semvec/pypi-packages huggingface dataset:
netmind-mcpserver-mcp 0.1.1
AThis is an MCP (Model Context Protocol) server specifically built to collect and serve ratings and reviews for other MCP servers deployed on the NetMind platform.
License: MIT
Requires Python: >=3.11

# NetMind MCPServer MCP

This is an MCP (Model Context Protocol) server that provides endpoints for managing and interacting with  [NetMind MCP Servers](https://netmind.ai/AIServices), including querying servers, retrieving server details, and handling user reviews and ratings.


## Components

### Tools

- query_server:  Query the server list with optional fuzzy name matching and pagination.
- get_server: Retrieves detailed information about a specific server by its name.
- add_update_rating_review: Adds or updates a rating and review for a specific server.
- Lists reviews and ratings for a specific server.

## Installation

### Requires [UV](https://github.com/astral-sh/uv) (Fast Python package and project manager)

If uv isn't installed.

```bash
# Using Homebrew on macOS
brew install uv
```

or

```bash
# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

### Environment Variables

You can obtain an API key from [Netmind](https://www.netmind.ai/user/apiToken)

- `NETMIND_API_TOKEN`: Your Netmind API key

### Cursor & Claude Desktop && Windsurf Installation

Add this tool as a mcp server by editing the Cursor/Claude/Windsurf config file.

```json
{
  "mcpServers": {
    "netmind-mcpserver-mcp": {
      "env": {
        "NETMIND_API_TOKEN": "XXXXXXXXXXXXXXXXXXXX"
      },
      "command": "uvx",
      "args": [
        "netmind-mcpserver-mcp"
      ]
    }
  }
}
```

#### Cursor

- On MacOS: `/Users/your-username/.cursor/mcp.json`
- On Windows: `C:\Users\your-username\.cursor\mcp.json`

#### Claude

- On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
- On Windows: `%APPDATA%/Claude/claude_desktop_config.json`


### Windsurf

- On MacOS: `/Users/your-username/.codeium/windsurf/mcp_config.json`
- On Windows: `C:\Users\your-username\.codeium\windsurf\mcp_config.json`
uxsimpp 0.0.1
UXsim++: Fast and simple traffic simulator for Python
License: MIT
Requires Python: >=3.9

# UXsim++: Fast and simple traffic simulator for Python

**UXsim++** (or **uxsimpp**) is a free, open-source mesoscopic network traffic flow simulator for Python. 
It simulates the movements of car travelers and traffic congestion in road networks. 
It is suitable for simulating large-scale (e.g., city-scale) traffic phenomena. 
UXsim++ would be especially useful for scientific and educational purposes because of its fast, simple, lightweight, and customizable features, but users are free to use UXsim++ for any purpose.

UXsim++ is a significantly faster variation of [**UXsim**](https://github.com/toruseo/UXsim), a pure Python-based traffic simulator.
Its functionalities and syntax are almost equivalent to UXsim.
Meanwhile, the internal simulation engine is thoroughly written in C++, making it 20 to 30 times faster than UXsim.
Thanks to pybind11, the C++ engine is fully accessible from Python codes without any dependencies.

This is alpha stage.
The codes and docs are work in progress.

## Main Features

## Examples

## Install

## Getting Started

```python
from uxsimpp import newWorld, Analyzer

W = newWorld(
    name="basic",
    deltan=5,
    tmax=1200,
    random_seed=42
)

W.addNode("orig1", 0, 0)
W.addNode("orig2", 0, 2)
W.addNode("merge", 1, 1)
W.addNode("dest", 2, 1)

W.addLink("link1", "orig1", "merge", 1000, 20, 0.2, 1)
W.addLink("link2", "orig2", "merge", 1000, 20, 0.2, 1)
W.addLink("link3", "merge", "dest", 1000, 20, 0.2, 1)

W.adddemand("orig1", "dest", 0, 1000, 0.45)
W.adddemand("orig2", "dest", 400, 1000, 0.6)

W.print_scenario_stats()

W.exec_simulation()
W.print_simple_results()

ana = Analyzer(W)
ana.plot_time_space_trajectories(["link1", "link3"])
ana.network_fancy()
```

## Further Reading

ArXiv preprint will be added

## Terms of Use & License

MIT License
mlpredictor-joaa 0.1.0
A simple machine learning package using scikit-learn
License: MIT
Requires Python: >=3.6

# MLPredictor

MLPredictor is a simple machine learning package that trains a RandomForest model using the Iris dataset and enables users to make predictions. The package is built using `scikit-learn` and is intended as a demonstration of packaging Python machine learning projects for distribution.

## Features

- Train a RandomForestClassifier on the Iris dataset.
- Make predictions on new data after training.
- Save and load trained models.

## Installation

You can install the package via **PyPI** or from **source**.

### Install from PyPI

```bash
pip install mlpredictor
```

### Install from Source (GitHub)

```bash
git clone https://github.com/ebimsv/mlpredictor.git
cd mlpredictor
pip install .
```

## Usage

After installation, you can use `MLPredictor` to train a model and make predictions.

### Example: Training and Making Predictions

```python
from mlpredictor import MLPredictor

# Initialize the predictor
predictor = MLPredictor()

# Train the model on the Iris dataset
predictor.train()

# Make a prediction on a sample input
sample_input = [5.1, 3.5, 1.4, 0.2]
prediction = predictor.predict(sample_input)

print(f"Predicted class: {prediction}")
```