Installation

PyRth is a Python package for analyzing thermal transient measurements using Network Identification by Deconvolution (NID) methods. It’s compatible with Windows, macOS, and Linux systems.

Hint

Not familiar with NID? See the theory overview.

Standard Installation

Install PyRth with pip directly from PyPI:

pip install PyRth

This command also works if you are using a Conda environment by using pip from within the Conda environment.

This is the recommended method for most users who want to use PyRth for thermal analysis. See Usage for a first walkthrough.

Development Installation

If you want to contribute to PyRth or modify the code, install it in editable mode:

git clone https://github.com/nizie002/PyRth
cd PyRth
pip install --editable .

This installation method allows you to make changes to the source code and have them immediately reflected without reinstalling the package.

To explore available modules, see the API reference.

Requirements

PyRth requires Python 3.11 or higher and depends on several scientific computing libraries.

Core Dependencies

The following Python packages are installed automatically with PyRth. Each plays a specific role in the thermal transient analysis pipeline:

  • numpy – for efficient array and matrix operations used throughout PyRth’s data structures and numerical routines.

  • scipy – provides certain algorithms needed during the analysis.

  • matplotlib – used to visualize RC networks, temperature curves, evaluation results, and structure functions.

  • numba – accelerates core numerical loops (e.g., differentiation, deconvolution) with just-in-time (JIT) compilation.

  • gmpy2 – allows PyRth to perform arbitrary-precision arithmetic for some Foster-to-Cauer transformations.

  • sklearn – used for sparse regression techniques such as sklearn.linear_model.LassoCV to extract reduced models.

These are installed automatically when running pip install PyRth.

Development Dependencies

For testing and contributing:

  • pytest — for running the test suite

Documentation Dependencies

To build the documentation locally:

  • sphinx

  • sphinx_rtd_theme

  • sphinx_proof

  • sphinx.ext.autodoc

  • sphinx.ext.napoleon

  • sphinx.ext.intersphinx

  • sphinx.ext.mathjax

You can install all of these with:

pip install .[dev,docs]

Verifying Installation

After installation, verify that PyRth is correctly installed:

import PyRth
print("PyRth successfully installed!")

To run a test evaluation, see the Getting Started Guide.