Metadata-Version: 2.1
Name: pyspeckle
Version: 0.4.0
Summary: Routines for analysis of laser speckle
Home-page: https://github.com/scottprahl/pyspeckle.git
Author: Scott Prahl
Author-email: scott.prahl@oit.edu
License: MIT
Description: pyspeckle
        =========
        
        .. image:: https://img.shields.io/pypi/v/pyspeckle.svg
           :target: https://pypi.org/project/pyspeckle/
        
        .. image:: https://colab.research.google.com/assets/colab-badge.svg
           :target: https://colab.research.google.com/github/scottprahl/pyspeckle/blob/master
        
        .. image:: https://mybinder.org/badge_logo.svg
           :target: https://mybinder.org/v2/gh/scottprahl/pyspeckle/master?filepath=docs
        
        .. image:: https://img.shields.io/badge/readthedocs-latest-blue.svg
           :target: https://pyspeckle2.readthedocs.io
        
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           :target: https://github.com/scottprahl/pyspeckle
        
        .. image:: https://img.shields.io/badge/MIT-license-yellow.svg
           :target: https://github.com/scottprahl/pyspeckle/blob/master/LICENSE.txt
        
        __________
        
        A collection of routines to track and analyze laser speckle.  This is a python
        port of SimSpeckle Matlab routines described in
        `Duncan & Kirkpatrick, "Algorithms for simulation of speckle (laser and otherwise)," in SPIE Vol. 6855 (2008) <https://www.researchgate.net/profile/Sean-Kirkpatrick-2/publication/233783056_Algorithms_for_simulation_of_speckle_laser_and_otherwise/links/09e4150b78c4e8fe5f000000/Algorithms-for-simulation-of-speckle-laser-and-otherwise.pdf>`_
        
        This implementation contains code for
        
            * 1D exponential and gaussian speckle 
            * 2D speckle algorithms
            * 3D speckle generation
        
        Using pyspeckle
        -------------------
        
        1. Install with ``pip``::
            
            pip install --user pyspeckle
        
        2. or `run this code in the cloud using Google Collaboratory <https://colab.research.google.com/github/scottprahl/pyspeckle/blob/master>`_ by selecting the Jupyter notebook that interests you.
        
        3. use `binder <https://mybinder.org/v2/gh/scottprahl/pyspeckle/master?filepath=docs>`_ which will create a new environment that allows you to run Jupyter notebooks.  This takes a bit longer to start, but it automatically installs ``pyspeckle``.
        
        4. clone the `pyspeckle github repository <https://github.com/scottprahl/pyspeckle>`_ and then add the repository to your ``PYTHONPATH`` environment variable
        
        
        License
        -------
        
        pyspeckle is licensed under the terms of the MIT license.
Keywords: speckle,objective,subjective,contrast,size,autocorrelation,ACF,PDF,laser
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.4
Description-Content-Type: text/x-rst
