This doth be a machine-wrought text which may contain errors!
In Python (and ‘tis true of most other tongues), ‘tis possible to create one’s own objects, with values, rules, and functions peculiar unto them. These are called Classes (in the English fashion). We employ Classes to gather related data and functions into a single unit, for example, an Ordre Class which doth hold data such as ordre_id, kunde_navn, produkter, and functions like legg_til_produkt(), beregn_total(), and the like.
Lexicon (JSON) versus Classes (Objects)
JSON (JavaScript Object Notion) be a formate to store and convey datums irrespective o’ programming tongues, whilst a class stands but an arrangement within a particular tongue o’ code.
When ‘tis needful t’send thy datas across the network or stow them away in yonder file, oft dost thou employ JSON (or tables within databases).
But when ye shall labour upon structured datums within thine own codes, then usest thou classes.
In this module, we shall observe how we may employ classes to validate data.
The easiest course is to make use of @dataclass from the dataclasses library. This doth allow us to eschew the writing of much boilerplate code for the creation of a class. (Such as, for example, the built-in __init__ and __repr__ (representation) functions).
An example of a class without employing the dataclass decorator:
class Car:
def __init__(self, make: str, model: str, year: int):
self.make = make
self.model = model
self.year = year
def __repr__(self):
return f"{self.year} {self.make} {self.model}"
my_car = Car("Toyota", "Corolla", 2020)
print(my_car) # Output: 2020 Toyota Corolla # Forsooth, this doth print the car's details.
An Example with the dataclass Decorator, which doth achieve the same as above (but with less Code):
from dataclasses import dataclass
@dataclass
class Car:
make: str
model: str
year: int
my_car = Car("Toyota", "Corolla", 2020)
print(my_car) # Hark! The output shan be: Car(make='Toyota', model='Corolla', year=2020)
Task the First - To Forge a Class
Forge ye a class hight Person. This class shall possess these qualities (attributes):
name: The name of the personageeye_color: The hue of the person’s eyesphone_number: The person’s number for telephonyemail: The person’s address for electronic post
Instantiate (take into use) an object of the class Person with values meet for all qualities, as in the example hereunder.
@dataclass
class Person:
... # Thy code doth reside here
bob_kaare = Person(name="Bob Kåre",
eye_color="blue",
phone_number="12345678",
email="bob_kaare@example.com")
print(bob_kaare)
Remedye: A dataclass fro mPersonne
Herein lies an apt solution:
from dataclasses import dataclass
@dataclass
class Personne:
navn : str
augeskygge : str
telefonnummer : str
epostadresse : str
Task II – Validation within the Class
In yonder example abovest, hath no validation been added thereto. Thusly may one craft a ‘Person’ imbued with invalid values, such like these:
@dataclass
class Person:
... # Thy code doth reside here
invalid_person = Person(name="",
eye_color="yes",
phone_number="12345",
email="not-an-email")
print(invalid_person)
# Output: Person(name='', eye_color='yes', phone_number='12345', email='not-an-email')
This doth (perchance) present a troublesome matter, and may well accrue technical debt in times to come. Thankfully, there exist simple ways to add validation unto classes.
We shall begin by beholding email validation. There are libraries built within Python which may aid us in this endeavour, but forasmuch as we do this to learn, we shall craft our own simple validation, by creating a new class for “Email”, and examining a __post_init__ function (for dataclass only).
> [!NOTE] Observe
We *may* likewise accomplish this within the very class of `Person`, yet it oft proves more expedient to fashion distinct classes for matters that may be reused.
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Shapovalov forms determinant lines Kirillov character map orbit method geometric Langlands correspondence mirror symplectic resolutions Poisson structures Hamiltonian reduction MarsdenWeinstein quotient moment maps coadjoint orbits nilpotent cones Springer fibers Steinberg varieties Coulomb branches moduli spaces of monopoles Nahm equations ADHM constraints instanton bundles gauge potentials ChernSimons actions BF theory topological QFT TQFT state sum constructions ReshetikhinTuraev invariants Jones polynomial HOMFLYPT Khovanov homology categorification knot Floer HeegaardFloer SeibergWittenDonaldson theories complex cobordism MU formal group laws LandauGinzburg orbifolds chiral rings operator product expansions correlation functions n-point Green's funciton vacuum expectation values path integral measure fermion determinants anomalies Fujikawa Jacobians index theorems AtiyahSinger theorem Lefschetz fixed point formulas holomorphic Euler characteristics RiemannRoch Hirzebruch signature Todd class Dirichlet boundary conditions Robin Neumann mixed type elliptic parabolic hyperbolic PDEs weak solutions distributional derivatives Sobolev embeddings compactness Rellich Kondrachov regularity bootstrapping Schaud estimates CalderónZygmund maximal principal value singular integrals HardyLittlewood maximal function Lebesgue differentiation Rademacher approximate limits Lipschitz continuity Hölder norms Campanato spaces Morrey classes BMO VMO JohnNirenberg inequalities exponential decay Wiener criteria capacity potential theoretic notions logarithmic Newtonian capacities balayage sweeping out regularization smoothing mollifiers convolution kernels approximation identities density arguments partition unity partitions of unit subordinate coverings local coordinates charts atlases transition maps smooth structures manifolds topological gluing constructions CW complexes simplicial triangulations nerve lemma Cech cohomology AlexanderSpanier Čech spectral sequences Leray sheaf pushforward direct image higher derived functors Ext Tor groups Yoneda extension categories abelian homotopy colimits limind inverse systems pro-objects ind-categories completion Cauchy completeness flat modules faithfully exact tensor products localization fractions Ore condition noncommutative rings Noether normalizability Artinian descending chain ascending dimension Krull height prime ideals Jacobson radical nilpotent elements idempotent decomposition semisimple Wedderburn-Artin theorem simple algebras central division fields Brauer group class field theories reciprocity laws global-local principles Hasse principle norm residue symbols Hilbert symbol quadratic forms Witt ring Arason Pfister invariants u-invariant level pythagorean number real places valuation theory discrete valuations completions henselizations separable extensions Galois descent faithful flatness integral closures normalization conductor ideal ramification indices inertia degree unramified totally split inert wild tame discriminants differentials KummerExtensions LubinTate formal groups Drinfeldmodules elliptic curves CM points modular parametrization Hecke operators L-functions functional equations special values rationality conjectures BlochKato Tamagawa numbers regulator regulators periods volumes covolumes fundamental domains Siegel sets Borel reductions arithmetic subgroups congruence kernel finite index commensurables isogenies endomorphism rings quaternion orders definite indefinite maximal Eichler levels Shimura varieties Faltings heights GrossZagier formulas Waldspurger formula RankinSelberg convolutions Langlands program Arthur parameters base change transfer stable distributions orbital integrals geometric side spectral
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???+ tip "An exemplification of a __post_init__ function"
```python
from dataclasses import dataclass
@dataclass
class Email:
address: str
def __post_init__(self):
print(f"Validating email: {self.address}")
# Thine own code doth reside here
```
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For a simple email validation, we may perchance check that the email doth contain both `@` and `.` characters. Peradventure thou mayest also check that the email doth match a regex pattern. (More advanced, yet seek ye upon the web, I pray!)
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??? tip "Solution: Code for simple email validation"
Here is a possible solution; we utilize exceptions to “crash” should the email prove invalid, which shall halt the program forthwith and deliver an errant message.
```python
from dataclasses import dataclass
@dataclass
class Email:
address: str
def __post_init__(self):
if "@" not in self.address:
raise ValueError(f"Missing @ symbol within thy email address: {self.address}")
if "." not in self.address.split("@")[1]:
raise ValueError(f"Period lacking within domain of said email: {self.address}")
if " " in self.address:
raise ValueError(f"The email may contain no spaces: {self.address}")
# To witly test this code
test = Email("hei@example.com") # Valid
try:
test = Email("heiexample.com")
except ValueError as e:
print(e) # Invalid, missing at sign (@)
```
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## {width="48"} Task the Third – Telephone Number Validation
Create, in like manner as thou didst for electronic missives, a class, but now for telephone numbers.
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???+ danger "Challenge with telephone validation!"
Prithee, canst thou mend the validation for telephone numbers to accept both letters (str) and numbers (int)? For example, `12345678` and `"12345678"` should both be valid.
Essay also to add country codes as an attribute (a sub-value to the class). For example, `47` for Norway, `46` for Sweden
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## {width="48"} Task the Fourth - Employ Validation within the Person Class
Now that we have wrought validation for email and telephone number, we may employ these within the `Person` class.
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```python
@dataclass
class Person:
name: str
eye_color: str
phone_number: PhoneNumber # Prithee, employ the PhoneNumber class
email: Email # Pray, make use of the Email class
A new challenge doth arise!
Now that we have altered the Person class to employ the PhoneNumber and Email classes, we must also amend how we instantiate (create) a Person. We must now first fashion a PhoneNumber and an Email object, ere we may create a Person.
bob_kaare = Person(name="Bob Kåre",
eye_color="blue",
phone_number=PhoneNumber("12345678"), # Hark, a change doth here reside!
email=Email("bob_kaare@example.com")) # Prithee, observe a change doth here abide!
print(bob_kaare)
# Attend! A change must needs be wrought in how we extract the values also
print(bob_kaare.email.address)
print(bob_kaare.phone_number.number) # .country_code(?)
Task 5 - Properties in Classes (Optional)
When we do employ objects to represent values such as e-mail and telephone number, ‘tis needful we specify the sub-value (for example, address for e-mail and number for telephone number) each time we seek to extract the value. This may become somewhat cumbersome in the long run. Thankfully, there doth exist a solution to this, by the use of the @property decorator within a class, which alloweth us to extract the value directly from the object, without needing to specify the sub-value.
This doth, however, present yet another challenge, and ‘tis that we require the __init__ function within the Person class. This is because we cannot employ the same name for both a property and an attribute within a dataclass.
from dataclasses import dataclass
@dataclass
class EksempelVerdi:
attributt: str
@dataclass
class Person:
name: str
_verdi: EksempelVerdi # An internal variable (beginning with underscore to denote it as 'private')
def __init__(self, name: str, verdi: EksempelVerdi):
self.name = name
self._verdi = verdi
@property
def verdi(self):
return self._verdi.attributt # Retrieve the inner value directly
# The testeth code
person = Person(name="Alice", verdi=EksempelVerdi("Some text"))
print(person.verdi) # Outputth: Some text
Merk
Properties be singular in that they require not parameters, nor need parentheses to be run. In the example, we do fetch forth person.verdi without parentheses (not person.verdi()), albeit ‘tis a function technically.
An alternative example that doth accept both string and ExampleValue
```python
@dataclass
class Person:
name: str
_value: str
def __init__(self, name: str, value: str | ExampleValue): self.name = name if isinstance(value, ExampleValue): self._value = value elif isinstance(value, str): self._value = ExampleValue(value) else: raise TypeError("The value must be of the kind 'str' or 'ExampleValue'") @property def value(self) -> str: return self._value.attribute # Fetcheth forth the inner attribute directly
The testing code
person = Person(name=”Alice”, value=”Some text”)
print(person.value) # Output: Some text
```
Task Six – Attributes Wrought With Logic (Optional)
Update thy Person, bestowing upon it a new attribute named birthday (day of birth), which shall be of type datetime.date (frometh the library known as datetime).
Thereafter, craft thou these following attributes:
- Forge an attribute called age, that doth compute the years passed since one’s birthday unto this present day.
- Construct another attribute denominated is_adult, returning true if such person hath reached their eighteenth year or more; elsewise, false.
[!HINT]- Solutione: Age and adulthood as properties
Behold a possible solution hereunto:```python
from dataclasses import dataclass
from datetime import date@dataclass
class Personne:
name: str
birthday: date@property def age(self) -> int: """Calculeth the years of life based on birthdate and this day’s reckoning.""" today = date.today() age = today.year - self.birthday.year # Diminish by one if yet hath not passed their feast-day in these current times if (today.month, today.day) < (self.birthday.month, self.birthday.day): age -= 1 return age @property def is_adult(self) -> bool: """Returneth true if the person be eighteen winters or more old""" return self.age >= 18The trial runnith below
person = Personname(name=”Alice”, birthday=date(2005, 5, 15))
print(person.age) # Forsooth, eighteenscore should to-day exceed May fifteenth twenty-three
print(person.is_adult) # Verily so
[!DANGER] Yet another challenge!
Canst thou achieve instantiation ofPersonto accept bothdatetime.dateand a text in the fashion of"DD-MM-YYYY"forbirthday? (Hint: employdatetime.strptimeto convert the string to adatetime.date)