def main():
"""
Main function.
Arguments:
None
Returns:
None
"""
command_args = parse_command_line()
# Import volume
sp_global_def.sxprint("Import volume.")
input_vol = sp_utilities.get_im(command_args.input_volume)
# Sanity checks
sanity_checks(command_args, input_vol)
try:
os.makedirs(command_args.output_dir)
except OSError:
sp_global_def.sxprint(
"Output directory already exists. No need to create it.")
else:
sp_global_def.sxprint("Created output directory.")
sp_global_def.write_command(command_args.output_dir)
output_prefix = os.path.join(command_args.output_dir, command_args.prefix)
# Filter volume if specified
if command_args.low_pass_filter_resolution is not None:
sp_global_def.sxprint("Filter volume to {0}A.".format(
command_args.low_pass_filter_resolution))
input_vol = sp_filter.filt_tanl(
input_vol,
old_div(command_args.pixel_size,
command_args.low_pass_filter_resolution),
command_args.low_pass_filter_falloff,
)
input_vol.write_image(output_prefix + "_filtered_volume.hdf")
else:
sp_global_def.sxprint("Skip filter volume.")
# Create a mask based on the filtered volume
sp_global_def.sxprint("Create mask")
density_threshold = -9999.0
nsigma = 1.0
if command_args.mol_mass:
density_threshold = input_vol.find_3d_threshold(
command_args.mol_mass, command_args.pixel_size)
sp_global_def.sxprint(
"Mask molecular mass translated into binary threshold: ",
density_threshold)
elif command_args.threshold:
density_threshold = command_args.threshold
elif command_args.nsigma:
nsigma = command_args.nsigma
else:
assert False
if command_args.edge_type == "cosine":
mode = "C"
elif command_args.edge_type == "gaussian":
mode = "G"
else:
assert False
mask_first = sp_morphology.adaptive_mask_scipy(
input_vol,
nsigma=nsigma,
threshold=density_threshold,
ndilation=command_args.ndilation,
nerosion=command_args.nerosion,
edge_width=command_args.edge_width,
allow_disconnected=command_args.allow_disconnected,
mode=mode,
do_approx=command_args.do_old,
do_fill=command_args.fill_mask,
do_print=True,
)
# Create a second mask based on the filtered volume
s_mask = None
s_density_threshold = 1
s_nsigma = 1.0
if command_args.second_mask is not None:
sp_global_def.sxprint("Prepare second mask")
s_mask = sp_utilities.get_im(command_args.second_mask)
s_density_threshold = -9999.0
s_nsigma = 1.0
if command_args.s_mol_mass:
s_density_threshold = input_vol.find_3d_threshold(
command_args.s_mol_mass, command_args.s_pixel_size)
sp_global_def.sxprint(
"Second mask molecular mass translated into binary threshold: ",
s_density_threshold,
)
elif command_args.s_threshold:
s_density_threshold = command_args.s_threshold
elif command_args.s_nsigma:
s_nsigma = command_args.s_nsigma
else:
assert False
elif command_args.second_mask_shape is not None:
sp_global_def.sxprint("Prepare second mask")
nx = mask_first.get_xsize()
ny = mask_first.get_ysize()
nz = mask_first.get_zsize()
if command_args.second_mask_shape == "cube":
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
s_mask = sp_utilities.model_blank(s_nx, s_ny, s_nz, 1)
elif command_args.second_mask_shape == "cylinder":
s_radius = command_args.s_radius
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
try:
s_mask = sp_utilities.model_cylinder(s_radius, s_nx, s_ny,
s_nz)
except RuntimeError as e:
sp_global_def.sxprint(
"An error occured! Please check the error log")
raise
elif command_args.second_mask_shape == "sphere":
s_radius = command_args.s_radius
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
try:
s_mask = sp_utilities.model_circle(s_radius, s_nx, s_ny, s_nz)
except RuntimeError as e:
sp_global_def.sxprint(
"An error occured! Please check the error log")
raise
else:
assert False
s_mask = sp_utilities.pad(s_mask, nx, ny, nz, 0)
if s_mask is not None:
sp_global_def.sxprint("Create second mask")
if command_args.s_edge_type == "cosine":
mode = "C"
elif command_args.s_edge_type == "gaussian":
mode = "G"
else:
assert False
s_mask = sp_morphology.adaptive_mask_scipy(
s_mask,
nsigma=s_nsigma,
threshold=s_density_threshold,
ndilation=command_args.s_ndilation,
nerosion=command_args.s_nerosion,
edge_width=command_args.s_edge_width,
allow_disconnected=command_args.s_allow_disconnected,
mode=mode,
do_approx=command_args.s_do_old,
do_fill=command_args.s_fill_mask,
do_print=True,
)
if command_args.s_invert:
s_mask = 1 - s_mask
sp_global_def.sxprint("Write outputs.")
mask_first.write_image(output_prefix + "_mask_first.hdf")
s_mask.write_image(output_prefix + "_mask_second.hdf")
masked_combined = mask_first * s_mask
masked_combined.write_image(output_prefix + "_mask.hdf")
else:
sp_global_def.sxprint("Write outputs.")
mask_first.write_image(output_prefix + "_mask.hdf")
def helicalshiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1):
nproc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("helical-shiftali_MPI")
max_iter = int(maxit)
if (myid == main_node):
infils = EMUtil.get_all_attributes(stack, "filament")
ptlcoords = EMUtil.get_all_attributes(stack, 'ptcl_source_coord')
filaments = ordersegments(infils, ptlcoords)
total_nfils = len(filaments)
inidl = [0] * total_nfils
for i in range(total_nfils):
inidl[i] = len(filaments[i])
linidl = sum(inidl)
nima = linidl
tfilaments = []
for i in range(total_nfils):
tfilaments += filaments[i]
del filaments
else:
total_nfils = 0
linidl = 0
total_nfils = bcast_number_to_all(total_nfils, source_node=main_node)
if myid != main_node:
inidl = [-1] * total_nfils
inidl = bcast_list_to_all(inidl, myid, source_node=main_node)
linidl = bcast_number_to_all(linidl, source_node=main_node)
if myid != main_node:
tfilaments = [-1] * linidl
tfilaments = bcast_list_to_all(tfilaments, myid, source_node=main_node)
filaments = []
iendi = 0
for i in range(total_nfils):
isti = iendi
iendi = isti + inidl[i]
filaments.append(tfilaments[isti:iendi])
del tfilaments, inidl
if myid == main_node:
print_msg("total number of filaments: %d" % total_nfils)
if total_nfils < nproc:
ERROR(
'number of CPUs (%i) is larger than the number of filaments (%i), please reduce the number of CPUs used'
% (nproc, total_nfils),
myid=myid)
# balanced load
temp = chunks_distribution([[len(filaments[i]), i]
for i in range(len(filaments))],
nproc)[myid:myid + 1][0]
filaments = [filaments[temp[i][1]] for i in range(len(temp))]
nfils = len(filaments)
#filaments = [[0,1]]
#print "filaments",filaments
list_of_particles = []
indcs = []
k = 0
for i in range(nfils):
list_of_particles += filaments[i]
k1 = k + len(filaments[i])
indcs.append([k, k1])
k = k1
data = EMData.read_images(stack, list_of_particles)
ldata = len(data)
sxprint("ldata=", ldata)
nx = data[0].get_xsize()
ny = data[0].get_ysize()
if maskfile == None:
mrad = min(nx, ny) // 2 - 2
mask = pad(model_blank(2 * mrad + 1, ny, 1, 1.0), nx, ny, 1, 0.0)
else:
mask = get_im(maskfile)
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(ldata):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], p['tx'], p['ty'],
p['mirror'], p['scale'])
if CTF:
from sp_filter import filt_ctf
from sp_morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
ctf_abs_sum = None
from sp_utilities import info
for im in range(ldata):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
qctf = data[im].get_attr("ctf_applied")
if qctf == 0:
data[im] = filt_ctf(fft(data[im]), ctf_params)
data[im].set_attr('ctf_applied', 1)
elif qctf != 1:
ERROR('Incorrectly set qctf flag', myid=myid)
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
else:
data[im] = fft(data[im])
del list_of_particles
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
tsnr = 1. / snr
for i in range(0, nx + 2, 2):
for j in range(ny):
temp.set_value_at(i, j, tsnr)
temp.set_value_at(i + 1, j, 0.0)
#info(ctf_2_sum)
Util.add_img(ctf_2_sum, temp)
#info(ctf_2_sum)
del temp
total_iter = 0
shift_x = [0.0] * ldata
for Iter in range(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n" % (total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in range(ldata):
Util.add_img(avg, fshift(data[im], shift_x[im]))
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF: tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0 / float(nima))
else:
tavg = model_blank(nx, ny)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask(tavg, mask, False)
tavg -= stat[0]
Util.mul_img(tavg, mask)
tavg.write_image("tavg.hdf", Iter)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
tavg = fft(tavg)
sx_sum = 0.0
nxc = nx // 2
for ifil in range(nfils):
"""
# Calculate filament average
avg = EMData(nx, ny, 1, False)
filnima = 0
for im in xrange(indcs[ifil][0], indcs[ifil][1]):
Util.add_img(avg, data[im])
filnima += 1
tavg = Util.mult_scalar(avg, 1.0/float(filnima))
"""
# Calculate 1D ccf between each segment and filament average
nsegms = indcs[ifil][1] - indcs[ifil][0]
ctx = [None] * nsegms
pcoords = [None] * nsegms
for im in range(indcs[ifil][0], indcs[ifil][1]):
ctx[im - indcs[ifil][0]] = Util.window(ccf(tavg, data[im]), nx,
1)
pcoords[im - indcs[ifil][0]] = data[im].get_attr(
'ptcl_source_coord')
#ctx[im-indcs[ifil][0]].write_image("ctx.hdf",im-indcs[ifil][0])
#print " CTX ",myid,im,Util.infomask(ctx[im-indcs[ifil][0]], None, True)
# search for best x-shift
cents = nsegms // 2
dst = sqrt(
max((pcoords[cents][0] - pcoords[0][0])**2 +
(pcoords[cents][1] - pcoords[0][1])**2,
(pcoords[cents][0] - pcoords[-1][0])**2 +
(pcoords[cents][1] - pcoords[-1][1])**2))
maxincline = atan2(ny // 2 - 2 - float(search_rng), dst)
kang = int(dst * tan(maxincline) + 0.5)
#print " settings ",nsegms,cents,dst,search_rng,maxincline,kang
# ## C code for alignment. @ming
results = [0.0] * 3
results = Util.helixshiftali(ctx, pcoords, nsegms, maxincline,
kang, search_rng, nxc)
sib = int(results[0])
bang = results[1]
qm = results[2]
#print qm, sib, bang
# qm = -1.e23
#
# for six in xrange(-search_rng, search_rng+1,1):
# q0 = ctx[cents].get_value_at(six+nxc)
# for incline in xrange(kang+1):
# qt = q0
# qu = q0
# if(kang>0): tang = tan(maxincline/kang*incline)
# else: tang = 0.0
# for kim in xrange(cents+1,nsegms):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# #print " A ", ifil,six,incline,kim,xl,ixl,dxl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# for kim in xrange(cents):
# dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# xl = -dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# xl = dst*tang+six+nxc
# ixl = int(xl)
# dxl = xl - ixl
# qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# if( qt > qm ):
# qm = qt
# sib = six
# bang = tang
# if( qu > qm ):
# qm = qu
# sib = six
# bang = -tang
#if incline == 0: print "incline = 0 ",six,tang,qt,qu
#print qm,six,sib,bang
#print " got results ",indcs[ifil][0], indcs[ifil][1], ifil,myid,qm,sib,tang,bang,len(ctx),Util.infomask(ctx[0], None, True)
for im in range(indcs[ifil][0], indcs[ifil][1]):
kim = im - indcs[ifil][0]
dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 +
(pcoords[cents][1] - pcoords[kim][1])**2)
if (kim < cents): xl = -dst * bang + sib
else: xl = dst * bang + sib
shift_x[im] = xl
# Average shift
sx_sum += shift_x[indcs[ifil][0] + cents]
# #print myid,sx_sum,total_nfils
sx_sum = mpi.mpi_reduce(sx_sum, 1, mpi.MPI_FLOAT, mpi.MPI_SUM,
main_node, mpi.MPI_COMM_WORLD)
if myid == main_node:
sx_sum = float(sx_sum[0]) / total_nfils
print_msg("Average shift %6.2f\n" % (sx_sum))
else:
sx_sum = 0.0
sx_sum = 0.0
sx_sum = bcast_number_to_all(sx_sum, source_node=main_node)
for im in range(ldata):
shift_x[im] -= sx_sum
#print " %3d %6.3f"%(im,shift_x[im])
#exit()
# combine shifts found with the original parameters
for im in range(ldata):
t1 = Transform()
##import random
##shix=random.randint(-10, 10)
##t1.set_params({"type":"2D","tx":shix})
t1.set_params({"type": "2D", "tx": shift_x[im]})
# combine t0 and t1
tt = t1 * init_params[im]
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi.mpi_barrier(mpi.MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from sp_utilities import file_type
if (file_type(stack) == "bdb"):
from sp_utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata,
nproc)
else:
from sp_utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, 0, ldata, nproc)
else:
send_attr_dict(main_node, data, par_str, 0, ldata)
if myid == main_node: print_end_msg("helical-shiftali_MPI")
def generate_helimic(refvol,
outdir,
pixel,
CTF=False,
Cs=2.0,
voltage=200.0,
ampcont=10.0,
nonoise=False,
rand_seed=14567):
from sp_utilities import model_blank, model_gauss, model_gauss_noise, pad, get_im
from random import random
from sp_projection import prgs, prep_vol
from sp_filter import filt_gaussl, filt_ctf
from EMAN2 import EMAN2Ctf
if os.path.exists(outdir):
ERROR(
"Output directory exists, please change the name and restart the program"
)
return
os.mkdir(outdir)
seed(rand_seed)
Util.set_randnum_seed(rand_seed)
angles = []
for i in range(3):
angles.append([0.0 + 60.0 * i, 90.0 - i * 5, 0.0, 0.0, 0.0])
nangle = len(angles)
volfts = get_im(refvol)
nx = volfts.get_xsize()
ny = volfts.get_ysize()
nz = volfts.get_zsize()
volfts, kbx, kby, kbz = prep_vol(volfts)
iprj = 0
width = 500
xstart = 0
ystart = 0
for idef in range(3, 6):
mic = model_blank(2048, 2048)
#defocus = idef*0.2
defocus = idef * 0.6 ##@ming
if CTF:
#ctf = EMAN2Ctf()
#ctf.from_dict( {"defocus":defocus, "cs":Cs, "voltage":voltage, "apix":pixel, "ampcont":ampcont, "bfactor":0.0} )
from sp_utilities import generate_ctf
ctf = generate_ctf(
[defocus, 2, 200, 1.84, 0.0, ampcont, defocus * 0.2, 80]
) ##@ming the range of astigmatism amplitude is between 10 percent and 22 percent. 20 percent is a good choice.
i = idef - 4
for k in range(1):
psi = 90 + 10 * i
proj = prgs(
volfts, kbz,
[angles[idef - 3][0], angles[idef - 3][1], psi, 0.0, 0.0], kbx,
kby)
proj = Util.window(proj, 320, nz)
mic += pad(proj, 2048, 2048, 1, 0.0, 750 * i, 20 * i, 0)
if not nonoise: mic += model_gauss_noise(30.0, 2048, 2048)
if CTF:
#apply CTF
mic = filt_ctf(mic, ctf)
if not nonoise:
mic += filt_gaussl(model_gauss_noise(17.5, 2048, 2048), 0.3)
mic.write_image("%s/mic%1d.hdf" % (outdir, idef - 3), 0)
def main():
progname = os.path.basename(sys.argv[0])
usage = progname + """ Input Output [options]
Generate three micrographs, each micrograph contains one projection of a long filament.
Input: Reference Volume, output directory
Output: Three micrographs stored in output directory
sxhelical_demo.py tmp.hdf mic --generate_micrograph --CTF --apix=1.84
Generate noisy cylinder ini.hdf with radius 35 pixels and box size 100 by 100 by 200
sxhelical_demo.py ini.hdf --generate_noisycyl --boxsize="100,100,200" --rad=35
Generate rectangular 2D mask mask2d.hdf with width 60 pixels and image size 200 by 200 pixels
sxhelical_demo.py mask2d.hdf --generate_mask --masksize="200,200" --maskwidth=60
Apply the centering parameters to bdb:adata, normalize using average and standard deviation outside the mask, and output the new images to bdb:data
sxhelical_demo.py bdb:adata bdb:data mask2d.hdf --applyparams
Generate run through example script for helicon
sxhelical_demo.py --generate_script --filename=run --seg_ny=180 --ptcl_dist=15 --fract=0.35
"""
parser = OptionParser(usage, version=SPARXVERSION)
# helicise the Atom coordinates
# generate micrographs of helical filament
parser.add_option(
"--generate_micrograph",
action="store_true",
default=False,
help=
"Generate three micrographs where each micrograph contains one projection of a long filament. \n Input: Reference Volume, output directory \n Output: Three micrographs containing helical filament projections stored in output directory"
)
parser.add_option("--CTF",
action="store_true",
default=False,
help="Use CTF correction")
parser.add_option("--apix",
type="float",
default=-1,
help="pixel size in Angstroms")
parser.add_option(
"--rand_seed",
type="int",
default=14567,
help=
"the seed used for generating random numbers (default 14567) for adding noise to the generated micrographs."
)
parser.add_option("--Cs",
type="float",
default=2.0,
help="Microscope Cs (spherical aberation)")
parser.add_option("--voltage",
type="float",
default=200.0,
help="Microscope voltage in KV")
parser.add_option("--ac",
type="float",
default=10.0,
help="Amplitude contrast (percentage, default=10)")
parser.add_option("--nonoise",
action="store_true",
default=False,
help="Do not add noise to the micrograph.")
# generate initial volume
parser.add_option("--generate_noisycyl",
action="store_true",
default=False,
help="Generate initial volume of noisy cylinder.")
parser.add_option(
"--boxsize",
type="string",
default="100,100,200",
help=
"String containing x , y, z dimensions (separated by comma) in pixels")
parser.add_option("--rad",
type="int",
default=35,
help="Radius of initial volume in pixels")
# generate 2D mask
parser.add_option("--generate_mask",
action="store_true",
default=False,
help="Generate 2D rectangular mask.")
parser.add_option(
"--masksize",
type="string",
default="200,200",
help=
"String containing x and y dimensions (separated by comma) in pixels")
parser.add_option("--maskwidth",
type="int",
default=60,
help="Width of rectangular mask")
# Apply 2D alignment parameters to input stack and output new images to output stack
parser.add_option(
"--applyparams",
action="store_true",
default=False,
help=
"Apply the centering parameters to input stack, normalize using average and standard deviation outside the mask, and output the new images to output stack"
)
# Generate run script
parser.add_option("--generate_script",
action="store_true",
default=False,
help="Generate script for helicon run through example")
parser.add_option("--filename",
type="string",
default="runhelicon",
help="Name of run script to generate")
parser.add_option("--seg_ny",
type="int",
default=180,
help="y-dimension of segment used for refinement")
parser.add_option(
"--ptcl_dist",
type="int",
default=15,
help=
"Distance in pixels between adjacent segments windowed from same filament"
)
parser.add_option(
"--fract",
type="float",
default=0.35,
help="Fraction of the volume used for applying helical symmetry.")
(options, args) = parser.parse_args()
if len(args) > 3:
sxprint("usage: " + usage)
sxprint("Please run '" + progname + " -h' for detailed options")
ERROR(
"Invalid number of parameters. Please see usage information above."
)
return
else:
if options.generate_script:
generate_runscript(options.filename, options.seg_ny,
options.ptcl_dist, options.fract)
if options.generate_micrograph:
if options.apix <= 0:
ERROR("Please enter pixel size.")
return
generate_helimic(args[0], args[1], options.apix, options.CTF,
options.Cs, options.voltage, options.ac,
options.nonoise, options.rand_seed)
if options.generate_noisycyl:
from sp_utilities import model_cylinder, model_gauss_noise
outvol = args[0]
boxdims = options.boxsize.split(',')
if len(boxdims) < 1 or len(boxdims) > 3:
ERROR(
"Enter box size as string containing x , y, z dimensions (separated by comma) in pixels. E.g.: --boxsize=\'100,100,200\'"
)
return
nx = int(boxdims[0])
if len(boxdims) == 1:
ny = nx
nz = nx
else:
ny = int(boxdims[1])
if len(boxdims) == 3:
nz = int(boxdims[2])
(model_cylinder(options.rad, nx, ny, nz) *
model_gauss_noise(1.0, nx, ny, nz)).write_image(outvol)
if options.generate_mask:
from sp_utilities import model_blank, pad
outvol = args[0]
maskdims = options.masksize.split(',')
if len(maskdims) < 1 or len(maskdims) > 2:
ERROR(
"Enter box size as string containing x , y dimensions (separated by comma) in pixels. E.g.: --boxsize=\'200,200\'"
)
return
nx = int(maskdims[0])
if len(maskdims) == 1:
ny = nx
else:
ny = int(maskdims[1])
mask = pad(model_blank(options.maskwidth, ny, 1, 1.0), nx, ny, 1,
0.0)
mask.write_image(outvol)
if options.applyparams:
from sp_utilities import get_im, get_params2D, set_params2D
from sp_fundamentals import cyclic_shift
stack = args[0]
newstack = args[1]
mask = get_im(args[2])
nima = EMUtil.get_image_count(stack)
for im in range(nima):
prj = get_im(stack, im)
alpha, sx, sy, mirror, scale = get_params2D(prj)
prj = cyclic_shift(prj, int(sx))
set_params2D(prj, [0.0, 0., 0.0, 0, 1])
stat = Util.infomask(prj, mask, False)
prj = (prj - stat[0]) / stat[1]
ctf_params = prj.get_attr("ctf")
prj.set_attr('ctf_applied', 0)
prj.write_image(newstack, im)
def prep_vol(vol, npad=2, interpolation_method=-1):
"""
Name
prep_vol - prepare the volume for calculation of gridding projections and generate the interpolants.
Input
vol: input volume for which projections will be calculated using prgs (interpolation_method=-1) or prgl (interpolation_method>0)
interpolation_method = -1 gridding
interpolation_method = 0 NN
interpolation_method = 1 trilinear
Output
volft: volume prepared for gridding projections using prgs
kb: interpolants (tabulated Kaiser-Bessel function) when the volume is cubic.
kbx,kby: interpolants along x, y and z direction (tabulated Kaiser-Bessel function) when the volume is rectangular
"""
# prepare the volume
Mx = vol.get_xsize()
My = vol.get_ysize()
Mz = vol.get_zsize()
# gridding
if interpolation_method == -1:
K = 6
alpha = 1.75
assert npad == 2
if (Mx == Mz & My == Mz):
M = vol.get_xsize()
# padd two times
N = M * npad
# support of the window
kb = Util.KaiserBessel(alpha, K, M / 2, K / (2. * N), N)
volft = vol.copy()
volft.divkbsinh(kb)
volft = volft.norm_pad(False, npad)
volft.do_fft_inplace()
volft.center_origin_fft()
volft.fft_shuffle()
return volft, kb
else:
Nx = Mx * npad
Ny = My * npad
Nz = Mz * npad
# support of the window
kbx = Util.KaiserBessel(alpha, K, Mx / 2, K / (2. * Nx), Nx)
kby = Util.KaiserBessel(alpha, K, My / 2, K / (2. * Ny), Ny)
kbz = Util.KaiserBessel(alpha, K, Mz / 2, K / (2. * Nz), Nz)
volft = vol.copy()
volft.divkbsinh_rect(kbx, kby, kbz)
volft = volft.norm_pad(False, npad)
volft.do_fft_inplace()
volft.center_origin_fft()
volft.fft_shuffle()
return volft, kbx, kby, kbz
else:
# NN and trilinear
assert interpolation_method >= 0
from sp_utilities import pad
volft = pad(vol, Mx * npad, My * npad, My * npad, 0.0)
volft.set_attr("npad", npad)
volft.div_sinc(interpolation_method)
volft = volft.norm_pad(False, 1)
volft.do_fft_inplace()
volft.center_origin_fft()
volft.fft_shuffle()
volft.set_attr("npad", npad)
return volft
def recons3d_4nn_ctf_MPI(
myid,
prjlist,
snr=1.0,
sign=1,
symmetry="c1",
finfo=None,
npad=2,
xysize=-1,
zsize=-1,
mpi_comm=None,
smearstep=0.0,
):
"""
recons3d_4nn_ctf - calculate CTF-corrected 3-D reconstruction from a set of projections using three Eulerian angles, two shifts, and CTF settings for each projeciton image
Input
stack: name of the stack file containing projection data, projections have to be squares
list_proj: list of projections to be included in the reconstruction or image iterator
snr: Signal-to-Noise Ratio of the data
sign: sign of the CTF
symmetry: point-group symmetry to be enforced, each projection will enter the reconstruction in all symmetry-related directions.
"""
if mpi_comm == None:
mpi_comm = mpi.MPI_COMM_WORLD
if type(prjlist) == list:
prjlist = sp_utilities.iterImagesList(prjlist)
if not prjlist.goToNext():
sp_global_def.ERROR("empty input list", "recons3d_4nn_ctf_MPI", 1)
imgsize = prjlist.image().get_xsize()
if prjlist.image().get_ysize() != imgsize:
imgsize = max(imgsize, prjlist.image().get_ysize())
dopad = True
else:
dopad = False
prjlist.goToPrev()
fftvol = EMAN2_cppwrap.EMData()
if smearstep > 0.0:
# if myid == 0: print " Setting smear in prepare_recons_ctf"
ns = 1
smear = []
for j in range(-ns, ns + 1):
if j != 0:
for i in range(-ns, ns + 1):
for k in range(-ns, ns + 1):
smear += [
i * smearstep, j * smearstep, k * smearstep, 1.0
]
# Deal with theta = 0.0 cases
prj = []
for i in range(-ns, ns + 1):
for k in range(-ns, ns + 1):
prj.append(i + k)
for i in range(-2 * ns, 2 * ns + 1, 1):
smear += [i * smearstep, 0.0, 0.0, float(prj.count(i))]
# if myid == 0: print " Smear ",smear
fftvol.set_attr("smear", smear)
weight = EMAN2_cppwrap.EMData()
if xysize == -1 and zsize == -1:
params = {
"size": imgsize,
"npad": npad,
"snr": snr,
"sign": sign,
"symmetry": symmetry,
"fftvol": fftvol,
"weight": weight,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctf", params)
else:
if xysize != -1 and zsize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = old_div(float(zsize), imgsize)
elif xysize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = 1.0
else:
rx = 1.0
ry = 1.0
rz = old_div(float(zsize), imgsize)
# There is an error here with sizeprojection PAP 10/22/2014
params = {
"size": sizeprojection,
"npad": npad,
"snr": snr,
"sign": sign,
"symmetry": symmetry,
"fftvol": fftvol,
"weight": weight,
"xratio": rx,
"yratio": ry,
"zratio": rz,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctf_rect", params)
r.setup()
# if not (finfo is None):
nimg = 0
while prjlist.goToNext():
prj = prjlist.image()
if dopad:
prj = sp_utilities.pad(prj, imgsize, imgsize, 1, "circumference")
# if params:
insert_slices(r, prj)
if not (finfo is None):
nimg += 1
finfo.write(" %4d inserted\n" % (nimg))
finfo.flush()
del sp_utilities.pad
if not (finfo is None):
finfo.write("begin reduce\n")
finfo.flush()
sp_utilities.reduce_EMData_to_root(fftvol, myid, comm=mpi_comm)
sp_utilities.reduce_EMData_to_root(weight, myid, comm=mpi_comm)
if not (finfo is None):
finfo.write("after reduce\n")
finfo.flush()
if myid == 0:
dummy = r.finish(True)
else:
if xysize == -1 and zsize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, imgsize)
else:
if zsize == -1:
fftvol = sp_utilities.model_blank(xysize, xysize, imgsize)
elif xysize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, zsize)
else:
fftvol = sp_utilities.model_blank(xysize, xysize, zsize)
return fftvol
def recons3d_4nn_ctf(
stack_name,
list_proj=[],
snr=1.0,
sign=1,
symmetry="c1",
verbose=0,
npad=2,
xysize=-1,
zsize=-1,
):
"""Perform a 3-D reconstruction using Pawel's FFT Back Projection algoritm.
Input:
stack_name - name of the stack file on a disk,
each image has to have the following attributes set:
psi, theta, phi, sx, sy, defocus,
list_proj - list of images from stack_name to be included in the reconstruction
symmetry -- Point group of the target molecule (defaults to "C1")
Return: 3d reconstructed volume image
Usage:
anglelist = getAngles("myangles.txt") # not yet written
vol = do_reconstruction(filepattern, start, end, anglelist, symmetry)
"""
# read first image to determine the size to use
if list_proj == []:
if type(stack_name) == bytes:
nima = EMAN2_cppwrap.EMUtil.get_image_count(stack_name)
else:
nima = len(stack_name)
list_proj = list(range(nima))
# read first image to determine the size to use
if type(stack_name) == bytes:
proj = EMAN2_cppwrap.EMData()
proj.read_image(stack_name, list_proj[0])
else:
proj = stack_name[list_proj[0]].copy()
# convert angles to transform (rotation) objects
# horatio active_refactoring Jy51i1EwmLD4tWZ9_00000_1
# active = proj.get_attr_default('active', 1)
size = proj.get_xsize()
if proj.get_ysize() != size:
size = max(size, proj.get_ysize())
dopad = True
else:
dopad = False
# reconstructor
fftvol = EMAN2_cppwrap.EMData()
weight = EMAN2_cppwrap.EMData()
params = {
"npad": npad,
"symmetry": symmetry,
"snr": snr,
"sign": sign,
"fftvol": fftvol,
"weight": weight,
}
if xysize == -1 and zsize == -1:
params["size"] = size
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctf", params)
else:
if xysize != -1 and zsize != -1:
rx = old_div(float(xysize), size)
ry = old_div(float(xysize), size)
rz = old_div(float(zsize), size)
elif xysize != -1:
rx = old_div(float(xysize), size)
ry = old_div(float(xysize), size)
rz = 1.0
else:
rx = 1.0
ry = 1.0
rz = old_div(float(zsize), size)
params["sizeprojection"] = size
params["xratio"] = rx
params["yratio"] = ry
params["zratio"] = rz
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctf_rect", params)
r.setup()
if type(stack_name) == bytes:
for i in range(len(list_proj)):
proj.read_image(stack_name, list_proj[i])
if dopad:
proj = sp_utilities.pad(proj, size, size, 1, "circumference")
insert_slices(r, proj)
else:
for i in range(len(list_proj)):
insert_slices(r, stack_name[list_proj[i]])
dummy = r.finish(True)
return fftvol
def recons3d_4nnw_MPI(
myid,
prjlist,
bckgdata,
snr=1.0,
sign=1,
symmetry="c1",
finfo=None,
npad=2,
xysize=-1,
zsize=-1,
mpi_comm=None,
smearstep=0.0,
fsc=None,
):
"""
recons3d_4nn_ctf - calculate CTF-corrected 3-D reconstruction from a set of projections using three Eulerian angles, two shifts, and CTF settings for each projeciton image
Input
stack: name of the stack file containing projection data, projections have to be squares
prjlist: list of projections to be included in the reconstruction or image iterator
bckgdata = [get_im("tsd.hdf"),read_text_file("data_stamp.txt")]
snr: Signal-to-Noise Ratio of the data
sign: sign of the CTF
symmetry: point-group symmetry to be enforced, each projection will enter the reconstruction in all symmetry-related directions.
"""
pass # IMPORTIMPORTIMPORT from sp_utilities import reduce_EMData_to_root, pad
pass # IMPORTIMPORTIMPORT from EMAN2 import Reconstructors
pass # IMPORTIMPORTIMPORT from sp_utilities import iterImagesList, set_params_proj, model_blank
pass # IMPORTIMPORTIMPORT from mpi import MPI_COMM_WORLD
pass # IMPORTIMPORTIMPORT import types
if mpi_comm == None:
mpi_comm = mpi.MPI_COMM_WORLD
if type(prjlist) == list:
prjlist = sp_utilities.iterImagesList(prjlist)
if not prjlist.goToNext():
sp_global_def.ERROR("empty input list", "recons3d_4nnw_MPI", 1)
imgsize = prjlist.image().get_xsize()
if prjlist.image().get_ysize() != imgsize:
imgsize = max(imgsize, prjlist.image().get_ysize())
dopad = True
else:
dopad = False
prjlist.goToPrev()
# Do the FSC shtick.
bnx = old_div(imgsize * npad, 2) + 1
if fsc:
pass # IMPORTIMPORTIMPORT from math import sqrt
pass # IMPORTIMPORTIMPORT from sp_utilities import reshape_1d
t = [0.0] * len(fsc)
for i in range(len(fsc)):
t[i] = min(max(fsc[i], 0.0), 0.999)
t = sp_utilities.reshape_1d(t, len(t), npad * len(t))
refvol = sp_utilities.model_blank(bnx, 1, 1, 0.0)
for i in range(len(fsc)):
refvol.set_value_at(i, t[i])
else:
refvol = sp_utilities.model_blank(bnx, 1, 1, 1.0)
refvol.set_attr("fudge", 1.0)
fftvol = EMAN2_cppwrap.EMData()
weight = EMAN2_cppwrap.EMData()
if smearstep > 0.0:
# if myid == 0: print " Setting smear in prepare_recons_ctf"
ns = 1
smear = []
for j in range(-ns, ns + 1):
if j != 0:
for i in range(-ns, ns + 1):
for k in range(-ns, ns + 1):
smear += [
i * smearstep, j * smearstep, k * smearstep, 1.0
]
# Deal with theta = 0.0 cases
prj = []
for i in range(-ns, ns + 1):
for k in range(-ns, ns + 1):
prj.append(i + k)
for i in range(-2 * ns, 2 * ns + 1, 1):
smear += [i * smearstep, 0.0, 0.0, float(prj.count(i))]
# if myid == 0: print " Smear ",smear
fftvol.set_attr("smear", smear)
if xysize == -1 and zsize == -1:
params = {
"size": imgsize,
"npad": npad,
"snr": snr,
"sign": sign,
"symmetry": symmetry,
"refvol": refvol,
"fftvol": fftvol,
"weight": weight,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctfw", params)
else:
if xysize != -1 and zsize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = old_div(float(zsize), imgsize)
elif xysize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = 1.0
else:
rx = 1.0
ry = 1.0
rz = old_div(float(zsize), imgsize)
# There is an error here with sizeprojection PAP 10/22/2014
params = {
"size": sizeprojection,
"npad": npad,
"snr": snr,
"sign": sign,
"symmetry": symmetry,
"fftvol": fftvol,
"weight": weight,
"xratio": rx,
"yratio": ry,
"zratio": rz,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4_ctf_rect", params)
r.setup()
# from utilities import model_blank, get_im, read_text_file
# bckgdata = [get_im("tsd.hdf"),read_text_file("data_stamp.txt")]
nnx = bckgdata[0].get_xsize()
nny = bckgdata[0].get_ysize()
bckgnoise = []
for i in range(nny):
prj = sp_utilities.model_blank(nnx)
for k in range(nnx):
prj[k] = bckgdata[0].get_value_at(k, i)
bckgnoise.append(prj)
datastamp = bckgdata[1]
if not (finfo is None):
nimg = 0
while prjlist.goToNext():
prj = prjlist.image()
try:
stmp = old_div(nnx, 0)
stmp = prj.get_attr("ptcl_source_image")
except:
try:
stmp = prj.get_attr("ctf")
stmp = round(stmp.defocus, 4)
except:
sp_global_def.ERROR(
"Either ptcl_source_image or ctf has to be present in the header.",
"recons3d_4nnw_MPI",
1,
myid,
)
try:
indx = datastamp.index(stmp)
except:
sp_global_def.ERROR("Problem with indexing ptcl_source_image.",
"recons3d_4nnw_MPI", 1, myid)
if dopad:
prj = sp_utilities.pad(prj, imgsize, imgsize, 1, "circumference")
prj.set_attr("bckgnoise", bckgnoise[indx])
insert_slices(r, prj)
if not (finfo is None):
nimg += 1
finfo.write(" %4d inserted\n" % (nimg))
finfo.flush()
del sp_utilities.pad
if not (finfo is None):
finfo.write("begin reduce\n")
finfo.flush()
sp_utilities.reduce_EMData_to_root(fftvol, myid, comm=mpi_comm)
sp_utilities.reduce_EMData_to_root(weight, myid, comm=mpi_comm)
if not (finfo is None):
finfo.write("after reduce\n")
finfo.flush()
if myid == 0:
dummy = r.finish(True)
else:
pass # IMPORTIMPORTIMPORT from sp_utilities import model_blank
if xysize == -1 and zsize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, imgsize)
else:
if zsize == -1:
fftvol = sp_utilities.model_blank(xysize, xysize, imgsize)
elif xysize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, zsize)
else:
fftvol = sp_utilities.model_blank(xysize, xysize, zsize)
return fftvol
def recons3d_4nn_MPI(
myid,
prjlist,
symmetry="c1",
finfo=None,
snr=1.0,
npad=2,
xysize=-1,
zsize=-1,
mpi_comm=None,
):
if mpi_comm == None:
mpi_comm = mpi.MPI_COMM_WORLD
if type(prjlist) == list:
prjlist = sp_utilities.iterImagesList(prjlist)
if not prjlist.goToNext():
sp_global_def.ERROR("empty input list", "recons3d_4nn_MPI", 1)
imgsize = prjlist.image().get_xsize()
if prjlist.image().get_ysize() != imgsize:
imgsize = max(imgsize, prjlist.image().get_ysize())
dopad = True
else:
dopad = False
prjlist.goToPrev()
fftvol = EMAN2_cppwrap.EMData()
weight = EMAN2_cppwrap.EMData()
if xysize == -1 and zsize == -1:
params = {
"size": imgsize,
"npad": npad,
"symmetry": symmetry,
"fftvol": fftvol,
"weight": weight,
"snr": snr,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4", params)
else:
if xysize != -1 and zsize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = old_div(float(zsize), imgsize)
elif xysize != -1:
rx = old_div(float(xysize), imgsize)
ry = old_div(float(xysize), imgsize)
rz = 1.0
else:
rx = 1.0
ry = 1.0
rz = old_div(float(zsize), imgsize)
params = {
"sizeprojection": imgsize,
"npad": npad,
"symmetry": symmetry,
"fftvol": fftvol,
"weight": weight,
"xratio": rx,
"yratio": ry,
"zratio": rz,
}
r = EMAN2_cppwrap.Reconstructors.get("nn4_rect", params)
r.setup()
if not (finfo is None):
nimg = 0
while prjlist.goToNext():
prj = prjlist.image()
if dopad:
prj = sp_utilities.pad(prj, imgsize, imgsize, 1, "circumference")
insert_slices(r, prj)
if not (finfo is None):
nimg += 1
finfo.write("Image %4d inserted.\n" % (nimg))
finfo.flush()
if not (finfo is None):
finfo.write("Begin reducing ...\n")
finfo.flush()
sp_utilities.reduce_EMData_to_root(fftvol, myid, comm=mpi_comm)
sp_utilities.reduce_EMData_to_root(weight, myid, comm=mpi_comm)
if myid == 0:
dummy = r.finish(True)
else:
if xysize == -1 and zsize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, imgsize)
else:
if zsize == -1:
fftvol = sp_utilities.model_blank(xysize, xysize, imgsize)
elif xysize == -1:
fftvol = sp_utilities.model_blank(imgsize, imgsize, zsize)
else:
fftvol = sp_utilities.model_blank(xysize, xysize, zsize)
return fftvol
