semvec/pypi-packages huggingface dataset:
dify-utils 0.1.0
A Python utility library for interacting with Dify API
Requires Python: >=3.11

# dify-utils

A Python utility library for interacting with Dify API.

## Installation

```bash
pip install dify-utils
```

## Quick Start

```python
from dify_utils import DifyClient, Credentials

# Initialize credentials
credentials = Credentials(api_key="your_api_key")

# Create a client
client = DifyClient(credentials)

# Use the client to interact with Dify API
# Example: Get all applications
applications = client.get_applications()
```

## Features

- **DifyClient**: A client for interacting with the Dify API
- **Credentials**: A class for managing API credentials
- **Comprehensive API Coverage**: Support for all major Dify API endpoints

## Documentation

For more detailed documentation, please visit [the documentation page](https://github.com/ivan-pikulin/dify_utils#readme).

## Development

### Setup

1. Clone the repository:
   ```bash
   git clone https://github.com/ivan-pikulin/dify_utils.git
   cd dify_utils
   ```

2. Create a virtual environment and install dependencies:
   ```bash
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e .
   ```

### Running Tests

```bash
# Install test dependencies
pip install -e ".[dev]"

# Run tests
pytest
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
projectionSVD 0.2.0
Projection into SVD space for genetic data
Requires Python: >=3.10

# projectionSVD (v0.2.0)
[![DOI](https://zenodo.org/badge/866019962.svg)](https://doi.org/10.5281/zenodo.13881621)\
`projectionSVD` is a small command-line program written in Python/Cython to project a dataset onto a principal component space based on genotype data. It takes binary PLINK files as genotype input and works with PCA output from programs like [`halkoSVD`](https://github.com/Rosemeis/halkoSVD), `PLINK`, and `PCAone`. `projectionSVD` requires estimated allele frequencies, eigenvalues and SNP loadings to perform the projection.

## Installation
```bash
# Option 1: Build and install via PyPI
pip install projectionSVD

# Option 2: Download source and install via pip
git clone https://github.com/Rosemeis/projectionSVD.git
cd projectionSVD
pip install .

# Option 3: Download source and install in a new Conda environment
git clone https://github.com/Rosemeis/projectionSVD.git
conda env create -f projectionSVD/environment.yml
conda activate projectionSVD
```
You can now run the program with the `projectionSVD` command. 


## Quick usage
```bash
# Check help message of the program
projectionSVD -h

# Perform projection using PCAone output
projectionSVD --bfile new --freqs old.afreq --eigvals old.eigvals --loadings old.loadings --threads 32 --out new

# Outputs eigenvectors of new dataset (new.eigvecs)
```

### Options
* `--freqs-col`, specify which column to use in frequency file (6)
* `--batch`, process projection in batches of specified number of SNPs (8192)
* `--raw`, only output eigenvectors without FID/IID