Hi, On Fri, Jan 25, 2019 at 12:45 PM Kirill Lomachenko < kirill.lomachenko@esrf.fr> wrote:

Dear Matt, I have a question and a comment regarding Larch. I would appreciate if you could have a look. 1) The flattening algorithm for XAS in Larch works differently with respect to Athena when linear post-edge function is used for normalization. In Larch the normalized spectrum is always fitted by a parabola for flattening, no matter which function was used for normalization. It works fine when the post-edge function is also a parabola, but when it is a straight line, the resulting flattened spectrum does not stick to Y=1, as it is expected to do (and as it does in Athena). My question is: is it a bug or a feature? Just in case, I made some minor modifications in the pre_edge.py file that allow to get the same results as in Athena when linear post-edge is used. Maybe these corrections are a bit clumsy, but it seems to work. If anyone needs, I can share.

Flattening is not well-defined, and probably not well-justified. I sort of view it as weakness that it is included in Larch at all, and yet I see many people use it. The definition may well differ from what Athena does. That's probably a clue of how poorly defined the process is. For sure, Larch has some improvements in pre-edge removal and normalization over Ifeffit. For example, Larch can use the Victoreen formula to (at least mostly) account for the expected decay in mu(E). It also has an implementation of Mback, and a modification to this (mback_norm) that is more like the cl_norm function in Ifeffit. In my opinion, this matching of mu(E) data to tabulated values has a lot of merit to it. FWIW, the Mback algorithm has a lot of subtle features - I find `mback_norm` to be more consistent and easier to use.

2) There is a typo in the larch web-manual in the description of the nnorm parameter of the pre_edge() function. It is stated that it is the number of terms in the fitting polynomial (i.e., 1+degree) whereas it seems that it is just its degree. So, nnorm=1 corresponds to a linear function and nnorm=2 to quadratic. In the gui help it is partially corrected, but the phrase "Default=3 (quadratic)" stays. It is not critical at all, but may be misleading for beginners...

Ah, sorry and thanks. The online doc is also different from the documentation string in the code (which is closer, but still not perfect). nnorm is the degree of the polynomial (0 for constant, 1 for line, 2 for quadratic, default=2). The word "order" for polynomial is apparently not that well-defined. I either learned it wrong long ago or mis-remembered. I'm in the process of trying to release the next version, so I'll make these changes soon.

Using the possibility, I would like to thank you for all the great work you are doing on Larch. It really helps for large datasets!

Great, glad it's useful. --Matt Newville