semvec/pypi-packages huggingface dataset:
rainbowneko 1.10.0
Neural network training and inference framework
Requires Python: >=3.8

# RainbowNeko Engine

[![PyPI](https://img.shields.io/pypi/v/rainbowneko)](https://pypi.org/project/rainbowneko/)
[![GitHub stars](https://img.shields.io/github/stars/IrisRainbowNeko/RainbowNekoEngine)](https://github.com/IrisRainbowNeko/RainbowNekoEngine/stargazers)
[![GitHub license](https://img.shields.io/github/license/IrisRainbowNeko/RainbowNekoEngine)](https://github.com/IrisRainbowNeko/RainbowNekoEngine/blob/master/LICENSE)
[![codecov](https://codecov.io/gh/IrisRainbowNeko/RainbowNekoEngine/branch/main/graph/badge.svg)](https://codecov.io/gh/IrisRainbowNeko/RainbowNekoEngine)
[![open issues](https://isitmaintained.com/badge/open/IrisRainbowNeko/RainbowNekoEngine.svg)](https://github.com/IrisRainbowNeko/RainbowNekoEngine/issues)


[📘English document](https://rainbownekoengine.readthedocs.io/en/latest/)
[📘中文文档](https://rainbownekoengine.readthedocs.io/zh-cn/latest/)

## Introduction
RainbowNeko Engine is a toolbox for pytorch based neural network training and inference. Multiple tasks and training strategies are built-in and highly expandable.


## Install

1. Install [pytorch](https://pytorch.org/)

2. Install with pip:
```bash
pip install rainbowneko
```

or
Install from source:
```bash
git clone https://github.com/IrisRainbowNeko/RainbowNekoEngine.git
cd RainbowNekoEngine
pip install -e .
```

3. To use xFormers to reduce VRAM usage and accelerate training:
```bash
# use conda
conda install xformers -c xformers

# use pip
pip install xformers>=0.0.17
```

## User guidance

### Start a new project
```bash
mkdir my_project
cd my_project
# Modified based on this project or start a new project and make initialization
nekoinit
```

### Training

Training scripts based on 🤗 Accelerate or Colossal-AI are provided.
+ For 🤗 Accelerate, you may need to [configure the environment](https://github.com/huggingface/accelerate/tree/main#launching-script) before launching the scripts.
+ For Colossal-AI, you can use [torchrun](https://pytorch.org/docs/stable/elastic/run.html) to launch the scripts.

```yaml
# with Accelerate
neko_train --cfg cfgs/train/cfg_file.py
# with Accelerate and only one GPU
neko_train_1gpu --cfg cfgs/train/cfg_file.py
```

### Inference
RainbowNeko Engine inference with workflow configuration file.

```yaml
neko_run --cfg cfgs/infer/cfg_file.py
```

### Tutorials

[📘English document](https://rainbownekoengine.readthedocs.io/en/latest/)
[📘中文文档](https://rainbownekoengine.readthedocs.io/zh-cn/latest/)

## Contributing

You are welcome to contribute more models and features to this toolbox!
aidrac 0.2.0
AIdrac - AI Out-of-Band Management with Sentinel AI. Alias for mcp-server-aidrac.
Requires Python: >=3.10
Keywords: aidrac, dtmf, hardware-guardian, idrac, ilo, ipmi, oob, sentinel, ultrasonic

# AIdrac

**AI-Powered Out-of-Band Management**

Alias for [`mcp-server-aidrac`](https://pypi.org/project/mcp-server-aidrac/).

```bash
pip install aidrac
```

The open alternative to iDRAC, iLO, and IPMI.

- DTMF telephone control
- Ultrasonic audio (ggwave)
- ntfy push notifications
- I-Poll AI messaging

Part of HumoticaOS - One love, one fAmIly!
OptimaLab35 1.8.4
User interface for optima35.
Requires Python: <3.14,>=3.9

**[OptimaLab35](https://code.boxyfoxy.net/CodeByMrFinchum/OptimaLab35)** is a graphical interface for the [optima35 library](https://code.boxyfoxy.net/CodeByMrFinchum/optima35), designed for efficient image and metadata management.

Developed on my [forgejo instance](https://code.boxyfoxy.net/CodeByMrFinchum), [GitLab](https://gitlab.com/CodeByMrFinchum) is used as backup.

### **Features**
- Resize, adjust brightness/contrast, and convert images to grayscale
- Add customizable text-based watermarks
- Manage EXIF data: add, copy, remove, and adjust timestamps
- Preview image adjustments in real time
- Theme selection (light, dark, auto)
- Automatic updates via PyPI

For more details, screenshots, and additional information, please see the [wiki](https://code.boxyfoxy.net/CodeByMrFinchum/OptimaLab35/wiki/Home).
arp-jarvis-artifactstore 0.3.8
JARVIS Artifact Store internal service for storing binary artifacts.
Requires Python: >=3.11

# JARVIS Artifact Store

Internal JARVIS service that stores binary artifacts and returns stable artifact references.
This is a JARVIS-only contract (not part of the ARP Standard).

## Requirements

- Python >= 3.11

## Install

```bash
python3 -m pip install -e .
```

## Run

```bash
python3 -m pip install -e .
arp-jarvis-artifactstore
```

> [!TIP]
> Use `bash src/scripts/dev_server.sh --host ... --port ... --reload` for dev convenience.

## Configuration

Environment variables:
- `JARVIS_ARTIFACT_DIR` (default `./artifacts/`)
- `JARVIS_ARTIFACT_MAX_SIZE_MB` (optional guardrail)
- `ARP_AUTH_*` (JWT auth settings, shared across JARVIS services)

Auth is enabled by default (JWT). To disable for local dev, set `ARP_AUTH_PROFILE=dev-insecure`
or `ARP_AUTH_MODE=disabled`. Health/version endpoints are always exempt.
If no `ARP_AUTH_*` env vars are set, the service defaults to the dev Keycloak issuer.

## API (v0.3.7)

Health/version:
- `GET /v1/health`
- `GET /v1/version`

Artifacts:
- `POST /v1/artifacts` -> `ArtifactRef`
- `GET /v1/artifacts/{artifact_id}` (raw bytes)
- `HEAD /v1/artifacts/{artifact_id}` (metadata headers)
- `GET /v1/artifacts/{artifact_id}/metadata` -> `ArtifactRef`

## Notes

- Filesystem storage by default.
- Metadata is tracked in a small SQLite index under the artifact directory.
arp-jarvis-eventstream 0.3.8
JARVIS Event Stream internal service for RunEvent storage and NDJSON streaming.
Requires Python: >=3.11

# JARVIS Event Stream

Internal JARVIS service that stores RunEvents and serves NDJSON streams.
This is a JARVIS-only contract (not part of the ARP Standard).

## Requirements

- Python >= 3.11

## Install

```bash
python3 -m pip install -e .
```

## Run

```bash
python3 -m pip install -e .
arp-jarvis-eventstream
```

> [!TIP]
> Use `bash src/scripts/dev_server.sh --host ... --port ... --reload` for dev convenience.

## Configuration

Environment variables:
- `JARVIS_EVENT_STORE_DB_URL` (default `sqlite:///./runs/jarvis_event_store.sqlite`)
- `JARVIS_EVENT_RETENTION_DAYS` (optional)
- `ARP_AUTH_*` (JWT auth settings, shared across JARVIS services)

Auth is enabled by default (JWT). To disable for local dev, set `ARP_AUTH_PROFILE=dev-insecure`
or `ARP_AUTH_MODE=disabled`. Health/version endpoints are always exempt.
If no `ARP_AUTH_*` env vars are set, the service defaults to the dev Keycloak issuer.

## API (v0.3.7)

Health/version:
- `GET /v1/health`
- `GET /v1/version`

Events:
- `POST /v1/run-events` -> `{ items: [{ run_id, seq }], next_seq_by_run: { run_id: seq } }`
- `GET /v1/runs/{run_id}/events` (NDJSON)
- `GET /v1/node-runs/{node_run_id}/events` (NDJSON)

Query params for streams:
- `cursor` (opaque, optional)
- `follow` (0/1, optional)
- `limit` (optional)
arp-jarvis-runstore 0.3.8
JARVIS Run Store internal service for Run and NodeRun persistence.
Requires Python: >=3.11

# JARVIS Run Store

Internal JARVIS service that persists `Run` and `NodeRun` state for the Coordinator.
This is a JARVIS-only contract (not part of the ARP Standard).

## Requirements

- Python >= 3.11

## Install

```bash
python3 -m pip install -e .
```

## Run

```bash
python3 -m pip install -e .
arp-jarvis-runstore
```

> [!TIP]
> Use `bash src/scripts/dev_server.sh --host ... --port ... --reload` for dev convenience.

## Configuration

Environment variables:
- `JARVIS_RUN_STORE_DB_URL` (default `sqlite:///./runs/jarvis_run_store.sqlite`)
- `JARVIS_RUN_STORE_MAX_SIZE_MB` (optional guardrail)
- `ARP_AUTH_*` (JWT auth settings, shared across JARVIS services)

Auth is enabled by default (JWT). To disable for local dev, set `ARP_AUTH_PROFILE=dev-insecure`
or `ARP_AUTH_MODE=disabled`. Health/version endpoints are always exempt.
If no `ARP_AUTH_*` env vars are set, the service defaults to the dev Keycloak issuer.

## API (v0.3.7)

Health/version:
- `GET /v1/health`
- `GET /v1/version`

Runs:
- `POST /v1/runs` -> `{ run: Run }`
- `GET /v1/runs/{run_id}`
- `PUT /v1/runs/{run_id}` -> `{ run: Run }`

NodeRuns:
- `POST /v1/node-runs` -> `{ node_run: NodeRun }`
- `GET /v1/node-runs/{node_run_id}`
- `PUT /v1/node-runs/{node_run_id}` -> `{ node_run: NodeRun }`
- `GET /v1/runs/{run_id}/node-runs?limit=100&page_token=...`

Idempotency:
- `POST` endpoints accept `idempotency_key` and will return the existing record if the key matches.

## Notes

- The store is owned by the Coordinator; no cross-component DB access.
- Uses SQLite by default for v0.3.7.
starlette-admin-beanie-backend 0.1.1
Beanie-ODM support for Starlette Admin
Requires Python: <3.14,>=3.9

# starlette-admin-beanie-backend

<div style="display: flex; gap: 10px">
<a href="https://github.com/arnabJ/starlette-admin-beanie-backend/actions">
    <img src="https://github.com/arnabJ/starlette-admin-beanie-backend/actions/workflows/publish.yml/badge.svg" alt="Publish">
</a>
<a href="https://pypi.org/project/starlette-admin-beanie-backend/">
    <img src="https://badge.fury.io/py/starlette-admin-beanie-backend.svg" alt="Package version">
</a>
<a href="https://pypi.org/project/starlette-admin-beanie-backend/">
    <img src="https://img.shields.io/pypi/pyversions/starlette-admin-beanie-backend" alt="Supported Python versions">
</a>
<a href="https://pepy.tech/projects/starlette-admin-beanie-backend">
<img src="https://static.pepy.tech/personalized-badge/starlette-admin-beanie-backend?period=total&units=INTERNATIONAL_SYSTEM&left_color=GRAY&right_color=RED&left_text=downloads" alt="PyPI Downloads">
</a>
</div>

🧪 A package to use Beanie-ODM as a backend with starlette-admin.

## ✴️ Documentation
**Follow the documentation [here](https://arnabJ.github.io/starlette-admin-beanie-backend) or check a quick guide below.**

## 🔧 Install
```bash
  pip install starlette-admin-beanie-backend
```

## ⚙️ Usage
```python
from starlette_admin_beanie_backend import Admin, ModelView
from .auth import AdminAuthProvider
from .models import User

def set_db_admin(app):
    # Create the Admin Interface
    admin = Admin(
        title="Test App",
        base_url="/admin",
        debug=True,
        auth_provider=AdminAuthProvider(),
    )
    
    # Add the Admin Views
    admin.add_view(ModelView(User, icon="fa fa-users"))

    # Mount app
    admin.mount_to(app)
```

## 🤝 Contribute
Contributions are welcome! Whether you’ve spotted a bug, have ideas to improve the Package, or want to extend functionality — I’d love your input. Please fork the repository, work on the dev-colab branch, and open a pull request when ready. Be sure to include clear commit messages and tests where applicable. Let’s build something great together!

- Submit PRs to `dev-colab`
- Please follow the coding style
---

## 🙏🏼 Credits
- jowilf (https://github.com/jowilf)
- BeanieODM (https://github.com/BeanieODM)
- pydantic (https://github.com/pydantic)
animethemes-webm-verifier 1.3
Verify WebM(s) Against AnimeThemes Encoding Standards
Requires Python: >=3.14

### Description

Verify WebM(s) Against AnimeThemes Encoding Standards

Executes a test suite on the input WebM(s) to verify compliance.

Test success/failure does **NOT** guarantee acceptance/rejection of submissions. In some tests, we are determining the correctness of our file properties. In other tests, we are flagging uncommon property values for inspection.

### Install

**Requirements:**

* FFmpeg
* Python >= 3.14

**Install:**

    pip install animethemes-webm-verifier

### Usage

    test_webm [-h] [--loglevel [{debug,info,error}]] [--groups [{format,video,audio} ...]] [file ...]

**File**

The WebM(s) to verify. If not provided, we will test all WebMs in the current directory.

**Groups**

The groups of tests that should be run.

The `format` group pertains to testing of the file format and context of streams.

The `video` group pertains to testing of the video stream of the file.

The `audio` group pertains to testing of the audio stream of the file.

By default, all test groups will be included.

**Logging**

Determines the level of the logging for the program.

`--loglevel error` will only output error messages.

`--loglevel info` will output error messages and script progression info messages.

`--loglevel debug` will output all messages, including variable dumps.
wuba_wos 0.1.2
五八WOS平台skd
Requires Python: >=3.10

## WOS

A Python client for the WOS (WuBa Object Storage) REST API.


### Usage
```python
from wos import WOS
# bucket: 存储桶名称; app_id: 应用ID; secret_id: 密钥ID
wos = WOS(bucket="your-bucket-name", app_id="your-app-id", secret_id="your-secret-id")
# 上传文件
result = wos.upload(filename = "test.txt", file_path = "test.txt")
# 删除文件
wos.delete(filename = "test.txt")
```
local-share-cli 0.1.7
Share files instantly over LAN with a QR code. Only one file at a time. Also, you can edit the 'template.html' file to customize your download page!
Requires Python: >=3.8

Installation : pip install local-share
Command to execute: share

Description:
            Share files via LAN. Theres also a template.html file if you want to customize your landing page! Only one file at a time.
openslide-bin 4.0.0.11
Binary build of OpenSlide
Requires Python: >=3.9
Keywords: OpenSlide, whole-slide image, virtual slide, library

# openslide-bin

[OpenSlide Python][] requires a copy of [OpenSlide][].  You could install
it from your package manager, or you could download binaries from the
OpenSlide website.  Either choice might be inconvenient, and depending on
your package manager, it might get you an old version of OpenSlide.

[openslide-bin][] is a pip-installable, self-contained build of OpenSlide
for Linux, macOS, and Windows.  It's built by the OpenSlide maintainers,
ships the same binaries as the OpenSlide website, and has no dependencies
you don't already have on your system.  And it always has the latest version
of OpenSlide.

[OpenSlide Python]: https://pypi.org/project/openslide-python/
[OpenSlide]: https://openslide.org/
[openslide-bin]: https://github.com/openslide/openslide-bin/

## Installing

Install with `pip install openslide-bin`.  OpenSlide Python ≥ 1.4.0 will
automatically find openslide-bin and use it.

openslide-bin is available for Python 3.9+ on the following platforms:

- Linux aarch64 and x86_64 with glibc 2.28+ (Debian, Fedora, RHEL 8+,
  Ubuntu, many others)
- macOS 11+ (arm64 and x86_64)
- Windows 10+ and Windows Server 2016+ (x64)

pip older than 20.3 cannot install openslide-bin, claiming that it `is not a
supported wheel on this platform`.  On platforms with these versions of pip
(RHEL 8), upgrade pip first with `pip install --upgrade pip`.

## Using

Use OpenSlide via [OpenSlide Python][].  The OpenSlide Python
[API documentation][] will get you started.

[API documentation]: https://openslide.org/api/python/

## Building from source

You should probably [build OpenSlide from source][openslide-build] instead.

The wheels are built by a [custom script][] that runs [Meson][] in builder
containers.  The source tarball includes all the source code and scripts,
and the wheels are built directly from the tarball, but the build cannot be
invoked from a [PEP 517][] frontend like `build` or `pip`.  If wheels are
not available for your system, building openslide-bin from source is not
likely to help, and you'll likely have better luck installing OpenSlide from
source directly.

[openslide-build]: https://github.com/openslide/openslide/#compiling
[custom script]: https://github.com/openslide/openslide-bin/#readme
[Meson]: https://mesonbuild.com/
[PEP 517]: https://peps.python.org/pep-0517/

## License

OpenSlide and openslide-bin are released under the terms of the
[GNU Lesser General Public License, version 2.1][lgpl].

openslide-bin includes components released under the LGPL 2.1 and other
compatible licenses.  A complete set of component licenses is installed in
the `licenses` subdirectory of openslide-bin's `dist-info` metadata.

OpenSlide and openslide-bin are distributed in the hope that they will be
useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Lesser
General Public License for more details.

[lgpl]: https://openslide.org/license/
colorswan 0.2.0
Precision color conversion library (sRGB to Oklab/Oklch)
Requires Python: >=3.7

# colorswan

**colorswan** is a precision color conversion library for Python, focused on converting sRGB to the perceptual color spaces **Oklab** and **Oklch**.

It provides accurate conversions using standard white points and matrices, suitable for color science, data visualization, and web design applications.

## Installation

```bash
pip install colorswan
```

## Usage

### Basic Conversion (Hex to Oklab/Oklch)

```python
from colorswan import OkColor

converter = OkColor()

# Convert from Hex (returns Oklab object by default)
result = converter.convert("#FF0000") 
print(result.L) 
# 0.6279...

# Request Oklch explicitly
oklch = converter.convert("#FF0000", return_type="oklch")
print(oklch.h)
# 29.23...

# Request both (legacy behavior)
all_results = converter.convert("#FF0000", return_type="all")
print(all_results['oklab'].L)
```

### Tuple Input

You can also pass RGB tuples (0-255).

```python
# Convert from RGB Tuple
white = converter.convert((255, 255, 255))
print(white.L)
# 1.0
```

### Using Constants

The library exposes standard color matrices and white points.

```python
from colorswan import constants

print(constants.WHITE_POINT_D65)
# (0.95047, 1.0, 1.08883)
```

## Features
- **Accurate**: Implements the official Oklab matrices (sRGB -> Linear -> XYZ -> LMS -> Oklab).
- **Zero Dependencies**: Pure Python implementation.
- **Easy to Use**: Simple API for common color tasks.

## License
MIT
bizdays 1.0.19
Functions to handle business days calculations
Requires Python: >=3.9

[![Downloads](https://img.shields.io/pypi/dm/bizdays.svg)](https://pypi.python.org/pypi/bizdays/)
[![Latest Version](https://img.shields.io/pypi/v/bizdays.svg)](https://pypi.python.org/pypi/bizdays/)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/bizdays.svg)](https://pypi.python.org/pypi/bizdays/)

# [python-bizdays](http://wilsonfreitas.github.io/python-bizdays/)

In several countries and markets, the accountability of the price of a financial
instrument, mainly bonds and derivatives, involves the use of different rules to
compute the way the days go by.
In Brazil, several financial instruments pay interest according to the business
days along their life cycle.
So, having a way to compute the number of business days between 2 dates is
fairly useful to price financial instruments.
**bizdays** was created to make it easier.

**bizdays** computes business days between two dates based on the definition of
nonworking days (usually holidays and weekends).
It also computes other collateral effects like adjust dates for the next or
previous business day, check whether a date is a business day, create sequences
of business days, and much more.

Several financial libraries compute the holidays, giving no option to users set
it by their own.
Furtherly, the financial calendar is usually a small feature of a huge library,
as quantlib, for example, and some users, including myself, don't want to put a
hand in such a huge library only to use the financial calendar.

**bizdays** is a pure Python module without strong dependencies,
what makes it appropriated for small projects.
driven-sql-tool 0.1.1
Simple wrapper (based on turbodbc) for most common MSSQL operations I face day-to-day
Requires Python: >=3.11

# SQL Tools

## Description

This is simply a turbodbc wrapper. To reduce boilerplate on routine SQL actions, making a code a little bit cleaner.

## Content

Core functionality include:
- `SQLConfig` : Collection of connection configuration options. With `TurbODBCOptions` for advanced options
- `Query` : Query object
- `QuerySequence` : Collection of Queries

```Python
from driven_sql_tool import SQLConfig, TurbODBCOptions, Query, QuerySequence
```

## Usage samples

```Python
# prepare sample instanced config ...
turbodbc_options = TurbODBCOptions(autocommit=False, use_async_io=False)
conf = SQLConfig(server=r'server.address', database=r'db', turbodbc_options=turbodbc_options)
# ... or simply set defaults
SQLConfig.default_server = r'default.server'
SQLConfig.default_database = r'default.database'
```

Most common actions (Data Query Language):
```Python
# regular querying
df_res = Query('SELECT * FROM db.schema.table', conf=conf).execute()
# not specifying conf attribute will grab class defaults
df_res = Query('SELECT * FROM db.schema.table').execute()
# the result is also stored in `data` property of `Query` object 
query = Query('SELECT * FROM db.schema.table')
query.execute()
df_res = query.data

# .sql file querying
df_res = Query('./path/to/file.sql').execute()
```

Parametrized actions 
```Python
# parametrized insertion ...
Query(
    """
        INSERT INTO db.schema.table
        ([ID], [field1], [field2], [date])
        VALUES (?, ?, ?, ?)
    """, 
    data=df_insert[['ID', 'field1', 'field2', 'date']]
).execute()
# ... or execution
Query('EXEC db.schema.sproc @p1=?, @p2=?', data=df_exec[['p1', 'p2']]).execute()
```

Running multiple queries
```Python
# prepare sequence of queries
queries = QuerySequence()

queries.case_1 = Query('SELECT * FROM db.schema.table', conf=conf_1)
queries.case_2 = Query('./query.sql', conf=conf_2)
queries.case_3 = Query('EXEC db.schema.sproc @p1=?', data=df_exec[['p1']])

# run multiple queries sequentially ...
queries.run_seq()
# ... or, alternatively, in parallel (`joblib`)
queries.run_par()

# then access `data` property
df_res_1 = queries.case_1.data
```
model-protocol-sdk 0.0.8
模型协议开发SDK
Requires Python: >=3.8

# Device Protocol SDK

无人机设备协议开发工具包，提供标准化接口实现多类型无人机设备的快速接入。

[![PyPI Version](https://img.shields.io/pypi/v/device-protocol-sdk)](https://pypi.org/project/device-protocol-sdk/)
[![Python Versions](https://img.shields.io/pypi/pyversions/device-protocol-sdk)](https://pypi.org/project/device-protocol-sdk/)

## 功能特性

- 🛠️ **协议无关抽象**：统一各类模型的控制接口
- 🔌 **连接池管理**：自动维护模型连接，支持多模型并行控制
- 🚀 **实时状态推送**：内置WebSocket状态推送工具
- 🔍 **动态协议发现**：运行时自动加载用户协议实现

## 安装

```bash
pip install model-protocol-sdk