(Obviously this is for culling the bad data after data reduction is complete-
doesn't do anything to prevent the ice ring from introducing error in the
scaling process. Not really what you were asking for, and maybe not even useful
now, given automatic outlier rejection in today's programs)
On 03/05/2021 04:00 PM, Edward A. Berry wrote:
For manually masking out the rings, you can use mtz2various to dump reflections
in resolution ranges without ice in ascii, cat them all together and use f2mtz
to read them into a new mtz file. Example attached.
You might also be able to do it using mtzutils to write out mtz files in
limited resolution ranges, and then to merge them back into one file.
On 03/04/2021 10:39 AM, Alexander Brown wrote:
Hi all,
I'm struggling with a dataset I have which shows very poor data quality around
3.6A, or exactly where I can see a significant ice ring in the images. I'm
trying to use mosflm to process the image files, and I have seen a previous
thread on the message board where it is recommended to turn on three tick boxes
for ice ring exclusion, but despite this, as I continue through the processing
sequence and use aimless, it still flags that the data is affected by an ice
ring at that resolution, which you can also see in the quality/resolution
graphs.
I have even tried making a mask in the moslfm viewer using the mask tool to
cover the entire ice ring, but to no avail.
Finally I did have a go at using EVAL which is mentioned in the original post
about ice rings, but it seems it depends on libgfortran3 packages which have
now been replaced with libgfortran5 and so I didn't get very far.
Is there a manual way to mask out certain data, or could there be something
with my data that is causing the automatic ice ring resolution not to be as
effective?
Thank you!
*Alex Brown*
PhD Student
School of Pharmacy
Biodiscovery Institute (previously Centre for Biomolecular sciences)
University of Nottingham
Nottingham
NG7 2RD
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