semvec/pypi-packages huggingface dataset:
easy-unet 1.0.2
A lightweight and flexible PyTorch library providing a modular UNet implementation with advanced attention and normalization blocks for fast image processing and deep learning development.
Requires Python: >=3.10
Keywords: UNet, PyTorch, deep learning, image segmentation, attention, normalization, machine learning, computer vision


# easy-unet: Modular UNet Backbone in PyTorch

A **lightweight and flexible PyTorch library** providing a **modular UNet implementation** with advanced attention and normalization blocks. Designed for fast experimentation and development across diverse image processing and deep learning tasks.

## 🚀 Features

- 🧱 **UNet backbone** with residual blocks and flexible channel multipliers  
- 🎯 **Advanced attention modules** including linear and flash attention for improved feature modeling  
- ⚙️ **Configurable architecture** for dropout, channels, and dimensions  
- 🧪 Modular and clean PyTorch codebase suitable for research and production  
- 🔄 Supports easy integration with diffusion models, segmentation, or any custom pipeline  

## 📦 Installation

You can access the [PyPI page](https://pypi.org/project/easy-unet/) or install the package directly.

```bash
pip install easy-unet

```

## 📁 Project Structure

```bash
easy-unet/
├── easy_unet/
│   ├── __init__.py
│   ├── module.py        # All architecture classes and logic
├── pyproject.toml
├── LICENSE
└── README.md

```

## 🚀 Quick Start

### Import and create the model

```python
import torch
from easy_unet import UNet

model = UNet(
    dim=64,
    dim_mults=(1, 2, 4, 8),
    channels=3,
    out_channels=1,
    dropout=0.1
)

x = torch.randn(1, 3, 256, 256)    # sample input
output = model(x)
print(output.shape)  # e.g., torch.Size([1, 3, 256, 256])

```

## ⚙️ Configuration Options

| Argument | Type | Default | Description |
|--|--|--|--|
| `dim` | `int` | `64` | Base number of feature channels |
| `dim_mults` | `tuple` | `(1, 2, 4, 8)` | Channel multipliers per U-Net stage |
| `channels` | `int` | `3` | Number of input channels (e.g., 3 for RGB) |
| `out_channels` | `int` | `1` | Number of output channels (e.g., 1 for binary and 2 or more for multi-class) |
| `dropout` | `float` | `0.0` | Dropout rate for regularization. |

## 🙋‍♂️ Author

Developed by [Mehran Bazrafkan](mailto:mhrn.bzrafkn.dev@gmail.com)

> Created for general-purpose use cases and research requiring flexible UNet architectures in PyTorch.

## ⭐️ Support & Contribute

If you find this project useful, please:

- ⭐️ Star the repo

- 🐛 Report issues

- 📦 Suggest features or improvements

## 🔗 Related Projects

- [diffusion-pytorch-lib · PyPI (by me)](https://pypi.org/project/diffusion-pytorch-lib/)
- [variational-autoencoder-pytorch-lib · PyPI (by me)](https://pypi.org/project/variational-autoencoder-pytorch-lib/)
- [convolutional-autoencoder-pytorch · PyPI (by me)](https://pypi.org/project/convolutional-autoencoder-pytorch/)

## 📜 License

This project is licensed under the terms of the [`MIT LICENSE`](https://github.com/MehranBazrafkan/easy-unet/blob/main/LICENSE).
vec-cluster 0.2.2
Inspect and cluster generic vector features for entities
Requires Python: >=3.10

# vec-cluster

Inspect and cluster generic vector features for entities. Lightweight CLI built with Typer + Rich.

- Repo: https://github.com/mikewong23571/vec-cluster
- License: MIT
- Python: 3.10+

## Install

```bash
uv venv
uv sync
```

## Usage

```bash
# Help
uv run vec-cluster -h

# Inspect (recommended first)
uv run vec-cluster inspect features.csv

# One-shot auto cluster
uv run vec-cluster auto features.csv
```

After publishing to PyPI you can run without installing:

```bash
uvx vec-cluster inspect features.csv
```

### Commands quick reference

- `inspect` — data health, distance distribution, HAC threshold suggestions.
- `auto` — HAC with suggested medium threshold, writes clusters + report.
- `hac` — hierarchical clustering (requires `--k` or `--threshold`).
- `kmeans` — k-means (requires `--k`).
- `dbscan` — DBSCAN (`--eps`, `--min-samples`, cluster=-1 => outlier).

## Development

```bash
uv run ruff format
uv run ruff check
uv run mypy src tests
uv run pytest          # full suite with coverage gate
```

Design/contract document: `docs/design.md`.
moleculekit 1.11.1
A molecule reading/writing and manipulation package.
Requires Python: >=3.9

# MoleculeKit

[![Conda](https://anaconda.org/acellera/moleculekit/badges/version.svg)](https://anaconda.org/acellera/moleculekit)

A molecule manipulation library

# Getting started

### Installing moleculekit

#### With pip

```
pip install moleculekit
```

#### With conda

[Installation Instructions](https://software.acellera.com/moleculekit/installation.html)

### Using moleculekit in ipython

Install ipython in the correct conda enviroment using the following command.

```
pip install ipython
```

Now you can start an ipython console with

```
ipython
```

In the ipython console you can now import any of the modules of moleculekit and use it as normal.

```python
from moleculekit.molecule import Molecule

mol = Molecule('3ptb')
mol.view()
```

### API

For the official documentation of the moleculekit API head over to https://software.acellera.com/moleculekit/index.html

### Issues

For any bugs or questions on usage feel free to use the issue tracker of this github repo.

### Dev

If you are using moleculekit without installing it by using the PYTHONPATH env var you will need to compile the C++ extensions in-place with the following command:

```
python setup.py build_ext --inplace
```

#### Building for WebAssembly

Install `emscripten` https://emscripten.org/docs/getting_started/downloads.html

```
conda create -n pyodide-build
conda activate pyodide-build
conda install python=3.11
pip install pyodide-build==0.25.1

# Activate the emscripten environment
cd ../emsdk
./emsdk install 3.1.46
./emsdk activate 3.1.46
source emsdk_env.sh
cd -

# Build the package
export PYO3_CROSS_INCLUDE_DIR="HACK"
export PYO3_CROSS_LIB_DIR="HACK"
rm -rf .pyodide-xbuildenv
pyodide build -o dist_pyodide
cp dist_pyodide/*.whl test_wasm/wheels/
cd test_wasm
python3 -m http.server
```

If you get an error at building about numpy missing, check this issue https://github.com/pyodide/pyodide/issues/4347

#### Debugging segmentation faults in Cython part

1. Put a reproducible script in a file like `segfault.py`
2. Modify setup.py to have `-g` as compile flag instead of `-O3`
3. Recompile extensions with `python setup.py build_ext --inplace`
4. Execute script with gdb like `gdb --args python segfault.py`
5. Execute `run` and then `bt 10` to show the backtrace
6. Have fun

## Citing MoleculeKit

If you use this software in your publication please cite:

Stefan Doerr, Matthew J. Harvey, Frank Noé, and Gianni De Fabritiis.
**HTMD: High-throughput molecular dynamics for molecular discovery.**
_Journal of Chemical Theory and Computation_, **2016**, _12_ (4), pp 1845–1852.
[doi:10.1021/acs.jctc.6b00049](http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00049)