"""
LiiS Circular Economy Simulator

Core calculations for CTI metrics and P&L projections.
"""

from dataclasses import dataclass
from pathlib import Path

import yaml


@dataclass
class CTIMetrics:
    """Circular Transition Indicator metrics."""

    eolrr: float  # End-of-Life Recovery Rate (0-1)
    pri: float  # Primary Resource Input (0-1, lower is better)
    lifetime_extension: float  # Ratio of actual to design lifetime
    units_recovered: int  # Absolute units recovered per cycle
    blended_cost: float  # EUR per unit (new + refurb weighted)


@dataclass
class PLProjection:
    """Profit & Loss projection."""

    revenue_per_unit: float
    cost_per_unit: float
    margin_per_unit: float
    margin_percent: float
    break_even_price: float


def load_product(name: str) -> dict:
    """Load product configuration from YAML."""
    config_path = Path(__file__).parent.parent / "products" / name / "config.yaml"
    if not config_path.exists():
        raise FileNotFoundError(f"Product config not found: {config_path}")
    return yaml.safe_load(config_path.read_text())


def calculate_cti(config: dict, distributed_units: int = 200) -> CTIMetrics:
    """
    Calculate CTI metrics for a product configuration.

    Args:
        config: Product configuration dict (from YAML)
        distributed_units: Number of units distributed in pilot

    Returns:
        CTIMetrics with calculated values
    """
    returns = config["return_assumptions"]
    design = config["design"]
    costs = config["costs"]

    # EoLRR = Return Rate × (Grade A + Grade B percentage)
    return_rate = returns["return_rate"]
    grade_a = returns["grade_distribution"]["a"]
    grade_b = returns["grade_distribution"]["b"]
    eolrr = return_rate * (grade_a + grade_b)

    # Units recovered
    units_recovered = int(distributed_units * eolrr)

    # PRI for Year 2 steady state (simplified)
    # Assumes monthly demand of distributed_units / 12
    monthly_demand = distributed_units / 12
    months_until_return = returns["months_until_return"]
    refurb_available = units_recovered / (months_until_return / 12)

    # New units needed = demand - refurb available
    new_units_monthly = max(0, monthly_demand - refurb_available)
    total_monthly = monthly_demand

    pri = new_units_monthly / total_monthly if total_monthly > 0 else 1.0

    # Lifetime Extension
    cycles = design["max_cycles"]
    use_per_cycle = design["use_cycle_years"]
    design_lifetime = design["lifetime_years"]
    lifetime_extension = (cycles * use_per_cycle) / design_lifetime

    # Blended cost
    mfg_cost = costs["manufacturing_unit_cost"]
    avg_refurb_cost = (
        costs["refurbishment"]["grade_a"] * grade_a
        + costs["refurbishment"]["grade_b"] * grade_b
    ) / (grade_a + grade_b)

    blended_cost = (
        new_units_monthly * mfg_cost + refurb_available * avg_refurb_cost
    ) / total_monthly

    return CTIMetrics(
        eolrr=round(eolrr, 3),
        pri=round(pri, 3),
        lifetime_extension=round(lifetime_extension, 2),
        units_recovered=units_recovered,
        blended_cost=round(blended_cost, 2),
    )


def calculate_pl(config: dict, cti: CTIMetrics) -> PLProjection:
    """
    Calculate P&L projection based on CTI metrics.

    Args:
        config: Product configuration dict
        cti: Calculated CTI metrics

    Returns:
        PLProjection with financial metrics
    """
    pricing = config["pricing"]
    costs = config["costs"]

    # Revenue per unit over lease term
    monthly_price = pricing["lease_monthly"]
    lease_months = pricing["lease_term_months"]
    revenue_per_unit = monthly_price * lease_months

    # Cost per unit (blended manufacturing + logistics)
    logistics_cost = costs["logistics"]["delivery"] + costs["logistics"]["return"]
    cost_per_unit = cti.blended_cost + logistics_cost

    # Margin
    margin_per_unit = revenue_per_unit - cost_per_unit
    margin_percent = (margin_per_unit / revenue_per_unit) * 100 if revenue_per_unit > 0 else 0

    # Break-even monthly price
    break_even_price = cost_per_unit / lease_months

    return PLProjection(
        revenue_per_unit=round(revenue_per_unit, 2),
        cost_per_unit=round(cost_per_unit, 2),
        margin_per_unit=round(margin_per_unit, 2),
        margin_percent=round(margin_percent, 1),
        break_even_price=round(break_even_price, 2),
    )


def format_report(config: dict, cti: CTIMetrics, pl: PLProjection) -> str:
    """Generate a markdown report for a product."""
    name = config["product"]["name"]

    report = f"""# CTI Analysis: {name}

## Summary

| Metric | Value | Target |
|--------|-------|--------|
| EoLRR | {cti.eolrr:.1%} | {config['cti_parameters']['target_eolrr']:.0%} |
| PRI (Year 2) | {cti.pri:.1%} | {config['cti_parameters']['target_pri_year2']:.0%} |
| Lifetime Extension | {cti.lifetime_extension:.1f}x | 1.0x |

## P&L Projection

| Metric | Value |
|--------|-------|
| Revenue per unit | EUR {pl.revenue_per_unit:.2f} |
| Cost per unit (blended) | EUR {pl.cost_per_unit:.2f} |
| Margin per unit | EUR {pl.margin_per_unit:.2f} |
| Margin % | {pl.margin_percent:.1f}% |
| Break-even price | EUR {pl.break_even_price:.2f}/month |

## Configuration

- **Lease price**: EUR {config['pricing']['lease_monthly']}/month
- **Lease term**: {config['pricing']['lease_term_months']} months
- **Manufacturing cost**: EUR {config['costs']['manufacturing_unit_cost']}
- **Return rate**: {config['return_assumptions']['return_rate']:.0%}

## Grade Distribution

| Grade | % | Refurb Cost |
|-------|---|-------------|
| A (reusable) | {config['return_assumptions']['grade_distribution']['a']:.0%} | EUR {config['costs']['refurbishment']['grade_a']} |
| B (refurbish) | {config['return_assumptions']['grade_distribution']['b']:.0%} | EUR {config['costs']['refurbishment']['grade_b']} |
| C (scrap) | {config['return_assumptions']['grade_distribution']['c']:.0%} | EUR {config['costs']['refurbishment']['grade_c']} |
"""
    return report


if __name__ == "__main__":
    # Example usage
    for product_name in ["besafe", "hamax"]:
        print(f"\n{'='*50}")
        print(f"Analyzing: {product_name}")
        print("=" * 50)

        config = load_product(product_name)
        cti = calculate_cti(config)
        pl = calculate_pl(config, cti)

        print(f"EoLRR: {cti.eolrr:.1%}")
        print(f"PRI: {cti.pri:.1%}")
        print(f"Lifetime Extension: {cti.lifetime_extension:.1f}x")
        print(f"Blended Cost: EUR {cti.blended_cost:.2f}")
        print(f"Margin: {pl.margin_percent:.1f}%")