semvec/pypi-packages huggingface dataset:
torrent-scrappers 0.0.1
Simple collection of scrappers of popular torrent sites.
Requires Python: >=3.10

# Torrent Scraper Collection

Collection of web scrappers of popular torrent sites.

Currently package provides scrappers for:
- [X] [1337x](https://1337x.to)
- [ ] [The Pirate Bay](https://thepiratebay.org/index.html)

---

## 🚀 Features

- Scrape torrent listings (name, seeders, leechers, size, uploader, etc.)
- Fetch magnet links
- Handle pagination and search filters (category, sorting)
- Easily extendable to other torrent sites
- Logs warnings for missing data, connection errors and timeouts


## Installation

You can install the package from pypi or clone the repository.

### Package

```bash
pip install torrent-scrappers
```

### Manual installation

🧱 Requirements

- Python 3.10+
- `requests`
- `beautifulsoup4`

#### Clone repository

```bash
git clone https://github.com/flisakl/torrent-scrappers
cd torrent-scrappers
```

#### Install dependencies

```bash
pip install requests beautifulsoup4
```

## 🛠 Usage

### Using built in scrapper
```python
from scrappers import Scrapper1337, SearchParams

scraper = Scrapper1337()

params = SearchParams(
    name="ubuntu",
    order_column="seeders",
    order_ascending=False
)

results = scraper.find_torrents(params, pages=(1, 2))
scraper.get_magnet_links(results)

for torrent in results:
    print(torrent.name, torrent.magnet)

```

### 🧩 Making your own scrapper

To add support for a new torrent site:

1. Subclass Scrapper
1. Implement the following methods:
    - get_request_data
    - parse_response
    - fetch_magnet_link - implement when magnet link can not be obtained on site's
    search page


### 🔧 Structures

#### TorrentInfo

Dataclass containing torrent informations

| **Field** | **Type** | **Description**                   |
|-----------|----------|-----------------------------------|
| name      | str      | Search term                       |
| url       | str      | URL for torrent detail page       |
| seeders   | int      | Number of seeders                 |
| leechers  | int      | Number of leechers                |
| size      | str      | Size of torrent's data            |
| uploader  | str      | Optional name of torrent uploader |
| magnet    | str      | Magnet link                       |

#### SearchParams

Immutable dataclass for filters

| **Field**       | **Type** | **Description**   |
|-----------------|----------|-------------------|
| name            | str      | Search term       |
| category        | str      | Optional category |
| order_column    | str      | Sorting column    |
| order_ascending | bool     | Sort order        |

If **order_column** is set to None, ordering will not be applied.
clipboard-text-translate 0.1.6
Program that collects text from the clipboard and translates it.
Requires Python: >=3.8
Keywords: writing, translate

# clipboard-text-translate

Program that translate text.

![logo](https://raw.githubusercontent.com/trucomanx/ClipboardTextTranslate/main/src/clipboard_text_translate/icons/logo.png)

## 1. Installing

### 1.1. Install from PyPI

To install the package from `PyPI`, follow the instructions below:


```bash
python -m pip install --upgrade pip
python -m pip install --upgrade supertools

pip install --upgrade clipboard-text-translate
```

Execute `pip show clipboard-text-correction` to see where it was installed.

#### Using

If the program was not added to the Linux start session, then to start, use the command below:

```bash
clipboard-text-translate-indicator
```


### 1.2. Add the program to Linux start session

Add the program to the bar indicator on Linux startup session.

```bash
clipboard-text-translate-indicator --autostart
```

## 2 Uninstall

```bash
pip uninstall clipboard-text-correction
```

## 3. Buy me a coffee

If you find this tool useful and would like to support its development, you can buy me a coffee!  
Your donations help keep the project running and improve future updates.  

[☕ Buy me a coffee](https://ko-fi.com/trucomanx) 

## 4. License

This project is licensed under the GPL license. See the `LICENSE` file for more details.
netsuite-client 0.1.1
A Python client library for interacting with NetSuite's REST and SuiteQL APIs
Requires Python: >=3.7

# NetSuite Client

A Python client library for interacting with NetSuite's REST and SuiteQL APIs.

## Features

- OAuth 1.0 authentication with NetSuite
- Support for REST API calls
- SuiteQL query execution
- Custom scriptlet execution

## Installation

```bash
pip install netsuite-client
```

## Quick Start

```python
from netsuite_client import NetSuiteClient

# Initialize the client
client = NetSuiteClient(
    realm="YOUR_REALM",
    account="YOUR_ACCOUNT",
    consumer_key="YOUR_CONSUMER_KEY",
    consumer_secret="YOUR_CONSUMER_SECRET",
    token_id="YOUR_TOKEN_ID",
    token_secret="YOUR_TOKEN_SECRET"
)

# Execute a SuiteQL query
results = client.get_suiteql_query("SELECT * FROM transaction WHERE tranDate = '2024-01-01'")

# Execute a custom scriptlet
results = client.get_scriptlet({"script": "123", "deploy": "1"})
```

## Authentication

This library uses OAuth 1.0 for authentication. You'll need to set up an integration record in your NetSuite account to get the required credentials:

1. Go to Setup > Integration > Manage Integrations > New
2. Enable Token-Based Authentication
3. Copy the Consumer Key and Consumer Secret
4. Create an Access Token and copy the Token ID and Token Secret

## Configuration

The following parameters are required to initialize the client:

- `realm`: Your NetSuite realm ID
- `account`: Your NetSuite account ID (the xxxxx part of xxxxx.app.netsuite.com)
- `consumer_key`: OAuth consumer key from your integration record
- `consumer_secret`: OAuth consumer secret from your integration record
- `token_id`: OAuth token ID from your access token
- `token_secret`: OAuth token secret from your access token

Optional parameters:
- `timeout`: Request timeout in seconds (default: 60)
- `logger`: Custom logger instance (default: standard logging)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the BSD 3-Clause License - see the [LICENSE](LICENSE) file for details.

## Credits

Created and maintained by Continuous AI, Inc.
rjfinance 0.1.1
A package for stock pattern extraction and matching using cosine similarity
Requires Python: >=3.7

# rjfinance

**A Python library for generating and matching stock price & volume patterns using cosine similarity.**

---

## 📖 Project Overview

**rjfinance** is a lightweight, experimental Python package crafted for traders, data scientists, and financial analysts eager to dive into pattern-based technical analysis. This library empowers you to identify recurring patterns in stock price and volume data, offering a streamlined way to explore market behavior. It helps you:  

- **Generate** historical price and volume patterns from CSV data, capturing key market movements.  
- **Store** these patterns in a reusable format for quick reference.  
- **Match** real-time stock data against your pattern library using cosine similarity, a robust mathematical approach.  
- **Predict** potential future percentage moves based on the closest historical match.  

**Why use rjfinance?**  
Analyzing stock patterns manually can be a tedious and error-prone task. **rjfinance** automates this process, saving you time and effort while delivering consistent results. By leveraging cosine similarity—a technique widely used in data science to measure vector similarity—it ensures accurate pattern matching, even across varying scales of data.  

**Who is it for?**  
- **Traders**: Perfect for backtesting pattern-driven strategies with historical data.  
- **Data Scientists**: Ideal for those experimenting with machine learning in financial markets.  
- **Analysts**: A handy tool for automating repetitive pattern-recognition workflows.  

> ⚠️ **Disclaimer:**  
> This library is designed for informational and educational purposes only. It is *not* intended as financial advice. Always conduct thorough research and consult professionals before making investment decisions.

---

## 🚀 Installation

Installing `rjfinance` is a breeze with `pip`:  

```bash
pip install rjfinance
spacebud 0.1.0
Add your description here
Requires Python: >=3.13

# spacebud

`spacebud` is a Python package for creating performance budgets following the ECSS‐E‐ST‐60‐10C standard, commonly used in space engineering projects.

## Features

- Simplifies performance budget creation aligned with ECSS‐E‐ST‐60‐10C.
- Supports standard performance error indices APE, MPE, RPE, PDE, PRE, and knowledge error indices AKE, MKE, RKE.
- Temporal, ensemble, or mixed interpretation of requirements.

## Installation

Install `spacebud` using pip:

```bash
pip install spacebud
```

## Usage

Here is a toy example of how to use `spacebud`:

Consider: 
* Array of temperature sensors reading on a time scale of 0.1
* Each sensor has white readout noise (Gaussian) where the readout noise level is around 2.0, but it depends on the sensor with a uniform ensemble distribution. 
* Each sensor has an unknown bias value which is fixed over time and the value is uniformly distributed according to supplier specification

```python
from spacebud import Budget, Gaussian

# Create a new performance budget
budget = Budget(name="Radiometric Uncertainty")

# Add contributor to the budget
budget.add(name="Sensor Noise", timescale=0.1, temporal_distribution=Gaussian(sigma=Uniform(1.8, 2.2)))
budget.add(name="Sensor Bias", timescale='inf', temporal_distribution=Fixed(value=Uniform(-0.1, 0.1)))

# Generate a report
report = budget.report()
print(report)
```

## Documentation

For documentation, visit [spacebud documentation](https://github.com/himbeles/spacebud).

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.