I agree I was too generous in my statement and you promptly caught it,
thanks!
phenix.refine does catch and deal with clearly nonsensical situations,
like having Fobs<=0 in refinement. So, saying "phenix.refine does
not use any data cutoff for refinement" was not precise, indeed. In
addition, phenix.refine automatically removes Fobs outliers based on
R.Read paper.
I don't see much sense having a term (0-Fcalc)**2 in least-squares
target or equivalent one in ML target. Implementing an intensity based
ML target function (or corresponding LS) would allow using Iobs<=0,
but this is not done yet, and this is a different story - your
original question below was about Fo (Fobs).
Do you have rock solid evidence that substituting missing (unmeasured)
Fobs with 0 would be better than just using actual set (Fobs>0) in
refinement? Or did I miss any relevant paper on this matter? I would
appreciate if you point me out. Unless I see a clear evidence that this
would improve things I wouldn't waste time on implementing it.
Unfortunately I don't have time right now for experimenting with this
myself.
Thanks!
Pavel.
On 5/17/10 6:52 AM, Ed Pozharski wrote:
On Fri, 2010-05-14 at 15:35 -0700, Pavel Afonine wrote:
phenix.refine does not use any data cutoff for refinement.
So was the Fo>0 hard-wired cutoff removed? I don't have the latest
version so I can't check myself.