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* '''clases_xx.hdf''' - [[EMAN2/ClassesFiles|Class-averages]] | * '''classes_xx.hdf''' - [[EMAN2/ClassesFiles|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 |
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If you are struggling with a failed refinement, look at the produced files in this order until you run into a problem, and that may give some clues as to what went wrong. | 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. |
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
Information on specific output files in 3-D single particle refinement runs (e2refine_easy.py and e2refinemulti.py)
This section documents the contents of all of the files produced by running e2refine.py in one of the refine_xx directories. This listing is in the order that the files are created.
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.
Information on specific output files from 2-D reference-free class-averaging (e2refine2d.py)
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 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