# Industry-specific utility functions
"""
Industry-specific utility functions for the Indy Hub module.
These functions handle industry job calculations, production metrics, etc.
"""

# Standard Library
import logging

logger = logging.getLogger(__name__)


def calculate_industry_metrics(jobs_data):
    """
    Calculate industry performance metrics from job data.

    Args:
        jobs_data: List of industry job data dictionaries

    Returns:
        dict: Calculated metrics including completion rates, efficiency, etc.
    """
    if not jobs_data:
        return {
            "total_jobs": 0,
            "completed_jobs": 0,
            "completion_rate": 0,
            "average_duration": 0,
        }

    total_jobs = len(jobs_data)
    completed_jobs = sum(1 for job in jobs_data if job.get("status") == "delivered")
    completion_rate = (completed_jobs / total_jobs) if total_jobs > 0 else 0

    return {
        "total_jobs": total_jobs,
        "completed_jobs": completed_jobs,
        "completion_rate": completion_rate,
        "average_duration": 0,  # TODO: Calculate based on job durations
    }


def optimize_production_chain(blueprint_data):
    """
    Analyze and suggest optimizations for production chains.

    Args:
        blueprint_data: List of blueprint data dictionaries

    Returns:
        dict: Optimization suggestions and recommendations
    """
    if not blueprint_data:
        return {"suggestions": [], "efficiency_score": 0}

    # TODO: Implement production chain optimization logic
    return {
        "suggestions": ["Consider improving ME/TE research"],
        "efficiency_score": 75,  # Placeholder
    }


def calculate_production_costs(blueprint_id, runs=1):
    """
    Calculate production costs for a blueprint.

    Args:
        blueprint_id: ID of the blueprint
        runs: Number of production runs

    Returns:
        dict: Cost breakdown including materials, fees, etc.
    """
    # TODO: Implement cost calculation logic
    return {"material_cost": 0, "facility_fees": 0, "total_cost": 0}


def estimate_completion_time(job_data):
    """
    Estimate completion time for industry jobs.

    Args:
        job_data: Job data dictionary

    Returns:
        int: Estimated completion time in seconds
    """
    # TODO: Implement time estimation logic based on job type, skills, etc.
    return 0
