semvec/pypi-packages huggingface dataset:
AdvSegLearn 1.0.5
Package for combining traditional and adversarial learning.
Requires Python: >=3.7

# AdvSegLearn
## Descriptor
 This package creates an easy to use api for training models on small datasets with adversarial learning.
 Important classes are:
 * Mutliclass_dataset - allows for both paired and unpaired images to be loaded, with built in support for initalized data augmentation and lazy loading data augmentation.
 * train_GAN - provides api to easily load in different models, datasets and loss functions to the model. Subclassing allows for easy modification of the training loop.
 * semi_supervised_loss - a loss function for the semisupervised training loop.
 * adversarial_loss - a loss function for hte unsupervised training loop.

# License 
This package is licensed under the [MIT LICENSE](LICENSE).
embdiv 0.1.0
Library for dividing the Embedding layer of the LLM.
Requires Python: >=3.9
Keywords: embeddings division nlp

# [Currently in development!]

# Embeddings-Division

Python script for dividing `Embedding` layer of LLM.

The solution was tested on meta-llama/Llama-3.2-1B.

# Installation

Installation from PyPI:

```bash
pip install embdiv
```

# Requirements

- Python 3.9+
- cachetools>=5.5.0,
- torch>=2.5.1,
- transformers>=4.45.2,

# Docs

[https://embeddings-division.readthedocs.io/en/latest/](https://embeddings-division.readthedocs.io/en/latest/)
abp-tools 0.1.2
ABP is an encryption algorithm.
Requires Python: >=3.9

# ABP Tools

![ABP Logo](https://mazegroup.org//wiki/images/e/ed/ABP_logo.png)

ABP (Additional Binary Patterns) is a string encryption algorithm, designed on March 31, 2025. It is based on the use of a table of patterns to transform a text in a reversible way.
_From the [MazeGroup Wiki page](https://mazegroup.org/wiki/index.php/ABP) (in french)._

## Installation

### With PyPi :

Use the command `pip install abp-tools`.

## Usage

### Importation

The code below imports ABP :
```py
from abp import *
```

### Encoding

The function `encode(text: str, patterns: list[list[int]]) -> bytes` takes the string to encode/encrypt and a list of list of numbers (the table of patterns), it returns a bytes string.

### Decoding

The function `decode(ciphertext: bytes, patterns: list[list[int]]) -> str` takes the bytes string and the table of patterns to decode, it returns a string.

### Key to patterns (K2P)

The function `generate_patterns(key: str, num_patterns: int = 100) -> list` takes a string key and a number of patterns to generate, it returns the table of patterns corresponding to the parameters.
bohri-calendar 0.1.0
A tool for generating Bohri calendar PDFs

# Time Travel PDF

A Python library for converting between Gregorian and Dawoodi Bohra calendars and generating beautiful PDF calendars.

## Features

- Convert dates between Gregorian and Dawoodi Bohra calendars
- Generate PDF calendars for both calendar systems
- Include important events and celebrations
- Support for different quality settings in PDF generation

## Installation

```bash
pip install dawoodi-bohra-calendar
```
system-info 2.0.3
Package for finding system hardware specifications
Requires Python: >=3.6

system_info
===========

- system_info is a package for finding system hardware specifications.
- It is cross-platform package, which you can run on Linux, Windows and macOS.

**Note:** For Linux system it requires sudo user password and dmidecode should be installed.

###### system_info provides following information ######

* Processor
* CPU (Total CPU count)
* Total Disk Space
* Available Space
* Host Name
* Ip
* Operating System
* OS Version
* CPU_Core
* HD Size
* Manufacturer
* Model
* Ram_Type
* Ram_Type
* HD Type
* Serial_Number

It can be used as
```python
from system_info import sysinfo
system_hardware_details = sysinfo.sysInfo
```
Sample result for Linux :
>{'Processor': 'Intel(R) Core(TM) i3-5005U CPU @ 2.00GHz', 'CPU': 4, 'Total Disk Space': '454GB', 'Available Space': '231GB', 'Host Name': 'XXXXX', 'Ip': 'xxx.xxx.xx.xxx', 'Operating System': 'Ubuntu', 'OS Version': '14.04', 'CPU_Core': '2', 'HD Size': '500GB', 'Manufacturer': 'Dell Inc.', 'Model': 'Latitude 3560', 'Ram_Type': 'DDR3', 'Ram_Size': '8 GB', 'HD_Type': 'SATA controller', 'Serial_Number': 'XXXXXXX'}

Sample result for Windows :
>{'Processor': 'Intel(R) Core(TM) i3-5005U CPU @ 2.00GHz', 'CPU': 1, 'Ip': 'xxx.xxx.xx.xxx', 'OS Version': '10.0.10240', 'Total Disk Space': '49 GB', 'HD Size': '49 GB', 'Available Space': '36 GB', 'HD_Type': None, 'Operating System': 'Microsoft Windows 10 Pro', 'Host Name': 'XXXXX', 'CPU_Core': '1', 'Manufacturer': 'Dell Inc.', 'Model': 'Latitude 3560', 'Ram_Type': DDR3, 'Ram_Size': '8 GB', 'Serial_Number': 'XXXXXXX'}

Sample result for MacOS :
>{'Processor': 'Intel(R) Core(TM) i5-4278U CPU @ 2.60GHz', 'CPU': 4, 'Operating System': 'Mac OS X', 'OS Version': '10.14.6', 'Manufacturer': 'Apple', 'Available Space': '891.64 GB', 'Host Name': 'XXXXX', 'Ip': 'xxx.xxx.xx.xxx', 'CPU_Core': '2', 'HD Size': '1 TB ', 'Total Disk Space': '1 TB ', 'Model': 'Mac mini', 'Ram_Type': 'DDR3', 'Ram_Size': '8 GB', 'HD_Type': 'SATA', 'Serial_Number': 'XXXXXXX'}
pymt5linux 1.0
MetaTrader5 for Linux
Requires Python: >=3.13

# pymt5linux

**Forked from [lucas-campagna/mt5linux](https://github.com/lucas-campagna/mt5linux). This is a up-to-date version which incorporates recent MT5 software updates. It works with Python 3.13.**

**As an alternative to the setup below, you can also opt to run a Docker image of MT5 with x11/noVNC remote access. Check out [mt5docker](https://github.com/hpdeandrade/mt5docker) for details.**

## How To Use

1. Install (as per [MT5 Linux user guide](https://www.metatrader5.com/en/terminal/help/start_advanced/install_linux)):
    * [Wine](https://www.winehq.org) in your Linux machine.
    * [Python for Windows](https://www.python.org) inside Wine.
    * [MT5 software](https://www.metatrader5.com) inside Wine.
    
2. Install MetaTrader5 package inside your Wine Python installation.

```
pip install MetaTrader5
```

3. Install pymt5linux package in both Wine and Linux Python versions.

```
pip install pymt5linux
```

4. Establish the connection.

* Open the MT5 terminal in Wine.

* Run the MT5 server in Wine.

`python -m pymt5linux <path/to/python.exe>`,

or if you want to specify the host and port:

`python -m pymt5linux --host localhost --port 8001 <path/to/python.exe>`

* Run your Linux script.

```python
# import the package
from pymt5linux import MetaTrader5

# establish the connection
mt5 = MetaTrader5()

# or if you specified the host and port in the previous step:
mt5 = MetaTrader5(host="localhost", port=8001)
```

5. Make sure you test with a DEMO account first, then have fun!

Most importantly, see MetaQuotes' [official documentation](https://www.mql5.com/en/docs/python_metatrader5).
box-manager 0.1.1
Orchestration tool for managing systemd services and process priorities in Debian systems.
Requires Python: >=3.8

# BoxManager
[![Python Package](https://github.com/rajatasusual/box-manager/actions/workflows/python-publish.yml/badge.svg)](https://github.com/rajatasusual/box-manager/actions/workflows/python-publish.yml)
[![PyPI version](https://badge.fury.io/py/box-manager.svg)](https://pypi.org/project/box-manager/)

BoxManager is a lightweight Python library for orchestrating systemd services and adjusting process priorities on Debian-based systems. Perfect for embedded systems, robotics, or any scenario where performance context switching is essential.

## Features
- ✅ Suspend/resume systemd services
- ✅ Deprioritize or prioritize running processes by keyword
- ✅ Automatically restore system state after execution
- ✅ YAML or JSON config support
- ✅ Decorator or imperative-style usage
- ✅ CLI for direct service and process control

---

## Installation
```bash
pip install box-manager
```

Or if developing locally:
```bash
git clone https://github.com/yourusername/box-manager.git
cd box-manager
pip install -e .
```

---

## Usage

### Basic Example
```python
from box_manager import BoxManager

manager = BoxManager()

@manager.with_orchestration(
    stop_services=["nginx"],
    deprioritize=["chrome"]
)
def critical_task():
    print("Doing performance-critical work...")

critical_task()
```

### Using Config File
```yaml
# config.yaml
orchestration:
  stop_services:
    - nginx
  start_services:
    - my_custom_service
  deprioritize:
    - chrome
  prioritize:
    - ros_node
```

```python
manager.load_config_and_orchestrate("config.yaml", func=my_task)
```

---

## CLI
Use the command line interface to quickly control services:

```bash
box-manager start nginx
box-manager stop nginx
box-manager status nginx
box-manager restart nginx
```

### Available Commands
- `start <service>`: Start a systemd service
- `stop <service>`: Stop a systemd service
- `status <service>`: Check if a service is active
- `restart <service>`: Restart a systemd service

---

## License
MIT License

---

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

---

## Author
Made with ☕ by [Rajat](mailto:rajatasusual@github.com)
do-cap-utils 0.2.1
A utilities library for Common AI Platform
Requires Python: >=3.11

# do-cap-utils

A library to simplify your use case with chat bot with LLMs. Perfect for Common AI Platform.

## Installation
 ```
 uv add do-cap-utils
 ```

## Usage

See `example.py` and `example_multiagents.py`.


### General

```python
from do_cap_utils.agent.base import BaseAgent
from do_cap_utils.agent.prebuilt import SummariserAgent
from do_cap_utils.tool import tool


# --------------------------------------------
# Create and run your own agent with some tool
# --------------------------------------------
@tool
def add_numbers(a: int, b: int):
    """Adds two numbers together"""
    return a + b


agent = BaseAgent(
    system_prompt="You are a helpful assistant that answers everything nicely (only that you can do or know)",
    tools=[add_numbers],
)
r = agent.chat_each_message("hello")
r = agent.chat_each_message("add two numbers for me")
r = agent.chat_each_message("2 and 4")
print(r)  # Inpect the output
print()

# You can use `chat_interact()` method to have an interactive session
agent.chat_interact()
```

Now, if you use `self.chat_interact()`, save the file and run this file in the command line.

```
uv run your_file.py
```

Or activate your python environment and run
```
python your_file.py
```

Example output:
```
================================ Human Message =================================

hello
================================== Ai Message ==================================

Hello! How can I assist you today?
================================ Human Message =================================

add two numbers for me
================================== Ai Message ==================================

Of course! Please provide the two numbers you'd like to add.
================================ Human Message =================================

2 and 4
================================== Ai Message ==================================
Tool Calls:
  add_numbers (call_kGDJ89gTwG6cTZFT7cWr1dBs)
 Call ID: call_kGDJ89gTwG6cTZFT7cWr1dBs
  Args:
    a: 2
    b: 4
================================= Tool Message =================================
Name: add_numbers

6
================================== Ai Message ==================================

The sum of 2 and 4 is 6.
```