3.9. ADR Dragon Position Set

• Smok w trakcie gry może być ustawiony w dowolne miejsce ekranu

• EN: Set new position to x=10, y=20

• PL: Ustaw nową pozycję smoka na x=10, y=20

3.9.1. Option 1

>>> dragon.fly(10, 20)
>>> dragon.teleport(10, 20)

Pros and Cons:

• Good: easy to use

• Good: encapsulation

• Good: easy to add validation if needed

• Good: easy to extend to 3D - add parameter with default value 0

• Bad: method names are too use-case specific

• Bad: arguments are implicit, require knowledge of an API what are the values provided as arguments

• Decision: rejected, too use-case specific names

Example:

>>> dragon.fly(10, 20)     # does the same, but different name
>>> hero.walk(10, 20)      # does the same, but different name
>>> snake.slide(10, 20)    # does the same, but different name
>>> fish.swim(10, 20)      # does the same, but different name

Use Case:

>>> mycache = DatabaseCache()
>>> mycache.insert()
>>> mycache.select()

>>> mycache = FilesystemCache()
>>> mycache.write()
>>> mycache.read()

>>> mycache = MemoryCache()
>>> mycache.store()
>>> mycache.retrieve()

3.9.2. Option 2

>>> dragon.set_position(10, 20)

Pros and Cons:

• Good: easy to use

• Good: encapsulation

• Good: easy to add validation if needed

• Good: easy to extend to 3D - add parameter with default value 0

• Bad: arguments are implicit, require knowledge of an API what are the values provided as arguments

• Decision: candidate

Example:

>>> dragon.set_position(10, 20)  # 2D, maybe
>>> dragon.set_position(10, 20, 30)  # 3D, maybe

>>> dragon.set_position(10, 20)     # ok
>>> hero.set_position(10, 20)       # ok
>>> snake.set_position(10, 20)      # ok

Use Case:

>>> mycache = DatabaseCache()
>>> mycache.set()
>>> mycache.get()

>>> mycache = FilesystemCache()
>>> mycache.set()
>>> mycache.get()

>>> mycache = MemoryCache()
>>> mycache.set()
>>> mycache.get()

3.9.3. Option 3

>>> dragon.set_position_xy(10, 20)

Pros and Cons:

• Good: verbose

• Good: does not require knowledge of an API what are the values provided as arguments

• Good: easy to use

• Good: encapsulation

• Good: easy to add validation if needed

• Bad: name set_position_xy() ties to 2D point

• Decision: rejected, ties to 2D point

Example:

>>> dragon.set_position_xy(10, 20)  # 2D, bad
>>> dragon.set_position_xy(10, 20, 30)  # 3D, bad

>>> dragon.set_position_xy(10, 20)  # 2D, bad
>>> dragon.set_position_xyz(10, 20, 30)  # 3D, bad

3.9.4. Option 4

>>> dragon.set_position(x=10, y=20)

Pros and Cons:

• Good: easy to use

• Good: arguments are explicit

• Good: encapsulation

• Good: easy to add validation if needed

• Good: easy to extend to 3D - add parameter with default value 0

• Decision: candidate

Example:

>>> dragon.set_position(x=10, y=20)  # 2D, ok
>>> dragon.set_position(x=10, y=20, z=30)  # 3D, ok

3.9.5. Option 5

>>> dragon.set(position_x=10, position_y=20)

Pros and Cons:

• Good: easy to use

• Good: arguments are explicit

• Good: easy to add validation if needed

• Bad: set() is too generic and allows for abuse

• Bad: encapsulation is in question

• Decision: rejected, possibility of abuse

Example:

>>> dragon.set(position_x=10, position_y=20)

Problem:

>>> dragon.set(name='Wawelski')
>>> dragon.set(health=50)
>>> dragon.set(position_x=50)
>>> dragon.set(position_y=100)

3.9.6. Option 6

>>> dragon.position_x = 10
>>> dragon.position_y = 20

Pros and Cons:

• Good: easy to use

• Good: arguments are explicit

• Good: can use property() for validation if needed

• Bad: violates encapsulation (OOP Principle)

• Decision: rejected, violates encapsulation

Use Case:

>>> model = KNearestNeighbors()
>>> model.k = 3
>>> model.weights = [0.75, 0.50, 0.25]

3.9.7. Option 7

>>> dragon.position.x = 10
>>> dragon.position.y = 20

Pros and Cons:

• Good: more or less easy to use (Simple is better than complex)

• Good: arguments are explicit

• Good: can use property() for validation if needed

• Good: namespace

• Good: more or less readable (Readability counts)

• Good: extensible, easy to refactor to 3D

• Bad: violates encapsulation - OOP good practices

• Bad: flat is better than nested (PEP 20)

• Bad: require knowledge of an API

• Bad: violates encapsulation (OOP Principle)

• Decision: rejected, violates encapsulation and Python convention (PEP 20)

Use Case:

>>> model = KNearestNeighbors()
>>> model.hyperparameters.k = 3
>>> model.hyperparameters.weights = [0.75, 0.50, 0.25]

3.9.8. Option 8

>>> dragon.position = (10, 20)

Pros and Cons:

• Good: easy to use

• Good: can use property() for validation if needed

• Bad: arguments are implicit

• Bad: require knowledge of an API

• Bad: always 2D

• Bad: not extensible, hard to refactor to 3D

• Bad: violates abstraction (OOP Principle)

• Bad: violates encapsulation (OOP Principle)

• Bad: violates Tell, Don't Ask (OOP Principle)

• Decision: rejected, violates OOP principles

Use Case:

>>> model = KNearestNeighbors()
>>> model.hyperparameters = (3, [0.75, 0.50, 0.25])

3.9.9. Option 9

>>> dragon.position = {'x':10, 'y':20}

Pros and Cons:

• Good: easy to use

• Good: can use property() for validation if needed

• Bad: arguments are implicit

• Bad: require knowledge of an API

• Bad: always 2D

• Bad: not extensible, hard to refactor to 3D

• Bad: violates abstraction (OOP Principle)

• Bad: violates encapsulation (OOP Principle)

• Bad: violates Tell, Don't Ask (OOP Principle)

• Decision: rejected, violates OOP principles

Use Case:

>>> model = KNearestNeighbors()
>>> model.hyperparameters = {'k':3, 'weights':[0.75, 0.50, 0.25]}

3.9.10. Option 10

>>> dragon.position = Position(x=10, y=20)

Pros and Cons:

• Good: easy to use

• Good: can use property() for validation if needed

• Good: arguments are explicit

• Good: readability

• Bad: require knowledge of an API

• Bad: extensible, easy to refactor to 3D

• Bad: violates abstraction (OOP Principle)

• Bad: violates encapsulation (OOP Principle)

• Bad: violates Tell, Don't Ask (OOP Principle)

• Decision: rejected, violates OOP principles

Use Case:

>>> model = KNearestNeighbors()
>>> model.hyperparameters = HyperParameters(k=3, weights=[0.75, 0.50, 0.25])

3.9.11. Option 11

>>> dragon @ Position(x=10, y=20)

Pros and Cons:

• Good: easy to use

• Good: using @ (matmul) it is easy to add validation

• Bad: @ (at) makes sense only in English

• Bad: require knowledge of an API

• Bad: extensible, easy to refactor to 3D

• Bad: violates abstraction (OOP Principle)

• Bad: violates encapsulation (OOP Principle)

• Bad: violates Tell, Don't Ask (OOP Principle)

• Decision: rejected, violates OOP principles, misleading for non-English speakers

Use Case:

>>> model = KNearestNeighbors()
>>> model << HyperParameters(k=3, weights=[0.75, 0.50, 0.25])

3.9.12. Decision

>>> dragon.set_position(x=10, y=20)

Pros and Cons:

• Good: easy to use

• Good: arguments are explicit

• Good: provides encapsulation

• Good: easy to add validation if needed

• Good: extensible, easy to refactor to 3D

Implementation:

>>> class Dragon:
...     def set_position(self, *, x: int, y: int) -> None:
...         ...