# analytics/monitoring_analysis.py

import pandas as pd

from metrics.vintage_metrics import (
    calculate_vintage_dpd,
    calculate_vintage_ncl
)


def generate_monitoring_table(df):

    dpd_30_mob3 = calculate_vintage_dpd(
        df=df,
        mob=3,
        dpd_threshold=30,
        metric_type="count"
    )

    dpd_30_mob6 = calculate_vintage_dpd(
        df=df,
        mob=6,
        dpd_threshold=30,
        metric_type="count"
    )

    dpd_60_mob6 = calculate_vintage_dpd(
        df=df,
        mob=6,
        dpd_threshold=60,
        metric_type="count"
    )

    ncl_y1 = calculate_vintage_ncl(
        df=df,
        mob=12
    )

    final_table = (
        dpd_30_mob3[
            ["booking_vintage", "dpd_rate"]
        ]
        .rename(
            columns={
                "dpd_rate": "30+@3"
            }
        )
    )

    final_table = final_table.merge(
        dpd_30_mob6[
            ["booking_vintage", "dpd_rate"]
        ].rename(
            columns={
                "dpd_rate": "30+@6"
            }
        ),
        on="booking_vintage",
        how="outer"
    )

    final_table = final_table.merge(
        dpd_60_mob6[
            ["booking_vintage", "dpd_rate"]
        ].rename(
            columns={
                "dpd_rate": "60+@6"
            }
        ),
        on="booking_vintage",
        how="outer"
    )

    final_table = final_table.merge(
        ncl_y1[
            ["booking_vintage", "ncl_rate"]
        ].rename(
            columns={
                "ncl_rate": "Yr1_NCL"
            }
        ),
        on="booking_vintage",
        how="outer"
    )

    return final_table