This package (COFE - kaa·fee) implements nonlinear dimensionality reduction with a circular constraint on the (dependent) principal components.
- The manuscript describing and applying COFE is now published in PLoS Biology: https://doi.org/10.1371/journal.pbio.3003196
- Free software: GNU General Public License v3
- 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.
- 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.
- Conda installed on your system. You can download and install conda from official Conda website.
- Install and
- Open a terminal or command prom
- Clone the COFE Repository
- Open a terminal or command prompt.
- Navigate to the directory where you want to install COFE.
- Clone the COFE repository from GitHub by running the following command:
git clone https://github.com/bharathananth/COFE.git
- Installation
Navigate to the COFE directory:
cd COFEInstall and switch to circular_ordering-env environment:
conda env create -f environment.yml conda activate circular_ordering-env
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 as cf
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.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.