aowangphilly / axiomathbf

A PyPi Mathematical Aid Package for Multivariate Calculus (MATH 200)

Axiomathbf is a Mathematical Aid package for Multivariate Calculus, used mainly for Jupyter Notebook due to outputs mainly are in Latex. But users can solve Multivariate Calculus problems using any Python environment. Axiomathbf can find the relative and absolute extrema of a function to calculating the gradient and directional derivative of a function.

I decided to create Axiomathbf when I began Math 200 at Drexel University in the winter. As a freshman, I wanted to implement what I learned from my CS courses. And math was what started my CS journey when I first programmed my TI-84.

Table of Content

• Technologies Used
• Installation
• How Does it Work?
• User Manual
• Further Goals
• License

Technologies Used

• Python
• Sympy - A computer algebra system written in pure Python
• Jupyter Notebook - an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text

Installation

git clone [email protected]:ow-wow-wang/axiomathbf.git

pip install -r requirements.txt

How Does it Work?

• For each module, I created a class related to the topic of the chapters, like creating the VectorFunction class to solve vector-related problems in chapters 11-12 in the Calculus Early Transcendentals textbook, which talk about Three-Dimensions Space, Vectors, and Vector-Valued Functions.
• I mainly used Sympy to derive, integrate, and other operations to solve these problems
• I also used OOP-design, as it allows me to write more clean code

Documentation

Find the documentation at https://aowangphilly.github.io/axiomathbf to learn how to use Axiomathbf

Further Goals

• I hope to create a Flask web application, so people who aren't familiar with programming can still solve MV problems
• I also want to create an mobile app that solves Linear Algebra problems using Sympy and OpenCV

License

• MIT

axiomathbf's Issues

Refactor multivariate_calculus.py:chain_rule(f,xf,yf,zf,respect,s)

I've selected multivariate_calculus.py:chain_rule(f,xf,yf,zf,respect,s) for refactoring, which is a unit of 16 lines of code and 6 parameters. Addressing this will make our codebase more maintainable and improve Better Code Hub's Keep Unit Interfaces Small guideline rating! 👍

Here's the gist of this guideline:

• Definition 📖
  Limit the number of parameters per unit to at most 4.
• Why❓
  Keeping the number of parameters low makes units easier to understand, test and reuse.
• How 🔧
  Reduce the number of parameters by grouping related parameters into objects. Alternatively, try extracting parts of units that require fewer parameters.

You can find more info about this guideline in Building Maintainable Software. 📖

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ℹ️ To know how many other refactoring candidates need addressing to get a guideline compliant, select some by clicking on the 🔲 next to them. The risk profile below the candidates signals (✅) when it's enough! 🏁

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Good luck and happy coding! ✨ 💯