usage: prog [options]

	This program is used to produce reference-free class averages from a population of mixed,
	unaligned particle images. These averages can be used to generate initial models or assess
	the structural variability of the data. They are not normally themselves used as part of
	the single particle reconstruction refinement process, which uses the raw particles in a
	reference-based classification approach. However, with a good structure, projections of
	the final 3-D model should be consistent with the results of this reference-free analysis.

	This program uses a fully automated iterative alignment/MSA approach. You should normally
	target a minimum of 10-20 particles per class-average, though more is fine.

	Default parameters should give a good start, but are likely not optimal for any given system.

	Note that it does have the --parallel option, but a few steps of the iterative process
	are not parallelized, so don't be surprised if multiple cores are not always active.

Option Type Description
--version None show program's version number and exit
--path str Path for the refinement, default=auto
--input str The name of the file containing the particle data
--ncls int Number of classes to generate
--normproj None Normalizes each projected vector into the MSA subspace. Note that this is different from normalizing the input images since the subspace is not expected to fully span the image
--fastseed None Will seed the k-means loop quickly, but may produce less consistent results. Always use this when generating >~100 classes.
--iter int The total number of refinement iterations to perform
--nbasisfp int Number of MSA basis vectors to use when classifying particles
--automask None Automasking during class-averaging to help with centering when particle density is high
--naliref int Number of alignment references to when determining particle orientations
--parallel, -P str Run in parallel, specify type:<option>=<value>:<option>:<value>
--centeracf None This option has been removed in favor of a new centering algorithm
--center str If the default centering algorithm (xform.center) doesn't work well, you can specify one of the others here (e2help.py processor center)
--check, -c None Checks the contents of the current directory to verify that e2refine2d.py command will work - checks for the existence of the necessary starting files and checks their dimensions. Performs no work
--verbose, -v int verbose level [0-9], higher number means higher level of verboseness
--maxshift int Maximum particle translation in x and y
--exclude str The named file should contain a set of integers, each representing an image from the input file to exclude.
--resume None This will cause a check of the files in the current directory, and the refinement will resume after the last completed iteration. It's ok to alter other parameters.
--initial str File containing starting class-averages. If not specified, will generate starting averages automatically
--minchange int Minimum number of particles that change group before deicding to terminate. Default = -1 (auto)
--simalign str The name of an 'aligner' to use prior to comparing the images (default=rotate_translate_tree)
--simaligncmp str Name of the aligner along with its construction arguments (default=ccc)
--simralign str The name and parameters of the second stage aligner which refines the results of the first alignment
--simraligncmp str The name and parameters of the comparitor used by the second stage aligner. (default=dot).
--simcmp str The name of a 'cmp' to be used in comparing the aligned images (default=ccc)
--shrink int Optionally shrink the input particles by an integer amount prior to computing similarity scores. For speed purposes. default=0, no shrinking
--classkeep float The fraction of particles to keep in each class, based on the similarity score generated by the --cmp argument (default=0.85).
--classkeepsig None Change the keep ('--keep') criterion from fraction-based to sigma-based.
--classiter int Number of iterations to use when making class-averages (default=5)
--classalign str If doing more than one iteration, this is the name and parameters of the 'aligner' used to align particles to the previous class average.
--classaligncmp str This is the name and parameters of the comparitor used by the fist stage aligner Default is dot.
--classralign str The second stage aligner which refines the results of the first alignment in class averaging. Default is None.
--classraligncmp str The comparitor used by the second stage aligner in class averageing. Default is dot:normalize=1.
--classaverager str The averager used to generate the class averages. Default is 'mean'.
--classcmp str The name and parameters of the comparitor used to generate similarity scores, when class averaging. Default is ccc'
--classnormproc str Normalization applied during class averaging
--classrefsf None Use the setsfref option in class averaging to produce better filtered averages.
--ppid int Set the PID of the parent process, used for cross platform PPID

For more information go to emanwiki/EMAN2/Programs/e2refine2d.