Hi Adam,
On Tue, Aug 14, 2018 at 7:07 AM Clark Adam Hugh (PSI)
Hi Matt
I recently had some problems incorporating the larch Cauchy wavelet transform into my program for rapid bulk processing of QEXAFS data (discussed http://millenia.cars.aps.anl.gov/pipermail/ifeffit/2014-April/007250.html). I particular after importing the relevant module into python the cauchy_wavelet(cwsam, kweight=kwwt, _larch=mylarch) function raises the error ‘not callable’. As a work around I have explicitly copied the module code for the definition into my own script and this seems to resolve the issue.
A minimal example that shows the problem would be helpful, but I think I can guess what is going on. Basically, the python API for lots of the XAFS (and plotting) functionality in larch should be cleaned-up. My focus has been on making the commands usable as larch commands, but the python API has lots of inconsistencies. That said, this works for me: import larch from larch_plugins.io import read_ascii from larch_plugins.xafs import autobk from larch_plugins.xafs.cauchy_wavelet import cauchy_wavelet import matplotlib.pyplot as plt _larch = larch.Interpreter() dat = read_ascii('feo_xafs.dat', _larch=_larch) autobk(dat, rbkg=0.9, kweight=2, _larch=_larch) cauchy_wavelet(dat, kweight=2, _larch=_larch) plt.imshow(dat.wcauchy_mag) plt.show() That is, I would bet that the 'not callable' you are seeing is because of the poorly exposed "cauchy_wavelet" call. Does that match what you are seeing?
I also notice that at present that it is not possible to apply a windowing function within the wavelet transform, is this just a case of windowing the K space data that is fed to the module? I would also have interest in trying to fit the EXAFS data in the wavelet space, has there been any progress in Larch for performing this?
Yes, right now there is not a window function applied. We could add that. From talking with you and a couple other people at the XAFS conference, and other folks since then, it does seem that adding the Morlet wavelet would be worth doing too. I'm not sure I have the code for this. Do you? You can definitely fit EXAFS in wavelet space, just using "feffit_transform(fitspace='w', ....)". There's an example of this at https://github.com/xraypy/xraylarch/blob/master/examples/feffit/doc_feffit_w...
As briefly discussed at the EXAFS conference another feature that I think would be very nice to include would be the possibility to use a Victoreen function for the post edge normalisation instead of a polynomial function. I also briefly discussed the application of the MBACK algorithm. Unless I am doing something incorrect, when trying to use the MBACK algorithm in Larch within python the following error is raised ‘ ‘XrayDB’ object has no attribute _getChantler’. In some cases it would be very interesting to apply the MBACK algorithm particularly when only a short data range is collected in some experiments.
Yes, I remember that conversation well, and agree that getting some better normalization using tabulated mu/rho is a good idea. And, sorry about the error from XrayDB in the released version. This is now fixed in github. I've been playing with normalization with tabulated mu/rho with the MBACK algorithm. I think there are potential issues with this, in that MBACK includes both a pre-edge that tries to compensate for Compton scattering leaking into the fluorescence signal *and* normalization using tabulated mu/rho. Especially when using a Victoreen pre-edge, I think that the compensation for Compton scattering can sort of cause instabilities, though I haven't explored this for a wide range of spectra. But I recently (like, in the past week) added a "mback_norm" function to do only the normalization using tabulated mu/rho. This is now in the github master branch and partially supported in the github-version of XAS_Viewer. But, this definitely needs more study and more work. If you're interested in looking at this in more detail, let me know or check out the github master repo. Or, if you're more interested in the wavelet transforms, please work on that! Cheers, --Matt