Metadata-Version: 1.2
Name: pywicta
Version: 0.3.dev1
Summary: PyWI CTA wrapper - a signal processing library for Imaging Atmospheric Cherenkov Telescopes
Home-page: https://github.com/jeremiedecock/pywi-cta
Author: Jeremie DECOCK and Tino Michael
Author-email: jd.jdhp@gmail.com
Maintainer: Jeremie DECOCK and Tino Michael
Maintainer-email: jd.jdhp@gmail.com
License: UNKNOWN
Download-URL: https://github.com/jeremiedecock/pywi-cta
Description: .. image:: https://travis-ci.org/jeremiedecock/pywi-cta.svg?branch=master
            :target: https://travis-ci.org/jeremiedecock/pywi-cta
        
        =================================
        PyWI CTA - A CTA wrapper for PyWI
        =================================
        
        Copyright (c) 2016-2018 Jeremie DECOCK (www.jdhp.org)
        
        * Web site: http://cta.pywi.org/
        * Online documentation: http://cta.pywi.org/docs/
        * Source code: https://github.com/jeremiedecock/pywi-cta
        * Issue tracker: https://github.com/jeremiedecock/pywi-cta/issues
        * PyWI-CTA on PyPI: https://pypi.org/project/pywicta/
        * PyWI-CTA on Anaconda Cloud: https://anaconda.org/jdhp/pywicta
        
        .. Former documentation: http://sap-cta-data-pipeline.readthedocs.io/en/latest/
        
        .. Former documentation: https://jeremiedecock.github.io/pywi-cta/
        
        Description
        ===========
        
        PyWI-CTA is a ctapipe_ wrapper for PyWI_.
        
        .. warning::
        
            This project is in beta stage.
        
        Features
        ========
        
        The PyWI and PyWI-CTA libraries contain:
        
        * wavelet transform and wavelet filtering functions for image multiresolution
          analysis and filtering;
        * additional filter to remove some image components (non-significant pixels
          clusters);
        * a set of generic filtering performance estimators (MSE, angular precision,
          energy loss, contamination rate, ...), some relying on the scikit-image
          Python library (supplementary estimators can be easily added to meet
          particular needs);
        * a graphical user interface to visualize the filtering process in the wavelet
          transformed space;
        * an Evolution Strategies (ES) algorithm known in the mathematical optimization
          community for its good convergence rate on generic derivative-free continuous
          global optimization problems (Beyer, H. G. (2013) "The theory of evolution
          strategies", Springer Science & Business Media);
        * additional tools to manage and monitor the parameter optimization.
        
        Dependencies
        ============
        
        .. Highly inspired by http://docs.astropy.org/en/stable/_sources/install.rst.txt
        
        PyWI-CTA has the following strict requirements:
        
        * `Python <https://www.python.org/>`_ 3.5 or 3.6
        * `Numpy <http://www.numpy.org/>`_
        * ctapipe_ 0.5.3
        
        PyWI-CTA also depends on other packages for optional features:
        
        * `Scipy <https://www.scipy.org/>`_
        * `Scikit-image <http://scikit-image.org/>`_
        * `Pillow (a.k.a. PIL) <https://pillow.readthedocs.io/en/latest/>`_ to read and write many image formats (PNG, JPEG, TIFF, ...)
        * `Astropy <http://www.astropy.org/>`_ to provide Fits file format
        * `Pandas <http://pandas.pydata.org/>`_
        * `Matplotlib <http://matplotlib.org/>`_ 1.5 or later to provide plotting functionality
        * PyWI_
        * `Cosmostat iSAP Sparce2D <http://www.cosmostat.org/software/isap/>`_
        
        However, note that these only need to be installed if those particular features
        are needed. `pywicta` will import even if these dependencies are not installed.
        
        .. _install:
        
        Installation
        ============
        
        PyWI-CTA and its dependencies may be installed using the *Anaconda* or
        *Miniconda* package system. We recommend creating a conda virtual environment
        first, to isolate the installed version and dependencies from your master
        environment (this is optional).
        
        The following command will set up a conda virtual environment, add the
        necessary package channels, and download PyWI-CTA and its dependencies. The
        file *environment.yml* can be found in this repository. 
        Note this is *beta* stage software and is not yet stable enough for end-users
        (expect large API changes until the first stable 1.0 release).
        
        ::
        
            conda env create -n pywi-cta -f environment.yml
            source activate pywi-cta
            pip install pywicta --no-deps
        
        If you have already installed *ctapipe* following the
        `official installation procedure <https://github.com/cta-observatory/ctapipe#installation-for-users>`_,
        you can add PyWI-CTA to the *cta* virtual environment like this::
        
            source activate cta
            pip install pywicta --no-deps
        
        Developers should follow the development install instructions found in the
        `documentation <https://jeremiedecock.github.io/pywi-cta/developer.html#getting-started-for-developers>`_.
        
        .. note::
        
            As *ctapipe* is not tested to work on Microsoft Windows systems, PyWI-CTA
            does not officially support these systems neither.
        
        .. note::
        
            The ``--no-deps`` flag is optional, but highly recommended otherwise pip
            will sometimes try to "help" you by upgrading PyWI-CTA dependencies like
            Numpy, which may not always be desired.
        
        Cosmostat iSAP Sparce2D installation
        ====================================
        
        1. Download http://www.cosmostat.org/wp-content/uploads/2014/12/ISAP_V3.1.tgz (see http://www.cosmostat.org/software/isap/)
        2. Unzip this archive, go to the "sparse2d" directory and compile the sparse2d
           library. It should generate two executables named ``mr_transform`` and ``mr_filter``::
        
            tar -xzvf ISAP_V3.1.tgz
            cd ISAP_V3.1/cxx
            tar -xzvf sparse2d_V1.1.tgz
            cd sparse2d
            compile the content of this directory
        
        An automated compilation and installation script for Linux is available
        `here <https://github.com/tino-michael/tino_cta/blob/master/grid/compile_mrfilter_pilot.sh>`_
        (author: `Tino Michael <https://github.com/tino-michael>`_).
        
        .. Also available in `utils/compile_isap_sparce2d.sh`
        
        Example
        =======
        
        1. Get a simtel file (e.g. from `there <https://forge.in2p3.fr/projects/cta_analysis-and-simulations/wiki/Monte_Carlo_Productions>`_)
        2. In your system terminal, from the directory that contains the sample image,
           type the following commands (where `SIMTEL_FILE` is the path to your simtel
           file)::
          
            pywicta-mrtransform -f common_hard_filtering -t 13.,1.5 -L mask --camid LSTCam --max-images 1 --plot SIMTEL_FILE
            pywicta-mrfilter -K -k -C1 -m3 -n4 -s2,4.5,3.5,3 --kill-isolated-pixels --camid LSTCam --max-images 1 --plot SIMTEL_FILE
        
        3. Type ``pywicta-mrtransform -h`` or ``pywicta-mrfilter -h`` to display the list of
           available options and their documentation.
        
        .. A "benchmark mode" can also be used to clean images and assess cleaning
        .. algorithms (it's still a bit experimental): use the additional option ``-b all``
        .. in each command (and put several fits files in input e.g. ``\*.fits``)
        
        IPython/Jupyter Notebooks
        =========================
        
        PyWI provide some Jupyter notebooks that can be used as examples or tutorials.
        
        * PyWI Notebooks on GitHub: https://github.com/jeremiedecock/pywi-cta-notebooks
        * PyWI Notebooks on Anaconda Cloud: https://anaconda.org/jdhp/notebooks
        
        Bug reports
        ===========
        
        To search for bugs or report them, please use the PyWI Bug Tracker at:
        
            https://github.com/jeremiedecock/pywi-cta/issues
        
        
        .. _PyWI: http://www.pywi.org/
        .. _ctapipe: https://github.com/cta-observatory/ctapipe
        .. _command prompt: https://en.wikipedia.org/wiki/Cmd.exe
        
Keywords: wavelet imaging cherenkov telescope array
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
