A Python library created to assist programmers with complex mathematical functions

Overview

libmaths

python License

libmaths was created not only as a learning experience for me, but as a way to make mathematical models in seconds for Python users using math in their code. With pre-programmed mathematical functions ranging from linear to sextic and more, graphing in your code will be a breeze.

Quick Demo


Installation

The package is available on PyPI. Install with:

pip install libmaths

or

pip3 install libmaths

libmaths only supports Python 3.8 and above, so please make sure you are on the newest version.

General Usage

There are many functions, but here is one example:

from libmaths import polynomial

After that, graphing a quadratic function is as simple as:

polynomial.quadratic(2, 4, 6)

If you need more assistance, examples are provided here.

General Information

libmaths was created by me, a 14-year old high schooler at Lynbrook High School 3 days ago on 2/20/2021. libmaths exists to help reduce the incapability to make quick and accurate models in Python within seconds. With a limited usage of external libraries and access to a multitude of functions, libmaths' variety is one of the many things that makes it unique. With the creation of this library, I hope to bring simplicity and accuracy together.

Documentation

I am currently working on getting the documentation out to a website. It will be added upon completion.

Mathematical Functions

The mathematical functions provided in libmaths are listed below:

  1. Graphable Functions

    • Linear
      • Slope Intercept Form
      • Point Slope Form
      • Constant
    • Polynomial
      • Standard Quadratic
      • Vertex Form Quadratic
      • Cubic
      • Quartic
      • Quintic
      • Sextic
    • Trigonometry
      • Sine
      • Cosine
      • Tangent
  2. Visualizeable Functions

    • Constant Graph
      • ReLU
      • Sigmoid
  3. Others

    • Output / Graphable Functions
      • Logarithmic
      • Absolute Value
      • Sigmoid -> Int Output
      • Relu -> Int Output
      • isPrime
      • isSquare
      • Divisor

Public References

r/Python : r/Python Post

Future Plans

In the future, I plan on adding several different complex functions.

Contributing

First, install the required libraries:

pip install -r requirements.txt

Please remember that I am a high school student with less than half a year of experience in Python programming. I already know you can do better than me! If you have any issues, suggestions, or requests, please feel free to contact me by opening an issue or on my linkedin which can be found in my profile page.

Thanks for contributing!

Resources

Over the three days spent in creating this library, I used plenty of resources which can be found in my code. You will see links under many of my functions which you can read about the concepts in.

Feedback, comments, or questions

If you have any feedback or something you would like to tell me, please do not hesitate to share! Feel free to comment here on github or reach out to me through [email protected]!

©Vinay Venkatesh 2021

You might also like...
Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Euro-Truck-Simulator-2-Lane-Assist Lane assist for ETS2, built with the ultra-fast-lane-detection model. This project was made possible by the amazing

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

An abstraction layer for mathematical optimization solvers.
An abstraction layer for mathematical optimization solvers.

MathOptInterface Documentation Build Status Social An abstraction layer for mathematical optimization solvers. Replaces MathProgBase. Citing MathOptIn

Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

Maths from examples - Learning advanced mathematical computations from examples This is the source code and data sets relevant to the paper Learning a

NaturalProofs: Mathematical Theorem Proving in Natural Language

NaturalProofs: Mathematical Theorem Proving in Natural Language NaturalProofs: Mathematical Theorem Proving in Natural Language Sean Welleck, Jiacheng

Framework that uses artificial intelligence applied to mathematical models to make predictions
Framework that uses artificial intelligence applied to mathematical models to make predictions

LiconIA Framework that uses artificial intelligence applied to mathematical models to make predictions Interface Overview Table of contents [TOC] 1 Ar

1st Solution For ICDAR 2021 Competition on Mathematical Formula Detection
1st Solution For ICDAR 2021 Competition on Mathematical Formula Detection

This project releases our 1st place solution on ICDAR 2021 Competition on Mathematical Formula Detection. We implement our solution based on MMDetection, which is an open source object detection toolbox based on PyTorch.

Official implementation for ICDAR 2021 paper "Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer"

Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer Description Convert offline handwritten mathematical expressi

PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集,包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

Comments
  • Updated logic in isPrime to stay consistent

    Updated logic in isPrime to stay consistent

    Comment says "from 2 to value / 2" however the code uses a loop that goes all of the way up to value. I updated the logic to be more consistent with the comment above it.

    opened by alecgirman 9
  • Use OOP to simplify code

    Use OOP to simplify code

    First and foremost, it's amazing to see a 14 year old writing a library. Keep up the good work, this is a great beginning! I hope this project gets traction, it could be very useful for school/college students for their maths assignment.

    In terms of the code, there are a few ways you could improve them. Making a polynomial class is probably more efficient and scalable than writing a function for every degree.

    How to write such class can be found at https://www.python-course.eu/polynomial_class_in_python.php

    TLDR : See the code below (taken from the page above)

    
    import numpy as np
    import matplotlib.pyplot as plt
    
    
    class Polynomial:
     
    
        def __init__(self, *coefficients):
            """ input: coefficients are in the form a_n, ...a_1, a_0 
            """
            self.coefficients = list(coefficients) # tuple is turned into a list
    
            
        def __repr__(self):
            """
            method to return the canonical string representation 
            of a polynomial.
       
            """
            return "Polynomial" + str(self.coefficients)
    
        
        def __call__(self, x):    
            res = 0
            for coeff in self.coefficients:
                res = res * x + coeff
            return res 
    
        
        def degree(self):
            return len(self.coefficients)   
    
        
        def __add__(self, other):
            c1 = self.coefficients[::-1]
            c2 = other.coefficients[::-1]
            res = [sum(t) for t in zip_longest(c1, c2, fillvalue=0)]
            return Polynomial(*res)
    
        
        def __sub__(self, other):
            c1 = self.coefficients[::-1]
            c2 = other.coefficients[::-1]
            
            res = [t1-t2 for t1, t2 in zip_longest(c1, c2, fillvalue=0)]
            return Polynomial(*res)
     
    
        def derivative(self):
            derived_coeffs = []
            exponent = len(self.coefficients) - 1
            for i in range(len(self.coefficients)-1):
                derived_coeffs.append(self.coefficients[i] * exponent)
                exponent -= 1
            return Polynomial(*derived_coeffs)
    
        
        def __str__(self):
            
            def x_expr(degree):
                if degree == 0:
                    res = ""
                elif degree == 1:
                    res = "x"
                else:
                    res = "x^"+str(degree)
                return res
    
            degree = len(self.coefficients) - 1
            res = ""
    
            for i in range(0, degree+1):
                coeff = self.coefficients[i]
                # nothing has to be done if coeff is 0:
                if abs(coeff) == 1 and i < degree:
                    # 1 in front of x shouldn't occur, e.g. x instead of 1x
                    # but we need the plus or minus sign:
                    res += f"{'+' if coeff>0 else '-'}{x_expr(degree-i)}"  
                elif coeff != 0:
                    res += f"{coeff:+g}{x_expr(degree-i)}" 
    
            return res.lstrip('+')    # removing leading '+'
    
    opened by subash774 1
  • fleshed out ArithmeticSeries and GeometricSeries classes

    fleshed out ArithmeticSeries and GeometricSeries classes

    Fixed an import error and fleshed out ArithmeticSeries and GeometricSeries classes. This could be a good demo for generators, class methods and inheritance for you. :)

    opened by atharva-naik 0
  • Opening new file series and adding Polynomial class to polynomial.py

    Opening new file series and adding Polynomial class to polynomial.py

    I have added a new file for series, which you can use to implement sin, cosine series, arithmetic, geometric, harmonic etc. types of series, and I have also added a polynomial class which I talked about in my reddit post. I have made comments that might help you understand classes a bit. Please feel free to contact me if you face any issues. Best of luck and keep it up !!

    opened by atharva-naik 0
Owner
Simple
14 year old programming enthusiast with a strong passion toward AI and Machine Learning.
Simple
NR-GAN: Noise Robust Generative Adversarial Networks

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Code and checkpoints for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling

Takuhiro Kaneko 59 Dec 11, 2022
Code for all the Advent of Code'21 challenges mostly written in python

Advent of Code 21 Code for all the Advent of Code'21 challenges mostly written in python. They are not necessarily the best or fastest solutions but j

4 May 26, 2022
This repository contains the code for TACL2021 paper: SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

SummaC: Summary Consistency Detection This repository contains the code for TACL2021 paper: SummaC: Re-Visiting NLI-based Models for Inconsistency Det

Philippe Laban 24 Jan 03, 2023
Keras implementation of AdaBound

AdaBound for Keras Keras port of AdaBound Optimizer for PyTorch, from the paper Adaptive Gradient Methods with Dynamic Bound of Learning Rate. Usage A

Somshubra Majumdar 132 Sep 23, 2022
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
CodeContests is a competitive programming dataset for machine-learning

CodeContests CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. It consists of pro

DeepMind 1.6k Jan 08, 2023
EMNLP'2021: Simple Entity-centric Questions Challenge Dense Retrievers

EntityQuestions This repository contains the EntityQuestions dataset as well as code to evaluate retrieval results from the the paper Simple Entity-ce

Princeton Natural Language Processing 119 Sep 28, 2022
Advances in Neural Information Processing Systems (NeurIPS), 2020.

What is being transferred in transfer learning? This repo contains the code for the following paper: Behnam Neyshabur*, Hanie Sedghi*, Chiyuan Zhang*.

Google Research 36 Aug 26, 2022
POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propagation including diffraction

POPPY: Physical Optics Propagation in Python POPPY (Physical Optics Propagation in Python) is a Python package that simulates physical optical propaga

Space Telescope Science Institute 132 Dec 15, 2022
Pytorch Lightning Implementation of SC-Depth Methods.

SC_Depth_pl: This is a pytorch lightning implementation of SC-Depth (V1, V2) for self-supervised learning of monocular depth from video. In the V1 (IJ

JiaWang Bian 216 Dec 30, 2022
Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
A PaddlePaddle implementation of STGCN with a few modifications in the model architecture in order to forecast traffic jam.

About This repository contains the code of a PaddlePaddle implementation of STGCN based on the paper Spatio-Temporal Graph Convolutional Networks: A D

Tianjian Li 1 Jan 11, 2022
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

75 Dec 29, 2022
This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

TreePartNet This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction". Depende

刘彦超 34 Nov 30, 2022
Label Hallucination for Few-Shot Classification

Label Hallucination for Few-Shot Classification This repo covers the implementation of the following paper: Label Hallucination for Few-Shot Classific

Yiren Jian 13 Nov 13, 2022
Image Fusion Transformer

Image-Fusion-Transformer Platform Python 3.7 Pytorch =1.0 Training Dataset MS-COCO 2014 (T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ram

Vibashan VS 68 Dec 23, 2022
NeurIPS-2021: Neural Auto-Curricula in Two-Player Zero-Sum Games.

NAC Official PyTorch implementation of NAC from the paper: Neural Auto-Curricula in Two-Player Zero-Sum Games. We release code for: Gradient based ora

Xidong Feng 19 Nov 11, 2022
Official pytorch code for SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal This is the official pytorch code for SSAT: A Symmetric Semantic-

ForeverPupil 57 Dec 13, 2022
NeuralDiff: Segmenting 3D objects that move in egocentric videos

NeuralDiff: Segmenting 3D objects that move in egocentric videos Project Page | Paper + Supplementary | Video About This repository contains the offic

Vadim Tschernezki 14 Dec 05, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022