Mesi is a tool to measure the similarity in a many-to-many fashion of long-form documents like Python source code or technical writing. The output can be useful in determining which of a collection of files are the most similar to each other.
Python 3.9+ and pipx are recommended, although Python 3.6+ and/or pip will also work.
pipx install mesiIf you'd like to test out Mesi before installing it, use the remote execution
feature of pipx, which will temporarily download Mesi and run it in an
isolated virtual environment.
pipx run mesi --helpFor a directory structure that looks like:
projects
├── project-one
│ ├── pyproject.toml
│ ├── deliverables
│ │ └── python_program.py
│ └── README.md
├── project-two
│ ├── pyproject.toml
│ ├── deliverables
│ │ └── python_program.py
│ └── README.md
│
where similarity should be measured between each project's
deliverables/python_program.py file, run the command:
mesi projects/*/deliverables/python_program.pyA lower distance in the produced table equates to a higher degree of similarity.
See the help menu (mesi --help) for additional options and configuration.
There are many algorithms to choose from when comparing string similarity! Mesi
implements all the
algorithms provided by
TextDistance. In general levenshtein
is never a bad choice, which is why it is the default.
Mesi uses tabulate for table
formatting. The table format can be configured with the --table-format option
to one of the formats
listed in tabulate's
documentation.
Mesi uses two primary dependencies for text similarity calculation: polyleven, and TextDistance. Polyleven is the default, as its singular implementation of Levenshtein distance can be faster in most situations. However, if a different edit distance algorithm is requested, TextDistance's implementations will be used.
Please use the GitHub issue tracker to submit bugs or request new features, options, or algorithms.
Distributed under the terms of the GPL v3 license, mesi is free and open source software.