def apply_ctf(projection_2d, ctf): """ Applies a CTF to a projection :param projection_2d: 2D Projection :param ctf: CTF :return: CTF filtered projection """ projection_filtered = fltr.filt_ctf(projection_2d, ctf) return projection_filtered
def gen_rings_ctf(prjref, nx, ctf, numr): """ Convert set of ffts of projections to Fourier rings with additional multiplication by a ctf The command returns list of rings """ from math import sin, cos, pi from sp_fundamentals import fft from sp_alignment import ringwe from sp_filter import filt_ctf mode = "F" wr_four = ringwe(numr, "F") cnx = nx // 2 + 1 cny = nx // 2 + 1 qv = pi / 180.0 refrings = [ ] # list of (image objects) reference projections in Fourier representation for i in range(len(prjref)): cimage = Util.Polar2Dm(filt_ctf(prjref[i], ctf, True), cnx, cny, numr, mode) # currently set to quadratic.... Util.Normalize_ring(cimage, numr, 0) Util.Frngs(cimage, numr) Util.Applyws(cimage, numr, wr_four) refrings.append(cimage) phi = prjref[i].get_attr('phi') theta = prjref[i].get_attr('theta') psi = prjref[i].get_attr('psi') n1 = sin(theta * qv) * cos(phi * qv) n2 = sin(theta * qv) * sin(phi * qv) n3 = cos(theta * qv) refrings[i].set_attr_dict({ "n1": n1, "n2": n2, "n3": n3, "phi": phi, "theta": theta, "psi": psi }) return refrings
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 shiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1, oneDx=False, search_rng_y=-1): number_of_proc = 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("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", myid=myid) if maskfile == None: mrad = min(nx, ny) mask = model_circle(mrad // 2 - 2, nx, ny) else: mask = get_im(maskfile) 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 from sp_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.mpi_barrier(mpi.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.mpi_reduce(sx_sum, 1, mpi.MPI_INT, mpi.MPI_SUM, main_node, mpi.MPI_COMM_WORLD) if not oneDx: sy_sum = mpi.mpi_reduce(sy_sum, 1, mpi.MPI_INT, mpi.MPI_SUM, main_node, mpi.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.mpi_reduce(not_zero, 1, mpi.MPI_INT, mpi.MPI_SUM, main_node, mpi.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.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, image_start, image_end, number_of_proc) else: from sp_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 main(): progname = os.path.basename(sys.argv[0]) usage = progname + " proj_stack output_averages --MPI" parser = OptionParser(usage, version=SPARXVERSION) parser.add_option("--img_per_group", type="int", default=100, help="number of images per group") parser.add_option("--radius", type="int", default=-1, help="radius for alignment") parser.add_option( "--xr", type="string", default="2 1", help="range for translation search in x direction, search is +/xr") parser.add_option( "--yr", type="string", default="-1", help= "range for translation search in y direction, search is +/yr (default = same as xr)" ) parser.add_option( "--ts", type="string", default="1 0.5", help= "step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional" ) parser.add_option( "--iter", type="int", default=30, help="number of iterations within alignment (default = 30)") parser.add_option( "--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)") parser.add_option("--thld_err", type="float", default=1.0, help="threshold of pixel error (default = 1.732)") parser.add_option( "--grouping", type="string", default="GRP", help= "do grouping of projections: PPR - per projection, GRP - different size groups, exclusive (default), GEV - grouping equal size" ) parser.add_option( "--delta", type="float", default=-1.0, help="angular step for reference projections (required for GEV method)" ) parser.add_option( "--fl", type="float", default=0.3, help="cut-off frequency of hyperbolic tangent low-pass Fourier filter") parser.add_option( "--aa", type="float", default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter") parser.add_option("--CTF", action="store_true", default=False, help="Consider CTF correction during the alignment ") parser.add_option("--MPI", action="store_true", default=False, help="use MPI version") (options, args) = parser.parse_args() myid = mpi.mpi_comm_rank(MPI_COMM_WORLD) number_of_proc = mpi.mpi_comm_size(MPI_COMM_WORLD) main_node = 0 if len(args) == 2: stack = args[0] outdir = args[1] else: sp_global_def.ERROR("Incomplete list of arguments", "sxproj_stability.main", 1, myid=myid) return if not options.MPI: sp_global_def.ERROR("Non-MPI not supported!", "sxproj_stability.main", 1, myid=myid) return if sp_global_def.CACHE_DISABLE: from sp_utilities import disable_bdb_cache disable_bdb_cache() sp_global_def.BATCH = True img_per_grp = options.img_per_group radius = options.radius ite = options.iter num_ali = options.num_ali thld_err = options.thld_err xrng = get_input_from_string(options.xr) if options.yr == "-1": yrng = xrng else: yrng = get_input_from_string(options.yr) step = get_input_from_string(options.ts) if myid == main_node: nima = EMUtil.get_image_count(stack) img = get_image(stack) nx = img.get_xsize() ny = img.get_ysize() else: nima = 0 nx = 0 ny = 0 nima = bcast_number_to_all(nima) nx = bcast_number_to_all(nx) ny = bcast_number_to_all(ny) if radius == -1: radius = nx / 2 - 2 mask = model_circle(radius, nx, nx) st = time() if options.grouping == "GRP": if myid == main_node: sxprint(" A ", myid, " ", time() - st) proj_attr = EMUtil.get_all_attributes(stack, "xform.projection") proj_params = [] for i in range(nima): dp = proj_attr[i].get_params("spider") phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[ "psi"], -dp["tx"], -dp["ty"] proj_params.append([phi, theta, psi, s2x, s2y]) # Here is where the grouping is done, I didn't put enough annotation in the group_proj_by_phitheta, # So I will briefly explain it here # proj_list : Returns a list of list of particle numbers, each list contains img_per_grp particle numbers # except for the last one. Depending on the number of particles left, they will either form a # group or append themselves to the last group # angle_list : Also returns a list of list, each list contains three numbers (phi, theta, delta), (phi, # theta) is the projection angle of the center of the group, delta is the range of this group # mirror_list: Also returns a list of list, each list contains img_per_grp True or False, which indicates # whether it should take mirror position. # In this program angle_list and mirror list are not of interest. proj_list_all, angle_list, mirror_list = group_proj_by_phitheta( proj_params, img_per_grp=img_per_grp) del proj_params sxprint(" B number of groups ", myid, " ", len(proj_list_all), time() - st) mpi_barrier(MPI_COMM_WORLD) # Number of groups, actually there could be one or two more groups, since the size of the remaining group varies # we will simply assign them to main node. n_grp = nima / img_per_grp - 1 # Divide proj_list_all equally to all nodes, and becomes proj_list proj_list = [] for i in range(n_grp): proc_to_stay = i % number_of_proc if proc_to_stay == main_node: if myid == main_node: proj_list.append(proj_list_all[i]) elif myid == main_node: mpi_send(len(proj_list_all[i]), 1, MPI_INT, proc_to_stay, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT, proc_to_stay, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) elif myid == proc_to_stay: img_per_grp = mpi_recv(1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) img_per_grp = int(img_per_grp[0]) temp = mpi_recv(img_per_grp, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) proj_list.append(list(map(int, temp))) del temp mpi_barrier(MPI_COMM_WORLD) sxprint(" C ", myid, " ", time() - st) if myid == main_node: # Assign the remaining groups to main_node for i in range(n_grp, len(proj_list_all)): proj_list.append(proj_list_all[i]) del proj_list_all, angle_list, mirror_list # Compute stability per projection projection direction, equal number assigned, thus overlaps elif options.grouping == "GEV": if options.delta == -1.0: ERROR( "Angular step for reference projections is required for GEV method" ) return from sp_utilities import even_angles, nearestk_to_refdir, getvec refproj = even_angles(options.delta) img_begin, img_end = MPI_start_end(len(refproj), number_of_proc, myid) # Now each processor keeps its own share of reference projections refprojdir = refproj[img_begin:img_end] del refproj ref_ang = [0.0] * (len(refprojdir) * 2) for i in range(len(refprojdir)): ref_ang[i * 2] = refprojdir[0][0] ref_ang[i * 2 + 1] = refprojdir[0][1] + i * 0.1 sxprint(" A ", myid, " ", time() - st) proj_attr = EMUtil.get_all_attributes(stack, "xform.projection") # the solution below is very slow, do not use it unless there is a problem with the i/O """ for i in xrange(number_of_proc): if myid == i: proj_attr = EMUtil.get_all_attributes(stack, "xform.projection") mpi_barrier(MPI_COMM_WORLD) """ sxprint(" B ", myid, " ", time() - st) proj_ang = [0.0] * (nima * 2) for i in range(nima): dp = proj_attr[i].get_params("spider") proj_ang[i * 2] = dp["phi"] proj_ang[i * 2 + 1] = dp["theta"] sxprint(" C ", myid, " ", time() - st) asi = Util.nearestk_to_refdir(proj_ang, ref_ang, img_per_grp) del proj_ang, ref_ang proj_list = [] for i in range(len(refprojdir)): proj_list.append(asi[i * img_per_grp:(i + 1) * img_per_grp]) del asi sxprint(" D ", myid, " ", time() - st) #from sys import exit #exit() # Compute stability per projection elif options.grouping == "PPR": sxprint(" A ", myid, " ", time() - st) proj_attr = EMUtil.get_all_attributes(stack, "xform.projection") sxprint(" B ", myid, " ", time() - st) proj_params = [] for i in range(nima): dp = proj_attr[i].get_params("spider") phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[ "psi"], -dp["tx"], -dp["ty"] proj_params.append([phi, theta, psi, s2x, s2y]) img_begin, img_end = MPI_start_end(nima, number_of_proc, myid) sxprint(" C ", myid, " ", time() - st) from sp_utilities import nearest_proj proj_list, mirror_list = nearest_proj( proj_params, img_per_grp, list(range(img_begin, img_begin + 1))) #range(img_begin, img_end)) refprojdir = proj_params[img_begin:img_end] del proj_params, mirror_list sxprint(" D ", myid, " ", time() - st) else: ERROR("Incorrect projection grouping option") return ########################################################################################################### # Begin stability test from sp_utilities import get_params_proj, read_text_file #if myid == 0: # from utilities import read_text_file # proj_list[0] = map(int, read_text_file("lggrpp0.txt")) from sp_utilities import model_blank aveList = [model_blank(nx, ny)] * len(proj_list) if options.grouping == "GRP": refprojdir = [[0.0, 0.0, -1.0]] * len(proj_list) for i in range(len(proj_list)): sxprint(" E ", myid, " ", time() - st) class_data = EMData.read_images(stack, proj_list[i]) #print " R ",myid," ",time()-st if options.CTF: from sp_filter import filt_ctf for im in range(len(class_data)): # MEM LEAK!! atemp = class_data[im].copy() btemp = filt_ctf(atemp, atemp.get_attr("ctf"), binary=1) class_data[im] = btemp #class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1) for im in class_data: try: t = im.get_attr( "xform.align2d") # if they are there, no need to set them! except: try: t = im.get_attr("xform.projection") d = t.get_params("spider") set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0]) except: set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0]) #print " F ",myid," ",time()-st # Here, we perform realignment num_ali times all_ali_params = [] for j in range(num_ali): if (xrng[0] == 0.0 and yrng[0] == 0.0): avet = ali2d_ras(class_data, randomize=True, ir=1, ou=radius, rs=1, step=1.0, dst=90.0, maxit=ite, check_mirror=True, FH=options.fl, FF=options.aa) else: avet = within_group_refinement(class_data, mask, True, 1, radius, 1, xrng, yrng, step, 90.0, ite, options.fl, options.aa) ali_params = [] for im in range(len(class_data)): alpha, sx, sy, mirror, scale = get_params2D(class_data[im]) ali_params.extend([alpha, sx, sy, mirror]) all_ali_params.append(ali_params) #aveList[i] = avet #print " G ",myid," ",time()-st del ali_params # We determine the stability of this group here. # stable_set contains all particles deemed stable, it is a list of list # each list has two elements, the first is the pixel error, the second is the image number # stable_set is sorted based on pixel error #from utilities import write_text_file #write_text_file(all_ali_params, "all_ali_params%03d.txt"%myid) stable_set, mir_stab_rate, average_pix_err = multi_align_stability( all_ali_params, 0.0, 10000.0, thld_err, False, 2 * radius + 1) #print " H ",myid," ",time()-st if (len(stable_set) > 5): stable_set_id = [] members = [] pix_err = [] # First put the stable members into attr 'members' and 'pix_err' for s in stable_set: # s[1] - number in this subset stable_set_id.append(s[1]) # the original image number members.append(proj_list[i][s[1]]) pix_err.append(s[0]) # Then put the unstable members into attr 'members' and 'pix_err' from sp_fundamentals import rot_shift2D avet.to_zero() if options.grouping == "GRP": aphi = 0.0 atht = 0.0 vphi = 0.0 vtht = 0.0 l = -1 for j in range(len(proj_list[i])): # Here it will only work if stable_set_id is sorted in the increasing number, see how l progresses if j in stable_set_id: l += 1 avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3]) if options.grouping == "GRP": phi, theta, psi, sxs, sy_s = get_params_proj( class_data[j]) if (theta > 90.0): phi = (phi + 540.0) % 360.0 theta = 180.0 - theta aphi += phi atht += theta vphi += phi * phi vtht += theta * theta else: members.append(proj_list[i][j]) pix_err.append(99999.99) aveList[i] = avet.copy() if l > 1: l += 1 aveList[i] /= l if options.grouping == "GRP": aphi /= l atht /= l vphi = (vphi - l * aphi * aphi) / l vtht = (vtht - l * atht * atht) / l from math import sqrt refprojdir[i] = [ aphi, atht, (sqrt(max(vphi, 0.0)) + sqrt(max(vtht, 0.0))) / 2.0 ] # Here more information has to be stored, PARTICULARLY WHAT IS THE REFERENCE DIRECTION aveList[i].set_attr('members', members) aveList[i].set_attr('refprojdir', refprojdir[i]) aveList[i].set_attr('pixerr', pix_err) else: sxprint(" empty group ", i, refprojdir[i]) aveList[i].set_attr('members', [-1]) aveList[i].set_attr('refprojdir', refprojdir[i]) aveList[i].set_attr('pixerr', [99999.]) del class_data if myid == main_node: km = 0 for i in range(number_of_proc): if i == main_node: for im in range(len(aveList)): aveList[im].write_image(args[1], km) km += 1 else: nl = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) nl = int(nl[0]) for im in range(nl): ave = recv_EMData(i, im + i + 70000) nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) nm = int(nm[0]) members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) ave.set_attr('members', list(map(int, members))) members = mpi_recv(nm, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) ave.set_attr('pixerr', list(map(float, members))) members = mpi_recv(3, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) ave.set_attr('refprojdir', list(map(float, members))) ave.write_image(args[1], km) km += 1 else: mpi_send(len(aveList), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) for im in range(len(aveList)): send_EMData(aveList[im], main_node, im + myid + 70000) members = aveList[im].get_attr('members') mpi_send(len(members), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) mpi_send(members, len(members), MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) members = aveList[im].get_attr('pixerr') mpi_send(members, len(members), MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) try: members = aveList[im].get_attr('refprojdir') mpi_send(members, 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) except: mpi_send([-999.0, -999.0, -999.0], 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD) sp_global_def.BATCH = False mpi_barrier(MPI_COMM_WORLD)
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 ali2d_single_iter( data, numr, wr, cs, tavg, cnx, cny, xrng, yrng, step, nomirror=False, mode="F", CTF=False, random_method="", T=1.0, ali_params="xform.align2d", delta=0.0, ): """ single iteration of 2D alignment using ormq if CTF = True, apply CTF to data (not to reference!) """ maxrin = numr[-1] # length ou = numr[-3] # maximum radius if random_method == "SCF": frotim = [sp_fundamentals.fft(tavg)] xrng = int(xrng + 0.5) yrng = int(yrng + 0.5) cimage = EMAN2_cppwrap.Util.Polar2Dm(sp_fundamentals.scf(tavg), cnx, cny, numr, mode) EMAN2_cppwrap.Util.Frngs(cimage, numr) EMAN2_cppwrap.Util.Applyws(cimage, numr, wr) else: # 2D alignment using rotational ccf in polar coords and quadratic interpolation cimage = EMAN2_cppwrap.Util.Polar2Dm(tavg, cnx, cny, numr, mode) EMAN2_cppwrap.Util.Frngs(cimage, numr) EMAN2_cppwrap.Util.Applyws(cimage, numr, wr) sx_sum = 0.0 sy_sum = 0.0 sxn = 0.0 syn = 0.0 mn = 0 nope = 0 mashi = cnx - ou - 2 for im in range(len(data)): if CTF: # Apply CTF to image ctf_params = data[im].get_attr("ctf") ima = sp_filter.filt_ctf(data[im], ctf_params, True) else: ima = data[im] if random_method == "PCP": sxi = data[im][0][0].get_attr("sxi") syi = data[im][0][0].get_attr("syi") nx = ny = data[im][0][0].get_attr("inx") else: nx = ima.get_xsize() ny = ima.get_ysize() alpha, sx, sy, mirror, dummy = sp_utilities.get_params2D( data[im], ali_params) alpha, sx, sy, dummy = sp_utilities.combine_params2( alpha, sx, sy, mirror, 0.0, -cs[0], -cs[1], 0) alphai, sxi, syi, scalei = sp_utilities.inverse_transform2( alpha, sx, sy) # introduce constraints on parameters to accomodate use of cs centering sxi = min(max(sxi, -mashi), mashi) syi = min(max(syi, -mashi), mashi) # The search range procedure was adjusted for 3D searches, so since in 2D the order of operations is inverted, we have to invert ranges txrng = search_range(nx, ou, sxi, xrng, "ali2d_single_iter") txrng = [txrng[1], txrng[0]] tyrng = search_range(ny, ou, syi, yrng, "ali2d_single_iter") tyrng = [tyrng[1], tyrng[0]] # print im, "B",cnx,sxi,syi,txrng, tyrng # align current image to the reference if random_method == "SHC": """Multiline Comment0""" # For shc combining of shifts is problematic as the image may randomly slide away and never come back. # A possibility would be to reject moves that results in too large departure from the center. # On the other hand, one cannot simply do searches around the proper center all the time, # as if xr is decreased, the image cannot be brought back if the established shifts are further than new range olo = EMAN2_cppwrap.Util.shc( ima, [cimage], txrng, tyrng, step, -1.0, mode, numr, cnx + sxi, cny + syi, "c1", ) ##olo = Util.shc(ima, [cimage], xrng, yrng, step, -1.0, mode, numr, cnx, cny, "c1") if data[im].get_attr("previousmax") < olo[5]: # [angt, sxst, syst, mirrort, peakt] = ormq(ima, cimage, xrng, yrng, step, mode, numr, cnx+sxi, cny+syi, delta) # print angt, sxst, syst, mirrort, peakt,olo angt = olo[0] sxst = olo[1] syst = olo[2] mirrort = int(olo[3]) # combine parameters and set them to the header, ignore previous angle and mirror [alphan, sxn, syn, mn] = sp_utilities.combine_params2(0.0, -sxi, -syi, 0, angt, sxst, syst, mirrort) sp_utilities.set_params2D(data[im], [alphan, sxn, syn, mn, 1.0], ali_params) ##set_params2D(data[im], [angt, sxst, syst, mirrort, 1.0], ali_params) data[im].set_attr("previousmax", olo[5]) else: # Did not find a better peak, but we have to set shifted parameters, as the average shifted sp_utilities.set_params2D(data[im], [alpha, sx, sy, mirror, 1.0], ali_params) nope += 1 mn = 0 sxn = 0.0 syn = 0.0 elif random_method == "SCF": sxst, syst, iref, angt, mirrort, totpeak = multalign2d_scf( data[im], [cimage], frotim, numr, xrng, yrng, ou=ou) [alphan, sxn, syn, mn] = sp_utilities.combine_params2(0.0, -sxi, -syi, 0, angt, sxst, syst, mirrort) sp_utilities.set_params2D(data[im], [alphan, sxn, syn, mn, 1.0], ali_params) elif random_method == "PCP": [angt, sxst, syst, mirrort, peakt] = ormq_fast(data[im], cimage, txrng, tyrng, step, numr, mode, delta) sxst = rings[0][0][0].get_attr("sxi") syst = rings[0][0][0].get_attr("syi") sp_global_def.sxprint(sxst, syst, sx, sy) dummy, sxs, sys, dummy = sp_utilities.inverse_transform2( -angt, sx + sxst, sy + syst) sp_utilities.set_params2D(data[im][0][0], [angt, sxs, sys, mirrort, 1.0], ali_params) else: if nomirror: [angt, sxst, syst, mirrort, peakt] = ornq(ima, cimage, txrng, tyrng, step, mode, numr, cnx + sxi, cny + syi) else: [angt, sxst, syst, mirrort, peakt] = ormq( ima, cimage, txrng, tyrng, step, mode, numr, cnx + sxi, cny + syi, delta, ) # combine parameters and set them to the header, ignore previous angle and mirror [alphan, sxn, syn, mn] = sp_utilities.combine_params2(0.0, -sxi, -syi, 0, angt, sxst, syst, mirrort) sp_utilities.set_params2D(data[im], [alphan, sxn, syn, mn, 1.0], ali_params) if mn == 0: sx_sum += sxn else: sx_sum -= sxn sy_sum += syn return sx_sum, sy_sum, nope
def align2d_direct3(input_images, refim, xrng=1, yrng=1, psimax=180, psistep=1, ou=-1, CTF=None): nx = input_images[0].get_xsize() if ou < 0: ou = old_div(nx, 2) - 1 mask = sp_utilities.model_circle(ou, nx, nx) nk = int(old_div(psimax, psistep)) nm = 2 * nk + 1 nc = nk + 1 refs = [None] * nm * 2 for i in range(nm): temp = sp_fundamentals.rot_shift2D(refim, (i - nc) * psistep) * mask refs[2 * i] = [ sp_fundamentals.fft(temp), sp_fundamentals.fft(sp_fundamentals.mirror(temp)), ] temp = sp_fundamentals.rot_shift2D(refim, (i - nc) * psistep + 180.0) * mask refs[2 * i + 1] = [ sp_fundamentals.fft(temp), sp_fundamentals.fft(sp_fundamentals.mirror(temp)), ] del temp results = [] mir = 0 for image in input_images: if CTF: ims = sp_filter.filt_ctf(sp_fundamentals.fft(image), image.get_attr("ctf")) else: ims = sp_fundamentals.fft(image) ama = -1.0e23 bang = 0.0 bsx = 0.0 bsy = 0.0 for i in range(nm * 2): for mirror_flag in [0, 1]: c = sp_fundamentals.ccf(ims, refs[i][mirror_flag]) w = EMAN2_cppwrap.Util.window(c, 2 * xrng + 1, 2 * yrng + 1) pp = sp_utilities.peak_search(w)[0] px = int(pp[4]) py = int(pp[5]) if pp[0] == 1.0 and px == 0 and py == 0: pass # XSH, YSH, PEAKV = 0.,0.,0. else: ww = sp_utilities.model_blank(3, 3) ux = int(pp[1]) uy = int(pp[2]) for k in range(3): for l in range(3): ww[k, l] = w[k + ux - 1, l + uy - 1] XSH, YSH, PEAKV = parabl(ww) # print i,pp[-1],XSH, YSH,px+XSH, py+YSH, PEAKV if PEAKV > ama: ama = PEAKV bsx = px + round(XSH, 2) bsy = py + round(YSH, 2) bang = i mir = mirror_flag # returned parameters have to be inverted bang = (old_div(bang, 2) - nc) * psistep + 180.0 * (bang % 2) bang, bsx, bsy, _ = sp_utilities.inverse_transform2( bang, (1 - 2 * mir) * bsx, bsy, mir) results.append([bang, bsx, bsy, mir, ama]) return results
def main(): from sp_utilities import get_input_from_string progname = os.path.basename(sys.argv[0]) usage = progname + " stack output_average --radius=particle_radius --xr=xr --yr=yr --ts=ts --thld_err=thld_err --num_ali=num_ali --fl=fl --aa=aa --CTF --verbose --stables" parser = OptionParser(usage, version=SPARXVERSION) parser.add_option("--radius", type="int", default=-1, help=" particle radius for alignment") parser.add_option( "--xr", type="string", default="2 1", help= "range for translation search in x direction, search is +/xr (default 2,1)" ) parser.add_option( "--yr", type="string", default="-1", help= "range for translation search in y direction, search is +/yr (default = same as xr)" ) parser.add_option( "--ts", type="string", default="1 0.5", help= "step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional (default: 1,0.5)" ) parser.add_option("--thld_err", type="float", default=0.7, help="threshld of pixel error (default = 0.75)") parser.add_option( "--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)") parser.add_option("--maxit", type="int", default=30, help="number of iterations for each xr (default = 30)") parser.add_option( "--fl", type="float", default=0.45, help= "cut-off frequency of hyperbolic tangent low-pass Fourier filter (default = 0.3)" ) parser.add_option( "--aa", type="float", default=0.2, help= "fall-off of hyperbolic tangent low-pass Fourier filter (default = 0.2)" ) parser.add_option("--CTF", action="store_true", default=False, help="Use CTF correction during the alignment ") parser.add_option("--verbose", action="store_true", default=False, help="print individual pixel error (default = False)") parser.add_option( "--stables", action="store_true", default=False, help="output the stable particles number in file (default = False)") parser.add_option( "--method", type="string", default=" ", help="SHC (standard method is default when flag is ommitted)") (options, args) = parser.parse_args() if len(args) != 1 and len(args) != 2: sxprint("Usage: " + usage) sxprint("Please run \'" + progname + " -h\' for detailed options") ERROR( "Invalid number of parameters used. Please see usage information above." ) return else: if sp_global_def.CACHE_DISABLE: from sp_utilities import disable_bdb_cache disable_bdb_cache() from sp_applications import within_group_refinement, ali2d_ras from sp_pixel_error import multi_align_stability from sp_utilities import write_text_file, write_text_row sp_global_def.BATCH = True xrng = get_input_from_string(options.xr) if options.yr == "-1": yrng = xrng else: yrng = get_input_from_string(options.yr) step = get_input_from_string(options.ts) class_data = EMData.read_images(args[0]) nx = class_data[0].get_xsize() ou = options.radius num_ali = options.num_ali if ou == -1: ou = nx / 2 - 2 from sp_utilities import model_circle, get_params2D, set_params2D mask = model_circle(ou, nx, nx) if options.CTF: from sp_filter import filt_ctf for im in range(len(class_data)): # Flip phases class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1) for im in class_data: im.set_attr("previousmax", -1.0e10) try: t = im.get_attr( "xform.align2d") # if they are there, no need to set them! except: try: t = im.get_attr("xform.projection") d = t.get_params("spider") set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0]) except: set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0]) all_ali_params = [] for ii in range(num_ali): ali_params = [] if options.verbose: ALPHA = [] SX = [] SY = [] MIRROR = [] if (xrng[0] == 0.0 and yrng[0] == 0.0): avet = ali2d_ras(class_data, randomize = True, ir = 1, ou = ou, rs = 1, step = 1.0, dst = 90.0, \ maxit = options.maxit, check_mirror = True, FH=options.fl, FF=options.aa) else: avet = within_group_refinement(class_data, mask, True, 1, ou, 1, xrng, yrng, step, 90.0, \ maxit = options.maxit, FH=options.fl, FF=options.aa, method = options.method) from sp_utilities import info #print " avet ",info(avet) for im in class_data: alpha, sx, sy, mirror, scale = get_params2D(im) ali_params.extend([alpha, sx, sy, mirror]) if options.verbose: ALPHA.append(alpha) SX.append(sx) SY.append(sy) MIRROR.append(mirror) all_ali_params.append(ali_params) if options.verbose: write_text_file([ALPHA, SX, SY, MIRROR], "ali_params_run_%d" % ii) """ avet = class_data[0] from sp_utilities import read_text_file all_ali_params = [] for ii in xrange(5): temp = read_text_file( "ali_params_run_%d"%ii,-1) uuu = [] for k in xrange(len(temp[0])): uuu.extend([temp[0][k],temp[1][k],temp[2][k],temp[3][k]]) all_ali_params.append(uuu) """ stable_set, mir_stab_rate, pix_err = multi_align_stability( all_ali_params, 0.0, 10000.0, options.thld_err, options.verbose, 2 * ou + 1) sxprint("%4s %20s %20s %20s %30s %6.2f" % ("", "Size of set", "Size of stable set", "Mirror stab rate", "Pixel error prior to pruning the set above threshold of", options.thld_err)) sxprint("Average stat: %10d %20d %20.2f %15.2f" % (len(class_data), len(stable_set), mir_stab_rate, pix_err)) if (len(stable_set) > 0): if options.stables: stab_mem = [[0, 0.0, 0] for j in range(len(stable_set))] for j in range(len(stable_set)): stab_mem[j] = [int(stable_set[j][1]), stable_set[j][0], j] write_text_row(stab_mem, "stable_particles.txt") stable_set_id = [] particle_pixerr = [] for s in stable_set: stable_set_id.append(s[1]) particle_pixerr.append(s[0]) from sp_fundamentals import rot_shift2D avet.to_zero() l = -1 sxprint("average parameters: angle, x-shift, y-shift, mirror") for j in stable_set_id: l += 1 sxprint(" %4d %4d %12.2f %12.2f %12.2f %1d" % (l, j, stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], int(stable_set[l][2][3]))) avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3]) avet /= (l + 1) avet.set_attr('members', stable_set_id) avet.set_attr('pix_err', pix_err) avet.set_attr('pixerr', particle_pixerr) avet.write_image(args[1]) sp_global_def.BATCH = False