semvec/pypi-packages huggingface dataset:
mtasanpystatus 6
Multi Theft Auto San Andreas Server Monitoring Library Using Python
Requires Python: >=3.6

# MTASANPyStatus

Multi Theft Auto San Andreas Server Monitoring Library

## Usage
Here are some key attributes you can access using mtasanpystatus:

| Attribute                     | Description                     | Example Value                     |
|-------------------------------|---------------------------------|-----------------------------------|
| `mtasanpystatus.address`      | Server IP address               | `'01.23.456.789'`                 |
| `mtasanpystatus.name`         | Server name                     | `'Ieoub MTA SERVER'`              |
| `mtasanpystatus.port`         | Server port                     | `22003`                           |
| `mtasanpystatus.game`         | Always "MTA:SA"                 | `'MTA:SA'`                        |
| `mtasanpystatus.gamemode`     | Current gamemode                | `'Freeroam'`                      |
| `mtasanpystatus.map`          | Current map                     | `'Los Santos'`                    |
| `mtasanpystatus.version`      | Server version                  | `'1.6'`                           |
| `mtasanpystatus.players`      | Online players count            | `5`                              |
| `mtasanpystatus.maxplayers`   | Max player slots                | `32`                             |
| `mtasanpystatus.join_link`    | Direct join link                | `'mtasa://01.23.456.789:22003'`   |
| `mtasanpystatus.playing_now_names` | List of player names [removed]       | `['Player1', 'Player2']`        |

Online Players names has been removed (version 4)



## Installation

Install the library [using pip](https://pypi.org/project/mtasanpystatus/):

```bash
pip install mtasanpystatus
pwlfit 0.2.0
Package for piecewise linear fitting of noisy data
Requires Python: >=3.8
Keywords: linear fitting, piecewise linear, astronomy, data analysis

# Piecewise Linear Fits to Noisy Data

[![PyPI package](https://img.shields.io/badge/pip%20install-pwlfit-brightgreen)](https://pypi.org/project/pwlfit/) [![GitHub Release](https://img.shields.io/github/v/release/dkirkby/pwlfit?color=green)](https://github.com/dkirkby/pwlfit/releases) [![Actions Status](https://github.com/dkirkby/pwlfit/workflows/Test/badge.svg)](https://github.com/dkirkby/pwlfit/actions) [![License](https://img.shields.io/github/license/dkirkby/pwlfit)](https://github.com/dkirkby/pwlfit/blob/main/LICENSE)

Use this package to perform efficient piecewise linear fits to noisy data.

## Installation

Install the [latest released version](https://github.com/dkirkby/pwlfit/releases/latest) from [pypi](https://pypi.org/project/pwlfit/) using:
```
pip install pwlfit
```
The required dependencies are numpy, scipy, yaml.

The changes with each version are documented [here](CHANGELOG.md).

## Quick Start

Fit some sample data included in this package using:
```
from pwlfit.util import read_sample_data
from pwlfit.grid import Grid
from pwlfit.driver import PWLinearFitter

x, y, ivar = read_sample_data('A')
grid = Grid(x, ngrid=100)

fitter = PWLinearFitter(grid)

fit = fitter(y, ivar)

plt.plot(x, y, '.')
plt.plot(fit.xknots, fit.yknots, 'o-');
```
to produce this plot:

![Fit to sample A data](https://github.com/dkirkby/pwlfit/blob/main/examples/Quickstart-sampleA.png?raw=true)

For more details see the Quickstart notebook: [readonly](https://github.com/dkirkby/pwlfit/blob/main/examples/Quickstart.ipynb) or [live (via google colab)](https://colab.research.google.com/github/dkirkby/pwlfit/blob/main/examples/Quickstart.ipynb).
human-memory 0.1.1
A Python SDK for storing structured memory objects in Supabase using OpenAI's API
Requires Python: >=3.7

# Memory SDK

A Python SDK for storing structured memory objects in Supabase using OpenAI's API.

[![Documentation](https://img.shields.io/badge/docs-website-blue)](https://docs-human-memory.vercel.app/)


## Documentation

For complete documentation, visit our [documentation website](https://docs-human-memory.vercel.app/).

## Installation

```bash
# Clone the repository
git clone https://github.com/deadcow-labs/human-memory.git
cd human-memory

# Install dependencies
pip install -r requirements.txt
```

## Requirements

Create a `requirements.txt` file with the following dependencies:

```
openai>=1.0.0
supabase>=2.0.0
python-dotenv>=1.0.0
```

## Configuration

The SDK requires the following environment variables:

- `OPENAI_API_KEY`: Your OpenAI API key
- `SUPABASE_URL`: Your Supabase project URL
- `SUPABASE_KEY`: Your Supabase API key

You can set these in your environment or use a `.env` file with python-dotenv.

## Supabase Setup

Create a `memories` table in your Supabase project with the following schema:

```sql
create table
  public.memories (
    id uuid primary key,
    user_id text not null,
    created_at timestamp with time zone not null,
    content text not null,
    reflection text not null,
    embedding vector(1536) not null,
    emotional_tone text not null,
    location jsonb not null,
    tags text[] not null
  );
```

Enable RLS (Row Level Security) on your table and create appropriate policies.

## Usage

```python
from memory_sdk import MemorySDK

# Initialize the SDK with a user ID
user_id = "user_123"
sdk = MemorySDK(user_id)

# Save a memory from a raw message
raw_message = "Today I made significant progress on my project and feel optimistic about finishing it soon."
memory = sdk.save(raw_message)

# The memory object contains all structured data:
print(f"Content: {memory.content}")
print(f"Reflection: {memory.reflection}")
print(f"Emotional tone: {memory.emotional_tone}")
```

See `example.py` for a complete usage example.

## Memory Structure

Each memory includes:

- `id`: UUID
- `user_id`: provided by the SDK user
- `created_at`: UTC timestamp
- `content`: short summary of the message
- `reflection`: insight about the user from the message
- `embedding`: vector from OpenAI embedding API
- `emotional_tone`: e.g. "hopeful", "anxious"
- `location`: dict with type and name
- `tags`: list of string topics or themes

## License

MIT
pyamdgpuinfo 2.1.7
AMD GPU stats
Requires Python: >=3.9

# pyamdgpuinfo

AMD GPU information

## Install

`pip install pyamdgpuinfo`

Only Linux is supported, using the AMDGPU driver.

The library is written using Cython, meaning that Cython and and a C compiler are needed to build and install from source. Additionally, libdrm development headers are required. 

Precompiled wheels for Python 3.9-3.13 are the default method of install. This means that you don't need Cython or any other dependencies to install from PyPi.

## Usage

Example:
```python
>>> import pyamdgpuinfo
>>> pyamdgpuinfo.detect_gpus()
1 # we have 1 device present, so it'll be at index 0
>>> first_gpu = pyamdgpuinfo.get_gpu(0) # returns a GPUInfo object
>>> vram_usage = first_gpu.query_vram_usage()
>>> vram_usage
3954978816 # number of bytes in use
```

All documentation is in the docstrings of each function/class.

Available functions are (see docstrings for more info):
* detect_gpus - Returns the number of GPUs available
* get_gpu - Returns a GPUInfo object for the device index specified


GPUInfo methods (see docstring for class overview)
* start_utilisation_polling - Starts polling GPU functional units for utilisation statistics
* stop_utilisation_polling - Stops utilisation polling
* query_utilisation - Queries utilisation of different GPU parts
* query_max_clocks - Queries max GPU clocks
* query_sclk - Queries shader (core) clock
* query_mclk - Queries memory clock
* query_vram_usage - Queries VRAM usage
* query_gtt_usage - Queries GTT usage
* query_temperature - Queries temperature
* query_load - Queries GPU load
* query_power - Queries power consumption
* query_northbridge_voltage - Queries northbridge voltage
* query_graphics_voltage - Queries graphics voltage


VRAM and GTT sizes are available as an attribute of GPUInfo.

## Mentions

Parts of this package were inspired by [radeontop](https://github.com/clbr/radeontop).

## License

GPLV3
memory-sdk 0.1.0
A Python SDK for storing structured memory objects in Supabase using OpenAI's API
Requires Python: >=3.7

# Memory SDK

A Python SDK for storing structured memory objects in Supabase using OpenAI's API.

## Installation

```bash
# Clone the repository
git clone https://github.com/deadcow-labs/human-memory.git
cd human-memory

# Install dependencies
pip install -r requirements.txt
```

## Requirements

Create a `requirements.txt` file with the following dependencies:

```
openai>=1.0.0
supabase>=2.0.0
python-dotenv>=1.0.0
```

## Configuration

The SDK requires the following environment variables:

- `OPENAI_API_KEY`: Your OpenAI API key
- `SUPABASE_URL`: Your Supabase project URL
- `SUPABASE_KEY`: Your Supabase API key

You can set these in your environment or use a `.env` file with python-dotenv.

## Supabase Setup

Create a `memories` table in your Supabase project with the following schema:

```sql
create table
  public.memories (
    id uuid primary key,
    user_id text not null,
    created_at timestamp with time zone not null,
    content text not null,
    reflection text not null,
    embedding vector(1536) not null,
    emotional_tone text not null,
    location jsonb not null,
    tags text[] not null
  );
```

Enable RLS (Row Level Security) on your table and create appropriate policies.

## Usage

```python
from memory_sdk import MemorySDK

# Initialize the SDK with a user ID
user_id = "user_123"
sdk = MemorySDK(user_id)

# Save a memory from a raw message
raw_message = "Today I made significant progress on my project and feel optimistic about finishing it soon."
memory = sdk.save(raw_message)

# The memory object contains all structured data:
print(f"Content: {memory.content}")
print(f"Reflection: {memory.reflection}")
print(f"Emotional tone: {memory.emotional_tone}")
```

See `example.py` for a complete usage example.

## Memory Structure

Each memory includes:

- `id`: UUID
- `user_id`: provided by the SDK user
- `created_at`: UTC timestamp
- `content`: short summary of the message
- `reflection`: insight about the user from the message
- `embedding`: vector from OpenAI embedding API
- `emotional_tone`: e.g. "hopeful", "anxious"
- `location`: dict with type and name
- `tags`: list of string topics or themes

## License

MIT