Metadata-Version: 1.1
Name: pywicta
Version: 0.1.dev6
Summary: PyWI CTA wrapper - a signal processing library for Imaging Atmospheric Cherenkov Telescopes
Home-page: http://www.jdhp.org/software_en.html#pywicta
Author: Jeremie DECOCK and Tino Michael
Author-email: jd.jdhp@gmail.com
License: UNKNOWN
Download-URL: http://www.jdhp.org/software_en.html#pywicta
Description-Content-Type: UNKNOWN
Description: ======================
        CTAPipe Wavelet Filter
        ======================
        
        Copyright (c) 2016,2017,2018 Jeremie DECOCK (www.jdhp.org) and Tino Michael
        
        * Online documentation: https://jdhp-sap.github.io/ctapipe-wavelet-filter/
        * Source code: https://github.com/jdhp-sap/ctapipe-wavelet-filter
        * Issue tracker: https://github.com/jdhp-sap/ctapipe-wavelet-filter/issues
        * CTAPipe wavelet filter on PyPI: https://pypi.python.org/pypi/ctapipe-wavelet-filter
        
        .. Former documentation: http://sap-cta-data-pipeline.readthedocs.io/en/latest/
        
        Description
        ===========
        
        Signal processing for gamma-ray science.
        
        Note:
        
            This project is in beta stage.
        
        
        Dependencies
        ============
        
        *  Python >= 3.0
        
        .. _install:
        
        Installation
        ============
        
        Gnu/Linux
        ---------
        
        You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a
        terminal)::
        
            pip install --pre ctapipe-wavelet-filter
            pip install --upgrade ctapipe-wavelet-filter
            pip uninstall ctapipe-wavelet-filter
        
        Or, if you have downloaded the SAp CTA data pipeline source code::
        
            python3 setup.py install
        
        .. There's also a package for Debian/Ubuntu::
        .. 
        ..     sudo apt-get install ctapipe-wavelet-filter
        
        Windows
        -------
        
        .. Note:
        .. 
        ..     The following installation procedure has been tested to work with Python
        ..     3.4 under Windows 7.
        ..     It should also work with recent Windows systems.
        
        You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a
        `command prompt`_)::
        
            py -m pip install --pre ctapipe-wavelet-filter
            py -m pip install --upgrade ctapipe-wavelet-filter
            py -m pip uninstall ctapipe-wavelet-filter
        
        Or, if you have downloaded the SAp CTA data pipeline source code::
        
            py setup.py install
        
        MacOSX
        -------
        
        .. Note:
        .. 
        ..     The following installation procedure has been tested to work with Python
        ..     3.5 under MacOSX 10.9 (*Mavericks*).
        ..     It should also work with recent MacOSX systems.
        
        You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a
        terminal)::
        
            pip install --pre ctapipe-wavelet-filter
            pip install --upgrade ctapipe-wavelet-filter
            pip uninstall ctapipe-wavelet-filter
        
        Or, if you have downloaded the SAp CTA data pipeline source code::
        
            python3 setup.py install
        
        Image cleaning guidelines
        =========================
        
        Here is the basic guidelines to clean images (and assess cleaning algorithms).
        
        Step 1
        ------
        
        Extract images from Simtel files, crop them, convert them to "regular" 2D
        images and write them into fits files (one fits file per image with the ADC
        signal in HDU0 and the photoelectron signal in HDU1):
        
        1. clone http://github.com/jdhp-sap/snippets
        2. check snippets/ctapipe/extract_and_crop_simtel_images.py on lines 64 and 66,
           these lines may need to be fixed
        3. in snippets/ctapipe run ``./extract_crop_and_plot_all_astri_images.sh ASTRI_SIMTEL_FILE``
        
        Step 1.4 generate a lot of fits files in your current directory ;
        its execution may be long (up to several hours) as the script is not optimized
        at all and many instructions are redundant (but this is not a big deal because
        you only need to run it once to generate your input files).
        
        Step 2
        ------
        
        Install mr_transform (the cosmostat wavelet transform tool):
        
        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 an executable named "mr_transform"::
        
            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
        
        Step 3
        ------
        
        Clean images generated in step 1:
        
        1. clone and install
           http://github.com/jdhp-sap/data-pipeline-standalone-scripts (see
           https://github.com/jdhp-sap/data-pipeline-standalone-scripts#installation)
        2. to clean one fits file (see for instance run_experiments.sh):
        
           - with Tailcut : in data-pipeline-standalone-scripts, run ``./pywicta/denoising/tailcut.py -T 0.75 -t 0.5 FITS_FILE`` (-T = max threshold, -t = min threshold, use the -h option to see command usage)
           - with FFT : in data-pipeline-standalone-scripts, run ``./pywicta/denoising/fft.py -s -t 0.02 FITS_FILE`` (-t = threshold in the Fourier space, use the -h option to see command usage)
           - with Wavelets : in data-pipeline-standalone-scripts, run ``./pywicta/denoising/wavelets_mrtrransform.py FITS_FILE`` (use the -h option to see command usage)
        
        3. instead of the step 3.2, the "benchmark mode" can be set to clean
           images and assess cleaning algorithms (it's still a bit experimental) : use
           the same instructions than for step 3.2 with the additional option "-b 1" in
           each command (and put several fits files in input e.g. "\*.fits")
        
        Step 4
        ------
        
        Optionally, plot some stats about scores:
        in data-pipeline-standalone-scripts/utils, use the plot_score_*.py scripts on
        the JSON files generated in step 3.3 (use the -h option to see command usage)
        
        
        Bug reports
        ===========
        
        To search for bugs or report them, please use the SAp Data Pipeline Standalone
        Scripts Bug Tracker at:
        
            https://github.com/jdhp-sap/sap-cta-data-pipeline/issues
        
        
        .. _SAp CTA data pipeline: http://www.jdhp.org/software_en.html#pywicta
        .. _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
