写一段python代码，读取一个csv文件，该文件没有表头，第一列为时间编号,编号以1,2,3……这样自然数的形式出现，第二列为充电量数值，将该数据集的前70%划分为训练集，其余部分设置为测试集，将该数据集所构成的时间序列利用EMD分解的方法进行分解，分解出的子序列中频率最高的3个子序列叠加后用lstm模型进行训练，其余的低频子序列叠加后用svr模型进行训练，然后用stacking方法的集成学习对总体利用lstm模型进行训练，并对总序列进行预测，并打印rmse作为评价指标，并分别绘制训练集的真实值和预测值图片以及测试集的真实值和预测值图片，两张图片的横坐标单位为time/h，纵坐标单位为kwh

好的，以下是实现上述任务的代码：

import numpy as np
import pandas as pd
from PyEMD import EMD
from sklearn import svm
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import LSTM, Dense
from matplotlib import pyplot as plt
from sklearn.ensemble import StackingRegressor

# 读取csv文件
data = pd.read_csv('data.csv', header=None)

# 划分训练集和测试集
train_size = int(len(data) * 0.7)
train_data = data[:train_size]
test_data = data[train_size:]

# 对数据进行EMD分解
emd = EMD()
imfs = emd(train_data[1].values)

# 找到频率最高的三个子序列
freqs = []
for i in range(len(imfs)):
    freqs.append(np.abs(np.fft.fft(imfs[i])))

freqs = np.array(freqs)
top_freqs = freqs.argsort()[::-1][:3]

# 叠加高频子序列用LSTM模型进行训练
X_train, y_train = [], []
for i in range(len(train_data) - 1):
    X_train.append(imfs[top_freqs, i])
    y_train.append(train_data[1].iloc[i + 1])

X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

model_lstm = Sequential()
model_lstm.add(LSTM(50, input_shape=(X_train.shape[1], 1)))
model_lstm.add(Dense(1))
model_lstm.compile(loss='mean_squared_error', optimizer='adam')
model_lstm.fit(X_train, y_train, epochs=50, batch_size=32)

# 叠加低频子序列用SVR模型进行训练
X_train, y_train = [], []
for i in range(len(train_data) - 1):
    X_train.append(np.sum(imfs[top_freqs[-1]:, i]))
    y_train.append(train_data[1].iloc[i + 1])

X_train, y_train = np.array(X_train), np.array(y_train)
model_svr = svm.SVR(kernel='linear', C=1.0, epsilon=0.2)
model_svr.fit(X_train.reshape(-1, 1), y_train)

# 集成学习
X_test = test_data[1].values[:-1]
y_test = test_data[1].values[1:]
X_test_imfs = emd(X_test)
X_test_high = np.sum(X_test_imfs[top_freqs], axis=0)
X_test_low = np.sum(X_test_imfs[top_freqs[-1]:], axis=0)

y_pred_high = model_lstm.predict(X_test_high.reshape(1, -1, 1)).flatten()
y_pred_low = model_svr.predict(X_test_low.reshape(-1, 1)).flatten()

estimators = [('lstm', model_lstm)]
reg = StackingRegressor(estimators=estimators, final_estimator=model_lstm)
X_train_imfs = emd(train_data[1].values)
X_train_high = np.sum(X_train_imfs[top_freqs], axis=0)
X_train_low = np.sum(X_train_imfs[top_freqs[-1]:], axis=0)
X_train_high = X_train_high[:-1].reshape(1, -1, 1)
X_train_low = X_train_low[:-1].reshape(-1, 1)
y_train = train_data[1].values[1:]
reg.fit([X_train_high, X_train_low], y_train)

y_pred = reg.predict([y_pred_high.reshape(1, -1, 1), y_pred_low.reshape(-1, 1)]).flatten()

# 计算RMSE
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print('RMSE:', rmse)

# 绘制训练集和测试集的真实值和预测值图像
plt.plot(train_data[0], train_data[1], label='True Train')
plt.plot(train_data.iloc[1:, 0], model_lstm.predict(X_train).flatten(), label='Pred Train')
plt.plot(test_data[0], test_data[1], label='True Test')
plt.plot(test_data.iloc[1:, 0], y_pred, label='Pred Test')
plt.xlabel('Time/h')
plt.ylabel('kwh')
plt.legend()
plt.show()

需要注意的是，由于该任务涉及到多个模型的训练和集成，代码实现较为复杂，这里只提供一个大致的框架，需要根据具体情况进行调整和优化。