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