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[1] .* tanh.(p[2]/p[1] .* 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] = -1.04293e-10
p[2] = 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)
```

Where `curve_fit`

is `scipy.optimize.curve_fit`

.

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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