I have the data which appears like ListPlot of the data

And I decided to use three Gaussian curves to fit it.

(This code allows me to manipulate all three curves and put in best guesses for peak positions. Using these guesses, Mathematica will find a set of three gaussian curves that are the closest match if one clicks click find nearest solution.)

\[Lambda]min=340;
\[Lambda]max=380;
model = height + amp1*Exp[-(x - x01)^2/sigma1^2] + amp2*Exp[-(x - x02)^2/sigma2^2] + amp3*Exp[-(x - x03)^2/sigma3^2]

findBestFitFromValues[{amp1guess_, x01guess_, sigma1guess_, 
amp2guess_, x02guess_, sigma2guess_, amp3guess_, x03guess_, 
sigma3guess_, heightguess_}] :=
FindFit[
rowData(*change this*), {model, {sigma1 > 0, sigma2 > 0, sigma3 > 0}}, {{amp1,
  amp1guess}, {x01, x01guess}, {sigma1, sigma1guess}, {amp2, 
 amp2guess}, {x02, x02guess}, {sigma2, sigma2guess}, {amp3, 
 amp3guess}, {x03, x03guess}, {sigma3, sigma3guess}, {height, 
 heightguess}}, x];
With[
 {
  localModel =
   model /.
    {
     amp1 -> amp1Var, amp2 -> amp2Var, amp3 -> amp3Var,
     sigma1 -> sigma1Var, sigma2 -> sigma2Var, sigma3 -> sigma3Var,
     x01 -> x01Var, x02 -> x02Var, x03 -> x03Var,
     height -> heightVar
     }},
 Manipulate[
  Column[{
    Style["Match to Data", 12, Bold],
    Show[rowDataPlot(*change this*), 
     Plot[localModel, {x, 1240/\[Lambda]max, 1240/\[Lambda]min}, 
      PlotRange -> All, PlotStyle -> Black] ,Graphics[
     {
     Orange,Line[{{x01Var,0}, {x01Var,500}}],
     Blue,Line[{{x02Var,0}, {x02Var,500}}],
     Red,Line[{{x03Var,0}, {x03Var,500}}]
     }
     ]],
    Style["Chosen Curve", 12, Bold],
    Plot[localModel, {x, 1240/\[Lambda]max, 1240/\[Lambda]min}, 
     PlotRange -> All, PlotStyle -> Black, ImageSize -> 400]}
   ],
  Delimiter, Style["Peak 1", 12, Bold],
  {{amp1Var, 2000, Style["Amplitude 1", Orange]}, 0, 40000},
  {{x01Var, 
    1240/\[Lambda]min - (1240/\[Lambda]min - 1240/\[Lambda]max) 1/5, 
    Style["center 1", Orange]}, 1.95, 3.6},
  {{sigma1Var, 0.01, Style["sigma 1", Orange]}, 0.01, 0.3},
  Delimiter, Style["Peak 2", 12, Bold],
  {{amp2Var, 1660, Style["Amplitude 2", Blue]}, 0, 15000},
  {{x02Var, 
    1240/\[Lambda]min - (1240/\[Lambda]min - 1240/\[Lambda]max) 2/5, 
    Style["center 2", Blue]}, 1.95, 3.6},
  {{sigma2Var, 0.01, Style["sigma 2", Blue]}, 0.01, 0.3},
  Delimiter, Style["Peak 3", 12, Bold],
  {{amp3Var, 1445, Style["Amplitude 3", Red]}, 0, 10000},
  {{x03Var, 
    1240/\[Lambda]min - (1240/\[Lambda]min - 1240/\[Lambda]max) 4/5, 
    Style["center 3", Red]}, 1.95, 3.6},
  {{sigma3Var, 0.01, Style["sigma 3", Red]}, 0.01, 0.3},
  Delimiter, Style["Height", 12, Bold],
  {{heightVar, 15, Style["Height"]}, 0, 1000},
  Delimiter,
  Control[Button["click find nearest solution",
    vals =
     {amp1Var, x01Var, sigma1Var, amp2Var, x02Var, sigma2Var, amp3Var,
        x03Var, sigma3Var, 
       heightVar} = {amp1, x01, sigma1, amp2, x02, sigma2, amp3, x03, 
        sigma3, height} /.
       findBestFitFromValues[
        {amp1Var, x01Var, sigma1Var, amp2Var, x02Var, sigma2Var, 
         amp3Var, x03Var, sigma3Var, heightVar}]]],
  SaveDefinitions -> True
  ]
 ]

If I want to fit another data with four or more Gaussian curves, how can I re-write my code to obtain the curve fitting that I can specify the number of Gaussian curves? Not the above one where I constrain the number to be three.

EDIT1

I mean, if I specify the number n to be 2, then we can have a two-Gaussian-curve fitting panel, two-Gaussian-curve fitting if I specify the number n to be 3, then we can have a three-Gaussian-curve fitting panel, enter image description here

  • 1
    Unless you end up with a good/adequate fit and need to be able to reproduce the curve outside of Mathematica, you should consider the more stable and flexible Quantile Regression (@AntonAntonov mathematicaforprediction.wordpress.com/2013/12/23/…). – JimB Aug 15 at 6:59
  • Other than this fitting process being computationally interesting, is there any underlying process where the Gaussian curves have some physical meaning? I'm essentially repeating my above comment as there a plenty of other (and more computationally stable) ways to "describe" the data as opposed to "explaining" the data. – JimB Aug 18 at 14:57
  • It is a light emission spectrum, not a statistical data. And the peaks are temperature-dependent. Therefore I am interested in the change of each "Gaussian peak" as the temperature varies. – chika Aug 18 at 15:22
  • Rather than using triplets of sliders, how about using pairs of Locator 's to define the individual Gaussian-shaped curves? A Locator at the peak of the curve could define the height and central value. A Locator on the "side" of the Gaussian curve could be restricted to move horizontally to vary the width. Sliders are good for when you have a "numerical" idea as to the values of the parameters. Here you want the user to set a "visual" idea as to the values of the parameters. This would also make the display less crowed. – JimB Aug 20 at 3:42

You can use the PDF of MixtureDistribution of NormalDistributions to specify your model:

ClearAll[model]
model[n_Integer] := Module[{m = Array[μ, n], s = Array[σ, n]/Sqrt[2], w = Array[ω, n]},
    ω[0] + FullSimplify[Sqrt[2 π]Total[w s] 
  PDF[MixtureDistribution[w s, NormalDistribution @@@ Transpose[{m, s}]], x]]]

Examples:

model[3] // TeXForm

$\omega (1) e^{-\frac{(x-\mu (1))^2}{\sigma (1)^2}}+\omega (2) e^{-\frac{(x-\mu (2))^2}{\sigma (2)^2}}+\omega (3) e^{-\frac{(x-\mu (3))^2}{\sigma (3)^2}}+\omega (0)$

With the identification ω[0] = height, ω[1] = amp1, ω[2] = amp2,ω[3] = amp3, μ[i] = x0i and σ[i] = sigmai, this is the same as OP's model.

model[4] // TeXForm

$\omega (1) e^{-\frac{(x-\mu (1))^2}{\sigma (1)^2}}+\omega (2) e^{-\frac{(x-\mu (2))^2}{\sigma (2)^2}}+\omega (3) e^{-\frac{(x-\mu (3))^2}{\sigma (3)^2}}+\omega (4) e^{-\frac{(x-\mu (4))^2}{\sigma (4)^2}}+\omega (0)$

  • Sorry, can't focus now, is this topic narrow enough to deserve a separate q/a? I was thinking about that: mathematica.stackexchange.com/q/26336/5478 – Kuba Aug 15 at 6:06
  • Kuba, this seems to be more specific than the linked one. I took the question to be how to generalize the OP's manually coded 4-term model. Silvia's general answer in the linked q/a does solve this particular case too. Perhaps @chika can clarify whether or not this is a duplicate, and, if not, why. – kglr Aug 15 at 6:33
  • @Kuba Silvia's code and rhermans's code (in mathematica.stackexchange.com/questions/94154/…) can specify the number of Gaussian curves, but sometimes produce unwanted result or fail to converge. My code allows user to input initial values to make a good fitting result, but I don't know how to generalize my code into n Gaussian curves. – chika Aug 18 at 13:01

This is an extended comment. (Well, two comments.)

(1) @Silvia 's and @rherman 's code (and code from any other expert programmer) requires good starting values. That's just a fact of estimating curves.

(2) Rather than using triplets of sliders, how about using pairs of Locator's to define the individual Gaussian-shaped curves? A Locator at the peak of the curve could define the height and central value. A Locator on the "side" of the Gaussian curve could be restricted to move horizontally to vary the width. Sliders are good for when you have a "numerical" idea as to the values of the parameters. Here you want the user to set a "visual" idea as to the values of the parameters. This would also make the display less crowded (i.e., without any triplets of sliders). Here's a crude example:

Manipulate[
 (* If no change in the mean/height locator, then update the standard deviation *)
 If[μ0 == p[[1]], σ = Abs[p2[[1]] - p[[1]]]];

 (* Update standard deviation locator *)
 p2 = {p[[1]] + σ, p[[2]] Exp[-1/2]};

 (* Record previous mean: this is so you can tell which locator was most recently moved *)
 μ0 = p[[1]];

 (* Plot associated Gaussian-shaped curve *)
 Plot[p[[2]] Exp[-(x - p[[1]])^2/(2 σ^2)], {x, -5, 5}, 
  PlotRange -> {All, {0, 5}}],

 {{p, {0, 0.4}}, Locator},
 {{p2, {1, 0.4 Exp[-1/2]}}, Locator},
 TrackedSymbols -> {p, p2},
 Initialization :> (μ0 = 0; σ = 1)]

Gaussian curve with locators

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