{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "01CjUc4lG-U9",
"outputId": "6304f65e-0751-491a-beef-d60e45e8ab0b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Function 'filter_data' called 1 times. Execution time: 0.0000 seconds.\n",
"Function 'transform_data' called 1 times. Execution time: 0.0000 seconds.\n",
"Processed Data: 102, Running Average: 102.00\n",
"Processed Data: 104, Running Average: 103.00\n",
"Processed Data: 106, Running Average: 104.00\n",
"Processed Data: 108, Running Average: 105.00\n",
"Processed Data: 110, Running Average: 106.00\n",
"Processed Data: 112, Running Average: 107.00\n",
"Processed Data: 114, Running Average: 108.00\n",
"Processed Data: 116, Running Average: 109.00\n",
"Processed Data: 118, Running Average: 110.00\n",
"Processed Data: 120, Running Average: 111.00\n",
"Processed Data: 122, Running Average: 112.00\n",
"Processed Data: 124, Running Average: 113.00\n",
"Processed Data: 126, Running Average: 114.00\n",
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"Processed Data: 130, Running Average: 116.00\n",
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"Processed Data: 300, Running Average: 201.00\n",
"Processing stopped after 100 items.\n"
]
}
],
"source": [
"import time\n",
"\n",
"# 裝飾器：記錄函數使用次數和執行時間\n",
"def logger(func):\n",
" \"\"\"記錄函數的使用次數和執行時間\"\"\"\n",
" usage_count = 0 # 用來計算函數被調用的次數\n",
" def wrapper(*args, **kwargs):\n",
" nonlocal usage_count\n",
" usage_count += 1\n",
" start_time = time.time() # 記錄函數開始執行的時間\n",
" result = func(*args, **kwargs) # 執行被裝飾的函數\n",
" end_time = time.time() # 記錄函數結束執行的時間\n",
" print(f\"Function '{func.__name__}' called {usage_count} times. Execution time: {end_time - start_time:.4f} seconds.\")\n",
" return result # 返回函數的執行結果\n",
" return wrapper\n",
"\n",
"# 數據過濾生成器\n",
"@logger\n",
"def filter_data(data_stream, threshold):\n",
" \"\"\"\n",
" 過濾數據流中小於或等於閾值的數據\n",
" :param data_stream: 數據流生成器\n",
" :param threshold: 閾值\n",
" :yield: 大於閾值的數據\n",
" \"\"\"\n",
" for data in data_stream:\n",
" if data > threshold:\n",
" yield data\n",
"\n",
"# 數據轉換生成器\n",
"@logger\n",
"def transform_data(data_stream, transform_func):\n",
" \"\"\"\n",
" 對數據流中的每個數據進行轉換\n",
" :param data_stream: 數據流生成器\n",
" :param transform_func: 用於轉換數據的函數\n",
" :yield: 轉換後的數據\n",
" \"\"\"\n",
" for data in data_stream:\n",
" yield transform_func(data)\n",
"\n",
"# 運行平均值計算（閉包）\n",
"def running_average():\n",
" \"\"\"\n",
" 計算運行平均值的閉包函數\n",
" :return: 可用於更新並返回當前平均值的函數\n",
" \"\"\"\n",
" total = 0 # 累積總和\n",
" count = 0 # 數據個數\n",
" def avg(new_value):\n",
" nonlocal total, count\n",
" total += new_value\n",
" count += 1\n",
" return total / count # 返回當前平均值\n",
" return avg\n",
"\n",
"# 數據生成器：模擬實時數據流\n",
"def data_stream():\n",
" \"\"\"模擬生成連續數據流\"\"\"\n",
" for i in range(1, 1000): # 數據範圍從1到999\n",
" yield i\n",
"\n",
"# 數據處理管道\n",
"def data_pipeline(threshold, transform_func, max_items):\n",
" \"\"\"\n",
" 數據處理管道，整合數據過濾、轉換與即時平均值計算\n",
" :param threshold: 過濾的閾值\n",
" :param transform_func: 數據轉換函數\n",
" :param max_items: 最大處理數據個數\n",
" \"\"\"\n",
" avg_calc = running_average() # 初始化運行平均值閉包\n",
" processed_count = 0 # 已處理數據計數\n",
" for transformed_data in transform_data(filter_data(data_stream(), threshold), transform_func):\n",
" # 顯示處理後的數據和當前運行平均值\n",
" print(f\"Processed Data: {transformed_data}, Running Average: {avg_calc(transformed_data):.2f}\")\n",
" processed_count += 1\n",
" if processed_count >= max_items: # 當處理數據達到max_items時停止\n",
" print(f\"Processing stopped after {processed_count} items.\")\n",
" break\n",
"\n",
"# 主程式\n",
"if __name__ == \"__main__\":\n",
" # 設定閾值為50，數據轉換為乘以2，最多處理100筆數據\n",
" data_pipeline(threshold=50, transform_func=lambda x: x * 2, max_items=100)\n"
]
}
]
}