# Difference between revisions of "Optimisation"

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and re-run the INTEGRATE and CORRECT steps. This has the advantage that the refined geometry parameters (from CORRECT) are recycled into INTEGRATE, which sometimes leads to better R-factors. | and re-run the INTEGRATE and CORRECT steps. This has the advantage that the refined geometry parameters (from CORRECT) are recycled into INTEGRATE, which sometimes leads to better R-factors. | ||

* Wilson outliers: look through the list of reflections labeled as "aliens" in [[CORRECT.LP]]. Decide whether they follow a slowly decaying non-Wilson distribution (resulting in many reflections with Z > 8 instead of almost none in the case of a Wilson distribution), or whether the top ones are true outliers. The latter occurs most often from ice reflections (these may even be there when no ice rings are visible). My personal rule of thumb is that when the integer parts of Z ("int(Z)") are the numbers 8, 9, ... n, but there are no reflections (or just a single one) with int(Z) = n+1, then I consider all reflections with Z > n+1 as outliers. These are then put (i.e. copied) into REMOVE.HKL, and [[CORRECT]] is re-run.<br /> It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more aliens should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges very very quickly. | * Wilson outliers: look through the list of reflections labeled as "aliens" in [[CORRECT.LP]]. Decide whether they follow a slowly decaying non-Wilson distribution (resulting in many reflections with Z > 8 instead of almost none in the case of a Wilson distribution), or whether the top ones are true outliers. The latter occurs most often from ice reflections (these may even be there when no ice rings are visible). My personal rule of thumb is that when the integer parts of Z ("int(Z)") are the numbers 8, 9, ... n, but there are no reflections (or just a single one) with int(Z) = n+1, then I consider all reflections with Z > n+1 as outliers. These are then put (i.e. copied) into REMOVE.HKL, and [[CORRECT]] is re-run.<br /> It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more aliens should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges very very quickly. | ||

− | * Another way to judge | + | * Another way to judge Wilson outliers is to identify resolution ranges that deviate from 1. in the table '''HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF ACENTRIC DATA''' in [[CORRECT.LP]]. "Aliens" that are put into REMOVE.HKL will lower the values in these resolution ranges! |

* SCALEPACK users: don't confuse this process of rejecting Wilson outliers with the iterative procedure of rejecting scaling outliers that is usually done when using SCALEPACK. Scaling outliers are handled non-iteratively in [[XDS]]; the only way to influence [[XDS]] in this respect is by modifying [[WFAC1]]. | * SCALEPACK users: don't confuse this process of rejecting Wilson outliers with the iterative procedure of rejecting scaling outliers that is usually done when using SCALEPACK. Scaling outliers are handled non-iteratively in [[XDS]]; the only way to influence [[XDS]] in this respect is by modifying [[WFAC1]]. |

## Revision as of 13:27, 19 November 2007

## General guidelines for obtaining a good result from XDS

- read XDS.INP
- for good indexing, follow XDS.INP#Keywords which affect whether indexing will succeed
- for good completeness read MINIMUM_ZETA

## Final polishing

- After running through all steps of XDS (including space group determination), one might want to

cp GXPARM.XDS XPARM.XDS mv CORRECT.LP CORRECT.LP.old grep -v REIDX XDS.INP > XDS.INP.new mv XDS.INP.new XDS.INP

and re-run the INTEGRATE and CORRECT steps. This has the advantage that the refined geometry parameters (from CORRECT) are recycled into INTEGRATE, which sometimes leads to better R-factors.

- Wilson outliers: look through the list of reflections labeled as "aliens" in CORRECT.LP. Decide whether they follow a slowly decaying non-Wilson distribution (resulting in many reflections with Z > 8 instead of almost none in the case of a Wilson distribution), or whether the top ones are true outliers. The latter occurs most often from ice reflections (these may even be there when no ice rings are visible). My personal rule of thumb is that when the integer parts of Z ("int(Z)") are the numbers 8, 9, ... n, but there are no reflections (or just a single one) with int(Z) = n+1, then I consider all reflections with Z > n+1 as outliers. These are then put (i.e. copied) into REMOVE.HKL, and CORRECT is re-run.

It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more aliens should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges very very quickly. - Another way to judge Wilson outliers is to identify resolution ranges that deviate from 1. in the table
**HIGHER ORDER MOMENTS OF WILSON DISTRIBUTION OF ACENTRIC DATA**in CORRECT.LP. "Aliens" that are put into REMOVE.HKL will lower the values in these resolution ranges! - SCALEPACK users: don't confuse this process of rejecting Wilson outliers with the iterative procedure of rejecting scaling outliers that is usually done when using SCALEPACK. Scaling outliers are handled non-iteratively in XDS; the only way to influence XDS in this respect is by modifying WFAC1.