Re: Ifeffit digest, Vol 1 #38 - 6 msgs
Hi Matt, first of all I'd like to say that I have been using Iffefit and Athena for a while and I found both packages very easy to learn and use. I've been following the discussion about how Ifeffit and also athena manage continuous scans and finer grid in energy space. We work in continuous scan mode at BioCAT, so our energy grid cannot be change during the scan as step scans usually do and our energy grid is aprox 1 eV per point, so when we transform this grid to k space we get a not even grid and for most of our point the grid is really smaller than 0.05 A-1. I was very curious about the statistics and how ifeffit and Athena calculate the interpolation. I took one of our scans taken at the Cu K Edge and I remove every other point from it. In order to preserve the grid in the very first region of K I just substitued the high K region from 6 to 13 A-1 with this "new" grid. I found that the results of the original data and the modified data are exactly the same, so the interpolation is not considering the missing points. I've also removed a group of three consecutive points of a goupr of five from the grid (in the same k region) and I found the same results. It will be very usefull if you could include into your package other method that consider every measured point. As you said in your last mail working with finer grid could be a non trivial change but if you could include a better interpolation method will be great. Attached you will fine the three files with the original data and the modified ones (senc-full.xmu; senc-1.xmu and senc-3.xmu). Thanks Raul Raul A. Barrea, PhD Senior Research Associate Illinois Institute of Technology 7900 S Cass Av. BioCAT/APS, Bldg 435B Argonne, IL 60439
Raul, Sam, Carlo, Bruce, Thanks for all the messages! OK, OK, I agree: Something should be done to better use finely gridded data from continuous scan / quick-exafs collection. I'll try to post pictures of Raul's data with current and spline interpolation in the next few days. Using spline interpolation will probably be better than 3-point interpolation, but it does seem that it still may not be enough to use all the data appropriately. I'm not entirely sure what the best way to bin the data is. I would've thought a rolling average with a Lorentzian or Gaussian would be about right, but I'm sure there are other ways to do it. What are Sam and Carlo/Jeff/SteveW using? If we can agree on a best way to handle the data, I'll include this in the spline() function. If you have these algorithms in Mathematica, python, or whatever, I'd be happy to try them out. Thanks, --Matt
participants (2)
-
Matt Newville
-
Raul Barrea