semvec/pypi-packages huggingface dataset:
timeir 0.0.3
python functions to extract and work with time.ir data
Requires Python: >=3.12

A few Python functions to extract data from https://time.ir .

Usage
-----
The `holiday_occasion` function will return the holiday occasion of the requested date, or `None` if the given date is not a holiday.
```Python
In [1]: from timeir import holiday_occasion

In [2]: holiday_occasion(1404, 1, 13)
Out[2]: 'جشن سیزده به در'

In [3]: holiday_occasion(1404, 1, 14)

In [4]: 
```

**Note:** This function fetches data from the time.ir website annually (one request per requested year) and caches the results for up to 3 months. See the source code for more details.
RahkaranPythonAPI 0.1.1
A Python client for the Rahkaran API
Requires Python: >=3.7

# Rahkaran API Client

A Python client library for interacting with the Rahkaran API.

## Installation

```bash
pip install rahkaran-api
```

## Quick Start

```python
import json
from rahkaran_api import RahkaranAPI, RahkaranConfig

# Create config object
config = RahkaranConfig(
    rahkaran_name="code",
    server_name="localhost",
    port="80",
    username="admin",
    password="admin"
)

# Initialize the client with config
client = RahkaranAPI(config)



# Make GET request for demonstration
try:
    response = client.get(
        "/General/AddressManagement/Services/AddressManagementWebService.svc/GetRegionalDivisionList"
    )
    print(json.dumps(response, ensure_ascii=False, indent=2))    
except Exception as e:
    print(f"Error occurred: {str(e)}")

# Data for creating a party
data = [{"Type ": 1, "FirstName": "Ehsan", "LastName": "Rezaei"}]
# Make Post request
try:
    response = client.post(
        "/General/PartyManagement/Services/PartyService.svc/GenerateParty",
        data
        )
    print(json.dumps(response, ensure_ascii=False, indent=2))    
except Exception as e:
    print(f"Error occurred: {str(e)}")

```

## Features

- Secure authentication with RSA encryption
- Automatic session management
- Connection pooling and retry mechanism
- Configurable SSL verification
- Comprehensive error handling
- Automatic rate limiting
- Request timeout configuration

## Development

To set up the development environment:

```bash
# Clone the repository
git clone https://github.com/yourusername/rahkaran-api.git
cd rahkaran-api

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"
```

## Running Tests

```bash
pytest tests/
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
RahkaranPythonAP 0.1.1
A Python client for the Rahkaran API
Requires Python: >=3.7

# Rahkaran API Client

A Python client library for interacting with the Rahkaran API.

## Installation

```bash
pip install rahkaran-api
```

## Quick Start

```python
import json
from rahkaran_api import RahkaranAPI, RahkaranConfig

# Create config object
config = RahkaranConfig(
    rahkaran_name="code",
    server_name="localhost",
    port="80",
    username="admin",
    password="admin"
)

# Initialize the client with config
client = RahkaranAPI(config)



# Make GET request for demonstration
try:
    response = client.get(
        "/General/AddressManagement/Services/AddressManagementWebService.svc/GetRegionalDivisionList"
    )
    print(json.dumps(response, ensure_ascii=False, indent=2))    
except Exception as e:
    print(f"Error occurred: {str(e)}")

# Data for creating a party
data = [{"Type ": 1, "FirstName": "Ehsan", "LastName": "Rezaei"}]
# Make Post request
try:
    response = client.post(
        "/General/PartyManagement/Services/PartyService.svc/GenerateParty",
        data
        )
    print(json.dumps(response, ensure_ascii=False, indent=2))    
except Exception as e:
    print(f"Error occurred: {str(e)}")

```

## Features

- Secure authentication with RSA encryption
- Automatic session management
- Connection pooling and retry mechanism
- Configurable SSL verification
- Comprehensive error handling
- Automatic rate limiting
- Request timeout configuration

## Development

To set up the development environment:

```bash
# Clone the repository
git clone https://github.com/yourusername/rahkaran-api.git
cd rahkaran-api

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"
```

## Running Tests

```bash
pytest tests/
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
digicircuit 0.1.0
A package for converting Boolean expressions to circuit diagrams
Requires Python: >=3.6

# DigiCircuit

DigiCircuit là một thư viện Python để chuyển đổi biểu thức Boolean thành sơ đồ mạch điện.

## Cài đặt

```bash
pip install digicircuit
```

## Cách sử dụng

1. Tạo file input với các biểu thức Boolean (ví dụ: `data.txt`):
```
1 A + B
2 A.B + C
```

2. Chạy lệnh để tạo sơ đồ mạch:
```bash
digicircuit data.txt
```

Các sơ đồ mạch sẽ được tạo trong thư mục `circuits` dưới dạng file LaTeX.

### Tùy chọn

- `--output_dir` hoặc `-o`: Chỉ định thư mục output (mặc định: `circuits`)
- `--scale` hoặc `-s`: Tỷ lệ của sơ đồ (mặc định: 1.0)

Ví dụ:
```bash
digicircuit data.txt --output_dir my_circuits --scale 1.5
```

## Yêu cầu

- Python >= 3.6
- numpy
- pandas
- openpyxl

## Giấy phép

MIT License
forestmq 0.2.1
Python client for ForestMQ

# ForestMQ Python

Python client for [ForestMQ](https://github.com/josefdigital/forestmq)

[Read The Docs](https://forestmq-python.readthedocs.io/en/latest/)

### Install
```
pip install forestmq
```

### Running ForestMQ
```
docker run -p 8005:8005 josefdigital/forestmq:0.6.2
```

### Examples
#### Using the provider API

```python
from forestmq import ForestMQ


def sync_example():
    fmq = ForestMQ(domain="http://localhost:8005")
    result = fmq.provider.send_msg_sync({
        "name": "Sync message",
    })
    print(result)

sync_example()  # {'queue_length': 38, 'message_size': 5120, 'message': {'name': 'Sync message'}}
```

Using the provider's async client
```python
import asyncio
from forestmq import ForestMQ

async def async_example():
    fmq = ForestMQ(domain="http://localhost:8005")
    result = await fmq.provider.send_msg({
        "name": "Async message!",
    })
    print(result)

asyncio.run(async_example())  # {'queue_length': 39, 'message_size': 5120, 'message': {'name': 'Async message!'}}
```
#### Using the consumer API
```python
import asyncio

from forestmq import ForestMQ


def callback(message: dict) -> None:
    print(f"Consumer message: {message['message']}")


if __name__ == "__main__":
    fmq = ForestMQ(domain="http://localhost:8005", interval=1)
    asyncio.run(fmq.consumer.poll_sync(callback))

```

Async consumer example
```python
import asyncio

from forestmq import ForestMQ


async def callback(message: dict) -> None:
    await asyncio.sleep(1)
    print(f"Consumer message: {message['message']}")


if __name__ == "__main__":
    fmq = ForestMQ(domain="http://localhost:8005", interval=1)
    asyncio.run(fmq.consumer.poll(callback))
```
PowerQuant 0.1.0
Quantitative analysis for power markets
Keywords: python, quantitative, risk, power, quant


# PowerQuant


[![PyPI version](https://badge.fury.io/py/[your_package_name_on_pypi].svg)](https://pypi.org/project/[your_package_name_on_pypi]/)
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Python Versions](https://img.shields.io/pypi/pyversions/[your_package_name_on_pypi])

**PowerQuant** is a Python package providing tools for the quantitative analysis of the electricity sector. It offers methods to explore data, perform calculations, and gain crucial insights into energy markets.

## Installation

You can install PowerQuant using pip:

```bash
pip install PowerQuant
```


<p align="center">
  <img src="images/logo_PowerQuant.png" width="200"/>
</p>
LePatrick 0.1.0
Augment images with Patrick imprints.
Requires Python: >=3.10

# LePatrick

Augment your images with Patrick Star!  
LePatrick is a Python package that applies fun and creative Patrick imprints to images using OpenCV.

---

### What It Does

LePatrick enhances images by pasting Patrick Star's figure onto your input image — great for data augmentation, or just plain fun.

### Features

- Add Patrick imprints with a single function
- Uses OpenCV and NumPy for high-performance image processing
- Installable via PyPI

---

## Installation

Install from [PyPI](https://pypi.org/project/LePatrick):

```bash
pip install LePatrick
```

## Example Usage
```python
import cv2
from LePatrick import Patrick

# Apply to your dataset
Patrick(
    source_dir = "data/train",
    num_outputs = 1000,
    num_patricks = 5
)
```

> [!NOTE]
> The `patrick.png` asset is bundled within the package and loaded internally.

## Requirements

- Python 3.10 or newer
- OpenCV 4.9.0.80
- NumPy 1.26.4

These are installed automatically when you install the package.