def gridrot_shift2D(image, ang=0.0, sx=0.0, sy=0.0, scale=1.0): """ Rotate and shift an image using gridding in Fourier space. """ from EMAN2 import Processor from fundamentals import fftip, fft nx = image.get_xsize() # split shift into integer and fractional parts isx = int(sx) fsx = sx - isx isy = int(sy) fsy = sy - isy # prepare npad = 2 N = nx * npad K = 6 alpha = 1.75 r = nx / 2 v = K / 2.0 / N kb = Util.KaiserBessel(alpha, K, r, v, N) image1 = image.copy( ) # This step is needed, otherwise image will be changed outside the function # divide out gridding weights image1.divkbsinh(kb) # pad and center image, then FFT image1 = image1.norm_pad(False, npad) fftip(image1) # Put the origin of the (real-space) image at the center image1.center_origin_fft() # gridding rotation image1 = image1.fouriergridrot2d(ang, scale, kb) if (fsx != 0.0 or fsy != 0.0): params = { "filter_type": Processor.fourier_filter_types.SHIFT, "x_shift": float(fsx), "y_shift": float(fsy), "z_shift": 0.0 } image1 = Processor.EMFourierFilter(image1, params) # put the origin back in the corner image1.center_origin_fft() # undo FFT and remove padding (window) image1 = fft(image1) image1 = image1.window_center(nx) Util.cyclicshift(image1, {"dx": isx, "dy": isy, "dz": 0}) return image1
def gridrot_shift2D(image, ang = 0.0, sx = 0.0, sy = 0.0, scale = 1.0): """ Rotate and shift an image using gridding on Fourier space. """ from EMAN2 import Processor from fundamentals import fftip, fft nx = image.get_xsize() # split shift into integer and fractional parts isx = int(sx) fsx = sx - isx isy = int(sy) fsy = sy - isy # prepare npad = 2 N = nx*npad K = 6 alpha = 1.75 r = nx/2 v = K/2.0/N kb = Util.KaiserBessel(alpha, K, r, v, N) image1 = image.copy() # This step is needed, otherwise image will be changed outside the function # divide out gridding weights image1.divkbsinh(kb) # pad and center image, then FFT image1 = image1.norm_pad(False, npad) fftip(image1) # Put the origin of the (real-space) image at the center image1.center_origin_fft() # gridding rotation image1 = image1.fouriergridrot2d(ang, scale, kb) if(fsx != 0.0 or fsy != 0.0): params = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : float(fsx), "y_shift" : float(fsy), "z_shift" : 0.0 } image1 = Processor.EMFourierFilter(image1, params) # put the origin back in the corner image1.center_origin_fft() # undo FFT and remove padding (window) image1 = fft(image1) image1 = image1.window_center(nx) Util.cyclicshift(image1,{"dx":isx,"dy":isy,"dz":0}) return image1
def cml_open_proj(stack, ir, ou, lf, hf, dpsi=1): from projection import cml_sinogram from utilities import model_circle, get_params_proj, model_blank, get_im from fundamentals import fftip from filter import filt_tanh # number of projections if type(stack) == type(""): nprj = EMUtil.get_image_count(stack) else: nprj = len(stack) Prj = [] # list of projections Ori = [ -1 ] * 4 * nprj # orientation intial (phi, theta, psi, index) for each projection for i in xrange(nprj): image = get_im(stack, i) # read initial angles if given try: Ori[4 * i], Ori[4 * i + 1], Ori[4 * i + 2], s2x, s2y = get_params_proj(image) except: pass if (i == 0): nx = image.get_xsize() if (ou < 1): ou = nx // 2 - 1 diameter = int(2 * ou) mask2D = model_circle(ou, nx, nx) if ir > 0: mask2D -= model_circle(ir, nx, nx) # normalize under the mask [mean_a, sigma, imin, imax] = Util.infomask(image, mask2D, True) image -= mean_a Util.mul_scalar(image, 1.0 / sigma) Util.mul_img(image, mask2D) # sinogram sino = cml_sinogram(image, diameter, dpsi) # prepare the cut positions in order to filter (lf: low freq; hf: high freq) ihf = min(int(2 * hf * diameter), diameter + (diameter + 1) % 2) ihf = ihf + (ihf + 1) % 2 # index ihf must be odd to take the img part ilf = max(int(2 * lf * diameter), 0) ilf = ilf + ilf % 2 # index ilf must be even to fall in the real part bdf = ihf - ilf + 1 # process lines nxe = sino.get_xsize() nye = sino.get_ysize() prj = model_blank(bdf, 2 * nye) pp = model_blank(nxe, 2 * nye) for li in xrange(nye): # get the line li line = Util.window(sino, nxe, 1, 1, 0, li - nye // 2, 0) # u2 (not improve the results) #line = filt_tanh(line, ou / float(nx), ou / float(nx)) # normalize this line [mean_l, sigma_l, imin, imax] = Util.infomask(line, None, True) line = (line - mean_l) / sigma_l # fft fftip(line) # filter (cut part of coef) and create mirror line Util.cml_prepare_line(prj, line, ilf, ihf, li, nye) # store the projection Prj.append(prj) return Prj, Ori
def do_volume_mrk02(ref_data): """ data - projections (scattered between cpus) or the volume. If volume, just do the volume processing options - the same for all cpus return - volume the same for all cpus """ from EMAN2 import Util from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD from filter import filt_table from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank from fundamentals import rops_table, fftip, fft import types # Retrieve the function specific input arguments from ref_data data = ref_data[0] Tracker = ref_data[1] iter = ref_data[2] mpi_comm = ref_data[3] # # For DEBUG # print "Type of data %s" % (type(data)) # print "Type of Tracker %s" % (type(Tracker)) # print "Type of iter %s" % (type(iter)) # print "Type of mpi_comm %s" % (type(mpi_comm)) if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD myid = mpi_comm_rank(mpi_comm) nproc = mpi_comm_size(mpi_comm) try: local_filter = Tracker["local_filter"] except: local_filter = False #========================================================================= # volume reconstruction if( type(data) == types.ListType ): if Tracker["constants"]["CTF"]: vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \ symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"]) else: vol = recons3d_4nn_MPI (myid, data,\ symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm) else: vol = data if myid == 0: from morphology import threshold from filter import filt_tanl, filt_btwl from utilities import model_circle, get_im import types nx = vol.get_xsize() if(Tracker["constants"]["mask3D"] == None): mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx) elif(Tracker["constants"]["mask3D"] == "auto"): from utilities import adaptive_mask mask3D = adaptive_mask(vol) else: if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"]) else: mask3D = (Tracker["constants"]["mask3D"]).copy() nxm = mask3D.get_xsize() if( nx != nxm): from fundamentals import rot_shift3D mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx) nxm = mask3D.get_xsize() assert(nx == nxm) stat = Util.infomask(vol, mask3D, False) vol -= stat[0] Util.mul_scalar(vol, 1.0/stat[1]) vol = threshold(vol) Util.mul_img(vol, mask3D) if( Tracker["PWadjustment"] ): from utilities import read_text_file, write_text_file rt = read_text_file( Tracker["PWadjustment"] ) fftip(vol) ro = rops_table(vol) # Here unless I am mistaken it is enough to take the beginning of the reference pw. for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**Tracker["upscale"] #write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt") if Tracker["constants"]["sausage"]: ny = vol.get_ysize() y = float(ny) from math import exp for i in xrange(len(ro)): ro[i] *= \ (1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2)) if local_filter: # skip low-pass filtration vol = fft( filt_table( vol, ro) ) else: if( type(Tracker["lowpass"]) == types.ListType ): vol = fft( filt_table( filt_table(vol, Tracker["lowpass"]), ro) ) else: vol = fft( filt_table( filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]), ro) ) del ro else: if Tracker["constants"]["sausage"]: ny = vol.get_ysize() y = float(ny) ro = [0.0]*(ny//2+2) from math import exp for i in xrange(len(ro)): ro[i] = \ (1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2)) fftip(vol) filt_table(vol, ro) del ro if not local_filter: if( type(Tracker["lowpass"]) == types.ListType ): vol = filt_table(vol, Tracker["lowpass"]) else: vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]) if Tracker["constants"]["sausage"]: vol = fft(vol) if local_filter: from morphology import binarize if(myid == 0): nx = mask3D.get_xsize() else: nx = 0 nx = bcast_number_to_all(nx, source_node = 0) # only main processor needs the two input volumes if(myid == 0): mask = binarize(mask3D, 0.5) locres = get_im(Tracker["local_filter"]) lx = locres.get_xsize() if(lx != nx): if(lx < nx): from fundamentals import fdecimate, rot_shift3D mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx) vol = fdecimate(vol, lx,lx,lx) else: ERROR("local filter cannot be larger than input volume","user function",1) stat = Util.infomask(vol, mask, False) vol -= stat[0] Util.mul_scalar(vol, 1.0/stat[1]) else: lx = 0 locres = model_blank(1,1,1) vol = model_blank(1,1,1) lx = bcast_number_to_all(lx, source_node = 0) if( myid != 0 ): mask = model_blank(lx,lx,lx) bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm) from filter import filterlocal vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc) if myid == 0: if(lx < nx): from fundamentals import fpol vol = fpol(vol, nx,nx,nx) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected. Util.mul_img(vol, mask3D) del mask3D # vol.write_image('toto%03d.hdf'%iter) else: vol = model_blank(nx,nx,nx) else: if myid == 0: #from utilities import write_text_file #write_text_file(rops_table(vol,1),"goo.txt") stat = Util.infomask(vol, mask3D, False) vol -= stat[0] Util.mul_scalar(vol, 1.0/stat[1]) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected. Util.mul_img(vol, mask3D) del mask3D # vol.write_image('toto%03d.hdf'%iter) # broadcast volume bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm) #========================================================================= return vol
def dovolume( ref_data ): from utilities import print_msg, read_text_row from filter import fit_tanh, filt_tanl from fundamentals import fshift from morphology import threshold # Prepare the reference in 3D alignment, this function corresponds to what do_volume does. # Input: list ref_data # 0 - mask # 1 - center flag # 2 - raw average # 3 - fsc result # Output: filtered, centered, and masked reference image # apply filtration (FSC) to reference image: global ref_ali2d_counter ref_ali2d_counter += 1 fl = ref_data[2].cmp("dot",ref_data[2], {"negative":0, "mask":ref_data[0]} ) print_msg("do_volume user function Step = %5d GOAL = %10.3e\n"%(ref_ali2d_counter,fl)) stat = Util.infomask(ref_data[2], ref_data[0], False) vol = ref_data[2] - stat[0] Util.mul_scalar(vol, 1.0/stat[1]) vol = threshold(vol) #Util.mul_img(vol, ref_data[0]) try: aa = read_text_row("flaa.txt")[0] fl = aa[0] aa=aa[1] except: fl = 0.4 aa = 0.2 msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa) print_msg(msg) from utilities import read_text_file from fundamentals import rops_table, fftip, fft from filter import filt_table, filt_btwl fftip(vol) try: rt = read_text_file( "pwreference.txt" ) ro = rops_table(vol) # Here unless I am mistaken it is enough to take the beginning of the reference pw. for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**0.5 vol = fft( filt_table( filt_tanl(vol, fl, aa), ro) ) msg = "Power spectrum adjusted\n" print_msg(msg) except: vol = fft( filt_tanl(vol, fl, aa) ) stat = Util.infomask(vol, ref_data[0], False) vol -= stat[0] Util.mul_scalar(vol, 1.0/stat[1]) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5) Util.mul_img(vol, ref_data[0]) if ref_data[1] == 1: cs = volf.phase_cog() msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n"%(cs[0], cs[1], cs[2]) print_msg(msg) volf = fshift(volf, -cs[0], -cs[1], -cs[2]) else: cs = [0.0]*3 return vol, cs
def cml_open_proj(stack, ir, ou, lf, hf, dpsi = 1): from projection import cml_sinogram from utilities import model_circle, get_params_proj, model_blank, get_im from fundamentals import fftip from filter import filt_tanh # number of projections if type(stack) == type(""): nprj = EMUtil.get_image_count(stack) else: nprj = len(stack) Prj = [] # list of projections Ori = [-1] * 4 * nprj # orientation intial (phi, theta, psi, index) for each projection for i in xrange(nprj): image = get_im(stack, i) # read initial angles if given try: Ori[4*i], Ori[4*i+1], Ori[4*i+2], s2x, s2y = get_params_proj(image) except: pass if(i == 0): nx = image.get_xsize() if(ou < 1): ou = nx // 2 - 1 diameter = int(2 * ou) mask2D = model_circle(ou, nx, nx) if ir > 0: mask2D -= model_circle(ir, nx, nx) # normalize under the mask [mean_a, sigma, imin, imax] = Util.infomask(image, mask2D, True) image -= mean_a Util.mul_scalar(image, 1.0/sigma) Util.mul_img(image, mask2D) # sinogram sino = cml_sinogram(image, diameter, dpsi) # prepare the cut positions in order to filter (lf: low freq; hf: high freq) ihf = min(int(2 * hf * diameter), diameter + (diameter + 1) % 2) ihf = ihf + (ihf + 1) % 2 # index ihf must be odd to take the img part ilf = max(int(2 * lf * diameter), 0) ilf = ilf + ilf % 2 # index ilf must be even to fall in the real part bdf = ihf - ilf + 1 # process lines nxe = sino.get_xsize() nye = sino.get_ysize() prj = model_blank(bdf, 2*nye) pp = model_blank(nxe, 2*nye) for li in xrange(nye): # get the line li line = Util.window(sino, nxe, 1, 1, 0, li-nye//2, 0) # u2 (not improve the results) #line = filt_tanh(line, ou / float(nx), ou / float(nx)) # normalize this line [mean_l, sigma_l, imin, imax] = Util.infomask(line, None, True) line = (line - mean_l) / sigma_l # fft fftip(line) # filter (cut part of coef) and create mirror line Util.cml_prepare_line(prj, line, ilf, ihf, li, nye) # store the projection Prj.append(prj) return Prj, Ori
def do_volume_mrk02(ref_data): """ data - projections (scattered between cpus) or the volume. If volume, just do the volume processing options - the same for all cpus return - volume the same for all cpus """ from EMAN2 import Util from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD from filter import filt_table from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank from fundamentals import rops_table, fftip, fft import types # Retrieve the function specific input arguments from ref_data data = ref_data[0] Tracker = ref_data[1] iter = ref_data[2] mpi_comm = ref_data[3] # # For DEBUG # print "Type of data %s" % (type(data)) # print "Type of Tracker %s" % (type(Tracker)) # print "Type of iter %s" % (type(iter)) # print "Type of mpi_comm %s" % (type(mpi_comm)) if (mpi_comm == None): mpi_comm = MPI_COMM_WORLD myid = mpi_comm_rank(mpi_comm) nproc = mpi_comm_size(mpi_comm) try: local_filter = Tracker["local_filter"] except: local_filter = False #========================================================================= # volume reconstruction if (type(data) == types.ListType): if Tracker["constants"]["CTF"]: vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \ symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"]) else: vol = recons3d_4nn_MPI (myid, data,\ symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm) else: vol = data if myid == 0: from morphology import threshold from filter import filt_tanl, filt_btwl from utilities import model_circle, get_im import types nx = vol.get_xsize() if (Tracker["constants"]["mask3D"] == None): mask3D = model_circle( int(Tracker["constants"]["radius"] * float(nx) / float(Tracker["constants"]["nnxo"]) + 0.5), nx, nx, nx) elif (Tracker["constants"]["mask3D"] == "auto"): from utilities import adaptive_mask mask3D = adaptive_mask(vol) else: if (type(Tracker["constants"]["mask3D"]) == types.StringType): mask3D = get_im(Tracker["constants"]["mask3D"]) else: mask3D = (Tracker["constants"]["mask3D"]).copy() nxm = mask3D.get_xsize() if (nx != nxm): from fundamentals import rot_shift3D mask3D = Util.window( rot_shift3D(mask3D, scale=float(nx) / float(nxm)), nx, nx, nx) nxm = mask3D.get_xsize() assert (nx == nxm) stat = Util.infomask(vol, mask3D, False) vol -= stat[0] Util.mul_scalar(vol, 1.0 / stat[1]) vol = threshold(vol) Util.mul_img(vol, mask3D) if (Tracker["PWadjustment"]): from utilities import read_text_file, write_text_file rt = read_text_file(Tracker["PWadjustment"]) fftip(vol) ro = rops_table(vol) # Here unless I am mistaken it is enough to take the beginning of the reference pw. for i in xrange(1, len(ro)): ro[i] = (rt[i] / ro[i])**Tracker["upscale"] #write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt") if Tracker["constants"]["sausage"]: ny = vol.get_ysize() y = float(ny) from math import exp for i in xrange(len(ro)): ro[i] *= \ (1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2)) if local_filter: # skip low-pass filtration vol = fft(filt_table(vol, ro)) else: if (type(Tracker["lowpass"]) == types.ListType): vol = fft( filt_table(filt_table(vol, Tracker["lowpass"]), ro)) else: vol = fft( filt_table( filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]), ro)) del ro else: if Tracker["constants"]["sausage"]: ny = vol.get_ysize() y = float(ny) ro = [0.0] * (ny // 2 + 2) from math import exp for i in xrange(len(ro)): ro[i] = \ (1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2)) fftip(vol) filt_table(vol, ro) del ro if not local_filter: if (type(Tracker["lowpass"]) == types.ListType): vol = filt_table(vol, Tracker["lowpass"]) else: vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]) if Tracker["constants"]["sausage"]: vol = fft(vol) if local_filter: from morphology import binarize if (myid == 0): nx = mask3D.get_xsize() else: nx = 0 nx = bcast_number_to_all(nx, source_node=0) # only main processor needs the two input volumes if (myid == 0): mask = binarize(mask3D, 0.5) locres = get_im(Tracker["local_filter"]) lx = locres.get_xsize() if (lx != nx): if (lx < nx): from fundamentals import fdecimate, rot_shift3D mask = Util.window( rot_shift3D(mask, scale=float(lx) / float(nx)), lx, lx, lx) vol = fdecimate(vol, lx, lx, lx) else: ERROR("local filter cannot be larger than input volume", "user function", 1) stat = Util.infomask(vol, mask, False) vol -= stat[0] Util.mul_scalar(vol, 1.0 / stat[1]) else: lx = 0 locres = model_blank(1, 1, 1) vol = model_blank(1, 1, 1) lx = bcast_number_to_all(lx, source_node=0) if (myid != 0): mask = model_blank(lx, lx, lx) bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm) from filter import filterlocal vol = filterlocal(locres, vol, mask, Tracker["falloff"], myid, 0, nproc) if myid == 0: if (lx < nx): from fundamentals import fpol vol = fpol(vol, nx, nx, nx) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected. Util.mul_img(vol, mask3D) del mask3D # vol.write_image('toto%03d.hdf'%iter) else: vol = model_blank(nx, nx, nx) else: if myid == 0: #from utilities import write_text_file #write_text_file(rops_table(vol,1),"goo.txt") stat = Util.infomask(vol, mask3D, False) vol -= stat[0] Util.mul_scalar(vol, 1.0 / stat[1]) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected. Util.mul_img(vol, mask3D) del mask3D # vol.write_image('toto%03d.hdf'%iter) # broadcast volume bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm) #========================================================================= return vol
def dovolume(ref_data): from utilities import print_msg, read_text_row from filter import fit_tanh, filt_tanl from fundamentals import fshift from morphology import threshold # Prepare the reference in 3D alignment, this function corresponds to what do_volume does. # Input: list ref_data # 0 - mask # 1 - center flag # 2 - raw average # 3 - fsc result # Output: filtered, centered, and masked reference image # apply filtration (FSC) to reference image: global ref_ali2d_counter ref_ali2d_counter += 1 fl = ref_data[2].cmp("dot", ref_data[2], { "negative": 0, "mask": ref_data[0] }) print_msg("do_volume user function Step = %5d GOAL = %10.3e\n" % (ref_ali2d_counter, fl)) stat = Util.infomask(ref_data[2], ref_data[0], False) vol = ref_data[2] - stat[0] Util.mul_scalar(vol, 1.0 / stat[1]) vol = threshold(vol) #Util.mul_img(vol, ref_data[0]) try: aa = read_text_row("flaa.txt")[0] fl = aa[0] aa = aa[1] except: fl = 0.4 aa = 0.2 msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % ( fl, aa) print_msg(msg) from utilities import read_text_file from fundamentals import rops_table, fftip, fft from filter import filt_table, filt_btwl fftip(vol) try: rt = read_text_file("pwreference.txt") ro = rops_table(vol) # Here unless I am mistaken it is enough to take the beginning of the reference pw. for i in xrange(1, len(ro)): ro[i] = (rt[i] / ro[i])**0.5 vol = fft(filt_table(filt_tanl(vol, fl, aa), ro)) msg = "Power spectrum adjusted\n" print_msg(msg) except: vol = fft(filt_tanl(vol, fl, aa)) stat = Util.infomask(vol, ref_data[0], False) vol -= stat[0] Util.mul_scalar(vol, 1.0 / stat[1]) vol = threshold(vol) vol = filt_btwl(vol, 0.38, 0.5) Util.mul_img(vol, ref_data[0]) if ref_data[1] == 1: cs = volf.phase_cog() msg = "Center x = %10.3f Center y = %10.3f Center z = %10.3f\n" % ( cs[0], cs[1], cs[2]) print_msg(msg) volf = fshift(volf, -cs[0], -cs[1], -cs[2]) else: cs = [0.0] * 3 return vol, cs
def filterlocal(ui, vi, m, falloff, myid, main_node, number_of_proc): from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv, mpi_send, mpi_recv from mpi import MPI_SUM, MPI_FLOAT, MPI_INT from utilities import bcast_number_to_all, bcast_list_to_all, model_blank, bcast_EMData_to_all, reduce_EMData_to_root from morphology import threshold_outside from filter import filt_tanl from fundamentals import fft, fftip if(myid == main_node): nx = vi.get_xsize() ny = vi.get_ysize() nz = vi.get_zsize() # Round all resolution numbers to two digits for x in xrange(nx): for y in xrange(ny): for z in xrange(nz): ui.set_value_at_fast( x,y,z, round(ui.get_value_at(x,y,z), 2) ) dis = [nx,ny,nz] else: falloff = 0.0 radius = 0 dis = [0,0,0] falloff = bcast_number_to_all(falloff, main_node) dis = bcast_list_to_all(dis, myid, source_node = main_node) if(myid != main_node): nx = int(dis[0]) ny = int(dis[1]) nz = int(dis[2]) vi = model_blank(nx,ny,nz) ui = model_blank(nx,ny,nz) bcast_EMData_to_all(vi, myid, main_node) bcast_EMData_to_all(ui, myid, main_node) fftip(vi) # volume to be filtered st = Util.infomask(ui, m, True) filteredvol = model_blank(nx,ny,nz) cutoff = max(st[2] - 0.01,0.0) while(cutoff < st[3] ): cutoff = round(cutoff + 0.01, 2) #if(myid == main_node): print cutoff,st pt = Util.infomask( threshold_outside(ui, cutoff - 0.00501, cutoff + 0.005), m, True) # Ideally, one would want to check only slices in question... if(pt[0] != 0.0): #print cutoff,pt[0] vovo = fft( filt_tanl(vi, cutoff, falloff) ) for z in xrange(myid, nz, number_of_proc): for x in xrange(nx): for y in xrange(ny): if(m.get_value_at(x,y,z) > 0.5): if(round(ui.get_value_at(x,y,z),2) == cutoff): filteredvol.set_value_at_fast(x,y,z,vovo.get_value_at(x,y,z)) mpi_barrier(MPI_COMM_WORLD) reduce_EMData_to_root(filteredvol, myid, main_node, MPI_COMM_WORLD) return filteredvol