semvec/pypi-packages huggingface dataset:
amberNPS 0.1.6
A python api to make lethal blood concentrations using amberNPS
License: MIT
Keywords: amberNPS, chemistry, toxicology


# amberNPS-api


amperNPS-api is a convenient python API to make predictions lethal blood concentrations (LBC) of new psychoactive substances (NPS). 

[amberNPS](https://ambernps.streamlit.app/) is a streamlit application developed by Tarcisio Nascimento Correa. For further details please the publication at [A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances](https://www.sciencedirect.com/science/article/pii/S2667118224000151)

## Installation

Install the latest version of amberNPS with pip:

```shell
pip install amberNPS
```

## Example usage

Import the amberNPS class, instatiate it then call the predict method with the SMILES string:
```pycon
>>> from amberNPS import amberNPS
>>> a = amberNPS()
>>> a = a.predict("Brc1cc2C(=NCc3nnc(C)n3c2cc1)c4ccccc4") # bromazolam
>>> preds
{'Drug Class': 'Benzodiazepines', 'LOLBC': 28.988149644904777,'LBC50': 151.32238377755087, 'HOLBC': 870.7153200659999}

```
Results are also stored as properties:

```pycon
>>> a.LOLBC
28.988149644904777
>>> a.LBC50
151.32238377755087
>>> a.HOLBC
870.7153200659999
>>> a.drug_class
'Benzodiazepines'
>>> a.smiles
'Brc1cc2C(=NCc3nnc(C)n3c2cc1)c4ccccc4'
```
The raw predictions (log10 of concentrations above) can also be accessed.

As a list:
```pycon
>>> a.lbc_preds
[1.0843620906924618, 0.36667941118940295, -0.3932936005597203]
```
Or using properties:
```pycon
>>> a.pLOLBC
1.0843620906924618
>>> a.pLBC50
0.36667941118940295
>>> a.pHOLBC
0.3932936005597203
```

You can also render the image of the structure (this may or may not work depending on IDE):
```pycon
a.structure
```
![example structure](assets/example.png)

## Documentation

The API reference is available at <https://ambernps-api.readthedocs.io/en/latest/amberNPS.html#module-amberNPS>.

## Contributing

- Feature ideas and bug reports are welcome on the [Issue Tracker](https://github.com/dpasin/amberNPS/issues).
- Fork the [source code](https://github.com/dpasin/amberNPS) on GitHub, make changes and file a pull request.

## License

PubChemPy is licensed under the [MIT license](https://github.com/dpasin/amberNPS/LICENSE).
pdf-bookmark-splitter 0.1.0
A tool for splitting PDF files based on bookmarks
License: MIT
Requires Python: >=3.6

# PDF Bookmark Splitter

A Python utility that splits a PDF file into multiple files based on its bookmarks.

## Features

- Splits PDF files using existing bookmarks
- Creates separate PDF files for each bookmark
- Maintains original PDF quality
- Preserves bookmark hierarchy

## Requirements

- Python 3.11+
- [uv](https://docs.astral.sh/uv/) - Fast Python package manager
- PyPDF (automatically installed with package)

## Installation

### Using uv (Recommended)

This project uses `uv` for dependency management. First, install uv:

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

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

Then clone and set up the project:

```bash
git clone https://github.com/leejss/pdf-bookmark-splitter.git
cd pdf-bookmark-splitter
uv sync
```

### Using pip (Alternative)

You can also install with pip:

```bash
pip install git+https://github.com/leejss/pdf-bookmark-splitter.git
```

## Usage

### With uv

```bash
uv run pdf-split input.pdf --output-dir chapters
```

### With pip installation

```bash
pdf-split input.pdf --output-dir chapters
```

### Options

- `--output-dir`: Directory to save split PDF files (default: `chapters`)

### Example

```bash
# Split PDF and save to 'output' directory
uv run pdf-split book.pdf --output-dir output
```

This will create separate PDF files in the specified directory, named according to the top-level bookmarks in the input PDF.
sap2000-mcp 0.1.0
Minimal MCP server to operate SAP2000 from AI agents
License: MIT
Requires Python: >=3.10

# sap2000-mcp

First minimal MCP server for controlling SAP2000 from an AI agent (e.g. Cursor AI, Claude, GPT).

Status: early preview. MCP/tool UX is evolving and most general-purpose agents won’t yet perform complex SAP2000 tasks reliably. We’ll iterate quickly to make agent workflows far more effective.

We welcome ideas: open an issue with feature requests, missing tools, or workflow suggestions.

Install (PyPI):
```bash
pip install sap2000-mcp
```

Install (dev):
```bash
python3 -m venv venv
source venv/bin/activate
pip install -e .
```

Use with Cursor (Settings → Features → MCP → Edit Config):
```json
{
  "mcpServers": {
    "sap2000-mcp": {
      "command": "/Users/you/path/to/engmcp/venv/bin/python",
      "args": ["-m", "sap2000_mcp"]
    }
  }
}
```

Notes:
- SAP2000 integration requires Windows (COM). On macOS, `sap2000_is_available` will be false.
- More tools and agent patterns coming soon. PRs and issues are appreciated.
mehta 1.1.6
A beginner-friendly telegram sdk providing intuitive command decorators.
License: MIT
Requires Python: >=3.7
Keywords: telegram bot sdk framework telethon pyTelegramBotAPI telebot bot-development decorator message-handling automation

**Note**: This package is a **wrapper** that uses [pyTeleBot](https://github.com/eternnoir/pyTelegramBotAPI) and [Telethon](https://github.com/LonamiWebs/Telethon) for Telegram-related features.    
When you install it, all required dependencies including [pyTeleBot](https://github.com/eternnoir/pyTelegramBotAPI), [Telethon](https://github.com/LonamiWebs/Telethon), and others are automatically installed to ensure full functionality and seamless integration.  
  
## Installation  
```sh  
pip install mehta  
```  
  
## Basic Setup  
```python  
from mehta import telegram  
  
bot = telegram()  
  
@bot.commands(['start'])  
def welcome(message):  
    return text("Hello World!")  
  
bot.run("BOT_TOKEN")  
```  
  

**Full Documention here**: [View on Github](https://github.com/realstarexx/mehta)