The parameters for the arctangent function are initially estimated using
Hi Ryan, There are a couple of different questions here. And it turns out that this is also coming up against a few small bugs in code that are in the process of being resolved ;). I think this is going to be a long answer, first the "how to do this", then a warning about the bug-let, then on pseudo-Voigt in general. First, on doing peak fitting with arc-tangents in Larix: the 'pick values from plot'
feature. However, modifying these parameters doesn't result in corresponding changes in the graph, making it difficult to ascertain if it aligns with the data points. Considering this, would it be acceptable to set the arctangent's amplitude to 1 (normalised edge jump) and position its centre a couple of eV below E0?
Following this, two pseudo-Voigt functions are introduced, with their
Yes, you can definitely change the initial values (and set bounds -- but be careful about setting these too tightly). Picking default values from the plot is meant to be a rough value anyway. The amplitude should be about 1 (and you could set this to be "positive"), and the center is "halfway up the edge". These values should refine well. Be careful using arc-tangents in general. I know that is what many people (including Farges et al) used. I find that "line + Lorentzian" for the main peak just works better. But, I also get that you're trying to reproduce that earlier work, which is fine. So, also: note that "fit baseline" in Larix Pre-Edge Peak Fitting fits that baseline and assigns fit components named "bline" and "bpeak" ("b" for baseline) -- these are in your model. If you want, you can simply delete these components of the model. parameters initially estimated.
Then, to replicate the conditions of '1.3 eV 2σ width and 45% Gaussian,' do I set the pseud_fraction to 0.45 and pseud_sigma to 2? I'm uncertain about where to input the 1.3 eV width and whether this choice is optimal, especially considering that the natural width of the atomic K level at the Mn edge is 1.16 eV (Krause, 1979).
Actually, to reproduce Farge's settings, use 'fraction' of 0.55. Here "fraction" is the fraction of the Lorentzian. Sigma is a little trickier, because I am not 100% certain of the definition of pseudo-Voigt that they used. As far as I can tell, most definitions (and the one we use) have sigma as the sigma for the Lorentzian (ie, HWFM), and set a sigma for the Gaussian so that the FWHM are the same for both Lorentzian and Gaussian. This is not really that well justified physically (more below), but a common approach. With all that, you should then set the sigma to be 0.65, so that 2*sigma is 1.3. I think that is all of the "technical bits" and mechanics of doing the fits. Second, on buglets: There are 2 bugs in the combination of lmfit 1.3.0 (latest) and Larix 0.9.75 (latest). I'm reasonable for both bugs. Lmfit 1.3.0 is brand new (so, if you're using an older version, wait for 1.3.1 before updating) and breaks the way Larix sets the "arctan" form of the step function. A fix is in the works. A completely separate bug in Larch 0.9.75 bug is the bigger problem, as save/load of Larix sessions with Peak Fitting results do not correctly restore. This is fixed in the development branch, and I hope to push out Larix 0.9.76 in a day or two which will fix both of these.
Finally, I couldn't find the specific paper, but the authors stated that due to the significant processing times required for Voigt functions, they opted for pseudo-Voigt functions to model instrumental and core-hole broadening factors. With improvements in processing times, are Voigt functions now the preferred choice, or does the pseudo-Voigt function still hold advantages over both?
Third: Voigt functions are a convolution of Lorentzian and Gaussian peaks. Historically, these have been used in X-ray powder diffraction analysis. The basic idea is that the energy profile of the X-ray source (say, tube or bend magnet) gives a broadly Gaussian-like profile of incident angles on a monochromator. Monochromators at beamlines have complicated profiles, but are also Gaussian-like (tails get suppressed quickly). In XRD, I think that the powder-i-ness of the sample is expected to give Lorentzian-like broadening of peaks. For XAS, it is the core-level width that gives a Lorentzian-like energy profile to a peak. Properly, the Voigt function needs the complex Faddeeva function (complex extension of an error function). In the old days (say pre-2000?), implementing this was considered either hard (say, if you were writing in C) or slow to run.... or, well, the people writing analysis codes were just lazy ;). So they (I think fullProf may be to blame) invented pseudo-Voigt as a fraction sum of a Lorentzian and Gaussian of the same FWHM. It must have worked well enough -- lots of people use this. Is there any justification for asserting that the FWHM are the same for these two components? Not really. Like you say, the Gamma (= 2*sigma) for the core-holes are at least nominally known, and for transition metal K-edges are around 1 to 1.5 eV. FWHM of the source should scale linearly with energy, and should also be around 1 eV, though at modern facilities ought to be dominated by the mono Darwin width, which is Gaussian-ish (and depends on which mono crystal is used). Is it hard or slow to calculate a Faddeeva function these days? Nope, not in Python: it is built into scipy by the kind of people who love to read Abramowitz and Stegun so that we don't have to. So, you would be welcome to use a "real" Voigt function: it does not have a "fraction" parameter but does add a "gamma" parameter which works to control "how Gaussian vs how Lorentzian" the result is. Or use pseudo-Voigt to go all in at "reproduce previous results". My experience with pre-edge peak fitting with data from modern X-ray beamlines (either a good collimating mirror or an insertion device source with divergence comparable to the mono Darwin width) is that a Voigt profile (even with the default "gamma=sigma", similar to fraction=0.5, and equivalent FWHM) gives lower fit residuals than Gaussian. Sometimes, if the fit and the data are really good, it seems like "real" Voigt does a better job than pseudo-Voigt. But that is at the "detection limit", and it would not change any interpreted result within estimated uncertainties. --Matt