KoopmanESN/TrajTrans/StateOnly/DeepKoopMan/DataProcess.py

import numpy as np
import torch

DataFileName = './DataLib/Data.csv'
Data = np.loadtxt(DataFileName, delimiter=',')

NumState = 13
NumControl = 3

Data = Data[:, :NumState]

# 标准化
DataMax = np.max(Data, axis=0)
DataMin = np.min(Data, axis=0)
DataNor = 2*(Data-DataMin)/(DataMax-DataMin)-1
DataMaxMin = np.vstack([DataMax, DataMin])

np.savetxt('./DataLib/DataMaxMin.csv', DataMaxMin, delimiter=',')

# 数据间隔固定为10s
deltat = 100
# 每个sample的长度为51，对应相邻两个时序5000s
seqlen = 51

# 数据特征
NumSample = DataNor.shape[0]
NumFea = DataNor.shape[1]

NumSampleTensor = NumSample - seqlen + 1
print(NumSampleTensor)
DataTensor = torch.zeros([NumSampleTensor, seqlen, NumFea])
# print(DataTensor.shape)

for iterSample in range(NumSampleTensor):
    index_start = iterSample
    index_end = iterSample + seqlen
    DataThis = DataNor[index_start:index_end, :]
    DataThisTensor = torch.from_numpy(DataThis)
    # # print(index_start)
    # # print(index_end)
    # # print(DataThis.shape)
    DataTensor[iterSample, :, :] = DataThisTensor
    print(iterSample)

# 打乱
idx = torch.randperm(DataTensor.size(0))
shuffData = DataTensor[idx]

DataFileNameTensor = './DataLib/DataSeqTensor.pt'
torch.save(shuffData, DataFileNameTensor)