semvec/pypi-packages huggingface dataset:
synkro 0.5.56
Curate, evaluate, and ship LLM datasets from any document
Requires Python: >=3.10
Keywords: dataset-generation, fine-tuning, llm, synthetic-data, training-data

# Synkro

![](https://static.scarf.sh/a.png?x-pxid=f08f2a53-e0cf-4291-83f4-b518f620bf69)
[![PyPI version](https://img.shields.io/pypi/v/synkro.svg?cacheSeconds=3600)](https://pypi.org/project/synkro/)
[![Downloads](https://static.pepy.tech/badge/synkro)](https://pepy.tech/project/synkro)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![Documentation](https://img.shields.io/badge/docs-synkro.sh-purple.svg)](https://synkro.sh/docs)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

## Installation

```bash
pip install synkro
```

## Quick Start

```python
from synkro import create_pipeline, DatasetType
from synkro.models import Google
from synkro.examples import EXPENSE_POLICY

pipeline = create_pipeline(
    model=Google.GEMINI_25_FLASH,
    grading_model=Google.GEMINI_25_PRO,
    dataset_type=DatasetType.CONVERSATION,
)

dataset = pipeline.generate(EXPENSE_POLICY, traces=50)
dataset.save("training.jsonl")
```

Or use the CLI:

```bash
synkro generate policy.pdf --traces 50

# Quick demo with built-in policy
synkro demo
```

## Features

- **Multiple dataset types** - Conversation, Instruction, Evaluation, Tool Calling
- **Auto grading & refinement** - Responses graded and refined until passing
- **Coverage tracking** - Track scenario diversity, identify gaps
- **Eval platform export** - LangSmith, Langfuse, Q&A formats
- **Any LLM** - OpenAI, Anthropic, Google, Ollama, vLLM
- **Any document** - PDF, DOCX, TXT, Markdown, URLs

## Documentation

Full documentation at **[synkro.sh/docs](https://synkro.sh/docs)**

- [Quickstart](https://synkro.sh/docs/quickstart)
- [Dataset Types](https://synkro.sh/docs/datasets/conversation)
- [Coverage Tracking](https://synkro.sh/docs/concepts/coverage)
- [Tool Calling](https://synkro.sh/docs/guides/tool-calling)
- [API Reference](https://synkro.sh/docs/api-reference/overview)
cautom 1.0.0
Highly customisable cellular automaton visualiser
Requires Python: >=3.8

Simple experiments with cellular automaton. Currently a customisable implementation of conways' game of life; will hopefully include interacive rule setting and beyond directly adjacent neighbour monitoring, along with more interesting visuals.

**Install:**

Homebrew:

```
brew tap Shiyuan-Chen-17/cautom
brew install cautom
```

Pip:

```
pip install --upgrade pip
pip install cautom
```
plainbook 0.0.20
Plain Language Notebooks
Requires Python: >=3.8
Keywords: notebook, jupyter, natural language, data science

# Plainbook: Natural Language Notebooks. 

Authors: 

Luca de Alfaro, dealfaro@acm.org

## Overview

Plain Language Notebooks (Plainbook) allow you to create notebooks where the cells are written in natural language. 
The natural language is automatically converted to code via AI, and executed.  The results of the execution are then displayed below the natural language cell. 

**In Plainbooks, natural language is the programming language.**
The goal of Plainbooks is to allow users to create and share notebooks in natural language, without having to write code, or understand the code that is shared with them. 
Actions to be performed are described in natural language, and the natural languate is retained, so users can edit it, improve it, and share it. 
We are building methods for verifying that the code implementation is faithful to the natural language description, so that users can trust the notebooks they receive from others.

Plain Language Notebooks have two type of cells: 

* **Action cells**, where the user describes in natural language the action to be performed (e.g., "Load the dataset from file data.csv and display the first 10 rows").  The system converts the description to code, executes it, and displays the results below the cell.

* **Comment cells**, where the user can add comments, section headers, and so forth, using markdown syntax. 

Differently from other notebook systems, Plainbooks are executed from start to end: random cell execution order, as in Jupyter notebooks, is not allowed.  This ensures that the results are obtained in the same order in which a human reader would read the notebook.

This project is in an early stage of development.

## Running Plainbook

```bash

plainbook path/to/notebook.nlb
``` 

(where `.nlb` is a Natural Language Notebook file; you can use other extensions if you wish). 
The file is created if it does not exist.
For a list of command-lien options, do: 

```bash
plainbook --help
```

If you want to be able to generate or check code from explanations, you need to add a Gemini API key in the settings (click on the gear icon in the top-right corner of the web interface).

## Development

Run with: 

```bash
python -m plainbook.main --debug
```

Running with the VSCode launch.json does not work, due to a VScode bug/quirk. 
See DEVELOP.md for development instructions, and see TODO.md for planned features.
mrx-runway 1.13.3b1
makina-runway
Requires Python: <3.12,>=3.8

# MRX Runway SDK

> _`mrx-runway` is the Runway SDK, a development tool that allows you to utilize the features of the Runway platform._

It provides:
- Logging model artifacts
- Creating datasets and dataset versions

## Usage Environment

| Supported Platform | Linux (AMD64, ARM64) / macOS (Intel, Apple Silicon) / Windows |
| ------------------ | ------------------------------------------------------------- |
| Python Version     | `3.8`, `3.9`, `3.10`                                          |

## Getting Started

We recommend installing our program in a new virtual environment.

Install Runway SDK with the following command.

```bash
python3 -m pip install --upgrade mrx-runway
```
openc3 7.0.0rc2
Python support for OpenC3 COSMOS
Requires Python: <4.0,>=3.10
Keywords: cosmos, command-and-control, embedded-systems, telemetry, spacecraft

## Python support for OpenC3 COSMOS

---

This project allows accessing the COSMOS API from the python programming language.
Additional functionality and support will be added over time.

---

## Installation

### For Users

```bash
pip install openc3
```

### For Development

See [README-DEV.md](README-DEV.md) for complete development setup.

#### Install Development Tools

Install UV, Ruff, and Just using pipx (recommended for isolated environments):

```bash
# Install pipx if not already installed
pip install --user pipx
pipx ensurepath

# Install development tools
pipx install uv
pipx install ruff
pipx install just
```

Or using your system package manager:

- **macOS**: `brew install uv ruff just`
- **Linux**: See [UV docs](https://docs.astral.sh/uv/getting-started/installation/), [Ruff docs](https://docs.astral.sh/ruff/installation/), [Just docs](https://github.com/casey/just#installation)
- **Windows**: See tool documentation links above

#### Quick Start

```bash
# Create virtual environment and install dependencies
cosmos/openc3/python % uv venv
cosmos/openc3/python % uv sync

# Run tests
cosmos/openc3/python % uv run pytest

# Run tests with coverage
cosmos/openc3/python % uv run coverage run -m pytest
cosmos/openc3/python % uv run coverage html
```