import typing
import pandas as pd
from banking_breakdown import types
import json
import re
import numpy as np


def _escape_string(to_escape: str):
    return to_escape.translate(str.maketrans({"&": r"\&"}))


def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame:
    stripped_df = pd.DataFrame(
        {'t': df["t"], 'value': df["balance"]})

    stripped_df.index = stripped_df['t']
    gb = stripped_df.groupby(pd.Grouper(freq='M'))

    result = gb.tail(1)['value'].reset_index()

    return result


def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
    df.index = df['t']
    income_df = df.loc[df['value'] > 0]
    expenses_df = df.loc[df['value'] < 0]

    income_df = income_df.groupby(pd.Grouper(freq='M'))[
        'value'].sum().reset_index().round(decimals=2)
    expenses_df = expenses_df.groupby(pd.Grouper(freq='M'))[
        'value'].sum().reset_index().round(decimals=2)

    t = income_df['t']
    income = income_df['value'].round(decimals=2)
    expenses = expenses_df['value'].round(decimals=2)
    net = (income + expenses).round(decimals=2)

    result_df = pd.DataFrame(
        {'t': t, 'income': income, 'expenses': expenses, 'net': net})

    return result_df


def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
    df = df.loc[df['value'] < 0]
    df = df.drop('t', axis=1)
    df = df.groupby(['category']).sum().reset_index()
    values = (df['value'] / df['value'].sum() * 100).to_numpy()
    values[-1] += 100 - np.sum(values)
    values = np.round(values, decimals=1)
    categories = [_escape_string(category) for category in df['category']]

    category_overview_df = pd.DataFrame(
        {"category": categories, "value": values})

    category_overview_df = category_overview_df.sort_values('value',
                                                            ascending=False)

    return category_overview_df


def _compute_expenses_by_category(complete_df: pd.DataFrame) -> pd.DataFrame:
    complete_df = complete_df.loc[complete_df['value'] < 0].copy()
    complete_df['value'] = -complete_df['value']
    complete_df.index = complete_df['t']

    complete_gb = complete_df.groupby(pd.Grouper(freq='M'))

    categories = complete_df['category'].unique()
    data_dict = {category: [] for category in categories}

    for (month_date, month_df) in complete_gb:
        month_df = month_df.drop('t', axis=1).reset_index().drop('t', axis=1)

        category_df = month_df.groupby(['category']).sum().reset_index()

        for _, row in category_df.iterrows():
            data_dict[row['category']].append(row['value'])

        non_listed = list(set(categories) - set(category_df['category']))
        for category in non_listed:
            data_dict[category].append(0)

    result = pd.DataFrame(data_dict)
    result = result.reindex(result.mean().sort_values(ascending=False).index,
                            axis=1)
    result = result.round(decimals=2)
    result['t'] = complete_gb.tail(1).drop('t', axis=1).reset_index()['t']

    return result


def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame:
    return pd.DataFrame({'t': df["t"],
                         'value': df["balance"]})


def parse_statement(filename: str) -> types.ReportData:
    df = pd.read_csv(filename)
    df["t"] = pd.to_datetime(df["t"], format='%Y-%m-%d')

    category_overview_df = _compute_category_overview(df)
    total_balance_df = _compute_total_balance(df)
    net_income_df = _compute_net_income(df)
    detailed_balance_df = _compute_detailed_balance(df)
    expenses_by_category_df = _compute_expenses_by_category(df)

    return types.ReportData(category_overview_df,
                            expenses_by_category_df,
                            net_income_df,
                            total_balance_df,
                            detailed_balance_df, )


def main():
    report_data = parse_statement("../res/bank_statement_2023_categorized.csv")


if __name__ == "__main__":
    main()