PYTHON 3

2. Random walk In this problem you will program a random walk and explore its properties. A two-dimensional random walk is the result of making random steps in both X- and y-directions. Tip: Start the program development for very small parameters of number of steps and walks and check your results every step. Built you program out of small elements, one expression and one line at a time, each of which you check. This will save you time later when you otherwise have a hard time finding bugs. 2.3 Explore the maximum distance the random walk gets away from the origin as a function of the number of steps taken. Make n = 2 steps for pe [4,5,... , 13), and each time make nwalk = 1000 walks to establish good statistics. For each of the 10 cases (one for each p) determine the distance r max-hist at which the maximum of the histogram of the maximum distance of the nwalk walks for this case with n steps is located. Then plot r max-hist vs. logion. 2. Random walk In this problem you will program a random walk and explore its properties. A two-dimensional random walk is the result of making random steps in both X- and y-directions. Tip: Start the program development for very small parameters of number of steps and walks and check your results every step. Built you program out of small elements, one expression and one line at a time, each of which you check. This will save you time later when you otherwise have a hard time finding bugs. 2.3 Explore the maximum distance the random walk gets away from the origin as a function of the number of steps taken. Make n = 2 steps for pe [4,5,... , 13), and each time make nwalk = 1000 walks to establish good statistics. For each of the 10 cases (one for each p) determine the distance r max-hist at which the maximum of the histogram of the maximum distance of the nwalk walks for this case with n steps is located. Then plot r max-hist vs. logion