Hi,
Thought I'd give my two cents before the experts chime in... I'm only a grad student right now though so make what you want out of it. I also haven't touched Larch for a while now since I got my xafs (Larch) functions working in python several months ago.
First off though, ntot1 = param(expr='nO1+nO2', min=5.0, max=8.5) ran without throwing an error in Larch for me... (See screenshot near the middle of this email) So I don't think anyone can really help without more details, but I want to comment on some things:
- The first thing is that you're missing a vary = True in the statement for ntot1. Since the default value for vary is False according to the website, I would add that in if you actually wanted it to be fitted for during the fitting process (https://xraypy.github.io/xraylarch/fitting/parameters.html).
- In addition, when you run ntot1 = param(expr='nO1+nO2', min=5.0, max=8.5), are nO1 and nO2 pre-defined? If not the computer's not going to know what to make of those variables.
- I'd recommend something along the lines of:
nO1 = param(7.0, min=5, max=7, vary=True)nO2 = param(1, vary=True),ntot1 = param(expr='nO1+nO2', min=5.0, max=8.5, vary=True)
I just tested this on my machine and it didn't throw any errors. (Though it also didn't throw any errors without the 2 fixes I recommended...):
I didn't think to check when I took that screenshot but with that line, the vary is in fact set to false:
larch> ntot1.vary
False
In terms of the errors on error calculation --
- What's the reduced chi-sq? What do you mean by very small? In physics you want that to be as close to one as possible. Though in xafs the reduced chi-sq apparently is typically around 100-1000 so I'm confused as to what you mean by "small". Also, if you're having issues with errorbars to begin with, I wouldn't trust the chi-sq since that calculation relies on how big the errorbars are. If the errors are NAN, who knows what the computer would do to divide those numbers...
- What do you mean by "errors are not reported"? After the fit, it's returned in feffit.residual, or something along those lines (http://cars9.uchicago.edu/xraylarch/fitting/results.html#fitting-results-sec)... I haven't actually used this myself, so I can't say more.
- (Just a comment...) I don't know if this would help but my physics professor didn't let me trust the reduced chi-sq and gof values I was getting until I found out how the errors were calculated. By default, the errors are calculated by assuming that the noise is white in R-space and that there's no signal at high-R, so the "signal" near your rmax is the noise. (http://cars9.uchicago.edu/xraylarch/xafs/utilities.html#_xafs.estimate_noise, second to last paragraph)
Hope this was at least somewhat helpful,
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Hanyu (Alice) Zhang
Master's in Materials science | 2019
Research in utilizing XAFS to match current theory of metallic glasses to experimental data
Academic achievements:
Honors societies: National Society of Collegiate Scholars | Tau Beta Pi | Pi Mu Epsilon
Other interests:
Clubs: Case Origami Circle | Case Rocket Team | CWRUbotix | Case Physics and Astronomy club | Math club