EMAN2 Output Files

This page documents all of the various files produced by various tasks and workflows in EMAN2. While the format of the actual files will be one of the standard EMAN2 supported image formats in most cases, these pages will explain the contents of files with specific standard names.

General File Information

• File formats & data storage conventions

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

Information on specific input/output files for different EMAN2 programs

For Single Particle Analysis (SPA)

For SPA refinement runs (e2refine_easy.py and e2refinemulti.py)

Input files:

• files specified via --input, --model for e2refine_easy

• files specified via --input and --model or --models

• strucfac.txt should normally be present in the project directory - This is a text file containing the ideal 1-D structure factor expected for the final map. Intensity as a function of spatial frequency.

Output files (in temporal order of creation), _xx denotes the iteration number:

• projections_xx.hdf, proj_stg1_xx.hdf - Map projection files

• simmx_xx.hdf, simmx_stg1_xx.hdf, proj_simmx_xx.hdf - Similarity matrix image files

• classify_xx.hdf - Classification matrix image files

• cls_result_xx.hdf - Class-Averaging Results matrix image files

• classes_xx.hdf - Class-averages

• threed_even_unmasked.hdf, threed_odd_unmasked.hdf - Reconstructions from the final completed iteration without masking or final filtration, suitable for use with ResMap

• threed_xx.hdf, threed_filt_xx.hdf, threed_mask_xx.hdf - 3-D reconstructions

If you are struggling with a failed refinement, look at the produced files in this order until you find something unexpected, and that may give some clues as to what went wrong.

For SPA 2-D reference-free class-averaging (e2refine2d.py)

You may also wish to look at: e2refine2d Input files:

• file specified via --input

Output files:

• 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 and aligned) to be used for the current iteration. Other than sorting/alingment, same as classes_XX files

• 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

For Single Particle Tomography (SPT, "subtomogram averaging")

For SPT initial model generation by hierarchical ascendant classification (HAC, e2spt_hac.py)

For SPT initial model generation by binary tree alignment (BTA, e2spt_binarytree.py)

For SPT initial model generation by self symmetry alignment (SSA, e2symsearch3d.py)

For SPT iterative refinement runs (e2spt_classaverage.py)

Input files:

• --input, subvolume stack in .hdf format

• --ref, if performing reference-based refinement, reference image in .hdf format

Output files:

• sptbt, spthac or sptssa subdirectories if --ref is not provided and the program generates initial models automatically. The corresponding directory will appear, with the files specified above in it, depending on whether --btref, --hacref or --ssaref are specified.

• aliptcls.hdf - Requires specifying --saveali. Stack of final aligned particles from last refinement iteration. If gold-standard refinement is on (by not supplying --goldstadardoff) the output includes aliptcls_even.hdf and aliptcls_odd.hdf.

• avgs.hdf - Requires specifying --savesteps. Stack of the averages produced in all iterations of refinement. If gold-standard refinement is on (by not supplying --goldstadardoff) avgs.hdf; will be the averages of the odd and even averages for each iteration, and additionalavgs_even.hdf and avgs_odd.hdf stacks will be generated containing the even and odd averages across iterations.

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

• final_avg.hdf - Final average of the final even and odd averages (or simply the final average of all the particles if gold-standard refinement is off).

• fsc_XX.txt - FSC between even and odd averages for the current iteration.

• initialrefs_fsc.txt - Initial FSC curve against the reference or between the initial even and odd models generated by the program if not reference is provided via --ref.

• parameters_sptclassavg.txt - Text file containing the executed command and the values used by the program for all parameters (including defaulted ones, not specified by the user).

• spt_cccs_XX.txt - Text file with sorted cross correlation coefficients. If gold-standard refinement is on, spt_cccs_XX_even.txt and spt_cccs_XX_odd.txt are generated.

• spt_meanccc.txt - Text file containing the mean cross correlation coefficient across iterations (this can help determine whether the mean score is improving or has plateaued or is degenerating). If gold-standard refinement is on, spt_meanccc_even.txt and spt_meanccc_odd.txt are generated.

• tomo_xforms_0.json - Json file with alignment parameters for all particles in the stack.

• tomo_xforms_0_avgali2ref.json - Json file with alignment parameters for the final average aligned to the reference if gold-standard refinement is off. This becomes tomo_xforms_0_oddali2even.json if gold-standard refinement is on.

For SPT multiple model refinement (e2spt_refinemulti.py)