pyhom 1.0.7

PyHom: a Python library for homogenization

Pyhom

In this repository, we present "PyHom: a Python library for homogenization".

This library is conceived for calculating the effective conductivity properties of composite materials with complex microstructures in a 2D framework. The backbone of the code relies on the theory described in [Cruz-González et al., 2024], and uses not only existing image processing modules, but also functionality and packages built by the authors. The main interest is using PyHom as a toolbox to study the core issues in the application of IDD and the IDD-based PCW.

For more information, please refer to the paper

1. Cruz-González, O., Cornaggia, R., Dartois, S., and Brenner, R. (2024). Accounting for spatial distribution in mean-field homogenization of particulate composites. International Journal of Solids and Structures, 294:112747. DOI 10.1016/j.ijsolstr.2024.112747

Keywords:
Microstructures, Particulate reinforced composites, Homogenization, RSA, Voronoï Diagram, Spatial distribution, Mean Field estimates, FFT-based solver.

Updates

• pyhom is now officially available on the Python Package Index. You can view the project on PyPI here https://pypi.org/project/pyhom/. (Sep 26, 2025)

• pyhom_core: Calculate the normalized effective conductivity properties of composite materials in 2D by varying the conductivity contrast parameter gamma. (Dec 17, 2024)

Installation

Recommended Method: Install from PyPI

1. Ensure you have Python 3.10 and pip installed.

2. Run the following command:

pip install pyhom

Alternative Method: For Developers

1. Python 3.10 and Git installed on your system.

2. Clone the repository in a local directory.

git clone https://github.com/olcruzgonzalez/pyhom.git
cd pyhom

3. Create and activate a virtual environment

• On Linux/macOS:

python3.10 -m venv myenv
source myenv/bin/activate

• On Windows:

py -3.10 -m venv myenv
myenv/Scripts/Activate.ps1

4. Install dependencies

(myenv) pip install -r requirements.txt

5. Use the library.

Basic Usage

In this section, we show how to use the library through some practical examples.

For demonstrating the most basic functionality of the python_core modulus, please, create a script basic_usage.py and use the following template:

# basic_usage.py
from pyhom.pyhom_core import Core

if __name__ == '__main__':
    coreJob = Core()
    coreJob.input_data(output_dir = 'results', input_file_path = 'input.yaml')
    coreJob.run()

💡 Notice that the only requirement to launch the code is to pass a YAML file as input. It defines various parameters related to the configuration of inclusions, cell properties, and other related settings.

Create a YAML file input.yaml and use the following template:

#-----------------------------------------------
# Input Parameters - PyHom Core
#---------------------------------------------

N_incl: 10 # Number of inclusions.
# default varying parameter
gamma: [2,5,10,20,50,100]
# fixed parameters
c_incl: [0.05] 
e_incl: [1]
theta_incl: '[0*np.pi/180]'
e_cell_RSA: [0.5]
theta_cell_RSA: '[0*np.pi/180]'
size_factor: [0.375]
security_factor: 1.02

# Further information
meta_x_incl: 
  value: false
  path: null
meta_image: 
  value: false
  path: null
hasReproducibility:
  value: False
  seed: null

Launch the code

python basic_usage.py

Built-in examples

We have incorporated built-in examples for different input_core.yaml files (see src/pyhom/built-in).

To do this, simply add the following line to basic_usage.py script. Notice that labelNumber is an integer between 1 and 8.

# basic_usage.py
from pyhom.pyhom_core import Core

if __name__ == '__main__':
    coreJob = Core()
    coreJob.get_built_in_dataset(labelNumber = "1") # NEW LINE !
    coreJob.input_data(output_dir = 'results', input_file_path = 'built-in_input/labelNumber_1/input_core.yaml') # UPDATE input_file_path!
    coreJob.run()

License

The template is available as open source under the terms of the MIT License.