timer-decorator 0.1.1

Provides a decorator to easily measure and optionally save the execution times.

ExecutionTimer

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.

Installation

• Install via pip:

pip install execution-timer

• Install via git clone:

git clone https://github.com/vgilabert94/execution-timer

Documentation

Class: ExecutionTimer

Parameters

• 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).

Methods

time_execution(self, func: Callable[..., Any] = None, *, return_measure: bool = False) -> Union[Callable[..., Any], Any]

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(self) -> dict[str, Optional[float]]

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_timedef reset_measured_time(self) -> None:

Reset the recorded execution times.

average_measured_time(self) -> dict[str, Optional[float]]:

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.

Examples

You can access a comprehensive examples notebook at the following link: examples/notebook.ipynb

Load packages:

import time
from execution_timer import ExecutionTimer

Example 1: Measuring a function with the default settings.

timer = ExecutionTimer()

@timer.time_execution
def sample_function(n):
    time.sleep(n)

print(sample_function(n=2))
print(timer.get_measured_time())

Output

None
{'sample_function': [2.0000783540003795]}

Example 4: Measuring N iterations

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

None
{'sample_function': [1.0000926779994188, 1.0000929059988266, 1.00007422499948, 1.0001207340010296, 1.000119641999845]}
{'sample_function': 1.00010003699972}

Example 6: Measuring a Method from a class

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=2))
print(sample.sample_method_x2(n=2))
print(timer.get_measured_time())

Output

(None, 2.0000849130010465)
None
{'SampleClass': {'sample_method': [2.0000849130010465], 'sample_method_x2': [4.000076272001024]}}

LICENSE

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Vicent Gilabert

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