2. Installation

Compatibility and Requirements

sarlib is fully compatible with Python 3.7 and above and runs on GNU/Linux, Unix, Windows, and macOS platforms.

Hardware requirements depend on the size of the dataset:

CPU: affects computation time only
RAM: must be sufficient to store and process datasets; recommended RAM capacity is at least twice the dataset size
GPU: not required, as sarlib uses only the CPU for computation

Python package dependencies:

numpy, scipy, statsmodels — for statistical analysis and robust linear regression validation
scikit-learn — for training and validating regression models
matplotlib — for plotting and visualization

To install sarlib, use pip to get it from PyPI:

pip install sarlib

This will automatically install all required dependencies, including:

Alternatively, you can install all dependencies manually:

pip install numpy matplotlib statsmodels scikit-learn scipy

Once installed, verify the installation by running:

python -c "import sarlib; print(sarlib.__version__)"

Since sarlib is a Python library, it must be imported into your script or Python environment before use. You can import the entire library:

import sarlib

Or import specific classes or functions:

from sarlib import <class_or_function_name>