Installation
============
The GLAM framework is written in Python and leverages high-performance libraries for spatial indexing (KD-trees) and medical image analysis[cite: 1]. It operates as a fully standalone extraction engine, meaning it does not require external radiomics packages to compute conventional texture matrices[cite: 2].

Prerequisites
-------------
Before installing GLAM, ensure you have the following requirements[cite: 3]:

* **Python**: Version 3.10 or higher[cite: 3].
* **Pip**: The Python package installer[cite: 3].
* **Virtual Environment**: It is highly recommended to use a virtual environment (e.g., ``venv`` or ``conda``) to avoid dependency conflicts[cite: 4].

Installing from TestPyPI
------------------------
Currently, the GLAM library is hosted on TestPyPI[cite: 5]. You can install it along with its standard dependencies using the following command:

.. code-block:: bash

    pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ glam_radiomics

.. note::
   Make sure you have activated your virtual environment before running this command!

Key Dependencies
----------------
When you install GLAM, the following core libraries are automatically integrated[cite: 7]:

* **NumPy & SciPy**: Provide the computational backbone for RDF calculations, spatial KD-trees, and Statistical Mechanics descriptors[cite: 7].
* **SimpleITK**: Handles the loading and normalization of 3D medical imaging formats like NIfTI (.nii.gz)[cite: 8].
* **Pandas**: Manages the structured output of multiscale Radial Distribution Functions and feature aggregation[cite: 9].
* **Scikit-image & Scikit-learn**: Powers the morphological marching cubes (surface area), K-Means clustering, and advanced geometric descriptors[cite: 10].

Verifying the Installation
--------------------------
To verify that GLAM is correctly installed, you can run a simple version check in your Python environment:

.. code-block:: python

    import glam_radiomics
    print(glam_radiomics.__version__)