utils.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import os

# 把虚部放在一起，实部放在一起,顺便归一化
def reshape_dim(a):
    temp = []
    a_mean = np.mean(a)
    a_std = np.std(a)

    for i in range(np.array(a).shape[0]):
        temp1 = []
        temp2 = []

        for j in a[i]:
            j_0 = (j[0] - a_mean) / a_std
            j_1 = (j[0] - a_mean) / a_std
            temp1.append(j_0)
            temp2.append(j_1)
        temp1.extend(temp2)
        temp.append(temp1)
    return np.array(temp).astype(np.float32)


def read_data(snr):
    # 读取数据
    data_output_imag = pd.read_csv(os.path.join('RxDistortData','%ddB_source_data_imag.csv'%snr), header=None).T
    data_output_real = pd.read_csv(os.path.join('RxDistortData','%ddB_source_data_real.csv'%snr), header=None).T
    data_input_imag = pd.read_csv(os.path.join('RxDistortData','%ddB_dis_data_imag.csv'%snr), header=None).T
    data_input_real = pd.read_csv(os.path.join('RxDistortData','%ddB_dis_data_real.csv'%snr), header=None).T
    print('读取csv完成')
    
    # 分开输入输出,实部虚部放一起
    X = pd.concat([data_input_real, data_input_imag], axis=1)
    Y = pd.concat([data_output_real, data_output_imag], axis=1)
    print('实部和虚部结合完成')

    # 序列化Y
    Y = Y.applymap(lambda x:1 if x>=0 else 0)
    print('序列化y完成')
    return np.array(X), np.array(Y)