def helicalshiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im, pad
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from pap_statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D, fshift
from utilities import get_params2D, set_params2D, chunks_distribution
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
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
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from time import time
from pixel_error import ordersegments
from math import sqrt, atan2, tan, pi
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(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), "ehelix_MPI", 1, 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)
print("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 filter import filt_ctf
from 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 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', "helicalshiftali_MPI", 1,
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_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node,
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_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if (file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata,
nproc)
else:
from 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 shiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1,
oneDx=False,
search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from pap_statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
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
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter = int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node=main_node)
list_of_particles = list(range(nima))
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node=main_node)
ny = bcast_number_to_all(ny, source_node=main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node=main_node)
if ctf_app > 0:
ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad // 2 - 2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from 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
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in range(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in range(len(data)):
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")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else:
ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in range(0, nx, 2):
for j in range(ny):
temp.set_value_at(i, j, snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im],
p['alpha'],
sx=p['tx'],
sy=p['ty'],
mirror=p['mirror'],
scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in range(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum = 0
sy_sum = 0
sx_sum_total = 0
sy_sum_total = 0
shift_x = [0.0] * len(data)
shift_y = [0.0] * len(data)
ishift_x = [0.0] * len(data)
ishift_y = [0.0] * len(data)
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 data:
Util.add_img(avg, 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 = EMData(nx, ny, 1, False)
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)
# 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)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum = 0
sy_sum = 0
if search_rng > 0: nwx = 2 * search_rng + 1
else: nwx = nx
if search_rng_y > 0: nwy = 2 * search_rng_y + 1
else: nwy = ny
not_zero = 0
for im in range(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im], tavg), nwx, 1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im], tavg), nwx, nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not (ishift_x[im] == 0.0)) or (not (ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node=main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total,
source_node=main_node)
sx_ave = round(float(sx_sum_total) / nima)
sy_ave = round(float(sy_sum_total) / nima)
for im in range(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": p1_x,
"y_shift": p1_y,
"z_shift": 0.0
}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node=main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n" % (time() - start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in range(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({
"type": "2D",
"alpha": 0,
"scale": t0.get_scale(),
"mirror": 0,
"tx": shift_x[im],
"ty": shift_y[im]
})
# combine t0 and t1
tt = t1 * t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if (file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def ali3d_MPI(stack, ref_vol, outdir, maskfile = None, ir = 1, ou = -1, rs = 1,
xr = "4 2 2 1", yr = "-1", ts = "1 1 0.5 0.25", delta = "10 6 4 4", an = "-1",
center = 0, maxit = 5, term = 95, CTF = False, fourvar = False, snr = 1.0, ref_a = "S", sym = "c1",
sort=True, cutoff=999.99, pix_cutoff="0", two_tail=False, model_jump="1 1 1 1 1", restart=False, save_half=False,
protos=None, oplane=None, lmask=-1, ilmask=-1, findseam=False, vertstep=None, hpars="-1", hsearch="73.0 170.0",
full_output = False, compare_repro = False, compare_ref_free = "-1", ref_free_cutoff= "-1 -1 -1 -1",
wcmask = None, debug = False, recon_pad = 4):
from alignment import Numrinit, prepare_refrings
from utilities import model_circle, get_image, drop_image, get_input_from_string
from utilities import bcast_list_to_all, bcast_number_to_all, reduce_EMData_to_root, bcast_EMData_to_all
from utilities import send_attr_dict
from utilities import get_params_proj, file_type
from fundamentals import rot_avg_image
import os
import types
from utilities import print_begin_msg, print_end_msg, print_msg
from mpi import mpi_bcast, mpi_comm_size, mpi_comm_rank, MPI_FLOAT, MPI_COMM_WORLD, mpi_barrier, mpi_reduce
from mpi import mpi_reduce, MPI_INT, MPI_SUM, mpi_finalize
from filter import filt_ctf
from projection import prep_vol, prgs
from statistics import hist_list, varf3d_MPI, fsc_mask
from numpy import array, bincount, array2string, ones
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
if myid == main_node:
if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "ali3d_MPI", 1)
os.mkdir(outdir)
mpi_barrier(MPI_COMM_WORLD)
if debug:
from time import sleep
while not os.path.exists(outdir):
print "Node ",myid," waiting..."
sleep(5)
info_file = os.path.join(outdir, "progress%04d"%myid)
finfo = open(info_file, 'w')
else:
finfo = None
mjump = get_input_from_string(model_jump)
xrng = get_input_from_string(xr)
if yr == "-1": yrng = xrng
else : yrng = get_input_from_string(yr)
step = get_input_from_string(ts)
delta = get_input_from_string(delta)
ref_free_cutoff = get_input_from_string(ref_free_cutoff)
pix_cutoff = get_input_from_string(pix_cutoff)
lstp = min(len(xrng), len(yrng), len(step), len(delta))
if an == "-1":
an = [-1] * lstp
else:
an = get_input_from_string(an)
# make sure pix_cutoff is set for all iterations
if len(pix_cutoff)<lstp:
for i in xrange(len(pix_cutoff),lstp):
pix_cutoff.append(pix_cutoff[-1])
# don't waste time on sub-pixel alignment for low-resolution ang incr
for i in range(len(step)):
if (delta[i] > 4 or delta[i] == -1) and step[i] < 1:
step[i] = 1
first_ring = int(ir)
rstep = int(rs)
last_ring = int(ou)
max_iter = int(maxit)
center = int(center)
nrefs = EMUtil.get_image_count( ref_vol )
nmasks = 0
if maskfile:
# read number of masks within each maskfile (mc)
nmasks = EMUtil.get_image_count( maskfile )
# open masks within maskfile (mc)
maskF = EMData.read_images(maskfile, xrange(nmasks))
vol = EMData.read_images(ref_vol, xrange(nrefs))
nx = vol[0].get_xsize()
## make sure box sizes are the same
if myid == main_node:
im=EMData.read_images(stack,[0])
bx = im[0].get_xsize()
if bx!=nx:
print_msg("Error: Stack box size (%i) differs from initial model (%i)\n"%(bx,nx))
sys.exit()
del im,bx
# for helical processing:
helicalrecon = False
if protos is not None or hpars != "-1" or findseam is True:
helicalrecon = True
# if no out-of-plane param set, use 5 degrees
if oplane is None:
oplane=5.0
if protos is not None:
proto = get_input_from_string(protos)
if len(proto) != nrefs:
print_msg("Error: insufficient protofilament numbers supplied")
sys.exit()
if hpars != "-1":
hpars = get_input_from_string(hpars)
if len(hpars) != 2*nrefs:
print_msg("Error: insufficient helical parameters supplied")
sys.exit()
## create helical parameter file for helical reconstruction
if helicalrecon is True and myid == main_node:
from hfunctions import createHpar
# create initial helical parameter files
dp=[0]*nrefs
dphi=[0]*nrefs
vdp=[0]*nrefs
vdphi=[0]*nrefs
for iref in xrange(nrefs):
hpar = os.path.join(outdir,"hpar%02d.spi"%(iref))
params = False
if hpars != "-1":
# if helical parameters explicitly given, set twist & rise
params = [float(hpars[iref*2]),float(hpars[(iref*2)+1])]
dp[iref],dphi[iref],vdp[iref],vdphi[iref] = createHpar(hpar,proto[iref],params,vertstep)
# get values for helical search parameters
hsearch = get_input_from_string(hsearch)
if len(hsearch) != 2:
print_msg("Error: specify outer and inner radii for helical search")
sys.exit()
if last_ring < 0 or last_ring > int(nx/2)-2 : last_ring = int(nx/2) - 2
if myid == main_node:
# import user_functions
# user_func = user_functions.factory[user_func_name]
print_begin_msg("ali3d_MPI")
print_msg("Input stack : %s\n"%(stack))
print_msg("Reference volume : %s\n"%(ref_vol))
print_msg("Output directory : %s\n"%(outdir))
if nmasks > 0:
print_msg("Maskfile (number of masks) : %s (%i)\n"%(maskfile,nmasks))
print_msg("Inner radius : %i\n"%(first_ring))
print_msg("Outer radius : %i\n"%(last_ring))
print_msg("Ring step : %i\n"%(rstep))
print_msg("X search range : %s\n"%(xrng))
print_msg("Y search range : %s\n"%(yrng))
print_msg("Translational step : %s\n"%(step))
print_msg("Angular step : %s\n"%(delta))
print_msg("Angular search range : %s\n"%(an))
print_msg("Maximum iteration : %i\n"%(max_iter))
print_msg("Center type : %i\n"%(center))
print_msg("CTF correction : %s\n"%(CTF))
print_msg("Signal-to-Noise Ratio : %f\n"%(snr))
print_msg("Reference projection method : %s\n"%(ref_a))
print_msg("Symmetry group : %s\n"%(sym))
print_msg("Fourier padding for 3D : %i\n"%(recon_pad))
print_msg("Number of reference models : %i\n"%(nrefs))
print_msg("Sort images between models : %s\n"%(sort))
print_msg("Allow images to jump : %s\n"%(mjump))
print_msg("CC cutoff standard dev : %f\n"%(cutoff))
print_msg("Two tail cutoff : %s\n"%(two_tail))
print_msg("Termination pix error : %f\n"%(term))
print_msg("Pixel error cutoff : %s\n"%(pix_cutoff))
print_msg("Restart : %s\n"%(restart))
print_msg("Full output : %s\n"%(full_output))
print_msg("Compare reprojections : %s\n"%(compare_repro))
print_msg("Compare ref free class avgs : %s\n"%(compare_ref_free))
print_msg("Use cutoff from ref free : %s\n"%(ref_free_cutoff))
if protos:
print_msg("Protofilament numbers : %s\n"%(proto))
print_msg("Using helical search range : %s\n"%hsearch)
if findseam is True:
print_msg("Using seam-based reconstruction\n")
if hpars != "-1":
print_msg("Using hpars : %s\n"%hpars)
if vertstep != None:
print_msg("Using vertical step : %.2f\n"%vertstep)
if save_half is True:
print_msg("Saving even/odd halves\n")
for i in xrange(100) : print_msg("*")
print_msg("\n\n")
if maskfile:
if type(maskfile) is types.StringType: mask3D = get_image(maskfile)
else: mask3D = maskfile
else: mask3D = model_circle(last_ring, nx, nx, nx)
numr = Numrinit(first_ring, last_ring, rstep, "F")
mask2D = model_circle(last_ring,nx,nx) - model_circle(first_ring,nx,nx)
fscmask = model_circle(last_ring,nx,nx,nx)
if CTF:
from filter import filt_ctf
from reconstruction_rjh import rec3D_MPI_noCTF
if myid == main_node:
active = EMUtil.get_all_attributes(stack, 'active')
list_of_particles = []
for im in xrange(len(active)):
if active[im]: list_of_particles.append(im)
del active
nima = len(list_of_particles)
else:
nima = 0
total_nima = bcast_number_to_all(nima, source_node = main_node)
if myid != main_node:
list_of_particles = [-1]*total_nima
list_of_particles = bcast_list_to_all(list_of_particles, source_node = main_node)
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
# create a list of images for each node
list_of_particles = list_of_particles[image_start: image_end]
nima = len(list_of_particles)
if debug:
finfo.write("image_start, image_end: %d %d\n" %(image_start, image_end))
finfo.flush()
data = EMData.read_images(stack, list_of_particles)
t_zero = Transform({"type":"spider","phi":0,"theta":0,"psi":0,"tx":0,"ty":0})
transmulti = [[t_zero for i in xrange(nrefs)] for j in xrange(nima)]
for iref,im in ((iref,im) for iref in xrange(nrefs) for im in xrange(nima)):
if nrefs == 1:
transmulti[im][iref] = data[im].get_attr("xform.projection")
else:
# if multi models, keep track of eulers for all models
try:
transmulti[im][iref] = data[im].get_attr("eulers_txty.%i"%iref)
except:
data[im].set_attr("eulers_txty.%i"%iref,t_zero)
scoremulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
pixelmulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
ref_res = [0.0 for x in xrange(nrefs)]
apix = data[0].get_attr('apix_x')
# for oplane parameter, create cylindrical mask
if oplane is not None and myid == main_node:
from hfunctions import createCylMask
cmaskf=os.path.join(outdir, "mask3D_cyl.mrc")
mask3D = createCylMask(data,ou,lmask,ilmask,cmaskf)
# if finding seam of helix, create wedge masks
if findseam is True:
wedgemask=[]
for pf in xrange(nrefs):
wedgemask.append(EMData())
# wedgemask option
if wcmask is not None:
wcmask = get_input_from_string(wcmask)
if len(wcmask) != 3:
print_msg("Error: wcmask option requires 3 values: x y radius")
sys.exit()
# determine if particles have helix info:
try:
data[0].get_attr('h_angle')
original_data = []
boxmask = True
from hfunctions import createBoxMask
except:
boxmask = False
# prepare particles
for im in xrange(nima):
data[im].set_attr('ID', list_of_particles[im])
data[im].set_attr('pix_score', int(0))
if CTF:
# only phaseflip particles, not full CTF correction
ctf_params = data[im].get_attr("ctf")
st = Util.infomask(data[im], mask2D, False)
data[im] -= st[0]
data[im] = filt_ctf(data[im], ctf_params, sign = -1, binary=1)
data[im].set_attr('ctf_applied', 1)
# for window mask:
if boxmask is True:
h_angle = data[im].get_attr("h_angle")
original_data.append(data[im].copy())
bmask = createBoxMask(nx,apix,ou,lmask,h_angle)
data[im]*=bmask
del bmask
if debug:
finfo.write( '%d loaded \n' % nima )
finfo.flush()
if myid == main_node:
# initialize data for the reference preparation function
ref_data = [ mask3D, max(center,0), None, None, None, None ]
# for method -1, switch off centering in user function
from time import time
# this is needed for gathering of pixel errors
disps = []
recvcount = []
disps_score = []
recvcount_score = []
for im in xrange(number_of_proc):
if( im == main_node ):
disps.append(0)
disps_score.append(0)
else:
disps.append(disps[im-1] + recvcount[im-1])
disps_score.append(disps_score[im-1] + recvcount_score[im-1])
ib, ie = MPI_start_end(total_nima, number_of_proc, im)
recvcount.append( ie - ib )
recvcount_score.append((ie-ib)*nrefs)
pixer = [0.0]*nima
cs = [0.0]*3
total_iter = 0
volodd = EMData.read_images(ref_vol, xrange(nrefs))
voleve = EMData.read_images(ref_vol, xrange(nrefs))
if restart:
# recreate initial volumes from alignments stored in header
itout = "000_00"
for iref in xrange(nrefs):
if(nrefs == 1):
modout = ""
else:
modout = "_model_%02d"%(iref)
if(sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',transmulti[im][iref])
fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index = group, npad = recon_pad)
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep=None
if vertstep is not None:
vstep=(vdp[iref],vdphi[iref])
print_msg("Old rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
print_msg("New rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)
else:
vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%itout),-1)
if save_half is True:
volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%itout),-1)
voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%itout),-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs,fl = ref_ali3d(ref_data)
vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res = fl
else:
res_msg = "Models filtered at resolution of: \t"
res = apix / fl
ares = array2string(array(res), precision = 2)
print_msg("%s%s\n\n"%(res_msg,ares))
bcast_EMData_to_all(vol[iref], myid, main_node)
# write out headers, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
# projection matching
for N_step in xrange(lstp):
terminate = 0
Iter = -1
while(Iter < max_iter-1 and terminate == 0):
Iter += 1
total_iter += 1
itout = "%03g_%02d" %(delta[N_step], Iter)
if myid == main_node:
print_msg("ITERATION #%3d, inner iteration #%3d\nDelta = %4.1f, an = %5.2f, xrange = %5.2f, yrange = %5.2f, step = %5.2f\n\n"%(N_step, Iter, delta[N_step], an[N_step], xrng[N_step],yrng[N_step],step[N_step]))
for iref in xrange(nrefs):
if myid == main_node: start_time = time()
volft,kb = prep_vol( vol[iref] )
## constrain projections to out of plane parameter
theta1 = None
theta2 = None
if oplane is not None:
theta1 = 90-oplane
theta2 = 90+oplane
refrings = prepare_refrings( volft, kb, nx, delta[N_step], ref_a, sym, numr, MPI=True, phiEqpsi = "Minus", initial_theta=theta1, delta_theta=theta2)
del volft,kb
if myid== main_node:
print_msg( "Time to prepare projections for model %i: %s\n" % (iref, legibleTime(time()-start_time)) )
start_time = time()
for im in xrange( nima ):
data[im].set_attr("xform.projection", transmulti[im][iref])
if an[N_step] == -1:
t1, peak, pixer[im] = proj_ali_incore(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],finfo)
else:
t1, peak, pixer[im] = proj_ali_incore_local(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],an[N_step],finfo)
#data[im].set_attr("xform.projection"%iref, t1)
if nrefs > 1: data[im].set_attr("eulers_txty.%i"%iref,t1)
scoremulti[im][iref] = peak
from pixel_error import max_3D_pixel_error
# t1 is the current param, t2 is old
t2 = transmulti[im][iref]
pixelmulti[im][iref] = max_3D_pixel_error(t1,t2,numr[-3])
transmulti[im][iref] = t1
if myid == main_node:
print_msg("Time of alignment for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
# gather scoring data from all processors
from mpi import mpi_gatherv
scoremultisend = sum(scoremulti,[])
pixelmultisend = sum(pixelmulti,[])
tmp = mpi_gatherv(scoremultisend,len(scoremultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
tmp1 = mpi_gatherv(pixelmultisend,len(pixelmultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
tmp = mpi_bcast(tmp,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
tmp1 = mpi_bcast(tmp1,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
tmp = map(float,tmp)
tmp1 = map(float,tmp1)
score = array(tmp).reshape(-1,nrefs)
pixelerror = array(tmp1).reshape(-1,nrefs)
score_local = array(scoremulti)
mean_score = score.mean(axis=0)
std_score = score.std(axis=0)
cut = mean_score - (cutoff * std_score)
cut2 = mean_score + (cutoff * std_score)
res_max = score_local.argmax(axis=1)
minus_cc = [0.0 for x in xrange(nrefs)]
minus_pix = [0.0 for x in xrange(nrefs)]
minus_ref = [0.0 for x in xrange(nrefs)]
#output pixel errors
if(myid == main_node):
from statistics import hist_list
lhist = 20
pixmin = pixelerror.min(axis=1)
region, histo = hist_list(pixmin, lhist)
if(region[0] < 0.0): region[0] = 0.0
print_msg("Histogram of pixel errors\n ERROR number of particles\n")
for lhx in xrange(lhist):
print_msg(" %10.3f %7d\n"%(region[lhx], histo[lhx]))
# Terminate if 95% within 1 pixel error
im = 0
for lhx in xrange(lhist):
if(region[lhx] > 1.0): break
im += histo[lhx]
print_msg( "Percent of particles with pixel error < 1: %f\n\n"% (im/float(total_nima)*100))
term_cond = float(term)/100
if(im/float(total_nima) > term_cond):
terminate = 1
print_msg("Terminating internal loop\n")
del region, histo
terminate = mpi_bcast(terminate, 1, MPI_INT, 0, MPI_COMM_WORLD)
terminate = int(terminate[0])
for im in xrange(nima):
if(sort==False):
data[im].set_attr('group',999)
elif (mjump[N_step]==1):
data[im].set_attr('group',int(res_max[im]))
pix_run = data[im].get_attr('pix_score')
if (pix_cutoff[N_step]==1 and (terminate==1 or Iter == max_iter-1)):
if (pixelmulti[im][int(res_max[im])] > 1):
data[im].set_attr('pix_score',int(777))
if (score_local[im][int(res_max[im])]<cut[int(res_max[im])]) or (two_tail and score_local[im][int(res_max[im])]>cut2[int(res_max[im])]):
data[im].set_attr('group',int(888))
minus_cc[int(res_max[im])] = minus_cc[int(res_max[im])] + 1
if(pix_run == 777):
data[im].set_attr('group',int(777))
minus_pix[int(res_max[im])] = minus_pix[int(res_max[im])] + 1
if (compare_ref_free != "-1") and (ref_free_cutoff[N_step] != -1) and (total_iter > 1):
id = data[im].get_attr('ID')
if id in rejects:
data[im].set_attr('group',int(666))
minus_ref[int(res_max[im])] = minus_ref[int(res_max[im])] + 1
minus_cc_tot = mpi_reduce(minus_cc,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
minus_pix_tot = mpi_reduce(minus_pix,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
minus_ref_tot = mpi_reduce(minus_ref,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
if (myid == main_node):
if(sort):
tot_max = score.argmax(axis=1)
res = bincount(tot_max)
else:
res = ones(nrefs) * total_nima
print_msg("Particle distribution: \t\t%s\n"%(res*1.0))
afcut1 = res - minus_cc_tot
afcut2 = afcut1 - minus_pix_tot
afcut3 = afcut2 - minus_ref_tot
print_msg("Particle distribution after cc cutoff:\t\t%s\n"%(afcut1))
print_msg("Particle distribution after pix cutoff:\t\t%s\n"%(afcut2))
print_msg("Particle distribution after ref cutoff:\t\t%s\n\n"%(afcut3))
res = [0.0 for i in xrange(nrefs)]
for iref in xrange(nrefs):
if(center == -1):
from utilities import estimate_3D_center_MPI, rotate_3D_shift
dummy=EMData()
cs[0], cs[1], cs[2], dummy, dummy = estimate_3D_center_MPI(data, total_nima, myid, number_of_proc, main_node)
cs = mpi_bcast(cs, 3, MPI_FLOAT, main_node, MPI_COMM_WORLD)
cs = [-float(cs[0]), -float(cs[1]), -float(cs[2])]
rotate_3D_shift(data, cs)
if(sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',transmulti[im][iref])
if(nrefs == 1):
modout = ""
else:
modout = "_model_%02d"%(iref)
fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index=group, npad=recon_pad)
if myid == main_node:
print_msg("3D reconstruction time for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
# Compute Fourier variance
if fourvar:
outvar = os.path.join(outdir, "volVar_%s.hdf"%(itout))
ssnr_file = os.path.join(outdir, "ssnr_%s"%(itout))
varf = varf3d_MPI(data, ssnr_text_file=ssnr_file, mask2D=None, reference_structure=vol[iref], ou=last_ring, rw=1.0, npad=1, CTF=None, sign=1, sym=sym, myid=myid)
if myid == main_node:
print_msg("Time to calculate 3D Fourier variance for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
varf = 1.0/varf
varf.write_image(outvar,-1)
else: varf = None
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep=None
if vertstep is not None:
vstep=(vdp[iref],vdphi[iref])
print_msg("Old rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
print_msg("New rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)
print_msg("Time to search and apply helical symmetry for model %i: %s\n\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
else:
vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%(itout)),-1)
if save_half is True:
volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%(itout)),-1)
voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%(itout)),-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
ref_data[4] = varf
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs,fl = ref_ali3d(ref_data)
vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res[iref] = fl
else:
res_msg = "Models filtered at resolution of: \t"
res[iref] = apix / fl
del varf
bcast_EMData_to_all(vol[iref], myid, main_node)
if compare_ref_free != "-1": compare_repro = True
if compare_repro:
outfile_repro = comp_rep(refrings, data, itout, modout, vol[iref], group, nima, nx, myid, main_node, outdir)
mpi_barrier(MPI_COMM_WORLD)
if compare_ref_free != "-1":
ref_free_output = os.path.join(outdir,"ref_free_%s%s"%(itout,modout))
rejects = compare(compare_ref_free, outfile_repro,ref_free_output,yrng[N_step], xrng[N_step], rstep,nx,apix,ref_free_cutoff[N_step], number_of_proc, myid, main_node)
# retrieve alignment params from all processors
par_str = ['xform.projection','ID','group']
if nrefs > 1:
for iref in xrange(nrefs):
par_str.append('eulers_txty.%i'%iref)
if myid == main_node:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else: send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node:
ares = array2string(array(res), precision = 2)
print_msg("%s%s\n\n"%(res_msg,ares))
dummy = EMData()
if full_output:
nimat = EMUtil.get_image_count(stack)
output_file = os.path.join(outdir, "paramout_%s"%itout)
foutput = open(output_file, 'w')
for im in xrange(nimat):
# save the parameters for each of the models
outstring = ""
dummy.read_image(stack,im,True)
param3d = dummy.get_attr('xform.projection')
g = dummy.get_attr("group")
# retrieve alignments in EMAN-format
pE = param3d.get_params('eman')
outstring += "%f\t%f\t%f\t%f\t%f\t%i\n" %(pE["az"], pE["alt"], pE["phi"], pE["tx"], pE["ty"],g)
foutput.write(outstring)
foutput.close()
del dummy
mpi_barrier(MPI_COMM_WORLD)
# mpi_finalize()
if myid == main_node: print_end_msg("ali3d_MPI")
def ali3d_MPI(stack,
ref_vol,
outdir,
maskfile=None,
ir=1,
ou=-1,
rs=1,
xr="4 2 2 1",
yr="-1",
ts="1 1 0.5 0.25",
delta="10 6 4 4",
an="-1",
center=0,
maxit=5,
term=95,
CTF=False,
fourvar=False,
snr=1.0,
ref_a="S",
sym="c1",
sort=True,
cutoff=999.99,
pix_cutoff="0",
two_tail=False,
model_jump="1 1 1 1 1",
restart=False,
save_half=False,
protos=None,
oplane=None,
lmask=-1,
ilmask=-1,
findseam=False,
vertstep=None,
hpars="-1",
hsearch="0.0 50.0",
full_output=False,
compare_repro=False,
compare_ref_free="-1",
ref_free_cutoff="-1 -1 -1 -1",
wcmask=None,
debug=False,
recon_pad=4,
olmask=75):
from alignment import Numrinit, prepare_refrings
from utilities import model_circle, get_image, drop_image, get_input_from_string
from utilities import bcast_list_to_all, bcast_number_to_all, reduce_EMData_to_root, bcast_EMData_to_all
from utilities import send_attr_dict
from utilities import get_params_proj, file_type
from fundamentals import rot_avg_image
import os
import types
from utilities import print_begin_msg, print_end_msg, print_msg
from mpi import mpi_bcast, mpi_comm_size, mpi_comm_rank, MPI_FLOAT, MPI_COMM_WORLD, mpi_barrier, mpi_reduce
from mpi import mpi_reduce, MPI_INT, MPI_SUM, mpi_finalize
from filter import filt_ctf
from projection import prep_vol, prgs
from statistics import hist_list, varf3d_MPI, fsc_mask
from numpy import array, bincount, array2string, ones
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
if myid == main_node:
if os.path.exists(outdir):
ERROR(
'Output directory exists, please change the name and restart the program',
"ali3d_MPI", 1)
os.mkdir(outdir)
mpi_barrier(MPI_COMM_WORLD)
if debug:
from time import sleep
while not os.path.exists(outdir):
print "Node ", myid, " waiting..."
sleep(5)
info_file = os.path.join(outdir, "progress%04d" % myid)
finfo = open(info_file, 'w')
else:
finfo = None
mjump = get_input_from_string(model_jump)
xrng = get_input_from_string(xr)
if yr == "-1": yrng = xrng
else: yrng = get_input_from_string(yr)
step = get_input_from_string(ts)
delta = get_input_from_string(delta)
ref_free_cutoff = get_input_from_string(ref_free_cutoff)
pix_cutoff = get_input_from_string(pix_cutoff)
lstp = min(len(xrng), len(yrng), len(step), len(delta))
if an == "-1":
an = [-1] * lstp
else:
an = get_input_from_string(an)
# make sure pix_cutoff is set for all iterations
if len(pix_cutoff) < lstp:
for i in xrange(len(pix_cutoff), lstp):
pix_cutoff.append(pix_cutoff[-1])
# don't waste time on sub-pixel alignment for low-resolution ang incr
for i in range(len(step)):
if (delta[i] > 4 or delta[i] == -1) and step[i] < 1:
step[i] = 1
first_ring = int(ir)
rstep = int(rs)
last_ring = int(ou)
max_iter = int(maxit)
center = int(center)
nrefs = EMUtil.get_image_count(ref_vol)
nmasks = 0
if maskfile:
# read number of masks within each maskfile (mc)
nmasks = EMUtil.get_image_count(maskfile)
# open masks within maskfile (mc)
maskF = EMData.read_images(maskfile, xrange(nmasks))
vol = EMData.read_images(ref_vol, xrange(nrefs))
nx = vol[0].get_xsize()
## make sure box sizes are the same
if myid == main_node:
im = EMData.read_images(stack, [0])
bx = im[0].get_xsize()
if bx != nx:
print_msg(
"Error: Stack box size (%i) differs from initial model (%i)\n"
% (bx, nx))
sys.exit()
del im, bx
# for helical processing:
helicalrecon = False
if protos is not None or hpars != "-1" or findseam is True:
helicalrecon = True
# if no out-of-plane param set, use 5 degrees
if oplane is None:
oplane = 5.0
if protos is not None:
proto = get_input_from_string(protos)
if len(proto) != nrefs:
print_msg("Error: insufficient protofilament numbers supplied")
sys.exit()
if hpars != "-1":
hpars = get_input_from_string(hpars)
if len(hpars) != 2 * nrefs:
print_msg("Error: insufficient helical parameters supplied")
sys.exit()
## create helical parameter file for helical reconstruction
if helicalrecon is True and myid == main_node:
from hfunctions import createHpar
# create initial helical parameter files
dp = [0] * nrefs
dphi = [0] * nrefs
vdp = [0] * nrefs
vdphi = [0] * nrefs
for iref in xrange(nrefs):
hpar = os.path.join(outdir, "hpar%02d.spi" % (iref))
params = False
if hpars != "-1":
# if helical parameters explicitly given, set twist & rise
params = [float(hpars[iref * 2]), float(hpars[(iref * 2) + 1])]
dp[iref], dphi[iref], vdp[iref], vdphi[iref] = createHpar(
hpar, proto[iref], params, vertstep)
# get values for helical search parameters
hsearch = get_input_from_string(hsearch)
if len(hsearch) != 2:
print_msg("Error: specify outer and inner radii for helical search")
sys.exit()
if last_ring < 0 or last_ring > int(nx / 2) - 2:
last_ring = int(nx / 2) - 2
if myid == main_node:
# import user_functions
# user_func = user_functions.factory[user_func_name]
print_begin_msg("ali3d_MPI")
print_msg("Input stack : %s\n" % (stack))
print_msg("Reference volume : %s\n" % (ref_vol))
print_msg("Output directory : %s\n" % (outdir))
if nmasks > 0:
print_msg("Maskfile (number of masks) : %s (%i)\n" %
(maskfile, nmasks))
print_msg("Inner radius : %i\n" % (first_ring))
print_msg("Outer radius : %i\n" % (last_ring))
print_msg("Ring step : %i\n" % (rstep))
print_msg("X search range : %s\n" % (xrng))
print_msg("Y search range : %s\n" % (yrng))
print_msg("Translational step : %s\n" % (step))
print_msg("Angular step : %s\n" % (delta))
print_msg("Angular search range : %s\n" % (an))
print_msg("Maximum iteration : %i\n" % (max_iter))
print_msg("Center type : %i\n" % (center))
print_msg("CTF correction : %s\n" % (CTF))
print_msg("Signal-to-Noise Ratio : %f\n" % (snr))
print_msg("Reference projection method : %s\n" % (ref_a))
print_msg("Symmetry group : %s\n" % (sym))
print_msg("Fourier padding for 3D : %i\n" % (recon_pad))
print_msg("Number of reference models : %i\n" % (nrefs))
print_msg("Sort images between models : %s\n" % (sort))
print_msg("Allow images to jump : %s\n" % (mjump))
print_msg("CC cutoff standard dev : %f\n" % (cutoff))
print_msg("Two tail cutoff : %s\n" % (two_tail))
print_msg("Termination pix error : %f\n" % (term))
print_msg("Pixel error cutoff : %s\n" % (pix_cutoff))
print_msg("Restart : %s\n" % (restart))
print_msg("Full output : %s\n" % (full_output))
print_msg("Compare reprojections : %s\n" % (compare_repro))
print_msg("Compare ref free class avgs : %s\n" % (compare_ref_free))
print_msg("Use cutoff from ref free : %s\n" % (ref_free_cutoff))
if protos:
print_msg("Protofilament numbers : %s\n" % (proto))
print_msg("Using helical search range : %s\n" % hsearch)
if findseam is True:
print_msg("Using seam-based reconstruction\n")
if hpars != "-1":
print_msg("Using hpars : %s\n" % hpars)
if vertstep != None:
print_msg("Using vertical step : %.2f\n" % vertstep)
if save_half is True:
print_msg("Saving even/odd halves\n")
for i in xrange(100):
print_msg("*")
print_msg("\n\n")
if maskfile:
if type(maskfile) is types.StringType: mask3D = get_image(maskfile)
else: mask3D = maskfile
else: mask3D = model_circle(last_ring, nx, nx, nx)
numr = Numrinit(first_ring, last_ring, rstep, "F")
mask2D = model_circle(last_ring, nx, nx) - model_circle(first_ring, nx, nx)
fscmask = model_circle(last_ring, nx, nx, nx)
if CTF:
from filter import filt_ctf
from reconstruction_rjh import rec3D_MPI_noCTF
if myid == main_node:
active = EMUtil.get_all_attributes(stack, 'active')
list_of_particles = []
for im in xrange(len(active)):
if active[im]: list_of_particles.append(im)
del active
nima = len(list_of_particles)
else:
nima = 0
total_nima = bcast_number_to_all(nima, source_node=main_node)
if myid != main_node:
list_of_particles = [-1] * total_nima
list_of_particles = bcast_list_to_all(list_of_particles,
source_node=main_node)
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
# create a list of images for each node
list_of_particles = list_of_particles[image_start:image_end]
nima = len(list_of_particles)
if debug:
finfo.write("image_start, image_end: %d %d\n" %
(image_start, image_end))
finfo.flush()
data = EMData.read_images(stack, list_of_particles)
t_zero = Transform({
"type": "spider",
"phi": 0,
"theta": 0,
"psi": 0,
"tx": 0,
"ty": 0
})
transmulti = [[t_zero for i in xrange(nrefs)] for j in xrange(nima)]
for iref, im in ((iref, im) for iref in xrange(nrefs)
for im in xrange(nima)):
if nrefs == 1:
transmulti[im][iref] = data[im].get_attr("xform.projection")
else:
# if multi models, keep track of eulers for all models
try:
transmulti[im][iref] = data[im].get_attr("eulers_txty.%i" %
iref)
except:
data[im].set_attr("eulers_txty.%i" % iref, t_zero)
scoremulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
pixelmulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
ref_res = [0.0 for x in xrange(nrefs)]
apix = data[0].get_attr('apix_x')
# for oplane parameter, create cylindrical mask
if oplane is not None and myid == main_node:
from hfunctions import createCylMask
cmaskf = os.path.join(outdir, "mask3D_cyl.mrc")
mask3D = createCylMask(data, olmask, lmask, ilmask, cmaskf)
# if finding seam of helix, create wedge masks
if findseam is True:
wedgemask = []
for pf in xrange(nrefs):
wedgemask.append(EMData())
# wedgemask option
if wcmask is not None:
wcmask = get_input_from_string(wcmask)
if len(wcmask) != 3:
print_msg(
"Error: wcmask option requires 3 values: x y radius")
sys.exit()
# determine if particles have helix info:
try:
data[0].get_attr('h_angle')
original_data = []
boxmask = True
from hfunctions import createBoxMask
except:
boxmask = False
# prepare particles
for im in xrange(nima):
data[im].set_attr('ID', list_of_particles[im])
data[im].set_attr('pix_score', int(0))
if CTF:
# only phaseflip particles, not full CTF correction
ctf_params = data[im].get_attr("ctf")
st = Util.infomask(data[im], mask2D, False)
data[im] -= st[0]
data[im] = filt_ctf(data[im], ctf_params, sign=-1, binary=1)
data[im].set_attr('ctf_applied', 1)
# for window mask:
if boxmask is True:
h_angle = data[im].get_attr("h_angle")
original_data.append(data[im].copy())
bmask = createBoxMask(nx, apix, ou, lmask, h_angle)
data[im] *= bmask
del bmask
if debug:
finfo.write('%d loaded \n' % nima)
finfo.flush()
if myid == main_node:
# initialize data for the reference preparation function
ref_data = [mask3D, max(center, 0), None, None, None, None]
# for method -1, switch off centering in user function
from time import time
# this is needed for gathering of pixel errors
disps = []
recvcount = []
disps_score = []
recvcount_score = []
for im in xrange(number_of_proc):
if (im == main_node):
disps.append(0)
disps_score.append(0)
else:
disps.append(disps[im - 1] + recvcount[im - 1])
disps_score.append(disps_score[im - 1] + recvcount_score[im - 1])
ib, ie = MPI_start_end(total_nima, number_of_proc, im)
recvcount.append(ie - ib)
recvcount_score.append((ie - ib) * nrefs)
pixer = [0.0] * nima
cs = [0.0] * 3
total_iter = 0
volodd = EMData.read_images(ref_vol, xrange(nrefs))
voleve = EMData.read_images(ref_vol, xrange(nrefs))
if restart:
# recreate initial volumes from alignments stored in header
itout = "000_00"
for iref in xrange(nrefs):
if (nrefs == 1):
modout = ""
else:
modout = "_model_%02d" % (iref)
if (sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',
transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection', transmulti[im][iref])
fscfile = os.path.join(outdir, "fsc_%s%s" % (itout, modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(
data,
sym,
fscmask,
fscfile,
myid,
main_node,
index=group,
npad=recon_pad)
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep = None
if vertstep is not None:
vstep = (vdp[iref], vdphi[iref])
print_msg(
"Old rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
hvals = processHelicalVol(vol[iref], voleve[iref],
volodd[iref], iref, outdir,
itout, dp[iref], dphi[iref],
apix, hsearch, findseam, vstep,
wcmask)
(vol[iref], voleve[iref], volodd[iref], dp[iref],
dphi[iref], vdp[iref], vdphi[iref]) = hvals
print_msg(
"New rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask(volodd[iref], voleve[iref], mask3D, rstep,
fscfile)
else:
vol[iref].write_image(
os.path.join(outdir, "vol_%s.hdf" % itout), -1)
if save_half is True:
volodd[iref].write_image(
os.path.join(outdir, "volodd_%s.hdf" % itout), -1)
voleve[iref].write_image(
os.path.join(outdir, "voleve_%s.hdf" % itout), -1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs, fl = ref_ali3d(ref_data)
vol[iref].write_image(
os.path.join(outdir, "volf_%s.hdf" % (itout)), -1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res = fl
else:
res_msg = "Models filtered at resolution of: \t"
res = apix / fl
ares = array2string(array(res), precision=2)
print_msg("%s%s\n\n" % (res_msg, ares))
bcast_EMData_to_all(vol[iref], myid, main_node)
# write out headers, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
# projection matching
for N_step in xrange(lstp):
terminate = 0
Iter = -1
while (Iter < max_iter - 1 and terminate == 0):
Iter += 1
total_iter += 1
itout = "%03g_%02d" % (delta[N_step], Iter)
if myid == main_node:
print_msg(
"ITERATION #%3d, inner iteration #%3d\nDelta = %4.1f, an = %5.2f, xrange = %5.2f, yrange = %5.2f, step = %5.2f\n\n"
% (N_step, Iter, delta[N_step], an[N_step], xrng[N_step],
yrng[N_step], step[N_step]))
for iref in xrange(nrefs):
if myid == main_node: start_time = time()
volft, kb = prep_vol(vol[iref])
## constrain projections to out of plane parameter
theta1 = None
theta2 = None
if oplane is not None:
theta1 = 90 - oplane
theta2 = 90 + oplane
refrings = prepare_refrings(volft,
kb,
nx,
delta[N_step],
ref_a,
sym,
numr,
MPI=True,
phiEqpsi="Minus",
initial_theta=theta1,
delta_theta=theta2)
del volft, kb
if myid == main_node:
print_msg(
"Time to prepare projections for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
for im in xrange(nima):
data[im].set_attr("xform.projection", transmulti[im][iref])
if an[N_step] == -1:
t1, peak, pixer[im] = proj_ali_incore(
data[im], refrings, numr, xrng[N_step],
yrng[N_step], step[N_step], finfo)
else:
t1, peak, pixer[im] = proj_ali_incore_local(
data[im], refrings, numr, xrng[N_step],
yrng[N_step], step[N_step], an[N_step], finfo)
#data[im].set_attr("xform.projection"%iref, t1)
if nrefs > 1:
data[im].set_attr("eulers_txty.%i" % iref, t1)
scoremulti[im][iref] = peak
from pixel_error import max_3D_pixel_error
# t1 is the current param, t2 is old
t2 = transmulti[im][iref]
pixelmulti[im][iref] = max_3D_pixel_error(t1, t2, numr[-3])
transmulti[im][iref] = t1
if myid == main_node:
print_msg("Time of alignment for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
# gather scoring data from all processors
from mpi import mpi_gatherv
scoremultisend = sum(scoremulti, [])
pixelmultisend = sum(pixelmulti, [])
tmp = mpi_gatherv(scoremultisend, len(scoremultisend), MPI_FLOAT,
recvcount_score, disps_score, MPI_FLOAT,
main_node, MPI_COMM_WORLD)
tmp1 = mpi_gatherv(pixelmultisend, len(pixelmultisend), MPI_FLOAT,
recvcount_score, disps_score, MPI_FLOAT,
main_node, MPI_COMM_WORLD)
tmp = mpi_bcast(tmp, (total_nima * nrefs), MPI_FLOAT, 0,
MPI_COMM_WORLD)
tmp1 = mpi_bcast(tmp1, (total_nima * nrefs), MPI_FLOAT, 0,
MPI_COMM_WORLD)
tmp = map(float, tmp)
tmp1 = map(float, tmp1)
score = array(tmp).reshape(-1, nrefs)
pixelerror = array(tmp1).reshape(-1, nrefs)
score_local = array(scoremulti)
mean_score = score.mean(axis=0)
std_score = score.std(axis=0)
cut = mean_score - (cutoff * std_score)
cut2 = mean_score + (cutoff * std_score)
res_max = score_local.argmax(axis=1)
minus_cc = [0.0 for x in xrange(nrefs)]
minus_pix = [0.0 for x in xrange(nrefs)]
minus_ref = [0.0 for x in xrange(nrefs)]
#output pixel errors
if (myid == main_node):
from statistics import hist_list
lhist = 20
pixmin = pixelerror.min(axis=1)
region, histo = hist_list(pixmin, lhist)
if (region[0] < 0.0): region[0] = 0.0
print_msg(
"Histogram of pixel errors\n ERROR number of particles\n"
)
for lhx in xrange(lhist):
print_msg(" %10.3f %7d\n" % (region[lhx], histo[lhx]))
# Terminate if 95% within 1 pixel error
im = 0
for lhx in xrange(lhist):
if (region[lhx] > 1.0): break
im += histo[lhx]
print_msg("Percent of particles with pixel error < 1: %f\n\n" %
(im / float(total_nima) * 100))
term_cond = float(term) / 100
if (im / float(total_nima) > term_cond):
terminate = 1
print_msg("Terminating internal loop\n")
del region, histo
terminate = mpi_bcast(terminate, 1, MPI_INT, 0, MPI_COMM_WORLD)
terminate = int(terminate[0])
for im in xrange(nima):
if (sort == False):
data[im].set_attr('group', 999)
elif (mjump[N_step] == 1):
data[im].set_attr('group', int(res_max[im]))
pix_run = data[im].get_attr('pix_score')
if (pix_cutoff[N_step] == 1
and (terminate == 1 or Iter == max_iter - 1)):
if (pixelmulti[im][int(res_max[im])] > 1):
data[im].set_attr('pix_score', int(777))
if (score_local[im][int(res_max[im])] < cut[int(
res_max[im])]) or (two_tail and score_local[im][int(
res_max[im])] > cut2[int(res_max[im])]):
data[im].set_attr('group', int(888))
minus_cc[int(res_max[im])] = minus_cc[int(res_max[im])] + 1
if (pix_run == 777):
data[im].set_attr('group', int(777))
minus_pix[int(
res_max[im])] = minus_pix[int(res_max[im])] + 1
if (compare_ref_free != "-1") and (ref_free_cutoff[N_step] !=
-1) and (total_iter > 1):
id = data[im].get_attr('ID')
if id in rejects:
data[im].set_attr('group', int(666))
minus_ref[int(
res_max[im])] = minus_ref[int(res_max[im])] + 1
minus_cc_tot = mpi_reduce(minus_cc, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
minus_pix_tot = mpi_reduce(minus_pix, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
minus_ref_tot = mpi_reduce(minus_ref, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
if (myid == main_node):
if (sort):
tot_max = score.argmax(axis=1)
res = bincount(tot_max)
else:
res = ones(nrefs) * total_nima
print_msg("Particle distribution: \t\t%s\n" % (res * 1.0))
afcut1 = res - minus_cc_tot
afcut2 = afcut1 - minus_pix_tot
afcut3 = afcut2 - minus_ref_tot
print_msg("Particle distribution after cc cutoff:\t\t%s\n" %
(afcut1))
print_msg("Particle distribution after pix cutoff:\t\t%s\n" %
(afcut2))
print_msg("Particle distribution after ref cutoff:\t\t%s\n\n" %
(afcut3))
res = [0.0 for i in xrange(nrefs)]
for iref in xrange(nrefs):
if (center == -1):
from utilities import estimate_3D_center_MPI, rotate_3D_shift
dummy = EMData()
cs[0], cs[1], cs[2], dummy, dummy = estimate_3D_center_MPI(
data, total_nima, myid, number_of_proc, main_node)
cs = mpi_bcast(cs, 3, MPI_FLOAT, main_node, MPI_COMM_WORLD)
cs = [-float(cs[0]), -float(cs[1]), -float(cs[2])]
rotate_3D_shift(data, cs)
if (sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',
transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',
transmulti[im][iref])
if (nrefs == 1):
modout = ""
else:
modout = "_model_%02d" % (iref)
fscfile = os.path.join(outdir, "fsc_%s%s" % (itout, modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(
data,
sym,
fscmask,
fscfile,
myid,
main_node,
index=group,
npad=recon_pad)
if myid == main_node:
print_msg("3D reconstruction time for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
# Compute Fourier variance
if fourvar:
outvar = os.path.join(outdir, "volVar_%s.hdf" % (itout))
ssnr_file = os.path.join(outdir, "ssnr_%s" % (itout))
varf = varf3d_MPI(data,
ssnr_text_file=ssnr_file,
mask2D=None,
reference_structure=vol[iref],
ou=last_ring,
rw=1.0,
npad=1,
CTF=None,
sign=1,
sym=sym,
myid=myid)
if myid == main_node:
print_msg(
"Time to calculate 3D Fourier variance for model %i: %s\n"
% (iref, legibleTime(time() - start_time)))
start_time = time()
varf = 1.0 / varf
varf.write_image(outvar, -1)
else:
varf = None
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep = None
if vertstep is not None:
vstep = (vdp[iref], vdphi[iref])
print_msg(
"Old rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
hvals = processHelicalVol(vol[iref], voleve[iref],
volodd[iref], iref, outdir,
itout, dp[iref], dphi[iref],
apix, hsearch, findseam,
vstep, wcmask)
(vol[iref], voleve[iref], volodd[iref], dp[iref],
dphi[iref], vdp[iref], vdphi[iref]) = hvals
print_msg(
"New rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask(volodd[iref], voleve[iref], mask3D,
rstep, fscfile)
print_msg(
"Time to search and apply helical symmetry for model %i: %s\n\n"
% (iref, legibleTime(time() - start_time)))
start_time = time()
else:
vol[iref].write_image(
os.path.join(outdir, "vol_%s.hdf" % (itout)), -1)
if save_half is True:
volodd[iref].write_image(
os.path.join(outdir, "volodd_%s.hdf" % (itout)),
-1)
voleve[iref].write_image(
os.path.join(outdir, "voleve_%s.hdf" % (itout)),
-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
ref_data[4] = varf
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs, fl = ref_ali3d(ref_data)
vol[iref].write_image(
os.path.join(outdir, "volf_%s.hdf" % (itout)), -1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res[iref] = fl
else:
res_msg = "Models filtered at resolution of: \t"
res[iref] = apix / fl
del varf
bcast_EMData_to_all(vol[iref], myid, main_node)
if compare_ref_free != "-1": compare_repro = True
if compare_repro:
outfile_repro = comp_rep(refrings, data, itout, modout,
vol[iref], group, nima, nx, myid,
main_node, outdir)
mpi_barrier(MPI_COMM_WORLD)
if compare_ref_free != "-1":
ref_free_output = os.path.join(
outdir, "ref_free_%s%s" % (itout, modout))
rejects = compare(compare_ref_free, outfile_repro,
ref_free_output, yrng[N_step],
xrng[N_step], rstep, nx, apix,
ref_free_cutoff[N_step],
number_of_proc, myid, main_node)
# retrieve alignment params from all processors
par_str = ['xform.projection', 'ID', 'group']
if nrefs > 1:
for iref in xrange(nrefs):
par_str.append('eulers_txty.%i' % iref)
if myid == main_node:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
send_attr_dict(main_node, data, par_str, image_start,
image_end)
if myid == main_node:
ares = array2string(array(res), precision=2)
print_msg("%s%s\n\n" % (res_msg, ares))
dummy = EMData()
if full_output:
nimat = EMUtil.get_image_count(stack)
output_file = os.path.join(outdir, "paramout_%s" % itout)
foutput = open(output_file, 'w')
for im in xrange(nimat):
# save the parameters for each of the models
outstring = ""
dummy.read_image(stack, im, True)
param3d = dummy.get_attr('xform.projection')
g = dummy.get_attr("group")
# retrieve alignments in EMAN-format
pE = param3d.get_params('eman')
outstring += "%f\t%f\t%f\t%f\t%f\t%i\n" % (
pE["az"], pE["alt"], pE["phi"], pE["tx"], pE["ty"],
g)
foutput.write(outstring)
foutput.close()
del dummy
mpi_barrier(MPI_COMM_WORLD)
# mpi_finalize()
if myid == main_node: print_end_msg("ali3d_MPI")
def shiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1, oneDx=False, search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
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
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter=int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node = main_node)
list_of_particles = range(nima)
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start: image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node = main_node)
ny = bcast_number_to_all(ny, source_node = main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node = main_node)
if ctf_app > 0: ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad//2-2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from 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
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in xrange(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in xrange(len(data)):
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")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else: ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in xrange(0,nx,2):
for j in xrange(ny):
temp.set_value_at(i,j,snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in xrange(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], sx=p['tx'], sy=p['ty'], mirror=p['mirror'], scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in xrange(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum=0
sy_sum=0
sx_sum_total=0
sy_sum_total=0
shift_x = [0.0]*len(data)
shift_y = [0.0]*len(data)
ishift_x = [0.0]*len(data)
ishift_y = [0.0]*len(data)
for Iter in xrange(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 data: Util.add_img(avg, 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 = EMData(nx, ny, 1, False)
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)
# 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)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum=0
sy_sum=0
if search_rng > 0: nwx = 2*search_rng+1
else: nwx = nx
if search_rng_y > 0: nwy = 2*search_rng_y+1
else: nwy = ny
not_zero = 0
for im in xrange(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im],tavg),nwx,1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im],tavg), nwx,nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not(ishift_x[im] == 0.0)) or (not(ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node = main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total, source_node = main_node)
sx_ave = round(float(sx_sum_total)/nima)
sy_ave = round(float(sy_sum_total)/nima)
for im in xrange(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : p1_x, "y_shift" : p1_y, "z_shift" : 0.0}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node = main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n"%(time()-start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in xrange(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({"type":"2D","alpha":0,"scale":t0.get_scale(),"mirror":0,"tx":shift_x[im],"ty":shift_y[im]})
# combine t0 and t1
tt = t1*t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if(file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else: send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def helicalshiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im, pad
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D, fshift
from utilities import get_params2D, set_params2D, chunks_distribution
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
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
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from time import time
from pixel_error import ordersegments
from math import sqrt, atan2, tan, pi
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(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 xrange(total_nfils): inidl[i] = len(filaments[i])
linidl = sum(inidl)
nima = linidl
tfilaments = []
for i in xrange(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 xrange(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), "ehelix_MPI", 1,myid)
# balanced load
temp = chunks_distribution([[len(filaments[i]), i] for i in xrange(len(filaments))], nproc)[myid:myid+1][0]
filaments = [filaments[temp[i][1]] for i in xrange(len(temp))]
nfils = len(filaments)
#filaments = [[0,1]]
#print "filaments",filaments
list_of_particles = []
indcs = []
k = 0
for i in xrange(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)
print "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 xrange(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 filter import filt_ctf
from 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 utilities import info
for im in xrange(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', "helicalshiftali_MPI", 1,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 xrange(0,nx+2,2):
for j in xrange(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 xrange(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 xrange(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 xrange(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 xrange(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 xrange(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_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node, 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 xrange(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 xrange(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_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if(file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata, nproc)
else:
from 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")
