e2refine
This program is the heart of single particle reconstruction in EMAN2. It embodies the concept of an iterative 3-D single particle reconstruction in a single step, starting with a 3-D starting model and a set of preprocessed particle data. The overall strategy is similar to that used in EMAN1, with a number of improvements for speed and accuracy. The general idea is that the 3-D orientation of each particle is determined by comparison to a set of projections of the current 3-D model. Particles in near-identical orientations are then aligned and averaged in 2-D. These averages are then used to construct a new 3-D model, which is then reprojected for use in the next cycle of refinement. This process of reference-based classification is somewhat unique to EMAN, and is one reason why it can converge so rapidly to the correct answer even with a poor starting model.
EMAN2 refinement has many more options than EMAN1, and permits much more precise control over the refinement process. This can be both a blessing and a curse. We suggest launching your refinements from the workflow interface which simplifies specifying all of the necessary options. For those in search of detail, we document everything here.
Command Line Arguments
General Options
|
--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 |
Options impacting the overall refinement
|
--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 |
Specify symmetry - choices are: c<n>, d<n>, h<n>, tet, oct, icos. Omit this option or specify 'c1' for asymmetric reconstructions. |
Options related to making projections
See also EMAN2/Programs/e2project3d
|
--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 |
|
--automask3d |
string |
The 5 parameters of the mask.auto3d processor, applied after 3D reconstruction. These paramaters 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. |
|
--simalign |
string |
The name of an 'aligner' to use prior to comparing the images |
|
--simaligncmp |
string |
Name of the aligner along with its construction arguments |
|
--simralign |
string |
The name and parameters of the second stage aligner which refines the results of the first alignment |
|
--simraligncmp |
string |
The name and parameters of the comparitor used by the second stage aligner. Default is dot. |
|
--simcmp |
string |
The name of a 'cmp' to be used in comparing the aligned images |
|
--simmask |
string |
A file containing a single 0/1 image to apply as a mask before comparison but after alignment |
|
--shrink |
int |
Optionally shrink the input particles by an integer amount prior to computing similarity scores. For speed purposes. |
|
--twostage |
int |
Optionally run a faster 2-stage similarity matrix, ~5-10x faster, generally same accuracy. Value specifies shrink factor for first stage, typ 1-3 |
|
--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) |
|
--classkeep |
float |
The fraction of particles to keep in each class, based on the similarity score generated by the --cmp argument. |
|
--classkeepsig |
bool |
Change the keep ('--keep') criterion from fraction-based to sigma-based. |
|
--classiter |
int |
The number of iterations to perform. Default is 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 comparitor 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. Default is None. |
|
--classraligncmp |
string |
The comparitor 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'. |
|
--classcmp |
string |
The name and parameters of the comparitor used to generate similarity scores, when class averaging. Default is 'dot:normalize=1' |
|
--classnormproc |
string |
Normalization applied during class averaging |
|
--classrefsf |
bool |
Use the setsfref option in class averaging to produce better filtered averages. |
|
--classautomask |
bool |
This will apply an 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 |
|
--recon |
string |
Reconstructor to use see e2help.py reconstructors -v |
|
--m3dkeep |
float |
The percentage of slices to keep in e2make3d.py |
|
--m3dkeepsig |
bool |
The standard deviation alternative to the --m3dkeep argument |
|
--m3dsetsf |
bool |
The standard deviation alternative to the --m3dkeep argument |
|
--m3diter |
int |
The number of times the 3D reconstruction should be iterated |
|
--m3dpreprocess |
string |
Normalization processor applied before 3D reconstruction |
|
--m3dpostprocess |
string |
Post processor to be applied to the 3D volume once the reconstruction is completed |
|
--lowmem |
bool |
Make limited use of memory when possible - useful on lower end machines |
-P |
--parallel |
string |
Run in parallel, specify type:<option>=<value>:<option>:<value> |
The refinement process produces a large number of different output files in databases within directories named refine_xx. The easiest way to browse these files is with EMAN2/Programs/e2display, the file browser. For documentation of the file contents, please see the items towards the bottom of this page.