semvec/pypi-packages huggingface dataset:
sanjaya-ninja 1.0.0.dev1770944934
Django Ninja app for dynamic reporting with pluggable data providers
Requires Python: >=3.12
Keywords: ag-grid, analytics, django, ninja, pivot, reporting

# sanjaya-ninja

Django Ninja app for dynamic reporting with pluggable data providers.

Mount this app in your Django project to get a full reporting API — dataset
discovery, filtered previews, AG Grid pivot, CSV/XLSX export, and saved
report management with sharing.

## Installation

```bash
uv pip install -e "packages/sanjaya-ninja[dev]"
```

## Quick start

```python
# settings.py
INSTALLED_APPS = [
    ...,
    "sanjaya_ninja",
]

SANJAYA_PROVIDERS = [
    "myproject.reporting.datasets",
]
```

```python
# urls.py / api.py
from ninja import NinjaAPI
from sanjaya_ninja.api import router as reporting_router

api = NinjaAPI()
api.add_router("v1/reporting", reporting_router)
```
mujoco-mjx 3.5.0
MuJoCo XLA (MJX)
Requires Python: >=3.9

# MuJoCo XLA (MJX)

[![PyPI Python Version][pypi-versions-badge]][pypi]
[![PyPI version][pypi-badge]][pypi]

[pypi-versions-badge]: https://img.shields.io/pypi/pyversions/mujoco-mjx
[pypi-badge]: https://badge.fury.io/py/mujoco-mjx.svg
[pypi]: https://pypi.org/project/mujoco-mjx/

This package is a re-implementation of the
[MuJoCo physics engine](https://github.com/google-deepmind/mujoco) in
[JAX](https://github.com/jax-ml/jax). This library is developed and maintained
by Google DeepMind, and is kept up-to-date with the latest developments in
MuJoCo itself.

The `mujoco-mjx` package is API-compatible with MuJoCo, but is missing some
features found in MuJoCo.  See our
[documentation](https://mujoco.readthedocs.io/en/stable/mjx.html) for more
details concerning feature parity.

## Installation

The recommended way to install this package is via [PyPI](https://pypi.org/project/mujoco-mjx/):

```sh
pip install mujoco-mjx
```

## Usage

Once installed, the package can be imported via `from mujoco import mjx`. Please
consult our [documentation](https://mujoco.readthedocs.io/en/stable/mjx.html)
for further detail on the package's API.

We recommend going through the tutorial notebook which introduces the MJX API
and trains a reinforcement learning policy in a few minutes: [![Open In
Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb)

## Versioning

The `major.minor.micro` portion of the version number matches the version of
MuJoCo that this library provides. Optionally, if we release updates to MJX that
target the same version of MuJoCo, a `.postN` suffix is added, for example
`3.0.1.post2` represents the second update to MJX for MuJoCo 3.0.1.

## License and Disclaimer

Copyright 2023 DeepMind Technologies Limited

MuJoCo and its libraries are licensed under the Apache License,
Version 2.0. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0.

This is not an officially supported Google product.
origin-miner 1.0.1
A high-performance forensic tool to detect non-human code contributions in Git history.
Requires Python: >=3.12

# 🕵️ Origin-Miner

[![Version](https://img.shields.io/badge/version-1.0.0-blue.svg)](https://github.com/youruser/origin-miner)
[![Python](https://img.shields.io/badge/python-3.10+-yellow.svg)](https://www.python.org/)

**Origin-Miner** is a high-performance Git forensic tool designed to detect non-human code contributions.

---

## 🚀 Key Features

* **Forensic Engine v1.0:** Uses weighted cognitive load, logical complexity, and Shannon entropy to calculate AI probability.
* **Velocity Metrics (LPM):** Tracks Lines Per Minute to detect impossible human typing speeds.
* **Multi-Core Engine:** Parallelized diff analysis for lightning-fast audits on large repositories.
* **Smart Caching:** Persistent SQLite database stores previous audits to avoid redundant computations.

## 📊 Interpreting the Results

| Score Range   | Classification     | Verdict                                |
|:--------------|:-------------------|:---------------------------------------|
| **0% - 25%**  | **Human Logic**    | Standard manual development.           |
| **51% - 75%** | **Suspicious**     | High probability of code injection.    |
| **> 75%**     | **🚨 AI DETECTED** | Statistically impossible human output. |

> **Note on Velocity:** A human typically operates at **10-40 LPM**. Anything consistently over **100 LPM** combined with high complexity is a major red flag.

## 🛠️ Installation

## Clone and Install

```bash
git clone [https://github.com/Hermit-commits-code/origin-check.git](https://github.com/Hermit-commits-code/origin-check.git)
cd origin-check

# Install the tool globally
uv tool install .

# Or run without installing
uv run miner.py --path .
```