utilities.get_im(args[2]), 0.5)
else:
m = sp_utilities.model_blank(nx, ny, nz)
outdir = args[3]
if os.path.exists(outdir) and myid == 0:
sp_global_def.ERROR('Output directory already exists!')
elif myid == 0:
os.makedirs(outdir)
sp_global_def.write_command(outdir)
sp_utilities.bcast_EMData_to_all(m, myid, main_node)
"""Multiline Comment0"""
freqvol, resolut = sp_statistics.locres(vi, ui, m, nk, cutoff,
options.step, myid, main_node,
number_of_proc)
if (myid == 0):
# Remove outliers based on the Interquartile range
output_volume(freqvol, resolut, options.apix, outdir,
options.prefix, options.fsc, options.out_ang_res, nx,
ny, nz, res_overall)
else:
cutoff = options.cutoff
vi = sp_utilities.get_im(args[0])
ui = sp_utilities.get_im(args[1])
nn = vi.get_xsize()
nx = nn
ny = nn
nz = nn
nk = int(options.wn)
if len(args) == 3:
m = sp_utilities.model_circle((nn - nk) // 2, nn, nn, nn)
outdir = args[2]
elif len(args) == 4:
m = sp_morphology.binarize(sp_utilities.get_im(args[2]), 0.5)
outdir = args[3]
if os.path.exists(outdir):
sp_global_def.ERROR('Output directory already exists!')
else:
os.makedirs(outdir)
sp_global_def.write_command(outdir)
mc = sp_utilities.model_blank(nn, nn, nn, 1.0) - m
vf = sp_fundamentals.fft(vi)
uf = sp_fundamentals.fft(ui)
"""Multiline Comment1"""
lp = int(nn / 2 / options.step + 0.5)
step = 0.5 / lp
freqvol = sp_utilities.model_blank(nn, nn, nn)
resolut = []
for i in range(1, lp):
fl = step * i
fh = fl + step
#print(lp,i,step,fl,fh)
v = sp_fundamentals.fft(sp_filter.filt_tophatb(vf, fl, fh))
u = sp_fundamentals.fft(sp_filter.filt_tophatb(uf, fl, fh))
tmp1 = EMAN2_cppwrap.Util.muln_img(v, v)
tmp2 = EMAN2_cppwrap.Util.muln_img(u, u)
do = EMAN2_cppwrap.Util.infomask(
sp_morphology.square_root(
sp_morphology.threshold(
EMAN2_cppwrap.Util.muln_img(tmp1, tmp2))), m, True)[0]
tmp3 = EMAN2_cppwrap.Util.muln_img(u, v)
dp = EMAN2_cppwrap.Util.infomask(tmp3, m, True)[0]
resolut.append([i, (fl + fh) / 2.0, dp / do])
tmp1 = EMAN2_cppwrap.Util.box_convolution(tmp1, nk)
tmp2 = EMAN2_cppwrap.Util.box_convolution(tmp2, nk)
tmp3 = EMAN2_cppwrap.Util.box_convolution(tmp3, nk)
EMAN2_cppwrap.Util.mul_img(tmp1, tmp2)
tmp1 = sp_morphology.square_root(sp_morphology.threshold(tmp1))
EMAN2_cppwrap.Util.mul_img(tmp1, m)
EMAN2_cppwrap.Util.add_img(tmp1, mc)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
EMAN2_cppwrap.Util.add_img(tmp3, mc)
EMAN2_cppwrap.Util.div_img(tmp3, tmp1)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
freq = (fl + fh) / 2.0
bailout = True
for x in range(nn):
for y in range(nn):
for z in range(nn):
if (m.get_value_at(x, y, z) > 0.5):
if (freqvol.get_value_at(x, y, z) == 0.0):
if (tmp3.get_value_at(x, y, z) < cutoff):
freqvol.set_value_at(x, y, z, freq)
bailout = False
else:
bailout = False
if (bailout): break
#print(len(resolut))
# remove outliers
output_volume(freqvol, resolut, options.apix, outdir, options.prefix,
options.fsc, options.out_ang_res, nx, ny, nz,
res_overall)
