runtimeponyo

Runtime Ponyo

alexh212- Alex Hmitti

as13909 - Aaron Stein

Rafinator123 - Rafael Nadal-Scala

jk021227 - Jhon Kim

Times the execution of functions while providing users to save and display recorded runtimes.

Installation

Import the provided functions from main.py into your Python script. from package_main.main import *

Functionalities

@tracker When this decorator is applied to a function, Runtime Ponyo begins to time that functions execution.

@tracker_print When this decorator is applied to a function, Runtime Ponyo begins to time that functions execution and displays the runtime upon completion.

'print_all_runtimes()' All runtimes recorded are displayed.

This only print the runtimes of functions that have been completed and tracked prior to the call of this function.

save_to_file(file.txt) This function saves the recorded runtimes to a specified file. If the file doesn't exist, it will be created with the given name, and the runtime data will be written to it.

'entire_runtime()' Calculates and prints the total runtime of the entire program.

Usage

• To use @tracker_print, apply the decorator to a function.

@tracker
def  example_function():
	for  i  in  range(1000):
		i  +=  1

• To use @tracker_print, apply the decorator to a function.

@tracker_print
def  example_function():
	for  i  in  range(1000):
		i  +=  1

• Insert the print_all_runtimes() function at the desired location in your program to use it.

analyzed_data  = complex_data_analysis(pd.concat(processed_chunks))

print_all_runtimes() # Track and print function runtimes

log_operations(datetime.now())
print(f"Analyzed Data: \n{analyzed_data}")

• Insert the save_to_file() function at the desired location in your program to use it.

with ThreadPoolExecutor(max_workers=4) as  executor:
	future_to_chunk  = {executor.submit(advanced_data_transformation, chunk): chunk  for  chunk  in  chunks}


save_to_file(my_runtimes.txt) # Save recorded runtimes to a specified file

processed_data  = [future.result() for  future  in  future_to_chunk]

• Insert the 'entire_runtime()' function at the desired location in your program to use it.

data  = np.random.exponential(scale=1.0, size=(1000, 2))
df  = pd.DataFrame(data, columns=['id', 'values'])

chunks  = np.array_split(df, 4)

entire_runtime() # Print the total runtime of the program

Contributing

Here are the steps you need to take if you would like to contribute to the project.

• Fork and Clone the project repository

• Install pipenv if not already.

• Use pipenv to install all necessary development dependencies. pipenv install --dev

• Set up a new virtual environment using Python. python -m venv env

• Import the main package and the specific functions you wish to test into your test file (_tests.py located in package/tests).

• Execute your tests using pytest within the pipenv environment

Development Policy

• Python 2/3 compatibility
• PEP8-compliance (with exceptions)
• Tests should be passing

Package Link in PyPI

https://pypi.org/project/runtimeponyo/

A little exercise to create a Python package, build it, test it, distribute it, and use it. See instructions for details.