Problem
Students often ask "what grade will I earn?" But that question is complicated because there are often many assignments still left to be graded and they are not all worth the same number of points. To help visualize the range of possibilities, you are going to make a graph with three line plots:
A running sum line of the maximum weighted points possible in the course (max_points).
A running sum line of the maximum possible weighted score in the course (max_score).
A running sum line of the minimum possible weighted score in the course (min_score).
If all assignments in the course were already graded, then the max_score and min_score lines would be the same; otherwise, the two lines show what happens if you scored perfect 100%s (max_score) and 0%s (min_score) on each assignment. Similarly, if you scored perfectly on every assignment in the entire course, then the max_points line would match exactly to the max_score line.
All of the lines need to be weighted appropriately, which means you will need to begin by calculating the total weighted points before the running sum lists (similar to the previous problem). The final result should have the values 0 to 100 on the Y-axis (representing the course grade), and each assignment on the X-axis.
Define a function predict_grades that consumes a user_token (a string) and a course_id (an integer) and returns nothing but creates a graph with the three running sums as described above. The function does not need a return statement, but must call the appropriate plotting function the plt.show() function the plt.title(text) function and both plt.xlabel(text) and plt.ylabel(text).