[Ifeffit] The difference of EXAFS oscillation when using Athena and when using Larch

Mon Apr 17 12:22:03 CDT 2017

Dear Matt

Thank you very much for your quick reply.

I attached three files: a raw data obtained from SPring-8 in Japan (PdO.q), .chik file obtained using Athena based on PdO.q (PdO.q.chik), and a python script I actually analyzed with Larch (EXAFSanalaysis.py).

(When opening PdO.q with Athena we use the plugin named "PFBL12C".)

If you put three files in the same directory and run “EXAFSanalysis.py”, you can see the previous PdO.png.

I am looking forward to hearing from you. 

Best,

Yuji

From: Ifeffit [mailto:ifeffit-bounces �� millenia.cars.aps.anl.gov] On Behalf Of Matt Newville
Sent: Tuesday, April 18, 2017 12:45 AM
To: XAFS Analysis using Ifeffit <ifeffit �� millenia.cars.aps.anl.gov>
Subject: Re: [Ifeffit] The difference of EXAFS oscillation when using Athena and when using Larch

Hi Yuji,

On Mon, Apr 17, 2017 at 9:49 AM, Yuji Mahara <mahara.yuji �� e.mbox.nagoya-u.ac.jp <mailto:mahara.yuji �� e.mbox.nagoya-u.ac.jp> > wrote:

Dear All, 

I am Yuji Mahara at Nagoya University, Japan.

I am using Athena in Demeter 0.9.24 package and Larch 0.9.33 on windows 10. 

At that time, I am worrying about the difference of EXAFS oscillation when using Athena and when using Larch.

k3-weighted Pd K-edge EXAFS and Fourier transform EXAFS spectrum for PdO bulk are shown in attached file(PdO.png).

When XAFS raw data is read using Athena and EXAFS oscillation is obtained, EXAFS spectrum is smooth and noise is small. On the other hand, using Larch, noise is noticeable in k space. 

This phenomenon occurred when analyzing other XAFS data as well.

I tried to analyze XAFS data by Athena and Larch with the almost same conditions.

Does this difference of EXAFS results arise from the difference in autobk's algorithm between Athena and Larch?

The analysis conditions are shown below. 

If someone knows this reason, please let me know.

There may be subtle differences in the results from autobk() from Ifeffit and autobk() from Larch, but they should not be very big.   If I recall correctly, the meaning of the values for the "clamps" is different between the two, but I don't think that accounts for the changes you're seeing.

Like you say, the differences seem to be more about the noise in the data which is much more noticeable in the data processed with Larch than with Athena, as if the data is being smoothed or interpolated differently.  if the data is very finely spaced or binned in some unexpected way from a QXAFS collection, that might help explain the difference, though I'm not sure of that or what behavior would be preferred.  

Is it possible to post one of the data files?

--Matt

Best regards,

Yuji Mahara

******************************************
■ Yuji Mahara
Nagoya University(Japan)  D3
Graduate School of Engineering
Furo-cho, Chikusa-ku, Nagoya-shi, Aichi, 466-8603, Japan
・Phone : +81-52-789-3192 <tel:%2B81-52-789-3192> 
・E-mail :  <mailto:mahara.yuji �� e.mbox.nagoya-u.ac.jp> mahara.yuji �� e.mbox.nagoya-u.ac.jp
******************************************

#####Analysis condition of Athena #####

e0 -->[ set e0 to a fraction of the edge step] --> 24355.9037323484 <tel:%28903%29%20732-3484> 

Normalization order: 3

Pre-edge range: from -150 to -60 eV

Normalization range: from 150 to 1100 eV

Rbkg: 1

k-weight used by spline: 2

Spline clamp: low=None, high=Strong

Spline range in k: from 0 to 17 

Fourier transform k-range: from 3 to 15, dk=0.5, window=’hanning’

#####Analysis condition of Larch #####

dat.e0 = 24355.9037323484 <tel:%28903%29%20732-3484>  <-- e0 was estimated by Athena

pre_edge(dat.e, dat.mu <http://dat.mu> , group=dat, e0=dat.e0, pre1=-150, pre2=-60, norm1=150, norm2=1100, nnorm=2, _larch=mylarch) 

autobk(dat.e, dat.mu <http://dat.mu> , group=dat, e0=dat.e0, rbkg=1, nclamp=5, clamp_lo=0, clamp_hi=24, kstep=0.05, kmin=0, kmax=17, kweight=2, win='hanning', _larch=mylarch) 

xftf(dat.k, dat.chi, group=dat, kmin=3, kmax=15, dk=0.5, window='hanning', kweight=3, _larch=mylarch)

#########################

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