Project: s41467-024-44864-5 | Function: read_file (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Reads instrumental noise data from MAT files and splits into training and validation datasets for noise pattern learning

**KEY TERMS:** {
  "noise": "instrumental noise measurements",
  "train_data": "training noise patterns",
  "valid_data": "validation noise patterns",
  "valid_ratio": "train-validation split ratio"
}

**CONTEXT CODE:** """
Author: HaoHe
Email: hehao@stu.xmu.edu.cn
Date: 2020-09-13
"""

import numpy as np
import os
import torch
import scipy.io as sio
from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt


class Read_data:
    def __init__(self, path, valid_ratio=20):
        self.path = path
        self.valid_ratio = valid_ratio

    def read_file(self):
        """
        Args:
            valid_ratio: the ratio of the validation sets
            path: the absolute path of the source file
        """
        # MASKED: Reads instrumental noise data from MAT files and splits into training and validation datasets for noise pattern learning implementation
        raise NotImplementedError("This function is masked for testing purposes.")


# 制作数据集
class Make_dataset(Dataset):
    def __init__(self, data):
        super(Make_dataset, self).__init__()
        self.data = data

    def __getitem__(self, index):
        feature = self.data[index]
        return feature

    def __len__(self):
        return np.size(self.data, 0)


if __name__ == '__main__':
    path = r'E:\PAPER\paper writing\Noise learning\Simulate datasets'
    reader = Read_data(path, 20)
    train_set, _ = reader.read_file()
    print(train_set.shape)
    x = np.mean(train_set[0], axis=0)
    print(x.shape)
    plt.plot(x.ravel())
    # test dataloader
    myset = Make_dataset(train_set)
    train_loader = DataLoader(dataset=myset, batch_size=100)
    print(len(train_loader))
    for batch_idx, raw in enumerate(train_loader):
        if batch_idx > 1:
            print(raw.shape)
            x = np.mean(raw.numpy(), axis=0)
            print(x.shape)
            plt.plot(x)
            plt.show()
            break


Complete the function implementation replacing "raise NotImplementedError".