Incomplete easy-to-use math solver and PDF generator.

Overview

Math Expert

Let me do your work

Preview

preview.mp4

Introduction

Math Expert is our (@salastro, @younis-tarek, @marawn-mogeb) math high school graduation project. The project tackles the problem of generating beautiful, quick, and useful mathematics. While most software can either only generate beautiful formatted PDF (i.e. LaTeX) or sufficiently solve mathematical problems (e.g. Wolfram|Alpha). There may be, however, alternatives to these tools, yet they can not fully grasp the potential of either of them or are slow and hard to use. Therefore, this project tries to do what others failed.

Inner Workings

Our approach was to create an easy-to-use graphical user interface (GUI) that uses different components to reach our goal. LaTeX is the main PDF generation backend due to its indubitable abilities and speed; it is the universal standard for mathematical notation. However, it is reasonably hard to use making it difficult to use in a short-term practical context. This makes the application even more useful. It was mainly interacted with through PyLaTeX; it provides a usable set of commands that make use of LaTeX's capabilities. The standard article document class with numbered math alignment environment and TikZ drawings was used.

Although both SymPy and NumPy were used, the focus was on SymPy due to its nature of symbolic manipulation and its alignment with the goals of the project. The latter is powerful in mathematical evaluations, which — although supported — is not the focus of this project. The results of all functions, other than Evaluate, are performed through SymPy. It provides more than one function to perform some of the operations at hand, but the one that proves to be the most effective is used. For example, there are integrate and manualintegrate, and although the latter can show steps (non-human-readable), the former was chosen for its wide variety of solutions.

Unlike the previous two, the choice of a GUI framework was not a straightforward decision. Kivy was a serious candidate, but due to its own unique syntax for designing being its bedrock and lack of some convenient Python capabilities use, it was not feasible. Another option was Tkinter, which is considered the main framework for Python. However, it is very lacking some modern UI design features and is not plain sailing in some considerable aspects. The final option was PyQt5, which is a Python binding for the Qt cross-platform framework. Basically utilizing all of the powerful aspects of the Qt framework, which avoids the aforementioned limitations, while maintaining a usable toolkit. A highly programmable interface that is easily integrable into other environments was the product of this decision.

Philosophy

Although the infamous it just works are spread throughout the codebase, which is not ideal for a structured project, the goal of our is to define a great code that follows best practices (e.g. PEP 8) to have a readable, maintainable, and legacy-free codebase to stand in the way of passing time for the longest. As such, a minimal amount of code is necessary to avoid using breakable functions; a suitable modus operandi is the suckless philosophy. On the other hand, extensibility and customizability are as important. Unix philosophy is the one method that is well-regarded as the jewel in the crown.

Object orientation was used due to its inheritance, encapsulation, and other proprieties; some of which can not be achieved through modularity alone resulting in a more complex codebase. In addition, the structural way PyLaTeX handles documents would make it even harder to avoid object-oriented programming, despite its known disadvantages. However, parts of SymPy and NumPy code were written more procedurally.

Codebase

As mentioned above, the goals of the code are minimalism, functionality, extensibility, and customizability. The program is divided into 3 separate files: gui.py for all of the UI elements, func.py for all the operations on documents, and main.py for the main program and linking of the two. This was to ease the switching of undesired modules and separate development based on the working context.

func.py was mainly structured as one class (MathDoc) with multiple methods for document manipulation (e.g. Inte, Diff, etc). The class is instantiated and used in main.py in the __name__ == "__main__" if statement after the imports outside the conditional; on every button click the corresponding method is called. In contrast, explicit mentions of gui.py are rare (besides the linkage ones) since all of its handlings is in the file itself.

Properties

Advantages

  • Easy-to-use
  • Fast
  • Accurate
  • Concise Formatting
  • Extensible
  • Programmable

Disadvantages

  • poor error handling
  • limited syntax input
  • limited operations
  • no-preview before add
  • undoable actions

Neutral

  • Unappealing UI
  • No indication when unsolvable

Future plans

See issues

Usage

Although the interface is obvious, some clarifications may need to be made.

  • First text input is the file name without extension
  • Second text input is the document title
  • Third text input is the author(s) title
  • Fourth (and last) text input is the mathematical expression to be operated on
    • Euler's number should be written as exp(x) instead of e^(x)
    • log is the natural logarithm.
    • Multiplication should be written in the form 2*x
  • After defining all the previous inputs, click Generate PDF
  • Choose the type of operation you want to perform, then click Generate PDF again

Dependences

Building

Running

COCOMO estimates

Using scc

───────────────────────────────────────────────────────────────────────────────
Language                 Files     Lines   Blanks  Comments     Code Complexity
───────────────────────────────────────────────────────────────────────────────
Python                       4       465       36        24      405         17
───────────────────────────────────────────────────────────────────────────────
Total                        4       465       36        24      405         17
───────────────────────────────────────────────────────────────────────────────
Estimated Cost to Develop (organic) $10,457
Estimated Schedule Effort (organic) 2.431055 months
Estimated People Required (organic) 0.382159
───────────────────────────────────────────────────────────────────────────────
Processed 19228 bytes, 0.019 megabytes (SI)
───────────────────────────────────────────────────────────────────────────────
Comments
  • Better logs

    Better logs

    They look ugly and increase loc with a lot of repetition. I am not used to using it so it will take me some time until I discover how to get around the logs.

    enhancement 
    opened by salastro 2
  • Code refactoring

    Code refactoring

    Some aspects of the code are written badly.

    For reference:

    • https://stackoverflow.com/questions/20873259/pyqt-how-to-dynamically-update-widget-property-on-outer-variable-value-change
    enhancement 
    opened by salastro 2
  • Automated tests

    Automated tests

    They basically exist in the __main__ in func.py For reference:

    • https://www.youtube.com/watch?v=DhUpxWjOhME
    • https://stackoverflow.com/questions/27954702/unittest-vs-pytest
    • https://docs.python.org/3/library/unittest.html
    • https://docs.pytest.org/
    enhancement 
    opened by salastro 1
  • Better define methods in `main.py`

    Better define methods in `main.py`

    Currently, exec is used, which is very unpythonic and inefficient. There should be a way to get all the functions in the func.py and link them accordingly.

        operations = ["inte", "diff", "lim", "fact", "sol",
                      "simp", "eval", "plot", "generate_pdf", "generate_latex"]
    
        for func in operations:
            exec(f"""
                \[email protected]()
                \ndef on_{func}_bt_clicked(self):
                \n    self.mathdoc.{func}(self.expression.toPlainText().\
                    replace(" ", ""))
            """)
    
    bug 
    opened by salastro 1
  • Show steps

    Show steps

    Since the functions that do not operate on a human level are more advanced in solving problems, it would be great if it is possible to check if the problem is solvable with human steps and then use the function that shows the results. Mainly integration and differentiation are my concern.

    enhancement 
    opened by salastro 0
  • Integration hangs

    Integration hangs

    Sometimes when integration is unsolvable (even by other more advanced calculators) it just halts.

    Possible solutions are:

    • restricting the computational resources of the process
    • adding a timeout (e.g. of 10 seconds) for the process
    • creating an external watchdog for the management of the program flow
    bug 
    opened by salastro 1
  • LaTeX dependency

    LaTeX dependency

    Tests on my machine went smoothly on both Linux and Windows. However, it was not as smooth for both @marawan-mogeb and @younis-tarek. The only unique thing in my setup on both OSs is having LaTeX (texlive-full on void Linux and MikTeX on Windows) while both do not. The size of a TeX distro is yet to be confirmed, but since there is no advanced PDF formatting (e.g. Arabic support) TinyTeX should work. @younis-tarek plans to further test this in the future.

    bug documentation 
    opened by salastro 2
Releases(0.2)
Owner
SalahDin Ahmed
A computers magician who uses simple spells.
SalahDin Ahmed
DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

DI-HPC: Decision Intelligence - High Performance Computation DI-HPC is an acceleration operator component for general algorithm modules in reinforceme

OpenDILab 185 Dec 29, 2022
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)

GraspNet Baseline Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020). [paper] [dataset] [API] [do

GraspNet 209 Dec 29, 2022
Image morphing without reference points by applying warp maps and optimizing over them.

Differentiable Morphing Image morphing without reference points by applying warp maps and optimizing over them. Differentiable Morphing is machine lea

Alex K 380 Dec 19, 2022
Simple Tensorflow implementation of "Adaptive Convolutions for Structure-Aware Style Transfer" (CVPR 2021)

AdaConv — Simple TensorFlow Implementation [Paper] : Adaptive Convolutions for Structure-Aware Style Transfer (CVPR 2021) Note This repository does no

Junho Kim 26 Nov 18, 2022
Arch-Net: Model Distillation for Architecture Agnostic Model Deployment

Arch-Net: Model Distillation for Architecture Agnostic Model Deployment The official implementation of Arch-Net: Model Distillation for Architecture A

MEGVII Research 22 Jan 05, 2023
Bootstrapped Representation Learning on Graphs

Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri

NerDS Lab :: Neural Data Science Lab 55 Jan 07, 2023
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
Code for MarioNette: Self-Supervised Sprite Learning, in NeurIPS 2021

MarioNette | Webpage | Paper | Video MarioNette: Self-Supervised Sprite Learning Dmitriy Smirnov, Michaël Gharbi, Matthew Fisher, Vitor Guizilini, Ale

Dima Smirnov 28 Nov 18, 2022
Code for "Unsupervised State Representation Learning in Atari"

Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This

Mila 217 Jan 03, 2023
This is a collection of all challenges in HKCERT CTF 2021

香港網絡保安新生代奪旗挑戰賽 2021 (HKCERT CTF 2021) This is a collection of all challenges (and writeups) in HKCERT CTF 2021 Challenges ID Chinese name Name Score S

10 Jan 27, 2022
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation(mCOLT/mRASP2), ACL2021

Contrastive Learning for Many-to-many Multilingual Neural Machine Translation(mCOLT/mRASP2), ACL2021 The code for training mCOLT/mRASP2, a multilingua

104 Jan 01, 2023
This repository is a basic Machine Learning train & validation Template (Using PyTorch)

pytorch_ml_template This repository is a basic Machine Learning train & validation Template (Using PyTorch) TODO Markdown 사용법 Build Docker 사용법 Anacond

1 Sep 15, 2022
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
Rethinking Nearest Neighbors for Visual Classification

Rethinking Nearest Neighbors for Visual Classification arXiv Environment settings Check out scripts/env_setup.sh Setup data Download the following fin

Menglin Jia 29 Oct 11, 2022
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

PowerGridworld provides users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training fr

National Renewable Energy Laboratory 37 Dec 17, 2022
Awesome Long-Tailed Learning

Awesome Long-Tailed Learning This repo pays specially attention to the long-tailed distribution, where labels follow a long-tailed or power-law distri

Stomach_ache 284 Jan 06, 2023
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Dirk Neuhäuser 6 Dec 08, 2022
Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

Point-BERT: Pre-Training 3D Point Cloud Transformers with Masked Point Modeling Created by Xumin Yu*, Lulu Tang*, Yongming Rao*, Tiejun Huang, Jie Zho

Lulu Tang 306 Jan 06, 2023
Action Segmentation Evaluation

Reference Action Segmentation Evaluation Code This repository contains the reference code for action segmentation evaluation. If you have a bug-fix/im

5 May 22, 2022
This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting

1 MAGNN This repo is the official implementation for Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting. 1.1 The frame

SZJ 12 Nov 08, 2022