semvec/pypi-packages huggingface dataset:
seqabpy 0.2.2
Sequential A/B Testing Framework in Python
Requires Python: >=3.7
Keywords: AB, AVI, GST, Sequential, Testing

# seqabpy
![python-package-build](https://github.com/NPodlozhniy/seqabpy/actions/workflows/python-package.yml/badge.svg)
[![python-package-coverage](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/NPodlozhniy/seqabpy/python-coverage-comment-action-data/endpoint.json)](https://htmlpreview.github.io/?https://github.com/NPodlozhniy/seqabpy/blob/python-coverage-comment-action-data/htmlcov/index.html)
![PyPI - Version](https://img.shields.io/pypi/v/seqabpy?label=pypi%20version&color=green)

Sequential A/B Testing Framework in Python

### Getting started

Easy installation via `pip`

```
$ pip install seqabpy
```

### Workflow

With each push to `master` building workflow is triggered that,
besides the build itself, checks linters, applies tests and measures the coverage.

What is more, when the tag is pushed, PyPI workflow is triggered,
that publishes a package and, in addition, builds GitHub release.


### Development

If you would like to contribute to the project yo can do the following


1. Copy the repo
```
$ git clone https://github.com/NPodlozhniy/seqabpy.git
```

2. Test the package
```
$ python -m pip install pytest coverage
$ coverage run --source=src --module pytest --verbose tests && coverage report --show-missing
```

3. Install requirements for developers
```
$ pip install -r requirements_dev.txt
```

4. Make changes and then release a new version
```
$ python -m build
$ python -m twine upload --repository testpypi dist/*
```
pyshowdown 0.3.1
A client library for the Pokémon Showdown! battle simulator.
Requires Python: >=3.9

<p align="center">
 <img src="https://raw.githubusercontent.com/ScottehMax/pyshowdown/main/assets/logo.png" width=300 alt="pyshowdown logo"/>
</p>

<p align="center">
  This is a client library for the Pokémon Showdown! battle simulator.
</p>

## Installation

```bash
pip install pyshowdown
```

## Usage

```python
from pyshowdown import client
import asyncio

if __name__ == "__main__":
    c = client.Client(
        username="username",
        password="password",
        url="wss://sim3.psim.us/showdown/websocket",
    )

    # System plugins (challstr, init, deinit, title, users) are automatically loaded
    # Load any additional custom plugins here if needed:
    # c.load_plugin("custom_plugin_name")

    loop = asyncio.get_event_loop()
    loop.run_until_complete(c.keep_connected())
```

## Documentation

Full documentation is available at [https://docs.scotteh.me/pyshowdown/](https://docs.scotteh.me/pyshowdown/).
doc-reviewer-mcp 0.2.1
MCP server for OCR-generated Markdown document review and diagnostics
Requires Python: >=3.10
Keywords: diagnostics, document-review, markdown, mcp, ocr

# doc-reviewer-mcp

MCP server for OCR-generated Markdown document review and diagnostics.

## Features

- **ToC Alignment**: Compares table of contents with actual headers using fuzzy matching
- **LaTeX Formula Validation**: Detects syntax errors and OCR noise in formulas
- **Image Link Checking**: Verifies referenced images exist with similar path suggestions
- **Git Integration**: Checkpoint, diff, and rollback support for safe batch modifications

## Installation

```bash
# Using uvx (recommended)
uvx doc-reviewer-mcp

# Using pip
pip install doc-reviewer-mcp
```

## Usage

### As MCP Server

Add to your MCP client configuration:

```json
{
  "mcpServers": {
    "doc-reviewer": {
      "command": "uvx",
      "args": ["doc-reviewer-mcp"]
    }
  }
}
```

### Available Tools

- `analyze_document` - Comprehensive document analysis
- `analyze_toc` - Table of contents alignment check
- `analyze_formulas` - LaTeX formula validation
- `analyze_images` - Image link verification
- `extract_headers` - Extract all markdown headers
- `extract_toc` - Extract ToC entries
- `git_checkpoint` - Create a checkpoint before changes
- `git_diff_summary` - View changes since checkpoint
- `git_rollback` - Rollback to previous checkpoint
- `git_history` - View commit history

## Development

```bash
# Clone the repository
git clone https://github.com/yourusername/doc-reviewer-mcp.git
cd doc-reviewer-mcp

# Install dependencies
uv sync

# Run locally
uv run doc-reviewer-mcp
```

## License

MIT
hydrobricks 0.8.6
A modular hydrological modelling framework
Requires Python: >=3.10

# hydrobricks

A modular hydrological modelling framework.

[![DOI](https://zenodo.org/badge/301952016.svg)](https://zenodo.org/badge/latestdoi/301952016)
[![Core Tests Linux](https://github.com/hydrobricks/hydrobricks/actions/workflows/core-tests-linux.yml/badge.svg)](https://github.com/hydrobricks/hydrobricks/actions/workflows/core-tests-linux.yml)
[![Python Wheels](https://github.com/hydrobricks/hydrobricks/actions/workflows/python-wheels.yml/badge.svg)](https://github.com/hydrobricks/hydrobricks/actions/workflows/python-wheels.yml)
[![codecov](https://codecov.io/gh/hydrobricks/hydrobricks/branch/main/graph/badge.svg?token=G1PBSK8EG2)](https://codecov.io/gh/hydrobricks/hydrobricks)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/hydrobricks/hydrobricks/main.svg)](https://results.pre-commit.ci/latest/github/hydrobricks/hydrobricks/main)
[![Core Doxygen](https://github.com/hydrobricks/hydrobricks/actions/workflows/core-doxygen.yml/badge.svg)](https://github.com/hydrobricks/hydrobricks/actions/workflows/core-doxygen.yml)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/639e5bb76690488f9aac5feb89722bfa)](https://www.codacy.com/gh/hydrobricks/hydrobricks/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=hydrobricks/hydrobricks&amp;utm_campaign=Badge_Grade)

## Install

Wheels are available to install from PyPI (https://pypi.org/project/hydrobricks/)

Install: ```pip install hydrobricks```

## Resources
*   **Documentation**: https://hydrobricks.readthedocs.io/en/latest/
*   How to build: https://github.com/hydrobricks/hydrobricks/wiki
*   Code documentation of the core: https://hydrobricks.github.io/hydrobricks-doc-core/
sfkit 0.0.262
Some CLI Tools For The Secure Multi-party Genomic Analysis Website
Requires Python: <4.0,>=3.10

## sfkit CLI

This is a collection of command-line interface tools to facilitate privacy perserving and secure genomics analyses with multiple parties. 

The full documentation, including installation instructions and tutorials can be found at [sfkit.readthedocs.io](https://sfkit.readthedocs.io/en/latest/).

The companion website is [sfkit.org](https://sfkit.org/), with a github repo at [github.com/hcholab/sfkit-website](https://github.com/hcholab/sfkit-website).

For questions or concerns, you can contact us at [support@sfkit.org](mailto:support@sfkit.org).
cacimbao 0.4.0
Base de dados brasileiras com dados reais para propósitos educacionais
Requires Python: >=3.12

# cacimbão

<img src="cacimbao_logo.svg" alt="cacimbão logo" width="125" align="right"/>

[![PyPI - Version](https://img.shields.io/pypi/v/cacimbao)](https://pypi.org/project/cacimbao/) [![Test](https://github.com/anapaulagomes/cacimbao/actions/workflows/ci.yml/badge.svg)](https://github.com/anapaulagomes/cacimbao/actions/workflows/ci.yml) [![DOI](https://zenodo.org/badge/975927667.svg)](https://doi.org/10.5281/zenodo.15640773)

Bases de dados brasileiras para fins educacionais 🤓 [demo](https://huggingface.co/spaces/anapaulagomes/pescadores-e-pescadoras-profissionais)

## Uso

Primeiro, instale o pacote com o PyPi ou interaja através do notebook <a target="_blank" href="https://marimo.app/l/8pi68g"><img src="https://marimo.io/shield.svg"/></a>

```bash
pip install cacimbao
```

### Base de dados disponíveis

Depois, você pode usar o pacote para ver as bases disponíveis:

```python
import cacimbao

cacimbao.list_datasets()
```

Se quiser ver mais detalhes sobre as base de dados disponíveis, você pode usar:

```python
import cacimbao

cacimbao.list_datasets(include_metadata=True)
```

### Carregando uma base de dados local

Carregue um dataset local:

```python
df = cacimbao.load_dataset("pescadores_e_pescadoras_profissionais")
```

### Carregando uma base de dados remota

Ou escolha um dataset para _download_:

```python
df = cacimbao.download_dataset("filmografia_brasileira")
```

### Escolha do formato do dataframe

Você pode também escolher qual o formato do dataframe na sua biblioteca preferida.
A biblioteca padrão é o Polars mas você pode trabalhar com Pandas também.

```python
df = cacimbao.download_dataset("filmografia_brasileira", df_format="pandas")
```

## O que é um cacimbão?

Veja o que é um cacimbão [aqui](https://www.youtube.com/watch?v=Ft8-XXILjgE).