e2refine2d

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.