Just looked at the algorithm, how it stores the average non-spot
through all the images.
What happens with dataset where the non-spot (e.g. background) changes
systematically through the dataset, i.e. anisotropic datasets or thin
crystals lying flat in a thin loop? How much worse is
This is a purely historical comment. In the 1970's we were faced with the
problem that if, as was then the practice, the reflection data were stored
on punched cards with one reflection per card, even small molecule
structures could be rather heavy to carry around. One of the innovations
Frank von Delft wrote:
Just looked at the algorithm, how it stores the average non-spot
through all the images.
What happens with dataset where the non-spot (e.g. background)
changes systematically through the dataset, i.e. anisotropic datasets
or thin crystals lying flat in a thin loop?
Dear Colleagues,
The main problem with a lossy compression that suppresses weak
spots is that those spots may be a tip-off to a misidentified
symmetry, so you may wish to keep some faithful copy of the
original diffraction image until you are very certain of having
the symmetry right.
That
James,
caseB was lossy compressed.
It is 10% smaller when compressed (gzip, bzip2), so it contains
significantly less information.
cheers,
Hans
James Holton schreef:
Ian Tickle wrote:
I found an old e-mail from James Holton where he suggested lossy
compression for diffraction images (as
So far I have gotten several votes based on the lossless compression
ratio of the images, but, before I reveal the answer to the CCP4BB I
remind everyone that the LOSSY compression ratio of the compressed
images is 34-fold! So bzip2 and gzip are now incredibly inefficient
methods of storage
Ian Tickle wrote:
I found an old e-mail from James Holton where he suggested lossy
compression for diffraction images (as long as it didn't change the
F's significantly!) - I'm not sure whether anything came of that!
Well, yes, something did come of this But I don't think Gerard
