In Python, how can I overcome the limitations of scipy.integrate.quad() to integrate the square of the Airy function between 0 and +infinity?
I’m trying to integrate the Airy function (of the first kind, that I’ll call Ai from now) squared, between 0 and infinity.
why results from stats.chisquare and stats.chi2_contingency are different even with the same data
I have an array named ‘data_count’, storing the counts of 9 digital groups. I used a Benford’s Law to generate a expected count array called ‘expected_counts’. I want to test whether two arrays have the same distribution. I have used the stats.chisquare and stats.chi2_contingency functions, but the results turned out quite different. The [scipy guide] (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2_contingency.html) says they should have the same results. Why it didn’t work on my case? Please help me, thanks a million.
Scipy Differentil Evolution: Components of solution are permutated near end of optimization
In using scipy´s differential evolution, I ran into a problem:
In the solution vector result.x for a significant number of steps before the optimization ends, the componentes are just permutated.