semvec/pypi-packages huggingface dataset:
ddcci-plasmoid-backend 0.1.10
Backend for ddcci-plasmoid
License: MIT
Requires Python: >=3.8,<4.0
Keywords: ddcci,brightmess,widget,kde,plasma

# KDE Plasma Widget for external monitor brightness adjustment

![Screenshot](https://raw.githubusercontent.com/davidhi7/ddcci-plasmoid/main/screenshots/banner.png)

* This widget allows you to adjust the brightness of external monitors. We accomplish that using [DDC/CI](https://en.wikipedia.org/wiki/Display_Data_Channel#DDC/CI), a protocol that allows your computer to control monitors and change options like the brightness or contrast.
* A seamless integration into the Plasma desktop is a major goal of this project. The widget is versatile and can be used as a standalone widget or integrated into the system tray.
* Notebook monitors are currently unsupported because they use different interfaces to communicate with the operating system.

## Requirements

* Python 3.8 or newer
* [fasteners](https://pypi.org/project/fasteners/)
* ddcutil 1.4.1 or newer (older versions may work but caused issues in the past)

## This package acts as the backend for the widget.
For a full installation guide, refer to the [GitHub repository](https://github.com/davidhi7/ddcci-plasmoid).
mni2mz3 1.0.0
Brain imaging surface mesh file format converter
License: MIT
Requires Python: >=3.7
Keywords: surface,mesh,MNI,obj,mz3

# mni2mz3

[![crates.io](https://img.shields.io/crates/v/mni2mz3?label=version)](https://crates.io/crates/mni2mz3)
[![MIT License](https://img.shields.io/github/license/fnndsc/mni2mz3)](https://github.com/FNNDSC/mni2mz3/blob/main/LICENSE)
[![test](https://github.com/FNNDSC/mni2mz3/actions/workflows/test.yml/badge.svg)](https://github.com/FNNDSC/mni2mz3/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/FNNDSC/mni2mz3/branch/master/graph/badge.svg?token=1YQRZWW95S)](https://codecov.io/gh/FNNDSC/mni2mz3)

Converts a file from MNI polygonal surface mesh format (`.obj`\*) or
vertex-wise data (e.g. curvature, cortical thickness, `*.txt`)
to [Surf-Ice MZ3 (`.mz3`)](https://github.com/neurolabusc/surf-ice/tree/master/mz3).
Useful for visualizing surfaces using [Surf-Ice](https://github.com/neurolabusc/surf-ice) or [NiiVue](https://github.com/niivue/niivue).

> [!WARNING]
> \*Not to be confused with Wavefront .obj, which is a different spec but with the same file extension.

## Installation

There are many ways to install and use `mni2mz3`. Linux, Mac, and Windows are supported.

### Using _ChRIS_

The easiest way to run `mni2mz3` is on [_ChRIS_](https://chrisproject.org), no installation needed.
Simply upload your data to a feed in https://app.chrisproject.org, then run `pl-mni2common`.

### Using Apptainer

`pl-mni2common` is a _ChRIS_ plugin wrapper for `mni2mz3`, which means you can
use its container image to run `mni2mz3`.

```shell
apptainer run docker://ghcr.io/fnndsc/pl-mni2common:latest mni2mz3 input.obj output.mz3
```

Call the wrapper script `mni2mz3` instead to do bulk processing on an input directory.

```shell
apptainer run docker://ghcr.io/fnndsc/pl-mni2common:latest mni2mz3 inputdir/ outputdir/
```

### Using [cargo-binstall](https://github.com/cargo-bins/cargo-binstall)

```shell
cargo binstall mni2mz3
```

### Using pip

```shell
pip install mni2mz3
```

### Manual Download

Select and download the right binary for your architecture and OS from GitHub Releases:
https://github.com/FNNDSC/mni2mz3/releases/latest

### Compile From Source

Install [Rust](https://rustup.rs), then run

```shell
cargo install mni2mz3
```

## Usage

```shell
# convert mesh
mni2mz3 surface_81920.obj surface.mz3

# convert data
mni2mz3 thickness.txt thickness.mz3
```

To do bulk conversions, use the [_ChRIS_ plugin wrapper](#using-chris).

## Details

- Output file will be gzip compressed.
- For surfaces, only triangle meshes are supported.
- For data, only 32-bit single-precision "float" is supported.

## Testing

It is recommended to install [cargo-nextest](https://nexte.st/).

```shell
cargo nextest run
```
geometric-algebra-attention 0.5.1
Library for geometric algebra-based attention mechanisms
License: MIT
Requires Python: >=3


[![PyPI](https://img.shields.io/pypi/v/geometric_algebra_attention.svg?style=flat)](https://pypi.org/project/geometric_algebra_attention/)
[![ReadTheDocs](https://img.shields.io/readthedocs/geometric_algebra_attention.svg?style=flat)](https://geometric_algebra_attention.readthedocs.io/en/latest/)

# Introduction

This is a library in development for implementing geometric algebra
attention mechanisms (as detailed in the paper [Geometric Algebra
Attention Networks for Small Point
Clouds](https://arxiv.org/abs/2110.02393) in tensorflow, keras,
pytorch, and jax.

# Installation

The latest tagged version of `geometric-algebra-attention` is
available on PyPI for installation via pip:

```
$ pip install geometric-algebra-attention
```

Alternatively, install the package from source:

```
$ git clone https://github.com/klarh/geometric_algebra_attention
$ pip install ./geometric_algebra_attention
```

# Documentation

The documentation is available as standard sphinx documentation:

```
$ cd doc
$ pip install -r requirements.txt
$ make html
```

Automatically-built documentation is available at
https://geometric-algebra-attention.readthedocs.io .

# Examples

Jupyter notebook examples for various backends are available in the
`examples` directory of the source repository.
flowws-keras-geometry 0.5.1
Stage-based scientific workflows for deep learning experiments with geometric algebra
License: MIT
Requires Python: >=3


# Flowws Modules for Geometric Algebra Attention Experiments

`flowws-keras-geometry` is a set of
[flowws](https://flowws.readthedocs.io) modules to train machine
learning models using geometric algebra operations in tensorflow and
keras. It contains modules for data sources, architectures for sample
tasks, and analysis and visualization of trained models.

## Installation

Install `flowws-keras-geometry` from source:

```
pip install git+https://github.com/klarh/flowws-keras-geometry.git#egg=flowws-keras-geometry
```

## API Documentation

Build the sphinx documentation from source:

```
git clone https://github.com/klarh/flowws-keras-geometry
cd flowws-keras-geometry/doc
pip install -r requirements.txt
make html
```
flowws-structure-pretraining 0.3.1
Stage-based scientific workflows for pretraining deep learning models on self-assembly data
License: MIT
Requires Python: >=3


# Flowws Modules for Self-supervised Pretraining

`flowws-structure-pretraining` is a set of
[flowws](https://flowws.readthedocs.io) modules to train machine
learning models on self-supervised pretraining tasks.
StringFuncs 0.0.3.217
A package for string manipulation, has
License: MIT

## StringFuncs README

This is more of a pratice package for general functions that I might use for basic string manipulation using python. I will be updating the package as I continue college. 

> That being said anybody can use the project and contrusctive critism is highly encouraged.

To access the functions simply install the package using: 

`pip install StringFuncs`

and import the functions like so:
 
`from StringFuncs import string_funcs`

You can now use the functions like so:

`print((string_funcs("This.is.a.Testing.String.3Cool3", separator=".")).chars_as_list())`

> or you can  

`print((string_funcs("Hello World!!!", separator=" ")).get_last_word())`

If you need help I have included doc strings detailing each function.
I am planning on adding more functions and improving existing functions as I go through college.
While this project is for string manipulation, I plan on adding a vast amount of functions that might prove useful.
nldesa 0.0.3
A package with tools to solve and analyse nonlinear differential equations Stability using DMD.
License: MIT
Requires Python: >=3.8
Keywords: differential equations,stability analysis,nonlinear systems,jax,odeint

# NLDESA - Nonlinear Differential Equation Stability Analysis

This is a Python package for the stability analysis of nonlinear differential equations using JAX [1] and pyDMD [2] [3].
The package is still in development and is not yet ready for use.

Authors: João Biu (joaopedrofbiu@tecnico.ulisboa.pt), Miguel Lameiras (miguel.lameiras@tecnico.ulisboa.pt)

[1] https://github.com/google/jax

[2] Demo et al., (2018). PyDMD: Python Dynamic Mode Decomposition. Journal of Open Source Software, 3(22), 530, https://doi.org/10.21105/joss.00530 (https://github.com/PyDMD/PyDMD.git)

## Installation
Using pip:
`pip install nldesa`

For developers
- Clone the repository
`cd NLDESA && pip install -e .`