Hello all. I’ve been trying to use
LsqFit to fit a hyperbolic tangent function to some data about terminal velocity in free fall. Using SciPy’s
curve_fit function, it seems to work fine. However, in Julia, it’s nowhere near the correct fit.
Here’s the function I’ve written to fit the curve -
function fit_terminal_velocity(data::CSV.File) # Create base model with random initial parameters p0 = rand(2) model(t, p) = p .* tanh.(p/p .* t) # We are fitting v(t), so lets get those values # We also want to go from t = 0 to t = t_max t = data.t .- minimum(data.t) y = data.y v = diff(y) ./ diff(t) tv = t[1:end-1] fitted_model = lsqfit.curve_fit(model, v, tv, p0) return fitted_model, model end
And it gives
p = -1.04293e-10 p = 0.00917912
Whereas Python gives
p = [-623.23056677, -319.5100746 ] , which fit the model correctly. For reference, the code in python is
f = lambda t, g, vt : vt * np.tanh(g/vt * t) popts, pcov = curve_fit(f, t[1:], v)
I think it may be something to do with the fact that LsqFit requires
p0 but SciPy doesn’t? I would really appreciate if anybody can help me figure out why curve fitting isn’t working in Julia properly. Thank you so much!
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