Welcome to scikit-rmt documentation!

Random Matrix Theory, or RMT, is the field of Statistics that analyses matrices that their entries are random variables.

This package offers classes, methods and functions to give support to RMT in Python. Includes a wide range of utils to work with different random matrix ensembles, random matrix spectral laws and estimation of covariance matrices. See documentation or visit the project page hosted by Github for further information on the features included in the package.

Contents:

• skrmt package
  • Subpackages
  • Module contents
• skrmt.ensemble package
  • Submodules
  • skrmt.ensemble.base_ensemble module
  • skrmt.ensemble.circular_ensemble module
  • skrmt.ensemble.gaussian_ensemble module
  • skrmt.ensemble.manova_ensemble module
  • skrmt.ensemble.misc module
  • skrmt.ensemble.spectral_law module
  • skrmt.ensemble.tracy_widom_approximator module
  • skrmt.ensemble.tridiagonal_utils module
  • skrmt.ensemble.utils module
  • skrmt.ensemble.wishart_ensemble module
  • Module contents
• skrmt.denoise package
  • Submodules
  • skrmt.denoise.metrics module
  • skrmt.denoise.mp_pca module
  • skrmt.denoise.utils module
  • Module contents

Indices and tables

• Index

• Module Index

Installation

Using a virtual environment is recommended to minimize the chance of conflicts. However, the global installation should work properly as well.

Local installation using venv (recommended)

Navigate to your project directory.

cd MyProject

Create a virtual environment (you can change the name “env”).

python3 -m venv env

Activate the environment “env”.

source env/bin/activate

Install using pip.

pip install scikit-rmt

You may need to use pip3.

pip3 install scikit-rmt

Global installation

Just install it using pip`or `pip3.

pip install scikit-rmt

Requirements

scikit-rmt depends on the following packages:

• numpy - The fundamental package for scientific computing with Python

• matplotlib - Plotting with Python

• scipy - Scientific computation in Python