api/routes/validate.py

"""
Validation Endpoint — Returns all hackathon validation answers as structured JSON.

GET /api/validate
GET /api/validate/{system}

Answers:
1. Time to full realization (scaling roadmap)
2. Number of dependent variables
3. Global energy impact percentage
4. Egypt-specific impact
5. Solution energy consumption
6. Solution CO2 emissions
7. Before/after computational efficiency
8. Net energy and CO2 after solution
"""
from __future__ import annotations

import time

from fastapi import APIRouter

from config import CFG
from src.grid.loader import load_network
from src.grid.power_flow import get_baseline, evaluate_topology
from src.quantum.qaoa_reconfig import solve_sa
from src.evaluation.metrics import (
    compute_impact,
    compute_solution_footprint,
    compute_net_benefit,
    compute_egypt_impact,
    compute_business_model,
    count_dependent_variables,
)

router = APIRouter()


@router.get("/validate/{system}")
@router.get("/validate")
def get_validation(system: str = "case33bw"):
    """Return all hackathon validation answers for the given system.

    This endpoint runs a quick optimisation and computes all metrics needed
    to answer the hackathon judges' questions.
    """
    # Load network and compute baseline
    net = load_network(system)
    baseline = get_baseline(net)

    if not baseline.get("converged"):
        return {"error": "Baseline power flow did not converge."}

    # Run quantum SA optimisation (lightweight)
    t0 = time.perf_counter()
    sa_result = solve_sa(net, n_iter=300, n_restarts=3, top_k=3)
    optimisation_time = time.perf_counter() - t0

    if "error" in sa_result:
        return {"error": f"Optimisation failed: {sa_result['error']}"}

    ev = evaluate_topology(net, sa_result["best_open_lines"])
    if not ev.get("converged"):
        return {"error": "Optimised topology did not converge."}

    # Compute all metrics
    impact = compute_impact(baseline, ev)
    footprint = compute_solution_footprint(optimisation_time)
    net_benefit = compute_net_benefit(impact, footprint)
    egypt = compute_egypt_impact(impact["loss_reduction_pct"])
    variables = count_dependent_variables()
    business = compute_business_model(impact)

    # Published comparison
    published = {
        "case33bw": {
            "base_loss_kw": 202.67,
            "optimal_loss_kw": 139.55,
            "optimal_reduction_pct": 31.15,
            "source": "Baran & Wu 1989, reproduced by PSO, GA, MILP",
        }
    }

    return {
        "system": system,

        # Q1: How can the solution be implemented?
        "implementation_plan": egypt["implementation_plan"],

        # Q2: Is it one-time or recurring? Per what?
        "usage_model": business["usage_model"],

        # Q3: Pricing and revenue
        "business_model": {
            "savings_per_feeder": business["savings_per_feeder"],
            "pricing_models": business["pricing_models"],
            "revenue_projections": business["revenue_projections"],
        },

        # Q4: Comparison against existing solutions
        "competitive_analysis": business["comparison_to_alternatives"],

        # Q5: Number of dependent variables
        "dependent_variables": variables,

        # Q6: Global energy impact
        "global_impact": egypt["global"],

        # Q7: Egypt-specific impact
        "egypt_impact": egypt["egypt"],
        "cairo_impact": egypt["cairo"],

        # Q8: Waste elimination (not solution-vs-consumption)
        "waste_elimination": {
            "baseline_waste_kwh_year": net_benefit["baseline_waste_kwh_year"],
            "optimized_waste_kwh_year": net_benefit["optimized_waste_kwh_year"],
            "waste_eliminated_kwh_year": net_benefit["waste_eliminated_kwh_year"],
            "waste_eliminated_pct": net_benefit["waste_eliminated_pct"],
            "solution_overhead_pct": net_benefit["solution_overhead_pct_of_savings"],
        },

        # Q9: Solution energy footprint
        "solution_footprint": footprint,

        # Q10: CO2 trustworthiness
        "trustworthiness": net_benefit["trustworthiness"],

        # Q11: Before/after comparison
        "before_after": {
            "baseline_loss_kw": impact["baseline_loss_kw"],
            "optimized_loss_kw": impact["optimized_loss_kw"],
            "loss_reduction_kw": impact["loss_reduction_kw"],
            "loss_reduction_pct": impact["loss_reduction_pct"],
            "baseline_min_voltage": impact["baseline_min_voltage"],
            "optimized_min_voltage": impact["optimized_min_voltage"],
            "voltage_violations_fixed": impact["voltage_violations_fixed"],
            "computation_time_sec": round(optimisation_time, 3),
        },

        # Comparison with published
        "published_comparison": published.get(system, {}),

        # Annual impact (single feeder)
        "annual_impact_single_feeder": {
            "energy_saved_mwh": impact["energy_saved_mwh_year"],
            "co2_saved_tonnes": impact["co2_saved_tonnes_year"],
            "cost_saved_usd": impact["cost_saved_usd_year"],
            "trees_equivalent": impact["equivalent_trees_planted"],
            "cars_removed": impact["equivalent_cars_removed"],
        },
    }