semvec/pypi-packages huggingface dataset:
omcp-manager 0.1.0
OMCP 管理器：用于管理模型上下文协议（MCP）服务器的包管理器
Requires Python: >=3.7

# OMCP Manager

OMCP Manager is a package manager for Model Context Protocol (MCP) servers. It provides a simple command-line interface to manage MCP server packages, including installation, uninstallation, and configuration.

## ⚠️ Security Warning

**Important Security Notice**: MCP tools may pose potential poisoning risks. Please be aware of the following security considerations:

1. Always verify the source and integrity of MCP packages before installation
2. Run MCP tools in isolated environments or containers
3. Regularly update packages to the latest secure versions
4. Monitor system resources and network activity
5. Use strong authentication and access controls
6. Keep your system and dependencies up to date

### Features

- List available MCP server packages
- Install and uninstall MCP server packages
- Manage package configurations
- Support for multiple runtimes (Node.js, Python)
- Client integration support (Claude, Cursor, etc.)

### Installation

```bash
# Clone the repository
git clone git@github.com:jinyalong/omcp.git
cd omcp

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements-dev.txt

# Install the package
pip install -e .
```

### Usage

```bash
# List available packages
omcp list

# Install a package
omcp install modelcontextprotocol@filesystem

# List installed packages
omcp installed

# Uninstall a package
omcp uninstall modelcontextprotocol@filesystem
```

### Development

To contribute to the project:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

### License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 探索更多

想要了解更多 MCP 服务器信息？请访问 [MCP服务器](https://mcpservers.cn) 获取更多资讯。
structureboost 0.7.0
Gradient Boosting and Probabilistic Regression with categorical structure
Requires Python: >=3.8

# StructureBoost

StructureBoost is a package to do Gradient Boosting in a manner that exploits the **structure** of categorical variables.

Have you ever used "US state" as a variable in a prediction problem and thought "Why can't my algorithm use the geography of the states to make better predictions?"

Or you've used the month of the year as a predictor, but noticed that January should border December the same way that June borders July. (or hour of the day, or day of the week)

StructureBoost can help.  Read the documentation and references below.  Or dive into some [examples](https://github.com/numeristical/structureboost/tree/master/examples)

## Video Lectures

There are some explanatory videos on the [Numeristical Youtube Channel](https://www.youtube.com/channel/UCfsbASar8nsLs4NbhQwuaVg)


## Documentation
[Read the Docs](https://structureboost.readthedocs.io/)

## References:

Lucena, B. "Exploiting Categorical Structure with Tree-Based Methods. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2949-2958, 2020. [http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf](http://proceedings.mlr.press/v108/lucena20a/lucena20a.pdf)

Lucena, B. "StructureBoost: Efficient Gradient Boosting for Structured Categorical Variables." [https://arxiv.org/abs/2007.04446](https://arxiv.org/abs/2007.04446)
dnaweaver 0.3.10
Make ordering and assembly plans for DNA sequences
Keywords: DNA, optimization, assembly, ordering, synthetic, biology

DNA Weaver
===========

DNA Weaver is a Python library to find optimal strategies for assembling large
DNA constructs. Given an arbitrary sequence, DNA Weaver will select the most
adapted commercial DNA providers, cloning methods and parts repositories
(depending on your preferences), and will design all necessary assembly fragments
and assembly steps.

DNA Weaver was written with versatility and extensibility in mind:
each DNA source and assembly method can be customized, and assembly plans can
be optimized with respect to total price, overall duration of the assembly,
or assembly success probabilities.

Infos
-----

**PIP installation:**

.. code:: bash

  pip install dnaweaver

**Web documentation:**

`<https://edinburgh-genome-foundry.github.io/DnaWeaver/>`_

**Github Page**

`<https://github.com/Edinburgh-Genome-Foundry/Dnaweaver>`_

**Live demo**

`<https://dnaweaver.genomefoundry.org>`_

**License:** MIT

Copyright 2021, 2025 Edinburgh Genome Foundry, University of Edinburgh


More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

DNA Weaver is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_ synthetic biology software suite for DNA design, manufacturing and validation.
mosaik-fmi 1.1.0.post0
An adapter for FMUs (ME or CS) in mosaik
Requires Python: >=3.9

==========
mosaik-fmi
==========

This mosaik-fmi adapter allows to couple FMUs, which are based on the FMI standard (https://fmi-standard.org) with mosaik.

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

* mosaik-fmi is based on the FMI++ library, which can be found at https://fmipp.sourceforge.io/
* The FMI++ python interface is used, which can be found at https://github.com/AIT-IES/py-fmipp. See this page also for details about installation and requirements of the python interface.

Test
====

The FMUs for the test is based on https://github.com/modelica/Reference-FMUs.

How to Use
==========
Specify simulator configurations within your scenario script::

    sim_config = {
        'FMI': {
            'python': 'mosaik_fmi.mosaik_fmi:FmuAdapter',
        },
        ...
    }

Initialize the FMU Simulator::

    fmu_sim = world.start('FMI',
                        integrator='integratorCK',
                        work_dir='path/to/the/fmu/directory',
                        fmu_name='FMU_name',
                        fmi_version='2',
                        fmi_type='cs',
                        logging_on=False,
                        instance_name='FMU_name',
                        step_size=60*60)

Instantiate FMU model entity::

    fmu_entity = fmu_sim.FMU_name.create(1,
                                 input_1=1.0,          #  FMU input variable
                                 input_2=2.0,          #  All variables are listed in modelDescription.xml
                                 ...
                                 )

Connect FMU output to another simulator's input::

    world.connect(fmu_entity[0], other_simulator,'fmu_output_1', 'simulator_input_1')

Selecting integrator

Refer to https://fmipp.readthedocs.io/projects/py-fmipp/en/latest/getting-started/model-exchange.html#classes-fmumodelexchangev1-and-fmumodelexchangev2 for choosing different integration algorithms


Getting help
============

If you need help, please visit the `mosaik-users mailing list`__ .

__ https://mosaik.offis.de/mailinglist
spritekit 0.2.2
2D scene+actor framework built on top of raylib
Requires Python: >=3.10

# PySpriteKit

> [!WARNING]
> Work in progress, see [TODO](#todo)

> [!NOTE]
> `pip install spritekit==0.2.2`

2D scene+actor framework based off Apple's SpriteKit. See [test.py](https://github.com/takeiteasy/PySpriteKit/blob/master/test.py) for working example.

## Features

- [X] Easy + Flexible
- [X] Scenes
- [X] Automatic batched rendering
- [X] Automatic asset cache
- [X] Line, Rectangle, Ellipse, Circle, Polygon
- [X] Sprites + SpriteSheets + Animation
- [X] Labels
- [X] Actions, Timer, Emitter
- [X] Music + Sounds

## TODO

- [ ] Input handling
- [ ] Event broadcasting
- [ ] Framebuffers + Shader classes
- [ ] Render scenes to Framebuffers
- [ ] 3D render to texture (displayed in 2D)
- [ ] 2D Physics
- [ ] Bezier curves
- [ ] Video + GIFs
- [ ] Headless mode
- [ ] Add examples
- [ ] Add documentation

## Requirements

```
transitions==0.9.2
pyglm==2.8.1
raudio==0.0.1
moderngl==5.12.0
numpy==2.2.4
pillow==11.1.0
pyglsl==0.0.8
```

## LICENSE

```
PySpriteKit

Copyright (C) 2025 George Watson

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
```