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→Wilson outliers (aliens)
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=== Wilson outliers (aliens) === | === Wilson outliers (aliens) === | ||
* Look through the list of reflections labeled as "aliens" at the bottom of [[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 arise most often from ice reflections (these may even be there when no ice rings are visible). <br /> 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 aliens (or just a single one) with int(Z) = n+1, then I consider all aliens with Z > n+1 as outliers. <br /> A different rule of thumb would be to simply consider aliens with Z of 20 or more as outliers (see [[Wishlist]]). This may be accomplished by | * Look through the list of reflections labeled as "aliens" at the bottom of [[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 arise most often from ice reflections (these may even be there when no ice rings are visible). Outliers should be put (i.e. copied) into REMOVE.HKL, and [[CORRECT]] then should be re-run.<br /> 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 aliens (or just a single one) with int(Z) = n+1, then I consider all aliens with Z > n+1 as outliers. <br /> A different rule of thumb would be to simply consider aliens with Z of 20 or more as outliers (see [[Wishlist]]). This may be accomplished by | ||
awk '/alien/ { if (strtonum($5) > 19) print $0 }' CORRECT.LP >> REMOVE.HKL | awk '/alien/ { if (strtonum($5) > 19) print $0 }' CORRECT.LP >> REMOVE.HKL | ||
It is useful to inspect the list of aliens after re-running CORRECT; maybe a few more of those should be put into REMOVE.HKL. But this process of rejecting Wilson outliers usually converges 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! | * 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]]. | ||