"""
Utility functions for the Media Planning Simulator
"""


def calculate_cpm(budget: float, impressions: int) -> float:
    """Calculate CPM (Cost Per Mille)"""
    if impressions == 0:
        return 0
    return (budget / impressions) * 1000


def calculate_ctr(clicks: int, impressions: int) -> float:
    """Calculate CTR (Click-Through Rate)"""
    if impressions == 0:
        return 0
    return (clicks / impressions) * 100


def calculate_cpc(spend: float, clicks: int) -> float:
    """Calculate CPC (Cost Per Click)"""
    if clicks == 0:
        return 0
    return spend / clicks


def calculate_conversion_rate(conversions: int, clicks: int) -> float:
    """Calculate Conversion Rate"""
    if clicks == 0:
        return 0
    return (conversions / clicks) * 100


def calculate_roi(revenue: float, investment: float) -> float:
    """Calculate ROI (Return on Investment)"""
    if investment == 0:
        return 0
    return ((revenue - investment) / investment) * 100


def calculate_roas(revenue: float, ad_spend: float) -> float:
    """Calculate ROAS (Return on Ad Spend)"""
    if ad_spend == 0:
        return 0
    return revenue / ad_spend


def calculate_clv(
    avg_purchase_value: float,
    purchase_frequency: float,
    customer_lifespan: float,
    acquisition_cost: float
) -> float:
    """Calculate CLV (Customer Lifetime Value)"""
    return (avg_purchase_value * purchase_frequency * customer_lifespan) - acquisition_cost


def format_currency_eur(value: float) -> str:
    """Format value as Euro currency"""
    return f"€{value:,.2f}"


def format_percentage_value(value: float, decimals: int = 2) -> str:
    """Format value as percentage string"""
    return f"{value:.{decimals}f}%"


def create_summary_dataframe(metrics: dict) -> pd.DataFrame:
    """Create a summary DataFrame from metrics dictionary"""
    summary_items = [
        ("Impression Totali", metrics.get("total_impressions", 0)),
        ("Click Totali", metrics.get("total_clicks", 0)),
        ("Conversioni Totali", metrics.get("total_conversions", 0)),
        ("Ricavi Totali (€)", metrics.get("total_revenue", 0)),
        ("Spesa Pubblicitaria (€)", metrics.get("total_ad_spend", 0)),
        ("Margine Netto (€)", metrics.get("net_margin", 0)),
        ("ROI (%)", metrics.get("roi", 0)),
        ("ROAS", metrics.get("roas", 0)),
        ("CPA (€)", metrics.get("cpa", 0)),
        ("CLV (€)", metrics.get("clv", 0)),
    ]
    return pd.DataFrame(summary_items, columns=["Metrica", "Valore"])