semvec/pypi-packages huggingface dataset:
bigtalk 7
Big Talk

| **Information and Evidence Unit**
| **Office of the Prosecutor**
| **Post Office Box 19519**
| **2500 CM The Hague**
| **The Netherlands** 
|


Hello Office of the Prosecutor, 

i write you in the context of communications and claims under art.15 of 
the Rome Statute. i want to inform the prosecutor that the king of the 
netherlands and his government are commiting 3 of the 5 crimes defined 
in the Rome Statute. 

The dutch government has introduced three new forced care laws, the Wfz 
(wet forensische zorg) for criminals, the WvGGZ (Wet verplichte GGZ) for 
the disturbed and addicted and the Wzd (Wet zorg en dwang) for the 
handicapped.

Both the chamber suggesting these laws to the king and the king himself 
have been informed about the fact the the substances administered under 
these laws are proven to be poison.
 
There is proof the medication used in forced treatments in the 
netherlands are poison, see copies of the ECHA <`European Chemical Agency`>
website for:

(1) ``haldol``
(2) ``clozapine``
(3) ``zyprexa``
(4) ``abilify``

With the mass scale torture with poison the king of the netherlands and 
his chamber:

(1) kills
(2) does grave bodily and mental harm
(3) makes impotent

on the following victim groups here in the Netherlands:

(1) verslaafden (addicts)
(2) bejaarden (elderly)
(3) ggz patienten (psychiatric patients)
(4) criminelen (criminials)
(5) gehandicapten (handicapped)

Since the members of the chamber and the king were aware that these medicine
are poison at the time they voted for this law and the day the Wfz law took
effect (1-1-2019), i ask the prosecutor to prosecute the king for making the
commiting of the above mentioned crimes (killing, torture and impotent making)
possible.
orthogonal 1.3.2
Orthogonal Graph Layout for Python 3
Keywords: pyut, graphics, layout

![](https://github.com/hasii2011/code-ally-basic/blob/master/developer/agpl-license-web-badge-version-2-256x48.png "AGPL")

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/hasii2011/orthogonal/tree/master.svg?style=shield)](https://dl.circleci.com/status-badge/redirect/gh/hasii2011/orthogonal/tree/master)
[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)
[![PyPI version](https://badge.fury.io/py/orthogonal.svg)](https://badge.fury.io/py/orthogonal)



# Orthogonal Drawing Algorithm

This project is a fork of [this project.](https://github.com/rawfh/orthogonal-drawing-algorithm)

## Developer Notes
This project uses [buildlackey](https://github.com/hasii2011/buildlackey) for day to day development builds

___

## Note
For all kind of problems, requests, enhancements, bug reports, etc.,
please drop me an e-mail.
Written by <a href="mailto:email@humberto.a.sanchez.ii@gmail.com?subject=Hello Humberto">Humberto A. Sanchez II</a>  (C) 2023



![Humberto's Modified Logo](https://raw.githubusercontent.com/wiki/hasii2011/gittodoistclone/images/SillyGitHub.png)

I am concerned about GitHub's Copilot project



I urge you to read about the
[Give up GitHub](https://GiveUpGitHub.org) campaign from
[the Software Freedom Conservancy](https://sfconservancy.org).

While I do not advocate for all the issues listed there I do not like that
a company like Microsoft may profit from open source projects.

I continue to use GitHub because it offers the services I need for free.  But, I continue
to monitor their terms of service.

Any use of this project's code by GitHub Copilot, past or present, is done
without my permission.  I do not consent to GitHub's use of this project's
code in Copilot.
lipidlibrarian 0.4.2
A Lipidomics Database Explorer Package
Requires Python: ~=3.11
Keywords: lipids, bioinformatics, development

# Lipid Librarian

## Build and Installation Instructions

### Python Virtual Environment

    make install install_optional

### Docker Container

    docker build -t lipidlibrarian -f Containerfile .

### OCI Container for Docker/Podman/Kubernetes

    buildah build -t lipidlibrarian -f Containerfile .

## Test Lipid Librarian

Run pytest in the git root directory with a venv activated with LipidLibrarian installed. It is highly recommended to run a local ALEX123 SQL Database for performance reasons (see below)

    pytest

## Run Lipid Librarian

### CLI

In case you installed Lipid Librarian into a 'venv' virtual environment, make sure you activate it before with `source venv/bin/activate`.

    lipidlibrarian "PC(18:1_20:0)" "PE 38:1" "816.6477;0.001;+H+" "Cholesterol" "SLM:000487065"
    lipidlibrarian path/to/file
    cat path/to/file | lipidlibrarian

### Docker

    docker run lipidlibrarian "PC(18:1_20:0)" "PE 38:1" "816.6477;0.001;+H+" "Cholesterol" "SLM:000487065"

### Podman

    podman run lipidlibrarian "PC(18:1_20:0)" "PE 38:1" "816.6477;0.001;+H+" "Cholesterol" "SLM:000487065"

### Import Python Package

```python
from lipidlibrarian.LipidQuery import *

query1 = LipidQuery("PC(18:1_20:0)").query()
query2 = LipidQuery("PE 38:1").query()
query3 = LipidQuery("816.6477;0.001;+H+").query()
query4 = LipidQuery("Cholesterol").query()
query5 = LipidQuery("SLM:000487065").query()

for lipid in query1:
    print(repr(lipid))

for lipid in query2:
    print(format(lipid, 'json'))

for lipid in query3:
    print(format(lipid, 'html'))

for lipid in query4:
    print(repr(lipid))

for lipid in query5:
    print(repr(lipid))
```

## Run a local ALEX123 SQL Database

```
podman run --detach --name alex123-sql --env MARIADB_ALLOW_EMPTY_ROOT_PASSWORD=1 --env MARIADB_USER=alex123 --env MARIADB_PASSWORD=alex123 --env MARIADB_DATABASE=alex123 -p 3306:3306  docker.io/library/mariadb:latest

sync_alex123_sql_database --sql mysql+pymysql://alex123:alex123@127.0.0.1:3306/alex123

lipidlibrarian --sql --sql-host 127.0.0.1 --sql-port 3306 --sql-user alex123 --sql-password alex123 --sql-database alex123 "PC(18:1_20:0)"
```
axio-framework 1.0.0
AI-native full-stack framework with compiler-driven code generation
Requires Python: >=3.11

# Axio Framework

AI-native full-stack framework with compiler-driven code generation.

## Overview

Axio allows developers to declare intent in Python DSL, which the compiler then transforms into a production-ready React + FastAPI application with an integrated AI agent runtime.

## Core Features

- **Declaration Over Implementation**: Define agents, views, and workflows in simple Python.
- **AI-Native**: Agents and reasoning loops are first-class citizens.
- **Compiler-Driven**: Generates typed TypeScript (React) and Python (FastAPI).
- **Hybrid Memory**: Built-in support for Vector, Graph, and Cache storage.
- **MCP Integration**: First-class support for Model Context Protocol tools.

## Installation

```bash
pip install axio-framework
```

## Quick Start

1. Create a new project:

   ```bash
   axio new my-app
   ```

2. Start development server:

   ```bash
   cd my-app
   axio dev
   ```

3. Build for production:

   ```bash
   axio build --prod
   ```

## Documentation

For detailed architectural overview, see `README_ADVANCED.md` or our online documentation.

## License

Apache 2.0
wanabi 1.5.8
a light authoring tool
Requires Python: >=3.10

# なにコレ？  
このソフトウェアはWEB小説の執筆用に作ったものです  
動作は軽いと思います、CPU:N100,RAM16GB,SSD搭載のminiPCで問題なく動くのを確認済みです  
EN.  
This software is made for write web novel.  
## 推奨環境(Recommended)  
CPU:intel N100  
RAM:16GB  
Storage: 128GB,SSD  
Python version: 3.10 or later  
上記は推奨環境で、それ以下のスペックでも動きますが、快適さは保証しません  
PythonはGIL無効化、またはJITコンパイル、もしくはその両方がされた環境での動作を保証しません  
if Python disable GIL,or JIT compile is enable,or BOTH is not supported  
# インストール方法(installation)  
Python3がインストールされている環境でターミナルを起動して  
### pip install wanabi  
上記のコマンドを入力するとインストールされます  
the above command execute Python3 installed computer  
## 使い方(How to use)  
ターミナルからwanabiと入力すると起動フォルダに設定ファイルを作成して起動します。  
起動フォルダに設定ファイルを格納するconfディレクトリが出来ることに注意してください  
設定を初期化したい場合confディレクトリを削除してください  
これを避けるためにはバッチファイルに起動コマンドを書いてそのショートカットを使用するとバッチファイルのあるフォルダにconfディレクトリが作成されます  
### English
If installed, in terminal command (wanabi) and enter  
if want english menu, conf/lang.txt is write "en" and restart  
In command run directory make conf directory  
If you want reset config, delete a conf directory  
if you not want conf directory, write "wanabi" in editor.bat and shortcut it.  
This method only conf directory make editor.bat directory  


Note  
----
当ソフトは現在64bitのWindows11環境でのみサポートしています  
This software is support only on Windows 11
# Amazonの欲しいものリスト(Amazon wish list)  
[モチベーションが上がります](https://www.amazon.jp/hz/wishlist/ls/1J2JK61UUSMOP?ref_=wl_share)  
[Donation Link](https://www.amazon.jp/hz/wishlist/ls/1J2JK61UUSMOP?ref_=wl_share)
-------------
# LICENSE  
----  
当ソフトはGPLv3+ライセンスで配布されます  
this software license is GPLv3+  
[GPLv3+](https://www.gnu.org/licenses/gpl-3.0.en.html)  
----  

----
ソースコードは[こちら](https://github.com/Ark-Bird/WriteAndWrite)
drf-docmint 1.0.0
A zero-runtime CLI tool for generating static API documentation for Django REST Framework projects.
Requires Python: >=3.10
Keywords: django, drf, documentation, api, openapi, postman, pdf, ast, static-analysis

# drf-docmint
A zero-runtime CLI tool for generating static API documentation for Django REST Framework projects.

-   Zero Config: Auto-detects serializers, views, and query parameters.
-   Formats: Markdown, HTML, PDF, OpenAPI (JSON/YAML), and Postman.
pytest-kafka-broker 0.7.1
Pytest plugin to run a single-broker Kafka cluster
Requires Python: >=3.11

# pytest-kafka-broker

[![Python Package Index status](https://img.shields.io/pypi/v/pytest-kafka-broker)](https://pypi.org/project/pytest-kafka-broker/)
[![codecov](https://codecov.io/gh/nasa-gcn/pytest-kafka-broker/graph/badge.svg?token=ZZbmpKQUlb)](https://codecov.io/gh/nasa-gcn/pytest-kafka-broker)
[![Documentation status](https://readthedocs.org/projects/pytest-kafka-broker/badge/?version=latest)](https://pytest-kafka-broker.readthedocs.io/en/latest/?badge=latest)

A Pytest plugin to run a single-broker Kafka cluster.

## Installation

Run this command to install the Python package:

    pip install pytest-kafka-broker

The only other dependency is Java. You must have a Java virtual machine installed.

## Usage

See [documentation](https://pytest-kafka-broker.readthedocs.io/).
epicsdev-rigol-scope 2.2.0
EPICS PVAccess server for RIGOL oscilloscopes
Requires Python: >=3.7

# epicsdev_rigol_scope.
Python-based EPICS PVAccess server for RIGOL oscilloscopes.
It is based on [p4p](https://epics-base.github.io/p4p/) and [epicsdev](https://github.com/ASukhanov/epicsdev) packages 
and it can run standalone on Linux, OSX, and Windows platforms.<br>
It was tested with RIGOL DHO924 on Linux.

## Installation
```pip install epicsdev_rigol_scope```<br>
For control GUI and plotting:
```pip install pypeto,pvplot```

## Run
To start: ```python -m epicsdev_rigol_scope -r'TCPIP::192.168.27.31::INSTR'```<br>
Control GUI:<br>
```python -m pypeto -c path_to_repository/config -f epicsdev_rigol_scope```<br>

![Control page](docs/pypet.jpg), ![Plots](docs/pvplot.jpg)

## Performance
The network interface of DHO924 is 100BASE-T.
The measured data output rate is 3.3 MPPS (6.6 MB/s) for SOCKET and 2.2 MPPS (4.4 MB/s) for INSTR visa resources.
mcDETECT 2.1.4
Uncovering the dark transcriptome in polarized neuronal compartments with mcDETECT
Requires Python: >=3.6

# mcDETECT

## Uncovering the dark transcriptome in polarized neuronal compartments with mcDETECT

#### Chenyang Yuan, Krupa Patel, Hongshun Shi, Hsiao-Lin Wang, Feng Wang, Ronghua Li, Yangping Li, Victor Corces, Hailing Shi, Sulagna Das, Jindan Yu, Peng Jin, Bing Yao* and Jian Hu*

mcDETECT is a computational framework designed to study the dark transcriptome related to polarized compartments in brain using *in situ* spatial transcriptomics (iST) data. It begins by examining the subcellular distribution of mRNAs in an iST sample. Each mRNA molecule is treated as a distinct point with its own 3D spatial coordinates considering the thickness of the sample. Unlike many cell-type marker genes, which are typically found within the nucleus or soma, compartmentalized mRNAs often form small aggregates outside the soma. mcDETECT uses a density-based clustering approach to identify these extrasomatic aggregates. This involves calculating the Euclidean distance between mRNA points and defining the neighborhood of each point within a specified search radius. Points are then categorized as core points, border points, or noise points based on their reachability from neighboring points. mcDETECT recognizes each connected bundle of core and border points as a mRNA aggregate. To minimize false positives, it excludes aggregates that substantially overlap with somata, which are estimated by dilating the nuclear masks derived from DAPI staining. mcDETECT then repeats this process for multiple granule markers, merging aggregates from different markers that exhibit high spatial overlap. After aggregating across all markers, an additional filtering step removes aggregates containing mRNAs from negative control genes, which are known to be enriched exclusively in nuclei and somata. The remaining aggregates are considered individual RNA granules. mcDETECT then computes the minimum enclosing sphere for each aggregate to connect neighboring mRNA molecules from all measured genes and summarizes their counts, thereby defining the spatial transcriptome profile of individual RNA granules.
pylegifrance 1.3.0b1
Librairie qui fournit des fonctions simples pour rechercher dans legifrance
Requires Python: >=3.12

# PyLegifrance

<!-- Build and License -->
[![GitHub Actions][ci-shield]][ci-url]

## À propos

Librairie python qui simplifie l'interrogation des contenus de legifrance en créant des fonctions pythons prêtes à l'emploi pour la recherche ou la consultation de textes légaux et réglementaires.  
Elle repose sur l'utilisation de pydantic pour gérer les structures de données d'interrogation et de réponse de l'API legifrance. A terme l'intégralité des fonctions de l'API legifrance seront disponibles sous forme de fonctions python. 

Si vous souhaitez accéder directement à une API simplifiée, c'est par ici : [https://lab.dassignies.law/api/docs](https://lab.dassignies.law/api/docs) 

Pour un serveur MCP Legifrance, c'est par ici : [https://github.com/rdassignies/mcp-server-legifrance](https://github.com/rdassignies/mcp-server-legifrance)

## Documentation

La documentation complète est disponible ici : [https://pylegifrance.github.io/pylegifrance/](https://pylegifrance.github.io/pylegifrance/)

Elle inclut :
- [Tutoriels](https://pylegifrance.github.io/pylegifrance/tutoriels/) - Apprentissage guidé pour les débutants
- [Guides pratiques](https://pylegifrance.github.io/pylegifrance/#installation) - Instructions d'installation et d'utilisation
- [Référence API](https://pylegifrance.github.io/pylegifrance/reference/) - Documentation technique détaillée
- [Explications](https://pylegifrance.github.io/pylegifrance/explanation/) - Concepts et roadmap

## Fonctionnalités principales

- Recherche dans les codes français
- Recherche dans les lois, ordonnances, décrets et arrêtés
- Recherche dans la jurisprudence judiciaire 

Pour des exemples d'utilisation, consultez la [documentation](https://pylegifrance.github.io/pylegifrance/guides-pratiques/)).

## Contact

Raphael d'Assignies - [LinkedIn](https://www.linkedin.com/in/dassignies/)  
Site web: [https://dassignies.law](https://dassignies.law)

**Contributeurs :**  
Frederic Ferrera - [LinkedIn](https://www.linkedin.com/in/fferrera31/)  
Site web: https://fredferre.com

<!-- MARKDOWN LINKS & IMAGES -->
[ci-shield]: https://github.com/pylegifrance/pylegifrance/actions/workflows/test.yml/badge.svg?style=for-the-badge
[ci-url]: https://github.com/pylegifrance/pylegifrance/actions
pycupra 0.2.13
A library to read and send vehicle data via Cupra/Seat portal using the same API calls as the MyCupra/MySeat mobile app.
Requires Python: >=3.12

# PyCupra

A library to read and send vehicle data via Cupra/Seat portal using the same API calls as the MyCupra/MySeat mobile app.

Fork of https://github.com/Farfar/seatconnect which in turn is a fork of:
Fork of https://github.com/lendy007/skodaconnect which in turn is a fork of:
https://github.com/robinostlund/volkswagencarnet

## Information

Retrieve statistics about your Cupra/Seat from the Cupra/Seat Connect online service

## Breaking changes

- The method vehicle.update(updateType) supports 3 different update types: 
   - updateType=0: Small update (=only get_basiccardata() and get_statusreport are called). If the last full update is more than 1100 seconds ago, then a full update is performed.
   - updateType=1: Full update (nearly all get-methods are called. The model images and the capabilitites are refreshed only every 2 hours.)
   - updateType=2: Like updateType=0, but ignoring the nightly update reduction

- Nightly update reduction: If nightly reduction is activated and the current time is within the time frame between 22:00 and 05:00, then vehicle.update(0) performs a full update, if the last full update is more than 1700 seconds ago. If that's not the case, vehicle.update(0) does nothing.

- PyCupra can ask the Seat/Cupra portal to send push notifications to PyCupra if the charging status or the climatisation status has changed or when the API has finished a request like lock or unlock vehicle, start or stop charging or change departure timers or ....

## Thanks to

- [RobinostLund](https://github.com/robinostlund/volkswagencarnet) for initial project for Volkswagen Carnet I was able to fork
- [Farfar](https://github.com/Farfar) for modifications related to electric engines
- [tanelvakker](https://github.com/tanelvakker) for modifications related to correct SPIN handling for various actions and using correct URLs also for MY2021
- [sdb9696](https://github.com/sdb9696) for the firebase-messaging package that is used in PyCupra with only minor modifications

### Example

For an extensive example, please use the code found in example/PyCupra.py.
When logged in the library will automatically create a vehicle object for every car registered to the account. Initially no data is fetched at all. Use the doLogin method and it will sign in with the credentials used for the class constructor. After a successful login, the tokens are stored in a json file. Later doLogin calls can use the token file instead of the credentials.
Method get_vehicles will fetch vehicle basic information and create Vehicle class objects for all associated vehicles in account.
To update all available data use the update_all method of the Connect class. This will call the update function for all registered vehicles, which in turn will fetch data from all available API endpoints.

The file *pycupra_credentials.json.demo* explains the data structure of the credentials file.
xy-health-measurement-sdk 2.2.7
小阳心健康测量SDK
Requires Python: <=3.11,>=3.9
Keywords: 健康测量, 生理测量, 情绪测量, 健康风险, 小阳科技

# 小阳心健康测量SDK

- [使用手册](https://measurement.xymind.cn/docs/sdk/python.html)

- [示例程序](https://github.com/xiaoyang-tech/measurement-python-client)

## 运行环境说明

自SDK >=2.2 版本起引入了部分C++库，这些库对OS环境和其自身依赖包可能存在较为严格的版本要求。

### 本地运行

如需本体环境调试，推荐版本如下：

- 运行平台
  - OS: Ubuntu 22.04 +
  - CPU: x86_64
- 依赖包
  - google protobuf 3.19.6
  - opencv 4.5.4

### Docker运行

当前SDK运行环境也提供了Docker镜像，具体用法可以参考[示例程序](https://github.com/xiaoyang-tech/measurement-python-client)。
gimle-hugin 0.2.0
Hugin - An agentic framework for creative reasoning tasks
Requires Python: >=3.12
Keywords: agentic-framework, agents, ai, anthropic, llm

# Hugin

[![CI](https://github.com/gimlelabs/gimle-hugin/actions/workflows/ci.yml/badge.svg)](https://github.com/gimlelabs/gimle-hugin/actions/workflows/ci.yml)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)

<p align="center">
  <img src="https://raw.githubusercontent.com/gimlelabs/gimle-hugin/main/hugin.gif" alt="Hugin demo" width="900" />
</p>

A framework for building agents with a focus on longer running, creative, reasoning tasks.

## Quick Start

```bash
pip install gimle-hugin
hugin create
```

## Documentation

Full documentation at **[hugin.gimlelabs.com](https://hugin.gimlelabs.com)**

- [Getting Started](https://hugin.gimlelabs.com/getting-started/)
- [Core Concepts](https://hugin.gimlelabs.com/concepts/)
- [CLI](https://hugin.gimlelabs.com/cli/) - Learn the command line interfaces
- [Examples](https://hugin.gimlelabs.com/examples/)
- [API Reference](https://hugin.gimlelabs.com/api/)

## Development

```bash
git clone https://github.com/gimlelabs/gimle-hugin.git
cd gimle-hugin
uv sync --all-extras
uv run pytest
```

## Vibe Coding

I am a big fan of vibe coding and one of the intentions of Hugin was to make a framework simple and clean enough for coding agents to use it to build their own agents and agentic apps.

As a show case of this, all the apps in the `apps` folder are 100% vibed (which does show a bit in terms the code quality 😄) using either Claude Code or Cursor.

Generally, I think they did a pretty good job, so I have left them as vibed and so far I haven't found any bugs in the agentic flows - but lots in the UI 😆.

Similarly all the tests and much of the CLI are mostly vibed - although this had to do more with time constraints than anything else.

### Claude Code Plugin

To help Claude Code (and other coding agents) build Hugin agents, there's a plugin with comprehensive guidance:

```bash
# Run Claude Code with the Hugin agent creator plugin
claude --plugin-dir ./skills/hugin-agent-creator
```

Then use the skills:
- `/hugin-agent-creator:hugin-guide` - Comprehensive guide for creating agents
- `/hugin-agent-creator:hugin-scaffold my_agent` - Generate starter files for a new agent

See the [plugin documentation](./skills/hugin-agent-creator/README.md) for more details.

## Why "Hugin"?

In old Norse, Hugin means thought and is the name of one of Odin's ravens. The other being Munin, meaning memory.
I think that is quite a fitting name for an agentic framework 😃
pyclim 1.0.1
A Python package that makes analysing climatological data easy, and pretty!
Requires Python: >=3.12
Keywords: climate, timeseries, plotting

# pyClim

## A Python module to plot climatological data

![Getting Started](./docs/pyClim_logo.png)

pyClim is a Python package that makes analysing climatological data easy, and pretty!

pyClim provides you with a set of Python functions so you can perform a complete analysis of your climatological data with publication-quality figures.

## Install

To install the pyClim package:

```
pip install pyclim
```

## Examples

An example script and a set of example data are provided so you can run the example script and check every kind of figure pyClim can produce. Some of them are shown below:

## Contact

Do you have any questions or suggestions? Have you found a bug? Open an issue or reach out:

Alejandro Rodríguez Sánchez

Email: ars.rodriguezs@gmail.com