import subprocess
import sys
import pandas as pd
import os
import numpy as np


try:
    dataset_path = sys.argv[1]
except Exception as e:
    print("Exception while retrieving dataset path")
    print(e)


def divide_dataset(dataset, path):
    """Split dataset to dev, train, test datasets. """

    print('Shuffle dataset...')
    shuf_path = 'data/Car_Prices_Poland_Kaggle_shuf.csv'
    os.system(f'tail -n +2 {path} | shuf > {shuf_path}')

    len1 = len(dataset) // 6
    len2 = (len1 * 2) + 1

    print('Dividing dataset...')
    os.system(f'head -n {len1} {shuf_path} > data/Car_Prices_Poland_Kaggle_dev.csv')
    os.system(f'head -n {len1} {shuf_path} | tail -n {len1} > data/Car_Prices_Poland_Kaggle_test.csv')
    os.system(f'tail -n +{len2} {shuf_path} > data/Car_Prices_Poland_Kaggle_train.csv')

    os.system(f'rm {shuf_path}')
    print("Len match: " + str(sum([len1 * 2, len2]) == len(dataset)))
    os.system('cat Car_Prices_Poland_Kaggle_train.csv | wc -l')
    os.system('cat Car_Prices_Poland_Kaggle_dev.csv | wc -l')
    os.system('cat Car_Prices_Poland_Kaggle_test.csv | wc -l')

    print('Dataset devided')


def normalize_dataset(dataset):
    """Drop unnecessary columns and set numeric values to [0,1] range"""

    print(f'--------------- Initial dataset length ---------------')
    print(len(dataset))

    # drop columns
    dataset.drop(columns=["Unnamed: 0", "generation_name"], inplace=True)
    dataset = dataset.dropna()

    # normalize numbers to [0, 1]
    for column in dataset.columns:
        if isinstance(dataset.iloc[1][column], np.int64) or isinstance(dataset.iloc[1][column], np.float64):
            dataset[column] = (dataset[column] - dataset[column].min()) / (dataset[column].max() - dataset[column].min())
    return dataset


cars = pd.read_csv(dataset_path)
df = pd.DataFrame(cars)
df = normalize_dataset(df)
divide_dataset(df, dataset_path)