semvec/pypi-packages huggingface dataset:
kgdata-core 4.0.2
Library to process dumps of knowledge graphs (Wikipedia, DBpedia, Wikidata)
Requires Python: >=3.10

# kgdata ![PyPI](https://img.shields.io/pypi/v/kgdata) ![Documentation](https://readthedocs.org/projects/kgdata/badge/?version=latest&style=flat)

KGData is a library to process dumps of Wikipedia, Wikidata. What it can do:

- Clean up the dumps to ensure the data is consistent (resolve redirect, remove dangling references)
- Create embedded key-value databases to access entities from the dumps.
- Extract Wikidata ontology.
- Extract Wikipedia tables and convert the hyperlinks to Wikidata entities.
- Create Pyserini indices to search Wikidata’s entities.
- and more

For a full documentation, please see [the website](https://kgdata.readthedocs.io/).

## Installation

From PyPI (using pre-built binaries):

```bash
pip install kgdata[spark]   # omit spark to manually specify its version if your cluster has different version
```
nonebot-plugin-afd 0.1.1
基于NoneBot2的爱发电订单审核进群插件
Requires Python: <4,>=3.10

# nonebot-plugin-afd

基于NoneBot的自动审核爱发电订单号进群的插件

## 安装

- 适配器
  - [`nonebot-adapter-onebot`](https://github.com/nonebot/adapter-onebot)
  - [`nonebot-adapter-afdian`](https://github.com/MineGraphCN/nonebot-adapter-afdian)

- NoneBot2 插件商店安装

  ```shell
  nb plugin install nonebot-plugin-afd
  ```

  启动 `NoneBot2` 项目：`nb run`

## 配置

```dotenv
# 插件配置
afd_token_dict = '{
    "群号": ["作者1user_id", "作者2user_id"]
}'

# 适配器配置
afdian_bots='[
    {
        "user_id": "爱发电开发者页面user_id",
        "token": "爱发电开发者页面token"
    }
]'
```

## 使用

1. 给Bot管理员身份
2. 设置加群验证方式为：`需要回答问题并由管理员审核`
3. 用户加群，**仅需**填写 `订单号` 即可（**不要填入其他任何字符**）
4. 有新的订单时，`Bot` 会自动推送至对应群聊

## 特别感谢

- [NoneBot2](https://github.com/nonebot/nonebot2)：开发框架。

## 贡献与支持

觉得好用可以给这个项目点个 `Star` 或者去 [爱发电](https://afdian.net/a/17TheWord) 投喂我。

有意见或者建议也欢迎提交 [Issues](https://github.com/17TheWord/nonebot-plugin-afd/issues)
和 [Pull requests](https://github.com/17TheWord/nonebot-plugin-afd/pulls) 。

## 开源许可

本项目使用 [MIT](./LICENSE) 作为开源许可证。
robotframework-keyta 1.2.3
A web UI for Robot Framework
Requires Python: >=3.10

# KeyTA
![PyPI - Version](https://img.shields.io/pypi/v/robotframework-keyta)
[![Downloads](https://static.pepy.tech/badge/robotframework-keyta)](https://pepy.tech/project/robotframework-keyta)
[![Monthly Downloads](https://static.pepy.tech/badge/robotframework-keyta/month)](https://pepy.tech/project/robotframework-keyta)
![Python - Versions](https://img.shields.io/pypi/pyversions/robotframework-keyta)
![GitHub - Commit Activity](https://img.shields.io/github/commit-activity/m/RF-KeyTA/KeyTA)

KeyTA is a web app designed for domain experts who want to leverage Robot Framework while avoiding the accidental complexity of code editors.

Install the Python package as usual:

```powershell
pip install robotframework-keyta
```

## English

Execute the command `keyta` (or `python -m keyta`). The KeyTA icon will appear in the taskbar. Click on the icon in order to open KeyTA in the default browser.

A tutorial for using the app will be [presented](https://robocon.io/program#online-keyta-a-simple-web-ui-for-robot-framework) at the online RoboCon 2025 on March 6th. Stay tuned!

## Deutsch

Nach der Ausführung des Befehls `keyta-de` (bzw. `python -m keyta.de`) erscheint das KeyTA-Logo in der Taskleiste. Mit einem Klick auf das Symbol wird KeyTA im Webbrowser geöffnet.

Die Verwendung der Webanwendung wird im [Tutorial](https://github.com/RF-KeyTA/KeyTA/blob/main/Tutorial.md) erklärt.

KeyTA wurde von imbus für einen Kunden entwickelt. Neue Features können bei imbus via E-Mail (testautomatisierung at imbus de) bestellt werden.
storylines 0.16
Line plots with Python & TikZ
Requires Python: >=2.7

# Line plots with Python & TikZ

StoryLines is a Python library to create plots in LaTeX format.

* `plot` - figure object
* `calc` - mathematical helpers
* `color` - blendable colors and colormaps
* `convert` - inches to centimeters etc.
* `cut` - remove redundant or unwanted points
* `fatband` - plot weights along line
* `files` - typeset TeX, rasterize PDF
* `group` - chunk sequences of points
* `png` - save and load RGB(A) graphics
* `proj` - project 3D object onto plane

## Installation

Optionally using a virtual environment:

    python3 -m venv venv
    source venv/bin/activate
    python3 -m pip install --upgrade pip

Either from PyPI:

    python3 -m pip install storylines

Or from the repository:

    git clone https://github.com/janberges/StoryLines
    python3 -m pip install -e StoryLines

## Documentation

The documentation and example scripts along with output can be found at
<https://io.janberges.de/StoryLines>.

Note that `storylines.module.member` is equivalent to `storylines.member`.

## License

This program is free software under the terms of the BSD Zero Clause License.

Copyright (C) 2016-2025 Jan Berges
ngsPETSc 0.1.1
NGSolve/Netgen interface to PETSc
Requires Python: <4.0,>=3.10

# ngsPETSc
[![ngsPETSc](https://github.com/UZerbinati/ngsPETSc/actions/workflows/ngsPETSc.yml/badge.svg)](https://github.com/UZerbinati/ngsPETSc/actions/workflows/ngsPETSc.yml)
[![Documentation Status](https://readthedocs.org/projects/ngspetsc/badge/?version=latest)](https://ngspetsc.readthedocs.io/en/latest/?badge=latest)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.07359/status.svg)](https://doi.org/10.21105/joss.07359)

ngsPETSc is an interface between PETSc and NGSolve/NETGEN that enables the use of NETGEN meshes and geometries in PETSc-based solvers while providing NGSolve users access to the wide array of linear, nonlinear solvers, and time-steppers available in PETSc.

## Installation
ngsPETSc is available on [PyPI](https://pypi.org/project/ngsPETSc/).
If you have PETSc installed be sure to set the `PETSC_DIR` and `PETSC_ARCH` environment variables to the required values.
You can install by running:
```bash
pip install ngsPETSc
```

## Getting started
To get started with ngsPETSc, check out the [documentation](https://ngspetsc.readthedocs.io/en/latest/).

## Development
If you already have NGSolve (with MPI support) and PETSc installed, you can install ngsPETSc via pip:
```bash
git clone https://github.com/UZerbinati/ngsPETSc.git
pip install ./ngsPETSc
```
Alternatively, you can also build PETSc, SLEPc, and NGSolve from source following the instructions in the [documentation](https://ngspetsc.readthedocs.io/en/latest/install.html).

### Testing
To test the installation, you can run the tests in the `tests` folder, via the Makefile in the root directory of the repository:
```bash
make test
```