e2refine2d_bispec

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