from matplotlib import numpy
import matplotlib.pyplot as plot
import numpy as np
import tensorflow as tf
import os

'''
这是一个处理data的类
作用是将后缀为.dat的文件按节点个数(head)解析为可用的十进制文件
需要传入的参数有：
head:节点头数   int类型
filename:放置.dat文件的根路径   string类型
'''


class DataDeal(object):
    voiceArray = []

    def __init__(self, head, filename):
        self.head = head
        self.name = filename
        '''创建一个获取文件夹下所有后缀名为.dat得文件'''
        L = []
        for root, dir, filename in os.walk(self.name):
            for file in filename:
                if os.path.splitext(file)[1] == '.dat':
                    L.append(file)
        self.array = L

    def __call__(self, head, filename, **kwargs):
        k = np.array(self.array)
        result = [[[] * self.head] * k.shape[0]]
        Z = []
        for file in self.array:
            # 构建文件名
            filename = self.name + '/' + file
            Z.append(filename)
        sum0 = [[] for i in range(self.head)]
        sum1 = [[] for i in range(self.head)]
        sum2 = [[] for i in range(self.head)]
        for filename in Z:
            with open(filename, 'rb') as f:
                # 解析为16进制数据
                bs = ['{:0>2X}'.format(x) for x in f.read()]
                # 将每一个数据分别转化为10进制数据，并存入voiceArray
                for x in bs:
                    # print(x)
                    x = int(x, 16)
                    self.voiceArray.append(x)

            # 将voiceArray转化为np的数组
            data = np.array(self.voiceArray)
            print(data.shape)
            # 创建一个二维空数组
            data_true = [[] for i in range(self.head)]
            # 数据按节点个数分成head个分别存入二维空数组的各行
            for i in range(self.head):
                data_true[i] = data[i::self.head]
            # 将二维空数组重新整合成np.array的形式
            data_true = np.array(data_true)

            z = 0
            if z % 12 == 0 or z % 12 == 1 or z % 12 == 2 or z % 12 == 3:
                sum0 = np.hstack((sum0, data_true))
            if z % 12 == 4 or z % 12 == 6 or z % 12 == 7 or z % 12 == 5:
                sum1 = np.hstack((sum1, data_true))
            if z % 12 == 9 or z % 12 == 10 or z % 12 == 11 or z % 12 == 8:
                sum2 = np.hstack((sum2, data_true))
            z=z+1
        #sum0 = np.array(sum0)
        #sum1 = np.array(sum1)
        #sum2 = np.array(sum2)
        print(sum0)
        print(sum1)
        print(sum2)
        return sum0, sum1, sum2


if __name__ == '__main__':
    data0= DataDeal(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
    print(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
'''    data0 = np.array(data0)
    data1 = np.array(data1)
    data2 = np.array(data2)
    print(data0.shape)
    print(data1.shape)
    print(data2.shape)'''

'''
    # 读取文件
    filename = r'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA\dds测试故障库4.6#0001.dat'
    with open(filename, 'rb') as f:
        # 解析为16进制数据
        bs = ['{:0>2X}'.format(x) for x in f.read()]
        # 将每一个数据分别转化为10进制数据，并存入voiceArray
        for x in bs:
            # print(x)
            x = int(x, 16)
            voiceArray.append(x)

    # 将voiceArray转化为np的数组
    data = np.array(voiceArray)
    print(data.shape)
    # 创建一个二维空数组
    data_true = [[] for i in range(9)]
    # 数据按节点个数分成head个分别存入二维空数组的各行
    for i in range(9):
        data_true[i] = data[i::9]
    # 将二维空数组重新整合成np.array的形式
    data_true = np.array(data_true)
    print(data_true)
'''