#!/usr/bin/env python
# coding: utf-8

# In[3]:


import pandas as pd


# In[4]:


from tms_data_interface import SQLQueryInterface
seq = SQLQueryInterface(schema="transactionschema")


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seq.execute_raw("show tables")


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query = """
    select  final.CUSTOMER_NUMBER_main as Focal_id,
            final.Credit_transaction_amount,
            final.SEGMENT,
            final.RISK,
            final.SAR_FLAG
    from 
    (
        (
            select subquery.CUSTOMER_NUMBER_1 as CUSTOMER_NUMBER_main,
                    subquery.Credit_transaction_amount
            from 
            (
                (
                    select customer_number as CUSTOMER_NUMBER_1, 
                            sum(transaction_amount) as Credit_transaction_amount
                    from 
                    (
                        select * 
                        from {trans_data} as trans_table 
                        left join {acc_data} as acc_table
                        on trans_table.benef_account_number = acc_table.account_number
                        where trans_table.transaction_desc = 'WIRE RELATED TRANSACTION'
                    )
                    where account_number not in ('None')
                    group by 1
                ) credit
            ) subquery
        ) main left join 
        (
            select subquery.CUSTOMER_NUMBER_3 as CUSTOMER_NUMBER_cust,
                    subquery.SEGMENT,
                    subquery.RISK,
                    case
                         when subquery.SAR_FLAG is NULL then 'N'
                         else subquery.SAR_FLAG
                    end as SAR_FLAG    
            from
            (
                (
                    select customer_number as CUSTOMER_NUMBER_3, 
                            business_segment as SEGMENT,
                            case
                               when RISK_CLASSIFICATION = 1 then 'Low Risk'
                               when RISK_CLASSIFICATION = 2 then 'Medium Risk'
                               when RISK_CLASSIFICATION = 3 then 'High Risk'
                               else 'Unknown Risk'
                            end AS RISK
                    from {cust_data}
                ) cd left join
                (
                    select customer_number as CUSTOMER_NUMBER_4, 
                            sar_flag as SAR_FLAG
                    from {alert_data}
                ) ad on cd.CUSTOMER_NUMBER_3 = ad.CUSTOMER_NUMBER_4
            ) subquery
        ) cust_alert on cust_alert.CUSTOMER_NUMBER_cust = main.CUSTOMER_NUMBER_main
    ) final
"""


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from tms_data_interface import SQLQueryInterface

class Scenario:
    seq = SQLQueryInterface(schema="transactionschema")

    def logic(self, **kwargs):
        row_list = self.seq.execute_raw(query.format(trans_data="transaction10m",
                                                    cust_data="customer_data_v1",
                                                    acc_data="account_data_v1",
                                                    alert_data="alert_data_v1")
                                       )
        cols = ["Focal_id", "Total_Wire_Deposit_Amt",
                "Segment", "Risk", "SAR_FLAG"]
        
        df = pd.DataFrame(row_list, columns = cols)
        df['Total_Wire_Deposit_Amt'] = df['Total_Wire_Deposit_Amt'].astype('int')
        # df['Segment'] = 'Individual'
        
        p98 = (
        df.groupby("Segment")["Total_Wire_Deposit_Amt"]
              .quantile(0.98)
              .reset_index()
              .rename(columns={"Total_Wire_Deposit_Amt": "P98_Value"})
        )
        print(p98)

        # Merge percentile back to main dataframe
        df = df.merge(p98, on="Segment", how="left")

        # Step 2: Identify population above 98th percentile
        high_pop = df["Total_Wire_Deposit_Amt"] > df["P98_Value"]
        print(high_pop)

        # Step 3: From this high-risk population, select 0.1% random sample
        sample_fraction = 0.1    # 0.1%
        high_pop_indices = df[high_pop].sample(frac=sample_fraction, random_state=42).index

        # Step 4: Assign SAR_FLAG
        df["SAR_FLAG"] = "N"              # default for all
        df.loc[high_pop_indices, "SAR_FLAG"] = "Y"   # assign Y to random 0.1% above 98th percentile

        return df


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# sen = Scenario()
# a = sen.logic()


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# a[a['SAR_FLAG'] == "Y"]


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#tst cmt