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usage: e2classaverage.py <output> [options]

	This program produces iterative class-averages, one of the secrets to EMAN's rapid convergence.
	Normal usage is to provide a stack of particle images and a classification matrix file defining
	class membership. Members of each class are then iteratively aligned to each other and averaged
	together with (optional) CTF correction.  It is also possible to use this program on all of the
	images in a single stack.

	

positional arguments:
  positionalargs

options:
  --version             show program's version number and exit
  --help-to-html        print this help message in html format
  --input INPUT         The name of the input particle stack
  --output OUTPUT       The name of the output class-average stack
  --oneclass ONECLASS   Create only a single class-average. Specify the number.
  --classmx CLASSMX     The name of the classification matrix specifying how particles in 'input' should be grouped. If omitted, all
                        particles will be averaged.
  --focused FOCUSED     Name of a reference projection file to read 1st iteration refine alignment references from.
  --ref REF             Reference image(s). Used as an initial alignment reference and for final orientation adjustment if present. Also
                        used to assign euler angles to the generated classes. This is typically the projections that were used for
                        classification.
  --storebad            Even if a class-average fails, write to the output. Forces 1->1 numbering in output
  --compressbits COMPRESSBITS
                        Bits to keep when writing class-averages with compression. 0->lossless floating point. Default 10 (3 significant
                        figures)
  --decayedge           Applies an edge decay to zero on the output class-averages. A very good idea if you plan on 3-D reconstruction.
  --resultmx RESULTMX   Specify an output image to store the result matrix. This contains 5 images where row is particle number. Rows in
                        the first image contain the class numbers and in the second image consist of 1s or 0s indicating whether or not
                        the particle was included in the class. The corresponding rows in the third, fourth and fifth images are the
                        refined x, y and angle (respectively) used in the final alignment, these are updated and accurate, even if the
                        particle was excluded from the class.
  --iter ITER           The number of iterations to perform. Default is 1.
  --prefilt             Filter each reference (c) to match the power spectrum of each particle (r) before alignment and comparison
  --prectf              Apply particle CTF to each reference before alignment
  --align ALIGN         This is the aligner used to align particles to the previous class average. Default is None.
  --aligncmp ALIGNCMP   The comparitor used for the --align aligner. Default is ccc.
  --ralign RALIGN       This is the second stage aligner used to refine the first alignment. This is usually the 'refine' aligner.
  --raligncmp RALIGNCMP
                        The comparitor used by the second stage aligner.
  --averager AVERAGER   The type of averager used to produce the class average.
  --setsfref            This will impose the 1-D structure factor of the reference on the class-average (recommended when a reference is
                        available)
  --cmp CMP             The comparitor used to generate quality scores for the purpose of particle exclusion in classes, strongly linked
                        to the keep argument.
  --keep KEEP           The fraction of particles to keep in each class.
  --keepsig             Causes the keep argument to be interpreted in standard deviations.
  --automask            Applies a 2-D automask before centering. Can help with negative stain data, and other cases where centering is
                        poor.
  --center CENTER       If the default centering algorithm (xform.center) doesn't work well, you can specify one of the others here
                        (e2help.py processor center), or the word 'nocenter' for no centering
  --bootstrap           Ignored. Present for historical reasons only.
  --normproc NORMPROC   Normalization processor applied to particles before alignment. Default is normalize.edgemean. If you want to turn
                        this option off specify 'None'
  --usefilt USEFILT     Specify a particle data file that has been low pass or Wiener filtered. Has a one to one correspondence with your
                        particle data. If specified will be used to align particles to the running class average, however the original
                        particle will be used to generate the actual final class average
  --idxcache            Ignored. Present for historical reasons.
  --dbpath DBPATH       Ignored. Present for historical reasons.
  --resample            If set, will perform bootstrap resampling on the particle data for use in making variance maps.
  --odd                 Used by EMAN2 when running eotests. Includes only odd numbered particles in class averages.
  --even                Used by EMAN2 when running eotests. Includes only even numbered particles in class averages.
  --parallel PARALLEL   parallelism argument
  --saveali             Writes aligned particle images to aligned.hdf. Normally resultmx produces more useful information. This can be
                        used for debugging.
  --verbose n, -v n     verbose level [0-9], higher number means higher level of verboseness
  --debug, -d           Print debugging information while the program is running. Default is off.
  --nofilecheck         Turns file checking off in the check functionality - used by e2refine.py.
  --check, -c           Performs a command line argument check only.
  --ppid PPID           Set the PID of the parent process, used for cross platform PPID
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