The "burden" of using lmfit is that one would have to run "pip install lmfit", which could take several seconds - perhaps even as long as it took you to read this message. To be most robust, you probably would want to allow a to vary. On the other hand, looping as shown here is clearly using 2 variables at a time, but it does ignore that a is sort of a varying parameter. Note that attempting to freeze a variable by setting the lower and upper bound to the value will cause confusion in the statistical analysis over whether there are 2 or 3 variables in the problem. Result = model.fit(ydata, params, x=xdata) import numpy as npimport nstantsimport timestarttime time.time()c speed of light. # test values for a, find one with lowest chi-squareīest_result, best_a, best_chisqr = None, None, 1e199 Something like this would do: import lmfit You can rate examples to help us improve the. Just for clarity, this sort of thing can be done easily with lmfit as the original question mentions. These are the top rated real world Python examples of scipyconstants.value extracted from open source projects.
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