[Ifeffit] restraints in larch

Mon Oct 8 12:38:11 CDT 2018

Hi Barbara,

Looking at this data I don't think Larch found a fit... Try overlaying a
plot of your fit with your data and see if the fit was any good.

I'd check the assumptions again and change the initial guesses around.
Since this is a multi-dimensional fit, the initial guesses could change the
results quite a bit. A small chi_sq in physics typically means that the
errorbars on the data is too big, though I don't know how that would factor
into XAFS... Where's your cutoff for the R-value when you're fitting? If
it's at a R where you are still expecting data, that might be the issue (?)

----------
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:*

*MASMC
<https://en.wikipedia.org/wiki/Missouri_Academy_of_Science,_Mathematics_and_Computing>/NWMSU
<https://www.nwmissouri.edu/>:* Associate of Science, 2014
*CWRU <http://case.edu/>:* Bachelor's of Science in Engineering Physics
<http://physics.case.edu/undergraduate-programs/undergrad-degree-programs/bsdegree-engrphys/>
, 2018
*Honors societies*: National Society of Collegiate Scholars | Tau Beta Pi
<http://www.tbp.org> | Pi Mu Epsilon <http://pme-math.org/>

*Other interests:*

*Clubs*: Case Origami Circle | Case Rocket Team <http://caserocketteam.org>
| CWRUbotix <http://cwrubotix.case.edu/> | Case Physics and Astronomy club
| Math club
*YouTube*: Chinese Operations Lead, Veritasium <http://veritasium.com> (5M
subscribers) | Saturday Vlogs <http://bit.ly/AliceZhang>

On Sun, Oct 7, 2018 at 5:56 PM Barbara Etschmann <
barbara.etschmann at monash.edu> wrote:

> Hi Matt and Hanyu
>
> Thanks - the latest version of larch runs wrt
> ntot1 = param(expr='nO1+nO2', min=5.0, max=8.5, vary=True)
>
> However, still getting odd gofs/errors:
> [[Statistics]]
>    nvarys, npts       = 6, 390
>    n_independent      = 11.186
>    chi_square         = 0.00080359
>    reduced chi_square = 0.000154956
>    r-factor           = 0.01850
>    Akaike info crit   = -94.7257
>    Bayesian info crit = -92.2378
> ....
>
>   delrO1         =  0.132140 +/- unknown   (init=  0.150000)
>    delrO2         = -0.446980 +/- unknown   (init= -0.500000)
>    enot           = -4.125159 +/- unknown   (init=  0.000000)
>    nO1            =  7.000000 +/- unknown   (init=  7.000000)
>    nO2            =  0.929177 +/- unknown   (init=  1.000000)
>    ntot1          =  7.929177 +/- unknown  = 'nO1+nO2'
>    ssO1           =  0.006499 +/- unknown   (init=  0.003000)
>  .....
> (Please ignore the large init value.)
>
> Running on a Mac 10.13.3.
> Happy to send the script & output.
> Thanks
> Barb.
>
>
>
>
>
>
> On Fri, 5 Oct 2018 at 23:02, Matt Newville <newville at cars.uchicago.edu>
> wrote:
>
>> Hi Hanyu, Barbara,
>>
>>
>> On Fri, Oct 5, 2018 at 12:55 AM Hanyu Zhang <hxz269 at case.edu> wrote:
>>
>>> 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:
>>>
>>>    1. 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).
>>>    2. 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.
>>>    3. 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...):
>>>
>>> [image: Screen Shot 2018-10-05 at 12.55.59 AM.png]
>>>
>>> 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 --
>>>
>>>    1. 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...
>>>    2. 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.
>>>    3. *(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,
>>> ----------
>>> 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:*
>>>
>>> *MASMC
>>> <https://en.wikipedia.org/wiki/Missouri_Academy_of_Science,_Mathematics_and_Computing>/NWMSU
>>> <https://www.nwmissouri.edu/>:* Associate of Science, 2014
>>> *CWRU <http://case.edu/>:* Bachelor's of Science in Engineering Physics
>>> <http://physics.case.edu/undergraduate-programs/undergrad-degree-programs/bsdegree-engrphys/>
>>> , 2018
>>> *Honors societies*: National Society of Collegiate Scholars | Tau Beta
>>> Pi <http://www.tbp.org> | Pi Mu Epsilon <http://pme-math.org/>
>>>
>>> *Other interests:*
>>>
>>> *Clubs*: Case Origami Circle | Case Rocket Team
>>> <http://caserocketteam.org> | CWRUbotix <http://cwrubotix.case.edu/> | Case
>>> Physics and Astronomy club | Math club
>>> *YouTube*: Chinese Operations Lead, Veritasium <http://veritasium.com> (5M
>>> subscribers) | Saturday Vlogs <http://bit.ly/AliceZhang>
>>>
>>>
>>>
>>> On Thu, Oct 4, 2018 at 8:04 PM Barbara Etschmann <
>>> barbara.etschmann at monash.edu> wrote:
>>>
>>>> Hello
>>>>
>>>> I'm trying to use larch to refine the coordination of Y in solution. I
>>>> need to use 2xO paths to fit the first shell and would like to restrain the
>>>> total coordination to be between 5.5 and 8.
>>>>
>>>> I can apply the constraint to each nO, but not the total, i.e.
>>>> nO1 = param(7.0, min=5, max=7, vary=True),
>>>> nO2 = param(1, vary=True),
>>>>  but
>>>> ntot1 = param(expr='nO1+nO2', min=5.0, max=8.5)
>>>> does not work.
>>>> How can I restrain the total coordination?
>>>>
>>>>
>> I think that should work.  But I can also believe it doesn't ;).   It may
>> require a recent version of Larch (0.39 or 0.40 and really lmfit).   I'll
>> try to verify that over the next couple of days.
>>
>>
>> Also, having some problems with the gofs and errors - the chi square is
>>>> rather small and often the errors are not reported and it is not clear why.
>>>>
>>>
>>
>> Hm, not sure. Can you post an example?
>> Cheers,
>>
>> --Matt
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