semvec/pypi-packages huggingface dataset:
llmdb 1.0.0
A simple interface to models.dev, the open database of AI model specs, pricing and capabilities
Requires Python: >=3.13
Keywords: llm, ai, models, api, database, openai, anthropic, claude, gpt

# llmdb

llmdb is a handy python package you can use to access the [models.dev](https://models.dev) comprehensive open-source database of AI model specifications, pricing and capabilities.

The database is not curated by me and all the data is retrieved from the models.dev API which you can also use directly.

## Installation

```bash
pip install llmdb
```

## Usage

The package exposes a `db` singleton that lazily fetches data from the API on first access.

```python
from llmdb import db

# Get a specific model by ID
model = db.models.get("gpt-4o")
print(f"{model.name}: {model.cost.input}$/M input tokens")

# Get a provider
provider = db.providers.get("openai")
print(f"{provider.name} has {len(provider.models)} models")
```

### Filtering

Filter models using field equality or custom predicates.

```python
# Filter by field values
reasoning_models = db.models.filter(reasoning=True).all()
open_source = db.models.filter(open_weights=True, reasoning=True).all()

# Filter with a custom predicate
cheap_models = db.models.filter_by(lambda m: m.cost and m.cost.input < 1.0).all()
```

### Sorting

Sort results by any field, including nested fields using dot notation.

```python
# Get newest models first
newest = db.models.sort_by("release_date", desc=True).all()

# Sort by input cost
by_cost = db.models.filter_by(lambda m: m.cost is not None).sort_by("cost.input").all()
```

### Indexed Lookups

Models are indexed by provider and family for fast access.

```python
# All models from a specific provider
openai_models = db.models.by_provider("openai").all()

# All models in a family
gpt_models = db.models.by_family("gpt").all()
```

### Chaining Queries

All query methods return a query builder, so you can chain them.

```python
result = (
    db.models
    .filter(reasoning=True, open_weights=True)
    .sort_by("release_date", desc=True)
    .first()
)
```

### Query Terminators

Queries are lazy until you call a terminator method.

```python
.all()    # Returns list of all matching items
.first()  # Returns first item or None
.count()  # Returns the count of matching items
```

### Refreshing Data

Data is fetched once and cached. Force a refresh from the API when needed.

```python
db.refresh()
print(f"Data fetched at: {db.fetched_at}")
```

## Acknowledgments

This package is a wrapper around the [models.dev](https://models.dev) database, created and maintained by [Anomaly](https://github.com/anomalyco/models.dev). All credit for the data goes to them.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
netbox-firmware 4.4.0
Firmware management in NetBox
Requires Python: >=3.10
Keywords: netbox, netbox-plugin, Firmware

# Netbox firmware Plugin

Een [Netbox](https://github.com/netbox-community/netbox) plugin voor het bijhouden van de firmwares en bios van de apparaten
Dit is een plugin geschreven door een student tijdens een stage dus tijdelijke updates kunnen nog gebeuren tijdens de geduurte van deze stage.

## Features

Een plugin voor het bijhouden van de firmware en bios versies die op uw devices en modules staan.
Dit is een simpele plugin die een basis legt voor eigen uitbreidingen

## Compatibility
| Netbox Version | Plugin Version |
|----------------|----------------|
|      4.2.4     |      2.7.0     |
|      4.3.x     |      4.x.x     |

## Installing

Officiele installeer instructies: [official Netbox plugin documentation](https://docs.netbox.dev/en/stable/plugins/#installing-plugins)

Je kan de plugin installeren via de github cli of via git
```bash
$ source /opt/netbox/venv/bin/activate
(venv) $ cd /opt/netbox/netbox/netbox/plugins
(venv) $ gh repo clone discedric/netbox_firmware
(venv) $ pip install ./netbox_firmware
```
Je kan deze nu ook installeren via pip
```bash
$ source /opt/netbox/venv/bin/activate
(venv) $ pip install netbox-firmware
```

```python
PLUGINS = [
    'netbox_firmware'
]
```

Je zal ook moeten database migrations en netbox search index moeten updaten:

```bash
(venv) $ cd /opt/netbox/netbox/
(venv) $ python3 manage.py migrate
(venv) $ python3 manage.py reindex --lazy
```
bonni 1.1.2
BONNI: Bayesian Optimization via Neural Network surrogates and Interior Point Optimization
Requires Python: <3.13,>=3.12

![title image](https://github.com/ymahlau/bonni/blob/main/docs/source/_static/bonni.png?raw=true)

[![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://bonni.readthedocs.io/en/latest/)
[![PyPI version](https://img.shields.io/pypi/v/bonni)](https://pypi.org/project/bonni/)

# BONNI: Bayesian Optimization via Neural Network surrogates and Interior Point Optimization

BONNI optimizes any black box function WITH gradient information. 
Especially in optimizations with many degree of freedom, gradient-information increases optimization speed. 
In the image, the surrogate fits the function almost perfectly with few observations.

![surrogate image](https://github.com/ymahlau/bonni/blob/main/docs/source/_static/surrogate.png?raw=true)

## Installation

You can install BONNI simply via 

```bash
pip install bonni
```
We recommend installing also the GPU-acceleration from JAX, which will massively increase speed:
```bash
pip install jax[cuda]
```


## Usage

BONNI provides a nice optimization wrapper similar to the scipy.minimize API:

```python
from bonni import optimize_bonni
from pathlib import Path
import numpy as np

def fn(x: np.ndarray):
    # Input function should return function value and gradient
    value = x[0] ** 2 + x[1]
    grad = np.asarray([2 * x[0], 1])
    return value, grad

xs, ys, gs = optimize_bonni(
    fn=fn,
    bounds=np.asarray([[-1, 1], [0, 1]], dtype=float),
    # BO requires some samples before iterations start. You can either explicitly provide 
    # previous fn evals via `xs=..., ys=..., gs=... or specify a number of random samples. 
    num_bonni_iterations=5,
    num_random_samples=2,
    direction="minimize",
    save_path=Path.cwd(), # save data as npz here
    seed=42,
)
```

Additionally, BONNI includes a convenient wrapper for IPOPT. The standard IPOPT package can be difficult to install/use, so we created a convenient wrapper shown below:

```python
from bonni import optimize_ipopt
xs, ys, gs = optimize_ipopt(
    fn=fn,
    x0=np.asarray([0.5, 0.5]),  # startpoint of optimization
    bounds=np.asarray([[-1, 1], [0, 1]], dtype=float),
    # IPOPT performs line search each iteration, such that the number 
    # of iterations and fn_eval may not be the same
    max_fn_eval=5,
    max_iterations=3,
    direction="maximize",
    save_path=Path.cwd(),
)
```

## Documentation

You can find the full extensive documentation of BONNI [here](https://bonni.readthedocs.io/en/latest/).


## Citation

If you find this repository helpful for your research, please consider citing:

TODO insert citation as soon as paper online.
remotivelabs-topology 0.12.0
RemotiveLabs RemotiveTopology framework allows developers to easily create and run ECU stubs and control their behavior
Requires Python: <4.0,>=3.10
Keywords: automotive, autotech, networking, CAN

# RemotiveTopology framework

RemotiveTopology framework is a python library that allows you to easily interact with RemotiveTopology. Using the framework you can create Behavioral Models or write test cases probing communication channels.

## Installation

```bash
pip install remotivelabs-topology
```

## Project Links

- [Documentation](https://docs.remotivelabs.com/apis/python/remotivelabs/topology)
- [Examples](https://github.com/remotivelabs/remotivelabs-topology-examples)
- [Issues](mailto:support@remotivelabs.com)
ccdt 2.1.172
AI数据转换工具箱

<div align="center">
  <img src="resources/LOGO.png" width="450"/>
  <center>

  [![PyPI](https://img.shields.io/badge/pypi-v1.0.0-blue)](https://pypi.org/project/ccdt/)
  [![license](https://img.shields.io/badge/license-GNU%20General%20Public%20License-green)](https://github.com/540717421/chipeak_cv_data_tool/blob/main/LICENSE)

  </center>
</div>


## 简介
简体中文| [English](README_EN.md)

&nbsp;&nbsp;&nbsp;&nbsp;chipeak_cv_data_tool主要用于多种数据集(计算机视觉)格式之间的相互转换,如coco、labelme等。另外还集成数据、文件、视频等文件常用的处理功能。


## 主要特性
1. 集成多种计算机视觉标注格式相互转换
2. 支持各类数据集的筛选,分配与切割
3. 集成多种文件、图像、视频处理功能
4. 模块可拓展,用户可自定义数据格式
5. 提供python模块包,命令行等便捷方式

## 安装

pip install ccdt

## 快速入门
请参考[快速入门文档](docs/get_started.md)学习 chipeak_cv_data_tool 的基本使用。


## 相关标注工具
labelme:https://github.com/wkentaro/labelme

## 命令行功能实现
* 数据支持集格式：
    - [x] labelme格式数据集 https://github.com/wkentaro/labelme
    - [x] COCO格式数据集
* 数据处理功能：
    - [x] 数据集标注文件相互转换
    - [x] 截取目标保存为图像(目前支持:矩形框,)
* 图像(文件)处理：
    - [x] 按比例或者文件数目划分数据(列如:文件分配或者训练测试集划分)    
* 视频处理：
    - [x] 视频切片成图像
    - [x] 图像合成为视频

## python模块功能实现
* 数据支持集格式：
    - [x] labelme格式数据集 https://github.com/wkentaro/labelme
    - [x] COCO格式数据集
* 数据处理功能：
    - [x] 数据集标注文件相互转换
    - [x] 截取目标保存为图像(目前支持:矩形框,)
* 图像(文件)处理：
    - [x] 按比例或者文件数目划分数据(列如:用于文件分配或者训练测试集划分)
* 视频处理：
    - [x] 视频切片成图像
    - [x] 图像合成为视频

## 开源许可证

该项目采用 [GNU General Public License v3.0 开源许可证](LICENSE)。

## 更新日志

最新的月度版本 v1.0 在 2021.11.08 发布。

已支持的数据集：

- [x] COCO数据集
- [x] Via标注数据集
- [x] Labelme标注数据集


已更新的功能：

- [x] labelme数据集抠图
- [x] coco数据集转labelme数据集
- [x] labelme数据集转coco数据集
- [x] 根据标签类别筛选labelme数据集

## 贡献指南

无

## 致谢

无

## 引用

```
@misc{ccdt2021,
  author =       {詹勇},
  title =        {{ccdt: Annotation conversion and file video processing tools}},
  howpublished = {url{https://github.com/chipeak/chipeak_cv_data_tool.git}},
  year =         {2023}
}
```
## ChiPeak的其它项目

- [ccdt](https://github.com/540717421/chipeak_data_tool): chipeak_cv_data_tool AI数据处理工具箱


## 欢迎加入 ChiPeak 社区

扫描下方的二维码可关注 芯峰科技团队的 [官方网站](http://http://www.chipeak.com/)，加入芯峰科技 团队的 [官方交流](http://www.chipeak.com/account/login)
<div align="center">
<img src="/resources/xf_rq_code.png" height="200" />
</div>
我们会在 ChiPeak 社区为大家

- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 ccdt 常用模块源码
- 📰 发布 ChiPeak 的相关新闻
- 🚀 介绍 ChiPeakDetection 开发的前沿算法
- 🏃 获取更高效的问题答疑和意见反馈
- 🔥 提供与各行各业开发者充分交流的平台

干货满满 📘，等你来撩 💗，ChiPeak 社区期待您的加入 👬

## 安全徽章
[![Security Status](https://www.murphysec.com/platform3/v31/badge/1673251865674670080.svg)](https://www.murphysec.com/console/report/1673251865636921344/1673251865674670080)
scrapy-poet 0.27.0
Page Object pattern for Scrapy
Requires Python: >=3.10

===========
scrapy-poet
===========

.. image:: https://img.shields.io/pypi/v/scrapy-poet.svg
   :target: https://pypi.python.org/pypi/scrapy-poet
   :alt: PyPI Version

.. image:: https://img.shields.io/pypi/pyversions/scrapy-poet.svg
   :target: https://pypi.python.org/pypi/scrapy-poet
   :alt: Supported Python Versions

.. image:: https://github.com/scrapinghub/scrapy-poet/workflows/tox/badge.svg
   :target: https://github.com/scrapinghub/scrapy-poet/actions
   :alt: Build Status

.. image:: https://codecov.io/github/scrapinghub/scrapy-poet/coverage.svg?branch=master
   :target: https://codecov.io/gh/scrapinghub/scrapy-poet
   :alt: Coverage report

.. image:: https://readthedocs.org/projects/scrapy-poet/badge/?version=stable
   :target: https://scrapy-poet.readthedocs.io/en/stable/?badge=stable
   :alt: Documentation Status

``scrapy-poet`` is the `web-poet`_ Page Object pattern implementation for Scrapy.
``scrapy-poet`` allows to write spiders where extraction logic is separated from the crawling one.
With ``scrapy-poet`` is possible to make a single spider that supports many sites with
different layouts.

Requires **Python 3.10+** and **Scrapy >= 2.6.0**.

Read the `documentation <https://scrapy-poet.readthedocs.io>`_  for more information.

License is BSD 3-clause.

* Documentation: https://scrapy-poet.readthedocs.io
* Source code: https://github.com/scrapinghub/scrapy-poet
* Issue tracker: https://github.com/scrapinghub/scrapy-poet/issues

.. _`web-poet`: https://github.com/scrapinghub/web-poet


Quick Start
***********

Installation
============

.. code-block::

    pip install scrapy-poet

Usage in a Scrapy Project
=========================

Add the following inside Scrapy's ``settings.py`` file:

-   Scrapy ≥ 2.10:

    .. code-block:: python

        ADDONS = {
            "scrapy_poet.Addon": 300,
        }

-   Scrapy < 2.10:

    .. code-block:: python

        DOWNLOADER_MIDDLEWARES = {
            "scrapy_poet.InjectionMiddleware": 543,
            "scrapy.downloadermiddlewares.stats.DownloaderStats": None,
            "scrapy_poet.DownloaderStatsMiddleware": 850,
        }
        REQUEST_FINGERPRINTER_CLASS = "scrapy_poet.ScrapyPoetRequestFingerprinter"
        SPIDER_MIDDLEWARES = {
            "scrapy_poet.RetryMiddleware": 275,
        }

Developing
==========

Setup your local Python environment via:

1. `pip install -r requirements-dev.txt`
2. `pre-commit install`

Now everytime you perform a `git commit`, these tools will run against the
staged files:

* `black`
* `isort`
* `flake8`

You can also directly invoke `pre-commit run --all-files` or `tox -e linters`
to run them without performing a commit.