import time def timing_decorator(original_function): @functools.wraps(original_function) def wrapper(*args, **kwargs): start_time = time.time() result = original_function(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Function '{original_function...
importthreadingimporttimedefdo_work():print("Thread starting.")time.sleep(1)# 模拟耗时操作print("Thread finishing.")if__name__=="__main__":threads=[]foriinrange(3):t=threading.Thread(target=do_work)threads.append(t)t.start()fortinthreads:t.join()# 等待所有线程完成print("All threads ...
import timedefsay_hello(): start_time = time.time() print("Hello, world!") end_time = time.time() execution_time = end_time - start_time print(f"Function say_hello executed in {execution_time} seconds")这样做确实可以实现我们的需求，但是这种改法第一太侵入式（修改原有...
Obtain the time before a function and again immediately after the function to calculate the execution time. Repeat this with each function to determine which function is taking the longest to execute. Below is an example of timing a DAQmx write. import nidaqmximport datetimewith nidaqmx.Task()...
Decorator that reports the execution time.''' pass @timethis defcountdown(n):whilen>0:n-=1 语法糖@标识了装饰器。 好了,让我们回到刚才的例子。我们将用装饰器做一些更典型的操作: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importtime ...
if callable(attribute): # Check if the attribute is a callable (e.g., a method) def wrapped_method(*args, **kwargs): start_time = time.time() result = attribute(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time ...
Toolformeasuring execution timeofsmall code snippets. Thismoduleavoids a numberofcommon trapsformeasuring execution times. See also Tim Peters' introduction to the Algorithms chapter inthe Python Cookbook, publishedbyO'Reilly.Library usage: see the Timerclass. ...
Function execution time: {end_time - start_time}{suffix}")returnresultreturnwrapperreturndecorator# 创建一个带参数的装饰器实例log_time_decorator=partial(log_decorator,prefix="[INFO] ",suffix=" seconds\n")@log_time_decoratordefslow_function(n):time.sleep(n)returnn*n# 调用函数slow_function(2)...
There are several advantages.One is performance: knowing that a string is immutable means we can allocate space for it at creation time, and the storage requirements are fixed and unchanging. This is also one of the reasons for the distinction between tuples and lists.Another advantage is that...
There are several advantages.One is performance: knowing that a string is immutable means we can allocate space for it at creation time, and the storage requirements are fixed and unchanging. This is also one of the reasons for the distinction between tuples and lists.Another advantage is that...