Installation#

There are different ways to install Radius Clustering:

  • From PyPI. This is the recommended way to install Radius Clustering. It will provide a stable version and pre-built packages are available for most platforms.

  • From the source. This is best for users who want the latest features and are comfortable building from source. This is also needed if you want to contribute to the project.

Warning

Radius Clustering is currently not available on PyPI, pending the organization acceptance on PyPI. You can install the package from the source by following the instructions. Please notice that the compilation stage requires a C and C++ compiler toolchain to be installed on your system.

Installing from PyPI#

Install the 64-bit version of Python 3, for instance from the official website.

Now create a virtual environment (venv) and install Radius Clustering. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

python -m venv rad-env
rad-env\Scripts\activate  # activate
pip install -U radius-clustering

In order to check your installation, you can use:

python -m pip show radius-clustering # show radius-clustering version and location
python -m pip freeze             # show all installed packages in the environment

Install Python 3 using homebrew (brew install python) or by manually installing the package from the official website.

Now create a virtual environment (venv) and install Radius Clustering. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

python -m venv rad-env
source rad-env/bin/activate  # activate
pip install -U radius-clustering

In order to check your installation, you can use:

python -m pip show radius-clustering  # show radius-clustering version and location
python -m pip freeze             # show all installed packages in the environment

Python 3 is usually installed by default on most Linux distributions. To check if you have it installed, try:

python3 --version
pip3 --version

If you don’t have Python 3 installed, please install python3 and python3-pip from your distribution’s package manager.

Now create a virtual environment (venv) and install Radius Clustering. Note that the virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

python3 -m venv rad-env
source rad-env/bin/activate  # activate
pip3 install -U radius-clustering

In order to check your installation, you can use:

python3 -m pip show radius-clustering  # show radius-clustering version and location
python3 -m pip freeze             # show all installed packages in the environment

Using an isolated environment such as pip venv or conda makes it possible to install a specific version of mds-clustering with pip or conda and its dependencies independently of any previously installed Python packages. In particular under Linux it is discouraged to install pip packages alongside the packages managed by the package manager of the distribution (apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choice prior to running any Python command whenever you start a new terminal session.

If you have not installed NumPy or SciPy yet, you can also install these using conda or pip. When using pip, please ensure that binary wheels are used, and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi).

Installing from the source#

Compiler Requirements#

To install Radius Clustering from the source, you need to have a C and C++ compiler and their respective toolchains installed on your system, depending on your operating system.

Install the correct version of Microsoft Visual C++ Build Tools for your Python version from the official website.

In Build Tools, install C++ toolchain. Ensure that it is added to the system PATH. You are now ready to install Radius Clustering from source.

Normally, you should have the necessary tools installed on your system as it comes with Xcode Command Line Tools, which is included when you first install Homebrew or Xcode.

To check if you have the necessary tools installed, try:

gcc --version
g++ --version

If you don’t have the necessary tools installed, you can install them directly from the App Store by getting Xcode. You may also be interested in installing Homebrew. See this tutorial for more information.

Normally, you should have the necessary tools installed on your system. To check if you have the necessary tools installed, try:

gcc --version
g++ --version

If you don’t have the necessary tools installed, you can install them using your distribution’s package manager. For instance, on Ubuntu, you can install them by running:

sudo apt-get update
sudo apt-get install build-essential

Installing Radius Clustering#

Now you have installed compilers toolchains requirements, you can build and install radius-clustering from the sources. You need to clone the repository and install the package using the following commands:

git clone git@github.com:lias-laboratory/radius_clustering.git # clone the repository
cd radius_clustering
python -m venv rad-env
source rad-env/bin/activate  # activate
python -m pip install .

To check your installation, you can use:

python -m pip show radius-clustering  # show radius-clustering version and location
python -m pip freeze             # show all installed packages in the environment
python -c "from radius_clustering import *; rad = RadiusClustering(); print(rad)"

If you want to contribute to the project, you will need to install the development dependencies. You can do this by running:

python -m pip install -e .[dev]

Alternatively, if you want to contribute only to the documentation, you can install the documentation dependencies by running:

python -m pip install -e .[docs]

Dependencies#

The minimum version of radius-clustering dependencies are listed below along with its purpose.

Dependency

Minimum version

Purpose

numpy

1.23.4

Build, Install

scipy

1.12.0

Build, Install

scikit-learn

1.2.2

Build, Install

cython

3.0.10

Build

setuptools

61.0.0

Build

pytest

8.3.3

Tests

ruff

0.2.1

Tests

black

24.3.0

Tests

matplotlib

3.6.2

Docs, Examples

sphinx

8.1.3

Docs

sphinx-copybutton

0.5.2

Docs

sphinx-rtd-theme

3.0.0

Docs

sphinx_design

0.6.1

Docs

sphinx_gallery

0.18.0

Docs

sphinx-prompt

1.9.0

Docs