Just a couple of housekeeping notes: I’m giving talks in Europe in a month at the following locations: Unité Matériaux et Transformations (UMET), Lille on May 16, hosted by Grégory Stoclet, Birmingham University, Birmingham on the 20th of May, hosted by Zoe Schnepp, Nottingham University, Nottingham, on the 23rd of May, hosted by Philip Moriarty, Between these dates, I’ll also be joining Zoe and Martin for beamtime at the Diamond synchrotron (beamline I11) between May 21 and May 23. Please feel free to stop me at these locations and say hi! For today, I’ve got another bit of data correction to show. I thought it might be interesting to put them all together and show you what difference it makes to an integrated scattering pattern. Many of the data corrections implemented are quite straightforward shifts and scalings, but some are more involved and have a greater effect on the scattering pattern.
Short news first; by going through the motions and waiting for Elsevier to get back to me, I have gotten permission (for the royal sum of 0.00 eurodollars) to repost one more paper from Polymer on my site. So that has now gone in the 2010 publications page here. Then it is time to give you something. For those who have to do their own data processing and would like to get my way of doing it, I have attached my data processing flowchart to this post. It is not a perfect method, but as far as I can tell it works quite well. If you are interested in getting the actual Python code that does all this work, drop me a line. Since the code is quite new, it does not support many strange detectors, so if support needs to be built for a particular detector, I’ll be happy to spend some of my time looking at whether it can be done. So there:imp_imagecorrect_and_imgint. Let me know if there are improvements, obscurities or if you have any other comments on this.
Dear scatterers, Those of you who have been reading this weblog for a while now, may remember the calculation of the sample self-absorption correction for plate-like samples. The result of this was a straightforward equation which could be used to correct the scattering of strongly absorbing samples (>30%) with a plate-like geometry. It was mentioned then, that the calculation of this correction for capillary samples is more complicated, but would be good to have. This sample self-absorption of a capillary will show up as a butterfly-shaped shadow on your scattering pattern. In the latest issue of J. Appl. Cryst., there is a new paper discussing exactly this. Sulyanov et al. have (programmed) a solution to calculate the sample self-absorption factor for cylindrical samples. The code they provide is available in Fortran, and I will spend some time to try to transcode this into Python in the near future. Judging from their solutions, I am happy I did not try to solve it. The solution seems to be a little bit more complicated than I thought. Additionally, in the same issue, Zeidler has a solution for samples of spherical geometry. While I have not encountered a problem requiring this solution before, it is certainly noteworthy, and may be of use to some of you doing scattering from suspended objects. Lastly, there is a new video of one of my latest short presentations online here, explaining a little about my work as well as the monte-carlo analysis method. It’s very short, and there will be a more detailed MC method explanation shortly (as I have promised for quite a while now). Scatter well!
(Sorry about the hiatus, there’s been a period filled with that noblest of Japanese traditions: paperwork!) If you want to do fitting of a 2D image, you want to preserve the information in the entire image. 2D fitting is quite computationally intensive, so you still want to reduce the number of pixels in your images. Methods I have seen published, are occasionally quite poor at preserving detail, but I’ve played with a type of binning (quite similar to the mathematical concept of k-d trees) that does preserve this: Behold the coolness in the following plots, which are different zoom levels of the same scattering pattern. On the vertical axis is the azimuthal angle, on the horizontal axis q (in reciprocal meters). An explanation will follow in the near future, but you’re welcome to write to me for the scoop. Pretty cool, huh! And since the errors of the intensity of each bin are known, fitting is not affected by the differences in area!
Hello dear readers, As a followup on my previous story, a colleague of mine sent me this paper that helps explain the standard deviation, standard error and confidence intervals. A useful, and funny read: Click here. The second noteworthy item is that I have, as of November 1st, started working at the National Institute for Materials Science (NIMS) as a (limited time) independent researcher. From now on, I will be working on a variety of stuff, which revolves around developing anisotropic SAS analysis methodologies. Naturally, I will post items of interest on this site as always, so keep checking in!. Also, don’t hesitate to leave a comment! B.
Everybody hates statistics … … but it can be of major importance in our small angle world. While very few papers on small-angle scattering discuss statistics, they can tell you whether your observations are real or just imaginary. In addition, statistics will let you know whether you have been able to describe your scattering pattern with your model or not. All in all, nice to have. I will briefly discuss two statistical concepts which could be of great use, as they have been for me. While I never really could understand all the concepts during statistics lectures at university (a situation which may sound familiar), I can try to explain some simple concepts. By the by, if you (dear reader) are a statistician, I would be happy to get in touch with you. The first concept is the most straightforward, and involves the uncertainties on fitting parameters. Secondly I will discuss statistics on collected intensity and how to retrieve them for a variety of detectors.