Metadata-Version: 2.1
Name: muDIC
Version: 0.0.6
Summary: A digital image correlation toolkit
Home-page: https://github.com/PolymerGuy/muDIC
Author: PolymerGuy
Author-email: sindre.n.olufsen@ntnu.no
License: UNKNOWN
Description: ![](documentation/logo.png)
        # µDIC [![CircleCI](https://circleci.com/gh/PolymerGuy/myDIC/tree/dev.svg?style=shield&circle-token=a243539092cfaffdffd7a59765c8b25827e55ab0)](https://circleci.com/gh/PolymerGuy/myDIC/tree/dev)
        
        
        
        A python toolkit for Digital Image Correlation (DIC)
        ## Overview
        This package provides a "batteries included" toolkit for 2D digital image correlation. The intended use in in the field of material research, for non-contact strain measurements.
        The implementation is based on B-splines, providing a very flexible framework where various degrees of continuity can be enforced etc.
        
        Typical usage is demonstrated in the provided examples
        
        
        This toolkit includes the following:
        * Image reader tools
        * Virtual lab
            * Speckle image generators
            * Image deformation tools
            * Noise injection
            * Image down-sampling
        * B-spline finite elements
            * Arbitrary polynomial order
            * Knot vectors can be manipulated
        * Meshing tools:
            * A light weight GUI for structured meshing
        * Image correlation routines:
            * Non linear least squares solver
        * Post processor
            * Calculates most popular strain measures
            * Light weight visualization
        * Logging
         
         
        
        
        ## Getting Started
        
        These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
        ### Prerequisites
        This toolkit is tested on Python 2.7x an 3.7 and need all dependencies listen in requirements.txt
        
        ### Installing
        
        #### Installing by a package manager:
        
        
        #### Installing by cloning the repos:
        Start to clone this repo to your preferred location:
        ```
        $ git init
        $ git clone https://github.com/PolymerGuy/muDIC.git
        ```
        
        
        We recommend that you always use virtual environments, either by virtualenv or by Conda env
        
        Virtual env:
        ```
        $ virtualenv myproject
        $ source /myproject/bin/activate
        $ pip install -r requirements.txt
        ```
        
        Conda env:
        ```
        $ conda create -n envname python=2.7
        $ source activate envname
        $ conda install --yes --file requirements.txt
        ```
        
        You can now run an example:
        ```
        $ python path_to_muDIC/Examples/phantomImages.py
        ```
        
        ## Running the tests
        
        The tests should always be launched to check your installation.
        These tests are integration and unit tests:
        
        
        ```
        $ unittests /tests
        ```
        
        ## Documentation
        Documentation is hosted by: LINK TO READ THE DOCS
        
        
        ## Our motivation
        The motivation for this work was the need for a transparent code which could be modified and extended easily, without digging deep into C or C++ source code. The implementation is pure python with the exception of third-party packages such as Scipy, Numy etc.
        
        
        ## Contributing
        Clone the repository, add your changes, add new tests and you are ready for a pull request
        
        ## Authors
        * **Sindre Olufsen** - *Implementation* - [PolymerGuy](https://github.com/polymerguy)
        * **Marius Endre Andersen** - *Wrote the Matlab code which was the starting point for this project*
        
        ## License
        This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
        
        ## Citing this project
        This project is described in the following paper and citation is highly appreciated
        
        [THE AWESOME PAPER TO BE PUBLISHED]
        
        
        
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
