| from __future__ import annotations |
|
|
| from dataclasses import dataclass |
| from typing import List |
|
|
|
|
| @dataclass |
| class ValidationResult: |
| is_valid: bool |
| errors: List[str] |
|
|
|
|
| def validate_scenario_payload(payload: dict) -> ValidationResult: |
| errors: List[str] = [] |
|
|
| weather = payload.get("weather_curve") |
| demand = payload.get("demand_curve") |
| leak_probability = payload.get("leak_probability") |
|
|
| if not isinstance(weather, list) or len(weather) != 24: |
| errors.append("weather_curve must have exactly 24 values.") |
| if not isinstance(demand, list) or len(demand) != 24: |
| errors.append("demand_curve must have exactly 24 values.") |
|
|
| if isinstance(weather, list): |
| for idx, value in enumerate(weather): |
| if not isinstance(value, (int, float)) or value < 0 or value > 1: |
| errors.append(f"weather_curve[{idx}] must be in range [0,1].") |
| break |
|
|
| if isinstance(demand, list): |
| for idx, value in enumerate(demand): |
| if not isinstance(value, (int, float)) or value <= 0: |
| errors.append(f"demand_curve[{idx}] must be > 0.") |
| break |
|
|
| if not isinstance(leak_probability, (int, float)) or leak_probability < 0 or leak_probability > 1: |
| errors.append("leak_probability must be in range [0,1].") |
|
|
| return ValidationResult(is_valid=not errors, errors=errors) |
|
|
|
|
| def normalize_curve(values: List[float], default: float) -> List[float]: |
| if not values or len(values) != 24: |
| return [default for _ in range(24)] |
| return [float(v) for v in values] |
|
|
