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 variant of the program uses rotational/translational invariants derived from the bispectrum
	of each particle.

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
--alignsort None This will align and sort the final class-averages based on mutual similarity.
--msamode str e2msa can use a variety of different dimensionality reduction algorithms, the default is Principal Component Analysis (PCA), but others are available, see e2msa.py
--basisrefs str Will use a set of existing class-averages/projections to generate the Eigenbasis for classification. This must be an image stack with the same dimensions as the particle data.
--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
--iter int The total number of refinement iterations to perform
--nbasisfp int Number of MSA basis vectors to use when classifying particles
--parallel, -P str Run in parallel, specify type:<option>=<value>:<option>:<value>
--threads int Number of threads to run in parallel on a single computer when multi-computer parallelism isn't useful
--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)
--verbose, -v int verbose level [0-9], higher number means higher level of verboseness
--classkeep float The fraction of particles to keep in each class, based on the similarity score generated by the --cmp argument (default=0.8).
--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=4)
--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
--ppid int Set the PID of the parent process, used for cross platform PPID