import os
import pandas as pd
import numpy as np
from src.dataloaders.utils import BaseDataset, expand_data
from torch.utils.data import Dataset
# from dataloaders.utils import load_sparse_data, load_data, to_sparse_bin_data

import matplotlib.pyplot as plt
import pickle as pk


base_dir = os.path.join(os.path.split(__file__)[0], '../../data/p53_origin/')


# if not os.path.exists(os.path.join(base_dir, 'K9_process.data')):
#     f = open(os.path.join(base_dir, 'K9.data'), 'r')
#     wf = open(os.path.join(base_dir, 'K9_process.data'), 'w')
#     for i, line in enumerate(f):
#         tmp = line[:-2].replace('?', '')
#         wf.write(tmp + '\n')
#         print(f'\r {i}/31420', end='')
#     wf.close()
#     f.close()


class P53(BaseDataset):
    def __init__(self, dtype='train'):
        super().__init__(base_dir, dtype)
