semvec/pypi-packages huggingface dataset:
DiscordOAuth-Helper 0.0.1
Helps with discord oauth2
Requires Python: >=3.7

# DiscordOAuth2
automatically follows through with the requests for Discord OAuth

***

## What I'm working on
- [x] Making the main function
  - [x] Make the auth url automatically, with the client_id, scopes, and a redirect uri
  - [x] Test the return of the url
  - [x] Add typing, docstrings, and follow [PEPs](https://peps.python.org) [8](https://peps.python.org/pep-0008/), [257](https://peps.python.org/pep-0257/), and [484](https://peps.python.org/pep-0484/)
    - [x] [PEP 8 (Style Guide For Python Guide)](https://peps.python.org/pep-0008/)
    - [x] [PEP 257 (Docstring Conventions)](https://peps.python.org/pep-0257/)
    - [x] [PEP 484 (Type Hints)](https://peps.python.org/pep-0484/)
- [ ] Succesfully upload to [PyPi.org](https://pypi.org)
pyconfman 0.1.0
Manage multiple and hierarchical configurations
Requires Python: >=3.10,<4.0

# Easy Configuration Manager for Python applications
## Configuration made easy

When you need some configuration, or you need sets of configuration 
to correspond to more stages such as development, test and production 
you may want to have a single configuration set and refer to it 
all over your application.

Then you may want to incorporate your set of configration buckets
into your CLI application.

## installation

### install the package on your environment
using pip:
```shell
$ pip install pyconfman
```

### Incorporate it into your application
If you use `click` library to manage your application CLI
there is already, `pyconfman` comes along with a simple function to add
the config group to your application's `cli.group`. 

For example, if your app has a command such as
```python
import click
@click.group()
def cli():
    """My amazing application"""

if __name__ == "__main__":
    cli()
```

you can incorporate the `confman` as following:

```python
import click
import pyconfman


@pyconfman.cli
@click.group()
def cli():
    """My amazing application"""


if __name__ == "__main__":
    cli()
```
This adds the configuration manager to your command line interface

## Usage and command line

```
$ myapp --help

Usage: myapp [OPTIONS] COMMAND [ARGS]...

Options:
  -C, --conf-dir TEXT  configuration directory: [~/.config/<myapp>]
  --help               Show this message and exit.

Commands:
  config  Read or modify configuration files.
  ...
  ...
```

### Get configs on the spot 

```shell
Usage: myapp config [OPTIONS] COMMAND [ARGS]...

  Read or modify configuration files.

Options:
  --help  Show this message and exit.

Commands:
  get   Print out configuration information.
  load  Load a full configuration storage from `path_to_json` file.
  set   Set a single value on a configuration storage
```
hcai-nova-server-nightly 0.1.15.dev202309131500
!Alpha Version! - This repository contains code to setup a computational backend server for the nova annotation tool. You can use this backend to train models of your choosing or create explanations for existing models.
Requires Python: >=3.6, <4

## Project
This project contains a nova backend that can be used to train models or generate explanations for the models on a remote server.
The server api can be used either from the UI itself or as a standalone tool to just interact with an existing nova database.
You can find the full documentation of the project [here](https://hcmlab.github.io/nova-server/docbuild/).
trustML 2.6.2
Trust for Machine Learning
Requires Python: >=3.7

<p align="center"><img src="https://github-production-user-asset-6210df.s3.amazonaws.com/7520973/249193988-89f07b66-f8b9-4df7-97ba-500f0cbebaab.png"></p>

_TrustML_ is a modular and extensible package to support the definition, assessment and monitoring of custom-built trustworthiness indicators for AI models. _TrustML_ allows data scientists to define trustworthiness indicators by selecting a set of metrics from a catalog of trustworthy-related metrics and grouping them into higher-level metric aggregations.

_TrustML_ also provides different assessment methods to compute and monitor the indicators previously defined. _TrustML_ enables and supports the development of trustworthy AI models, aiming to provide assistance not only during their construction phase, but also in production environments, as a mechanism to continuously monitor such trust and enable mitigation activities when required.

The package makes use of existing packages meant to compute each of the included trustworthiness metrics, check the requirements.txt file in the root of the source code repository for details.

The API documentation is available on https://martimanzano.github.io/TrustML/.
The wiki with tutorials on the package's usage and extension is available on https://github.com/martimanzano/TrustML/wiki/Home/.

The _TrustML_ package is free software distributed under the Apache License 2.0. If you are interested in participating in this project, please use the [GitHub repository](https://github.com/martimanzano/TrustML); and review the [Contributing page](https://github.com/martimanzano/trustML/blob/modular/CONTRIBUTING.md), the [Code of Conduct](https://github.com/martimanzano/trustML/blob/modular/CODE_OF_CONDUCT.md) and the specific [Contributing articles](https://github.com/martimanzano/trustML/tree/modular/contributing) all contributions are welcomed.
xsectron 0.0.0
A cross section calculation handbook for students
Requires Python: >=3.9

# cross_section
[![DOI](https://zenodo.org/badge/662117505.svg)](https://zenodo.org/badge/latestdoi/662117505)
[![Test package](https://github.com/dachengx/cross_section/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/dachengx/cross_section/actions/workflows/pytest.yml)
[![PyPI version shields.io](https://img.shields.io/pypi/v/xsectron.svg)](https://pypi.python.org/pypi/xsectron/)
[![CodeFactor](https://www.codefactor.io/repository/github/dachengx/cross_section/badge)](https://www.codefactor.io/repository/github/dachengx/cross_section)
[![Documentation Status](https://readthedocs.org/projects/xsectron/badge/?version=latest)](https://xsectron.readthedocs.io/en/latest/?badge=latest)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/dachengx/cross_section/main.svg)](https://results.pre-commit.ci/latest/github/dachengx/cross_section/main)

A cross section calculation handbook for students

<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.

The python codes in this repository are licensed under MIT license.

## What are the goals of this handbook?

0. Unified notation system
1. Derivation of cross section, tree-level and a bit loop
2. Electromagnetism and weak interaction covered
3. Reference of textbooks for each step
4. Reference of literatures for final numerical results
5. Comparison to latest experiment results
6. Focus on simplified theory
7. Python codes provided

## The short-term plan: process to cover

1. neutrino - lepton
2. neutrino - quark & nucleus
3. WIMP - nucleon
4. dark photon - electron
5. axion - electron

## How to generate the handbook

inside `note` folder, run

```
latexmk -C && latexmk --lualatex main.tex --shell-escape
```

## Credit of template usage

The template used in LaTex codes is modified based on [Physics Book Template](https://www.overleaf.com/latex/templates/physics-book-template/ncpnbrqpttwv), published at Overleaf under [Creative Commons CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/), by [Charles Averill](https://github.com/CharlesAverill).


0.0.0 / 2023-09-13
------------------
* Bump to v0.0.0 by @dachengx in https://github.com/dachengx/cross_section/pull/1

New Contributors
* @dachengx made their first contribution in https://github.com/dachengx/cross_section/pull/1
horde-client-rahuldshetty 1.0.0
Easy-to-use Python Interface for KoboldAI Horde.
Requires Python: >=3.7


<!-- PROJECT LOGO -->
<br />
<div align="center">
  <a href="https://github.com/rahuldshetty/horde-client">
    <img src="images/logo.png" alt="Logo" width="100" height="90">
  </a>

  <h3 align="center">Horde Client
</h3>

  <p align="center">
    Easy-to-use Python Interface for KoboldAI Horde.
    <br />
    <a href="#"><strong>Explore the docs »</strong></a>
    <br />
    <br />
    <a href="#">View Demo</a>
    ·
    <a href="https://github.com/rahuldshetty/horde-client/issues">Report Bug</a>
    ·
    <a href="https://github.com/rahuldshetty/horde-client/issues">Request Feature</a>
  </p>
</div>

<!-- ABOUT THE PROJECT -->
## About The Project

<!-- [![Product Name Screen Shot][product-screenshot]](https://example.com) -->

This project provides Python Client to interact with [KoboldAI Horde](https://horde.koboldai.net) service, which is crowdsourced distributed cluster program that offers Image generation and Text Generation workers.

* Simple & Easy-to-use.
* Support for Text Generation & Image Generation (WIP).
* More Examples! (on the way)

> Note: There is an official Python SDK under development from Haidra-org: https://github.com/Haidra-Org/horde-sdk.

<!-- ROADMAP -->
## Roadmap

- [X] Text Generation Support
- [ ] Image Generation Support
- [ ] Asynchronous Client
- [ ] Readthedocs integration
- [ ] Tests

Missing Something? Raise a [request](https://github.com/rahuldshetty/horde-client/issues) 

<!-- CONTRIBUTING -->
## Contributing

Any contributions you make are **greatly appreciated**.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request