• EMAN2
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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) 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 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") 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:

• 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) the output includes 'avgs_even.hdf' and 'avgs_odd.hdf'.

classmx_0.hdf final_avg.hdf fsc_0.txt parameters_sptclassavg.txt results.txt spt_cccs_0.txt spt_meanccc.txt subset4.hdf tomo_xforms_0.json tomo_xforms_0_avgAli2ref.json