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First change the function combinedTransformation to combinedTransformation[{x_, y_}] = {sx (prx (-Cos[theta]) + prx + pry Sin[theta]) + psx (-sx) + psx + sx x Cos[theta] - sx y Sin[theta] + sx vecx …

Assuming that data is obtained from translated and/or rotated models Clear[a, c] p = {x, y, z}; P = {X, Y, Z}; Txyz = z^2 - a^2 + (c - (x^2 + y^2)^(1/2))^2 /. Thread[p -> RollPitchYawMatrix[{alpha, be …

It seems that the best fit to pCD data is a gaussian bell as can be depicted. Hopelessly we don't have pCD data... pIDmm = {{1, 4.}, {2, 4.}, {3, 0.}, {4, 5.}, {5, 4.}, {6, 8.}, {7, 12.}, {8, 11.}, { …

Using the language resources adata = {{0, 1}, {2, 0.88}, {6, .69}, {10, .53}, {20, .28}, {30, .15}, {50, .043}, {70, .012}, {90, 0}, {120, 0}, {150, 0}, {200, 0}}; bdata = {{0, 0}, {2, 0.12}, {6, .29 …

Fitting explicitly the real and imaginary parts. mx[fr_, s_, t_] := s/(1 + t^2 fr^2) my[fr_, s_, t_] := s t fr/(1 + t^2 fr^2) frk = Transpose[data][[1]]; zk = Transpose[data][[2]]; zkr = Re[zk]; zki = …

The modified script below doesn't gives formal errors but keeps a lot of time processing before outputting some messages now linked to numerical problems. eqns = {n'[t] == a L - b n[t]^2 - c n[t]^3 , …

To eliminate noise in the data, it is best to perform a smoothing and not an interpolation. With the procedure that follows, three splines are adjusted one for each dimension, being that the degree of …

As a product of visual inspection, taking data from $\approx 80$ to $120$ and using the model $$ f(a,b,\sigma_1,\sigma_2,x_1,x_2,x)=a e^{-\left(\frac{x-x_1}{\sigma_1}\right)^2}+b e^{-\left(\frac{x-x_2 …

This is an extension for the excellent answer from @Ulrich Neumann considering $$m\ddot x=-kx^{\alpha}-c\dot x-mg$$ instead of $$m\ddot x=-kx-c\dot x-mg$$ tx = Map[{#[[1]], #[[2]]/100} &, data] {tc, x …

Clear[obj] data2 = {{{0, 0.2025}, {2, 0.76}, {3, 1.28}, {4, 2.2}, {5, 2.75}, {6, 2.95}, {6.5, 3.055}}, {{0, 441.337}, {2, 400.435}, {3, 300.326}, {4, 219.348}, {5, 120.109}, {6, 50}, {6.5, 25}}, {{0, …

With xmin = Min@data[[All, 1]]; xmax = Max@data[[All, 1]]; ymin = Min@data[[All, 2]]; ymax = Max@data[[All, 2]]; After scaling datanew = Table[{(data[[i, 1]] - xmin)/(xmax - xmin), (data[[i, 2]] - ym …

A = Uncompress[FromCharacterCode[ Flatten[ImageData[Import["https://i.sstatic.net/s6rlR.png"], "Byte"]]]]; B = Uncompress[FromCharacterCode[ Flatten[ImageData[Import["https://i.sstatic.net/jylyd.pn …

Another way to do the fitting. 1 - We will using a custom-made second order interpolation as giving in f[t]. … This facilitates an undesirable over-fitting. …

Introducing few modifications in the actual script, we found using MATHEMATICA version 12.0.0 running under Linux, Follows the script fitness[a00_?NumberQ, c00_?NumberQ, d00_?NumberQ, s0_?NumberQ, b …