根据上述数据建立，lstm-transformer模型，帮我写出python代码

基于给定的时间序列数据，我们可以构建一个结合LSTM（长短期记忆网络）和Transformer的模型。这里是一个基本的Python代码示例，使用Keras库，假设我们有pandas DataFrame `df`，其中包含时间序列数据（比如`year`, `total_precipitation` 和 `variability_index`列）：

# 导入所需库
import numpy as np
import pandas as pd
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, LSTM, Dense, Embedding, TransformerEncoder, TransformerEncoderLayer
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
from sklearn.preprocessing import MinMaxScaler

# 加载并预处理数据
df = pd.read_csv('your_data.csv')  # 替换为实际文件路径
scaler = MinMaxScaler()
df['total_precipitation'] = scaler.fit_transform(df[['total_precipitation']])
data = df.values.reshape(-1, 1)

# 定义序列长度和步长
sequence_length = 12  # 可能需要调整以适应数据
step = 1

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
train, test = data[:train_size], data[train_size:]

# 创建生成器
train_generator = TimeseriesGenerator(train, train, length=sequence_length, batch_size=32, stride=step)
test_generator = TimeseriesGenerator(test, test, length=sequence_length, batch_size=32, stride=step)

# 定义嵌入层
input_dim = len(df.columns) - 1  # 减去time列，因为它是索引
embedding_layer = Embedding(input_dim, 16, input_length=sequence_length)

# LSTM部分
lstm_input = embedding_layer(train[:, :-1])  # 输入序列，除去最后一个元素作为目标
lstm_output = LSTM(64)(lstm_input)

# Transformer部分
transformer_layers = TransformerEncoderLayer(d_model=64, num_heads=2, dropout=0.1)
transformer_encoder = TransformerEncoder(transformer_layers, sequence_length)

# 将LSTM输出展平后传入Transformer
flat_lstm_output = tf.keras.layers.Flatten()(lstm_output)
encoded_sequence = transformer_encoder(flat_lstm_output)

# 合并LSTM和Transformer的输出
combined = tf.keras.layers.concatenate([lstm_output, encoded_sequence])

# 输出层
output = Dense(1)(combined)  # 预测降水量

# 构建完整的模型
model = Model(inputs=lstm_input, outputs=output)

# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')

# 训练模型
model.fit(train_generator, epochs=50, validation_data=test_generator)

请注意，这只是一个基础框架，实际应用中可能需要调整超参数、优化方案或添加更多的层次结构。此外，你需要确保已安装所需的库（如tensorflow等）。