Exported fitted curve data as Table

Question

How can I export my fitted plot results as a txt file with two column XY without any brackets or graphics comments... I tried the following line, but I didn't get two column...

Export["firtcurve.dat", Flatten /@ fitplot2, "Table", "FieldSeparators" -> ""]

Here my data

data={{0.0058, 103686.}, {0.00635, 99133.2}, {0.00692, 96768.4}, {0.00747, 91096.8}, {0.00803, 91584.4}, {0.00858, 90998}, {0.00913, 93469.2}, {0.0097, 92494.8}, {0.01026, 92280.4}, {0.01081, 92167.6}, {0.01136, 98385.6}, {0.01192, 95647.6}, {0.01248, 99023.2}, {0.01303, 103776}, {0.01358, 100053.}, {0.01413, 99689.2}, {0.01469, 96788.4}, {0.01525, 90226.4}, {0.0158, 88250}, {0.01635, 75089.2}, {0.01691, 70733.2}, {0.01747, 62443.6}, {0.01802, 53762.8}, {0.01857, 46584}, {0.01913, 40684.4}, {0.01969, 34428.}, {0.02024, 32338.5}, {0.0208, 28251.}, {0.02135, 25399.}, {0.02191, 24325.4}, {0.02246, 22329.8}, {0.02302, 20324.6}, {0.02357, 19741.7}, {0.02413, 18336.1}, {0.02468, 18015.7}, {0.02523, 17504}, {0.02579, 17462.}, {0.02635, 16806.}, {0.0269, 16959.3}, {0.02745, 17069.6}, {0.02801, 15096.1}, {0.02856, 15410.6}, {0.02912, 14848.7}, {0.02967, 13276.7}, {0.03023, 12602}, {0.03078, 12084.3}, {0.03134, 11693.2}, {0.03189, 10419.9}, {0.03245, 10220.2}, {0.033, 9399.24}, {0.01437, 83937.6}, {0.01573, 75249.6}, {0.01712, 61748.4}, {0.0185, 50326.8}, {0.01989, 38519.4}, {0.02125, 30555.7}, {0.02262, 24798.4}, {0.02402, 20976.5}, {0.0254, 17888.6}, {0.02677, 16839.2}, {0.02814, 15443.6}, {0.02951, 13593.7}, {0.03089, 12432}, {0.03227, 10577.2}, {0.03363, 9237.3}, {0.035, 7825.44}, {0.03637, 6582}, {0.03775, 5730.63}, {0.03912, 4945.22}, {0.04049, 4257.93}, {0.04187, 3768.53}, {0.04325, 3553.86}, {0.04462, 2954.67}, {0.04599, 2681.2}, {0.04736, 2420.49}, {0.04875, 2063.76}, {0.05012, 1861.36}, {0.05149, 1647.01}, {0.05287, 1530.26}, {0.05424, 1451.87}, {0.05561, 1275.58}, {0.05699, 1202.98}, {0.05836, 1057.94}, {0.05975, 992.04}, {0.06112, 938.712}, {0.06249, 835.932}, {0.06386, 832.986}, {0.06524, 719.376}, {0.06662, 657.75}, {0.06799, 591.407}, {0.06936, 586.143}, {0.07074, 538.37}, {0.07211, 540.723}, {0.07348, 484.061}, {0.07486, 418.047}, {0.07624, 381.774}, {0.07762, 358.336}, {0.07898, 330.041}, {0.08035, 317.571}, {0.08165, 292.333}, {0.05481, 1326.86}, {0.06003, 1021.67}, {0.06533, 747.138}, {0.07058, 558.727}, {0.07592, 438.387}, {0.08116, 328.292}, {0.08632, 251.455}, {0.09165, 200.327}, {0.09693, 162.102}, {0.10209, 132.823}, {0.1073, 103.748}, {0.11267, 81.5521}, {0.11791, 70.8106}, {0.12313, 56.6837}, {0.12837, 49.1627}, {0.13363, 43.4262}, {0.13892, 38.9658}, {0.14415, 34.4442}, {0.1494, 30.7966}, {0.15474, 26.9218}, {0.16001, 25.2626}, {0.16522, 24.965}, {0.17051, 20.3799}, {0.17573, 19.5228}, {0.18094, 16.7343}, {0.18617, 17.8135}, {0.19148, 17.7965}, {0.19673, 17.0199}, {0.20191, 15.4306}, {0.20717, 15.4462}, {0.21245, 13.247}, {0.21772, 13.931}, {0.2229, 13.0009}, {0.22813, 12.8896}, {0.23333, 13.5703}, {0.23857, 11.7486}, {0.24386, 12.1296}, {0.24907, 11.688}, {0.25429, 12.5605}, {0.25949, 11.0021}, {0.26468, 10.4639}, {0.2699, 10.8949}, {0.27517, 10.4332}, {0.28034, 11.0561}, {0.28556, 10.8001}, {0.29082, 10.4613}, {0.29609, 9.0205}, {0.3011, 9.54147}, {0.3059, 11.3875}}

Here my code and fitting function :

mu0 = 4*Pi*10^(-7);
Ms = 0.0926 ;
bH = 2.91*10^8 ;


Heff[q_, l_, Hi_] := Hi*(1 + (l)^2*(q*10^(10))^2)
p[q_, l_, Hi_] := Ms/Heff[q, l, Hi]
Vp[r_] := 4/3*Pi*(r*10^(-9))^3
FF[q_, r_] := 
 9*SphericalBesselJ[1, q*10^(10)*r*10^(-9)]^2/(q*10^(10)*r*10^(-9))^2
RH[q_, l_, Hi_] := (1/4)*p[q, l, Hi]^2*(2 + 1/(1 + p[q, l, Hi])^(0.5))
RM[q_, l_, Hi_] := ((1 + p[q, l, Hi])^(0.5) - 1)/2
f[r_, pp_, R_] := (1/(r*10^(-9)*pp))*
  Exp[-(Log[r*10^(-9)] - Log[R*10^(-9)])^2/(2*pp^2)]

Sigma1[q_, l_, Hi_, pp_, R_, k_, bck_, aa_, bb_] := 
 NIntegrate[
   k*(aa*RM[q, l, Hi] + bb*RH[q, l, Hi]) *FF[q, r]*f[r, pp, R]*9*
    Vp[r]^2, {r, 0.1, 100}] + bck



 model2 = Sigma1[xx, l, 0.5, 0.15, 18.5, k, 10, 0, 1];
    result = Normal[
       NonlinearModelFit[Select[data, First[#] > 0.02 &], 
        model2, {{l, 1.64*10^(-9)}, {k, 5*10^(42)}}, xx]];
    singlefit = 
      NonlinearModelFit[Select[data, First[#] > 0.02 &], 
       model2, {{l, 1.64*10^(-9)}, {k, 5*10^(42)}}, xx];

    singlefit["BestFitParameters"];
    singlefit["EstimatedVariance"];
    singlefit["ParameterErrors"];
    singlefit["ParameterConfidenceIntervals"];
    MatrixForm[singlefit["CorrelationMatrix"]];
    Print[singlefit["ParameterTable"]];

    fitplot2 = Plot[result, {xx, 0.001, 0.3},
       PlotRange -> All,
       ScalingFunctions -> {"Log", "Log"},
       PlotStyle -> {Thickness[0.005], Red}
       ];

    Export["firtcurve.dat", Flatten /@ fitplot2, "Table", 
     "FieldSeparators" -> ""]
    FilePrint["firtcurve.dat"]

Answer 1

In your code, "result" is returned from the function NonlinearModelFit. Instead of applying Normal, you could create a new variable "resultf"

...
resultf= ...NonLinearModelFit[__]..; 
result= Normal@resultf...
...

Now, with the function resultf, you use Table to create your list of values with the appropriate granularity.

...
exportData= Table[{i, resultf[i]}, {i, ...}]; 
Export["  ", exportData, ...]

To me it seems like you want to export the diagram and the values in one go which could explain your comment about graphics etc.