semvec/pypi-packages huggingface dataset:
dummypypi2 0.1.34a0
Dummy package to test direct publishing from GitHub to PyPi
Requires Python: >=3.8
Keywords: dummy, github, pypi, python, test

# DummyPyPi2
Dummy package to test direct publishing from GitHub to PyPi.

![Tests](https://github.com/bwolleswinkel/DummyPyPi2/actions/workflows/run-tests.yml/badge.svg)
configparser-typed 0.1.0
A typed, validated wrapper around Python's built-in configparser
Requires Python: >=3.10
Keywords: config, ini, configparser, typed, dataclass, pydantic, validation, configuration

# typed-configparser

A small, modern Python library that provides a typed, validated wrapper around Python's built-in `configparser`.

## What it does

`typed-configparser` lets you load INI configuration files into typed dataclasses or Pydantic models with automatic type conversion and validation. It's thin, explicit, and predictable—no magic, no auto-loading, no framework overhead.

## Installation

```bash
pip install typed-configparser
```

Or install from source:

```bash
git clone https://github.com/PierrunoYT/typed-configparser.git
cd typed-configparser
pip install -e .
```

## Example

Given an INI file:

```ini
# app.ini
[server]
port = 8000
debug = true
```

And a dataclass:

```python
from dataclasses import dataclass
from typed_configparser import load_section

@dataclass
class ServerConfig:
    port: int
    debug: bool = False

config = load_section("app.ini", "server", ServerConfig)

assert isinstance(config.port, int)  # True
assert isinstance(config.debug, bool)  # True
```

## Features

- **Typed section loading**: Map INI sections to dataclasses or Pydantic models
- **Type conversion**: Automatic conversion for `int`, `float`, `bool`, `str`, `list`, `dict`, and `Path`
- **Validation**: Clear errors for missing keys, unknown keys, and type mismatches
- **Optional fields**: Support for `Optional[T]` and default values
- **Python 3.10+**: Full type hints throughout with `py.typed` marker
- **Zero dependencies**: Uses only Python standard library

## Non-goals

This library intentionally does **not** provide:

- Environment variable support
- Multiple file merging
- Remote configuration loading
- CLI tools
- Plugin systems
- Section inheritance
- Logging framework integration

If you need these features, this library is not for you.

## Development

### Running Tests

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=typed_configparser
```

### Type Checking

```bash
mypy typed_configparser/
```

### Linting

```bash
ruff check .
```

## Publishing

See [PUBLISHING.md](PUBLISHING.md) for detailed instructions on building and publishing this package to PyPI.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

MIT License - see the LICENSE file for details.

## Links

- **PyPI**: https://pypi.org/project/typed-configparser/
- **Source Code**: https://github.com/PierrunoYT/typed-configparser
- **Issue Tracker**: https://github.com/PierrunoYT/typed-configparser/issues
- **Changelog**: https://github.com/PierrunoYT/typed-configparser/releases
endnote-generator 0.1.0
A tool to generate EndNote XML libraries from PDF files using DOI metadata.
Requires Python: >=3.8

# EndNote Library Generator

![App Icon](icon.png)


A Python tool that scans a directory of PDFs, automatically identifies their DOIs, fetches bibliographic metadata from Crossref, and generates an EndNote-compatible XML library with file attachments.

![App Main Window](main_window.png)

## Features

- **PDF Scanning**: Recursive logic to find all PDFs in a folder.
- **DOI Identification**: Regex-based extraction of DOIs from the first few pages of each PDF.
- **Metadata Fetching**: Retrieves Title, Author, Year, Journal, Volume, Issue, Pages from Crossref API.
- **EndNote XML Export**: Generates a rich XML file that can be imported directly into EndNote.
- **File Attachments**: Links the original PDF files to the created EndNote records.

## Installation

1. Clone or download this repository.
2. Install the required dependencies:

```bash
pip install -r requirements.txt
```

## Usage

### Using the Python Package

```python
from endnote_generator import LibraryGenerator

# Initialize
generator = LibraryGenerator()

# Process a folder containing PDFs
df = generator.process_directory("path/to/your/pdf_folder")

# View results
print(df)

# Save header to EndNote XML
generator.save_library("MyLibrary.xml")
```

### Using the GUI

To run the modern graphical interface:

```bash
python gui_app.py
```

- Select the folder containing your PDFs.
- Select where to save the EndNote XML.
- Click **Generate Library**.

### Using the Jupyter Notebook

Open `demo.ipynb` to see a step-by-step interactive demonstration.


## Importing into EndNote

1. Open EndNote.
2. Go to **File** -> **Import** -> **File...**
3. Choose the generated `MyLibrary.xml` file.
4. Set **Import Option** to `EndNote generated XML`.
5. Click **Import**.

## Project Structure

- `src/endnote_generator/`: Source code package.
- `src/endnote_generator/pdf_processor.py`: Handles PDF text extraction and DOI finding.
- `src/endnote_generator/metadata_fetcher.py`: Connects to Crossref API.
- `src/endnote_generator/endnote_writer.py`: Generates the XML output.
- `src/endnote_generator/library_manager.py`: Main orchestrator.
gwaslab-agent 0.1.0
GWASLab Agent
Requires Python: <3.13,>=3.12

## GWASLab-Agent

**GWASLab-Agent** is an LLM-powered framework for automated GWAS summary statistics processing, quality control, transformation, and visualization.  
It extends the original **GWASLab** Python package with intelligent planning, multi-step workflow generation, and agent-driven execution.

GWASLab-Agent is designed to serve as an *autonomous GWAS assistant*, capable of interpreting user instructions, planning complex operations, managing file paths, and producing publication-ready summaries and figures.

<img width="1090" height="536" alt="image" src="https://github.com/user-attachments/assets/bf9b085e-5f14-43fc-9106-1ac765762db8" />



---

## Installation

1. Create a new environment (recommended)
```
# Create a clean environment with Python 3.12
conda create -n gwaslab-agent python=3.12

# Activate it
conda activate gwaslab-agent
```

2. Install GWASLab and GWASLab-Agent
```bash
pip install gwaslab
pip install gwaslab_agent
```

---

## Design of GWASLab-Agent

### SmartSumstats Object

At the core of GWASLab-Agent is the **SmartSumstats** object — an LLM-enhanced wrapper around `gl.Sumstats`.  
It integrates five coordinated sub-agents:

- **Loader** — detects file formats, parses paths, handles chromosome patterns  
- **Planner** — constructs optimal multi-step workflows  
- **Worker** — executes tasks, QC steps, and visualizations  
- **PathManager** — manages input/output paths and reference resources  
- **Summarizer** — generates structured summaries and Methods-section text  

Together, these sub-agents enable fully automated GWAS workflows with minimal user input.

---


---

## Citation

(Coming soon — please cite GWASLab and GWASLab-Agent once the corresponding manuscripts or preprints are available.)