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**   Compressive Sensing Simulation Framework (CSSF)                         **
**                                                                           **
**   22/11 2012 - Version 1.1                                                **
**                                                                           **
**   A Python package developed by:                                          **
**     Christian Schou Oxvig                                                 **
**     Patrick Steffen Pedersen                                              **
**                                                                           **
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¤¤¤  Introduction
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The present Python package features several reconstruction algorithms to be
used in compressive sensing signal reconstruction in addition to a tool-box for
evaluating the performance of reconstruction algorithms. It has been developed
as part of the 8th semester project "Fast Parallel Reconstruction Algorithms
for Applied Compressive Sensing" on the Elite Program in Wireless Communication
at Aalborg University (AAU), Denmark.

The paper "SURPASSING THE THEORETICAL 1-NORM PHASE TRANSITION IN COMPRESSIVE
SENSING BY TUNING THE SMOOTHED L0 ALGORITHM" is based on the work done
throughout the project.



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¤¤¤  Installation
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CSSF has been tested with the Enthought Python Distribution (EPD) 7.2-2
(64-bit) on Linux. It may or may not work with other Python distributions
and/or operating systems.

Dependencies for CSSF are:
  - Numpy
  - Scipy
  - h5py

To install CSSF, copy the "cssf" folder (including all sub folders) to a local
directory which is part of your PYTHONPATH.



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¤¤¤  Usage
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CSSF includes several Python packages which are all loaded by importing cssf in
a Python module or script. See the init file (__init__.py) for further details
on included Python packages.



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¤¤¤  Documentation
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Documentation of included packages/modules/functions is provided through Python
docstrings. See the individual init files (__init__.py) belonging to the
included Python packages for an introduction to the functionality they
provide.

