pyMechT

pyMechT: A Python package for mechanics of soft tissues - Published in JOSS (2025)

https://github.com/ankushaggarwal/pymecht

Science Score: 100.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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Found .zenodo.json file
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DOI references
Found 1 DOI reference(s) in JOSS metadata
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Academic publication links
Links to: joss.theoj.org
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Committers with academic emails
3 of 5 committers (60.0%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Scientific Fields

Mathematics Computer Science - 88% confidence

Last synced: 4 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: ankushaggarwal
License: mit
Language: Python
Default Branch: master
Size: 1.63 MB

Statistics

Stars: 5
Watchers: 2
Forks: 5
Open Issues: 2
Releases: 10

Created over 4 years ago · Last pushed 11 months ago

Metadata Files

Readme Contributing License Citation

pyMechT

pyMechT is a Python package for simulating the mechanical response of soft biological tissues. The ethos of pyMechT is to create simplified virtual experimental setups, rather than finite element analyses. Thus, varying parameters and running simulations is much faster, making it feasible to perform Bayesian inference and Markov Chain Monte Carlo analyses. A short overview is provided in the video below.

Documentation

Find the full documentation at https://pymecht.readthedocs.io/en/latest/.

Structure

pyMechT is a collection of modules for:

MatModel: defining material models
SampleExperiment: simulating experiments, such as uniaxial extension, biaxial extension, and inflation-extension. Simulations can be either disp_controlled or force_controlled
ParamDict: a custom dictionary class of a datastructure called Param, which facilitates storing/varying/fitting parameters
ParamFitter: fitting parameters to experimental data
RandomParameters and MCMC: Bayesian inference by running Monte Carlo (MC) and Markov chain Monte Carlo (MCMC) simulations

Structure of pyMechT{{caption=Structure of pyMechT}

This package is developed and maintained by the Computational Biomechanics Research Group at the University of Glasgow.

Required dependencies are: * matplotlib * numpy * pandas * pyDOE * scipy * torch * sympy * tqdm

Installation

Step 1 (optional): Create a virtual environment

To create an environment in Anaconda, execute: sh conda create -n pymecht python=3.9 ipykernel Python3.9 is suggested, although any of the currently-supported versions of Python will also work.

To activate this virtual environment, execute: sh conda activate pymecht This is an optional, but recommended, step. There are other options for creating and managing environments (such as venv or virtualenv)

Step 2: Install via pip

User

pyMechT can be installed directly from PyPI via pip by using: ```sh pip install pymecht ```

Developer

To install as a devloper, it is recommended to fork from the repo and clone this fork locally. ### *Step 2.1 Fork from ankushaggarwal/pymecht* To fork a branch, head to the [Github repository](https://github.com/ankushaggarwal/pymecht) and click the fork button in the top right-hand corner. ### *Step 2.2 Clone the forked repo* To clone this repo locally, use the ```sh git clone ``` where `` can be replaced by either the https or ssh addresses of the forked repo. ### *Step 2.3 Install developer version of pyMechT* To install a developer version of pyMechT, navigate to the locally cloned repo and execute: ```sh pip install -e . ``` An editable version of pyMechT is now installed. All local changes to the cloned source code files will be reflected when pyMechT is imported.

Step 3: Check installation

Ensure that pyMechT has been installed by executing: sh pip list The package and version should be visible in the resulting list.

Contributing to pyMechT

See the contributing guidelines CONTRIBUTING.md for information on submitting issues and pull requests.

Owner

Login: ankushaggarwal
Kind: user

Repositories: 3
Profile: https://github.com/ankushaggarwal

JOSS Publication

pyMechT: A Python package for mechanics of soft tissues

Published

February 12, 2025

DOI

10.21105/joss.07490

Volume 10, Issue 106, Page 7490