"""
寓信ETL
财务权责明细表
按月、权责、费用科目、应收、应付、实收、实付、待收、待付划分账单明细
"""

import pandas as pd
from sqlalchemy import create_engine, text
from sqlalchemy.types import NVARCHAR, Date, DECIMAL, INTEGER
from datetime import datetime, timedelta
from tqdm import tqdm
import json
from dateutil.relativedelta import relativedelta
import calendar
from urllib.parse import quote_plus
from utils import load_config, truncate_target_db, update_column_comment
import json


debug = False
if debug:
    debug_condition = ''
    pass
else:
    debug_condition = ''
    pass

cols = {
    'contract_id': [NVARCHAR(32), '租赁合同ID'],
    'fee_subject_id': [NVARCHAR(32), '费用类型ID'],
    'fee_direction': [INTEGER, '费用方向'],
    'themonth': [NVARCHAR(32), '账单明细ID'],
    'kind': [NVARCHAR(16), '类型'],
    'money': [DECIMAL(14, 2), '金额'],
}


def query_total(conn) -> int:
    query = """
        select 
            count(1)
        from yuxin_finance.fin_finance_bill_detail
        where is_delete=0 and is_valid=1 and biz_type=2 {debug_condition}
        """.format(debug_condition=debug_condition)
    return conn.execute(text(query)).scalar()


def extract(conn, batch_size, i) -> pd.DataFrame:
    """ This API extracts data from
    """
    query = """
        select biz_id, fee_subject_id, fee_direction, begin_time, end_time, original_money, occurred_money
        from yuxin_finance.fin_finance_bill_detail
        where is_delete=0 and is_valid=1 and biz_type=2 {debug_condition}
        limit {batch_size} offset {offset}
        """.format(batch_size=batch_size, offset=i, debug_condition=debug_condition)
    source_data = pd.read_sql(query, con=conn)
    return source_data


def transform(conn, data) -> pd.DataFrame:
    """ Transforms the dataset into desired structure and filters
    """

    df_data = []
    for index, row in data.iterrows():
        begin_date = row['begin_time']
        end_date = row['end_time']

        # Calculate the number of days between the two dates
        num_days = (end_date - begin_date).days + 1
        num_months = (end_date.year - begin_date.year) * 12 + (end_date.month - begin_date.month) + 1
        # Calculate the amount per day
        if num_days == 0:
            num_days = 1
        elif num_days < 0:
            num_days = abs(num_days)
        original_money_per_day = row['original_money'] / num_days
        occurred_money_per_day = row['occurred_money'] / num_days

        original_money_remainder = row['original_money']
        occurred_money_remainder = row['occurred_money']
        current_month = begin_date
        while current_month <= end_date:
            first_day = 1
            last_day = calendar.monthrange(current_month.year, current_month.month)[1]
            if current_month.month == begin_date.month and current_month.year == begin_date.year:
                first_day = begin_date.day
            if current_month.month == end_date.month and current_month.year == end_date.year:
                last_day = end_date.day
            num_days_month = last_day - first_day + 1

            if current_month.month == end_date.month and current_month.year == end_date.year:
                # keep remainder in the last day
                original_money = original_money_remainder
                occurred_money = occurred_money_remainder
            else:
                original_money = original_money_per_day * num_days_month
                original_money_remainder -= original_money
                occurred_money = occurred_money_per_day * num_days_month
                occurred_money_remainder -= occurred_money
            original_money = round(original_money, 2)
            occurred_money = round(occurred_money, 2)

            def append_row(_kind, _money):
                new_row = {
                    'contract_id': row['biz_id'],
                    'fee_subject_id': row['fee_subject_id'],
                    'fee_direction': row['fee_direction'],
                    'themonth': current_month.date(),
                    'kind': _kind,
                    'money': _money,
                }
                df_data.append(new_row)

            if row['fee_direction'] == 1:
                append_row('应收', original_money)
                append_row('实收', occurred_money)
                append_row('待收', original_money-occurred_money)
            else:
                append_row('应付', original_money)
                append_row('实付', occurred_money)
                append_row('待付', original_money-occurred_money)

            # Increment the current date by one month
            current_month += relativedelta(months=1)

        pass

    columns = [k for k, v in cols.items()]
    df = pd.DataFrame(df_data, columns=columns)
    return df


def load(conn, df: pd.DataFrame, target_db) -> None:
    """ Loads data into a MySQL database
    """
    # Define the column types for the table
    dtypes = {key: value[0] for key, value in cols.items()}
    # create target table with df.dtypes
    df.to_sql(target_db, con=conn, if_exists='append',
              index=False, dtype=dtypes)


def etl():
    config = load_config()

    target_db = 'bi_bill_detail_month_'

    connection_string = 'mysql+pymysql://{username}:{pwd}@{host}:{port}/{db}?charset=utf8'\
        .format(username=config['mysql']['username'],
                pwd=quote_plus(config['mysql']['password']),
                host=config['mysql']['host'],
                port=config['mysql']['port'],
                db=config['mysql']['db'])

    engine = create_engine(connection_string)

    with engine.begin() as conn:
        # Get the total number of rows
        total = query_total(conn)
        # Define the batch size
        batch_size = 100

        truncate_target_db(conn, target_db)

        if debug:
            total = 200
            batch_size = 100
        print('total', total)

        # Write the data to the table in batches
        for i in tqdm(range(0, total, batch_size)):
            data = extract(conn, batch_size, i)
            data = transform(conn, data)

            if debug:
                print(data.head())
            load(conn, data, target_db)

        update_column_comment(conn, target_db, cols)


etl()