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Myth Hash

Nox Tests on Multiple Python Versions Coverage Python Versions License Last Commit Contributors Issues

Myth Hash is a Python package designed to create human-readable hashes that are not only functional but also cognitively engaging. A key feature of this tool is its multilingual support, enabling the generation of hashes in multiple languages. The concept behind these human-readable hashes is to enhance communication and recognition by creating hashes that form vivid, memorable mental images. The goal is to make the hash as human-compatible as possible, ensuring it effectively serves its purpose in an intuitive and user-friendly manner.

Features

  • Character Name Generation: Generates a unique fantasy name based on an input string.
  • Supported Languages: Currently supports English (en) and German (de).
  • CLI Support: Easily generate names via the command line.
  • Library Usage: Integrate Myth Hash into your Python projects.
  • Customizable Data: Modify the included JSON files to customize the generated names.

Installation

You can install Myth Hash using pip:

pip install myth-hash

Usage

Command Line Interface (CLI)

After installing the package, you can use the myth-hash command to generate fantasy names.

Basic Example:

myth-hash "The moon whispered secrets, but only the cats understood."

Example Output:

exotic-thoughtful-Griffin

Using as a Library

You can also use Myth Hash within your Python code:

Example:

import myth_hash

# Generate a character name using the hash_name function
name_parts = myth_hash.hash_name("The moon whispered secrets, but only the cats understood.", "en")
print(name_parts)  # Output: ('exotic', 'thoughtful', 'Griffin')

# Access individual parts of the name
physical_attr, personality_attr, mystical_character = name_parts
print(f"Physical Attribute: {physical_attr}")
print(f"Personality Trait: {personality_attr}")
print(f"Mystical Character: {mystical_character}")

Performance and Collisions of the Algorithm in Version 0.1.0

In a test with 1,000,000 generated names, the hash_name algorithm produced the following results:

  • Total generated names: 1,000,000
  • Actual duplicates: 119,949
  • Uniqueness rate: 88.01%

What Do These Results Mean?

  • Collisions: The actual number of duplicates (119,949) resulting in a uniqueness rate of 88.01%. This means roughly 12% of the generated names were duplicates.
  • Implications: While the algorithm is robust, users should be aware that with large datasets, the likelihood of name collisions increases.

Future Improvements

  • Improved Uniqueness: Add more attributes and characters to reduce the likelihood of name collisions.
  • Additional Languages: Add support for more languages with help from the community.

Contributing

Contributions are welcome! Please fork the repository, create a new branch for your feature or bug fix, and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.