| Re: Minimization: Determine a constant across data sets [message #79806] |
Tue, 10 April 2012 11:24  |
Craig Markwardt
Messages: 1869
Registered: November 1996
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Senior Member |
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On Tuesday, April 10, 2012 10:58:52 AM UTC-4, justinr...@gmail.com wrote:
> On Thursday, April 5, 2012 10:42:40 PM UTC-4, Justin wrote:
>> Hi all!
>>
>> I have several data sets that follow the form:
>>
>> data = A* e^(-t/t0)+ y
>>
>> I suspect EVERY data set to have the same t0, but different A & y values. (ie: (A1,y1),(A2,y2)...)
>> I can use MPFITFUN to fit A,t0 and y, but the routine determines a least chi-squared such that t0 is different for every data set.
>>
>> It would seem simple enough to fix t0 in MPFITFUN if I knew what the value was beforehand, but I don't :) Is there a way to minimize t0 across several data sets such that A & y are allowed to vary, but t0 is tied to every data set?
>>
>> I tried doing this in grids, but it was very computationally time consuming to search an unknown gridspace of t0 in an double for loop.
>>
>>
>> Thanks!
>> Justin
>
>
>
> On Thursday, April 5, 2012 10:42:40 PM UTC-4, Justin wrote:
>> Hi all!
>>
>> I have several data sets that follow the form:
>>
>> data = A* e^(-t/t0)+ y
>>
>> I suspect EVERY data set to have the same t0, but different A & y values. (ie: (A1,y1),(A2,y2)...)
>> I can use MPFITFUN to fit A,t0 and y, but the routine determines a least chi-squared such that t0 is different for every data set.
>>
>> It would seem simple enough to fix t0 in MPFITFUN if I knew what the value was beforehand, but I don't :) Is there a way to minimize t0 across several data sets such that A & y are allowed to vary, but t0 is tied to every data set?
>>
>> I tried doing this in grids, but it was very computationally time consuming to search an unknown gridspace of t0 in an double for loop.
>>
>>
>> Thanks!
>> Justin
>
> Awesome, I think I got it. Took a while to get the dimensions to agree.
>
> result=mpfitfun('myfun',x,y(*,*),1.,guess,bestnorm=chisq)
>
> FUNCTION myfun, X, P
> ;create the function from the inputs
> s=size(p)
> cols=(s(1)-1)/2
> model=dblarr(cols,n_elements(x))
> for i=0, cols-1 do begin
> model(i,*)=[P[i]* EXP(-x/P[cols])+P[i+cols+1] ]
> endfor
> RETURN, model
Now you are an advanced MPFIT user :-)
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