semvec/pypi-packages huggingface dataset:
gja 0.3
Gauss-Jordan assistant
Requires Python: >=3.8

# Gauss-Jordan Assistant (gja)

Enabling live demonstration of the Gauss-Jordan algorithm in a terminal/console.

## Installation

```
pip install gja
```

## Description

The idea of this project is to enable live demonstration of using the
Gauss-Jordan algorithm to solve systems of linear equations,
using a simple console.
This project is developed on Windows but should work using other operating systems.

![Example of row operation](screenshot.png "Example of row operation")

## Why?

I teach an introductory course in Linear Algebra by videoconference
to students who are not math majors; the focus is on calculations,
not proofs.
As I teach remotely, I share the content of my screen.
Many of the examples require, at one stage or another, that
Gaussian elimination on a matrix be performed.

In my experience, using slides with all the content
written up in advance (which is what I have mostly done until recently)
is not a good way to maintain student engagement.
My hand writing (either using a mouse or a tablet) on a computer screen
is absolutely atrocious, which makes it difficult to do "live" examples.

I have also found that a small fraction of students do not seem to understand
the basic step of Gaussian elimination, even when I attempt to
provide additional explanations via email.

This tool, which I named the Gauss-Jordan Assistant (or GJA),
allows me to do live computations on my screen in a legible way.
I have tried to design it so that each computation includes some
visual clues reinforcing some aspects of Gaussian elimination.

Using the GJA, I can ask students to suggest what should be the
next step in the computation, just as easily as I could
do it in a traditional classroom with a blackboard. If they suggest
something that is incorrect, rather than telling them up front,
I can type in their suggestion, observe the result, and follow with
relevant explanations go wrong.


## Bonus

It is possible to save the result of all the steps using LaTeX
format, for easy inclusion (without typos!) in any LaTeX document.

## Requirements

- Python 3.8+
- Rich (https://pypi.org/project/rich/).  Rich is a fantastic project.

To work properly on Windows (which is what I use), you need the following:

- Windows Terminal (https://github.com/Microsoft/Terminal), which you can
  get from the Microsoft Store.

![Available commands](help.png "Available commands")
cadishi 1.1.4
High performance distance histogram calculation framework for CPUs and GPUs

CADISHI
=======

Introduction
------------

CADISHI \-- CAlculation of DIStance HIstograms \-- is a software package
that enables scientists to compute (Euclidean) distance histograms
efficiently. Any sets of objects that have 3D Cartesian coordinates may
be used as input, for example, atoms in molecular dynamics datasets or
galaxies in astrophysical contexts. CADISHI drives the high-performance
kernels pydh (CPU) and cudh (GPU, optional) to do the actual histogram
computation. The kernels pydh and cudh are part of CADISHI and are
written in C++ and CUDA.

For more information, we refer to our publication:

K. Reuter, J. Koefinger; CADISHI: Fast parallel calculation of
particle-pair distance histograms on CPUs and GPUs; [Comp. Phys. Comm.
(236), 274 (2019)](https://doi.org/10.1016/j.cpc.2018.10.018).

A preprint of the paper is available on
[arXiv.org](https://arxiv.org/abs/1808.01478).

Documentation
-------------

Documentation is available at [http://cadishi.readthedocs.io/en/latest/
\<http://cadishi.readthedocs.io/en/latest/\>](). Alternatively, you may
access the local copy at [doc/html/index.html]{.title-ref} after having
cloned the repository.

License and Citation
--------------------

The CADISHI package is released under the permissive MIT license. See
the file [LICENSE.txt]{.title-ref} for details.

Copyright 2015-2019 Klaus Reuter (MPCDF), Juergen Koefinger (MPIBP)

In case you're using CADISHI for own academic or non-academic research,
we kindly request that you cite CADISHI in your publications and
presentations. We suggest the following citation as appropriate:

K. Reuter, J. Koefinger; CADISHI: Fast parallel calculation of
particle-pair distance histograms on CPUs and GPUs; Computer Physics
Communications (2018); \<<https://doi.org/10.1016/j.cpc.2018.10.018>\>.