示例#1
0
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
示例#2
0
def main():
	from logger import Logger, BaseLogger_Files
        arglist = []
        i = 0
        while( i < len(sys.argv) ):
            if sys.argv[i]=='-p4pg':
                i = i+2
            elif sys.argv[i]=='-p4wd':
                i = i+2
            else:
                arglist.append( sys.argv[i] )
                i = i+1
	progname = os.path.basename(arglist[0])
	usage = progname + " stack  outdir  <mask> --focus=3Dmask --radius=outer_radius --delta=angular_step" +\
	"--an=angular_neighborhood --maxit=max_iter  --CTF --sym=c1 --function=user_function --independent=indenpendent_runs  --number_of_images_per_group=number_of_images_per_group  --low_pass_frequency=.25  --seed=random_seed"
	parser = OptionParser(usage,version=SPARXVERSION)
	parser.add_option("--focus",                         type   ="string",        default ='',                    help="bineary 3D mask for focused clustering ")
	parser.add_option("--ir",                            type   = "int",          default =1, 	                  help="inner radius for rotational correlation > 0 (set to 1)")
	parser.add_option("--radius",                        type   = "int",          default =-1,	                  help="particle radius in pixel for rotational correlation <nx-1 (set to the radius of the particle)")
	parser.add_option("--maxit",	                     type   = "int",          default =25, 	                  help="maximum number of iteration")
	parser.add_option("--rs",                            type   = "int",          default =1,	                  help="step between rings in rotational correlation >0 (set to 1)" ) 
	parser.add_option("--xr",                            type   ="string",        default ='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 ='0.25',                help="step size of the translation search in both directions direction, search is -xr, -xr+ts, 0, xr-ts, xr ")
	parser.add_option("--delta",                         type   ="string",        default ='2',                   help="angular step of reference projections")
	parser.add_option("--an",                            type   ="string",        default ='-1',	              help="angular neighborhood for local searches")
	parser.add_option("--center",                        type   ="int",           default =0,	                  help="0 - if you do not want the volume to be centered, 1 - center the volume using cog (default=0)")
	parser.add_option("--nassign",                       type   ="int",           default =1, 	                  help="number of reassignment iterations performed for each angular step (set to 3) ")
	parser.add_option("--nrefine",                       type   ="int",           default =0, 	                  help="number of alignment iterations performed for each angular step (set to 0)")
	parser.add_option("--CTF",                           action ="store_true",    default =False,                 help="do CTF correction during clustring")
	parser.add_option("--stoprnct",                      type   ="float",         default =3.0,                   help="Minimum percentage of assignment change to stop the program")
	parser.add_option("--sym",                           type   ="string",        default ='c1',                  help="symmetry of the structure ")
	parser.add_option("--function",                      type   ="string",        default ='do_volume_mrk05',     help="name of the reference preparation function")
	parser.add_option("--independent",                   type   ="int",           default = 3,                    help="number of independent run")
	parser.add_option("--number_of_images_per_group",    type   ="int",           default =1000,                  help="number of groups")
	parser.add_option("--low_pass_filter",               type   ="float",         default =-1.0,                  help="absolute frequency of low-pass filter for 3d sorting on the original image size" )
	parser.add_option("--nxinit",                        type   ="int",           default =64,                    help="initial image size for sorting" )
	parser.add_option("--unaccounted",                   action ="store_true",    default =False,                 help="reconstruct the unaccounted images")
	parser.add_option("--seed",                          type   ="int",           default =-1,                    help="random seed for create initial random assignment for EQ Kmeans")
	parser.add_option("--smallest_group",                type   ="int",           default =500,                   help="minimum members for identified group")
	parser.add_option("--sausage",                       action ="store_true",    default =False,                 help="way of filter volume")
	parser.add_option("--chunkdir",                      type   ="string",        default ='',                    help="chunkdir for computing margin of error")
	parser.add_option("--PWadjustment",                  type   ="string",        default ='',                    help="1-D power spectrum of PDB file used for EM volume power spectrum correction")
	parser.add_option("--protein_shape",                 type   ="string",        default ='g',                   help="protein shape. It defines protein preferred orientation angles. Currently it has g and f two types ")
	parser.add_option("--upscale",                       type   ="float",         default =0.5,                   help=" scaling parameter to adjust the power spectrum of EM volumes")
	parser.add_option("--wn",                            type   ="int",           default =0,                     help="optimal window size for data processing")
	parser.add_option("--interpolation",                 type   ="string",        default ="4nn",                 help="3-d reconstruction interpolation method, two options trl and 4nn")
	(options, args) = parser.parse_args(arglist[1:])
	if len(args) < 1  or len(args) > 4:
    		print "usage: " + usage
    		print "Please run '" + progname + " -h' for detailed options"
	else:

		if len(args)>2:
			mask_file = args[2]
		else:
			mask_file = None

		orgstack                        =args[0]
		masterdir                       =args[1]
		global_def.BATCH = True
		#---initialize MPI related variables
		from mpi import mpi_init, mpi_comm_size, MPI_COMM_WORLD, mpi_comm_rank,mpi_barrier,mpi_bcast, mpi_bcast, MPI_INT,MPI_CHAR
		sys.argv = mpi_init(len(sys.argv),sys.argv)
		nproc    = mpi_comm_size(MPI_COMM_WORLD)
		myid     = mpi_comm_rank(MPI_COMM_WORLD)
		mpi_comm = MPI_COMM_WORLD
		main_node= 0
		# import some utilities
		from utilities import get_im,bcast_number_to_all,cmdexecute,write_text_file,read_text_file,wrap_mpi_bcast, get_params_proj, write_text_row
		from applications import recons3d_n_MPI, mref_ali3d_MPI, Kmref_ali3d_MPI
		from statistics import k_means_match_clusters_asg_new,k_means_stab_bbenum
		from applications import mref_ali3d_EQ_Kmeans, ali3d_mref_Kmeans_MPI  
		# Create the main log file
		from logger import Logger,BaseLogger_Files
		if myid ==main_node:
			log_main=Logger(BaseLogger_Files())
			log_main.prefix = masterdir+"/"
		else:
			log_main =None
		#--- fill input parameters into dictionary named after Constants
		Constants		                         ={}
		Constants["stack"]                       = args[0]
		Constants["masterdir"]                   = masterdir
		Constants["mask3D"]                      = mask_file
		Constants["focus3Dmask"]                 = options.focus
		Constants["indep_runs"]                  = options.independent
		Constants["stoprnct"]                    = options.stoprnct
		Constants["number_of_images_per_group"]  = options.number_of_images_per_group
		Constants["CTF"]                         = options.CTF
		Constants["maxit"]                       = options.maxit
		Constants["ir"]                          = options.ir 
		Constants["radius"]                      = options.radius 
		Constants["nassign"]                     = options.nassign
		Constants["rs"]                          = options.rs 
		Constants["xr"]                          = options.xr
		Constants["yr"]                          = options.yr
		Constants["ts"]                          = options.ts
		Constants["delta"]               		 = options.delta
		Constants["an"]                  		 = options.an
		Constants["sym"]                 		 = options.sym
		Constants["center"]              		 = options.center
		Constants["nrefine"]             		 = options.nrefine
		#Constants["fourvar"]            		 = options.fourvar 
		Constants["user_func"]           		 = options.function
		Constants["low_pass_filter"]     		 = options.low_pass_filter # enforced low_pass_filter
		#Constants["debug"]              		 = options.debug
		Constants["main_log_prefix"]     		 = args[1]
		#Constants["importali3d"]        		 = options.importali3d
		Constants["myid"]	             		 = myid
		Constants["main_node"]           		 = main_node
		Constants["nproc"]               		 = nproc
		Constants["log_main"]            		 = log_main
		Constants["nxinit"]              		 = options.nxinit
		Constants["unaccounted"]         		 = options.unaccounted
		Constants["seed"]                		 = options.seed
		Constants["smallest_group"]      		 = options.smallest_group
		Constants["sausage"]             		 = options.sausage
		Constants["chunkdir"]            		 = options.chunkdir
		Constants["PWadjustment"]        		 = options.PWadjustment
		Constants["upscale"]             		 = options.upscale
		Constants["wn"]                  		 = options.wn
		Constants["3d-interpolation"]    		 = options.interpolation
		Constants["protein_shape"]    		     = options.protein_shape 
		# -----------------------------------------------------
		#
		# Create and initialize Tracker dictionary with input options
		Tracker = 			    		{}
		Tracker["constants"]       = Constants
		Tracker["maxit"]           = Tracker["constants"]["maxit"]
		Tracker["radius"]          = Tracker["constants"]["radius"]
		#Tracker["xr"]             = ""
		#Tracker["yr"]             = "-1"  # Do not change!
		#Tracker["ts"]             = 1
		#Tracker["an"]             = "-1"
		#Tracker["delta"]          = "2.0"
		#Tracker["zoom"]           = True
		#Tracker["nsoft"]          = 0
		#Tracker["local"]          = False
		#Tracker["PWadjustment"]   = Tracker["constants"]["PWadjustment"]
		Tracker["upscale"]         = Tracker["constants"]["upscale"]
		#Tracker["upscale"]        = 0.5
		Tracker["applyctf"]        = False  #  Should the data be premultiplied by the CTF.  Set to False for local continuous.
		#Tracker["refvol"]         = None
		Tracker["nxinit"]          = Tracker["constants"]["nxinit"]
		#Tracker["nxstep"]         = 32
		Tracker["icurrentres"]     = -1
		#Tracker["ireachedres"]    = -1
		#Tracker["lowpass"]        = 0.4
		#Tracker["falloff"]        = 0.2
		#Tracker["inires"]         = options.inires  # Now in A, convert to absolute before using
		Tracker["fuse_freq"]       = 50  # Now in A, convert to absolute before using
		#Tracker["delpreviousmax"] = False
		#Tracker["anger"]          = -1.0
		#Tracker["shifter"]        = -1.0
		#Tracker["saturatecrit"]   = 0.95
		#Tracker["pixercutoff"]    = 2.0
		#Tracker["directory"]      = ""
		#Tracker["previousoutputdir"] = ""
		#Tracker["eliminated-outliers"] = False
		#Tracker["mainiteration"]  = 0
		#Tracker["movedback"]      = False
		#Tracker["state"]          = Tracker["constants"]["states"][0] 
		#Tracker["global_resolution"] =0.0
		Tracker["orgstack"]        = orgstack
		#--------------------------------------------------------------------
		# import from utilities
		from utilities import sample_down_1D_curve,get_initial_ID,remove_small_groups,print_upper_triangular_matrix,print_a_line_with_timestamp
		from utilities import print_dict,get_resolution_mrk01,partition_to_groups,partition_independent_runs,get_outliers
		from utilities import merge_groups, save_alist, margin_of_error, get_margin_of_error, do_two_way_comparison, select_two_runs, get_ali3d_params
		from utilities import counting_projections, unload_dict, load_dict, get_stat_proj, create_random_list, get_number_of_groups, recons_mref
		from utilities import apply_low_pass_filter, get_groups_from_partition, get_number_of_groups, get_complementary_elements_total, update_full_dict
		from utilities import count_chunk_members, set_filter_parameters_from_adjusted_fsc, adjust_fsc_down, get_two_chunks_from_stack
		####------------------------------------------------------------------
		#
		# Get the pixel size; if none, set to 1.0, and the original image size
		from utilities import get_shrink_data_huang
		if(myid == main_node):
			line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
			print(line+"Initialization of 3-D sorting")
			a = get_im(orgstack)
			nnxo = a.get_xsize()
			if( Tracker["nxinit"] > nnxo ):
				ERROR("Image size less than minimum permitted $d"%Tracker["nxinit"],"sxsort3d.py",1)
				nnxo = -1
			else:
				if Tracker["constants"]["CTF"]:
					i = a.get_attr('ctf')
					pixel_size = i.apix
					fq = pixel_size/Tracker["fuse_freq"]
				else:
					pixel_size = 1.0
					#  No pixel size, fusing computed as 5 Fourier pixels
					fq = 5.0/nnxo
					del a
		else:
			nnxo = 0
			fq = 0.0
			pixel_size = 1.0
		nnxo = bcast_number_to_all(nnxo, source_node = main_node)
		if( nnxo < 0 ):
			mpi_finalize()
			exit()
		pixel_size = bcast_number_to_all(pixel_size, source_node = main_node)
		fq         = bcast_number_to_all(fq, source_node = main_node)
		if Tracker["constants"]["wn"]==0:
			Tracker["constants"]["nnxo"]          = nnxo
		else:
			Tracker["constants"]["nnxo"]          = Tracker["constants"]["wn"]
			nnxo                                  = Tracker["constants"]["nnxo"]
		Tracker["constants"]["pixel_size"]        = pixel_size
		Tracker["fuse_freq"]                      = fq
		del fq, nnxo, pixel_size
		if(Tracker["constants"]["radius"] < 1):
			Tracker["constants"]["radius"]  = Tracker["constants"]["nnxo"]//2-2
		elif((2*Tracker["constants"]["radius"] +2) > Tracker["constants"]["nnxo"]):
			ERROR("Particle radius set too large!","sxsort3d.py",1,myid)
####-----------------------------------------------------------------------------------------
		# Master directory
		if myid == main_node:
			if masterdir =="":
				timestring = strftime("_%d_%b_%Y_%H_%M_%S", localtime())
				masterdir ="master_sort3d"+timestring
			li =len(masterdir)
			cmd="{} {}".format("mkdir", masterdir)
			os.system(cmd)
		else:
			li=0
		li = mpi_bcast(li,1,MPI_INT,main_node,MPI_COMM_WORLD)[0]
		if li>0:
			masterdir = mpi_bcast(masterdir,li,MPI_CHAR,main_node,MPI_COMM_WORLD)
			import string
			masterdir = string.join(masterdir,"")
		if myid ==main_node:
			print_dict(Tracker["constants"],"Permanent settings of 3-D sorting program")
		######### create a vstack from input stack to the local stack in masterdir
		# stack name set to default
		Tracker["constants"]["stack"]       = "bdb:"+masterdir+"/rdata"
		Tracker["constants"]["ali3d"]       = os.path.join(masterdir, "ali3d_init.txt")
		Tracker["constants"]["ctf_params"]  = os.path.join(masterdir, "ctf_params.txt")
		Tracker["constants"]["partstack"]   = Tracker["constants"]["ali3d"]  # also serves for refinement
		if myid == main_node:
			total_stack = EMUtil.get_image_count(Tracker["orgstack"])
		else:
			total_stack = 0
		total_stack = bcast_number_to_all(total_stack, source_node = main_node)
		mpi_barrier(MPI_COMM_WORLD)
		from time import sleep
		while not os.path.exists(masterdir):
				print  "Node ",myid,"  waiting..."
				sleep(5)
		mpi_barrier(MPI_COMM_WORLD)
		if myid == main_node:
			log_main.add("Sphire sort3d ")
			log_main.add("the sort3d master directory is "+masterdir)
		#####
		###----------------------------------------------------------------------------------
		# Initial data analysis and handle two chunk files
		from random import shuffle
		# Compute the resolution 
		#### make chunkdir dictionary for computing margin of error
		import user_functions
		user_func  = user_functions.factory[Tracker["constants"]["user_func"]]
		chunk_dict = {}
		chunk_list = []
		if myid == main_node:
			chunk_one = read_text_file(os.path.join(Tracker["constants"]["chunkdir"],"chunk0.txt"))
			chunk_two = read_text_file(os.path.join(Tracker["constants"]["chunkdir"],"chunk1.txt"))
		else:
			chunk_one = 0
			chunk_two = 0
		chunk_one = wrap_mpi_bcast(chunk_one, main_node)
		chunk_two = wrap_mpi_bcast(chunk_two, main_node)
		mpi_barrier(MPI_COMM_WORLD)
		######################## Read/write bdb: data on main node ############################
	   	if myid==main_node:
			if(orgstack[:4] == "bdb:"):	cmd = "{} {} {}".format("e2bdb.py", orgstack,"--makevstack="+Tracker["constants"]["stack"])
			else:  cmd = "{} {} {}".format("sxcpy.py", orgstack, Tracker["constants"]["stack"])
	   		cmdexecute(cmd)
			cmd = "{} {} {}".format("sxheader.py  --params=xform.projection", "--export="+Tracker["constants"]["ali3d"],orgstack)
			cmdexecute(cmd)
			cmd = "{} {} {}".format("sxheader.py  --params=ctf", "--export="+Tracker["constants"]["ctf_params"],orgstack)
			cmdexecute(cmd)
		mpi_barrier(MPI_COMM_WORLD)	   		   	
		########-----------------------------------------------------------------------------
		Tracker["total_stack"]              = total_stack
		Tracker["constants"]["total_stack"] = total_stack
		Tracker["shrinkage"]                = float(Tracker["nxinit"])/Tracker["constants"]["nnxo"]
		Tracker["radius"]                   = Tracker["constants"]["radius"]*Tracker["shrinkage"]
		if Tracker["constants"]["mask3D"]:
			Tracker["mask3D"] = os.path.join(masterdir,"smask.hdf")
		else:
			Tracker["mask3D"]  = None
		if Tracker["constants"]["focus3Dmask"]:
			Tracker["focus3D"] = os.path.join(masterdir,"sfocus.hdf")
		else:
			Tracker["focus3D"] = None
		if myid == main_node:
			if Tracker["constants"]["mask3D"]:
				mask_3D = get_shrink_3dmask(Tracker["nxinit"],Tracker["constants"]["mask3D"])
				mask_3D.write_image(Tracker["mask3D"])
			if Tracker["constants"]["focus3Dmask"]:
				mask_3D = get_shrink_3dmask(Tracker["nxinit"],Tracker["constants"]["focus3Dmask"])
				st = Util.infomask(mask_3D, None, True)
				if( st[0] == 0.0 ):  ERROR("sxrsort3d","incorrect focused mask, after binarize all values zero",1)
				mask_3D.write_image(Tracker["focus3D"])
				del mask_3D
		if Tracker["constants"]["PWadjustment"] !='':
			PW_dict              = {}
			nxinit_pwsp          = sample_down_1D_curve(Tracker["constants"]["nxinit"],Tracker["constants"]["nnxo"],Tracker["constants"]["PWadjustment"])
			Tracker["nxinit_PW"] = os.path.join(masterdir,"spwp.txt")
			if myid == main_node:  write_text_file(nxinit_pwsp,Tracker["nxinit_PW"])
			PW_dict[Tracker["constants"]["nnxo"]]   = Tracker["constants"]["PWadjustment"]
			PW_dict[Tracker["constants"]["nxinit"]] = Tracker["nxinit_PW"]
			Tracker["PW_dict"]                      = PW_dict
		mpi_barrier(MPI_COMM_WORLD)
		#-----------------------From two chunks to FSC, and low pass filter-----------------------------------------###
		for element in chunk_one: chunk_dict[element] = 0
		for element in chunk_two: chunk_dict[element] = 1
		chunk_list =[chunk_one, chunk_two]
		Tracker["chunk_dict"] = chunk_dict
		Tracker["P_chunk0"]   = len(chunk_one)/float(total_stack)
		Tracker["P_chunk1"]   = len(chunk_two)/float(total_stack)
		### create two volumes to estimate resolution
		if myid == main_node:
			for index in xrange(2): write_text_file(chunk_list[index],os.path.join(masterdir,"chunk%01d.txt"%index))
		mpi_barrier(MPI_COMM_WORLD)
		vols = []
		for index in xrange(2):
			data,old_shifts = get_shrink_data_huang(Tracker,Tracker["constants"]["nxinit"], os.path.join(masterdir,"chunk%01d.txt"%index), Tracker["constants"]["partstack"],myid,main_node,nproc,preshift=True)
			vol             = recons3d_4nn_ctf_MPI(myid=myid, prjlist=data,symmetry=Tracker["constants"]["sym"], finfo=None)
			if myid == main_node:
				vol.write_image(os.path.join(masterdir, "vol%d.hdf"%index))
			vols.append(vol)
			mpi_barrier(MPI_COMM_WORLD)
		if myid ==main_node:
			low_pass, falloff,currentres = get_resolution_mrk01(vols,Tracker["constants"]["radius"],Tracker["constants"]["nxinit"],masterdir,Tracker["mask3D"])
			if low_pass >Tracker["constants"]["low_pass_filter"]: low_pass= Tracker["constants"]["low_pass_filter"]
		else:
			low_pass    =0.0
			falloff     =0.0
			currentres  =0.0
		bcast_number_to_all(currentres,source_node = main_node)
		bcast_number_to_all(low_pass,source_node   = main_node)
		bcast_number_to_all(falloff,source_node    = main_node)
		Tracker["currentres"]                      = currentres
		Tracker["falloff"]                         = falloff
		if Tracker["constants"]["low_pass_filter"] ==-1.0:
			Tracker["low_pass_filter"] = min(.45,low_pass/Tracker["shrinkage"]) # no better than .45
		else:
			Tracker["low_pass_filter"] = min(.45,Tracker["constants"]["low_pass_filter"]/Tracker["shrinkage"])
		Tracker["lowpass"]             = Tracker["low_pass_filter"]
		Tracker["falloff"]             =.1
		Tracker["global_fsc"]          = os.path.join(masterdir, "fsc.txt")
		############################################################################################
		if myid == main_node:
			log_main.add("The command-line inputs are as following:")
			log_main.add("**********************************************************")
		for a in sys.argv:
			if myid == main_node:log_main.add(a)
		if myid == main_node:
			log_main.add("number of cpus used in this run is %d"%Tracker["constants"]["nproc"])
			log_main.add("**********************************************************")
		from filter import filt_tanl
		### START 3-D sorting
		if myid ==main_node:
			log_main.add("----------3-D sorting  program------- ")
			log_main.add("current resolution %6.3f for images of original size in terms of absolute frequency"%Tracker["currentres"])
			log_main.add("equivalent to %f Angstrom resolution"%(Tracker["constants"]["pixel_size"]/Tracker["currentres"]/Tracker["shrinkage"]))
			log_main.add("the user provided enforced low_pass_filter is %f"%Tracker["constants"]["low_pass_filter"])
			#log_main.add("equivalent to %f Angstrom resolution"%(Tracker["constants"]["pixel_size"]/Tracker["constants"]["low_pass_filter"]))
			for index in xrange(2):
				filt_tanl(get_im(os.path.join(masterdir,"vol%01d.hdf"%index)), Tracker["low_pass_filter"],Tracker["falloff"]).write_image(os.path.join(masterdir, "volf%01d.hdf"%index))
		mpi_barrier(MPI_COMM_WORLD)
		from utilities import get_input_from_string
		delta       = get_input_from_string(Tracker["constants"]["delta"])
		delta       = delta[0]
		from utilities import even_angles
		n_angles    = even_angles(delta, 0, 180)
		this_ali3d  = Tracker["constants"]["ali3d"]
		sampled     = get_stat_proj(Tracker,delta,this_ali3d)
		if myid ==main_node:
			nc = 0
			for a in sampled:
				if len(sampled[a])>0:
					nc += 1
			log_main.add("total sampled direction %10d  at angle step %6.3f"%(len(n_angles), delta)) 
			log_main.add("captured sampled directions %10d percentage covered by data  %6.3f"%(nc,float(nc)/len(n_angles)*100))
		number_of_images_per_group = Tracker["constants"]["number_of_images_per_group"]
		if myid ==main_node: log_main.add("user provided number_of_images_per_group %d"%number_of_images_per_group)
		Tracker["number_of_images_per_group"] = number_of_images_per_group
		number_of_groups = get_number_of_groups(total_stack,number_of_images_per_group)
		Tracker["number_of_groups"] =  number_of_groups
		generation     =0
		partition_dict ={}
		full_dict      ={}
		workdir =os.path.join(masterdir,"generation%03d"%generation)
		Tracker["this_dir"] = workdir
		if myid ==main_node:
			log_main.add("---- generation         %5d"%generation)
			log_main.add("number of images per group is set as %d"%number_of_images_per_group)
			log_main.add("the initial number of groups is  %10d "%number_of_groups)
			cmd="{} {}".format("mkdir",workdir)
			os.system(cmd)
		mpi_barrier(MPI_COMM_WORLD)
		list_to_be_processed = range(Tracker["constants"]["total_stack"])
		Tracker["this_data_list"] = list_to_be_processed
		create_random_list(Tracker)
		#################################
		full_dict ={}
		for iptl in xrange(Tracker["constants"]["total_stack"]):
			 full_dict[iptl]    = iptl
		Tracker["full_ID_dict"] = full_dict
		################################# 	
		for indep_run in xrange(Tracker["constants"]["indep_runs"]):
			Tracker["this_particle_list"] = Tracker["this_indep_list"][indep_run]
			ref_vol =  recons_mref(Tracker)
			if myid == main_node: log_main.add("independent run  %10d"%indep_run)
			mpi_barrier(MPI_COMM_WORLD)
			Tracker["this_data_list"]          = list_to_be_processed
			Tracker["total_stack"]             = len(Tracker["this_data_list"])
			Tracker["this_particle_text_file"] = os.path.join(workdir,"independent_list_%03d.txt"%indep_run) # for get_shrink_data
			if myid == main_node: write_text_file(Tracker["this_data_list"], Tracker["this_particle_text_file"])
			mpi_barrier(MPI_COMM_WORLD)
			outdir  = os.path.join(workdir, "EQ_Kmeans%03d"%indep_run)
			ref_vol = apply_low_pass_filter(ref_vol,Tracker)
			mref_ali3d_EQ_Kmeans(ref_vol, outdir, Tracker["this_particle_text_file"], Tracker)
			partition_dict[indep_run]=Tracker["this_partition"]
		Tracker["partition_dict"]    = partition_dict
		Tracker["total_stack"]       = len(Tracker["this_data_list"])
		Tracker["this_total_stack"]  = Tracker["total_stack"]
		###############################
		do_two_way_comparison(Tracker)
		###############################
		ref_vol_list = []
		from time import sleep
		number_of_ref_class = []
		for igrp in xrange(len(Tracker["two_way_stable_member"])):
			Tracker["this_data_list"]      = Tracker["two_way_stable_member"][igrp]
			Tracker["this_data_list_file"] = os.path.join(workdir,"stable_class%d.txt"%igrp)
			if myid == main_node:
				write_text_file(Tracker["this_data_list"], Tracker["this_data_list_file"])
			data,old_shifts = get_shrink_data_huang(Tracker,Tracker["nxinit"], Tracker["this_data_list_file"], Tracker["constants"]["partstack"], myid, main_node, nproc, preshift = True)
			volref          = recons3d_4nn_ctf_MPI(myid=myid, prjlist = data, symmetry=Tracker["constants"]["sym"], finfo = None)
			ref_vol_list.append(volref)
			number_of_ref_class.append(len(Tracker["this_data_list"]))
			if myid == main_node:
				log_main.add("group  %d  members %d "%(igrp,len(Tracker["this_data_list"])))
		Tracker["number_of_ref_class"] = number_of_ref_class
		nx_of_image = ref_vol_list[0].get_xsize()
		if Tracker["constants"]["PWadjustment"]:
			Tracker["PWadjustment"] = Tracker["PW_dict"][nx_of_image]
		else:
			Tracker["PWadjustment"] = Tracker["constants"]["PWadjustment"]	 # no PW adjustment
		if myid == main_node:
			for iref in xrange(len(ref_vol_list)):
				refdata    = [None]*4
				refdata[0] = ref_vol_list[iref]
				refdata[1] = Tracker
				refdata[2] = Tracker["constants"]["myid"]
				refdata[3] = Tracker["constants"]["nproc"]
				volref     = user_func(refdata)
				volref.write_image(os.path.join(workdir,"volf_stable.hdf"),iref)
		mpi_barrier(MPI_COMM_WORLD)
		Tracker["this_data_list"]           = Tracker["this_accounted_list"]
		outdir                              = os.path.join(workdir,"Kmref")  
		empty_group, res_groups, final_list = ali3d_mref_Kmeans_MPI(ref_vol_list,outdir,Tracker["this_accounted_text"],Tracker)
		Tracker["this_unaccounted_list"]    = get_complementary_elements(list_to_be_processed,final_list)
		if myid == main_node:
			log_main.add("the number of particles not processed is %d"%len(Tracker["this_unaccounted_list"]))
			write_text_file(Tracker["this_unaccounted_list"],Tracker["this_unaccounted_text"])
		update_full_dict(Tracker["this_unaccounted_list"], Tracker)
		#######################################
		number_of_groups    = len(res_groups)
		vol_list            = []
		number_of_ref_class = []
		for igrp in xrange(number_of_groups):
			data,old_shifts = get_shrink_data_huang(Tracker, Tracker["constants"]["nnxo"], os.path.join(outdir,"Class%d.txt"%igrp), Tracker["constants"]["partstack"],myid,main_node,nproc,preshift = True)
			volref          = recons3d_4nn_ctf_MPI(myid=myid, prjlist = data, symmetry=Tracker["constants"]["sym"], finfo=None)
			vol_list.append(volref)

			if( myid == main_node ):  npergroup = len(read_text_file(os.path.join(outdir,"Class%d.txt"%igrp)))
			else:  npergroup = 0
			npergroup = bcast_number_to_all(npergroup, main_node )
			number_of_ref_class.append(npergroup)

		Tracker["number_of_ref_class"] = number_of_ref_class
		
		mpi_barrier(MPI_COMM_WORLD)
		nx_of_image = vol_list[0].get_xsize()
		if Tracker["constants"]["PWadjustment"]:
			Tracker["PWadjustment"]=Tracker["PW_dict"][nx_of_image]
		else:
			Tracker["PWadjustment"]=Tracker["constants"]["PWadjustment"]	

		if myid == main_node:
			for ivol in xrange(len(vol_list)):
				refdata     =[None]*4
				refdata[0] = vol_list[ivol]
				refdata[1] = Tracker
				refdata[2] = Tracker["constants"]["myid"]
				refdata[3] = Tracker["constants"]["nproc"] 
				volref = user_func(refdata)
				volref.write_image(os.path.join(workdir,"volf_of_Classes.hdf"),ivol)
				log_main.add("number of unaccounted particles  %10d"%len(Tracker["this_unaccounted_list"]))
				log_main.add("number of accounted particles  %10d"%len(Tracker["this_accounted_list"]))
				
		Tracker["this_data_list"]    = Tracker["this_unaccounted_list"]   # reset parameters for the next round calculation
		Tracker["total_stack"]       = len(Tracker["this_unaccounted_list"])
		Tracker["this_total_stack"]  = Tracker["total_stack"]
		number_of_groups             = get_number_of_groups(len(Tracker["this_unaccounted_list"]),number_of_images_per_group)
		Tracker["number_of_groups"]  =  number_of_groups
		while number_of_groups >= 2 :
			generation     +=1
			partition_dict ={}
			workdir =os.path.join(masterdir,"generation%03d"%generation)
			Tracker["this_dir"] = workdir
			if myid ==main_node:
				log_main.add("*********************************************")
				log_main.add("-----    generation             %5d    "%generation)
				log_main.add("number of images per group is set as %10d "%number_of_images_per_group)
				log_main.add("the number of groups is  %10d "%number_of_groups)
				log_main.add(" number of particles for clustering is %10d"%Tracker["total_stack"])
				cmd ="{} {}".format("mkdir",workdir)
				os.system(cmd)
			mpi_barrier(MPI_COMM_WORLD)
			create_random_list(Tracker)
			for indep_run in xrange(Tracker["constants"]["indep_runs"]):
				Tracker["this_particle_list"] = Tracker["this_indep_list"][indep_run]
				ref_vol                       = recons_mref(Tracker)
				if myid == main_node:
					log_main.add("independent run  %10d"%indep_run)
					outdir = os.path.join(workdir, "EQ_Kmeans%03d"%indep_run)
				Tracker["this_data_list"]   = Tracker["this_unaccounted_list"]
				#ref_vol=apply_low_pass_filter(ref_vol,Tracker)
				mref_ali3d_EQ_Kmeans(ref_vol,outdir,Tracker["this_unaccounted_text"],Tracker)
				partition_dict[indep_run]   = Tracker["this_partition"]
				Tracker["this_data_list"]   = Tracker["this_unaccounted_list"]
				Tracker["total_stack"]      = len(Tracker["this_unaccounted_list"])
				Tracker["partition_dict"]   = partition_dict
				Tracker["this_total_stack"] = Tracker["total_stack"]
			total_list_of_this_run          = Tracker["this_unaccounted_list"]
			###############################
			do_two_way_comparison(Tracker)
			###############################
			ref_vol_list        = []
			number_of_ref_class = []
			for igrp in xrange(len(Tracker["two_way_stable_member"])):
				Tracker["this_data_list"]      = Tracker["two_way_stable_member"][igrp]
				Tracker["this_data_list_file"] = os.path.join(workdir,"stable_class%d.txt"%igrp)
				if myid == main_node: write_text_file(Tracker["this_data_list"], Tracker["this_data_list_file"])
				mpi_barrier(MPI_COMM_WORLD)
				data,old_shifts  = get_shrink_data_huang(Tracker,Tracker["constants"]["nxinit"],Tracker["this_data_list_file"],Tracker["constants"]["partstack"],myid,main_node,nproc,preshift = True)
				volref           = recons3d_4nn_ctf_MPI(myid=myid, prjlist = data, symmetry=Tracker["constants"]["sym"],finfo= None)
				#volref = filt_tanl(volref, Tracker["constants"]["low_pass_filter"],.1)
				if myid == main_node:volref.write_image(os.path.join(workdir,"vol_stable.hdf"),iref)
				#volref = resample(volref,Tracker["shrinkage"])
				ref_vol_list.append(volref)
				number_of_ref_class.append(len(Tracker["this_data_list"]))
				mpi_barrier(MPI_COMM_WORLD)
			Tracker["number_of_ref_class"]      = number_of_ref_class
			Tracker["this_data_list"]           = Tracker["this_accounted_list"]
			outdir                              = os.path.join(workdir,"Kmref")
			empty_group, res_groups, final_list = ali3d_mref_Kmeans_MPI(ref_vol_list,outdir,Tracker["this_accounted_text"],Tracker)
			# calculate the 3-D structure of original image size for each group
			number_of_groups                    =  len(res_groups)
			Tracker["this_unaccounted_list"]    = get_complementary_elements(total_list_of_this_run,final_list)
			if myid == main_node:
				log_main.add("the number of particles not processed is %d"%len(Tracker["this_unaccounted_list"]))
				write_text_file(Tracker["this_unaccounted_list"],Tracker["this_unaccounted_text"])
			mpi_barrier(MPI_COMM_WORLD)
			update_full_dict(Tracker["this_unaccounted_list"],Tracker)
			vol_list = []
			for igrp in xrange(number_of_groups):
				data,old_shifts = get_shrink_data_huang(Tracker,Tracker["constants"]["nnxo"], os.path.join(outdir,"Class%d.txt"%igrp), Tracker["constants"]["partstack"], myid, main_node, nproc,preshift = True)
				volref = recons3d_4nn_ctf_MPI(myid=myid, prjlist = data, symmetry=Tracker["constants"]["sym"],finfo= None)
				vol_list.append(volref)

			mpi_barrier(MPI_COMM_WORLD)
			nx_of_image=ref_vol_list[0].get_xsize()
			if Tracker["constants"]["PWadjustment"]:
				Tracker["PWadjustment"] = Tracker["PW_dict"][nx_of_image]
			else:
				Tracker["PWadjustment"] = Tracker["constants"]["PWadjustment"]	

			if myid == main_node:
				for ivol in xrange(len(vol_list)):
					refdata    = [None]*4
					refdata[0] = vol_list[ivol]
					refdata[1] = Tracker
					refdata[2] = Tracker["constants"]["myid"]
					refdata[3] = Tracker["constants"]["nproc"] 
					volref     = user_func(refdata)
					volref.write_image(os.path.join(workdir, "volf_of_Classes.hdf"),ivol)
				log_main.add("number of unaccounted particles  %10d"%len(Tracker["this_unaccounted_list"]))
				log_main.add("number of accounted particles  %10d"%len(Tracker["this_accounted_list"]))
			del vol_list
			mpi_barrier(MPI_COMM_WORLD)
			number_of_groups            = get_number_of_groups(len(Tracker["this_unaccounted_list"]),number_of_images_per_group)
			Tracker["number_of_groups"] =  number_of_groups
			Tracker["this_data_list"]   = Tracker["this_unaccounted_list"]
			Tracker["total_stack"]      = len(Tracker["this_unaccounted_list"])
		if Tracker["constants"]["unaccounted"]:
			data,old_shifts = get_shrink_data_huang(Tracker,Tracker["constants"]["nnxo"],Tracker["this_unaccounted_text"],Tracker["constants"]["partstack"],myid,main_node,nproc,preshift = True)
			volref          = recons3d_4nn_ctf_MPI(myid=myid, prjlist = data, symmetry=Tracker["constants"]["sym"],finfo= None)
			nx_of_image     = volref.get_xsize()
			if Tracker["constants"]["PWadjustment"]:
				Tracker["PWadjustment"]=Tracker["PW_dict"][nx_of_image]
			else:
				Tracker["PWadjustment"]=Tracker["constants"]["PWadjustment"]	
			if( myid == main_node ):
				refdata    = [None]*4
				refdata[0] = volref
				refdata[1] = Tracker
				refdata[2] = Tracker["constants"]["myid"]
				refdata[3] = Tracker["constants"]["nproc"]
				volref     = user_func(refdata)
				#volref    = filt_tanl(volref, Tracker["constants"]["low_pass_filter"],.1)
				volref.write_image(os.path.join(workdir,"volf_unaccounted.hdf"))
		# Finish program
		if myid ==main_node: log_main.add("sxsort3d finishes")
		mpi_barrier(MPI_COMM_WORLD)
		from mpi import mpi_finalize
		mpi_finalize()
		exit()
示例#3
0
def do_volume_mrk03(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_4nnw_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"])
			vol = recons3d_4nnw_MPI(myid, data, Tracker["bckgnoise"], 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 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 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)
			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)
			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
示例#4
0
def main():
	import	global_def
	from	optparse 	import OptionParser
	from	EMAN2 		import EMUtil
	import	os
	import	sys
	from time import time

	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()
	
	from mpi          import mpi_init, mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD, MPI_TAG_UB
	from mpi          import mpi_barrier, mpi_send, mpi_recv, mpi_bcast, MPI_INT, mpi_finalize, MPI_FLOAT
	from applications import MPI_start_end, within_group_refinement, ali2d_ras
	from pixel_error  import multi_align_stability
	from utilities    import send_EMData, recv_EMData
	from utilities    import get_image, bcast_number_to_all, set_params2D, get_params2D
	from utilities    import group_proj_by_phitheta, model_circle, get_input_from_string

	sys.argv = mpi_init(len(sys.argv), sys.argv)
	myid = mpi_comm_rank(MPI_COMM_WORLD)
	number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
	main_node = 0

	if len(args) == 2:
		stack  = args[0]
		outdir = args[1]
	else:
		ERROR("incomplete list of arguments", "sxproj_stability", 1, myid=myid)
		exit()
	if not options.MPI:
		ERROR("Non-MPI not supported!", "sxproj_stability", myid=myid)
		exit()		 

	if global_def.CACHE_DISABLE:
		from utilities import disable_bdb_cache
		disable_bdb_cache()
	global_def.BATCH = True

	#if os.path.exists(outdir):  ERROR('Output directory exists, please change the name and restart the program', "sxproj_stability", 1, myid)
	#mpi_barrier(MPI_COMM_WORLD)

	
	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:
			print "  A  ",myid,"  ",time()-st
			proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
			proj_params = []
			for i in xrange(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
			print "  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 xrange(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, MPI_TAG_UB, MPI_COMM_WORLD)
				mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT, proc_to_stay, MPI_TAG_UB, MPI_COMM_WORLD)
			elif myid == proc_to_stay:
				img_per_grp = mpi_recv(1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
				img_per_grp = int(img_per_grp[0])
				temp = mpi_recv(img_per_grp, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
				proj_list.append(map(int, temp))
				del temp
			mpi_barrier(MPI_COMM_WORLD)
		print "  C  ",myid,"  ",time()-st
		if myid == main_node:
			# Assign the remaining groups to main_node
			for i in xrange(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","sxproj_stability",1)
		from 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 xrange(len(refprojdir)):
			ref_ang[i*2]   = refprojdir[0][0]
			ref_ang[i*2+1] = refprojdir[0][1]+i*0.1

		print "  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)
		"""
		print "  B  ",myid,"  ",time()-st

		proj_ang = [0.0]*(nima*2)
		for i in xrange(nima):
			dp = proj_attr[i].get_params("spider")
			proj_ang[i*2]   = dp["phi"]
			proj_ang[i*2+1] = dp["theta"]
		print "  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 xrange(len(refprojdir)):
			proj_list.append(asi[i*img_per_grp:(i+1)*img_per_grp])
		del asi
		print "  D  ",myid,"  ",time()-st
		#from sys import exit
		#exit()


	#   Compute stability per projection
	elif options.grouping == "PPR":
		print "  A  ",myid,"  ",time()-st
		proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
		print "  B  ",myid,"  ",time()-st
		proj_params = []
		for i in xrange(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)
		print "  C  ",myid,"  ",time()-st
		from utilities import nearest_proj
		proj_list, mirror_list = nearest_proj(proj_params, img_per_grp, range(img_begin, img_begin+1))#range(img_begin, img_end))
		refprojdir = proj_params[img_begin: img_end]
		del proj_params, mirror_list
		print "  D  ",myid,"  ",time()-st
	else:  ERROR("Incorrect projection grouping option","sxproj_stability",1)
	"""
	from utilities import write_text_file
	for i in xrange(len(proj_list)):
		write_text_file(proj_list[i],"projlist%06d_%04d"%(i,myid))
	"""

	###########################################################################################################
	# Begin stability test
	from 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 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 xrange(len(proj_list)):
		print "  E  ",myid,"  ",time()-st
		class_data = EMData.read_images(stack, proj_list[i])
		#print "  R  ",myid,"  ",time()-st
		if options.CTF :
			from filter import filt_ctf
			for im in xrange(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 xrange(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 xrange(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 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 xrange(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, sys = 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:
			print  " 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 xrange(number_of_proc):
			if i == main_node :
				for im in xrange(len(aveList)):
					aveList[im].write_image(args[1], km)
					km += 1
			else:
				nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
				nl = int(nl[0])
				for im in xrange(nl):
					ave = recv_EMData(i, im+i+70000)
					nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
					nm = int(nm[0])
					members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
					ave.set_attr('members', map(int, members))
					members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
					ave.set_attr('pixerr', map(float, members))
					members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
					ave.set_attr('refprojdir', map(float, members))
					ave.write_image(args[1], km)
					km += 1
	else:
		mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
		for im in xrange(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, MPI_TAG_UB, MPI_COMM_WORLD)
			mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
			members = aveList[im].get_attr('pixerr')
			mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
			try:
				members = aveList[im].get_attr('refprojdir')
				mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
			except:
				mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)

	global_def.BATCH = False
	mpi_barrier(MPI_COMM_WORLD)
	from mpi import mpi_finalize
	mpi_finalize()
示例#5
0
def ali3d_MPI(stack, ref_vol, outdir, maskfile = None, ir = 1, ou = -1, rs = 1, 
	    xr = "4 2 2 1", yr = "-1", ts = "1 1 0.5 0.25", delta = "10 6 4 4", an = "-1",
	    center = 0, maxit = 5, term = 95, CTF = False, fourvar = False, snr = 1.0,  ref_a = "S", sym = "c1", 
	    sort=True, cutoff=999.99, pix_cutoff="0", two_tail=False, model_jump="1 1 1 1 1", restart=False, save_half=False,
	    protos=None, oplane=None, lmask=-1, ilmask=-1, findseam=False, vertstep=None, hpars="-1", hsearch="73.0 170.0",
	    full_output = False, compare_repro = False, compare_ref_free = "-1", ref_free_cutoff= "-1 -1 -1 -1",
	    wcmask = None, debug = False, recon_pad = 4):

	from alignment      import Numrinit, prepare_refrings
	from utilities      import model_circle, get_image, drop_image, get_input_from_string
	from utilities      import bcast_list_to_all, bcast_number_to_all, reduce_EMData_to_root, bcast_EMData_to_all 
	from utilities      import send_attr_dict
	from utilities      import get_params_proj, file_type
	from fundamentals   import rot_avg_image
	import os
	import types
	from utilities      import print_begin_msg, print_end_msg, print_msg
	from mpi	    import mpi_bcast, mpi_comm_size, mpi_comm_rank, MPI_FLOAT, MPI_COMM_WORLD, mpi_barrier, mpi_reduce
	from mpi	    import mpi_reduce, MPI_INT, MPI_SUM, mpi_finalize
	from filter	 import filt_ctf
	from projection     import prep_vol, prgs
	from statistics     import hist_list, varf3d_MPI, fsc_mask
	from numpy	  import array, bincount, array2string, ones

	number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
	myid	   = mpi_comm_rank(MPI_COMM_WORLD)
	main_node = 0
	if myid == main_node:
		if os.path.exists(outdir):  ERROR('Output directory exists, please change the name and restart the program', "ali3d_MPI", 1)
		os.mkdir(outdir)
	mpi_barrier(MPI_COMM_WORLD)

	if debug:
		from time import sleep
		while not os.path.exists(outdir):
			print  "Node ",myid,"  waiting..."
			sleep(5)

		info_file = os.path.join(outdir, "progress%04d"%myid)
		finfo = open(info_file, 'w')
	else:
		finfo = None
	mjump = get_input_from_string(model_jump)
	xrng	= get_input_from_string(xr)
	if  yr == "-1":  yrng = xrng
	else	  :  yrng = get_input_from_string(yr)
	step	= get_input_from_string(ts)
	delta       = get_input_from_string(delta)
	ref_free_cutoff = get_input_from_string(ref_free_cutoff)	
	pix_cutoff = get_input_from_string(pix_cutoff)
	
	lstp = min(len(xrng), len(yrng), len(step), len(delta))
	if an == "-1":
		an = [-1] * lstp
	else:
		an = get_input_from_string(an)
	# make sure pix_cutoff is set for all iterations
	if len(pix_cutoff)<lstp:
		for i in xrange(len(pix_cutoff),lstp):
			pix_cutoff.append(pix_cutoff[-1])
	# don't waste time on sub-pixel alignment for low-resolution ang incr
	for i in range(len(step)):
		if (delta[i] > 4 or delta[i] == -1) and step[i] < 1:
			step[i] = 1

	first_ring  = int(ir)
	rstep       = int(rs)
	last_ring   = int(ou)
	max_iter    = int(maxit)
	center      = int(center)

	nrefs   = EMUtil.get_image_count( ref_vol )
	nmasks = 0
	if maskfile:
		# read number of masks within each maskfile (mc)
		nmasks   = EMUtil.get_image_count( maskfile )
		# open masks within maskfile (mc)
		maskF   = EMData.read_images(maskfile, xrange(nmasks))
	vol     = EMData.read_images(ref_vol, xrange(nrefs))
	nx      = vol[0].get_xsize()

	## make sure box sizes are the same
	if myid == main_node:
		im=EMData.read_images(stack,[0])
		bx = im[0].get_xsize()
		if bx!=nx:
			print_msg("Error: Stack box size (%i) differs from initial model (%i)\n"%(bx,nx))
			sys.exit()
		del im,bx
	
	# for helical processing:
	helicalrecon = False
	if protos is not None or hpars != "-1" or findseam is True:
		helicalrecon = True
		# if no out-of-plane param set, use 5 degrees
		if oplane is None:
			oplane=5.0
	if protos is not None:
		proto = get_input_from_string(protos)
		if len(proto) != nrefs:
			print_msg("Error: insufficient protofilament numbers supplied")
			sys.exit()
	if hpars != "-1":
		hpars = get_input_from_string(hpars)
		if len(hpars) != 2*nrefs:
			print_msg("Error: insufficient helical parameters supplied")
			sys.exit()
	## create helical parameter file for helical reconstruction
	if helicalrecon is True and myid == main_node:
		from hfunctions import createHpar
		# create initial helical parameter files
		dp=[0]*nrefs
		dphi=[0]*nrefs
		vdp=[0]*nrefs
		vdphi=[0]*nrefs
		for iref in xrange(nrefs):
			hpar = os.path.join(outdir,"hpar%02d.spi"%(iref))
			params = False
			if hpars != "-1":
				# if helical parameters explicitly given, set twist & rise
				params = [float(hpars[iref*2]),float(hpars[(iref*2)+1])]
			dp[iref],dphi[iref],vdp[iref],vdphi[iref] = createHpar(hpar,proto[iref],params,vertstep)

	# get values for helical search parameters
	hsearch = get_input_from_string(hsearch)
	if len(hsearch) != 2:
		print_msg("Error: specify outer and inner radii for helical search")
		sys.exit()

	if last_ring < 0 or last_ring > int(nx/2)-2 :	last_ring = int(nx/2) - 2

	if myid == main_node:
	#	import user_functions
	#	user_func = user_functions.factory[user_func_name]

		print_begin_msg("ali3d_MPI")
		print_msg("Input stack		 : %s\n"%(stack))
		print_msg("Reference volume	    : %s\n"%(ref_vol))	
		print_msg("Output directory	    : %s\n"%(outdir))
		if nmasks > 0:
			print_msg("Maskfile (number of masks)  : %s (%i)\n"%(maskfile,nmasks))
		print_msg("Inner radius		: %i\n"%(first_ring))
		print_msg("Outer radius		: %i\n"%(last_ring))
		print_msg("Ring step		   : %i\n"%(rstep))
		print_msg("X search range	      : %s\n"%(xrng))
		print_msg("Y search range	      : %s\n"%(yrng))
		print_msg("Translational step	  : %s\n"%(step))
		print_msg("Angular step		: %s\n"%(delta))
		print_msg("Angular search range	: %s\n"%(an))
		print_msg("Maximum iteration	   : %i\n"%(max_iter))
		print_msg("Center type		 : %i\n"%(center))
		print_msg("CTF correction	      : %s\n"%(CTF))
		print_msg("Signal-to-Noise Ratio       : %f\n"%(snr))
		print_msg("Reference projection method : %s\n"%(ref_a))
		print_msg("Symmetry group	      : %s\n"%(sym))
		print_msg("Fourier padding for 3D      : %i\n"%(recon_pad))
		print_msg("Number of reference models  : %i\n"%(nrefs))
		print_msg("Sort images between models  : %s\n"%(sort))
		print_msg("Allow images to jump	: %s\n"%(mjump))
		print_msg("CC cutoff standard dev      : %f\n"%(cutoff))
		print_msg("Two tail cutoff	     : %s\n"%(two_tail))
		print_msg("Termination pix error       : %f\n"%(term))
		print_msg("Pixel error cutoff	  : %s\n"%(pix_cutoff))
		print_msg("Restart		     : %s\n"%(restart))
		print_msg("Full output		 : %s\n"%(full_output))
		print_msg("Compare reprojections       : %s\n"%(compare_repro))
		print_msg("Compare ref free class avgs : %s\n"%(compare_ref_free))
		print_msg("Use cutoff from ref free    : %s\n"%(ref_free_cutoff))
		if protos:
			print_msg("Protofilament numbers	: %s\n"%(proto))
			print_msg("Using helical search range   : %s\n"%hsearch) 
		if findseam is True:
			print_msg("Using seam-based reconstruction\n")
		if hpars != "-1":
			print_msg("Using hpars		  : %s\n"%hpars)
		if vertstep != None:
			print_msg("Using vertical step    : %.2f\n"%vertstep)
		if save_half is True:
			print_msg("Saving even/odd halves\n")
		for i in xrange(100) : print_msg("*")
		print_msg("\n\n")
	if maskfile:
		if type(maskfile) is types.StringType: mask3D = get_image(maskfile)
		else:				  mask3D = maskfile
	else: mask3D = model_circle(last_ring, nx, nx, nx)

	numr	= Numrinit(first_ring, last_ring, rstep, "F")
	mask2D  = model_circle(last_ring,nx,nx) - model_circle(first_ring,nx,nx)

	fscmask = model_circle(last_ring,nx,nx,nx)
	if CTF:
		from filter	 import filt_ctf
	from reconstruction_rjh import rec3D_MPI_noCTF

	if myid == main_node:
		active = EMUtil.get_all_attributes(stack, 'active')
		list_of_particles = []
		for im in xrange(len(active)):
			if active[im]:  list_of_particles.append(im)
		del active
		nima = len(list_of_particles)
	else:
		nima = 0
	total_nima = bcast_number_to_all(nima, source_node = main_node)

	if myid != main_node:
		list_of_particles = [-1]*total_nima
	list_of_particles = bcast_list_to_all(list_of_particles, source_node = main_node)

	image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)

	# create a list of images for each node
	list_of_particles = list_of_particles[image_start: image_end]
	nima = len(list_of_particles)
	if debug:
		finfo.write("image_start, image_end: %d %d\n" %(image_start, image_end))
		finfo.flush()

	data = EMData.read_images(stack, list_of_particles)

	t_zero = Transform({"type":"spider","phi":0,"theta":0,"psi":0,"tx":0,"ty":0})
	transmulti = [[t_zero for i in xrange(nrefs)] for j in xrange(nima)]

	for iref,im in ((iref,im) for iref in xrange(nrefs) for im in xrange(nima)):
		if nrefs == 1:
			transmulti[im][iref] = data[im].get_attr("xform.projection")
		else:
			# if multi models, keep track of eulers for all models
			try:
				transmulti[im][iref] = data[im].get_attr("eulers_txty.%i"%iref)
			except:
				data[im].set_attr("eulers_txty.%i"%iref,t_zero)

	scoremulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)] 
	pixelmulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)] 
	ref_res = [0.0 for x in xrange(nrefs)] 
	apix = data[0].get_attr('apix_x')

	# for oplane parameter, create cylindrical mask
	if oplane is not None and myid == main_node:
		from hfunctions import createCylMask
		cmaskf=os.path.join(outdir, "mask3D_cyl.mrc")
		mask3D = createCylMask(data,ou,lmask,ilmask,cmaskf)
		# if finding seam of helix, create wedge masks
		if findseam is True:
			wedgemask=[]
			for pf in xrange(nrefs):
				wedgemask.append(EMData())
			# wedgemask option
			if wcmask is not None:
				wcmask = get_input_from_string(wcmask)
				if len(wcmask) != 3:
					print_msg("Error: wcmask option requires 3 values: x y radius")
					sys.exit()

	# determine if particles have helix info:
	try:
		data[0].get_attr('h_angle')
		original_data = []
		boxmask = True
		from hfunctions import createBoxMask
	except:
		boxmask = False

	# prepare particles
	for im in xrange(nima):
		data[im].set_attr('ID', list_of_particles[im])
		data[im].set_attr('pix_score', int(0))
		if CTF:
			# only phaseflip particles, not full CTF correction
			ctf_params = data[im].get_attr("ctf")
			st = Util.infomask(data[im], mask2D, False)
			data[im] -= st[0]
			data[im] = filt_ctf(data[im], ctf_params, sign = -1, binary=1)
			data[im].set_attr('ctf_applied', 1)
		# for window mask:
		if boxmask is True:
			h_angle = data[im].get_attr("h_angle")
			original_data.append(data[im].copy())
			bmask = createBoxMask(nx,apix,ou,lmask,h_angle)
			data[im]*=bmask
			del bmask
	if debug:
		finfo.write( '%d loaded  \n' % nima )
		finfo.flush()
	if myid == main_node:
		# initialize data for the reference preparation function
		ref_data = [ mask3D, max(center,0), None, None, None, None ]
		# for method -1, switch off centering in user function

	from time import time	

	#  this is needed for gathering of pixel errors
	disps = []
	recvcount = []
	disps_score = []
	recvcount_score = []
	for im in xrange(number_of_proc):
		if( im == main_node ):  
			disps.append(0)
			disps_score.append(0)
		else:		  
			disps.append(disps[im-1] + recvcount[im-1])
			disps_score.append(disps_score[im-1] + recvcount_score[im-1])
		ib, ie = MPI_start_end(total_nima, number_of_proc, im)
		recvcount.append( ie - ib )
		recvcount_score.append((ie-ib)*nrefs)

	pixer = [0.0]*nima
	cs = [0.0]*3
	total_iter = 0
	volodd = EMData.read_images(ref_vol, xrange(nrefs))
	voleve = EMData.read_images(ref_vol, xrange(nrefs))

	if restart:
		# recreate initial volumes from alignments stored in header
		itout = "000_00"
		for iref in xrange(nrefs):
			if(nrefs == 1):
				modout = ""
			else:
				modout = "_model_%02d"%(iref)	
	
			if(sort): 
				group = iref
				for im in xrange(nima):
					imgroup = data[im].get_attr('group')
					if imgroup == iref:
						data[im].set_attr('xform.projection',transmulti[im][iref])
			else: 
				group = int(999) 
				for im in xrange(nima):
					data[im].set_attr('xform.projection',transmulti[im][iref])
			
			fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))

			vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index = group, npad = recon_pad)

			if myid == main_node:
				if helicalrecon:
					from hfunctions import processHelicalVol

					vstep=None
					if vertstep is not None:
						vstep=(vdp[iref],vdphi[iref])
					print_msg("Old rise and twist for model %i     : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
					hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
								dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
					(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
					print_msg("New rise and twist for model %i     : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
					# get new FSC from symmetrized half volumes
					fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)
				else:
					vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%itout),-1)

				if save_half is True:
					volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%itout),-1)
					voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%itout),-1)

				if nmasks > 1:
					# Read mask for multiplying
					ref_data[0] = maskF[iref]
				ref_data[2] = vol[iref]
				ref_data[3] = fscc
				#  call user-supplied function to prepare reference image, i.e., center and filter it
				vol[iref], cs,fl = ref_ali3d(ref_data)
				vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
				if (apix == 1):
					res_msg = "Models filtered at spatial frequency of:\t"
					res = fl
				else:
					res_msg = "Models filtered at resolution of:       \t"
					res = apix / fl	
				ares = array2string(array(res), precision = 2)
				print_msg("%s%s\n\n"%(res_msg,ares))	
			
			bcast_EMData_to_all(vol[iref], myid, main_node)
			# write out headers, under MPI writing has to be done sequentially
			mpi_barrier(MPI_COMM_WORLD)

	# projection matching	
	for N_step in xrange(lstp):
		terminate = 0
		Iter = -1
 		while(Iter < max_iter-1 and terminate == 0):
			Iter += 1
			total_iter += 1
			itout = "%03g_%02d" %(delta[N_step], Iter)
			if myid == main_node:
				print_msg("ITERATION #%3d, inner iteration #%3d\nDelta = %4.1f, an = %5.2f, xrange = %5.2f, yrange = %5.2f, step = %5.2f\n\n"%(N_step, Iter, delta[N_step], an[N_step], xrng[N_step],yrng[N_step],step[N_step]))
	
			for iref in xrange(nrefs):
				if myid == main_node: start_time = time()
				volft,kb = prep_vol( vol[iref] )

				## constrain projections to out of plane parameter
				theta1 = None
				theta2 = None
				if oplane is not None:
					theta1 = 90-oplane
					theta2 = 90+oplane
				refrings = prepare_refrings( volft, kb, nx, delta[N_step], ref_a, sym, numr, MPI=True, phiEqpsi = "Minus", initial_theta=theta1, delta_theta=theta2)
				
				del volft,kb

				if myid== main_node:
					print_msg( "Time to prepare projections for model %i: %s\n" % (iref, legibleTime(time()-start_time)) )
					start_time = time()
	
				for im in xrange( nima ):
					data[im].set_attr("xform.projection", transmulti[im][iref])
					if an[N_step] == -1:
						t1, peak, pixer[im] = proj_ali_incore(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],finfo)
					else:
						t1, peak, pixer[im] = proj_ali_incore_local(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],an[N_step],finfo)
					#data[im].set_attr("xform.projection"%iref, t1)
					if nrefs > 1: data[im].set_attr("eulers_txty.%i"%iref,t1)
					scoremulti[im][iref] = peak
					from pixel_error import max_3D_pixel_error
					# t1 is the current param, t2 is old
					t2 = transmulti[im][iref]
					pixelmulti[im][iref] = max_3D_pixel_error(t1,t2,numr[-3])
					transmulti[im][iref] = t1

				if myid == main_node:
					print_msg("Time of alignment for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
					start_time = time()


			# gather scoring data from all processors
			from mpi import mpi_gatherv
			scoremultisend = sum(scoremulti,[])
			pixelmultisend = sum(pixelmulti,[])
			tmp = mpi_gatherv(scoremultisend,len(scoremultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
			tmp1 = mpi_gatherv(pixelmultisend,len(pixelmultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
			tmp = mpi_bcast(tmp,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
			tmp1 = mpi_bcast(tmp1,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
			tmp = map(float,tmp)
			tmp1 = map(float,tmp1)
			score = array(tmp).reshape(-1,nrefs)
			pixelerror = array(tmp1).reshape(-1,nrefs) 
			score_local = array(scoremulti)
			mean_score = score.mean(axis=0)
			std_score = score.std(axis=0)
			cut = mean_score - (cutoff * std_score)
			cut2 = mean_score + (cutoff * std_score)
			res_max = score_local.argmax(axis=1)
			minus_cc = [0.0 for x in xrange(nrefs)]
			minus_pix = [0.0 for x in xrange(nrefs)]
			minus_ref = [0.0 for x in xrange(nrefs)]
			
			#output pixel errors
			if(myid == main_node):
				from statistics import hist_list
				lhist = 20
				pixmin = pixelerror.min(axis=1)
				region, histo = hist_list(pixmin, lhist)
				if(region[0] < 0.0):  region[0] = 0.0
				print_msg("Histogram of pixel errors\n      ERROR       number of particles\n")
				for lhx in xrange(lhist):
					print_msg(" %10.3f     %7d\n"%(region[lhx], histo[lhx]))
				# Terminate if 95% within 1 pixel error
				im = 0
				for lhx in xrange(lhist):
					if(region[lhx] > 1.0): break
					im += histo[lhx]
				print_msg( "Percent of particles with pixel error < 1: %f\n\n"% (im/float(total_nima)*100))
				term_cond = float(term)/100
				if(im/float(total_nima) > term_cond): 
					terminate = 1
					print_msg("Terminating internal loop\n")
				del region, histo
			terminate = mpi_bcast(terminate, 1, MPI_INT, 0, MPI_COMM_WORLD)
			terminate = int(terminate[0])	
			
			for im in xrange(nima):
				if(sort==False):
					data[im].set_attr('group',999)
				elif (mjump[N_step]==1):
					data[im].set_attr('group',int(res_max[im]))
				
				pix_run = data[im].get_attr('pix_score')			
				if (pix_cutoff[N_step]==1 and (terminate==1 or Iter == max_iter-1)):
					if (pixelmulti[im][int(res_max[im])] > 1):
						data[im].set_attr('pix_score',int(777))

				if (score_local[im][int(res_max[im])]<cut[int(res_max[im])]) or (two_tail and score_local[im][int(res_max[im])]>cut2[int(res_max[im])]):
					data[im].set_attr('group',int(888))
					minus_cc[int(res_max[im])] = minus_cc[int(res_max[im])] + 1

				if(pix_run == 777):
					data[im].set_attr('group',int(777))
					minus_pix[int(res_max[im])] = minus_pix[int(res_max[im])] + 1

				if (compare_ref_free != "-1") and (ref_free_cutoff[N_step] != -1) and (total_iter > 1):
					id = data[im].get_attr('ID')
					if id in rejects:
						data[im].set_attr('group',int(666))
						minus_ref[int(res_max[im])] = minus_ref[int(res_max[im])] + 1	
						
				
			minus_cc_tot = mpi_reduce(minus_cc,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)	
			minus_pix_tot = mpi_reduce(minus_pix,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD) 	
			minus_ref_tot = mpi_reduce(minus_ref,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
			if (myid == main_node):
				if(sort):
					tot_max = score.argmax(axis=1)
					res = bincount(tot_max)
				else:
					res = ones(nrefs) * total_nima
				print_msg("Particle distribution:	     \t\t%s\n"%(res*1.0))
				afcut1 = res - minus_cc_tot
				afcut2 = afcut1 - minus_pix_tot
				afcut3 = afcut2 - minus_ref_tot
				print_msg("Particle distribution after cc cutoff:\t\t%s\n"%(afcut1))
				print_msg("Particle distribution after pix cutoff:\t\t%s\n"%(afcut2)) 
				print_msg("Particle distribution after ref cutoff:\t\t%s\n\n"%(afcut3)) 
					
						
			res = [0.0 for i in xrange(nrefs)]
			for iref in xrange(nrefs):
				if(center == -1):
					from utilities      import estimate_3D_center_MPI, rotate_3D_shift
					dummy=EMData()
					cs[0], cs[1], cs[2], dummy, dummy = estimate_3D_center_MPI(data, total_nima, myid, number_of_proc, main_node)				
					cs = mpi_bcast(cs, 3, MPI_FLOAT, main_node, MPI_COMM_WORLD)
					cs = [-float(cs[0]), -float(cs[1]), -float(cs[2])]
					rotate_3D_shift(data, cs)


				if(sort): 
					group = iref
					for im in xrange(nima):
						imgroup = data[im].get_attr('group')
						if imgroup == iref:
							data[im].set_attr('xform.projection',transmulti[im][iref])
				else: 
					group = int(999) 
					for im in xrange(nima):
						data[im].set_attr('xform.projection',transmulti[im][iref])
				if(nrefs == 1):
					modout = ""
				else:
					modout = "_model_%02d"%(iref)	
				
				fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))
				vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index=group, npad=recon_pad)
	
				if myid == main_node:
					print_msg("3D reconstruction time for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
					start_time = time()
	
				# Compute Fourier variance
				if fourvar:
					outvar = os.path.join(outdir, "volVar_%s.hdf"%(itout))
					ssnr_file = os.path.join(outdir, "ssnr_%s"%(itout))
					varf = varf3d_MPI(data, ssnr_text_file=ssnr_file, mask2D=None, reference_structure=vol[iref], ou=last_ring, rw=1.0, npad=1, CTF=None, sign=1, sym=sym, myid=myid)
					if myid == main_node:
						print_msg("Time to calculate 3D Fourier variance for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
						start_time = time()
						varf = 1.0/varf
						varf.write_image(outvar,-1)
				else:  varf = None

				if myid == main_node:
					if helicalrecon:
						from hfunctions import processHelicalVol

						vstep=None
						if vertstep is not None:
							vstep=(vdp[iref],vdphi[iref])
						print_msg("Old rise and twist for model %i     : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
						hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
									dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
						(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
						print_msg("New rise and twist for model %i     : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
						# get new FSC from symmetrized half volumes
						fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)

						print_msg("Time to search and apply helical symmetry for model %i: %s\n\n"%(iref, legibleTime(time()-start_time)))
						start_time = time()
					else:
						vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%(itout)),-1)

					if save_half is True:
						volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%(itout)),-1)
						voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%(itout)),-1)

					if nmasks > 1:
						# Read mask for multiplying
						ref_data[0] = maskF[iref]
					ref_data[2] = vol[iref]
					ref_data[3] = fscc
					ref_data[4] = varf
					#  call user-supplied function to prepare reference image, i.e., center and filter it
					vol[iref], cs,fl = ref_ali3d(ref_data)
					vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
					if (apix == 1):
						res_msg = "Models filtered at spatial frequency of:\t"
						res[iref] = fl
					else:
						res_msg = "Models filtered at resolution of:       \t"
						res[iref] = apix / fl	
	
				del varf
				bcast_EMData_to_all(vol[iref], myid, main_node)
				
				if compare_ref_free != "-1": compare_repro = True
				if compare_repro:
					outfile_repro = comp_rep(refrings, data, itout, modout, vol[iref], group, nima, nx, myid, main_node, outdir)
					mpi_barrier(MPI_COMM_WORLD)
					if compare_ref_free != "-1":
						ref_free_output = os.path.join(outdir,"ref_free_%s%s"%(itout,modout))
						rejects = compare(compare_ref_free, outfile_repro,ref_free_output,yrng[N_step], xrng[N_step], rstep,nx,apix,ref_free_cutoff[N_step], number_of_proc, myid, main_node)

			# retrieve alignment params from all processors
			par_str = ['xform.projection','ID','group']
			if nrefs > 1:
				for iref in xrange(nrefs):
					par_str.append('eulers_txty.%i'%iref)

			if myid == main_node:
				from utilities import recv_attr_dict
				recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
				
			else:	send_attr_dict(main_node, data, par_str, image_start, image_end)

			if myid == main_node:
				ares = array2string(array(res), precision = 2)
				print_msg("%s%s\n\n"%(res_msg,ares))
				dummy = EMData()
				if full_output:
					nimat = EMUtil.get_image_count(stack)
					output_file = os.path.join(outdir, "paramout_%s"%itout)
					foutput = open(output_file, 'w')
					for im in xrange(nimat):
						# save the parameters for each of the models
						outstring = ""
						dummy.read_image(stack,im,True)
						param3d = dummy.get_attr('xform.projection')
						g = dummy.get_attr("group")
						# retrieve alignments in EMAN-format
						pE = param3d.get_params('eman')
						outstring += "%f\t%f\t%f\t%f\t%f\t%i\n" %(pE["az"], pE["alt"], pE["phi"], pE["tx"], pE["ty"],g)
						foutput.write(outstring)
					foutput.close()
				del dummy
			mpi_barrier(MPI_COMM_WORLD)


#	mpi_finalize()	

	if myid == main_node: print_end_msg("ali3d_MPI")
示例#6
0
文件: sxali2d.py 项目: cryoem/eman2
def main():
	progname = os.path.basename(sys.argv[0])
	usage = progname + " stack outdir <maskfile> --ir=inner_radius --ou=outer_radius --rs=ring_step --xr=x_range --yr=y_range --ts=translation_step --dst=delta --center=center --maxit=max_iteration --CTF --snr=SNR --Fourvar=Fourier_variance --Ng=group_number --Function=user_function_name --CUDA --GPUID --MPI"
	parser = OptionParser(usage,version=SPARXVERSION)
	parser.add_option("--ir",       type="float",  default=1,             help="inner radius for rotational correlation > 0 (set to 1)")
	parser.add_option("--ou",       type="float",  default=-1,            help="outer radius for rotational correlation < nx/2-1 (set to the radius of the particle)")
	parser.add_option("--rs",       type="float",  default=1,             help="step between rings in rotational correlation > 0 (set to 1)" ) 
	parser.add_option("--xr",       type="string", default="4 2 1 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 ")
	parser.add_option("--ts",       type="string", default="2 1 0.5 0.25",help="step of translation search in both directions")
	parser.add_option("--nomirror", action="store_true", default=False,   help="Disable checking mirror orientations of images (default False)")
	parser.add_option("--dst",      type="float",  default=0.0,           help="delta")
	parser.add_option("--center",   type="float",  default=-1,            help="-1.average center method; 0.not centered; 1.phase approximation; 2.cc with Gaussian function; 3.cc with donut-shaped image 4.cc with user-defined reference 5.cc with self-rotated average")
	parser.add_option("--maxit",    type="float",  default=0,             help="maximum number of iterations (0 means the maximum iterations is 10, but it will automatically stop should the criterion falls")
	parser.add_option("--CTF",      action="store_true", default=False,   help="use CTF correction during alignment")
	parser.add_option("--snr",      type="float",  default=1.0,           help="signal-to-noise ratio of the data (set to 1.0)")
	parser.add_option("--Fourvar",  action="store_true", default=False,   help="compute Fourier variance")
	#parser.add_option("--Ng",       type="int",          default=-1,      help="number of groups in the new CTF filteration")
	parser.add_option("--function", type="string",       default="ref_ali2d",  help="name of the reference preparation function (default ref_ali2d)")
	#parser.add_option("--CUDA",     action="store_true", default=False,   help="use CUDA program")
	#parser.add_option("--GPUID",    type="string",    default="",         help="ID of GPUs available")
	parser.add_option("--MPI",      action="store_true", default=False,   help="use MPI version ")
	parser.add_option("--rotational", action="store_true", default=False, help="rotational alignment with optional limited in-plane angle, the parameters are: ir, ou, rs, psi_max, mode(F or H), maxit, orient, randomize")
	parser.add_option("--psi_max",  type="float",        default=180.0,   help="psi_max")
	parser.add_option("--mode",     type="string",       default="F",     help="Full or Half rings, default F")
	parser.add_option("--randomize",action="store_true", default=False,   help="randomize initial rotations (suboption of friedel, default False)")
	parser.add_option("--orient",   action="store_true", default=False,   help="orient images such that the average is symmetric about x-axis, for layer lines (suboption of friedel, default False)")
	parser.add_option("--template", type="string",       default=None,    help="2D alignment will be initialized using the template provided (only non-MPI version, default None)")
	parser.add_option("--random_method",   type="string", default="",   help="use SHC or SCF (default standard method)")

	(options, args) = parser.parse_args()

	if len(args) < 2 or len(args) > 3:
		print "usage: " + usage
		print "Please run '" + progname + " -h' for detailed options"
	elif(options.rotational):
		from applications import ali2d_rotationaltop
		global_def.BATCH = True
		ali2d_rotationaltop(args[1], args[0], options.randomize, options.orient, options.ir, options.ou, options.rs, options.psi_max, options.mode, options.maxit)
	else:
		if args[1] == 'None': outdir = None
		else:		          outdir = args[1]

		if len(args) == 2: mask = None
		else:              mask = args[2]
		

		if global_def.CACHE_DISABLE:
			from utilities import disable_bdb_cache
			disable_bdb_cache()
		
		global_def.BATCH = True
		if  options.MPI:
			from applications import ali2d_base
			from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
			sys.argv = mpi_init(len(sys.argv),sys.argv)

			number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
			myid = mpi_comm_rank(MPI_COMM_WORLD)
			main_node = 0

			if(myid == main_node):
				import subprocess
				from logger import Logger, BaseLogger_Files
				#  Create output directory
				log = Logger(BaseLogger_Files())
				log.prefix = os.path.join(outdir)
				cmd = "mkdir "+log.prefix
				outcome = subprocess.call(cmd, shell=True)
				log.prefix += "/"
			else:
				outcome = 0
				log = None
			from utilities       import bcast_number_to_all
			outcome  = bcast_number_to_all(outcome, source_node = main_node)
			if(outcome == 1):
				ERROR('Output directory exists, please change the name and restart the program', "ali2d_MPI", 1, myid)

			dummy = ali2d_base(args[0], outdir, mask, options.ir, options.ou, options.rs, options.xr, options.yr, \
				options.ts, options.nomirror, options.dst, \
				options.center, options.maxit, options.CTF, options.snr, options.Fourvar, \
				options.function, random_method = options.random_method, log = log, \
				number_of_proc = number_of_proc, myid = myid, main_node = main_node, mpi_comm = MPI_COMM_WORLD,\
				write_headers = True)
		else:
			print " Non-MPI is no more in use, try MPI option, please."
			"""
			from applications import ali2d
			ali2d(args[0], outdir, mask, options.ir, options.ou, options.rs, options.xr, options.yr, \
				options.ts, options.nomirror, options.dst, \
				options.center, options.maxit, options.CTF, options.snr, options.Fourvar, \
				-1, options.function, False, "", options.MPI, \
				options.template, random_method = options.random_method)
	    	"""
		global_def.BATCH = False

		if options.MPI:
			from mpi import mpi_finalize
			mpi_finalize()
示例#7
0
def shiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1, oneDx=False, search_rng_y=-1):  
	from applications import MPI_start_end
	from utilities    import model_circle, model_blank, get_image, peak_search, get_im
	from utilities    import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
	from statistics   import varf2d_MPI
	from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
	from utilities    import get_params2D, set_params2D
	from utilities    import print_msg, print_begin_msg, print_end_msg
	import os
	import sys
	from mpi 	  	  import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
	from mpi 	  	  import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
	from mpi 	  	  import MPI_SUM, MPI_FLOAT, MPI_INT
	from EMAN2	  	  import Processor
	from time         import time	
	
	number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
	myid = mpi_comm_rank(MPI_COMM_WORLD)
	main_node = 0
		
	ftp = file_type(stack)

	if myid == main_node:
		print_begin_msg("shiftali_MPI")

	max_iter=int(maxit)

	if myid == main_node:
		if ftp == "bdb":
			from EMAN2db import db_open_dict
			dummy = db_open_dict(stack, True)
		nima = EMUtil.get_image_count(stack)
	else:
		nima = 0
	nima = bcast_number_to_all(nima, source_node = main_node)
	list_of_particles = range(nima)
	
	image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
	list_of_particles = list_of_particles[image_start: image_end]

	# read nx and ctf_app (if CTF) and broadcast to all nodes
	if myid == main_node:
		ima = EMData()
		ima.read_image(stack, list_of_particles[0], True)
		nx = ima.get_xsize()
		ny = ima.get_ysize()
		if CTF:	ctf_app = ima.get_attr_default('ctf_applied', 2)
		del ima
	else:
		nx = 0
		ny = 0
		if CTF:	ctf_app = 0
	nx = bcast_number_to_all(nx, source_node = main_node)
	ny = bcast_number_to_all(ny, source_node = main_node)
	if CTF:
		ctf_app = bcast_number_to_all(ctf_app, source_node = main_node)
		if ctf_app > 0:	ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)

	if maskfile == None:
		mrad = min(nx, ny)
		mask = model_circle(mrad//2-2, nx, ny)
	else:
		mask = get_im(maskfile)

	if CTF:
		from filter import filt_ctf
		from morphology   import ctf_img
		ctf_abs_sum = EMData(nx, ny, 1, False)
		ctf_2_sum = EMData(nx, ny, 1, False)
	else:
		ctf_2_sum = None

	from global_def import CACHE_DISABLE
	if CACHE_DISABLE:
		data = EMData.read_images(stack, list_of_particles)
	else:
		for i in xrange(number_of_proc):
			if myid == i:
				data = EMData.read_images(stack, list_of_particles)
			if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)


	for im in xrange(len(data)):
		data[im].set_attr('ID', list_of_particles[im])
		st = Util.infomask(data[im], mask, False)
		data[im] -= st[0]
		if CTF:
			ctf_params = data[im].get_attr("ctf")
			ctfimg = ctf_img(nx, ctf_params, ny=ny)
			Util.add_img2(ctf_2_sum, ctfimg)
			Util.add_img_abs(ctf_abs_sum, ctfimg)

	if CTF:
		reduce_EMData_to_root(ctf_2_sum, myid, main_node)
		reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
	else:  ctf_2_sum = None
	if CTF:
		if myid != main_node:
			del ctf_2_sum
			del ctf_abs_sum
		else:
			temp = EMData(nx, ny, 1, False)
			for i in xrange(0,nx,2):
				for j in xrange(ny):
					temp.set_value_at(i,j,snr)
			Util.add_img(ctf_2_sum, temp)
			del temp

	total_iter = 0

	# apply initial xform.align2d parameters stored in header
	init_params = []
	for im in xrange(len(data)):
		t = data[im].get_attr('xform.align2d')
		init_params.append(t)
		p = t.get_params("2d")
		data[im] = rot_shift2D(data[im], p['alpha'], sx=p['tx'], sy=p['ty'], mirror=p['mirror'], scale=p['scale'])

	# fourier transform all images, and apply ctf if CTF
	for im in xrange(len(data)):
		if CTF:
			ctf_params = data[im].get_attr("ctf")
			data[im] = filt_ctf(fft(data[im]), ctf_params)
		else:
			data[im] = fft(data[im])

	sx_sum=0
	sy_sum=0
	sx_sum_total=0
	sy_sum_total=0
	shift_x = [0.0]*len(data)
	shift_y = [0.0]*len(data)
	ishift_x = [0.0]*len(data)
	ishift_y = [0.0]*len(data)

	for Iter in xrange(max_iter):
		if myid == main_node:
			start_time = time()
			print_msg("Iteration #%4d\n"%(total_iter))
		total_iter += 1
		avg = EMData(nx, ny, 1, False)
		for im in data:  Util.add_img(avg, im)

		reduce_EMData_to_root(avg, myid, main_node)

		if myid == main_node:
			if CTF:
				tavg = Util.divn_filter(avg, ctf_2_sum)
			else:	 tavg = Util.mult_scalar(avg, 1.0/float(nima))
		else:
			tavg = EMData(nx, ny, 1, False)                               

		if Fourvar:
			bcast_EMData_to_all(tavg, myid, main_node)
			vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)

		if myid == main_node:
			if Fourvar:
				tavg    = fft(Util.divn_img(fft(tavg), vav))
				vav_r	= Util.pack_complex_to_real(vav)

			# normalize and mask tavg in real space
			tavg = fft(tavg)
			stat = Util.infomask( tavg, mask, False ) 
			tavg -= stat[0]
			Util.mul_img(tavg, mask)
			# For testing purposes: shift tavg to some random place and see if the centering is still correct
			#tavg = rot_shift3D(tavg,sx=3,sy=-4)
			tavg = fft(tavg)

		if Fourvar:  del vav
		bcast_EMData_to_all(tavg, myid, main_node)

		sx_sum=0 
		sy_sum=0 
		if search_rng > 0: nwx = 2*search_rng+1
		else:              nwx = nx
		
		if search_rng_y > 0: nwy = 2*search_rng_y+1
		else:                nwy = ny

		not_zero = 0
		for im in xrange(len(data)):
			if oneDx:
				ctx = Util.window(ccf(data[im],tavg),nwx,1)
				p1  = peak_search(ctx)
				p1_x = -int(p1[0][3])
				ishift_x[im] = p1_x
				sx_sum += p1_x
			else:
				p1 = peak_search(Util.window(ccf(data[im],tavg), nwx,nwy))
				p1_x = -int(p1[0][4])
				p1_y = -int(p1[0][5])
				ishift_x[im] = p1_x
				ishift_y[im] = p1_y
				sx_sum += p1_x
				sy_sum += p1_y

			if not_zero == 0:
				if (not(ishift_x[im] == 0.0)) or (not(ishift_y[im] == 0.0)):
					not_zero = 1

		sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)  

		if not oneDx:
			sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)

		if myid == main_node:
			sx_sum_total = int(sx_sum[0])
			if not oneDx:
				sy_sum_total = int(sy_sum[0])
		else:
			sx_sum_total = 0	
			sy_sum_total = 0

		sx_sum_total = bcast_number_to_all(sx_sum_total, source_node = main_node)

		if not oneDx:
			sy_sum_total = bcast_number_to_all(sy_sum_total, source_node = main_node)

		sx_ave = round(float(sx_sum_total)/nima)
		sy_ave = round(float(sy_sum_total)/nima)
		for im in xrange(len(data)): 
			p1_x = ishift_x[im] - sx_ave
			p1_y = ishift_y[im] - sy_ave
			params2 = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : p1_x, "y_shift" : p1_y, "z_shift" : 0.0}
			data[im] = Processor.EMFourierFilter(data[im], params2)
			shift_x[im] += p1_x
			shift_y[im] += p1_y
		# stop if all shifts are zero
		not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)  
		if myid == main_node:
			not_zero_all = int(not_zero[0])
		else:
			not_zero_all = 0
		not_zero_all = bcast_number_to_all(not_zero_all, source_node = main_node)

		if myid == main_node:
			print_msg("Time of iteration = %12.2f\n"%(time()-start_time))
			start_time = time()

		if not_zero_all == 0:  break

	#for im in xrange(len(data)): data[im] = fft(data[im])  This should not be required as only header information is used
	# combine shifts found with the original parameters
	for im in xrange(len(data)):		
		t0 = init_params[im]
		t1 = Transform()
		t1.set_params({"type":"2D","alpha":0,"scale":t0.get_scale(),"mirror":0,"tx":shift_x[im],"ty":shift_y[im]})
		# combine t0 and t1
		tt = t1*t0
		data[im].set_attr("xform.align2d", tt)  

	# write out headers and STOP, under MPI writing has to be done sequentially
	mpi_barrier(MPI_COMM_WORLD)
	par_str = ["xform.align2d", "ID"]
	if myid == main_node:
		from utilities import file_type
		if(file_type(stack) == "bdb"):
			from utilities import recv_attr_dict_bdb
			recv_attr_dict_bdb(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
		else:
			from utilities import recv_attr_dict
			recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
		
	else:           send_attr_dict(main_node, data, par_str, image_start, image_end)
	if myid == main_node: print_end_msg("shiftali_MPI")				
示例#8
0
def helicalshiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1):
	from applications import MPI_start_end
	from utilities    import model_circle, model_blank, get_image, peak_search, get_im, pad
	from utilities    import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
	from statistics   import varf2d_MPI
	from fundamentals import fft, ccf, rot_shift3D, rot_shift2D, fshift
	from utilities    import get_params2D, set_params2D, chunks_distribution
	from utilities    import print_msg, print_begin_msg, print_end_msg
	import os
	import sys
	from mpi 	  	  import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
	from mpi 	  	  import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
	from mpi 	  	  import MPI_SUM, MPI_FLOAT, MPI_INT
	from time         import time	
	from pixel_error  import ordersegments
	from math         import sqrt, atan2, tan, pi
	
	nproc = mpi_comm_size(MPI_COMM_WORLD)
	myid = mpi_comm_rank(MPI_COMM_WORLD)
	main_node = 0
		
	ftp = file_type(stack)

	if myid == main_node:
		print_begin_msg("helical-shiftali_MPI")

	max_iter=int(maxit)
	if( myid == main_node):
		infils = EMUtil.get_all_attributes(stack, "filament")
		ptlcoords = EMUtil.get_all_attributes(stack, 'ptcl_source_coord')
		filaments = ordersegments(infils, ptlcoords)
		total_nfils = len(filaments)
		inidl = [0]*total_nfils
		for i in xrange(total_nfils):  inidl[i] = len(filaments[i])
		linidl = sum(inidl)
		nima = linidl
		tfilaments = []
		for i in xrange(total_nfils):  tfilaments += filaments[i]
		del filaments
	else:
		total_nfils = 0
		linidl = 0
	total_nfils = bcast_number_to_all(total_nfils, source_node = main_node)
	if myid != main_node:
		inidl = [-1]*total_nfils
	inidl = bcast_list_to_all(inidl, myid, source_node = main_node)
	linidl = bcast_number_to_all(linidl, source_node = main_node)
	if myid != main_node:
		tfilaments = [-1]*linidl
	tfilaments = bcast_list_to_all(tfilaments, myid, source_node = main_node)
	filaments = []
	iendi = 0
	for i in xrange(total_nfils):
		isti = iendi
		iendi = isti+inidl[i]
		filaments.append(tfilaments[isti:iendi])
	del tfilaments,inidl

	if myid == main_node:
		print_msg( "total number of filaments: %d"%total_nfils)
	if total_nfils< nproc:
		ERROR('number of CPUs (%i) is larger than the number of filaments (%i), please reduce the number of CPUs used'%(nproc, total_nfils), "ehelix_MPI", 1,myid)

	#  balanced load
	temp = chunks_distribution([[len(filaments[i]), i] for i in xrange(len(filaments))], nproc)[myid:myid+1][0]
	filaments = [filaments[temp[i][1]] for i in xrange(len(temp))]
	nfils     = len(filaments)

	#filaments = [[0,1]]
	#print "filaments",filaments
	list_of_particles = []
	indcs = []
	k = 0
	for i in xrange(nfils):
		list_of_particles += filaments[i]
		k1 = k+len(filaments[i])
		indcs.append([k,k1])
		k = k1
	data = EMData.read_images(stack, list_of_particles)
	ldata = len(data)
	print "ldata=", ldata
	nx = data[0].get_xsize()
	ny = data[0].get_ysize()
	if maskfile == None:
		mrad = min(nx, ny)//2-2
		mask = pad( model_blank(2*mrad+1, ny, 1, 1.0), nx, ny, 1, 0.0)
	else:
		mask = get_im(maskfile)

	# apply initial xform.align2d parameters stored in header
	init_params = []
	for im in xrange(ldata):
		t = data[im].get_attr('xform.align2d')
		init_params.append(t)
		p = t.get_params("2d")
		data[im] = rot_shift2D(data[im], p['alpha'], p['tx'], p['ty'], p['mirror'], p['scale'])

	if CTF:
		from filter import filt_ctf
		from morphology   import ctf_img
		ctf_abs_sum = EMData(nx, ny, 1, False)
		ctf_2_sum = EMData(nx, ny, 1, False)
	else:
		ctf_2_sum = None
		ctf_abs_sum = None



	from utilities import info

	for im in xrange(ldata):
		data[im].set_attr('ID', list_of_particles[im])
		st = Util.infomask(data[im], mask, False)
		data[im] -= st[0]
		if CTF:
			ctf_params = data[im].get_attr("ctf")
			qctf = data[im].get_attr("ctf_applied")
			if qctf == 0:
				data[im] = filt_ctf(fft(data[im]), ctf_params)
				data[im].set_attr('ctf_applied', 1)
			elif qctf != 1:
				ERROR('Incorrectly set qctf flag', "helicalshiftali_MPI", 1,myid)
			ctfimg = ctf_img(nx, ctf_params, ny=ny)
			Util.add_img2(ctf_2_sum, ctfimg)
			Util.add_img_abs(ctf_abs_sum, ctfimg)
		else:  data[im] = fft(data[im])

	del list_of_particles		

	if CTF:
		reduce_EMData_to_root(ctf_2_sum, myid, main_node)
		reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
	if CTF:
		if myid != main_node:
			del ctf_2_sum
			del ctf_abs_sum
		else:
			temp = EMData(nx, ny, 1, False)
			tsnr = 1./snr
			for i in xrange(0,nx+2,2):
				for j in xrange(ny):
					temp.set_value_at(i,j,tsnr)
					temp.set_value_at(i+1,j,0.0)
			#info(ctf_2_sum)
			Util.add_img(ctf_2_sum, temp)
			#info(ctf_2_sum)
			del temp

	total_iter = 0
	shift_x = [0.0]*ldata

	for Iter in xrange(max_iter):
		if myid == main_node:
			start_time = time()
			print_msg("Iteration #%4d\n"%(total_iter))
		total_iter += 1
		avg = EMData(nx, ny, 1, False)
		for im in xrange(ldata):
			Util.add_img(avg, fshift(data[im], shift_x[im]))

		reduce_EMData_to_root(avg, myid, main_node)

		if myid == main_node:
			if CTF:  tavg = Util.divn_filter(avg, ctf_2_sum)
			else:    tavg = Util.mult_scalar(avg, 1.0/float(nima))
		else:
			tavg = model_blank(nx,ny)

		if Fourvar:
			bcast_EMData_to_all(tavg, myid, main_node)
			vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)

		if myid == main_node:
			if Fourvar:
				tavg    = fft(Util.divn_img(fft(tavg), vav))
				vav_r	= Util.pack_complex_to_real(vav)
			# normalize and mask tavg in real space
			tavg = fft(tavg)
			stat = Util.infomask( tavg, mask, False )
			tavg -= stat[0]
			Util.mul_img(tavg, mask)
			tavg.write_image("tavg.hdf",Iter)
			# For testing purposes: shift tavg to some random place and see if the centering is still correct
			#tavg = rot_shift3D(tavg,sx=3,sy=-4)

		if Fourvar:  del vav
		bcast_EMData_to_all(tavg, myid, main_node)
		tavg = fft(tavg)

		sx_sum = 0.0
		nxc = nx//2
		
		for ifil in xrange(nfils):
			"""
			# Calculate filament average
			avg = EMData(nx, ny, 1, False)
			filnima = 0
			for im in xrange(indcs[ifil][0], indcs[ifil][1]):
				Util.add_img(avg, data[im])
				filnima += 1
			tavg = Util.mult_scalar(avg, 1.0/float(filnima))
			"""
			# Calculate 1D ccf between each segment and filament average
			nsegms = indcs[ifil][1]-indcs[ifil][0]
			ctx = [None]*nsegms
			pcoords = [None]*nsegms
			for im in xrange(indcs[ifil][0], indcs[ifil][1]):
				ctx[im-indcs[ifil][0]] = Util.window(ccf(tavg, data[im]), nx, 1)
				pcoords[im-indcs[ifil][0]] = data[im].get_attr('ptcl_source_coord')
				#ctx[im-indcs[ifil][0]].write_image("ctx.hdf",im-indcs[ifil][0])
				#print "  CTX  ",myid,im,Util.infomask(ctx[im-indcs[ifil][0]], None, True)
			# search for best x-shift
			cents = nsegms//2
			
			dst = sqrt(max((pcoords[cents][0] - pcoords[0][0])**2 + (pcoords[cents][1] - pcoords[0][1])**2, (pcoords[cents][0] - pcoords[-1][0])**2 + (pcoords[cents][1] - pcoords[-1][1])**2))
			maxincline = atan2(ny//2-2-float(search_rng),dst)
			kang = int(dst*tan(maxincline)+0.5)
			#print  "  settings ",nsegms,cents,dst,search_rng,maxincline,kang
			
			# ## C code for alignment. @ming
 			results = [0.0]*3;
 			results = Util.helixshiftali(ctx, pcoords, nsegms, maxincline, kang, search_rng,nxc)
			sib = int(results[0])
 			bang = results[1]
 			qm = results[2]
			#print qm, sib, bang
			
			# qm = -1.e23	
# 				
# 			for six in xrange(-search_rng, search_rng+1,1):
# 				q0 = ctx[cents].get_value_at(six+nxc)
# 				for incline in xrange(kang+1):
# 					qt = q0
# 					qu = q0
# 					if(kang>0):  tang = tan(maxincline/kang*incline)
# 					else:        tang = 0.0
# 					for kim in xrange(cents+1,nsegms):
# 						dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# 						xl = dst*tang+six+nxc
# 						ixl = int(xl)
# 						dxl = xl - ixl
# 						#print "  A  ", ifil,six,incline,kim,xl,ixl,dxl
# 						qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# 						xl = -dst*tang+six+nxc
# 						ixl = int(xl)
# 						dxl = xl - ixl
# 						qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# 					for kim in xrange(cents):
# 						dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
# 						xl = -dst*tang+six+nxc
# 						ixl = int(xl)
# 						dxl = xl - ixl
# 						qt += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# 						xl =  dst*tang+six+nxc
# 						ixl = int(xl)
# 						dxl = xl - ixl
# 						qu += (1.0-dxl)*ctx[kim].get_value_at(ixl) + dxl*ctx[kim].get_value_at(ixl+1)
# 					if( qt > qm ):
# 						qm = qt
# 						sib = six
# 						bang = tang
# 					if( qu > qm ):
# 						qm = qu
# 						sib = six
# 						bang = -tang
					#if incline == 0:  print  "incline = 0  ",six,tang,qt,qu
			#print qm,six,sib,bang
			#print " got results   ",indcs[ifil][0], indcs[ifil][1], ifil,myid,qm,sib,tang,bang,len(ctx),Util.infomask(ctx[0], None, True)
			for im in xrange(indcs[ifil][0], indcs[ifil][1]):
				kim = im-indcs[ifil][0]
				dst = sqrt((pcoords[cents][0] - pcoords[kim][0])**2 + (pcoords[cents][1] - pcoords[kim][1])**2)
				if(kim < cents):  xl = -dst*bang+sib
				else:             xl =  dst*bang+sib
				shift_x[im] = xl
							
			# Average shift
			sx_sum += shift_x[indcs[ifil][0]+cents]
			
			
		# #print myid,sx_sum,total_nfils
		sx_sum = mpi_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node, MPI_COMM_WORLD)
		if myid == main_node:
			sx_sum = float(sx_sum[0])/total_nfils
			print_msg("Average shift  %6.2f\n"%(sx_sum))
		else:
			sx_sum = 0.0
		sx_sum = 0.0
		sx_sum = bcast_number_to_all(sx_sum, source_node = main_node)
		for im in xrange(ldata):
			shift_x[im] -= sx_sum
			#print  "   %3d  %6.3f"%(im,shift_x[im])
		#exit()


			
	# combine shifts found with the original parameters
	for im in xrange(ldata):		
		t1 = Transform()
		##import random
		##shix=random.randint(-10, 10)
		##t1.set_params({"type":"2D","tx":shix})
		t1.set_params({"type":"2D","tx":shift_x[im]})
		# combine t0 and t1
		tt = t1*init_params[im]
		data[im].set_attr("xform.align2d", tt)
	# write out headers and STOP, under MPI writing has to be done sequentially
	mpi_barrier(MPI_COMM_WORLD)
	par_str = ["xform.align2d", "ID"]
	if myid == main_node:
		from utilities import file_type
		if(file_type(stack) == "bdb"):
			from utilities import recv_attr_dict_bdb
			recv_attr_dict_bdb(main_node, stack, data, par_str, 0, ldata, nproc)
		else:
			from utilities import recv_attr_dict
			recv_attr_dict(main_node, stack, data, par_str, 0, ldata, nproc)
	else:           send_attr_dict(main_node, data, par_str, 0, ldata)
	if myid == main_node: print_end_msg("helical-shiftali_MPI")				
示例#9
0
文件: filter.py 项目: cpsemmens/eman2
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
示例#10
0
def main():

	def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
		if mirror:
			m = 1
			alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)
		else:
			m = 0
			alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)
		return  alpha, sx, sy, m
	
	progname = os.path.basename(sys.argv[0])
	usage = progname + " prj_stack  --ave2D= --var2D=  --ave3D= --var3D= --img_per_grp= --fl=0.2 --aa=0.1  --sym=symmetry --CTF"
	parser = OptionParser(usage, version=SPARXVERSION)

	parser.add_option("--ave2D",		type="string"	   ,	default=False,				help="write to the disk a stack of 2D averages")
	parser.add_option("--var2D",		type="string"	   ,	default=False,				help="write to the disk a stack of 2D variances")
	parser.add_option("--ave3D",		type="string"	   ,	default=False,				help="write to the disk reconstructed 3D average")
	parser.add_option("--var3D",		type="string"	   ,	default=False,				help="compute 3D variability (time consuming!)")
	parser.add_option("--img_per_grp",	type="int"         ,	default=10   ,				help="number of neighbouring projections")
	parser.add_option("--no_norm",		action="store_true",	default=False,				help="do not use normalization")
	parser.add_option("--radiusvar", 	type="int"         ,	default=-1   ,				help="radius for 3D var" )
	parser.add_option("--npad",			type="int"         ,	default=2    ,				help="number of time to pad the original images")
	parser.add_option("--sym" , 		type="string"      ,	default="c1" ,				help="symmetry")
	parser.add_option("--fl",			type="float"       ,	default=0.0  ,				help="stop-band frequency (Default - no filtration)")
	parser.add_option("--aa",			type="float"       ,	default=0.0  ,				help="fall off of the filter (Default - no filtration)")
	parser.add_option("--CTF",			action="store_true",	default=False,				help="use CFT correction")
	parser.add_option("--VERBOSE",		action="store_true",	default=False,				help="Long output for debugging")
	#parser.add_option("--MPI" , 		action="store_true",	default=False,				help="use MPI version")
	#parser.add_option("--radiuspca", 	type="int"         ,	default=-1   ,				help="radius for PCA" )
	#parser.add_option("--iter", 		type="int"         ,	default=40   ,				help="maximum number of iterations (stop criterion of reconstruction process)" )
	#parser.add_option("--abs", 			type="float"       ,	default=0.0  ,				help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
	#parser.add_option("--squ", 			type="float"       ,	default=0.0  ,				help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
	parser.add_option("--VAR" , 		action="store_true",	default=False,				help="stack on input consists of 2D variances (Default False)")
	parser.add_option("--decimate",     type="float",           default=1.0,                 help="image decimate rate, a number large than 1. default is 1")
	parser.add_option("--window",       type="int",             default=0,                   help="reduce images to a small image size without changing pixel_size. Default value is zero.")
	#parser.add_option("--SND",			action="store_true",	default=False,				help="compute squared normalized differences (Default False)")
	parser.add_option("--nvec",			type="int"         ,	default=0    ,				help="number of eigenvectors, default = 0 meaning no PCA calculated")
	parser.add_option("--symmetrize",	action="store_true",	default=False,				help="Prepare input stack for handling symmetry (Default False)")
	
	(options,args) = parser.parse_args()
	#####
	from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD, MPI_TAG_UB
	from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
	from applications import MPI_start_end
	from reconstruction import recons3d_em, recons3d_em_MPI
	from reconstruction	import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
	from utilities import print_begin_msg, print_end_msg, print_msg
	from utilities import read_text_row, get_image, get_im
	from utilities import bcast_EMData_to_all, bcast_number_to_all
	from utilities import get_symt

	#  This is code for handling symmetries by the above program.  To be incorporated. PAP 01/27/2015

	from EMAN2db import db_open_dict
	
	if options.symmetrize :
		try:
			sys.argv = mpi_init(len(sys.argv), sys.argv)
			try:	
				number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
				if( number_of_proc > 1 ):
					ERROR("Cannot use more than one CPU for symmetry prepration","sx3dvariability",1)
			except:
				pass
		except:
			pass

		#  Input
		#instack = "Clean_NORM_CTF_start_wparams.hdf"
		#instack = "bdb:data"
		instack = args[0]
		sym = options.sym
		if( sym == "c1" ):
			ERROR("Thre is no need to symmetrize stack for C1 symmetry","sx3dvariability",1)

		if(instack[:4] !="bdb:"):
			stack = "bdb:data"
			delete_bdb(stack)
			cmdexecute("sxcpy.py  "+instack+"  "+stack)
		else:
			stack = instack

		qt = EMUtil.get_all_attributes(stack,'xform.projection')

		na = len(qt)
		ts = get_symt(sym)
		ks = len(ts)
		angsa = [None]*na
		for k in xrange(ks):
			delete_bdb("bdb:Q%1d"%k)
			cmdexecute("e2bdb.py  "+stack+"  --makevstack=bdb:Q%1d"%k)
			DB = db_open_dict("bdb:Q%1d"%k)
			for i in xrange(na):
				ut = qt[i]*ts[k]
				DB.set_attr(i, "xform.projection", ut)
				#bt = ut.get_params("spider")
				#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
			#write_text_row(angsa, 'ptsma%1d.txt'%k)
			#cmdexecute("e2bdb.py  "+stack+"  --makevstack=bdb:Q%1d"%k)
			#cmdexecute("sxheader.py  bdb:Q%1d  --params=xform.projection  --import=ptsma%1d.txt"%(k,k))
			DB.close()
		delete_bdb("bdb:sdata")
		cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
		#cmdexecute("ls  EMAN2DB/sdata*")
		a = get_im("bdb:sdata")
		a.set_attr("variabilitysymmetry",sym)
		a.write_image("bdb:sdata")


	else:

		sys.argv = mpi_init(len(sys.argv), sys.argv)
		myid     = mpi_comm_rank(MPI_COMM_WORLD)
		number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
		main_node = 0

		if len(args) == 1:
			stack = args[0]
		else:
			print( "usage: " + usage)
			print( "Please run '" + progname + " -h' for detailed options")
			return 1

		t0 = time()
	
		# obsolete flags
		options.MPI = True
		options.nvec = 0
		options.radiuspca = -1
		options.iter = 40
		options.abs = 0.0
		options.squ = 0.0

		if options.fl > 0.0 and options.aa == 0.0:
			ERROR("Fall off has to be given for the low-pass filter", "sx3dvariability", 1, myid)
		if options.VAR and options.SND:
			ERROR("Only one of var and SND can be set!", "sx3dvariability", myid)
			exit()
		if options.VAR and (options.ave2D or options.ave3D or options.var2D): 
			ERROR("When VAR is set, the program cannot output ave2D, ave3D or var2D", "sx3dvariability", 1, myid)
			exit()
		#if options.SND and (options.ave2D or options.ave3D):
		#	ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
		#	exit()
		if options.nvec > 0 :
			ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
			exit()
		if options.nvec > 0 and options.ave3D == None:
			ERROR("When doing PCA analysis, one must set ave3D", "sx3dvariability", myid=myid)
			exit()
		import string
		options.sym = options.sym.lower()
		 
		if global_def.CACHE_DISABLE:
			from utilities import disable_bdb_cache
			disable_bdb_cache()
		global_def.BATCH = True

		if myid == main_node:
			print_begin_msg("sx3dvariability")
			print_msg("%-70s:  %s\n"%("Input stack", stack))
	
		img_per_grp = options.img_per_grp
		nvec = options.nvec
		radiuspca = options.radiuspca

		symbaselen = 0
		if myid == main_node:
			nima = EMUtil.get_image_count(stack)
			img  = get_image(stack)
			nx   = img.get_xsize()
			ny   = img.get_ysize()
			if options.sym != "c1" :
				imgdata = get_im(stack)
				try:
					i = imgdata.get_attr("variabilitysymmetry")
					if(i != options.sym):
						ERROR("The symmetry provided does not agree with the symmetry of the input stack", "sx3dvariability", myid=myid)
				except:
					ERROR("Input stack is not prepared for symmetry, please follow instructions", "sx3dvariability", myid=myid)
				from utilities import get_symt
				i = len(get_symt(options.sym))
				if((nima/i)*i != nima):
					ERROR("The length of the input stack is incorrect for symmetry processing", "sx3dvariability", myid=myid)
				symbaselen = nima/i
			else:  symbaselen = nima
		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)
		Tracker ={}
		Tracker["nx"]  =nx
		Tracker["ny"]  =ny
		Tracker["total_stack"]=nima
		if options.decimate==1.:
			if options.window !=0:
				nx = options.window
				ny = options.window
		else:
			if options.window ==0:
				nx = int(nx/options.decimate)
				ny = int(ny/options.decimate)
			else:
				nx = int(options.window/options.decimate)
				ny = nx
		symbaselen = bcast_number_to_all(symbaselen)
		if radiuspca == -1: radiuspca = nx/2-2

		if myid == main_node:
			print_msg("%-70s:  %d\n"%("Number of projection", nima))
		
		img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
		"""
		if options.SND:
			from projection		import prep_vol, prgs
			from statistics		import im_diff
			from utilities		import get_im, model_circle, get_params_proj, set_params_proj
			from utilities		import get_ctf, generate_ctf
			from filter			import filt_ctf
		
			imgdata = EMData.read_images(stack, range(img_begin, img_end))

			if options.CTF:
				vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
			else:
				vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)

			bcast_EMData_to_all(vol, myid)
			volft, kb = prep_vol(vol)

			mask = model_circle(nx/2-2, nx, ny)
			varList = []
			for i in xrange(img_begin, img_end):
				phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
				ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
				if options.CTF:
					ctf_params = get_ctf(imgdata[i-img_begin])
					ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
				diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
				diff2 = diff*diff
				set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
				varList.append(diff2)
			mpi_barrier(MPI_COMM_WORLD)
		"""
		if options.VAR:
			#varList = EMData.read_images(stack, range(img_begin, img_end))
			varList = []
			this_image = EMData()
			for index_of_particle in xrange(img_begin,img_end):
				this_image.read_image(stack,index_of_particle)
				varList.append(image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF))
		else:
			from utilities		import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
			from utilities		import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
			from utilities		import model_blank, nearest_proj, model_circle
			from applications	import pca
			from statistics		import avgvar, avgvar_ctf, ccc
			from filter		    import filt_tanl
			from morphology		import threshold, square_root
			from projection 	import project, prep_vol, prgs
			from sets		    import Set

			if myid == main_node:
				t1 = time()
				proj_angles = []
				aveList = []
				tab = EMUtil.get_all_attributes(stack, 'xform.projection')
				for i in xrange(nima):
					t     = tab[i].get_params('spider')
					phi   = t['phi']
					theta = t['theta']
					psi   = t['psi']
					x     = theta
					if x > 90.0: x = 180.0 - x
					x = x*10000+psi
					proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
				t2 = time()
				print_msg("%-70s:  %d\n"%("Number of neighboring projections", img_per_grp))
				print_msg("...... Finding neighboring projections\n")
				if options.VERBOSE:
					print "Number of images per group: ", img_per_grp
					print "Now grouping projections"
				proj_angles.sort()

			proj_angles_list = [0.0]*(nima*4)
			if myid == main_node:
				for i in xrange(nima):
					proj_angles_list[i*4]   = proj_angles[i][1]
					proj_angles_list[i*4+1] = proj_angles[i][2]
					proj_angles_list[i*4+2] = proj_angles[i][3]
					proj_angles_list[i*4+3] = proj_angles[i][4]
			proj_angles_list = bcast_list_to_all(proj_angles_list, myid, main_node)
			proj_angles = []
			for i in xrange(nima):
				proj_angles.append([proj_angles_list[i*4], proj_angles_list[i*4+1], proj_angles_list[i*4+2], int(proj_angles_list[i*4+3])])
			del proj_angles_list

			proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp, range(img_begin, img_end))

			all_proj = Set()
			for im in proj_list:
				for jm in im:
					all_proj.add(proj_angles[jm][3])

			all_proj = list(all_proj)
			if options.VERBOSE:
				print "On node %2d, number of images needed to be read = %5d"%(myid, len(all_proj))

			index = {}
			for i in xrange(len(all_proj)): index[all_proj[i]] = i
			mpi_barrier(MPI_COMM_WORLD)

			if myid == main_node:
				print_msg("%-70s:  %.2f\n"%("Finding neighboring projections lasted [s]", time()-t2))
				print_msg("%-70s:  %d\n"%("Number of groups processed on the main node", len(proj_list)))
				if options.VERBOSE:
					print "Grouping projections took: ", (time()-t2)/60	, "[min]"
					print "Number of groups on main node: ", len(proj_list)
			mpi_barrier(MPI_COMM_WORLD)

			if myid == main_node:
				print_msg("...... calculating the stack of 2D variances \n")
				if options.VERBOSE:
					print "Now calculating the stack of 2D variances"

			proj_params = [0.0]*(nima*5)
			aveList = []
			varList = []				
			if nvec > 0:
				eigList = [[] for i in xrange(nvec)]

			if options.VERBOSE: 	print "Begin to read images on processor %d"%(myid)
			ttt = time()
			#imgdata = EMData.read_images(stack, all_proj)
			img     = EMData()
			imgdata = []
			for index_of_proj in xrange(len(all_proj)):
				img.read_image(stack, all_proj[index_of_proj])
				dmg = image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF)
				#print dmg.get_xsize(), "init"
				imgdata.append(dmg)
			if options.VERBOSE:
				print "Reading images on processor %d done, time = %.2f"%(myid, time()-ttt)
				print "On processor %d, we got %d images"%(myid, len(imgdata))
			mpi_barrier(MPI_COMM_WORLD)

			'''	
			imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
			if options.fl > 0.0:
				for k in xrange(len(imgdata2)):
					imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
			if options.CTF:
				vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
			else:
				vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
			if myid == main_node:
				vol.write_image("vol_ctf.hdf")
				print_msg("Writing to the disk volume reconstructed from averages as		:  %s\n"%("vol_ctf.hdf"))
			del vol, imgdata2
			mpi_barrier(MPI_COMM_WORLD)
			'''
			from applications import prepare_2d_forPCA
			from utilities import model_blank
			for i in xrange(len(proj_list)):
				ki = proj_angles[proj_list[i][0]][3]
				if ki >= symbaselen:  continue
				mi = index[ki]
				phiM, thetaM, psiM, s2xM, s2yM = get_params_proj(imgdata[mi])

				grp_imgdata = []
				for j in xrange(img_per_grp):
					mj = index[proj_angles[proj_list[i][j]][3]]
					phi, theta, psi, s2x, s2y = get_params_proj(imgdata[mj])
					alpha, sx, sy, mirror = params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror_list[i][j])
					if thetaM <= 90:
						if mirror == 0:  alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, phiM-phi, 0.0, 0.0, 1.0)
						else:            alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, 180-(phiM-phi), 0.0, 0.0, 1.0)
					else:
						if mirror == 0:  alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(phiM-phi), 0.0, 0.0, 1.0)
						else:            alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(180-(phiM-phi)), 0.0, 0.0, 1.0)
					set_params2D(imgdata[mj], [alpha, sx, sy, mirror, 1.0])
					grp_imgdata.append(imgdata[mj])
					#print grp_imgdata[j].get_xsize(), imgdata[mj].get_xsize()

				if not options.no_norm:
					#print grp_imgdata[j].get_xsize()
					mask = model_circle(nx/2-2, nx, nx)
					for k in xrange(img_per_grp):
						ave, std, minn, maxx = Util.infomask(grp_imgdata[k], mask, False)
						grp_imgdata[k] -= ave
						grp_imgdata[k] /= std
					del mask

				if options.fl > 0.0:
					from filter import filt_ctf, filt_table
					from fundamentals import fft, window2d
					nx2 = 2*nx
					ny2 = 2*ny
					if options.CTF:
						from utilities import pad
						for k in xrange(img_per_grp):
							grp_imgdata[k] = window2d(fft( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa) ),nx,ny)
							#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
							#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
					else:
						for k in xrange(img_per_grp):
							grp_imgdata[k] = filt_tanl( grp_imgdata[k], options.fl, options.aa)
							#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
							#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
				else:
					from utilities import pad, read_text_file
					from filter import filt_ctf, filt_table
					from fundamentals import fft, window2d
					nx2 = 2*nx
					ny2 = 2*ny
					if options.CTF:
						from utilities import pad
						for k in xrange(img_per_grp):
							grp_imgdata[k] = window2d( fft( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1) ) , nx,ny)
							#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
							#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)

				'''
				if i < 10 and myid == main_node:
					for k in xrange(10):
						grp_imgdata[k].write_image("grp%03d.hdf"%i, k)
				'''
				"""
				if myid == main_node and i==0:
					for pp in xrange(len(grp_imgdata)):
						grp_imgdata[pp].write_image("pp.hdf", pp)
				"""
				ave, grp_imgdata = prepare_2d_forPCA(grp_imgdata)
				"""
				if myid == main_node and i==0:
					for pp in xrange(len(grp_imgdata)):
						grp_imgdata[pp].write_image("qq.hdf", pp)
				"""

				var = model_blank(nx,ny)
				for q in grp_imgdata:  Util.add_img2( var, q )
				Util.mul_scalar( var, 1.0/(len(grp_imgdata)-1))
				# Switch to std dev
				var = square_root(threshold(var))
				#if options.CTF:	ave, var = avgvar_ctf(grp_imgdata, mode="a")
				#else:	            ave, var = avgvar(grp_imgdata, mode="a")
				"""
				if myid == main_node:
					ave.write_image("avgv.hdf",i)
					var.write_image("varv.hdf",i)
				"""
			
				set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
				set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])

				aveList.append(ave)
				varList.append(var)

				if options.VERBOSE:
					print "%5.2f%% done on processor %d"%(i*100.0/len(proj_list), myid)
				if nvec > 0:
					eig = pca(input_stacks=grp_imgdata, subavg="", mask_radius=radiuspca, nvec=nvec, incore=True, shuffle=False, genbuf=True)
					for k in xrange(nvec):
						set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
						eigList[k].append(eig[k])
					"""
					if myid == 0 and i == 0:
						for k in xrange(nvec):
							eig[k].write_image("eig.hdf", k)
					"""

			del imgdata
			#  To this point, all averages, variances, and eigenvectors are computed

			if options.ave2D:
				from fundamentals import fpol
				if myid == main_node:
					km = 0
					for i in xrange(number_of_proc):
						if i == main_node :
							for im in xrange(len(aveList)):
								aveList[im].write_image(options.ave2D, km)
								km += 1
						else:
							nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
							nl = int(nl[0])
							for im in xrange(nl):
								ave = recv_EMData(i, im+i+70000)
								"""
								nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
								nm = int(nm[0])
								members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
								ave.set_attr('members', map(int, members))
								members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
								ave.set_attr('pix_err', map(float, members))
								members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
								ave.set_attr('refprojdir', map(float, members))
								"""
								tmpvol=fpol(ave, Tracker["nx"],Tracker["nx"],Tracker["nx"])								
								tmpvol.write_image(options.ave2D, km)
								km += 1
				else:
					mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
					for im in xrange(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, MPI_TAG_UB, MPI_COMM_WORLD)
						mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
						members = aveList[im].get_attr('pix_err')
						mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
						try:
							members = aveList[im].get_attr('refprojdir')
							mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
						except:
							mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
						"""

			if options.ave3D:
				from fundamentals import fpol
				if options.VERBOSE:
					print "Reconstructing 3D average volume"
				ave3D = recons3d_4nn_MPI(myid, aveList, symmetry=options.sym, npad=options.npad)
				bcast_EMData_to_all(ave3D, myid)
				if myid == main_node:
					ave3D=fpol(ave3D,Tracker["nx"],Tracker["nx"],Tracker["nx"])
					ave3D.write_image(options.ave3D)
					print_msg("%-70s:  %s\n"%("Writing to the disk volume reconstructed from averages as", options.ave3D))
			del ave, var, proj_list, stack, phi, theta, psi, s2x, s2y, alpha, sx, sy, mirror, aveList

			if nvec > 0:
				for k in xrange(nvec):
					if options.VERBOSE:
						print "Reconstruction eigenvolumes", k
					cont = True
					ITER = 0
					mask2d = model_circle(radiuspca, nx, nx)
					while cont:
						#print "On node %d, iteration %d"%(myid, ITER)
						eig3D = recons3d_4nn_MPI(myid, eigList[k], symmetry=options.sym, npad=options.npad)
						bcast_EMData_to_all(eig3D, myid, main_node)
						if options.fl > 0.0:
							eig3D = filt_tanl(eig3D, options.fl, options.aa)
						if myid == main_node:
							eig3D.write_image("eig3d_%03d.hdf"%k, ITER)
						Util.mul_img( eig3D, model_circle(radiuspca, nx, nx, nx) )
						eig3Df, kb = prep_vol(eig3D)
						del eig3D
						cont = False
						icont = 0
						for l in xrange(len(eigList[k])):
							phi, theta, psi, s2x, s2y = get_params_proj(eigList[k][l])
							proj = prgs(eig3Df, kb, [phi, theta, psi, s2x, s2y])
							cl = ccc(proj, eigList[k][l], mask2d)
							if cl < 0.0:
								icont += 1
								cont = True
								eigList[k][l] *= -1.0
						u = int(cont)
						u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node, MPI_COMM_WORLD)
						icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)

						if myid == main_node:
							u = int(u[0])
							print " Eigenvector: ",k," number changed ",int(icont[0])
						else: u = 0
						u = bcast_number_to_all(u, main_node)
						cont = bool(u)
						ITER += 1

					del eig3Df, kb
					mpi_barrier(MPI_COMM_WORLD)
				del eigList, mask2d

			if options.ave3D: del ave3D
			if options.var2D:
				from fundamentals import fpol 
				if myid == main_node:
					km = 0
					for i in xrange(number_of_proc):
						if i == main_node :
							for im in xrange(len(varList)):
								tmpvol=fpol(varList[im], Tracker["nx"], Tracker["nx"],1)
								tmpvol.write_image(options.var2D, km)
								km += 1
						else:
							nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
							nl = int(nl[0])
							for im in xrange(nl):
								ave = recv_EMData(i, im+i+70000)
								tmpvol=fpol(ave, Tracker["nx"], Tracker["nx"],1)
								tmpvol.write_image(options.var2D, km)
								km += 1
				else:
					mpi_send(len(varList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
					for im in xrange(len(varList)):
						send_EMData(varList[im], main_node, im+myid+70000)#  What with the attributes??

			mpi_barrier(MPI_COMM_WORLD)

		if  options.var3D:
			if myid == main_node and options.VERBOSE:
				print "Reconstructing 3D variability volume"

			t6 = time()
			radiusvar = options.radiusvar
			if( radiusvar < 0 ):  radiusvar = nx//2 -3
			res = recons3d_4nn_MPI(myid, varList, symmetry=options.sym, npad=options.npad)
			#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
			if myid == main_node:
				from fundamentals import fpol
				res =fpol(res, Tracker["nx"], Tracker["nx"], Tracker["nx"])
				res.write_image(options.var3D)

			if myid == main_node:
				print_msg("%-70s:  %.2f\n"%("Reconstructing 3D variability took [s]", time()-t6))
				if options.VERBOSE:
					print "Reconstruction took: %.2f [min]"%((time()-t6)/60)

			if myid == main_node:
				print_msg("%-70s:  %.2f\n"%("Total time for these computations [s]", time()-t0))
				if options.VERBOSE:
					print "Total time for these computations: %.2f [min]"%((time()-t0)/60)
				print_end_msg("sx3dvariability")

		global_def.BATCH = False

		from mpi import mpi_finalize
		mpi_finalize()