
Security News
Node.js TSC Declines to Endorse Feature Bounty Program
The Node.js TSC opted not to endorse a feature bounty program, citing concerns about incentives, governance, and project neutrality.
Provides a decorator to easily measure and optionally save the execution times.
ExecutionTimer is a utility class for measuring execution times of functions or methods in Python. It provides a decorator to easily measure and optionally save the execution times.
pip install timer-decorator
git clone https://github.com/vgilabert94/execution-timer
time_execution
return_measure
(bool, optional): Whether to return the measured time along with the function result (default is False).nanoseconds
(bool, optional): Whether to use nanoseconds resolution for timing measurements (default is False).n_iter
(int, optional): The number of iterations to execute the function/method (default is 1). If n_iter > 1 and return_measure=True: result of the function will be the last execution.return_average
(bool, optional): Whether to return the average time when measuring over multiple iterations (default is True). Only is used when n_iter > 1.return_measure
=False and return_average
=False: a message will be printed for each iteration.n_iter
> 1 and return_measure
=True: the result will be the last function result.n_iter
> 1 and return_measure
=True and return_average
=False: the result will be the last function result with a list of times for each iteration.ExecutionTimer
save_measure
(bool, optional): Flag to determine if measurement results should be saved (default is True).nanoseconds
(bool, optional): Flag to use nanoseconds resolution for timing measurements (default is False).n_iter
: (int, optional): Number of iterations to execute the function/method (default is 1).n_iter
> 1 and return_measure
=True: the result will be the last function result.n_iter
> 1 and return_measure
=False: the time printed will be the last execution result.time_execution
Decorator method to measure the execution time of a function or method.
func
(callable, optional): Function or method to be timed. If None, returns a decorator function.return_measure
(bool, optional): Flag to indicate if the measured time should be returned along with the function result.print_measure
(bool, optional): Flag to indicate if the measured time should be printed.Returns:
wrapper
(callable): Decorated function that measures the execution time of func
.get_measured_time
Retrieve the recorded execution times.
Returns:
dict
: Dictionary containing the measured execution times. Keys are function or method names, and values are lists of measured times.reset_measured_time
Reset the recorded execution times.
average_measured_time
Calculate the average execution times for all recorded functions or methods.
Returns:
dict
: A dictionary where keys are function or class names, and values are the average execution times in seconds. If no times are recorded, the value will be None.You can access a comprehensive examples notebook at the following link: examples/notebook.ipynb
Load packages:
import time
from execution_timer import ExecutionTimer, time_execution
timer = ExecutionTimer()
@timer.time_execution
def sample_function(n):
time.sleep(n)
print(sample_function(n=1))
print(timer.get_measured_time())
Output
None
{'sample_function': [1.0000783540003795]}
@time_execution
def sample_function(n):
time.sleep(n)
print(sample_function(n=1))
Output
The 'sample_function' function was executed in 1.00085 seconds.
None
timer = ExecutionTimer(n_iter=5)
@timer.time_execution
def sample_function(n):
time.sleep(n)
print(sample_function(n=1))
print(timer.get_measured_time())
print(timer.average_measured_time())
Output
The 'sample_function' function was executed in 1.00098 seconds.
None
{'sample_function': [1.0010039510007118, 1.001013477000015, 1.001078371999938, 1.0008607439995103, 1.0009819289998632]}
{'sample_function': 1.0009876946000076}
@time_execution(n_iter=5)
def sample_function(n):
time.sleep(n)
print(sample_function(n=1))
Output
The 'sample_function' function was executed in 1.00030 seconds.
None
timer = ExecutionTimer()
class SampleClass:
def __init__(self):
pass
@timer.time_execution(return_measure=True)
def sample_method(self, n):
time.sleep(n)
@timer.time_execution
def sample_method_x2(self, n):
time.sleep(2*n)
sample = SampleClass()
print(sample.sample_method(n=1))
print(sample.sample_method_x2(n=1))
print(timer.get_measured_time())
Output
(None, 1.0002362490004089)
The 'sample_method_x2' function was executed in 2.00203 seconds.
None
{'SampleClass': {'sample_method': [1.0002362490004089], 'sample_method_x2': [2.0020319710001786]}}
Distributed under the MIT License. See LICENSE.txt for more information.
FAQs
Provides a decorator to easily measure and optionally save the execution times.
We found that timer-decorator demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
The Node.js TSC opted not to endorse a feature bounty program, citing concerns about incentives, governance, and project neutrality.
Research
Security News
A look at the top trends in how threat actors are weaponizing open source packages to deliver malware and persist across the software supply chain.
Security News
ESLint now supports HTML linting with 48 new rules, expanding its language plugin system to cover more of the modern web development stack.