# Path: data/utils_data.py
# FOLDER_REAL_DATA = join(dirname(__file__), 'real_data')
# FOLDER_SIMULATOR_INPUT = join(dirname(__file__), 'simulator_input')
# FOLDER_REAL_DATA_ANALYSIS = join(FOLDER_REAL_DATA, 'analysis')
# FOLDER_SIMULATOR_LOG = join(pardir, 'experiments/results')
# FILE_ANONYMIZED_DATASET = join(FOLDER_REAL_DATA, 'anonymized_dataset.csv')
# FILE_REAL_LOG = join(FOLDER_REAL_DATA, 'transaction_log.csv')
# FILE_SIMULATOR_LOG = join(FOLDER_SIMULATOR_LOG, 'transaction_log.csv')
# def get_dataset(file):
# def get_real_dataset():
# def get_simulated_dataset(result_idx):
# def get_real_data_stats():
# def get_simulated_data_stats(result_idx):
# def get_data_stats(datasets):
# def get_grouped_prob(group_by, col_name):
# def get_transaction_dist(col_name):
# def plot_hist_num_transactions(trans_frac, col_name):
# def plot_bar_trans_prob(trans_frac, col_name, file_name=None):


from datetime import datetime
from currency_converter import CurrencyConverter
from pytz import timezone, country_timezones
from data import utils_data
import numpy as np
import pandas
"""
Let this run if you have the original dataset

    anonymized_dataset.csv
    
to produce the preprocessed log file of transactions

    transaction_log.csv
    
"""


# Path: data/utils_data.py
# FOLDER_REAL_DATA = join(dirname(__file__), 'real_data')
# FOLDER_SIMULATOR_INPUT = join(dirname(__file__), 'simulator_input')
# FOLDER_REAL_DATA_ANALYSIS = join(FOLDER_REAL_DATA, 'analysis')
# FOLDER_SIMULATOR_LOG = join(pardir, 'experiments/results')
# FILE_ANONYMIZED_DATASET = join(FOLDER_REAL_DATA, 'anonymized_dataset.csv')
# FILE_REAL_LOG = join(FOLDER_REAL_DATA, 'transaction_log.csv')
# FILE_SIMULATOR_LOG = join(FOLDER_SIMULATOR_LOG, 'transaction_log.csv')
# def get_dataset(file):
# def get_real_dataset():
# def get_simulated_dataset(result_idx):
# def get_real_data_stats():
# def get_simulated_data_stats(result_idx):
# def get_data_stats(datasets):
# def get_grouped_prob(group_by, col_name):
# def get_transaction_dist(col_name):
# def plot_hist_num_transactions(trans_frac, col_name):
# def plot_bar_trans_prob(trans_frac, col_name, file_name=None):


# Path: data/preprocess_data_raw.py
from datetime import datetime
from currency_converter import CurrencyConverter
from pytz import timezone, country_timezones
from data import utils_data
import numpy as np
import pandas

"""
Let this run if you have the original dataset

    anonymized_dataset.csv
    
to produce the preprocessed log file of transactions

    transaction_log.csv
    
"""


dataset = pandas.read_csv(utils_data.FILE_ANONYMIZED_DATASET)