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
Temporal-Relational CrossTransformers

Temporal-Relational Cross-Transformers (TRX) This repo contains code for the method introduced in the paper: Temporal-Relational CrossTransformers for

83 Dec 12, 2022
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems - NAACL 2021

Fine-grained Post-training for Multi-turn Response Selection Implements the model described in the following paper Fine-grained Post-training for Impr

Janghoon Han 83 Dec 20, 2022
Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption

⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor

Lukas Hedegaard 21 Dec 22, 2022
The implementation of the paper "A Deep Feature Aggregation Network for Accurate Indoor Camera Localization".

A Deep Feature Aggregation Network for Accurate Indoor Camera Localization This is the PyTorch implementation of our paper "A Deep Feature Aggregation

9 Dec 09, 2022
PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency"

Improving Generation and Evaluation of Visual Stories via Semantic Consistency PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluat

Adyasha Maharana 28 Dec 08, 2022
Repository of our paper 'Refer-it-in-RGBD' in CVPR 2021

Refer-it-in-RGBD This is the repository of our paper 'Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images' in CVPR 2021 Pape

Haolin Liu 34 Nov 07, 2022
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

collie_recs Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Coll

ShopRunner 97 Jan 03, 2023
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
level1-image-classification-level1-recsys-09 created by GitHub Classroom

level1-image-classification-level1-recsys-09 ❗ 주제 설명 COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델 ?

6 Mar 17, 2022
A simple log parser and summariser for IIS web server logs

IISLogFileParser A basic parser tool for IIS Logs which summarises findings from the log file. Inspired by the Gist https://gist.github.com/wh13371/e7

2 Mar 26, 2022
A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Project Name: Which Bollywood Celebrity You look like A Deep learning based streamlit web app which can tell with which bollywood celebrity your face

BAPPY AHMED 20 Dec 28, 2021
[NeurIPS 2020] Official repository for the project "Listening to Sound of Silence for Speech Denoising"

Listening to Sounds of Silence for Speech Denoising Introduction This is the repository of the "Listening to Sounds of Silence for Speech Denoising" p

Henry Xu 40 Dec 20, 2022
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

LancoPKU 25 Dec 11, 2022
Does Pretraining for Summarization Reuqire Knowledge Transfer?

Pretraining summarization models using a corpus of nonsense

Approximately Correct Machine Intelligence (ACMI) Lab 12 Dec 19, 2022
Implementation of average- and worst-case robust flatness measures for adversarial training.

Relating Adversarially Robust Generalization to Flat Minima This repository contains code corresponding to the MLSys'21 paper: D. Stutz, M. Hein, B. S

David Stutz 13 Nov 27, 2022
Spatial Action Maps for Mobile Manipulation (RSS 2020)

spatial-action-maps Update: Please see our new spatial-intention-maps repository, which extends this work to multi-agent settings. It contains many ne

Jimmy Wu 27 Nov 30, 2022
Depth image based mouse cursor visual haptic

Depth image based mouse cursor visual haptic How to run it. Install pyqt5. Install python modules pip install Pillow pip install numpy For illustrati

Xiong Jie 17 Dec 20, 2022
Project for music generation system based on object tracking and CGAN

Project for music generation system based on object tracking and CGAN The project was inspired by MIDINet: A Convolutional Generative Adversarial Netw

1 Nov 21, 2021
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals

LapDepth-release This repository is a Pytorch implementation of the paper "Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals" M

Minsoo Song 205 Dec 30, 2022
Python Fanduel API (2021) - Lineup Automation

Southpaw is a python package that provides access to the Fanduel API. Optimize your DFS experience by programmatically updating your lineups, analyzin

Brandin Canfield 13 Jan 04, 2023