semvec/pypi-packages huggingface dataset:
latex-clean-fig 0.2.0
A CLI tool to clean unused figure files in LaTeX projects
Requires Python: <4.0,>=3.13

<h1 align="center">
    latex-clean-fig
</h1>

<p align="center">
    <img alt="PyPI version" src="https://img.shields.io/pypi/v/latex-clean-fig.svg" />
    <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/latex-clean-fig.svg">
    <a href="https://pepy.tech/project/latex-clean-fig">
        <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/latex-clean-fig.svg">
    </a>
    <img alt="Downloads" src="https://static.pepy.tech/badge/latex-clean-fig">
    <img alt="latex-clean-fig" src="https://github.com/firefly-cpp/latex-clean-fig/actions/workflows/test.yml/badge.svg" />
</p>

<p align="center">
    <img alt="Repository size" src="https://img.shields.io/github/repo-size/firefly-cpp/latex-clean-fig" />
    <img alt="License" src="https://img.shields.io/github/license/firefly-cpp/latex-clean-fig.svg" />
    <img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/w/firefly-cpp/latex-clean-fig.svg">
    <a href="https://isitmaintained.com/project/firefly-cpp/latex-clean-fig">
        <img alt="Percentage of issues still open" src="https://isitmaintained.com/badge/open/firefly-cpp/latex-clean-fig.svg">
    </a>
    <a href="https://isitmaintained.com/project/firefly-cpp/latex-clean-fig">
        <img alt="Average time to resolve an issue" src="https://isitmaintained.com/badge/resolution/firefly-cpp/latex-clean-fig.svg">
    </a>
    <img alt="GitHub contributors" src="https://img.shields.io/github/contributors/firefly-cpp/latex-clean-fig.svg"/>
</p>

<p align="center">
    <a href="#-motivation">🎯 Motivation</a> •
    <a href="#-installation">📦 Installation</a> •
    <a href="#-license">🔑 License</a>
</p>

## 🎯 Motivation

The package provides a simple command-line tool to help authors clean up unused image files in a project directory before submitting a paper. Multiple versions of figures often accumulate in the folder during the writing process, making it cluttered and difficult to manage. This tool scans the LaTeX file for figures included using the \includegraphics command and compares them against the image files in the specified folder. It identifies unused images and removes them, leaving only the files referenced in the LaTeX document. This is especially useful for ensuring the project directory remains tidy and submission-ready.

## 📦 Installation

### pip

Install `latex-clean-fig` with pip:

```sh
pip install latex-clean-fig
```

### How to use?

```sh
clean-fig TEX_FILE FOLDER
```
where:

- `TEX_FILE`: Path to your LaTeX file.
- `FOLDER`: Path to the folder containing image files.

This will scan TEX_FILE for included figures and remove any unused image files from the FOLDER directory.

## 🔑 License

This package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.

## Disclaimer

This package is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
pytest-collect-markers 0.2.0
A pytest plugin to collect and output test markers to JSON
Requires Python: >=3.10

# pytest-collect-markers

A pytest plugin that collects test markers and outputs them to a JSON file.

## Installation

```bash
pip install pytest-collect-markers
```

## Usage

Run pytest with the `--collect-markers` flag to collect all markers from your tests:

```bash
pytest --collect-markers
```

This will create a `markers.json` file containing a mapping of test node IDs to their markers.

### Options

| Option | Default | Description |
|--------|---------|-------------|
| `--collect-markers` | `False` | Enable marker collection |
| `--markers-output` | `markers.json` | Output file path for collected markers |

### Example

```bash
pytest --collect-markers --markers-output=my-markers.json
```

### Output Format

The output JSON file maps test node IDs to their list of markers:

```json
{
  "tests/test_example.py::test_slow_operation": ["slow", "integration"],
  "tests/test_example.py::test_fast_unit": ["unit"]
}
```

## Development

```bash
# Install with uv
uv sync

# Run tests
uv run pytest
```

## License

MIT
paper-siphon 0.3.0
Extract clean Markdown from academic PDFs - like drinking through a straw
Requires Python: >=3.11

# Paper Siphon

**Extract clean Markdown from academic PDFs** - like drinking through a straw.

Academic papers come with artifacts: awkward page breaks, mangled tables, or even line numbers.
Paper Siphon filters them out, leaving you with clean, readable Markdown.

```
paper-siphon paper.pdf
```

That's it. Your paper is now `paper.md`.

---

## Features

- **Smart whitespace** - Collapses excessive blank lines, normalizes spacing
- **Table preservation** - Keeps your data tables intact and formatted
- **Formula support** - Optional enrichment for mathematical expressions
- **Line number removal** - Automatically strips the margin numbers (when present)
- **VLM pipeline** - Use vision-language models for complex layouts
- **Apple Silicon acceleration** - MLX support for fast processing on M-series Macs

## Installation

```bash
# With uv (recommended)
uv pip install paper-siphon

# With pip
pip install paper-siphon
```

For Apple Silicon acceleration (optional):

```bash
uv pip install paper-siphon[mlx]
```

## Usage

### Quick start (no install)

```bash
uvx paper-siphon paper.pdf                # Run directly with uvx
```

### Basic

```bash
paper-siphon paper.pdf                    # Creates paper.md
paper-siphon paper.pdf -o notes.md        # Custom output path
```

### From URL (including arXiv)

```bash
paper-siphon https://arxiv.org/pdf/1706.03762.pdf
```

**Tip:** For arXiv papers, just change `/abs/` to `/pdf/` in the URL:
```
https://arxiv.org/abs/1706.03762  →  https://arxiv.org/pdf/1706.03762.pdf
```

(That's "Attention Is All You Need" - the Transformer paper)

### Advanced

```bash
paper-siphon --vlm paper.pdf              # Use VLM for complex layouts
paper-siphon --enrich-formula paper.pdf   # Enable formula enrichment
paper-siphon --no-mlx --vlm paper.pdf     # VLM without MLX acceleration
paper-siphon -v paper.pdf                 # Verbose logging
```

## How It Works

Paper Siphon uses [Docling](https://github.com/DS4SD/docling) for PDF parsing, then applies
post-processing to clean up common academic paper artifacts:

1. **PDF parsing** - Extracts structure, text, and tables
2. **Line number filtering** - Removes standalone 1-4 digit numbers (common in journal formats)
3. **Whitespace normalization** - Collapses multiple blank lines

## Options

| Flag | Description |
|------|-------------|
| `-o, --output` | Output file path (default: input with `.md` extension) |
| `--vlm` | Use VLM pipeline for complex layouts |
| `--mlx/--no-mlx` | Toggle MLX acceleration (Apple Silicon, default: on) |
| `--enrich-formula` | Enable formula enrichment (slow, CPU-bound) |
| `-v, --verbose` | Enable debug logging |

## Development

```bash
# Clone and install
git clone https://github.com/mrshu/paper-siphon.git
cd paper-siphon
uv sync --dev

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=paper_siphon
```

## License

MIT

---

*Stop wrestling with PDFs. Just siphon the good stuff.*
helics-apps 3.7.0
Meta-package to install helics (ARCHIVED, pip install helics instead)
Requires Python: >=3.7
Keywords: helics, co-simulation

# HELICS Apps (Archived)

**This has been replaced by [pyhelics](https://github.com/GMLC-TDC/pyhelics) - the final version of this package uploaded has been changed to be equivalent to `pip install helics`**

[HELICS](https://github.com/GMLC-TDC/HELICS) is a cross-platform co-simulation framework that enables multiple
simulation tools to exchange data and stay synchronized in time to create larger simulations. This is a set of
binary wheels for macOS, Windows, and Linux for installing the collection of HELICS apps utilities. If you want
the Python interface for writing HELICS programs in Python, see the [helics](https://pypi.org/project/helics/)
pip package.

## Installation
At minimum for the Linux binary wheels to work properly, [pip 19.0 is required](https://packaging.python.org/specifications/platform-compatibility-tags/#manylinux-compatibility-support).
Regardless of your platform, you should probably upgrade pip with `python -m pip install --upgrade` to make
sure the right binary package is found. If you aren't on a supported platform, you will need to compile
the HELICS apps from source or check if your system package manager has a copy of them.

After upgrading pip, running `python -m pip install helics-apps` should fetch a binary wheel if one is available for your platform.

### Binary distributions
This package provides pre-compiled binary wheels for the following operating systems

* Linux 64-bit (manylinux2010 compatible -- glibc 2.12+, most popular distributions released since May 2010)
* macOS Universal2 -- compatible with 10.9+ for Intel macs and 11+ for Apple Silicon macs
* Windows 32/64-bit

### Source distributions
An attempt has been made at providing a source distribution that will compile HELICS from source. Although minimal
support can be provided (e.g. you will need to troubleshoot issues yourself), PRs fixing issues are welcome.

Certain features may not be available when building from source, including the ZeroMQ core and any features that
rely on Boost (such as the interprocess core or web server).

If for some reason using a binary wheel is not an option but a source wheel is and you want to replicate the way the
supported binary wheels are built, set the `CIBUILDWHEEL` environment variable to tell the binary wheel build to
create a wheel from a pre-built HELICS release. Note that you *must* have an internet connection when installing
for this to work, and that it will *only work on glibc x86_64 Linux, 32/64-bit Windows, and macOS systems.

## Release
HELICS is distributed under the terms of the BSD-3 clause license. All new
contributions must be made under this license. [LICENSE](LICENSE)

SPDX-License-Identifier: BSD-3-Clause