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
Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

About this repository This repo contains an Pytorch implementation for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Netwo

wxDai 7 Oct 14, 2022
Diffusion Normalizing Flow (DiffFlow) Neurips2021

Diffusion Normalizing Flow (DiffFlow) Reproduce setup environment The repo heavily depends on jam, a personal toolbox developed by Qsh.zh. The API may

76 Jan 01, 2023
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
Volsdf - Volume Rendering of Neural Implicit Surfaces

Volume Rendering of Neural Implicit Surfaces Project Page | Paper | Data This re

Lior Yariv 221 Jan 07, 2023
DABO: Data Augmentation with Bilevel Optimization

DABO: Data Augmentation with Bilevel Optimization [Paper] The goal is to automatically learn an efficient data augmentation regime for image classific

ElementAI 24 Aug 12, 2022
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques Installation PyPI pip install colossalai Install

HPC-AI Tech 7.1k Jan 03, 2023
Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 07, 2023
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

36 Jan 05, 2023
Wide Residual Networks (WideResNets) in PyTorch

Wide Residual Networks (WideResNets) in PyTorch WideResNets for CIFAR10/100 implemented in PyTorch. This implementation requires less GPU memory than

Jason Kuen 296 Dec 27, 2022
Official implementation of Monocular Quasi-Dense 3D Object Tracking

Monocular Quasi-Dense 3D Object Tracking Monocular Quasi-Dense 3D Object Tracking (QD-3DT) is an online framework detects and tracks objects in 3D usi

Visual Intelligence and Systems Group 441 Dec 20, 2022
A repository for generating stylized talking 3D and 3D face

style_avatar A repository for generating stylized talking 3D faces and 2D videos. This is the repository for paper Imitating Arbitrary Talking Style f

Haozhe Wu 191 Dec 22, 2022
OBG-FCN - implementation of 'Object Boundary Guided Semantic Segmentation'

OBG-FCN This repository is to reproduce the implementation of 'Object Boundary Guided Semantic Segmentation' in http://arxiv.org/abs/1603.09742 Object

Jiu XU 3 Mar 11, 2019
A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

One-Stage Visual Grounding ***** New: Our recent work on One-stage VG is available at ReSC.***** A Fast and Accurate One-Stage Approach to Visual Grou

Zhengyuan Yang 118 Dec 05, 2022
Pytorch Implementation of PointNet and PointNet++++

Pytorch Implementation of PointNet and PointNet++ This repo is implementation for PointNet and PointNet++ in pytorch. Update 2021/03/27: (1) Release p

Luigi Ariano 1 Nov 11, 2021
👐OpenHands : Making Sign Language Recognition Accessible (WiP 🚧👷‍♂️🏗)

👐 OpenHands: Sign Language Recognition Library Making Sign Language Recognition Accessible Check the documentation on how to use the library: ReadThe

AI4Bhārat 69 Dec 12, 2022
A modular domain adaptation library written in PyTorch.

A modular domain adaptation library written in PyTorch.

Kevin Musgrave 225 Dec 29, 2022
A framework that allows people to write their own Rocket League bots.

YOU PROBABLY SHOULDN'T PULL THIS REPO Bot Makers Read This! If you just want to make a bot, you don't need to be here. Instead, start with one of thes

543 Dec 20, 2022
Official PyTorch Implementation of Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition, ICCV 2021

Official PyTorch Implementation of Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition, ICCV 2021

26 Dec 07, 2022
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Thomas Neumann 117 Nov 27, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022