semvec/pypi-packages huggingface dataset:
posebusters 0.6.4
PoseBusters: Plausibility checks for generated molecule poses.
Requires Python: >=3.9
Keywords: posebusters, docking, tests, metric

![posebusters_banner](https://github.com/maabuu/posebusters/blob/b5f4c2caed1499c2b73f5297a08e60ec7d263c61/docs/source/_static/logo_banner.png?raw=true "PoseBusters")

PoseBusters: Plausibility checks for generated molecule poses.

### Paper in [Chemical Science](https://pubs.rsc.org/en/content/articlelanding/2024/sc/d3sc04185a) and preprint on [arXiv](https://arxiv.org/abs/2308.05777)

## Installation

```bash
# install with pip from PyPI
pip install posebusters
```
<!-- # install with conda from conda-forge
conda install posebusters -c conda-forge -->

## Usage

<!-- ### Command line usage -->

```bash

# Check generated molecule pose.
bust molecule_pred.sdf
bust molecule_a.sdf molecule_b.sdf
bust molecule_*.sdf

# Check new ligand generated for a given protein.
bust ligand_pred.sdf -p mol_cond.pdb

# Check re-docked ligand (a pose that should recover the ligand in a given protein-ligand crystal complex).
bust ligand_pred.sdf -l mol_true.sdf -p protein.pdb

# Check any of the three by providing a csv with files to check together
bust -t file_table.csv
```

<!-- ### Python API

```python
from dockbusters import DockBuster

# check re-docked ligand
DockBuster().bust(ligand_pred_file, ligand_crystal_file, protein_crystal_file)

# check docked ligand
DockBuster().bust(ligand_pred_file, protein_crystal_file)

# check molecule
DockBuster().bust(ligand_pred_file, protein_crystal_file)
``` -->

## Documentation

Documentation is available at [https://posebusters.readthedocs.io](https://posebusters.readthedocs.io).

For more information about the tests and for a study using PoseBusters to compare docking methods, refer to our [paper](http://dx.doi.org/10.1039/D3SC04185A) or [preprint](https://arxiv.org/abs/2308.05777):

```
@article{buttenschoen2024posebusters,
  title = {{{PoseBusters}}: {{AI-based}} Docking Methods Fail to Generate Physically Valid Poses or Generalise to Novel Sequences},
  shorttitle = {{{PoseBusters}}},
  author = {Buttenschoen, Martin and Morris, Garrett M. and Deane, Charlotte M.},
  year = "2024",
  journal = "Chemical Science",
  volume = "15",
  issue = "9",
  pages = "3130-3139",
  publisher = "The Royal Society of Chemistry",
  doi = "10.1039/D3SC04185A",
  url = "http://dx.doi.org/10.1039/D3SC04185A",
}
```

The data used for the paper is available at [https://zenodo.org/record/8278563](https://zenodo.org/record/8278563).

## Feedback & Contact

We welcome all feedback. For code issues, please open an issue. For other inquiries contact us by email.

## Thanks

This program uses software written by other people. Notably:

- RDKit - [https://github.com/rdkit/rdkit](https://github.com/rdkit/rdkit)
- Pandas - [https://github.com/pandas-dev/pandas](https://github.com/pandas-dev/pandas)
mkdocs-likec4 1.1.1
LikeC4 for MkDocs
Requires Python: >=3.10

# mkdocs-likec4

MkDocs plugin for embedding [LikeC4](https://likec4.dev/) architecture diagrams.

## Quick Start

1. Ensure `likec4` and `graphviz` are available on the build system
2. Install the `mkdocs-likec4` plugin via `pip`:
  ```shell
  pip install mkdocs-likec4
  ```
3. Add the plugin to your `mkdocs.yml`:
  ```yaml
  plugins:
    - search
    - mkdocs-likec4
  ```
4. Start embedding views in your markdown:
  ````markdown
  ```likec4-view
  <your-view-id>
  ```
  ````

## Documentation

For detailed instructions, configuration options and a demo, please read the **[documentation](https://doubleslashde.github.io/mkdocs-likec4/)**.

## Development

### Local setup

Run `./local-preview` in your terminal to build and run a MkDocs server with the plugin installed,
serving on <http://127.0.0.1:8000/>.

## Releasing

Manually trigger the `release` workflow via GitHub Actions, which will auto-bump the plugin version and perform the release process.
PyPi publishing is set up via [trusted publishing](https://docs.pypi.org/trusted-publishers/).

## License

mkdocs-likec4 is licensed under the [Apache License, Version 2.0](LICENSE).
torchfont 0.8.1
Datasets, Transforms and Models specific to Vector Fonts
Requires Python: >=3.10
Keywords: pytorch, vector-fonts, fonts, glyphs, google-fonts, ttf, otf

# TorchFont

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Python Version](https://img.shields.io/pypi/pyversions/torchfont)](https://pypi.org/project/torchfont/)
[![CI](https://github.com/torchfont/torchfont/actions/workflows/ci.yml/badge.svg)](https://github.com/torchfont/torchfont/actions)
[![Documentation](https://readthedocs.org/projects/torchfont/badge/?version=latest)](https://torchfont.readthedocs.io/)
[![PyPI Downloads](https://static.pepy.tech/personalized-badge/torchfont?period=total&units=INTERNATIONAL_SYSTEM&left_color=GRAY&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/torchfont)
[![PyPI version](https://img.shields.io/pypi/v/torchfont)](https://pypi.org/project/torchfont/)
[![Rust](https://img.shields.io/badge/Rust-2024-orange?logo=rust)](https://www.rust-lang.org/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.3+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/)

TorchFont is an **unofficial** library based on PyTorch for deep learning with vector fonts.
It is not affiliated with or endorsed by the PyTorch project.

## Installation

The package requires Python 3.10+ and PyTorch 2.3+.

Install TorchFont with **uv**:

```bash
uv add torchfont
```

Or with **pip**:

```bash
pip install torchfont
```

## Citation

If you find TorchFont useful in your work, please consider citing the following BibTeX entry:

```bibtex
@software{fujioka2025torchfont,
    title        = {TorchFont: A PyTorch-based library for Vector Fonts},
    author       = {Takumu Fujioka},
    year         = 2025,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/torchfont/torchfont}}
}
```
puters 0.1.0
Add your description here
Requires Python: >=3.11

# puters

Initial PyPI package scaffold for `puters`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```