[Ifeffit] Athena interpolation when removing mono glitches

Matt Newville newville at cars.uchicago.edu
Fri Aug 12 08:24:38 CDT 2016

Hi Michael,

On Fri, Aug 12, 2016 at 6:56 AM, Michael Gaultois <mike.g at usask.ca> wrote:

> Dear members of the Ifeffit list,
> I recently collected some EXAFS data with some significant monochromator
> glitches that I am looking to remove. I have used a python script
> graciously written by the beamline scientist to remove the offending
> regions, but when I import the data into Athena, Athena does some funny
> business in an attempt to join together the regions outside of the data
> gap. (See the bending away in the dataset and/or attached image.) I have
> confirmed by plotting with other software that the strange step-like
> behaviour in the mu(E) is present only after importing into Athena (the raw
> data is fine).
> I have looked through the mailing list archives and also the user manual,
> but can't seem to find anything that explains it, or other people who have
> experienced this problem in the past. From what I can determine, Athena
> joins together the segments to obtain a linear interpolation in the
> norm(E)? This leads to a warping in the mu(E).
> ==How does Athena try to treat this data?==

Yes, it does linear interpolation between two points around the value.

> I was wondering if other people have had similar issues, and what steps
> can be taken to remedy the problem. For example, replacing removed data
> points with artificial points along a linear interpolation would be
> possible, but the act of adding artificial points that don't exist is
> concerning to me.
> ==What is the best way to treat data with mono glitches to reduce spurious
> features not intrinsic to the sample?==
Your data was very finely spaced in energy, and fairly noisy.  The good
news is that your rebinned data was much less noisy shows much less of a
problem when deglitched.  That's the data to use.

But a little more of what happened with your noisy, fine-energy-grid data.
For the glitch around 13950 eV, it turns out that the two endpoints
remaining were both fairly low compared to the mean value, so the resulting
chi(k) goes strongly negative.   For reference, the glitch around 14075 eV
had endpoints much closer to the mean, and so the resulting chi(k) in that
region is closer to zero and less obviously "completely wrong".

FWIW, I got this far then saw Bruce's answer.  I think we're saying the
same thing.

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