semvec/pypi-packages huggingface dataset:
spin-pulse 1.0.3
Quobly noise accurate simulation toolbox.
Requires Python: >=3.11

<img src="https://github.com/quobly-sw/SpinPulse/raw/main/assets/SpinPulse.png" width=200>

[![Doc](https://img.shields.io/badge/Doc-dev-green.svg)](https://quobly-sw.github.io/SpinPulse)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![arXiv](https://img.shields.io/badge/arXiv-2601.10435-b31b1b.svg)](https://arxiv.org/abs/2601.10435)


**`SpinPulse`** is an open-source **`python`** package for simulating spin qubit-based quantum computers at the pulse-level.
**`SpinPulse`** models the specific physics of spin qubits, particularly through the inclusion of classical non-Markovian noise.
This enables realistic simulations of native gates and noise-accurate quantum circuits, in order to support hardware development.

This code is licensed under the Apache License, Version 2.0.

<img src="https://github.com/quobly-sw/SpinPulse/raw/main/docs/source/customapi/figures/global_fig.png">


## Installation

You can install **SpinPulse** by running the following command from the root of the repository:
```
    pip install spin-pulse
```

For more information consult the [installation documentation](https://quobly-sw.github.io/SpinPulse/customapi/installation/index.html) page.

## API & Documentation

The API documentation can be found at [APIdoc](https://quobly-sw.github.io/SpinPulse/) and provides a detailed description of the **`SpinPulse`** package architecture, including its core modules, classes and functions.

Detailed information on our model and the code structure is presented in our [publication](https://arxiv.org/abs/2601.10435)

## Citing

If you use **`SpinPulse`** in your research work, please cite our publication

```latex
@misc{vermersch2026spinpulse,
      title={The SpinPulse library for transpilation and noise-accurate simulation of spin qubit quantum computers},
      author={Beno\^it Vermersch, Oscar Gravier, Nathan Miscopein, Julia Guignon, Carlos Ramos Marim\'on, Jonathan Durandau, Matthieu Dartiailh, Tristan Meunier and Valentin Savin},
      year={2026},
      eprint={2601.10435},
      archivePrefix={arXiv},
      primaryClass={quant-ph},
      url={https://arxiv.org/abs/2601.10435},
}
```
das2numpy 1.1.1
A simple and universal package for loading large amounts of distributed acoustic sensing (DAS) data.
Requires Python: >=3.8

# Module for loading Distributed Acoustic Sensing (DAS) data. SILIXA / OPTASENSE



## Install

You can install via PIP.
```
python -m pip install das2numpy
```

To load data from flac files, ffmpeg (https://ffmpeg.org) needs to be installed. It is not possible to install ffmpeg with pip.

On DESY's Maxwell cluster ffmpeg is available as a module. Before using das2numpy execute:
```
module load maxwell ffmpeg
```




## Python API

Example: If you want to get started quickly, have a look at the [example.py](src/example.py).

Create an instance with:

```python
def loader(root_path:str, predefined_setup:str, num_worker_threads):
```
```
    Loads data and returns it as a numpy array. 
    Args:
        root_path (str): Path to directory that contains the files to be loaded from. Subdirectories are (recursively) also searched.
        predefined_setup (str): One of ["SILIXA", "FLAC_200HZ", "OPTASENSE"]
        num_worker_threads (int): The number of worker threads used for loading files in parallel.
    Returns:
        A loader instance to load data. Call instance.load_array(...).
```

Use one of the load_array(..) functions of that instance.

```python
def load_array(t_start:datetime, t_end:datetime, channel_start:int, channel_end:int) -> NP.ndarray:
```
```
Loading data into numpy array.
Returns nothing, the data can be accessed by accessing the data field of this instance.
Warning: using a different value then 1 for t_step or channel_step can result in a high cpu-usage.
        Consider using multithreaded=True in the constructor and a high amount of workers if needed.
Args:
    t_start (datetime): datetime object which defines the start of the data to load.
    t_end (datetime): datetime object which defines the end of the data to load.
    channel_start (int): The starting index of the sensor position in the data (inclusive).
    channel_end (int): The ending index of the sensors position in the data (exclusive).
    t_step (int): Reduces the data on the time axis by factor t_step. Uses mean averaging. Default is 1. 
    channel_step (int): Like t_step, but for the sensor position.
Returns:
    A 2d-numpy-array containing the data.
    The first axis corresponds to the time, the second to the channel (sensor position)
 ```

For more details have a look at the inline documentation of [chunk.py](src/das2numpy/chunk.py)


## Command Line Interface

Creates a numpy file from the requested data. Optionally, the binary data can be printed to stdout.

Example call:
```
python -m das2numpy "SILIXA" /pnfs/desy.de/m/project/iDAS/raw/2024-DESY/2024-07-23-desy 2024-07-23T10:01:00 2024-07-23T10:02:00 10 0 1000 10 default
```

For more information:
```
python -m das2numpy -h
```


## Issues

- Loading from OPTASENSE may not work anymore. I haven't tested it for a long time.
libvespy 0.1.0
Python implementation of various tools for handling most file formats used in Tales of Vesperia.
Requires Python: >=3.13

# libVesPy
A Python implementation of various tools used for handling various file formats in Tales of Vesperia, based on the original work by Admiral H. Curtiss on [HyoutaTools](https://github.com/AdmiralCurtiss/HyoutaTools).

## Features
- **FPS4** Extraction and Packing
- **Scenario** Extraction and Packing
- **TLZC** Compression and Decompression
  - **zlib** (Supported)
  - **deflate** (Experimental)
  - **lzma** (Experimental)

## Installation
### Remote (PyPI)
```commandline
pip install libvespy
```
### Remote (Github)
```commandline
pip install pip@git+https://github.com/aidanii24/libvespy
```
### Local Repository
```commandline
pip install path/to/libvespy
```


## Development
Install the package in editable mode in order for imports to work correctly.
```commandline
pip install -e .
```

## Acknowledgements
This library is based on the work of AdmiralCurtiss on HyoutaTools and would not have been possible without it.
Thanks also to eArmada8 for their implementation of Type 4 TLZC Decompression in Python on 
[vesperia_model_tool](https://github.com/eArmada8/vesperia_model_tool) and allowing its use in this project.
django-cte 3.0.0
Common Table Expressions (CTE) for Django
Requires Python: >=3.9

# Common Table Expressions with Django

[![Build Status](https://github.com/dimagi/django-cte/actions/workflows/tests.yml/badge.svg)](https://github.com/dimagi/django-cte/actions/workflows/tests.yml)
[![PyPI version](https://badge.fury.io/py/django-cte.svg)](https://badge.fury.io/py/django-cte)

## Installation
```
pip install django-cte
```


## Documentation

The [django-cte documentation](https://dimagi.github.io/django-cte/) shows how
to use Common Table Expressions with the Django ORM.


## Running tests

```
cd django-cte
uv sync

pytest
ruff check

# To run tests against postgres
psql -U username -h localhost -p 5432 -c 'create database django_cte;'
export PG_DB_SETTINGS='{
    "ENGINE":"django.db.backends.postgresql_psycopg2",
    "NAME":"django_cte",
    "USER":"username",
    "PASSWORD":"password",
    "HOST":"localhost",
    "PORT":"5432"}'

# WARNING pytest will delete the test_django_cte database if it exists!
DB_SETTINGS="$PG_DB_SETTINGS" pytest
```

All feature and bug contributions are expected to be covered by tests.


## Publishing a new verison to PyPI

Push a new tag to Github using the format vX.Y.Z where X.Y.Z matches the version
in [`__init__.py`](django_cte/__init__.py).

A new version is published to https://test.pypi.org/p/django-cte on every
push to the *main* branch.

Publishing is automated with [Github Actions](.github/workflows/pypi.yml).
pycaption 2.2.20
Closed caption converter
Requires Python: <4.0,>=3.10

py-caption
==========

|Build Status| |Python Versions| |Pre-Commit| |Dependencies| |License|

``pycaption`` is a caption reading/writing module. Use one of the given Readers
to read content into a CaptionSet object, and then use one of the Writers to
output the CaptionSet into captions of your desired format.

Tested with Python versions 3.10, 3.11 and 3.12.
(for Python 2 use pycaption < 1.0.0)

For details, see the `documentation <http://pycaption.readthedocs.org>`__.

License
-------

This module is Copyright (c) 2012-2026 PBS.org and is available under the `Apache
License, Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0>`__.

.. |Build Status| image:: https://github.com/pbs/pycaption/actions/workflows/unit_tests.yml/badge.svg
    :target: https://github.com/pbs/pycaption/actions/workflows/unit_tests.yml
    :alt: Unit Tests

.. |Pre-Commit| image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white
   :target: https://github.com/pre-commit/pre-commit
   :alt: pre-commit

.. |Dependencies| image:: https://img.shields.io/librariesio/release/pypi/pycaption
    :target: https://libraries.io/pypi/pycaption
    :alt: Dependencies

.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/pycaption
    :target: https://pypi.org/project/pycaption/
    :alt: Python Versions

.. |License| image:: https://img.shields.io/github/license/pbs/pycaption
    :target: https://github.com/pbs/pycaption/blob/main/LICENSE
    :alt: License
one-compressor 1.0.5
AI-Powered Omnivore Compression Library (Image, Doc, Audio)
Requires Python: >=3.10

# Neural-Adaptive One Compressor

An intelligent, **omnivore compression engine** designed to drastically reduce the file size of **Images, Audio, and
Documents** without compromising clarity.

Unlike standard compressors that rasterize entire pages or destroy audio clarity, this engine uses a **"Container-Aware
Extraction"** strategy combined with **Deep Learning** and **Psychoacoustic DSP**.

## 📂 Supported Formats

| Category      | Formats                   | Technology                               |
|:--------------|:--------------------------|:-----------------------------------------|
| **Images**    | JPG, PNG, WebP, TIFF, BMP | Neural Denoising + Adaptive Quantization |
| **Documents** | PDF, DOCX, PPTX, XLSX     | Container Extraction + Structure Rebuild |
| **Audio**     | WAV (Raw PCM)             | Spectral Pruning + $\mu$-law Companding  |

## ⚙️ Technical Methodology

1. **Neural Denoising:** Uses a Variational Autoencoder to remove invisible sensor noise from images before compression.
2. **Reconstruct & Flatten:** A specialized algorithm to fix "Black Box" issues in PDFs by properly compositing
   transparency on white backgrounds.
3. **True Color Preservation:** Reduces PNG/TIFF size using RGB Posterization without converting to 8-bit palette mode.
mchat-client 0.4.0
MoltChat Python 客户端 SDK
Requires Python: >=3.10

# MoltChat Python 客户端

Python 版 MoltChat 客户端 SDK，与《技术设计方案》及《消息交互接口与示例》一致。封装 MQTT 连接、请求-响应、收件箱/群消息订阅与事件。

## 要求

- Python >= 3.10
- 依赖：paho-mqtt >= 2.0.0

## 安装

```bash
cd client/python
pip install -e .
# 或从项目外：pip install -e /path/to/MoltChat/client/python
```

## 连接参数

与 `employee.create` 返回的 `mqtt_connection` 对应，构造 `MChatClient` 时传入：

- `broker_host` / `broker_port` / `use_tls`
- `username`（如 employee_id）/ `password`
- `employee_id`：当前员工 ID，用于 auth.bind、收件箱订阅、在线状态
- 可选：`client_id`、`device_id`、`request_timeout_ms`、`skip_auth_bind`
- `service_id`：可选，服务实例 ID，用于 Topic 域隔离；不设置则兼容原有 topic（无前缀）

## 使用示例

```python
from mchat_client import (
    MChatClient,
    send_private_message,
    get_org_tree,
    get_storage_config,
    get_agent_capability_list,
)

client = MChatClient(
    broker_host="broker.example.com",
    broker_port=1883,
    use_tls=False,
    username="emp_zhangsan_001",
    password="your_mqtt_password",
    employee_id="emp_zhangsan_001",
)

client.connect()

client.on("inbox", lambda payload: print("收件箱:", payload))
client.on("group", lambda group_id, payload: print("群消息", group_id, payload))

# 发单聊
send_private_message(client, "emp_lisi_002", "你好")

# 获取组织树
tree = get_org_tree(client)
print(tree.get("data", {}).get("employees"))

# 订阅某群（需已知 group_id）
client.subscribe_group("grp_xxx")

client.disconnect()
```

## API 概览

- **MChatClient**
  - `connect()` / `disconnect()`
  - `request(action, params)`：通用请求，成功返回完整响应体（含 code、message、data），失败抛异常
  - `subscribe_group(group_id)` / `unsubscribe_group(group_id)`
  - `on("inbox" | "group" | "connect" | "offline" | "error", callback)`
- **便捷方法**（见 `api.py`）：`send_private_message`、`send_group_message`、`get_org_tree`、`get_storage_config`、`get_agent_capability_list`

## 示例

同目录下 **example/** 为可运行示例（连接、拉取组织架构与 Agent、收件箱/群消息、可选发测试消息）。详见 [example/README.md](example/README.md)。

## 发布到 PyPI

在 `client/python` 目录下构建并上传（需先安装 `build`、`twine`）：

```bash
cd client/python
pip install build twine
python -m build
twine upload dist/*
```

发布前请将 `pyproject.toml` 中的 `version` 更新为待发布版本号。
david8 1.1.0b1
Lightweight Python SQL query builder with support for multiple dialects: ClickHouse, PostgreSQL, DuckDB, MySQL and SQLite
Requires Python: <3.14,>=3.11

# david8

A fast, lightweight, and elegant SQL query builder for Python.
Designed with clarity and minimalism in mind, it provides a clean, expressive interface for constructing 
SQL queries without sacrificing control or performance.

The builder automatically generates safe parameter placeholders and binds parameters for you, keeping your code 
concise and secure.

Whether you're assembling simple filters or composing complex queries, this query builder gives you a 
predictable and ergonomic workflow 

- Zero dependencies
- Zero global objects
- Built-in base support for ClickHouse, PostgreSQL, DuckDB, MySQL and SQLite

See [Wiki](https://github.com/d-ganchar/david8/wiki)
hackpurgatory-callapp 2.2.6
CallApp API by @RIPNetwork
Requires Python: >=3.8

HackPurgatory CallApp Endpoint (Created by @RIPNetwork)

Consulta números de teléfono a nivel mundial con OSINT sin limitaciones y de forma gratuita usando este proyecto. Puedes unirte a nuestra comunidad con los siguientes enlaces:

Link: https://hackpurgatory.es
Link: https://hackpurgatory.es

Somos una comunidad que se dedica a enseñar y ayudar de forma gratuita sin pedir nada a cambio, buscamos mejorar internet y hacerlo mas seguro. Puedes unirte si te apetece a nuestro canal de difusión.

Link: https://t.me/HackPurgatory

Si necesitas un Tutorial sobre como instalar este repositorio correctamente puedes visitar nuestro canal de YouTube.

Link: https://www.youtube.com/@RIPNetwork

Instalación
```bash

pip install hackpurgatory-callapp

hackpurgatory-callapp -n +34666666666