slowpca

Slowpca is a package to perform principal component analysis based on correlation or covariance. This Repository is a only an example of python packaging.

The method was published in:

Authors, …, Title of publication goes here, in preparation

We kindly ask you to cite these articles if you use this software package for published works.

Features

• performs PCA
• easy to install
• …

Installation

So far the package is only published to PyPI. For installing it to within a python environment simple call:

python3 -m pip install slowpca

or for the latest dev version

# via ssh key
python3 -m pip install git+ssh://git@github.com/moldyn/python-packaging.git

# , or via password-based login
python3 -m pip install git+https://github.com/moldyn/python-packaging.git

Usage

CI - Usage Directly from the Command Line

In general one can call the module directly by its entry point $ slowpca or by calling the module $ python -m slowpca. The latter method is preferred to ensure using the desired python environment. For help simply call $ python -m slowpca --help.

Module - Inside a Python Script

from slowpca import estimate_pca

# Load file
# X is np.ndarray of shape (n_samples, n_features)
proj, evecs = estimate_pca(X)
...

""".. include:: ../../README.md"""
from slowpca.slowpca import perform_pca

__version__ = '0.0.1'

Sub-modules

slowpca.slowpca

Performing PCA based on covariance/corrleation.