#!/usr/bin/python3

# TODO format output

import pandas as pd
import argparse

# setup CLI parsing
parser = argparse.ArgumentParser(
    prog='xmpp-expenses-report',
    description='Generate a report from a CSV file generated by the XMPP expense bot.',
    epilog='')

parser.add_argument('-f', '--filename')
parser.add_argument('-n', '--nexp')
parser.add_argument('-l', '--lang')
args = parser.parse_args()

# localize

sentences = { 'it':
                [f'Ciao, ecco le ultime {args.nexp} spese dal',
                 f'Bilancio',
                 f'Bilancio per ogni utente',
                 f'Uscite',
                 f'Uscite per ogni utente',
                 f'Entrate',
                 f'Entrate per ogni utente'
                 ],
              'en':
                [f'Hi, here\'s the latest {args.nexp} expenses since',
                 f'Total balance',
                 f'Balance for every user',
                 f'Sum of expenses',
                 f'Sum of expenses for every user',
                 f'Sum of incoming',
                 f'Sum of incoming for every user']}


# load CSV
df = pd.read_csv(args.filename)
df['dt'] = pd.to_datetime(df['Time'])
df['FloatValue'] = df[['Value']].astype(float)

recent = df.sort_values(by='dt', ascending=False)
startdate = recent.dt.iloc[-1].date()

# latest N expenses
print('{0} {1}:\n'.format(
    sentences[args.lang][0],
    startdate))

print(
    recent[['ID', 'Time', 'Value', 'User', 'Notes']]
    .head(int(args.nexp))   # only print latest args.nexp expenses
    .to_string(index=False))

# balance
ntot = recent.FloatValue.sum()

print('\n{0}:\n{1:.2f} euro'.format(
    sentences[args.lang][1],
    ntot))

usertot = pd.DataFrame(recent[['User','FloatValue']].groupby('User', group_keys=True).sum())

print('\n{0}:'.format(sentences[args.lang][2]))

for user,value in usertot.iterrows():
    print('{0}: {1:.2f} euro'.format(user,abs(value.values[0])))

# outgoing
outgoing = recent.loc[recent['FloatValue'] < 0].FloatValue.sum()
userOut = pd.DataFrame(recent[['User','FloatValue']].loc[recent['FloatValue'] < 0].groupby('User', group_keys=True).sum())

print('\n{0}:\n{1:.2f} euro'.format(
    sentences[args.lang][3],
    abs(outgoing)))

print('\n{0}:'.format(sentences[args.lang][4]))

for user,value in userOut.iterrows():
    print('{0}: {1:.2f} euro'.format(user,abs(value.values[0])))

# incoming
incoming = recent.loc[recent['FloatValue'] >= 0].FloatValue.sum()
userIn = pd.DataFrame(recent[['User','FloatValue']].loc[recent['FloatValue'] >= 0].groupby('User', group_keys=True).sum())

print('\n{0}:\n{1:.2f} euro'.format(
    sentences[args.lang][5],
    abs(incoming)))

print('\n{0}:'.format(sentences[args.lang][6]))

for user,value in userIn.iterrows():
    print('{0}: {1:.2f} euro'.format(user,abs(value.values[0])))