2.4. Dragon ADR Init Position¶

Set Dragon's initial position to x=50, y=120

2.4.1. Option 1¶

>>> dragon = Dragon('Wawelski', 50, 120)

Pros and Cons:

Good: easy to use
Bad: not explicit enough
Bad: requires knowledge of API to answer what are those numbers
Bad: It can suggest, that x and y are gold and hit points
Bad: It can suggest, that x and y are width and height of an texture
Bad: Not extensible to 3D
Decision: rejected, not explicit

Example:

>>> dragon = Dragon('Wawelski', 0, 0)  # 2D
>>> dragon = Dragon('Wawelski', 0, 0, 0)  # 3D
>>> dragon = Dragon('Wawelski', 'img/dragon/alive.png', 0, 0)

>>> dragon = Dragon('Wawelski', None, None)  # default point
>>> dragon = Dragon('Wawelski', 'img/dragon/alive.png', None, None)

Use Case:

>>> knn = KNearestNeighbors(3)  # ok
>>> knn = KNearestNeighbors(3, [1,2,3])  # bad

2.4.2. Option 2¶

>>> dragon = Dragon('Wawelski', x=50, y=120)

Pros and Cons:

Good: easy to use
Good: short argument names
Good: verbose in this example
Good: you can assign None by default to set default point
Good: extensible, easy to add z with default value 0
Good: Extensible to 3D
Bad: It can suggest, that x and y are gold and hit points
Bad: It can suggest, that x and y are width and height of an texture
Decision: rejected, not explicit enough

Example:

>>> dragon = Dragon('Wawelski', x=0, y=0)
>>> dragon = Dragon('Wawelski', t='img/dragon/alive.png', x=0, y=0)

>>> dragon = Dragon('Wawelski', x=None, y=None)
>>> dragon = Dragon('Wawelski', t='img/dragon/alive.png', x=None, y=None)

Use Case:

>>> knn = KNearestNeighbors(k=3)  # ok
>>> knn = KNearestNeighbors(k=3, w=[1,2,3])  # bad

2.4.3. Option 3¶

>>> dragon = Dragon('Wawelski', posx=50, posy=120)

Pros and Cons:

Good: simple, easy to use
Good: you can assign None by default to set default point
Good: extensible, easy to add posz with default value 0
Good: Extensible to 3D
Bad: not verbose
Decision: rejected, not explicit enough

Example:

>>> dragon = Dragon('Wawelski', posx=0, posy=0)  # maybe, bad
>>> dragon = Dragon('Wawelski', posx=0, posy=0, posz=0)  # maybe, bad

Use Case:

>>> knn = KNearestNeighbors(k=3, wgt=[1,2,3])  # maybe, bad

2.4.4. Option 4¶

>>> dragon = Dragon('Wawelski', positionx=50, positiony=120)

Pros and Cons:

Good: simple, easy to use
Good: you can assign None by default to set default point
Good: extensible, easy to add positionz with default value 0
Good: Extensible to 3D
Bad: CamelCase positionX and positionY does not conform to PEP-8
Decision: candidate, but names could be better

Example:

>>> dragon = Dragon('Wawelski', positionx=0, positiony=0)  # maybe
>>> dragon = Dragon('Wawelski', positionx=0, positiony=0, positionz=0)  # maybe

Use Case:

>>> knn = KNearestNeighbors(k=3, weights=[1,2,3])  # ok

>>> df.plot(kind='line', subplots=True, color='grey', sharey=True)  # bad

2.4.5. Option 5¶

>>> dragon = Dragon('Wawelski', position_x=50, position_y=120)

Pros and Cons:

Good: simple, easy to use
Good: you can assign None by default to set initial point
Good: extensible, easy to add position_z with default value 0
Good: backward compatible
Good: Extensible to 3D
Decision: candidate

>>> dragon = Dragon('Wawelski', position_x=0, position_y=0)  # ok
>>> dragon = Dragon('Wawelski', position_x=0, position_y=0, position_z=0)  # ok

Use Case:

>>> df.plot(kind='line', subplots=True, color='grey', share_y=True)       # ok
>>> df.plot(kind='line', sub_plots=True, color='grey', share_y=True)      # ok

>>> df.plot(kind='line', sub_plots=True, color='grey', share_y_axis=True) # ok
>>> df.plot(kind='line', sub_plots=True, color='grey', share_axis_y=True) # ok

2.4.6. Option 6¶

>>> dragon = Dragon('Wawelski', (50, 120))
>>> dragon = Dragon('Wawelski', position=(50, 120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple, easy to use
Good: you can assign None to set default position
Good: can set only one axis to None
Good: always has to pass both x and y coordinates together
Bad: always has to pass both x and y coordinates together
Bad: you have to know that first is x and second is y
Bad: order is important, you cannot change it
Bad: unpacking
Bad: not extensible, position will always be 2D
Bad: could be refactored to 3D using regexp: pattern = r'[\(\[(\s*?:\d+|None\s*)\s*,\s*(\s*?:\d+|None\s*)[\)\]]'
Decision: rejected, not extensible

Example:

>>> dragon = Dragon('Wawelski', (0, 0))              # bad
>>> dragon = Dragon('Wawelski', (0, 0, 0))           # bad
>>> dragon = Dragon('Wawelski', [0, 0])              # bad
>>> dragon = Dragon('Wawelski', [0, 0, 0])           # bad

>>> dragon = Dragon('Wawelski', position=(0, 0))     # maybe
>>> dragon = Dragon('Wawelski', position=(0, 0, 0))  # maybe
>>> dragon = Dragon('Wawelski', position=[0, 0])     # maybe
>>> dragon = Dragon('Wawelski', position=[0, 0, 0])  # maybe

>>> dragon = Dragon('Wawelski', (None, None))                   # bad
>>> dragon = Dragon('Wawelski', (None, None, None))             # bad
>>> dragon = Dragon('Wawelski', position=(None, None))          # bad, maybe
>>> dragon = Dragon('Wawelski', position=(None, None, None))    # bad, maybe

>>> pt = (None, None)
>>> pt = (None, None, None)

>>> pt = (50, 120)
>>> pt = (50, 120, 0)
>>> pt = [50, 120]
>>> pt = [50, 120, 0]

Use Case:

>>> np.random.randint(0, 10, (3, 3))  # bad
>>> np.random.randint(0, 10, size=(3, 3))  # ok

>>> pt = (50, 120)
>>>
>>> pt[0], pt[1]
(50, 120)

>>> x, y = (50, 120)
>>>
>>> x, y
(50, 120)

2.4.7. Option 7¶

>>> dragon = Dragon('Wawelski', {'x':50, 'y':120})
>>> dragon = Dragon('Wawelski', position={'x':50, 'y':120})

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: you can assign None by default to set default point
Good: order is not important
Good: always has to pass both x and y
Good: possible to extend to 3D with refactoring
Good: easier to refactor than tuple - pattern = r'\{\s*"x"\s*:\s*(?:\d+|None)\s*,\s*"y"\s*:\s*(?:\d+|None)\s*\}'
Bad: always has to pass both x and y
Bad: unpacking
Bad: not extensible, position will always be 2D
Decision: rejected, not extensible

Example:

>>> dragon = Dragon('Wawelski', {'x':0, 'y':0})         # bad, maybe
>>> dragon = Dragon('Wawelski', {'x':0, 'y':0, 'z':0})  # bad, maybe

>>> dragon = Dragon('Wawelski', position={'x':0, 'y':0})         # maybe
>>> dragon = Dragon('Wawelski', position={'x':0, 'y':0, 'z':0})  # maybe

>>> dragon = Dragon('Wawelski', {'x':None, 'y':None})                     # bad, maybe
>>> dragon = Dragon('Wawelski', {'x':None, 'y':None, 'z':None})           # bad, maybe
>>> dragon = Dragon('Wawelski', position={'x':None, 'y':None})            # bad, maybe
>>> dragon = Dragon('Wawelski', position={'x':None, 'y':None, 'z':None})  # bad, maybe

>>> pt = {'x':None, 'y':None}
>>> pt = {'x':None, 'y':None, 'z':None}

>>> pt = {'x':50, 'y':120}
>>> pt = {'x':50, 'y':120, 'z':0}

Use Case:

>>> pt = {'x':50, 'y':120}
>>>
>>> pt['x']
50
>>> pt['y']
120

2.4.8. Option 8¶

>>> from collections import namedtuple
>>>
>>>
>>> Position = namedtuple('Point', ['x', 'y'])
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple, easy to use
Good: always has to pass both x and y
Good: possible to extend to 3D (Python will crash if z not found)
Good: keyword argument is not required, class name is verbose enough
Good: lightweight, in the end this is a tuple
Bad: always has to pass both x and y
Bad: not extensible, position will always be 2D
Decision: rejected, could be done better

Use Case:

>>> pt = Point(x=50, y=120)
>>>
>>> pt[0], pt[1]
(50, 120)
>>>
>>> pt.x, pt.y
(50, 120)

2.4.9. Option 9¶

>>> from typing import NamedTuple
>>>
>>>
>>> class Position(NamedTuple):
...     x: int
...     y: int
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple, easy to use
Good: verbose
Good: you can assign None by default to set default position
Good: very easy to extend to 3D
Good: keyword argument is not required, class name is verbose enough
Good: lightweight, in the end this is a tuple
Decision: candidate

Use Case:

>>> pt = Point(x=50, y=120)
>>>
>>> pt[0], pt[1]
(50, 120)
>>>
>>> pt.x, pt.y
(50, 120)

2.4.10. Option 10¶

>>> from typing import TypedDict
>>>
>>>
>>> class Position(TypedDict):
...     x: int
...     y: int
>>>
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple
Good: you can assign position=None by default to set default position
Good: relatively easy to extend to 3D
Good: keyword argument is not required, class name is verbose enough
Bad: TypeDict does not support default values
Decision: rejected, better than dict, does not support default values

Use Case:

>>> pt = Point(x=50, y=120)
>>>
>>> pt['x']
50
>>> pt['y']
120

2.4.11. Option 11¶

>>> from typing import TypedDict, Required, NotRequired
>>>
>>>
>>> class Position(TypedDict):
...     x: Required[int]
...     y: Required[int]
...     z: NotRequired[int]
>>>
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Good: data is stored together (x and y coordinates)
Good: simple
Good: you can assign position=None by default to set default position
Good: relatively easy to extend to 3D
Good: keyword argument is not required, class name is verbose enough
Bad: today we need to make decision, that game will eventually be 3D
Bad: TypeDict does not support default values
Decision: rejected, does not support default values

Use Case:

>>> pt = Point(x=50, y=120)
>>>
>>> pt['x']
50
>>> pt['y']
120

2.4.12. Option 12¶

>>> class Position:
...     x: int
...     y: int
...
...     def __init__(self, x: int = 0, y: int = 0) -> None:
...         self.x = x
...         self.y = y
>>>
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: very common pattern
Good: easy to use
Good: faster than dataclasses
Good: more explicit than dataclass
Good: easy to extend to 3D
Good: can set default values
Good: keyword argument is not required, class name is verbose enough
Bad: allows creation of not existing attributes
Bad: allows for attribute mutation
Decision: maybe, has some limitation

Use Case:

>>> pt = Point(x=1, y=2)
>>>
>>> pt.x, pt.y
(1, 2)
>>>
>>> pt.x = 10            # ok
>>> pt.y = 20            # ok
>>> pt.notexisting = 30  # ok

2.4.13. Option 13¶

>>> class Position:
...     __slots__ = ('x', 'y')
...     x: int
...     y: int
...
...     def __init__(self, x: int = 0, y: int = 0) -> None:
...         self.x = x
...         self.y = y
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: common pattern
Good: easy to use
Good: more explicit than dataclass
Good: easy to extend to 3D
Good: can set default values
Good: keyword argument is not required, class name is verbose enough
Good: slots make class lighter and faster
Bad: too complex for now
Bad: allows for attribute mutation
Decision: maybe, too complex for now

Use Case:

>>> pt = Point(x=1, y=2)
>>>
>>> pt.x, pt.y
(1, 2)
>>>
>>> pt.x = 10             # ok
>>> pt.y = 20             # ok
>>> pt.notexisting = 30   # error

2.4.14. Option 14¶

>>> from dataclasses import dataclass
>>>
>>>
>>> @dataclass
... class Position:
...     x: int
...     y: int
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple, easy to use
Good: verbose
Good: you can assign None to set default position
Good: very easy to extend to 3D
Good: keyword argument is not required, class name is verbose enough
Bad: allows creation of not existing attributes
Bad: allows for attribute mutation
Decision: maybe, has some limitation

Use Case:

>>> pt = Point(x=1, y=2)
>>>
>>> pt.x, pt.y
(1, 2)
>>>
>>> pt.x = 10             # ok
>>> pt.y = 20             # ok
>>> pt.notexisting = 30   # ok

2.4.15. Option 15¶

>>> from dataclasses import dataclass
>>>
>>>
>>> @dataclass(frozen=True, slots=True)
... class Position:
...     x: int = 0
...     y: int = 0
>>>
>>> dragon = Dragon('Wawelski', Position(50, 120))
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))
>>> dragon = Dragon('Wawelski', position=Position(50, 120))
>>> dragon = Dragon('Wawelski', position=Position(x=50, y=120))

Pros and Cons:

Good: data is stored together (x and y coordinates)
Good: simple, easy to use
Good: verbose
Good: you can assign None by default to set default position
Good: very easy to extend to 3D
Good: keyword argument is not required, class name is verbose enough
Good: is faster and leaner than simple dataclass
Good: does not allow for attribute mutation
Good: does not allow for attribute creation
Good: slots make class lighter and faster
Bad: more complicated than mutable dataclasses
Decision: candidate

Use Case:

>>> pt = Point(x=1, y=2)
>>>
>>> pt.x, pt.y
(1, 2)
>>>
>>> pt.x = 10             # error
>>> pt.y = 20             # error
>>> pt.notexisting = 30   # error

2.4.16. Decision¶

>>> class Dragon:
...     def __init__(name: str, /, *, position_x: int, position_y: int) -> None:
...         ...
>>>
>>>
>>> dragon = Dragon('Wawelski', position_x=50, position_y=120)

Pros and Cons:

Good: simple
Good: explicit
Good: verbose
Good: extensible

2.4.17. Future¶

>>> class Dragon:
...     def __init__(name: str, /, *, position: Position) -> None:
...         ...
>>>
>>>
>>> dragon = Dragon('Wawelski', Position(x=50, y=120))

Choices: NameTuple, dataclass(frozen=True, slots=True)
Good: explicit
Good: verbose
Good: extensible
Bad: to complicated for now