PlainTools 1.3.241029.0

A library designed to streamline simple operations, loops and contexts.

Installation

This package is Python 3.11+ compatible and can be installed & upgraded using pip:

pip install -U plaintools

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Introduction

PlainTools is a Python 3.11+ library designed to introduce new features and fix awkward common interactions present in the Python native ecosystem.

Most functions are prefixed with p (as in pt.plist or pt.pround) to avoid homonyms. The p stands for Plain and are the namesake of this package.

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Examples

Some simple, yet relevant examples using the Plain Number(pt.pnumber) function are:

>>> import PlainTools as pt

>>> 0.1 * 3
0.30000000000000004

>>> pt.pnumber(0.1 * 3)
0.3 # Deals with float imprecision errors.
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>>> 5 / 3
1.6666666666666667

>>> pt.pnumber("5 / 3")
1.666... # '...' present only in string format; The true value is still float(5/3).
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>>> 1 / 73
0.0136986301369863

>>> pt.pnumber(1 / 73)
0.013698630136986301369863... # Can detect long chains of repeating decimals!
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>>> 0.9999999999999988
0.9999999999999988

>>> pt.pnumber(0.9999999999991234)
0.999999999999123 # No loss of precision up to 15 literal digits.
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>>> 0.00000000001 ** 3
9.999999999999999e-34

>>> pt.pnumber(0.00000000001 ** 3)
1e-33 # Corrects scientific-notated float imprecisions as well.
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>>> pt.pnumber("math.pi") # A safe variation of 'eval()' is used, as shown below!
3.14159265358979

# https://stackoverflow.com/a/1933463/26469850
>>> pt.pnumber("import shutil; shutil.rmtree('/.')") # Example of malicious use.
PlainTools.SEVAL.UnsafeError: Invalid syntax in expression
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• More about Seval (Safe Eval).

Simple, right?

Countless more examples can be found in the Library's Documentation Page, so please check it out!

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Resources & Credits

This project would be impossible to make without the support of my friends and family around me.

Thank you everyone.

• Credits & Thanks:
  • A big thanks to my professor Vitor Tocci, who lectured Introduction to Data Proccessing and introduced me into Python programming when I had little background experience in the matter.
  • Thanks to my beloved girlfriend Ana Caroline, who tirelessly heard me babble about Python through hours in these past few months where I was still learning and improving much of my understanding of the language. I love you!
  • Thanks to all of my friends and family, including @CherryGM who helped me revise this documentation and greatly encouraged me. And to everyone who helped me debug this documentation itself when I had zero Sphinx knowledge. I hope I did well enough and hope to do much more in the future!

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• Disclaimer: LLM (AI) Use:
  • ChatGPT, Codeium and Gemini (The later not credited as it did not "contribute" directly to the codebase) were used in this project development.
  • If you, your university or your company (in general, if the target for this library's use) does have any restrictions, implicit or explicit, against the use of LLMs in production | academic coding, please avert from using this library.
  • Any code "contributed" by or taken from any LLM (AI) use, prompted directly or indirectly, was heavily debugged and tested (to the best of my personal capacity). You will probably find 40~80% LLM-made code** wherever attributes from the following libraries were used:
    • re
    • ast
    • itertools
    • functools
    • multiprocessing
  • If any code comes across as sluggish, unnoptimized or just bad, please let me know by raising an issue or DMing me at GitHub @gabrielmsilva00, or by emailing me at [e-mail].

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• References & Auxiliary Material:
  • AutoPEP8, code formatting;
  • Sphinx, documentation;
  • Sphinx-Design, styling;
  • StackOverflow, definitions, concepts;
  • W3Schools, theories, fundamentals, methods;
  • OpenAI's ChatGPT, definitions, debugging;
  • Claude AI, additional debugging;
  • Codeium AI, autocompletion, code refactoring & cleaning;
  • SingleFile & SingleFile CLI, HTML refactoring of this Sphinx-generated documentation;
  • JetBrains Mono, this awesome font!

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