![]() Params = model.make_params(offset=375, scale=5000, decay=4) Return offset + scale * np.exp(-decay* x) # Define the function that we want to fit to the data With that, you would set up and execute the fit like this: import numpy as np scipy curve_fit can, but it requires some work to extract them - a pity.Īllow me to recommend trying lmfit (disclaimer: I am an author). I don't know if Igor will give you those. But when comparing fit results, that everyday knowledge cannot be assumed, and you have to include uncertainties. Of course, in everyday conversation, 376.91 and 702.45 are very different, mostly because simply stating a value to 2 decimal places implies accuracy at approximately that scale (the distance between New York and Tokyo isġ0,850 km but is not really 10,847,024,31 cm - that might be the distance between bus stops in the two cities). P0=-2.6744) are different, what is it that leads to conclude those values are actually different? Well, when comparing fit results, it is always important to include uncertainties in the fitted parameters. Popt, pcov = curve_fit(lambda t, a, b, c: a * np.exp(-b * t) + c, x, y) I use curve_fit as follow: from scipy.optimize import curve_fit ![]() Values calculated by curve_fit: No optimal values found I used the curve_fit function, but the values that I get are nowhere near the ones calculated by Igor software. I use a software "Igor Pro" that calculates the values for me, but want a Python implementation. I want to calculate the values of P0, P1 and P2. I have a set of x and y data and I want to use exponential regression to find the line that best fits those set of points. ![]()
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