e2refine_easy

and e2refine_evenodd.py. Major features of this program:

input

string

The name of the image file containing the particle data

model

string

The map to use as a starting point for refinement

OR

startfrom

string

Path to an existing refine_xx directory to continue refining from. Alternative to --input and --model.

Standard Options:

Short

Name

Type

Description

targetres

float

Target resolution in A of this refinement run. Usually works best in at least two steps (low/medium resolution, then final resolution) when starting with a poor starting model. Usually 3-4 iterations is sufficient.

speed

int

(1-7) Balances speed vs precision. Larger values sacrifice a bit of potential resolution for significant speed increases. 1 may yield slightly better results but come with a significant performance penalty. default=5

sym

bool

Specify symmetry - choices are: c<n>, d<n>, tet, oct, icos.

breaksym

bool

If selected, reconstruction will be asymmetric with sym= specifying a known pseudosymmetry, not an imposed symmetry.

iter

int

The total number of refinement iterations to perform. Default=auto

mass

float

The ~mass of the particle in kilodaltons, used to run normalize.bymass. Due to resolution effects, not always the true mass.

apix

float

The angstrom per pixel of the input particles. This argument is required if you specify the --mass argument. If unspecified (set to 0), the convergence plot is generated using either the project apix, or if not an apix of 1.

classkeep

float

The fraction of particles to keep in each class, based on the similarity score. (default=0.9 -> 90%%)

classautomask

bool

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

prethreshold

bool

Applies a threshold to the volume just before generating projections. A sort of aggressive solvent flattening for the reference.

m3dkeep

float

The fraction of slices to keep in e2make3d.py. Default=0.8 -> 80%%

m3dpostprocess

string

Default=none. An arbitrary post-processor to run after all other automatic processing. Maps are autofiltered, so a low-pass filter should not normally be used here.

-P

parallel

string

Run in parallel, specify type:<option>=<value>:<option>=<value>. See http://blake.bcm.edu/emanwiki/EMAN2/Parallel

threads

int

Number of threads to run in parallel on a single computer when multi-computer parallelism isn't useful

Complete Options, including advanced options:

Short

Name

Type

Description

input

string

Image stack containing phase-flipped particles used for alignment

inputavg

string

Optional file containing alternate version of the particles to use for reconstruction after alignment

model

string

The map to use as a starting point for refinement

startfrom

string

Path to an existing refine_xx directory to continue refining from. Alternative to --input and --model.

targetres

float

Target resolution in A of this refinement run. Usually works best in at least two steps (low/medium resolution, then final resolution) when starting with a poor starting model. Usually 3-4 iterations is sufficient.

speed

int

(1-7) Balances speed vs precision. Larger values sacrifice a bit of potential resolution for significant speed increases. Set to 1 when really pushing resolution. Set to 7 for initial refinements. default=5

sym

bool

Specify symmetry - choices are: c<n>, d<n>, tet, oct, icos.

breaksym

bool

If selected, reconstruction will be asymmetric with sym= specifying a known pseudosymmetry, not an imposed symmetry.

tophat

bool

Instead of imposing a final Wiener filter, use a tophat filter (similar to Relion). Sharper features, but may exaggerate.

treeclassify

bool

Classify using a binary tree.

m3dold

bool

Use the traditional e2make3d program instead of the new e2make3dpar program

iter

int

The total number of refinement iterations to perform. Default=auto

mass

float

The ~mass of the particle in kilodaltons, used to run normalize.bymass. Due to resolution effects, not always the true mass.

apix

float

The angstrom per pixel of the input particles. Normally set to 0, which will read the value from the header of the input file

sep

int

The number of classes each particle can contribute towards (normally 1). Increasing will improve SNR, but produce rotational blurring.

classkeep

float

The fraction of particles to keep in each class, based on the similarity score. (default=0.9 -> 90%%)

classautomask

bool

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

prethreshold

bool

Applies a threshold to the volume just before generating projections. A sort of aggressive solvent flattening for the reference.

eulerrefine

bool

Refines Euler angles of class-averages before reconstruction

m3dkeep

float

The fraction of slices to keep in e2make3d.py. Default=0.8 -> 80%%

m3dpostprocess

string

Default=none. An arbitrary post-processor to run after all other automatic processing. Maps are autofiltered, so a low-pass filter should not normally be used here.

-P

parallel

string

Run in parallel, specify type:<option>=<value>:<option>=<value>. See http://blake.bcm.edu/emanwiki/EMAN2/Parallel

threads

int

Number of threads to run in parallel on a single computer when multi-computer parallelism isn't useful

path

string

The name of a directory where results are placed. Default = create new refine_xx

-v

verbose

int

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

usefilt

string

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.

automaskexpand

int

Default=boxsize/20. Specify number of voxels to expand mask before soft edge. Use this if low density peripheral features are cut off by the mask.

automask3d

string

Default=auto. Specify as a processor, eg - mask.auto3d:threshold=1.1:radius=30:nshells=5:nshellsgauss=5.

automask3d2

string

Default=none. If specified, this mask will be multiplied by the result of the first mask, eg - using mask.soft to mask out the center of a virus.

projector

bool

Default=standard. Projector to use with parameters.

orientgen

string

Default=auto. Orientation generator for projections, eg - eman:delta=5.0:inc_mirror=0:perturb=1

simalign

string

Default=auto. The name of an 'aligner' to use prior to comparing the images

simaligncmp

string

Default=auto. Name of the aligner along with its construction arguments

simralign

string

Default=auto. The name and parameters of the second stage aligner which refines the results of the first alignment

simraligncmp

string

Default=auto. The name and parameters of the comparitor used by the second stage aligner.

simcmp

string

Default=auto. The name of a 'cmp' to be used in comparing the aligned images

simmask

string

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

shrink

int

Default=auto. Optionally shrink the input particles by an integer amount prior to computing similarity scores. For speed purposes. 0 -> no shrinking

shrinks1

int

The level of shrinking to apply in the first stage of the two-stage classification process. Default=0 (autoselect)

prefilt

bool

Default=auto. Filter each reference (c) to match the power spectrum of each particle (r) before alignment and comparison. Applies both to classification and class-averaging.

cmpdiff

bool

Used only in binary tree classification. Use a mask that focus on the difference of two children.

treeincomplete

int

Used only in binary tree classification. Incompleteness of the tree on each level.Default=0

classkeepsig

bool

Change the keep ('--keep') criterion from fraction-based to sigma-based.

classiter

int

Default=auto. The number of iterations to perform.

classalign

string

Default=auto. 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

Default=auto. This is the name and parameters of the comparitor used by the fist stage aligner.

classralign

string

Default=auto. The second stage aligner which refines the results of the first alignment in class averaging.

classraligncmp

string

Default=auto. The comparitor used by the second stage aligner in class averageing.

classaverager

string

Default=auto. The averager used to generate the class averages. Default is auto.

classcmp

string

Default=auto. The name and parameters of the comparitor used to generate similarity scores, when class averaging.

classnormproc

string

Default=auto. Normalization applied during class averaging

classrefsf

bool

Use the setsfref option in class averaging. This matches the filtration of the class-averages to the projections for easier comparison. Disabled when ampcorrect=flatten is used.

pad

int

Default=auto. To reduce Fourier artifacts, the model is typically padded by ~25 percent - only applies to Fourier reconstruction

recon

bool

Default=auto. Reconstructor to use see e2help.py reconstructors -v

m3dkeepsig

bool

Default=auto. The standard deviation alternative to the --m3dkeep argument

m3dsetsf

string

Default=auto. Name of a file containing a structure factor to apply after refinement

m3dpreprocess

string

Default=auto. Normalization processor applied before 3D reconstruction

ampcorrect

bool

Will perform amplitude correction via the specified method. 'flatten' requires a target resolution better than 8 angstroms (experimental). 'none' will disable amplitude correction (experimental).

classweight

bool

Alter the weight of each class in the reconstruction (experimental).

sqrtnorm

bool

If set, the sqrt of the number of particles in each class will be used to weight the direct fourier inversion.

lowmem

bool

Default=auto. Make limited use of memory when possible - useful on lower end machines

ppid

int

Set the PID of the parent process, used for cross platform PPID

To run this program, you would normally specify only the following options: Use these 2 when starting a new "gold standard" refinement from scratch:

Use this, when you have already achieved sufficient resolution to validate the gold-standard, and you are trying to improve resolution/quality:

Then a subset of these:

Optional:

Details about the refinement, and parameters which have automatically been selected are discussed in report/index.html