--version

bool

show program's version number and exit

-h

--help

bool

show this help message and exit

-c

--check

bool

Checks the contents of the current directory to verify that e2refine.py command will work - checks for the existence of the necessary starting files and checks their dimensions.

-v

--verbose

int

verbose level [0-9], higner number means higher level of verboseness

--iter

int

The total number of refinement iterations to perform

--startiter

int

If a refinement crashes, this can be used to pick it up where it left off. This should NOT be used to change parameters, but only to resume an incomplete run.

--model

string

The name 3D image that will seed the refinement

--input

string

The name of the image containing the particle data

--usefilt

string

Note: some unresolved bugs may exist with this option (6/2011) 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 in projection matching routines, and elsewhere.

--path

string

The name of a directory where results are placed. If not specified (suggested), will use a path of the form refine_xx.

--mass

float

The mass of the particle in kilodaltons, used to run normalize.bymass. If unspecified nothing happens. Requires the --apix argument.

--apix

float

The angstrom per pixel of the input particles. This argument is required if you specify the --mass argument. If unspecified, the convergence plot is generated using either the project apix, or an apix of 1.

--sym

string

Symmetry to be imposed throughout: c<n>, d<n>, h<n>, tet, oct, icos. Omit this option or specify 'c1' for asymmetric reconstructions.

--lowmem

bool

Make limited use of memory when possible. Slight speed penalty.

-P

--parallel

string

Run in parallel, specify type:<option>=<value>:<option>:<value>. See EMAN2/Parallel

--projector

string

Projector to use. 'standard' is the default

--orientgen

string

The orientation generation argument for e2project3d.py. Typically something like: --orientgen=eman:delta=2.0:inc_mirror=0

--twostage

int

Optionally run a faster 2-stage similarity matrix, ~5-30x faster, almost identical results. Value specifies shrink factor for first stage, typ 1-3

--shrink

int

Optionally shrink the input particles by an integer amount prior to computing similarity scores. For speed. If used with --twostage, this specifies the second stage shrink factor.

--simcmp

string

The name of a comparator to be used in comparing the aligned images

--simalign

string

The name of an aligner to use prior to comparing the images

--simaligncmp

string

Name and options for a comparator to use in first stage alignment for classification

--simralign

string

The name and parameters of the second stage aligner which refines the results of the first alignment. Currently this is either not specified or is 'refine'.

--simraligncmp

string

The name and parameters of the comparator used by the second stage aligner. Default is dot.

--simmask

string

A file containing a single 0/1 image to apply as a mask before comparison but after alignment

--prefilt

bool

Filter each reference (c) to match the power spectrum of each particle (r) before alignment and comparison

--sep

int

The number of classes a particle can contribute towards (default is 1). This is a precursor to full maximum-liklihood.

--classiter

int

The number of iterations to perform. Default is 1. Larger values reduce model/noise bias, but slightly decrease resolution.

--classcmp

string

The name and parameters of the comparitor used to generate similarity scores, when class averaging. Default is 'dot:normalize=1'

--classalign

string

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

string

This is the name and parameters of the comparator used by the fist stage aligner Default is dot.

--classralign

string

The second stage aligner which refines the results of the first alignment in class averaging. Currently this is either not specified or is 'refine'.

--classraligncmp

string

The comparator used by the second stage aligner in class averageing. Default is dot:normalize=1.

--classaverager

string

The averager used to generate the class averages. Default is 'mean'.

--classkeep

float

The fraction of particles to keep in each class, based on the similarity score generated by the --cmp argument (see also --classkeepsig).

--classkeepsig

bool

Change the keep ('--keep') criterion from fraction-based to sigma-based. eg - with this set, 1.0 would correspond to discarding particles more than 1 standard deviation from the mean

--classnormproc

string

Normalization processor and options applied during class averaging. Typically 'normalize.edgemean'

--classrefsf

bool

This will impose the 1-D structure factor of each model projection onto the corresponding class-average to improve its filtration. This is an alternative to Wiener filtration if the map resolution is regulated.

--classautomask

bool

Experimental. This will apply a 2-D automask to the class-average during iterative alignment for better accuracy. The final class averages are unmasked.

--pad

int

To reduce Fourier artifacts, the model is typically padded by ~25% - only applies to Fourier reconstruction. Please read EMAN2/BoxSize

--recon

string

reconstructor to use. Main choices are 'fourier' or 'wiener_fourier'

--m3dkeep

float

The percentage of slices to keep in e2make3d.py

--m3dkeepsig

bool

Similar to classkeepsig above, changes the meaning of --m3dkeep to be in terms of standard deviations

--m3dsetsf

bool

Filters the final 3-D map to match the precomputed structure factor (stored in the project database). Normally used with a --m3dpostprocess=filter.lowpass.* option

--m3diter

int

The number of times the 3D reconstruction should be iterated. 2 and 3 are the only valid values. 2 is faster and normally has sufficient accuracy.

--m3dpreprocess

string

Normalization processor applied before 3D reconstruction

--m3dpostprocess

string

Post processor to be applied to the 3D volume once the reconstruction is completed

--automask3d

string

The 5 parameters of the mask.auto3d processor, applied after 3D reconstruction. These parameters are, in order: isosurface threshold,radius,nshells and ngaussshells. From e2proc3d.py you could achieve the same thing using --process=mask.auto3d:threshold=1.1:radius=30:nshells=5:ngaussshells=5. e2help.py processors -v2 for more information on mask.auto3d.