semvec/pypi-packages huggingface dataset:
rna-clustering-algorithms 0.1.4
A small example package
Requires Python: >=3.8


# Enhanced Scanpy Clustering

A collection of tools and workflows to improve clustering analysis in single-cell RNA-seq data using [Scanpy](https://scanpy.readthedocs.io/).

## Features

- Advanced clustering algorithms and parameter tuning
- Visualization enhancements for cluster interpretation
- Utilities for preprocessing and quality control
- Integration with AnnData objects

## Installation

```bash
pip install enhanced-scanpy-clustering
```
Or, for development:
```bash
git clone https://github.com/Terrell-byte/enhanced-scanpy-clustering.git
cd enhanced-scanpy-clustering
pip install -e .
```

## Usage

Describe the main ways to use the package here.

### Command-line usage

```bash
# Run the main workflow
python -m enhanced_scanpy_clustering.main --input <input_file> --output <output_dir>

# Additional options
python -m enhanced_scanpy_clustering.main \
    --input <input_file> \
    --output <output_dir> \
    --algorithm dbscan_base \
    --neighbors 15 \
    --resolution 0.5 \
    --random-state 42
```

### Python usage

```python
import scanpy as sc
import enhanced_scanpy_clustering.clustering as cl

# Option 1: Use the cluster function
cl.cluster(adata, algorithm="...", key_added='...')

# Option 2: Use direct Scanpy integration
cl.enable_scanpy_integration()
```

> **Note:** 
> - The package requires AnnData objects as input
> - Recommended to normalize and preprocess your data before clustering
> - Set random seed for reproducible results

### Examples

```python
import scanpy as sc
import enhanced_scanpy_clustering.clustering as cl

# Load adata with you data set
adata = sc.read_h5ad('YourFile.h5ad')  

# 1: Using only neccesary paremeters with algo "DBScan_Base"
cl.cluster(adata, algorithm='DBScan_Base', key_added='dbscan_labels')

# 2: Using Scanpy integration
cl.enable_scanpy_integration()

sc.tl.DBScan_Base(adata, key_added='dbscan_labels')

# 3: Additional parameters for fine-tuning
cl.cluster(
    adata,
    algorithm='DBScan_Base',
    key_added='dbscan_labels',
    n_neighbors=15,
    resolution=0.5,
    random_state=42
)
```


## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
perlin-noise-dhex 0.1.0
Python implementation of Perlin noise generator
Requires Python: >=3.7

# perlin_noise_dhex

Python implementation of Perlin noise generator by Exityx (DH - DataHandler)

## Installation

```bash
pip install perlin_noise_dhex
```

## Usage

```python
from perlin_noise_dhex import CreateNewPerlinNoise

# Create noise generator
noise = CreateNewPerlinNoise(seed=20, octave=2, amp=7, period=24)

matrix = noise.createPerlin()

noise.showGraph(matrix)

```

## License

MIT