import argparse

from pathlib import Path

from app import App

def parse_arguments():

parser = argparse.ArgumentParser(description='Liability Cash Flow Generation Tool')

parser.add_argument('--log',

action='store_true',

help='Log messages')

parser.add_argument('--debug',

action='store_true',

help='Save files in various stages into csv files')

parser.add_argument('--debug_dir',

required=False,

help='Dir where to save CSV files to')

parser.add_argument('--dbpwd',

required=False,

help='Password to SQL Server (for Linux ODBC drivers)')

args = parser.parse_args()

return args.log, args.debug, args.debug_dir, args.dbpwd

if __name__ == '__main__':

"""

This small application demonstrates how it's to read from a CSV file and

save it into a database.

NOTE:

There is some overhead here with date type (datetime64) when read

from a database which is relevant only in Linux and ok in Windows.

params: Supply password as a params when executed for Linux systems.

No need in windows as you can save the password in the ODBC

connection settings

"""

# Parse arguments

logging_enabled, debug_enabled, debug_dir, dbpwd = parse_arguments()

print(f'Logging:{logging_enabled}')

print(f'Debug:{debug_enabled}')

print(f'Debug dir:{debug_dir}')

# Initialise a global app

# this will hold the state and components of the app

app = App(log_flag=logging_enabled,

debug_flag=debug_enabled,

debug_dir=debug_dir,

dbpwd=dbpwd)

# Main implementation

csv_path = Path('~/docker/test/exchange-rates/data')

daily_csv_file = 'daily.csv'

# Read file to a DataFrame

csv_df = app.get_csv_data(csv_path.joinpath(daily_csv_file))

csv_df = csv_df.astype({'Date': 'datetime64'})

csv_df = csv_df.rename(columns={'Date': 'RateDate'})

csv_df = csv_df.fillna(0)

# Check the data

app.debug_df('Rates from CSV:', csv_df)

# Save Rates Into DB

app.save_rates_into_db(csv_df)