Returns ------- VladModel A frozen dataclass with all fields populated.
The code is written as a ( parse_vladmodels_spec ) together with a tiny helper class ( VladModel ). You can drop it into any Python project (or copy‑paste it into a Jupyter notebook) and start using it right away. 1️⃣ What the feature does | Step | Action | |------|--------| | 1️⃣ Parse | Splits the input string into its logical parts: brand , model name , model code , width and height . | | 2️⃣ Validate | Checks that the numeric parts are actually numbers and that the brand is the expected one ( vladmodels ). | | 3️⃣ Enrich | Computes a derived metric – area ( width × height ) – which is often useful for sizing, shipping, UI layout, etc. | | 4️⃣ Return | Gives you a clean, typed object ( VladModel ) that you can query like model.brand , model.area , etc. | | 5️⃣ Extend | The implementation is deliberately short but documented and type‑annotated, so you can easily add more derived fields (volume, aspect‑ratio, …) later. | 2️⃣ The code from __future__ import annotations from dataclasses import dataclass from typing import Tuple, List vladmodels katya y117 47 154
# Optional sanity‑check (you can adjust the limits to your domain) if not (0 < width < 10_000 and 0 < height < 10_000): raise ValueError(f"Unreasonable dimensions: width mm × height mm") Returns ------- VladModel A frozen dataclass with all
Expected format (case‑insensitive): "<brand> <name> <code> <width> <height>" Example: "vladmodels katya y117 47 154" 1️⃣ What the feature does | Step |
@dataclass(frozen=True, slots=True) class VladModel: """A tiny data‑class representing a single VladModels product.""" brand: str # e.g. "vladmodels" name: str # e.g. "katya" code: str # e.g. "y117" width_mm: int # first numeric value (mm) height_mm: int # second numeric value (mm)
pytest test_vladmodel_parser.py If you just need the area without the extra ceremony:
