Cyclic Ordering with Feature Extraction

This package (COFE - kaa·fee) implements nonlinear dimensionality reduction with a circular constraint on the (dependent) principal components.

• Preprint: https://doi.org/10.1101/2024.03.13.584582
• Free software: GNU General Public License v3

Features

• Assigns time-labels to high-dimensional data representing an underlying rhythmic process
• Identifies features in the data that contribute to the temporal reordering
• Regularized unsupervised machine learning approach with automated choice of hyperparameters.

Installation

• Prerequisites

  • Python 3.9 or better installed on your system. You can download and install Python from the official Python website.
  • Git installed on your system. You can download and install Git from the official Git website.

• Clone the COFE Repository

  1. Open a terminal or command prompt.
  2. Navigate to the directory where you want to install COFE.
  3. Clone the COFE repository from GitHub by running the following command:

  git clone https://github.com/bharathananth/COFE.git

• Installation

  1. Navigate to the COFE directory:

     cd COFE

  2. You can install COFE and its dependencies by running the following command:

     python -m pip install .

• Verify Installation

  To verify that COFE is installed correctly, you can try importing it in a Python environment. Open a Python interpreter or create a new Python script, and then try importing COFE:

  import COFE.analyse
  import COFE.plot
  import COFE.scpca

Getting Started

You can get started with COFE by running it on synthetic data, as illustrated in the Jupyter notebook synthetic_data_example.ipynb located in the docs/ folder.

For detailed usage, refer to the docstrings of the COFE functions.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.