semvec/pypi-packages huggingface dataset:
symmetr 0.10.0
Package for determining symmetry properties of crystals.

Python package for determining symmetry properties of crystals. See [bitbucket
        repository](https://bitbucket.org/zeleznyj/linear-response-symmetry) for more details.
        
        The code internally uses [findsym](https://stokes.byu.edu/iso/isolinux.php) to determine the symmetry of a given crystal. Since findsym only works on Linux, most features of Symmetr will also only work on Linux. However, it is possible to run the code on any platform through [Docker](https://bitbucket.org/zeleznyj/linear-response-symmetry/wiki/docker). The Windows Subsystem for Linux could likely be used for running the code, but this has not been tested. 
        
        Currently both Python2 and Python3 are supported, however, the support for Python2 will likely be dropped in the future.
        
        This code is still in development and may contain bugs! Use with caution!
vcfpy 0.14.2
Python 3 VCF library with good support for both reading and writing
Requires Python: >=3.10

[![pypi](https://img.shields.io/pypi/v/vcfpy.svg)](https://pypi.python.org/pypi/vcfpy)
[![bioconda](https://img.shields.io/conda/dn/bioconda/vcfpy.svg?label=Bioconda)](https://bioconda.github.io/recipes/vcfpy/README.html)
[![CI](https://github.com/bihealth/vcfpy/actions/workflows/main.yml/badge.svg)](https://github.com/bihealth/vcfpy/actions/workflows/main.yml)
[![Documentation Status](https://readthedocs.org/projects/vcfpy/badge/?version=latest)](https://vcfpy.readthedocs.io/en/latest/?badge=latest)
[![Publication in The Journal of Open Source Software](http://joss.theoj.org/papers/edae85d90ea8a49843dbaaa109e47cba/status.svg)](http://joss.theoj.org/papers/10.21105/joss.00085)

# VCFPy

Python 3 VCF library with good support for both reading and writing

- Free software: MIT license
- Documentation: <https://vcfpy.readthedocs.io>.

## Features

- Support for reading and writing VCF v4.3
- Interface to `INFO` and `FORMAT` fields is based on `dict` allows for easier modification than PyVCF (also I find this more pythonic)
- Read (and jump in) and write BGZF files just using `vcfpy`

## Why another VCF parser for Python!

I've been using PyVCF with quite some success in the past. However, the
main bottleneck of PyVCF is when you want to modify the per-sample
genotype information. There are some issues in the tracker of PyVCF but
none of them can really be considered solved. I tried several hours to
solve these problems within PyVCF but this never got far or towards a
complete rewrite...

For this reason, VCFPy was born and here it is!

## What's the State?

VCFPy is the result of two full days of development plus some
maintenance work later now (right now). I'm using it in several projects
but it is not as battle-tested as PyVCF.

## Why Python 3 Only?

As I'm only using Python 3 code, I see no advantage in carrying around
support for legacy Python 2 and maintaining it. At a later point when
VCFPy is known to be stable, Python 2 support might be added if someone
contributes a pull request.
rl-bandits 0.1.0
A modular library for multi-armed bandit algorithms
Requires Python: >=3.9

# RL Bandits

A modular Python library for multi-armed bandit algorithms, designed for research and experimentation.

## Features

- **Environments**: Stationary and non-stationary k-armed bandits
- **Agents**: ε-greedy, UCB, gradient bandit algorithms
- **Evaluation**: Single and multi-run experiment framework
- **Utilities**: Plotting, seeding, and configuration management

## Installation

```bash
# Clone the repository
git clone https://github.com/meeran03/rl-bandits.git
cd rl-bandits

# Install in development mode
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"
```

## Quick Start

```python
from rlbandits import Bandit, EpsilonGreedyAgent, run_multi_experiment, plot_curves

# Create environment and agent
bandit_kwargs = {"k": 10, "reward_std": 1.0}
agent = EpsilonGreedyAgent(k=10, epsilon=0.1)

# Run experiment
results = run_multi_experiment(
    bandit_class=Bandit,
    bandit_kwargs=bandit_kwargs,
    agent=agent,
    steps=1000,
    runs=200
)

# Plot results
plot_curves(results, title="ε-Greedy Performance")
```

## Project Structure

```
rl-bandits/
├── README.md
├── pyproject.toml
├── requirements.txt
├── rlbandits/
│   ├── __init__.py
│   ├── envs/                    # Bandit environments
│   │   ├── stationary_bandit.py
│   │   └── nonstationary_bandit.py
│   ├── agents/                  # Bandit algorithms
│   │   ├── epsilon_greedy.py
│   │   ├── ucb.py
│   │   └── gradient_bandit.py
│   ├── core/                    # Experiment framework
│   │   ├── runner.py
│   │   ├── eval.py
│   │   └── utils.py
│   └── experiments/             # Configuration files
├── notebooks/                   # Jupyter notebooks
├── tests/                       # Unit tests
└── .github/workflows/           # CI/CD
```

## Algorithms Implemented

- **Greedy**: Pure greedy action selection
- **ε-Greedy**: Epsilon-greedy with exploration
- **UCB**: Upper Confidence Bound
- **Gradient Bandit**: Preference-based policy gradient

## Development

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

# Run tests
pytest

# Format code
black rlbandits/ tests/

# Type checking
mypy rlbandits/
```

## License

MIT License
graph-id-db 1.1.1
A database project for graph identification and processing
Requires Python: <4.0,>=3.9

# GraphID DB

## Installation

### From pypi
```
pip install graph-id-db
```

### From github
```
git clone git@github.com:kmu/graphid-db.git
conda create -n graph-id-db python=3.11
conda activate graph-id-db
python -m build
pip install dist/*.whl
```
approck-imagesize 1.4.1
Getting image size from png/jpeg/jpeg2000/gif file
Requires Python: >=3.10

imagesize
=============

.. image:: https://travis-ci.org/shibukawa/imagesize_py.svg?branch=master
    :target: https://travis-ci.org/shibukawa/imagesize_py

This module analyzes JPEG/JPEG 2000/PNG/GIF/TIFF/SVG/Netpbm/WebP image headers and returns image size or DIP.

.. code:: python

   import imagesize

   width, height = imagesize.get("test.png")
   print(width, height)

   width, height = imagesize.getDPI("test.png")
   print(width, height)

This module is a pure Python module. You can use file like object like file or something like ``io.BytesIO``.

API
-----

* ``imagesize.get(filepath)``

  Returns image size (width, height).

* ``imagesize.getDPI(filepath)``

  Returns image DPI (width, height).

Benchmark
------------

It only parses headers, and ignores pixel data. So it is much faster than Pillow.

.. list-table::
   :header-rows: 1

   - * module
     * result
   - * imagesize (pure Python)
     * 1.077 seconds per 100 000 times
   - * Pillow
     * 10.569 seconds per 100 000 times

I tested on MacBookPro (2014/Core i7) with 125kB PNG files.

Development
---------------

Run test with the following command:

.. code:: bash

   python -m unittest

License
-----------

MIT License

Thanks
----------

I referred to the following code:

* http://markasread.net/post/17551554979/get-image-size-info-using-pure-python-code
* https://stackoverflow.com/questions/8032642/how-to-obtain-image-size-using-standard-python-class-without-using-external-lib

I use sample image from here:

* https://www.nightprogrammer.org/development/multipage-tiff-example-download-test-image-file/

Thank you for feedback:

* tk0miya (https://github.com/tk0miya)
* shimizukawa (https://github.com/shimizukawa)
* xantares (https://github.com/xantares)
* Ivan Zakharyaschev (https://github.com/imz)
* Jon Dufresne (https://github.com/jdufresne)
* Geoff Lankow (https://github.com/darktrojan)
* Hugo (https://github.com/hugovk)
* Jack Cherng (https://github.com/jfcherng)
* Tyler A. Young (https://github.com/s3cur3)
* Mark Browning (https://github.com/mabrowning)
* ossdev07 (https://github.com/ossdev07)
* Nicholas-Schaub (https://github.com/Nicholas-Schaub)
* Nuffknacker (https://github.com/Nuffknacker) 
* Hannes Römer (https://github.com/hroemer)
* mikey (https://github.com/ffreemt)
* Marco (https://github.com/marcoffee)
* ExtReMLapin (https://github.com/ExtReMLapin)