phenix.auto_sharpen expected filtration of input map
Hi, So far I have liked very much the results of phenix.auto_sharpen, but one thing is not yet clear to me: how does it expect the input map to be filtered? Would be something like: a) Unsharpened, unfiltered map (e.g. the simple average of the half-maps) b) Unsharpened, SSNR-weighted (Cref) (Rosenthal & Henderson, JMB 2003) c) Doesn't matter / other? Thanks in advance! Best wishes, -- Ricardo Diogo Righetto
Hi Ricardo,
If you are using phenix.auto_sharpen with just a map and resolution as
inputs, then it will (by default) apply an overall sharpening B-factor up
to the resolution limit you supply, and then a blurring B-factor beyond
that. This means that any filtering applied to the input map will make a
difference. I haven't tested systematically, but my guess is that the
better the map you start with with the better the map you'll get. So I
would recommend starting with the weighted map as in Rosenthal and
Henderson. In this case phenix.auto_sharpen will basically be identifying
the overall sharpening that optimizes the clarity and connectivity of the
map.
If you sharpen using a model, then a different sharpening is applied at
each resolution (basically as in Rosenthal and Henderson, but based on the
match between model and map, not between two half-maps), so the starting
map should make only a small difference.
All the best,
Tom T
On Wed, Jan 24, 2018 at 6:00 AM, Ricardo Righetto wrote: Hi, So far I have liked very much the results of phenix.auto_sharpen, but one
thing is not yet clear to me: how does it expect the input map to be
filtered? Would be something like: a) Unsharpened, unfiltered map (e.g. the simple average of the half-maps)
b) Unsharpened, SSNR-weighted (Cref) (Rosenthal & Henderson, JMB 2003)
c) Doesn't matter / other? Thanks in advance! Best wishes, --
Ricardo Diogo Righetto _______________________________________________
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Thomas C Terwilliger
Laboratory Fellow, Los Alamos National Laboratory
Senior Scientist, New Mexico Consortium
100 Entrada Dr, Los Alamos, NM 87544
Email: [email protected]
Tel: 505-431-0033
Hi Tom, Yes, I meant the case with just a single map and a resolution value as input. Thanks a lot for the clarification! Cheers, -- Ricardo Diogo Righetto 2018-01-24 14:45 GMT+01:00 Tom Terwilliger < [email protected]>:
Hi Ricardo,
If you are using phenix.auto_sharpen with just a map and resolution as inputs, then it will (by default) apply an overall sharpening B-factor up to the resolution limit you supply, and then a blurring B-factor beyond that. This means that any filtering applied to the input map will make a difference. I haven't tested systematically, but my guess is that the better the map you start with with the better the map you'll get. So I would recommend starting with the weighted map as in Rosenthal and Henderson. In this case phenix.auto_sharpen will basically be identifying the overall sharpening that optimizes the clarity and connectivity of the map.
If you sharpen using a model, then a different sharpening is applied at each resolution (basically as in Rosenthal and Henderson, but based on the match between model and map, not between two half-maps), so the starting map should make only a small difference.
All the best, Tom T
On Wed, Jan 24, 2018 at 6:00 AM, Ricardo Righetto < [email protected]> wrote:
Hi,
So far I have liked very much the results of phenix.auto_sharpen, but one thing is not yet clear to me: how does it expect the input map to be filtered?
Would be something like:
a) Unsharpened, unfiltered map (e.g. the simple average of the half-maps) b) Unsharpened, SSNR-weighted (Cref) (Rosenthal & Henderson, JMB 2003) c) Doesn't matter / other?
Thanks in advance!
Best wishes,
-- Ricardo Diogo Righetto
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-- Thomas C Terwilliger Laboratory Fellow, Los Alamos National Laboratory Senior Scientist, New Mexico Consortium 100 Entrada Dr, Los Alamos, NM 87544 https://maps.google.com/?q=100+Entrada+Dr,+Los+Alamos,+NM+87544&entry=gmail&source=g Email: [email protected] Tel: 505-431-0033
participants (2)
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Ricardo Righetto -
Tom Terwilliger