def spruce_up_var_m( refdata ): from utilities import print_msg from utilities import model_circle, get_im from filter import filt_tanl, filt_gaussl from morphology import threshold import os numref = refdata[0] outdir = refdata[1] fscc = refdata[2] total_iter = refdata[3] varf = refdata[4] mask = refdata[5] ali50S = refdata[6] if ali50S: mask_50S = get_im( "mask-50S.spi" ) if fscc is None: flmin = 0.4 aamin = 0.1 else: flmin,aamin,idmin=minfilt( fscc ) aamin = aamin msg = "Minimum tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fflmin, aamin) print_msg(msg) for i in xrange(numref): volf = get_im( os.path.join(outdir, "vol%04d.hdf"% total_iter) , i ) if(not (varf is None) ): volf = volf.filter_by_image( varf ) volf = filt_tanl(volf, flmin, aamin) stat = Util.infomask(volf, mask, True) volf -= stat[0] Util.mul_scalar(volf, 1.0/stat[1]) nx = volf.get_xsize() stat = Util.infomask(volf,model_circle(nx//2-2,nx,nx,nx)-model_circle(nx//2-6,nx,nx,nx), True) volf -= stat[0] Util.mul_img( volf, mask ) volf = threshold(volf) volf = filt_gaussl( volf, 0.4) if ali50S: if i==0: v50S_0 = volf.copy() v50S_0 *= mask_50S else: from applications import ali_vol_3 from fundamentals import rot_shift3D v50S_i = volf.copy() v50S_i *= mask_50S params = ali_vol_3(v50S_i, v50S_0, 10.0, 0.5, mask=mask_50S) volf = rot_shift3D( volf, params[0], params[1], params[2], params[3], params[4], params[5], 1.0) volf.write_image( os.path.join(outdir, "volf%04d.hdf"%total_iter), i )
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try: local_filter = Tracker["local_filter"]
except: local_filter = False
#=========================================================================
# volume reconstruction
if( type(data) == types.ListType ):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if(Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx)
elif(Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"])
else: mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if( nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx)
nxm = mask3D.get_xsize()
assert(nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if( Tracker["PWadjustment"] ):
from utilities import read_text_file, write_text_file
rt = read_text_file( Tracker["PWadjustment"] )
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft( filt_table( vol, ro) )
else:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = fft( filt_table( filt_table(vol, Tracker["lowpass"]), ro) )
else:
vol = fft( filt_table( filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]), ro) )
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0]*(ny//2+2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if(myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node = 0)
# only main processor needs the two input volumes
if(myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if(lx != nx):
if(lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx)
vol = fdecimate(vol, lx,lx,lx)
else: ERROR("local filter cannot be larger than input volume","user function",1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
else:
lx = 0
locres = model_blank(1,1,1)
vol = model_blank(1,1,1)
lx = bcast_number_to_all(lx, source_node = 0)
if( myid != 0 ): mask = model_blank(lx,lx,lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc)
if myid == 0:
if(lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx,nx,nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx,nx,nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if (mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try:
local_filter = Tracker["local_filter"]
except:
local_filter = False
#=========================================================================
# volume reconstruction
if (type(data) == types.ListType):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if (Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(
int(Tracker["constants"]["radius"] * float(nx) /
float(Tracker["constants"]["nnxo"]) + 0.5), nx, nx, nx)
elif (Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if (type(Tracker["constants"]["mask3D"]) == types.StringType):
mask3D = get_im(Tracker["constants"]["mask3D"])
else:
mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if (nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(
rot_shift3D(mask3D, scale=float(nx) / float(nxm)), nx, nx,
nx)
nxm = mask3D.get_xsize()
assert (nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if (Tracker["PWadjustment"]):
from utilities import read_text_file, write_text_file
rt = read_text_file(Tracker["PWadjustment"])
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1, len(ro)):
ro[i] = (rt[i] / ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft(filt_table(vol, ro))
else:
if (type(Tracker["lowpass"]) == types.ListType):
vol = fft(
filt_table(filt_table(vol, Tracker["lowpass"]), ro))
else:
vol = fft(
filt_table(
filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"]), ro))
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0] * (ny // 2 + 2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if (type(Tracker["lowpass"]) == types.ListType):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if (myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node=0)
# only main processor needs the two input volumes
if (myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if (lx != nx):
if (lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(
rot_shift3D(mask, scale=float(lx) / float(nx)), lx, lx,
lx)
vol = fdecimate(vol, lx, lx, lx)
else:
ERROR("local filter cannot be larger than input volume",
"user function", 1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
else:
lx = 0
locres = model_blank(1, 1, 1)
vol = model_blank(1, 1, 1)
lx = bcast_number_to_all(lx, source_node=0)
if (myid != 0): mask = model_blank(lx, lx, lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal(locres, vol, mask, Tracker["falloff"], myid, 0,
nproc)
if myid == 0:
if (lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx, nx, nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx, nx, nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def do_volume_mrk03(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nnw_MPI # recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try: local_filter = Tracker["local_filter"]
except: local_filter = False
#=========================================================================
# volume reconstruction
if( type(data) == types.ListType ):
if Tracker["constants"]["CTF"]:
#vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
# symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
vol = recons3d_4nnw_MPI(myid, data, Tracker["bckgnoise"], Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if(Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx)
elif(Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"])
else: mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if( nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx)
nxm = mask3D.get_xsize()
assert(nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if not local_filter:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"])
if local_filter:
from morphology import binarize
if(myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node = 0)
# only main processor needs the two input volumes
if(myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if(lx != nx):
if(lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx)
vol = fdecimate(vol, lx,lx,lx)
else: ERROR("local filter cannot be larger than input volume","user function",1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
else:
lx = 0
locres = model_blank(1,1,1)
vol = model_blank(1,1,1)
lx = bcast_number_to_all(lx, source_node = 0)
if( myid != 0 ): mask = model_blank(lx,lx,lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc)
if myid == 0:
if(lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx,nx,nx)
vol = threshold(vol)
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx,nx,nx)
"""
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
"""
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def spruce_up_var_m(refdata):
from utilities import print_msg
from utilities import model_circle, get_im
from filter import filt_tanl, filt_gaussl
from morphology import threshold
import os
numref = refdata[0]
outdir = refdata[1]
fscc = refdata[2]
total_iter = refdata[3]
varf = refdata[4]
mask = refdata[5]
ali50S = refdata[6]
if ali50S:
mask_50S = get_im("mask-50S.spi")
if fscc is None:
flmin = 0.4
aamin = 0.1
else:
flmin, aamin, idmin = minfilt(fscc)
aamin = aamin
msg = "Minimum tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fflmin, aamin)
print_msg(msg)
for i in xrange(numref):
volf = get_im(os.path.join(outdir, "vol%04d.hdf" % total_iter), i)
if (not (varf is None)): volf = volf.filter_by_image(varf)
volf = filt_tanl(volf, flmin, aamin)
stat = Util.infomask(volf, mask, True)
volf -= stat[0]
Util.mul_scalar(volf, 1.0 / stat[1])
nx = volf.get_xsize()
stat = Util.infomask(
volf,
model_circle(nx // 2 - 2, nx, nx, nx) -
model_circle(nx // 2 - 6, nx, nx, nx), True)
volf -= stat[0]
Util.mul_img(volf, mask)
volf = threshold(volf)
volf = filt_gaussl(volf, 0.4)
if ali50S:
if i == 0:
v50S_0 = volf.copy()
v50S_0 *= mask_50S
else:
from applications import ali_vol_3
from fundamentals import rot_shift3D
v50S_i = volf.copy()
v50S_i *= mask_50S
params = ali_vol_3(v50S_i, v50S_0, 10.0, 0.5, mask=mask_50S)
volf = rot_shift3D(volf, params[0], params[1], params[2],
params[3], params[4], params[5], 1.0)
volf.write_image(os.path.join(outdir, "volf%04d.hdf" % total_iter), i)
