列表2显示了汇总统计的主程序。可以看到，程序创建了summary_statistics类对象，然后调用其中的方法。该程序导入NumPy库以生成一维数组，并使用time模块中的clock()方法来计算程序的运行时间。
import time import numpy as np from class_summary_statistics import SummaryStatistics def main(one_dimensional_array): # 创建汇总统计类对象 summary_statistics = SummaryStatistics() # 计算观测数 number_observation = summary_statistics.calculate_number_observation(one_dimensional_array) print("Number of Observation: {} ".format(number_observation)) # 计算算术平均值 arithmetic_mean = summary_statistics.calculate_arithmetic_mean(one_dimensional_array, number_observation) print("Arithmetic Mean: {} ".format(arithmetic_mean)) # 计算中值 median = summary_statistics.calculate_median(one_dimensional_array, number_observation) print("Median: {} ".format(median)) # 计算样本标准差 sample_standard_deviation = summary_statistics.calculate_sample_standard_deviation(one_dimensional_array, number_observation, arithmetic_mean) print("Sample Standard Deviation: {} ".format(sample_standard_deviation)) if __name__ == '__main__': start_time = time.clock() one_dimensional_array = np.arange(100000000, dtype=np.float64) main(one_dimensional_array) end_time = time.clock() print("Program Runtime: {} seconds".format(round(end_time - start_time, 1)))列表2. 使用NumPy数组的汇总统计类对象代码
当行数达到1百万时，汇总统计主程序将得到以下结果：
观测数： 1000000 算术平均值： 499999.5 中值： 499999.5 样本标准差： 288675.27893349814 程序运行时间： 1.3秒结合asyncio异步库的NumPy数组
列表3显示了结合Python asyncio异步库的汇总统计asyncio类对象代码。请注意，main()方法使用calculate_number_observation()作为第一个也是唯一任务来启动事件循环异步进程。
import sys import time import traceback import asyncio from math import sqrt class SummaryStatisticsAsyncio(object): """ 使用asyncion库计算观测数、算术平均值、中值和样本标准差 """ def __init__(self): pass async def calculate_number_observation(self, one_dimensional_array): """ 计算观测数 :参数 one_dimensional_array: numpy一维数组 :返回值 空 """ try: print('start calculate_number_observation() procedure') await asyncio.sleep(0) number_observation = one_dimensional_array.size print("Number of Observation: {} ".format(number_observation)) print("观测数： {} ".format(number_observation)) await self.calcuate_arithmetic_mean(one_dimensional_array, number_observation) print("finished calculate_number_observation() procedure") except Exception: self.print_exception_message() async def calcuate_arithmetic_mean(self, one_dimensional_array, number_observation): """ 计算算术平均值 :参数 one_dimensional_array: numpy一维数组 :参数 number_observation: 观测数 :返回值 空 """ try: print('start calcuate_arithmetic_mean() procedure') await self.calculate_median(one_dimensional_array, number_observation) sum_result = 0.0 await asyncio.sleep(0) for i in range(number_observation): sum_result += one_dimensional_array[i] arithmetic_mean = sum_result / number_observation print("Arithmetic Mean: {} ".format(arithmetic_mean)) await self.calculate_sample_standard_deviation(one_dimensional_array, number_observation, arithmetic_mean) print("finished calcuate_arithmetic_mean() procedure") except Exception: self.print_exception_message() async def calculate_median(self, one_dimensional_array, number_observation): """ 计算中值 :参数 one_dimensional_array: numpy一维数组 :参数 number_observation: 观测数 :返回值 空 """ try: print('starting calculate_median()') await asyncio.sleep(0) one_dimensional_array.sort() half_position = number_observation // 2 if not number_observation % 2: median = (one_dimensional_array[half_position - 1] + one_dimensional_array[half_position]) / 2.0 else: median = one_dimensional_array[half_position] print("Median: {} ".format(median)) print("finished calculate_median() procedure") except Exception: self.print_exception_message() async def calculate_sample_standard_deviation(self, one_dimensional_array, number_observation, arithmetic_mean): """ 计算样本标准差 :参数 one_dimensional_array: numpy一维数组 :参数 number_observation: 观测数 :参数 arithmetic_mean: 算术平均值 :返回值 空 """ try: print('start calculate_sample_standard_deviation() procedure') await asyncio.sleep(0) sum_result = 0.0 for i in range(number_observation): sum_result += pow((one_dimensional_array[i] - arithmetic_mean), 2) sample_variance = sum_result / (number_observation - 1) sample_standard_deviation = sqrt(sample_variance) print("Sample Standard Deviation: {} ".format(sample_standard_deviation)) print("finished calculate_sample_standard_deviation() procedure") except Exception: self.print_exception_message() def print_exception_message(self, message_orientation = "horizontal"): """ 打印完整异常消息 :参数 message_orientation: 水平或垂直 :返回值 空 """ try: exc_type, exc_value, exc_tb = sys.exc_info() file_name, line_number, procedure_name, line_code = traceback.extract_tb(exc_tb)[-1] time_stamp = " [Time Stamp]: " + str(time.strftime("%Y-%m-%d %I:%M:%S %p")) file_name = " [File Name]: " + str(file_name) procedure_name = " [Procedure Name]: " + str(procedure_name) error_message = " [Error Message]: " + str(exc_value) error_type = " [Error Type]: " + str(exc_type) line_number = " [Line Number]: " + str(line_number) line_code = " [Line Code]: " + str(line_code) if (message_orientation == "horizontal"): print( "An error occurred:{};{};{};{};{};{};{}".format(time_stamp, file_name, procedure_name, error_message, error_type, line_number, line_code)) elif (message_orientation == "vertical"): print( "An error occurred:\n{}\n{}\n{}\n{}\n{}\n{}\n{}".format(time_stamp, file_name, procedure_name, error_message, error_type, line_number, line_code)) else: pass except Exception: pass def main(self, one_dimensional_array): """ 启动事件循环异步进程 :参数 one_dimensional_array: numpy一维数组 """ try: ioloop = asyncio.get_event_loop() tasks = [ioloop.create_task(self.calculate_number_observation(one_dimensional_array))] wait_tasks = asyncio.wait(tasks) ioloop.run_until_complete(wait_tasks) ioloop.close() except Exception:列表3. 结合Python异步库的汇总统计asyncio类对象代码