User-Defined Types.pdf

Code will outlive your time working on it

Legacy Code
A codebase where you do not have direct communication with
the original authors

YOU
Tasks completed in
a month

Fast-forward a few years........

?
Future
Collaborator
Tasks completed in
a month

Future
Collaborator
Tasks completed in
a month

You have a duty to deliver value in a timely
manner

You have a duty to make it easy for future
collaborators to deliver value in a timely manner

Make it easy for your future collaborator

Do you want future collaborators to thank you
for your foresight ........

Robust Python
Building a Vocabulary with User-Defined Types
Patrick Viafore
HSV.py Lunchtime Talk
4/18/2023

A Robust Codebase
A robust codebase is full of code that is clean and
maintainable, and is resilient and error-free in spite of
constant change

How do you build your codebase without
knowing what's in the future?

Your codebase is your most effective tool of
communication and collaboration with other
developers

Software Engineering is simultaneously
archaeology and time-travel

Let your codebase be your Rosetta Stone

https://learning.oreilly.com/get-learning/?code=ROBUSTP21

Typechecking User Deﬁned Types
Extensibility Building a Safety Net

User-deﬁned types are a way for you to deﬁne
your own vocabulary

The abstractions you choose communicate to
the future

def print_receipt(
order: Order,
restaurant: tuple[str, int, str]):
total = (order.subtotal *
(1 + tax[restaurant[2]]))
print(Receipt(restaurant[0], total))

def print_receipt(
order: Order,
restaurant: Restaurant):
total = (order.subtotal *
(1 + tax[restaurant.city]))
print(Receipt(restaurant.name, total))

User-deﬁned types unify mental models

User-deﬁned types make it easier to reason
about a codebase

User-deﬁned types make it harder to make
errors

I want to focus on the "why" we use user-deﬁned
types, not "how" to create them

MOTHER_SAUCES = ("Béchamel",
"Velouté",
"Espagnole",
"Tomato",
"Hollandaise")

def create_daughter_sauce(
mother_sauce: str,
extra_ingredients: list[str]):
# ...
create_daughter_sauce(MOTHER_SAUCE[0],
["Onion"])

mother_sauce: str,
# ...
# What was MOTHER_SAUCE[0] again?
create_daughter_sauce(MOTHER_SAUCE[0],
["Onion"])

mother_sauce: str,
# ...
create_daughter_sauce("Bechamel",
["Onion"])

mother_sauce: str,
# ...
create_daughter_sauce("BBQ Sauce",
["Onion"])

mother_sauce: str,
# ...
create_daughter_sauce("BBQ Sauce",
["Onion"])
Wrong

mother_sauce: str,
# ...
create_daughter_sauce("Béchamel",
["Onion"])

Restrict your choices with enumerations

from enum import Enum
class MotherSauce(Enum):
BÉCHAMEL = "Béchamel"
VELOUTÉ = "Velouté"
ESPAGNOLE = "Espagnole"
TOMATO = "Tomato"
HOLLANDAISE = "Hollandaise"
MotherSauce.BÉCHAMEL

mother_sauce: MotherSauce,
# ...
create_daughter_sauce(MotherSauce.BÉCHAMEL,
["Onion"])

Catch mistakes with static analysis

Use enumerations to prevent collaborators from
using incorrect values

Use enumerations to simplify choices

Author's Name
Recipe
Ingredient List
Author's Life
Story
# of Servings
Recipe Name

Online Recipe
Author's Name
Recipe
Ingredient List
Author's Life
Story
# of Servings
Recipe Name

Data classes represent a relationship between
data

@dataclass
class OnlineRecipe:
name: str
author_name: str
author_life_story: str
number_of_servings: int
ingredients: list[Ingredient]
recipe: str

recipe = OnlineRecipe(
"Pasta With Sausage",
"Pat Viafore",
"When I was 15, I remember ......",
6,
["Rigatoni", ..., "Basil", "Sausage"],
"First, brown the sausage ...."
)

recipe.name
>>> "Pasta With Sausage"
recipe.number_of_servings
>>> 6

Data classes represent heterogeneous data

Heterogeneous data
● Heterogeneous data is data that may be multiple different
types (such as str, int, list[Ingredient], etc.)
● Typically not iterated over -- you access a single ﬁeld at a time

# DO NOT DO THIS
recipe = {
"name": "Pasta With Sausage",
"author": "Pat Viafore",
"story": "When I was 15, I remember ....",
"number_of_servings": 6,
"ingredients": ["Rigatoni", ..., "Basil"],
"recipe": "First, brown the sausage ...."
}

# is life story the right key name?
put_on_top_of_page(recipe["life_story"])
# What type is recipe?
def double_recipe(recipe: dict):
# .... snip ....

Any time a developer has to trawl through the
codebase to answer a question about data, it
wastes time and increases frustration

This will create mistakes and incorrect
assumptions, leading to bugs

recipe: OnlineRecipe = create_recipe()
# type checker will catch problems
put_on_top_of_page(recipe.life_story)
def double_recipe(recipe: OnlineRecipe):
# .... snip ....

Use data classes to group data together and
reduce errors when accessing

You communicate intent and prevent future
developers from making errors

Data classes aren't appropriate for all
heterogeneous data

Invariants
● Fundamental truths throughout your codebase
● Developers will depend on these truths and build
assumptions on them
● These are not universal truths in every possible system, just
your system

Invariants
● Sauce will never be put on top of other toppings
(cheese is a topping in this scenario).
● Toppings may go above or below cheese.
● Pizza will have at most only one sauce.
● Dough radius can be only whole numbers.
● The radius of dough may be only between 15 and 30 cm

@dataclass
class Pizza:
radius_in_cm: int
toppings: list[str]

pizza = Pizza(15, ["Tomato Sauce",
"Mozzarella",
"Pepperoni"])
# THIS IS BAD!
pizza.radius_in_cm = 1000
pizza.toppings.append("Alfredo Sauce")

class Pizza:
def __init__(self, radius_in_cm: int,
toppings: list[str])
assert 15 <= radius_in_cm <= 30
sauces = [t for t in toppings
if is_sauce(t)]
assert len(sauces) <= 1
self.__radius_in_cm = radius_in_cm
sauce = sauces[:1]
self.__toppings = sauce +
[t for t in toppings if not is_sauce(t)]

class Pizza:
if is_sauce(t)]
sauce = sauces[:1]
INVARIANT
CHECKING

# Now an exception
pizza = Pizza(1000, ["Tomato Sauce",
"Mozzarella",
"Pepperoni"])

class Pizza:
if is_sauce(t)]
sauce = sauces[:1]
"Private"
Members

# Linters will catch this error
# Also a runtime error
pizza.__radius_in_cm = 1000
pizza.__toppings.append("Alfredo Sauce")

Classes create invariants that developers cannot
easily modify

Classes must always preserve these invariants

class Pizza:
# ... snip ...
def add_topping(self, topping: str):
if is_sauce(topping) and self.has_sauce():
raise TooManySaucesError()
if is_sauce(topping):
self.__toppings.insert(0, topping)
else:
self.__toppings.append(topping)

Give future collaborators solid classes to reason
upon

Classes allow you to group inter-related data
and preserve invariants across their lifetime

@dataclass
class OnlineRecipe:
...
def condense(self):
# ...
def scale_to(self, servings: int)
# ...

The Paradox of Code Interfaces

You have one chance to get your interface
right....

.....but you won't know it's right until it's used.

What happens if you don't get it right

When it all goes wrong
● Duplicated functionality
● Broken mental models
● Reduced testing

@dataclass
class Ingredient:
name: str
units: ImperialMeasure # cup, tbsp, or tsp
quantity: int

class Recipe:
...
def add_ingredient(self, ing: Ingredient):
if self.contains(ing):
???
else:
self.ingredients.append(ingredients)

class Recipe:
...
def add_ingredient(self, ing: Ingredient):
matched = self._find_ingredient(ing)
if matched:
matched += ing
else:
self.ingredients.append(ingredients)

class Ingredient:
def __add__(self, rhs: Ingredient):
assert self.name == rhs.name
converted = rhs.convert(self.units)
return Ingredient(self.name,
self.units,
(self.quantity +
converted.quantity))

def reserve_table(table: Table):
assert not table.is_reserved()
table.hold()
wait_for_user_confirmation()
if is_confirmed():
table.reserve()
table.remove_hold()

def reserve_table(table: Table):
assert not table.is_reserved()
with table.hold():
wait_for_user_confirmation()
if is_confirmed():
table.reserve()

from contextlib import contextmanager
class Table:
...
@contextmanager
def hold(self):
...

@contextmanager
def hold(self):
# .. hold logic ...
try:
yield
finally:
table.remove_hold()

Sub-typing is a relationship between types

A sub-type has all the same behaviors as a
super-type (it may also customize some
behaviors)

class Rectangle:
def __init__(self, height: int, width: int):
self.__height = height
self.__width = width
def set_width(self, width: int):
self.__width = width
def set_height(self, height: int):
self.__height = height
# ... snip getters ...

class Square(Rectangle):
def __init__(self, side_length: int):
self.set_height(side_length)
def set_height(self, side_length: int):
self.__height = side_length
self.__width = side_length
def set_width(self, side_length: int):
self.__height = side_length
self.__width = side_length

What I've just shown you has a very subtle error.

FOOD_TRUCK_AREA_SIZES = [
Rectangle(1, 20),
Rectangle(5, 5),
Rectangle(20, 30)
]

Yes, a square is a rectangle (geometrically
speaking)

Is a square substitutable for a rectangle?

FOOD_TRUCK_AREA_SIZES = [
Rectangle(1, 20),
Square(5),
Rectangle(20, 30)
]

def double_food_truck_area_widths():
for ft_shape in FOOD_TRUCK_AREA_SIZES:
old_size = ft_shape.get_width()
ft_shape.set_width(old_size * 2)

# What happens when this is a square?

old_height = ft_shape.get_height()
# Is this a reasonable assert?
assert ft_shape.get_height() == old_height

Developers will write code based on the
constraints of the superclass

Do not let subclasses violate those constraints

Someone changing a super-class should not
need to know about all possible sub-classes

Substitutability
● Do not strengthen pre-conditions
● Do not weaken post-conditions
● Do not raise new types of exceptions
○ Looking at you, NotImplementedError
● Overridden functions almost always should call super()

Types of sub-typing
● Inheritance
● Duck Typing
● Protocols
● Plug-ins
● etc.

How you subtype will inﬂuence how easy it is to
make errors as code changes

Who Am I?
Principal Software Engineer
Cloud Software Group
Owner of Kudzera, LLC
Author of Robust Python
Organizer of HSV.py

Contact Me
@PatViaforever
pat@kudzera.com

User-Defined Types.pdf

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