def main():
import global_def
from optparse import OptionParser
from EMAN2 import EMUtil
import os
import sys
from time import time
progname = os.path.basename(sys.argv[0])
usage = progname + " proj_stack output_averages --MPI"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--img_per_group",
type="int",
default=100,
help="number of images per group")
parser.add_option("--radius",
type="int",
default=-1,
help="radius for alignment")
parser.add_option(
"--xr",
type="string",
default="2 1",
help="range for translation search in x direction, search is +/xr")
parser.add_option(
"--yr",
type="string",
default="-1",
help=
"range for translation search in y direction, search is +/yr (default = same as xr)"
)
parser.add_option(
"--ts",
type="string",
default="1 0.5",
help=
"step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional"
)
parser.add_option(
"--iter",
type="int",
default=30,
help="number of iterations within alignment (default = 30)")
parser.add_option(
"--num_ali",
type="int",
default=5,
help="number of alignments performed for stability (default = 5)")
parser.add_option("--thld_err",
type="float",
default=1.0,
help="threshold of pixel error (default = 1.732)")
parser.add_option(
"--grouping",
type="string",
default="GRP",
help=
"do grouping of projections: PPR - per projection, GRP - different size groups, exclusive (default), GEV - grouping equal size"
)
parser.add_option(
"--delta",
type="float",
default=-1.0,
help="angular step for reference projections (required for GEV method)"
)
parser.add_option(
"--fl",
type="float",
default=0.3,
help="cut-off frequency of hyperbolic tangent low-pass Fourier filter")
parser.add_option(
"--aa",
type="float",
default=0.2,
help="fall-off of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--CTF",
action="store_true",
default=False,
help="Consider CTF correction during the alignment ")
parser.add_option("--MPI",
action="store_true",
default=False,
help="use MPI version")
(options, args) = parser.parse_args()
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from mpi import mpi_barrier, mpi_send, mpi_recv, mpi_bcast, MPI_INT, mpi_finalize, MPI_FLOAT
from applications import MPI_start_end, within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import send_EMData, recv_EMData
from utilities import get_image, bcast_number_to_all, set_params2D, get_params2D
from utilities import group_proj_by_phitheta, model_circle, get_input_from_string
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 2:
stack = args[0]
outdir = args[1]
else:
ERROR("incomplete list of arguments", "sxproj_stability", 1, myid=myid)
exit()
if not options.MPI:
ERROR("Non-MPI not supported!", "sxproj_stability", myid=myid)
exit()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
#if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "sxproj_stability", 1, myid)
#mpi_barrier(MPI_COMM_WORLD)
img_per_grp = options.img_per_group
radius = options.radius
ite = options.iter
num_ali = options.num_ali
thld_err = options.thld_err
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else: yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
if radius == -1: radius = nx / 2 - 2
mask = model_circle(radius, nx, nx)
st = time()
if options.grouping == "GRP":
if myid == main_node:
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[
"psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
# Here is where the grouping is done, I didn't put enough annotation in the group_proj_by_phitheta,
# So I will briefly explain it here
# proj_list : Returns a list of list of particle numbers, each list contains img_per_grp particle numbers
# except for the last one. Depending on the number of particles left, they will either form a
# group or append themselves to the last group
# angle_list : Also returns a list of list, each list contains three numbers (phi, theta, delta), (phi,
# theta) is the projection angle of the center of the group, delta is the range of this group
# mirror_list: Also returns a list of list, each list contains img_per_grp True or False, which indicates
# whether it should take mirror position.
# In this program angle_list and mirror list are not of interest.
proj_list_all, angle_list, mirror_list = group_proj_by_phitheta(
proj_params, img_per_grp=img_per_grp)
del proj_params
print " B number of groups ", myid, " ", len(
proj_list_all), time() - st
mpi_barrier(MPI_COMM_WORLD)
# Number of groups, actually there could be one or two more groups, since the size of the remaining group varies
# we will simply assign them to main node.
n_grp = nima / img_per_grp - 1
# Divide proj_list_all equally to all nodes, and becomes proj_list
proj_list = []
for i in xrange(n_grp):
proc_to_stay = i % number_of_proc
if proc_to_stay == main_node:
if myid == main_node: proj_list.append(proj_list_all[i])
elif myid == main_node:
mpi_send(len(proj_list_all[i]), 1, MPI_INT, proc_to_stay,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT,
proc_to_stay, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
elif myid == proc_to_stay:
img_per_grp = mpi_recv(1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
img_per_grp = int(img_per_grp[0])
temp = mpi_recv(img_per_grp, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
proj_list.append(map(int, temp))
del temp
mpi_barrier(MPI_COMM_WORLD)
print " C ", myid, " ", time() - st
if myid == main_node:
# Assign the remaining groups to main_node
for i in xrange(n_grp, len(proj_list_all)):
proj_list.append(proj_list_all[i])
del proj_list_all, angle_list, mirror_list
# Compute stability per projection projection direction, equal number assigned, thus overlaps
elif options.grouping == "GEV":
if options.delta == -1.0:
ERROR(
"Angular step for reference projections is required for GEV method",
"sxproj_stability", 1)
from utilities import even_angles, nearestk_to_refdir, getvec
refproj = even_angles(options.delta)
img_begin, img_end = MPI_start_end(len(refproj), number_of_proc, myid)
# Now each processor keeps its own share of reference projections
refprojdir = refproj[img_begin:img_end]
del refproj
ref_ang = [0.0] * (len(refprojdir) * 2)
for i in xrange(len(refprojdir)):
ref_ang[i * 2] = refprojdir[0][0]
ref_ang[i * 2 + 1] = refprojdir[0][1] + i * 0.1
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
# the solution below is very slow, do not use it unless there is a problem with the i/O
"""
for i in xrange(number_of_proc):
if myid == i:
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
mpi_barrier(MPI_COMM_WORLD)
"""
print " B ", myid, " ", time() - st
proj_ang = [0.0] * (nima * 2)
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
proj_ang[i * 2] = dp["phi"]
proj_ang[i * 2 + 1] = dp["theta"]
print " C ", myid, " ", time() - st
asi = Util.nearestk_to_refdir(proj_ang, ref_ang, img_per_grp)
del proj_ang, ref_ang
proj_list = []
for i in xrange(len(refprojdir)):
proj_list.append(asi[i * img_per_grp:(i + 1) * img_per_grp])
del asi
print " D ", myid, " ", time() - st
#from sys import exit
#exit()
# Compute stability per projection
elif options.grouping == "PPR":
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
print " B ", myid, " ", time() - st
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[
"psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
print " C ", myid, " ", time() - st
from utilities import nearest_proj
proj_list, mirror_list = nearest_proj(
proj_params, img_per_grp,
range(img_begin, img_begin + 1)) #range(img_begin, img_end))
refprojdir = proj_params[img_begin:img_end]
del proj_params, mirror_list
print " D ", myid, " ", time() - st
else:
ERROR("Incorrect projection grouping option", "sxproj_stability", 1)
"""
from utilities import write_text_file
for i in xrange(len(proj_list)):
write_text_file(proj_list[i],"projlist%06d_%04d"%(i,myid))
"""
###########################################################################################################
# Begin stability test
from utilities import get_params_proj, read_text_file
#if myid == 0:
# from utilities import read_text_file
# proj_list[0] = map(int, read_text_file("lggrpp0.txt"))
from utilities import model_blank
aveList = [model_blank(nx, ny)] * len(proj_list)
if options.grouping == "GRP":
refprojdir = [[0.0, 0.0, -1.0]] * len(proj_list)
for i in xrange(len(proj_list)):
print " E ", myid, " ", time() - st
class_data = EMData.read_images(stack, proj_list[i])
#print " R ",myid," ",time()-st
if options.CTF:
from filter import filt_ctf
for im in xrange(len(class_data)): # MEM LEAK!!
atemp = class_data[im].copy()
btemp = filt_ctf(atemp, atemp.get_attr("ctf"), binary=1)
class_data[im] = btemp
#class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
try:
t = im.get_attr(
"xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
#print " F ",myid," ",time()-st
# Here, we perform realignment num_ali times
all_ali_params = []
for j in xrange(num_ali):
if (xrng[0] == 0.0 and yrng[0] == 0.0):
avet = ali2d_ras(class_data,
randomize=True,
ir=1,
ou=radius,
rs=1,
step=1.0,
dst=90.0,
maxit=ite,
check_mirror=True,
FH=options.fl,
FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1,
radius, 1, xrng, yrng, step,
90.0, ite, options.fl,
options.aa)
ali_params = []
for im in xrange(len(class_data)):
alpha, sx, sy, mirror, scale = get_params2D(class_data[im])
ali_params.extend([alpha, sx, sy, mirror])
all_ali_params.append(ali_params)
#aveList[i] = avet
#print " G ",myid," ",time()-st
del ali_params
# We determine the stability of this group here.
# stable_set contains all particles deemed stable, it is a list of list
# each list has two elements, the first is the pixel error, the second is the image number
# stable_set is sorted based on pixel error
#from utilities import write_text_file
#write_text_file(all_ali_params, "all_ali_params%03d.txt"%myid)
stable_set, mir_stab_rate, average_pix_err = multi_align_stability(
all_ali_params, 0.0, 10000.0, thld_err, False, 2 * radius + 1)
#print " H ",myid," ",time()-st
if (len(stable_set) > 5):
stable_set_id = []
members = []
pix_err = []
# First put the stable members into attr 'members' and 'pix_err'
for s in stable_set:
# s[1] - number in this subset
stable_set_id.append(s[1])
# the original image number
members.append(proj_list[i][s[1]])
pix_err.append(s[0])
# Then put the unstable members into attr 'members' and 'pix_err'
from fundamentals import rot_shift2D
avet.to_zero()
if options.grouping == "GRP":
aphi = 0.0
atht = 0.0
vphi = 0.0
vtht = 0.0
l = -1
for j in xrange(len(proj_list[i])):
# Here it will only work if stable_set_id is sorted in the increasing number, see how l progresses
if j in stable_set_id:
l += 1
avet += rot_shift2D(class_data[j], stable_set[l][2][0],
stable_set[l][2][1],
stable_set[l][2][2],
stable_set[l][2][3])
if options.grouping == "GRP":
phi, theta, psi, sxs, sys = get_params_proj(
class_data[j])
if (theta > 90.0):
phi = (phi + 540.0) % 360.0
theta = 180.0 - theta
aphi += phi
atht += theta
vphi += phi * phi
vtht += theta * theta
else:
members.append(proj_list[i][j])
pix_err.append(99999.99)
aveList[i] = avet.copy()
if l > 1:
l += 1
aveList[i] /= l
if options.grouping == "GRP":
aphi /= l
atht /= l
vphi = (vphi - l * aphi * aphi) / l
vtht = (vtht - l * atht * atht) / l
from math import sqrt
refprojdir[i] = [
aphi, atht,
(sqrt(max(vphi, 0.0)) + sqrt(max(vtht, 0.0))) / 2.0
]
# Here more information has to be stored, PARTICULARLY WHAT IS THE REFERENCE DIRECTION
aveList[i].set_attr('members', members)
aveList[i].set_attr('refprojdir', refprojdir[i])
aveList[i].set_attr('pixerr', pix_err)
else:
print " empty group ", i, refprojdir[i]
aveList[i].set_attr('members', [-1])
aveList[i].set_attr('refprojdir', refprojdir[i])
aveList[i].set_attr('pixerr', [99999.])
del class_data
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(aveList)):
aveList[im].write_image(args[1], km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('pixerr', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
ave.write_image(args[1], km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node, im + myid + 70000)
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
members = aveList[im].get_attr('pixerr')
mpi_send(members, len(members), MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
except:
mpi_send([-999.0, -999.0, -999.0], 3, MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
global_def.BATCH = False
mpi_barrier(MPI_COMM_WORLD)
from mpi import mpi_finalize
mpi_finalize()
def main():
import global_def
from optparse import OptionParser
from EMAN2 import EMUtil
import os
import sys
from time import time
progname = os.path.basename(sys.argv[0])
usage = progname + " proj_stack output_averages --MPI"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--img_per_group",type="int" , default=100 , help="number of images per group" )
parser.add_option("--radius", type="int" , default=-1 , help="radius for alignment" )
parser.add_option("--xr", type="string" , default="2 1", help="range for translation search in x direction, search is +/xr")
parser.add_option("--yr", type="string" , default="-1", help="range for translation search in y direction, search is +/yr (default = same as xr)")
parser.add_option("--ts", type="string" , default="1 0.5", help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional")
parser.add_option("--iter", type="int" , default=30, help="number of iterations within alignment (default = 30)" )
parser.add_option("--num_ali", type="int" , default=5, help="number of alignments performed for stability (default = 5)" )
parser.add_option("--thld_err", type="float" , default=1.0, help="threshold of pixel error (default = 1.732)" )
parser.add_option("--grouping" , type="string" , default="GRP", help="do grouping of projections: PPR - per projection, GRP - different size groups, exclusive (default), GEV - grouping equal size")
parser.add_option("--delta", type="float" , default=-1.0, help="angular step for reference projections (required for GEV method)")
parser.add_option("--fl", type="float" , default=0.3, help="cut-off frequency of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--aa", type="float" , default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--CTF", action="store_true", default=False, help="Consider CTF correction during the alignment ")
parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
(options,args) = parser.parse_args()
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD, MPI_TAG_UB
from mpi import mpi_barrier, mpi_send, mpi_recv, mpi_bcast, MPI_INT, mpi_finalize, MPI_FLOAT
from applications import MPI_start_end, within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import send_EMData, recv_EMData
from utilities import get_image, bcast_number_to_all, set_params2D, get_params2D
from utilities import group_proj_by_phitheta, model_circle, get_input_from_string
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 2:
stack = args[0]
outdir = args[1]
else:
ERROR("incomplete list of arguments", "sxproj_stability", 1, myid=myid)
exit()
if not options.MPI:
ERROR("Non-MPI not supported!", "sxproj_stability", myid=myid)
exit()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
#if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "sxproj_stability", 1, myid)
#mpi_barrier(MPI_COMM_WORLD)
img_per_grp = options.img_per_group
radius = options.radius
ite = options.iter
num_ali = options.num_ali
thld_err = options.thld_err
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else : yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
if radius == -1: radius = nx/2-2
mask = model_circle(radius, nx, nx)
st = time()
if options.grouping == "GRP":
if myid == main_node:
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp["psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
# Here is where the grouping is done, I didn't put enough annotation in the group_proj_by_phitheta,
# So I will briefly explain it here
# proj_list : Returns a list of list of particle numbers, each list contains img_per_grp particle numbers
# except for the last one. Depending on the number of particles left, they will either form a
# group or append themselves to the last group
# angle_list : Also returns a list of list, each list contains three numbers (phi, theta, delta), (phi,
# theta) is the projection angle of the center of the group, delta is the range of this group
# mirror_list: Also returns a list of list, each list contains img_per_grp True or False, which indicates
# whether it should take mirror position.
# In this program angle_list and mirror list are not of interest.
proj_list_all, angle_list, mirror_list = group_proj_by_phitheta(proj_params, img_per_grp=img_per_grp)
del proj_params
print " B number of groups ",myid," ",len(proj_list_all),time()-st
mpi_barrier(MPI_COMM_WORLD)
# Number of groups, actually there could be one or two more groups, since the size of the remaining group varies
# we will simply assign them to main node.
n_grp = nima/img_per_grp-1
# Divide proj_list_all equally to all nodes, and becomes proj_list
proj_list = []
for i in xrange(n_grp):
proc_to_stay = i%number_of_proc
if proc_to_stay == main_node:
if myid == main_node: proj_list.append(proj_list_all[i])
elif myid == main_node:
mpi_send(len(proj_list_all[i]), 1, MPI_INT, proc_to_stay, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT, proc_to_stay, MPI_TAG_UB, MPI_COMM_WORLD)
elif myid == proc_to_stay:
img_per_grp = mpi_recv(1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
img_per_grp = int(img_per_grp[0])
temp = mpi_recv(img_per_grp, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
proj_list.append(map(int, temp))
del temp
mpi_barrier(MPI_COMM_WORLD)
print " C ",myid," ",time()-st
if myid == main_node:
# Assign the remaining groups to main_node
for i in xrange(n_grp, len(proj_list_all)):
proj_list.append(proj_list_all[i])
del proj_list_all, angle_list, mirror_list
# Compute stability per projection projection direction, equal number assigned, thus overlaps
elif options.grouping == "GEV":
if options.delta == -1.0: ERROR("Angular step for reference projections is required for GEV method","sxproj_stability",1)
from utilities import even_angles, nearestk_to_refdir, getvec
refproj = even_angles(options.delta)
img_begin, img_end = MPI_start_end(len(refproj), number_of_proc, myid)
# Now each processor keeps its own share of reference projections
refprojdir = refproj[img_begin: img_end]
del refproj
ref_ang = [0.0]*(len(refprojdir)*2)
for i in xrange(len(refprojdir)):
ref_ang[i*2] = refprojdir[0][0]
ref_ang[i*2+1] = refprojdir[0][1]+i*0.1
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
# the solution below is very slow, do not use it unless there is a problem with the i/O
"""
for i in xrange(number_of_proc):
if myid == i:
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
mpi_barrier(MPI_COMM_WORLD)
"""
print " B ",myid," ",time()-st
proj_ang = [0.0]*(nima*2)
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
proj_ang[i*2] = dp["phi"]
proj_ang[i*2+1] = dp["theta"]
print " C ",myid," ",time()-st
asi = Util.nearestk_to_refdir(proj_ang, ref_ang, img_per_grp)
del proj_ang, ref_ang
proj_list = []
for i in xrange(len(refprojdir)):
proj_list.append(asi[i*img_per_grp:(i+1)*img_per_grp])
del asi
print " D ",myid," ",time()-st
#from sys import exit
#exit()
# Compute stability per projection
elif options.grouping == "PPR":
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
print " B ",myid," ",time()-st
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp["psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
print " C ",myid," ",time()-st
from utilities import nearest_proj
proj_list, mirror_list = nearest_proj(proj_params, img_per_grp, range(img_begin, img_begin+1))#range(img_begin, img_end))
refprojdir = proj_params[img_begin: img_end]
del proj_params, mirror_list
print " D ",myid," ",time()-st
else: ERROR("Incorrect projection grouping option","sxproj_stability",1)
"""
from utilities import write_text_file
for i in xrange(len(proj_list)):
write_text_file(proj_list[i],"projlist%06d_%04d"%(i,myid))
"""
###########################################################################################################
# Begin stability test
from utilities import get_params_proj, read_text_file
#if myid == 0:
# from utilities import read_text_file
# proj_list[0] = map(int, read_text_file("lggrpp0.txt"))
from utilities import model_blank
aveList = [model_blank(nx,ny)]*len(proj_list)
if options.grouping == "GRP": refprojdir = [[0.0,0.0,-1.0]]*len(proj_list)
for i in xrange(len(proj_list)):
print " E ",myid," ",time()-st
class_data = EMData.read_images(stack, proj_list[i])
#print " R ",myid," ",time()-st
if options.CTF :
from filter import filt_ctf
for im in xrange(len(class_data)): # MEM LEAK!!
atemp = class_data[im].copy()
btemp = filt_ctf(atemp, atemp.get_attr("ctf"), binary=1)
class_data[im] = btemp
#class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0,-d["tx"],-d["ty"],0,1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
#print " F ",myid," ",time()-st
# Here, we perform realignment num_ali times
all_ali_params = []
for j in xrange(num_ali):
if( xrng[0] == 0.0 and yrng[0] == 0.0 ):
avet = ali2d_ras(class_data, randomize = True, ir = 1, ou = radius, rs = 1, step = 1.0, dst = 90.0, maxit = ite, check_mirror = True, FH=options.fl, FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1, radius, 1, xrng, yrng, step, 90.0, ite, options.fl, options.aa)
ali_params = []
for im in xrange(len(class_data)):
alpha, sx, sy, mirror, scale = get_params2D(class_data[im])
ali_params.extend( [alpha, sx, sy, mirror] )
all_ali_params.append(ali_params)
#aveList[i] = avet
#print " G ",myid," ",time()-st
del ali_params
# We determine the stability of this group here.
# stable_set contains all particles deemed stable, it is a list of list
# each list has two elements, the first is the pixel error, the second is the image number
# stable_set is sorted based on pixel error
#from utilities import write_text_file
#write_text_file(all_ali_params, "all_ali_params%03d.txt"%myid)
stable_set, mir_stab_rate, average_pix_err = multi_align_stability(all_ali_params, 0.0, 10000.0, thld_err, False, 2*radius+1)
#print " H ",myid," ",time()-st
if(len(stable_set) > 5):
stable_set_id = []
members = []
pix_err = []
# First put the stable members into attr 'members' and 'pix_err'
for s in stable_set:
# s[1] - number in this subset
stable_set_id.append(s[1])
# the original image number
members.append(proj_list[i][s[1]])
pix_err.append(s[0])
# Then put the unstable members into attr 'members' and 'pix_err'
from fundamentals import rot_shift2D
avet.to_zero()
if options.grouping == "GRP":
aphi = 0.0
atht = 0.0
vphi = 0.0
vtht = 0.0
l = -1
for j in xrange(len(proj_list[i])):
# Here it will only work if stable_set_id is sorted in the increasing number, see how l progresses
if j in stable_set_id:
l += 1
avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3] )
if options.grouping == "GRP":
phi, theta, psi, sxs, sys = get_params_proj(class_data[j])
if( theta > 90.0):
phi = (phi+540.0)%360.0
theta = 180.0 - theta
aphi += phi
atht += theta
vphi += phi*phi
vtht += theta*theta
else:
members.append(proj_list[i][j])
pix_err.append(99999.99)
aveList[i] = avet.copy()
if l>1 :
l += 1
aveList[i] /= l
if options.grouping == "GRP":
aphi /= l
atht /= l
vphi = (vphi - l*aphi*aphi)/l
vtht = (vtht - l*atht*atht)/l
from math import sqrt
refprojdir[i] = [aphi, atht, (sqrt(max(vphi,0.0))+sqrt(max(vtht,0.0)))/2.0]
# Here more information has to be stored, PARTICULARLY WHAT IS THE REFERENCE DIRECTION
aveList[i].set_attr('members', members)
aveList[i].set_attr('refprojdir',refprojdir[i])
aveList[i].set_attr('pixerr', pix_err)
else:
print " empty group ",i, refprojdir[i]
aveList[i].set_attr('members',[-1])
aveList[i].set_attr('refprojdir',refprojdir[i])
aveList[i].set_attr('pixerr', [99999.])
del class_data
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(aveList)):
aveList[im].write_image(args[1], km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('pixerr', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
ave.write_image(args[1], km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node,im+myid+70000)
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
members = aveList[im].get_attr('pixerr')
mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
global_def.BATCH = False
mpi_barrier(MPI_COMM_WORLD)
from mpi import mpi_finalize
mpi_finalize()
def main():
from utilities import get_input_from_string
progname = os.path.basename(sys.argv[0])
usage = progname + " stack output_average --radius=particle_radius --xr=xr --yr=yr --ts=ts --thld_err=thld_err --num_ali=num_ali --fl=fl --aa=aa --CTF --verbose --stables"
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--radius", type="int", default=-1, help=" particle radius for alignment")
parser.add_option("--xr", type="string" , default="2 1", help="range for translation search in x direction, search is +/xr (default 2,1)")
parser.add_option("--yr", type="string" , default="-1", help="range for translation search in y direction, search is +/yr (default = same as xr)")
parser.add_option("--ts", type="string" , default="1 0.5", help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional (default: 1,0.5)")
parser.add_option("--thld_err", type="float", default=0.75, help="threshld of pixel error (default = 0.75)")
parser.add_option("--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)")
parser.add_option("--maxit", type="int", default=30, help="number of iterations for each xr (default = 30)")
parser.add_option("--fl", type="float" , default=0.3, help="cut-off frequency of hyperbolic tangent low-pass Fourier filter (default = 0.3)")
parser.add_option("--aa", type="float" , default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter (default = 0.2)")
parser.add_option("--CTF", action="store_true", default=False, help="Use CTF correction during the alignment ")
parser.add_option("--verbose", action="store_true", default=False, help="print individual pixel error (default = False)")
parser.add_option("--stables", action="store_true", default=False, help="output the stable particles number in file (default = False)")
parser.add_option("--method", type="string" , default=" ", help="SHC (standard method is default when flag is ommitted)")
(options, args) = parser.parse_args()
if len(args) != 1 and len(args) != 2:
print "usage: " + usage
print "Please run '" + progname + " -h' for detailed options"
else:
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
from applications import within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import write_text_file, write_text_row
global_def.BATCH = True
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else : yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
class_data = EMData.read_images(args[0])
nx = class_data[0].get_xsize()
ou = options.radius
num_ali = options.num_ali
if ou == -1: ou = nx/2-2
from utilities import model_circle, get_params2D, set_params2D
mask = model_circle(ou, nx, nx)
if options.CTF :
from filter import filt_ctf
for im in xrange(len(class_data)):
# Flip phases
class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
im.set_attr("previousmax", -1.0e10)
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
all_ali_params = []
for ii in xrange(num_ali):
ali_params = []
if options.verbose:
ALPHA = []
SX = []
SY = []
MIRROR = []
if( xrng[0] == 0.0 and yrng[0] == 0.0 ):
avet = ali2d_ras(class_data, randomize = True, ir = 1, ou = ou, rs = 1, step = 1.0, dst = 90.0, \
maxit = options.maxit, check_mirror = True, FH=options.fl, FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1, ou, 1, xrng, yrng, step, 90.0, \
maxit = options.maxit, FH=options.fl, FF=options.aa, method = options.method)
from utilities import info
#print " avet ",info(avet)
for im in class_data:
alpha, sx, sy, mirror, scale = get_params2D(im)
ali_params.extend([alpha, sx, sy, mirror])
if options.verbose:
ALPHA.append(alpha)
SX.append(sx)
SY.append(sy)
MIRROR.append(mirror)
all_ali_params.append(ali_params)
if options.verbose:
write_text_file([ALPHA, SX, SY, MIRROR], "ali_params_run_%d"%ii)
"""
avet = class_data[0]
from utilities import read_text_file
all_ali_params = []
for ii in xrange(5):
temp = read_text_file( "ali_params_run_%d"%ii,-1)
uuu = []
for k in xrange(len(temp[0])):
uuu.extend([temp[0][k],temp[1][k],temp[2][k],temp[3][k]])
all_ali_params.append(uuu)
"""
stable_set, mir_stab_rate, pix_err = multi_align_stability(all_ali_params, 0.0, 10000.0, options.thld_err, options.verbose, 2*ou+1)
print "%4s %20s %20s %20s %30s %6.2f"%("", "Size of set", "Size of stable set", "Mirror stab rate", "Pixel error prior to pruning the set above threshold of",options.thld_err)
print "Average stat: %10d %20d %20.2f %15.2f"%( len(class_data), len(stable_set), mir_stab_rate, pix_err)
if( len(stable_set) > 0):
if options.stables:
stab_mem = [[0,0.0,0] for j in xrange(len(stable_set))]
for j in xrange(len(stable_set)): stab_mem[j] = [int(stable_set[j][1]), stable_set[j][0], j]
write_text_row(stab_mem, "stable_particles.txt")
stable_set_id = []
particle_pixerr = []
for s in stable_set:
stable_set_id.append(s[1])
particle_pixerr.append(s[0])
from fundamentals import rot_shift2D
avet.to_zero()
l = -1
print "average parameters: angle, x-shift, y-shift, mirror"
for j in stable_set_id:
l += 1
print " %4d %4d %12.2f %12.2f %12.2f %1d"%(l,j, stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], int(stable_set[l][2][3]))
avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3] )
avet /= (l+1)
avet.set_attr('members', stable_set_id)
avet.set_attr('pix_err', pix_err)
avet.set_attr('pixerr', particle_pixerr)
avet.write_image(args[1])
global_def.BATCH = False
def main():
from utilities import get_input_from_string
progname = os.path.basename(sys.argv[0])
usage = (
progname
+ " stack output_average --radius=particle_radius --xr=xr --yr=yr --ts=ts --thld_err=thld_err --num_ali=num_ali --fl=fl --aa=aa --CTF --verbose --stables"
)
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--radius", type="int", default=-1, help=" particle radius for alignment")
parser.add_option(
"--xr",
type="string",
default="2 1",
help="range for translation search in x direction, search is +/xr (default 2,1)",
)
parser.add_option(
"--yr",
type="string",
default="-1",
help="range for translation search in y direction, search is +/yr (default = same as xr)",
)
parser.add_option(
"--ts",
type="string",
default="1 0.5",
help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional (default: 1,0.5)",
)
parser.add_option("--thld_err", type="float", default=0.75, help="threshld of pixel error (default = 0.75)")
parser.add_option(
"--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)"
)
parser.add_option("--maxit", type="int", default=30, help="number of iterations for each xr (default = 30)")
parser.add_option(
"--fl",
type="float",
default=0.3,
help="cut-off frequency of hyperbolic tangent low-pass Fourier filter (default = 0.3)",
)
parser.add_option(
"--aa", type="float", default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter (default = 0.2)"
)
parser.add_option("--CTF", action="store_true", default=False, help="Use CTF correction during the alignment ")
parser.add_option(
"--verbose", action="store_true", default=False, help="print individual pixel error (default = False)"
)
parser.add_option(
"--stables",
action="store_true",
default=False,
help="output the stable particles number in file (default = False)",
)
parser.add_option(
"--method", type="string", default=" ", help="SHC (standard method is default when flag is ommitted)"
)
(options, args) = parser.parse_args()
if len(args) != 1 and len(args) != 2:
print "usage: " + usage
print "Please run '" + progname + " -h' for detailed options"
else:
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
from applications import within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import write_text_file, write_text_row
global_def.BATCH = True
xrng = get_input_from_string(options.xr)
if options.yr == "-1":
yrng = xrng
else:
yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
class_data = EMData.read_images(args[0])
nx = class_data[0].get_xsize()
ou = options.radius
num_ali = options.num_ali
if ou == -1:
ou = nx / 2 - 2
from utilities import model_circle, get_params2D, set_params2D
mask = model_circle(ou, nx, nx)
if options.CTF:
from filter import filt_ctf
for im in xrange(len(class_data)):
# Flip phases
class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
im.set_attr("previousmax", -1.0e10)
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
all_ali_params = []
for ii in xrange(num_ali):
ali_params = []
if options.verbose:
ALPHA = []
SX = []
SY = []
MIRROR = []
if xrng[0] == 0.0 and yrng[0] == 0.0:
avet = ali2d_ras(
class_data,
randomize=True,
ir=1,
ou=ou,
rs=1,
step=1.0,
dst=90.0,
maxit=options.maxit,
check_mirror=True,
FH=options.fl,
FF=options.aa,
)
else:
avet = within_group_refinement(
class_data,
mask,
True,
1,
ou,
1,
xrng,
yrng,
step,
90.0,
maxit=options.maxit,
FH=options.fl,
FF=options.aa,
method=options.method,
)
from utilities import info
# print " avet ",info(avet)
for im in class_data:
alpha, sx, sy, mirror, scale = get_params2D(im)
ali_params.extend([alpha, sx, sy, mirror])
if options.verbose:
ALPHA.append(alpha)
SX.append(sx)
SY.append(sy)
MIRROR.append(mirror)
all_ali_params.append(ali_params)
if options.verbose:
write_text_file([ALPHA, SX, SY, MIRROR], "ali_params_run_%d" % ii)
"""
avet = class_data[0]
from utilities import read_text_file
all_ali_params = []
for ii in xrange(5):
temp = read_text_file( "ali_params_run_%d"%ii,-1)
uuu = []
for k in xrange(len(temp[0])):
uuu.extend([temp[0][k],temp[1][k],temp[2][k],temp[3][k]])
all_ali_params.append(uuu)
"""
stable_set, mir_stab_rate, pix_err = multi_align_stability(
all_ali_params, 0.0, 10000.0, options.thld_err, options.verbose, 2 * ou + 1
)
print "%4s %20s %20s %20s %30s %6.2f" % (
"",
"Size of set",
"Size of stable set",
"Mirror stab rate",
"Pixel error prior to pruning the set above threshold of",
options.thld_err,
)
print "Average stat: %10d %20d %20.2f %15.2f" % (len(class_data), len(stable_set), mir_stab_rate, pix_err)
if len(stable_set) > 0:
if options.stables:
stab_mem = [[0, 0.0, 0] for j in xrange(len(stable_set))]
for j in xrange(len(stable_set)):
stab_mem[j] = [int(stable_set[j][1]), stable_set[j][0], j]
write_text_row(stab_mem, "stable_particles.txt")
stable_set_id = []
particle_pixerr = []
for s in stable_set:
stable_set_id.append(s[1])
particle_pixerr.append(s[0])
from fundamentals import rot_shift2D
avet.to_zero()
l = -1
print "average parameters: angle, x-shift, y-shift, mirror"
for j in stable_set_id:
l += 1
print " %4d %4d %12.2f %12.2f %12.2f %1d" % (
l,
j,
stable_set[l][2][0],
stable_set[l][2][1],
stable_set[l][2][2],
int(stable_set[l][2][3]),
)
avet += rot_shift2D(
class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3]
)
avet /= l + 1
avet.set_attr("members", stable_set_id)
avet.set_attr("pix_err", pix_err)
avet.set_attr("pixerr", particle_pixerr)
avet.write_image(args[1])
global_def.BATCH = False