Harmonia Occulta: A Computational Study of Robert Fludd's Monochordum Mundi

Overview

This repository contains the code and data for the paper "The Completed Harmony: A Computational Pilot Study of Musical Encodings in Robert Fludd's Monochordum Mundi." The study applies computational methods to analyze the harmonic structure of Robert Fludd's 17th-century cosmological diagrams, with a focus on the Monochordum Mundi.

Key Findings

• Musical Encoding: Identification of a statistically significant (p < 0.001) 16:15 interval at the Earth node in Fludd's diagram
• Kepler Connection: The measured interval matches the 16:15 ratio Kepler assigned to Earth's orbital eccentricity
• Textual Analysis: Detection of a statistically significant over-representation of musical characters in related Rosicrucian texts

Repository Structure

harmonia-occulta/
├── data/                   # Raw and processed data
│   ├── images/            # Source images of Fludd's works
│   ├── measurements/      # Extracted measurements and coordinates
│   └── text/              # Source texts and character frequency data
├── notebooks/             # Jupyter notebooks for analysis
│   ├── omr_pipeline.ipynb # Optical Music Recognition pipeline
│   └── text_analysis.ipynb # Statistical text analysis
├── src/                   # Source code
│   ├── omr/               # Optical Music Recognition tools
│   └── analysis/          # Statistical analysis tools
├── outputs/               # Generated outputs and visualizations
├── docs/                  # Documentation and supplementary materials
└── environment.yml        # Conda environment specification

Getting Started

Prerequisites

• Python 3.8+
• Conda (recommended) or pip

Installation

1. Clone the repository:

   git clone https://github.com/AlkaiDynamics/Harmonia-Occulta.git
   cd Harmonia-Occulta

2. Create and activate the conda environment:

   conda env create -f environment.yml
   conda activate harmonia-occulta

3. Install the package in development mode:

   pip install -e .

Usage

Running the OMR Pipeline

python -m src.omr.pipeline --input data/images/monochordum_mundi.tif --output outputs/measurements/

Reproducing the Analysis

1. Launch Jupyter Lab:

   jupyter lab

2. Open and run the notebooks in the notebooks/ directory

Data

Source images are derived from the Wellcome Collection's digital archive of Robert Fludd's works. Processed data and measurements are included in the data/ directory.

License

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

Citation

If you use this work in your research, please cite:

@article{sherer2025completed,
  title={The Completed Harmony: A Computational Pilot Study of Musical Encodings in Robert Fludd's Monochordum Mundi},
  author={Sherer, Morgan H.},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}

Acknowledgements

• The Wellcome Collection for providing digital access to Fludd's works
• The broader digital humanities and computational musicology communities for their tools and methodologies