MechMicroMan · rhysgt · Jan 24, 2025 · Jan 25, 2025 · Jan 25, 2025 · Jan 26, 2025
diff --git a/.travis.yml b/.travis.yml
diff --git a/CHANGES.txt b/CHANGES.txt
diff --git a/INSTALL b/INSTALL
diff --git a/MANIFEST.in b/MANIFEST.in
diff --git a/MILESTONES b/MILESTONES
diff --git a/README.md b/README.md
@@ -1,100 +1,27 @@
-# OpenPIV
-[![Build Status](https://travis-ci.org/OpenPIV/openpiv-python.svg?branch=master)](https://travis-ci.org/OpenPIV/openpiv-python)
-![Build and upload to PyPI](https://github.com/OpenPIV/openpiv-python/workflows/Build%20and%20upload%20to%20PyPI/badge.svg)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4409178.svg)](https://doi.org/10.5281/zenodo.4409178)
-
-
-
-![PyPI](https://img.shields.io/pypi/v/openpiv)
-[![Anaconda-Server Badge](https://anaconda.org/alexlib/openpiv/badges/version.svg)](https://anaconda.org/alexlib/openpiv)
-
-
-OpenPIV consists in a Python and Cython modules for scripting and executing the analysis of 
-a set of PIV image pairs. In addition, a Qt and Tk graphical user interfaces are in 
-development, to ease the use for those users who don't have python skills.
-
-## Warning
-
-The OpenPIV python version is still in its *beta* state. This means that
-it still might have some bugs and the API may change. However, testing and contributing
-is very welcome, especially if you can contribute with new algorithms and features.
-
-
-## Test it without installation
-Click the link - thanks to BinderHub, Jupyter and Conda you can now get it in your browser with zero installation:
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openpiv/openpiv-python/master?filepath=openpiv%2Fexamples%2Fnotebooks%2Ftutorial1.ipynb)
+# OpenPIV XL
 
+The authors aknowledge the authors of [OpenPIV](https://github.com/OpenPIV/openpiv-python), from which this package is derived. OpenPIV should be cited with this [DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4409178).
 
+This package is designed to perform DIC on large images (> 10,000 x 10,000 px) using chunking and GPU acceleration.
 
+Using the GPU, DIC correlation from a sub-window of 2048x2048 to 16x16 with no overlap on a (16000x16000) px image takes around a minute.
 
 ## Installing
 
-Use PyPI: <https://pypi.python.org/pypi/OpenPIV>:
-
-    pip install openpiv
-
-
-## Or `conda` 
-
-    conda install -c alexlib openpiv
-
-
-### To build from source
-
-Download the package from the Github: https://github.com/OpenPIV/openpiv-python/archive/master.zip
-or clone using git
+Download the package and run
 
-    git clone https://github.com/OpenPIV/openpiv-python.git
+`pip install -e .`
 
-Using distutils create a local (in the same directory) compilation of the Cython files:
+OR
 
-    python setup.py build_ext --inplace
+`pip install git+https://github.com/MechMicroMan/openpiv-python.git`
 
-Or for the global installation, use:
+Note: you will need a Nvidia GPU which supports CUDA 12x
 
-    python setup.py install 
 
+## License and copyright
 
-## Documentation
-
-The OpenPIV documentation is available on the project web page at <http://openpiv.readthedocs.org>
-
-## Demo notebooks 
-
-1. [Tutorial Notebook 1](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/tutorial1.ipynb)
-2. [Tutorial notebook 2](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/tutorial2.ipynb)
-3. [Dynamic masking tutorial](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/masking_tutorial.ipynb)
-4. [Multipass with Windows Deformation](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/window_deformation_comparison.ipynb)
-5. [Multiple sets in one notebook](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/all_test_cases_sample.ipynb)
-6. [3D PIV](https://nbviewer.org/github/OpenPIV/openpiv-python-examples/blob/main/notebooks/PIV_3D_example.ipynb)
-
-
-These and many additional examples are in another repository: [OpenPIV-Python-Examples](https://github.com/OpenPIV/openpiv-python-examples)
-
-
-## Contributors
-
-1. [Alex Liberzon](http://github.com/alexlib)
-2. [Roi Gurka](http://github.com/roigurka)
-3. [Zachary J. Taylor](http://github.com/zjtaylor)
-4. [David Lasagna](http://github.com/gasagna)
-5. [Mathias Aubert](http://github.com/MathiasAubert)
-6. [Pete Bachant](http://github.com/petebachant)
-7. [Cameron Dallas](http://github.com/CameronDallas5000)
-8. [Cecyl Curry](http://github.com/leycec)
-9. [Theo Käufer](http://github.com/TKaeufer)
-10. [Andreas Bauer](https://github.com/AndreasBauerGit)
-11. [David Bohringer](https://github.com/davidbhr)
-12. [Erich Zimmer](https://github.com/ErichZimmer)
-13. [Peter Vennemann](https://github.com/eguvep)
-14. [Lento Manickathan](https://github.com/lento234) 
-
+This package is licenses under the GNU General Public License v3.0
 
 Copyright statement: `smoothn.py` is a Python version of `smoothn.m` originally created by D. Garcia [https://de.mathworks.com/matlabcentral/fileexchange/25634-smoothn], written by Prof. Lewis and available on Github [https://github.com/profLewis/geogg122/blob/master/Chapter5_Interpolation/python/smoothn.py]. We include a version of it in the `openpiv` folder for convenience and preservation. We are thankful to the original authors for releasing their work as an open source. OpenPIV license does not relate to this code. Please communicate with the authors regarding their license. 
 
-## How to cite this work
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4409178.svg)](https://doi.org/10.5281/zenodo.4409178)
-
-
-
-