How can slice the time-series data with multi-feat

Question

I have the formatted dataset which looks like a matrix[NxM] where N = 40 total number of cycles(time-stamps) and M = 1440 pixels. For every cycle, I have 1440 pixel values corresponding to 1440 pixels. I've used different models to predict pixel values of future cycles based on past 10 cycles.

The problem is I couldn't achieve proper continuous plot after training NN most probably due to bad data split technique I've used via train_test_split but never tried by TimeSeriesSplit as following :

trainX, testX, trainY, testY = train_test_split(trainX,trainY, test_size=0.2 , shuffle=False) 

1st problem is considering I've used shuffle=False and expect that 0.2 of from end of data will be considered as test data and I can get plot them correctly but I couldn't because: 1) unfortunately I miss 10 cycles because of history function def create_dataset() which check the the past 10 cycles to forecast the future one. as you can see here:

def create_dataset(dataset,data_train,look_back=1):
    dataX,dataY = [],[]
    print("Len:",len(dataset)-look_back-1)

    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), :]
        dataX.append(a)
        dataY.append(data_train[i + look_back,  :])
    return np.array(dataX), np.array(dataY)

look_back = 10
trainX,trainY = create_dataset(data_train,Y_train, look_back=look_back)

2) I also can't access the number of cycles which are considered as test data , therefore when I plot it starts from 0 ! instead of continue from end cycle of train data!

my expectation result after right data slice is something cen led me to catch following continuous plot which I made manually by paint in Windows 7:

Any advice would be greatly appreciated.