openpiv-python

OpenPIV

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PyPI

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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

Installing

Use PyPI: https://pypi.python.org/pypi/OpenPIV:

pip install openpiv

Or conda

conda install -c conda-forge openpiv

To build from source

Download the package from the Github: https://github.com/OpenPIV/openpiv-python/archive/master.zip or clone using git

git clone https://github.com/OpenPIV/openpiv-python.git

Using distutils create a local (in the same directory) compilation of the Cython files:

python setup.py build_ext --inplace

Or for the global installation, use:

python setup.py install 

Documentation

The OpenPIV documentation is available on the project web page at http://openpiv.readthedocs.org

Demo notebooks

  1. Tutorial Notebook 1
  2. Tutorial notebook 2
  3. Dynamic masking tutorial
  4. Multipass with Windows Deformation
  5. Multiple sets in one notebook
  6. 3D PIV

These and many additional examples are in another repository: OpenPIV-Python-Examples

Contributors

  1. Alex Liberzon
  2. Roi Gurka
  3. Zachary J. Taylor
  4. David Lasagna
  5. Mathias Aubert
  6. Pete Bachant
  7. Cameron Dallas
  8. Cecyl Curry
  9. Theo Käufer
  10. Andreas Bauer
  11. David Bohringer
  12. Erich Zimmer
  13. Peter Vennemann

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