Lab 05: Python Lists, Trees

Files: lab05.zip

Starter Files

Download lab05.zip. Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.

Topics

Consult this section if you need a refresher on the material for this lab. It's okay to skip directly to the questions and refer back here should you get stuck.

>>> list_of_ints = [1, 2, 3, 4]
>>> list_of_bools = [True, True, False, False]
>>> nested_lists = [1, [2, 3], [4, [5]]]

>>> lst = [6, 5, 4, 3, 2, 1, 0]
>>> lst[0]
6
>>> lst[3]
3
>>> lst[-1] # Same as lst[6]
0

>>> lst[:3]   # Start index defaults to 0
[6, 5, 4]
>>> lst[3:]   # End index defaults to len(lst)
[3, 2, 1, 0]
>>> lst[::-1]   # Make a reversed copy of the entire list
[0, 1, 2, 3, 4, 5, 6]
>>> lst[::2]  # Skip every other; step size defaults to 1 otherwise
[6, 4, 2, 0]

number_tree = tree(1,
          [tree(2),
          tree(3,
                [tree(4),
                tree(5)]),
          tree(6,
                [tree(7)])])

   1
 / | \
2  3  6
  / \  \
 4   5  7

label(branches(number_tree)[1])

def print_tree(t, indent=0):

  """Print a representation of this tree in which each node is
  indented by two spaces times its depth from the root.

  >>> print_tree(tree(1))
  1
  >>> print_tree(tree(1, [tree(2)]))
  1
    2
  >>> numbers = tree(1, [tree(2), tree(3, [tree(4), tree(5)]), tree(6, [tree(7)])])
  >>> print_tree(numbers)
  1
    2
    3
      4
      5
    6
      7
  """
  print('  ' * indent + str(label(t)))
  for b in branches(t):
      print_tree(b, indent + 1)

Required Questions

Lists

Q1: Factors List

Write factors_list, which takes a number n and returns a list of its factors in ascending order.

def factors_list(n):
    """Return a list containing all the numbers that divide `n` evenly, except
    for the number itself. Make sure the list is in ascending order.

    >>> factors_list(6)
    [1, 2, 3]
    >>> factors_list(8)
    [1, 2, 4]
    >>> factors_list(28)
    [1, 2, 4, 7, 14]
    """
    all_factors = []
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q factors_list

Q2: Flatten

Write a function flatten that takes a list and "flattens" it. The list could be a deep list, meaning that there could be a multiple layers of nesting within the list. Think about how we can write a function that can account for any number of nested lists.

For example, one use case of flatten could be the following:

>>> lst = [1, [[2], 3], 4, [5, 6]]
>>> flatten(lst)
[1, 2, 3, 4, 5, 6]

Make sure your solution does not mutate the input list.

Hint: you can check if something is a list by using the built-in type function. For example:

>>> type(3) == list
False
>>> type([1, 2, 3]) == list
True

def flatten(s):
    """Returns a flattened version of list s.

    >>> flatten([1, 2, 3])     # normal list
    [1, 2, 3]
    >>> x = [1, [2, 3], 4]     # deep list
    >>> flatten(x)
    [1, 2, 3, 4]
    >>> x # Ensure x is not mutated
    [1, [2, 3], 4]
    >>> x = [[1, [1, 1]], 1, [1, 1]] # deep list
    >>> flatten(x)
    [1, 1, 1, 1, 1, 1]
    >>> x
    [[1, [1, 1]], 1, [1, 1]]
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q flatten

Data Abstraction

Say we have an abstract data type for cities. A city has a name, a latitude coordinate, and a longitude coordinate.

Our data abstraction has one constructor:

• make_city(name, lat, lon): Creates a city object with the given name, latitude, and longitude.

We also have the following selectors in order to get the information for each city:

• get_name(city): Returns the city's name
• get_lat(city): Returns the city's latitude
• get_lon(city): Returns the city's longitude

Here is how we would use the constructor and selectors to create cities and extract their information:

>>> provo = make_city('Provo', 111, 40)
>>> get_name(provo)
'Provo'
>>> get_lat(provo)
111
>>> new_york = make_city('New York City', 74, 40)
>>> get_lon(new_york)
40

All of the selector and constructor functions can be found in the lab file, if you are curious to see how they are implemented. However, the point of data abstraction is that we do not need to know how an abstract data type is implemented, but rather just how we can interact with and use the data type.

Q3: Distance

We will now implement the function distance, which computes the distance between two city objects. Recall that the distance between two coordinate pairs (x1, y1) and (x2, y2) can be found by calculating the sqrt of (x1 - x2)**2 + (y1 - y2)**2. We have already imported sqrt for your convenience.

Hint: Use the latitude and longitude of a city as its x & y coordinates; you'll need to use the selectors to access this info!

from math import sqrt
def distance(city_a, city_b):
    """
    >>> city_a = make_city('city_a', 0, 1)
    >>> city_b = make_city('city_b', 0, 2)
    >>> distance(city_a, city_b)
    1.0
    >>> city_c = make_city('city_c', 6.5, 12)
    >>> city_d = make_city('city_d', 2.5, 15)
    >>> distance(city_c, city_d)
    5.0
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q distance

Q4: Closer City (OPTIONAL)

Next, implement closer_city, a function that takes a latitude, longitude, and two cities, and returns the name of the city that is relatively closer to the provided latitude and longitude.

You may only use the selectors and constructors introduced above and the distance function you just defined for this question.

Hint: How can you use your distance function to find the distance between the given location and each of the given cities?

def closer_city(lat, lon, city_a, city_b):
    """
    Returns the name of either city_a or city_b, whichever is closest to
    coordinate (lat, lon). If the two cities are the same distance away
    from the coordinate, consider city_b to be the closer city.

    >>> provo = make_city('Provo', 40.23, 111.66)
    >>> rexburg = make_city('Rexburg', 43.82, 111.79)
    >>> closer_city(40.76, 111.89, provo, rexburg)
    'Provo'
    >>> bucharest = make_city('Bucharest', 44.43, 26.10)
    >>> vienna = make_city('Vienna', 48.20, 16.37)
    >>> closer_city(41.29, 174.78, bucharest, vienna)
    'Bucharest'
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q closer_city

Q5: Don't violate the abstraction barrier!

Note: this question has no code-writing component (if you implemented the previous two questions correctly).

When writing functions that use an data abstraction, we should use the constructor(s) and selector(s) whenever possible instead of assuming the data abstraction's implementation. Relying on a data abstraction's underlying implementation is known as violating the abstraction barrier, and we never want to do this!

It's possible that you passed the doctests for the previous questions even if you violated the abstraction barrier. To check whether or not you did so, run the following command:

Use Ok to test your code:

python3 ok -q check_city_abstraction

The check_city_abstraction function exists only for the doctest, which swaps out the implementations of the original abstraction with something else, runs the tests from the previous two parts, then restores the original abstraction.

The nature of the abstraction barrier guarantees that changing the implementation of an data abstraction shouldn't affect the functionality of any programs that use that data abstraction, as long as the constructors and selectors were used properly.

If you passed the Ok tests for the previous questions but not this one, the fix is simple! Just replace any code that violates the abstraction barrier with the appropriate constructor or selector.

Make sure that your functions pass the tests with both the first and the second implementations of the data abstraction and that you understand why they should work for both before moving on.

Trees

Q6: Finding Berries!

The squirrels on campus need your help! There are a lot of trees on campus and the squirrels would like to know which ones contain berries. Define the function berry_finder, which takes in a tree and returns True if the tree contains a node with the value 'berry' and False otherwise.

Hint: To iterate through each of the branches of a particular tree, you can consider using a for loop to get each branch.

def berry_finder(t):
    """Returns True if t contains a node with the value 'berry' and
    False otherwise.

    >>> scrat = tree('berry')
    >>> berry_finder(scrat)
    True
    >>> sproul = tree('roots', [tree('branch1', [tree('leaf'), tree('berry')]), tree('branch2')])
    >>> berry_finder(sproul)
    True
    >>> numbers = tree(1, [tree(2), tree(3, [tree(4), tree(5)]), tree(6, [tree(7)])])
    >>> berry_finder(numbers)
    False
    >>> t = tree(1, [tree('berry',[tree('not berry')])])
    >>> berry_finder(t)
    True
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q berry_finder

Q7: Sprout Leaves

Define a function sprout_leaves that takes in a tree, t, and a list of leaves, leaves. It produces a new tree that is identical to t, but where each old leaf node has new branches, one for each leaf in leaves.

For example, say we have the tree t = tree(1, [tree(2), tree(3, [tree(4)])]):

  1
 / \
2   3
    |
    4

If we call sprout_leaves(t, [5, 6]), the result is the following tree:

     1
   /   \
  2     3
 / \    |
5   6   4
       / \
      5   6

def sprout_leaves(t, leaves):
    """Sprout new leaves containing the data in leaves at each leaf in
    the original tree t and return the resulting tree.

    >>> t1 = tree(1, [tree(2), tree(3)])
    >>> print_tree(t1)
    1
      2
      3
    >>> new1 = sprout_leaves(t1, [4, 5])
    >>> print_tree(new1)
    1
      2
        4
        5
      3
        4
        5

    >>> t2 = tree(1, [tree(2, [tree(3)])])
    >>> print_tree(t2)
    1
      2
        3
    >>> new2 = sprout_leaves(t2, [6, 1, 2])
    >>> print_tree(new2)
    1
      2
        3
          6
          1
          2
    """
    "*** YOUR CODE HERE ***"

Use Ok to test your code:

python3 ok -q sprout_leaves

Q8: Don't violate the abstraction barrier!

Note: this question has no code-writing component (if you implemented the previous two questions correctly).

When writing functions that use an data abstraction, we should use the constructor(s) and selector(s) whenever possible instead of assuming the data abstraction's implementation. Relying on a data abstraction's underlying implementation is known as violating the abstraction barrier, and we never want to do this!

It's possible that you passed the doctests for the previous questions even if you violated the abstraction barrier. To check whether or not you did so, run the following command:

Use Ok to test your code:

python3 ok -q check_abstraction

The check_abstraction function exists only for the doctest, which swaps out the implementations of the original abstraction with something else, runs the tests from the previous two parts, then restores the original abstraction.

The nature of the abstraction barrier guarantees that changing the implementation of an data abstraction shouldn't affect the functionality of any programs that use that data abstraction, as long as the constructors and selectors were used properly.

If you passed the Ok tests for the previous questions but not this one, the fix is simple! Just replace any code that violates the abstraction barrier with the appropriate constructor or selector.

Make sure that your functions pass the tests with both the first and the second implementations of the data abstraction and that you understand why they should work for both before moving on.

Submit

Submissions will be in Canvas.

It is highly recommended that you test your code before you submit it. To run all of the tests for the required questions, type:

python3 ok