semvec/pypi-packages huggingface dataset:
onyx-otc 0.3.1
Onyx otc python client and documentation
Requires Python: <4.0,>=3.11

# Onyx Websocket API v2

[![PyPI version](https://badge.fury.io/py/onyx-otc.svg)](https://badge.fury.io/py/onyx-otc)
[![Python versions](https://img.shields.io/pypi/pyversions/onyx-otc.svg)](https://pypi.org/project/onyx-otc)
[![Python downloads](https://img.shields.io/pypi/dd/onyx-otc.svg)](https://pypi.org/project/onyx-otc)
[![build](https://github.com/Onyx-Capital-Technology/onyx-otc/actions/workflows/build.yml/badge.svg)](https://github.com/Onyx-Capital-Technology/onyx-otc/actions/workflows/build.yml)


The websocket API v2 support both JSON and [Protobuf](https://protobuf.dev/) (binary) encoding. The protobuf encoding is more efficient and faster than JSON encoding.


| Environment | Protocol | URL                                  |
|-------------|----------|--------------------------------------|
| Production  | JSON     | wss://ws.onyxhub.co/stream/v2        |
| Production  | Protobuf | wss://ws.onyxhub.co/stream/v2/binary |
| UAT  | JSON     | wss://ws.uat.onyxhub.co/stream/v2        |
| UAT  | Protobuf | wss://ws.uat.onyxhub.co/stream/v2/binary |


## Installation

Install the library via pip:

```bash
pip install onyx-otc
```

To install the library with command line support

```bash
pip install onyx-otc[cli]
```


## Example

Install the library with command line support and run the client:

```bash
onyx --help
```

Stream tickers for a list of product symbols.

```bash
onyx stream -t ebob -t brt
```

Stream tradable quotes for a list of contract symbols.

```bash
onyx stream -r brtm25@ice -r ebobm25@ice
```

## Protobuf support

The binary endpoints uses protobuf encoding and the definitions are available in the [protos/onyx_otc/v2](https://github.com/Onyx-Capital-Technology/onyx-otc/tree/main/protos/onyx_otc/v2) directory.

## Other resources

* [Onyx Flux Rest API docs](https://api.onyxhub.co/v1/docs)
* [Onyx Flux web app](https://www.onyxcapitalgroup.com/flux)
PyL360 0.1.10
A package for interfacing with Life360
Requires Python: >=3.6



# PyL360 ![Static Badge](https://img.shields.io/badge/0.1.5-green?logo=%5C&label=PyPI&link=https%3A%2F%2Fpypi.org%2Fproject%2FPyL360%2F)

This is a Python library to interact with Life360, primarily to read data.


## Usage
First install the package by running `pip install PyL360`. Example to print out a list of all users and their current location in all your circles
```py
from PyL360 import L360Client

if __name__ == '__main__':
	client = L360Client(
		username="sammy@gmail.com",
		password="my-secure-password"
	)

	client.Authenticate()
	circles = client.GetCircles().circles

	for circle in circles:
		for p in circle.GetDetails().members:
			if (p.location is not None):
				print("{} is at ({},{})".format(p.firstName, p.location.latitude, p.location.longitude))
			else:
				print("{} cannot be located".format(p.firstName))

```

Example to print out a list of all the places in all your circles along with their locations
```py
from PyL360 import L360Client

if __name__ == '__main__':
	client = L360Client(
		username="sammy@gmail.com",
		password="my-secure-password"
	)

	client.Authenticate()
	circles = client.GetCircles().circles

	for circle in circles:
		for place in client.GetPlaces(circle.id).places:
			if (p.location is not None):
					print("{} is at ({},{})".format(p.firstName, p.location.latitude, p.location.longitude))
				else:
					print("{} cannot be located".format(p.firstName))
```
ab-agent 0.1.1
A small example package
Requires Python: >=3.9

# AB SDK

**The fastest way to build powerful AI agents.**  
Built for developers who want to create custom, Gemini-powered AI agents in just a few lines of code.

---

## 🚀 Features

- ⚡ Fast agent setup with minimal boilerplate
- 🧠 Powered by Google Gemini
- 🔧 Add custom tools and extend with ease
- 🛠️ Built with Python, clean and modular
- 🧪 Perfect for prototyping and production use

---

## 📦 Installation

```bash
pip install ab-agents
```
jax-triton 0.3.0
JAX + OpenAI Triton integration
Requires Python: >=3.10

# jax-triton

![PyPI version](https://img.shields.io/pypi/v/jax-triton)

The `jax-triton` repository contains integrations between [JAX](https://github.com/jax-ml/jax) and [Triton](https://github.com/openai/triton).

Documentation can be found [here](https://jax-ml.github.io/jax-triton).

*This is not an officially supported Google product.*

## Quickstart

The main function of interest is `jax_triton.triton_call` for applying Triton
functions to JAX arrays, including inside `jax.jit`-compiled functions. For
example, we can define [a kernel from the Triton
tutorial](https://triton-lang.org/main/getting-started/tutorials/01-vector-add.html#sphx-glr-getting-started-tutorials-01-vector-add-py):

```python
import triton
import triton.language as tl


@triton.jit
def add_kernel(
    x_ptr,
    y_ptr,
    length,
    output_ptr,
    block_size: tl.constexpr,
):
  """Adds two vectors."""
  pid = tl.program_id(axis=0)
  block_start = pid * block_size
  offsets = block_start + tl.arange(0, block_size)
  mask = offsets < length
  x = tl.load(x_ptr + offsets, mask=mask)
  y = tl.load(y_ptr + offsets, mask=mask)
  output = x + y
  tl.store(output_ptr + offsets, output, mask=mask)
```

Then we can apply it to JAX arrays using `jax_triton.triton_call`:

```python
import jax
import jax.numpy as jnp
import jax_triton as jt

def add(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
  out_shape = jax.ShapeDtypeStruct(shape=x.shape, dtype=x.dtype)
  block_size = 8
  return jt.triton_call(
      x,
      y,
      x.size,
      kernel=add_kernel,
      out_shape=out_shape,
      grid=(x.size // block_size,),
      block_size=block_size)

x_val = jnp.arange(8)
y_val = jnp.arange(8, 16)
print(add(x_val, y_val))
print(jax.jit(add)(x_val, y_val))
```

See [the examples
directory](https://github.com/jax-ml/jax-triton/tree/main/examples), especially
[fused_attention.py](https://github.com/jax-ml/jax-triton/blob/main/examples/fused_attention.py)
and [the fused attention
ipynb](https://github.com/jax-ml/jax-triton/blob/main/examples/JAX_%2B_Triton_Flash_Attention.ipynb).

## Installation

```bash
$ pip install jax-triton
```

Make sure you have a CUDA-compatible `jax` installed. For example you could run:
```bash
$ pip install "jax[cuda12]"
```

`jax-triton` currently requires building the latest version of `triton`
[from source](https://triton-lang.org/main/getting-started/installation.html#from-source).

## Development

To develop `jax-triton`, you can clone the repo with:
```bash
$ git clone https://github.com/jax-ml/jax-triton.git
```
and do an editable install with:
```bash
$ cd jax-triton
$ pip install -e .
```
To run the `jax-triton` tests, you'll need `pytest`:
```bash
$ pip install pytest
$ pytest tests/
```
Matrix-Alt-Dashboard 0.0.2
Package contains code to run the agent dashboard for Alt Agentss
Requires Python: >=3.9

# Matrix-Alt-Dashboard

**Matrix-Alt-Dashboard** is an extension package to the [Matrix-Alt-Core](https://pypi.org/project/Matrix-Alt-Core/) framework, designed to add a dashboard to your visualization dashboard for your alt agents. The dashboard will run at http://localhost:9000/
Alt-Dashboard 0.0.1
Package contains code to run the agent dashboard for Alt Agentss
Requires Python: >=3.9

# Matrix-Alt-Objectlocalization

**Matrix-Alt-Objectlocalization** is an extension package to the [Matrix-Alt-Core](https://pypi.org/project/Matrix-Alt-Core/) framework, designed to add localization and inference capabilities to your Alt Agents 
MIT License

Copyright (c) 2025 The Matrix 488

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Matrix-Alt-ObjectLocalization 0.0.1
Extension for the Matrix-Alt-Core package to add object localization tools
Requires Python: >=3.9

# Matrix-Alt-Objectlocalization

**Matrix-Alt-Objectlocalization** is an extension package to the [Matrix-Alt-Core](https://pypi.org/project/Matrix-Alt-Core/) framework, designed to add localization and inference capabilities to your Alt Agents