import numpy as np

import os

# import tensorflow as tf

import h5py

def load_h5_all(file, is_training):

    hf = h5py.File(file, 'r+')

    label = hf['label'][:][:]

    num_samples = len(label)

    train_size = num_samples - test_size

    feat = hf['feature'][:][:, :]

    gene = hf['gene_name'][:]

    sample = hf['sample'][:]

    print('%s has data:', feat.shape)

    # train_dataset = tf.data.Dataset.from_tensor_slices((feat[:train_size, :], label[:train_size]))     #not using now

    # test_dataset = tf.data.Dataset.from_tensor_slices((feat[-test_size:], label[-test_size:]))     #not using now

    # train_dataset = tf.data.Dataset.from_generator((feat0[:train_size, :], label[:train_size]))

    # test_dataset = tf.data.Dataset.from_generator((feat0[-test_size:], label[-test_size:]))

    return feat, label, gene, sample

if __name__ == '__main__':

    m_rna, label, gene, sample_id = load_h5_all('../data_process/tcga.h5', True)