runtimeponyo 0.0.3

A pacakge that allows the user to record and print out neatly the runtime of function and also the entire program.

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.

1. Fork and Clone the project repository

2. Install pipenv if not already.

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

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

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

6. 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.