semvec/pypi-packages huggingface dataset:
pyghgaq 0.1.2
A package to calculate GHG emissions from aquatic ecosystems
Requires Python: >=3.12

# PyGHGaq 

This package contains different methods and properties to estimate GHG (CH<sub>4</sub> and CO<sub>2</sub>) emissions and concentrations for aquatic ecosystems

Implemented methods:
* Flux through the water-air interface
* Gas transfer coefficient (k<sub>600</sub>) using different models:
    * MacIntyre et al. 2010 (positive bouyancy, negative bouyancy and mixed).
    * Cole and Caraco 1998.
    * Vachon and Prairie 2013.
* Calculations to transfor k<sub>600</sub> to k<sub>gas</sub> and viceversa.
* Schmidt number.
* Henry's coefficient corrected by temperature and salt.
* Saturation concentration in water.


## Installation
```sh
pip install pyghgaq
```

## Usage

```python
import pyghgaq
import numpy as np

temp = np.random.random(30)
u = np.random.random(30)
cw = np.random.random(30)
cgas = np.random.random(30)
hcpch4 = pyghgaq.henry_coefficient("CH4", temp_c=temp)
k600_ms = pyghgaq.k600("VP2013", u10_ms=1, area_km2=1)
kgas_ms = pyghgaq.k600_to_kx("CO2", temp, k600_ms, u)
co2flux = pyghgaq.atm_diff_flux(cgas, cw, kgas_ms)
```

### Units

Units are managed using `Pint`. They can be changed by default for all the package as
```python
    from pyghgaq.functions.units import DEFAULT_INPUT_UNITS

    DEFAULT_INPUT_UNITS["WindSpeed"] = "m/s"
    DEFAULT_INPUT_UNITS["kgas"] = "m/d"
```
or by function as:
```python
    atmf = pyg.atm_diff_flux(0, 1, 2, units={"kgas": "m/s"})
```

## IMPORTANT
This package is still under delvelopment
maria-summarize-text-ai-assistant 0.1.6
A simple AI-powered text summarization helper using Groq API.

# maria_summarize_text_ai_assistant

A simple Python library to summarize long text using an AI API (Groq).

## Features
- AI-powered text summarization  
- Clean API design  
- Docstrings included  
- Easy to use in Flask or any Python app  
- Automatic `.env` file support for API key configuration

---

## Installation

```bash
pip install maria_summarize_text_ai_assistant
```

The package automatically installs `groq` and `python-dotenv` dependencies.

---

## Usage

### Method 1: Using .env file (Recommended)

1. Create a `.env` file in your project directory:
```
GROQ_API_KEY=your_actual_groq_api_key_here
```

2. Use the library in your code:
```python
from maria_summarize_text_ai_assistant import summarize_text

long_text = "Artificial intelligence is transforming industries..."
summary = summarize_text(long_text)
print(summary)
```

### Method 2: Set environment variable manually

```python
import os
from maria_summarize_text_ai_assistant import summarize_text

# Set your API key in environment
os.environ["GROQ_API_KEY"] = "YOUR_GROQ_API_KEY"

long_text = "Artificial intelligence is transforming industries..."
summary = summarize_text(long_text)
print(summary)
```

---

## Publishing Updates

Update `setup.py` version number, then rebuild your package:
```bash
python setup.py sdist bdist_wheel
```

Upload to PyPI:
```bash
python -m twine upload dist/*
```
exe2txt 0.1.0
Convert Windows EXE/DLL files into text representations for LLM prompting.
Requires Python: >=3.10
Keywords: dll, exe, llm, pe, reverse-engineering, windows

# exe2txt

Convert Windows EXE/DLL files into text representations for LLM prompting.

## Installation

### Using uv (recommended)

```bash
# Install as a tool
uv tool install exe2txt

# Or run directly without installing
uvx exe2txt <file.exe>
```

### Using pip

```bash
pip install exe2txt
```

## Usage

### Command Line

```bash
# Analyze an EXE file
exe2txt example.exe

# Save output to a file
exe2txt example.exe -o output.txt
```

### As a Library

```python
from exe2txt import analyze_pe

# Analyze a PE file
info = analyze_pe("example.exe")

# Get text representation
print(info.to_text())

# Access structured data
print(f"Machine type: {info.machine_type}")
print(f"Entry point: 0x{info.entry_point:08X}")

for section in info.sections:
    print(f"Section: {section.name}")

for imp in info.imports:
    print(f"Import: {imp.dll_name}")
```

## Features

- Parse PE file headers and metadata
- Extract section information
- List imported DLLs and functions
- List exported functions
- Generate LLM-friendly text output

## Development

### Setup

```bash
# Clone the repository
git clone https://github.com/crlotwhite/exe2txt.git
cd exe2txt

# Create virtual environment and install dependencies
uv sync

# Install in development mode
uv pip install -e .
```

### Running Tests

```bash
uv run pytest
```

### Building

```bash
uv build
```

## License

MIT License