pytimers 2.1

Timing of functions, methods or block of codes made easy.

PyTimers

Python micro library for easy to use code timing with the elapsed times logged using the standard logging library.

Requires Python 3.6+.

How to Install

# pip install pytimers

Usage Example

The library allows you to measure the run time of your code in two way. Using decorators and using context manager to measure run time of any code block.

Decorator Timer

import logging
from time import sleep

from pytimers import timer


logging.basicConfig(level=logging.INFO)

@timer
def func(*args: int):
    print("Hello from func.")
    sleep(1)
    return sum(args)


if __name__ == '__main__':
    func(1, 2, 3)

Hello from func.
INFO:timer:Finished function func in 1.0018878109985963s.

PyTimers leverage python library wrapt to make sure decorators can be applied to all function, methods, static methods and also class methods while not changing the callable signature in any way.

Block Timer

To measure time of any code not enclosed in a callable object you can use timer context manager as follows.

import logging
from time import sleep

from pytimers import timer


logging.basicConfig(level=logging.INFO)

if __name__ == '__main__':
    with timer:
        print("Hello from code block.")
        sleep(1)

Hello from code block.
INFO:timer:Finished code block in 1.0027356049977243s.

Block of code can also be named to increase log readability.

import logging
from time import sleep

from pytimers import timer


logging.basicConfig(level=logging.INFO)

if __name__ == '__main__':
    with timer.named("data processing pipeline"):
        print("Hello from code block.")
        sleep(1)

Hello from code block.
INFO:timer:Finished data processing pipeline in 1.0051407059945632s.

Timer context manager also allows you to stack context managers freely without a worry of interference.

import logging
from time import sleep

from pytimers import timer


logging.basicConfig(level=logging.INFO)

if __name__ == '__main__':
    with timer.named("data collecting pipeline"):
        print("Hello from code block n.1.")
        sleep(1)
        with timer.named("data processing pipeline"):
            print("Hello from code block n.2.")
            sleep(1)

Hello from code block n.1.
Hello from code block n.2.
INFO:timer:Finished data processing pipeline in 1.0008160540019162s.
INFO:timer:Finished data collecting pipeline in 2.0029643359885085s.

Timer Custom Configuration

If you need to customize the log message template or logging level you can create your own timer using provided Timer class.

import logging
from time import sleep

from pytimers import Timer


logging.basicConfig(level=logging.INFO)

custom_timer = Timer(
    log_template="Processing ${name} took ${duration}s.",
    log_level=logging.WARNING,
)

@custom_timer
def func(*args: int):
    print("Hello from func.")
    sleep(1)
    return sum(args)


if __name__ == '__main__':
    func(1, 2, 3)

Hello from func.
WARNING:timer:Processing function func took 1.0028993980085943s.

Build Status

PyPi