Using curve_fit to fit a parameter that varies with x












0















So I am trying to use Scipy curve_fit to fit the Fuchs-Sondheimer formula to some resistivity (y) against sample thickness (x) data. The formula looks like this:



y = (rho_0)*(1 + 3l/8x)



Where rho_0 is constant for all x values but l varies for each value of x.



My problem is curve_fit returns a single value for both rho_0 l and I can't fathom how to get it to return different optimum values of l for each x value. I'm twisting my mind trying to write functions within functions but I'm sure theres an easy way of going about this that I'm missing.



Thank you for your help and I hope I've not worded that too confusingly.






python scipy curve-fitting







share|improve this question























  • Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

    – Cleb
    Nov 24 '18 at 21:13






  • 1





    "Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

    – Warren Weckesser
    Nov 24 '18 at 21:34








  • 1





    So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

    – Warren Weckesser
    Nov 24 '18 at 21:40











  • I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

    – James Phillips
    Nov 24 '18 at 21:45
















0















So I am trying to use Scipy curve_fit to fit the Fuchs-Sondheimer formula to some resistivity (y) against sample thickness (x) data. The formula looks like this:



y = (rho_0)*(1 + 3l/8x)



Where rho_0 is constant for all x values but l varies for each value of x.



My problem is curve_fit returns a single value for both rho_0 l and I can't fathom how to get it to return different optimum values of l for each x value. I'm twisting my mind trying to write functions within functions but I'm sure theres an easy way of going about this that I'm missing.



Thank you for your help and I hope I've not worded that too confusingly.






python scipy curve-fitting







share|improve this question























  • Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

    – Cleb
    Nov 24 '18 at 21:13






  • 1





    "Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

    – Warren Weckesser
    Nov 24 '18 at 21:34








  • 1





    So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

    – Warren Weckesser
    Nov 24 '18 at 21:40











  • I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

    – James Phillips
    Nov 24 '18 at 21:45














0












0








0








So I am trying to use Scipy curve_fit to fit the Fuchs-Sondheimer formula to some resistivity (y) against sample thickness (x) data. The formula looks like this:



y = (rho_0)*(1 + 3l/8x)



Where rho_0 is constant for all x values but l varies for each value of x.



My problem is curve_fit returns a single value for both rho_0 l and I can't fathom how to get it to return different optimum values of l for each x value. I'm twisting my mind trying to write functions within functions but I'm sure theres an easy way of going about this that I'm missing.



Thank you for your help and I hope I've not worded that too confusingly.






python scipy curve-fitting







share|improve this question














So I am trying to use Scipy curve_fit to fit the Fuchs-Sondheimer formula to some resistivity (y) against sample thickness (x) data. The formula looks like this:



y = (rho_0)*(1 + 3l/8x)



Where rho_0 is constant for all x values but l varies for each value of x.



My problem is curve_fit returns a single value for both rho_0 l and I can't fathom how to get it to return different optimum values of l for each x value. I'm twisting my mind trying to write functions within functions but I'm sure theres an easy way of going about this that I'm missing.



Thank you for your help and I hope I've not worded that too confusingly.






python scipy curve-fitting




python scipy curve-fitting






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 24 '18 at 21:02









Daniel MarchantDaniel Marchant

42




42













  • Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

    – Cleb
    Nov 24 '18 at 21:13






  • 1





    "Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

    – Warren Weckesser
    Nov 24 '18 at 21:34








  • 1





    So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

    – Warren Weckesser
    Nov 24 '18 at 21:40











  • I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

    – James Phillips
    Nov 24 '18 at 21:45



















  • Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

    – Cleb
    Nov 24 '18 at 21:13






  • 1





    "Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

    – Warren Weckesser
    Nov 24 '18 at 21:34








  • 1





    So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

    – Warren Weckesser
    Nov 24 '18 at 21:40











  • I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

    – James Phillips
    Nov 24 '18 at 21:45

















Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

– Cleb
Nov 24 '18 at 21:13





Please provide some code and data, then it will be much easier to help. Right now it is not entirely clear to me what the actual problem is.

– Cleb
Nov 24 '18 at 21:13




1




1





"Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

– Warren Weckesser
Nov 24 '18 at 21:34







"Where rho_0 is constant for all x values but l varies for each value of x." So you mean y = f(x) = rho_0*(1 + 3*I(x)/(8*x))? curve_fit requires a model with a finite number of parameters. It can't find an arbitrary function I(x).

– Warren Weckesser
Nov 24 '18 at 21:34






1




1





So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

– Warren Weckesser
Nov 24 '18 at 21:40





So you have to decide on a parametrized family of functions that defines the possible forms of the function I(x), and then express the function passed to curve_fit in terms of those parameters.

– Warren Weckesser
Nov 24 '18 at 21:40













I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

– James Phillips
Nov 24 '18 at 21:45





I see another potential problem. If you have three data points, you will have four parameters to be fitted - and curve_fit will give an error that there are more parameters than data points..

– James Phillips
Nov 24 '18 at 21:45












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