On Python 2

In the Wright-Fisher Model, assuming no mutation, migration or selection, the number of alleles in generation t+1, given the number of alleles in generation t follows a binomial distribution, i.e, in which N is the population size, and Hence, the random variable X_ft+1 follows a binomial distribution. This can be implemented using np.random.binomial. P1 1. Using binomial random function in python, write a function that given an initial frequency of allele A and a given population size N, simulates the population history (i.e., an array of counts of allele A over different genereations) for a given number of generations. For example a possible output of your function wF mode1(N,f,t), in which N and t are integers and f is a float, for N-10e,f-0.1,t-18 could be the array [10,5,4,8,11,15,19,20,22,27] 2. Modify your function so that if allele A goes to fixation before reaching the specified number of generations t, i.e, fA-0 or fA-1, the iteration stops and only returns the history up to the point when the allele got fixed 3. Plot results of 10 different runs of this simulation