Hi Kiril,
Yes, this is a common issue, especially for data from some beamlines that do a continuous or fly-scan data collection (I'm 100% in favor of this). There is a function for that. See larch.xafs.rebin_xafs:
https://urldefense.us/v3/__https://github.com/xraypy/xraylarch/blob/master/l...
For the data re-binning to each point in the output, this can do either a boxcar average (which might reduce resolution) or use a centroid value (which might do less resolution reduction). Either of these should be better (in the sense of "use more of the input data") than a simple interpolation.
The Larix GUI will prompt users to use this to re-bin XAFS data that looks like it has too many points (more than 1200 points) or too fine an energy spacing (< 0.75eV) in the high-energy portion of the spectra. So, I think your data would be noticed as "needs rebinning".
--Matt
PS: suggestions for better approaches welcome!
________________________________
From: Kirill LOMACHENKO via Ifeffit
Sent: Friday, November 22, 2024 12:10 PM
To: ifeffit@millenia.cars.aps.anl.gov
Cc: Kirill LOMACHENKO
Subject: [Ifeffit] Larch: noise in chi(k) - another try
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Dear Matt and dear all,
seems that my original message has been cut by the mailing server. I try
again, this time without attachments.
I have encountered an issue processing oversampled EXAFS data with
Larch. In my example the energy step is 0.33 eV in all parts of the
scan, including the EXAFS. When I use the default procedure in Larch
(pre_edge and then autobk), I get the noise level in chi(k) that is
higher than I would expect. Strangely, rebinning of mu(E) using
rebin_xafs function does not help, and even increases the noise. The
lowest level of noise can be obtained when rebinning the data in k
(starting from the "raw" oversampled chi(E) provided by Larch). In this
last case, the result is noise-wise very similar to Athena's default.
I think that the high level of noise after the default Larch processing
can be due to the fact that Larch does not seem to do averaging when
converting the data from E to k. Instead, it does interpolation with
UnivariateSpline which by design is not supposed to decrease the noise
when applied to oversampled data. Is this correct? Would it be worth
changing?
The reason why the rebinning in E does not decrease the noise is not
clear to me. Do you have any idea?
Has anyone run into similar issues? Any comments and suggestions are
welcome.
Thank you
Best regards,
Kirill
