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eman2:e3make3d_point [2026/03/20 16:25] steveludtkeeman2:e3make3d_point [2026/03/20 17:45] (current) steveludtke
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-===== Preview of e3_make3d_point for better 3-D reconstructions =====+===== Preview of e3make3d_point for better 3-D reconstructions =====
  
 **THE METHOD DESCRIBED ON THIS PAGE IS NOT YET PUBLISHED, IT IS PROVIDED TO GIVE PEOPLE THE OPPORTUNITY TO TRY IT OUT AS WE MAKE FINAL IMPROVEMENTS**  **THE METHOD DESCRIBED ON THIS PAGE IS NOT YET PUBLISHED, IT IS PROVIDED TO GIVE PEOPLE THE OPPORTUNITY TO TRY IT OUT AS WE MAKE FINAL IMPROVEMENTS** 
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 ==== If you are starting with an EMAN2/3 project ==== ==== If you are starting with an EMAN2/3 project ====
-  * Run a standard e2spa_refine.py on your phase-flipped particle data +  * Run a standard //e2spa_refine.py// on your phase-flipped particle data 
-  * Run exactly the same command with the "--pointrecon 500option added to the command-line, then compare the results of the 2 refinements.+  * Run exactly the same command with the //--pointrecon 500// option added to the command-line, then compare the results of the 2 refinements.
   * 500 is the number of first-stage points to use in the reconstruction as described above.   * 500 is the number of first-stage points to use in the reconstruction as described above.
  
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     * This involves making a copy of your data (with compression). It is creating a complete EMAN2 project folder, so for a large project will take a little time.       * This involves making a copy of your data (with compression). It is creating a complete EMAN2 project folder, so for a large project will take a little time.  
   * cd eman3   * cd eman3
-  * e3make3d_point.py sets/fromstar__ctf_flip.lst --volout fromstar.hdf:12 --gaussout fromstar.txt --volfiltlp 3 --initpoints 500 -v 2+  * e3make3d_point.py sets/fromstar%%__%%ctf_flip.lst --volout fromstar.hdf:12 --gaussout fromstar.txt --volfiltlp 3 --initpoints 500 -v 2
     * volfiltlp is the resolution in Å the final map will be (Gaussian) filtered to. If you decide the map is over/under filtered the final volume can easily be recreated from the other output file.     * volfiltlp is the resolution in Å the final map will be (Gaussian) filtered to. If you decide the map is over/under filtered the final volume can easily be recreated from the other output file.
   * The output volume is fromstar.hdf, which can be opened with e2display.py or Chimera   * The output volume is fromstar.hdf, which can be opened with e2display.py or Chimera
   * If you wish to generate even/odd maps for a GSFSC calculation, you can run the program twice with the --class 0 (even) option on the first run and the --class 1 (odd) option on the second run (with modified output filenames).   * If you wish to generate even/odd maps for a GSFSC calculation, you can run the program twice with the --class 0 (even) option on the first run and the --class 1 (odd) option on the second run (with modified output filenames).
   * Alternatively you could run an EMAN2 style iterative refinement with point-based reconstruction, eg:   * Alternatively you could run an EMAN2 style iterative refinement with point-based reconstruction, eg:
-    * e2spa_refine.py --ptcl sets/fromstar__ctf_flip.lst --ref <starting model> --parallel thread:32:<fast scratch folder> --threads 32 --sym <symmetry> --res <start res> --niter 2 --tophat local --pointrecon 500 -v 1+    * e2spa_refine.py --ptcl sets/fromstar%%__%%ctf_flip.lst --ref <starting model> --parallel thread:32:<fast scratch folder> --threads 32 --sym <symmetry> --res <start res> --niter 2 --tophat local --pointrecon 500 -v 1
     * You may need to look at a full EMAN2 tutorial to pick specific options for your case. The only change is the addition of the --pointrecon option.     * You may need to look at a full EMAN2 tutorial to pick specific options for your case. The only change is the addition of the --pointrecon option.
  
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     * This involves making a copy of your data (with compression). It is creating a complete EMAN2 project folder, so for a large project will take a little time.       * This involves making a copy of your data (with compression). It is creating a complete EMAN2 project folder, so for a large project will take a little time.  
   * cd eman3   * cd eman3
-  * e3make3d_point.py sets/fromcs__ctf_flip.lst --volout fromcs.hdf:12 --gaussout fromcs.txt --volfiltlp 3 --initpoints 500 -v 2+  * e3make3d_point.py sets/fromcs%%__%%ctf_flip.lst --volout fromcs.hdf:12 --gaussout fromcs.txt --volfiltlp 3 --initpoints 500 -v 2
     * volfiltlp is the resolution in Å the final map will be (Gaussian) filtered to. If you decide the map is over/under filtered the final volume can easily be recreated from the other output file.     * volfiltlp is the resolution in Å the final map will be (Gaussian) filtered to. If you decide the map is over/under filtered the final volume can easily be recreated from the other output file.
   * The output volume is fromcs.hdf, which can be opened with e2display.py or Chimera   * The output volume is fromcs.hdf, which can be opened with e2display.py or Chimera
   * You can also test a standard EMAN2 style iterative refinement with point-based reconstruction, eg:   * You can also test a standard EMAN2 style iterative refinement with point-based reconstruction, eg:
-    * e2spa_refine.py --ptcl sets/fromcs__ctf_flip.lst --ref <starting model> --parallel thread:32:<fast scratch folder> --threads 32 --sym <symmetry> --res <start res> --niter 2 --tophat local --pointrecon 500 -v 1+    * e2spa_refine.py --ptcl sets/fromcs%%__%%ctf_flip.lst --ref <starting model> --parallel thread:32:<fast scratch folder> --threads 32 --sym <symmetry> --res <start res> --niter 2 --tophat local --pointrecon 500 -v 1
     * You may need to look at a full EMAN2 tutorial to pick specific options for your case. The only change is the addition of the --pointrecon option.     * You may need to look at a full EMAN2 tutorial to pick specific options for your case. The only change is the addition of the --pointrecon option.
  
  
eman2/e3make3d_point.1774023931.txt.gz · Last modified: by steveludtke