EMAN2 Concepts and Conventions

• Particle box-size and refinement speed, what size to use

• 3D and 2D Transformations (rotation, translation, etc.)

• Symmetry in EMAN2, and generating projections in an asymmetric unit

• File Formats, Data storage conventions, and the internal database (what is BDB: ?)

• Using EMAN2 in environments with NFS or other network-shared directories

• CTF model used in EMAN2

• Parameters/Metadata appearing in EMData attributes (and stored on disk with the image), accessed with get_attr and set_attr

• Parameters/Metadata for applications, not associated with a specific image, stored in bdb:project

Modular functions in EMAN2

Many aspects of EMAN2 are modular, meaning new functions can be easily added for a variety of tasks. These categories are used in the GUI and from the command-line, and are not just useful for programmers. For example, the '--process' option in e2proc2d.py and e2proc3d.py uses 'processors' as defined below.

• Overview of how to use modular functions

• Processors - Filters, masks and other basic image processing operations

• Comparators (cmp) - Similarity metrics (cmp) for comparing two images/volumes

• Aligners - Algorithms for aligning one image/volume to another

• Averagers - Methods for averaging many images/volumes together

• Projectors - Generate 2-D projections of 3-D volumes (or 1-D projections of 2-D images)

• Reconstructors - Generate 3-D volumetric reconstructions from sets of 2-D images and orientation paramters

• Analyzers - Perform various analyses on sets of images/volumes

• Symmetries - Permissible symmetries to use in EMAN2

• Orientation Generators (orientgen) - Methods for generating sets of orientations filling an asymmetric unit

Information on specific files used in 3-D single particle refinement

This section documents the contents of all of the files produced by running e2refine.py in one of the refine_xx directories.

• Map projection files (projections_xx, proj_stg1_xx)

• Similarity matrix image files (simmx_xx, simmx_stg1_xx, proj_simmx_xx)

• Classification matrix image files (classify_xx)

• Class-Averaging Results matrix image files (cls_result_xx)

• Class-averages (classes_xx)

• 3-D reconstructions (threed_xx, threed_filt_xx, threed_mask_xx)

Information on specific files used in 2-D reference-free class-averaging

You may also wish to look at: e2refine2d

• input_fp - rotational/translational invariants for each particle

• input_fp_basis - MSA basis vectors (images) from input_fp

• input_fp_basis_proj - MSA subspace projections of the input_fp invariants

• classmx_00 - Initial classification of particles, same format as classmx above

• classes_init - Initial set of class-averages from invariant method (not very good usually)

• allrefs_XX - All of the references (sorted) to be used for the current iteration

• basis_XX - MSA basis from allrefs_xx

• aliref_XX - Subset of allrefs used for alignment of raw particles

• simmx_XX - Similarity matrix in same format as simmx above

• input_XX_proj - Aligned particles projected into basis_XX subspace

• classmx_XX - Classification matrix for the current iteration (as above)

• classes_XX - Class averages at the end of the iteration. The highest numbered classes_XX file is the final output of the program