mjhjmtu

I have the data which appears like

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,

if I specify the number n to be 3, then we can have a three-Gaussian-curve fitting panel,

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

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

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

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

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)]