Classrooms

Let’s consider the hypothetical scenario of a group of students who have taken a programming exam. The professor responsible of that exam is interested in knowing the pairs of students who got similar grades and were seating next to each other. The information available is the following:

  • A grades dictionary: A dict where:

    • the key is the Student Id (int)

    • the value is a list with the name (str) and the grade (float) of that student for that exam.

    For example:

    
>>> gradesD = {
... 107: ['JBB', 0.0],
... 227: ['JJJ', 6.5],
... 321: ['MMP', 5.5],
... 423: ['SCC', 7.5],
... 555: ['FCC', 5.0],
... 727: ['ABC', 0.0],
... 811: ['GMM', 8.0],
... 923: ['MRP', 10.0],
... }

  • A class list: This is a 2-dimension list representing the locations where the students were seating during the exam. More precisely it is a list of rows. Each row in turn is a list of Student Ids (int). For example:

    
>>> cl51 = [
... [227, 423, 811, 107], 
... [923, 727, 321, 555],
... ]

Please implement the following Python function in the classrooms module (classrooms.py file):

classrooms.gen_closeL(gradesD, classroomL, eps)

such that

given

  • gradesD, a dict of grades as described above

  • classroomL, a list representing a classroom as described above. It is garanteed that all Student Id in this list appear as keys in gradesD.

  • eps, a float value that determines when two grades are similar

returns a list where the elements are also list one for each row in classroomL. Each sublist contains a tuple for each pair of students in that row seating next to each other whose grades difference is equal or less than eps. This tuple has 4 components:

  1. The name of the first student of the pair (the seating on the left side from our point of view).

  2. The position in the row where he/she was seating.

  3. The name of the student next to the first one (the seating on the right side from our point of view).

  4. The absolute value of the difference of their grades.

Note

Seat positions are counted from left to right starting at 1.

For example, the function should behave as follows:


>>> gradesD = {
... 107: ['JBB', 0.0],
... 155: ['GWV', 5.5],
... 227: ['JJJ', 6.5],
... 275: ['AWV', 7.5],
... 321: ['MMP', 5.5],
... 325: ['BWV', 2.5],
... 423: ['SCC', 7.5],
... 490: ['BWV', 9.0],
... 555: ['FCC', 5.0],
... 727: ['ABC', 0.0],
... 811: ['GMM', 8.0],
... 923: ['MRP', 10.0],
... }

>>> cl51 = [
... [227, 423, 811, 107, 490],
... [155, 275, 325],
... [923, 727, 321, 555],
... ]

>>> suspL = gen_closeL(gradesD, cl51, 1.0)

>>> corrL = [
... [('JJJ', 1, 'SCC', 1.0), ('SCC', 2, 'GMM', 0.5)],
... [],
... [('MMP', 3, 'FCC', 0.5)],
... ]

Notice that the second sublist in corrL is the empty list. That’s because 155’s grade is not close to 275’s and in turn 275’s grade is not close to 325’s grade.

More doctests are available at the gen_closeL.test file.