semvec/pypi-packages huggingface dataset:
1Password-Utility 0.1.2
1Password_Utility is a Python package providing utility functions for interacting with 1Password via the command-line interface (CLI). It simplifies the process of retrieving credentials from 1Password vaults within your Python scripts, making it easier to automate tasks and securely manage sensitive information.
Requires Python: >=3.6

# 1Password_Utility 🚀

A collection of utility functions for interacting with 1Password via the command-line interface (CLI).

## Features ✨

- Retrieve credentials (username and password) from 1Password vaults securely within your Python scripts. 🔒
- Simplifies the process of automating tasks and managing sensitive information. 🤖

## Prerequisites 📋

- **1Password:** You need to have 1Password installed and set up on your system. 
- **1Password CLI (`op`):**  Install the 1Password CLI by following the instructions on the [1Password Developer website](https://developer.1password.com/docs/cli/get-started). 🛠️

## Installation 📥

```bash
pip install 1Password-Utility
```
## Usage

```python
onepassword = __import__('1Password-Utility')

# Retrieve credentials from a 1Password item
item_uuid = "your_item_uuid"  # Replace with the actual UUID of your 1Password item
username, password = onepassword_utils.get_credentials_from_1password(item_uuid)

if username and password:
    print(f"Username: {username}")
    print(f"Password: {password}")
    # ... Use the credentials in your script 🎉
else:
    print("Failed to retrieve credentials.") 😕
```
## Important:⚠️

Make sure you have the 1Password CLI (op) installed and configured correctly before using this package.
Handle the retrieved credentials securely within your scripts. Avoid printing them to the console or storing them in plain text. 🛡️

## Contributing 🤝
Contributions are welcome! Please feel free to submit pull requests or open issues if you find any bugs or have suggestions for improvements. 

## License 📄
This project is licensed under the MIT License - see the LICENSE file for details.   


**Key points:**

- **Clear sections:** The README is organized into clear sections with headings to make it easy to navigate.
- **Prerequisites:** The prerequisites section explicitly mentions the need for 1Password and the 1Password CLI.
- **Installation:** Provides the `pip install` command for easy installation.
- **Usage:** Includes a concise code example demonstrating how to use the `get_credentials_from_1password` function.
- **Important notes:** Highlights important considerations for security and setup.
- **Contributing and License:** Encourages contributions and specifies the license under which the project is released.

Remember to replace placeholders like `your_item_uuid` and `your-username` with your actual values. 

You can further enhance the README by adding more detailed explanations, additional usage examples, or information about any other functions you might include in your `onepassword_utils` package in the future. 

Let me know if you have any other questions or need help with further refinements!
graphs-HDoubleH 0.1.2
A small example package
Requires Python: >=3.7

# HDoubleH's Graphs Algorithm Library

This package implements two graph algorithms in python, including **Dijkstra's Shortest Path Algorithm** and **Tarjan's Algorithm for Strongly Connected Components**.

Graphs are data structures which connect nodes identified by labels. 

- **Dijkstra's Shortest Path Algorithm**: This computes the shortest path from a single source node to all the other nodes in a graph. It will find the shortest path from a single source node to all other nodes in a weighted graph with non-negative edge weights. It operates by iteratively expanding the node with the smallest known distance and updating the distances to its neighbors.

- **Tarjan's Algorithm for Strongly Connected Components**: This algorithm detects all the strongly connected components (SCCs) in a directed graph using a depth-first search. Each SCC is a maximal subgraph where every node is reachable from every other node in the same subgraph. 

## Usage

**Dijkstra's Shortest Path Algorithm**- python src/test.py graph.txt <br />
**Tarjan's Algorithm for Strongly Connected Components**- python src/test_tarjan.py graph.txt

### Installation 
Package can be installed with - pip3 install graphs_HDoubleH
dcs-bios-connector 1.4
Allows easy access to dcs bios state and sending of commands
Requires Python: >=3.6

# DCS Bios Connector [Python Library]

This is a python library that allows you to listen to DCS Bios events. This can be imported into any python and used for projects.

This library uses [DCS Bios Skunkworks](https://github.com/DCS-Skunkworks/dcs-bios)

A helpful tool is [DCS Bort](https://github.com/DCS-Skunkworks/Bort/releases/tag/v0.3.0) to see button/switch names for each piece of data for a given aircraft. In the `Usage` section below, I am using the `FLAPS_SW` for the `F/A-18_Hornet`. Bort helps you see the name in DCS Bios for every piece of data, it also shows the values in realtime to help debug and see what data you are looking for.

# Installation
`pip install dcs-bios-connector`

## Usage
```
from dcs_bios_connector import DcsBiosConnector
bios = DcsBiosConnector()
bios.connect()

# ============= Example: Detect when flap switch changes and its new value =====================
def handle_flaps_change(value, controlInformation, dataInformation):
    print("Flaps have been changed to: ", value)

bios.on("FLAP_SW", handle_flaps_change)


# ============= Example: Callback method to run when flaps are set to 2 =====================
def handle_flaps_lowered():
    print("That flaps have been lowered! Yeah")

bios.on("FLAP_SW:0", handle_flaps_lowered)


# ================= Example: Setting flap switch to position 2 (Flaps to auto) ==========================
bios.send("FLAP_SW 2")
```
pycryto 0.1.0
A simple cryptography library using AES and PKCS7 padding
Requires Python: >=3.6

# pycryptography

A simple encryption library using AES and PKCS7 padding.

## Installation

```bash
pip install pycryptography

## Usage
```xml
from encryption import encrypt, decrypt

# Define your key and plaintext
key = "thisisaverysecretkey1234"
plain_text = "Hello, World!"

# Encrypt the plaintext
encrypted_text = encrypt(plain_text, key)
print(f"Encrypted: {encrypted_text}")

# Decrypt the ciphertext
decrypted_text = decrypt(encrypted_text, key)
print(f"Decrypted: {decrypted_text}")
```
document-sorter 0.1.0
A package to automatically sort documents into folders based on content similarity
Requires Python: >=3.6

# Document Sorter

Document Sorter is a Python package that automatically organizes documents into folders based on their content similarity. It supports various file types including PDF, DOCX, XLSX, CSV, TXT, MD, and TEX.

## Features

- Searches for documents in a specified directory
- Extracts text from various file types
- Clusters documents based on content similarity using the elbow method
- Allows users to specify the number of clusters
- Supports custom keywords for folder names
- Sorts documents into folders named after the most relevant keyword for each cluster or user-specified keywords
- Supports dry run mode for testing
- Provides verbose output option for detailed information during execution

## Installation

You can install Document Sorter using pip:

```
pip install document-sorter
```

## Usage

After installation, you can use the Document Sorter from the command line:

```
document-sorter [directory_path] [OPTIONS]
```

### Options

- `directory_path`: The directory to search for documents (default: current directory)
- `--dry-run`: Perform a dry run without moving files
- `--verbose`: Print detailed information during execution
- `--clusters N`: Specify the number of clusters to use
- `--keywords KEYWORD1 KEYWORD2 ...`: Specify custom keywords for folder names

## Examples

1. Basic usage (current directory):
   ```
   document-sorter
   ```

2. Specify a directory with verbose output:
   ```
   document-sorter /path/to/documents --verbose
   ```

3. Perform a dry run:
   ```
   document-sorter /path/to/documents --dry-run
   ```

4. Specify the number of clusters:
   ```
   document-sorter /path/to/documents --clusters 5
   ```

5. Use custom keywords:
   ```
   document-sorter /path/to/documents --keywords work personal projects research
   ```

6. Combine options:
   ```
   document-sorter /path/to/documents --verbose --clusters 4 --keywords work personal projects research
   ```

## Behavior

- If neither `--clusters` nor `--keywords` are specified, the script will automatically determine the optimal number of clusters and generate keywords based on document content.
- If `--clusters` is specified without `--keywords`, the script will use the specified number of clusters and generate keywords based on document content.
- If `--keywords` is specified without `--clusters`, the script will automatically determine the optimal number of clusters and use the provided keywords for folder names.
- If both `--clusters` and `--keywords` are specified, the script will use the specified number of clusters and the provided keywords for folder names.

## Requirements

Document Sorter requires Python 3.6 or later. For a full list of dependencies, see the `requirements.txt` file.

## License

This project is licensed under the MIT License.

## Contributing

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