Metadata-Version: 2.1
Name: OpenPIV
Version: 0.21.9
Summary: UNKNOWN
Home-page: UNKNOWN
License: UNKNOWN
Description: # OpenPIV
        [![Build Status](https://travis-ci.org/OpenPIV/openpiv-python.svg?branch=master)](https://travis-ci.org/OpenPIV/openpiv-python)
        [![Build status](https://ci.appveyor.com/api/projects/status/4ht2vwvur22jmn6b?svg=true)](https://ci.appveyor.com/project/alexlib/openpiv-python)
        [![DOI](https://zenodo.org/badge/4213/OpenPIV/openpiv-python.svg)](https://zenodo.org/badge/latestdoi/4213/OpenPIV/openpiv-python)
        
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/openpiv/badges/version.svg)](https://anaconda.org/conda-forge/openpiv)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/openpiv/badges/platforms.svg)](https://anaconda.org/conda-forge/openpiv)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/openpiv/badges/license.svg)](https://anaconda.org/conda-forge/openpiv)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/openpiv/badges/downloads.svg)](https://anaconda.org/conda-forge/openpiv)
        [![Anaconda-Server Badge](https://anaconda.org/conda-forge/openpiv/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
        
        
        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 graphical user interface is in 
        development, to ease the use for those users who don't have python skills.
        
        ## Warning
        
        The OpenPIV python version is still in 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.
        
        Development is currently done on a Linux/Mac OSX environment, but as soon as possible 
        Windows will be tested. If you have access to one of these platforms
        please test the code. 
        
        ## 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)
        
        
        
        
        ## Installing
        
        Use PyPI: <https://pypi.python.org/pypi/OpenPIV>:
        
            pip install cython numpy 
            pip install openpiv --pre
        
        `--pre` because sometimes we push pre-releases
        
        ## 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 
        
        
        ### Latest developments
        
        Latest developments go into @alexlib repository <https://github.com/alexlib/openpiv-python>
        
        ## 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/blob/master/openpiv/examples/notebooks/tutorial1.ipynb)
        2. [Tutorial notebook 2](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python/blob/master/openpiv/examples/notebooks/tutorial2.ipynb)
        3. [Dynamic masking tutorial](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python/blob/master/openpiv/examples/notebooks/masking_tutorial.ipynb)
        4. [Multipass tutorial with WiDiM](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python/blob/master/openpiv/examples/notebooks/tutorial_multipass.ipynb)
        5. [Multipass with Windows Deformation](https://nbviewer.jupyter.org/github/OpenPIV/openpiv-python/blob/master/openpiv/examples/notebooks/window_deformation_comparison.ipynb)
        
        
        ## 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) 
        
        
        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. 
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: Environment :: X11 Applications
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
